pharaouk/stack-v2-python-with-content-chunk1

repo_name stringlengths 5 114	repo_url stringlengths 24 133	snapshot_id stringlengths 40 40	revision_id stringlengths 40 40	directory_id stringlengths 40 40	branch_name stringclasses 209 values	visit_date timestamp[ns]	revision_date timestamp[ns]	committer_date timestamp[ns]	github_id int64 9.83k 683M ⌀	star_events_count int64 0 22.6k	fork_events_count int64 0 4.15k	gha_license_id stringclasses 17 values	gha_created_at timestamp[ns]	gha_updated_at timestamp[ns]	gha_pushed_at timestamp[ns]	gha_language stringclasses 115 values	files listlengths 1 13.2k	num_files int64 1 13.2k
atomic-waffle/config-files	https://github.com/atomic-waffle/config-files	4bfa64071043023fda14de4b1d33f89ecaa3ee18	4a45d225155fcce7a230d8bf83e8c109e3dad7f2	dec4ca043520dd2a9c2673a5ff012f02490bff15	refs/heads/master	2023-03-11T10:55:32.463496	2021-02-17T18:42:57	2021-02-17T18:42:57	337,500,651	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5668991804122925, "alphanum_fraction": 0.5903614163398743, "avg_line_length": 36.5476188659668, "blob_id": "fd86c9e503e38e91fe06943d830435e8a635ad92", "content_id": "2f113c669870c0a4dad516977ac0d4438400bcf1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1577, "license_type": "no_license", "max_line_length": 78, "num_lines": 42, "path": "/bin/move_window", "repo_name": "atomic-waffle/config-files", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python3\nimport subprocess\nimport sys\n# calibration\ncal = 4\n# direction, as argument from user input (l, r, u, d / h+, h-, v+, v-)\ndirection = sys.argv[1]\n# move step size \nmv = -1 if direction in [\"l\", \"d\", \"h-\", \"v-\"] else 1\n\ndef get(command):\n return subprocess.check_output([\"/bin/bash\", \"-c\", command])\\\n .decode(\"utf-8\")\n\ndef execute(command):\n subprocess.call([\"/bin/bash\", \"-c\", command])\n# find the top shift (height of the panel = resolution - working area)\nres_output = get(\"xrandr\").split(); idf = res_output.index(\"current\")\nres = (int(res_output[idf+1]), int(res_output[idf+3].replace(\",\", \"\")))[-1]\ntopshift = int(res) - int(get(\"wmctrl -d\").split()[8].split(\"x\")[-1])+cal\n# find frontmost window\ndef get_windowid():\n cmd = \"xprop -root\"\n frontmost = [l for l in get(cmd).splitlines() if\\\n \"ACTIVE_WINDOW(WINDOW)\" in l][0].split()[-1]\n return frontmost[:2]+\"0\"+frontmost[2:]\n# get window geometry, create move command\nset_w = [w.split()[0:6] for w in get(\"wmctrl -lG\").splitlines()\\\n if get_windowid() in w][0]\nset_w[0] = \"wmctrl -ir \"+set_w[0]+\" -e 0\"\nset_w.pop(1)\n\nif direction in [\"l\", \"r\"]:\n set_w[1] = str(int(set_w[1])+mv); set_w[2] = str(int(set_w[2])-topshift) \nelif direction in [\"u\", \"d\"]:\n set_w[2] = str(int(set_w[2])-topshift-mv) \nelif direction in [\"v-\", \"v+\"]:\n set_w[2] = str(int(set_w[2])-topshift); set_w[4] = str(int(set_w[4])+mv)\nelif direction in [\"h-\", \"h+\"]:\n set_w[2] = str(int(set_w[2])-topshift); set_w[3] = str(int(set_w[3])+mv)\n\nexecute((\",\").join(set_w))\n" }, { "alpha_fraction": 0.4399999976158142, "alphanum_fraction": 0.47999998927116394, "avg_line_length": 4.75, "blob_id": "7c99ef9fba143f7d157f7d8820c3679263f0200c", "content_id": "14a7f9e8bde80e28802d211b75623f7e8fbb62ff", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 25, "license_type": "no_license", "max_line_length": 8, "num_lines": 4, "path": "/bin/mpd_starter.sh", "repo_name": "atomic-waffle/config-files", "src_encoding": "UTF-8", "text": "if [ 1 ]\nthen \n\tmpd\nfi\n\n\n" }, { "alpha_fraction": 0.782608687877655, "alphanum_fraction": 0.782608687877655, "avg_line_length": 22, "blob_id": "a55420f57f6b3616b017db7870faf8584564eda5", "content_id": "3ce81c850fa4deecdb8892f496ed38bf387576e2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 23, "license_type": "no_license", "max_line_length": 22, "num_lines": 1, "path": "/README.md", "repo_name": "atomic-waffle/config-files", "src_encoding": "UTF-8", "text": "# configs and programs\n" } ]	3
arbabiha/Particles2PDEs	https://github.com/arbabiha/Particles2PDEs	a89ff21fe7eb1dd9462ef72a9b5fb62ea9e9bf15	6a0a791926cf30c16fbb017b6c5b2ceea8a312b2	d4f4b1ffbb9160f9ef1cd0aade5f0809c304c5ba	refs/heads/master	2023-01-05T13:21:07.949449	2020-11-06T17:21:06	2020-11-06T17:21:06	292,632,911	8	1	null	null	null	null	null	[ { "alpha_fraction": 0.6140252351760864, "alphanum_fraction": 0.6268572807312012, "avg_line_length": 28.407285690307617, "blob_id": "5c3bd38eeb4239c0e615c143fc7a6312b8e57c56", "content_id": "85eaa04ffec4e866a957932145c8fb4c97705b7b", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8884, "license_type": "permissive", "max_line_length": 108, "num_lines": 302, "path": "/thehood/model_library.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\"\nLibrary of neural nets for learning PDEs.\n\nH. Arbabi, Aptil 2020, [email protected].\n\"\"\"\n\n\n\n\nimport numpy as np\nimport time\nimport tensorflow as tf\nimport tensorflow.keras as keras\nfrom tensorflow.keras.layers import Dense, Activation\nfrom tensorflow.keras.layers import Concatenate, Conv1D\n\n\nimport BarLegacy as BL\n\n# tf.keras.backend.set_floatx('float32')\n# tf.keras.backend.set_floatx('float64')\n\ndef Discretized_PDE_net(n_grid:int, n_stencil: int, n_conv=3, n_neurons=32):\n \"\"\"A CNN model for learning Burgers PDE.\n\n E.g. the model with n_stencil=3 learns du_j/dt=f(u_j,u_{j-1},u_{j+1}).\n\n Args:\n n_grid: size of the (periodically-padded) input grid\n n_stencil: the kernel size of the first convolutional layer, \n AND the stencil size of the finite difference\n n_conv: total number of convolutional layers\n n_neurons: size of hidden layers\n\n Returns:\n tf.keras.model that maps the field u (input) to u_t (output). \n \"\"\"\n u=tf.keras.Input(shape=(n_grid,),name=\"input_field\")\n\n u_embeded=stencil_embedding(u,n_stencil)\n\n\n\n # 1st convolution+ activation layers\n clayer1= Conv1D(n_neurons,1,padding='valid',name='convolution_1')\n u_out = clayer1(u_embeded)\n u_out = tf.keras.activations.relu(u_out)\n\n for layer_ind in range(n_conv-2):\n clayer= Conv1D(n_neurons,1,padding='valid',name='convolution_'+str(layer_ind+2))\n u_out = clayer(u_out)\n u_out = tf.keras.activations.relu(u_out)\n \n clayer= Conv1D(1,1,padding='valid',name='convolution_'+str(n_conv))\n u_out = clayer(u_out)\n\n\n return tf.keras.Model(u,u_out)\n \n\ndef Functional_PDE_net(n_grid: int, dx: float, n_stencil: int, \n n_conv=3, n_neurons=32):\n \"\"\"A functional model for learning PDEs.\n\n The model computes u_x,u_xx via finite difference and then\n at each x models u_t=f(u,u_x,u_xx) with trainable neural net.\n\n Args:\n n_grid: size of the (periodically-padded) input grid\n dx: uniform grid spacing\n n_stencil: the kernel size of the first convolutional layer, \n AND the stencil size of the finite difference\n n_conv: total number of convolutional layers\n n_neurons: size of hidden layers\n\n Returns:\n tf.keras.model that maps the field u (input) to u_t (output). \n \"\"\"\n\n u=tf.keras.Input(shape=(n_grid,),name=\"input_field\")\n\n # fixed layer for u_xx\n laplacian_layer = finite_diff_layer(dx,2,n_stencil)\n u_xx= laplacian_layer(u)\n\n # fixed layer for u_x\n laplacian_layer = finite_diff_layer(dx,1,n_stencil)\n u_x= laplacian_layer(u)\n\n # putting u,u_x and u_xx together\n us = tf.stack((u,u_x,u_xx),axis=-1,name='stack_u_ux_uxx')\n\n\n # 1st convolution+ activation layers\n clayer1= Conv1D(n_neurons,1,padding='valid',name='convolution_1')\n u_out = clayer1(us)\n u_out = tf.keras.activations.relu(u_out)\n\n for layer_ind in range(n_conv-2):\n clayer= Conv1D(n_neurons,1,padding='valid',name='convolution_'+str(layer_ind+2))\n u_out = clayer(u_out)\n u_out = tf.keras.activations.relu(u_out)\n \n clayer= Conv1D(1,1,padding='valid',name='convolution_'+str(n_conv))\n u_out = clayer(u_out)\n return tf.keras.Model(u,u_out)\n\ndef Burgers_PDE_greybox(n_grid: int, dx: float, nu: float, n_stencil: int, n_conv=3):\n \"\"\"A CNN model for learning Burgers PDE only uux part.\n\n The model computes u_x,u_xx via finite difference and then\n at each x models u_t=f(u,u_x,u_xx) with trainable neural net.\n\n Args:\n n_grid: size of the (periodically-padded) input grid\n dx: uniform grid spacing\n n_stencil: the kernel size of the first convolutional layer, \n AND the stencil size of the finite difference\n n_conv: total number of convolutional layers\n nu: viscosity\n\n Returns:\n tf.keras.model that maps the field u (input) to u_t (output). \n \"\"\"\n\n u=tf.keras.Input(shape=(n_grid,),name=\"input_field\")\n\n \n\n # fixed layer for u_xx\n laplacian_layer = finite_diff_layer(dx,2,n_stencil)\n u_xx= laplacian_layer(u)\n # print(u_xx[...,tf.newaxis].shape)\n\n # fixed layer for u_x\n ux_layer = finite_diff_layer(dx,1,n_stencil)\n u_x= ux_layer(u)\n\n # putting u u_x together\n us = tf.stack((u,u_x),axis=-1,name='stack_u_ux_uxx')\n\n\n # 1st convolution+ activation layers\n clayer1= Conv1D(32,1,padding='valid',name='convolution_1')\n u_out = clayer1(us)\n u_out = tf.keras.activations.relu(u_out)\n\n for layer_ind in range(n_conv-2):\n clayer= Conv1D(32,1,padding='valid',name='convolution_'+str(layer_ind+2))\n u_out = clayer(u_out)\n u_out = tf.keras.activations.relu(u_out)\n \n clayer= Conv1D(1,1,padding='valid',name='convolution_'+str(n_conv))\n u_out = clayer(u_out) + nu * u_xx[...,tf.newaxis]\n \n\n return tf.keras.Model(u,u_out)\n\n\n\n\n\ndef Burgers_PDE(n_grid: int, dx: float, n_stencil: int, nu: float):\n \"\"\"A standard discretization of Burgers.\n\n The model computes u_t= - uu_x+ nuu_xx.\n\n Args:\n n_grid: size of the (periodically-padded) input grid\n dx: uniform grid spacing\n n_stencil: the kernel size of the first convolutional layer, \n AND the stencil size of the finite difference\n ns: resolutaion rate of interpolation\n nu: viscosity\n\n Returns:\n tf.keras.model that maps the field u (input) to u_t (output). \n \"\"\"\n\n u=tf.keras.Input(shape=(n_grid,),name=\"input_field\")\n\n # fixed layer for u_xx\n laplacian_layer = finite_diff_layer(dx,2,n_stencil)\n u_xx= laplacian_layer(u)\n\n # fixed layer for u_x\n laplacian_layer = finite_diff_layer(dx,1,n_stencil)\n u_x= laplacian_layer(u)\n\n # putting u,u_x and u_xx together\n u_t =- uu_x + nu u_xx \n\n\n return tf.keras.Model(u,u_t)\n \n\n\n\n\n\nclass finite_diff_layer(tf.keras.layers.Layer):\n \"\"\"A layer of frozen finite difference on uniform periodic grid.\"\"\"\n\n def __init__(self, dx: float, derivative_order: int, stencil_size: int):\n \"\"\"Constructor.\n\n Args:\n dx: spacing between grid points\n derivative_order: larger than 0\n stencil_size: at this point we only accept odd numbers \n \"\"\"\n super(finite_diff_layer, self).__init__()\n assert stencil_size % 2 ==1, \"I accept only odd stencil size\"\n self.stencil_size=stencil_size\n\n int_grid= np.arange(-stencil_size//2+1,stencil_size//2+1,1)\n local_grid = int_grid* dx # local position of points\n\n damethod=BL.Method.FINITE_DIFFERENCES\n standard_coeffs= BL.coefficients(local_grid,damethod,derivative_order)\n \n # self.coeffs=tf.constant(standard_coeffs,dtype=tf.float64,name='df_coeffs_O'+str(derivative_order))\n self.coeffs=tf.constant(standard_coeffs,dtype=tf.float32,name='df_coeffs_O'+str(derivative_order))\n\n def build(self, input_shape):\n pass\n\n def call(self,u):\n u_embeded=stencil_embedding(u, self.stencil_size)\n return tf.einsum('s,bxs->bx', self.coeffs, u_embeded)\n\n\n\n\n\n\n\n\n\ndef stencil_embedding(inputs:tf.Tensor,stencil_width:int)-> tf.Tensor:\n \"\"\"Embedding the input data with the size of stencil.\n\n Args:\n inputs: values of the field on a periodic 1d grid, shape=(...,x)\n stencil_width: width of the stencil\n\n Returns:\n tensor of shape (...,x,stencil_width) the values of stencil nodes\n \"\"\"\n if stencil_width % 2 ==1:\n npad = stencil_width//2\n else:\n raise NotImplementedError('only accept odd stencil size')\n\n\n padded_inputs=tf.concat([inputs[:,-npad:],inputs,inputs[:,:npad]],axis=-1)\n\n # we add (y,depth) dimension to fake an image\n embedded=tf.image.extract_patches(padded_inputs[...,tf.newaxis,tf.newaxis],\n sizes=[1,stencil_width,1,1],\n strides=[1, 1, 1, 1],\n rates=[1, 1, 1, 1],\n padding='VALID',name='stencil_embeded')\n\n\n return tf.squeeze(embedded,axis=-2,name='squeeeze_me') # remove (y,) dimension\n\n\n\n\ndef fdm_2nd_der(nx:int,dx:float,ns:int):\n \"\"\"Computing the finite-diff ccoefficients for 2nd derivative.\n \n Args:\n nx: number of grid points\n dx: (uniform) grid spacing\n ns: (odd) stencil width\n \n Returns:\n alpha: tf.tensor of shape (nx,s) where alpha[ix,:] is the\n finite diff coefficients for estimating d2u/dx2 with O(h2) error.\n \"\"\"\n \n assert ns % 2 ==1, \"I accept only odd stencil width\"\n\n int_grid= np.arange(-ns//2+1,ns//2+1,1)\n local_grid = int_grid* dx # local position of points\n\n\n damethod=BL.Method.FINITE_DIFFERENCES\n\n a= BL.coefficients(local_grid,damethod,2)\n\n return tf.constant(a,dtype=tf.float32,name='a_xx')\n\n\n\n\n\nif __name__=='__main__':\n\n print('pass?')\n\n\n\n" }, { "alpha_fraction": 0.5705607533454895, "alphanum_fraction": 0.6042056083679199, "avg_line_length": 24.77108383178711, "blob_id": "68acd8872bbdd90d424c8075e7067df61fa2502a", "content_id": "fef18ea1f8809b73f72967282ccdfe4f801aba30", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4280, "license_type": "permissive", "max_line_length": 91, "num_lines": 166, "path": "/gap_tooth.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\" \nIllustrating gap-tooth method and data generation for Burgers model. \n\nFrom the paper\n[1]: \"Particles to PDEs parsimoniously\" by Arbabi & Kevrekidis 2020\n\nGap-tooth method is based on \n[2]: \"The Gap-Tooth Method in Particle Simulations\" by Gear, Lu & Kevrekidis 2003\n\n\nHassan Arbabi, [email protected] \nApril 4, 2020\n\"\"\"\n\n\nimport numpy as np\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport timeit\n\n\nfrom sys import path\npath.append('./thehood/')\n\nimport BurgersGapTooth as BGT\nimport CFDroutines as CR\n\n\n\ndef gaptooth_vs_truth(nu=0.05, N=128, Z=10000.0, alpha=.1):\n \"\"\"Running gap-tooth and comapre vs finite volume.\n\n Generates fig 1(b) in the paper.\n \n Args:\n nu: viscosity\n N: numbeer of teeth\n Z: resolution factor, i.e., number of particles per unit mass\n alpha: fraction of space occupied by teeth\n\n \"\"\"\n np.random.seed(41)\n\n rho0=lambda x: 1-np.sin(x)/2\n\n\n tsys = BGT.BurgersGapToothSystem(alpha=alpha,N=N,nu=nu)\n tsys.initialize(Z/alpha,rho0=rho0)\n tsys.run_gap_tooth(dt = .002, Nt = 1000,save_history=True)\n grid_g,rho_g,t_g=tsys.tooth_center_x,tsys.rho_history,tsys.rho_history_t\n rho_g=np.stack(rho_g)\n\n dx = grid_g[1]-grid_g[0]\n\n def FiniteVolume(t,y):\n dydt= - CR.WENO_FVM_convection(y,dx) + nu * CR.diffusion_term(y,dx)\n return dydt\n\n u0 = rho0(grid_g) \n Sol = solve_ivp(FiniteVolume,[0,t_g[-1]],u0,method='BDF',t_eval=t_g,max_step=0.01)\n rho_truth=Sol.y.T\n\n\n x_padded = np.concatenate((grid_g,2grid_g[-1:]-grid_g[-2:-1]))\n periodic_pad = lambda u: np.concatenate([u,u[:,-1:]],axis=-1)\n\n plt.figure(figsize=[4,2])\n plt.subplot(1,2,1)\n plt.contourf(x_padded,t_g,periodic_pad(rho_g),30,cmap='jet')\n plt.yticks([0,2]),plt.xticks([0,2np.pi],['0','$2\\pi$'])\n plt.title(r'$\\rho(x,t)$'+'\\n gap tooth')\n plt.colorbar()\n plt.xlabel(r'$x$'),plt.ylabel(r'$t$')\n\n er = rho_g-rho_truth\n rMSE = np.mean(er*2)/np.var(rho_truth)\n\n plt.subplot(1,2,2)\n plt.contourf(x_padded,t_g,periodic_pad(er),30,cmap='jet')\n plt.yticks([0,2]),plt.xticks([0,2np.pi],['0','$2\\pi$'])\n plt.title('error \\n rMSE={:.1e}'.format(rMSE))\n plt.colorbar()\n plt.xlabel(r'$x$'),plt.ylabel(r'$t$')\n\n plt.tight_layout()\n plt.savefig('Burgers_gaptooth.png',dpi=450)\n\n\ndef basic_traj():\n pass\n\n\n\ndef generate_data():\n \"\"\"Generates 12 trajectories of gap-tooth simulation with random initial conditions.\"\"\"\n\n ntraj=12\n alpha = .1\n N =128\n Zk = 500\n Z = Zk1000\n nu =.05\n Nt = 1000\n dt =.002\n\n rho_histories=[]\n teeth_histograms=[]\n rho0s=[]\n\n for k in range(ntraj):\n print(10'--')\n print('k='+str(k))\n rho0=get_random_rho()\n tsys=BGT.BurgersGapToothSystem(alpha=alpha,N=N,nu=nu)\n tsys.initialize(Z,rho0=rho0)\n rho0s.append(rho0(tsys.tooth_center_x)) \n print('# of particles='+str(tsys.particle_count))\n tsys.run_gap_tooth(dt=dt,Nt=Nt,save_inner_histograms=True)\n\n rho_history = np.stack(tsys.rho_history,axis=1).T\n rho_histories.append(rho_history)\n\n histograms_history = np.stack(tsys.inner_histograms_history)\n teeth_histograms.append(histograms_history)\n\n np.savez('BurgersGT_Z{}k_N{}_n{}_'.format(Zk,N,ntraj)+'.npz',\n Density=rho_histories,t=tsys.t,\n Z=Z,alpha=alpha,nu=nu,x=tsys.tooth_center_x,\n rho0s=np.stack(rho0s),teeth_histograms=teeth_histograms) \n\n\ndef get_random_rho():\n \"\"\"Generates a positive random profile.\n\n Returns:\n a callable that is positive everywhere on (0,2pi).\n \"\"\"\n\n N= 20\n A = np.random.rand(N)-.5\n phi,l=np.random.rand(N)2np.pi,np.random.randint(1,high=7,size=N)\n\n def rho(x):\n y = 0\n for k in range(N):\n y = y + A[k]np.sin(l[k]x + phi[k])\n return y\n \n # make it positive\n x = np.linspace(0,2np.pi,num=128)\n r = rho(x)\n rmin = np.min(r)\n if rmin<0.05:\n rho2= lambda x: rho(x) + np.abs(rmin) + .1\n else:\n rho2 = rho\n\n return rho2\n\nif __name__ == \"__main__\":\n ttin = timeit.default_timer()\n gaptooth_vs_truth()\n generate_data()\n\n print('whole run took {} seconds'.format(timeit.default_timer() - ttin))\n\n\n" }, { "alpha_fraction": 0.6126201152801514, "alphanum_fraction": 0.6255745887756348, "avg_line_length": 30.467105865478516, "blob_id": "9dda92d9c135d74767da310c19057a69f1005cb8", "content_id": "00d08a50d135038324bce5f12b5c4f2f60cb6fb0", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4786, "license_type": "permissive", "max_line_length": 109, "num_lines": 152, "path": "/thehood/diffusion_maps.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\"A simple implementation of diffusion maps and geometric harmonics.\n\nFrom Coifman, Ronald R., and Stéphane Lafon. 'Diffusion maps' \nApplied and computational harmonic analysis 21.1 (2006): 5-30.\nand \"Geometric harmonics: a novel tool \nfor multiscale out-of-sample extension of empirical functions.\" Applied and \nComputational Harmonic Analysis 21.1 (2006): 31-52.\n\n\nThis code is not optimized for speed, but it can handel arbitrary distance function. \nFor a more optimized implementation see\nhttps://github.com/jmbr/diffusion-maps .\n\n\n\nH. Arbabi, [email protected].\n\"\"\"\n\nimport numpy as np\nfrom scipy.spatial.distance import pdist, squareform, cdist\nimport scipy.sparse\n\n\nclass Manifold_Model:\n \"\"\"A simple object for computing diffusion maps and geometric harmonics.\"\"\"\n\n def __init__(self, X, distance_function, alpha = 1 ):\n \"\"\"Constructor.\n\n Args:\n X: data points (observaions x features)\n distance function: a callable that takes two data points and spits out their distance\n alpha: operator parameter (1:Laplace-Beltrami, 1/2:Fokker-Planck, 0: Normalized Graph Laplacian) \n \"\"\"\n self.X =X\n self.alpha=alpha\n self.distance_function= distance_function\n self.distance_matrix = squareform(pdist(self.X,metric=self.distance_function)) \n\n def compute_diffusion_maps(self, epsilon=None, num_pairs = 10):\n \"\"\"Computing the first 10 diffusion maps.\n\n Args:\n epsilon: the kernel width, if None the median of distance matrices is used\n num_pairs: number of eigen-vector -values for output\n \n Returns:\n matrix of diffusion map coordiantes and their eigenvalues\n \"\"\"\n\n if epsilon is None:\n epsilon=np.median(self.distance_matrix)\n \n self.epsilon=epsilon\n\n self.W=np.exp(-(self.distance_matrix2)/self.epsilon)\n\n d=np.sum(self.W, axis=1)self.alpha\n d_inv = 1.0/d\n W_bar=d_inv[:,np.newaxis] (self.W * d_inv[np.newaxis,:])\n \n row_sums = W_bar.sum(axis=1)\n W_hat = W_bar / row_sums[:, np.newaxis]\n \n eigenvalues, v_unsrtd = np.linalg.eig(W_hat)\n id_eigvalue=np.argsort(eigenvalues)[::-1]\n\n self.phis = np.real(v_unsrtd[:,id_eigvalue][:,:num_pairs])\n self.eigenvalues = np.real(eigenvalues[id_eigvalue][:num_pairs])\n\n return self.phis,self.eigenvalues\n\n def compute_geometric_harmonics(self,dim=None,epsilon_interp=None):\n \"\"\"Computing the basis for geometric harmonic interpolation.\"\"\"\n \n if epsilon_interp is None:\n epsilon_interp= 50 * np.median(self.distance_matrix)\n print('eps interp set to {:.1e}'.format(epsilon_interp))\n self.epsilon_interp = epsilon_interp\n\n W=np.exp(-(self.distance_matrix2)/self.epsilon_interp)\n\n eigenvalues, v_unsrtd = np.linalg.eigh(W)\n id_eigvalue=np.argsort(eigenvalues)[::-1]\n\n if dim is None:\n ratio = eigenvalues/eigenvalues[0]\n dim = ratio[ratio>1e-6].shape[0]\n print('cut-off dim is '+str(dim))\n\n self.psis = v_unsrtd[:,id_eigvalue][:,:dim]\n self.psi_eigvales = eigenvalues[id_eigvalue][:dim]\n\n\n def interpolate_geometric_harmonics(self,X, f):\n \"\"\"Using the Geometric Harmonics to interpolate the diffusion map coordinates.\n\n Args:\n X: new data points\n f: the function (values on self.X) to be interpolated\n\n Returns:\n the value of diffusion map coordinates for new data points.\n \"\"\"\n if not hasattr(self, 'psis'):\n print('computing the basis ...')\n self.compute_geometric_harmonics()\n \n new_distance = cdist(X,self.X,metric=self.distance_function)\n W=np.exp(-(new_distance2)/self.epsilon_interp) \n\n a = self.psis.T @ f\n\n extended_psis = W @ (self.psis / self.psi_eigvales[np.newaxis,:]) \n\n extended_f = extended_psis @ a\n\n return extended_f\n\n\n\n\n\n\n\ndef BasicDiffusionMaps(W,alpha=1):\n \"\"\"Computing first 10 diffusion maps.\n \n Args:\n W: affinity matrix of data points\n alpha: operator parameter (1:Laplace-Beltrami, 1/2:Fokker-Planck, 0: Normalized Graph Laplacian)\n \n Returns:\n phis: the vector of diffusion maps\n lams: the corresponding eigenvalues\n \"\"\"\n \n d=np.sum(W, axis=1)*alpha\n d_inv = 1.0/d\n W_bar=d_inv[:,np.newaxis] (W * d_inv[np.newaxis,:])\n \n\n row_sums = W_bar.sum(axis=1)\n W_hat = W_bar / row_sums[:, np.newaxis]\n \n\n eigenvalues, v_unsrtd = np.linalg.eig(W_hat)\n id_eigvalue=np.argsort(eigenvalues)[::-1]\n phis = v_unsrtd[:,id_eigvalue]\n eigenvalues = eigenvalues[id_eigvalue]\n\n return phis[:,:10],eigenvalues[:10]\n\n\n\n" }, { "alpha_fraction": 0.5860602855682373, "alphanum_fraction": 0.6194279789924622, "avg_line_length": 26.910072326660156, "blob_id": "e12601255b82fdfe582f685c528429fa6afab0fe", "content_id": "7422d0aa199bd7deedab8e22d58d80d6b3385ba4", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7762, "license_type": "permissive", "max_line_length": 105, "num_lines": 278, "path": "/learn_PDE_density.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\"\nLearning PDEs for density field of particles.\n\nFrom \"Particles to PDEs Parsimoniously\" by Arbabi & Kevrekidis 2020\n\nH. Arbabi, August 2020, [email protected].\n\"\"\"\n\nimport numpy as np\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport time\nimport scipy.io as sio\nimport importlib\nimport tensorflow as tf\nimport tensorflow.keras as keras\nfrom scipy.integrate import solve_ivp\nfrom sklearn.model_selection import train_test_split\nfrom scipy.ndimage import gaussian_filter\n\nfrom sys import path\npath.append('./thehood/')\n\nimport model_library as ML\nimport CFDroutines as CR\n\n\nplt.rc('text', usetex=True)\n\nfont = {'family' : 'serif',\n 'size' : 8}\n\nmatplotlib.rc('font', *font)\n\ndef prep_data(filename = 'BurgersGT_Z500k_N128_n12.npz',\n ti=[0,10],\n smoothing_sigma=1):\n \"\"\"Load and preprocess microscopic data.\n \n Args:\n filename: name of file with gap_tooth data\n ti: index of trajectories to pick up\n smoothing_sigma: the STD of teh gaussian filter\n \n Returns:\n x: space grid\n t: time grid\n v: density field\n dvdt: density field time derivative\n rho0s: initial conditions (not gap-tooth particle estimates)\n data_tag: string to distinguish\n \"\"\"\n\n Data=np.load(filename)\n x=Data['x'].astype('float32')\n Density=Data['Density'][ti[0]:ti[1]]\n rho0s=Data['rho0s'][ti[0]:ti[1]]\n t = Data['t']\n\n \n smoother= lambda u: gaussian_filter(u,smoothing_sigma,mode='wrap')\n v_temp = np.apply_along_axis(smoother,2,Density)\n\n dvdt,v = [],[]\n dt = t[1]-t[0]\n\n for batch in range(v_temp.shape[0]):\n vt_temp= (v_temp[batch,1:,:]-v_temp[batch,:-1,:])/dt\n dvdt.append(vt_temp)\n v.append(v_temp[batch,:-1,:])\n\n dvdt = np.concatenate(dvdt,axis=0)\n v = np.concatenate(v,axis=0)\n\n data_tag = '_sigma'+str(smoothing_sigma)+'_N'+str(Density.shape[-1])\n\n return x,t,v,dvdt,rho0s.squeeze(),data_tag\n\n\ndef learn_functional_model(x,t,v,dvdt,rho0s,data_tag):\n \"\"\"Learning the functional form of the PDE via neural net.\n \n Args:\n x: space grid\n t: time grid\n v: density field\n dvdt: density field time derivative\n data_tag: string to distinguish\n\n Returns:\n trained keras model mapping v to dvdt\n \"\"\"\n\n print(200'=')\n print('learning a functional model of the PDE ...')\n model_tag='_arch1o'+data_tag\n\n x_train,x_test,y_train,y_test=train_test_split(v,dvdt,train_size=.85,random_state=42)\n\n n_grid = x.shape[0]\n dx = x[1]-x[0]\n\n nn_model = ML.Functional_PDE_net(n_grid,dx,3,n_conv=2,n_neurons=48)\n\n adam_opt=tf.keras.optimizers.Adam(learning_rate=.001)\n nn_model.compile(optimizer=adam_opt,loss='mse')\n\n PDEfit_history=nn_model.fit(x_train,y_train,\n batch_size=64,epochs=256,\n verbose=0,validation_split=.1)\n\n plt.figure(figsize=[3,2.5])\n plt.plot(PDEfit_history.history['loss']/np.var(y_test),label='training loss')\n plt.plot(PDEfit_history.history['val_loss']/np.var(y_test),label='validation loss')\n plt.yscale('log')\n plt.legend(),plt.tight_layout()\n plt.savefig('fit'+model_tag,dpi=350)\n\n eval_loss=nn_model.evaluate(x=x_test,y=y_test,verbose=0)\n eval_lossp=100eval_loss/np.var(y_test)\n print('test loss %',eval_lossp )\n\n # nn_model.save('./models/nn'+model_tag)\n return nn_model\n\ndef learn_discretized_model(x,t,v,dvdt,rho0s,data_tag):\n \"\"\"Learning the discretized form of the PDE via neural net.\n \n Args:\n x: space grid\n t: time grid\n v: density field\n dvdt: density field time derivative\n data_tag: string to distinguish\n\n Returns:\n trained keras model mapping v to dvdt\n \"\"\"\n\n print(200'=')\n print('learning a discretized model of the PDE ...')\n model_tag='_arch2o'+data_tag\n\n x_train,x_test,y_train,y_test=train_test_split(v,dvdt,train_size=.85,random_state=42)\n\n n_grid = x.shape[0]\n\n nn_model = ML.Discretized_PDE_net(n_grid,3,n_conv=3,n_neurons=48)\n\n adam_opt=tf.keras.optimizers.Adam(learning_rate=.001)\n nn_model.compile(optimizer=adam_opt,loss='mse')\n\n PDEfit_history=nn_model.fit(x_train,y_train,\n batch_size=64,epochs=256,\n verbose=0,validation_split=.1)\n\n plt.figure(figsize=[3,2.5])\n plt.plot(PDEfit_history.history['loss']/np.var(y_test),label='training loss')\n plt.plot(PDEfit_history.history['val_loss']/np.var(y_test),label='validation loss')\n plt.yscale('log')\n plt.legend(),plt.tight_layout()\n plt.savefig('fit'+model_tag,dpi=350)\n\n eval_loss=nn_model.evaluate(x=x_test,y=y_test,verbose=0)\n eval_lossp=100eval_loss/np.var(y_test)\n print('test loss %',eval_lossp )\n return nn_model\n\n\ndef test_models(nn1,nn2,x,t,v,dvdt,rho0s,data_tag):\n \"\"\"Testing nn models in estimating dvdt and trajectory predictions.\n \n \n Args:\n models: list of nn models\n x: space grid\n t: time grid\n v: density field\n dvdt: density field time derivative\n data_tag: string to distinguish\n\n Returns:\n saves comparison figures\n \"\"\"\n\n RHS1=lambda t,u: nn1.predict(u[np.newaxis,:]).squeeze()\n RHS2=lambda t,u: nn2.predict(u[np.newaxis,:]).squeeze()\n\n k = 200\n\n # dvdt plots\n plt.figure(figsize=[6.75/2,1.7])\n plt.subplot(1,2,1)\n plt.plot(x,dvdt[k],'k',label='gap tooth')\n plt.plot(x,RHS1(0,v[k]))\n plt.subplot(1,2,2)\n plt.plot(x,dvdt[k],'k',label='gap tooth')\n plt.plot(x,RHS2(0,v[k]))\n\n plt.savefig('ddensity_dt.png',dpi=450)\n\n # trajectory pred\n v_gt = v[::10]\n t_eval = t[:-1:10]\n u0_truth = rho0s\n\n dx = x[1]-x[0]\n\n def Standard_FV(t,y):\n dydt= - CR.WENO_FVM_convection(y,dx) + .05 CR.diffusion_term(y,dx)\n return dydt\n\n v_truth = solve_ivp(Standard_FV,[0,t_eval[-1]],u0_truth,method='BDF',t_eval=t_eval,max_step=0.01).y.T\n\n\n v_nn1 = solve_ivp(RHS1,[0,t_eval[-1]],u0_truth,method='BDF',t_eval=t_eval,max_step=0.01).y.T\n v_nn2 = solve_ivp(RHS2,[0,t_eval[-1]],u0_truth,method='BDF',t_eval=t_eval,max_step=0.01).y.T\n\n\n plt.figure(figsize=[6.75,2])\n\n plt.subplot(1,4,1)\n plt.contourf(x,t_eval,v_truth,30,cmap='jet')\n plt.colorbar()\n plt.yticks([0,2]),plt.xticks([0,2np.pi],['0',r'$2\\pi$'])\n plt.title(r'$\\rho(x,t)$'+'\\n truth')\n plt.xlabel(r'$x$'),plt.ylabel(r'$t$')\n\n er0 = v_truth - v_gt\n rmse0 = np.mean(er02)/np.var(v_truth)\n\n plt.subplot(1,4,2)\n plt.contourf(x,t_eval,er0,30,cmap='jet')\n plt.colorbar()\n plt.yticks([0,2]),plt.xticks([0,2np.pi],['0',r'$2\\pi$'])\n plt.title('gap-tooth error \\n rMSE={:.1e}'.format(rmse0))\n plt.xlabel(r'$x$'),plt.ylabel(r'$t$')\n\n er1 = v_truth - v_nn1\n rmse1 = np.mean(er1*2)/np.var(v_truth)\n\n plt.subplot(1,4,3)\n plt.contourf(x,t_eval,er1,30,cmap='jet')\n plt.colorbar()\n plt.yticks([0,2]),plt.xticks([0,2np.pi],['0',r'$2\\pi$'])\n plt.title('arch. 1 error \\n rMSE={:.1e}'.format(rmse1))\n plt.xlabel(r'$x$'),plt.ylabel(r'$t$')\n\n er2 = v_truth - v_nn2\n rmse2 = np.mean(er2*2)/np.var(v_truth)\n\n plt.subplot(1,4,4)\n plt.contourf(x,t_eval,er2,30,cmap='jet')\n plt.colorbar()\n plt.yticks([0,2]),plt.xticks([0,2np.pi],['0',r'$2\\pi$'])\n plt.title('arch. 2 error \\n rMSE={:.1e}'.format(rmse2))\n plt.xlabel(r'$x$'),plt.ylabel(r'$t$')\n\n plt.tight_layout()\n plt.savefig('u_traj.png',dpi=450)\n\n\n\n\n\n\nif __name__=='__main__':\n ttin=time.time()\n\n train_data = prep_data(smoothing_sigma=1)\n nn1=learn_functional_model(train_data)\n nn2=learn_discretized_model(train_data)\n\n test_data=prep_data(smoothing_sigma=1,ti=[11,12])\n test_models(nn1,nn2,test_data)\n\n print( 'run took {} seconds'.format(time.time()-ttin) )\n\n\n\n" }, { "alpha_fraction": 0.7756806015968323, "alphanum_fraction": 0.7858439087867737, "avg_line_length": 80, "blob_id": "0e4dd4bb39140676464cfaa443b242c30e17f9f5", "content_id": "9c99172280bc8c7f9b90b5d295c0ff3cfc96b433", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2755, "license_type": "permissive", "max_line_length": 881, "num_lines": 34, "path": "/README.md", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "# Particles2PDEs\n\nSource code for \"Particles to PDEs Parsimoniously\" by Arbabi & Kevrekidis 2020\n\nUsing first prinicples typically leads to microscopic evolution laws for physical, chemical and biological systems (e.g. lattice dynamics in crystals, molecular interaction in reactions or neuron response in tissue). Yet some of these systems may also admit coarse-grained evolution laws, e.g. in the form of PDEs, which can result in huge savings in computation. We propose a frameowrk for 1) identifying the coarse-grained variable from data and 2) finding the PDE that governs that variable evolution. The example we use is a model of collective particle motion that leads to the Burgers PDE at the coarse-level description.\n\nThe below figures, taken from the above paper, shows the setup for discovering the coarse variable from particle data. We collect particle distributions (<img src=\"https://render.githubusercontent.com/render/math?math=\\mu_i, i=1,2,\\ldots,m\">) from simulations. Thinking of each distribution as a data point, we hypothesize that the data cloud lies close to a low-dimensional manifold. The coordinates of that manifold, equipped with unbalanced optimal transport distance <img src=\"https://render.githubusercontent.com/render/math?math=d_W\">, and mined via Diffusion Maps (kernel matrix <img src=\"https://render.githubusercontent.com/render/math?math=W\">), are our candidates for coarse-grained variable. In the example, the discovered coordinate is one-to-one with density field (proportional to zeroth moment <img src=\"https://render.githubusercontent.com/render/math?math=M_0\">).\n\n\n\n<img src=\"../master/thehood/sketch1.png\" width=\"750\">\n<img src=\"../master/thehood/distances_and_moments.png\" width=\"750\">\n\n## main files:\n\ngap_tooth* illustrates the gap-tooth scheme for parsimonious simulations of particle dynamics (Fig. 1 in the paper).\n\nlearn_PDE_density builds and trains neural nets that learn the right-hand-side of PDEs for the denisty (a priori known coarse-grained variable).\n\nVariable Identification uses unnormalized/unbalanced optimal transport distance + diffusion maps to learn the coarse-grained variable from particle data.\n\nlearn_PDE_phi builds and trains neural nets that learn the right-hand-side of PDEs for the denisty (coarse-grained variable discovered from data).\n\n## files under 'thehood':\n\nunbalanced_transport: implementations of Chizat et al. 2018 and Gangbo et al. 2019 (analytical) formulations of unbalanced optimal transport.\n\nBurgersGapTooth: the gap-tooth implementation of the particle model leading to Burgers PDE.\n\nmodel_library: collection of neural net models for learning PDEs.\n\n## dependencies\n\n[TensorFlow >=2.0](https://www.tensorflow.org/install)\n\n" }, { "alpha_fraction": 0.6007168292999268, "alphanum_fraction": 0.613312840461731, "avg_line_length": 33.199649810791016, "blob_id": "b2bc7a26fecaeca1dc25b896df8a1223e4ec763a", "content_id": "c45357f417144b39f450a884db6312d6d565bf73", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 19530, "license_type": "permissive", "max_line_length": 147, "num_lines": 571, "path": "/thehood/BurgersGapTooth.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\" \nAn implementaion of gap-tooth scheme for Burgers equation.\n\nBased on \"The Gap-Tooth Method in Particle Simulations\" by Gear, Lu & Kevrekidis 2003\nHassan Arbabi, [email protected] \nApril 4, 2020\n\"\"\"\n\n\n\n\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport timeit\nimport os\nimport scipy.fftpack as fftpack\nfrom scipy.linalg import toeplitz\nimport scipy.integrate\nimport pickle\n\n\n\nclass BurgersGapToothSystem:\n \"\"\"Class of gap tooth modeling of Burgers system.\n\n\n Attributes:\n teeth: list of arrays; i-th array is the particle positons in tooth i\n cavities: list of arrays; i-th array is the anti-particle positons in tooth i\n\n right_outflux_particle: list of arrays; i-th array is the extrusion of particles getting out of tooth i\n right_outflux_anti: list of arrays; i-th array is the extrusion of anti_particles getting out of tooth i\n left_outflux_particle: list of arrays; i-th array is the extrusion of particles getting out of tooth i\n left_outflux_anti: list of arrays; i-th array is the extrusion of anti_particles getting out of tooth i\n\n adjacency_mat: an array; the i-th row is [index of patch to the left of patch i, i, index of right patch]\n left_boundary: list of left boundary positions\n right_boundary: list of right boundary positions\n t: list of time stamps so far \n .....\n \"\"\"\n \n def __init__(self, N=40, alpha = 1, domain_bounds=[0,2np.pi], nu=0.05, dt = None ):\n \"\"\"Creating a uniform grid of teeth on a periodic domain.\n\n Args:\n N: number of teeth\n alpha: gap length; 1 no gap between teeth, 0 infinitely thin teeth\n domain_bounds: bounds of the domain duh\n nu: viscosity\n \"\"\"\n self.N = N\n self.dt=dt\n self.alpha=alpha\n self.nu = nu\n self.t = [0]\n self.domain_bounds=domain_bounds\n D = (domain_bounds[1]-domain_bounds[0])/N\n self.tooth_center_x = np.arange(domain_bounds[0],domain_bounds[1],D)\n self.tooth_width = alphaD\n self.left_boundaries = self.tooth_center_x - self.tooth_width/2\n self.right_boundaries = self.tooth_center_x + self.tooth_width/2\n\n # only 2 neighbors are required\n self.adjacency_mat = np.stack((np.arange(-1,N-1),np.arange(N),np.arange(1,N+1)),axis=1)\n self.adjacency_mat[N-1,2] = 0\n\n # flux resitriution coeffs\n self.a11 = 1- self.alpha*2\n self.a10 = - self.alpha (1-self.alpha)/2\n self.a12 = self.alpha * (1+self.alpha)/2\n \n def initialize(self, Z: float, rho0=lambda x: 1-np.sin(np.pix)/2, inner_histogram_size = 10):\n \"\"\"Lift density to particle positions.\n\n Args:\n Z: resolution, i.e., number of particles representing one unit of mass\n rho: initial density profile callable or numpy array\n inner_histogram_size: number of bins for tooth histograms\n\n Return:\n teeth\n \"\"\"\n self.Z=Z \n self.teeth = []\n\n if callable(rho0):\n rho_vals = rho0(self.tooth_center_x)\n else:\n rho_vals = rho0\n\n for xc,itooth in zip(self.tooth_center_x,range(self.N)):\n rho_tooth = rho_vals[itooth]\n tooth_bounds=[xc-self.tooth_width/2,xc+self.tooth_width/2]\n patch,_=lift_from_density(self.Z,rho_tooth,tooth_bounds)\n self.teeth.append(patch)\n \n self.cavities = [np.array([]) for k in range(self.N)]\n\n self.particle_history = [self.teeth]\n self.particle_history_t = [0]\n\n self.compute_density()\n self.rho_history = [self.rho]\n self.rho_history_t = [0]\n \n self.inner_histogram_size=inner_histogram_size\n self.compute_inner_histograms()\n self.inner_histograms_history = [self.inner_histograms]\n self.inner_histograms_t=[0]\n\n def compute_density(self):\n \"\"\"Computing density of each tooth.\"\"\"\n self.rho = np.array([(tooth.size/self.Z)/self.tooth_width for tooth in self.teeth])\n\n def compute_inner_histograms(self):\n \"\"\"Computes the histogram of particles within each tooth.\"\"\"\n self.inner_histograms=[]\n for tooth,lb,rb in zip(self.teeth,self.left_boundaries,self.right_boundaries):\n self.inner_histograms.append(np.histogram(tooth,bins=self.inner_histogram_size,range=[lb,rb])[0])\n\n @property \n def how_many_redistributions(self):\n \"\"\"Number of redistributions so that every (anti)-particle ends up inside teeth.\"\"\"\n self.compute_density()\n u_max = np.max(self.rho)\n jump_max = max(u_maxself.dt,4np.sqrt(2self.nuself.dt))\n return int(jump_max/(self.tooth_width/2)+1)\n\n @property\n def total_mass(self):\n self.compute_density()\n return np.sum(self.rho (self.domain_bounds[1]-self.domain_bounds[0])/self.N)\n\n @property\n def particle_count(self):\n \"\"\"No. of particles in teeth.\"\"\"\n each_tooth=[p.size for p in self.teeth]\n return sum(each_tooth)\n \n @property\n def anti_count(self):\n \"\"\"No of anti-particles in teeth.\"\"\"\n each_tooth=[p.size for p in self.cavities]\n return sum(each_tooth)\n\n @property\n def particle_count_outflux(self):\n \"\"\"No of particles in outflux.\"\"\"\n right=[p.size for p in self.right_outflux_particle]\n left=[p.size for p in self.left_outflux_particle]\n return sum(right)+sum(left)\n \n\n @property\n def anti_count_ouflux(self):\n \"\"\"No of particles in teeth.\"\"\"\n right=[p.size for p in self.right_outflux_anti]\n left=[p.size for p in self.left_outflux_anti]\n return sum(right)+sum(left)\n\n\n def update_teeth(self):\n \"\"\"Updating the state of particles within teeth via Burgers dynamics.\n \n This update takes each particle from x_t to x_{t+1} but does not \n include the implementaion of influx or computation of outflux. \n \"\"\"\n \n self.teeth = [Burgers_tooth_update(tooth,self.nu,self.tooth_width,self.dt,self.Z) for tooth in self.teeth]\n \n \n def compute_outflux(self):\n \"\"\"Computes the outgoing particles and anti-particles from all teeth.\n \n From patches, done for all patches, by the patches ;).\n It looks at teeth, identifies what particles have gone out of teeth,\n and puts their extrusion in right_outflux and left_outlfux.\"\"\"\n\n # not efficient duh \n self.right_outflux_particle = [x[x>right_bdry]-right_bdry for x,right_bdry in zip(self.teeth,self.right_boundaries)]\n self.left_outflux_particle = [x[x<left_bdry]-left_bdry for x,left_bdry in zip(self.teeth,self.left_boundaries)]\n self.teeth=[patch[(patch<right_bdry)&(patch>left_bdry)] \\\n for patch,left_bdry,right_bdry in zip(self.teeth,self.left_boundaries,self.right_boundaries)]\n \n\n\n\n self.right_outflux_anti = [x[x>right_bdry]-right_bdry for x,right_bdry in zip(self.cavities,self.right_boundaries)]\n self.left_outflux_anti = [x[x<left_bdry]-left_bdry for x,left_bdry in zip(self.cavities,self.left_boundaries)]\n self.cavities=[patch[(patch<right_bdry)&(patch>left_bdry)] \\\n for patch,left_bdry,right_bdry in zip(self.cavities,self.left_boundaries,self.right_boundaries)]\n return\n\n\n\n def _interpolate_influx(self,outflux):\n \"\"\"Interpolates one outflux into three influxes.\n \n IMPORTANT: indices show order in the flow direction: 0 upstream,\n 1 current tooth, 2 downstream\n \"\"\"\n\n n = outflux.size\n n10 = int(- self.a10 * n)\n n12 = int(self.a12 * n)\n\n # in order to preserve mass we compute n11 this way\n n11 = n - n12 + n10\n # print(str(n)+'--->'+str(n10)+','+str(n11)+','+str(n12))\n\n np.random.shuffle(outflux)\n I_upstream_anti = outflux[:n10] # this one makes particles anti-particles and vice versa\n I_self = outflux[:n11]\n I_downstream = outflux[n-n12:n] # avoiding special behavior when n12=0\n\n\n return I_upstream_anti,I_self,I_downstream\n\n def _distribute_outflux(self,O1,I0,I1,I2):\n \"\"\"Breaking outflux O1 into influxes I0,I1,I2.\"\"\"\n I_upstream_anti,I_self,I_downstream=self._interpolate_influx(O1)\n I0.append(I_upstream_anti)\n I1.append(I_self)\n I2.append(I_downstream)\n\n\n def compute_influx(self):\n \"\"\"Computes the influx of particles and anti-particles into each tooth.\n \n Uses the quadratic interpolation formula to redistribute the outflux \n into influxes.\n \"\"\"\n self.left_influx_particle = [ [] for i in range(self.N) ]\n self.left_influx_anti = [ [] for i in range(self.N) ]\n self.right_influx_particle = [ [] for i in range(self.N) ]\n self.right_influx_anti = [ [] for i in range(self.N) ]\n\n\n # right-going fluxes\n upstream,downstream = self.adjacency_mat[:,0],self.adjacency_mat[:,2]\n\n for j in range(self.N):\n # particles\n self._distribute_outflux( self.right_outflux_particle[j],\\\n self.right_influx_anti[upstream[j]],self.right_influx_particle[j],self.right_influx_particle[downstream[j]])\n\n self._distribute_outflux( self.right_outflux_anti[j],\\\n self.right_influx_particle[upstream[j]],self.right_influx_anti[j],self.right_influx_anti[downstream[j]])\n\n\n\n # left-going fluxes\n upstream,downstream = self.adjacency_mat[:,2],self.adjacency_mat[:,0]\n \n for j in range(self.N):\n # particles\n self._distribute_outflux(self.left_outflux_particle[j],\\\n self.left_influx_anti[upstream[j]],self.left_influx_particle[j], self.left_influx_particle[downstream[j]])\n\n\n\n # anti-particles\n self._distribute_outflux(self.left_outflux_anti[j],\\\n self.left_influx_particle[upstream[j]],self.left_influx_anti[j], self.left_influx_anti[downstream[j]])\n\n\n\n # TODO: consider moving this to the loop\n self.left_influx_particle=[np.concatenate(x) for x in self.left_influx_particle]\n self.left_influx_anti=[np.concatenate(x) for x in self.left_influx_anti]\n self.right_influx_particle=[np.concatenate(x) for x in self.right_influx_particle]\n self.right_influx_anti=[np.concatenate(x) for x in self.right_influx_anti]\n\n \n\n def collect_influx(self):\n \"\"\"Merge influxes with each tooth and cavity.\"\"\"\n \n # loop seems more comprehensible than list comprehension\n for j in range(self.N):\n incoming_particles_l= self.left_influx_particle[j] + self.right_boundaries[j]\n incoming_particles_r= self.right_influx_particle[j] + self.left_boundaries[j]\n \n self.teeth[j] = np.concatenate((self.teeth[j],incoming_particles_l,incoming_particles_r)) \n\n incoming_anti_l= self.left_influx_anti[j] + self.right_boundaries[j]\n incoming_anti_r= self.right_influx_anti[j] + self.left_boundaries[j]\n\n self.cavities[j] = np.concatenate((self.cavities[j],incoming_anti_l,incoming_anti_r))\n\n\n def fuse_cavity_tooth(self):\n \"\"\"Annihilate anti-particles and particles within each tooth.\"\"\"\n self.teeth=[annihilate_particles(tooth,cavity) for tooth,cavity in zip(self.teeth,self.cavities) ]\n self.cavities = [np.array([]) for k in range(self.N)]\n \n \n def redistribute_particles(self, max_iter = 5):\n \"\"\"Redistributes particles and anti-particles till they land inside teeth.\n \n Repeates outflux, influx computation and merge with tooth until all particles or anti-particle are inside the teeth or max_iter is reached.\n \"\"\"\n for j in range(max_iter):\n self.compute_outflux()\n if (self.anti_count_ouflux + self.particle_count_outflux)==0:\n break\n self.compute_influx()\n self.collect_influx()\n # self.visualize_teeth()\n\n if j==max_iter-1: \n print('max iteration reached for redistribution process')\n\n\n def run_gap_tooth(self, dt: float, Nt = 1, \n show_runtime=True, \n save_history=True, \n save_particle_history=False,\n save_inner_histograms=False):\n \"\"\"Continue simulating the system.\n \n Args:\n dt: time step\n Nt: number of time steps\n show_runtime: show how much time for whole run has elapsed\n save_history: saves the history of the rho evolution to self.rho_history\n save_particle_history: saves the history of particle position in teeth -- MEMORY EXPENSIVE\n tooth_histograms: the number of bins used for histograms within each tooth,\n if None, those histograms are not computed or saved.\n \"\"\"\n\n self.dt = dt\n ttin = timeit.default_timer()\n\n n_redist = self.how_many_redistributions * 4\n\n for _ in range(Nt):\n self.t.append(self.t[-1]+self.dt)\n self.update_teeth()\n self.redistribute_particles(max_iter=n_redist)\n self.fuse_cavity_tooth()\n \n if save_history:\n self.compute_density()\n self.rho_history.append(self.rho)\n self.rho_history_t.append(self.t[-1])\n\n if save_inner_histograms:\n self.compute_inner_histograms()\n self.inner_histograms_history.append(self.inner_histograms)\n self.inner_histograms_t.append(self.t[-1])\n\n if save_particle_history:\n self.particle_history.append(self.teeth)\n self.particle_history_t.append(self.t[-1])\n \n \n\n\n if show_runtime:\n print('run took {} seconds'.format(timeit.default_timer() - ttin))\n\n\n\ndef lift_from_density(Z: int, rho: float, bin_edges):\n \"\"\"Lifting density funcation to particle positions for a single tooth.\n\n This is Hassan's understanding of the paper; not optimized but understandable.\n\n Args:\n Z: resolution, i.e., number of particles representing one unit of mass\n rho: value of density within bin\n bin_edges: duh\n n_bin: how many bins put into the domain and used for lifting\n \n Returns:\n x_particles: array of particle positions\n Z_new: the adjusted value of Z\n \"\"\"\n\n bin_edges = np.array(bin_edges)\n bin_width=bin_edges[1]-bin_edges[0]\n x_bin = (bin_edges[:-1]+bin_edges[1:])/2 # center of the bin\n\n mass_bin = rho * bin_width\n n_particles_bin = np.floor(mass_bin * Z )\n\n\n x_particles= []\n\n r = np.random.rand(int(n_particles_bin)) -.5 # n_particles_bin random numbers in [-.5,.5]\n x_particles = x_bin+ bin_width * r\n\n\n # we have slightly changed Z so we report back the new Z\n Z_new = x_particles.size / np.sum(mass_bin)\n\n\n return x_particles,Z_new\n\n\ndef annihilate_particles(tooth,cavity):\n \"\"\"Cancel particles in tooth with anti-particles in cavity.\n \n Finds the closest particle in tooth to each anti-particle in cavity\n and deletes that particle.\n \n Args:\n tooth: array of particle positions\n cavity: array of anti-particle positions\n \n Returns:\n array of remaining particles\n \"\"\"\n\n # not efficient\n for j in range(cavity.size):\n dist = np.abs(cavity[j]-tooth)\n ind_close = np.argmin(dist)\n tooth=np.delete(tooth,ind_close)\n\n return tooth\n\ndef Burgers_tooth_update(x_particles: np.ndarray, nu: float, tooth_width: float, dt: float, Z: float):\n \"\"\"Updates particles positions according to viscous Burgers.\n \n Args:\n x_particles: array of particle positions\n nu: viscosity\n tooth_width: width of the teeth\n dt: time step\n Z: resolution factor, i.e., number of particles per unit mass\n \"\"\"\n\n n_partciles = x_particles.size\n mass_tooth = n_partciles/Z \n rho_tooth = mass_tooth/tooth_width\n u_drift = rho_tooth/2\n \n jump = u_driftdt + np.random.randn(n_partciles)np.sqrt(2nudt)\n x_particles=x_particles + jump\n return x_particles\n\n\n\n\ndef compute_truth_Burgers(rho_0, nu: float = 0.05, t: float=0, \n ngrid = None):\n \"\"\"Computing the truth density of Burgers via Cole-Hopf transform.\n\n Be careful: sometimes lead to artificially irregular approximations.\n \n Args:\n rho_0: the density profile at t=0, either callable or array\n nu: viscosity\n t: time\n ngrid: size of the grid on [0,2pi], overwritten if rho_0 is array\n \n Returns:\n the grid and density profile\n \"\"\"\n \n if not callable(rho_0):\n density = rho_0\n ngrid=rho_0.shape[0]\n x = np.arange(0,1-.5/ngrid,1/ngrid) * 2* np.pi \n else:\n x = np.arange(0,1-.5/ngrid,1/ngrid) * 2* np.pi \n density = rho_0(x)\n\n \n\n cumdensity = np.cumsum(density) * 2 * np.pi / ngrid\n total_mass = cumdensity[-1]\n r = np.exp( total_mass/4/np.pi/nu * x - cumdensity / 2 / nu )\n a_k = fftpack.fft(r)\n\n N = a_k.size\n k = np.concatenate((np.arange(0,N/2),np.arange(-N/2,0)))\n f = np.exp(nu * (1jk-total_mass/4/np.pi/nu)2 t) * a_k\n r = fftpack.ifft(f)\n rx = fftpack.ifft(1jkf)\n rho = total_mass / 2 / np.pi - 2 * nu * np.real(rx / r)\n\n return x,rho\n\n\n\n\n\n\n\n\ndef quick_run(rho0: callable, N = 32, Z = 1000, nu = .05, alpha = .1, dt = .002, Nt = 1000):\n \"\"\"Runing the Burgers example.\n \n This is to be used for quick data generation without the need to save the solver.\n\n Args:\n rho: the initial density profile on [0,2pi)\n N: no. of teeth\n Z: resolution factor, i.e., no. of particles representing each unit of mass\n nu: viscosity\n alpha: fraction of space covered by teeth\n dt: time step\n Nt: number of time steps\n\n Returns:\n the grid of tooth (x of center), the density evaluated at the end of simulation\n\n \"\"\"\n\n\n tsys=BurgersGapToothSystem(alpha=alpha,N=N,nu=nu)\n tsys.initialize(Z,rho0=rho0)\n print('no. of particles:'+str(tsys.particle_count))\n\n tsys.run_gap_tooth(dt=dt,Nt=Nt)\n tsys.compute_density()\n return tsys.tooth_center_x,tsys.rho\n\ndef parameter_dependence():\n \"\"\"Plots dependence of solution based on no. of teeth and resolution factor.\n\n This is fig. 2 in the paper.\n \"\"\"\n\n rho0=lambda x: 1-np.sin(x)/2\n\n # vary the grid size \n Z0 = 100000\n Ns = [16,32,64]\n\n grid_N,sol_N=[],[]\n\n\n # for N in Ns:\n # grid,sol=quick_run(rho0,N=N,Z=Z0)\n # grid_N.append(grid),sol_N.append(sol)\n\n\n # vary the resolution factors \n Zs = [1000,10000,100000]\n N0 = 128\n\n grid_Z,sol_Z=[],[]\n\n\n for Z in Zs:\n grid,sol=quick_run(rho0,N=N0,Z=Z)\n grid_Z.append(grid),sol_Z.append(sol)\n\n # # # da truth\n x,rho=compute_truth_Burgers(rho0,t=2)\n x[x>np.pi]=x[x>np.pi]-2np.pi\n ix = np.argsort(x)\n x_truth,rho_truth = x[ix],rho[ix]\n\n np.savez('../data/Burgers_benchmark13',\\\n grid_N=grid_N, sol_N=sol_N, \n Ns=Ns,grid_truth=x_truth,sol_truth=rho_truth,\\\n grid_Z=grid_Z, sol_Z=sol_Z, Zs=Zs, N0=N0,Z0=Z0)\n\n\n\nif __name__ == \"__main__\":\n ttin = timeit.default_timer()\n parameter_dependence()\n print('whole run took {} seconds'.format(timeit.default_timer() - ttin))\n\n\n" }, { "alpha_fraction": 0.5906279683113098, "alphanum_fraction": 0.6049001216888428, "avg_line_length": 32.624000549316406, "blob_id": "5da167ed7a7c6a2c32903388d3cc1229982ea720", "content_id": "1b057db1dc2a7e44996b9441da77ab471829c6e5", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4204, "license_type": "permissive", "max_line_length": 103, "num_lines": 125, "path": "/thehood/unbalanced_transport.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\" Unbalanced optimal transport.\n\nTwo methods for calculation of optimal transport distance \nbetween two distributions with uneqaul masses.\n\nHassan Arbabi, July 2020, [email protected]\n\"\"\"\n\n#TODO: implement the UW1 explicit difference from Osher paper\n\n\nimport numpy as np\nimport scipy.linalg as linalg\n\n\n\n\ndef sinkhorn_log(mu,nu,c,epsilon, \n options={'niter':1000, 'tau':-0.5, 'rho':np.inf}):\n \"\"\"Computing stabilized sinkhorn over log domain with acceleration.\n\n Adapted from \"SCALING ALGORITHMS FOR UNBALANCED OPTIMAL TRANSPORT PROBLEMS\" by Chizat et al, 2017\n and the MATLAB code at 'https://github.com/gpeyre/2017-MCOM-unbalanced-ot' by G. Peyre.\n\n Args:\n mu: a histogram vector (marginal)\n nu: a histogram vector (marginal)\n c: cost matrix\n epsilon: the entropic regularization strength\n options: \n niter: max number of the iteration \n tau: is avering step and negative values usually lead to acceleration\n rho: the strngth of mass variation constraint. np.inf results in classical transport\n but finite values allow mass variation\n \n Returns:\n gamma: the coupling distribution\n Wprimal: the primal transport distance of iterations\n Wdual: its dual\n err: the difference with previous iterate or marginal violation\n Wdistance: the Wasserstein distance at the end (with the entropy term removed)\n \"\"\"\n\n for key,val in zip(['tau','rho','niter'],[-.5,np.inf,500]):\n options.setdefault(key, val)\n rho,tau,niter = options['rho'],options['tau'],options['niter']\n\n lam = rho/(rho+epsilon)\n if rho==np.inf:\n lam=1.0\n\n H1 = np.ones_like(mu)\n H2 = np.ones_like(nu)\n\n ave = lambda tau, u, u1: tauu+(1-tau)u1\n\n lse = lambda A: np.log(np.sum(np.exp(A),axis=1))\n M = lambda u,v:(-c+u[:,np.newaxis]@H2[np.newaxis,:] + H1[:,np.newaxis]@v[np.newaxis,:] )/epsilon\n\n # kullback divergence\n H = lambda p: -np.sum( p.flatten()(np.log(p.flatten()+1e-20)-1) )\n KL = lambda h,p: np.sum( h.flatten()* np.log( h.flatten()/p.flatten() ) - h.flatten()+p.flatten())\n KLd = lambda u,p: np.sum( p.flatten()( np.exp(-u.flatten()) -1) )\n dotp = lambda x,y: np.sum(xy); \n\n err,Wprimal,Wdual = [],[],[]\n u = np.zeros_like(mu)\n v = np.zeros_like(nu)\n\n for _ in range(niter):\n u1=u\n u = ave(tau, u, lamepsilonnp.log(mu) - lamepsilonlse( M(u,v) ) + lamu )\n v = ave(tau, v, lamepsilonnp.log(nu) - lamepsilonlse( M(u,v).T) + lamv )\n gamma = np.exp(M(u,v))\n\n if rho==np.inf: \n Wprimal.append(dotp(c,gamma) - epsilonH(gamma))\n Wdual.append( dotp(u,mu) + dotp(v,nu) - epsilonnp.sum(gamma) )\n err.append( np.linalg.norm( np.sum(gamma,axis=1)-mu ) )\n else:\n Wprimal.append( dotp(c,gamma) - epsilonH(gamma) \\\n + rhoKL(np.sum(gamma,axis=1),mu) \\\n + rhoKL(np.sum(gamma,axis=0),nu) )\n\n Wdual.append( -rhoKLd(u/rho,mu) - rhoKLd(v/rho,nu) \\\n - epsilonnp.sum( gamma))\n err.append(np.linalg.norm(u-u1, ord=1) )\n \n WDistance = Wprimal[-1]+epsilonH(gamma)\n\n return gamma,Wprimal,Wdual,err,WDistance\n\n\ndef unbalanced_Wasserstein_L1(mu,nu,x = None,alpha = 1):\n \"\"\"\n Compute the unnormalized L^1 Wasserstein metric for two distributions on the same domain.\n\n Adapted from \"Unnormalized optimal transport\" by Gangbo et al, 2019. See refs within.\n\n Args:\n mu,nu: vector of histogram values on the same (uniformly-sized) bins\n x : the grid for histogram bin centers\n alpha: the inverse of the weight of mass difference in the objective function\n larger alpha means more mass generation/destruction is allowed\n \n Returns:\n the unnormalized L^1 Wasserstein metric between mu and nu\n\n \"\"\"\n\n N = mu.size\n \n if x is None:\n x = np.linspace(0,1,N)\n\n dx = x[1]-x[0]\n\n mass_diff = np.sum((mu-nu) dx) \n\n Integrand = np.abs( np.cumsum(mu-nu) - x * mass_diff )\n\n\n UW1 = np.sum(Integrand * dx) + (1/alpha)* np.abs( mass_diff )\n\n return UW1\n\n" }, { "alpha_fraction": 0.5042237639427185, "alphanum_fraction": 0.5526255965232849, "avg_line_length": 20.786069869995117, "blob_id": "cbc2464303434b71f848db1fce75e084f00a43ca", "content_id": "781f63b34d1540026bff84c1f3fc66b456a9cc8d", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8760, "license_type": "permissive", "max_line_length": 77, "num_lines": 402, "path": "/thehood/CFDroutines.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\"Some routines for solving 1D PDES\"\"\"\n\n\nimport numpy as np \nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport timeit\nimport scipy.io as sio\nfrom scipy.integrate import solve_ivp\n\n\n\n\n\ndef test_flux_WENO():\n x = np.linspace(0,2np.pi,num=200,endpoint=False)\n h = x[1]-x[0]\n b = 1.5\n u = np.sin(bx)\n\n\n dfdx_truth=b* np.sin(bx)np.cos(bx)\n dfdx_WENO=WENO_FVM_convection(u,h)\n\n plt.figure(figsize=[6.5,4])\n plt.plot(x,dfdx_truth,'x')\n plt.plot(x,dfdx_WENO,'--')\n # plt.ylim(-1,1.1)\n # plt.xlim(-.01,.05)\n\n plt.savefig('./figs/WENO_flux.png')\n\n\ndef WENO_FVM_convection(V,h,flux='Gudonov'):\n \"\"\" Finite-volume approximation of momentum flux using WENO.\n\n\n Args:\n V: solution values on 1d periodic grid\n h: (uniform) grid size\n\n Returns:\n array of same size as V holding values of d(v2)/dx\n \"\"\"\n\n # periodify\n V = np.concatenate((V[-3:],V,V[0:3]),axis=0)\n\n # compute v+ and v- at the right boundaries of each cell \n v1 = V[0:-5]\n v2 = V[1:-4]\n v3 = V[2:-3]\n v4 = V[3:-2]\n v5 = V[4:-1]\n v6 = V[5:]\n\n v_minus = WENO_reconstruct(v1,v2,v3,v4,v5)\n v_plus = WENO_reconstruct(v6,v5,v4,v3,v2)\n\n\n if flux is 'Gudonov':\n F = Gudonov_v2(v_minus,v_plus)\n else:\n raise NotImplementedError('flux method not known or implemented yet')\n\n\n # flux balance\n\n dFdx = (1/h)(F[1:]-F[:-1])\n\n\n\n return dFdx\n\n\ndef Gudonov_v2(v_minus,v_plus):\n \"\"\"Computing Gudonov method for flux f(v)=v^2/2.\"\"\"\n\n\n F=np.zeros(v_minus.shape)\n\n # when v- is smaller than v+\n leftD = np.where(v_minus<=v_plus)\n\n F[leftD]=np.minimum(v_minus[leftD]2,v_plus[leftD]2)\n\n x_cross=np.where(v_minusv_plus<0)\n F[x_cross]=0\n\n\n\n\n # when v- is larger than v+\n rightD= np.where(v_minus>v_plus) # optimize this later\n\n F[rightD]=np.maximum(v_minus[rightD]2,v_plus[rightD]2)\n\n return F/2\n\ndef WENO_reconstruct(v1,v2,v3,v4,v5):\n \"\"\"Reconstructing value of field v at boundary nodes.\n\n\n Args:\n v1: array holding values at node i-2\n v2: array holding values at node i-1\n v3: array holding values at node i\n v4: array holding values at node i+1\n v5: array holding values at node i+2\n\n Returns:\n array of values at (boundary) node i+1/2\n \"\"\"\n\n\n\n # combining three stenccils\n phi1 = v1/3 - 7v2/6 + 11v3/6\n phi2 =-v2/6 + 5v3/6 + v4/3\n phi3 = v3/3 + 5v4/6 - v5/6\n\n # measures of smoothness for each stencil (larger the S --> less smooth)\n S1 = (13/12)(v1-2v2+v3)2+(1/4)(v1-4v2+3v3)*2\n S2 = (13/12)(v2-2v3+v4)2+(1/4)(v2-v4)*2\n S3 = (13/12)(v3-2v4+v5)2+(1/4)(3v3-4v4+v5)2\n\n # deciding the weights at each point\n V = np.stack((v1,v2,v3,v4,v5),axis=1)\n EPS = np.amax(V,axis=1)2 * 1e-6 + 1e-99\n\n # non-normalized weights\n a1 = 0.1/ (S1+EPS)2\n a2 = 0.6/ (S2+EPS)2\n a3 = 0.3/ (S3+EPS)*2\n\n # combine the stencils\n v = (a1phi1 + a2phi2 + a3phi3)/(a1+a2+a3)\n\n \n return v\n\n\n\n\n\ndef linear_adv_WENO(C,h,U):\n \"\"\"Computing the advection term via WENO.\n \n Args:\n C: the field to be advected\n h: grid spacing\n U: velocity field\n \n Returns:\n np.array containing U* dC/dx\n \n (use this for advection by a given velocity field)\n see Osher & Fedkiw, Level Set Methods, 2003\n \n \"\"\"\n\n N = C.shape[0]\n adv_term=np.zeros(N)\n\n \n if not isinstance(U, (np.ndarray)):\n U = U * np.ones(N)\n \n\n # index of points with left and right updwind direction\n LeftD =np.where(U>=0)\n RightD=np.where(U<0)\n\n # periodify\n C = np.concatenate((C[-3:],C,C[0:3]),axis=0)\n\n\n\n # divided difference at all points\n D = (C[1:]-C[:-1])/h\n\n v1 = D[0:-5]\n v2 = D[1:-4]\n v3 = D[2:-3]\n v4 = D[3:-2]\n v5 = D[4:-1]\n v6 = D[5:]\n\n\n\n # now reconstruct the actual derivative using WENO\n vx_left = WENO_reconstruct(v1[LeftD],v2[LeftD],v3[LeftD],\\\n v4[LeftD],v5[LeftD])\n vx_right = WENO_reconstruct(v6[RightD],v5[RightD],v4[RightD],\\\n v3[RightD],v2[RightD])\n\n adv_term[LeftD] =vx_left * U[LeftD]\n adv_term[RightD]=vx_right* U[RightD]\n\n\n return adv_term\n\ndef diffusion_term(V,h,nu=1):\n # central finite-difference to approximate the diffusion term\n # periodify\n V = np.concatenate((V[-1:],V,V[0:1]),axis=0)\n D2V = (-2V[1:-1] + V[0:-2] + V[2:]) / (h2)\n return D2Vnu\n\ndef test_diffusion():\n x = np.linspace(0,2np.pi,num=200,endpoint=False)\n h = x[1]-x[0]\n\n\n c0 = np.sin(x) + np.cos(x)\n\n\n\n diff_cd= diffusion_term(c0,h,nu=1)\n diff_tru = -np.sin(x) - np.cos(x)\n\n\n plt.figure(figsize=[6.5,4])\n plt.plot(x,diff_cd,'x')\n plt.plot(x,diff_tru,'--')\n # plt.ylim(-1,1.1)\n # plt.xlim(-.01,.05)\n\n plt.savefig('./figs/diff_test.png')\n\n\ndef test_WENOderivative():\n x = np.linspace(0,2np.pi,num=200,endpoint=False)\n h = x[1]-x[0]\n c0 = np.sin(x) + 2.1np.cos(2x)\n\n U0 = 1\n # U = U0 np.ones(x.shape) # constant velocity\n\n adv_WENO = linear_adv_WENO(c0,h,U0)\n adv_tru = (np.cos(x)-4.2np.sin(2x)) U0\n\n plt.figure(figsize=[6.5,4])\n plt.plot(x,adv_WENO,'x')\n plt.plot(x,adv_tru,'--')\n # plt.ylim(-1,1.1)\n # plt.xlim(-.01,.05)\n\n plt.savefig('./figs/WENO_der_test.png')\n\n\n\ndef solve_ivp_TVD(F,tspan,y0,tval,max_step=.01):\n \"\"\"Solving initial value problem with a total variation method.\n \n Args:\n F: callable that takes (t,y) and returns ydot\n tsapn: time span of integration\n u0: initial condition\n tval: values of t at which the solution is recorded\n\n \n Returns:\n Y: the array ntn where n is the dimension of y0\n \"\"\"\n nt = len(tval)\n Y = np.zeros((nt,y0.shape[0]))\n\n assert tspan[0] <= tval[0], 'tspan should contain tvals'\n assert tspan[-1] >= tval[-1], 'tspan should contain tvals'\n\n tstart = tspan[0]\n\n for j in range(nt):\n # re-compute the dt\n interval = tval[j]-tstart\n nstep = int(interval//max_step)\n\n if nstep !=0:\n dt = interval/nstep\n \n for k in range(nstep):\n y0 = TVD_RK3(F,tstart+kdt,y0,dt)\n\n Y[j]=y0\n tstart=tval[j]\n\n return Y\n \n\n\n\ndef TVD_RK3(F,t,u,dt):\n \"\"\"Total variation Runge-Kutta for time marching hyperbolic PDEs.\n \n Args:\n F: callable that takes (t,u) and returns udot\n t: time\n dt: time step size\n u: solution at time t0\n\n Returns:\n u_next: solution at time t0+dt\n \n \"\"\"\n # Total Variation Diminishing (TVD) Runge-Kutta of order 3\n # for solving udot=F(t,u)\n # see Shu 2009, \n # or Osher-Fedkiw 2003 for more detail\n\n u1 = u + dtF(t,u)\n u2 = (3/4)u + (1/4)u1 + (1/4)dtF(t+dt,u1)\n u_next = (1/3)u + (2/3) u2 + (2/3)dtF(t+dt/2,u2)\n\n return u_next\n\n\ndef linear_advection_test():\n # testing WENO in linear advection\n\n x = np.linspace(0,2np.pi,num=100,endpoint=False)\n h = x[1]-x[0]\n\n t = np.linspace(0,10,301)\n\n def WaveS(x):\n s=np.zeros(x.shape)\n s[x>2]=1\n s[x>3]=.5\n return s\n\n c0 = WaveS(x)\n U0 = .1\n U = U0 np.ones(x.shape) # constant velocity\n\n\n # ode RHS\n rhs = lambda t,y: -1.0linear_adv_WENO(y,h,V0=U)\n\n\n Sol = solve_ivp(rhs,(np.amin(t),np.amax(t)),c0,method='RK45',\\\n t_eval=t,max_step=.1 )\n\n\n V = np.transpose(Sol.y)\n dt = t[1]-t[0]\n\n plt.figure(figsize=[6.5,4])\n plt.subplot(2,2,1)\n j = 0\n Vtruth = WaveS(x-U0jdt)\n plt.plot(x,V[j,:])\n plt.plot(x,Vtruth,'--')\n plt.xlabel('$X$'),plt.ylabel('$V$')\n plt.title('initial condition')\n plt.ylim(-1,1)\n\n\n plt.subplot(2,2,2)\n j = 100\n Vtruth = WaveS(x-U0jdt)\n plt.plot(x,V[j,:])\n plt.plot(x,Vtruth,'--')\n plt.xlabel('$X$'),plt.ylabel('$V$')\n plt.title('t='+str(t[j]))\n plt.ylim(-1,1)\n\n plt.subplot(2,2,3)\n j = 200\n Vtruth = WaveS(x-U0jdt)\n plt.plot(x,V[j,:])\n plt.plot(x,Vtruth,'--')\n plt.xlabel('$X$'),plt.ylabel('$V$')\n plt.title('t='+str(t[j]))\n plt.ylim(-1,1)\n\n\n plt.subplot(2,2,4)\n j = 300\n Vtruth = WaveS(x-U0jdt)\n plt.plot(x,V[j,:])\n plt.plot(x,Vtruth,'--')\n plt.xlabel('$X$'),plt.ylabel('$V$')\n plt.title('t='+str(t[j]))\n plt.ylim(-1,1)\n\n plt.subplots_adjust(wspace=.4,hspace=.6)\n plt.savefig('./figs/WENO_lin_avd.png')\n\nif __name__=='__main__':\n print('Jesus take the wheel')\n\n # V = np.arange(0,10)\n # V = np.concatenate((V[-3:],V,V[:3]),axis=0)\n # print(V)\n # linear_advection_test()\n # test_WENOderivative()\n # test_diffusion()\n # test_flux_WENO()\n # test_Burgers_WENO()\n test_WENOderivative()\n\n\n" } ]	8
mingtak/funlog.portlet.profile	https://github.com/mingtak/funlog.portlet.profile	daa7b5996ba1f05f163a6aa35138050ff6586ef5	c9d5a268c7bfa6e6622386e70bb859c096d2a632	e29861464f3e8079e4f2ccb93e14205e43f1902e	refs/heads/master	2021-01-21T22:29:09.425776	2015-03-23T14:37:08	2015-03-23T14:37:08	32,737,509	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7727272510528564, "alphanum_fraction": 0.7878788113594055, "avg_line_length": 32, "blob_id": "bd9bf6ee470b1b416f585f11faa9d3dfb50d4e0e", "content_id": "7daa3993dfb84eb7e5456ed127de242783f5b63b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 198, "license_type": "no_license", "max_line_length": 64, "num_lines": 6, "path": "/funlog/portlet/profile/__init__.py", "repo_name": "mingtak/funlog.portlet.profile", "src_encoding": "UTF-8", "text": "from zope.i18nmessageid import MessageFactory\nProfileMessageFactory = MessageFactory('funlog.portlet.profile')\n\n\ndef initialize(context):\n \"\"\"Initializer called when used as a Zope 2 product.\"\"\"\n" } ]	1
mr-billyu/git_notes	https://github.com/mr-billyu/git_notes	6751f6b8f9f07d6287b8cf2745c595b01179cf2c	8974fd7a6bc874b74b175116f327cfd742936d5f	8bc932e2e78ede4d38f09d1bf58c977b41005ae4	refs/heads/master	2020-04-18T15:56:28.782148	2019-05-03T19:13:07	2019-05-03T19:13:07	167,622,649	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6641981601715088, "alphanum_fraction": 0.6682839393615723, "avg_line_length": 18.191177368164062, "blob_id": "d8b1b5eeb25d0d0e4101677ffe2573e949b2efb0", "content_id": "ea9b59d9c256ddd92148d68dfcf8326fc9e14469", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 3916, "license_type": "no_license", "max_line_length": 187, "num_lines": 204, "path": "/single_user_git.md", "repo_name": "mr-billyu/git_notes", "src_encoding": "UTF-8", "text": "#Git Notes\n\n##Local\n\n###Create repository\n\nTo create a git repository, switch to the parent directory and run `git init project-name`. The following example creates a git_notes repository under the parent directory ~/Applications.\n\n cd ~/Applications\n git init git_notes\n\nTo create a git repository for an existing directory, switch to the directory and run `git init`. This will create the `.git` file.\n\n cd ~/Applications/Project_x\n git init\n\n###Display status of repository\n\n git status\n\n###Display files tracked by git\n\n git ls-files\n\n###Ignore files\n\nInstruct git to ignore a file, enter the file name into `.gitignore`. The .gitignore file should be committed.\n\n###Stage files to be commited\n\n git add file\n\n###Unstage file\n\nChange file back to untracted status.\n\n git reset HEAD file\n\n###Get fresh copy of file\n\nChange file back to last commit.\n\n git checkout file\n\n###Add file to git\n\n git add file\n\n###Commit files\n\nIt is wise to run `git status` before and after a commit.\n\n git commit \n git commit file\n\n###Display commit messages for current branch\n\n git log\n git log --oneline\n\n###Display commit messages for file\n\n git log file\n git log --oneline file\n\n###Display branches\n\n git branch\n\n###Create branch\n\n git branch develop\n\n###Change branch\n\n git commit -a # Save work in current branch\n git checkout branch-name\n\n###Delete branch\n\n git branch -d branch-name\n\n###Merge develop branch to master\n\n git commit -a # Save work in develop branch\n git checkout master \n git merge develop\n\n###Update branch with changes from master\n\n git checkout branch\n git commit -a # Save work in branch\n git merge master \n\n###Show file differences\n\nNote: `d` is an alias for `difftool`.\n\nDisplay differences between working directory and index.\n\n git d filename\n\nDisplay differences between index and last commit.\n\n git d --cached filename\n\nDisplay differences between working directory and last commit.\n\n git d HEAD filename\n\nDisplay differences between two branches.\n\n git d master develop filename\n\n###Display specified version of file\n\n git show SHA:file\n\n###Undo merge\n\n git reset --merge ORIG_HEAD\n\n###Rename file or directory\n\nChange the file name and prepare it for commit.\n\n git mv 'original file/dir' 'renamed file/dir'\n\n###Remove file\n\nRemove the file from the working directory and stages the deletion.\n\n git rm file\n\n###Git tags\n\nList all tags.\n\n git tag\n\nAdd tag to current commit.\n\n git tag -a v1.0 -m \"v1.0 of file\|project\|etc\"\n\nAdd tag to SHA commit.\n\n git tag -a v1.0 -m \"v1.0 of file\|project\|etc\" SHA\n\nDisplays detail about tag v1.0.\n \n git show v1.0\n\nDelete tag v1.0.\n\n git tag -d v1.0\n\n###List git configuration\n\n git config -l\n\n###Configure difftool\n\n git config --global diff.tool gvimdiff\n git config --global difftool.prompt false\n git config --global alias.d difftool\n\n##Remote\n\nRemote can be another server, cloud server, GitHub, etc\n\n###View existing remotes\n\n git remote -v\n\n###Rename remote\n\nRename remote origin to server.\n\n git remote rename origin server\n\n###GitHub\n\nSign on GitHub and create repository for git_notes.\n\n####Associate local repository with GitHub repository.\n\n git remote add server https://github.com/mr-billyu/git_notes.git\n\n####Push local branches to GitHub repository.\n\n git push server master\n git push server develop\n\n####Clone a repository\n\nChange to the directory that will be the parent of the clone. ie. ~/Applications\n\n git clone url\n\nThe clone directory will have been created under the parent directory. ie. ~/Applications/clone_dir\n\n##Git Reference\n\n<https://git-scm.com/docs/>\n\n" }, { "alpha_fraction": 0.7422680258750916, "alphanum_fraction": 0.75, "avg_line_length": 42, "blob_id": "b4d6558b6c4096246edc2013903530756a522957", "content_id": "41f0c950758459d2e0e63142f5755cc1a407a7ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 388, "license_type": "no_license", "max_line_length": 199, "num_lines": 9, "path": "/build_single_user_git.py", "repo_name": "mr-billyu/git_notes", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python3\n\nimport subprocess\n\n# Create HTML with Code Highlighting and Sidebar\ncmd = 'pandoc -o /home/pi/Applications/git_notes/single_user_git.html single_user_git.md -s --self-contained --toc --toc-depth=4 -c sidebar.css --template=/home/pi/Applications/Markdown/toc.template'\ncmd = cmd.split()\nresults = subprocess.check_output(cmd)\nprint(str(results, encoding='UTF-8'))\n\n" } ]	2
ahollyer/python-exercises	https://github.com/ahollyer/python-exercises	913888de761cd0282707e5a1a2cc9cd11cbac105	6b97276174dd3f3d9cb5a03608740421d51fbebd	c021a68c1d0a977d0ad8deff0e0d0ab7aaaea105	refs/heads/master	2021-01-19T21:18:51.458363	2017-06-03T19:00:11	2017-06-03T19:00:11	88,637,227	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6459096670150757, "alphanum_fraction": 0.6581196784973145, "avg_line_length": 39.95000076293945, "blob_id": "995a2cb5ed4d4840460d32d5113c93f7787e9faa", "content_id": "a39f66736f77cfb95f2dc1cdc2cde4f049c691a6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 819, "license_type": "no_license", "max_line_length": 141, "num_lines": 20, "path": "/degree_conversion.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# degree_conversion.py\n# by Aspen\n# Asks for degrees and units, converts between Fahrenheit and Celsius, and prints the converted temperature\n\nprint(\"*\\nINSTRUCTIONS: This program converts between Celsius and Fahrenheit. First, enter your units, then enter the temperature.\\n\\n\")\n\ndef convert():\n input_units = input(\"Starting units? Please type C or F, then press Enter: \").upper()\n input_temp = float(input(\"Please type the temperature (ex. 32), then press Enter: \"))\n\n if input_units == \"C\":\n converted_units = \"F\"\n converted_temp = input_temp (9/5) + 32\n elif input_units == \"F\":\n converted_units = \"C\"\n converted_temp = (input_temp - 32) * (5/9)\n print(\"Your converted temperature is:\", converted_temp, converted_units)\n\nif __name__ == \"__main__\":\n convert()\n" }, { "alpha_fraction": 0.6384040117263794, "alphanum_fraction": 0.6408977508544922, "avg_line_length": 27.64285659790039, "blob_id": "a3aabf4b6d0b7dd93f538d69731557f0a9d6b07e", "content_id": "502c701036d0b987c9aebc6d4e2922b3f5c346a2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 401, "license_type": "no_license", "max_line_length": 87, "num_lines": 14, "path": "/banner.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# banner.py\n# By Aspen\n# Prints a string with a box around it. The box stretches to fit the size of the string\n\nprint(\"\\n*\\nINSTRUCTIONS: Enter text to create a banner with that message\\n\\n\")\n\nmessage = input(\"What do you want your banner to say? \")\n\ndef print_banner(str):\n width = len(str) + 4\n print(\"\" * width)\n print(\"* \" + str + \" \")\n print(\"\" * width)\nprint_banner(message)\n" }, { "alpha_fraction": 0.5126582384109497, "alphanum_fraction": 0.577531635761261, "avg_line_length": 22.407407760620117, "blob_id": "a546c5fbb21e43049f8883c65dc1fb21ea80fb04", "content_id": "86f12440423b2e20a864b3633de2213c77f4c9a5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 632, "license_type": "no_license", "max_line_length": 95, "num_lines": 27, "path": "/turtle_graphics/night_sky.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# night_sky.py\n# By Aspen\n# Draws a random night sky full of stars\n\nfrom turtle import \nimport random\nimport shapes\n\ndef draw_sky():\n bgcolor('#001220')\n pretty_colors = ['#efe9f4', '#fff', '#e05cb2', '#5fbff9', '#1cefff', '#bd5ce0', '#9368ff' ]\n\n for i in range(60):\n speed(10)\n penup()\n x = random.randint(-250, 250)\n setx(x)\n y = random.randint(-250, 250)\n sety(y)\n pendown()\n star_color = pretty_colors[random.randint(0, len(pretty_colors)-1)]\n star_size = random.randint(5, 20)\n shapes.draw_star(star_size, star_color)\n\n mainloop()\n\ndraw_sky()\n" }, { "alpha_fraction": 0.5832321047782898, "alphanum_fraction": 0.589307427406311, "avg_line_length": 23.205883026123047, "blob_id": "c012eb0c593d988068e221f661da3518602531e3", "content_id": "7dcfbf186cb10c1ab23255e4f1db3f0a429418f3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 823, "license_type": "no_license", "max_line_length": 68, "num_lines": 34, "path": "/file-io-exercises/histograms.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# histograms.py\n# By Aspen\n# histogram module for ex_3.py\n\nimport string\n\ndef letter_histogram (word):\n histogram = {}\n for char in word:\n if char in histogram:\n histogram[char] += 1\n else:\n histogram[char] = 1\n return histogram\n\n\ndef word_histogram(text):\n stripped_text = text.translate(string.punctuation)\n print(stripped_text)\n histogram = {}\n for word in stripped_text.lower().split():\n if word in histogram:\n histogram[word] += 1\n else:\n histogram[word] = 1\n # To print histogram\n # for key, value in histogram.items():\n # print(key + \":\", value)\n return histogram\n\nif __name__ == '__main__':\n ans = input(\"Feed me a string, and I'll count the characters: \")\n ans = letter_histogram(ans)\n print(ans)\n" }, { "alpha_fraction": 0.510869562625885, "alphanum_fraction": 0.510869562625885, "avg_line_length": 25.285715103149414, "blob_id": "581152d16c5d2f6bc429e8734263525c6ffe2b3d", "content_id": "e18ddea7cc2d5193f900b334532bd3915db7c28f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 368, "license_type": "no_license", "max_line_length": 70, "num_lines": 14, "path": "/function_exercises/ex8_ex9.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# Asks the user to play again\n\ndef play_again():\n while True:\n answer = input(\"Do you want to play again? Y or N? \").upper()\n if answer == \"Y\":\n return True\n elif answer == \"N\":\n return False\n else:\n print(\"\\nThat is not a valid answer. Please type y or n.\")\n\nif __name__ == \"__main__\":\n play_again()\n" }, { "alpha_fraction": 0.6332518458366394, "alphanum_fraction": 0.6332518458366394, "avg_line_length": 30.461538314819336, "blob_id": "fc295b34567190c4ef4473178ff98dd50078fee8", "content_id": "cd9e07193d5e2395e5eb244ab0acb1896b769d12", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 818, "license_type": "no_license", "max_line_length": 230, "num_lines": 26, "path": "/madlib2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# madlib.py\n# by Aspen Hollyer\n# Asks for input as strings, generates and prints a madlib string\n\n# Get words from user\ndef get_input():\n words = {}\n words['name'] = input(\"Give me a name: \")\n words['subject'] = input(\"Give me a school subject: \")\n words['pet'] = input(\"Give me an animal: \")\n words['power'] = input(\"Give me a superpower: \")\n words['food'] = input(\"Give me a food: \")\n words['num'] = input(\"Give me a number: \")\n return words\n\n# Print madlib with user's words\ndef madlib(dict_of_words):\n print(\"%(name)s's %(pet)s ate some magical %(food)s that granted him the power of %(power)s. It also made him a genius! He went on to major in %(subject)s and win a nobel prize at the age of %(num)s. The end.\" % dict_of_words)\n\n\ndef main():\n\n words = get_input()\n madlib(words)\n\nmain()\n" }, { "alpha_fraction": 0.6690763235092163, "alphanum_fraction": 0.6730923652648926, "avg_line_length": 37.90625, "blob_id": "fc6f669f9cc87804e0af54d1108c5ae3a6200b6f", "content_id": "bcaf5b2bf3b7c0e174ceeceeb0a0e995f43be67c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1249, "license_type": "no_license", "max_line_length": 72, "num_lines": 32, "path": "/dictionary_exercises/word_histogram.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# word_histogram.py\n# By Aspen\n\n# INSTRUCTIONS: Write a word histogram function that accepts\n# a paragraph of text and returns a dictionary containing the\n# tally of how many times each word was used in the text.\n\nimport string\n\ndef word_histogram(text):\n stripped_text = text.translate(string.punctuation)\n histogram = {}\n for word in stripped_text.lower().split():\n if word in histogram:\n histogram[word] += 1\n else:\n histogram[word] = 1\n # To print histogram\n #for key, value in histogram.items():\n # print(key + \":\", value)\n return histogram\n\nif __name__ == '__main__':\n word_histogram(\"Congress returns Tuesday from its spring recess,\\\n facing yet another down-to-the-wire spate of deal-making — and a\\\n White House anxious to claim its first major legislative win. On\\\n Friday night, the funding measure lawmakers approved last year\\\n to keep the federal government running will expire. The timing\\\n leaves members of the House and Senate just four days to reach\\\n a new agreement to fund the government, or risk a partial\\\n shutdown of federal agencies on Saturday — the 100th day of\\\n Donald Trump's presidency.\")\n" }, { "alpha_fraction": 0.5090909004211426, "alphanum_fraction": 0.6340909004211426, "avg_line_length": 26.5, "blob_id": "8def96385244765c863951d521c75927b580b4d0", "content_id": "67b3db556e054449b7c8c3f59372fbc98338c711", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2200, "license_type": "no_license", "max_line_length": 70, "num_lines": 80, "path": "/turtle_graphics/dc_logo.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# dc_logo.py\n# By Aspen\n# Draws the DigitalCrafts logo\n\nfrom turtle import \nimport random\nimport shapes\n\n\ndef draw_logo():\n hideturtle()\n penup()\n speed(9)\n setpos(-250, 100)\n pendown()\n shapes.draw_trapezoid(100, pcolor='#7EC04B', fcolor='#7EC04B')\n shapes.draw_rhombus(50, pcolor='#5D8842', fcolor='#5D8842')\n shapes.draw_triangle_down(50, pcolor='#7EC04B', fcolor='#7EC04B')\n right(60)\n forward(50)\n right(60)\n shapes.draw_triangle_up(50, pcolor='#2A94CE', fcolor='#2A94CE')\n forward(50)\n shapes.draw_rhombus(50, pcolor='#1771B5', fcolor='#1771B5')\n right(60)\n shapes.draw_trapezoid(100, pcolor='#2A94CE', fcolor='#2A94CE')\n back(150)\n right(60)\n shapes.draw_triangle_down(50, pcolor='#1B67A1', fcolor='#1B67A1')\n forward(50)\n right(60)\n shapes.draw_triangle_down(50, pcolor='#467866', fcolor='#467866')\n forward(50)\n right(60)\n shapes.draw_triangle_down(50, pcolor='#1F84B1', fcolor='#1F84B1')\n forward(50)\n right(60)\n shapes.draw_triangle_down(40, pcolor='#278DC9', fcolor='#278DC9')\n penup()\n forward(60)\n right(120)\n pendown()\n shapes.draw_triangle_up(40, pcolor='#A6AAAB', fcolor='#A6AAAB')\n right(120)\n forward(10)\n right(120)\n shapes.draw_triangle_down(50, pcolor='#646B6F', fcolor='#646B6F')\n shapes.draw_triangle_up(50, pcolor='#C08258', fcolor='#C08258')\n right(300)\n shapes.draw_triangle_up(50, pcolor='#939597', fcolor='#939597')\n forward(50)\n right(120)\n shapes.draw_triangle_up(50, pcolor='#FBB231', fcolor='#FBB231')\n forward(50)\n right(120)\n back(50)\n shapes.draw_rhombus_mirror(50, pcolor='#F27830', fcolor='#F27830')\n right(120)\n forward(50)\n right(180)\n penup()\n back(100)\n right(60)\n forward(50)\n left(60)\n pendown()\n shapes.draw_trapezoid(100, pcolor='#F79E34', fcolor='#F79E34')\n right(60)\n forward(50)\n right(60)\n shapes.draw_triangle_up(50, pcolor='#BBBDBF', fcolor='#BBBDBF')\n forward(50)\n left(120)\n shapes.draw_rhombus_mirror(50, pcolor='#A6AAAB', fcolor='#A6AAAB')\n right(180)\n shapes.draw_trapezoid(100, pcolor='#BBBDBF', fcolor='#BBBDBF')\n\n mainloop()\n\ndraw_logo()\n" }, { "alpha_fraction": 0.5604395866394043, "alphanum_fraction": 0.5686812996864319, "avg_line_length": 30.65217399597168, "blob_id": "a2baa40117cb2024b492f477a27629855afeb71d", "content_id": "635a3f6d5f0eaaedafff1614b3868e446d367318", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 728, "license_type": "no_license", "max_line_length": 108, "num_lines": 23, "path": "/day_of_week.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# day_of_week.py\n# by Aspen\n# Accepts an integer 0-6 and prints the corresponding weekday as a string\n\ndef main():\n\n def day_of_week():\n days = [\"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\"]\n\n while True:\n try:\n num = int(input(\"Enter a number from 0-6: \"))\n print(\"You chose {num}. The corresponding weekday is {day}.\".format(num=num, day=days[num]))\n break\n except ValueError:\n print(\"Your input must be a number. Try again.\")\n except IndexError:\n print(\"The number you chose has no corresponding weekday. Please choose a number from 0-6.\")\n\n\n day_of_week()\n\nmain()\n" }, { "alpha_fraction": 0.6279342770576477, "alphanum_fraction": 0.6936619877815247, "avg_line_length": 25.625, "blob_id": "670383e47ebea4a161a2abc20243c51082eb1dde", "content_id": "68a7e43fb0eebdb631380c23d1145faa5e7ef463", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 852, "license_type": "no_license", "max_line_length": 68, "num_lines": 32, "path": "/dictionary_exercises/ex_1.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# ex_1.py\n# By Aspen\n# Modifies a simple dictionary\n\nphonebook_dict = {\n 'Alice': '703-493-1834',\n 'Bob': '857-384-1234',\n 'Elizabeth': '484-584-2923'\n}\n\nprint(\"\\nDictionary Exercises: Phonebook\")\nprint(\"phonebook_dict = \", phonebook_dict)\n\nprint(\"\\n1. Print Elizabeth's phone number:\")\nprint(\"Elizabeth's Number bracket:\", phonebook_dict['Elizabeth'])\nprint(\"Elizabeth's Number .get():\", phonebook_dict.get('Elizabeth'))\n\nprint(\"\\n2. Add an entry to the dictionary.\")\nphonebook_dict['Kareem'] = '968-345-2345'\nprint(phonebook_dict)\n\nprint(\"\\n3. Delete Alice's phone entry.\")\ndel phonebook_dict['Alice']\nprint(phonebook_dict)\n\nprint(\"\\n4. Change Bob's phone number.\")\nphonebook_dict['Bob'] = '968-345-2345'\nprint(phonebook_dict)\n\nprint(\"\\n5. Print all the phonebook entries.\")\nfor key, value in phonebook_dict.items():\n print(key + \": \" + value)\n" }, { "alpha_fraction": 0.6373239159584045, "alphanum_fraction": 0.6408450603485107, "avg_line_length": 26.047618865966797, "blob_id": "949b991c7dd7d48365b022df3af6f549e4ae2d85", "content_id": "85b2a8ca9bca768940ebfb494d88e8e1d55c394c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 568, "license_type": "no_license", "max_line_length": 68, "num_lines": 21, "path": "/dictionary_exercises/letter_histogram.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# letter_histogram.py\n# By Aspen\n\n# INSTRUCTIONS: Write a histogram function that takes a word as\n# input and returns a dictionary containing the tally of how\n# many times each letter of the alphabet was used in the word.\n\ndef letter_histogram (word):\n histogram = {}\n for char in word:\n if char in histogram:\n histogram[char] += 1\n else:\n histogram[char] = 1\n return histogram\n\n\nif __name__ == '__main__':\n ans = input(\"Feed me a string, and I'll count the characters: \")\n ans = letter_histogram(ans)\n print(ans)\n" }, { "alpha_fraction": 0.5442739129066467, "alphanum_fraction": 0.5602124929428101, "avg_line_length": 26.770492553710938, "blob_id": "851f4593e603538a658b5ab18db373510c1ec7ff", "content_id": "20641926ccd15ec3131944a9f335cc626ff689e1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1694, "license_type": "no_license", "max_line_length": 137, "num_lines": 61, "path": "/guess_the_number.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# guess_the_number.py\n# By Aspen\n# Generates a random number 0-100, asks for guesses, and prints whether the guess was too high or low. Accepts 5 guesses before win/lose.\n\nimport random\n\n# assign a secret number\ndef get_secret_num():\n secret_num = random.randint(1, 100)\n return secret_num\n\n# prompt the user to guess the secret number\ndef get_guess():\n while True:\n try:\n guess_num = int(input(\"What's the number? \"))\n if 0 < guess_num < 101:\n return guess_num\n else:\n print(\"Your number must be between 1 and 100. Try again.\")\n except ValueError:\n print(\"Your guess must be an integer (ex: 5). Try again.\")\n\n# compare user's guess to secret number\ndef compare(s, g):\n tries = 4\n win = False\n while not win and tries > 0:\n if g == s:\n print(\"You win!!\")\n win = True\n elif g > s:\n print(\"Nope, too high. Guesses left:\", tries)\n tries -= 1\n g = get_guess()\n elif g < s:\n print(\"Nope, too low. Guesses left:\", tries)\n tries -=1\n g = get_guess()\n print(\"You ran out of guesses! You lose.\")\n\n# ask user to play again\ndef play_again():\n play = input(\"Do you want to play again? Yes or no: \").lower()\n if play == 'yes':\n main()\n elif play == 'no':\n print(\"Okay, bye!\")\n else:\n print(\"That answer doesn't make sense.\")\n play_again()\n\ndef main():\n print(\"I'm thinking of a number between 1 and 100. You get 5 tries to guess the number!\\n\")\n\n snum = get_secret_num()\n gnum = get_guess()\n compare(snum, gnum)\n play_again()\n\nmain()\n" }, { "alpha_fraction": 0.6430155038833618, "alphanum_fraction": 0.6452327966690063, "avg_line_length": 27.1875, "blob_id": "961683e270c7f79a0d57e538c49b7540b775b0dd", "content_id": "94564aa55998050ad20176d2bd68673e027ffe44", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 451, "license_type": "no_license", "max_line_length": 75, "num_lines": 16, "path": "/file-io-exercises/ex_1.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# ex_1.py\n# By Aspen\n# INSTRUCTIONS: Write a program that prompts the user to enter a file name,\n# reads the contents of the file, and prints it to the screen.\n\ndef read_my_file():\n print(\"\\nThis program prints the contents of your file to the screen.\")\n my_file = input('Enter the name of the file to print: ')\n\n with open(my_file, 'r') as fh:\n contents = fh.read()\n\n print(contents)\n\nif __name__ == '__main__':\n read_my_file()\n" }, { "alpha_fraction": 0.6487935781478882, "alphanum_fraction": 0.6487935781478882, "avg_line_length": 38.26315689086914, "blob_id": "38155ccbed3ab38265b50d7740b1f07655d3d95a", "content_id": "cefa862d48bceeff97b0dab5ea07fc02dd5860e5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 746, "license_type": "no_license", "max_line_length": 283, "num_lines": 19, "path": "/madlib.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# madlib.py\n# by Aspen Hollyer\n# Asks for input as strings, generates and prints a madlib string\n\ndef main():\n\n name = input(\"Give me a name: \")\n subject = input(\"Give me a school subject: \")\n pet = input(\"Give me an animal: \")\n power = input(\"Give me a superpower: \")\n food = input(\"Give me a food: \")\n num = input(\"Give me a number: \")\n\n def madlib(name, subject, pet, power, food, num):\n print(\"{name}'s {pet} ate some magical {food} that granted him the power of {power}. It also made him a genius! He went on to major in {subject} and win a nobel prize at the age of {num}. The end.\".format(name=name, pet=pet, food=food, power=power, subject=subject, num=num))\n\n madlib(name, subject, pet, power, food, num)\n\nmain()\n" }, { "alpha_fraction": 0.6494845151901245, "alphanum_fraction": 0.6529209613800049, "avg_line_length": 28.100000381469727, "blob_id": "90566b9c34bb803c9b79e2d8aaace30ef18e40d0", "content_id": "a591d20f1d8b5ba6b2341bc455807ea2e70f1931", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 291, "license_type": "no_license", "max_line_length": 106, "num_lines": 10, "path": "/hello2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# hello2.py\n# by Aspen Hollyer\n# Asks for a name as input and prints the name in all caps as well as the number of characters in the name\n\ndef main():\n name = input(\"What is your name? \").upper()\n print(\"HELLO,\", name)\n print(\"YOUR NAME HAS\", len(name), \"LETTERS IN IT! AWESOME!\")\n\nmain()\n" }, { "alpha_fraction": 0.5050504803657532, "alphanum_fraction": 0.5959596037864685, "avg_line_length": 14.230769157409668, "blob_id": "86cfd10c870369539d2ebcf02cff754a73392e39", "content_id": "9847e775729b7eaddb1deb3107c0c560eb56720c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 198, "license_type": "no_license", "max_line_length": 20, "num_lines": 13, "path": "/turtle_graphics/draw_square.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "from turtle import \n\ndef draw_square():\n forward(100)\n right(90)\n forward(100)\n right(90)\n forward(100)\n right(90)\n forward(100)\n draw_square()\n mainloop()\ndraw_square()\n" }, { "alpha_fraction": 0.6208718419075012, "alphanum_fraction": 0.6552179455757141, "avg_line_length": 17.463415145874023, "blob_id": "2a38121cb7b1294c9539c091079cb537b46de7c7", "content_id": "49c4b716d7974c9a9450b9b8b690847714c022e0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 757, "license_type": "no_license", "max_line_length": 77, "num_lines": 41, "path": "/breakwhile.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# breakwhile.py\n# Aspen Hollyer\n\n# MITx 6.001x Practice problems for understanding while loops\n\n# Practice using break statement\nnum = 10\nwhile True:\n if num < 7:\n print('Breaking out of loop')\n break\n print(num)\n num -= 1\nprint('Outside of loop')\n\n\n# Convert the following into code that uses a while loop.\n# print 2\n# prints 4\n# prints 6\n# prints 8\n# prints 10\n# prints Goodbye!\n\nnum = 0\nwhile num < 10:\n num += 2\n print(num)\nprint('Goodbye!')\n\n# Write a while loop that sums the values 1 through end, inclusive. end is a\n# variable that we define for you. So, for example, if we define end to be 6,\n# your code should print out the result: 21.\n\nend = 6\nnum = 1\ntotal = 0\nwhile num <= end:\n total += num\n num += 1\nprint(total)\n" }, { "alpha_fraction": 0.5483871102333069, "alphanum_fraction": 0.5648592710494995, "avg_line_length": 29.35416603088379, "blob_id": "c56f6fb69c053cb0029d603eab452261f8a4f8bc", "content_id": "3134a3a6946b8caed5a4a3d6297e81ce347e3259", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1457, "license_type": "no_license", "max_line_length": 159, "num_lines": 48, "path": "/guess_game_2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# guess_game_2.py\n# By Aspen\n# A program that guesses a number from 1-100 chosen by you.\n\ndef guess():\n playing = True\n min = 1\n max = 100\n guess_count = 0\n\n while playing:\n\n # Update guess with new min/max values\n current_guess = int((min + max)/2)\n # Accumulate guesses\n guess_count += 1\n # Guess a number\n print(\"Is it {current_guess}?\".format(current_guess=current_guess))\n # Ask user if guess is correct\n answer1 = input(\"Type Y or N: \").lower()\n\n if answer1 == 'y':\n # Print win message\n print(\"I win! Your number is {current_guess}. It only took me {guess_count} guesses.\".format(current_guess=current_guess, guess_count=guess_count))\n # Stop playing\n playing = False\n\n elif answer1 == 'n':\n # Ask user if guess was too high or too low\n print(\"Was my guess too high or too low?\")\n answer2 = input(\"Type H or L: \").lower()\n # Update range of possible guesses to reflect user input\n if answer2 == 'h':\n max = current_guess - 1\n elif answer2 == 'l':\n min = current_guess + 1\n\n\ndef play():\n input(\"Ready? Don't tell me your number. Press ENTER to begin. \")\n guess()\n\ndef main():\n print(\"\\n\\nINSTRUCTIONS: Hello. I am GuessBot. Choose a number from 1-100. I will try to guess your number.\\n\\n\")\n\n play()\n\nmain()\n" }, { "alpha_fraction": 0.5709219574928284, "alphanum_fraction": 0.6063829660415649, "avg_line_length": 30.33333396911621, "blob_id": "c1399228e2eef73f6d23110014a8c60ddbece202", "content_id": "066d831136dd3ac651dadd18ba79d47327fa9d70", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 282, "license_type": "no_license", "max_line_length": 65, "num_lines": 9, "path": "/multiplication_table.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# multiplication_table.py\n# By Aspen\n# Uses a loop to print the multiplication table for numbers 1-10.\n\nprint(\"\\n9. Multiplication Table:\")\nfor i in range(1, 11):\n print(\"Multiples of\", str(i))\n for j in range(1, 11):\n print(str(i) + \" \" + str(j) + \" = \" + str(ij))\n" }, { "alpha_fraction": 0.7306064963340759, "alphanum_fraction": 0.737658679485321, "avg_line_length": 39.514286041259766, "blob_id": "71b964b789d33d52be041c4e32a076d5238d933c", "content_id": "4713477fa773444ebd230a924317171107e232a8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1418, "license_type": "no_license", "max_line_length": 180, "num_lines": 35, "path": "/README.md", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# Python Programming Exercises\n\n---------\n### What is this?\n\nThis repository contains various programs written in Python 3. These are learning exercises.\n\n-------\n### Running the Programs\n\nEach program has a specification at the beginning of the script. To run an interactive program, download the file and run it on the command line. Some interactive programs include:\n\n madlib.py - Fill in words to generate a silly madlib.\n* guess_the_number.py - Try to determine a random number 1-100 within 5 guesses.\n* tip_calc2.py - Supply the bill amount, number of guests, and tip percentage to receive the cost per guest.\n* degree_converter.py - Supply a temperature in Celsius or Fahrenheit and have it converted.\n* caesar_cipher.py - Provide a string and shift key to encrypt or decrypt messages.\n* banner.py - Supply a message and the program will print a banner with your message.\n* guess_game_2.py - Think of a random number, and the program will try to guess your number.\n\n---------\n### To Do's\n\n- [ ] Add exception handling to degree_conversion.py\n- [x] Format tip_calc.py to display 2 decimal places\n- [x] Complete Bonus Problem in list_exercises.py\n- [x] Complete Ceasar Cipher in string_exercises.py\n- [ ] Create alternate Caesar Cipher using ord()/chr()\n- [ ] Add exception handling to guess_game_2.py\n\n----------\nIf you have questions or want to learn together, feel free to e-mail me at [email protected]\n\nCheers!\nAspen\n" }, { "alpha_fraction": 0.5774134993553162, "alphanum_fraction": 0.5828779339790344, "avg_line_length": 20.115385055541992, "blob_id": "5f83c76d8e31f2aadc0daae86a7dff8db55cc137", "content_id": "458adc53ed4d99fa4036398ed7c94854975b4336", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 549, "license_type": "no_license", "max_line_length": 74, "num_lines": 26, "path": "/file-io-exercises/ex_4.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# ex_4.py\n# By Aspen\n# INSTRUCTIONS: Write a program that takes a JSON file name as input and\n# plots the X,Y data.\n\nimport json\nimport matplotlib.pyplot as p\n\ndef plot_data(source):\n with open(source, 'r') as fh:\n obj = json.load(fh)\n\n xs = []\n ys = []\n\n for i in obj['data']:\n xs.append(i[0])\n ys.append(i[1])\n\n p.plot(xs, ys)\n p.show()\n\nif __name__ == '__main__':\n print(\"\\nThis program accepts a .JSON file and plots the X,Y data.\\n\")\n my_file = input(\"Enter the file name: \")\n plot_data(my_file)\n" }, { "alpha_fraction": 0.5775368809700012, "alphanum_fraction": 0.6064438223838806, "avg_line_length": 19.37423324584961, "blob_id": "3f442177cef9c682b8afb6b00b2d19c34eb22a24", "content_id": "3cc00d16f2a6ccf69c679757a8a391ce5d525ae7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3321, "license_type": "no_license", "max_line_length": 76, "num_lines": 163, "path": "/turtle_graphics/shapes.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# shapes.by\n# By Aspen\n# A module for drawing shapes with the built-in turtle module for Python3\n\nfrom turtle import \n\n# 1. An equilateral triangle\ndef draw_triangle_up(size, pcolor='black', fcolor='white', wait=False):\n pencolor(pcolor)\n fillcolor(fcolor)\n begin_fill()\n for i in range(3):\n forward(size)\n left(120)\n end_fill()\n if wait:\n mainloop()\n#draw_triangle_up()\n\ndef draw_triangle_down(size, pcolor='black', fcolor='white', wait=False):\n pencolor(pcolor)\n fillcolor(fcolor)\n begin_fill()\n for i in range(3):\n forward(size)\n right(120)\n end_fill()\n if wait:\n mainloop()\n#draw_triangle_down()\n\n# 2. A square\ndef draw_square(size, color, wait=False):\n pencolor(color)\n fillcolor(color)\n for i in range(4):\n forward(size)\n left(90)\n if wait:\n mainloop()\n#draw_square()\n\n# 3. A pentagon\ndef draw_pentagon(size, color, wait=False):\n pencolor(color)\n fillcolor(color)\n for i in range(5):\n forward(size)\n left(72)\n if wait:\n mainloop()\n#draw_pentagon(100, 'black', wait=True)\n\n# 4. A hexagon\ndef draw_hexagon(size, color, wait=False):\n pencolor(color)\n fillcolor(color)\n for i in range(6):\n forward(size)\n left(60)\n if wait:\n mainloop()\n#draw_hexagon()\n\n# 5. An octagon\ndef draw_octagon(size, wait=False):\n pencolor(color)\n fillcolor(color)\n for i in range(8):\n forward(size)\n left(45)\n if wait:\n mainloop()\n#draw_octagon()\n\n# 6. A star\ndef draw_star(size, color, wait=False):\n pencolor(color)\n for i in range(5):\n forward(size)\n right(144)\n if wait:\n mainloop()\n#draw_star(200, 'black')\n\n# 7. A circle\ndef draw_circle(size, color, wait=False):\n pencolor(color)\n fillcolor(color)\n circle(size)\n mainloop()\n#draw_circle()\n\n# BONUS STUFF\ndef draw_trapezoid(size, pcolor='black', fcolor='white', wait=False):\n pencolor(pcolor)\n fillcolor(fcolor)\n begin_fill()\n forward(size)\n left(120)\n forward(size/2)\n left(60)\n forward(size/2)\n left(60)\n forward(size/2)\n left(120)\n forward(size)\n end_fill()\n if wait:\n mainloop()\n#draw_trapezoid(100, pcolor='blue', fcolor='lightblue')\n\ndef draw_trapezoid_mirror(size, pcolor='black', fcolor='white', wait=False):\n pencolor(pcolor)\n fillcolor(fcolor)\n begin_fill()\n forward(size/2)\n right(120)\n forward(size/2)\n right(60)\n forward(size/2)\n right(60)\n forward(size)\n right(120)\n forward(size)\n end_fill()\n if wait:\n mainloop()\n#draw_trapezoid(100, pcolor='blue', fcolor='lightblue')\n\ndef draw_rhombus(size, pcolor='black', fcolor='white', wait=False):\n pencolor(pcolor)\n fillcolor(fcolor)\n begin_fill()\n forward(size)\n left(120)\n forward(size)\n left(60)\n forward(size)\n left(120)\n forward(size)\n left(60)\n end_fill()\n if wait:\n mainloop()\n#draw_rhombus(100)\n\ndef draw_rhombus_mirror(size, pcolor='black', fcolor='white', wait=False):\n pencolor(pcolor)\n fillcolor(fcolor)\n begin_fill()\n forward(size)\n right(120)\n forward(size)\n right(60)\n forward(size)\n right(120)\n forward(size)\n right(60)\n end_fill()\n if wait:\n mainloop()\n#draw_rhombus(100)\n" }, { "alpha_fraction": 0.5810564756393433, "alphanum_fraction": 0.6156648397445679, "avg_line_length": 21.875, "blob_id": "b4bf007b16025238caee5987a51b202bee7c3505", "content_id": "64b40dbb5ebf14fcba3c64ee711b49b7658c9eec", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 549, "license_type": "no_license", "max_line_length": 60, "num_lines": 24, "path": "/triangle_numbers.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# triangle_numbers.py\n# By Aspen\n# A program that prints the first 100 triangle numbers.\n\n# What is a triangle number? See:\n# https://www.mathsisfun.com/algebra/triangular-numbers.html\n\n# Recursive solution\ndef print_triangle_nums(n):\n if n == 0:\n print(\"Done!\")\n else:\n num = n (n+1) / 2\n print(num)\n print_triangle_nums(n-1)\nprint_triangle_nums(100)\n\n# For loop\ndef print_triangle_nums2(p):\n for i in range(1, p+1):\n num = i * (i+1) / 2\n print(num)\n print(\"Done!\")\nprint_triangle_nums2(100)\n" }, { "alpha_fraction": 0.6319702863693237, "alphanum_fraction": 0.6617100238800049, "avg_line_length": 15.8125, "blob_id": "12a5fd0cd3c8aff95405110d0d3d55de55386972", "content_id": "c87afc5705fb87d88005e5bb454e43611c4bd534", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 269, "license_type": "no_license", "max_line_length": 40, "num_lines": 16, "path": "/function_exercises/ex7.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# Plots Celsius to Fahrenheit conversion\n\nimport matplotlib.pyplot as plot\n\ndef t(x):\n return x * (9/5) + 32\n\nc = list(range(0, 100))\nf = []\nfor x in c:\n f.append(t(x))\n\nplot.plot(c, f)\nplot.xlabel('Degrees Celsius')\nplot.ylabel('Degrees Fahrenheit')\nplot.show()\n" }, { "alpha_fraction": 0.5088408589363098, "alphanum_fraction": 0.5206286907196045, "avg_line_length": 16.55172348022461, "blob_id": "1a08c55ed778372859f86259497a1c830c875aac", "content_id": "443f40105a05e36e4d0a355cac67dcb7da1fc787", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1018, "license_type": "no_license", "max_line_length": 58, "num_lines": 58, "path": "/phonebook.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "#!/usr/bin/env\n\n# phonebook.py\n# By Aspen\n# A command line program to manage a phone book.\n\ndef lookup_entry():\n print('lookup')\n return\n\ndef set_entry():\n print('set')\n return\n\ndef delete_entry():\n print('delete')\n return\n\ndef list_entries():\n print('list')\n return\n\ndef quit():\n print('quit')\n in_use = False\n\ndef use_phonebook():\n in_use = True\n\n functions = {\n '1': lookup_entry,\n '2': set_entry,\n '3': delete_entry,\n '4': list_entries,\n '5': quit\n }\n\n instructions = 'Electronic Phone Book\\n\\\n =====================\\n\\\n 1. Look up an entry\\n\\\n 2. Set an entry\\n\\\n 3. Delete an entry\\n\\\n 4. List all entries\\n\\\n 5. Quit\\n'\n\n\n while in_use:\n print(instructions)\n\n ans = str(input(\"What do you want to do (1-5)? \"))\n \n if ans in functions:\n functions[ans]()\n else:\n print(\"That is not a valid input. Try again.\")\n\nif __name__ == '__main__':\n use_phonebook()\n" }, { "alpha_fraction": 0.650306761264801, "alphanum_fraction": 0.6625766754150391, "avg_line_length": 27.34782600402832, "blob_id": "c873cd15ea77e3f688522d1f2ca7827d912e5533", "content_id": "e87e36ef3b717913e40def3bb385956b0a29240d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 652, "license_type": "no_license", "max_line_length": 77, "num_lines": 23, "path": "/file-io-exercises/bonus_challenge.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# bonus_challenge.py\n# By Aspen\n# INSTRUCTIONS: Write a program that writes to an in-memory file and keeps\n# track of how many characters/bytes were added and prints that information\n# to the screen. Continue adding characters until your program dies.\n\n# 1. At what count did your computer crash?\n\n# 2. What kind of error did you get?\n\n# 3. Did your program crash earlier or later than expected? Why do you think?\n\ndef crash():\n fh = open('crash.txt', 'w')\n counter = 0\n while True:\n fh.write('c' * (10 counter))\n counter += 10 counter\n print('Characters Written:', counter)\n\n\nif __name__ == '__main__':\n crash()\n" }, { "alpha_fraction": 0.6653322577476501, "alphanum_fraction": 0.6749399304389954, "avg_line_length": 39.290321350097656, "blob_id": "f4961088d896facf4471c7da638526a618919fa8", "content_id": "0bbe2a2f04480cfca41a96889ea9da165110994e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1249, "license_type": "no_license", "max_line_length": 220, "num_lines": 31, "path": "/tip_calc2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# tip_calc.py\n# by Aspen\n# Accepts a bill amount, service level, and number of patrons. Prints price per patron including appropriate tip.\n\nprint(\"\\n*\\nINSTRUCTIONS: This program accepts your bill amount, number of patrons, and level of satisfaction with your service. It splits your check and returns the price per patron including appropriate tip.\\n\\n\")\n\ndef main():\n # ask for bill amount\n bill = float(input(\"What is the amount of your bill? $\"))\n\n # ask for number of patrons\n num_guests = int(input(\"How many people are splitting the check? \"))\n\n # ask for satisfaction level\n service = input(\"\\nHow was the service?\\ngood\\nfair\\nbad\\nPlease type one of the above options, then press Enter. \").lower()\n\n\n # calculate tip amount\n tips = {\"good\" : 0.20,\n \"fair\" : 0.15,\n \"bad\" : 0.10}\n tip_percentage = tips[service]\n tip = bill tip_percentage\n\n # calculate cost per guest\n cost_per_guest = (bill + tip) / num_guests\n\n # print message with tip amount\n print(\"\\nYour bill was ${bill:.2f}, and your service was {service}. You should tip ${tip:.2f}.\\n\\nEach guest owes ${cost_per_guest:.2f}.\".format(bill=bill, service=service, tip=tip, cost_per_guest=cost_per_guest))\n\nmain()\n" }, { "alpha_fraction": 0.4771241843700409, "alphanum_fraction": 0.5424836874008179, "avg_line_length": 14.300000190734863, "blob_id": "84a47a2aaf3e38f313e44cd09b88266b9455b06b", "content_id": "548e2ab4d1442b298b2014e97073541113c528f1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 153, "license_type": "no_license", "max_line_length": 32, "num_lines": 10, "path": "/1_to_10.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# 1_to_10.py\n# By Aspen\n# prints 1-10 using a while loop\n\ndef main():\n count = 0\n while count < 10:\n count += 1\n print(count)\nmain()\n" }, { "alpha_fraction": 0.6775994300842285, "alphanum_fraction": 0.685275673866272, "avg_line_length": 34.82500076293945, "blob_id": "2c897667fe7fb646492e92af0d42b973ec61656f", "content_id": "4da42adf58ce5bdf5fe5bb5cf52a652cb3866bda", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1435, "license_type": "no_license", "max_line_length": 76, "num_lines": 40, "path": "/dictionary_exercises/bonus_challenge.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# bonus_challenge.py\n# By Aspen\n\n# INSTRUCTIONS: Given a histogram tally (one returned from either\n# letter_histogram or word_histogram), print the top 3 words or letters.\n\nimport string\n\ndef word_histogram(text):\n stripped_text = text.translate(string.punctuation)\n histogram = {}\n for word in stripped_text.lower().split():\n if word in histogram:\n histogram[word] += 1\n else:\n histogram[word] = 1\n # To print histogram\n # for key, value in histogram.items():\n # print(key + \":\", value)\n return histogram\n\ndef top_3(histogram):\n # Generate list of words sorted by values in descending order\n sorted_dict = sorted(histogram, key=histogram.__getitem__, reverse=True)\n # To print top 3\n # print(sorted_dict[:3])\n return sorted_dict[:3]\n\nif __name__ == '__main__':\n hist = word_histogram(\"Congress returns Tuesday from its spring\\\n recess,facing yet another down-to-the-wire spate of deal-making\\\n and a White House anxious to claim its first major legislative\\\n win. On Friday night, the funding measure lawmakers approved\\\n last year to keep the federal government running will expire.\\\n The timing leaves members of the House and Senate just four\\\n days to reach a new agreement to fund the government, or risk\\\n a partial shutdown of federal agencies on Saturday — the 100th\\\n day of Donald Trump's presidency.\")\n\n top_3(hist)\n" }, { "alpha_fraction": 0.5341365337371826, "alphanum_fraction": 0.5401606559753418, "avg_line_length": 23.899999618530273, "blob_id": "3051937ebf408ecf886a4de95fb11033626fa763", "content_id": "9d14ace56d2919fbe578666dddd96e87ea8eb6da", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 498, "license_type": "no_license", "max_line_length": 66, "num_lines": 20, "path": "/coins.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# coins.py\n# By Aspen\n# Accumulates coins until the user tells the program to stop\n\ndef main():\n want_coins = True\n coins = 0\n\n while want_coins:\n print(\"You have {0} coins.\".format(coins))\n answer = input(\"Do you want another? Yes or no: \").lower()\n if answer == \"yes\":\n coins += 1\n elif answer == \"no\":\n print(\"Okay, bye!\")\n want_coins = False\n else:\n print(\"That answer makes no sense. Try again.\")\n\nmain()\n" }, { "alpha_fraction": 0.6369958519935608, "alphanum_fraction": 0.6383866667747498, "avg_line_length": 28.95833396911621, "blob_id": "0478e8515543beb70b3634d018288c4c0d3fbfeb", "content_id": "a2b9fbd59c88b20118d245ed9d8c1a2d6911df54", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 719, "license_type": "no_license", "max_line_length": 75, "num_lines": 24, "path": "/file-io-exercises/ex_3.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# ex_3.py\n# By Aspen\n# INSTRUCTIONS: Write a program that prompts the user to enter a file name,\n# then prints the letter histogram and the word histogram of the file.\n\nimport histograms as h\n\ndef print_histograms(file):\n with open(file, 'r') as fh:\n content = fh.read()\n\n letter_hist_dict = h.letter_histogram(content)\n print(\"Histogram of Characters:\")\n for key, val in letter_hist_dict.items():\n print(\"{}: {}\".format(key, val))\n\n word_hist_dict = h.word_histogram(content)\n print(\"Histogram of Words:\")\n for key, val in word_hist_dict.items():\n print(\"{}: {}\".format(key, val))\n\nif __name__ == '__main__':\n ans = input('Enter the file name: ')\n print_histograms(ans)\n" }, { "alpha_fraction": 0.5472496747970581, "alphanum_fraction": 0.5592383742332458, "avg_line_length": 25.754716873168945, "blob_id": "9e88bbcd89d41457dcba53d52c5d0dc7437ac991", "content_id": "c81ddbb6c5ff231b32d6da549db3b3c5a0e48fe1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1418, "license_type": "no_license", "max_line_length": 69, "num_lines": 53, "path": "/string_exercises.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# string_exercises.py\n# By Aspen\n# A collection of basic exercises on string_exercises\n\nstr = \"doubt kills more dreams than failure ever will\"\nprint(\"\\nInitial string:\")\nprint(str, \"\\n\")\n\n# 1. Uppercase a string\nprint(\"\\n1. Uppercase a string:\")\nprint(str.upper(), \"\\n\")\n\n# 2. Capitalize a string\nprint(\"\\n2. Capitalize a string:\")\nprint(str.capitalize(), \"\\n\")\n\n# 3. Reverse a string\nprint(\"\\n3. Reverse a string:\")\ndef reverse_str(message):\n listified = list(message)\n listified.reverse()\n new_string = \"\".join(listified)\n return new_string\nprint(reverse_str(str))\n\n# 4. Leetspeak\nprint(\"\\n4. Translate to Leetspeak:\")\ndef leetspeak(str):\n translation_key = { 'A' : '4',\n 'E' : '3',\n 'G' : '6',\n 'I' : '1',\n 'O' : '0',\n 'S' : '5',\n 'T' : '7' }\n str = str.upper()\n translated = \"\"\n for char in str:\n if char in translation_key:\n translated += translation_key[char]\n else:\n translated += char\n return translated\nprint(leetspeak(str), \"\\n\")\n\n# 5. Long-long vowels\nprint(\"\\n5. Long-long vowels:\")\ndef long_vowels(str):\n str = str.replace('ee', 'eeeee')\n str = str.replace('oo', 'ooooo')\n return str\nprint(\"Normal: A good man feels happy.\")\nprint(long_vowels(\"Long-long vowels: A good man feels happy.\"), \"\\n\")\n" }, { "alpha_fraction": 0.5666666626930237, "alphanum_fraction": 0.621052622795105, "avg_line_length": 15.285714149475098, "blob_id": "51cfb9851f30285cb462c8e83cdecc71eeb03907", "content_id": "048ff90b8abdecc4e6e5bfb2922ab366c23de950", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 570, "license_type": "no_license", "max_line_length": 60, "num_lines": 35, "path": "/for-loops.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# for-loops.py\n# Aspen Hollyer\n\n# MITx 6.001x for loop practice.\n\n# Convert the following into code that uses a for loop.\n# prints 2\n# prints 4\n# prints 6\n# prints 8\n# prints 10\n# prints \"Goodbye!\"\n\nfor i in range(2, 11, 2):\n print(i)\nprint(\"Goodbye!\")\n\n\n# Convert the following code into code that uses a for loop.\n# prints \"Hello!\"\n# prints 10\n# prints 8\n# prints 6\n# prints 4\n# prints 2\n\nprint(\"Hello!\")\nfor i in range(10, 1, -2):\n print(i)\n\nfor variable in range(20):\n if variable % 4 == 0:\n print(variable)\n if variable % 16 == 0:\n print('Foo!')\n" }, { "alpha_fraction": 0.5545712113380432, "alphanum_fraction": 0.6009922027587891, "avg_line_length": 24.196428298950195, "blob_id": "eece44d64b472550e82d809e68ad0ba372e2e5c1", "content_id": "35e1ee28752cb74773b46ebe707d67d687390523", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2822, "license_type": "no_license", "max_line_length": 93, "num_lines": 112, "path": "/list_exercises.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# list-exercises.py\n# By Aspen\n# a collection of brief programming exercises on lists\n\nimport random\n\nnum_list = list(range(-5, 10))\nrandom.shuffle(num_list)\nprint(\"The list of numbers is:\", num_list)\n\n# 1. Sum the Numbers\n# Given a list of numbers, print their sum.\nex1_sum = 0\nfor num in num_list:\n ex1_sum += num\nprint(\"1. Sum the Numbers:\", ex1_sum)\n\n# 2. Largest Number\nex2_largest = 0\nfor num in num_list:\n if num > ex2_largest:\n ex2_largest = num\nprint(\"2. Largest Number:\", ex2_largest)\n\n# 3. Smallest Number\nex3_smallest = num_list[0]\nfor num in num_list:\n if num < ex3_smallest:\n ex3_smallest = num\nprint(\"3. Smallest Number:\", ex3_smallest)\n\n# 4. Even Numbers\nprint(\"4. Even Numbers:\")\nfor num in num_list:\n if num % 2 == 0:\n print(\"\\t\", num)\n\n# 5. Positive Numbers\nprint(\"5. Positive Numbers:\")\nfor num in num_list:\n if num > 0:\n print(\"\\t\", num)\n\n# 6. Positive Numbers II\nex6_list = []\nfor num in num_list:\n if num > 0:\n ex6_list.append(num)\nprint(\"6. List of Positive Numbers:\", ex6_list)\n\n# 7. Multiply a List\nex7_list = []\ndef multiply_list(list, factor):\n for num in list:\n ex7_list.append(num * factor)\n return ex7_list\nprint(\"7. Multiply a List:\\n\", multiply_list(num_list, 2))\n\n# 8. Multiply Vectors\nex8_list1 = [2, 4, 5]\nex8_list2 = [2, 3, 6]\nproduct = []\ndef multiply_vectors(a, b):\n for i in range(0, len(a)):\n product.append(a[i] * b[i])\n return product\nprint(\"8. Multiply Vectors:\\n\", multiply_vectors(ex8_list1, ex8_list2))\n\n# 9. Matrix Addition\nex9_list1 = [[1, 3], [2, 4]]\nex9_list2 = [[5,2], [1, 0]]\nex9_sum = []\ndef add_matrices(a, b):\n # Iterate over list\n for i in range(0, len(a)):\n # Iterate over sub-list\n temp = []\n for j in range(0, len(a[i])):\n temp.append(a[i][j] + b[i][j])\n ex9_sum.append(temp)\n return ex9_sum\nprint(\"9. Matrix Addition:\\n\", add_matrices(ex9_list1, ex9_list2))\n\n# 10. Matrix Addition II\n# see above\nprint(\"10. Matrix Addition II: (see exercise 9)\")\n\n# 11. De-dup\nex11_original = [1, 2, 2, 3, 4, 5, 6, 4, 7, 5, 5]\nex11_deduped = []\ndef dedup(list):\n for num in ex11_original:\n if not num in ex11_deduped:\n ex11_deduped.append(num)\n return ex11_deduped\nprint(\"11. De-dup:\\nOriginal List:\", ex11_original, \"\\nDe-Duped List:\", dedup(ex11_original))\n\n# Bonus: Matrix Multiplication\nbonus1 = [[2, -2], [5, 3]]\nbonus2 = [[-1, 4], [7, -6]]\ndef multiply_matrices(a, b):\n product = []\n for i in range(len(a)):\n temp = []\n for j in range(len(a[i])):\n sum = 0\n for k in range(len(b[i])):\n sum += (a[i][k] * b[k][j])\n temp.append(sum)\n product.append(temp)\n return product\nprint(\"Bonus - Matrix Multiplication:\", multiply_matrices(bonus1, bonus2))\n" }, { "alpha_fraction": 0.5857987999916077, "alphanum_fraction": 0.5986193418502808, "avg_line_length": 25.6842098236084, "blob_id": "7b7a6e0350ba316ceaff04a90c2c3069e102d075", "content_id": "f5d6602190cc59d43322a4067ea3ff90e0182abb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1014, "license_type": "no_license", "max_line_length": 68, "num_lines": 38, "path": "/dictionary_exercises/ex_2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# ex_2.py\n# By Aspen\n# Work with a nested dictionary\n\nramit = {\n 'name': 'Ramit',\n 'email': '[email protected]',\n 'interests': ['movies', 'tennis'],\n 'friends': [\n {\n 'name': 'Jasmine',\n 'email': '[email protected]',\n 'interests': ['photography', 'tennis']\n },\n {\n 'name': 'Jan',\n 'email': '[email protected]',\n 'interests': ['movies', 'tv']\n }\n ]\n}\n\nprint(\"\\nNested Dictionary Exercises: Ramit\")\nprint(\"ramit = \", ramit)\n\nprint(\"\\n1. Write an expression that gets Ramit's email address.\")\nprint(\"ramit.get('email'):\", ramit.get('email'))\nprint(\"ramit['email']:\", ramit['email'])\n\nprint(\"\\n2. Write an expression that gets Ramit's first interest.\")\nprint(\"ramit['interests'][0]:\", ramit['interests'][0])\n\nprint(\"\\n3. Write an expression that gets Jasmine's email address.\")\nprint(\"ramit['friends'][0]['email']:\", ramit['friends'][0]['email'])\n\nprint(\"\\n4. Write an expression that gets Jan's second interest.\")\nprint(\"ramit['friends'][1]['interests'][1]:\", \\\n ramit['friends'][1]['interests'][1])\n" }, { "alpha_fraction": 0.5212159752845764, "alphanum_fraction": 0.5535148978233337, "avg_line_length": 24.063491821289062, "blob_id": "d4cc7a4afaf5ff91e7543c987a539b9d44d098d1", "content_id": "e6a7775dce1cf235b0a03e9f4d2f8c32859acfda", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1579, "license_type": "no_license", "max_line_length": 72, "num_lines": 63, "path": "/loop_exercises.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# loop_exercises.py\n# By Aspen\n# A collection of brief programming exercises with loops\n\n# 1. 1 to 10\nprint(\"\\n1. 1 to 10:\")\nfor i in range(1, 11):\n print(\"\\t\", i)\n\n# 2. n to m\nprint(\"\\n2. n to m:\")\nstart = int(input(\"Start from: \"))\nend = int(input(\"End on: \"))\ndef count_loop(start, end):\n for i in range(start, end+1):\n print(\"\\t\", i)\ncount_loop(start, end)\n\n# 3. Odd Numbers\nprint(\"\\n3. Odd Numbers:\")\nfor i in range(1, 10, 2):\n print(\"\\t\", i)\n\n# 4. Print a Square\nprint(\"\\n4. Print a Square:\")\nunit = \"\"\nside = 5\nfor i in range(0, side):\n print(unit side)\n\n# 5. Print a Square II\nprint(\"\\n5. Print a Square II:\")\nside = int(input(\"How long is each side? Enter an integer: \"))\nfor i in range(0, side):\n print(unit * side)\n\n# 6. Print a Box\nprint(\"\\n6. Print a Box:\")\nheight = int(input(\"How tall is the box? Enter an integer: \"))\nwidth = int(input(\"How wide is the box? Enter an integer: \"))\ndef print_box(w, h):\n print(\"\" w)\n for i in range(0, (h-2)):\n print(\"\" + (\" \" (w-2)) + \"\")\n print(\"\" * w)\nprint_box(width, height)\n\n# 7. Print a Triangle\n(print(\"\\n7. Print a Triangle:\"))\nprint(\" \" * 3 + \"\" 1 + \" \" * 3)\nprint(\" \" * 2 + \"\" 3 + \" \" * 2)\nprint(\" \" * 1 + \"\" 5 + \" \" * 1)\nprint(\" \" * 0 + \"\" 7 + \" \" * 0)\n\n# 8. Print a Triangle II\nprint(\"\\n8. Print a Triangle II:\")\nheight = int(input(\"What is the triangle's height? Enter an integer: \"))\ndef print_triangle(h):\n num_stars = 1\n for i in range(0, h):\n print(\" \" * (h-i) + \"\" num_stars + \" \" * (h-i))\n num_stars += 2\nprint_triangle(height)\n" }, { "alpha_fraction": 0.5774134993553162, "alphanum_fraction": 0.5871281027793884, "avg_line_length": 27.894737243652344, "blob_id": "93ee2d0ba32e97f11da1bc8899da4911a48edff7", "content_id": "e3ec767427f7ba48d3ed737819ec6e386c309ec2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1647, "license_type": "no_license", "max_line_length": 78, "num_lines": 57, "path": "/caesar_cipher.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python3\n\n# caesar_cipher.py\n# By Aspen\n# Accepts a string and amount to shift, returns shifted string\n\n# 6. Caesar Cipher\n\n# Create a list of characters to use indices for shifting\n## NOTE: 4/24/17 can use string instead of list:\n## string.ascii_lowercase\nCHARS = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n\n# Choose a string to translate\nstr = input(\"Enter a string to shift: \").lower()\n\n\n# Determine the amount to shift\nshift_key = int(input(\"How much do you want to shift? Enter an integer.\"))\n\n\n\n# Create a function that accepts the string & shift key\ndef c_shift(str, shift_key):\n # Create an empty string to add shifted chars\n shifted_str = \"\"\n\n # Iterate over each character in the input string\n for char in str:\n\n # Don't shift spaces or punctuation; just add to shifted_str\n try:\n i = chars.index(char)\n except ValueError:\n shifted_str += char\n continue\n\n # Apply the shift key\n try:\n c = chars[i + shift_key]\n except IndexError:\n if i + shift_key < 0:\n c = chars[26 + (i + shift_key)]\n elif i + shift_key > 25:\n c = chars[i + shift_key - 26]\n\n # Append the shifted character to shifted_str\n shifted_str += c\n return shifted_str\n\nprint(str)\nprint(\"Shifted:\", c_shift(str, shift_key))\nprint(\"Unshifted:\", c_shift(c_shift(str, shift_key), -shift_key))\n\npuzzle = \"lbh zhfg hayrnea jung lbh unir yrnearq\"\nprint(\"\\nPuzzle:\", puzzle + \"\\nDeciphered:\", c_shift(puzzle, -13))\n" }, { "alpha_fraction": 0.4808126389980316, "alphanum_fraction": 0.48758465051651, "avg_line_length": 19.136363983154297, "blob_id": "e97ef644339be919ec7f9c799478d312b654fcde", "content_id": "798fa3b10c46e1dd35fac018b00629f64d51d6b3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 443, "license_type": "no_license", "max_line_length": 55, "num_lines": 22, "path": "/error_handling/error_test.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python3\n\ndef catch_error ():\n while True:\n try:\n x = int(input(\"Please enter an integer: \"))\n\n except ValueError:\n raise ValueError(\"This is not an integer.\")\n\n except MemoryError:\n print(\"Ran out of memory.\")\n\n else:\n x += 13\n print(x)\n\n finally:\n print(\"Going around again!\")\n\nif __name__ == '__main__':\n catch_error()\n" }, { "alpha_fraction": 0.5132948160171509, "alphanum_fraction": 0.5202311873435974, "avg_line_length": 32.269229888916016, "blob_id": "d355fc4afef414d6a06ff7d4f48660ecd820ca64", "content_id": "8592844481e922a115044816f1d113addcce04fd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 865, "license_type": "no_license", "max_line_length": 110, "num_lines": 26, "path": "/work_or_sleep_in.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# work_or_sleep_in.py\n# by Aspen\n# Accepts a number, converts it to the corresponding weekday, and prints an instruction to sleep in or get up.\n\ndef main():\n\n def work_or_sleep():\n days = [\"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\"]\n\n while True:\n try:\n num = int(input(\"Enter a number from 0-6: \"))\n if 0 < num < 6:\n print(\"It's \", days[num], \". Go to work!\", sep=\"\")\n else:\n print(\"It's \", days[num], \". Go back to bed!\", sep=\"\")\n break\n except ValueError:\n print(\"Your input must be a number. Try again.\")\n except IndexError:\n print(\"The number you chose has no corresponding weekday. Please choose a number from 0-6.\")\n\n\n work_or_sleep()\n\nmain()\n" }, { "alpha_fraction": 0.6122449040412903, "alphanum_fraction": 0.6326530575752258, "avg_line_length": 28.399999618530273, "blob_id": "f007259ec30890e60488fc1b846b7e413a64966d", "content_id": "e9fbfd942eb780d5bf2b621d15f71e01cae9276a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 441, "license_type": "no_license", "max_line_length": 140, "num_lines": 15, "path": "/secs_converter.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# secs_converter.py\n# By Aspen & Sammy\n# Asks for a number of seconds and prints the corresponding number of hours, days, and years\n\ndef main():\n\n secs = int(input(\"How many seconds does it take? \"))\n mins = secs / 60\n hours = mins / 60\n days = hours / 24\n years = days / 365\n\n print(\"It takes\", secs, \"seconds. That's a lot of time.\\nThat's\", hours, \"hours.\\nThat's\", days, \"days.\\nThat's\", years, \"years.\\nWow!\")\n\nmain()\n" }, { "alpha_fraction": 0.6739130616188049, "alphanum_fraction": 0.6739130616188049, "avg_line_length": 22, "blob_id": "52425ae7a436e90d05ca25200feeec51032e47f7", "content_id": "17917dcdf000b49e001e2f9c1008105dfd771f7c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 92, "license_type": "no_license", "max_line_length": 38, "num_lines": 4, "path": "/function_exercises/ex1.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# Accepts a name and prints a greeting\n\ndef hello(name):\n print(\"Hello {}\".format(name))\n" }, { "alpha_fraction": 0.625201940536499, "alphanum_fraction": 0.631663978099823, "avg_line_length": 22.80769157409668, "blob_id": "e7d2072865122bd7884716230f6a3f5ed57346ac", "content_id": "1f8bb9c534bfd625419251a29b785c938c01690d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 619, "license_type": "no_license", "max_line_length": 56, "num_lines": 26, "path": "/caesar_cipher2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python3\n\n# caesar_cipher2.py\n\n# Accepts a rotation amount and string, returns a string\n# shifted by the rotation amount\n\nimport string\nimport sys\n\ndef caesar_cipher (rotation, text):\n answer = \"\"\n for t in text:\n t = t.lower()\n if t in string.ascii_lowercase:\n index = string.ascii_lowercase.index(t)\n index = index - rotation\n answer += string.ascii_lowercase[index]\n answer += t\n return answer\n\nif __name__ == \"__main__\":\n rotation = int(sys.argv[1])\n text = sys.argv[2]\n answer = caesar_cipher(rotation, text)\n print('Answer: {}'.format(answer))\n" }, { "alpha_fraction": 0.5692307949066162, "alphanum_fraction": 0.6000000238418579, "avg_line_length": 20.66666603088379, "blob_id": "0f77c0a4d3d7c84ccdea4507af538105b3d8f16e", "content_id": "f542eff7c377972fb30727e07736985cc82b1e8e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 195, "license_type": "no_license", "max_line_length": 62, "num_lines": 9, "path": "/factor_a_number.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# factor_a_number.py\n# By Aspen\n# Accepts an integer and prints its factors in ascending order\n\ndef factor(n):\n for i in range(1, n+1):\n if n % i == 0:\n print(i)\nfactor(120)\n" }, { "alpha_fraction": 0.6253870129585266, "alphanum_fraction": 0.6269350051879883, "avg_line_length": 27.086956024169922, "blob_id": "400d4b3dc53f0ca1f51ba06888f88c779aa53d61", "content_id": "fc808479ad40f4ef3140837b726df5494bd6699a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 646, "license_type": "no_license", "max_line_length": 75, "num_lines": 23, "path": "/file-io-exercises/ex_2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# ex_2.py\n# By Aspen\n# INSTRUCTIONS: Write a program that prompts the user to enter a file name,\n# prompts the user to enter content, and saves the content to the file.\n\ndef write_my_file():\n print(\"\\nThis program accepts a file name and content, then creates\\\n the file for you.\\n\")\n\n name = input(\"What should we call the file? \")\n content = input(\"What should we put in the file? \")\n\n with open(name, 'w') as fh:\n fh.write(content)\n\n print('\\nDone! Here are the contents of', name + \":\\n\")\n with open(name, 'r') as fh:\n content = fh.read()\n\n print(content)\n\nif __name__ == '__main__':\n write_my_file()\n" } ]	44
yann-a/ASCADv2-Internship	https://github.com/yann-a/ASCADv2-Internship	86063795f6f745a849a0166a4ec066eadcf9f6e7	655518535e369bc817adc1d0c08a2a077c3ba220	5080f9be4381d56ddd0a3791bce396a8a0cdccdb	refs/heads/main	2023-07-14T15:43:57.244908	2021-08-23T22:34:24	2021-08-23T22:34:24	399,164,460	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6159420013427734, "alphanum_fraction": 0.6231883764266968, "avg_line_length": 12.800000190734863, "blob_id": "c05a4a1fe9ad2bbac50e38125ebf4513c2aa3db3", "content_id": "aac4ad78398beb0bb35b0cf0786d65038a07223f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 138, "license_type": "no_license", "max_line_length": 30, "num_lines": 10, "path": "/scripts/snr_beta.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute SNR for beta\n\nfrom utils.snr import snr\n\nsnr(\n 'beta_mask',\n nb_files=1,\n save=False,\n output_path='snr/beta.npy'\n)\n" }, { "alpha_fraction": 0.5705289840698242, "alphanum_fraction": 0.5922544002532959, "avg_line_length": 29.24761962890625, "blob_id": "b2097ab174a1c7abaa8922253d33d533b919ab54", "content_id": "6bb0cd80e5ed4a8227e063a36bc33be1e7cde6a0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3176, "license_type": "no_license", "max_line_length": 105, "num_lines": 105, "path": "/scripts/utils/lda.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Computes LDA and PI\nimport numpy as np\nimport scalib.modeling as mdl\nimport matplotlib.pyplot as plt\nimport pickle\n\nfrom tqdm import tqdm\n\nfrom utils.multilabelize import multilabelize\n\n# Variables\ntraces_path = 'data/traces{}.npy'\nmetadata_path = 'data/metadata{}.npz'\nnb_traces_per_file = 99_999\nnb_files = 1\nlda_batch_size = 10_000\n\ndef lda(\n target_label,\n snr_path,\n nb_poi=2800,\n lda_dim=28,\n nb_bytes=1,\n nb_classes=256,\n can_be_zero=True,\n nb_files_train=1,\n gemm_mode=1,\n save=False,\n output_path=None\n):\n pois = [np.argsort(np.load(snr_path.format(index_byte)))[-nb_poi:] for index_byte in range(nb_bytes)]\n\n lda = mdl.MultiLDA(\n [nb_classes] * nb_bytes,\n [lda_dim] * nb_bytes,\n pois,\n gemm_mode\n )\n\n for index_train_file in tqdm(range(nb_files_train)):\n traces_train = np.load(traces_path.format(index_train_file + 1), mmap_mode='r')\n metadata_train = np.load(metadata_path.format(index_train_file + 1))\n multilabel_train = multilabelize(\n metadata_train['masks'][:],\n metadata_train['keys'][:],\n metadata_train['plaintext'][:]\n )\n\n l = multilabel_train[target_label][:-1].astype(np.uint16)\n if not can_be_zero:\n l -= 1\n\n for s in tqdm(range(0, nb_traces_per_file, lda_batch_size)):\n traces = traces_train[s:min(s + lda_batch_size, nb_traces_per_file), :].astype(np.int16)\n labels = l[s:min(s + lda_batch_size, nb_traces_per_file), :]\n lda.fit_u(traces, labels)\n del traces\n del traces_train\n\n lda.solve(done=True)\n\n if save:\n if output_path is None:\n print('No filename provided. Skipping saving')\n else:\n with open(output_path, 'wb') as output_file:\n pickle.dump(lda, output_file)\n\n return lda\n\ndef pi(\n lda,\n target_byte,\n nb_bytes=1,\n nb_classes=256,\n can_be_zero=True,\n null_threshold=1e-20,\n nb_files_train=2,\n nb_files_test=1\n):\n pis = [[] for _ in range(nb_bytes)]\n for index_test_file in tqdm(range(nb_files_train, nb_files_train + nb_files_test)):\n traces_test = np.load(traces_path.format(index_test_file + 1), mmap_mode='r')\n metadata_test = np.load(metadata_path.format(index_test_file + 1))\n multilabel_test = multilabelize(\n metadata_test['masks'][:20_000],\n metadata_test['keys'][:20_000],\n metadata_test['plaintext'][:20_000]\n )\n\n probas = list(lda.predict_proba(traces_test[:20_000].astype(np.int16)))\n labels = multilabel_test[target_byte][:len(probas)]\n\n if not can_be_zero:\n labels -= 1\n\n for index_byte in range(nb_bytes):\n prs = probas[index_byte]\n prs[np.where(prs < null_threshold)] = null_threshold\n pi = np.mean(np.log2(prs[np.arange(len(labels)), labels]))\n pis[index_byte].append(pi)\n\n for index_byte in range(nb_bytes):\n pi = np.log2(nb_classes) + np.mean(np.array(pis[index_byte]))\n print(f'For byte no {index_byte}, pi = {pi} with maximum {np.log2(nb_classes)}')\n" }, { "alpha_fraction": 0.6555023789405823, "alphanum_fraction": 0.6650717854499817, "avg_line_length": 18, "blob_id": "59ba97a740b0dbf3d85b84af98b857b75746e101", "content_id": "245facccbd528b1488739f6d68678b6cf8737d5d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 209, "license_type": "no_license", "max_line_length": 48, "num_lines": 11, "path": "/scripts/lda_permvalue.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute and pickle LDA for the permuted values\n\nfrom utils.lda import lda, pi\n\nlda(\n 'sbox_masked',\n 'snr/permvalue/permvalue{}.npy',\n nb_bytes=16,\n save=False,\n output_path='lda/permvalue'\n)\n" }, { "alpha_fraction": 0.6274510025978088, "alphanum_fraction": 0.6274510025978088, "avg_line_length": 14.300000190734863, "blob_id": "f2c081bf20338441e9e53f191656e7ebd2772731", "content_id": "84df78ab8d18fb2c680794dd9bfe259da49782a6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 153, "license_type": "no_license", "max_line_length": 33, "num_lines": 10, "path": "/scripts/lda_beta.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute and pickle LDA for beta\n\nfrom utils.lda import lda, pi\n\nlda(\n 'beta_mask',\n 'snr/beta.npy',\n save=False,\n output_path='lda/beta'\n)\n" }, { "alpha_fraction": 0.2617669999599457, "alphanum_fraction": 0.5793588757514954, "avg_line_length": 49.75396728515625, "blob_id": "5e7b4d2f5f91298fa99867fa21a2c40ebebe0353", "content_id": "1956ce2155beb8829604c87ccb7f01a7c195b0ea", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6395, "license_type": "no_license", "max_line_length": 149, "num_lines": 126, "path": "/scripts/utils/multilabelize.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Retrieve masks and permutations. Adapted from https://github.com/ANSSI-FR/ASCAD/blob/58c527fdd82c5c5896d61eeb019cccdd628248eb/ASCAD_generate.py\nimport numpy as np\n\nlog_table = np.array([\n 0, 0, 25, 1, 50, 2, 26, 198, 75, 199, 27, 104, 51, 238, 223, 3,\n 100, 4, 224, 14, 52, 141, 129, 239, 76, 113, 8, 200, 248, 105, 28, 193,\n 125, 194, 29, 181, 249, 185, 39, 106, 77, 228, 166, 114, 154, 201, 9, 120,\n 101, 47, 138, 5, 33, 15, 225, 36, 18, 240, 130, 69, 53, 147, 218, 142,\n 150, 143, 219, 189, 54, 208, 206, 148, 19, 92, 210, 241, 64, 70, 131, 56,\n 102, 221, 253, 48, 191, 6, 139, 98, 179, 37, 226, 152, 34, 136, 145, 16,\n 126, 110, 72, 195, 163, 182, 30, 66, 58, 107, 40, 84, 250, 133, 61, 186,\n 43, 121, 10, 21, 155, 159, 94, 202, 78, 212, 172, 229, 243, 115, 167, 87,\n 175, 88, 168, 80, 244, 234, 214, 116, 79, 174, 233, 213, 231, 230, 173, 232,\n 44, 215, 117, 122, 235, 22, 11, 245, 89, 203, 95, 176, 156, 169, 81, 160,\n 127, 12, 246, 111, 23, 196, 73, 236, 216, 67, 31, 45, 164, 118, 123, 183,\n 204, 187, 62, 90, 251, 96, 177, 134, 59, 82, 161, 108, 170, 85, 41, 157,\n 151, 178, 135, 144, 97, 190, 220, 252, 188, 149, 207, 205, 55, 63, 91, 209,\n 83, 57, 132, 60, 65, 162, 109, 71, 20, 42, 158, 93, 86, 242, 211, 171,\n 68, 17, 146, 217, 35, 32, 46, 137, 180, 124, 184, 38, 119, 153, 227, 165,\n 103, 74, 237, 222, 197, 49, 254, 24, 13, 99, 140, 128, 192, 247, 112, 7\n])\n\nalog_table = np.array([\n 1, 3, 5, 15, 17, 51, 85, 255, 26, 46, 114, 150, 161, 248, 19, 53,\n 95, 225, 56, 72, 216, 115, 149, 164, 247, 2, 6, 10, 30, 34, 102, 170,\n 229, 52, 92, 228, 55, 89, 235, 38, 106, 190, 217, 112, 144, 171, 230, 49,\n 83, 245, 4, 12, 20, 60, 68, 204, 79, 209, 104, 184, 211, 110, 178, 205,\n 76, 212, 103, 169, 224, 59, 77, 215, 98, 166, 241, 8, 24, 40, 120, 136,\n 131, 158, 185, 208, 107, 189, 220, 127, 129, 152, 179, 206, 73, 219, 118, 154,\n 181, 196, 87, 249, 16, 48, 80, 240, 11, 29, 39, 105, 187, 214, 97, 163,\n 254, 25, 43, 125, 135, 146, 173, 236, 47, 113, 147, 174, 233, 32, 96, 160,\n 251, 22, 58, 78, 210, 109, 183, 194, 93, 231, 50, 86, 250, 21, 63, 65,\n 195, 94, 226, 61, 71, 201, 64, 192, 91, 237, 44, 116, 156, 191, 218, 117,\n 159, 186, 213, 100, 172, 239, 42, 126, 130, 157, 188, 223, 122, 142, 137, 128,\n 155, 182, 193, 88, 232, 35, 101, 175, 234, 37, 111, 177, 200, 67, 197, 84,\n 252, 31, 33, 99, 165, 244, 7, 9, 27, 45, 119, 153, 176, 203, 70, 202,\n 69, 207, 74, 222, 121, 139, 134, 145, 168, 227, 62, 66, 198, 81, 243, 14,\n 18, 54, 90, 238, 41, 123, 141, 140, 143, 138, 133, 148, 167, 242, 13, 23,\n 57, 75, 221, 124, 132, 151, 162, 253, 28, 36, 108, 180, 199, 82, 246, 1\n])\n\n\n# Multiplication function in GF(2^8)\ndef multGF256(a, b):\n return alog_table[(log_table[a] + log_table[b]) % 255]\n\nAES_Sbox = np.array([\n 0x63, 0x7C, 0x77, 0x7B, 0xF2, 0x6B, 0x6F, 0xC5, 0x30, 0x01, 0x67, 0x2B, 0xFE, 0xD7, 0xAB, 0x76,\n 0xCA, 0x82, 0xC9, 0x7D, 0xFA, 0x59, 0x47, 0xF0, 0xAD, 0xD4, 0xA2, 0xAF, 0x9C, 0xA4, 0x72, 0xC0,\n 0xB7, 0xFD, 0x93, 0x26, 0x36, 0x3F, 0xF7, 0xCC, 0x34, 0xA5, 0xE5, 0xF1, 0x71, 0xD8, 0x31, 0x15,\n 0x04, 0xC7, 0x23, 0xC3, 0x18, 0x96, 0x05, 0x9A, 0x07, 0x12, 0x80, 0xE2, 0xEB, 0x27, 0xB2, 0x75,\n 0x09, 0x83, 0x2C, 0x1A, 0x1B, 0x6E, 0x5A, 0xA0, 0x52, 0x3B, 0xD6, 0xB3, 0x29, 0xE3, 0x2F, 0x84,\n 0x53, 0xD1, 0x00, 0xED, 0x20, 0xFC, 0xB1, 0x5B, 0x6A, 0xCB, 0xBE, 0x39, 0x4A, 0x4C, 0x58, 0xCF,\n 0xD0, 0xEF, 0xAA, 0xFB, 0x43, 0x4D, 0x33, 0x85, 0x45, 0xF9, 0x02, 0x7F, 0x50, 0x3C, 0x9F, 0xA8,\n 0x51, 0xA3, 0x40, 0x8F, 0x92, 0x9D, 0x38, 0xF5, 0xBC, 0xB6, 0xDA, 0x21, 0x10, 0xFF, 0xF3, 0xD2,\n 0xCD, 0x0C, 0x13, 0xEC, 0x5F, 0x97, 0x44, 0x17, 0xC4, 0xA7, 0x7E, 0x3D, 0x64, 0x5D, 0x19, 0x73,\n 0x60, 0x81, 0x4F, 0xDC, 0x22, 0x2A, 0x90, 0x88, 0x46, 0xEE, 0xB8, 0x14, 0xDE, 0x5E, 0x0B, 0xDB,\n 0xE0, 0x32, 0x3A, 0x0A, 0x49, 0x06, 0x24, 0x5C, 0xC2, 0xD3, 0xAC, 0x62, 0x91, 0x95, 0xE4, 0x79,\n 0xE7, 0xC8, 0x37, 0x6D, 0x8D, 0xD5, 0x4E, 0xA9, 0x6C, 0x56, 0xF4, 0xEA, 0x65, 0x7A, 0xAE, 0x08,\n 0xBA, 0x78, 0x25, 0x2E, 0x1C, 0xA6, 0xB4, 0xC6, 0xE8, 0xDD, 0x74, 0x1F, 0x4B, 0xBD, 0x8B, 0x8A,\n 0x70, 0x3E, 0xB5, 0x66, 0x48, 0x03, 0xF6, 0x0E, 0x61, 0x35, 0x57, 0xB9, 0x86, 0xC1, 0x1D, 0x9E,\n 0xE1, 0xF8, 0x98, 0x11, 0x69, 0xD9, 0x8E, 0x94, 0x9B, 0x1E, 0x87, 0xE9, 0xCE, 0x55, 0x28, 0xDF,\n 0x8C, 0xA1, 0x89, 0x0D, 0xBF, 0xE6, 0x42, 0x68, 0x41, 0x99, 0x2D, 0x0F, 0xB0, 0x54, 0xBB, 0x16\n])\n\nG = np.array([0x0C, 0x05, 0x06, 0x0b, 0x09, 0x00, 0x0a, 0x0d, 0x03, 0x0e, 0x0f, 0x08, 0x04, 0x07, 0x01, 0x02])\n\n\ndef permIndices(i, m0, m1, m2, m3):\n x0, x1, x2, x3 = m0 & 0x0f, m1 & 0x0f, m2 & 0x0f, m3 & 0x0f\n return G[G[G[G[(15-i) ^ x0] ^ x1] ^ x2] ^ x3]\n\n\ndef multilabelize(masks, keys, plaintexts):\n def mult_sbox_mask_f(target_byte):\n ind = permIndices(target_byte, masks[:, 0], masks[:, 1], masks[:, 2], masks[:, 3])\n alpha = masks[:, 18]\n beta = masks[:, 17]\n\n mask_size = masks.shape[0]\n S = AES_Sbox[plaintexts[np.arange(mask_size), ind] ^ keys[np.arange(mask_size), ind]]\n\n return multGF256(alpha, S) ^ beta\n\n def mult_sbox_mask_with_perm_f(target_byte):\n alpha = masks[:, 18]\n beta = masks[:, 17]\n\n S = AES_Sbox[plaintexts[:, target_byte] ^ keys[:, target_byte]]\n\n return multGF256(alpha, S) ^ beta\n\n def permind_f(target_byte):\n return permIndices(target_byte, masks[:, 0], masks[:, 1], masks[:, 2], masks[:, 3])\n\n def alpha_mask_f():\n return masks[:, 18]\n\n def beta_mask_f():\n return masks[:, 17]\n\n y_alpha = alpha_mask_f()\n y_beta = beta_mask_f()\n y_sbox = []\n y_sbox_with_perm = []\n y_permind = []\n\n for i in range(16):\n y_sbox.append(mult_sbox_mask_f(i))\n y_sbox_with_perm.append(mult_sbox_mask_with_perm_f(i))\n y_permind.append(permind_f(i))\n\n y_sbox = np.transpose(y_sbox)\n y_sbox_with_perm = np.transpose(y_sbox_with_perm)\n y_permind = np.transpose(y_permind)\n\n multilabel_type = np.dtype([\n (\"alpha_mask\", np.uint8, (1,)),\n (\"beta_mask\", np.uint8, (1,)),\n (\"sbox_masked\", np.uint8, (16,)),\n (\"sbox_masked_with_perm\", np.uint8, (16,)),\n (\"perm_index\", np.uint8, (16,))\n ])\n multilabel = np.array([(y_alpha[n], y_beta[n], y_sbox[n], y_sbox_with_perm[n], y_permind[n]) for n in range(len(masks))], dtype=multilabel_type)\n\n return multilabel\n" }, { "alpha_fraction": 0.6320754885673523, "alphanum_fraction": 0.6556603908538818, "avg_line_length": 16.66666603088379, "blob_id": "e0016956eefe85cabed35196efaa8f7e2522113e", "content_id": "deed1815b8cad73fb5f061f698de958deb4a742a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 212, "license_type": "no_license", "max_line_length": 47, "num_lines": 12, "path": "/scripts/snr_permindex.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Comopute SNR for the permutation indexes\n\nfrom utils.snr import snr\n\nsnr(\n 'perm_index',\n nb_files=1,\n nb_classes=16,\n nb_bytes=16,\n save=False,\n output_path='snr/permindex/permindex{}.npy'\n)\n" }, { "alpha_fraction": 0.60326087474823, "alphanum_fraction": 0.625, "avg_line_length": 14.333333015441895, "blob_id": "bb3481f42e4ceca314169b37fd3c7bbca19016de", "content_id": "d69d666297feb78aefb35859c4441141e5844912", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 184, "license_type": "no_license", "max_line_length": 31, "num_lines": 12, "path": "/scripts/snr_alpha.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute SNR for alpha\n\nfrom utils.snr import snr\n\nsnr(\n 'alpha_mask',\n nb_classes=255,\n can_be_zero=False,\n nb_files=1,\n save=False,\n output_path='snr/alpha.npy'\n)\n" }, { "alpha_fraction": 0.6375501751899719, "alphanum_fraction": 0.6656626462936401, "avg_line_length": 35, "blob_id": "886cf0327643fcf2499d5ff415f1a2f64a97f283", "content_id": "81cd2e975afac63016c4f6c6b8fabf136cb10a21", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2988, "license_type": "no_license", "max_line_length": 166, "num_lines": 83, "path": "/scripts/attack.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Run the attack and draw key rank accuracy by number of traces\nimport numpy as np\nimport pickle\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\n\nfrom utils.multilabelize import multilabelize as multilabelizer, multGF256, AES_Sbox\nimport scalib.postprocessing.rankestimation as rk\n\ntraces = np.load('data/traces1.npy', mmap_mode='r')\nmetadata = np.load('data/metadata1.npz')\n\nkey_ranks = []\nmin_traces = 0\nmax_traces = 3000\ntraces_step = 10\n\nfor nb_traces in tqdm(range(min_traces, max_traces + 1, traces_step)):\n multilabelize = multilabelizer(\n metadata['masks'][:nb_traces],\n metadata['keys'][:nb_traces],\n metadata['plaintext'][:nb_traces]\n )\n\n # Draw key and artificially replace the dataset's key with it\n k = np.random.randint(0, 256, 16)\n plaintexts = metadata['keys'][:nb_traces] ^ metadata['plaintext'][:nb_traces] ^ k\n\n # Get alpha\n lda_alpha = pickle.load(open('lda/alpha', 'rb'))\n prs_alpha = list(lda_alpha.predict_proba(traces[:nb_traces].astype(np.int16)))[0]\n\n max_alpha = np.argmax(prs_alpha, axis=1)+1\n true_alpha = metadata['masks'][:nb_traces, 18]\n\n # Get beta\n lda_beta = pickle.load(open('lda/beta', 'rb'))\n prs_beta = list(lda_beta.predict_proba(traces[:nb_traces].astype(np.int16)))[0]\n\n max_beta = np.argmax(prs_beta, axis=1)\n true_beta = metadata['masks'][:nb_traces, 17]\n\n # Get permindex\n lda_permindex = pickle.load(open('lda/permindex', 'rb'))\n prs_permindex = np.array(list(lda_permindex.predict_proba(traces[:nb_traces].astype(np.int16))))\n\n max_permindexes = np.argmax(prs_permindex, axis=2).T\n true_permindexes = multilabelize['perm_index']\n\n # Get permvalues\n lda_permvalue = pickle.load(open('lda/permvalue', 'rb'))\n prs_permvalue = np.array(list(lda_permvalue.predict_proba(traces[:nb_traces].astype(np.int16))))\n\n max_permvalue = np.argmax(prs_permvalue, axis=2).T\n true_permvalue = multilabelize['sbox_masked']\n\n probas = np.zeros((nb_traces, 256, 16))\n index_manip = np.argmax(prs_permindex, axis=0)\n\n # Attack\n for target_byte in range(16):\n for key_byte in range(256):\n C = multGF256(max_alpha, AES_Sbox[plaintexts[:, target_byte] ^ key_byte]) ^ max_beta\n proba_c = prs_permvalue[index_manip[:, target_byte], np.arange(nb_traces), C]\n\n probas[:, key_byte, target_byte] = proba_c\n\n probas = probas / np.sum(probas, axis=1)[:, None]\n key_probability = np.sum(np.log(probas), axis=0)\n\n # Estimate key rank and store it\n lmin, l, lmax = rk.rank_accuracy(-key_probability.T, k)\n\n key_ranks.append(l)\n\n# Plot key rank accuracy\nplt.plot(range(min_traces, max_traces + 1, traces_step), key_ranks)\nprint(key_ranks)\nplt.xlabel('Number of traces')\nplt.ylabel('Key rank estimation')\nplt.yscale('log', base=2)\nplt.yticks([np.float(20), np.float(216), np.float(232), np.float(248), np.float(264), np.float(280), np.float(296), np.float(2112), np.float(2**128)])\nplt.show()\n" }, { "alpha_fraction": 0.6150000095367432, "alphanum_fraction": 0.6299999952316284, "avg_line_length": 15.666666984558105, "blob_id": "9879462917298ab02a2a7cc3b86f4df6cafe2ac6", "content_id": "7be557e6c9290cf979b2b0e97688681ce748bc3c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 200, "license_type": "no_license", "max_line_length": 34, "num_lines": 12, "path": "/scripts/lda_alpha.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute and pickle LDA for alpha\n\nfrom utils.lda import lda, pi\n\nlda(\n 'alpha_mask',\n 'snr/alpha.npy',\n nb_classes=255,\n can_be_zero=False,\n save=False,\n output_path='lda/alpha'\n)\n" }, { "alpha_fraction": 0.630081295967102, "alphanum_fraction": 0.6504064798355103, "avg_line_length": 17.923076629638672, "blob_id": "0e60347952f8372b6c922b2021736f8d7657f99b", "content_id": "ebe68ed3f6af45e3fdb1ec832a4460384e604627", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 246, "license_type": "no_license", "max_line_length": 52, "num_lines": 13, "path": "/scripts/lda_permindex.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute and pickle LDA for the permutation indexes\n\nfrom utils.lda import lda, pi\n\nlda(\n 'perm_index',\n 'snr/permindex/permindex{}.npy',\n nb_bytes=16,\n nb_classes=16,\n lda_dim=5,\n save=False,\n output_path='lda/permindex'\n)\n" }, { "alpha_fraction": 0.6130719184875488, "alphanum_fraction": 0.6287581920623779, "avg_line_length": 26.321428298950195, "blob_id": "2d007d7ea317fd0c090da64b516dbe1172149529", "content_id": "7090dfbfabb4b11755d50086917f73f0c3628178", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1530, "license_type": "no_license", "max_line_length": 130, "num_lines": 56, "path": "/scripts/utils/snr.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute SNR\nimport numpy as np\nimport scalib.metrics as mts\nimport matplotlib.pyplot as plt\n\nfrom tqdm import tqdm\n\nfrom utils.multilabelize import multilabelize\n\n# Variables\ntraces_path = 'data/traces{}.npy'\nmetadata_path = 'data/metadata{}.npz'\nnb_values_per_trace = 1_000_000\n\n# SNR function\ndef snr(\n target_label,\n nb_traces=99_999,\n nb_classes=256,\n nb_bytes=1,\n can_be_zero=True,\n nb_files=8,\n plot=True,\n save=False,\n output_path=None\n):\n # Extract SNR\n snr = mts.SNR(nc=nb_classes, ns=nb_values_per_trace, np=nb_bytes)\n\n for index_file in tqdm(range(nb_files)):\n traces = np.load(traces_path.format(index_file + 1), mmap_mode='r')\n metadata = np.load(metadata_path.format(index_file + 1))\n\n multilabel = multilabelize(metadata['masks'][:nb_traces], metadata['keys'][:nb_traces], metadata['plaintext'][:nb_traces])\n\n traces = traces[:nb_traces].astype(np.int16)\n labels = np.reshape(multilabel[target_label][:nb_traces], newshape=(nb_traces, nb_bytes)).astype(np.uint16)\n\n if not can_be_zero:\n labels -= 1\n\n snr.fit_u(traces, labels)\n\n snr_val = snr.get_snr()\n\n if save:\n if output_path is None:\n print('No filename provided. Skipping saving')\n else:\n for index_byte in range(nb_bytes):\n np.save(output_path.format(index_byte), snr_val[index_byte])\n\n if plot:\n for index_byte in range(nb_bytes):\n plt.plot(snr_val[index_byte])\n plt.show()\n" }, { "alpha_fraction": 0.7557692527770996, "alphanum_fraction": 0.8038461804389954, "avg_line_length": 56.77777862548828, "blob_id": "3bb8a8e5f986e4c9142039e481a17dbe264b918d", "content_id": "188177782a2e009ee2409c24fc3777034a206390", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 520, "license_type": "no_license", "max_line_length": 223, "num_lines": 9, "path": "/README.md", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# ASCADv2-Internship\n\nThis repository holds the code I wrote for my internship at UCLouvain on SCA against ASCADv2.\n\nIt relies on SCALib, a python library developped by Olivier Bronchain.\n\nAll the code contained in this repository is mine, except the one in `scripts/utils/multilabelize.py` which I adapted from [https://github.com/ANSSI-FR/ASCAD/blob/58c527fdd82c5c5896d61eeb019cccdd628248eb/ASCAD_generate.py].\n\nI intend to make this repository more reproducible in the future, but for now I haven't written instructions for it.\n" }, { "alpha_fraction": 0.6402116417884827, "alphanum_fraction": 0.6560846567153931, "avg_line_length": 16.18181800842285, "blob_id": "24e12d1e1a93da0d119effafcc0b7c27c5cc0a9f", "content_id": "678f962edbfa1f78720cc00592799b6cfe86181b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 189, "license_type": "no_license", "max_line_length": 47, "num_lines": 11, "path": "/scripts/snr_permvalue.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute SNR for the permuted values\n\nfrom utils.snr import snr\n\nsnr(\n 'sbox_masked',\n nb_files=1,\n nb_bytes=16,\n save=False,\n output_path='snr/permvalue/permvalue{}.npy'\n)\n" }, { "alpha_fraction": 0.636686384677887, "alphanum_fraction": 0.6485207080841064, "avg_line_length": 39.238094329833984, "blob_id": "031e4346ba05f9f346bebf2adeab3f15be9093f2", "content_id": "7fadd7db1fa31025a3f7ada83ff6b56771fcd230", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 845, "license_type": "no_license", "max_line_length": 93, "num_lines": 21, "path": "/scripts/h5_to_npy.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Transform ANSSI's h5 files to npy/npz files, used for the attack\n\nimport numpy as np\nimport h5py as h5\nfrom tqdm import tqdm\n\nbase_filename = 'ascadv2-stm32-conso-raw-traces{}.{}'\nN = 8\n\nfor index_file in tqdm(range(N, N+1)):\n with h5.File(base_filename.format(index_file, 'h5')) as data:\n traces = np.array(data['traces'])\n masks = np.array(data['metadata'][:, 'masks'])\n keys = np.array(data['metadata'][:, 'key'])\n plaintext = np.array(data['metadata'][:, 'plaintext'])\n ciphertext = np.array(data['metadata'][:, 'ciphertext'])\n\n with open('data/traces{}.npy'.format(index_file), 'wb') as npyfile:\n np.save(npyfile, traces)\n with open('data/metadata{}.npz'.format(index_file), 'wb') as npyfile:\n np.savez(npyfile, masks=masks, keys=keys, plaintext=plaintext, ciphertext=ciphertext)\n" } ]	14
MrCodeGuy/Jerry1.2	https://github.com/MrCodeGuy/Jerry1.2	2ce5f25f7a6aef1e4cb38ecbba8f3c9f244d3bc1	b78027ba6a36de77e25bf2f5d1c17030238895ee	bf086a61a5097837ac136faf7f125d11b5406fab	refs/heads/master	2022-12-11T19:27:45.533724	2020-08-25T16:18:59	2020-08-25T16:18:59	290,264,190	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5936739444732666, "alphanum_fraction": 0.6072297692298889, "avg_line_length": 46.3220329284668, "blob_id": "34ef9dbe30a50b51dd3719c299b0559d187af006", "content_id": "e07a9b4bf3fbcd49d1837a2ed03e859d171e5b94", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2877, "license_type": "no_license", "max_line_length": 203, "num_lines": 59, "path": "/Jerry 1.2.py", "repo_name": "MrCodeGuy/Jerry1.2", "src_encoding": "UTF-8", "text": "def favor(): # ask to define favor\r\n favor = int(input(\"Do you need any help from me? (reply with a positive number for yes and a negative number for no do not type zero).\")) # says do you need help\r\n \r\n if favor < 0: # if the favor is less than zero\r\n print(\"Bye! More upgrades will come!\") # say bye! more upgrades will come\r\n\r\n \r\n else: #if not\r\n print(\"The favor would be a fun shape\") # says the favor would be a fun shape\r\n import turtle # first step on bringing the turtle\r\n import random\r\n fred = turtle.Pen()# second step on bringing the turtle\r\n colorlist = [\"red\",\"orange\",\"yellow\",\"green\",\"blue\",\"purple\",\"pink\"] # make a list called colorlist\r\n fred.shape(\"turtle\") # Change the shape of the turtle to a turtle\r\n fred.speed(0) # sets speed to zero\r\n \r\n\r\n for i in range(456): # repeat 456 times\r\n col=random.choice (colorlist)# pick a random color from color list\r\n fred.color(col) # change color to col\r\n fred.left(78) # turns left 78 degrees\r\n fred.forward(90) # moves forward 90 pixels\r\n\r\n \r\n favor2 = int(input(\" Do you want another favor? Type a negative number for no and a positive number for yes\")) # asks if you want another favor\r\n if favor2 > 0: #if you asked for the favor\r\n print(\"Ok! The other favor is a game called Jerry Guesses\") # print the message\r\n print (\" The rules are simple. I will choose a number and all you need to do is guess a number which is greater than the number you can use the numbers from the range of 1-11\")# print the message\r\n numberlist = [ \"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\"] # create list\r\n\r\n \r\n for i in range(3): # repeat 3 times\r\n number = random.choice(numberlist) # pick a number in number list\r\n guess = int(input(\" Pick your number!\")) # say pick your number\r\n print(\"Good Job! The number was...\") # say the number was\r\n print(number) # say the number\r\n \r\n \r\n \r\n print(\"Bye! More upgrades will come!\") # says bye! more upgrades will come \r\n \r\n\r\n\r\nname = input (\"What is your name?\") # ask what is your name\r\nprint ( \"Hello\" , name) # says hello your name\r\nprint (\"My name is Jerry!\") # says his name is Jerry\r\nwelness = 0 # the variable wellness equals 0\r\nwellness = int (input (\"How are you doing? (respond as a positive number for well and a negetive number for not well do not choose zero)\")) # asks if you are doing well\r\n\r\n\r\nif wellness > 0: # if the variable wellness is greater than zero\r\n print(\"Awesome!\") # say awesome\r\n favor() # do the function favor\r\n\r\n \r\n \r\nelse: # if not\r\n print(\"Hope you get better!\") # says hope you get better\r\n favor() # do the function favor\r\n \r\n\r\n \r\n\r\n\r\n \r\n\r\n" }, { "alpha_fraction": 0.6857143044471741, "alphanum_fraction": 0.7428571581840515, "avg_line_length": 16.5, "blob_id": "115595673f472dfa84e0203634b5dd9d6b24309f", "content_id": "b72f36c5f9b8284730a6b402c5986021cd4dcc48", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 35, "license_type": "no_license", "max_line_length": 23, "num_lines": 2, "path": "/README.md", "repo_name": "MrCodeGuy/Jerry1.2", "src_encoding": "UTF-8", "text": "# Jerry1.2\nThe third of the Jerrys\n" } ]	2
vilcans/LD24	https://github.com/vilcans/LD24	87a4aefbbcceb0b33a30fcac64193de577582e14	b957acc0880f96af07dfa57a07f447f6bef962f0	74d838e11813a196a5a83fa12bc6669aede280a0	refs/heads/master	2021-01-22T16:18:21.173203	2012-09-14T08:50:39	2012-09-14T08:50:39	null	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.4834834933280945, "alphanum_fraction": 0.48848849534988403, "avg_line_length": 20.717391967773438, "blob_id": "75032a07620c25ce984fafbf13b555af8d68ea7d", "content_id": "73a32314dd6cde28f5b227b37d30d2f3e9d2583d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 999, "license_type": "no_license", "max_line_length": 97, "num_lines": 46, "path": "/bin/make-levels.py", "repo_name": "vilcans/LD24", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\nimport yaml\nimport sys\nimport json\n\nlevels = yaml.load_all(sys.stdin)\n\nprint 'levels = [];'\n\ntypes = {\n 'p': 'pawn',\n 'b': 'bishop',\n 'r': 'rook',\n 'n': 'knight',\n 'k': 'king',\n 'q': 'queen',\n}\n\nfor number, data in enumerate(levels, 1):\n\n print 'levels[%d] = function(board) {' % number\n\n board = data['board']\n rows = board.split(' ')\n assert len(rows) == 8\n assert all(len(row) == 8 for row in rows)\n rows.reverse()\n for r in rows:\n print '// ' + r\n\n for r in range(8):\n for c in range(8):\n p = rows[r][c]\n if p == '.':\n continue\n type = types[p.lower()]\n team = 'Piece.WHITE' if p.islower() else 'Piece.BLACK'\n print 'board.addPiece(new Piece({type: %r, team: %s}), board.getSquare(%d, %d));' % (\n type, team, r, c\n )\n\n print 'return %s;' % json.dumps({\n 'description': data['description']\n })\n print '}'\n" }, { "alpha_fraction": 0.7477797269821167, "alphanum_fraction": 0.75133216381073, "avg_line_length": 23.478260040283203, "blob_id": "df8659382d8c2edfd2b288cac8a79730971b7bd1", "content_id": "d65cc17da2bf61a7d32c82a498567309a8b42546", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 563, "license_type": "no_license", "max_line_length": 75, "num_lines": 23, "path": "/bin/release", "repo_name": "vilcans/LD24", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\nexport RELEASES_MASTER_REPO=filur:releases/LD24.git\nexport GIT_WORK_TREE=$PWD\nexport GIT_DIR=$PWD/releases.git\n\nif [ ! -e $GIT_DIR ]; then\n git clone --bare $RELEASES_MASTER_REPO $GIT_DIR\nfi\ngit config core.bare false\ngit config remote.origin.fetch '+refs/heads/:refs/remotes/origin/'\ngit fetch\ngit reset --mixed origin/master # fast-forward\n\ngit status\ngit add -fA site/ nginx.conf\ngit status\necho 'Starting a shell so you can look around. Exit the shell to continue.'\nbash\necho 'Press enter to make the release'\nread\ngit commit\ngit push origin\n" } ]	2
cookingaunt/First	https://github.com/cookingaunt/First	79c78b540e33ddb68d2d9e706edcd3514ffe4091	64420417e26b1ab8df79ba202cd526d8ecbaf86d	eb58ed67222c962c7a4abb3a117b493121b4e9a9	refs/heads/master	2022-12-06T15:12:36.127482	2020-08-26T11:06:52	2020-08-26T11:06:52	290,471,091	3	1	null	null	null	null	null	[ { "alpha_fraction": 0.800000011920929, "alphanum_fraction": 0.800000011920929, "avg_line_length": 16.5, "blob_id": "8955d7e174959f993e2a6f540f916730a1675c1e", "content_id": "5f6954ccde50e5384017b1ace0de1a23d0b4292a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 35, "license_type": "no_license", "max_line_length": 26, "num_lines": 2, "path": "/README.md", "repo_name": "cookingaunt/First", "src_encoding": "UTF-8", "text": "# First\nmy first github repository\n" }, { "alpha_fraction": 0.7749999761581421, "alphanum_fraction": 0.7749999761581421, "avg_line_length": 40, "blob_id": "45191c9d00216f41c357f3cfc65e9abfaece0bb5", "content_id": "99d221b475dfe73b71281c0eaf81a5766d27af24", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 40, "license_type": "no_license", "max_line_length": 40, "num_lines": 1, "path": "/first_git.py", "repo_name": "cookingaunt/First", "src_encoding": "UTF-8", "text": "print(\"this is my first GitHub attempt\")" } ]	2
mosherbd/devcontainer-base	https://github.com/mosherbd/devcontainer-base	b2fe8fa9a8d970bc4daacb583d3663c6904aef4b	1abc31d585353d292a716bf0df7502e1d85bf0d0	0e49509197197dd729987432818ac79e45f31009	refs/heads/main	2023-02-25T05:59:57.737736	2021-10-30T11:56:09	2021-10-30T11:56:09	331,085,551	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7698412537574768, "alphanum_fraction": 0.7936508059501648, "avg_line_length": 41, "blob_id": "d01df7b981c24cd5af3fba546fb72a87dbb88341", "content_id": "ce024a07c97321359aabf3215b4be6ae014edec0", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": true, "language": "Shell", "length_bytes": 252, "license_type": "permissive", "max_line_length": 114, "num_lines": 6, "path": "/dotnet/utils/dotnet-install.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/sh\n\ncurl https://packages.microsoft.com/config/ubuntu/21.04/packages-microsoft-prod.deb -o packages-microsoft-prod.deb\ndpkg -i packages-microsoft-prod.deb\nrm packages-microsoft-prod.deb\napt-helper --no-upgrade apt-transport-https dotnet-sdk-5.0\n" }, { "alpha_fraction": 0.7507987022399902, "alphanum_fraction": 0.7571884989738464, "avg_line_length": 19.933332443237305, "blob_id": "3b9681c878314d91f75f1fe223121cbc1972bd10", "content_id": "048deb024c7fb838877d33443aa268b7167cd3f3", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 313, "license_type": "permissive", "max_line_length": 52, "num_lines": 15, "path": "/base/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM ubuntu\nLABEL maintainer=\"[email protected]\"\nLABEL LABEL version=\"1.0\"\nLABEL description=\"base development container image\"\n\nARG USERNAME=\"docker\"\n\nENV DEVCONTAINER_UTILS_PATH=/usr/local/bin\n\nCOPY utils/* ${DEVCONTAINER_UTILS_PATH}/\n\nRUN apt-helper sudo curl && \\\n\tadduser-nopasswd-sudo.sh ${USERNAME}\n\nUSER ${USERNAME}" }, { "alpha_fraction": 0.7563451528549194, "alphanum_fraction": 0.7664974331855774, "avg_line_length": 34.90909194946289, "blob_id": "abbc21f0b41bc6603b334f95b8b13dc41a0a7f5d", "content_id": "92f1264da00df1a67f667c06a45afeea254c1a26", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 394, "license_type": "permissive", "max_line_length": 195, "num_lines": 11, "path": "/gui/README.md", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "# dev-base-gui\n\n## Overview\nThis is a ubuntu-based docker image that provides the required dependencies to forward x11 clients from a docker container. Use this Dockerfile to extend this functionality to ubuntu base images.\n\n## X11 Servers\n### Windows\n- VcXSrv: https://sourceforge.net/projects/vcxsrv/\n- Xming https://sourceforge.net/projects/xming/\n### OSX\n- XQuartz: https://www.xquartz.org/" }, { "alpha_fraction": 0.5279411673545837, "alphanum_fraction": 0.5367646813392639, "avg_line_length": 16.921052932739258, "blob_id": "1e1404e1581e153337124c4e526dcc45fc452808", "content_id": "20efaea15b0f38b3d9014e58dd382508e779f642", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 680, "license_type": "permissive", "max_line_length": 56, "num_lines": 38, "path": "/node/utils/npm-install.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\nNODE_VERSION=node\nNPM_VERSION=\nPACKAGES=()\nwhile [[ $# -gt 0 ]]; do\n key=\"$1\"\n case $key in\n --node-version)\n NODE_VERSION=$2\n shift # past argument\n shift # past value\n ;;\n --npm-version)\n NPM_VERSION=$2\n shift # past argument\n shift # past value\n ;;\n ) # unknown option\n PACKAGES+=(\"$1\") # save it in an array for later\n shift # past argument\n ;;\n esac\ndone\n\n. nvm-install.sh\n\n. nvm-activate.sh\n\nnvm install ${NODE_VERSION}\n\nif [[ ! -z ${NPM_VERSION} ]]; then\n npm install -g npm@${NPM_VERSION}\nfi\n\nif [[ ${#PACKAGES[@]} -gt 0 ]]; then\n npm install \"${PACKAGES[@]}\"\nfi" }, { "alpha_fraction": 0.8469387888908386, "alphanum_fraction": 0.8469387888908386, "avg_line_length": 48.5, "blob_id": "f08e51ee6ec0384678f99f7b4bb5c179ae221a8c", "content_id": "a01d43314cb9e80a6b7f21815ea7db55c4881744", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 98, "license_type": "permissive", "max_line_length": 61, "num_lines": 2, "path": "/cpp/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\nRUN sudo apt-helper --no-upgrade build-essential gdb valgrind" }, { "alpha_fraction": 0.642201840877533, "alphanum_fraction": 0.6513761281967163, "avg_line_length": 42.599998474121094, "blob_id": "021d00dc936873f3605653a57a1a3a86dbdd4923", "content_id": "42c85e34c99250ceedfb91f2ab42826d6d4c9346", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 218, "license_type": "permissive", "max_line_length": 118, "num_lines": 5, "path": "/node/utils/nvm-install.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/sh\n\nNVM_VERSION=$(curl -s https://api.github.com/repos/nvm-sh/nvm/tags \| grep name -m 1 \| xargs \| cut -c7- \| sed 's/,$//')\n\ncurl -o- https://raw.githubusercontent.com/nvm-sh/nvm/${NVM_VERSION}/install.sh \| bash\n" }, { "alpha_fraction": 0.6783784031867981, "alphanum_fraction": 0.6932432651519775, "avg_line_length": 36.04999923706055, "blob_id": "231663117182d6a6745a906c9df6565aace269c3", "content_id": "62d1e615a531d3dc46afbc7b427916733736539e", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 740, "license_type": "permissive", "max_line_length": 146, "num_lines": 20, "path": "/azureagent/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\n\nARG TARGETARCH=amd64\nARG AGENT_VERSION=2.185.1\n\nWORKDIR /home/docker/azp\n\nCOPY utils/ ${DEVCONTAINER_UTILS_PATH}/\n\nRUN curl -LsS https://aka.ms/InstallAzureCLIDeb \| sudo bash && \\\n if [ \"$TARGETARCH\" = \"amd64\" ]; then \\\n AZP_AGENTPACKAGE_URL=https://vstsagentpackage.azureedge.net/agent/${AGENT_VERSION}/vsts-agent-linux-x64-${AGENT_VERSION}.tar.gz; \\\n else \\\n AZP_AGENTPACKAGE_URL=https://vstsagentpackage.azureedge.net/agent/${AGENT_VERSION}/vsts-agent-linux-${TARGETARCH}-${AGENT_VERSION}.tar.gz; \\\n fi; \\\n curl -LsS \"$AZP_AGENTPACKAGE_URL\" \| tar -xz; \\\n sudo ./bin/installdependencies.sh && \\\n sudo apt-helper --clean\n\nENTRYPOINT [ \"bash\", \"-c\", \"azureagent-start.sh\" ]" }, { "alpha_fraction": 0.7576923370361328, "alphanum_fraction": 0.7769230604171753, "avg_line_length": 26.88888931274414, "blob_id": "f5a6b9b2b830b05c97a0b904694866456ee79199", "content_id": "776dc4f060dd9e97d8e4555558413dcf70691680", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 260, "license_type": "permissive", "max_line_length": 64, "num_lines": 9, "path": "/gui/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\r\n\r\nLABEL maintainer=\"[email protected]\"\r\nLABEL LABEL version=\"1.0\"\r\nLABEL description=\"containerized gui development image\"\r\n\r\nENV DISPLAY=host.docker.internal:0.0\r\n\r\nRUN sudo apt-helper --no-install-recommends xorg libgl1-mesa-glx\r\n" }, { "alpha_fraction": 0.5, "alphanum_fraction": 0.5, "avg_line_length": 18, "blob_id": "4c1f8369ac474bbdc10eac5d7b2ea8044ed65a3f", "content_id": "62de4e9e2f66c7d6eda17fd3edecd135cf3b3e23", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 94, "license_type": "permissive", "max_line_length": 52, "num_lines": 5, "path": "/node/utils/nvm-activate.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/sh\n\nexport NVM_DIR=\"${HOME}/.nvm\"\n\n[ -s \"${NVM_DIR}/nvm.sh\" ] && \\. \"${NVM_DIR}/nvm.sh\"" }, { "alpha_fraction": 0.6292906403541565, "alphanum_fraction": 0.6384439468383789, "avg_line_length": 24.764705657958984, "blob_id": "fad5c4438daeb4bc2b9ec5f24241215c015ddc27", "content_id": "bdb6bbc6ce0eb5ba914aa350e65264514bf20314", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 437, "license_type": "permissive", "max_line_length": 70, "num_lines": 17, "path": "/base/utils/adduser-nopasswd-sudo.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/sh\n\n# Adds a sudo user that does not require a password.\n\n# Requires sudo\n\nif [ \"$#\" -ne 1 ]; then\n echo \"usage: ${0} [username]\"\n exit 1\nfi\n\nUSERNAME=\"$1\"\nadduser --disabled-password --gecos '' ${USERNAME}\nadduser ${USERNAME} sudo\necho '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers\necho 'USER=\"'${USERNAME}'\"; export USER' >> /home/${USERNAME}/.bashrc\necho 'USER=\"'${USERNAME}'\"; export USER' >> /home/${USERNAME}/.profile" }, { "alpha_fraction": 0.7304075360298157, "alphanum_fraction": 0.7304075360298157, "avg_line_length": 28.380952835083008, "blob_id": "05468ab9fc7b57dcf2659f6fa107d0cd0fc2f457", "content_id": "ca90f03d9f4a29458c0f398a3ca1b99f4d88fe0a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 638, "license_type": "permissive", "max_line_length": 197, "num_lines": 21, "path": "/java/README.md", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "# devcontainer-java\r\n\r\n## Overview\r\n\r\nThis Dockerfile extends the brandonmosher/devcontainer-base container image with sdkman, java sdk, ant, ivy, maven, and gradle. Specific versions for each can easily be specified in the Dockerfile.\r\n\r\n## Utils\r\n\r\nThe following utils are copied to DEVCONTAINER_UTILS_PATH.\r\n\r\n### ivy_install.sh \r\n\r\nAutomates installation of the specified version of ivy\r\n\r\n ivy_install.sh [ivy version] [ivy jar dest filepath]\r\n\r\n### sdk_install.sh\r\n\r\nWrapper for sdkman to install multiple sdks in a single command and clean up junk files to reduce container size.\r\n\r\n sdk_install.sh [\"[sdk [version]]\", ...]\r\n" }, { "alpha_fraction": 0.7987477779388428, "alphanum_fraction": 0.7987477779388428, "avg_line_length": 52.238094329833984, "blob_id": "44d1dbec16561b95b81f9313f8c80d86fe5a021d", "content_id": "07a29e0d301f023bd2f8db4699cc4e42f885b1f6", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1118, "license_type": "permissive", "max_line_length": 478, "num_lines": 21, "path": "/base/README.md", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "# devcontainer-base\n\n## Overview\n\nThis Dockerfile builds a ubuntu container image intended as a common base for language-specific or project-specific development images. The goal is to eliminate duplication of boilerplate configuration needed to make the container more suitable for interactive use. This includes setting the timezone and locale, and creating a non-root sudo user. No assumptions are made about derived containers i.e. no compilers, version control, build system, libraries, etc... are included.\n\n## Utils\n\nThe following utils are copied to DEVCONTAINER_UTILS_PATH. DEVCONTAINER_UTILS_PATH defaults to /usr/local/bin making the utils available from $PATH. Furhtermore, DEVCONTAINER_UTILS_PATH is set as a persistent environment variable so that derived images may use these utils and add additional utils as needed.\n\n### adduser-nopassword-sudo.sh\n\nAdds a sudo user that requires no password for quick and easy ad-hoc container changes\n\n adduser-nopassword-sudo.sh [username]\n\n### apt-install.sh\n\nCombines apt update/install/dist-upgrade/cleanup tasks\n\n apt-install.sh [options] [package names]\n" }, { "alpha_fraction": 0.5910151600837708, "alphanum_fraction": 0.5921820402145386, "avg_line_length": 26.190475463867188, "blob_id": "a235c807fa4baf21cb9005b21b3390fa2dffd7c4", "content_id": "ec7c79e6ad832bd69b9cc503933f2a2d74208a2b", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1714, "license_type": "permissive", "max_line_length": 76, "num_lines": 63, "path": "/update.py", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python3\n\nimport glob\nimport re\nfrom pathlib import Path\nimport os\nfrom subprocess import Popen, PIPE\n\nupdate_build = set(Path('update_build').read_text().splitlines())\nupdate_push = set(Path('update_push').read_text().splitlines())\n\ndockerfilepaths = [Path(dfp) for dfp in glob.glob(\".//Dockerfile\")]\n\nm = {}\nfor dockerfilepath in dockerfilepaths:\n dockerfiletext = dockerfilepath.open('r').read()\n f = str(dockerfilepath.parent)\n t = re.match(r'^FROM.', dockerfiletext).group()\n if '-' not in t:\n continue\n t = t.split('-')[1]\n if f not in m:\n m[f] = set()\n m[f].add(t)\n\ncompleted = set()\ndef build(target, tag):\n if (target in completed) or (target not in update_build):\n return\n if target in m:\n for dep in m[target]:\n build(dep, tag)\n \n print(\"Building {}...\".format(target))\n p = Popen([\n 'docker',\n 'build',\n '--no-cache',\n '-t',\n 'brandonmosher/devcontainer-{}:{}'.format(target, tag),\n Path(target).resolve()\n ], stdout=PIPE, stderr=PIPE, stdin=PIPE)\n out = Path('{}.out'.format(target))\n out.open('wb').write(p.stderr.read())\n out.open('ab').write(p.stdout.read())\n \n if target not in update_push:\n return\n \n cmd = 'docker push brandonmosher/devcontainer-{}:{}'.format(target, tag)\n print(\"Pushing {}...\".format(target))\n p = Popen([\n 'docker',\n 'push',\n 'brandonmosher/devcontainer-{}:{}'.format(target, tag)\n ], stdout=PIPE, stderr=PIPE, stdin=PIPE)\n out.open('ab').write(p.stderr.read())\n out.open('ab').write(p.stdout.read())\n\n completed.add(target)\n\nfor target in m:\n build(target, 'latest')\n\n" }, { "alpha_fraction": 0.7684887647628784, "alphanum_fraction": 0.7749196290969849, "avg_line_length": 25, "blob_id": "0929813fb9d7dfeeae1863e0bb28f3e31ae108a7", "content_id": "6a46c7904544cfc06e046be5d03c2dd429eeec56", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 311, "license_type": "permissive", "max_line_length": 78, "num_lines": 12, "path": "/node/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\n\nLABEL maintainer=\"[email protected]\"\nLABEL LABEL version=\"1.0\"\nLABEL description=\"node development container image\"\n\nENV NODE_VERSION node\nENV NPM_VERSION latest\n\nCOPY utils/* ${DEVCONTAINER_UTILS_PATH}/\n\nRUN npm-install.sh --node-version ${NODE_VERSION} --npm-version ${NPM_VERSION}" }, { "alpha_fraction": 0.8627451062202454, "alphanum_fraction": 0.8627451062202454, "avg_line_length": 50, "blob_id": "c9240a1646a9a4bdad5eaeb30382fed1be30388a", "content_id": "606b44059b50c0fd84bda2cc40b491673994f9d2", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 102, "license_type": "permissive", "max_line_length": 85, "num_lines": 2, "path": "/README.md", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "# devcontainers\nDocker containers suitable for a general purpose interactive development environment.\n" }, { "alpha_fraction": 0.5431720614433289, "alphanum_fraction": 0.5523576140403748, "avg_line_length": 21.68055534362793, "blob_id": "e8e852e1732638eff6d38ce5e074cb92a51c7416", "content_id": "4ae0a873b5b7e86b6713d6dab31ceb592bd66fd1", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 1633, "license_type": "permissive", "max_line_length": 133, "num_lines": 72, "path": "/base/utils/apt-helper", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\n# Runs updates/install/upgrade/cleanup tasks for docker\n\nRUN_STAGES=()\nSKIP_STAGES=()\nPACKAGES=()\nwhile [[ $# -gt 0 ]]; do\n key=\"$1\"\n case $key in\n --update\|--install\|--upgrade\|--clean)\n RUN_STAGES+=(\"${1:2}\")\n shift # past argument\n ;;\n --no-update\|--no-install\|--no-upgrade\|--no-clean)\n SKIP_STAGES+=(\"${1:5}\")\n shift # past argument\n ;;\n ) # unknown option\n PACKAGES+=(\"$1\") # save it in an array for later\n shift # past argument\n ;;\n esac\ndone\n\nIMPLICIT_ALL=\n\nrun () {\n (([[ -n ${IMPLICIT_ALL} ]] \|\| [[ \" ${RUN_STAGES[]} \" =~ \" ${1} \" ]]) && (! [[ \" ${SKIP_STAGES[]} \" =~ \" ${1} \" ]])) && return 0\n return -1\n}\n\nif run install && [[ ${#PACKAGES[@]} -eq 0 ]]; then\n echo \"usage: apt-helper [--update] [--install] [--upgrade] [--clean] [package names]\"\n exit 1\nfi\n\nif [[ ${#RUN_STAGES[@]} -eq 0 ]]; then\n echo \"Implicitly performing all non-specified operations...\"\n IMPLICIT_ALL=true\nfi\n\nset -- \"${PACKAGES[@]}\"\n\nif run update; then\n echo \"Updating...\"\n apt-get update\n # if [[ -n $(find /var/lib/apt/lists/ -maxdepth 0 -empty) ]]; then\n # echo \"Updating...\"\n # apt-get update\n # else\n # echo \"Already updated.\"\n # fi\nfi\n\nif run install; then\n echo \"Installing...\"\n DEBIAN_FRONTEND=noninteractive apt-get install -y \"$@\"\nfi\n\nif run upgrade; then\n echo \"Upgrading...\"\n DEBIAN_FRONTEND=noninteractive apt-get dist-upgrade -y\nfi\n\nif run clean; then\n echo \"Cleaning...\"\n apt-get clean autoclean\n apt-get autoremove -y\n rm -rf /var/lib/apt/lists/\n rm -rf /tmp/\nfi\n" }, { "alpha_fraction": 0.7364341020584106, "alphanum_fraction": 0.7441860437393188, "avg_line_length": 27.77777862548828, "blob_id": "2315657a4ef52c71069373a0e6f8a23ff7ebb641", "content_id": "036f6bd62b3b6bc05ad95a86b8c12ec6feab0079", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 258, "license_type": "permissive", "max_line_length": 58, "num_lines": 9, "path": "/playwright/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-node\n\nLABEL maintainer=\"[email protected]\"\nLABEL LABEL version=\"1.0\"\nLABEL description=\"playwright development container image\"\n\nRUN . nvm-activate.sh && \\\n npx playwright install-deps && \\\n apt-helper --no-update --no-upgrade git" }, { "alpha_fraction": 0.8214285969734192, "alphanum_fraction": 0.8452380895614624, "avg_line_length": 41.5, "blob_id": "17bb5add363813b57cccbdb7ced3e2087cb30f5c", "content_id": "aa0b9a6aad3aa744f40870324ab5370f68272052", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 84, "license_type": "permissive", "max_line_length": 47, "num_lines": 2, "path": "/python/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\nRUN apt-helper --no-upgrade python3 python3-pip" }, { "alpha_fraction": 0.801886796951294, "alphanum_fraction": 0.801886796951294, "avg_line_length": 20.399999618530273, "blob_id": "c4934778ecced1f96846ac7b3ecf841adbe96e21", "content_id": "724be64c1faae0bf3f27e07eec550deeae49313a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 106, "license_type": "permissive", "max_line_length": 40, "num_lines": 5, "path": "/dotnet/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\n\nCOPY utils/ ${DEVCONTAINER_UTILS_PATH}/\n\nRUN sudo dotnet-install.sh" }, { "alpha_fraction": 0.7095761299133301, "alphanum_fraction": 0.7174254059791565, "avg_line_length": 31.526315689086914, "blob_id": "50ef8676c396892187aa01bc51b35c6bc4942f61", "content_id": "f0ff0b3c4db0ef30e67c6f0758b7c85469cbc088", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 637, "license_type": "permissive", "max_line_length": 89, "num_lines": 19, "path": "/java/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\r\n\r\nLABEL maintainer=\"[email protected]\"\r\nLABEL LABEL version=\"1.0\"\r\nLABEL description=\"java development container image\"\r\n\r\nARG JAVA_VERSION=\"java\"\r\nARG ANT_VERSION=\"ant\"\r\nARG MAVEN_VERSION=\"maven\"\r\nARG GRADLE_VERSION=\"gradle\"\r\nARG IVY_VERSION=2.5.0\r\nARG IVY_JAR_DEST_FILEPATH=~/.sdkman/candidates/ant/current/lib/\r\nARG VERSION_CONTROL=git\r\n\r\nCOPY utils/* ${DEVCONTAINER_UTILS_PATH}/\r\n\r\nRUN sudo apt-helper --no-upgrade zip unzip gpg ${VERSION_CONTROL} && \\\r\n sdk-install.sh ${JAVA_VERSION} ${ANT_VERSION} ${MAVEN_VERSION} ${GRADLE_VERSION} && \\\r\n ivy-install.sh ${IVY_VERSION} ${IVY_JAR_DEST_FILEPATH}\r\n" }, { "alpha_fraction": 0.6689059734344482, "alphanum_fraction": 0.6756237745285034, "avg_line_length": 27.16216278076172, "blob_id": "381c602c9d79f00fd164c7b015badb03ad0d2599", "content_id": "c9a56d3af1f5f205ef8b9bf2c4e01359a45215e4", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 1042, "license_type": "permissive", "max_line_length": 101, "num_lines": 37, "path": "/java/utils/ivy-install.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\n# Automates installation of the specified version of ivy\n\n# Requires curl, gpg, tar packages\n\nif [ \"$#\" -ne 2 ]; then\n echo \"usage: ${0} [ivy version] [ivy jar dest filepath]\"\n exit 1\nfi\n\ncd ~\n\nANT_HOST=https://downloads.apache.org/ant\nANT_KEYS_FILENAME=KEYS\n\nIVY_VERSION=\"${1}\"\nIVY_JAR_DEST_FILEPATH=\"${2}\"\n\nIVY_HOST=${ANT_HOST}/ivy/${IVY_VERSION}\nIVY_TAR_FILENAME=apache-ivy-${IVY_VERSION}-bin.tar.gz\nIVY_JAR_SRC_FILEPATH=${IVY_TAR_FILENAME/-bin.tar.gz/}/ivy-${IVY_VERSION}.jar\nIVY_ASC_FILENAME=${IVY_TAR_FILENAME}.asc\n\ncurl -O -J -L ${IVY_HOST}/${IVY_TAR_FILENAME}\ncurl -O -J -L ${IVY_HOST}/${IVY_ASC_FILENAME}\ncurl -O -J -L ${ANT_HOST}/${ANT_KEYS_FILENAME}\n\ngpg --import ${ANT_KEYS_FILENAME}\nif ! gpg --verify ${IVY_ASC_FILENAME} ${IVY_TAR_FILENAME}; then\n echo \"error: gpg could not be verified\"\n exit 1\nfi\n\nmkdir -p ${IVY_JAR_DEST_FILEPATH}\ntar -xvf ${IVY_TAR_FILENAME} -C ${IVY_JAR_DEST_FILEPATH} --strip-components=1 ${IVY_JAR_SRC_FILEPATH}\nrm -rf ${IVY_TAR_FILENAME} ${IVY_ASC_FILENAME} ${ANT_KEYS_FILENAME}\n" }, { "alpha_fraction": 0.6814988255500793, "alphanum_fraction": 0.6978922486305237, "avg_line_length": 22.08108139038086, "blob_id": "b60c35eaeeab38ad2da6aa88225b10cd4e2697d6", "content_id": "5d288083b04c3f3453ab1944a044b2d8cec42292", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 854, "license_type": "permissive", "max_line_length": 75, "num_lines": 37, "path": "/java/utils/sdk-install.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\n# Wrapper for sdkman to install multiple sdks in a single command \n# and clean up junk files to reduce container size.\n\n# Requires packages zip, unzip, curl\n\n# Args are double-quoted strings containing sdkman candidates\nif [ \"$#\" -eq 0 ]; then\n echo \"usage: ${0} [\"[sdk [version]]\", ...]\"\n echo 'example: '${0}' \"java 11.0.9.hs-adpt\" \"gradle 6.8\" \"maven\" \"ivy\"'\n exit 1\nfi\n\ncd ~\n\nCONFIG=\"sdkman_auto_answer=false\nsdkman_auto_selfupdate=false\nsdkman_insecure_ssl=false\nsdkman_curl_connect_timeout=14\nsdkman_curl_max_time=20\nsdkman_beta_channel=false\nsdkman_debug_mode=false\nsdkman_colour_enable=true\nsdkman_auto_env=false\"\n\ncurl -s \"https://get.sdkman.io\" \| bash\nsource .sdkman/bin/sdkman-init.sh\necho \"${CONFIG}\" > ~/.sdkman/etc/config\n\nfor arg in \"$@\"\ndo\n sdk install ${arg}\ndone\n\nrm -rf .sdkman/archives/\nrm -rf .sdkman/tmp/\n" }, { "alpha_fraction": 0.7454545497894287, "alphanum_fraction": 0.7454545497894287, "avg_line_length": 24.090909957885742, "blob_id": "fbf043d8a8a82cc1a429a0e94d508932168817ba", "content_id": "0395288ac1314038bf74ea115b6a2e1647eec043", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 275, "license_type": "permissive", "max_line_length": 151, "num_lines": 11, "path": "/azureagent/README.md", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "# Run\n\n```\ndocker run -e AZP_URL=<Azure Instance URL> -e AZP_TOKEN=<Azure User Token> -e AZP_AGENT_NAME=mydockeragent brandonmosher/devcontainer-azureagent:latest\n```\n\n# Variants\n\n## dotnetcore\n\nDepends on dotnet/utils and node/utils, so docker build must be run with PATH=.." } ]	23
saeid-h/Traffic-Tracker	https://github.com/saeid-h/Traffic-Tracker	1f4a100b7d8c42bbb90ef3b81aa456e46f458497	f1aac40b1503b6bce971649bf9bd90689c11644b	7c6dae75d6f7c57023d0c627383815d18b301eeb	refs/heads/main	2023-04-16T20:31:18.749496	2021-04-26T23:50:16	2021-04-26T23:50:16	361,922,307	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6914688944816589, "alphanum_fraction": 0.7126362919807434, "avg_line_length": 22.714284896850586, "blob_id": "1ae512ac1f5ddb5cbde4f9ab4e06f7137aece3ea", "content_id": "bdccc55eed43f611a391a92509edc8a725720176", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1559, "license_type": "permissive", "max_line_length": 319, "num_lines": 63, "path": "/README.md", "repo_name": "saeid-h/Traffic-Tracker", "src_encoding": "UTF-8", "text": "# Traffic Tracker\r\n\r\nThis is a rough estimation of traffic flow using OpenCV library.\r\nIt was tested by Python 3.9 and OpenCV 4.0\r\n\r\n\r\n\r\n# Requirements\r\n\r\nMake sure you have installed the following requirements:\r\n\r\n- Python3.9\r\n- OpenCV 4.0\r\n- numpy, imutils, argparse\r\n\r\n```bash\r\ngit clone https://github.com/saeid-h/Traffic-Tracker.git\r\n# If you do not have python3.9, install it. \r\n# It might be working with other version of Python, but it's not tested before.\r\n\r\n# Make virtual environment\r\nvirtualenv -p /usr/bin/python3.9 venv\r\n# Activate the environment\r\nsource venv/bin/activate\r\npip install <libraries>\r\n\r\n```\r\n\r\n# Demo\r\n\r\nTry the [main.py]() \r\n\r\n```bash\r\npython main.py --video-file $YOURVIDEO \r\n```\r\n\r\nYou may turn off the layer by adding the following switches:\r\n\r\n```bash\r\npython main.py --video-file $YOURVIDEO --no-speed-check\r\n```\r\n\r\n```bash\r\npython main.py --video-file $YOURVIDEO --no-heatmap\r\n```\r\n\r\n```bash\r\npython main.py --video-file $YOURVIDEO --no-car-detection\r\n```\r\n\r\nThere hyper paramers that you can change them:\r\n```\r\n--blur-window <int>\r\n--threshold <int>\r\n--object-area <int>\r\n--invalid-length <int>\r\n--dilation-kernel <int>\r\n--dilation-iter <int>\r\n--heatmap-threshold <int>\r\n--speed-limit <int>\r\n```\r\n\r\nYou may also download a sample video [here](https://spaceeco-my.sharepoint.com/personal/ovunc_spacee_com/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fovunc%5Fspacee%5Fcom%2FDocuments%2Ftraffic%2Emp4&parent=%2Fpersonal%2Fovunc%5Fspacee%5Fcom%2FDocuments&originalPath=aHR0cHM6Ly9zcGFjZWVjby1teS5zaGFyZXBvaW50LmNvbS86djovZy9wZXJzb25hbC9vdnVuY19zcGFjZWVfY29tL0VmNWxMci16NHpkTWtZMUQ5Tl9jYU9zQlYxTjNfUEJzZkg1WUlVT0hyVEdQb1E%5FcnRpbWU9YmE0Z0pnc0oyVWc)\r\n\r\n" }, { "alpha_fraction": 0.5406201481819153, "alphanum_fraction": 0.5631387233734131, "avg_line_length": 44.95121765136719, "blob_id": "5c9158f724ddd9a2a97d25c50e1b6f285284dff4", "content_id": "171a107fc37223d75837b91f220821df3852a07e", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5773, "license_type": "permissive", "max_line_length": 175, "num_lines": 123, "path": "/main.py", "repo_name": "saeid-h/Traffic-Tracker", "src_encoding": "UTF-8", "text": "'''\r\n#############################################################################\r\n#\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\r\n# Copyright (c) 2021 under MIT License #\r\n# Code by Saeid Hosseinipoor <https://saeid-h.github.io/>\t\t #\r\n# All rights reserved.\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\r\n#\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\r\n############################################################################# \r\n\r\nReferences:\r\n https://www.learnpythonwithrune.org/opencv-counting-cars-a-simple-approach/\r\n https://towardsdatascience.com/build-a-motion-heatmap-videousing-opencv-with-python-fd806e8a2340\r\n https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/\r\n'''\r\n\r\nimport cv2\r\nimport imutils\r\nimport numpy as np\r\nimport argparse\r\nfrom collections import OrderedDict\r\nfrom tracker import CentroidTracker\r\n\r\nRED = (0, 0, 255)\r\nGREEN = (0, 255, 0)\r\nPPF_to_MPH = 15.0\r\n\r\nif __name__ == \"__main__\":\r\n parser = argparse.ArgumentParser(fromfile_prefix_chars='@', description=\"\")\r\n\r\n parser.add_argument('--video-file', default='traffic.mp4', help='')\r\n parser.add_argument('--blur-window', type=int, default=5, help='')\r\n parser.add_argument('--threshold', type=int, default=25, help='')\r\n parser.add_argument('--object-area', type=int, default=180, help='')\r\n parser.add_argument('--invalid-length', type=int, default=3, help='')\r\n parser.add_argument('--dilation-kernel', type=int, default=3, help='')\r\n parser.add_argument('--dilation-iter', type=int, default=3, help='')\r\n parser.add_argument('--heatmap-threshold', type=int, default=3, help='')\r\n parser.add_argument('--no-heatmap', action=\"store_true\", help='')\r\n parser.add_argument('--no-car-detection', action=\"store_true\", help='')\r\n parser.add_argument('--no-speed-check', action=\"store_true\", help='')\r\n parser.add_argument('--speed-limit', type=int, default=60, help='')\r\n parser.add_argument('--save-video', default=None, help='')\r\n \r\n args = parser.parse_args()\r\n\r\n blur_window = args.blur_window\r\n threshold = args.threshold\r\n object_area = args.object_area\r\n invalid_length = args.invalid_length\r\n dilation_kernel = np.ones((args.dilation_kernel, args.dilation_kernel), 'uint8')\r\n dilation_iter = args.dilation_iter\r\n heatmap_threshold = args.heatmap_threshold\r\n video_file = args.video_file\r\n\r\n ct = CentroidTracker(500)\r\n\r\n cap = cv2.VideoCapture(video_file)\r\n first_iteration_indicator = True\r\n while cap.isOpened():\r\n _, frame = cap.read()\r\n\r\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n gray = cv2.GaussianBlur(gray, (blur_window, blur_window), 0)\r\n\r\n if first_iteration_indicator:\r\n last_frame = gray\r\n accum_image = np.zeros_like(gray)\r\n first_iteration_indicator = False\r\n last_tracked_boxes = OrderedDict()\r\n last_tracked_centroids = OrderedDict()\r\n continue\r\n \r\n delta_frame = cv2.absdiff(last_frame, gray)\r\n last_frame = gray\r\n thresh1 = cv2.threshold(delta_frame, threshold, 255, cv2.THRESH_BINARY)[1]\r\n thresh = cv2.dilate(thresh1, dilation_kernel, iterations=dilation_iter)\r\n if thresh.max() == 0: continue\r\n\r\n if not args.no_car_detection or not args.no_speed_check:\r\n contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\n contours = imutils.grab_contours(contours)\r\n total_number_cars = 0\r\n rects = []\r\n for contour in contours:\r\n (x, y, w, h) = cv2.boundingRect(contour)\r\n if w < invalid_length or h < invalid_length:\r\n continue\r\n if not args.no_car_detection:\r\n total_number_cars += 1\r\n cv2.rectangle(frame, (x, y), (x + w, y + h), GREEN, 2)\r\n if not args.no_speed_check:\r\n rects.append([x, y, x+h, y+w])\r\n\r\n if not args.no_speed_check:\r\n ct.update(rects)\r\n tracked_boxes = ct.boxes\r\n tracked_centroids = ct.objects\r\n for objectID in tracked_centroids.keys():\r\n if not objectID in last_tracked_centroids.keys():\r\n continue\r\n speed_pixel_per_frame = np.sqrt((tracked_centroids[objectID][0]-last_tracked_centroids[objectID][0])2 + \r\n (tracked_centroids[objectID][1]-last_tracked_centroids[objectID][1])2)\r\n speed_MPH = speed_pixel_per_frame * PPF_to_MPH\r\n if speed_MPH > args.speed_limit:\r\n cv2.rectangle(frame, (tracked_boxes[objectID][0], tracked_boxes[objectID][1]), (tracked_boxes[objectID][2], tracked_boxes[objectID][3]), RED, 2)\r\n # cv2.putText(frame, \"{:4.0f} mph\".format(speed_MPH) , (tracked_boxes[objectID][0]-5, tracked_boxes[objectID][1]-5), cv2.FONT_HERSHEY_PLAIN, 1, RED, 2)\r\n last_tracked_boxes = tracked_boxes.copy()\r\n last_tracked_centroids = tracked_centroids.copy()\r\n \r\n if not args.no_heatmap:\r\n accum_image = cv2.addWeighted(accum_image, 0.98, thresh, 0.1, 0.0)\r\n color_image_video = cv2.applyColorMap(accum_image, cv2.COLORMAP_HOT)\r\n frame = cv2.addWeighted(color_image_video, 0.5, frame, 0.8, 0.0)\r\n if not args.no_car_detection:\r\n cv2.putText(frame, \"Total Number of Cars: {}\".format(total_number_cars) , (800, 700), cv2.FONT_HERSHEY_PLAIN, 2, GREEN, 2) \r\n\r\n cv2.imshow(\"Car counter\", frame)\r\n \r\n if cv2.waitKey(1) & 0xFF == ord('q'):\r\n break\r\n\r\n cap.release()\r\n cv2.destroyAllWindows()" } ]	2
jetamartin/flask_pet_adoption_website	https://github.com/jetamartin/flask_pet_adoption_website	ea28e218efdc512aac5c51524c11c699f96e06ba	db2e6a371229744416fba45288b9b910c6191268	d43f6aafc53ff287cc6631bd8cc0660bfcf2598e	refs/heads/master	2023-03-15T01:35:00.637177	2020-04-27T21:56:38	2020-04-27T21:56:38	259,465,551	0	0	null	2020-04-27T21:55:13	2020-04-27T21:57:14	2021-03-20T03:37:38	Python	[ { "alpha_fraction": 0.4944134056568146, "alphanum_fraction": 0.6927374005317688, "avg_line_length": 16.047618865966797, "blob_id": "f63c0144e9720ab58c7807ce2d0ce5160f2c3ee6", "content_id": "c108dd8743431bd58cfd89418c9045742278598b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 358, "license_type": "no_license", "max_line_length": 26, "num_lines": 21, "path": "/templates/requirements.txt", "repo_name": "jetamartin/flask_pet_adoption_website", "src_encoding": "UTF-8", "text": "astroid==2.3.3\nblinker==1.4\nclick==7.1.1\ncolorama==0.4.3\nFlask==1.1.2\nFlask-DebugToolbar==0.11.0\nFlask-SQLAlchemy==2.4.1\nFlask-WTF==0.14.3\nisort==4.3.21\nitsdangerous==1.1.0\nJinja2==2.11.2\nlazy-object-proxy==1.4.3\nMarkupSafe==1.1.1\nmccabe==0.6.1\npsycopg2-binary==2.8.5\npylint==2.4.4\nsix==1.14.0\nSQLAlchemy==1.3.16\nWerkzeug==1.0.1\nwrapt==1.11.2\nWTForms==2.3.1\n" }, { "alpha_fraction": 0.5025799870491028, "alphanum_fraction": 0.5077399611473083, "avg_line_length": 22.609756469726562, "blob_id": "b73b3c2cdff18f39d8360a6a897585eef6433d89", "content_id": "4441d5c30e406a2dad46893f5e2226416edc715f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 969, "license_type": "no_license", "max_line_length": 104, "num_lines": 41, "path": "/models.py", "repo_name": "jetamartin/flask_pet_adoption_website", "src_encoding": "UTF-8", "text": "\"\"\"Demo file showing off a model for SQLAlchemy.\"\"\"\n\nfrom flask_sqlalchemy import SQLAlchemy\n\ndb = SQLAlchemy()\n\n\ndef connect_db(app):\n \"\"\"Connect to database.\"\"\"\n\n db.app = app\n db.init_app(app)\n\nclass Pet(db.Model):\n \"\"\"Pets for adoption\"\"\"\n\n __tablename__ = \"pets\"\n\n id = db.Column(db.Integer,\n primary_key = True,\n autoincrement = True)\n\n name = db.Column(db.String(20),\n nullable = False)\n\n species = db.Column(db.Text, \n nullable = False)\n\n photo_url = db.Column(\n db.String(200), \n default='https://mylostpetalert.com/wp-content/themes/mlpa-child/images/nophoto.gif'\n )\n\n age = db.Column(db.Float,\n nullable = True)\n\n notes = db.Column(db.Text,\n nullable = True)\n \n available = db.Column(db.Boolean,\n default = True)\n\n" }, { "alpha_fraction": 0.5706638097763062, "alphanum_fraction": 0.5770878195762634, "avg_line_length": 31.75438690185547, "blob_id": "425958b215dbe40563d6322e35ac9357afd6730a", "content_id": "dabcfe10edfc7c51f50902b739ed70cc58d59a75", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1868, "license_type": "no_license", "max_line_length": 135, "num_lines": 57, "path": "/forms.py", "repo_name": "jetamartin/flask_pet_adoption_website", "src_encoding": "UTF-8", "text": "# \"\"\"Forms for our demo Flask app.\"\"\"\n\nfrom flask_wtf import FlaskForm\nfrom wtforms.widgets.html5 import URLInput, Input\n# from wtforms.widgets.html5 import URLInput, Input\n\nfrom wtforms import StringField, FloatField, TextAreaField, Field, BooleanField, SelectField\nfrom wtforms.validators import InputRequired, Optional, Regexp, NumberRange, AnyOf, URL\n\n\nclass AddPetForm(FlaskForm):\n \"\"\"Form for adding a new Pet.\"\"\"\n\n name = StringField(\"Name:\", \n validators=[\n InputRequired(\"Pet name can't be blank\")],\n render_kw={\"placeholder\": \"Enter your pet's name\"} \n )\n\n\n species = SelectField(\n \"Species\",\n choices=[(\"cat\", \"Cat\"), (\"dog\", \"Dog\"), (\"snake\", \"Snake\"), (\"guinea pig\", \"Guinea Pig\"), (\"pig\", \"Pig\"), (\"mouse\", \"Mouse\")],\n render_kw={\"placeholder\": \"test\"}\n )\n\n photo_url = StringField('Photo_URL:', \n validators = [\n Optional(),\n URL('URL must be a valid link')],\n render_kw={\"placeholder\": \"Enter URL for pet's picture (e.g., https://..)\"} \n ) \n# Regexp('^(http\|https):\\/\\/[\\w.\\-]+(\\.[\\w.\\-]+)+.*$', 0,\n # 'URL must be a valid link')])\n\n age = FloatField(\"Age:\", \n validators = [NumberRange(min=0, max=30, message=\"Pet's age can only be between 0 and 30\")],\n render_kw={\"placeholder\": \"Enter your pets age in years (e.g., 1 or 1.5 etc)\"}\n )\n\n notes = TextAreaField(\"Notes:\",\n render_kw={\"placeholder\": \"Enter any notes you thing are pertinent\"}\n )\n\n \n\nclass EditPetForm(FlaskForm):\n \"\"\" Form for editing subset of fields for a pet \"\"\"\n\n photo_url = StringField('Photo_URL:', \n validators = [\n Optional(),\n URL('URL must be a valid link')]) \n\n notes = TextAreaField(\"Notes:\")\n\n available = BooleanField(\"Available?\")\n\n" }, { "alpha_fraction": 0.5969681143760681, "alphanum_fraction": 0.6853110194206238, "avg_line_length": 53.657142639160156, "blob_id": "eae39fe967fe510b74c41396c3ed943fbf32d269", "content_id": "c863ddf972c98be061e9f68cbd0878ca1d9c210e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1913, "license_type": "no_license", "max_line_length": 160, "num_lines": 35, "path": "/seed.py", "repo_name": "jetamartin/flask_pet_adoption_website", "src_encoding": "UTF-8", "text": "from app import db\nfrom models import Pet\n\ndb.drop_all()\ndb.create_all()\n\nPet.query.delete()\n\npet1 = Pet(name=\"Bacon\", species = \"pig\", age=3, available = True,\n photo_url = \"https://images.unsplash.com/photo-1516467508483-a7212febe31a?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1952&q=80\", \n notes = \"Bacon is a sweet adorable pig. He loves to eat\")\n\npet2 = Pet(name=\"Monty\", species = \"snake\", age=4, available = True,\n photo_url = \"https://images.unsplash.com/photo-1585095595274-aeffce35511a?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1950&q=80\", \n notes = \"Monty is a Ball pythons. He eats two mice per week\")\n\npet3 = Pet(name=\"Templeton\", species = \"rat\", age=1, available = True,\n photo_url = \"https://images.unsplash.com/photo-1550697851-920b181d8ca8?ixlib=rb-1.2.1&auto=format&fit=crop&w=1950&q=80\", \n notes = \"Templeton loves his cheese and lots of attention\")\n\npet4 = Pet(name=\"Blanca\", species = \"cat\", age=4, available = True,\n photo_url = \"https://images.unsplash.com/photo-1573148164257-8a2b173be464?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1050&q=80\", \n notes = \"Blanca is a sweet kitty who loves chest and chin rubs\")\n\npet5 = Pet(name=\"Ruffy\", species = \"dog\", age=1.5, available = True,\n photo_url = \"https://images.unsplash.com/photo-1502673530728-f79b4cab31b1?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1950&q=80\", \n notes = \"Ruffy is well trained. He's a snuggler\")\n\npet6 = Pet(name=\"Garth\", species = \"Guinea Pig\", age=1, available = True,\n photo_url = \"https://animals.sandiegozoo.org/sites/default/files/2019-04/animals_guineapig-domestic.jpg\", \n notes = \"Garth is chubby little Guinea Pig who loves his kibbles \")\n\n\ndb.session.add_all([pet1, pet2, pet3, pet4, pet5, pet6])\ndb.session.commit()\n" }, { "alpha_fraction": 0.6430678367614746, "alphanum_fraction": 0.6445427536964417, "avg_line_length": 30.292306900024414, "blob_id": "0419d6d3e132449cc275ba9ec12902b8a11e89ae", "content_id": "43a8ae366d8fa147be2293809d0b19388f79ad47", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2034, "license_type": "no_license", "max_line_length": 92, "num_lines": 65, "path": "/app.py", "repo_name": "jetamartin/flask_pet_adoption_website", "src_encoding": "UTF-8", "text": "from flask import Flask, render_template, flash, redirect, render_template\nfrom flask_debugtoolbar import DebugToolbarExtension\nfrom models import db, connect_db, Pet\n\nfrom forms import AddPetForm\nfrom forms import EditPetForm\n\n\napp = Flask(__name__)\napp.config[\"SECRET_KEY\"] = \"my-secret-word\"\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = 'postgresql:///pet_adoption'\napp.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\n\ndebug = DebugToolbarExtension(app)\n\nconnect_db(app)\n\n\[email protected](\"/\")\ndef homepage():\n \"\"\"Show listing of all pets\"\"\"\n pets = Pet.query.all()\n return render_template(\"index.html\", pets = pets)\n\n\[email protected](\"/add\", methods=[\"GET\", \"POST\"])\ndef add_pet():\n \"\"\"Display Form to add a new pet.\"\"\"\n\n form = AddPetForm()\n\n if form.validate_on_submit():\n name = form.name.data.strip().capitalize()\n species = form.species.data.strip().capitalize()\n \n photo_url = form.photo_url.data if form.photo_url.data else None\n age = form.age.data\n notes = form.notes.data.strip()\n flash(f\"Added {name} the {species} as a new pet\", \"success-msg\")\n new_pet = Pet(name=name, species=species, photo_url=photo_url, age=age, notes=notes)\n db.session.add(new_pet)\n db.session.commit()\n return redirect(\"/\")\n\n else:\n return render_template(\"add_pet.html\", form=form)\n\[email protected](\"/<int:pet_id>/edit\", methods=[\"GET\", \"POST\"])\ndef edit_pet(pet_id):\n \"\"\"Display Pet detail page with form to edit some info on Pets.\"\"\"\n pet = Pet.query.get_or_404(pet_id)\n form = EditPetForm(obj = pet)\n\n if form.validate_on_submit():\n pet.photo_url = form.photo_url.data\n pet.notes = form.notes.data.strip()\n pet.available = form.available.data\n flash(f\"Updated '{pet.name}' the {pet.species}'s profile\", 'success-msg')\n db.session.add(pet)\n db.session.commit()\n # import pdb; pdb.set_trace()\n return redirect(\"/\")\n\n else:\n return render_template(\"edit_pet.html\", form=form, pet=pet)\n" } ]	5
algo-trading-python-airflow/udemy_algo_trading_airflow	https://github.com/algo-trading-python-airflow/udemy_algo_trading_airflow	dc42228f5efc5938b3caf33b83dbc89f5519fa1b	2fab8a52ceb968bb7bdbc0d82cdd0b550481c2dd	b915bb1dd60091d6f50e8d684d28ab974cb0e8e8	refs/heads/main	2023-03-24T10:15:09.508318	2021-02-25T10:34:40	2021-02-25T10:34:40	337,430,008	4	4	null	null	null	null	null	[ { "alpha_fraction": 0.581892192363739, "alphanum_fraction": 0.5890132188796997, "avg_line_length": 24.86842155456543, "blob_id": "321a90cae86301f9e77ae08c7f0de073c3cbf95d", "content_id": "0c37c095ed82fc3a1cd735ce0b44e944453e3ad1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 983, "license_type": "no_license", "max_line_length": 74, "num_lines": 38, "path": "/airflow/dags/update_stock_prices.py", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "from airflow import DAG\nfrom airflow.operators.python import PythonOperator\nfrom airflow.utils.dates import days_ago\nfrom utils import db, tinkoff\nfrom datetime import timedelta\nimport os\n\nDAG_ID = os.path.basename(__file__).replace('.pyc', '').replace('.py', '')\nCONN_ID = 'postgres_stocks'\n\ndefault_args = {\n 'owner': 'airflow',\n 'depends_on_past': False,\n 'start_date': days_ago(1),\n 'retries': 2,\n 'retry_delay': timedelta(minutes=5),\n 'email_on_failure': False,\n 'email_on_retry': False,\n}\n\nwith DAG(\n dag_id=DAG_ID,\n default_args=default_args,\n schedule_interval='5 1 * * ',\n) as dag:\n\n update_stock_prices_aapl = PythonOperator(\n task_id='update_stock_prices_aapl',\n python_callable=db.load_df_to_db,\n op_kwargs={\n 'connector': CONN_ID,\n 'df': tinkoff.get_data_by_ticker_and_period(\n 'AAPL',\n 2,\n ).tail(1),\n 'table_name': 'aapl',\n }\n )\n" }, { "alpha_fraction": 0.586840808391571, "alphanum_fraction": 0.5902273654937744, "avg_line_length": 27.3150691986084, "blob_id": "a7571d96c47dfad486096a4c7df9363239c8f653", "content_id": "5f0f7538db51669c6023aa5b99e36a1d3038f3fd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2067, "license_type": "no_license", "max_line_length": 100, "num_lines": 73, "path": "/airflow/plugins/utils/db.py", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "import psycopg2\nfrom io import StringIO\nimport csv\nfrom airflow.hooks.base import BaseHook\nimport pandas as pd\n\n\ndef _get_db_url(connector: str) -> str:\n connection = BaseHook.get_connection(connector)\n\n return f'user={connection.login} password={connection.password} host={connection.host} ' \\\n f'port={connection.port} dbname={connection.schema}'\n\n\ndef load_df_to_db(connector: str, df: pd.DataFrame, table_name: str) -> None:\n buffer = StringIO()\n df.to_csv(buffer, index=False, sep='\|', na_rep='NUL', quoting=csv.QUOTE_MINIMAL,\n header=False, float_format='%.8f', doublequote=False, escapechar='\\\\')\n buffer.seek(0)\n copy_query = f\"\"\"\n COPY {table_name}({','.join(df.columns)})\n FROM STDIN\n DELIMITER '\|'\n NULL 'NUL'\n \"\"\"\n conn = psycopg2.connect(dsn=_get_db_url(connector))\n with conn.cursor() as cursor:\n cursor.copy_expert(copy_query, buffer)\n conn.commit()\n conn.close()\n\n\ndef get_data_from_price_table(connector: str, table_name: str, filter_: str = None) -> pd.DataFrame:\n query = f\"\"\"\n SELECT time,\n open,\n high,\n low,\n close,\n volume\n FROM {table_name}\n {filter_}\n \"\"\"\n with psycopg2.connect(dsn=_get_db_url(connector)) as conn:\n data = pd.read_sql(query, conn)\n\n return data\n\n\ndef get_data_from_signal_table(connector: str, filter_: str) -> pd.DataFrame:\n query = f\"\"\"\n SELECT time,\n position,\n strategy_type,\n ticker\n FROM signal\n {filter_}\n \"\"\"\n with psycopg2.connect(dsn=_get_db_url(connector)) as conn:\n data = pd.read_sql(query, conn)\n\n return data\n\n\ndef get_last_price_from_price_table(connector: str, table_name: str) -> float:\n data = get_data_from_price_table(\n connector,\n table_name,\n f\"WHERE time = (SELECT max(time) FROM {table_name})\"\n )\n data = data.sort_values(by='time', ascending=False)\n\n return data.iloc[0]['close']\n" }, { "alpha_fraction": 0.5896057486534119, "alphanum_fraction": 0.59617680311203, "avg_line_length": 32.47999954223633, "blob_id": "77e8414372fee0e8a79a11dbca0b0d766eb384c9", "content_id": "dbfc66a550d59c43eb53cc5e2b25a7db324dbac9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1674, "license_type": "no_license", "max_line_length": 84, "num_lines": 50, "path": "/airflow/plugins/utils/strategy.py", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport numpy as np\nfrom utils import db\nfrom typing import Callable\n\n\ndef apply_strategy(\n connector: str,\n source_table_name: str,\n ticker: str,\n strategy_func: Callable,\n op_kwargs: dict\n) -> None:\n data = db.get_data_from_price_table(connector, source_table_name)\n signal = strategy_func(data, op_kwargs).assign(ticker=ticker)\n db.load_df_to_db(connector, signal, 'signal')\n\n\ndef cross_sma_strategy(\n data: pd.DataFrame,\n sma_short: int,\n sma_long: int,\n) -> pd.DataFrame:\n data['sma_short'] = data['close'].rolling(sma_short).mean()\n data['sma_long'] = data['close'].rolling(sma_long).mean()\n\n data['position'] = np.where(data['sma_short'] > data['sma_long'], 1, -1)\n\n return data[['time', 'position']].tail(1).assign(strategy_type='sma')\n\n\ndef bollinger_bands_strategy(\n data: pd.DataFrame,\n sma: int,\n dev: int,\n) -> pd.DataFrame:\n data['sma'] = data['close'].rolling(sma).mean()\n std = data['close'].rolling(sma).std() dev\n data['lower'] = data['sma'] - std\n data['upper'] = data['sma'] + std\n\n data['distance'] = data['close'] - data['sma']\n data['position'] = np.where(data['close'] < data['lower'], 1, np.nan)\n data['position'] = np.where(data['close'] > data['upper'], -1, data['position'])\n data['position'] = np.where(data['distance'] * data['distance'].shift(1) < 0,\n 0, data['position'])\n data['position'] = data['position'].ffill().fillna(0)\n data['position'] = data['position'].astype('int8')\n\n return data[['time', 'position']].tail(1).assign(strategy_type='bollinger')\n" }, { "alpha_fraction": 0.5991129279136658, "alphanum_fraction": 0.6028659343719482, "avg_line_length": 28.494949340820312, "blob_id": "7da629676dd513360ff2cf8b079011dd0ac4d03d", "content_id": "dba93e05a8307aa4299cbe4ae6065a6bf9e89436", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2931, "license_type": "no_license", "max_line_length": 94, "num_lines": 99, "path": "/airflow/plugins/utils/tinkoff.py", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "import tinvest\nimport pandas as pd\nfrom configparser import ConfigParser\nfrom datetime import datetime, timedelta\nfrom typing import Optional\n\n\ndef _get_api_params_from_config() -> dict:\n config_parser = ConfigParser()\n config_parser.read('/usr/local/airflow/tinkoff.cfg')\n\n return {\n 'token': config_parser.get('core', 'TOKEN_TINKOFF'),\n 'use_sandbox': config_parser.get('core', 'USE_SANDBOX')\n }\n\n\ndef get_figi_from_ticker(ticker: str) -> str:\n client = tinvest.SyncClient(_get_api_params_from_config())\n ticker_data = client.get_market_search_by_ticker(ticker)\n return ticker_data.payload.instruments[0].figi\n\n\ndef get_data_by_ticker_and_period(\n ticker: str,\n period_in_days: int = 365,\n freq: tinvest.CandleResolution = tinvest.CandleResolution.day\n) -> pd.DataFrame:\n client = tinvest.SyncClient(_get_api_params_from_config())\n figi = get_figi_from_ticker(ticker)\n\n raw_data = client.get_market_candles(\n figi,\n datetime.now() - timedelta(days=period_in_days),\n datetime.now() - timedelta(days=1),\n freq,\n )\n\n return pd.DataFrame(\n data=(\n (\n candle.time,\n candle.o,\n candle.h,\n candle.l,\n candle.c,\n candle.v,\n ) for candle in raw_data.payload.candles\n ),\n columns=(\n 'time',\n 'open',\n 'high',\n 'low',\n 'close',\n 'volume',\n )\n )\n\n\ndef get_position_by_ticker(ticker: str) -> Optional[tinvest.PortfolioPosition]:\n client = tinvest.SyncClient(_get_api_params_from_config())\n positions = client.get_portfolio().payload.positions\n\n filtered_positions = list(filter(lambda x: x.ticker.lower() == ticker.lower(), positions))\n if len(filtered_positions) == 0:\n return None\n return filtered_positions[0]\n\n\ndef create_limit_order_by_figi(\n figi: str,\n lots: int,\n price: float,\n op_type: str = 'Buy'\n) -> None:\n if op_type not in ('Buy', 'Sell'):\n raise ValueError('Operation type must be Sell or Buy with upper-case first letter')\n client = tinvest.SyncClient(_get_api_params_from_config())\n client.post_orders_limit_order(\n figi=figi,\n body=tinvest.schemas.LimitOrderRequest(\n lots=lots,\n operation=tinvest.schemas.OperationType(value=op_type),\n price=price\n )\n )\n\n\ndef get_current_balance(currency_type: str) -> float:\n client = tinvest.SyncClient(*_get_api_params_from_config())\n currencies = client.get_portfolio_currencies().payload.currencies\n\n filtered_currencies = list(\n filter(lambda x: x.currency.lower() == currency_type.lower(), currencies)\n )\n if len(filtered_currencies) == 0:\n return 0.0\n return float(filtered_currencies[0].balance)\n\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.8142076730728149, "alphanum_fraction": 0.8142076730728149, "avg_line_length": 90.5, "blob_id": "2270bc8a923d6571ca928aac192495e68fd92e6b", "content_id": "c7e38a8a6ba3c084e06d62eacdc03169e03c6113", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 236, "license_type": "no_license", "max_line_length": 153, "num_lines": 2, "path": "/README.md", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "# udemy_algo_trading_airflow\nРепозиторий для курса [Инфраструктура для Алготрейдинга с Python и Apache Airflow](https://www.udemy.com/course/algo-trading-python-airflow) на Udemy.com\n" }, { "alpha_fraction": 0.5532389879226685, "alphanum_fraction": 0.5673864483833313, "avg_line_length": 32.349998474121094, "blob_id": "b0a3a21e12bd8d87a256bcce65f777d4e5d70546", "content_id": "4e4a5dd2c0cb42f8527d305332751d1957d8d201", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1343, "license_type": "no_license", "max_line_length": 92, "num_lines": 40, "path": "/airflow/plugins/utils/order.py", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport numpy as np\nfrom utils import tinkoff, db\nimport math\n\n\ndef create_limit_order_by_signals(\n connector: str,\n ticker: str,\n weights: dict,\n threshold: float = 0.5\n) -> None:\n signals = db.get_data_from_signal_table(\n connector,\n f\"WHERE time = (SELECT max(time) FROM signal) AND lower(ticker) = lower('{ticker}')\"\n )\n if len(signals) == 0:\n return\n signals['weights'] = signals['strategy_type'].map(weights)\n signals['weighted_position'] = signals['weights'] signals['position']\n position = tinkoff.get_position_by_ticker(ticker)\n last_price = db.get_last_price_from_price_table(connector, ticker.lower())\n\n if signals['weighted_position'].sum() > threshold:\n if not position:\n tinkoff.create_limit_order_by_figi(\n tinkoff.get_figi_from_ticker(ticker),\n int(tinkoff.get_current_balance('USD') * 0.1 // last_price),\n math.ceil(last_price * 100) / 100.0,\n 'Buy'\n )\n\n elif signals['weighted_position'].sum() < -threshold:\n if position:\n tinkoff.create_limit_order_by_figi(\n position.figi,\n position.lots,\n math.floor(last_price * 100) / 100.0,\n 'Sell'\n )\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5743119120597839, "alphanum_fraction": 0.5844036936759949, "avg_line_length": 24.34883689880371, "blob_id": "933fde933eed2881ac36739a2e4bcfaf4731d5e6", "content_id": "0ef73c6533e10473197fb0cb8cf2ffa6cec8bfdc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1090, "license_type": "no_license", "max_line_length": 74, "num_lines": 43, "path": "/airflow/dags/strategy/cross_sma_strategy.py", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "from airflow import DAG\nfrom airflow.operators.python import PythonOperator\nfrom airflow.utils.dates import days_ago\nfrom utils import strategy\nfrom datetime import timedelta\nimport os\n\nDAG_ID = os.path.basename(__file__).replace('.pyc', '').replace('.py', '')\nCONN_ID = 'postgres_stocks'\n\nSMA_SHORT = 50\nSMA_LONG = 200\n\ndefault_args = {\n 'owner': 'airflow',\n 'depends_on_past': False,\n 'start_date': days_ago(1),\n 'retries': 2,\n 'retry_delay': timedelta(minutes=5),\n 'email_on_failure': False,\n 'email_on_retry': False,\n}\n\nwith DAG(\n dag_id=DAG_ID,\n default_args=default_args,\n schedule_interval='10 1 * * ',\n) as dag:\n\n cross_sma_aapl = PythonOperator(\n task_id='cross_sma_aapl',\n python_callable=strategy.apply_strategy,\n op_kwargs={\n 'connector': CONN_ID,\n 'source_table_name': 'aapl',\n 'ticker': 'AAPL',\n 'strategy_func': strategy.cross_sma_strategy,\n 'op_kwargs': {\n 'sma_short': SMA_SHORT,\n 'sma_long': SMA_LONG,\n }\n }\n )\n" }, { "alpha_fraction": 0.620514452457428, "alphanum_fraction": 0.6325817704200745, "avg_line_length": 28.990476608276367, "blob_id": "f7ee0c2b34f81525c68b3734424ffcfccb67cf4f", "content_id": "8db474a5834359b8b34262a51ae85f59f0a9f569", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 3149, "license_type": "no_license", "max_line_length": 134, "num_lines": 105, "path": "/airflow/Dockerfile", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "FROM python:3.8-slim\nLABEL maintainer=\"Puckel_\"\n\n# Never prompt the user for choices on installation/configuration of packages\nENV DEBIAN_FRONTEND noninteractive\nENV TERM linux\n\n# Airflow\nARG AIRFLOW_VERSION=2.0.1\nARG AIRFLOW_USER_HOME=/usr/local/airflow\nENV AIRFLOW_HOME=${AIRFLOW_USER_HOME}\nARG AIRFLOW_DEPS=\"\"\nARG PYTHON_DEPS=\"\"\nARG PYTHON_VERSION=3.8\nARG CONSTRAINT_URL=\"https://raw.githubusercontent.com/apache/airflow/constraints-${AIRFLOW_VERSION}/constraints-3.8.txt\"\n\n# Define ru_RU.\nENV LANGUAGE ru_RU.UTF-8\nENV LANG ru_RU.UTF-8\nENV LC_ALL ru_RU.UTF-8\nENV LC_CTYPE ru_RU.UTF-8\nENV LC_MESSAGES ru_RU.UTF-8\n\n# Disable noisy \"Handling signal\" log messages:\n# ENV GUNICORN_CMD_ARGS --log-level WARNING\n\nARG BUILD_DEPS=\" \\\n freetds-dev \\\n libkrb5-dev \\\n libsasl2-dev \\\n libssl-dev \\\n libffi-dev \\\n libpq-dev \\\n git \\\n unixodbc-dev \\\n\"\n\nRUN set -ex \\\n && apt-get update -yqq \\\n && apt-get upgrade -yqq \\\n && apt-get install -yqq --no-install-recommends \\\n ${BUILD_DEPS} \\\n freetds-bin \\\n build-essential \\\n default-libmysqlclient-dev \\\n apt-utils \\\n curl \\\n rsync \\\n netcat \\\n locales \\\n gnupg2 \\\n && sed -i 's/^# ru_RU.UTF-8 UTF-8$/ru_RU.UTF-8 UTF-8/g' /etc/locale.gen \\\n && locale-gen \\\n && update-locale LANG=ru_RU.UTF-8 LC_ALL=ru_RU.UTF-8 \\\n && useradd -ms /bin/bash -d ${AIRFLOW_USER_HOME} airflow \\\n && pip install -U pip setuptools wheel \\\n && pip install pytz \\\n && pip install pyOpenSSL \\\n && pip install ndg-httpsclient \\\n && pip install pyodbc \\\n && pip install tinvest \\\n && pip install psycopg2 \\\n && pip install pyasn1 \\\n && pip install pymssql \\\n && pip install mysqlclient \\\n && pip install xmltodict \\\n && pip install ldap3 \\\n && pip install apache-airflow[crypto,celery,postgres,vertica,odbc,password]==${AIRFLOW_VERSION} --constraint \"${CONSTRAINT_URL}\" \\\n && pip install 'apache-airflow-providers-papermill' \\\n && pip install 'apache-airflow-providers-postgres' \\\n && pip install 'apache-airflow-providers-vertica' \\\n && apt install gcc -y \\\n && apt install default-libmysqlclient-dev -y \\\n && pip install 'apache-airflow-providers-mysql' \\\n && pip install 'apache-airflow-providers-microsoft-mssql' \\\n && pip install redis \\\n && pip install flask-bcrypt \\\n && pip install papermill \\\n && pip install jupyter \\\n && if [ -n \"${PYTHON_DEPS}\" ]; then pip install ${PYTHON_DEPS}; fi \\\n && apt-get purge --auto-remove -yqq ${BUILD_DEPS} \\\n && apt-get autoremove -yqq --purge \\\n && apt-get clean \\\n && rm -rf \\\n /var/lib/apt/lists/ \\\n /tmp/* \\\n /var/tmp/* \\\n /usr/share/man \\\n /usr/share/doc \\\n /usr/share/doc-base\n\n\nRUN mkdir -p /var/log/airflow /var/spool/airflow \\\n && chown -R airflow: ${AIRFLOW_USER_HOME} \\\n && chown -R airflow: /var/log/airflow \\\n && chown -R airflow: /var/spool/airflow\n\nRUN mkdir -p /var/notebooks/output \\\n && chown -R airflow: /var/notebooks/output\n\nEXPOSE 8080 5555 8793\n\nUSER airflow\nWORKDIR ${AIRFLOW_USER_HOME}\nENTRYPOINT [\"./entrypoint.sh\"]\n" }, { "alpha_fraction": 0.7142857313156128, "alphanum_fraction": 0.7142857313156128, "avg_line_length": 15.333333015441895, "blob_id": "4964ee64d6da0371464cf1221f80ef6264e757d5", "content_id": "89e560c685529642e06fdc1494b158f048e9ba73", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 98, "license_type": "no_license", "max_line_length": 49, "num_lines": 6, "path": "/airflow/entrypoint.sh", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "#!/bin/sh\n\nairflow db init\n\nairflow scheduler \\\n & exec airflow webserver --pid /tmp/airflow.pid\n" } ]	9
agalindom/Luigi-Wine-Reviews	https://github.com/agalindom/Luigi-Wine-Reviews	d450f6e0d4ae1ad80e6cb7f0ae598ffa97a4c098	99a1d439a751c21e293885002f5a27ae8eceef29	c4e1237466240e06eef5b684319162afc236b125	refs/heads/master	2020-07-29T15:24:33.713263	2019-11-13T19:58:06	2019-11-13T19:58:06	209,855,809	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7444717288017273, "alphanum_fraction": 0.7444717288017273, "avg_line_length": 24.4375, "blob_id": "ac04513e0e3e3dc8c6f517b8b03d385725d86b18", "content_id": "46f43fc0d67c234c787443db078eafead5faf68c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 815, "license_type": "no_license", "max_line_length": 157, "num_lines": 32, "path": "/README.md", "repo_name": "agalindom/Luigi-Wine-Reviews", "src_encoding": "UTF-8", "text": "# Overview\n\n- The script contains an simple ML luigi-pipeline used to predict wine scores based on the taster's reviews\n\n# Libraries\n\n* pandas\n* numpy\n* sklearn\n* matplotlib\n* luigi\n* nltk\n\n# Get started\n\nFirst of all you need to download the dataset which is located in:\n\n* [Kaggle Wine Reviews](https://www.kaggle.com/zynicide/wine-reviews)\n\nAfter you download the file on your local machine, store it in:\n\n* `tac/data_root/raw/` \n\nSecond, in order to get the pipeline running, first you need to install the setup.py file to create the tac module, for this, go to the command line and run:\n\n* `pip install .`\n\nThen again on the command line run:\n\n* `luigi --module tac.ml-pipeline EvaluateModel --input_dir tac/data_root/raw/{FILENAME.csv}`\n\nAnd voilá, just wait until the pipeline finishes to see the results.\n" }, { "alpha_fraction": 0.5615585446357727, "alphanum_fraction": 0.5769948959350586, "avg_line_length": 33.92608642578125, "blob_id": "2b7a9fc8f0a09269b8da623e9a68e8e963b8be97", "content_id": "cc38d5d0beac8e9aed7c964b094b5232c049e43b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8033, "license_type": "no_license", "max_line_length": 106, "num_lines": 230, "path": "/tac/ml-pipeline.py", "repo_name": "agalindom/Luigi-Wine-Reviews", "src_encoding": "UTF-8", "text": "import os\nimport re\nimport luigi\nimport nltk\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Rectangle\nfrom nltk.corpus import stopwords\nfrom nltk.stem.porter import PorterStemmer\nfrom nltk.tokenize import RegexpTokenizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n\n\nclass Preprocessing(luigi.Task):\n input_dir = luigi.Parameter()\n\n def run(self):\n df = pd.read_csv(self.input_dir)\n df = df.drop('Unnamed: 0', axis = 1)\n\n # drop unnecessary columns\n df2 = df.drop(['region_2', 'designation', 'taster_twitter_handle',\n 'title', 'winery', 'variety', 'taster_name',\n 'region_1', 'country'], axis = 1)\n\n #\n mean_price = df2.price.mean()\n df2['price'] = df2['price'].fillna(mean_price)\n\n #df2 = df2.dropna()\n df2.to_csv(self.output().path)\n\n\n def output(self):\n target = luigi.LocalTarget('tac/data_root/task0.csv')\n\n return target\n\n\n\nclass Features(luigi.Task):\n input_dir = luigi.Parameter()\n\n def requires(self):\n return Preprocessing(self.input_dir)\n\n def run(self):\n #apply lowercase and get only the letters\n df = pd.read_csv(self.input().path)\n df.drop('Unnamed: 0', axis = 1, inplace = True)\n df['description'] = df['description'].str.lower()\n df['description']= df['description'].apply(\n lambda x: re.sub('[^a-zA-Z]',' ', x))\n\n #tokenize the descriptions file\n tokenizer = RegexpTokenizer(r'\\w+')\n tokens = df['description'].apply(tokenizer.tokenize)\n\n #apply stopwords and stemming\n stopw = stopwords.words('english')\n ps = PorterStemmer()\n tokens = tokens.apply(\n lambda x:[word for word in x if not word in stopw]\n )\n tokens = tokens.apply(lambda x: [ps.stem(word) for word in x])\n df = df.assign(description = tokens.apply(lambda x: ' '.join(x)))\n df2 = pd.get_dummies(df, columns = ['province'])\n #apply CountVectorizer to the descriptions to get a number matrix\n vect = CountVectorizer(\n analyzer='word', token_pattern=r'\\w+',max_features=600\n )\n xx = vect.fit_transform(df2['description']).toarray()\n xx = pd.DataFrame(xx)\n data = pd.concat([df2, xx], axis = 1)\n data = data.drop(['description'], axis = 1)\n\n #save data to file\n data.to_csv(self.output().path)\n\n def output(self):\n target = luigi.LocalTarget('tac/data_root/task1.csv')\n\n return target\n\n\nclass Split(luigi.Task):\n input_dir = luigi.Parameter()\n evaluate = luigi.BoolParameter(default=False)\n\n def requires(self):\n return Features(self.input_dir)\n\n def run(self):\n df = pd.read_csv(self.input().path)\n df.drop('Unnamed: 0', axis = 1, inplace = True)\n #split into target and data\n target = df['points'].values\n data = df.drop(['points'], axis = 1)\n\n #train-test split\n X_train, X_test, y_train, y_test = train_test_split(data, target,\n test_size = 0.2, random_state = 1234)\n if self.evaluate:\n X_train, X_test, y_train, y_test = train_test_split(data, target,\n test_size = 0.2, random_state = 1234)\n # output test/train splits\n X_test.to_csv(self.output()[2].path)\n pd.Series(y_test).to_csv(self.output()[3].path)\n\n X_train.reset_index(drop = True, inplace = True)\n X_train.to_csv(self.output()[0].path)\n pd.Series(y_train).to_csv(self.output()[1].path)\n\n def output(self):\n targets = [luigi.LocalTarget('tac/data_root/X_train.csv'),\n luigi.LocalTarget('tac/data_root/y_train.csv')]\n\n if self.evaluate:\n targets.append(luigi.LocalTarget('tac/data_root/X_test.csv'))\n targets.append(luigi.LocalTarget('tac/data_root/y_test.csv'))\n\n return targets\n\n\nclass TrainModel(luigi.Task):\n input_dir = luigi.Parameter()\n\n def requires(self):\n return Split(self.input_dir)\n\n def run(self):\n X = pd.read_csv(self.input()[0].path)\n X.drop('Unnamed: 0', axis = 1, inplace = True)\n y = pd.read_csv(self.input()[1].path, header=None)\n y = y[1].values\n\n #specify and fit model\n gb = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.1,\n max_depth=5, max_features='sqrt',\n min_samples_leaf=15, min_samples_split=10,\n loss='huber', random_state =5)\n gb.fit(X, y)\n\n f = open(self.output().path, 'wb')\n pickle.dump(gb, f)\n f.close()\n\n def output(self):\n return luigi.LocalTarget('tac/data_root/model.pickle')\n\n\nclass EvaluateModel(luigi.Task):\n input_dir = luigi.Parameter()\n name = luigi.Parameter(default= \"results_plot.png\")\n\n def requires(self):\n return [TrainModel(self.input_dir),\n Split(self.input_dir, True)]\n\n def run(self):\n GBModel = pickle.load(open(self.input()[0].path, 'rb'))\n X = pd.read_csv(self.input()[1][2].path)\n X.drop('Unnamed: 0', axis = 1, inplace = True)\n y = pd.read_csv(self.input()[1][3].path, header = None)\n y = y[1].values\n\n #define function to get grphic results of the model\n def plot_loss_rmse(train_score, test_score, path = None, title = None):\n '''\n Plot of the loss of the GBRegressor for training and test set along with the rmse of the model\n\n :train_score: loss of the model e.g. gb.train_score_\n :test_score: loss at each iteration, type np.float64\n '''\n fig = plt.figure(figsize = (8, 10))\n fig.suptitle('{} (RMSE: {:.4f})'.format(title, rmsle(y, preds)), fontsize = 15)\n ax = fig.add_subplot(111)\n fig.subplots_adjust(top = .93, bottom = 0.4)\n ax1 = ax.twinx()\n xaxis = np.arange(3000) + 1\n\n train = '#ff7f0e'\n test = '#1f77b4'\n\n labels = ['Training Loss', 'Test Loss']\n handles = [Rectangle((2,2),5,5,color=c) for c in [train, test]]\n\n ax1.plot(xaxis, train_score)\n ax.plot(xaxis, test_score, color = '#ff7f0e')\n ax.set_ylabel('Test Loss', fontsize = 15, labelpad = 20)\n ax1.set_ylabel('Train Loss', fontsize = 15, labelpad = 20)\n ax.set_xlabel('Boosting Iterations', fontsize = 15, labelpad = 20)\n ax.legend(handles, labels, loc = 'upper right', frameon = True, shadow = True,\n handlelength = 2.5, borderaxespad = 2, fontsize = 'medium');\n ax.grid()\n # fig.savefig(path)\n\n #define rmsle function for evaluation\n def rmsle(y, y_pred):\n '''\n Returns the root mean square error of the predictons.\n It expects an array containing the model predictions.\n\n :return: RMSE value of the predictions.\n '''\n return np.sqrt(mean_squared_error(y, y_pred))\n\n preds = GBModel.predict(X)\n\n #prepare test_score for plot\n test_score = np.zeros((3000,), dtype=np.float64)\n\n for i, y_pred in enumerate(GBModel.staged_predict(X)):\n test_score[i] = GBModel.loss_(y, y_pred)\n\n plot_loss_rmse(GBModel.train_score_, test_score,\n title = 'Model')\n\n return plt.savefig(\"{}/{}\".format('tac/data_root/', self.name))\n\n\n def output(self):\n return luigi.LocalTarget(self.name)\n" } ]	2
EarlPaquibol/Phyton	https://github.com/EarlPaquibol/Phyton	761a9943121816e75e4b185f4b317daa5553b0f6	3cfb0fbaefedb0487c49d7c292fbb228f4c7601f	ad8f4ccc6a4741dbff2391f3ea71c3a9eb8577b6	refs/heads/master	2020-08-26T16:09:11.677324	2019-11-08T08:05:10	2019-11-08T08:05:10	217,067,619	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5591397881507874, "alphanum_fraction": 0.6129032373428345, "avg_line_length": 23.799999237060547, "blob_id": "4e262f04425b7a2d85a0d8a137420fbf836e6699", "content_id": "42f302af5a67b55685b7c436faee28f40d1af57b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 372, "license_type": "no_license", "max_line_length": 66, "num_lines": 15, "path": "/Exercises/6th kyu/Multiples_3_5.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "#If we list all the natural numbers below 10 that are multiples of\n#3 or 5, we get 3, 5, 6 and 9. The sum of these multiples is 23.\n\ndef solution(number):\n total = 0\n for e in list(range(1,number)):\n if e%3 == 0 or e%5 == 0:\n total += e\n return total\n### there was no need to create a list\n\nprint(solution(10))\n\nfor e in range(10):\n print(e)\n" }, { "alpha_fraction": 0.5304136276245117, "alphanum_fraction": 0.5352798104286194, "avg_line_length": 30.538461685180664, "blob_id": "444095392ea695ff159ac0c8c39966a5be2121e0", "content_id": "814b54ce8ad0849f0fdc6b4df4fa5d2bd16e7903", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 411, "license_type": "no_license", "max_line_length": 120, "num_lines": 13, "path": "/Exercises/6th kyu/Encode().py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def duplicate_encode(word):\n# hello = []\n# for e in word.lower():\n# if word.lower().count(e) > 1:\n# hello.append(')')\n# else:\n# hello.append('(')\n# return ''.join(hello)\n\ndef duplicate_encode(word):\n return ''.join([')' if word.lower().count(e) > 1 else '(' for e in word.lower()]) ###ey another one liner from me\n\nprint(duplicate_encode(\"Receder\"))\n\n" }, { "alpha_fraction": 0.47858941555023193, "alphanum_fraction": 0.501259446144104, "avg_line_length": 15.541666984558105, "blob_id": "9d23aaae3e89629bc8df74b0be9362dc7a7c7c2a", "content_id": "ee6f2fbe34f9082ff54f65b3d6b27dd59b8779c4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 397, "license_type": "no_license", "max_line_length": 46, "num_lines": 24, "path": "/Exercises/7th kyu+/SquareNum.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def is_square(n):\n if n < 0:\n return False\n else:\n an = int(n*(1/2))\n if anan == n:\n return True\n else:\n return False\n\nprint(is_square(36))\n\n\n# import math\n# def is_square(n):\n# if n < 0:\n# return False\n# sqrt = math.sqrt(n)\n# return sqrt.is_integer()\n\n\n# import math\n# def is_square(n):\n# return n > -1 and math.sqrt(n) % 1 == 0;\n" }, { "alpha_fraction": 0.5807228684425354, "alphanum_fraction": 0.6060240864753723, "avg_line_length": 20.28205108642578, "blob_id": "fad2f089285c9f89d813ad83191909ea67e6c0bd", "content_id": "b723e2889b0d1a674ff17cad0dc83ff6026ad4ea", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 830, "license_type": "no_license", "max_line_length": 127, "num_lines": 39, "path": "/SQL/SQLite_demo.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "import sqlite3\nfrom Employee import Employee\n\nconn = sqlite3.connect('employee.db') #memory\n\nc = conn.cursor()\n\n# c.execute(\"\"\"CREATE TABLE employees('\n# first text,\n# last text,\n# pay integer\n# ) \"\"\")\n\n\nemp_1 = Employee('Lrae', 'Sexy', 60000)\nemp_2 = Employee('Rizza', 'Yang', 65000)\n\n\n# c.execute(\"INSERT INTO employees VALUES (?, ?, ?)\", (emp_1.first, emp_1.last, emp_1.pay))\n# conn.commit()\n\n# c.execute(\"INSERT INTO employees VALUES (:first, :last, :pay)\", {'first': emp_2.first, 'last': emp_2.last, 'pay': emp_2.pay})\n# conn.commit()\n\n\nc.execute(\"SELECT * FROM employees WHERE last=?\", ('Pogi', ))\nprint(c.fetchall())\n\nc.execute(\"SELECT * FROM employees WHERE last=:last\", {'last': 'Sexy'})\nprint(c.fetchall())\n\n\n\n# c.fetchmany(3)\n# c.fetchone()\n\n\nconn.commit()\nconn.close()\n" }, { "alpha_fraction": 0.43103447556495667, "alphanum_fraction": 0.5517241358757019, "avg_line_length": 22.200000762939453, "blob_id": "46b939cd9a0999b1ad390eed0dd6e2ad9a35c372", "content_id": "8c7f18ceb88c804983ce6776e0763ed76b6e8d53", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 232, "license_type": "no_license", "max_line_length": 57, "num_lines": 10, "path": "/Exercises/5th kyu/Move_zeroes.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def move_zeros(array):\n a = array.count(0)\n while array.count(0) > 1:\n array.remove(0)\n while array.count(0) != a:\n array.append(0)\n print(array)\n\n\nmove_zeros([9,0.0,0,9,1,2,0,1,0,1,0.0,3,0,1,9,0,0,0,0,9])\n" }, { "alpha_fraction": 0.41436463594436646, "alphanum_fraction": 0.45303866267204285, "avg_line_length": 21.5, "blob_id": "7fca3060d2631b5338b83575ccdc20a7d7333668", "content_id": "22858ec6673124f7f5782d9caee9f5f9ab7ba516", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 181, "license_type": "no_license", "max_line_length": 29, "num_lines": 8, "path": "/Exercises/7th kyu+/Get_middle.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def get_middle(s):\n length = len(s)\n if length % 2 == 0:\n a = int(length/2 - 1)\n return s[a:length-a]\n else:\n a = length//2 + 1\n return s[a-1]\n\n" }, { "alpha_fraction": 0.6792079210281372, "alphanum_fraction": 0.6881188154220581, "avg_line_length": 27.05555534362793, "blob_id": "993229cb23daa67c447b40fdbf4a0d5771e57a11", "content_id": "08e61a1441b11cc72340e2587bc9ab3ca437e3b4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1010, "license_type": "no_license", "max_line_length": 156, "num_lines": 36, "path": "/Tutorial/Basic/Dictionaries.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "#dictionary is like a class. you can create properties.\n\nstudent = {'name': 'Earl', 'age': 21, 'courses': 'Software Engineering'}\nprint(student)\n\n\nprint(student['name']) ##when key does not exist, it will return an error\nprint(student.get('phone', 'Key does not exist')) ##when key does not exist, it will return none. Second argument == msg displayed when key is not found\n\nstudent['phone'] = '3091209' ##adds a key to the dictionary student\nprint(student.get('phone', 'Key does not exist'))\n\nstudent['name'] = 'Pogi'\nprint(student['name'])\n\nstudent.update({'name': 'Earl Pogi', 'sex': 'Male'})\nprint(student)\n\ndel student['age']\nprint(student)\n\n\nSex = student.pop('sex')\n# print(Sex)\n# print(student)\n\nprint(len(student)) #gets the number of keys\nprint(student.keys()) #gets the keys of the dict\nprint(student.values()) #gets the values of each key\nprint(student.items()) #gets both keys and values by pair tuples\n\n\n# print(new_dict)\n\nfor key, value in student.items():\n print(key, value)\n" }, { "alpha_fraction": 0.6228765845298767, "alphanum_fraction": 0.6364665627479553, "avg_line_length": 32.96154022216797, "blob_id": "a939da33ad2f58c2f03d3db415f742b86b48ce98", "content_id": "4cd9283e0142777e7b301353395bf3c0e288e165", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 883, "license_type": "no_license", "max_line_length": 128, "num_lines": 26, "path": "/Exercises/7th kyu+/Artur.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# King Arthur and his knights are having a New Years party. Last year Lancelot was jealous of Arthur,\n# because Arthur had a date and Lancelot did not, and they started a duel.\n# To prevent this from happening again, Arthur wants to make\n# sure that there are at least as many women as men at this year's party. He gave you a list of integers of all the party goers.\n# Arthur needs you to return true if he needs to invite more women or false if he is all set.\n\ndef invite_more_women(attendees):\n men = 0\n women = 0\n for sex in attendees:\n if sex == -1:\n women += 1\n else:\n men += 1\n if men > women:\n return True\n else:\n return False\n\nprint(invite_more_women([1, 1, 1]))\n\n\ndef invite_more_women(attendees):\n return sum(attendees) > 0 #ONELINER\n\nprint(invite_more_women([1, 1, 1]))\n" }, { "alpha_fraction": 0.4728260934352875, "alphanum_fraction": 0.5271739363670349, "avg_line_length": 17.399999618530273, "blob_id": "b7c958a3ecb65a5e7b29268545a9cab4717dd08a", "content_id": "be3152366da7de602b5867bed6cc338636689025", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 184, "license_type": "no_license", "max_line_length": 41, "num_lines": 10, "path": "/Exercises/7th kyu+/Odd-Recurring.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "#find the odd number that recurs the most\n#unsolved\ndef find_it(seq):\n for e in seq:\n if e % 2 != 0:\n pass\n return e\n\n\nprint(find_it([1, 2, 3, 4, 5, 5, 5, 6]))\n" }, { "alpha_fraction": 0.4359550476074219, "alphanum_fraction": 0.47191011905670166, "avg_line_length": 14.344827651977539, "blob_id": "55a18d32ed78903a18575be108c1cba09c01c7b3", "content_id": "f652257ddcd4a78e5c3d05cf29f092f85d407efb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 445, "license_type": "no_license", "max_line_length": 60, "num_lines": 29, "path": "/Tutorial/Basic/Loops-Iterations.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "nums = [1, 2, 3, 4, 5]\n\nfor e in nums:\n if e == 3:\n print('Found it!')\n break\n print(e)\n\nfor e in nums:\n if e == 3:\n print('Found it!')\n continue #skips to the next iteration\n print(e)\n\n\nfor i in range(1, 11):\n print(i)\n\nx = 0\n\nwhile x < 10:\n print(x)\n x += 1\n\n# fruits = ['apple', 'banana', 'orange']\n# x = 0\n# for fruit in range(len(fruits)):\n# print(fruits[x])\n# x += 1\n" }, { "alpha_fraction": 0.5164835453033447, "alphanum_fraction": 0.5494505763053894, "avg_line_length": 18.157894134521484, "blob_id": "6d8a192fd7281831dce88deb6a923f5f227a7680", "content_id": "7e8beb729c3464480874d515fa620e82efb72d22", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 364, "license_type": "no_license", "max_line_length": 80, "num_lines": 19, "path": "/Exercises/6th kyu/Sum_digits.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "import functools\n\ndef digital_root(n):\n n = [int(e) for e in str(n)]\n while len(n) > 1:\n new = functools.reduce(lambda x,y: x+y, n)\n n = [int(e) for e in str(new)]\n return sum(n)\n\n\n\n\nprint(digital_root(166))\n\n\n# def digital_root(n):\n# return n%9 or n and 9\n\n# If n % 9 != 0 return n % 9. Otherwise return n (if n == 0) or 9 (if n != 0).\n" }, { "alpha_fraction": 0.39915966987609863, "alphanum_fraction": 0.462184876203537, "avg_line_length": 13, "blob_id": "fe80c3c74099ee2cceddb318bc657ee861371060", "content_id": "d463f43879b0a8fec42f19fa00eb4d219a50c62e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 238, "license_type": "no_license", "max_line_length": 27, "num_lines": 17, "path": "/Exercises/7th kyu+/Fibonacci.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def fibo(num):\n n1 = 0\n n2 = 1\n counter = 0\n fib_list = []\n while counter < num:\n nth = n1 +n2\n n1 = n2\n n2 = nth\n fib_list.append(n1)\n counter+=1\n return fib_list\n\n\n\n\nprint(fibo(100))\n" }, { "alpha_fraction": 0.39881831407546997, "alphanum_fraction": 0.41137370467185974, "avg_line_length": 27.16666603088379, "blob_id": "5126ff562af987159cb6ce49ec31a6c1ce51d652", "content_id": "37f700626121af15d1a3445a69989e430e4bcadc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1354, "license_type": "no_license", "max_line_length": 55, "num_lines": 48, "path": "/Exercises/5th kyu/Directions.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def dirReduc(arr):\n if len(arr) == 0:\n return arr\n dup = True\n i = 0\n while dup == True:\n if len(arr)-1 == i:\n dup = False\n elif len(arr) == 0:\n dup = False\n elif arr[i] == 'NORTH' and arr[i+1] == 'SOUTH':\n arr.remove('NORTH')\n arr.remove('SOUTH')\n i = 0\n elif arr[i] == 'EAST' and arr[i+1] == 'WEST':\n arr.remove('EAST')\n arr.remove('WEST')\n i = 0\n elif arr[i] == 'SOUTH' and arr[i+1] == 'NORTH':\n arr.remove('SOUTH')\n arr.remove('NORTH')\n i = 0\n elif arr[i] == 'WEST' and arr[i+1] == 'EAST':\n arr.remove('EAST')\n arr.remove('WEST')\n i = 0\n else:\n i += 1\n return arr\n\n\nprint(dirReduc([\"NORTH\", \"SOUTH\"]))\n\n\n\n # for i,e in enumerate(arr):\n # if e == 'NORTH' and arr[i+1] == 'SOUTH':\n # arr.remove('NORTH')\n # arr.remove('SOUTH')\n # elif e == 'EAST' and arr[i+1] == 'WEST':\n # arr.remove('EAST')\n # arr.remove('WEST')\n # elif e == 'SOUTH' and arr[i+1] == 'NORTH':\n # arr.remove('SOUTH')\n # arr.remove('NORTH')\n # elif e == 'WEST' and arr[i+1] == 'EAST':\n # arr.remove('EAST')\n # arr.remove('WEST')\n\n\n" }, { "alpha_fraction": 0.5144357085227966, "alphanum_fraction": 0.5275590419769287, "avg_line_length": 22.090909957885742, "blob_id": "ba7d11d40f8b680fed1fe54477710a3037366e79", "content_id": "da0aeadf7d37d3d4b771e0a3309566b41834eaea", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 762, "license_type": "no_license", "max_line_length": 88, "num_lines": 33, "path": "/Exercises/7th kyu+/Square_digits.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def square_digits(num):\n# num = str(num)\n# num = list(num)\n# new_list = []\n# for e in num:\n# new_list += [int(e)]\n# squared_list = []\n# for i in new_list:\n# squared_list += [ii]\n# again = []\n# for x in squared_list:\n# again += [str(x)]\n# again = ''.join(again)\n# return int(again)\n\n\n\ndef square_digitss(num):\n return int(''.join(str(x) for x in [ii for i in [int(e) for e in list(str(num))]]))\n\nprint(square_digitss(9119))\n\n#good answer in kata\n# def square_digits(num):\n# num = str(num)\n# ans = ''\n# for i in num:\n# ans += str(int(i)2)\n# return int(ans)\n\ndef square_digits(num):\n return int(''.join(str(int(i)2) for i in str(num)))\nprint(square_digits(9119))\n" }, { "alpha_fraction": 0.3580645024776459, "alphanum_fraction": 0.41129031777381897, "avg_line_length": 23.799999237060547, "blob_id": "320b82ff60b86166c1bf91bc91ffeabede95e998", "content_id": "0d4c96b64daffcb436f192203c646c08f09591bc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 620, "license_type": "no_license", "max_line_length": 56, "num_lines": 25, "path": "/Exercises/6th kyu/Outlier.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def find_outlier(integers):\n# odd = 0\n# even = 0\n# for e in integers:\n# if e%2 == 0:\n# even += 1\n# else:\n# odd += 1\n# if even > 1:\n# for e in integers:\n# if e%2 != 0:\n# return e\n# elif odd > 1:\n# for e in integers:\n# if e%2 == 0:\n# return e\n\ndef find_outlier(int):\n odds = [x for x in int if x%2!=0]\n evens= [x for x in int if x%2==0]\n return odds[0] if len(odds)<len(evens) else evens[0]\n\n\n\nprint(find_outlier([2, 4, 0, 100, 4, 11, 2602, 36]))\n" }, { "alpha_fraction": 0.5669811367988586, "alphanum_fraction": 0.5726414918899536, "avg_line_length": 27.594594955444336, "blob_id": "6e94571171054998ea9be02f05960bbd3f10f627", "content_id": "149090206f97238db78710710026df1ccafa04c3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1060, "license_type": "no_license", "max_line_length": 136, "num_lines": 37, "path": "/Tutorial/FileDemo/FileDemo.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "### File objects\n\n\n# f = open('test.txt', 'r') #opens file for reading. w for writing, will overwrite. r+ for read and write. a for appending, will add.\n# print(f.name)\n# f.close()\n\n\n# with open('test.txt', 'r') as f: #automatically closes the text file\n# f_contents = f.read() #read(x) wherein x is the number of characters you want to read\n # print(f_contents)\n # f_contents = f.readline() #readlines() every line\n # print(f_contents, end='')\n # f_contents = f.readline()\n # print(f_contents, end='')\n # for lines in f:\n # print(lines, end='')\n #f.seek(x) wherein x will be the place where it starts to read\n\n\n\n# with open('test2.txt', 'w') as f:\n# f.write('pogi')\n# f.seek(0)\n# f.write('earl')\n\n\nwith open('test.txt', 'r') as rf:\n with open('test_copy.txt', 'w') as wf:\n size_to_read = 100\n rf_chunk = rf.read(size_to_read)\n while len(rf_chunk) > 0:\n wf.write(rf_chunk)\n rf_chunk = rf.read(size_to_read)\n\n# for lines in rf:\n# wf.write(lines)\n\n\n" }, { "alpha_fraction": 0.48968106508255005, "alphanum_fraction": 0.48968106508255005, "avg_line_length": 21.20833396911621, "blob_id": "73a37670f6872a7249fa547ac493d5b57a0ed711", "content_id": "fdde15f43923893adffefa5030f140d4747a76fc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 533, "license_type": "no_license", "max_line_length": 73, "num_lines": 24, "path": "/Exercises/7th kyu+/NoVowels.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def disemvowel(string):\n# for e in string:\n# if ('a' == e or 'e' == e or 'i' == e or 'o' == e or 'u' == e or\n# 'A' == e or 'E' == e or 'I' == e or 'O' == e or 'U' == e):\n# string = string.replace(e, '')\n# return string\n\n\n\n\n\n# def disemvowel(string):\n# return \"\".join(c for c in string if c not in \"aeiouAEIOU\")\n\n\n\n\ndef disemvowel(string):\n c = ''\n for c in string:\n if c not in \"aeiouAEIOU\":\n c += c\n print (c)\nprint(disemvowel(\"This website is for losers LOL!\"))\n" }, { "alpha_fraction": 0.541015625, "alphanum_fraction": 0.6171875, "avg_line_length": 29.117647171020508, "blob_id": "bef665d73192268dd9f108694712df30f17064e4", "content_id": "b9f99757b7363a92cda629c71cd8a996dfff6757", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 512, "license_type": "no_license", "max_line_length": 88, "num_lines": 17, "path": "/Exercises/6th kyu/IQTest.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def iq_test(numbers):\n numbers = [int(e) for e in numbers.split(' ')]\n odd = [x for x in numbers if x%2 != 0]\n even = [x for x in numbers if x%2 == 0]\n return numbers.index(even[0])+1 if sum(odd) > sum(even) else numbers.index(odd[0])+1\n\n\n\nprint(iq_test(\"2 4 7 8 10\"))\n\n\n# def iq_test(numbers):\n# e = [int(i) % 2 == 0 for i in numbers.split()]\n\n# return e.index(True) + 1 if e.count(True) == 1 else e.index(False) + 1\n\n#https://www.codewars.com/kata/552c028c030765286c00007d/solutions/python\n" }, { "alpha_fraction": 0.5402414202690125, "alphanum_fraction": 0.5744466781616211, "avg_line_length": 23.219512939453125, "blob_id": "32ae603a97d1485446ebdfbe915c9efa0d174b57", "content_id": "0109136adebfd42c383672e3911acdcf16ed6c38", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 994, "license_type": "no_license", "max_line_length": 100, "num_lines": 41, "path": "/Exercises/7th kyu+/DescendingOrder.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def Descending_Order(num):\n# st = str(num)\n# my_total = []\n# # for num in st:\n# # my_total += [int(num)]\n# my_total = [int(num) for num in st]\n# my_total.sort()\n# my_total.reverse()\n# st = str(my_total)\n# st = st[1:-1]\n# st = st.replace(', ', '')\n# num = int(st)\n# print(num)\n### same funcs, just tried to make it cleaner\n\n\n\n\ndef Descending_Orderr(num):\n string_num = str(num)\n list_num = [int(num) for num in string_num]\n list_num.sort()\n list_num.reverse()\n st = str(list_num)\n num = int(st[1:-1].replace(', ', ''))\n return num\n\ndef Descending_Order(num):\n s = str(num)\n s = list(s) ##no need to convert to list, sorted automatically converts a string into a list\n s = sorted(s)\n s = reversed(s)\n s = ''.join(s)\n print(s)\n return int(s)\n\ndef Descending_Order(num):\n return int(''.join(sorted(str(num), reverse=True)))\n\nDescending_Orderr(134454236546645)\nDescending_Order(134454236546645)\n\n" }, { "alpha_fraction": 0.8032786846160889, "alphanum_fraction": 0.8032786846160889, "avg_line_length": 60, "blob_id": "353b82f947581b7d7f41027bf1bd502e9ded2a64", "content_id": "66b88435721df4855274ea75c24aacb480dc8f1d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 61, "license_type": "no_license", "max_line_length": 60, "num_lines": 1, "path": "/README.md", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# This repo contains all things about my knowledge on Phyton\n" }, { "alpha_fraction": 0.5670391321182251, "alphanum_fraction": 0.5782122611999512, "avg_line_length": 20.918367385864258, "blob_id": "1612b747f29b66805e3718c48aa2bff5ebddb532", "content_id": "927c82796997205e7342a9d34285c9765e0e5123", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1074, "license_type": "no_license", "max_line_length": 115, "num_lines": 49, "path": "/Exercises/6th kyu/Persistence.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# Write a function, persistence, that takes in a positive parameter num and returns its multiplicative persistence,\n# which is the number of times you must multiply the digits in num until you reach a single digit.\n\n\n\n# def persistence(num):\n# if len(str(num)) == 1:\n# return 0\n# num = [int(e) for e in str(num)]\n# digits = functools.reduce(lambda x,y:xy, num)\n# counter = 1\n# while len(str(digits)) > 1:\n# digits = [int(e) for e in str(digits)]\n# num = functools.reduce(lambda x,y:xy, digits)\n# digits = num\n# counter += 1\n# return counter\n\n\n\n\nimport functools\ndef persistence(n):\n nums = [int(x) for x in str(n)]\n sist = 0\n while len(nums) > 1:\n newNum = functools.reduce(lambda x, y: x * y, nums)\n nums = [int(x) for x in str(newNum)]\n sist = sist + 1\n return sist\n\n\nprint(persistence(25))\n\n\n\n\n\n# new = []\n# for e in str(total):\n# new += [int(e)]\n\n# num = 98\n# total = []\n# for e in str(num):\n# total += [int(e)]\n\n# num = [int(e) for e in str(num)]\n# print(total)\n" }, { "alpha_fraction": 0.4935988485813141, "alphanum_fraction": 0.49644380807876587, "avg_line_length": 20.303030014038086, "blob_id": "749413f84e64f35213605e3c837c3d84012769b8", "content_id": "b6a12f3611016c56885440dfa79a560642026be3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 703, "license_type": "no_license", "max_line_length": 65, "num_lines": 33, "path": "/Exercises/7th kyu+/DNA.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# DNA_strand (\"ATTGC\") # return \"TAACG\"\n\ndef DNA_strand(dna):\n dna = list(dna)\n for i,e in enumerate(dna):\n if e == 'A':\n dna[i] = 'T'\n elif e == 'T':\n dna[i] = 'A'\n elif e == 'G':\n dna[i] = 'C'\n elif e == 'C':\n dna[i] = 'G'\n return ''.join(dna)\n\n\n\nprint(DNA_strand('ATAGC'))\n\n# def DNA_strand(dna):\n# return dna.translate(string.maketrans(\"ATCG\",\"TAGC\"))\n # Python 3.4 solution \|\| you don't need to import anything :)\n # return dna.translate(str.maketrans(\"ATCG\",\"TAGC\"))\n\npairs = {'A':'T','T':'A','C':'G','G':'C'}\ndef DNA_strands(dna):\n for x in dna:\n print(pairs[x])\n\n\n\n\nprint(DNA_strands('ATAGC'))\n" }, { "alpha_fraction": 0.4045092761516571, "alphanum_fraction": 0.4615384638309479, "avg_line_length": 15.32608699798584, "blob_id": "6d8c97fc01028b73fb5b81f80c17835149fec7ca", "content_id": "cacf397cfd5c3bb2e05937fd904db162fc9237c2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 754, "license_type": "no_license", "max_line_length": 47, "num_lines": 46, "path": "/Exercises/6th kyu/Equal_sides_array.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def find_even_index(arr):\n arr = list(arr)\n if (sum(arr) == arr[-1]):\n return arr.index(arr[-1])\n print(arr)\n num = arr\n a = 0\n i = 0\n while a != sum(num):\n if sum(num) == 0:\n return -1\n if a == sum(arr[1:]):\n return i\n a += num[0]\n num.remove(num[0])\n i += 1\n if (sum(arr) == 0):\n return 0\n return -1\n\n\ndef find_even_index(arr):\n for i in range(len(arr)):\n if sum(arr[:i]) == sum(arr[i+1:]):\n return i\n return -1\n\n\n\n\n\n\nprint(find_even_index([10,-80,10,10,15,35,20]))\n\n\n\n\n\n# num = [1,100,50,-51,1,1]\n# print(sum(num))\n# a = num[0]\n# num.remove(num[0])\n# print(a, sum(num))\n# a += num[1]\n# num.remove(num[1])\n# print(a, sum(num))\n\n\n\n" }, { "alpha_fraction": 0.6442952752113342, "alphanum_fraction": 0.6778523325920105, "avg_line_length": 17.625, "blob_id": "a64c43e8094aeb938200dec29a76351d3bd5e1aa", "content_id": "f592745c351a0cea153c320c933838c87c5ffdb2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 149, "license_type": "no_license", "max_line_length": 37, "num_lines": 8, "path": "/Tutorial/Basic/Enumerate.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "fruits = ['apple', 'banana', 'mango']\nname = 'Earl'\n\nobj1 = enumerate(fruits)\nobj2 = enumerate(name)\nprint(obj1)\nprint(list(obj1))\nprint(list(obj2))\n" }, { "alpha_fraction": 0.5321100950241089, "alphanum_fraction": 0.5779816508293152, "avg_line_length": 23.22222137451172, "blob_id": "6f068264f6e7f75670fb2f6183293bcfe2535093", "content_id": "f2c07644dd5c94b991149ba2f100ca32629a8da4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 436, "license_type": "no_license", "max_line_length": 67, "num_lines": 18, "path": "/Exercises/7th kyu+/Highest_lowest.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def high_and_low(numbers):\n# # numbers = numbers.split(' ')\n# numbers = list(map(int, numbers.split(' ')))\n# numbers.sort()\n# numbers = \"{}\".format(numbers[-1]) + \" {}\".format(numbers[0])\n# return numbers\n\n\n\n# print(high_and_low('5 3 2 221 321'))\n\n\n\ndef high_and_low(numbers):\n numbers = list(map(int, numbers.split(' ')))\n return f'{max(numbers)}' + f' {min(numbers)}'\n\nprint(high_and_low('5 3 2 221 321'))\n" }, { "alpha_fraction": 0.4324817657470703, "alphanum_fraction": 0.4562043845653534, "avg_line_length": 26.299999237060547, "blob_id": "b789ac22e94e7dfa3a34fda2875801b5df59e6d4", "content_id": "9fa0a4ba7a4a5cbc0a8c26e24bb99d48f958abe9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 548, "license_type": "no_license", "max_line_length": 60, "num_lines": 20, "path": "/Exercises/6th kyu/Find_odd.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def find_it(seq):\n# new = []\n# for index, value in enumerate(seq):\n# if seq.count(value)%2 != 0:\n# new += [seq.count(value)]\n# a = max(new)\n# for i,e in enumerate(seq):\n# if seq.count(e) == a:\n# a = seq[i]\n# print('retard')\n# return a\n\ndef find_it(seq):\n odd = [seq.count(e) for e in seq if seq.count(e)%2 != 0]\n for i,e in enumerate(seq):\n if seq.count(e) == max(odd):\n n = seq[i]\n return n\n\nprint(find_it([3,3,3,3,4,4,4,4,4]))\n\n\n" }, { "alpha_fraction": 0.5431235432624817, "alphanum_fraction": 0.5524475574493408, "avg_line_length": 24.235294342041016, "blob_id": "480bf4cebffcb88ca1f84224800d683d53d7f889", "content_id": "62d1e155a020c93dec9530fd47ce948a52186fa2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 429, "license_type": "no_license", "max_line_length": 81, "num_lines": 17, "path": "/OOP/Classes.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "class Pogi:\n\n def __init__(self, first, last, sex):\n self.first = first\n self.last = last\n self.sex = sex\n\n def display_info(self):\n if self.sex == 'M':\n print(f'Hi, {self.first} {self.last}! Is your phone number 69? Hihe')\n else:\n print(f'Hi, {self.first} {self.last}! Is your phone number beach?')\n\n\n\nperson1 = Pogi('Earl', 'Paquibol', 'M')\nperson1.display_info()\n" }, { "alpha_fraction": 0.49253731966018677, "alphanum_fraction": 0.5149253606796265, "avg_line_length": 14.764705657958984, "blob_id": "51aec9041264c72c2f8c1dc16f81b906044ce5c1", "content_id": "f79a1dc4810fcb681520d586c7380148f128aeb2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 268, "license_type": "no_license", "max_line_length": 47, "num_lines": 17, "path": "/Exercises/7th kyu+/SumBetween.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def get_sum(a,b):\n if a<b:\n return sum(range(a,b+1))\n else:\n return sum(range(b,a+1))\n\n\n\n\nprint(get_sum(1,0))\n\ndef get_sum(a,b):\n return sum(range(min(a, b), max(a, b) + 1))\n\ndef get_sum(a,b):\n if a>b : a,b = b,a\n return sum(range(a,b+1))\n" }, { "alpha_fraction": 0.45041322708129883, "alphanum_fraction": 0.4793388545513153, "avg_line_length": 22, "blob_id": "0e7a7abe4bf54d3b913c958f5c314d103025dabb", "content_id": "dedfb4809348962fa211604ef4c497cd0f404f37", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 484, "license_type": "no_license", "max_line_length": 104, "num_lines": 21, "path": "/Exercises/6th kyu/Ten_minute_walk.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "pairs = {'s': 1, 'n': -1, 'e': 2, 'w': -2}\ndef isValidWalk(walk):\n if len(walk) != 10:\n return False\n total = 0\n for e in walk:\n total += pairs[e]\n return total == 0\n\n\n\n\nprint(isValidWalk(['s', 'n', 's', 'n', 'e', 'w', 'w', 'w', 'w', 'w']))\n\n\n# pairs = {'s': 1, 'n': -1, 'e': 2, 'w': -2}\n# for x in pairs:\n# print(pairs[x])\n\ndef isValidWalk(walk):\n return len(walk) == 10 and walk.count('n') == walk.count('s') and walk.count('e') == walk.count('w')\n\n" }, { "alpha_fraction": 0.6036036014556885, "alphanum_fraction": 0.6081081032752991, "avg_line_length": 19.18181800842285, "blob_id": "a2001e0ff2896ef5f6964946bded7d56db1f2c02", "content_id": "4a950dc3aee095af7edfa674727d10908cebbdff", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 222, "license_type": "no_license", "max_line_length": 44, "num_lines": 11, "path": "/Exercises/6th kyu/Dubstep.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def song_decoder(song):\n song = song.split(\"WUB\")\n while song.count('') != 0:\n song.remove('')\n return ' '.join(song)\n\n\n\n\nprint(song_decoder(\"AWUBWUBWUBBWUBWUBWUBC\"))\n # => WE ARE THE CHAMPIONS MY FRIEND\n" }, { "alpha_fraction": 0.5587044358253479, "alphanum_fraction": 0.5587044358253479, "avg_line_length": 16.64285659790039, "blob_id": "71ff032f5748ac337229f73e4efbc269bd4cc6c9", "content_id": "80ee821b6b6b3ab0c9bfa4e1d5d7eab3b013e447", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 247, "license_type": "no_license", "max_line_length": 48, "num_lines": 14, "path": "/Exercises/7th kyu+/Shortest_word.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def find_short(s):\n# length = []\n# for word in s.split(' '):\n# length += [len(word)]\n# return min(length)\n\n\n\n\n\ndef find_short(s):\n return min(len(x) for x in s.split(' '))\n\nprint(find_short(\"hello world a askjhdaskdhas\"))\n" }, { "alpha_fraction": 0.6843373775482178, "alphanum_fraction": 0.6891566514968872, "avg_line_length": 26.66666603088379, "blob_id": "7922f07b5c8713a8f1f2009e7d0fa5fb89bdb3df", "content_id": "b53095a486b40d6976de05449bea8ad769f04ff5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 415, "license_type": "no_license", "max_line_length": 118, "num_lines": 15, "path": "/Exercises/7th kyu+/VowelCount.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def getCount(inputStr):\n return inputStr.count('a') + inputStr.count('e') + inputStr.count('i') + inputStr.count('o') + inputStr.count('u')\n\n\nprint(getCount('aeioudasjhgsajgdkjhsdahkjgadsfghkjadfs'))\n\n\ndef getCount(inputStr):\n num_vowels = 0\n for char in inputStr:\n if char in \"aeiouAEIOU\":\n num_vowels += 1\n return num_vowels\n\nprint(getCount('aeioudasjhgsajgdkjhsdahkjgadsfghkjadfs'))\n" }, { "alpha_fraction": 0.5433962345123291, "alphanum_fraction": 0.6150943636894226, "avg_line_length": 19.384614944458008, "blob_id": "f5a1339bc1f5bab13ed7e5be8dbde19b6fc45f19", "content_id": "628bec28ec06a757afe40fc7c2472235225d17cc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 265, "license_type": "no_license", "max_line_length": 52, "num_lines": 13, "path": "/Tutorial/Basic/Intergers.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "num = 3\nprint(type(num)) #prints the type of the variable\n\nprint(3/2) #prints 1.5\nprint(3//2) #prints 1\nprint(abs(-2))\nprint(round(2.35, 1)) #round off to 1 decimal place\n\n\na = \"300\"\n## error >>>>>>> print(a + 3)\na = int(a) #convert string to int\nprint(a+3)\n" }, { "alpha_fraction": 0.46028512716293335, "alphanum_fraction": 0.4684317708015442, "avg_line_length": 22.33333396911621, "blob_id": "89a5b5ba2b38e2348cb079dd3a5f0abba9fa1dc7", "content_id": "7eead3447546263e5ca0389eb2d476bd855fe72b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 491, "license_type": "no_license", "max_line_length": 94, "num_lines": 21, "path": "/Exercises/5th kyu/Pig_latin.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def pig_it(text):\n text = text.split (' ')\n new = []\n bruh = []\n for word in text:\n if word not in ('!@#$%^&()'):\n new.append(word[1:] + word[0] + 'ay')\n else:\n bruh.append(word)\n bruh.append(' ')\n bruh = ''.join(nigga)\n new = ' '.join(new)\n text = f'{new}{nigga}'\n return text\n\n\n\n\ndef pig_it(text):\n lst = text.split()\n return ' '.join( [word[1:] + word[:1] + 'ay' if word.isalpha() else word for word in lst])\n\n" }, { "alpha_fraction": 0.492337167263031, "alphanum_fraction": 0.5268199443817139, "avg_line_length": 19, "blob_id": "ace0472ee3816e2be99b59f4f4244167a1b81129", "content_id": "a4c07fd38bd8284a4a8a6d8bfd2cb8f05655d197", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 522, "license_type": "no_license", "max_line_length": 62, "num_lines": 26, "path": "/Exercises/5th kyu/Scramblies.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def scramble(s1, s2):\n if len(s2) > len(s1):\n return False\n return len([char for char in s2 if char in s1]) == len(s2)\n\nprint(scramble('cedewaraaossoqqyt', 'codewars'))\n\n\n\n\n# def scramble(s1, s2):\n# for char in s2:\n# if char in s1:\n# s1 = s1.replace(char, '', 1)\n# else:\n# return False\n# return True\n\n\n# def scramble(s1, s2):\n# for char in s2:\n# if char not in s1:\n# return False\n# return True\n\n# print(scramble('katas', 'steak'))\n\n\n" }, { "alpha_fraction": 0.37377050518989563, "alphanum_fraction": 0.4229508340358734, "avg_line_length": 16.941177368164062, "blob_id": "855a5a0d8d85325d7f41a8f5930ba5793582923b", "content_id": "94147485f571a103d987da722d88c53e80687a0a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 305, "license_type": "no_license", "max_line_length": 30, "num_lines": 17, "path": "/Exercises/6th kyu/BinaryNum.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def countBits(n):\n binary = []\n while n > 0:\n if n%2 == 0:\n binary.append(0)\n n = n/2\n elif n%2 != 0:\n n -= 1\n n = n/2\n binary.append(1)\n return sum(binary)\n\n\n# def countBits(n):\n# return bin(n).count(\"1\")\n\ncountBits(1234)\n" }, { "alpha_fraction": 0.5929203629493713, "alphanum_fraction": 0.6283186078071594, "avg_line_length": 19.545454025268555, "blob_id": "2905365877ecdf72c10afdc48d0cf6aafbfcbb5f", "content_id": "00ab14f94a8cb1e73201b9962fbb41910bb39cf8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 226, "license_type": "no_license", "max_line_length": 46, "num_lines": 11, "path": "/Tutorial/Intermediate/Lamba.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "###filter, map, reduce\n\n\nimport functools\nsample = [1, 2, 3, 4, 5, 6]\nx = list(filter(lambda x: (x%2==0), sample))\ny = list(map(lambda x: xx, sample))\nz = functools.reduce(lambda x, y: x+y, sample)\nprint(x)\nprint(y)\nprint(z)\n" }, { "alpha_fraction": 0.5729349851608276, "alphanum_fraction": 0.5887522101402283, "avg_line_length": 20.074073791503906, "blob_id": "c0a832314d6da0e762a07f2076c72c05ec7f2cbd", "content_id": "0c0a2eb88832aaf1b852f35a42deb8c4a32c3dcf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 569, "license_type": "no_license", "max_line_length": 58, "num_lines": 27, "path": "/Exercises/7th kyu+/PigLatin.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "#move the first letter of the word to the last and add ay\n\nadd = \"ay\"\nword = \"EarlP\"\ntemp_firstletter = (word[0])\npig_latin = word[1:] + temp_firstletter + add\nprint(pig_latin)\n\n#sentence\n#define things first\ndef pigLatin(string_):\n new_ = string_.split(\" \", -1)\n print(new_)\n x = 0\n total = \"\"\n delim = \" \"\n for e in new_:\n temp_firstletter = (new_[0+x][0])\n pig_latin = new_[0+x][1:] + temp_firstletter + add\n total += pig_latin + delim\n x += 1\n return total\n\n\n\nsentence = \"Dave is retarded\"\nprint(pigLatin(sentence))\n" }, { "alpha_fraction": 0.5683760643005371, "alphanum_fraction": 0.5712250471115112, "avg_line_length": 21.645160675048828, "blob_id": "d2d9c3ea550fb02f870cd3976e77fb37cb830d06", "content_id": "a11027ee3eeaef1933b3a4a1a70850a2c60d2316", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 702, "license_type": "no_license", "max_line_length": 56, "num_lines": 31, "path": "/Exercises/6th kyu/Alphabet.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "import string\nalphabet = dict.fromkeys(string.ascii_lowercase)\ni = 1\nfor x in alphabet:\n alphabet[x] = str(i)\n i += 1\n\n\n# sentence = \"The sunset sets at twelve o' clock.\"\n# sentence = list(sentence.lower().split(' '))\n# new = []\n# for word in sentence:\n# for char in word:\n# if not char in d:\n# break\n# new.append(d[char])\n\n# print(new)\n\ndef alphabet_position(text):\n text = list(text.lower().split(' '))\n num = []\n for word in text:\n for char in word:\n if not char in alphabet:\n break\n num.append(alphabet[char])\n num = ' '.join(num)\n return num\n\nalphabet_position(\"The sunset sets at twelve o' clock.\")\n" }, { "alpha_fraction": 0.5833333134651184, "alphanum_fraction": 0.5866666436195374, "avg_line_length": 14.789473533630371, "blob_id": "ddec8258dab710d247bfeb633abb0769a135e0f1", "content_id": "cbc043f0ab423e41aaa325a0ccc70e5f7764eaa6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 300, "license_type": "no_license", "max_line_length": 50, "num_lines": 19, "path": "/Exercises/7th kyu+/Isogram.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def is_isogram(string):\n# for e in string.lower():\n# if string.lower().count(e) > 1:\n# return False\n# return True\n\n\ndef is_isogram(string):\n return len(string) == len(set(string.lower()))\n\n\n\nprint(is_isogram('qwerty'))\n\n\n\nstring = 'qwertyyq'\nq = set(string)\nprint(q)\n" }, { "alpha_fraction": 0.6244444251060486, "alphanum_fraction": 0.6288889050483704, "avg_line_length": 18.565217971801758, "blob_id": "18e11a88e90eb48a781408ef9ec724aa95406c7b", "content_id": "9de4796404514fdc247f71c75d01214bb97ae5ee", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 450, "license_type": "no_license", "max_line_length": 47, "num_lines": 23, "path": "/Tutorial/Basic/Functions.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def empty_function():\n pass\n\ndef hello_func(greeting):\n return f'{greeting} function!'\n\na = hello_func('Hi')\nprint(a)\n\ndef hello_func_default(greeting, name = 'You'):\n return f'{greeting} {name}!'\n\na = hello_func_default('sup')\nprint(a)\n\n\ndef student_info(args, kwargs):\n print(args) #prints tuples\n print(kwargs) #prints dict\n\ncourses = ['Math', 'Biology']\ninfo = {'name': 'Earl', 'age': 21}\nstudent_info(courses, *info)\n" }, { "alpha_fraction": 0.5690423250198364, "alphanum_fraction": 0.5757238268852234, "avg_line_length": 18.91111183166504, "blob_id": "5e5b317b03a5e4cb7b629d3020684e00e99e0d83", "content_id": "1f1098fffe52c52ae8eadffd57db07865687a09e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 898, "license_type": "no_license", "max_line_length": 59, "num_lines": 45, "path": "/Exercises/7th kyu+/JadenCased.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# string = 'hello hello world'\n# string = string.split()\n# for e in string:\n# e.capitalize()\n# return ''.join(e.capitalize() for e in string.split())\n\n\n\n# def Jaden_casing(string):\n# string = string.split()\n# for e in string:\n# if e[0].islower() == True:\n# return False\n# return True\n\n\n# print(Jaden_casing('Hello World Wigga'))\n\n# def toJadenCase(string):\n# string = string.split()\n# for i, e in enumerate(string):\n# string[i] = e[0].upper() + e[1:]\n# return ' '.join(string)\n\n\n\n# print(toJadenCase('hello world wigga'))\n\n\n# def toJadenCase(string):\n# s = []\n# for e in string.split():\n# s += e[0].upper() + e[1:] + ' '\n# return ''.join(s[:-1])\n\n# print(toJadenCase('hello world wigga'))\n\n\n\n\n\ndef toJadenCase(string):\n return ' '.join(e.capitalize() for e in string.split())\n\nprint(toJadenCase('hello world wigga'))\n\n\n" }, { "alpha_fraction": 0.5555555820465088, "alphanum_fraction": 0.5679012537002563, "avg_line_length": 22.14285659790039, "blob_id": "59c1cf74951b11d7bf6853ba844ece270b16759f", "content_id": "a5af4d7aa2973312ffe38712351e3443b873a754", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 324, "license_type": "no_license", "max_line_length": 65, "num_lines": 14, "path": "/Exercises/6th kyu/Duplicate_count.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def duplicate_count(text):\n text = text.lower()\n counter = 0\n for e in text:\n if text.count(e) > 1:\n text = text.replace(e, '')\n counter += 1\n return counter\n\nprint(duplicate_count('abcbc'))\n\n\ndef duplicate_count(s):\n return len([c for c in set(s.lower()) if s.lower().count(c)>1])\n" }, { "alpha_fraction": 0.6847673058509827, "alphanum_fraction": 0.6953455805778503, "avg_line_length": 21.140625, "blob_id": "db6621af041fdb6fe8518d4cffde6908df86c948", "content_id": "f9366b423539c90fa9378398ccb391b45dc9f44d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1418, "license_type": "no_license", "max_line_length": 85, "num_lines": 64, "path": "/Tutorial/Basic/Lists.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "courses = [\"History\", \"Math\", \"Physics\", \"Compsci\"]\nprint(len(courses)) #prints length of lists\n\nprint(courses[-1]) #prints the last value in the list\nprint(courses[:2]) #prints first to second value\n\ncourses.append(\"Art\")\nprint(courses)\n\npopped = courses.pop() #removes art or last value from the list\ncourses.insert(0, \"Art\")\nprint(popped)\n\ncourses_2 = [\"Education\"]\ncourses.extend(courses_2) #extend for each item, append or insert for list to list\nprint(courses)\n\ncourses.remove('Math')\ncourses.remove('Education')\ncourses.remove('Art')\nprint(courses)\ncourses.reverse()\nprint(courses)\ncourses.sort()\nprint(courses)\nnum = [1,5,3,8,2]\nnum.sort(reverse=True) #sort can take an arguement, reverse\nprint(num)\n### use sorted when you need to keep the original value\nnum_sort = sorted(num)\n\n###\nprint(min(num))\nprint(max(num))\nprint(sum(num))\n\ncourses = [\"History\", \"Math\", \"Physics\", \"Compsci\"]\nprint(courses.index('Compsci'))\nprint('Art' in courses)\nprint('Math' in courses)\n\nfor course in courses:\n print(course)\n\nfor index, course in enumerate(courses):\n print(index, course)\n\nfor index, course in enumerate(courses, start=1):\n print(index, course)\n\ncourse_str = ', '.join(courses)\nprint(course_str)\n\n\na = list(range(1,100))\n\n\n\nfruits = ['banana', 'apple', 'cyka']\nfruits = '-'.join(fruits)\nprint(fruits)\n\ndef Descending_Order(num):\n return int(''.join(sorted(str(num), reverse=True)))\n\n" }, { "alpha_fraction": 0.5916115045547485, "alphanum_fraction": 0.6070640087127686, "avg_line_length": 14.100000381469727, "blob_id": "277a1ff47f8e87edcd4f64b43f45b0d8f54e9d52", "content_id": "24585dff26a3e3069e14ee8d515983ec0c50c66e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 453, "license_type": "no_license", "max_line_length": 51, "num_lines": 30, "path": "/Tutorial/Basic/Conditionals-IfElse.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "language = 'Phyton'\n\nif language == 'Phyton':\n print('Language is Phyton')\nelif languange == 'Java':\n print('Language is Java')\nelse:\n print('idk that language')\n\n\nlogged_in = False\nif not logged_in:\n print('login re')\nelse:\n print('welcome')\n\na = [1,2,3]\nb = [1,2,3]\nprint(a==b)\n\nprint(id(a))\nprint(id(b))\nprint(a is b) #is operator does is id(a) == id(b)\n\ncondition = 0\n\nif condition:\n print('is treu')\nelse:\n print('iz false')\n" }, { "alpha_fraction": 0.4566929042339325, "alphanum_fraction": 0.4711286127567291, "avg_line_length": 17.14285659790039, "blob_id": "75705dbc1d5ed2775e67fbd362ea3ca5b350943f", "content_id": "7b84ce61ca7b9d40ebc467c10c51486ffd88910e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 762, "license_type": "no_license", "max_line_length": 71, "num_lines": 42, "path": "/Exercises/7th kyu+/Mumble.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# accum(\"RqaEzty\") -> \"R-Qq-Aaa-Eeee-Zzzzz-Tttttt-Yyyyyyy\"\n\n# def accum(s):\n# total = \"\"\n# word = \"\"\n# num = 1\n# for e in s:\n# for i in range(0,num):\n# word += e\n# num += 1\n# total += word[0].upper() + word[0:-1].lower() + '-'\n# word = \"\"\n# return total[:-1]\n\n# print(accum('NANI'))\n\n\n##possible solutions\n\n# def accum(s):\n# total = \"\"\n# i = 0\n# for letter in s:\n# total += letter.upper() + letter.lower()i + '-'\n# i += 1\n# print(total[:-1])\n\n\n\n\ndef accum(s):\n return '-'.join(c.upper() + c.lower() * i for i, c in enumerate(s))\n\n\nprint(accum('eaarl'))\nname = 'E'\na = name.lower()5\nprint(a)\n\n# sample = 'abc'\n# for i, c in enumerate(sample):\n# print(i, c)\n" }, { "alpha_fraction": 0.5373665690422058, "alphanum_fraction": 0.5504151582717896, "avg_line_length": 22.27777862548828, "blob_id": "c2799bea2845a5dbfab4e35afc6e48ceb05eb1be", "content_id": "f7e861539e934c7f9b542aa1281b1da999cbc98a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 843, "license_type": "no_license", "max_line_length": 109, "num_lines": 36, "path": "/Exercises/7th kyu+/Exes_Ohes.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# Check to see if a string has the same amount of 'x's and 'o's.\n# The method must return a boolean and be case insensitive. The string can contain any char.\n\n\n# def xo(s):\n# x = 0\n# o = 0\n# for e in s:\n# if e == 'x':\n# x += 1\n# elif e == 'o':\n# o += 1\n# if x == o:\n# return True\n# else:\n# return False\n\n\n# print(xo('xoxoxoxo;lkjashddsakhjdsaox'))\n\n# def xoo(s):\n# s = list(s.lower())\n# return s.count('x') == s.count('o')\n\n\n# print(xoo('xoxoxXosakjdhsakjoOOxxkjlashdkhasdjas'))\n\n\ndef xo(s):\n return list(s.lower()).count('x') == list(s.lower()).count('o') ##Oct 26, 2019 4am. I became a onelinerr\n\n##THERE WAS NO NEED TO CONVERT TO LIST!!!\ndef xo(s):\n return s.lower().count('x') == s.lower().count('o')\n\nprint(xo('xoxoxoxo;lkjashddsakhjdsaox'))\n\n\n\n\n\n" }, { "alpha_fraction": 0.5270270109176636, "alphanum_fraction": 0.6891891956329346, "avg_line_length": 28.600000381469727, "blob_id": "db4c6d91fa1263fbf14f58b978b6aaa4d580e091", "content_id": "2522bdb95555c28ed16fde659f9deba6908df02b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 148, "license_type": "no_license", "max_line_length": 89, "num_lines": 5, "path": "/Exercises/5th kyu/Human_time.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def make_readable(seconds):\n return '{0:02}:{1:02}:{2:02}'.format(int(seconds/3600),int(seconds/60%60),seconds%60)\n\n\nprint(make_readable(86399))\n" }, { "alpha_fraction": 0.6457356810569763, "alphanum_fraction": 0.6532333493232727, "avg_line_length": 26.35897445678711, "blob_id": "7a214ca2d35b5db4a6c5257f495e6530cd7d0200", "content_id": "fbbad0b17d7e0703bcba0974ec965df7aa43d308", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1067, "license_type": "no_license", "max_line_length": 87, "num_lines": 39, "path": "/Tutorial/Basic/Strings.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "msg = \"Hello world\"\nprint(msg) #prints the message\n\nprint(msg[0]) #prints the first letter in the string\n\nprint(len(msg)) #length of message\n\nprint(msg[0:5]) #prints 0 to 4 index\nprint(msg[:5]) #automatically assumes start at index 0\nprint(msg[6:]) #prints the last word\nprint(msg.lower()) #prints msg in lowercase, upper() for uppercase\n\nprint(msg.count('o')) #prints the count of o's in msg\nprint(msg.find('world')) #prints the index where world starts\n\nnew_message = msg.replace(\"world\", \"bro\") #replace world with bro\nprint(new_message)\n\ngreeting = \"Hello\"\nname = \"Earl\"\nmessage = greeting + ', ' + name\nprint(message)\nmessage = \"{}, {}. My bro!\".format(greeting, name) #formatting strings\nprint(message)\n\nmessage = f\"{greeting}, {name}. My Fstring!\"\nprint(message)\n\n\n #to see what else you can do with a string pass it to the directory\nprint(dir(message))\n\n #to see what it does\nprint(help(str))\nprint(help(str.lower))\n\n\nhelloworld = \"Hello world\"\nprint(\"Hello\" in helloworld)\n" }, { "alpha_fraction": 0.5568862557411194, "alphanum_fraction": 0.56886225938797, "avg_line_length": 28.352941513061523, "blob_id": "c1f5a5b860f024556100af70b62feba0ec68a21d", "content_id": "fd7bb7b2bec53fee34f9ba598b5742ae2eeb1c58", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 501, "license_type": "no_license", "max_line_length": 108, "num_lines": 17, "path": "/Exercises/6th kyu/Reverse_five.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def spin_words(sentence):\n sentence = sentence.split()\n new = []\n for e in sentence:\n if len(e) > 4:\n new.append(e[::-1])\n else:\n new.append(e)\n return(' '.join(new))\n\nprint(spin_words(\"Hello my friend\"))\n\ndef spin_words(sentence):\n return(' '.join([''.join(reversed(e)) if len(e) > 4 else e for e in sentence.split()])) ###my one lin3r\n\ndef spin_words(sentence):\n return ' '.join(word if len(word)<5 else word[::-1] for word in sentence.split())\n\n\n" }, { "alpha_fraction": 0.6697965860366821, "alphanum_fraction": 0.6744914054870605, "avg_line_length": 20.299999237060547, "blob_id": "67b1a7cfa824197907423e8ba3cc2fd9d6a045f4", "content_id": "71f32abe630be9401c331b9a5d0044fdc4be0232", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 639, "license_type": "no_license", "max_line_length": 65, "num_lines": 30, "path": "/Tutorial/Basic/Tuples_Sets.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "### tuple works by replacing list [] with ()\n### tuple meaning immutable cannot be mutated/modified\n\nlist_one = (1, 2, 3)\nprint(list_one)\n\n\n### sets\n### sets do not allow duplicates, print out at any order, uses {}\n\nset_one = {'Math', 'Physics'}\nset_two = {'Math', 'Physical Education'}\n\nprint(set_one.intersection(set_two)) #prints the same values\nprint(set_one.difference(set_two))\nnew_set = set_one.union(set_two)\nprint(new_set)\n\n\n###declaring empty list\nempty_list = []\nempty_list = list()\n\n#empty tuple\nempty_tuple = ()\nempty_tuple = tuple()\n\n#empty set\nempty_set = set()\nempty_set = {} ###>> this creates a disctionary not a setb\n" }, { "alpha_fraction": 0.4360119104385376, "alphanum_fraction": 0.5654761791229248, "avg_line_length": 25.8799991607666, "blob_id": "cc47af2f81082a4614cbda832020fd34dfdfa2b3", "content_id": "d1f65ed8bf147fe9477a6420e5044d99d7a90d1e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 689, "license_type": "no_license", "max_line_length": 87, "num_lines": 25, "path": "/Exercises/6th kyu/Playing_digits.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# dig_pow(89, 1) should return 1 since 8¹ + 9² = 89 = 89 1\n# dig_pow(92, 1) should return -1 since there is no k such as 9¹ + 2² equals 92 * k\n# dig_pow(695, 2) should return 2 since 6² + 9³ + 5⁴= 1390 = 695 * 2\n# dig_pow(46288, 3) should return 51 since 4³ + 6⁴+ 2⁵ + 8⁶ + 8⁷ = 2360688 = 46288 * 51\n\n\n# def dig_pow(a, b):\n# temp = [int(e) for e in str(a)]\n# total = 0\n# for i, e in enumerate(temp, start=b):\n# total += e*i\n# return int(total/a) if total%a == 0 else -1\n\n\n# print(dig_pow(92, 1))\n\ndef dig_pow(n, p):\n s = 0\n for i,c in enumerate(str(n), start=1):\n s += pow(int(c),i)\n return s/n if s%n==0 else -1\n\n\n\nprint(dig_pow(92, 1))\n" }, { "alpha_fraction": 0.5126262903213501, "alphanum_fraction": 0.5202020406723022, "avg_line_length": 12.199999809265137, "blob_id": "fd6349475c6f45f7c80e213ad1f8098a73b50ef9", "content_id": "44063c8f2d18d37410929b7c27993e655d9beef9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 396, "license_type": "no_license", "max_line_length": 52, "num_lines": 30, "path": "/Exercises/7th kyu+/Calcu.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "#sample calcu program\na = 4\nb = 2\noperator = '+'\n\ndef add(a, b): #functions must be declared first\n print(a+b)\n\ndef mul(a, b):\n print(ab)\n\ndef div(a, b):\n print(a/b)\n\ndef sub(a, b):\n print(a-b)\n\ndef pow(a):\n print(a*2)\n\nif operator == '+':\n add(a,b)\nelif operator == '-':\n sub(a,b)\nelif operator == '':\n mul(a,b)\nelif operator == '/':\n div(a,b)\nelse:\n pow(a)\n" }, { "alpha_fraction": 0.7209302186965942, "alphanum_fraction": 0.7209302186965942, "avg_line_length": 20.5, "blob_id": "746afe46191258cbb42129e3e26c78f55f4a3ff0", "content_id": "9ba179ef2a0a200a06045e7ca017fa3e5acc11b8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 172, "license_type": "no_license", "max_line_length": 43, "num_lines": 8, "path": "/Tutorial/Importing libraries/Importing_Libs.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "import Sample_module as sm\n#from Sample_module import find_index, test\n\n\ncourses = ['Math', 'Education', 'History']\n\nindex = sm.find_index(courses, 'History')\nprint(index)\n" }, { "alpha_fraction": 0.5415162444114685, "alphanum_fraction": 0.5848375558853149, "avg_line_length": 23.086956024169922, "blob_id": "6440fe5586765c7e60d16ac8a51501eeeca04f01", "content_id": "beae981306d61a13fbc0969ee0f943b77e24ab77", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 554, "license_type": "no_license", "max_line_length": 83, "num_lines": 23, "path": "/OOP/Variables.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "class Employee:\n\n raise_amount = 1.04\n num_emps = 0\n\n def __init__(self, first, last, pay):\n self.first = first\n self.last = last\n self.email = first + '.' + last + '@email.com'\n self.pay = pay\n Employee.num_emps += 1\n\n def fullname(self):\n return '{} {} {}'.format(self.first, self.last, self.raise_amount*self.pay)\n\nemp_1 = Employee('Pogi', 'Earl', 50000)\nemp_2 = Employee('Test', 'Employee', 60000)\n\nprint(emp_1.fullname())\nemp_1.raise_amount = 1.05\nprint(emp_1.fullname())\n\nprint(emp_1.num_emps)\n" } ]	55
mastier/net-surveyor	https://github.com/mastier/net-surveyor	a8dcca3b2b2611e0063d7907caece20c05f7d7f0	07c775e9804029ef70661168a046e10e35b75535	2e3e78c47b12db25e2a0b2fda07538f7e82cecbc	refs/heads/master	2022-04-22T07:01:29.058649	2020-04-23T08:47:12	2020-04-23T08:47:12	null	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5564202070236206, "alphanum_fraction": 0.5581495761871338, "avg_line_length": 26.879518508911133, "blob_id": "0657714e4e7409107a0b47e2a8c038b45f103d3a", "content_id": "1f9168c0487800e2de3faeb9325a2d2607dbc91c", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2313, "license_type": "permissive", "max_line_length": 115, "num_lines": 83, "path": "/report_html.py", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "#!/usr/bin/python3\n\nimport json\nimport os\nfrom optparse import OptionParser\n\ndef get_ports(netmap):\n ports = []\n for host in get_hosts(netmap):\n for port in netmap['machines'][host]['ports']:\n if port not in ports:\n ports.append(port)\n ports.sort()\n return ports\n\ndef get_hosts(netmap):\n hosts = list(netmap['machines'])\n hosts.sort()\n return hosts\n\ndef get_port_data(netmap, host, port):\n data = {}\n keys = ['chassis', 'port', 'vlan', 'descr']\n pdata = netmap['machines'][host]['ports'][port]\n try:\n for key in keys:\n data[key] = pdata[key]\n except KeyError:\n pass\n return str(data)\n\ndef write_header(f):\n f.write(\"<html><body>\\n\")\n\ndef write_footer(f):\n f.write(\"</body></html>\")\n\ndef write_table_header(f, ports):\n f.write(\"<table border=1><tr>\")\n f.write(\"<td>Hostname</td>\")\n for port in ports:\n f.write(\"<td>\" + port + \"</td>\")\n f.write(\"</tr>\\n\")\n\ndef write_table_footer(f):\n f.write(\"</tr></table>\\n\")\n\ndef write_table_row(f, netmap, host, ports):\n f.write(\"<tr>\")\n f.write(\"<td>\" + host + \"</td>\")\n for port in ports:\n try:\n f.write(\"<td>\" + get_port_data(netmap, host, port) + \"</td>\")\n except KeyError:\n f.write(\"<td>\" + \"Not connected\" + \"</td>\")\n f.write(\"</tr>\\n\") \n\ndef render_html(netmap, outfile):\n ports = get_ports(netmap)\n hosts = get_hosts(netmap)\n with open(outfile, 'w') as f:\n write_header(f)\n write_table_header(f, ports)\n for host in hosts:\n write_table_row(f, netmap, host, ports)\n write_table_footer(f)\n write_footer(f)\n\ndef main(options):\n netmap = {}\n with open(options.infile) as f:\n netmap = json.load(f)\n render_html(netmap, options.outfile) \n\nif __name__ == \"__main__\":\n usage = \"usage: %prog [options] arg1 arg2\"\n parser = OptionParser(usage=usage)\n parser.add_option(\"-i\", \"--input\",\n action=\"store\", type=\"string\", dest=\"infile\", default='netmap.json', help=\"Input file\") \n parser.add_option(\"-o\", \"--output\",\n action=\"store\", type=\"string\", dest=\"outfile\", default='netmap.html', help=\"Output file\")\n (options, args) = parser.parse_args() \n main(options)" }, { "alpha_fraction": 0.7129735946655273, "alphanum_fraction": 0.7267508506774902, "avg_line_length": 28.03333282470703, "blob_id": "99802e0455c19b2919be1154b6da9f43a2145e56", "content_id": "f8a411f11871e0dc7e953d3fdb3a6962c32dcfac", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 871, "license_type": "permissive", "max_line_length": 126, "num_lines": 30, "path": "/collect-lldp-ssh.sh", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "#!/usr/bin/env bash\ninput_file=${1:-machines.txt}\nif [ ! -f $input_file ];then\n echo \"Missing machines list\"\n exit 1\nfi\ndownload_dir='/tmp/lldp'\n\nrm -rf $download_dir\nmkdir -p $download_dir\nscript=$(mktemp)\n\ncat > $script << \"EOF\"\n#!/bin/bash\nfor interface in `ls /sys/kernel/debug/i40e`\n do echo \"lldp stop\" > /sys/kernel/debug/i40e/${interface}/command\ndone\n\napt install lldpd -y\nsleep 10\nlldpcli show neighbors details -f json > /tmp/lldp_output.json\nEOF\n\nwhile read -r machine; do\n echo $machine\n scp -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null $script $machine:/tmp/\n ssh -n -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null $machine \"chmod 700 $script; sudo $script; rm $script\"\n scp -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null $machine:'/tmp/lldp_output.json' $download_dir/$machine.json\ndone < machines.txt\nexit 0\n" }, { "alpha_fraction": 0.7224478721618652, "alphanum_fraction": 0.7303005456924438, "avg_line_length": 29.024391174316406, "blob_id": "7382a6b16b3a4087d8358533bdadaae49b7808a6", "content_id": "d0aa4f878863518257083a2717894affee672454", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 3693, "license_type": "permissive", "max_line_length": 257, "num_lines": 123, "path": "/README.md", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "# NET-SURVEYOR\n\n## About Net-Surveyor\nNet-Surveyor is an LLDP based tool which purpose is to collect, agggregate and visualise network topology data from cloud environment, thus simplifying network configuration and troubleshooting.\n\nNet-Surveyor bases on [Link Layer Doscovery Protocol](https://en.wikipedia.org/wiki/Link_Layer_Discovery_Protocol) - LLDP - data about VLANs, switch port connectivity and link aggregation.\n\n## Usage\n\nNet-Surveyor works with multiple sources of LLDP data. Currently the preferred way is using [juju](https://jaas.ai/) and [magpie](https://jaas.ai/u/openstack-charmers-next/magpie).\n\nThe tool generates 3 types of reports:\n- HTML web page with a table of all machines and interfaces with LLDP data in appropriate cells\n- SVG network topology map with the same information as above\n- Python NetworkX graph\n\n### Juju \n\nNet-Surveyor can collect LLDP data from an existing juju environment. Please note that it will install LLDP on all the machines.\n\n1. Clone the Net-Surveyor repository\n2. Collect LLDP data:\n```\n./collect-lldp-juju.py -i \n```\nThis collects the data by default into `/tmp/lldp/`. `-i` installs LLDP on the machines. Note that if this is fresh LLDP install, this step may need to be repeated as LLDP data is collected over time, based on incoming LLDP PDUs.\n\n3. Build topology from the collected data:\n```\n./build_netmap.py -o netmap.json\n```\nThis step merges the collected data into single JSON file.\n\n4. Create report:\n```\n./report_html.py -i netmap.json -o netmap.html\n```\n\n### Juju and magpie\n\n1. Create a [juju](https://jaas.ai/) [magpie](https://jaas.ai/u/openstack-charmers-next/magpie) bundle for your environment. Magpie charm can be deployed on both bare metal machines and containers. Make sure to enable LLDP collection on bare metal machines:\n```\nseries: \"bionic\"\nmachines:\n '1':\n constraints: tags=foundation-nodes\n '2': \n constraints: tags=foundation-nodes\nservices:\n magpie-bare:\n charm: \"cs:~openstack-charmers-next/magpie\"\n series: \"bionic\"\n num_units: 2\n constraints: spaces=oam-space\n bindings:\n \"\": oam-space\n options:\n check_dns: true\n check_iperf: false\n check_bonds: \"bond0,bond1,bond2\"\n use_lldp: true # required to be true\n check_port_description: false\n to:\n - 1\n - 2\n```\n2. Deploy the bundle:\n```\njuju deploy ./magpie-bundle.yaml\n```\n3. Wait for the bundle to deploy\n4. Clone the Net-Surveyor repository\n5. Collect LLDP data:\n```\n./collect-lldp-juju.py\n```\nThis collects the data by default into `/tmp/lldp/`\n\n6. Build topology from the collected data:\n```\n./build_netmap.py -o netmap.json\n```\nThis step merges the collected data into single JSON file.\n\n7. Create report:\n```\n./report_html.py -i netmap.json -o netmap.html\n```\n\n### SSH\n\n1. Create txt fle conatining hostnames of all machines to connect to, eg: `machines.txt`\n2. Collect LLDP data:\n```\n./collect-lldp-ssh.sh machines.txt\n```\nThis collects the data by default into `/tmp/lldp/`\n\n3. Build topology from the collected data:\n```\n./build_netmap.py -o netmap.json\n```\nThis step merges the collected data into single JSON file.\n\n4. Create report:\n```\n./report_html.py -i netmap.json -o netmap.html\n```\n\n\n## Future work\n\n1. Update and publish plugin to collect [MAAS](maas.io) comissioning data.\n2. Improve SVG graph to group host interfaces into VLANs\n3. Add VLAN and host filtering for report generation\n4. Create Net-Surveyor SNAP\n5. Create Net-Surveyor server and integrate with Magpie for single bundle deployment\n\n## Bugs and feature requests\n\nUse issues reporting feature in this repository to report bugs and feature requests.\n\nAll contributions are heartily welcome.\n" }, { "alpha_fraction": 0.6245681643486023, "alphanum_fraction": 0.6295585632324219, "avg_line_length": 36.228572845458984, "blob_id": "dc89a84e0677cd362453aa4414d5a54989574565", "content_id": "e27c17289872f9a6e165858601490025b32bb63a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2605, "license_type": "permissive", "max_line_length": 138, "num_lines": 70, "path": "/collect-lldp-juju.py", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "#!/usr/bin/python3\n\nimport json\nimport os\nimport shutil\nimport tempfile\nimport subprocess\nfrom optparse import OptionParser\n\ndef get_model_machies():\n raw = subprocess.check_output('juju machines --format json',shell=True)\n ret = []\n json_machines = json.loads(raw)\n for id in json_machines['machines']:\n name = json_machines['machines'][id]['display-name']\n ret.append( (id,name) )\n return ret\n\ndef write_collector_script(install_lldp = True):\n ftmp, ftmpname = tempfile.mkstemp()\n header = \"#!/bin/bash\\n\"\n install = \"\"\"\n for interface in `ls /sys/kernel/debug/i40e`\n do echo \"lldp stop\" > /sys/kernel/debug/i40e/${interface}/command\n done\n apt install lldpd -y;\n \"\"\"\n collect = \"lldpcli show neighbors details -f json > /tmp/lldp_output.json\\n\"\n body = header\n if install_lldp:\n body += install\n body += \"\\n\"\n body += collect\n os.write(ftmp,body.encode('utf-8'))\n os.close(ftmp)\n return ftmpname\n\ndef copy_script(machine, script_name):\n subprocess.run(\"juju scp {script_name} {machine}:{script_name}\".format(machine = machine, script_name = script_name), shell = True)\n\ndef run_script(machine, script_name):\n subprocess.run(\"juju ssh {machine} \\\"chmod 700 {script_name}; sudo {script_name}; rm {script_name}\\\"\"\n .format(machine = machine, script_name = script_name), shell = True)\n\ndef collect_data(machine_id, hostname, work_dir):\n subprocess.run(\"juju scp {machine_id}:/tmp/lldp_output.json {work_dir}/{hostname}.json\"\n .format(machine_id = machine_id, hostname = hostname, work_dir = work_dir), shell = True)\n\ndef main(options):\n if os.path.isdir(options.work_dir):\n shutil.rmtree(options.work_dir)\n os.mkdir(options.work_dir)\n script_name = write_collector_script(options.install_lldp)\n for machine in get_model_machies():\n id = machine[0]\n hostname = machine[1]\n copy_script(id, script_name)\n run_script(id, script_name)\n collect_data(id, hostname, options.work_dir)\n os.remove(script_name)\n\nif __name__ == \"__main__\":\n usage = \"usage: %prog [options] arg1 arg2\"\n parser = OptionParser(usage=usage)\n parser.add_option(\"-d\", \"--dir\",\n action=\"store\", type=\"string\", dest=\"work_dir\", default=\"/tmp/lldp\", help=\"Output directory\")\n parser.add_option(\"-i\", \"--install\",\n action=\"store_true\", dest=\"install_lldp\", default=False, help=\"Install LLDP tools first\") \n (options, args) = parser.parse_args() \n main(options)" }, { "alpha_fraction": 0.586584210395813, "alphanum_fraction": 0.5880114436149597, "avg_line_length": 31.859375, "blob_id": "f723e99ac3b4e669e734f2511084539861f9bb78", "content_id": "77311d023c2a5d29b65b688065f849b8423b6d39", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2102, "license_type": "permissive", "max_line_length": 115, "num_lines": 64, "path": "/report_graph.py", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "#!/usr/bin/python3\n\nimport json\nimport os\nfrom optparse import OptionParser\nimport networkx as nx\nimport matplotlib.pyplot as plt\n\ndef get_hosts(netmap):\n hosts = list(netmap['machines'])\n hosts.sort()\n return hosts\n\ndef get_switches(netmap):\n hosts = list(netmap['switches'])\n hosts.sort()\n return hosts\n\ndef render_graph(netmap, outfile):\n colors = {'host': 'blue',\n 'switch': 'green',\n 'iface': 'red',\n 'port': 'red',\n }\n\n G = nx.Graph()\n color_map = []\n label_map = {}\n for host in get_hosts(netmap):\n G.add_node(host)\n color_map.append(colors['host'])\n label_map[host] = host\n for port in netmap['machines'][host]:\n G.add_node(host + port)\n color_map.append(colors['iface'])\n G.add_edge(host, host + port)\n label_map[host + port] = port\n for switch in get_switches(netmap):\n G.add_node(switch)\n color_map.append(colors['switch'])\n for link in netmap['links']:\n G.add_node(link['destination_host'] + link['destination_port'])\n color_map.append(colors['port'])\n G.add_edge(link['destination_host'], link['destination_host'] + link['destination_port'])\n G.add_edge(link['source_host'] + link['source_port'], link['destination_host'] + link['destination_port'])\n nx.draw(G, labels=label_map, node_color=color_map, with_labels=True)\n #plt.savefig(outfile)\n plt.show()\n\ndef main(options):\n netmap = {}\n with open(options.infile) as f:\n netmap = json.load(f)\n render_graph(netmap, options.outfile) \n\nif __name__ == \"__main__\":\n usage = \"usage: %prog [options] arg1 arg2\"\n parser = OptionParser(usage=usage)\n parser.add_option(\"-i\", \"--input\",\n action=\"store\", type=\"string\", dest=\"infile\", default='netmap.json', help=\"Input file\") \n parser.add_option(\"-o\", \"--output\",\n action=\"store\", type=\"string\", dest=\"outfile\", default='netmap.png', help=\"Output file\")\n (options, args) = parser.parse_args() \n main(options)" }, { "alpha_fraction": 0.5676092505455017, "alphanum_fraction": 0.5686375498771667, "avg_line_length": 37.88999938964844, "blob_id": "fd74133363231074d8e7441e4b77e8ccd65eadcf", "content_id": "ed27b955d92808f1f201ff30a6bdfe99c48de31c", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3890, "license_type": "permissive", "max_line_length": 116, "num_lines": 100, "path": "/build_netmap.py", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "#!/usr/bin/python3\n\nimport json\nimport os\nfrom optparse import OptionParser\n\ndef init_netmap(netmap):\n netmap['vlans'] = []\n netmap['machines'] = {}\n netmap['switches'] = {}\n netmap['links'] = []\n\ndef add_vlan(netmap, vlan):\n if vlan not in netmap['vlans']:\n netmap['vlans'].append(vlan)\n\ndef add_switch_port(netmap, switch, pdata):\n if switch not in netmap['switches']:\n netmap['switches'][switch] = {}\n if 'ports' not in netmap['switches'][switch]:\n netmap['switches'][switch]['ports'] = {}\n if pdata['port'] not in netmap['switches'][switch]['ports']:\n netmap['switches'][switch]['ports'][pdata['port']] = pdata\n if 'vlans' not in netmap['switches'][switch]:\n netmap['switches'][switch]['vlans'] = {}\n if 'vlan' in pdata:\n vlan_id = pdata['vlan']\n else:\n vlan_id = 'untagged'\n if vlan_id not in netmap['switches'][switch]['vlans']:\n netmap['switches'][switch]['vlans'][vlan_id] = []\n netmap['switches'][switch]['vlans'][vlan_id].append(pdata['port']) \n\ndef add_host_port(netmap, hostname, ifname, pdata):\n if hostname not in netmap['machines']:\n netmap['machines'][hostname] = {}\n if 'ports' not in netmap['machines'][hostname]:\n netmap['machines'][hostname]['ports'] = {}\n if ifname not in netmap['machines'][hostname]['ports']:\n netmap['machines'][hostname]['ports'][ifname] = pdata \n if 'vlans' not in netmap['machines'][hostname]:\n netmap['machines'][hostname]['vlans'] = {}\n if 'vlan' in pdata:\n vlan_id = pdata['vlan']\n else:\n vlan_id = 'untagged'\n if vlan_id not in netmap['machines'][hostname]['vlans']:\n netmap['machines'][hostname]['vlans'][vlan_id] = []\n netmap['machines'][hostname]['vlans'][vlan_id].append(ifname)\n\ndef add_link(netmap, hostname, host_port, switch_name, switch_port):\n link = {'source_host': hostname, \n 'source_port': host_port, \n 'destination_host': switch_name, \n 'destination_port': switch_port, \n }\n netmap['links'].append(link)\n\ndef parse_machine_file(netmap, work_dir, fname):\n hostname=fname.split('.')[0]\n with open(work_dir + \"/\" + fname) as f:\n data = json.load(f)\n for iface in data['lldp']['interface']:\n for ifname in iface.keys():\n iface_lldp = iface[ifname]\n pdata = {}\n pdata['raw'] = iface_lldp\n pdata['descr'] = iface_lldp['port']['descr']\n pdata['port'] = iface_lldp['port']['id']['value']\n if 'vlan' in iface_lldp:\n vid = iface_lldp['vlan']['vlan-id']\n pdata['vlan'] = vid\n add_vlan(netmap, vid)\n for chassis_name in iface_lldp['chassis'].keys():\n pdata['chassis'] = chassis_name\n add_switch_port(netmap, chassis_name, pdata)\n add_host_port(netmap, hostname, ifname, pdata)\n add_link(netmap, hostname, ifname, chassis_name, pdata['port'])\n\ndef populate_netmap(netmap, work_dir):\n for fname in os.listdir(work_dir):\n parse_machine_file(netmap, work_dir, fname)\n\ndef main(options):\n netmap = {}\n init_netmap(netmap)\n populate_netmap(netmap, options.work_dir)\n with open(options.outfile, 'w') as outfile:\n json.dump(netmap, outfile)\n \n\nif __name__ == \"__main__\":\n usage = \"usage: %prog [options] arg1 arg2\"\n parser = OptionParser(usage=usage)\n parser.add_option(\"-d\", \"--dir\",\n action=\"store\", type=\"string\", dest=\"work_dir\", default=\"/tmp/lldp\", help=\"Input directory\")\n parser.add_option(\"-o\", \"--output\",\n action=\"store\", type=\"string\", dest=\"outfile\", default='netmap.json', help=\"Output file\")\n (options, args) = parser.parse_args() \n main(options)\n\n" }, { "alpha_fraction": 0.4242086112499237, "alphanum_fraction": 0.4287407696247101, "avg_line_length": 44.53333282470703, "blob_id": "84e764d45c50e9d14165e5f88589c63b65d3c978", "content_id": "b3f69c6379163e93a34dfd287b3a8212ffa77bf1", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 14342, "license_type": "permissive", "max_line_length": 170, "num_lines": 315, "path": "/report_svg.py", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "#!/usr/bin/python3\n\nimport json\nimport os\nfrom optparse import OptionParser\n\nimport svgwrite\nfrom svgwrite import mm, px \n\ndef get_hosts(netmap):\n hosts = list(netmap['machines'])\n hosts.sort()\n return hosts\n\ndef get_switches_left(netmap):\n hosts = list(netmap['switches'])\n hosts.sort()\n return hosts[0::2]\n\ndef get_switches_right(netmap):\n hosts = list(netmap['switches'])\n hosts.sort()\n return hosts[1::2]\n\ndef init_side_pane(netmap, items):\n meta = {\n 'max_width': 0,\n 'max_height': 0,\n 'count' : len(items),\n 'items': items\n } \n for item in items:\n meta[item] = {}\n meta[item]['width'] = len(item)\n meta[item]['vlans'] = {}\n meta[item]['ports'] = []\n for vlan in netmap['switches'][item]['vlans']:\n meta[item]['vlans'][vlan] = {'ports': []}\n for port in netmap['switches'][item]['vlans'][vlan]:\n if len(item) > meta[item]['width']:\n meta[item]['width'] = len(item)\n meta[item]['vlans'][vlan]['ports'].append(port)\n meta[item]['ports'].append(port)\n meta[item][port] = {}\n meta[item]['height'] = len(netmap['switches'][item]['ports'])+len(netmap['switches'][item]['vlans'])\n if meta[item]['width'] > meta['max_width']:\n meta['max_width'] = meta[item]['width']\n if meta[item]['height'] > meta['max_height']:\n meta['max_height'] = meta[item]['height'] \n return meta\n\ndef init_center_pane(netmap, items, peers):\n meta = {\n 'max_width': 0,\n 'max_height': 0,\n 'count' : len(items),\n 'items': items\n } \n for item in items:\n meta[item] = {}\n meta[item]['width'] = len(item)\n meta[item]['ports_left'] = []\n meta[item]['ports_right'] = []\n meta[item]['vlans'] = {}\n meta[item]['ports'] = []\n port_left_max_width=0\n port_right_max_width=0\n for vlan in netmap['machines'][item]['vlans']:\n meta[item]['vlans'][vlan] = {'ports': []} \n for port in netmap['machines'][item]['vlans'][vlan]: \n if netmap['machines'][item]['ports'][port]['chassis'] in peers['left']:\n meta[item]['ports_left'].append(port)\n meta[item]['ports'].append(port)\n meta[item]['vlans'][vlan]['ports'].append(port)\n meta[item][port] = {'align': 'left'}\n if len(port) > port_left_max_width:\n port_left_max_width = len(port)\n if netmap['machines'][item]['ports'][port]['chassis'] in peers['right']:\n meta[item]['ports_right'].append(port)\n meta[item]['ports'].append(port)\n meta[item][port] = {'align': 'right'} \n if len(port) > port_right_max_width:\n port_right_max_width = len(port)\n\n if meta[item]['width'] < port_right_max_width + port_left_max_width:\n meta[item]['width'] = port_right_max_width + port_left_max_width\n\n if meta[item]['width'] > meta['max_width']:\n meta['max_width'] = meta[item]['width'] \n\n meta[item]['height'] = max(len(meta[item]['ports_left']),\n len(meta[item]['ports_right'])\n ) + len(netmap['machines'][item]['vlans'])\n if meta[item]['height'] > meta['max_height']:\n meta['max_height'] = meta[item]['height']\n return meta\n\ndef prepare_topology_metadata(netmap):\n meta = {}\n items = {}\n items['left'] = get_switches_left(netmap)\n items['right'] = get_switches_right(netmap)\n items['center'] = get_hosts(netmap)\n meta['left'] = init_side_pane( netmap, items['left'])\n meta['right'] = init_side_pane( netmap, items['right'])\n meta['center'] = init_center_pane( netmap, items['center'], items )\n return meta\n\ndef prepare_placement_metadata(meta, options):\n max_height = 0\n for side in ['left', 'center', 'right']:\n height = (meta[side]['max_height'] * options['port_height'] + options['item_label_height'] + options['item_hspace'])meta[side]['count']\n if height > max_height:\n max_height = height\n \n meta['left']['x'] = options['img_left_padding']\n meta['center']['x'] = meta['left']['x'] + meta['left']['max_width'] options['col_width'] + options['item_vspace']\n meta['right']['x'] = meta['center']['x'] + meta['center']['max_width'] * options['col_width'] + options['item_vspace']\n for side in ['left', 'center', 'right']:\n meta[side]['box_height'] = max_height / meta[side]['count']\n y = 0\n for item in meta[side]['items']:\n box_x = meta[side]['x']\n box_y = meta[side]['box_height']y\n meta[side][item]['width'] = meta[side]['max_width'] options['col_width']\n meta[side][item]['height'] = meta[side][item]['height'] * options['port_height'] + options['item_label_height'] \n meta[side][item]['x'] = box_x\n meta[side][item]['y'] = box_y + (meta[side]['box_height'] - meta[side][item]['height']) / 2\n if side == 'center':\n for col in ['left', 'right']:\n dy = 0\n for port in meta[side][item]['ports_' + col]:\n meta[side][item][port]['width'] = (meta[side]['max_width'] / 2) * options['col_width']\n meta[side][item][port]['height'] = options['port_height']\n meta[side][item][port]['y'] = meta[side][item]['y'] + options['item_label_height'] + dy * meta[side][item][port]['height'] \n meta[side][item][port]['anchor_y'] = meta[side][item][port]['y'] + meta[side][item][port]['height'] / 2\n if col == 'left':\n meta[side][item][port]['x'] = meta[side][item]['x']\n meta[side][item][port]['anchor_x'] = meta[side][item]['x']\n if col == 'right':\n meta[side][item][port]['x'] = meta[side][item]['x'] + (meta[side]['max_width'] / 2) * options['col_width']\n meta[side][item][port]['anchor_x'] = meta[side][item][port]['x'] + meta[side][item][port]['width']\n dy += 1 \n else:\n dy = 0\n for port in meta[side][item]['ports']:\n meta[side][item][port]['width'] = meta[side]['max_width'] * options['col_width']\n meta[side][item][port]['height'] = options['port_height']\n meta[side][item][port]['x'] = meta[side][item]['x'] \n meta[side][item][port]['y'] = meta[side][item]['y'] + options['item_label_height'] + dy * meta[side][item][port]['height'] \n meta[side][item][port]['anchor_y'] = meta[side][item][port]['y'] + meta[side][item][port]['height'] / 2\n if side == 'left':\n meta[side][item][port]['anchor_x'] = meta[side][item]['x'] + meta[side][item][port]['width']\n if side == 'right':\n meta[side][item][port]['anchor_x'] = meta[side][item]['x']\n dy += 1 \n y += 1\n return meta\n\ndef port_color(netmap, item, port):\n if item in netmap['machines']:\n port_data = netmap['machines'][item]['ports'][port]\n if item in item in netmap['switches']:\n port_data = netmap['switches'][item]['ports'][port]\n if 'vlan' in port_data:\n vlan_id = int(port_data['vlan'])\n else:\n vlan_id = 0\n return vlan_color(vlan_id)\n\ndef vlan_color(vlan_id):\n if vlan_id == 0:\n return 'gray'\n rev = int(str(vlan_id)[::-1])\n color = {}\n color['r'] = (rev * (( rev / 10 ) % 10)) % 256\n color['g'] = (rev * (( rev / 100 ) % 10)) % 256\n color['b'] = (rev * (( rev / 1000 ) % 10)) % 256\n return svgwrite.rgb(color['r'], color['g'], color['b'])\n\ndef render_svg(netmap, outfile):\n dwg = svgwrite.Drawing(filename=outfile, debug=True)\n shapes = dwg.add(dwg.g(id='shapes'))\n options = {\n 'col_width' : 2.2,\n 'port_height' : 5,\n 'item_hspace' : 1,\n 'item_vspace' : 100,\n 'img_left_padding' : 10,\n 'item_label_height' : 5,\n 'format' : {\n 'line_width': 3,\n 'left' : {\n 'item' : {\n 'fill': 'none',\n 'stroke': 'black',\n },\n 'item_label' : {\n 'text_anchor': 'middle'\n },\n 'port' : {\n 'stroke': 'black'\n },\n 'port_label' : {\n 'text_anchor': 'middle'\n },\n },\n 'center' : {\n 'item' : {\n 'fill': 'none',\n 'stroke': 'black'\n },\n 'item_label' : {\n 'text_anchor': 'middle'\n }, \n 'port' : {\n 'stroke': 'black'\n },\n 'port_label' : {\n 'text_anchor': 'middle'\n }, \n },\n 'right' : {\n 'item' : {\n 'fill': 'none',\n 'stroke': 'green'\n },\n 'item_label' : {\n 'text_anchor': 'middle'\n },\n 'port' : {\n 'stroke': 'black'\n },\n 'port_label' : {\n 'text_anchor': 'middle'\n }, \n },\n }\n }\n topology = prepare_topology_metadata(netmap)\n meta = prepare_placement_metadata(topology, options)\n unit = mm\n for side in ['left', 'center', 'right']:\n for item in meta[side]['items']:\n rect = dwg.rect(insert = (\n meta[side][item]['x'] * unit, \n meta[side][item]['y'] * unit), \n size = (\n meta[side][item]['width'] * unit, \n meta[side][item]['height'] * unit),\n *options['format'][side]['item'])\n label = dwg.text(item, \n insert = (\n (meta[side][item]['x'] + meta[side][item]['width'] / 2) unit, \n (meta[side][item]['y'] + options['item_label_height'])unit),\n options['format'][side]['item_label']\n )\n shapes.add(rect)\n shapes.add(label)\n for port in meta[side][item]['ports']:\n dwg_opts = options['format'][side]['port'].copy()\n if 'fill' not in dwg_opts:\n dwg_opts['fill'] = port_color(netmap, item, port)\n rect = dwg.rect(insert=(meta[side][item][port]['x'] unit, meta[side][item][port]['y'] * unit), \n size=(meta[side][item][port]['width'] * unit, meta[side][item][port]['height'] * unit),\n *dwg_opts)\n label = dwg.text(port, \n insert=(\n (meta[side][item][port]['x'] + meta[side][item][port]['width'] / 2 ) unit, \n (meta[side][item][port]['y'] + options['item_label_height']) * unit),\n *options['format'][side]['port_label']\n )\n shapes.add(rect)\n shapes.add(label)\n for link in netmap['links']:\n if link['source_host'] == item and link['source_port'] == port:\n for dst_side in ['left', 'center', 'right']:\n for dst_item in meta[dst_side]['items']:\n if dst_item == link['destination_host']:\n for dst_port in meta[dst_side][dst_item]['ports']:\n if dst_port == link['destination_port']:\n if 'line_stroke' not in options['format']:\n stroke = port_color(netmap, item, port)\n else:\n stroke = options['format']['line_stroke']\n line = dwg.line(\n start = (\n meta[side][item][port]['anchor_x'] unit,\n meta[side][item][port]['anchor_y'] * unit,\n ),\n end = (\n meta[dst_side][dst_item][dst_port]['anchor_x'] * unit,\n meta[dst_side][dst_item][dst_port]['anchor_y'] * unit,\n ),\n stroke_width = options['format']['line_width'],\n stroke = stroke\n )\n shapes.add(line) \n dwg.save()\n\ndef main(options):\n netmap = {}\n with open(options.infile) as f:\n netmap = json.load(f)\n render_svg(netmap, options.outfile)\n\nif __name__ == \"__main__\":\n usage = \"usage: %prog [options] arg1 arg2\"\n parser = OptionParser(usage=usage)\n parser.add_option(\"-i\", \"--input\",\n action=\"store\", type=\"string\", dest=\"infile\", default='netmap.json', help=\"Input file\") \n parser.add_option(\"-o\", \"--output\",\n action=\"store\", type=\"string\", dest=\"outfile\", default='netmap.svg', help=\"Output file\")\n (options, args) = parser.parse_args() \n main(options)" } ]	7
TurcanuVlad/For-x-in-range	https://github.com/TurcanuVlad/For-x-in-range	8c75365ef53b73b875697cf1299ac7b6bfad398f	487dcfa7e1b43beff7d8fe2575c5f6078499cbe1	b80506ba1d111b2329615f94cfe56f0148741a92	refs/heads/main	2023-01-12T06:31:47.424971	2020-11-08T16:04:14	2020-11-08T16:04:14	311,099,923	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5733333230018616, "alphanum_fraction": 0.6133333444595337, "avg_line_length": 23.66666603088379, "blob_id": "d2c4ad96987c54ff2d396aaee8431e5d71d1fe94", "content_id": "8408d356547c106fc01eb5d81b54cf070b2b629a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 75, "license_type": "no_license", "max_line_length": 35, "num_lines": 3, "path": "/Problema_5.py", "repo_name": "TurcanuVlad/For-x-in-range", "src_encoding": "UTF-8", "text": "n=int(input(\"Dati numarul dorit=\"))\r\nfor n in range(0,n+1,2):\r\n print(n)" }, { "alpha_fraction": 0.506771981716156, "alphanum_fraction": 0.568848729133606, "avg_line_length": 18.136363983154297, "blob_id": "3edfddf447b1afe507088268aa574e83f79db347", "content_id": "72495afc973d83ea3f2382819e8bfb1087565edd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 886, "license_type": "no_license", "max_line_length": 45, "num_lines": 44, "path": "/Problema_6.py", "repo_name": "TurcanuVlad/For-x-in-range", "src_encoding": "UTF-8", "text": "n=int(input(\"Dati numarul dorit= \"))\r\na=1\r\ns=0\r\ns1=0\r\ns2=0\r\ns3=0\r\ns4=0\r\ns5=0\r\np=1\r\nd=0\r\nfor a in range(1,n+1,1):\r\n s=a+s\r\nprint(\"Raspunsul la primul exemplu= \", s)\r\n\r\nn=int(input(\"Dati numarul dorit= \"))\r\nfor a in range(1,n+1,1):\r\n s1=((a-1)a)+s1\r\nprint(\"Raspunsul la al 2 exemplu este= \", s1)\r\n\r\nn=int(input(\"Dati numarul dorit= \"))\r\nfor a in range(1,n+1,1):\r\n p=a\r\n s2+=p\r\nprint(\"Raspunsul la al 3 exemplu este= \", s2)\r\n\r\nn=int(input(\"Dati numarul dorit= \"))\r\nfor a in range(1,n+1,1):\r\n a=str(a)\r\n a=int(a+\"2\")\r\n s3=s3+a\r\nprint(\"Raspunsul la exemplul 4 este= \", s3)\r\n\r\nn=int(input(\"Dati numarul dorit= \"))\r\nfor a in range(1,n+1,1):\r\n d=a/(a+1)\r\n s4=s4+d\r\nprint(\"Raspunsul la al 5 exemplu este= \", s4)\r\n\r\nn=int(input(\"Dati numarul dorit= \"))\r\nfor a in range(1,n+1,1):\r\n a=str(a)\r\n a=int(\"2\"+a)\r\n s5=s5+a\r\nprint(\"Raspunsul la al 6 exemplu= \", s5)\r\n" }, { "alpha_fraction": 0.6000000238418579, "alphanum_fraction": 0.6266666650772095, "avg_line_length": 28.399999618530273, "blob_id": "69ab749c26f56e2f936e9549272fc89200d78f64", "content_id": "4f87d0b292e7aebe4394a1c8f7e428dd85e85f1c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 150, "license_type": "no_license", "max_line_length": 44, "num_lines": 5, "path": "/Problema_4.py", "repo_name": "TurcanuVlad/For-x-in-range", "src_encoding": "UTF-8", "text": "a=int(input(\"Dati inceputul intervalului=\"))\r\nb=int(input(\"Dati sfirsitul intervalului=\"))\r\nfor a in range(a,b+1,1):\r\n if a%2!=0:\r\n print(a)" }, { "alpha_fraction": 0.4757281541824341, "alphanum_fraction": 0.5339806079864502, "avg_line_length": 24.25, "blob_id": "fd8cb136fc5d81e616f055292e22167afa74f499", "content_id": "d54136e2b543439d99a69f7e53b2c7380e66c8cb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 103, "license_type": "no_license", "max_line_length": 35, "num_lines": 4, "path": "/Problema_3.py", "repo_name": "TurcanuVlad/For-x-in-range", "src_encoding": "UTF-8", "text": "n=int(input(\"Dati numarul dorit=\"))\r\nfor n in range(1,n,1):\r\n if n%3==0 or n%5==0:\r\n print(n)" } ]	4
ccollado7/UNSAM_IA	https://github.com/ccollado7/UNSAM_IA	a5e8d1d605dacc612875e9bf0b595d73cbe0b06d	9cad4afbe61349f02524000cfa514032e5f17c09	294c686afaf7d4c4afedd52d09e3e2e75e3c0687	refs/heads/master	2022-11-14T05:18:47.006608	2020-07-07T20:47:23	2020-07-07T20:47:23	null	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7319042682647705, "alphanum_fraction": 0.7897721529006958, "avg_line_length": 62.048484802246094, "blob_id": "17f48b9821b6282ffb3a4bbe515b72dacecfedb6", "content_id": "767ee876588423761415e01c10ae1865446a77f2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 10461, "license_type": "no_license", "max_line_length": 339, "num_lines": 165, "path": "/README.md", "repo_name": "ccollado7/UNSAM_IA", "src_encoding": "UTF-8", "text": "# Repositorio de la materia Aprendizaje Automático (UNSAM)\n\n## 1er cuatrimestre de 2020\n\n### Notebooks\n\nLos notebooks que forman parte del repositorio contienen las partes prácticas de las clases, tanto de los martes como de los jueves, y ejercicios. En principio, cada notebook de clase tiene asociado una serie de ejercicios.\n\n**\n\n### Diapositivas de las clases\n\nLas diapositivas están disponibles siguiendo los links que aparecen abajo.\n\n[Martes 10 de marzo](https://drive.google.com/file/d/1tWty4OfYgU3LRR1FzH358l5IKz2KQfvM/view). Introducción a la materia, aprendizaje automático y probabilidad.\n\n[Martes 17 de marzo](https://drive.google.com/file/d/1oFB76Vz5Szl6FWjRKAuAsoevDebgYIW_/view). Probabilidades bayesianas; funciones de probabilidad; uso del teorema de Bayes.\n\n[Jueves 2 de abril](https://drive.google.com/file/d/10cjqw1yuE-m17FS5lxOjDz8JL8sQra_0/view). Distribución normal y multinormal. Teorema Central del Límite.\n\n[Martes 7 de abril](https://drive.google.com/file/d/1Td6j5amKZBLiEPeTdibAC3IMPNa7mcjv/view). Modelos lineales. Regresión lineal y polinomial. Estimadores de máxima verosimilitud.\n\n[Martes 14 de abril](https://drive.google.com/file/d/1D1pUlX9E8jQl7IKTbjH7FistQyx_Myh8/view). Sobreajuste. Validación cruzada (K-fold, Leave One Out CV). Regularización (Ridge, Lasso).\n\n[Jueves 16 de abril](https://drive.google.com/open?id=1Dq9lvzY8iI9DAsmkQunq9JlubiK4SoxJ). Algunos detalles sobre cuadrados minimos, BGD y SGD.\n\n[Martes 21 de abril](https://drive.google.com/file/d/1DoWLNQUPJkrPTlS53YUdSLZumQn7bFDd/view). Visión bayesiana de la regresión con modelos lineales. Comparación de modelos. Clasificación: conceptos generales y funciones discriminativas. Discriminante lineal de Fischer. Perceptrón.\n\n[Jueves 30 de abril](https://drive.google.com/file/d/1WFnVMjwRVhUGBmAgpsUSSgRE1C-AQnI8/view). Repaso de clasificación y algunos conceptos sobre regresión logística.\n\n[Martes 5 de mayo](https://drive.google.com/file/d/1P2roqsFsORRWRjfqdLopzfzTATqS4fPA/view). Más de clasificación con modelos lineales. Modelos discriminativos. Regresión logística. IRLS.\n\n[Martes 12 de mayo](https://drive.google.com/file/d/1yFjeezVKlIXggt7DlFkBdXc-3hUeHOCN/view). Support Vector Machines (SVM). Problema dual y truco del kernel. SVM para clasificación y para regresión.\n\n[Martes 19 de mayo](https://drive.google.com/file/d/1NBmAnucQMYI6FAVaQ0dFNVc2PeJ0N8EL/view). Modelos generativos. Naive Bayes. Combinación de modelos. Árboles de decisión.\n\n[Martes 26 de mayo](https://drive.google.com/file/d/1J8m5yNWQkicV6pIap4PHjUIZOq68wHdz/view). Métodos de ensemble. Descomposición sesgo-varianza. Comités. Bagging. Boosting (AdaBoost y Gradient Boost). Stacking.\n\n[Martes 02 de junio](https://drive.google.com/file/d/1ILpXY9TUEVWXGXwM2q7r4fEMPqBGvBRB/view). Comentario sobre tratamiento de incertezas del target en Modelos Lineales.\n\n[Martes 9 de junio](https://drive.google.com/file/d/1GLH1R78PII3dr5UO6i0gaoz_A5P_Bekh/view?usp=sharing). Redes neuronales.\n\nMartes 23 de junio. [Transfer-Learning](https://drive.google.com/file/d/1FSQbNU-6WMdaTsPdWPNG-4pbNHLCF9zG/view?usp=sharing). [Redes Generativas Antagónicas (GAN)](https://drive.google.com/file/d/1miiPjds60ot3t04pCkuaGrgZ7wBNCEAP/view?usp=sharing). [RNNs](https://drive.google.com/file/d/1Gw6luuDlCCDw_7zfVMN8hXCso_vo2LHg/view?usp=sharing).\n\nJueves 2 de julio.<br/>\nYamila Barrera (Aristas), [\"Modelos generativos para fragancias\"](https://docs.google.com/presentation/d/16ZM79NhlEuN47ZeexD7ABkHQWkxzmq2kTRhbd0Cs_Z0/edit?usp=sharing).</br>\nEzequiel Álvarez (ICAS), [\"Intelligent Arxiv\"](https://drive.google.com/file/d/1HlSQgHc7rxQhBJgHu7G1Wk6ygVJg8-tb/view?usp=sharing).\n\n### Videos\n\nVideos de las clases virtuales\n\n__Martes 17 de marzo.__<br>\n[Primera parte (YouTube)](https://www.youtube.com/watch?v=WkpgXdN4gF8&feature=youtu.be)<br>\n[Segunda parte (Google Meet)](https://drive.google.com/file/d/1qCTc-uttzxjA3KY6M1vvKSxX9OAikGxN/view); [transcripción del chat](https://drive.google.com/file/d/1aOj1kF2Dd6LoP_AM4JB1XDd1mz3_m7hV/view?usp=sharing)\n\n__Jueves 19 de marzo.__<br>\n[Video](https://drive.google.com/file/d/1-Jg3EfaBzeMZcYBieG3Lo8EObOedBC3n/view);\n[transcripción del chat](https://drive.google.com/file/d/13DE3rf6X4EtExi-zTFyeixBuO91OK8Sd/view?usp=sharing)\n\n__Jueves 26 de marzo.__<br>\n[Video](https://drive.google.com/file/d/1EPb5TmGpaxdUVkPKNCBZrCX9-iLBbUws/view);\n[transcripción del chat](https://drive.google.com/file/d/1s2VuABeFMY7jj0k2BkLegCpLmxRFt8_Z/view?usp=sharing)\n\n__Jueves 2 de abril.__<br>\n[Video](https://drive.google.com/file/d/1GMCZ9RXDsDfxb1OtOCZyW7CpRs6oP_2w/view);\n[transcripción del chat](https://drive.google.com/file/d/1TkSw2TCSxXi9nhvhOK82H_O57WOkTnO3/view)\n\n__Martes 7 de abril.__<br>\n[Video](https://drive.google.com/file/d/1XIqKWD5L7F0xhPKkLQXJYZNelhy6z0Y2/view);\n[transcripción del chat](https://drive.google.com/file/d/1DQltVnPu_7F_WLqR9oYdSiACszRBPPfu/view)\n\n__Martes 14 de abril.__<br>\n[Video](https://drive.google.com/file/d/1tU4bR6EXmr3nHupAus1aXesu85TGKYJW/view);\n[transcripción del chat](https://drive.google.com/file/d/1T-ZyxwA3vTkAklgmvGUcUhf3o4msCIyE/view)\n\n__Jueves 16 de abril.__<br>\n[Video](https://drive.google.com/file/d/1XXQI7TDqMbJPkbJJOpVaLTVkV1pJYNSS/view);\n[transcripción del chat](https://drive.google.com/file/d/167PmPXCoAWFV_dERbRzLC0dBOwvEfWKN/view)\n\n__Martes 21 de abril.__<br>\n[Video](https://drive.google.com/file/d/1ge_a9ukMpl3_pRSCgeIF7rjex3gGVq4O/view);\n[transcripción del chat](https://drive.google.com/file/d/1FG_vZh6TFRtUY6K46CXflROLVW60CLmH/view)\n\n__Jueves 23 de abril.__<br>\nConsulta con Manu. [Video](https://drive.google.com/file/d/13kzi_M5hE8bKp5-fGQR60oYOf-n6VweJ/view); [chat](https://drive.google.com/file/d/1BOQoBVkqHJmhHA9_Um2p5OvAVDTHLxD3/view)<br>\nConsulta con Nacho. [Video](https://drive.google.com/file/d/1rP3tCtWnXSTKDNiAq5pRG15oAlwyTXOT/view); [chat](https://drive.google.com/file/d/18DuPbEdJNL1HVxvb5lFZ0YxrWfdVQEm-/view)<br>\nConsulta con Rodrigo. [Video](https://drive.google.com/file/d/1kYjWvwCxPJyvNxnpUHbwx0SauGylsA8y/view); [chat](https://drive.google.com/file/d/1ZxKsRW_t3LO3nX6Own5AO6_4-UJwzYkR/view)\n\n__Jueves 30 de abril.__<br>\n[Video](https://drive.google.com/file/d/1wHjec7ncIwPhos3Gg4e657Vb_GlazpWr/view);\n[transcripción del chat](https://drive.google.com/file/d/1fZsAIC27btj7JIQ-umx2s_RP54z5ttbE/view)\n\n__Martes 5 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1f56X7pXaroaDotMGv3XpDgAVlc9O2zZQ/view);\n[transcripción del chat](https://drive.google.com/file/d/1MoSPD9MDYXyMQHISQCZNK0ICEuJ_thXk/view)\n\n__Jueves 7 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1rh6r5vYv8t_mna51HN5KCQfWE-aDCaRx/view);\n[transcripción del chat](https://drive.google.com/file/d/1SJOX_3yS8IM3oG50bVEjeNRcPri71cxe/view)\n\n__Martes 12 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1esKkeyfF1X4ys_e6jz09CBQzgkJJ0HrI/view);\n[transcripción del chat](https://drive.google.com/file/d/1SzEF3nkY_gsqf7DQUgTAaraBbY3Msnxp/view)\n\n__Jueves 14 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1229GmCEnDCO-MJr02_He7-f6221l8wLA/view);\n[transcripción del chat](https://drive.google.com/file/d/1RW1x6iLgYKRVXmp4aCtRi3zMA6OLtUWp/view)\n\n__Martes 19 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1y3KsVKVWwKgLnq_jVgLKp7ZphXDjLtzC/view);\n[transcripción del chat](https://drive.google.com/file/d/1ejjo6do3S0xqjyaetLyJCCmWwZzdeV0Q/view)\n\n__Jueves 21 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1pjikEy8hdpoj4GMVZM_6uPUJxBLHb0d7/view);\n[transcripción del chat](https://drive.google.com/file/d/1YyxzcYlXGwQmzgK5kDoeFqG_xvmx2iMb/view)\n\n__Martes 26 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1fS3emr9LjCbLrk9yFxMlkL1ecJanmA0g/view);\n[transcripción del chat](https://drive.google.com/file/d/1u--_3sgp6O4ap4jgv2AdsSml33dTjtBI/view)\n\n__Jueves 28 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1tWl7AhIPORWUiRW6eZprK9UCtNi33NW6/view);\n[transcripción del chat](https://drive.google.com/file/d/1Xrae7jSdTAqb7UfG5TfQSoqi2zfE9t6R/view)\n\n__Martes 02 de junio.__<br>\n[Video](https://drive.google.com/file/d/1uBxUyPuxcNBpZM3K-GgNMhWFh09I6IZU/view);\n[transcripción del chat](https://drive.google.com/file/d/1iZgQ58VI_X8RptUnvCjfZ6n_ZMEk4BCA/view)\n\n__Jueves 04 de junio.__<br>\n[Video](https://drive.google.com/file/d/19OrUvG7xXZCvjS1Fam6kwPFZMyPvRImI/view);\n[transcripción del chat](https://drive.google.com/file/d/1E59AAJSZGGjM0nrNZy-Olo_F-0ZgOgbd/view)\n\n__Martes 09 de junio.__<br>\n[Video](https://drive.google.com/file/d/1qCZUfab9mp3u2z7z02y9AIQMW2T31gcp/view);\n[transcripción del chat](https://drive.google.com/file/d/1GipnUXVCv6mh-lW3a860j8pEGv8xv7er/view)\n\n__Jueves 11 de junio.__<br>\n[Video](https://drive.google.com/file/d/1CK22nkykTYXJi9dr39_YiV67OtCDU-Pc/view);\n[transcripción del chat](https://drive.google.com/file/d/1VUODSCa8YLAqEn7yVkHXca9S94ElFRmJ/view)\n\n__Martes 16 de junio.__<br>\n[Video](https://drive.google.com/file/d/1qVWf_pdbVtZjBu_9ImoRbqx2aXlujLmL/view);\n[transcripción del chat](https://drive.google.com/file/d/1fYvQ8yc76yaSHG3f_1VENaTtCaUdy49A/view)\n\n__Jueves 18 de junio.__<br>\n[Video](https://drive.google.com/file/d/1Ah6qz_4KcdxmT0v__uMMNCr38EnRsgav/view);\n[transcripción del chat](https://drive.google.com/file/d/1ZWWN2TxhIDdZUxg0J5QLJGwDub0pJZXp/view)\n\n__Martes 23 de junio.__<br>\n[Video](https://drive.google.com/file/d/1WUF91z-9RTQ-ywcMnUgPV4Wnitu7xq5m/view);\n[transcripción del chat](https://drive.google.com/file/d/1DJ_aZJqrUzU0X-hU9S9ewz2pG8UH6_RC/view)\n\n__Jueves 25 de junio.__<br>\nConsulta con Manu. [Video](https://drive.google.com/file/d/19QtKBzqDXLi_FMvHrYvrj6iJJwzZ5WnN/view?usp=sharing); [chat](https://drive.google.com/file/d/1ipWf_CBqFRZ_A8hs4k0DAb0SA77_ESB4/view?usp=sharing)<br>\nConsulta con Nacho. [Video](https://drive.google.com/file/d/14Hh76EaFQPyv5T3DaYKXg59crKQ8RR2o/view); [chat](https://drive.google.com/file/d/1GJtKHYpX0LUU0w0BzmIu0NXPk3MYsU67/view)<br>\nConsulta con Rodrigo. [Video](https://drive.google.com/file/d/1AY1unWJhq3NwV3xei-oODnQf3Iulv5O1/view); [chat](https://drive.google.com/file/d/1q0KAEL30BAfTQ0zhz8IJjBWJAkSJ-zM9/view)\n\n__Martes 30 de junio.__<br>\n[Video](https://drive.google.com/file/d/17Nr7BJ-shrJ9d9Ncq_xPvbZ0Kz60wajv/view);\n[transcripción del chat](https://drive.google.com/file/d/1GuNd06vvCMUTR05rjedcpx-Y_tSjG9eK/view)\n\n__Jueves 02 de julio: Cierre de la materia e invitados especiales.__<br>\n[Video](https://drive.google.com/file/d/1HSj0TKx9PquFkTs-7j0mT1eS68vwK-9Q/view);\n[transcripción del chat](https://drive.google.com/file/d/1m5LXiS9JjsO7U-V7WDrd9OCbbQMI0AO_/view)\n" }, { "alpha_fraction": 0.6088825464248657, "alphanum_fraction": 0.6442216038703918, "avg_line_length": 30.727272033691406, "blob_id": "7ce97922b67a2e46dd45402d6b20d1a5d0ebd8ca", "content_id": "f9ca86379debb8f85ef69e39eb1ab8449214a6a4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2094, "license_type": "no_license", "max_line_length": 149, "num_lines": 66, "path": "/entregas/deJesusJoaquin/ej_1.py", "repo_name": "ccollado7/UNSAM_IA", "src_encoding": "UTF-8", "text": "import numpy as np\nimport pandas as pd\n\n### Ejercicio 1 - Probabilidad condicional #####\n\n## Se puede calcular todo lo que se pide sin necesidad de usar el teo de bayes\n\n\ndf=pd.read_csv('student-mat.csv')\nprint(df.head())\n\n##########################\n## Sin teorema de Bayes ##\n##########################\n\n## Primero debo saber cuantos alumnos sacaron grade mayor a 12\ngrade_mayor_12_bool = df['G3'] >= 12\n\ndf_grade_mayor_12 = df[grade_mayor_12_bool]\n\ntotal_grade_mayor_12 = len(df_grade_mayor_12) # total de casos de alumnos con grade mayor a 12\n\n# sobre df_grade_mayor_12, debo calcular calcular cuantos faltaron menos de 3 veces\ndf_menos_de_3_bool = df_grade_mayor_12['absences'] < 3\ndf_menos_de_3 = df_grade_mayor_12[df_menos_de_3_bool] ## dataframe con todos los que faltaron menos de 3 y sacaron mas de 12\n\nn_menos_de_3 = len(df_menos_de_3)\n\n\n\n# probabilidad\npp = float(n_menos_de_3)/total_grade_mayor_12\nprint(\"SIN TEOREMA DE BAYES\")\nprint(\"La probabilidad que se pide es %i/%i = %.2f\" % (n_menos_de_3, total_grade_mayor_12, pp))\nprint(\"\\n\")\n\n##########################\n## Con teorema de Bayes ##\n##########################\n\n# B = faltar menos de 3\n# A = sacar mas de 60%\n# Necesito P(B\|A) = P(A\|B) pB/pA\n\nn_total = len(df) # cantidad total de alumnos\nn_grade_mayor_12 = len(df_grade_mayor_12) # cantidad de alumnos que sacaron mayor o igual a 12\n\ndf_menos_de_3_bool = df['absences'] < 3 #a diferencia de antes, ahora lo calculo sobre el conjunto total de alumnos, no sobre aquellos con mayor a 12\ndf_menos_de_3 = df[df_menos_de_3_bool]\n\nn_menos_de_3 = len(df_menos_de_3)\n\npB = float(n_menos_de_3)/n_total #proba de faltar menos de 3\npA = float(total_grade_mayor_12) / n_total #proba de sacar mas de 12\n\n# Ahora a calcular P(A\|B)\n\ndf_condicionada = df_menos_de_3[df_menos_de_3['G3'] >= 12]\npA_B = float(len(df_condicionada))/ len(df_menos_de_3)\npB_A = pA_B * pB/pA # Teorema Bayes\n\nprint(\"CON TEOREMA DE BAYES\")\nprint(\"La probabilidad que se pide es P(B\|A) = P(A\|B) * pB/pA = %.2f * %.2f/%.2f = %.2f\" % (pA_B, pB, pA, pB_A ))\nprint(\"\\n\")\n\nprint(\"Ambas coinciden? : %s\" % (pp == pB_A))\n" } ]	2
KongL1013/datacom	https://github.com/KongL1013/datacom	e9f5650740cbf459f4b0b429d1d0af02664d4f83	d70dd5214d6f64c5d95d74434133f8c0299d3bb7	750e5ff5735d7e40d4621d141403bb2372baa235	refs/heads/master	2020-09-28T14:50:51.798043	2019-12-14T07:56:49	2019-12-14T07:56:49	226,799,944	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.4810086190700531, "alphanum_fraction": 0.5217576622962952, "avg_line_length": 32.56428527832031, "blob_id": "ef129d05c45910dd081dce1bbce22829aaa73a5c", "content_id": "ed4b2164da289c80e642791cfba9534cf37059e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9527, "license_type": "no_license", "max_line_length": 109, "num_lines": 280, "path": "/PycharmProjects/data_com/test12/nfold_xg_lg1.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n# In[2]:\n\n\npd.set_option('display.max_columns', None)\n\n\n# In[3]:\n\n\nwith open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n\n# In[4]:\n\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n\n# In[5]:\n\n\nwith open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n\nprint(len(data1),len(data))\n\n\n\ndata['topic_sim0_max']=data1['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data1['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data1['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_std']=data1['topic_sim0'].apply(lambda x:x[3])\ndata['topic_sim0_num']=data1['topic_sim0'].apply(lambda x:x[4])\n\ndata['topic_sim1_max']=data1['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data1['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data1['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_std']=data1['topic_sim1'].apply(lambda x:x[3])\ndata['topic_sim1_num']=data1['topic_sim1'].apply(lambda x:x[4])\ndata['topic_sim1_max1']=data1['topic_sim1'].apply(lambda x:x[5])\ndata['topic_sim1_min1']=data1['topic_sim1'].apply(lambda x:x[6])\n\n\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\nfill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\nfill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\nfill_null(data, 'topic_sim1_max1', -2)\nfill_null(data, 'topic_sim1_min1', -2)\n\n\ndata=data.drop(['follow_topic','inter_topic','topic','title_t1','title_t2','desc_t1','desc_t2'],axis=1)\n\n\nprint(len(data)-1141683)\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\n# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import accuracy_score\nimport gc\n\nfrom lightgbm import LGBMClassifier\n\nfrom sklearn.model_selection import StratifiedKFold\n\nn_fold = 5\nskf = StratifiedKFold(n_splits=n_fold, random_state=2020, shuffle=False)\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\ny_pred = np.zeros([X_evaluate.shape[0],2])\n\n\n\ncnt = 1\n\n\n\nloaded_model1 = pickle.load(open(\"model_xgboost1.pickle.dat\", \"rb\"))\ntotal_xg_pred1 = loaded_model1.predict_proba(X_evaluate)\nprint(\"total_xg_pred1\",total_xg_pred1[:5,:])\nloaded_model_bgm1 = pickle.load(open(\"LGBMClassifier2.pickle.dat\", \"rb\"))\ntotal_bgm_pred2 = loaded_model_bgm1.predict_proba(X_evaluate)\nprint(\"total_xg_pred1\",total_bgm_pred2[:5,:])\n\ny_pred += total_xg_pred1\ny_pred += total_bgm_pred2\n\nprint(\"y_pred\",y_pred[:5,:]/2)\n\nfor index ,(train_index,test_index) in enumerate(skf.split(X , y)): #训练数据五折\n if cnt < 3:\n print(\"pass = {}\".format(cnt))\n cnt += 1\n pass\n else:\n print(\"start train time = {cnt}\".format(cnt=cnt))\n print(\"train_index = \",train_index)\n print(\"train_index_% = \", len(train_index)/len(X))\n train_x, test_x, train_y, test_y = X[train_index], X[test_index], y[\n train_index], y[test_index]\n print(\"start train\")\n if cnt%2 != 0:\n print(\"model_xgboost\")\n model = XGBClassifier(\n max_depth=10,\n learning_rate=0.01,\n n_estimators=2000,\n min_child_weight=5, # 5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n )\n model.fit(train_x, train_y,\n eval_metric='auc',\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n print(\"save {} model!!!\".format(cnt))\n pickle.dump(model, open(\"model_xgboost{}.pickle.dat\".format(cnt), \"wb\"))\n else:\n print(\"LGBMClassifier\")\n model = LGBMClassifier(boosting_type='gbdt',\n task='train',\n num_leaves=2 ** 9 - 1,\n num_iterations=2000,\n learning_rate=0.01,\n n_estimators=2000,\n max_bin=425,\n subsample_for_bin=50000,\n objective='binary',\n min_split_gain=0,\n min_child_weight=5,\n min_child_samples=10,\n feature_fraction=0.9,\n feature_fraction_bynode=0.8,\n drop_rate=0.05,\n subsample=0.8,\n subsample_freq=1,\n colsample_bytree=1,\n reg_alpha=3,\n reg_lambda=5,\n seed=1000,\n n_jobs=-1,\n silent=True\n )\n # 建议使用CV的方式训练预测。\n model.fit(train_x,\n train_y,\n eval_names=['train'],\n eval_metric={'auc'},\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n print(\"save {} model!!!\".format(cnt))\n pickle.dump(model, open(\"LGBMClassifier{}.pickle.dat\".format(cnt), \"wb\"))\n\n gc.collect() # 垃圾清理，内存清理\n\n y_pred_test = model.predict(test_x)\n predictions = [round(value) for value in y_pred_test]\n accuracy = accuracy_score(test_y, predictions)\n print(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n y_pred += model.predict_proba(X_evaluate)\n print('len X_evaluate =',len(X_evaluate))\n print(\"len y_pred=\",len(y_pred))\n print(\"y_pred\")\n print(y_pred[:5, :]/cnt)\n cnt += 1\n\ny_pred = y_pred/n_fold\n\n\n# print(\"start xgboost\")\n# model_xgboost = XGBClassifier(\n# max_depth=10,\n# learning_rate=0.01,\n# n_estimators=2500,\n# min_child_weight=5, #5\n# max_delta_step=0,\n# subsample=0.8,\n# colsample_bytree=0.7,\n# reg_alpha=0,\n# reg_lambda=0.4,\n# scale_pos_weight=0.8,\n# silent=True,\n# objective='binary:logistic',\n# missing=None,\n# eval_metric='auc',\n# seed=1440,\n# gamma=0,\n# n_jobs=-1\n# # nthread=40\n# )\n# model_xgboost.fit(X_train,y_train,\n# eval_metric='auc',\n# eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n# #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n# early_stopping_rounds=50)\n\n\n\n# save model to file\n# print(\"save model!!!\")\n# pickle.dump(model_xgboost, open(\"model_xgboost1208.pickle.dat\", \"wb\"))\n#\n#\n# print('START TO SAVE RESULT!!!!!!!!!!!!!')\n# y_pred_test = model_xgboost.predict(X_test)\n# predictions = [round(value) for value in y_pred_test]\n# accuracy = accuracy_score(y_test, predictions)\n# print(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n#\n#\n# X_evaluate = data[2593669:].drop(['label'], axis=1).values\n# y_pred = model_xgboost.predict_proba(X_evaluate)\n# print(\"y_pred\")\n# print(y_pred[:5,:])\n#\n\n\ntest = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\ntest.columns = ['问题id', '用户id', '邀请创建时间']\nprint(len(test))\n# 用于保存提交结果\nresult_append = test[['问题id', '用户id', '邀请创建时间']]\nresult_append['Score'] = y_pred[:, 1]\nprint(result_append.head())\nresult_append.to_csv('result_xg_lg.txt', header=False, index=False, sep='\\t')\n\n" }, { "alpha_fraction": 0.5737146735191345, "alphanum_fraction": 0.6189990043640137, "avg_line_length": 27.367149353027344, "blob_id": "0edbe8d95c60f11e688794606b4411f9ff394af6", "content_id": "b444361a834e0860338b723403847a39bda576d4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5966, "license_type": "no_license", "max_line_length": 150, "num_lines": 207, "path": "/PycharmProjects/data_com/test12/xgboost_merge.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n# In[2]:\n\n\npd.set_option('display.max_columns', None)\n\n\n# In[3]:\n\n\nwith open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n\n# In[4]:\n\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n\n# In[5]:\n\n\nwith open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n\n# In[8]:\n\n\nprint(len(data1),len(data))\n\n\n# In[9]:\n\n\ndata['topic_sim0_max']=data1['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data1['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data1['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_std']=data1['topic_sim0'].apply(lambda x:x[3])\ndata['topic_sim0_num']=data1['topic_sim0'].apply(lambda x:x[4])\n\ndata['topic_sim1_max']=data1['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data1['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data1['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_std']=data1['topic_sim1'].apply(lambda x:x[3])\ndata['topic_sim1_num']=data1['topic_sim1'].apply(lambda x:x[4])\ndata['topic_sim1_max1']=data1['topic_sim1'].apply(lambda x:x[5])\ndata['topic_sim1_min1']=data1['topic_sim1'].apply(lambda x:x[6])\n\n\n# In[10]:\n\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\nfill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\nfill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\nfill_null(data, 'topic_sim1_max1', -2)\nfill_null(data, 'topic_sim1_min1', -2)\n\n\n# In[13]:\n\n\ndata=data.drop(['follow_topic','inter_topic','topic','title_t1','title_t2','desc_t1','desc_t2'],axis=1)\n\n\n# In[40]:\n\n\n#data=data.drop(['topic_sim0_max','topic_sim0_min','topic_sim0_avg','topic_sim1_max','topic_sim1_min','topic_sim1_avg'],axis=1)\n\n\n# In[41]:\n\n\n#data = data.drop(['topic_gz', 'topic_int', 't_invi', 't_quest', 'desc_quest_w', 'desc_quest_sw', 'desc_tit_w', 'desc_tit_sw', 'topic_quest'], axis=1)\n\n# 缺省值处理\n\n# ## 模型训练\n\n# In[18]:\n\n\nprint(len(data)-1141683)\n\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\n\n\nprint(\"load model from file\")\nloaded_model = pickle.load(open(\"model_xgboost.pickle.dat\", \"rb\"))\ntotal_xg_pred = loaded_model.predict_proba(X)\ntotal_xg_pred_1 = pd.DataFrame(total_xg_pred[:,1],columns=['xg_pred1'])\ntotal_xg_pred_2 = pd.DataFrame(total_xg_pred[:,1],columns=['xg_pred2'])\n\ndata = pd.concat([data,total_xg_pred_1],axis=1)\ndata = pd.concat([data,total_xg_pred_2],axis=1)\nprint(data.head())\n\nprint(\"start lgb merge\")\nX = data[:2593669].drop(['label'], axis=1).values\nloaded_model_bgm = pickle.load(open(\"model_lgb1203.pickle.dat\", \"rb\"))\ntotal_bgm_pred = loaded_model_bgm.predict_proba(X)\ntotal_bgm_pred_1 = pd.DataFrame(total_bgm_pred[:,1],columns=['bgm_pred1'])\ntotal_bgm_pred_2 = pd.DataFrame(total_bgm_pred[:,1],columns=['bgm_pred2'])\ndata = pd.concat([data,total_bgm_pred_1],axis=1)\ndata = pd.concat([data,total_bgm_pred_2],axis=1)\nprint(data.head())\n\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nprint(\"X = \",X[:5])\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\n\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import accuracy_score\nprint(\"start xgboost\")\nmodel_xgboost = XGBClassifier(\n max_depth=10,\n learning_rate=0.01,\n n_estimators=3000,\n min_child_weight=5, #5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n)\nmodel_xgboost.fit(X_train,y_train,\n eval_metric='auc',\n eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n# save model to file\npickle.dump(model_xgboost, open(\"model_xgboost_bgm_merge.pickle.dat\", \"wb\"))\n\n# # save model to file\n\nprint('START TO SAVE RESULT!!!!!!!!!!!!!')\ny_pred_test = model_xgboost.predict(X_test)\npredictions = [round(value) for value in y_pred_test]\naccuracy = accuracy_score(y_test, predictions)\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\ny_pred = model_xgboost.predict_proba(X_evaluate)\nprint(\"y_pred\")\nprint(y_pred[:5,:])\n\n\ntest = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\ntest.columns = ['问题id', '用户id', '邀请创建时间']\nprint(len(test))\n# 用于保存提交结果\nresult_append = test[['问题id', '用户id', '邀请创建时间']]\nresult_append['Score'] = y_pred[:, 1]\nprint(result_append.head())\nresult_append.to_csv('result_merge_1203.txt', header=False, index=False, sep='\\t')\n\n\n" }, { "alpha_fraction": 0.5847499370574951, "alphanum_fraction": 0.6017491817474365, "avg_line_length": 34.71806335449219, "blob_id": "21f3ee7747a396eb21189d7c0de164bc0412a128", "content_id": "2f38e0ca801f613fa3a57fb8f0dea83066fb3bf0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8164, "license_type": "no_license", "max_line_length": 156, "num_lines": 227, "path": "/PycharmProjects/data_com/data_process/process.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport numpy as np\nimport pickle\nimport gc\nfrom tqdm import tqdm_notebook\nimport os\nimport time\n\npd.set_option('display.max_columns',1000)\npd.set_option('display.width', 1000)\npd.set_option('display.max_colwidth',1000)\n\n\n\n\n\ntic = time.time()\n# 减少内存占用\ndef reduce_mem_usage(df):\n \"\"\" iterate through all the columns of a dataframe and modify the data type\n to reduce memory usage.\n \"\"\"\n start_mem = df.memory_usage().sum() / 1024 2\n print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n\n for col in df.columns:\n col_type = df[col].dtype\n\n if col_type != object:\n c_min = df[col].min()\n c_max = df[col].max()\n if str(col_type)[:3] == 'int':\n if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n df[col] = df[col].astype(np.int8)\n elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n df[col] = df[col].astype(np.int16)\n elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n df[col] = df[col].astype(np.int32)\n elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n df[col] = df[col].astype(np.int64)\n\n end_mem = df.memory_usage().sum() / 1024 2\n print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n\n return df\n\n# 解析列表，重编码id\ndef parse_str(d):\n return np.array(list(map(float, d.split())))\n\ndef parse_list_1(d):\n if d == '-1':\n return [0]\n return list(map(lambda x: int(x[1:]), str(d).split(',')))\n\ndef parse_list_2(d):\n if d == '-1':\n return [0]\n return list(map(lambda x: int(x[2:]), str(d).split(',')))\n\ndef parse_map(d):\n if d == '-1':\n return {}\n return dict([int(z.split(':')[0][1:]), float(z.split(':')[1])] for z in d.split(','))\n\nPATH = '../kanshanbei'\nSAVE_PATH = '../pkl'\nif not os.path.exists(SAVE_PATH):\n print('create dir: %s' % SAVE_PATH)\n os.mkdir(SAVE_PATH)\n\n######################### single_word\nsingle_word = pd.read_csv(os.path.join(PATH, 'single_word_vectors_64d.txt'),\n names=['id', 'embed'], sep='\\t')\nprint(single_word.head())\nsingle_word['embed'] = single_word['embed'].apply(parse_str)\nsingle_word['id'] = single_word['id'].apply(lambda x: int(x[2:]))\nprint(single_word.head())\nprint('single_word')\nwith open('../pkl/single_word.pkl', 'wb') as file:\n pickle.dump(single_word, file)\n\ndel single_word\ngc.collect() #gc 垃圾回收模块\n\n\n\n######################### word\nword = pd.read_csv(os.path.join(PATH,'word_vectors_64d.txt'),names=['id','embed'],sep='\\t')\nprint('word')\nprint(word.head())\nprint(word['embed'][1])\nprint(type(word['embed'][1]))\nword['embed'] = word['embed'].apply(parse_str) #把str转为float\nwith open(os.path.join(SAVE_PATH,'word.pkl'),'wb') as file:\n pickle.dump(word,file)\ndel word\ngc.collect()\n\n\n######################## topic\ntopic = pd.read_csv(os.path.join(PATH, 'topic_vectors_64d.txt'),\n names=['id', 'embed'], sep='\\t')\nprint('topic')\nprint(topic.head())\n\ntopic['embed'] = topic['embed'].apply(parse_str)\ntopic['id'] = topic['id'].apply(lambda x: int(x[1:]))\ntopic.head()\n\nwith open('../pkl/topic.pkl', 'wb') as file:\n pickle.dump(topic, file)\n\ndel topic\ngc.collect()\n\n######################## invite\n\n\ninvite_info = pd.read_csv(os.path.join(PATH, 'invite_info_0926.txt'),\n names=['question_id', 'author_id', 'invite_time', 'label'], sep='\\t')\ninvite_info_evaluate = pd.read_csv(os.path.join(PATH, 'invite_info_evaluate_1_0926.txt'),\n names=['question_id', 'author_id', 'invite_time'], sep='\\t')\nprint('invite_info')\nprint(invite_info.head())\ninvite_info['day'] = invite_info['invite_time']\\\n .apply(lambda x: int(x.split('-')[0][1:])).astype(np.int8)\ninvite_info['invite_hour'] = invite_info['invite_time'].apply(lambda x: int(x.split('-')[1][1:])).astype(np.int8)\n\ninvite_info_evaluate['invite_day'] = invite_info_evaluate['invite_time'].apply(lambda x: int(x.split('-')[0][1:])).astype(np.int16)\ninvite_info_evaluate['invite_hour'] = invite_info_evaluate['invite_time'].apply(lambda x: int(x.split('-')[1][1:])).astype(np.int8)\n\ninvite_info = reduce_mem_usage(invite_info)\n\nwith open('../pkl/invite_info.pkl', 'wb') as file:\n pickle.dump(invite_info, file)\n\nwith open('../pkl/invite_info_evaluate.pkl', 'wb') as file:\n pickle.dump(invite_info_evaluate, file)\n\ndel invite_info, invite_info_evaluate\ngc.collect()\n\n\n################################ member\n\nmember_info = pd.read_csv(os.path.join(PATH, 'member_info_0926.txt'),\n names=['author_id', 'gender', 'keyword', 'grade', 'hotness', 'reg_type','reg_plat','freq',\n 'A1', 'B1', 'C1', 'D1', 'E1', 'A2', 'B2', 'C2', 'D2', 'E2',\n 'score', 'topic_attent', 'topic_interest'], sep='\\t')\nprint('member_info')\nprint(member_info.head())\n\n\nmember_info['topic_attent'] = member_info['topic_attent'].apply(parse_list_1)\nmember_info['topic_interest'] = member_info['topic_interest'].apply(parse_map)\n\nmember_info = reduce_mem_usage(member_info)\n\nwith open('../pkl/member_info.pkl', 'wb') as file:\n pickle.dump(member_info, file)\n\ndel member_info\ngc.collect()\n\n\n##################### question_info\n\nquestion_info = pd.read_csv(os.path.join(PATH, 'question_info_0926.txt'),\n names=['question_id', 'question_time', 'title_sw_series', 'title_w_series', 'desc_sw_series', 'desc_w_series', 'topic'], sep='\\t')\nprint(question_info.head())\n\nquestion_info['title_sw_series'] = question_info['title_sw_series'].apply(parse_list_2)#.apply(sw_lbl_enc.transform).apply(list)\nquestion_info['title_w_series'] = question_info['title_w_series'].apply(parse_list_1)#.apply(w_lbl_enc.transform).apply(list)\nquestion_info['desc_sw_series'] = question_info['desc_sw_series'].apply(parse_list_2)#.apply(sw_lbl_enc.transform).apply(list)\nquestion_info['desc_w_series'] = question_info['desc_w_series'].apply(parse_list_1)#.apply(w_lbl_enc.transform).apply(list)\nquestion_info['topic'] = question_info['topic'].apply(parse_list_1)# .apply(topic_lbl_enc.transform).apply(list)\n\nquestion_info['question_day'] = question_info['question_time'].apply(lambda x: int(x.split('-')[0][1:])).astype(np.int16)\nquestion_info['question_hour'] = question_info['question_time'].apply(lambda x: int(x.split('-')[1][1:])).astype(np.int8)\ndel question_info['question_time']\ngc.collect()\n\nprint(question_info.head())\n\nquestion_info = reduce_mem_usage(question_info)\n\nwith open('../pkl/question_info.pkl', 'wb') as file:\n pickle.dump(question_info, file)\n\ndel question_info\ngc.collect()\n\n\n################################ answer\n#%%time\nprint('answer')\nanswer_info = pd.read_csv(os.path.join(PATH, 'answer_info_0926.txt'),\n names=['answer_id', 'question_id', 'author_id', 'answer_time', 'content_sw_series', 'content_w_series',\n 'excellent', 'recommend', 'round_table', 'figure', 'video',\n 'num_word', 'num_like', 'num_unlike', 'num_comment',\n 'num_favor', 'num_thank', 'num_report', 'num_nohelp', 'num_oppose'], sep='\\t')\nanswer_info.head()\n\nanswer_info['content_sw_series'] = answer_info['content_sw_series'].apply(parse_list_2)\nanswer_info['content_w_series'] = answer_info['content_w_series'].apply(parse_list_1)\nanswer_info.head()\n\n\nanswer_info['answer_day'] = answer_info['answer_time'].apply(lambda x: int(x.split('-')[0][1:])).astype(np.int16)\nanswer_info['answer_hour'] = answer_info['answer_time'].apply(lambda x: int(x.split('-')[1][1:])).astype(np.int8)\ndel answer_info['answer_time']\ngc.collect()\n\n\nanswer_info = reduce_mem_usage(answer_info)\n\n\n\nwith open('../pkl/answer_info.pkl', 'wb') as file:\n pickle.dump(answer_info, file)\n\ndel answer_info\ngc.collect()\ntoc = time.time()\nprint('Used time: %d' % int(toc-tic))\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.6845007538795471, "alphanum_fraction": 0.6916542649269104, "avg_line_length": 33.21938705444336, "blob_id": "c8455ba951fb88796f9de7d0a00819a54663e669", "content_id": "b3c21bb272d14d6bb293dbc8fdaeb05b365059e1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6949, "license_type": "no_license", "max_line_length": 267, "num_lines": 196, "path": "/PycharmProjects/data_com/data_process/gen_feat.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import warnings\nwarnings.filterwarnings('ignore')\n\nimport pandas as pd\nimport numpy as np\nimport pickle\nimport gc\nimport os\nimport time\nimport multiprocessing as mp\n\nfrom sklearn.preprocessing import LabelEncoder\n\ntic = time.time()\n\nSAVE_PATH = './feats'\nif not os.path.exists(SAVE_PATH):\n print('create dir: %s' % SAVE_PATH)\n os.mkdir(SAVE_PATH)\n\n\n################## member_info: 用户特征¶\n\nwith open('../pkl/member_info.pkl', 'rb') as file:\n member_info = pickle.load(file)\nmember_info.head(2)\n\n# 原始类别特征\nmember_cat_feats = ['gender', 'freq', 'A1', 'B1', 'C1', 'D1', 'E1', 'A2', 'B2', 'C2', 'D2', 'E2']\nfor feat in member_cat_feats:\n member_info[feat] = LabelEncoder().fit_transform(member_info[feat])\n\n# 用户关注和感兴趣的topic数\nmember_info['num_atten_topic'] = member_info['topic_attent'].apply(len)\nmember_info['num_interest_topic'] = member_info['topic_interest'].apply(len)\n\n\ndef most_interest_topic(d):\n if len(d) == 0:\n return -1\n return list(d.keys())[np.argmax(list(d.values()))]\n\n# 用户最感兴趣的topic\nmember_info['most_interest_topic'] = member_info['topic_interest'].apply(most_interest_topic)\nmember_info['most_interest_topic'] = LabelEncoder().fit_transform(member_info['most_interest_topic'])\n\n\ndef get_interest_values(d):\n if len(d) == 0:\n return [0]\n return list(d.values())\n\n\n# 用户topic兴趣值的统计特征\nmember_info['interest_values'] = member_info['topic_interest'].apply(get_interest_values)\nmember_info['min_interest_values'] = member_info['interest_values'].apply(np.min)\nmember_info['max_interest_values'] = member_info['interest_values'].apply(np.max)\nmember_info['mean_interest_values'] = member_info['interest_values'].apply(np.mean)\nmember_info['std_interest_values'] = member_info['interest_values'].apply(np.std)\n\n# 汇总\nfeats = ['author_id', 'gender', 'freq', 'A1', 'B1', 'C1', 'D1', 'E1', 'A2', 'B2', 'C2', 'D2', 'E2', 'score']\nfeats += ['num_atten_topic', 'num_interest_topic', 'most_interest_topic']\nfeats += ['min_interest_values', 'max_interest_values', 'mean_interest_values', 'std_interest_values']\nmember_feat = member_info[feats]\n\nmember_feat.head(3)\n\nmember_feat.to_hdf('./feats/member_feat.h5', key='data')\n\ndel member_feat, member_info\ngc.collect()\n\n\n################## question_info: 问题特征\n\nwith open('../pkl/question_info.pkl', 'rb') as file:\n question_info = pickle.load(file)\n\nquestion_info.head(2)\n\n\n# title、desc词计数，topic计数\nquestion_info['num_title_sw'] = question_info['title_sw_series'].apply(len)\nquestion_info['num_title_w'] = question_info['title_w_series'].apply(len)\nquestion_info['num_desc_sw'] = question_info['desc_sw_series'].apply(len)\nquestion_info['num_desc_w'] = question_info['desc_w_series'].apply(len)\nquestion_info['num_qtopic'] = question_info['topic'].apply(len)\n\nfeats = ['question_id', 'num_title_sw', 'num_title_w', 'num_desc_sw', 'num_desc_w', 'num_qtopic', 'question_hour']\nfeats += []\nquestion_feat = question_info[feats]\n\nquestion_feat.head(3)\n\nquestion_feat.to_hdf('./feats/question_feat.h5', key='data')\n\ndel question_info, question_feat\ngc.collect()\n\n################# member_info & question_info: 用户和问题的交互特征\n\nwith open('../pkl/invite_info.pkl', 'rb') as file:\n invite_info = pickle.load(file)\nwith open('../pkl/invite_info_evaluate.pkl', 'rb') as file:\n invite_info_evaluate = pickle.load(file)\nwith open('../pkl/member_info.pkl', 'rb') as file:\n member_info = pickle.load(file)\nwith open('../pkl/question_info.pkl', 'rb') as file:\n question_info = pickle.load(file)\n\n\n# 合并 author_id，question_id\ninvite = pd.concat([invite_info, invite_info_evaluate])\ninvite_id = invite[['author_id', 'question_id']]\ninvite_id['author_question_id'] = invite_id['author_id'] + invite_id['question_id']\ninvite_id.drop_duplicates(subset='author_question_id',inplace=True)\ninvite_id_qm = invite_id.merge(member_info[['author_id', 'topic_attent', 'topic_interest']], 'left', 'author_id').merge(question_info[['question_id', 'topic']], 'left', 'question_id')\ninvite_id_qm.head(2)\n\n\n# 分割 df，方便多进程跑\ndef split_df(df, n):\n chunk_size = int(np.ceil(len(df) / n))\n return [df[ichunk_size:(i+1)chunk_size] for i in range(n)]\n\ndef gc_mp(pool, ret, chunk_list):\n del pool\n for r in ret:\n del r\n del ret\n for cl in chunk_list:\n del cl\n del chunk_list\n gc.collect()\n\n# 用户关注topic和问题 topic的交集\ndef process(df):\n return df.apply(lambda row: list(set(row['topic_attent']) & set(row['topic'])),axis=1)\n\npool = mp.Pool()\nchunk_list = split_df(invite_id_qm, 100)\nret = pool.map(process, chunk_list)\ninvite_id_qm['topic_attent_intersection'] = pd.concat(ret)\ngc_mp(pool, ret, chunk_list)\n\n\n# 用户感兴趣topic和问题 topic的交集\ndef process(df):\n return df.apply(lambda row: list(set(row['topic_interest'].keys()) & set(row['topic'])),axis=1)\n\npool = mp.Pool()\nchunk_list = split_df(invite_id_qm, 100)\nret = pool.map(process, chunk_list)\ninvite_id_qm['topic_interest_intersection'] = pd.concat(ret)\ngc_mp(pool, ret, chunk_list)\n\n# 用户感兴趣topic和问题 topic的交集的兴趣值\ndef process(df):\n return df.apply(lambda row: [row['topic_interest'][t] for t in row['topic_interest_intersection']],axis=1)\n\npool = mp.Pool()\nchunk_list = split_df(invite_id_qm, 100)\nret = pool.map(process, chunk_list)\ninvite_id_qm['topic_interest_intersection_values'] = pd.concat(ret)\ngc_mp(pool, ret, chunk_list)\n\n\n# 交集topic计数\ninvite_id_qm['num_topic_attent_intersection'] = invite_id_qm['topic_attent_intersection'].apply(len)\ninvite_id_qm['num_topic_interest_intersection'] = invite_id_qm['topic_interest_intersection'].apply(len)\n\n\n# 交集topic兴趣值统计\ninvite_id_qm['topic_interest_intersection_values'] = invite_id_qm['topic_interest_intersection_values'].apply(lambda x: [0] if len(x) == 0 else x)\ninvite_id_qm['min_topic_interest_intersection_values'] = invite_id_qm['topic_interest_intersection_values'].apply(np.min)\ninvite_id_qm['max_topic_interest_intersection_values'] = invite_id_qm['topic_interest_intersection_values'].apply(np.max)\ninvite_id_qm['mean_topic_interest_intersection_values'] = invite_id_qm['topic_interest_intersection_values'].apply(np.mean)\ninvite_id_qm['std_topic_interest_intersection_values'] = invite_id_qm['topic_interest_intersection_values'].apply(np.std)\n\n\nfeats = ['author_question_id', 'num_topic_attent_intersection', 'num_topic_interest_intersection', 'min_topic_interest_intersection_values', 'max_topic_interest_intersection_values', 'mean_topic_interest_intersection_values', 'std_topic_interest_intersection_values']\nfeats += []\nmember_question_feat = invite_id_qm[feats]\nmember_question_feat.head(3)\n\nmember_question_feat.to_hdf('./feats/member_question_feat.h5', key='data')\n\ndel invite_id_qm, member_question_feat\ngc.collect()\n\ntoc = time.time()\nprint('Used time: %d' % int(toc-tic))\n\n\n################## \n\n" }, { "alpha_fraction": 0.4515170753002167, "alphanum_fraction": 0.4894971251487732, "avg_line_length": 34.8203125, "blob_id": "065adc5ab6d1ef3492e90a91f11eb1f8363beaee", "content_id": "2141821cd61bcad05f51c5d257fe660c6deb3110", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4767, "license_type": "no_license", "max_line_length": 105, "num_lines": 128, "path": "/PycharmProjects/data_com/test12/nfold_xgboost.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import lightgbm as lgb\r\nimport pickle\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nimport seaborn as sns\r\nfrom xgboost import XGBClassifier\r\nfrom sklearn.metrics import accuracy_score\r\nimport gc\r\nfrom lightgbm import LGBMClassifier\r\nfrom sklearn.model_selection import StratifiedKFold\r\n\r\npd.set_option('display.max_columns', None)\r\n\r\nwith open('train20.pkl', 'rb') as file:\r\n data = pickle.load(file)\r\nprint(data.head())\r\n\r\nX = data[:2593669].drop(['label'], axis=1).values\r\ny = data[:2593669]['label'].values\r\n\r\nn_fold = 5\r\nsteps = 2500\r\n\r\nskf = StratifiedKFold(n_splits=n_fold, random_state=0, shuffle=False)\r\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\r\ny_pred = np.zeros([X_evaluate.shape[0],2])\r\ncnt = 1\r\n\r\nfor index ,(train_index,test_index) in enumerate(skf.split(X , y)): #训练数据五折\r\n print(\"start train time = {cnt}\".format(cnt=cnt))\r\n print(\"train_index = \",train_index)\r\n print(\"train_index_% = \", len(train_index)/len(X))\r\n train_x, test_x, train_y, test_y = X[train_index], X[test_index], y[\r\n train_index], y[test_index]\r\n print(\"start train\")\r\n if True:\r\n print(\"model_xgboost\")\r\n model = XGBClassifier(\r\n max_depth=10,\r\n learning_rate=0.01,\r\n n_estimators=steps,\r\n min_child_weight=5, # 5\r\n max_delta_step=0,\r\n subsample=0.8,\r\n colsample_bytree=0.7,\r\n reg_alpha=0,\r\n reg_lambda=0.4,\r\n scale_pos_weight=0.8,\r\n silent=True,\r\n objective='binary:logistic',\r\n missing=None,\r\n eval_metric='auc',\r\n seed=1440,\r\n gamma=0,\r\n n_jobs=-1\r\n # nthread=40\r\n )\r\n model.fit(train_x, train_y,\r\n eval_metric='auc',\r\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\r\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\r\n early_stopping_rounds=50)\r\n print(\"save {} model!!!\".format(cnt))\r\n pickle.dump(model, open(\"all_xgboost{}.pickle.dat\".format(cnt), \"wb\"))\r\n else:\r\n print(\"LGBMClassifier\")\r\n model = LGBMClassifier(boosting_type='gbdt',\r\n task='train',\r\n num_leaves=2 ** 9 - 1,\r\n num_iterations=steps,\r\n learning_rate=0.01,\r\n n_estimators=2000,\r\n max_bin=425,\r\n subsample_for_bin=50000,\r\n objective='binary',\r\n min_split_gain=0,\r\n min_child_weight=5,\r\n min_child_samples=10,\r\n feature_fraction=0.9,\r\n feature_fraction_bynode=0.8,\r\n drop_rate=0.05,\r\n subsample=0.8,\r\n subsample_freq=1,\r\n colsample_bytree=1,\r\n reg_alpha=3,\r\n reg_lambda=5,\r\n seed=1000,\r\n silent=True\r\n )\r\n # 建议使用CV的方式训练预测。\r\n model.fit(train_x,\r\n train_y,\r\n eval_names=['train'],\r\n eval_metric={'auc'},\r\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\r\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\r\n early_stopping_rounds=50)\r\n print(\"save {} model!!!\".format(cnt))\r\n pickle.dump(model, open(\"LGBM1212Classifier{}.pickle.dat\".format(cnt), \"wb\"))\r\n\r\n gc.collect() # 垃圾清理，内存清理\r\n\r\n y_pred_test = model.predict(test_x)\r\n predictions = [round(value) for value in y_pred_test]\r\n accuracy = accuracy_score(test_y, predictions)\r\n print(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\r\n\r\n y_pred += model.predict_proba(X_evaluate)\r\n print('len X_evaluate =',len(X_evaluate))\r\n print(\"len y_pred=\",len(y_pred))\r\n print(\"y_pred\")\r\n print(y_pred[:5, :]/cnt)\r\n cnt += 1\r\n\r\ny_pred = y_pred/n_fold\r\n\r\n\r\nwith open('test1.pkl', 'rb') as file:\r\n result_append = pickle.load(file)\r\n\r\nprint(data[2593669:].head())\r\n\r\nresult_append['Score'] = y_pred[:, 1]\r\n\r\nprint(result_append.head())\r\n\r\nresult_append.to_csv('all_xg_result1_nfold.txt', header=False, index=False, sep='\\t')\r\n" }, { "alpha_fraction": 0.5418858528137207, "alphanum_fraction": 0.5813435912132263, "avg_line_length": 37.27906799316406, "blob_id": "84100a1b9acd998f92a96b287ffbca0ee71fe930", "content_id": "a1324b5802c44e94bfb447b16803696f665043df", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5016, "license_type": "no_license", "max_line_length": 116, "num_lines": 129, "path": "/PycharmProjects/data_com/test12/xgboost_thread.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\ndef do_xgboost():\n from xgboost import XGBClassifier\n global model_xgboost\n import lightgbm as lgb\n import pickle\n import numpy as np\n import pandas as pd\n from sklearn.model_selection import train_test_split\n import seaborn as sns\n\n pd.set_option('display.max_columns', None)\n\n with open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n def fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n def fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n with open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n print(len(data1), len(data))\n\n data['topic_sim0_max'] = data1['topic_sim0'].apply(lambda x: x[0])\n data['topic_sim0_avg'] = data1['topic_sim0'].apply(lambda x: x[1])\n data['topic_sim0_min'] = data1['topic_sim0'].apply(lambda x: x[2])\n data['topic_sim0_std'] = data1['topic_sim0'].apply(lambda x: x[3])\n data['topic_sim0_num'] = data1['topic_sim0'].apply(lambda x: x[4])\n\n data['topic_sim1_max'] = data1['topic_sim1'].apply(lambda x: x[0])\n data['topic_sim1_avg'] = data1['topic_sim1'].apply(lambda x: x[1])\n data['topic_sim1_min'] = data1['topic_sim1'].apply(lambda x: x[2])\n data['topic_sim1_std'] = data1['topic_sim1'].apply(lambda x: x[3])\n data['topic_sim1_num'] = data1['topic_sim1'].apply(lambda x: x[4])\n data['topic_sim1_max1'] = data1['topic_sim1'].apply(lambda x: x[5])\n data['topic_sim1_min1'] = data1['topic_sim1'].apply(lambda x: x[6])\n\n fill_null(data, 'topic_sim0_max', -2)\n fill_null(data, 'topic_sim0_avg', -2)\n fill_null(data, 'topic_sim0_min', -2)\n fill_null(data, 'topic_sim0_std', -2)\n fill_null(data, 'topic_sim0_num', -2)\n fill_null(data, 'topic_sim1_max', -2)\n fill_null(data, 'topic_sim1_avg', -2)\n fill_null(data, 'topic_sim1_min', -2)\n fill_null(data, 'topic_sim1_std', -2)\n fill_null(data, 'topic_sim1_num', -2)\n fill_null(data, 'topic_sim1_max1', -2)\n fill_null(data, 'topic_sim1_min1', -2)\n\n data = data.drop(['follow_topic', 'inter_topic', 'topic', 'title_t1', 'title_t2', 'desc_t1', 'desc_t2'], axis=1)\n\n print(len(data) - 1141683)\n\n from sklearn.model_selection import train_test_split\n from sklearn.metrics import roc_auc_score\n # 划分训练集和测试集\n # y_train = data[:train.shape[0]]['label'].values\n # X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n # X_test = data[train.shape[0]:].drop(['label'], axis=1).values\n X = data[:2593669].drop(['label'], axis=1).values\n y = data[:2593669]['label'].values\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\n print(\"start xgboost\")\n model_xgboost = XGBClassifier(\n max_depth=13,\n learning_rate=0.01,\n n_estimators=2000,\n min_child_weight=5,\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n )\n try:\n model_xgboost.fit(X_train, y_train,\n eval_metric='auc',\n eval_set=[(X_train, y_train), (X_test, y_test)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n # save model to file\n pickle.dump(model_xgboost, open(\"model_xgboost.pickle.dat\", \"wb\"))\n except KeyboardInterrupt:\n print('START TO SAVE RESULT!!!!!!!!!!!!!')\n # load model from file\n # loaded_model = pickle.load(open(\"model_xgboost.pickle.dat\", \"rb\"))\n y_pred_test = model_xgboost.predict(X_test)\n predictions = [round(value) for value in y_pred_test]\n accuracy = roc_auc_score(y_test, predictions)\n print(\"FINALL Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n X_evaluate = data[2593669:].drop(['label'], axis=1).values\n y_pred = model_xgboost.predict_proba(X_evaluate)\n # y_pred = model_lgb.predict_proba(X_evaluate)\n pd.DataFrame(y_pred[:, 1], columns=['y_pred'])\n test = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\n test.columns = ['问题id', '用户id', '邀请创建时间']\n print(len(test))\n # 用于保存提交结果\n result_append = test[['问题id', '用户id', '邀请创建时间']]\n result_append['Score'] = y_pred[:, 1]\n print(result_append.head())\n result_append.to_csv('result.txt', header=False, index=False, sep='\\t')\n raise\nif __name__ == '__main__':\n do_xgboost()\n\n\n\n\n" }, { "alpha_fraction": 0.597749650478363, "alphanum_fraction": 0.6385372877120972, "avg_line_length": 25.33333396911621, "blob_id": "b1ecd66aeb649faf0287389396399f6a32f296c6", "content_id": "a43abe5a51b95c153ad360b431264b9c79176459", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 711, "license_type": "no_license", "max_line_length": 66, "num_lines": 27, "path": "/PycharmProjects/data_com/test12/merge.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import pickle\nimport numpy as np\nimport pandas as pd\n\n\n# data1 = pd.read_csv('result_xgboost.txt', header=None, sep='\\t')\ndata2 = pd.read_csv('result1.txt', header=None, sep='\\t')\n# print(data1.head())\n# print(data2.head())\n# data1[3] = (data1[3]+data2[3])/2\n# print(data1.head())\n#\n#\n# data1.to_csv('result.txt', header=False, index=False, sep='\\t')\ntest = data2.head(5)\ntest.columns = ['q','w','e','r']\ntest =pd.concat( [test,pd.DataFrame(np.arange(8),columns=['w'])])\nprint(test)\ntest['w'] =np.arange(5)\ndata = np.arange(9.0,step = 1.2)\ntt = pd.DataFrame(data,columns=['q'])\n# print(tt)\n# print(data)\n# print(data2.iloc[:3,3])\n# print(np.c_[data,data2.iloc[:3,3]])\ncc = pd.concat([test,tt],axis=0)\nprint(cc)\n" }, { "alpha_fraction": 0.7011070251464844, "alphanum_fraction": 0.7380073666572571, "avg_line_length": 28, "blob_id": "bfda3b07ba4faf512a105beed106573f9158e80f", "content_id": "37b67978cf305e313eda160fca2c931ffbabe727", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 813, "license_type": "no_license", "max_line_length": 82, "num_lines": 28, "path": "/PycharmProjects/data_com/test12/1213merge.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import accuracy_score\nimport gc\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import StratifiedKFold\n\npd.set_option('display.max_columns', None)\n\nwith open('train20.pkl', 'rb') as file:\n data = pickle.load(file)\nprint(data.head())\n\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\ny_pred = np.zeros([X_evaluate.shape[0],2])\n\n\nfor i in range():\n loaded_model1 = pickle.load(open(\"all_xgboost{}.pickle.dat \".format(i), \"rb\"))\n total_xg_pred1 = loaded_model1.predict_proba(X_evaluate)\n\n" }, { "alpha_fraction": 0.4991398751735687, "alphanum_fraction": 0.6147942543029785, "avg_line_length": 28.97222137451172, "blob_id": "ff8c19ca6d2e0b7508456e1b9eea0a94a1bd81f9", "content_id": "c53a86644149a10cff23758dfa91eb787b49fc08", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7673, "license_type": "no_license", "max_line_length": 150, "num_lines": 252, "path": "/PycharmProjects/data_com/test12/lgbmTest4.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n# In[2]:\n\n\npd.set_option('display.max_columns', None)\n\n\n# In[3]:\n\n\nwith open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n\n# In[4]:\n\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n\n# In[5]:\n\n\nwith open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n\n# In[8]:\n\n\nprint(len(data1),len(data))\n\n\n# In[9]:\n\n\ndata['topic_sim0_max']=data1['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data1['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data1['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_std']=data1['topic_sim0'].apply(lambda x:x[3])\ndata['topic_sim0_num']=data1['topic_sim0'].apply(lambda x:x[4])\n\ndata['topic_sim1_max']=data1['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data1['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data1['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_std']=data1['topic_sim1'].apply(lambda x:x[3])\ndata['topic_sim1_num']=data1['topic_sim1'].apply(lambda x:x[4])\ndata['topic_sim1_max1']=data1['topic_sim1'].apply(lambda x:x[5])\ndata['topic_sim1_min1']=data1['topic_sim1'].apply(lambda x:x[6])\n\n\n# In[10]:\n\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\nfill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\nfill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\nfill_null(data, 'topic_sim1_max1', -2)\nfill_null(data, 'topic_sim1_min1', -2)\n\n\n# In[13]:\n\n\ndata=data.drop(['follow_topic','inter_topic','topic','title_t1','title_t2','desc_t1','desc_t2'],axis=1)\n\n\n# In[40]:\n\n\n#data=data.drop(['topic_sim0_max','topic_sim0_min','topic_sim0_avg','topic_sim1_max','topic_sim1_min','topic_sim1_avg'],axis=1)\n\n\n# In[41]:\n\n\n#data = data.drop(['topic_gz', 'topic_int', 't_invi', 't_quest', 'desc_quest_w', 'desc_quest_sw', 'desc_tit_w', 'desc_tit_sw', 'topic_quest'], axis=1)\n\n\n# In[14]:\n\n\ndata.head(2)\n\n\n# 缺省值处理\n\n# ## 模型训练\n\n# In[18]:\n\n\nprint(len(data)-1141683)\n\n\n# In[19]:\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\n\n# In[21]:\n\n\nmodel_lgb = LGBMClassifier(boosting_type='gbdt',\n task='train',\n num_leaves=29-1,\n num_iterations=2000,\n learning_rate=0.01,\n n_estimators=2000,\n max_bin=425,\n subsample_for_bin=50000,\n objective='binary',\n min_split_gain=0,\n min_child_weight=5,\n min_child_samples=10,\n feature_fraction=0.9,\n feature_fraction_bynode=0.8,\n drop_rate=0.05,\n subsample=0.8,\n subsample_freq=1,\n colsample_bytree=1,\n reg_alpha=3,\n reg_lambda=5,\n seed=1000,\n n_jobs=4,\n silent=True\n )\n# 建议使用CV的方式训练预测。\nmodel_lgb.fit(X_train,\n y_train,\n eval_names=['train'],\n eval_metric={'auc'},\n eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=100)\n\n\n# In[22]:\n\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\n\n\n# In[23]:\n\n\ny_pred = model_lgb.predict_proba(X_evaluate)\n\n\n# [1]\ttrain's auc: 0.763875\ttrain's binary_logloss: 0.465954\tvalid_1's auc: 0.763206\tvalid_1's binary_logloss: 0.46561\n# Training until validation scores don't improve for 50 rounds\n# [2]\ttrain's auc: 0.769659\ttrain's binary_logloss: 0.464518\tvalid_1's auc: 0.769093\tvalid_1's binary_logloss: 0.464178\n# [3]\ttrain's auc: 0.77108\ttrain's binary_logloss: 0.463213\tvalid_1's auc: 0.770482\tvalid_1's binary_logloss: 0.462878\n# [4]\ttrain's auc: 0.771961\ttrain's binary_logloss: 0.461849\tvalid_1's auc: 0.771291\tvalid_1's binary_logloss: 0.461519\n# [5]\ttrain's auc: 0.773232\ttrain's binary_logloss: 0.460513\tvalid_1's auc: 0.772525\tvalid_1's binary_logloss: 0.460188\n# [6]\ttrain's auc: 0.773901\ttrain's binary_logloss: 0.459217\tvalid_1's auc: 0.773155\tvalid_1's binary_logloss: 0.458898\n# [7]\ttrain's auc: 0.774043\ttrain's binary_logloss: 0.457969\tvalid_1's auc: 0.77331\tvalid_1's binary_logloss: 0.457653\n# [8]\ttrain's auc: 0.774356\ttrain's binary_logloss: 0.456738\tvalid_1's auc: 0.773593\tvalid_1's binary_logloss: 0.456426\n# [9]\ttrain's auc: 0.774624\ttrain's binary_logloss: 0.455526\tvalid_1's auc: 0.773802\tvalid_1's binary_logloss: 0.455221\n# [10]\ttrain's auc: 0.774838\ttrain's binary_logloss: 0.454347\tvalid_1's auc: 0.773992\tvalid_1's binary_logloss: 0.454047\n# \n# [1]\ttrain's auc: 0.763775\ttrain's binary_logloss: 0.46588\tvalid_1's auc: 0.763507\tvalid_1's binary_logloss: 0.465891\n# Training until validation scores don't improve for 50 rounds\n# [2]\ttrain's auc: 0.769223\ttrain's binary_logloss: 0.464457\tvalid_1's auc: 0.768826\tvalid_1's binary_logloss: 0.464472\n# [3]\ttrain's auc: 0.770967\ttrain's binary_logloss: 0.463157\tvalid_1's auc: 0.770461\tvalid_1's binary_logloss: 0.463174\n# [4]\ttrain's auc: 0.772809\ttrain's binary_logloss: 0.461796\tvalid_1's auc: 0.772294\tvalid_1's binary_logloss: 0.461816\n# [5]\ttrain's auc: 0.773698\ttrain's binary_logloss: 0.460459\tvalid_1's auc: 0.773271\tvalid_1's binary_logloss: 0.460482\n# [6]\ttrain's auc: 0.773922\ttrain's binary_logloss: 0.459161\tvalid_1's auc: 0.773457\tvalid_1's binary_logloss: 0.459188\n# [7]\ttrain's auc: 0.774355\ttrain's binary_logloss: 0.457907\tvalid_1's auc: 0.773902\tvalid_1's binary_logloss: 0.457936\n# [8]\ttrain's auc: 0.774441\ttrain's binary_logloss: 0.45667\tvalid_1's auc: 0.774017\tvalid_1's binary_logloss: 0.4567\n# [9]\ttrain's auc: 0.774521\ttrain's binary_logloss: 0.455468\tvalid_1's auc: 0.774074\tvalid_1's binary_logloss: 0.455502\n# [10]\ttrain's auc: 0.77456\ttrain's binary_logloss: 0.454296\tvalid_1's auc: 0.774088\tvalid_1's binary_logloss: 0.454333\n\n# In[18]:\n\n\ny_pred\n\n\n# In[103]:\n\n\npd.DataFrame(y_pred[:, 1], columns=['y_pred'])\n\n\n# In[24]:\n\n\ntest = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\ntest.columns = ['问题id', '用户id', '邀请创建时间']\nprint(len(test))\n# 用于保存提交结果\nresult_append = test[['问题id', '用户id', '邀请创建时间']]\n\n\n# In[25]:\n\n\nresult_append['Score'] = y_pred[:, 1]\n\n\n# In[26]:\n\n\nresult_append.head()\n\n\n# In[27]:\n\n\nresult_append.to_csv('result.txt', header=False, index=False, sep='\\t')\n\n\n# In[ ]:\n\n\n\n\n" }, { "alpha_fraction": 0.5716162919998169, "alphanum_fraction": 0.6177179217338562, "avg_line_length": 29.847972869873047, "blob_id": "c1550a6e69ac2eeeb87a4733982151dd641134d9", "content_id": "a659d70ae46efc73a387f3999b1550303dc85404", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9326, "license_type": "no_license", "max_line_length": 150, "num_lines": 296, "path": "/PycharmProjects/data_com/test12/xgboost4.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n# In[2]:\n\n\npd.set_option('display.max_columns', None)\n\n\n# In[3]:\n\n\nwith open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n\n# In[4]:\n\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n\n# In[5]:\n\n\nwith open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n\n# In[8]:\n\n\nprint(len(data1),len(data))\n\n\n# In[9]:\n\n\ndata['topic_sim0_max']=data1['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data1['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data1['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_std']=data1['topic_sim0'].apply(lambda x:x[3])\ndata['topic_sim0_num']=data1['topic_sim0'].apply(lambda x:x[4])\n\ndata['topic_sim1_max']=data1['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data1['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data1['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_std']=data1['topic_sim1'].apply(lambda x:x[3])\ndata['topic_sim1_num']=data1['topic_sim1'].apply(lambda x:x[4])\ndata['topic_sim1_max1']=data1['topic_sim1'].apply(lambda x:x[5])\ndata['topic_sim1_min1']=data1['topic_sim1'].apply(lambda x:x[6])\n\n\n# In[10]:\n\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\nfill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\nfill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\nfill_null(data, 'topic_sim1_max1', -2)\nfill_null(data, 'topic_sim1_min1', -2)\n\n\n# In[13]:\n\n\ndata=data.drop(['follow_topic','inter_topic','topic','title_t1','title_t2','desc_t1','desc_t2'],axis=1)\n\n\n# In[40]:\n\n\n#data=data.drop(['topic_sim0_max','topic_sim0_min','topic_sim0_avg','topic_sim1_max','topic_sim1_min','topic_sim1_avg'],axis=1)\n\n\n# In[41]:\n\n\n#data = data.drop(['topic_gz', 'topic_int', 't_invi', 't_quest', 'desc_quest_w', 'desc_quest_sw', 'desc_tit_w', 'desc_tit_sw', 'topic_quest'], axis=1)\n\n# 缺省值处理\n\n# ## 模型训练\n\n# In[18]:\n\n\nprint(\"len(data) = \",len(data)-1141683)\n\n\n# In[19]:\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\n\n'''\nparameters = {\n 'max_depth': [5, 10, 15, 20, 25],\n 'learning_rate': [0.01, 0.02, 0.05, 0.1],\n 'n_estimators': [1000,2000],\n 'min_child_weight': [0, 2, 5, 10, 20, 50],\n 'max_delta_step': [0, 0.2, 0.6, 1, 2],\n 'subsample': [0.6, 0.7, 0.8, 0.85, 0.95],\n 'colsample_bytree': [0.5, 0.6, 0.7, 0.8, 0.9],\n 'reg_alpha': [0, 0.25, 0.5, 0.75, 1],\n 'reg_lambda': [0.2, 0.4, 0.6, 0.8, 1],\n 'scale_pos_weight': [0.2, 0.4, 0.6, 0.8, 1]\n}\n\n\nimport xgboost as xgb\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import GridSearchCV\nfrom xgboost import XGBClassifier\n\n#\n# model_xgboost = XGBClassifier(\n# max_leaf_nodes=29-1,\n# # max_depth=15,\n# learning_rate=0.01,\n# n_estimators=2000,\n# min_child_weight=5,\n# max_delta_step=0,\n# subsample=0.8,\n# colsample_bytree=0.7,\n# reg_alpha=0,\n# reg_lambda=0.4,\n# scale_pos_weight=0.8,\n# silent=True,\n# objective='binary:logistic',\n# missing=None,\n# eval_metric='auc',\n# seed=1440,\n# gamma=0,\n# n_jobs=-1\n# # nthread=40 #auto detect\n# )\n# # model_xgboost.fit(X_train,y_train)\n# gsearch = GridSearchCV(model_xgboost, param_grid=parameters, scoring='roc_auc', cv=5,n_jobs=-1)\n# gsearch.fit(X_train, y_train)\n#\n# print(\"Best score: %0.3f\" % gsearch.best_score_)\n# print(\"Best parameters set:\")\n# best_parameters = gsearch.best_estimator_.get_params()\n# for param_name in sorted(parameters.keys()):\n# print(\"\\t%s: %r\" % (param_name, best_parameters[param_name]))\n#\n#\n# y_pred = gsearch.predict(X_test)\n# predictions = [round(value) for value in y_pred]\n# accuracy = roc_auc_score(y_test,predictions)\n# print(\"FINALL Accuracy: %.2f%%\" % (accuracy * 100.0))\n'''\n\n#start test\nimport xgboost as xgb\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import GridSearchCV\nfrom xgboost import XGBClassifier\nfrom sklearn import metrics\nfrom sklearn.preprocessing import MinMaxScaler #最大最小归一化\nfrom sklearn.preprocessing import StandardScaler #标准化\nfrom sklearn.model_selection import cross_val_score\nimport matplotlib.pyplot as plt\n\n'''\n#cvresult.shape[0]是其中我们用的树的个数，cvresult的结果是一个DataFrame.\ndef tun_parameters(train_x, train_y): # 通过这个函数，确定树的个数\n xgb1 = XGBClassifier(learning_rate=0.01, n_estimators=2000, max_depth=10, min_child_weight=1, gamma=0, subsample=0.8,\n colsample_bytree=0.8, objective='binary:logistic', scale_pos_weight=1, eval_metric='auc',seed=1440)\n modelfit(xgb1, train_x, train_y)\n\ndef modelfit(alg, X, y, useTrainCV=True, cv_folds=5, early_stopping_rounds=50):\n if useTrainCV:\n xgb_param = alg.get_xgb_params()\n xgtrain = xgb.DMatrix(X, label=y)\n cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\n metrics='auc', early_stopping_rounds=early_stopping_rounds)\n print(\"cvresult = \",cvresult)\n print('n_estimators=', cvresult.shape[0])\n alg.set_params(n_estimators=cvresult.shape[0])\n\n # Fit the algorithm on the data\n alg.fit(X, y,\n eval_metric='auc',\n eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n\n # Predict training set:\n dtrain_predictions = alg.predict(X)\n dtrain_predprob = alg.predict_proba(X)[:, 1]\n\n # Print model report:\n print(\"\\nModel Report\")\n print(\"Accuracy : %.4g\" % metrics.accuracy_score(y, dtrain_predictions))\n print(\"AUC Score (Train): %f\" % metrics.roc_auc_score(y, dtrain_predprob))\n\n # feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)\n # feat_imp.plot(kind='bar', title='Feature Importances')\n # plt.ylabel('Feature Importance Score')\n # plt.show()\n\ntun_parameters(X_train, y_train)\n'''\n#end test\n\n\n# max_depth 和 min_child_weight 参数调优\nprint(\"max_depth and min_child_weight\")\nparam_test1 = {\n 'max_depth':range(5,15,1),\n 'min_child_weight':range(1,10,1)\n}\ngsearch1 = GridSearchCV(estimator = XGBClassifier(learning_rate =0.01, n_estimators=200, max_depth=10,\n min_child_weight=1, gamma=0, subsample=0.8,colsample_bytree=0.8,\\\n objective= 'binary:logistic', scale_pos_weight=1, seed=1440),\n param_grid = param_test1,scoring='roc_auc',iid=False, cv=5)\ngsearch1.fit(X_train,y_train)\nprint(\"gsearch1.grid_scores_ = \",gsearch1.grid_scores_)\nprint(\"gsearch1.best_params_ = \", gsearch1.best_params_)\nprint(\"gsearch1.best_score_ = \", gsearch1.best_score_)\n# #gamma参数调优\n# print(\"gamma\")\n# param_test3 = {\n# 'gamma': [i / 10.0 for i in range(0, 5)]\n# }\n# gsearch3 = GridSearchCV(\n# estimator=XGBClassifier(learning_rate=0.1, n_estimators=160, max_depth=9, min_child_weight=1, gamma=0,\n# subsample=0.8, colsample_bytree=0.8, objective='binary:logistic', nthread=8,\n# scale_pos_weight=1, seed=27), param_grid=param_test3, scoring='roc_auc', n_jobs=-1,\n# iid=False, cv=5)\n# gsearch3.fit(X_train,y_train)\n# print(gsearch3.grid_scores_, gsearch3.best_params_, gsearch3.best_score_)\n\n#略\n\n\n\n\n\n\n\n\n# X_evaluate = data[2593669:].drop(['label'], axis=1).values\n#\n# # y_pred = model_lgb.predict_proba(X_evaluate)\n#\n# pd.DataFrame(y_pred[:, 1], columns=['y_pred'])\n#\n# test = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\n# test.columns = ['问题id', '用户id', '邀请创建时间']\n# print(len(test))\n# # 用于保存提交结果\n# result_append = test[['问题id', '用户id', '邀请创建时间']]\n# result_append['Score'] = y_pred[:, 1]\n# print(result_append.head())\n# result_append.to_csv('result.txt', header=False, index=False, sep='\\t')\n#\n\n" }, { "alpha_fraction": 0.5530909299850464, "alphanum_fraction": 0.6065454483032227, "avg_line_length": 25.95098114013672, "blob_id": "7c1ac9ef03b56e9451f2aa437ad04c2bd3b166f5", "content_id": "43f14545469f066deecac31c82b8235bbbf35ba7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5592, "license_type": "no_license", "max_line_length": 150, "num_lines": 204, "path": "/PycharmProjects/data_com/test12/xgboost2.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n# In[2]:\n\n\npd.set_option('display.max_columns', None)\n\n\n# In[3]:\n\n\nwith open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n\n# In[4]:\n\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n\n# In[5]:\n\n\nwith open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n\n# In[8]:\n\n\nprint(len(data1),len(data))\n\n\n# In[9]:\n\n\ndata['topic_sim0_max']=data1['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data1['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data1['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_std']=data1['topic_sim0'].apply(lambda x:x[3])\ndata['topic_sim0_num']=data1['topic_sim0'].apply(lambda x:x[4])\n\ndata['topic_sim1_max']=data1['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data1['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data1['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_std']=data1['topic_sim1'].apply(lambda x:x[3])\ndata['topic_sim1_num']=data1['topic_sim1'].apply(lambda x:x[4])\ndata['topic_sim1_max1']=data1['topic_sim1'].apply(lambda x:x[5])\ndata['topic_sim1_min1']=data1['topic_sim1'].apply(lambda x:x[6])\n\n\n# In[10]:\n\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\nfill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\nfill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\nfill_null(data, 'topic_sim1_max1', -2)\nfill_null(data, 'topic_sim1_min1', -2)\n\n\n# In[13]:\n\n\ndata=data.drop(['follow_topic','inter_topic','topic','title_t1','title_t2','desc_t1','desc_t2'],axis=1)\n\n\n# In[40]:\n\n\n#data=data.drop(['topic_sim0_max','topic_sim0_min','topic_sim0_avg','topic_sim1_max','topic_sim1_min','topic_sim1_avg'],axis=1)\n\n\n# In[41]:\n\n\n#data = data.drop(['topic_gz', 'topic_int', 't_invi', 't_quest', 'desc_quest_w', 'desc_quest_sw', 'desc_tit_w', 'desc_tit_sw', 'topic_quest'], axis=1)\n\n# 缺省值处理\n\n# ## 模型训练\n\n# In[18]:\n\n\nprint(len(data)-1141683)\n\n\n# In[19]:\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\nparameters = {\n 'max_depth': [10,15, 20, 25, 35],\n 'learning_rate': [0.01, 0.02, 0.05, 0.1],\n 'n_estimators': [1000,2000],\n 'min_child_weight': [0, 2, 5, 10, 20, 50],\n 'max_delta_step': [0, 0.2, 0.6, 1, 2],\n 'subsample': [0.6, 0.7, 0.8, 0.85, 0.95],\n 'colsample_bytree': [0.5, 0.6, 0.7, 0.8, 0.9],\n 'reg_alpha': [0, 0.25, 0.5, 0.75, 1],\n 'reg_lambda': [0.2, 0.4, 0.6, 0.8, 1],\n 'scale_pos_weight': [0.2, 0.4, 0.6, 0.8, 1]\n}\n\n\nimport xgboost as xgb\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import GridSearchCV\nfrom xgboost import XGBClassifier\n\n\nmodel_xgboost = XGBClassifier(\n # max_leaf_nodes=2*9-1,\n max_depth=15,\n learning_rate=0.01,\n n_estimators=2000,\n min_child_weight=5,\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=False,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40 #auto detect\n)\n# model_xgboost.fit(X_train,y_train)\ngsearch = GridSearchCV(model_xgboost, param_grid=parameters, scoring='roc_auc', cv=5,n_jobs=-1)\ngsearch.fit(X_train, y_train)\n\nprint(\"Best score: %0.3f\" % gsearch.best_score_)\nprint(\"Best parameters set:\")\nbest_parameters = gsearch.best_estimator_.get_params()\nfor param_name in sorted(parameters.keys()):\n print(\"\\t%s: %r\" % (param_name, best_parameters[param_name]))\n\n\ny_pred = gsearch.predict(X_test)\npredictions = [round(value) for value in y_pred]\naccuracy = roc_auc_score(y_test,predictions)\nprint(\"FINALL Accuracy: %.2f%%\" % (accuracy 100.0))\n\n\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\n\n# y_pred = model_lgb.predict_proba(X_evaluate)\n\npd.DataFrame(y_pred[:, 1], columns=['y_pred'])\n\ntest = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\ntest.columns = ['问题id', '用户id', '邀请创建时间']\nprint(len(test))\n# 用于保存提交结果\nresult_append = test[['问题id', '用户id', '邀请创建时间']]\nresult_append['Score'] = y_pred[:, 1]\nprint(result_append.head())\nresult_append.to_csv('result.txt', header=False, index=False, sep='\\t')\n\n\n" }, { "alpha_fraction": 0.5927634835243225, "alphanum_fraction": 0.6070908308029175, "avg_line_length": 35.45132827758789, "blob_id": "98d704ae340d7f93a68e38ae4e03577ba5ffd8e7", "content_id": "74aca4456339f9d5e3f5030e329b576b243a9f1b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4118, "license_type": "no_license", "max_line_length": 120, "num_lines": 113, "path": "/PycharmProjects/data_com/data_process/baseline.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import warnings\nwarnings.filterwarnings('ignore')\n\nimport pandas as pd\nimport numpy as np\nimport gc\nimport pickle\nimport time\n\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import StratifiedKFold\n\nfrom catboost import CatBoostClassifier, Pool\n\ntic = time.time()\n\nwith open('../pkl/invite_info.pkl', 'rb') as file:\n invite_info = pickle.load(file)\nwith open('../pkl/invite_info_evaluate.pkl', 'rb') as file:\n invite_info_evaluate = pickle.load(file)\n\n\nmember_feat = pd.read_hdf('./feats/member_feat.h5', key='data') # 0.689438\nquestion_feat = pd.read_hdf('./feats/question_feat.h5', key='data') # 0.706848\n\n\nmember_question_feat = pd.read_hdf('./feats/member_question_feat.h5', key='data') # 719116 d12\ninvite_info['author_question_id'] = invite_info['author_id'] + invite_info['question_id']\ninvite_info_evaluate['author_question_id'] = invite_info_evaluate['author_id'] + invite_info_evaluate['question_id']\n\n\ntrain = invite_info.merge(member_feat, 'left', 'author_id')\ntest = invite_info_evaluate.merge(member_feat, 'left', 'author_id')\n\ntrain = train.merge(question_feat, 'left', 'question_id')\ntest = test.merge(question_feat, 'left', 'question_id')\n\ntrain = train.merge(member_question_feat, 'left', 'author_question_id')\ntest = test.merge(member_question_feat, 'left', 'author_question_id')\n\ndel member_feat, question_feat, member_question_feat\ngc.collect()\n\ndrop_feats = ['question_id', 'author_id', 'author_question_id', 'invite_time', 'label', 'invite_day']\n\nused_feats = [f for f in train.columns if f not in drop_feats]\nprint(len(used_feats))\nprint(used_feats)\n\ntrain_x = train[used_feats].reset_index(drop=True)\ntrain_y = train['label'].reset_index(drop=True)\ntest_x = test[used_feats].reset_index(drop=True)\n\npreds = np.zeros((test_x.shape[0], 2))\nscores = []\nhas_saved = False\nimp = pd.DataFrame()\nimp['feat'] = used_feats\n\nkfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\nfor index, (tr_idx, va_idx) in enumerate(kfold.split(train_x, train_y)):\n print('' 30)\n X_train, y_train, X_valid, y_valid = train_x.iloc[tr_idx], train_y.iloc[tr_idx], train_x.iloc[va_idx], train_y.iloc[\n va_idx]\n cate_features = []\n train_pool = Pool(X_train, y_train, cat_features=cate_features)\n eval_pool = Pool(X_valid, y_valid, cat_features=cate_features)\n if not has_saved:\n cbt_model = CatBoostClassifier(iterations=10000,\n learning_rate=0.1,\n eval_metric='AUC',\n use_best_model=True,\n random_seed=42,\n logging_level='Verbose',\n task_type='GPU',\n devices='0',\n early_stopping_rounds=300,\n loss_function='Logloss',\n depth=12,\n )\n cbt_model.fit(train_pool, eval_set=eval_pool, verbose=100)\n # with open('./models/fold%d_cbt_v1.mdl' % index, 'wb') as file:\n # pickle.dump(cbt_model, file)\n else:\n with open('./models/fold%d_cbt_v1.mdl' % index, 'rb') as file:\n cbt_model = pickle.load(file)\n\n imp['score%d' % (index + 1)] = cbt_model.feature_importances_\n\n score = cbt_model.best_score_['validation']['AUC']\n scores.append(score)\n print('fold %d round %d : score: %.6f \| mean score %.6f' % (\n index + 1, cbt_model.best_iteration_, score, np.mean(scores)))\n preds += cbt_model.predict_proba(test_x)\n\n del cbt_model, train_pool, eval_pool\n del X_train, y_train, X_valid, y_valid\n import gc\n\n gc.collect()\n\n# mdls.append(cbt_model)\n\nimp.sort_values(by='score1', ascending=False)\n\nresult = invite_info_evaluate[['question_id', 'author_id', 'invite_time']]\nresult['result'] = preds[:, 1] / 5\nresult.head()\n\nresult.to_csv('./result.txt', sep='\\t', index=False, header=False)\n\ntoc = time.time()\nprint('Used time: %d' % int(toc - tic))" }, { "alpha_fraction": 0.6106017231941223, "alphanum_fraction": 0.6389684677124023, "avg_line_length": 25.58730125427246, "blob_id": "0d4b87cc2556c28687b1e643e452a8bf9714a94f", "content_id": "c8b4659704a66b1fd50cef0274bae1f773d25a62", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3490, "license_type": "no_license", "max_line_length": 70, "num_lines": 126, "path": "/PycharmProjects/data_com/test12/test/1212split.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\r\n# coding: utf-8\r\n\r\n# In[1]:\r\n\r\n# import lightgbm as lgb\r\nimport pickle\r\nimport numpy as np\r\nimport pandas as pd\r\n# from sklearn.model_selection import train_test_split\r\n# import seaborn as sns\r\n# from xgboost import XGBClassifier\r\n# from sklearn.metrics import accuracy_score\r\nimport gc\r\n# from lightgbm import LGBMClassifier\r\n# from sklearn.model_selection import StratifiedKFold\r\n\r\npd.set_option('display.max_columns', None)\r\n\r\n# with open('train20.pkl', 'rb') as file:\r\n# data = pickle.load(file)\r\n# print(data.head())\r\n\r\n# testdata = data[:1000]\r\n\r\n# with open('test1213.pkl','rb') as file:\r\n# # pickle.dump(testdata,file)\r\n# data = pickle.load(file)\r\n# # print(data.head())\r\n# isnull_index = data['title_ans_t1_max'].isnull()\r\n# # print(isnull_index)\r\n# data_isnull = data[isnull_index]\r\n# data_notnull = data[~isnull_index]\r\n# print(len(data_notnull['title_ans_t1_max']))\r\n# print(len(data_notnull.index))\r\n# print(data_notnull['title_ans_t1_max'])\r\n# print(data_isnull['title_ans_t1_max'])\r\n#\r\n# data_isnull.reset_index(drop=True)\r\n# data_notnull = data_notnull.reset_index(drop=True)\r\n# print(data_notnull['title_ans_t1_max'])\r\n# print(data_isnull['title_ans_t1_max'])\r\n# print(data_isnull.columns)\r\n#\r\n# with open('index.txt','w') as file:\r\n# # pickle.dump(data_isnull.columns,file)\r\n# for i in data_isnull.columns:\r\n# file.write(str(i)+'\\t')\r\n\r\na = pd.Series(np.arange(1,2,0.1),index=None,name='str0.1')\r\nb = pd.Series(np.arange(0,10),index=np.arange(10),name='kk')\r\nc = pd.Series(np.arange(20,30),index=np.arange(10),name='big')\r\n\r\ntest = pd.DataFrame()\r\ntest = test.append(a)\r\ntest = test.append(b)\r\ntest = test.append(c)\r\ntest[2]['kk'] = np.nan\r\n# print(test)\r\n# print(test[2]['kk'])\r\n# print(test.iloc[1,2])\r\n# print(test[2])\r\n\r\ndef pp(d):\r\n print('ddddddd')\r\n print(d)\r\n # print(d[1])\r\n # print(pd.isnull(d[1]))\r\n# ee = pd.isnull(test.iloc[1,2])\r\n# test.apply(pp,axis=1)\r\n# print(ee)\r\n# dd = test.apply(lambda x:x if pd.isnull(x[1]) else x/2,axis=0)\r\n# print(dd)\r\n# index_null = test[2].isnull()\r\n# select = test[index_null]\r\n# print(select)\r\n\r\n# test = test.drop(columns=[1,2],axis=1)\r\n# print(test)\r\n\r\n# data_notnull = data.drop(index=isnull_index,axis=0)\r\n# print(len(data_notnull))\r\n# unan = data.head().apply(lambda x )\r\n\r\n\r\ntest2 = pd.DataFrame(np.random.rand(8, 4),columns=['a','b','c','d'])\r\n\r\ntest2['b'][2] = np.nan\r\ntest2['b'][3] = np.nan\r\ntest2['b'][4] = np.nan\r\ntest2['b'][5] = np.nan\r\ntest2['b'][6] = np.nan\r\nprint(test2)\r\n\r\nindex = test2['b'].isnull()\r\nee = test2[index]\r\nprint(ee)\r\nselect_index = np.random.choice(test2[index].index,3,replace=False)\r\nprint(np.sort(select_index))\r\ndata = test2.iloc[select_index,:]\r\nprint(data)\r\n# print(data.iloc[1,:])\r\n# print(data)\r\n# print(data[data['b'].isnull()])\r\n# print(select_index)\r\n# print(test2.iloc[select_index,:])\r\n# data1 = test2.drop(index=index,axis=0)\r\n# data2 = test2.drop([0,2,3],axis=0)\r\n# test2 = test2.reset_index()\r\n# data1 = data1.reset_index()\r\n# data2 = data2.reset_index()\r\n# # print(data1)\r\n\r\n# data = pd.concat([data1,data2],axis=0)\r\n# # print(data)\r\n# data = data.sort_values(by=\"index\" , ascending=True)\r\n# print(data)\r\n\r\n# data1 = pd.DataFrame(np.arange(0,))\r\n# print(test2[:2]['a'])\r\n# test_isnull_index = test2['b'].isnull()\r\n# print(test_isnull_index)\r\n# test_isnull = test2[test_isnull_index]\r\n# print(test_isnull)\r\n# test_isnull = test_isnull.reindex(index=np.arange(len(test_isnull)))\r\n# print(test_isnull)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n" }, { "alpha_fraction": 0.5125778913497925, "alphanum_fraction": 0.5521082878112793, "avg_line_length": 33.06938934326172, "blob_id": "5746fd350f91ce62f817b7bafa6112c34068bf96", "content_id": "74fa056577c616a9415d359812e94960dcb8bdb6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8420, "license_type": "no_license", "max_line_length": 105, "num_lines": 245, "path": "/PycharmProjects/data_com/test12/test/1213nfold_mydata.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import accuracy_score\nimport gc\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import StratifiedKFold\n\npd.set_option('display.max_columns', None)\n\nwith open('train20.pkl', 'rb') as file:\n data = pickle.load(file)\nprint(data.head())\n\n\n\n# X = data[:2593669].drop(['label'], axis=1).values\nX = data.drop(['label'], axis=1).values\nprint(\"load model from file\")\nloaded_model1 = pickle.load(open(\"all_xgboost1.pickle.dat\", \"rb\"))\ntotal_xg_pred1 = loaded_model1.predict_proba(X)\nloaded_model2 = pickle.load(open(\"all_xgboost2.pickle.dat\", \"rb\"))\ntotal_xg_pred2 = loaded_model2.predict_proba(X)\nloaded_model3 = pickle.load(open(\"all_xgboost3.pickle.dat\", \"rb\"))\ntotal_xg_pred3 = loaded_model3.predict_proba(X)\ntotal_xg_pred_1 = pd.DataFrame(total_xg_pred1[:,1],columns=['xg_pred1'])\ntotal_xg_pred_2 = pd.DataFrame(total_xg_pred2[:,1],columns=['xg_pred2'])\ntotal_xg_pred_3 = pd.DataFrame(total_xg_pred3[:,1],columns=['xg_pred3'])\n\ndata = pd.concat([data,total_xg_pred_1],axis=1)\ndata = pd.concat([data,total_xg_pred_2],axis=1)\ndata = pd.concat([data,total_xg_pred_3],axis=1)\nprint(data.head())\n\nprint(\"start lgb merge\")\nloaded_model_bgm1 = pickle.load(open(\"all_xgboost4.pickle.dat\", \"rb\"))\ntotal_bgm_pred1 = loaded_model_bgm1.predict_proba(X)\nloaded_model_bgm2 = pickle.load(open(\"all_xgboost5.pickle.dat\", \"rb\"))\ntotal_bgm_pred2 = loaded_model_bgm2.predict_proba(X)\ntotal_bgm_pred_1 = pd.DataFrame(total_bgm_pred1[:,1],columns=['bgm_pred1'])\ntotal_bgm_pred_2 = pd.DataFrame(total_bgm_pred2[:,1],columns=['bgm_pred2'])\ndata = pd.concat([data,total_bgm_pred_1],axis=1)\ndata = pd.concat([data,total_bgm_pred_2],axis=1)\nprint(data.head())\n\n\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\n# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import accuracy_score\nimport gc\n\nfrom lightgbm import LGBMClassifier\n\n\n\nfrom sklearn.model_selection import StratifiedKFold\n\nn_fold = 5\nsteps = 2300\ndepth = 3\n\n\nskf = StratifiedKFold(n_splits=n_fold, random_state=2020, shuffle=False)\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\ny_pred = np.zeros([X_evaluate.shape[0],2])\ncnt = 1\n\n\n'''\nfor index ,(train_index,test_index) in enumerate(skf.split(X , y)): #训练数据五折\n print(\"start train time = {cnt}\".format(cnt=cnt))\n print(\"train_index = \",train_index)\n print(\"train_index_% = \", len(train_index)/len(X))\n train_x, test_x, train_y, test_y = X[train_index], X[test_index], y[\n train_index], y[test_index]\n print(\"start training\")\n # if cnt%2 != 0:\n if True:\n print(\"model_xgboost\")\n model = XGBClassifier(\n max_depth=depth,\n learning_rate=0.001,\n n_estimators=steps,\n min_child_weight=5, # 5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n )\n model.fit(train_x, train_y,\n eval_metric='auc',\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=2000)\n print(\"save {} model!!!\".format(cnt))\n pickle.dump(model, open(\"./model/mydata_model_xgboost{}.pickle.dat\".format(cnt), \"wb\"))\n else:\n print(\"LGBMClassifier\")\n model = LGBMClassifier(boosting_type='gbdt',\n task='train',\n num_leaves=2 ** depth - 1,\n num_iterations=steps,\n learning_rate=0.01,\n n_estimators=2000,\n max_bin=425,\n subsample_for_bin=50000,\n objective='binary',\n min_split_gain=0,\n min_child_weight=5,\n min_child_samples=10,\n feature_fraction=0.9,\n feature_fraction_bynode=0.8,\n drop_rate=0.05,\n subsample=0.8,\n subsample_freq=1,\n colsample_bytree=1,\n reg_alpha=3,\n reg_lambda=5,\n seed=1000,\n # n_jobs=4,\n silent=True\n )\n # 建议使用CV的方式训练预测。\n model.fit(train_x,\n train_y,\n eval_names=['train'],\n eval_metric={'auc'},\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n print(\"save {} model!!!\".format(cnt))\n pickle.dump(model, open(\"./model/mydata_LGBMClassifier{}.pickle.dat\".format(cnt), \"wb\"))\n\n gc.collect() # 垃圾清理，内存清理\n\n y_pred_test = model.predict(test_x)\n predictions = [round(value) for value in y_pred_test]\n accuracy = accuracy_score(test_y, predictions)\n print(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n y_pred += model.predict_proba(X_evaluate)\n print('len X_evaluate =',len(X_evaluate))\n print(\"len y_pred=\",len(y_pred))\n print(\"y_pred\")\n print(y_pred[:5, :]/cnt)\n cnt += 1\n\ny_pred = y_pred/n_fold\n'''\n\n###sigle xgboost\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\n\n\nprint(\"start xgboost\")\nmodel_xgboost = XGBClassifier(\n max_depth=2,\n learning_rate=0.001,\n n_estimators=2200,\n min_child_weight=5, #5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n)\nmodel_xgboost.fit(X_train,y_train,\n eval_metric='auc',\n eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=200)\n# save model to file\npickle.dump(model_xgboost, open(\"./model/model_xgboost.pickle2.dat\", \"wb\"))\n\n\n# model_xgboost = pickle.load(open(\"./model/model_xgboost.pickle.dat\", \"rb\"))\n\n\ny_pred_test = model_xgboost.predict(X_test)\npredictions = [round(value) for value in y_pred_test]\naccuracy = accuracy_score(y_test, predictions)\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\n\ny_pred = model_xgboost.predict_proba(X_evaluate)\n\n\n###sigle xgboost\n\n\n\nwith open('test1.pkl', 'rb') as file:\n result_append = pickle.load(file)\n\nprint(data[2593669:].head())\n\nresult_append['Score'] = y_pred[:, 1]\n\nprint(result_append.head())\n\nresult_append.to_csv('./model/result1213_mydata2.txt', header=False, index=False, sep='\\t')\n\n" }, { "alpha_fraction": 0.6242665648460388, "alphanum_fraction": 0.662824809551239, "avg_line_length": 31.033557891845703, "blob_id": "8aeee94235a1d5d0974206e753fd477398e1be8b", "content_id": "86a5365d443ace36e6467d74d51d26dd333c45c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4790, "license_type": "no_license", "max_line_length": 157, "num_lines": 149, "path": "/PycharmProjects/data_com/test12/load_model.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n\npd.set_option('display.max_columns', None)\n\n\nwith open('train15.pkl', 'rb') as file:\n data = pickle.load(file)\n\nprint(data.head())\n\ndata=data.drop(['u_topic_0_c','u_topic_0_d','u_topic_0_z','u_topic_1_c','u_topic_1_d','u_topic_1_z', 'u_topic_ans_c','u_topic_ans_d','u_topic_ans_z'],axis=1)\n\ndata=data.drop(['topic','follow_topic','inter_topic','topic_n'],axis=1)\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n# def fill_null(data, col, null_value):\n# use_avg = data[data[col] != null_value][col].mean()\n# data.loc[data[col] == null_value, col] = use_avg\n# return data\n# def fill_null_nan(data, col, null_value):\n# use_avg = np.nan\n# data.loc[data[col] == null_value, col] = use_avg\n# return data\n\n\n\n#\ndata['topic_sim0_max']=data['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_num']=data['topic_sim0'].apply(lambda x:x[3])\n\ndata['topic_sim1_max']=data['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_num']=data['topic_sim1'].apply(lambda x:x[3])\n\n# data['topic_sim0_max']=data['topic_sim0'].apply(lambda x:x[0])\n# data['topic_sim0_avg']=data['topic_sim0'].apply(lambda x:x[1])\n# data['topic_sim0_min']=data['topic_sim0'].apply(lambda x:x[2])\n# data['topic_sim0_std']=data['topic_sim0'].apply(lambda x:x[3])\n# data['topic_sim0_num']=data['topic_sim0'].apply(lambda x:x[4])\n#\n# data['topic_sim1_max']=data['topic_sim1'].apply(lambda x:x[0])\n# data['topic_sim1_avg']=data['topic_sim1'].apply(lambda x:x[1])\n# data['topic_sim1_min']=data['topic_sim1'].apply(lambda x:x[2])\n# data['topic_sim1_std']=data['topic_sim1'].apply(lambda x:x[3])\n# data['topic_sim1_num']=data['topic_sim1'].apply(lambda x:x[4])\n# data['topic_sim1_max1']=data['topic_sim1'].apply(lambda x:x[5])\n# data['topic_sim1_min1']=data['topic_sim1'].apply(lambda x:x[6])\n#\n\n\ndata=data.drop(['topic_sim0','topic_sim1'],axis=1)\n\n\n# data['topic_sim0_max']=fill_null_nan()\n# data['topic_sim0_avg']=data['topic_sim0'].apply(lambda x:x[1])\n# data['topic_sim0_min']=data['topic_sim0'].apply(lambda x:x[2])\n# data['topic_sim0_num']=data['topic_sim0'].apply(lambda x:x[3])\n#\n# data['topic_sim1_max']=data['topic_sim1'].apply(lambda x:x[0])\n# data['topic_sim1_avg']=data['topic_sim1'].apply(lambda x:x[1])\n# data['topic_sim1_min']=data['topic_sim1'].apply(lambda x:x[2])\n# data['topic_sim1_num']=data['topic_sim1'].apply(lambda x:x[3])\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\n# fill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\n\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\n# fill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\n# fill_null(data, 'topic_sim1_max1', -2)\n# fill_null(data, 'topic_sim1_min1', -2)\n\nprint(data.head())\n\n\nprint(len(data)-1141683)\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\n\n\nprint(\"load model from file\")\nmodel_xgboost = pickle.load(open(\"model_xgboost1205.pickle.dat\", \"rb\"))\n\nfrom sklearn.metrics import accuracy_score\ny_pred_test = model_xgboost.predict(X_test)\npredictions = [round(value) for value in y_pred_test]\naccuracy = accuracy_score(y_test, predictions)\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n\n\nprint('START TO SAVE RESULT!!!!!!!!!!!!!')\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\n\ny_pred = model_xgboost.predict_proba(X_evaluate)\nprint(\"y_pred\")\nprint(y_pred[:5,:])\n\n\nwith open('test1.pkl', 'rb') as file:\n result_append = pickle.load(file)\n\nprint(data[2593669:].head())\n\nresult_append['Score'] = y_pred[:, 1]\n\nprint(result_append.head())\n\nresult_append.to_csv('result1205.txt', header=False, index=False, sep='\\t')" }, { "alpha_fraction": 0.5143929719924927, "alphanum_fraction": 0.5602836608886719, "avg_line_length": 29.12986946105957, "blob_id": "adfdded78f4d3652fb657dfa5043bac1eb59fed2", "content_id": "18601495e358b087d2a57375fb74b7e961c2896e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2397, "license_type": "no_license", "max_line_length": 97, "num_lines": 77, "path": "/PycharmProjects/data_com/test12/test/test1212.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\r\n# coding: utf-8\r\n\r\nimport lightgbm as lgb\r\nimport pickle\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nimport seaborn as sns\r\nfrom xgboost import XGBClassifier\r\nfrom sklearn.metrics import accuracy_score\r\nimport gc\r\n\r\npd.set_option('display.max_columns', None)\r\n\r\nwith open('train20.pkl', 'rb') as file:\r\n data = pickle.load(file)\r\nprint(data.head())\r\n\r\nX = data[:2593669].drop(['label'], axis=1).values\r\ny = data[:2593669]['label'].values\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\r\n\r\nprint(\"start xgboost\")\r\nmodel = XGBClassifier(\r\n max_depth=10,\r\n learning_rate=0.01,\r\n n_estimators=2000,\r\n min_child_weight=5, # 5\r\n max_delta_step=0,\r\n subsample=0.8,\r\n colsample_bytree=0.7,\r\n reg_alpha=0,\r\n reg_lambda=0.4,\r\n scale_pos_weight=0.8,\r\n silent=True,\r\n objective='binary:logistic',\r\n missing=None,\r\n eval_metric='auc',\r\n seed=1440,\r\n gamma=0,\r\n n_jobs=-1\r\n # nthread=40\r\n )\r\nmodel.fit(X_train, y_train,\r\n eval_metric='auc',\r\n eval_set=[(X_train, y_train), (X_test, y_test)], # , (X_test, y_test)\r\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\r\n early_stopping_rounds=50)\r\ncnt = 1212\r\nprint(\"save {} model!!!\".format(cnt))\r\npickle.dump(model, open(\"model_xgboost{}.pickle.dat\".format(cnt), \"wb\"))\r\n\r\nprint('START TO SAVE RESULT!!!!!!!!!!!!!')\r\ny_pred_test = model.predict(X_test)\r\npredictions = [round(value) for value in y_pred_test]\r\naccuracy = accuracy_score(y_test, predictions)\r\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\r\n\r\nprint('START TO SAVE RESULT!!!!!!!!!!!!!')\r\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\r\ny_pred = model.predict_proba(X_evaluate)\r\nprint(\"y_pred\")\r\nprint(y_pred[:5,:])\r\n\r\n\r\nwith open('test1.pkl', 'rb') as file:\r\n result_append = pickle.load(file)\r\n\r\nprint(data[2593669:].head())\r\n\r\nresult_append['Score'] = y_pred[:, 1]\r\n\r\nprint(result_append.head())\r\n\r\nresult_append.to_csv('result1212_xgboost.txt', header=False, index=False, sep='\\t')\r\n" }, { "alpha_fraction": 0.5354499220848083, "alphanum_fraction": 0.5663069486618042, "avg_line_length": 35.52117156982422, "blob_id": "4bbc05838f0b1ba5066a1d6a28951cdddf7891d6", "content_id": "3802b68a6dede13c1bccb858c1fd49f3d752b9ac", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 11267, "license_type": "no_license", "max_line_length": 145, "num_lines": 307, "path": "/PycharmProjects/data_com/test12/test/mydata2.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import accuracy_score\nimport gc\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import StratifiedKFold\n\npd.set_option('display.max_columns', None)\n\nwith open('train20.pkl', 'rb') as file:\n data = pickle.load(file)\n# print(data.head())\n\n\ndata_use = data[:2593669]\ndata_evaluate = data[2593669:]\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\n\nisnull_index = data_use['title_ans_t1_max'].isnull()\n# print(isnull_index)\ndata_isnull = data_use[isnull_index]\ndata_notnull = data_use[~isnull_index]\n\n# print(len(data_notnull['title_ans_t1_max']))\nprint('len(data_notnull.index) = ',len(data_notnull.index))\nprint('len(data_isnull.index) = ',len(data_isnull.index))\n\n\nevaluate_isnull_index = data_evaluate['title_ans_t1_max'].isnull()\nevaluate_isnull = data_evaluate[evaluate_isnull_index]\nevaluate_notnull = data_evaluate[~evaluate_isnull_index]\n\n\nprint(\"evaluate_isnull = \",len(evaluate_isnull))\nprint(\"evaluate_notnull = \",len(evaluate_notnull))\n\nchoice_scale_drop = round((len(evaluate_isnull)/len(data_evaluate) len(data_use)-len(data_isnull))/(-1+len(evaluate_isnull)/len(data_evaluate)))\nchoice_scale_isnull = len(data_isnull) - choice_scale_drop\nprint(\"scale = {}%\".format(len(evaluate_isnull)/len(data_evaluate)))\nprint(\"choice_scale_isnull\",choice_scale_isnull)\nprint(\"selct scale = \",choice_scale_isnull/(len(data_use)-choice_scale_drop))\n\nselect_index_isnull = np.random.choice(data_use[data_use['title_ans_t1_max'].isnull()].index,choice_scale_isnull,replace=False)\n# print(np.sort(select_index_isnull))\nprint(len(select_index_isnull))\n\nselect_data_isnull = data_use.iloc[select_index_isnull,:]\nprint(\"select_data_isnull = \",len(select_data_isnull))\n# print(select_data_isnull['title_ans_t1_max'][:1000])\nprint(\"select_data_isnull() = \",select_data_isnull['title_ans_t1_max'].isnull().sum())\n\ndata_notnull = data_notnull.reset_index()\nselect_data_isnull = select_data_isnull.reset_index()\n# print(select_data_isnull.head(20))\n\n\n\n# print(\"data_notnull\",len(data_notnull[data_notnull['title_ans_t1_max'].isnull()]))\n# print(\"select_data_isnull\",len(select_data_isnull['title_ans_t1_max'].isnull().index))\ntrain_data = pd.concat([data_notnull,select_data_isnull],axis=0)\nprint(\"train_data\",len(train_data))\n# print('before sort\\n',train_data.head(40))\ntrain_data = train_data.sort_values(by=\"index\" , ascending=True)\n# print('after sort\\n',train_data.head(40))\n\n\nprint(\"len(train_data) =\",len(train_data))\nprint(\"isnull() = \",len(train_data[train_data['title_ans_t1_max'].isnull()]))\nafter_scale = len(train_data[train_data['title_ans_t1_max'].isnull()])/len(train_data)\nprint(\"scale = {}\".format(len(evaluate_isnull)/len(data_evaluate)))\nprint(\"after selct train data = {}\".format(after_scale))\n#train\n\nX = train_data.drop(['label','index'],axis=1).values\ny = train_data['label'].values\n\n'''\nX_train, X_test, y_train, y_test = train_test_split(X_isnull, y_isnull, test_size=0.1)\n\n\nprint(\"start isnull_model_xgboost\")\nisnull_model_xgboost = XGBClassifier(\n max_depth=depth,\n learning_rate=0.01,\n n_estimators=steps,\n min_child_weight=5, #5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n)\nisnull_model_xgboost.fit(X_train,y_train,\n eval_metric='auc',\n eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=200)\n# save model to file\npickle.dump(isnull_model_xgboost, open(\"./model/mydata1_isnull_xgboost.pickle.dat\", \"wb\"))\n\n\n# model_xgboost = pickle.load(open(\"./model/model_xgboost.pickle.dat\", \"rb\"))\n\n\ny_pred_test = isnull_model_xgboost.predict(X_test)\npredictions = [round(value) for value in y_pred_test]\naccuracy = accuracy_score(y_test, predictions)\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy 100.0))\n\nX_train, X_test, y_train, y_test = train_test_split(X_notnull, y_notnull, test_size=0.1)\nprint(\"start notnull_model_xgboost\")\nnotnull_model_xgboost = XGBClassifier(\n max_depth=depth,\n learning_rate=0.01,\n n_estimators=steps,\n min_child_weight=5, #5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n)\nnotnull_model_xgboost.fit(X_train,y_train,\n eval_metric='auc',\n eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=200)\n# save model to file\npickle.dump(notnull_model_xgboost, open(\"./model/mydata1_notnull_xgboost.pickle.dat\", \"wb\"))\n\n\n# model_xgboost = pickle.load(open(\"./model/model_xgboost.pickle.dat\", \"rb\"))\n\n\ny_pred_test = notnull_model_xgboost.predict(X_test)\npredictions = [round(value) for value in y_pred_test]\naccuracy = accuracy_score(y_test, predictions)\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n'''\n\n\nfrom sklearn.model_selection import StratifiedKFold\n\nn_fold = 5\nsteps = 2000\ndepth = 10\n\n\nskf = StratifiedKFold(n_splits=n_fold, random_state=2020, shuffle=False)\n\n# isnul 5 fold\n# X_evaluate = data[2593669:].drop(['label'], axis=1).values\npred_y = np.zeros([data_evaluate.shape[0],2])\ncnt = 1\n\nfor index ,(train_index,test_index) in enumerate(skf.split(X , y)): #训练数据五折\n print(\"start train time = {cnt}\".format(cnt=cnt))\n print(\"train_index = \",train_index)\n print(\"train_index_% = \", len(train_index)/len(X))\n train_x, test_x, train_y, test_y = X[train_index], X[test_index], y[\n train_index], y[test_index]\n print(\"start training\")\n # if cnt%2 != 0:\n if True:\n print(\"model_xgboost\")\n model = XGBClassifier(\n max_depth=10,\n learning_rate=0.01,\n n_estimators=steps,\n min_child_weight=5, # 5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n )\n model.fit(train_x, train_y,\n eval_metric='auc',\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=200)\n print(\"save {} model!!!\".format(cnt))\n pickle.dump(model, open(\"./model2/sample_mydata_model_xgboost{}.pickle.dat\".format(cnt), \"wb\"))\n else:\n print(\"LGBMClassifier\")\n model = LGBMClassifier(boosting_type='gbdt',\n task='train',\n num_leaves=2 ** depth - 1,\n num_iterations=steps,\n learning_rate=0.01,\n n_estimators=2000,\n max_bin=425,\n subsample_for_bin=50000,\n objective='binary',\n min_split_gain=0,\n min_child_weight=5,\n min_child_samples=10,\n feature_fraction=0.9,\n feature_fraction_bynode=0.8,\n drop_rate=0.05,\n subsample=0.8,\n subsample_freq=1,\n colsample_bytree=1,\n reg_alpha=3,\n reg_lambda=5,\n seed=1000,\n # n_jobs=4,\n silent=True\n )\n # 建议使用CV的方式训练预测。\n model.fit(train_x,\n train_y,\n eval_names=['train'],\n eval_metric={'auc'},\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n print(\"save {} model!!!\".format(cnt))\n pickle.dump(model, open(\"./model/mydata_LGBMClassifier{}.pickle.dat\".format(cnt), \"wb\"))\n\n gc.collect() # 垃圾清理，内存清理\n\n y_pred_test = model.predict(test_x)\n predictions = [round(value) for value in y_pred_test]\n accuracy = accuracy_score(test_y, predictions)\n print(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n pred_y += model.predict_proba(X_evaluate)\n print('len X_evaluate =',len(X_evaluate))\n print(\"len y_pred=\",len(pred_y))\n print(\"y_pred\")\n print(pred_y[:5, :]/cnt)\n cnt += 1\n\npred_y = (pred_y/n_fold)[:,1]\n\n#save data\n\n\n# isnull_pred = isnull_model_xgboost.predict_proba(evaluate_X_isnull)[:,1]\n# print(\"isnull_pred\\n\",isnull_pred)\n# notnull_pred = notnull_model_xgboost.predict_proba(evaluate_X_notnull)[:,1]\n# print(\"notnull_pred\\n\",notnull_pred)\n\n# store_isnull = pd.DataFrame()\n# store_notnull = pd.DataFrame()\n# store_isnull['index'] = evaluate_isnull['index']\n# store_isnull['y_pred'] = isnull_pred\n# print(store_isnull.head())\n#\n# store_notnull['index'] = evaluate_notnull['index']\n# store_notnull['y_pred'] = notnull_pred\n# print(store_notnull.head())\n#\n#\n# store = pd.concat([store_isnull,store_notnull],axis=0)\n# print('before sort',store.head(20))\n# store = store.sort_values(by=\"index\" , ascending=True)\n# print('after sort',store.head(40))\n#\n# y_pred = store['y_pred'].values\n\n########store data\nwith open('test1.pkl', 'rb') as file:\n result_append = pickle.load(file)\n\n# print(data[2593669:].head())\n\nresult_append['Score'] = pred_y\n\nprint(result_append.head())\n\nresult_append.to_csv('./model2/mydata_sample.txt', header=False, index=False, sep='\\t')\n\n" }, { "alpha_fraction": 0.5969184041023254, "alphanum_fraction": 0.6368651390075684, "avg_line_length": 25.540403366088867, "blob_id": "5b0f6f69a5f1727a9b8fc8bbf6735baae5f107a6", "content_id": "7c39ecc284a79ddefb327ec9fc069fabdfd5019d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5349, "license_type": "no_license", "max_line_length": 150, "num_lines": 198, "path": "/PycharmProjects/data_com/test12/xgboost_fix.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n# In[2]:\n\n\npd.set_option('display.max_columns', None)\n\n\n# In[3]:\n\n\nwith open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n\n# In[4]:\n\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n\n# In[5]:\n\n\nwith open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n\n# In[8]:\n\n\nprint(len(data1),len(data))\n\n\n# In[9]:\n\n\ndata['topic_sim0_max']=data1['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data1['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data1['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_std']=data1['topic_sim0'].apply(lambda x:x[3])\ndata['topic_sim0_num']=data1['topic_sim0'].apply(lambda x:x[4])\n\ndata['topic_sim1_max']=data1['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data1['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data1['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_std']=data1['topic_sim1'].apply(lambda x:x[3])\ndata['topic_sim1_num']=data1['topic_sim1'].apply(lambda x:x[4])\ndata['topic_sim1_max1']=data1['topic_sim1'].apply(lambda x:x[5])\ndata['topic_sim1_min1']=data1['topic_sim1'].apply(lambda x:x[6])\n\n\n# In[10]:\n\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\nfill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\nfill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\nfill_null(data, 'topic_sim1_max1', -2)\nfill_null(data, 'topic_sim1_min1', -2)\n\n\n# In[13]:\n\n\ndata=data.drop(['follow_topic','inter_topic','topic','title_t1','title_t2','desc_t1','desc_t2'],axis=1)\n\n\n# In[40]:\n\n\n#data=data.drop(['topic_sim0_max','topic_sim0_min','topic_sim0_avg','topic_sim1_max','topic_sim1_min','topic_sim1_avg'],axis=1)\n\n\n# In[41]:\n\n\n#data = data.drop(['topic_gz', 'topic_int', 't_invi', 't_quest', 'desc_quest_w', 'desc_quest_sw', 'desc_tit_w', 'desc_tit_sw', 'topic_quest'], axis=1)\n\n# 缺省值处理\n\n# ## 模型训练\n\n# In[18]:\n\n\nprint(len(data)-1141683)\n\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\nX_xg_evaluate = data[2593669:].drop(['label'], axis=1).values\n\nprint(\"load model from file\")\nloaded_model = pickle.load(open(\"model_xgboost.pickle.dat\", \"rb\"))\ntotal_xg_pred = loaded_model.predict_proba(X)\n\n##start\ny_xg_pred = loaded_model.predict_proba(X_xg_evaluate)\n\nget = np.r_[total_xg_pred,y_xg_pred]\nprint(get)\nprint(\"len get = \",len(get))\nprint(\"len data =\",len(data))\n# y_xg_pred1 = pd.DataFrame(get,columns=['pred1'])\n# y_xg_pred2 = pd.DataFrame(get,columns=['pred2'])\n\n##end\n\n\ntotal_xg_pred_1 = pd.DataFrame(get[:,1],columns=['pred1'])\ntotal_xg_pred_2 = pd.DataFrame(get[:,1],columns=['pred2'])\n\nprint(total_xg_pred[:5])\ndata = pd.concat([data,total_xg_pred_1],axis=1)\ndata = pd.concat([data,total_xg_pred_2],axis=1)\nprint(data.head())\n\n################################################################################\n\n# print(\"load model from file\")\n# loaded_model = pickle.load(open(\"model_xgboost.pickle.dat\", \"rb\"))\n# total_xg_pred = loaded_model.predict_proba(X)\n# total_xg_pred_1 = pd.DataFrame(total_xg_pred[:,1],columns=['pred1'])\n# print(total_xg_pred[:5])\n\n\n# data['pred1'] = pd.concat([data['pred1'],y_xg_pred1],axis=0)\n# data['pred2'] = pd.concat([data['pred2'],y_xg_pred2],axis=0)\n# print(\"after merge\")\n# print(data.head())\n# print(data.tail())\n###################################################################################################\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nprint(\"X = \",X[:5])\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\nprint(\"start lgb merge\")\nmodel_lgb = pickle.load(open(\"model_lgb1203.pickle.dat\", \"rb\"))\n\nfrom sklearn.metrics import accuracy_score\n\nprint('START TO SAVE RESULT!!!!!!!!!!!!!')\ny_pred_test = model_lgb.predict(X_test)\npredictions = [round(value) for value in y_pred_test]\naccuracy = accuracy_score(y_test, predictions)\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\ny_pred = model_lgb.predict_proba(X_evaluate)\nprint(\"y_pred\")\nprint(y_pred[:5,:])\n\n\ntest = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\ntest.columns = ['问题id', '用户id', '邀请创建时间']\nprint(len(test))\n# 用于保存提交结果\nresult_append = test[['问题id', '用户id', '邀请创建时间']]\nresult_append['Score'] = y_pred[:, 1]\nprint(result_append.head())\nresult_append.to_csv('result_fix.txt', header=False, index=False, sep='\\t')\n\n\n" } ]	18
linylgithub/data_structure	https://github.com/linylgithub/data_structure	0367a44474a5654ecd8d199d2d5925fecbb48ab1	1e87a8229df03a8682b430956189518bf5ddc2dd	db4b46e25b66bb46e3c23c8817bf01527497d5d7	refs/heads/master	2020-03-26T07:32:20.817770	2018-08-14T02:50:38	2018-08-14T02:50:38	144,659,238	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.4725162088871002, "alphanum_fraction": 0.47558894753456116, "avg_line_length": 22.983606338500977, "blob_id": "f886d95086cbfa93a69c2357b12601d6012dada8", "content_id": "26748203ee01f032b46cdb0e13408c53cfa5822c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3053, "license_type": "no_license", "max_line_length": 55, "num_lines": 122, "path": "/link_list/cycle_double_link_list.py", "repo_name": "linylgithub/data_structure", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# -- coding: utf-8 --\n\nfrom __future__ import unicode_literals\nfrom __future__ import absolute_import\n\n# 双向循环链表\n\n# 节点\nclass Node:\n def __init__(self, value):\n self.data = value\n self.next = None\n self.pre = None\n\n# 双向循环链表\nclass CycleDoubleLinkList:\n def __init__(self):\n self._head = Node(None)\n self._head.next = self._head\n self._head.prev = self._head\n self._rear = self._head\n\n # 插入\n def insert(self, index, value):\n if index < 0:\n print '插入位置有误'\n return\n n = Node(value)\n cur = self._head\n for i in range(index - 1):\n cur = cur.next\n if cur == self._head:\n print '插入位置有误'\n return\n n.next = cur.next\n cur.next.prev = n\n cur.next = n\n n.prev = cur\n if n.next = self.head:\n self._rear = n\n\n # 删除\n def remove(self,index):\n if self.empty():\n print '链表是空的'\n if index <= 0 and index > self.length():\n print '删除的位置有误'\n return \n cur = self._head\n for i in range(index - 1):\n cur = cur.next\n n = cur.next\n cur.next.next.prev = cur\n cur.next = cur.next.next\n if cur.next == self._head:\n self._rear = cur\n del n \n\n # 判断是否空链表\n def empty(self):\n return self._head.next == self._head\n\n def travel(self):\n cur = self._head.next\n print '正向输出'\n while cur != self._head:\n print(cur.data)\n cur = cur.next\n print '逆向输出'\n while cur.prev != self._head:\n cur = cur.prev\n print(cur.data)\n\n def search(self, value):\n cur = self._head.next\n index = 1\n while cur != self._head:\n if cur.data == value:\n print 'index:%d,value:%d'%(index,value)\n return\n cur = cur.next\n index += 1\n\n print '没有该元素'\n\n def clear(self):\n cur = self._head.next\n while cur != self._head:\n temp = cur\n cur = cur.next\n del temp\n self._head.next = self._head\n self._head.prev = self._head\n\n def appendleft(self,value):\n n = Node(value)\n self._head.next.prev = n\n n.next = self._head.next\n self._head.next = n\n n.prev = self._head\n if n.next == self._head:\n self._rear = n\n\n def appendright(self,value):\n n = Node(value)\n self._rear.next.prev = n\n n.next = self._rear.next\n self._rear.next = n\n n.prev = self._rear\n self._rear = n\n\n def length(self):\n cur = self._head.next\n count = 0 \n while cur != self._head:\n cur = cur.next\n count += 1\n return count\n\nif __name__ == '__main__':\n link = CycleDoubleLinkList()\n\n\n\n" } ]	1
FelicityPictures/Pokemon_GO	https://github.com/FelicityPictures/Pokemon_GO	3a45e8b7d602fd84ab802c248d39afb9e9908011	d93d1a934179a472c248e2c6514dd8b17d7201f1	68393cfbaabc84e07674e0e74308fe50cf64d2e3	refs/heads/master	2018-12-31T09:02:55.978544	2016-07-21T22:13:34	2016-07-21T22:13:34	63,491,030	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5802469253540039, "alphanum_fraction": 0.5873944163322449, "avg_line_length": 25.534482955932617, "blob_id": "fd422f1fe37dff55ca793e2c86bc79cb1355b69f", "content_id": "450ae45d6cfc97ab46abaf07e98d8f332065a13e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1539, "license_type": "no_license", "max_line_length": 61, "num_lines": 58, "path": "/db.py", "repo_name": "FelicityPictures/Pokemon_GO", "src_encoding": "UTF-8", "text": "from pymongo import MongoClient\n\nconnection = MongoClient()\ndb = connection[\"pokemon\"]\n\n#for restarting database. Delete after testing\n#db.pokemon.drop()\n\n\"\"\"\nCOLLECTIONS\n(assuming all locations fit into a rectangle\npokemon: name, angle, startx, starty, endx, endy\n\"\"\"\n\ndef add_pokemon(name,lat,lng):\n \"\"\"\n Adds new location of a Pokemon\n Params: name - name of Pokemon\n lat - latitude of point where found\n lng - longitude of point where found\n Returns: True if insertion successful\n False if otherwise\n \"\"\"\n p = {\"name\":name,\n \"lat\":lat,\n \"lng\":lng}\n db.pokemon.insert(p)\n return True\n\ndef add_by_array(name,array):\n array = string_to_array(array)\n for point in array:\n add_pokemon(name,point[0],point[1])\n\ndef string_to_array(string):\n arr = []\n temp = \"\"\n string = string[1:(len(string)-1)]\n while len(string)>0:\n if string.index(',') == 0:\n string = string[1:]\n if string.index('[') == 0:\n temp = string[1:string.index(']')]\n add = [float(temp[:temp.index(',')]),\n float(temp[temp.index(',')+1:])]\n arr.append(add)\n string = string[string.index(']')+1:]\n return arr\n\ndef get_pokemon(name):\n \"\"\"\n Params: name - name of the Pokemon\n Returns: all the locations of where a Pokemon was found\n \"\"\"\n ret = list(db.pokemon.find({\"name\":name}))\n if not ret:\n return \"Pokemon has no current locations to be found\"\n return ret\n" }, { "alpha_fraction": 0.5229591727256775, "alphanum_fraction": 0.581632673740387, "avg_line_length": 19.63157844543457, "blob_id": "35acaac74cca3a8a4ce9c6dd1c41f7cc321964c0", "content_id": "24142b6669c8586d0c651972ea1df20182d47de7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 392, "license_type": "no_license", "max_line_length": 49, "num_lines": 19, "path": "/dbtest.py", "repo_name": "FelicityPictures/Pokemon_GO", "src_encoding": "UTF-8", "text": "import db\n\n\"\"\"\ndb.add_pokemon(\"Dragonite\",\"2\",'3')\ndb.add_pokemon(\"Dragonite\",\"5\",'2')\ndb.add_pokemon(\"Bulbasaur\",'5','3')\n\nprint db.get_pokemon(\"Dragonite\")\nprint db.get_pokemon(\"Meowth\")\n\nDitto = [['1','2'],['3','4'],['6','7'],['2','0']]\ndb.add_by_array(\"Ditto\",Ditto)\n\nprint db.get_pokemon(\"Ditto\")\n\"\"\"\n\ns = \"[[1.2,2],[3,4],[6,7],[2,0]]\"\nprint \"original: \" + s\nprint db.string_to_array(s)\n" }, { "alpha_fraction": 0.5799999833106995, "alphanum_fraction": 0.5928571224212646, "avg_line_length": 24, "blob_id": "8734cad7582af63c464ea3202a69838585f58f4a", "content_id": "5e0cc2d2c9964e8751607ef265718436d20178aa", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 700, "license_type": "no_license", "max_line_length": 49, "num_lines": 28, "path": "/app.py", "repo_name": "FelicityPictures/Pokemon_GO", "src_encoding": "UTF-8", "text": "from flask import Flask, render_template, request\nimport json\n#imports database\nimport db\n\napp=Flask(__name__)\n\[email protected]('/', methods=[\"POST\"])\[email protected]('/home', methods=[\"POST\"])\ndef home():\n if request.method == \"POST\":\n find = request.form.get('find')\n \n return render_template('index.html')\n\[email protected]('/add', methods=[\"POST\"])\ndef add():\n if request.method == \"POST\":\n data = request.form.get('locations')\n sep = data.index('\|')\n name = data[:sep]\n locations = data[(sep+1):]\n db.add_by_array(name,locations)\n return render_template('add.html')\n\nif __name__=='__main__':\n app.debug=True\n app.run(host='0.0.0.0', port=5111)\n" } ]	3
rungsiman/dorest	https://github.com/rungsiman/dorest	668ee791879b76d825114eed465e8949db0e2697	480f0cf6369777beb6209e67e6515343ce826281	bbb0bea88da01da3db8fb4d657e6a640e16915a5	refs/heads/main	2023-01-27T18:51:52.654408	2020-12-06T13:39:36	2020-12-06T13:39:36	316,101,787	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6197856068611145, "alphanum_fraction": 0.6261943578720093, "avg_line_length": 39.10280227661133, "blob_id": "2208bc9728dec58e24bf8c063a1da06b9335aaab", "content_id": "0ca3079f7ce87dac8cb3848f6a67b5bbeebee714", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8582, "license_type": "permissive", "max_line_length": 142, "num_lines": 214, "path": "/dorest/decorators.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Dorest's endpoint and Django-related decorators\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nimport importlib\nfrom functools import wraps\nfrom typing import Any, Callable, List, Union\n\nfrom django.conf import settings\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom rest_framework import status\nfrom rest_framework.request import Request\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom dorest.glossary import Glossary\n\n\ndef endpoint(methods: List[str], default: bool = False, throttle: str = 'base', requires: Union[tuple, list] = None,\n include_request: str = None) -> Callable[..., Any]:\n \"\"\"Intercept requests and transform them into function calls with arguments\n\n :param methods: HTTP request method\n :param default: Set as module's default endpoint in case no specific function or class name is specified in the request\n :param throttle: Throttle type\n :param requires: A list of required access permissions\n :param include_request: Include 'request' parameter in the function call\n :return: An output of the function as Django REST Framework's Response object,\n or the target function if called directly (not as an endpoint)\n \"\"\"\n\n endpoint.meta = locals()\n\n def decorator(func: Callable[..., Any]) -> Callable[..., Any]:\n @wraps(func)\n def wrapper(args, kwargs) -> Any:\n if len(args) == 1 and isinstance(args[0], Request):\n try:\n request = args[0]\n\n # Extract request's body from requests using methods other than GET\n body = {key: value for key, value in request.data.items()}\n parameters = request.META[Glossary.META_ENDPOINT.value].parse({dict(request.GET), body})\n\n if include_request is not None:\n parameters[include_request] = request\n\n # A request is handled differently based on how the endpoint is defined (as a class or a function)\n if Glossary.META_CLASS.value in request.META and request.META[Glossary.META_CLASS.value] is not None:\n return Response({'data': func(request.META[Glossary.META_CLASS.value][1], parameters)}, status=status.HTTP_200_OK)\n else:\n return Response({'data': func(parameters)}, status=status.HTTP_200_OK)\n\n except TypeError as error:\n return Response({'detail': str(error)}, status=status.HTTP_400_BAD_REQUEST)\n\n else:\n return func(args, *kwargs)\n\n if hasattr(settings, 'DOREST'):\n throttle_class = settings.DOREST.get('DEFAULT_THROTTLE_CLASSES', None)\n\n if throttle_class is not None and len(throttle_class) > 0:\n throttle_class_branch = [node.split('.') for node in throttle_class]\n wrapper.throttle_classes = [getattr(importlib.import_module('.'.join(node[:-1])), node[-1]) for node in throttle_class_branch]\n\n wrapper.meta = endpoint.meta\n\n if requires is not None:\n wrapper.permission_classes = requires\n\n return wrapper\n\n return decorator\n\n\ndef require(validators: Union[tuple, list]):\n \"\"\"Django's standard permission_required decorator does not recognize 'user' in 'request'\n received from Django rest_framework's APIView, resulting in the user being treated as anonymous.\n\n This custom implementation solves the issue, as well as removes a redirect URL since\n this channel of communication does not provide a user interface.\n\n :param: validators: A tuple of permission validators.\n By default, a user must have all required permissions to perform an action.\n However, if only one permission was needed, set 'or' as the first member of the tuple.\n\n For example:\n (p1, p2, ('or', p3, p4))\n In this case, a user must have permissions p1 and p2, and she must also have p3 or p4\n in order to perform an action.\n \"\"\"\n\n def decorator(func):\n def wrapper(args, kwargs):\n \"\"\"Receive HTTP request handler (function) of Django rest_framework's APIView class.\n ---\n\n args: [0] an object of a class inherited from Django's view\n [1] an object of rest_framework.request.Request\n \"\"\"\n\n if 'request' in kwargs and _require_operator(validators, kwargs):\n return func(args, kwargs)\n elif _require_operator(validators, args[1], args[0], kwargs):\n return func(args, kwargs)\n else:\n return Response({'detail': _('Permission required')}, status=403)\n\n return wrapper\n\n return decorator\n\n\ndef _require_operator(validators: Union[tuple, list], request: Request, view: APIView, kwargs) -> bool:\n \"\"\"Validate and applie AND operator on the results produced by the validators\n\n :param validators: A tuple of validators\n :param request: A request sent from Django REST framework\n :param view: Django REST framework API view\n :param kwargs: Validator's argument\n :return: Validation result\n \"\"\"\n\n def validate(validator):\n if type(validator) is tuple or type(validator) is list:\n return _require_operator(validator, request, view)\n\n elif type(validator) is str:\n return request.user.has_perm(validator)\n\n else:\n validator_instance = validator()\n\n try:\n return validator_instance.has_permission(request, view, *kwargs)\n except TypeError:\n return validator_instance.has_permission(request, view)\n\n # 'operator_or()' returns a list with 'or' as its first member\n if type(validators) is not tuple and type(validators) is not list:\n return validate(validators)\n\n elif validators[0] == 'or':\n return any([validate(v) for v in validators[1:]])\n\n elif validators[0] == 'not':\n return not validate(validators[1])\n\n else:\n return all([validate(v) for v in validators])\n\n\ndef operator_or(args):\n \"\"\"Another form of 'or' operator in 'permissions._require_operator'.\n Instead of setting the first member of the tuple as 'or', e.g., 'permissions.require(p1, p2, (\"or\", p3, p4))',\n this function offers a different format that does not include an operator as a member.\n\n For convenience, import this function as '_or'.\n The above example can then be written as 'permissions.require(p1, p2, _or(p3, p4))'.\n \"\"\"\n\n return ('or',) + args\n\n\ndef operator_not(validator):\n \"\"\"Another form of 'not' operator in 'permissions._require_operator'.\n\n Warning: While 'permissions.operator_or' accepts multiple arguments, this operator accepts only one validator\n \"\"\"\n\n return 'not', validator\n\n\ndef mandate(*params):\n \"\"\"Check whether all required request parameters are included in an HTTP request to an APIView\n :param params: parameter 'fields' contains required request parameters\n parameter 'strict_fields' are 'fields' that cannot be blank\n \"\"\"\n\n def decorator(func):\n def wrapper(args, *kwargs):\n \"\"\"Receive HTTP request handler (function) of Django rest_framework's APIView class.\"\"\"\n\n # Assign rest_framework.request.Request object to 'request' variable, which is to be validated\n request = args[0] if isinstance(args[0], Request) else args[1]\n\n missings = []\n blanks = []\n fields = params['fields'] if 'fields' in params else [] + params['strict_fields'] if 'strict_fields' in params else []\n strict_fields = params['strict_fields'] if 'strict_fields' in params else []\n\n for kwarg in fields:\n if kwarg not in request.data and kwarg not in request.GET:\n missings.append(kwarg)\n\n for kwarg in strict_fields:\n if kwarg in request.data and len(request.data[kwarg]) == 0:\n blanks.append(kwarg)\n\n if len(missings) > 0 or len(blanks):\n return Response({'detail': _('Parameters missing or containing blank value'),\n 'missing': missings,\n 'blank': blanks}, status=400)\n\n return func(args, *kwargs)\n\n return wrapper\n\n return decorator\n" }, { "alpha_fraction": 0.6387904286384583, "alphanum_fraction": 0.6418615579605103, "avg_line_length": 34.87288284301758, "blob_id": "7bc4224cd6321c588f969cef8f90254c9a6ad271", "content_id": "2a8e06b7a52d445d3ad3f04ba4afb22f5351dc64", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4233, "license_type": "permissive", "max_line_length": 128, "num_lines": 118, "path": "/dorest/interfaces.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"The generic architecture\n\nThe generic mechanism forwards API calls to a preconfigured set of backend functions.\nAn basic use case is when an API should support multiple database backends.\nFor example, the following packages provides connectors for databases 'dba' and 'dbb':\n~~~~~~~~~~~~~~~~~~~~~~~\n + gen\n \|- __init__.py\n \|- connector.py\n + db\n \| + dba\n \| \|- __init__.py\n \| \|- connector.py\n \| + dbb\n \| \|- __init__.py\n \| \|- connector.py\n ...\n~~~~~~~~~~~~~~~~~~~~~~~\n\nTo make package 'gen' generic, import 'register' function from this module and implement it in 'gen/__init__.py':\n---\n generic.register('db', to=__name__, using='dba')\n---\nThe first parameter indicates the root package of the backends, and the 'using' parameter indicates which backend is being used.\n\nSuppose there is a function defined in 'gen/connector.py' that inserts data to a database and returns record ID:\n---\n def insert(title: str, article: str, author: str) -> str:\n return generic.resolve(insert)(title, article, author, datetime.now())\n---\nOnce received an API call, the 'generic.resolve' function will look for the registered configuration ('generic.register')\nin the same and parent packages, then match the caller ('gen.connector.insert') to the backend ('db.dba.connector.insert').\n\nThus, the 'insert' function in 'db/dba/connector.py' should be:\n---\n def insert(title: str, article: str, author: str, created: datetime) -> str:\n return dba_client.insert({'title': title, 'article': article, 'author': author, 'created': created})\n---\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\n\nimport importlib\nimport sys\nfrom typing import Union\nfrom types import ModuleType\n\n\ndef _get_module(module: Union[str, ModuleType]) -> ModuleType:\n return getattr(sys.modules, module, importlib.import_module(module)) if isinstance(module, str) else module\n\n\ndef _locate_package(branch: str) -> Union[str, None]:\n path = branch.split('.')\n\n for i in range(len(path) - 1, 0, -1):\n active_path = '.'.join(path[:i])\n\n try:\n package = importlib.import_module('%s.package' % active_path)\n\n if hasattr(package, '__package'):\n return package.__name__[:-8]\n\n except ModuleNotFoundError:\n continue\n\n return None\n\n\ndef bind(backend: Union[str, ModuleType], , to: Union[str, ModuleType], using: str,\n bypass_package: bool = False) -> None:\n \"\"\"Register a backends' package by setting the attribute '__interface' to the module\n\n :param backend: The backends' package\n :param to: The root of the interface package (usually __name__)\n :param using: The selected backend\n :param bypass_package: Only locate backends in PYTHONPATH\n :return: None\n \"\"\"\n\n package = _locate_package(_get_module(to).__name__)\n\n if isinstance(backend, str) and package is not None and not bypass_package:\n backend = '%s.%s' % (package, backend)\n\n setattr(_get_module(to), '__interface', {'package': _get_module(backend), 'driver': using})\n\n\ndef resolve(caller: Union[callable, str, ModuleType]) -> callable:\n \"\"\"Call the preconfigured backend that matches the caller\n\n :param caller: The caller function or a branch indicating the function\n :return: The backend function\n \"\"\"\n\n if callable(caller):\n branch = '%s.%s' % (caller.__module__, caller.__name__)\n else:\n branch = getattr(sys.modules, caller, importlib.import_module(caller)).__name__ \\\n if isinstance(caller, str) else caller.__name__\n\n path = branch.split('.')\n depth, target = None, None\n\n # Locate the registered configuration ('interfaces.bind') in the same and parent packages\n for i in range(len(path) - 1, 0, -1):\n active_path = '.'.join(path[:i])\n module = getattr(sys.modules, active_path, importlib.import_module(active_path))\n\n if hasattr(module, '__interface'):\n depth, target = i, getattr(module, '__interface')\n\n return getattr(importlib.import_module('%s.%s.%s' % (target['package'].__name__, target['driver'],\n '.'.join(path[depth:-1]))), path[-1])\n" }, { "alpha_fraction": 0.719298243522644, "alphanum_fraction": 0.7368420958518982, "avg_line_length": 21.799999237060547, "blob_id": "6eab4aaf23f251fca32ce05e16511f54a0824486", "content_id": "3e5741a1f41c91c9873bde30987541ac222618d8", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 228, "license_type": "permissive", "max_line_length": 56, "num_lines": 10, "path": "/dist.sh", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\nrm -r build\nrm -r dist\nrm -r dorest.egg-info\n\npython3 -m pip install --user --upgrade setuptools wheel\npython3 setup.py sdist bdist_wheel\npython3 -m pip install --user --upgrade twine\npython3 -m twine upload dist/\n" }, { "alpha_fraction": 0.7727272510528564, "alphanum_fraction": 0.7727272510528564, "avg_line_length": 21, "blob_id": "acc21a461360a95ee8c807cf74ed6239a1af6610", "content_id": "bb31b1868b2123b69ea0a3716d622e7c5ed1ebc7", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 22, "license_type": "permissive", "max_line_length": 21, "num_lines": 1, "path": "/dorest/__init__.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "from . import verbose\n" }, { "alpha_fraction": 0.8055555820465088, "alphanum_fraction": 0.8055555820465088, "avg_line_length": 17, "blob_id": "1eac1a5e35ccecbb27a86a10dafc239fc586c15e", "content_id": "157fa43483489b9c6c07c5b065997cacbd64cd17", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 36, "license_type": "permissive", "max_line_length": 26, "num_lines": 2, "path": "/README.md", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "# dorest\nDjango Open REST Framework\n" }, { "alpha_fraction": 0.6558896899223328, "alphanum_fraction": 0.6573969125747681, "avg_line_length": 44.132652282714844, "blob_id": "231edea1fcf05cdf38f7a993eda50e394d0a3f8f", "content_id": "3bfde5ed5e7e90c9429dbcbbb8fffd24b5c12175", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 13269, "license_type": "permissive", "max_line_length": 151, "num_lines": 294, "path": "/dorest/struct.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Structured-endpoint manager\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nimport importlib\nimport inspect\nimport re\nimport os\nimport sys\nfrom functools import partial\nfrom typing import Any, Callable, List, Tuple, Type, Union\nfrom types import ModuleType\n\nfrom django.conf import settings\nfrom django.core.handlers.wsgi import WSGIRequest\nfrom django.urls import include, path, re_path, resolve\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.exceptions import MethodNotAllowed\nfrom rest_framework.permissions import AllowAny\nfrom rest_framework.response import Response\n\nfrom dorest.glossary import Glossary\nfrom dorest.meta import Endpoint\nfrom dorest import meta\n\n\ndef _get_module(module: Union[str, ModuleType]) -> ModuleType:\n return getattr(sys.modules, module, importlib.import_module(module)) if isinstance(module, str) else module\n\n\ndef _get_endpoint(method: str, branch: str, module: ModuleType = None) -> Tuple[Callable[..., Any], Union[Type, None]]:\n \"\"\"Resolve the target endpoint from an API request\n\n A branch, within tree-structure packages, may either include a module or an endpoint function as a leaf.\n\n This function first assumes that the branch only include hierarchical packages as its nodes and the target module as its leaf.\n In the 'try' clause, the function will try to look for any default endpoints that matches the request method;\n if not found, it will try to find redirect operators that matches the request method, then look for the endpoint within the redirected module.\n\n If the branch contains an endpoint function as a leaf, the 'importlib.import_module' will fail to import and skip to the 'except' clause.\n\n :param method: An endpoint may accept multiple HTTP methods, the 'method' parameter specifies which method to be looking for\n :param branch: A string that indicates a branch containing packages as nodes and a module, function, or class as a leaf (e.g. 'pkg_a.pkg_b.leaf')\n :param module: A direct reference to the module which contains the endpoint. If set, the function will skip 'importlib.import_module'\n :return: A tuple containing the target endpoint function and, if applicable, its class (current version only support endpoint functions)\n \"\"\"\n\n try:\n imported_module, endpoint_function = importlib.import_module(branch), None\n\n for func in [obj for obj in [getattr(imported_module, attr) for attr in dir(imported_module)]\n if hasattr(obj, 'meta') and getattr(obj, '__module__', None) == imported_module.__name__]:\n if func.meta['default']:\n return func, None\n endpoint_function = func\n\n if endpoint_function is not None:\n return endpoint_function, None\n\n else:\n obj = getattr(imported_module, Glossary.REDIRECT.value)[method.lower()]\n\n if inspect.isfunction(obj):\n return obj, None\n else:\n return _get_endpoint(method, getattr(imported_module, Glossary.REDIRECT.value)[method.lower()].__name__)\n\n except ModuleNotFoundError:\n path = branch.split('.')\n target_function = path[-1]\n imported_module = importlib.import_module('.'.join(path[:-1])) if module is None else module\n obj = getattr(imported_module, target_function, None)\n\n if inspect.isfunction(obj) and hasattr(obj, 'meta'):\n if method in obj.meta['methods']:\n return obj, None\n else:\n raise MethodNotAllowed(method)\n\n else:\n raise AttributeError(\"Could not find endpoint '%s' with HTTP request method '%s' in '%s'\" % (target_function, method, module))\n\n\ndef walk_endpoints(branch: str, reduce: bool = False) -> dict:\n \"\"\"Walk and generate descriptions of structured API endpoints\n\n The structure of the returned dictionary is as followed:\n ---\n {\n 'package': {\n 'subpackage': {\n 'module': {\n '': [ ... function descriptions ... ]\n }\n }\n }\n }\n ---\n\n In case the structural organization of an API results in some packages containing only one subpackage or module (other than the package's\n __init__.py), with the 'reduce' parameter true, all sequences of these packages will be merged in the returned dictionary.\n When reduced, the above example will become:\n ---\n {\n 'package/subpackage/module': {\n '': [ ... function descriptions ... ]\n }\n }\n ---\n\n :param branch: The root of structured API endpoints (or a part of interest within the structure)\n :param reduce: Reduce the returned dictionary\n :return: A description of the tree\n \"\"\"\n\n def attach_endpoint(sub_api_tree: dict, sub_branch: List[str], endpoint: callable) -> None:\n if len(sub_branch):\n sub_branch_head = sub_branch.pop(0)\n\n if sub_branch_head not in sub_api_tree:\n sub_api_tree[sub_branch_head] = dict()\n\n attach_endpoint(sub_api_tree[sub_branch_head], sub_branch, endpoint)\n\n else:\n if '' not in sub_api_tree:\n sub_api_tree[''] = [Endpoint(endpoint).rest(brief=True)]\n else:\n sub_api_tree[''].append(Endpoint(endpoint).rest(brief=True))\n\n root_module = importlib.import_module(branch)\n root_path = os.path.dirname(root_module.__file__)\n\n # Generate a list of all Python scripts\n py_files = [os.path.join(dirpath, filename) for dirpath, dirnames, filenames in os.walk(root_path) for filename in filenames\n if os.path.splitext(filename)[1] == '.py']\n\n # Generate a list of all modules for import from the list of Python scripts\n modules = [('%s.%s' % (branch, py_file.replace(os.path.dirname(root_module.__file__), '').replace('__init__', '').replace('.py', '')\n .replace('/', '.').strip('.'))).strip('.') for py_file in py_files]\n\n # Remove the root endpoint\n modules = [module for module in modules if module != branch]\n api_tree = dict()\n\n # Fill the API tree with descriptions of the functions\n for module in modules:\n sub_module = importlib.import_module(module)\n funcs = [obj for obj in [getattr(sub_module, attr) for attr in dir(sub_module)]\n if hasattr(obj, 'meta') and hasattr(obj, '__module__') and obj.__module__ == sub_module.__name__]\n sub_branch = module.replace(branch, '').strip('.')\n\n for func in funcs:\n attach_endpoint(api_tree, sub_branch.split('.'), func)\n\n return api_tree if not reduce else Endpoint.reduce(api_tree)\n\n\n@api_view(['DELETE', 'GET', 'PATCH', 'POST', 'PUT'])\n@permission_classes([AllowAny])\ndef _reply(request: WSGIRequest, topic: str, message: Union[dict, str], status: int) -> Response:\n \"\"\"Create Django REST Framework's Response object from a text message\n\n :param request: A request sent from Django REST Framework (despite not being used, this parameter is mandatory)\n :param topic: The topic of the message\n :param message: The message\n :param status: HTTP status code\n :return: Django REST Framework's Response object\n \"\"\"\n\n return Response({topic: message}, status=status)\n\n\ndef handle(request: WSGIRequest, root: Union[str, ModuleType]) -> Response:\n \"\"\"Handle a redirected request from Django REST Framework, call the target endpoint and gets the result, then create a wrapped response\n\n This function also provides special responses, which are either an API structure or a detailed description of specific endpoint function.\n To get an API structure, append '?' after a parent endpoint.\n\n For example, suppose an API is structured as followed:\n ~~~~~~~~~~~~~~~~~~~~~~~\n + parent_pkg\n \| + pkg_a\n \| \|- module_a.py\n \| \|- module_b.py\n \| + pkg_b\n \| \| + pkg_c\n \| \| \|- module_c.py\n ~~~~~~~~~~~~~~~~~~~~~~~\n and the root of the structured endpoints is 'http://domain/api/parent_pkg', which points to the 'parent_pkg' package,\n a GET request with URI 'http://domain/api/parent_pkg?' will return the description of the 'parent_pkg' package, including its\n subpackages, modules, and endpoint functions. 'http://domain/api/parent_pkg/pkg_a?' will return only those within the 'pkg_a' package.\n\n In case the structural organization of an API results in some packages containing only one subpackage or module (other than the package's\n __init__.py), appending the 'reduce' key to the request URI will merge all sequences of these packages. In the above example,\n 'http://domain/api/parent_pkg?&reduce' will return the API structure with 'pkg_b/pkg_c/module_c' keyword.\n\n To get a description of a particular endpoint function, append '?' to the URI, e.g., 'http://domain/api/parent_pkg/pkg_a/module_a/func?'\n\n :param request: A request sent from Django REST Framework\n :param root: The root package of the structured endpoints\n :return: Django REST Framework's Response object\n \"\"\"\n\n # 'request.GET' is a QueryDict. When appending '?' to the URI, '' becomes a dictionary key\n if '*' in request.GET:\n route = re.sub(r'\\.\\$', '', resolve(request.path_info).route)\n branch = ('%s.%s' % (root if isinstance(root, str) else root.__name__, re.sub(r'^/%s' % route, '', request.path).replace('/', '.'))).strip('.')\n return _reply(request, 'api', walk_endpoints(branch, 'reduce' in request.GET), status.HTTP_200_OK)\n\n else:\n route = re.sub(r'\\.\\$', '', resolve(request.path_info).route)\n branch = '%s.%s' % (root if isinstance(root, str) else root.__name__, re.sub(r'^/%s' % route, '', request.path).replace('/', '.'))\n\n # Given the request URI and method, try to find the target endpoint\n try:\n endpoint, request.META[Glossary.META_CLASS.value] = _get_endpoint(request.method, branch, root if isinstance(root, str) else None)\n request.META[Glossary.META_ENDPOINT.value] = meta.Endpoint(endpoint)\n except MethodNotAllowed as error:\n return _reply(request, 'detail', error.detail, status.HTTP_403_FORBIDDEN)\n except AttributeError as error:\n return _reply(request, 'detail', str(error), status.HTTP_403_FORBIDDEN)\n\n if '' in request.GET:\n return _reply(request, 'help', request.META[Glossary.META_ENDPOINT.value].rest(brief='brief' in request.GET), status.HTTP_200_OK)\n else:\n view = api_view([request.method])(endpoint)\n\n if hasattr(settings, 'REST_FRAMEWORK'):\n throttle_rates = settings.REST_FRAMEWORK.get('DEFAULT_THROTTLE_RATES', None)\n\n if throttle_rates is not None and getattr(endpoint, 'meta')['throttle'] in throttle_rates:\n view.view_class.throttle_custom_scope = getattr(endpoint, 'meta')['throttle']\n\n return view(request)\n\n\ndef redirect(, methods: List[str], at: Union[str, ModuleType], to: [str, ModuleType]) -> None:\n \"\"\"Redirect an API request to the target module containing endpoints\n\n To redirect an API request, call this function in the redirecting module\n Since the call is in the redirecting module, the 'at' parameter should normally be '__name__', e.g.:\n ---\n redirect(methods=['GET'], at=__name__, to='api.target.func')\n ---\n\n This function sets the redirect attribute of the module in which it is called.\n The attribute will later be evaluated and acted upon by the 'handle' function as it processes API requests.\n\n :param methods: HTTP methods that this redirect rule applies\n :param at: The redirecting module\n :param to: The target module\n :return: None\n \"\"\"\n\n caller, target = _get_module(at), _get_module(to)\n [setattr(caller, Glossary.REDIRECT.value, {getattr(caller, Glossary.REDIRECT.value, {}), {method.lower(): target}}) for method in methods]\n\n\ndef bind(package: Union[str, ModuleType], , to: Union[str, ModuleType], url: str = r'.') -> None:\n \"\"\"Bind a package of structured endpoints to a manager\n\n ...\n\n :param package: The package of structured endpoints\n :param to: The target module\n :param url: URL path to the target module\n :return: None\n \"\"\"\n\n pkg, anchor = _get_module(package), _get_module(to)\n setattr(anchor, 'urlpatterns',\n getattr(anchor, 'urlpatterns', []) + [re_path(url, csrf_exempt(partial(handle, root=pkg)))])\n\n\ndef extend(pattern: str, , at: Union[str, ModuleType], to: Union[str, ModuleType]) -> None:\n \"\"\"Extend 'urlpatterns' to handle requests other than those handled by Dorest managers\n\n The extended patterns are defined in a separate file indicated in the 'to' parameter.\n\n :param pattern: URL pattern according to Django URL handling specification\n :param at: The source module\n :param to: The extension module\n :return: None\n \"\"\"\n\n caller = _get_module(at)\n setattr(caller, 'urlpatterns', getattr(caller, 'urlpatterns', []) + [path(pattern, include(to))])\n" }, { "alpha_fraction": 0.5575234293937683, "alphanum_fraction": 0.5594582557678223, "avg_line_length": 40.73291778564453, "blob_id": "e6f7c11107f5a42cb067b5514d0c6ce5f35aeb58", "content_id": "47663fd3563c652c2185a45e8440e066e7d61954", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6719, "license_type": "permissive", "max_line_length": 120, "num_lines": 161, "path": "/dorest/packages.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Package manager\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nimport importlib\nimport sys\nimport os\nfrom functools import partial\nfrom typing import Union\nfrom types import ModuleType\n\nfrom django.core.handlers.wsgi import WSGIRequest\nfrom django.urls import re_path\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.permissions import AllowAny\nfrom rest_framework.response import Response\n\nfrom . import conf as _conf, struct as _struct, resources as dr_resources\n\nDEFAULT_STRUCTURE = {'struct': 'endpoints', 'resources': 'resources',\n 'conf': 'conf', 'private': 'private', 'templates': 'templates'}\n\n\ndef _get_module(module: Union[str, ModuleType]) -> ModuleType:\n return getattr(sys.modules, module, importlib.import_module(module)) if isinstance(module, str) else module\n\n\ndef bind(site: Union[str, ModuleType], , to: Union[str, ModuleType], url: str = None) -> None:\n def resolve_module(key: str, partial_path: Union[bool, str]) -> str:\n if partial_path is None:\n return '%s.%s' % (package.__name__.replace('.package', ''), DEFAULT_STRUCTURE[key].replace('/', '.'))\n else:\n return '%s.%s' % (package.__name__.replace('.package', ''), partial_path.replace('/', '.'))\n\n def resolve_path(key: str, package_patch: str, partial_path: Union[bool, str]) -> str:\n if partial_path is None:\n return '%s%s' % (package_patch, DEFAULT_STRUCTURE[key])\n else:\n return '%s%s' % (package_patch, partial_path)\n\n site, caller = _get_module(site), _get_module(to)\n target_dir = site.__file__.replace('__init__.py', '')\n\n packages = []\n package_root_dirs = []\n\n for root, dirs, files in os.walk(target_dir):\n if all(root not in prd for prd in package_root_dirs):\n if 'package.py' in files:\n package_root_dirs.append(root)\n\n for prd in package_root_dirs:\n root = prd.replace(target_dir, '').replace('/', '.')\n package = importlib.import_module('%s.%s.package' % (site.__name__, root))\n pkg_attr = getattr(package, '__package')\n packages.append(package)\n\n if not pkg_attr['bound']:\n for key in ('struct', 'resources'):\n pkg_attr[key] = resolve_module(key, pkg_attr[key])\n\n for key in ('conf', 'private', 'templates'):\n pkg_attr[key] = resolve_path(key, package.__file__.replace('package.py', ''), pkg_attr[key])\n\n pkg_attr['bound'] = True\n setattr(package, '__package', pkg_attr)\n setattr(package, '__conf', _conf.load_dir(pkg_attr['conf']))\n pkg_conf = getattr(package, '__conf')\n\n try:\n pkg_struct = importlib.import_module(pkg_attr['struct'])\n\n if pkg_conf['urls']['struct'] is None:\n _struct.bind(pkg_struct, to=caller, url='%s%s/' % ('' if url is None else '%s/' % url.strip('/'),\n pkg_conf['urls']['root'].strip('/')))\n else:\n _struct.bind(pkg_struct, to=caller, url='%s%s/%s/' % ('' if url is None else '%s/' % url.strip('/'),\n pkg_conf['urls']['root'].strip('/'),\n pkg_conf['urls']['struct'].strip('/')))\n except ModuleNotFoundError:\n pkg_struct = None\n\n if pkg_struct is not None:\n try:\n pkg_res = importlib.import_module(pkg_attr['resources'])\n dr_resources.bind(pkg_res, to=pkg_struct)\n except ModuleNotFoundError:\n pass\n \n key = pkg_conf['urls']['root'].strip('/') if 'urls' in pkg_conf and 'root' in pkg_conf['urls'] else root\n setattr(package, '__struct', {key: pkg_struct})\n \n setattr(caller, 'urlpatterns',\n getattr(caller, 'urlpatterns', []) + [re_path('%s$' % ('' if url is None else '%s/' % url.strip('/')),\n csrf_exempt(partial(walk, root=site)))])\n setattr(site, '__packages', packages)\n\n\ndef link(module: Union[str, ModuleType], , path: str = None, struct: str = None, resources: str = None,\n conf: str = None, private: str = None,\n templates: str = None) -> None:\n package = _get_module(module)\n\n if not hasattr(package, '__package'):\n setattr(package, '__package', {'bound': False,\n 'path': path, 'struct': struct, 'resources': resources,\n 'conf': conf, 'private': private, 'templates': templates})\n\n\ndef requires(module: Union[str, ModuleType], , permissions: Union[list, tuple] = None) -> None:\n package = _get_module(module)\n setattr(package, '__permissions', permissions)\n\n\ndef call(branch: str, , by: Union[str, ModuleType]) -> callable:\n caller = _get_module(by).__name__\n path = caller.split('.')\n root = None\n\n for i in range(len(path), 0, -1):\n try:\n package = importlib.import_module('%s.package' % '.'.join(path[:i]))\n \n if hasattr(package, '__package'):\n root = '.'.join(package.__name__.split('.')[:-2])\n\n except ModuleNotFoundError:\n continue\n \n if root is None:\n raise Exception(\"Function 'packages.call' must be called from within a package\")\n\n branch = branch.split('.')\n return getattr(importlib.import_module('%s.%s' % (root, '.'.join(branch[:-1]))), branch[-1])\n\n\n@api_view(['GET'])\n@permission_classes([AllowAny])\ndef walk(request: WSGIRequest, root: Union[str, ModuleType]) -> Response:\n site = _get_module(root)\n package_endpoints = {}\n\n for package in getattr(site, '__packages', []):\n permissions = getattr(package, '__permissions', [])\n\n if all(permission().has_permission(request, api_view()(walk)) for permission in permissions):\n for key, pkg_struct in getattr(package, '__struct', {}).items():\n package_endpoints[key] = _struct.walk_endpoints(pkg_struct.__name__, 'reduce' in request.GET)\n\n # print(request.user)\n # print(getattr(package, '__permissions'))\n # print(getattr(package, '__permissions')[0]().has_permission(request, api_view()(walk)))\n # print(api_view()(walk))\n\n return Response({'packages': package_endpoints}, status=status.HTTP_200_OK)\n" }, { "alpha_fraction": 0.6908817887306213, "alphanum_fraction": 0.6943888068199158, "avg_line_length": 28.791044235229492, "blob_id": "ef884dc3769528451c4e4081704b816d9bfd6271", "content_id": "dafe3f974df598639a6e425f816db2c276d658c8", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1996, "license_type": "permissive", "max_line_length": 105, "num_lines": 67, "path": "/dorest/permissions.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Implementations of additional Django REST Framework permission validators\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nfrom django.core.exceptions import ObjectDoesNotExist\n\nfrom rest_framework.permissions import BasePermission\n\n\nclass OR(BasePermission):\n \"\"\"'Or' operator for rest_framework's permission_classes\"\"\"\n\n def __init__(self, args):\n self.validators = args\n\n def __call__(self, args, *kwargs):\n return self\n\n def has_permission(self, request, view):\n return any(validator().has_permission(request, view) for validator in self.validators)\n\n\nclass NOT(BasePermission):\n \"\"\"'Not' operator for rest_framework's permission_classes\"\"\"\n\n def __init__(self, validator):\n self.validator = validator\n\n def __call__(self, args, kwargs):\n return self\n\n def has_permission(self, request, view):\n return not self.validator().has_permission(request, view)\n\n\nclass IsAccountOwner(BasePermission):\n \"\"\"Validate whether the target account in the request URL is owned by the user sending the request\"\"\"\n\n def has_permission(self, request, view, kwargs):\n try:\n return kwargs['username'] == request.user.username\n except ObjectDoesNotExist:\n return False\n\n\nclass IsAccountManager(BasePermission):\n \"\"\"Validate whether the account sending the request has the permission to manage other accounts\"\"\"\n\n def has_permission(self, request, view):\n return len(request.user.groups.filter(name='user_manager')) > 0\n\n\nclass IsDemoUser(BasePermission):\n \"\"\"Validate whether the account is for demonstration\"\"\"\n\n def has_permission(self, request, view):\n return len(request.user.groups.filter(name='user_demo')) > 0\n\n\nclass IsTrustedUser(BasePermission):\n \"\"\"Validate whether the account is trusted\"\"\"\n\n def has_permission(self, request, view):\n return len(request.user.groups.filter(name='user_trusted')) > 0\n" }, { "alpha_fraction": 0.6939759254455566, "alphanum_fraction": 0.7024096250534058, "avg_line_length": 26.66666603088379, "blob_id": "8fe6b8be0f17c96de8a0056c439286af09cfaa5e", "content_id": "eecc670f2b2ac4eae7f74337e8b5e55fefb1a78e", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 830, "license_type": "permissive", "max_line_length": 106, "num_lines": 30, "path": "/dorest/exceptions.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Exceptions for Dorest\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nfrom typing import List\n\nfrom django.utils.translation import gettext_lazy as _\n\nfrom rest_framework import status\nfrom rest_framework.exceptions import APIException\n\n\nclass ObjectNotFound(APIException):\n status_code = status.HTTP_404_NOT_FOUND\n default_detail = _('Object not found')\n default_code = 'object_not_found'\n\n\nclass UnsupportedFileTypeError(Exception):\n \"\"\"A configuration file must be in either YAML or JSON formats\"\"\"\n\n def __init__(self, path: str, supported_types: List[str]):\n self.path = path\n self.supported_types = supported_types\n\n def __str__(self):\n return \"Unsupported file type for '%s'. Accept: %s\" % (self.path, ', '.join(self.supported_types))\n" }, { "alpha_fraction": 0.6830247044563293, "alphanum_fraction": 0.6851851940155029, "avg_line_length": 41.07792282104492, "blob_id": "120bab6f047f63e095d375673c835868dc445c21", "content_id": "e5c8d6913512d1410cbb98b63ed26d8e6e2825f5", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3240, "license_type": "permissive", "max_line_length": 141, "num_lines": 77, "path": "/dorest/resources.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Structured API endpoints resource manager\n\nThis manager associates structured API endpoints with their resources, which must also be structured according to the API structure.\n\nFor example, suppose there is a function 'bar' in a module 'foo', which is located in 'api_root_pkg.subpkg' (the actual path would be\n'.../api_root_pkg/subpkg/foo.py').\n\nFunction 'bar' needs to access an input file 'input.txt'. To use this resource manager, the 'input.txt' must be stored as\n'.../res_root/subpkg/foo/bar/input.txt', with '.../res_root' registered as the root directory of the structured API endpoints.\n\nOnce 'input.txt' is stored in the directory, calling 'resolve' with 'bar' and 'input.txt' as its parameters will return the path to the file:\n---\n def bar(...):\n with open(resources.resolve(bar, 'input.txt), 'r') as file:\n ...\n---\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nimport importlib\nimport os\nimport sys\nfrom typing import Union\nfrom types import ModuleType\n\n\ndef _get_module(module: Union[str, ModuleType]) -> ModuleType:\n return getattr(sys.modules, module, importlib.import_module(module)) if isinstance(module, str) else module\n\n\ndef bind(package: Union[str, ModuleType], *, to: Union[str, ModuleType]) -> None:\n \"\"\"Bind a root resource directory to structured API endpoints\n\n In general, this function should be called in '__init__.py' of the root package of the structured API endpoints.\n The functions will then add '__resources' attribute to the module for the 'resolve' function to use as reference.\n\n :param package: The root package of the designated resources in Python's import format\n :param to: The root package of the structured API endpoints\n :return: None\n \"\"\"\n\n setattr(_get_module(to), '__resources', _get_module(package))\n\n\ndef resolve(obj: Union[callable, str, ModuleType], path: str = None) -> str:\n \"\"\"Resolve the path where resources corresponding to the given function or module are stored\n\n :param obj: The caller function or module\n :param path: A sub-path to the target resource (usually filename) within the resolved path\n :return: A path to the target resource\n \"\"\"\n\n if callable(obj):\n branch = '%s.%s' % (obj.__module__, obj.__name__)\n else:\n branch = getattr(sys.modules, obj, importlib.import_module(obj)).__name__ if isinstance(obj, str) else obj.__name__\n\n levels = branch.split('.')\n res_level, res_module = None, None\n\n # Find a reference to the root path of the resources registered using the 'register' function\n # starting from the lowest-level package of the 'obj' (function or module)\n for i in range(len(levels) - 1, 0, -1):\n sub_branch = '.'.join(levels[:i])\n attr = getattr(sys.modules, sub_branch, importlib.import_module(sub_branch))\n\n if hasattr(attr, '__resources'):\n res_level, res_module = i, getattr(attr, '__resources')\n\n if res_module is None:\n raise ModuleNotFoundError(\"Cannot locate resource directory of '%s'\" % str(obj))\n\n return '%s/%s%s' % (os.path.dirname(res_module.__file__), '/'.join(levels[res_level:]),\n '/%s' % path if path is not None else '')\n" }, { "alpha_fraction": 0.6778810024261475, "alphanum_fraction": 0.6806427240371704, "avg_line_length": 32.75423812866211, "blob_id": "c82099c24ffc126e0b0f9349cb2c9eb3d96e7b8a", "content_id": "8cdfa415c3f1296ab886b38241720305ebbf491c", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3983, "license_type": "permissive", "max_line_length": 124, "num_lines": 118, "path": "/dorest/conf.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Configuration manager\n\nThe 'resolve' function reads all YAML and JSON configurations in the directory specified in a Django project's setting file.\nThe function returns a dictionary of a tree with configuration file names as the first level.\n\nFor example, suppose there is a file named 'foo.yaml' with the following content:\n---\ngreet:\n message: Hello!\n---\nCalling 'configs.resolve(\"foo.greet\")' would return a dictionary '{message: \"Hello!\"}',\nand calling 'configs.resolve(\"foo.greet.message\")' would return a string \"Hello!\".\n\nThe path to the configuration directory is stored in 'DOREST[\"CONFIGS\"][\"PATH\"]' in Django project's setting file.\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nimport importlib\nimport json\nimport re\nimport sys\nfrom os import listdir\nfrom os.path import isfile, join\nfrom typing import Any, Dict, List, Union\nfrom types import ModuleType\n\nimport yaml\nfrom django.conf import settings\n\nfrom .exceptions import UnsupportedFileTypeError\n\n\nSUPPORTED_FILE_TYPES = ['yml', 'yaml', 'json']\n\n\ndef _get_module(module: Union[str, ModuleType]) -> ModuleType:\n return getattr(sys.modules, module, importlib.import_module(module)) if isinstance(module, str) else module\n\n\ndef _load(path: str) -> dict:\n \"\"\"Select appropriate loader (YAML or JSON) based on file type\n\n :param path: Path to the configuration file\n :return: An unprocessed configuration dictionary\n \"\"\"\n\n if re.search(r'\\.(yml\|yaml)$', path):\n return yaml.safe_load(open(path, 'r'))\n\n elif re.search(r'\\.json$', path):\n return json.load(path)\n\n else:\n raise UnsupportedFileTypeError(path, SUPPORTED_FILE_TYPES)\n\n\ndef _traverse(tree: Dict[str, Any], active_branch: List[str]) -> Any:\n \"\"\"Traverse the tree-structure configuration dictionary\n\n :param tree: The configuration dictionary\n :param active_branch: A list of node names in the branch of interest\n :return: A subtree or value of a leaf\n \"\"\"\n if len(active_branch) > 1:\n return _traverse(tree[active_branch[0]], active_branch[1:])\n else:\n return tree[active_branch[0]]\n\n\ndef _resolve_package(module: Union[str, ModuleType]) -> Any:\n package_branch = _get_module(module).__name__.split('.')\n pkg = None\n\n for i in range(len(package_branch) - 1, 0, -1):\n sub_branch = '.'.join(package_branch[:i])\n\n try:\n module = importlib.import_module('%s.package' % sub_branch)\n\n if hasattr(module, '__package'):\n pkg = module\n except ModuleNotFoundError:\n continue\n\n return getattr(pkg, '__conf')\n\n\ndef load_dir(path: str) -> dict:\n \"\"\"Load configurations from YAML or JSON files in a directory\n\n :param path: Path to the configuration directory\n :return: An unprocessed configuration dictionary\n \"\"\"\n return {re.sub(r'\\.(%s)$' % '\|'.join(SUPPORTED_FILE_TYPES), '', file): _load(join(path, file)) for file in listdir(path)\n if isfile(join(path, file)) and re.search(r'\\.(%s)$' % '\|'.join(SUPPORTED_FILE_TYPES), file)}\n\n\ndef resolve(branch: str = None, at: Union[str, ModuleType] = None) -> Any:\n \"\"\"Retrieve a subtree or value of specified node or leaf within the tree-structure configuration dictionary\n\n If 'package' is None then return the API's common configuration; otherwise, return the package's configuration\n\n :param at: A module (usually where the caller function belongs) which is part of a package\n In case the module contains the caller function, pass '__name__' to this argument\n :param branch: A branch to node or leaf of interest\n :return: A subtree or value of a leaf\n \"\"\"\n if at is None:\n return _traverse(_conf, branch.split('.')) if branch is not None else _conf\n else:\n pkg_conf = _resolve_package(at)\n return _traverse(pkg_conf, branch.split('.')) if branch is not None else pkg_conf\n\n\n_conf = load_dir(settings.DOREST['CONFIGS']['PATH'])\n" } ]	11
kpenar/LBBT-calculator	https://github.com/kpenar/LBBT-calculator	f08879e7990fb106f21ae43238d55b4b3287d603	38dde2e590b5484ff169919a86f9504ed87ea80b	f2b8f27a48efdcbb4a8b7415434e925e48b36b0a	refs/heads/main	2023-03-10T07:41:12.523969	2021-02-22T22:00:47	2021-02-22T22:00:47	341,349,045	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5413306355476379, "alphanum_fraction": 0.6381048560142517, "avg_line_length": 27.235294342041016, "blob_id": "cbde2ad7a95a5e2536217ee5f973bb9096e39d51", "content_id": "4f3d48b4e0762e6094174ad641b0156b2d941342", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 999, "license_type": "no_license", "max_line_length": 155, "num_lines": 34, "path": "/test.py", "repo_name": "kpenar/LBBT-calculator", "src_encoding": "UTF-8", "text": "import unittest\r\nimport sys\r\nfrom calculator import Calculator\r\n\r\n# Requirements\r\n# Minimum property purchase price\tMaximum property purchase price\tStamp Duty rate (only applies to the part of the property price falling within each band)\r\n# £0\t£250,000\t0%\r\n# £250,001\t£325,000\t5%\r\n# £325,001\t£750,000\t10%\r\n# Over £750,000\t \t12%\r\n\r\nclass TestCalc(unittest.TestCase):\r\n\r\n def testFirstBand(self):\r\n\r\n calcObj = Calculator(200000)\r\n self.assertEqual(calcObj.Calc(),0,\"Should be Zero\")\r\n\r\n def testSecondBand(self):\r\n \r\n calcObj = Calculator(275000)\r\n self.assertEqual(calcObj.Calc(),1250,\"Should be 1250\")\r\n \r\n def testThirdBand(self):\r\n \r\n calcObj = Calculator(500000)\r\n self.assertEqual(calcObj.Calc(),21250,\"Should be 21250\")\r\n \r\n def testForthBand(self):\r\n \r\n calcObj = Calculator(900000)\r\n self.assertEqual(calcObj.Calc(),64250,\"Should be 64250\")\r\nif __name__ == '__main__':\r\n unittest.main()" }, { "alpha_fraction": 0.5269121527671814, "alphanum_fraction": 0.5382436513900757, "avg_line_length": 17.61111068725586, "blob_id": "c68dd89c8a441c9557fe0d23e9078ad8b7e4c114", "content_id": "662fa280420e82b737572b3955b0cb146b3dd6a9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 353, "license_type": "no_license", "max_line_length": 59, "num_lines": 18, "path": "/main.py", "repo_name": "kpenar/LBBT-calculator", "src_encoding": "UTF-8", "text": "\r\n__author__ = \"Krzysztof Penar\"\r\n__version__ = \"0.1.0\"\r\n\r\n\r\nimport sys\r\nfrom calculator import Calculator\r\n\r\ndef main():\r\n for arg in sys.argv[1:]:\r\n priceArg = int(arg)\r\n calcObj = Calculator(priceArg)\r\n print(calcObj.Calc())\r\n \r\n\r\n\r\nif __name__ == \"__main__\":\r\n \"\"\" This is executed when run from the command line \"\"\"\r\n main()" }, { "alpha_fraction": 0.4039370119571686, "alphanum_fraction": 0.5110236406326294, "avg_line_length": 28.975608825683594, "blob_id": "5e6eff1ec297e55553c3607e8e95fca0f8459f7d", "content_id": "b19de549547d25c6c6d742d504a191e41d4ced6f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1277, "license_type": "no_license", "max_line_length": 155, "num_lines": 41, "path": "/calculator.py", "repo_name": "kpenar/LBBT-calculator", "src_encoding": "UTF-8", "text": "# Requirements\r\n# Minimum property purchase price\tMaximum property purchase price\tStamp Duty rate (only applies to the part of the property price falling within each band)\r\n# £0\t£250,000\t0%\r\n# £250,001\t£325,000\t5%\r\n# £325,001\t£750,000\t10%\r\n# Over £750,000\t \t12%\r\n\r\nclass Calculator:\r\n price = ()\r\n def __init__(self, price):\r\n self.price = price\r\n \r\n def Calc(self):\r\n firstBand = 0 \r\n secondBand = 0 \r\n thirdBand = 0 \r\n forthband = 0 \r\n #This is first band ciurrently zero \r\n if(self.price <= 250000):\r\n firstBand = 0\r\n\r\n if(self.price > 250000 ): \r\n if(self.price > 325000):\r\n secondBand = 75000 * (5/100)\r\n else:\r\n taxable = self.price - 250000\r\n secondBand = taxable * (5/100)\r\n\r\n if(self.price > 325000):\r\n if(self.price > 750000):\r\n thirdBand = 425000 * (10/100)\r\n else:\r\n taxable = self.price - 325000\r\n thirdBand = taxable * (10/100)\r\n\r\n if(self.price > 750000): \r\n taxable = self.price - 750000\r\n forthband = taxable * (12/100)\r\n\r\n \r\n return (firstBand + secondBand + thirdBand +forthband)\r\n" } ]	3
Brady31027/fb_chatbot_lyla_gallery	https://github.com/Brady31027/fb_chatbot_lyla_gallery	a5f3dffd0bcc698cffb5d51070896640fd36139d	d0029322a76e8ebd935cfb34ebd00265d15b48f5	426ed6e96e7520aec0704e94b79cc188855bde4b	refs/heads/master	2021-01-12T03:52:47.701604	2017-01-08T11:06:45	2017-01-08T11:06:45	78,279,934	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6428571343421936, "alphanum_fraction": 0.6428571343421936, "avg_line_length": 29.33333396911621, "blob_id": "a4f9e5588c1b94019ce37c589ad88c5c7e4bbbe2", "content_id": "80f98afa2b5ea497fe7d2c13d32a96c4af2140a9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 182, "license_type": "no_license", "max_line_length": 48, "num_lines": 6, "path": "/firebase/README.md", "repo_name": "Brady31027/fb_chatbot_lyla_gallery", "src_encoding": "UTF-8", "text": "[Frameworks] </br> \nhttps://github.com/ozgur/python-firebase </br> \n<br/> \n[Installation] </br> \nsudo pip install requests </br> \nsudo pip install python-firebase </br>\n" }, { "alpha_fraction": 0.7238636612892151, "alphanum_fraction": 0.730681836605072, "avg_line_length": 32.88461685180664, "blob_id": "4a2002634de5f47da7c732b32e99d2e5fdf3e167", "content_id": "046badd1c437981fecc6077d5824077f5fa9266d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 880, "license_type": "no_license", "max_line_length": 88, "num_lines": 26, "path": "/firebase/get.py", "repo_name": "Brady31027/fb_chatbot_lyla_gallery", "src_encoding": "UTF-8", "text": "from firebase import firebase\nfirebase = firebase.FirebaseApplication('https://lyla-gallery-db.firebaseio.com/', None)\n\n# followings are query samples\n\ntable_result = firebase.get('/gallery', None)\nfirst_data_result = firebase.get('/gallery/1', None)\nfirst_title_result = firebase.get('/gallery/1/title', None)\nfirst_subtitle_result = firebase.get('/gallery/1/subtitle', None)\nfirst_img_url_result = firebase.get('/gallery/1/img_url', None)\nfirst_story_result = firebase.get('/gallery/1/story', None)\n\n'''\nprint \"title: %s\"%(first_title_result)\nprint \"subtitle: %s\"%(first_subtitle_result)\nprint \"img url: %s\"%(first_img_url_result)\nprint \"story: %s\"%(first_story_result)\n'''\n\n# actually we don't have to query data level by level\n# use hirachical query by applying the second parameter\n# samples\nmultilevel_data = firebase.get('/gallery/1', 'title')\n'''\nprint multilevel_data\n'''" } ]	2
peterchen94/test_repo	https://github.com/peterchen94/test_repo	fc2554ace0ba4600eda20821c5aeaccc20ff10b8	76f9e6e8e7f13c41a294d996884a436f6e9c5175	01dcc5f8fae03963d8ad9133dd512f69381e9f4d	refs/heads/master	2018-11-01T02:20:23.179773	2018-07-28T17:59:08	2018-07-28T17:59:08	142,698,241	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5761957764625549, "alphanum_fraction": 0.6462736129760742, "avg_line_length": 19.428571701049805, "blob_id": "1350fd52ad0fae5f0299927b41dd1054ba047cf0", "content_id": "27cb5fcacf4948ef196cf63cd4836ad16c523328", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 899, "license_type": "no_license", "max_line_length": 76, "num_lines": 42, "path": "/breakout_peter.py", "repo_name": "peterchen94/test_repo", "src_encoding": "UTF-8", "text": "import pygame\nimport time\nSCREEN_SIZE = [640,480]\n\nBRICK_WIDTH = 60\nBRICK_HEIGHT = 15\nPADDLE_WIDTH = 60\nPADDLE_HEIGHT = 15\nBALL_DIAMETER = 12\nBALL_RADIUS = BALL_DIAMETER/2\n\nMAX_PADDLE_X = SCREEN_SIZE[0] - PADDLE_WIDTH\nMAX_BALL_X = SCREEN_SIZE[0] - BALL_DIAMETER\nMAX_BALL_Y = SCREEN_SIZE[1] - BALL_DIAMETER\n\nPADDLE_Y = SCREEN_SIZE[1] - PADDLE_HEIGHT - 8\n\nBLACK = (0,0,0)\nWHITE = (255,255,255)\nBLUE = (0,0,255)\nBRICK_COLOR = (200,200,0)\n\nSTATE_HIT_BALL= 0\nSTATE_PLAYING = 1\nSTATE_WON = 2\nSTATE_GAME_OVER = 3\n\n\n \nscreen = pygame.display.set_mode(SCREEN_SIZE)\npygame.display.set_caption('Breakout')\n\nclock = pygame.time.Clock()\n \nlives = 3\nscore = 0\nstate = 0\n \npaddle = pygame.Rect(300,PADDLE_Y,PADDLE_WIDTH,PADDLE_HEIGHT)\nball = pygame.Rect(300,PADDLE_Y - BALL_DIAMETER,BALL_DIAMETER,BALL_DIAMETER)\ntime.sleep(10)\nball_V = [5,-5] \n \n \n \n " }, { "alpha_fraction": 0.5218446850776672, "alphanum_fraction": 0.5740291476249695, "avg_line_length": 30.440000534057617, "blob_id": "ff3fda3a6072596a61a10a8a99b5591fa6ce26e9", "content_id": "35bead1521865af8653ad6b5dd3f3e225a4b0b30", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 824, "license_type": "no_license", "max_line_length": 101, "num_lines": 25, "path": "/breakout.py", "repo_name": "peterchen94/test_repo", "src_encoding": "UTF-8", "text": "from visual import \nimport math \nscene.range=10\nbricks = [box(pos=vector(i-5,9,0),lenght=1,height=1, width=0, color=color.white)for i in range(0,11)]\nball=sphere(color=color.cyan, pos=vector(0,0,0), radius=0.2)\nball.velocity = vector(0,-1,0)\ndelta_t= 0.01\npanel=box(pos=(0,-9,0), length=2, height=0.5, width=0, color=color.white)\npanel.velocity = vector(1,0,0)\n \n \nwhile True:\n rate(600)\n \n ball.pos= ball.pos + ball.velocitydelta_t\n if ball.y<10:\n ball.velocity.y=-ball.velocity.y\n if distance(ball.pos, panel.pos) <= 0.5:\n ball.velocity.y= -ball.velocity.y\n if scene.kb.keys:\n s=scene.kb.getkey()\n if s == 'right':\n panel.pos.x = panel.pos.x + 0.2\n if s == 'left':\n panel.pos.x = panel.pos.x - 0.2\n \n \n " }, { "alpha_fraction": 0.7586206793785095, "alphanum_fraction": 0.7586206793785095, "avg_line_length": 13.5, "blob_id": "28aa7e6adf867846dd045cc3df8db41ea2692bb9", "content_id": "97f22685c917963985df67f0f3e7ce012e45f068", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 29, "license_type": "no_license", "max_line_length": 16, "num_lines": 2, "path": "/README.md", "repo_name": "peterchen94/test_repo", "src_encoding": "UTF-8", "text": "# test_repo\nfor random stuff\n" } ]	3
wzx1997/ao_lai_shangcheng	https://github.com/wzx1997/ao_lai_shangcheng	136030fdad54dc545cbf421381a27f978f669971	c1224c2ad3ff86846abd86c5224dcf18d24782dd	25bfe13293f7eb839f54a5984be0c6950dd02211	refs/heads/master	2022-11-17T22:08:59.979799	2020-06-30T09:06:06	2020-06-30T09:06:06	275,747,512	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7174887657165527, "alphanum_fraction": 0.7174887657165527, "avg_line_length": 19.272727966308594, "blob_id": "0667aa11926e0dd3a3566d92ca64dd2ee7272dd5", "content_id": "ba68dfe1044c48c3eefd36066ba71a36924763b6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 223, "license_type": "no_license", "max_line_length": 46, "num_lines": 11, "path": "/page/main_page.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from selenium.webdriver.common.by import By\n\nfrom base.base_action import BaseAction\n\n\nclass MainPage(BaseAction):\n\n me_button=By.ID,\"com.yunmall.lc:id/tab_me\"\n\n def click_me(self):\n self.click(self.me_button)\n" }, { "alpha_fraction": 0.6738544702529907, "alphanum_fraction": 0.6738544702529907, "avg_line_length": 19.63888931274414, "blob_id": "9538e3d3f78a98d6161cda09242c10ea0b747684", "content_id": "775485c08af40beb9139768beb4c55f50d8c1241", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 742, "license_type": "no_license", "max_line_length": 41, "num_lines": 36, "path": "/page/page.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from page.about_page import AboutPage\nfrom page.login_page import LoginPage\nfrom page.main_page import MainPage\nfrom page.my_page import MyPage\nfrom page.setting_page import SettingPage\nfrom page.sing_in_page import SingInPage\n\n\nclass Page:\n\n def __init__(self,driver):\n self.driver=driver\n\n @property\n def main(self):\n return MainPage(self.driver)\n\n @property\n def login(self):\n return LoginPage(self.driver)\n\n @property\n def my(self):\n return MyPage(self.driver)\n\n @property\n def sing_in(self):\n return SingInPage(self.driver)\n\n @property\n def setting(self):\n return SettingPage(self.driver)\n\n @property\n def about(self):\n return AboutPage(self.driver)" }, { "alpha_fraction": 0.7029520273208618, "alphanum_fraction": 0.7029520273208618, "avg_line_length": 30.941177368164062, "blob_id": "3fe61c402162d38031bf30657f179a8435e0e17d", "content_id": "210c066b84d9f9dad833be161fdeb0ee4c1d6c47", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 542, "license_type": "no_license", "max_line_length": 70, "num_lines": 17, "path": "/page/sing_in_page.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from selenium.webdriver.common.by import By\n\nfrom base.base_action import BaseAction\n\nclass SingInPage(BaseAction):\n account_text = By.ID, \"com.yunmall.lc:id/logon_account_textview\"\n password_text = By.ID, \"com.yunmall.lc:id/logon_password_textview\"\n login_button = By.ID, \"com.yunmall.lc:id/logon_button\"\n\n def input_account(self, text):\n self.input(self.account_text, text)\n\n def input_password(self, text):\n self.input(self.password_text, text)\n\n def click_login(self):\n self.click(self.login_button)" }, { "alpha_fraction": 0.725321888923645, "alphanum_fraction": 0.729613721370697, "avg_line_length": 24.55555534362793, "blob_id": "d51173a50ffc0e66a411b71a1ff125508530b7ad", "content_id": "711883daf4e5c98cd8d7564317742a1ab2af1b43", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 233, "license_type": "no_license", "max_line_length": 50, "num_lines": 9, "path": "/page/login_page.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from selenium.webdriver.common.by import By\nfrom base.base_action import BaseAction\n\nclass LoginPage(BaseAction):\n\n login_text=By.ID,\"com.yunmall.lc:id/textView1\"\n\n def click_login(self):\n self.click(self.login_text) " }, { "alpha_fraction": 0.7647058963775635, "alphanum_fraction": 0.7647058963775635, "avg_line_length": 38, "blob_id": "a62220e5577010b72ab12b42dd301769f89f4bf7", "content_id": "ed975e2338152ad3a3d3b6494c076d712f3c5796", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 272, "license_type": "no_license", "max_line_length": 66, "num_lines": 7, "path": "/page/setting_page.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from selenium.webdriver.common.by import By\nfrom base.base_action import BaseAction\n\nclass SettingPage(BaseAction):\n version_button=By.ID,\"com.yunmall.lc:id/setting_about_yunmall\"\n def version(self):\n self.find_element_with_scroll(self.version_button).click()" }, { "alpha_fraction": 0.7227979302406311, "alphanum_fraction": 0.7227979302406311, "avg_line_length": 31.25, "blob_id": "a1dc87238bd56b7d0c487f55d92f119c68ca2f86", "content_id": "e5a9e84a7d81befaf0c5d898912f8e64ffa0b7dd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 386, "license_type": "no_license", "max_line_length": 70, "num_lines": 12, "path": "/page/my_page.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from selenium.webdriver.common.by import By\nfrom base.base_action import BaseAction\n\nclass MyPage(BaseAction):\n\n my_name_test=By.ID,\"com.yunmall.lc:id/tv_user_nikename\"\n setting_button=By.ID,\"com.yunmall.lc:id/ymtitlebar_left_btn_image\"\n\n def my_name(self):\n return self.find_element(self.my_name_test).text\n def setting(self):\n self.click(self.setting_button)" }, { "alpha_fraction": 0.6334792375564575, "alphanum_fraction": 0.6345732808113098, "avg_line_length": 28.516128540039062, "blob_id": "1f21f9a6888868761f57c60fd31d8309a6e84af7", "content_id": "8eab5560d67dab5571001bb93a242ae71a41417c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 914, "license_type": "no_license", "max_line_length": 82, "num_lines": 31, "path": "/scripts/test_singin.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "import time\nimport pytest\n\nfrom base.base_analyze import analyze_file\nfrom base.base_driver import init_driver\nfrom page.page import Page\n\n\nclass TestLogin:\n\n def setup(self):\n self.driver = init_driver(no_reset=False)\n self.page=Page(self.driver)\n def teardown(self):\n time.sleep(5)\n self.driver.quit()\n\n @pytest.mark.parametrize(\"args\", analyze_file(\"test_login.yaml\",\"test_login\"))\n def test_login(self,args):\n account=args[\"account\"]\n password=args[\"password\"]\n toast=args[\"toast\"]\n self.page.main.click_me()\n self.page.login.click_login()\n self.page.sing_in.input_account(account)\n self.page.sing_in.input_password(password)\n self.page.sing_in.click_login()\n if toast is None:\n assert self.page.my.my_name() == account\n else:\n assert self.page.sing_in.is_toast_exist(toast)" }, { "alpha_fraction": 0.6657754182815552, "alphanum_fraction": 0.6657754182815552, "avg_line_length": 27.846153259277344, "blob_id": "5890054ab12789304d64c0e90fe7171af97597ca", "content_id": "0427590731f6b33a306e0937db787a80e50d72c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 390, "license_type": "no_license", "max_line_length": 57, "num_lines": 13, "path": "/scripts/test_version.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from base.base_driver import init_driver\nfrom page.page import Page\n\n\nclass TestVersion:\n def teardown(self):\n self.driver=init_driver()\n self.page=Page(self.driver)\n def test_version(self):\n self.page.my.setting()\n self.page.setting.version()\n self.page.about.click_update()\n assert self.page.about.is_toast_exist(\"当前已是最新版本\")" } ]	8
markedwinharvey/file_walker	https://github.com/markedwinharvey/file_walker	4f31bd73695c47fa8e6ad32ba32564c2b3d79101	b32bd1f2fcfcc04bbc942c82e58848b39a820dcb	556064111c6ca638824463baddb2a6740e1f091d	refs/heads/master	2021-01-20T12:45:01.379182	2018-02-01T23:53:57	2018-02-01T23:53:57	62,860,549	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5469619035720825, "alphanum_fraction": 0.5566166639328003, "avg_line_length": 24.82655906677246, "blob_id": "fdfee903643c2206e939f6d71c441353c6c39c4f", "content_id": "865c00aaf3f18235f721968e29f27f98e076df8f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9529, "license_type": "no_license", "max_line_length": 139, "num_lines": 369, "path": "/filewalker/filewalker.py", "repo_name": "markedwinharvey/file_walker", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n'''\nfilewalker.py\n\nDepth-first filesystem traversal and logging\n\nfilewalker.py returns tuple of objects: \n\tfiles \t(node list)\n\tdirs \t(node list)\n\tftree \t(node tree)\n\n\nInstallation:\n \n\tpython setup.py install\n\n\nUsage:\n\n\t#!/usr/bin/env python\n\timport filewalker as fw\n\t\n\troot='/Users/CountChocula'\t\t#optional kwarg (default is current directory)\n\tmax_depth=None\t\t\t\t\t#optional kwarg (accepts integers; default is None; max_depth=0 scans only root)\n\tprint_all=False\t\t\t\t\t#optional kwarg; print files and messages (default is True)\n\n\tfiles, dirs, ftree = fw.walk( root=root, max_depth=max_depth, print_all=print_all )\n\n\nUse the returned data (output as tuple): \n\n\tfor f in files:\t\t\t\t\t\t\t\t#files is list of file nodes (in order of assessment)\n\t\tprint f.name, f.rel, f.abs, f.size\t\t#rel=relative path, abs=absolute path\n\t\t\n\tfor d in dirs:\t\t\t\t\t\t\t\t#dirs is list of dir nodes\n\t\tprint d.name, d.rel, d.abs, d.size\n\n\nInteractive Usage:\n\n\t$ python\n\t>>> import filewalker\n\t>>> filewalker.walk()\n\t\n\t>>> files, dirs, ftree = filewalker.walk()\n\t>>> ftree\n\t<filewalker.filewalker.file_tree instance at 0x10cf74c20>\n\t>>> dir(ftree.root)\n\t['__doc__', '__init__', '__module__', 'abs', 'children', 'depth', 'fsize', 'name', 'parent', 'rel', 'size', 'type']\n\t\n'''\n\nimport subprocess as sp\nimport os\nimport sys\nimport operator\n\n#--------------------------#\n#--- class declarations ---#\n#--------------------------#\n\nclass file_tree():\n\tdef __init__(self,root):\n\t\tself.root=root\n\nclass file():\n\tdef __init__(self, name, rel, abs, size, parent, depth):\n\t\tself.name\t= name\n\t\tself.rel\t= rel\n\t\tself.abs\t= abs\n\t\tself.size\t= size\n\t\tself.parent\t= parent\n\t\tself.depth\t= depth\n\t\tself.type\t= 'f'\n\t\tself.fsize\t= format_size(size)\n\t\tself.fsize0\t= self.fsize.split(' ')[0]\n\t\tself.fsize1 = self.fsize.split(' ')[1]\n\nclass dir():\n\tdef __init__(self, name, rel, abs, size, parent, depth):\n\t\tself.name\t\t= name\n\t\tself.rel\t\t= rel\n\t\tself.abs\t\t= abs\n\t\tself.children\t= []\n\t\tself.size \t\t= size\n\t\tself.parent\t\t= parent\n\t\tself.depth\t\t= depth\n\t\tself.type\t\t= 'd'\n\t\tself.fsize\t\t= '0 B'\n\t\tself.fsize0 \t= self.fsize.split(' ')[0]\n\t\tself.fsize1 \t= self.fsize.split(' ')[1]\n\nclass _error():\n\tdef __init__(self, node_path, error):\n\t\tself.node_path = node_path\n\t\tself.error = error\n\n\n#-------------------------#\n#---- misc. functions ----#\n#-------------------------#\n\ndef exit():\n\tprint; print 'Exiting...'; print\n\tsys.exit()\n\n\ndef ps(msg):\n\t''' print messages if print_all == True '''\n\tif print_all:\n\t\tprint msg\n\n\ndef get_root(kwargs):\n\t'''confirm valid path for kwarg 'root'; if unspecified, use root = cwd '''\n\t\n\troot = kwargs.get('root')\n\tif not root:\n\t\troot = sp.Popen(['pwd'],stdout=sp.PIPE).communicate()[0].replace('\\n','')\n\telse:\n\t\tif not os.path.exists(root):\n\t\t\tps('not a viable path')\n\t\t\texit()\n\treturn os.path.abspath(root)\n\n\ndef get_max_depth(kwargs):\n\tmax_depth = kwargs.get('max_depth')\n\tif max_depth is not None:\n\t\ttry:\n\t\t\tmax_depth = int(max_depth)\n\t\texcept:\n\t\t\tps('invalid depth')\n\t\t\texit()\n\t\tif max_depth < 0:\n\t\t\tmax_depth = 0\n\treturn max_depth\n\n\ndef format_size(num):\n\t''' Return formatted file size of input num as string; e.g., 14156348 --> 14.1 MB'''\n\trnum = str(int(num))[::-1]\n\tslist = zip( [rnum[x:x+3][::-1] for x in range(0,len(rnum), 3) ] , 'B KB MB GB TB'.split(' ') )\n\tsize = slist[-1][0]\n\tif len(slist) > 1:\n\t\tsize += '.' + slist[-2][0][0]\n\treturn '%s %s' % (size, slist[-1][1])\n\n\n#------------------------#\n#------ FILEWALKER ------#\n#------------------------#\ndef walk(kwargs):\n\t'''accept kwargs: \n\t\t'root'\t\t\t(default: current dir)\n\t\t'max_depth' \t(default: None [i.e., traverse entire tree]; accepts integers)\n\t\t'print_all' \t(default: True; print all messages to stdout)\n\t\n\tRecursively walk a file hierarchy, depth-first. \n\tEach file/dir encountered becomes a node added to the file_tree, \n\t\tand appended to the respective `files` list or `dirs` list\n\tAll nodes are accessible from successive `children` lists starting at the root node. \n\t\n\t'''\t\t\n\tglobal print_all\n\tprint_all = False if kwargs.get('print_all') == False else True\n\t\n\n\tps('#--------------------------------#\\n#-------- filewalker.py ---------#\\n#--------------------------------#')\n\t\n\t#----------------------------#\n\t#---initialize some things---#\n\t#----------------------------#\n\tcurr_depth = 0\n\tall_file_list = []\n\tall_dir_list = []\n\tall_error_list = []\n\tcurr_node_list = []\n\tlength_dict = {}\n\t\n\t\n\t\n\troot = get_root(kwargs)\n\tmax_depth = get_max_depth(*kwargs)\n\t\n\tnew_node = dir(\t\t\t#make root node\n\t\tname\t= root.split('/')[-1],\n\t\trel\t\t= '',\n\t\tabs\t\t= root,\n\t\tsize\t= 0,\n\t\tparent \t= '',\n\t\tdepth\t= -1\n\t)\n\t\n\tf_tree = file_tree(new_node)\n\tcurr_node_list.append(new_node)\t\n\t\n\tps('Scanning: %s' % root)\n\tif (kwargs):\n\t\tps(' keyword args: %s' % ', '.join([x+': '+str(kwargs[x]) for x in kwargs]) )\n\tps(root.split('/')[-1]+' (root dir)')\n\t\n\t\n\t#----------------------------#\n\t#------ begin recursion -----#\n\t#----------------------------#\n\t\n\tdef do_walk(curr_depth, curr_node_list):\n\t\t\n\t\tthis_dir_path = curr_node_list[-1].abs\n\t\t\n\t\tcmd = 'ls \"%s\"\t' % str(this_dir_path)\n\t\tfolder_contents = [x for x in sp.Popen([cmd],stdout=sp.PIPE,shell=True).communicate()[0].split('\\n') if x]\n\t\t\n\t\tif not folder_contents:\n\t\t\tcurr_node_list.pop()\n\t\t\treturn curr_depth - 1, curr_node_list\n\t\t\n\t\tfile_size_sum = 0\n\t\t\n\t\tfor this_node in folder_contents:\n\t\t\t\n\t\t\tthis_node_path = os.path.abspath(os.path.join(this_dir_path,this_node))\n\t\t\t\n\t\t\tthis_node_rel_path = this_node_path[len(root)+1:]\t#convert to rel path\n\t\t\t\t\n\t\t\tif not os.path.isdir(this_node_path):\t\t\n\t\t\t\t#------------------------#\n\t\t\t\t#----- node is file -----#\n\t\t\t\t#------------------------#\n\t\t\t\t\n\t\t\t\ttry:\n\t\t\t\t\tsize = os.path.getsize(this_node_path)\n\t\t\t\texcept Exception as e:\n\t\t\t\t\tprint 'Warning: found error\\n\\t%s' % e\n\t\t\t\t\tnew_error = _error(this_node_path, e)\n\t\t\t\t\tall_error_list.append(new_error)\n\t\t\t\t\tcontinue\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\tnew_node = file(\n\t\t\t\t\tname\t= this_node,\n\t\t\t\t\trel\t\t= this_node_rel_path,\n\t\t\t\t\tabs\t\t= this_node_path,\n\t\t\t\t\tsize\t= size,\n\t\t\t\t\tparent \t= curr_node_list[-1],\n\t\t\t\t\tdepth \t= curr_depth\n\t\t\t\t)\n\t\t\t\tall_file_list.append(new_node)\n\t\t\n\t\t\t\tcurr_node_list[-1].children.append(new_node)\n\t\t\t\t\n\t\t\t\t#msg = ' 'curr_depth+'\|'+this_node\n\t\t\t\t#ps( msg + (30-len(msg))' '+ ' f_ (%s %s)' % ( new_node.fsize0+' '(5-len(new_node.fsize0)) , new_node.fsize1) )\n\t\t\t\t\n\t\t\t\tfile_size_sum += size\n\t\t\t\t\n\t\t\telse:\t\t\t\t\t\t\t\t\t\n\t\t\t\t#------------------------#\n\t\t\t\t#----- node is dir ------#\n\t\t\t\t#------------------------#\n\t\t\t\t\t\t\t\n\t\t\t\tnew_node = dir(\n\t\t\t\t\tname\t= this_node,\n\t\t\t\t\trel\t\t= this_node_rel_path,\n\t\t\t\t\tabs\t\t= this_node_path,\n\t\t\t\t\tsize\t= 0,\n\t\t\t\t\tparent\t= curr_node_list[-1],\n\t\t\t\t\tdepth \t= curr_depth\n\t\t\t\t)\n\t\t\t\tall_dir_list.append(new_node)\n\t\t\t\t\n\t\t\t\tcurr_node_list[-1].children.append(new_node)\n\t\t\t\t\n\t\t\t\tmsg = ' 'curr_depth+'\|'+this_node\n\t\t\t\tps(msg+(30-len(msg))' '+'_d')\n\t\t\t\t\n\t\t\t\tif max_depth is None or curr_depth < max_depth:\n\t\t\t\t\t'''recurse through directories'''\n\t\t\t\t\tcurr_depth+=1\n\t\t\t\t\tcurr_node_list.append(new_node)\n\t\t\t\t\tcurr_depth, curr_node_list = do_walk(curr_depth, curr_node_list)\t\t\t\t\t\n\t\t\t\telse:\n\t\t\t\t\n\t\t\t\t\treturn curr_depth -1, curr_node_list\n\t\telse:\t\n\t\t\t'''contents of folder explored; calculate size of total contents'''\n\t\t\tcontents_size = 0\t\t\t\n\t\t\tfor child in curr_node_list[-1].children:\n\t\t\t\tcontents_size += child.size\n\t\t\t\tnlength = len(child.name)\n\t\t\t\tif nlength in length_dict.keys():\n\t\t\t\t\tlength_dict[nlength] += 1\t\t\t\t\n\t\t\t\telse:\n\t\t\t\t\tlength_dict[nlength] = 1\n\t\t\tcurr_node_list[-1].size = contents_size\n\t\t\tfsize = format_size(contents_size)\n\t\t\tcurr_node_list[-1].fsize = fsize\n\t\t\tcurr_node_list[-1].fsize0 = fsize.split(' ')[0]\n\t\t\tcurr_node_list[-1].fsize1 = fsize.split(' ')[1]\n\t\t\tcurr_node_list.pop()\n\t\t\n\t\treturn curr_depth - 1, curr_node_list\n\tcurr_depth, curr_node_list = do_walk(curr_depth, curr_node_list)\n\t\n\t\n\tmaxlength = max(length_dict.keys())\n\t\n\t\n\tif print_all:\n\t\tprint\n\t\tprint 'File tree with contents sizes:'\n\t\tprint '------------------------------'\n\t\tprint f_tree.root.name + ' (root)'\n\t\tdef post_walk(node):\n\t\t\tfor child in node.children:\n\t\t\t\tspacer = (' ' if child.type == 'f' else '-')(maxlength - len(child.name))\n\t\t\t\tprint '%s\|%s %s \|%s %s%s %s' % (' 'child.depth, child.name, spacer, child.type, ' '*(5-len(child.fsize0)),child.fsize0, child.fsize1 )\n\t\t\t\tif child.type == 'd':\n\t\t\t\t\tpost_walk(child)\n\t\t\n\t\tpost_walk(f_tree.root)\n\t\n\t\n\t#-----------------------------------------#\n\t#----- print largest dirs and files ------#\n\t#-----------------------------------------#\n\t\n\tprint '#------ Error list: ------#'\n\tfor error in all_error_list:\n\t\tprint 'Error on \\\"%s\\\": %s' % (error.node_path, error.error)\n\tprint '#-------------------------#'\n\tprint\n\t\n\tprint '#----- Largest dirs: -----#'\n\tlarge_dirs = [x for x in sorted(all_dir_list,key=operator.attrgetter('size'))[::-1]][:10]\n\tfor d in large_dirs:\n\t\tprint d.abs\n\t\tprint ' '+d.fsize\n\tprint '#-------------------------#'\t\n\tprint\n\t\n\tprint '#----- Largest files: -----#'\n\tlarge_files = [x for x in sorted(all_file_list,key=operator.attrgetter('size'))[::-1]][:10]\n\tfor f in large_files:\n\t\tprint f.abs\n\t\tprint ' '+f.fsize\n\tprint '#-------------------------#'\n\t\n\tprint 'Total directories: %d' \t% len( all_dir_list )\n\tprint 'Total files: %d' \t\t% len( all_file_list ) \n\t\n\ttfs = sum([ x.size for x in all_file_list ])\t# tfs = total file(s) size\n\tfsize = format_size(tfs)\n\t\n\tprint 'Total file(s) size: %s' % fsize\n\t\n\t#------------------------------#\n\t#--- output from filewalker\t---#\n\t#------------------------------#\n\treturn all_file_list, all_dir_list, f_tree\n\t\n\n\ndef main():\n\twalk()\nif __name__ == '__main__':\n\tmain()" }, { "alpha_fraction": 0.666132926940918, "alphanum_fraction": 0.6757405996322632, "avg_line_length": 24.510204315185547, "blob_id": "83b67b27526bbb71c202c2380b86eed26207710a", "content_id": "2ec6c8820ac1f7f9f7af2bb6de20985a748ba22c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1249, "license_type": "no_license", "max_line_length": 117, "num_lines": 49, "path": "/readme.md", "repo_name": "markedwinharvey/file_walker", "src_encoding": "UTF-8", "text": "<h4>filewalker</h4>\n\n<h5>filewalker.py</h5>\n\nDepth-first filesystem traversal and logging\n\nfilewalker.py returns tuple of objects: \n\tfiles \t(node list)\n\tdirs \t(node list)\n\tftree \t(node tree)\n\n\nInstallation:\n \n\tpython setup.py install\n\n\nUsage:\n\n\t#!/usr/bin/env python\n\timport filewalker as fw\n\t\n\troot='/Users/CountChocula'\t\t#optional kwarg (default is current directory)\n\tmax_depth=None\t\t\t\t\t#optional kwarg (accepts integers; default is None; max_depth=0 scans only root)\n\tprint_all=False\t\t\t\t\t#optional kwarg; print files and messages (default is True)\n\n\tfiles, dirs, ftree = fw.walk( root=root, max_depth=max_depth, print_all=print_all )\n\n\nUse the returned data (output as tuple): \n\n\tfor f in files:\t\t\t\t\t\t\t\t#files is list of file nodes (in order of assessment)\n\t\tprint f.name, f.rel, f.abs, f.size\t\t#rel=relative path, abs=absolute path\n\t\t\n\tfor d in dirs:\t\t\t\t\t\t\t\t#dirs is list of dir nodes\n\t\tprint d.name, d.rel, d.abs, d.size\n\n\nInteractive Usage:\n\n\t$ python\n\t>>> import filewalker as fw\n\t>>> fw.walk()\n\t\n\t>>> files, dirs, ftree = fw.walk()\n\t>>> ftree\n\t<filewalker.filewalker.file_tree instance at 0x10cf74c20>\n\t>>> dir(ftree.root)\n\t['__doc__', '__init__', '__module__', 'abs', 'children', 'depth', 'fsize', 'name', 'parent', 'rel', 'size', 'type']`" }, { "alpha_fraction": 0.6234309673309326, "alphanum_fraction": 0.6317991614341736, "avg_line_length": 16.14285659790039, "blob_id": "dd6432e97d2a235b84d2e9c17d5f503823873b02", "content_id": "1624719152669be2d5e500cc055e72d15f071fe6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 239, "license_type": "no_license", "max_line_length": 46, "num_lines": 14, "path": "/setup.py", "repo_name": "markedwinharvey/file_walker", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\nfrom setuptools import setup\ndef main():\n\n\tsetup(\n\t\tname='filewalker',\n\t\tversion='0.1',\n\t\tdescription='walk and log a file hierarchy',\n\t\tpackages=['filewalker'],\n\t\tauthor='meh',\t\n\t)\n\nif __name__ == '__main__':\n\tmain()" } ]	3
chandrimahere/KNearestNeighbour-Classifier	https://github.com/chandrimahere/KNearestNeighbour-Classifier	eae9f4abc25497f040f7d2f487f68bb1abcaf5b0	b1b68bf24d72ecb349f237583e43c6fbe1433888	e05fbb52ec276998c4144f4fb9ed91c316f918d9	refs/heads/master	2020-08-22T10:34:57.255241	2019-11-03T19:25:48	2019-11-03T19:25:48	216,375,555	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.48393890261650085, "alphanum_fraction": 0.49289098381996155, "avg_line_length": 28.10769271850586, "blob_id": "e079fa62c8a658806b82122e997aaa8457728387", "content_id": "22d87d217bd08b25357eac7e1fdf5dee6104a31c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1899, "license_type": "no_license", "max_line_length": 95, "num_lines": 65, "path": "/Weightedknn.py", "repo_name": "chandrimahere/KNearestNeighbour-Classifier", "src_encoding": "UTF-8", "text": "import operator\nfrom collections import Counter\n\nimport numpy as np\n\n\nclass KNearestNeighbors:\n def __init__(self,k,weights):\n self.k=k\n self.result=[]\n self.result_new=[]\n self.weights=weights\n\n\n def fit(self,X_train,y_train):\n self.X_train=X_train\n self.y_train=y_train\n print(\"Training Done\")\n\n def predict(self,X_test):\n for j in X_test:\n distance={}\n new_dist={}\n counter=1\n for i in self.X_train:\n c=0\n for k in range(len(j)):\n c=c+(j[k]-i[k])2\n if self.weights == \"uniform\":\n distance[counter] = c 1 / 2\n elif self.weights == \"distance\":\n new_dist[counter] = 1 /(c ** 1 / 2)\n counter=counter+1\n if self.weights == \"uniform\":\n distance = sorted(distance.items(), key=operator.itemgetter(1))\n self.result.append(self.classify(distance[:self.k]))\n\n\n\n elif self.weights == \"distance\":\n new_dist = sorted(new_dist.items(), key=operator.itemgetter(1), reverse=True)\n self.result_new.append(self.classify_new(new_dist[:self.k]))\n\n\n if self.weights == \"uniform\":\n return self.result\n elif self.weights == \"distance\":\n return self.result_new\n def classify(self,distance):\n label=[]\n for i in distance:\n label.append(self.y_train[i[0]])\n\n return Counter(label).most_common()[0][0]\n def classify_new(self,distance):\n y = np.unique(self.y_train)\n [s]=len(y)\n for i in range(len(y)):\n sum = 0\n for j in distance:\n if y[i] == self.y_train[i[0]]:\n sum=sum+i[1]\n s[i] = sum\n\n return y[max(s)]\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.7468926310539246, "alphanum_fraction": 0.7638418078422546, "avg_line_length": 28.16666603088379, "blob_id": "1e50b075b9f488d855f55454ba3d90c33afeb650", "content_id": "4ff0c90776ffe8b072f7c36abe99e6f6ad611376", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 885, "license_type": "no_license", "max_line_length": 66, "num_lines": 30, "path": "/knn_test2.py", "repo_name": "chandrimahere/KNearestNeighbour-Classifier", "src_encoding": "UTF-8", "text": "import numpy as np\nimport pandas as pd\nfrom KNearestRegressor import KNearestRegressors\nfrom sklearn.metrics import r2_score\nfrom sklearn.metrics import accuracy_score\n\n\n#taking inputs\ndata=pd.read_csv('Social_Network_Ads.csv')\nX=data.iloc[:,2:4].values\ny=data.iloc[:,-1].values\n#using train_test_split function\nfrom sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n\n#using StandardScaler function\nfrom sklearn.preprocessing import StandardScaler\nscaler=StandardScaler()\nX_train=scaler.fit_transform(X_train)\nX_test=scaler.transform(X_test)\n\n#an object of knn\nknn=KNearestRegressors(k=5)\nknn.fit(X_train,y_train)\n\n#using the predict() function\nknn.predict(np.array(X_test).reshape(len(X_test), len(X_test[0])))\n\nprint(round((r2_score(y_test, y_pred) * 100)))\nprint(round((accuracy_score(y_test,y_pred)100)))\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.6991221308708191, "alphanum_fraction": 0.7102953195571899, "avg_line_length": 26.15217399597168, "blob_id": "d4f3be2dcd4119250de9d3babde200b296f829ab", "content_id": "36f9df3dacb136ffe497cc3f1967b10fa19f53c8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1253, "license_type": "no_license", "max_line_length": 84, "num_lines": 46, "path": "/knn_test.py", "repo_name": "chandrimahere/KNearestNeighbour-Classifier", "src_encoding": "UTF-8", "text": "import numpy as np\nimport pandas as pd\nfrom KNearestNeighbor import KNearestNeighbors\n\n#taking inputs\ndata=pd.read_csv('Social_Network_Ads.csv')\ndata['Gender'] = data['Gender'].replace({'Male': 0, 'Female': 1})\nX=data.iloc[:,1:4].values\ny=data.iloc[:,-1].values\n\nprint(X.shape)\nprint(y.shape)\n\n#using train_test_split function\nfrom sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n\n#using StandardScaler function\nfrom sklearn.preprocessing import StandardScaler\nscaler=StandardScaler()\nX_train=scaler.fit_transform(X_train)\nX_test=scaler.transform(X_test)\n\n#an object of knn\nknn=KNearestNeighbors(k=5)\nknn.fit(X_train,y_train)\n\n#using the predict() function\nknn.predict(np.array(X_test).reshape(len(X_test), len(X_test[0])))\n\n#defining a function to check the output\ndef predict_new():\n age=int(input(\"Enter the age\"))\n salary=int(input(\"Enter the salary\"))\n gender = int(input(\"Enter the gender,type '0' for Male or type '1' for female\"))\n X_new=np.array([[age],[gender],[salary]]).reshape(1,3)\n X_new=scaler.transform(X_new)\n result=knn.predict(X_new)\n if result==0:\n print(\"Will not purchase\")\n else:\n print(\"Will purchase\")\n\n\n\npredict_new()\n\n\n\n\n" }, { "alpha_fraction": 0.5216919779777527, "alphanum_fraction": 0.5325379371643066, "avg_line_length": 25.81818199157715, "blob_id": "93de65550af6d09caca981977cb685a06cc385ce", "content_id": "f0be2cc8351bee396d9fc8293c7d1b94aac7dad8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 922, "license_type": "no_license", "max_line_length": 69, "num_lines": 33, "path": "/KNearestNeighbor.py", "repo_name": "chandrimahere/KNearestNeighbour-Classifier", "src_encoding": "UTF-8", "text": "import operator\r\nfrom collections import Counter\r\n\r\nclass KNearestNeighbors:\r\n def __init__(self,k):\r\n self.k=k\r\n self.result=[]\r\n\r\n\r\n def fit(self,X_train,y_train):\r\n self.X_train=X_train\r\n self.y_train=y_train\r\n print(\"Training Done\")\r\n\r\n def predict(self,X_test):\r\n for j in X_test:\r\n distance={}\r\n counter=1\r\n for i in self.X_train:\r\n c=0\r\n for k in range(len(j)):\r\n c=c+(j[k]-i[k])2\r\n distance[counter]=c*1/2\r\n counter=counter+1\r\n distance=sorted(distance.items(),key=operator.itemgetter(1))\r\n self.result.append(self.classify(distance=distance[:self.k]))\r\n return self.result\r\n def classify(self,distance):\r\n label=[]\r\n for i in distance:\r\n label.append(self.y_train[i[0]])\r\n\r\n return Counter(label).most_common()[0][0]\r\n\r\n\r\n" }, { "alpha_fraction": 0.8399999737739563, "alphanum_fraction": 0.8399999737739563, "avg_line_length": 61.5, "blob_id": "fe2c0078c93ad36fec31a0c4bb0f396b246077b0", "content_id": "410bc40ed2bf2c509958453992eb6bb8e764a7ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 125, "license_type": "no_license", "max_line_length": 93, "num_lines": 2, "path": "/README.md", "repo_name": "chandrimahere/KNearestNeighbour-Classifier", "src_encoding": "UTF-8", "text": "# KNearestNeighbour-Classifier\nThis code depicts how the knn algorithm works and also shows the decision boundary condition.\n" } ]	5
declarativesystems/pipelines_example_pythonenv	https://github.com/declarativesystems/pipelines_example_pythonenv	87f0f80774e3c43256ceca2c0e6dff1a9c6ffc34	968e7b946b14d56a75e8d604d5fc737c2b466468	ff8679f7a0b8dafdc160f4fc341280859ec00297	refs/heads/master	2023-03-28T16:38:31.835973	2021-04-08T01:49:31	2021-04-08T01:49:31	340,528,955	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6988322138786316, "alphanum_fraction": 0.7062077522277832, "avg_line_length": 35.155555725097656, "blob_id": "d09cf438e3bbd473bf306a65b3b85355f19921e4", "content_id": "da2934a9cfacb71a0f37083f49a5b7ed6baaea63", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1627, "license_type": "no_license", "max_line_length": 94, "num_lines": 45, "path": "/setup.py", "repo_name": "declarativesystems/pipelines_example_pythonenv", "src_encoding": "UTF-8", "text": "# Copyright 2021 Declarative Systems Pty Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport setuptools\nimport pipelines_example_pythonenv.version\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"pipelines_example_pythonenv\",\n version=pipelines_example_pythonenv.version.get_version(),\n author=\"Geoff Williams\",\n author_email=\"[email protected]\",\n description=\"example pip packaging\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/declarativesystems/pipelines_example_pythonenv\",\n packages=setuptools.find_packages(),\n # pick from https://pypi.org/classifiers/\n classifiers=[\n \"Development Status :: 2 - Pre-Alpha\",\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Operating System :: OS Independent\",\n ],\n python_requires='>=3.6',\n entry_points={\n \"console_scripts\": (['pipelines_example_pythonenv=pipelines_example_pythonenv:main'],)\n },\n include_package_data=True,\n install_requires=[\n ]\n)\n" }, { "alpha_fraction": 0.4803921580314636, "alphanum_fraction": 0.7009803652763367, "avg_line_length": 15.319999694824219, "blob_id": "a3160d9c23f5b90cde981d901273c02777cb4709", "content_id": "47d599d0bd843181eb892dc1b71cb26ee33692e4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 408, "license_type": "no_license", "max_line_length": 24, "num_lines": 25, "path": "/requirements.txt", "repo_name": "declarativesystems/pipelines_example_pythonenv", "src_encoding": "UTF-8", "text": "bleach==3.3.0\ncertifi==2020.12.5\ncffi==1.14.5\nchardet==4.0.0\ncolorama==0.4.4\ncryptography==3.4.6\ndocutils==0.16\nidna==2.10\njeepney==0.6.0\nkeyring==22.0.1\npackaging==20.9\npkginfo==1.7.0\npycparser==2.20\nPygments==2.8.0\npyparsing==2.4.7\nreadme-renderer==28.0\nrequests==2.25.1\nrequests-toolbelt==0.9.1\nrfc3986==1.4.0\nSecretStorage==3.3.1\nsix==1.15.0\ntqdm==4.57.0\ntwine==3.3.0\nurllib3==1.26.3\nwebencodings==0.5.1\n" }, { "alpha_fraction": 0.6933333277702332, "alphanum_fraction": 0.6933333277702332, "avg_line_length": 14.050000190734863, "blob_id": "1cdfcbb788d1ba8e938a57ce7ead24d540868781", "content_id": "22f01eaeeb51c8eadbd66d63d87a0d50165ea549", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Makefile", "length_bytes": 300, "license_type": "no_license", "max_line_length": 44, "num_lines": 20, "path": "/Makefile", "repo_name": "declarativesystems/pipelines_example_pythonenv", "src_encoding": "UTF-8", "text": "dist: env\n\t. env/bin/activate; \\\n\tpython setup.py sdist bdist_wheel\n\nenv:\n\tvirtualenv env ; \\\n\t. env/bin/activate; \\\n\tpip install -r requirements.txt\n\n\npublish: dist\n\t. env/bin/activate; \\\n\tpython setup.py bdist_wheel upload -r local\n\nclean:\n\trm -rf env\n\trm -rf build\n\trm -rf dist\n\n.PHONY: clean dist" }, { "alpha_fraction": 0.675000011920929, "alphanum_fraction": 0.6968749761581421, "avg_line_length": 28.18181800842285, "blob_id": "1048e18019485e41b8603293bc486d67ba6fbd2b", "content_id": "b9770eacf565a9db1c3e36b64c8357fd24fdfa46", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 320, "license_type": "no_license", "max_line_length": 67, "num_lines": 11, "path": "/pipelines_example_pythonenv/version.py", "repo_name": "declarativesystems/pipelines_example_pythonenv", "src_encoding": "UTF-8", "text": "import os\n\nversion = \"0.0.0\"\n\ndef get_version():\n # set in pipelines environment\n build_number = os.environ.get(\"build_number\", False)\n\n # python is picky about how version numbers are handled\n # see https://www.python.org/dev/peps/pep-0440\n return f\"{version}+{build_number}\" if build_number else version" } ]	4
sidneijp/luizalabs-employee-manager	https://github.com/sidneijp/luizalabs-employee-manager	dfb234b9c70061ca61d0685a2cd07b3762dbb46f	98783a185ffa1f3f4180eeb34e46cf6d2c3cae1f	07f7613a41a9fc0237965814395df888271dbecd	refs/heads/master	2023-01-27T16:40:24.635909	2018-04-13T15:34:54	2018-04-13T15:34:54	317,712,554	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.727053165435791, "alphanum_fraction": 0.727053165435791, "avg_line_length": 23.352941513061523, "blob_id": "575ef1c4ecc7a86bcb77edecfe3a266b9f366f8b", "content_id": "103db72dffde0eef23b0f6b77ea702a000014d2d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 414, "license_type": "no_license", "max_line_length": 51, "num_lines": 17, "path": "/core/admin.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "from django.contrib import admin\n\nfrom core.models import Department, Employee\n\n\nclass DepartmentAdmin(admin.ModelAdmin):\n search_fields = ('name',)\n\n\nclass EmployeeAdmin(admin.ModelAdmin):\n list_display = ('email', 'name', 'department',)\n search_fields = ('email', 'name',)\n list_filter = ('department',)\n\n\nadmin.site.register(Department, DepartmentAdmin)\nadmin.site.register(Employee, EmployeeAdmin)\n" }, { "alpha_fraction": 0.5342960357666016, "alphanum_fraction": 0.5516245365142822, "avg_line_length": 34.512821197509766, "blob_id": "78e88cb68895cb21d834c8ce2e0838f97fcd5c9b", "content_id": "956b33615ee914ee8ee07d53a3b4f552c539cf6d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1385, "license_type": "no_license", "max_line_length": 168, "num_lines": 39, "path": "/core/migrations/0001_initial.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "# Generated by Django 2.0.4 on 2018-04-12 19:13\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Department',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=160, verbose_name='Name')),\n ],\n options={\n 'verbose_name': 'Department',\n 'verbose_name_plural': 'Departments',\n },\n ),\n migrations.CreateModel(\n name='Employee',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=160, verbose_name='Name')),\n ('email', models.EmailField(max_length=254, unique=True, verbose_name='E-mail')),\n ('department', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='core.Department', verbose_name='Department')),\n ],\n options={\n 'verbose_name': 'Employee',\n 'verbose_name_plural': 'Employees',\n },\n ),\n ]\n" }, { "alpha_fraction": 0.6486761569976807, "alphanum_fraction": 0.6737949848175049, "avg_line_length": 24.188034057617188, "blob_id": "543508be5c7dda8f3f1402d72dbdc1ad1a4013dd", "content_id": "2ae71aec2f5d61df568fead44567dc519bd70436", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2946, "license_type": "no_license", "max_line_length": 178, "num_lines": 117, "path": "/README.md", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "# employee-manager\n___\n[![CircleCI](https://circleci.com/bb/sidnei/employee-manager/tree/develop.svg?style=svg&circle-token=a05a6fd2a24f3465225aba2e23a648f9a7ee1ea8)](https://circleci.com/bb/sidnei/employee-manager/tree/develop)\n\nA Django Admin panel to manage employees' data and an API to list, add and remove employees.\n\n## Installation\n\n### Dependecies\n\n- Python 3.6\n- Django 2.0\n- pipenv 11.10.0\n\nThe project uses `pipenv` instead of `pip` to manage python - so there is no `requirements.txt`. To install, run as root:\n\n```# pip install -U pipenv```\n\nNow to install python packages dependencies:\n\n```$ make install```\n\nTo the application and the API will be available at http://localhost:8000 and the admin interface at http://localhost:8000/admin.\n\n```$ make run```\n\nA initial superuser is created:\n\n```\nusername:admin\n\npassword:employee123\n\n```\n\n### Development\n\nInstall the `dev-packages` to be able to run tests, generate coverage report, run linter, etc. \n\n```$ make dev-install```\n\nTo run automated tests:\n\n```$ make test```\n\nor run in \"watcher mode\":\n\n```$ make testd```\n\nto generate tests coverage:\n\n```$ make coverage```\n\nthen to see the report result:\n\n```make report```\n\nor for \"HTML fashion\" report:\n\n```make html```\n\nThe HTML report will be available at `./htmlcov/index.html` - open it with a web browser.\n\n## API example (list)\n\nURL schema:\n\n```http://localhost:8000/employee/[employee_email/][?department=department_name]```\n\nwith curl\n```curl -u username:password -H \"Content-Type: application/json\" http://localhost:8000/employee/[employee_email/][?department=department_name]```\n\n\n### Examples\n\nTo create some employees:\n\n```curl -u admin:employee123 -X POST -d '{\"name\": \"Jack\", \"email\": \"[email protected]\", \"department\":\"IT\"}' -H \"Content-Type: application/json\" http://localhost:8000/employee/'```\n\n```curl -u admin:employee123 -X POST -d '{\"name\": \"John\", \"email\": \"[email protected]\", \"department\":\"IT\"}' -H \"Content-Type: application/json\" http://localhost:8000/employee/'```\n\nTo edit a employee:\n\n```curl -u admin:employee123 -X PUT -d '{\"name\": \"Jackson\", \"email\": \"[email protected]\", \"department\":\"RH\"}' -H \"Content-Type: application/json\" http://localhost:8000/employee/[email protected]/```\n\nTo get a employee:\n\n```curl -u admin:employee123 -X GET -H \"Content-Type: application/json\" http://localhost:8000/employee/[email protected]/```\n\nTo list all employees:\n\n```curl -u admin:employee123 -X GET -H \"Content-Type: application/json\" http://localhost:8000/employee/```\n\nResponse sample: \n\n```\n[\n {\n \"name\": \"Jackson\",\n \"email\": \"[email protected]\",\n \"department\": \"RH\"\n },\n {\n \"name\": \"John\",\n \"email\": \"[email protected]\",\n \"department\": \"IT\"\n }\n]\n```\n\nTo list all employees by department:\n\n```curl -u admin:employee123 -X GET -H \"Content-Type: application/json\" http://localhost:8000/employee/?department=IT```\n\nTo delete a employee:\n\n```curl -u admin:employee123 -X DELETE -H \"Content-Type: application/json\" http://localhost:8000/employee/[email protected]/```" }, { "alpha_fraction": 0.7396313548088074, "alphanum_fraction": 0.7396313548088074, "avg_line_length": 21.842105865478516, "blob_id": "dfff8d4830dcb52ebfd3633e9164f4dac7d86403", "content_id": "979a0a72440ac736db4686cd2ace31159c07183f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 434, "license_type": "no_license", "max_line_length": 59, "num_lines": 19, "path": "/conftest.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "import pytest\n\nfrom employee_manager.tests.factories import UserFactory\n\n\[email protected]\ndef user_credentials():\n user = UserFactory.create()\n password = 'test_password'\n user.set_password(password)\n user.save()\n return user, password\n\n\[email protected]\ndef authenticated_client(client, user_credentials):\n user, password = user_credentials\n client.login(username=user.username, password=password)\n return client\n" }, { "alpha_fraction": 0.7394594550132751, "alphanum_fraction": 0.7481080889701843, "avg_line_length": 14.677966117858887, "blob_id": "829134ec6d0e3f2cc7687dbe9c618d62723ff69c", "content_id": "bd182bb3b0a6377171f532fa377c12564e3afb99", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Makefile", "length_bytes": 925, "license_type": "no_license", "max_line_length": 55, "num_lines": 59, "path": "/Makefile", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "SHELL:=/bin/bash\nPORT:=8000\nMANAGE:=`pipenv --venv`/bin/python manage.py\n\nrequirements:\n\tpipenv install\n\ndev-requirements:\n\tpipenv install --dev\n\nload-initial-data:\n\t$(MANAGE) loaddata employee_manager/fixtures/auth.json\n\t$(MANAGE) loaddata core/fixtures/department.json\n\nrun:\n\t$(MANAGE) runserver 0.0.0.0:$(PORT)\n\nmigrate:\n\t$(MANAGE) migrate\n\ncollectstatic:\n\t$(MANAGE) collectstatic --noinput\n\nshell:\n\t$(MANAGE) shell\n\ntest:\n\tpy.test\n\ntestd:\n\tptw\n\ncoverage:\n\tcoverage run -m py.test\n\nreport:\n\tcoverage report\n\nhtml:\n\t@coverage html\n\t@echo \"Generated coverage HTML report at ./htmlcov\"\n\nclean:\n\t@rm -f .coverage\n\t@rm -rf htmlcov/\n\t@echo \"Cleaned coverage report files\"\n\npull:\n\tgit pull origin\n\ninstall: requirements migrate load-initial-data\n\tcp settings.ini.sample settings.ini\n\ndev-install: dev-requirements migrate load-initial-data\n\tcp settings.ini.sample settings.ini\n\nupdate:\tpull install\n\ndev-update:\tpull dev-install\n" }, { "alpha_fraction": 0.7045454382896423, "alphanum_fraction": 0.7134740352630615, "avg_line_length": 34.71014404296875, "blob_id": "c7327bbcdf47935c66ae6d006e11923e26e278b2", "content_id": "af378e63560ae88f3d08ef4df19eec4c37243ba7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2464, "license_type": "no_license", "max_line_length": 76, "num_lines": 69, "path": "/core/api/tests/test_views.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "import pytest\n\nfrom core.models import Employee\nfrom . import factories\n\n\[email protected]_db\ndef test_create_employee(authenticated_client, department):\n employee = factories.EmployeeFactory.build(department=department)\n data = {\n 'name': employee.name,\n 'email': employee.email,\n 'department': employee.department.name,\n }\n response = authenticated_client.post('/employee/', data)\n assert response.status_code == 201\n last_inserted_employee = Employee.objects.last()\n assert last_inserted_employee.email == employee.email\n\n\[email protected]_db\ndef test_list_employee(authenticated_client, employees):\n expected_amount = Employee.objects.all().count()\n response = authenticated_client.get('/employee/')\n assert response.status_code == 200\n amount = len(response.json())\n assert expected_amount == amount\n\n\[email protected]_db\ndef test_list_filter_employee(authenticated_client, departments, employees):\n department = departments.pop()\n expected_amount = Employee.objects.filter(department=department).count()\n data = {'department': department.name}\n response = authenticated_client.get('/employee/', data)\n assert response.status_code == 200\n json_response = response.json()\n amount = len(json_response)\n assert expected_amount == amount\n assert department.name in [_.get('department') for _ in json_response]\n\n\[email protected]_db\ndef test_get_employee(authenticated_client, employee):\n response = authenticated_client.get('/employee/%s/' % employee.email)\n assert response.status_code == 200\n json_response = response.json()\n assert employee.email == json_response.get('email')\n assert employee.department.name == json_response.get('department')\n expected_fields = ('name', 'email', 'department')\n fields = json_response.keys()\n for expected_field in expected_fields:\n assert expected_field in fields\n assert len(expected_fields) == len(fields)\n\n\[email protected]_db\ndef test_delete_employee(authenticated_client, employee):\n response = authenticated_client.delete('/employee/%s/' % employee.email)\n assert response.status_code == 204\n invalid_id = -1\n response = authenticated_client.delete('/employee/%s/' % invalid_id)\n assert response.status_code == 404\n\n\[email protected]_db\ndef test_unauthorized_api_call(client, employees):\n response = client.get('/employee/')\n assert response.status_code == 401\n" }, { "alpha_fraction": 0.6090047359466553, "alphanum_fraction": 0.6113743782043457, "avg_line_length": 37.3636360168457, "blob_id": "dfd2682051f9ec9f44189cb3bd78d96f42f9d566", "content_id": "8f75356eaf131f7d448fa1cf13968c8ed7a85366", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 422, "license_type": "no_license", "max_line_length": 61, "num_lines": 11, "path": "/core/api/utils.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "def use_serializer(serializer_class):\n def decorator(view):\n def wrapper(args, kwargs):\n self = args[0]\n original_serializer_class = self.serializer_class\n self.serializer_class = serializer_class\n response = view(args, *kwargs)\n self.serializer_class = original_serializer_class\n return response\n return wrapper\n return decorator\n" }, { "alpha_fraction": 0.7346072196960449, "alphanum_fraction": 0.747346043586731, "avg_line_length": 32.64285659790039, "blob_id": "33fc6535edd219dfdcb0b740d3adef4e4409b364", "content_id": "307b8e95a133b0ec653ed277176bd38864be97cd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 471, "license_type": "no_license", "max_line_length": 73, "num_lines": 14, "path": "/core/api/viewsets.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "from rest_framework import viewsets\n\nfrom core.api.serializers import EmployeeSerializer\nfrom core.api.filters import EmployeeFilter\nfrom core.api.utils import use_serializer\nfrom core.models import Employee\n\n\nclass EmployeeViewSet(viewsets.ModelViewSet):\n queryset = Employee.objects.all()\n serializer_class = EmployeeSerializer\n filter_class = EmployeeFilter\n lookup_field = 'email'\n lookup_value_regex = '[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+'\n" }, { "alpha_fraction": 0.8157894611358643, "alphanum_fraction": 0.8157894611358643, "avg_line_length": 21.799999237060547, "blob_id": "ba93d5eec2f10e9c2dee67c3f37f64e789257f10", "content_id": "23d1fecb1fe98a2a95705baaeb5db30fd22c143e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 228, "license_type": "no_license", "max_line_length": 45, "num_lines": 10, "path": "/core/api/urls.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "from django.urls import include, path\nfrom rest_framework import routers\n\nfrom core.api.viewsets import EmployeeViewSet\n\n\nrouter = routers.DefaultRouter()\nrouter.register(r'employee', EmployeeViewSet)\n\nurlpatterns = router.urls\n" }, { "alpha_fraction": 0.5378972887992859, "alphanum_fraction": 0.591687023639679, "avg_line_length": 21.72222137451172, "blob_id": "04d7e0944407004b1a6cdbe5950d6848f7ef7aa5", "content_id": "e609802dbfc1e153c5731352152d124dbe6f53ce", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 409, "license_type": "no_license", "max_line_length": 85, "num_lines": 18, "path": "/core/migrations/0002_auto_20180412_2251.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "# Generated by Django 2.0.4 on 2018-04-12 22:51\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('core', '0001_initial'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='department',\n name='name',\n field=models.CharField(max_length=160, unique=True, verbose_name='Name'),\n ),\n ]\n" }, { "alpha_fraction": 0.722352921962738, "alphanum_fraction": 0.722352921962738, "avg_line_length": 21.36842155456543, "blob_id": "3112a45e7810b475c14f24e5ae26737cfc221fbf", "content_id": "498522a9b5967f4a727b761a95879110b8579c9c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 425, "license_type": "no_license", "max_line_length": 59, "num_lines": 19, "path": "/core/api/tests/factories.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "import factory\n\nfrom core.models import Department, Employee\n\n\nclass DepartmentFactory(factory.django.DjangoModelFactory):\n name = factory.Faker('name')\n\n class Meta:\n model = Department\n\n\nclass EmployeeFactory(factory.django.DjangoModelFactory):\n name = factory.Faker('name')\n email = factory.Faker('email')\n department = factory.SubFactory(DepartmentFactory)\n\n class Meta:\n model = Employee\n" }, { "alpha_fraction": 0.8090909123420715, "alphanum_fraction": 0.8090909123420715, "avg_line_length": 26.5, "blob_id": "0eef295f1e584f0f0033b22582120dc248a0b97a", "content_id": "aee135c24da7b35ac3a607ede0ee81e07d664135", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "INI", "length_bytes": 110, "license_type": "no_license", "max_line_length": 48, "num_lines": 4, "path": "/pytest.ini", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "[pytest]\nDJANGO_SETTINGS_MODULE=employee_manager.settings\npython_files=tests.py test*.py\ndata_file=.coverage\n" }, { "alpha_fraction": 0.6225930452346802, "alphanum_fraction": 0.6302952766418457, "avg_line_length": 25.86206817626953, "blob_id": "c826ccc20dd8199db971f25b84b3cccc2f2e1d81", "content_id": "c403a12c23630250d66f6caa429fd1a6e8981d7e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 779, "license_type": "no_license", "max_line_length": 69, "num_lines": 29, "path": "/core/models.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "from django.db import models\nfrom django.utils.translation import ugettext_lazy as _\n\n\nclass Department(models.Model):\n name = models.CharField(_('Name'), max_length=160, unique=True)\n\n class Meta:\n verbose_name = _('Department')\n verbose_name_plural = _('Departments')\n\n def __str__(self):\n return self.name\n\n\nclass Employee(models.Model):\n name = models.CharField(_('Name'), max_length=160)\n email = models.EmailField(_('E-mail'), unique=True)\n department = models.ForeignKey(\n Department, on_delete=models.SET_NULL, null=True, blank=True,\n verbose_name=_('Department')\n )\n\n class Meta:\n verbose_name = _('Employee')\n verbose_name_plural = _('Employees')\n\n def __str__(self):\n return self.name\n" }, { "alpha_fraction": 0.7727272510528564, "alphanum_fraction": 0.7854545712471008, "avg_line_length": 21, "blob_id": "8b55ccb89d8d2ad889d574fcb577a6b84e332752", "content_id": "be8ded8109415dd3aebf2e4ef0d89dd980ad3cbd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 550, "license_type": "no_license", "max_line_length": 67, "num_lines": 25, "path": "/core/api/tests/conftest.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "import pytest\n\nfrom .factories import DepartmentFactory, EmployeeFactory\n\n\[email protected]\ndef department():\n return DepartmentFactory.create()\n\n\[email protected]\ndef departments():\n return DepartmentFactory.create_batch(2)\n\n\[email protected]\ndef employee(department):\n return EmployeeFactory.create(department=department)\n\n\[email protected]\ndef employees(departments):\n department_1, department_2 = departments\n EmployeeFactory.create_batch(2, department=department_1)\n return EmployeeFactory.create_batch(2, department=department_2)\n" }, { "alpha_fraction": 0.70703125, "alphanum_fraction": 0.70703125, "avg_line_length": 22.272727966308594, "blob_id": "f18cd8e63bbe8c5574f9a856485657740446bfb8", "content_id": "a85f638346eacad605deb562662ebebaad1375bb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 256, "license_type": "no_license", "max_line_length": 67, "num_lines": 11, "path": "/core/api/filters.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "import django_filters\n\nfrom core.models import Employee\n\n\nclass EmployeeFilter(django_filters.FilterSet):\n department = django_filters.CharFilter(name=\"department__name\")\n\n class Meta:\n model = Employee\n fields = ('name', 'department')\n" }, { "alpha_fraction": 0.7002725005149841, "alphanum_fraction": 0.7002725005149841, "avg_line_length": 25.214284896850586, "blob_id": "561d5440835dc1abae4faf0f722b98c4700d73d9", "content_id": "2e7f0d2d8d38dfc47636a9aedbf751e69887c2c5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 367, "license_type": "no_license", "max_line_length": 65, "num_lines": 14, "path": "/core/api/serializers.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "from rest_framework import serializers\n\nfrom core.models import Department, Employee\n\n\nclass EmployeeSerializer(serializers.HyperlinkedModelSerializer):\n department = serializers.SlugRelatedField(\n slug_field='name',\n queryset=Department.objects.all(),\n )\n\n class Meta:\n model = Employee\n fields = ('name', 'email', 'department')\n" }, { "alpha_fraction": 0.639418363571167, "alphanum_fraction": 0.6476714611053467, "avg_line_length": 25.925926208496094, "blob_id": "23a66b1d11e1c8f775b56dbcf2dfbf71b2f9a00d", "content_id": "6b2f245acbfab4039a0efa4c6445817195a93f5c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5089, "license_type": "no_license", "max_line_length": 91, "num_lines": 189, "path": "/employee_manager/settings.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "\"\"\"\nDjango settings for employee_manager project.\n\nGenerated by 'django-admin startproject' using Django 2.0.4.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/2.0/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/2.0/ref/settings/\n\"\"\"\n\nfrom decouple import config, Csv\nimport os\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = config(\n 'SECRET_KEY', default='wnyyx5y0q)0m(zh221a3r4&&=xlz=xzt$s#_s+6i9k3x)#i1gd'\n)\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = config('DEBUG', default=False, cast=bool)\n\nALLOWED_HOSTS = config(\n 'ALLOWED_HOSTS', default='localhost, 127.0.0.1', cast=Csv()\n)\n\n\n# Application definition\n\nINSTALLED_APPS = [\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n\n 'rest_framework',\n 'django_filters',\n\n 'core',\n]\n\nMIDDLEWARE = [\n 'django.middleware.security.SecurityMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.locale.LocaleMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n]\n\nROOT_URLCONF = 'employee_manager.urls'\n\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n 'DIRS': [],\n 'APP_DIRS': True,\n 'OPTIONS': {\n 'context_processors': [\n 'django.template.context_processors.debug',\n 'django.template.context_processors.request',\n 'django.contrib.auth.context_processors.auth',\n 'django.contrib.messages.context_processors.messages',\n ],\n },\n },\n]\n\nWSGI_APPLICATION = 'employee_manager.wsgi.application'\n\n\n# Database\n# https://docs.djangoproject.com/en/2.0/ref/settings/#databases\n\n_DB_DEFAULTS = {\n 'django.db.backends.sqlite3': {\n 'HOST': '',\n 'PORT': '',\n 'NAME': 'db.sqlite3',\n 'USER': '',\n 'PASSWORD': '',\n },\n 'django.db.backends.postgresql': {\n 'HOST': 'localhost',\n 'PORT': '5432',\n 'NAME': 'db.sqlite3',\n 'USER': 'postgres',\n 'PASSWORD': '',\n },\n 'django.db.backends.mysql': {\n 'HOST': 'localhost',\n 'PORT': '3306',\n 'NAME': 'db.sqlite3',\n 'USER': 'root',\n 'PASSWORD': '',\n },\n}\n\n_DB_ENGINE = 'django.db.backends.%s' % config('DB_ENGINE', default='sqlite3')\n\nDATABASES = {\n 'default': {\n 'ENGINE': _DB_ENGINE,\n 'HOST': config('DB_HOST', default=_DB_DEFAULTS[_DB_ENGINE]['HOST']),\n 'PORT': config('DB_PORT', default=_DB_DEFAULTS[_DB_ENGINE]['PORT']),\n 'NAME': config('DB_NAME', default=_DB_DEFAULTS[_DB_ENGINE]['NAME']),\n 'USER': config('DB_USER', default=_DB_DEFAULTS[_DB_ENGINE]['USER']),\n 'PASSWORD': config(\n 'DB_PASSWORD', default=_DB_DEFAULTS[_DB_ENGINE]['PASSWORD']\n ),\n }\n}\n\n\n# Password validation\n# https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators\n\nAUTH_PASSWORD_VALIDATORS = [\n {\n 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n },\n]\n\n\n# Internationalization\n# https://docs.djangoproject.com/en/2.0/topics/i18n/\n\nLANGUAGE_CODE = config('LANGUAGE_CODE', default='en-us')\n\nTIME_ZONE = config('TIME_ZONE', default='UTC')\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/2.0/howto/static-files/\n\nSTATIC_URL = '/static/'\n\n\n# Django REST Framework settings\n# http://www.django-rest-framework.org\n\nREST_FRAMEWORK = {\n 'DEFAULT_AUTHENTICATION_CLASSES': (\n 'rest_framework.authentication.BasicAuthentication',\n 'rest_framework.authentication.SessionAuthentication',\n ),\n 'DEFAULT_PERMISSION_CLASSES': (\n 'rest_framework.permissions.IsAuthenticated',\n ),\n 'DEFAULT_FILTER_BACKENDS': (\n 'django_filters.rest_framework.DjangoFilterBackend',\n ),\n 'DEFAULT_RENDERER_CLASSES': (\n 'rest_framework.renderers.JSONRenderer',\n ),\n}\n\n# Custom settings for DEBUG=True\nif DEBUG:\n REST_FRAMEWORK['DEFAULT_RENDERER_CLASSES'] += (\n 'rest_framework.renderers.BrowsableAPIRenderer',\n )\n" }, { "alpha_fraction": 0.675000011920929, "alphanum_fraction": 0.675000011920929, "avg_line_length": 19, "blob_id": "f038a455f057cb9ae3e7e136eb482eb01ada44b7", "content_id": "9d2774e0055d6e563f4bf3b6515d29135a317a8e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 40, "license_type": "no_license", "max_line_length": 30, "num_lines": 2, "path": "/core/tests.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "def test_pytest_sanity_test():\n pass\n" } ]	18
ranta09/spelling-correction	https://github.com/ranta09/spelling-correction	5a873916d455bfb736e8cdce3f4d4504dae2421b	b42713da13e41b63e9a90609be0a4a79611e75ae	099076fd1e1757b76db76b06b8670ce791b5b732	refs/heads/master	2022-11-24T23:31:57.200775	2020-07-31T10:25:42	2020-07-31T10:25:42	null	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.44498777389526367, "alphanum_fraction": 0.4498777389526367, "avg_line_length": 41.05263137817383, "blob_id": "524d0f99cbdc0518eb8f2e8e6f62de7d2d9cbc47", "content_id": "ac0cf8d508c46fa68d6e504cde241da5b5d0cb23", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 818, "license_type": "no_license", "max_line_length": 84, "num_lines": 19, "path": "/correct.py", "repo_name": "ranta09/spelling-correction", "src_encoding": "UTF-8", "text": "\r\n################ follow @09programmer on facebook ################\r\n\r\nfrom textblob import TextBlob # install libraries\r\n\r\nfile=open(\"programmer.txt\", \"r+\") # open file in read mode \r\na=file.read() \r\n\r\nprint(\"original text : \"+str(a)) #print original text\r\n\r\nb=TextBlob(a) #convert the data type of textblob \r\nprint(\"corrected text : \"+str(b.correct())) # print corrected text\r\n\r\nfile.close() #close file\r\n\r\nd=open(\"programmer.txt\",\"w\") # open file in write mode\r\nd.write(str(b.correct())) # update file\r\nd.close() #close file\r\n\r\n################ follow @09programmer on facebook ################" }, { "alpha_fraction": 0.8237410187721252, "alphanum_fraction": 0.8237410187721252, "avg_line_length": 138, "blob_id": "db77cd81539d5a24a42ddbd7c654d447960cfac5", "content_id": "c3c7766cd58fbbb662f4dcd3181a1d372075b955", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 278, "license_type": "no_license", "max_line_length": 255, "num_lines": 2, "path": "/README.md", "repo_name": "ranta09/spelling-correction", "src_encoding": "UTF-8", "text": "# spelling-correction\nPython - Spelling Check. Checking of spelling is a basic requirement in any text processing or analysis. The python package pyspellchecker provides us this feature to find the words that may have been mis-spelled and also suggest the possible corrections.\n" } ]	2
danielseow234/flask-taskmaster	https://github.com/danielseow234/flask-taskmaster	c8fa53de6736f2491ce9e11c3f206c5f7603f857	564a8094229ab2938fe345d825d597cc122a8a10	bb8e10d2600aef6b0699c52b1aafccf77c6ed6ca	refs/heads/main	2022-12-27T22:36:05.822990	2020-10-08T06:26:17	2020-10-08T06:26:17	302,059,289	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6063264012336731, "alphanum_fraction": 0.612174391746521, "avg_line_length": 36.25742721557617, "blob_id": "312d63551fd4c0b214fcafe5b3dd68c60f9ae6a0", "content_id": "9185f808b9fa2e5f663bdd3458b19478527b8317", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3762, "license_type": "no_license", "max_line_length": 77, "num_lines": 101, "path": "/app.py", "repo_name": "danielseow234/flask-taskmaster", "src_encoding": "UTF-8", "text": "from flask import Flask, flash, render_template, request, redirect, url_for\nimport bcrypt, uuid\nfrom datetime import datetime\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///data.db'\ndb = SQLAlchemy(app)\napp.secret_key = b'_5#y2L\"F4Q8z\\n\\xec]/'\n\nclass User(db.Model):\n id = db.Column(db.String, nullable=False, primary_key=True)\n username = db.Column(db.String(40), nullable=False, unique=True)\n password = db.Column(db.String(200), nullable=False)\n\nclass Todo(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(40), nullable=False, unique=False)\n task = db.Column(db.String(200), nullable=False)\n completed = db.Column(db.Integer, default=0)\n date_created = db.Column(db.DateTime, default=datetime.utcnow)\n\[email protected]('/', methods=['POST', 'GET'])\ndef login():\n if request.method == 'POST':\n form_username = request.form['username']\n form_password = request.form['password']\n bytes_password = form_password.encode('utf-8')\n user_search = User.query.filter_by(username=form_username).first()\n if user_search != None:\n if bcrypt.checkpw(bytes_password, user_search.password):\n return redirect(url_for('home', id=user_search.id))\n else:\n flash('Password was incorrect.')\n else:\n flash('User not found.')\n return render_template('login.html')\n\[email protected]('/signup', methods=['POST', 'GET'])\ndef signup():\n if request.method == 'POST':\n new_username = request.form['username']\n new_password = request.form['password']\n bytes_password = new_password.encode('utf-8')\n hashed_password = bcrypt.hashpw(bytes_password, bcrypt.gensalt())\n user_check = User.query.filter_by(username=new_username).first()\n u = uuid.uuid4()\n new_user = User(id=u.hex, username=new_username, password=hashed_password)\n print(u.hex)\n if user_check == None:\n try:\n db.session.add(new_user)\n db.session.commit()\n return redirect('/')\n except:\n return 'There was an issue adding your account.'\n else:\n flash('Username is taken. Try again.')\n return render_template('signup.html')\n\[email protected]('/home/<id>', methods=['POST', 'GET'])\ndef home(id):\n user = User.query.filter_by(id=id).first()\n tasks = Todo.query.filter_by(username=user.username).all()\n if request.method == 'POST':\n task = request.form['task']\n task_username = user.username\n new_task = Todo(task=task, username=task_username)\n try:\n db.session.add(new_task)\n db.session.commit()\n return redirect(url_for('home', id=user.id))\n except:\n return 'There was an issue adding your task'\n return render_template('home.html', user=user, tasks=tasks, id=id)\n\[email protected]('/home/<id>/delete/<idt>')\ndef delete(id, idt):\n task_to_delete = Todo.query.get_or_404(idt)\n try:\n db.session.delete(task_to_delete)\n db.session.commit()\n return redirect(url_for('home', id=id))\n except:\n return 'There was a problem deleting that task.'\n\[email protected]('/home/<id>/update/<idt>', methods=['GET', 'POST'])\ndef update(id, idt):\n item = Todo.query.get_or_404(idt)\n if request.method == 'POST':\n item.task = request.form['task']\n try:\n db.session.commit()\n return redirect(url_for('home', id=id))\n except:\n return \"There was an issue updating your task\"\n else:\n return render_template('update.html', item=item, id=id)\n\nif __name__ == \"__main__\":\n app.run(debug=True)" }, { "alpha_fraction": 0.7798690795898438, "alphanum_fraction": 0.7798690795898438, "avg_line_length": 151.75, "blob_id": "fac825804b1fb4ebdaaf697889bd9edcefc87e74", "content_id": "b4748887e5a88d50bffdf6c179c061750b14c340", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1222, "license_type": "no_license", "max_line_length": 487, "num_lines": 8, "path": "/README.md", "repo_name": "danielseow234/flask-taskmaster", "src_encoding": "UTF-8", "text": "# flask-taskmaster\nSimple flask website that allows user to create and remove tasks on a todo list.\n\nI used the Flask python package as the backend of the website. I didn't use any CSS as I was kinda lazy so the website looks really blank. I bodged a simple login and sign up system that stores the user details and tasks on an SQLite database, and I used the SQLAlchemy module in python. Flask has a module named Flask-login, which adds user authentication to pages, and adds many more features. Since I didn't use that I had to come up with a way to secure the website for users. \n\nAt first, the link to a specific user's tasks were linked using \"/home/'username'\". This would be terrible, as other users could simply find out another user's username, and access their home page. To counter this I implemented a randomly generated user ID, that could be used for the link instead like \"/home/'user ID'\". This isn't the most secure method, as a user's ID could be copied from their web browser, or it could be pulled from the database if there were a breach in security.\n\nIf I were to re-do this project, I would definitely implement the flask-login module, and I would use the login-authenticator to secure users' data.\n" } ]	2
MasterOfTheMojave/JetBrains-Academy	https://github.com/MasterOfTheMojave/JetBrains-Academy	f164f32b88b8efbc2e43a64577c06d6937e4d3ca	318c5506a99eb79c25446fb258c9b9f6615ea4d0	98e6e2edf7133c72d400bb0bc4666db748bd2b90	refs/heads/master	2023-07-14T17:53:32.473142	2021-08-19T19:01:39	2021-08-19T19:01:39	288,516,488	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.3663688004016876, "alphanum_fraction": 0.3663688004016876, "avg_line_length": 35.20588302612305, "blob_id": "55390b18b3c62613d82a0041bd79d32c39aa9633", "content_id": "7f1f677161dc81d9b008aa2475af5161f6ef4069", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1247, "license_type": "no_license", "max_line_length": 48, "num_lines": 34, "path": "/Zookeeper/Stage_2_4.py", "repo_name": "MasterOfTheMojave/JetBrains-Academy", "src_encoding": "UTF-8", "text": "# For the second stage, you will need to develop\n# an animal printer. Your program should display\n# the animal identified in the code field.\n#\n# Please, don't remove the r character at the\n# start of the code template. It's a part of the\n# string and it's important. So, the string\n# should start with r\"\"\" sequence. This “r” at\n# the beginning stands for “raw” and allows\n# various characters to be used in a string\n# without escaping. For instance, “\\” in a\n# non-raw string should be escaped as\n# follows: “\\\\”.\n\nprint(\"\"\"\nSwitching on camera from habitat with camels...\n ___.-''''-.\n/___ @ \|\n',,,,. \| _.'''''''._\n ' \| / \\\\\n \| \\ _.-' \\\\\n \| '.-' '-.\n \| ',\n \| '',\n ',,-, ':;\n ',,\| ;,, ,' ;;\n ! ; !'',,,',',,,,'! ; ;:\n : ; ! ! ! ! ; ; :;\n ; ; ! ! ! ! ; ; ;,\n ; ; ! ! ! ! ; ; \n ; ; ! ! ! ! ; ;\n ;,, !,! !,! ;,;\n /_I L_I L_I /_I\nYey, our little camel is sunbathing!\"\"\")\n" }, { "alpha_fraction": 0.7358490824699402, "alphanum_fraction": 0.7358490824699402, "avg_line_length": 32.25, "blob_id": "6a060d931d32d650f92f5b9050ef9eec0ab6ac22", "content_id": "1b3f7eaada160be450b0f452ab5a377ab27bc045", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 265, "license_type": "no_license", "max_line_length": 63, "num_lines": 8, "path": "/Zookeeper/Stage_1_4.py", "repo_name": "MasterOfTheMojave/JetBrains-Academy", "src_encoding": "UTF-8", "text": "# To begin with, you will develop a simple printer.\n# Your program should display the text from the output example.\n\nprint(\"I do love animals!\")\nprint(\"Start looking after animals...\")\nprint(\"Deer looks fine.\")\nprint(\"Bat looks happy.\")\nprint(\"Lion looks healthy.\")" } ]	2
IBM/MAX-Inception-ResNet-v2	https://github.com/IBM/MAX-Inception-ResNet-v2	995356bcfdac5667e212ae8bfa61551fd14c81d5	a23c1f6ee543feefc0df1959c02f07cc44fd0999	e75f56c8c5c81750303373d5e9c8cf4861c19246	refs/heads/master	2023-05-27T15:44:50.038755	2021-06-11T04:56:06	2021-06-11T04:56:06	124,603,311	28	24	Apache-2.0	2018-03-09T23:15:53	2023-04-12T12:00:36	2023-05-23T00:40:57	Python	[ { "alpha_fraction": 0.6740947365760803, "alphanum_fraction": 0.7013927698135376, "avg_line_length": 34.900001525878906, "blob_id": "e5c5d5efc8510000987838d0ef71a33d281d4b8e", "content_id": "abfcef3c32e614e2b146f9dadd3c8ca36153d808", "detected_licenses": [ "Apache-2.0", "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1795, "license_type": "permissive", "max_line_length": 87, "num_lines": 50, "path": "/config.py", "repo_name": "IBM/MAX-Inception-ResNet-v2", "src_encoding": "UTF-8", "text": "#\n# Copyright 2018-2019 IBM Corp. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n# Flask settings\nDEBUG = False\n\n# Flask-restplus settings\nRESTPLUS_MASK_SWAGGER = False\nSWAGGER_UI_DOC_EXPANSION = 'none'\n\n# API Metadata\nAPI_TITLE = 'MAX Image Classifier - Inception ResNet v2'\nAPI_DESC = 'Identify objects in images using a third-generation deep residual network.'\nAPI_VERSION = '1.2.0'\n\n# Model settings\nkeras_builtin_models = {\n 'inception_v3': {'size': (299, 299), 'license': 'Apache v2'},\n 'inception_resnet_v2': {'size': (299, 299), 'license': 'Apache v2'},\n 'xception': {'size': (299, 299), 'license': 'MIT'},\n 'resnet50': {'size': (224, 224), 'license': 'MIT'}\n}\n\n# default model\nMODEL_NAME = 'inception_resnet_v2'\nDEFAULT_MODEL_PATH = 'assets/{}.h5'.format(MODEL_NAME)\nMODEL_INPUT_IMG_SIZE = keras_builtin_models[MODEL_NAME]['size']\nMODEL_LICENSE = keras_builtin_models[MODEL_NAME]['license']\n\nMODEL_META_DATA = {\n 'id': '{}-keras-imagenet'.format(MODEL_NAME.lower()),\n 'name': '{} Keras Model'.format(MODEL_NAME),\n 'description': '{} Keras model trained on ImageNet'.format(MODEL_NAME),\n 'type': 'image_classification',\n 'license': MODEL_LICENSE,\n 'source': 'https://developer.ibm.com/exchanges/models/all/max-inception-resnet-v2/'\n}\n" }, { "alpha_fraction": 0.42465752363204956, "alphanum_fraction": 0.6575342416763306, "avg_line_length": 13.600000381469727, "blob_id": "8b07ef70e384b19a8d7136da1ac0fc1cdd2880c7", "content_id": "7080eacc4dbd4741f766da22b22f9bd51f78d9d6", "detected_licenses": [ "Apache-2.0", "MIT" ], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 73, "license_type": "permissive", "max_line_length": 16, "num_lines": 5, "path": "/requirements-test.txt", "repo_name": "IBM/MAX-Inception-ResNet-v2", "src_encoding": "UTF-8", "text": "pytest==6.2.0\nrequests==2.25.0\nflake8==3.8.4\nPillow==8.2.0\nbandit==1.6.2\n" }, { "alpha_fraction": 0.6827309131622314, "alphanum_fraction": 0.7309237122535706, "avg_line_length": 48.79999923706055, "blob_id": "2f847136e96f48018024c289394b2f115ef6b015", "content_id": "677ce3b5d460d652889b1d59c3089b708484a0dd", "detected_licenses": [ "Apache-2.0", "MIT", "CC0-1.0" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 498, "license_type": "permissive", "max_line_length": 145, "num_lines": 10, "path": "/samples/README.md", "repo_name": "IBM/MAX-Inception-ResNet-v2", "src_encoding": "UTF-8", "text": "# Sample Assets\n\n## Images\n\nAll test images are from [Pexels](https://www.pexels.com) and licensed under a [CC0 License](https://creativecommons.org/publicdomain/zero/1.0/).\n\n* [`dog.jpg`](https://www.pexels.com/photo/adorable-animal-animal-photography-beagle-452772/)\n* [`cat.jpg`](https://www.pexels.com/photo/cat-whiskers-kitty-tabby-20787/)\n* [`shuttle.jpg`](https://www.pexels.com/photo/flight-sky-earth-space-2166/)\n* [`pizza.jpg`](https://www.pexels.com/photo/baked-pepperoni-pizza-774487/)\n" }, { "alpha_fraction": 0.6808567643165588, "alphanum_fraction": 0.7212851643562317, "avg_line_length": 42.94117736816406, "blob_id": "2d3bf31997bda1afb9a5c0c216ec4057370fddc0", "content_id": "c5c31a6a6b24471ee10cfecfee97e2c7c2c6cd75", "detected_licenses": [ "Apache-2.0", "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 7470, "license_type": "permissive", "max_line_length": 543, "num_lines": 170, "path": "/README.md", "repo_name": "IBM/MAX-Inception-ResNet-v2", "src_encoding": "UTF-8", "text": "[![Build Status](https://travis-ci.org/IBM/MAX-Inception-ResNet-v2.svg?branch=master)](https://travis-ci.org/IBM/MAX-Inception-ResNet-v2) [![Website Status](https://img.shields.io/website/http/max-inception-resnet-v2.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud/swagger.json.svg?label=api+demo)](http://max-inception-resnet-v2.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud)\n\n[<img src=\"docs/deploy-max-to-ibm-cloud-with-kubernetes-button.png\" width=\"400px\">](http://ibm.biz/max-to-ibm-cloud-tutorial)\n\n# IBM Code Model Asset Exchange: Inception-ResNet-v2 Image Classifier\n\nThis repository contains code to instantiate and deploy an image classification model. This model recognizes the 1000 different classes of objects in the [ImageNet 2012 Large Scale Visual Recognition Challenge](http://www.image-net.org/challenges/LSVRC/2012/). The model consists of a deep convolutional net using the Inception-ResNet-v2 architecture that was trained on the ImageNet-2012 data set. The input to the model is a 299x299 image, and the output is a list of estimated class probabilities.\n\nThe model is based on the [Keras built-in model for Inception-ResNet-v2](https://keras.io/applications/#inceptionresnetv2). The model files are hosted on [IBM Cloud Object Storage](https://max-cdn.cdn.appdomain.cloud/max-inception-resnet-v2/1.0/assets.tar.gz). The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the [IBM Code Model Asset Exchange](https://developer.ibm.com/code/exchanges/models/) and the public API is powered by [IBM Cloud](https://ibm.biz/Bdz2XM).\n\n## Model Metadata\n\| Domain \| Application \| Industry \| Framework \| Training Data \| Input Data Format \|\n\| ------------- \| -------- \| -------- \| --------- \| --------- \| -------------- \| \n\| Vision \| Image Classification \| General \| Keras \| [ImageNet](http://www.image-net.org/) \| Image (RGB/HWC)\| \n\n## References\n\n* _C. Szegedy, S. Ioffe, V. Vanhoucke, A. Alemi_, [\"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning\"](https://arxiv.org/abs/1602.07261), CoRR (abs/1602.07261), 2016.\n* [Keras Applications](https://keras.io/applications/#inceptionresnetv2)\n\n## Licenses\n\n\| Component \| License \| Link \|\n\| ------------- \| -------- \| -------- \|\n\| This repository \| [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) \| [LICENSE](LICENSE) \|\n\| Model Weights \| [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) \| [Keras Inception-ResNet-v2](https://keras.io/applications/#inceptionresnetv2)\|\n\| Model Code (3rd party) \| [MIT](https://opensource.org/licenses/MIT) \| [Keras LICENSE](https://github.com/keras-team/keras/blob/master/LICENSE)\|\n\| Test assets \| Various \| [Samples README](samples/README.md) \|\n\n## Pre-requisites:\n\n* `docker`: The [Docker](https://www.docker.com/) command-line interface. Follow the [installation instructions](https://docs.docker.com/install/) for your system.\n* The minimum recommended resources for this model is 2GB Memory and 2 CPUs.\n* If you are on x86-64/AMD64, your CPU must support [AVX](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) at the minimum.\n\n# Deployment options\n\n* [Deploy from Quay](#deploy-from-quay)\n* [Deploy on Red Hat OpenShift](#deploy-on-red-hat-openshift)\n* [Deploy on Kubernetes](#deploy-on-kubernetes)\n* [Run Locally](#run-locally)\n\n## Deploy from Quay\n\nTo run the docker image, which automatically starts the model serving API, run:\n\n```\n$ docker run -it -p 5000:5000 quay.io/codait/max-inception-resnet-v2\n```\n\nThis will pull a pre-built image from the Quay.io container registry (or use an existing image if already cached locally) and run it.\nIf you'd rather checkout and build the model locally you can follow the [run locally](#run-locally) steps below.\n\n## Deploy on Red Hat OpenShift\n\nYou can deploy the model-serving microservice on Red Hat OpenShift by following the instructions for the OpenShift web console or the OpenShift Container Platform CLI [in this tutorial](https://developer.ibm.com/tutorials/deploy-a-model-asset-exchange-microservice-on-red-hat-openshift/), specifying `quay.io/codait/max-inception-resnet-v2` as the image name.\n\n## Deploy on Kubernetes\n\nYou can also deploy the model on Kubernetes using the latest docker image on Quay.\n\nOn your Kubernetes cluster, run the following commands:\n\n```\n$ kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Inception-ResNet-v2/master/max-inception-resnet-v2.yaml\n```\n\nThe model will be available internally at port `5000`, but can also be accessed externally through the `NodePort`.\n\nA more elaborate tutorial on how to deploy this MAX model to production on [IBM Cloud](https://ibm.biz/Bdz2XM) can be found [here](http://ibm.biz/max-to-ibm-cloud-tutorial).\n\n## Run Locally\n\n1. [Build the Model](#1-build-the-model)\n2. [Deploy the Model](#2-deploy-the-model)\n3. [Use the Model](#3-use-the-model)\n4. [Development](#4-development)\n5. [Cleanup](#5-cleanup)\n\n### 1. Build the Model\n\nClone this repository locally. In a terminal, run the following command:\n\n```\n$ git clone https://github.com/IBM/MAX-Inception-ResNet-v2.git\n```\n\nChange directory into the repository base folder:\n\n```\n$ cd MAX-Inception-ResNet-v2\n```\n\nTo build the docker image locally, run: \n\n```\n$ docker build -t max-inception-resnet-v2 .\n```\n\nAll required model assets will be downloaded during the build process. _Note_ that currently this docker image is CPU only (we will add support for GPU images later).\n\n\n### 2. Deploy the Model\n\nTo run the docker image, which automatically starts the model serving API, run:\n\n```\n$ docker run -it -p 5000:5000 max-inception-resnet-v2\n```\n\n### 3. Use the Model\n\nThe API server automatically generates an interactive Swagger documentation page. Go to `http://localhost:5000` to load it. From there you can explore the API and also create test requests.\n\nUse the `model/predict` endpoint to load a test image (you can use one of the test images from the `samples` folder) and get predicted labels for the image from the API.\n\n![Swagger Doc Screenshot](docs/swagger-screenshot.png)\n\nYou can also test it on the command line, for example:\n\n```\n$ curl -F \"image=@samples/dog.jpg\" -X POST http://localhost:5000/model/predict\n```\n\nYou should see a JSON response like that below:\n\n```json\n{\n \"status\": \"ok\",\n \"predictions\": [\n {\n \"label_id\": \"n02088364\",\n \"label\": \"beagle\",\n \"probability\": 0.44505545496941\n },\n {\n \"label_id\": \"n02089867\",\n \"label\": \"Walker_hound\",\n \"probability\": 0.3902231156826\n },\n {\n \"label_id\": \"n02089973\",\n \"label\": \"English_foxhound\",\n \"probability\": 0.02027696929872\n },\n {\n \"label_id\": \"n02088632\",\n \"label\": \"bluetick\",\n \"probability\": 0.010103852488101\n },\n {\n \"label_id\": \"n02088238\",\n \"label\": \"basset\",\n \"probability\": 0.001649746671319\n }\n ]\n}\n```\n\n### 4. Development\n\nTo run the Flask API app in debug mode, edit `config.py` to set `DEBUG = True` under the application settings. You will then need to rebuild the docker image (see [step 1](#1-build-the-model)).\n\n### 5. Cleanup\n\nTo stop the Docker container, type `CTRL` + `C` in your terminal.\n\n## Resources and Contributions\n \nIf you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions [here](https://github.com/CODAIT/max-central-repo).\n" }, { "alpha_fraction": 0.684302568435669, "alphanum_fraction": 0.6917359232902527, "avg_line_length": 33.65151596069336, "blob_id": "09cbc41af0bca22d815768f9159458e1a4b7a33e", "content_id": "fc10a8f8ec3fbd88eec84a8f569c1d16dbabe3bf", "detected_licenses": [ "Apache-2.0", "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2287, "license_type": "permissive", "max_line_length": 90, "num_lines": 66, "path": "/core/model.py", "repo_name": "IBM/MAX-Inception-ResNet-v2", "src_encoding": "UTF-8", "text": "#\n# Copyright 2018-2019 IBM Corp. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nfrom PIL import Image\nfrom keras.backend import clear_session\nfrom keras import models\nfrom keras.preprocessing.image import img_to_array\nfrom keras.applications import imagenet_utils\nimport io\nimport numpy as np\nimport logging\nfrom flask import abort\nfrom config import DEFAULT_MODEL_PATH, MODEL_INPUT_IMG_SIZE, MODEL_META_DATA as model_meta\nfrom maxfw.model import MAXModelWrapper\n\nlogger = logging.getLogger()\n\n\nclass ModelWrapper(MAXModelWrapper):\n\n MODEL_META_DATA = model_meta\n\n def __init__(self, path=DEFAULT_MODEL_PATH):\n logger.info('Loading model from: {}...'.format(path))\n clear_session()\n\n self.model = models.load_model(path)\n # this seems to be required to make Keras models play nicely with threads\n self.model._make_predict_function()\n logger.info('Loaded model: {}'.format(self.model.name))\n\n def _read_image(self, image_data):\n try:\n image = Image.open(io.BytesIO(image_data)).convert('RGB')\n return image\n except IOError as e:\n logger.error(str(e))\n abort(400, \"The provided input is not a valid image (PNG or JPG required).\")\n\n def _pre_process(self, image, target, mode='tf'):\n image = image.resize(target)\n image = img_to_array(image)\n image = np.expand_dims(image, axis=0)\n image = imagenet_utils.preprocess_input(image, mode=mode)\n return image\n\n def _post_process(self, preds):\n return imagenet_utils.decode_predictions(preds)[0]\n\n def _predict(self, x):\n x = self._pre_process(x, target=MODEL_INPUT_IMG_SIZE)\n preds = self.model.predict(x)\n return self._post_process(preds)\n" }, { "alpha_fraction": 0.4027777910232544, "alphanum_fraction": 0.6527777910232544, "avg_line_length": 13.399999618530273, "blob_id": "1db4d02824862ce63fa4e20cb5ca160c9348f500", "content_id": "32140a0ec8bc81be169cca83b6c021202819a15b", "detected_licenses": [ "Apache-2.0", "MIT" ], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 72, "license_type": "permissive", "max_line_length": 18, "num_lines": 5, "path": "/requirements.txt", "repo_name": "IBM/MAX-Inception-ResNet-v2", "src_encoding": "UTF-8", "text": "numpy==1.17.5\ntensorflow==1.15.4\nPillow==8.2.0\nh5py==2.9.0\nkeras==2.1.4\n" } ]	6
JunshuTedLiu/KnowledgeGlobe	https://github.com/JunshuTedLiu/KnowledgeGlobe	0699a74ba52b4b1f847ebfe64812e7f4843eab08	55f6ecaea3fca6af06ec834b79552597b40b705a	4182803b97a507dbd0596bceaa3c692c720a44c8	refs/heads/master	2022-08-13T17:40:10.988403	2022-08-03T09:28:35	2022-08-03T09:28:35	161,742,498	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5712318420410156, "alphanum_fraction": 0.5947972536087036, "avg_line_length": 64.36000061035156, "blob_id": "b18291a31b16a51d6125bc8a32cdf55f2d358aa6", "content_id": "2940d9cfffc9da4ccd66554bf2977d9c1997701a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "HTML", "length_bytes": 6537, "license_type": "no_license", "max_line_length": 450, "num_lines": 100, "path": "/old-version/contact-us.html", "repo_name": "JunshuTedLiu/KnowledgeGlobe", "src_encoding": "UTF-8", "text": "<!DOCTYPE html>\n<html>\n <head>\n <title>Contact Us - Knowledge Globe</title>\n <link rel=\"stylesheet\" href=\"style.css\">\n <link href=\"https://fonts.googleapis.com/css?family=Archivo+Black\|Archivo+Narrow:400,400i,500,500i,600,600i,700,700i\|Archivo:400,400i,500,500i,600,600i,700,700i&subset=latin-ext,vietnamese\" rel=\"stylesheet\">\n <link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/[email protected]/animate.min.css\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n <!--[if lt IE 9]>\n <script src=\"http://html5shim.googlecode.com/svn/trunk/html5.js\">\n </script>\n <![endif]-->\n <meta name=\"description\" content=\"Knowledge Globe, a.k.a. KnoGlo, is a powerful tool to analyze and visualize the distribution of research interests for the selected topic and year. You can see how these research interests distributed by subjects, keywords, countries, publishers, and publication types.\">\n </head>\n\n <body>\n <nav>\n<!-- <strong>Knowledge Globe</strong> / dataset preview (beta) / how we do it? / update history / blog / contact us-->\n <ul>\n <li><a href=\"index.html\"><img src=\"assets/KnoGlo.png\" class=\"icon\" width=100></a></li>\n <li><a href=\"visualizations.html\">visualizations</a></li>\n <li><a href=\"how-this-works.html\">how this works?</a></li>\n <li><a href=\"dataset-examples.html\">dataset examples</a></li>\n <li><a href=\"source-codes.html\">source codes</a></li>\n <li><a href=\"our-vision.html\">our vision</a></li>\n <li><strong><a href=\"contact-us.html\">contact us</a></strong></li>\n </ul>\n </nav>\n <div class=\"page\">\n <h1 class=\"large-title\">\n About the KnoGlo team.\n </h1>\n <p>\n And how to reach us out.\n </p>\n <hr>\n <p class=\"paragraph-head\">\n Knowledge Globe is a final project for the Data Management and Data Analysis course offered by the <strong>University of Chicago.</strong> Who made and participated in this project are students and faculties from UChicago's brand-new graduate program, <strong class=\"extreme-highlight\">Digital Studies of Language, Culture, and History.</strong>\n </p>\n \n <p>\n Learn more about UChicago's <a href=\"https://digitalstudies.uchicago.edu/\">Digital Studies</a> program.\n </p>\n<!--\n <dir class=\"numbers-group\">\n <dir class=\"number-and-caption\">\n <p class=\"number\"><strong>A.D. 1636</strong></p>\n <p class=\"number-caption\">Harvard University is the oldest university in the United States, established in the year 1636.</p>\n </dir>\n <dir class=\"number-and-caption\">\n <p class=\"number\"><strong>≈ 5,300</strong></p>\n <p class=\"number-caption\">As of 2015, there are 5,300 colleges and universities in the United States.</p>\n </dir>\n </dir>\n-->\n <h2>\n Who are we?\n </h2>\n \n <p>\n <strong>Junshu Liu</strong> is the founder and director of this project. He is a graduate student in UChicago majoring Digital Studies of Language, Culture, and History. Learn more about Junshu and his past projects at <a href=\"https://junshuliu.com\">junshuliu.com</a>.\n </p>\n <p>\n Professor <strong>Jeffrey R. Tharsen</strong> is the instructor of the Data Analysis course. He is one of the advisors for this project. He is an excellent person who masters not only data science but also East Asian language and culture. With the help from Dr. Tharsen, I learned a lot of R programming skills, and he helped me a lot Learn more about Dr. Tharsen at his website, <a href=\"http://tharsen.net\">tharsen.net</a>.\n </p>\n <p>\n Professor <strong>Miller Prosser</strong> is the instructor of the Data Management course. He is also the advisors for Knowledge Globe. Dr. Prosser is also a professional data analyst that proficient in a lot of data management software and tools. As a beginner for data science, I learned a lot with the help of Dr. Prosser in the past 2 months. <a href=\"https://ochre.uchicago.edu/directory/miller-prosser\">Learn more</a> about Dr. Prosser.\n </p>\n <h2>Contact Us</h2>\n <p>We treasure your advice and feedback. You're welcomed to contact us with your valuable feedback to make our product better.</p>\n <dir class=\"numbers\">\n <dir class=\"number-and-caption centered-numbers\">\n <p class=\"number-small\"><strong>Junshu Liu</strong></p>\n <p class=\"number-caption\"><a href=\"mailto:[email protected]?Subject=Hi,%20I%20have%20some%20questions&#47advices%20in%20regard%20to%20Knowledge%20Globe\" target=\"_top\">[email protected]</a></p>\n <p class=\"number-caption\">Graduate student, software developer, graphic and UI/UX designer.</p>\n </dir>\n <dir class=\"number-and-caption centered-numbers\">\n <p class=\"number-small\"><strong>Jeffrey Tharsen</strong></p>\n <p class=\"number-caption\"><a href=\"mailto:[email protected]?Subject=Hi,%20I%20have%20some%20questions&#47advices%20in%20regard%20to%20Knowledge%20Globe\" target=\"_top\">[email protected]</a></p>\n <p class=\"number-caption\">Computational Scientist for the Digital Humanities in UChicago.</p>\n </dir>\n <dir class=\"number-and-caption centered-numbers\">\n <p class=\"number-small\"><strong>Miller Prosser</strong></p>\n <p class=\"number-caption\"><a href=\"mailto:[email protected]?Subject=Hi,%20I%20have%20some%20questions&#47advices%20in%20regard%20to%20Knowledge%20Globe\" target=\"_top\">[email protected]</a></p>\n <p class=\"number-caption\">Research Database Specialist in UChicago.</p>\n </dir>\n </dir>\n <hr>\n </div>\n </body>\n \n <footer>\n © 2018-2019 Knowledge Globe. All rights reserved.\n <br>\n <a href=\"https://www.junshuliu.com\">Junshu Liu</a>,\n <a href=\"http://tharsen.net\">Jeffrey R. Tharsen</a>, <a href=\"https://ochre.uchicago.edu/directory/miller-prosser\">Miller Prosser</a>.\n <hr>\n <a href=\"contact-us.html\">Contact Us</a>\n </footer>\n</html>" }, { "alpha_fraction": 0.5993953943252563, "alphanum_fraction": 0.6139893531799316, "avg_line_length": 65.625, "blob_id": "5b280075859537c0ddacf55434499cf403314862", "content_id": "3a35afd099b8605792e3926c15033cd1e6e28274", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "HTML", "length_bytes": 9605, "license_type": "no_license", "max_line_length": 669, "num_lines": 144, "path": "/corpus-and-NLP/case-studies.html", "repo_name": "JunshuTedLiu/KnowledgeGlobe", "src_encoding": "UTF-8", "text": "<!DOCTYPE html>\n<html lang=\"en\">\n\n<head>\n <meta charset=\"UTF-8\">\n <meta http-equiv=\"X-UA-Compatible\" content=\"IE=edge\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1, shrink-to-fit=no\">\n <meta http-equiv=\"x-ua-compatible\" content=\"ie=edge\">\n <!-- <link rel=\"stylesheet\" href=\"css/bootstrap.min.css\">-->\n <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css\" integrity=\"sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T\" crossorigin=\"anonymous\">\n <!-- Additional stylesheet link goes here -->\n <link rel=\"stylesheet\" type=\"text/css\" href=\"../css/boot.css\">\n <title>Case Studies - Corpus & NLP - Knowledge Globe</title>\n\n <script>\n $(document).ready(function() {\n $(\".dropdown-toggle\").dropdown();\n });\n </script>\n</head>\n\n<body>\n <nav class=\"navbar fixed-top navbar-expand-lg navbar-custom\">\n <a class=\"navbar-brand\" href=\"../index.html\">\n <!-- instead of link to index.html, use # for just jumping back to the top of the page.-->\n <img src=\"../assets/KnoGlo2white.png\" class=\"align-top\" style=\"width: 70px;\">\n </a>\n <button class=\"navbar-toggler\" type=\"button\" data-toggle=\"collapse\" data-target=\"#navbarNav\">\n <span class=\"navbar-toggler-icon\"></span>\n </button>\n <div class=\"collapse navbar-collapse\" id=\"navbarNav\">\n <ul class=\"navbar-nav\">\n <li class=\"nav-item active\">\n <a class=\"nav-link\" href=\"../index.html\">Home</a>\n <!-- instead of link to index.html, use # for just jumping back to the top of the page.-->\n </li>\n <li class=\"nav-item dropdown\">\n <a class=\"nav-link dropdown-toggle\" href=\"#schedule\" id=\"navbarDropdown\" role=\"button\" data-toggle=\"dropdown\" aria-haspopup=\"true\" aria-expanded=\"false\">Statistical Metadata Visualization</a>\n <div class=\"dropdown-menu\">\n <a class=\"dropdown-item\" href=\"../stat-metadata-visual/how-to-use.html\">How to Use?</a>\n <a class=\"dropdown-item\" href=\"../stat-metadata-visual/case-studies.html\">Visualization Case Studies</a>\n <a class=\"dropdown-item\" href=\"../stat-metadata-visual/implementation.html\">Implementation & Source Code</a>\n <a class=\"dropdown-item\" href=\"../source-code/Statistical-Metadata-Visualization/Web/knoglo2-statistics-list.html\">Web Version</a>\n </div>\n </li>\n <li class=\"nav-item dropdown\">\n <a class=\"nav-link dropdown-toggle\" href=\"#schedule\" id=\"navbarDropdown\" role=\"button\" data-toggle=\"dropdown\" aria-haspopup=\"true\" aria-expanded=\"false\">Corpus Generator & NLP Analysis</a>\n <div class=\"dropdown-menu\">\n <a class=\"dropdown-item\" href=\"../corpus-and-NLP/how-to-use.html\">How to Use?</a>\n <a class=\"dropdown-item\" href=\"../corpus-and-NLP/case-studies.html\">Case Studies</a>\n <a class=\"dropdown-item\" href=\"../corpus-and-NLP/implementation.html\">Implementation & Source Code</a>\n </div>\n </li>\n <li class=\"nav-item\">\n <a class=\"nav-link\" href=\"../who-cares.html\">Who Cares?</a>\n </li>\n <li class=\"nav-item\">\n <a class=\"nav-link\" href=\"../about-knoglo.html\">About KnoGlo</a>\n </li>\n </ul>\n </div>\n </nav>\n \n<!--\n <div class=\"jumbotron\" style=\"margin-top: 140px;\">\n <h1>Who cares?</h1> \n</div>\n-->\n \n <div style=\"margin-top: 140px;\">\n <h4 class=\"container-fluid\">Corpus Generator & NLP Analysis</h4>\n <hr>\n </div>\n \n <div class=\"jumbotron jumbotron-fluid\" style=\"background-color: #ffffff\">\n <div class=\"container\" style=\"\">\n <h2 class=\"display-2\">Case Studies.</h2>\n<!-- <hr>-->\n <p class=\"lead\">See KnoGlo’s corpus generator and NLP analysis tool in action and what those results mean to us.</p>\n\n </div>\n </div>\n \n\n \n \n\n <div class=\"jumbotron jumbotron-fluid\" style=\"background-color: #f2f2f2\">\n <div class=\"container\">\n <!-- <h1 class=\"display-4\">An exciting final week.</h1>-->\n <!-- <hr class=\"my-2\">-->\n <h2>Example 1. See how the Internet has evolved in the past 50 years.</h2>\n <p class=\"lead\">Keywords: <span class=\"text-monospace\">Internet</span>, <span class=\"text-monospace\">ARPANET</span>, and other related. Year: from <span class=\"text-monospace\">1969</span> to <span class=\"text-monospace\">2019</span>. 5 years as a gap.</p>\n <hr>\n <h5>Generating Corpus</h5>\n <p>There are 55 text documents inside the <a href=\"https://github.com/JunshuTedLiu/KnowledgeGlobe/tree/master/source-code/Corpus-Generator-and-NLP/Corpus%20Examples/Corpus%20Example%201%20-%20Computer%20and%20Internet\">corpus</a>. For each document, there are up to 50 titles and abstracts about a selected topic published in a specific year.</p>\n <p>For this research, I picked several keywords and a time range of 1969-2019 (5 years as a gap). Selected keywords include <span class=\"text-monospace\">Internet</span>, <span class=\"text-monospace\">ARPANET</span>, <span class=\"text-monospace\">computer network</span>, <span class=\"text-monospace\">computer</span>, and <span class=\"text-monospace\">World Wide Web</span>.</p>\n <p>When I was making the corpus, I found something interesting - there were NO results for the “World Wide Web” until 1994.</p>\n <h5>NLP Topic Modeling</h5>\n <p>I made a few attempts on different numbers of topics. When I increased the number of topics, while the word cloud seemed like it could provide more results of the top <span class=\"font-italic\">n</span> words, some of them are constantly repeating between topics. 4 topics return a fairly satisfying result in this case.</p>\n <p>View the complete topic modeling result with 4 topics by running <a href=\"https://github.com/JunshuTedLiu/KnowledgeGlobe/blob/master/source-code/Corpus-Generator-and-NLP/NLP%20Topic%20Modeling%20Example%201.ipynb\">this IPYNB file</a> in Jupyter Notebook.</p>\n <p class=\"small\">This file has the output results for Example 1. Do not re-run the code, or the old results will be overwritten.</p>\n <p>Note: after changing the topic number, be sure to change the row and columns for the distribution of word counts by dominant topic, word cloud, and word count & importance of topic keywords to match your topic numbers (i.e., 9 topics with a 3x3 visualization).</p>\n <h5>Compare with Statistical Metadata Results</h5>\n <p>We can compare the result of topic modeling from our corpus and the original subject and keyword tags from Springer. Although the topic modeling results might not contain everything that is included in Springer’s tags since my free API plan only allows me to get 50 titles and abstracts for each text file, we can find some topics share in common, like the “Medicine & Health” subject tag correlated to the top <span class=\"font-italic\">n</span> words, such as <span class=\"text-monospace\">protein</span>, <span class=\"text-monospace\">gene</span>, and <span class=\"text-monospace\">cell</span>, in one of the topics from the topic modeling result.</p>\n </div>\n </div>\n\n <div class=\"jumbotron jumbotron-fluid\" style=\"background-color: #f2f2f2\">\n <div class=\"container\">\n <!-- <h1 class=\"display-4\">An exciting final week.</h1>-->\n <!-- <hr class=\"my-2\">-->\n <h2>Example 2. Medicine in Japan.</h2>\n <p class=\"lead\">Keyword: <span class=\"text-monospace\">medicine</span>. Country: <span class=\"text-monospace\">Japan</span>. Year: from <span class=\"text-monospace\">1989</span> to <span class=\"text-monospace\">2019</span>. 5 years as a gap.</p>\n <hr>\n<!-- <p>Japan is one of the countries that is leading the medicine industry.</p>-->\n <p>Coming soon. Please check back later.</p>\n </div>\n </div>\n\n <footer class=\"bg-footer text-white d-none d-lg-block p-2\">\n <div class=\"container\">\n <div class=\"row\">\n <div class=\"col small\">\n\n\n <!-- <img src=\"assets/knowledgeglobe2white.png\" class=\"align-top\" style=\"width: 200px;\">-->\n <span>© 2018-2019 Knowledge Globe. All rights reserved.</span>\n <br>\n <span>Created by <a href=\"https://junshuliu.com\">Junshu Liu</a>. Advised by <a href=\"https://ochre.uchicago.edu/directory/david-schloen\">David Schloen</a>, <a href=\"http://tharsen.net/\">Jeffrey Tharsen</a>, and <a href=\"https://ochre.uchicago.edu/directory/miller-prosser\">Miller Prosser</a>.</span>\n </div>\n\n\n </div>\n </div>\n </footer>\n\n <script src=\"https://code.jquery.com/jquery-3.3.1.slim.min.js\" integrity=\"sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo\" crossorigin=\"anonymous\"></script>\n <script src=\"https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js\" integrity=\"sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1\" crossorigin=\"anonymous\"></script>\n <script src=\"https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js\" integrity=\"sha384-JjSmVgyd0p3pXB1rRibZUAYoIIy6OrQ6VrjIEaFf/nJGzIxFDsf4x0xIM+B07jRM\" crossorigin=\"anonymous\"></script>\n <!-- Additional JS goes here -->\n</body>\n\n</html>" }, { "alpha_fraction": 0.6720043420791626, "alphanum_fraction": 0.6957538723945618, "avg_line_length": 43.119049072265625, "blob_id": "23ab7596c475dfe02dbf43b6a96e69f5f8e1c398", "content_id": "e1c6cf7fd63d1057197a7f61419f01a6f9ca23ad", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "R", "length_bytes": 5558, "license_type": "no_license", "max_line_length": 183, "num_lines": 126, "path": "/source-code/Statistical-Metadata-Visualization/Python-and-R/KnoGlo-Visualization.R", "repo_name": "JunshuTedLiu/KnowledgeGlobe", "src_encoding": "UTF-8", "text": "# setwd(\"~/Documents/GitHub/KnowledgeGlobe/source-code/Analysis-and-Visualization\")\n\n# Every time before you switch to a new folder, you need to go back to the upper menu.\nsetwd(\"../\") # NOT FOR THE FIRST TIME OF USE!\n\n# Set your work directory, which should be the folder that contains the datasets that you want to visualize.\nsetwd(\"keyword_computer_year_1988\")\n\n# Save CSV tables\n\nsubject <- read.csv('statistics_subject.csv', stringsAsFactors = FALSE)\nkeyword <- read.csv('statistics_keyword.csv', stringsAsFactors = FALSE)\npub <- read.csv('statistics_pub.csv', stringsAsFactors = FALSE)\nyear <- read.csv('statistics_year.csv', stringsAsFactors = FALSE)\ncountry <- read.csv('statistics_country.csv', stringsAsFactors = FALSE)\ntype <- read.csv('statistics_type.csv', stringsAsFactors = FALSE)\n\nkeywordInput <- readline(prompt=\"Enter the topic (keyword) name that you've used in KnowledgeGlobe.py: \")\nkeywordInput\n\nyearInput <- readline(prompt=\"Enter the year that you've used in KnowledgeGlobe.py: \")\nyearInput\n\n# Visualizations\n\n# 1. subject\n\n# Bar Plot\npar(mar=c(12,4,4,1)) # apply margins since the name will be so long\nxcor <- barplot(subject$count, main = paste(\"Subjects of publications about\", keywordInput, \"in\", yearInput), names.arg=c(subject$value), las = 2, cex.names = 0.6)\ntext(x = xcor, y = subject$count, label = subject$count, pos = 3, cex = 0.8, col = \"red\")\ndev.off()\n# The 'mar' argument of 'par' sets the width of the margins in the order: 'bottom', 'left', 'top', 'right'. The default is to set 'left' to 4,\n# https://stackoverflow.com/questions/2807060/r-is-plotting-labels-off-the-page\n\n# Pie Chart\n# Not recommended! Unless you have paid API license with full access\n# The Basic plan for Springer API will only return the top 20 results for the statistics for each attribute\nslices <- c(subject$count)\nlbls <- c(subject$value)\npct <- round(slices/sum(slices)100)\nlbls <- paste(lbls, pct) # add percents to labels \nlbls <- paste(lbls,\"%\",sep=\"\") # ad % to labels \npie(slices,labels = lbls, col=rainbow(length(lbls)), main=c(\"Subjects of publications about \", keywordInput, \" in \", yearInput))\n\n# 2. keyword\n\n# Bar Plot\nxcor1 <- barplot(keyword$count, main = c(\"Keywords of publications about \", keywordInput, \" in \", yearInput), names.arg=c(keyword$value), las = 2)\ntext(x = xcor1, y = keyword$count, label = keyword$count, pos = 3, cex = 0.8, col = \"red\")\n\n# Pie Chart\n# Not recommended! Unless you have paid API license with full access\n# The Basic plan for Springer API will only return the top 20 results for the statistics for each attribute\nslices1 <- c(keyword$count)\nlbls1 <- c(keyword$value)\npct1 <- round(slices1/sum(slices1)100)\nlbls1 <- paste(lbls1, pct1) # add percents to labels\nlbls1 <- paste(lbls1,\"%\",sep=\"\") # ad % to labels\npie(slices1,labels = lbls1, col=rainbow(length(lbls1)), main=c(\"Keywords of publications about \", keywordInput, \" in \", yearInput))\n\n# 3. pub\nbarplot(pub$count, main = c(\"Publishers of the journals about \", keywordInput, \" in \", yearInput), names.arg=c(pub$value), col=rainbow(20, start=.7, end=.1), las = 2, cex.names = 0.9)\n\n# 4. year (total)\nyear$count\nbarplot(year$count, main = c(\"Quantities of publications about \", keywordInput, \" for each year\"), names.arg=c(year$value), las = 2)\n\n# 5. country\nbarplot(country$count, main = c(\"Quantities of publications about \", keywordInput, \" for each countries in \", yearInput), names.arg=c(country$value), las = 2)\n\n# 6. type\nslices2 <- c(type$count)\nlbls2 <- c(type$value)\npct2 <- round(slices2/sum(slices2)*100)\nlbls2 <- paste(lbls2, pct2) # add percents to labels\nlbls2 <- paste(lbls2,\"%\",sep=\"\") # ad % to labels\npie(slices2,labels = lbls2, col=rainbow(length(lbls2)), main=\"Types of publications about \", keywordInput, \" in \", yearInput)\n\ndev.off()\n\n# put everything together in one place.\n# (Need to fix the margin problem)\npar(mfrow = c(2,2))\n# be sure to run the code above.\n# 1. subject\nxcor <- barplot(subject$count, main = paste(\"Subjects of publications about\", keywordInput, \"in\", yearInput), names.arg=c(subject$value), las = 2, cex.names = 0.6)\ntext(x = xcor, y = subject$count, label = subject$count, pos = 3, cex = 0.8, col = \"red\")\n# 2. keyword\nxcor1 <- barplot(keyword$count, main = c(\"Keywords of publications about \", keywordInput, \" in \", yearInput), names.arg=c(keyword$value), las = 2)\ntext(x = xcor1, y = keyword$count, label = keyword$count, pos = 3, cex = 0.8, col = \"red\")\n# 3. pub\nbarplot(pub$count, main = c(\"Publishers of the journals about \", keywordInput, \" in \", yearInput), names.arg=c(pub$value), col=rainbow(20, start=.7, end=.1), las = 2, cex.names = 0.9)\n# 5. country\nbarplot(country$count, main = c(\"Quantities of publications about \", keywordInput, \" for each countries in \", yearInput), names.arg=c(country$value), las = 2)\n\ndev.off()\n\n# now, let's make something fancy!\n\n# R bokeh\n# Learn more at http://hafen.github.io/rbokeh\n# install.packages(\"rbokeh\")\nlibrary(rbokeh)\n\n# country\n# p <- figure(width = 1000, height = 600) %>%\n# ly_points(value, count, data = country,\n# color = value, glyph = value,\n# hover = list(value, count)) %>%\n# x_axis(angle = 45)\n# p\n\n# this looks better. I swapped the x and y axis.\n\n# country\np1 <- figure(width = 1000, height = 600) %>%\n ly_points(count, value, data = country,\n color = value, glyph = value,\n hover = list(count, value)) %>%\n theme_legend(background_fill_alpha = 0.25)\np1\n\n# Feel free to use any other visualization frameworks like ggplot.\n# install.packages(\"ggplot2\")\nlibrary(ggplot2)" }, { "alpha_fraction": 0.6923938989639282, "alphanum_fraction": 0.7013587355613708, "avg_line_length": 39.5625, "blob_id": "7de7878bdae4a5c8500d83eb9a6c2303361e2c59", "content_id": "0e642ee78ec28cd7cca5184e3751a7247fa41874", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7139, "license_type": "no_license", "max_line_length": 324, "num_lines": 176, "path": "/source-code/Statistical-Metadata-Visualization/Python-and-R/KnoGlo-Dataset-Generator-keyword-and-year.py", "repo_name": "JunshuTedLiu/KnowledgeGlobe", "src_encoding": "UTF-8", "text": "# Big thanks to Lanfei Liu, who built a tool in Python using Springer Metadata API and other APIs in order to generate literature review data.\n# Lanfei's code: github.com/lanfeiliu/SpringerAPI-ElsevierAPI_LiteratureReviewTable\n\n# By learning her code, I figured out how to gather data from the Springer metadata JSON output using Python with filters (query), manage these data, and create & write CSV files by columns.\n\n# I rewrote mostly everything for our own research for the Knowledge Globe project, while I kept her part of the code for querying, which is filtering by topic and year by user input. Once I figured out how she implements the query, I could add a new feature that could allow the user to filter the data by other conditions.\n\n# In the JSON metadata, \"records\" and \"facets\" has the same structure. Lanfei's code helped me to understand how to gather and organize the data. However, I rewrote these part of the code because we need to use the \"facets\" data for statistics (\"counts\"), instead of \"records\" that she was using.\n# The method of getting these data is the same as Lanfei's, but since we are making completely different things, I made some changes in the Springer_Article class - I made several lists directly for the columns in all datasets, instead of combining data in a string.\n\n### KNOWLEDGE GLOBE\n\n# KnowledgeGlobe.py\n# Junshu Liu\n\nimport unittest\nimport requests\nimport json\nimport csv\nimport os\n\nprint \"\\nKNOWLEDGE GLOBE\\n===============\\nSee how scholars around the world leading the evolution of knowledge.\\n\"\n\nprint \"Statistical Metadata Visualization. Filtered by keyword and year.\\n\"\n\nkeyword = raw_input(\"Say a topic. Could be a single word or phase.\\nTOPIC: \")\n\ndate_int = int(raw_input(\"\\nWhat range of time would you like to see? Name that year.\\nYEAR: \"))\n\n# Create target Directory if don't exist\nif not os.path.exists(\"keyword_%s_year_%i\" % (keyword,int(date_int))):\n os.mkdir(\"keyword_%s_year_%i\" % (keyword,int(date_int)))\n print 'Created directory \"keyword_%s_year_%i\"' % (keyword,int(date_int))\nelse:\n print 'A directory with the keyword \"%s\" and year \"%i\" already exists.\\nPlease try another keyword and year.\\nOr, delete the one that you had, and make a new one to get latest results.' % (keyword,int(date_int))\n\nspringer_keyword = \"?q=(\"+ \"%22\" + keyword.replace(\" \", \"%20\") + \"%22\" + \"%20AND%20year:\" + str(date_int) + \")\"\n\n\nspringer_api_key = raw_input(\"\\nEnter your Springer API key.\\nNeed help? Visit https://dev.springernature.com/ for more details.\\nAPI key: \")\n\nbase_url_springer = 'http://api.springer.com/metadata/json'\n\nurl_params_springer = {}\nurl_params_springer[\"api_key\"] = springer_api_key\nurl_params_springer[\"p\"] = 50 # June 2019: Looks like the maximum is 50 for the free plan. \| December 2018: If put 200: 10 results will be returned. This doesn't affect for counts. I've tested this line of code and changed this value to 300 and 400, and the outputs are the same.\n\n# try:\n# \tfor_springer = open('statistics.txt').read()\n# \td_springer = json.loads(for_springer)\n# \tprint \"\\n=== ERROR: You have already have a saved results of Springer Metadata API in your directly. ===\\n\"\n# \tprint \"\\nIn order to not mess up your previously saved output result and dataset files, we are asking you to\"\n# \tprint \"\\ndelete or move your previous saved raw output and dataset files to the other place first,\"\n# \tprint \"\\nthen you can make a new request.\"\n# \tprint \"\\nYour API Key hasn't been used at this time, so don't worry. That doesn't count.\"\n\n# except:\n\t# d_springer = requests.get(base_url_springer + springer_keyword\n\t# \t\t\t\t\t\t\t,params=url_params_springer).json()\n\t#\n\t# print \"\\n=== See the result of Springer Metadata API in your directory. ===\\n\"\n\t#\n\t# fr_springer = open(\"statistics.txt\",\"w\")\n\t# fr_springer.write(json.dumps(d_springer))\n\t# fr_springer.close()\n\nd_springer = requests.get(base_url_springer + springer_keyword\n\t\t\t\t\t\t\t,params=url_params_springer).json()\n\nprint \"\\n=== See the result of Springer Metadata API in your directory. ===\\n\"\n\nfr_springer = open(\"keyword_%s_year_%i/statistics.txt\" % (keyword,int(date_int)),\"w\")\nfr_springer.write(json.dumps(d_springer)) # write the json output into the text file.\nfr_springer.close()\n\nclass Springer_Article():\n def __init__(self, facets={}):\n self.facets = facets\n\n def count(self):\n count_lst = [i['count'].encode('utf-8') for i in self.facets['values']]\n # combine_name = ', '.join(count_lst)\n return count_lst\n # return combine_name\n\n def value(self):\n value_lst = [i['value'].encode('utf-8') for i in self.facets['values']]\n # combine_name = ', '.join(value_lst)\n return value_lst\n # return combine_name\n\n\nfacet_cat_insts = [Springer_Article(facets) for facets in d_springer['facets']] # 6 items, means 6 attributes in \"facets\".\n\ncount = 0\n\nfor facets in facet_cat_insts:\n count = count + 1 # is 6\n\ncountlst = [i.count() for i in facet_cat_insts]\ncountlst_subject = countlst[0]\ncountlst_keyword = countlst[1]\ncountlst_pub = countlst[2]\ncountlst_year = countlst[3]\ncountlst_country = countlst[4]\ncountlst_type = countlst[5]\nvaluelst = [i.value() for i in facet_cat_insts]\nvaluelst_subject = valuelst[0]\nvaluelst_keyword = valuelst[1]\nvaluelst_pub = valuelst[2]\nvaluelst_year = valuelst[3]\nvaluelst_country = valuelst[4]\nvaluelst_type = valuelst[5]\n\nfile0=open('keyword_%s_year_%i/statistics_subject.csv' % (keyword,int(date_int)),'wb')\nwriter=csv.writer(file0)\n\nwriter.writerow(['count','value'])\nwriter.writerows(zip(countlst_subject, valuelst_subject))\n\nprint '-------- Created dataset file \"statistics_subject.csv\" -----------'\n\n# ---\n\nfile1=open('keyword_%s_year_%i/statistics_keyword.csv' % (keyword,int(date_int)),'wb')\nwriter=csv.writer(file1)\n\nwriter.writerow(['count','value'])\nwriter.writerows(zip(countlst_keyword, valuelst_keyword))\n\nprint '-------- Created dataset file \"statistics_keyword.csv\" -----------'\n\n# ---\n\nfile2=open('keyword_%s_year_%i/statistics_pub.csv' % (keyword,int(date_int)),'wb')\nwriter=csv.writer(file2)\n\nwriter.writerow(['count','value'])\nwriter.writerows(zip(countlst_pub, valuelst_pub))\n\nprint '-------- Created dataset file \"statistics_pub.csv\" -----------'\n\n# ---\n\nfile3=open('keyword_%s_year_%i/statistics_year.csv' % (keyword,int(date_int)),'wb')\nwriter=csv.writer(file3)\n\nwriter.writerow(['count','value'])\nwriter.writerows(zip(countlst_year, valuelst_year))\n\nprint '-------- Created dataset file \"statistics_year.csv\" -----------'\n\n# ---\n\nfile4=open('keyword_%s_year_%i/statistics_country.csv' % (keyword,int(date_int)),'wb')\nwriter=csv.writer(file4)\n\nwriter.writerow(['count','value'])\nwriter.writerows(zip(countlst_country, valuelst_country))\n\nprint '-------- Created dataset file \"statistics_country.csv\" -----------'\n\n# ---\n\nfile5=open('keyword_%s_year_%i/statistics_type.csv' % (keyword,int(date_int)),'wb')\nwriter=csv.writer(file5)\n\nwriter.writerow(['count','value'])\nwriter.writerows(zip(countlst_type, valuelst_type))\n\nprint '-------- Created dataset file \"statistics_type.csv\" -----------\\n'\n\n### For test purpose.\n# print subject_count\n# print count\n# print countlst_year\n" }, { "alpha_fraction": 0.7124773859977722, "alphanum_fraction": 0.7124773859977722, "avg_line_length": 60.5, "blob_id": "0e5dab563db7b757a0ef9966a263889f2e628fb2", "content_id": "f702ff7480dac17fa84bdf5d49628547fcb8188d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1106, "license_type": "no_license", "max_line_length": 382, "num_lines": 18, "path": "/README.md", "repo_name": "JunshuTedLiu/KnowledgeGlobe", "src_encoding": "UTF-8", "text": "# Knowledge Globe\n\n<p>See how researchers around the world leading the evolution of knowledge. No matter when and where.<p>\n\n<hr>\n\n<p>\n We present a new way to see how knowledge evolves by looking at what researchers around the world put their effort on specific industries in a range of time.\n </p>\n <p>\n By looking for what people were thinking and doing academically and professionally, we could observe the evolution in specific areas of study, and even how this world changes from a global and historical perspective.\n </p>\n\n<p>\n <strong>Introducing <strong class=\"extreme-highlight\">Knowledge Globe </strong>(KnoGlo),</strong> a powerful tool to <strong>analyze and visualize</strong> the distribution of research interests for the selected topic and year. You can see how these research interests distributed by <strong>subjects, keywords, countries, publishers, and publication types.</strong>\n </p>\n\n<p>Learn more at <a href=\"https://junshutedliu.github.io/KnowledgeGlobe/\">KnoGlo's official website.</a></p>" }, { "alpha_fraction": 0.7088035941123962, "alphanum_fraction": 0.7151994109153748, "avg_line_length": 42.573768615722656, "blob_id": "f345fd05614b32f8283e834a42e71dccb6b33eb7", "content_id": "9518ab0d1606f8e057a2382b82791f459667a5f1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5316, "license_type": "no_license", "max_line_length": 324, "num_lines": 122, "path": "/source-code/Corpus-Generator-and-NLP/KnoGlo-Corpus-Generator-keyword-and-year.py", "repo_name": "JunshuTedLiu/KnowledgeGlobe", "src_encoding": "UTF-8", "text": "# Big thanks to Lanfei Liu, who built a tool in Python using Springer Metadata API and other APIs in order to generate literature review data.\n# Lanfei's code: github.com/lanfeiliu/SpringerAPI-ElsevierAPI_LiteratureReviewTable\n\n# By learning her code, I figured out how to gather data from the Springer metadata JSON output using Python with filters (query), manage these data, and create & write CSV files by columns.\n\n# I rewrote mostly everything for our own research for the Knowledge Globe project, while I kept her part of the code for querying, which is filtering by topic and year by user input. Once I figured out how she implements the query, I could add a new feature that could allow the user to filter the data by other conditions.\n\n# In the JSON metadata, \"records\" and \"facets\" has the same structure. Lanfei's code helped me to understand how to gather and organize the data. However, I rewrote these part of the code because we need to use the \"facets\" data for statistics (\"counts\"), instead of \"records\" that she was using.\n# The method of getting these data is the same as Lanfei's, but since we are making completely different things, I made some changes in the Springer_Article class - I made several lists directly for the columns in all datasets, instead of combining data in a string.\n\n### KNOWLEDGE GLOBE\n\n# KnowledgeGlobe.py\n# Junshu Liu\n\nimport unittest\nimport requests\nimport json\nimport csv\nimport os\n\nprint \"\\nKNOWLEDGE GLOBE\\n===============\\nSee how researchers around the world leading the evolution of knowledge. No matter when and where.\\n\"\n\nprint \"Corpus Generator. Filtered by keyword and year.\\n\"\n\ncorpus_path = raw_input(\"Where do you want to store your content for this time?\\nEnter your corpus name. Make a new one, or enter an existing one.\\nCORPUS NAME: \")\nif not os.path.exists(\"Corpus_%s\" % corpus_path):\n os.mkdir(\"corpus_%s\" % corpus_path)\n print 'Created a new directory \"corpus_%s\" as your corpus.' % corpus_path\nelse:\n print 'A corpus directory \"corpus_%s\" already exists.\\nYour document will now be stored in this corpus,' % corpus_path\n\nif not os.path.exists(\"raw_JSON_for_corpus_%s\" % corpus_path):\n os.mkdir(\"raw_JSON_for_corpus_%s\" % corpus_path)\n print 'We also made another directory, \"Raw_JSON_%s\", to store the raw JSON output.' % corpus_path\nelse:\n print 'as well as the raw JSON output in \"raw_JSON_for_corpus_%s\".' % corpus_path\n\n\nkeyword = raw_input(\"\\nSay a topic. Could be a single word or phase.\\nTOPIC: \")\n\ndate_int = int(raw_input(\"\\nWhat range of time would you like to see? Name that year.\\nYEAR: \"))\n\nspringer_keyword = \"?q=(\"+ \"%22\" + keyword.replace(\" \", \"%20\") + \"%22\" + \"%20AND%20year:\" + str(date_int) + \")\"\n\n\nspringer_api_key = raw_input(\"\\nEnter your Springer API key.\\nNeed help? Visit https://dev.springernature.com/ for more details.\\nAPI key: \")\n\nbase_url_springer = 'http://api.springer.com/metadata/json'\n\nurl_params_springer = {}\nurl_params_springer[\"api_key\"] = springer_api_key\nurl_params_springer[\"p\"] = 50 # If put 200: 10 results will be returned. This doesn't affect for counts. I've tested this line of code and changed this value to 300 and 400, and the outputs are the same.\n\n# try:\n# \tfor_springer = open('raw_JSON_%s_%i.txt' % (keyword,int(date_int))).read()\n# \td_springer = json.loads(for_springer)\n#\n# except:\n# \td_springer = requests.get(base_url_springer + springer_keyword\n# \t\t\t\t\t\t\t\t,params=url_params_springer).json()\n#\n# \tprint \"\\n=== See the result of Springer Metadata API in your directory. ===\\n\"\n#\n# \tfr_springer = open('raw_JSON_%s_%i.txt' % (keyword,int(date_int)),\"w\")\n# \tfr_springer.write(json.dumps(d_springer))\n# \tfr_springer.close()\n\nd_springer = requests.get(base_url_springer + springer_keyword\n\t\t\t\t\t\t\t,params=url_params_springer).json()\n\nprint \"\\n=== See the result of Springer Metadata API in your directory. ===\\n\"\n\nfr_springer = open('raw_JSON_for_corpus_%s/raw_JSON_%s_%i.txt' % (corpus_path,keyword,int(date_int)),\"w\")\nfr_springer.write(json.dumps(d_springer))\nfr_springer.close()\n\nclass Springer_Article():\n def __init__(self, records={}):\n\t\tself.records = records\n\t\tself.title = records['title'].encode('utf-8')\n\t\tself.abstract = records['abstract'].encode('utf-8')[8:]# get rid of the word \"Abstract\"\n\n# facet_cat_insts = [Springer_Article(facets) for facets in d_springer['facets']] # 6 items, means 6 attributes in \"facets\".\n\narticle_insts = [Springer_Article(records) for records in d_springer['records']]\n\n# for i in article_insts:\n# \tif i.title in titlelst:\n# \t\tarticle_insts.remove(i)\n\n# count = 0\n#\n# for records in article_insts:\n# \tcount = count + 1\n\n# #Create Springer csv file\ntitlelst = [i.title for i in article_insts]\nabstractlst = [i.abstract for i in article_insts]\n\n# file1=open('abstract_full.csv','wb')\n# writer=csv.writer(file1)\n#\n# writer.writerow(['title','abstract'])\n# writer.writerow(zip(titlelst,abstractlst))\n\n\n\nalllistwithtuples = zip(titlelst,abstractlst)\n\nallinonestring = ' '.join([i for sub in alllistwithtuples for i in sub])\n\ntitlestr = \"\".join(allinonestring)\n\n# print zip(titlelst,abstractlst)\n\n# print titlestr\n\nfile1=open('Corpus_%s/title_and_abstract_%s_%i.txt' % (corpus_path,keyword,int(date_int)),'wb')\nfile1.write(titlestr)\n\nprint '--------Created text file \"title_and_abstract_%s_%i.txt\"-----------\\n' % (keyword,int(date_int))\n" } ]	6
lemeryb/Car-Class	https://github.com/lemeryb/Car-Class	5593d4760e617add482f96735e6e68f529ffe7ed	05b6edaa4cb4f20d8f04f986eb601f20b22d080e	4288c6be3d29aa479b7820fd7ea5f6053f768ed2	refs/heads/master	2020-09-08T06:49:17.027096	2019-11-12T02:42:46	2019-11-12T02:42:46	221,050,596	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6166666746139526, "alphanum_fraction": 0.6722221970558167, "avg_line_length": 34.599998474121094, "blob_id": "4794210756fe817fa6cd2661f63733f8876f7fd7", "content_id": "d0ed3087c98cee01438b609db1ad7d248f8edb86", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 180, "license_type": "no_license", "max_line_length": 77, "num_lines": 5, "path": "/change-color.py", "repo_name": "lemeryb/Car-Class", "src_encoding": "UTF-8", "text": "from main import Car\n\npaint_car = input(\"Enter the color you want the car to be painted: \").title()\ncar = Car(\"Tesla\",\"S\",paint_car,\"2019\",\"$60,000\",\"0\")\ncar.print_description()\n\n\n" }, { "alpha_fraction": 0.5764706134796143, "alphanum_fraction": 0.5973856449127197, "avg_line_length": 27.185184478759766, "blob_id": "1ee0633e36abc9999e8bb035304b3f0795cf34f0", "content_id": "9b093af4c2db8fadfecca04b4a1a8397d641b6d5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 765, "license_type": "no_license", "max_line_length": 77, "num_lines": 27, "path": "/main.py", "repo_name": "lemeryb/Car-Class", "src_encoding": "UTF-8", "text": "# Title Car Class\n# Author: Benjamin Lemery\n# Date: 11/11/19\n# This program demonstrates how to use classes.\n# README: Start the program from change-color.py and/or odometer_increment.py\n\n\nclass Car:\n\n def __init__(self,make,model,color,year,price,mileage):\n self.make = make\n self.model = model\n self.color = color\n self.year = year\n self.price = price\n self.mileage = mileage\n\n def print_description(self):\n print(\"Manufacturer: \" + self.make)\n print(\"Model: \" + self.model)\n print(\"Color: \" + self.color)\n print(\"Year: \" + self.year)\n print(\"Price: \" + self.price)\n print(\"Mileage: \" + self.mileage)\n\ncar = Car(\"Tesla\",\"S\",\"Green\",\"2019\",\"$60,000\",\"0\")\ncar.print_description()\n\n\n\n\n" }, { "alpha_fraction": 0.5864332318305969, "alphanum_fraction": 0.6236323714256287, "avg_line_length": 27.5625, "blob_id": "11e9f968543b4b70444a2a9402949c2574d232aa", "content_id": "9fd4d73433394594f55fb916a10124baed5aa1ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 457, "license_type": "no_license", "max_line_length": 112, "num_lines": 16, "path": "/odometer_increment.py", "repo_name": "lemeryb/Car-Class", "src_encoding": "UTF-8", "text": "from main import Car\nimport time\nmileage = 0\n\nbegin_driving = int(input(\"Would you like to begin driving your new car?\\nPress 1 to drive.\\nPress 2 to exit.\"))\n\nif begin_driving == 1:\n print(\"Starting the car..\")\n while mileage < 3:\n time.sleep(1)\n mileage += 1\n print(\"Miles Driven: \" + str(mileage))\n car = Car(\"Tesla\",\"S\",\"Green\",\"2019\",\"$60,000\",str(mileage))\n car.print_description()\nelif begin_driving == 2:\n exit()\n" } ]	3
tjtgzxs/showCode	https://github.com/tjtgzxs/showCode	6ff4843a18789f18db93d876806613adddf10afa	e72d1691c386f06d9cd9658ffc8ca6cde9083790	a770848f2079bab6ef828d78659d8e56a5945986	refs/heads/master	2020-06-24T12:30:54.264099	2019-08-14T01:51:07	2019-08-14T01:51:07	198,963,370	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5524017214775085, "alphanum_fraction": 0.5829694271087646, "avg_line_length": 24.41666603088379, "blob_id": "c03b4c3782a20cf523c5681b3311b0da879fafd2", "content_id": "97c44eb6660b5c5068f574aaef9bc288fa1ab2ae", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 916, "license_type": "no_license", "max_line_length": 85, "num_lines": 36, "path": "/addNumber/addNum.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "\"\"\"\nTalk is Cheap show me the code. --Linus Torvalds\nadd red number at a pic right head\n\"\"\"\nfrom PIL import Image,PSDraw,ImageDraw,ImageFont\nimport sys\nclass handle_pic(object):\n \"\"\"\n use pillow to handle picture\n \"\"\"\n def __init__(self,pic_path):\n self._pic=self.get_pic(pic_path)\n self._num=self.get_num()\n\n @classmethod\n def get_pic(cls,pic_path):\n im = Image.open(pic_path)\n return im\n\n @classmethod\n def get_num(cls):\n return 4\n\n def draw(self):\n base=self._pic.convert('RGBA')\n txt=Image.new(\"RGBA\",base.size,(255,255,255,0))\n fnt=ImageFont.truetype(\"./DroidSans.ttf\",size=100)\n d=ImageDraw.Draw(txt)\n d.text((base.size[0]0.9, 10), str(self._num),fill=(255, 0, 0, 255),font=fnt)\n out = Image.alpha_composite(base, txt)\n\n out.show()\n\nif __name__==\"__main__\":\n pic=handle_pic('./4.jpeg')\n pic.draw()\n\n" }, { "alpha_fraction": 0.6260504126548767, "alphanum_fraction": 0.6260504126548767, "avg_line_length": 20.636363983154297, "blob_id": "5688651deabeee5cc98cae7e4acf4aef40ced76b", "content_id": "267a755b4f7b09dcfc0c4750bf86f87f4c04f222", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 238, "license_type": "no_license", "max_line_length": 81, "num_lines": 11, "path": "/predis.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "import redis\nimport settings\n\n\nclass predis():\n def __init__(self):\n self._r = redis.Redis(host=settings.REDIS_HOST, port=settings.REDIS_PORT)\n\n def rpush(self, code):\n self._r.rpush('codes', code)\n return True\n" }, { "alpha_fraction": 0.5316140055656433, "alphanum_fraction": 0.5465890169143677, "avg_line_length": 24.5744686126709, "blob_id": "657e8b9d1910f557b4cfaf09fca39b57f5d6dbab", "content_id": "82ce10f272539ab5baa61430465e38d14069f460", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1202, "license_type": "no_license", "max_line_length": 106, "num_lines": 47, "path": "/bargin_code/bargin.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "\"\"\"\nTalk is Cheap show me the code. --Linus Torvalds\n# task 01 generate 200 bargin codes\n# task 02 save code to mysql\n# task 03 save code to redis\n\"\"\"\nimport sys\nimport random\nimport string\nsys.path.append(\"..\")\nfrom sql import Sql\nfrom predis import predis\n\nclass bargin(object):\n def __init__(self):\n self._code = string.ascii_letters\n self._code += (\"\".join([str(x) for x in range(0, 10)]))\n\n def gene_code(self):\n code = \"\"\n for i in range(4):\n for j in range(4):\n code += random.choice(self._code) # random.choice return a random item in list/set/string\n if (i <= 2): code += \"-\"\n return code\n\n def generateList(self, num):\n list = []\n for i in range(num):\n code = self.gene_code()\n while code in list:\n code = self.gene_code()\n list.append(self.gene_code())\n return list\n\n def insert_code(self, codes):\n for code in codes:\n Sql.insert('codes', code=code)\n r=predis()\n r.rpush(code=code)\n return True\n\n\nif __name__ == \"__main__\":\n b = bargin()\n codes = b.generateList(200)\n b.insert_code(codes)\n" }, { "alpha_fraction": 0.5115044116973877, "alphanum_fraction": 0.5150442719459534, "avg_line_length": 23.565217971801758, "blob_id": "710b5fe34e48bded9c3c99619e63982322ebfe5f", "content_id": "a1ec41d263d270880397c0d82f0ecfdf52e11cf7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 565, "license_type": "no_license", "max_line_length": 55, "num_lines": 23, "path": "/statistic_word/english_text.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "\"\"\"\nTalk is Cheap show me the code. --Linus Torvalds\n# statistic words of an English text\n\"\"\"\nclass statistic():\n def statistic(self,path):\n with open(path,'r') as o:\n lines=o.read().replace(\"\\n\",\"\")\n statistic_count={}\n words=lines.split(\" \")\n for word in words:\n if(not word in statistic_count.keys()):\n statistic_count[word] =0\n statistic_count[word]+=1\n print(statistic_count)\n\n\n\n\n\nif __name__==\"__main__\":\n s=statistic()\n s.statistic(\"./test.txt\")\n" }, { "alpha_fraction": 0.5134615302085876, "alphanum_fraction": 0.517307698726654, "avg_line_length": 27.88888931274414, "blob_id": "a742cfe2dea8d4047c69e34baba19cb6a236152e", "content_id": "a6ea286d5bca4e5f4e623b41fa772134e94eadf6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 520, "license_type": "no_license", "max_line_length": 49, "num_lines": 18, "path": "/statistic_word_v2/statistic.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "\"\"\"\nTalk is Cheap show me the code. --Linus Torvalds\n# statistic words of an English text in directory\n\"\"\"\nimport glob\nimport os\nif __name__==\"__main__\":\n txts=glob.glob(os.path.join(\"test\",\".txt\"))\n word_dict={}\n for txt in txts:\n with open(txt,'r') as o:\n lines=o.read().replace(\"\\n\",\" \")\n words=lines.split(\" \")\n for word in words:\n if not word in word_dict.keys():\n word_dict[word]=0\n word_dict[word]+=1\n print(word_dict)\n" }, { "alpha_fraction": 0.5720930099487305, "alphanum_fraction": 0.5906976461410522, "avg_line_length": 25.9375, "blob_id": "0c460c853bf54dde15db46822ba98489e6082c4f", "content_id": "7ab332ec9ab82f3b504b305314e27d55173e487c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 430, "license_type": "no_license", "max_line_length": 55, "num_lines": 16, "path": "/handle_pic/handle.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "import glob\nimport os\nfrom PIL import Image\n\nclass handle_pic(object):\n def getPicPath(self,path):\n images=glob.glob(os.path.join(path,'.jpg'))\n for image in images:\n im=Image.open(image)\n im.thumbnail((1920, 1280))#Resolution of xs\n print(im.format, im.size, im.mode)\n im.save(image,\"JPEG\")\n\nif __name__==\"__main__\":\n handle=handle_pic()\n handle.getPicPath(\".\")" }, { "alpha_fraction": 0.5957179069519043, "alphanum_fraction": 0.5969773530960083, "avg_line_length": 29.538461685180664, "blob_id": "782f2205670c526c5a6807f7b725e79cc77e6dfd", "content_id": "ab24de1635ee472ed38c1f7b44b92824e5f02666", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 794, "license_type": "no_license", "max_line_length": 140, "num_lines": 26, "path": "/sql.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "import mysql.connector\nimport settings\n\nMYSQL_HOSTS = settings.MYSQL_HOSTS\nMYSQL_USER = settings.MYSQL_USER\nMYSQL_PASSWORD = settings.MYSQL_PASSWORD\nMYSQL_PORT = settings.MYSQL_PORT\nMYSQL_DB = settings.MYSQL_DB\n\ncnx = mysql.connector.connect(user=MYSQL_USER, password=MYSQL_PASSWORD, host=MYSQL_HOSTS, database=MYSQL_DB,auth_plugin='mysql_native_password')\ncur = cnx.cursor(buffered=True)\n\n\nclass Sql:\n @classmethod\n def insert(cls, table, kw):\n sql_key = \"\"\n sql_value = \"\"\n for key, value in kw.items():\n sql_key += \"`\" + str(key) + \"` \"\n sql_value += str(value)\n sql = \"INSERT INTO \" + str(table) + \" (\" + sql_key + \") \" \\\n + \" VALUES ('\" + str(sql_value) + \"') \"\n dd=1\n cur.execute(sql)\n cnx.commit()\n" }, { "alpha_fraction": 0.578199028968811, "alphanum_fraction": 0.7203791737556458, "avg_line_length": 22.55555534362793, "blob_id": "3998ac6db62ac1b6cc5e3048c265c393ff2c2416", "content_id": "af97e594d6bfb38e8b25c34d3417f3972fad7461", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 211, "license_type": "no_license", "max_line_length": 30, "num_lines": 9, "path": "/settings.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "# mysql configuration\nMYSQL_HOSTS = '192.168.99.100'\nMYSQL_USER = \"root\"\nMYSQL_PASSWORD = '123456'\nMYSQL_PORT = 3306\nMYSQL_DB = 'discount'\n# redis configuration\nREDIS_HOST='192.168.99.100'\nREDIS_PORT = 6379" }, { "alpha_fraction": 0.49940547347068787, "alphanum_fraction": 0.5136741995811462, "avg_line_length": 28.034482955932617, "blob_id": "c03377adb1766a5a31c0d8315fbf92feb1682e3d", "content_id": "a47a2328c3497d8625d30b89e486f474e85825e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 841, "license_type": "no_license", "max_line_length": 94, "num_lines": 29, "path": "/check_code/check.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "import glob\nimport os\nimport re\ndef get_codes_in_dir(path):\n #test\n\n\n files=glob.glob(os.path.join(path,\".py\")) # Get all py files in directory\n total_count=0\n comment_count=0\n space_count=0\n code_count=0\n for file in files:\n with open(file,'r') as f:\n lines=f.readlines()\n for line in lines:\n total_count+=1\n regex1=re.compile(r\"(\\s)#\")\n regex2=re.compile(r\"(\\s)$\")\n if regex1.match(line):\n comment_count+=1\n elif regex2.match(line):\n space_count+=1\n else:\n code_count+=1\n return {\"code\":code_count,\"comment\":comment_count,\"space\":space_count,'total':total_count}\nif __name__==\"__main__\":\n count=get_codes_in_dir(\".\")\n print(count)" } ]	9
oliveira086/Liga	https://github.com/oliveira086/Liga	e0fc855603ce76131d96651978f7308aa41c4f82	145bf08f753ea880ddb69edda88b2680b1ebe3a7	362110824176245aef542782d9a2f4ac78f693d1	refs/heads/master	2020-12-27T20:35:27.798369	2020-02-14T16:16:06	2020-02-14T16:16:06	238,044,307	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.41754597425460815, "alphanum_fraction": 0.48748868703842163, "avg_line_length": 28.83783721923828, "blob_id": "834ab9df0adaa262ef298d5797477a0b7ef9cd21", "content_id": "9afa81691f44f167c681baf59a8e81805c40ea73", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3317, "license_type": "no_license", "max_line_length": 158, "num_lines": 111, "path": "/objeto.py", "repo_name": "oliveira086/Liga", "src_encoding": "UTF-8", "text": "from PIL import Image\nfrom PIL import ImageFont\nfrom PIL import ImageDraw\nimport telepot\nfrom telepot.loop import MessageLoop\nimport sys\nimport time\n\nbot = telepot.Bot('552538744:AAE5r1s7wRAHrdxHq6xYWUcRLnfVgar3xQo')\ndef handle(msg):\n content_type, chat_type, chat_id = telepot.glance(msg)\n\n if '/' in msg['text']:\n mensagemRecebida = msg['text'][1:]\n mensagemTratada = mensagemRecebida.split('/')\n\n lista = [\n {\n 'ativo': mensagemTratada[0],\n 'posicao': [90, 350],\n 'porcetagem': mensagemTratada[10],\n 'posicaoPorcentagem': [90, 405],\n 'cor':(0,255,0)\n },\n {\n 'ativo': mensagemTratada[1],\n 'posicao': [310, 350],\n 'porcetagem': mensagemTratada[11],\n 'posicaoPorcentagem': [310, 405],\n 'cor':(0,255,0)\n },\n\n {\n 'ativo': mensagemTratada[2],\n 'posicao': [535, 350],\n 'porcetagem': mensagemTratada[12],\n 'posicaoPorcentagem': [535, 405],\n 'cor':(0,255,0)\n },\n {\n 'ativo': mensagemTratada[3],\n 'posicao': [200, 520],\n 'porcetagem': mensagemTratada[13],\n 'posicaoPorcentagem': [200, 575],\n 'cor':(0,255,0)\n },\n {\n 'ativo': mensagemTratada[4],\n 'posicao': [430, 520],\n 'porcetagem': mensagemTratada[14],\n 'posicaoPorcentagem': [420, 575],\n 'cor':(0,255,0)\n },\n {\n 'ativo': mensagemTratada[5],\n 'posicao': [95, 840],\n 'porcetagem': mensagemTratada[15],\n 'posicaoPorcentagem': [80, 900],\n 'cor':(255,0,0)\n },\n {\n 'ativo': mensagemTratada[6],\n 'posicao': [310, 840],\n 'porcetagem': mensagemTratada[16],\n 'posicaoPorcentagem': [310, 900],\n 'cor':(255,0,0)\n },\n {\n 'ativo': mensagemTratada[7],\n 'posicao': [535, 840],\n 'porcetagem': mensagemTratada[17],\n 'posicaoPorcentagem': [530, 900],\n 'cor':(255,0,0)\n },\n {\n 'ativo': mensagemTratada[8],\n 'posicao': [200, 1010],\n 'porcetagem': mensagemTratada[18],\n 'posicaoPorcentagem': [200, 1070],\n 'cor':(255,0,0)\n },\n {\n 'ativo': mensagemTratada[9],\n 'posicao': [430, 1010],\n 'porcetagem': mensagemTratada[19],\n 'posicaoPorcentagem': [420, 1070],\n 'cor':(255,0,0)\n },\n ]\n\n \n imagem = Image.open('fundo.jpg')\n imagem.save('pronto.jpg')\n\n for i in range(10):\n imagem = Image.open('pronto.jpg')\n draw = ImageDraw.Draw(imagem)\n font = ImageFont.truetype(\"arial.ttf\", 30)\n\n draw.text((lista[i]['posicao'][0],lista[i]['posicao'][1]), '{}'.format(lista[i]['ativo']), (12, 12, 12), font=font)\n draw.text((lista[i]['posicaoPorcentagem'][0], lista[i]['posicaoPorcentagem'][1]), '{}'.format(lista[i]['porcetagem']), lista[i]['cor'], font=font)\n\n imagem.save('pronto.jpg')\n \n\n\nMessageLoop(bot, handle).run_as_thread()\nprint ('Online')\n\nwhile 1:\n time.sleep(10)\n\t\n\n\n\n" }, { "alpha_fraction": 0.5313059091567993, "alphanum_fraction": 0.6141068339347839, "avg_line_length": 32.681034088134766, "blob_id": "ce7a9490a9e0e980c2b6b76228fc38537e74d9d2", "content_id": "d5a293cea305515b86f2b13219fff8f40764b2c3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3918, "license_type": "no_license", "max_line_length": 114, "num_lines": 116, "path": "/index.py", "repo_name": "oliveira086/Liga", "src_encoding": "UTF-8", "text": "from PIL import Image\nfrom PIL import ImageFont\nfrom PIL import ImageDraw\nimport sys\nimport time\nimport telepot\nfrom telepot.loop import MessageLoop\nimport requests\n\nbot = telepot.Bot('552538744:AAE5r1s7wRAHrdxHq6xYWUcRLnfVgar3xQo')\ndef handle(msg):\n content_type, chat_type, chat_id = telepot.glance(msg)\n\n if msg['text'] == 'start':\n\t bot.sendMessage(chat_id,'''Digite a lista de ativos que desejas adicionar, contendo as seguintes informações:\n\t\t ID do ativo, porcetagem (alta ou baixa) e a sua cotação atual, exemplo:\nPETRA3 / +1,41% / R$ 12,99\nJBS3 / -0,41% / R$ 2,99''')\n if '/' in msg['text']:\n\t mensagem = msg['text']\n\t #print(mensagem)\n\t\t#=== Recortar a string e armazenar valores =====\n\t ativoUm = mensagem[1:6]\n\t ativoDois = mensagem[7:12]\n\t ativoTres = mensagem[13:18]\n\t ativoQuatro = mensagem[19:24]\n\t ativoCinco = mensagem[25:30]\n\t ativoSeis = mensagem[31:36]\n\t ativoSete = mensagem[37:42]\n\t ativoOito = mensagem[43:48]\n\t ativoNove = mensagem[49:54]\n\t ativoDez = mensagem[55:60]\n\t \n\t IMAGEM = Image.open(\"fundo.jpg\")\n\t draw = ImageDraw.Draw(IMAGEM)\n\t\t# font = ImageFont.truetype(<font-file>, <font-size>)\n\t font = ImageFont.truetype(\"arial.ttf\", 30)\n\t\t\n\t draw.text((95, 350), '{}'.format(ativoUm), (0, 0, 0), font=font)\n\t draw.text((310, 350), '{}'.format(ativoDois), (0, 0, 0), font=font)\n\t draw.text((535, 350), '{}'.format(ativoTres), (0, 0, 0), font=font)\n\t draw.text((200, 520), '{}'.format(ativoQuatro), (0, 0, 0), font=font)\n\t draw.text((430, 520), '{}'.format(ativoCinco), (0, 0, 0), font=font)\n\t\t\n\t draw.text((95, 840), '{}'.format(ativoSeis), (0, 0, 0), font=font)\n\t draw.text((310, 840), '{}'.format(ativoSete), (0, 0, 0), font=font)\n\t draw.text((535, 840), '{}'.format(ativoOito), (0, 0, 0), font=font)\n\t draw.text((200, 1010), '{}'.format(ativoNove), (0, 0, 0), font=font)\n\t draw.text((430, 1010), '{}'.format(ativoDez), (0, 0, 0), font=font)\n\t\t\n\t img.save('pronto.jpg')\n\t\n if '>' in msg['text']:\n\t mensagem = msg['text']\n\t porcentagemUm = mensagem[1:7]\n\t porcentagemDois = mensagem[8:14]\n\t porcentagemTres = mensagem[15:21]\n\t porcentagemQuatro = mensagem[22:28]\n\t porcentagemCinco = mensagem[29:35]\n\t porcentagemSeis = mensagem[36:42]\n\t porcentagemSete = mensagem[43:49]\n\t porcentagemOito = mensagem[50:56]\n\t porcentagemNove = mensagem[57:63]\n\t porcentagemDez = mensagem[64:70]\n\t img = Image.open(\"balanço.jpg\")\n\t draw = ImageDraw.Draw(img)\n\t\t# font = ImageFont.truetype(<font-file>, <font-size>)\n\t font = ImageFont.truetype(\"arial.ttf\", 30)\n\t\t\n\t draw.text((80, 405), '{}'.format(porcentagemUm), (0, 255, 0), font=font)\n\t draw.text((310, 405), '{}'.format(porcentagemDois), (0, 255, 0), font=font)\n\t draw.text((535, 405), '{}'.format(porcentagemTres), (0, 255, 0), font=font)\n\t draw.text((200, 575), '{}'.format(porcentagemQuatro), (0, 255, 0), font=font)\n\t draw.text((420, 575), '{}'.format(porcentagemCinco), (0, 255, 0), font=font)\n\t\t\n\t draw.text((80, 900), '{}'.format(porcentagemSeis), (255,0,0), font=font)\n\t draw.text((310, 900), '{}'.format(porcentagemSete), (255,0,0), font=font)\n\t draw.text((530, 900), '{}'.format(porcentagemOito), (255,0,0), font=font)\n\t draw.text((200, 1070), '{}'.format(porcentagemNove), (255,0,0), font=font)\n\t draw.text((420, 1070), '{}'.format(porcentagemDez), (255,0,0), font=font)\n\t \n\t img.save('pronto.jpg')\n\t\t\n\t\t\n\t url = \"https://api.telegram.org/bot552538744:AAE5r1s7wRAHrdxHq6xYWUcRLnfVgar3xQo/sendPhoto\";\n\t files = {'photo': open('pronto.jpg', 'rb')}\n\t data = {'chat_id' : chat_id}\n\t r= requests.post(url, files=files, data=data)\n\t print(r.status_code, r.reason, r.content)\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\nMessageLoop(bot, handle).run_as_thread()\nprint ('Online')\n\n# Keep the program running.\nwhile 1:\n time.sleep(10)\n\t\n\t\n\t\n" } ]	2
harkiratdhanoa/mammograms	https://github.com/harkiratdhanoa/mammograms	38b9f982c57438d72ed76b81d1dc46fb0dc49839	7bda7ec43cf610021c7408f61dd0b73d1b6dab1e	954fb092fcd8809e9f50e8f19bef57faa037bf70	refs/heads/master	2021-10-20T07:33:36.536496	2019-02-26T15:31:31	2019-02-26T15:31:31	172,738,252	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6795398592948914, "alphanum_fraction": 0.6894001364707947, "avg_line_length": 21.943395614624023, "blob_id": "518cb136cc7f8eba4afbff39d74ca51a15640255", "content_id": "24937f1a9790e6b9e6f4ed73d43da921fcc4830b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1217, "license_type": "no_license", "max_line_length": 73, "num_lines": 53, "path": "/embeddings.py", "repo_name": "harkiratdhanoa/mammograms", "src_encoding": "UTF-8", "text": "from gensim.models import Word2Vec\nfrom sklearn.decomposition import PCA\nfrom matplotlib import pyplot\n\nimport nltk\n\nfrom nltk.corpus import stopwords\n\nstopw = stopwords.words('english')\nstopw.append('also')\n\ndef readFile(file):\n f=open(file,'r',encoding='utf-8')\n text=f.read()\n \n sentences=nltk.sent_tokenize(text)\n \n data = []\n \n for sent in sentences:\n words = nltk.word_tokenize(sent)\n words = [w.lower() for w in words if len(w)>2 and w not in stopw]\n data.append(words)\n \n return data\n\nsentences = readFile(\"bollywood.txt\")\n\n# train model\nmodel = Word2Vec(sentences, min_count=1)\nprint(model)\n# summarize vocabulary\nwords = list(model.wv.vocab)\nprint(words)\n# access vector for one word\nprint(model['actress'])\n# save model\nmodel.save('model.bin')\n# load model\nnew_model = Word2Vec.load('model.bin')\nprint(new_model)\n\n\n# fit a 2d PCA model to the vectors\nX = model[model.wv.vocab]\npca = PCA(n_components=2)\nresult = pca.fit_transform(X)\n# create a scatter plot of the projection\npyplot.scatter(result[:, 0], result[:, 1])\nwords = list(model.wv.vocab)\nfor i, word in enumerate(words):\n\tpyplot.annotate(word, xy=(result[i, 0], result[i, 1]))\npyplot.show()\n\n" } ]	1
arizuk/misc	https://github.com/arizuk/misc	53b1268cdf9a85dbcf8ad8c7d7bc06dd171a2f35	a0d92a0e0f1263b4456a2c8e49fdba1816f1113f	1924be662cc2d728254030d2bbc4773bab6c4899	refs/heads/master	2020-01-18T22:45:03.077753	2020-01-18T13:41:41	2020-01-18T13:41:41	82,464,098	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7368420958518982, "alphanum_fraction": 0.7368420958518982, "avg_line_length": 18, "blob_id": "47c056d690dee7ad08876401d45fa8cea58b7c3c", "content_id": "f70ba1b0845c1efacb07ba12b20c39ee8b98b180", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 19, "license_type": "no_license", "max_line_length": 18, "num_lines": 1, "path": "/python/namespace_packages/hogehoge/__init__.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "from . import fuga\n" }, { "alpha_fraction": 0.5461847186088562, "alphanum_fraction": 0.5542168617248535, "avg_line_length": 17.2439022064209, "blob_id": "c11c6b15017ab03ff155eff3655a48fe153ef48f", "content_id": "02d6b2043afef4e06aba8721efca2a0ad7283852", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 747, "license_type": "no_license", "max_line_length": 51, "num_lines": 41, "path": "/python/queue/main.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "from queue import Queue, Empty\nfrom threading import Thread\nfrom time import sleep\n\n\nTHREAD_POOL_SIZE = 4\n\ndef worker(worker_queue):\n while not worker_queue.empty():\n try:\n item = worker_queue.get(block=False)\n except Empty:\n break\n else:\n sleep(1)\n print(item)\n worker_queue.task_done()\n\n\ndef main():\n worker_queue = Queue()\n\n for i in range(1, 20, 2):\n worker_queue.put(i)\n\n threads = [\n Thread(target=worker, args=(worker_queue,))\n for _ in range(THREAD_POOL_SIZE)\n ]\n\n for thread in threads:\n thread.start()\n\n worker_queue.join()\n\n while threads:\n threads.pop().join()\n\n\nif __name__ == \"__main__\":\n main()" }, { "alpha_fraction": 0.5782155394554138, "alphanum_fraction": 0.5816918015480042, "avg_line_length": 11, "blob_id": "392ed9305b1ec8a898d00d82114cd8767fda996c", "content_id": "f32820dbb2999427455b37619f5240e2384e5ed5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 863, "license_type": "no_license", "max_line_length": 45, "num_lines": 72, "path": "/book/3_4_queue_via_stack.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "class Stack\n attr_accessor :min, :top\n\n def push(val)\n min = top&.min \|\| val\n node = StackNode.new(val, [val, min].min)\n node.next = top\n self.top = node\n end\n\n def pop\n return unless top\n val = top.val\n self.top = top.next\n return val\n end\n\n def min\n top&.min\n end\n\n def peek\n top.val\n end\nend\n\n\nclass StackNode\n attr_accessor :val, :next, :min\n\n def initialize(val, min)\n @val = val\n @min = min\n end\nend\n\n\nclass Queue\n def initialize\n @newest = Stack.new\n @oldest = Stack.new\n end\n\n def add(val)\n @newest.push(val)\n end\n\n def remove\n val = @oldest.pop\n return val if val\n\n while val = @newest.pop\n @oldest.push(val)\n end\n @oldest.pop\n end\nend\n\n\nq = Queue.new\n\nn = 5\n\nn.times do \|\|\n val = rand(20)\n puts \"push #{val}\"\n q.add(val)\nend\n\nn.times do\n puts \"pop #{q.remove()}\"\nend" }, { "alpha_fraction": 0.4559585452079773, "alphanum_fraction": 0.46891191601753235, "avg_line_length": 16.590909957885742, "blob_id": "056254b21b4772ffa13178355bb5f771612fb371", "content_id": "06e5c9209a1387ef82f447a7b60d5e2be70648ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 386, "license_type": "no_license", "max_line_length": 36, "num_lines": 22, "path": "/python/__get__/main.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "class A:\n def __init__(self, val):\n self.val = val\n\n def __get__(self, obj, objtype):\n print(\"__get__ called\")\n return self.val\n\n def __set__(self, obj, val):\n print(\"__set__ called\")\n self.val = val\n return self.val\n\nclass Klass:\n a = A(10)\n\n\nif __name__ == \"__main__\":\n a = A(1)\n klass = Klass()\n klass.a\n klass.a = 20" }, { "alpha_fraction": 0.5245901346206665, "alphanum_fraction": 0.5245901346206665, "avg_line_length": 14.25, "blob_id": "bf060ae4d2b02e4ca82eceb0cb2a67c9851167ed", "content_id": "febf3c1e46890c9867536063ac7db0c8467d6968", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 61, "license_type": "no_license", "max_line_length": 26, "num_lines": 4, "path": "/python/namespace_packages/ng.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "import hoge\n\nif __name__ == \"__main__\":\n hoge.fuga.hoge()\n" }, { "alpha_fraction": 0.6129032373428345, "alphanum_fraction": 0.6303763389587402, "avg_line_length": 18.578947067260742, "blob_id": "7ee40bef653f49289bda89f2b5ce3c77f7e96352", "content_id": "6eb023a5bd104bd43f94086b9d3c9e9affef93b8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 744, "license_type": "no_license", "max_line_length": 43, "num_lines": 38, "path": "/ruby/quicksort.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "# Runtime: O(N+logN) # average\ndef quicksort(array)\n quicksort_inner(array, 0, array.length-1)\nend\n\ndef quicksort_inner(array, left, right)\n index = partition(array, left, right)\n if left < index-1\n quicksort_inner(array, left, index-1)\n end\n if index < right\n quicksort_inner(array, index, right)\n end\nend\n\ndef partition(array, left, right)\n lr = left..right\n mid = (left+right)/2\n pivot = array[mid]\n\n while left <= right\n left += 1 while array[left] < pivot\n right -= 1 while array[right] > pivot\n\n if left <= right\n tmp = array[left]\n array[left] = array[right]\n array[right] = tmp\n left += 1\n right -= 1\n end\n end\n left\nend\n\narray = 10.times.map { rand(30) }\nquicksort(array)\np array\n" }, { "alpha_fraction": 0.45072993636131287, "alphanum_fraction": 0.48905110359191895, "avg_line_length": 11.767441749572754, "blob_id": "5c50a5492b2f3473a4f87693f9e5b8571373391d", "content_id": "99334a3e4dc1c4d543ecd562da47a489db6d1151", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 548, "license_type": "no_license", "max_line_length": 39, "num_lines": 43, "path": "/book/4_7_build_order.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "edges = [\n [1, 4],\n [6, 2],\n [2, 4],\n [6, 1],\n [4, 3],\n # [4, 6]\n]\n\nh = {}\ne = {}\n(1..6).each do \|n\|\n h[n] = 0\n e[n] = []\nend\nedges.each do \|a, b\|\n h[a] += 1\n e[b] << a\nend\n\n# toporigical sort\nqueue = []\nh.each do \|k, v\|\n queue << k if v == 0\nend\n\nans = []\nwhile queue.size > 0\n node = queue.shift\n ans << node\n e[node].each do \|parent\|\n h[parent] -= 1\n if h[parent] == 0\n queue << parent\n end\n end\nend\n\nif ans.size == h.values.size\n p ans.map { \|n\| ('a'.ord + n-1).chr }\nelse\n puts \"Circular dependency detected\"\nend" }, { "alpha_fraction": 0.5882353186607361, "alphanum_fraction": 0.5882353186607361, "avg_line_length": 16, "blob_id": "d76102b7dba7ba9e5c542bb5d743a278a7f861d8", "content_id": "135885955b803869a2ecad63607727fb7c05795d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 34, "license_type": "no_license", "max_line_length": 21, "num_lines": 2, "path": "/python/namespace_packages/hogehoge/fuga.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "def hoge():\n print(\"hogehoge\")\n" }, { "alpha_fraction": 0.6434316635131836, "alphanum_fraction": 0.6621983647346497, "avg_line_length": 17.700000762939453, "blob_id": "ceb2bcf1abb80401509a097fdb1f79afdad1ab03", "content_id": "dc8dd1f6bef1b68a61463ec46d083cff9f8b40c3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 373, "license_type": "no_license", "max_line_length": 43, "num_lines": 20, "path": "/python/pytest/test_main.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "import pytest\n\nfrom primes import is_prime\n\[email protected]()\ndef prime_numbers():\n return [1, 3, 11]\n\[email protected]()\ndef non_prime_numbers():\n return [4, 8, 9]\n\ndef test_is_prime_true(prime_numbers):\n for n in prime_numbers:\n assert is_prime(n)\n\n\ndef test_is_prime_false(non_prime_numbers):\n for n in non_prime_numbers:\n assert not is_prime(n)" }, { "alpha_fraction": 0.5494853258132935, "alphanum_fraction": 0.5724465847015381, "avg_line_length": 16.54166603088379, "blob_id": "18f2284eaf987a87260734777da3607baa3aabaa", "content_id": "945d031cb739a50ecac36d722edcaf959cd26e99", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 1263, "license_type": "no_license", "max_line_length": 63, "num_lines": 72, "path": "/book/2_5.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "require './lib/linked_list'\n\nmodule Backward\n def self.to_int(a)\n ret = 0\n d = 1\n while a\n ret += a.data * d\n d = 10\n a = a.next\n end\n ret\n end\n\n\n def self.to_linked_list(a)\n node = nil\n while a > 0\n val = a % 10\n if node\n node.append_to_tail(val)\n else\n node = LinkedList::Node.new(val)\n end\n a /= 10\n end\n node\n end\nend\n\nmodule Forward\n def self.to_int(a)\n ret = 0\n while a\n ret = 10\n ret += a.data\n a = a.next\n end\n ret\n end\n\n\n def self.to_linked_list(a)\n head = nil\n while a > 0\n val = a % 10\n node = LinkedList::Node.new(val)\n node.next = head\n head = node\n a /= 10\n end\n head\n end\nend\n\n\na = LinkedList::Node.new(7).append_to_tail(1).append_to_tail(6)\nb = LinkedList::Node.new(5).append_to_tail(9).append_to_tail(2)\np a\np b\nputs(Backward.to_int(a))\nputs(Backward.to_int(b))\n\nans = Backward.to_int(a) + Backward.to_int(b)\np Backward.to_linked_list(ans)\n\na = LinkedList::Node.new(6).append_to_tail(1).append_to_tail(7)\nb = LinkedList::Node.new(2).append_to_tail(9).append_to_tail(5)\nputs(Forward.to_int(a))\nputs(Forward.to_int(b))\nans = Forward.to_int(a) + Forward.to_int(b)\np Forward.to_linked_list(ans)\n" }, { "alpha_fraction": 0.46952009201049805, "alphanum_fraction": 0.4915693998336792, "avg_line_length": 24.700000762939453, "blob_id": "e8c085369496556cec744215064346c83cb1c947", "content_id": "0681d2605236bf2053dc04273380524ffc8d7028", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Java", "length_bytes": 771, "license_type": "no_license", "max_line_length": 64, "num_lines": 30, "path": "/book/1_3/URLify.java", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "public class URLify {\n public static void replaceSpaces(char[] str, int trueLength) {\n int spaceCount = 0, index, i = 0;\n for (i=0; i<trueLength; i++) {\n if (str[i] == ' ') {\n spaceCount += 1;\n }\n }\n index = trueLength + spaceCount * 2;\n if (trueLength < str.length) str[trueLength] = '\\0';\n for (i = trueLength-1; i>=0; i--) {\n if (str[i] == ' ') {\n str[index-1] = '0';\n str[index-2] = '2';\n str[index-3] = '%';\n index = index-3;\n } else {\n str[index-1] = str[i];\n index--;\n }\n }\n }\n\n public static void main(String args[]) {\n char[] str = \"aiu eo test \".toCharArray();\n replaceSpaces(str, 11);\n String s = new String(str);\n System.out.println(s);\n }\n}\n" }, { "alpha_fraction": 0.5454545617103577, "alphanum_fraction": 0.5454545617103577, "avg_line_length": 15.5, "blob_id": "39dc10a0f1e6c4bc9cee4d7c6ecbe13f2e2913f3", "content_id": "b6fdbfdf3b5d76a120bed09d02cc019d9354f6fe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 66, "license_type": "no_license", "max_line_length": 26, "num_lines": 4, "path": "/python/namespace_packages/ok.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "from hoge import fuga\n\nif __name__ == \"__main__\":\n fuga.hoge()\n" }, { "alpha_fraction": 0.5446428656578064, "alphanum_fraction": 0.555059552192688, "avg_line_length": 15.390243530273438, "blob_id": "ea491a0e592288c681c8312cb5273051e228c7d9", "content_id": "a62c530b67974a86eea110c80a704f8513da7c15", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 672, "license_type": "no_license", "max_line_length": 30, "num_lines": 41, "path": "/book/1_4.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "def one_repalce(a, b)\n cnt = 0\n a.length.times do \|i\|\n cnt += 1 if a[i] != b[i]\n return false if cnt > 1\n end\n true\nend\n\ndef one_remove(a, b)\n j = 0\n a.length.times do \|i\|\n if a[i] != b[j]\n return false if i != j\n else\n j += 1\n end\n end\n true\nend\n\ndef one_away(a, b)\n if a.length == b.length\n one_repalce(a, b)\n elsif a.length == b.length+1\n one_remove(a, b)\n elsif a.length == b.length-1\n one_remove(b, a)\n else\n false\n end\nend\n\n\np one_away(\"pale\", \"ple\")\np one_away(\"pales\", \"pale\")\np one_away(\"pale\", \"bale\")\np one_away(\"pale\", \"pales\")\np one_away(\"pale\", \"pale\")\np one_away(\"pale\", \"bae\")\np one_away(\"pale\", \"paless\")\n" }, { "alpha_fraction": 0.7625899314880371, "alphanum_fraction": 0.7625899314880371, "avg_line_length": 26.799999237060547, "blob_id": "03d2f3aa6939341b22417a69eedcef150aac2161", "content_id": "a939b6d50e6fdb342bc6b40d64f268866f7858a2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 278, "license_type": "no_license", "max_line_length": 154, "num_lines": 10, "path": "/python/flake8/main.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "TOO_LONG_LINE = \"aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\"\n\n\ndef greeting(name):\n print(\"Hello {}\".format(name))\n\n\nif __name__ == \"__main__\":\n name = \"Taro\"\n greeting(name)\n" }, { "alpha_fraction": 0.5826377272605896, "alphanum_fraction": 0.5876460671424866, "avg_line_length": 10.764705657958984, "blob_id": "f10adea770058915ade9069a2d9da2713a4ab12a", "content_id": "cdaff6262a2b5f1b43a5731b213ed3ead3afe294", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 599, "license_type": "no_license", "max_line_length": 45, "num_lines": 51, "path": "/book/3_2.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "class Stack\n attr_accessor :min, :top\n\n def push(val)\n min = top&.min \|\| val\n node = StackNode.new(val, [val, min].min)\n node.next = top\n self.top = node\n end\n\n def pop\n return unless top\n val = top.val\n self.top = top.next\n return val\n end\n\n def min\n top&.min\n end\n\n def peek\n top.val\n end\nend\n\n\nclass StackNode\n attr_accessor :val, :next, :min\n\n def initialize(val, min)\n @val = val\n @min = min\n end\nend\n\n\n\nstack = Stack.new\n\nn = 5\n\nn.times do \|\|\n val = rand(20)\n puts \"push #{val}\"\n stack.push(val)\nend\n\nn.times do\n p [stack.min, stack.pop]\nend" }, { "alpha_fraction": 0.6656891703605652, "alphanum_fraction": 0.6656891703605652, "avg_line_length": 17, "blob_id": "8aa29977ba48e98412bfe09fe2e9a9c00244e9b6", "content_id": "874b8ca064bb5c16e44c3ca8ee5f5c1c2c3ccb8d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 341, "license_type": "no_license", "max_line_length": 34, "num_lines": 19, "path": "/book/2_3.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "require_relative 'lib/linked_list'\n\nhead = LinkedList::Node.new('a')\nd_node = nil\n%w(b c d e f).each do \|c\|\n node = head.append_to_tail(c)\n d_node = node if c == 'd'\nend\n\ndef delete_node(node)\n return false unless node.next\n next_nd = node.next\n node.data = next_nd.data\n node.next = next_nd.next\n true\nend\n\ndelete_node(d_node)\np head" }, { "alpha_fraction": 0.5720469951629639, "alphanum_fraction": 0.5769944190979004, "avg_line_length": 15.680412292480469, "blob_id": "7361af54139685255445c0be1022cf883a806d38", "content_id": "a9a30b46459004ef93e18f2d0009d5fe6ace0063", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 1617, "license_type": "no_license", "max_line_length": 69, "num_lines": 97, "path": "/book/4_11_random_node.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "require './lib/print_binary_tree'\n\nIndex = Struct.new(:val)\ndef find_by_index(node, index, curr_index = Index.new(-1))\n curr_index.val += 1\n # p \"Search #{index} node=#{node.val} curr_index=#{curr_index.val}\"\n return node if index == curr_index.val\n if node.left\n found = find_by_index(node.left, index, curr_index)\n return found if found\n end\n if node.right\n found = find_by_index(node.right, index, curr_index)\n return found if found\n end\nend\n\nclass BST\n attr_accessor :root\n\n def initialize\n @root = nil\n @size = 0\n end\n\n def add(val)\n @size += 1\n if @root\n @root.add(val)\n else\n @root = TreeNode.new(val)\n end\n end\n\n def find(val)\n @root&.find(val)\n end\n\n def random_node\n idx = rand(@size)\n find_by_index(@root, idx)\n end\nend\n\nclass TreeNode\n attr_accessor :left, :right, :val\n def initialize(val)\n @val = val\n end\n\n def add(val)\n if @val < val\n if @right\n @right.add(val)\n else\n @right = TreeNode.new(val)\n end\n else\n if @left\n @left.add(val)\n else\n @left = TreeNode.new(val)\n end\n end\n end\n\n def find(val)\n return self if @val == val\n if val <= @val\n @left&.find(val)\n else\n @right&.find(val)\n end\n end\n\n def inspect\n left = @left ? \" left=#{@left}\" : ''\n right = @right ? \" right=#{@right}\" : ''\n \"#<val=#{@val}#{left}#{right}>\"\n end\n\n def to_s\n inspect\n end\nend\n\ntree = BST.new\n5.times do \|v\|\n val = rand(20)\n tree.add(val)\nend\nprint_binary_tree(tree.root)\n\nputs \"#Get a node randomly\"\n5.times do\n p tree.random_node&.val\nend" }, { "alpha_fraction": 0.5947775840759277, "alphanum_fraction": 0.603481650352478, "avg_line_length": 12.269230842590332, "blob_id": "e50e174ff2f4798c7dbe552dfddbb12fe1097253", "content_id": "17af07523faa63576614cccd7d4b59b424c54742", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 1034, "license_type": "no_license", "max_line_length": 47, "num_lines": 78, "path": "/book/3_3_stack_of_plates.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "class Stack\n attr_accessor :size, :top\n\n def initialize\n @size = 0\n end\n\n\n def push(val)\n self.size += 1\n node = StackNode.new(val)\n node.next = top\n self.top = node\n end\n\n def pop\n return unless top\n self.size -= 1\n val = top.val\n self.top = top.next\n return val\n end\n\n def peek\n top.val\n end\nend\n\n\nclass StackNode\n attr_accessor :val, :next\n\n def initialize(val)\n @val = val\n end\nend\n\nclass SetOfStack\n attr_reader :capacity, :stacks\n def initialize(capacity)\n @capacity = capacity\n @stacks = []\n end\n\n def push(val)\n stack = @stacks.first\n if stack.nil? \|\| stack.size >= capacity\n stack = Stack.new\n @stacks.unshift(stack)\n end\n stack.push(val)\n end\n\n def pop\n stack = @stacks[0]\n val = stack.pop()\n @stacks.shift if stack.size == 0\n val\n end\nend\n\n\n\nstack = SetOfStack.new(3)\n\nn = 5\n\nn.times do \|\|\n val = rand(20)\n puts \"push #{val}\"\n stack.push(val)\nend\n\np \"The number of stack is #{stack.stacks.size}\"\n\nn.times do\n p stack.pop\nend" }, { "alpha_fraction": 0.501075267791748, "alphanum_fraction": 0.5311827659606934, "avg_line_length": 11.94444465637207, "blob_id": "f273084136004ca3ead5e398c2170a712fb4032d", "content_id": "361bcea4fa0a2864315510214280be5c90442c58", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 465, "license_type": "no_license", "max_line_length": 38, "num_lines": 36, "path": "/book/4_7_build_order2.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "# a depends b\nedges = [\n [1, 4],\n [6, 2],\n [2, 4],\n [6, 1],\n [4, 3],\n # [4, 6]\n]\n\nnodes = (1..6).to_a\n\ne = {}\nnodes.each do \|n\|\n e[n] = []\nend\nedges.each do \|a, b\|\n e[a] << b\nend\n\ndef dfs(n, edges, processed, ans)\n processed[n] = true\n edges[n].each do \|next_n\|\n next if processed[next_n]\n dfs(next_n, edges, processed, ans)\n end\n ans << n\nend\n\nans = []\nprocessed = {}\nnodes.each do \|n\|\n next if processed[n]\n dfs(n, e, processed, ans)\nend\np ans" }, { "alpha_fraction": 0.5564388036727905, "alphanum_fraction": 0.5643879175186157, "avg_line_length": 16.24657440185547, "blob_id": "ce2584d1ca9d3b4e5d08631b991ad529a7a7f4ac", "content_id": "e075d9570a981513102b6f1a503f1bca9b1ffb27", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 1258, "license_type": "no_license", "max_line_length": 54, "num_lines": 73, "path": "/book/4_4_check_balanced.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "def printer(root)\n cur_nodes = []\n cur_nodes.push(root)\n\n while cur_nodes.size > 0\n puts cur_nodes.map { \|n\| n&.val \|\| '-' }.join(\" \")\n\n next_nodes = []\n cur_nodes.each do \|node\|\n next_nodes.push(node&.left)\n next_nodes.push(node&.right)\n end\n break if next_nodes.all?(&:nil?)\n cur_nodes = next_nodes\n end\nend\n\nMIN = -2\n\ndef balanced?(node)\n check_height(node) != MIN\nend\n\ndef check_height(node)\n return -1 unless node\n l = check_height(node.left)\n return MIN if l==MIN\n r = check_height(node.left)\n return MIN if r==MIN\n return MIN if (l-r).abs() > 1\n [l, r].max + 1\nend\n\nclass TreeNode\n attr_reader :left, :right, :val\n def initialize(val)\n @val = val\n end\n\n def add(val)\n if @val < val\n if @right\n @right.add(val)\n else\n @right = TreeNode.new(val)\n end\n else\n if @left\n @left.add(val)\n else\n @left = TreeNode.new(val)\n end\n end\n end\n\n def inspect\n left = @left ? \" left=#{@left}\" : ''\n right = @right ? \" right=#{@right}\" : ''\n \"#<val=#{@val}#{left}#{right}>\"\n end\n\n def to_s\n inspect\n end\nend\n\nnode = TreeNode.new(rand(20))\n5.times do \|v\|\n val = rand(20)\n node.add(val)\nend\nprinter(node)\nputs \"blanced #{balanced?(node)}\"" }, { "alpha_fraction": 0.621730387210846, "alphanum_fraction": 0.6297786831855774, "avg_line_length": 11.146341323852539, "blob_id": "aaa0ab653bd7c0dc748b696bedfb92d32fdd080c", "content_id": "59dabbc4710c4bf8b34af7a996e93bc68c3172ae", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 497, "license_type": "no_license", "max_line_length": 30, "num_lines": 41, "path": "/ruby/stack.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "class StackNode\n attr_reader :data\n attr_accessor :next\n def initialize(data)\n @data = data\n end\nend\n\nclass Stack\n attr_accessor :top\n def initialize\n @top = nil\n end\n\n def push(data)\n node = StackNode.new(data)\n node.next = top\n self.top = node\n end\n\n def pop\n node = top\n return unless node\n self.top = node.next\n node.data\n end\nend\n\n\ndef main\n stack = Stack.new\n stack.push(1)\n stack.push(2)\n stack.push(3)\n\n 4.times do\n p stack.pop\n end\nend\n\nmain" }, { "alpha_fraction": 0.7272727489471436, "alphanum_fraction": 0.7272727489471436, "avg_line_length": 33, "blob_id": "e6e1b3ec1703976ea6f035e7ad75807774b643db", "content_id": "d095fba10a84339c7ed290b2351ac4452ef90c04", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 33, "license_type": "no_license", "max_line_length": 33, "num_lines": 1, "path": "/python/pytest/run_test.sh", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "coverage run --source . -m pytest" }, { "alpha_fraction": 0.5211678743362427, "alphanum_fraction": 0.5211678743362427, "avg_line_length": 16.564102172851562, "blob_id": "9dcb92c071bdf91482d0a776969975a18369d5f7", "content_id": "0ecb1d6d6ce1b4545e6327e6676cc089eefbc16f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 685, "license_type": "no_license", "max_line_length": 43, "num_lines": 39, "path": "/book/lib/linked_list.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "module LinkedList\n class Node\n attr_accessor :next, :data\n def initialize(data)\n @data = data\n end\n\n def append_to_tail(data)\n if self.next == nil\n self.next = Node.new(data)\n else\n self.next.append_to_tail(data)\n end\n self\n end\n\n def self.delete_node(head, data)\n node = head\n prev = nil\n\n return node.next if node.data == data\n\n while node.next\n prev = node\n node = node.next\n if node.data == data\n prev.next = node.next\n break\n end\n end\n head\n end\n\n def to_s\n \"<Node #{@data}> => #{self.next}\"\n end\n alias :inspect :to_s\n end\nend\n" }, { "alpha_fraction": 0.6847826242446899, "alphanum_fraction": 0.6847826242446899, "avg_line_length": 45, "blob_id": "6a598cf5f6b7ff6d7b3cd4471fc9d98aac8e3e33", "content_id": "173e0588005a317897ff44fc9769a0c1fd491d26", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Makefile", "length_bytes": 92, "license_type": "no_license", "max_line_length": 76, "num_lines": 2, "path": "/mruby-link-test/Makefile", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "hello: hello.c\n\tgcc -I ./mruby/include -L ./mruby/build/host/lib -lmruby ./hello.c -o hello\n" }, { "alpha_fraction": 0.5583832263946533, "alphanum_fraction": 0.5658682584762573, "avg_line_length": 16.363636016845703, "blob_id": "2a5fbefc73bf7f528582016ba34506fb5c9d9fe1", "content_id": "f1d3321307c8f5a36ed7e5f6136421d20c151e68", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 1336, "license_type": "no_license", "max_line_length": 54, "num_lines": 77, "path": "/book/4_5_bst.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "def printer(root)\n cur_nodes = []\n cur_nodes.push(root)\n\n while cur_nodes.size > 0\n puts cur_nodes.map { \|n\| n&.val \|\| '-' }.join(\" \")\n\n next_nodes = []\n cur_nodes.each do \|node\|\n next_nodes.push(node&.left)\n next_nodes.push(node&.right)\n end\n break if next_nodes.all?(&:nil?)\n cur_nodes = next_nodes\n end\nend\n\nMIN = -2\n\ndef bst?(node)\n check_max(node) != MIN\nend\n\ndef check_max(node)\n return -1 unless node\n l = check_max(node.left)\n r = check_max(node.right)\n return MIN if l==MIN\n return MIN if r==MIN\n return MIN unless l <= node.val\n return MIN unless r == -1 \|\| node.val < r\n [r, node.val].max\nend\n\nclass TreeNode\n attr_accessor :left, :right, :val\n def initialize(val)\n @val = val\n end\n\n def add(val)\n if @val < val\n if @right\n @right.add(val)\n else\n @right = TreeNode.new(val)\n end\n else\n if @left\n @left.add(val)\n else\n @left = TreeNode.new(val)\n end\n end\n end\n\n def inspect\n left = @left ? \" left=#{@left}\" : ''\n right = @right ? \" right=#{@right}\" : ''\n \"#<val=#{@val}#{left}#{right}>\"\n end\n\n def to_s\n inspect\n end\nend\n\nnode = TreeNode.new(rand(20))\n5.times do \|v\|\n val = rand(20)\n node.add(val)\nend\nprinter(node)\nputs \"bst #{bst?(node)}\"\nnode.val = 2\nprinter(node)\nputs \"bst #{bst?(node)}\"" }, { "alpha_fraction": 0.6167664527893066, "alphanum_fraction": 0.6167664527893066, "avg_line_length": 17.55555534362793, "blob_id": "d6230c91f75f1b7ca24af275f39910cc5cbcd98f", "content_id": "55b00cd1bb7a892f788b73b107c9a76a88e3224a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C", "length_bytes": 167, "license_type": "no_license", "max_line_length": 47, "num_lines": 9, "path": "/mruby-link-test/hello.c", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "#include \"mruby.h\"\n#include \"mruby/compile.h\"\n\nint main(void)\n{\n mrb_state *mrb = mrb_open();\n mrb_load_string(mrb, \"puts 'hello world'\");\n mrb_close(mrb);\n}\n" }, { "alpha_fraction": 0.5797101259231567, "alphanum_fraction": 0.5797101259231567, "avg_line_length": 16.25, "blob_id": "477fc7efb32193236fd81c415ff07becdc1a7579", "content_id": "46fceb842dc36158d07465ec19af8e58631eb177", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 69, "license_type": "no_license", "max_line_length": 26, "num_lines": 4, "path": "/python/namespace_packages/ok2.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "import hogehoge\n\nif __name__ == \"__main__\":\n hogehoge.fuga.hoge()\n" }, { "alpha_fraction": 0.5418719053268433, "alphanum_fraction": 0.5862069129943848, "avg_line_length": 15.052631378173828, "blob_id": "089f19925c3cbe7dc84c8f38aa39dd91fb1927e3", "content_id": "30fa89eeea79dc2d24cf8da56699209853774cd1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 609, "license_type": "no_license", "max_line_length": 48, "num_lines": 38, "path": "/book/2_4.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "require_relative 'lib/linked_list'\n\nhead = LinkedList::Node.new(1)\n[11, 12, 11, 1, 1, 8, 3, 16, 24, 10].each do \|v\|\n head.append_to_tail(v)\nend\n\ndef parition(head, pivot)\n left = nil\n left_end = nil\n right = nil\n\n node = head\n while node\n next_node = node.next\n\n if node.data < pivot\n node.next = left\n left = node\n left_end \|\|= left\n else\n node.next = right\n right = node\n end\n\n node = next_node\n end\n\n return right unless left\n left_end.next = right\n left\nend\n\n[1, 3, 5, 8, 11, 13, 30].each do \|p\|\n head = parition(head, p)\n puts \"Pivot #{p}\"\n p head\nend" }, { "alpha_fraction": 0.5451613068580627, "alphanum_fraction": 0.5526881814002991, "avg_line_length": 15.333333015441895, "blob_id": "a4d4ec2df336573792d1c2477d22e724857f412d", "content_id": "8ca0ea09e22e1891da82c910c23b5e0dfe6735bf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 930, "license_type": "no_license", "max_line_length": 47, "num_lines": 57, "path": "/book/4_3_list_of_depths.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "def printer(root)\n cur_nodes = []\n cur_nodes.push(root)\n\n while cur_nodes.size > 0\n puts cur_nodes.map(&:val).join(\" \")\n\n next_nodes = []\n cur_nodes.each do \|node\|\n next_nodes.push(node.left) if node.left\n next_nodes.push(node.right) if node.right\n end\n cur_nodes = next_nodes\n end\nend\n\n\nclass TreeNode\n attr_reader :left, :right, :val\n def initialize(val)\n @val = val\n end\n\n def add(val)\n if @val < val\n if @right\n @right.add(val)\n else\n @right = TreeNode.new(val)\n end\n else\n if @left\n @left.add(val)\n else\n @left = TreeNode.new(val)\n end\n end\n end\n\n def inspect\n left = @left ? \" left=#{@left}\" : ''\n right = @right ? \" right=#{@right}\" : ''\n \"#<val=#{@val}#{left}#{right}>\"\n end\n\n def to_s\n inspect\n end\nend\n\nnode = TreeNode.new(rand(20))\n10.times do \|v\|\n val = rand(20)\n node.add(val)\nend\n\nprinter(node)" }, { "alpha_fraction": 0.382536381483078, "alphanum_fraction": 0.382536381483078, "avg_line_length": 17.538461685180664, "blob_id": "1dcf2f3f722254442fc5d50d5b8b8b68f6a2199d", "content_id": "3843872cd159a8cc0b9d47b926b92e4e275f2a21", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 481, "license_type": "no_license", "max_line_length": 51, "num_lines": 26, "path": "/python/super/main.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "class A:\n def __init__(self):\n print(\"A __init__\")\n super().__init__()\n\n\nclass B:\n def __init__(self):\n print(\"B __init__\")\n super().__init__()\n\nclass C(A, B):\n def __init__(self):\n print(\"C __init__\")\n super().__init__()\n\n\nif __name__ == \"__main__\":\n c = C()\n print(\"MRO: \", [x.__name__ for x in C.__mro__])\n\n # -> % python main.py\n # C __init__\n # A __init__\n # B __init__\n # MRO: ['C', 'A', 'B', 'object']" }, { "alpha_fraction": 0.6048564910888672, "alphanum_fraction": 0.620309054851532, "avg_line_length": 18.29787254333496, "blob_id": "db16082bababdb268d695a549ec7ad9203fddbee", "content_id": "c680740e6f26416d5c81074f7ae37c2155bd3430", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 906, "license_type": "no_license", "max_line_length": 51, "num_lines": 47, "path": "/ruby/mergesort.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "# Runtime: O(N+logN)\n# Memory: O(N)\ndef mergesort(array)\n helper = Array.new(array.length)\n mergesort_inner(array, helper, 0, array.length-1)\nend\n\ndef mergesort_inner(array, helper, low, high)\n return if low == high\n\n mid = (low+high) / 2\n mergesort_inner(array, helper, low, mid)\n mergesort_inner(array, helper, mid+1, high)\n merge(array, helper, low, mid, high)\nend\n\ndef merge(array, helper, low, mid, high)\n for i in low..high\n helper[i] = array[i]\n end\n\n left = low\n right = mid+1\n current = low\n\n while left <= mid && right <= high do\n if helper[left] <= array[right]\n array[current] = helper[left]\n left += 1\n current += 1\n else\n array[current] = helper[right]\n right += 1\n current += 1\n end\n end\n\n for i in left..mid\n array[current] = helper[i]\n current += 1\n end\nend\n\narray = 10.times.map { rand(30) }\np array\nmergesort(array)\np array" }, { "alpha_fraction": 0.5922208428382874, "alphanum_fraction": 0.6198243498802185, "avg_line_length": 14.326923370361328, "blob_id": "19af16b7d2dcb73e86582c4dbba87fb488f13037", "content_id": "decf98dbc8181481a8c7b89d53817f0d34085665", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 797, "license_type": "no_license", "max_line_length": 51, "num_lines": 52, "path": "/book/2_2.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "require_relative 'lib/linked_list'\n\nhead = LinkedList::Node.new(1)\nhead.append_to_tail(2)\nhead.append_to_tail(3)\nhead.append_to_tail(4)\nhead.append_to_tail(5)\nhead.append_to_tail(6)\n# p list\n# list = Node.delete_node(list, 2)\n# p list\n\n\n# recusirve\ndef rec(node, k, &block)\n if node.next\n nth = rec(node.next, k, &block) + 1\n else\n nth = 0\n end\n\n if nth == k\n yield node\n end\n nth\nend\n\n3.times do \|i\|\n rec(head, i) do \|n\|\n puts \"the #{i}th to last element is #{n.data}\"\n end\nend\n\ndef find_kth_element(head, k)\n p1 = head\n p2 = head\n # move p1 k nodes into the list\n k.times do\n p2 = p2.next\n end\n\n while p2.next\n p2 = p2.next\n p1 = p1.next\n end\n p1\nend\n\n4.times do \|i\|\n node = find_kth_element(head, i)\n puts \"the #{i}th to last element is #{node.data}\"\nend\n" }, { "alpha_fraction": 0.5840455889701843, "alphanum_fraction": 0.5868945717811584, "avg_line_length": 21, "blob_id": "cb41307a0686223e62d7e6e1e3a3882c8c5091f1", "content_id": "3f8e90b30e50311cdeb3a53e6eded4b23a269932", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 351, "license_type": "no_license", "max_line_length": 54, "num_lines": 16, "path": "/book/lib/print_binary_tree.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "def print_binary_tree(root)\n cur_nodes = []\n cur_nodes.push(root)\n\n while cur_nodes.size > 0\n puts cur_nodes.map { \|n\| n&.val \|\| '-' }.join(\" \")\n\n next_nodes = []\n cur_nodes.each do \|node\|\n next_nodes.push(node&.left)\n next_nodes.push(node&.right)\n end\n break if next_nodes.all?(&:nil?)\n cur_nodes = next_nodes\n end\nend" } ]	33
sochis/send-gmail-to-slack	https://github.com/sochis/send-gmail-to-slack	511da0627e01a4dc53548207b729cfa72e94257d	895ed51c9df318da8ef075de0c3d886823c942c1	03b069e4cff6aee6568505b10503f9bd0c700e9e	refs/heads/master	2020-03-19T23:30:28.324306	2018-06-15T06:26:18	2018-06-15T06:26:18	137,008,786	2	0	null	null	null	null	null	[ { "alpha_fraction": 0.5810147523880005, "alphanum_fraction": 0.5810147523880005, "avg_line_length": 32.94444274902344, "blob_id": "bc0c1a4c029b0505efe7c80e0d97aa3d5ee0bce4", "content_id": "993560f21f4d1a590766d20b11673490d18a97d1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 611, "license_type": "no_license", "max_line_length": 105, "num_lines": 18, "path": "/execute_test.py", "repo_name": "sochis/send-gmail-to-slack", "src_encoding": "UTF-8", "text": "from gmail.getmail import GetMailContent\nfrom slack.send_slack import SendSlack\n\n\nclass Execute():\n def send_gmail_to_slack(self):\n get_mail = GetMailContent()\n mails = get_mail.get_mail_list()['messages']\n for mail in mails:\n content = get_mail.get_mail_content(mail['id'])\n parse = get_mail.parse_mail(content)\n text = 'From : ' + parse['from'] + '\\n' + 'date : ' + parse['date'] + '\\n \\n' + parse['body']\n SendSlack().send_slack(parse['subject'], text)\n\n\nif __name__ == '__main__':\n execute = Execute()\n execute.send_gmail_to_slack()\n" }, { "alpha_fraction": 0.5973348617553711, "alphanum_fraction": 0.6031286120414734, "avg_line_length": 35.74468231201172, "blob_id": "f3dde8dc3470384518794fc67a10dfffbfa6e976", "content_id": "73cb50ebfdb36869dda910537e72fe5a6eae80df", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1726, "license_type": "no_license", "max_line_length": 77, "num_lines": 47, "path": "/gmail/authorization.py", "repo_name": "sochis/send-gmail-to-slack", "src_encoding": "UTF-8", "text": "import json\nimport webbrowser\nfrom googleapiclient.discovery import build\nfrom httplib2 import Http\nfrom oauth2client.file import Storage\nfrom oauth2client.client import OAuth2WebServerFlow\n\n\nclass Credential():\n\n def readonly_credential(self):\n return Credential.pass_credential(self, 'readonly')\n\n def modify_credential(self):\n return Credential.pass_credential(self, 'modify')\n\n def send_credential(self):\n return Credential.pass_credential(self, 'send')\n\n def full_credential(self):\n return Credential.pass_credential(self, 'full')\n\n def pass_credential(self, scope_type):\n SCOPES = 'https://www.googleapis.com/auth/gmail.' + scope_type\n if (scope_type == 'full'):\n SCOPES = 'https://mail.google.com/'\n storage = Storage('gmail/credentials.json')\n credential = storage.get()\n if credential is None or credential.invalid:\n auth_info = json.load(open('gmail/client_secret.json'))\n info = auth_info['installed']\n flow = OAuth2WebServerFlow(client_id=info['client_id'],\n client_secret=info['client_secret'],\n scope=SCOPES,\n redirect_uri=info['redirect_uris'][0])\n auth_url = flow.step1_get_authorize_url()\n webbrowser.open(auth_url)\n code = input(\"input code : \")\n credential = flow.step2_exchange(code)\n storage = storage.put(credential)\n http = credential.authorize(Http())\n\n gmail_service = build('gmail', 'v1', http=http)\n return gmail_service\n\n if __name__ == '__main__' :\n pass_credential(1,\"full\")" }, { "alpha_fraction": 0.5338208675384521, "alphanum_fraction": 0.5338208675384521, "avg_line_length": 19.296297073364258, "blob_id": "ddc073a147ea6d292f2fc9079ffe74156a597c0e", "content_id": "fbb920489c630e01e0a0ad431960d8d123498f03", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 547, "license_type": "no_license", "max_line_length": 58, "num_lines": 27, "path": "/slack/send_slack.py", "repo_name": "sochis/send-gmail-to-slack", "src_encoding": "UTF-8", "text": "from slackclient import SlackClient\n\nfrom slack.slack_info import (\n SLACK_TOKEN,\n CHANNEL,\n USERNAME\n)\n\n\nclass SendSlack():\n\n def send_slack(self, title, text):\n sc = SlackClient(SLACK_TOKEN)\n\n attachments = [{\n 'title': title,\n 'text': text,\n 'fallback': text\n }]\n\n sc.api_call(\n \"chat.postMessage\",\n channel=CHANNEL,\n user=USERNAME,\n attachments=attachments,\n text=\"you have unread mails in your gmail box\"\n )" }, { "alpha_fraction": 0.5572519302368164, "alphanum_fraction": 0.7251908183097839, "avg_line_length": 20.83333396911621, "blob_id": "5422745b6045f10288ab5855245302609b124c37", "content_id": "d375bd917afe026357235134d606bf7342b304e3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 131, "license_type": "no_license", "max_line_length": 31, "num_lines": 6, "path": "/requirements.txt", "repo_name": "sochis/send-gmail-to-slack", "src_encoding": "UTF-8", "text": "google-api-python-client==1.6.2\ngoogle-auth==1.5.0\ngoogle-auth-httplib2==0.0.3\nhtml5lib==1.0.1\nhttplib2==0.11.3\nslackclient==1.0.5\n" }, { "alpha_fraction": 0.5374592542648315, "alphanum_fraction": 0.5439739227294922, "avg_line_length": 33.13888931274414, "blob_id": "682205d42fc168b42d2b6ae931251295836a196d", "content_id": "00c293c87fef49ef558c7762f860c0b1701cf34c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1228, "license_type": "no_license", "max_line_length": 89, "num_lines": 36, "path": "/gmail/getmail.py", "repo_name": "sochis/send-gmail-to-slack", "src_encoding": "UTF-8", "text": "from gmail.authorization import Credential\nimport base64\n\n\nclass GetMailContent():\n\n def get_mail_list(self):\n return self.service.users().messages().list(userId='me', q=\"is:unread\").execute()\n\n def get_mail_content(self, id):\n return self.service.users().messages().get(userId='me', id=id).execute()\n\n def parse_mail(self, content):\n mail = {}\n\n if 'parts' in content['payload'].keys():\n raw_body = content['payload']['parts'][0]['body']['data']\n else:\n raw_body = content['payload']['body']['data']\n mail['body'] = base64.urlsafe_b64decode(raw_body).decode('utf-8')\n mail['snippet'] = content['snippet']\n\n headers = content['payload']['headers']\n for header in headers:\n if header['name'] == 'From':\n mail['from'] = header['value']\n elif header['name'] == 'To':\n mail['to'] = header['value']\n elif header['name'] == 'Subject':\n mail['subject'] = header['value']\n elif header['name'] == 'Date':\n mail['date'] = header['value']\n return mail\n\n def __init__(self):\n self.service = Credential().readonly_credential()" } ]	5
snirav/FragranticaNotes	https://github.com/snirav/FragranticaNotes	2aaafd870294e60ff546c8340242ff5f380b22b9	b5443e4efa41fc3e080371bf3bd8eb5d14ca1308	aebef1793fbf5757e3aeb478c9f96ed591ff0e2b	refs/heads/master	2021-01-13T01:54:05.670490	2015-08-08T19:52:26	2015-08-08T19:52:26	39,312,169	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6367098093032837, "alphanum_fraction": 0.6610814929008484, "avg_line_length": 47.62963104248047, "blob_id": "70de4a0f9d05a004c8fde8c1a0505e67a6c75eaa", "content_id": "a1190286efd36cc991e1f5ddfc62aacd313a5d73", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1313, "license_type": "no_license", "max_line_length": 131, "num_lines": 27, "path": "/UserFragCrawl.py", "repo_name": "snirav/FragranticaNotes", "src_encoding": "UTF-8", "text": "__author__ = 'Nirav'\n\nimport urllib2\nfrom bs4 import BeautifulSoup\n# import numpy as np\n\nuserList = [614510, 581227]\nfor userID in userList:\n userUrl = 'http://www.fragrantica.com/member/' + str(userID)\n\n userPerfumes = urllib2.urlopen(userUrl, timeout=10)\n userPerfumeSoup = BeautifulSoup(userPerfumes)\n perfumeHas = [int(a.attrs['id'][2:]) for a in userPerfumeSoup.findAll('ul', attrs={'id': 'imam-sortable'})[0].findAll('li')]\n perfumeWants = [int(a.attrs['id'][2:]) for a in userPerfumeSoup.findAll('ul', attrs={'id' :'zelim-sortable'})[0].findAll('li')]\n perfumeHad = [int(a.attrs['id'][2:]) for a in userPerfumeSoup.findAll('ul', attrs={'id': 'imao-sortable'})[0].findAll('li')]\n\n userLoveHateUrl = 'http://www.fragrantica.com/ajax.php?view=emotion&id=' + str(userID)\n userLoveHatePerfumes = urllib2.urlopen(userLoveHateUrl, timeout=10)\n userLoveHateSoup = BeautifulSoup(userLoveHatePerfumes)\n loveHateList = [[],[],[]]\n lovelikedislikeCount = -1\n for loveHate in userLoveHateSoup.findAll('body')[0]:\n if loveHate.name == 'h3':\n lovelikedislikeCount += 1\n elif loveHate.name == 'a':\n if loveHate.attrs['href'] != None:\n loveHateList[lovelikedislikeCount].append(int(loveHate.attrs['href'].split('.')[0].split('-')[-1]))\n" }, { "alpha_fraction": 0.41322314739227295, "alphanum_fraction": 0.41746705770492554, "avg_line_length": 39.70000076293945, "blob_id": "d644a1cfd9a5e07056b623a1f24c99df2ad20dde", "content_id": "23b930b14569d72821562cc09b6d97108f573fa9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4477, "license_type": "no_license", "max_line_length": 114, "num_lines": 110, "path": "/FragranceCrawlerApp.py", "repo_name": "snirav/FragranticaNotes", "src_encoding": "UTF-8", "text": "import Tkinter as tk\nimport tkMessageBox\nfrom FragCrawlThreadCreation import \n\nclass fragranceCrawler(tk.Frame):\n def __init__(self, master):\n # Initialize window using the parent's constructor\n tk.Frame.__init__(self,\n master,\n width=400,\n height=300)\n # Set the title\n self.master.title('Perfume Crawler')\n \n # This allows the size specification to take effect\n self.pack_propagate(0)\n \n # We'll use the flexible pack layout manager\n self.pack()\n \n # Fill the Website List\n self.webSiteList=['http://www.fragrantica.com']\n # Fill the designers List\n self.designerEntries=['Azzaro',\n 'Bond-No-9',\n 'Boucheron',\n 'Britney-Spears',\n 'Burberry',\n 'Bvlgari',\n 'Cacharel',\n 'Calvin-Klein',\n 'Carolina-Herrera',\n 'Cartier',\n 'Chanel',\n 'Creed',\n 'Davidoff',\n 'Dior',\n 'Dolce%26Gabbana',\n 'Donna-Karan',\n 'Elizabeth-Arden',\n 'Escada',\n 'Estee-Lauder',\n 'Giorgio-Armani',\n 'Givenchy',\n 'Gucci',\n 'Guerlain',\n 'Guy-Laroche',\n 'Gwen-Stefani',\n 'Hermes',\n 'Hugo-Boss',\n 'Jean-Paul-Gaultier',\n 'Jennifer-Lopez',\n 'Kenzo',\n 'Lacoste',\n 'Lancome',\n 'Liz-Claiborne',\n 'Mariah-Carey',\n 'Moschino',\n 'Nina-Ricci',\n 'Paco-Rabanne',\n 'Paris-Hilton',\n 'Perry-Ellis',\n 'Prada',\n 'Ralph-Lauren',\n 'Rochas',\n 'Thierry-Mugler',\n 'Tommy-Hilfiger',\n 'Vera-Wang',\n 'Versace',\n 'Victoria%60s-Secret',\n 'Yves-Saint-Laurent']\n # Website Selection Menu\n self.defaultWebsite = tk.StringVar()\n self.defaultWebsite.set('Please Select the Websites to crawl')\n self.websiteDropdown = tk.OptionMenu(self,\n self.defaultWebsite, self.webSiteList)\n \n # Designer Selection Menu\n self.defaultDesigner = tk.StringVar()\n self.defaultDesigner.set('Please Select the sites to crawl')\n self.designerList = tk.OptionMenu(self,\n self.defaultDesigner, \"All\",\n *self.designerEntries)\n \n # Declaring the buttons and linking the functions\n self.textBox=tk.Text(xscrollcommand=set(), yscrollcommand=set(), height=5,width=5)\n \n self.crawl_button = tk.Button(self,\n text='Crawl Designer',\n command=self.initiateCrawl, height=2, width=15)\n\n # Put the controls on the form\n \n self.websiteDropdown.pack(fill=tk.X, side=tk.TOP)\n self.designerList.pack(fill=tk.X, side=tk.TOP)\n self.crawl_button.pack(fill=tk.X, side=tk.TOP)\n self.textBox.pack(fill=tk.X, side=tk.TOP)\n \n # Crawl handling routine\n def initiateCrawl(self): \n crawl(self.defaultWebsite.get().title().lower(), self.defaultDesigner.get().title(), self.designerEntries)\n tkMessageBox.showinfo(\"Crawling Status\", \"Crawling Completed Successfully!!\")\n self.textBox.insert('1.0', \"Crawling Completed Successfully!!\",\"a\")\n \n def run(self):\n ''' Run the app ''' \n self.mainloop()\n \napp = fragranceCrawler(tk.Tk())\napp.run()\n" }, { "alpha_fraction": 0.5731329917907715, "alphanum_fraction": 0.5802032947540283, "avg_line_length": 42.50961685180664, "blob_id": "39d6f515ebe9ba5502601b679dcd9029c4af9416", "content_id": "b2db346172a02fe6dd2a16b6d61b97df041cb666", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4526, "license_type": "no_license", "max_line_length": 129, "num_lines": 104, "path": "/FragCrawlThreadCreation.py", "repo_name": "snirav/FragranticaNotes", "src_encoding": "UTF-8", "text": "__author__ = 'Nirav'\n\nimport threading\nimport urllib2\nfrom bs4 import BeautifulSoup\nimport numpy as np\n# import pandas as pd\n\n\n# Spider class for running the threads for all the designers\nclass mySpider(threading.Thread):\n\n def __init__(self,siteUrl,designerName):\n threading.Thread.__init__(self)\n self.siteUrl = siteUrl\n self.designerName = designerName\n\n def run(self):\n print \"Starting \" + self.designerName\n getNextlevelUrls(self.siteUrl, self.designerName)\n print \"Exiting \" + self.designerName\n\n\ndef getNextlevelUrls(siteUrl, designerName):\n # f = codecs.open('C:\\\\Perfumes\\\\'+designerName+'.txt','w',encoding='utf8')\n notesDictionary = {}\n try:\n designerResponse = urllib2.urlopen(siteUrl + '/designers/' + designerName + '.html', timeout=10)\n except urllib2.URLError, e:\n print \"Oops, timed out?\", e\n designerSoup = BeautifulSoup(designerResponse)\n links = designerSoup.findAll('a')\n perfumeCount = 0\n print designerName\n # f.write(designerName +'\\n')\n for link in links:\n if link.get('href') != None:\n if link['href'].find('/perfume/'+designerName) != -1:\n\n perfumeCount += 1\n\n perfumeID = str(link['href'].split('-')[-1].split('.')[0])\n # print(perfumeID)\n print(link.img.get('alt').split(' ', 1)[1])\n userListUrl = siteUrl+'/' + '/ajax.php?view=whoHasHadWant&perfume_id=' + perfumeID\n try:\n userListResponse = urllib2.urlopen(userListUrl,timeout=10)\n except urllib2.URLError, e:\n print \"URL Fetch time-out\",e\n userListSoup = BeautifulSoup(userListResponse)\n perfumeWhoHasHadSigWantCurr = [int(tag.string) for tag in (userListSoup.find('p').findAll('b'))]\n # print perfumeWhoHasHadSigWantCurr\n\n perfumeUrl = siteUrl+'/'+link['href']\n try:\n perfumeResponse = urllib2.urlopen(perfumeUrl,timeout=10)\n except urllib2.URLError, e:\n print \"URL Fetch time-out\",e\n perfumeSoup = BeautifulSoup(perfumeResponse)\n\n perfumeNotesTitle = perfumeSoup.findAll('span',attrs={'class':'rtgNote'})\n noteList = [perfumeTitle.img.get('alt') for perfumeTitle in perfumeNotesTitle]\n noteID = [int(perfumeTitle.attrs['title']) for perfumeTitle in perfumeNotesTitle]\n # print(noteList)\n # print(noteID)\n idx = 0\n for ith in noteID:\n if not(ith in notesDictionary):\n notesDictionary[ith] = noteList[idx]\n idx += 1\n perfumeMenWomen = np.sign(np.array([perfumeSoup.title.text.find(' men'), perfumeSoup.title.text.find(' women')]))\n # print(perfumeMenWomen)\n perfumeVotes = perfumeSoup.findAll('table',attrs={'class':'voteLS'})\n perfumeLongevityVotes = np.array([int(a.string) for a in perfumeVotes[0].findAll('td', attrs={'class':'ndSum'})])\n # print(perfumeLongevityVotes)\n perfumeSillageVotes = np.array([int(a.string) for a in perfumeVotes[1].findAll('td', attrs={'class':'ndSum'})])\n # print(perfumeSillageVotes)\n perfumeAttrs = np.append(perfumeMenWomen, np.append(perfumeLongevityVotes, perfumeSillageVotes))\n perfumeNotes = perfumeSoup.findAll('div',attrs={'id':'userMainNotes'})\n\n # perfumeNotesStr = str(perfumeNotes[0].attrs[u'title'])\n # notesVec = np.zeros(400, dtype = int)\n # perfumeNotesStrSplit = perfumeNotesStr.split(';')\n # for notes in perfumeNotesStrSplit :\n # individualNotes = notes.split(':')\n # individualNotesInt = np.array([int(str_tmp) for str_tmp in individualNotes])\n # notesVec[individualNotesInt[0]] = individualNotesInt[1]\n # print(notesVec)\n\n\ndef crawl(url, designer, designerList):\n threads = []\n if designer == 'All':\n for designer in designerList:\n try:\n t = mySpider(url, designer)\n threads.append(t)\n except Exception as errtxt:\n print errtxt\n else:\n getNextlevelUrls(url, designer)\n [t.start() for t in threads]\n [t.join() for t in threads]\n print \"Main Thread Completed!!\" \n" } ]	3
jorendorff/dht	https://github.com/jorendorff/dht	012546ff00a336cbe7c988b12ccab20315d2eb3d	3c893214dfe7f9fb26c9e59a565c3ad651784aa2	8cea6304cbbdae079cf1d762bc0140b473072cf8	refs/heads/master	2020-05-31T20:55:09.255169	2016-02-05T07:14:20	2016-02-05T07:14:20	3,507,865	19	5	null	null	null	null	null	[ { "alpha_fraction": 0.5639333724975586, "alphanum_fraction": 0.5703738331794739, "avg_line_length": 19.60194206237793, "blob_id": "b5103538c53ae87ee2d4407f0e5471360ecf3902", "content_id": "0ccd1386115e6734b303a2c6a91458e516d324be", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6366, "license_type": "permissive", "max_line_length": 90, "num_lines": 309, "path": "/tables.cpp", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "#include \"tables.h\"\n#include <cstring>\n\nusing namespace std;\n\n\n// === OpenTable\n\nOpenTable::OpenTable() {\n table = new Entry[8];\n mask = 7;\n live_count = 0;\n nonempty_count = 0;\n}\n\nOpenTable::~OpenTable() {\n delete[] table;\n}\n\nOpenTable::Entry \nOpenTable::lookup(KeyArg key)\n{\n hashcode_t h = hash(key);\n size_t i = h & mask;\n h >>= 3;\n while (!isEmpty(table[i].key)) {\n if (table[i].key == key)\n return &table[i];\n i = (i + (h \| 1)) & mask;\n }\n return NULL;\n}\n\nconst OpenTable::Entry \nOpenTable::lookup(KeyArg key) const\n{\n return const_cast<OpenTable >(this)->lookup(key);\n}\n\nvoid\nOpenTable::rehash(size_t new_capacity)\n{\n Entry old_table = table;\n Entry old_table_end = table + mask + 1;\n table = new Entry[new_capacity];\n mask = new_capacity - 1;\n live_count = 0;\n nonempty_count = 0;\n for (Entry p = old_table; p != old_table_end; ++p) {\n if (isLive(p->key))\n set(p->key, p->value);\n }\n delete[] old_table;\n}\n\nsize_t\nOpenTable::byte_size(ByteSizeOption) const\n{\n return sizeof(this) + (mask + 1) sizeof(Entry);\n}\n\nsize_t\nOpenTable::size() const\n{\n return live_count;\n}\n\nbool\nOpenTable::has(KeyArg key) const\n{\n return lookup(key) != NULL;\n}\n\nValue\nOpenTable::get(KeyArg key) const\n{\n const Entry e = lookup(key);\n return e ? e->value : Value();\n}\n\nvoid\nOpenTable::set(KeyArg key, ValueArg value)\n{\n hashcode_t h = hash(key);\n size_t i = h & mask;\n h >>= 3;\n while (isLive(table[i].key)) {\n if (table[i].key == key) {\n table[i].value = value;\n return;\n }\n i = (i + (h \| 1)) & mask;\n }\n\n bool tomb = isTombstone(table[i].key);\n table[i].key = key;\n table[i].value = value;\n live_count++;\n if (!tomb)\n nonempty_count++;\n if (nonempty_count > (mask + 1) max_fill_ratio())\n rehash((mask + 1) << 1);\n}\n\nbool\nOpenTable::remove(KeyArg key)\n{\n Entry e = lookup(key);\n if (!e)\n return false;\n makeTombstone(e->key);\n live_count--;\n if (mask > 7 && live_count < (mask + 1) min_fill_ratio())\n rehash((mask + 1) >> 1);\n return true;\n}\n\n\n// === DenseTable\n\n#ifdef HAVE_SPARSEHASH\n\nDenseTable::DenseTable()\n{\n Key k;\n makeEmpty(k);\n map.set_empty_key(k);\n makeTombstone(k);\n map.set_deleted_key(k);\n}\n\nsize_t\nDenseTable::byte_size(ByteSizeOption) const\n{\n return sizeof(this) + sizeof(std::pair<const Key, Value>) map.bucket_count();\n}\n\nsize_t\nDenseTable::size() const\n{\n return map.size();\n}\n\nbool\nDenseTable::has(KeyArg key) const\n{\n return map.find(key) != map.end();\n}\n\nValue\nDenseTable::get(KeyArg key) const\n{\n Map::const_iterator it = map.find(key);\n return (it == map.end() ? Value() : it->second);\n}\n\nvoid\nDenseTable::set(KeyArg key, ValueArg value)\n{\n map[key] = value;\n}\n\nbool\nDenseTable::remove(KeyArg key)\n{\n Map::iterator it = map.find(key);\n if (it == map.end())\n return false;\n map.erase(it);\n size_t n = map.bucket_count();\n if (n > 32 && map.size() <= n / 8)\n map.resize(0);\n return true;\n}\n\n#endif // HAVE_SPARSEHASH\n\n\n// === CloseTable\n\nCloseTable::CloseTable()\n{\n size_t buckets = initial_buckets();\n table = new EntryPtr[buckets];\n memset(table, 0, buckets * sizeof(EntryPtr));\n table_mask = buckets - 1;\n entries_capacity = size_t(buckets * fill_factor());\n entries = new Entry[entries_capacity];\n entries_length = 0;\n live_count = 0;\n}\n\nCloseTable::~CloseTable()\n{\n delete[] table;\n delete[] entries;\n}\n\nCloseTable::Entry \nCloseTable::lookup(KeyArg key, hashcode_t h)\n{\n for (Entry e = table[h & table_mask]; e; e = e->chain) {\n if (e->key == key)\n return e;\n }\n return NULL;\n}\n\nconst CloseTable::Entry \nCloseTable::lookup(KeyArg key) const {\n return const_cast<CloseTable >(this)->lookup(key, hash(key));\n}\n\nvoid\nCloseTable::rehash(size_t new_table_mask)\n{\n size_t new_capacity = size_t((new_table_mask + 1) * fill_factor());\n EntryPtr new_table = new EntryPtr[new_table_mask + 1];\n memset(new_table, 0, (new_table_mask + 1) sizeof(EntryPtr));\n Entry new_entries = new Entry[new_capacity];\n\n Entry q = new_entries;\n for (Entry p = entries, end = entries + entries_length; p != end; p++) {\n if (!isEmpty(p->key)) {\n hashcode_t h = hash(p->key) & new_table_mask;\n q->key = p->key;\n q->value = p->value;\n q->chain = new_table[h];\n new_table[h] = q;\n q++;\n }\n }\n\n delete[] table;\n delete[] entries;\n table = new_table;\n table_mask = new_table_mask;\n entries = new_entries;\n entries_capacity = new_capacity;\n entries_length = live_count;\n}\n\nsize_t\nCloseTable::byte_size(ByteSizeOption option) const\n{\n return sizeof(this)\n + (table_mask + 1) sizeof(EntryPtr)\n + (option == BytesAllocated ? entries_capacity : entries_length) * sizeof(Entry);\n}\n\nsize_t\nCloseTable::size() const\n{\n return live_count;\n}\n\nbool\nCloseTable::has(KeyArg key) const\n{\n return lookup(key) != NULL;\n}\n\nValue\nCloseTable::get(KeyArg key) const\n{\n const Entry e = lookup(key);\n return e ? e->value : Value();\n}\n\nvoid\nCloseTable::set(KeyArg key, ValueArg value)\n{\n hashcode_t h = hash(key);\n Entry e = lookup(key, h);\n if (e) {\n e->value = value;\n } else {\n if (entries_length == entries_capacity) {\n // If the table is more than 1/4 deleted entries, simply rehash in\n // place to free up some space. Otherwise, grow the table.\n rehash(live_count >= entries_capacity * 0.75\n ? (table_mask << 1) \| 1\n : table_mask);\n }\n h &= table_mask;\n live_count++;\n e = &entries[entries_length++];\n e->key = key;\n e->value = value;\n e->chain = table[h];\n table[h] = e;\n }\n}\n\nbool\nCloseTable::remove(KeyArg key)\n{\n // If an entry exists for the given key, empty it.\n Entry e = lookup(key, hash(key));\n if (e == NULL)\n return false;\n live_count--;\n makeEmpty(e->key);\n\n // If many entries have been removed, shrink the table.\n if (table_mask > initial_buckets() && live_count < entries_length min_vector_fill())\n rehash(table_mask >> 1);\n return true;\n}\n" }, { "alpha_fraction": 0.4968442916870117, "alphanum_fraction": 0.5218830704689026, "avg_line_length": 23.65561294555664, "blob_id": "94710112a0994678110b108fbba18f9ce9693f37", "content_id": "eb93b645a3c622f368436fd16f91708d9da77ab4", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 9665, "license_type": "permissive", "max_line_length": 108, "num_lines": 392, "path": "/hashbench.cpp", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "#include <cstring>\n#include <cmath>\n#include <stdint.h>\n#include <iostream>\n#include <iomanip>\n#ifdef HAVE_GETTIMEOFDAY\n#include <sys/time.h>\n#else\n#include <windows.h>\n#endif\n#include \"tables.h\"\n\nusing namespace std;\n\n// === Code for measuring speed\n//\n// Instead of producing a single number, we want to produce several data\n// points. Then we'll plot them, and we'll be able to see noise, nonlinearity,\n// and any other nonobvious weirdness.\n\n// Run a Test of size n once. Return the elapsed time in seconds.\ntemplate <class Test>\ndouble measure_single_run(size_t n)\n{\n Test test;\n test.setup(n);\n\n#ifdef HAVE_GETTIMEOFDAY\n struct timeval t0, t1;\n gettimeofday(&t0, NULL);\n#else\n LARGE_INTEGER f, t0, t1;\n if (!QueryPerformanceFrequency(&f))\n abort();\n if (!QueryPerformanceCounter(&t0))\n abort();\n#endif\n\n test.run(n);\n\n#ifdef HAVE_GETTIMEOFDAY\n gettimeofday(&t1, NULL);\n return t1.tv_sec - t0.tv_sec + 1e-6 * (t1.tv_usec - t0.tv_usec);\n#else\n if (!QueryPerformanceCounter(&t1))\n abort();\n return double(t1.QuadPart - t0.QuadPart) / double(f.QuadPart);\n#endif\n}\n\nconst double min_run_seconds = 0.1;\nconst double max_run_seconds = 1.0;\n\n// Run several Tests of different sizes. Write results to stdout.\n//\n// We intentionally don't scale the test size exponentially, because hash\n// tables can have nonlinear performance-falls-off-a-cliff points (table\n// resizes) that occur at exponentially spaced intervals. We want to make sure\n// we don't miss those.\n//\ntemplate <class Test>\nvoid run_time_trials()\n{\n cout << \"[\\n\";\n\n // Estimate how many iterations per second we can do.\n double estimated_speed;\n for (size_t n = 1; ; n = 2) {\n double dt = measure_single_run<Test>(n);\n if (dt >= min_run_seconds) {\n estimated_speed = n / dt;\n break;\n }\n }\n\n // Now run trials of increasing size and print the results.\n double total = 0;\n const int trials = Test::trials();\n for (int i = 0; i < trials; i++) {\n double target_dt = min_run_seconds + double(i) / (trials - 1) (max_run_seconds - min_run_seconds);\n size_t n = size_t(ceil(estimated_speed * target_dt));\n double dt = measure_single_run<Test>(n);\n cout << \"\\t\\t[\" << n << \", \" << dt << (i < trials - 1 ? \"],\" : \"]\") << endl;\n }\n\n cout << \"\\t]\";\n}\n\n\n// === Tests\n\nstruct GoodTest {\n static int trials() { return 10; }\n};\n\nstruct SquirrelyTest {\n static int trials() { return 25; }\n};\n\ntemplate <class Table>\nstruct InsertLargeTest : SquirrelyTest {\n Table table;\n void setup(size_t) {}\n void run(size_t n) {\n Key k = 1;\n for (size_t i = 0; i < n; i++) {\n table.set(k, k);\n k = k * 1103515245 + 12345;\n }\n }\n};\n\n// This test repeatedly builds a table of pseudorandom size (an exponential\n// distribution with median size 100), then discards the table and starts over.\n// It stops when it has done n total inserts.\n//\n// For a given n, the workload is deterministic.\n//\n// It would be simpler to repeatedly build tables of a particular\n// size. However, all the implementations have particular sizes at which they\n// rehash, an expensive operation that is meant to be amortized across all\n// the other inserts. The benchmark should not reward implementations for\n// having any particular rehashing threshold; so we build tables of a variety\n// of sizes.\n//\ntemplate <class Table>\nstruct InsertSmallTest : GoodTest {\n void setup(size_t) {}\n void run(size_t n) {\n Key k = 1;\n while (n) {\n Table table;\n do {\n table.set(k, k);\n k = k * 1103515245 + 12345;\n } while (--n && k % 145 != 0);\n }\n }\n};\n\ntemplate <class Table>\nstruct LookupHitTest : GoodTest {\n enum { M = 8675309 + 1 }; // jenny's number, a prime, plus 1\n Table table;\n size_t errors;\n\n void setup(size_t n) {\n Key k = 1;\n for (size_t i = 0; i < n; i++) {\n table.set(k, k);\n k = k * 31 % M;\n if (k == 1)\n break;\n }\n errors = 0;\n }\n\n void run(size_t n) {\n Key k = 1;\n for (size_t i = 0; i < n; i++) {\n if (table.get(k) != k)\n abort();\n k = k * 31 % M;\n }\n }\n};\n\ntemplate <class Table>\nstruct LookupMissTest : GoodTest {\n enum { M = 8675309 + 1 }; // jenny's number, a prime, plus 1\n Table table;\n size_t errors;\n\n void setup(size_t n) {\n Key k = 1;\n for (size_t i = 0; i < n; i++) {\n table.set(k, k);\n k = k * 31 % M;\n if (k == 1)\n break;\n }\n errors = 0;\n }\n\n void run(size_t n) {\n Key k = 1;\n for (size_t i = 0; i < n; i++) {\n if (table.get(k + M) != 0)\n abort();\n k = k * 31 % M;\n }\n }\n};\n\n// This test adds and removes entries from a table in FIFO order.\ntemplate <class Table>\nstruct WorklistTest : GoodTest {\n Table table;\n Key r, w;\n\n void setup(size_t) {\n r = 1;\n w = 1;\n for (int i = 0; i < 700; i++) {\n table.set(w, w);\n w = w * 1103515245 + 12345;\n }\n }\n\n void run(size_t n) {\n for (size_t i = 0; i < n; i++) {\n table.set(w, w);\n w = w * 1103515245 + 12345;\n\n if (!table.remove(r))\n abort();\n r = r * 1103515245 + 12345;\n }\n }\n};\n\ntemplate <class Table>\nstruct DeleteTest : SquirrelyTest {\n Table table;\n\n void setup(size_t n) {\n while (n % 7 == 0 \|\| n % 11 == 0)\n n++;\n\n Key k = 0;\n for (size_t i = 0; i < n; i++) {\n table.set(k + 1, 0);\n k = (k + 7) % n;\n }\n }\n\n void run(size_t n) {\n while (n % 7 == 0 \|\| n % 11 == 0)\n n++;\n\n Key k = 0;\n for (size_t i = 0; i < n; i++) {\n if (!table.remove(k + 1))\n abort();\n k = (k + 11) % n;\n }\n }\n};\n\ntemplate <class Table>\nstruct LookupAfterDeleteTest : GoodTest {\n Table table;\n\n enum { Size = 50000 };\n\n void setup(size_t n) {\n for (size_t i = 1; i <= Size; i++)\n table.set(i, i);\n for (size_t i = 1; i <= Size; i++) {\n if ((i & 0xff) != 0)\n table.remove(i);\n }\n }\n\n void run(size_t n) {\n for (size_t i = 1; i <= n; i++) {\n Key k = i % Size;\n if (table.get(k) != ((k & 0xff) == 0 ? k : Value()))\n abort();\n }\n }\n};\n\ntemplate <template <class> class Test>\nvoid run_speed_test()\n{\n cout << '{' << endl;\n\n#ifdef HAVE_SPARSEHASH\n cout << \"\\t\\\"DenseTable\\\": \";\n run_time_trials<Test<DenseTable> >();\n cout << ',' << endl;\n#endif\n\n cout << \"\\t\\\"OpenTable\\\": \";\n run_time_trials<Test<OpenTable> >();\n cout << ',' << endl;\n\n cout << \"\\t\\\"CloseTable\\\": \";\n run_time_trials<Test<CloseTable> >();\n cout << endl;\n\n cout << \"}\";\n}\n\nvoid run_one_speed_test(const char name)\n{\n if (strcmp(name, \"InsertLargeTest\") == 0)\n run_speed_test<InsertLargeTest>();\n else if (strcmp(name, \"InsertSmallTest\") == 0)\n run_speed_test<InsertSmallTest>();\n else if (strcmp(name, \"LookupHitTest\") == 0)\n run_speed_test<LookupHitTest>();\n else if (strcmp(name, \"LookupMissTest\") == 0)\n run_speed_test<LookupMissTest>();\n else if (strcmp(name, \"WorklistTest\") == 0)\n run_speed_test<WorklistTest>();\n else if (strcmp(name, \"DeleteTest\") == 0)\n run_speed_test<DeleteTest>();\n else if (strcmp(name, \"LookupAfterDeleteTest\") == 0)\n run_speed_test<LookupAfterDeleteTest>();\n else {\n cerr << \"No such test: \" << name << endl;\n return;\n }\n cout << endl;\n}\n\nvoid run_all_speed_tests()\n{\n cout << \"{\" << endl;\n\n cout << \"\\\"InsertLargeTest\\\": \";\n run_speed_test<InsertLargeTest>();\n cout << \",\" << endl;\n\n cout << \"\\\"InsertSmallTest\\\": \";\n run_speed_test<InsertSmallTest>();\n cout << \",\" << endl;\n\n cout << \"\\\"LookupHitTest\\\": \";\n run_speed_test<LookupHitTest>();\n cout << \",\" << endl;\n\n cout << \"\\\"LookupMissTest\\\": \";\n run_speed_test<LookupMissTest>();\n cout << \",\" << endl;\n\n cout << \"\\\"WorklistTest\\\": \";\n run_speed_test<WorklistTest>();\n cout << \",\" << endl;\n\n cout << \"\\\"DeleteTest\\\": \";\n run_speed_test<DeleteTest>();\n cout << \",\" << endl;\n\n cout << \"\\\"LookupAfterDeleteTest\\\": \";\n run_speed_test<LookupAfterDeleteTest>();\n\n cout << \"}\" << endl;\n}\n\nvoid measure_space(ByteSizeOption opt)\n{\n#ifdef HAVE_SPARSEHASH\n DenseTable ht0;\n#endif\n OpenTable ht1;\n CloseTable ht2;\n\n for (int i = 0; i < 100000; i++) {\n cout << i << '\\t'\n#ifdef HAVE_SPARSEHASH\n << ht0.byte_size(opt) << '\\t'\n#else\n << 1 << '\\t'\n#endif\n << ht1.byte_size(opt) << '\\t' << ht2.byte_size(opt) << endl;\n\n#ifdef HAVE_SPARSEHASH\n ht0.set(i + 1, i);\n#endif\n ht1.set(i + 1, i);\n ht2.set(i + 1, i);\n }\n}\n\nint main(int argc, const char argv) {\n if (argc == 2 && (strcmp(argv[1], \"-m\") == 0 \|\| strcmp(argv[1], \"-w\") == 0)) {\n measure_space(argv[1][1] == 'm' ? BytesAllocated : BytesWritten);\n } else if (argc == 1) {\n //cout << measure_single_run<LookupHitTest<OpenTable> >(1000000) << endl;\n run_all_speed_tests();\n } else if (argc == 2) {\n run_one_speed_test(argv[1]);\n } else {\n cerr << \"usage:\\n \" << argv[0] << \"\\n \" << argv[0] << \" -m\\n \" << argv[0] << \" -w\\n\";\n return 1;\n }\n\n return 0;\n}\n" }, { "alpha_fraction": 0.5738714337348938, "alphanum_fraction": 0.6094391345977783, "avg_line_length": 32.227272033691406, "blob_id": "1318dbf869864b5e4d53404b30a0e9d1ab8dd309", "content_id": "b1122da79a4e857bf89ae5b57768b0d16287141a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1462, "license_type": "permissive", "max_line_length": 98, "num_lines": 44, "path": "/plot.py", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "from __future__ import division\nimport sys\nfrom matplotlib.pyplot import \nimport numpy\n\ndef main(filename, outfilename):\n data = numpy.genfromtxt(filename)\n\n # plot the graph and save it\n if filename == 'figure-1-data.txt':\n suptitle('Memory allocated (log/log plot)')\n ylabel('bytes of memory allocated')\n elif filename == 'figure-2-data.txt':\n suptitle('Memory written (log/log plot)')\n ylabel('bytes of memory written')\n xlabel('number of entries')\n\n index = data[:,0]\n series1 = data[:,1]\n series2 = data[:,2]\n series3 = data[:,3]\n loglog(index, series1, '-', color='#cccccc', label='dense_hash_map (open addressing)')\n loglog(index, series2, 'b-', label='open addressing')\n loglog(index, series3, 'r-', label='Close table')\n legend(loc='upper left')\n savefig(outfilename, format='png')\n\n # compute and print summary information about which is bigger\n r1 = []\n r2 = []\n for i, _, s1, s2 in data:\n if s1 > s2:\n r1.append(s1/s2)\n else:\n r2.append(s2/s1)\n\n r1avg = sum(r1)/len(r1) - 1 if len(r1) else 0\n r2avg = sum(r2)/len(r2) - 1 if len(r2) else 0\n r1f = len(r1)/len(data)\n r2f = len(r2)/len(data)\n print(\"Implementation 1 takes up more space {:.1%} of the time, by {:.1%}\".format(r1f, r1avg))\n print(\"Implementation 2 takes up more space {:.1%} of the time, by {:.1%}\".format(r2f, r2avg))\n\nmain(sys.argv[1], sys.argv[2])\n" }, { "alpha_fraction": 0.5858041048049927, "alphanum_fraction": 0.5893980264663696, "avg_line_length": 32.727272033691406, "blob_id": "e11c82aa41644e255dae35b8222480e6a3d0755a", "content_id": "36c0c7e1cb781b678c24a5950f2d630994ed0c2a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1113, "license_type": "permissive", "max_line_length": 104, "num_lines": 33, "path": "/plot_speed.py", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "from __future__ import division\nimport sys\nimport matplotlib.pyplot as plt\nimport numpy\nimport json\n\ndef main(filename):\n with open(filename) as f:\n data = json.load(f)\n\n # plot the graph and save it\n for testname, results in data.items():\n fig = plt.figure()\n fig.suptitle(testname)\n axes = fig.gca()\n axes.set_ylabel('speed (operations/second)')\n hi = max(max(x/y for x, y in series) for series in results.values())\n axes.set_ylim(bottom=0, top=hi * 1.2)\n axes.set_xlabel('number of operations')\n\n def show(data, args, kwargs):\n xs = [x for x, y in data]\n ys = [x/y for x, y in data]\n axes.plot(xs, ys, args, *kwargs)\n\n if 'DenseTable' in results:\n show(results['DenseTable'], '-o', color='#cccccc', label='dense_hash_map (open addressing)')\n show(results['OpenTable'], 'b-o', label='open addressing')\n show(results['CloseTable'], 'r-o', label='Close table')\n axes.legend(loc='best')\n fig.savefig(testname + \"-speed.png\", format='png')\n\nmain(sys.argv[1])\n" }, { "alpha_fraction": 0.7191780805587769, "alphanum_fraction": 0.7277397513389587, "avg_line_length": 28.183332443237305, "blob_id": "00158063ed08dcb545b4f3fbab8d1aae8647c467", "content_id": "8ce73bd000b341286794ab6897bc2eaf1b11249f", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Makefile", "length_bytes": 1752, "license_type": "permissive", "max_line_length": 91, "num_lines": 60, "path": "/Makefile", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "CXX=g++-apple-4.2\nCXXFLAGS=-O3 -g -Isparsehash-install/include -DNDEBUG -DHAVE_GETTIMEOFDAY -DHAVE_SPARSEHASH\n\n# To run plot.py, you need Python with matplotlib. Set the python executable to\n# use below.\n#\n# If you have MacPorts, you can:\n# sudo port install py27-matplotlib\n# This doesn't install matplotlib in your Mac's system python installation.\n# Instead it installs a copy of python in /opt/local/bin (or wherever you've\n# configured ports to install stuff) and that copy has matplotlib.\n#\nPYTHON=/opt/local/bin/python2.7\n\nSPEED_IMAGES=\\\n InsertSmallTest-speed.png \\\n InsertLargeTest-speed.png \\\n LookupHitTest-speed.png \\\n LookupMissTest-speed.png \\\n WorklistTest-speed.png \\\n DeleteTest-speed.png \\\n LookupAfterDeleteTest-speed.png\n\nall: figure-1.png figure-2.png $(SPEED_IMAGES)\n\nfigure-1.png: figure-1-data.txt plot.py\n\t$(PYTHON) plot.py $< $@\n\nfigure-2.png: figure-2-data.txt plot.py\n\t$(PYTHON) plot.py $< $@\n\nfigure-1-data.txt: hashbench\n\t./hashbench -m > $@\n\nfigure-2-data.txt: hashbench\n\t./hashbench -w > $@\n\n$(SPEED_IMAGES): hashbench-data.txt plot_speed.py\n\t$(PYTHON) plot_speed.py $<\n\nhashbench-data.txt: hashbench\n\t./hashbench > $@\n\nhashbench: hashbench.o tables.o\n\t$(CXX) -o $@ $^\n\nhashbench.o: hashbench.cpp tables.h sparsehash-install/include/sparsehash/dense_hash_map\n\t$(CXX) $(CXXFLAGS) -o $@ -c $<\n\ntables.o: tables.cpp tables.h sparsehash-install/include/sparsehash/dense_hash_map\n\t$(CXX) $(CXXFLAGS) -o $@ -c $<\n\nsparsehash-sources/configure:\n\tsvn checkout http://sparsehash.googlecode.com/svn/trunk/ sparsehash-sources\n\nsparsehash-install/include/sparsehash/dense_hash_map: sparsehash-sources/configure\n\tcd sparsehash-sources && \\\n\t\t./configure --prefix=$(PWD)/sparsehash-install && \\\n\t\tmake && \\\n\t\tmake install\n\n" }, { "alpha_fraction": 0.6471967697143555, "alphanum_fraction": 0.6559368968009949, "avg_line_length": 28.503145217895508, "blob_id": "25072b5728aa54f34fdd694e16a80e3f0987276a", "content_id": "48c8b14710c6340134c57349990a17d2d398a59d", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4691, "license_type": "permissive", "max_line_length": 83, "num_lines": 159, "path": "/tables.h", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "#ifndef tables_h_\n#define tables_h_\n\n#include <stdint.h>\n#include <cstdlib>\n#ifdef HAVE_SPARSEHASH\n#include <sparsehash/dense_hash_map>\n#endif\n\n// === Keys and values (common definitions used by both hash table implementations)\n\n// The keys to be stored in our hash tables are 64-bit values. However two keys\n// are set aside: Key(0) indicates that a record is empty, and Key(-1) indicates\n// that the record has been deleted.\n\ntypedef uint64_t Key;\ntypedef Key KeyArg;\ntypedef uint64_t Value;\ntypedef Value ValueArg;\ntypedef uint32_t hashcode_t;\n\ninline hashcode_t hash(KeyArg k) { return k; }\n\n// A key is either \"live\" (that is, an actual value), empty, or a tombstone.\n// For a given key, exactly one of the three predicates\n// isLive/isEmpty/isTombstone is true. The implementation of isLive below is\n// equivalent to !isEmpty(k) && !isTombstone(k) but much* faster; it is the\n// only fancy thing in this program.\n\ninline bool isEmpty(KeyArg k) { return k == 0; }\ninline void makeEmpty(Key &k) { k = 0; }\ninline bool isTombstone(KeyArg k) { return k == Key(-1); }\ninline void makeTombstone(Key &k) { k = Key(-1); }\ninline bool isLive(KeyArg k) { return ((k + 1) & ~1) != 0; }\n\nenum ByteSizeOption { BytesAllocated, BytesWritten };\n\n\n#ifdef HAVE_SPARSEHASH\n// === DenseTable\n// The dense_hash_map type from Google sparsehash, included to give a baseline.\n\nclass DenseTable {\nprivate:\n struct Hasher {\n size_t operator()(KeyArg key) const { return hash(key); }\n };\n\n typedef google::dense_hash_map<Key, Value, Hasher> Map;\n Map map;\n\npublic:\n DenseTable();\n\n size_t byte_size(ByteSizeOption option) const;\n size_t size() const;\n bool has(KeyArg key) const;\n Value get(KeyArg key) const;\n void set(KeyArg key, ValueArg value);\n bool remove(KeyArg key);\n};\n#endif // HAVE_SPARSEHASH\n\n\n// === OpenTable\n// A simple hash table with open addressing.\n// See <https://en.wikipedia.org/wiki/Hash_table#Open_addressing>.\n//\nclass OpenTable {\n struct Entry {\n Key key;\n Value value;\n\n Entry() { makeEmpty(key); }\n };\n\n Entry table; // power-of-2-sized flat hash table\n size_t live_count; // number of live entries\n size_t nonempty_count; // number of live and tombstone entries\n size_t mask; // size of table, in elements, minus 1\n\n static double min_fill_ratio() { return 0.25; }\n static double max_fill_ratio() { return 0.75; }\n\n inline Entry lookup(KeyArg key);\n inline const Entry * lookup(KeyArg key) const;\n\n void rehash(size_t new_capacity);\n\npublic:\n OpenTable();\n ~OpenTable();\n\n size_t byte_size(ByteSizeOption option) const;\n size_t size() const;\n bool has(KeyArg key) const;\n Value get(KeyArg key) const;\n void set(KeyArg key, ValueArg value);\n bool remove(KeyArg key);\n};\n\n\n// === CloseTable\n// A vector combined with a very simple hash table for fast lookup.\n// Tyler Close proposed this.\n//\nclass CloseTable {\nprivate:\n // The number of buckets in the table initially.\n // This must be a power of two.\n static size_t initial_buckets() { return 4; }\n\n // The maximum load factor (mean number of entries per bucket).\n // It is an invariant that\n // entries_capacity == floor((table_mask + 1) * fill_factor()).\n //\n // This fill factor was chosen to make the size of the entries\n // array, in bytes, close to a power of two. (sizeof(Entry)\n // is 24 on both 32-bit and 64-bit systems.)\n //\n static double fill_factor() { return 8.0 / 3.0; }\n\n // The minimum permitted value of (live_count / entries_length).\n // If that ratio drops below this value, we shrink the table.\n static double min_vector_fill() { return 0.25; }\n\n struct Entry {\n Key key;\n Value value;\n Entry chain;\n };\n\n typedef Entry EntryPtr;\n\n EntryPtr table; // power-of-2-sized hash table\n size_t table_mask; // size of table, in elements, minus one\n Entry entries; // data vector, an array of Entry objects\n size_t entries_capacity; // size of entries, in elements\n size_t entries_length; // number of initialized entries\n size_t live_count; // entries_length less empty (removed) entries\n\n inline Entry * lookup(KeyArg key, hashcode_t h);\n inline const Entry * lookup(KeyArg key) const;\n void rehash(size_t new_table_mask);\n\npublic:\n CloseTable();\n ~CloseTable();\n\n size_t byte_size(ByteSizeOption option) const;\n size_t size() const;\n bool has(KeyArg key) const;\n Value get(KeyArg key) const;\n void set(KeyArg key, ValueArg value);\n bool remove(KeyArg key);\n};\n\n\n#endif // tables_h_\n" }, { "alpha_fraction": 0.754811704158783, "alphanum_fraction": 0.7623431086540222, "avg_line_length": 55.904762268066406, "blob_id": "4c85a604d8f3df41db268edde52969e628b6d6c9", "content_id": "fdaf16f7c367426f45476b45e01e7d8bad0acf25", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2390, "license_type": "permissive", "max_line_length": 318, "num_lines": 42, "path": "/README.md", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "dht - Deterministic hash tables\n===============================\n\nThis repo contains the source code I used to evaluate deterministic hash table performance in the specific context of implementing the ECMAScript Map object for modern JS engines.\n\nUpdates will be posted to [the deterministic hash tables page](https://wiki.mozilla.org/User:Jorend/Deterministic_hash_tables).\n\nTo run benchmarks on Mac/Linux:\n\n* Install matplotlib. If you have MacPorts, you can do `sudo port install py27-matplotlib` but note that this doesn't install matplotlib in your Mac's system python installation. Instead it installs a copy of python in /opt/local/bin (or wherever you've configured ports to install stuff) and that copy has matplotlib.\n* Install Subversion. You need it for Sparsehash.\n* Edit the Makefile to set CXX, CXXFLAGS, and PYTHON to values that will work on your system.\n* `make`\n* Wait. The benchmarks take a while to run.\n\nTo run benchmarks on Windows:\n\nOn Windows, you're going to use a batch file, because Windows is awesome.\n\n* You have to have Visual Studio.\n* Install Python(x,y) from [http://code.google.com/p/pythonxy/](http://code.google.com/p/pythonxy/). I used Python(x,y) version 2.7.2.1.\n* Open a Visual Studio Command Line (All Programs → Microsoft Visual Studio 2010 → Visual Studio Tools → Visual Studio Command Prompt (2010).\n* cd to the dht directory.\n* Run build.bat.\n* Wait. The benchmarks take a while to run.\n\nWhat you get\n\n* figure-1.png shows how much memory each implementation allocates. figure-1-data.txt is the raw data.\n* figure-2.png shows how much memory each implementation uses (that is, how much of the allocated memory is actually accessed). figure-2-data.txt is the raw data.\n* The images InsertSmallTest-speed.png and friends show how fast each implementation is at each test. Higher is better. The file hashbench-data.txt contains the raw data for all these graphs. It's JSON.\n\n\n## License\n\nThe code in this repository was written for the sole purpose of evaluating the data structures.\n[The results page](https://wiki.mozilla.org/User:Jorend/Deterministic_hash_tables)\ntells how this specific goal affected the code.\nThis is not a general-purpose hash table library. Don't expect too much!\n\nThat said, feel free to use everything in this repository under the terms of\n[the MIT license](http://opensource.org/licenses/MIT).\n" } ]	7
mohammad9975/Fish-Recognition-androidApp	https://github.com/mohammad9975/Fish-Recognition-androidApp	07cc07e08a2c5df54ef5aa392774c8694009b271	c604dc3da64877d2484286d8e2e92d15174d1c2e	472dfd1978ec1911184a90a0b5862b37bdbc4294	refs/heads/master	2022-12-25T03:36:17.395099	2020-09-11T01:46:09	2020-09-11T01:46:09	294,244,519	2	0	null	null	null	null	null	[ { "alpha_fraction": 0.5732254981994629, "alphanum_fraction": 0.5991015434265137, "avg_line_length": 32.35185241699219, "blob_id": "3404d651958d1081d4c830cb48e288c36fba8349", "content_id": "662b15f28327be1c36bce3ab7e8815836ec1edb0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5565, "license_type": "no_license", "max_line_length": 132, "num_lines": 162, "path": "/fishClassification.py", "repo_name": "mohammad9975/Fish-Recognition-androidApp", "src_encoding": "UTF-8", "text": "import glob\r\nimport cv2\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.model_selection import train_test_split\r\nfrom tensorflow.keras.utils import to_categorical\r\nfrom tensorflow.keras import models\r\nfrom tensorflow.keras import layers\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nfrom tensorflow.keras.optimizers import SGD,Adam\r\n# from tensorflow.keras.applications import VGG16\r\n# import pickle\r\nfrom tensorflow.keras.losses import CosineSimilarity\r\nfrom random_eraser import get_random_eraser\r\nimport datetime\r\n\r\n## show results by tensorboard\r\nlog_dir = 'logs\\\\fit\\\\' + datetime.datetime.now().strftime('%Y%m%d-%H%M%S')\r\ntensorboar_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir)\r\n\r\nepoches = 100\r\n\r\naug= ImageDataGenerator(rotation_range=30,\r\n width_shift_range=.1,\r\n height_shift_range=.1,\r\n shear_range=.2,\r\n zoom_range=.1,\r\n horizontal_flip=True,\r\n fill_mode='nearest'\r\n # preprocessing_function=get_random_eraser(v_l=0, v_h=1)\r\n )\r\n\r\ndata=[]\r\nlabels=[]\r\nfor i,item in enumerate(glob.glob(r'C:\\Users\\mohammad\\Desktop\\preprocessod images\\\\')):\r\n \r\n img=cv2.imread(item)\r\n r_img=cv2.resize(img,(128,128))\r\n\r\n data.append(r_img)\r\n\r\n label=item.split('\\\\')[-2].split('.')[0]\r\n # print(label)\r\n labels.append(label)\r\n\r\n if i%100==0:\r\n print('Notice {} of data processod'.format(i))\r\n # if i==200:\r\n # break\r\n \r\ndata=np.array(data)/255\r\n\r\n## onehot encoding\r\nle= LabelEncoder()\r\nlabels=le.fit_transform(labels)\r\n\r\nlabels=to_categorical(labels,13)\r\n\r\n## using class weight because of unbalancing\r\nclassTotals=labels.sum(axis=0)\r\nclassWeight=classTotals.max()/classTotals\r\nclassWeight={0:classWeight[0],1:classWeight[1],2:classWeight[2],3:classWeight[3],4:classWeight[4],5:classWeight[5],6:classWeight[6],\r\n 7:classWeight[7],8:classWeight[8],9:classWeight[9],10:classWeight[10],11:classWeight[11],12:classWeight[12]}\r\n\r\n## split data to train and test data\r\nX_train,X_test,y_train,y_test=train_test_split(data,labels,test_size=.2)\r\n\r\n\r\n\r\n# with open(r'C:\\Users\\mohammad\\Documents\\python code\\practise deeplearning\\dog_cat.pkl','rb') as f:\r\n# X_train,X_test,y_train,y_test=pickle.load(f)\r\n\r\n\r\n# baseModel= VGG16(\r\n# weights='imagenet',\r\n# include_top=False,\r\n# input_tensor=layers.Input(shape=(32,32,3))\r\n# )\r\n\r\n# for layer in baseModel.layers:\r\n# layer.trainable=False\r\n\r\n## bulid CNN from scratch\r\n\r\n# Network= models.Sequential([\r\n# layers.Conv2D(32,(8,8),activation='relu',input_shape=(32,32,3)),\r\n# layers.BatchNormalization(),\r\n# layers.MaxPool2D((2,2)),\r\n\r\n# # layers.Conv2D(64,(3,3),activation='relu'),\r\n# # layers.BatchNormalization(),\r\n# # layers.MaxPool2D((2,2)),\r\n\r\n# layers.Conv2D(64,(5,5),activation='relu'),\r\n# # baseModel,\r\n# layers.BatchNormalization(),\r\n# layers.MaxPool2D((4,4)),\r\n# layers.Flatten(),\r\n\r\n# layers.Dense(128,activation='relu'),\r\n# layers.Dropout(.5),\r\n# layers.Dense(64,activation='relu'),\r\n# layers.Dense(13,activation='softmax')\r\n# ])\r\n \r\n## transfer learning\r\nbase_model = tf.keras.applications.MobileNet(input_tensor=layers.Input(shape=(128,128,3)),include_top=False)\r\nbase_model.trainable = False\r\n\r\nNetwork = tf.keras.Sequential([\r\n base_model,\r\n# using BatchNormalization\r\n# layers.BatchNormalization(),\r\n tf.keras.layers.GlobalAveragePooling2D(),\r\n layers.Dense(64,activation='relu'),\r\n # using dropOut\r\n layers.Dropout(.2),\r\n layers.Dense(32,activation='relu'),\r\n # using dropOut\r\n layers.Dropout(.2),\r\n tf.keras.layers.Dense(13, activation='softmax')\r\n])\r\n\r\n## using learning rae decay\r\nopt= SGD(lr=0.01,decay=0.001/epoches)\r\n\r\nNetwork.compile(optimizer=opt,\r\n # loss='binary_crossentropy',\r\n loss=CosineSimilarity(axis=1),\r\n metrics=['accuracy'])\r\n\r\n\r\nH = Network.fit_generator(aug.flow(X_train,y_train,batch_size=32),\r\n epochs=epoches,validation_data=(X_test,y_test),\r\n steps_per_epoch=len(X_train)//32,class_weight=classWeight,callbacks=[tensorboar_callback])\r\n\r\nloss,acc=Network.evaluate(X_test,y_test)\r\nprint('accuracy : {:.2f}'.format(acc))\r\n\r\n\r\nprint(Network.summary())\r\n# Network.save('out_CNN.h5')\r\n\r\n## Convert the model.\r\n# converter = tf.lite.TFLiteConverter.from_keras_model(model)\r\n# tflite_model = converter.convert()\r\n\r\n## Save the TF Lite model.\r\n# with tf.io.gfile.GFile('model.tflite', 'wb') as f:\r\n# f.write(tflite_model)\r\n\r\nplt.plot(np.arange(epoches),H.history['accuracy'],label='train_accuracy')\r\nplt.plot(np.arange(epoches),H.history['val_accuracy'],label='test_accuracy')\r\nplt.plot(np.arange(epoches),H.history['loss'],label='loss')\r\nplt.plot(np.arange(epoches),H.history['val_loss'],label='val_loss')\r\n\r\nplt.legend()\r\nplt.xlabel='epoches'\r\nplt.ylabel='accuracy/loss'\r\nplt.show()\r\n" }, { "alpha_fraction": 0.7638819217681885, "alphanum_fraction": 0.7943971753120422, "avg_line_length": 63.32258224487305, "blob_id": "2f210ab3ce357e60aeeef8e34eff9cc624f43937", "content_id": "e702e4e423e5a33ecc9452e652ebb3238096a99b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2011, "license_type": "no_license", "max_line_length": 268, "num_lines": 31, "path": "/README.md", "repo_name": "mohammad9975/Fish-Recognition-androidApp", "src_encoding": "UTF-8", "text": "# Fish-Recognition-androidApp\nFish Image Recognition in Android App \n\nIn this project, we are going to develop an android app that uses mobile phone camera to classify 15 different types of fishes realtime.\n\n## Data Collection and Machine Learning Pipeline\n\nThere isn’t a comprehensive labeled dataset available for fish species so I built my own dataset of 120+ images from scratch. \nI collected images using a few different methods outlined below.\n\n### Data Sources:\n\n1) Bing image search\n2) instagram by hashtag (i.e. #browntrout)\n\nAfter collecting the images, I manually reviewed all of the photos to ensure that the images matched their respective species label and removed any miscellaneous images that didn’t belong (fishing equipment, a river, tackle box, incorrectly labeled fish species, etc).\nThe data collection and cleaning phase took about one weeks. Now equipped with 120+ images across 15 fish species, I had an initial image dataset to start building a deep learning fish identification model. \nAlthough there were so many images that I ignore them because data collection is a time-consuming process.\n\n### Building the Deep Learning Model\nRather than creating a CNN from scratch, we’ll use a pre-trained model and perform transfer learning to customize this model with our new dataset. The pre-trained model we’re going to use is MobileNet, and we'll fine tune on our images.\n\n### Develop Android App\nAfter we’ve built our TensorFlow model with transfer learning, we’ll use the TFLite converter to create a mobile-ready model variant. The model will then be used in an Android application that recognizes images captured by the camera realtime.\n\n## Results\nAfter 100 epochs, the test accuracy is about 71 percent for 15 specious. to reach a better result, we need to increase our data number to 500+. lack of data is a bottleneck in deep learning!\n\n\n\n<img src=\"https://user-images.githubusercontent.com/51213071/92834467-4f379f00-f3ef-11ea-9b42-971cda9a2715.png\" width=\"300\">\n\n\n\n\n\n" } ]	2
Scienceseb/Pairwise-data-augmentation-PyTorch	https://github.com/Scienceseb/Pairwise-data-augmentation-PyTorch	d74cc85cdb884b2dd176f3fb0795a5cbce00edd8	2438a619bcd3b03dbcd330a15b96a71d2019a894	f6f7a07aa561f0b53e5dc457688a270602339ff5	refs/heads/master	2020-08-29T00:27:11.090567	2019-10-27T14:25:06	2019-10-27T14:25:06	217,865,017	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7884615659713745, "alphanum_fraction": 0.7884615659713745, "avg_line_length": 50.900001525878906, "blob_id": "53c77cf460a609b1d7f3a043fbc6ec02a4072a38", "content_id": "5d68c6c44e5feba17f844c5435c6aaec95237a6a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 520, "license_type": "no_license", "max_line_length": 202, "num_lines": 10, "path": "/README.md", "repo_name": "Scienceseb/Pairwise-data-augmentation-PyTorch", "src_encoding": "UTF-8", "text": "# Pair-wise-data-augmentation\nTransform class for pairwise data augmentation, flip, random crop and random rotation, ColorJitter (possibly useful for denoising, e.g., dehazing, deraining, etc.), you can add your transformation also.\n\nThis code is very useful if you work with pair of images as input ou output, you can make the same data augmentation\non both images, if you have three images you can easily extend the code for that situation. \n\nCode was made for: deep learning using PyTorch\n\nYou need:\nPyTorch and PIL\n\n" }, { "alpha_fraction": 0.6149703860282898, "alphanum_fraction": 0.625201940536499, "avg_line_length": 31, "blob_id": "791e225713c0ab9eed62925e6ad91883ea2aae0a", "content_id": "a4b5a7437f17c6700a37c6aa4f41574bcce8b607", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1857, "license_type": "no_license", "max_line_length": 167, "num_lines": 58, "path": "/code.py", "repo_name": "Scienceseb/Pairwise-data-augmentation-PyTorch", "src_encoding": "UTF-8", "text": "import random\nimport torchvision.transforms.functional as TF\nimport torchvision.transforms as transforms\nimport PIL\nfrom PIL import Image\n\n\n#Transform class for pairwise data augmentation, flip, random crop, random rotation, ColorJitter (possibly useful for denoising), you can add your transformation also.\nclass PAIR_TRANFORMATIONS(object, h_flip=False, v_flip=False, Random_crop=False, Random_rotation_degree=0, ColorJitter=[0,0,0,0]):\n\n def __call__(self, input, target):\n\n\n #Horizontal flip\n if h_flip:\n flip = random.random() < 0.5\n if flip:\n input=input.transpose(Image.FLIP_LEFT_RIGHT)\n target = target.transpose(Image.FLIP_LEFT_RIGHT)\n\n\n #Vertical flip\n if v_flip:\n flip = random.random() < 0.5\n if flip:\n input = input.transpose(Image.FLIP_TOP_BOTTOM)\n target = target.transpose(Image.FLIP_TOP_BOTTOM)\n\n\n #RandomCrop\n if Random_crop:\n i, j, h, w = transforms.RandomCrop.get_params(input, output_size=(224, 224))\n input = TF.crop(input, i, j, h, w)\n target = TF.crop(target, i, j, h, w)\n\n\n #RandomRotation\n i = transforms.RandomRotation.get_params(angle)\n input = TF.rotate(input, i)\n target = TF.rotate(target, i)\n\n\n\n #ColorJitter\n b,c,s,h=transforms.ColorJitter.get_params(ColorJitter[0],ColorJitter[1],ColorJitter[2],ColorJitter[3])\n\n input=TF.adjust_brightness(input,b)\n input=TF.adjust_contrast(input,c)\n input = TF.adjust_saturation(input, s)\n input = TF.adjust_hue(input, h)\n\n target=TF.adjust_brightness(target,b)\n target=TF.adjust_contrast(target,c)\n target = TF.adjust_saturation(target, s)\n target = TF.adjust_hue(target, h)\n\n\n return input, target\n\n" } ]	2
SaurabhG02/Algo-Problems	https://github.com/SaurabhG02/Algo-Problems	fcf204cd7694fca2a8f30b576e59cabbe2ef68da	5ea2ad6befdcf303cc68db0547612426ad5433da	2936c1d8183a3130eb4d7e6e7c7ac6d85eae947c	refs/heads/master	2022-12-02T14:09:23.611866	2020-08-19T16:15:10	2020-08-19T16:15:10	288,772,434	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5278884172439575, "alphanum_fraction": 0.5537848472595215, "avg_line_length": 12.184210777282715, "blob_id": "9dd870c5331174c58d26ac6a9e71b8d43465ac02", "content_id": "1ec3599664491c9d4a3f3d3d67fb2034d27f0f40", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 502, "license_type": "no_license", "max_line_length": 39, "num_lines": 38, "path": "/2ndProb.py", "repo_name": "SaurabhG02/Algo-Problems", "src_encoding": "UTF-8", "text": "def fillheight(input, i , height): \n\t\n\t\n\tif height[i] != 0: \n\t\treturn\n\t\n\t\n\tif input[i] == -1: \n\t\theight[i] = 1\n\t\treturn\n\n\t \n\tif height[input[i]] == 0: \n\t\tfillheight(input, input[i] , height) \n\n\t\n\theight[i] = height[input[i]] + 1\n\n \ndef findNodes(input): \n\tn = len(input) \n\t \n\theight = [0 for i in range(n)] \n\n\t\n\tfor i in range(n): \n\t\tfillheight(input, i, height) \n\n\t \n\tht = height[0] \n\tfor i in range(1,n): \n\t\tht = max(ht, height[i]) \n\n\treturn ht \n\n\ninput = [-1 , 0, 4, 0, 3] \nprint(findNodes(input))\n\n" } ]	1
angel-penchev/portfolio	https://github.com/angel-penchev/portfolio	9ba380987d4ae3b5c5da01b255760b438f3771dc	1fa9476d74e8c49a73d7ea022f9cd9d1ecfa5cae	54eedc2c4427616622f819d5b0b797163843b3a4	refs/heads/master	2022-12-27T06:45:01.013390	2020-06-14T15:28:11	2020-06-14T15:28:11	202,220,897	1	0	MIT	2019-08-13T20:42:15	2020-06-14T15:33:29	2022-12-09T23:05:46	CSS	[ { "alpha_fraction": 0.5392199754714966, "alphanum_fraction": 0.5503944158554077, "avg_line_length": 32.07246398925781, "blob_id": "d4e23507bac7e93087c41451993fcb332b7bbfb6", "content_id": "44370a484dc6289700f8eaa11a0697b2dc9aaddf", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 4564, "license_type": "permissive", "max_line_length": 121, "num_lines": 138, "path": "/scripts/navigation.js", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "function implementNavigation() {\n let current_section_slide = 0;\n let current_project_slide = 0;\n\n const section_slider = document.getElementsByClassName(\"section-slider\")[0].getElementsByClassName(\"slide\");\n const project_slider = section_slider[1].getElementsByClassName(\"slider\")[0].getElementsByClassName(\"project-slide\");\n const section_nav = document.getElementsByClassName(\"navigation\")[0];\n const nav_arrow_sections = document.getElementsByClassName(\"navigation-arrows-sections\")[0];\n const nav_arrow_projects = document.getElementsByClassName(\"navigation-arrows-project\")[0];\n\n function refreshView() {\n for (let i = 0; i < section_slider.length; i++) {\n section_slider[i].classList.remove(\"is-active\");\n }\n\n for (let i = 0; i < project_slider.length; i++) {\n project_slider[i].classList.remove(\"is-active\");\n }\n\n section_slider[current_section_slide].classList.add(\"is-active\");\n project_slider[current_project_slide].classList.add(\"is-active\");\n }\n\n section_nav.getElementsByClassName(\"nav-technologies\")[0].onclick = () => {\n current_section_slide = 0;\n refreshView();\n };\n\n section_nav.getElementsByClassName(\"nav-projects\")[0].onclick = () => {\n current_section_slide = 1;\n refreshView();\n };\n\n section_nav.getElementsByClassName(\"nav-contact\")[0].onclick = () => {\n current_section_slide = 2;\n refreshView();\n };\n\n nav_arrow_sections.getElementsByClassName(\"up\")[0].onclick = () => {\n current_section_slide -= 1;\n if (current_section_slide < 0) {\n current_section_slide = section_slider.length - 1;\n }\n refreshView();\n };\n\n nav_arrow_sections.getElementsByClassName(\"down\")[0].onclick = () => {\n current_section_slide += 1;\n if (current_section_slide > section_slider.length - 1) {\n current_section_slide = 0;\n }\n refreshView();\n };\n\n nav_arrow_projects.getElementsByClassName(\"left\")[0].onclick = () => {\n current_project_slide -= 1;\n if (current_project_slide < 0) {\n current_project_slide = project_slider.length - 1;\n }\n refreshView();\n };\n\n nav_arrow_projects.getElementsByClassName(\"right\")[0].onclick = () => {\n current_project_slide += 1;\n if (current_project_slide > project_slider.length - 1) {\n current_project_slide = 0;\n }\n refreshView();\n };\n\n document.getElementsByClassName(\"section-slider\")[0].addEventListener('touchstart', handleTouchStart, false);\n document.getElementsByClassName(\"section-slider\")[0].addEventListener('touchmove', handleTouchMove, false);\n\n var xDown = null;\n var yDown = null;\n\n function getTouches(evt) {\n return evt.touches;\n }\n\n function handleTouchStart(evt) {\n const firstTouch = getTouches(evt)[0];\n xDown = firstTouch.clientX;\n yDown = firstTouch.clientY;\n };\n\n function handleTouchMove(evt) {\n if (!xDown \|\| !yDown) {\n return;\n }\n\n var xUp = evt.touches[0].clientX;\n var yUp = evt.touches[0].clientY;\n\n var xDiff = xDown - xUp;\n var yDiff = yDown - yUp;\n\n if (Math.abs(xDiff) > Math.abs(yDiff)) {/most significant/\n if (xDiff > 0) {\n /* left swipe /\n current_project_slide -= 1;\n if (current_project_slide < 0) {\n current_project_slide = project_slider.length - 1;\n }\n refreshView();\n } else {\n / right swipe /\n current_project_slide += 1;\n if (current_project_slide > project_slider.length - 1) {\n current_project_slide = 0;\n }\n refreshView();\n }\n } else {\n if (yDiff > 0) {\n / up swipe /\n current_section_slide -= 1;\n if (current_section_slide < 0) {\n current_section_slide = section_slider.length - 1;\n }\n refreshView();\n } else {\n / down swipe /\n current_section_slide += 1;\n if (current_section_slide > section_slider.length - 1) {\n current_section_slide = 0;\n }\n refreshView();\n }\n }\n\n / reset values /\n xDown = null;\n yDown = null;\n };\n\n refreshView();\n}\n" }, { "alpha_fraction": 0.772990882396698, "alphanum_fraction": 0.7771334052085876, "avg_line_length": 62.578948974609375, "blob_id": "2a74f5d97796b14178118e68d752ec6e3109b6a6", "content_id": "ec96898a686694c00a2a147294a2ee8db1a2e77e", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1208, "license_type": "permissive", "max_line_length": 472, "num_lines": 19, "path": "/data/README.md", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "# Portfolio Profile Generator\nA simple script which uses your Github profile info to quickly generate a .json file with all the portfolio information required.\n\n## How to use\n```bash\n# Install the required python modules\n$ pip install -r requirements.txt\n\n# Execute the generation script\n$ python3 ./generate_profile.py\n```\nYou will be prompted to input your Github and LinkedIn usernames, as well as your contact email address. Upon complition a [profile.json](./profile.json) file will be created, containing all the information needed for the portfolio.\n\n## Imprtant notice\nBe sure to double check all the technology/language arrays outputed in order to resolve any descrepencies. The script will look for all the languages used in all repos and match this list against [a language reference](./language-reference/language-reference.json) to convert them to valid language objects (containing \"data\" and \"image\" to be displayed in the page). If the languge is missing from the reference, it will not be outputed in [profile.json](./profile.json).\n\n## License\n[![License](http://img.shields.io/:license-mit-blue.svg?style=flat-square)](http://angel-penchev.mit-license.org)<br>\nCopyright 2019 © Angel Penchev" }, { "alpha_fraction": 0.54334557056427, "alphanum_fraction": 0.5457875728607178, "avg_line_length": 27.241378784179688, "blob_id": "2b8d54cc888267d99b0f92a24a58de1e278453aa", "content_id": "d7e80659bc9a18f3f029585aed1ef1c9f2774401", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 819, "license_type": "permissive", "max_line_length": 91, "num_lines": 29, "path": "/data/language_reference/generate_language_reference.py", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "import os\nimport json\n\n\ndef main():\n save_file(\"language_reference.json\", generate_data(\"../../img/icons/technologies\"))\n\ndef generate_data(directory):\n directory_contents = [x[0] for x in os.walk(directory)]\n directory_contents.pop(0)\n languages = []\n for language in directory_contents:\n title = language.replace(\"../../img/icons/technologies/\", \"\")\n languages.append({\n \"title\": title,\n \"img\": language.replace(\"../.\", \"\") + \"/\" + title + \"-original.svg\"\n })\n\n return languages\n\n\ndef save_file(filename, data):\n with open(filename, \"w\") as outfile:\n json.dump(data, outfile)\n print(\"Language catalog generated successfully!\")\n\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.5658981204032898, "alphanum_fraction": 0.5681753754615784, "avg_line_length": 30.09734535217285, "blob_id": "ec1329ec54bc2b2b49abb531e7c80af085311694", "content_id": "88b3cbdeaa78258edc70a3a0b895eee42d045a9a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3513, "license_type": "permissive", "max_line_length": 100, "num_lines": 113, "path": "/data/generate_profile.py", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "# For \"AttributeError: 'dict' object has no attribute 'iteritems'\" check the solution provided here:\n# https://stackoverflow.com/questions/30418481/error-dict-object-has-no-attribute-iteritems\n\nimport json\nfrom github import Github\nfrom bunch import bunchify\n\ndef main():\n github_username = input(\"What is your Github username? : \")\n linkedin_username = input(\"What is your LinkedIn username? (skip if the same) : \")\n if (not linkedin_username):\n linkedin_username = github_username\n \n save_file(\"profile.json\", generate_data(github_username, linkedin_username))\n\ndef generate_data(github_username, linkedin_username):\n g = Github()\n user = g.get_user(github_username)\n email = user.email or input(\"What is your Email address? : \")\n repositories = g.search_repositories(query=\"user:%s\" % github_username, sort=\"updated\")\n language_reference_dict = json.load(open(\"language_reference/language_reference.json\", \"r\"))\n language_reference = []\n for language in language_reference_dict:\n language_reference.append(bunchify(language))\n data = {}\n\n data[\"user\"] = {\n \"name\": user.name,\n \"description\": user.bio,\n \"picture\": user.avatar_url,\n \"location\": user.location,\n \"email\": email,\n \"links\": [\n {\n \"title\": \"Github\",\n \"img\": \"./img/icons/technologies/github/github-original.svg\",\n \"href\": \"https://github.com/%s\" % github_username\n },\n {\n \"title\": \"LinkedIn\",\n \"img\": \"./img/icons/technologies/linkedin/linkedin-original.svg\",\n \"href\": \"https://linkedin.com/in/%s\" % linkedin_username\n },\n {\n \"title\": \"Resume\",\n \"img\": \"./img/icons/misc/resume/resume.png\",\n \"href\": \"\"\n },\n {\n \"title\": \"Email\",\n \"img\": \"./img/icons/misc/email/email.png\",\n \"href\": \"mailto:%s\" % email\n }\n ]\n }\n\n content = [];\n for repo in repositories:\n languages = repo.get_languages()\n for language in languages:\n if not(language_catalog(language_reference, language) in content):\n content.append(language_catalog(language_reference, language))\n \n data[\"technologies\"] = [\n {\n \"title\": \"Uncategorised\",\n \"content\": content\n }\n ]\n \n\n\n data[\"projects\"] = []\n for repo in repositories:\n languages = []\n for language in repo.get_languages():\n languages.append(language_catalog(language_reference, language))\n\n data[\"projects\"].append({\n \"title\": repo.name.capitalize(),\n \"description\": repo.description,\n \"image\": \"./img/profile.jpg\",\n \"languages\": languages,\n \"href_repository\": repo.html_url,\n \"href_preview\": repo.homepage\n })\n\n \n return data\n\n\ndef get_repo_languages(repo):\n languages = []\n for language in repo.get_languages():\n languages.append(language)\n return languages\n\n\ndef language_catalog(reference, language):\n for i in reference:\n if i.title.lower() == language.lower():\n i.title = language\n return i\n\n\ndef save_file(filename, data):\n with open(filename, \"w\") as outfile:\n json.dump(data, outfile)\n print(\"Profile generated successfully!\")\n\n\nif __name__ == '__main__':\n main()" }, { "alpha_fraction": 0.7546717524528503, "alphanum_fraction": 0.7570675611495972, "avg_line_length": 68.5999984741211, "blob_id": "ab48cfda80b790cdbbb037f1d634cc48aa5b461d", "content_id": "ce8e2e24bbb50cc102f84621c165c7805de98ce8", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2088, "license_type": "permissive", "max_line_length": 359, "num_lines": 30, "path": "/README.md", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "# Portfolio\nWebpage containing all a list of all the developer technologies I am familiar with and all the significant project I have contributed to. The hosted version of the portfolio could be found [here](https://angel-penchev.github.io/portfolio/).<br>\n![AppVeyor](https://img.shields.io/appveyor/ci/angel-penchev/portfolio?style=flat-square)\n![Libraries.io dependency status for GitHub repo](https://img.shields.io/librariesio/github/angel-penchev/portfolio?style=flat-square)\n[![License](http://img.shields.io/:license-mit-blue.svg?style=flat-square)](http://angel-penchev.mit-license.org)\n\n## Portfolio Contents\nSummary of all the contents included in the portfolio website. The hosted version of the portfolio could be found [here](https://angel-penchev.github.io/portfolio/).\n### Technologies\nFront-End:* HTML5, CSS, SASS, JavaScript, TypeScript, AngularJS<br>\nBack-End: Python, NodeJS, Dart, C/C++<br>\nDatabase: MySQL<br>\nDeployment: Git, Docker<br>\n\n### Projects\n* Librarity<br>\n Web on-demand library service implementing a database of books, as well as an user system for a personalized experience.\n\n* Readability<br>\n Mobile application (Android/iOS) which captures text using the device's camera, recognizes it using Firebase ML Kit and sends it to an Bluetooth connected embedded Arduino device which converts it letter by letter to braille alphabet.\n\n* Text Art Studio<br>\n Simple web-hosted tool for creation of art using ASCII characters and exporting it to clipboard for later use.\n \n## How to use the template\nAll the personal data shown in the webpage is taken from [data/profile.json](./data/profile.json), so it is easy to change the information to match your profile. The [data/generate-profile.py](./data/generate-profile.py) script could be used for the automation of this process. How to use information is included in the [data/README.md](./data/README.md).\n\n## License\n[![License](http://img.shields.io/:license-mit-blue.svg?style=flat-square)](http://angel-penchev.mit-license.org)<br>\nCopyright 2019 © Angel Penchev" }, { "alpha_fraction": 0.5649271607398987, "alphanum_fraction": 0.5719053149223328, "avg_line_length": 40.721519470214844, "blob_id": "4c9254cda713fec8ac0d6ef36d3b4c1601f32068", "content_id": "9ccdb2f942695ff5efb71908a2484edde347cec9", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 3296, "license_type": "permissive", "max_line_length": 135, "num_lines": 79, "path": "/scripts/information.js", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "function loadJSON(callback) {\n var xobj = new XMLHttpRequest();\n xobj.overrideMimeType(\"application/json\");\n xobj.open('GET', './data/profile.json', true);\n xobj.onreadystatechange = function () {\n if (xobj.readyState == 4 && xobj.status == \"200\") {\n // Required use of an anonymous callback as .open will NOT return a value but simply returns undefined in asynchronous mode\n callback(JSON.parse(xobj.responseText));\n }\n };\n xobj.send(null);\n}\n\nfunction injectDataInDocument(data) {\n injectProfile();\n injectTechnologies();\n injectProjects();\n injectContact();\n\n function injectProfile() {\n profile = document.getElementsByClassName(\"profile\")[0];\n profile.getElementsByClassName(\"picture\")[0].src = data.user.picture;\n profile.getElementsByClassName(\"name\")[0].innerHTML = data.user.name;\n profile.getElementsByClassName(\"description\")[0].innerHTML = data.user.description;\n profile.getElementsByClassName(\"location\")[0].innerHTML = data.user.location;\n\n data.user.links.forEach((link) => {\n profile.getElementsByClassName(\"links\")[0].innerHTML +=\n `<a href=\"${ link.href }\" target=\"_blank\" class=\"profile-link\">\n <img src=\"${ link.img }\" alt=\"${link.title}\">\n </a>`\n })\n }\n\n function injectTechnologies() {\n let slide = document.getElementsByClassName(\"technologies\")[0].getElementsByClassName(\"content\")[0];\n\n for (let i = 0; i < data.technologies.length; i++) {\n section = data.technologies[i];\n slide.innerHTML +=\n `<div class=\"subgroup\"> \n <h2 class=\"title\">${ section.title }</h2>\n <div class=\"languages\"></div>\n </div>`\n\n languages = slide.getElementsByClassName(\"subgroup\")[i].getElementsByClassName(\"languages\")[0];;\n section.languages.forEach((language) => {\n languages.innerHTML +=\n `<img src=\"${ language.img }\" alt=\"${ language.title }\">`\n })\n }\n \n }\n\n function injectProjects() {\n let slider = document.getElementsByClassName(\"projects\")[0].getElementsByClassName(\"slider\")[0];\n \n for (let i = 0; i < data.projects.length; i++) {\n project = data.projects[i]\n slider.innerHTML +=\n `<div class=\"project-slide\">\n <img src=\"${ project.image }\" alt=\"${ project.title }\" class=\"project-image\">\n <h1 class=\"project-title\">${ project.title }</h1>\n <div class=\"project-languages\"></div>\n <div class=\"project-description\"><p>${ project.description }</p></div>\n </div>`\n\n project_languages = slider.getElementsByClassName(\"project-slide\")[i].getElementsByClassName(\"project-languages\")[0];\n project.languages.forEach((language) => {\n project_languages.innerHTML +=\n `<img src=\"${ language.img }\" alt=\"${ language.title }\">`\n })\n }\n }\n\n function injectContact() {\n document.getElementsByName(\"contact-form\")[0].setAttribute(\"action\", \"https://formspree.io/\" + data.user.email);\n }\n}\n" }, { "alpha_fraction": 0.5808823704719543, "alphanum_fraction": 0.5808823704719543, "avg_line_length": 16.125, "blob_id": "e536166c39f476f9f22e7b574cc8d5bc9bccc988", "content_id": "ffd5f016e0c94b1e2bc63d51c10ed5885722f29f", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 136, "license_type": "permissive", "max_line_length": 39, "num_lines": 8, "path": "/scripts/main.js", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "function main() {\n loadJSON((response) => {\n injectDataInDocument(response);\n implementNavigation();\n });\n}\n\nmain();" } ]	7
httaotao/kevintao-for-python	https://github.com/httaotao/kevintao-for-python	ba91c1901dc23b3807f17dfea26559494847af48	f661c5930e2df346d470b8a1a77acb9672638583	d6e21a3622c482d7637d2cd5ea815ea04f7c7c20	refs/heads/master	2017-12-21T05:43:26.809563	2016-12-14T05:57:36	2016-12-14T05:57:36	76,429,280	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6353413462638855, "alphanum_fraction": 0.6538152694702148, "avg_line_length": 13.658227920532227, "blob_id": "466596233eec7ae65cb2b7bfb8a31a7277601e0f", "content_id": "aab15ca9d2124598e4bdbfa3de108bc306beb980", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1599, "license_type": "no_license", "max_line_length": 73, "num_lines": 79, "path": "/python基础编程/标准输入输出.txt", "repo_name": "httaotao/kevintao-for-python", "src_encoding": "GB18030", "text": "#!/usr/bin/python\r\n#enconding:utf8\r\n\r\nimport sys\r\nfd=sys.stdin\r\ndata=fd.read()\r\nsys.stdout.write(data+'\\n')\r\n#print data,\r\n\r\n这是python 标准输入输出，crtl+D结束\r\nsys.stdout.write 加'\\n'是最后换行，默认最后没有换行的\r\n而print默认最后会有\\n，而加了逗号就不会\r\n\r\n\r\n#!/usr/bin/python\r\n#encoding:utf8\r\n\r\nimport sys\r\ninput=sys.stdin\r\n\r\ndef lineCount(f):\r\n n=0\r\n for i in f:\r\n n=n+1\r\n return n\r\nprint lineCount(input)\r\n\r\n这是模拟linux的wc功能\r\ncat /etc/passwd \|python 2_stdin.py\r\n输出结果就是文件的行数\r\n\r\n也可以直接执行文件，输出标准输入的行数，ctrl+D结束 \r\n\r\nsys.stderr.write:\r\nHelp on built-in function write:\r\nwrite(...)\r\n write(str) -> None. Write string str to file.\r\n \r\n Note that due to buffering, flush() or close() may be needed beforefs\r\n the file on disk reflects the data written.\r\n\r\n标准输出：\r\n在ipython进行\r\n\r\n\r\nimport sys\r\nf=open('/tmp/3.txt','w')\r\nf.write('sdffsd')\r\nf.close()\r\ncat /tmp/3.txt\r\n注意，这里用write不能写Int，只能用字符串\r\n\r\n但是如果用\r\nprint >>f,123\r\n就可以把数值123输入进文件\r\n\r\n\r\n\r\n\r\nfor i in xrange(1,11):\r\n sys.stdout.write(\"%d\\n\" %i)\r\n这里stdout采用格式化输出\r\n\r\n\r\n\r\n#!/usr/bin/python\r\n\r\nimport sys\r\nimport time\r\n\r\nfor i in xrange(1,11):\r\n sys.stdout.write(\"%d\" %i)\r\n sys.stdout.flush()\r\n time.sleep(1)\r\n\r\n这里是每隔1秒输出\r\n这里采用了sys.stdoout.flush()来清空输出buffer\r\n也可以采用python -u 来输出\r\n如果都不加，那么则会停留10秒后才全部输出\r\n\r\n\r\n\r\n\r\n" }, { "alpha_fraction": 0.539638876914978, "alphanum_fraction": 0.5503009557723999, "avg_line_length": 14.806358337402344, "blob_id": "a7c3da769f3ad554816402c27206ba0fba526ab1", "content_id": "14913ce0ce13e498914001f4aa8b14d6c3dfbdc2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6985, "license_type": "no_license", "max_line_length": 94, "num_lines": 346, "path": "/python基础编程/类和对象.txt", "repo_name": "httaotao/kevintao-for-python", "src_encoding": "GB18030", "text": "#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n\r\n def way(parameter):\r\n parameter.color='black'\r\n print parameter.__age\r\n print \"parameter is %s\" %parameter.color\r\n\r\ntom=People()\r\nprint tom._People__age\r\n\r\n这是调用类的私有属性\r\n注意，这里的tom._People 是一个下划线，而私有属性是两个下划线\r\n----------------------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n\r\n def way(self):\r\n parameter.color='black'\r\n print parameter.__age\r\n print \"parameter is %s\" %parameter.color\r\n\r\ntom=People()\r\ntom.way()\r\n\r\n这是调用类的公有方法\r\n\r\n\r\n-------------------------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n\r\n def way(parameter):\r\n parameter.color='black'\r\n print parameter.__age\r\n print \"parameter is %s\" %parameter.color\r\n \r\nren=People()\r\nprint ren.color\r\nren.way()\r\nprint ren._People__age\r\nren.color='白人'\r\nprint ren.color\r\nprint People.color\r\n\r\n这里的class 就是定义类，而 __双下划线开头的就是类的私有属性，私有属性不能在类的外面调用\r\n下面的def 是类的方法，但是要调用才可以\r\n\r\nren._People__age 这个方法是调用类的私有属性，但一般不建议这样写\r\n\r\n#coding:utf8 是为了支持中文\r\n类的命名，默认规则是一个单词，第一个字母大写\r\n\r\n注意，这里ren.color='白人'不会改变原来类的属性，因为ren已经是一个对象了，具体化了，类似于临时更改而已，但再次调用，后面的print People.color还是yellow\r\n\r\n\r\n--------------------------------------\r\n\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def way(parameter):\r\n parameter.color='black'\r\n print parameter.__age\r\n print \"parameter is %s\" %parameter.color\r\n\r\n def __talk(self):\r\n print \"ABC\"\r\n\r\n def test(self):\r\n self.__talk()\r\n\r\n def test2(self):\r\n print \"Test is ......\"\r\n\r\n cm=classmethod(test2)\r\n\r\njack=People()\r\njack.test()\r\nPeople.cm()\r\n\r\n这里的 __talk 是私有方法\r\n\r\n\r\n这里classmethod 是类方法，就是可以直接在类的外面调用方法，而不需要具体的实例，因为如果没有classmethod,\r\nPeople.test2()是不行的\r\n\r\n------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def way(parameter):\r\n parameter.color='black'\r\n print parameter.__age\r\n print \"parameter is %s\" %parameter.color\r\n\r\n def __talk(self):\r\n print \"ABC\"\r\n\r\n def test(self):\r\n self.__talk()\r\n\r\n def test2(self):\r\n print \"Test is ......\"\r\n # self.test3()\r\n\r\n def test3():\r\n print \"This is test3\"\r\n print People.color\r\n\r\n cm=classmethod(test2)\r\n sm=staticmethod(test3)\r\n\r\njack=People()\r\n#People.test3()\r\nPeople.sm()\r\n\r\n\r\nPeople.sm()这是调用静态方法，类似于‘全局函数’，因为没有参数的，默认无法调用\r\n而使用self.test3()，则会提示test3没有参数\r\n\r\n------------------------------------------------\r\n @classmethod\r\n def test2(self):\r\n print \"Test is ......\"\r\n # self.test3()\r\n\r\n @staticmethod\r\n def test3():\r\n print \"This is test3\"\r\n print People.color\r\n\r\n\r\njack=People()\r\nPeople.test2()\r\nPeople.test3()\r\n\r\n这是使用装饰器来调用，注意，@classmethod和@staticmethod只对下面的方法有效\r\n\r\n-------------------------------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n class chinese(object):\r\n print \"I am chinese\"\r\n\r\njack=People.chinese()\r\n\r\n这是外部类调用内部类\r\n\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n class chinese(object):\r\n #print \"I am chinese\"\r\n name=\"I am a chinese\"\r\n\r\njack=People.chinese()\r\nprint jack.name\r\n\r\n这是调用内部类的属性\r\n\r\n这里还可以这样写：\r\njack=People()\r\njack_name=People.chinese()\r\nprint jack_name.name\r\n\r\n这样写也可以：\r\nprint People.chinese.name\r\n\r\n或者 print People.chinese().name\r\n\r\n\r\n---------------------------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def __str__(self):\r\n return \"I am a chinese\"\r\n def think(self):\r\n print \"abc\"\r\n def __talk(self):\r\n print \"sdf\"\r\n\r\njack=People()\r\nprint jack\r\n\r\n这是调用 __str__ ，这是内置方法，不需要调用函数名，print就会默认执行\r\n注意，__str__ 不能用print ，要使用return\r\n\r\n注意，方法和函数一样，但方法需要self做为第一个参数\r\nthink是公有方法\r\n__talk是私有方法\r\n注意，私有方法是双下划线\r\n--------------------------------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def __str__(self):\r\n return \"I am a chinese\"\r\n\r\n def __init__(self):\r\n self.color='black'\r\n\r\n\r\njack=People()\r\nprint jack.color\r\nprint People.color\r\n\r\n这里的结果是 \r\nblack\r\nyellow\r\n\r\n也就是说，如果类实例化了，会自动执行__init__ 初始化函数\r\n也叫构造函数\r\n\r\n\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def __str__(self):\r\n return \"I am a chinese\"\r\n\r\n def __init__(self,c='white'):\r\n self.color=c\r\n\r\n\r\njack=People()\r\nprint jack.color\r\nprint People.color\r\n\r\n结果是：\r\nwhite\r\nyellow\r\n这里通过类来调用的属性没改变\r\n\r\n\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def __str__(self):\r\n return \"I am a chinese\"\r\n\r\n def __init__(self,c='white'):\r\n self.color=c\r\n\r\n\r\njack=People('green')\r\nprint jack.color\r\nprint People.color\r\n\r\n结果是\r\ngreen\r\nyellow\r\n\r\n-------------------------------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def __str__(self):\r\n return \"I am a chinese\"\r\n\r\n def __init__(self,c='white'):\r\n self.color=c\r\n self.fd.open('/etc/hosts')\r\n \r\n def __del__(self):\r\n self.fd.close()\r\n\r\njack=People('green')\r\nprint jack.color\r\nprint People.color\r\n\r\n析构函数，在脚本最后执行，一般用于释放对象空间\r\n\r\n---\r\nimport gc\r\n\r\ngc.collect()\r\n\r\npython的默认回收机制\r\n__init__是构造函数，用于初始化类的内部状态\r\n-------------------------------------------\r\n\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass Myclass(object):\r\n var1='类的公有属性'\r\n __var2='类的私有属性'\r\n\r\n def fun1(self):\r\n self.var3='对象的公有属性'\r\n self.__var4='对象的私有属性'\r\n var5='函数的局部变量'\r\n" }, { "alpha_fraction": 0.7727272510528564, "alphanum_fraction": 0.7727272510528564, "avg_line_length": 21, "blob_id": "4ac38d3f731f239cafad08b7cd86bebf28fc28b2", "content_id": "a1f3cd5eeb1b9dda51bbd6faf95cfb5e54968c9f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 22, "license_type": "no_license", "max_line_length": 21, "num_lines": 1, "path": "/README.md", "repo_name": "httaotao/kevintao-for-python", "src_encoding": "UTF-8", "text": "# kevintao-for-python\n" }, { "alpha_fraction": 0.5582557916641235, "alphanum_fraction": 0.603110671043396, "avg_line_length": 16.639896392822266, "blob_id": "3f533ffc92e6214b4dccb5b8f2c5a50a94f96ce3", "content_id": "57d4c8a37c6a8267ddfdff5f275840d61435a9d8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9693, "license_type": "no_license", "max_line_length": 87, "num_lines": 386, "path": "/python基础编程/函数.txt", "repo_name": "httaotao/kevintao-for-python", "src_encoding": "GB18030", "text": "#! /usr/bin/python\r\n\r\ndef fun():\r\n sth=raw_input('please input something:')\r\n try:\r\n if type(int(sth))==type(1):\r\n print\"%s is a number\" %sth\r\n except:\r\n print \"%s is not a number\" %sth\r\n\r\nfun()\r\n\r\n\r\n这里的try是异常处理，就是如果type(int(sth))是int，那就输出下面的，否则就输出不是number\r\n\r\ndef 是函数定义，注意函数定义后面一样需要冒号\r\nfun()是函数调用\r\n\r\n这个函数的作用是判断输入的是否是数字\r\n\r\n\r\n#! /usr/bin/python\r\nimport sys\r\ndef isNum(s):\r\n for i in s:\r\n if i in '0123456789':\r\n pass\r\n else:\r\n print\"%s is not a number\" %s\r\n break\r\n else:\r\n print\"%s is a number\" %s\r\n\r\nisNum(sys.argv[1])\r\n\r\n这个函数是通过外部输入参数来判断的，就是调用程序的时候给出参数来判断，而sys.argv[1]是输入的第一个参数\r\n\r\npython 11.py aaa \r\n这里就是判断输入的参数aaa是否是数字\r\n\r\n注意，这里一定要给出参数，否则会出现索引越界的现象\r\nIndexError: list index out of range\r\n\r\n另外这里sys是模块，import是插入模块\r\n\r\n另外这里只能判断输入的第一个参数，如果调用的时候是python 11.py 123 aaa 后面的参数aaa就没法判断了\r\n\r\n\r\n\r\n#! /usr/bin/python\r\n\r\nimport sys\r\nimport os\r\n\r\ndef isNUm(s):\r\n for i in s:\r\n if i in '0123456789':\r\n pass\r\n else:\r\n break\r\n else:\r\n print s\r\n\r\nfor i in os.listdir('/proc'):\r\n isNUm(i)\r\n这里是查看系统所有的pid，是通过查看文件/proc来查看的\r\n注意这里的os.listdir的作用是把所有的文件名称变为列表的元素，这里通过查看列表的每个元素来判断\r\n\r\n\r\n\r\n\r\ndef fun(x,y=100)\r\n print x+y\r\n\r\nfun(2,3)\r\n这里的是定义默认参数，注意这里的结果是5，如果不给3，则结果是102\r\n另外，这里定义参数，不能def fun(x=1,y)：因为定义默认参数，一定要从右往左定义\r\n\r\n\r\n#! /usr/bin/python\r\n\r\nimport sys\r\nimport os\r\n\r\ndef isNUm(s):\r\n for i in s:\r\n if i not in '0123456789':\r\n return True\r\n return False\r\nfor i in os.listdir('/proc'):\r\n if isNUm(i):\r\n print i\r\n\r\n\r\n#------------------------------------------------------------\r\n\r\n#! /usr/bin/python\r\nimport sys\r\nimport os\r\n\r\ndef isNUm(s):\r\n if s.isdigit():\r\n return True\r\n return False\r\n\r\nfor i in os.listdir('/proc'):\r\n if isNUm(i):\r\n print i\r\n\r\n这里两种写法都是对前面的优化\r\n\r\n\r\n多类型传值\r\n\r\n\r\ndef fun(x,y,z):\r\n return x+y+z\r\n\r\nt=(1,2,3)\r\nfun(t)\r\na=[1,2,3]\r\nfun(a)\r\n\r\n这两个就是多类型传值，如果没有，则认为是一个参数\r\n\r\n也可以这样写：\r\nt1=(1,2)\r\nfun(1,t1)\r\n\r\n但注意，不能写成这样：\r\nfun(t1,1)，会显示SyntaxError: only named arguments may follow expression\r\n\r\n如果直接赋值，可以这样写：\r\nfun(x=1,y=2,z=3),x,y,z的顺序可以随意，但是不能写成fun(a=1,b=2,c=3),要和形参的名称一致\r\n\r\n另外还可以使用字典赋值：\r\ndic={'x':1,'y':2,'z':3}\r\nfun(dic) ,这里要两个\r\n\r\n\r\n\r\n冗余参数\r\ndef fun(x,argv,kwargv):\r\n a=(1,2,3)\r\n print x\r\n print argv\r\n print kwargv\r\n\r\n\r\n后面的argv和*kwargv是冗余参数，也就是说，函数fun一定要给出形参x的值，而后面两个不一样\r\n另外，argv是一个元组，而*是字典，另外argv和kwargv名称可以改变\r\n\r\n因此调用的时候\r\nfun(a,1,32,'asd',(123,23),{'k':1},('aa','bb'))\r\n结果就是：\r\n(1,2,3)\r\n(1, 32, 'asd', (123, 23), {'k': 1}, 'aa', 'bb')\r\n{}\r\n\r\n\r\n\r\n而fun(a,1,32,'asd',(123,23),{'k':1},('aa','bb'))会出错，因此一定要是字典，而如果字典没有，则会默认为元组的元素，例如\r\nfun(a,1,32,'asd',(123,23),{'k':1})，结果是：\r\n(1, 2, 3)\r\n(1, 32, 'asd', (123, 23), {'k': 1})\r\n{}\r\n\r\n\r\n而fun(a,1,32,'asd',(123,23),{'k':1})，结果是：\r\n(1, 2, 3)\r\n(1, 32, 'asd', (123, 23))\r\n{'k': 1}\r\n\r\n\r\n\r\n函数递归：\r\ndef factorial(n):\r\n if n==0\r\n return 1\r\n else:\r\n return nfactorial(n-1)\r\nprint factorial(100)\r\n\r\n\r\nimport os\r\nos.path.isdir('/tmp') 判断/tmp是不是目录，如果是就返回True，否则就返回False。但是注意这里不能判断目录是否存在，不存在也是返回False\r\n\r\nos.path.isfile()\r\n与os.path.isdir类似，但是这里是判断文件\r\n\r\n\r\n#! /usr/bin/python\r\n\r\nimport os\r\nimport sys\r\n\r\ndef print_file(path):\r\n lsdir=os.listdir(path)\r\n dirs=[i for i in lsdir if os.path.isdir(os.path.join(path,i))]\r\n files=[i for i in lsdir if os.path.isfile(os.path.join(path,i))]\r\n if files:\r\n for f in files:\r\n print os.path.join(path,f)\r\n if dirs:\r\n for d in dirs:\r\n print_file(os.path.join(path,d))\r\n\r\nprint_file(sys.argv[1])\r\n\r\n\r\n这个函数的作用是遍历目录中的所有文件\r\n\r\n这里的os.listdir是把指定目录下的名称全部变成列表\r\n然后用lsdir和dirs找出文件和目录\r\n\r\n[i for i in lsdir if os.path.isdir(os.path.join(path,i))] 判断是否是文件，用for遍历，用if来判断\r\n\r\nif files就是如果文件不为空，则遍历files里面的文件的名称，然后用print输出文件所在路径，注意，这里要连接参数path才是完成路径\r\n\r\n如果是目录，就继续调用函数来递归找到文件\r\n\r\n执行如下：\r\npython 4.py .\r\n这里的 “.” 就是当前目录，也可以python 4.py /etc/\r\n\r\n\r\n\r\n函数 reduce:\r\nreduce(function, sequence[, initial]) -> value\r\n例子：\r\nreduce(lambda x, y: x+y, [1, 2, 3, 4, 5])\r\n函数reduce的作用是调用函数来处理后面的序列，这里的函数可以是匿名函数，也可以是function\r\n\r\n匿名函数：\r\nlamba\r\n\r\nr=lamba x,y:x+y\r\nr(3,5) 结果是15\r\n\r\n\r\n内置函数：\r\ndivmod:\r\n divmod(x, y) -> (div, mod)\r\n Return the tuple ((x-x%y)/y, x%y). Invariant: divy + mod == x.\r\n\r\n返回值，前面是商，后面是余数\r\n\r\ndivmod(5,2) 结果是2，1\r\ndivmod(10,3) 结果是3，1\r\n\r\npow：\r\npow(x, y[, z]) -> number\r\n \r\n With two arguments, equivalent to xy. With three arguments,\r\n equivalent to (xy) % z, but may be more efficient (e.g. for longs).\r\n\r\n\r\n如果是两个参数，则是x^y,如果是三个参数，则是x^y%z\r\n例如pow(2,3) 结果是8，而pow(2,3,4) 结果是0\r\n\r\n\r\nround:\r\n四舍五入\r\nprint round(12,3256,3) 保留三位有效数字\r\n\r\n注意，round函数是先把数值转换为float,然后再取舍，因此容易有精度不准的问题\r\n\r\n\r\ncallable:\r\n\r\ncallable(object) -> bool\r\n \r\n Return whether the object is callable (i.e., some kind of function).\r\n Note that classes are callable, as are instances with a __call__() method.\r\n\r\n判断是否可被调用，返回bool值\r\n\r\nisinstance:\r\nisinstance(object, class-or-type-or-tuple) -> bool\r\n isinstance(x, (A, B, ...))\r\n\r\n判断是否是给定的类型，与type用法类似\r\n\r\nisinstance(123,list) 判断数值123是否是list,返回的是bool\r\nisinstance主要用于判断是否是同一个类，比type方便\r\n\r\n\r\ncmp:\r\ncmp(x, y) -> integer\r\n \r\n Return negative if x<y, zero if x==y, positive if x>y.\r\n\r\n比较两个对象，返回数值\r\n可以用来比较数值，也可以比较字符串\r\n\r\ncomplex:\r\n转换为复数\r\n例如 complex('12') 结果是 12+0j\r\n注意，不能是字母\r\n\r\n\r\nhex:\r\nhex(number) -> string\r\n \r\n Return the hexadecimal representation of an integer or long integer.\r\n\r\n把数值转换成16进制的数\r\n例如hex(123) 结果是'0x7b'\r\neval(hex(10)) 把十六进制重新转换为十进制，这里不能用int，因为用hex的是字符串，而int不能是字符串\r\n\r\noct:\r\n把数值转换成八进制，用法和hex一样\r\n\r\neval:\r\neval(source[, globals[, locals]]) -> value\r\n\r\neval(\"['a','b',1]\") 结果是列表['a','b',1]\r\n\r\nchr:\r\n chr(i) -> character\r\n \r\n Return a string of one character with ordinal i; 0 <= i < 256.\r\n\r\n返回数值对应的ASCII的字符，例如chr(123) 结果是‘{’\r\n\r\nord：\r\nord(c) -> integer\r\n \r\n Return the integer ordinal of a one-character string.\r\n\r\n和chr相反，返回字符对应的ASCII，返回的是整数\r\n\r\n\r\nfilter:\r\nfilter(function or None, sequence) -> list, tuple, or string\r\n \r\n Return those items of sequence for which function(item) is true. If\r\n function is None, return the items that are true. If sequence is a tuple\r\n or string, return the same type, else return a list.\r\n\r\n用function来处理后面的sequence序列，function可以为空，也就是None\r\n\r\ndef f(x):\r\n if x%2==0:\r\n return True\r\n\r\nfilter(f,range(10))\r\n\r\n这个是过滤出偶数，结果是[0, 2, 4, 6, 8]\r\n\r\n\r\nzip:\r\nzip(seq1 [, seq2 [...]]) -> [(seq1[0], seq2[0] ...), (...)]\r\n Return a list of tuples, where each tuple contains the i-th element\r\n from each of the argument sequences. The returned list is truncated\r\n in length to the length of the shortest argument sequence.\r\n\r\n\r\na1=['1','2','3']\r\nb1=['a','b','c']\r\nc1=['I','II','III']\r\nzip(a1,b1,c1)结果是[('1', 'a', 'I'), ('2', 'b', 'II'), ('3', 'c', 'III')]\r\n\r\n如果c1变成['I','II']，那么结果就是[('1', 'a', 'I'), ('2', 'b', 'II')]\r\n结果取最短完整的\r\n\r\n\r\n\r\nmap:\r\n map(function, sequence[, sequence, ...]) -> list\r\n这个函数的作用是zip和filter的综合，如果function为None，则和zip一样，如果有function，则和filter一样\r\n\r\ndef f(x,y):\r\n return xy\r\n\r\na1=[1,2,3]\r\nb1=[4,5,6]\r\nmap(a1,b1) 结果是[4,10,18]\r\n\r\nmap(lambda x,y:xy,range(10),range(10)) 结果是[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\r\n\r\n\r\n\r\n列表表达式：\r\n[i2+10 for i in range(10)] 结果是[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]\r\n[i2+10 for i in range(10) if i%3==0] 结果是[10, 16, 22, 28]\r\n\r\n\r\n\r\n" }, { "alpha_fraction": 0.5095477104187012, "alphanum_fraction": 0.5095477104187012, "avg_line_length": 25.52777862548828, "blob_id": "9ac13e742b59dc3685e310134363a1e3a0fbfe38", "content_id": "af4718fd1ceccc1184ccfc3dddf0d93000488f4f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1095, "license_type": "no_license", "max_line_length": 56, "num_lines": 36, "path": "/python基础编程/Optparse.txt", "repo_name": "httaotao/kevintao-for-python", "src_encoding": "GB18030", "text": "#!/usr/bin/python\r\n\r\nfrom optparse import OptionParser\r\n\r\nparser=OptionParser()\r\nparser.add_option('-c','--char','--chars',\r\n dest=\"chars\",\r\n action=\"store_true\",\r\n default=False,\r\n help=\"only conut chars\")\r\n\r\nparser.add_option('-w','--word','--words',\r\n dest=\"words\",\r\n action=\"store_true\",\r\n default=False,\r\n help=\"only conut words\")\r\nparser.add_option('-l','--line','--lines',\r\n dest=\"lines\",\r\n action=\"store_true\",\r\n default=False,\r\n help=\"only conut lines\")\r\noptions,args=parser.parse_args()\r\nprint options,args\r\n\r\n这是专门用来做参数选项模块的\r\n'-c','--char'这些是参数选项\r\n\r\noptions是存放参数信息，args是存放文件信息\r\n\r\npython OptionParser.py -l -cw \r\n结果是：\r\n{'chars': True, 'lines': True, 'words': True} []\r\n\r\npython OptionParser.py -l -cw a b\r\n{'chars': True, 'lines': True, 'words': True} ['a', 'b']\r\n文件名称放到列表里面\r\n\r\n\r\n" }, { "alpha_fraction": 0.5515303015708923, "alphanum_fraction": 0.5515303015708923, "avg_line_length": 15.662983894348145, "blob_id": "c65cf62564f1f0a60775425386fa21845cdf3231", "content_id": "65f699c0eb4018ecab7db53ce2f56111ef9e30da", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3720, "license_type": "no_license", "max_line_length": 74, "num_lines": 181, "path": "/python基础编程/继承.txt", "repo_name": "httaotao/kevintao-for-python", "src_encoding": "GB18030", "text": "#! /usr/bin/python\r\n\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"I am a People\"\r\n\r\nclass chinese(People):\r\n pass\r\n\r\ncn=chinese()\r\nprint cn.color\r\ncn.think()\r\n\r\n这是类的继承，cn是类chinese的实例化，但可以继承类People的属性和方法\r\n\r\n\r\n#! /usr/bin/python\r\n\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"I am a People\"\r\n\r\n def __init__(self):\r\n self.dwell='Earth'\r\n\r\nclass chinese(People):\r\n pass\r\n\r\ncn=chinese()\r\nprint cn.dwell\r\ncn.think()\r\n\r\n\r\n这是调用继承的初始化函数\r\n\r\n------------------------------------\r\n#! /usr/bin/python\r\n\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"I am a People\"\r\n\r\n def __init__(self,c):\r\n self.dwell='Earth'\r\n print self.dwell\r\n\r\nclass chinese(People):\r\n def __init__(self):\r\n People.__init__(self,'c')\r\n\r\n\r\ncn=chinese()\r\n\r\n这是调用父类的初始化函数，这是多参数的\r\n----------------------------------------\r\n#! /usr/bin/python\r\n\r\nclass people:\r\n color='yellow'\r\n\r\na=people()\r\nprint a.color\r\n\r\n这是传统的类的命名方法，之前的都是new style\r\n-----------------------------------------------\r\n#! /usr/bin/python\r\n\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"I am a People\"\r\n\r\n def __init__(self,c):\r\n self.dwell='Earth'\r\n print self.dwell\r\n\r\nclass chinese(People):\r\n def __init__(self):\r\n super(chinese,self).__init__('red')\r\n\r\ncn=chinese()\r\n\r\n这里也可以使用super调用父类\r\n注意，这里的类的命名不能使用传统的方式，就是没有object\r\n另外，这里的参数，也不需要写成super(chinese,self).__init__(self,'red')\r\n\r\n继承里面，如果父类和子类有相同的名称，那么实际上就是在修改父类的方法\r\n\r\n------------------------------------------------------\r\n#! /usr/bin/python\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"I am a %s People\" %self.dwell\r\n\r\n def __init__(self):\r\n self.dwell='Earth'\r\n print self.dwell\r\n\r\nclass Martin(object):\r\n color='red'\r\n\r\n def __init__(self):\r\n self.dwell='Martin'\r\n\r\n\r\nclass chinese(People,Martin):\r\n def __init__(self):\r\n People.__init__(self)\r\n\r\ncn=chinese()\r\ncn.think()\r\n\r\n这是多重继承\r\n\r\n\r\n#! /usr/bin/python\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"I am a %s People\" %self.dwell\r\n\r\n def __init__(self):\r\n self.dwell='Earth'\r\n print self.dwell\r\n\r\nclass Martin(object):\r\n color='red'\r\n\r\n def __init__(self):\r\n self.dwell='Martin'\r\n print self.dwell\r\n\r\n def think(self):\r\n print \"I am a %s People\" %self.dwell\r\n\r\nclass chinese(Martin,People):\r\n pass\r\n\r\ncn=chinese()\r\ncn.think()\r\n\r\n这里如果Martin没有think方法，那么会调用People的think，但是如果有，那就调用Martin的，根据类chinese的参数的先后循序\r\n\r\n当父类中出现多个自定义的__init__方法时，多重继承只执行第一个类的__init__方法\r\n\r\n#! /usr/bin/python\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"color is %s\" %self.color\r\n print \"I am a %s People\" %self.dwell\r\n\r\n def __init__(self):\r\n self.dwell='Earth'\r\n print self.dwell\r\n\r\nclass Martin(object):\r\n color='red'\r\n\r\n def __init__(self):\r\n self.dwell='Martin'\r\n print self.dwell\r\n\r\nclass chinese(Martin,People):\r\n pass\r\n\r\ncn=chinese()\r\ncn.think()\r\nprint cn.color\r\n这里因为Martin没有think的方法，因为调用的是People的think的方法，但参数是Martin的\r\n\n\r\n\r\n" } ]	6
GhalyahF/django_cart1	https://github.com/GhalyahF/django_cart1	919f00753230a0a72a32c2e4b470710a6026aa8f	af1b41d05abf819952ae09c52ecfa7c8c3c1934b	a70b4c1ef13f15675ff6cdb28a8e91e6607243d1	refs/heads/master	2020-04-11T04:35:33.224729	2019-10-22T22:47:29	2019-10-22T22:47:29	161,517,494	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7333333492279053, "alphanum_fraction": 0.7333333492279053, "avg_line_length": 25.25, "blob_id": "2e523c30abca47acdd2e13c6174be097fb527efd", "content_id": "3df4945d2731115bb46e27da2819143b306010d6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 105, "license_type": "no_license", "max_line_length": 49, "num_lines": 4, "path": "/art_project/art/context_processors.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from .models import Category\n\ndef get_navbar(request):\n return {\"categories\": Category.objects.all()}\n" }, { "alpha_fraction": 0.6985592842102051, "alphanum_fraction": 0.7037310600280762, "avg_line_length": 35.09333419799805, "blob_id": "4ef592efd02b95e2b2a83f81f62daf2d77698dbf", "content_id": "530c034a86417868233f706d4940db224d0594e9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2707, "license_type": "no_license", "max_line_length": 89, "num_lines": 75, "path": "/art_project/cart/views.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from django.shortcuts import render, redirect, get_object_or_404\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib import messages\nfrom django.urls import reverse\nimport datetime\nfrom cart.extras import generate_order_id\nfrom art.models import Product, Profile\nfrom cart.models import OrderItem, Order\n\n\ndef get_pending_order(request):\n user_profile = get_object_or_404(Profile, user=request.user)\n order= Order.objects.filter(owner=user_profile, is_ordered=False)\n if order:\n return order[0]\n else:\n messages.info(request, \"Your cart is empty!\")\n\n@login_required\ndef add_to_cart(request, kwargs):\n user_profile= get_object_or_404(Profile, user=request.user)\n product= Product.objects.filter(id=kwargs.get('item_id', \"\")).first()\n if product in request.user.profile.merchandise.all():\n messages.info(request, 'You already own this product')\n order_item, status = OrderItem.objects.get_or_create(product=product)\n user_order, status = Order.objects.get_or_create(owner=user_profile,is_ordered=False)\n user_order.items.add(order_item)\n if status:\n user_order.ref_code = generate_order_id()\n user_order.save()\n messages.info(request, 'item added to cart')\n return redirect (reverse('cart:order_summary'))\n\n@login_required\ndef delete_from_cart(request, item_id):\n item_to_delete= OrderItem.objects.filter(pk=item_id)\n if item_to_delete.exists():\n item_to_delete[0].delete()\n messages.info(request, 'Item has been deleted successfully')\n return redirect(reverse('cart:order_summary'))\n\n@login_required\ndef order_details(request, kwargs):\n existing_order = get_pending_order(request)\n context = {\n 'order': existing_order\n }\n return render(request, 'order_summary.html', context)\n\n@login_required\ndef checkout(request, *kwargs):\n\n order_to_purchase = get_pending_order(request)\n order_to_purchase.is_ordered= True\n order_to_purchase.save()\n order_to_purchase.date_ordered=datetime.datetime.now()\n\n order_items= order_to_purchase.items.all()\n order_items.update(is_ordered= True, date_ordered= datetime.datetime.now())\n\n user_profile= get_object_or_404(Profile, user=request.user)\n order_products = [item.product for item in order_items]\n user_profile.merchandise.add(order_products)\n user_profile.save()\n messages.info(request, \"Thank you for your purhcase!\")\n # order_to_purchase.exclude(?????)\n return redirect(reverse('profile'))\n\n context = {\n 'order': order_to_purchase,\n 'order_items': order_items,\n 'user_profile': user_profile,\n }\n\n return render(request, 'checkout.html', context)\n" }, { "alpha_fraction": 0.5452322959899902, "alphanum_fraction": 0.5579462051391602, "avg_line_length": 38.32692337036133, "blob_id": "69f273f7215dd3fe54e00e5a872032c25beee9f0", "content_id": "10e89fbe0188b0a3fa1ada0048bf10e27892b8a6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2045, "license_type": "no_license", "max_line_length": 137, "num_lines": 52, "path": "/art_project/art/migrations/0001_initial.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "# Generated by Django 2.1.4 on 2018-12-08 02:14\n\nfrom django.conf import settings\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Category',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('category', models.CharField(max_length=150)),\n ('slug', models.SlugField(max_length=200, unique=True)),\n ('description', models.TextField(default='art')),\n ],\n options={\n 'verbose_name': 'category',\n 'verbose_name_plural': 'categories',\n },\n ),\n migrations.CreateModel(\n name='Product',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=100)),\n ('photo', models.ImageField(blank=True, null=True, upload_to='')),\n ('description', models.TextField()),\n ('price', models.DecimalField(decimal_places=2, max_digits=5)),\n ('category', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='products', to='art.Category')),\n ],\n options={\n 'ordering': ('category',),\n },\n ),\n migrations.CreateModel(\n name='Profile',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('merchandise', models.ManyToManyField(blank=True, to='art.Product')),\n ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),\n ],\n ),\n ]\n" }, { "alpha_fraction": 0.7065989971160889, "alphanum_fraction": 0.710659921169281, "avg_line_length": 32.965518951416016, "blob_id": "d68d066b989934a9a06a9f235f5bb392f87d9338", "content_id": "3cf84863a9658f041e59edd9f64e352f756d02fc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 985, "license_type": "no_license", "max_line_length": 80, "num_lines": 29, "path": "/art_project/cart/models.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from django.db import models\nfrom django.contrib.auth.models import User\nfrom art.models import Product, Profile\n\n\nclass OrderItem(models.Model):\n product= models.OneToOneField(Product, on_delete=models.SET_NULL, null=True)\n is_ordered= models.BooleanField(default=False)\n date_added= models.DateTimeField(auto_now=True)\n date_ordered= models.DateTimeField(null=True)\n\n def __str__(self):\n return self.product.name\n\nclass Order(models.Model):\n ref_code= models.CharField(max_length=15)\n owner= models.ForeignKey(Profile, on_delete= models.SET_NULL, null=True)\n is_ordered= models.BooleanField(default=False)\n items= models.ManyToManyField(OrderItem)\n date_ordered= models.DateTimeField(auto_now=True)\n\n def get_cart_items(self):\n return self.items.all()\n\n def get_cart_total(self):\n return sum([item.product.price for item in self.items.all()])\n\n def __str__(self):\n return '{0}-{1}'.format(self.owner, self.ref_code)\n" }, { "alpha_fraction": 0.652814507484436, "alphanum_fraction": 0.6543256640434265, "avg_line_length": 29.4252872467041, "blob_id": "44b9daf90fe07f1cd48b3e0d575a3c63a7bdc4f0", "content_id": "10efea0e2864ad4ae36d2f88533173fe166cc913", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2647, "license_type": "no_license", "max_line_length": 87, "num_lines": 87, "path": "/art_project/art/views.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from django.shortcuts import render, redirect, get_object_or_404\nfrom django.contrib.auth import login,logout, authenticate\nfrom django.contrib import messages\nfrom .models import \nfrom cart.models import \nfrom .forms import \n\ndef home(request):\n categories= Category.objects.all()\n products=Product.objects.all().order_by('price')\n\n context={\n 'categories': categories,\n 'products': products\n }\n return render (request, 'home.html', context)\n\ndef product_list(request):\n product_list= Product.objects.all()\n filtered_orders= Order.objects.filter(owner=request.user.profile, is_ordered=False)\n current_order_products= []\n if filtered_orders.exists():\n user_order= filtered_orders[0]\n user_order_items= user_order.items.all()\n current_order_products= [product.product for product in user_order_items]\n context={\n 'product_list': product_list,\n 'current_order_products': current_order_products,\n }\n return render(request, \"product_list.html\", context)\n\ndef product_category(request, slug):\n category= Category.objects.get(slug=slug)\n products=Product.objects.all()\n context={\n 'category': category,\n 'products': products\n }\n return render(request, 'cat_details.html', context)\n\ndef user_register(request):\n form=UserRegister()\n if request.method == 'POST':\n form= UserRegister(request.POST)\n if form.is_valid():\n user= form.save(commit=False)\n\n user.set_password(user.password)\n user.save()\n\n login(request, user)\n return redirect ('home' )\n context={\n \"form\": form,\n }\n\n return render(request, 'register.html', context)\n\ndef user_login(request):\n form= UserLogin()\n if request.method == 'POST':\n form = UserLogin(request.POST)\n if form.is_valid():\n username= form.cleaned_data['username']\n password= form.cleaned_data['password']\n auth_user= authenticate(username=username, password=password)\n if auth_user is not None:\n login(request, auth_user)\n messages.success(request, \"Welcome Back!\")\n return redirect('home')\n\n context={\n \"form\": form,\n }\n return render(request, 'login.html', context)\n\ndef user_logout(request):\n logout(request)\n return render (request, \"logout.html\")\n\ndef user_profile(request):\n user_profile= Profile.objects.filter(user=request.user).first()\n user_orders= Order.objects.filter(is_ordered=True, owner= user_profile)\n context={\n 'user_orders': user_orders\n }\n return render (request, 'profile.html', context)\n" }, { "alpha_fraction": 0.5272727012634277, "alphanum_fraction": 0.5848484635353088, "avg_line_length": 18.41176414489746, "blob_id": "a02d83b0f7e9339ee8eece6807a982a24221e35c", "content_id": "8f434a19bc67fedcac61335a6896c6b82e5fcae6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 330, "license_type": "no_license", "max_line_length": 47, "num_lines": 17, "path": "/art_project/cart/migrations/0003_remove_orderitem_quantity.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "# Generated by Django 2.1.4 on 2018-12-11 14:37\n\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('cart', '0002_orderitem_quantity'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='orderitem',\n name='quantity',\n ),\n ]\n" }, { "alpha_fraction": 0.6981664299964905, "alphanum_fraction": 0.6981664299964905, "avg_line_length": 36.31578826904297, "blob_id": "e6250c7e33963cae28b8bb573b63d6f854fbb669", "content_id": "7f6d31171554235ce36a26d4b4b9ce952f9ff2c6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 709, "license_type": "no_license", "max_line_length": 75, "num_lines": 19, "path": "/art_project/art_project/urls.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from django.contrib import admin\nfrom django.urls import path, include\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\nfrom art import views\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('register/', views.user_register, name='register'),\n path('login/', views.user_login, name='user_login'),\n path('logout/', views.user_logout, name='user_logout'),\n path('profile/', views.user_profile, name= 'profile'),\n path('', views.home, name= 'home'),\n path('category/<slug:slug>/', views.product_category, name='category'),\n path('', include('cart.urls', namespace='cart')),\n]\n\nurlpatterns+=static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n" }, { "alpha_fraction": 0.708737850189209, "alphanum_fraction": 0.708737850189209, "avg_line_length": 33.33333206176758, "blob_id": "295e8eeb4a642e379be8d2d1b95fe992fe7e6a55", "content_id": "c1853738bf17fe3a131ceae3cda82291a18b1dde", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 412, "license_type": "no_license", "max_line_length": 75, "num_lines": 12, "path": "/art_project/cart/urls.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from django.urls import path\nfrom cart import views\n\napp_name= 'cart'\n\nurlpatterns=[\npath('add-to-cart/<item_id>', views.add_to_cart, name=\"add_to_cart\"),\npath('item/delete/<item_id>', views. delete_from_cart, name='delete_item'),\n# path('success/', views.success, name='purchase_success'),\npath('order_summary/', views.order_details, name='order_summary'),\npath('checkout/', views.checkout, name='checkout'),\n]\n" }, { "alpha_fraction": 0.380952388048172, "alphanum_fraction": 0.6666666865348816, "avg_line_length": 13, "blob_id": "7724ea1a0e4d249d435176554311d2f5a3b60589", "content_id": "4a643892fd0b2c346a824c9152c64009804f9dce", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 42, "license_type": "no_license", "max_line_length": 14, "num_lines": 3, "path": "/art_project/requirements.txt", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "Django==2.1.11\nPillow==5.3.0\npytz==2018.7\n" }, { "alpha_fraction": 0.7239263653755188, "alphanum_fraction": 0.7300613522529602, "avg_line_length": 31.600000381469727, "blob_id": "586a12b5d1699cbf3ecb6a6ae9376bec23aff235", "content_id": "85ab8f7d32f2348d1b4ff61ceccf4104bf35bf6e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 163, "license_type": "no_license", "max_line_length": 56, "num_lines": 5, "path": "/bin/django-admin.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "#!/Users/ghalyahalansari/Development/art/art/bin/python3\nfrom django.core import management\n\nif __name__ == \"__main__\":\n management.execute_from_command_line()\n" }, { "alpha_fraction": 0.6970474720001221, "alphanum_fraction": 0.7041078209877014, "avg_line_length": 30.15999984741211, "blob_id": "e87b2deeb4420ef5955f4b468db78cd3006bfe6c", "content_id": "3ea3755b3595600cc4c897cf789308b498f8b690", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1558, "license_type": "no_license", "max_line_length": 93, "num_lines": 50, "path": "/art_project/art/models.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from django.db import models\nfrom django.contrib.auth.models import User\nfrom django.conf import settings\nfrom django.utils.text import slugify\nfrom django.db.models.signals import post_save\n\nclass Category(models.Model):\n category= models.CharField(max_length=150)\n slug= models.SlugField(max_length= 200, unique= True)\n description= models.TextField(default=\"art\")\n\n class Meta:\n verbose_name= 'category'\n verbose_name_plural= 'categories'\n\n def __str__(self):\n return self.category\n\n def save(self, args, *kwargs):\n self.slug= slugify(self.category)\n super(Category, self).save(args, *kwargs)\n\n\n\nclass Product(models.Model):\n name= models.CharField(max_length= 100)\n category= models.ForeignKey(Category, on_delete= models.PROTECT, related_name='products')\n photo= models.ImageField(null=True, blank=True)\n description= models.TextField()\n price= models.DecimalField(max_digits=5, decimal_places=2)\n\n class Meta:\n ordering=('category',)\n\n def __str__(self):\n return self.name\n\nclass Profile(models.Model):\n user= models.OneToOneField(User, on_delete= models.CASCADE)\n merchandise= models.ManyToManyField(Product, blank=True)\n\n def __str__(self):\n return self.user.username\n\ndef post_save_profile_create(sender, instance, created, args, **kwargs):\n if created:\n Profile.objects.get_or_create(user=instance)\n user_profile, created= Profile.objects.get_or_create(user=instance)\n\npost_save.connect(post_save_profile_create, sender=User)\n" } ]	11
dbsousa01/AI-challenge	https://github.com/dbsousa01/AI-challenge	a09da283012c3c97a684dad010a7fe4b8c9d2c2c	0fa150d3d3c1abafe4243dba4550ba1a35300469	d70fbec03a5972c66fa03d2a029ed30cbaff7cae	refs/heads/master	2020-04-13T02:48:59.364421	2019-01-19T12:42:29	2019-01-19T12:42:29	162,912,947	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6903262734413147, "alphanum_fraction": 0.7009158730506897, "avg_line_length": 30.370370864868164, "blob_id": "ec90f804dcb3facdfa07241aabf95b8c800eed76", "content_id": "bac050e193d878342601b2664dfe5dd3335c7b48", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3494, "license_type": "no_license", "max_line_length": 118, "num_lines": 108, "path": "/langid/task1.py", "repo_name": "dbsousa01/AI-challenge", "src_encoding": "UTF-8", "text": "# Task 1 is to train an algorithm in order to distinguish en, es, pt\r\n\r\n## TF-IDF Score at Word level implementation\r\n## TF-IDF score represents the relative importance of a term in the document and the entire corpus. \r\n#TF-IDF score is composed by two terms: the first computes the normalized Term Frequency (TF), the second term is the \r\n#Inverse Document Frequency (IDF), computed as the logarithm of the number of the documents in the corpus divided by \r\n#the number of documents where the specific term appears.\r\n\r\n#TF(t) = (Number of times term t appears in a document) / (Total number of terms in the document)\r\n#IDF(t) = log_e(Total number of documents / Number of documents with term t in it)\r\n\r\nfrom sklearn import model_selection, preprocessing, metrics\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nfrom sklearn.naive_bayes import MultinomialNB\r\n\r\nimport pandas as pd \r\nimport numpy as np \r\nimport os\r\n\r\nPATH = os.getcwd()\r\ntexts_pt = []\r\nlabels = []\r\ntexts_en = []\r\ntexts_es = []\r\nX_test = []\r\n#Open file and populate arrays to create dataset - pt\r\nwith open(PATH + '/langid-data/task1/data.pt') as fp:\r\n\tfor i, line in enumerate(fp):\r\n\t\ttexts_pt.append(line.split(\"\\n\")[0])\r\n\t\tif i == 100000: #might be enough for a dataset to this problem - ? Should introduce a shuffle read in the future\r\n\t\t\tbreak\r\n\r\nfp.close()\r\n\r\n#Open file and populate arrays to create dataset - en\r\nwith open(PATH + '/langid-data/task1/data.en') as fp:\r\n\tfor i, line in enumerate(fp):\r\n\t\ttexts_en.append(line.split(\"\\n\")[0])\r\n\t\tif i == 100000: #might be enough for a dataset to this problem - ?\r\n\t\t\tbreak\r\n\r\nfp.close()\r\n\r\n#Open file and populate arrays to create dataset - es\r\nwith open(PATH + '/langid-data/task1/data.es') as fp:\r\n\tfor i, line in enumerate(fp):\r\n\t\ttexts_es.append(line.split(\"\\n\")[0])\r\n\t\tif i == 100000: #might be enough for a dataset to this problem - ?\r\n\t\t\tbreak\r\n\r\nfp.close()\r\n\r\n## Test set\r\nwith open(PATH + \"/langid.test\") as fp:\r\n\tfor i, line in enumerate(fp):\r\n\t\t\tX_test.append(line.split(\"\\n\")[0])\r\n\t\t\t\r\nfp.close()\r\n\r\nlabel_pt = ['pt'] * len(texts_pt)\r\nlabel_en = ['en'] * len(texts_en)\r\nlabel_es = ['es'] * len(texts_es)\r\n\r\ntexts = texts_pt\r\ntexts.extend(texts_en)\r\ntexts.extend(texts_es)\r\nlabels = label_pt\r\nlabels.extend(label_en)\r\nlabels.extend(label_es)\r\n\r\n#print(len(texts))\r\n#print(len(labels))\r\n\r\ntrainDF = pd.DataFrame()\r\ntrainDF['text'] = texts\r\ntrainDF['label'] = labels\r\n#print(trainDF)\r\n\r\n\r\n# split the dataset into training and validation datasets \r\nX_train, X_valid, y_train, y_valid = model_selection.train_test_split(trainDF['text'], trainDF['label'])\r\n\r\n# label encode the target variable \r\nencoder = preprocessing.LabelEncoder()\r\ny_train = encoder.fit_transform(y_train)\r\ny_valid = encoder.fit_transform(y_valid)\r\n\r\n#Calculate the TF-IDF words vector\r\ntfidf_vect = TfidfVectorizer(analyzer='word', token_pattern=r'\\w{1,}', max_features=1000000)\r\ntfidf_vect.fit(trainDF['text'])\r\nxtrain_tfidf = tfidf_vect.transform(X_train)\r\nxvalid_tfidf = tfidf_vect.transform(X_valid)\r\nxtest_tfidf = tfidf_vect.transform(X_test)\r\n\r\n# Naive Bayes on Word Level TF IDF Vectors\r\nclf = MultinomialNB()\r\nclf.fit(xtrain_tfidf, y_train)\r\n\r\npredictions = clf.predict(xvalid_tfidf)\r\n\r\nacc = metrics.accuracy_score(predictions, y_valid)\r\nprint(\"Accuracy of the model:\", acc) # It's around 86%\r\n\r\n#Run with the test set\r\npredictions = clf.predict(xtest_tfidf)\r\npredictions = encoder.inverse_transform(predictions)\r\n\r\nnp.savetxt('task1-result.txt', predictions, fmt='%s')" }, { "alpha_fraction": 0.7777777910232544, "alphanum_fraction": 0.782282292842865, "avg_line_length": 52.279998779296875, "blob_id": "bc0cda11ae977f1d53a4e4273b6c28fc29d1cfb8", "content_id": "41b950e143e5c13f69119ef4e971679e5ed4c365", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1332, "license_type": "no_license", "max_line_length": 204, "num_lines": 25, "path": "/README.md", "repo_name": "dbsousa01/AI-challenge", "src_encoding": "UTF-8", "text": "# AI coding challenge - NLP\n\n## Description\n\nThe first thing you need to be able to do is to identify these languages properly.\n\nWe need you to build a service that provided with a text, identifies the language in which it is \nwritten, and provides that answer.\nWe provide you with the initial repo to implement this, and some requirements that the service \nshould satisfy. \n\nWhether you choose to implement an existing approach or compile one, make sure you document it\nand explain your reasoning.\n\n### Tasks\n\n1 - Implement a Language Identification service that returns the language code of the language in which the text is written. The provided data and test will\ntarget Spanish (ES), Portuguese (PT-PT) and English (EN)\n\n2 - Train the system to distinguish between language variants. In this case we wish to distinguish between European Portuguese (PT-PT) and Brazilian Portuguese (PT-BR)\n\n3(not completed) - Implement a deep learning model (recommended: a BILSTM tagger) to detect code switching (language mixture) and return both a list of tokens and a list with one language label per token.\nTo simplify we are going to focus on English and Spanish, so you only need to return for each token either 'en', 'es' or 'other'\n\nSee more information about tasks 1 and 2 in langid folder, and about task3 in code_switching folder\n" } ]	2
okest12/leetCode	https://github.com/okest12/leetCode	084707f27b6e296242417cc973ddd120213f8044	7baac8041385ea34cd0a48ac601274c4b339cda9	7e1f80bb213bf630c68d605aee134f016c58c4da	refs/heads/master	2020-09-12T02:22:54.563549	2020-01-20T02:36:01	2020-01-20T02:36:01	222,269,820	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.4282907545566559, "alphanum_fraction": 0.4675835072994232, "avg_line_length": 19.659574508666992, "blob_id": "74f82a375dc900e923a3e344db809125d5a1cc98", "content_id": "b3e0d27d8db85517c38bdd80e10d342d3dd8f88e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1018, "license_type": "no_license", "max_line_length": 44, "num_lines": 47, "path": "/add_two_number.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "class listNode(object):\r\n def __init__(self, x):\r\n self.value = x\r\n self.next = None\r\n\r\n\r\ndef add_two_number(s1, s2):\r\n l1 = create_list_node(s1)\r\n l2 = create_list_node(s2)\r\n result = dummy = listNode(-1)\r\n carry = 0\r\n while l1 or l2 or carry:\r\n sum = carry\r\n if l1:\r\n sum += l1.value\r\n l1 = l1.next\r\n if l2:\r\n sum += l2.value\r\n l2 = l2.next\r\n if sum >= 10:\r\n carry = 1\r\n sum -= 10\r\n else:\r\n carry = 0\r\n dummy.next = listNode(sum)\r\n dummy = dummy.next\r\n return result.next\r\n\r\n\r\ndef print_list_node(l):\r\n s = ''\r\n while l:\r\n s = str(l.value) + s\r\n l = l.next\r\n print(s)\r\n\r\n\r\ndef create_list_node(s):\r\n result = dummy = listNode(-1)\r\n for i in range(len(s), 0, -1):\r\n dummy.next = listNode(int(s[i - 1]))\r\n dummy = dummy.next\r\n return result.next\r\n\r\n\r\nl3 = add_two_number(\"12345\", \"67890\")\r\nprint_list_node(l3)\r\n" }, { "alpha_fraction": 0.49813199043273926, "alphanum_fraction": 0.5205479264259338, "avg_line_length": 27.740739822387695, "blob_id": "e4f013dd9c50a58bb5c191d341175f1e541de1d4", "content_id": "b99ac6f802de96a50bb01ac001125487a3da4de3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 803, "license_type": "no_license", "max_line_length": 60, "num_lines": 27, "path": "/median_after_each_input.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "import heapq\r\ndef median_after_each_input(datas):\r\n minHeap = []\r\n maxHeap = []\r\n median = 0\r\n for data in datas:\r\n if data >= median:\r\n heapq.heappush(minHeap, data)\r\n else:\r\n heapq.heappush(maxHeap, -data)\r\n\r\n if len(maxHeap) >= len(minHeap) + 2:\r\n heapq.heappush(minHeap, -heapq.heappop(maxHeap))\r\n elif len(minHeap) >= len(maxHeap) + 2:\r\n heapq.heappush(maxHeap, -heapq.heappop(minHeap))\r\n\r\n if len(maxHeap) == len(minHeap):\r\n median = (-maxHeap[0] + minHeap[0])/2\r\n elif len(maxHeap) > len(minHeap):\r\n median = -maxHeap[0]\r\n else:\r\n median = minHeap[0]\r\n print(data, end=':')\r\n print(median)\r\n\r\n\r\nmedian_after_each_input([5,4,2,-3,7,9,0,1,3,8])\r\n" }, { "alpha_fraction": 0.4866071343421936, "alphanum_fraction": 0.5133928656578064, "avg_line_length": 23.11111068725586, "blob_id": "ba665b6346cd906e1c9a1a971e60fd890e755e99", "content_id": "c23f1e9a204620f2e7681effb84901f43dc2bf3c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 224, "license_type": "no_license", "max_line_length": 43, "num_lines": 9, "path": "/find_addend.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "def find_addend(nums, target):\r\n c_index = {}\r\n for i, c in enumerate(nums):\r\n if target - c in c_index:\r\n return [c_index[target - c], i]\r\n c_index[c] = i\r\n\r\n\r\nprint(find_addend([1,3,5,7],10))" }, { "alpha_fraction": 0.5223745107650757, "alphanum_fraction": 0.5403467416763306, "avg_line_length": 43.71369171142578, "blob_id": "b8989bbd1ab4106f4c9cbad17514660f8fca02d7", "content_id": "b1f31d6b7878377ae4ae67e6e248a1781c14e7ba", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 22034, "license_type": "no_license", "max_line_length": 116, "num_lines": 482, "path": "/make_tools_package.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\r\n\"\"\"\r\n2016/12/21 first version by @author: [email protected]\r\n This is the file to make tools package automatically\r\n2017/01/13 update to add new tools, and support selecting multiple files with pattern, by Ye Sheng\r\n2017/02/09 add link.txt and notes.txt for auto_helper tool, by Ye Sheng\r\n2017/02/24 add text_styles.py for edit_dialog highlight text, by Ye Sheng\r\n2017/05/15 add new mmt_check and remove old mmt_check_tool, by Ye Sheng\r\n2017/05/18 update template and example list, by Ye Sheng\r\n2017/06/12 add tools/lib/advanced_listbox.py, by Ye Sheng\r\n2017/06/16 add tools/lib/log_info_dialog.py, by Ye Sheng\r\n2017/07/14 add test_base/version.py, by Ye Sheng\r\n2017/07/14 add tools/log_view/graphic_dialog.py, by Ye Sheng\r\n2017/07/24 add log_view.ico, by Ye Sheng\r\n2017/07/25 add auto_helper.ico, mmt_check.ico, by Ye Sheng\r\n2017/08/23 update to display code number of lines, by Ye Sheng\r\n2017/08/31 add robot_runner, by Ye Sheng\r\n2017/09/19 add function get_version() for tools, and move this file from tools to tools/lib, by Ye Sheng\r\n2017/11/09 add tool log_processor and script_runner, by Ye Sheng\r\n2017/11/23 add tool file_converter, remove tool log_processor, by Ye Sheng\r\n2017/11/06 add tti_tracer_getter, by Ye Sheng\r\n2018/01/12 update to add up_main, and move target files to ./bin, by Ye Sheng\r\n2018/01/16 update to support auto upgrade, by Ye Sheng\r\n2018/01/19 make_package support source_code=False parameter, by Ye Sheng\r\n2018/03/02 improve usability by tool_main_file, tool_depend_file and released_tool_list, by Ye Sheng\r\n2018/03/05 add tk_app_template, by Ye Sheng\r\n2018/03/05 add extract_logs, by Wang Yixiang\r\n2018/03/06 add tool_name.txt in package, by Ye Sheng\r\n2018/03/06 use tool_name as tools subfolder, by Ye Sheng\r\n2018/03/14 avoid updating SHARED_FOLDER if local version is older, by Ye Sheng\r\n2018/09/14 add file auto_helper_item.py, by Ye Sheng\r\n2019/03/29 change for python2 to python3 migration phase I, by Ye Sheng\r\n2019/04/04 change for python2 to python3 migration phase II, by Ye Sheng\r\n2019/04/24 add new tool log_collector, by Ye Sheng\r\n2019/05/15 update xlrd, xlwt file list, by Ye Sheng\r\n2019/07/05 add new tool code_tree, add calculator as demo, by Ye Sheng\r\n\"\"\"\r\n\r\nimport os\r\nimport zipfile\r\nimport sys\r\nimport hashlib\r\nimport shutil\r\n\r\n_md5 = hashlib.md5 if sys.version_info.major == 2 else lambda x: hashlib.md5(x.encode('utf8'))\r\n\r\nsuite_root = os.path.abspath(os.path.dirname(__file__)) + '/..' * 6\r\nsys.path.append(suite_root)\r\nroot = os.path.abspath(os.path.dirname(__file__) + '/../..')\r\nroot in sys.path and sys.path.remove(root)\r\nroot in sys.path or sys.path.insert(0, root)\r\n\r\nif sys.version_info.major == 2:\r\n DEFAULT_SHARED_FOLDER = r'\\\\beeefsn01.china.nsn-net.net\\DCM_project\\qch2043\\up_tools'\r\nelse:\r\n DEFAULT_SHARED_FOLDER = r'\\\\beeefsn01.china.nsn-net.net\\DCM_project\\qch2043\\up_tools_3'\r\n\r\ntestsuite_file_list = (\r\n ('__init__.py', ''),\r\n ('../../../../tool_name.txt', ''),\r\n ('../../../../testsuite/__init__.py', ''),\r\n ('../../../../testsuite/DCM/__init__.py', ''),\r\n ('../../../../testsuite/DCM/libraries/__init__.py', ''),\r\n)\r\n\r\nup_lib_file_list = ('up_lib_file_list_place_holder',)\r\n\r\nxlwt_lib_file_list = (\r\n (('xlwt/', '.py$', 1), ''),\r\n ('xlwt/excel-formula.g', ''),\r\n)\r\n\r\nxlrd_lib_file_list = (\r\n (('xlrd/', '.py$', 1), ''),\r\n)\r\n\r\nexample_file_list = (\r\n ('examples/example1_001.py', ''),\r\n ('examples/example2_001.py', ''),\r\n ('examples/example3_001.py', ''),\r\n ('examples/example5_pmi_ri_cqi.py', ''),\r\n ('examples/example5_pmi_ri_cqi_001.py', ''),\r\n ('examples/example_set.py', ''),\r\n ('examples/example_set_1_test_base.py', ''),\r\n ('examples/example_set_2_test_log.py', ''),\r\n ('examples/example_set_5_sdata.py', ''),\r\n ('examples/example_set_6_mmt.py', ''),\r\n ('examples/example_set_7_common.py', ''),\r\n ('examples/__init__.py', ''),\r\n)\r\n\r\ntemplate_file_list = (\r\n ('template/__init__.py', ''),\r\n ('template/FeatureID.py', ''),\r\n ('template/FeatureID_CaseID.py', ''),\r\n)\r\n\r\n\r\ntools_lib_file_list = (\r\n ('tools/__init__.py', ''),\r\n ('tools/lib/__init__.py', ''),\r\n (('tools/lib/tkdnd2.8/', '..dll', 2), ''),\r\n (('tools/lib/tkdnd2.8/', '..tcl', 2), ''),\r\n ('tools/lib/TkDND.py', ''),\r\n ('tools/lib/TwoSideScrolledText.py', ''),\r\n ('tools/lib/wait_dialog.py', ''),\r\n ('tools/lib/edit_dialog.py', ''),\r\n ('tools/lib/text_styles.py', ''),\r\n ('tools/lib/get_strings_dialog.py', ''),\r\n ('tools/lib/advanced_listbox.py', ''),\r\n ('tools/lib/super_extractor.py', ''),\r\n ('tools/lib/log_info_dialog.py', ''),\r\n ('tools/lib/select_log_dialog.py', ''),\r\n ('tools/lib/log_summary_dialog.py', ''),\r\n ('tools/lib/tk_app.py', ''),\r\n ('tools/lib/make_tools_package.py', ''),\r\n ('tools/lib/check.gif', ''),\r\n ('tools/lib/uncheck.gif', ''),\r\n (('tools/lib/robot/', '.py$', 3), ''),\r\n (('tools/lib/robot/', '.html$', 3), ''),\r\n (('tools/lib/robot/', '.js$', 3), ''),\r\n (('tools/lib/robot/', '.css$', 3), ''),\r\n (('tools/lib/robot_3/', '.py$', 3), ''),\r\n (('tools/lib/robot_3/', '.html$', 3), ''),\r\n (('tools/lib/robot_3/', '.js$', 3), ''),\r\n (('tools/lib/robot_3/', '.css$', 3), ''),\r\n (('tools/lib/psutil/', '.py$', 1), ''),\r\n (('tools/lib/psutil/', '.pyd$', 1), ''),\r\n (('tools/lib/psutil_3/', '.py$', 1), ''),\r\n (('tools/lib/psutil_3/', '.pyd$', 1), ''),\r\n)\r\n\r\n# if sys.version_info.major == 2:\r\n# tools_lib_file_list += (\r\n# (('tools/lib/psutil/', '.py$', 1), ''),\r\n# (('tools/lib/psutil/', '.pyd$', 1), ''),\r\n# )\r\n# else:\r\n# tools_lib_file_list += (\r\n# (('tools/lib/psutil_3/', '.py$', 1), ''),\r\n# (('tools/lib/psutil_3/', '.pyd$', 1), ''),\r\n# )\r\n\r\nsys_info_file_list = (\r\n # ('bin/sys_info.bat', '../../../../sys_info.bat'),\r\n ('test_base/sys_info.ico', ''),\r\n ('test_base/sys_info.gif', ''),\r\n ('test_base/sys_info.py', '')\r\n)\r\n\r\nlog_runner_file_list = (\r\n # ('bin/log_runner.bat', '../../../../log_runner.bat'),\r\n ('tools/log_runner/__init__.py', ''),\r\n ('tools/log_runner/log_runner.py', ''),\r\n ('tools/log_runner/log_runner.ico', ''),\r\n ('tools/log_runner/log_runner.gif', ''),\r\n)\r\n\r\nlog_view_file_list = (\r\n # ('bin/log_view.bat', '../../../../log_view.bat'),\r\n ('tools/log_view/__init__.py', ''),\r\n ('tools/log_view/log_view.py', ''),\r\n ('tools/log_view/graphic_dialog.py', ''),\r\n ('tools/log_view/log_view.ico', ''),\r\n ('tools/log_view/log_view.gif', '')\r\n)\r\n\r\nmmt_check_file_list = (\r\n # ('bin/mmt_check.bat', '../../../../mmt_check.bat'),\r\n ('tools/mmt_check/__init__.py', ''),\r\n ('tools/mmt_check/mmt_check.py', ''),\r\n ('tools/mmt_check/mmt_check.ico', ''),\r\n ('tools/mmt_check/mmt_check.gif', ''),\r\n ('tools/mmt_check/Tshark_MMT_sample.txt', ''),\r\n)\r\n\r\nauto_helper_file_list = (\r\n # ('bin/auto_helper.bat', '../../../../auto_helper.bat'),\r\n ('tools/auto_helper/__init__.py', ''),\r\n ('tools/auto_helper/auto_helper.py', ''),\r\n ('tools/auto_helper/auto_helper_item.py', ''),\r\n ('tools/auto_helper/auto_helper.ini', ''),\r\n ('tools/auto_helper/log_column_tool.py', ''),\r\n ('tools/auto_helper/time_tool.py', ''),\r\n ('tools/auto_helper/sdata_tool.py', ''),\r\n ('tools/auto_helper/link.txt', ''),\r\n ('tools/auto_helper/notes.txt', ''),\r\n ('tools/auto_helper/auto_helper.ico', ''),\r\n ('tools/auto_helper/auto_helper.gif', ''),\r\n ('tools/auto_helper/Tshark_MMT_sample.txt', ''),\r\n (('../../../../testsuite/DCM/user_plane/template/', '.robot$', 0), ''),\r\n (('../../../../testsuite/DCM/user_plane/resources/', '.robot$', 0), ''),\r\n (('../../../../testsuite/DCM/user_plane/17A/sdata_lrc_edit/', 'SDATA_edit_tool_LTEAPI1220HD_1.0.0.xls', 0), ''),\r\n (('../../../../resources/DCM/', '.robot$', 0), ''),\r\n)\r\n\r\nrobot_runner_file_list = (\r\n # ('bin/robot_runner.bat', '../../../../robot_runner.bat'),\r\n ('tools/robot_runner/__init__.py', ''),\r\n ('tools/robot_runner/robot_runner.py', ''),\r\n ('tools/robot_runner/robot_runner.ico', ''),\r\n ('tools/robot_runner/robot_runner.gif', ''),\r\n ('tools/robot_runner/sample.robot', ''),\r\n)\r\n\r\nscript_runner_file_list = (\r\n # ('bin/script_runner.bat', '../../../../script_runner.bat'),\r\n ('tools/script_runner/__init__.py', ''),\r\n ('tools/script_runner/script_runner.py', ''),\r\n ('tools/script_runner/script_runner.ico', ''),\r\n ('tools/script_runner/script_runner.gif', ''),\r\n ('tools/script_runner/scripts/__init__.py', ''),\r\n ('tools/script_runner/scripts/sample.py', ''),\r\n ('tools/script_runner/scripts/lib/__init__.py', ''),\r\n ('tools/script_runner/scripts/lib/common.py', ''),\r\n)\r\n\r\nfile_converter_file_list = (\r\n # ('bin/file_converter.bat', '../../../../file_converter.bat'),\r\n ('tools/file_converter/__init__.py', ''),\r\n ('tools/file_converter/file_converter.py', ''),\r\n ('tools/file_converter/file_converter.ico', ''),\r\n ('tools/file_converter/file_converter.gif', ''),\r\n ('tools/file_converter/tti_tracer_getter.py', ''),\r\n)\r\n\r\nup_main_file_list = (\r\n # ('bin/up_main.bat', '../../../../up_main.bat'),\r\n ('tools/up_main/__init__.py', ''),\r\n ('tools/up_main/up_main.py', ''),\r\n ('tools/up_main/up_tools.py', ''),\r\n ('tools/up_main/up_main.ico', ''),\r\n)\r\n\r\ntk_app_template_file_list = (\r\n # ('bin/tk_app_template.bat', '../../../../tk_app_template.bat'),\r\n ('tools/tk_app_template/tk_app_template.py', ''),\r\n ('tools/tk_app_template/tk_app_template.ico', ''),\r\n ('tools/tk_app_template/tk_app_template.gif', ''),\r\n)\r\n\r\ncode_tree_file_list = (\r\n ('tools/code_tree/code_tree.py', ''),\r\n ('tools/code_tree/code_tree.ico', ''),\r\n ('tools/code_tree/code_tree.gif', ''),\r\n)\r\n\r\ncalculator_file_list = (\r\n # ('bin/tk_app_template.bat', '../../../../tk_app_template.bat'),\r\n ('tools/calculator/calculator.py', ''),\r\n ('tools/calculator/calculator.ico', ''),\r\n ('tools/calculator/calculator.gif', ''),\r\n)\r\n\r\nlog_collector_file_list = (\r\n ('tools/log_collector/log_collector.py', ''),\r\n ('tools/log_collector/log_collector.ini', ''),\r\n ('tools/log_collector/log_collector.ico', ''),\r\n ('tools/log_collector/log_collector.gif', ''),\r\n (('tools/log_collector/scripts/', '.\\.py', 0), ''),\r\n (('tools/log_collector/scripts/', '.\\.exe', 0), ''),\r\n)\r\n\r\nextract_logs_file_list = (\r\n # ('bin/extract_logs.bat', '../../../../extract_logs.bat'),\r\n ('tools/extract_logs/get_logs.py', ''),\r\n ('tools/extract_logs/extract_logs.py', ''),\r\n ('tools/extract_logs/extract_logs.ico', ''),\r\n ('tools/extract_logs/extract_logs.gif', ''),\r\n)\r\n\r\ntool_main_file = {\r\n 'auto_helper': (up_lib_file_list, auto_helper_file_list),\r\n 'mmt_check': (up_lib_file_list, mmt_check_file_list),\r\n 'log_view': (up_lib_file_list, log_view_file_list),\r\n 'log_runner': (up_lib_file_list, log_runner_file_list),\r\n 'robot_runner': (robot_runner_file_list,),\r\n 'script_runner': (up_lib_file_list, script_runner_file_list,),\r\n 'file_converter': (up_lib_file_list, file_converter_file_list,),\r\n 'up_main': (up_lib_file_list, up_main_file_list),\r\n 'sys_info': (sys_info_file_list,),\r\n 'extract_logs': (extract_logs_file_list,),\r\n 'log_collector': (log_collector_file_list,),\r\n 'code_tree': (code_tree_file_list,),\r\n 'tk_app_template': (tk_app_template_file_list,),\r\n 'calculator': (calculator_file_list,),\r\n\r\n}\r\n\r\ntool_depend_file = {\r\n 'auto_helper': (testsuite_file_list, tools_lib_file_list, example_file_list, template_file_list,\r\n xlrd_lib_file_list, xlwt_lib_file_list),\r\n 'mmt_check': (testsuite_file_list, tools_lib_file_list,),\r\n 'log_view': (testsuite_file_list, tools_lib_file_list, xlrd_lib_file_list, xlwt_lib_file_list),\r\n 'log_runner': (testsuite_file_list, tools_lib_file_list, xlrd_lib_file_list, xlwt_lib_file_list),\r\n 'robot_runner': (testsuite_file_list, tools_lib_file_list,),\r\n 'script_runner': (testsuite_file_list, tools_lib_file_list,),\r\n 'file_converter': (testsuite_file_list, tools_lib_file_list,),\r\n 'up_main': (testsuite_file_list, tools_lib_file_list),\r\n 'sys_info': (testsuite_file_list, ),\r\n 'extract_logs': (testsuite_file_list, tools_lib_file_list),\r\n 'log_collector': (testsuite_file_list, tools_lib_file_list,),\r\n 'code_tree': (testsuite_file_list, tools_lib_file_list,),\r\n 'tk_app_template': (testsuite_file_list, tools_lib_file_list),\r\n 'calculator': (testsuite_file_list, tools_lib_file_list),\r\n}\r\n\r\nreleased_tool_list = (\r\n ('up_tools', ('auto_helper', 'mmt_check', 'log_view', 'log_runner', 'robot_runner', 'script_runner',\r\n 'file_converter', 'up_main')),\r\n ('log_processor', ('extract_logs', 'file_converter', 'script_runner', 'log_view', 'log_collector',\r\n 'up_main')),\r\n ('code_tools', ('auto_helper', 'mmt_check', 'robot_runner', 'code_tree', 'up_main')),\r\n ('robot_runner', ('robot_runner', 'up_main')),\r\n ('calculator', ('calculator', 'robot_runner', 'up_main')),\r\n ('tk_app_template', ('tk_app_template', 'up_main')),\r\n)\r\n\r\n\r\ndef get_version(tool_name=None):\r\n import re\r\n import inspect\r\n base_version, sub_version, sub_version_count = '', '', 0\r\n up_root = os.path.abspath(os.path.dirname(__file__)) + '/../..'\r\n version_file_list = []\r\n if tool_name not in tool_main_file:\r\n tool_name = 'up_main'\r\n tool_name = tool_name or os.path.basename(inspect.stack()[1][1]).replace('.pyc', '').replace('.py', '')\r\n for file_list in tool_main_file[tool_name]:\r\n if file_list == ('up_lib_file_list_place_holder',):\r\n from test_base.version import Version\r\n base_version = Version().get_version()[0] + '.'\r\n else:\r\n for item in file_list:\r\n if type(item[0]) is str:\r\n version_file_list.append(item[0])\r\n for filename in version_file_list:\r\n if filename.endswith('.py'):\r\n filename = os.path.join(up_root, filename)\r\n if not os.path.exists(filename):\r\n filename += 'c'\r\n with open(filename, 'rb' if filename.endswith('pyc') else 'r') as rf:\r\n for line in rf:\r\n if filename.endswith('pyc') and sys.version_info.major != 2:\r\n line = str(line)\r\n m = re.match(r\"(..)?(\\d\\d\\d\\d)/(\\d\\d)/(\\d\\d)\", line)\r\n if m:\r\n version = m.group(2) + m.group(3) + m.group(4)\r\n if version > sub_version:\r\n sub_version = version\r\n sub_version_count = 1\r\n elif version == sub_version:\r\n sub_version_count += 1\r\n m = re.match(r\"\\sfrom tools\\.lib\\.(\\w+)\", line)\r\n if m and m.group(1) != 'make_tools_package':\r\n add_file = os.path.join(up_root, \"tools/lib/\" + m.group(1) + \".py\")\r\n if add_file not in version_file_list:\r\n version_file_list.append(add_file)\r\n if filename.endswith('pyc'):\r\n for module in re.findall(r\"tools\\.lib\\.([a-zA-Z_]+)[Rr]\", line):\r\n if module != 'make_tools_package':\r\n add_file = os.path.join(up_root, \"tools/lib/\" + module + \".py\")\r\n if add_file not in version_file_list:\r\n version_file_list.append(add_file)\r\n return base_version + sub_version + \".%d\" % sub_version_count\r\n\r\n\r\ndef update_up_lib_file_list(file_list):\r\n if 'up_lib_file_list_place_holder' in file_list:\r\n from test_base.version import file_list as up_lib_files\r\n file_list = [i for i in file_list if i != 'up_lib_file_list_place_holder']\r\n for filename, _ in up_lib_files + (('test_base/version.py', ''),):\r\n add_item = filename, ''\r\n if add_item not in file_list:\r\n file_list.append(add_item)\r\n return file_list\r\n\r\n\r\ndef make_package(tool_name=None, source_code=False):\r\n from test_base.common import find_all_files\r\n up_root = os.path.abspath(os.path.dirname(__file__)) + '/../..'\r\n robot_root = os.path.abspath(os.path.dirname(__file__)) + '/..' * 6\r\n\r\n for release_name, tool_list in released_tool_list:\r\n if tool_name and release_name not in tool_name and release_name != tool_name:\r\n continue\r\n with open(os.path.join(suite_root, 'tool_name.txt'), 'w') as wf:\r\n wf.write(release_name)\r\n # for tool, file_list, tool_list in up_tool_list:\r\n all_file_list = []\r\n for tool in tool_list:\r\n for file_list in tool_main_file[tool] + tool_depend_file[tool]:\r\n for item in file_list:\r\n if item not in all_file_list:\r\n all_file_list.append(item)\r\n num_of_lines = 0\r\n num_of_lines_detail = {}\r\n tool_zip_name = os.path.join(up_root, 'bin/' + release_name + '.zip')\r\n zip_obj = zipfile.ZipFile(tool_zip_name, 'w', zipfile.ZIP_DEFLATED)\r\n all_file_list = update_up_lib_file_list(all_file_list)\r\n for name, target_name in dict(all_file_list).items():\r\n # up_root = C:\\robotlte_trunk\\testsuite\\DCM\\libraries\\UP\\\r\n # case1: name = 'tools/auto_helper/auto_helper.py', arcname = ''\r\n # case2: name = ('../../../../resources/DCM/', '.robot$', 0), arcname = ''\r\n # case3: name = 'mmt_check.bat', arcname = '../../../../mmt_check.bat'\r\n if type(name) is str:\r\n name = (os.path.join(up_root, name),)\r\n else:\r\n name = find_all_files(os.path.join(up_root, name[0]), pattern=name[1], recurse=True, depth=name[2])\r\n for source_name in name:\r\n source_name = os.path.abspath(source_name)\r\n arc_name = target_name or source_name\r\n arc_name = release_name + '/' + os.path.relpath(os.path.join(up_root, arc_name), robot_root)\r\n if source_name.endswith('.py'):\r\n path = os.path.relpath(os.path.dirname(source_name), robot_root)\r\n if 'xlrd' not in path and 'xlwt' not in path:\r\n if sys.version_info.major == 2:\r\n with open(source_name) as rf:\r\n lines = len(rf.readlines())\r\n else:\r\n with open(source_name, encoding='utf8') as rf:\r\n lines = len(rf.readlines())\r\n num_of_lines_detail.setdefault(path, 0)\r\n num_of_lines_detail[path] += lines\r\n num_of_lines += lines\r\n if (not source_code and source_name.endswith('.py') and 'sample' not in source_name\r\n and '\\\\scripts\\\\' not in source_name\r\n and 'UP\\\\tools\\\\' in source_name):\r\n import compileall\r\n if sys.version_info.major != 2:\r\n compileall.compile_file(source_name, force=True, legacy=True)\r\n else:\r\n compileall.compile_file(source_name, force=True)\r\n source_name += 'c'\r\n arc_name += 'c'\r\n if 'psutil' in source_name:\r\n print(source_name, arc_name)\r\n zip_obj.write(source_name, arc_name)\r\n\r\n print(\"\\nTool: %s, code number of lines: %d\" % (release_name, num_of_lines))\r\n for folder, lines in sorted(num_of_lines_detail.items()):\r\n if lines > 0:\r\n print(\"\\t%s: %s\" % (folder, lines))\r\n zip_obj.close()\r\n with open(tool_zip_name + '.ver.txt', 'w') as wf, open(tool_zip_name, 'rb') as rf:\r\n for t in tool_list:\r\n wf.write(\"%s: %s\\n\" % (t, get_version(t)))\r\n wf.write(\"md5: %s\\n\" % hashlib.md5(rf.read()).hexdigest())\r\n if os.path.exists(DEFAULT_SHARED_FOLDER):\r\n bin_path = os.path.join(DEFAULT_SHARED_FOLDER, 'bin')\r\n if not os.path.exists(bin_path):\r\n os.mkdir(bin_path, 0o777)\r\n old_version_file = os.path.join(bin_path, release_name + '.zip.ver.txt')\r\n if os.path.exists(old_version_file):\r\n with open(old_version_file) as rf:\r\n for line in rf:\r\n if ':' in line:\r\n tool, old_version = line.rstrip(\"\\n\").split(\": \")\r\n if tool == 'md5':\r\n continue\r\n new_version = get_version(tool)\r\n print(new_version)\r\n old_version = [int(i) for i in old_version.split('.')]\r\n new_version = [int(i) for i in new_version.split('.')]\r\n if sorted([old_version, new_version]) != [old_version, new_version]:\r\n info = (\"The version of %s on shared_folder is newer!\\n%s\\n\"\r\n \"Update anyway?(y/n)\"\r\n % (tool, old_version_file))\r\n input_check = raw_input(info) if sys.version_info.major == 2 else input(info)\r\n if input_check not in ('y', 'Y'):\r\n raise Exception(info)\r\n for filename in (tool_zip_name, tool_zip_name + '.ver.txt'):\r\n shutil.copy(filename, os.path.join(DEFAULT_SHARED_FOLDER, 'bin'))\r\n print(\"\\nCopy %s files to shared_folder done.\\n\" % release_name)\r\n\r\n\r\nif __name__ == '__main__':\r\n # make_package(tool_name='log_processor')\r\n # make_package(tool_name='calculator')\r\n make_package()\r\n for t_name in tool_main_file:\r\n print(\"%s version: %s\" % (t_name, get_version(t_name)))\r\n" }, { "alpha_fraction": 0.4954545497894287, "alphanum_fraction": 0.5090909004211426, "avg_line_length": 25.5, "blob_id": "cf2576e6d85d626176eb7f79076b5ddadefcafd0", "content_id": "55afe31ad1498d70aac34a617c421254372568f9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 440, "license_type": "no_license", "max_line_length": 50, "num_lines": 16, "path": "/longest_sub_string.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "def length_of_longest_substring(s):\r\n start = 0\r\n max_len = 0\r\n max_start = 0\r\n c_index = {}\r\n for i, c in enumerate(s):\r\n if c in c_index:\r\n start = max(start, c_index[c] + 1)\r\n c_index[c] = i\r\n if i - start + 1 >= max_len:\r\n max_len = i - start + 1\r\n max_start = start\r\n return s[max_start:max_start+max_len]\r\n\r\n\r\nprint(length_of_longest_substring(\"aefbacbcedec\"))\r\n" }, { "alpha_fraction": 0.5473933815956116, "alphanum_fraction": 0.564150333404541, "avg_line_length": 42.75757598876953, "blob_id": "0e89522dca3a862d74f2208dd574a32520f3de24", "content_id": "06146b2a11731f9f15918b5aa701caf3a140eb50", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5908, "license_type": "no_license", "max_line_length": 120, "num_lines": 132, "path": "/make_tools_exe.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\r\n\"\"\"\r\n2018/01/10 first version by @author: [email protected]\r\n This is the file to make tools exe package automatically\r\n2018/01/16 update to support auto upgrade, by Ye Sheng\r\n2018/03/05 add tk_app_template, by Ye Sheng\r\n2018/03/05 add extract_logs, by Wang Yixiang\r\n2018/03/06 include pythonw.exe and python.exe into package, by Ye Sheng\r\n2018/03/06 use tool_name as tools subfolder, use up_main.exe as application name, by Ye Sheng\r\n2018/03/15 fix issue of packing additional files into target exe file, by Ye Sheng\r\n2019/03/29 change for python2 to python3 migration phase I, by Ye Sheng\r\n2019/04/24 add debug_flag, by Ye Sheng\r\n2019/07/05 add calculator as demo, by Ye Sheng\r\n\"\"\"\r\n\r\nimport os\r\nimport zipfile\r\nimport re\r\nimport sys\r\nimport shutil\r\nfrom make_tools_package import DEFAULT_SHARED_FOLDER\r\n\r\nroot = os.path.abspath(os.path.dirname(__file__) + '/../..')\r\nroot in sys.path and sys.path.remove(root)\r\nroot in sys.path or sys.path.insert(0, root)\r\nfrom test_base.common import find_all_files\r\n\r\n# input_file_pattern = r'C:\\robotlte_trunk\\testsuite\\DCM\\libraries\\UP\\{name}.zip'\r\n# output_file_pattern = r'C:\\robotlte_trunk\\testsuite\\DCM\\libraries\\UP\\{name}_exe.zip'\r\n\r\nPYTHON_PATH = r'c:\\python27' if sys.version_info.major == 2 else r'c:\\python36'\r\n\r\n\r\nmake_exe_cmd = (PYTHON_PATH + r'\\scripts\\pyinstaller ..\\up_main\\up_tools.py {console} -y '\r\n r'-n {app_name} '\r\n r'-i {lib_path}\\..\\up_main\\up_main.ico '\r\n r'--distpath {folder}\\dist '\r\n r'--workpath {folder}\\build '\r\n r'--specpath {folder}\\build')\r\n\r\ntools_list = [{'name': 'up_tools',\r\n 'input':\r\n ((r'.\\bin\\up_tools.zip', '..'),\r\n (PYTHON_PATH + r'\\lib\\site-packages\\matplotlib_one_folder.zip', '')),\r\n 'output': r'.\\bin\\up_tools_exe.zip'},\r\n\r\n {'name': 'robot_runner',\r\n 'input': ((r'.\\bin\\robot_runner.zip', '..'),),\r\n 'output': r'.\\bin\\robot_runner_exe.zip'},\r\n\r\n {'name': 'log_processor',\r\n 'input': ((r'.\\bin\\log_processor.zip', '..'),\r\n (PYTHON_PATH + r'\\lib\\site-packages\\matplotlib_one_folder.zip', '')),\r\n 'output': r'.\\bin\\log_processor_exe.zip'},\r\n\r\n {'name': 'code_tools',\r\n 'input': ((r'.\\bin\\code_tools.zip', '..'),),\r\n 'output': r'.\\bin\\code_tools_exe.zip'},\r\n\r\n {'name': 'tk_app_template',\r\n 'input': ((r'.\\bin\\tk_app_template.zip', '..'),),\r\n 'output': r'.\\bin\\tk_app_template_exe.zip'},\r\n\r\n {'name': 'calculator',\r\n 'input': ((r'.\\bin\\calculator.zip', '..'),),\r\n 'output': r'.\\bin\\calculator_exe.zip'},\r\n\r\n ]\r\n\r\n\r\ndef unzip_file(zipfilename, unziptodir, strip_level=0):\r\n if not os.path.exists(unziptodir):\r\n os.mkdir(unziptodir, 0o777)\r\n zfobj = zipfile.ZipFile(zipfilename)\r\n for name in zfobj.namelist():\r\n name = name.replace('\\\\', '/')\r\n new_name = name\r\n for i in range(strip_level):\r\n new_name = re.sub('^.?/', '', new_name)\r\n if new_name.endswith('/'):\r\n os.mkdir(os.path.join(unziptodir, new_name))\r\n elif new_name:\r\n ext_filename = os.path.join(unziptodir, new_name)\r\n ext_dir = os.path.dirname(ext_filename)\r\n if not os.path.exists(ext_dir):\r\n os.makedirs(ext_dir, 0o777)\r\n outfile = open(ext_filename, 'wb')\r\n outfile.write(zfobj.read(name))\r\n outfile.close()\r\n\r\n\r\ndef make_exe(tools=None, debug_flag=False):\r\n\r\n if type(tools) is not tuple:\r\n tools = (tools,)\r\n tmp_folder = os.environ[\"TMP\"]\r\n for tool in tools_list:\r\n tool_name = tool['name']\r\n if not tools or tool_name in tools:\r\n working_folder = os.path.join(tmp_folder, 'up_tools')\r\n tools_folder = os.path.join(tmp_folder, 'up_tools/dist/%s' % tool_name)\r\n print(\"Use pyinstaller to generate up_main.exe in folder: %s\" % tools_folder)\r\n console = '-c' if debug_flag else '-w'\r\n os.system(make_exe_cmd.format(folder=working_folder, app_name=tool_name, console=console,\r\n lib_path=os.path.dirname(__file__)))\r\n for input_file, sub_folder in tool.get('input', []):\r\n input_file = os.path.join(root, input_file)\r\n folder = os.path.join(tools_folder, sub_folder)\r\n print(\"Unzip %s to folder: %s\" % (input_file, folder))\r\n unzip_file(input_file, folder)\r\n # shutil.copy(PYTHON_PATH + r\"\\pythonw.exe\", tools_folder)\r\n # shutil.copy(PYTHON_PATH + r\"\\python.exe\", tools_folder)\r\n shutil.move(os.path.join(tools_folder, \"%s.exe\" % tool_name),\r\n os.path.join(tools_folder, \"up_main.exe\"))\r\n output_file = os.path.join(root, tool['output'])\r\n print(\"Create output file %s from %s\" % (output_file, tools_folder))\r\n target_zip_obj = zipfile.ZipFile(output_file, 'w', zipfile.ZIP_DEFLATED)\r\n for file_name in find_all_files(tools_folder, depth=-1):\r\n target_zip_obj.write(file_name, arcname=os.path.relpath(file_name, start=os.path.dirname(tools_folder)))\r\n target_zip_obj.close()\r\n if os.path.exists(DEFAULT_SHARED_FOLDER):\r\n shutil.copy(output_file, os.path.join(DEFAULT_SHARED_FOLDER, 'bin'))\r\n\r\nif __name__ == '__main__':\r\n make_exe('up_tools')\r\n # make_exe('robot_runner')\r\n # make_exe('tk_app_template')\r\n make_exe('log_processor', debug_flag=False)\r\n make_exe('code_tools', debug_flag=False)\r\n # make_exe('calculator', debug_flag=False)\r\n make_exe(debug_flag=False)\r\n print(\"out folder: %s\" % os.path.join(DEFAULT_SHARED_FOLDER, 'bin'))\r\n" }, { "alpha_fraction": 0.5515506863594055, "alphanum_fraction": 0.5616093873977661, "avg_line_length": 25.744186401367188, "blob_id": "ecf526ffb276686162dc4bb59ebc0c25204e7c92", "content_id": "02f3829a9741a09fabeab3034defee4f7e99ff5b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1193, "license_type": "no_license", "max_line_length": 68, "num_lines": 43, "path": "/longest_palindrome.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "def longest_palindrome(s):\r\n result = ''\r\n for i in range(len(s)):\r\n for j in range(i, len(s)):\r\n sub_str = s[i:j + 1]\r\n if is_palindrom(sub_str) and len(sub_str) > len(result):\r\n result = sub_str\r\n return result\r\n\r\n\r\ndef is_palindrom(s):\r\n if s == s[::-1]:\r\n return True\r\n return False\r\n\r\n\r\ndef find_palindrome(s, l, r):\r\n while l >= 0 and r < len(s) and s[l] == s[r]:\r\n l -= 1\r\n r += 1\r\n return s[l+1:r]\r\n\r\n\r\ndef longest_palindrome_optimize(s):\r\n result = ''\r\n for i in range(len(s)-1):\r\n if 2 * (len(s) - i) - 1 <= len(result):break\r\n temp_s = find_palindrome(s, i - 1, i + 1)\r\n if len(temp_s) > len(result): result = temp_s\r\n temp_s = find_palindrome(s, i, i + 1)\r\n if len(temp_s) > len(result): result = temp_s\r\n return result\r\n\r\n\r\nprint(longest_palindrome(\"abcbab\"))\r\nprint(longest_palindrome(\"aaaa\"))\r\nprint(longest_palindrome(\"aaaacc\"))\r\nprint(longest_palindrome(\"abvd\"))\r\n\r\nprint(longest_palindrome_optimize(\"abcbab\"))\r\nprint(longest_palindrome_optimize(\"aaaa\"))\r\nprint(longest_palindrome_optimize(\"aaaacc\"))\r\nprint(longest_palindrome_optimize(\"abcd\"))\r\n" }, { "alpha_fraction": 0.4127569794654846, "alphanum_fraction": 0.49393779039382935, "avg_line_length": 33.16666793823242, "blob_id": "1022fb76ac20587f7c74c1a98fa36b797b2146dd", "content_id": "d591c980a717f5d46d4990e85ce44c5c11923375", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1897, "license_type": "no_license", "max_line_length": 89, "num_lines": 54, "path": "/middle_number_of_two_list.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "def middle_number(s1, s2):\r\n a, b = sorted((s1, s2), key=len)\r\n len_a, len_b = len(a), len(b)\r\n len_half = int((len_a + len_b - 1) / 2)\r\n lo, hi = 0, len_a - 1\r\n while lo < hi:\r\n i = int((lo + hi) / 2)\r\n j = len_half - i - 1\r\n if a[i] >= b[j]:\r\n hi = i\r\n else:\r\n lo = i + 1\r\n second_middle_offset = (len_a + len_b + 1) % 2\r\n i = lo\r\n #if i == 0 and (len_half + 1 < len_b) and (a[i] > b[len_half + 1]):\r\n #result = b[len_half:len_half+2]\r\n if i == (len_a - 1) and (len_half - len_a >= 0) and (a[i] < b[len_half - len_a]):\r\n result = b[len_half - len_a: len_half - len_a + 2]\r\n else:\r\n result = sorted(a[i:i + 2] + b[len_half - i: len_half - i + 2])\r\n return (result[0] + result[second_middle_offset]) / 2\r\n\r\n\r\ndef generate_sequence(mask, size):\r\n s1 = []\r\n s2 = []\r\n for index in range(size):\r\n if mask & (0x1 << index):\r\n s1.append(index)\r\n else:\r\n s2.append(index)\r\n return s1, s2\r\n\r\n\r\ndef test(size):\r\n for mask in range(1, (0x1 << size) - 1):\r\n s1, s2 = generate_sequence(mask, size)\r\n expected = (size - 1) / 2\r\n actual = middle_number(s1, s2)\r\n if expected != actual:\r\n print(s1)\r\n print(s2)\r\n print(\"Expected:%.1f, Actual:%.1f\" % (expected, actual))\r\n return\r\n\r\n\r\ntest(10)\r\ntest(9)\r\nprint(\"Expected:2, Actual:\", middle_number([2], [1,3])) #2\r\nprint(\"Expected:2.5, Actual:\", middle_number([1,2], [3,4])) #2, 3\r\nprint(\"Expected:16, Actual:\", middle_number([1,12,15,26,38], [2,13,17,30,45])) #15, 17\r\nprint(\"Expected:17, Actual:\", middle_number([1,12,15,26,38], [2,13,17,30,45,50])) #17\r\nprint(\"Expected:10.5, Actual:\", middle_number([1,2,5,6,8], [13,17,30,45,50])) #8, 13\r\nprint(\"Expected:9.5, Actual:\", middle_number([1,2,5,6,8,9,10], [13,17,30,45,50])) #9, 10" } ]	8
leoneckert/people-also-bought	https://github.com/leoneckert/people-also-bought	7bd8bdb89ab70ccb5b090e28ac981b223e0a1342	611b30ae215936efb6c46008d6019a059cdc3e71	5b00540dd6f524659daa7a82ec59282d8cf2b1cd	refs/heads/master	2021-01-19T09:51:41.010205	2017-02-16T06:33:18	2017-02-16T06:33:18	82,144,203	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5988826751708984, "alphanum_fraction": 0.6067039370536804, "avg_line_length": 33.71844482421875, "blob_id": "659e94f3eed294d7a3337bf8322a040da0fb80ba", "content_id": "d12c84fa4750f9c5d8137be9edfb937360f75c00", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3580, "license_type": "no_license", "max_line_length": 96, "num_lines": 103, "path": "/amazonImageChain.py", "repo_name": "leoneckert/people-also-bought", "src_encoding": "UTF-8", "text": "from selenium import webdriver\nimport time, os, sys, random, urllib\n# import random\n# import ConfigParser\n# from bs4 import BeautifulSoup\n# import urllib\n# from selenium.webdriver.support.ui import WebDriverWait # available since 2.4.0\n\ndriver = None\noutput_dir = None\n\ndef type_word(input_elem, word):\n for letter in word:\n input_elem.send_keys(letter)\n # time.sleep(0.1)\n\ndef open_amazon():\n global driver\n driver = webdriver.Firefox()\n url = \"https://amazon.com\"\n driver.get(url)\n\ndef make_output_dir(word):\n global output_dir\n ts = int(time.time())\n output_dir = str(ts) + \"_\" + word\n if not os.path.isdir(output_dir):\n os.mkdir(output_dir)\n\ndef navigate_to_first_item(word):\n global driver\n search_element = driver.find_element_by_css_selector(\"#twotabsearchtextbox\")\n type_word(search_element, word) \n search_button = driver.find_element_by_css_selector(\"div.nav-search-submit input.nav-input\")\n search_button.click()\n time.sleep(2)\n results = driver.find_elements_by_css_selector(\"a.s-access-detail-page\")\n random_result = random.choice(results)\n random_result.click()\n\ndef get_main_image(count):\n global driver\n global output_dir\n # image = None\n # image = driver.find_element_by_css_selector(\"img#landingImage\")\n # url = image.get_attribute(\"src\")\n # urllib.urlretrieve(url, output_dir + \"/\" + \"{:06d}\".format(count) + \".jpg\")\n try:\n image = driver.find_element_by_css_selector(\"img#landingImage\")\n url = image.get_attribute(\"src\")\n urllib.urlretrieve(url, output_dir + \"/\" + \"{:06d}\".format(count) + \".jpg\")\n except:\n print \"landingImage didnt work\"\n try:\n image = driver.find_element_by_css_selector(\"img#landingimage\")\n url = image.get_attribute(\"src\")\n urllib.urlretrieve(url, output_dir + \"/\" + \"{:06d}\".format(count) + \".jpg\")\n except:\n print \"landingimage didnt work\"\n try:\n image = driver.find_element_by_css_selector(\"img#imgBlkFront\")\n url = image.get_attribute(\"src\")\n urllib.urlretrieve(url, output_dir + \"/\" + \"{:06d}\".format(count) + \".jpg\")\n except:\n print \"imgBlkFront didnt work\"\n\ndef open_next_item():\n global driver\n the_link = None\n headlines = driver.find_elements_by_css_selector(\"h2.a-carousel-heading\")\n for headline in headlines:\n if headline.text == \"Customers Who Bought This Item Also Bought\":\n # if headline.text == \"Sponsored Products Related To This Item\":\n parent = headline.find_element_by_xpath('./..')\n items = parent.find_elements_by_css_selector(\"li.a-carousel-card\")\n while len(items) == 0:\n parent = parent.find_element_by_xpath('./..')\n items = parent.find_elements_by_css_selector(\"li.a-carousel-card\")\n print \"in while loop\"\n random_item = random.choice(items)\n the_link = random_item.find_element_by_css_selector(\"a.a-link-normal\")\n break\n print the_link\n driver.get(the_link.get_attribute(\"href\"))\n\nif __name__ == \"__main__\":\n open_amazon()\n time.sleep(2)\n query = sys.argv[1]\n make_output_dir(query)\n navigate_to_first_item(query)\n time.sleep(5)\n get_main_image(0)\n time.sleep(2)\n count = 1\n while True:\n open_next_item()\n time.sleep(2)\n get_main_image(count)\n time.sleep(2)\n count += 1\n # if count > 10:\n # break\n\n\n\n\n" }, { "alpha_fraction": 0.761904776096344, "alphanum_fraction": 0.761904776096344, "avg_line_length": 20, "blob_id": "4b6bec773e3ed97a8370e495678e4b5865047d50", "content_id": "6993d9e051d276fce51bc45b775e43812f544880", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 21, "license_type": "no_license", "max_line_length": 20, "num_lines": 1, "path": "/README.md", "repo_name": "leoneckert/people-also-bought", "src_encoding": "UTF-8", "text": "# people-also-bought\n" } ]	2
kaiwei12014/git-test	https://github.com/kaiwei12014/git-test	9577d23fcaa6f4949794b6433df88c0612e15035	2045409b639c9d72e12ce72b7d24268104d5032e	36d0020eca917acbf02d064aaabc019c7f559a61	refs/heads/master	2020-03-14T22:44:12.046763	2018-05-09T08:37:40	2018-05-09T08:37:40	131,827,810	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7419354915618896, "alphanum_fraction": 0.7419354915618896, "avg_line_length": 14.5, "blob_id": "1ce18459e9f0dc987554af9674cef230f1506c02", "content_id": "62f4efc48679cfa071f10382bef3acaa73e1e0bc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 31, "license_type": "no_license", "max_line_length": 20, "num_lines": 2, "path": "/hello.py", "repo_name": "kaiwei12014/git-test", "src_encoding": "UTF-8", "text": "print('hello world')\nhi python\n" } ]	1
wenyaxinluoyang/statistical-learning-methods	https://github.com/wenyaxinluoyang/statistical-learning-methods	9864a33ff6edcbe094a47998174da2d9623e86d2	a417921d0ea3e9596b58092b0cea270de5dfee78	0f119f7189538ddf8434311047d00021d252561c	refs/heads/master	2020-09-07T01:08:54.989708	2020-03-06T10:25:35	2020-03-06T10:25:35	220,611,134	2	0	null	null	null	null	null	[ { "alpha_fraction": 0.5364238619804382, "alphanum_fraction": 0.5492173433303833, "avg_line_length": 33.78010559082031, "blob_id": "2c832a8bfbcf3df01aeac68d73133adfdadbea9a", "content_id": "fefb1edfe794b5738731e6c8e8732aededce9281", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7398, "license_type": "no_license", "max_line_length": 108, "num_lines": 191, "path": "/chapter_five/CART.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\nimport pickle\n\n'''\n分类与回归树 (classification and regression tree)\n是二叉树\n回归树：用平方误差最小化准则\n分类树：用基尼指数最小化准则\n基尼指数：\nsum(pk * (1-pk) = 1 - sum(pkpk)\n某个样本点被错误分类的概率.\n样本点属于第k类的概率为pk，它被分错的概率为(1-pk)\n对于二类分类问题，若样本点属于第1个类的概率为p\nGini = p(1-p) + (1-p)[1-(1-p)] = p(1-p) + p(1-p) = 2p(1-p)\n'''\n\nimport math\nimport pandas as pd\nimport numpy as np\n\nINF = float('inf')\n\nclass Node:\n def __init__(self, fea_name, split_value):\n self.fea_name = fea_name\n self.split_value = split_value\n self.left = None\n self.right = None\n self.output = None\n\n# 平方误差\ndef square(value1, value2):\n return math.pow((value1-value2),2 )\n\n# 构建回归树, 特征是连续型变量，输出也是连续型变量\ndef build_regression_tree(df, target, thresold):\n if df is None: return None\n if df.empty: return None\n # 样本量小于某个数的时候停止分裂\n if df.shape[0]<=thresold: return None\n columns = df.columns.values.tolist()\n columns.remove(target)\n if columns is None: return None\n min_square_loss = INF # 最小平方损失\n split_col = None # 切分变量\n split_value = None # 切分值\n left_data = None # 左训练集\n right_data = None # 右训练集\n for col in columns:\n values = df[col].values.tolist()\n length = len(values)\n target_values = df[target].values.tolist()\n if len(list(set(values)))==1: continue\n for value in list(set(values)):\n left_target = [target_values[i] for i in range(length) if values[i]<=value]\n right_target = [target_values[i] for i in range(length) if values[i]>value]\n if len(left_target)==0 or len(right_target)==0: continue\n aver1 = np.mean(left_target)\n aver2 = np.mean(right_target)\n sum1 = sum2 = 0\n for y in left_target: sum1 += square(y, aver1)\n for y in right_target: sum2 += square(y, aver2)\n if sum1 + sum2 < min_square_loss:\n split_col = col\n split_value = value\n left_data = df[df[col] <= value]\n right_data = df[df[col] > value]\n if split_col is None: return None\n tree = Node(split_col, split_value)\n print(split_col, split_value)\n # 递归构建左右子树\n tree.output = np.average(df[target].values.tolist())\n tree.left = build_regression_tree(left_data, target, thresold)\n tree.right = build_regression_tree(right_data, target, thresold)\n return tree\n\n\n# 构建分类树, 特征是离散的\ndef build_class_tree(df, set_x, target):\n if df is None: return None\n if df.empty: return None\n # 如果某个数据集中，所有分类都为同一分类，则不需再划分,因为对于任何特征，其基尼系数都是0\n target_values = df[target].values.tolist()\n if len(set(target_values)) == 1:\n node = Node('', '')\n node.output = target_values[0]\n return node\n columns = df.columns.values.tolist()\n columns.remove(target)\n min_gini = INF\n fea_name = None\n split_value = None\n left_df = None\n right_df = None\n for col in columns:\n value_of_col = set_x[col]\n values = df[col].values.tolist()\n if len(list(set(values))) == 1:\n continue\n # 枚举特征col可取的每一个值\n for value in value_of_col:\n df1 = df[df[col] == value]\n df2 = df[df[col] != value]\n fm1 = df1.shape[0]df1.shape[0]\n fm2 = df2.shape[0]df2.shape[0]\n temp1 = df1[target].value_counts().reset_index()\n temp2 = df2[target].value_counts().reset_index()\n sum = 0\n for _, count in zip(temp1['index'].values.tolist(), temp1[target].values.tolist()):\n sum += (countcount)/fm1\n gini1 = 1 - sum\n sum = 0\n for _, count in zip(temp2['index'].values.tolist(), temp2[target].values.tolist()):\n sum += (countcount)/fm2\n gini2 = 1 - sum\n gini = df1.shape[0]/df.shape[0]gini1 + df2.shape[0]/df.shape[0]gini2\n #print(f'特征值={col}; 取值={value}; 基尼指数={gini_df_col}')\n if gini < min_gini:\n min_gini = gini\n fea_name = col\n split_value = value\n left_df = df1\n right_df = df2\n if fea_name is None: return None\n tree = Node(fea_name, split_value)\n tree.output = target_counts = df[target].value_counts()[0]\n tree.left = build_class_tree(left_df, set_x, target)\n tree.right = build_class_tree(right_df, set_x, target)\n return tree\n\ndef get_data():\n set_x = {\n \"age\":('青年','中年', '老年'),\n \"have_job\":('是', '否'),\n \"have_house\":('是', '否'),\n \"credit_detail\":('一般', '好', '非常好')}\n set_y = ['是', '否']\n age = ['青年','青年','青年','青年','青年', '中年', '中年', '中年', '中年', '中年', '老年', '老年', '老年', '老年', '老年']\n have_job = ['否', '否', '是', '是', '否', '否', '否', '是', '否', '否', '否', '否', '是', '是', '否']\n have_house = ['否', '否', '否', '是', '否', '否', '否', '是', '是', '是', '是', '是', '否', '否', '否']\n credit_detail = ['一般', '好', '好', '一般', '一般', '一般', '好', '好', '非常好', '非常好', '非常好', '好', '好', '非常好', '一般']\n y = ['否', '否', '是', '是', '否', '否', '否', '是', '是', '是', '是', '是', '是', '是', '否']\n df = pd.DataFrame()\n df['age'] = age\n df['have_job'] = have_job\n df['have_house'] = have_house\n df['credit_detail'] = credit_detail\n df['Y'] = y\n return df, set_x, set_y\n\ndef display(tree, level=1):\n if tree is not None:\n print(f'特征名:{tree.fea_name} 特征划分值:{tree.split_value} 最大分类:{tree.output} 层次:{level}')\n if tree.left is not None:\n display(tree.left, level+1)\n if tree.right is not None:\n display(tree.right, level+1)\n\ndef regression_predict(test_data, root):\n predict_target = []\n for index, row in test_data.iterrows():\n temp = root\n output = 0\n while temp is not None:\n value = row[temp.fea_name]\n output = temp.output\n if value <= temp.split_value: temp = temp.left\n else: temp = temp.right\n predict_target.append(output)\n test_data['predict_target'] = predict_target\n return test_data\n\n\nif __name__ == '__main__':\n df, set_x, set_y = get_data()\n # train_data, test_data = get_data2()\n # tree = build_regression_tree(train_data, target='pm2.5', thresold=50)\n # print('build tree success')\n # f = open('./data/model.pkl', 'wb')\n # pickle.dump(tree, f)\n # display(tree)\n # f = open('./data/model.pkl', 'rb')\n # tree = pickle.load(f)\n # result = regression_predict(test_data, tree)\n # result = regression_predict(test_data, tree)\n # result.to_csv('result.csv', index=False)\n # result.to_csv('./data/result2.csv')\n #df, set_x, set_y = get_data()\n tree = build_class_tree(df, set_x, 'Y')\n display(tree)\n\n" }, { "alpha_fraction": 0.5418760180473328, "alphanum_fraction": 0.5690954923629761, "avg_line_length": 21.349056243896484, "blob_id": "02227cf129cd10d136e6f38c1542f31ba264e01f", "content_id": "ef1053e06090d888d91daebd8016d1561814ca2a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3136, "license_type": "no_license", "max_line_length": 70, "num_lines": 106, "path": "/chapter_two/Perceptron.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\nimport numpy as np\nimport random\n'''\n李航：《统计学习方法》第二章数据理论的代码实现\n感知机学习算法的原始形式\n\n模型模型:\nyi = sign(wxi + b)\nsign(t) t>0, sign(t)=1; t<0, sign(t)=-1\n解决方案：\nwx + b = 0\n找到一个超平面，把正实例点和负实例点划分到超平面两侧使得\ny(i)=+1 wx(i)+b>0\ny(i)=-1 wx(i)+b<0\n\n损失函数，误分类点到超平面的距离\n对误分类的点来说，-y(i)(wx(i)+b) > 0 \n因此误分类点xi到超平面的距离是\n-1/\|w\|y(i)(wx(i))+b)\n\nM是误分类点的集合\nsign(wx + b)的损失函数为\nxi属于M\nyi取值-1,1 \nL(w,b) = -SUM(yi* (wxi + b)) \n\n损失函数中w,b未知，我们要找到使损失函数最小的模型参数w,b，即感知机的模型\n\n使用梯度下降不断地极小化目标函数，极小话过程中不是一次使M中所有误分类点的梯度下降，\n而是一次随机选取一个误分类点使其梯度下降：\n对w求偏导： -SUM(yixi)\n对b求偏导： -SUM(yi)\n\n设学习速率为learning_rate（0，1], 随机选择\nw + learningyixi) -> w\nb + learningyi) -> b\n'''\n\n# f(x) = sign(x) x>0返回1，x<0返回-1\ndef sign(value):\n if value>0: return 1\n elif value<0: return -1\n\n# 确定参数后，就有模型函数\ndef func(x, w, b):\n return sign(np.dot(w.T,x) + b)\n\n# 求解最佳模型参数\ndef perceptron(x_list, y_list, learning_rate, w, b):\n # w, b = init_params()\n # 前提是训练集线性可分，否则感知机算法不收敛\n count = 0\n while True:\n error_x_list, error_y_list = error_point(x_list, y_list, w, b)\n if len(error_x_list) == 0:\n break\n # 进行梯度下降算法\n w, b = SGD(learning_rate, error_x_list, error_y_list, w, b)\n count = count + 1\n print('第', count , '次梯度下降: w =', w, ', b =', b)\n return w,b\n\n# 获取当前模型的误分类点\ndef error_point(x_list, y_list, w, b):\n y_predict_list = []\n for x in x_list:\n y_predict_list.append(func(x, w, b))\n error_x_list = []\n error_y_list = []\n for x, y, y_predict in zip(x_list, y_list, y_predict_list):\n if y != y_predict:\n error_x_list.append(x)\n error_y_list.append(y)\n return error_x_list, error_y_list\n\n# 梯度下降, 随机选取一个误判点进行梯度下降\ndef SGD(learning_rate, error_x_list, error_y_list, w, b):\n index = random.randint(0, len(error_x_list)-1)\n x = error_x_list[index]\n y = error_y_list[index]\n w = w + learning_rateyx\n b = b + learning_ratey\n return w,b\n\nif __name__ == '__main__':\n x1 = np.array([3,3]).reshape(2,1)\n x2 = np.array([4,3]).reshape(2,1)\n x3 = np.array([1,1]).reshape(2,1)\n x_list = [x1, x2, x3]\n y_list = [1, 1, -1]\n w = np.zeros((2,1))\n b = 0\n learning_rate = 1\n w, b = perceptron(x_list, y_list, learning_rate, w, b)\n print(w, b)\n\n\n'''\n算法的解有： \nw = (1,1) b = -3\nw = (2,1) b = -5\nw = (1,0) b = -2\nw = (3,1) b = -5\n感知机学习算法由于采用不同的初值或选择不同的误分类点，解可以不同\n'''\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.6157380938529968, "alphanum_fraction": 0.6214818954467773, "avg_line_length": 27.557376861572266, "blob_id": "1076499703e3d25bcf85f6bb02177eb0e1e4a57c", "content_id": "f8b717648a9922a6501e5610646feafa45a317fc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1861, "license_type": "no_license", "max_line_length": 114, "num_lines": 61, "path": "/chapter_eight/AdaBoost.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "import math\nimport numpy as np\n\n\n# 基础分类器\ndef basic_classify_pred(am_list, feature):\n pass\n\n\n# 分类误差率\ndef classify_missing_rate(classifier, weights, fea_mat, label_mat, am_list):\n summary = 0\n row_num, col_num = np.shape(fea_mat)\n for i in range(row_num):\n is_equal = 1 if basic_classify_pred(am_list, fea_mat[i]) != label_mat[i] else 0\n summary += weights[i] is_equal\n return summary\n\n# 计算基础分类器的系数\ndef get_classify_coefficient(em):\n am = 0.5math.log((1-em)/em)\n return am\n\n# 计算规范化因子\ndef get_normal_factor(classifier, weights, am, fea_mat, label_mat, am_list):\n zm = 0\n row_num, col_num = np.shape(fea_mat)\n for i in range(row_num):\n zm += weights[i] math.exp(-am * label_mat[i] * basic_classify_pred(am_list, fea_mat[i]))\n return zm\n\n# 更新权重\ndef update_weight(classifier, weights, em, am, zm, fea_mat, label_mat, am_list):\n length = len(weights)\n new_weights = []\n for i in range(length):\n new_weights[i] = (weights[i]/zm) * math.exp(-am * label_mat[i] * basic_classify_pred(am_list, fea_mat[i]))\n weights = new_weights\n return new_weights,\n\n\n# AdaBoost算法分类\ndef AdaBoost_Classify(fea_mat, label_mat, m):\n row_num, col_num = np.shape(fea_mat)\n # 初始化训练数据的权重分布，开始每个样本的权重都一样\n weights = np.array([1/row_num]row_num)\n cnt = 0\n am_list = []\n while cnt < m:\n em = classify_missing_rate(None, weights, fea_mat, label_mat, am_list)\n am = get_classify_coefficient(em)\n am_list.append(am)\n zm = get_normal_factor(None, weights, am, fea_mat, label_mat, am_list)\n weights = update_weight(None, weights, em, am, zm, fea_mat, label_mat, am_list)\n cnt += 1\n return am_list\n\n\n\nif __name__ == '__main__':\n pass" }, { "alpha_fraction": 0.5523401498794556, "alphanum_fraction": 0.5707395672798157, "avg_line_length": 31.160919189453125, "blob_id": "125e7e04e2b9139023de6d011b8cd56c06aabaf6", "content_id": "22b07ed35dd0b991043e486013cbc2dbf33bf296", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6200, "license_type": "no_license", "max_line_length": 75, "num_lines": 174, "path": "/chapter_three/KdTree.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\n'''\n实现kd树算法\n'''\n\nimport numpy as np\nimport math\nimport copy\n\nINF = float('inf') # 无穷大\n\n# 节点，里面应该有属于它的样本点，左孩子，右孩子\nclass Node:\n def __init__(self, point, split_value, dimension_index):\n self.point = point\n self.split_value = split_value\n self.dimension_index = dimension_index\n self.left = None\n self.right = None\n\n# 递归构建kd树\ndef build_kd_tree(x_list, k, depth=0):\n if len(x_list)==0: return None\n dimension_index = depth%k # 维度索引\n x_list.sort(key=lambda x: x[dimension_index][0])\n split_index = len(x_list)//2 # 切分点索引\n point = x_list[split_index] # 切分点\n split_value = point[dimension_index][0] # 切分值\n left_list = x_list[0: split_index]\n right_list = x_list[split_index+1: len(x_list)]\n root = Node(point, split_value, dimension_index) # 创建根节点, 随后递归构建左右子树\n root.left = build_kd_tree(left_list, k, depth+1)\n root.right = build_kd_tree(right_list, k, depth+1)\n return root # 返回当前根节点\n\n# 打印二叉树\ndef display(root, level=0, note='root'):\n if root is None: return\n print('level =', level, 'note =', note)\n print('对第', root.dimension_index, '维，以该坐标轴的中位数为分点，以垂直与该维，并过切分点的超平面做划分')\n print('切分值为:', root.split_value)\n print('落在该超平面上的点有:')\n point = root.point\n print(point)\n print(''50)\n display(root.left, level+1, note='left') # 打印左子树\n display(root.right, level+1, note='right') # 打印右子树\n\n# p>=1，当p=2，称为欧氏距离\n# p=1, 称为曼哈顿距离\n# p趋于正无穷, 它氏各个坐标距离的最大值\ndef distance(x1, x2, p):\n sum = 0\n for vector1, vector2 in zip(x1, x2):\n v1 = vector1[0]\n v2 = vector2[0]\n sum += math.pow(abs(v1-v2), p)\n return math.pow(sum, 1/p) # 开p次方根\n\n\n# 寻找目标叶子节点\ndef find_target_leaf_node(root, target):\n nearest_dis, nearest_node = INF, None\n node = copy.deepcopy(root)\n path = []\n while node is not None:\n path.append(node)\n point = node.point # 切分空间的超平面上的点\n dis = distance(point, target, 2)\n if dis < nearest_dis:\n nearest_dis = dis\n nearest_node = node\n dimension_index = node.dimension_index\n split_value = node.split_value\n if target[dimension_index][0] <= split_value:\n node = node.left\n else:\n node = node.right\n return nearest_dis, nearest_node, path\n\n\ndef find_target_leaf_node_two(root, target, k=1):\n k_nearest = [(distance(root.point, target, 2), root)]\n node = copy.deepcopy(root)\n path = []\n while node is not None:\n path.append(node)\n k_nearest.sort(key=lambda dis: dis[0]) # 按照距离从小到大排序\n point = node.point\n dis = distance(point, target, 2)\n if dis < k_nearest[-1][0]:\n if len(k_nearest)<k: k_nearest.append((dis, node))\n else:\n k_nearest.pop()\n k_nearest.append((dis, node))\n dimension_index = node.dimension_index\n split_value = node.split_value\n if target[dimension_index][0]<= split_value:\n node = node.left\n else:\n node = node.right\n return k_nearest, path\n\n# kd树搜索，搜索最近邻\ndef kd_tree_search_nearest(root, target):\n # 先找到目标叶子节点\n nearest_dis, nearest_node, path = find_target_leaf_node(root, target)\n # 回溯\n while len(path)!=0:\n node = path.pop()\n split_value = node.split_value\n dimension_index = node.dimension_index\n # 目标点与当前最近点距离的半径的圆与其另一边的子空间有交点\n if abs(target[dimension_index][0]-split_value) < nearest_dis:\n # 原来进入的是左子树，则需要搜索右子树\n if target[dimension_index][0]<=split_value:\n temp_node = node.right\n else:\n temp_node = node.left\n if temp_node is not None:\n path.append(temp_node)\n dis = distance(target, temp_node.point, 2)\n if dis < nearest_dis:\n nearest_dis = dis\n nearest_node = temp_node\n return nearest_dis, nearest_node\n\n# 寻找target的k近邻, 比最近邻多了一个排序数组\ndef kd_tree_search_knearest(root, target, k):\n k_nearest, path = find_target_leaf_node_two(root, target, k)\n while(len(path)) != 0:\n node = path.pop()\n split_value = node.split_value\n dimension_index = node.dimension_index\n k_nearest.sort(key=lambda dis: dis[0])\n if abs(target[dimension_index][0]-split_value) < k_nearest[-1][0]:\n if target[dimension_index][0]<=split_value:\n temp_node = node.right\n else:\n temp_node = node.left\n if temp_node is not None:\n path.append(temp_node)\n dis = distance(target, temp_node.point, 2)\n if dis < k_nearest[-1][0]:\n if len(k_nearest)<k: k_nearest.append((dis, temp_node))\n else:\n k_nearest.pop()\n k_nearest.append((dis, temp_node))\n return k_nearest\n\n# 获取例3.2数据\ndef example_three_two():\n x1 = np.array([2,3]).reshape((2,1))\n x2 = np.array([5,4]).reshape((2,1))\n x3 = np.array([9,6]).reshape((2,1))\n x4 = np.array([4,7]).reshape((2,1))\n x5 = np.array([8,1]).reshape((2,1))\n x6 = np.array([7,2]).reshape((2,1))\n x_list = [x1, x2, x3, x4, x5, x6]\n return x_list\n\n\nif __name__ == '__main__':\n x_list = example_three_two()\n root = build_kd_tree(x_list, k=2)\n # display(root)\n target = np.array([1,5]).reshape((2,1))\n k_nearest = kd_tree_search_knearest(root, target, k=3)\n # 求距离target最近的前k个点\n for item in k_nearest:\n print('distance:', item[0])\n print('point:', item[1].point)\n print('-'50)\n\n\n" }, { "alpha_fraction": 0.5549180507659912, "alphanum_fraction": 0.5699453353881836, "avg_line_length": 33.00934600830078, "blob_id": "7dbb40c90b995b0592e7d73dd27be66256cf5050", "content_id": "f2a98e4569c85013f98ad4b3f25f22e6caed53e2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4144, "license_type": "no_license", "max_line_length": 103, "num_lines": 107, "path": "/chapter_four/naive_bayes.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\nimport pandas as pd\nimport numpy as np\n'''\n朴素贝叶斯算法\n\nP(A)是 A 的先验概率，之所以称为“先验”是因为它不考虑任何 B 方面的因素。\nP(A\|B)是已知 B 发生后 A 的条件概率，也由于得自 B 的取值而被称作 A 的后验概率。\nP(B\|A)是已知 A 发生后 B 的条件概率，也由于得自 A 的取值而被称作 B 的后验概率。\nP(B)是 B 的先验概率，也作标淮化常量（normalizing constant）\n'''\n\n# probability\n# 计算先验概率\n# lambda_value 为 0的时候是极大似然估计，为1时是拉普拉斯平滑\ndef proba_of_class(y_list, set_y, lambda_value):\n total = len(y_list)\n count_of_class = {key:0 for key in set_y}\n for y in y_list:\n count_of_class[y] = count_of_class[y] + 1\n probability_of_class = {}\n for value, count in count_of_class.items():\n probability_of_class[value] = (count+lambda_value)/(total+len(set_y)lambda_value)\n return count_of_class, probability_of_class\n\n\n# 计算条件概率, 在y=ck时，特征向量的第i个特征有j个取值\n# 计算先验概率和条件概率\n# set_x 每维特征的取值集合\ndef condition_proba(df, set_x, set_y, lambda_value):\n # 在分类为c的条件下，计算第j个特征取每个值的概率\n count_of_class, probability_of_class = proba_of_class(df['Y'].values.tolist(), set_y, lambda_value)\n columns = df.columns.values.tolist()\n columns.remove('Y')\n condition_proba_dict = {y: [] for y in set_y}\n for class_name in set_y:\n # 把分类为class_name的特征向量聚合在一起\n data = df[df.Y == class_name]\n array = []\n for col_id, column in enumerate(columns):\n proba = dict()\n value_count = {value:0 for value in set_x[col_id]}\n values = data[column].value_counts()\n values = values.reset_index()\n values['index'] = values['index'].astype(type(set_x[col_id][0]))\n total = 0\n for _, row in values.iterrows():\n value = row['index']\n count = row[column]\n total = total + count\n value_count[value] = count\n for value, count in value_count.items():\n proba[value] = (count + lambda_value)/(total+len(set_x[col_id])lambda_value)\n array.append(proba)\n condition_proba_dict[class_name] = array\n return probability_of_class, condition_proba_dict\n\n\n# 预测\ndef predict(x, set_x, probability_of_class, condition_proba_dict):\n class_list = list(probability_of_class.keys())\n target = None\n max_proba = 0\n for c in class_list:\n mul = 1\n for i, value in enumerate(x):\n proba = condition_proba_dict[c][i]\n mul = mul * proba[value]\n mul = mul * probability_of_class[c]\n print(mul)\n if mul > max_proba:\n max_proba = mul\n target = c\n return max_proba, target\n\n\n# 书本例4.1数据\ndef get_data():\n df = pd.DataFrame()\n df['X1'] = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3]\n df['X2'] = ['S', 'M', 'M', 'S', 'S', 'S', 'M', 'M', 'L', 'L', 'L', 'M', 'M', 'L', 'L']\n df['Y'] = [-1,-1,1,1,-1,-1,-1,1,1,1,1,1,1,1,-1]\n return df\n\n\n\nif __name__ == '__main__':\n df = get_data()\n set_x = [(1,2,3), ('S', 'M', 'L')]\n set_y = [-1, 1]\n lambda_value = 1\n probability_of_class, condition_proba_dict = condition_proba(df, set_x, set_y, lambda_value)\n for c in set_y:\n print(f'P(Y={c}) =', probability_of_class[c])\n for c in set_y:\n proba_list = condition_proba_dict[c]\n for index,proba in enumerate(proba_list):\n for key,value in proba.items():\n print(f'在分类{c}下, 第{index+1}个特征，取值为{key}的概率为{value}')\n print(''20)\n print('-'20)\n test_x = [2, 'S']\n print(predict(test_x, set_x, probability_of_class, condition_proba_dict))\n #condition_proba(x_list, y_list)\n # count_of_class, probability_of_class = proba_of_class(y_list)\n # print(count_of_class, probability_of_class)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5602409839630127, "alphanum_fraction": 0.5674698948860168, "avg_line_length": 19.268293380737305, "blob_id": "b2fdaa8db2d5b48af12ba9d284d63dbce1267284", "content_id": "09d1a05647a9bdb645f494e118eea9f9331cbb4f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 880, "license_type": "no_license", "max_line_length": 80, "num_lines": 41, "path": "/chapter_seven/Kernel.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "import numpy as np\nimport math\n\n# 核函数\nclass Kernel_Func():\n\n # 计算\n def calculate(self, x, z):\n pass\n\n # p代表范数\n def normalForm(x, y, p):\n dis_value = 0\n for x_value, y_value in zip(x, y):\n dis_value += math.pow(y_value - x_value, p)\n return math.pow(dis_value, 1 / p)\n\n# 多项式核函数\nclass Polynomial_Kernel_Func(Kernel_Func):\n\n def __init(self, p):\n self.__p = p\n\n def calculate(self, x, z,):\n return math.pow(x z + 1, self.__p)\n\n\n# 高斯核函数\nclass Gaussian_Kernel_Func(Kernel_Func):\n\n def __init__(self, sigma):\n self.__sigma = sigma\n\n def calculate(self, x, z):\n return math.exp(-self.normalForm(x, z, 2)/(2self.__sigmaself.__sigma))\n\n# 线性核函数\nclass Linear_Kernel_Func(Kernel_Func):\n\n def calculate(self, x, z):\n return np.dot(x,z)[0]" }, { "alpha_fraction": 0.503125011920929, "alphanum_fraction": 0.5625, "avg_line_length": 16.66666603088379, "blob_id": "bdd415b0805bea7b784047d82f982b5daf1a084c", "content_id": "eb2ce8f4c099fa5cae6662f47b17a1712a163ebf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 402, "license_type": "no_license", "max_line_length": 40, "num_lines": 18, "path": "/chapter_three/KNN.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\n'''\nk近邻算法\n'''\n\nimport math\n\n# p>=1，当p=2，称为欧氏距离\n# p=1, 称为曼哈顿距离\n# p趋于正无穷, 它氏各个坐标距离的最大值\ndef distrance(self, x1, x2, p):\n sum = 0\n for vector1, vector2 in zip(x1, x2):\n v1 = vector1[0]\n v2 = vector2[0]\n sum += math.pow(abs(v1-v2), p)\n return math.pow(sum, 1/p) # 开p次方根\n\n\n" }, { "alpha_fraction": 0.5119877457618713, "alphanum_fraction": 0.5383438467979431, "avg_line_length": 35.53416061401367, "blob_id": "693d7838928ecc8efb95969cc6c4975625961310", "content_id": "8fcdcd2d2298c44b6486dbbaa5c200be5f4cf352", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6471, "license_type": "no_license", "max_line_length": 124, "num_lines": 161, "path": "/chapter_seven/SVM.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "import random\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport copy\nfrom chapter_seven.Kernel import \n\n\n# 随机获取一个不等i的索引号\ndef get_index_random(i, size):\n j = i\n while i == j:\n j = random.randint(0, size-1)\n return j\n\n# 获取a2_new的上下界\ndef get_alpha_limit(a1_old, a2_old, y1, y2, c):\n if y1 != y2:\n low, high = max(0, a2_old - a1_old), min(c, c + a2_old - a1_old)\n else:\n low, high = max(0, a2_old + a1_old - c), min(c, a2_old + a1_old)\n return low, high\n\n# 其中g(x) = sum(aiyiK(xi,x)) + b\ndef func_g(alphas, b, fea_mat, label_list, index, kernel_func):\n label_mat = np.array(label_list).reshape(len(label_list), 1)\n alphas_mat = np.array(alphas).reshape(len(alphas), 1)\n temp = alphas_mat label_mat # alphas_mat 和 label_mat均是列向量，此处向量temp为两向量对应位置想成\n array = []\n current_sample = fea_mat[index]\n for sample in fea_mat:\n t = kernel_func.calculate(current_sample, sample.reshape((-1, 1)))\n array.append(t)\n result = np.dot(np.array(array), temp)[0] + b\n return result\n\n# Ei = g(xi) - yi,\ndef get_e(alphas, b, fea_mat, label_list, index, kernel_func):\n result = func_g(alphas, b, fea_mat, label_list, index, kernel_func)\n return result - label_list[index]\n\n\n# 序列最小化算法(sequential minimal optimization, SMO)\ndef smo(fea_mat, label_mat, c=1, epsilon=1e-5, max_iter=20, kernel_func=Linear_Kernel_Func()):\n label_list = [label[0] for label in label_mat]\n row_num, col_num = fea_mat.shape\n # 初始化拉格朗日乘子\n alphas = np.zeros((row_num, 1)) # row_num 行，1列的数据\n # 初始化偏置\n b = 0\n iter_cnt = 0\n while iter_cnt < max_iter:\n # 外层循环代表选择第一个变量\n for i in range(row_num):\n gi = func_g(alphas, b, fea_mat, label_list, i, kernel_func)\n ei = gi - label_list[i]\n '''\n 选取的a1, 应该违反KKT条件（李航统计学习方法，说挑选违反KKT条件最严重的一个）\n 停机条件，停机：所有拉格朗日乘子满足KKT条件\n yig(xi) >= 1 , ai = 0\n yig(xi) = 1 , 0 < ai < c\n yig(xi) <= 1, ai = c\n 若ai < c, 则 0 <= ai < c, 若满足KKT条件则 yig(xi) >= 1, 考虑精度, yig(xi)>=1-epsilon\n 则需要违反KKT条件，则yig(xi)<1-epsilon, 则 yig(xi)-1 < -epsilon\n 若ai > 0, 则 0 < ai <=c, 若满足KKT条件则 yig(xi) <= 1, 考虑精度，yig(xi)<=1+epsilon\n 则需要违反KKT条件，则yig(xi)>1+epsilon, 则 yig(xi)-1 > epsilon\n '''\n if (label_list[i]ei<-epsilon and alphas[i]<c) or (label_list[i]ei>epsilon and alphas[i]>0):\n '''\n 挑选a2, 李航统计学习方法，说挑选 abs(e1 - e2)最大的，在这里是随机挑选的，\n 当然也可以计算所有的e, 然后选abs(e1 - e2)最大的作为a2\n '''\n j = get_index_random(i, row_num)\n gj = func_g(alphas, b, fea_mat, label_list, j, kernel_func)\n ej = gj - label_list[j]\n\n alpha_i_old = alphas[i]\n alpha_j_old = alphas[j]\n\n # 计算alpha_j_new的取值范围\n low, high = get_alpha_limit(alpha_i_old, alpha_j_old, label_list[i], label_list[j], c)\n\n kii = kernel_func.calculate(fea_mat[i], fea_mat[i].reshape((-1,1)))\n kij = kernel_func.calculate(fea_mat[i], fea_mat[j].reshape((-1,1)))\n kjj = kernel_func.calculate(fea_mat[j], fea_mat[j].reshape((-1,1)))\n\n alpha_j_new = alpha_j_old + label_list[j](ei - ej)/(kii + kjj + 2kij)\n if alpha_j_new < low: alpha_j_new = low\n if alpha_j_new > high: alpha_j_new = high\n\n if abs(alpha_j_new - alpha_j_old) < 1e-6: continue\n\n alpha_i_new = alpha_i_old + label_list[i]label_list[j](alpha_j_old - alpha_j_new)\n\n\n # 更新两个拉格朗日乘子\n alphas[i] = alpha_i_new\n alphas[j] = alpha_j_new\n\n # 更新b的值\n bi_new = -ei - label_list[i]kii(alpha_i_new-alpha_i_old) - label_list[j]kij(alpha_j_new-alpha_j_old) + b\n bj_new = -ej - label_list[i]kij(alpha_i_new-alpha_i_old) - label_list[j]kjj(alpha_j_new-alpha_j_old) + b\n if 0<alpha_i_new<c and 0<alpha_j_new<c:\n b = bi_new\n else:\n b = (bi_new + bj_new)/2\n iter_cnt += 1\n # 由w = sum(aiyixi)\n w = np.zeros(fea_mat[0].shape) # 行向量\n for i in range(row_num):\n w += alphas[i]label_list[i]fea_mat[i]\n # 转为列向量\n w = w.reshape((-1,1))\n return alphas, w, b\n\n\n# 数据集1，线性可分\ndef get_data1():\n # 创建40个点\n fea_mat = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]\n label_mat = np.array([-1]20 + [1]20).reshape((40, 1))\n # plt.scatter(fea_mat[:, 0], fea_mat[:, 1], c = [label[0] for label in label_mat])\n # plt.show()\n return fea_mat, label_mat\n\n# 显示数据集1的效果\ndef show_effect1():\n fea_mat, label_mat = get_data1()\n alphas, w, b = smo(fea_mat, label_mat, max_iter=200)\n print(w)\n print(b)\n plt.scatter(fea_mat[:, 0], fea_mat[:, 1], c=[label[0] for label in label_mat])\n x_list = list(range(-5, 5, 1))\n y_list = [(-w[0][0] * x - b) / w[1][0] for x in x_list]\n plt.plot(x_list, y_list, label='分隔超平面', color='red')\n plt.show()\n\n# 数据集2，近似线性可分\ndef get_data2():\n fea_mat = np.r_[np.random.randn(20,2), np.random.randn(20,2) + [2,2]]\n label_mat = np.array([-1]20 + [1]20).reshape((-1, 1))\n # plt.scatter(fea_mat[:, 0], fea_mat[:, 1], c = [label[0] for label in label_mat])\n # plt.show()\n return fea_mat, label_mat\n\ndef show_effect2():\n fea_mat, label_mat = get_data2()\n alphas, w, b = smo(fea_mat, label_mat, max_iter=200)\n print(w)\n print(b)\n plt.scatter(fea_mat[:, 0], fea_mat[:, 1], c=[label[0] for label in label_mat])\n x_list = list(range(-5, 5, 1))\n y_list = [(-w[0][0] * x - b) / w[1][0] for x in x_list]\n plt.plot(x_list, y_list, label='分隔超平面', color='red')\n plt.show()\n\n\nif __name__ == '__main__':\n # show_effect1()\n # get_data2()\n show_effect2()" }, { "alpha_fraction": 0.5426052808761597, "alphanum_fraction": 0.5514201521873474, "avg_line_length": 21.173913955688477, "blob_id": "33566cca9f6474f44fdddfffdc005ec4b05b2b63", "content_id": "f6e448e5536641a88526a5676aaefd6725151132", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1113, "license_type": "no_license", "max_line_length": 56, "num_lines": 46, "path": "/chapter_one/loss_function.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\n'统计学常用的损失函数'\n\n\nclass ZeroOne:\n '''\n 0-1，实际值和预测值相等，返回1，否则返回0\n '''\n # 计算代价函数\n def cal_loss(self, y_actual, y_predict):\n cost = 0\n for actual, predict in zip(y_actual, y_predict):\n cost = self.cal(actual, predict)\n return cost\n\n def cal(self, actual, predict):\n if actual == predict:\n return 0\n else:\n return 1\n\nclass Quadratic:\n '''\n 平方损失函数\n '''\n def cal_loss(self, y_actual, y_predict):\n cost = 0\n for actual, predict in zip(y_actual, y_predict):\n cost += self.cal(actual, predict)\n return cost\n\n def cal(self, actual, predict):\n return (actual-predict)(actual-predict)\n\nclass Absolute:\n '''\n 绝对损失函数\n '''\n def cal_loss(self, y_actual, y_predict):\n cost = 0\n for actual, predict in zip(y_actual, y_predict):\n cost = cost + self.cal(actual, predict)\n\n def cal(self, actual, predict):\n return abs(actual-predict)\n\n" }, { "alpha_fraction": 0.5271453857421875, "alphanum_fraction": 0.5487449169158936, "avg_line_length": 26.612903594970703, "blob_id": "b7cf50261284c961159311af5aefd943be3a8034", "content_id": "17f9a4c64c1f68cbce358e712ccb04ac492649e3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1853, "license_type": "no_license", "max_line_length": 85, "num_lines": 62, "path": "/chapter_two/DualPerceptron.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\nimport numpy as np\nimport random\n'''\n感知机学习算法的对偶形式\n考虑每个样本点更新的次数，那么现在次数变成我们的参数\n'''\n\ndef sign(value):\n if value>0: return 1\n elif value<0: return -1\n\n# 计算xj xi 备用\ndef cal_gram_matrix(x_list):\n size = len(x_list)\n matrix = np.zeros((size, size))\n for i in range(size):\n for j in range(size):\n matrix[i][j] = np.dot(x_list[i].T, x_list[j])\n return matrix\n\ndef func(x_list, x_index, y_list, n_list, learning_rate, matrix):\n result = 0\n temp = 0\n size = len(x_list)\n for j in range(size):\n result = result + n_list[j]learning_ratey_list[j]matrix[j][x_index]\n temp = temp + n_list[j]learning_ratey_list[j] # b的值\n result = result + temp\n return result\n\n# 感知机学习算法的对偶形式\ndef dual_perceptron(x_list, y_list, n_list, learning_rate, matrix):\n count = 0\n while True:\n size = len(x_list)\n flag = False # 标记是否存在误分类点\n for i in range(size):\n if y_list[i]func(x_list, i, y_list, n_list, learning_rate, matrix) <= 0:\n n_list[i] += 1\n flag = True\n break\n if flag==False:\n break\n count = count + 1\n print('第', count, '次迭代:')\n print(n_list)\n print('-'50)\n return n_list\n\nif __name__ == '__main__':\n x1 = np.array([3, 3]).reshape(2, 1)\n x2 = np.array([4, 3]).reshape(2, 1)\n x3 = np.array([1, 1]).reshape(2, 1)\n x_list = [x1, x2, x3]\n y_list = [1, 1, -1]\n n_list = [0, 0, 0]\n learning_rate = 1\n matrix = cal_gram_matrix(x_list)\n n_list = dual_perceptron(x_list, y_list, n_list, learning_rate, matrix)\n print('params (n_list:', n_list, ', learning_rate: ', learning_rate, ')')\n\n" }, { "alpha_fraction": 0.5398218035697937, "alphanum_fraction": 0.5476190447807312, "avg_line_length": 30.920000076293945, "blob_id": "8d963b74530b91d11d953b42a0d400b001348533", "content_id": "fc6661b71e6f47759a54f477c603ff969243b871", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7904, "license_type": "no_license", "max_line_length": 108, "num_lines": 225, "path": "/chapter_five/decision_tree.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\n'决策树'\nimport pandas as pd\nimport math\nimport copy\n\n\n# 树的节点\nclass Node():\n def __init__(self, sign, fea_name, fea_value, data):\n self.fea_name = fea_name\n self.sign = sign\n self.fea_value = fea_value\n self.child_list = []\n self.data = data\n\n\n\n# 信息增益\ndef igain(x_list, value_of_x, y_list, value_of_y, hy):\n return hy - condition_entropy(x_list, value_of_x, y_list, value_of_y)\n\n# 信息增益比\ndef igain_ratio(x_list, value_of_x, y_list, value_of_y, hy):\n h = condition_entropy(x_list, value_of_x, y_list, value_of_y)\n hx, value_count = entropy(x_list, value_of_x)\n return (hy-h)/hx\n\n# 计算条件熵\ndef condition_entropy(x_list, value_of_x, y_list, value_of_y):\n hx, value_count = entropy(x_list, value_of_x) # x的经验熵，以及x取各种值的计数\n temp = {x: [] for x in value_of_x} # 每种x的取值下，对应的y值\n for x,y in zip(x_list, y_list):\n temp[x].append(y)\n total_x = len(x_list)\n h = 0\n for x, y_belong_x in temp.items():\n # 计算X=x的条件下, y的经验熵\n hyx, value_count_y = entropy(y_belong_x, value_of_y)\n h = h + (value_count[x]/total_x)hyx\n return h\n\n# 计算经验熵\ndef entropy(value_list, value_of_x):\n value_count = {value:0 for value in value_of_x}\n total = len(value_list)\n for value in value_list:\n value_count[value] = value_count[value] + 1\n h = 0\n for value in value_of_x:\n count = value_count[value]\n p = count/total\n if count == 0: continue\n else: h = h + pmath.log(p, 2)\n h = -h\n return h, value_count\n\n\n# 特征的信息增益比\ndef igain_of_fea(df, set_x, set_y):\n columns = df.columns.values.tolist()\n columns.remove('Y')\n # 每种特征的信息增益\n fea_infor_gain = {col:0 for col in columns}\n y_list = df['Y'].values.tolist()\n hy, value_count_y = entropy(y_list, set_y)\n for col in columns:\n fea_infor_gain[col] = igain(df[col].values.tolist(), set_x[col], y_list, set_y, hy)\n print(fea_infor_gain)\n print('Y的熵', hy)\n return fea_infor_gain\n\ndef igain_ratio_of_fea(df, set_x, set_y):\n columns = df.columns.values.tolist()\n columns.remove('Y')\n # 每种特征的信息增益\n fea_infor_ratio_gain = {col: 0 for col in columns}\n y_list = df['Y'].values.tolist()\n hy, value_count_y = entropy(y_list, set_y)\n for col in columns:\n fea_infor_ratio_gain[col] = igain_ratio(df[col].values.tolist(), set_x[col], y_list, set_y, hy)\n print(fea_infor_ratio_gain)\n print('Y的熵', hy)\n return fea_infor_ratio_gain\n\ndef get_data():\n set_x = {\n \"age\":('青年','中年', '老年'),\n \"have_job\":('是', '否'),\n \"have_house\":('是', '否'),\n \"credit_detail\":('一般', '好', '非常好')}\n set_y = ['是', '否']\n age = ['青年','青年','青年','青年','青年', '中年', '中年', '中年', '中年', '中年', '老年', '老年', '老年', '老年', '老年']\n have_job = ['否', '否', '是', '是', '否', '否', '否', '是', '否', '否', '否', '否', '是', '是', '否']\n have_house = ['否', '否', '否', '是', '否', '否', '否', '是', '是', '是', '是', '是', '否', '否', '否']\n credit_detail = ['一般', '好', '好', '一般', '一般', '一般', '好', '好', '非常好', '非常好', '非常好', '好', '好', '非常好', '一般']\n y = ['否', '否', '是', '是', '否', '否', '否', '是', '是', '是', '是', '是', '是', '是', '否']\n df = pd.DataFrame()\n df['age'] = age\n df['have_job'] = have_job\n df['have_house'] = have_house\n df['credit_detail'] = credit_detail\n df['Y'] = y\n return df, set_x, set_y\n\n\n# 使用ID3构建决策树\ndef ID3(df, set_x, set_y, threshold):\n if df.empty: return None\n y_unique = df['Y'].values.tolist()\n # 训练数据集所有实例都属于同一类，则为单节点树\n if len(set(y_unique)) == 1:\n tree = Node(y_unique[0], None, None, df)\n return tree\n # 特征集是空集\n feature_names = df.columns.values.tolist()\n feature_names.remove('Y')\n temp = df['Y'].value_counts()\n temp = temp.reset_index()\n max_count_y = temp.loc[0]['index']\n if len(feature_names) == 0:\n tree = Node(max_count_y, None, None, df)\n return tree\n fea_infor_gain = igain_of_fea(df, set_x, set_y)\n max_infor_gain = 0\n chose_fea = None\n for key,value in fea_infor_gain.items():\n if value > max_infor_gain:\n max_infor_gain = value\n chose_fea = key\n # 最大信息增益小于阈值\n if max_infor_gain < threshold:\n tree = Node(max_count_y, None, None, df)\n return tree\n tree = Node(max_count_y, chose_fea, None, df)\n for index, data in df.groupby([chose_fea]):\n sub_df = data.drop([chose_fea], axis=1)\n child = ID3(sub_df, set_x, set_y, threshold)\n child.fea_value = index\n tree.child_list.append(child)\n return tree\n\n\n# 使用C4.5算法构建生成树, 用信息增益比来选择特征\ndef C45(df, set_x, set_y, threshold):\n if df.empty: return None\n y_unique = df['Y'].values.tolist()\n # 训练数据集所有实例都属于同一类\n if len(set(y_unique)) == 1:\n tree = Node(y_unique[0], None, None, df)\n return tree\n feature_names = df.columns.values.tolist()\n feature_names.remove('Y')\n temp = df['Y'].value_counts()\n temp = temp.reset_index()\n max_count_y = temp.loc[0]['index']\n # 特征集是空集\n if len(feature_names) == 0:\n tree = Node(max_count_y, None, None, df)\n return tree\n fea_infor_ratio_gain = igain_ratio_of_fea(df, set_x, set_y)\n max_infor_gain = 0\n chose_fea = None\n for key, value in fea_infor_ratio_gain.items():\n if value > max_infor_gain:\n max_infor_gain = value\n chose_fea = key\n if max_infor_gain < threshold:\n tree = Node(max_count_y, None, None, df)\n return tree\n tree = Node(max_count_y, chose_fea, None, df)\n for index, data in df.groupby([chose_fea]):\n sub_df = data.drop([chose_fea], axis=1)\n child = C45(sub_df, set_x, set_y, threshold)\n child.fea_value = index\n tree.child_list.append(child)\n return tree\n\n\ndef display(tree, level=1):\n if tree is not None:\n print('第', level, '层')\n print('选取的特征是:', tree.fea_name)\n print('上一层特征值:', tree.fea_value)\n print('该特征下，最大的分类:', tree.sign)\n print('='50)\n for child in tree.child_list:\n display(child, level+1)\n\n# 剪枝\ndef cut_branch(tree, alfa, set_y):\n if tree is not None:\n # 计算该节点的经验熵\n data = tree.data\n h, value_count = entropy(data['Y'].values.tolist(), set_y)\n result = data.shape[0]h\n if len(tree.child_list) != 0:\n sum = 0\n leaf_node_num = 0\n for child in tree.child_list:\n value, num, node = cut_branch(child, alfa, set_y)\n sum += value\n leaf_node_num += num\n # sum为当前节点统领下的叶子节点的\n if result+alfa < sum+alfaleaf_node_num:\n tree.child_list = [] # 进行减枝\n return result, 1, tree\n else:\n return sum, leaf_node_num, tree\n else: # 是叶子节点\n return result, 1, tree\n\n\nif __name__ == '__main__':\n df, set_x, set_y = get_data()\n igain_of_fea(df, set_x, set_y)\n igain_ratio_of_fea(df, set_x, set_y)\n tree = ID3(df, set_x, set_y, 0.001)\n #tree = C45(df, set_x, set_y, 0.001)\n display(tree)\n #cost, is_leaf, tree = cut_branch(tree, 0.5, set_y)\n #print('-'10, '减枝后的树', '-'10)\n #display(tree)\n # infor_gain_ratio_of_fea(df, set_x, set_y)\n" }, { "alpha_fraction": 0.5555555820465088, "alphanum_fraction": 0.5944444537162781, "avg_line_length": 19.05555534362793, "blob_id": "194179e1042aaadb4ab3e8ba8e949abf769211bc", "content_id": "dee2375fdeed78022ff49ac454e20529d1e66856", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 370, "license_type": "no_license", "max_line_length": 56, "num_lines": 18, "path": "/chapter_five/temp.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "import math\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# 绘制 f(x) = xlog(x)的曲线\n\ndef func(x):\n return -xmath.log(x, 2)\n\ndef draw_func():\n x_list = np.linspace(0.00001, 1, 200, endpoint=True)\n y_list = [func(x) for x in x_list]\n plt.plot(x_list, y_list, lw=2.0, label='xlog(x,2)')\n plt.show()\n\n\nif __name__ == '__main__':\n draw_func()" } ]	12
shan-mathi/InterviewBit	https://github.com/shan-mathi/InterviewBit	c94e091f728b9d18d55e86130756824a3637a744	6688e4ff54d56cf75297bb72ce67926b40e45127	f85cc3fb482f1b71e7a749e1bcdbe90ba78fd059	refs/heads/main	2023-06-29T10:43:29.712472	2021-08-05T19:06:53	2021-08-05T19:06:53	364,321,855	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.4197530746459961, "alphanum_fraction": 0.4444444477558136, "avg_line_length": 26.7391300201416, "blob_id": "9e9e8f259c284dc8b2e64d1212e494dbfe389e93", "content_id": "45dd20d6e5c8044a6c4c7606cd74e304f3da72bb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 648, "license_type": "no_license", "max_line_length": 62, "num_lines": 23, "path": "/array_peak.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @return an integer\n def perfectPeak(self, A):\n n = len(A)\n\n prefix = [0]n\n suffix = [0]n\n prefix[0] = A[0]\n suffix[-1]=A[-1]\n\n for i in range(1,n):\n prefix[i] = max(prefix[i-1], A[i])\n #print(prefix)\n \n for i in reversed(range(n-1)):\n suffix[i] = min(suffix[i+1], A[i])\n #print(suffix)\n for i in range(1,n-1):\n if prefix[i]==A[i] and prefix[i-1]!=prefix[i]:\n if A[i]==suffix[i] and suffix[i+1]!=suffix[i]:\n return 1\n return 0\n\n \n" }, { "alpha_fraction": 0.3187499940395355, "alphanum_fraction": 0.36666667461395264, "avg_line_length": 15.821428298950195, "blob_id": "597ce47377f8f3f29dc961d0f6a143f0584d63e1", "content_id": "8e5bb21fbf15606341d500e683753437e3b31cf8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 480, "license_type": "no_license", "max_line_length": 51, "num_lines": 28, "path": "/roman_to_integer.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\n\n#brute force\ndef roman_to_integer(n):\n\n ans=0\n\n hash = {'M':1000,\n 'D':500,\n 'C':100,\n 'L':50,\n 'X':10,\n 'V':5,\n 'I':1}\n i=0\n while( i< len(n)):\n if i+1<len(n) and hash[n[i+1]]> hash[n[i]]:\n ans+= hash[n[i+1]] - hash[n[i]]\n i+=2\n else:\n ans+= hash[n[i]]\n i+=1\n return ans\n\n\n\nn = str(input())\nprint(roman_to_integer(n))\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.430402934551239, "alphanum_fraction": 0.430402934551239, "avg_line_length": 27.526315689086914, "blob_id": "0bf5d528f32e389e9a4ab53e919eb7af88abb08b", "content_id": "2e412934e3f72da1a04c83e714129b675529d427", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 546, "license_type": "no_license", "max_line_length": 49, "num_lines": 19, "path": "/level_order tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": " def level_order(self):\n if self is None:\n return []\n queue = [root]\n next_queue=[]\n level=[]\n result = []\n while not queue:\n for root in queue:\n level.append(root.data)\n if root.left:\n next_queue.append(root.left)\n if root.right:\n next_queue.append(root.right)\n result.append(level)\n level=[]\n queue=next_queue[:]\n next_queue=[]\n return result\n" }, { "alpha_fraction": 0.4240986704826355, "alphanum_fraction": 0.42979127168655396, "avg_line_length": 22.863636016845703, "blob_id": "70fe8d48dde64e8cf5f44069e63a722625ff0141", "content_id": "7a2d4da4481d0decc3cb3cb6a11e521409fb8054", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1054, "license_type": "no_license", "max_line_length": 52, "num_lines": 44, "path": "/palindrome_partitions_BT.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def palindrom_part(s):\n ans=[]\n\n def all_pallindromes(A):\n \"\"\"\n using the most optimal method i.e O(sqrt(n))\n\n :param A: a number\n :return: list of all its factors\n \"\"\"\n ans = []\n for i in range(len(A)):\n for j in range(i + 1, len(A) + 1):\n if A[i:j] == A[i:j][::-1]:\n ans.append(A[i:j])\n return ans\n\n all = all_pallindromes(s)\n\n def back_track( subset=[], booll=[0]len(all)):\n ss = \"\".join(subset)\n\n if ss ==s:\n if subset not in ans:\n ans.append(subset[:])\n return\n\n for i in range(len(all)):\n if not booll[i]:\n subset.append(all[i])\n booll[i]=1\n else:\n continue\n if len(\"\".join(subset))<=len(s):\n back_track(subset, booll)\n\n booll[i]=0\n subset.pop()\n back_track()\n return (ans)\n\nif __name__ ==\"__main__\":\n s = str(input())\n print(palindrom_part(s))\n\n\n\n\n" }, { "alpha_fraction": 0.40445268154144287, "alphanum_fraction": 0.41372913122177124, "avg_line_length": 24.66666603088379, "blob_id": "8ec66dd724af007b9a99d0e4725cf25f9401391b", "content_id": "10e47de734edfc5bac11f4c8334c532bfb639c72", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 539, "license_type": "no_license", "max_line_length": 36, "num_lines": 21, "path": "/version_master.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "#trick is to use split function\n\nclass Solution:\n # @param A : string\n # @param B : string\n # @return an integer\n def compareVersion(self, A, B):\n a = A.split('.')\n b = B.split('.')\n if len(a)>len(b):\n b+=['0'](len(a)-len(b))\n else:\n a+=['0'](len(b)-len(a))\n for i,j in zip(a,b):\n if int(i)==int(j):\n continue\n elif int(i)>int(j):\n return 1\n elif int(i)<int(j):\n return -1\n return 0\n" }, { "alpha_fraction": 0.5515947341918945, "alphanum_fraction": 0.5628517866134644, "avg_line_length": 18, "blob_id": "20c9ebce3238540676519c3a234bc5304b9692a9", "content_id": "21560e728058160a5b9ae872ce74d67e9e99132e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1066, "license_type": "no_license", "max_line_length": 61, "num_lines": 56, "path": "/threading_queue.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import deque\nimport time\nimport threading\n\nclass Queue():\n def __init__(self):\n self.Q = deque()\n def dequeue(self):\n return self.Q.pop()\n def enqueue(self,val):\n self.Q.appendleft(val)\n def size(self):\n return len(self.Q)\n def peek(self):\n return self.Q[-1]\n def is_empty(self):\n return len(self.Q)==0\n\nQ = Queue()\ndef Place_order(order):\n for order in orders:\n print(\"Placing order for:\",order)\n\n Q.enqueue(order)\n time.sleep(0.5)\n\ndef Serve_order():\n time.sleep(1)\n while not Q.is_empty():\n order = Q.dequeue()\n print(\"Now serving \",order)\n time.sleep(1)\n\n\n\n\n\n\nif __name__ == \"__main__\":\n t = time.time()\n orders = ['pizza','samosa','pasta','biryani','burger']\n\n T1 = threading.Thread(target=Place_order, args=(orders,))\n T2 = threading.Thread(target=Serve_order)\n\n\n\n T1.start()\n\n T2.start()\n\n T1.join()\n T2.join()\n\n print(\"done in : \", time.time() - t)\n print(\"Hah... I am done with all my work now!\")\n\n\n" }, { "alpha_fraction": 0.5994194746017456, "alphanum_fraction": 0.6037735939025879, "avg_line_length": 30.31818199157715, "blob_id": "140b91e6e41aa0ea2f1235651b30107fba87a9ce", "content_id": "08c9466ceef13c36e14381707a7f5f2aefb6e1bd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 689, "license_type": "no_license", "max_line_length": 70, "num_lines": 22, "path": "/coverPoints.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def coverPoints(A, B):\n \"\"\"\n my approach. since it can go diagonal, we can shift the point\n in such a way it needs to travel only the longest side of the \n rectange. hence we keep adding the longest side values.\n \n :param A: x coordinates of all the points\n :param B: y coordinates of all the y points\n :return: minimum distance to reach to all the diestination points\n \"\"\"\n l = len(A)\n dist = []\n for i in range(1, l):\n x_dis = abs(A[i] - A[i - 1])\n y_dis = abs(B[i] - B[i - 1])\n dist.append(max(x_dis, y_dis))\n return sum(dist)\n\n\nA = list(map(int,input().split()))\nB = list(map(int,input().split()))\nprint(coverPoints(A,B))\n" }, { "alpha_fraction": 0.46038252115249634, "alphanum_fraction": 0.4617486298084259, "avg_line_length": 23.518518447875977, "blob_id": "3fe21116e7c837af99dbde835a36f04d014423b1", "content_id": "eae33eedbcbfe307f1fd45a879b61a5dfe5b68b4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 732, "license_type": "no_license", "max_line_length": 55, "num_lines": 27, "path": "/Construct Binary Tree From Inorder And Preorder.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n#\tdef __init__(self, x):\n#\t\tself.val = x\n#\t\tself.left = None\n#\t\tself.right = None\n\nclass Solution:\n\t# @param A : list of integers\n\t# @param B : list of integers\n\t# @return the root node in the tree\n\tdef buildTree(self, pre_o, in_o):\n\n if not in_o:\n return None\n \n for i in pre_o:\n if i in in_o:\n v = i\n pre_o.remove(i)\n break\n \n root = TreeNode(v)\n id = in_o.index(v)\n root.left = self.buildTree(pre_o, in_o[:id])\n root.right = self.buildTree(pre_o, in_o[id+1:])\n return root\n \n \n \n \n \n \n\t \n\t \n" }, { "alpha_fraction": 0.44800883531570435, "alphanum_fraction": 0.4502212405204773, "avg_line_length": 22.421052932739258, "blob_id": "700d4f5178163574c39219a6bfd002069e60ec14", "content_id": "971ee7ced7d1b31437e35e32a3fb281f2a700584", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 904, "license_type": "no_license", "max_line_length": 43, "num_lines": 38, "path": "/partition_linked_lists.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n#\tdef __init__(self, x):\n#\t\tself.val = x\n#\t\tself.next = None\n\nclass Solution:\n\t# @param A : head node of linked list\n\t# @param B : integer\n\t# @return the head node in the linked list\n\tdef partition(self, A, B):\n\n\t \n\t if A.val>= B:\n\t prev = ListNode(None)\n\t prev.next = A\n\n else:\n prev = A\n\t itr = A\n\t set =0\n while itr.next:\n if itr.next.val<B and set:\n \n temp = itr.next.next\n itr.next.next= prev.next\n prev.next = itr.next\n itr.next =temp\n if itr.next.val<B and not set:\n itr = itr.next\n \n else:\n set=1\n itr = itr.next\n if prev.val ==None:\n return prev.next\n else:\n return prev\n\t \n" }, { "alpha_fraction": 0.43167027831077576, "alphanum_fraction": 0.4403470754623413, "avg_line_length": 27.8125, "blob_id": "36ef93b8902b0e7b0ffe19ce5fa93104ad72f6df", "content_id": "d1e32478c82e5aae41449930868d49369205b80d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 461, "license_type": "no_license", "max_line_length": 53, "num_lines": 16, "path": "/prefix_equation_stacks.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import deque\nclass Solution:\n # @param A : list of strings\n # @return an integer\n def evalRPN(self, A):\n \n stack = deque()\n for i in A:\n if i not in [\"+\",\"-\",\"\",\"/\"]:\n stack.append(i)\n else:\n val1 = stack.pop()\n val2 = stack.pop()\n sol = str(int(eval(val2 + i + val1)))\n stack.append(sol)\n return stack.pop()\n" }, { "alpha_fraction": 0.29588431119918823, "alphanum_fraction": 0.3270300328731537, "avg_line_length": 22.657894134521484, "blob_id": "1775291c316c06d91b5e5e95155e39e3523d179b", "content_id": "53d9bc02aab193da0705d64efb7a2d5f7cc84362", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 899, "license_type": "no_license", "max_line_length": 64, "num_lines": 38, "path": "/three_arrays.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "INT_MAX = 2147483647\nclass Solution:\n # @param A : tuple of integers\n # @param B : tuple of integers\n # @param C : tuple of integers\n # @return an integer\n def minimize(self, A,B,C):\n i,j,k=0,0,0\n #a,b,c=0,0,0\n res= INT_MAX\n while( i<len(A) and j<len(B) and k<len(C)):\n if min(A[i],B[j],C[k]) == A[i]:\n a=1\n b=0\n c=0\n elif min(A[i],B[j],C[k]) == B[j]:\n b=1\n a=0\n c=0\n else:\n c=1\n a=0\n b=0\n \n if max(A[i],B[j],C[k]) - min(A[i],B[j],C[k]) <= res:\n res = max(A[i],B[j],C[k]) - min(A[i],B[j],C[k])\n \n if a:\n i+=1\n elif b:\n j+=1\n elif c:\n k+=1\n \n \n \n \n return res\n" }, { "alpha_fraction": 0.4378698170185089, "alphanum_fraction": 0.44378697872161865, "avg_line_length": 21.86206817626953, "blob_id": "8f74c626b43e3ecf58f9845b74cf4ace44f3a332", "content_id": "e6b381ead69c995fd6098a1821966606b56ef3c1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 676, "license_type": "no_license", "max_line_length": 46, "num_lines": 29, "path": "/remove_kthnode_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @param B : integer\n # @return the head node in the linked list\n def removeNthFromEnd(self, A, B):\n \n itr = A\n len=0\n while itr:\n len+=1\n itr = itr.next\n \n if B>=len:\n return A.next\n \n itr=A\n count=0\n while itr:\n count+=1\n if count == len - B:\n itr.next = itr.next.next\n return A\n itr = itr.next\n \n" }, { "alpha_fraction": 0.26722338795661926, "alphanum_fraction": 0.30062630772590637, "avg_line_length": 13.9375, "blob_id": "71a463428039a0f0838124e4dc994bb48a5e4461", "content_id": "f716292b2268b3a6c5b94e8b7451ff990c642bf8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 479, "license_type": "no_license", "max_line_length": 30, "num_lines": 32, "path": "/set_zeroes.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "\ndef set_zeros(A):\n R = len(A)\n C = len(A[0])\n r = []\n c = [1]C\n for i in range(R):\n if 0 in A[i]:\n\n for j in range(C):\n if A[i][j]==0:\n c[j]=0\n A[i]=[0]C\n else:\n r.append(i)\n\n for i in r:\n for j in range(C):\n if not c[j]:\n A[i][j]=0\n return A\n\n\n\n\n\n\nA= [ [1, 0, 1],\n [1, 1, 1],\n [1, 0, 1] ]\n\n\nprint(set_zeros(A))\n" }, { "alpha_fraction": 0.45537757873535156, "alphanum_fraction": 0.4965675175189972, "avg_line_length": 25.75, "blob_id": "2dace252b42aaee4bd3cd79a8337c8ff9da76b19", "content_id": "721b609d3d55ca503a3be8a743a89236bea76fa5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 437, "license_type": "no_license", "max_line_length": 89, "num_lines": 16, "path": "/integer_to_roman.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\n\n#brute force\ndef integer_to_roman(n):\n\n ones=[\"\",\"I\",\"II\",\"III\",\"IV\",\"V\",\"VI\",\"VII\",\"VIII\",\"IX\"]\n tens=[\"\",\"X\",\"XX\",\"XXX\",\"XL\",\"L\",\"LX\",\"LXX\",\"LXXX\",\"XC\"]\n hundreds = [\"\",\"C\",\"CC\",\"CCC\",\"CD\",\"D\",\"DC\",\"DCC\",\"DCCC\",\"CM\"]\n thousands =[\"\",\"M\",\"MM\",\"MMM\"]\n\n ans = thousands[n//1000] + hundreds[(n%1000)//100] + tens[(n%100)//10] + ones[(n%10)]\n\n return ans\n\nn = int(input())\nprint(integer_to_roman(n))\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.43048128485679626, "alphanum_fraction": 0.4385026693344116, "avg_line_length": 22.399999618530273, "blob_id": "e7d5a46c41748c13ebea240163d560b0f017bb0b", "content_id": "b7b12c235056c312e5be9362792b103c1d25fca5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 374, "license_type": "no_license", "max_line_length": 34, "num_lines": 15, "path": "/diff_K_Hmaps.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import Counter\nclass Solution:\n # @param A : tuple of integers\n # @param B : integer\n # @return an integer\n def diffPossible(self, A, B):\n a = list(A)\n a.sort()\n ans={}\n for i in a:\n if i in ans:\n return 1\n else:\n ans[B+i]=1\n return 0\n \n\n \n" }, { "alpha_fraction": 0.37457045912742615, "alphanum_fraction": 0.41237112879753113, "avg_line_length": 25.454545974731445, "blob_id": "434e9f9e28f65f0b9cb12783fb58eb4f1b161c44", "content_id": "ce06e36f898a53f1bfbf569d737b3e20cd9423a3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 291, "license_type": "no_license", "max_line_length": 40, "num_lines": 11, "path": "/number of binary search tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : integer\n # @return an integer\n def numTrees(self, A):\n n=A\n dp=[0] * (n + 1)\n dp[0],dp[1] = 1, 1\n for i in range(2, n + 1):\n for j in range(1, i + 1):\n dp[i] += dp[i-j]dp[j-1]\n return dp[n]\n" }, { "alpha_fraction": 0.3464399576187134, "alphanum_fraction": 0.3613177537918091, "avg_line_length": 24.432432174682617, "blob_id": "74a27365e0b183d504096b77e5cc3ef6ff1682e0", "content_id": "8098ede2185334ffca7c672fb1a3539fe8996403", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 941, "license_type": "no_license", "max_line_length": 35, "num_lines": 37, "path": "/first&last_occurence.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : tuple of integers\n # @param B : integer\n # @return a list of integers\n def searchRange(self, A, B):\n ans = []\n \n low = 0\n high = len(A) - 1\n res =-1\n # first occurence\n while (low <= high):\n mid = (low + high) // 2\n if A[mid] > B:\n high = mid - 1\n elif A[mid] < B:\n low = mid + 1\n elif A[mid] == B:\n res = mid\n high = mid - 1\n ans.append(res)\n \n low = 0\n high = len(A) - 1\n res = -1\n # second occurence\n while (low <= high):\n mid = (low + high) // 2\n if A[mid] > B:\n high = mid - 1\n elif A[mid] < B:\n low = mid + 1\n elif A[mid] == B:\n res = mid\n low = mid+1\n ans.append(res)\n return ans\n" }, { "alpha_fraction": 0.5079365372657776, "alphanum_fraction": 0.5079365372657776, "avg_line_length": 24.200000762939453, "blob_id": "262ece7d63e4937756afd0be8819899118dd2a55", "content_id": "62710709a5cc4f958fe56f45dc2e7d6907d8930f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 756, "license_type": "no_license", "max_line_length": 64, "num_lines": 30, "path": "/Remove Half Nodes.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @return the root node in the tree\n def solve(self, A):\n if not A:\n return\n \n if (not A.left and A.right) or (not A.right and A.left):\n\n \n return self.solve(self.delNode(A))\n \n A.left = self.solve(A.left)\n A.right = self.solve(A.right)\n \n return A\n \n def delNode(self, A):\n #for sure the deleted node will have a single child\n if A.left and not A.right:\n return A.left\n else:\n return A.right\n" }, { "alpha_fraction": 0.36681222915649414, "alphanum_fraction": 0.42794761061668396, "avg_line_length": 6.161290168762207, "blob_id": "52ce2d532a472698aca0aa7e828ace4e5d1200d9", "content_id": "5eb8dcf5d3b881cece67b43d0a4d2f4c3576df4a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 229, "license_type": "no_license", "max_line_length": 32, "num_lines": 31, "path": "/GCD.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "\ndef gcd(A,B):\n A,B = max(A,B), min(A,B)\n if B==0:\n return 1\n\n while(A%B!=0):\n temp=B\n\n B= A%B\n A=temp\n\n\n\n return B\nprint(gcd(56,120))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprint(excel_colimn_base(468096))\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.2648221254348755, "alphanum_fraction": 0.2905138432979584, "avg_line_length": 21, "blob_id": "a5f3100257cdbed1aa9c4ef63ce0c9b51d536262", "content_id": "9c1f8d95e41ea1f708f40fdf0866f8a108f379f6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 506, "license_type": "no_license", "max_line_length": 34, "num_lines": 23, "path": "/remove_duplicates_atmost_twice.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @return an integer\n def removeDuplicates(self, A):\n i=1\n t=0\n f=1\n \n while i<len(A):\n if A[i]==A[t] and f<2:\n t+=1\n A[t]=A[i]\n f+=1\n i+=1\n elif A[i]==A[t]:\n i+=1\n f+=1\n elif A[i]!=A[t]:\n t+=1\n A[t]=A[i]\n i+=1\n f=1\n return t+1\n" }, { "alpha_fraction": 0.44893112778663635, "alphanum_fraction": 0.45130640268325806, "avg_line_length": 22.38888931274414, "blob_id": "538665388c9313561a78733b2854997da2a4ce3f", "content_id": "7448481c96e366e47c3be1b8aca87b75fec368b1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 421, "license_type": "no_license", "max_line_length": 51, "num_lines": 18, "path": "/plusOne.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @return a list of integers\n def plusOne(self, A):\n \"\"\"\n \n :param A: an array that represntes a number\n :return: a list\n \"\"\"\n l = len(A)\n n_str=''\n for i in A:\n n_str+=str(i)\n n_incr = str(int(n_str)+1)\n ans=[]\n for i in n_incr:\n ans.append(int(i))\n return ans\n" }, { "alpha_fraction": 0.49364790320396423, "alphanum_fraction": 0.503629744052887, "avg_line_length": 23.46666717529297, "blob_id": "180a970c12620939202e19b49f3eb5f065fd5db8", "content_id": "7b6ac2d5f24b13071a301fee77e5f19c90fc5dd7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1102, "license_type": "no_license", "max_line_length": 63, "num_lines": 45, "path": "/rat_maze_BT.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def isValid(x, y, n, arr):\n if x<n and y<n:\n if arr[x][y]==1:\n return True\n else:\n return False\n else:\n return False\n\ndef RatinMaze(x, y, arr, solarr, n):\n #base condition\n if x==n-1 and y==n-1:\n solarr[x][y]='1'\n return True\n\n\n if isValid(x, y, n, arr):\n solarr[x][y]='1'\n #checking right path\n if RatinMaze(x, y+1, arr, solarr, n):\n return True\n #checking if down path is valid\n if RatinMaze(x+1, y, arr, solarr, n):\n return True\n\n #if neither is true then we backtrack and change solarr\n solarr[x][y]='0'\n return False\n return False\n\nif __name__ == \"__main__\":\n n = int(input(\"enter the size of maze\"))\n print(\"enter values\")\n arr=[]\n for i in range(n):\n arr.append(list(map(int,input().split())))\n solarr=[]\n for i in range(n):\n s = '0'n\n solarr.append(list(s))\n if RatinMaze(0,0, arr,solarr,n):\n for i in range(n):\n print(\" \".join(solarr[i]))\n else:\n print(\"no solution\")\n\n" }, { "alpha_fraction": 0.42598187923431396, "alphanum_fraction": 0.44108760356903076, "avg_line_length": 21.066667556762695, "blob_id": "2cea947273ac03e10181188665955e52521a5d5c", "content_id": "47a4027b7142aeecbcf8ccfef938690a393cde9b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 331, "license_type": "no_license", "max_line_length": 41, "num_lines": 15, "path": "/largest_number_formed.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def arrange(A):\n li = list(map(str,A))\n li.sort()\n l = len(A)\n for i in range(l):\n for j in range(i,l):\n first = li[i]+li[j]\n second = li[j]+li[i]\n if second>first:\n li[j],li[i]= li[i], li[j]\n ans = \"\".join(li)\n return int(ans)\n\n\nprint(arrange([0,0,0,0,0]))\n" }, { "alpha_fraction": 0.46974697709083557, "alphanum_fraction": 0.4774477481842041, "avg_line_length": 23.567567825317383, "blob_id": "9bc3ea8f222b2c541f0adac9d9147bbac9b7d5ef", "content_id": "ada8743ee98a6d47f0903b2494ec6148fb53ab6c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 909, "license_type": "no_license", "max_line_length": 143, "num_lines": 37, "path": "/Kth_smallest_BST.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n#\tdef __init__(self, x):\n#\t\tself.val = x\n#\t\tself.left = None\n#\t\tself.right = None\n\nclass Solution:\n\t# @param A : root node of tree\n\t# @param B : integer\n\t# @return an integer\n\tdef kthsmallest(self, A, B):\n\t k=[0]\n\t ans=[0]\n def in_order(A):\n elements=[]\n \n #print left of subtree go all the way to leaf node\n if A.left:\n elements += in_order(A.left)\n \n k[0]+=1\n if k[0]==B:\n ans[0]= A.val\n \n \n \n v = A.val\n elements.append(v)\n \n if A.right:\n elements += in_order(A.right) #we are returning elements so we are basically appeningn the returned value of those sub nodes\n \n return elements\n \n l = in_order(A)\n return ans[0]\n" }, { "alpha_fraction": 0.5549450516700745, "alphanum_fraction": 0.5563187003135681, "avg_line_length": 29.33333396911621, "blob_id": "54eef6b92d0dea189cce79be2163407619b9dcff", "content_id": "382cd105924360af622ba95190ca1d4012b07495", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 728, "license_type": "no_license", "max_line_length": 73, "num_lines": 24, "path": "/swap_every_two_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @return the head node in the linked list\n def swapPairs(self, A):\n if A is None or A.next is None:\n return A\n temp = ListNode(-1)\n temp.next = A\n \n current = temp\n while current.next is not None and current.next.next is not None:\n first = current.next\n second = current.next.next\n first.next = second.next\n current.next = second\n current.next.next = first\n current = current.next.next\n return temp.next\n" }, { "alpha_fraction": 0.29472774267196655, "alphanum_fraction": 0.33707866072654724, "avg_line_length": 22.02083396911621, "blob_id": "046ff4f6bea4643e3ffb990c604e1d6f59819619", "content_id": "4e25b6a7156cb8b8f322c1c5c4be311d10415993", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1157, "license_type": "no_license", "max_line_length": 37, "num_lines": 48, "path": "/flip bits.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : string\n # @return a list of integers\n def flip(self, A):\n #naive approach\n #-1 2 9 -2 4 5 -10 11\n # l r\n #1 0 0 1 1 0 0 0 \n #1 1 1 0 0 1 1 1\n\n #edge case = 0 (done)\n # edge case = 10101010\n\n\n if A.count('1')==len(A):\n return []\n \n\n left,right = -1,-1\n count, max_count = 0,0\n reset = True\n currleft = 0\n\n for i in range(len(A)):\n if A[i]=='0':\n if reset:\n currleft = i\n reset = False\n count+=1\n \n else:\n if count>max_count:\n max_count = count\n left = currleft+1\n right = i\n \n if count>0:\n count-=1\n else:\n count=0\n reset = True\n\n if count>max_count:\n max_count = count\n left = currleft+1\n right = i+1\n\n return [left,right]\n \n\n \n\n\n \n \n\n" }, { "alpha_fraction": 0.4344472885131836, "alphanum_fraction": 0.47814908623695374, "avg_line_length": 15.913043022155762, "blob_id": "b20f896b7304dfd9b528c0341b8386a2a1bcf20c", "content_id": "918b95a88226d3a30a4fa199e9c22322e2ad39e9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 389, "license_type": "no_license", "max_line_length": 50, "num_lines": 23, "path": "/power_function.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def pow(x, n, d):\n if n==0 and x!=0:\n return 1%d\n elif n==0 and x==0:\n return 0\n ans = power(x, n)\n return ans % d\n\n#main power function\ndef power(x, n):\n if n==1:\n return x\n elif n%2==0:\n return power(x, n // 2) ** 2\n elif n%2==1:\n return power(x,(n+1)//2)power(x,(n-1)//2)\n\n\n\n\nx,n,d = map(long,input().split())\n\nprint(pow(x,n,d))\n" }, { "alpha_fraction": 0.42741936445236206, "alphanum_fraction": 0.4375, "avg_line_length": 22.619047164916992, "blob_id": "68a91ac1302c0dcc171c876fdc0ad8c3b3a9a739", "content_id": "fe2a837939d451cac22391fbcdb4a141f4c5bcf1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 496, "license_type": "no_license", "max_line_length": 52, "num_lines": 21, "path": "/find_all_factors.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : integer\n # @return a list of integers\n def allFactors(self, A):\n \"\"\"\n using the most optimal method i.e O(sqrt(n))\n \n :param A: a number\n :return: list of all its factors\n \"\"\"\n \n s = int(A0.5)\n ans=[]\n\n for i in range(1,s+1):\n if A%i==0:\n ans.append(i)\n if A/i not in ans:\n ans.append(int(A/i))\n ans.sort()\n return ans\n" }, { "alpha_fraction": 0.4223363399505615, "alphanum_fraction": 0.44672656059265137, "avg_line_length": 23.838708877563477, "blob_id": "892074d6382115b7d8a2ad11bd84cc0e59d23d14", "content_id": "763e6e116fbce1a75742dc947cf065eabce01fce", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 779, "license_type": "no_license", "max_line_length": 63, "num_lines": 31, "path": "/longest increasing subseq.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n\t# @param A : tuple of integers\n\t# @return an integer\n\tdef lis(self, A):\n\t\t#dayummm this solution is damn smart!\n\t\t#so basically sort the list and find the lcs of this and that\n\n\t\tB = sorted(set(A))\n\t\tn = len(A)\n\t\tm = len(B)\n\t\tdp = [[0 for i in range(n+1)] for j in range(m+1)]\n\n\t\tfor j in range(1,m+1):\n\t\t\tfor i in range(1,n+1):\n\t\t\t\tif A[i-1]==B[j-1]:\n\t\t\t\t\tdp[j][i] = 1 + dp[j-1][i-1]\n\t\t\t\telse:\n\t\t\t\t\tdp[j][i] = max(dp[j-1][i],dp[j][i-1])\n\t\treturn dp[m][n]\n\t\n\tdef solve_method2(self, A):\n\t\t\n n = len(A)\n dp = [1]n\n \n for i in range(1,n):\n for j in range(0,i):\n if A[j] < A[i]:\n dp[i] = max(dp[i] , dp[j] + 1)\n \n return dp\n \n" }, { "alpha_fraction": 0.4718446731567383, "alphanum_fraction": 0.48155340552330017, "avg_line_length": 15.433333396911621, "blob_id": "34b83bd825c6cc61b2a4b20bb957664d239d8904", "content_id": "81ad696431e15e430fd552fa7b7f7427ddf21549", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 515, "license_type": "no_license", "max_line_length": 57, "num_lines": 30, "path": "/word_rank_without_rep.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\nimport string\ndef lex_rank(A):\n \"\"\"\n approach: interesting\n \n :param A: a word\n :return: the rank of the word when arranged lexically\n \"\"\"\n\n p = 0\n alp = [i for i in A]\n\n alp.sort()\n print(alp)\n ans = 0\n l = len(A)\n while p < l:\n search = A[p]\n p += 1\n i = alp.index(search)\n alp.remove(search)\n if i!=0:\n\n ans+= imath.factorial(l-p)\n \n return ans+1\n\n\nprint(lex_rank('DTNGJPURFHYEW'))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.43807339668273926, "alphanum_fraction": 0.45183485746383667, "avg_line_length": 27.399999618530273, "blob_id": "bfef147d96e420c8efdfa8c0ebc7d3755cfd153a", "content_id": "5c5d913818b178a0decf04d25589b05a2e0a342e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 872, "license_type": "no_license", "max_line_length": 94, "num_lines": 30, "path": "/max distance.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : tuple of integers\n # @return an integer\n def maximumGap(self, A):\n \"\"\"\n suffix array= it collects the maximum elements like a filter dine during zone refining\n and we compare this to our origianl array(A).\n if A[i]<S[j].. then a bigger number is waiting for us in the end. j+=1\n else:\n we crossed a big element therefore i+=1\n\n also \n O(N) yay!\n \"\"\"\n n = len(A)\n S = [0]len(A)\n S[-1] = A[-1]\n for i in reversed(range(n-1)):\n S[i] = max(S[i+1],A[i])\n \n #S is the suffix array\n i,j=0,0\n ans_max = 0\n while i<n and j<n:\n if A[i]<=S[j]:\n ans_max = max(ans_max, j-i)\n j+=1\n else:\n i+=1\n return ans_max\n\n\n \n\n" }, { "alpha_fraction": 0.43393146991729736, "alphanum_fraction": 0.43882545828819275, "avg_line_length": 27.049999237060547, "blob_id": "02c93263f1931d6a17c4cb47b75935475d2d03ab", "content_id": "b6fdfe66a3b9aeab91bf066aea79e531612c9532", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 613, "license_type": "no_license", "max_line_length": 50, "num_lines": 20, "path": "/largest_area_histogram.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import deque\nclass Solution:\n # @param A : list of integers\n # @return an integer\n def largestRectangleArea(self, A):\n area=0\n stack = deque()\n \n for i, h in enumerate(A):\n start = i\n while stack and stack[-1][1]>h:\n index, height = stack.pop()\n area = max(area, height(i-index))\n start = index\n stack.append((start,h))\n \n for i, h in stack:\n area = max(area, h(len(A)-i))\n \n return area\n \n \n \n \n" }, { "alpha_fraction": 0.5183066129684448, "alphanum_fraction": 0.5480549335479736, "avg_line_length": 15.39622688293457, "blob_id": "b405317e0d348f9263373f49216fee5a67ff17c1", "content_id": "a227b01e42dadd2d27c442db5c1e2df8d580379c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 874, "license_type": "no_license", "max_line_length": 91, "num_lines": 53, "path": "/Max_non_negative.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def Max_non_negative(A):\n \"\"\"\n aim is to travesre the list and flag the points that are negative, ans clculate the sum\n that exceeds the previous sum and change the indices accordingly\n to print the reqd. sunset.\n\n :param A:\n :return: returns a continous list of maximum non negative number\n \"\"\"\n \n \"\"\"\n Example Input\nInput 1:\n\n A = [1, 2, 5, -7, 2, 3]\nInput 2:\n\n A = [10, -1, 2, 3, -4, 100]\n\n\nExample Output\nOutput 1:\n\n [1, 2, 5]\nOutput 2:\n\n [100]\n \"\"\"\n\n\n set=[]\n summ=[]\n all_set=[]\n for j in A:\n if j<0:\n\n summ.append(sum(set))\n all_set.append(set)\n set=[]\n else:\n set.append(j)\n summ.append(sum(set))\n all_set.append(set)\n\n i = summ.index(max(summ))\n return all_set[i]\n\n\n\n return ans\n\nA = list(map(int,input().split()))\nprint(Max_non_negative(A))\n\n\n\n\n\n" }, { "alpha_fraction": 0.47187501192092896, "alphanum_fraction": 0.5062500238418579, "avg_line_length": 15.8421049118042, "blob_id": "2bd7c2ff292a3374540446dcbd1e662a49482b8d", "content_id": "b83774d35fd24362c5dff0d1f71c255f7f899d6d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 320, "license_type": "no_license", "max_line_length": 57, "num_lines": 19, "path": "/find_all_primes_tillA.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# seive of Eratosthenes--- more optimised O(sqrt(n)logn)\ndef seive(A):\n\n sei = [True](A+1)\n for i in range(2,int(A0.5)+1):\n\n\n for j in range(i2,A+1,i):\n sei[j]=False\n ans=[]\n\n\n for i,v in enumerate(sei[2:]):\n if v:\n ans.append(i+2)\n return ans\n\n\nprint(seive(25))\n" }, { "alpha_fraction": 0.4480712115764618, "alphanum_fraction": 0.45103856921195984, "avg_line_length": 21.46666717529297, "blob_id": "e1c983594d584a738efd1b8196ad56b2435c66cd", "content_id": "c74a1d4bbbe7faa530c28b265ffeea8f6bb67ee3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 674, "license_type": "no_license", "max_line_length": 60, "num_lines": 30, "path": "/detect_loop_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\n#using Floyd's technique\n\nclass Solution:\n # @param A : head node of linked list\n # @return the first node in the cycle in the linked list\n def detectCycle(self, A):\n fp,sp = A,A\n s=0\n while fp and fp.next:\n fp = fp.next.next\n sp = sp.next\n \n if fp==sp:\n s=1\n break\n if not s:\n return None\n \n fp = A\n while fp!=sp:\n fp = fp.next\n sp = sp.next\n \n return fp\n" }, { "alpha_fraction": 0.4365781843662262, "alphanum_fraction": 0.4375614523887634, "avg_line_length": 29.96875, "blob_id": "40640a2477dc41a042621ade6c53c4bfa02d5c94", "content_id": "58bb6413e1752c5bf06ae9977fbcaae85d389599", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2034, "license_type": "no_license", "max_line_length": 105, "num_lines": 64, "path": "/cousins_binary_tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @param B : integer\n # @return a list of integers\n def solve(self, A, B):\n \"\"\"\n logic: im setting a queue that contains all the nodes of the same level\n i.e both siblings + cousins\n so im colecting all the parents of siblings and cousins\n if a particular parent's child is B then the brothers kids will be the cousins\n \n \"\"\"\n if not A:\n return []\n \n queue=[A]\n #level = [] #will store values in the same level #2d array, siblings will also be grouped togther\n next_queue=[]\n #result=[]\n #l=0\n parent_node = []\n ans=[]\n \n while queue: #atleast one node (or one child)\n for node in queue:\n #level.append(node)\n \n if node.left:\n if node.left.val==B:\n parent_node = queue\n parent_node.remove(node)\n next_queue=[]\n break\n next_queue.append(node.left)\n if node.right:\n if node.right.val==B:\n parent_node = queue\n parent_node.remove(node)\n next_queue=[]\n break\n next_queue.append(node.right)\n #print(next_queue)\n \n queue = next_queue\n next_queue=[]\n #result.append(level)\n #level=[]\n #print(parent_node)\n \n for i in parent_node:\n if i.left:\n ans.append(i.left.val)\n if i.right:\n ans.append(i.right.val)\n #print(ans)\n \n return ans\n \n \n \n \n" }, { "alpha_fraction": 0.4043927788734436, "alphanum_fraction": 0.44702842831611633, "avg_line_length": 18.820512771606445, "blob_id": "9d39e118116b9b6b2ec54668ae818eb45a50948d", "content_id": "4bb03dd3d7bb6a2f0f4d928dfecaf0dcf6c6d9ad", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 774, "license_type": "no_license", "max_line_length": 47, "num_lines": 39, "path": "/merge_intervals.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "\ndef insert(intervals, new_interval):\n ans=[]\n p=0\n for j,i in enumerate(intervals):\n if new_interval[p]<i[1]:\n if p!=1:\n sub_ans=[i[0]]\n p+=1\n elif new_interval[p]<i[0]:\n sub_ans.append(new_interval[p])\n ans.append(sub_ans)\n ans.extend(intervals[j:])\n break\n elif new_interval[p]<i[1]:\n sub_ans.append(i[1])\n ans.append(sub_ans)\n ans.extend(intervals[j+1:])\n break\n\n elif not p:\n ans.append(i)\n return ans\n\n\n\n\n\n\n\n\n\n\n\nintervals =[[1,2],[3,5],[6,7],[8,10],[12,16]]\nnew_intervals=[4,9]\n#[[1, 2], [3, 10], [12, 16]]\n\n\nprint(insert(intervals,new_intervals))\n" }, { "alpha_fraction": 0.4647887349128723, "alphanum_fraction": 0.49295774102211, "avg_line_length": 34.5, "blob_id": "4c6a30886e4e12404af8ce55b7425025d75c4a22", "content_id": "75050e77309ef63f999308f152f099641460e0b7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 213, "license_type": "no_license", "max_line_length": 88, "num_lines": 6, "path": "/pascal triangle.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n def generate(self, A: int) -> List[List[int]]:\n l=[]\n for i in range(A):\n l.append([1 if j==0 or j==i else l[i-1][j]+l[i-1][j-1] for j in range(i+1)])\n return l\n" }, { "alpha_fraction": 0.4404057562351227, "alphanum_fraction": 0.45224007964134216, "avg_line_length": 20.363636016845703, "blob_id": "3c42071ceeddb47e09e42c8ba015897fa013726c", "content_id": "03808720a8eed9a910feee021fcf1cf532426198", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1183, "license_type": "no_license", "max_line_length": 74, "num_lines": 55, "path": "/justified_allignment_text.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\n\n#brute force\ndef justified_text_alignment(A,B):\n \"\"\"\n\n :param A: a list of words from a senctence\n :param B: L number of characters regquired per line\n :return: n lines that are aligned and justifed equally spaced\n \"\"\"\n\n main=[]\n sub=[]\n ans=[]\n for i in A:\n if sub==[]:\n sub.append(i)\n l = len(i)\n elif l+len(i)+1<=B:\n sub.append(i)\n l +=len(i)+1\n elif l+len(i)+1>B:\n main.append(sub)\n sub=[i]\n l=len(i)\n main.append(sub)\n\n for line in main:\n words = len(line)\n c_count = len(\"\".join(line))\n extras = B - c_count\n if words==1:\n a = line[0] + ' '(B-len(line[0]))\n else:\n space = extras//(words-1)\n rem = extras%(words-1)\n k=0\n while (rem>0):\n line[k] = line[k]+ ' '\n rem-=1\n k+=1\n a = (\" \"space).join(line)\n\n\n ans.append(a)\n\n\n\n\n return ans #cooooooooool\n\n\nA = [\"the\", \"quick\", \"brown\", \"fox\", \"jumped\", \"over\", \"the\",\"lazy\",\"fox\"]\nB=24\nprint(justified_text_alignment(A,B))\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.35083797574043274, "alphanum_fraction": 0.3631284832954407, "avg_line_length": 25.322580337524414, "blob_id": "ea3b90e0c3fa0722c8c7a10011fe8f777feeaf31", "content_id": "4f15150746444241b5fb4b1bc18e45ae3fa3dcc7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 895, "license_type": "no_license", "max_line_length": 80, "num_lines": 31, "path": "/Subarray with given XOR.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @param B : integer\n # @return an integer\n def solve(self, A, B):\n count = 0\n xor =0\n freq = {}\n \"\"\"\n a = [4,2,2,6,4] k =6\n b = [ ] b is overall xor value a is the prefix xor value..... \n k will be the sufix xor value a = b^k we have calculated the freq of all\n prefix values.. k is the sufix value.\n freq = {(xor val): freq}\n \"\"\"\n \n for i in A:\n xor = xor^i\n \n if xor == B:\n count+=1\n \n if xor^B in freq:\n count+=freq[xor^B]\n \n if xor in freq:\n freq[xor]+=1\n else:\n freq[xor]=1\n \n return count\n \n \n \n \n \n \n\n" }, { "alpha_fraction": 0.4444444477558136, "alphanum_fraction": 0.469696968793869, "avg_line_length": 12.310344696044922, "blob_id": "8b91ca00276d2826b2d40a8326c3dbc2eb746b12", "content_id": "9a7cd0ed644dff62b7cd18f1d8f1be0ebfabd717", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 396, "license_type": "no_license", "max_line_length": 34, "num_lines": 29, "path": "/all_thrice_excpet_one.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "\n\n#brute force\ndef all_thrice_excpet_one(A):\n ans=0\n\n for i in reversed(range(32)):\n count=0\n for j in range(len(A)):\n if A[j]&(1<<i):\n count+=1\n if count%3!=0:\n ans += 1<<i\n return ans\n\n\n\n\n\n\n #id = f.values().index(1)\n #return f.keys()[id]\n\n\n\n\n\n\n\nA = list(map(int,input().split()))\nprint(all_thrice_excpet_one(A))\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5163204669952393, "alphanum_fraction": 0.5222551822662354, "avg_line_length": 31.799999237060547, "blob_id": "758ff3038399eb61f34e77dae61ba98a87d7bccf", "content_id": "97f27cd34afb24fcc175d593ca571699a4d04886", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 337, "license_type": "no_license", "max_line_length": 98, "num_lines": 10, "path": "/gray_code.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : integer\n # @return a list of integers\n def grayCode(self, A):\n ans=[]\n x = 2*A\n for i in range(x): #divide word into twos...\n j = i//2\n ans.append(i^j) #gray code will be the XOR of the number with its integer half value\n return ans\n \n" }, { "alpha_fraction": 0.30102789402008057, "alphanum_fraction": 0.37298092246055603, "avg_line_length": 23.321428298950195, "blob_id": "1151be7c4bcfb168897525a70b9e3228334b61cb", "content_id": "7e3265c27d70bb2a533e8f22778438ab62e97ef6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 681, "license_type": "no_license", "max_line_length": 58, "num_lines": 28, "path": "/anti diagonals.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of list of integers\n # @return a list of list of integers\n def diagonal(self, A):\n \"\"\"\n 1 2 3 5\n 4 5 6 7\n 7 8 9 8\n 9 1 2 3\n [[0,0], sum = 0\n [0,1],[1,0], sum = 1\n [0,2],[1,1],[2,0], sum = 2\n [1,2],[2,1], sum = 3\n [2,2]] sum = 4\n\n table of size N will have 2n - 1 i.e 0,1,2,...2n-1\n\n \n \"\"\"\n ans=[]\n n=len(A)\n for i in range(2n-1):\n sub=[]\n for j in range(n):\n if j<n and i-j>=0 and i-j<n:\n sub.append(A[j][i-j])\n ans.append(sub)\n return ans\n" }, { "alpha_fraction": 0.34419551491737366, "alphanum_fraction": 0.36659878492355347, "avg_line_length": 23.549999237060547, "blob_id": "d6f7da1f5606f7a550ed6fab2b66d94bebb3a0ae", "content_id": "5b15eaced5c2613945eabb6f168c1f70cb75b751", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 491, "license_type": "no_license", "max_line_length": 36, "num_lines": 20, "path": "/sliding_window_atmost_ones.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @param B : integer\n # @return an integer\n def solve(self, A, k):\n start,good,bad,res = 0,0,0,0\n \n for i in A:\n if i==1:\n good+=1\n else:\n bad+=1\n while(bad>k):\n if A[start]==1:\n good-=1\n else:\n bad-=1\n start+=1\n res = max(res, good+bad)\n return res\n" }, { "alpha_fraction": 0.3237597942352295, "alphanum_fraction": 0.33681461215019226, "avg_line_length": 16.227272033691406, "blob_id": "46b173054ea13cf7d1e4b56490bab8f4ab9dcd5c", "content_id": "5bb102576a34ddfa91d5c9abc16b1d1bf0e27f18", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 383, "license_type": "no_license", "max_line_length": 38, "num_lines": 22, "path": "/2-Sum Binary Tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": " def t2Sum(self,B):\n\n ans = [0]\n h={}\n\n def in_order(A, B):\n if A == None:\n return\n\n in_order(A.left, B)\n\n if A.data in h:\n ans[0] += 1\n else:\n h[B - A.data] = A.data\n\n in_order(A.right, B)\n\n return\n\n in_order(self, B)\n return ans[0]\n" }, { "alpha_fraction": 0.41734617948532104, "alphanum_fraction": 0.4188287556171417, "avg_line_length": 23.14285659790039, "blob_id": "331d2b89dcce8a62da1b2c05079201208076b534", "content_id": "712f586f0f4a222fd09e375ea71e692e456a212a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1349, "license_type": "no_license", "max_line_length": 58, "num_lines": 49, "path": "/deep_copy_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list with a random pointer.\n# class RandomListNode:\n# def __init__(self, x):\n# self.label = x\n# self.next = None\n# self.random = None\n\nclass Solution:\n # @param head, a RandomListNode\n # @return a RandomListNode\n def copyRandomList(self, head):\n \n #return head\n #copy list\n itr = head\n while itr:\n copy = RandomListNode(itr.label)\n temp = itr.next\n itr.next = copy\n copy.next = temp\n itr = temp\n \n itr = head\n while itr:\n itr.next.random = itr.random.next\n itr = itr.next.next\n #create copy list\n ans = head.next\n itr = ans\n while itr or itr.next.next:\n itr.next = itr.next.next\n itr = itr.next\n \n return ans\n\n \n \n \n\"\"\" def copy_list(self, head):\n itr = head\n c = RandomListNode(0)\n copy = c\n \n while itr:\n copy.label = itr.label\n copy.next = RandomListNode(0)\n itr = itr.next\n copy = copy.next\n return c\"\"\"\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "alpha_fraction": 0.4969325065612793, "alphanum_fraction": 0.49897751212120056, "avg_line_length": 31.600000381469727, "blob_id": "5fb745db742d949151a4b3eb7340b008bd643a1e", "content_id": "dec461c04db1d42126222df3d59fc0514aa5325c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 489, "license_type": "no_license", "max_line_length": 41, "num_lines": 15, "path": "/order of people height.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @param B : list of integers\n # @return a list of integers\n def order(self, A, B):\n line = dict(zip(A,B))\n correct_order= ['x']len(A)\n index = list(range(len(A)))\n while correct_order.count('x')>0:\n for i in sorted(line):\n count = line[i]\n id = index[count]\n index.pop(count)\n correct_order[id] = i\n return correct_order\n" }, { "alpha_fraction": 0.3450789749622345, "alphanum_fraction": 0.34750911593437195, "avg_line_length": 23.33333396911621, "blob_id": "b7b973be5cd111f86e8b2800e1fa4e183cc35f47", "content_id": "6872726f1452b604d60d5a8adb090c06d427d201", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 823, "license_type": "no_license", "max_line_length": 47, "num_lines": 33, "path": "/shortest_unique_prefix.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n\t# @param A : list of strings\n\t# @return a list of strings\n\tdef prefix(self, Input):\n\t \"\"\"\n\t Input: [zebra, dog, duck, dove]\n Output: {z, dog, du, dov}\n where we can see that\n zebra = z\n dog = dog\n duck = du\n dove = dov\n\t \"\"\"\n\t ans=[]\n\t \n\t for word in Input:\n i=0\n while i<len(word):\n prev=i\n for other in Input:\n if other==word:\n continue\n else:\n if word[:i]==other[:i]:\n i+=1\n break\n if i==prev:\n break\n\t \n ans.append(word[:i])\n \n #print(ans)\n return ans\n\t \n\t \n" }, { "alpha_fraction": 0.35172414779663086, "alphanum_fraction": 0.3751724064350128, "avg_line_length": 15.860465049743652, "blob_id": "bdf921c424d1d5530e16498b73dc37df38cc848c", "content_id": "193ca93c3659171431c7e7d776edd4656dd6a358", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 725, "license_type": "no_license", "max_line_length": 45, "num_lines": 43, "path": "/smalles_next_largest_number.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def next_largest_number(A):\n \"\"\"\n \n :param A: a string numner\n \n :return: the smallest next largest number\n \"\"\"\n\n if len(A)==1:\n return -1\n l=len(A)-2\n ans=\"\"\n while(l>=0):\n l1 = [int(i) for i in A[l:]]\n if max(l1)==l1[0]:\n l-=1\n continue\n else:\n ctrl = l1[0]\n l1.sort()\n ans+=A[:l]\n for i in l1:\n if i>ctrl:\n ans+=str(i)\n l1.remove(i)\n break\n for j in l1:\n ans+=str(j)\n break\n if l>=0:\n return ans\n else:\n return -1\n\n\n\n\n\n\n\n\nA = str(input())\nprint(next_largest_number(A))\n" }, { "alpha_fraction": 0.4031413495540619, "alphanum_fraction": 0.4267015755176544, "avg_line_length": 21.47058868408203, "blob_id": "d4e95acabc282cf496b503f05739c6c008cb10aa", "content_id": "9e52a0f05937f7fc595f8cee00b879009e5ffbfc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 382, "license_type": "no_license", "max_line_length": 68, "num_lines": 17, "path": "/hotel.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n def hotel(self, arrive, depart, K):\n events = [(t, 1) for t in arrive] + [(t, 0) for t in depart]\n events = sorted(events)\n\n guests = 0\n\n for event in events:\n if event[1] == 1:\n guests += 1\n else:\n guests -= 1\n\n if guests > K:\n return 0\n\n return 1\n" }, { "alpha_fraction": 0.4238845109939575, "alphanum_fraction": 0.44750654697418213, "avg_line_length": 24.214284896850586, "blob_id": "2ced885f13d24749b80564af1cfd645823fe34f6", "content_id": "4879ec397770e357337a4e665e3fb5d7491c596e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 762, "license_type": "no_license", "max_line_length": 46, "num_lines": 28, "path": "/add_2_linked_lists.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @param B : head node of linked list\n # @return the head node in the linked list\n def addTwoNumbers(self, A, B):\n l1=A\n l2=B\n carry=0\n cur=temp=ListNode(0)\n \n while l1 or l2 or carry:\n if l1:\n carry+=l1.val\n l1=l1.next\n if l2:\n carry+=l2.val\n l2=l2.next\n cur.next=ListNode(carry%10)\n cur=cur.next\n \n carry=carry//10\n return temp.next\n \n \n \n \n" }, { "alpha_fraction": 0.4678899049758911, "alphanum_fraction": 0.4694189727306366, "avg_line_length": 23.22222137451172, "blob_id": "b8f32211d289a783f4db9115c285f5cf0f324808", "content_id": "95b7e87d2c858da8105f93ff11a89b9c1c95ea2e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 654, "license_type": "no_license", "max_line_length": 110, "num_lines": 27, "path": "/maximum sum path(binary tree).py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : root node of tree\n # @return an integer\n def __init__(self):\n self.maX = float('-inf')\n \n \n def maxPathSum(self, A):\n #using DFS\n #global maX\n\n \n\n def dfs(node):\n \n if not node:\n return 0\n \n left_val = dfs(node.left)\n right_val = dfs(node.right)\n cur_val = node.val\n\n self.maX = max(left_val+cur_val , right_val+cur_val, cur_val,left_val+right_val+cur_val, self.maX)\n return max(left_val, right_val) + cur_val\n \n ans = dfs(A)\n return self.maX\n" }, { "alpha_fraction": 0.5709571242332458, "alphanum_fraction": 0.5742574334144592, "avg_line_length": 26.545454025268555, "blob_id": "64fddffa531b06d198f9f0404417f84f912cf6b3", "content_id": "94fc483757b0cab8b6e30a30b920c1017ab6941f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 303, "license_type": "no_license", "max_line_length": 88, "num_lines": 11, "path": "/largest_coprime_divisor.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\nclass Solution:\n # @param A : integer\n # @param B : integer\n # @return an integer\n def cpFact(self, A, B):\n #largets factor of A is A, if A is divisdible by A then remove the common factor\n while(math.gcd(A,B)!=1):\n A= A//math.gcd(A,B)\n\n return A\n" }, { "alpha_fraction": 0.4926387369632721, "alphanum_fraction": 0.4994337558746338, "avg_line_length": 30.5, "blob_id": "a97607fedd9e01894af057dfa34418c61c19b745", "content_id": "73e4e1019f590c6aec19bd5c5b7d0a13f7281a1e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 883, "license_type": "no_license", "max_line_length": 76, "num_lines": 28, "path": "/overlapping intervals.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for an interval.\n# class Interval:\n# def __init__(self, s=0, e=0):\n# self.start = s\n# self.end = e\n\nclass Solution:\n # @param intervals, a list of Intervals\n # @return a list of Interval\n def merge(self, intervals):\n ans=[]\n intervals.sort(key = lambda x: x.start)\n \n ans.append(intervals[0])\n #print(intervals[0])\n for i in intervals[1:]:\n prev_interval = ans[-1]\n if prev_interval.end >= i.start:\n #cool overlapping, but partially or fully overlapping? hmm..\n if prev_interval.end>=i.end:\n #fully overlapping no changes\n continue\n else:\n #partial overlapping\n prev_interval.end = i.end\n else:\n ans.append(i)\n return ans\n\n" }, { "alpha_fraction": 0.4342857003211975, "alphanum_fraction": 0.4628571569919586, "avg_line_length": 23.928571701049805, "blob_id": "b2ebd9e422372b4d2e08af5c5645f32b80d924fd", "content_id": "4dca0ea824e36df76c286d3af32eef21e0ad2200", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 350, "license_type": "no_license", "max_line_length": 48, "num_lines": 14, "path": "/min jumps array dp.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import sys\nsys.setrecursionlimit(106)\nclass Solution:\n # @param A : list of integers\n # @return an integer\n def jump(self, A):\n if A==[]:\n return -1\n\n if A[0]>len(A):\n return 1+self.jump(A[1:])\n else:\n return 1 + min(self.jump(A[1:]),\n self.jump(A[A[0]:]))\n\n" }, { "alpha_fraction": 0.4664764702320099, "alphanum_fraction": 0.4907275438308716, "avg_line_length": 18.799999237060547, "blob_id": "516326a048d69d0af5b156e63746b07eb755824a", "content_id": "34622e566f0534e5c0f0a73f3a780ed9f6937868", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 701, "license_type": "no_license", "max_line_length": 63, "num_lines": 35, "path": "/kth_smallest.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import Counter\nINT_MAX = 2147483647\n#brute force\ndef kth_smallest(a,k):\n l = len(a)\n low =min(a)\n high = max(a)\n\n while(low<=high):\n mid = low + (high - low)//2\n count_less, count_equal =0,0\n\n for i in range(l):\n if a[i]< mid:\n count_less+=1\n elif a[i]==mid:\n count_equal+=1\n\n if count_less<k and (count_less + count_equal) >=k:\n return mid\n\n elif count_less>= k:\n high = mid-1\n\n elif (count_less < k and count_less + count_equal < k):\n low = mid+1\n\n\n\n\n\na = list(map(int,input().split(', ')))\nk = int(input())\n\nprint(kth_smallest(a, k))\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.48816028237342834, "alphanum_fraction": 0.49544626474380493, "avg_line_length": 23.136363983154297, "blob_id": "a1de8532147028ef0bdbee69940735bfaeb4ef28", "content_id": "66743528250b2611c6d5aa8d425eec5a856a1811", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 549, "license_type": "no_license", "max_line_length": 40, "num_lines": 22, "path": "/min length tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @return an integer\n lenn=float('inf')\n def minDepth(self, A, l=1):\n if not A:\n return 0\n \n if not A.left and not A.right:\n self.lenn = min(self.lenn,l)\n \n self.minDepth(A.left, l+1)\n self.minDepth(A.right, l+1)\n \n return self.lenn\n \n \n" }, { "alpha_fraction": 0.29742613434791565, "alphanum_fraction": 0.3012392818927765, "avg_line_length": 21.542856216430664, "blob_id": "37dc910baaf6f1551fb8e143c652a24dc55af56a", "content_id": "ac2a28b2d77c9151c5f4791cf384a2d5a5f9f7e8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1049, "license_type": "no_license", "max_line_length": 38, "num_lines": 35, "path": "/right view tree bfs.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @return a list of integers\n def solve(self, A):\n depth={}\n def traversal(A,l=0):\n if not A:\n return\n \n if l not in depth:\n depth[l]= [A.val]\n \n else:\n depth[l].append(A.val)\n \n traversal(A.left,l+1)\n traversal(A.right,l+1)\n \n return\n \n traversal(A)\n ans=[]\n for i in depth:\n ans.append(depth[i][-1])\n \n \n \n return ans\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "alpha_fraction": 0.38732394576072693, "alphanum_fraction": 0.38967135548591614, "avg_line_length": 22.66666603088379, "blob_id": "775219b844ed4547a95e3248316e6921eb9ad7cd", "content_id": "8c334b99210aa1c286eff40f3a35053f72625329", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 426, "license_type": "no_license", "max_line_length": 55, "num_lines": 18, "path": "/check_parenthesis_stack.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "s = Stack()\nstrr = str(input())\ndef parant_check(strr,s):\n map = {']':'[','}':'{',')':'('}\n for i in strr:\n if i in ['[','{','(']:\n s.push(i)\n elif i in [']','}',')']:\n if not s.is_empty() and s.peek() == map[i]:\n s.pop()\n else:\n return False\n if s.size()==0:\n return True\n else:\n return False\n\nprint(parant_check(strr,s))\n" }, { "alpha_fraction": 0.4400978088378906, "alphanum_fraction": 0.44498777389526367, "avg_line_length": 29.769229888916016, "blob_id": "754ba8e7ce2cfdde3c8f4ab4b1df82da8e9dff97", "content_id": "5e3c90037fe28d6f17c983c5889e15d965492046", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 409, "license_type": "no_license", "max_line_length": 56, "num_lines": 13, "path": "/anagram_hashing.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : tuple of strings\n # @return a list of list of integers\n def anagrams(self, A):\n ref = {}\n #A = list(A)\n for i in range(len(A)):\n if \"\".join(sorted(A[i])) in ref:\n ref[\"\".join(sorted(A[i]))].append(i + 1)\n else:\n ref[\"\".join(sorted(A[i]))] = [i + 1]\n \n return list(ref.values())\n \n" }, { "alpha_fraction": 0.40391677618026733, "alphanum_fraction": 0.40391677618026733, "avg_line_length": 24.576923370361328, "blob_id": "fcd4ec0f528d766375d5021bec79e6b8647d8689", "content_id": "b1d59e82e3e4e0c4a5b2b4332ff61539dcbfc479", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 817, "license_type": "no_license", "max_line_length": 46, "num_lines": 26, "path": "/merge tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @param B : root node of tree\n # @return the root node in the tree\n def solve(self, A, B):\n if A is None and B is None:\n return None\n if A and not B:\n return A\n if not A and B:\n return B\n \n #print(A.val)\n A.val = A.val + B.val\n #print(A.val)\n A.left = self.solve(A.left, B.left)\n A.right = self.solve(A.right, B.right)\n \n return A\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "alpha_fraction": 0.4289044141769409, "alphanum_fraction": 0.4312354326248169, "avg_line_length": 23.787878036499023, "blob_id": "5bbee2e9db426a8844e40af2f1ebc8c5e85fdb01", "content_id": "3c722c338f7aba8470933fd63a0612eb72756815", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 858, "license_type": "no_license", "max_line_length": 43, "num_lines": 33, "path": "/Path to Given Node.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @param B : integer\n # @return a list of integers\n def __init__(self):\n self.path = []\n def solve(self, A, B):\n\n #path.append(A.val)\n def findPath(node, key):\n if not node: #or path[-1]==key:\n return\n \n self.path.append(node.val)\n if node.val != key:\n findPath(node.left,key)\n findPath(node.right, key)\n if self.path[-1]!=key:\n self.path.pop()\n \n \n\n return\n \n findPath(A,B)\n return self.path\n\n \n \n \n" }, { "alpha_fraction": 0.39620253443717957, "alphanum_fraction": 0.41012659668922424, "avg_line_length": 27.730770111083984, "blob_id": "245eab575e60a72ccad32b323387b1254d900ea9", "content_id": "f3c649556a4f732dc696f0401347da2b546eff5a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 790, "license_type": "no_license", "max_line_length": 50, "num_lines": 26, "path": "/next_greater_stack.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import collections\nclass Solution:\n # @param A : list of integers\n # @return a list of integers\n def nextGreater(self, A):\n stack = collections.deque()\n ans = collections.deque()\n \n l = len(A) - 1\n while l >= 0:\n if stack and A[l] < stack[-1]:\n ans.appendleft(stack[-1])\n \n elif stack and A[l] >= stack[-1]:\n while stack and stack[-1] <= A[l]:\n stack.pop()\n if stack:\n ans.appendleft(stack[-1])\n else:\n ans.appendleft(-1)\n \n elif len(stack) == 0:\n ans.appendleft(-1)\n stack.append(A[l])\n l -= 1\n return ans\n \n \n \n" }, { "alpha_fraction": 0.48049280047416687, "alphanum_fraction": 0.48049280047416687, "avg_line_length": 23.210525512695312, "blob_id": "4079ddbcae708311e1681610ddca9806966fe68b", "content_id": "f9cf11cb1246506c2010c0cce0ae3016b23f0da8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 487, "license_type": "no_license", "max_line_length": 46, "num_lines": 19, "path": "/reverse_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @return the head node in the linked list\n def reverseList(self, A):\n prev = None\n itr = A\n while itr:\n nextt = itr.next\n itr.next = prev\n prev = itr\n itr = nextt\n A = prev\n return A\n\n \n \n" }, { "alpha_fraction": 0.5963541865348816, "alphanum_fraction": 0.6223958134651184, "avg_line_length": 11.600000381469727, "blob_id": "6045cd3f5d15ad5ae8f6cdefaac086a071ecf0ab", "content_id": "9dc61f040065f20abc35ce3c9ecbaa18931abf0d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 384, "license_type": "no_license", "max_line_length": 67, "num_lines": 30, "path": "/number_of_unique_paths.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\nimport string\ndef number_of_unique_paths(A,B):\n \"\"\"\n\n :param A: grid length\n :param B: grid breadth\n :return: number of unique ways to reach final destination\n \"\"\"\n a= A-1\n b=B-1\n ans = math.factorial(a+b)/(math.factorial(b)math.factorial(a))\n return ans\nprint(number_of_unique_paths(3,3))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprint(excel_colimn_base(468096))\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.40105077624320984, "alphanum_fraction": 0.4115586578845978, "avg_line_length": 17.766666412353516, "blob_id": "b978636adabf775ddd778010ad37aa8a0a3cfa92", "content_id": "923bb4020877caa416139968746cdb42c01cdc7d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 571, "license_type": "no_license", "max_line_length": 61, "num_lines": 30, "path": "/JSON_indendation.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\n\n#brute force\ndef json_indendation(S):\n \"\"\"\n\n :param S: a string\n :return: an indendation answer to prettify the string\n \"\"\"\n ans= S[0] + '\\n\\t'\n count=1\n for i in S[1:]:\n if i =='{' or i =='[':\n\n ans+= '\\n'+ '\\t'count + i+ '\\n' + '\\t'(count+1)\n count+=1\n elif i==',':\n ans+=',\\n' + '\\t'count\n elif i=='}' or i ==']':\n count-=1\n ans+= '\\n'+ '\\t'count + i\n else:\n ans+=i\n return ans\n\n\n\nS= str(input())\n\nprint(json_indendation(S))\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.3915211856365204, "alphanum_fraction": 0.4164588451385498, "avg_line_length": 24.0625, "blob_id": "fbf06ecd1731d4eaa6ea6cd7d4b71dc08eb07866", "content_id": "0d4afeb1854d76b2b2c50bd0d82082ef41b54f50", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 401, "license_type": "no_license", "max_line_length": 38, "num_lines": 16, "path": "/modular_expressions_recursive.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : integer\n # @param B : integer\n # @param C : integer\n # @return an integer\n def Mod(self, A, B, C):\n if B==0 and A==0:\n return 0\n if B==0:\n return 1\n if B%2==0:\n res = self.Mod(A, B//2, C)\n return (res*2)%C\n else:\n res = self.Mod(A,B-1, C)\n return ((A%C)res)%C\n" }, { "alpha_fraction": 0.37945792078971863, "alphanum_fraction": 0.4022824466228485, "avg_line_length": 28.20833396911621, "blob_id": "833a297e9c174e4b245e9c6dd1fb6b872fe84b49", "content_id": "6b11ac4ea77f9decd6b371d2d8be60f8cc4fc1cf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 701, "license_type": "no_license", "max_line_length": 69, "num_lines": 24, "path": "/Tushar's bday Dp.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : tuple of integers\n # @param B : tuple of integers\n # @param C : tuple of integers\n # @return an integer\n def solve(self, A, B, C):\n cap= max(A)\n n = len(B)\n dp = [[0 for i in range(cap+1)] for j in range(n+1)]\n \n for i in range(cap+1):\n dp[0][i] = float('inf')\n #dp[1][i] = i\n \n for i in range(1, n+1):\n for j in range(1,cap+1):\n if j>=B[i-1]:\n dp[i][j]= min(dp[i-1][j], C[i-1]+dp[i][j-B[i-1]])\n else:\n dp[i][j]= dp[i-1][j]\n summ=0\n for c in A:\n summ+=dp[n][c]\n return summ\n" }, { "alpha_fraction": 0.5590476393699646, "alphanum_fraction": 0.569523811340332, "avg_line_length": 19.173076629638672, "blob_id": "136e3b725f5b796cebd2817c6be7aa6ca8d3aba1", "content_id": "44cd8200d1abe668d186bac977c446f4f87f5f28", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1050, "license_type": "no_license", "max_line_length": 42, "num_lines": 52, "path": "/print_binary_queue.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import deque\nimport time\nimport threading\n\nclass Queue():\n def __init__(self):\n self.Q = deque()\n def dequeue(self):\n return self.Q.pop()\n def enqueue(self,val):\n self.Q.appendleft(val)\n def size(self):\n return len(self.Q)\n def peek(self):\n return self.Q[-1]\n def is_empty(self):\n return len(self.Q)==0\n\nQ = Queue()\ndef Place_order(order):\n for order in orders:\n print(\"Placing order for:\",order)\n\n Q.enqueue(order)\n time.sleep(0.5)\n\ndef Serve_order():\n time.sleep(1)\n while not Q.is_empty():\n order = Q.dequeue()\n print(\"Now serving \",order)\n time.sleep(1)\n\n\n\n\n\ndef produce_binary_numbers(n):\n numbers_queue = Queue()\n numbers_queue.enqueue(\"1\")\n\n for i in range(n):\n front = numbers_queue.peek()\n print(\" \", front)\n numbers_queue.enqueue(front + \"0\")\n numbers_queue.enqueue(front + \"1\")\n\n numbers_queue.dequeue()\n\n\nif __name__ == '__main__':\n produce_binary_numbers(20)\n\n" }, { "alpha_fraction": 0.5273159146308899, "alphanum_fraction": 0.5558194518089294, "avg_line_length": 35.5217399597168, "blob_id": "9c4f97c850148650264d8a78688f60e9b5dd2c2e", "content_id": "a252b03c5b74ce0c65113cb87d0be3b794216e2f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 842, "license_type": "no_license", "max_line_length": 200, "num_lines": 23, "path": "/square_root.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# this is using the famous babylon method to find the square root\n\"\"\" \nMake an initial guess. Guess any positive number x0.\nImprove the guess. Apply the formula x1 = (x0 + S / x0) / 2. The number x1 is a better approximation to sqrt(S).\nIterate until convergence. Apply the formula xn+1 = (xn + S / xn) / 2 until the process converges. Convergence is achieved when the digits of xn+1 and xn agree to as many decimal places as you desire.\n\"\"\"\n\nclass Solution:\n # @param A : integer\n # @return an integer\n def sqrt(self, A):\n if A==0:\n return 0\n elif A in range(1,4):\n return 1\n else:\n x = A//2\n x1 = (x + A/x)/2\n while(int(x)>int(x1)):\n x = x1\n x1 = (x1 + A/x1)/2\n if int(x)==int(x1):\n return int(x)\n\n\n" }, { "alpha_fraction": 0.30315789580345154, "alphanum_fraction": 0.35789474844932556, "avg_line_length": 20.11111068725586, "blob_id": "8262e43a5ac521e9356501a5462eb5a0d159e15a", "content_id": "cef0063536f1f16c800341c73d586b839009a814", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 950, "license_type": "no_license", "max_line_length": 47, "num_lines": 45, "path": "/atoi.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : string\n # @return ascii to integer\n def atoi(self, a):\n \"\"\"\n with overflow conditons,\n \n \n :param S: a string numerical\n :return: converted integer\n \"\"\"\n pos=True\n if a[0]=='+':\n pos=True\n a=a[1:]\n elif a[0]=='-':\n pos = False\n a=a[1:]\n \n \n ref = '0123456789'\n l = 0\n ans=0\n c=0\n while(l<len(a)):\n if a[l] in ref:\n c+=1\n l += 1\n elif a[l] not in ref:\n break\n for i in range(c):\n ans+= ref.index(a[i])(10(c-i-1))\n \n \n \n \n if not pos:\n ans=-1\n if (abs(ans) > ((1 << 31) - 1)):\n return -12147483648\n return ans\n if (abs(ans) > ((1 << 31) - 1)):\n return 2147483647\n \n return ans\n" }, { "alpha_fraction": 0.5085877776145935, "alphanum_fraction": 0.5114504098892212, "avg_line_length": 28.11111068725586, "blob_id": "162ad9599a7392f05bf415643c20769299fe9124", "content_id": "2128c677b752877111ff691417be3768f53bd882", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1048, "license_type": "no_license", "max_line_length": 142, "num_lines": 36, "path": "/N_queens.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def N_queens(n):\n ans=[]\n\n def back_track(col_array=[]):\n if len(col_array) == n: #GOAL ===> if our column length reaches n\n ans.append(col_array[:])\n return\n\n for i in range(n): #CHOICES ===> here the choice are all the column values\n col_array.append(i)\n if isValid(col_array): #CONSTRAINTS ====> sub function; ensure that no thing is right below, and nothing is diagonally opposite\n back_track(col_array)\n\n col_array.pop()\n\n def isValid(col_array):\n row_index = len(col_array)-1\n for i in range(row_index):\n dif = abs(col_array[i] - col_array[row_index])\n if dif==0 or dif== row_index - i:\n return False\n return True\n\n back_track()\n return ans\n\nif __name__ == \"__main__\":\n n = int(input())\n main=[]\n queens = N_queens(n)\n for i in queens:\n sub=[]\n for j in i:\n sub.append(\".\"j+ 'Q'+\".\"(n-1-j))\n main.append(sub)\n print(main)\n" }, { "alpha_fraction": 0.5146805047988892, "alphanum_fraction": 0.5354058742523193, "avg_line_length": 16.90625, "blob_id": "91f1b7eaea0789a70839799ad691f1e607217958", "content_id": "67ae90dcd30cbe0651dddbb2cea94e6b01500cee", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 579, "license_type": "no_license", "max_line_length": 92, "num_lines": 32, "path": "/primesum.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def primesum(A):\n \"\"\"\n every even number can be represented by sum of 2 prime numbers.\n here we create a list of all primes less than A. and check if i and A-i are in the list.\n :param A: even postive number\n :return: a pair of prime numbers\n \"\"\"\n if A == 4:\n return [2, 2]\n\n sei = [False]2+ [True](A-1)\n for i in range(2,int(A0.5)+1):\n\n\n for j in range(i2,A+1,i):\n sei[j]=False\n\n\n for i in range(len(ans)):\n if ans[A-i] and ans[i]:\n return [i,A-i]\n\n\n\n\n\n\n\n\n\nA = int(input())\nprint(primesum(A))\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.4445350766181946, "alphanum_fraction": 0.46655791997909546, "avg_line_length": 34.30303192138672, "blob_id": "0c43eb4515126767684d343077b96988fa68baaf", "content_id": "06274f1353badc26fcd9077868eadf84b136d11f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1226, "license_type": "no_license", "max_line_length": 82, "num_lines": 33, "path": "/median 2 sorted array.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\n if len(nums1)>len(nums2):\n nums1,nums2 = nums2, nums1\n \n \n x = len(nums1)\n y = len(nums2)\n \n flag = (x+y)%2==0\n \n high = x\n low=0\n while low<=high:\n xPartition = (low+high)//2\n yPartition = (x+y+1)//2 - xPartition\n \n maxLeftX = float('-inf') if xPartition==0 else nums1[xPartition-1]\n minRightX = float('inf') if xPartition==x else nums1[xPartition]\n \n maxLeftY = float('-inf') if yPartition==0 else nums2[yPartition-1]\n minRightY = float('inf') if yPartition==y else nums2[yPartition]\n \n if maxLeftX<=minRightY and maxLeftY<=minRightX:\n if not flag:\n return max(maxLeftX,maxLeftY )\n else:\n return (max(maxLeftX,maxLeftY) + min(minRightX,minRightY))/2\n \n elif maxLeftX>minRightY:\n high = xPartition-1\n else:\n low = xPartition+1\n \n \n \n \n \n" }, { "alpha_fraction": 0.3919716775417328, "alphanum_fraction": 0.3943329453468323, "avg_line_length": 25, "blob_id": "0012ae73d476cbfb996fd0a4233e96251f70e1ed", "content_id": "11dec40f14ea2ca6b000e523ffff826d20b1702d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 847, "license_type": "no_license", "max_line_length": 93, "num_lines": 27, "path": "/validate_BST.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @return an integer\n def isValidBST(self, A):\n \n def sub_validate(A, upper=float('-inf'), lower=float('inf')):\n if not A:\n return True\n \n if A.val>=upper or A.val<=lower:\n return False\n \n return sub_validate(A.left, A.val, lower) and sub_validate(A.right, upper, a.val)\n \n \n if sub_validate(A):\n return 1\n \n else:\n return 0\n \n \n \n\n \n \n \n \n \n \n \n \n\n \n\n \n" }, { "alpha_fraction": 0.397550106048584, "alphanum_fraction": 0.40089085698127747, "avg_line_length": 22.405405044555664, "blob_id": "b34d54deef766a0df8cc41f5edaf04f20f9efd04", "content_id": "4dba1d5bedee87716ca2a3adb9e944d22921f841", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 898, "license_type": "no_license", "max_line_length": 40, "num_lines": 37, "path": "/verticalOrderTraversalTre.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @return a list of list of integers\n \n def verticalOrderTraversal(self, A):\n vr={}\n def vertical(A,c=0):\n if not A:\n return\n if c in vr:\n \n vr[c].append(A.val)\n #print(self.vr)\n else:\n vr[c]=[A.val]\n #print(self.vr)\n \n vertical(A.left, c+1)\n vertical(A.right, c-1)\n \n vertical(A)\n c = list(vr.keys())\n ans=[]\n c.sort()\n for i in reversed(c):\n \n ans.append(vr[i])\n #print(ans)\n \n return ans\n \n \n \n \n" }, { "alpha_fraction": 0.37605804204940796, "alphanum_fraction": 0.38210397958755493, "avg_line_length": 22.558822631835938, "blob_id": "66d5202b1c0522d69d2815723e797a1a53f354bc", "content_id": "ded19a1e699d54d6f6eccc5c5bb9a656e0a6fe6c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 827, "license_type": "no_license", "max_line_length": 44, "num_lines": 34, "path": "/sum root to leaf.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @return an integer\n ans=[]\n def sumNumbers(self, A):\n \n def summer(A,s=''):\n if not A:\n return\n \n s += str(A.val)\n \n if not A.right and not A.left:\n print(s)\n self.ans.append(s)\n #return\n \n if A.left:\n summer(A.left,s)\n if A.right:\n summer(A.right,s)\n \n s = s[:-1]\n \n #return\n summer(A)\n return (sum(map(int,self.ans)))%1003\n \n \n" }, { "alpha_fraction": 0.413943350315094, "alphanum_fraction": 0.47058823704719543, "avg_line_length": 14.551724433898926, "blob_id": "acb0851829b54383e1a5564c22edc3d9d77c66a7", "content_id": "bcb9e3e235e9a2f003bcec69aa5b0c9ea4f95cf0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 459, "license_type": "no_license", "max_line_length": 34, "num_lines": 29, "path": "/counting_triangles.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import Counter\nINT_MAX = 2147483647\n#brute force\ndef counting_triangles(a):\n a.sort()\n l = len(a)\n count=0\n for i in range(l-2):\n s0 = i+1\n s1 = l-1\n while(s0<s1):\n if a[i]+a[s0]> a[s1]:\n count+= s1 - s0\n s1-=1\n\n else:\n s0 += 1\n return count\n\n\n return count\n\n\n\n\n\n\na = list(map(int,input().split()))\nprint(counting_triangles(a))\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.4719334840774536, "alphanum_fraction": 0.4844074845314026, "avg_line_length": 19.04166603088379, "blob_id": "ce5b12b5df7b76318d1bd4e15701037343d6da45", "content_id": "0144ea25390d06546e12807ebebf54cef17dacbf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 481, "license_type": "no_license", "max_line_length": 38, "num_lines": 24, "path": "/subsets_duplicates.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def subset_dup(arr=[1,2,2]):\n ans = []\n arr.sort()\n\n def back_tracking(i=0, subset=[]):\n # goal\n if i == len(arr):\n if subset not in ans:\n ans.append(subset[:])\n return\n\n back_tracking(i + 1, subset)\n subset.append(arr[i])\n back_tracking(i + 1, subset)\n subset.pop()\n\n # function calling\n back_tracking()\n\n ans.sort()\n return ans\n\nif __name__ ==\"__main__\":\n print(subset_dup())\n" }, { "alpha_fraction": 0.38357487320899963, "alphanum_fraction": 0.3874396085739136, "avg_line_length": 22.35714340209961, "blob_id": "0b26a46afcf4d7518006447ea938d8ef89eb3b05", "content_id": "4668876dbb4ae569169f608d295c3038c702508b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1035, "license_type": "no_license", "max_line_length": 46, "num_lines": 42, "path": "/reorder_linked_lists.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @return the head node in the linked list\n def reorderList(self, A):\n count =0\n mid = A\n itr = A\n while itr:\n if count%2==1:\n mid = mid.next\n itr = itr.next\n count+=1\n \n prev = None\n itr = mid\n while itr:\n temp = itr.next\n itr.next = prev\n prev = itr\n itr = temp\n rev = prev\n ib = rev\n ia = A\n while ib:\n tempa = ia.next\n if tempa == None:\n ia = ib\n return A\n if tempa ==ib:\n return A\n ia.next = ib\n tempb = ib.next\n ib.next = tempa\n ia = tempa\n ib = tempb\n return A\n \n \n \n \n \n \n" }, { "alpha_fraction": 0.4867549538612366, "alphanum_fraction": 0.5, "avg_line_length": 32.55555725097656, "blob_id": "0a95b70821c7d5bffd40d81d20ccbb7f0f12a00e", "content_id": "85fbc912b23c6748e32db60d05180cb1bb66ef54", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 302, "license_type": "no_license", "max_line_length": 69, "num_lines": 9, "path": "/rotate_matrix.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of list of integers\n # @return the same list modified\n def rotate(self, A):\n l = len(A)\n for i in range(l//2):\n A[i], A[l-i-1] = A[l-i-1], A[i]\n ans = [[A[j][i] for j in range(l)] for i in range(len(A[0]))]\n return ans\n" }, { "alpha_fraction": 0.3896551728248596, "alphanum_fraction": 0.40344828367233276, "avg_line_length": 21.30769157409668, "blob_id": "e1f5baac0a84144430be185f1d25d5cf9d9d744d", "content_id": "0335800f0b76e1edb2146d4a93ba41c1fc58f58e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 290, "license_type": "no_license", "max_line_length": 35, "num_lines": 13, "path": "/remove_duplicates.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @return an integer\n def removeDuplicates(self, a):\n\n q=0\n t=1\n for i in range(1,len(a)):\n if a[i]!=a[q]:\n q=t\n a[i],a[t]=a[t],a[i]\n t+=1\n return t\n" }, { "alpha_fraction": 0.3741573095321655, "alphanum_fraction": 0.3797752857208252, "avg_line_length": 21.243244171142578, "blob_id": "2e2aca7d542218abb37bc0a3d73ba184a857e657", "content_id": "f92399f3ac3c2ed712850639bab20bd9307ea2ef", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 890, "license_type": "no_license", "max_line_length": 46, "num_lines": 37, "path": "/rotate_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @param B : integer\n # @return the head node in the linked list\n def rotateRight(self, A, B):\n itr = A\n len = 0\n while itr:\n len += 1\n itr = itr.next\n n = B%len\n if n==0:\n return A\n n = len - n\n\n\n \n count =0\n itr = A\n while itr:\n count+=1\n if count ==n:\n var = itr.next\n itr.next = None\n itr = var\n continue\n elif itr.next==None:\n itr.next = A\n return var\n \n itr = itr.next\n\n \n \n \n \n \n \n" }, { "alpha_fraction": 0.40257880091667175, "alphanum_fraction": 0.47277936339378357, "avg_line_length": 21.516128540039062, "blob_id": "efac006d4b0a17e97eb291246fcada94b1ad850b", "content_id": "5e2b611d54243e7c39dea0c60b015f4d46a9d63c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 698, "license_type": "no_license", "max_line_length": 83, "num_lines": 31, "path": "/integer_division_bit_manipulation.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "\"\"\"\napproach is we are trying to represnt the dividend in a binary format. say 36/3 =12\n12 is 1100 in binary\nso we cal also say that 3(8 + 4 + 0 + 0) = 36\nso we keep removing the biggest power of 2 b that is less than A.\n\"\"\"\n\nclass Solution:\n # @param A : integer\n # @param B : integer\n # @return an integer\n def divide(self, A, B):\n if A==-2147483648 and B==-1:\n return 2147483647\n\n sign=1\n \n if (A<0)^(B<0):\n sign =-1\n \n a,b = abs(A),abs(B)\n\n \n res=0\n while( a>=b):\n x=0\n while(a >= b<<1<<x):\n x+=1\n res += 1<<x\n a-= b<<x\n return ressign\n" }, { "alpha_fraction": 0.4285714328289032, "alphanum_fraction": 0.43877550959587097, "avg_line_length": 23.55555534362793, "blob_id": "1991cfa47171043dbb934714edb7a4257730241e", "content_id": "ca6245d89a5d7c1ae471f8b1ad70c07493798022", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 686, "license_type": "no_license", "max_line_length": 59, "num_lines": 27, "path": "/binary_sort_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @return the head node in the linked list\n def solve(self, A):\n #tryin to over write instead of new memory location\n itr = A\n count=[0,0]\n while itr:\n count[itr.val]+=1\n itr = itr.next\n \n itr = A\n i=0\n while itr:\n if count[i]==0:\n i+=1\n else:\n itr.val = i\n count[i]-=1\n itr = itr.next\n return A\n \n \n\n" }, { "alpha_fraction": 0.49840256571769714, "alphanum_fraction": 0.5175718665122986, "avg_line_length": 31.947368621826172, "blob_id": "717029776038e37509b0a74dd01fd548fdbc5c94", "content_id": "3903903d934e0687d3b9a7f171985143e936b848", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 626, "license_type": "no_license", "max_line_length": 89, "num_lines": 19, "path": "/Maximum Absolute Difference.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @return an integer\n def maxArr(self, A):\n #Maximum Absolute Difference\n \"\"\"\n trick open the absolute and try to see how to optimise keeping the compleity low.\n f(i,j) = \| A[i] - A[j] \| + \| i - j \|\n :param A: a list og numbers both pos and negative\n :return: using the above function generate the max value\n \"\"\"\n l1 =[]\n l2 = []\n for i,v in enumerate(A):\n l1.append(v+i)\n l2.append(v-i)\n m1 = max(l1) - min(l1)\n m2 = max(l2)- min(l2)\n return (max(m1,m2))\n" }, { "alpha_fraction": 0.38793104887008667, "alphanum_fraction": 0.4166666567325592, "avg_line_length": 26.799999237060547, "blob_id": "f1f2977a0f17130b8c51e8d64e9ef3afc8b3e736", "content_id": "d6947142a98dfd7126263528f06b7711b466caef", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 696, "license_type": "no_license", "max_line_length": 58, "num_lines": 25, "path": "/palindrome_without_alphaN.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import string\nclass Solution:\n # @param A : string\n # @return an integer\n def isPalindrome(self, s):\n s = s.lower()\n l = len(s)\n low=0\n high = l-1\n ref=\"abcdefghijklmnopqrstuvwxyz0123456789\"\n while(low<=high):\n if s[low] in ref and s[high] in ref:\n if s[low]==s[high]:\n low+=1\n high-=1\n else:\n return 0\n elif s[low] not in ref and s[high] not in ref:\n low+=1\n high-=1\n elif s[low] not in ref:\n low+=1\n elif s[high] not in ref:\n high-=1\n return 1\n\n" }, { "alpha_fraction": 0.4577586352825165, "alphanum_fraction": 0.48362070322036743, "avg_line_length": 24.217391967773438, "blob_id": "0d23204eb54f9beac22bf46f189a759ceafbe71d", "content_id": "87c6d0b6f709958a6b26fcc0bf7409d67d099793", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1160, "license_type": "no_license", "max_line_length": 79, "num_lines": 46, "path": "/Spiral.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def spiral(A):\n \"\"\"\n there will be 4 pointers called top,bottom,left, right\n keeping one fixed the others will travers all following a single while loop\n doing the same way as following a aprial staircase\n ther will also be 4 directoion\n 0: l to r\n 1: u to d\n 2: r to l\n 3: d to u\n\n :param A: a square matrix\n :return: a linear array with the spiral elements\n \"\"\"\n l=len(A)\n top,left=0,0\n right = len(A[0])-1\n bottom = l-1\n dir=0\n ans=[]\n\n while(top<=bottom and left<=right):\n\n if dir==0:\n for i in range(left,right+1):\n ans.append(A[top][i])\n top+=1\n dir+=1\n elif dir==1:\n for i in range(top,bottom+1):\n ans.append(A[i][right])\n right-=1\n dir+=1\n elif dir==2:\n #r to l\n for i in range(right,left-1,-1):\n ans.append(A[bottom][i])\n bottom-=1\n dir+=1\n elif dir==3:\n #d to u\n for i in range(bottom,top-1,-1):\n ans.append(A[i][left])\n left+=1\n dir=0\n return ans\n" }, { "alpha_fraction": 0.31661441922187805, "alphanum_fraction": 0.34639498591423035, "avg_line_length": 29.380952835083008, "blob_id": "383ca32b2a69f3affd370bd5637ce7365747b5c3", "content_id": "d77f9f0d60a108473cc3fd99cab3c0d83d82c95f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 638, "license_type": "no_license", "max_line_length": 62, "num_lines": 21, "path": "/three_sum_closest.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "INT_MAX = 2147483647\nclass Solution:\n # @param A : list of integers\n # @param B : integer\n # @return an integer\n def threeSumClosest(self, A,B):\n i = 0,\n ans = A[0] + A[1] + A[2]\n A.sort()\n for i in range(len(A) - 2):\n l, r = i+1, len(A) - 1\n while l < r:\n if abs(ans - B) > abs(A[i] + A[l] + A[r] - B):\n ans = A[i] + A[l] + A[r]\n if A[i] + A[l] + A[r] > B:\n r -= 1\n elif A[i] + A[l] + A[r] < B:\n l += 1\n else:\n return B\n return ans\n" }, { "alpha_fraction": 0.45719844102859497, "alphanum_fraction": 0.45719844102859497, "avg_line_length": 25.912281036376953, "blob_id": "860aa47c93ddcfa2457b124d1056a44b9f325725", "content_id": "57feae955a67e59996783b02e0d7d3acab4fb8bd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1542, "license_type": "no_license", "max_line_length": 62, "num_lines": 57, "path": "/delete_duplicates_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @return the head node in the linked list\n def deleteDuplicates(self, A):\n prev_prev = None\n prev = A\n current = A.next\n \n while current:\n if current.val != prev.val:\n prev_prev = prev\n prev = current\n current = current.next\n else:\n while current and current.val == prev.val:\n prev = current\n current = current.next\n if prev_prev != None: prev_prev.next = current\n if prev_prev == None: A = current\n prev = current\n if current: current = current.next\n return A\n \ndef delete_duplicate_my_solution(A):\n clock = None\n itr = A\n prev = None\n while itr:\n if clock and itr.val == clock:\n if prev:\n prev.next = itr.next\n itr = itr.next\n if itr == None:\n return A\n\n elif itr.next == None:\n return A\n\n\n elif itr.val != itr.next.val:\n if prev == None:\n A = itr\n prev = itr\n clock = None\n itr = itr.next\n elif itr.val == itr.next.val:\n\n clock = itr.val\n if prev:\n prev.next = itr.next\n itr = itr.next \n" }, { "alpha_fraction": 0.36666667461395264, "alphanum_fraction": 0.36851853132247925, "avg_line_length": 20.360000610351562, "blob_id": "42a59fc61eee96cb6790b65f730a0805eaeec918", "content_id": "5b104e9088aebd4a0cbd359cb809f73c1a591e6b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 540, "license_type": "no_license", "max_line_length": 37, "num_lines": 25, "path": "/path_directory_stacks.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import deque\nclass Solution:\n # @param A : string\n # @return a strings\n def simplifyPath(self, A):\n a = A.split('/')\n \n stack = deque()\n for i in a:\n if i in [\"\",\" \",\".\"]:\n continue\n elif i==\"..\":\n if len(stack)>0:\n stack.pop()\n else:\n continue\n else:\n stack.append(i)\n \n \n final = \"/\" + '/'.join(stack)\n \n \n \n return final\n\n \n" }, { "alpha_fraction": 0.3057607114315033, "alphanum_fraction": 0.3264401853084564, "avg_line_length": 23.178571701049805, "blob_id": "825bad3ff57957a61e4b051fca02637cf364ee84", "content_id": "ea19a5e2cc5e5f5532a83c3db838265fbf058022", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 677, "license_type": "no_license", "max_line_length": 47, "num_lines": 28, "path": "/binary_addition.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : string\n # @param B : string\n # @return a strings\n def addBinary(self, A, B):\n cy=0\n ans=''\n if len(B)>len(A):\n A,B = B,A\n \n B = '0'(len(A)-len(B)) + B\n \n A = A[::-1]\n B= B[::-1]\n for a,b in zip(A,B):\n if int(a) + int(b) +cy >2:\n ans+= '1'\n cy=1\n elif int(a) + int(b) +cy ==2:\n ans +='0'\n cy=1\n elif int(a) + int(b) +cy <2:\n ans+= str(int(a) + int(b) +cy )\n cy=0\n if cy:\n ans+='1'\n ans= ans[::-1]\n return int(ans)\n" }, { "alpha_fraction": 0.4359712302684784, "alphanum_fraction": 0.4532374143600464, "avg_line_length": 22.925926208496094, "blob_id": "b175f7325ce8f5a66fd5807fb862975069902bd3", "content_id": "b97ed9e7fb6c0eb82e1f5fa09dbe10e939d58324", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 695, "license_type": "no_license", "max_line_length": 53, "num_lines": 27, "path": "/sorted_Array_to_BST.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : tuple of integers\n # @return the root node in the tree\n\n \n def sortedArrayToBST(self, A):\n #2 pointer method\n # ^ ^\n # [1,2,3,4,5,6,7,8,9]\n if not A:\n return None\n \n A = list(A)\n \n mid = len(A)//2\n root = TreeNode(A[mid])\n root.right = self.sortedArrayToBST(A[mid+1:])\n root.left = self.sortedArrayToBST(A[:mid])\n \n return root\n \n\n \n \n \n" }, { "alpha_fraction": 0.35561877489089966, "alphanum_fraction": 0.37837839126586914, "avg_line_length": 20.96875, "blob_id": "eb6ee2195914418feb783c4b4ed7ba7821f66512", "content_id": "db42bff15fad8807637ee1ad18d9b5fd37ff327d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 703, "license_type": "no_license", "max_line_length": 46, "num_lines": 32, "path": "/letter_phone_BT.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def letter_phone(s):\n ans=[]\n dic= {'1':'1',\n '2':'abc',\n '3':'def',\n '4':'ghi',\n '5':'jkl',\n '6':'mno',\n '7':'pqrs',\n '8':'tuv',\n '9':'wxyz',\n '0':'0'}\n def back_tracking(pos=0,sub_Set=''):\n\n if len(sub_Set)==len(s): #GOAL\n ans.append(sub_Set)\n return\n\n for i in dic[s[pos]]: #CHOICES\n sub_Set+=i\n if pos<len(s): #CONSTRAINT\n back_tracking(pos+1,sub_Set)\n\n #pos-=1\n sub_Set = sub_Set[:-1]\n\n back_tracking()\n return ans\n\nif __name__ ==\"__main__\":\n s = str(input())\n print(letter_phone(s))\n" }, { "alpha_fraction": 0.608433723449707, "alphanum_fraction": 0.6120482087135315, "avg_line_length": 21.432432174682617, "blob_id": "7df4c3c709b5e2fee27cd182a3170d4cb87c5d80", "content_id": "9a197934eae8e7a682fd06ea4ab1bce7a765cbd1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 830, "license_type": "no_license", "max_line_length": 71, "num_lines": 37, "path": "/longest path nary tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import deque\nclass Solution:\n\t# @param A : list of integers\n\t# @return an integer\n\tdef solve(self, A):\n\t\n\t\tdef adjEdges(A):\n\t\t\tedges = {v: [] for v in range(len(A))}\n\n\t\t\tfor i, n in enumerate(A):\n\t\t\t\tif n == -1:\n\t\t\t\t\tstart = i\n\t\t\t\t\tcontinue\n\t\t\t\tedges[i].append(n)\n\t\t\t\tedges[n].append(i)\n\t\t\treturn start, edges\n\n\t\tdef BFS(edges, starting, flag):\n\t\t\tQ = deque()\n\t\t\tQ.appendleft((starting,0))\n\t\t\tvisited = [False]len(edges)\n\t\t\tvisited[starting]=True\n\t\t\twhile Q:\n\t\t\t\tnode, d = Q.pop()\n\t\t\t\tneighb = edges[node]\n\t\t\t\tfor n in neighb:\n\t\t\t\t\tif not visited[n]:\n\t\t\t\t\t\tQ.appendleft((n,d+1))\n\t\t\t\t\t\tvisited[n]=True\n\t\t\t#last node will be the node with the largest distance from root node\n\t\t\t#i.e node\n\t\t\tif not flag:\n\t\t\t\treturn node\n\t\t\treturn d\n\t\t\n\t\tstart, edges = adjEdges(A)\n\t\treturn BFS(edges,BFS(edges, start,False), True)\n" }, { "alpha_fraction": 0.5261437892913818, "alphanum_fraction": 0.5555555820465088, "avg_line_length": 16, "blob_id": "ccc801f0ccc2d3d3c08335ca80c2db3bc7c953d5", "content_id": "7516c7b6596a3dd73fc327ed1054778d84ab5fb2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 306, "license_type": "no_license", "max_line_length": 43, "num_lines": 18, "path": "/number_of_trailing_zeros.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\nimport string\ndef number_of_trailing_zeros(A):\n \"\"\"\n \n :param A: integer A>0\n :return: number of trailing zeros in A!\n \"\"\"\n \n pow = int(math.log(A,5))\n ans=0\n while(pow):\n ans+= A//(5*pow)\n pow-=1\n\n return ans\n\nprint(number_of_trailing_zeros(9247))\n" }, { "alpha_fraction": 0.39387309551239014, "alphanum_fraction": 0.4018964171409607, "avg_line_length": 20.421875, "blob_id": "58db5f048db1d3e759d0b2385fe173c7fe61e47a", "content_id": "8d6caa0f85d648bd525185cf68a333655698738e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1371, "license_type": "no_license", "max_line_length": 61, "num_lines": 64, "path": "/word_rank_with_rep.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\nclass Solution:\n # @param A : string\n # @return an integer\n \n def fac(self,li):\n p=1\n for i in li:\n if i>1:\n p= math.factorial(i)\n return p\n\n def duplicate(self,i,alp):\n \n d=alp.copy()\n s = list(alp.keys())[i]\n if d[s]>1:\n d[s]-=1\n else:\n d.pop(s)\n v = list(d.values())\n ans = math.factorial(sum(v)) / self.fac(v)\n \n return ans\n \n def findRank(self, A):\n \"\"\"\n approach: interesting\n \n :param A: a word\n :return: the rank of the word when arranged lexically\n \"\"\"\n \n p = 0\n alpha = [i for i in A]\n alpha.sort()\n keys=list(set(alpha))\n keys.sort()\n v= [alpha.count(i) for i in keys]\n alp = dict(zip(keys,v))\n \n\n \n ans = 0\n l = len(A)\n while p < l:\n search = A[p]\n p += 1\n i = list(alp.keys()).index(search)\n \n \n if i!=0:\n v= list(alp.values())\n #ans+= i* math.factorial(sum(v))/fac(v)\n for j in range(i):\n ans+= self.duplicate(j,alp)\n \n if alp[search]>1:\n alp[search]-=1\n else:\n alp.pop(search)\n \n \n return ans+1\n" } ]	97
xtracthub/temp-xtract-maps	https://github.com/xtracthub/temp-xtract-maps	0e2e5b812eba0f8c4ad47bfd69276b93e7f7c4f6	f4343bfa09a086f22ca2c19708ca76bb8e1cd872	51f3234ac21d0d6dd1e75b1fdab631735b195b7f	refs/heads/master	2020-06-18T08:28:16.247881	2019-07-10T15:41:11	2019-07-10T15:41:11	196,232,594	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.627289354801178, "alphanum_fraction": 0.666208803653717, "avg_line_length": 30.65217399597168, "blob_id": "11ae4a4b55404c619fed6b079a75b109f331e461", "content_id": "4eb93c8e9cdeb266eb41f581331cdccd003e3e33", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2184, "license_type": "no_license", "max_line_length": 79, "num_lines": 69, "path": "/tests/xtract_maps_tests.py", "repo_name": "xtracthub/temp-xtract-maps", "src_encoding": "UTF-8", "text": "import unittest\nimport os\nimport sys\n\nos.chdir(\"..\")\ncurrent_directory = os.getcwd()\nsys.path.append(current_directory + '/code')\n\nfrom xtract_maps_main import extract_map_metadata\n\n\nimg1 = current_directory + '/tests/test_imgs/CAIBOX_2009_map.jpg'\nimg2 = current_directory + '/tests/test_imgs/Bigelow2015_map.jpg'\nimg3 = current_directory + '/tests/test_imgs/GOMECC2_map.jpg'\nimg4 = current_directory + '/tests/test_imgs/Marion_Dufresne_map_1991_1993.jpg'\nimg5 = current_directory + '/tests/test_imgs/Oscar_Dyson_map.jpg'\nimg6 = current_directory + '/tests/test_imgs/P16S_2014_map.jpg'\nimg7 = current_directory + '/tests/test_imgs/us_states.png'\n\n\n# Test cases for xtract-maps. Very naively only checks whether it outputs\n# something or not.\nclass MapTests(unittest.TestCase):\n def setUp(self):\n pass\n\n def test_img1(self):\n img1_metadata = extract_map_metadata(img1)\n for i in range(3):\n self.assertTrue(img1_metadata[i])\n\n def test_img2(self):\n img2_metadata = extract_map_metadata(img2)\n self.assertTrue(img2_metadata[0])\n self.assertTrue(img2_metadata[1])\n self.assertFalse(img2_metadata[2])\n\n def test_img3(self):\n img3_metadata = extract_map_metadata(img3)\n self.assertTrue(img3_metadata[0])\n self.assertTrue(img3_metadata[1])\n self.assertFalse(img3_metadata[2])\n\n def test_img4(self):\n img4_metadata = extract_map_metadata(img4)\n self.assertTrue(img4_metadata[0])\n self.assertTrue(img4_metadata[1])\n self.assertFalse(img4_metadata[2])\n\n def test_img5(self):\n img5_metadata = extract_map_metadata(img5)\n self.assertTrue(img5_metadata[0])\n self.assertTrue(img5_metadata[1])\n self.assertFalse(img5_metadata[2])\n\n def test_img6(self):\n img6_metadata = extract_map_metadata(img6)\n for i in range(3):\n self.assertTrue((img6_metadata[i]))\n\n def test_img7(self):\n img7_metadata = extract_map_metadata(img7)\n self.assertFalse(img7_metadata[0])\n self.assertFalse(img7_metadata[1])\n self.assertTrue(img7_metadata[2])\n\n\nif __name__ == \"__main__\":\n unittest.main()\n" }, { "alpha_fraction": 0.7773019075393677, "alphanum_fraction": 0.7773019075393677, "avg_line_length": 20.227272033691406, "blob_id": "cc8c0ca0a4ed4a2e4a50176e1f248b8805f2a1d1", "content_id": "0807299aa8a9ad5ad12d61e54bd5eba959fd0f6a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 467, "license_type": "no_license", "max_line_length": 50, "num_lines": 22, "path": "/code/Dockerfile", "repo_name": "xtracthub/temp-xtract-maps", "src_encoding": "UTF-8", "text": "FROM python:latest\n\nMAINTAINER Ryan Wong\n\nRUN apt-get update\n\n# Copy files\nCOPY unionfind.py /\nCOPY contouring.py /\nCOPY text_extraction.py /\nCOPY coordinate_extraction.py /\nCOPY border_extraction.py /\nCOPY location_extraction.py /\nCOPY xtract_maps_main.py /\n\n# Install dependencies\nRUN pip install numpy opencv-python Pillow shapely\nRUN apt install tesseract-ocr -y\nRUN apt install libtesseract-dev -y\nRUN pip install pytesseract\n\nCMD [\"python\", \"xtract_maps_main.py\"]\n" } ]	2
hedgeneck/HedgePanel	https://github.com/hedgeneck/HedgePanel	4d60651d1316d4cc764b43b1f927ef272631b566	e47fcc6bde4682e3282b633de6775b6dd3cb74e6	f99816252511df0a74a42667ba67b8ee3bc66b12	refs/heads/master	2020-07-26T03:20:49.462430	2019-09-21T23:39:22	2019-09-21T23:39:22	208,517,280	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7469879388809204, "alphanum_fraction": 0.7469879388809204, "avg_line_length": 29.272727966308594, "blob_id": "8db88b8d96fee15981da723d8c17a0fa70501fb0", "content_id": "0411c15fcbdcb066d1c1228942af3addefd9bab1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 332, "license_type": "no_license", "max_line_length": 62, "num_lines": 11, "path": "/HedgePanel/__init__.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "from flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'dbfd46992a739efa9b8f10ebc97338b4'\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///storage.db'\napp.config['TEMPLATES_AUTO_RELOAD'] = True\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb = SQLAlchemy(app)\n\nfrom HedgePanel import routes" }, { "alpha_fraction": 0.5884605050086975, "alphanum_fraction": 0.5955701470375061, "avg_line_length": 41.52325439453125, "blob_id": "3754a365766039fc18c507cf55909faf81e7a2eb", "content_id": "7c23a264bd04da3a1e0baba73bbbc28676dd2393", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "R", "length_bytes": 3683, "license_type": "no_license", "max_line_length": 91, "num_lines": 86, "path": "/R/teste2.R", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "ISO-8859-1", "text": "library(httr)\nlibrary(stringr)\n\nurl = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\n\nresponse = POST(url, \n body = list(\n dData1 = \"09/09/2019\" ),\n encode = \"form\")\npage = content(response, \"text\", encoding = \"iso-8859-1\")\n\n# esse pattern pega toda a parte da tabela referente a DI\npattern = paste0(\n \"<tr>\\\\s<td>DI1[^<]</td>\\\\s\", #primeiro TD com nome do ativo sendo DI\n \"<td[^>]>[A-Z]\\\\d{2}[^<]</td>\\\\s\", # segundo TD contendo Vencimento\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # terceiro TD com Preço de Ajuste Anterior\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # quarto TD contendo Preço de Ajuste Atual\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # quinto TD contendo Variação\n \"<td[^>]>[\\\\d.,-]</td>\\\\s</tr>\", # Valor do Ajuste por Contrato\n \"(?:\\\\s<tr>\\\\s<td></td>\\\\s\", #Começa segunda série de TD, sem nada\n \"<td[^>]>[A-Z]\\\\d{2}[^<]</td>\\\\s\", # Vencimento\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # TD com Preço de Ajuste Anterior\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # TD contendo Preço de Ajuste Atual\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # TD contendo Variação\n \"<td[^>]>[\\\\d.,-]</td>\\\\s</tr>)\" # Valor do Ajuste por Contrato\n)\n\n# quando não for extrair nenhum grupo usa str_extract() e str_extract_all()\n# quando for extrair grupos, melhor usar str_match() e str_match_all()\ntotal_exp = str_extract(string = page, pattern = pattern)[[1]]\n\n# esse padrão serve para dividir cada table row em parte de um vetor\npattern = paste0(\n \"<tr>\\\\s<td>[^<]</td>\\\\s\", #primeiro TD com nome do ativo sendo DI\n \"<td[^>]>[A-Z]\\\\d{2}[^<]</td>\\\\s\", # segundo TD contendo Vencimento\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # terceiro TD com Preço de Ajuste Anterior\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # quarto TD contendo Preço de Ajuste Atual\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # quinto TD contendo Variação\n \"<td[^>]>[\\\\d.,-]</td>\\\\s</tr>\" # Valor do Ajuste por Contrato\n)\n\nparts = as.vector(str_extract_all(string = total_exp, pattern = pattern)[[1]])\n\n# esse padrão serve para extrair os groupings do vetor de table rows\npattern = paste0(\n \"<tr>\\\\s<td>[^<]</td>\\\\s\", #primeiro TD com nome do ativo sendo DI\n \"<td[^>]>([A-Z]\\\\d{2}[^<])</td>\\\\s\", # segundo TD contendo Vencimento\n \"<td[^>]>([\\\\d.,-])</td>\\\\s\", # terceiro TD com Preço de Ajuste Anterior\n \"<td[^>]>([\\\\d.,-])</td>\\\\s\", # quarto TD contendo Preço de Ajuste Atual\n \"<td[^>]>([\\\\d.,-])</td>\\\\s\", # quinto TD contendo Variação\n \"<td[^>]>([\\\\d.,-])</td>\\\\s</tr>\" # Valor do Ajuste por Contrato\n)\n\nmatches = str_match_all(string = parts, pattern = pattern)\n\nmatriz = matrix(unlist(matches),nrow=length(matches),byrow=T)\n\ndados = data.frame(matriz[,-1], stringsAsFactors = F)\n\n# essa solução para adicionar data adiciona no fim, portanto dá para melhorar\ndados['Data'] = dt\n\n# apenas para dar uma olhada e ver que ficou tudo factor\n# str(dados)\n\ncolnames(dados) = c(\"Vencimento\", \"PA_Anterior\", \"PA_Atual\", \"Variacao\", \"VAPC\")\n\ndados$Vencimento = gsub(\" \",\"\",dados$Vencimento)\n# se não escapar o . dá pau. Pode usar [.] tambem\ndados$PA_Anterior = gsub(\"\\\\.\",\"\",dados$PA_Anterior)\ndados$PA_Anterior = gsub(\",\",\".\",dados$PA_Anterior)\ndados$PA_Anterior = as.numeric(dados$PA_Anterior)\n\ndados$PA_Atual = gsub(\"\\\\.\",\"\",dados$PA_Atual)\ndados$PA_Atual = gsub(\",\",\".\",dados$PA_Atual)\ndados$PA_Atual = as.numeric(dados$PA_Atual)\n\ndados$Variacao = gsub(\"\\\\.\",\"\",dados$Variacao)\ndados$Variacao = gsub(\",\",\".\",dados$Variacao)\ndados$Variacao = as.numeric(dados$Variacao)\n\ndados$VAPC = gsub(\"\\\\.\",\"\",dados$VAPC)\ndados$VAPC = gsub(\",\",\".\",dados$VAPC)\ndados$VAPC = as.numeric(dados$VAPC)\n\n# writeLines(total_exp, \"outfile.txt\")\n" }, { "alpha_fraction": 0.640070915222168, "alphanum_fraction": 0.6835106611251831, "avg_line_length": 27.149999618530273, "blob_id": "bac876d22ae9cb5dbfe94a21c9f2d8b8683da326", "content_id": "b662b99ac56b4f25ee22539c8fe74911cb0ca315", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1137, "license_type": "no_license", "max_line_length": 90, "num_lines": 40, "path": "/testes/01_numeros_aleatorios.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "import random\nimport pylab\nimport numpy as np \nimport matplotlib.pyplot as plt \n\nx = random.sample([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 4)\n\nprint(x)\n\n\n\n# Tirei as informações desse site\n# https://www.geeksforgeeks.org/random-walk-implementation-python/\n# Para instalar o numpy e o matplotlib tem que primeiro sair do enviroment\n# >>> env\\Scripts\\deactivate\n# agora temos que primeiramente atualizar o pip\n# > python -m pip install --upgrade pip\n# Agora instala o numpy\n# > pip install numpy\n# Agora instala o matplotlib\n# Quando estamos dentro do interpretador e queremos chamar um script externo python usamos\n# exec(open(\"03_random_walk_2d.py\").read())\n\n### NUMPY\n# permite escrever matrizes, transpor, etc\n\n\n### tipos básicos de python\n# x = [1,2,5,4] LIST\n\t# [41, 'aaa', 32, ['a', 'b']] # listas podem ser heterogeneas, e aninhadas \n# t = (42, 1, 5) TUPLE - igual a lista, mas imutavel\n\t# x = 41, 37, 31 - outra maneira de inicializar\n# s = {1, 2, 4, 7} SET - não permite valores repetidos\n# d = {'a': 42, 'b': 717} DICTIONARY\n\n### referenciando tipos básicos de python\n# x[0] é 1\n# t[0] é 42\n# s[0] não funciona\n# d['a'] é 42\n\n\n" }, { "alpha_fraction": 0.6558139324188232, "alphanum_fraction": 0.6883720755577087, "avg_line_length": 18.636363983154297, "blob_id": "611c5f08dc4dc3c9faeea9e07013f782ed72474f", "content_id": "f3becef0e4f1c8d362d99119d1ddf37c1da49108", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 219, "license_type": "no_license", "max_line_length": 57, "num_lines": 11, "path": "/testes/06_function_random_walk.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "import numpy as np\n\n# vamos fazer a função random walk como uma função python\n\ndef random_walk(start=0, n=10, sigma=1):\n\trw = [start]\n\n\tfor i in range(1,n):\n\t\trw.append(rw[-1]+np.random.normal(0, sigma))\n\n\treturn rw" }, { "alpha_fraction": 0.6478873491287231, "alphanum_fraction": 0.7065727710723877, "avg_line_length": 25.6875, "blob_id": "c7140291eb5cb6306946cfa0c5603898521f6144", "content_id": "6fd8a7ace63a992f9ef04ad7f0e61e667171adc3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 432, "license_type": "no_license", "max_line_length": 93, "num_lines": 16, "path": "/testes/05_random_wak_caseiro.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "import numpy as np\n\n# uma maneira de iniciar um array com 1 elemento igual a 100 é np.array(100)\nrw = [np.array(100)]\n# np.array([1, 2, 3]) se quiser array com mais de 1\n# eu pensava em iniciar rw como um numpy array por causa do output da função normal mas acabo\n# de descobrir que a função normal com dimensão 1 retorna um float\n\nrw = [100]\n\nn = 100\n\nfor i in range(1,n):\n\trw.append(rw[-1]+np.random.normal(0, 1))\n\nprint(rw)" }, { "alpha_fraction": 0.8524590134620667, "alphanum_fraction": 0.8524590134620667, "avg_line_length": 29.5, "blob_id": "2a6a7f0f2eb43775a4310206b2f2e51a3e712ac1", "content_id": "05406e4882e825ebe8ac17fa382bed27854351c0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 62, "license_type": "no_license", "max_line_length": 47, "num_lines": 2, "path": "/README.md", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "# HedgePanel\nPainel com indicadores financeiros e econômicos\n" }, { "alpha_fraction": 0.5869289636611938, "alphanum_fraction": 0.6085025668144226, "avg_line_length": 26.578947067260742, "blob_id": "ba5c42aadd295ab103c478c9e7c6163bdd367870", "content_id": "a56c6b749421d0f262134d5fb2efcdc7a633aaaf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "R", "length_bytes": 1576, "license_type": "no_license", "max_line_length": 83, "num_lines": 57, "path": "/R/CETIP_CDI.R", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "\nler.CETIP.CDI = function(dt){\n stopifnot(is(dt, \"Date\"), length(dt) == 1)\n url = format(dt, \"ftp://ftp.cetip.com.br/MediaCDI/%Y%m%d.txt\")\n txt = try(readLines(url), silent = T)\n if(is(txt, \"try-error\")){\n stop(paste0(\"erro ao ler o CDI para a data \", dt))\n }\n txt = gsub(\" \", \"\", txt)\n cdi = as.numeric(txt)/100\n return(cdi)\n}\n\ncarregar.CETIP.CDI = function(dt){\n cdi = ler.CETIP.CDI(dt)\n dados = data.frame(data = dt, valor = cdi, stringsAsFactors = F)\n \n sql = \"INSERT INTO cdi_cetip (data, valor) VALUES (:data, :valor)\"\n r = try(dbSendQuery(conn, sql, params=dates.to.string(dados)))\n if(!is(r, \"try-error\")){\n message(\"CDI CETIP carregado com sucesso para a data \", format(dt, \"%Y-%m-%d\"))\n dbClearResult(r)\n } else {\n stop(\"Erro ao carregar CDI CETIP para a data \", format(dt, \"%Y-%m-%d\"))\n }\n}\n\nultima.data.CETIP.CDI = function(){\n dbGetQuery(conn, \"SELECT max(data) FROM CDI_CETIP\")[1,1]\n}\n\natualizar.CETIP.CDI = function(){\n message(\"Atualizando CDI CETIP...\")\n datas = dias.uteis.desde(ultima.data.CETIP.CDI())\n if(length(datas)==0){\n message(\"Nada a ser feito.\")\n } else {\n for(i in 1:length(datas)){\n try(carregar.CETIP.CDI(datas[i]))\n }\n }\n \n}\n# dt = as.Date(\"2018-07-17\", \"%Y-%m-%d\")\n# \n# ler.CETIP.CDI(dt)\n# \n# library(bizdays)\n# cal = create.calendar(\"ANBIMA\", holidaysANBIMA, weekdays=c(\"saturday\", \"sunday\"))\n# \n# datas = bizdays::bizseq(as.Date(\"2018-07-01\"), as.Date(\"2018-07-19\"), \"ANBIMA\")\n# \n# for(i in 1:length(datas)){\n# dt = datas[i]\n# cdi = ler.CETIP.CDI(dt)\n# print(dt)\n# print(cdi)\n# }\n\n\n\n" }, { "alpha_fraction": 0.5506808161735535, "alphanum_fraction": 0.6853252649307251, "avg_line_length": 27.782608032226562, "blob_id": "a5394d3140b4f919f87cd0ce8f7b06bafe4ed9ed", "content_id": "6c798705a0d24b17a192de9b6214d03ccb5dc358", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 670, "license_type": "no_license", "max_line_length": 86, "num_lines": 23, "path": "/testes/04_algumas_coisas_numericas.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "import numpy as np \n\nx = np.random.normal(1.01, 0.03, 5)\n# array([0.99444773, 0.94140399, 1.05218897, 1.00534229, 1.01679733])\n# notar como o resultado é um array, explorando melhor\n\ntype(x)\n# <class 'numpy.ndarray'>\n\ny = list(range(11, 17))\n# em python 3 range é um objeto iterator, então é necessário convertê-lo para lista\n# [11, 12, 13, 14, 15, 16] - ele só vai até 16\n\n\nz = [1] 5\n# para criar uma lista com o elemento repetidas vezes\n# z é [1, 1, 1, 1, 1]\n\ns = np.random.random(10)\n\nmu, sigma = 0, 0.1 # mean and standard deviation\ns2 = np.random.normal(mu, sigma, 5)\n# https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.random.normal.html" }, { "alpha_fraction": 0.5707497596740723, "alphanum_fraction": 0.6383315920829773, "avg_line_length": 22.674999237060547, "blob_id": "22bff0d2f3295599b4c0e3114a9da3f66546bf4b", "content_id": "3d637df5699f86c362f3324a738f47be0f419909", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "R", "length_bytes": 1894, "license_type": "no_license", "max_line_length": 93, "num_lines": 80, "path": "/R/teste.R", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "install.packages(\"httr\")\nlibrary(httr)\nlibrary(stringr)\n\nurl = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\n\nr = POST(\n url = url,\n body = list(\n dData1 = \"opa\"\n )\n)\n\nr = POST(\n url = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\n)\n\n\ntxt = content(r, as = \"text\", encoding = \"UTF-8\")\n\n\n\nr <- GET(url)\ncontent(r, as = \"text\", encoding = \"UTF-8\")\ncontent(r)\n\n\n\nurl = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\nquery = list('form[dData1]'=\"2014-11-01\")\nresponse = POST(url, body = query)\npage = content(response, \"text\", encoding = \"iso-8859-1\")\ncat(page, \"outfile.txt\")\nwriteLines(page, \"outfile.txt\")\n\n\n\n\n\n\nurl = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\nresponse = POST(url, body = list(\n dData1 = \"09/09/2019\"\n))\nresponse = POST(url, body = list(\n dData1 = 11/09/2019\n))\npage = content(response, \"text\", encoding = \"iso-8859-1\")\nwriteLines(page, \"outfile.html\")\n\n\n\n\n\n\nurl = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\nquery = list('form[dData1]'=\"dData1=11%2F09%2F2019\")\nresponse = POST(url, body = query)\npage = content(response, \"text\", encoding = \"iso-8859-1\")\nwriteLines(page, \"outfile.html\")\n\n\n\n\n\nurl = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\nresponse = POST(url, \n body = list(\n dData1 = \"09/09/2019\" ),\n encode = \"form\")\npage = content(response, \"text\", encoding = \"iso-8859-1\")\nwriteLines(page, \"outfile.html\")\n\n\nstrings <- c(\"Home: 219 733 8965. Work: 229-293-8753 \",\n \"banana pear apple\", \"595 794 7569 / 387 287 6718\")\nphone <- \"([2-9][0-9]{2})[- .]([0-9]{3})[- .]([0-9]{4})\"\n\na = str_extract_all(strings, phone)\nb = str_match_all(strings, phone)\n" }, { "alpha_fraction": 0.6530232429504395, "alphanum_fraction": 0.6632558107376099, "avg_line_length": 29.742856979370117, "blob_id": "e86e87a1825e4367f9f985d24af37464251a6091", "content_id": "dd59dcf01971b2000ebf307467f804182e88b3c2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1079, "license_type": "no_license", "max_line_length": 131, "num_lines": 35, "path": "/HedgePanel/routes.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "from flask import render_template\nfrom HedgePanel import app\nfrom HedgePanel.models import CDI_CETIP\nimport numpy as np\nfrom datetime import datetime, date\n\[email protected](\"/\")\[email protected](\"/home\")\ndef home():\n\treturn render_template('home.html')\n\[email protected](\"/about\")\ndef about():\n\treturn render_template('about.html')\n\n\[email protected](\"/cdi_media_cetip\")\ndef cdi_media_cetip(chartID = 'char_id', chart_type = 'line', chart_height = 400):\n\tz = CDI_CETIP.query.all()\n\tn = len(z)\n\n\tdados = [[0,0] for i in range(n)]\n\n\tfor i in range(n):\n\t\tdt = datetime.strptime(z[i].data, \"%Y-%m-%d\").date()\n\t\tdt = int(datetime.combine(dt, datetime.min.time()).timestamp())1000\n\t\tdados[i][0] = dt\n\t\tdados[i][1] = z[i].valor\n\n\tchart = {\"renderTo\": chartID, \"type\": chart_type, \"height\": chart_height,}\n\tseries = [{\"name\": 'CDI', \"data\": dados}]\n\ttitle = {\"text\": 'CDI média diária (%)'}\n\txAxis = {\"type\": 'datetime'}\n\tyAxis = {\"title\": {\"text\": 'CDI média diária (%)'}}\n\treturn render_template('cdi_media_cetip.html', chartID=chartID, chart=chart, series=series, title=title, xAxis=xAxis, yAxis=yAxis)" }, { "alpha_fraction": 0.6309148073196411, "alphanum_fraction": 0.64826500415802, "avg_line_length": 17.647058486938477, "blob_id": "5adaae61c1776b5ca0e2c5da94f6695d694d57f4", "content_id": "47e78637456934a04a36ab53024ad5f3cffb9464", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "R", "length_bytes": 634, "license_type": "no_license", "max_line_length": 67, "num_lines": 34, "path": "/R/CARGA_BDM.R", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "library(RSQLite)\nlibrary(bizdays)\ncal = bizdays::calendars()\n\n\nconectar.bdm = function(){ \n dbConnect(dbDriver(\"SQLite\"), \"../HedgePanel/storage.db\")\n}\n\ndesconectar.bdm = function(conn){\n dbDisconnect(conn)\n}\n\ndias.uteis.desde = function(date){\n if(is.na(date)) date = as.Date(\"2012-08-20\")\n datas = bizseq(date, Sys.Date()-1, \"Brazil/ANBIMA\")\n datas[datas != date]\n}\n\ndates.to.string = function(df){\n for(i in 1:ncol(df)){\n if(class(df[,i]) == \"Date\") df[,i] = format(df[,i], \"%Y-%m-%d\")\n }\n return(df)\n}\n\n\nsource(\"CETIP_CDI.R\")\n\n\nconn = conectar.bdm()\natualizar.CETIP.CDI()\natualizar.B3.CONTRATOS.DI()\ndbDisconnect(conn)\n" }, { "alpha_fraction": 0.5633187890052795, "alphanum_fraction": 0.5715927481651306, "avg_line_length": 41.24271774291992, "blob_id": "d028448169eb529158d2d0fd7a5ec18d0ae94554", "content_id": "e2cb570ab612d6f828678ba652f36461ae31677a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "R", "length_bytes": 4369, "license_type": "no_license", "max_line_length": 166, "num_lines": 103, "path": "/R/B3_CONTRATOS_DI.R", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "ISO-8859-1", "text": "library(stringr)\nlibrary(httr)\n\nler.B3.CONTRATOS.DI = function(dt){\n stopifnot(is(dt, \"Date\"), length(dt) == 1)\n url = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\n response = POST(url, \n body = list(\n dData1 = format(dt,\"%d/%m/%y\") ),\n encode = \"form\")\n page = content(response, \"text\", encoding = \"iso-8859-1\")\n pattern = paste0(\n \"<tr>\\\\s<td>DI1[^<]</td>\\\\s\", #primeiro TD com nome do ativo sendo DI\n \"<td[^>]>[A-Z]\\\\d{2}[^<]</td>\\\\s\", # segundo TD contendo Vencimento\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # terceiro TD com Preço de Ajuste Anterior\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # quarto TD contendo Preço de Ajuste Atual\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # quinto TD contendo Variação\n \"<td[^>]>[\\\\d.,-]</td>\\\\s</tr>\", # Valor do Ajuste por Contrato\n \"(?:\\\\s<tr>\\\\s<td></td>\\\\s\", #Começa segunda série de TD, sem nada\n \"<td[^>]>[A-Z]\\\\d{2}[^<]</td>\\\\s\", # Vencimento\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # TD com Preço de Ajuste Anterior\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # TD contendo Preço de Ajuste Atual\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # TD contendo Variação\n \"<td[^>]>[\\\\d.,-]</td>\\\\s</tr>)\" # Valor do Ajuste por Contrato\n )\n total_exp = str_extract(string = page, pattern = pattern)[[1]]\n if(is.na(total_exp)){\n stop(paste0(\"erro ao ler contratos DI para a data \", dt))\n }\n pattern = paste0(\n \"<tr>\\\\s<td>[^<]</td>\\\\s\", #primeiro TD com nome do ativo sendo DI\n \"<td[^>]>[A-Z]\\\\d{2}[^<]</td>\\\\s\", # segundo TD contendo Vencimento\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # terceiro TD com Preço de Ajuste Anterior\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # quarto TD contendo Preço de Ajuste Atual\n \"<td[^>]>[\\\\d.,-]</td>\\\\s\", # quinto TD contendo Variação\n \"<td[^>]>[\\\\d.,-]</td>\\\\s</tr>\" # Valor do Ajuste por Contrato\n )\n parts = as.vector(str_extract_all(string = total_exp, pattern = pattern)[[1]])\n pattern = paste0(\n \"<tr>\\\\s<td>[^<]</td>\\\\s\", #primeiro TD com nome do ativo sendo DI\n \"<td[^>]>([A-Z]\\\\d{2}[^<])</td>\\\\s\", # segundo TD contendo Vencimento\n \"<td[^>]>([\\\\d.,-])</td>\\\\s\", # terceiro TD com Preço de Ajuste Anterior\n \"<td[^>]>([\\\\d.,-])</td>\\\\s\", # quarto TD contendo Preço de Ajuste Atual\n \"<td[^>]>([\\\\d.,-])</td>\\\\s\", # quinto TD contendo Variação\n \"<td[^>]>([\\\\d.,-])</td>\\\\s</tr>\" # Valor do Ajuste por Contrato\n )\n matches = str_match_all(string = parts, pattern = pattern)\n matriz = matrix(unlist(matches),nrow=length(matches),byrow=T)\n dados = data.frame(matriz[,-1], stringsAsFactors = F)\n \n colnames(dados) = c(\"Vencimento\", \"PA_Anterior\", \"PA_Atual\", \"Variacao\", \"VAPC\")\n \n dados['Data'] = dt\n \n dados$Vencimento = gsub(\" \",\"\",dados$Vencimento)\n\n dados$PA_Anterior = gsub(\"\\\\.\",\"\",dados$PA_Anterior)\n dados$PA_Anterior = gsub(\",\",\".\",dados$PA_Anterior)\n dados$PA_Anterior = as.numeric(dados$PA_Anterior)\n \n dados$PA_Atual = gsub(\"\\\\.\",\"\",dados$PA_Atual)\n dados$PA_Atual = gsub(\",\",\".\",dados$PA_Atual)\n dados$PA_Atual = as.numeric(dados$PA_Atual)\n \n dados$Variacao = gsub(\"\\\\.\",\"\",dados$Variacao)\n dados$Variacao = gsub(\",\",\".\",dados$Variacao)\n dados$Variacao = as.numeric(dados$Variacao)\n \n dados$VAPC = gsub(\"\\\\.\",\"\",dados$VAPC)\n dados$VAPC = gsub(\",\",\".\",dados$VAPC)\n dados$VAPC = as.numeric(dados$VAPC)\n \n return(dados)\n}\n\ncarregar.B3.CONTRATOS.DI = function(dt){\n dados = ler.B3.CONTRATOS.DI(dt)\n \n sql = \"INSERT INTO b3_contratos_di (vencimento, pa_anterior, pa_atual, variacao, vapc, data) VALUES (:Vencimento, :PA_Anterior, :PA_Atual, :Variacao, :VAPC, :Data)\"\n r = try(dbSendQuery(conn, sql, params=dates.to.string(dados)))\n if(!is(r, \"try-error\")){\n message(\"B3_CONTRATOS_DI carregadoS com sucesso para a data \", format(dt, \"%Y-%m-%d\"))\n dbClearResult(r)\n } else {\n stop(\"Erro ao carregar B3_CONTRATOS_DI para a data \", format(dt, \"%Y-%m-%d\"))\n }\n}\n\nultima.data.B3.CONTRATOS.DI = function(){\n dbGetQuery(conn, \"SELECT max(data) FROM B3_CONTRATOS_DI\")[1,1]\n}\n\natualizar.B3.CONTRATOS.DI = function(){\n message(\"Atualizando B3 CONTRATOS DI...\")\n datas = dias.uteis.desde(ultima.data.B3.CONTRATOS.DI())\n if(length(datas)==0){\n message(\"Nada a ser feito.\")\n } else {\n for(i in 1:length(datas)){\n try(carregar.B3.CONTRATOS.DI(datas[i]))\n }\n }\n}\n" }, { "alpha_fraction": 0.6883116960525513, "alphanum_fraction": 0.6883116960525513, "avg_line_length": 27.090909957885742, "blob_id": "efaa7e3be358e2f0c4557b7a7f868caa044707f0", "content_id": "b255147adefbe32493c3445c416663ff1cd4acad", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 308, "license_type": "no_license", "max_line_length": 57, "num_lines": 11, "path": "/HedgePanel/models.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "from HedgePanel import db\n\nclass CDI_CETIP(db.Model):\n\t__tablename__ = \"CDI_CETIP\"\n\n\tid = db.Column(db.Integer, primary_key=True)\n\tdata = db.Column(db.String, unique=True, nullable=False)\n\tvalor = db.Column(db.String, nullable=False)\n\n\tdef __repr__(self):\n\t\treturn f\"CDI_CETIP('{self.data}': '{self.valor}')\"" } ]	13
skmatz/pytorch-lightning	https://github.com/skmatz/pytorch-lightning	336b83e74a10999f38629be966208f39a4b780e1	fc6d4027334b8869f02a3bdca0a0846f1cf79928	33bfdf36cc7d16c3c70c2ef8fa98d5c178c10fa2	refs/heads/master	2021-04-21T04:51:40.213877	2021-03-09T09:49:59	2021-03-09T09:49:59	345,974,442	0	0	Apache-2.0	2021-03-09T10:53:44	2021-03-09T10:25:26	2021-03-09T10:27:39	null	[ { "alpha_fraction": 0.6492230892181396, "alphanum_fraction": 0.6617252826690674, "avg_line_length": 29.42934799194336, "blob_id": "ddcaf0de59f07cefca938db5bbc883c0a9cd7679", "content_id": "52f585409e8650262b054a0944ac2300282b04ea", "detected_licenses": [ "Apache-2.0", "LicenseRef-scancode-proprietary-license" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5599, "license_type": "permissive", "max_line_length": 89, "num_lines": 184, "path": "/tests/accelerators/test_dp.py", "repo_name": "skmatz/pytorch-lightning", "src_encoding": "UTF-8", "text": "# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport torch\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\n\nimport pytorch_lightning as pl\nimport tests.helpers.pipelines as tpipes\nimport tests.helpers.utils as tutils\nfrom pytorch_lightning.callbacks import EarlyStopping\nfrom pytorch_lightning.core import memory\nfrom tests.helpers import BoringModel, RandomDataset\nfrom tests.helpers.datamodules import ClassifDataModule\nfrom tests.helpers.runif import RunIf\nfrom tests.helpers.simple_models import ClassificationModel\n\nPRETEND_N_OF_GPUS = 16\n\n\nclass CustomClassificationModelDP(ClassificationModel):\n\n def _step(self, batch, batch_idx):\n x, y = batch\n logits = self(x)\n return {'logits': logits, 'y': y}\n\n def training_step(self, batch, batch_idx):\n out = self._step(batch, batch_idx)\n loss = F.cross_entropy(out['logits'], out['y'])\n return loss\n\n def validation_step(self, batch, batch_idx):\n return self._step(batch, batch_idx)\n\n def test_step(self, batch, batch_idx):\n return self._step(batch, batch_idx)\n\n def validation_step_end(self, outputs):\n self.log('val_acc', self.valid_acc(outputs['logits'], outputs['y']))\n\n def test_step_end(self, outputs):\n self.log('test_acc', self.test_acc(outputs['logits'], outputs['y']))\n\n\n@RunIf(min_gpus=2)\ndef test_multi_gpu_early_stop_dp(tmpdir):\n \"\"\"Make sure DDP works. with early stopping\"\"\"\n tutils.set_random_master_port()\n\n dm = ClassifDataModule()\n model = CustomClassificationModelDP()\n\n trainer_options = dict(\n default_root_dir=tmpdir,\n callbacks=[EarlyStopping(monitor='val_acc')],\n max_epochs=50,\n limit_train_batches=10,\n limit_val_batches=10,\n gpus=[0, 1],\n accelerator='dp',\n )\n\n tpipes.run_model_test(trainer_options, model, dm)\n\n\n@RunIf(min_gpus=2)\ndef test_multi_gpu_model_dp(tmpdir):\n tutils.set_random_master_port()\n\n trainer_options = dict(\n default_root_dir=tmpdir,\n max_epochs=1,\n limit_train_batches=10,\n limit_val_batches=10,\n gpus=[0, 1],\n accelerator='dp',\n progress_bar_refresh_rate=0,\n )\n\n model = BoringModel()\n\n tpipes.run_model_test(trainer_options, model)\n\n # test memory helper functions\n memory.get_memory_profile('min_max')\n\n\n@RunIf(min_gpus=2)\ndef test_dp_test(tmpdir):\n tutils.set_random_master_port()\n\n dm = ClassifDataModule()\n model = CustomClassificationModelDP()\n trainer = pl.Trainer(\n default_root_dir=tmpdir,\n max_epochs=2,\n limit_train_batches=10,\n limit_val_batches=10,\n gpus=[0, 1],\n accelerator='dp',\n )\n trainer.fit(model, datamodule=dm)\n assert 'ckpt' in trainer.checkpoint_callback.best_model_path\n results = trainer.test(datamodule=dm)\n assert 'test_acc' in results[0]\n\n old_weights = model.layer_0.weight.clone().detach().cpu()\n\n results = trainer.test(model, datamodule=dm)\n assert 'test_acc' in results[0]\n\n # make sure weights didn't change\n new_weights = model.layer_0.weight.clone().detach().cpu()\n\n assert torch.all(torch.eq(old_weights, new_weights))\n\n\nclass ReductionTestModel(BoringModel):\n\n def train_dataloader(self):\n return DataLoader(RandomDataset(32, 64), batch_size=2)\n\n def val_dataloader(self):\n return DataLoader(RandomDataset(32, 64), batch_size=2)\n\n def test_dataloader(self):\n return DataLoader(RandomDataset(32, 64), batch_size=2)\n\n def add_outputs(self, output, device):\n output.update({\n \"reduce_int\": torch.tensor(device.index, dtype=torch.int, device=device),\n \"reduce_float\": torch.tensor(device.index, dtype=torch.float, device=device),\n })\n\n def training_step(self, batch, batch_idx):\n output = super().training_step(batch, batch_idx)\n self.add_outputs(output, batch.device)\n return output\n\n def validation_step(self, batch, batch_idx):\n output = super().validation_step(batch, batch_idx)\n self.add_outputs(output, batch.device)\n return output\n\n def test_step(self, batch, batch_idx):\n output = super().test_step(batch, batch_idx)\n self.add_outputs(output, batch.device)\n return output\n\n def training_epoch_end(self, outputs):\n assert outputs[0][\"loss\"].shape == torch.Size([])\n assert outputs[0][\"reduce_int\"].item() == 0 # mean([0, 1]) = 0\n assert outputs[0][\"reduce_float\"].item() == 0.5 # mean([0., 1.]) = 0.5\n\n\n@RunIf(min_gpus=2)\ndef test_dp_training_step_dict(tmpdir):\n \"\"\" This test verifies that dp properly reduces dictionaries \"\"\"\n model = ReductionTestModel()\n model.training_step_end = None\n model.validation_step_end = None\n model.test_step_end = None\n\n trainer = pl.Trainer(\n default_root_dir=tmpdir,\n max_epochs=1,\n limit_train_batches=1,\n limit_val_batches=1,\n limit_test_batches=1,\n gpus=2,\n accelerator='dp',\n )\n trainer.fit(model)\n" } ]	1
n04hk/Python_Zusammenfassung	https://github.com/n04hk/Python_Zusammenfassung	b118e967d5d5547ad3eb88f9570cb7c9de45d443	923fadb28ab4609450e532f08de41dc4bf4913d1	315788ed9c3727acca394ad107b0a55285a7ddc4	refs/heads/master	2020-04-24T20:28:43.656148	2019-04-28T13:52:31	2019-04-28T13:52:31	172,245,211	1	0	null	null	null	null	null	[ { "alpha_fraction": 0.5850340127944946, "alphanum_fraction": 0.646258533000946, "avg_line_length": 28.399999618530273, "blob_id": "91df6266200493a820008f420489c5e09004ac32", "content_id": "2444c49e05e3c481a09463a7e086e320b40ce757", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 147, "license_type": "no_license", "max_line_length": 51, "num_lines": 5, "path": "/listings/v6_klassen5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n speed_of_light = 299792458 # Klassen-Variable\n\n def __init__(self):\n self.name = 'unbekannt' # Instanz-Variable\n" }, { "alpha_fraction": 0.5748031735420227, "alphanum_fraction": 0.5905511975288391, "avg_line_length": 24.399999618530273, "blob_id": "bf47ec355f87c8a71d7fa1bc8cdb2daeb1da0608", "content_id": "202b4e336337fede980e59df682a35a22682b505", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 127, "license_type": "no_license", "max_line_length": 33, "num_lines": 5, "path": "/listings/v9_matplotlib8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "with plt.xkcd():\n plt.figure()\n plt.plot(np.random.randn(10))\n plt.xlabel('Zeit (s)')\n plt.ylabel('Amplitude (V)')\n" }, { "alpha_fraction": 0.6567164063453674, "alphanum_fraction": 0.6567164063453674, "avg_line_length": 15.75, "blob_id": "7d5c65a653e63eb8ff4692afcc1d9c5d30c5a1f7", "content_id": "c87cd8b89b2a798bfebc22e68ea32b91820d4df4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 67, "license_type": "no_license", "max_line_length": 28, "num_lines": 4, "path": "/listings/v3_datei6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "f = open('mailaenderli.txt')\ntext = f.read()\nf.close()\nprint(text)\n" }, { "alpha_fraction": 0.5390008687973022, "alphanum_fraction": 0.5635407567024231, "avg_line_length": 29.83783721923828, "blob_id": "62faee12bf3217ec1f1e056ffc3af4e0636c6087", "content_id": "0a4867aeb51ecba0aff99174c48e82078a791c32", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1141, "license_type": "no_license", "max_line_length": 100, "num_lines": 37, "path": "/listings/v7_vererbung10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class SuperKlasse:\n def __init__(self):\n self.pub = 'public Variable'\n self._prot = 'protected Variable'\n self.__priv = 'private Variable'\n\n def pub_func(self):\n print('public Methode')\n\n def _prot_func(self):\n print('protected Methode')\n\n def __priv_func(self):\n print('private Methode')\n\nclass SubKlasse(SuperKlasse):\n def __init__(self):\n self.pub_func()\n self._prot_func()\n self.__priv_func() # nicht erreichbar, kann in der Subklasse wiederbenutzt werden\n\n\nsub = SubKlasse() # Ausgabe:\n# public Methode\n# protected Methode\n\n# ---------------------------------------------------------------------------\n# AttributeError Traceback (most recent call last)\n# <ipython-input-12-479270c9858a> in <module>\n# ----> 1 sub = SubKlasse()\n\n# <ipython-input-11-1794f0b16121> in __init__(self)\n# 3 self.pub_func()\n# 4 self._prot_func()\n# ----> 5 self.__priv_func() # nicht erreichbar, kann in der Subklasse wiederbenutzt werden\n\n# AttributeError: 'SubKlasse' object has no attribute '_SubKlasse__priv_func'\n" }, { "alpha_fraction": 0.7714285850524902, "alphanum_fraction": 0.7714285850524902, "avg_line_length": 34, "blob_id": "2d6f02dba3533e8d095787b536474783bf83e818", "content_id": "7c63bcebf48fff499f53edd73a86139073761d5d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 35, "license_type": "no_license", "max_line_length": 34, "num_lines": 1, "path": "/listings/v3_exception8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "raise ValueError('Falscher Wert.')\n" }, { "alpha_fraction": 0.31147539615631104, "alphanum_fraction": 0.44262295961380005, "avg_line_length": 29.5, "blob_id": "1af526a72ad77fab35d831adb1803435e3720b28", "content_id": "1a8f507d785a57767f633c6e2373f0452ca5a949", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 61, "license_type": "no_license", "max_line_length": 42, "num_lines": 2, "path": "/listings/v5_ra24.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.findall(r'\\b(?<!#)\\d+', '#10, #25, 66')\n# Ausgabe: ['66']\n" }, { "alpha_fraction": 0.7058823704719543, "alphanum_fraction": 0.7058823704719543, "avg_line_length": 16, "blob_id": "41dc54aeb38d3fff9dd4ca1e4a629aac1a5ee968", "content_id": "ac9a3140f77a189f55b9785cfad1711e1761f6c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 17, "license_type": "no_license", "max_line_length": 16, "num_lines": 1, "path": "/listings/v8_numpy12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "np.linalg.inv(M)\n" }, { "alpha_fraction": 0.4084506928920746, "alphanum_fraction": 0.4084506928920746, "avg_line_length": 13.199999809265137, "blob_id": "f0e193513710aad8fe548c8f92d4823483a56dae", "content_id": "b2f2712f500b7b496f3371570bdefdaf201edd27", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 71, "license_type": "no_license", "max_line_length": 18, "num_lines": 5, "path": "/listings/v7_vererbung14.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "d = D() # Ausgabe:\n# D.__init__\n# B.__init__\n# C.__init__\n# A.__init__\n" }, { "alpha_fraction": 0.48653846979141235, "alphanum_fraction": 0.49038460850715637, "avg_line_length": 26.36842155456543, "blob_id": "bc6033966ccc1bdcc72defd69407ebc77a3f7639", "content_id": "9949beedbc0025b87583d58c51054ffc4c803a16", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 520, "license_type": "no_license", "max_line_length": 90, "num_lines": 19, "path": "/listings/v6_my_module.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\nprint('Dies ist {}:\\n__name__ = {}'.format(__file__, __name__))\n\nclass MeineKlasse:\n def __init__(self, name):\n self.name = name\n\n def gruss(self):\n print('Hallo', self.name)\n\n# -----------------------------------------------------------------------------\nif __name__ == '__main__':\n k = MeineKlasse('Python')\n k.gruss()\n\n# Konsolen-Ausgabe:\n# Dies ist C:/Users/Noah/Documents/GitHub/Python_Zusammenfassung/listings/v6_my_module.py:\n# __name__ = __main__\n# Hallo Python\n" }, { "alpha_fraction": 0.3513513505458832, "alphanum_fraction": 0.45945945382118225, "avg_line_length": 17.5, "blob_id": "5388f4f87046a97d2329eb948436106ec4175f16", "content_id": "f08efa648694b626f29cebb65347fe73346f093a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 37, "license_type": "no_license", "max_line_length": 20, "num_lines": 2, "path": "/listings/v8_numpy15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "v = np.array([5, 6])\n# array([5, 6])\n" }, { "alpha_fraction": 0.4035087823867798, "alphanum_fraction": 0.5087719559669495, "avg_line_length": 27.5, "blob_id": "80c83298d81d5bc225b7fac494fc04a8a49f5d68", "content_id": "69dc28ba8ce410a451d310f0f3df6efaf4c6855a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 57, "license_type": "no_license", "max_line_length": 35, "num_lines": 2, "path": "/listings/v4_tupel20.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "list(map(lambda x: xx, [1, 2, 3]))\n# Ausgabe: [1, 4, 9]\n" }, { "alpha_fraction": 0.4030612111091614, "alphanum_fraction": 0.5408163070678711, "avg_line_length": 23.5, "blob_id": "1a7240ddeed6a4b20380c7bd28612d67761d2816", "content_id": "eff4fbece4848ba466dcba5cb61304c6e6e4d54f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 196, "license_type": "no_license", "max_line_length": 39, "num_lines": 8, "path": "/listings/v8_numpy1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr1 = np.array([1, 2, 3])\nprint(arr1) # Ausgabe: [1 2 3]\narr2 = np.array([[1, 2, 3], [4, 5, 6]])\nprint(arr2) # Ausgabe:\n# [[1 2 3]\n# [4 5 6]]\narr2.ndim # Ausgabe: 2\narr2.shape # Ausgabe: (2, 3)\n" }, { "alpha_fraction": 0.4029304087162018, "alphanum_fraction": 0.43223443627357483, "avg_line_length": 33.125, "blob_id": "abde9d51b6a8488b4e75e460c7ae871c51208de2", "content_id": "d5f19b04b88e59d59a6a622e4bb5b39d1771f451", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 273, "license_type": "no_license", "max_line_length": 77, "num_lines": 8, "path": "/listings/v4_iter6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "next(iterator)\n# Ausgabe:\n# ---------------------------------------------------------------------------\n# StopIteration Traceback (most recent call last)\n# <ipython-input-8-4ce711c44abc> in <module>()\n# ----> 1 next(iterator)\n#\n# StopIteration:\n" }, { "alpha_fraction": 0.5755102038383484, "alphanum_fraction": 0.6693877577781677, "avg_line_length": 23.5, "blob_id": "5ea2d4c74000d7365aa86e52c75db35fb5158d04", "content_id": "4538cfb1c88ff5ba345ed57d7a2db6206aec8667", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 245, "license_type": "no_license", "max_line_length": 38, "num_lines": 10, "path": "/listings/v9_matplotlib2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "plt.figure()\nax1 = plt.subplot(2, 1, 1)\nax1.plot(np.random.randn(10));\nax1.set_xlabel('X1')\nax1.set_ylabel('Y1')\nax2 = plt.subplot(2, 1, 2, sharex=ax1)\nax2.plot(np.random.randn(10));\nax2.set_xlabel('X2')\nax2.set_ylabel('Y2')\nplt.tight_layout();\n" }, { "alpha_fraction": 0.758865237236023, "alphanum_fraction": 0.758865237236023, "avg_line_length": 69.5, "blob_id": "ad55936498f7e68d7359a63676002b7850dbb636", "content_id": "db74d7016ed513639ffb104836c8ce1c9c55a306", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 141, "license_type": "no_license", "max_line_length": 102, "num_lines": 2, "path": "/listings/v3_strings19.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "spruch.replace('sollten', 'muessten')\n# Ausgabe: 'Wir muessten heute das tun,\\nvon dem wir uns morgen wuenschen\\nes gestern getan zu haben.'\n" }, { "alpha_fraction": 0.6590909361839294, "alphanum_fraction": 0.7045454382896423, "avg_line_length": 13.666666984558105, "blob_id": "8faa18d2f98cbbf6668746f3eb14d1682f9893f9", "content_id": "4a8f897a6da2a7ff89ab107357ff3f9203cc1552", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 44, "license_type": "no_license", "max_line_length": 14, "num_lines": 3, "path": "/listings/v2_if1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "if Bedingung:\n Anweisung1\n Anweisung2\n" }, { "alpha_fraction": 0.409326434135437, "alphanum_fraction": 0.5440414547920227, "avg_line_length": 37.599998474121094, "blob_id": "ef1cf619721283778bb95928253be13523b90b5a", "content_id": "1a1032af99bb89fc8da1d6adb2937fcee14114d7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 193, "license_type": "no_license", "max_line_length": 54, "num_lines": 5, "path": "/listings/v8_numpy5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr = np.arange(8)\nprint(arr) # Ausgabe: array([0, 1, 2, 3, 4, 5, 6, 7])\ns = arr[2:5] # array([2, 3, 4])\ns[0] = 13 # modifiziert auch arr\nprint(arr) # Ausgabe: array([0, 1, 13, 3, 4, 5, 6, 7])\n" }, { "alpha_fraction": 0.3712121248245239, "alphanum_fraction": 0.5454545617103577, "avg_line_length": 32, "blob_id": "10fbd33c26fb5030d158f4e5f1197642f2664dd3", "content_id": "a476e7e5cecd6e42b26390b1273d8d56e44dc2d8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 132, "license_type": "no_license", "max_line_length": 67, "num_lines": 4, "path": "/listings/v4_list11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "menge = {xy for x in range(6) for y in range(6)}\n\nprint(menge)\n# Ausgabe: set([0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 20, 25])\n" }, { "alpha_fraction": 0.2982456088066101, "alphanum_fraction": 0.7368420958518982, "avg_line_length": 10.399999618530273, "blob_id": "60adf7ab0f5c2e6974a346356125b2b46e910954", "content_id": "4a55b5b8feca06ffb3c224e9e510a2614ab626cf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 57, "license_type": "no_license", "max_line_length": 15, "num_lines": 5, "path": "/listings/v3_strings2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "Menge,Name,Wert\n3,R1,1500\n7,R2,100\n2,R3,22000\n5,R4,47000\n" }, { "alpha_fraction": 0.7307692170143127, "alphanum_fraction": 0.7307692170143127, "avg_line_length": 25, "blob_id": "9eaea0ab7eb30fb6c40326396a57a29a0a186f4f", "content_id": "9e91929d5c9d4b222ad385cfad9b265851f7086e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 78, "license_type": "no_license", "max_line_length": 32, "num_lines": 3, "path": "/listings/v4_iter3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "iterator = iter(liste)\nprint(type(iterator))\n# Ausgabe: <type 'listiterator'>\n" }, { "alpha_fraction": 0.4895833432674408, "alphanum_fraction": 0.5208333134651184, "avg_line_length": 17, "blob_id": "1e2108a281c6eb3fb656cf7d08e49126f4136877", "content_id": "f5aea2e8b55b94ae43dc0ed5b86459a8422d7e81", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 288, "license_type": "no_license", "max_line_length": 80, "num_lines": 16, "path": "/listings/v4_iter10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def counter():\n n = 0\n while True:\n wert = yield n # next() liefert None zurueck, send(x) liefert x zurueck\n if wert is not None:\n n = wert\n else:\n n += 1\n\nc = counter()\nnext(c)\n# Ausgabe: 0\nc.send(50)\n# Ausgabe: 50\nnext(c)\n# Ausgabe: 51\n" }, { "alpha_fraction": 0.3726707994937897, "alphanum_fraction": 0.5155279636383057, "avg_line_length": 22, "blob_id": "1287a2cad82f660c9f48b803320c7f16515f6601", "content_id": "23abcc736ed86f6e142b503c7a093d1fd654530d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 161, "license_type": "no_license", "max_line_length": 67, "num_lines": 7, "path": "/listings/v4_list10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "menge = set()\nfor x in range(6):\n for y in range(6):\n menge.add(xy)\n\nprint(menge)\n# Ausgabe: set([0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 20, 25])\n" }, { "alpha_fraction": 0.7291666865348816, "alphanum_fraction": 0.7291666865348816, "avg_line_length": 18.200000762939453, "blob_id": "5da2afccdd0fcb5ac0f8a30b9682cf18b78ce524", "content_id": "fb9aef4180cfe3c4bd44973afb4721e460de196c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 96, "license_type": "no_license", "max_line_length": 41, "num_lines": 5, "path": "/listings/v6_klassen15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def quadrieren(x):\n return xx\n\nclass MeineKlasse:\n quadrieren = staticmethod(quadrieren)\n" }, { "alpha_fraction": 0.3448275923728943, "alphanum_fraction": 0.4482758641242981, "avg_line_length": 28, "blob_id": "1a6781a5900b18642c912e37d11449e7cbc5c4af", "content_id": "75d05cfb41d43d227040a942bca7f1326ed828e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 29, "license_type": "no_license", "max_line_length": 28, "num_lines": 1, "path": "/listings/v8_numpy2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr = np.array([1., 2., 3.])\n" }, { "alpha_fraction": 0.6352941393852234, "alphanum_fraction": 0.7215686440467834, "avg_line_length": 62.75, "blob_id": "940f8376c282e42f604e2142f51be74883c07ae5", "content_id": "04727575cf866db73e06a20729521ab1054d390d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 255, "license_type": "no_license", "max_line_length": 83, "num_lines": 4, "path": "/listings/v6_klassen8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x.speed_of_light = 10 # hier wird eine neue Instanz-Variable erzeugt\nprint('x:', x.speed_of_light) # Ausgabe: x: 10\nprint('y:', y.speed_of_light) # Ausgabe: y: 299792458\nprint('MeineKlasse:', MeineKlasse.speed_of_light) # Ausgabe: MeineKlasse: 299792458\n" }, { "alpha_fraction": 0.4701492488384247, "alphanum_fraction": 0.5373134613037109, "avg_line_length": 25.799999237060547, "blob_id": "36d98074c151ca4aba373a55e252074d06cf7db1", "content_id": "07cbce2a48fa923b89c221b648e45a1a368c40af", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 268, "license_type": "no_license", "max_line_length": 52, "num_lines": 10, "path": "/listings/v9_matplotlib5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x = y = np.linspace(-3, 3, 100)\nX, Y = np.meshgrid(x, y)\nZ1 = np.exp(-X2 - Y2)\nZ2 = np.exp(-(X - 1)2 - (Y - 1)2)\nZ = (Z1 - Z2) * 2\n\nfig, ax = plt.subplots()\nCS = ax.contourf(X, Y, Z, 20, cmap=plt.cm.coolwarm);\ncbar = fig.colorbar(CS);\ncbar.ax.set_ylabel('C');\n" }, { "alpha_fraction": 0.6984127163887024, "alphanum_fraction": 0.7301587462425232, "avg_line_length": 20, "blob_id": "e5e29fec2be52c44f7d7e3ea12fca464bc368cb6", "content_id": "58c7a027ae4296c81ac09fcfd85eb2869e6be61d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 63, "license_type": "no_license", "max_line_length": 40, "num_lines": 3, "path": "/listings/v4_list2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "quadratzahlen = [nn for n in range(11)]\n\nprint(quadratzahlen)\n" }, { "alpha_fraction": 0.4830508530139923, "alphanum_fraction": 0.5508474707603455, "avg_line_length": 38.33333206176758, "blob_id": "0bd9ebb05b1d97297ee00d89990a69dba3e18a66", "content_id": "49cb3c3562c1661a36c5afacc29591568d315321", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 118, "license_type": "no_license", "max_line_length": 47, "num_lines": 3, "path": "/listings/v3_strings9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "messung = {'spannung': 24, 'strom': 2.5}\n'U = {spannung}, I = {strom}'.format(messung)\n# Ausgabe: 'U = 24, I = 2.5'\n" }, { "alpha_fraction": 0.6554622054100037, "alphanum_fraction": 0.6722689270973206, "avg_line_length": 18.83333396911621, "blob_id": "9013e5408ca6140fe5f3ed2da5a49ccc7fbb48ee", "content_id": "dce39cf5a68fc8ee388ea62b60ed3893b0ab56e7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 119, "license_type": "no_license", "max_line_length": 38, "num_lines": 6, "path": "/listings/v6_klassen16.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n @staticmethod\n def quadrieren(x):\n return xx\n\nMeineKlasse.quadrieren(3) # Ausgabe: 9\n" }, { "alpha_fraction": 0.6602451801300049, "alphanum_fraction": 0.6602451801300049, "avg_line_length": 26.190475463867188, "blob_id": "45ee7c96ba5cbcd89731cbb37480c11e61521117", "content_id": "cfb973e0267d2813b7e1f9552641721dd1fd913b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 571, "license_type": "no_license", "max_line_length": 72, "num_lines": 21, "path": "/listings/v2_func13.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "modul = 'Python' # globale Variable\n\ndef anmeldung():\n print(modul) # Variable existiert bereits ausserhalb der Funktion\n\nanmeldung() # Ausgabe: Python\n\ndef wechseln():\n modul = 'C++' # erstellt eine neue lokale Variable\n print('lokal:', modul)\n\nwechseln() # Ausgabe: lokal: C++\nprint('global:', modul) # Ausgabe: global: Python\n\ndef wirklich_wechseln():\n global modul #referenzieren auf die globale Variable\n modul = 'C++'\n print('lokal:', modul)\n\nwirklich_wechseln() # Ausgabe: lokal: C++\nprint('global:', modul) # Ausgabe: global: C++\n" }, { "alpha_fraction": 0.6320754885673523, "alphanum_fraction": 0.650943398475647, "avg_line_length": 20.200000762939453, "blob_id": "daf1109b8d3a637441f366a660d750831e65d9f7", "content_id": "02370c935a7967ed21c38e5021d94418ba5b98b5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 106, "license_type": "no_license", "max_line_length": 52, "num_lines": 5, "path": "/listings/v3_exception3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "eingabe = '10 Fr.'\ntry:\n x = int(eingabe)\nexcept:\n print('Oops! Irgendein Fehler ist aufgetreten.')\n" }, { "alpha_fraction": 0.6623376607894897, "alphanum_fraction": 0.6623376607894897, "avg_line_length": 37.5, "blob_id": "aa972f2d0e4fdfc3c12abc48c32a040c6c53d8da", "content_id": "c610578d418b3124302a4604f1941b2ca08fe537", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 77, "license_type": "no_license", "max_line_length": 42, "num_lines": 2, "path": "/listings/v6_klassen10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = MeineKlasse('Wall-E') # name='Wall-E'\n# Ausgabe: Wall-E wurde erstellt.\n" }, { "alpha_fraction": 0.48022598028182983, "alphanum_fraction": 0.6384180784225464, "avg_line_length": 43.25, "blob_id": "fbf9a4425f7d6d60a39653c3ffaba4a10ca7de3c", "content_id": "3c2c5553e38c24a110e07771207b6b57ff54407f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 177, "license_type": "no_license", "max_line_length": 88, "num_lines": 4, "path": "/listings/v4_list7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = ', '.join(['{:.0f}km={:.0f}mi'.format(km, mi) for km, mi in zip(kilometer, meilen)])\n\nprint(s)\n# Ausgabe: 30km=19mi, 50km=31mi, 60km=37mi, 80km=50mi, 100km=62mi, 120km=75mi\n" }, { "alpha_fraction": 0.6829268336296082, "alphanum_fraction": 0.6829268336296082, "avg_line_length": 23.600000381469727, "blob_id": "7d01ab9255660c1124be8592e1c5e6841fec9fde", "content_id": "3d8ead0a69c0fad809cd0caaabcb526748106040", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 123, "license_type": "no_license", "max_line_length": 35, "num_lines": 5, "path": "/listings/v3_datei8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "with open('mailaenderli.txt') as f:\n zeilen = f.readlines()\nprint(zeilen)\nfor zeile in zeilen:\n print(zeile.strip())\n" }, { "alpha_fraction": 0.739130437374115, "alphanum_fraction": 0.739130437374115, "avg_line_length": 22, "blob_id": "22e51b014891e36197006ab64564262e046f45ae", "content_id": "c82ba800295a701f1fd65581cead0f09802e7779", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 23, "license_type": "no_license", "max_line_length": 22, "num_lines": 1, "path": "/listings/v6_klassen4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "objekt = MeineKlasse()\n" }, { "alpha_fraction": 0.375, "alphanum_fraction": 0.4642857015132904, "avg_line_length": 27, "blob_id": "71ce0e63aec76e57d893bdbd418049897f7807b4", "content_id": "aa83b2c0e29c78fb7b17e33e4a898be2cc16b030", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 56, "license_type": "no_license", "max_line_length": 42, "num_lines": 2, "path": "/listings/v4_tupel15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste.sort()\nliste # Ausgabe: [1, 2, 3, 4, 5]\n" }, { "alpha_fraction": 0.6138613820075989, "alphanum_fraction": 0.6336633563041687, "avg_line_length": 19.200000762939453, "blob_id": "8d7e70e2478bd8697d22633d6c809184c26edf17", "content_id": "46b86e0b224e95364692802a3d9583fc1422698b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 101, "license_type": "no_license", "max_line_length": 28, "num_lines": 5, "path": "/listings/v3_strings14.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "bin_data = 'A'.encode(utf-8)\nprint(bin_data)\n# Ausgabe: b'A'\nbin_data.decode('utf-8')\n# Ausgabe: 'A'\n" }, { "alpha_fraction": 0.6327683329582214, "alphanum_fraction": 0.6723163723945618, "avg_line_length": 21.125, "blob_id": "c09af4ba30ed7f40f2903d3c96f00e847e0d4169", "content_id": "29fd805f319b6b7c1cc49dae42648b06d285d13e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 354, "license_type": "no_license", "max_line_length": 41, "num_lines": 16, "path": "/listings/v8_numpy13.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\nt = np.arange(100)/100\ns1 = np.sin(2np.pit)\ns2 = s1 + np.random.randn(s1.shape)/4\n\nplt.figure()\nplt.plot(t, s1, '.-', label='Simulation')\nplt.plot(t, s2, '.-', label='Messung')\nplt.xlabel('Zeit (s)')\nplt.ylabel('Amplitude')\nplt.grid(True)\nplt.legend()\nplt.tight_layout()\nplt.savefig('diagramm.pdf')\n" }, { "alpha_fraction": 0.5478261113166809, "alphanum_fraction": 0.5652173757553101, "avg_line_length": 20.5625, "blob_id": "ca6c9836e1278d09217f21ea28287ab11a7fd79f", "content_id": "e31b019af31b23688b5c64cc088a2470c6e6ed5e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 345, "license_type": "no_license", "max_line_length": 56, "num_lines": 16, "path": "/listings/v5_ra2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "m = re.match(r'[a-z]+', 'hallo welt!')\nprint(m)\n# Ausgabe: <re.Match object; span=(0, 5), match='hallo'>\n\nif m is not None:\n print('group:', m.group())\n print('start:', m.start())\n print('end:', m.end())\n print('span:', m.span())\nelse:\n print('keine Uebereinstimmung')\n# Ausgabe:\n# group: hallo\n# start: 0\n# end: 5\n# span: (0, 5)\n" }, { "alpha_fraction": 0.4557721018791199, "alphanum_fraction": 0.49025487899780273, "avg_line_length": 43.46666717529297, "blob_id": "704ed362b5de870e3ec851cf2ccd2b00470c3f6e", "content_id": "cbb9c9bb56963c9cb82ef6ceea78bdedcf5edcb9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 667, "license_type": "no_license", "max_line_length": 77, "num_lines": 15, "path": "/listings/v6_klassen21.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "objekt.__priv # Ausgabe:\n# ---------------------------------------------------------------------------\n# AttributeError Traceback (most recent call last)\n# <ipython-input-5-9bcc05561362> in <module>\n# ----> 1 objekt.__priv\n#\n# AttributeError: 'MeineKlasse' object has no attribute '__priv'\n\nobjekt.__priv_funktion() # Ausgabe:\n# ---------------------------------------------------------------------------\n# AttributeError Traceback (most recent call last)\n# <ipython-input-16-6e6c693108e6> in <module>\n# ----> 1 objekt.__priv_funktion()\n#\n# AttributeError: 'MeineKlasse' object has no attribute '__priv_funktion'\n" }, { "alpha_fraction": 0.6422287225723267, "alphanum_fraction": 0.6598240733146667, "avg_line_length": 36.88888931274414, "blob_id": "bc88a27a6de234373dd4c25037e67d72ed9d7487", "content_id": "b27bb1214ae6feef24c71ad90ad786e2a5d8451b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 341, "license_type": "no_license", "max_line_length": 64, "num_lines": 9, "path": "/listings/v9_interpolate3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "f_lin = interpld(x, y) # lineare Interpolation\nf_cub = interpld(x, y, kind='cubic') # kubische Interpolation\nxnew = np.linspace(0, 10, num=101, endpoint=True)\nplt.figure()\nplt.plot(x, y, 'o', label='Stuetzwerte')\nplt.plot(xnew, f_lin(xnew), '-', label='linear')\nplt.plot(xnew, f_cub(xnew), '--', label='kubisch')\nplt.legend()\nplt.show()\n" }, { "alpha_fraction": 0.4399999976158142, "alphanum_fraction": 0.5199999809265137, "avg_line_length": 11.5, "blob_id": "b0fec7740d813df298f501d406e7166434956520", "content_id": "d0f473356314e5cddc632cc14fd9a631920c37c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 25, "license_type": "no_license", "max_line_length": 15, "num_lines": 2, "path": "/listings/v3_strings13.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "ord('A')\n# Ausgabe: (65)\n" }, { "alpha_fraction": 0.6271186470985413, "alphanum_fraction": 0.694915235042572, "avg_line_length": 28.5, "blob_id": "6dac7438f8d97cea7fafe37c7beb4bf80e7c3eb2", "content_id": "fb1f2ed877f7ba596b6e3fdbc1d06929f6340467", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 59, "license_type": "no_license", "max_line_length": 45, "num_lines": 2, "path": "/listings/v9_matplotlib4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "plt.figure()\nplt.step(np.arange(15), np.random.randn(15));\n" }, { "alpha_fraction": 0.5, "alphanum_fraction": 0.5727272629737854, "avg_line_length": 26.5, "blob_id": "098c5be830bc7ba19056694de23068c1869889dc", "content_id": "0345fc9d94c0e11062f564e2ccc094bb05ac5ee2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 110, "license_type": "no_license", "max_line_length": 32, "num_lines": 4, "path": "/listings/v3_exception1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "int('bla') => ValueError\n5/0 => ZeroDivisionError\na[1000] => IndexError\n10 + 'Fr.' => TypeError\n" }, { "alpha_fraction": 0.6041666865348816, "alphanum_fraction": 0.6822916865348816, "avg_line_length": 47, "blob_id": "e0d6ee304da4b1f876276a2439f966954ea16581", "content_id": "144bdfb558049dd3e09561509e12cbaf2f17c195", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 192, "license_type": "no_license", "max_line_length": 57, "num_lines": 4, "path": "/listings/v2_func6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "resultat1 = summe(2, 3)\nresultat2 = summe(a=10, b=2) # Schluesselwortparameter\nresultat3 = summe(b=2, a=10) # Reihenfolge ist egal\nresultat4 = summe(20, b=4) # zuerst die namelosen\n" }, { "alpha_fraction": 0.4761904776096344, "alphanum_fraction": 0.5595238208770752, "avg_line_length": 41, "blob_id": "854ba984e45c20d9b9d08e2170bf7987f85f637f", "content_id": "c7dfc12d32c6626cf92e00ef6f7bd4d95cce9490", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 84, "license_type": "no_license", "max_line_length": 65, "num_lines": 2, "path": "/listings/v4_tupel22.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "list(filter(lambda x: True if x >= 0 else False, [5, -8, 3, -1]))\n# Ausgabe: [5, 3]\n" }, { "alpha_fraction": 0.4000000059604645, "alphanum_fraction": 0.5066666603088379, "avg_line_length": 36.5, "blob_id": "e2ac30c5f7e48b2f0eca1902cff12d28c2b5d962", "content_id": "7cfe97b0ecd7f843f79f2a5c5cae5890f7da32a7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 75, "license_type": "no_license", "max_line_length": 42, "num_lines": 2, "path": "/listings/v3_strings5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'U = {0}, I = {1}'.format(spannung, strom)\n# Ausgabe: 'U = 12.56, I = 0.5'\n" }, { "alpha_fraction": 0.6470588445663452, "alphanum_fraction": 0.6470588445663452, "avg_line_length": 21.66666603088379, "blob_id": "e11d597d1c7022b3f6694558ec1b3640beaa58aa", "content_id": "fb1ade3725caa5cd8a6481d05ede77273c5e927f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 68, "license_type": "no_license", "max_line_length": 35, "num_lines": 3, "path": "/listings/v3_datei7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "with open('mailaenderli.txt') as f:\n text = f.read()\nprint(text)\n" }, { "alpha_fraction": 0.7222222089767456, "alphanum_fraction": 0.7222222089767456, "avg_line_length": 62, "blob_id": "ff5e1cdf9ebcf34e1c53706a3a14db1fbdaffeea", "content_id": "55298814c0fd5a0bd588c93ad139967a649df4ee", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 126, "license_type": "no_license", "max_line_length": 73, "num_lines": 2, "path": "/listings/v5_ra20.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.sub(r'\\bschoen\\b', 'herrlich', 'Das Wetter ist schoen oder unschoen.')\n# Ausgabe: 'Das Wetter ist herrlich oder unschoen.'\n" }, { "alpha_fraction": 0.4516128897666931, "alphanum_fraction": 0.5161290168762207, "avg_line_length": 22.25, "blob_id": "88472dd25dc9d8f726058cf6417589cb6203d465", "content_id": "73117959b8e53cdb3fc176a1f074520434777998", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 93, "license_type": "no_license", "max_line_length": 24, "num_lines": 4, "path": "/listings/v4_tupel3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x, y, z = 1, 2, 3\nprint(x) # Ausgabe: 1\nprint(y) # Ausgabe: 2\nprint(z) # Ausgabe: 3\n" }, { "alpha_fraction": 0.6969696879386902, "alphanum_fraction": 0.6969696879386902, "avg_line_length": 15.5, "blob_id": "158ab0fb0c676540dbc9db7aa5662bd05fd815c7", "content_id": "f5c22d2677793684afcb4c3280b06e1b2457a6c2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 33, "license_type": "no_license", "max_line_length": 20, "num_lines": 2, "path": "/listings/v3_datei10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "import glob\nglob.glob('.ipynb')\n" }, { "alpha_fraction": 0.7444444298744202, "alphanum_fraction": 0.7444444298744202, "avg_line_length": 29, "blob_id": "52a07db43066c4044ac51b50f69717553d481755", "content_id": "67b4df36803ea7d2ff2283643e5057677979b704", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 90, "license_type": "no_license", "max_line_length": 34, "num_lines": 3, "path": "/listings/v4_tupel5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "vorname, nachname = t\nprint(vorname) # Ausgabe: Peter\nprint(nachname) # Ausgabe: Mueller\n" }, { "alpha_fraction": 0.406047523021698, "alphanum_fraction": 0.53995680809021, "avg_line_length": 29.866666793823242, "blob_id": "59b3ec78c8106fdf7d5c711f7cf627c39f376f67", "content_id": "e591ab6af37742317041fccdb77b1c9859a92b2f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 463, "license_type": "no_license", "max_line_length": 66, "num_lines": 15, "path": "/listings/v3_strings15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'Python ist eine Schlange.'.split()\n# Ausgabe: ['Python', 'ist', 'eine', 'Schlange.']\n\ncsv = '1;2000;30.3;44;505'\ncsv.split(';')\n# Ausgabe: ['1', '2000', '30.3', '44', '505']\n\ncsv.split(';', maxsplit=2) # max. zwei Trennungen von links her\n# Ausgabe: ['1', '2000', '30.3;44;505']\n\ncsv.rsplit(';', maxsplit=2) # max. zwei Trennungen von rechts her\n# Ausgabe: ['1;2000;30.3', '44', '505']\n\n'1;2;;;;3;4'.split(';')\n# Ausgabe: ['1', '2', '', '', '', '3', '4']\n" }, { "alpha_fraction": 0.5359116196632385, "alphanum_fraction": 0.5425414443016052, "avg_line_length": 31.321428298950195, "blob_id": "3809aa12a4611d40826060fe3a8052bbd855d479", "content_id": "056795e9a2ca13ab4a4a86876783ee5c51586b5c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 905, "license_type": "no_license", "max_line_length": 89, "num_lines": 28, "path": "/listings/v7_vererbung12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Fahrzeug:\n def __init__(self, antrieb, kwargs):\n print('Fahrzeug.__init__(),', 'kwargs =', kwargs)\n super().__init__(kwargs)\n self.antrieb = antrieb\n\nclass Computer:\n def __init__(self, display, kwargs):\n print('Computer.__init__(),', 'kwargs =', kwargs)\n super().__init__(kwargs)\n self.display = display\n\nclass Tesla(Fahrzeug, Computer):\n def __init__(self, display, dual_motor, kwargs):\n print('Tesla.__init__()')\n super().__init__(\n antrieb='elektrisch',\n display=display,\n kwargs\n )\n self.dual_motor = dual_motor\n\n\nt = Tesla(display='17 Zoll', dual_motor=True) # Ausgabe:\n# Tesla.__init__()\n# Fahrzeug.__init__(), kwargs = {'display': '17 Zoll'}\n# Computer.__init__(), kwargs = {}\nt.__dict__ # Ausgabe: {'display': '17 Zoll', 'antrieb': 'elektrisch', 'dual_motor': True}\n" }, { "alpha_fraction": 0.6219512224197388, "alphanum_fraction": 0.6707317233085632, "avg_line_length": 12.333333015441895, "blob_id": "f05f3a5db5f10608919552686935c20138639f4a", "content_id": "cce414860496912635e36d354b76ea13d8837184", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 82, "license_type": "no_license", "max_line_length": 14, "num_lines": 6, "path": "/listings/v2_if2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "if Bedingung:\n Anweisung1\n Anweisung2\nelse:\n Anweisung3\n Anweisung4 \n" }, { "alpha_fraction": 0.4878048896789551, "alphanum_fraction": 0.4878048896789551, "avg_line_length": 40, "blob_id": "91728793015e069f24c08cb5daa5283b34865b20", "content_id": "6fb9c92b8b75b229eca5dba491dac2da2c878a04", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 82, "license_type": "no_license", "max_line_length": 48, "num_lines": 2, "path": "/listings/v4_iter2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "print('__iter__():', hasattr(liste, '__iter__'))\n# Ausgabe: ('__iter__():', True)\n" }, { "alpha_fraction": 0.44368600845336914, "alphanum_fraction": 0.511945366859436, "avg_line_length": 40.85714340209961, "blob_id": "2a9b73f1348abdf38defd8041ed551546d496668", "content_id": "c9914de7c14010f8b40073a4cf5145f7de2e5384", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 293, "license_type": "no_license", "max_line_length": 73, "num_lines": 7, "path": "/listings/v4_tupel16.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = [2, 5, 3, 4, 1]\nsortiert = sorted(liste, reverse=True)\nprint('Liste:', liste) # Ausgabe: ('Liste:', [2, 5, 3, 4, 1])\nprint('sortiert:', sortiert) # Ausgabe: ('sortiert:', [5, 4, 3, 2, 1])\n\nliste.sort(reverse=True)\nliste # Ausgabe: [5, 4, 3, 2, 1]\n" }, { "alpha_fraction": 0.4951923191547394, "alphanum_fraction": 0.629807710647583, "avg_line_length": 28.714284896850586, "blob_id": "1c9326e10cc7f24f626df184d8c8dc3f8b46782c", "content_id": "c73e2cf871138dd3c06f84273d169acfccce1cd7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 208, "license_type": "no_license", "max_line_length": 77, "num_lines": 7, "path": "/listings/v4_list6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "temp = []\nfor km, mi in zip(kilometer, meilen):\n temp.append('{:.0f}km={:.0f}mi'.format(km, mi))\ns = ', '.join(temp)\n\nprint(s)\n# Ausgabe: 30km=19mi, 50km=31mi, 60km=37mi, 80km=50mi, 100km=62mi, 120km=75mi\n" }, { "alpha_fraction": 0.3738937973976135, "alphanum_fraction": 0.491150438785553, "avg_line_length": 21.600000381469727, "blob_id": "ba83a1c41a4779baf8a3fb722ed12b3c4f274086", "content_id": "c8523d9ece321a80b9b72d18530797441fa91a4b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 452, "license_type": "no_license", "max_line_length": 55, "num_lines": 20, "path": "/listings/v4_tupel18.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "from collections import deque\nliste = deque([1, 2, 3])\nprint(liste) # Ausgabe: deque([1, 2, 3])\nliste.rotate(1)\nprint(liste) # Ausgabe: deque([3, 1, 2])\nendlich_lang = deque(maxlen=5)\nfor n in range(10):\n endlich_lang.append(n)\n print(list(endlich_lang))\n# Ausgabe:\n# [0]\n# [0, 1]\n# [0, 1, 2]\n# [0, 1, 2, 3]\n# [0, 1, 2, 3, 4]\n# [1, 2, 3, 4, 5]\n# [2, 3, 4, 5, 6]\n# [3, 4, 5, 6, 7]\n# [4, 5, 6, 7, 8]\n# [5, 6, 7, 8, 9]\n" }, { "alpha_fraction": 0.6308724880218506, "alphanum_fraction": 0.6308724880218506, "avg_line_length": 17.625, "blob_id": "6589f4280e84ed63100bcc0cd5d32c906f564a29", "content_id": "ce30143620e924c0092c56e1c042e9955265f417", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 149, "license_type": "no_license", "max_line_length": 38, "num_lines": 8, "path": "/listings/v3_exception6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "try:\n f = open('datei.txt')\nexcept IOError:\n print('Kann Datei nicht oeffnen.')\nelse:\n print('Datei schliessen.')\n f.close()\nprint('Ende')\n" }, { "alpha_fraction": 0.6480000019073486, "alphanum_fraction": 0.6480000019073486, "avg_line_length": 24, "blob_id": "218f78ee5a5f4809c903e6fd2c0f176b7520a09a", "content_id": "e8338fe87f5cef7d1be07b22febd8cf4683cb404", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 500, "license_type": "no_license", "max_line_length": 60, "num_lines": 20, "path": "/listings/v6_klassen3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n '''Diese Klasse hat nicht viel drin.'''\n pass\n\nMeineKlasse.__doc__\n# Ausgabe: 'Diese Klasse hat nicht viel drin.'\nhelp(MeineKlasse)\n# Ausgabe:\n# Help on class MeineKlasse in module __main__:\n# \n# class MeineKlasse(builtins.object)\n# \| Diese Klasse hat nicht viel drin.\n# \|\n# \| Data descriptors defined here:\n# \|\n# \| __dict__\n# \| dictionary for instance variables (if defined)\n# \|\n# \| __weakref__\n# \| list of weak references to the object (if defined)\n" }, { "alpha_fraction": 0.45928338170051575, "alphanum_fraction": 0.4918566644191742, "avg_line_length": 17.058822631835938, "blob_id": "2c10f054294959e6cf5d4d30239d8f2bf913ac6b", "content_id": "d40c5389eb40be9dfdfc8857be572c711d269893", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 307, "license_type": "no_license", "max_line_length": 47, "num_lines": 17, "path": "/listings/v5_ra5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "for m in re.finditer(r'[a-z]+', 'hallo welt!'):\n print('---')\n print('group:', m.group())\n print('start:', m.start())\n print('end:', m.end())\n print('span:', m.span())\n# Ausgabe:\n# ---\n# group: hallo\n# start: 0\n# end: 5\n# span: (0, 5)\n# ---\n# group: welt\n# start: 6\n# end: 10\n# span: (6, 10)\n" }, { "alpha_fraction": 0.5089552402496338, "alphanum_fraction": 0.5223880410194397, "avg_line_length": 25.799999237060547, "blob_id": "bae7b6a3a28454a0967d266a156cb07196c2964f", "content_id": "296d8f9f535aa8f4b7c119760079e9c8b447725b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1340, "license_type": "no_license", "max_line_length": 76, "num_lines": 50, "path": "/listings/v6_klassen9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n '''Beschreibung der Klasse.'''\n speed_of_light = 299792458\n\n def __init__(self, name):\n '''Diese Methode initialisiert die Variablen.'''\n self.name = name\n print(self.name, 'wurde erstellt.')\n\n def __del__(self):\n '''Diese Methode raeumt alles auf bevor es zerstoert wird.'''\n print(self.name, 'wurde zerstoert.')\n\n def hallo(self):\n '''Sagt Hallo.'''\n print('Hallo', self.name)\n\nhelp(MeineKlasse)\n# Ausgabe:\n# Help on class MeineKlasse in module __main__:\n#\n# class MeineKlasse(builtins.object)\n# \| MeineKlasse(name)\n# \|\n# \| Beschreibung der Klasse.\n# \|\n# \| Methods defined here:\n# \|\n# \| __del__(self)\n# \| Diese Methode raeumt alles auf bevor es zerstoert wird.\n# \|\n# \| __init__(self, name)\n# \| Diese Methode initialisiert die Variablen.\n# \|\n# \| hallo(self)\n# \| Sagt Hallo.\n# \|\n# \| ----------------------------------------------------------------------\n# \| Data descriptors defined here:\n# \|\n# \| __dict__\n# \| dictionary for instance variables (if defined)\n# \|\n# \| __weakref__\n# \| list of weak references to the object (if defined)\n# \|\n# \| ----------------------------------------------------------------------\n# \| Data and other attributes defined here:\n# \|\n# \| speed_of_light = 299792458\n" }, { "alpha_fraction": 0.4735202491283417, "alphanum_fraction": 0.585669755935669, "avg_line_length": 31.100000381469727, "blob_id": "1daa07bb0784aeb208d3f094f9cd01ca5bea16fc", "content_id": "9b27de117f76ef8ff4b3de64e6582ccb4501f57b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 642, "license_type": "no_license", "max_line_length": 59, "num_lines": 20, "path": "/listings/v6_klassen27.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Konto:\n def __init__(self, guthaben, iban):\n self.guthaben = guthaben\n self.iban = iban\n\n def __float__(self):\n return float(self.guthaben)\n\n def __add__(self, other):\n return self.guthaben + other.guthaben\n\n def __sub__(self, other):\n return self.guthaben - other.guthaben\n\nk1 = Konto(50, 'CH42 4738 2934 9267 0878 5')\nk2 = Konto(23, 'CH27 1036 5802 2994 9234 3')\nprint('float(k1) =', float(k1)) # Ausgabe: float(k1) = 50.0\nprint('float(k2) =', float(k2)) # Ausgabe: float(k2) = 23.0\nprint('k1 + k2 =', k1 + k2) # Ausgabe: k1 + k2 = 73\nprint('k1 - k2 =', k1 - k2) # Ausgabe: k1 - k2 = 27\n" }, { "alpha_fraction": 0.4191419184207916, "alphanum_fraction": 0.4983498454093933, "avg_line_length": 29.299999237060547, "blob_id": "e5139744ffd741def4615dd7c91cd27d4050d218", "content_id": "bd4018ace44a1781247ab2fe306cf8608209160f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 303, "license_type": "no_license", "max_line_length": 56, "num_lines": 10, "path": "/listings/v4_tupel8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = ['a', 'b', 'c']\nliste = liste + [1, 2] # zu vermeiden, sehr langsam\nliste\n# Ausgabe: ['a', 'b', 'c', 1, 2]\nliste += [3, 4] # viel schneller\nliste\n# Ausgabe: ['a', 'b', 'c', 1, 2, 1, 2, 3, 4]\nliste.extend([5, 6]) # noch schneller\nliste\n# Ausgabe: ['a', 'b', 'c', 1, 2, 1, 2, 3, 4, 3, 4, 5, 6]\n" }, { "alpha_fraction": 0.7333333492279053, "alphanum_fraction": 0.7333333492279053, "avg_line_length": 51.5, "blob_id": "5e62b7b0134f440781d6f57e68696096d61129b8", "content_id": "d07669662aaf39029989368d936220e143812ab6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 105, "license_type": "no_license", "max_line_length": 53, "num_lines": 2, "path": "/listings/v6_klassen20.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "print(objekt._prot) # Ausgabe: Ich bin protected.\nobjekt._prot_funktion() # Ausgabe: Ich bin protected.\n" }, { "alpha_fraction": 0.3333333432674408, "alphanum_fraction": 0.5185185074806213, "avg_line_length": 39.5, "blob_id": "f37bc08702f219ed05b1d793fb30b07d70d037b0", "content_id": "de57744c401a49ed46286333b88978ca70a66d04", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 81, "license_type": "no_license", "max_line_length": 42, "num_lines": 2, "path": "/listings/v3_strings6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'U = {0:.2f}, U = {0:.f}'.format(spannung)\n# Ausgabe: 'U = 12.56, U = 12.560000'\n" }, { "alpha_fraction": 0.6390977501869202, "alphanum_fraction": 0.6390977501869202, "avg_line_length": 43.33333206176758, "blob_id": "c5a71e64fd99494075bf2b20b7f5d3be9b59ab73", "content_id": "5fcaf46598d4a627a5faece6fe9fcb0df25e3b41", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 133, "license_type": "no_license", "max_line_length": 45, "num_lines": 3, "path": "/listings/v3_datei3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "inhalt = f.read() # gesamte Datei lesen\ninhalt = f.read(n) # n Zeichen lesen\nzeilen = f.readlines() # Liste aller Zeilen\n" }, { "alpha_fraction": 0.5625, "alphanum_fraction": 0.6625000238418579, "avg_line_length": 25.66666603088379, "blob_id": "fadabdf6ca3e11a64c6fb2b888027de2519a168d", "content_id": "2960d0842f02256b6455168cf1c6b9d7aa7e3cb0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 80, "license_type": "no_license", "max_line_length": 31, "num_lines": 3, "path": "/listings/v3_strings10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "lokale_variable = 13\nf'Wert = {lokale_variable:.3f}'\n# Ausgabe: 'Wert = 13.000'\n" }, { "alpha_fraction": 0.5545454621315002, "alphanum_fraction": 0.5727272629737854, "avg_line_length": 21, "blob_id": "aa2fe895c394dcd7a5d160514e34466783314188", "content_id": "5518027c8dd9a6a12a45d214275c2ee5eb3009f9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 110, "license_type": "no_license", "max_line_length": 38, "num_lines": 5, "path": "/listings/v2_func7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def rosen(farbe='rot'):\n print('Rosen sind ' + farbe + '.')\n\nrosen() # Aufruf 1\nrosen('gelb') # Aufruf 2\n" }, { "alpha_fraction": 0.4479166567325592, "alphanum_fraction": 0.6666666865348816, "avg_line_length": 23, "blob_id": "5d30aeb28413741929ff9e1ee51a64d92b53956c", "content_id": "d16aac42d7f8ee50e1eeed1b8192a88d283d00ba", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 96, "license_type": "no_license", "max_line_length": 41, "num_lines": 4, "path": "/listings/v4_list4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "kilometer = [30, 50, 60, 80, 100, 120]\nmeilen = [km0.621371 for n in kilometer]\n\nprint(meilen)\n" }, { "alpha_fraction": 0.4852941036224365, "alphanum_fraction": 0.6029411554336548, "avg_line_length": 33, "blob_id": "a88c40268da85870f6964e63ad96a8cfe710cd0d", "content_id": "89580c184522c59ac822463d1b5402ad95d15728", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 68, "license_type": "no_license", "max_line_length": 45, "num_lines": 2, "path": "/listings/v9_interpolate2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x = np.linspace(0, 10, num=11, endpoint=True)\ny = np.cos(-x2/9.0)\n" }, { "alpha_fraction": 0.5680473446846008, "alphanum_fraction": 0.5857987999916077, "avg_line_length": 20.125, "blob_id": "07351ab64a87f4aaa2a4ca61c7be7f3af9448ef7", "content_id": "18eff35508796804cc5415651136f98ed0127c7e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 169, "license_type": "no_license", "max_line_length": 30, "num_lines": 8, "path": "/listings/v3_exception4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "eingabe = '10Fr.'\ntry:\n x = int(eingabe)\n y = 1/x\nexcept ValueError as e:\n print('Oops! ' + str(e))\nexcept ZeroDivisionError as e:\n print('Oops! ' + str(e))\n" }, { "alpha_fraction": 0.6666666865348816, "alphanum_fraction": 0.6666666865348816, "avg_line_length": 22.25, "blob_id": "d65f9eef7af69550b119bdaf23e34c7211ca724f", "content_id": "8e14f0cb6127cd84147caf1c9a27c594be734536", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 93, "license_type": "no_license", "max_line_length": 29, "num_lines": 4, "path": "/listings/v6_klassen7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x.name = 'Hans'\ny.name = 'Peter'\nprint(x.name) # Ausgabe: Hans\nprint(y.name) #Ausgabe: Peter\n" }, { "alpha_fraction": 0.5248227119445801, "alphanum_fraction": 0.5319148898124695, "avg_line_length": 16.625, "blob_id": "1f4c4d4b4feb84f16922bdb30480e2b23b0f954c", "content_id": "1ea50cffba4c5c8d2fd426fdc84a72cc06eab097", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 141, "license_type": "no_license", "max_line_length": 33, "num_lines": 8, "path": "/listings/v6_klassen2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n i = 0\n\n def __init__(self, name):\n self.name = name\n\n def gruss(self):\n print('Hallo', self.name)\n" }, { "alpha_fraction": 0.5977011322975159, "alphanum_fraction": 0.5977011322975159, "avg_line_length": 28, "blob_id": "a98205245b512a60a66bd663c01ad7388c1fef32", "content_id": "31c86e60f7164cc1ff6c6bbde4a8a2d0f04e146d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 87, "license_type": "no_license", "max_line_length": 44, "num_lines": 3, "path": "/listings/v5_ra4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = re.findall(r'[a-z]+', 'hallo welt!')\nprint(liste)\n# Ausgabe: ['hallo', 'welt']\n" }, { "alpha_fraction": 0.5901639461517334, "alphanum_fraction": 0.6065573692321777, "avg_line_length": 14.25, "blob_id": "a8a0cd1f28dbd7e3aa0dd8326d71669e2b253e11", "content_id": "3e10cca74584dbe6955e53ceb0d0a81126bf1296", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 61, "license_type": "no_license", "max_line_length": 16, "num_lines": 4, "path": "/listings/v2_while3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "while Bedingung:\n Anweisung1\n if Fehler:\n break\n" }, { "alpha_fraction": 0.5657894611358643, "alphanum_fraction": 0.5745614171028137, "avg_line_length": 18, "blob_id": "c5e30c3d9ebc6ee5055e493b8ef620143c0b93cb", "content_id": "4f41d589ce1d921cce6b7dabeb0c2af153585984", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 228, "license_type": "no_license", "max_line_length": 29, "num_lines": 12, "path": "/listings/v4_iter7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def fibonacci_zahlen():\n a = 0\n b = 1\n while True:\n yield b\n a, b = b, a + b\n\nprint(type(fibonacci_zahlen))\n# Ausgabe: <type 'function'>\nf = fibonacci_zahlen()\nprint(type(f))\n# Ausgabe: <type 'generator'>\n" }, { "alpha_fraction": 0.3448275923728943, "alphanum_fraction": 0.5, "avg_line_length": 6.733333110809326, "blob_id": "fad251071acc7999acffdca978069a8a80926960", "content_id": "5baf8a748abf500cc88a4360e4c6b8a92392dd0b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 116, "license_type": "no_license", "max_line_length": 19, "num_lines": 15, "path": "/listings/v4_iter8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "next(f)\n# Ausgabe: 1\nfor n in range(10):\n print(next(f))\n# Ausgabe:\n# 1\n# 2\n# 3\n# 5\n# 8\n# 13\n# 21\n# 34\n# 55\n# 89\n" }, { "alpha_fraction": 0.4399999976158142, "alphanum_fraction": 0.5600000023841858, "avg_line_length": 24, "blob_id": "541a992c20887cf47171c23905216c1adfd7a118", "content_id": "15467a4232627dab01d6363c4cfb60655b94ce94", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 150, "license_type": "no_license", "max_line_length": 27, "num_lines": 6, "path": "/listings/v3_strings7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'{:>8.2f}'.format(sapnnung)\n# Ausgabe: ' 12.56'\n'{:<8.2f}'.format(spannung)\n# Ausgabe: '12.56 '\n'{:^8.2f}'.format(spannung)\n# Ausgabe: ' 12.56 '\n" }, { "alpha_fraction": 0.5979999899864197, "alphanum_fraction": 0.6420000195503235, "avg_line_length": 26.77777862548828, "blob_id": "ddffff87d486791e59a15254b9cae11201b658bc", "content_id": "36bb634a69262cecdf78222744e03d1fdbf9da70", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 500, "license_type": "no_license", "max_line_length": 77, "num_lines": 18, "path": "/listings/v6_klassen25.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Bank:\n def __init__(self):\n self.__guthaben = 0\n\n @property\n def guthaben(self):\n print('Das Guthaben wurde abgefragt.')\n return self.__guthaben\n\n @guthaben.setter\n def guthaben(self, n):\n self.__guthaben = n\n print('Das Guthaben wurde auf {} geaendert.'.format(self.__guthaben))\n\nk = Bank()\nk.guthaben = 1000000 # Ausgabe: Das Guthaben wurde auf 1000000 geaendert.\nprint(k.guthaben) # Ausgabe: Das Guthaben wurde abgefragt.\n# Ausgabe: 1000000\n" }, { "alpha_fraction": 0.5175438523292542, "alphanum_fraction": 0.5526315569877625, "avg_line_length": 18, "blob_id": "b5b1d4f68d69af6cba2a68669d422f1702318410", "content_id": "6799da5552f15234626f8acd529b2067218da2a8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 114, "license_type": "no_license", "max_line_length": 26, "num_lines": 6, "path": "/listings/v9_matplotlib7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x = np.logspace(-2, 1, 20)\ns = np.exp(-x)\n\nfig, ax = plt.subplots()\nax.loglog(x, s, '.-');\nax.grid(which='both');\n" }, { "alpha_fraction": 0.3636363744735718, "alphanum_fraction": 0.3787878751754761, "avg_line_length": 21, "blob_id": "01e00587f34f23a21611fe389455bf34c0e3fcf5", "content_id": "533abe01bf5e566e1b48f643dba64a33b6f365ff", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 66, "license_type": "no_license", "max_line_length": 36, "num_lines": 3, "path": "/listings/v4_tupel12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste[3:] = ['D', 'E']\nliste\n# Ausgabe: ['a', 'B', 'c', 'D', 'E']\n" }, { "alpha_fraction": 0.473053902387619, "alphanum_fraction": 0.4790419042110443, "avg_line_length": 22.85714340209961, "blob_id": "11f278981ef73c4f3c4d8b2dd20c1f0458f2e773", "content_id": "db7a78edd97c5a9129fdb4fc9f4d43c092bd8ec7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 167, "license_type": "no_license", "max_line_length": 36, "num_lines": 7, "path": "/listings/v4_tupel7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = ['a', 'b', 'c']\nliste.append('X') # rechts\nliste\n# Ausgabe: ['a', 'b', 'c', 'X']\nliste.insert(2, 'Y') # mit Index\nliste\n# Ausgabe: ['a', 'b', 'Y', 'c', 'X']\n" }, { "alpha_fraction": 0.5400000214576721, "alphanum_fraction": 0.5600000023841858, "avg_line_length": 15.666666984558105, "blob_id": "78ed9a3d38d80fa16ff8f4e15b340be3b6b417e5", "content_id": "ba926e72a7aa7033347db629e68f9e6b72279b97", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 50, "license_type": "no_license", "max_line_length": 25, "num_lines": 3, "path": "/listings/v4_tupel2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "t = (5,)\nprint(type(t))\n# Ausgabe: <type 'tuple'>\n" }, { "alpha_fraction": 0.6106870174407959, "alphanum_fraction": 0.6870229244232178, "avg_line_length": 31.75, "blob_id": "2206bc56f850fffad0f835cc7e69efc3eca5dda7", "content_id": "83a0527b0b38664722e779a0167e210efdedb757", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 131, "license_type": "no_license", "max_line_length": 49, "num_lines": 4, "path": "/listings/v9_matplotlib3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\nax1.plot(np.random.randn(10));\nax2.plot(np.random.randn(10));\nfig.tight_layout()\n" }, { "alpha_fraction": 0.6026490330696106, "alphanum_fraction": 0.6026490330696106, "avg_line_length": 49.33333206176758, "blob_id": "af4968696d276dbe43f310b6df22d941c363c675", "content_id": "48370b9876355fcb1867179ecd9041a4ce0fd69f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 151, "license_type": "no_license", "max_line_length": 69, "num_lines": 3, "path": "/listings/v5_ra6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = re.split(r'\\W+', 'Nun, dies ist ein (einfaches) Beispiel.')\nprint(liste)\n# Ausgabe: ['Nun', 'dies', 'ist', 'ein', 'einfaches', 'Beispiel', '']\n" }, { "alpha_fraction": 0.6785714030265808, "alphanum_fraction": 0.7142857313156128, "avg_line_length": 13, "blob_id": "7edce0a09f0c4daf5c110e0bce19f1c24cab1368", "content_id": "ba810afc1e6849df6de522720e55c9835b845eb4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 84, "license_type": "no_license", "max_line_length": 14, "num_lines": 6, "path": "/listings/v4_iter5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "next(iterator)\n# Ausgabe: 1\nnext(iterator)\n# Ausgabe: 2\nnext(iterator)\n# Ausgabe: 3\n" }, { "alpha_fraction": 0.6957831382751465, "alphanum_fraction": 0.7198795080184937, "avg_line_length": 32.20000076293945, "blob_id": "4c5337ec47471b7e4bc03f689bfa43be6f4679a1", "content_id": "a2109d2f1da4a49ee803c22a8adb37440c387588", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 332, "license_type": "no_license", "max_line_length": 69, "num_lines": 10, "path": "/listings/v6_klassen30.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "import sys\nsys.path.append('scripts')\nprint('\\n'.join(sys.path)) # Liste der Suchorte\n# Ausgabe: alle in Frage kommenden Verzeichnisse\n\nfrom my_other_module import Bank # Ausgabe:\n# Dies ist scripts\\my_other_module.py:\n# __name__ = my_other_module\nb = Bank()\nb.guthaben = 500.0 # Ausgabe: Das Guthaben wurde auf 500.0 geaendert.\n" }, { "alpha_fraction": 0.4868420958518982, "alphanum_fraction": 0.4868420958518982, "avg_line_length": 75, "blob_id": "04c0516d6dcd9fbbd518fd32ddad92a761508874", "content_id": "d7514e7d227de5e0f350704032538ed6304a1e00", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 76, "license_type": "no_license", "max_line_length": 75, "num_lines": 1, "path": "/listings/v7_vererbung15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "D.mro() # Ausgabe: [__main__.D, __main__.B, __main__.C, __main__.A, object]\n" }, { "alpha_fraction": 0.3076923191547394, "alphanum_fraction": 0.5, "avg_line_length": 38, "blob_id": "ad0941c051a2f8635fdb9635927163086cd930d3", "content_id": "d1c9e85eb722c05e31bfa9e97ef0317d6f181e63", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 78, "license_type": "no_license", "max_line_length": 53, "num_lines": 2, "path": "/listings/v4_tupel21.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "list(map(lambda x,y: x + y, [1, 2, 3], [10, 20, 30]))\n# Ausgabe: [11, 22, 33]\n" }, { "alpha_fraction": 0.688524603843689, "alphanum_fraction": 0.7213114500045776, "avg_line_length": 14.25, "blob_id": "c2ced5db12ef3f3c27143fb1c6861525958c4f04", "content_id": "5331c6a8d63c9af68b72b4514628d2da59c52207", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 61, "license_type": "no_license", "max_line_length": 24, "num_lines": 4, "path": "/listings/v2_for2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "for Variable in Sequenz:\n Anweisung1\nelse:\n Anweisung2\n" }, { "alpha_fraction": 0.534246563911438, "alphanum_fraction": 0.5753424763679504, "avg_line_length": 35.5, "blob_id": "c478dba7628bdcfa557fdb72e5fff12c994006b7", "content_id": "8cb319e2da404737b2fece3819aa77d259dcd83c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 73, "license_type": "no_license", "max_line_length": 42, "num_lines": 2, "path": "/listings/v2_func12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "punkt = {'x':1, 'y':2, 'z':2}\ndistanz(punkt) # Dictionary entpacken\n" }, { "alpha_fraction": 0.3563218414783478, "alphanum_fraction": 0.5862069129943848, "avg_line_length": 42.5, "blob_id": "fae96e9d7051f3f5a75a793412a39914adbfe1bd", "content_id": "db8f227d39fad9aeb9334d723e60e257a496de1f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 174, "license_type": "no_license", "max_line_length": 70, "num_lines": 4, "path": "/listings/v8_numpy9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "t = linspace(1, 3, 5)\nnp.log10(t) # Ausgabe: array([0., 0.17609, 0.30103, 0.39794, 0.47712])\nnp.cumsum(t) # Ausgabe: array([1., 2.5, 4.5, 7., 10.])\nnp.mean(t) # Ausgabe: 2.0\n" }, { "alpha_fraction": 0.47987616062164307, "alphanum_fraction": 0.6099071502685547, "avg_line_length": 25.91666603088379, "blob_id": "5de86b651dff496f1844b813d663c09762752867", "content_id": "da38e27dba86ef34f3425865eeb3ffc17eb16df0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 323, "license_type": "no_license", "max_line_length": 72, "num_lines": 12, "path": "/listings/v6_klassen26.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Konto:\n def __init__(self, guthaben, iban):\n self.guthaben = guthaben\n self.iban = iban\n\n def __str__(self):\n return 'IBAN: {}\\nGuthaben: {}'.format(self.iban, self.guthaben)\n\nk = Konto(50, 'CH42 4738 2934 9267 0878 5')\nprint(k) # Ausgabe:\n# IBAN: CH42 4738 2934 9267 0878 5\n# Guthaben: 50\n" }, { "alpha_fraction": 0.692307710647583, "alphanum_fraction": 0.7142857313156128, "avg_line_length": 17.200000762939453, "blob_id": "5c2609434ac8bb9d1613e514699caa2684a0fd7d", "content_id": "ac68cf6a4eea731e9de9f6d56c1a4379ee1ecf1f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 91, "license_type": "no_license", "max_line_length": 29, "num_lines": 5, "path": "/listings/v4_list1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "quadratzahlen = []\nfor n in range(11):\n quadratzahlen.append(nn)\n\nprint(quadratzahlen)\n" }, { "alpha_fraction": 0.7323943376541138, "alphanum_fraction": 0.7417840361595154, "avg_line_length": 41.599998474121094, "blob_id": "0a3b64d28f8627f1dc3a18f0f1389aa1baf67919", "content_id": "0e99c86589f8384179ea130ebb46a0de7f48ced2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 213, "license_type": "no_license", "max_line_length": 85, "num_lines": 5, "path": "/listings/v6_klassen29.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "from my_module import MeineKlasse\n# Ausgabe: Dies ist C:\\Users\\Noah\\switchdrive\\Python\\vorlesung\\w06\\code\\my_module.py:\n# __name__ = my_module\nm = MeineKlasse('Python User')\nm.gruss() # Ausgabe: Hallo Python User\n" }, { "alpha_fraction": 0.39423078298568726, "alphanum_fraction": 0.45192307233810425, "avg_line_length": 16.33333396911621, "blob_id": "19c373d4ea19b50324ec6027de8184e9ed274a15", "content_id": "7ff54998b78228a2f72f76c9e31a96b9a5d5bea0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 104, "license_type": "no_license", "max_line_length": 21, "num_lines": 6, "path": "/listings/v4_iter1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = [1, 2, 3]\nfor element in liste:\n print(element)\n# Ausgabe: 1\n# 2\n# 3\n" }, { "alpha_fraction": 0.6335078477859497, "alphanum_fraction": 0.6335078477859497, "avg_line_length": 16.363636016845703, "blob_id": "a9917c8217df7e3a0032853ee92ef542fd15c754", "content_id": "880f2d749166e408bc4544fdad07ae6dc247f001", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 191, "license_type": "no_license", "max_line_length": 32, "num_lines": 11, "path": "/listings/v7_vererbung4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Person:\n def func(self):\n print('Person')\n\nclass Angestellter(Person):\n def func(self):\n print('Angestellter')\n\n\na = Angestellter()\na.func() # Ausgabe: Angestellter\n" }, { "alpha_fraction": 0.6436781883239746, "alphanum_fraction": 0.6436781883239746, "avg_line_length": 16.399999618530273, "blob_id": "05ae2bc88c0037c36665af946e9c710856c1b4da", "content_id": "ec89d4d6c58eec16cd906d0983329f727bc1f9c5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 87, "license_type": "no_license", "max_line_length": 23, "num_lines": 5, "path": "/listings/v3_strings21.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'Passwort'.lower()\n# Ausgabe: 'passwort'\n\n'Passwort'.upper()\n# Ausgabe: 'PASSWORT'\n" }, { "alpha_fraction": 0.6044444441795349, "alphanum_fraction": 0.6044444441795349, "avg_line_length": 15.071428298950195, "blob_id": "4e64a46252d349803acaaa02f2daf86517bbda3c", "content_id": "d0f186fb61d13d9c77e8bc62c27e7b06e8c0a40b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 225, "license_type": "no_license", "max_line_length": 29, "num_lines": 14, "path": "/listings/v7_vererbung5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Person:\n def func(self):\n print('Person')\n\nclass Angestellter(Person):\n def func(self):\n super().func()\n print('Angestellter')\n\n\na = Angestellter()\na.func() # Ausgabe:\n# Person\n# Angestellter\n" }, { "alpha_fraction": 0.6197183132171631, "alphanum_fraction": 0.6549295783042908, "avg_line_length": 46.33333206176758, "blob_id": "664fa3257ccaf1c75d3ffb7e6e2f5ee21aab7f4b", "content_id": "f12270138be532352276ff047beb9b39c46140a2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 142, "license_type": "no_license", "max_line_length": 69, "num_lines": 3, "path": "/listings/v5_ra10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "resultat = re.subn(r'\\d+', '<Zahl>', '3 Stuecke kosten 250 Franken.')\nprint(resultat)\n# Ausgabe: ('<Zahl> Stuecke kosten <Zahl> Franken.', 2)\n" }, { "alpha_fraction": 0.5995850563049316, "alphanum_fraction": 0.6452282071113586, "avg_line_length": 29.125, "blob_id": "fb44595b8a1bc0a7c8fa7311783b7d9e92e03d3d", "content_id": "c25343ac9b0ea306b9625870686e61bacf3b4dd0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 482, "license_type": "no_license", "max_line_length": 77, "num_lines": 16, "path": "/listings/v6_klassen23.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Bank:\n def __init__(self):\n self.__guthaben = 0\n\n def get_guthaben(self):\n print('Das Guthaben wurde abgefragt.')\n return self.__guthaben\n\n def set_guthaben(self, n):\n self.__guthaben = n\n print('Das Guthaben wurde auf {} geaendert.'.format(self.__guthaben))\n\nk = Bank()\nk.set_guthaben(1000000) # Ausgabe: Das Guthaben wurde auf 1000000 geaendert.\nprint(k.get_guthaben()) # Ausgabe: Das Guthaben wurde abgefragt.\n# Ausgabe: 1000000\n" }, { "alpha_fraction": 0.604938268661499, "alphanum_fraction": 0.604938268661499, "avg_line_length": 19.25, "blob_id": "fa52f620cc7a06adc96eb31f504fcc3ea15918a1", "content_id": "9872e9a8a0760e47cb9400dbe254f1d36e2aa305", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 81, "license_type": "no_license", "max_line_length": 37, "num_lines": 4, "path": "/listings/v3_exception2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "try:\n x = int(input('Zahl eingeben: '))\nexcept:\n print('Falsche Eingabe!')\n" }, { "alpha_fraction": 0.6000000238418579, "alphanum_fraction": 0.6000000238418579, "avg_line_length": 9, "blob_id": "b5edb7b78a9afb4090c5c7e2af15be876bfbee90", "content_id": "3e597aa499f22d3807c1f7dc7f32011520ed1b2f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 10, "license_type": "no_license", "max_line_length": 9, "num_lines": 1, "path": "/listings/v3_datei5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "f.close()\n" }, { "alpha_fraction": 0.5207956433296204, "alphanum_fraction": 0.5551537275314331, "avg_line_length": 41.53845977783203, "blob_id": "d7198355c5a687cf7d91feb87ebdaf03ff832d71", "content_id": "0e34701b50791901804d19c68ce567984f5f48e7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 553, "license_type": "no_license", "max_line_length": 90, "num_lines": 13, "path": "/listings/v4_tupel17.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = ['laenger', 'lang', 'am laengsten']\nsorted(liste, key=len)\n# Ausgabe: ['lang', 'laenger', 'am laengsten']\n\n# nur [1]-tes Element (stabile Sortierung)\nliste = [('a', 3), ('a', 2), ('c', 1), ('b', 1)]\nfrom operator import itemgetter\nsorted(liste, key=itemgetter(1))\n# Ausgabe: [('c', 1), ('b', 1), ('a', 2), ('a', 3)]\nsorted(liste, key=lambda x: x[1])\n# Ausgabe: [('c', 1), ('b', 1), ('a', 2), ('a', 3)]\nsorted(liste) # zuerst nach dem ersten Unterelement sortieren, dann nach dem zweiten, ...\n# Ausgabe: [('a', 2), ('a', 3), ('b', 1), ('c', 1)]\n" }, { "alpha_fraction": 0.4776119291782379, "alphanum_fraction": 0.5223880410194397, "avg_line_length": 32.5, "blob_id": "94f20a1bb6dcab32a4db03cabc0e998e50d6ad20", "content_id": "8ebf79026408790d23551560ab8d92629909bcc1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 67, "license_type": "no_license", "max_line_length": 47, "num_lines": 2, "path": "/listings/v5_ra22.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.findall(r'[A-Za-z]+(?!\\d+)\\b', 'abc123 cde')\n# Ausgabe: ['cde']\n" }, { "alpha_fraction": 0.4780219793319702, "alphanum_fraction": 0.4780219793319702, "avg_line_length": 90, "blob_id": "e4a4fe15eb9bf156c24ecf20757514ef54b588fd", "content_id": "7217b624822cfdc7e1b5ed9e4da120ce46f858cb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 182, "license_type": "no_license", "max_line_length": 113, "num_lines": 2, "path": "/listings/v5_ra15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = re.split(r'(\\W+)', 'Nun, dies ist ein (simples) Beispiel.')\nprint(liste) # Ausgabe: ['Nun', ', ', 'dies', ' ', 'ist', ' ', 'ein', ' (', 'simples', ') ', 'Beispiel', '.', '']\n" }, { "alpha_fraction": 0.4624277353286743, "alphanum_fraction": 0.4624277353286743, "avg_line_length": 42.25, "blob_id": "cb6dd17df2c0bc1f096f37666a57f1542fda7af5", "content_id": "d56c0a8d38c7a62e9fd9c6fa04d7fe0865090485", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 173, "license_type": "no_license", "max_line_length": 51, "num_lines": 4, "path": "/listings/v4_iter4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "print('__iter__():', hasattr(iterator, '__iter__'))\nprint('__next__():', hasattr(iterator, '__next__'))\n# Ausgabe: ('__iter__():', True)\n# ('__next__():', False)\n" }, { "alpha_fraction": 0.699999988079071, "alphanum_fraction": 0.699999988079071, "avg_line_length": 9, "blob_id": "0b73d219c0b15d83c058bce1a16d0a1d5209048a", "content_id": "7bf9b7856bfa24a99e0776ca06bb4f552270bf97", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 20, "license_type": "no_license", "max_line_length": 15, "num_lines": 2, "path": "/listings/v8_numpy11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "np.transpose(M)\nM.T\n" }, { "alpha_fraction": 0.6695652008056641, "alphanum_fraction": 0.730434775352478, "avg_line_length": 13.375, "blob_id": "f2bfa581f2f4b332765afa3d79010a2c0bdc9991", "content_id": "4fdd40205206b73d1239dcf9cd23ce33a6635da0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 115, "license_type": "no_license", "max_line_length": 16, "num_lines": 8, "path": "/listings/v2_if3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "if Bedingung1:\n Anweisung1\nelif Bedingung2:\n Anweisung2\nelif Bedingung3:\n Anweisung3\nelse:\n Anweisung4\n" }, { "alpha_fraction": 0.4128205180168152, "alphanum_fraction": 0.46666666865348816, "avg_line_length": 31.5, "blob_id": "a4b70458fc4304cf47002a3e6ed8f6aadcfc7ec7", "content_id": "fd0649e64a0abbd1e78b336bb40b67b538cdbfbe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 390, "license_type": "no_license", "max_line_length": 73, "num_lines": 12, "path": "/listings/v4_tupel14.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = [2, 5, 3, 4, 1]\nsortiert = sorted(liste)\nprint('Liste:', liste) # Ausgabe: ('Liste:', [2, 5, 3, 4, 1])\nprint('sortiert:', sortiert) # Ausgabe: ('sortiert:', [1, 2, 3, 4, 5])\n\nt = (5,4,3)\nsortiert = sorted(t)\nsortiert # Ausgabe: [3, 4, 5]\n\ns = 'python'\nsortiert = sorted(s)\nsortiert # Ausgabe: ['h', 'n', 'o', 'p', 't', 'y']\n" }, { "alpha_fraction": 0.6035503149032593, "alphanum_fraction": 0.6745561957359314, "avg_line_length": 27.16666603088379, "blob_id": "566e1e1a9b70c6a3748fc0555615418d8229398c", "content_id": "04e0bb0139141f0cfea0c598ce9c57ae47523e40", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 169, "license_type": "no_license", "max_line_length": 42, "num_lines": 6, "path": "/listings/v7_vererbung9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "a = Angestellter('Max', 123456) # Ausgabe:\n# __init__() von Person\n# __init__() von Angestellter\n\nprint(a.name) # Ausgabe: Max\nprint(a.personalnummer) # Ausgabe: 123456\n" }, { "alpha_fraction": 0.5473684072494507, "alphanum_fraction": 0.5684210658073425, "avg_line_length": 46.5, "blob_id": "e8d46a7236ad303307c1223f4071f91825a2fe7e", "content_id": "e7a4f3454c31334c443737d6269513141097b52c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 95, "license_type": "no_license", "max_line_length": 47, "num_lines": 2, "path": "/listings/v3_datei4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "f.write('hello') # String schreiben\nf.writelines(['1', '2']) # Liste von Strings\n" }, { "alpha_fraction": 0.5458996295928955, "alphanum_fraction": 0.5630354881286621, "avg_line_length": 27.172412872314453, "blob_id": "f703814b56342371eaef2b988877e256488b1001", "content_id": "5fe6be2d75013f0db405aa072bd4b69400282820", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 817, "license_type": "no_license", "max_line_length": 101, "num_lines": 29, "path": "/listings/scripts/my_other_module.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\nprint('Dies ist {}:\\n__name__ = {}'.format(__file__, __name__))\n\nclass Bank:\n def __init__(self):\n self.__guthaben = 0\n\n @property\n def guthaben(self):\n print('Das Guthaben wurde abgefragt.')\n return self.__guthaben\n\n @guthaben.setter\n def guthaben(self, n):\n self.__guthaben = n\n print('Das Guthaben wurde auf {} geaendert.'.format(self.__guthaben))\n\n# --- Klasse testen -----------------------------------------------------------\nif __name__ == '__main__':\n b = Bank()\n b.guthaben = 1000\n print(b.guthaben)\n\n# Konsolen-Ausgabe:\n# Dies ist C:/Users/Noah/Documents/GitHub/Python_Zusammenfassung/listings/scripts/my_other_module.py:\n# __name__ = __main__\n# Das Guthaben wurde auf 1000 geaendert.\n# Das Guthaben wurde abgefragt.\n# 1000\n" }, { "alpha_fraction": 0.49712643027305603, "alphanum_fraction": 0.5574712753295898, "avg_line_length": 25.769229888916016, "blob_id": "b2994baf6bd3838c7c19447a86c38dfd150a3f38", "content_id": "4713bb050056121325d3088b2d4134d59cf09484", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 348, "license_type": "no_license", "max_line_length": 51, "num_lines": 13, "path": "/listings/v5_ra11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "m = re.search(r'(\\d+) ([a-z]+)', '123 hallo welt!')\nif m is not None:\n print('groups():', m.groups())\n print('group(0):', m.group(0))\n print('group(1):', m.group(1))\n print('group(2):', m.group(2))\nelse:\n print('keine Uebereinstimmung')\n# Ausgabe:\n# groups(): ('123', 'hallo')\n# group(0): 123 hallo\n# group(1): 123\n# group(2): hallo\n" }, { "alpha_fraction": 0.4811320900917053, "alphanum_fraction": 0.5943396091461182, "avg_line_length": 20.200000762939453, "blob_id": "453db88ddb50330212c1f236d38627192ca71bb7", "content_id": "58e077f8f49d52922b156e25361fa46c1320a30f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 106, "license_type": "no_license", "max_line_length": 60, "num_lines": 5, "path": "/listings/v5_ra17.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "for m in re.finditer(r'\\d+(V\|A)', '230V und 10A bei 23Ohm'):\n print(m.group())\n# Ausgabe:\n# 230V\n# 10A\n" }, { "alpha_fraction": 0.5873016119003296, "alphanum_fraction": 0.6507936716079712, "avg_line_length": 41, "blob_id": "67cf80c8e9ac7905fb08d83af811f5f5986ba0dc", "content_id": "725169ab07013a4d2b986c34d8f1031305b88456", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 126, "license_type": "no_license", "max_line_length": 70, "num_lines": 3, "path": "/listings/v5_ra8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = re.sub(r'\\d+', '<Zahl>', '3 Stuecke kosten 250 Franken.', count=1)\nprint(s)\n# Ausgabe: <Zahl> Stuecke kosten 250 Franken.\n" }, { "alpha_fraction": 0.7356321811676025, "alphanum_fraction": 0.7356321811676025, "avg_line_length": 42.5, "blob_id": "fe887749f105bd351bda43ebc993035162731ce3", "content_id": "56c3d9e5c6593e08a68871731f797f47672e8e3c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 87, "license_type": "no_license", "max_line_length": 51, "num_lines": 2, "path": "/listings/v6_klassen11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "del s # loescht nur die Referenz s auf das Objekt.\n# Ausgabe: Wall-E wurde zerstoert.\n" }, { "alpha_fraction": 0.5197368264198303, "alphanum_fraction": 0.5197368264198303, "avg_line_length": 46.5, "blob_id": "7cebbb83aad829859962ff657baa95cb519845e5", "content_id": "9090c5f874583349566c2d606ef6b45da3f1296a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 760, "license_type": "no_license", "max_line_length": 75, "num_lines": 16, "path": "/listings/v6_klassen22.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "objekt.__dict__ # Ausgabe:\n# {'pub': 'Hier macht jeder was er will.',\n# '_prot': 'Ich bin protected.',\n# '_MeineKlasse__priv': 'Ich bin privat.'}\n\ndir(objekt) # Ausgabe:\n# ['_MeineKlasse__priv', '_MeineKlasse__priv_funktion', '__class__',\n# '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__',\n# '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__',\n# '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__',\n# '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__',\n# '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_prot',\n# '_prot_funktion', 'pub', 'pub_funktion']\n\nobjekt._MeineKlasse__priv # Ausgabe: 'Ich bin privat.'\nobjekt._MeineKlasse__priv_funktion() # Ausgabe: Ich bin privat.\n" }, { "alpha_fraction": 0.7599999904632568, "alphanum_fraction": 0.7599999904632568, "avg_line_length": 49, "blob_id": "313cbd159dd7a918e47464c4ae9373944c10d143", "content_id": "f0897129d576c204b9234bd747eb49acad34ca62", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 50, "license_type": "no_license", "max_line_length": 49, "num_lines": 1, "path": "/listings/v6_klassen14.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "MeineKlasse.hallo(self=s) # Ausgabe: Hallo Wall-E\n" }, { "alpha_fraction": 0.7278106212615967, "alphanum_fraction": 0.7396449446678162, "avg_line_length": 41.25, "blob_id": "99b3bb820b3ba2dd02cd2811ddaebf8763a04320", "content_id": "c49311c9fc1d91e5c7277d6a0f32d31ad8b8ecd3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 169, "license_type": "no_license", "max_line_length": 47, "num_lines": 4, "path": "/listings/v2_func1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "import time # time.time(), time.sleep()\nimport math # math.pi, math,cos(), math.log10()\nimport zipfile # ZIP-Dateien manipulieren\nimport socket # UDP-/TCP-Kommunikation\n" }, { "alpha_fraction": 0.8039215803146362, "alphanum_fraction": 0.8039215803146362, "avg_line_length": 24.5, "blob_id": "43bbebb5dfa9a05a8fcebd12314b684a14973168", "content_id": "54d928d89e5bf02d4bcb56ed3c02d2bae0462421", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 51, "license_type": "no_license", "max_line_length": 34, "num_lines": 2, "path": "/listings/v2_func2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def Funktionsname(Parameterliste):\n Anweisungen\n" }, { "alpha_fraction": 0.3760683834552765, "alphanum_fraction": 0.5384615659713745, "avg_line_length": 57.5, "blob_id": "5912393532ca3d5b780e6d691ab54f612f67105a", "content_id": "ddc5139a04496f762dcb6e76c2ecb2eacb1872f5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 117, "license_type": "no_license", "max_line_length": 85, "num_lines": 2, "path": "/listings/v5_ra16.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = re.sub(r'(\\d+)/(\\d+)/(\\d+)', r'\\2.\\1.\\3', '03/20/2019') # mit Gruppen-Referenzen\nprint(s) # Ausgabe: 20.03.2019\n" }, { "alpha_fraction": 0.6691729426383972, "alphanum_fraction": 0.6917293071746826, "avg_line_length": 43.33333206176758, "blob_id": "92d6dd0349b43f430eeb37a142110d1329c8ad6e", "content_id": "a15f1b2aa587ee7229c6b1894dd90c85b59d7bf4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 133, "license_type": "no_license", "max_line_length": 85, "num_lines": 3, "path": "/listings/v4_iter9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "gen = (ii for i in range(1, 10)) # wie List Comprehension, aber mit runden Klammern\nprint(type(gen))\n# Ausgabe: <type 'generator'>\n" }, { "alpha_fraction": 0.6194030046463013, "alphanum_fraction": 0.641791045665741, "avg_line_length": 21.33333396911621, "blob_id": "9400136710e71bd9569e25bf0c218a9f99159aad", "content_id": "5fa95ca3fc636ed9f2634ecbd9c2f49ee9ac8233", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 134, "license_type": "no_license", "max_line_length": 39, "num_lines": 6, "path": "/listings/v3_exception5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "eingabe = '10Fr.'\ntry:\n x = int(eingabe)\n y = 1/x\nexcept (ValueError, ZeroDivisionError):\n print('Oops! Bitte wiederholen.')\n" }, { "alpha_fraction": 0.540772557258606, "alphanum_fraction": 0.6008583903312683, "avg_line_length": 18.41666603088379, "blob_id": "a90378ee7e0165a4fed1d3ae34c02b35e387b405", "content_id": "85fde0933ffdbf49a5435327fbe9a3f8f26882c9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 233, "license_type": "no_license", "max_line_length": 42, "num_lines": 12, "path": "/listings/v9_integrate2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "from scipy.integrate import trapz\n\nx = np.array([0, 1, 1.5, 1.8, 2, 3, 4, 5])\ny = np.cos(x) + 2\n\nplt.figure()\nplt.plot(x, y, 'o-', label='Samples')\nplt.ylim(0, 1.1np.max(y))\nplt.grid(True)\nplt.xlabel('x')\nplt.ylabel('y')\nplt.show()\n" }, { "alpha_fraction": 0.682692289352417, "alphanum_fraction": 0.682692289352417, "avg_line_length": 33.66666793823242, "blob_id": "6b5c1298cf99f0ec6a0d70e2b0dd914178e3190c", "content_id": "f042f3f8ba101ddccf0c44c4a8d56f7f49d2513c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 208, "license_type": "no_license", "max_line_length": 82, "num_lines": 6, "path": "/listings/v2_func14.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def meine_funktion(a, b):\n '''Gibt die Argumente a und b in umgekehrter Reihenfolge als Tupel zurueck.'''\n return(b, a)\n\nmeine_funktion.__doc__ # Ausgabe: 'Gibt die Arguemnte ...'\nhelp(meine_funktion)\n" }, { "alpha_fraction": 0.6803519129753113, "alphanum_fraction": 0.6862170100212097, "avg_line_length": 23.35714340209961, "blob_id": "833292b70e6c8bfef4b0df353507271b7ce67cb7", "content_id": "8a324a47856e4ec95bb87f3cc1699210cb1d02b2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 341, "license_type": "no_license", "max_line_length": 79, "num_lines": 14, "path": "/listings/v4_list5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "fruechte = ['Apfel', 'Erdbeer', 'Clementine', 'Kokosnuss', 'Birne', 'Himbeere']\n\n# konventionell:\nfruechte_abc = []\nfor frucht in fruechte:\n if frucht[0] in 'ABC':\n fruechte_abc.append(frucht)\n\nprint(fruechte_abc)\n\n# mit Listen-Abstraktion:\nfruechte_abc = [frucht for frucht in fruechte if frucht[0] in 'ABC']\n\nprint(fruechte_abc)\n" }, { "alpha_fraction": 0.6000000238418579, "alphanum_fraction": 0.6342105269432068, "avg_line_length": 62.33333206176758, "blob_id": "942cd9e0e5c14811b818cb7f7b1129dc7c1bb295", "content_id": "d1111e75117e51c3801408f0f4b9d0daf2262d79", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 380, "license_type": "no_license", "max_line_length": 82, "num_lines": 6, "path": "/listings/v5_ra13.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = re.findall(r'(\\w+)=(\\w+)', 'Jahrgang=1930, Name=Hans und Ort=Rappi')\nprint(liste) # Ausgabe: [('Jahrgang', '1930'), ('Name', 'Hans'), ('Ort', 'Rappi')]\nliste = re.findall(r'Ort=(\\w+)', 'Jahrgang=1930, Name=Hans und Ort=Rappi')\nprint(liste) # Ausgabe: ['Rappi']\nliste = re.findall(r'(dum)\\1', 'dumdum') # mit Rueckwaertsreferenz der Gruppe\nprint(liste) # Ausgabe: ['dum']\n" }, { "alpha_fraction": 0.681614339351654, "alphanum_fraction": 0.7354260087013245, "avg_line_length": 54.75, "blob_id": "e68de7e9a9a985bf15bc12a3db7942191b8c78f7", "content_id": "ede84a1d5544c632d68ca8174bc59b2845b96ffd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 223, "license_type": "no_license", "max_line_length": 91, "num_lines": 4, "path": "/listings/v4_tupel6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "vorname, nachname, adresse = ('Peter', 'Mueller', 'Oberseestrasse 10', 8640, 'Rapperswil')\nprint(vorname) # Ausgabe: Peter\nprint(nachname) # Ausgabe: Mueller\nprint(adresse) # Ausgabe: Oberseestrasse 10, 8640, Rapperswil\n" }, { "alpha_fraction": 0.44692736864089966, "alphanum_fraction": 0.49162012338638306, "avg_line_length": 21.375, "blob_id": "9cac975392920abfdbb56df5bce5b5069700fb2e", "content_id": "1bd66d007b388af586c8c5cdab4ec19544b4c75d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 179, "license_type": "no_license", "max_line_length": 37, "num_lines": 8, "path": "/listings/v2_func10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def distanz(x, y, z):\n print('x =', x)\n print('y =', y)\n print('z =', z)\n return (x2 + y2 + z2)0.5\n\nposition = (2, 3, 6)\ndistanz(position) # Tupel entpacken\n" }, { "alpha_fraction": 0.8444444537162781, "alphanum_fraction": 0.8444444537162781, "avg_line_length": 29, "blob_id": "1e7d4dfdd083e2cbbe2b51853e000de520d44b97", "content_id": "cd54a8d4d2c638a1a0621ceff2985ea6419b869b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 90, "license_type": "no_license", "max_line_length": 38, "num_lines": 3, "path": "/listings/v9_interpolate1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.interpolate import interpld\n" }, { "alpha_fraction": 0.3581395447254181, "alphanum_fraction": 0.4976744055747986, "avg_line_length": 22.88888931274414, "blob_id": "1643bbcf4248892888847a78ec5a127acfbc21ce", "content_id": "51f84d43b0b8d58da6a21b64fd6f86d4cf2737a4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 215, "license_type": "no_license", "max_line_length": 33, "num_lines": 9, "path": "/listings/v3_strings11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = 'Python'\ns.center(20, '=')\n# Ausgabe: '=======Python======'\ns.ljust(20, '-')\n# Ausgabe: 'Python--------------'\ns.rjust(20, '')\n# Ausgabe: '************Python'\n'1234'.zfill(20)\n# Ausgabe: '000000000000001234'\n" }, { "alpha_fraction": 0.7250000238418579, "alphanum_fraction": 0.75, "avg_line_length": 19, "blob_id": "a5061559ba9a3c62c7cb0a0ed80af9147e85c2dc", "content_id": "606915220267f12a03dcee0cd2f603b4b447eeb7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 40, "license_type": "no_license", "max_line_length": 24, "num_lines": 2, "path": "/listings/v2_for1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "for Variable in Sequenz:\n Anweisung1\n" }, { "alpha_fraction": 0.7314814925193787, "alphanum_fraction": 0.7314814925193787, "avg_line_length": 53, "blob_id": "ad2509b9ce79f60fd1b136484a66b56381edb1ea", "content_id": "82c246ef81f4cd3efe8ce8667fa425ac13a40025", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 108, "license_type": "no_license", "max_line_length": 62, "num_lines": 2, "path": "/listings/v6_klassen19.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "objekt.pub = 'Hier macht jeder was er will.'\nobjekt.pub_funktion() # Ausgabe: Hier macht jeder was er will.\n" }, { "alpha_fraction": 0.6578947305679321, "alphanum_fraction": 0.6578947305679321, "avg_line_length": 21.799999237060547, "blob_id": "67f4335ada128f48b4d637ee61d79b917a65ca50", "content_id": "b37d1d4cef305f533a2646e66e2cde472d5441d6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 114, "license_type": "no_license", "max_line_length": 52, "num_lines": 5, "path": "/listings/v3_strings16.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "csv = '''Dies ist\nein mehrzeiliger\nText.'''\ncsv.splitlines()\n# Ausgabe: ['Dies ist', 'ein mehrzeiliger', 'Text.']\n" }, { "alpha_fraction": 0.5841584205627441, "alphanum_fraction": 0.6188119053840637, "avg_line_length": 66.33333587646484, "blob_id": "014ba7013f6922b919217abd549825b754faab48", "content_id": "3602a2ff15ff27882dfa238a458d15826de9f537", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 202, "license_type": "no_license", "max_line_length": 99, "num_lines": 3, "path": "/listings/v5_ra18.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.findall(r'^\\w+', 'Hallo Welt') # Ausgabe: ['Hallo']\nre.findall(r'^\\w+', 'Erste Zeile\\nZweite Zeile', flags=re.MULTILINE) # Ausgabe: ['Erste', 'Zweite']\nre.findall(r'\\A\\d', '123456') # Ausgabe: ['1']\n" }, { "alpha_fraction": 0.35555556416511536, "alphanum_fraction": 0.7111111283302307, "avg_line_length": 44, "blob_id": "faee3a90d64f196c97f1888503b745abc97a6994", "content_id": "3ce04db3de7b5d98d0a3cc174101ee524cb5294a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 45, "license_type": "no_license", "max_line_length": 44, "num_lines": 1, "path": "/listings/v9_integrate3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "trapz(x=x, y=y) # Ausgabe: 9.125227959734184\n" }, { "alpha_fraction": 0.7297297120094299, "alphanum_fraction": 0.7297297120094299, "avg_line_length": 17.5, "blob_id": "c2921a34b0c2d576fc1196f0661c888c8277e51d", "content_id": "aecaf7ebbcf506e41559913af8546731b363a251", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 37, "license_type": "no_license", "max_line_length": 27, "num_lines": 2, "path": "/listings/v7_vererbung2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Angestellter(Person):\n pass\n" }, { "alpha_fraction": 0.6785714030265808, "alphanum_fraction": 0.6785714030265808, "avg_line_length": 36.33333206176758, "blob_id": "d3f71f1fd2aecd4b64320575c695512719e368f1", "content_id": "4ade1f3f3068919558d2b247ce17938840285183", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 112, "license_type": "no_license", "max_line_length": 47, "num_lines": 3, "path": "/listings/v3_datei2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "open(file, mode='r', buffering=, encoding=None,\n errors=None, newline=None, closefd=True,\n opener=None)\n" }, { "alpha_fraction": 0.6333333253860474, "alphanum_fraction": 0.6333333253860474, "avg_line_length": 44, "blob_id": "c2d316d05859d081506a3ca00bd2d7a2e986e4b1", "content_id": "6d859da729a81902c7031185fb79053b90369128", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 90, "license_type": "no_license", "max_line_length": 54, "num_lines": 2, "path": "/listings/v5_ra21.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.findall(r'\\w+(?=.doc)', 'bericht.doc dokument.doc')\n# Ausgabe: ['bericht', 'dokument']\n" }, { "alpha_fraction": 0.5589743852615356, "alphanum_fraction": 0.6615384817123413, "avg_line_length": 23.375, "blob_id": "ec6287d1ecc0b57abd1691d2d43a2bfec11aa06f", "content_id": "3fff2032cb19df64d7aa2a9ee792d9028cd7b595", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 195, "license_type": "no_license", "max_line_length": 54, "num_lines": 8, "path": "/listings/v6_klassen17.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n speed_of_light = 299792458\n\n @classmethod\n def c0(cls):\n print('Speed of light =', cls.speed_of_light)\n\nMeineKlasse.c0() # Ausgabe: Speed of light = 299792458\n" }, { "alpha_fraction": 0.6428571343421936, "alphanum_fraction": 0.6428571343421936, "avg_line_length": 41, "blob_id": "14cb9644c9a7655d252e8061b9f6fe65bd29e0e5", "content_id": "1a0a12a24b3cb32058fa03814be0febb08f58f82", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 84, "license_type": "no_license", "max_line_length": 52, "num_lines": 2, "path": "/listings/v7_vererbung7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "p = Person('Laura') # Ausgabe: __init__() von Person\nprint(p.name) # Ausgabe: Laura\n" }, { "alpha_fraction": 0.4399999976158142, "alphanum_fraction": 0.5199999809265137, "avg_line_length": 11.5, "blob_id": "467dc2456db8ff3b8abf0c99cb7b15f93c21faea", "content_id": "cc2364e96e3487b10cc99ec413fa47a18144fa6d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 25, "license_type": "no_license", "max_line_length": 16, "num_lines": 2, "path": "/listings/v3_strings12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "chr(65)\n# Ausgabe: ('A')\n" }, { "alpha_fraction": 0.4215686321258545, "alphanum_fraction": 0.5392156839370728, "avg_line_length": 24.5, "blob_id": "156bf6f411c5c72902ac66f0991e6244c8f7c372", "content_id": "3fb0b32f3fffd91f3b33aadabad5604ee6ae9e63", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 102, "license_type": "no_license", "max_line_length": 40, "num_lines": 4, "path": "/listings/v3_strings4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "spannung = 12.56\nstrom = 0.5\n'U = {}, I = {}'.format(spannung, strom)\n# Ausgabe: 'U = 12.56, I = 0.5'\n" }, { "alpha_fraction": 0.6190476417541504, "alphanum_fraction": 0.6190476417541504, "avg_line_length": 20, "blob_id": "0cf32d1d1f78ee7d9670f98c3af4693d1c655d87", "content_id": "d467b99cc16d9b181cb61db3ab33fcb6692933b0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 42, "license_type": "no_license", "max_line_length": 24, "num_lines": 2, "path": "/listings/v2_func4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def gruss(name):\n print('Hallo', name)\n" }, { "alpha_fraction": 0.30128204822540283, "alphanum_fraction": 0.41025641560554504, "avg_line_length": 25, "blob_id": "b01a47ceacde761b0cedfcba9e785efe064a9a92", "content_id": "9bbc32c77507662ab386112318480434d5b7953e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 156, "license_type": "no_license", "max_line_length": 37, "num_lines": 6, "path": "/listings/v8_numpy8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr1 = np.ones((3, 2)) # shape=(3, 2)\narr2 = np.array([1, 2]) # shape=(2,)\narr1 + arr2 # Ausgabe:\n# array([[2., 3.],\n# [2., 3.],\n# [2., 3.]])\n" }, { "alpha_fraction": 0.5975610017776489, "alphanum_fraction": 0.6219512224197388, "avg_line_length": 12.666666984558105, "blob_id": "eafc14d35e11085e88844d365da9c426fd78b51f", "content_id": "95bc4fcb68fad07376125581d66da8722f59d7b5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 82, "license_type": "no_license", "max_line_length": 16, "num_lines": 6, "path": "/listings/v2_while4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "while Bedingung:\n Anweisung1\n if Fehler:\n break\nelse:\n Anweisung2\n" }, { "alpha_fraction": 0.7147766351699829, "alphanum_fraction": 0.7147766351699829, "avg_line_length": 31.33333396911621, "blob_id": "048db728737b77db98f46d12c1b95e00fa5abf6f", "content_id": "c63ef193fdd365d96566052feadf3b7df164531a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 291, "license_type": "no_license", "max_line_length": 55, "num_lines": 9, "path": "/listings/v3_strings20.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = ' Dieser String sollte saubere Enden haben. \\n'\nprint(s)\n# Ausgabe: Dieser String sollte saubere Enden haben.\n\ns.strip()\n# Ausgabe: 'Dieser String sollte saubere Enden haben.'\n\n'Ein Satz ohne Satzzeichen am Schluss?'.rstrip('.!?')\n# Ausgabe: 'Ein Satz ohne Satzzeichen am Schluss'\n" }, { "alpha_fraction": 0.6893203854560852, "alphanum_fraction": 0.7038834691047668, "avg_line_length": 16.16666603088379, "blob_id": "75f00c0a859ecdf1fffd6db45f6aa3859563edc5", "content_id": "f2ddfc64c77c4e2a28c06b8aa568ca88ae09e7c6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 206, "license_type": "no_license", "max_line_length": 38, "num_lines": 12, "path": "/listings/v3_strings18.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "spruch = '''Wir sollten heute das tun,\nvon dem wir uns morgen wuenschen\nes gestern getan zu haben.'''\n\n'morgen' in spruch\n# Ausgabe: True\n\nspruch.find('heute')\n# Ausgabe: 12\n\nspruch.count('en')\n#Ausgabe: 4\n" }, { "alpha_fraction": 0.6703296899795532, "alphanum_fraction": 0.6703296899795532, "avg_line_length": 21.75, "blob_id": "404e502b67a7dc83bd61fca990140715ee7c6f40", "content_id": "e86fb9cd8b2b57068b57b9fc13fc527b460fcc7a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 91, "license_type": "no_license", "max_line_length": 58, "num_lines": 4, "path": "/listings/v3_exception7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "try:\n welt_retten()\nfinally:\n print('Dinge, die so oder so gemacht werden muessen.')\n" }, { "alpha_fraction": 0.5882353186607361, "alphanum_fraction": 0.6203208565711975, "avg_line_length": 13.384614944458008, "blob_id": "1fef8d5889f679dd41e0e9933654384e23708211", "content_id": "8481ed57eee64a02587c3faa294fac115cf172ba", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 187, "license_type": "no_license", "max_line_length": 27, "num_lines": 13, "path": "/listings/v7_vererbung3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Person:\n var = 123\n\n def func(self):\n print('Person')\n\nclass Angestellter(Person):\n pass\n\n\na = Angestellter()\nprint(a.var) # Ausgabe: 123\na.func() # Ausgabe: Person\n" }, { "alpha_fraction": 0.5345911979675293, "alphanum_fraction": 0.5849056839942932, "avg_line_length": 25.5, "blob_id": "6e772685c7255e17b1203cec46dc6e0be930ed40", "content_id": "d0b7aacba374b0a4b256ffe2a6c38a0b987bb53a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 159, "license_type": "no_license", "max_line_length": 57, "num_lines": 6, "path": "/listings/v5_ra9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def func(m):\n return '(' + m.group() + ')'\n\ns = re.sub(r'\\d+', func, '3 Stuecke kosten 250 Franken.')\nprint(s)\n# Ausgabe: (3) Stuecke kosten (250) Franken.\n" }, { "alpha_fraction": 0.4000000059604645, "alphanum_fraction": 0.5600000023841858, "avg_line_length": 24, "blob_id": "53837051d338052cb55521cba79791c222b5c66f", "content_id": "7af1fdfe524696491281316f52e459047791733a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 75, "license_type": "no_license", "max_line_length": 24, "num_lines": 3, "path": "/listings/v8_numpy4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr[0, 0] # Ausgabe: 1.0\narr[2, 0] # Ausgabe: 7.0\narr[0, 2] # Ausgabe: 3.0\n" }, { "alpha_fraction": 0.7297297120094299, "alphanum_fraction": 0.7297297120094299, "avg_line_length": 36, "blob_id": "edbf0039f75a9a461f6967f7bb01680484eb8958", "content_id": "b8d2b5318dbf0a8e6e72f85442bfb3a5bd0ba41f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 74, "license_type": "no_license", "max_line_length": 37, "num_lines": 2, "path": "/listings/v2_func3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def begruessung(vorname, nachname):\n print('Hallo', vorname, nachname)\n" }, { "alpha_fraction": 0.7142857313156128, "alphanum_fraction": 0.7142857313156128, "avg_line_length": 13, "blob_id": "2a74ebd17762a8823ef2b170fd46056bbcd1ec52", "content_id": "f54e4b1f230a9a9c1e372a87ccb16188acf4de80", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 28, "license_type": "no_license", "max_line_length": 18, "num_lines": 2, "path": "/listings/v6_klassen1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n pass\n" }, { "alpha_fraction": 0.3863636255264282, "alphanum_fraction": 0.3863636255264282, "avg_line_length": 16.600000381469727, "blob_id": "b911decf4491a84872960a77c7de7caf4f742b15", "content_id": "fd07f2d1dc33bc3653542e04843fa52042c94057", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 88, "license_type": "no_license", "max_line_length": 25, "num_lines": 5, "path": "/listings/v3_strings17.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "''.join(['a', 'b', 'c'])\n# Ausgabe: 'abc'\n\n','.join(['a', 'b', 'c'])\n# Ausgabe: 'a,b,c'\n" }, { "alpha_fraction": 0.4838709533214569, "alphanum_fraction": 0.5080645084381104, "avg_line_length": 40.33333206176758, "blob_id": "50c258841eb1c33c21fd8e077687b62bfec7799d", "content_id": "a959cb51f8c33250bc7fafc237cf292a9f1436a1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 124, "license_type": "no_license", "max_line_length": 56, "num_lines": 3, "path": "/listings/v4_tupel19.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "summe = lambda x,y: x + y\nprint(type(summe)) # Ausgabe: <type 'function'>\nsumme(2, 3) # Ausgabe: 5\n" }, { "alpha_fraction": 0.5185185074806213, "alphanum_fraction": 0.5185185074806213, "avg_line_length": 26, "blob_id": "e02753edc39bb961579b2e8c04b46a20fa1ba92c", "content_id": "206b60cdfc4bd1861d66a748cacb662af89c2711", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 108, "license_type": "no_license", "max_line_length": 38, "num_lines": 4, "path": "/listings/v7_vererbung6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Person:\n def __init__(self, name):\n self.name = name\n print('__init__() von Person')\n" }, { "alpha_fraction": 0.375, "alphanum_fraction": 0.546875, "avg_line_length": 31, "blob_id": "dadbabf4e6849991a6b68362c6064f410e7cf821", "content_id": "5d178dee2fbedd30dc0d37c29370fc7b8c94063d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 128, "license_type": "no_license", "max_line_length": 65, "num_lines": 4, "path": "/listings/v4_tupel23.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "from functools import reduce\n\n# (((10 + 20)/2 + 30)/2 + 40)/2\nreduce(lambda x, y: (x + y)/2, [10, 20, 30, 40]) # Ausgabe: 31\n" }, { "alpha_fraction": 0.5093333125114441, "alphanum_fraction": 0.5120000243186951, "avg_line_length": 30.25, "blob_id": "b05a55213cf11e0812268873b3c555f923d71e83", "content_id": "87f61d9dcea18d474c580d52534e7b20583e4973", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 375, "license_type": "no_license", "max_line_length": 52, "num_lines": 12, "path": "/listings/v4_tupel13.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = ['a', 'b', 'c', 'd', 'e', 'f']\n\nelement = liste.pop() # letztes Element rechts\nprint(element) # Ausgabe: f\nliste # Ausgabe: ['a', 'b', 'c', 'd', 'e']\n\nelement = liste.pop(0) # mit Index\nprint(element) # Ausgabe: a\nliste # Ausgabe: ['b', 'c', 'd', 'e']\n\nliste.remove('c') # mit einem bestimmten Wert\nliste # Ausgabe: ['b', 'd', 'e']\n" }, { "alpha_fraction": 0.5728155374526978, "alphanum_fraction": 0.5970873832702637, "avg_line_length": 28.428571701049805, "blob_id": "952b960669058297d18e0f7d44a099dc2735e0a1", "content_id": "743473da0c39ed94664a7cd3c3c9869565747a86", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 206, "license_type": "no_license", "max_line_length": 73, "num_lines": 7, "path": "/listings/v2_func9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def mittelwert(a, args): # a ist zwingend\n print('a =', 1)\n print('args =', args) # die restlichen Argumente sind im Tupel args\n a += sum(args)\n return a/len(args) + 1\n\nmittelwert(2, 3, 7)\n" }, { "alpha_fraction": 0.5428571701049805, "alphanum_fraction": 0.5685714483261108, "avg_line_length": 20.875, "blob_id": "ce751f8b2140c893646247bbb58942f94f0b6801", "content_id": "46ff3eaffae216bd5d137ccc6634cfe1f1c41a3c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 350, "license_type": "no_license", "max_line_length": 56, "num_lines": 16, "path": "/listings/v5_ra3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "m = re.search(r'[a-z]+', '123 hallo welt!')\nprint(m)\n# Ausgabe: <re.Match object; span=(4, 9), match='hallo'>\n\nif m is not None:\n print('group:', m.group())\n print('start:', m.start())\n print('end:', m.end())\n print('span:', m.span())\nelse:\n print('keine Uebereinstimmung')\n# Ausgabe:\n# group: hallo\n# start: 4\n# end: 9\n# span: (4, 9)\n" }, { "alpha_fraction": 0.601123571395874, "alphanum_fraction": 0.6423221230506897, "avg_line_length": 28.66666603088379, "blob_id": "74ab525ca2bbfd266db9be2d437412b4d7990866", "content_id": "959af6075a047238b9aabe77041123bf7d6db208", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 534, "license_type": "no_license", "max_line_length": 77, "num_lines": 18, "path": "/listings/v6_klassen24.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Bank:\n def __init__(self):\n self.__guthaben = 0\n\n def __get_guthaben(self):\n print('Das Guthaben wurde abgefragt.')\n return self.__guthaben\n\n def __set_guthaben(self, n):\n self.__guthaben = n\n print('Das Guthaben wurde auf {} geaendert.'.format(self.__guthaben))\n\n guthaben = property(__get_guthaben, __set_guthaben)\n\nk = Bank()\nk.guthaben = 1000000 # Ausgabe: Das Guthaben wurde auf 1000000 geaendert.\nprint(k.guthaben) # Ausgabe: Das Guthaben wurde abgefragt.\n# Ausgabe: 1000000\n" }, { "alpha_fraction": 0.71875, "alphanum_fraction": 0.75, "avg_line_length": 15, "blob_id": "12c85537c4ab3a22d4fbdb97b452ea2d802bceeb", "content_id": "2e2a3b18d94abb13865aa06d7673c412a6dc5733", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 32, "license_type": "no_license", "max_line_length": 16, "num_lines": 2, "path": "/listings/v2_while1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "while Bedingung:\n Anweisung1\n" }, { "alpha_fraction": 0.43421053886413574, "alphanum_fraction": 0.5131579041481018, "avg_line_length": 24.33333396911621, "blob_id": "0e7d3936521e2bcc8210b9f6410d4a6086da7b28", "content_id": "2a8800e7b2982c2ce9f58636c560bf50cb5c08e9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 228, "license_type": "no_license", "max_line_length": 37, "num_lines": 9, "path": "/listings/v9_matplotlib6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x = y = np.linspace(-3, 3, 100)\nX, Y = np.meshgrid(x, y)\nZ1 = np.exp(-X2 - Y2)\nZ2 = np.exp(-(X - 1)2 - (Y - 1)2)\nZ = (Z1 - Z2) * 2\n\nfig, ax = plt.subplots()\nCS = ax.contour(X, Y, Z);\nax.clabel(CS, inline=1, fontsize=8);\n" }, { "alpha_fraction": 0.6376811861991882, "alphanum_fraction": 0.6570048332214355, "avg_line_length": 11.176470756530762, "blob_id": "f36a3188508f041a58513c5430626cb4a8718799", "content_id": "7b47474408a7ea3bb382c5e2b34f46f116e8223d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 207, "license_type": "no_license", "max_line_length": 18, "num_lines": 17, "path": "/listings/v3_strings22.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'255'.isdigit()\n# Ausgabe: True\n\n'hallo'.isalpha()\n# Ausgabe: True\n\n'Gleis7'.isalnum()\n# Ausgabe: True\n\n'klein'.islower()\n# Ausgabe: True\n\n'GROSS'.isupper()\n# Ausgabe: True\n\n'Haus'.istitle()\n# Ausgabe: True\n" }, { "alpha_fraction": 0.5616438388824463, "alphanum_fraction": 0.6027397513389587, "avg_line_length": 42.79999923706055, "blob_id": "72c50a12f498ea96204d692cdcc0e354905f1f20", "content_id": "9aa94236ec545d25e89809d69a4338b0e59720bf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 219, "license_type": "no_license", "max_line_length": 64, "num_lines": 5, "path": "/listings/v5_ra12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "m = re.search(r'(?P<zahl>\\d+) (?P<wort>\\w+)', '123 hallo welt!')\nprint(m.group('zahl')) # Ausgabe: 123\nprint(m.group('wort')) # Ausgabe: hallo\nm.groupdict() # als Dictionary\n# Ausgabe: {'zahl': '123', 'wort': 'hallo'}\n" }, { "alpha_fraction": 0.3055555522441864, "alphanum_fraction": 0.48148149251937866, "avg_line_length": 35, "blob_id": "3787b6bafedfec58d90e02fbbb49bd55b433db5c", "content_id": "18992dab511d6cfea4494d6fc179df29ed4a1a68", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 108, "license_type": "no_license", "max_line_length": 53, "num_lines": 3, "path": "/listings/v8_numpy7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])\narr[2:5] = 9\nprint(arr) # Ausgabe: array([1, 2, 9, 9, 9, 6, 7, 8])\n" }, { "alpha_fraction": 0.5888158082962036, "alphanum_fraction": 0.6019737124443054, "avg_line_length": 24.33333396911621, "blob_id": "545ba731632ad7b93fbb67653e3a9ef8eff012c0", "content_id": "cc2bed20aa10d4b417a867124616a5d1247b65e8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 304, "license_type": "no_license", "max_line_length": 50, "num_lines": 12, "path": "/listings/v3_datei9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "personen = ['Alice', 'Bob', 'Charlie']\nwith open('rangliste.txt', 'w') as f:\n for n, person in enumerate(personen, start=1):\n f.write(str(n) + '. ' + person + '\\n')\n\n# Ueberpruefen\nwith open('rangliste.txt') as f:\n print(f.read())\n\n# Ausgabe: 1. Alice\n# Ausgabe: 2. Bob\n# Ausgabe: 3.Charlie\n" }, { "alpha_fraction": 0.2769230902194977, "alphanum_fraction": 0.4307692348957062, "avg_line_length": 31.5, "blob_id": "de92932fd36ce8b8d9125411486cf46ad29077b0", "content_id": "4ed25e39ded7ad0e9f3aad45161b78371d05229f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 65, "license_type": "no_license", "max_line_length": 40, "num_lines": 2, "path": "/listings/v5_ra23.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.findall(r'(?<=#)\\d+', '#10, #25, 66')\n# Ausgabe: ['10', '25']\n" }, { "alpha_fraction": 0.6329966187477112, "alphanum_fraction": 0.6329966187477112, "avg_line_length": 41.42856979370117, "blob_id": "ace57ee61ae0d46f28b9771d28bd0bb1558721ea", "content_id": "6660ba92ad8c200bc33676d949cb9574900b431d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 297, "license_type": "no_license", "max_line_length": 58, "num_lines": 7, "path": "/listings/v7_vererbung8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Angestellter(Person):\n def __init__(self, name, personalnummer):\n # Initialisierungsmethode der Superklasse aufrufen\n super().__init__(name)\n # oder Person.__init__(self, name)\n self.personalnummer = personalnummer\n print('__init__() von Angestellter')\n" }, { "alpha_fraction": 0.6111111044883728, "alphanum_fraction": 0.7222222089767456, "avg_line_length": 17, "blob_id": "a9ecfee6da8efcede52629a97bbebf6fe8a73ef5", "content_id": "c4c572c3a46642460727ed3a74485ced09683fa0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 18, "license_type": "no_license", "max_line_length": 17, "num_lines": 1, "path": "/listings/v8_numpy3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr[axis0, axis1]\n" }, { "alpha_fraction": 0.5409836173057556, "alphanum_fraction": 0.5792349576950073, "avg_line_length": 60, "blob_id": "4c1e4de31959c19a8b2de09c9fc2b82deaf0f22e", "content_id": "c772679990a966567882c51d1af76b5f9603df2e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 183, "license_type": "no_license", "max_line_length": 81, "num_lines": 3, "path": "/listings/v5_ra19.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.findall(r'\\w+$', 'Hallo Welt') # Ausgabe: ['Welt']\nre.findall(r'\\w+$', 'Punkt A\\nPunkt B', flags=re.MULTILINE) # Ausgabe: ['A', 'B']\nre.findall(r'\\d\\Z', '123456') # Ausgabe: ['6']\n" }, { "alpha_fraction": 0.47457626461982727, "alphanum_fraction": 0.6525423526763916, "avg_line_length": 18.66666603088379, "blob_id": "0f2746d6d4c6647003a14fe343fb3e34d1e56d56", "content_id": "1512cc475b726fc57254757423e9c25bc844c7a8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 118, "license_type": "no_license", "max_line_length": 38, "num_lines": 6, "path": "/listings/v4_list3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "kilometer = [30, 50, 60, 80, 100, 120]\nmeilen = []\nfor km in kilometer:\n meilen.append(km0.621371)\n\nprint(meilen)\n" }, { "alpha_fraction": 0.6083333492279053, "alphanum_fraction": 0.6416666507720947, "avg_line_length": 39, "blob_id": "98bcbd2bd347cacb4df3d0797d05e3ba6c6a95db", "content_id": "846d1eaa72199a489717e864748e474215c187a8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 120, "license_type": "no_license", "max_line_length": 61, "num_lines": 3, "path": "/listings/v5_ra7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = re.sub(r'\\d+', '<Zahl>', '3 Stuecke kosten 250 Franken.')\nprint(s)\n# Ausgabe: <Zahl> Stuecke kosten <Zahl> Franken.\n" }, { "alpha_fraction": 0.6172839403152466, "alphanum_fraction": 0.6419752836227417, "avg_line_length": 15.199999809265137, "blob_id": "c4a5c6ff702e19e92bd00a81db7acbc84fe42b5b", "content_id": "d7dd61554d90f25dc3795c47ae32a70a653bdf21", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 81, "license_type": "no_license", "max_line_length": 16, "num_lines": 5, "path": "/listings/v2_while2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "while Bedingung:\n Anweisung1\n if Ausnahme:\n continue\n Anweisung2\n" }, { "alpha_fraction": 0.5161290168762207, "alphanum_fraction": 0.5161290168762207, "avg_line_length": 6.75, "blob_id": "e8a4116979f8d83afe7076b96129c55b940917d4", "content_id": "5d79551bcc4d50a1872801d2623221610dacead8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 62, "license_type": "no_license", "max_line_length": 14, "num_lines": 8, "path": "/listings/v7_vererbung11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class A:\n pass\n\nclass B:\n pass\n\nclass C(A, B):\n pass\n" }, { "alpha_fraction": 0.6129032373428345, "alphanum_fraction": 0.6209677457809448, "avg_line_length": 61, "blob_id": "c219782e5296e29e9158682d54c4a0d1a2e0fbee", "content_id": "c4812835eb9423d18fc4a5c038f4a31d24a9435d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 124, "license_type": "no_license", "max_line_length": 79, "num_lines": 2, "path": "/listings/v5_ra14.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = re.findall(r'((dum)\\2)', 'dumdum') # (dum) ist jetzt die zweite Gruppe\nprint(liste) # Ausgabe: [('dumdum', 'dum')]\n" }, { "alpha_fraction": 0.4527363181114197, "alphanum_fraction": 0.5174129605293274, "avg_line_length": 19.100000381469727, "blob_id": "433c6e1194c8b2678532c71d6aceea82745e1806", "content_id": "d480c3d6f9a9c4527d23003122d685e6817b2dc9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 201, "license_type": "no_license", "max_line_length": 69, "num_lines": 10, "path": "/listings/v2_func15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x = [1, 2, 3]\ny = [7, 8, 9]\n\ndef foo(a, b):\n a.append(4) # Objekt veraendern\n b = [10, 11, 12] # lokale Variable b referenziert neues Objekt\n\nfoo(x, y)\nprint('x =', x)\nprint('y =', y)\n" }, { "alpha_fraction": 0.4000000059604645, "alphanum_fraction": 0.5, "avg_line_length": 19, "blob_id": "d0cc2d062f1ab052994e1d2bdf56602dc49cff6d", "content_id": "0187233f3bafb98984e6f428f8587437d3041161", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 20, "license_type": "no_license", "max_line_length": 19, "num_lines": 1, "path": "/listings/v8_numpy6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = arr[2:5].copy()\n" }, { "alpha_fraction": 0.4303797483444214, "alphanum_fraction": 0.5063291192054749, "avg_line_length": 38.5, "blob_id": "42273c741af067e97672a8d056b8ab75acf8b4da", "content_id": "e647c7251fc3fe3e6e765adc7270b82a0e408353", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 79, "license_type": "no_license", "max_line_length": 46, "num_lines": 2, "path": "/listings/v3_strings8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'U = {u}, I = {i}'.format(u=spannung, i=strom)\n# Ausgabe: 'U = 12.56, I = 0.5'\n" }, { "alpha_fraction": 0.5, "alphanum_fraction": 0.6708074808120728, "avg_line_length": 25.83333396911621, "blob_id": "7fc44ad278dea620ba70ba57b160f7d5e77b9d64", "content_id": "20aea04806327285b0d23dc7dc844f5e047d28bc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 322, "license_type": "no_license", "max_line_length": 53, "num_lines": 12, "path": "/listings/v9_integrate4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "from scipy.integrate import quad\n\ndef func(x):\n return np.sin(x)2\n\n# Integration mit Gauss-Quadratur\ny, abserr = quad(func, a=0, b=np.pi)\nprint('y =', y, '; err=', abserr) # Ausgabe:\n# y = 1.5707963267948966 ; err= 1.743934249004316e-14\n# Analytische Loesung\nprint('y =', np.pi/2) # Ausgabe:\n# y = 1.5707963267948966\n" }, { "alpha_fraction": 0.5416666865348816, "alphanum_fraction": 0.6666666865348816, "avg_line_length": 35, "blob_id": "8b98660ee7687940b82414b3894f3a1c4df31b36", "content_id": "8c9f7a5d25e91b4a87dfa0968ddb20dae7adff8c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 144, "license_type": "no_license", "max_line_length": 53, "num_lines": 4, "path": "/listings/v6_klassen6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x = MeineKlasse()\ny = MeineKlasse()\nprint('x:', x.speed_of_light) # Ausgabe: x: 299792458\nprint('y:', y.speed_of_light) # Ausgabe: y: 299792458\n" }, { "alpha_fraction": 0.6442307829856873, "alphanum_fraction": 0.6442307829856873, "avg_line_length": 19.799999237060547, "blob_id": "1713d70a493ae9cbe51a346fd76b50d8c34e7b2c", "content_id": "bf7412a2b6c199ab8bb557a35088db4d9f05a865", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 104, "license_type": "no_license", "max_line_length": 42, "num_lines": 5, "path": "/listings/v6_klassen12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = MeineKlasse('Wall-E') # name='Wall-E'\ns.hallo()\n# Ausgabe:\n# Wall-E wurde erstellt.\n# Hallo Wall-E\n" }, { "alpha_fraction": 0.5625, "alphanum_fraction": 0.5625, "avg_line_length": 15, "blob_id": "2ac1b207808101d3adeadbfbe01ed13ab128333c", "content_id": "d9808200a3197c6e145f0db4ba6b5af54d3d16f4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 48, "license_type": "no_license", "max_line_length": 25, "num_lines": 3, "path": "/listings/v4_tupel1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "t = ()\nprint(type(t))\n# Ausgabe: <type 'tuple'>\n" }, { "alpha_fraction": 0.3931034505367279, "alphanum_fraction": 0.548275887966156, "avg_line_length": 31.22222137451172, "blob_id": "60e25d8109b3bcf1eb6c47b9e9a778d7b0554051", "content_id": "3a07e313320e6cb0b00ec8c06cb56f4b08c69915", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 290, "license_type": "no_license", "max_line_length": 56, "num_lines": 9, "path": "/listings/v3_strings3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "spannung = 12.56\nstrom = 0.5\nN = 10\nprint('N = %d, U = %f, I = %.3f' % (N, spannung, strom))\n# Ausgabe: N = 10, U = 12.560000, I = 0.500\nprint('U = %g' % spannung) # generelles Format\n# Ausgabe: U = 12.56\nprint('X = 0x%04X, Y = 0x%04X' % (7, 15)) # hex\n# Ausgabe: X = 0x0007, Y = 0x000F\n" }, { "alpha_fraction": 0.675000011920929, "alphanum_fraction": 0.6850000023841858, "avg_line_length": 39, "blob_id": "562b2f61f1bd3868c01a57f55fca90d7d84c6998", "content_id": "6f4d80e13ff991133ef020dd041a344e168e23b1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 200, "license_type": "no_license", "max_line_length": 84, "num_lines": 5, "path": "/listings/v2_func11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def einfache_funktion(x, *kwargs):\n print('x =', x)\n print('kwargs =', kwargs) # die restlichen Argumente sind im Dictionary kwargs\n\neinfache_funktion(x='Hallo', farbe='rot', durchmesser=10)\n" }, { "alpha_fraction": 0.7428571581840515, "alphanum_fraction": 0.7428571581840515, "avg_line_length": 20, "blob_id": "82f94af7042ba17628760126c71bee16f39fa228", "content_id": "ee7e84705eee18be52ff3e057c8e8b1b1c0757de", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 315, "license_type": "no_license", "max_line_length": 47, "num_lines": 15, "path": "/listings/v3_datei11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "import os\nfull_path = os.path.abspath('mailaenderli.txt')\nprint(full_path)\n# Ausgabe: kompletter Pfad der datei\n\nos.path.isfile(full_path)\n# Ausgabe: True\n\nos.path.isdir(full_path)\n# Ausgabe: False\n\nos.path.getsize(full_path)\nos.path.split(full_path)\nos.path.splitext(full_path)\nos.path.join('ordner', 'datei.txt')\n" }, { "alpha_fraction": 0.5789473652839661, "alphanum_fraction": 0.5789473652839661, "avg_line_length": 18, "blob_id": "92c197a69794ddf27dbdeaa2730087b38efd7e4f", "content_id": "db040e53eee580b0771c55d186d73b8d540b65d7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 57, "license_type": "no_license", "max_line_length": 31, "num_lines": 3, "path": "/listings/v4_tupel4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "t = 'Peter', 'Mueller'\nt\n# Ausgabe: ('Peter', 'Mueller')\n" }, { "alpha_fraction": 0.5720000267028809, "alphanum_fraction": 0.5720000267028809, "avg_line_length": 40.66666793823242, "blob_id": "c4e5ffdee5df42d4dbba7fd4ad7160d5236bb054", "content_id": "d5280de70301a4dadd0fcdd15d11c5f203921cfd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 250, "license_type": "no_license", "max_line_length": 43, "num_lines": 6, "path": "/listings/v3_datei1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "f = open('dokument.txt') # lesen\nf = open('dokument.txt', 'r') # lesen\nf = open('dokument.txt', 'w') # schreiben\nf = open('dokument.txt', 'a') # anhaengen\nf = open('dokument.txt', 'rb') # binaer\nf = open('dokument.txt', 'wb') # binaer\n" } ]	192
kaktojosko/BIG_rock_paper_scissors	https://github.com/kaktojosko/BIG_rock_paper_scissors	d19e930a0786a69cb757d205507ea0b33593ccdc	57474616ce7fadf470fc66188cad6f2430f9c09f	f2b2eb92d4737268c634696e46c1ee2fac5fe50a	refs/heads/main	2023-01-19T20:51:32.675719	2020-11-28T13:16:07	2020-11-28T13:16:07	316,729,907	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5108920931816101, "alphanum_fraction": 0.5176348686218262, "avg_line_length": 34.60759353637695, "blob_id": "19a18b73541eacbf505126f1add735c0fa5f77fd", "content_id": "d9c1f0ab84b4824939926050f0e80a479ca4a881", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5964, "license_type": "no_license", "max_line_length": 106, "num_lines": 158, "path": "/my_test.py", "repo_name": "kaktojosko/BIG_rock_paper_scissors", "src_encoding": "UTF-8", "text": "# РАБОТАЕТ ОКЕЙ\r\nimport random\r\nex = False\r\nname = input('Enter your name: ')\r\nprint('Hello,', name)\r\nscore1 = 0\r\n# Пустой ввод = камень, ножницы и бумага в игре\r\n# Ввод из rock, paper даст все 15 игровых фигур\r\nmoves = [x for x in input().split(',')]\r\n\r\nprint(\"Okay, let's start\")\r\nwinning_cases = {\r\n 'water': ['snake', 'human', 'tree', 'wolf', 'sponge', 'paper', 'air'],\r\n 'dragon': ['human', 'tree', 'wolf', 'sponge', 'paper', 'air', 'water'],\r\n 'devil': ['tree', 'wolf', 'sponge', 'paper', 'air', 'water', 'dragon'],\r\n 'lightning': ['wolf', 'sponge', 'paper', 'air', 'water', 'dragon', 'devil'],\r\n 'gun': ['sponge', 'paper', 'air', 'water', 'dragon', 'devil', 'lightning'],\r\n 'rock': ['paper', 'air', 'water', 'dragon', 'devil', 'lightning', 'gun'],\r\n 'fire': ['air', 'water', 'dragon', 'devil', 'lightning', 'gun', 'rock'],\r\n 'scissors': ['water', 'dragon', 'devil', 'lightning', 'gun', 'rock', 'fire'],\r\n 'snake': ['dragon', 'devil', 'lightning', 'gun', 'rock', 'fire', 'scissors'],\r\n 'human': ['devil', 'lightning', 'gun', 'rock', 'fire', 'scissors', 'snake'],\r\n 'tree': ['lightning', 'gun', 'rock', 'fire', 'scissors', 'snake', 'human'],\r\n 'wolf': ['gun', 'rock', 'fire', 'scissors', 'snake', 'human', 'tree'],\r\n 'sponge': ['rock', 'fire', 'scissors', 'snake', 'human', 'tree', 'wolf'],\r\n 'paper': ['rock', 'fire', 'scissors', 'snake', 'human', 'tree', 'wolf', 'sponge'],\r\n 'air': ['fire', 'scissors', 'snake', 'human', 'tree', 'wolf', 'sponge', 'paper']\r\n}\r\n\r\nlosing_cases = {\r\n 'water': ['scissors', 'fire', 'rock', 'hun', 'lightning', 'devil', 'dragon'],\r\n 'dragon': ['snake', 'scissors', 'fire', 'rock', 'gun', 'lightning', 'devil'],\r\n 'devil': ['tree', 'human', 'snake', 'scissors', 'fire', 'rock', 'gun'],\r\n 'gun': ['wolf', 'tree', 'human', 'snake', 'scissors', 'fire', 'rock'],\r\n 'rock': ['sponge', 'wolf', 'tree', 'human', 'snake', 'scissors', 'fire'],\r\n 'fire': ['paper', 'sponge', 'wolf', 'tree', 'human', 'snake', 'scissors'],\r\n 'scissors': ['air', 'paper', 'sponge', 'wolf', 'tree', 'human', 'snake'],\r\n 'snake': ['water', 'air', 'paper', 'sponge', 'wolf', 'tree', 'human'],\r\n 'human': ['dragon', 'water', 'air', 'paper', 'sponge', 'wolf', 'tree'],\r\n 'tree': ['devil', 'dragon', 'water', 'air', 'paper', 'sponge', 'wolf'],\r\n 'wolf': ['lightning', 'devil', 'dragon', 'water', 'air', 'paper', 'sponge'],\r\n 'sponge': ['gun', 'lightning', 'devil', 'dragon', 'water', 'air', 'paper'],\r\n 'paper': ['rock', 'gun', 'lightning', 'devil', 'dragon', 'water', 'air'],\r\n 'air': ['fire', 'rock', 'gun', 'lightning', 'devil', 'dragon', 'water'],\r\n 'lightning': ['tree', 'human', 'snake', 'scissors', 'fire', 'rock', 'gun']\r\n}\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\nwith open('rating.txt', 'a+') as file:\r\n file.seek(0)\r\n lines = file.readlines()\r\n for line in lines:\r\n if line.startswith(name):\r\n score1 = int(line[len(name) + 1:])\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef check_name(name):\r\n exists = False\r\n with open('rating.txt', 'a+') as file:\r\n file.seek(0)\r\n lines = file.readlines()\r\n for line in lines:\r\n if name in line:\r\n exists = True\r\n\r\n if not exists:\r\n file.seek(0)\r\n file.write(name + ' ' + str(score1) + '\\n')\r\n\r\n\r\ncheck_name(name)\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef rating(z):\r\n with open('rating.txt', 'a+') as file:\r\n file.seek(0)\r\n lines = file.readlines()\r\n for line in lines:\r\n if line.startswith(z):\r\n print('Your rating:', score1)\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef ez_win(x):\r\n if len(moves) == 1:\r\n win = {'scissors': 'rock', 'rock': 'paper', 'paper': 'scissors'}\r\n print('Sorry, but the computer chose ' + win[x])\r\n else:\r\n print('Sorry, but the computer chose ' + random.choice(losing_cases[x]))\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef ez_lose(x):\r\n global score1\r\n if len(moves) == 1:\r\n lose = {'rock': 'scissors', 'scissors': 'paper', 'paper': 'rock'}\r\n loser: str = lose[x]\r\n print('Well done. The computer chose {} and failed'.format(loser))\r\n else:\r\n print('Well done. The computer chose {} and failed'.format(random.choice(winning_cases[x])))\r\n score1 += 100\r\n change_score()\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef draw(x):\r\n global score1\r\n tie = {'rock': 'rock', 'scissors': 'scissors', 'paper': 'paper', 'water': 'water', 'dragon': 'dragon',\r\n 'devil': 'devil',\r\n 'gun': 'gun', 'fire': 'fire', 'snake': 'snake',\r\n 'human': 'human', 'tree': 'tree', 'wolf': 'wolf', 'sponge': 'sponge',\r\n 'air': 'air', 'lightning': 'lightning'\r\n }\r\n print('There is a draw ({})'.format(tie[x]))\r\n score1 += 50\r\n change_score()\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef mainloop(y):\r\n global ex, name\r\n r_int = random.randint(1, 3)\r\n if r_int == 1 and y in winning_cases and y != '!exit':\r\n ez_win(y)\r\n elif r_int == 2 and y in winning_cases and y != '!exit':\r\n ez_lose(y)\r\n elif r_int == 3 and y in winning_cases and y != '!exit':\r\n draw(y)\r\n elif y == '!exit':\r\n print('Bye!')\r\n ex = True\r\n elif y == '!rating':\r\n rating(name)\r\n else:\r\n print('Invalid input')\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef change_score():\r\n global name, score1\r\n with open('rating.txt', 'a+') as f2:\r\n f2.write(name + ' ' + str(score1))\r\n with open('rating.txt', 'w+') as f2:\r\n counter = 0\r\n lines = f2.readlines()\r\n for line in lines:\r\n if line.startswith(name) and counter < 1:\r\n line = ''\r\n counter += 1\r\n f2.write(name + ' ' + str(score1))\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\nwhile ex is False:\r\n player_move = input().lower()\r\n mainloop(player_move)\r\n" } ]	1
reporter-law/automated_writing	https://github.com/reporter-law/automated_writing	c796afa949800f4907309be437698105613a32a7	9f134b15d5e994166bd350c769ed23ccffc7c5af	c531b30bea67bd7abb0e56aa56b733fd2ba73192	refs/heads/master	2022-11-28T22:58:14.377736	2020-08-17T09:06:08	2020-08-17T09:06:08	288,129,801	1	0	null	null	null	null	null	[ { "alpha_fraction": 0.5846500992774963, "alphanum_fraction": 0.5959367752075195, "avg_line_length": 21.200000762939453, "blob_id": "7e2035d527e7afa76bbe831b5cbf235e96066d32", "content_id": "b2d80e5564046959e358304aa953a2fb479711f5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 547, "license_type": "no_license", "max_line_length": 65, "num_lines": 20, "path": "/中国知网文献检索分析/__init__.py", "repo_name": "reporter-law/automated_writing", "src_encoding": "UTF-8", "text": "\"\"\"程序说明\"\"\"\n# -- coding: utf-8 --\n# Author: cao wang\n# Datetime : 2020\n# software: PyCharm\n# 收获:\nimport time\nimport logging\n\n\ndef start_logger():\n \"\"\"日志初始化设置、文件名（时间）、DEBUG为调试级别(级别导致输出内容的不同）、日志的记录格式、日期格式\"\"\"\n\n logging.basicConfig( #filename='daily_report_error_%s.log' %\n\n #datetime.strftime(datetime.now(), '%m%d%Y_%H%M%S'),\n\n level=logging.DEBUG,\n format='%(asctime)s %(message)s',\n datefmt='%m-%d %H:%M:%S')" }, { "alpha_fraction": 0.5138194561004639, "alphanum_fraction": 0.5276389122009277, "avg_line_length": 33.0099983215332, "blob_id": "ff7e6383e44626cb3ea0e6dd250276f1a754a961", "content_id": "2574c1ff37295cd0d8be9f0f960d6ba1c7b289fe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8158, "license_type": "no_license", "max_line_length": 120, "num_lines": 200, "path": "/中国知网文献检索分析/文章深入检索.py", "repo_name": "reporter-law/automated_writing", "src_encoding": "UTF-8", "text": "\"\"\"程序说明\"\"\"\n# -- coding: utf-8 --\n# Author: cao wang\n# Datetime : 2020\n# software: PyCharm\n# 收获:\nimport time\nimport logging\n\n\ndef start_logger():\n\n \"\"\"日志初始化设置、文件名（时间）、DEBUG为调试级别(级别导致输出内容的不同）、日志的记录格式、日期格式\"\"\"\n time.sleep(1)\n\n logging.basicConfig( #filename='daily_report_error_%s.log' %\n\n #datetime.strftime(datetime.now(), '%m%d%Y_%H%M%S'),\n\n level=logging.DEBUG,\n format='%(asctime)s %(message)s',\n datefmt='%m-%d %H:%M:%S')\n\n\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom urllib.parse import urljoin\nimport time\nimport random\nimport json\n#import csv\n\n# 设置谷歌驱动器的环境\noptions = webdriver.ChromeOptions()\n# 设置chrome不加载图片，提高速度\noptions.add_experimental_option(\"prefs\", {\"profile.managed_default_content_settings.images\": 2})\n# 创建一个谷歌驱动器\noptions.add_argument('--headless')\nbrowser = webdriver.Chrome(options=options)\nurl = 'http://wap.cnki.net/touch/web/guide'\nwait = WebDriverWait(browser, 20)\n\n\n# 声明一个全局列表，用来存储字典\ndata_list = []\n\n\ndef start_spider(page,keyword):\n \"\"\"请求url\"\"\"\n browser.get(url)\n # 显示等待输入框是否加载完成\n WebDriverWait(browser, 1000).until(\n EC.presence_of_all_elements_located(\n (By.ID, 'keyword')\n )\n )\n # 找到输入框的id，并输入python关键字\n browser.find_element_by_id('keyword').click()\n browser.find_element_by_id('keyword_ordinary').send_keys(keyword)\n # 输入关键字之后点击搜索\n browser.find_element_by_class_name('btn-search ').click()\n\n\n\n # print(browser.page_source)\n # 显示等待文献是否加载完成\n WebDriverWait(browser, 1000).until(EC.presence_of_all_elements_located((By.CLASS_NAME, 'g-search-body')))\n\n # 声明一个标记，用来标记翻页几页\n try:\n count = 1\n while True:\n # 显示等待加载更多按钮加载完成\n WebDriverWait(browser, 1000).until(\n EC.presence_of_all_elements_located(\n (By.CLASS_NAME, 'c-company__body-item-more')\n )\n )\n # 获取加载更多按钮\n Btn = browser.find_element_by_class_name('c-company__body-item-more')\n # 显示等待该信息加载完成\n WebDriverWait(browser, 1000).until(\n EC.presence_of_all_elements_located(\n (By.XPATH,\n '//div[@id=\"searchlist_div\"]/div[{}]/div[@class=\"c-company__body-item\"]'.format(2 * count - 1))\n )\n )\n # 获取在div标签的信息，其中format(2count-1)是因为加载的时候有显示多少条\n # 简单的说就是这些div的信息都是奇数\n divs = browser.find_elements_by_xpath(\n '//div[@id=\"searchlist_div\"]/div[{}]/div[@class=\"c-company__body-item\"]'.format(2 count - 1))\n # 遍历循环\n for div in divs:\n # 获取文献的题目\n name = div.find_element_by_class_name('c-company__body-title').text\n # 获取文献的作者\n author = div.find_element_by_class_name('c-company__body-author').text\n # # 获取文献的摘要\n # content = div.find_element_by_class_name('c-company__body-content').text\n # 获取文献的来源和日期、文献类型等\n text = div.find_element_by_class_name('c-company__body-name').text.split()\n if (len(text) == 3 and text[-1] == '优先') or len(text) == 2:\n # 来源\n source = text[0]\n # 日期\n datetime = text[1]\n # 文献类型\n literature_type = None\n else:\n source = text[0]\n datetime = text[2]\n literature_type = text[1]\n # 获取下载数和被引数\n temp = div.find_element_by_class_name('c-company__body-info').text.split()\n # 下载数\n download = temp[0].split('：')[-1]\n # 被引数\n cite = temp[1].split('：')[-1]\n\n # ----------2020-3-18修改----------#\n # 文献链接\n link = div.find_element_by_class_name('c-company-top-link').get_attribute('href')\n # 拼接\n link = urljoin(browser.current_url, link)\n # 获取关键字（需要访问该文献，url就是上面获取到的link）\n # browser.get(link) # 这行和下面那行不推荐使用，因为句柄的问题，会报错\n # browser.back()\n # 打印查看下未访问新窗口时的句柄\n # print(browser.current_window_handle)\n js = 'window.open(\"%s\");' % link\n # 每次访问链接的时候适当延迟\n time.sleep(random.uniform(1, 2))\n browser.execute_script(js)\n # 打印查看窗口的句柄，对比看下当前的句柄是哪个\n # 结果是原先窗口的句柄，而不是新打开窗口的句柄，因为和上面打印的句柄一样\n # print(browser.current_window_handle)\n # 切换句柄到新打开的窗口，browser.window_handles是查看全部的句柄\n # browser.switch_to_window是切换句柄\n browser.switch_to_window(browser.window_handles[1])\n # 获取关键字（使用xpath）\n key_worlds = browser.find_elements_by_xpath(\n '//div[@class=\"c-card__paper-name\"][contains(text(), \"关键词\")]/following-sibling::div[1]/a')\n key_worlds = ','.join(map(lambda x: x.text, key_worlds))\n # ----------2020-3-19修改----------#\n # 获取文献的摘要\n content = browser.find_element_by_class_name('c-card__aritcle').text\n # ----------2020-3-19修改----------#\n # 获取信息完之后先关闭当前窗口再切换句柄到原先的窗口\n browser.close()\n browser.switch_to_window(browser.window_handles[0])\n # 注：切换句柄参考该文章，感谢该博主：https://blog.csdn.net/DongGeGe214/article/details/52169761\n # ----------2020-3-18修改----------#\n\n # 声明一个字典存储数据\n data_dict = {'name': name, 'author': author, 'content': content, 'source': source, 'datetime': datetime,\n 'literature_type': literature_type, 'download': download, 'cite': cite, 'link': link,\n 'key_worlds': key_worlds}\n print(data_dict)\n data_list.append(data_dict)\n\n # 如果Btn按钮（就是加载更多这个按钮）没有找到（就是已经到底了），就退出\n if not Btn:\n break\n else:\n Btn.click()\n # 如果到了爬取的页数就退出\n if count == page: # 0的作用就是到底，当btn不存在\n break\n\n count += 1\n\n # 延迟两秒，我们不是在攻击服务器\n time.sleep(2)\n except:\n pass\n # 全部爬取结束后退出浏览器\n browser.quit()\n\n\n\n\ndef main(keyword):\n \"\"\"主函数\"\"\"\n start_spider(eval(input('请输入要爬取的页数（如果需要全部爬取请输入0）：')),keyword)\n\n # 将数据写入json文件中\n\n with open('data_json.json', 'a+', encoding='utf-8') as f:\n json.dump(data_list, f, ensure_ascii=False, indent=4)\n print('json文件写入完成')\n\n\n\n\nif __name__ == '__main__':\n list_ = ['侦查讯问告知']\n for i in list_:\n main(i)\n" }, { "alpha_fraction": 0.8805969953536987, "alphanum_fraction": 0.8805969953536987, "avg_line_length": 32.5, "blob_id": "cd9dac46f58fa45169c61f67711f078797b5bc7a", "content_id": "198aedee2a9f2790495b0cfbf60728ff1b6a384f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 151, "license_type": "no_license", "max_line_length": 46, "num_lines": 2, "path": "/README.md", "repo_name": "reporter-law/automated_writing", "src_encoding": "UTF-8", "text": "# automated_writing\n第一个cnki的检索程序并不是我写的，只是搬过来用的，具有出自哪里暂时不记得了，等记得了加上\n" }, { "alpha_fraction": 0.5469270944595337, "alphanum_fraction": 0.5526441335678101, "avg_line_length": 27.71232795715332, "blob_id": "b964405b340cffc75449fe7686d920d17ea2d350", "content_id": "aecfe5728ea7591e475e78fa80e59a5f7d72443b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2305, "license_type": "no_license", "max_line_length": 91, "num_lines": 73, "path": "/参考文献写入/参考文献写入.py", "repo_name": "reporter-law/automated_writing", "src_encoding": "UTF-8", "text": "\"\"\"程序说明\"\"\"\n# -- coding: utf-8 --\n# Author: cao wang\n# Datetime : 2020\n# software: PyCharm\n# 收获:\n\nimport logging\nimport time\n#from 常用设置.文字处理.文件格式转化.读取ddp import check_file_types as check\n#from 常用设置.文字处理.docx读取.docx内容 import read_docx as read\nimport os\nimport glob\nimport docx\nimport re\nimport pprint\nimport mammoth\nfrom lxml import etree\n\n\nclass Reference_Find():\n def __init__(self,input):\n self.input = input\n\n\n\n def docx_html(self):\n \"\"\"这是由于脚注无法读取\"\"\"\n \"\"\"转为html\"\"\"\n with open(self.input, \"rb\") as docx_file:\n result = mammoth.convert_to_html(docx_file)\n html = result.value # The generated HTML\n #messages = result.messages # Any messages, such as warnings during conversion\n temp = self.input.split(\".\")[0]+\".html\"\n with open(temp, \"a\")as f:\n f.write(html)\n return temp\n\n\n def references_write_to_docx(self):\n \"\"\"首先提取脚注，然后写入\"\"\"\n temp = self.docx_html()\n print(temp)\n parser = etree.HTMLParser(encoding=\"gbk\")\n text = etree.parse(temp, parser=parser)\n content = text.xpath(\"/html/body/ol[10]//text()\")\n #print(content)\n references = []\n for i in content:\n if i == \"↑\" or \"说明：\" in i:\n pass\n else:\n try:\n text = i.split(\"；\")\n for i in text:\n references.append(i.replace(\"参见\",\"\"))\n except:\n references.append(i.replace(\"参见\", \"\"))\n #写入docx\n document = docx.Document(self.input)\n docText2 = [paragraph.text for paragraph in document.paragraphs]\n p = document.paragraphs[len(docText2)-1]\n #document.add_page_break() # 另起一页\n p.add_run(\"\\n\")\n p.add_run(\"参考文献\\n\")\n for i,text in enumerate(references):\n i = i+1\n p.add_run(\"[%d]\"%i+text+\"\\n\")\n document.save(self.input)\n\n\ninput=r\"C:\\Users\\lenovo\\Desktop\\法官量刑的限度：从危险驾驶罪的量刑情节影响因子谈起 - 副本.docx\"\nReference_Find(input).references_write_to_docx()\n\n\n\n" }, { "alpha_fraction": 0.533281147480011, "alphanum_fraction": 0.5489428639411926, "avg_line_length": 24.85714340209961, "blob_id": "57e702d668781de70c1eb3df6518c901ddc8d56d", "content_id": "8714e290d090e750a8755f08029eeb987140cf5c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1419, "license_type": "no_license", "max_line_length": 92, "num_lines": 49, "path": "/中国知网文献检索分析/数据清洗.py", "repo_name": "reporter-law/automated_writing", "src_encoding": "UTF-8", "text": "\"\"\"程序说明\"\"\"\n# -- coding: utf-8 --\n# Author: cao wang\n# Datetime : 2020\n# software: PyCharm\n# 收获:\nimport time\nimport logging\nimport json\nimport re\n\n\ndef start_logger():\n \"\"\"日志初始化设置、文件名（时间）、DEBUG为调试级别(级别导致输出内容的不同）、日志的记录格式、日期格式\"\"\"\n\n logging.basicConfig( #filename='daily_report_error_%s.log' %\n\n #datetime.strftime(datetime.now(), '%m%d%Y_%H%M%S'),\n\n level=logging.DEBUG,\n format='%(asctime)s %(message)s',\n datefmt='%m-%d %H:%M:%S')\nstart_logger()\nset_ =[]\n\"\"\"\nwith open('titles.txt', 'r+', encoding='utf-8') as f:\n words = f.readlines()\n for word in words:\n word.replace('[','').replace(']','').replace(\"'\",'').replace('[','').replace(']','')\n #print(word)\n\n print(type(word))\n words = word.strip().replace('[','').replace(']','').replace(\"'\",'')\n abs = words.split(',')\n for i in abs:\n set_.append(i)\n print(len(set_))\n set_q = set(set_)\n print(set_q)\n print(len(set_q))#257,215\\208\\95\\62\n\"\"\"\nwith open('/项目/论文写作自动化/中国知网文献检索分析/data_json.json', 'r+', encoding='utf-8') as f:\n words = f.read()\n pattern = re.compile('\"name\":(.?),')\n word = re.findall(pattern,words)\n print(word)\n print(len(word))\n set_word = set(word)\n print(len(set_word))\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5539793372154236, "alphanum_fraction": 0.5614330172538757, "avg_line_length": 27.86805534362793, "blob_id": "3ef1fed7cd7691a31e58f80d3e2757ee8299e105", "content_id": "316df7716123408d88f042d565bdb8a5f0ce90d1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4435, "license_type": "no_license", "max_line_length": 128, "num_lines": 144, "path": "/中国知网文献检索分析/(未完成）检索结果分析.py", "repo_name": "reporter-law/automated_writing", "src_encoding": "UTF-8", "text": "\"\"\"程序说明\"\"\"\n# -- coding: utf-8 --\n# Author: cao wang\n# Datetime : 2020\n# software: PyCharm\n# 收获:电脑端知网无法检索，只能手机端\n\nimport logging\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom lxml import etree\nimport time\n\n\nlogging.disable(logging.DEBUG)\ndef start_logger():\n \"\"\"日志初始化设置、文件名（时间）、DEBUG为调试级别(级别导致输出内容的不同）、日志的记录格式、日期格式\"\"\"\n\n logging.basicConfig( #filename='daily_report_error_%s.log' %\n\n #datetime.strftime(datetime.now(), '%m%d%Y_%H%M%S'),\n\n level=logging.DEBUG,\n format='%(asctime)s %(message)s',\n datefmt='%m-%d %H:%M:%S')\nstart_logger()\ndef search_result(keyword):\n \"\"\"返回检索初步结果\"\"\"\n browser = webdriver.Chrome()\n wait = WebDriverWait(browser, 20)\n\n \"\"\"网页获取\"\"\"\n browser.get('http://wap.cnki.net/touch/web/guide')\n click = wait.until(EC.presence_of_element_located((By.XPATH, '//[@id=\"keyword\"]')))\n click.click()\n input_wprd = wait.until(EC.presence_of_element_located((By.XPATH, '//[@id=\"keyword_ordinary\"]')))\n input_wprd.clear()\n input_wprd.send_keys(keyword)\n button = wait.until(EC.presence_of_element_located((By.XPATH, '//[@id=\"searchbody_ordinary\"]/div/div[1]/div/div[1]/a[2]')))\n button.click()\n #time.sleep(1111)\n time.sleep(11111)\n try:\n while True:\n Btn = wait.until(EC.element_to_be_clickable((By.CLASS_NAME, 'c-company__body-item-more')))\n\n if Btn:\n browser.execute_script('window.scrollTo(0,document.body.scrollHeight)')\n Btn.click()\n\n else:\n with open('xxxxxxxxx.txt', \"a+\", encoding='utf-8')as f:\n f.write(browser.page_source)\n return browser.page_source\n except:\n with open('xxxxxxxxx.txt', \"a+\", encoding='utf-8')as f:\n f.write(browser.page_source)\n return browser.page_source\n\n\n\n\n\n\ndef get_page(html):\n \"\"\"解析函数：首先要进行返回html\"\"\"\n\n html_ = etree.HTML(html)\n contents = html_.xpath('//*[@id=\"searchlist_div\"]/div')\n # 内容遍历每一页\n print(contents)\n\n # logging.info(contents.xpath('/div//text()'))\n data = []\n for texts in contents:\n # logging.info(texts)\n \"\"\"对每一页进行内容提取\"\"\"\n titles = ''.join(\n texts.xpath('//div[@class=\"c-company__body-title c-company__body-title-blue\"]//text()')).strip()\n # print(titles)\n title = [i.strip() for i in titles.split('\\n')]\n print(len(title))\n print(title[0] + '\\n')\n with open('titles.txt', \"a+\", encoding='utf-8')as f:\n f.write(str(title) + \"\\n\")\n\n\n\n\n \"\"\"\n 其余内容\n authors = ''.join(texts.xpath('//div[@class=\"c-company__body-author\"]//text()')).replace(' ','')\n author = [i.strip() for i in authors.split('\\n') if i !='']\n print(author)\n\n\n contents = ''.join(texts.xpath('//div[@class=\"c-company__body-content\"]//text()'))\n content = [i.strip().replace(' ', '') for i in contents.split('\\n') if i !='']\n #print(content)\n\n\n times = ''.join(texts.xpath('//span[@class=\"color-green\"]//text()'))\n time_ = [i.strip().replace(' ', '').replace('\\xa0',',') for i in times.split('\\n') if i !='']\n #print(time_)\n\n downloads = ''.join(texts.xpath('//a[@class=\"c-company__body-info\"]//text()'))\n download = [i.replace('\\xa0','-').replace(' ','') for i in downloads.split('\\n') if i !='']\n downloads = []\n for i in download:\n if i == '':\n pass\n else:\n downloads.append(i)\n print(downloads)\n\n\n\n\n data_list = []\n for t,a,c,ti,d in zip(title,author,content,time_,download):\n dict_datas={'title':t,'author':a,'content':c,'time':ti,'download':d}\n data_list.append(dict_datas)\n print(data_list)\n \"\"\"\n # time.sleep(11111)\n\n\n\n\ndef main(keyword):\n \"\"\"主函数\"\"\"\n html = search_result(keyword)\n get_page(html)\n\n\n\n\n\nif __name__ == \"__main__\":\n list_ = ['权利告知','米兰达规则','犯罪嫌疑人知情权','侦查讯问告知']\n for i in list_:\n main(i)\n\n\n" } ]	6
wolfmib/ja_pandas	https://github.com/wolfmib/ja_pandas	0988a026199843f4a28f0e73d9d98d93973954ed	1454746f4e999efdae6b9597d49b61090c392f1a	9099853e43e87acd78abd22ded16a5cf27f7b70a	refs/heads/master	2022-12-11T09:48:14.778989	2020-01-10T03:33:19	2020-01-10T03:33:19	232,946,063	0	0	null	2020-01-10T02:10:32	2020-01-10T03:33:27	2021-06-02T00:54:40	Python	[ { "alpha_fraction": 0.5278500914573669, "alphanum_fraction": 0.5325351357460022, "avg_line_length": 28.015151977539062, "blob_id": "49d100f306284824c1b073ee8e2f2c80084b0c09", "content_id": "7bd11fad85bf49f23afafb49c76a7b551b682206", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1921, "license_type": "no_license", "max_line_length": 82, "num_lines": 66, "path": "/ja_pandas.py", "repo_name": "wolfmib/ja_pandas", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport ja_language as ja_lan \n\n\n\n\nclass ja_pandas:\n def __init__(self):\n self.name = \"ja_pandas\"\n self.description = \"C'est utili pour moi-meme development purpose\"\n \n def set_df(self,input_df):\n self.df = input_df.copy()\n \n def _obtenir_df(self):\n return self.df\n\n def rename_col(self,origin_col_norm,after_col_norm,option_df=_obtenir_df()):\n\n # __cnanged_dict Format:\n # {origin_name : after_name}\n __changed_dict = {origin_col_norm:after_col_norm}\n option_df.rename(columns=__changed_dict,inplace=True)\n return option_df.copy\n \n\n\nif __name__ == \"__main__\":\n\n try:\n ja_lan_df = pd.read_pickle('ja_lan_env.pkl')\n apply_lan = ja_lan_df['ja_lan'][0]\n print(\"[INFO]: Your apply language is {%s}\"%apply_lan)\n except:\n print(\"[INFO]: No ja_lan_env.pkl found !\")\n print(\"Set language as default 'English' \")\n\n ja_lan = ja_lan.language_translator()\n ja_lan.set_language_code(apply_lan)\n\n\n print(ja_lan.print(\"Test the class: ja_pandas\"))\n # Changer le norm ###########################\n name_data_list = [\"Johnny\",\"Jean\",\"Jason\",\"Douge\"]\n age_data_list = [32,25,27,45]\n # Creer la DataFrame\n test_df = pd.DataFrame({'norm_de_familiy':name_data_list,'age':age_data_list})\n print(test_df)\n \n # Change le norm de col\n test_df.rename(columns={'age': 'col_age_changed'},inplace=True)\n print(test_df)\n\n\n # test avec agent\n print(\"---------------------------------------------------------\")\n ja_pd = ja_pandas()\n test_df = pd.DataFrame({'norm_de_familiy':name_data_list,'age':age_data_list})\n print(test_df)\n print(\"-----------\\n\\n\")\n test_df = ja_pd.rename_col('age','after_age',test_df)\n print(test_df)\n print(\"-----------\\n\\n\")\n\n\n #######################################################################\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.6721311211585999, "alphanum_fraction": 0.6721311211585999, "avg_line_length": 13.75, "blob_id": "ed16570521fa9191eee12bb87808cb0540c92e44", "content_id": "c7c3a4a4f8b8433c5a986804297726332b8fd777", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 61, "license_type": "no_license", "max_line_length": 41, "num_lines": 4, "path": "/README.md", "repo_name": "wolfmib/ja_pandas", "src_encoding": "UTF-8", "text": "# ja_pandas\nC'est un peu example avec Pandas package.\n\n---\n\n\n" }, { "alpha_fraction": 0.5270270109176636, "alphanum_fraction": 0.5337837934494019, "avg_line_length": 41.14285659790039, "blob_id": "524d850a43a7f4e37fad7738ef5b68f01949e820", "content_id": "5d137f6ccfce9c7c713deda1419355e8c8a58878", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 296, "license_type": "no_license", "max_line_length": 71, "num_lines": 7, "path": "/ja_freeze.sh", "repo_name": "wolfmib/ja_pandas", "src_encoding": "UTF-8", "text": "\necho \"[Jason]: Start to crate requirements.txt\"\npip3 freeze > requirements.txt\n\necho \"[Jason]: Finish.. you can install this in other device by typing\"\necho \"----------------------------------------\"\necho \"pip or pip3 install -r requirements.txt\"\necho \"----------------------------------------\"\n" } ]	3
vmurashko322/dinamo-03-07-2021	https://github.com/vmurashko322/dinamo-03-07-2021	d6e525c0b741a2d5cc29693a2755d78af4e16091	cfb236506ed8b2100762a5ca5dd816983a3eda8c	c8813539dab930b2a7d7230e183704aee16a6709	refs/heads/main	2023-06-10T13:51:50.425342	2021-07-05T14:03:50	2021-07-05T14:03:50	382,679,860	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6570320129394531, "alphanum_fraction": 0.6653734445571899, "avg_line_length": 31.018632888793945, "blob_id": "a3902e2c3ebb799fee3883495f62f5297062c50c", "content_id": "8a3301a532795b659c9fdb5da983484de06ed248", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5160, "license_type": "no_license", "max_line_length": 120, "num_lines": 161, "path": "/main/views.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "import json\nimport io\nimport requests\nfrom django.http import JsonResponse, Http404\nfrom django.shortcuts import get_object_or_404, redirect\n\nfrom django.views.decorators.csrf import csrf_exempt\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.renderers import JSONRenderer\nfrom rest_framework.response import Response\nfrom rest_framework.reverse import reverse_lazy\nfrom rest_framework.views import APIView\n\nfrom main.models import ProductModel, Car, User, City, Country, Material\nfrom main.serializers import ProductSerializerModel, ProductSerializer, CarSerializer, UserSerializer, CitySerializer, \\\n CountrySerializer, MaterialSerializer\n\n\nclass A:\n def __init__(self, name, username, email):\n self.name = name\n self.username = username\n self.email = email\n\n def get_name(self):\n return self.name\n\n\nuser = requests.get('https://jsonplaceholder.typicode.com/users/1')\na1 = user.json()\na = A(a1['name'], a1['username'], a1['email'])\n\nres = json.dumps(a.__dict__)\ncontent = JSONRenderer().render(a.__dict__)\n\nd = {'id': 1, 'name': 'Johan'}\n_json = json.dumps(d)\n\n\n@api_view(['GET', 'POST'])\ndef product(request):\n confeti = ProductModel.objects.all()\n data = ProductSerializer(confeti, many=True)\n if request.method == \"POST\":\n serializer = ProductSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(data.data)\n # if request.method == 'POST':\n # serializer = ProductSerializerModel(data=request.data)\n # if serializer.is_valid():\n # serializer.save()\n # return Response(serializer.data, status=201)\n # confeti = ProductModel.objects.all()\n # serializer = ProductSerializerModel(confeti, many=True)\n # return Response(serializer.data)\n\n\n@csrf_exempt\n@api_view(['GET', 'POST', 'PUT'])\ndef product_detail(request, pk):\n prod = get_object_or_404(ProductModel, pk=pk)\n if request.method == \"GET\":\n serializer = ProductSerializerModel(prod)\n return Response(serializer.data, status=status.HTTP_200_OK)\n elif request.method == 'PUT':\n serializer = ProductSerializerModel(instance=prod, data=request.data, partial=True)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=201)\n else:\n return Response(serializer.errors, status=201)\n\n\n@api_view(['GET', 'POST', 'PUT'])\ndef product2_detail(request, pk):\n prod = get_object_or_404(Car, pk=pk)\n serializer = CarSerializer(prod)\n print(serializer.data)\n if request.method == 'PUT':\n serializer = CarSerializer(instance=prod, partial=True, data=request.data)\n ######\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n else:\n return Response(serializer.errors)\n return Response(serializer.data)\n\n\ndef get_object(request):\n try:\n return ProductModel.objects.all()\n except:\n return Http404\n\n\nclass ProductNew(APIView):\n\n def get(self, request, format=None):\n obj = get_object(request)\n serializer = ProductSerializer(obj, many=True)\n return Response(serializer.data)\n\n def post(self, request, format=None):\n serializer = ProductSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return redirect(reverse_lazy('main:product'))\n return Response(serializer.errors, status=status.HTTP_404_NOT_FOUND)\n\n\nclass UserView(APIView):\n def get(self, request, format=None):\n model = User.objects.all()\n serializer = UserSerializer(model, many=True)\n return Response(serializer.data)\n\n def post(self, request, format=None):\n serializer = UserSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return redirect(reverse_lazy('main:product'))\n return Response(serializer.errors, status=status.HTTP_404_NOT_FOUND)\n\n\nclass CityView(APIView):\n def get(self, request):\n obj = City.objects.all()\n serializer = CitySerializer(obj, many=True)\n return Response(serializer.data)\n\n\nclass CountryView(APIView):\n def get(self, request):\n obj = Country.objects.all()\n serializer = CountrySerializer(obj, many=True)\n return Response(serializer.data)\n\n def post(self, request):\n serializer = CountrySerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n\n\nclass ShowMaterial(APIView):\n def get(self, request):\n obj = Material.objects.all()\n serializer = MaterialSerializer(obj, many=True)\n return Response(serializer.data)\n\n def post(self, request):\n serializer = MaterialSerializer(data=request.data)\n print(request.data)\n if serializer.is_valid():\n print(serializer.validated_data, ' из вью ')\n serializer.save()\n return Response(status=200)\n return Response(serializer.errors)\n" }, { "alpha_fraction": 0.6672598123550415, "alphanum_fraction": 0.6841636896133423, "avg_line_length": 25.761905670166016, "blob_id": "2386f6bfae10e5d052d1f4e96795ac98c71088a9", "content_id": "c890a20fa2ae3d24ad99f596a9692b15984e7314", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1162, "license_type": "no_license", "max_line_length": 73, "num_lines": 42, "path": "/main/models.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.db import models\n\n\n# Create your models here.\nclass ProductModel(models.Model):\n title = models.CharField(max_length=150)\n price = models.DecimalField(max_digits=6, decimal_places=2)\n\n\nclass Car(models.Model):\n name = models.CharField(max_length=10)\n product = models.ForeignKey('ProductModel', on_delete=models.CASCADE)\n\n\nclass User(models.Model):\n name = models.CharField(max_length=150)\n product = models.ForeignKey('ProductModel', on_delete=models.CASCADE)\n\n def __str__(self):\n return self.name\n\n\nclass Material(models.Model):\n A = (\n ('железо', 'железо'),\n ('ртуть', 'ртуть'),\n ('что-то', 'что-то')\n\n )\n title = models.ManyToManyField('ProductModel')\n model = models.CharField(max_length=150, choices=A, default='железо')\n user = models.OneToOneField('User', on_delete=models.CASCADE)\n\n\nclass City(models.Model):\n name = models.CharField(max_length=150)\n population = models.PositiveIntegerField()\n\n\nclass Country(models.Model):\n name = models.CharField(max_length=150)\n city = models.ForeignKey(\"City\", on_delete=models.CASCADE)\n" }, { "alpha_fraction": 0.741410493850708, "alphanum_fraction": 0.741410493850708, "avg_line_length": 41.61538314819336, "blob_id": "55b9262a8ace0000985ae2a05ec0e924f51beb64", "content_id": "8f7149b101e3c59d98bc61768396d0323403252f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 553, "license_type": "no_license", "max_line_length": 79, "num_lines": 13, "path": "/dinamo/urls.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.contrib import admin\nfrom django.urls import path\nfrom rest_framework.urlpatterns import format_suffix_patterns\nfrom dinamo.views import ShowPlayer, ShowPlayers, PlayerView\n\napp_name = 'dinamo'\nurlpatterns = [\n path('all_players/', ShowPlayers.as_view(), name='show_players'),\n path('player/', ShowPlayer.as_view(), name='ShowPlayers'),\n path('create_player/', PlayerView.as_view(), name='PlayerView'),\n path('create_player/<int:pk>/', PlayerView.as_view(), name='UpdatePlayer'),\n]\nurlpatterns=format_suffix_patterns(urlpatterns)" }, { "alpha_fraction": 0.5425295829772949, "alphanum_fraction": 0.5551035404205322, "avg_line_length": 41.25, "blob_id": "2a8e7f41627ca47e19ffd9d898bb1a178a063018", "content_id": "d2c629b40f5eb0009e3c020d774b1e1b3b45a3b2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2742, "license_type": "no_license", "max_line_length": 152, "num_lines": 64, "path": "/main/migrations/0001_initial.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "# Generated by Django 3.2.4 on 2021-07-04 14:02\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='City',\n fields=[\n ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=150)),\n ('population', models.PositiveIntegerField()),\n ],\n ),\n migrations.CreateModel(\n name='ProductModel',\n fields=[\n ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('title', models.CharField(max_length=150)),\n ('price', models.DecimalField(decimal_places=2, max_digits=6)),\n ],\n ),\n migrations.CreateModel(\n name='User',\n fields=[\n ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=150)),\n ('product', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.productmodel')),\n ],\n ),\n migrations.CreateModel(\n name='Material',\n fields=[\n ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('model', models.CharField(choices=[('железо', 'железо'), ('ртуть', 'ртуть'), ('что-то', 'что-то')], default='железо', max_length=150)),\n ('title', models.ManyToManyField(to='main.ProductModel')),\n ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='main.user')),\n ],\n ),\n migrations.CreateModel(\n name='Country',\n fields=[\n ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=150)),\n ('city', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.city')),\n ],\n ),\n migrations.CreateModel(\n name='Car',\n fields=[\n ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=10)),\n ('product', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.productmodel')),\n ],\n ),\n ]\n" }, { "alpha_fraction": 0.653923511505127, "alphanum_fraction": 0.6780683994293213, "avg_line_length": 25.891891479492188, "blob_id": "b136f3feaf800c8605bdeee89110b93c7d0ac263", "content_id": "398182eb05b6e6163bfc289d29488852c98c44f9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 994, "license_type": "no_license", "max_line_length": 67, "num_lines": 37, "path": "/dinamo/models.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.db import models\n\n\n# Create your models here.\n\nclass Stadium(models.Model):\n title = models.CharField(max_length=100)\n country = models.CharField(max_length=100)\n city = models.CharField(max_length=100)\n street = models.CharField(max_length=100)\n description = models.TextField()\n team = models.OneToOneField('Team', on_delete=models.CASCADE)\n\n def __str__(self):\n return self.title\n\nclass Team(models.Model):\n title = models.CharField(max_length=100)\n player = models.ForeignKey('Players', on_delete=models.CASCADE)\n\n def __str__(self):\n return self.title\n\nclass Players(models.Model):\n name = models.CharField(max_length=100)\n surname = models.CharField(max_length=100)\n # team = models.ManyToManyField('Team')\n\n def __str__(self):\n return self.name\n\nclass Sponcor(models.Model):\n title = models.CharField(max_length=100)\n teams = models.ManyToManyField('Team')\n\n def __str__(self):\n return self.title" }, { "alpha_fraction": 0.6503928303718567, "alphanum_fraction": 0.6574074029922485, "avg_line_length": 34.28712844848633, "blob_id": "b7f0b37a33bb0bc6f77cb067887a1cd6fb006f96", "content_id": "f174985fb1806e8e6de798fb71a2cdc2a6705b27", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3564, "license_type": "no_license", "max_line_length": 98, "num_lines": 101, "path": "/main/serializers.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from rest_framework import serializers\n\nfrom main.models import ProductModel, User, City, Country, Material\n\n\nclass ProductSerializer(serializers.Serializer):\n title = serializers.CharField(max_length=150)\n price = serializers.DecimalField(max_digits=6, decimal_places=2)\n\n def create(self, validated_data):\n \"\"\"\n Create and return a new `Snippet` instance, given the validated data.\n \"\"\"\n return ProductModel.objects.create(validated_data)\n\n def update(self, instance, validated_data):\n instance.title = validated_data.get('title', instance.title)\n instance.price = validated_data.get('price', instance.price)\n instance.save()\n return instance\n\n\nclass ProductSerializerModel(serializers.ModelSerializer):\n class Meta:\n model = ProductModel\n fields = '__all__'\n\n def create(self, validated_data):\n return ProductModel.objects.create(validated_data)\n\n\nclass CarSerializer(serializers.Serializer):\n name = serializers.CharField(max_length=15, read_only=True)\n product = ProductSerializer()\n\n def update(self, instance, validated_data):\n instance.name = validated_data.get('name', instance.name)\n instance.save()\n return instance\n\n\nclass UserSerializer(serializers.Serializer):\n name = serializers.CharField(max_length=150)\n product = ProductSerializer()\n\n def create(self, validated_data):\n product = ProductModel(dict(validated_data['product']))\n test = ProductModel.objects.filter(title=product.title, price=product.price)\n if test:\n validated_data['product'] = test[0]\n else:\n validated_data['product'] = product\n product.save()\n return User.objects.create(validated_data)\n\n # product=ProductModel.objects.get_or_create(dict(validated_data['product']))\n # validated_data['product']=product[0]\n # return User.objects.create(validated_data)\n\n\nclass CitySerializer(serializers.Serializer):\n name = serializers.CharField(max_length=150)\n population = serializers.IntegerField()\n\n\nclass CountrySerializer(serializers.Serializer):\n name = serializers.CharField(max_length=150)\n city = CitySerializer()\n\n def create(self, validated_data):\n city = City.objects.create(dict(validated_data['city']))\n base = City.objects.filter(name=city.name, population=city.population)\n print(base)\n print(base.values())\n print(base[0])\n if base:\n validated_data['city'] = base[0]\n else:\n validated_data['city'] = city\n city.save()\n return Country.objects.create(validated_data)\n\n\nclass MaterialSerializer(serializers.Serializer):\n title = ProductSerializer(many=True)\n model = serializers.CharField(max_length=150)\n user = UserSerializer()\n\n def create(self, validated_data):\n product_list = []\n if validated_data.get('title'):\n for i in validated_data['title']:\n prod = ProductModel.objects.get_or_create(i)\n product_list.append(prod[0])\n if validated_data.get('user'):\n prod_user = ProductModel.objects.get_or_create(validated_data['user']['product'])[0]\n user = User.objects.create(name=validated_data[\"user\"]['name'], product=prod_user)\n if validated_data.get('model'):\n material = Material.objects.create(model=validated_data['model'], user=user)\n material.title.set(product_list)\n return material\n" }, { "alpha_fraction": 0.5781126022338867, "alphanum_fraction": 0.5844568014144897, "avg_line_length": 20.372880935668945, "blob_id": "f4535b5cf07b36ef690e0229c4be30c56d82ae53", "content_id": "afeab8857c92702194fa9858fe84e8a0b66f598e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1261, "license_type": "no_license", "max_line_length": 69, "num_lines": 59, "path": "/less_drf.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "import requests\nimport json\n\n#\n# user = requests.get('https://jsonplaceholder.typicode.com/users/5')\n# print(type(user))\n\n\n# name = {'name': 'Vader'}\n#\n# n_json=json.dumps(name)\n# print(type(n_json))\n# l_json=json.loads(n_json)\n# print(type(l_json))\n#\n# _json = json.dumps(name)\n# print(type(_json))\n# print(_json)\n# _dict = json.loads(_json.encode())\n# print(_dict)\n\n# with open(\"first_json.json\", 'w') as file:\n# name=json.dumps(name, indent=4)\n# file.write(name)\n# d=''\n# with open(\"first_json.json\", 'r') as file:\n# d=json.load(file)\n# print(d)\n# from rest_framework.renderers import JSONRenderer\n\n# class A:\n# def __init__(self, name, username, email):\n# self.name = name\n# self.username = username\n# self.email = email\n#\n# def get_name(self):\n# return self.name\n#\n#\n# user = requests.get('https://jsonplaceholder.typicode.com/users/1')\n# a1 = user.json()\n# a = A(a1['name'], a1['username'], a1['email'])\n#\n# res=json.dumps(a.__dict__)\n# content = JSONRenderer().render(res.__dict__)\n# print(content)\n\n# d = {'id': 1, 'name': 'Johan'}\n# _json=json.dumps(d)\n# print(type(_json))\n\n# a = {\"A\", \"B\", \"C\"}\n# b = {\"C\", \"D\", \"E\"}\n# print(type(a))\n# print(a \| b)\n# print(a & b)\n# print(a ^ b)\n# print(a - b)\n" }, { "alpha_fraction": 0.7158176898956299, "alphanum_fraction": 0.737265408039093, "avg_line_length": 30.08333396911621, "blob_id": "534e291a35792b5bb95a003cc5600af93ca1fed9", "content_id": "ed12dc0a4b97bca145e9753e6c9383138dcf6cbd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1119, "license_type": "no_license", "max_line_length": 56, "num_lines": 36, "path": "/dinamo/serializers.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from rest_framework import serializers\n\nfrom dinamo.models import Players\n\n\nclass PlayersSerializer(serializers.Serializer):\n name = serializers.CharField(max_length=100)\n surname = serializers.CharField(max_length=100)\n\n def create(self, validated_data):\n return Players.objects.create(**validated_data)\n\n def update(self, instance, validated_data):\n instance.name = validated_data.pop('name')\n instance.surname = validated_data.pop('surname')\n instance.save()\n return instance\n\n\nclass TeamSerializer(serializers.Serializer):\n title = serializers.CharField(max_length=100)\n player = PlayersSerializer()\n\n\nclass StadiumSerializer(serializers.Serializer):\n title = serializers.CharField(max_length=100)\n country = serializers.CharField(max_length=100)\n city = serializers.CharField(max_length=100)\n street = serializers.CharField(max_length=100)\n description = serializers.CharField()\n team = TeamSerializer()\n\n\nclass SponcorSerializer(serializers.Serializer):\n title = serializers.CharField(max_length=100)\n teams = TeamSerializer(many=True)\n" }, { "alpha_fraction": 0.6335549354553223, "alphanum_fraction": 0.6339460015296936, "avg_line_length": 34.51388931274414, "blob_id": "e741d17398c0a0c5edf9aaadfef2569a28329588", "content_id": "7cdb254461de8db3043035611ef206bd3737c293", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2557, "license_type": "no_license", "max_line_length": 115, "num_lines": 72, "path": "/dinamo/views.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.shortcuts import render, redirect\n\n# Create your views here.\nfrom django.views import generic\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom dinamo.models import Team, Players, Stadium, Sponcor\nfrom dinamo.serializers import PlayersSerializer, StadiumSerializer, TeamSerializer, SponcorSerializer\n\n\nclass ShowPlayers(generic.ListView):\n model = Team\n template_name = 'dinamo/all_players.html'\n\n def post(self, request):\n player = Players.objects.filter(name=request.POST['name_players'])\n try:\n t = player.values('id')\n print(player[0].__dict__)\n print(t)\n result = []\n for i in t:\n teams = Team.objects.get(pk=i['id'])\n print(teams)\n result.append(teams)\n context = {\"teams\": result}\n print(result)\n return render(request, 'dinamo/all_players.html', context)\n except:\n return render(request, 'dinamo/all_players.html')\n\n\nclass ShowPlayer(APIView):\n def get(self, request):\n stadium = Stadium.objects.all()\n players = Players.objects.all()\n team = Team.objects.all()\n sponcor = Sponcor.objects.all()\n serializer_player = PlayersSerializer(players, many=True)\n serializer_stadium = StadiumSerializer(stadium, many=True)\n serializer_team = TeamSerializer(team, many=True)\n serializer_sponcor = SponcorSerializer(sponcor, many=True)\n\n res = {\"players\": serializer_player.data, 'stadium': serializer_stadium.data, 'team': serializer_team.data,\n \"sponcor\": serializer_sponcor.data}\n return Response(res)\n\n\nclass PlayerView(APIView):\n def get(self, request, pk=None):\n if not pk:\n player = Players.objects.all()\n serializer = PlayersSerializer(player, many=True)\n else:\n player = Players.objects.get(pk=pk)\n serializer = PlayersSerializer(player)\n return Response(serializer.data)\n\n def post(self, request, pk):\n serializer = PlayersSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors)\n\n def put(self, request, pk):\n player = Players.objects.get(pk=pk)\n serializer = PlayersSerializer(player, data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n" }, { "alpha_fraction": 0.6895161271095276, "alphanum_fraction": 0.6948924660682678, "avg_line_length": 48.599998474121094, "blob_id": "8ad038e4c97afac0e335648387d6ac438e0bc5e1", "content_id": "f414cee3cc73b57204a676376ee425c911729f09", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 744, "license_type": "no_license", "max_line_length": 118, "num_lines": 15, "path": "/main/urls.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.contrib import admin\nfrom django.urls import path, include\nfrom .views import product, product_detail, product2_detail, ProductNew, UserView, CityView, CountryView, ShowMaterial\n\napp_name = 'main'\nurlpatterns = [\n path('product/', product, name='product'),\n path('product_detail/<int:pk>/', product_detail, name='product_detail'),\n path('product2/<int:pk>/', product2_detail, name='product2_detail'),\n path('product_class/', ProductNew.as_view(), name='ProductNew'),\n path('user_class/', UserView.as_view(), name='UserView'),\n path('city/', CityView.as_view(), name='CityView'),\n path('country/', CountryView.as_view(), name='CountryView'),\n path('material/', ShowMaterial.as_view(), name='MaterialView'),\n]\n" }, { "alpha_fraction": 0.8115941882133484, "alphanum_fraction": 0.8115941882133484, "avg_line_length": 24.75, "blob_id": "822b0773d8f4725eb91e66b356b2013c4ffb92ed", "content_id": "cf0b813a00df45e789c67d91254435ee4ede7ab2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 207, "license_type": "no_license", "max_line_length": 57, "num_lines": 8, "path": "/dinamo/admin.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.contrib import admin\n\nfrom dinamo.models import Stadium, Team, Players, Sponcor\n\nadmin.site.register(Stadium)\nadmin.site.register(Team)\nadmin.site.register(Players)\nadmin.site.register(Sponcor)\n\n" }, { "alpha_fraction": 0.8135592937469482, "alphanum_fraction": 0.8135592937469482, "avg_line_length": 25.33333396911621, "blob_id": "2f51c3bcd1b627de01b24e82d21766e45533f831", "content_id": "f85a6de8f4437be2c84db015949f12e6491dc336", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 236, "license_type": "no_license", "max_line_length": 57, "num_lines": 9, "path": "/main/admin.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.contrib import admin\n\n# Register your models here.\nfrom main.models import ProductModel, Car, User, Material\n\nadmin.site.register(ProductModel)\nadmin.site.register(Car)\nadmin.site.register(User)\nadmin.site.register(Material)" } ]	12
jconnolly814/ThinkFull	https://github.com/jconnolly814/ThinkFull	ed66430395d1d50c6aa9674e865266f335595903	2f06b09f3ff61f6b9ee2d6bd514769f6ca885987	8bf7d6a5da118c24c6ae36d0264c1f3af6a1fea3	refs/heads/master	2021-01-10T09:00:24.073120	2015-10-25T23:25:00	2015-10-25T23:25:00	43,975,056	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6678321957588196, "alphanum_fraction": 0.6968032121658325, "avg_line_length": 27.18309783935547, "blob_id": "244cf45aeb78d442e9134387c7042453abb1ec89", "content_id": "774cf2b61431c414c92e9fc589917f33bc2e2f86", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2002, "license_type": "no_license", "max_line_length": 119, "num_lines": 71, "path": "/multivariate.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\n\nloansData = pd.read_csv(\"/Users/jenniferconnolly/Documents/ThinkFul/projects/multivar/LoanStats3d.csv\")\n\nloansData['income'] = loansData['annual_inc'].astype(int)[1]\n\n\n\nloansData['interest']= loansData['int_rate'].map(lambda a: round(float(a.rstrip('%')) / 100, 4))[1]\ny = loansData['interest']\n\n\nX= loansData['income']\nX =sm.add_constant(X)\n\n\nprint \"\\n model #1\"\n\nmodel = sm.OLS(y,X).fit()\nprint model.summary()\n\nprint '\\n model #2'\n\nloansData['homeOwn'] = pd.Categorical(loansData['home_ownership']).labels\n\nX= loansData[['income','homeOwn']]\nX =sm.add_constant(X)\n\nmodel2 = sm.OLS(y,X).fit()\nprint model2.summary()\n\n\nprint '\\nmodel #2 interaction homeownership'\n\nincome = loansData['income']\nhomeOwn= loansData['homeOwn']\n\nloansData['interaction'] = income * homeOwn\n\nX= loansData[['income','homeOwn','interaction']]\n#X= loansData[['income','interaction']]\nX =sm.add_constant(X)\nmodel2a = sm.OLS(y,X).fit()\nprint model2a.summary()\n\nxx1, xx2 = np.meshgrid(np.linspace(X.income.min(), X.income.max(), 100),\n np.linspace(X.interaction.min(), X.interaction.max(), 100))\n# plot the hyperplane by evaluating the parameters on the grid\nZ = model2a.params[0] + model2a.params[1] * xx1 + model2a.params[2] * xx2\n\n# create matplotlib 3d axes\nfig = plt.figure(figsize=(12, 8))\nax = Axes3D(fig, azim=-115, elev=15)\n\n# plot hyperplane\nsurf = ax.plot_surface(xx1, xx2, Z, cmap=plt.cm.RdBu_r, alpha=0.6, linewidth=0)\n\n# plot data points - points over the HP are white, points below are black\nresid = y - model2a.predict(X)\nax.scatter(X[resid >= 0].income, X[resid >= 0].interaction, y[resid >= 0], color='black', alpha=1.0, facecolor='white')\nax.scatter(X[resid < 0].income, X[resid < 0].interaction, y[resid < 0], color='black', alpha=1.0)\n\n# set axis labels\nax.set_xlabel('income')\nax.set_ylabel('interest')\nax.set_zlabel('interaction')\nplt.show()\n\n" }, { "alpha_fraction": 0.6013767123222351, "alphanum_fraction": 0.6514393091201782, "avg_line_length": 23.538461685180664, "blob_id": "11df9a4f9e34632db090db6d320fba9ad4ac49a9", "content_id": "a35b7dbd9eaefde7dffda6da653331e0f5aa2672", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1598, "license_type": "no_license", "max_line_length": 50, "num_lines": 65, "path": "/stats.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import pandas as pd\nfrom scipy import stats\n\n\ndata = '''Region, Alcohol, Tobacco\nNorth, 6.47, 4.03\nYorkshire, 6.13, 3.76\nNortheast, 6.19, 3.77\nEast Midlands, 4.89, 3.34\nWest Midlands, 5.63, 3.47\nEast Anglia, 4.52, 2.92\nSoutheast, 5.89, 3.20\nSouthwest, 4.79, 2.71\nWales, 5.27, 3.53\nScotland, 6.08, 4.51\nNorthern Ireland, 4.02, 4.56'''\n\ndata = data.splitlines()\ndata = [i.split(', ') for i in data]\n\ncolumn_names = data[0] # this is the first row\ndata_rows = data[1::] # all of the folling rows\n\n\ndf = pd.DataFrame(data_rows, columns=column_names)\n\ndf['Alcohol'] = df['Alcohol'].astype(float)\ndf['Tobacco'] = df['Tobacco'].astype(float)\n\nmean_alc = df['Alcohol'].mean()\nmedian_alc = df['Alcohol'].median()\nmode_alc = stats.mode(df['Alcohol'])\n\nrange_alc =max(df['Alcohol']) - min(df['Alcohol'])\nstd_alc = df['Alcohol'].std()\nvar_alc = df['Alcohol'].var()\n\n\nprint '\\n'\n\nprint \"Alcohol results are\"\nprint \"Mean = {0}\".format(mean_alc)\nprint \"Median = {0}\".format(median_alc)\nprint \"Mode = {0}\".format(mode_alc)\nprint \"Range = {0}\".format(range_alc)\nprint \"STD = {0}\".format(std_alc)\nprint \"Var = {0}\".format(var_alc)\n\n\nmean_tob = df['Tobacco'].mean()\nmeadian_tob = df['Tobacco'].median()\nmode_tob = stats.mode(df['Tobacco'])\n\nrange_tob =max(df['Tobacco']) - min(df['Tobacco'])\nstd_tob = df['Tobacco'].std()\nvar_tob = df['Tobacco'].var()\n\nprint ' \\n'\nprint \"Tobacco results are\"\nprint \"Mean = {0}\".format(mean_tob)\nprint \"Median = {0}\".format(meadian_tob)\nprint \"Mode = {0}\".format(mode_tob)\nprint \"Range = {0}\".format(range_tob)\nprint \"STD = {0}\".format(std_tob)\nprint \"Var = {0}\".format(var_tob)\n\n\n\n" }, { "alpha_fraction": 0.7257346510887146, "alphanum_fraction": 0.7266250848770142, "avg_line_length": 21.93877601623535, "blob_id": "c496542a5eae4f75d8845146116cc4c45d9eea05", "content_id": "db5a0372f26d923888975aaf454b14d9f4cd7a54", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1123, "license_type": "no_license", "max_line_length": 91, "num_lines": 49, "path": "/prob_lending_club.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import matplotlib.pyplot as plt\nimport pandas as pd\nimport scipy.stats as stats\nimport os\n\nloansData = pd.read_csv('https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')\n\n\n\nloansData.dropna(inplace=True)\nloansData.boxplot(column='Amount.Funded.By.Investors')\nplt.show()\n\nloansData.dropna(inplace=True)\nloansData.boxplot(column='Amount.Requested')\nplt.show()\n\n\ndf = pd.DataFrame(loansData)\nAFBI= df.loc[:, ['Amount.Funded.By.Investors']]\nAR= df.loc[:, ['Amount.Requested']]\n\n\nbox = AFBI.plot(kind ='box')\nplt.savefig(str('box_AFBI_loansData.jpeg'))\nplt.show()\n\nhist = AFBI.plot(kind ='hist')\nplt.savefig(str('hist_AFBI_loansData.jpeg'))\nplt.show()\n\nplt.figure()\ngraph = stats.probplot(loansData['Amount.Funded.By.Investors'], dist=\"norm\", plot=plt)\nplt.savefig(str('qq_AFBI_loansData.jpeg'))\nplt.show()\n\n\nbox = AR.plot(kind ='box')\nplt.savefig(str('box_AR_loansData.jpeg'))\nplt.show()\n\nhist = AR.plot(kind ='hist')\nplt.savefig(str('hist_AR_loansData.jpeg'))\nplt.show()\n\nplt.figure()\ngraph = stats.probplot(loansData['Amount.Requested'], dist=\"norm\", plot=plt)\nplt.savefig(str('qq_AR_loansData.jpeg'))\nplt.show()" }, { "alpha_fraction": 0.6118780374526978, "alphanum_fraction": 0.6362760663032532, "avg_line_length": 42.87323760986328, "blob_id": "0a7300136266b736971c79766126e0ad69495fc6", "content_id": "8eaa33a373f265646da3868eb7f056f2e4e9075d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3115, "license_type": "no_license", "max_line_length": 116, "num_lines": 71, "path": "/database.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import sqlite3 as lite\nimport pandas as pd\n\n# Here you connect to the database. The `connect()` method returns a connection object.\ncon = lite.connect('C:\\Users\\JConno02\\projects\\sqlite\\getting_started.db')\n# Select all rows and print the result set one row at a time\nwith con:\n cur = con.cursor()\n cur.execute(\"DROP TABLE IF EXISTS cities \")\n cur.execute(\"DROP TABLE IF EXISTS weather \")\n\n cur.execute(\"CREATE TABLE cities (name TEXT, state TEXT)\")\n cur.execute(\n \"CREATE TABLE weather (city TEXT, year INTEGER, warm_month TEXT, cold_month TEXT, average_high INTEGER)\")\n\n cur.execute(\"INSERT INTO cities VALUES('Houston', 'TX')\")\n cur.execute(\"INSERT INTO cities VALUES('New York City','NY')\")\n cur.execute(\"INSERT INTO cities VALUES('Boston', 'MA')\")\n cur.execute(\"INSERT INTO cities VALUES('Chicago', 'IL')\")\n cur.execute(\"INSERT INTO cities VALUES('Miami', 'FL')\")\n cur.execute(\"INSERT INTO cities VALUES('Dallas', 'TX')\")\n cur.execute(\"INSERT INTO cities VALUES('Seattle', 'WA')\")\n cur.execute(\"INSERT INTO cities VALUES('Portland', 'OR')\")\n cur.execute(\"INSERT INTO cities VALUES('San Francisco', 'CA')\")\n cur.execute(\"INSERT INTO cities VALUES('Los Angeles', 'CA')\")\n\n cur.execute(\"INSERT INTO weather VALUES('New York City', 2013 ,'July', 'January', 62)\")\n cur.execute(\"INSERT INTO weather VALUES('Boston', 2013, 'July','January',59)\")\n cur.execute(\"INSERT INTO weather VALUES('Chicago', 2013, 'July','January',59)\")\n cur.execute(\"INSERT INTO weather VALUES('Miami', 2013, 'August','January',84)\")\n cur.execute(\"INSERT INTO weather VALUES('Dallas', 2013, 'July','January',77)\")\n cur.execute(\"INSERT INTO weather VALUES('Seattle', 2013, 'July','January',61)\")\n cur.execute(\"INSERT INTO weather VALUES('Portland', 2013, 'July','December',63)\")\n cur.execute(\"INSERT INTO weather VALUES('San Francisco', 2013,'September','December',64)\")\n cur.execute(\"INSERT INTO weather VALUES('Los Angeles', 2013,'September','December', 75)\")\n cur.execute(\"SELECT name, state, warm_month FROM cities INNER JOIN weather ON name = city ORDER BY warm_month\")\n # cur.execute(\"SELECT city, state,warm_month FROM weather ORDER BY warm_month\")\n rows = cur.fetchall()\n cols = [desc[0] for desc in cur.description]\n df = pd.DataFrame(rows, columns=cols)\n\njuly_df = df[df['warm_month'].isin(['July'])]\nnm_state = july_df.loc[:, ['name', 'state']]\n\ncount = len(nm_state)\nindex = count - 1\ntracker = index\npartb = ''\n\nwhile tracker > -1:\n if tracker == index:\n b = (nm_state.iloc[int(tracker)][:2])\n bb = b.values\n for value in bb:\n y = bb[0]\n z = bb[1]\n start = \"The cities that are warmest in July are: {0} {1}\".format(y, z)\n tracker -= 1\n else:\n b = (nm_state.iloc[int(tracker)][:2])\n bb = b.values\n partc = partb.replace(\"and \", \",\")\n for value in bb:\n y = bb[0]\n z = bb[1]\n\n partb = \"{0} and {1} {2}\".format(partc, y, z)\n final = \"{0}{1}\".format(start, partb)\n\n tracker -= 1\nprint \"{0}.\".format(final)\n" }, { "alpha_fraction": 0.5952476859092712, "alphanum_fraction": 0.6335078477859497, "avg_line_length": 26.430938720703125, "blob_id": "10eb6d175c7b88cf09a33d0618f2c3a18bc3aea7", "content_id": "3647434d843d8baf834bfd7836889732245a041b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4966, "license_type": "no_license", "max_line_length": 228, "num_lines": 181, "path": "/education.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "# Import libraries\nfrom bs4 import BeautifulSoup\nimport requests\nimport pandas as pd\nimport sqlite3 as lite\nimport matplotlib.pyplot as plt\nimport csv\nimport numpy as np\n#import statsmodels.api as sm\n\nurl = \"http://web.archive.org/web/20110514112442/http://unstats.un.org/unsd/demographic/products/socind/education.htm\"\n\nr = requests.get(url)\n\n# Use Beautiful Soup to Parse the HTML\nsoup_data = BeautifulSoup(r.content)\n\n\n\n# Select tables we are interested in\n# pull all data with class tcont\n\n\nsoup_tag = soup_data('table')[6].tr.td\n#for row in soup_tag:\n #print row\nsoup_table = soup_tag('table')[1].tr.td.div\n#for row in soup_table:\n #print row\nraw_table = soup_table('table')[0]\n#for row in raw_table:\n #print row\n\n\n\ncol_name = []\n\nfor j in raw_table('tr'):\n if j.get('class', ['Null'])[0] == 'lheader':\n for td in j.find_all('td'):\n if td.get_text() != '':\n col_name.append(td.get_text())\n break\n\n\ncountry_table = pd.DataFrame(columns=col_name)\n#print country_table\n\nedu_counter = 0\nfor j in raw_table('tr'):\n row_data = []\n if j.get('class', ['Null']) == 'tcont':\n row_data.append(j.find('td').get_text())\n for td in j.find_all('td')[1:]:\n if td.get('align'):\n row_data.append(td.get_text())\n else:\n row_data.append(j.find('td').get_text())\n for td in j.find_all('td')[1:]:\n if td.get('align'):\n row_data.append(td.get_text())\n if len(row_data) == len(col_name):\n country_table.loc[edu_counter] = row_data\n edu_counter += 1\n\n\n\nsublistdf =country_table[['Total', 'Men', 'Women']]\nnumeric_country_table= pd.to_numeric(sublistdf , errors='coerce')\ncountry_table[['Total', 'Men', 'Women']]= numeric_country_table[['Total', 'Men', 'Women']]\n\n#print numeric_country_table\n\n\ncountry_gdp = pd.DataFrame(\n columns=['country_name', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009',\n '2010'])\n\n\n# convert school years to integers\n\ncon = lite.connect('education.db')\ncur = con.cursor()\n\n\n\n# create table to hold school data\nwith con:\n cur.execute(\"DROP TABLE IF EXISTS school_years\")\n cur.execute('CREATE TABLE school_years (country_name, _Year, _Total, _Men, _Women)')\n\nwith con:\n cur.execute(\"DROP TABLE IF EXISTS gdp\")\n cur.execute(\n 'CREATE TABLE gdp (country_name text, _1999 numeric, _2000 numeric, _2001 numeric, _2002 numeric, _2003 numeric, _2004 numeric, _2005 numeric, _2006 numeric, _2007 numeric, _2008 numeric, _2009 numeric, _2010 numeric)')\n\nwith open('E:\\\\ThinkFul\\\\ny\\\\ny.gdp.mktp.cd_Indicator_en_csv_v2.csv', 'rU') as inputFile:\n next(inputFile)\n next(inputFile)\n header = next(inputFile)\n #print header\n inputReader = csv.reader(inputFile)\n gdp_row_num = 0\n for line in inputReader:\n row_data = [line[0]]\n row_data.extend(line[43:-5])\n country_gdp.loc[gdp_row_num] = row_data\n gdp_row_num += 1\n with con:\n cur.execute(\n 'INSERT INTO gdp (country_name, _1999, _2000, _2001, _2002, _2003, _2004, _2005, _2006, _2007, _2008, _2009, _2010) VALUES (\"' +\n line[0] + '\",\"' + '\",\"'.join(line[43:-5]) + '\");')\n\n\n\nsublist_gdp_df = country_gdp[country_gdp.columns[1:-1]]\nnumeric_country_table= pd.to_numeric(sublist_gdp_df, errors='coerce')\ncountry_gdp[country_gdp.columns[1:-1]]=numeric_country_table\n\ncountry_table['GDP']=0\n\n\n#print country_gdp['GDP']\n#print country_gdp\n#print country_table\n\ngdplist =[]\nfor i in range(edu_counter):\n rowid = country_gdp[country_gdp['country_name'] == country_table['Country or area'][i]].index\n if len(rowid) > 0:\n row_index = rowid.tolist()[0]\n try:\n gdp = float(country_gdp[country_table['Year'][i]][row_index]) ##extracts year from country table they pull the year, row index from gdp table and assigns that value back to GDP col of the country tab;e\n gdplist.append(gdp)\n except ValueError:\n gdp ='nan'\n gdplist.append(gdp)\n\n else:\n gdp ='nan'\n gdplist.append(gdp)\n\n#print gdplist\n#print len(gdplist)\n#print len(country_table['GDP'])\n\ncountry_table['GDP']= gdplist\n\n\ngdp = country_table['GDP'].map(lambda x: 0 if x =='nan'else float(x))\nschool_years = country_table['Year'].map(lambda x: int(x))\nlog_gdp = gdp.map(lambda x: np.log(x))\n\nprint len (gdp)\nprint len(log_gdp)\nprint len(school_years)\n\ncolors = np.random.rand(len(gdp))\nplt.scatter(log_gdp, school_years, c=colors)\nplt.show()\n\n\ny = np.matrix(gdp).transpose()\nx = np.matrix(school_years).transpose()\n\nX = sm.add_constant(x)\nmodel = sm.OLS(y,X)\nresults = model.fit()\n\ny = np.matrix(log_gdp).transpose()\nx = np.matrix(school_years).transpose()\n\nX = sm.add_constant(x)\nmodel2 = sm.OLS(y,X)\nresults2 = model2.fit()\n\nsm.graphics.tsa.plot_acf(log_gdp) #Autocorrelation\nplt.show()\n\nsm.graphics.tsa.plot_pacf(log_gdp) #Partial Autocorrelation\nplt.show()\n\n" }, { "alpha_fraction": 0.7641857266426086, "alphanum_fraction": 0.767870306968689, "avg_line_length": 27.893617630004883, "blob_id": "ad068a76d1b018d08e3e89c5ca27d25e724f56af", "content_id": "3c32d8e31d8e56ed8459c4ef9d91ff7143aa1854", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1357, "license_type": "no_license", "max_line_length": 123, "num_lines": 47, "path": "/time_series.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm\n\n\ndf = pd.read_csv(\"/Users/jenniferconnolly/Documents/ThinkFul/projects/multivar/LoanStats3d.csv\",header=0, low_memory=False)\n\n# converts string to datetime object in pandas:\ndf['issue_d_format'] = pd.to_datetime(df['issue_d'])\ndfts = df.set_index('issue_d_format')\nyear_month_summary = dfts.groupby(lambda x : x.year * 100 + x.month).count()\nloan_count_summary = year_month_summary['issue_d']\n\nprint loan_count_summary\n\n\nplt.plot(loan_count_summary)\nplt.ylabel(\"Number of Loans\")\nplt.xlabel(\"Month\")\nplt.show()\n\n### data is not stationary; transform by getting the difference\nloan_count_sum_diff = loan_count_summary.diff()\nplt.plot(loan_count_sum_diff)\nplt.ylabel(\"Number of Loans\")\nplt.xlabel(\"Difference\")\nplt.show()\n\nsm.graphics.tsa.plot_acf(loan_count_summary) #Autocorrelation\n\nplt.show()\n\nsm.graphics.tsa.plot_pacf(loan_count_summary) #Partial Autocorrelation\n\nplt.show()\n\nsm.graphics.tsa.plot_acf(loan_count_sum_diff) #Autocorrelation\n\nplt.show()\n\nsm.graphics.tsa.plot_pacf(loan_count_sum_diff) #Partial Autocorrelation\n\nplt.show()\n\n### Yes there are autocorrelated structures\n# The autocorrelations is quickly decaying but with persistent partial autocorrelations therefore the model is best\n# served by an MA terms to match the lags of significant autocorrelations." }, { "alpha_fraction": 0.6467532515525818, "alphanum_fraction": 0.6893506646156311, "avg_line_length": 28.15151596069336, "blob_id": "1f4fff5746dff86b8e88efc7ab02671417734b42", "content_id": "57f924b41c1e4c01123f1e498a2c8ea84ca1d346", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1925, "license_type": "no_license", "max_line_length": 131, "num_lines": 66, "path": "/logistic_regression.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom numpy import loadtxt, where\nfrom pylab import scatter, show, legend, xlabel, ylabel\n\n# Function for log regression: equations : p(x) = 1/(1 + e^(intercept + 0.087423(FicoScore) ? 0.000174(LoanAmount))\ndef logistic_function(FicoScore, LoanAmount, threshold, coeff, ):\n interest_rate = -(coeff[2]) - (coeff[1]FicoScore) -(coeff[0]LoanAmount)\n prob = (1 / (1+ interest_rate*(coeff[2] + coeff[1] FicoScore + coeff[0] * LoanAmount)))\n lines=plt.plot()\n if prob > threshold:\n p = 1\n else:\n p = 0\n return prob, p\n\n\nloansData = pd.read_csv('/Users/jenniferconnolly/Documents/ThinkFul/projects/graphs/loansData_clean.csv')\n\nCleanIR = loansData['Interest.Rate'].map(lambda a: round(float(a.rstrip('%')) / 100, 4))\nt = CleanIR[0:5]\n# print t\n\nCleanLL = loansData['Loan.Length'].map(lambda a: round(int(a.rstrip('months'))))\nu = CleanLL[0:5]\n# print u\n\nCleanFR_a = loansData['FICO.Range'].map(lambda a: a.split('-')[0])\nv = CleanFR_a[0:5]\n# print v\n\nloansData['IR_TF'] = (CleanIR.map(lambda x: 0 if x < .12 else 1))\n\nloansData['intercept'] = 1.0\n\ndf = pd.DataFrame(loansData)\n\ndf['FICO.SCORE'] = (CleanFR_a.astype(int))\n\nind_vars = ['Amount.Funded.By.Investors', 'FICO.SCORE', 'intercept']\nprint ind_vars\n\nlogit = sm.Logit(loansData['IR_TF'], loansData[ind_vars])\nresult = logit.fit()\nprint result\ncoeff = result.params\nprint coeff\nthreshold = 0.7\n\nprob = logistic_function(720, 10000,threshold ,coeff)[0]\npredict =logistic_function(720, 10000,threshold ,coeff)[1]\n\n\nprint prob\nprint predict\n\n\n\nprint \"The probability of getting a 12% or less interest loan of $10,000 with a 720 FICO score is: {0}% \".format(float(prob * 100))\n\nif predict ==1:\n print \"You will likely get your loan funded if the threshold is 70%\"\nelse:\n print \"You loan will likely not be funded if the threshold is 70%\"\n\n" }, { "alpha_fraction": 0.613043487071991, "alphanum_fraction": 0.6486956477165222, "avg_line_length": 19.872726440429688, "blob_id": "c63c90058586f81c57b850ad48d584306e8d5d06", "content_id": "1611ab5be9261dbfcbd043fa6ec0984a0a10c563", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1150, "license_type": "no_license", "max_line_length": 100, "num_lines": 55, "path": "/prob.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import collections\nimport scipy\nimport scipy.stats\nimport matplotlib.pyplot as plt\nimport os\n\ntestlist = [1, 4, 5, 6, 9, 9, 9]\nx = [1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4, 4, 4, 4, 5, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 9, 9]\n\nprint \"start testlist summary\"\nc = collections.Counter(testlist)\ncount_sum = sum(c.values())\n\nfor k, v in c.iteritems():\n print \"The frequency of number \" + str(k) + \" is \" + str(float(v) / count_sum)\n\nplt.boxplot(testlist)\n\n\nplt.savefig(str('box_testlist.jpeg'))\nplt.show()\n\nplt.hist(testlist, histtype='bar')\nplt.savefig(str('hist_testlist.jpeg'))\nplt.show()\n\nplt.figure()\n\ngraph1 = scipy.stats.probplot(testlist, dist=\"norm\", plot=plt)\n\nplt.savefig(str('qq_testlist.jpeg'))\nplt.show()\n\n\nprint \"start x data summary\"\nz = collections.Counter(x)\ncount_sum = sum(z.values())\n\nfor k, v in z.iteritems():\n print \"The frequency of number \" + str(k) + \" is \" + str(float(v) / count_sum)\n\nplt.boxplot(x)\n\nplt.savefig(str('box_x.jpeg'))\nplt.show()\n\nplt.hist(x, histtype='bar')\n\nplt.savefig(str('hist_x.jpeg'))\nplt.show()\n\ngraph2 = scipy.stats.probplot(x, dist=\"norm\", plot=plt)\n\nplt.savefig(str('qq_x.jpeg'))\nplt.show()\n\n\n" }, { "alpha_fraction": 0.6761904954910278, "alphanum_fraction": 0.7020407915115356, "avg_line_length": 20.58823585510254, "blob_id": "c4a55aa824b1fcedf32b9412b5a18bd068ac5f40", "content_id": "4a976dd40a51ffc9fc4296e05b5bdd4135b89109", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1470, "license_type": "no_license", "max_line_length": 93, "num_lines": 68, "path": "/linear_regression.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import numpy as np\nimport pandas as pd\nfrom pandas.tools.plotting import scatter_matrix\n\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm\n\nloansData = pd.read_csv('https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')\n# Drop null rows\nloansData.dropna(inplace=True)\n\n\na=loansData['Interest.Rate'][0:5]\nb =loansData['Loan.Length'][0:5]\nc = loansData['FICO.Range'][0:5]\n#print c\n\nCleanIR = loansData['Interest.Rate'].map(lambda a: round(float(a.rstrip('%')) / 100, 4))\nt=CleanIR[0:5]\n#print t\n\nCleanLL = loansData['Loan.Length'].map(lambda a: round(int(a.rstrip('months'))))\nu= CleanLL[0:5]\n#print u\n\nCleanFR_a = loansData['FICO.Range'].map(lambda a: a.split('-')[0])\nv= CleanFR_a[0:5]\n#print v\n\ndf = pd.DataFrame(loansData)\ndf['FICO.SCORE']= (CleanFR_a.astype(int))\n\n#print df\n\nhist = df['FICO.SCORE'].plot(kind ='hist')\nplt.show()\n\n\nj=scatter_matrix(df, alpha=0.05, figsize=(10,10))\nplt.show()\nk= pd.scatter_matrix(df, alpha=0.05, figsize=(10,10), diagonal='hist')\nplt.show()\n\n\n#df.to_csv('C:\\Users\\JConno02\\projects\\graphs\\loansData_clean.csv', header=True, index=False)\n\nintrate = CleanIR\nloanamt = df['Amount.Requested']\nfico = df['FICO.SCORE']\n\n\n\ny = np.matrix(intrate).transpose()\nprint y\n# The independent variables shaped as columns\nx1 = np.matrix(fico).transpose()\nx2 = np.matrix(loanamt).transpose()\n\nx = np.column_stack([x1,x2])\nprint x\n\nX = sm.add_constant(x)\nmodel = sm.OLS(y,X)\nf = model.fit()\n\noutput = f.summary()\n\nprint output\n\n\n" } ]	9
archius11/checkio-mission-four-to-the-floor	https://github.com/archius11/checkio-mission-four-to-the-floor	98595b94350a5b403ba478ad114a53b5acdf9d1b	7c1140eb891751dbba3a88c99a74054edb13ea1d	d39c1f94a2674bb993a77dad200219be577feccd	refs/heads/master	2020-12-11T14:12:44.834106	2019-12-12T09:44:57	2019-12-12T09:44:57	233,870,226	1	0	null	2020-01-14T15:17:43	2019-12-12T09:45:03	2019-12-12T09:44:59	null	[ { "alpha_fraction": 0.2836935222148895, "alphanum_fraction": 0.29404059052467346, "avg_line_length": 33.39189147949219, "blob_id": "a5f02e7bd1503d999a1bfe302d13fb3e59b829ac", "content_id": "c13fe063c12c7d0d9a2b19c1a0b3103de5131798", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 7635, "license_type": "no_license", "max_line_length": 74, "num_lines": 222, "path": "/editor/animation/init.js", "repo_name": "archius11/checkio-mission-four-to-the-floor", "src_encoding": "UTF-8", "text": "//Dont change it\n//Dont change it\nrequirejs(['ext_editor_io', 'jquery_190', 'raphael_210'],\n function (extIO, $) {\n function fourToTheFloorCanvas(dom, data) {\n\n if (! data) {\n return\n }\n\n const input = data.in\n\n /----------------------------------------------\n \n attr\n \n ----------------------------------------------/\n const attr = {\n rect: {\n background: {\n 'fill': 'black',\n },\n },\n axis: {\n 'stroke-width': '0.7px',\n 'stroke': 'white',\n },\n circle: {\n center: {\n 'stroke-width': '1px',\n 'stroke': 'white',\n 'fill': 'white',\n },\n surface: {\n 'stroke-width': 0,\n 'fill': '#545454',\n 'opacity': '0.7',\n },\n },\n rectangle: {\n 'stroke-width': 1.5,\n 'stroke': 'white',\n },\n scale: {\n figure: {\n h: {\n 'fill': 'white',\n 'stroke': 'white',\n 'stroke-width': 0,\n },\n v: {\n 'fill': 'white',\n 'stroke': 'white',\n 'stroke-width': 0,\n 'text-anchor': 'end',\n },\n },\n line: {\n 'stroke-width': '0.7px',\n 'stroke': 'white',\n },\n },\n }\n\n /----------------------------------------------\n \n * paper\n \n ----------------------------------------------/\n const [os_l, os_r, os_t, os_b] = [25, 15, 10, 15]\n const [os_w, os_h] = [os_l+os_r, os_t+os_b]\n const graph_length = 300\n const paper = Raphael(dom, graph_length+os_w, \n graph_length+os_h, 0, 0);\n\n /----------------------------------------------\n \n * calculate values\n \n ----------------------------------------------/\n const [rectangle, circles] = input\n const [w, h] = rectangle\n let [max_x, max_y, min_x, min_y] = [0, 0, 0, 0]\n\n for (let [cx, cy, r] of circles) {\n max_x = Math.max(cx+r, max_x)\n max_y = Math.max(cy+r, max_y)\n min_x = Math.min(cx-r, min_x)\n min_y = Math.min(cy-r, min_y)\n }\n\n const right = Math.max(max_x, w)\n const left = Math.min(min_x, 0)\n const top = Math.max(max_y, h)\n const bottom = Math.min(min_y, 0)\n const pos_max = Math.max(right, top)\n const neg_min = Math.min(0, left, bottom) -1\n\n // scale\n let grain = 0\n\n if (pos_max >= 1000) {\n grain = 500\n } else if (pos_max >= 100) {\n grain = 50\n } else if (pos_max >= 10) {\n grain = 5\n } else {\n grain = 1\n }\n\n const scale_max = Math.ceil(pos_max / (grain2)) grain2\n const scales = [scale_max / 2, scale_max]\n\n const margin = grain 0.3\n const graph_neg_size\n = Math.ceil(neg_min / (grain)) * grain + margin\n const graph_pos_size\n = Math.ceil(pos_max / (grain)) * grain + margin\n\n // ratio\n const ratio = graph_length / (graph_neg_size + graph_pos_size)\n\n /----------------------------------------------\n \n background rect\n \n ----------------------------------------------/\n paper.rect(os_l, os_t, graph_length, graph_length).attr(\n attr.rect.background)\n\n /----------------------------------------------\n \n * circles (surface)\n \n ----------------------------------------------/\n for (const [cx, cy, r] of circles) {\n paper.circle(\n os_l+(graph_neg_size+cx)ratio,\n os_t+(graph_pos_size-cy)ratio, rratio).attr(\n attr.circle.surface)\n }\n\n /----------------------------------------------\n \n axis\n \n ----------------------------------------------/\n paper.path(['M', os_l, os_t+graph_pos_sizeratio,\n 'h', graph_length]).attr(attr.axis)\n paper.path(['M', os_l+graph_neg_sizeratio, os_t,\n 'v', graph_length]).attr(attr.axis)\n\n /----------------------------------------------\n \n * rectangle\n \n ----------------------------------------------/\n paper.rect(\n os_l+(graph_neg_sizeratio),\n os_t+(graph_pos_size-h)ratio, wratio, hratio).attr(\n attr.rectangle)\n\n /----------------------------------------------\n \n * circles (center)\n \n ----------------------------------------------/\n for (const [cx, cy, r] of circles) {\n paper.circle(\n os_l+(graph_neg_size+cx)ratio,\n os_t+(graph_pos_size-cy)ratio, 1).attr(\n attr.circle.center)\n }\n\n /----------------------------------------------\n \n * scale figures\n \n ----------------------------------------------/\n // 0\n paper.text(os_l+(graph_neg_size)ratio-5,\n os_t+graph_pos_sizeratio+10, 0).attr(\n attr.scale.figure.v)\n\n for (const s of scales) {\n // horizontal\n paper.text(os_l+(graph_neg_size+s)ratio,\n os_t+graph_pos_sizeratio+10, s).attr(\n attr.scale.figure.h)\n paper.path(['M', os_l+(graph_neg_size+s)ratio,\n os_t+graph_pos_sizeratio, 'v', 5]).attr(\n attr.scale.line)\n\n // vertical\n paper.text(os_l+(graph_neg_size)ratio-5,\n os_t+(graph_pos_size-s)ratio, s).attr(\n attr.scale.figure.v)\n paper.path(['M', os_l+(graph_neg_size)ratio,\n os_t+(graph_pos_size-s)ratio, 'h', -4]).attr(\n attr.scale.line)\n }\n }\n\n var $tryit;\n\n var io = new extIO({\n multipleArguments: true,\n functions: {\n python: 'is_covered',\n js: 'isCovered'\n },\n animation: function($expl, data){\n fourToTheFloorCanvas(\n $expl[0],\n data\n )\n }\n });\n io.start();\n }\n);\n" }, { "alpha_fraction": 0.2821376323699951, "alphanum_fraction": 0.4153733551502228, "avg_line_length": 31.836538314819336, "blob_id": "5feb691f222ed93b1f673703f9093b9c17c104cf", "content_id": "fe691deae3c723750b243cde8c90b88dc2847707", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6830, "license_type": "no_license", "max_line_length": 111, "num_lines": 208, "path": "/verification/tests.py", "repo_name": "archius11/checkio-mission-four-to-the-floor", "src_encoding": "UTF-8", "text": "from math import sqrt\nfrom itertools import combinations\nfrom collections import defaultdict\nfrom random import choice\n\n\ndef judge(room, sensors):\n corners = (0, 0), (0, room[1]), (room[0], 0), (room[0], room[1])\n uncovered_dots = defaultdict(int)\n [uncovered_dots.update({d: 0}) for d in corners]\n\n if len(sensors) == 1: return all([is_in_range(d, sensors[0]) for d in uncovered_dots])\n\n for s1, s2 in combinations(sensors, 2):\n dots = sensors_intersections(s1, s2)\n if dots:\n for x, y in dots:\n if 0 <= x <= room[0] and 0 <= y <= room[1]: uncovered_dots[(x, y)] = 0\n\n for sensor in sensors:\n intersections = borders_intersections(room, sensor)\n for dot in intersections:\n uncovered_dots[dot] = 0\n\n for sensor in sensors:\n for dot in uncovered_dots:\n uncovered_dots[dot] += 1 if is_in_range(dot, sensor) else 0\n\n [print(k, v) for k, v in uncovered_dots.items()]\n for (x, y), n in uncovered_dots.items():\n if n < 1: return False\n if n == 1 and (x, y) not in corners: return False\n if n == 2 and 0 < x < room[0] and 0 < y < room[1]: return False\n return True\n\n\ndef is_in_range(dot, sensor):\n x, y, r = sensor\n x0, y0 = dot\n return sqrt((x-x0)2 + (y-y0)2) < r + 10e-6\n\n\ndef borders_intersections(room, sensor):\n dots = []\n xr, yr = room\n x, y, r = sensor\n if x <= r:\n dy = round(sqrt(r2 - x2),6)\n if y + dy <= yr: dots.append((0, y+dy))\n if y - dy >= 0: dots.append((0, y-dy))\n if x + r >= xr:\n dy = round(sqrt(r2 - (xr-x)2), 6)\n if y + dy <= yr: dots.append((xr, y+dy))\n if y - dy >= 0: dots.append((xr, y-dy))\n if y <= r:\n dx = round(sqrt(r2 - y2), 6)\n if x + dx <= xr: dots.append((x+dx, 0))\n if x - dx >= 0: dots.append((x-dx, 0))\n if y + r >= yr:\n dx = round(sqrt(r2 - (yr-y)2), 6)\n if x + dx <= xr: dots.append((x+dx, yr))\n if x - dx >= 0: dots.append((x-dx, yr))\n return dots\n\n\ndef sensors_intersections(sensor1, sensor2):\n dots = []\n x1, y1, r1 = sensor1\n x2, y2, r2 = sensor2\n d = round(sqrt((x2-x1)2 + (y2-y1)2), 6)\n if r1 + r2 >= d > abs(r1-r2):\n\n a = (r12 - r22 + d2)/(2d)\n h = sqrt(r12 - a2)\n\n x_d = x1 + a(x2 - x1)/d\n y_d = y1 + a(y2 - y1)/d\n\n x_int_1 = round(x_d + h(y2 - y1)/d, 6)\n y_int_1 = round(y_d - h(x2 - x1)/d, 6)\n\n x_int_2 = round(x_d - h(y2 - y1)/d, 6)\n y_int_2 = round(y_d + h(x2 - x1)/d, 6)\n\n dots.extend([(x_int_1, y_int_1), (x_int_2, y_int_2)])\n return dots\n\n\ndef make_random_test_test(n):\n tests = []\n for _ in range(n):\n h = choice(range(100, 1100, 50))\n w = h * choice(range(1, 5))\n x_interval = list(range(0, w+5, w//20))\n y_interval = list(range(0, h+5, h//20))\n r_interval = list(range(h//10, h, h//20))\n sensors_num = choice(range(5, 11))\n sensors = []\n for _ in range(sensors_num):\n x = choice(x_interval)\n x_interval.remove(x)\n y = choice(y_interval)\n y_interval.remove(y)\n r = choice(r_interval)\n r_interval.remove(r)\n sensors.append([x, y, r])\n tests.append(\n {\n \"input\": [[w, h], sensors],\n \"answer\": judge([w, h], sensors)\n }\n )\n return tests\n\n\nTESTS = {\n \"Basics\": [\n {\n \"input\": [[200, 150], [[100, 75, 130]]],\n \"answer\": True\n },\n {\n \"input\": [[200, 150], [[50, 75, 100], [150, 75, 100]]],\n \"answer\": True\n },\n {\n \"input\": [[200, 150], [[50, 75, 100], [150, 25, 50], [150, 125, 50]]],\n \"answer\": False,\n },\n {\n \"input\": [[200, 150], [[100, 75, 100], [0, 40, 60], [0, 110, 60], [200, 40, 60], [200, 110, 60]]],\n \"answer\": True\n },\n {\n \"input\": [[200, 150], [[100, 75, 100], [0, 40, 50], [0, 110, 50], [200, 40, 50], [200, 110, 50]]],\n \"answer\": False\n },\n {\n \"input\": [[200, 150], [[100, 75, 110], [105, 75, 110]]],\n \"answer\": False\n },\n {\n \"input\": [[200, 150], [[100, 75, 110], [105, 75, 20]]],\n \"answer\": False\n },\n {\n \"input\": [[3, 1], [[1, 0, 2], [2, 1, 2]]],\n \"answer\": True\n },\n {\n \"input\": [[30, 10], [[0, 10, 10], [10, 0, 10], [20, 10, 10], [30, 0, 10]]],\n \"answer\": True\n },\n {\n \"input\": [[30, 10], [[0, 10, 8], [10, 0, 7], [20, 10, 9], [30, 0, 10]]],\n \"answer\": False\n }\n ],\n \"Extra\": [\n {\n \"input\": [[8, 6], [[4, 3, 5]]],\n \"answer\": True\n },\n {\n \"input\": [[2000, 1000], [[0, 0, 500], [500, 0, 500], [1000, 0, 500], [1500, 0, 500],\n [2000, 0, 500], [0, 500, 500], [500, 500, 500], [1000, 500, 500],\n [1500, 500, 500], [2000, 500, 500]]],\n \"answer\": False\n },\n {\n \"input\": [[4000, 1000], [[0, 500, 1600], [2000, 100, 500], [2100, 900, 500],\n [2500, 200, 500], [2600, 800, 500], [4000, 0, 1200]]],\n \"answer\": False\n },\n {\n \"input\": [[4000, 1000], [[0, 500, 1600], [2000, 100, 500], [2100, 900, 500],\n [2500, 200, 500], [2600, 800, 500], [4000, 0, 1200], [4000, 500, 200]]],\n \"answer\": False\n },\n {\n \"input\": [[4000, 1000], [[0, 500, 1600], [2000, 100, 500], [2100, 900, 500],\n [2500, 200, 500], [2600, 800, 500], [4000, 500, 1200], [1600, 500, 600]]],\n \"answer\": True\n },\n {\n \"input\": [[4000, 1000], [[0, 500, 1600], [2000, 100, 500], [2100, 900, 500],\n [2500, 200, 500], [2600, 800, 500], [4000, 600, 1200], [1600, 500, 600]]],\n \"answer\": False\n },\n {\n \"input\": [[100, 100], [[50, 50, 65], [25, 25, 25], [25, 75, 25], [75, 25, 25], [75, 75, 25]]],\n \"answer\": False\n },\n {\n \"input\": [[100, 100], [[50, 50, 65], [5, 5, 25], [5, 95, 25], [95, 5, 25], [95, 95, 25]]],\n \"answer\": True\n },\n {\n \"input\": [[800, 800], [[0, 0, 500], [0, 800, 500], [800, 0, 500], [800, 800, 500]]],\n \"answer\": False\n },\n {\n \"input\": [[800, 800], [[0, 0, 570], [0, 800, 500], [800, 0, 500], [800, 800, 570]]],\n \"answer\": True\n }\n ],\n \"Randoms\": make_random_test_test(10)\n}\n" }, { "alpha_fraction": 0.7777777910232544, "alphanum_fraction": 0.7777777910232544, "avg_line_length": 35, "blob_id": "8cc379f595f51499cf3c3415c32f34af6eeb86d7", "content_id": "1433d7d26ad9715c662997ee16afb37e0c8ea4a7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 36, "license_type": "no_license", "max_line_length": 35, "num_lines": 1, "path": "/README.md", "repo_name": "archius11/checkio-mission-four-to-the-floor", "src_encoding": "UTF-8", "text": "# checkio-mission-four-to-the-floor\n" } ]	3
AndrzejStecyk/Jars	https://github.com/AndrzejStecyk/Jars	6cdc99e939577e154c4357fa362b83002fa7fa6a	d2a5f2f6d64355a8d56ec6194e78c5ad5a68fd57	748e322fc416e28d3aa403db04116e17787ec7d4	refs/heads/master	2023-04-04T20:54:15.834325	2021-04-25T17:38:41	2021-04-25T17:38:41	361,395,374	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.8017241358757019, "alphanum_fraction": 0.8017241358757019, "avg_line_length": 57, "blob_id": "5b937dda2ceb9cde66b29aec58cd2709b274a5cf", "content_id": "09a235084f47551d19101636fba00f6df75d7848", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 241, "license_type": "no_license", "max_line_length": 138, "num_lines": 4, "path": "/README.md", "repo_name": "AndrzejStecyk/Jars", "src_encoding": "UTF-8", "text": "# Jars\n\nŻeby skrypt działał poprawnie, na maszynie lokalniej musi istnieć instancja mongoDb.\nZałożenie jest takie, że przy użyciu opcji TRANSFER używamy opcji --source i --destination a do DEPOSIT\|WITHDRAW\|LIST\|HISTORY opcji --jar.\n" }, { "alpha_fraction": 0.5769087672233582, "alphanum_fraction": 0.580260694026947, "avg_line_length": 26.397958755493164, "blob_id": "a06c095bb6e0525c21b5050eccbe9ab4c0603b7f", "content_id": "97b919ba98d6058037691af1bbf99487fa8294c5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2685, "license_type": "no_license", "max_line_length": 102, "num_lines": 98, "path": "/db.py", "repo_name": "AndrzejStecyk/Jars", "src_encoding": "UTF-8", "text": "import pymongo\nimport datetime\nfrom bson.objectid import ObjectId\nfrom bson import errors as bson_errors\n\nmyclient = pymongo.MongoClient(\"mongodb://localhost:27017/\")\nmydb = myclient['TestTask']\n\n\ndef add_hitory(source, dest, operation, amount):\n mycol = mydb[\"history\"]\n data = {'source': source,\n 'destination': dest,\n 'operation': operation,\n 'amount': amount,\n 'date': datetime.datetime.now()}\n mycol.insert_one(data)\n\n\ndef create_jar(currency=None):\n mycol = mydb[\"jars\"]\n id = mycol.insert_one({'balance': 0,\n 'currency': currency})\n add_hitory(None, str(id.inserted_id), \"Creation\", 0)\n return id.inserted_id\n\n\ndef deposit(jar_id, amount, transfer=False):\n mycol = mydb[\"jars\"]\n jar = mycol.find_one(ObjectId(jar_id))\n final_amount = amount + jar['balance']\n mycol.find_one_and_update(\n {\"_id\": ObjectId(jar_id)},\n {\"$set\": {\"balance\": final_amount}\n }, upsert=True\n )\n if not transfer:\n add_hitory(None, jar_id, 'Deposit', amount)\n\n\ndef withdraw(jar_id, amount, transfer=False):\n mycol = mydb[\"jars\"]\n try:\n jar = mycol.find_one(ObjectId(jar_id))\n except bson_errors.InvalidId:\n print(\"Invalid Jar ID\")\n exit(-1)\n\n if jar['balance'] >= amount:\n final_amount = jar['balance'] - amount\n mycol.find_one_and_update(\n {\"_id\": ObjectId(jar_id)},\n {\"$set\": {\"balance\": final_amount}\n }, upsert=True\n )\n if not transfer:\n add_hitory(None, jar_id, 'Withdraw', amount)\n else:\n return \"Jar has not enough funds. Operation is canceled\"\n\n\ndef transfer(source, destination, amount):\n withdraw(source, amount, transfer=True)\n deposit(destination, amount, transfer=True)\n add_hitory(source, destination, \"Transfer\", amount)\n\n\ndef get_jar(jar_id=None):\n query = None\n mycol = mydb[\"jars\"]\n list_of_jars = list()\n try:\n if jar_id:\n query = {'_id': ObjectId(jar_id)}\n except bson_errors.InvalidId:\n print(\"Invalid Jar ID\")\n exit(-1)\n\n results = mycol.find(query)\n for x in results:\n list_of_jars.append({'id': str(x['_id']), 'balance': x['balance'], 'currency': x['currency']})\n return list_of_jars\n\n\ndef get_history(jar_id=None):\n query = None\n mycol = mydb[\"history\"]\n history = list()\n if jar_id:\n query = {'$or': [{'destination': jar_id}, {'source': jar_id}]}\n\n results = mycol.find(query)\n\n for x in results:\n del x['_id']\n x['date'] = x['date'].strftime(\"%d-%b-%Y (%H:%M:%S.%f)\")\n history.append(x)\n return history\n" }, { "alpha_fraction": 0.5785991549491882, "alphanum_fraction": 0.5817933082580566, "avg_line_length": 32.45801544189453, "blob_id": "5f1ecf40346c27223422f1afe1590d443b84ae0e", "content_id": "27423aa5c4dccdc1d83d5839d53cdb6c3c8b842a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4383, "license_type": "no_license", "max_line_length": 93, "num_lines": 131, "path": "/jars.py", "repo_name": "AndrzejStecyk/Jars", "src_encoding": "UTF-8", "text": "import db\nfrom optparse import OptionParser\nimport json\n\n\ndef create_a_jar(currency):\n if currency:\n db.create_jar(currency.upper())\n else:\n db.create_jar()\n\n\ndef make_a_deposit(jar_id, amount, currency):\n if amount and jar_id and len(jar_id) == 24:\n jar = db.get_jar(jar_id)\n try:\n if currency:\n if jar[0]['currency'] == currency.upper():\n db.deposit(jar_id, abs(amount))\n print(\"Deposit successful\")\n else:\n print(\"Currency do not match currency of destination account\")\n elif not jar[0]['currency']:\n db.deposit(jar_id, abs(amount))\n print(\"Deposit successful\")\n else:\n print(\"Currency do not match currency of destination account\")\n except IndexError:\n print(\"Jar ID does not exist\")\n else:\n parser.print_help()\n\n\ndef make_a_withdraw(jar_id, amount):\n if amount and jar_id and len(jar_id) == 24:\n db.withdraw(jar_id, abs(amount))\n print(\"Withdraw successful\")\n else:\n parser.print_help()\n\n\ndef make_a_transfer(source, destination, amount):\n if amount and source and destination and len(source) == 24 and len(\n destination) == 24:\n source_jar = db.get_jar(source)\n dest_jar = db.get_jar(destination)\n try:\n if source_jar[0]['currency'] == dest_jar[0]['currency']:\n db.transfer(source, destination, abs(amount))\n print(\"Transfer successful\")\n else:\n print(\"Accounts have different currencies. Operation Canceled\")\n except IndexError:\n print(\"Jar ID does not exist\")\n\n\ndef show_history(jar_id, order_by, desc):\n if jar_id and len(jar_id) == 24:\n hist_data = db.get_history(jar_id)\n else:\n hist_data = db.get_history(jar_id)\n\n if order_by:\n sorted_hist_data = sorted(hist_data, key=lambda i: i[order_by.lower()], reverse=desc)\n print(json.dumps(sorted_hist_data))\n else:\n print(json.dumps(hist_data))\n\n\nif __name__ == '__main__':\n parser = OptionParser()\n\n parser.add_option(\"-o\", \"--operation\", dest=\"operation\", type=str,\n help=\"[CREATE\|DEPOSIT\|WITHDRAW\|TRANSFER\|LIST\|HISTORY]\")\n\n parser.add_option(\"-j\", \"--jar\", dest=\"jar_id\", type=str,\n help=\"id of the jar for\", default=False)\n\n parser.add_option(\"-a\", \"--amount\", dest=\"amount\", type=int,\n help=\"Amount of funds for specified operation\")\n\n parser.add_option(\"-s\", \"--source\", dest=\"source\", type=str,\n help=\"Source jar for funds transfer\")\n\n parser.add_option(\"-d\", \"--destination\", dest=\"destination\", type=str,\n help=\"Destination jar for funds transfer\")\n\n parser.add_option(\"-c\", \"--currency\", dest=\"currency\", type=str,\n help=\"PLN\|USD\|EUR\")\n\n parser.add_option(\"--order_by\", dest=\"order_by\", type=str,\n help=\"\|ID\|OPERATION\|AMOUNT\")\n\n parser.add_option(\"--desc\", dest=\"desc\", action=\"store_true\", default=False,\n help=\"Flag enables descending sorting\")\n\n (options, args) = parser.parse_args()\n\n if not options.operation:\n parser.print_help()\n exit()\n\n allowed_currencies = ['PLN', 'USD', 'EUR']\n allowed_order_by = ['ID', 'OPERATION', 'AMOUNT']\n\n if options.currency and options.currency.upper() not in allowed_currencies:\n parser.print_help()\n exit()\n if options.order_by and options.order_by.upper() not in allowed_order_by:\n parser.print_help()\n exit()\n\n if options.operation.upper() == \"CREATE\":\n create_a_jar(options.currency)\n\n elif options.operation.upper() == \"DEPOSIT\":\n make_a_deposit(options.jar_id, options.amount, options.currency)\n\n elif options.operation.upper() == \"WITHDRAW\":\n make_a_withdraw(options.jar_id, options.amount)\n\n elif options.operation.upper() == \"TRANSFER\":\n make_a_transfer(options.source, options.destination, options.amount)\n\n elif options.operation.upper() == \"LIST\":\n print(json.dumps(db.get_jar()))\n\n elif options.operation.upper() == \"HISTORY\":\n show_history(options.jar_id, options.order_by, options.desc)\n else:\n parser.print_help()\n" }, { "alpha_fraction": 0.6304849982261658, "alphanum_fraction": 0.6304849982261658, "avg_line_length": 15.037036895751953, "blob_id": "5f2ac997567d306be1041e02c02271b95d5ab697", "content_id": "f5cefab596175e6e64affcadc6f44aa5595b526c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 433, "license_type": "no_license", "max_line_length": 35, "num_lines": 27, "path": "/test_jars.py", "repo_name": "AndrzejStecyk/Jars", "src_encoding": "UTF-8", "text": "from unittest import TestCase\nimport db\n\n\nclass Test(TestCase):\n def test_create_a_jar(self):\n self.fail()\n\n\nclass Test(TestCase):\n def test_make_a_deposit(self):\n self.fail()\n\n\nclass Test(TestCase):\n def test_make_a_withdraw(self):\n self.fail()\n\n\nclass Test(TestCase):\n def test_make_a_transfer(self):\n self.fail()\n\n\nclass Test(TestCase):\n def test_show_history(self):\n self.fail()\n" } ]	4
Nabarun21/CMS-ECAL_TPGAnalysis	https://github.com/Nabarun21/CMS-ECAL_TPGAnalysis	5e72de38a2a13e3ee80fdc3ccda87b3abda62251	4de52926331d6162e3b2eba3bf9a723f6975e12e	e1587e4483afcaf3b72a36be9f2d4df6c361e7fe	refs/heads/master	2021-05-31T16:25:53.350687	2016-03-31T16:10:40	2016-03-31T16:10:40	null	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7222222089767456, "alphanum_fraction": 0.7626262903213501, "avg_line_length": 65, "blob_id": "0120e34a13ec4a764ed2445e04b61e7bf07f14cc", "content_id": "9dd6e6d3739badefba1144ad224531ea6946be6a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 198, "license_type": "no_license", "max_line_length": 155, "num_lines": 3, "path": "/TriggerAnalysis/python/__init__.py", "repo_name": "Nabarun21/CMS-ECAL_TPGAnalysis", "src_encoding": "UTF-8", "text": "#Automatically created by SCRAM\nimport os\n__path__.append(os.path.dirname(os.path.abspath(__file__).rsplit('/EcalPFG/TriggerAnalysis/',1)[0])+'/cfipython/slc6_amd64_gcc491/EcalPFG/TriggerAnalysis')\n" }, { "alpha_fraction": 0.6931216716766357, "alphanum_fraction": 0.7460317611694336, "avg_line_length": 17.899999618530273, "blob_id": "31ec58170ea623256c0897cd2d2e7ef4f672433b", "content_id": "753c18a62006ccf72505b4c1f09980f6c579bce4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 189, "license_type": "no_license", "max_line_length": 88, "num_lines": 10, "path": "/Scripts/TriggerAnalysis/produceplotonCAFqueues.sh", "repo_name": "Nabarun21/CMS-ECAL_TPGAnalysis", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\ncd /afs/cern.ch/work/n/ndev/CMSSW_7_5_6/src/CMS-ECAL_TPGAnalysis/Scripts/TriggerAnalysis\n\n\neval `scramv1 runtime -csh`\n\n./makeTrigPrimPlots.sh -r 254833 -a _test_package\n\nexit\n" }, { "alpha_fraction": 0.6881579160690308, "alphanum_fraction": 0.7197368144989014, "avg_line_length": 35.17460250854492, "blob_id": "057128b75b4cdf6303a4e12ac7aea7f4d4972b1c", "content_id": "a930e485b863a8d056897905a3fc08171e73a8fa", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2280, "license_type": "no_license", "max_line_length": 169, "num_lines": 63, "path": "/README.md", "repo_name": "Nabarun21/CMS-ECAL_TPGAnalysis", "src_encoding": "UTF-8", "text": "# CMS-ECAL_TPGAnalysis (8_0_2)\n\n##This houses the latest version of the Level 1 Ecal TPG analysis of CMS\n======================================================================\nHow to run:\n\nGet the CMSSW release area\n```bash\ncmsrel CMSSW_8_0_2\ncd CMSSW_8_0_2/src\ncmsenv\n```\n\nCheckout the repository\n```\ngit cms-init\ngit clone https://github.com/cms-ecal-L1TriggerTeam/CMS-ECAL_TPGAnalysis.git\nscram b\n``` \n\n\nBEFORE YOU RUN, you need to make sure you change the directory/path names to your directories or directories you want results in.\n\n `cd CMS-ECAL_TPGAnalysis/Scripts/TriggerAnalysis`\n\nIn particular the files you need to modify are : `mergeTPGAnalysis.sh; makeTrigPrimPlots.sh ; convertAllPlots.py ;produceplotonCAFqueues.sh `\n\n `cd ../../TPGPlotting/plots` and also modify path in `makeTPGPlots.sh`\n \n \n ###Running the analysis\n This has been tested on lxplus. After you have changed all the pathnames above:\n \n To run, e.g. on 266423 ZeroBias dataset\n ```./runTPGbatch_AAA.sh lxbatch 266423 /Cosmics/Commissioning2016-v1/RAW 80X_dataRun2_HLT_v6 -1 False```\n \n i.e.```./runTPGbatch_AAA.sh lxbatch run_number das_dataset_path Global_Tag Num_of_events_to_be_processed(-1 defaults to all events) MC_or_data(False for data)```\n \n This will run the jobs on the queue \"cmscaf1nd\", If you do not have permissions to run on the queue modify runTPGbatch_AAA.sh and change `queue=cmscaf1nd` appropiately\n \n To locally test if jobs are running:\n `cd log_and_results\n cmsRun runTPG_cfg_2.py`\n \n Once jobs are done, merge the output files.\n\n ```./mergeTPGAnalysis.sh -r 266423 -m log_and_results/266423-_ZeroBias_Run2015C-v1_RAW-batch/results/ -a testing```\n \n `-a` option is optional if u want run on the same set of data several times and want to call each merged output a different name\n\n To make plots:`./makeTrigPrimPlots.sh -r 266423 -a testing`\n\n The plots will be created in the folder: `Scripts/TriggerAnalysis/Commisioning2016`\n \n To make thumbnails: `python convertAllPlots.py -r 266423 -a testing_eg12`\n\n Copy it to your html_folder to look at it on the web.\n \n\n \nSomemore plotting options and instructions on this (partially outdated) twiki here:\n\nhttps://twiki.cern.ch/twiki/bin/viewauth/CMS/EcalPFGCode#Trigger_Primitive_Analysis\n\n" } ]	3
lunerip/Tarea-07	https://github.com/lunerip/Tarea-07	30c05fb2912146a938a8d4546b76020466e37485	f5eed57d87e9fad6f7fc4c37a573652eed39f9cd	bbf2eaef71f65f0e7ddc0316e67fbcd00d9adf84	refs/heads/master	2021-07-18T19:18:05.657860	2017-10-26T00:39:36	2017-10-26T00:39:36	null	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.4589524567127228, "alphanum_fraction": 0.4920133650302887, "avg_line_length": 31.835365295410156, "blob_id": "b44b0d8d91e39adcb2090bfb4d204299b9a92f68", "content_id": "4791835066328138571100cf265dd72bc8bc50d7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5402, "license_type": "no_license", "max_line_length": 118, "num_lines": 164, "path": "/Listas.py", "repo_name": "lunerip/Tarea-07", "src_encoding": "UTF-8", "text": "# encoding UTF-8\n# AUTOR: Luis Enrique Neri Pérez\n# DESCRIPCIÓN: Programa que muestra varios ejemplos donde se refleje que cada función funciona adecuadamente\n\n#PRIMER EJERCICIO ---------------------------------------------------------------------------------------------------\n\ndef ejercicio1(original):\n nueva = []\n acumulador = 0\n comprobador = sum(original)\n while not acumulador == comprobador:\n for dato in original:\n acumulador = acumulador + dato\n nueva.append(acumulador)\n print(\" -La lista\",original,\"devuelve\",nueva)\n\n\ndef funcion1():\n print(\"EJERCICIO #1: Regresa la suma acumulada de los datos\")\n print(\"--------------------------------------------------------------\")\n prueba1 = [1, 2, 3]\n ejercicio1(prueba1)\n prueba2 = [1, 2, 3, 4]\n ejercicio1(prueba2)\n prueba3 = []\n ejercicio1(prueba3)\n prueba4 = [5]\n ejercicio1(prueba4)\n print(\"\\n\")\n\n#SEGUNDO EJERCICIO ---------------------------------------------------------------------------------------------------\ndef ejercicio2(original):\n nueva = []\n nueva = nueva + original\n nueva.remove(nueva[0])\n nueva.remove(nueva[-1])\n print(\" -La lista\", original, \"devuelve\", nueva)\n\n\ndef funcion2():\n print(\"EJERCICIO #2: Regresa la lista sin el primer ni el último elemento \")\n print(\"--------------------------------------------------------------\")\n prueba1 = [1, 2, 3]\n ejercicio2(prueba1)\n prueba2 = [1, 2, 3, 4]\n ejercicio2(prueba2)\n prueba3 = [\"Primer\", \"Hola\", \"Mundo\", \"Último\"]\n ejercicio2(prueba3)\n prueba4 = [0,4,4,4,0]\n ejercicio2(prueba4)\n print(\"\\n\")\n\n#TERCER EJERCICIO ---------------------------------------------------------------------------------------------------\n\ndef ejercicio3(original):\n nueva = sorted(original)\n if original == nueva:\n print(\" -¿La lista\", original, \"está ordenada?:\", True)\n else:\n print(\" -¿La lista\", original, \"está ordenada?:\", False)\n\ndef funcion3():\n print(\"EJERCICIO #3: Regresa True si la lista está ordenenada o False si no lo está \")\n print(\"--------------------------------------------------------------------------------\")\n prueba1 = [1, 2, 3, 4]\n ejercicio3(prueba1)\n prueba2 = [\"A\", \"B\", \"C\", \"D\"]\n ejercicio3(prueba2)\n prueba3 = [2, 3, 1 ,5]\n ejercicio3(prueba3)\n prueba4 = [\"X\", \"B\", \"Y\", \"D\"]\n ejercicio3(prueba4)\n print(\"\\n\")\n\n#CUARTO EJERCICIO ---------------------------------------------------------------------------------------------------\n\ndef ejercicio4(prueba1, prueba2):\n original = list(prueba1.upper())\n nueva= list(prueba2.upper())\n nueva.reverse()\n if original == nueva:\n print(\" -¿La cadena\", prueba1, \"y\", prueba2, \"son anagramas?:\", True)\n else:\n print(\" -¿La cadena\" , prueba1, \"y\", prueba2 , \"son anagramas?:\", False)\n\ndef funcion4():\n print(\"EJERCICIO #4: Regresa True si las dos palabras son anagramas o False si no lo son \")\n print(\"--------------------------------------------------------------------------------\")\n prueba1 = \"Roma\"\n prueba2 = \"Amor\"\n ejercicio4(prueba1,prueba2)\n prueba1 = \"Hola\"\n prueba2 = \"Hello\"\n ejercicio4(prueba1, prueba2)\n prueba1 = \"Reto\"\n prueba2 = \"Oter\"\n ejercicio4(prueba1, prueba2)\n prueba1 = \"Pedro\"\n prueba2 = \"Peter\"\n ejercicio4(prueba1, prueba2)\n print(\"\\n\")\n\n#QUINTO EJERCICIO ---------------------------------------------------------------------------------------------------\n\ndef ejercicio5(original):\n for k in range(len(original)-1):\n if original.count(original[k]) >1:\n print(\" -¿La lista\" , original , \"repite elementos?:\", True)\n return True\n print(\" -¿La lista\", original, \"repite elementos?:\", False)\n return False\n\ndef funcion5():\n print(\"EJERCICIO #5: Regresa True si alguno de los datos están repetidos o False si no lo están \")\n print(\"------------------------------------------------------------------------------------------\")\n prueba1 = [1,2,3,4]\n ejercicio5(prueba1)\n prueba2 = [1,2,3,2]\n ejercicio5(prueba2)\n prueba3 = [\"a\", \"b\", \"c\", \"d\"]\n ejercicio5(prueba3)\n prueba4 = []\n ejercicio5(prueba4)\n print(\"\\n\")\n\n#SEXTO EJERCICIO ---------------------------------------------------------------------------------------------------\n\ndef ejercicio6(original):\n duplicado = []\n duplicado = duplicado + original\n for indice in range(len(original)-1):\n datoRep = original[indice]\n if original.count(datoRep) >1:\n while not original.count(datoRep) == 0:\n original.remove(datoRep)\n original.insert(indice,datoRep)\n print(\"Para la lista\", duplicado, \"se regresa\", original)\n\ndef funcion6():\n print(\"EJERCICIO #6: Si un elemento en la lista se repite, este de quitara dejando solo uno\")\n print(\"------------------------------------------------------------------------------------------\")\n prueba1 = [10, 30, 20, 30]\n ejercicio6(prueba1)\n prueba2 = [1,8,3,4,3]\n ejercicio6(prueba2)\n prueba2 = [1,2,3,4]\n ejercicio6(prueba2)\n prueba2 = [1,1]\n ejercicio6(prueba2)\n\n\n#Función Main que despliega todos los ejemplos del código\ndef main():\n print(\"TAREA 07: EJEMPLOS DE USO DE LISTAS\")\n print(\"\\n\")\n funcion1()\n funcion2()\n funcion3()\n funcion4()\n funcion5()\n funcion6()\n\n\nmain()" } ]	1
JeremySMorgan/2DGraphSearch	https://github.com/JeremySMorgan/2DGraphSearch	b5725a41a5f11a5fbd7f7a3c00c437df02808c70	4cea66bbd925bbcea1bca0e1ae15679947cc2788	99270a45001614d8db5a5aae3f52555b765fd1de	refs/heads/master	2018-04-29T06:03:42.344040	2017-11-28T23:05:09	2017-11-28T23:05:09	92,177,718	11	0	null	null	null	null	null	[ { "alpha_fraction": 0.5574135780334473, "alphanum_fraction": 0.5672240853309631, "avg_line_length": 34.595237731933594, "blob_id": "516337888c3d9a91248660ec873c98bbf485c963", "content_id": "a0e03379a94c0eeae3d4cc6c803cb950376db4b1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4485, "license_type": "no_license", "max_line_length": 136, "num_lines": 126, "path": "/Shapes.py", "repo_name": "JeremySMorgan/2DGraphSearch", "src_encoding": "UTF-8", "text": "import pygame\nimport math\n\nclass RectangleSpace(object):\n\n def __init__(self,screen,x,y,x_width,y_width,passable,occupied,border_color,high_granularity):\n\n ''' x,y are absolute '''\n\n self.screen = screen\n\n self.x = x\n self.y = y\n\n self.x_center = int(x + (x_width/2))\n self.y_center = int(y + (y_width/2))\n\n self.x_width = x_width\n self.y_width = y_width\n\n self.border_color = border_color\n self.occupied = occupied\n self.passable = passable\n self.color = None\n\n self.high_granularity = high_granularity\n if not high_granularity:\n self.myfont = pygame.font.SysFont(\"monospace\", 15)\n\n self.label = None\n\n def update_state(self,occupied):\n if self.passable:\n self.occupied = occupied\n else:\n raise ValueError(\"Cannot call update_state on unpassable Rectangle\")\n\n def setColor(self,color):\n self.color = color\n\n def draw(self):\n if self.color is None:\n raise ValueError(\"Cannot draw Rectangle, color has not been set\")\n pygame.draw.rect(self.screen, self.color, (self.x,self.y,self.x_width,self.y_width))\n if not self.high_granularity:\n pygame.draw.rect(self.screen, self.border_color, (self.x,self.y,self.x_width,self.y_width),1)\n\n if not self.label is None:\n if not self.high_granularity:\n self.screen.blit(self.label, (self.x_center-self.x_width/5, self.y_center-self.y_width/5))\n\n if self.occupied:\n circle_x = int(self.x + (self.x_width/2) )\n circle_y = int(self.y + (self.y_width/2) )\n pygame.draw.circle(self.screen, (0,0,0), (circle_x,circle_y), int(self.x_width/10), 0)\n\n def set_text(self,text):\n if not self.high_granularity:\n self.label = self.myfont.render(str(text), 1, (5,5,0))\n\n\n def __str__(self):\n ''' returns the index position of rectangle '''\n returnStr = \"<Rect object> ( XY -> (\"+str(int(self.x/self.x_width))+\",\"+str(int(self.y/self.y_width))+\"))\"\n return returnStr\n\n\nclass Arrow(object):\n\n def __init__(self,screen,srcRect,dstRect,color):\n\n # Pygame vars\n self.screen = screen\n\n self.xSrc = srcRect.x_center\n self.ySrc = srcRect.y_center\n self.xDst = dstRect.x_center\n self.yDst = dstRect.y_center\n\n self.color = color\n self.alive = True\n\n def update_state(self,alive):\n self.alive = alive\n\n def setColor(self,color):\n self.color = color\n\n # BUG: arrow oriented incorrectly when going left\n def draw(self):\n if self.alive:\n\n # line_deg is angle(deg) between slope of line and x axis (poited to the right)\n # arr_deg is the angle between the line and the arrow heads\n # A is length of one arrow 'head'\n # B is the proportion between length of A and length of line\n arr_deg = 20\n line_length = math.sqrt( (self.yDst - self.ySrc)2 + (self.xDst - self.xSrc)2 )\n line_deg = 0\n if (self.xDst-self.xSrc) == 0:\n if (self.yDst - self.ySrc) < 0:\n line_deg = -90\n else:\n line_deg = 90\n else:\n line_deg = 180 - math.degrees(math.atan((self.yDst - self.ySrc)/(self.xDst-self.xSrc)))\n\n B = 5\n A = line_length/B\n\n # for left arr head, (x-Acos(line_deg - arr_deg), y-Asin(line_deg - arr_deg))\n # for right arr head, (x-Asin((90-arr_deg) - line_deg), y-Acos((90-arr_deg) - line_deg)\n left_x = self.xDst - (Amath.cos(math.radians(line_deg - arr_deg )))\n left_y = self.yDst - (Amath.sin(math.radians(line_deg - arr_deg )))\n right_x = self.xDst - (Amath.sin(math.radians((90-arr_deg) - line_deg)))\n right_y = self.yDst - (Amath.cos(math.radians((90-arr_deg) - line_deg)))\n\n arr_head_lines = [(self.xDst,self.yDst),(left_x,left_y),(right_x,right_y),(self.xDst,self.yDst)]\n pygame.draw.polygon(self.screen,self.color,arr_head_lines,0)\n\n line = [(self.xSrc,self.ySrc), (self.xDst,self.yDst)]\n pygame.draw.lines(self.screen, self.color, False, line, 1)\n\n def __str__(self):\n returnStr = \"Arrow from (\"+str(int(self.xSrc))+\",\"+str(int(self.ySrc))+\") to ( \"+str(int(self.xDst))+\",\"+str(int(self.yDst))+\")\"\n return returnStr\n" }, { "alpha_fraction": 0.5458471775054932, "alphanum_fraction": 0.5511627793312073, "avg_line_length": 34, "blob_id": "a6f6528c56be3a13d799465a7d6f23dbee3675b4", "content_id": "4100b420e4a961a3cf8920d795ae1126a42b04ef", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 18060, "license_type": "no_license", "max_line_length": 248, "num_lines": 516, "path": "/ScreenUtils.py", "repo_name": "JeremySMorgan/2DGraphSearch", "src_encoding": "UTF-8", "text": "import pygame\nimport math\nimport sys\nimport time\nfrom PIL import Image\nfrom Shapes import RectangleSpace\nfrom Shapes import Arrow\nfrom Graph import Graph\nfrom Graph import Node\n\nclass ScreenUtils(object):\n\n def __init__(self,screen,clock,fps,rectangle_spacing,colors,high_granularity):\n\n # Pygame Vars\n self.screen = screen\n self.clock = clock\n self.fps = fps\n\n # Colors\n self.background_color = colors[0]\n self.un_passable_color = colors[1]\n self.occupied_color = colors[2]\n self.un_occupied_color = colors[3]\n self.rectangle_border_color = colors[4]\n self.start_color = colors[5]\n self.end_color = colors[6]\n\n # Rectangle Geometry\n self.num_horiz_rectangles = rectangle_spacing[0]\n self.num_vert_rectangles = rectangle_spacing[1]\n self.rectangle_width = rectangle_spacing[2]\n self.rectangle_height = rectangle_spacing[3]\n\n # Shape Objects\n self.Rectangles = []\n self.Arrows = []\n self.Nodes = []\n\n # Graph\n self.graph = Graph()\n self.startRect = None\n self.startNode = None\n self.endRect = None\n self.endNode = None\n self.startXYIndex = None\n self.endXYIndex = None\n self.obstacles = []\n\n # high_granularity of rectangles results in intensive cpu demands, which\n # causes font rendering failures\n self.high_granularity = high_granularity\n\n\n def image_initialization(self,image_src):\n\n ''' Takes image file name as param.\n - gets the number of horizantal, vertical rectangles from VariableUtils\n - analyzes the image, if an area on the image (defined by granularity dictated from\n rectangle width/height) has an average darkness greater than\n (max_darkness)/(min darkness threshold), ((min darkness threshold) ~ .5),\n then the rectangle representing that area is considered impassable,\n this is reflected in node aswell\n - Green is represents the start rectangle. the start rectangle is\n the closest rectangle to the centroid of all sufficiently green pixels\n - Red is represents the end rectangle. the end rectangle is\n the closest rectangle to the centroid of all sufficiently green pixels\n - Input image is translated to size of desired screen (VariableUtils.SCREEN_WIDTH/HEIGHT)\n '''\n\n # Image variables\n image = Image.open(image_src)\n image_rgb = image.convert('RGB')\n\n # image dimensions\n image_width, image_height = image.size\n image_x_slice_width = image_width / self.num_horiz_rectangles\n image_y_slice_height = image_height / self.num_vert_rectangles\n\n # if r_ave,g_ave,b_ave are all less than darkness_threshold, area is considered obstacle\n darkness_threshold = 50\n\n # if ave value is above this threshold, then value is considred live\n light_threshold = 150\n\n # Red, Green pixel locations for start and end locations\n g_pixel_locations = []\n r_pixel_locations = []\n\n for n_x in range(self.num_horiz_rectangles):\n\n row = []\n\n for n_y in range(self.num_vert_rectangles):\n\n image_x0 = int(n_x * image_x_slice_width)\n image_x = int((n_x * (image_x_slice_width)) + image_x_slice_width)\n\n image_y0 = int(n_y * image_y_slice_height)\n image_y = int((n_y * (image_y_slice_height )) + image_y_slice_height)\n\n pixel_count = 0\n r_ave = 0\n g_ave = 0\n b_ave = 0\n\n # x,y give us absolute pixel location of image\n for x in range(image_x0,image_x,1):\n for y in range(image_y0,image_y,1):\n\n r,g,b = image_rgb.getpixel((x,y))\n pixel_count += 1\n\n r_ave += r\n g_ave += g\n b_ave += b\n\n #if r > light_threshold and g<100:\n # print(\"Red found at: \",x,\",\",y,\", \\t\|\\t r:\",r,\"g:\",g,\"b:\",b, \"\\t\|\\t(r > light_threshold):\",(r > light_threshold),\"(g < darkness_threshold):\",(g < darkness_threshold),\"(b < darkness_threshold):\",(b < darkness_threshold))\n\n # Test for red\n if (r > light_threshold) and (g < darkness_threshold) and (b < darkness_threshold):\n r_pixel_locations.append([x,y])\n\n # Test for green\n if (r<darkness_threshold) and (g>light_threshold) and (b<darkness_threshold):\n g_pixel_locations.append([x,y])\n\n r_ave /= pixel_count\n g_ave /= pixel_count\n b_ave /= pixel_count\n\n passable = True\n occupied = False\n\n # test to see if area is occupied\n if (r_ave < darkness_threshold) and (g_ave < darkness_threshold) and (b_ave < darkness_threshold):\n passable = False\n\n # Rectangle x,y location\n rect_x = n_x * self.rectangle_width\n rect_y = n_y * self.rectangle_height\n\n # Create Rectangle\n rect = RectangleSpace(self.screen,rect_x,rect_y,self.rectangle_width, \\\n self.rectangle_height,passable,occupied,\n self.rectangle_border_color,self.high_granularity)\n\n rect.setColor(self.background_color)\n row.append(rect)\n\n # Rectangle is not passable, thus no node is created\n if not passable:\n rect.setColor(self.un_passable_color)\n self.obstacles.append((n_x,n_y))\n\n # Rectangle is passable, thus we create a node\n else:\n xyIndex = [n_x,n_y]\n\n write_out = \"Node at: \"+str(n_x)+\",\"+str(n_y)+\"\\t\"\n #sys.stdout.write(write_out)\n\n self.Nodes.append(Node(\"-\",xyIndex))\n\n self.Rectangles.append(row)\n\n start_ave = [0,0]\n target_ave = [0,0]\n\n for xy in g_pixel_locations:\n start_ave[0] += xy[0]\n start_ave[1] += xy[1]\n start_ave[0] /= len(g_pixel_locations)\n start_ave[1] /= len(g_pixel_locations)\n\n for xy in r_pixel_locations:\n target_ave[0] += xy[0]\n target_ave[1] += xy[1]\n target_ave[0] /= len(r_pixel_locations)\n target_ave[1] /= len(r_pixel_locations)\n\n print( (\"end xy: \",target_ave) )\n\n start_index = self.getIndexXYFromAbsXY(start_ave[0],start_ave[1])\n end_index = self.getIndexXYFromAbsXY(target_ave[0],target_ave[1])\n\n self.startXYIndex = start_index\n self.endXYIndex = end_index\n\n self.makeGraph()\n\n\n def manual_initialization(self,obstacles=[],start_index=None,end_index=None):\n\n for (x,y) in obstacles:\n if self.isValidLocation(x,y):\n self.obstacles.append((x,y))\n else:\n pass\n\n ''' Set obstacle rectangles using an array of tuples of the form [(X1,Y1),(X2,Y2), ...]\n where Xn and Ym (n,m are arbitrary numbers) describing the index of a impassable rectangle,\n then creates rectangle objects, ginally creat a graph using these rectangles '''\n\n for x in range(self.num_horiz_rectangles):\n\n row = []\n\n for y in range(self.num_vert_rectangles):\n\n rect_x = x * self.rectangle_width\n rect_y = y * self.rectangle_height\n\n occupied = False\n passable = True\n if (x,y) in self.obstacles:\n passable = False\n\n rect = RectangleSpace(self.screen,rect_x,rect_y,self.rectangle_width,self.rectangle_height,passable,occupied,self.rectangle_border_color,self.high_granularity)\n rect.setColor(self.background_color)\n row.append(rect)\n\n # Rectangle is not passable, thus no node is created\n if not passable:\n rect.setColor(self.un_passable_color)\n\n # Rectangle is passable, thus we create a node\n else:\n value = Node.XYIndexToNodeValue(x,y)\n xyIndex = [x,y]\n self.Nodes.append(Node(value,xyIndex))\n\n self.Rectangles.append(row)\n\n if not type(start_index) == type(None):\n self.startXYIndex = start_index\n else:\n raise ValueError(\"ScreenUtils.manual_initialization() parameter error: start index must be provided\")\n\n if not type(end_index) == type(None):\n self.endXYIndex = end_index\n else:\n raise ValueError(\"ScreenUtils.manual_initialization() parameter error: end index must be provided\")\n\n self.makeGraph()\n\n\n\n def makeGraph(self):\n\n ''' Makes graph. The rectangles must have all been created first '''\n\n for x in range(self.num_horiz_rectangles):\n for y in range(self.num_vert_rectangles):\n\n # origin node must be at a valid spot\n if self.isValidLocation(x,y):\n\n # Add rectangles neighbors to graph\n # offset - [leftright,updown]\n offsets = [ [1,0],[-1,0],[0,1],[0,-1] ]\n\n for offset in offsets:\n\n up_down = offset[1]\n right_left = offset[0]\n\n target_x = x + right_left\n target_y = y + up_down\n\n if self.isValidLocation(target_x,target_y):\n\n srcNode = self.getNodeFromXYIndex(x,y)\n trgtNode = self.getNodeFromXYIndex(target_x,target_y)\n\n srcNode.add_neighbor(trgtNode)\n self.graph.add_connection( srcNode, trgtNode )\n\n # Start and End nodes are set here if graph is created from image_initialization()\n\n # Set start node if it has not been set already\n if type(self.startNode) == type(None):\n if type(self.startXYIndex) == type(None):\n raise ValueError(\"ScreenUtil.makeGraph(): startNode has not been set\")\n else:\n self.setStartWithXYIndex(self.startXYIndex[0],self.startXYIndex[1])\n\n # Set end node if it has not been set already\n if type(self.endNode) == type(None):\n if type(self.endXYIndex) == type(None):\n raise ValueError(\"ScreenUtil.makeGraph(): endNode has not been set\")\n else:\n self.setEndWithXYIndex(self.endXYIndex[0],self.endXYIndex[1])\n\n\n def nodeExistsFromXYIndex(self,x,y):\n\n ''' returns whether a node exists a specified xy index '''\n\n for node in self.Nodes:\n if node.x == x and node.y == y:\n return True\n return False\n\n def getNodeFromXYIndex(self,x,y):\n\n ''' returns node with specified xy index '''\n\n for node in self.Nodes:\n if node.x == x and node.y == y:\n return node\n print(\"Fatal Error:\",x,\",\",y,\"has no associated node\")\n return None\n\n\n def setRectangleColorAndTextFromGraphResults(self,tentative_values):\n max_weight = -1\n min_weight = 0\n\n # Get max_weight\n for node in tentative_values:\n if not type(node) == None:\n # Exclude nodes with infinite value\n if tentative_values[node] < math.inf:\n if tentative_values[node] > max_weight:\n max_weight = tentative_values[node]\n else:\n raise ValueError(\"Invalid return from Graph.Dijkstra, node not returned: \",str(node))\n\n for node in tentative_values:\n rect = self.getRectFromNode(node)\n\n # make sure we have a valid rectangle\n if not type(rect) == None:\n\n if not self.high_granularity:\n rect.set_text(tentative_values[node])\n\n # dont change color of start and end nodes\n if not (node.amStart or node.amEnd):\n\n # dont change color of unvisited nodes\n if tentative_values[node] < math.inf:\n\n node_weight = tentative_values[node]\n\n # Scale weight between 0-255\n min_color = 250\n max_color = 0\n colorValue = (((node_weight - min_weight) * (max_color - min_color)) / (max_weight - min_weight)) + min_color\n\n color = (min_color-int(colorValue),int(colorValue),0)\n rect.setColor(color)\n else:\n if node.amStart:\n rect.setColor(self.start_color)\n\n if not self.high_granularity:\n rect.set_text(\"Start\")\n else:\n rect.setColor(self.end_color)\n if not self.high_granularity:\n rect.set_text(\"End\")\n else:\n raise ValueError(\"Invalid return from Graph.Dijkstra, rect not created from: \",str(node))\n\n\n\n def solveShortestPath(self,algo):\n\n ''' Find shortest path from beggining rectangle to end rectangle using paramaterized algorithm '''\n\n if self.startNode == None or self.endNode == None or self.startRect == None or self.endRect == None or self.current_rect == None:\n raise ValueError(\"Unable to solveDijkstra(), start, end or current_rect have not been set\")\n\n if not str(algo).lower() in self.graph.path_algorithms:\n raise ValueError(algo,\"is either not implemented or not a valid algorithm\")\n\n tentative_values, path = self.graph.shortestPath(algo)\n\n path = list(reversed(path))\n\n print(\"Length of path\",len(path))\n\n path_iterator = 1\n done_drawing_arrows = False\n\n self.setRectangleColorAndTextFromGraphResults(tentative_values)\n\n while True:\n\n # check for quit events\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit(); sys.exit();\n\n # erase the screen\n self.screen.fill(self.background_color)\n\n if not done_drawing_arrows:\n\n fromNode = path[path_iterator-1]\n toNode = path[path_iterator]\n\n fromRect = self.getRectFromNode(fromNode)\n toRect = self.getRectFromNode(toNode)\n\n self.changeCurrent(toRect)\n\n arrow_color = (255,255,255)\n tempArrow = Arrow(self.screen,fromRect,toRect,arrow_color)\n self.Arrows.append(tempArrow)\n self.draw()\n # update the screen\n pygame.display.update()\n\n path_iterator += 1\n if (path_iterator == len(path)):\n done_drawing_arrows = True\n\n self.clock.tick(self.fps)\n\n\n\n def changeCurrent(self,newCurrentRect):\n self.current_rect.update_state(False)\n self.current_rect = newCurrentRect\n self.current_rect.update_state(True)\n\n\n def getIndexXYFromAbsXY(self,x,y):\n\n ''' returns xy index of rectangle with xy absoulute coordinates on the screen '''\n\n x_index = int(x / self.rectangle_width)\n y_index = int(y / self.rectangle_height)\n return (x_index,y_index)\n\n def getRectFromNode(self,node):\n\n ''' Returns a rectangle object given a node '''\n\n return self.Rectangles[node.x][node.y]\n\n\n def setStartWithXYIndex(self,x,y):\n\n ''' Set the start location for the graph search. X, Y must be indexes '''\n\n if self.isValidLocation(x,y):\n self.startRect = self.getRectFromXYIndex(x,y)\n self.startRect.setColor(self.start_color)\n self.startRect.update_state(True)\n self.current_rect = self.startRect\n self.startNode = self.getNodeFromXYIndex(x,y)\n self.startNode.setAmStart(True)\n else:\n raise ValueError(\"Invalid input to ScreenUtils.setStart()\")\n\n def setEndWithXYIndex(self,x,y):\n\n ''' set end point of graph '''\n\n print( (\"setting end node with xy index x:\",x,\",y:\",y) )\n\n if self.isValidLocation(x,y):\n self.endRect = self.getRectFromXYIndex(x,y)\n self.endRect.setColor(self.end_color)\n self.endNode = self.getNodeFromXYIndex(x,y)\n self.endNode.setAmEnd(True)\n for node in self.Nodes:\n node.setEnd(x,y)\n\n else:\n raise ValueError(\"Invalid input to ScreenUtils.setEnd()\")\n\n def getRectFromXYIndex(self,x,y):\n\n ''' return Rectangle object given its xy index '''\n\n return self.Rectangles[x][y]\n\n def isValidLocation(self,x_rect,y_rect):\n\n ''' Returns whether a rectangle's indexed location is valid '''\n\n # location cannot be impassable\n if (x_rect,y_rect) in self.obstacles:\n return False\n\n # xRect must be 0 or greater\n if x_rect < 0:\n return False\n\n # x rect must be less than num_horiz_rectangles, to account for zero indexing\n if x_rect >= self.num_horiz_rectangles:\n return False\n\n # xRect must be 0 or greater\n if y_rect < 0:\n return False\n\n # x rect must be less than num_horiz_rectangles, to account for zero indexing\n if y_rect >= self.num_vert_rectangles:\n return False\n\n return True\n\n def draw(self):\n for i in self.Rectangles:\n for j in i:\n j.draw()\n\n for a in self.Arrows:\n a.draw()\n" }, { "alpha_fraction": 0.5533245801925659, "alphanum_fraction": 0.5579328536987305, "avg_line_length": 28.590909957885742, "blob_id": "735b04ec6036d17308619e14b09e5b8f1732d377", "content_id": "b458bc0d6517c85ccf93f005f18a7aeaf1d3e829", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9114, "license_type": "no_license", "max_line_length": 124, "num_lines": 308, "path": "/Graph.py", "repo_name": "JeremySMorgan/2DGraphSearch", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\nfrom collections import defaultdict\nimport copy\nimport math\n\nclass Node(object):\n\n def __init__(self, value,xyIndex, xyEndIndex=[-1,-1]):\n self.neighbors = []\n self.value = value\n self.x = xyIndex[0]\n self.y = xyIndex[1]\n self.xEnd = xyEndIndex[0]\n self.yEnd = xyEndIndex[1]\n self.amStart = False\n self.amEnd = False\n self.parent = None\n\n def setAmStart(self,amStart):\n ''' Designates the node as the start node '''\n self.amStart = amStart\n\n def setAmEnd(self,amEnd):\n ''' Designates the node as the end node '''\n self.amEnd = amEnd\n\n def setEnd(self,xIndex,yIndex):\n self.xEnd = xIndex\n self.yEnd = yIndex\n\n def add_neighbor(self,node):\n self.neighbors.append(node)\n\n def getNeighbors(self):\n return self.neighbors\n\n\n def getDijkstraVerticeWeight(self, node):\n\n ''' Dijkstra Search has equal weights for all edges '''\n return 1\n\n def nodeXY(self):\n ''' debugging tool. returns nodes XY coordinates '''\n returnStr = \"(\" +str(self.x)+\",\"+str(self.y)+\")\"\n return returnStr\n\n def __str__(self):\n ''' returns the index position of node in the '(x,y)', where x and y are the indexes of the node'''\n returnStr = \"<Node object> ( XY -> (\"+str(self.x)+\",\"+str(self.y)+ \")\"\n if self.amStart:\n returnStr += \" \| start -> \"+str(self.amStart)\n if self.amEnd:\n returnStr += \" \| end -> \"+ str(self.amEnd)\n returnStr += \")\"\n return returnStr\n\n def getAStarHeuristicWeight(self, trgtNode):\n\n if not type(trgtNode) == type(self):\n raise ValueError(\"TargetNode is not a Node\")\n\n if self.yEnd < 0 or self.xEnd < 0:\n raise ValueError(\"End Node has not been set\")\n\n dx = math.fabs(trgtNode.x - self.xEnd)\n dy = math.fabs(trgtNode.y - self.yEnd)\n D = 1\n h = D * (dx + dy)\n\n return h\n\n @staticmethod\n def XYIndexToNodeValue(x,y):\n returnStr = \"(\"+str(x)+\",\"+str(y)+\")\"\n return returnStr\n\n\nclass Graph(object):\n\n def __init__(self, connections=[],directed=False):\n self._graph = defaultdict(set)\n self._directed = directed\n self.add_connections(connections)\n self.path_algorithms = [\"dijkstra\",\"astar\"]\n\n\n def add_connections(self, connections):\n \"\"\" Add connections (list of tuple pairs) to graph \"\"\"\n\n for node1, node2 in connections:\n self.add(node1, node2)\n\n def add_connection(self, node1, node2):\n \"\"\" Add connection between node1 and node2 \"\"\"\n\n self._graph[node1].add(node2)\n if not self._directed:\n self._graph[node2].add(node1)\n\n def get_connections(self,node):\n \"\"\" Return a Nodes connections \"\"\"\n\n return self._graph[node]\n\n def remove(self, node):\n \"\"\" Remove all references to node \"\"\"\n\n for n, cxns in self._graph.iteritems():\n try:\n cxns.remove(node)\n except KeyError:\n pass\n try:\n del self._graph[node]\n except KeyError:\n pass\n\n def shortestPath(self,algo):\n\n # 1) Assign to each node a tentative distance value (0 for initial, inf for all others)\n # 2) Create a set of visited nodes. Starts with initial node\n # 3) For the current node, consider all of its unvisited neighbors and calulate\n # (distance to the current node) + (dustance from current node to neighbor)\n # if this calculated value is less than their tentative value, replace the tentative value with this new value\n # 4) Mark the current node visited\n # 5) if the destination node is marked visited, the search is done\n # 6) set the unvisited node marked with the smallest tentative distance as the next 'current node' and repeat from 3\n\n\n if algo == \"dijkstra\":\n return self.dijkstra_search()\n\n elif algo == \"astar\":\n return self.astar_search()\n\n else:\n print(\"unknown search algorithm.\")\n\n def astar_search(self):\n\n print(\"in a star search\")\n\n graph = self\n nodes = set(graph._graph)\n initial_node = None\n target_node = None\n\n # Create 'visited' dictanary <- thats spelled wrong\n visited = set()\n tentative_values = {}\n\n # initialize node tentative values\n for node in nodes:\n\n # Set tentative value of node to inf\n tentative_values[node] = math.inf\n\n if node.amStart:\n # Initial node has tentative value of 0\n tentative_values[node] = 0\n initial_node = node\n initial_node.parent = None\n\n if node.amEnd:\n target_node = node\n\n current_node = initial_node\n not_done = True\n\n while not_done:\n\n # Mark the current node visited\n visited.add(current_node)\n\n # Set new tentative values for current node's neighbors\n for neighbor in current_node.getNeighbors():\n\n # h = heuristic\n # g = cost of movement\n\n h = current_node.getAStarHeuristicWeight(neighbor)\n g = 1\n\n f = g + h\n # total weight from init to neighbor. if this value is less than\n # the current tentative weight of the neighbor, then the tentative weight is\n # updated with this value\n newTentativeValue = f\n\n if newTentativeValue < tentative_values[neighbor]:\n tentative_values[neighbor] = newTentativeValue\n neighbor.parent = current_node\n\n min_distance = math.inf\n nextNode = None\n\n for node in nodes:\n if node not in visited:\n if tentative_values[node] < min_distance:\n nextNode = node\n min_distance = tentative_values[node]\n\n current_node = nextNode\n if current_node == None:\n raise ValueError(\"Next Node Not Found\")\n\n if current_node == target_node:\n not_done = False\n\n path = self.getPath(target_node, [])\n\n return tentative_values, path\n\n\n def dijkstra_search(self):\n\n graph = self\n nodes = set(graph._graph)\n initial_node = None\n target_node = None\n\n # Create 'visited' dictanary <- thats spelled wrong\n visited = set()\n tentative_values = {}\n\n # initialize node tentative values\n for node in nodes:\n\n # Set tentative value of node to inf\n tentative_values[node] = math.inf\n\n if node.amStart:\n\n # Initial node has tentative value of 0\n tentative_values[node] = 0\n initial_node = node\n initial_node.parent = None\n\n if node.amEnd:\n target_node = node\n\n current_node = initial_node\n not_done = True\n\n while not_done:\n\n # Mark the current node visited\n visited.add(current_node)\n\n # Set new tentative values for current node's neighbors\n for neightbor in current_node.getNeighbors():\n\n # vertice weight between current_node and neighbor\n distanceToNeighbor = current_node.getDijkstraVerticeWeight(neightbor)\n\n # total weight from init to neighbor. if this value is less than\n # the current tentative weight of the neighbor, then the tentative weight is\n # updated with this value\n newTentativeValue = tentative_values[current_node] + distanceToNeighbor\n\n if newTentativeValue < tentative_values[neightbor]:\n tentative_values[neightbor] = newTentativeValue\n neightbor.parent = current_node\n\n min_distance = math.inf\n nextNode = None\n\n for node in nodes:\n if node not in visited:\n if tentative_values[node] < min_distance:\n\n nextNode = node\n min_distance = tentative_values[node]\n\n current_node = nextNode\n if current_node == None:\n raise ValueError(\"Next Node Not Found\")\n\n if current_node == target_node:\n not_done = False\n\n\n path = self.getPath(target_node,[])\n\n return tentative_values,path\n\n\n def getPath(self,node,path):\n if node.parent == None:\n return path\n\n else:\n path.append(node)\n return self.getPath(node.parent,path)\n\n\n def is_connected(self, node1, node2):\n \"\"\" Is node1 directly connected to node2 \"\"\"\n\n return node1 in self._graph and node2 in self._graph[node1]\n\n def tester(self):\n pass\n\n def __str__(self):\n return '{}({})'.format(self.__class__.__name__, dict(self._graph))\n" }, { "alpha_fraction": 0.5010225176811218, "alphanum_fraction": 0.6134969592094421, "avg_line_length": 22.238094329833984, "blob_id": "a2b29805909462443169e96a4aabe599f2b7453e", "content_id": "303ac8edd2d3016b9fcca71e9ade1c42b79167c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 489, "license_type": "no_license", "max_line_length": 44, "num_lines": 21, "path": "/VariableUtils.py", "repo_name": "JeremySMorgan/2DGraphSearch", "src_encoding": "UTF-8", "text": "\nclass VariableUtils(object):\n FPS = 90\n\n SCREEN_WIDTH = 900\n SCREEN_HEIGHT = 900\n\n HIGH_GRANULARITY = True\n\n HORIZ_RECTS = 60\n VERT_RECTS = 60\n\n RECT_WIDTH = SCREEN_WIDTH / HORIZ_RECTS\n RECT_HEIGHT = SCREEN_HEIGHT / VERT_RECTS\n\n BACKGROUND_COLOR = (255,255,255)\n UN_PASSABLE_COLOR = (0,0,0)\n OCCUPIED_COLOR = (0,255,0)\n UN_OCCUPIED_COLOR = (190,190,190)\n RECTANGLE_BORDER_COLOR = (0,0,0)\n START_COLOR = (0, 255, 255)\n END_COLOR = (0, 255,255)\n" }, { "alpha_fraction": 0.7621178030967712, "alphanum_fraction": 0.7710663676261902, "avg_line_length": 43.70000076293945, "blob_id": "15630f80fe66b62f9fc5f9634f7dcbcc73c1efde", "content_id": "293c882a86b7852433c1b03287eeb89795ad8af7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1341, "license_type": "no_license", "max_line_length": 170, "num_lines": 30, "path": "/README.md", "repo_name": "JeremySMorgan/2DGraphSearch", "src_encoding": "UTF-8", "text": "# 2DGraphSearch\nDijkstra, A* Search Algorithms displayed using pygame3 and PIL image analysis.\nThis program is either analyzes images to create a corresponding graph or manually read obstacle locations from preprogrammed xy coordinates\n\n### A\n- A search from image <br>\n![A* Search](https://github.com/JeremySMorgan/2DGraphSearch/blob/master/gifs/astar.gif)\n\n### Dijkstra\n- Dijkstra search from image<br> \n![Dijkstra](https://github.com/JeremySMorgan/2DGraphSearch/blob/master/gifs/dijkstra_image_read.gif)\n- Dijkstra search from manually added obstacles<br> \n![Dijkstra](https://github.com/JeremySMorgan/2DGraphSearch/blob/master/gifs/dijkstra.gif)\n\n### Notes\n- This project is written in python3\n- Low granularity image reads result in intensive cpu processing demands. Pygame's\n image rendering fails when creating a graph with more than ~3500 nodes, if this happens\n `HIGH_GRANULARITY` must be set to `True` to prevent crashes.\n- The image analysis program sets the start position to the centroid of all sufficiently green pixels, and the end location to the centroid of all sufficiently red pixels\n\n### Dependencies\n- pygame: `pip3 install pygame`\n- pillow: `pip3 install pillow`\n\n### To Do\n- ~~Image input (PIL) for graph construction~~\n- ~~A* heuristic weight algorithm revision~~\n- ~~Fix arrow head orientation~~\n- D* Search\n" }, { "alpha_fraction": 0.5140284299850464, "alphanum_fraction": 0.5978524684906006, "avg_line_length": 36.493507385253906, "blob_id": "9767f2abfee3d6ec9a0ba0b63d9bd9a00627eb06", "content_id": "3a3c934859512a9e81c8e47e5c7b3705fd585700", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2887, "license_type": "no_license", "max_line_length": 131, "num_lines": 77, "path": "/main.py", "repo_name": "JeremySMorgan/2DGraphSearch", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\n\"\"\" main.py: \"\"\"\n\n__author__ = \"Jeremy Morgan\"\n__date__ = \"May 17, 2017\"\n\nimport pygame\nimport sys\nimport time\nfrom Shapes import RectangleSpace\nfrom VariableUtils import VariableUtils\nfrom ScreenUtils import ScreenUtils\n\ndef main():\n\n # Pygame Constants\n fps = VariableUtils.FPS\n screen_dimensions = (VariableUtils.SCREEN_WIDTH,VariableUtils.SCREEN_HEIGHT)\n\n # Pygame Objects and Creation\n pygame.init()\n screen = pygame.display.set_mode(screen_dimensions)\n clock = pygame.time.Clock()\n high_granularity = VariableUtils.HIGH_GRANULARITY\n\n # Rectangle and Screen Colors\n background_color = VariableUtils.BACKGROUND_COLOR\n un_passable_color = VariableUtils.UN_PASSABLE_COLOR\n occupied_color = VariableUtils.OCCUPIED_COLOR\n un_occupied_color = VariableUtils.UN_OCCUPIED_COLOR\n rectangle_border_color = VariableUtils.RECTANGLE_BORDER_COLOR\n start_color = VariableUtils.START_COLOR\n end_color = VariableUtils.END_COLOR\n colors = [background_color, un_passable_color, occupied_color, un_occupied_color, rectangle_border_color,start_color,end_color]\n\n # Rectangle Spacing Constants\n num_horiz_rectangles = VariableUtils.HORIZ_RECTS\n num_vert_rectangles = VariableUtils.VERT_RECTS\n rectangle_width = VariableUtils.RECT_WIDTH\n rectangle_height = VariableUtils.RECT_HEIGHT\n rectangle_spacing = [num_horiz_rectangles,num_vert_rectangles,rectangle_width,rectangle_height]\n\n # ScreenUtil to modify screen\n screen_modifier = ScreenUtils(screen,clock,fps,rectangle_spacing,colors,high_granularity)\n\n\n # To initialize manually:\n '''\n obstacles = [(5, 8), (1, 1), (2, 2), (2, 3), (2, 4), (2, 5), (2, 6), (3, 1), (4, 2), (5, 5), \\\n (6, 1), (7, 1), (8, 1), (9, 1), (6, 7), (3, 24), (30, 12), (40, 22), (35, 19), (25, 16), (17, 41), \\\n (15, 2), (15, 3), (15, 4), (15, 5), (15, 6), (15, 7), (15, 8), (15, 9), \\\n (15, 10), (15, 11), (15, 12), (15, 14), (15, 15), (15, 16), (15, 17), (15, 18), \\\n (16, 20), (16, 21), (16, 22), (16, 24), (16, 25), (16, 26), (16, 27), (16, 28), \\\n (17, 4), (17, 13), (17, 5), (17, 6), (17, 7), (17, 8), (17, 9), (17, 10), \\\n (17, 11), (17, 12), (17, 14), (17, 15), (17, 16), (17, 17), (17, 18), \\\n (18, 21), (18, 22), (18, 24), (18, 25), (18, 26), (18, 27), (18, 28), (18, 29), (5, 2)\n ]\n obstacles = []\n start = (3, 15)\n end = (38, 17)\n screen_modifier.manual_initialization(obstacles, start, end)\n '''\n # To initialize using image\n image_src = \"map.png\"\n screen_modifier.image_initialization(image_src)\n\n\n # Available: 'dijkstra', 'astar'\n algorithm = \"astar\"\n screen_modifier.solveShortestPath(algorithm)\n\n\nif __name__ == '__main__':\n print(\"Beggning Program\")\n main()\n print(\"Program finished\")\n" } ]	6
SangminOut/DMS-Sanic	https://github.com/SangminOut/DMS-Sanic	7fb877b2a772de808b7391428e151b2a2645c59d	bbb65c584711fa23dbf0455300307c2acceba013	dafafb8f65cd93dd1f6567d9b8e431e31f19ae68	refs/heads/master	2020-06-15T06:11:33.116199	2019-07-14T12:57:11	2019-07-14T12:57:58	null	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5738045573234558, "alphanum_fraction": 0.5738045573234558, "avg_line_length": 20.863636016845703, "blob_id": "7bb83d0baa185fe756143ee095e6f0fecca1c70b", "content_id": "de272d9f96fe77147c3b2527b8dad48277fe174a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 481, "license_type": "permissive", "max_line_length": 53, "num_lines": 22, "path": "/dms/views/apply/music.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass MusicApplyView(HTTPMethodView):\n def get(self, request: Request, weekday: int):\n \"\"\"\n Response Music Apply Status\n \"\"\"\n pass\n\n def post(self, request: Request, weekday: int):\n \"\"\"\n Apply Music\n \"\"\"\n pass\n\n def delete(self, request: Request, weekday: int):\n \"\"\"\n Delete Music apply on the weekday\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.5927051901817322, "alphanum_fraction": 0.5927051901817322, "avg_line_length": 19.5625, "blob_id": "8d582cb2deab86af854805d997cdcf7af3b8c7d2", "content_id": "ddaaf99a5e56b70858abeca497fd50bc5e96b274", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 329, "license_type": "permissive", "max_line_length": 44, "num_lines": 16, "path": "/dms/views/apply/stay.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass StayApplyView(HTTPMethodView):\n async def get(self, request: Request):\n \"\"\"\n Response Stay Apply Status\n \"\"\"\n pass\n\n async def post(self, response: Request):\n \"\"\"\n Apply Stay\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.6713747382164001, "alphanum_fraction": 0.6713747382164001, "avg_line_length": 33.25806427001953, "blob_id": "08740858498e033252f21c339e8654727c9606f2", "content_id": "6a6497bc38fed7d40c9bcc0992f03ead5ed27fb2", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1062, "license_type": "permissive", "max_line_length": 117, "num_lines": 31, "path": "/dms/models/point.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from datetime import datetime\n\nfrom extension import db\n\n\nclass PointItemModel(db.Model):\n __tablename__ = 'point_item'\n\n id: int = db.Column(db.Integer(), primary_key=True)\n reason: str = db.Column(db.String())\n point: int = db.Column(db.Integer())\n type: bool = db.Column(db.Boolean())\n\n\nclass PointHistoryModel(db.Model):\n __tablename__ = 'point_history'\n\n id: int = db.Column(db.Integer(), primary_key=True)\n student_id: str = db.Column(db.String(), db.ForeignKey('student.username', ondelete='CASCADE'))\n point_id: int = db.Column(db.Integer(), db.ForeignKey('point_item.id', ondelete='CASCADE'))\n point_date: datetime = db.Column(db.DateTime())\n\n\nclass PointStatusModel(db.Model):\n __tablename__ = 'point_status'\n\n student_id: str = db.Column(db.String(), db.ForeignKey('student.username', ondelete='CASCADE'), primary_key=True)\n good_point: int = db.Column(db.Integer())\n bad_point: int = db.Column(db.Integer())\n penalty_level: int = db.Column(db.Integer())\n penalty_status: bool = db.Column(db.Boolean())\n" }, { "alpha_fraction": 0.6681614518165588, "alphanum_fraction": 0.6681614518165588, "avg_line_length": 21.299999237060547, "blob_id": "b1a24bfd904ad0e3339b1a06c17351da4765ce1d", "content_id": "5e71bef5550c61a2a23bd037c596ecd90a7fe0cd", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 223, "license_type": "permissive", "max_line_length": 41, "num_lines": 10, "path": "/dms/views/report/facility_report.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass FacilityReportView(HTTPMethodView):\n def post(self, request: Request):\n \"\"\"\n Report Broken Facility\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.7802503705024719, "alphanum_fraction": 0.7802503705024719, "avg_line_length": 36.842105865478516, "blob_id": "bd76b6c4a68df20714821a60b538229aca6e6425", "content_id": "c52599731a75b7571caa952bc64c124ceb20b227", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 719, "license_type": "permissive", "max_line_length": 95, "num_lines": 19, "path": "/dms/views/apply/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.blueprints import Blueprint\n\napply_blueprint = Blueprint(\n name='apply_blueprint',\n url_prefix='/apply',\n)\n\nfrom views.apply.extension import ExtensionApplyView, ExtensionMapView\napply_blueprint.add_route(ExtensionApplyView.as_view(), '/extension/<time:int>')\napply_blueprint.add_route(ExtensionMapView.as_view(), '/extension/map/<time:int>/<class_:int>')\n\nfrom views.apply.goingout import GoingoutApplyView\napply_blueprint.add_route(GoingoutApplyView.as_view(), '/goingout')\n\nfrom views.apply.music import MusicApplyView\napply_blueprint.add_route(MusicApplyView.as_view(), '/music/<weekday:int>')\n\nfrom views.apply.stay import StayApplyView\napply_blueprint.add_route(StayApplyView.as_view(), '/stay')\n" }, { "alpha_fraction": 0.7156862616539001, "alphanum_fraction": 0.7156862616539001, "avg_line_length": 21.66666603088379, "blob_id": "cd24acd5b0131c6f63704a08591e8c14de17e1b1", "content_id": "81f6cf5475315bafa196ed538da4e903f4cb09b0", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 204, "license_type": "permissive", "max_line_length": 55, "num_lines": 9, "path": "/dms/views/meal/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic import Blueprint\n\nmeal_blueprint = Blueprint(\n name='meal_blueprint',\n url_prefix='/meal',\n)\n\nfrom views.meal.meal import MealView\nmeal_blueprint.add_route(MealView.as_view(), '/<date>')\n" }, { "alpha_fraction": 0.8280922174453735, "alphanum_fraction": 0.8280922174453735, "avg_line_length": 38.75, "blob_id": "13f39716dc1f26e3d04a67777993d856a8279810", "content_id": "2ef9b9ef02a4d0b508aa69524ea8f2bac7264aa1", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 477, "license_type": "permissive", "max_line_length": 97, "num_lines": 12, "path": "/dms/models/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from extension import db\n\nfrom models.account import StudentModel, UnsignedStudentModel\nfrom models.apply import ExtensionApplyModel, GoingoutApplyModel, MusicApplyModel, StayApplyModel\nfrom models.notice import NoticeModel, RuleModel\nfrom models.point import PointItemModel, PointHistoryModel, PointStatusModel\nfrom models.report import FacilityReportModel\n\n\nasync def init_db():\n await db.set_bind('postgres://postgres@localhost/dms-sanic')\n await db.gino.create_all()\n" }, { "alpha_fraction": 0.6884328126907349, "alphanum_fraction": 0.6884328126907349, "avg_line_length": 29.339622497558594, "blob_id": "16401d83ddf342d59e153db18fb88210785f3db9", "content_id": "e2cd9c954a3532d94193b8c26eabede32192d76e", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1608, "license_type": "permissive", "max_line_length": 100, "num_lines": 53, "path": "/dms/models/apply.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from datetime import datetime\nfrom typing import Any\n\nfrom const import EXTENSION_CLASS, EXTENSION_TIME, GOINGOUT_STATUS, MUSIC_APPLY_WEEKDAY, STAY_STATUS\nfrom extension import db\nfrom utils import kst_now\n\n\nclass BaseApplyModel(db.Model):\n __abstract__ = True\n\n id: int = db.Column(db.Integer(), primary_key=True)\n student_id: str = db.Column(db.String, db.ForeignKey('student.username', ondelete='CASCADE'))\n apply_date = db.Column(db.DateTime(), default=kst_now)\n\n @classmethod\n async def query_by_id(cls, id: Any):\n return await cls.query.where(cls.id == id).gino.first()\n\n @classmethod\n async def query_by_student(cls, student_id: str):\n return await cls.query.where(cls.student_id == student_id).gino.first()\n\n\nclass ExtensionApplyModel(BaseApplyModel):\n __tablename__ = 'apply_extension'\n\n time: int = db.Column(db.Enum(EXTENSION_TIME))\n class_: int = db.Column(db.Enum(EXTENSION_CLASS))\n seat: int = db.Column(db.Integer())\n\n\nclass GoingoutApplyModel(BaseApplyModel):\n __tablename__ = 'apply_goingout'\n\n go_out_date: datetime = db.Column(db.DateTime())\n come_back_date: datetime = db.Column(db.DateTime())\n reason: str = db.Column(db.String())\n status: str = db.Column(db.Enum(GOINGOUT_STATUS))\n\n\nclass MusicApplyModel(BaseApplyModel):\n __tablename__ = 'apply_music'\n\n day: int = db.Column(db.Enum(MUSIC_APPLY_WEEKDAY))\n singer: str = db.Column(db.String())\n song_name: str = db.Column(db.String())\n\n\nclass StayApplyModel(BaseApplyModel):\n __tablename__ = 'apply_stay'\n\n status: int = db.Column(db.Enum(STAY_STATUS))\n" }, { "alpha_fraction": 0.6544821858406067, "alphanum_fraction": 0.6570931077003479, "avg_line_length": 25.720930099487305, "blob_id": "5a43454b27306755f34ba2d424db64ca9f1a2816", "content_id": "6f424624527187146eb3a114eb9c9dbbafc8b6c3", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1149, "license_type": "permissive", "max_line_length": 75, "num_lines": 43, "path": "/dms/models/account.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from uuid import uuid4\n\nfrom extension import db\n\n\nclass BaseStudent(db.Model):\n __abstract__ = True\n\n name: str = db.Column(db.String())\n number: int = db.Column(db.Integer(), unique=True)\n email: str = db.Column(db.String(), unique=True)\n\n @classmethod\n async def query_by_number(cls, number: int):\n return await cls.query.where(cls.number == number).gino.first()\n\n\nclass StudentModel(BaseStudent):\n __tablename__ = 'student'\n\n username: str = db.Column(db.String(), primary_key=True)\n password: str = db.Column(db.String())\n\n @classmethod\n async def query_by_username(cls, username: str):\n return await cls.query.where(cls.username == username).gino.first()\n\n\ndef generate_uuid():\n while True:\n uuid = str(uuid4())[:5]\n if not UnsignedStudentModel.query_by_uuid(uuid):\n return uuid\n\n\nclass UnsignedStudentModel(BaseStudent):\n __tablename__ = 'unsigned_student'\n\n uuid: str = db.Column(db.String(), default=generate_uuid, unique=True)\n\n @classmethod\n async def query_by_uuid(cls, uuid: str):\n return await cls.query.where(cls.uuid == uuid).gino.first()\n" }, { "alpha_fraction": 0.6456310749053955, "alphanum_fraction": 0.6456310749053955, "avg_line_length": 19.600000381469727, "blob_id": "a54a06b1000bdccf0fc543a47b772713c34ba19c", "content_id": "0e18fba334ea2bfda410814087f855d621cef680", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 206, "license_type": "permissive", "max_line_length": 38, "num_lines": 10, "path": "/dms/views/report/bug_report.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass BugReportView(HTTPMethodView):\n def post(self, request: Request):\n \"\"\"\n Report Bug\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.681034505367279, "alphanum_fraction": 0.681034505367279, "avg_line_length": 33.79999923706055, "blob_id": "e0b87728bb73e39a85e249fbb32d532af906a97d", "content_id": "3106eb8de6dc0652892f0fab0dbd6e90b8d86611", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 348, "license_type": "permissive", "max_line_length": 99, "num_lines": 10, "path": "/dms/models/report.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from extension import db\n\n\nclass FacilityReportModel(db.Model):\n __tablename__ = 'facility_report'\n\n id: int = db.Column(db.Integer(), primary_key=True)\n student_id: str = db.Column(db.String(), db.ForeignKey('student.username', ondelete='CASCADE'))\n room_number: int = db.Column(db.Integer())\n content: str = db.Column(db.String())\n" }, { "alpha_fraction": 0.5952380895614624, "alphanum_fraction": 0.5952380895614624, "avg_line_length": 20, "blob_id": "1bf5b92f5b3614b7dde079cf628368b24be79a92", "content_id": "83db326b6128024d947b39614a86d7a447238087", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 336, "license_type": "permissive", "max_line_length": 44, "num_lines": 16, "path": "/dms/views/account/auth.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass AuthView(HTTPMethodView):\n async def post(self, request: Request):\n \"\"\"\n Auth and Generate JWT Token\n \"\"\"\n pass\n\n async def patch(self, request: Request):\n \"\"\"\n Refresh Access Token\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.6551724076271057, "alphanum_fraction": 0.6551724076271057, "avg_line_length": 22.200000762939453, "blob_id": "9aa7af4837dcfdc7d758b1b3f4a09df517e82f03", "content_id": "eef0f0ebc1210f1fe090d6a960ad8e5376c60b61", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 232, "license_type": "permissive", "max_line_length": 47, "num_lines": 10, "path": "/dms/views/meal/meal.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass MealView(HTTPMethodView):\n def get(self, request: Request, date: str):\n \"\"\"\n Response meal on parameter date\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.5848214030265808, "alphanum_fraction": 0.5848214030265808, "avg_line_length": 21.399999618530273, "blob_id": "41588ed4dd87df9e5d0bc4271d7f5602d1081dd1", "content_id": "273ee086a01eaca07d931eef2add7a57aca16cbb", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 672, "license_type": "permissive", "max_line_length": 66, "num_lines": 30, "path": "/dms/views/apply/extension.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass ExtensionApplyView(HTTPMethodView):\n async def get(self, request: Request, time: int):\n \"\"\"\n Response Extension Position Which Applied\n \"\"\"\n pass\n\n async def post(self, request: Request, time: int):\n \"\"\"\n Apply Extension\n \"\"\"\n pass\n\n async def delete(self, request: Request, time: int):\n \"\"\"\n Delete Extension\n \"\"\"\n pass\n\n\nclass ExtensionMapView(HTTPMethodView):\n async def get(self, request: Request, time: int, class_: int):\n \"\"\"\n Response Extension Map\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.601123571395874, "alphanum_fraction": 0.601123571395874, "avg_line_length": 18.77777862548828, "blob_id": "16da2d091ff29f93c8fc66bfee2c4eb04bae1d69", "content_id": "585d3d4f44bd948f52b06be32ab06f358b37c9e4", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 356, "license_type": "permissive", "max_line_length": 50, "num_lines": 18, "path": "/dms/views/notice/rule.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass RuleListView(HTTPMethodView):\n def get(self, request: Request):\n \"\"\"\n Response Rule List\n \"\"\"\n pass\n\n\nclass RuleView(HTTPMethodView):\n def get(self, request: Request, rule_id: int):\n \"\"\"\n Response Rule\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.6521739363670349, "alphanum_fraction": 0.6521739363670349, "avg_line_length": 19.700000762939453, "blob_id": "5e982a5c24eb2e16f3b21faf3687a957ee661d08", "content_id": "52f084ae9654388a63e5dc507081ea6708fd73ed", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 207, "license_type": "permissive", "max_line_length": 50, "num_lines": 10, "path": "/dms/__main__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from asyncio import get_event_loop\n\nfrom app import create_app\nfrom models import init_db\n\nif __name__ == '__main__':\n get_event_loop().run_until_complete(init_db())\n app = create_app()\n\n app.run()\n" }, { "alpha_fraction": 0.7222222089767456, "alphanum_fraction": 0.7314814925193787, "avg_line_length": 20.600000381469727, "blob_id": "7ce6f77e0317d7c1aa448710f29244281fd45a18", "content_id": "58c32dabc844eb9adb0d32e01a1b861f9a70b84e", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 108, "license_type": "permissive", "max_line_length": 49, "num_lines": 5, "path": "/dms/utils.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from datetime import datetime, timedelta\n\n\ndef kst_now():\n return datetime.utcnow() + timedelta(hours=9)\n" }, { "alpha_fraction": 0.5764331221580505, "alphanum_fraction": 0.5764331221580505, "avg_line_length": 18.625, "blob_id": "acca34f0785f251650c81a26f263a2620fdfdceb", "content_id": "3e81ac7c2efb649e9afafe69ac114a51f162fc39", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 314, "license_type": "permissive", "max_line_length": 43, "num_lines": 16, "path": "/dms/views/account/signup.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass SignupView(HTTPMethodView):\n async def get(self, request: Request):\n \"\"\"\n Check Is UUID valid\n \"\"\"\n pass\n\n async def post(self, request: Request):\n \"\"\"\n Signup\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.7559999823570251, "alphanum_fraction": 0.7559999823570251, "avg_line_length": 34.71428680419922, "blob_id": "082842f7eaa8e90502d3d16f783d3c374aed44b9", "content_id": "daa13c509809669d6be88e05f2940430af026a3f", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 500, "license_type": "permissive", "max_line_length": 75, "num_lines": 14, "path": "/dms/views/notice/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic import Blueprint\n\nnotice_blueprint = Blueprint(\n name='notice_blueprint',\n url_prefix='/notice',\n)\n\nfrom views.notice.notice import NoticeListView, NoticeView\nnotice_blueprint.add_route(NoticeListView.as_view(), '/notice')\nnotice_blueprint.add_route(NoticeView.as_view(), '/notice/<notice_id:int>')\n\nfrom views.notice.rule import RuleListView, RuleView\nnotice_blueprint.add_route(RuleListView.as_view(), '/rule')\nnotice_blueprint.add_route(RuleView.as_view(), '/rule/<rule_id:int>')\n" }, { "alpha_fraction": 0.48032787442207336, "alphanum_fraction": 0.5409836173057556, "avg_line_length": 13.186046600341797, "blob_id": "a2b29ea6caaa5c925dffe6f5f29eb19d24be6261", "content_id": "5b0b4753f57f9b09d68a74e8ad7218b8ff8ce898", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 610, "license_type": "permissive", "max_line_length": 32, "num_lines": 43, "path": "/dms/const.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from enum import Enum\n\n\nclass EXTENSION_TIME(Enum):\n ELEVEN = 11\n TWELVE = 12\n\n\nclass EXTENSION_CLASS(Enum):\n GA = 0\n NA = 1\n DA = 2\n RA = 3\n FLOOR_2 = 4\n FLOOR_3_SCHOOL_SIDE = 5\n FLOOR_3_DORMITORY_SIDE = 6\n FLOOR_4_SCHOOL_SIDE = 7\n FLOOR_4_DORMITORY_SIDE = 8\n FLOOR_5 = 9\n FLOOR_3_WATER = 10\n\n\nclass GOINGOUT_STATUS(Enum):\n BEFORE = 0\n ING = 1\n AFTER = 2\n\n\nclass MUSIC_APPLY_WEEKDAY(Enum):\n MON = 0\n TUE = 1\n WED = 2\n THU = 3\n FRI = 4\n SAT = 5\n SUN = 6\n\n\nclass STAY_STATUS(Enum):\n FRI_OUT = 0\n SAT_OUT = 1\n SAT_IN = 2\n STAY = 3\n" }, { "alpha_fraction": 0.7765362858772278, "alphanum_fraction": 0.7765362858772278, "avg_line_length": 28.83333396911621, "blob_id": "544436b2f531a93a4aa803c633910e2b1e3c03b6", "content_id": "eee518bc65440328d66fe11515cd5ad879cd59c6", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 358, "license_type": "permissive", "max_line_length": 69, "num_lines": 12, "path": "/dms/views/report/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic import Blueprint\n\nreport_blueprint = Blueprint(\n name='report_blueprint',\n url_prefix='/report',\n)\n\nfrom views.report.bug_report import BugReportView\nreport_blueprint.add_route(BugReportView.as_view(), '/bug')\n\nfrom views.report.facility_report import FacilityReportView\nreport_blueprint.add_route(FacilityReportView.as_view(), '/facility')\n" }, { "alpha_fraction": 0.6120218634605408, "alphanum_fraction": 0.6120218634605408, "avg_line_length": 19.33333396911621, "blob_id": "9d96e408c4244ea27363e64d8d39f1d4e125616c", "content_id": "188388f4524f2861b5a5f2d5196ac1c34a8a76ca", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 366, "license_type": "permissive", "max_line_length": 52, "num_lines": 18, "path": "/dms/views/notice/notice.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass NoticeListView(HTTPMethodView):\n def get(self, request: Request):\n \"\"\"\n Response Notice List\n \"\"\"\n pass\n\n\nclass NoticeView(HTTPMethodView):\n def get(self, request: Request, notice_id: int):\n \"\"\"\n Response Notice\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.7858508825302124, "alphanum_fraction": 0.7858508825302124, "avg_line_length": 23.904762268066406, "blob_id": "0a70f4b3c749a404ce710b2459d3524e4699a476", "content_id": "61c65cb9aedec0f5cbfc3012abcc7c9d9bf32a1f", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 523, "license_type": "permissive", "max_line_length": 43, "num_lines": 21, "path": "/dms/views/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic import response\nfrom sanic.blueprints import Blueprint\nfrom sanic.request import Request\n\nfrom views.account import account_blueprint\nfrom views.apply import apply_blueprint\nfrom views.meal import meal_blueprint\nfrom views.notice import notice_blueprint\nfrom views.report import report_blueprint\n\nblueprint_group = Blueprint.group(\n account_blueprint,\n apply_blueprint,\n meal_blueprint,\n notice_blueprint,\n report_blueprint,\n)\n\n\nasync def ping(request: Request):\n return response.text('pong!')\n" }, { "alpha_fraction": 0.760869562625885, "alphanum_fraction": 0.760869562625885, "avg_line_length": 28.272727966308594, "blob_id": "edaa2411e2c41f88d7bbc3e532c19ee79e8c8f7f", "content_id": "7efa65f015e92b6db5356f17c532784204e1ed02", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 322, "license_type": "permissive", "max_line_length": 60, "num_lines": 11, "path": "/dms/views/account/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic import Blueprint\n\naccount_blueprint = Blueprint(\n name='account_blueprint',\n url_prefix='/account',\n)\n\nfrom views.account.auth import AuthView\nfrom views.account.signup import SignupView\naccount_blueprint.add_route(AuthView.as_view(), '/auth')\naccount_blueprint.add_route(SignupView.as_view(), '/signup')\n" }, { "alpha_fraction": 0.68544602394104, "alphanum_fraction": 0.68544602394104, "avg_line_length": 19.285715103149414, "blob_id": "435a8037d87b673b9f691b8e011a14abb6f83e67", "content_id": "0484bc7c8f595ff09afde55a5d1d128c31b987e9", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 426, "license_type": "permissive", "max_line_length": 67, "num_lines": 21, "path": "/dms/models/notice.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from datetime import datetime\n\nfrom extension import db\nfrom utils import kst_now\n\n\nclass BaseNoticeModel(db.Model):\n __abstract__ = True\n\n id: int = db.Column(db.Integer(), primary_key=True)\n post_date: datetime = db.Column(db.DateTime(), default=kst_now)\n title: str = db.Column(db.String())\n content: str = db.Column(db.String())\n\n\nclass NoticeModel(db.Model):\n pass\n\n\nclass RuleModel(db.Model):\n pass\n" }, { "alpha_fraction": 0.6569037437438965, "alphanum_fraction": 0.6569037437438965, "avg_line_length": 17.384614944458008, "blob_id": "0a50120de09e41534bde60868d814dba718bf428", "content_id": "ce5402a2f2b791cf228911612b2798be2d7e2cd3", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 239, "license_type": "permissive", "max_line_length": 50, "num_lines": 13, "path": "/dms/app.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic import Sanic\n\nfrom views import blueprint_group\n\n\ndef create_app() -> Sanic:\n app_ = Sanic()\n\n from views import ping\n app_.add_route(ping, '/ping', methods=['GET'])\n app_.blueprint(blueprint_group)\n\n return app_\n" }, { "alpha_fraction": 0.5837104320526123, "alphanum_fraction": 0.5837104320526123, "avg_line_length": 20.047618865966797, "blob_id": "a9c435b71e061bb242774f13f193907c0837567e", "content_id": "d1eceaa1670102ba202759cca6b91880c4612722", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 442, "license_type": "permissive", "max_line_length": 46, "num_lines": 21, "path": "/dms/views/apply/goingout.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass GoingoutApplyView(HTTPMethodView):\n async def get(self, request: Request):\n \"\"\"\n Response Goingout Apply Status\n \"\"\"\n pass\n\n async def post(self, request: Request):\n \"\"\"\n Apply Goingout\n \"\"\"\n pass\n\n async def delete(self, reqeuest: Request):\n \"\"\"\n Delete Goingout Apply\n \"\"\"\n" } ]	27
zM4loy/mp3-to-wav	https://github.com/zM4loy/mp3-to-wav	371cbb6ce672a9f6f7633282203168d34ac16f24	94cdbb3e1516dce3e8acb64729361fb4305a3dd8	4cb806a419b66b047f805b8a3cfd20cf559d41ad	refs/heads/main	2023-08-17T14:31:35.224671	2021-09-29T22:57:37	2021-09-29T22:57:37	411,852,446	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7388535141944885, "alphanum_fraction": 0.7643312215805054, "avg_line_length": 32.105262756347656, "blob_id": "fb501f10fdb5c783d71eda46880d4da6a053b73c", "content_id": "a72524c5939e3207e2ae687e05e1d881ad95e16b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 637, "license_type": "no_license", "max_line_length": 66, "num_lines": 19, "path": "/mp3-to-wav.py", "repo_name": "zM4loy/mp3-to-wav", "src_encoding": "UTF-8", "text": "#import da lib pydub para manipular áudios\nfrom pydub import AudioSegment\n\n#definindo a função que converte o mp3 para wav\n#a função recebe como parâmetro o path do mp3\n#o path do output wav\n#e o volume em decibéis\ndef convert(path_mp3, path_wav, volume):\n #criando variavel audio que recebe o arquivo mp3\n audio = AudioSegment.from_mp3(path_mp3)\n\n #definindo a taxa de amostragem para 16000\n audio = audio.set_frame_rate(16000)\n\n #definindo o volume a partir do número de decibéis\n audio = audio.apply_gain(volume)\n\n #exportando o arquivo convertido em wav para o path especificado\n audio.export(path_wav, format=\"wav\")" }, { "alpha_fraction": 0.7777777910232544, "alphanum_fraction": 0.7925925850868225, "avg_line_length": 66.5, "blob_id": "2ae3bc6e370f559c5d91c1d9452feb03fa1b655d", "content_id": "80947c007e11e43325e07a155769290c2df63852", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 135, "license_type": "no_license", "max_line_length": 121, "num_lines": 2, "path": "/README.md", "repo_name": "zM4loy/mp3-to-wav", "src_encoding": "UTF-8", "text": "# mp3-to-wav\nA music format converter from mp3 to wav, where various music characteristics such as volume and bit rate can be changed.\n" } ]	2
laohixdxm/jan30	https://github.com/laohixdxm/jan30	da20a7ca6e5de112aa92bce995cb839570c5ca62	c53c55900cb55380ab5e8cfb31430270a04be629	df5e03735d771a881ef447ab9e40c139f887b86d	refs/heads/master	2020-04-23T23:28:30.506282	2014-02-01T18:27:03	2014-02-01T18:27:03	null	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.703180193901062, "alphanum_fraction": 0.7208480834960938, "avg_line_length": 34.375, "blob_id": "88dc1eff5410be661369bc472f3b061a9dc9786e", "content_id": "cd41e5c505dba094ed1d4b0c0c3f9c12de19deaa", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 283, "license_type": "no_license", "max_line_length": 120, "num_lines": 8, "path": "/gmail_draft.py", "repo_name": "laohixdxm/jan30", "src_encoding": "UTF-8", "text": "import imaplib\nimport time\nimport email\n\nconn = imaplib.IMAP4_SSL('imap.gmail.com', port = 993)\nconn.login('[email protected]', 'abcd86237232')\nconn.select('[Gmail]/Drafts')\nconn.append(\"[Gmail]/Drafts\", 'subject', imaplib.Time2Internaldate(time.time()), str(email.message_from_string('TEST')))\n" }, { "alpha_fraction": 0.6132075190544128, "alphanum_fraction": 0.6179245114326477, "avg_line_length": 18.272727966308594, "blob_id": "318726fa60bb6ff0fa15fc58673c5c64d722784d", "content_id": "85cd9b2637f2ef286c465b2586a7c4cff119ac0f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 212, "license_type": "no_license", "max_line_length": 49, "num_lines": 11, "path": "/cpp_list.py", "repo_name": "laohixdxm/jan30", "src_encoding": "UTF-8", "text": "import sys\n\nfp = open(\"/home/min/tmp/git_info/cpp_list\", \"r\")\nfor line in fp.readlines():\n#\tline.strip()\n#\tprint \"hello %s\" % line\n\tsys.stdout.write(\"%s\" % line)\nfp.close()\n\n#line = \"hello1\\n\"\n#print \"%s\" % line\n" }, { "alpha_fraction": 0.6967792510986328, "alphanum_fraction": 0.6999214291572571, "avg_line_length": 25.33333396911621, "blob_id": "f0ecd19c65d5c34b55b9f06a5a4da943212f8075", "content_id": "8b3044eed990f8be1bc1d18aa64918f2fe373fa3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1273, "license_type": "no_license", "max_line_length": 68, "num_lines": 48, "path": "/gmail_sender.py", "repo_name": "laohixdxm/jan30", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\nfrom smtplib import SMTP\nfrom smtplib import SMTPException\nfrom email.mime.text import MIMEText\nimport sys\n\n#Global variables\nEMAIL_SUBJECT = \"Email from Python script\"\nEMAIL_RECEIVERS = ['[email protected]']\nEMAIL_SENDER = '[email protected]'\nGMAIL_SMTP = \"smtp.gmail.com\"\nGMAIL_SMTP_PORT = 587\nTEXT_SUBTYPE = \"plain\"\n\ndef listToStr(lst):\n\t\"\"\"This method makes comma seperated list item string\"\"\"\n\treturn ','.join(lst)\n\ndef send_email(content, pswd):\n\t\"\"\"This method sends an email\"\"\"\n\n\t#Create the message\n\tmsg = MIMEText(content, TEXT_SUBTYPE)\n\tmsg[\"Subject\"] = EMAIL_SUBJECT\n\tmsg[\"From\"] = EMAIL_SENDER\n\tmsg[\"To\"] = listToStr(EMAIL_RECEIVERS)\n\n\ttry:\n\t smtpObj = SMTP(GMAIL_SMTP, GMAIL_SMTP_PORT)\n\t #Identify yourself to GMAIL EMSMTP server.\n \t smtpObj.ehlo()\n\t #Put SMTP connection in TLS mode all ehlo again\n\t smtpObj.starttls()\n\t smtpObj.ehlo()\n \t #Login to service\n\t smtpObj.login(user=EMAIL_SENDER, password=pswd)\n\t #send email\n \t smtpObj.sendmail(EMAIL_SENDER, EMAIL_RECEIVERS, msg.as_string())\n\t #close connection and session\n\t smtpObj.quit()\n\texcept SMTPException as error:\n\t print \"Errro: unable to send email : {err}\".format(err=error)\n\t\ndef main(pswd):\n\tsend_email(\"hello\", pswd)\n\nif __name__ == \"__main__\":\n\tmain(sys.argv[1])\n\t\n\t \n\n\n\n" } ]	3
sashadixie/ultimate-trash	https://github.com/sashadixie/ultimate-trash	a488d7a41065965088d87695df48cb407f66780c	82cc7eb0fbacdbc1cc807cd91778dce513aa4da7	5b432c60165dafaadbfd64f23c49c199d675bd07	refs/heads/main	2023-07-02T00:33:30.672853	2021-08-11T18:22:14	2021-08-11T18:22:14	395,080,797	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6614906787872314, "alphanum_fraction": 0.6677018404006958, "avg_line_length": 34.77777862548828, "blob_id": "dedb71bc00c65a64bce73ccfcc0689e61d5fcf32", "content_id": "e4e78a5f9d4dda08e0951593fe3a75fb08faa26d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 322, "license_type": "no_license", "max_line_length": 112, "num_lines": 9, "path": "/test.py", "repo_name": "sashadixie/ultimate-trash", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport os\n\ndf = pd.read_excel(\"empty.xlsx\", index_col=None, na_values=['NA'], usecols=None, names=None, header=None).values\nfor idx, val in enumerate(df):\n filetochange = str(idx + 1) + \".png\"\n newname = val[0] + \".png\"\n if os.path.exists(filetochange):\n os.rename(filetochange, newname)\n" }, { "alpha_fraction": 0.6795580387115479, "alphanum_fraction": 0.6850828528404236, "avg_line_length": 37.85714340209961, "blob_id": "5445de3c4e53abcc917e0727d0a7abddeb455a32", "content_id": "71a770678eb240ed0fdf5d8dfc7a62f1c7b12f11", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 543, "license_type": "no_license", "max_line_length": 92, "num_lines": 14, "path": "/js/index.js", "repo_name": "sashadixie/ultimate-trash", "src_encoding": "UTF-8", "text": "const XLSX = require('xlsx');\nconst fs = require('fs')\nconst workbook = XLSX.readFile('empty.xlsx');\nconst sheet_name_list = workbook.SheetNames;\nconst worksheet = workbook.Sheets[workbook.SheetNames[0]];\nconst columnA = Object.keys(worksheet).filter(x => /^A\\d+/.test(x)).map(x => worksheet[x].v)\nconsole.log(columnA)\ncolumnA.forEach((element, idx) => {\n const path = `${idx + 1}.png`\n if (fs.existsSync(path)) {\n fs.renameSync(path, `${element}.png`)\n }\n});\n// console.log(XLSX.utils.sheet_to_json(workbook.Sheets[sheet_name_list[0]]))" } ]	2
nyibelunger/helloapp	https://github.com/nyibelunger/helloapp	2acd3757430e0372285c776e3dfa5aff026b9b81	3532bf71655f96e872e3ad269238b13a7fa6ec67	62240174e208d10ab54eede59364ccac507e3cc4	refs/heads/master	2017-12-05T02:23:14.252615	2017-01-31T15:07:56	2017-01-31T15:07:56	80,531,192	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6764934062957764, "alphanum_fraction": 0.687354564666748, "avg_line_length": 39.3125, "blob_id": "abf91f1a6bd4d8020677292632d622f6343335a3", "content_id": "49fda9dcba6a3d7abd5d913512a1920c40055c8d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1306, "license_type": "no_license", "max_line_length": 85, "num_lines": 32, "path": "/models.py", "repo_name": "nyibelunger/helloapp", "src_encoding": "UTF-8", "text": "from django.db import models\nfrom django.utils.encoding import smart_text as smart_unicode\nfrom datetime import datetime\n\nclass Pacient(models.Model):\n ##Základní údaje\n frst_name = models.CharField(max_length=256,verbose_name=\"Jméno\")\n scnd_name = models.CharField(max_length=256)\n email = models.EmailField(max_length=120, default='')\n #Datum přidání do databáze\n timestamp = models.DateTimeField(auto_now_add=True, auto_now=False, null=True)\n #Datum poslední úpravy\n updated = models.DateTimeField(auto_now_add=False, auto_now=True, null=True)\n is_healthy = models.BooleanField(default=True,verbose_name=\"Je plně zdráv.\")\n date_born = models.DateTimeField(auto_now_add=True, blank=True, null=True)\n\n CHOICES_POHLAVI = (\n (\"1\" , 'Muž'),\n (\"2\", 'Žena'),\n (None , 'Nevybráno'),\n )\n pohlavi = models.CharField(max_length=120, choices=CHOICES_POHLAVI, default=None)\n\n def __str__(self):\n return smart_unicode(\"Pacient: %s %s\" %(self.frst_name, self.scnd_name))\n\nclass RodAnam(models.Model):\n pacient = models.ForeignKey(Pacient, on_delete=models.CASCADE, default=None)\n ma_ra = models.BooleanField(default=False, verbose_name=\"Má RA:\")\n\n def __str__(self):\n return smart_unicode(\"%s má RA\" % self.pacient)" }, { "alpha_fraction": 0.6721527576446533, "alphanum_fraction": 0.6774193644523621, "avg_line_length": 32.043479919433594, "blob_id": "fa0e841776a2b87af809ebd1e041c521ea0f3a9d", "content_id": "db69a38d43aab37a727c30697013ae78751cc301", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1521, "license_type": "no_license", "max_line_length": 112, "num_lines": 46, "path": "/views.py", "repo_name": "nyibelunger/helloapp", "src_encoding": "UTF-8", "text": "from django.shortcuts import render, render_to_response, RequestContext, loader, HttpResponse, get_object_or_404\nfrom django.views.generic import TemplateView\nfrom .forms import PacientForm\nfrom.models import Pacient\n\n\nclass HomePageView(TemplateView):\n #aka pacient_list\n def get(self, request,*kwargs):\n return render(request, 'howdy/index.html', context=None)\n\n\nclass AboutPageView(TemplateView):\n template_name = 'howdy/about.html'\n\n#class PacientPageView(TemplateView):\n# form = PacientForm\n# template_name = 'howdy/pacient.html'\n\n#def PacientPageView(request):\n# form = PacientForm()\n# return render(request, 'howdy/pacient.html', {'form': form})\n\ndef pacient_new(request):\n form = PacientForm(request.POST or None)\n if form.is_valid():\n save_it = form.save(commit=False)\n save_it.save()\n\n return render_to_response(\"howdy/pacient.html\",\n locals(),\n context_instance=RequestContext(request))\n\ndef pacient_list(request):\n pacient_all = Pacient.objects.all()\n template = loader.get_template('howdy/index.html')\n context = {\n 'pacient_all': pacient_all,\n }\n return HttpResponse(template.render(context, request))\n\ndef pacient_detail(request, pacient_id):\n pacient = get_object_or_404(Pacient, pk = pacient_id)\n context = {'pacient': pacient}\n return render(request, 'howdy/pacient_detail.html', context)\n #return HttpResponse(\"<h3>Pacient číslo:\"+ pacient_id + \"</h3>\")" }, { "alpha_fraction": 0.5588055849075317, "alphanum_fraction": 0.5770871639251709, "avg_line_length": 41.07692337036133, "blob_id": "336262c42675cba52c210f71994230b7f81accdd", "content_id": "88e1a0a98a8410d27f989069727c49fd98f264f0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1647, "license_type": "no_license", "max_line_length": 136, "num_lines": 39, "path": "/migrations/0001_initial.py", "repo_name": "nyibelunger/helloapp", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 -*-\n# Generated by Django 1.9.5 on 2017-01-30 18:06\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Pacient',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('frst_name', models.CharField(max_length=256)),\n ('scnd_name', models.CharField(max_length=256)),\n ('email', models.EmailField(default='', max_length=120)),\n ('timestamp', models.DateTimeField(auto_now_add=True, null=True)),\n ('updated', models.DateTimeField(auto_now=True, null=True)),\n ('is_healthy', models.BooleanField(default=True, verbose_name='Je plně zdráv.')),\n ('date_born', models.DateTimeField(auto_now_add=True, null=True)),\n ('pohlavi', models.CharField(choices=[('1', 'Muž'), ('2', 'Žena'), (None, 'Nevybráno')], default=None, max_length=120)),\n ],\n ),\n migrations.CreateModel(\n name='RodAnam',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('ma_ra', models.BooleanField(default=False, verbose_name='Má RA:')),\n ('pacient', models.ForeignKey(default=None, on_delete=django.db.models.deletion.CASCADE, to='howdy.Pacient')),\n ],\n ),\n ]\n" }, { "alpha_fraction": 0.6354343891143799, "alphanum_fraction": 0.6439523100852966, "avg_line_length": 31.66666603088379, "blob_id": "986f88079161d11cc07a5894b0373a4d0d97ee4d", "content_id": "8699284fdb754f4fb26aadd3cf75e08b4c7718c5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 587, "license_type": "no_license", "max_line_length": 84, "num_lines": 18, "path": "/urls.py", "repo_name": "nyibelunger/helloapp", "src_encoding": "UTF-8", "text": "from howdy import views\nfrom django.conf.urls import url, patterns, include\n\napp_name = 'howdy'\n\nurlpatterns = [\n #url(r'^$', views.HomePageView.as_view()),\n url(r'^$', views.pacient_list, name='pacient_list'),\n url(r'^/about/$', views.AboutPageView.as_view(), name= 'about_me'),\n\n # pacient_list/new_pacient\n url(r'^/pacient_new/$', views.pacient_new, name='pacient_new'),\n #url(r'^pacient/$', views.PacientPageView.as_view(), name= 'pacient'),\n\n #pacient_list/pacient_new/123/\n url(r'^/(?P<pacient_id>[0-9]+)/$', views.pacient_detail, name='pacient_detail'),\n\n]" }, { "alpha_fraction": 0.7102272510528564, "alphanum_fraction": 0.7113636136054993, "avg_line_length": 35.70833206176758, "blob_id": "50be420ae0bb34437e3237688489c283fd7dbe93", "content_id": "001782814b95770619c401c451727b141f69c1eb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 880, "license_type": "no_license", "max_line_length": 123, "num_lines": 24, "path": "/forms.py", "repo_name": "nyibelunger/helloapp", "src_encoding": "UTF-8", "text": "from django.forms import ModelForm, RadioSelect\nfrom howdy.models import Pacient, RodAnam\nfrom django import forms\nfrom django.contrib.admin import widgets\nfrom django.forms import DateTimeField, DateTimeInput\n\n\nclass PacientForm(ModelForm):\n type = forms.ChoiceField(choices=Pacient.CHOICES_POHLAVI, widget=RadioSelect)\n type2 = forms.MultipleChoiceField(required=False, widget=forms.CheckboxSelectMultiple, choices=Pacient.CHOICES_POHLAVI)\n event_date = forms.DateTimeField(widget=forms.widgets.DateTimeInput())\n\n class Meta:\n model = Pacient\n fields = ['frst_name','scnd_name', 'email','pohlavi','is_healthy']\n\n def __init__(self):\n super(PacientForm, self).__init__()\n self.fields['date_born'].widget = widgets.AdminDateWidget()\n\nclass RaForm(ModelForm):\n class Meta:\n model = RodAnam\n fields = ['ma_ra']" }, { "alpha_fraction": 0.7777777910232544, "alphanum_fraction": 0.7777777910232544, "avg_line_length": 23.08333396911621, "blob_id": "fbb0e65e161ed0ff2a86dc724347da50f3aeedec", "content_id": "12c81e51e69a07f9d5d16b3edf84dcaedfcc947b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 289, "license_type": "no_license", "max_line_length": 42, "num_lines": 12, "path": "/admin.py", "repo_name": "nyibelunger/helloapp", "src_encoding": "UTF-8", "text": "from django.contrib import admin\n\n# Register your models here.\nfrom .models import Pacient\nfrom howdy.forms import PacientForm\n\nclass PacientAdmin(admin.ModelAdmin):\n class Meta:\n model = Pacient\n\nadmin.site.register(Pacient, PacientAdmin)\n#třeba registrovat RA, ale DO pacienta" } ]	6
marquavious/pinterest_challenge	https://github.com/marquavious/pinterest_challenge	c39b7b276ac473abeefe4f42ca6d11fe413a6cca	b111150a4bf45a7f500ef9e156f38f6900195b8d	0877e48dcb5e882791b6e5133d2222cb7680c6a5	refs/heads/master	2021-03-24T11:55:43.618534	2017-10-06T23:16:24	2017-10-06T23:16:24	105,960,741	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6424526572227478, "alphanum_fraction": 0.6516305208206177, "avg_line_length": 35.57857131958008, "blob_id": "4e412d3536b2af134824224d59735dda77b6eac3", "content_id": "fb3d116f89dd0a40199af712307dbf059f409e21", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5121, "license_type": "no_license", "max_line_length": 145, "num_lines": 140, "path": "/app.py", "repo_name": "marquavious/pinterest_challenge", "src_encoding": "UTF-8", "text": "from flask import Flask,request\nimport time\nimport datetime\n\napp = Flask(__name__)\n\n# Home page\[email protected]('/')\ndef home():\n return \"\"\" Hello, Pinterest! All the avalible routes are here:\\n\n /hello\\n\n /date\\n\n /compute\\n\\n\n Here are preformatted urls based off the challenge\\n\n /hello?firstname=tien&lastname=nguyen&gender=m\\n\n /compute?num1=5&num2=3&operation=subtract\\n\n /date\\n\\n\n Thank you for sending me this challenge. Enjoy!!\n \"\"\"\n\n# /hello route, GET\[email protected]('/hello', methods = ['GET'])\ndef hello():\n\n # Grabs parameters from the URL\n firstName = request.args.get('firstname')\n lastName = request.args.get('lastname')\n gender = request.args.get('gender')\n\n # Checks to make sure all parameters are filled out, if not return error\n if urlHasAllParameters([firstName,lastName,gender]) is False:\n return \"Please make sure the url is correctly formatted and all parameters are filled.\"\n\n # Created this check because there have been errors\n try:\n # If this goes through sucessfuly, the variables will be converted\n firstName = str(firstName)\n lastName = str(lastName)\n gender = str(gender)\n # If not, return error\n except AttributeError:\n return \"Unable to process greeting. Please check the format of the url.\"\n\n # Trim the strings and give them proper formatting\n firstName = firstName.replace(\" \", \"\").title()\n lastName = lastName.replace(\" \", \"\").title()\n gender = gender.replace(\" \", \"\").lower()\n\n # Length check to make sure the user enterd the correct things even after the formatting\n if len(firstName) <= 0 or len(lastName) <= 0 or len(gender) <= 0:\n return \"Please make sure the url is correctly formatted and all parameters are filled.\"\n\n # Returns the correct prefix based on the gender parameter\n # Check the 'returnGenderPrefix' fucntion\n prefix = returnGenderPrefix(gender)\n\n # Formats the above variables and returns\n return \"Hello {}{} {}!\".format(prefix,firstName,lastName)\n\n# /compute route, GET\[email protected]('/compute', methods = ['GET'])\ndef compute():\n\n # Grabs parameters from the URL\n num1 = request.args.get('num1')\n num2 = request.args.get('num2')\n operation = request.args.get('operation')\n\n # Checks to make sure all parameters are filled out if not, return error\n if urlHasAllParameters([num1,num2,operation]) is False:\n return \"Please make sure the url is correctly formatted and all parameters are filled.\"\n\n # Trims the strings\n num1 = num1.replace(\" \", \"\")\n num2 = num2.replace(\" \", \"\")\n operation = operation.replace(\" \", \"\")\n\n # Error check that the variables can be converted\n try:\n # If this goes through sucessfuly, the variables will be converted\n num1 = float(num1)\n num2 = float(num2)\n # If not, return error\n except ValueError:\n return \"Recieved an uncomputable value. Please make sure the url is correctly formatted and all parameters are filled.\"\n\n # Here we create the result if the 'computeValues'function and cast it to a string.\n computedValue = str(computeValues(num1,num2,operation))\n\n # Here we return the result if the 'computeValues'function in a nice to read format.\n return \"Here is the computed value result: {}\".format(computedValue)\n\n# /date route, GET\[email protected]('/date', methods = ['GET'])\ndef date():\n\n # Use datetime to retrive the current date and then format it\n currentDate = datetime.datetime.now().strftime(\"%Y-%m-%d\")\n\n # Return date\n return \"Current date: {}\".format(currentDate)\n\n# This function checks to make sure all parameters are filled out\ndef urlHasAllParameters(parameters):\n for param in parameters:\n if param == None :\n return False\n return True\n\n# This fucntion takes in a string and returns a name prefix\ndef returnGenderPrefix(gender):\n if gender == \"m\" or gender == \"male\":\n return \"Mr.\"\n elif gender == \"f\" or gender == \"female\":\n return \"Mrs.\"\n elif gender == \"n\" or gender == \"none\":\n return \"non-binary human named \"\n else:\n return \"human being named \"\n\n# This function takes in two integers and a operation string then returns the result\ndef computeValues(num1,num2,operation):\n if operation == \"add\":\n return round(num1 + num2, 2)\n elif operation == \"subtract\" or operation == \"-\":\n return round(num1 - num2, 2)\n elif operation == \"multiply\" or operation == \"\":\n return round(num1 num2, 2)\n elif operation == \"divide\" or operation == \"/\":\n # Edge case check, no division my 0 is allowed\n # The answer would be the first number\n if num2 == 0:\n return num1\n # Else return the result\n return round(num1 / num2, 2)\n # Edge case check\n return \"ERROR. Your number could not be computed. Please check the URL is in this format:\\n /hello?num1=INT&num2=INT&operation=OPERATION\"\n\nif __name__ == '__main__':\n app.run(port=5000, debug=True)\n" }, { "alpha_fraction": 0.7188577055931091, "alphanum_fraction": 0.7341211438179016, "avg_line_length": 53.89189147949219, "blob_id": "67bc07262d6507b1d8540486e08267c0a17d9cc3", "content_id": "771f1635872db459d8f8ef1709e694b4b92bfe55", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2079, "license_type": "no_license", "max_line_length": 213, "num_lines": 37, "path": "/readme.md", "repo_name": "marquavious/pinterest_challenge", "src_encoding": "UTF-8", "text": "![N\|jjkn](https://business.pinterest.com/profiles/contrib/pin/badge-wordmark.svg)\n# Pinterest take-home assignment\n\n#### Thank you so much for giving me this fun challenge! It challenged my googling skills and the ability to utilize my resources. I very much enjoyed this assignment!\n# Steps to get it running\n ##### 1. Unzip and navigate to the folder in the terminal\n ##### 1. If you don't have Flask installed, while in the folder run ```pip3.5 install flask```\n ##### 3. Once that's installed, run the application with ```python3.5 app.py```\n ##### 4. On the home screen, you'll see the available options that will allow you to use and test out the application\n\n### Now, if you don't feel like doing all the above, I hosted it on a server for you! :D\n### Here is the link: [https://pintrest-challenge.herokuapp.com/](https://pinterest-challenge.herokuapp.com/)\n\n\n# Challenge\n\n```\nBuild a simple REST-based web server in Scala or Python that supports the following features:\n#1\nRespond to requests of the form “/hello?firstname={first name}&lastname={last name}&gender={m/f}” and respond with “Hello Mr {First Name} {Last Name}” or “Hello Ms {First Name} {Last Name}” depending on the gender\nExample: the request “/hello?firstname=tien&lastname=nguyen&gender=m” returns “Hello Mr Tien Nguyen”\n#3\nRespond to requests of the form “/compute?num1={num1}&num2={num2}&operator={add/subtract/multiply/divide}” and respond with the result\nExample: the request “/hello?num1=5&num2=3&operation=subtract” returns “2” (5-3=2)\n#4\nRespond to requests of the form “/date” with the current date in the form “yyyy-mm-dd”\nExample: “/date” returns “2017-09-20”\n```\n\n# Edge cases considered\n - Bad Url formatting\n - Missing parameters\n - If user is non-binary\n \n# Assumptions\n- All numbers are accepted, not just integers. \n- In the challenge description, the URL for computing is stated with the `/compute` path, however, the example given uses the `/hello` path. I just went with the `/compute` pathway as this makes for a cleaner API.\n" } ]	2
nnodyy/obrazky	https://github.com/nnodyy/obrazky	97714adebe6b73155ee8fd45e44c3acec4dee65a	7a9bcd481f929ade23fed4f161535994af917e79	84e8a0f8408172ce4ad3061c618caafd0fd39b02	refs/heads/master	2021-01-24T00:04:23.698564	2018-02-25T18:03:10	2018-02-25T18:03:10	122,752,615	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7777777910232544, "alphanum_fraction": 0.7777777910232544, "avg_line_length": 12, "blob_id": "8f43ed1dc01b64fe025175bc00f3eaebfa6ce043", "content_id": "8ad5c93bae317f403dbd698395046163fcec94da", "detected_licenses": [ "Apache-2.0" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 27, "license_type": "permissive", "max_line_length": 15, "num_lines": 2, "path": "/README.md", "repo_name": "nnodyy/obrazky", "src_encoding": "UTF-8", "text": "# obrazky\nignore pictures \n" }, { "alpha_fraction": 0.6477987170219421, "alphanum_fraction": 0.7106918096542358, "avg_line_length": 15.666666984558105, "blob_id": "1ff2d017d1db912c549d36bf9ce8c1969983ce4c", "content_id": "7d96b0976d46dcf17751835d17ce1a5bf4a8cc37", "detected_licenses": [ "Apache-2.0" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 159, "license_type": "permissive", "max_line_length": 50, "num_lines": 9, "path": "/obrazky.py", "repo_name": "nnodyy/obrazky", "src_encoding": "UTF-8", "text": "from turtle import forward, left, right, getcanvas\r\n\r\nforward(50)\r\nleft(60)\r\nforward(50)\r\nright(60)\r\nforward(50)\r\n\r\ngetcanvas().postscript(file='obrazky.ps')\r\n" } ]	2
shadowteias/Mighty	https://github.com/shadowteias/Mighty	f5cf78865aacbf765ec4b72a7b71b36c69c33ec2	7305b0531c2cd0297c3ea2927068256dbac19dc6	747781704150f718d6f7d10a2fda813b07761eb9	refs/heads/master	2020-04-10T17:42:03.093791	2019-01-10T01:49:36	2019-01-10T01:49:36	161,181,407	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.45750582218170166, "alphanum_fraction": 0.4789765477180481, "avg_line_length": 34.94533920288086, "blob_id": "9896abe9574dc75554aa0a652c2dd0fc700c0576", "content_id": "49a819e47d182a7e4714a6075e404f5fedbb4e5f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 13306, "license_type": "no_license", "max_line_length": 107, "num_lines": 311, "path": "/player.py", "repo_name": "shadowteias/Mighty", "src_encoding": "UTF-8", "text": "# import \nimport re, random\nimport gamefunctions as gf\n\n# 변수 정의\nmighty = ['S', 14]\njoker = ['joker', 0]\n# giruda\n# grave\nshapes = ['C', 'D', 'H', 'S']\n\n#게임 플레이 과정에서 정해진 기루다, 마이티 같은거 어케 자동으로 못 가져오나... 인풋으로 안넣고도.\n\nclass Player(object):\n \"\"\"\n 나는\n 핸드가 있어야 하고\n 내 점수카드를 인식해야 하고\n 남의 카드 낸걸 카운팅 해야 하고\n 누가 점수카드를 몇 장 가졌는지 알아야 하고\n 지금이 몇 라운드인지 알아야 하고\n 기루나 조커콜이나 마이티 카드를 알아야 하고\n 조커나 마이티가 나왔는지 알아야 하고\n 카드를 선택해서 낼 줄 알아야 하고\n 남이 어떤 카드가 소진됬는지 알아야 하고\n 자기 자리를 기준으로 주공이 어딨는지, 프랜드가 어딨는지 포지션(자리)를 알아야 하고\n \n 공약을 할 줄 알아야 하고\n 프랜드를 선택할줄 알아야 하고\n \n 야당/주공/프랜드일때 플레이하는법을 알아야 하고\n 이에 따라 카드를 선택해서 낼 줄 알아야 하고\n 내 정체가 드러났는지 알아야 하고\n \n 어쩌면\n 내 누적 점수를 기억해야 할 수도 있고\n 세이브/로드로 불러오기가 가능해야 할지도\n \n 나는 아래 정보로 공약을 하고,\n 내 핸드, 남의 공약\n \n 나는 아래 정보로 카드를 내요.\n 역할(주공,프랜드,야당), 핸드, 자리(누가누가옆에앉았나)/차례, 라운드카드, \n 몇번째턴인지, 프랜드가 밝혀졌는지, 내가 프랜드인데 그게 드러났는지(혹은 의심받는지...이건 어케 구현하냐;;),\n 그동안 나온 히스토리(grave를 기억하면 되겠군), 누가 무슨 카드가 떨어졌는지(남의 카드를 의심할 줄 알아야 하나?)\n \n \"\"\"\n \n def __init__(self):\n \"\"\"\n 이건 AI플레이어잆니다\n \"\"\"\n self.name = \"\" #이름. 예를 들어 윤희\n self.hand = [] #내가 가진 카드\n self.field = [] #내 현재 점수카드\n self.score = 0 #내 현재 점수\n self.role = [0,0] #00:야당, 01:프랜드, 10: 주공\n self.grave = []\n# self. #의심도(?) 누가누가 프랜드같은지... 추론은 어케 해야 하냐\n# 피하고 싶은 상황: 눈앞에서 뻔히 프랜드같은놈이 주공한테 점수 퍼주는데 프랜드인지 모르고 걔한테 점수 주는 짓.\n# 혹은 야당끼리 협력 못하고 모두 서로를 프랜드로 보고 자기 점수만 늘리려고 하는 전략.\n# 피하기 위한 정보: 누가 무슨 카드를 냈나. 그 때 상황이 (필드가) 어땠나. \n \n def setMighty(self,giruda):\n if giruda == 'S':\n mighty = ['D', 14]\n else: mighty = ['S', 14]\n return mighty\n\n \n def pledge(self, others): #공약... 쉽지 않겠는걸\n oath = [\"\",1]\n #핸드에서 한 카드 종류가 몇개인지\n #핸드에 점카는 몇개인지\n #핸드에 특수카드(마이티(빨마), 조커, 조커콜)는 몇갠지, 어떤 조합인지...\n a = gf.shapeCounter(self.hand)\n b = max(a)\n maxIndex1 = a.index(b)\n if a.count(b) == 3 and gf.dealCount(self.hand) >5 and others[1] <17:\n oath[0] = \"noGiru\"\n if others[1] < 5: oath[1] = 12\n elif others[0] != 'noGiru':\n oath[1] = others[1]\n else:\n oath[1] = others[1]+1\n elif a.count(b) == 2: # 33 또는 44 또는 55다, \n maxIndex2 = a[maxIndex1+1:].index(b) + maxIndex1 + 1\n shapeCards1 = gf.shapeCollector(self.hand, shapes[maxIndex1])\n shapeCards2 = gf.shapeCollector(self.hand, shapes[maxIndex2])\n # print(shapeCards1)\n # print(shapeCards2)\n # print(b)\n if b in [3,4] and gf.dealCount(self.hand) > 6:\n oath[0] = \"noGiru\"\n if others[1] < 5: oath[1] = 12\n elif others[0] != 'noGiru':\n oath[1] = others[1]\n else:\n oath[1] = others[1]+1\n else: #b = 5임, 두 모양으로 5개씩 <Error!!!!> b==4인 경우도 있구나! \n sum1 = 0\n sum2 = 0\n for i in range(b):\n sum1 += shapeCards1[i][1]\n sum2 += shapeCards2[i][1]\n if sum1 >= sum2 and int((sum1+6)/4) > others[1]:\n oath[0] = shapes[maxIndex1]\n if others[0] != 'noGiru': oath[1] = others[1] + 1\n else: oath[1] = others[1] + 2\n elif int((sum2+6)/4) > others[1]:\n oath[0] = shapes[maxIndex2]\n if others[0] != 'noGiru': oath[1] = others[1] + 1\n else: oath[1] = others[1] + 2\n else: pass\n elif b >= 5: # 최고 많은 카드가 한 모양일때. 아마 대다수의 경우. 6,7,8,9,10이 가능하다. 졸라많네.\n sum1 = 0\n shapeCards1 = gf.shapeCollector(self.hand, shapes[maxIndex1])\n for i in range(b):\n sum1 += shapeCards1[i][1]\n if int((sum1+6)/4) > others[1]:\n oath[0] = shapes[maxIndex1]\n if others[0] != 'noGiru': oath[1] = others[1] + 1\n else: oath[1] = others[1] + 2\n else: pass\n else: pass\n return oath\n \n# if max(shapeCounter(self.hand)) >=4:\n# if shapeCounter(self.hand).index(max(shapeCounter(self.hand)))\n# oath[0] = random.choice([\"H\",\"S\",\"C\",\"D\",\"noGiru\"])\n# oath[1] = int(8random.random()random.random()) + 13\n #random.randrange(13,21)\n\n def kingGrave(self,grave, giruda):\n \"\"\" 여기 tf 로 대체해야 할 부분임. 룰 베이스로 할까... \"\"\"\n graveCandidate = self.hand + grave\n mighty = self.setMighty(giruda)\n if mighty in graveCandidate:\n graveCandidate.remove(mighty)\n if joker in graveCandidate:\n graveCandidate.remove(joker)\n \n #아래에 노기루가 고려 안되어있다 젠장..... shapeCounter에도 그렇고 아래 로직에도 그렇고....\n if giruda == 'noGiru':\n while len(graveCandidate) > len(grave):\n giruMax = 0\n findMaxIndex = 0\n for i in graveCandidate:\n if i[1] > giruMax:\n giruMax = i[1]\n findMaxIndex = graveCandidate.index(i)\n graveCandidate.pop(findMaxIndex)\n elif (len(graveCandidate) - gf.shapeCounter(graveCandidate, giruda)) > len(grave): #기루 아닌게 버리기 충분할때\n tempList = []\n print('giru delete: '+ str(giruda))\n for i in graveCandidate:\n if i[0] == giruda: \n tempList.append(i)\n for i in tempList:\n graveCandidate.remove(i)#논기루만 남긴다\n print(i)\n\n while len(graveCandidate) > len(grave): #버릴만큼 남을때까지\n findMax = 0\n findMaxIndex = 0\n for i in graveCandidate: #최대값 카드를 찾는 loop\n if i[1] > findMax:\n findMax = i[1]\n findMaxIndex = graveCandidate.index(i)\n graveCandidate.pop(findMaxIndex) #여기 버릴만큼만 남았다\n \n else: #기루 아닌게 버리기 부족할때. 논기루 1개 놔두고 나머지는 기루에서 버린다. \n giruset = gf.shapeCollector(graveCandidate, giruda)\n nonGiruset = graveCandidate +[]\n if len(giruset) != len(graveCandidate): #기루 아닌것도 들고있을때 있을 때\n for k in range(len(graveCandidate) - len(giruset) - 1): #기루중에서 최종적으로 남길 걸 없앤다\n giruMax = 0\n findMaxIndex = 0\n for i in giruset:\n if i[1] > giruMax:\n giruMax = i[1]\n findMaxIndex = giruset.index(i)\n if i in nonGiruset: nonGiruset.remove(i) #하는김에 논기루셋 만들어놓기\n giruset.pop(findMaxIndex)\n for k in range(len(nonGiruset)-1): #논기루 최대 하나만 남겨야 해\n findMin = 15\n findMinIndex = 0\n for i in nonGiruset:\n if i[1] < findMin:\n findMin = i[1]\n findMinIndex = nonGiruset.index(i)\n nonGiruset.pop(findMinIndex) #논기루 min 제거한다.\n graveCandidate = giruset + nonGiruset #여기 버릴만큼만 남았다\n else: #논기루 없을때 (다 기루임 ㄷㄷ)\n for k in range(len(graveCandidate) - len(grave)): #무덤 갯수만큼 남겨야 함, 맥스를 쓰레기 후보에서 제거\n giruMax = 0\n findMaxIndex = 0\n for i in graveCandidate:\n if i[1] > giruMax:\n giruMax = i[1]\n findMaxIndex = graveCandidate.index(i)\n graveCandidate.pop(findMaxIndex)#여기 버릴만큼만 남았다\n \n self.hand = self.hand + grave\n for i in graveCandidate:\n self.hand.remove(i)\n graveAgain = graveCandidate\n return graveAgain\n\n \n def callFriend(self, giruda):\n mighty, jokerCall = gf.setMightyJokercall(giruda)\n if mighty not in self.hand:\n return mighty\n elif joker not in self.hand:\n return joker\n elif giruda == 'noGiru':\n if ['S', 14] not in self.hand:\n return ['S', 14]\n elif ['H', 14] not in self.hand:\n return ['H', 14]\n elif ['C', 14] not in self.hand:\n return ['C', 14]\n elif ['D', 14] not in self.hand:\n return ['D', 14]\n elif ['C', 13] not in self.hand:\n return ['C', 13]\n elif ['D', 13] not in self.hand:\n return ['D', 13]\n elif ['H', 13] not in self.hand:\n return ['H', 13]\n elif ['S', 13] not in self.hand:\n return ['S', 13]\n elif ['C', 12] not in self.hand:\n return ['S', 12]\n elif [giruda, 14] not in self.hand:\n return [giruda, 14]\n elif [giruda, 13] not in self.hand:\n return [giruda, 13]\n elif [giruda, 12] not in self.hand:\n return [giruda, 12]\n elif [giruda, 11] not in self.hand:\n return [giruda, 11]\n elif [giruda, 10] not in self.hand:\n return [giruda, 10]\n elif [giruda, 9] not in self.hand:\n return [giruda, 9]\n elif [giruda, 8] not in self.hand:\n return [giruda, 8]\n elif [giruda, 7] not in self.hand:\n return [giruda, 7]\n elif [giruda, 6] not in self.hand:\n return [giruda, 6]\n else: return \"noFriend\"\n print(\"친구고르기\")\n \n def possibleOptions(self, turnShape, mighty): \n if turnShape == 'all':\n count = 0\n else:\n count = gf.shapeCounter(self.hand, turnShape)\n if count > 0:\n options = gf.shapeCollector(self.hand, turnShape)\n if mighty in self.hand:\n options.append(mighty)\n if joker in self.hand:\n options.append(joker)\n else:\n options = self.hand.copy()\n return options\n\n def sayShape(self):\n # when startCard is 'joker', then first player needs to say shape.\n return 'S'\n\n\n def pickCard(self, bits, turnShape, mighty): #젤어렵겠다... AI 구현 with TF\n# print('카드 고르기')\n# self.hand[random.randrange(0,len(self.hand))]\n myOptions = self.possibleOptions(turnShape, mighty)\n myPick = myOptions[random.randrange(0,len(myOptions))]\n self.hand.remove(myPick)\n return myPick\n\n def jokerCallPick(self, bits, turnShape, mighty):\n if joker in self.hand:\n self.hand.remove(joker)\n return joker\n else:\n return self.pickCard(bits, turnShape, mighty)\n# # 인풋은 뭐뭐여야 할까\n# # 내가 선일때, 선 아닐때를 구분해야 할까...\n# if len(self.hand) > 0:\n# return self.hand.pop()\n# else: \n# print('noCardError')\n# return False\n# # list.count(s) #리스트 내 s를 세는 함수\n\n\n\n\n\"\"\"\n용어 수정할까나\n 주공 President, Declarer\n 공약 bidding, bid\n 선 lead\n 야당 defender\n\n선을 뭐라고 부르지...roundShape?\n\"\"\"" }, { "alpha_fraction": 0.48272016644477844, "alphanum_fraction": 0.5104050636291504, "avg_line_length": 39.443607330322266, "blob_id": "1cb76d6ffbbf973b783b34e5aee0569537304412", "content_id": "d52e3894d7a8408d6ea1970467b5d81f75a49a3c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5882, "license_type": "no_license", "max_line_length": 111, "num_lines": 133, "path": "/user.py", "repo_name": "shadowteias/Mighty", "src_encoding": "UTF-8", "text": "# import\nimport gamefunctions as gf\n\nmighty = ['S', 14]\njoker = ['joker', 0]\nshapes = ['C', 'D', 'H', 'S']\n\nclass User(object):\n \n def __init__(self):\n \"\"\"\n 이건 프로그램을 사용하는 게이머(닝겐)입니답.\n \"\"\"\n self.name = \"ningen\"\n self.hand = []\n self.field = []\n self.score = 0 \n self.role = [0,0] #00:야당, 01:프랜드, 10: 주공\n self.grave = []\n# 함수명은 동일하게 가자, 내부 구현만 다르면 될것 같아\n\n### 이하 복붙 영역 ###\n def setMighty(self,giruda):\n if giruda == 'S':\n mighty = ['D', 14]\n else: mighty = ['S', 14]\n return mighty\n \n def pledge(self, others): #공약... 쉽지 않겠는걸\n biddingYesNo = \"\"\n myBidding = [\"\",0]\n biddingYesNo = input(self.name + ', will you pledge? (y/n) ')\n while not(biddingYesNo in ['y', 'n']):\n biddingYesNo = input('try again, will you pledge? (y/n) ')\n if biddingYesNo == \"n\":\n return myBidding\n else:\n if others[0] != 'noGiru':\n minBidding = others[1] + 1\n else:\n minBidding = others[1] + 2\n myBidding[0] = input('choose shape, one of ' + str(shapes) + \" or 'noGiru' :\")\n while not(myBidding[0] in shapes) and not(myBidding[0] == 'noGiru'):\n myBidding[0] = input('try again, one of ' + str(shapes) + \" or 'noGiru' :\")\n\n\n if myBidding[0] != 'noGiru':\n myBidding[1] = int(input('Choose number 비트윈 ' + str(minBidding) + '~20 : '))\n while myBidding[1] < minBidding or myBidding[1] > 20:\n myBidding[1] = int(input('try again, Choose number 비트윈 ' + str(minBidding) + '~20 : '))\n else:\n myBidding[1] = int(input('Choose number 비트윈 ' + str(minBidding - 1) + '~20 : '))\n while myBidding[1] < minBidding-1 or myBidding[1] > 20:\n myBidding[1] = int(input('try again, Choose number 비트윈 ' + str(minBidding - 1) + '~20 : '))\n print(self.name + ', your bidding is ')\n return myBidding\n\n #핸드에서 한 카드 종류가 몇개인지\n #핸드에 점카는 몇개인지\n #핸드에 특수카드(마이티(빨마), 조커, 조커콜)는 몇갠지, 어떤 조합인지...\n # print('this is your hand: ' + str(self.hand))\n # while not(oath[0] in [\"H\",\"S\",\"C\",\"D\",\"noKiru\"]):\n # oath[0] = input(self.name + ', Pick one of those \"H\",\"S\",\"C\",\"D\",\"noKiru\" : ')\n # if not(oath[0] in [\"H\",\"S\",\"C\",\"D\",\"noKiru\"]): print('Plz choice again')\n # while not(oath[1] in [1,13,14,15,16,17,18,19,20]): #21은 패스\n # oath[1] = int(input('How many do you pledge? (1 for pass, 13~20 to pledge)'))\n # if not(oath[1] in [1,13,14,15,16,17,18,19,20]): print('Plz say again')\n # #random.randrange(13,21)\n \n def friendCall(self):\n print(\" << 친구고르기 >> \")\n print(self.name + ', This is your hand: ' +str(self.hand))\n userFriend = [\" \", 0]\n userPick[0] = input('Choose your friend! say one of ' + str(shapes) + ', or \"joker\"')\n while not(userFriend[0] in shapes) and not(userFriend[0] == 'joker'):\n userPick[0] = input('try gain. Choose one of ' + str(shapes) + ', or \"joker\"')\n if userPick[0] == \"joker\":\n return joker\n else:\n userPick[1] = int(input('Choose number of 2 ~ 14 (14 for A, 13 for K, ...)'))\n while not(userFriend[1] in [2,3,4,5,6,7,8,9,10,11,12,13,14]):\n userPick[1] = int(input('try again. one of 2 ~ 14 (14 for A, 13 for K, ...)'))\n return userPick\n\n def possibleOptions(self, turnShape, mighty): \n if turnShape == 'all':\n count = 0\n else:\n count = gf.shapeCounter(self.hand, turnShape)\n if count > 0:\n options = gf.shapeCollector(self.hand, turnShape)\n if mighty in self.hand:\n options += mighty\n if joker in self.hand:\n options += joker\n else:\n options = self.hand.copy()\n return options\n\n def sayShape(self):\n # when startCard is 'joker', then first player needs to say shape.\n return 'S'\n\n def pickCard(self, bits, turnShape, mighty): #젤어렵겠다... AI 구현 with TF\n# print('카드 고르기')\n# self.hand[random.randrange(0,len(self.hand))]\n#상황에 따라 보기를 주고 그 중에 고르라고 하는게 맞을것 같다. pickable card.\n if len(self.hand) <= 0:\n print('noCardError')\n return False\n \n #선 이 뭔지 파악(선이 뭔지 인자로 받아야) (혹은 내가 선인지)\n #선 과 같은 카드 있는지 확인 (내 핸드는 내가 알지 self.hand 임)\n #선 과 같은 카드 있으면 그 중에 선택(선, 마이티, 조커)\n #선 과 같은 카드 없으면 모든 핸드중에 선택\n \n print(self.name + ', This is your hand: ' +str(self.hand))\n userPick = \" \"\n while not(userPick in self.hand):\n print(\"your hand is 0 ~ \" + str(len(self.hand) - 1))\n userPick = int(input('Witch card will you choose? Tell me index '))\n # if not(userPick in self.hand): print('Plz choice again')\n if userPick > len(self.hand) - 1: print('Plz choice again')\n else: userPick = self.hand[userPick]\n self.hand.remove(userPick)\n return userPick\n \n def jokerCallPick(self, bits, turnShape, mighty):\n if joker in self.hand:\n self.hand.remove(joker)\n return joker\n else:\n return self.pickCard(bits, turnShape, mighty)\n\n\n\n" }, { "alpha_fraction": 0.43467336893081665, "alphanum_fraction": 0.46510329842567444, "avg_line_length": 28.368852615356445, "blob_id": "a0cd788f83c51a836e228dd759ac0608ff48c3ae", "content_id": "9aee93b0e3cf7ad83db12ee775e0e4f26ef678cd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4452, "license_type": "no_license", "max_line_length": 102, "num_lines": 122, "path": "/environment.py", "repo_name": "shadowteias/Mighty", "src_encoding": "UTF-8", "text": "# import \nimport re, random\nimport gamefunctions as gf\n\njoker = ['joker', 0]\nshapes = ['C', 'D', 'H', 'S']\n\nclass Environment(object):\n \"\"\"\n 나는\n 모든 플레이어에게 보이는 상황이다.\n - 그래서 grave 처럼 Declarer와 Defender에게 보이는 것이 다른 정보는 저장하지 않는다.\n 이 상황은 Player의 pickCard method에 불려가게 될 것이므로 상황을 card 이름과 bit로 둘 다 저장하고 있는다.\n\n 모든 플레이어에게 같은 것\n - score\n - 어느 자리가 여당인지\n - roundRecord\n - roundNumber\n - roundLead (선)\n - 몇 번 플레이어가 어디인지\n - 어느 자리가 선인지\n - giruda\n - \n\n 플레이어마다 다른 것\n - hand\n - grave(king과 나머지가 다르다)\n - \n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n\t\tfor the tf input, it is needed to fix input data form.\n\n\t\tself.role [53bit]\n\t\tgrave [53bit]\n\t\troundRecord [ 53bit * 4 ]\n\t\troundNumber [ 10bit ]\n\t\thand [53bit]\n\t\tother's shape [4bit * 4] (C,D,H,S)\n\n\t\tpledge [4bit(shape) + int(num) ]\n\t\tlocation [4bit] (내 기준 자리임. 내 다음에 여당 있으면 [1,0,0,0]. 여당 둘이면 [1,0,1,0])\n\t\t (0끼리는 구분 안되어도 됨. 근데 의심을 하려면 뭔가 구분자가 필요할까? 모르겠다..)\n\t\t + [4bit] (start of each round, 시작사람이 1, 내가 시작이면 [0,0,0,0])\n\t\tscore [4 ints]\n\t\t\"\"\"\n self.rule = \"defalt\"\n self.playerNumber = 5\n\n self.role = [0,0,0,0,0] # 밝혀진 것만, 처음에 Declarer, 게임중에 Friend\n self.scores = [0,0,0,0,0] #턴 넘버에 매칭\n self.locations = [0,1,2,3,4] #players[?]에 넣을 숫자.\n self.field = [] #필드에 나와있는 숫자\n\n self.roundShape = 'all'\n self.roundTrun = []\n\n self.giruda = \"\"\n self.bidding = 0\n\n self.mighty = ['S', 14]\n self.jokerCall = ['C',3]\n\n self.roundCards = list(range(0,self.playerNumber))\n\n self.bits = list(range(0,478)) # 비트화된 정보 for tf\n \"\"\"\n bits 0~4 role\n 5~9 giruda [c,d,h,s,nogiru]\n 10~14 scores\n 15~19 locations # 이건 아닌가... 각자가 다른 입장일텐데 위치에 대해선.\n 20~24 roundStart\n ...\n \"\"\"\n\n# self. #의심도(?) 누가누가 프랜드같은지... 추론은 어케 해야 하냐\n# 피하고 싶은 상황: 눈앞에서 뻔히 프랜드같은놈이 주공한테 점수 퍼주는데 프랜드인지 모르고 걔한테 점수 주는 짓.\n# 혹은 야당끼리 협력 못하고 모두 서로를 프랜드로 보고 자기 점수만 늘리려고 하는 전략.\n# 피하기 위한 정보: 누가 무슨 카드를 냈나. 그 때 상황이 (필드가) 어땠나. \n \n def setGiruda(self,giruda):\n self.giruda = giruda\n if giruda == 'S':\n self.mighty = ['D', 14]\n elif giruda == 'C':\n self.jokerCall = ['H',3]\n if giruda in shapes:\n giruIndex = shapes.index(giruda)\n bits[5+giruIndex] = 1 # 5~9: giruda\n else:\n bits[9] = 1\n\n def setDeclarer(self,kingNo):\n self.role[kingNo] = 1\n\n def setFriend(self,friendNo):\n self.role[friendNo] = 1\n\n def initialize(self): #단순히 모든 변수 다 초기처럼 똑같이 맞추면 되겠군.\n self.rule = \"defalt\"\n self.playerNumber = 5\n\n self.role = [0,0,0,0,0] # 밝혀진 것만, 처음에 Declarer, 게임중에 Friend\n self.scores = [0,0,0,0,0] #턴 넘버에 매칭\n self.locations = [0,1,2,3,4] #players[?]에 넣을 숫자.\n self.field = [] #필드에 나와있는 숫자\n\n self.roundShape = 'all'\n self.roundTrun = []\n\n self.giruda = \"\"\n self.bidding = 0\n\n self.mighty = ['S', 14]\n self.jokerCall = ['C',3]\n\n self.roundCards = list(range(0,self.playerNumber))\n\n self.bits = list(range(0,478))" }, { "alpha_fraction": 0.5183362364768982, "alphanum_fraction": 0.5397682189941406, "avg_line_length": 27.636363983154297, "blob_id": "4a4478fe7d99fcfe066369671fedd1bbba29b4fc", "content_id": "6d81f6ec1093dd23664d31a2228cf3ebc6b208c1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6829, "license_type": "no_license", "max_line_length": 93, "num_lines": 220, "path": "/gamefunctions.py", "repo_name": "shadowteias/Mighty", "src_encoding": "UTF-8", "text": "# 다양한 functions for handling cards and game.\n\n\"\"\"\n\n\"\"\"\n\nimport re, random\n\nshapes = ['C', 'D', 'H', 'S']\n\n\ndef setMightyJokercall(giruda = 'H'):\n\tmighty = ['S', 14]\n\tjokerCall = ['C', 3]\n\tif giruda == 'S':\n\t\tmighty = ['D', 14]\n\telif giruda == 'C':\n\t\tjokerCall = ['H', 3]\n\telse:\n\t\tpass\n\treturn mighty, jokerCall\nmighty, jokerCall = setMightyJokercall()\njoker = ['joker', 0]\n\n#test function run\ndef printA():\n\tprint('print A, to test')\n\ndef ruleSetting(playerNum = 5, ruleName = 'defalt'):\n \"\"\"\n 플레이어 수와 룰을 묻는다.\n AI끼리 대전하는 룰인지, 사람이 같이 하는 룰인지 확인.\n \"\"\"\n playerNumber = playerNum\n if ruleName == 'defalt':\n sets = [playerNumber, ruleName]\n return sets\n elif ruleName == \"AIs\":\n sets = [playerNumber, ruleName]\n return sets\n else:\n print(str(ruleName) + ', this rule is not ready')\n\ndef deckGenerator(ruleName = 'defalt'):\n\tif ruleName == 'defalt':\n\t\tdeck = []\n\t\tfor i in 'C', 'D', 'H', 'S':\n\t\t for k in list(range(2, 15)):\n\t\t deck.append([i,k])\n\t\tdeck.append(['joker', 0])\n\t\treturn deck\n\t\t# random.shuffle(deck)\n\telse:\n\t\tprint(str(ruleName) + ', this rule is not ready')\t\n\ndef shapeCounter(hand, shape = \"all\"):\n \"\"\"\n 'hand' is a list of cards, card is something like [\"S\", 2].\n when 'shape' is \"all\", return is ['C', 'D', 'H', 'S', mighty, joker]\n if 'shape' is one of ['C', 'D', 'H', 'S'], it return number of that shape.\n \"\"\"\n # 딸마가 있다는건 마이티랑 같은 모양이 시작카드일 때 그모양이 마이티뿐이면 그거 낸다는거지?\n # 근데 선카가 다른 카드여도 마이티는 낼 수 있는건가? 그치?\n # 낼 수 있는 카드는 [0:3] 중에 1 이상인것, 그리고 마이티, 그리고 조커.\n shapes = [0,0,0,0,0,0]\n for i in range(len(hand)):\n if hand[i][0] == 'C':\n shapes[0] += 1\n elif hand[i][0] == 'D':\n shapes[1] += 1\n elif hand[i][0] == 'H':\n shapes[2] += 1\n elif hand[i][0] == 'S':\n shapes[3] += 1\n if hand[i] == mighty: \n shapes[4] += 1\n if hand[i] == joker:\n shapes[5] += 1\n if shape == 'all':\n return shapes\n elif shape == 'C':\n return shapes[0]\n elif shape == 'D':\n return shapes[1]\n elif shape == 'H':\n return shapes[2]\n elif shape == 'S':\n return shapes[3]\n else: print('input error!!!')\n\ndef shapeCollector(hand, shape):\n result = []\n for i in hand:\n if i[0] == shape: result += [i]\n# print(result)\n return result\n\ndef numCounter(hand, num):\n result = 0\n for i in hand:\n if i[1] == num: result += 1\n return result\n\n\n#set mighty and jokercall from giruda\n\n\ndef handout(players, deck, grave): #hand out cards to AI player\n random.shuffle(deck)\n for i in range(len(players)):\n \t# global players[i].hand\n players[i].hand = deck[i10 + 0:i10 + 10]\n# print(players[i].hand)\n grave = deck[len(players)10:]\n return grave\n \ndef dealCount(playersHand):\n count = 0\n for j in range(10):\n if int(playersHand[j][1]) >=10: \n count += 1\n if playersHand[j] == ['S', 14]: count -= 2 # mighty 면 점수 -1\n if playersHand[j] == ['joker', 0]: count -= 1 # joker는 -1\n# print(count)\n return count\n\ndef roundTrun(turn, startPosition): #이 함수는 아래 함수로 대체될것 같다... \n a = list(range(len(turn)))\n for i in range(len(turn)):\n a[i] = turn[startPosition]\n startPosition += 1\n if startPosition == len(turn):\n startPosition = 0\n return a\n\ndef turnSetting(position, turnStart): #턴스타트는 int, turn은 list\n roundTurn = position.copy()\n while turnStart != roundTurn[0]:\n roundTurn.append(roundTurn[0])\n roundTurn.pop(0)\n return roundTurn\n\ndef card2bits(cardList):\n bits = ['0'] 53\n bitIndex = 0\n for i in cardList:\n if i[0] == shapes[0]:\n bitIndex += 0\n bitIndex += i[1]-2\n elif i[0] == shapes[1]:\n bitIndex += 13\n bitIndex += i[1]-2\n elif i[0] == shapes[2]:\n bitIndex += 26\n bitIndex += i[1]-2\n elif i[0] == shapes[3]:\n bitIndex += 39\n bitIndex += i[1]-2\n elif i[0] == 'joker':\n bitIndex = 52\n else: \n print('!! Error card is in this list !!')\n break\n bits[bitIndex] += 1\n return bits\n\n\ndef roundWin(roundCards, mighty, giruda, jokerCall, validJoker): # validJoker: 첫턴0, 중간턴1, 끝턴0\n calculateScore = 0\n for i in range(len(roundCards)):\n if roundCards[i][1] >= 10:\n calculateScore += 1 #해당라운드의 점수지롱.\n if mighty in roundCards:\n return roundCards.index(mighty), calculateScore\n elif (joker in roundCards) and (roundCards[0] != jokerCall) and (validJoker == 1) :\n return roundCards.index(joker), calculateScore\n elif giruda != 'noGiru' : \n #이제부턴 마이티도 조커도 없으니 기루다 싸움이다. 근데 기루다가 있는지 먼저 물어보는게 예의지.\n if shapeCounter(roundCards, giruda) > 0: # 기루다가 라운드 카드중에 나왔을 때\n giruSet = shapeCollector(roundCards, giruda)\n maxCount = 0\n winCard = []\n for i in giruSet:\n if i[1] > maxCount:\n maxCount = i[1]\n winCard = i\n return roundCards.index(winCard), calculateScore\n else: #기루다 없어\n sunSet = shapeCollector(roundCards, roundCards[0][0]) #선 카드 모음\n maxCount = 0\n winCard = []\n for i in sunSet:\n if i[1] > maxCount:\n maxCount = i[1]\n winCard = i\n return roundCards.index(winCard), calculateScore\n else: # 노기루여?\n sunSet = shapeCollector(roundCards, roundCards[0][0]) #선 카드 모음\n maxCount = 0\n winCard = []\n for i in sunSet:\n if i[1] > maxCount:\n maxCount = i[1]\n winCard = i\n return roundCards.index(winCard), calculateScore\n\ndef finalWin(scores, roles):\n \"\"\"\n input: scores(list), roles(list)\n output: declarerScore(int), defenderScore(int)\n \"\"\"\n declarerScore = 0 #여당점수\n defenderScore = 0 #야당점수\n if len(scores) != len(roles):\n print('error Input in finalWin')\n for i in range(len(roles)):\n if roles[i] == 0:\n defenderScore += scores[i]\n declarerScore = 20 - defenderScore # 처음에 무덤에 들어간 점카도 여당이 먹은거니까\n return declarerScore, defenderScore" }, { "alpha_fraction": 0.6827957034111023, "alphanum_fraction": 0.725806474685669, "avg_line_length": 25.571428298950195, "blob_id": "dd16347fd1d76bad4520f014acfaf5387829c593", "content_id": "3e22e6ff500a1849d78add3e525cb3e48cbc2832", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 186, "license_type": "no_license", "max_line_length": 94, "num_lines": 7, "path": "/README.md", "repo_name": "shadowteias/Mighty", "src_encoding": "UTF-8", "text": "\"# Mighty\" \n\n# perpose\n\nTo teach myself with personal project to make card game 'Mighty'.\n\nI'll use python with tensorflow. Due is the end of Feb. 2019 (started on middle of Dec. 2018). " } ]	5
RKelley1/MovieReviewsBackend	https://github.com/RKelley1/MovieReviewsBackend	3c3884a5d4b699deeff3feb1f27e785e7c7382f3	c3dc5b7c45c3ea0c76e538c8cb8a42e2b7b668ba	bfd6ddb091f8ac633bb5b25f8addad7bd2af8ef8	refs/heads/master	2022-12-05T08:46:51.265344	2020-08-14T18:26:07	2020-08-14T18:26:07	287,597,636	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7388724088668823, "alphanum_fraction": 0.7388724088668823, "avg_line_length": 38.70588302612305, "blob_id": "921e3c311e8ed85fd83709abe5cf5ee3e347c04a", "content_id": "35edb06cd39ab958577a04026afb952732196ece", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 674, "license_type": "no_license", "max_line_length": 69, "num_lines": 17, "path": "/moviereviews/app/urls.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from django.urls import path\n\nfrom moviereviews.app.api import userView\nfrom moviereviews.app.api import commentView\nfrom moviereviews.app.api import movieView\n\nurlpatterns = [\n path('users/home', userView.get_home),\n path('users/get', userView.get_all_users),\n path('users/get/user', userView.get_user),\n path('user/create', userView.create_user),\n path('comments/get/movie', commentView.get_comments_for_movie),\n path('comments/get/user', commentView.get_comments_for_user),\n path('comments/update/upvotes', commentView.update_upvote_count),\n path('comments/create', commentView.create_comment),\n path('movies/create', movieView.create_movie) \n]" }, { "alpha_fraction": 0.6895999908447266, "alphanum_fraction": 0.7024000287055969, "avg_line_length": 31.894737243652344, "blob_id": "b7394a41be590b782d45642e5a905df5c5b0ad05", "content_id": "07e6c35eec6a645e810d5601fa3c2c2e8babd292", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 625, "license_type": "no_license", "max_line_length": 62, "num_lines": 19, "path": "/moviereviews/app/models.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from django.db import models\nfrom django.contrib.auth.models import User\n\nclass Movie(models.Model):\n title = models.CharField(max_length=150, unique=True)\n \n def __str__(self):\n return self.title\n\nclass Comment(models.Model):\n #each comment has a relationship to 1 movie and 1 user \n movie = models.ForeignKey(Movie, on_delete=models.CASCADE)\n user = models.ForeignKey(User, on_delete=models.CASCADE)\n content = models.TextField(max_length=350)\n upvotes = models.IntegerField()\n date = models.DateTimeField(auto_now_add=True, blank=True)\n\n def __str__(self):\n return self.content\n" }, { "alpha_fraction": 0.6909986734390259, "alphanum_fraction": 0.7026422023773193, "avg_line_length": 36.233333587646484, "blob_id": "28d91f78bceb6feb7dc0451ad9386e1a8b22f1be", "content_id": "e3af705cbb171ee4ccb479cd116ae731a1a3f9f9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2233, "license_type": "no_license", "max_line_length": 98, "num_lines": 60, "path": "/moviereviews/app/api/commentView.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated\nfrom moviereviews.app.serializers import CommentSerializer\nfrom django.shortcuts import get_list_or_404, get_object_or_404\nfrom django.contrib.auth.models import User\nfrom moviereviews.app.models import Comment, Movie\n\n\n@api_view(['POST'])\n@permission_classes([IsAuthenticated])\ndef create_comment(request, format=None):\n userID = int(request.data['userID'])\n movieID = int(request.data['movieID'])\n content = request.data['content']\n upvotes = int(request.data['upvotes'])\n user = get_object_or_404(User, pk=userID)\n movie = get_object_or_404(Movie, pk=movieID)\n try:\n comment = Comment.objects.create(movie=movie, user=user, content=content, upvotes=upvotes)\n serializer = CommentSerializer(comment, many=False)\n return Response({\n 'status': 201,\n 'payload': serializer.data\n })\n except Exception as e:\n raise(e)\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef get_comments_for_movie(request, format=None):\n movieID = int(request.GET.get('movie_id', None))\n comments = get_list_or_404(Comment, movie_id=movieID)\n serializer = CommentSerializer(comments, many=True)\n return Response(serializer.data) \n\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef get_comments_for_user(request, format=None):\n userID = int(request.GET.get('user_id', None))\n comments = get_list_or_404(Comment, user_id=userID)\n serializer = CommentSerializer(comments, many=True)\n return Response(serializer.data)\n\n@api_view(['PUT'])\n@permission_classes([IsAuthenticated])\ndef update_upvote_count(request, format=None):\n commentID = int(request.data['commentID'])\n update = request.data['update']\n comment = get_object_or_404(Comment, pk=commentID)\n serializer = CommentSerializer(comment, many=False)\n if update == 'INCREMENT':\n comment.upvotes += 1\n comment.save()\n return Response(serializer.data)\n if update == 'DECREMENT':\n comment.upvotes -= 1\n comment.save()\n return Response(serializer.data)" }, { "alpha_fraction": 0.6727272868156433, "alphanum_fraction": 0.6777272820472717, "avg_line_length": 35.081966400146484, "blob_id": "b1f5e3ee283a8002fd6e3bc6ad22c7e18106095e", "content_id": "a6b8ccc6899e5612405ca32d75290b45196bfdba", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2200, "license_type": "no_license", "max_line_length": 96, "num_lines": 61, "path": "/moviereviews/app/api/userView.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated\nfrom moviereviews.app.serializers import UserSerializer\nfrom django.shortcuts import get_object_or_404\nfrom django.contrib.auth.models import User\nfrom django.core.exceptions import ValidationError\nfrom moviereviews.app.api.helpers.validators import validateEmail\n# model imports\n# from moviereviews.app.models import User\n\nfrom moviereviews.app.api.helpers.validators import validateEmail\n\n\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef get_all_users(request, format=None):\n \n if request.method == 'GET':\n users = User.objects.all()\n serializer = UserSerializer(users, many=True)\n return Response(serializer.data)\n else:\n raise('Only a get request may be performed on this endpoint')\n\n@api_view(['GET'])\ndef get_home(request, format=None):\n return Response(\"Hello this is the api home\")\n\n@api_view(['POST'])\ndef create_user(request, format=None):\n ''' TODO: \n 1.) check if email is unique\n 2.) check if email fits requirments (client side)\n\n '''\n if request.method == 'POST':\n username = request.data['username']\n email = request.data['email']\n password = request.data['password']\n if (validateEmail(email)):\n user = User.objects.create_user(username=username, email=email, password=password)\n user.save()\n serializer = UserSerializer(user, many=False)\n return Response({'status': '201',\n 'payload': serializer.data})\n else:\n raise(ValidationError('Email is not valid'))\n\n# Need to create user_login, user_logout etc...\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef get_user(request, format=None):\n if request.method == 'GET':\n userID = int(request.GET.get('user_id', None))\n user = get_object_or_404(User, pk=userID)\n serializer = UserSerializer(user, many=False)\n return Response(serializer.data)\n else:\n raise('Only a GET request may be performed on this endpoint')" }, { "alpha_fraction": 0.7864077687263489, "alphanum_fraction": 0.7864077687263489, "avg_line_length": 24.75, "blob_id": "a95e4be0ceff06f68918d37f2c010cac7f84bd32", "content_id": "96d198768a3b46ba962a78eba6b66d8330c7b4e2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 206, "license_type": "no_license", "max_line_length": 46, "num_lines": 8, "path": "/moviereviews/app/api/authView.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom moviereviews.app.models import User\n\n\n@api_view(['POST'])\ndef generate_token(request, format=None):\n pass\n" }, { "alpha_fraction": 0.7808219194412231, "alphanum_fraction": 0.7808219194412231, "avg_line_length": 35.75, "blob_id": "1b798b17baf8fa0492cfc171079ba0ebe1f56e22", "content_id": "34725d677cf3ec616883a6fc9b32f5ef94cf5fde", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 146, "license_type": "no_license", "max_line_length": 56, "num_lines": 4, "path": "/moviereviews/instructions.md", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "for deployment:\n create postgresql user in psql cli\n create database moviereviewsdb\n create superuser // python manage.py createsuperuser" }, { "alpha_fraction": 0.6984785795211792, "alphanum_fraction": 0.702627956867218, "avg_line_length": 29.16666603088379, "blob_id": "1b7dd6ea2a577911b01c0d0516cd91a69ce8e4e9", "content_id": "84e13ecfbe1154d89e8f8e90af1907e3874c4d88", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 723, "license_type": "no_license", "max_line_length": 66, "num_lines": 24, "path": "/moviereviews/app/api/movieView.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated\nfrom moviereviews.app.serializers import MovieSerializer\n\nfrom moviereviews.app.models import Movie\n\n@api_view(['POST'])\n@permission_classes([IsAuthenticated])\ndef create_movie(request, format=None):\n title = request.data['title']\n\n movie, created = Movie.objects.get_or_create(title=title)\n\n if(created):\n movie.save()\n serializer = MovieSerializer(movie)\n return Response({\n 'status': 201,\n 'payload': serializer.data\n })\n \n else:\n raise(Exception('Movie has already been created'))" }, { "alpha_fraction": 0.6827371716499329, "alphanum_fraction": 0.6827371716499329, "avg_line_length": 30.75, "blob_id": "eab1ec328cda1c46f1ec045e7512ad394ceed42c", "content_id": "ccdc0566c92ed2982cea6687cddcf27744191ec7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 643, "license_type": "no_license", "max_line_length": 59, "num_lines": 20, "path": "/moviereviews/app/serializers.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from rest_framework import serializers\nfrom .models import Comment, Movie\nfrom django.contrib.auth.models import User\n\nclass UserSerializer(serializers.ModelSerializer):\n class Meta:\n model = User\n fields = ['username', 'email', 'id']\n\nclass MovieSerializer(serializers.ModelSerializer):\n class Meta:\n model = Movie\n fields = ['title']\n\nclass CommentSerializer(serializers.ModelSerializer):\n movie = MovieSerializer(many=False, read_only=True)\n user = UserSerializer(many=False, read_only=True)\n class Meta:\n model = Comment\n fields = ['id','movie','user', 'content','upvotes']\n " }, { "alpha_fraction": 0.6760563254356384, "alphanum_fraction": 0.6760563254356384, "avg_line_length": 27.91666603088379, "blob_id": "70dff3bc7b8e7fd743a54b27c9d2141cb11b4fe3", "content_id": "315e6e07e9a61d836245f0b26ecc87ccc00bff49", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 355, "license_type": "no_license", "max_line_length": 46, "num_lines": 12, "path": "/moviereviews/app/views.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from django.shortcuts import render\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\n# Create your views here.\n\n@api_view(['GET'])\ndef home(request, format=None):\n routes_list = {\n \"get_all_users\": \"/api/get_all_users\"\n }\n if request.method == 'GET':\n return Response(routes_list)\n " }, { "alpha_fraction": 0.5098901391029358, "alphanum_fraction": 0.5340659618377686, "avg_line_length": 24.33333396911621, "blob_id": "272ebeab455d7e260ae856835e950d596b643eb0", "content_id": "4333a4950bc3c51dfd351be60c0df82c5b61a1d1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 455, "license_type": "no_license", "max_line_length": 54, "num_lines": 18, "path": "/moviereviews/todo.md", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "*TODO\n\n Routes*\n A.) User\n 1.) create user\n 2.) delete a user\n 3.) get a list of users\n 4.) get a user's specific information\n B.) Movie\n 1.) create movie\n 2.) get movie details\n 3.) return a list of movies\n\n C.) Comment\n 1.) create comment\n 2.) get a list of comments for a certain movie\n 3.) get a list of comments from a certain user\n 4.) delete comment" } ]	10
Kota-Yamaguchi/MD_bandit	https://github.com/Kota-Yamaguchi/MD_bandit	bffc128b17ef577cb653111d57a07ec5fdc93677	b2390c6fa3551a1250dcf5909fce070a4444301f	60e196f2b7032a137c75b160acc45c8702461d90	refs/heads/master	2022-12-01T06:25:44.250428	2020-08-12T11:24:08	2020-08-12T11:24:08	286,666,653	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7655986547470093, "alphanum_fraction": 0.7723439931869507, "avg_line_length": 16.969696044921875, "blob_id": "28a384a6f22314739b94e1ee9da77eb4ac9b3bd9", "content_id": "ac2fec01949384915996203ac6b8376a79b7b693", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1055, "license_type": "no_license", "max_line_length": 75, "num_lines": 33, "path": "/README.md", "repo_name": "Kota-Yamaguchi/MD_bandit", "src_encoding": "UTF-8", "text": "# MD_bandit\n\n[環境構築]\nconda create -n [仮想環境名] python=3.6 \nsource activate [仮想環境名]\n\nconda install -c conda-forge mdanalysis\n\n仮想環境のMDAnalysisのanalysisの中にpca_mod.pyを設置する。\n\n・MDAnalysisの場所の確認方法\n import MDAnalysis\n \n print(MDAnalysis.__file__)\n \n\n\n[使用方法]\n\npython bandit.py -s {gro} -t {xtc} -r {gro}　-s {bandit reaction coordinate}\n-s トポロジーファイルにあたるもの,GRO形式で\n-t 解析したいトラジェクトリ\n-r ターゲットとなるGROファイル\n-s バンディットスコアにしたい反応座標を指定する\n\n\n[説明]\n\nPCA,RMSD、ContactMapの３つの反応座標のどれが一番良いかをバンディットアルゴリズムで探索する\n\nPaCSスコアを計算　→ preバンディットスコアを計算　→ バンディットスコアを計算\n\nバンディットスコア　＝ preバンディットスコア - 1サイクル前のpreバンディットスコア　+ 忘却率 * 今までのバンディットスコア\n" }, { "alpha_fraction": 0.5760576128959656, "alphanum_fraction": 0.5805580615997314, "avg_line_length": 36.533782958984375, "blob_id": "31386bd7da36ba446c62ccd7d6fb911b270ec3a1", "content_id": "5f43e5114ed1c6b4149d98b3f2e83290bdc6c9f1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5555, "license_type": "no_license", "max_line_length": 147, "num_lines": 148, "path": "/bandit/analysis_impl.py", "repo_name": "Kota-Yamaguchi/MD_bandit", "src_encoding": "UTF-8", "text": "import numpy as np\nfrom MDAnalysis.analysis import align\nfrom MDAnalysis.analysis.rms import rmsd\nimport MDAnalysis as mda\nfrom MDAnalysis import Universe\nfrom MDAnalysis.lib.util import get_weights, deprecate\nimport matplotlib.pyplot as plt\n#plt.switch_backend(\"tkagg\")\nimport os\nfrom MDAnalysis.analysis.pca import PCA\nfrom argparse import ArgumentParser\nimport sys\nfrom .analysis import Analysis\n\ndef get_option():\n argparser = ArgumentParser()\n argparser.add_argument(\"-o\",\"--option\",action='store_true')\n argparser.add_argument(\"-s\",\"--setting\", help=\"gro file.\")\n argparser.add_argument(\"-f\",\"--force\", nargs=\"\", help=\"trajectory.\")\n argparser.add_argument(\"-r\",\"--reference\", help=\"referense .\")\n argparser.add_argument(\"-fi\",\"--fitting\", help=\"fitting trajectory.\")\n argparser.add_argument(\"--rmsd\",help=\"what atoms you want to calculate ??\")\n argparser.add_argument(\"--pca\",help=\"what atoms you want to calculate ??\")\n argparser.add_argument(\"--path\", default=\".\")\n args = argparser.parse_args()\n return args\n\n#cudir = os.getcwd()\n\nclass Analysis_impl2(Analysis):\n def __init__(self): \n self.option = get_option()\n path = self.option.path\n self.cudir = path\n if self.option.option == True:\n self.top = self.option.setting\n obje = self.option.reference\n\n if len(self.option.force)==1:\n traj = self.option.force\n elif len(self.option.force) != 1:\n traj = self.option.force\n\n print(np.array(traj).shape)\n self.ref = Universe(self.top, obje)\n #traj = traj[0]\n print(traj)\n trj = Universe(self.top, traj)\n print(\"fitting\")\n align.AlignTraj(trj, self.ref, select=\"{}\".format(self.option.fitting),filename=\"alin.xtc\").run()\n print(\"fited\")\n self.trj = Universe(self.top, \"alin.xtc\")\n print(\"align\")\n print(self.trj.trajectory) \n else:\n self.top = None\n self.trj = None\n self.ref = None \n self.pca_eigen_vec = None\n # a=map(lambda i : np.array(i), self.trj.select_atoms(\"name CA\").trajectory)\n # trj = np.array(list(a))\n # b=map(lambda i : trj[i].reshape(trj.shape[1]3),range(trj.shape[0]))\n # self.traj_row = np.array(list(b))\n # print(self.traj_row.shape)\n \n def root_mean_square_deviation(self):\n rmsd1=np.array([])\n print(\"calculating rmsd1\")\n for ts in self.trj.trajectory:\n a =rmsd(self.trj.select_atoms(\"{}\".format(self.option.rmsd)).positions, self.ref.select_atoms(\"{}\".format(self.option.rmsd)).positions)\n rmsd1 = np.append(rmsd1, a)\n print(rmsd1)\n return rmsd1\n\n\n def PCA_(self, n_components=30, select_=\"name CA\"):\n #if self.option.option != True: \n #pc_space = np.array([])\n if self.option.option == True:\n if os.path.exists(self.cudir+\"/eigvec.npy\") ==True:\n pca = PCA(self.trj ,select=\"{}\".format(select_)).run()\n eig_vec = np.load(self.cudir+\"/eigvec.npy\")\n #eig_vec = pre_eigen_vec\n PC=pca.transform(self.trj.select_atoms(\"{}\".format(select_)), n_components=n_components,eigen_vec = eig_vec)\n print(\"axis\",PC.shape)\n #pc_space = np.append(pc_space, PC, axis =0)\n #PC = np.dot(self.traj_row, pre_eigen_vec)\n \n else:\n print(\"You don't have eigen vector, make eigvec \")\n pca = PCA(self.trj ,select=\"{}\".format(select_)).run()\n PC=pca.transform(self.trj.select_atoms(\"{}\".format(select_)), n_components=n_components)\n eig_vec = pca.p_components\n np.save(self.cudir+\"/eigvec.npy\", eig_vec)\n #pc_space = np.append(pc_space, PC, axis =1)\n \n print(\"tar\",PC.shape) \n PC_ref=pca.transform(self.ref.select_atoms(\"{}\".format(select_)), n_components=n_components, eigen_vec = eig_vec)\n print(\"ref\",PC_ref.shape)\n PC_ref=PC_ref.reshape(len(PC_ref[0]))\n print(\"ref reshape\",PC_ref.shape)\n \n pcnorm = np.linalg.norm(PC, axis =1)\n PCnormalize = [PC[i]/pcnorm[i] for i in range(len(pcnorm))]\n PCnormalize = np.array(PCnormalize)\n\n pcrefnorm = np.linalg.norm(PC_ref) \n PCnormalize_ref = PC_ref/pcrefnorm\n PC_dif = np.dot(PCnormalize,PCnormalize_ref)\n \n PC_dif = np.abs(PC_dif-1)\n print(\"dif\",PC_dif)\n return PC, PC_dif, eig_vec \n\n\n\n def ranking(self, RC, rank = 10, reverse = True):\n hist = RC\n rank_argv = []\n top_rank = sorted(hist, reverse = reverse)[:int(rank)]\n for n in range(len(Series(top_rank))):\n for i in range(len(hist)):\n if hist[i] == Series(top_rank)[n]:\n rank_argv.append(i)\n score = np.array([rank_argv, top_rank])\n return score\n\n def translater(self, top, xtc, frame, number):\n traj = md.load_xtc(xtc, top=top)\n traj = traj[frame]\n traj.save_gro(\"candi{0}_{1}.gro\".format(number,frame))\n\n def main(self):\n if self.option.rmsd != None:\n rmsd=self.root_mean_square_deviation()\n return rmsd\n if self.option.pca != None:\n pca ,pca_dif, eigvec = self.PCA_(select_=self.option.pca)\n return pca_dif\nif __name__ == \"__main__\":\n a = analysis()\n RC=a.main()\n np.save(\"RC.npy\",RC)\n #pca,eigvec = a.PCA_()\n \n #plt.plot(rmsd,color=\"r\")\n #plt.plot(pca.T[0],color=\"b\")\n #plt.show()\n" }, { "alpha_fraction": 0.6592572927474976, "alphanum_fraction": 0.6655334830284119, "avg_line_length": 31.965517044067383, "blob_id": "bbbc905cdd8fed6f8a9d7c97eb74ab7bcf00145e", "content_id": "f843433bb1e3283651e675994167e84dc2d80da5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3824, "license_type": "no_license", "max_line_length": 89, "num_lines": 116, "path": "/bandit/bandit.py", "repo_name": "Kota-Yamaguchi/MD_bandit", "src_encoding": "UTF-8", "text": "import numpy as np\nimport os\nfrom analysis_impl2 import Analysis_impl2 \nimport sys\nclass Bandit:\n\tdef __init__(self, trj, top, ref):\n\t\tself.cudir = os.getcwd()\n\t\tself.forgettingRate = 0.8\n\t\tself.rankNumber = 100\n\t\tself.banditScoreName = \"ContactMap\"\n\t\tself.RC_list =np.array( [\"RMSD\", \"ContactMap\", \"PCA\"])\n\t\t\n\t\tif os.path.exists(self.cudir+\"/prob.npy\") == False:\n\t\t\tself.RC_prob = np.zeros(self.RC_list.shape)\n\t\t\tself.RC_prob += 1/self.RC_list.shape[0]\n\t\t\n\t\telse:\n\t\t\tself.RC_prob = np.load(self.cudir+\"/prob.npy\",allow_pickle=True)\n\t\t\t\n\t\tif os.path.exists(self.cudir+\"/Score.npy\") == True:\n\t\t\tself.score = np.load(self.cudir+\"/Score.npy\",allow_pickle=True)[0]\n\t\t\tself.choice = max(self.score)\n\t\telse:\n\t\t\tself.score = {\"RMSD\": 0,\n\t\t\t\t\"ContactMap\":0,\n\t\t\t\t\"PCA\":0}\n\t\t\tchoice = np.random.choice(self.RC_list, p = self.RC_prob) \n\t\n\t\tself.RC = Analysis_impl2(trj, top, ref)\t\t\n\n\t\tself.calcRC = {\"RMSD\" : (lambda i : self.RC.rootMeanSquareDeviation(i)) } \n\t\tself.calcRC[\"ContactMap\"] = lambda i : self.RC.contactMap(i)\n\t\tself.calcRC[\"PCA\"] = lambda i : self.RC.pca(i)\n\t\t\n\n\n\tdef run(self,banditScoreName):\n\t\tprint(\"Probability of one reaction coordinate {}\".format(self.RC_prob))\t\t\n\t\tchoice = np.random.choice(self.RC_list, p = self.RC_prob) \n\t\tresult = self.calcRC[choice](None)\n\t\tprint(result.shape)\n\t\trank = self.RC.ranking(result,rank=self.rankNumber)\n\n\t\twith open(self.cudir+\"/rank.txt\", \"w\") as f:\n\t\t\t\tf.write(str(rank.T))\t\n\n\n\n\t\trank_argv = rank[0]\n\t\trank_argv = [int(n) for n in rank_argv ]\n\t\trankerValue = self.calcRC[banditScoreName](rank_argv)\n\t\ttopRanker = np.max(rankerValue)\n\n\t\tself._score(topRanker, choice)\n\t\t#if os.path.exists(self.cudir+\"/preBanditScore.npy\") == False:\n\t\t#\t\n\t\t#\tnp.save(self.cudir+\"/preBanditScore.npy\", topRanker)\n\t\t#\tdelta = topRanker\n\t\t#\tself.score[choice] = delta\n\t\t#\tnp.save(self.cudir+\"/Score.npy\", [self.score])\n\t\t#else:\n\t\t#\tpreBanditScore = np.load(self.cudir+\"/preBanditScore.npy\",allow_pickle=True)\n\t\t#\tdelta = topRanker - preBanditScore\n\t\t#\tpreScore = np.load(self.cudir+\"/Score.npy\",allow_pickle=True)[0]\n\t\t#\tself.score[choice] = delta\n\t\t#\tfor i in preScore:\n\t\t#\t\tself.score[i]+=(self.learningRate * preScore[i])\t\n\t\t#\tnp.save(self.cudir+\"/Score.npy\", [self.score])\n\t\t\n\t\tself._updateProbabilitySoftmax(self.score, tau=10)\n\t\t\t\n\tdef _updateProbabilitySoftmax(self, score, tau = 10):\n\t\tprint(\"Update Probability \")\n\t\tn = 0\n\t\tsigma = 0\n\t\tfor i in self.RC_list:\n\t\t\tsigma += np.exp(score[i]/tau)\n\t\tfor i in self.RC_list:\n\t\t\tprint(\"{} : {}\".format(i ,np.exp(score[i]/tau)/sigma))\n\t\t\tself.RC_prob[n] = np.exp(score[i]/tau)/sigma\n\t\t\tn+=1\n\t\tnp.save(self.cudir+\"/prob.npy\", self.RC_prob)\n\t\treturn self.RC_prob\n\t\t\n\n\tdef _score(self, Ranker, choiceRC):\n\t\tif os.path.exists(self.cudir+\"/preBanditScore.npy\") == False:\n\t\t\t\n\t\t\tnp.save(self.cudir+\"/preBanditScore.npy\", Ranker)\n\t\t\tdelta = Ranker\n\t\t\tself.score[choiceRC] = delta\n\t\t\tnp.save(self.cudir+\"/Score.npy\", [self.score])\n\t\telse:\n\t\t\tpreBanditScore = np.load(self.cudir+\"/preBanditScore.npy\",allow_pickle=True)\n\t\t\tdelta = Ranker - preBanditScore\n\t\t\tpreScore = np.load(self.cudir+\"/Score.npy\",allow_pickle=True)[0]\n\t\t\tself.score[choiceRC] = delta\n\t\t\tfor i in preScore:\n\t\t\t\tself.score[i]+=(self.forgettingRate * preScore[i])\t\n\t\t\tnp.save(self.cudir+\"/Score.npy\", [self.score])\n\n\t\t\n\t\t\nif __name__==\"__main__\":\n\tfrom argparse import ArgumentParser\n\tdef get_option():\n\t\targparser = ArgumentParser()\n\t\targparser.add_argument(\"-s\",\"--setting\", help=\"gro file.\")\n\t\targparser.add_argument(\"-t\",\"--traj\", nargs=\"\", help=\"trajectory.\")\n\t\targparser.add_argument(\"-r\",\"--reference\", help=\"referense .\")\n\t\targparser.add_argument(\"-b\",\"--banditscore\",default=\"ContactMap\", help=\"BanditScore .\")\n\t\targs = argparser.parse_args()\n\t\treturn args\n\toption = get_option()\n\tbandit = Bandit(option.traj, option.setting ,option.reference)\n\tbandit.run(option.banditscore)\n" }, { "alpha_fraction": 0.7213482856750488, "alphanum_fraction": 0.7258427143096924, "avg_line_length": 25.147058486938477, "blob_id": "8c38381f9fb91a2999fc99cb4df91f3b200f423a", "content_id": "ad164ff33107f2c6b8e72f60376ca0732e60ccd7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 890, "license_type": "no_license", "max_line_length": 56, "num_lines": 34, "path": "/bandit/analysis.py", "repo_name": "Kota-Yamaguchi/MD_bandit", "src_encoding": "UTF-8", "text": "import numpy as np\nfrom MDAnalysis.analysis import align\nfrom MDAnalysis.analysis.rms import rmsd\nimport MDAnalysis as mda\nfrom MDAnalysis import Universe\n#from MDAnalysis.lib.util import get_weights, deprecate\nimport matplotlib.pyplot as plt\n#plt.switch_backend(\"tkagg\")\nimport os\nfrom MDAnalysis.analysis.pca import PCA\n#from argparse import ArgumentParser\n#import sys\n\nclass Analysis():\n\tdef rootMeanSquareDeviation(self):\n\t\treturn null\n\n\n\tdef pca(self, n_components=30, select_=\"name CA\"):\n\t\treturn null, null, null \n\n\tdef contactMap(self):\n\t\treturn null\n\n\tdef ranking(self, RC, rank = 10, reverse = True):\n\t\thist = RC\n\t\trank_argv = []\n\t\ttop_rank = sorted(hist, reverse = reverse)[:int(rank)]\n\t\tfor n in range(len(Series(top_rank))):\n\t\t\tfor i in range(len(hist)):\n\t\t\t\tif hist[i] == Series(top_rank)[n]:\n\t\t\t\t\trank_argv.append(i)\n\t\tscore = np.array([rank_argv, top_rank])\n\t\treturn score\n\n" }, { "alpha_fraction": 0.6695603728294373, "alphanum_fraction": 0.682581901550293, "avg_line_length": 38.888267517089844, "blob_id": "d0475135b16e08486ab2ba0de472292c717fbf38", "content_id": "f5334758979a5942ea156c4dbe5627e219a24706", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7142, "license_type": "no_license", "max_line_length": 79, "num_lines": 179, "path": "/bandit/pca_mod.py", "repo_name": "Kota-Yamaguchi/MD_bandit", "src_encoding": "UTF-8", "text": "# -- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding:utf-8 --\n# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4\n#\n# MDAnalysis --- https://www.mdanalysis.org\n# Copyright (c) 2006-2017 The MDAnalysis Development Team and contributors\n# (see the file AUTHORS for the full list of names)\n#\n# Released under the GNU Public Licence, v2 or any higher version\n#\n# Please cite your use of MDAnalysis in published work:\n#\n# R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler,\n# D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein.\n# MDAnalysis: A Python package for the rapid analysis of molecular dynamics\n# simulations. In S. Benthall and S. Rostrup editors, Proceedings of the 15th\n# Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy.\n# doi: 10.25080/majora-629e541a-00e\n#\n# N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein.\n# MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations.\n# J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787\n#\n\nr\"\"\"Principal Component Analysis (PCA) --- :mod:`MDAnalysis.analysis.pca`\n=====================================================================\n\n:Authors: John Detlefs\n:Year: 2016\n:Copyright: GNU Public License v3\n\n.. versionadded:: 0.16.0\n\nThis module contains the linear dimensions reduction method Principal Component\nAnalysis (PCA). PCA sorts a simulation into 3N directions of descending\nvariance, with N being the number of atoms. These directions are called\nthe principal components. The dimensions to be analyzed are reduced by only\nlooking at a few projections of the first principal components. To learn how to\nrun a Principal Component Analysis, please refer to the :ref:`PCA-tutorial`.\n\nThe PCA problem is solved by solving the eigenvalue problem of the covariance\nmatrix, a :math:`3N \\times 3N` matrix where the element :math:`(i, j)` is the\ncovariance between coordinates :math:`i` and :math:`j`. The principal\ncomponents are the eigenvectors of this matrix.\n\nFor each eigenvector, its eigenvalue is the variance that the eigenvector\nexplains. Stored in :attr:`PCA.cumulated_variance`, a ratio for each number of\neigenvectors up to index :math:`i` is provided to quickly find out how many\nprincipal components are needed to explain the amount of variance reflected by\nthose :math:`i` eigenvectors. For most data, :attr:`PCA.cumulated_variance`\nwill be approximately equal to one for some :math:`n` that is significantly\nsmaller than the total number of components, these are the components of\ninterest given by Principal Component Analysis.\n\nFrom here, we can project a trajectory onto these principal components and\nattempt to retrieve some structure from our high dimensional data.\n\nFor a basic introduction to the module, the :ref:`PCA-tutorial` shows how\nto perform Principal Component Analysis.\n\n.. _PCA-tutorial:\n\nPCA Tutorial\n------------\n\nThe example uses files provided as part of the MDAnalysis test suite\n(in the variables :data:`~MDAnalysis.tests.datafiles.PSF` and\n:data:`~MDAnalysis.tests.datafiles.DCD`). This tutorial shows how to use the\nPCA class.\n\nFirst load all modules and test data\n\n >>> import MDAnalysis as mda\n >>> import MDAnalysis.analysis.pca as pca\n >>> from MDAnalysis.tests.datafiles import PSF, DCD\n\nGiven a universe containing trajectory data we can perform Principal Component\nAnalyis by using the class :class:`PCA` and retrieving the principal\ncomponents.\n\n >>> u = mda.Universe(PSF, DCD)\n >>> PSF_pca = pca.PCA(u, select='backbone')\n >>> PSF_pca.run()\n\nInspect the components to determine the principal components you would like\nto retain. The choice is arbitrary, but I will stop when 95 percent of the\nvariance is explained by the components. This cumulated variance by the\ncomponents is conveniently stored in the one-dimensional array attribute\n``cumulated_variance``. The value at the ith index of `cumulated_variance`\nis the sum of the variances from 0 to i.\n\n >>> n_pcs = np.where(PSF_pca.cumulated_variance > 0.95)[0][0]\n >>> atomgroup = u.select_atoms('backbone')\n >>> pca_space = PSF_pca.transform(atomgroup, n_components=n_pcs)\n\nFrom here, inspection of the ``pca_space`` and conclusions to be drawn from the\ndata are left to the user.\n\nClasses and Functions\n---------------------\n\n.. autoclass:: PCA\n.. autofunction:: cosine_content\n\n\"\"\"\nfrom __future__ import division, absolute_import\nfrom six.moves import range\nimport warnings\n\nimport numpy as np\nimport scipy.integrate\n\nfrom MDAnalysis import Universe\nfrom MDAnalysis.analysis.align import _fit_to\nfrom MDAnalysis.lib.log import ProgressMeter\n\nfrom .base import AnalysisBase\nfrom .pca import PCA\n\nclass PCA_MOD(PCA):\n def transform(self, atomgroup, n_components=None, start=None, stop=None,\n step=None,eigen_vec=None):\n \"\"\"Apply the dimensionality reduction on a trajectory\n\n Parameters\n ----------\n atomgroup: MDAnalysis atomgroup/ Universe\n The atomgroup or universe containing atoms to be PCA transformed.\n n_components: int, optional\n The number of components to be projected onto, Default none: maps\n onto all components.\n start: int, optional\n The frame to start on for the PCA transform. Default: None becomes\n 0, the first frame index.\n stop: int, optional\n Frame index to stop PCA transform. Default: None becomes n_frames.\n Iteration stops before* this frame number, which means that the\n trajectory would be read until the end.\n step: int, optional\n Number of frames to skip over for PCA transform. Default: None\n becomes 1.\n\n Returns\n -------\n pca_space : array, shape (number of frames, number of components)\n\n .. versionchanged:: 0.19.0\n Transform now requires that :meth:`run` has been called before,\n otherwise a :exc:`ValueError` is raised.\n \"\"\"\n if np.any(eigen_vec) != None:\n self.p_components = eigen_vec\n if not self._calculated:\n raise ValueError('Call run() on the PCA before using transform')\n\n if isinstance(atomgroup, Universe):\n atomgroup = atomgroup.atoms\n\n if(self._n_atoms != atomgroup.n_atoms):\n raise ValueError('PCA has been fit for'\n '{} atoms. Your atomgroup'\n 'has {} atoms'.format(self._n_atoms,\n atomgroup.n_atoms))\n if not (self._atoms.types == atomgroup.types).all():\n warnings.warn('Atom types do not match with types used to fit PCA')\n\n traj = atomgroup.universe.trajectory\n start, stop, step = traj.check_slice_indices(start, stop, step)\n n_frames = len(range(start, stop, step))\n\n dim = (n_components if n_components is not None else\n self.p_components.shape[1])\n\n dot = np.zeros((n_frames, dim))\n\n for i, ts in enumerate(traj[start:stop:step]):\n xyz = atomgroup.positions.ravel() - self.mean\n dot[i] = np.dot(xyz, self.p_components[:, :n_components])\n\n return dot\n\n\n" }, { "alpha_fraction": 0.6403831839561462, "alphanum_fraction": 0.6462785601615906, "avg_line_length": 31.830644607543945, "blob_id": "0fb51e4e494045ed54b43ccc95218f428fe195d8", "content_id": "ceb9fbeb9292208e7d515b948a6e4aa774581eb1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4071, "license_type": "no_license", "max_line_length": 114, "num_lines": 124, "path": "/bandit/analysis_impl2.py", "repo_name": "Kota-Yamaguchi/MD_bandit", "src_encoding": "UTF-8", "text": "import numpy as np\n#from MDAnalysis.analysis import align\nfrom MDAnalysis.analysis.rms import rmsd\nimport MDAnalysis as mda\nfrom MDAnalysis import Universe\nfrom pandas import Series\n#from MDAnalysis.lib.util import get_weights, deprecate\n#import matplotlib.pyplot as plt\n#plt.switch_backend(\"tkagg\")\nimport os\nfrom MDAnalysis.analysis.pca_mod import PCA_MOD as PCA\n#from argparse import ArgumentParser\nimport sys\nfrom analysis import Analysis\nfrom scipy.spatial import distance\n\nclass Analysis_impl2(Analysis):\n\tdef __init__(self , trj, top, ref): \n\t\tself.cudir = os.getcwd()\n\t\tself.ref = Universe(top, ref)\n\t\tself.trj = Universe(top, trj)\n\n\n\tdef rootMeanSquareDeviation(self, rank_argv=None ,part = \"name CA\"):\n\t\trmsd1=np.array([])\n\t\tprint(\"calculating rmsd1\")\n\t\tfor ts in self.trj.trajectory:\n\t\t\ta =rmsd(self.trj.select_atoms(\"{}\".format(part)).positions, self.ref.select_atoms(\"{}\".format(part)).positions)\n\t\t\trmsd1 = np.append(rmsd1, a)\n\t\tprint(rmsd1)\n\n\t\tif rank_argv != None:\n\t\t\treturn rmsd1[rank_argv]\n\t\t\n\t\treturn rmsd1\n\t\n\tdef contactMap(self, rank_argv=None):\n\t\tprint(\"calculate ContactMap \")\n\t\td = map(lambda i : np.array(i), self.trj.trajectory)\n\t\td = np.array(list(d))\n\t\t\n\t\tdist_M = map(lambda i : distance.cdist(d[i], d[i] ,metric=\"euclidean\"), range(d.shape[0]))\n\t\tdist_M = np.array(list(dist_M))\n\t\t\n\t\td_ref = map(lambda i : np.array(i), self.ref.trajectory)\n\t\td_ref = np.array(list(d_ref))\n\t\tref_dist_M = map(lambda i : distance.cdist(d_ref[i], d_ref[i] ,metric=\"euclidean\"), range(d_ref.shape[0]))\n\t\tref_dist_M = np.array(list(ref_dist_M))\n\t\t\n\t\thist_dif = []\n\t\tfor i in range(dist_M.shape[0]):\n\t\t\tdist_dif = np.sum((dist_M[i] -ref_dist_M)**2)\n\t\t\thist_dif.append(dist_dif)\n\t\thist_dif = np.array(hist_dif)\n\t\thist_dif = self.min_max(hist_dif)\t\n\t\tif np.all(rank_argv) != None:\n\t\t\treturn hist_dif[rank_argv]\t\t\t\t\n\t\n\t\treturn hist_dif\n\n\tdef pca(self, comp=30, select_=\"name CA\",rank_argv=None):\n #if self.option.option != True: \n #pc_space = np.array([])\n\n\t\tn_comp = 30\n\t\tprint(\"calculate Principle Component\")\n\t\tif os.path.exists(self.cudir+\"/eigvec.npy\") ==True:\n\t\t\tpca = PCA(self.trj ,select=\"{}\".format(select_)).run()\n\t\t\teig_vec = np.load(self.cudir+\"/eigvec.npy\")\n #eig_vec = pre_eigen_vec\n\t\t\tPC=pca.transform(self.trj.select_atoms(\"{}\".format(select_)), n_components=n_comp,eigen_vec = eig_vec)\n\t\t\tprint(\"PC:{}\".format(PC.shape))\n\t#pc_space = np.append(pc_space, PC, axis =0)\n #PC = np.dot(self.traj_row, pre_eigen_vec)\n \n\t\telse:\n\t\t\tprint(\"You don't have eigen vector, make eigvec \")\n\t\t\tpca = PCA(self.trj ,select=\"{}\".format(select_)).run()\n\t\t\tPC=pca.transform(self.trj.select_atoms(\"{}\".format(select_)), n_components=n_comp)\n\t\t\teig_vec = pca.p_components\n\t\t\tprint(\"PC:{}\".format(PC.shape))\n\t\t\tnp.save(self.cudir+\"/eigvec.npy\", eig_vec)\n #pc_space = np.append(pc_space, PC, axis =1)\n \n\t\tPC_ref=pca.transform(self.ref.select_atoms(\"{}\".format(select_)), n_components=n_comp, eigen_vec = eig_vec)\n\t\tPC_ref=PC_ref.reshape(len(PC_ref[0]))\n\t\tdot_dif = PC - PC_ref\n\t\tscore = np.sum(np.abs(dot_dif),axis =1)\n\t\tprint(\"PC score {}\".format(score))\n\t\treturn score\n # \n\t#\tpcnorm = np.linalg.norm(PC, axis =1)\n\t#\tPCnormalize = [PC[i]/pcnorm[i] for i in range(len(pcnorm))]\n\t#\tPCnormalize = np.array(PCnormalize)\n#\n#\t\tpcrefnorm = np.linalg.norm(PC_ref) \n#\t\tPCnormalize_ref = PC_ref/pcrefnorm\n#\t\tPC_dif = np.dot(PCnormalize,PCnormalize_ref)\n # \n#\t\tPC_dif = np.abs(PC_dif-1)\n#\t\tif rank_argv != None:\n # return PC_dif[rank_argv]\n#\t\treturn PC_dif #eig_vec \n\t\t\n\t\t\t\t\n\n\n\tdef min_max(self, x, axis=None):\n\t\tmin = x.min(axis=axis, keepdims=True)\n\t\tmax = x.max(axis=axis, keepdims=True)\n\t\tresult = (x-min)/(max-min)\n\t\treturn result\n\n\n\tdef ranking(self, RC, rank = 10, reverse = True):\n\t\thist = RC\n\t\trank_argv = []\n\t\ttop_rank = sorted(hist, reverse = reverse)[:int(rank)]\n\t\tfor n in range(len(Series(top_rank))):\n\t\t\tfor i in range(len(hist)):\n\t\t\t\tif hist[i] == Series(top_rank)[n]:\n\t\t\t\t\trank_argv.append(int(i))\n\t\tscore = np.array([rank_argv, top_rank])\n\t\treturn score\n" } ]	6
alexletu/traditional_ml_models	https://github.com/alexletu/traditional_ml_models	6abca5e339f74b02aae0fefd4f88fd971c622148	5c7c0d6c8a7ba31f4423843235ac21eb1ef6a97b	fcb6777c2aafb8590ad398492a3317c1ac74f288	refs/heads/main	2023-01-20T12:23:58.889113	2020-11-24T09:28:45	2020-11-24T09:28:45	315,579,542	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5733481049537659, "alphanum_fraction": 0.5769838690757751, "avg_line_length": 39.42172622680664, "blob_id": "02fc98baea9410b90ab95723f6d12dae53cb4bdf", "content_id": "291f6ab4b4c5083d8a2d288afb1071b2c2e4a6b4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 12652, "license_type": "no_license", "max_line_length": 139, "num_lines": 313, "path": "/decision_trees/decision_tree_starter.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "from collections import Counter\nimport numpy as np\nimport random\nfrom utils import extract_column, majority, find_index\n\ndef gen_clustered_thresholds(data, idx):\n sorted_data = data[data[:, idx].argsort()]\n idy = data.shape[1] - 1\n thresholds = []\n if len(data) == 1:\n return [data[0][idx]]\n for i in range(1, len(sorted_data)):\n prev = data[i - 1]\n curr = data[i]\n prev_y = prev[idy]\n curr_y = curr[idy]\n curr_x = curr[idx]\n prev_x = prev[idx]\n if prev_y != curr_y:\n thresholds.append((curr_x + prev_x) / 2)\n return thresholds\n\n\ndef gen_quantitative_thresholds(data, idx):\n xs = extract_column(data, idx)\n return [np.mean(xs)]\n\n\ndef gen_quantitative_thresholds_exact(data, idx):\n thresholds = gen_clustered_thresholds(data, idx)\n\n best_purification = -1\n best_threshold = None\n for threshold in thresholds:\n purification = DecisionTree.gini_purification(data, idx, False, [threshold])\n if purification > best_purification:\n best_purification = purification\n best_threshold = threshold\n return [best_threshold] if best_threshold is not None else gen_quantitative_thresholds(data, idx)\n\n\nclass Node:\n \"\"\"\n depth is an integer\n split_idx denotes the feature being split on 0 - d\n IF leaf node, then split_idx is None\n split_thresholds denotes:\n categories to split on -> len(children) = len(thresholds)\n quantitative data to split on -> len(children) = len(thresholds) + 1\n IF leaf node, then split_thresholds is None\n split_categorical is a boolean, denotes if this node follows categorical or quantitative splitting\n IF leaf node, then split_categorical is None\n is_leaf is a boolean denoting if this is leaf node or not\n children is a list of children\n IF leaf, children will be the empty list\n label denotes the class which this node represents\n IF not leaf, label will be None\n \"\"\"\n def __init__(self, depth, feature_names = None, class_names = None):\n self.depth = depth\n self.split_idx = None\n self.split_thresholds = None\n self.split_categorical = None\n self.is_leaf = None\n self.children = []\n self.label = None\n self.feature_names = feature_names\n self.class_names = class_names\n\n def set_leaf(self, label):\n self.is_leaf = True\n self.label = label\n\n def set_internal(self, idx, categorical, thresholds, children, label):\n self.split_idx = idx\n self.split_categorical = categorical\n self.split_thresholds = thresholds\n self.is_leaf = False\n self.children = children\n self.label = label\n\n def __repr__(self):\n offset = \"\\n\" + self.depth * \" \"\n s = \"\"\n if self.is_leaf:\n s += offset + \"Leaf Node\"\n s += offset + \" Depth = \" + str(self.depth)\n s += offset + \" Label = \" + (str(self.label) if self.class_names is None else self.class_names[int(self.label)])\n return s\n else:\n s += offset + \"Internal Node\"\n s += offset + \" Depth = \" + str(self.depth)\n s += offset + \" Split on feat = \" + (str(self.split_idx) if self.feature_names is None else self.feature_names[self.split_idx])\n s += offset + \" Categorical split = \" + str(self.split_categorical)\n s += offset + \" Categories/Thresholds = \" + str(self.split_thresholds)\n for child in self.children:\n s += child.__repr__()\n return s\n def string(self):\n offset = \"\\n\" + self.depth * \" \"\n s = \"\"\n if self.is_leaf:\n s += offset + \"Leaf Node\"\n s += offset + \" Depth = \" + str(self.depth)\n s += offset + \" Label = \" + (str(self.label) if self.class_names is None else self.class_names[int(self.label)])\n return s\n else:\n s += offset + \"Internal Node\"\n s += offset + \" Depth = \" + str(self.depth)\n s += offset + \" Split on feat = \" + (str(self.split_idx) if self.feature_names is None else self.feature_names[self.split_idx])\n s += offset + \" Categorical split = \" + str(self.split_categorical)\n s += offset + \" Categories/Thresholds = \" + str(self.split_thresholds)\n return s\n\nclass DecisionTree:\n def __init__(self, type_map, categories_map, feature_names = None, class_names = None):\n self.type_map = type_map\n self.categories_map = categories_map\n self.root = None\n self.bag_size = -1\n self.feature_names = feature_names\n self.class_names = class_names\n\n @staticmethod\n def gini_impurity(y):\n \"\"\"\n Input: 2d array of class labels (column vector)\n Output float number that represents the gini impurity. Low is good.\n \"\"\"\n cnt = Counter(y.flatten())\n gini = 0\n total = len(y)\n for elem in list(cnt): # elements of cnt are classes\n gini += (cnt[elem] / total) ** 2\n return 1 - gini\n\n\n @staticmethod\n def gini_purification(data, idx, categorical, thresholds):\n \"\"\"\n Input: data (last column is label column), feature to split on, whether or not the split is categorical,\n list of thresholds or categories\n Output: the change in gini-impurity, lower (negative) is good.\n \"\"\"\n idy = data.shape[1] -1\n y = extract_column(data, idy)\n x = extract_column(data, idx)\n gini_before = DecisionTree.gini_impurity(y)\n gini_after = 0\n\n # joined represents two columns, left is idx feature, right is the label\n joined = np.hstack((x,y))\n total = len(joined)\n\n if categorical:\n for category in thresholds:\n split_data = joined[joined[:, 0] == category]\n new_y = extract_column(split_data, 1)\n gini_after += DecisionTree.gini_impurity(new_y) * len(new_y)\n else:\n for thresh in thresholds:\n split_data_below = joined[joined[:, 0] < thresh]\n joined = joined[joined[:, 0] >= thresh]\n new_y = extract_column(split_data_below, 1)\n gini_after += DecisionTree.gini_impurity(new_y) * len(new_y)\n new_y = extract_column(joined, 1)\n gini_after += DecisionTree.gini_impurity(new_y) * len(new_y)\n gini_after /= total\n\n return gini_before - gini_after\n\n def split(self, data, idx, categorical, thresholds):\n \"\"\"\n Input: data (last column is label column), feature to split on, whether or not the split is categorical,\n list of thresholds or categories\n Output: a python list of numpy 2-d arrays representing the split data based on idx and thresholds\n \"\"\"\n total_data = []\n if categorical:\n for category in thresholds:\n split_data = data[data[:, idx] == category]\n total_data.append(split_data)\n else:\n for thresh in thresholds:\n split_data_below = data[data[:, idx] < thresh]\n data = data[data[:, idx] >= thresh]\n total_data.append(split_data_below)\n total_data.append(data)\n return total_data\n \n def segmenter(self, data):\n \"\"\"\n Input: data matrix, last column are labels\n Output returns the feature to split on (idx), categorical or not, the categories or thresholds to split on,\n the best purification\n \"\"\"\n best_idx = -1\n best_categorical = None\n best_thresholds = []\n best_purification = -1\n # for each feature\n sampled_features = np.random.choice(a=list(range(0, data.shape[1] - 1)), size=self.bag_size ,replace=False)\n for idx in sampled_features:\n feature_type = self.type_map[idx]\n if feature_type == 'categorical':\n thresholds = self.categories_map[idx] # these are really categories\n purification = DecisionTree.gini_purification(data, idx, True, thresholds)\n categorical = True\n elif feature_type == 'clustered':\n thresholds = gen_clustered_thresholds(data, idx)\n purification = DecisionTree.gini_purification(data, idx, False, thresholds)\n categorical = False\n else:\n #thresholds = gen_quantitative_thresholds(data, idx) #use with spam\n thresholds = gen_quantitative_thresholds_exact(data, idx) #use with titanic\n purification = DecisionTree.gini_purification(data, idx, False, thresholds)\n categorical = False\n if purification > best_purification:\n best_idx = idx\n best_categorical = categorical\n best_thresholds = thresholds\n best_purification = purification\n return best_idx, best_categorical, best_thresholds, best_purification\n\n def fit_helper(self, data, current_depth, max_depth, min_samples, node):\n if data.shape[0] == 0:\n node.is_leaf = True\n return\n if current_depth >= max_depth or len(data) < min_samples:\n label = majority(data)\n node.set_leaf(label)\n return\n else:\n idx, categorical, thresholds, purification = self.segmenter(data)\n if purification <= .0001 or thresholds is None:\n label = majority(data)\n node.set_leaf(label)\n return\n split_data = self.split(data, idx, categorical, thresholds)\n new_children = [Node(current_depth + 1, self.feature_names, self.class_names) for _ in range(len(split_data))]\n label = majority(data)\n node.set_internal(idx, categorical, thresholds, new_children, label)\n for i in range(len(split_data)):\n node.children[i].label = label\n self.fit_helper(split_data[i], current_depth + 1, max_depth, min_samples, node.children[i])\n\n def fit(self, data, max_depth, min_samples, bag_size = None):\n if bag_size is None:\n self.bag_size = data.shape[1] - 1\n else:\n self.bag_size = bag_size\n self.root = Node(0, self.feature_names, self.class_names)\n self.fit_helper(data, 0, max_depth, min_samples, self.root)\n return\n\n def predict_helper(self, x, node, verbose):\n offset = node.depth * \" \"\n if verbose:\n print(node.string())\n\n if node.is_leaf:\n return node.label\n\n idx = node.split_idx\n categorical = node.split_categorical\n thresholds = node.split_thresholds\n\n if categorical:\n categories = thresholds\n x_val = x[idx]\n child = categories.index(x_val)\n if verbose:\n print(offset + \" Observed value: \", x_val)\n return self.predict_helper(x, node.children[child], verbose)\n else:\n x_val = x[idx]\n child = find_index(x_val, thresholds)\n if verbose:\n print(offset + \" Observed value: \", x_val)\n return self.predict_helper(x, node.children[child], verbose)\n\n def predict(self, X, verbose = False):\n predictions = []\n for x in X:\n prediction = self.predict_helper(x, self.root, verbose)\n predictions.append(prediction)\n return np.array([predictions]).T\n\n def __repr__(self):\n return self.root.__repr__()\n\n\nclass RandomForest:\n def __init__(self, trees, sample_size, bag_size, type_map, categories_map, seed):\n self.trees = [DecisionTree(type_map, categories_map) for _ in range(trees)]\n self.sample_size = sample_size\n self.seed = seed\n self.bag_size = bag_size\n\n def fit(self, data, max_depth, min_samples):\n random.seed(self.seed)\n for tree in self.trees:\n sample = data[np.random.randint(data.shape[0], size=self.sample_size), :]\n tree.fit(sample, max_depth, min_samples, self.bag_size)\n return\n \n def predict(self, X):\n tree_predictions = []\n for tree in self.trees:\n tree_predictions.append(tree.predict(X))\n tree_predictions = np.hstack(tree_predictions)\n final_predictions = [Counter(ensemble).most_common(1)[0][0] for ensemble in tree_predictions]\n return np.array(final_predictions).T\n" }, { "alpha_fraction": 0.5748559236526489, "alphanum_fraction": 0.5989813804626465, "avg_line_length": 36.49748611450195, "blob_id": "7887dc114d128529856236506a3615492613e620", "content_id": "cc5054f3e1d99c392ee5a82571ff2e784fd4c305", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7461, "license_type": "no_license", "max_line_length": 210, "num_lines": 199, "path": "/logistic_regression/wine.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import io\nfrom save_csv import results_to_csv\nfrom sklearn.preprocessing import StandardScaler\nimport time\n\n\ndef permute_dictionaries(data, labels, rand=0):\n perm = np.random.RandomState(seed=rand).permutation(data.shape[0])\n return data[perm], labels[perm]\n\ntemp = io.loadmat(\"data.mat\")\ndata = temp[\"X\"]\nlabels = temp[\"y\"]\ndata, labels = permute_dictionaries(data, labels)\nprint(\"Loaded wine data.\")\n\ntrain_data, valid_data = data[:5500], data[5500:]\ntrain_labels, valid_labels = labels[:5500], labels[5500:]\nscaler = StandardScaler()\ntrain_data = scaler.fit_transform(train_data)\nvalid_data = scaler.transform(valid_data)\ntrain_data = np.hstack((train_data, np.ones((train_data.shape[0], 1))))\nvalid_data = np.hstack((valid_data, np.ones((valid_data.shape[0], 1))))\n\ndef gen_c_values(low_exp, high_exp):\n return [10i for i in range(low_exp, high_exp + 1)]\n\ndef error_rate(prediction, actual):\n return np.count_nonzero(prediction - actual) / prediction.shape[0]\n\ndef print_info(title, time, alpha, c, epsilon, n_samples, n_test, n_iterations, final_loss, train_error, test_error, decay = None):\n print(\"****** - %s - ******\" % title)\n print(\" Time elapsed: %f\" % time)\n print(\" Learning rate: %f\" % alpha)\n print(\" Regularization value: %f\" % c)\n if decay is not None:\n print(\" Decay Rate: %.10f\" % decay)\n print(\" Epsilon value: %f\" % epsilon)\n print(\" Training Samples: %d\" % n_samples)\n print(\" Test samples: %d\" % n_test)\n print(\" Total iterations: %d\" % n_iterations)\n print(\" Final Loss: %f\" % final_loss)\n print(\" Final train error %f\" % train_error)\n print(\" Final error test error %f\" % test_error)\n return\n\ndef plot_data(iterations, losses, clr, title):\n fig = plt.figure()\n fig.set_size_inches(10, 5)\n fig.set_dpi(100)\n plt.subplot(1, 1, 1)\n plt.plot(iterations, losses, label=\"Loss\", color=clr, marker='.', linestyle='dashed',linewidth=1, markersize=1)\n plt.legend()\n plt.title(title + \" vs Iterations\")\n plt.xlabel(\"Iterations\")\n plt.ylabel(\"Loss\")\n plt.show()\n\n\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\n\ndef sigmoid(gamma):\n gamma[gamma <= -709] = -700\n gamma[gamma >= 709] = 700\n return 1/(1 + np.e(-gamma))\n\ndef log_loss(z, y):\n return -y * np.log(z + 10*-300) - (1 - y) np.log(1 - z + 10*-300)\n\ndef obj(act, pred, w, c):\n return np.mean(log_loss(pred, act)) + (c / 2) np.linalg.norm(w)\n\ndef calc_gradient(X, y, predictions, w, c):\n n = X.shape[0]\n dz = predictions - y\n dw = (1 / n) * np.matmul(X.T, dz) + c * w\n return dw\n\ndef classify(data, w):\n probabilities = np.where(sigmoid(np.matmul(data, w)) > .5, 1, 0)\n return probabilities\n\ndef train_batch(X, x_labels, alpha, c, epsilon):\n w = np.zeros((13, 1))\n iteration = 0\n loss = np.inf\n iterations = []\n losses = []\n w_prev = np.ones((13,1))\n while not np.allclose(w_prev, w) and loss >= epsilon:\n x_predictions = sigmoid(np.matmul(X, w))\n dw = calc_gradient(X, x_labels, x_predictions, w, c)\n w_prev = w\n w = w - alpha * dw\n loss = obj(x_labels, x_predictions, w, c)\n iteration += 1\n if iteration % 10 == 0:\n iterations.append(iteration)\n losses.append(loss)\n print(loss)\n iterations.append(iteration)\n losses.append(loss)\n return w, iterations, losses\n\ndef train_sgd(X, x_labels, alpha, c, epsilon, decay=0):\n w = np.zeros((13, 1))\n iteration = 0\n loss = np.inf\n iterations = []\n losses = []\n i = 0\n w_prev = np.ones((13, 1))\n while not np.allclose(w_prev, w) and loss >= epsilon:\n #out of all x choose an index i, convert it to matrix\n x_i = np.array([X[i]])\n x_i_prediction = sigmoid(np.matmul(x_i, w))\n #convert the label to the single index i, convert to matrix\n l_i = x_labels[i]\n\n dw = calc_gradient(x_i, l_i, x_i_prediction, w, c)\n w = w - alpha * dw\n all_predictions = sigmoid(np.matmul(X, w))\n loss = obj(x_labels, all_predictions, w, c)\n alpha = 1 / (1 + decay iteration)\n if iteration % 10 == 0:\n iterations.append(iteration)\n losses.append(loss)\n print(loss)\n iteration += 1\n i += 1\n if i == X.shape[0]:\n X, x_labels = permute_dictionaries(X, x_labels, rand=np.random.randint(0, 100))\n i = 0\n\n iterations.append(iteration)\n losses.append(loss)\n return w, iterations, losses\n\ndef part_a(alpha, c, epsilon):\n start = time.time()\n w, iterations, losses = train_batch(train_data, train_labels, alpha, c, epsilon)\n time_elapsed = time.time() - start\n error = error_rate(classify(valid_data, w), valid_labels)\n train_error = error_rate(classify(train_data, w), train_labels)\n print_info(\"Summary Batch Gradient Decent\", time_elapsed, alpha, c, epsilon, train_data.shape[0], valid_data.shape[0], iterations[len(iterations) - 1],losses[len(losses) - 1] ,train_error, error)\n plot_data(iterations, losses, 'red', \"Batch Gradient Decent\")\n\ndef part_b(alpha, c, epsilon):\n start = time.time()\n w, iterations, losses = train_sgd(train_data, train_labels, alpha, c, epsilon)\n time_elapsed = time.time() - start\n error = error_rate(classify(valid_data, w), valid_labels)\n train_error = error_rate(classify(train_data, w), train_labels)\n print_info(\"Summary Stochastic Gradient Decent\", time_elapsed, alpha, c, epsilon, train_data.shape[0], valid_data.shape[0], iterations[len(iterations) - 1], losses[len(losses) - 1],train_error, error)\n plot_data(iterations, losses, 'blue', 'Stochastic Gradient Descent Loss')\n\ndef part_c(alpha, c, epsilon, decay):\n start = time.time()\n w, iterations, losses = train_sgd(train_data, train_labels, alpha, c, epsilon, decay)\n time_elapsed = time.time() - start\n error = error_rate(classify(valid_data, w), valid_labels)\n train_error = error_rate(classify(train_data, w), train_labels)\n print_info(\"SGD w/ decreasing learning rate\", time_elapsed, alpha, c, epsilon, train_data.shape[0], valid_data.shape[0], iterations[len(iterations) - 1], losses[len(losses) - 1], train_error, error, decay)\n plot_data(iterations, losses, 'green', 'SGD w/ Decreasing Dearning Date Loss')\n\ndef kaggle(alpha, c, epsilon):\n X = temp[\"X\"]\n test = temp[\"X_test\"]\n x_labels = temp[\"y\"]\n X, x_labels = permute_dictionaries(X, x_labels)\n X = scaler.transform(X)\n test = scaler.transform(test)\n X = np.hstack((X, np.ones((X.shape[0], 1))))\n test = np.hstack((test, np.ones((test.shape[0], 1))))\n w = np.zeros((13, 1))\n\n epoch = 0\n loss = np.inf\n while loss >= epsilon:\n x_predictions = sigmoid(np.matmul(X, w))\n\n dw = calc_gradient(X, x_labels, x_predictions, w, c)\n w = w - alpha * dw\n loss = obj(x_labels, x_predictions, w, c)\n epoch += 1\n if epoch % 1000 == 0:\n print(loss)\n test_predictions = classify(test, w)\n print(test_predictions)\n results_to_csv(test_predictions.flatten())\n\npart_a(.02, 0, .0356)\npart_b(.02, 0, .0356)\npart_c(.02, 0, .0356, .0000000001)\n\n#kaggle(1, 0, .03465751782558396)" }, { "alpha_fraction": 0.7456730604171753, "alphanum_fraction": 0.7591345906257629, "avg_line_length": 39.80392074584961, "blob_id": "4363b8918b1ca3bca4f244672d87d7029f3396a1", "content_id": "0b53d2cddd891f92e734388c691f256618b4d74d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2080, "license_type": "no_license", "max_line_length": 119, "num_lines": 51, "path": "/svms/mnist.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import sys\nif sys.version_info[0] < 3:\n raise Exception(\"Python 3 not detected.\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm\nfrom scipy import io\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.kernel_approximation import Nystroem\nfrom save_csv import results_to_csv\n\nfor data_name in [\"mnist\"]:\n data = io.loadmat(\"data/%s_data.mat\" % data_name)\n print(\"\\nloaded %s data!\" % data_name)\n fields = \"test_data\", \"training_data\", \"training_labels\"\n for field in fields:\n print(field, data[field].shape)\n\ndef permute_dictionaries(training_data, training_labels):\n\t#takes two dictionaries and permutes both while keeping consistency\n\tperm = np.random.RandomState(seed=70).permutation(len(training_data))\n\treturn (training_data[perm], training_labels[perm])\n\ntotal_data = io.loadmat(\"data/%s_data.mat\" % \"mnist\")\n\nindex = 60000\ntotal_training_data = total_data[\"training_data\"] / float(255)\ntotal_training_data_labels = total_data[\"training_labels\"]\ntotal_training_data, total_training_data_labels = permute_dictionaries(total_training_data, total_training_data_labels)\ntest_data = total_data[\"test_data\"] / float(255)\n\nfeature_map_nystroem = Nystroem(gamma = .05, n_components = 25000)\nfeatures_training = feature_map_nystroem.fit_transform(total_training_data)\nfeatures_test = feature_map_nystroem.transform(test_data)\n\nprint(\"mnist_training_data\", features_training.shape)\nprint(\"mnist_training_data_labels\", total_training_data_labels.shape)\nprint(\"mnist_test_data\", features_test.shape)\n\ndef problem5(training_data, training_data_labels, test_data, C_value):\t\n\tclassifier = svm.LinearSVC(dual = False, random_state = 10, C = C_value)\n\n\tclassifier.fit(training_data, np.ravel(training_data_labels))\n\n\tpredict_training_results = classifier.predict(training_data)\n\tprint(accuracy_score(np.ravel(training_data_labels), np.ravel(predict_training_results)))\n\tpredict_test_results = classifier.predict(test_data)\n\tresults_to_csv(predict_test_results)\n\n\nproblem5(features_training, total_training_data_labels, features_test, 5)" }, { "alpha_fraction": 0.5962145328521729, "alphanum_fraction": 0.6100946664810181, "avg_line_length": 39.19480514526367, "blob_id": "84c7ecde711a38f847585d1e612b524ebbf269a2", "content_id": "a38aecfed53af8a7209b9a789fbd7dd1f70f7c47", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3170, "license_type": "no_license", "max_line_length": 115, "num_lines": 77, "path": "/decision_trees/titanic_utils.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "from utils import extract_column\r\nfrom sklearn.impute import SimpleImputer\r\nimport numpy as np\r\nimport re\r\nimport pandas\r\n\r\ndef preprocess_titanic(data, include_labels):\r\n imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\r\n imp_mode = SimpleImputer(missing_values=np.nan, strategy='most_frequent')\r\n\r\n columns = [0 for _ in range(9)]\r\n\r\n current_col = extract_column(data, 5).flatten()\r\n current_col = [re.sub('[^0-9]', '', ticket) for ticket in current_col] #remove all letters\r\n current_col = [float(ticket) if ticket != '' else float(np.nan) for ticket in current_col] #convert to float\r\n current_col = np.array([current_col]).T\r\n current_col = imp_mean.fit_transform(current_col)\r\n columns[5] = current_col\r\n\r\n for col in [0, 1, 8]:\r\n current_col = extract_column(data, col)\r\n current_col =imp_mode.fit_transform(current_col)\r\n columns[col] = (current_col)\r\n for col in [2, 3, 4, 6]:\r\n current_col = extract_column(data, col)\r\n current_col = imp_mean.fit_transform(current_col)\r\n columns[col] = (current_col)\r\n\r\n current_col = extract_column(data, 7).flatten()\r\n current_col = [re.sub('[^A-z]', '', cabin)[0] if isinstance(cabin, str) else cabin for cabin in current_col]\r\n class_col = extract_column(data, 0).flatten()\r\n current_col = ['B' if not isinstance(current_col[i], str) and class_col[i] == 1 else current_col[i] for i in\r\n range(len(current_col))]\r\n current_col = ['D' if not isinstance(current_col[i], str) and class_col[i] == 2 else current_col[i] for i in\r\n range(len(current_col))]\r\n current_col = ['F' if not isinstance(current_col[i], str) and class_col[i] == 3 else current_col[i] for i in\r\n range(len(current_col))]\r\n current_col = np.array([current_col]).T\r\n columns[7] = current_col\r\n\r\n if include_labels:\r\n columns.append(extract_column(data, 9))\r\n return np.hstack(tuple(columns))\r\n\r\ndef load_titanic_data():\r\n path_train = 'datasets/titanic/titanic_training.csv'\r\n data = pandas.read_csv(path_train, delimiter=',', dtype=None)\r\n path_test = 'datasets/titanic/titanic_testing_data.csv'\r\n test_data = pandas.read_csv(path_test, delimiter=',', dtype=None)\r\n data = data[data.survived >= 0]\r\n y = data.values[:, [0]] # label = survived\r\n X = data.values[:, range(1, 10)]\r\n header = list(data.columns)\r\n header.append(header.pop(0))\r\n #print(header)\r\n data = np.hstack((X, y))\r\n #print(data.shape)\r\n #print(test_data.shape)\r\n test_data = test_data.values\r\n class_names =['died', 'survived']\r\n return data, test_data, header, class_names\r\n\r\ndef gen_maps(data):\r\n type_map = {}\r\n categories_map = {}\r\n\r\n for i in [0, 1, 7, 8]:\r\n type_map[i] = 'categorical'\r\n for i in [2, 3, 4, 6]:\r\n type_map[i] = 'quantitative'\r\n type_map[5] = 'clustered'\r\n\r\n categories_map[0] = [1, 2, 3]\r\n categories_map[1] = ['male', 'female']\r\n categories_map[7] = list(set(extract_column(data, 7).flatten()))\r\n categories_map[8] = ['S', 'C', 'Q']\r\n return type_map, categories_map" }, { "alpha_fraction": 0.7242571115493774, "alphanum_fraction": 0.7598479390144348, "avg_line_length": 42.83333206176758, "blob_id": "be74aab8e602bd738b49a413a5dc9c327f6f6c3f", "content_id": "9c040b984746db35cff002123f428b4c1ed18b17", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2894, "license_type": "no_license", "max_line_length": 127, "num_lines": 66, "path": "/svms/cifar10.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import sys\nif sys.version_info[0] < 3:\n raise Exception(\"Python 3 not detected.\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm\nfrom scipy import io\nfrom sklearn.metrics import accuracy_score\nfrom skimage.color import rgb2gray\nfrom skimage.feature import hog\nfrom sklearn.preprocessing import StandardScaler\nfrom save_csv import results_to_csv\n\nfor data_name in [\"cifar10\"]:\n data = io.loadmat(\"data/%s_data.mat\" % data_name)\n print(\"\\nloaded %s data!\" % data_name)\n fields = \"test_data\", \"training_data\", \"training_labels\"\n for field in fields:\n print(field, data[field].shape)\n\ndef permute_dictionaries(training_data, training_labels):\n\t#takes two dictionaries and permutes both while keeping consistency\n\tperm = np.random.RandomState(seed=70).permutation(len(training_data))\n\treturn (training_data[perm], training_labels[perm])\n\ncifar10_total_data = io.loadmat(\"data/%s_data.mat\" % \"cifar10\")\n\ncifar10_training_data = cifar10_total_data[\"training_data\"]\ncifar10_training_data_labels = cifar10_total_data[\"training_labels\"]\ncifar10_training_data, cifar10_training_data_labels = permute_dictionaries(cifar10_training_data, cifar10_training_data_labels)\ncifar10_test_data = cifar10_total_data[\"test_data\"]\n\n\ncifar10_uncompressed = np.array([np.transpose(pic.reshape(3,32,32), (1,2,0)) for pic in cifar10_training_data])\ncifar10_test_uncompressed = np.array([np.transpose(pic.reshape(3,32,32), (1,2,0)) for pic in cifar10_test_data])\n#print(cifar10_uncompressed[0].shape)\ncifar10_uncompressed_grey = np.array([rgb2gray(pic) for pic in cifar10_uncompressed])\ncifar10_test_uncompressed_grey = np.array([rgb2gray(pic) for pic in cifar10_test_uncompressed])\n#print(cifar10_uncompressed_grey[0].shape)\n\nhog_features = np.array([hog(image = pic) for pic in cifar10_uncompressed_grey])\nhog_test_features =np.array([hog(image = pic) for pic in cifar10_test_uncompressed_grey])\n\nscaler = StandardScaler()\nhog_features = scaler.fit_transform(hog_features)\nhog_test_features = scaler.transform(hog_test_features)\n\nprint(\"cifar10_training_data\", hog_features.shape)\nprint(\"cifar10_training_data_labels\", cifar10_training_data_labels.shape)\nprint(\"cifar10_test_data\", hog_test_features.shape)\n\ndef problem6(training_data, training_data_labels, test_data, linear, C_Value = 0):\n\n\tclassifier = svm.LinearSVC(dual = False, random_state = 10, verbose = 1, max_iter = 1000000)\n\n\tif(not linear):\n\t\tclassifier = svm.SVC(kernel = \"linear\", random_state = 10, verbose = 0)\n\n\tclassifier.fit(training_data, np.ravel(training_data_labels))\n\n\tpredict_training_results = classifier.predict(training_data)\n\tprint(accuracy_score(np.ravel(training_data_labels), np.ravel(predict_training_results)))\n\tpredict_test_results = classifier.predict(test_data)\n\tresults_to_csv(predict_test_results)\n\nproblem6(hog_features, cifar10_training_data_labels, hog_test_features, True, 0)\n\n" }, { "alpha_fraction": 0.644722044467926, "alphanum_fraction": 0.6596013307571411, "avg_line_length": 42.42683029174805, "blob_id": "1cfc284935b1e1f58fdf062558cae1ff70437526", "content_id": "928e2d2191d88e7c04856b393d4ae5d36a1cd08f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7124, "license_type": "no_license", "max_line_length": 120, "num_lines": 164, "path": "/discriminant_analysis/spam.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import numpy as np\nimport gc\nfrom scipy import io\nfrom scipy.stats import multivariate_normal\nfrom save_csv import results_to_csv\n\ndef sparse_to_np(sparse):\n temp = []\n for samp in range(sparse.shape[0]):\n row = sparse[samp].toarray()[0]\n temp.append(row)\n return np.asarray(temp)\ndef permute_dictionaries(data, labels, rand=25):\n perm = np.random.RandomState(seed=rand).permutation(training_data.shape[0])\n return data[perm], labels[perm]\n\n\ngc.enable()\n\nspam_data = io.loadmat(\"spam-data/spam_data.mat\")\nprint(\"Loaded spam data.\")\ntraining_data = sparse_to_np(spam_data[\"training_data\"])\ntraining_labels = spam_data[\"training_labels\"]\n\ntraining_data, training_labels = permute_dictionaries(training_data, training_labels)\ntraining_data, validation_data = training_data[:4138], training_data[4138:]\ntraining_labels, validation_labels = training_labels[:4138], training_labels[4138:]\n\nclasses = [0, 1]\nn, features = training_data.shape\n\nprint(\"\\nTraining data: \", training_data.shape)\nprint(\"Training data labels: \", training_labels.shape)\nprint(\"Validation data: \", validation_data.shape)\nprint(\"Validation labels: \", validation_labels.shape)\n\ndef empirical_mean(partitioned_data):\n return {k : np.sum(partitioned_data[k], 0, keepdims=True).transpose() / len(partitioned_data[k]) for k in classes}\n\ndef empirical_cov(partitioned_data):\n return {k : np.cov(partitioned_data[k].T, bias=True) for k in classes}\n\ndef calc_priors(partitioned_data, total):\n return {k: partitioned_data[k].shape[0] / total for k in classes}\n\ndef partition_data(data, labels):\n partitioned = {k: [] for k in classes}\n for sample_num in range(data.shape[0]):\n k = labels[sample_num][0]\n sample_features = data[sample_num]\n partitioned[k].append(sample_features)\n for k in classes:\n partitioned[k] = np.asarray(partitioned[k])\n return partitioned\n\ndef error_rate(prediction, actual):\n assert len(prediction) == len(actual)\n return np.count_nonzero(prediction - actual) / prediction.shape[0]\n\ndef classify(distributions, samples, priors):\n all_predictions = {}\n for key in samples.keys():\n predictions = []\n for sample in samples[key]:\n ll = {k: 0 for k in classes}\n for k in classes:\n sample = np.array(sample)\n ll[k] = distributions[k].logpdf(sample) + np.log(priors[k])\n predictions.append(max(ll, key=lambda key: ll[key]))\n all_predictions[key] = predictions\n return all_predictions\n\ndef pool_cov(covariances, priors):\n cov = np.zeros(covariances[0].shape)\n for k in classes:\n cov += priors[k] * covariances[k]\n return cov\ndef LDA(means, covariances, priors, inputs, c=0.0):\n pooled_cov = pool_cov(covariances, priors)\n pooled_cov += np.eye(features) * c * np.trace(pooled_cov)\n distributions = {k: multivariate_normal(means[k].flatten(), pooled_cov, allow_singular=True) for k in classes}\n return classify(distributions, inputs, priors)\ndef QDA(means, covariances, priors, inputs, c=0.0):\n temp_covariances, distributions = {}, {}\n for k in classes:\n temp_covariances[k] = np.eye(features) * c * np.trace(covariances[k]) + covariances[k]\n distributions[k] = multivariate_normal(means[k].flatten(), temp_covariances[k], allow_singular=True)\n return classify(distributions, inputs, priors)\n\n\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\ndef test_QDA(training_data, training_labels, validation_data, validation_labels, c=0.0):\n partitioned_training_data = partition_data(training_data, training_labels)\n means = empirical_mean(partitioned_training_data)\n covariances = empirical_cov(partitioned_training_data)\n priors = calc_priors(partitioned_training_data, training_data.shape[0])\n samples = {'validation' : validation_data}\n predictions = QDA(means, covariances, priors, samples, c)\n return error_rate(np.array([predictions['validation']]).T, validation_labels)\n\ndef test_LDA(training_data, training_labels, validation_data, validation_labels, c=0.0):\n partitioned_training_data = partition_data(training_data, training_labels)\n means = empirical_mean(partitioned_training_data)\n covariances = empirical_cov(partitioned_training_data)\n priors = calc_priors(partitioned_training_data, training_data.shape[0])\n samples = {'validation' : validation_data}\n predictions = LDA(means, covariances, priors, samples, c)\n return error_rate(np.array([predictions['validation']]).T, validation_labels)\n\n#print(test_QDA(training_data, training_labels, validation_data, validation_labels, .00064))\n#print(test_LDA(training_data, training_labels, validation_data, validation_labels))\n\ndef kaggle(c):\n data = sparse_to_np(spam_data[\"training_data\"])\n labels = spam_data[\"training_labels\"]\n test_data = sparse_to_np(spam_data[\"test_data\"])\n partitioned_data = partition_data(data, labels)\n\n means = empirical_mean(partitioned_data)\n partitioned_covariances = empirical_cov(partitioned_data)\n priors = calc_priors(partitioned_data, len(data))\n samples = {'training' : data}\n\n predictions = QDA(means, partitioned_covariances, priors, samples, c)\n train_predictions = predictions['training']\n #test_predictions = predictions['test']\n print(error_rate(np.array([train_predictions]).T, labels))\n #results_to_csv(np.array(test_predictions))\n #return\n\n#print(kaggle(.00064))\n#0.0004833252779120348 train error with 5000 @ .00064 no prior weighting (~95% test accuracy)\n\ndef opt_c_value(training_data, training_labels, validation_data, validation_labels, c_values):\n results = {}\n for c in c_values:\n results[c] = k_fold(training_data, training_labels, 5, c)\n print(\"Error rate \", results[c], \" achieved with c value: \", c)\n best_c = min(results, key=lambda key: results[key])\n print(\"Optimal c_value was \", best_c, \" with error: \", results[best_c])\n return best_c\n\ndef k_fold(data, labels, k, c):\n data, labels = permute_dictionaries(data, labels, np.random.randint(0,10000))\n data_partitions = np.array_split(data, k)\n label_partitions = np.array_split(labels, k)\n errors = []\n for k in range(k):\n validation_data = data_partitions[0]\n validation_labels = label_partitions[0]\n training_data = np.concatenate(data_partitions[1:])\n training_labels = np.concatenate(label_partitions[1:])\n\n error = test_QDA(training_data, training_labels, validation_data, validation_labels, c)\n\n data_partitions = np.roll(data_partitions, 1)\n label_partitions = np.roll(label_partitions, 1)\n errors.append(error)\n return sum(errors) / k\n\n#opt_c_value(training_data, training_labels, validation_data, validation_labels, np.arange(.0006, .0007, .0001))\n\n\n" }, { "alpha_fraction": 0.586129367351532, "alphanum_fraction": 0.6214480400085449, "avg_line_length": 35.99390411376953, "blob_id": "6878e852ea63c2daea4d0ed78c620440e06edc40", "content_id": "f7322233f06ebd299ae9937dc5c8f508ef2f494c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6229, "license_type": "no_license", "max_line_length": 102, "num_lines": 164, "path": "/decision_trees/spam_runner.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import numpy as np\r\nfrom utils import extract_column, results_to_csv, error_rate, plot_data\r\nfrom spam_utils import load_spam\r\nfrom decision_tree_starter import DecisionTree, RandomForest\r\n\r\n#RandomForest(trees, sample_size, bag_size, type_map, categories_map, seed)\r\n# fit(data, max_depth, min_samples)\r\n#\r\n#DecisionTree(type_map, categories_map)\r\n# fit(data, max_depth, min_samples, bag_size = None)\r\n#\r\n#100, 500, 70 .. 20 -> depth 36: .031884 or 26: .03478\r\n# classifier = RandomForest(100, 500, 70, type_map, categories_map, 20)\r\n\r\n\r\ndef plot_q2_5_3():\r\n data, test_data, feature_names, class_names = load_spam()\r\n type_map = dict((i, 'quantitative') for i in range(data.shape[1]))\r\n categories_map = {}\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:4137], data[4137:]\r\n\r\n train_accuracies = []\r\n valid_accuracries =[]\r\n depths = list(range(1,41))\r\n\r\n for max_depth in range(1,41):\r\n print(\"Computing max depth: \", max_depth)\r\n idy = valid.shape[1] - 1\r\n classifier = DecisionTree(type_map, categories_map, feature_names, class_names)\r\n classifier.fit(data, max_depth, 10)\r\n train_pred = classifier.predict(data)\r\n valid_pred = classifier.predict(valid)\r\n train_actual = extract_column(data, idy)\r\n valid_actual = extract_column(valid, idy)\r\n train_acc = 1 - error_rate(train_pred, train_actual)\r\n valid_acc = 1 -error_rate(valid_pred, valid_actual)\r\n train_accuracies.append(train_acc)\r\n valid_accuracries.append(valid_acc)\r\n\r\n plot_data(depths, train_accuracies, valid_accuracries, 'r', 'b', 'Training/Validation Accuracies')\r\n return\r\n\r\ndef q_2_5_2():\r\n data, test_data, feature_names, class_names = load_spam()\r\n type_map = dict((i, 'quantitative') for i in range(data.shape[1]))\r\n categories_map = {}\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:4137], data[4137:]\r\n\r\n classifier = DecisionTree(type_map, categories_map, feature_names, class_names)\r\n classifier.fit(data, 8, 15)\r\n samp_point = np.array([valid[0]])\r\n classifier.predict(samp_point, True)\r\n\r\n samp_point = np.array([valid[1]])\r\n classifier.predict(samp_point, True)\r\n\r\ndef kaggle():\r\n \"\"\"\r\n #run featurize.py with 5000 samples\r\n data, test_data, feature_names, class_names = load_spam()\r\n type_map = dict((i, 'quantitative') for i in range(data.shape[1]))\r\n categories_map = {}\r\n\r\n classifier = RandomForest(100, 500, 70, type_map, categories_map, 20)\r\n classifier.fit(data, 36, 10)\r\n predictions = classifier.predict(test_data)\r\n pred_train = classifier.predict(data)\r\n actual = extract_column(data, 9)\r\n print(error_rate(pred_train, actual))\r\n results_to_csv(predictions.flatten())\r\n\r\n #TESTING DECISION TREE\r\n data, test_data, feature_names, class_names = load_spam()\r\n type_map = dict((i, 'quantitative') for i in range(data.shape[1]))\r\n categories_map = {}\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:4137], data[4137:]\r\n\r\n best_i = -1\r\n best_error = 1\r\n for i in range(2, 50):\r\n classifier = DecisionTree(type_map, categories_map, feature_names, class_names)\r\n classifier.fit(data, 36, 10)\r\n predictions = classifier.predict(valid)\r\n actual = extract_column(valid, valid.shape[1] - 1)\r\n error = error_rate(predictions, actual)\r\n print(i, error)\r\n if error < best_error:\r\n best_error = error\r\n best_i = i\r\n print(best_i, best_error)\r\n # Best at depth 14 with error 0.11594202898550725\r\n \"\"\"\r\n \"\"\"\r\n data, test_data, feature_names, class_names = load_spam()\r\n type_map = dict((i, 'quantitative') for i in range(data.shape[1]))\r\n categories_map = {}\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:4137], data[4137:]\r\n\r\n best_i = -1\r\n best_error = 1\r\n best_j = -1\r\n print(\"Bagging, depth, error\")\r\n for i in range(10, 50):\r\n for j in range(30, 31):\r\n classifier = RandomForest(300, 300, i, type_map, categories_map, 20)\r\n classifier.fit(data, j, 10)\r\n predictions = classifier.predict(valid)\r\n actual = extract_column(valid, 9)\r\n error = error_rate(predictions, actual)\r\n print(i, j, error)\r\n if error < best_error:\r\n best_error = error\r\n best_i = i\r\n best_j = j\r\n print(best_i, best_j, best_error)\r\n \"\"\"\r\n return\r\n\r\ndef q2_4():\r\n print(\"****RUNNING SPAM DATA SET**\")\r\n data, test_data, feature_names, class_names = load_spam()\r\n type_map = dict((i, 'quantitative') for i in range(data.shape[1]))\r\n categories_map = {}\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:4137], data[4137:]\r\n idy = data.shape[1] - 1\r\n\r\n classifier = DecisionTree(type_map, categories_map)\r\n classifier.fit(data, 14 , 10)\r\n train_predictions = classifier.predict(data)\r\n train_actual = extract_column(data, idy)\r\n valid_predictions = classifier.predict(valid)\r\n valid_actual = extract_column(valid, idy)\r\n\r\n print(\"Decision Tree training Accuracies: \", error_rate(train_predictions, train_actual))\r\n print(\"Decision Tree Validation Accuracies: \", error_rate(valid_predictions, valid_actual))\r\n\r\n classifier = RandomForest(300, 300, 2, type_map, categories_map, 20)\r\n classifier.fit(data, 10, 10)\r\n\r\n train_predictions = classifier.predict(data)\r\n train_actual = extract_column(data, idy)\r\n valid_predictions = classifier.predict(valid)\r\n valid_actual = extract_column(valid, idy)\r\n\r\n print(\"Random Forest training Accuracies: \", error_rate(train_predictions, train_actual))\r\n print(\"Random Forest Validation Accuracies: \", error_rate(valid_predictions, valid_actual))\r\n\r\n return\r\n\r\nif __name__ == \"__main__\":\r\n #plot_q2_5_3()\r\n #q_2_5_2()\r\n #kaggle()\r\n q2_4()" }, { "alpha_fraction": 0.6084234714508057, "alphanum_fraction": 0.6226522326469421, "avg_line_length": 27.81355857849121, "blob_id": "67b8d9f3dd8ca057ef221b44dbeb94e6d20bc507", "content_id": "a6caef550c3f29a4b87598f24869d39d34f5348b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1757, "license_type": "no_license", "max_line_length": 125, "num_lines": 59, "path": "/decision_trees/utils.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import numpy as np\r\nimport pandas\r\nfrom collections import Counter\r\nimport matplotlib.pyplot as plt\r\n\r\ndef results_to_csv(predictions):\r\n predictions = predictions.astype(int)\r\n df = pandas.DataFrame({'Category': predictions})\r\n df.index += 1\r\n df.to_csv('submission.csv', index_label='Id')\r\n\r\ndef sparse_to_np(sparse):\r\n temp = []\r\n for samp in range(sparse.shape[0]):\r\n row = sparse[samp].toarray()[0]\r\n temp.append(row)\r\n return np.asarray(temp)\r\n\r\ndef majority(data):\r\n idy = data.shape[1] - 1\r\n y = extract_column(data, idy)\r\n cnt = Counter(y.flatten())\r\n return cnt.most_common(1)[0][0]\r\n\r\ndef error_rate(prediction, actual):\r\n prediction = np.array(prediction.flatten())\r\n actual = np.array(actual.flatten())\r\n return np.count_nonzero(prediction - actual) / prediction.shape[0]\r\n\r\n\r\ndef extract_column(data, col):\r\n \"\"\"\r\n Extracts col column from data matrix\r\n Outputs: a 2d array (column vector)\r\n \"\"\"\r\n return data[:, [col]]\r\n\r\n\r\ndef find_index(val, thresholds):\r\n i = 0\r\n while i < len(thresholds):\r\n threshold = thresholds[i]\r\n if val < threshold:\r\n return i\r\n i += 1\r\n return i\r\n\r\ndef plot_data(depths, training, validation, clr_tr, clr_vld, title):\r\n fig = plt.figure()\r\n fig.set_size_inches(10, 5)\r\n fig.set_dpi(100)\r\n plt.subplot(1, 1, 1)\r\n plt.plot(depths, training, label=\"training\", color=clr_tr, marker='.', linestyle='dashed',linewidth=1, markersize=1)\r\n plt.plot(depths, validation, label=\"validation\", color=clr_vld, marker='.', linestyle='dashed',linewidth=1, markersize=1)\r\n plt.legend()\r\n plt.title(title + \" vs max depth\")\r\n plt.xlabel(\"Max depth\")\r\n plt.ylabel(\"Accuracy\")\r\n plt.show()" }, { "alpha_fraction": 0.6397821307182312, "alphanum_fraction": 0.6571576595306396, "avg_line_length": 40.462364196777344, "blob_id": "35c4f2819b1ad51eda75ec3096215e15226460ab", "content_id": "44b0c28b9cce3d98d1277bb5c953ca0a25dcedc7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7712, "license_type": "no_license", "max_line_length": 125, "num_lines": 186, "path": "/discriminant_analysis/mnist.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import numpy as np\nimport gc\nfrom scipy import io\nfrom scipy.stats import multivariate_normal\nimport cv2\nfrom save_csv import results_to_csv\nSZ = 28\n\nwinSize = (28, 28)\nblockSize = (12, 12)\nblockStride = (4, 4)\ncellSize = (12, 12)\nnbins = 9\nderivAperture = 1\nwinSigma = -1.\nhistogramNormType = 0\nL2HysThreshold = 0.2\ngammaCorrection = 1\nnlevels = 64\nsignedGradients = True\n\nhog = cv2.HOGDescriptor(winSize, blockSize, blockStride, cellSize, nbins, derivAperture, winSigma, histogramNormType,\n L2HysThreshold, gammaCorrection, nlevels, signedGradients)\n\ndef permute_dictionaries(data, labels, rand=25):\n perm = np.random.RandomState(seed=rand).permutation(training_data.shape[0])\n return data[perm], labels[perm]\n\ndef deskew(img):\n m = cv2.moments(img)\n if abs(m['mu02']) < 1e-2:\n return img.copy()\n skew = m['mu11']/m['mu02']\n M = np.float32([[1, skew, -0.5SZskew], [0, 1, 0]])\n img = cv2.warpAffine(img,M,(SZ, SZ),flags=cv2.WARP_INVERSE_MAP \| cv2.INTER_LINEAR)\n return img\n\ndef deskew_all(data):\n new_data = []\n for i in range(data.shape[0]):\n row_img = data[i]\n img = row_img.reshape((28, 28))\n new_data.append(np.array((hog.compute(deskew(img)))).flatten())\n return np.array(new_data)\n\ngc.enable()\n\nmnist_data = io.loadmat(\"mnist-data/mnist_data.mat\")\nprint(\"Loaded mnist data.\")\ntraining_data = mnist_data[\"training_data\"]\ntraining_labels = mnist_data[\"training_labels\"]\n\ntraining_data, training_labels = permute_dictionaries(training_data, training_labels,1000)\ntraining_data = deskew_all(training_data)\n\ntraining_data, validation_data = training_data[:50000], training_data[50000:]\ntraining_labels, validation_labels = training_labels[:50000], training_labels[50000:]\n\nclasses = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\nn, features = training_data.shape\n\nprint(\"\\nTraining data: \", training_data.shape)\nprint(\"Training data labels: \", training_labels.shape)\nprint(\"Validation data: \", validation_data.shape)\nprint(\"Validation labels: \", validation_labels.shape)\n\ndef empirical_mean(partitioned_data):\n return {k : np.sum(partitioned_data[k], 0, keepdims=True).transpose() / len(partitioned_data[k]) for k in classes}\n\ndef empirical_cov(partitioned_data):\n return {k : np.cov(partitioned_data[k].T, bias=True) for k in classes}\n\ndef calc_priors(partitioned_data, total):\n return {k: partitioned_data[k].shape[0] / total for k in classes}\n\ndef partition_data(data, labels):\n partitioned = {k: [] for k in classes}\n for sample_num in range(data.shape[0]):\n k = labels[sample_num][0]\n sample_features = data[sample_num]\n partitioned[k].append(sample_features)\n for k in classes:\n partitioned[k] = np.asarray(partitioned[k])\n return partitioned\n\ndef error_rate(prediction, actual):\n assert len(prediction) == len(actual)\n return np.count_nonzero(prediction - actual) / prediction.shape[0]\n\ndef classify(distributions, samples, priors):\n all_predictions = {}\n for key in samples.keys():\n predictions = []\n for sample in samples[key]:\n ll = {k: 0 for k in classes}\n for k in classes:\n sample = np.array(sample)\n ll[k] = distributions[k].logpdf(sample) + np.log(priors[k])\n predictions.append(max(ll, key=lambda key: ll[key]))\n all_predictions[key] = predictions\n return all_predictions\n\ndef pool_cov(covariances, priors):\n cov = np.zeros(covariances[0].shape)\n for k in classes:\n cov += priors[k] covariances[k]\n return cov\n\ndef LDA(means, covariances, priors, inputs, c=0.0):\n pooled_cov = pool_cov(covariances, priors)\n pooled_cov += np.eye(features) * c * np.trace(pooled_cov)\n distributions = {k: multivariate_normal(means[k].flatten(), pooled_cov, allow_singular=True) for k in classes}\n return classify(distributions, inputs, priors)\ndef QDA(means, covariances, priors, inputs, c=0.0):\n temp_covariances, distributions = {}, {}\n for k in classes:\n temp_covariances[k] = np.eye(features) * c * np.trace(covariances[k]) + covariances[k]\n distributions[k] = multivariate_normal(means[k].flatten(), temp_covariances[k], allow_singular=True)\n return classify(distributions, inputs, priors)\n\n\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\ndef test_QDA(training_data, training_labels, validation_data, validation_labels, c=0.0):\n partitioned_training_data = partition_data(training_data, training_labels)\n means = empirical_mean(partitioned_training_data)\n covariances = empirical_cov(partitioned_training_data)\n priors = calc_priors(partitioned_training_data, training_data.shape[0])\n samples = {'validation' : validation_data}\n predictions = QDA(means, covariances, priors, samples, c)\n return error_rate(np.array([predictions['validation']]).T, validation_labels)\n\ndef test_LDA(training_data, training_labels, validation_data, validation_labels, c=0.0):\n partitioned_training_data = partition_data(training_data, training_labels)\n means = empirical_mean(partitioned_training_data)\n covariances = empirical_cov(partitioned_training_data)\n priors = calc_priors(partitioned_training_data, training_data.shape[0])\n samples = {'validation' : validation_data}\n predictions = LDA(means, covariances, priors, samples, c)\n return error_rate(np.array([predictions['validation']]).T, validation_labels)\n\n#print(test_QDA(training_data, training_labels, validation_data, validation_labels, .0001))\n\ndef kaggle(c):\n data = deskew_all(mnist_data[\"training_data\"])\n labels = mnist_data[\"training_labels\"]\n test_data = deskew_all(mnist_data[\"test_data\"])\n partitioned_data = partition_data(data, labels)\n\n means = empirical_mean(partitioned_data)\n partitioned_covariances = empirical_cov(partitioned_data)\n priors = calc_priors(partitioned_data, len(data))\n samples = {'training' : data, 'test' : test_data}\n\n predictions = QDA(means, partitioned_covariances, priors, samples, c)\n train_predictions = predictions['training']\n test_predictions = predictions['test']\n print(error_rate(np.array([train_predictions]).T, labels))\n results_to_csv(np.array(test_predictions))\n return\n#kaggle(.0001)\n\ndef opt_c_value_QDA(training_data, training_labels, validation_data, validation_labels, c_values):\n results = {}\n for c in c_values:\n results[c] = test_QDA(training_data,training_labels,validation_data,validation_labels,c)\n print(\"Error rate \", results[c], \" achieved with c value: \", c)\n best_c = min(results, key=lambda key: results[key])\n print(\"Optimal c_value was \", best_c, \" with error: \", results[best_c])\n return best_c, results[best_c]\n\ndef opt_c_value_LDA(training_data, training_labels, validation_data, validation_labels, c_values):\n results = {}\n for c in c_values:\n results[c] = test_LDA(training_data,training_labels,validation_data,validation_labels,c)\n print(\"Error rate \", results[c], \" achieved with c value: \", c)\n best_c = min(results, key=lambda key: results[key])\n print(\"Optimal c_value was \", best_c, \" with error: \", results[best_c])\n return best_c\n\ndef gen_c_values(low_exp, high_exp):\n return [10i for i in range(low_exp, high_exp + 1)]\n\nprint(opt_c_value_QDA(training_data, training_labels, validation_data, validation_labels, np.arange(.00047, .008, .0000001)))\n" }, { "alpha_fraction": 0.7415074110031128, "alphanum_fraction": 0.7494692206382751, "avg_line_length": 37.408164978027344, "blob_id": "857c1dbdc905462beb851cfddbfa2ecd1a4a0919", "content_id": "9e74345fe37eb6dda47134de5368eaa544821f23", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1884, "license_type": "no_license", "max_line_length": 115, "num_lines": 49, "path": "/svms/spam.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import sys\nif sys.version_info[0] < 3:\n raise Exception(\"Python 3 not detected.\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm\nfrom scipy import io\nfrom sklearn.metrics import accuracy_score\nfrom save_csv import results_to_csv\n\nfor data_name in [\"spam\"]:\n data = io.loadmat(\"data/%s_data.mat\" % data_name)\n print(\"\\nloaded %s data!\" % data_name)\n fields = \"test_data\", \"training_data\", \"training_labels\"\n for field in fields:\n print(field, data[field].shape)\n\ndef permute_dictionaries(training_data, training_labels):\n\t#takes two dictionaries and permutes both while keeping consistency\n\tperm = np.random.RandomState(seed=100).permutation(training_data.shape[0])\n\treturn (training_data[perm], training_labels[perm])\n\nspam_total_data = io.loadmat(\"data/%s_data.mat\" % \"spam\")\n\nspam_training_data = spam_total_data[\"training_data\"]\nspam_training_data_labels = spam_total_data[\"training_labels\"]\nspam_training_data, spam_training_data_labels = permute_dictionaries(spam_training_data, spam_training_data_labels)\nspam_test_data = spam_total_data[\"test_data\"]\nprint(\"train\")\nprint(spam_training_data)\nprint(\"test\")\nprint(spam_test_data)\n\nprint(\"spam_training_data\", spam_training_data.shape)\nprint(\"spam_training_data_labels\", spam_training_data_labels.shape)\nprint(\"spam_test_data\", spam_test_data.shape)\n\ndef problem6(training_data, training_data_labels, test_data, C_Value = 0):\n\n\tclassifier = svm.LinearSVC(random_state = 40, C = 10 C_Value)\n\n\tclassifier.fit(training_data, np.ravel(training_data_labels))\n\n\tpredict_training_results = classifier.predict(training_data)\n\tprint(accuracy_score(np.ravel(training_data_labels), np.ravel(predict_training_results)))\n\tpredict_test_results = classifier.predict(test_data)\n\tresults_to_csv(predict_test_results)\n\nproblem6(spam_training_data, spam_training_data_labels, spam_test_data, 1)\n\n\n" }, { "alpha_fraction": 0.655183732509613, "alphanum_fraction": 0.663385808467865, "avg_line_length": 33.2471923828125, "blob_id": "946fceb4b466015681b7c95f2fca8a4cea51cbc9", "content_id": "e9d9701ca32eabcc8b383961b9206773ccff7f5c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3048, "license_type": "no_license", "max_line_length": 90, "num_lines": 89, "path": "/discriminant_analysis/featurize2.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "'''\n************** PLEASE READ ***********\n\nScript that reads in spam and ham messages and converts each training example\ninto a feature vector\n\nCode intended for UC Berkeley course CS 189/289A: Machine Learning\n\nRequirements:\n-scipy ('pip install scipy')\n\nTo add your own features, create a function that takes in the raw text and\nword frequency dictionary and outputs a int or float. Then add your feature\nin the function 'def generate_feature_vector'\n\nThe output of your file will be a .mat file. The data will be accessible using\nthe following keys:\n -'training_data'\n -'training_labels'\n -'test_data'\n\nPlease direct any bugs to [email protected]\n'''\n\nimport glob\nimport scipy.io\nimport numpy as np\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nNUM_TRAINING_EXAMPLES = 5172\nNUM_TEST_EXAMPLES = 5857\n\nBASE_DIR = './'\nSPAM_DIR = 'spam/'\nHAM_DIR = 'ham/'\nTEST_DIR = 'test/'\n\n# This method generates a design matrix with a list of filenames\n# Each file is a single training example\ndef generate_design_fit_matrix(filenames, vectorizer):\n print(\"Fitting and transforming training data\")\n all_text = []\n for filename in filenames:\n with open(filename, 'r', encoding='utf-8', errors='ignore') as f:\n try:\n text = f.read() # Read in text from file\n except Exception as e:\n # skip files we have trouble reading.\n continue\n text = text.replace('\\r\\n', ' ') # Remove newline character\n all_text.append(text)\n\n design_matrix = vectorizer.fit_transform(all_text)\n return design_matrix\n\ndef generate_design_matrix(test_filenames, vectorizer):\n print(\"Tranforming test data\")\n all_text = []\n for filename in test_filenames:\n with open(filename, 'r', encoding='utf-8', errors='ignore') as f:\n try:\n text = f.read() # Read in text from file\n except Exception as e:\n # skip files we have trouble reading.\n continue\n text = text.replace('\\r\\n', ' ') # Remove newline character\n all_text.append(text)\n return vectorizer.transform(all_text)\n\n# ********** Script starts here ***********\n# DO NOT MODIFY ANYTHING BELOW\nvectorizer = TfidfVectorizer(max_features=5000, norm='l2', sublinear_tf=True)\n\nspam_filenames = glob.glob(BASE_DIR + SPAM_DIR + '.txt')\nham_filenames = glob.glob(BASE_DIR + HAM_DIR + '.txt')\nX = generate_design_fit_matrix(spam_filenames + ham_filenames, vectorizer)\n# Important: the test_filenames must be in numerical order as that is the\n# order we will be evaluating your classifier\ntest_filenames = [BASE_DIR + TEST_DIR + str(x) + '.txt' for x in range(NUM_TEST_EXAMPLES)]\ntest_design_matrix = generate_design_matrix(test_filenames, vectorizer)\n\n\nY = np.array([1]len(spam_filenames) + [0]len(ham_filenames)).reshape((-1, 1))\n\nfile_dict = {}\nfile_dict['training_data'] = X\nfile_dict['training_labels'] = Y\nfile_dict['test_data'] = test_design_matrix\nscipy.io.savemat('spam_data.mat', file_dict)\n" }, { "alpha_fraction": 0.642092764377594, "alphanum_fraction": 0.642092764377594, "avg_line_length": 27.964284896850586, "blob_id": "fccd0b765111feed190f5830af468b640270c40c", "content_id": "13444758d5951782a5a06fcb8ac541298fd12e99", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 841, "license_type": "no_license", "max_line_length": 60, "num_lines": 28, "path": "/decision_trees/spam_utils.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "from utils import extract_column\r\nfrom sklearn.impute import SimpleImputer\r\nimport numpy as np\r\nimport re\r\nimport pandas\r\nimport scipy.io\r\nfrom utils import sparse_to_np\r\n\r\ndef load_spam():\r\n path_train = 'datasets/spam-dataset/spam_data.mat'\r\n data = scipy.io.loadmat(path_train)\r\n X = data['training_data']\r\n X = sparse_to_np(X)\r\n y = data['training_labels']\r\n Z = data['test_data']\r\n Z = sparse_to_np(Z)\r\n #feature_names = data['feature_names']\r\n feature_names = []\r\n class_names = [\"Ham\", \"Spam\"]\r\n #print(X.shape)\r\n #print(y.shape)\r\n #print(Z.shape)\r\n data = np.hstack((X, y))\r\n\r\n feature_path = 'datasets/spam-dataset/feature_names.mat'\r\n feature_dict = scipy.io.loadmat(feature_path)\r\n feature_names = feature_dict['feature_names']\r\n return data, Z, feature_names, class_names\r\n\r\n" }, { "alpha_fraction": 0.6015936136245728, "alphanum_fraction": 0.6343048810958862, "avg_line_length": 35.56692886352539, "blob_id": "777e1d9e21e152c95e7003f5c00eabe031c3b007", "content_id": "4760fb0687b7665d4302f4d82bc2f08595bb9325", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4769, "license_type": "no_license", "max_line_length": 99, "num_lines": 127, "path": "/decision_trees/titanic_runner.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import numpy as np\r\nfrom utils import extract_column, results_to_csv, error_rate\r\nfrom decision_tree_starter import DecisionTree, RandomForest\r\nfrom titanic_utils import preprocess_titanic, load_titanic_data, gen_maps\r\n\r\n#RandomForest(trees, sample_size, bag_size, type_map, categories_map, seed)\r\n# fit(data, max_depth, min_samples)\r\n#\r\n#DecisionTree(type_map, categories_map)\r\n# fit(data, max_depth, min_samples, bag_size = None)\r\n#\r\n#100, 500, 70 .. 20 -> depth 36: .031884 or 26: .03478\r\n# classifier = RandomForest(100, 500, 70, type_map, categories_map, 20)\r\n\r\ndef q_2_6():\r\n data, test_data, feature_names, class_names = load_titanic_data()\r\n data = preprocess_titanic(data, True)\r\n\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:800], data[800:]\r\n\r\n type_map, categories_map = gen_maps(data)\r\n classifier = DecisionTree(type_map, categories_map, feature_names, class_names)\r\n classifier.fit(data, 3, 10)\r\n print(classifier)\r\n\r\ndef kaggle():\r\n data, test_data, feature_names, class_names = load_titanic_data()\r\n data = preprocess_titanic(data, True)\r\n test = preprocess_titanic(test_data, False)\r\n\r\n type_map, categories_map = gen_maps(data)\r\n classifier = DecisionTree(type_map, categories_map)\r\n\r\n classifier.fit(data, 4, 10)\r\n predictions = classifier.predict(test)\r\n pred_train = classifier.predict(data)\r\n actual = extract_column(data, 9)\r\n print(error_rate(pred_train, actual))\r\n results_to_csv(predictions.flatten())\r\n \"\"\"\r\n\r\n data, test_data, feature_names, class_names = load_titanic_data()\r\n data = preprocess_titanic(data, True)\r\n\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:800], data[800:]\r\n\r\n type_map, categories_map = gen_maps(data)\r\n classifier = DecisionTree(type_map, categories_map)\r\n\r\n #TESTING FOR BEST RANDOM FOREST\r\n best_i = -1\r\n best_error = 1\r\n best_j = -1\r\n print(\"Bagging, depth, error\")\r\n for i in range(1, 9):\r\n for j in range(1,20,1):\r\n classifier = RandomForest(300, 300, i, type_map, categories_map, 20)\r\n classifier.fit(data, j, 10)\r\n predictions = classifier.predict(valid)\r\n actual = extract_column(valid, 9)\r\n error = error_rate(predictions, actual)\r\n print(i, j, error)\r\n if error < best_error:\r\n best_error = error\r\n best_i = i\r\n best_j = j\r\n print(best_i, best_j, best_error)\r\n # best recorded is 2 10 at error .165829 (300 trees 300 samples)\r\n # best recorded is 2 5 at error .175879 (300 trees 300 samples)\r\n\r\n #TESTING FOR BEST DECISION TREE\r\n best_i = -1\r\n print(\"depth, error\")\r\n for i in range(1, 40):\r\n classifier = DecisionTree(type_map, categories_map,feature_names, class_names)\r\n classifier.fit(data, i, 10)\r\n predictions = classifier.predict(valid)\r\n actual = extract_column(valid, 9)\r\n error = error_rate(predictions, actual)\r\n print(i, j, error)\r\n if error < best_error:\r\n best_error = error\r\n best_i = i\r\n print(best_i, best_error)\r\n #best recorded at 4 at point .1758\r\n \"\"\"\r\ndef q_2_4():\r\n print(\"***RUNNING TITANIC DATA SET***\")\r\n\r\n data, test_data, feature_names, class_names = load_titanic_data()\r\n data = preprocess_titanic(data, True)\r\n\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:800], data[800:]\r\n idy = data.shape[1] - 1\r\n\r\n type_map, categories_map = gen_maps(data)\r\n classifier = DecisionTree(type_map, categories_map)\r\n classifier.fit(data, 4, 10)\r\n train_predictions = classifier.predict(data)\r\n train_actual = extract_column(data, idy)\r\n valid_predictions = classifier.predict(valid)\r\n valid_actual = extract_column(valid, idy)\r\n\r\n print(\"Decision Tree training Accuracies: \", error_rate(train_predictions, train_actual))\r\n print(\"Decision Tree Validation Accuracies: \", error_rate(valid_predictions, valid_actual))\r\n\r\n classifier = RandomForest(300, 300, 2, type_map, categories_map, 20)\r\n classifier.fit(data, 10, 10)\r\n train_predictions = classifier.predict(data)\r\n train_actual = extract_column(data, idy)\r\n valid_predictions = classifier.predict(valid)\r\n valid_actual = extract_column(valid, idy)\r\n\r\n print(\"Random Forest training Accuracies: \", error_rate(train_predictions, train_actual))\r\n print(\"Random Forest Validation Accuracies: \", error_rate(valid_predictions, valid_actual))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n #q_2_6()\r\n #kaggle()\r\n q_2_4()" } ]	13
jperilla/Machine-Learning	https://github.com/jperilla/Machine-Learning	53c800eb18af4ddf30e04708c549c8d8c90b8147	407275c2fca57c1f78ae11ef4c73ff87d4eef73a	1640e4b68f86c1f809b2539f74c7850e1406dba8	refs/heads/master	2020-07-10T16:04:21.396542	2020-06-22T18:30:16	2020-06-22T18:30:16	204,306,544	2	0	null	null	null	null	null	[ { "alpha_fraction": 0.6546216011047363, "alphanum_fraction": 0.660359799861908, "avg_line_length": 40.06369400024414, "blob_id": "f6b32428cd04db1288540869617019ae7eb5b42c", "content_id": "c55394a2a88eee52dd23f1e71cf2d1b686adf773", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6448, "license_type": "no_license", "max_line_length": 119, "num_lines": 157, "path": "/Clustering and Feature Selection/runAlgorithms.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import sys\n\nimport pandas as pd\n\nimport naiveBayes as nb\nfrom kMeansClustering import KMeansClustering\nfrom machineLearningUtilities import split_test_train, one_hot_encoder, get_num_similarities, \\\n calculate_silhouette_coefficient\nfrom stepwiseForwardSelection import StepwiseForwardSelection\n\n\ndef run_on_glass(file):\n \"\"\"\n This function runs the SFS Algorithm wrapping Naive Bayes to reduce the feature set\n and then runs a K-means clustering algorithm to cluster the data. To test k-means as\n a classifier Naive Bayes is run again with the cluster labels generated by K-means.\n :param file: The file name that includes the data\n :return: Nothing\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Glass data set...\")\n # Read in glass data\n df_glass = pd.read_csv(file, header=None)\n df_glass.columns = [\"Id\", \"RI\", \"Na\", \"Mg\", \"Al\", \"Si\", \"K\", \"Ca\", \"Ba\", \"Fe\", \"Class\"]\n\n # This data set has no missing values, so we will skip that step\n\n # Drop Id\n df_glass_all = df_glass.drop('Id', axis=1)\n\n # One hot code the classes in order to use my previous naive bayes algorithm\n df_glass_encoded = one_hot_encoder(df_glass_all, [\"Class\"])\n\n # Split into test and training sets\n x_test, x_train = split_test_train(df_glass_encoded)\n x_test = x_test.reset_index(drop=True)\n x_train = x_train.reset_index(drop=True)\n\n # Run stepwise forward selection to reduce the feature set\n print(\"Running SFS on Glass data set...\")\n features = df_glass_all.columns.values.tolist()[0:9]\n print(\"All features...\")\n print(features)\n sfs = StepwiseForwardSelection(features, x_train.iloc[:, 0:9], x_test.iloc[:, 0:9],\n x_train[\"Class_1\"], x_test[\"Class_1\"], nb.learn, nb.test)\n optimized_feature_set = sfs.run()\n\n cluster_and_classify(optimized_feature_set, x_test, x_train)\n\n\ndef run_on_iris(file):\n \"\"\"\n This function runs the SFS Algorithm wrapping Naive Bayes to reduce the feature set\n and then runs a K-means clustering algorithm to cluster the data. To test k-means as\n a classifier Naive Bayes is run again with the cluster labels generated by K-means.\n :param file: The file name that includes the data\n :return: Nothing\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Iris data set...\")\n # Read in iris data\n df_iris = pd.read_csv(file, header=None)\n df_iris.columns = [\"sepal length in cm\", \"sepal width in cm\", \"petal length in cm\", \"petal width in cm\", \"Class\"]\n\n # This data set has no missing values, so we will skip that step\n\n # One hot code the classes in order to use my previous naive bayes algorithm\n df_iris_encoded = one_hot_encoder(df_iris, [\"Class\"])\n\n # Split into test and training sets\n x_test, x_train = split_test_train(df_iris_encoded)\n x_test = x_test.reset_index(drop=True)\n x_train = x_train.reset_index(drop=True)\n\n # Run stepwise forward selection to reduce the feature set\n print(\"Run Stepwise forward selection to reduce the feature set on Iris...\")\n print(\"All features...\")\n features = df_iris_encoded.columns.values.tolist()[0:4]\n print(features)\n sfs = StepwiseForwardSelection(features, x_train.iloc[:, 0:4], x_test.iloc[:, 0:4],\n x_train[\"Class_Iris-virginica\"], x_test[\"Class_Iris-virginica\"], nb.learn, nb.test)\n optimized_feature_set = sfs.run()\n\n cluster_and_classify(optimized_feature_set, x_test, x_train)\n\n\ndef run_on_spambase(file):\n \"\"\"\n This function runs the SFS Algorithm wrapping Naive Bayes to reduce the feature set\n and then runs a K-means clustering algorithm to cluster the data. To test k-means as\n a classifier Naive Bayes is run again with the cluster labels generated by K-means.\n :param file: The file name that includes the data\n :return: Nothing\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Spambase data set...\")\n # Read in spambase data\n df_spambase = pd.read_csv(file, header=None)\n df_spambase.rename(columns={57: 'Class'}, inplace=True)\n\n # This data set has no missing values, so we will skip that step\n\n # Split into test and training sets\n x_test, x_train = split_test_train(df_spambase)\n x_test = x_test.reset_index(drop=True)\n x_train = x_train.reset_index(drop=True)\n\n features = df_spambase.columns.values.tolist()[0:56]\n print(\"Running SFS on Spambase...\")\n print(\"All features...\")\n print(features)\n sfs = StepwiseForwardSelection(features, x_train.iloc[:, 0:56], x_test.iloc[:, 0:56],\n x_train[\"Class\"], x_test[\"Class\"], nb.learn, nb.test)\n optimized_feature_set = sfs.run()\n\n cluster_and_classify(optimized_feature_set, x_test, x_train)\n\n\ndef cluster_and_classify(optimized_feature_set, x_test, x_train):\n \"\"\"This function run the clustering and classification algorithms and\n tests clusters with the silhouette coffecient\"\"\"\n # Use k-means to cluster data\n print(\"Running K Means on Glass data set with optimized feature set...\")\n km = KMeansClustering(x_train[optimized_feature_set], 2)\n labels = km.run()\n # Train the training data with the cluster labels using Naive Bayes\n print(\"Training with Naive Bayes with k-means labels...\")\n model = nb.learn(pd.Series(labels), x_train[optimized_feature_set])\n # Test the naive bayes classifier on test data\n print(\"Testing Naive Bayes Classifier with cluster labels\")\n predictions = nb.test(x_test[optimized_feature_set], model)\n print(\"Naive Bayes Classifier Performance = \" + str(get_num_similarities(labels, predictions) / len(labels) 100))\n # Find the silhouette coefficient of the clusters\n print(\"Calculating the silhouette coefficient...\")\n sc = calculate_silhouette_coefficient(x_train[optimized_feature_set], labels)\n print(\"Silhouette Coefficient = \" + str(sc))\n\n\nif __name__== \"__main__\":\n if len(sys.argv) < 3:\n print(\"Please add options for data set (iris, glass, spambase) then file path when running this script\")\n print(\"Example: python runAlgorithms iris ./data/iris.data\")\n exit()\n\n if sys.argv[1] == \"glass\":\n run_on_glass(sys.argv[2])\n exit()\n\n if sys.argv[1] == \"iris\":\n run_on_iris(sys.argv[2])\n exit()\n\n if sys.argv[1] == \"spambase\":\n run_on_spambase(sys.argv[2])\n exit()\n\n exit()\n\n" }, { "alpha_fraction": 0.8518518805503845, "alphanum_fraction": 0.8518518805503845, "avg_line_length": 53, "blob_id": "4e5acdd5816c891a45ae8c1b27b1efc8d2f57ef9", "content_id": "4f7d39d3e4399cd045c72ec73dd26c08cc890f34", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 108, "license_type": "no_license", "max_line_length": 88, "num_lines": 2, "path": "/README.md", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "# Machine-Learning\nRepository for Machine Learning Projects from Johns Hopkins and Memorial Sloan Kettering\n" }, { "alpha_fraction": 0.5440627336502075, "alphanum_fraction": 0.5530246496200562, "avg_line_length": 30.880952835083008, "blob_id": "8d4a54cbc23d62c987b90a5f92486e50e9dfcb08", "content_id": "a494b722c5df88820b8b5929b5f4d80b9829076c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2678, "license_type": "no_license", "max_line_length": 98, "num_lines": 84, "path": "/Logistic Regression/logisticRegression.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import math\nimport random\n\nfrom machineLearningUtilities import get_num_similarities\n\n\nclass LogisticRegression:\n def __init__(self, features, x_train, x_train_classes, x_test, x_test_classes):\n self.features = features\n self.training_classes = x_train_classes\n self.train = x_train\n self.test = x_test\n self.test_classes = x_test_classes\n self.max_iterations = 50\n self.eta = 0.01 # learning rate\n self.weights = [0] * len(self.features)\n\n def learn(self):\n \"\"\"\n This function runs the logistic regression algorithm on the training set provided\n \"\"\"\n print(\"Running Logistic Regression...\")\n for j, value in enumerate(self.features):\n self.weights[j] = random.uniform(-1.0, 1.0)\n\n print(f\"Starting weights = {self.weights}\")\n converged = False\n i = 0\n while not converged:\n weight_deltas = [0] * len(self.features)\n\n for x_index, x_values in self.train.iterrows():\n o = 0\n for j, value in enumerate(self.features):\n o += self.weights[j] * float(x_values[j])\n\n y = self.sigmoid(o)\n\n for j, value in enumerate(self.features):\n weight_deltas[j] += (self.training_classes[x_index] - y) * float(x_values[j])\n\n for j, value in enumerate(self.features):\n self.weights[j] += self.eta * weight_deltas[j]\n\n if i > self.max_iterations:\n converged = True\n\n i += 1\n\n print(f\"Ending eights = {self.weights}\")\n\n def validate(self):\n \"\"\"\n This function determines accuracy of model using the test data set\n and applying the linear function using the weights\n \"\"\"\n print(\"Testing...\")\n predictions = []\n for x_index, x_values in self.test.iterrows():\n # Calculate linear value by adding up x values and their weights\n o = 0\n for j, value in enumerate(self.features):\n o += float(x_values[j]) * self.weights[j]\n\n y = self.sigmoid(o)\n\n if y > 0.5:\n predictions.append(1)\n else:\n predictions.append(0)\n\n return get_num_similarities(predictions, self.test_classes) / len(self.test_classes) * 100\n\n @staticmethod\n def sigmoid(x):\n \"\"\"\n This function calculates the logistic sigmoid of x\n :param x: The value\n :return: The logistic sigmoid\n \"\"\"\n try:\n return 1 / (1 + math.exp(-x))\n except OverflowError:\n return float('inf')\n" }, { "alpha_fraction": 0.5622423887252808, "alphanum_fraction": 0.5766693949699402, "avg_line_length": 32.92307662963867, "blob_id": "b28eefc5ec0d0dcfac5ae0690e52c34d5604cc09", "content_id": "18bdb5961394f26e6acd81fc61993b1c451be104", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4852, "license_type": "no_license", "max_line_length": 113, "num_lines": 143, "path": "/K-Nearest Neighbors/runAlgorithms.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import sys\nimport statistics\n\nimport pandas as pd\n\nfrom kNearestNeighbors import KNearestNeighbors\nfrom machineLearningUtilities import split_into_random_stratified_groups, one_hot_encoder\n\n\ndef run_on_ecoli(file, k):\n \"\"\"\n This function runs k-nearest neighbors on the Ecoli data set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Ecoli data set...\")\n df_ecoli = pd.read_csv(file, header=None, sep='\\s+')\n\n df_ecoli.columns = [\"Id\", \"mcg\", \"gvh\", \"lip\", \"chg\", \"aac\", \"alm1\", \"alm2\", \"Class\"]\n # This data set has no missing values, so we will skip that step\n\n # Drop Id\n df_ecoli = df_ecoli.drop(\"Id\", axis=1)\n\n run_k_nearest_neighbor_experiments(df_ecoli, k, True, classification=True)\n\n\ndef run_on_image(file, k):\n \"\"\"\n This function runs k-nearest neighbors on the Image Segmentation data set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Image Segmentation data set...\")\n df_image = pd.read_csv(file, header=None)\n print(df_image.head())\n\n df_image.columns = [\"Class\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\",\n \"16\", \"17\", \"18\", \"19\"]\n\n run_k_nearest_neighbor_experiments(df_image, k, True, classification=True)\n\n\ndef run_on_computer(file, k):\n \"\"\"\n This function runs k-nearest neighbors on the Computer Hardware data set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Computer Hardware data set...\")\n df_computer = pd.read_csv(file, header=None)\n print(df_computer.head())\n\n df_computer.columns = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"Class\", \"9\"]\n\n # One hot encode categorical values\n df_computer = one_hot_encoder(df_computer, [\"0\", \"1\"])\n print(df_computer.head())\n\n run_k_nearest_neighbor_experiments(df_computer, k, False, classification=False)\n\n\ndef run_on_forest(file, k):\n \"\"\"\n This function runs k-nearest neighbors on the Forest Fires data set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Forest Fires data set...\")\n df_forest = pd.read_csv(file, header=None)\n print(df_forest.head())\n\n df_forest.columns = []\n\n run_k_nearest_neighbor_experiments(df_forest, k, False, classification=False)\n\n\ndef run_k_nearest_neighbor_experiments(df, k, run_condensed, classification=True):\n # Split dataset 5-fold stratified\n print(f\"Size of total dataset = {len(df)}\")\n train1, train2, train3, train4, train5 = split_into_random_stratified_groups(df)\n # Run five experiments, using one of the sets as a test set each time\n k_scores = []\n k_condensed_scores = []\n datasets = [train1, train2, train3, train4, train5]\n for i, d in enumerate(datasets):\n print(\"-------------\")\n print(f\"Experiment #{i + 1}\")\n print(\"-------------\")\n df_test = datasets[i]\n print(len(df_test))\n training_sets = datasets.copy()\n del training_sets[i]\n df_train = pd.concat(training_sets)\n print(len(df_train))\n\n # Run K-Nearest Neighbors\n print(f\"k = {k}\")\n print(\"Running k nearest neighbors...\")\n knn = KNearestNeighbors(df_test, k, df.columns, classification)\n accuracy = knn.run(df_train)\n print('Percent accurate: ' + repr(accuracy) + '%')\n k_scores.append(accuracy)\n\n if run_condensed:\n # Run Condensed K-Nearest Neighbors\n knn = KNearestNeighbors(df_test, k, df.columns, classification)\n accuracy = knn.run_condensed(df_train)\n print('Percent accurate: ' + repr(accuracy) + '%')\n k_condensed_scores.append(accuracy)\n\n print(\"----------------------------------------\")\n print(f\"Averages over 5 experiments where k={k}\")\n print(\"----------------------------------------\")\n print(f\"k-Nearest Neighbors = {statistics.mean(k_scores)}\")\n if run_condensed:\n print(f\"Condensed k-Nearest Neighbors = {statistics.mean(k_condensed_scores)}\")\n\n\nif __name__== \"__main__\":\n if len(sys.argv) < 4:\n print(\"Please add options for data set (--ecoli, --image, --computer or --forest) \"\n \"and value of k then file path when running this script\")\n print(\"Example: python runAlgorithms --ecoli 3 ./data/iris.data\")\n exit()\n\n if sys.argv[1] == \"--ecoli\":\n run_on_ecoli(sys.argv[3], int(sys.argv[2]))\n exit()\n\n if sys.argv[1] == \"--image\":\n run_on_image(sys.argv[3], int(sys.argv[2]))\n exit()\n\n if sys.argv[1] == \"--computer\":\n run_on_computer(sys.argv[3], int(sys.argv[2]))\n exit()\n\n if sys.argv[1] == \"--forest\":\n run_on_forest(sys.argv[3], int(sys.argv[2]))\n exit()\n\n exit()\n\n" }, { "alpha_fraction": 0.5569592118263245, "alphanum_fraction": 0.573803186416626, "avg_line_length": 32.917293548583984, "blob_id": "a39ac21c75a6f62098449e522d462c81e5aae76c", "content_id": "dbdc0cfe3169a2ac662257117f39c06ee86286fa", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4512, "license_type": "no_license", "max_line_length": 113, "num_lines": 133, "path": "/Decision Trees/runAlgorithms.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import sys\nimport statistics\n\nimport pandas as pd\n\nfrom DecisionTree import DecisionTree\nfrom machineLearningUtilities import split_into_random_stratified_groups\n\n\ndef run_id3_on_abalone(file, prune=False):\n \"\"\"\n This function runs ID3 algorithm on the abalone data set, which is a classification set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Abalone data set...\")\n df_abalone = pd.read_csv(file, header=None)\n df_abalone.columns = [\"Sex\", \"Length\", \"Diameter\", \"Height\", \"Whole\", \"Shucked\",\n \"Viscera\", \"Shell\", \"Class\"]\n\n # This data set has no missing values, so we will skip that step\n\n # Encode Sex column\n df_abalone.loc[df_abalone[\"Sex\"] == \"M\", \"Sex\"] = 0\n df_abalone.loc[df_abalone[\"Sex\"] == \"F\", \"Sex\"] = 1\n df_abalone.loc[df_abalone[\"Sex\"] == \"I\", \"Sex\"] = 2\n print(df_abalone.head())\n\n run_id3_decision_tree(df_abalone, prune)\n\n\ndef run_id3_on_car(file, prune=False):\n \"\"\"\n This function runs ID3 algorithm on the car evaluation data set, which is a classification set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Car Evaluation data set...\")\n df_car = pd.read_csv(file, header=None)\n df_car.columns = [\"buying\", \"maint\", \"doors\", \"persons\", \"lug_boot\", \"safety\", \"Class\"]\n\n # This data set has no missing values, so we will skip that step\n\n print(df_car.head())\n\n run_id3_decision_tree(df_car, prune)\n\n\ndef run_id3_on_image(file, prune=False):\n \"\"\"\n This function runs the ID3 algorithm on the Image Segmentation data set, which is a classification set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Image Segmentation data set...\")\n df_image = pd.read_csv(file, header=None)\n df_image.columns = [\"Class\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\",\n \"16\", \"17\", \"18\", \"19\"]\n print(df_image.head())\n\n run_id3_decision_tree(df_image, prune)\n\n\ndef run_id3_decision_tree(df, prune=False):\n # Split dataset 5-fold stratified\n print(f\"Size of total dataset = {len(df)}\")\n train1, train2, train3, train4, train5 = split_into_random_stratified_groups(df)\n datasets = [train1, train2, train3, train4, train5]\n scores = []\n pruned_scores = []\n for i, d in enumerate(datasets):\n print(\"-------------\")\n print(f\"Experiment #{i + 1}\")\n print(\"-------------\")\n\n # Use one subset as a test set\n df_test = datasets[i]\n print(f\"Test set size = {len(df_test)}\")\n training_sets = datasets.copy()\n\n # Create a training set from remaining subsets\n del training_sets[i]\n df_train = pd.concat(training_sets)\n print(f\"Training set size = {len(df_train)}\")\n\n # Build the decision tree from the training set\n id3 = DecisionTree(df_train)\n id3.build_id3_tree()\n #id3.print_tree()\n\n # Test the decision tree\n accuracy = id3.validate(id3.root, df_test)\n print('Percent accurate: ' + repr(accuracy) + '%')\n scores.append(accuracy)\n\n # If pruning is turned on, test pruned tree accuracy\n if prune:\n p_accuracy = id3.validate_pruned_tree(df_test)\n print('Pruned Tree Percent Accurate: ' + repr(p_accuracy) + '%')\n pruned_scores.append(p_accuracy)\n\n print(\"----------------------------\")\n print(f\"Averages over 5 experiments\")\n print(\"----------------------------\")\n print(f\"ID3 Decision Tree Averages = {statistics.mean(scores)}%\")\n if prune:\n print(f\"Pruned ID3 Decision Tree Averages = {statistics.mean(pruned_scores)}%\")\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 3:\n print(\"Please add options for data set (--abalone --car --image --computer --forest --wine) \"\n \"and the full file path when running this script\")\n print(\"Example: python runAlgorithms.py --classification ./data/abalone.data\")\n exit()\n\n prune = False\n if len(sys.argv) == 4:\n prune = True if sys.argv[3] == \"-p\" else False\n\n if sys.argv[1] == \"--abalone\":\n run_id3_on_abalone(sys.argv[2], prune=prune)\n exit()\n\n if sys.argv[1] == \"--car\":\n run_id3_on_car(sys.argv[2], prune=prune)\n exit()\n\n if sys.argv[1] == \"--image\":\n run_id3_on_image(sys.argv[2], prune=prune)\n exit()\n\n exit()\n\n" }, { "alpha_fraction": 0.601307213306427, "alphanum_fraction": 0.6055363416671753, "avg_line_length": 37.776119232177734, "blob_id": "2e060ee258338b4bcf2fc2d11c9a32373d6dabf0", "content_id": "8829035408fb600b7af2cd45c2e568aa693809d4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2601, "license_type": "no_license", "max_line_length": 96, "num_lines": 67, "path": "/Clustering and Feature Selection/stepwiseForwardSelection.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "from machineLearningUtilities import get_num_similarities\n\n\nclass StepwiseForwardSelection:\n \"\"\"\n This clas performs Stepwise Forward Selection for feature selection\n \"\"\"\n def __init__(self, all_features, d_train, d_valid, c_train, c_test, f_learn, f_perf):\n \"\"\"\n This function initialize the parameters for SFS\n :param all_features: full set of features\n :param d_train: training data\n :param d_valid: validation data\n :param c_train: training classes\n :param c_test: validation classes\n :param f_learn: The learning function\n :param f_perf: The performance monitoring function\n \"\"\"\n self.features = all_features\n self.train = d_train\n self.valid = d_valid\n self.train_classes = c_train\n self.valid_classes = c_test\n self.learn = f_learn\n self.validate = f_perf\n self.base_performance = -float(\"inf\")\n\n def run(self):\n \"\"\"\n This function runs the SFS algorithm for feature selection\n :return: The set reduced set of features\n \"\"\"\n print(\"Running StepWise Forward Selection...\")\n features_0 = []\n while self.features:\n best_performance = -float(\"inf\")\n for feature in self.features:\n features_0.append(feature)\n hypothesis = self.learn(self.train_classes, self.train[features_0])\n current_performance = self.test_performance(self.valid[features_0], hypothesis)\n if current_performance > best_performance:\n best_performance = current_performance\n best_feature = feature\n features_0.remove(feature)\n\n if best_performance > self.base_performance:\n self.base_performance = best_performance\n self.features.remove(best_feature)\n features_0.append(best_feature)\n else:\n break\n\n print(\"Final best performance...\")\n print(f\"{self.base_performance 100}%\")\n print(\"Final feature set\")\n print(features_0)\n return features_0\n\n def test_performance(self, data, hypothesis):\n \"\"\"\n This function measures performance using the validation function\n :param data: The data to test on\n :param hypothesis: The model to use\n :return: The performance as a percentage correct\n \"\"\"\n predictions = self.validate(data, hypothesis)\n return get_num_similarities(self.valid_classes, predictions) / len(self.valid_classes)\n\n\n\n" }, { "alpha_fraction": 0.5696234703063965, "alphanum_fraction": 0.5713924765586853, "avg_line_length": 33.911502838134766, "blob_id": "12d0870f1912c32f87a2094d6059165de3ea8ebd", "content_id": "66629c6e7a5d3f08a8f2379dd8fd1b1cfa04826c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3957, "license_type": "no_license", "max_line_length": 105, "num_lines": 113, "path": "/K-Nearest Neighbors/kNearestNeighbors.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import operator\nimport statistics\n\nimport pandas as pd\nfrom machineLearningUtilities import euclidean_distance, get_percent_similarities, get_mean_squared_error\n\n\nclass KNearestNeighbors:\n \"\"\"\n This class implements the k nearest neighbors algorihtm\n \"\"\"\n def __init__(self, test, k, columns, classification=True):\n \"\"\"\n This function initialize the k nearest neighbors class with data and value of k\n :param test: The training dataset\n :param test: The test dataset\n :param k: k = number of clusters to create\n \"\"\"\n self.test = test\n self.k = k\n self.columns = columns\n self.classification = classification\n self.predictions = []\n\n def run(self, train):\n \"\"\"\n This function runs the k nearest neighbors algorithm\n \"\"\"\n for i, x in self.test.iterrows():\n neighbors = self.get_k_nearest_neighbors(train, x)\n result = self.get_prediction(neighbors)\n self.predictions.append(result)\n # print('> predicted=' + repr(result) + ', actual=' + repr(x[\"Class\"]))\n\n if self.classification:\n accuracy = get_percent_similarities(self.test[\"Class\"], self.predictions)\n else:\n accuracy = get_mean_squared_error(self.test[\"Class\"], self.predictions)\n\n return accuracy\n\n def run_condensed(self, train):\n \"\"\"\n This function runs the condensed k nearest neighbors algorithm\n \"\"\"\n print(\"Running Condensed k-Nearest Neighbors...\")\n Z = pd.DataFrame(columns=self.columns)\n # Loop through data adding only misclassified data\n # Sample data to shuffle it, selecting x at random\n for i, x in train.sample(frac=1).iterrows():\n additions = False\n if len(Z) <= self.k:\n Z = Z.append(x)\n additions = True\n else:\n z_closest = self.get_closest_point(x, Z)\n if z_closest[\"Class\"] != x[\"Class\"]:\n Z = Z.append(z_closest)\n additions = True\n\n if not additions:\n break\n\n return self.run(Z)\n\n def get_k_nearest_neighbors(self, train, test_instance):\n \"\"\" This function finds the k-nearest neighbors of the test instance in the training set\n :param: test_instance\n :returns: The k-nearest neighbors\n \"\"\"\n # Find distances from each\n distances = []\n for i, x in train.iterrows():\n distance = euclidean_distance(test_instance.drop(labels=['Class']), x.drop(labels=['Class']))\n distances.append((x, distance))\n\n # Sort distances ascending\n distances.sort(key=operator.itemgetter(1))\n\n # Find neighbors by taking k-nearest from the top\n neighbors = []\n for x in range(self.k):\n neighbors.append(distances[x][0])\n\n return neighbors\n\n def get_prediction(self, neighbors):\n \"\"\" This function gets the class based on votes for class in neighbors \"\"\"\n if self.classification:\n class_votes = {}\n for x in range(len(neighbors)):\n vote = neighbors[x][\"Class\"]\n if vote in class_votes:\n class_votes[vote] += 1\n else:\n class_votes[vote] = 1\n\n sorted_votes = sorted(class_votes, key=operator.itemgetter(1), reverse=True)\n return sorted_votes[0]\n else:\n return statistics.mean(x[\"Class\"] for x in neighbors)\n\n @staticmethod\n def get_closest_point(x, Z):\n min_distance = float('inf')\n closest = None\n for i, z in Z.iterrows():\n distance = euclidean_distance(x.drop(labels=['Class']), z.drop(labels=['Class']))\n if distance < min_distance:\n min_distance = distance\n closest = z\n\n return closest\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5238657593727112, "alphanum_fraction": 0.5322145223617554, "avg_line_length": 42.112728118896484, "blob_id": "fc1a8957c3b3b38e896b30a2bc9707703254dc30", "content_id": "e0f692d123ffbc2438d236d16b46a1204340d655", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 11858, "license_type": "no_license", "max_line_length": 119, "num_lines": 275, "path": "/Reinforcement Learning/raceTrack.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import copy\nimport pickle\nimport random\n\nimport numpy as np\n\nfrom car import Car\n\n\nclass RaceTrack:\n \"\"\"\n This class implements the race track algorithm\n \"\"\"\n\n def __init__(self, track, track_name, start_position_x, start_position_y, max_x, max_y):\n \"\"\"\n This function initializes the Race Track environment by setting the track, start and finish positions\n and setting the car to it's initial state. This function also renders the initial environment to the terminal\n :param track: The grid of the track with # = wall, S = possible start positions, F = possible finish positions,\n and . = open track\n :param start_position_x: The x value of the chosen start position\n :param start_position_y: The y value fo the chosen start position\n \"\"\"\n # Initialize track\n self.track_max_x = int(max_x)\n self.track_max_y = int(max_y)\n self.track_name = str(track_name)\n print(track_name)\n self.track = list()\n for row in track:\n self.track.append(list(row))\n self.start_state = Car(start_position_x, start_position_y, 0, 0, self.track_max_x, self.track_max_y)\n self.car = Car(start_position_x, start_position_y, 0, 0, self.track_max_x, self.track_max_y)\n self.cost = 0\n self.wall = '#'\n self.finish = 'F'\n self.num_crashes = 0\n self.__set_car_to_start()\n self.render_state()\n\n # Initialize MDP\n self.value_matrix = dict()\n self.actions = [tuple((0, -1)), tuple((-1, -1)), tuple((-1, 0)),\n tuple((1, 0)), tuple((0, 1)), tuple((1, 1)), tuple((1, -1)), tuple((-1, 1))]\n self.transitions = [1, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8] # based on probability of acceleration working\n self.rewards = dict()\n self.gamma = 0.9\n self.policy = None\n\n def run_racetrack_on_value_iteration(self, restart_on_crash=True):\n \"\"\"\n This function was used to run the race track simulator using the policy created in with the value iteration\n algorithm. It reads the policy in from a file, so that you do not need to rerun the value iteration\n each time.\n \"\"\"\n # Read in the policy created by value iteration\n print(\"Reading in policy created by value iteration...\")\n with open(f\"policy_{self.track_name}\", \"rb\") as file:\n self.policy = pickle.load(file)\n print(self.policy)\n\n # Run the race track simulation\n print(\"Running racetrack simulation...\")\n finished = False\n self.__set_car_to_start()\n current_movement_x, current_movement_y = self.__choose_action(self.car.get_state())\n while not finished and self.cost < 1000:\n print(f'a = {current_movement_x}, {current_movement_y}')\n print(f\"Starting state = {self.car.get_state()}\")\n self.car.accelerate(current_movement_x, current_movement_y)\n print(f'Current position = {self.car.position.x}, {self.car.position.y}')\n print(f'Current velocity = {self.car.velocity.x}, {self.car.velocity.y}')\n print(f'COST = {self.cost}')\n if self.car_has_crashed(self.car):\n print('THE CAR HAS CRASHED $$@@##$@$%!!')\n if restart_on_crash:\n self.__set_car_to_start()\n else:\n self.car.set_to_last_position()\n self.car.reset_velocity()\n self.num_crashes += 1\n current_movement_x, current_movement_y = self.__choose_action(self.car.get_state())\n\n if self.car_has_crossed_the_finish(self.car):\n print('CROSSED THE FINISH LINE!!!!!')\n finished = True\n else:\n self.cost += 1\n\n self.render_state()\n\n def run_racetrack_on_sarsa(self, episodes, restart_on_crash, epsilon=0.1, learning_rate=0.1):\n print(\"Running racetrack simulation with SARSA...\")\n self.__initialize_states()\n q = self.__init_q()\n for episode in range(episodes):\n print(f\"Episode: {episode}\")\n self.__set_car_to_start()\n action = self.__epsilon_greedy(q, epsilon, len(self.actions))\n finished = False\n self.cost = 0\n while not finished and self.cost < 20:\n current_state = self.car.get_state()\n reward = self.__get_reward(current_state, action)\n print(f'a = {action}')\n print(f\"Current state = {current_state}\")\n successor_car = Car(current_state[0], current_state[1],\n current_state[2], current_state[3],\n self.track_max_x, self.track_max_y)\n successor_car.accelerate(action[0], action[1])\n successor_state = successor_car.get_state()\n print(f'Successor state = {successor_state}')\n print(f'COST = {self.cost}')\n if self.car_has_crashed(successor_car):\n print('THE CAR HAS CRASHED $$@@##$@$%!!')\n if restart_on_crash:\n successor_car.set_state(tuple((self.start_state.position.x,\n self.start_state.position.y,\n 0, 0)))\n else:\n successor_car.set_to_last_position()\n\n if self.car_has_crossed_the_finish(successor_car):\n print('CROSSED THE FINISH LINE!!!!!')\n finished = True\n else:\n self.cost += 1\n\n successor_action = self.__epsilon_greedy(q, epsilon, len(self.actions))\n q[tuple((current_state, action))] = q[tuple((current_state, action))] + \\\n learning_rate * \\\n (reward + self.gamma \n q[tuple((successor_state, successor_action))] -\n q[tuple((current_state, action))])\n self.car = successor_car\n action = successor_action\n\n self.render_state()\n\n def car_has_crashed(self, car):\n try:\n crashed = True if self.track[car.position.x][car.position.y] == self.wall else False\n return crashed\n except IndexError:\n return True\n\n def car_has_crossed_the_finish(self, car):\n return True if self.track[car.position.x][car.position.y] == self.finish else False\n\n def learn_value_iteration(self):\n self.__value_iteration__()\n\n def render_state(self):\n \"\"\"\n This function renders the current state of the race track to the screen with @ representing the current\n position of the car\n \"\"\"\n print(f\"Position = ({self.car.position.x}, {self.car.position.y})\")\n print(f\"Velocity = ({self.car.velocity.x}, {self.car.velocity.y})\")\n print(f\"Current cost = {self.cost}\")\n self.track[self.car.position.x][self.car.position.y] = '@'\n for row in self.track:\n print(\"\".join(row))\n\n def __set_car_to_start(self):\n print(\"Moving to Start Position\")\n self.car.position = self.start_state.position\n\n def __value_iteration__(self, epsilon=0.001):\n policy = dict()\n self.__initialize_states()\n\n # Run value iteration\n converged = False\n max_iterations = 20\n t = 0\n q = dict()\n previous_value_matrix = None\n while not converged and t < max_iterations:\n t += 1\n print(f\"Iterating: t = {t}\")\n for state, value in self.value_matrix.items():\n for a_index, action in enumerate(self.actions):\n if previous_value_matrix:\n q[tuple((state, action))] += self.__get_reward(state, action) + \\\n self.gamma \\\n sum(self.__transition(i)\n * previous_value_matrix[self.__apply_action(state, a)]\n for i, a in enumerate(self.actions))\n else:\n q[tuple((state, action))] = self.__get_reward(state, action)\n\n q_for_this_state = {k: v for (k, v) in q.items() if k[0] == state}\n policy[state] = max(q_for_this_state, key=q_for_this_state.get)[1]\n self.value_matrix[state] = q_for_this_state[(state, policy[state])]\n\n # Calculate convergence\n if previous_value_matrix:\n diff = self.__get_max_difference(self.value_matrix, previous_value_matrix)\n print(f\"Max Difference = {diff}\")\n if diff < epsilon:\n print(\"Converging...\")\n converged = True\n\n previous_value_matrix = copy.deepcopy(self.value_matrix)\n\n # Write policy to file\n with open(f\"policy_{self.track_name}\", \"wb\") as file:\n pickle.dump(policy, file, protocol=pickle.HIGHEST_PROTOCOL)\n\n return policy\n\n def __initialize_states(self):\n # Initialize the value matrix to all zeros\n for r_index, row in enumerate(self.track):\n for c_index, cell in enumerate(row):\n for i in range(-5, 6):\n for j in range(-5, 6):\n state = tuple((r_index, c_index, i, j))\n self.value_matrix[state] = 0.0\n self.rewards[state] = self.__calculate_reward(c_index, r_index)\n\n def __calculate_reward(self, c_index, r_index):\n return -1 if self.track[r_index][c_index] == self.wall else \\\n (2 if self.track[r_index][c_index] == self.finish else 1)\n\n @staticmethod\n def __get_max_difference(value_matrix, previous_value_matrix):\n max_difference = 0\n for state, value in value_matrix.items():\n diff = value - previous_value_matrix[state]\n if diff > max_difference:\n max_difference = diff\n return max_difference\n\n def __transition(self, action):\n return self.transitions[action]\n\n def __get_reward(self, state, action):\n car = Car(state[0], state[1], state[2], state[3], self.track_max_x, self.track_max_y)\n car.accelerate(action[0], action[1])\n try:\n reward = self.rewards[car.get_state()]\n return reward\n except KeyError:\n return -1 # Went off board\n\n def __apply_action(self, state, action):\n car = Car(state[0], state[1], state[2], state[3], self.track_max_x, self.track_max_y)\n car.accelerate(action[0], action[1])\n state = car.get_state()\n return state\n\n def __choose_action(self, state):\n print(f'Choosing action from current state {state}')\n return self.policy[state]\n\n def __epsilon_greedy(self, q, epsilon, num_actions):\n if np.random.rand() < epsilon:\n q_for_this_state = {k: v for (k, v) in q.items() if k[0] == self.car.get_state()}\n action = max(q_for_this_state, key=q_for_this_state.get)[1]\n else:\n action = self.actions[np.random.randint(0, num_actions)]\n print(f\"Chosen action = {action}\")\n return action\n\n def __init_q(self):\n q = dict()\n for state, value in self.value_matrix.items():\n for a_index, action in enumerate(self.actions):\n q[tuple((state, action))] = random.randint(0, 100)\n\n print(f\"Q = {q}\")\n\n return q\n\n\n" }, { "alpha_fraction": 0.5162436366081238, "alphanum_fraction": 0.5263959169387817, "avg_line_length": 25.226667404174805, "blob_id": "0c4d48c2f27ee22e7da1cb4cba70591503caf4de", "content_id": "f1026ab35c1e1e9deaf03f163a587d2747720a4e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1970, "license_type": "no_license", "max_line_length": 90, "num_lines": 75, "path": "/Reinforcement Learning/car.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "class Car:\n\n def __init__(self, x, y, vx, vy, max_x, max_y):\n self.position = CarPosition(x, y)\n self.last_position = CarPosition(x, y)\n self.velocity = CarVelocity(vx, vy)\n self.last_velocity = CarVelocity(vx, vy)\n self.max_x = max_x\n self.max_y = max_y\n\n def accelerate(self, ax, ay):\n self.last_position = CarPosition(self.position.x, self.position.y)\n self.last_velocity = CarVelocity(self.velocity.x, self.velocity.y)\n self.velocity.accelerate(ax, ay)\n self.__move()\n\n def set_to_last_position(self):\n self.position = self.last_position\n\n def reset_velocity(self):\n self.velocity.x = 0\n self.velocity.y = 0\n\n def get_state(self):\n return tuple((self.position.x, self.position.y, self.velocity.x, self.velocity.y))\n\n def set_state(self, state):\n self.position.x = state[0]\n self.position.y = state[1]\n self.velocity.x = state[2]\n self.velocity.y = state[3]\n\n def __move(self):\n self.position.x += self.velocity.x\n self.position.y += self.velocity.y\n\n # Limit to max values on track\n if self.position.x < 0:\n self.position.x = 0\n\n if self.position.x >= self.max_x:\n self.position.x = self.max_x - 1\n\n if self.position.y < 0:\n self.position.y = 0\n\n if self.position.y >= self.max_y:\n self.position.y = self.max_y - 1\n\n\nclass CarPosition:\n def __init__(self, x, y):\n self.x = x\n self.y = y\n\n\nclass CarVelocity:\n def __init__(self, vx, vy):\n self.x = vx\n self.y = vy\n\n def accelerate(self, ax, ay):\n if self.x + ax > 5:\n self.x = 5\n elif self.x + ax < -5:\n self.x = -5\n else:\n self.x += ax\n\n if self.y + ay > 5:\n self.y = 5\n elif self.y + ay < -5:\n self.y = -5\n else:\n self.y += ay\n\n\n\n" }, { "alpha_fraction": 0.5967450141906738, "alphanum_fraction": 0.6380349397659302, "avg_line_length": 45.71831130981445, "blob_id": "07b2ab2ce6bf5ef3397938f736cd489c4058de44", "content_id": "47e66bf0c8d346a29b48a633d850a4a7d9c0cd35", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3318, "license_type": "no_license", "max_line_length": 109, "num_lines": 71, "path": "/Clustering and Feature Selection/naiveBayes.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "\ndef learn(classes, data):\n \"\"\"\n This function creates the naive bayes model.\n :param classes: the set of actual class values\n :param data: the set of data\n :return: the model, which consists of P(C=0), P(C=1), P(F=1\|C=0), P(F=0\|C=0), P(F=1\|C=1) and P(F=0\|C=1)\n \"\"\"\n # Create the model\n value_counts = classes.value_counts().to_dict()\n num_samples = len(classes)\n prob_0 = value_counts[0] / num_samples if 0 in value_counts else 0\n prob_1 = value_counts[1] / num_samples if 1 in value_counts else 1\n feature_probs_f0_given0 = []\n feature_probs_f1_given0 = []\n feature_probs_f0_given1 = []\n feature_probs_f1_given1 = []\n zero_class_indexes = [i for i, x in enumerate(classes) if x == 0]\n one_class_indexes = [i for i, x in enumerate(classes) if x == 1]\n num_zero_classes = len(zero_class_indexes)\n num_one_classes = len(one_class_indexes)\n for index, colName in enumerate(data.columns):\n f_value_counts_given_0 = data.iloc[zero_class_indexes, index].value_counts().to_dict()\n f_value_counts_given_1 = data.iloc[one_class_indexes, index].value_counts().to_dict()\n prob_f0_given_c0 = f_value_counts_given_0[0] / num_zero_classes if 0 in f_value_counts_given_0 else 0\n prob_f1_given_c0 = f_value_counts_given_0[1] / num_zero_classes if 1 in f_value_counts_given_0 else 0\n prob_f0_given_c1 = f_value_counts_given_1[0] / num_one_classes if 0 in f_value_counts_given_1 else 0\n prob_f1_given_c1 = f_value_counts_given_1[1] / num_one_classes if 1 in f_value_counts_given_1 else 0\n feature_probs_f0_given0.append(prob_f0_given_c0)\n feature_probs_f1_given0.append(prob_f1_given_c0)\n feature_probs_f0_given1.append(prob_f0_given_c1)\n feature_probs_f1_given1.append(prob_f1_given_c1)\n\n return (prob_0, prob_1, feature_probs_f0_given0,\n feature_probs_f1_given0, feature_probs_f0_given1,\n feature_probs_f1_given1)\n\n\ndef test(data, prob_0, prob_1, feature_probs_f0_given0,\n feature_probs_f1_given0, feature_probs_f0_given1,\n feature_probs_f1_given1):\n \"\"\"\n This function makes predictions for the naive bayes algorithm\n :param data: The data to create the model from for naive bayes\n :param prob_0: P(C=0)\n :param prob_1: P(C=1)\n :param feature_probs_f0_given0: P(F=0\|C=0)\n :param feature_probs_f1_given0: P(F=1\|C=0)\n :param feature_probs_f0_given1: P(F=0\|C=1)\n :param feature_probs_f1_given1: P(F=1\|C=1)\n :return: the predictions made on the set of values, based on the model given\n \"\"\"\n # Calculate predictions by calculating the probability of each\n # C=1 and C=0 in each sample(row)\n predictions = []\n for index, row in data.iterrows():\n c0_product = prob_0\n c1_product = prob_1\n for feature_index, feature in enumerate(row):\n if feature == 0:\n c0_product = feature_probs_f0_given0[feature_index]\n c1_product = feature_probs_f0_given1[feature_index]\n else:\n c0_product = feature_probs_f1_given0[feature_index]\n c1_product = feature_probs_f1_given1[feature_index]\n\n if c0_product > c1_product:\n predictions.append(0)\n else:\n predictions.append(1)\n\n return predictions\n" }, { "alpha_fraction": 0.6023854613304138, "alphanum_fraction": 0.6235091090202332, "avg_line_length": 36.068180084228516, "blob_id": "f918ee1a03b0ebd1e1c80c79baf1adb8b0b8405a", "content_id": "554d25fc16c5feaf4462cc301aa6c487c833f3da", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 17942, "license_type": "no_license", "max_line_length": 115, "num_lines": 484, "path": "/Classification/algorithms.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n# Import packages\nimport pandas as pd\nimport numpy as np\n\n\n# # Utilities\n\ndef one_hot_encoder(df, columns):\n \"\"\"\n This function one hot encodes a set of data\n :param df: data to encode\n :param columns: columns to encode\n :return: one hot encoded data\n \"\"\"\n one_hot = pd.get_dummies(df, columns=columns, prefix=columns)\n df = df.drop(columns, axis=1)\n return one_hot\n\n\ndef split_test_train(df):\n \"\"\"\n This function splits test and train sets by 2/3 and 1/3 randomly\n :param df: data to split\n :return: train and test set\n \"\"\"\n train = df.sample(frac=2 / 3)\n test = df.loc[~df.index.isin(train.index), :]\n return train, test\n\n\ndef get_num_similarities(array1, array2):\n \"\"\"\n This function compares two arrays for similiaries and returns number of equal elements\n :param array1: first array to compare\n :param array2: second array to compare\n :return: the number of similar elements\n \"\"\"\n a = np.array(array1)\n b = np.array(array2)\n return np.sum(a == b)\n\n\n# # Winnow-2\n# TODO: move this to a class\ndef promote(X, w, alpha):\n \"\"\"\n This function performs promotion for the winnow-2 algorithm\n :param X: the set of features values for the sample\n :param w: the current weight array\n :param alpha: alpha hyperparamter\n :return: new weight array\n \"\"\"\n for index, x in enumerate(X):\n if x == 1:\n w[index] = alpha\n\n return w\n\n\ndef demote(X, w, alpha):\n \"\"\"\n This function performs demotion for the winnow-2 algorithm\n :param X: the set of features values for the sample\n :param w: the current weight array\n :param alpha: alpha hyperparamter\n :return: new weight array\n \"\"\"\n for index, x in enumerate(X):\n if x == 1:\n w[index] /= alpha\n\n return w\n\n\ndef make_prediction(X, w, theta):\n \"\"\"\n This function makes predictions for the winnow-2 algorithms\n :param X: the set of feature values for the sample\n :param w: the current weight array\n :param theta: theta hyperparameter\n :return: prediction 1 or 0\n \"\"\"\n sum = 0\n for index, x in enumerate(X):\n sum += w[index] int(x)\n\n if sum > theta:\n return 1\n\n return 0\n\n\ndef winnow_2(X, w, cls, theta, alpha):\n \"\"\"\n This function implements the winnow-2 algorithm for a single example\n :param X: the feature set\n :param w: the weights\n :param cls: the correct class\n :param theta: theta hyperparameter\n :param alpha: alpha hyperparameter\n :return: new weights and prediction\n \"\"\"\n # Make the prediction on X\n prediction = make_prediction(X, w, theta)\n\n # Test prediction against class\n if prediction == cls:\n return w, prediction\n\n if prediction == 0 and cls == 1:\n w = promote(X, w, alpha)\n else:\n w = demote(X, w, alpha)\n\n return w, prediction\n\n\ndef train_winnow2(X_train, classes, alpha, theta):\n \"\"\"\n Run winnow-2 on the training set\n :param X_train: the training set\n :param classes: the set of actual classes to be compared to predictions\n :param alpha: the alpha hyperparameter\n :param theta: the theta hyperparameter\n :return: new weight array\n \"\"\"\n\n print(\"\\n\")\n print(\"WINNOW 2\")\n print(\"\\n\")\n\n # Initialize w to all 1s\n w = [1] * (X_train.shape[1])\n print('w = ' + str(w))\n print(\"Number of attributes = \" + str(len(w)))\n\n # TRAIN\n predictions = []\n for index, row in X_train.iterrows():\n (w, p) = winnow_2(row, w, classes[index], theta, alpha)\n predictions.append(p)\n\n # Compare predictions to actual\n print(\"Training Predictions (% Correct)\")\n print(get_num_similarities(classes, predictions) / len(X_train) * 100)\n\n return w\n\n\ndef test_winnow2(X_test, classes, alpha, theta, w):\n \"\"\"\n Run winnow-2 on the tes set, with the weights\n created by the training function\n :param X_test: the test set\n :param classes: the set of actual classes to be compared to predictions\n :param alpha: the alpha hyperparameter\n :param theta: the theta hyperparameter\n :param w: new weight array\n \"\"\"\n predictions = []\n for index, row in X_test.iterrows():\n (w, p) = winnow_2(row, w, classes[index], theta, alpha)\n predictions.append(p)\n\n # Compare predictions to actual\n print(\"Testing Predictions (% Correct)\")\n print(get_num_similarities(classes, predictions) / len(X_test) * 100)\n\n\n# # Naive Bayes\n# TODO: move this to a class\ndef get_naive_bayes_model(classes, features):\n \"\"\"\n This function creates the naive bayes model.\n :param classes: the set of actual class values\n :param features: the set of feature values for each sample\n :return: the model, which consists of P(C=0), P(C=1), P(F=1\|C=0), P(F=0\|C=0), P(F=1\|C=1) and P(F=0\|C=1)\n \"\"\"\n # Create the model\n value_counts = classes.value_counts().to_dict()\n num_samples = len(classes)\n prob_0 = value_counts[0] / num_samples if 0 in value_counts else 0\n prob_1 = value_counts[1] / num_samples if 1 in value_counts else 1\n feature_probs_f0_given0 = []\n feature_probs_f1_given0 = []\n feature_probs_f0_given1 = []\n feature_probs_f1_given1 = []\n zero_class_indexes = [i for i, x in enumerate(classes) if x == 0]\n one_class_indexes = [i for i, x in enumerate(classes) if x == 1]\n num_zero_classes = len(zero_class_indexes)\n num_one_classes = len(one_class_indexes)\n for index, colName in enumerate(features.columns):\n f_value_counts_given_0 = features.iloc[zero_class_indexes, index].value_counts().to_dict()\n f_value_counts_given_1 = features.iloc[one_class_indexes, index].value_counts().to_dict()\n prob_f0_given_c0 = f_value_counts_given_0[0] / num_zero_classes if 0 in f_value_counts_given_0 else 0\n prob_f1_given_c0 = f_value_counts_given_0[1] / num_zero_classes if 1 in f_value_counts_given_0 else 0\n prob_f0_given_c1 = f_value_counts_given_1[0] / num_one_classes if 0 in f_value_counts_given_1 else 0\n prob_f1_given_c1 = f_value_counts_given_1[1] / num_one_classes if 1 in f_value_counts_given_1 else 0\n feature_probs_f0_given0.append(prob_f0_given_c0)\n feature_probs_f1_given0.append(prob_f1_given_c0)\n feature_probs_f0_given1.append(prob_f0_given_c1)\n feature_probs_f1_given1.append(prob_f1_given_c1)\n\n print(\"THE MODEL\")\n print(\"Probability of C = 0:\", prob_0)\n print(\"Probability of C = 1:\", prob_1)\n print(\"Probabilities of f = 0, given c = 0:\", feature_probs_f0_given0)\n print(\"Probabilities of f = 1, given c = 0:\", feature_probs_f1_given0)\n print(\"Probabilities of f = 0, given c = 1:\", feature_probs_f0_given1)\n print(\"Probabilities of f = 1, given c = 1:\", feature_probs_f1_given1)\n return (prob_0, prob_1, feature_probs_f0_given0,\n feature_probs_f1_given0, feature_probs_f0_given1,\n feature_probs_f1_given1)\n\n\ndef naive_bayes_make_predictions(data, prob_0, prob_1, feature_probs_f0_given0,\n feature_probs_f1_given0, feature_probs_f0_given1,\n feature_probs_f1_given1):\n \"\"\"\n This function makes predictions for the naive bayes algorithm\n :param data: The data to create the model from for naive bayes\n :param prob_0: P(C=0)\n :param prob_1: P(C=1)\n :param feature_probs_f0_given0: P(F=0\|C=0)\n :param feature_probs_f1_given0: P(F=1\|C=0)\n :param feature_probs_f0_given1: P(F=0\|C=1)\n :param feature_probs_f1_given1: P(F=1\|C=1)\n :return: the predictions made on the set of values, based on the model given\n \"\"\"\n\n # Calculate predictions by calculating the probability of each \n # C=1 and C=0 in each sample(row)\n predictions = []\n for index, row in data.iterrows():\n c0_product = prob_0\n c1_product = prob_1\n for feature_index, feature in enumerate(row):\n if feature == 0:\n c0_product = feature_probs_f0_given0[feature_index]\n c1_product = feature_probs_f0_given1[feature_index]\n else:\n c0_product = feature_probs_f1_given0[feature_index]\n c1_product = feature_probs_f1_given1[feature_index]\n\n if c0_product > c1_product:\n predictions.append(0)\n else:\n predictions.append(1)\n\n return predictions\n\n\ndef naive_bayes(train_classes, test_classes, X_train, X_test):\n \"\"\"\n This function runs the naive bayes algorithm on a training set, then a test set\n and compares predictions with actual classes and outputs the predictions\n :param train_classes: the training set of classes\n :param test_classes: the test set of classes\n :param X_train: the training datset\n :param X_test: the test dataset\n \"\"\"\n\n print(\"\\n\")\n print(\"NAIVE BAYES\")\n print(\"\\n\")\n\n (prob_0, prob_1,\n feature_probs_f0_given0,\n feature_probs_f1_given0,\n feature_probs_f0_given1,\n feature_probs_f1_given1) = get_naive_bayes_model(train_classes, X_train)\n\n # TRAIN\n predictions_train = naive_bayes_make_predictions(X_train, prob_0, prob_1,\n feature_probs_f0_given0,\n feature_probs_f1_given0,\n feature_probs_f0_given1,\n feature_probs_f1_given1)\n\n # Compare training predictions to actual\n print(\"\\n\")\n print(\"TRAINING PREDICTIONS\")\n print(predictions_train)\n print(\"Training Predictions (% Correct)\")\n print(get_num_similarities(train_classes, predictions_train) / len(X_train) * 100)\n\n # TEST\n predictions = naive_bayes_make_predictions(X_test, prob_0, prob_1,\n feature_probs_f0_given0,\n feature_probs_f1_given0,\n feature_probs_f0_given1,\n feature_probs_f1_given1)\n\n # Compare training predictions to actual\n print(\"\\n\")\n print(\"TEST PREDICTIONS\")\n print(predictions)\n print(\"Testing Predictions (% Correct)\")\n print(get_num_similarities(test_classes, predictions) / len(X_test) * 100)\n\n\ndef run_both_breast():\n \"\"\"\n This function runs the Winnow-2 algorithm and the naive bayes algorithm\n on the breast cancer dataset and allows you to tune theta and alpha\n :param theta: the theta hyperparameter\n :param alpha: the alpha hyperparameter\n \"\"\"\n\n print(\"\\n\")\n print(\"\\n\")\n print(\"BREAST DATASET\")\n print(\"\\n\")\n print(\"\\n\")\n\n # Read in breast cancer data\n df_breast = pd.read_csv('./data/breast-cancer-wisconsin.data', header=None)\n\n # This data has nine attributes including the class attribute (which is Benign = 2, Malignant = 4)\n df_breast.columns = [\"Sample Id\", \"Clump Thickness\", \"Uniformity of Cell Size\", \"Uniformity of Cell Shape\",\n \"Marginal Adhesion\", \"Single Epithelial Cell Size\", \"Bare Nuclei\", \"Bland Chromatin\",\n \"Normal Nucleoli\", \"Mitoses\", \"Class: Benign or Malignant\"]\n\n # Find missing values and remove them, since there are so few\n # The documentation notes that there are16 missing values in group 1 and 6 denoted by '?'\n # I found 16 values in Group 6\n # Since there are so few missing values I dropped those rows\n df_breast_all = df_breast[df_breast[\"Bare Nuclei\"] != '?']\n\n # Drop Sample Id\n df_breast_all = df_breast_all.drop('Sample Id', axis=1)\n\n # Generate boolean classifiers (0 = Benign, 1 = Malignant)\n df_breast_all.loc[df_breast_all[\"Class: Benign or Malignant\"] == 2, \"Class: Benign or Malignant\"] = 0\n df_breast_all.loc[df_breast_all[\"Class: Benign or Malignant\"] == 4, \"Class: Benign or Malignant\"] = 1\n\n # Split breast dataset\n X_breast_train, X_breast_test = split_test_train(df_breast_all)\n print(\"Sample size = \", len(df_breast_all))\n print(\"Training set size = \", len(X_breast_train))\n print(\"Test set size = \", len(X_breast_test))\n\n # Run Winnow-2 on breast\n theta = 38 # Chosen based on box plot of sums\n alpha = 1.6 # Started with 2 and tuned until I got the best result\n print(\"theta = \" + str(theta))\n print(\"alpha = \" + str(alpha))\n w = train_winnow2(X_breast_train.iloc[:, 0:9], X_breast_train.iloc[:, 9], alpha, theta)\n test_winnow2(X_breast_test.iloc[:, 0:9], X_breast_test.iloc[:, 9], alpha, theta, w)\n\n # One hot encode\n columns_to_encode = df_breast_all.columns.values.tolist()\n del columns_to_encode[9]\n df_breast_encoded = one_hot_encoder(df_breast_all, columns_to_encode)\n\n # Split breast dataset\n X_breast_train_encoded, X_breast_test_encoded = split_test_train(df_breast_encoded)\n\n naive_bayes(X_breast_train_encoded[\"Class: Benign or Malignant\"],\n X_breast_test_encoded[\"Class: Benign or Malignant\"],\n X_breast_train_encoded.iloc[:, 1:90],\n X_breast_test_encoded.iloc[:, 1:90])\n\n\ndef run_both_iris():\n \"\"\"\n This function runs the Winnow-2 algorithm and the naive bayes algorithm\n on the iris dataset\n :return:\n \"\"\"\n print(\"\\n\")\n print(\"\\n\")\n print(\"IRIS DATASET\")\n print(\"\\n\")\n print(\"\\n\")\n # Read in Iris dataset and set column names\n df_iris = pd.read_csv('./data/iris.data', header=None)\n df_iris.columns = [\"sepal length (cm)\", \"sepal width (cm)\", \"petal length (cm)\", \"petal width (cm)\", \"Class\"]\n df_iris[df_iris[\"Class\"] == \"Iris-virginica\"].head()\n\n # Generate boolean classifiers (0 = not-Iris-virginica, 1 = Iris-virginica)\n df_iris.loc[df_iris[\"Class\"] == \"Iris-virginica\", \"Class_Bool\"] = 1\n df_iris.loc[df_iris[\"Class\"] != \"Iris-virginica\", \"Class_Bool\"] = 0\n\n # Split iris dataset\n X_iris_train, X_iris_test = split_test_train(df_iris)\n print(\"Sample size = \", len(df_iris))\n print(\"Training set size = \", len(X_iris_train))\n print(\"Test set size = \", len(X_iris_test))\n X_iris_train.head()\n\n # Run Winnow-2 on Iris, I made this a two class problem by running\n # it for Iris-virginica (1) or not-Iris-virginica (0)\n # Given more time, I would write the algorithm to handle all three classes\n theta = 16 # Chosen based on box plot of sums\n alpha = 2 # Started with 2 and tuned until I got the best result\n print(\"theta = \" + str(theta))\n print(\"alpha = \" + str(alpha))\n w = train_winnow2(X_iris_train.iloc[:, 0:4], X_iris_train.iloc[:, 5], alpha, theta)\n test_winnow2(X_iris_test.iloc[:, 0:4], X_iris_test.iloc[:, 5], alpha, theta, w)\n\n # Run Naive Bayes on Iris\n # I encoded these by hand based on the box plots that I created\n sepal_length_cond_1 = df_iris[\"sepal length (cm)\"] > 6\n sepal_length_cond_0 = df_iris[\"sepal length (cm)\"] <= 6\n sepal_width_cond_1 = (df_iris[\"sepal width (cm)\"] > 2.7) & (df_iris[\"sepal width (cm)\"] < 3.25)\n sepal_width_cond_0 = (df_iris[\"sepal width (cm)\"] <= 2.7) \| (df_iris[\"sepal width (cm)\"] >= 3.25)\n petal_length_cond_1 = df_iris[\"petal length (cm)\"] > 5\n petal_length_cond_0 = df_iris[\"petal length (cm)\"] <= 5\n petal_width_cond_1 = df_iris[\"petal width (cm)\"] > 1.5\n petal_width_cond_0 = df_iris[\"petal width (cm)\"] <= 1.5\n\n df_iris_encoded = df_iris.copy()\n df_iris_encoded.loc[sepal_length_cond_1, \"sepal_length_cond_bool\"] = 1\n df_iris_encoded.loc[sepal_length_cond_0, \"sepal_length_cond_bool\"] = 0\n df_iris_encoded.loc[sepal_width_cond_1, \"sepal_width_cond_bool\"] = 1\n df_iris_encoded.loc[sepal_width_cond_0, \"sepal_width_cond_bool\"] = 0\n df_iris_encoded.loc[petal_length_cond_1, \"petal_length_cond_bool\"] = 1\n df_iris_encoded.loc[petal_length_cond_0, \"petal_length_cond_bool\"] = 0\n df_iris_encoded.loc[petal_width_cond_1, \"petal_width_cond_bool\"] = 1\n df_iris_encoded.loc[petal_width_cond_0, \"petal_width_cond_bool\"] = 0\n df_iris_encoded.head()\n\n # Split iris dataset\n X_iris_train, X_iris_test = split_test_train(df_iris_encoded)\n print(\"Sample size = \", len(df_iris))\n print(\"Training set size = \", len(X_iris_train))\n print(\"Test set size = \", len(X_iris_test))\n X_iris_train.head()\n\n naive_bayes(X_iris_train.iloc[:, 5], X_iris_test.iloc[:, 5], X_iris_train.iloc[:, 6:], X_iris_test.iloc[:, 6:])\n\n\ndef run_both_votes():\n \"\"\"This function runs the Winnow-2 algorithm and the naive bayes algorithm\n on the votes dataset\n \"\"\"\n print(\"\\n\")\n print(\"\\n\")\n print(\"VOTES DATASET\")\n print(\"\\n\")\n df_vote = pd.read_csv('./data/house-votes-84.data', header=None)\n df_vote.head()\n\n # Generate boolean classifiers (0 = republican, 1 = democrat)\n df_vote.loc[df_vote[0] == \"republican\", 0] = 0\n df_vote.loc[df_vote[0] == \"democrat\", 0] = 1\n\n # Generate boolean classifiers (0 = n, 1 = y)\n # Set ? to n, since there is no good way to impute votes\n df_vote.replace('n', 0, inplace=True)\n df_vote.replace('y', 1, inplace=True)\n df_vote.replace('?', 0, inplace=True)\n df_vote.head()\n\n # Split vote dataset\n X_vote_train, X_vote_test = split_test_train(df_vote)\n print(\"Sample size = \", len(df_vote))\n print(\"Training set size = \", len(X_vote_train))\n print(\"Test set size = \", len(X_vote_test))\n X_vote_train.head()\n\n # Run Winnow-2\n theta = 8.25 # Chosen based on box plot of sums\n alpha = 2 # Started with 2 and tuned until I got the best result\n print(\"theta = \" + str(theta))\n print(\"alpha = \" + str(alpha))\n w = train_winnow2(X_vote_train.iloc[:, 1:], X_vote_train.iloc[:, 0], alpha, theta)\n test_winnow2(X_vote_test.iloc[:, 1:], X_vote_test.iloc[:, 0], alpha, theta, w)\n\n # Run Naive Bayes\n naive_bayes(X_vote_train[0], X_vote_test[0], X_vote_train.iloc[:, 1:16], X_vote_test.iloc[:, 1:16])\n\n\nif __name__== \"__main__\":\n run_both_breast()\n run_both_iris()\n run_both_votes()\n\n" }, { "alpha_fraction": 0.6343877911567688, "alphanum_fraction": 0.6401979923248291, "avg_line_length": 28.769229888916016, "blob_id": "d6f584698b468566ffeb2e803a61e1f279042c2f", "content_id": "4780d1eb4e95a60ed74cfdffa69c9c4c84dbc4f0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4647, "license_type": "no_license", "max_line_length": 101, "num_lines": 156, "path": "/Clustering and Feature Selection/machineLearningUtilities.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import random\nimport pandas as pd\nimport numpy as np\n\n\ndef one_hot_encoder(df, columns):\n \"\"\"\n This function one hot encodes a set of data\n :param df: data to encode\n :param columns: columns to encode\n :return: one hot encoded data\n \"\"\"\n one_hot = pd.get_dummies(df, columns=columns, prefix=columns)\n df = df.drop(columns, axis=1)\n return one_hot\n\n\ndef split_test_train(df):\n \"\"\"\n This function splits test and train sets by 2/3 and 1/3 randomly\n :param df: data to split\n :return: train and test set\n \"\"\"\n train = df.sample(frac=2/3)\n test = df.loc[~df.index.isin(train.index), :]\n return train, test\n\n\ndef split_into_random_groups(df, k):\n \"\"\"\n This function splits a data set into k random groups\n :param df: data to split\n :param k: the number of groups\n :return: array of groups\n \"\"\"\n groups = []\n remaining = df.copy()\n if k > 0:\n for i in range(1, k):\n group = remaining.sample(frac=1/(k-(i-1)))\n remaining = remaining.loc[~remaining.index.isin(group.index), :]\n groups.append(group)\n\n groups.append(remaining)\n\n return groups\n\n\ndef generate_random_numbers(r, n):\n \"\"\"\n This function generates random integers in range(1:r)\n :param r: the range to generate random integers in\n :param n: the number of integers to generate\n :return: array of random numbers\n \"\"\"\n randoms = []\n for x in range(1, n+1):\n randoms.append(random.randint(1, r))\n\n return randoms\n\n\ndef get_num_similarities(array1, array2):\n \"\"\"\n This function compares two arrays for similiaries and returns number of equal elements\n :param array1: first array to compare\n :param array2: second array to compare\n :return: the number of similar elements\n \"\"\"\n a = np.array(array1)\n b = np.array(array2)\n return np.sum(a == b)\n\n\ndef get_euclidean_distance(array1, array2):\n \"\"\"\n This function computes the euclidean distance between two arrays\n :param array1: first array\n :param array2: second array\n :return: distance\n \"\"\"\n return np.linalg.norm(array1-array2)\n\n\ndef calculate_silhouette_coefficient(X, labels):\n \"\"\"\n This function calculates the silhouette coefficient of a data set, and it's labels for clustering\n :param X: The dataset\n :param labels: cluster labels\n :return: silhouette coefficient\n \"\"\"\n return np.mean(get_silhouettes(X, labels))\n\n\ndef get_mean_intra_cluster_distances(x, index, X, labels):\n \"\"\" \n This function gets the mean of all of the distances to each sample in the \n cluster from x\n :param x: The selected sample\n :param index: the index of the selected sample\n :param X: the sample dataset\n :param labels: the set of labels for data points (which cluster it's in)\n \"\"\"\n distances = []\n x_cluster = labels[index]\n rows_in_cluster = [i for i, value in enumerate(labels) if value == x_cluster]\n for i, sample in X.ix[rows_in_cluster].iterrows():\n # Find the distance to x\n d = get_euclidean_distance(x.values, sample.values)\n distances.append(d)\n \n return np.mean(distances)\n\n\ndef get_nearest_cluster_distance(x, index, X, labels):\n \"\"\"\n This function gets the distance to the cluster nearest to\n x, that is not the cluster that x is in.\n :param x: The data point to test distances from\n :param index: The index of the datapoint\n :param X: The data set\n :param labels: The cluster labels of each data point\n :return:\n \"\"\"\n minimum_distance = float('inf')\n clusters = set(labels)\n x_cluster = labels[index]\n for c in clusters:\n if c != x_cluster:\n distances = []\n rows_in_cluster = [i for i, value in enumerate(labels) if value == c]\n for i, sample in X.ix[rows_in_cluster].iterrows():\n distance = get_euclidean_distance(sample.values, x.values)\n distances.append(distance)\n\n mean = np.mean(distances)\n if mean < minimum_distance:\n minimum_distance = mean\n\n return minimum_distance\n\n\ndef get_silhouettes(X, labels):\n \"\"\" This function calculates the silhouette coefficient for each sample\n :param X: The dataset\n :param labels: cluster labels\n :return: silhouette coefficient\n \"\"\"\n silhouettes = []\n for index, x in X.iterrows():\n A = get_mean_intra_cluster_distances(x, index, X, labels)\n B = get_nearest_cluster_distance(x, index, X, labels)\n silhouette = (B - A) / np.maximum(A, B)\n silhouettes.append(np.nan_to_num(silhouette))\n\n return silhouettes\n\n\n\n" }, { "alpha_fraction": 0.5750651955604553, "alphanum_fraction": 0.5911139249801636, "avg_line_length": 39.02531814575195, "blob_id": "ba50ec2592c4a88d86853f099634a88f0d667a46", "content_id": "6133fca0e735b71191179cb3f4b263c5ffa3a96c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 12649, "license_type": "no_license", "max_line_length": 153, "num_lines": 316, "path": "/Logistic Regression/runAlgorithms.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import sys\nimport statistics\n\nimport pandas as pd\n\nfrom logisticRegression import LogisticRegression\nfrom machineLearningUtilities import split_into_random_stratified_groups, one_hot_encoder\nfrom naiveBayes import NaiveBayes\n\n\ndef run_naive_bayes(df, num_features):\n \"\"\"\n This function runs naive on the data frame and outputs statistics from five experiments\n :param df: The data set to run the algorithm on=\n :param num_features: The number of features in this dataset\n \"\"\"\n # Split dataset 5-fold stratified\n print(f\"Size of total dataset = {len(df)}\")\n train1, train2, train3, train4, train5 = split_into_random_stratified_groups(df)\n datasets = [train1, train2, train3, train4, train5]\n nb_scores = []\n for i, d in enumerate(datasets):\n print(\"-------------\")\n print(f\"Experiment #{i + 1}\")\n print(\"-------------\")\n\n # Use one subset as a test set\n df_test = datasets[i]\n print(f\"Test set size = {len(df_test)}\")\n training_sets = datasets.copy()\n\n # Create a training set from remaining subsets\n del training_sets[i]\n df_train = pd.concat(training_sets)\n print(f\"Training set size = {len(df_train)}\")\n\n # Create Naive Bayes\n nb = NaiveBayes(df_train.iloc[:, 0:num_features],\n df_train.iloc[:, num_features],\n df_test.iloc[:, 0:num_features],\n df_test.iloc[:, num_features])\n\n # Train with naive bayes\n nb.learn()\n\n # Test the accuracy of naive bayes\n nb_accuracy = nb.validate()\n print('Naive Bayes Percent accurate: ' + repr(nb_accuracy) + '%')\n nb_scores.append(nb_accuracy)\n\n return statistics.mean(nb_scores)\n\n\ndef run_logistic_regression(df, num_features):\n \"\"\"\n This function runs logistic regression on the data frame and outputs statistics from five experiments\n :param df: The data set to run the algorithm on=\n :param num_features: The number of features in this dataset\n \"\"\"\n # Split dataset 5-fold stratified\n print(f\"Size of total dataset = {len(df)}\")\n train1, train2, train3, train4, train5 = split_into_random_stratified_groups(df)\n datasets = [train1, train2, train3, train4, train5]\n lg_scores = []\n for i, d in enumerate(datasets):\n print(\"-------------\")\n print(f\"Experiment #{i + 1}\")\n print(\"-------------\")\n\n # Use one subset as a test set\n df_test = datasets[i]\n print(f\"Test set size = {len(df_test)}\")\n training_sets = datasets.copy()\n\n # Create a training set from remaining subsets\n del training_sets[i]\n df_train = pd.concat(training_sets)\n print(f\"Training set size = {len(df_train)}\")\n\n # Create Logistic Regression\n lg = LogisticRegression(df_train.columns[0:num_features],\n df_train.iloc[:, 0:num_features],\n df_train.iloc[:, num_features],\n df_test.iloc[:, 0:num_features],\n df_test.iloc[:, num_features])\n\n # Train with logistic regression\n lg.learn()\n\n # Test the logistic regression accuracy\n lg_accuracy = lg.validate()\n print('Logistic Regression Percent accurate: ' + repr(lg_accuracy) + '%')\n lg_scores.append(lg_accuracy)\n\n return statistics.mean(lg_scores)\n\n\ndef run_on_breast(file):\n \"\"\"\n This function runs logistic regression and naive bayes classifier on the breast data set, it encodes\n the classes to Benign = 0, Malignant = 1, and removes missing values\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Breast data set...\")\n df_breast = pd.read_csv(file, header=None)\n df_breast.columns = [\"Sample Id\", \"Clump Thickness\", \"Uniformity of Cell Size\", \"Uniformity of Cell Shape\",\n \"Marginal Adhesion\", \"Single Epithelial Cell Size\", \"Bare Nuclei\", \"Bland Chromatin\",\n \"Normal Nucleoli\", \"Mitoses\", \"Class\"]\n\n # Find missing values and remove them, since there are so few\n # The documentation notes that there are16 missing values in group 1 and 6 denoted by '?'\n # I found 16 values in Group 6\n # Since there are so few missing values I dropped those rows\n df_breast = df_breast[df_breast[\"Bare Nuclei\"] != '?']\n\n # Drop Sample Id\n df_breast = df_breast.drop('Sample Id', axis=1)\n\n # Generate boolean classifiers (0 = Benign, 1 = Malignant)\n df_breast.loc[df_breast[\"Class\"] == 2, \"Class\"] = 0\n df_breast.loc[df_breast[\"Class\"] == 4, \"Class\"] = 1\n\n # One hot encode breast data set for naive bayes\n columns_to_encode = df_breast.columns.values.tolist()\n del columns_to_encode[9]\n df_breast_encoded = one_hot_encoder(df_breast, columns_to_encode)\n\n lg_averages = run_logistic_regression(df_breast, 9)\n nb_averages = run_naive_bayes(df_breast_encoded, 9)\n\n print(\"----------------------------\")\n print(f\"Averages over 5 experiments\")\n print(\"----------------------------\")\n print(f\"Logistic Regression Averages = {lg_averages}%\")\n print(f\"Naive Bayes Averages = {nb_averages}%\")\n\n\ndef run_on_glass(file):\n \"\"\"\n This function runs logistic regression and naive bayes classifier on the glass data set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Glass data set...\")\n # Read in glass data\n df_glass = pd.read_csv(file, header=None)\n df_glass.columns = [\"Id\", \"RI\", \"Na\", \"Mg\", \"Al\", \"Si\", \"K\", \"Ca\", \"Ba\", \"Fe\", \"Class\"]\n\n # This data set has no missing values, so we will skip that step\n\n # Drop Id\n df_glass = df_glass.drop('Id', axis=1)\n\n # Encode the class\n df_glass = one_hot_encoder(df_glass, ['Class'])\n df_glass = df_glass.rename(columns={\"Class_1\": \"Class\"})\n df_glass = df_glass.drop(columns=['Class_2', 'Class_3', 'Class_5', 'Class_6', 'Class_7'])\n print(df_glass.head())\n\n # One hot code the data set for naive bayes\n columns_to_encode = df_glass.columns.values.tolist()\n del columns_to_encode[9]\n df_glass_encoded = one_hot_encoder(df_glass, columns_to_encode)\n df_glass_encoded = df_glass_encoded[[c for c in df_glass_encoded if c not in ['Class']] + ['Class']]\n\n lg_averages = run_logistic_regression(df_glass, 9)\n nb_averages = run_naive_bayes(df_glass_encoded, len(df_glass_encoded.columns) - 1)\n\n print(\"----------------------------\")\n print(f\"Averages over 5 experiments\")\n print(\"----------------------------\")\n print(f\"Logistic Regression Averages = {lg_averages}%\")\n print(f\"Naive Bayes Averages = {nb_averages}%\")\n\n\ndef run_on_iris(file):\n \"\"\"\n This function runs logistic regression and naive bayes classifier on the iris data set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Iris data set...\")\n # Read in iris data\n df_iris = pd.read_csv(file, header=None)\n df_iris.columns = [\"sepal length (cm)\", \"sepal width (cm)\", \"petal length (cm)\", \"petal width (cm)\", \"Class\"]\n\n # Generate boolean classifiers (0 = not-Iris-virginica, 1 = Iris-virginica)\n df_iris.loc[df_iris[\"Class\"] == \"Iris-virginica\", \"Class_Bool\"] = 1\n df_iris.loc[df_iris[\"Class\"] != \"Iris-virginica\", \"Class_Bool\"] = 0\n df_iris = df_iris.drop(columns=['Class'])\n df_iris = df_iris.rename(columns={\"Class_Bool\": \"Class\"})\n\n # I encoded these by hand based on the box plots that I created\n sepal_length_cond_1 = df_iris[\"sepal length (cm)\"] > 6\n sepal_length_cond_0 = df_iris[\"sepal length (cm)\"] <= 6\n sepal_width_cond_1 = (df_iris[\"sepal width (cm)\"] > 2.7) & (df_iris[\"sepal width (cm)\"] < 3.25)\n sepal_width_cond_0 = (df_iris[\"sepal width (cm)\"] <= 2.7) \| (df_iris[\"sepal width (cm)\"] >= 3.25)\n petal_length_cond_1 = df_iris[\"petal length (cm)\"] > 5\n petal_length_cond_0 = df_iris[\"petal length (cm)\"] <= 5\n petal_width_cond_1 = df_iris[\"petal width (cm)\"] > 1.5\n petal_width_cond_0 = df_iris[\"petal width (cm)\"] <= 1.5\n\n df_iris_encoded = df_iris.copy()\n df_iris_encoded.loc[sepal_length_cond_1, \"sepal_length_cond_bool\"] = 1\n df_iris_encoded.loc[sepal_length_cond_0, \"sepal_length_cond_bool\"] = 0\n df_iris_encoded.loc[sepal_width_cond_1, \"sepal_width_cond_bool\"] = 1\n df_iris_encoded.loc[sepal_width_cond_0, \"sepal_width_cond_bool\"] = 0\n df_iris_encoded.loc[petal_length_cond_1, \"petal_length_cond_bool\"] = 1\n df_iris_encoded.loc[petal_length_cond_0, \"petal_length_cond_bool\"] = 0\n df_iris_encoded.loc[petal_width_cond_1, \"petal_width_cond_bool\"] = 1\n df_iris_encoded.loc[petal_width_cond_0, \"petal_width_cond_bool\"] = 0\n\n df_iris_encoded = df_iris_encoded.drop(columns=['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'])\n df_iris_encoded = df_iris_encoded[['sepal_length_cond_bool', 'sepal_width_cond_bool', 'petal_length_cond_bool', 'petal_width_cond_bool', 'Class']]\n\n lg_averages = run_logistic_regression(df_iris, 4)\n nb_averages = run_naive_bayes(df_iris_encoded, 4)\n\n print(\"----------------------------\")\n print(f\"Averages over 5 experiments\")\n print(\"----------------------------\")\n print(f\"Logistic Regression Averages = {lg_averages}%\")\n print(f\"Naive Bayes Averages = {nb_averages}%\")\n\n\ndef run_on_soybean(file):\n print(\"_______________________________\")\n print(\"Reading in Soybean data set...\")\n # Read in soybean data\n df_soybean = pd.read_csv(file, header=None)\n\n # Generate boolean classifiers (0 = not-Iris-virginica, 1 = Iris-virginica)\n df_soybean = df_soybean.rename(columns={35: 'Class'})\n df_soybean.loc[df_soybean[\"Class\"] == \"D1\", \"Class\"] = 0\n df_soybean.loc[df_soybean[\"Class\"] == \"D2\", \"Class\"] = 0\n df_soybean.loc[df_soybean[\"Class\"] == \"D3\", \"Class\"] = 1\n df_soybean.loc[df_soybean[\"Class\"] == \"D4\", \"Class\"] = 1\n\n # One hot encode breast data set for naive bayes\n columns_to_encode = df_soybean.columns.values.tolist()\n del columns_to_encode[35]\n df_soybean_encoded = one_hot_encoder(df_soybean, columns_to_encode)\n\n lg_averages = run_logistic_regression(df_soybean, 35)\n nb_averages = run_naive_bayes(df_soybean_encoded, 35)\n\n print(\"----------------------------\")\n print(f\"Averages over 5 experiments\")\n print(\"----------------------------\")\n print(f\"Logistic Regression Averages = {lg_averages}%\")\n print(f\"Naive Bayes Averages = {nb_averages}%\")\n\n\ndef run_on_votes(file):\n print(\"_______________________________\")\n print(\"Reading in Votes data set...\")\n # Read in votes data\n df_vote = pd.read_csv(file, header=None)\n\n # Generate boolean classifiers (0 = republican, 1 = democrat)\n df_vote.loc[df_vote[0] == \"republican\", 0] = 0\n df_vote.loc[df_vote[0] == \"democrat\", 0] = 1\n\n # Generate boolean classifiers (0 = n, 1 = y)\n # Set ? to n, since there is no good way to impute votes\n df_vote.replace('n', 0, inplace=True)\n df_vote.replace('y', 1, inplace=True)\n df_vote.replace('?', 0, inplace=True)\n print(df_vote.head())\n\n # Rename and reorder columns to work with algorithms\n df_vote = df_vote.rename(columns={0: 'Class', 1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6,\n 8: 7, 9: 8, 10: 9, 11: 10, 12: 11, 13: 12, 14: 13, 15: 14,\n 16: 15})\n df_vote = df_vote[[c for c in df_vote if c not in ['Class']] + ['Class']]\n print(df_vote.head())\n\n lg_averages = run_logistic_regression(df_vote, 16)\n nb_averages = run_naive_bayes(df_vote, 16)\n\n print(\"----------------------------\")\n print(f\"Averages over 5 experiments\")\n print(\"----------------------------\")\n print(f\"Logistic Regression Averages = {lg_averages}%\")\n print(f\"Naive Bayes Averages = {nb_averages}%\")\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 3:\n print(\"Please add options for data set (--breast --glass --iris --soybean --vote) \"\n \"and the full file path when running this script\")\n print(\"Example: python runAlgorithms.py --breast ./data/breast.data\")\n exit()\n\n if sys.argv[1] == \"--breast\":\n run_on_breast(sys.argv[2])\n exit()\n\n if sys.argv[1] == \"--glass\":\n run_on_glass(sys.argv[2])\n exit()\n\n if sys.argv[1] == \"--iris\":\n run_on_iris(sys.argv[2])\n exit()\n\n if sys.argv[1] == \"--soybean\":\n run_on_soybean(sys.argv[2])\n exit()\n\n if sys.argv[1] == \"--votes\":\n run_on_votes(sys.argv[2])\n exit()\n\n exit()\n\n" }, { "alpha_fraction": 0.5721003413200378, "alphanum_fraction": 0.6056874394416809, "avg_line_length": 47.021507263183594, "blob_id": "bc67951e12b6cbdeba065744c682d96c86ca5446", "content_id": "ca2cf6c680d0d421558b8a1b17ca4f67b5b429aa", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4466, "license_type": "no_license", "max_line_length": 113, "num_lines": 93, "path": "/Logistic Regression/naiveBayes.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "from machineLearningUtilities import get_num_similarities\n\n\nclass NaiveBayesModel:\n def __init__(self, p0, p1, f0p0, f1p0, f0p1, f1p1):\n \"\"\"\n This class holds the model for the Naive Bayes algorithm\n :param p0: The probability of class = 0 in the entire data set\n :param p1: The probability of class = 1 in the entire data set\n :param fp00: An array that holds probabilities of f = 0, given c = 0\n :param fp10: An array that holds probabilities of f = 1, given c = 0\n :param fp01: An array that holds probabilities of f = 0, given c = 1\n :param fp11: An array that holds probabilities of f = 1, given c = 1\n \"\"\"\n self.prob_0 = p0\n self.prob_1 = p1\n self.feature_probs_f0_given0 = f0p0\n self.feature_probs_f1_given0 = f1p0\n self.feature_probs_f0_given1 = f0p1\n self.feature_probs_f1_given1 = f1p1\n\n\nclass NaiveBayes:\n def __init__(self, train, training_classes, test, test_classes):\n self.training_classes = training_classes\n self.test_classes = test_classes\n self.train = train\n self.test = test\n self.model = None\n self.predictions = []\n\n def learn(self):\n \"\"\"\n This function creates the naive bayes model.\n \"\"\"\n print(\"Training Naive Bayes...\")\n # Create the model\n value_counts = self.training_classes.value_counts().to_dict()\n num_samples = len(self.training_classes)\n prob_0 = value_counts[0] / num_samples if 0 in value_counts else 0\n prob_1 = value_counts[1] / num_samples if 1 in value_counts else 1\n feature_probs_f0_given0 = []\n feature_probs_f1_given0 = []\n feature_probs_f0_given1 = []\n feature_probs_f1_given1 = []\n zero_class_indexes = [i for i, x in enumerate(self.training_classes) if x == 0]\n one_class_indexes = [i for i, x in enumerate(self.training_classes) if x == 1]\n num_zero_classes = len(zero_class_indexes)\n num_one_classes = len(one_class_indexes)\n for index, colName in enumerate(self.train.columns):\n f_value_counts_given_0 = self.train.iloc[zero_class_indexes, index].value_counts().to_dict()\n f_value_counts_given_1 = self.train.iloc[one_class_indexes, index].value_counts().to_dict()\n prob_f0_given_c0 = f_value_counts_given_0[0] / num_zero_classes if 0 in f_value_counts_given_0 else 0\n prob_f1_given_c0 = f_value_counts_given_0[1] / num_zero_classes if 1 in f_value_counts_given_0 else 0\n prob_f0_given_c1 = f_value_counts_given_1[0] / num_one_classes if 0 in f_value_counts_given_1 else 0\n prob_f1_given_c1 = f_value_counts_given_1[1] / num_one_classes if 1 in f_value_counts_given_1 else 0\n feature_probs_f0_given0.append(prob_f0_given_c0)\n feature_probs_f1_given0.append(prob_f1_given_c0)\n feature_probs_f0_given1.append(prob_f0_given_c1)\n feature_probs_f1_given1.append(prob_f1_given_c1)\n\n self.model = NaiveBayesModel(prob_0, prob_1, feature_probs_f0_given0,\n feature_probs_f1_given0, feature_probs_f0_given1,\n feature_probs_f1_given1)\n\n def validate(self):\n \"\"\"\n This function makes predictions for the naive bayes algorithm\n \"\"\"\n print(\"Testing Naive Bayes Accuracy...\")\n if not self.model:\n print(\"Please call the function train first!\")\n return\n\n # Calculate predictions by calculating the probability of each\n # C=1 and C=0 in each sample(row)\n for index, row in self.test.iterrows():\n c0_product = self.model.prob_0\n c1_product = self.model.prob_1\n for feature_index, feature in enumerate(row):\n if feature == 0:\n c0_product = self.model.feature_probs_f0_given0[feature_index]\n c1_product = self.model.feature_probs_f0_given1[feature_index]\n else:\n c0_product = self.model.feature_probs_f1_given0[feature_index]\n c1_product = self.model.feature_probs_f1_given1[feature_index]\n\n if c0_product > c1_product:\n self.predictions.append(0)\n else:\n self.predictions.append(1)\n\n return get_num_similarities(self.predictions, self.test_classes) / len(self.test_classes) * 100\n" }, { "alpha_fraction": 0.6352075934410095, "alphanum_fraction": 0.6476175785064697, "avg_line_length": 29.773584365844727, "blob_id": "1b58297aa59c9eb916c080d22b70cc4a525ac5ca", "content_id": "9a66962d82beb1f34d2928eae6fb72787fef9797", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6527, "license_type": "no_license", "max_line_length": 101, "num_lines": 212, "path": "/K-Nearest Neighbors/machineLearningUtilities.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import math\nimport random\nimport pandas as pd\nimport numpy as np\n\n\ndef one_hot_encoder(df, columns):\n \"\"\"\n This function one hot encodes a set of data\n :param df: data to encode\n :param columns: columns to encode\n :return: one hot encoded data\n \"\"\"\n one_hot = pd.get_dummies(df, columns=columns, prefix=columns)\n df = df.drop(columns, axis=1)\n return one_hot\n\n\ndef split_test_train(df):\n \"\"\"\n This function splits test and train sets by 2/3 and 1/3 randomly\n :param df: data to split\n :return: train and test set\n \"\"\"\n train = df.sample(frac=2/3)\n test = df.loc[~df.index.isin(train.index), :]\n return train, test\n\n\ndef split_into_random_groups(df, k):\n \"\"\"\n This function splits a data set into k random groups\n :param df: data to split\n :param k: the number of groups\n :return: array of groups\n \"\"\"\n groups = []\n remaining = df.copy()\n if k > 0:\n for i in range(1, k):\n group = remaining.sample(frac=1/(k-(i-1)))\n remaining = remaining.loc[~remaining.index.isin(group.index), :]\n groups.append(group)\n\n groups.append(remaining)\n\n return groups\n\n\ndef split_into_random_stratified_groups(df):\n \"\"\"\n This function splits the data frame into 5 stratified groups for 5-fold cross validation\n :param df: The dataframe\n :param test: The test set\n :param train1: Training set 1\n :param train2: Training set 2\n :param train3: Training set 3\n :param train4: Training set 4\n :return:\n \"\"\"\n train1 = pd.DataFrame(columns=df.columns.values.tolist())\n train2 = pd.DataFrame(columns=df.columns.values.tolist())\n train3 = pd.DataFrame(columns=df.columns.values.tolist())\n train4 = pd.DataFrame(columns=df.columns.values.tolist())\n test = pd.DataFrame(columns=df.columns.values.tolist())\n\n unique_classes = df.Class.unique()\n for className in unique_classes:\n groups = split_into_random_groups(df.loc[df.Class == className], 5)\n train1 = train1.append(groups[0])\n train2 = train2.append(groups[1])\n train3 = train3.append(groups[2])\n train4 = train4.append(groups[3])\n test = test.append(groups[4])\n\n return train1, train2, train3, train4, test\n\n\ndef generate_random_numbers(r, n):\n \"\"\"\n This function generates random integers in range(1:r)\n :param r: the range to generate random integers in\n :param n: the number of integers to generate\n :return: array of random numbers\n \"\"\"\n randoms = []\n for x in range(1, n+1):\n randoms.append(random.randint(1, r))\n\n return randoms\n\n\ndef get_num_similarities(array1, array2):\n \"\"\"\n This function compares two arrays for similiaries and returns number of equal elements\n :param array1: first array to compare\n :param array2: second array to compare\n :return: the number of similar elements\n \"\"\"\n a = np.array(array1)\n b = np.array(array2)\n return np.sum(a == b)\n\n\ndef get_mean_squared_error(array1, array2):\n \"\"\"\n This function calculates the mean squared error between the two arrays\n :param array1: first array to compare\n :param array2: second array to compare\n :return: the mean squared error\n \"\"\"\n return np.mean((array1 - array2)*2 / 100)\n\n\ndef get_percent_similarities(array1, array2):\n \"\"\"\n This function compares two arrays for similiaries and returns number of equal elements\n :param array1: first array to compare\n :param array2: second array to compare\n :return: the number of similar elements\n \"\"\"\n a = np.array(array1)\n b = np.array(array2)\n return np.sum(a == b) / len(a) 100\n\n\ndef euclidean_distance(array1, array2):\n \"\"\"\n This function computes the euclidean distance between two arrays\n :param array1: first array\n :param array2: second array\n :return: distance\n \"\"\"\n length = len(array1)\n distance = 0\n for x in range(length):\n distance += pow((array1[x] - array2[x]), 2)\n return math.sqrt(distance)\n\n\ndef calculate_silhouette_coefficient(X, labels):\n \"\"\"\n This function calculates the silhouette coefficient of a data set, and it's labels for clustering\n :param X: The dataset\n :param labels: cluster labels\n :return: silhouette coefficient\n \"\"\"\n return np.mean(get_silhouettes(X, labels))\n\n\ndef get_mean_intra_cluster_distances(x, index, X, labels):\n \"\"\" \n This function gets the mean of all of the distances to each sample in the \n cluster from x\n :param x: The selected sample\n :param index: the index of the selected sample\n :param X: the sample dataset\n :param labels: the set of labels for data points (which cluster it's in)\n \"\"\"\n distances = []\n x_cluster = labels[index]\n rows_in_cluster = [i for i, value in enumerate(labels) if value == x_cluster]\n for i, sample in X.ix[rows_in_cluster].iterrows():\n # Find the distance to x\n d = get_euclidean_distance(x.values, sample.values)\n distances.append(d)\n \n return np.mean(distances)\n\n\ndef get_nearest_cluster_distance(x, index, X, labels):\n \"\"\"\n This function gets the distance to the cluster nearest to\n x, that is not the cluster that x is in.\n :param x: The data point to test distances from\n :param index: The index of the datapoint\n :param X: The data set\n :param labels: The cluster labels of each data point\n :return:\n \"\"\"\n minimum_distance = float('inf')\n clusters = set(labels)\n x_cluster = labels[index]\n for c in clusters:\n if c != x_cluster:\n distances = []\n rows_in_cluster = [i for i, value in enumerate(labels) if value == c]\n for i, sample in X.ix[rows_in_cluster].iterrows():\n distance = get_euclidean_distance(sample.values, x.values)\n distances.append(distance)\n\n mean = np.mean(distances)\n if mean < minimum_distance:\n minimum_distance = mean\n\n return minimum_distance\n\n\ndef get_silhouettes(X, labels):\n \"\"\" This function calculates the silhouette coefficient for each sample\n :param X: The dataset\n :param labels: cluster labels\n :return: silhouette coefficient\n \"\"\"\n silhouettes = []\n for index, x in X.iterrows():\n A = get_mean_intra_cluster_distances(x, index, X, labels)\n B = get_nearest_cluster_distance(x, index, X, labels)\n silhouette = (B - A) / np.maximum(A, B)\n silhouettes.append(np.nan_to_num(silhouette))\n\n return silhouettes\n\n\n\n" }, { "alpha_fraction": 0.7446808218955994, "alphanum_fraction": 0.7517730593681335, "avg_line_length": 22.66666603088379, "blob_id": "5d7884ef703a220bd4604884cd8b1082e40258de", "content_id": "29789025f077168b127893c9055505fb40f48140", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 141, "license_type": "no_license", "max_line_length": 57, "num_lines": 6, "path": "/Clustering and Feature Selection/README.md", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "# Programming Project 2\n\nTo run use the following command from the root directory:\n```.env\npython runAlgorithms.py iris ./data/iris/data \n```" }, { "alpha_fraction": 0.5718494057655334, "alphanum_fraction": 0.5787234306335449, "avg_line_length": 38.153846740722656, "blob_id": "f790e46f3000509fe348fc879bcc14a933f72d1e", "content_id": "9a14a6d2ed8e4b3b4002d69b4386800ae178af63", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6110, "license_type": "no_license", "max_line_length": 124, "num_lines": 156, "path": "/Neural Networks/backpropgationNeuralNetwork.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import math\nimport random\n\nfrom machineLearningUtilities import get_num_similarities\n\n\nclass BackpropagationNeuralNetwork:\n \"\"\"\n This class performs the backpropagation algorithm to train an artificial neural network\n \"\"\"\n def __init__(self, features, x_train, x_train_classes, x_test, x_test_classes, num_hidden=1):\n self.features = features\n self.training_classes = x_train_classes\n self.train = x_train\n self.test = x_test\n self.test_classes = x_test_classes\n self.num_iterations = 20\n self.eta = 0.01 # learning rate\n self.network = list()\n self.num_hidden_layers = num_hidden\n\n def learn(self):\n \"\"\"\n This function runs the backpropagation algorithm on the training set provided\n \"\"\"\n print(\"Running Backpropagation...\")\n\n print(\"Initializing Network...\")\n num_inputs = len(self.train.columns) - 1\n hidden = [{'w': [random.uniform(-1.0, 1.0) for i in range(num_inputs + 1)]} for i in range(self.num_hidden_layers)]\n self.network.append(hidden)\n num_outputs = len(set([c for c in self.training_classes]))\n output = [{'w': [random.uniform(-1.0, 1.0) for i in range(self.num_hidden_layers + 1)]} for i in range(num_outputs)]\n self.network.append(output)\n\n print(\"Training Network...\")\n for iteration in range(self.num_iterations):\n error = 0\n for x_index, row in self.train.iterrows():\n outputs = self.forward_propagate(row)\n expected = [0 for i in range(num_outputs)]\n expected[int(self.training_classes[x_index])] = 1\n error += sum([(expected[i] - outputs[i]) ** 2 for i in range(len(expected))])\n self.backward_propagate_error(expected)\n self.update_weights(row)\n print('>iteration=%d, learning rate=%.3f, error=%.3f' % (iteration, self.eta, error))\n\n def make_predictions(self):\n predictions = list()\n for i, row in self.test.iterrows():\n prediction = self.predict(row)\n predictions.append(prediction)\n\n print(\"Predicted Classes = \")\n print(predictions)\n\n print(\"Expected Classes = \")\n print(list(self.test_classes))\n\n return get_num_similarities(predictions, self.test_classes) / len(self.test_classes) * 100\n\n def predict(self, row):\n \"\"\"\n This function determines accuracy of model using the test data set\n and applying the linear function using the weights\n \"\"\"\n outputs = self.forward_propagate(row)\n return outputs.index(max(outputs))\n\n @staticmethod\n def sigmoid(x):\n \"\"\"\n This function calculates the logistic sigmoid of x and is used as the transfer function\n :param x: The value\n :return: The logistic sigmoid\n \"\"\"\n try:\n return 1.0 / (1.0 + math.exp(-x))\n except OverflowError:\n return float('inf')\n\n @staticmethod\n def activate(weights, inputs):\n \"\"\"\n This function performs activation by calculating the weighted sum of the\n inputs plus the bias\n :param weights: the current weight vector with bias as the last element\n :param inputs: a vector of inputs\n :return: The activation\n \"\"\"\n activation = weights[-1]\n for i in range(len(weights) - 1):\n activation += weights[i] * float(inputs[i])\n return activation\n\n @staticmethod\n def transfer_derivative(y):\n \"\"\"\n This function calculates the derivative to be used as the slope of an output value\n :param y: The output value\n :return: The derivative / slope\n \"\"\"\n return y * (1.0 - y)\n\n def forward_propagate(self, inputs):\n \"\"\"\n This function implements forward propagation, the inputs are taken and outpuss\n are calculated by first running the activation function and then the transfer (sigmoid)\n function using the activation, each new input is appended to the output layer and\n returned\n :param inputs: The input vector\n :return: The output layer\n \"\"\"\n for layer in self.network:\n new_inputs = []\n for neuron in layer:\n x = self.activate(neuron['w'], inputs)\n neuron['output'] = self.sigmoid(x)\n new_inputs.append(neuron['output'])\n inputs = new_inputs\n return inputs\n\n def backward_propagate_error(self, expected):\n \"\"\"\n This function performs backpropagation by calcuating error betweeen the expected outputs\n and the actual outputs of forward propagation. These error calcuations are propagated back\n throught the network from output to hidden layer, looking for where the error occurred.\n :param expected:\n :return:\n \"\"\"\n for i in reversed(range(len(self.network))):\n layer = self.network[i]\n errors = list()\n if i != len(self.network) - 1:\n for j in range(len(layer)):\n error = 0.0\n for neuron in self.network[i + 1]:\n error += (neuron['w'][j] * neuron['delta'])\n errors.append(error)\n else:\n for j in range(len(layer)):\n neuron = layer[j]\n errors.append(expected[j] - neuron['output'])\n for j in range(len(layer)):\n neuron = layer[j]\n neuron['delta'] = errors[j] * self.transfer_derivative(neuron['output'])\n\n def update_weights(self, row):\n for i in range(len(self.network)):\n inputs = row[:-1]\n if i != 0:\n inputs = [neuron['output'] for neuron in self.network[i - 1]]\n for neuron in self.network[i]:\n for j in range(len(inputs)):\n neuron['w'][j] += self.eta * float(neuron['delta']) * float(inputs[j])\n neuron['w'][-1] += self.eta * float(neuron['delta'])\n\n\n" }, { "alpha_fraction": 0.6258193850517273, "alphanum_fraction": 0.631281852722168, "avg_line_length": 43.282257080078125, "blob_id": "0751c20b32ef733f985ed0e02291a7741cf94690", "content_id": "598244cc94ffb7d7bbdc5f1a0bb7d881546475d6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5492, "license_type": "no_license", "max_line_length": 121, "num_lines": 124, "path": "/Reinforcement Learning/runAlgorithms.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import re\nimport sys\n\nfrom raceTrack import RaceTrack\n\n\ndef run_race_track_simulator_value_iteration(restart_on_crash, track_file_path, run_value_iteration=False):\n \"\"\"\n This function runs the race track simulator\n :param restart_on_crash: True if you should continue from nearest current location on crash, False restart at\n starting position on crash\n :param track_file_path: The file path to the race track text file\n :param run_value_iteration: True if you want to run the value iteration to create the policy, if False\n it will attempt to read the policy from a file\n \"\"\"\n print(f\"Running Race Track simulator with options: Restart on crash? {'yes' if restart_on_crash else 'no'}, \"\n f\"Track file: {track_file_path}\")\n\n print(\"Race Track\")\n with open(track_file_path, \"r\") as file:\n max_x, max_y = file.readline().split(',')\n race_track = file.read().splitlines()\n print(race_track)\n starting_positions = find_starting_positions(race_track)\n track_simulator = RaceTrack(race_track, track_file_path[track_file_path.rindex('/')+1:],\n starting_positions[1][0], starting_positions[1][1], max_x, max_y)\n if run_value_iteration:\n track_simulator.learn_value_iteration()\n else:\n track_simulator.run_racetrack_on_value_iteration(restart_on_crash)\n\n\ndef run_race_track_simulator_sarsa(restart_on_crash, track_file_path, run_value_iteration=False):\n \"\"\"\n This function runs the race track simulator\n :param restart_on_crash: True if you should continue from nearest current location on crash, False restart at\n starting position on crash\n :param track_file_path: The file path to the race track text file\n :param run_value_iteration: True if you want to run the value iteration to create the policy, if False\n it will attempt to read the policy from a file\n \"\"\"\n print(f\"Running Race Track simulator with options: Restart on crash? {'yes' if restart_on_crash else 'no'}, \"\n f\"Track file: {track_file_path}\")\n\n print(\"Race Track\")\n with open(track_file_path, \"r\") as file:\n max_x, max_y = file.readline().split(',')\n race_track = file.read().splitlines()\n print(race_track)\n starting_positions = find_starting_positions(race_track)\n track_simulator = RaceTrack(race_track, track_file_path[track_file_path.rindex('/') + 1:],\n starting_positions[1][0], starting_positions[1][1], max_x, max_y)\n\n track_simulator.run_racetrack_on_sarsa(3, restart_on_crash)\n\n\ndef find_starting_positions(track):\n \"\"\"\n This function takes a track and finds the instances of S and returns these start positions with a list of tuples\n of (x, y) pair values\n :param track: A list of strings that make up a grid that has a set of S values somewhere that indicate the\n starting line\n :return: A list of tuples of (x,y) pair values that represent possible starting positions\n \"\"\"\n return find_positions(track, 'S')\n\n\ndef find_finish_positions(track):\n \"\"\"\n This function takes a track and finds the instances of F and returns these finish positions with a list of tuples\n of (x, y) pair values\n :param track: A list of strings that make up a grid that has a set of S values somewhere that indicate the\n finish line\n :return: A list of tuples of (x,y) pair values that represent possible finish positions\n \"\"\"\n return find_positions(track, 'S')\n\n\ndef find_positions(list_of_sentences, word):\n \"\"\"\n This function finds all occurrences of a word in a list of sentences\n :param list_of_sentences: The list of sentences to find occurrences of a word in\n :param word: The word or character to find\n :return: A list of tuples with all positions\n \"\"\"\n start = list()\n for index, row in enumerate(list_of_sentences):\n for match in re.finditer('S', row):\n start.append(tuple((index, match.start())))\n\n return start\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 5:\n print(\"Please add options (--crash-continue --crash-restart) and (--value or --sarsa) \"\n \"and (--create-policy --run)\"\n \"and the full file path to the race track file\")\n print(\"Example: python runAlgorithms.py --crash-continue --value --run ./data/L-track.txt \")\n exit()\n\n if sys.argv[1] == \"--crash-continue\":\n if sys.argv[2] == \"--value\" and sys.argv[3] == \"--create-policy\":\n run_race_track_simulator_value_iteration(False, sys.argv[4], True)\n exit()\n elif sys.argv[2] == \"--value\" and sys.argv[3] == \"--run\":\n run_race_track_simulator_value_iteration(False, sys.argv[4], False)\n exit()\n elif sys.argv[2] == \"--sarsa\":\n run_race_track_simulator_sarsa(False, sys.argv[4])\n exit()\n\n if sys.argv[1] == \"--crash-restart\":\n if sys.argv[2] == \"--value\" and sys.argv[3] == \"--create-policy\":\n run_race_track_simulator_value_iteration(True, sys.argv[4], True)\n exit()\n elif sys.argv[2] == \"--value\" and sys.argv[3] == \"--run\":\n run_race_track_simulator_value_iteration(True, sys.argv[4], False)\n exit()\n elif sys.argv[2] == \"--sarsa\":\n run_race_track_simulator_sarsa(True, sys.argv[4])\n exit()\n\n exit()\n\n" }, { "alpha_fraction": 0.538469135761261, "alphanum_fraction": 0.5417410135269165, "avg_line_length": 35.94505310058594, "blob_id": "4b00a0020209f42496547710e1c9efcb95d4c5b1", "content_id": "5793d2ca342d1da08f82cdd792684626ec6afce5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 10086, "license_type": "no_license", "max_line_length": 108, "num_lines": 273, "path": "/Decision Trees/DecisionTree.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import math\nfrom collections import deque\n\nfrom machineLearningUtilities import get_num_similarities\n\n\nclass Node:\n def __init__(self, feature=None, value=None):\n self.feature = feature\n self.value = value\n self.label = None\n self.children = []\n self.next = None\n\n\nclass DecisionTree:\n \"\"\"\n This class implements a decision tree algorithm\n \"\"\"\n def __init__(self, train):\n self.train = train\n self.unique_labels = set(train[\"Class\"])\n self.root = None\n\n def build_id3_tree(self):\n \"\"\"\n This function builds the id3 decision tree from the root\n \"\"\"\n features = [col for col in self.train.columns if col != \"Class\"]\n self.root = self.build_id3_branch(self.train, features)\n\n def build_id3_branch(self, subset, features, value=None):\n \"\"\"\n This function builds a subset of the id3 decision tree.\n :param subset: The current subset\n :param features: The features left\n :param value: The value that led to this subset\n :return: the root of the current subset\n \"\"\"\n\n # Return if only one class left\n class_labels_left = set(subset[\"Class\"])\n if len(class_labels_left) == 1:\n leaf = Node(value=value)\n leaf.label = list(class_labels_left)[0]\n return leaf\n\n # Return if last feature\n if len(features) == 1:\n # print(\"Creating Leaf\")\n leaf = Node(value=value)\n leaf.label = self.mode(subset, \"Class\")\n return leaf\n\n # Otherwise, create a new decision node\n decision = Node(value=value)\n\n # Calculate the entropy I(c1, c2, ... ck) of the subset\n entropy = self.get_entropy(subset, \"Class\")\n # print(f\"entropy = {entropy}\")\n if entropy == 0:\n return None\n\n # Find the feature with the most information gain\n largest_gain_ratio = 0\n feature_largest_gain_ratio = None\n # print(features)\n for feature in features:\n\n # Find the expected entropy for this feature\n expected_entropy = self.get_expected_entropy(subset, feature)\n # print(f\"Expected Entropy for feature [{feature}] = {expected_entropy}\")\n\n # Calculate the Gain Ratio for this feature\n information_value = self.get_information_value(subset, feature)\n gain_ratio = (entropy - expected_entropy) / information_value\n # print(f\"Gain for feature {feature} = {gain_ratio}\")\n if gain_ratio > largest_gain_ratio:\n largest_gain_ratio = gain_ratio\n feature_largest_gain_ratio = feature\n\n # print(f\"Feature with largest gain = {feature_largest_gain_ratio} at {largest_gain_ratio}\")\n\n # For this feature, create a child node for each feature value\n decision.feature = feature_largest_gain_ratio\n feature_value_counts = subset[feature_largest_gain_ratio].value_counts()\n features.remove(feature_largest_gain_ratio)\n\n # Sort feature values\n sorted_fvs = []\n for key in sorted(feature_value_counts.keys()):\n sorted_fvs.append(key)\n\n for fv in sorted_fvs:\n child_subset = subset.loc[subset[feature_largest_gain_ratio] == fv]\n child_tree = self.build_id3_branch(child_subset, features, value=fv)\n if child_tree:\n decision.children.append(child_tree)\n\n return decision\n\n def get_entropy(self, data, column):\n entropy = 0\n num_samples = len(data)\n label_counts = data[column].value_counts()\n for label in self.unique_labels:\n if label in label_counts:\n num_this_label = label_counts[label]\n class_prob = num_this_label / num_samples\n entropy += (-class_prob * math.log2(class_prob))\n\n return entropy\n\n def get_expected_entropy(self, data, column):\n expected_entropy = 0\n num_samples = len(data)\n feature_value_counts = data[column].value_counts()\n unique_feature_values = set(data[column])\n for fv in unique_feature_values:\n num_this_feature_value = feature_value_counts[fv]\n feature_in_sample_prob = num_this_feature_value / num_samples\n expected_entropy += (feature_in_sample_prob\n * self.get_entropy_partition(data, column, fv, num_this_feature_value))\n\n return expected_entropy\n\n def get_entropy_partition(self, data, column, fv, num_total):\n entropy_partition = 0\n class_values_in_fv = data.loc[data[column] == fv, [\"Class\"]][\"Class\"]\n label_counts_in_fv = class_values_in_fv.value_counts()\n for label in self.unique_labels:\n if label in label_counts_in_fv:\n num_this_label = label_counts_in_fv[label]\n class_in_fv_prob = num_this_label / num_total\n entropy_partition += (-class_in_fv_prob * math.log2(class_in_fv_prob))\n\n return entropy_partition\n\n @staticmethod\n def get_information_value(data, column):\n iv = 0\n num_samples = len(data)\n feature_value_counts = data[column].value_counts()\n unique_feature_values = set(data[column])\n for fv in unique_feature_values:\n num_this_feature_value = feature_value_counts[fv]\n feature_in_sample_prob = num_this_feature_value / num_samples\n iv += (-feature_in_sample_prob * math.log2(feature_in_sample_prob))\n\n return iv\n\n def print_tree(self):\n \"\"\"\n This function prints the decision tree\n \"\"\"\n print(\"Printing Decision Tree...\")\n if self.root:\n nodes = deque()\n nodes.append(self.root)\n while len(nodes) > 0:\n node = nodes.popleft()\n if node:\n print(f\"Decision = {node.feature}\")\n if node.children:\n print(node.children)\n for child in node.children:\n if child:\n if child.value:\n print(f\"({child.value})\")\n\n if child.label:\n print(\"---Leaf Node---\")\n print(f\"Class Label = {child.label}\")\n else:\n nodes.append(child)\n else:\n print(\"Decision Tree has not been built.\")\n\n def validate_pruned_tree(self, data):\n pruned_tree = self.prune(self.root, data)\n return self.validate(pruned_tree, data)\n\n def prune(self, node, data):\n \"\"\"\n This function prunes the tree and validates accuracy against the validation set\n :return: pruned tree accuracy\n \"\"\"\n if len(node.children) == 0:\n return\n\n if len(data) == 0:\n return\n\n # Loop through children and flag for pruning\n for child in node.children:\n subset = data[data[node.feature] == child.value]\n self.prune(child, subset)\n\n # Test if pruning improves accuracy\n if self.can_prune(node, data):\n node.children = None\n node.label = self.mode(data, 'Class')\n print(f\"Pruning node, replacing with leaf node, label = {node.label}\")\n\n return node\n\n def can_prune(self, node, data):\n\n if len(data) == 0:\n return True\n\n correct = 0\n majority_class = self.mode(data, \"Class\")\n for index, row in data.iterrows():\n if row['Class'] == majority_class:\n correct += 1\n\n pruned_accuracy = (float(correct) / len(data) * 100)\n node_accuracy = self.validate(node, data)\n if pruned_accuracy >= node_accuracy:\n return True\n\n return False\n\n @staticmethod\n def validate(root, df_test):\n \"\"\"\n This function makes predictions on a test set, based on the decision tree built\n :param root: The root of the current tree\n :param df_test: The test set\n :return: accuracy\n \"\"\"\n if root:\n predictions = []\n for index, row in df_test.iterrows():\n # print(row)\n leaf_found = False\n node = root\n class_label = None\n while not leaf_found:\n # Check if node is a leaf\n if node.label:\n leaf_found = True\n class_label = node.label\n elif node.children:\n # Loop through current node's children\n for child in node.children:\n # print(f\"Decision = {node.feature}\")\n # print(f\"Child value = {child.value}\")\n if isinstance(child.value, str) and row[node.feature] == child.value:\n node = child\n break\n elif row[node.feature] <= child.value:\n node = child\n break\n elif child == node.children[-1]: # This is the last child\n node = child\n else:\n print(\"Something went wrong\")\n leaf_found = True\n\n # print(f\"Prediction = {class_label}\")\n predictions.append(class_label)\n\n # Compare predictions and actual class labels\n # print(f\"Comparing {len(predictions)} predictions against {len(df_test['Class'])} class labels...\")\n accuracy = get_num_similarities(df_test[\"Class\"], predictions) / len(predictions) * 100\n return accuracy\n\n @staticmethod\n def mode(subset, column):\n label_value_counts = subset[column].value_counts()\n return label_value_counts.argmax()\n" } ]	19
themmyloluwaa/complex_fibonacci	https://github.com/themmyloluwaa/complex_fibonacci	625f181b52155664a56057590d43d7e04cebd6ee	eaa130ba8c935eac68398a3512e52f771cdd0f78	6f270269b01010e0446bfe7a864d46e77e61947d	refs/heads/main	2023-03-13T09:45:47.739366	2021-03-03T10:03:38	2021-03-03T10:03:38	342,226,981	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6207584738731384, "alphanum_fraction": 0.6307384967803955, "avg_line_length": 22.85714340209961, "blob_id": "221cd01c0c0327f6cf8e05082db4cd59c504e303", "content_id": "e27ebd9fd1162c0c6541383fb65a4eb5b1f6c51f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 501, "license_type": "no_license", "max_line_length": 66, "num_lines": 21, "path": "/worker/index.py", "repo_name": "themmyloluwaa/complex_fibonacci", "src_encoding": "UTF-8", "text": "import redis\nimport os\n\nREDIS_HOST = os.environ['REDIS_HOST']\nREDIS_PORT = os.environ['REDIS_PORT']\n\nredisClient = redis.Redis(host=REDIS_HOST, port=REDIS_PORT, db=0,)\n\n\ndef fib(index):\n if(index < 2):\n return 1\n return fib(index - 1) + fib(index - 2)\n\n\ndef sub(name: 'str'):\n pubsub = redisClient.pubsub()\n pubsub.subscribe('insert')\n for message in pubsub.listen():\n if message.get('type') == 'message':\n redisClient.hset('values', fib(message.get('value')))\n" }, { "alpha_fraction": 0.7868852615356445, "alphanum_fraction": 0.7868852615356445, "avg_line_length": 19.66666603088379, "blob_id": "f9bd35f6f8b8aaa2826186a4dec38d9f1ce9b4b4", "content_id": "f51297a1040bb791efc440e0634c4cd48829fe9f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 61, "license_type": "no_license", "max_line_length": 49, "num_lines": 3, "path": "/nginx/Dockerfile.dev", "repo_name": "themmyloluwaa/complex_fibonacci", "src_encoding": "UTF-8", "text": "FROM nginx\n\nCOPY ./default.conf etc/nginx/conf.d/default.conf" }, { "alpha_fraction": 0.8235294222831726, "alphanum_fraction": 0.8235294222831726, "avg_line_length": 58, "blob_id": "e18dba31d3441166bb5afe5b91a7671a549a54c6", "content_id": "fdce18c28d869bf240e2e62367b074f6bd7c995c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 119, "license_type": "no_license", "max_line_length": 97, "num_lines": 2, "path": "/README.md", "repo_name": "themmyloluwaa/complex_fibonacci", "src_encoding": "UTF-8", "text": "# complex_fibonacci\nA dockerized multi container application built in react and nodejs with postgres, redis and nginx \n" } ]	3
bruabas/pymy	https://github.com/bruabas/pymy	0b1ce02b5433f68aa900611edee85d33a56f9a9e	5b9d1a3c95d5bab466911a904d83e03526ed0ef6	681a860a1aed380d63e760edeb091886b845cfe6	refs/heads/master	2021-09-03T14:31:58.243231	2018-01-09T19:46:04	2018-01-09T19:46:04	116,853,549	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.4379595220088959, "alphanum_fraction": 0.4453119933605194, "avg_line_length": 29.981855392456055, "blob_id": "de28e80ff559bcf4a895d776d7e0bba66bcef56b", "content_id": "292181716f36634940792f45494cde3e6fecd768", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C", "length_bytes": 15369, "license_type": "no_license", "max_line_length": 123, "num_lines": 496, "path": "/src/pymy.c", "repo_name": "bruabas/pymy", "src_encoding": "UTF-8", "text": "\n#include <mysql.h>\n#include <Python.h>\n#include <stdlib.h>\n#include \"structmember.h\"\n#include \"datetime.h\"\n\ntypedef struct {\n PyObject_HEAD\n MYSQL con; // MySQL connection handler\n} Database_Object;\n\ntypedef struct {\n PyObject_HEAD\n PyObject fields; // Tuple with field names (Python strings)\n int types; // Types of MySQL individual fields\n MYSQL_RES res; // MySQL stored result \n MYSQL_ROW last; // last read row from result\n unsigned row; // last row index\n unsigned num_rows; // total number of rows in result\n} Table_Object;\n\nvoid \nTable_dealloc(PyObject self)\n{\n Table_Object s = (Table_Object)self;\n mysql_free_result(s->res);\n Py_XDECREF(s->fields);\n free(s->types);\n Py_TYPE(self)->tp_free(self);\n}\n\nPyObject \nconvert_mysql_value(char cvalue, unsigned type)\n{\n PyObject res;\n if(cvalue == NULL) {\n /\n NULL values return None type\n /\n Py_INCREF(Py_None);\n return Py_None;\n }\n\n switch(type) {\n /\n * Integer types.\n /\n case MYSQL_TYPE_TINY:\n case MYSQL_TYPE_SHORT:\n case MYSQL_TYPE_LONG:\n case MYSQL_TYPE_INT24:\n case MYSQL_TYPE_LONGLONG:\n res = PyInt_FromString(cvalue, NULL, 10);\n break;\n\n /\n * Floating point types.\n /\n case MYSQL_TYPE_DECIMAL:\n case MYSQL_TYPE_NEWDECIMAL:\n case MYSQL_TYPE_FLOAT:\n case MYSQL_TYPE_DOUBLE:\n res = PyFloat_FromDouble(atof(cvalue));\n break;\n\n /\n * Date types.\n /\n case MYSQL_TYPE_DATE:\n res = PyDate_FromDate(atoi(cvalue), atoi(cvalue+5), atoi(cvalue+8));\n break;\n\n case MYSQL_TYPE_TIME:\n res = PyTime_FromTime(atoi(cvalue), atoi(cvalue+3), atoi(cvalue+6), 0);\n break;\n\n case MYSQL_TYPE_DATETIME:\n res = PyDateTime_FromDateAndTime(atoi(cvalue), atoi(cvalue+5), atoi(cvalue+8),\n atoi(cvalue+11), atoi(cvalue+14), atoi(cvalue+17), 0);\n break;\n\n case MYSQL_TYPE_TIMESTAMP:\n res = PyDateTime_FromDateAndTime(atoi(cvalue), atoi(cvalue+5), atoi(cvalue+8),\n atoi(cvalue+11), atoi(cvalue+14), atoi(cvalue+17), atoi(cvalue+20));\n break;\n\n /\n * All the rest are kept as strings\n /\n default:\n res = PyString_FromString(cvalue);\n break;\n }\n\n return res;\n}\n\nPyObject \nTable_getitem(PyObject self, Py_ssize_t index)\n{\n Table_Object s = (Table_Object)self;\n if(index != s->row) {\n /\n * Read a different row from MySQL stored result.\n /\n if(index >= s->num_rows) {\n /\n * Out of range\n /\n PyErr_SetString(PyExc_IndexError, \"Row out of range\");\n return NULL;\n }\n\n mysql_data_seek(s->res, index);\n s->row = index;\n s->last = mysql_fetch_row(s->res);\n }\n\n /\n * Create a dictionary with the fields and values.\n /\n PyObject item = PyDict_New();\n if(item == NULL) return NULL;\n\n unsigned i;\n unsigned n = PyTuple_Size(s->fields);\n for(i=0; i<n; i++) {\n PyObject key = PyTuple_GetItem(s->fields, i);\n PyObject value = convert_mysql_value(s->last[i], s->types[i]);\n if(value == NULL) {\n Py_DECREF(item);\n return NULL;\n }\n if(PyDict_SetItem(item, key, value) < 0) {\n Py_DECREF(item);\n return NULL;\n }\n }\n\n return item; \n}\n\nPyObject \nTable_getcolumn(PyObject self, PyObject args)\n{\n Table_Object s = (Table_Object)self;\n char field;\n\n if(!PyArg_ParseTuple(args, \"s\", &field)) return NULL;\n\n /\n Find the desired field into the fields list\n /\n unsigned i;\n unsigned n = PyTuple_Size(s->fields);\n for(i=0; i<n; i++) {\n char f = PyString_AS_STRING(PyTuple_GET_ITEM(s->fields, i));\n if(strcmp(field, f) == 0) break;\n }\n if(i >= n) {\n PyErr_SetString(PyExc_IndexError, \"Unknown field\");\n return NULL;\n }\n\n PyObject col = PyTuple_New(s->num_rows);\n if(col == NULL) return NULL;\n\n unsigned j;\n mysql_data_seek(s->res, 0);\n for(j=0; j<s->num_rows; j++) {\n s->row = j;\n s->last = mysql_fetch_row(s->res);\n PyTuple_SetItem(col, j, convert_mysql_value(s->last[i], s->types[i]));\n }\n\n return col;\n}\n\nPy_ssize_t \nTable_getsize(PyObject self)\n{\n Table_Object s = (Table_Object)self;\n return s->num_rows;\n}\n\n/\n Table can work as a (imutable) sequence.\n /\nstatic PySequenceMethods Table_as_sequence = {\n Table_getsize, / sq_length /\n 0, / sq_concat /\n 0, / sq_repeat /\n Table_getitem, / sq_item /\n 0, / sq_ass_item /\n 0, / sq_contains /\n 0, / sq_inplace_concat /\n 0 / sq_inplace_repeat /\n};\n\n/\n * Members for Table Object\n /\nstatic PyMemberDef Table_members[] = {\n { \"fields\", T_OBJECT, offsetof(Table_Object, fields), READONLY, \"Field names\" },\n { NULL }\n};\n\n/\n * C Methods for Table Object\n /\nstatic PyMethodDef Table_methods[] = {\n { \"column\", (PyCFunction)Table_getcolumn, METH_VARARGS, \"Tuple with all values of the column\" },\n { NULL }\n};\n\n/\n * Table Type definition for Python\n /\nstatic PyTypeObject Table_Type = {\n PyVarObject_HEAD_INIT(NULL, 0)\n \"pymy.Table\", / tp_name /\n sizeof(Table_Object), / tp_basicsize /\n 0, / tp_itemsize /\n (destructor)Table_dealloc, / tp_dealloc /\n 0, / tp_print /\n 0, / tp_getattr /\n 0, / tp_setattr /\n 0, / tp_compare /\n 0, / tp_repr /\n 0, / tp_as_number /\n &Table_as_sequence, / tp_as_sequence /\n 0, / tp_as_mapping /\n 0, / tp_hash /\n 0, / tp_call /\n 0, / tp_str /\n 0, / tp_getattro /\n 0, / tp_setattro /\n 0, / tp_as_buffer /\n Py_TPFLAGS_DEFAULT \| Py_TPFLAGS_BASETYPE, / tp_flags /\n \"A Table with MySQL Query Results\", / tp_doc /\n 0, / tp_traverse /\n 0, / tp_clear /\n 0, / tp_richcompare /\n 0, / tp_weaklistoffset /\n 0, / tp_iter /\n 0, / tp_iternext /\n Table_methods, / tp_methods /\n Table_members, / tp_members /\n 0, / tp_getset /\n 0, / tp_base /\n 0, / tp_dict /\n 0, / tp_descr_get /\n 0, / tp_descr_set /\n 0, / tp_dictoffset /\n 0, / tp_init /\n 0, / tp_alloc /\n 0, / tp_new /\n};\n\nstatic PyObject \nDatabase_new(PyTypeObject type, PyObject args, PyObject keywords)\n{\n Database_Object self = (Database_Object)type->tp_alloc(type, 0);\n if(self != NULL) {\n self->con = mysql_init(NULL);\n if(self->con == NULL) {\n PyErr_SetString(PyExc_RuntimeError, mysql_error(self->con));\n Py_XDECREF(self);\n return NULL;\n }\n }\n\n return (PyObject )self;\n}\n\nvoid\nDatabase_dealloc(PyObject self)\n{\n Database_Object s = (Database_Object)self;\n if(s->con != NULL)\n mysql_close(s->con);\n Py_TYPE(self)->tp_free(self);\n}\n\nstatic int\nDatabase_init(Database_Object self, PyObject args, PyObject keywords)\n{\n MYSQL res;\n static char keys[] = { \"database\", \"host\", \"user\", \"password\", NULL };\n char db;\n char host = \"localhost\";\n char user = \"root\";\n char pwd = \"\";\n \n if(!PyArg_ParseTupleAndKeywords(args, keywords, \"s\|sss\", keys, &db, &host, &user, &pwd))\n return -1;\n \n Py_BEGIN_ALLOW_THREADS\n res = mysql_real_connect(self->con, host, user, pwd, db, 0, NULL, 0);\n Py_END_ALLOW_THREADS\n\n if(res == NULL) {\n PyErr_SetString(PyExc_RuntimeError, mysql_error(self->con));\n return -1;\n }\n\n return 0;\n}\n\nstatic PyObject \nDatabase_query(PyObject self, PyObject args)\n{\n char query;\n int nok;\n MYSQL_RES res;\n Database_Object s = (Database_Object)self;\n\n /\n * Send query to MySQL server\n /\n if(!PyArg_ParseTuple(args, \"s\", &query)) return NULL;\n Py_BEGIN_ALLOW_THREADS\n nok = mysql_query(s->con, query);\n Py_END_ALLOW_THREADS\n\n if(nok) {\n PyErr_SetString(PyExc_RuntimeError, mysql_error(s->con));\n return NULL;\n }\n\n /\n * Read back results (store to memory).\n /\n Py_BEGIN_ALLOW_THREADS\n res = mysql_store_result(s->con);\n Py_END_ALLOW_THREADS\n\n if(res == NULL) {\n PyErr_SetString(PyExc_RuntimeError, mysql_error(s->con));\n return NULL;\n }\n\n /\n * Count results.\n /\n unsigned rows = mysql_num_rows(res);\n unsigned fields = mysql_field_count(s->con);\n if((rows == 0) \|\| (fields == 0)) {\n /\n * No results.\n /\n mysql_free_result(res);\n Py_RETURN_NONE;\n }\n\n /\n * Create new Table object.\n /\n Table_Object result = PyObject_New(Table_Object, &Table_Type);\n if(result == NULL) {\n mysql_free_result(res);\n return NULL;\n }\n\n result->num_rows = rows;\n result->res = res;\n result->row = -1;\n result->last = NULL;\n\n /\n Create new Tuple with fields' names\n /\n result->fields = PyTuple_New(fields);\n result->types = malloc(fields sizeof(int));\n unsigned i;\n for(i=0; ;i++) {\n MYSQL_FIELD f = mysql_fetch_field(res);\n if(f == NULL) break;\n PyTuple_SetItem(result->fields, i, PyString_FromString(f->name));\n result->types[i] = (int)f->type;\n }\n\n return (PyObject)result;\n}\n\nstatic PyObject \nDatabase_exec(PyObject self, PyObject args)\n{\n char query;\n int nok;\n Database_Object s = (Database_Object)self;\n\n /\n Send query to MySQL server\n /\n if(!PyArg_ParseTuple(args, \"s\", &query)) return NULL;\n Py_BEGIN_ALLOW_THREADS\n nok = mysql_query(s->con, query);\n Py_END_ALLOW_THREADS\n if(nok) {\n PyErr_SetString(PyExc_RuntimeError, mysql_error(s->con));\n return NULL;\n }\n\n /\n * Return number of affected rows.\n /\n return PyInt_FromLong(mysql_affected_rows(s->con));\n}\n\n/\n * C Methods for Connection Object\n /\nstatic PyMethodDef Database_methods[] = {\n { \"query\", (PyCFunction)Database_query, METH_VARARGS, \"Execute a SQL query and return a result (None) if no result\" },\n { \"execute\", (PyCFunction)Database_exec, METH_VARARGS, \"Execute a SQL command and return the number of affected rows\" },\n { NULL }\n};\n\n/\n * Connection Type definition for Python\n /\nstatic PyTypeObject Database_Type = {\n PyVarObject_HEAD_INIT(NULL, 0)\n \"pymy.Database\", / tp_name /\n sizeof(Database_Object), / tp_basicsize /\n 0, / tp_itemsize /\n (destructor)Database_dealloc, / tp_dealloc /\n 0, / tp_print /\n 0, / tp_getattr /\n 0, / tp_setattr /\n 0, / tp_compare /\n 0, / tp_repr /\n 0, / tp_as_number /\n 0, / tp_as_sequence /\n 0, / tp_as_mapping /\n 0, / tp_hash /\n 0, / tp_call /\n 0, / tp_str /\n 0, / tp_getattro /\n 0, / tp_setattro /\n 0, / tp_as_buffer /\n Py_TPFLAGS_DEFAULT \| Py_TPFLAGS_BASETYPE, / tp_flags /\n \"MySQL Database Connection abstraction\", / tp_doc /\n 0, / tp_traverse /\n 0, / tp_clear /\n 0, / tp_richcompare /\n 0, / tp_weaklistoffset /\n 0, / tp_iter /\n 0, / tp_iternext /\n Database_methods, / tp_methods /\n 0, / tp_members /\n 0, / tp_getset /\n 0, / tp_base /\n 0, / tp_dict /\n 0, / tp_descr_get /\n 0, / tp_descr_set /\n 0, / tp_dictoffset /\n (initproc)Database_init, / tp_init /\n 0, / tp_alloc /\n Database_new, / tp_new /\n};\n\nstatic PyMethodDef module_methods[] = {\n {NULL} / Sentinel /\n};\n\n#ifndef PyMODINIT_FUNC/ declarations for DLL import/export /\n#define PyMODINIT_FUNC void\n#endif\nPyMODINIT_FUNC\ninitpymy(void)\n{\n PyObject module;\n\n /\n Initialize new object types \n /\n if (PyType_Ready(&Database_Type) < 0) return;\n if (PyType_Ready(&Table_Type) < 0) return;\n\n /\n * Create a Python module\n /\n module = Py_InitModule3(\"pymy\", module_methods, \"MySQL wrapper module\");\n if (module == NULL) return;\n\n /\n * Add Connection class into module's namespace.\n /\n Py_INCREF(&Database_Type);\n Py_INCREF(&Table_Type);\n PyModule_AddObject(module, \"Database\", (PyObject )&Database_Type);\n\n PyDateTime_IMPORT ;\n}\n\n" }, { "alpha_fraction": 0.6940279006958008, "alphanum_fraction": 0.7048144936561584, "avg_line_length": 35.20000076293945, "blob_id": "3bd87cd595b2efc2e549ef5f4c5c442d21f01f7a", "content_id": "619100ea5a7393a468f81237c25c0515fce60822", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 3801, "license_type": "no_license", "max_line_length": 339, "num_lines": 105, "path": "/README.md", "repo_name": "bruabas/pymy", "src_encoding": "UTF-8", "text": "# pymy -- a Python MySQL connector\n\n\n## What is it\npymy is a Pythonic wrapper for connecting and sending queries to a MariaDB or MySQL database. Once it is not an ORM nor a SQL parser, you must enjoy SQL as much as you enjoy Python itself! And neither it adheres to any PEP database connection specification, but -- it is simple, neat and useful, doing a good job most of the times.\n\nYou can query data as simply as \n```python\nimport pymy\ndb = pymy.Database('test_db', user='myuser', password='pwd1234')\nres = db.query('select * from Table1')\nfor row in res:\n print 'id = {}, name = {}'.format(row['id'], row['name'])\n```\n\nor execute a SQL command...\n```python\nimport pymy\ndb = pymy.Database('test_db', user='myuser', password='pwd1234')\nres = db.execute('insert into Table1 (phone) values (\"551112345678\")')\nprint '{} rows affected'.format(res)\n```\n\n## Installation Instructions\nI've only installed in Linux, but with some effort it should be possible to install it on MacOS and Windows (using Cygwin). -- Help wanted here.\n\nDependencies\n+ libmysqlclient-dev\n+ python2.7\n\nClone the repository and execute setup.py\n```bash\ngit clone https://github.com/bruabas/pymy.git\ncd pymy\npython setup.py build\nsudo python setup.py install\n```\n\n## Roadmap\n- [x] convert MySQL numeric types into Python Numeric Types\n- [x] convert MySQL date/time types into Python datetime objects\n- [ ] convert BLOB data into bytearray() or something\n- [ ] provide a Generator for quering huge sets of data (no memory copy)\n- [ ] make a Python3 version\n- [ ] make installers for systems other than Linux\n\n\n## The API in a minute\n### pymy.Database class\n* constructor take one compulsory parameter, the database name\n* host address, user name and password are optional. If not supplied, address is supposed to be 'localhost', user is 'root' and password is blank.\n\n```python\ndb = pymy.Database(name='MyDatabase',\n host = '192.168.1.23',\n user = 'nonono',\n password = '1234')\n```\non success it returns a pymy.Database object that you will use to interact with the database. An exception (RuntimeException) is raised if any error occurs. \n\nTwo methods are available to acess the database: query() and execute()\n\n### pymy.Database.query( str )\n* query() takes a string as a SQL query that returns a result set (usually a SELECT clause)\n* Executes the SQL query on the server, returning a result set (pymy.Table) or None if the query returned no values.\n* Errors in SQL syntax or communication errors raise an exception\n\n### pymy.Database.execute( str )\n* execute() takes a string as a SQL query not returning a result set (clauses like INCLUDE, DELETE, etc)\n* Executes the SQL query on the server, returning the number of affected rows.\n* Errors in SQL syntax or communication errors raise an exception\n\n### pymy.Table class\n* is an iterable, list-like object, containing the results of a Database.query( )\n```python\n# result is a pymy.Table\n# you can use it as an iterable\nresult = db.query('select * from Employees')\nfor item in result:\n\tdo_something_with(item)\n\t\n# or as a list-like\nitem = result[2]\n```\n* its items are dictionaries with the keys corresponding to the field names\n```python\n# result is a pymy.Table\n# item is a dictionary\nresult = db.query('select FirstName, LastName from Clients')\nitem = result[2]\nname = item['FirstName']\n\n# or\nname = result[2]['FirstName']\n```\n\n### pymy.Table.column ( str )\n* This method returns a tuple with the values of all rows corresponding with the desired column\n```python\n# result is a pymy.Table\n# ages is a tuple\nresult = db.query('select * from Clients')\nages = result.column('age')\n# age is something like ( 34, 45, 33, 20, 60, 55 )\n```\n" }, { "alpha_fraction": 0.5947136282920837, "alphanum_fraction": 0.5991189479827881, "avg_line_length": 27.1875, "blob_id": "0f2aa70e8eef56c7907ed80cd9af44201f966b7b", "content_id": "5563458b7cb943d2416b7a26d5f25d4e913b41b3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 454, "license_type": "no_license", "max_line_length": 59, "num_lines": 16, "path": "/setup.py", "repo_name": "bruabas/pymy", "src_encoding": "UTF-8", "text": "\nfrom distutils.core import setup, Extension\nfrom subprocess import check_output\n\ncompile_args = check_output(['mysql_config', '--cflags'])\nlink_args = check_output(['mysql_config', '--libs'])\n\nsources = [ 'src/pymy.c' ]\n\next = Extension( 'pymy',\n sources,\n extra_compile_args = compile_args.split(),\n extra_link_args = link_args.split())\n\nsetup(\n name=\"pymy\", version=\"0.2\",\n ext_modules=[ ext ])\n\n\n" } ]	3
oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool	https://github.com/oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool	770770bcadbe40d6f0b117194b097ff8c57ba473	672f54bb527373d700b38d0389202526d748d12e	4baff9ce95c57b9196410f02d24e84a3df4e94d9	refs/heads/master	2022-02-10T05:04:27.726998	2018-11-05T13:40:10	2018-11-05T13:40:10	null	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.4493597149848938, "alphanum_fraction": 0.5098952054977417, "avg_line_length": 23.428571701049805, "blob_id": "7fabcfea5256ade29c7337cbca7b46d654817035", "content_id": "370953849c51be8589285bad3220554694035980", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 879, "license_type": "permissive", "max_line_length": 68, "num_lines": 35, "path": "/Quart2Euler.py", "repo_name": "oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool", "src_encoding": "UTF-8", "text": " ##四元数转Euler角##\nimport sys\nimport math\ndef quart2Euler (nums):\n Euler = []\n w = float(nums[0]) # img items: x,y,z real items :w #\n x = float(nums[1])\n y = float(nums[2])\n z = float(nums[3])\n theta1 = math.atan2(2 (w * x + y * z), 1 - 2 * (x * x + y * y))\n if theta1 < 0:\n theta1 += 2 * math.pi\n temp = w * y - z * x\n if temp >= 0.5:\n temp = 0.5\n elif temp <= -0.5:\n temp = -0.5\n else:\n pass\n theta2 = math.asin(2 * temp)\n theta3 = math.atan2(2 * (w * z + x * y), 1 - 2 * (z * z + y * y))\n if theta3 < 0:\n theta3 += 2 * math.pi\n ##弧度转角度##\n roll = theta1 * 180 / math.pi\n Euler.append(roll)\n pitch = theta2 * 180 / math.pi\n Euler.append(pitch)\n yaw = theta3 * 180 / math.pi\n Euler.append(yaw)\n return Euler\n\nif __name__ == '__main__':\n quart1 = (1,0,0,0)\n print(quart2Euler(quart1))\n\n\n\n" }, { "alpha_fraction": 0.579155683517456, "alphanum_fraction": 0.5936675667762756, "avg_line_length": 33.45454406738281, "blob_id": "f10a086fc7b540552dbb07d8c87bf9183b5a3306", "content_id": "e60408c469042e14dda4a8d75c40a5d930b90bc3", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 786, "license_type": "permissive", "max_line_length": 107, "num_lines": 22, "path": "/Euler2Quart.py", "repo_name": "oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool", "src_encoding": "UTF-8", "text": "import math\nimport numpy as np\ndef Euler2quatern(nums): ##input : 欧拉角列表 [roll,pitch,yaw],返回一个四元数列表\n\n roll = nums[0] / 2\n pitch = nums[1] / 2\n yaw = nums[2] / 2\n\n w = math.cos(roll) * math.cos(pitch) * math.cos(yaw) + math.sin(roll) * math.sin(pitch) * math.sin(yaw)\n\n x = math.sin(roll) * math.cos(pitch) * math.cos(yaw) - math.cos(roll) * math.sin(pitch) * math.sin(yaw)\n\n y = math.cos(roll) * math.sin(pitch) * math.cos(yaw) + math.sin(roll) * math.cos(pitch) * math.sin(yaw)\n\n z = math.cos(roll) * math.cos(pitch) * math.sin(yaw) + math.sin(roll) * math.sin(pitch) * math.cos(yaw)\n qua = [w, x, y, z]\n return qua\ndef main():\n testeuler = [0, 0, 0]\n print(Euler2quatern(testeuler))\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.42750608921051025, "alphanum_fraction": 0.4946221113204956, "avg_line_length": 34.345176696777344, "blob_id": "edbd19515f8692c5a6de1a67dfae8454bc3548cf", "content_id": "e4d805f03e33395e0a2b6445e86a2c15a2cdedbf", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7306, "license_type": "permissive", "max_line_length": 111, "num_lines": 197, "path": "/QuartClass.py", "repo_name": "oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool", "src_encoding": "UTF-8", "text": "import sys\nimport math\nimport cv2\nimport numpy as np\n\"\"\"\nThis is a quaternion class that calculates the quaternion to the rotation matrix, the rotation vector, \nand the rotation of the two quaternions based on the correlation method provided. \nThis is a key step in the virtual human project.\n© Weinan Gan. All Rights Reserved.\n\"\"\"\nclass Quart():\n def __init__(self,nums):\n self.w = float(nums[0])\n self.x = float(nums[1])\n self.y = float(nums[2])\n self.z = float(nums[3])\n def Quart2Euler(self): #四元数计算欧拉角#\n Euler = []\n w = self.w\n x = self.x\n y = self.y\n z = self.z\n theta1 = math.atan2(2 * (w * x + y * z), 1 - 2 * (x * x + y * y))\n if theta1 < 0:\n theta1 += 2math.pi\n temp = wy - zx\n if temp >= 0.5:\n temp = 0.5\n elif temp <= -0.5:\n temp = -0.5\n else:\n pass\n theta2 = math.asin(2 temp)\n theta3 = math.atan2(2 * (w * z + x * y), 1 - 2 * (z * z + y * y))\n if theta3 < 0:\n theta3 += 2math.pi\n ##弧度转角度##\n roll = theta1 180 / math.pi\n Euler.append(roll)\n pitch = theta2 * 180 / math.pi\n Euler.append(pitch)\n yaw = theta3 * 180 / math.pi\n Euler.append(yaw)\n return Euler\n def Euler2quatern(nums): ##input : 欧拉角列表 [roll,pitch,yaw]\n roll = nums[0] / 2\n pitch = nums[1] / 2\n yaw = nums[2] / 2\n w = math.cos(roll) math.cos(pitch) * math.cos(yaw) + math.sin(roll) * math.sin(pitch) * math.sin(yaw)\n x = math.sin(roll) * math.cos(pitch) * math.cos(yaw) - math.cos(roll) * math.sin(pitch) * math.sin(yaw)\n y = math.cos(roll) * math.sin(pitch) * math.cos(yaw) + math.sin(roll) * math.cos(pitch) * math.sin(yaw)\n z = math.cos(roll) * math.cos(pitch) * math.sin(yaw) + math.sin(roll) * math.sin(pitch) * math.cos(yaw)\n qua = [w, x, y, z]\n return qua\n \"\"\"\n The transform from quart to rotvector\n \"\"\"\n\n def Quart2RotationMatrix(self): ##四元数计算旋转矩阵##\n R = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 0]], dtype=float)\n w = self.w\n x = self.x\n y = self.y\n z = self.z\n # calculate element of matrix\n R[0][0] = np.square(w) + np.square(x) - np.square(y) - np.square(z)\n R[0][1] = 2 * (x * y + w * z)\n R[0][2] = 2 * (x * z - w * y)\n R[1][0] = 2 * (x * y - w * z)\n R[1][1] = np.square(w) - np.square(x) + np.square(y) - np.square(z)\n R[1][2] = 2 * (w * x + y * z)\n R[2][0] = 2 * (x * z + w * y)\n R[2][1] = 2 * (y * z - w * x)\n R[2][2] = np.square(w) - np.square(x) - np.square(y) + np.square(z)\n return R\n def rotMat2rotvector(R): ##旋转矩阵转旋转向量##\n vector = cv2.Rodrigues(R)[0]\n v =[]\n for i in range(3):\n v.append(vector[i][0])\n return v\n def Quart2RotationVector(self): ##四元数计算旋转向量##\n R = self.Quart2RotationMatrix()\n v = Quart.rotMat2rotvector(R)\n return v\n\n \"\"\"\n the transform from rotvector to quartern\n \"\"\"\n\n def rotvector2rotMat(v): ##旋转向量转旋转矩阵##\n v1=np.array([[0],[0],[0]],dtype=float)\n v1[0][0] = v[0]\n v1[1][0] = v[1]\n v1[2][0] = v[2]\n Matrix = cv2.Rodrigues(v1)\n return Matrix[0]\n def rotMat2quatern(R): ##旋转矩阵转四元数###\n # this function can transform the rotation matrix into quatern\n q = np.zeros(4)\n K = np.zeros([4, 4])\n K[0, 0] = 1 / 3 * (R[0, 0] - R[1, 1] - R[2, 2])\n K[0, 1] = 1 / 3 * (R[1, 0] + R[0, 1])\n K[0, 2] = 1 / 3 * (R[2, 0] + R[0, 2])\n K[0, 3] = 1 / 3 * (R[1, 2] - R[2, 1])\n K[1, 0] = 1 / 3 * (R[1, 0] + R[0, 1])\n K[1, 1] = 1 / 3 * (R[1, 1] - R[0, 0] - R[2, 2])\n K[1, 2] = 1 / 3 * (R[2, 1] + R[1, 2])\n K[1, 3] = 1 / 3 * (R[2, 0] - R[0, 2])\n K[2, 0] = 1 / 3 * (R[2, 0] + R[0, 2])\n K[2, 1] = 1 / 3 * (R[2, 1] + R[1, 2])\n K[2, 2] = 1 / 3 * (R[2, 2] - R[0, 0] - R[1, 1])\n K[2, 3] = 1 / 3 * (R[0, 1] - R[1, 0])\n K[3, 0] = 1 / 3 * (R[1, 2] - R[2, 1])\n K[3, 1] = 1 / 3 * (R[2, 0] - R[0, 2])\n K[3, 2] = 1 / 3 * (R[0, 1] - R[1, 0])\n K[3, 3] = 1 / 3 * (R[0, 0] + R[1, 1] + R[2, 2])\n # print(R)\n # print(\"**********\")\n # print(K)\n D, V = np.linalg.eig(K)\n # print(K)\n pp = 0\n for i in range(1, 4):\n if(D[i] > D[pp]):\n pp = i\n # print(D[pp])\n # print(D)\n q = V[:, pp]\n q = np.array([q[3], q[0], q[1], q[2]])\n return q\n def rotvector2quart(v): ##旋转向量转四元数##\n rotmatrix = Quart.rotvector2rotMat(v)\n q = Quart.rotMat2quatern(rotmatrix)\n return q\n\n\n \"\"\"\n loss about VirtualHuman's action \n \"\"\"\n def calculateloss1(self,nums): ##第一种方法，input：一个四元数元组\n R1 = np.matrix(self.Quart2RotationMatrix())\n quart2 = Quart(nums)\n R2 = np.matrix(quart2.Quart2RotationMatrix())\n reverse_R1 = R1.I\n R3 = reverse_R1 R2 ##这里本应该用转置的，但是由于浮点数计算误差带来问题，故选择求逆函数##\n loss_vector = Quart.rotMat2rotvector(np.array(R3))\n loss1 = (loss_vector[0][0]2 + loss_vector[1][0]2 + loss_vector[2][0]2)0.5\n return loss1\n def calculateloss2(self ,predic_v ): ##第二种方法,input predic_v = [a,b,c]##\n truth_v = self.Quart2RotationVector()\n loss_v_list = []\n for i in range(3):\n loss_v_list.append(truth_v[i][0]-predic_v[i])\n loss2 = (loss_v_list[0]2 + loss_v_list[1]2 + loss_v_list[2]2)0.5\n return loss2\n\n def Dicar_trans(self,nums1): ###笛卡尔坐标转换这里用不到 input:调用对象旧坐标 output：新的坐标##\n Matrix = self.Quart2RotationMatrix()\n newcordinate = []\n oldcordinate = np.array(nums1)\n for i in range(len(Matrix)):\n newcordinate.append(np.dot(Matrix[i],oldcordinate))\n return newcordinate\ndef main():\n testquart = (-0.707100,0.707100,0.000000,0.000000)\n old = (0,0,1)\n quart = Quart(testquart)\n print(quart.Quart2RotationVector())\n print(quart.Quart2RotationMatrix())\n print(quart.Dicar_trans(old))\n \n\n \"\"\"\n testquart = (-0.707100, 0.707100, 0.000000, 0.000000)\n testeuler = ((270.00109895412885/180)math.pi, -0.0, 0.0)\n quart = Quart(testquart)\n print(\"欧拉角为：\",quart.Quart2Euler())\n print(\"四元数为：\",Quart.Euler2quatern(testeuler))\n \"\"\"\n \"\"\"\n predict_quart = (-0.707100,0.707100,0.000000,0.000000)\n truth_quart = (-0.707100,0.707100,0.000000,0.000000)\n quart = Quart(predict_quart)\n print(\"lossFuncition1的损失为：\",quart.calculateloss1(truth_quart))\n \"\"\"\n \"\"\"\n testquart = (1, 0, 0, 0)\n quart = Quart(testquart)\n print(quart.Quart2RotationVector())\n print(Quart.rotvector2quart([0,0,0]))\n v_test = [1,0,0]\n print(\"lossFunction2的损失为：\",quart.calculateloss2(v_test))\n \"\"\"\n\nif __name__ == \"__main__\":\n main()\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.8223684430122375, "alphanum_fraction": 0.8223684430122375, "avg_line_length": 379, "blob_id": "2b4766b8efefd08633fcc6f4b4d863725eb5a2ee", "content_id": "5a022fba7beb1b545a7b02ef8e5ef3d0e48021df", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 796, "license_type": "permissive", "max_line_length": 734, "num_lines": 2, "path": "/README.md", "repo_name": "oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool", "src_encoding": "UTF-8", "text": "# 欧拉角四元数旋转向量旋转矩阵旋转损失\nThis is my internship project in NetEase game AI Lab: multi-modal virtual human interaction, through the text to predict the virtual human action and then generate animation through Unity rendering, enhance the interactive experience of virtual characters. This is the calculation tool I used to refer to the Python version written independently by the relevant C code, including quaternions, rotation vectors, Euler angles, and the loss function between the predicted rotation and the true rotation. This part I listed separately as a calculation tool for everyone to use in the game AI experiment development process. QuartClass is a class that integrates all functions. I have tested all the features and it is stable and feasible.\n" }, { "alpha_fraction": 0.46081870794296265, "alphanum_fraction": 0.5426900386810303, "avg_line_length": 29.464284896850586, "blob_id": "a2faf44db32ee18e8fd2102377e0d421a0809ef6", "content_id": "704282a7e63d67b7b723aa17d60998538f703cfe", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 855, "license_type": "permissive", "max_line_length": 69, "num_lines": 28, "path": "/Quart2RotationVector.py", "repo_name": "oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool", "src_encoding": "UTF-8", "text": "import numpy as np\nimport cv2\ndef quart2Rotationvector(nums):\n R = np.array([[0,0,0],[0,0,0],[0,0,0]],dtype=float)\n#print(RotationMatrix)\n w = float(nums[0]) # img items: x,y,z\n # real items :w #\n x = float(nums[1])\n y = float(nums[2])\n z = float(nums[3])\n#calculate element of matrix\n R[0][0] = np.square(w) + np.square(x) - np.square(y) - np.square(z)\n R[0][1] = 2(xy + wz)\n R[0][2] = 2(xz - wy)\n R[1][0] = 2(xy - wz)\n R[1][1] = np.square(w) - np.square(x) + np.square(y) - np.square(z)\n R[1][2] = 2(wx + yz)\n R[2][0] = 2(xz + wy)\n R[2][1] = 2(yz - wx)\n R[2][2] = np.square(w) - np.square(x) - np.square(y) + np.square(z)\n vector = cv2.Rodrigues(R)\n return vector[0]\n\n\n\nif __name__ == '__main__':\n testquart = (-0.707100,0.707100,0.000000,0.000000)\n print(quart2Rotationvector(*testquart))\n\n\n" } ]	5
mudsill/nys-re	https://github.com/mudsill/nys-re	6ada28ee27244f055433d8e06df0a731a8a8436c	aedaf103f143b11feba15a6a11eb9ddaea66d434	def3460f3e53dc79c03a1f46c34f5fb4aaadb672	refs/heads/master	2020-03-28T17:09:01.737172	2018-09-21T13:34:27	2018-09-21T13:34:27	null	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6932153105735779, "alphanum_fraction": 0.6932153105735779, "avg_line_length": 27.25, "blob_id": "f5b4fe5381dbe523ebe6a594165c57e6eddb6594", "content_id": "5c1ee6babfd4c80811972c5d1fcf69f21d37ce9c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 339, "license_type": "no_license", "max_line_length": 113, "num_lines": 12, "path": "/nys.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "from app import create_app, db\n\n\napp = create_app()\n\n\[email protected]_context_processor\ndef make_shell_context():\n from app import db\n from app.models import Filer, Disclosure, Entity, Link\n from app.engine import Engine\n return {'db': db, 'Filer': Filer, 'Disclosure': Disclosure, 'Entity': Entity, 'Link': Link, 'Engine': Engine}\n" }, { "alpha_fraction": 0.6706587076187134, "alphanum_fraction": 0.6706587076187134, "avg_line_length": 10.928571701049805, "blob_id": "253e2e836c2a8a0a00f7b25085c555d200ee8396", "content_id": "867470d621ba8d3a31d344907989771f3a7aa5a5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 167, "license_type": "no_license", "max_line_length": 31, "num_lines": 14, "path": "/app/cli.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "import click\n\nfrom engine import Engine\n\n\[email protected]()\ndef cli():\n pass\n\n\[email protected]()\ndef normalize():\n engine = Engine()\n engine.normalize_entities()\n" }, { "alpha_fraction": 0.5307454466819763, "alphanum_fraction": 0.5389900207519531, "avg_line_length": 32.45977020263672, "blob_id": "1e507bec2441d2a744f3730844aa405af07bfda3", "content_id": "944486925a98ce653c8b955333401939ea6e55e9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2911, "license_type": "no_license", "max_line_length": 103, "num_lines": 87, "path": "/app/engine.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "import logging\nimport requests\n\nfrom datetime import datetime\n\nfrom lxml import html\nfrom sqlalchemy import update\nfrom Levenshtein import distance\n\nfrom app import db\nfrom app.config import Config\nfrom app.models import Entity, Link, Filer, Disclosure\n\nlogging.basicConfig(level=logging.DEBUG)\n\n\nclass Engine:\n CORPORATE_SUFFIXES = ['corporation', 'incorporated', 'corp', 'co', 'inc',\n 'llc']\n ELECTED_OFFICIALS_URL = 'http://www.elections.ny.gov:8080/reports/rwservlet?cmdkey=nysboe_incumbnt'\n\n\n @property\n def app(self):\n if not hasattr(self, '_app'):\n self._app = create_app()\n return self._app\n\n\n @property\n def logger(self):\n if not hasattr(self, '_logger'):\n self._logger = logging.getLogger(self.__class__.__name__)\n return self._logger\n\n\n def normalize_entities(self):\n entities = Entity.query.yield_per(5000).enable_eagerloads(False)\n for entity in entities:\n name = entity.name.strip().lower()\n name_check = name.split(', ')\n last_name_check = name_check[0].split(' ')\n aliases = []\n if len(name_check) == 2 and len(last_name_check) == 1:\n # Check if this is a person\n first_names = ' '.join(name_check[1:])\n last_name = name_check[0]\n normalized_name = '%s %s' % (first_names, last_name)\n else:\n # Now check if this is a corporation\n name_partition = name.split(' ')\n name_suffix = name_partition[-1]\n not_found = True\n for suffix in self.CORPORATE_SUFFIXES:\n if distance(str(name_suffix), str(suffix)) <= 2:\n not_found = False\n if not_found:\n continue\n normalized_name = name_partition[:-1]\n aliases.append(name)\n details = {'aliases': aliases}\n updated = datetime.utcnow()\n self.logger.debug('Updating [%s]...', entity.uuid)\n db.session.query(Entity).filter(Entity.uuid == entity.uuid) \\\n .update({'name': normalized_name, 'details': details,\n 'updated': updated})\n db.session.commit()\n\n\n\n def stage_disclosures(self):\n disclosures = db.session(Disclosure).yield_per(5000) \\\n .enable_eagerloads(False)\n entities = db.session(Entity).yield_per(5000) \\\n .enable_eagerloads(False) \\\n .filter(Entity.type.ilike('%real estate%'))\n for disclosure in disclosures:\n updated = datetime.utcnow()\n contributor = disclosure.contributor\n\n\n def stage_filers(self):\n pass\n\n\n def stage_link(self):\n pass\n" }, { "alpha_fraction": 0.7142857313156128, "alphanum_fraction": 0.7142857313156128, "avg_line_length": 16.5, "blob_id": "5cec00a850c15e5ba36580ef131f176bbf2f4c43", "content_id": "7050780d75cdd1343d3e4901a6646ad7a1958f63", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 35, "license_type": "no_license", "max_line_length": 25, "num_lines": 2, "path": "/README.md", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "# nys_re\nNYS Real Estate Who's Who\n" }, { "alpha_fraction": 0.7198879718780518, "alphanum_fraction": 0.7198879718780518, "avg_line_length": 20.636363983154297, "blob_id": "b575c597dcc47d84105015c1db1d9d74cd308024", "content_id": "067ba8ec59459ad148c7328a4b4d3c4705432428", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 714, "license_type": "no_license", "max_line_length": 45, "num_lines": 33, "path": "/app/__init__.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "import logging\n\nfrom flask import Flask, request, current_app\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\nfrom flask_bootstrap import Bootstrap\n\nfrom config import Config\n\ndb = SQLAlchemy()\nmigrate = Migrate()\nbootstrap = Bootstrap()\n\n\ndef create_app(config_class=Config):\n \"\"\" \"\"\"\n app = Flask(__name__)\n app.config.from_object(config_class)\n\n db.init_app(app)\n migrate.init_app(app, db)\n bootstrap.init_app(app)\n\n from app import models\n\n from app.routes import bp as main_bp\n app.register_blueprint(main_bp)\n\n stream_handler = logging.StreamHandler()\n stream_handler.setLevel(logging.INFO)\n app.logger.addHandler(stream_handler)\n\n return app\n" }, { "alpha_fraction": 0.6690475940704346, "alphanum_fraction": 0.6738095283508301, "avg_line_length": 27, "blob_id": "985051b12ab922b0ba5146258daff92339130841", "content_id": "7f632ad2afc731f351709848a32ce6f3cc978d55", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 420, "license_type": "no_license", "max_line_length": 65, "num_lines": 15, "path": "/app/config.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "import os\n\nfrom dotenv import load_dotenv\n\nbasedir = os.path.abspath(os.path.dirname(__file__))\nload_dotenv(os.path.join('/home/anabase/nyd/nys-re/.env'))\n\n\nclass Config:\n SECRET_KEY = os.environ.get('SECRET_KEY') or 'a;sdlfkja3lkja;sdlfkjas;dlkf'\n SQLALCHEMY_DATABASE_URI = os.environ.get('DATABASE_URL') or \\\n 'sqlite:///' + os.path.join(basedir, 'app.db')\n SQLALCHEMY_TRACK_MODIFICATIONS = False\n\n ENTITIES_PER_PAGE = 25\n" }, { "alpha_fraction": 0.6524448990821838, "alphanum_fraction": 0.6529242396354675, "avg_line_length": 33.196720123291016, "blob_id": "059c31b6bea340ff4a1820c8741a084d332e8bd6", "content_id": "18f3f3e0e12a3fdc72ae92896c9fc7198c7eb775", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2086, "license_type": "no_license", "max_line_length": 80, "num_lines": 61, "path": "/app/models.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "from datetime import datetime\n\nfrom app import db\n\n\nclass ToDictMixin:\n @property\n def to_dict(self):\n return {c.name.split('.')[-1]: getattr(self, c.name) \\\n for c in self.__table__.columns}\n\n\nclass Entity(ToDictMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.Text)\n uuid = db.Column(db.Text)\n type = db.Column(db.Text)\n sector = db.Column(db.Text)\n timestamp = db.Column(db.DateTime, default=datetime.utcnow)\n updated = db.Column(db.DateTime, default=datetime.utcnow)\n details = db.Column(db.JSON)\n\n\nclass Link(ToDictMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n node_a = db.Column(db.Text)\n node_b = db.Column(db.Text)\n type = db.Column(db.Text)\n details = db.Column(db.JSON)\n updated = db.Column(db.DateTime, default=datetime.utcnow)\n\n\nclass Filer(ToDictMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n created = db.Column(db.DateTime, index=True, default=datetime.utcnow)\n run_id = db.Column(db.Text)\n uuid = db.Column(db.Text, unique=True)\n filer_id = db.Column(db.Text, index=True, unique=True)\n name = db.Column(db.Text, index=True)\n address = db.Column(db.Text, index=True)\n status = db.Column(db.Text)\n disclosures = db.relationship('Disclosure', backref='payer', lazy='dynamic')\n\n def __repr__(self):\n return '<Filer {}>'.format(self.filer_id)\n\n\nclass Disclosure(ToDictMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n created = db.Column(db.DateTime, index=True, default=datetime.utcnow)\n run_id = db.Column(db.Text)\n uuid = db.Column(db.Text, unique=True)\n filer_id = db.Column(db.Text)\n filer_uuid = db.Column(db.Text, db.ForeignKey('filer.uuid'), index=True)\n filing_year = db.Column(db.Integer)\n contributor = db.Column(db.Text, index=True)\n address = db.Column(db.Text, index=True)\n amount = db.Column(db.Float)\n date = db.Column(db.Date)\n report_code = db.Column(db.Text)\n schedule = db.Column(db.Text)\n" }, { "alpha_fraction": 0.599078357219696, "alphanum_fraction": 0.6006144285202026, "avg_line_length": 38.85714340209961, "blob_id": "9ceffced806f1801fc4f689f98cbd40032f4b779", "content_id": "05f06f71c5c9cebf4acc8f9be7a3b059570b001e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1953, "license_type": "no_license", "max_line_length": 87, "num_lines": 49, "path": "/app/routes.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "from flask import Blueprint, render_template, current_app, request, url_for\nfrom flask_paginate import Pagination, get_page_parameter\nfrom sqlalchemy import func, or_\n\nfrom app import db\nfrom app.models import Entity, Link\n\nbp = Blueprint('main', __name__)\n\n\[email protected]('/')\[email protected]('/index')\ndef index():\n q = request.args.get('q')\n search = q and q is not None\n\n page = request.args.get(get_page_parameter(), default=1, type=int)\n entities = db.session.query(Entity.name, Entity.uuid, Entity.type)\\\n .paginate(page, current_app.config['ENTITIES_PER_PAGE'], False).items\n total = db.session.query(func.count(Entity.uuid)).scalar()\n pagination = Pagination(page=page, total=total, search=search,\n record_name='entities',\n per_page=current_app.config['ENTITIES_PER_PAGE'],\n bs_version=4,\n inner_window=5)\n return render_template('index.html', title='Home', entities=entities,\n pagination=pagination)\n\n\[email protected]('/entity/<string:e_uuid>')\ndef profile(e_uuid):\n entity = Entity.query.filter_by(uuid=e_uuid).first()\n links = Link.query.filter(or_(Link.node_a == e_uuid, Link.node_b == e_uuid)).all()\n links_with_names = []\n for link in links:\n link = link.to_dict\n link['node_a'] = db.session.query(Entity.name, Entity.uuid) \\\n .filter(Entity.uuid == link.get('node_a')).first().name\n link['node_b'] = db.session.query(Entity.name, Entity.uuid) \\\n .filter(Entity.uuid == link.get('node_b')).first().name\n links_with_names.append(link)\n title = 'Profile: %s' % entity.name\n return render_template('profile.html', title=title, entity=entity,\n links=links_with_names)\n\n\[email protected]('/about')\ndef about():\n return render_template('about.html', title='About')\n" } ]	8
PaoloTCS/DjangoWebProjectGitHubTCS	https://github.com/PaoloTCS/DjangoWebProjectGitHubTCS	931dc640bcd0ecd4fdd0b5182a14127a73e026f8	0ee4f85fd577d88523e35547f9ddad59ef141d24	308c751c864c966a530ded15b4ae1626d338e944	refs/heads/master	2019-07-10T06:18:37.542548	2017-05-12T11:29:53	2017-05-12T11:29:53	90,991,311	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7446808218955994, "alphanum_fraction": 0.7446808218955994, "avg_line_length": 14.666666984558105, "blob_id": "60c337cdc2c194a3532ecc9254bd11a609594479", "content_id": "af4e4d0f6e70203f93cd9bd951a3cbadc14db72d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 47, "license_type": "no_license", "max_line_length": 38, "num_lines": 3, "path": "/DjangoWebProjectGitHubTCS/DjangoWebProjectGitHubTCS/__init__.py", "repo_name": "PaoloTCS/DjangoWebProjectGitHubTCS", "src_encoding": "UTF-8", "text": "\"\"\"\nPackage for DjangoWebProjectGitHubTCS.\n\"\"\"\n" } ]	1
nunixnunix04/league-table-maker	https://github.com/nunixnunix04/league-table-maker	4b529637c06839a679ddffd4386b575e93284ef9	ad63a4fa72e30e589b19d39b632926413c4fe268	b5eb75b47c4110f1832ecb630294f42be98f40f2	refs/heads/master	2020-12-05T02:55:58.212749	2020-01-05T23:25:24	2020-01-05T23:25:24	231,989,190	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7537155151367188, "alphanum_fraction": 0.7664543390274048, "avg_line_length": 46.099998474121094, "blob_id": "77d651cedb962e23cfdb2947a0cd19f3facb6467", "content_id": "f8cc29ea4db0c438503a6832b26e2c463d844190", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 471, "license_type": "no_license", "max_line_length": 104, "num_lines": 10, "path": "/README.md", "repo_name": "nunixnunix04/league-table-maker", "src_encoding": "UTF-8", "text": "# League Table Maker\nMakes a detailed league table csv file from soccer season data.\n\n# How to Use\n1) Get the csv files from the league you want from the following website: https://datahub.io/sports-data\n2) Place the csv files in a folder named after the country of that league.\n3) Open up the file standings.py\n4) Change the variables 'country' and 'season' to your choosing.\n5) Run the program.\n6) The csv table has been created under the same folder as your raw data.\n" }, { "alpha_fraction": 0.605980396270752, "alphanum_fraction": 0.6189467906951904, "avg_line_length": 24.815603256225586, "blob_id": "372e88c44c59ca8e9f067da94e80213da607ca36", "content_id": "a618eeb01d613062879bbe4b3fa69f4ced3f7a9a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3779, "license_type": "no_license", "max_line_length": 114, "num_lines": 141, "path": "/standings.py", "repo_name": "nunixnunix04/league-table-maker", "src_encoding": "UTF-8", "text": "import csv\r\n\r\n# SETTINGS\r\ncountry = \"spain\"\r\nseason = \"1819\" # Use the following format (eg: 2018-19 --> \"1819\")\r\n\r\n\r\nfilename = country + \"/\" + \"season-\" + season + \"_csv.csv\"\r\n\r\n# Creates team\r\ndef create_team_list(reader):\r\n\tteam_list = []\r\n\tfor row in reader:\r\n\t\tteam_name = row[2]\r\n\t\tif team_name not in team_list:\r\n\t\t\tteam_list.append(team_name)\r\n\tdel team_list[0]\r\n\treturn team_list\r\n\r\nclass Team():\r\n\t\r\n\tdef __init__(self, team, reader):\r\n\t\tself.team = team\r\n\t\tself.reader = reader\r\n\t\tself.matches = self.get_list_of_matches()\r\n\t\tself.wins = self.get_wins()\r\n\t\tself.draws = self.get_draws()\r\n\t\tself.losses = self.get_losses()\r\n\t\tself.points = self.get_points()\r\n\t\tself.goals_for = self.get_goals_for()\r\n\t\tself.goals_against = self.get_goals_against()\r\n\t\tself.goal_difference = self.get_goal_difference()\r\n\r\n\tdef get_list_of_matches(self):\r\n\t\tlist_of_matches = []\r\n\t\tfor row in self.reader:\r\n\t\t\tif (self.team == row[2]):\r\n\t\t\t\trow.append(\"H\")\r\n\t\t\t\tlist_of_matches.append(row)\r\n\t\t\telif (self.team == row[3]):\r\n\t\t\t\trow.append(\"A\")\r\n\t\t\t\tlist_of_matches.append(row)\r\n\t\treturn list_of_matches\r\n\r\n\tdef get_wins(self):\r\n\t\twins = 0\r\n\t\tfor match in self.matches:\r\n\t\t\tif (match[6] == match[-1]):\r\n\t\t\t\twins += 1\r\n\t\treturn wins\r\n\r\n\tdef get_draws(self):\r\n\t\tdraws = 0\r\n\t\tfor match in self.matches:\r\n\t\t\tif match[6] == \"D\":\r\n\t\t\t\tdraws += 1\r\n\t\treturn draws\r\n\r\n\tdef get_losses(self):\r\n\t\tlosses = 0\r\n\t\tfor match in self.matches:\r\n\t\t\tif (match[6] != match[-1]) and (match[6] != \"D\"):\r\n\t\t\t\tlosses += 1\r\n\t\treturn losses\r\n\r\n\tdef get_points(self):\r\n\t\tpoints = (3 * self.wins) + (self.draws)\r\n\t\treturn points\r\n\r\n\r\n\tdef get_goals_for(self):\r\n\t\tgoals_for = 0\r\n\t\tfor match in self.matches:\r\n\t\t\tif (match[-1] == \"H\"):\r\n\t\t\t\tgoals_for += int(match[4])\r\n\t\t\telif (match[-1] == \"A\"):\r\n\t\t\t\tgoals_for += int(match[5])\r\n\t\treturn goals_for\r\n\r\n\tdef get_goals_against(self):\r\n\t\tgoals_against = 0\r\n\t\tfor match in self.matches:\r\n\t\t\tif (match[-1] == \"H\"):\r\n\t\t\t\tgoals_against += int(match[5])\r\n\t\t\telif (match[-1] == \"A\"):\r\n\t\t\t\tgoals_against += int(match[4])\r\n\t\treturn goals_against\r\n\r\n\tdef get_goal_difference(self):\r\n\t\tgoal_difference = self.goals_for - self.goals_against\r\n\t\treturn goal_difference\r\n\t\r\n\tdef print_list_of_matches(self):\r\n\t\tfor match in self.matches:\r\n\t\t\tprint(match[2] + \" \" + match[4] + \" - \" + match[5] + \" \" + match[3])\r\n\r\n\tdef print_season_summary(self):\r\n\t\tprint(\"Team: \" + self.team)\r\n\t\tprint(\"\\tGames Played: \" + str(len(self.matches)))\r\n\t\tprint(\"\\tWins: \" + str(self.wins))\r\n\t\tprint(\"\\tDraws: \" + str(self.draws))\r\n\t\tprint(\"\\tLosses: \" + str(self.losses))\r\n\t\tprint(\"\\tGoals For: \" + str(self.goals_for))\r\n\t\tprint(\"\\tGoals Against: \" + str(self.goals_against))\r\n\t\tprint(\"\\tGoal Difference: \" + str(self.goal_difference))\r\n\r\nleague = {}\r\nteam_objects = []\r\nwith open(filename) as f:\r\n\treader = csv.reader(f)\r\n\tteam_list = create_team_list(reader)\r\n\r\nfor team in team_list:\r\n\twith open(filename) as f:\r\n\t\treader = csv.reader(f)\r\n\t\tleague[team] = Team(team, reader)\r\n\t\tteam_objects.append(league[team])\r\n\r\nteam_objects_sorted = sorted(team_objects, key=lambda x: (x.points, x.goal_difference, x.goals_for), reverse=True)\r\n\r\nleague_table = [[\"#\",\"Team\",\"GP\",\"W\",\"D\",\"L\",\"GF\",\"GA\",\"GD\",\"Points\"]]\r\n\r\nfor i in range(len(team_objects_sorted)):\r\n\tteam_row = [\r\n\ti+1,\r\n\tteam_objects_sorted[i].team,\r\n\tlen(team_objects_sorted[i].matches),\r\n\tteam_objects_sorted[i].wins,\r\n\tteam_objects_sorted[i].draws,\r\n\tteam_objects_sorted[i].losses,\r\n\tteam_objects_sorted[i].goals_for,\r\n\tteam_objects_sorted[i].goals_against,\r\n\tteam_objects_sorted[i].goal_difference,\r\n\tteam_objects_sorted[i].points\r\n\t]\r\n\tleague_table.append(team_row)\r\n\r\noutfile_name = country + \"/\" + \"league_table_\" + filename[-12:-8] + \".csv\"\r\nwith open(outfile_name,\"w\",newline=\"\") as f:\r\n\twriter = csv.writer(f)\r\n\twriter.writerows(league_table)" } ]	2
beatcode/neuro_network	https://github.com/beatcode/neuro_network	bd0d6a41e53c95e26e2b024293c521a76edfa39a	7017b486223250f63c4dbc762bcdf0b0b06b63f4	1d00c5af1f3572c7606c32167260e8002943af27	refs/heads/master	2021-01-12T11:32:54.807627	2016-11-26T22:57:01	2016-11-26T22:57:01	72,949,980	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6774193644523621, "alphanum_fraction": 0.6774193644523621, "avg_line_length": 21.14285659790039, "blob_id": "b43cf755b91dd12593489c086ac67d9e8b5befb6", "content_id": "7fc73f5fb63f1df0c1d4926ae4d611c3d2d67c22", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "PHP", "length_bytes": 155, "license_type": "no_license", "max_line_length": 89, "num_lines": 7, "path": "/ajax/PythonCall_play.php", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "<?php\n\n$input = $_POST['input'];\n$command = escapeshellcmd(\"python /var/www/html/neuronal_network/python/play.py $input\");\n$temp = passthru($command);\n\n?>\n" }, { "alpha_fraction": 0.5134692192077637, "alphanum_fraction": 0.5400437116622925, "avg_line_length": 24.677570343017578, "blob_id": "5aceca73b8351c1df467feb4352a2f1e2305c358", "content_id": "68398ea0b08eef46b58f2c494c7de18465a70aa2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 5497, "license_type": "no_license", "max_line_length": 183, "num_lines": 214, "path": "/js/script.js", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "$(document).ready(function() {\n $(\".chk\").click(function() {\n if ($(\".chk\").is(\":checked\")) {\n alert('In Entwicklung');\n \n clear_content();\n } else {\n weights()\n clear_content();\n }\n });\n});\n\nfunction delete_file(){\n $.ajax({\n type: 'POST',\n url: \"/neuronal_network/ajax/PythonCall_clear.php\",\n success: function(data) {\n alert(\"Gewichtungen gelösccht\");\n clear_content();\n }\n });\n}\n\nfunction weights(){\n $.ajax({\n type: 'POST',\n url: \"/neuronal_network/ajax/PythonCall_weights.php\",\n success: function(data) {\n alert(\"Gewichtungen errechnet\");\n // set_output(data);\n }\n });\n}\n\nfunction python() {\n\n set_player();\n\n if ($(\".chk\").is(\":checked\")) {\n\n $.ajax({\n type: 'POST',\n url: \"/neuronal_network/ajax/PythonCall_Train.php\",\n data: \"input=\" + $(\"#input\").val() + \"&output=\" + $(\"#output\").val(),\n success: function(data) {\n\n // set_output(data);\n }\n });\n\n } else {\n\n $.ajax({\n type: 'POST',\n url: \"/neuronal_network/ajax/PythonCall_play.php\",\n data: \"input=\" + ReadInput(),\n success: function(data) {\n $(\"#calc\").val(data);\n $(\"#input\").val(data);\n set_output(data);\n }\n });\n }\n}\n\n\nfunction set_player() {\n\n player = $(\"#player\").val();\n\n if (player == \"1\") {\n $(\"#player\").val(\"2\");\n } else if (player == \"2\") {\n $(\"#player\").val(\"1\");\n }\n}\n\nfunction ReadInput() {\n\n feld1 = document.getElementById(\"1\").value;\n feld2 = document.getElementById(\"2\").value;\n feld3 = document.getElementById(\"3\").value;\n feld4 = document.getElementById(\"4\").value;\n feld5 = document.getElementById(\"5\").value;\n feld6 = document.getElementById(\"6\").value;\n feld7 = document.getElementById(\"7\").value;\n feld8 = document.getElementById(\"8\").value;\n feld9 = document.getElementById(\"9\").value;\n\n var result = [feld1, feld2, feld3, feld4, feld5, feld6, feld7, feld8, feld9];\n return result;\n}\n\n\nfunction clear_content() {\n\n $(\"#input\").val(\"0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5\");\n $(\"#output\").val(\"\");\n $(\"#calc\").val(\"\");\n $(\"#zug\").val(\"0\");\n $(\"#player\").val(\"1\");\n\n var input = \"0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5\";\n set_output(input);\n\n}\n\n\nfunction set_output(val) {\n\n var output = val;\n output = output.replace(/\\s/g, \"\");\n var splits = output.split(\",\");\n\n setval_compute('0', '0', '1', splits[0]);\n setval_compute('0', '1', '2', splits[1]);\n setval_compute('0', '2', '3', splits[2]);\n setval_compute('1', '0', '4', splits[3]);\n setval_compute('1', '1', '5', splits[4]);\n setval_compute('1', '2', '6', splits[5]);\n setval_compute('2', '0', '7', splits[6]);\n setval_compute('2', '1', '8', splits[7]);\n setval_compute('2', '2', '9', splits[8]);\n}\n\n\nfunction get_zug() {\n return $(\"#zug\").val();\n}\n\nfunction count_Zug() {\n\n counter = $(\"#zug\").val();\n counter++;\n $(\"#zug\").val(counter);\n}\n\nfunction get_value() {\n var player = $(\"#player\").val();\n if (player == \"1\") {\n return \"1.0\";\n } else if (player == \"2\") {\n return \"0.0\";\n }\n\n}\n\n\n\nfunction setval_human(row, cell, feld, wert) {\n\n var wert = get_value();\n\n act_field = document.getElementById(feld).value;\n\n // training\n if ($(\".chk\").is(\":checked\")) {\n\n\n // Play\n if (act_field != '0.5') {\n\n } else if (wert == '0.0') {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<h2><i class=\"fa fa-circle-thin\"></i></h2> <input type = \"hidden\" id=\"' + feld + '\" value=\"' + wert + '\" >';\n } else if (wert == '1.0') {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<h2><i class=\"fa fa-times\"></i></h2> <input type = \"hidden\" id=\"' + feld + '\" value=\"' + wert + '\" >';\n } else {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<input type = \"hidden\" id=\"' + feld + '\" value=\"0.5\" >';\n }\n\n // aktueller output wird für den nächsten zug zum input\n if (get_zug() != 0) {\n $(\"#input\").val($(\"#output\").val());\n }\n\n // Output wird neu vom aktuellers Situation gelesen.\n $(\"#output\").val(ReadInput());\n count_Zug();\n\n // An Python senden\n python()\n\n } else {\n\n // Play\n if (act_field != '0.5') {\n\n } else if (wert == '0.0') {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<h2><i class=\"fa fa-circle-thin\"></i></h2> <input type = \"hidden\" id=\"' + feld + '\" value=\"' + wert + '\" >';\n } else if (wert == '1.0') {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<h2><i class=\"fa fa-times\"></i></h2> <input type = \"hidden\" id=\"' + feld + '\" value=\"' + wert + '\" >';\n } else {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<input type = \"hidden\" id=\"' + feld + '\" value=\"0.5\" >';\n }\n\n $(\"#output\").val(ReadInput());\n count_Zug();\n set_player();\n python();\n }\n\n}\n\nfunction setval_compute(row, cell, feld, wert) {\n if (wert == 0.0) {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<h2><i class=\"fa fa-circle-thin\"></i></h2> <input type = \"hidden\" id=\"' + feld + '\" value=\"' + wert + '\" >';\n } else if (wert == 1.0) {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<h2><i class=\"fa fa-times\"></i></h2> <input type = \"hidden\" id=\"' + feld + '\" value=\"' + wert + '\" >';\n } else {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<input type = \"hidden\" id=\"' + feld + '\" value=\"0.5\" >';\n }\n\n}" }, { "alpha_fraction": 0.6624277234077454, "alphanum_fraction": 0.6965317726135254, "avg_line_length": 38.1136360168457, "blob_id": "07a284ba6b6e8dbe52b1ea0343d3280705c273a2", "content_id": "2ff1480c22b763595a448a2754656bb3277a70a5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1730, "license_type": "no_license", "max_line_length": 144, "num_lines": 44, "path": "/python/SaveWeights.py", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "#!/usr/bin/python\n# -- coding: ascii --\n\n#Quelle: https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.pea0nt22f\nfrom numpy import exp, array, random, dot\nimport numpy as np\nimport sys, os\nfrom NeuralNetwork import NeuralNetwork\nfrom NeuralNetwork import NeuronLayer\n\nif __name__ == \"__main__\":\n\n #Seed the random number generator\n random.seed(1)\n\n # Create layer 1 (4 neurons, each with 3 inputs)\n layer1 = NeuronLayer(10, 9)\n\n # Create layer 2 (a single neuron with 4 inputs)\n layer2 = NeuronLayer(9, 10)\n\n # Parameter Uebergabe von PHP Script\n input_parameter = \"1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\"\n \n # Parameter konvertieren I\n input_int = input_parameter.split(',')\n\n # Parameter konvertieren II\n input = np.array(input_int, dtype=np.float32)\n\n # Combine the layers to create a neural network\n neural_network = NeuralNetwork(layer1, layer2, input)\n \n # Lade die Input und Output Arrays\n training_set_inputs = np.loadtxt('/var/www/html/neuronal_network/data/input_1.txt', delimiter=\",\")\n training_set_outputs = np.loadtxt('/var/www/html/neuronal_network/data/output_1.txt', delimiter=\",\")\n \n # Train the neural network using the training set.\n # Do it x times and make small adjustments each time.\n neural_network.train(training_set_inputs, training_set_outputs, 350000)\n\n # Erstelle die neuen Array mit den Gewichtungen\n np.savetxt('/var/www/html/neuronal_network/data/weights_layer1.txt', neural_network.get_weights('1'), delimiter=\",\")\n np.savetxt('/var/www/html/neuronal_network/data/weights_layer2.txt', neural_network.get_weights('2'), delimiter=\",\")\n \n " }, { "alpha_fraction": 0.6557527780532837, "alphanum_fraction": 0.6759486198425293, "avg_line_length": 41.98684310913086, "blob_id": "41cf432f1cc8cc437e669a381854cd76adfe10f4", "content_id": "71e91dd4b29836332f00c10cb5e1bf772c494783", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3268, "license_type": "no_license", "max_line_length": 144, "num_lines": 76, "path": "/python/NeuralNetwork.py", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "#!/usr/bin/python\n# -- coding: ascii --\n\n#Quelle: https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.pea0nt22f\nfrom numpy import exp, array, random, dot\nimport numpy as np\nimport sys, os\n\n\nclass NeuronLayer():\t\n\t\tdef __init__(self, number_of_neurons, number_of_inputs_per_neuron):\n \t\t\tself.synaptic_weights = 2 * random.random((number_of_inputs_per_neuron, number_of_neurons)) - 1\n\nclass NeuralNetwork():\n def __init__(self, layer1, layer2, input):\n\n self.input = input\n self.layer1 = layer1\n self.layer2 = layer2\n\n # The Sigmoid function, which describes an S shaped curve.\n # We pass the weighted sum of the inputs through this function to\n # normalise them between 0 and 1.\n def __sigmoid(self, x):\n return 1 / (1 + exp(-x))\n\n # The derivative of the Sigmoid function.\n # This is the gradient of the Sigmoid curve.\n # It indicates how confident we are about the existing weight.\n def __sigmoid_derivative(self, x):\n return x * (1 - x)\n\n\t\t\n # We train the neural network through a process of trial and error.\n # Adjusting the synaptic weights each time.\n def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):\n\t\t\t\n for iteration in xrange(number_of_training_iterations):\n # Pass the training set through our neural network\n output_from_layer_1, output_from_layer_2 = self.think(training_set_inputs)\n\n # Calculate the error for layer 2 (The difference between the desired output\n # and the predicted output).\n layer2_error = training_set_outputs - output_from_layer_2\n layer2_delta = layer2_error * self.__sigmoid_derivative(output_from_layer_2)\n\n # Calculate the error for layer 1 (By looking at the weights in layer 1,\n # we can determine by how much layer 1 contributed to the error in layer 2).\n layer1_error = layer2_delta.dot(self.layer2.synaptic_weights.T)\n layer1_delta = layer1_error * self.__sigmoid_derivative(output_from_layer_1)\n\n # Calculate how much to adjust the weights by\n layer1_adjustment = training_set_inputs.T.dot(layer1_delta)\n layer2_adjustment = output_from_layer_1.T.dot(layer2_delta)\n\n # Adjust the weights.\n self.layer1.synaptic_weights += layer1_adjustment\n self.layer2.synaptic_weights += layer2_adjustment\n\n\n # The neural network thinks.\n def think(self, inputs):\n\toutput_from_layer1 = self.__sigmoid(dot(inputs, self.layer1.synaptic_weights))\n\toutput_from_layer2 = self.__sigmoid(dot(output_from_layer1, self.layer2.synaptic_weights))\n\treturn output_from_layer1, output_from_layer2\n\n def set_weights(self):\n\tself.layer1.synaptic_weights = np.loadtxt('/var/www/html/neuronal_network/data/weights_layer1.txt', delimiter=\",\")\n\tself.layer2.synaptic_weights = np.loadtxt('/var/www/html/neuronal_network/data/weights_layer2.txt', delimiter=\",\")\n\t\t\n\t\t# The neural network prints its weights\n def get_weights(self, layer):\n \tif layer == '1':\n return self.layer1.synaptic_weights\n \telif layer == '2':\n\t return self.layer2.synaptic_weights\n\n" }, { "alpha_fraction": 0.6958299279212952, "alphanum_fraction": 0.7424366474151611, "avg_line_length": 39.766666412353516, "blob_id": "8b4008f4d1bba599f71c5f92e6a14cee695a5e6b", "content_id": "740c6b8df4ad2bb815dee68564be9f6f12d807c3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1231, "license_type": "no_license", "max_line_length": 169, "num_lines": 30, "path": "/README.md", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "# neuroal_network\n\nZiel: Erste Gehversuche mit dem Einsatz eines einfachen Neuronalen Netzwerks am Beispile Tic Tac Toe. \n\nLive Demo\n=========\nhttp://107.170.159.73/neuronal_network/web.php\n\nEinstellungen\n=============\n9 Input \| 9 Output \| 1 Hiddenschicht mit 10 Neuronen \| 3 ausgeprägte Games in Form von Trainingssätzen\n\nTodo (In Entwicklung)\n====\nEin Trainingsmodus mit welchem dem Netz eigene Trainingssätze mitgegeben werden kann. \n - Input.txt \| Output.txt und Gewichtungen.txt löschen.\n - Spieler spielt im Trainingsmodus für beide Spieler einen Zug und sendet diesen an das Netzwerk.\n - Jeder gespielte Zug soll durch das senden an das Netzwerk als Trainingssatz in 2 txt Dateien Input.txt und Output.txt angefügt werden.\n - Das Netzwerk soll im Anschluss alle Traininssätze laden und sich trainieren. Die errechneten Gewichtungen sollen in der Datei Gewichtungen.txt abgespecheicht werden. \n \n\nPython Testen\n=============\n$ python tic.py \"0.5,0,0,0,0,0,0,0,0\"\n\nSollte Fehlerfrei 1.0, 1.0, 0.0, 0.1, 0.0, 0.3, 1.0, 1.0, 1.0 zurück geben.\n\nQuelle Python Code \n==================\nhttps://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.pea0nt22f\n" }, { "alpha_fraction": 0.7080292105674744, "alphanum_fraction": 0.7080292105674744, "avg_line_length": 18.571428298950195, "blob_id": "14f2d3cd0e61d6bf8e405cd9e2b7988750411451", "content_id": "aa6c34ef440e0e6ac86a01c8256b989d1f07b738", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "PHP", "length_bytes": 137, "license_type": "no_license", "max_line_length": 98, "num_lines": 7, "path": "/ajax/PythonCall_weights.php", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "<?php\n\n\n$command = escapeshellcmd(\"/usr/bin/python /var/www/html/neuronal_network/python/SaveWeights.py\");\n$output = exec($command);\n\n?>\n" }, { "alpha_fraction": 0.644859790802002, "alphanum_fraction": 0.644859790802002, "avg_line_length": 24.625, "blob_id": "49333021f56206ea9d554f24b87eef65f68f1e78", "content_id": "c54360a1ae3697ec3436d8512fc9d212e2142461", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "PHP", "length_bytes": 214, "license_type": "no_license", "max_line_length": 113, "num_lines": 8, "path": "/ajax/PythonCall_Train.php", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "<?php\n\n$input = $_POST['input'];\n$output = $_POST['output'];\n$command = escapeshellcmd(\"/usr/bin/python /var/www/html/neuronal_network/python/SetTraining.py $input $output\");\n$output = exec($command);\n\n?>\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5831601023674011, "alphanum_fraction": 0.5914760828018188, "avg_line_length": 26.514286041259766, "blob_id": "9ac9a4eee5352d4d05ace747310d2f2407594367", "content_id": "0731741b778ed0ce0dc59e6fff2b22fd2d531f54", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 962, "license_type": "no_license", "max_line_length": 86, "num_lines": 35, "path": "/python/SetTraining.py", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "#!/usr/bin/python\n# -- coding: ascii --\n\nfrom numpy import exp, array, random, dot\nimport numpy as np\nimport sys, os\n\nif __name__ == \"__main__\":\n\n\n \n # # Parameter Uebergabe von PHP Script\n input_parameter = sys.argv[1]\n output_parameter = sys.argv[2]\n\n \n # # Parameter konvertieren I\n input_int = input_parameter.split(',')\n output_int = output_parameter.split(',')\n\n \n # # Parameter konvertieren II\n input = np.array(input_int, dtype=np.float32)\n output = np.array(output_int, dtype=np.float32)\n\n \n \n f_handleI = file('/var/www/html/neuronal_network/data/input_1.txt', 'a')\n np.savetxt(f_handleI, np.matrix(input), delimiter=\",\", fmt=\"%s\", newline='\\n')\n f_handleI.close()\n\n\n f_handleO = file('/var/www/html/neuronal_network/data/output_1.txt', 'a')\n np.savetxt(f_handleO, np.matrix(output), delimiter=\",\", fmt=\"%s\", newline='\\n')\n f_handleO.close()" }, { "alpha_fraction": 0.7190082669258118, "alphanum_fraction": 0.7190082669258118, "avg_line_length": 29.25, "blob_id": "22847516f90ad2fa7a6c9ad12ecdcd837e24b19b", "content_id": "ea4f74d5001f468292b19743e5732ec45e7c9df0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "PHP", "length_bytes": 121, "license_type": "no_license", "max_line_length": 83, "num_lines": 4, "path": "/ajax/PythonCall_clear.php", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "<?php\n$command = escapeshellcmd(\"python /var/www/html/neuronal_network/python/clear.py\");\n$temp = passthru($command);\n?>\n" }, { "alpha_fraction": 0.6517571806907654, "alphanum_fraction": 0.6757188439369202, "avg_line_length": 33.66666793823242, "blob_id": "54fd674db1d6ffa09227ba6fabc7b958c983715f", "content_id": "74fd54365df26c74652eecd70e0b8ac9df39bd61", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 626, "license_type": "no_license", "max_line_length": 144, "num_lines": 18, "path": "/python/clear.py", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "#!/usr/bin/python\n# -- coding: ascii --\n\n#Quelle: https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.pea0nt22f\nfrom numpy import exp, array, random, dot\nimport numpy as np\nimport sys, os\n\nif __name__ == \"__main__\":\n\n f = open('/var/www/html/neuronal_network/data/input_1.txt', 'w')\n f.close()\n f = open('/var/www/html/neuronal_network/data/output_1.txt', 'w')\n f.close()\n f = open('/var/www/html/neuronal_network/data/weights_layer1.txt', 'w')\n f.close()\n f = open('/var/www/html/neuronal_network/data/weights_layer2.txt', 'w')\n f.close()\n " } ]	10
fstrub95/writers	https://github.com/fstrub95/writers	474b9fa31fb4bce8c0352c9e4928a491a5dfc73b	19834487453f596c23d22f9cf5f5e7f60309fb2a	89dc5bd11ab772bd97eee0a41d67ebe0cd0b55fa	refs/heads/master	2020-03-23T18:39:50.114265	2018-07-07T00:08:32	2018-07-07T00:08:32	null	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.4369220733642578, "alphanum_fraction": 0.45475560426712036, "avg_line_length": 41.91304397583008, "blob_id": "328f7e5588d9d8c15d3e9eb92186d3b1f9530001", "content_id": "d911d8a775b65b7010887a1926fd20e00f5aecdd", "detected_licenses": [ "Apache-2.0" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3028, "license_type": "permissive", "max_line_length": 111, "num_lines": 69, "path": "/ang_writer/Methods/write_core_ang.py", "repo_name": "fstrub95/writers", "src_encoding": "UTF-8", "text": "import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport re\r\n\r\n\r\nclass AngWriter:\r\n def write_header(self, ang_in, ang_out):\r\n with open(ang_in, 'r') as input_ang, open(ang_out, 'w') as output_ang:\r\n for line in input_ang:\r\n if line.startswith(\"#\"):\r\n output_ang.write(line)\r\n\r\n def replace_phase(self, segment, ang_in, ang_out):\r\n seg = plt.imread(segment)\r\n seg_c = np.concatenate(seg, axis=0) # convert into a column array\r\n phase = np.copy(seg_c).astype(int)\r\n phase[np.where(seg_c == 255)] = 1\r\n phase[np.where(seg_c < 255)] = 2\r\n ang = pd.read_csv(ang_in, delim_whitespace=True, comment='#', names={'euler1', 'euler2', 'euler3', 'x',\r\n 'y', 'iq', 'ci', 'fit', 'phase',\r\n 'col9', 'col10', 'col11', 'col12',\r\n 'col13'})\r\n ang.iloc[ang_in[:, 6] < 0] = 0 # remove any negative confidence index\r\n ang.iloc[:, 7] = phase\r\n ang.to_csv(ang_out, index=False, header=False, sep='\\t', mode='a', float_format='%.5f')\r\n\r\n\r\nclass Phase:\r\n def __init__(self, number):\r\n self.number = number\r\n self.materialName = ''\r\n self.formula = ''\r\n self.info = ''\r\n self.symmetry = 0\r\n self.latticeConstants = [0] * 6 # 3 constants & 3 angles\r\n self.numberFamilies = []\r\n self.hklFamilies = []\r\n self.elasticConstants = []\r\n self.categories = []\r\n\r\n\r\nclass PhaseHeader:\r\n def _init_(self, filename_in, filename_out): # read and store info about input .ang file\r\n with open(filename_in) as file_in:\r\n for line in file_in:\r\n if line.startswith(\"#\"):\r\n tokens = re.split('\\s+', line.strip())\r\n if tokens[1] == 'Phase':\r\n self.phaseId = float(tokens[2])\r\n elif tokens[1] == 'MaterialName':\r\n self.materialName = str(tokens[2])\r\n elif tokens[1] == 'Formula':\r\n self.formula = str(tokens[2])\r\n # on s'en tape de 'Info', jamais rempli\r\n elif tokens[1] == 'Symmetry':\r\n self.symmetry = int(tokens[2])\r\n elif tokens[1] == 'LatticeConstants':\r\n values = [0] * 6\r\n values[0] = float(tokens[2])\r\n values[1] = float(tokens[3])\r\n values[2] = float(tokens[4])\r\n values[3] = float(tokens[5])\r\n values[4] = float(tokens[6])\r\n values[5] = float(tokens[7])\r\n # Code shit with phase properties using class phase\r\n\r\n with open(filename_out) as file_out:\r\n file_out.write()" }, { "alpha_fraction": 0.6764705777168274, "alphanum_fraction": 0.6764705777168274, "avg_line_length": 31.75, "blob_id": "6470e5f8d6f065662afa586f2334f2150b4fd1e6", "content_id": "f9591265137cc00716445b66356facc7cfe2a094", "detected_licenses": [ "Apache-2.0" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 272, "license_type": "permissive", "max_line_length": 89, "num_lines": 8, "path": "/ang_writer/Modify_ang.py", "repo_name": "fstrub95/writers", "src_encoding": "UTF-8", "text": "import os\r\nfrom Methods.write_core_ang import AngWriter\r\n\r\nang_dir = \"C:\\\\Users\\\\SteveJobs\\\\PycharmProjects\\\\ang_writer\\\\Data\\\\\"\r\nang_in = \"test.ang\"\r\nang_out = \"test_out.ang\"\r\n\r\nang_object = AngWriter((os.path.join(ang_dir, ang_in)), (os.path.join(ang_dir, ang_out)))\r\n\r\n" } ]	2
Ethan-mxc/xzday	https://github.com/Ethan-mxc/xzday	b188924752e86dfdda3927d7a4b070479596d382	6cd2fe61aa0d50745471ab25549c35c2fee3c9c7	216ea71ca5511c2e4eb7eb04cf6b707c104e5085	refs/heads/master	2020-04-02T10:36:37.502260	2019-07-28T02:08:49	2019-07-28T02:08:49	154,346,913	2	0	null	null	null	null	null	[ { "alpha_fraction": 0.6927223801612854, "alphanum_fraction": 0.7196765542030334, "avg_line_length": 19.66666603088379, "blob_id": "3922a11602f311922ebb403fd6542ca123b1227c", "content_id": "6e175d6c71f26fe40c9d0087c09d0a1d814f42e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 371, "license_type": "no_license", "max_line_length": 54, "num_lines": 18, "path": "/src/jiuye/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "'''\nCreated on 2018-1-4\n\n@author: corey\n'''\n#--coding:utf-8--\nimport xlrd\nfrom django.shortcuts import render\nfrom django.template.context_processors import request\nimport sys\ndef indexView(req):\n return render(req,'baseindex.html',locals())\n\ndef more(req):\n return render(req,'moreindex.html',locals())\n\ndef page_not_found(req):\n return render(req,'404.html')" }, { "alpha_fraction": 0.5879895687103271, "alphanum_fraction": 0.6052219271659851, "avg_line_length": 36.54901885986328, "blob_id": "88d6e565ebbb69edc8ed526ab9becd0efc9284b5", "content_id": "045833f75f1ed6036473b4099f4f3aaa8cebda58", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1915, "license_type": "no_license", "max_line_length": 132, "num_lines": 51, "path": "/src/forum/migrations/0003_auto_20180623_0910.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n# Generated by Django 1.11.2 on 2018-06-23 01:10\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('forum', '0002_auto_20180125_1210'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='application',\n name='receiver',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='appli_receiver', to='online.User'),\n ),\n migrations.AlterField(\n model_name='application',\n name='sender',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='appli_sender', to='online.User'),\n ),\n migrations.AlterField(\n model_name='comment',\n name='author',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='online.User'),\n ),\n migrations.AlterField(\n model_name='message',\n name='receiver',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='message_receiver', to='online.User'),\n ),\n migrations.AlterField(\n model_name='message',\n name='sender',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='message_sender', to='online.User'),\n ),\n migrations.AlterField(\n model_name='post',\n name='author',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='post_author', to='online.User'),\n ),\n migrations.AlterField(\n model_name='post',\n name='last_response',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='online.User'),\n ),\n ]\n" }, { "alpha_fraction": 0.511303186416626, "alphanum_fraction": 0.5166223645210266, "avg_line_length": 35.17073059082031, "blob_id": "f8d50ebbd232f7268a45e6645601935422548154", "content_id": "6e1f99bb9321e6849f1e75f4263d6bed065b2217", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1512, "license_type": "no_license", "max_line_length": 96, "num_lines": 41, "path": "/src/tools/iponline.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#--coding:utf-8--\nfrom tools.link import \nfrom online.models import \nfrom home.models import \nimport time\ndef pingall():\n computes = Compute.objects.all()\n for compute in computes:\n if(ping_ip(compute.ip)):\n Compute.objects.filter(ip = compute.ip).update(state = \"开机\")\n try:\n activecompute = Compute.objects.get(ip = compute.ip)\n except:\n print 'err'\n else:\n \n if (activecompute.activetime!=None):\n activetime = int(activecompute.activetime)+1\n Compute.objects.filter(ip = compute.ip).update(activetime = str(activetime))\n else:\n Compute.objects.filter(ip = compute.ip).update(activetime = '0')\n else:\n Compute.objects.filter(ip = compute.ip).update(state = \"关机\")\n try:\n idletcompute = Compute.objects.get(ip = compute.ip)\n except:\n print \"err\"\n else:\n if (idletcompute.idletime!=None):\n idletime = int(idletcompute.idletime)+1\n \n Compute.objects.filter(ip = compute.ip).update(idletime = str(idletime))\n else:\n Compute.objects.filter(ip = compute.ip).update(idletime = '0')\n return 1\ndef runpingall():\n while True:\n pingall()\n time.sleep(10) \nif __name__ == \"__main__\": \n runpingall()\n \n " }, { "alpha_fraction": 0.47608405351638794, "alphanum_fraction": 0.5109521746635437, "avg_line_length": 25.011627197265625, "blob_id": "50d2e2a658c8ff43b3187f47fa9f6f3ae798fb9c", "content_id": "112c7ddcc4810eb94d03252b91cd5640143beed2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2349, "license_type": "no_license", "max_line_length": 84, "num_lines": 86, "path": "/src/resume_tool/date_extractor.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# encoding:utf-8\nimport re\n\n# date_str1 2010.09\n#date_str1 = u\"[0-9 年/.]+\"\ndate_str1 = u\"[0-9]{4} [年/.-]+[0-9]{1,2}[月]\"\ndate_pattern1 = re.compile(date_str1)\n\n# 解决9/1990和09/1992这些case\ndate_str2 = u\"[0-9]{1,2}[月/.-]+19{1}[0-9]{2}[年]\|[0-9]{1,2}[月/.-]+20{1}[0-9]{2}[年]\"\ndate_pattern2 = re.compile(date_str2)\n\n# 将items（list）所有空元素，过滤掉\ndef drop_null(items):\n result = []\n for item in items:\n if len(item.strip()) == 0:\n continue\n result.append(item.strip())\n return result\n\n# 抽取日期信息,并将日期按照顺序排列存入list\n# str必须保证unicode编码\ndef date_extract(str):\n result_list = []\n date_list = date_pattern1.findall(str)\n for d in date_list:\n source = d\n d = d.replace(\"19\", \" \")\n d = d.replace(\"20\", \" \")\n items_tmp = d.split(\" \")\n items = drop_null(items_tmp)\n\n j = 0\n for i in range(0, len(items)):\n k = 0\n pre = ''\n m = j + 2\n if source[j] != items[i][k]:\n while (j < m and j < len(source)):\n pre += source[j]\n j = j + 1\n j = j + len(items[i])\n items[i] = pre + items[i]\n # print items[i]\n #str = str.replace(items[i], '') #注释\n result_list.append(items[i])\n\n result_list.extend(date_pattern2.findall(str))\n return result_list\n\ndef get_education_number_from_date(input_str):\n date_size = 0;\n size = len(date_extract(input_str))\n if size % 2 == 0:\n date_size = size / 2\n else:\n date_size = (size + 1) / 2\n return date_size\n\n\ndef process(input_file_path):\n items = []\n items.__sizeof__()\n for line in open(input_file_path, 'r'):\n try:\n line = line.strip().decode('utf-8') # 设置编码格式\n except:\n line = line.strip().decode('gb2312') \n print line\n # normalize_date(line)\n date_list = date_extract(line)\n for d in date_list:\n print d\n print 'education number: '\n print get_education_number_from_date(line)\n print '-------'\n\n\ndef main():\n print 'this is main'\n input_file_path = 'data/samples'\n result_dic = process(input_file_path)\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.685512363910675, "alphanum_fraction": 0.685512363910675, "avg_line_length": 34.5, "blob_id": "958d792ed7a85ee76493d7d215eed6bce3100afa", "content_id": "b51552f50cebe39caabc40b85b004c244c252212", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 283, "license_type": "no_license", "max_line_length": 73, "num_lines": 8, "path": "/src/resume/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom resume import views\napp_name = 'resume'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^uploadFile/$',views.upload_file,name = 'uploadFile'),\n url(r'^uploadPFile/$', views.upload_file_person, name='uploadPFile'),\n]" }, { "alpha_fraction": 0.6416666507720947, "alphanum_fraction": 0.6416666507720947, "avg_line_length": 29.125, "blob_id": "420a195c0a714fdab971144fb206c33363ea6e32", "content_id": "d7f15a1d43a8bc54dee772747d6fb4481b9ca050", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 240, "license_type": "no_license", "max_line_length": 48, "num_lines": 8, "path": "/src/question/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom question import views\napp_name = 'question'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^info/$',views.info,name = 'info'),\n url(r'^list/$',views.list,name = 'list'),\n]" }, { "alpha_fraction": 0.5555555820465088, "alphanum_fraction": 0.5681818127632141, "avg_line_length": 21, "blob_id": "62bb137aab12f28f2a54b4e923690f8c9d10d2d1", "content_id": "bdcd87a5b7fc367362acf7be55d2bbd582df550b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 432, "license_type": "no_license", "max_line_length": 41, "num_lines": 18, "path": "/src/bgtool/docprint.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "GB18030", "text": "# -- coding: cp936 --\n__author__ = 'corey'\n# -- coding:utf-8 --\nimport docx\n\ndef printdoc():\n \n document = docx.Document(\"./1.docx\")\n #打印每个段落的内容\n for paragraph in document.paragraphs:\n print paragraph.text\n #打印每个表格的内容\n for table in document.tables:\n for row in table.rows:\n for cell in row.cells:\n print cell.text\n \nprintdoc()\n" }, { "alpha_fraction": 0.6147859692573547, "alphanum_fraction": 0.6342412233352661, "avg_line_length": 35.85714340209961, "blob_id": "269ededa130c173d34c3b7fa18e8a2e4948033e9", "content_id": "a1d109f2ec46450c97d07c4e02e90b4bbd8ea548", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 265, "license_type": "no_license", "max_line_length": 74, "num_lines": 7, "path": "/src/tools/mail.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#--coding:utf-8--\nfrom django.core.mail import send_mail\ndef send_mymail(subject,body,send_to):\n print send_mail(subject,body, '[email protected]', send_to, fail_silently=False)\n return 1\nif __name__ == \"__main__\": \n send_mymail('我是谁', '他', ['[email protected]'])" }, { "alpha_fraction": 0.536644697189331, "alphanum_fraction": 0.5428968667984009, "avg_line_length": 35.40506362915039, "blob_id": "d5594e0c0874d41f73bf2d39c9840737ad56bfd1", "content_id": "b1c34e6ac9ea5f326e4828cb2cc578b115eb9b98", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2891, "license_type": "no_license", "max_line_length": 143, "num_lines": 79, "path": "/src/intr/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#--coding:utf-8--\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import \nfrom online.models import User\nimport sys \nreload(sys) \nsys.setdefaultencoding('utf8') \n\n\ndef isflag(req):\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(condition) \n user_info['job'] = r.job\n user_info['edu'] = r.edu\n user_info['comp'] = r.comp\n user_info['myjob'] = r.myjob\n user_info['mylocal'] = r.mylocal\n user_info['mycomp'] = r.mycomp\n user_info['myintr'] = r.myintr\n user_info['mymoney'] = r.mymoney\n if cmp(r.comp,\"\")!=0 or cmp(r.edu,\"\")!=0 or cmp(r.job,\"\")!=0:\n flag = 0\n \n else:\n flag = 1\n \n if cmp(r.mycomp,\"\")!=0 or cmp(r.mymoney,\"\")!=0 or cmp(r.mylocal,\"\")!=0 or cmp(r.myintr,\"\")!=0 or cmp(r.myjob,\"\")!=0:\n xzflag = 0\n else:\n xzflag = 1 \n return flag,xzflag,user_info\n \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.session['islogin'] == True:\n flag,xzflag,user_info=isflag(req)\n return render(req,\"intr_index.html\", locals())\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n \ndef money(req):\n if req.method == 'POST':\n mycomp = req.POST['mycomp']\n myjob = req.POST['myjob']\n mylocal = req.POST['mylocal']\n mymoney = req.POST['mymoney']\n myintr = req.POST['myintr']\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(condition)\n beans = r.beans\n beans +=10\n User.objects.filter(name=user_info['name']).update(beans=beans,myjob=myjob,mycomp=mycomp,mylocal=mylocal,mymoney=mymoney,myintr=myintr)\n flag,xzflag,user_info=isflag(req)\n return render(req,\"intr_index.html\", locals()) \n \ndef info(req):\n if req.method == 'POST':\n comp = req.POST['comp']\n job = req.POST['job']\n edu = req.POST['edu']\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(*condition)\n beans = r.beans\n beans +=10\n User.objects.filter(name=user_info['name']).update(beans=beans,job=job,comp=comp,edu=edu)\n flag,xzflag,user_info=isflag(req)\n return render(req,\"intr_index.html\", locals()) \n\n" }, { "alpha_fraction": 0.4760802388191223, "alphanum_fraction": 0.4934413433074951, "avg_line_length": 37.6716423034668, "blob_id": "978d16967dbcd8dd2157de8588dafd8ba60afacc", "content_id": "5998b023ca51b95374d4c8b75384141ba4173eb9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2704, "license_type": "no_license", "max_line_length": 114, "num_lines": 67, "path": "/src/company/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#--coding:utf-8--\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import \nfrom online.models import User\nimport json\nimport sys \nreload(sys) \nsys.setdefaultencoding('utf8') \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.session['islogin'] == True:\n return render(req,\"com_index.html\", locals())\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.method == 'POST':\n msg = \"模式错误\"\n return render(req,\"msg.html\", locals()) \n \n \ndef info(req):\n if req.method == 'POST' or req.method =='GET':\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(*condition)\n if r.beans<1:\n msg = '用户豆不够，请充值'\n return render(req,'msg.html', locals())\n else:\n try:\n if req.method == 'POST':\n text=req.POST.get(\"comtext\",'')\n else:\n text = req.GET.get('comtext')\n my_db = MynewcoderDB()\n sqlstr = \"SELECT from company where 标题 like '%\"+text+\"%'\"\n infos = my_db.getInfo(sqlstr)\n compintr = infos[0]\n infos = infos[0]\n info = infos[1]\n jstemp = ['百度', '腾讯', '阿里巴巴']\n jsdtemp = [[84, 66, 1000, '百度', '百度'], [88, 60, 1000, '腾讯', '腾讯'], [86, 64, 1000, '阿里巴巴', '阿里巴巴']]\n try:\n jsname = json.dumps(jstemp)\n data = [float(compintr[8]) * 20, float(compintr[9]) * 20, 1000, compintr[1], compintr[1]]\n if not compintr[1] in jstemp:\n jsdtemp.append(data)\n jstemp.append(compintr[1])\n jsdata = json.dumps(jsdtemp)\n except:\n pass\n beans = r.beans - 0\n User.objects.filter(name=user_info['name']).update(beans=beans)\n req.session['beans'] = beans\n my_db.close()\n return render(req,\"com_index.html\", locals()) \n except Exception,e:\n msg = '没有该公司，请重新查询'\n return render(req,\"msg.html\", locals())\n\n" }, { "alpha_fraction": 0.48055499792099, "alphanum_fraction": 0.4842681288719177, "avg_line_length": 31.044025421142578, "blob_id": "14b353fd385ce1d87aa50f9c2f799aa0ca05fdad", "content_id": "b00f16eff25ce674356ebb165385ba8736769598", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "HTML", "length_bytes": 5291, "license_type": "no_license", "max_line_length": 243, "num_lines": 159, "path": "/src/forum/templates/post_detail.html", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "{% extends \"./base.html\" %}\n\n<link rel=\"stylesheet\" type=\"text/css\" href=\"/static/css/post_detail.css\"> \t\t\t\n\n\n{% block body %}\n\n<!-- zoopda navigation start -->\n\n<!-- End -->\n\n<div id=\"all_post\">\n\t<div class=\"post\"> \n\t\t<div class=\"avatar\">\n\t\t<img src=\"{{ post.author.avatar}}\" border=\"0\"><br>\n 用户名：{{ post.author.username }}<br>\n\t\t积分：{{ post.author.levels }}<br>\n\t\t{% if user.username %}\n {% ifnotequal post.author.username user.username %}\n {%if not post.author\|checkfriend:user %}\n\t\t\t\t <a href=\"{% url 'make_friend' user.username post.author.username %}\">加好友</a>\n\t\t {% else %}\n\t\t\t\t <a href=\"{% url 'send_message' post.author.id %}\">发消息</a>\n\t\t {% endif %}\n\t\t {% endifnotequal %}\n\t\t{% endif %}\n\t\t</div>\n\t\t<div class=\"content\">\n\t\t\t<font size=\"8\" face=\"arial\" color=\"red\">标题：{{ post.title }}</font>\n\t\t\t<br>{% autoescape off %}\n\t\t\t\t\t\t{{post.content}}\n\t\t\t\t\t{% endautoescape %}\n\t\t</div>\n\t</div>\n\t<!-- -------------------------评论------------------------- start -->\n\t<div class=\"well\">\n <div class=\"vmaig-comment\">\n <div class=\"vmaig-comment-tx\">\n {% if user.img%}\n <img src=\"{{user.img}}\" width=\"40\"></img>\n {%else%}\n <img src=\"http://vmaig.qiniudn.com/image/tx/tx-default.jpg\" width=\"40\"></img>\n {%endif%}\n </div>\n <div class=\"vmaig-comment-edit clearfix\">\n <form id=\"vmaig-comment-form\" method=\"post\" role=\"form\">\n {% csrf_token %}\n <textarea id=\"comment2\" name=\"comment\" class=\"form-control\" rows=\"4\" placeholder=\"请输入评论限200字!\"></textarea>\n <button type=\"submit\" class=\"btn btn-vmaig-comments pull-right\">提交</button>\n </form>\n </div>\n <ul>\n {% for comment in comment_list%}\n <li>\n\t <div class=\"comment\">\n <div class=\"avatar_comment\">\n {% if comment.author.avatar %}\n \t<img src={{comment.author.avatar}} width=\"40\"></img><br>\n {%else%}\n \t<img src=\"http://vmaig.qiniudn.com/image/tx/tx-default.jpg\" width=\"40\"></img><br>\n {%endif%}\n \t用户名：{{ comment.author.username }}<br>\n\t\t\t积分：{{ comment.author.levels }}<br>\n\t\t\t{% if user.username %}\n\t\t\t {% ifnotequal comment.author.username user.username %}\n\t\t \t{%if not comment.author\|checkfriend:user %}\n\t\t\t\t <a href=\"{% url 'make_friend' user.username comment.author.username %}\">加好友</a>\n {% else %}\n\t\t\t\t <a href=\"{% url 'send_message' comment.author.id %}\">发消息</a>\n\t\t {% endif %}\n\t\t\t {% endifnotequal %}\n\t\t\t{% endif %}\n </div>\n <div class=\"content_comment\">\n <p>{% if comment.comment_parent %}回复{{comment.comment_parent.author}} 发表于{{comment.comment_parent.created_at\|date:\"Y-m-d H:i:s\"}}的内容《{{comment.comment_parent.content}}》; {% endif %}{{comment.created_at\|date:\"Y-m-d H:i:s\" }}</p>\n <p>\n 评论：\n {% autoescape on%}\n {{ comment.content }}\n {% endautoescape %}\n </p>\n \n </div>\n\t <div class=\"floor\">\n\t\t{% ifequal forloop.counter 1%} \n\t\t沙发\n\t\t{% else %}\n\t\t\t{% ifequal forloop.counter 2%} \n\t\t\t\t板凳\n\t\t\t{% else %}\n\t\t\t\t{{ forloop.counter}}楼\n\t\t\t{% endifequal %}\n\t\t{% endifequal %}\n\t\t<a href=\"javascript:showDivFun({{comment.pk}})\">评论</a>\n\t </div>\n </div>\n </li>\n {% endfor%}\n </ul>\n </div>\n</div>\n\n<script src=\"/static/js/jquery.min.js\" language=\"javascript\" type=\"text/javascript\"></script>\n<script language=\"javascript\" type=\"text/javascript\">\n//弹出调用的方法\ncommentid=0;\nfunction showDivFun(comment_id){\n {% if not user.is_authenticated %}\n alert(\"请登录后评论！\")\n location.reload();\n {% endif %}\n commentid=comment_id;\n document.getElementById('popDiv').style.display='block';\n \n}\n//关闭事件\nfunction closeDivFun(){\n \n document.getElementById('popDiv').style.display='none';\n $.ajax({\n type:\"POST\",\n url:\"/makecomment/\",\n data:{\"comment\":$(\"#comment\").val(),\"comment_id\":commentid,\"post_id\":{{post.pk}},},\n //beforeSend:function(xhr){\n //xhr.setRequestHeader(\"X-CSRFToken\", $.cookie('csrftoken')); \n //},\n success:function(data,textStatus){\n\t\t\n location.reload();\n }\n\n });\n \n \n}\n \n $('#vmaig-comment-form').submit(function(){\n\t{% if not user.is_authenticated %}\n alert(\"请登录后评论！\")\n return false;\n {% endif %}\n $.ajax({\n type:\"POST\",\n url:\"{% url 'make_comment'%}\",\n data:{\"comment\":$(\"#comment2\").val(),\"post_id\":{{post.pk}}},\n success:function(data,textStatus){\n location.reload();\n }\n \n });\n return false;\n }); \n</script>\n <!-- -------------------------评论------------------------- end -->\n\t\n</div>\n\n\n{% endblock %} \n \n \n" }, { "alpha_fraction": 0.6269841194152832, "alphanum_fraction": 0.6269841194152832, "avg_line_length": 49.46666717529297, "blob_id": "f8527cf49bcd8f15f00fbefa18216f765f52c902", "content_id": "98a9d40843594c60e3df2a56279eb05c3cb7e94f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 756, "license_type": "no_license", "max_line_length": 88, "num_lines": 15, "path": "/src/forum/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom forum.views import \nfrom forum import \napp_name = 'forum'\nurlpatterns = [\n url(r'index',index.as_view(),name='index'),\n url(r'^columns/$',views.columnall,name='column_all'),\n url(r'^column/(?P<column_pk>\\d+)/$',views.columndetail,name='column_detail'),\n url(r'^postdetail/(?P<post_pk>\\d+)/$', views.postdetail, name='post_detail'), \n url(r'^postlist/$', UserPostView.as_view(), name='user_post'),\n url(r'^post_create/$', PostCreate.as_view(), name='post_create'), \n url(r'^post_update/(?P<pk>\\d+)/$', PostUpdate.as_view(), name='post_update'), \n url(r'^post_delete/(?P<pk>\\d+)/$', PostDelete.as_view(), name='post_delete'), \n url(r'^search/$', SearchView.as_view(), name='search'),\n]" }, { "alpha_fraction": 0.5634675025939941, "alphanum_fraction": 0.5789473652839661, "avg_line_length": 25.387754440307617, "blob_id": "9d13287f8f84472922f2e318c21a96e67b37faaa", "content_id": "189281a3bacc0c2fe4c9a670f6f48a5b2b0e2fa2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1426, "license_type": "no_license", "max_line_length": 70, "num_lines": 49, "path": "/src/resume_tool/gwread.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# encoding:utf-8\nimport sys\nfrom codeinit import \nimport jieba\nimport xlrd\nreload(sys)\nsys.setdefaultencoding('utf8')\ninput_encode = 'utf-8'\n# 构建专业知识库\n\n# 抽取技能信息,并将日期按照顺序排列存入list\ndef word_extract(str):\n wordcut = WordCut(str)\n word_dict = {}\n for word, flag in wordcut.cutWords():\n if flag == \"n\" or flag == \"vn\":\n if word not in word_dict:\n word_dict[word] = 1\n else:\n word_dict[word] += 1\n if flag == \"eng\":\n if word not in word_dict:\n word_dict[word] = 10\n else:\n word_dict[word] += 10\n\n import operator\n word_dict = sorted(word_dict.items(), key=operator.itemgetter(1))\n for ktemp,vtemp in word_dict:\n print ktemp,vtemp\n\ndef readexcel(keyword,filename = \"lagou.xls\"):\n book = xlrd.open_workbook(filename)#得到Excel文件的book对象，实例化对象\n sheet0 = book.sheet_by_index(0) # 通过sheet索引获得sheet对象\n nrows = sheet0.nrows # 获取行总数\n ncols = sheet0.ncols #获取列总数\n words = \"\"\n for i in range(0,nrows):\n print i\n row_data = sheet0.row_values(i) # 获得第1行的数据列表\n if row_data[0] == keyword:\n words = words+row_data[11]\n word_extract(words)\n\n\ndef run():\n readexcel(\"测试\")\nrun()" }, { "alpha_fraction": 0.672535240650177, "alphanum_fraction": 0.672535240650177, "avg_line_length": 42.769229888916016, "blob_id": "55df3f384795f58995b5b2a052714dde07be1ba3", "content_id": "850ef22d43839229187eac5c7707f449917941fc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 568, "license_type": "no_license", "max_line_length": 72, "num_lines": 13, "path": "/src/online/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom online import views\napp_name = 'online'\nurlpatterns = [\n url(r'^login/$',views.login,name = 'login'),\n url(r'^forget/$',views.forget,name = 'forget'),\n url(r'^register/$',views.register,name = 'register'),\n url(r'^changepasswd/$',views.changepasswd,name = 'changepasswd'),\n url(r'^gotologin/$', views.gotologin, name='gotologin'),\n url(r'^logout', views.logout,name = 'logout'),\n url(r'^getpasswd',views.getpasswd,name = 'getpasswd'),\n url(r'^changehead_image',views.changehead_image,'changehead_image'),\n]" }, { "alpha_fraction": 0.5486012101173401, "alphanum_fraction": 0.5576102137565613, "avg_line_length": 23.811763763427734, "blob_id": "34c3b2cdba2abd8c5afeb00ef6befe1ba7576af4", "content_id": "50047da3c8ecb4324c74a2d95dc86cc429d7ca90", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2247, "license_type": "no_license", "max_line_length": 83, "num_lines": 85, "path": "/src/resume_tool/project_extractor.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# encoding:utf-8\nimport re\nimport sys\nfrom tools import \nreload(sys)\nsys.setdefaultencoding('utf8')\n\ninput_encode = 'utf-8'\n# 构建专业知识库\nmajor_set = []\nscore = {}\n\ndic = \"\"\nif cmp(get_os(),\"n\")==0:\n dic = sys.path[0]+\"\\\\resume_tool\\\\project_dic\"\nelse:\n dic = sys.path[0]+\"/resume_tool/project_dic\"\n \nfor v in open(dic, 'r'):\n vs = v.split()\n major_set.append(vs[0])\n score[vs[0]] = vs[1]\n \n\n# 将items（list）所有“!!!”元素，过滤掉\ndef drop_null(items):\n result = []\n for item in items:\n if item.strip() == \"!!!\":\n continue\n result.append(item.strip())\n return result\n\n\n# 抽取专业信息,并将日期按照顺序排列存入list\ndef project_extract(str):\n result_list = []\n score_list = []\n for major in major_set:\n if str.__contains__(major):\n result_list.append(major)\n # 歧义消解水利水利水电工程归并为水利水电工程\n result_list = list(set(result_list))\n for i in range(0, len(result_list)):\n for j in range(0, len(result_list)):\n if i == j:\n continue\n if result_list[i].__contains__(result_list[j]):\n result_list[j] = '!!!'\n result_list = drop_null(result_list)\n\n # 歧序消解\n if len(result_list) > 1:\n result_dic = {}\n for item in result_list:\n index = str.find(item)\n result_dic[item] = index\n result_list = sorted(result_dic.items(), key=lambda d: d[1], reverse=False)\n result_list = [v[0] for v in result_list]\n for resultemp in result_list:\n score_list.append(score[resultemp])\n # print type(result_list)\n return result_list,score_list\n\n\ndef process(input_file_path):\n for line in open(input_file_path, 'r'):\n try:\n line = line.strip().decode('utf-8') # 设置编码格式\n except:\n line = line.strip().decode('gb2312') \n school_list,score_list = project_extract(line)\n for d in school_list:\n print d,score[d]\n #print '-------'\n\n\ndef main():\n print 'this is main'\n input_file_path = 'lcy.txt'\n result_dic = process(input_file_path)\n\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.5954500436782837, "alphanum_fraction": 0.5998021960258484, "avg_line_length": 37.42748260498047, "blob_id": "6ad9702dba24f71dfdbd417b8a8b2a1ddfcc12e2", "content_id": "422a7993201f23f9ea8a6ee6874bc9c5551b8405", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5315, "license_type": "no_license", "max_line_length": 121, "num_lines": 131, "path": "/src/forum/models.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#coding:utf-8\nfrom django.db import models\nfrom django.conf import settings\nfrom django.contrib.auth.models import AbstractUser\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.db.models import signals\nfrom online.models import \nimport datetime\nfrom django.core.urlresolvers import reverse\n# Create your models here.\n\nclass Nav(models.Model):\n name = models.CharField(max_length=40,verbose_name=u'导航条')\n url = models.CharField(max_length=200,blank=True,null=True,verbose_name=u'指向地址')\n create_time = models.DateTimeField(u'创建时间',default=datetime.datetime.now)\n class Meta:\n db_table = 'nav'\n verbose_name_plural = verbose_name = u\"导航条\"\n ordering = ['-create_time']\n\n def __unicode__(self):\n return self.name \n\nclass Column(models.Model): #板块\n name = models.CharField(max_length=30)\n description = models.TextField()\n img = models.CharField(max_length=200,default='/static/tx/default.jpg',verbose_name=u'图标')\n post_number = models.IntegerField(default=0) #主题数\n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n\n class Meta:\n db_table = 'column'\n ordering = ['-post_number']\n verbose_name_plural = u'板块'\n \n def __unicode__(self):\n return self.name\n\n def get_absolute_url(self):\n return \"../../forum\"+reverse('column_detail',urlconf = \"forum.urls\",kwargs={'column_pk': self.pk })\n \nclass PostType(models.Model): #文章类型\n type_name = models.CharField(max_length=30)\n description = models.TextField()\n created_at = models.DateTimeField(default=datetime.datetime.now)\n class Meta:\n db_table = 'posttype'\n verbose_name_plural = u'主题类型'\n def __unicode__(self):\n return self.type_name\n\nclass Post(models.Model): #文章\n title = models.CharField(max_length=30)\n author = models.ForeignKey(User,related_name='post_author') #作者\n column = models.ForeignKey(Column) #所属板块\n type_name = models.ForeignKey(PostType) #文章类型\n content = models.TextField()\n \n view_times = models.IntegerField(default=0) #浏览次数\n responce_times = models.IntegerField(default=0) #回复次数\n last_response = models.ForeignKey(User) #最后回复者\n \n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n \n \n class Meta:\n db_table = 'post'\n ordering = ['-created_at']\n verbose_name_plural = u'主题'\n \n def __unicode__(self):\n return self.title\n \n def description(self):\n return u'%s 发表了主题《%s》' % (self.author, self.title)\n \n def get_absolute_url(self):\n return \"../../forum\"+reverse('post_detail',urlconf = \"forum.urls\",kwargs={'post_pk': self.pk })\n \n \nclass Comment(models.Model): #评论 \n post = models.ForeignKey(Post) \n author = models.ForeignKey(User) \n comment_parent = models.ForeignKey('self', blank=True, null=True,related_name='childcomment') \n content = models.TextField()\n \n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n \n class Meta:\n db_table = 'comment'\n ordering = ['created_at']\n verbose_name_plural = u'评论'\n \n def __unicode__(self):\n return self.content\n\n def description(self):\n return u'%s 回复了您的帖子(%s) R:《%s》' % (self.author,self.post, self.content)\n \n def get_absolute_url(self):\n return \"../../forum\"+reverse('post_detail',urlconf = \"forum.urls\",kwargs= { 'post_pk': self.post.pk })\n \nclass Message(models.Model): #好友消息\n sender = models.ForeignKey(User,related_name='message_sender') #发送者\n receiver = models.ForeignKey(User,related_name='message_receiver') #接收者\n content = models.TextField()\n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n \n def description(self):\n return u'%s 给你发送了消息《%s》' % (self.sender, self.content)\n\n class Meta:\n db_table = 'message'\n verbose_name_plural = u'消息'\n \nclass Application(models.Model): #好友申请\n sender = models.ForeignKey(User,related_name='appli_sender') #发送者\n receiver = models.ForeignKey(User,related_name='appli_receiver') #接收者\n status = models.IntegerField(default=0) #申请状态 0:未查看 1:同意 2:不同意\n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n def description(self):\n return u'%s 申请加好友' % self.sender \n\n class Meta:\n db_table = 'application'\n verbose_name_plural = u'好友申请'\n \n\n\n \n \n\n\n\n\n" }, { "alpha_fraction": 0.5009669661521912, "alphanum_fraction": 0.5196619033813477, "avg_line_length": 41.694190979003906, "blob_id": "0eedbbb347c02eaa71cb4ff8209f4e0f3520e16a", "content_id": "ec36f2ba1358a831292b9c4b630933df2360e3bb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 14363, "license_type": "no_license", "max_line_length": 269, "num_lines": 327, "path": "/src/vip/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#--coding:utf-8--\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import \nfrom online.models import User\nimport json\nimport sys\nimport time\nimport re\nfrom django.views.decorators.cache import cache_page\nreload(sys)\nsys.setdefaultencoding('utf8')\n\n\n#确定身份函数\ndef comfirm(req):\n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n user_role = user_info['role']\n if user_role != '管理员':\n msg = '您不是管理员'\n return 0\n else:\n return 1\n\ndef index(req):\n if comfirm(req):\n my_db = MynewcoderDB()\n sql = \"select from vip\"\n infos = my_db.getInfo(sql)\n results = []\n for infos1_row in infos:\n results.append({'id':infos1_row[0],'vip':infos1_row[2]})\n sql_share = \"select * from share\"\n infos_share = my_db.getInfo(sql_share)\n resultsshare = []\n for infos_row in infos_share:\n resultsshare.append({'id':infos_row[0],'old':infos_row[1],'new':infos_row[2],'start_time':infos_row[3],'end_time':infos_row[4],'dingdanhao':infos_row[5],'invitation':infos_row[6],'change_time':infos_row[7]})\n my_db.close()\n return render(req,'index.html',locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\n\ndef vip_edit_action(req):\n if comfirm(req):#确认身份\n res_id = req.POST.get('res_id','0')\n res_vipid = req.POST.get('res_vipid','999999')\n res_mail = req.POST.get('res_mail','0')\n res_create_time = req.POST.get('res_create_time','2010-1-10')\n res_change_time = time.strftime('%Y-%m-%d %X', time.localtime() )\n res_deadline = req.POST.get('res_deadline','2020-1-1')\n my_db = MynewcoderDB()\n if res_id == '0':\n sql2 = \"insert into vip (vipid,vip,create_time,change_time,deadline)values('\" + res_vipid + \"','\" + res_mail + \"','\" + res_create_time + \"','\" + res_change_time + \"','\" + res_deadline + \"')\"\n infos2 = my_db.execute(sql2)\n my_db.commit()\n #已经存在的会员查询\n sql = \"select * from vip\"\n infos = my_db.getInfo(sql)\n results = []\n for infos1_row in infos:\n results.append({'id': infos1_row[0], 'vip': infos1_row[2]})\n #接下去是订单表查询\n sql_share = \"select * from share\"\n infos_share = my_db.getInfo(sql_share)\n resultsshare = []\n for infos_row in infos_share:\n resultsshare.append(\n {'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n my_db.close()\n return render(req, 'index.html', locals())\n\n sql2 = \"update vip set vipid='\"+res_vipid+\"' ,vip='\"+res_mail+\"', create_time ='\"+res_create_time+\"',change_time= '\"+res_change_time+\"',deadline='\"+res_deadline+\"' where id= '\"+res_id+\"' \"\n infos2 = my_db.execute(sql2)\n my_db.commit()\n sql4 = \"select * from vip where id =\"+res_id+\" \"\n infos4 = my_db.getInfo(sql4)\n results = []\n for infos1_row in infos4:\n results.append(\n {'id': infos1_row[0], 'vipid': infos1_row[1], 'vip': infos1_row[2], 'create_time': infos1_row[3],\n 'change_time': infos1_row[4], 'deadline': infos1_row[5]})\n return render(req, 'member_edit.html', locals())\n my_db.close()\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef order_edit_action(req):\n if comfirm(req):#确认身份\n res_id = req.POST.get('id','0')\n res_old = req.POST.get('old','999999')\n res_new = req.POST.get('new','0')\n res_start_time = req.POST.get('start_time','2010-1-10')\n res_end_time = req.POST.get('end_time','2010-1-10')\n res_dingdanhao = req.POST.get('dingdanhao','2020-1-1')\n res_invitation = req.POST.get('invitation', '2020-1-1')\n res_change_time = time.strftime('%Y-%m-%d %X', time.localtime() )\n my_db = MynewcoderDB()\n if res_id == '0':\n sql2 = \"insert into share (old,new,start_time,end_time,dingdanhao,invitation,change_time)values('\" + res_old + \"','\" + res_new + \"','\" + res_start_time + \"','\" + res_end_time + \"','\" + res_dingdanhao + \"','\" + res_invitation + \"','\" + res_change_time + \"')\"\n infos2 = my_db.execute(sql2)\n my_db.commit()\n #已经存在的会员查询\n sql = \"select * from share\"\n infos = my_db.getInfo(sql)\n resultsshare = []\n for infos1_row in infos:\n resultsshare.append({'id': infos1_row[0], 'dingdanhao': infos1_row[5]})\n #接下去是订单表查询\n sql_share = \"select * from vip\"\n infos_share = my_db.getInfo(sql_share)\n results = []\n for infos_row in infos_share:\n results.append({'id': infos_row[0], 'vipid': infos_row[1], 'vip': infos_row[2], 'create_time': infos_row[3],'change_time': infos_row[4], 'deadline': infos_row[5]})\n my_db.close()\n return render(req, 'index.html', locals())\n\n sql3 = \"update share set old='\"+res_old+\"', new ='\"+res_new+\"',start_time= '\"+res_start_time+\"',end_time='\"+res_end_time+\"',dingdanhao='\"+res_dingdanhao+\"',invitation='\"+res_invitation+\"',change_time='\"+res_change_time+\"' where id= '\"+res_id+\"' \"\n infos3 = my_db.execute(sql3)\n my_db.commit()\n sql4 = \"select * from share where id =\"+res_id+\" \"\n infos4 = my_db.getInfo(sql4)\n results = []\n for infos_row in infos4:\n results.append(\n {'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n return render(req, 'share_edit.html', locals())\n my_db.close()\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef show(req,res_id):\n if comfirm(req):\n try:\n my_db = MynewcoderDB()\n sql = \"select * from vip where id =\"+res_id+\" \"\n infos = my_db.getInfo(sql)\n results = []\n for infos_row in infos:\n results.append({'id': infos_row[0], 'vipid': infos_row[1], 'vip': infos_row[2], 'create_time': infos_row[3],\n 'change_time': infos_row[4], 'deadline': infos_row[5]})\n my_db.close()\n return render(req,'member_edit.html',locals())\n except Exception as e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef order_show(req,res_id):\n if comfirm(req):\n try:\n my_db = MynewcoderDB()\n sql = \"select * from share where id =\"+res_id+\" \"\n infos = my_db.getInfo(sql)\n results = []\n for infos_row in infos:\n results.append({'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n my_db.close()\n return render(req,'share_edit.html',locals())\n except Exception as e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef search(req):\n if comfirm(req):\n try:\n text = req.POST['vip']\n my_db = MynewcoderDB()\n sql1 = \"select * from vip where vip LIKE '%\"+text+\"%'\"\n infos1 = my_db.getInfo(sql1)\n results = []\n for infos1_row in infos1:\n results.append({'id':infos1_row[0],'vipid':infos1_row[1],'vip':infos1_row[2],'create_time':infos1_row[3],'change_time':infos1_row[4],'deadline':infos1_row[5]})\n my_db.close()\n return render(req,'member_edit.html',locals())\n except Exception as e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef share_search(req):\n if comfirm(req):\n try:\n text = req.POST['share_dingdanhao']\n my_db = MynewcoderDB()\n sql1 = \"select * from share where dingdanhao ='\"+text+\"'\"\n infos1 = my_db.getInfo(sql1)\n results = []\n for infos_row in infos1:\n results.append({'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n my_db.close()\n return render(req,'share_edit.html',locals())\n except Exception as e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\",locals())\n else:\n msg = \"请求错误\"\n return render(req, \"msg.html\", locals())\n\ndef vip_edit(req,res_id):\n if comfirm(req):#确认身份\n if str(res_id) == '0':\n return render(req,'new_vip_edit.html')\n try:\n my_db = MynewcoderDB()\n sql1 = \"select * from vip where id = \"+res_id+\" \"\n infos1 = my_db.getInfo(sql1)\n results = []\n for infos1_row in infos1:\n results.append({'id':infos1_row[0],'vipid':infos1_row[1],'vip':infos1_row[2],'create_time':infos1_row[3],'change_time':infos1_row[4],'deadline':infos1_row[5]})\n my_db.close()\n res = results[0]\n return render(req,'vip_edit.html',locals())\n except Exception as e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef order_edit(req,res_id):\n if comfirm(req):#确认身份\n if str(res_id) == '0':\n return render(req,'new_order_edit.html')\n try:\n my_db = MynewcoderDB()\n sql1 = \"select * from share where id = \"+res_id+\" \"\n infos1 = my_db.getInfo(sql1)\n results = []\n for infos_row in infos1:\n results.append({'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n my_db.close()\n res = results[0]\n return render(req,'order_edit.html',locals())\n except Exception as e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef delete(req,res_id):\n if comfirm(req):\n try:\n my_db = MynewcoderDB()\n #vip表的删除\n sql1 = \"delete from vip where id =\"+res_id+\" \"\n my_db.execute(sql1)\n my_db.commit()\n #############\n sql = \"select * from vip\"\n infos = my_db.getInfo(sql)\n results = []\n for infos1_row in infos:\n results.append({'id':infos1_row[0],'vipid':infos1_row[1],'vip':infos1_row[2],'create_time':infos1_row[3],'change_time':infos1_row[4],'deadline':infos1_row[5]})\n ####################\n sql_share = \"select * from share\"\n infos_share = my_db.getInfo(sql_share)\n resultsshare = []\n for infos_row in infos_share:\n resultsshare.append(\n {'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n my_db.close()\n return render(req,\"index.html\",locals())\n except Exception as e:\n msg = '没有找到相关信息'\n return render(req,\"msg\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef order_delete(req,res_id):\n if comfirm(req):\n try:\n my_db = MynewcoderDB()\n #vip表的删除\n sql1 = \"delete from share where id =\"+res_id+\" \"\n my_db.execute(sql1)\n my_db.commit()\n #####################\n sql = \"select * from share\"\n infos = my_db.getInfo(sql)\n resultsshare = []\n for infos_row in infos:\n resultsshare.append({'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n #####################\n sql_share = \"select * from vip\"\n infos_share = my_db.getInfo(sql_share)\n results = []\n for infos1_row in infos_share:\n results.append(\n {'id':infos1_row[0],'vipid':infos1_row[1],'vip':infos1_row[2],'create_time':infos1_row[3],'change_time':infos1_row[4],'deadline':infos1_row[5]})\n my_db.close()\n return render(req,\"index.html\",locals())\n except Exception as e:\n msg = '没有找到相关信息'\n return render(req,\"msg\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n" }, { "alpha_fraction": 0.5809018611907959, "alphanum_fraction": 0.5895225405693054, "avg_line_length": 29.15999984741211, "blob_id": "d8863d7c6f6533cf381f5bcdfa796b3180431882", "content_id": "c8bd5597c2474cadcd777c9a959957cd2c9486f8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1584, "license_type": "no_license", "max_line_length": 97, "num_lines": 50, "path": "/src/resume_tool/eml2txt.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -- coding:utf8 --\n\nimport email.utils\nimport sys\nimport logging\n\nimport chardet\nimport html2text\nimport re\nfrom tools import \naddsys()\n\n\ndef get_charset(message):\n return message.get_content_charset()\n\n\ndef convert_eml_to_txt(msg):\n try:\n logging.debug('Converting email to txt: ' + str(file))\n for par in msg.walk():\n if not par.is_multipart(): # 这里要判断是否是multipart，是的话，里面的数据是无用的\n charset = get_charset(par)\n if charset == None:\n mailContent = par.get_payload(decode=True)\n else:\n mailContent = par.get_payload(decode=True).decode(charset, 'replace')\n return mailContent\n\n except Exception, e:\n logging.error('Error in file: ' + file + str(e))\n return \"\"\n\n\n# 将所有email文档转换为txt格式\ndef handle_emlfiles(emlfile):\n fp = open(emlfile, \"r\")\n msg = email.message_from_file(fp) # 创建消息对象\n emltext = 'content:{}'.format(convert_eml_to_txt(msg))\n # print chardet.detect(emltext)\n if (chardet.detect(emltext)['encoding'] == 'GB2312'):\n str_file = html2text.html2text(emltext.decode(\"gbk\", 'ignore').encode(\"utf-8\", 'ignore'))\n if ((chardet.detect(emltext)['encoding'] == 'utf-8') or (\n chardet.detect(emltext)['encoding'] == 'UTF-8-SIG')):\n str_file = html2text.html2text(emltext)\n #print str_file\n return str_file\n # for t in str_file:\n # txt = re.sub(r'[# \| ]?', '', t) # drop #\n # return txt\n" }, { "alpha_fraction": 0.5557677745819092, "alphanum_fraction": 0.5630252361297607, "avg_line_length": 32.32051467895508, "blob_id": "d4766ff2d12ef1396093045e66e0c73aaa3dfdd6", "content_id": "59b6c73e0592895722b96b753fa34db397f93473", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2648, "license_type": "no_license", "max_line_length": 105, "num_lines": 78, "path": "/src/question/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#--coding:utf-8--\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import \nfrom online.models import User\nfrom collections import Counter\nimport json\nimport sys\nimport time \nreload(sys) \nsys.setdefaultencoding('utf8') \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n \n if req.session['islogin'] == True:\n return render(req, \"question_index.html\")\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n\n#读大段然后内存处理\ndef info(req):\n if req.method == 'GET':\n data = ['review-frontend','acm-solutions','review-java','front-end-interview','nine-chapter']\n type = req.GET.get('type')\n if type == None:\n type = '1'\n page = req.GET.get('page')\n if page == None:\n page = '1'\n sqlstr = \"select * from question where type = '\"+data[int(type)]+\"' limit \"+str(int(page)-1)+\",1\"\n countsqlstr = \"select * from question where type = '\"+data[int(type)] +\"'\"\n queinfo = que_sql(sqlstr)\n maxnumber = len(que_sql(countsqlstr))\n maxlist = []\n pagenumber = int(page)\n maxlist.append(1)\n if maxnumber>3:\n if pagenumber < 3:\n maxlist.append(2)\n maxlist.append(3)\n elif pagenumber > maxnumber-2:\n maxlist.append(maxnumber-2)\n maxlist.append(maxnumber-1)\n else:\n for i in range(pagenumber-1,pagenumber+2):\n maxlist.append(i)\n maxlist.append(maxnumber)\n else:\n number = maxnumber\n for i in range(1,number+1):\n maxlist.append(i)\n print maxlist\n return render(req, \"question_info.html\", locals()) \n\ndef list(req):\n if req.method == 'GET':\n data = ['review-frontend','acm-solutions','review-java','front-end-interview','nine-chapter']\n type = req.GET.get('type')\n if type == None:\n type = '1'\n sqlstr = \"select * from question where type = '\"+data[int(type)]+\"'\"\n data = que_sql(sqlstr)\n return render(req, \"question_list.html\", locals()) \n\ndef que_sql(sqlstr,):\n my_db = MynewcoderDB()\n infos = my_db.getInfo(sqlstr)\n print sqlstr\n my_db.close()\n return infos \n \n\n\n\n" }, { "alpha_fraction": 0.5699234008789062, "alphanum_fraction": 0.5929118990898132, "avg_line_length": 23.85714340209961, "blob_id": "e19509f4a8e95ec2f842e58d9be8c4a0883d12cf", "content_id": "45ee65a740eacbd096b3c1d7c0f4fcd90a7dc2ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1044, "license_type": "no_license", "max_line_length": 51, "num_lines": 42, "path": "/src/tools/dbcon.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "'''\nCreated on 2018-1-11\n\n@author: corey\n'''\nimport platform\nimport MySQLdb\nimport jieba.posseg as pseg\nimport jieba.analyse as ana\nimport jieba\nclass MynewcoderDB:\n def __init__(self):\n self.db_conn= MySQLdb.connect(\n host='118.24.92.135',\n port = 3306,\n user='meng',\n passwd='123456',\n db ='newcoder',\n charset='utf8',\n )\n self.db_cur = self.db_conn.cursor()\n def execute(self,sqlstr):\n self.db_cur.execute(sqlstr)\n def getInfo(self,sqlstr):\n sqlstr = sqlstr.decode(\"utf-8\")\n self.execute(sqlstr)\n return self.db_cur.fetchall()\n def commit(self):\n self.db_conn.commit()\n def close(self):\n self.db_cur.close()\n self.db_conn.close()\n\nclass WordCut:\n def __init__(self,sentence):\n self.sentence = sentence\n def cutWords(self):\n self.words = pseg.cut(self.sentence)\n return self.words\n def top(self,num=20):\n words = ana.extract_tags(self.sentence,num)\n return self.words\n" }, { "alpha_fraction": 0.5396512746810913, "alphanum_fraction": 0.5584926605224609, "avg_line_length": 48.7342643737793, "blob_id": "f7e42b36706577485f899ebee39537369e1c1855", "content_id": "7e8e44b8281429398fcce7b53bada7c0b4c29774", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7112, "license_type": "no_license", "max_line_length": 171, "num_lines": 143, "path": "/src/forum/migrations/0001_initial.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n# Generated by Django 1.10 on 2018-01-25 02:38\nfrom __future__ import unicode_literals\n\nimport datetime\nfrom django.conf import settings\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Application',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('status', models.IntegerField(default=0)),\n ('created_at', models.DateTimeField(auto_now_add=True)),\n ('updated_at', models.DateTimeField(auto_now=True)),\n ('receiver', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='appli_receiver', to=settings.AUTH_USER_MODEL)),\n ('sender', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='appli_sender', to=settings.AUTH_USER_MODEL)),\n ],\n options={\n 'db_table': 'application',\n 'verbose_name_plural': '\\u597d\\u53cb\\u7533\\u8bf7',\n },\n ),\n migrations.CreateModel(\n name='Column',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=30)),\n ('description', models.TextField()),\n ('img', models.CharField(default=b'/static/tx/default.jpg', max_length=200, verbose_name='\\u56fe\\u6807')),\n ('post_number', models.IntegerField(default=0)),\n ('created_at', models.DateTimeField(auto_now_add=True)),\n ('updated_at', models.DateTimeField(auto_now=True)),\n ('manager', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='column_manager', to=settings.AUTH_USER_MODEL)),\n ('parent', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='childcolumn', to='forum.Column')),\n ],\n options={\n 'ordering': ['-post_number'],\n 'db_table': 'column',\n 'verbose_name_plural': '\\u677f\\u5757',\n },\n ),\n migrations.CreateModel(\n name='Comment',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('content', models.TextField()),\n ('created_at', models.DateTimeField(auto_now_add=True)),\n ('updated_at', models.DateTimeField(auto_now=True)),\n ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),\n ('comment_parent', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='childcomment', to='forum.Comment')),\n ],\n options={\n 'ordering': ['created_at'],\n 'db_table': 'comment',\n 'verbose_name_plural': '\\u8bc4\\u8bba',\n },\n ),\n migrations.CreateModel(\n name='Message',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('content', models.TextField()),\n ('created_at', models.DateTimeField(auto_now_add=True)),\n ('updated_at', models.DateTimeField(auto_now=True)),\n ('receiver', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='message_receiver', to=settings.AUTH_USER_MODEL)),\n ('sender', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='message_sender', to=settings.AUTH_USER_MODEL)),\n ],\n options={\n 'db_table': 'message',\n 'verbose_name_plural': '\\u6d88\\u606f',\n },\n ),\n migrations.CreateModel(\n name='Nav',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=40, verbose_name='\\u5bfc\\u822a\\u6761')),\n ('url', models.CharField(blank=True, max_length=200, null=True, verbose_name='\\u6307\\u5411\\u5730\\u5740')),\n ('create_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='\\u521b\\u5efa\\u65f6\\u95f4')),\n ],\n options={\n 'ordering': ['-create_time'],\n 'db_table': 'nav',\n 'verbose_name': '\\u5bfc\\u822a\\u6761',\n 'verbose_name_plural': '\\u5bfc\\u822a\\u6761',\n },\n ),\n migrations.CreateModel(\n name='Post',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('title', models.CharField(max_length=30)),\n ('content', models.TextField()),\n ('view_times', models.IntegerField(default=0)),\n ('responce_times', models.IntegerField(default=0)),\n ('created_at', models.DateTimeField(auto_now_add=True)),\n ('updated_at', models.DateTimeField(auto_now=True)),\n ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='post_author', to=settings.AUTH_USER_MODEL)),\n ('column', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='forum.Column')),\n ('last_response', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),\n ],\n options={\n 'ordering': ['-created_at'],\n 'db_table': 'post',\n 'verbose_name_plural': '\\u4e3b\\u9898',\n },\n ),\n migrations.CreateModel(\n name='PostType',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('type_name', models.CharField(max_length=30)),\n ('description', models.TextField()),\n ('created_at', models.DateTimeField(default=datetime.datetime.now)),\n ],\n options={\n 'db_table': 'posttype',\n 'verbose_name_plural': '\\u4e3b\\u9898\\u7c7b\\u578b',\n },\n ),\n migrations.AddField(\n model_name='post',\n name='type_name',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='forum.PostType'),\n ),\n migrations.AddField(\n model_name='comment',\n name='post',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='forum.Post'),\n ),\n ]\n" }, { "alpha_fraction": 0.6170940399169922, "alphanum_fraction": 0.6393162608146667, "avg_line_length": 28, "blob_id": "085f16d6af02f2e8fe7a6156373878a777b39109", "content_id": "7e3f68ed7a92d3c2d20a9dcb6187f309571bbc2e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 635, "license_type": "no_license", "max_line_length": 51, "num_lines": 20, "path": "/src/resume_tool/run.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\nfrom all_extractor2 import \nfrom main import \nimport sys\ndef runresume(input):\n #处理多线程问题\n import pythoncom\n pythoncom.CoInitialize()\n handle_document(input.decode('utf-8','ignore'))\n filenames = input.split(\".\")\n filename = filenames[0]\n for i in range(1,len(filenames)-1):\n filename = filename+filenames[i]\n result_list = process(filename+'.txt')\n return result_list,filename+'.txt'\n \nif __name__ == '__main__':\n filename = r\"\\1111114北京工业大学-李小龙-硕士-数字媒体专业历.doc\"\n path = os.getcwd()\n runresume(path+filename)\n \n" }, { "alpha_fraction": 0.6198630332946777, "alphanum_fraction": 0.6198630332946777, "avg_line_length": 31.44444465637207, "blob_id": "129f9d2d3bf58ce126577ca6b20eaaa81ee8e678", "content_id": "fd70728454274b690607a7511190ce83747554ef", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 292, "license_type": "no_license", "max_line_length": 57, "num_lines": 9, "path": "/src/school/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom school import views\napp_name = 'school'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^info/$',views.info,name = 'info'),\n url(r'^all/$',views.all,name = 'all'),\n url(r'^test_one/$', views.test_one, name='test_one'),\n]\n" }, { "alpha_fraction": 0.5962733030319214, "alphanum_fraction": 0.6035196781158447, "avg_line_length": 20.46666717529297, "blob_id": "916af44ef1ecee189411041644c05e9302a06667", "content_id": "a4172f7c3bb4c18de23296d2224e42114a954ac3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1016, "license_type": "no_license", "max_line_length": 52, "num_lines": 45, "path": "/src/resume_tool/skill_extractor.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# encoding:utf-8\nimport re\nimport sys\nfrom bgtool.codeinit import \nfrom tools import \nreload(sys)\nsys.setdefaultencoding('utf8')\n\ninput_encode = 'utf-8'\n# 构建专业知识库\n\n# 抽取技能信息,并将日期按照顺序排列存入list\ndef skill_extract(str):\n result_list = []\n dic = \"\"\n if cmp(get_os(),\"n\")==0:\n dic = sys.path[0]+\"\\\\resume_tool\\\\skill_dic\"\n else:\n dic = sys.path[0]+\"/resume_tool/skill_dic\"\n jieba.load_userdict(dic)\n wordcut = WordCut(str)\n for word, flag in wordcut.cutWords():\n if cmp(flag,\"skill\")==0:\n if word not in result_list:\n result_list.append(word)\n return result_list\n\n\ndef process(input_file_path):\n with open(input_file_path, \"r\") as f:\n text = f.read()\n skill_list = skill_extract(text)\n for skill in skill_list:\n print skill\n\n\n\ndef main():\n print 'this is main'\n input_file_path = 'lcy.txt'\n result_dic = process(input_file_path)\n\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.5870736241340637, "alphanum_fraction": 0.5969479084014893, "avg_line_length": 24.31818199157715, "blob_id": "17df535e864ba27e5c0339cb97a9f86c5ce328bf", "content_id": "b5fb9801c4339d9a4a53868e77b68da43cd5800e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1324, "license_type": "no_license", "max_line_length": 84, "num_lines": 44, "path": "/src/resume_tool/school_ship_extractor.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# encoding:utf-8\nimport re\n\ndegree_str1 = u\"(一等奖学金\|二等奖学金\|三等奖学金\|优秀毕业生\|优良毕业生\|国家励志奖学金\|学生标兵\|优秀学生\|优秀本科生\|三好学生\|优秀学生干部)\"\ndegree_pattern1 = re.compile(degree_str1)\n\n# 抽取学位信息,并将日期按照顺序排列存入list\ndef ship_extract(str):\n result_list = []\n school_list = degree_pattern1.findall(str)\n for d in school_list:\n # print d\n result_list.append(d)\n return result_list\n\n# 会破坏信息排布的顺序\n# 该函数并没有被用到\ndef remove_duplicate_data(degree_list):\n degree_dic = {}\n for k in degree_list:\n if k not in degree_dic:\n degree_dic.setdefault(k, 0)\n else:\n degree_list.remove(k) #去除第一个k对象\n return degree_list\n\ndef process(input_file_path):\n for line in open(input_file_path, 'r'):\n try:\n line = line.strip().decode('utf-8') # 设置编码格式\n except:\n line = line.strip().decode('gb2312') \n degree_list = ship_extract(line)\n for d in degree_list:\n print d\n print '-------'\n\ndef main():\n print 'this is main'\n input_file_path = 'lcy.txt'\n result_dic = process(input_file_path)\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.6614583134651184, "alphanum_fraction": 0.6614583134651184, "avg_line_length": 26.571428298950195, "blob_id": "f48e0821c5b797932b93e44d83002e7a61aaffab", "content_id": "c0fc01580658691076f0b184fd88abae820a11fb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 192, "license_type": "no_license", "max_line_length": 48, "num_lines": 7, "path": "/src/company/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom company import views\napp_name = 'company'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^info/$',views.info,name = 'info'),\n]" }, { "alpha_fraction": 0.739393949508667, "alphanum_fraction": 0.7515151500701904, "avg_line_length": 32.20000076293945, "blob_id": "67e2c5e74ba289f1836ba2da4374b82ee9fa922a", "content_id": "fea09f53bbeb3d1cf6d8933c0af738a7d3c56350", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 165, "license_type": "no_license", "max_line_length": 55, "num_lines": 5, "path": "/src/manager/models.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.db import models#do not understand only key\n\nclass TestData(models.Model):\n info_id = models.IntegerField()\n info = models.CharField(max_length=50)" }, { "alpha_fraction": 0.5875942707061768, "alphanum_fraction": 0.6010058522224426, "avg_line_length": 27.404762268066406, "blob_id": "46873db7f9b5da2ba246a6766f023fd44f10a742", "content_id": "9804df2dbe594b738c0407956a94db335aa6874c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1219, "license_type": "no_license", "max_line_length": 90, "num_lines": 42, "path": "/src/resume_tool/doc2txt.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -- coding:utf8 --\n\nimport sys\nimport logging\nimport chardet\nfrom tools import \n\naddsys()\n\n\n# Load MS Word document,change to txt and return the document object #import win32com\ndef convert_doc_to_txt(file): # takes in a filename and returns a word document object\n try:\n logging.debug('Converting word to txt: ' + str(file))\n wordapp = win32com.client.Dispatch('Word.Application')\n wordapp.Visible = False\n # 后缀名doc切词\n if file[-4:] == '.doc':\n doc2t = file[:-4]\n elif file[-5:] == '.docx':\n doc2t = file[:-5]\n print file\n wordapp.Documents.Open(file, False, False)# FileName, ConfirmConversions, ReadOnly\n wordapp.ActiveDocument.SaveAs(doc2t, FileFormat=2)\n wordapp.ActiveDocument.Close()\n fin = open(doc2t + '.txt', 'r')\n strfile = fin.read()\n #print strfile\n return strfile\n\n except Exception, e:\n logging.error('Error in file: ' + str(e))\n return \"\"\n\n\n# 将word文档转换为txt格式\ndef handle_docfiles(docfile):\n if docfile[-4:] == '.DOC':\n doc_file = docfile[:-4] + '.doc'\n else:\n doc_file = docfile\n convert_doc_to_txt(doc_file)\n" }, { "alpha_fraction": 0.6206896305084229, "alphanum_fraction": 0.6416791677474976, "avg_line_length": 25.68000030517578, "blob_id": "adf94bc5314bacb207b619df50fb7d19bbeae8ba", "content_id": "506c8a427b4454278e1a47a0e6ff1019d09104b0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 707, "license_type": "no_license", "max_line_length": 97, "num_lines": 25, "path": "/src/resume_tool/html2txt.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -- coding:utf8 --\n\nimport sys\nimport logging\n\nimport re\nimport chardet\nimport html2text\nfrom tools import \n\naddsys()\n\n# 将word文档转换为txt格式\ndef handle_htmlfiles(htmlfile):\n fin = open(htmlfile, 'r')\n strfile = fin.read()\n #print chardet.detect(strfile)\n # 文本格式的编码方式统一为utf-8\n if (chardet.detect(strfile)['encoding'] == 'GB2312'):\n str_file = html2text.html2text(strfile.decode(\"gbk\", 'ignore').encode(\"utf-8\", 'ignore'))\n if ((chardet.detect(strfile)['encoding'] == 'utf-8') or (\n chardet.detect(strfile)['encoding'] == 'UTF-8-SIG')):\n str_file = html2text.html2text(strfile)\n #print str_file\n return str_file\n" }, { "alpha_fraction": 0.5322144031524658, "alphanum_fraction": 0.5463707447052002, "avg_line_length": 50.30583572387695, "blob_id": "1289f0dd55511074306d40a7e7155d29107af99f", "content_id": "49218f97e13fe04e000518f81becdfe10073aba5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 26007, "license_type": "no_license", "max_line_length": 346, "num_lines": 497, "path": "/src/person/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#--coding:utf-8--\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import \nfrom online.models import User\nfrom collections import Counter\nimport json\nimport sys\nimport time\nimport xlrd\nreload(sys) \nsys.setdefaultencoding('utf-8')\ndef index(req):\n if req.method == 'GET':\n qz = 1\n req.session['qz'] = 1\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n \n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(condition)\n if r.myresume != None and len(r.myresume)!=0:\n return render(req, \"person_index.html\",locals())\n else:\n jl = 1\n return render(req, \"person_index.html\", locals())\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n\ndef index2(req):\n if req.method == 'GET':\n qz = 0\n req.session['qz'] = 0\n try:\n islogin = req.session['islogin']\n except Exception, e:\n msg = '请登录'\n return render(req, 'msg.html', locals())\n\n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(condition)\n if r.myresume != None and len(r.myresume) != 0:\n return render(req, \"person_index.html\", locals())\n else:\n jl = 1\n return render(req, \"person_index.html\", locals())\n else:\n msg = '请登录'\n return render(req, 'msg.html', locals())\n#读大段然后内存处理\ndef info(req):\n if req.method == 'POST':\n qz = req.session['qz']\n try:\n comp = req.POST['comp']\n except:\n comp = \"\"\n try:\n job = req.POST['job']\n except:\n job = \"\"\n try:\n becomp = req.POST['becomp']\n except:\n becomp = \"\"\n try:\n bejob = req.POST['bejob']\n except:\n bejob = \"\"\n edu = req.POST['edu']\n major = req.POST['major']\n degree = req.POST['degree']\n\n email=req.session['user_info']['email']\n sqlstr = \"INSERT INTO querylog (email,comp,job,becomp,bejob,edu,major,degree) VALUES ('\"+email+\"','\"+ comp+\"','\"+job+\"','\"+becomp+\"','\"+bejob+\"','\"+edu+\"','\"+major+\"','\"+degree+\"')\"\n my_db = MynewcoderDB()\n infos = my_db.execute(sqlstr)\n my_db.commit()\n my_db.close()\n\n \n \n comp_list = []\n pos_list = []\n degree_list = []\n edu_list = []\n major_list = []\n detail_list = []\n myedunum = 0\n #该大学去的最多岗位和公司,公司UI和岗位UI\n if len(edu)!=0:\n import random\n key = random.randint(1,30000)\n keynum = random.randint(200,1000)\n \n #为空测试结果一致\n sqlstr = \"select bz_job.company,bz_job.position from bz_job where bz_job.name in (select name from bz_edu where bz_edu.edu like '%\"+edu.encode(\"utf-8\")+\"%' and bz_edu.major like '%\"+major.encode(\"utf-8\")+\"%' and bz_edu.degree like '%\"+degree+\"%')\"\n eduinfo = person_sql(sqlstr)\n if len(eduinfo)<200:\n sqlstr = \"select bz_job.company,bz_job.position from bz_job where bz_job.name in (select name from bz_edu where bz_edu.edu like '%\"+edu.encode(\"utf-8\")+\"%' and bz_edu.degree like '%\"+degree.encode(\"utf-8\")+\"%')\"\n eduinfo = person_sql(sqlstr)\n if len(eduinfo)<200:\n sqlstr = \"select bz_job.company,bz_job.position from bz_job where bz_job.name in (select name from bz_edu where bz_edu.degree like '%\"+degree.encode(\"utf-8\")+\"%') limit \"+str(key)+\",\"+str(keynum)\n eduinfo = person_sql(sqlstr)\n for tcomp,tpos in eduinfo:\n if len(tcomp) != 0:\n comp_list.append(tcomp)\n if len(tpos) != 0:\n pos_list.append(tpos) \n compnumber = int(len(eduinfo)33.14)\n pos_data,pos_num,pos_t_data,pos_t_num = data_single_handle(pos_list)\n comp_data,comp_num,comp_t_data,comp_t_num = data_single_handle(comp_list)\n \n if len(comp)!=0:\n #该公司的统计信息\n sqlstr = \"select bz_edu.degree, bz_edu.edu ,bz_edu.major from bz_edu where bz_edu.name in (select name from bz_job where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' and bz_job.position like '%\" + job.encode(\"utf-8\") + \"%')\"\n compinfo = person_sql(sqlstr)\n if len(compinfo)<200:\n sqlstr = \"select bz_edu.degree, bz_edu.edu ,bz_edu.major from bz_edu where bz_edu.name in (select name from bz_job where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%')\"\n compinfo = person_sql(sqlstr)\n edunumber = int(len(compinfo)33.14)\n \n for td,te,tm in compinfo:\n if len(td) != 0:\n degree_list.append(td)\n if len(te) != 0:\n edu_list.append(te)\n if len(tm) != 0:\n major_list.append(tm)\n degree_list = change_dic(degree_list)\n a = {}\n if len(edu)!=0:\n templist = Counter(edu_list) \n myedunum = templist[edu]\n a['name'] = edu\n a['value'] = int(myedunum3.143) \n edu_list = change_dic(edu_list)\n if len(a)!=0 and myedunum!=0:\n edu_list.pop(-1)\n edu_list.append(a)\n degree_t_data = degree_list\n degree_data = json.dumps(degree_list) \n \n major_data,major_num,major_t_data,major_t_num = data_single_handle(major_list)\n major_list = change_dic(major_list)\n \n #该公司以前的人都去哪了，即关联公司\n sqlstr = \"select company,count(company) from bz_job where name in (select bz_job.name from bz_job where bz_job.company like '%\"+ comp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ comp.encode(\"utf-8\")+\"%' group by company ORDER BY COUNT(company) DESC LIMIT 5\"\n lcomp_data,lcomp_num,lcomp_t_data,lcomp_t_num,lcomp_number = data_handle(sqlstr)\n #这是个人项目经历\n if len(edu)!=0:\n num = 4\n tcount = 0\n while(num):\n tcount = tcount+1\n sqlstr = \"select from bz_job where bz_job.name in (SELECT * FROM (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\"+comp.encode(\"utf-8\")+\"%' and bz_edu.edu like '%\"+edu+\"%' and bz_edu.name=bz_job.name ORDER BY bz_job.time desc limit \"+str(tcount)+\",1) AS s)\"\n detailinfo = person_sql(sqlstr)\n if len(detailinfo)>1 and len(detailinfo)<10:\n detail_list.append(detailinfo)\n num = num -1\n if tcount > 20:\n break\n \n if len(job)!=0: \n sqlstr = \"select position,count(position) from bz_job where bz_job.position!='' and name in (select bz_job.name from bz_job where bz_job.position like '%\"+ job.encode(\"utf-8\")+\"%') and bz_job.position not like '%\"+ job.encode(\"utf-8\")+\"%' group by position ORDER BY COUNT(position) DESC LIMIT 5\"\n ljob_data,ljob_num,ljob_t_data,ljob_t_num,ljob_number= data_handle(sqlstr)\n sqlstr = \"select position from bz_job where bz_job.position!='' and name in (select bz_job.name from bz_job where bz_job.position like '%\"+ job.encode(\"utf-8\")+\"%') and bz_job.position not like '%\"+ job.encode(\"utf-8\")+\"%' group by position ORDER BY COUNT(position)\"\n ljob_number = int(len(person_sql(sqlstr))3.143)\n if len(becomp)!=0:\n sqlstr = \"select company,count(company) from bz_job where bz_job.company!='' and name in (select bz_job.name from bz_job where bz_job.company like '%\"+ becomp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ becomp.encode(\"utf-8\")+\"%' group by company ORDER BY COUNT(company) DESC LIMIT 5\"\n bcomp_data,bcomp_num,bcomp_t_data,bcomp_t_num,bcomp_number = data_handle(sqlstr)\n if len(comp)!=0:\n sqlstr = \"select company from bz_job where bz_job.company!='' and name in (select bz_job.name from bz_job where bz_job.company like '%\" + becomp.encode(\n \"utf-8\") + \"%') and bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' \"\n print sqlstr\n tempnumber = int(len(person_sql(sqlstr)) * 3.14 * 3)\n if comp not in bcomp_t_data:\n bcomp_t_data.append(comp)\n bcomp_t_num.append(tempnumber)\n bcomp_data = json.dumps(bcomp_t_data)\n bcomp_num = json.dumps(bcomp_t_num)\n\n\n\n sqlstr = \"select company from bz_job where bz_job.company!='' and name in (select bz_job.name from bz_job where bz_job.company like '%\"+ becomp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ becomp.encode(\"utf-8\")+\"%'\"\n bcomp_number = int(len(person_sql(sqlstr)) * 3.14 * 3)\n if len(bejob)!=0:\n sqlstr = \"select position,count(position) from bz_job where bz_job.position!='' and name in (select bz_job.name from bz_job where bz_job.position like '%\"+ bejob.encode(\"utf-8\")+\"%') and bz_job.position not like '%\"+ bejob.encode(\"utf-8\")+\"%' group by position ORDER BY COUNT(position) DESC LIMIT 5\"\n bjob_data,bjob_num,bjob_t_data,bjob_t_num,bjob_number = data_handle(sqlstr)\n sqlstr = \"select position from bz_job where bz_job.position!='' and name in (select bz_job.name from bz_job where bz_job.position like '%\"+ bejob.encode(\"utf-8\")+\"%') and bz_job.position not like '%\"+ bejob.encode(\"utf-8\")+\"%'\"\n bjob_number = int(len(person_sql(sqlstr)) * 3.14 * 3)\n #缺少验证信息\n return render(req, \"person_info.html\", locals()) \n\n\n#这个方法直接sql全部处理完 \ndef test(req):\n if req.method == 'GET': \n comp = req.GET.get('comp')\n if comp == None:\n comp = \"\"\n job = req.GET.get('job')\n if job == None:\n job = \"\"\n edu = req.GET.get('edu')\n if edu == None:\n edu = \"\"\n major = req.GET.get('major')\n if major == None:\n major = \"\"\n degree = req.GET.get('degree')\n if degree == None:\n degree = \"\"\n becomp =req.GET.get('becomp')\n if becomp == None:\n becomp = \"\"\n bejob = req.GET.get('bejob')\n if bejob == None:\n bejob = \"\"\n comp_list = []\n pos_list = []\n degree_list = []\n edu_list = []\n major_list = []\n detail_list = []\n myedunum = 0\n #该大学去的最多岗位和公司,公司UI和岗位UI\n if len(edu)!=0:\n import random\n key = random.randint(1,30000)\n keynum = random.randint(200,1000)\n \n #为空测试结果一致\n sqlstr = \"select bz_job.company,bz_job.position from bz_job where bz_job.name in (select name from bz_edu where bz_edu.edu like '%\"+edu.encode(\"utf-8\")+\"%' and bz_edu.major like '%\"+major.encode(\"utf-8\")+\"%' and bz_edu.degree like '%\"+degree+\"%')\"\n eduinfo = person_sql(sqlstr)\n if len(eduinfo)<200:\n sqlstr = \"select bz_job.company,bz_job.position from bz_job where bz_job.name in (select name from bz_edu where bz_edu.edu like '%\"+edu.encode(\"utf-8\")+\"%' and bz_edu.degree like '%\"+degree.encode(\"utf-8\")+\"%')\"\n eduinfo = person_sql(sqlstr)\n if len(eduinfo)<200:\n sqlstr = \"select bz_job.company,bz_job.position from bz_job where bz_job.name in (select name from bz_edu where bz_edu.degree like '%\"+degree.encode(\"utf-8\")+\"%') \"\n eduinfo = person_sql(sqlstr)\n for tcomp,tpos in eduinfo:\n if len(tcomp) != 0:\n comp_list.append(tcomp)\n if len(tpos) != 0:\n pos_list.append(tpos) \n compnumber = int(len(eduinfo)33.14)\n pos_data,pos_num,pos_t_data,pos_t_num = data_single_handle(pos_list)\n comp_data,comp_num,comp_t_data,comp_t_num = data_single_handle(comp_list)\n \n if len(comp)!=0:\n sqlstr = \"select * from company where 标题 like '%\" + comp.encode(\"utf-8\") + \"%'\"\n compintr = person_sql(sqlstr)\n compintr = compintr[0]\n jstemp = ['百度','腾讯','阿里巴巴']\n jsdtemp =[[84,66,1000,'百度','百度'],[88,60,1000,'腾讯','腾讯'],[86,64,1000,'阿里巴巴','阿里巴巴']]\n\n try:\n jsname = json.dumps(jstemp)\n data = [float(compintr[8]) * 20, float(compintr[9]) * 20, 1000, compintr[1], compintr[1]]\n if not compintr[1] in jstemp:\n jsdtemp.append(data)\n jstemp.append(compintr[1])\n jsdata = json.dumps(jsdtemp)\n except:\n compintr = []\n #该公司的统计信息\n sqlstr = \"select bz_edu.degree, bz_edu.edu ,bz_edu.major from bz_edu where bz_edu.name in (select name from bz_job where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' and bz_job.position like '%\" + job.encode(\"utf-8\") + \"%')\"\n compinfo = person_sql(sqlstr)\n if len(compinfo)<200:\n sqlstr = \"select bz_edu.degree, bz_edu.edu ,bz_edu.major from bz_edu where bz_edu.name in (select name from bz_job where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%')\"\n compinfo = person_sql(sqlstr)\n edunumber = int(len(compinfo)33.14)\n \n for td,te,tm in compinfo:\n if len(td) != 0:\n degree_list.append(td)\n if len(te) != 0:\n edu_list.append(te)\n if len(tm) != 0:\n major_list.append(tm)\n degree_list = change_dic(degree_list)\n a = {}\n if len(edu)!=0:\n templist = Counter(edu_list) \n myedunum = templist[edu]\n a['name'] = edu\n a['value'] = int(myedunum3.143) \n edu_list = change_dic(edu_list)\n if len(a)!=0 and myedunum!=0:\n edu_list.pop(-1)\n edu_list.append(a)\n degree_t_data = degree_list\n degree_data = json.dumps(degree_list)\n \n major_data,major_num,major_t_data,major_t_num = data_single_handle(major_list)\n major_list = change_dic(major_list)\n \n #该公司以前的人都去哪了，即关联公司\n sqlstr = \"select company,count(company) from bz_job where name in (select bz_job.name from bz_job where bz_job.company like '%\"+ comp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ comp.encode(\"utf-8\")+\"%' group by company ORDER BY COUNT(company) DESC LIMIT 5\"\n lcomp_data,lcomp_num,lcomp_t_data,lcomp_t_num,lcomp_number = data_handle(sqlstr)\n sqlstr = \"select company from bz_job where name in (select bz_job.name from bz_job where bz_job.company like '%\"+ comp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ comp.encode(\"utf-8\")+\"%'\"\n lcomp_number = int(len(person_sql(sqlstr))3.143) \n #这是个人项目经历\n if len(edu)!=0:\n num = 4\n tcount = 0\n while(num):\n tcount = tcount+1\n sqlstr = \"select * from bz_job where bz_job.name in (SELECT * FROM (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\"+comp.encode(\"utf-8\")+\"%' and bz_edu.edu like '%\"+edu+\"%' and bz_edu.name=bz_job.name ORDER BY bz_edu.time desc limit \"+str(tcount)+\",1) AS s)\"\n detailinfo = person_sql(sqlstr)\n if len(detailinfo)>1 and len(detailinfo)<10:\n detail_list.append(detailinfo)\n num = num -1\n if tcount > 20:\n while(num):\n sqlstr = \"select * from bz_job where bz_job.name in (SELECT * FROM (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\"+comp.encode(\"utf-8\")+\"%' and bz_edu.degree like '%\"+degree+\"%' and bz_edu.name=bz_job.name ORDER BY bz_edu.time desc limit \"+str(tcount)+\",1) AS s)\"\n detailinfo = person_sql(sqlstr)\n tcount = tcount+1\n if len(detailinfo)>1 and len(detailinfo)<10:\n detail_list.append(detailinfo)\n num = num -1\n if tcount >100:\n num = 0\n break\n sqlstr = \"select position from bz_job where position!='' and bz_job.name in (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' and bz_edu.edu like '%\" + edu.encode(\"utf-8\") + \"%' and bz_job.name = bz_edu.name)\"\n test_number = int(len(person_sql(sqlstr))3.143)\n \n if test_number<2003.143:\n sqlstr = \"select position from bz_job where position!='' and bz_job.name in (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' and bz_edu.degree like '%\" + degree.encode(\"utf-8\") + \"%' and bz_job.name = bz_edu.name )\"\n test_number = int(len(person_sql(sqlstr))3.143) \n sqlstr = \"select position,count(position) from bz_job where position!='' and bz_job.name in (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' and bz_edu.degree like '%\" + degree.encode(\"utf-8\") + \"%') group by position ORDER BY COUNT(position) DESC LIMIT 5\"\n test_data,test_num,test_t_data,test_t_num,testnumber = data_handle(sqlstr)\n else:\n sqlstr = \"select position,count(position) from bz_job where position!='' and bz_job.name in (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' and bz_edu.edu like '%\" + edu.encode(\"utf-8\") + \"%' and bz_job.name = bz_edu.name) group by position ORDER BY COUNT(position) DESC LIMIT 5\"\n test_data,test_num,test_t_data,test_t_num,testnumber = data_handle(sqlstr)\n\n \n if len(job)!=0: \n sqlstr = \"select position,count(position) from bz_job where bz_job.position!='' and name in (select bz_job.name from bz_job where bz_job.position like '%\"+ job.encode(\"utf-8\")+\"%') and bz_job.position not like '%\"+ job.encode(\"utf-8\")+\"%' group by position ORDER BY COUNT(position) DESC LIMIT 5\"\n ljob_data,ljob_num,ljob_t_data,ljob_t_num,ljob_number= data_handle(sqlstr)\n\n \n if len(becomp)!=0:\n sqlstr = \"select company,count(company) from bz_job where bz_job.company!='' and name in (select bz_job.name from bz_job where bz_job.company like '%\"+ becomp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ becomp.encode(\"utf-8\")+\"%' group by company ORDER BY COUNT(company) DESC LIMIT 5\"\n bcomp_data,bcomp_num,bcomp_t_data,bcomp_t_num,bcomp_number = data_handle(sqlstr)\n sqlstr = \"select company from bz_job where bz_job.company!='' and name in (select bz_job.name from bz_job where bz_job.company like '%\"+ becomp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ becomp.encode(\"utf-8\")+\"%'\"\n bcomp_number = int(len(person_sql(sqlstr))33.14) \n \n if len(bejob)!=0:\n sqlstr = \"select position,count(position) from bz_job where bz_job.position!='' and name in (select bz_job.name from bz_job where bz_job.position like '%\"+ bejob.encode(\"utf-8\")+\"%') and bz_job.position not like '%\"+ bejob.encode(\"utf-8\")+\"%' group by position ORDER BY COUNT(position) DESC LIMIT 5\"\n bjob_data,bjob_num,bjob_t_data,bjob_t_num,bjob_number = data_handle(sqlstr)\n\n #添加了关键字浮动\n word_list =readexcel(comp)\n word_list = json.dumps(word_list)\n\n #添加了公司信息详情\n if len(comp)!=0:\n sqlstr =\"select count(college_college.college_name) from bz_edu,college_college where bz_edu.name in (select name from bz_job where bz_job.company like '%\"+comp+\"%' and bz_job.position like '%\"+job+\"%') and bz_edu.edu = college_college.college_name GROUP BY college_college.isTwo;\"\n twoinfo = person_sql(sqlstr)\n if len(twoinfo) == 1:\n sqlstr = \"select count(college_college.college_name) from bz_edu,college_college where bz_edu.name in (select name from bz_job where bz_job.company like '%\" + comp + \"%') and bz_edu.edu = college_college.college_name GROUP BY college_college.isTwo;\"\n twoinfo = person_sql(sqlstr)\n twodata = []\n twotemp = {}\n twotemp['name'] = \"非211学校\"\n twotemp['value'] = int(int(twoinfo[0][0])3.14)\n twodata.append(twotemp)\n twotemp = {}\n twotemp['name'] = \"211学校\"\n twotemp['value'] = int(int(twoinfo[1][0])3.14)\n twodata.append(twotemp)\n twodata = json.dumps(twodata)\n\n sqlstr =\"select count(college_college.college_name) from bz_edu,college_college where bz_edu.name in (select name from bz_job where bz_job.company like '%\"+comp+\"%' and bz_job.position like '%\"+job+\"%') and bz_edu.edu = college_college.college_name GROUP BY college_college.isNine;\"\n nineinfo = person_sql(sqlstr)\n if len(nineinfo) == 1:\n sqlstr = \"select count(college_college.college_name) from bz_edu,college_college where bz_edu.name in (select name from bz_job where bz_job.company like '%\" + comp + \"%') and bz_edu.edu = college_college.college_name GROUP BY college_college.isNine;\"\n nineinfo = person_sql(sqlstr)\n ninedata = []\n ninetemp = {}\n ninetemp['name'] = \"非985学校\"\n ninetemp['value'] = int(int(nineinfo[0][0])3.14)\n ninedata.append(ninetemp)\n ninetemp = {}\n ninetemp['name'] = \"985学校\"\n ninetemp['value'] = int(int(nineinfo[1][0])3.14)\n ninedata.append(ninetemp)\n ninedata = json.dumps(ninedata)\n\n excelFile = 'maindata.xls'\n data = xlrd.open_workbook(sys.path[0] + \"//\" + excelFile)\n table = data.sheets()[0]\n nrows = table.nrows\n ncols = table.ncols\n mainlist = []\n for i in xrange(nrows-100, nrows):\n rowValues = table.row_values(i)\n if str(rowValues[1]).find(str(comp))>=0:\n mainlist.append(rowValues)\n #缺少验证信息\n return render(req, \"person_info.html\", locals()) \n \ndef person_sql(sqlstr,):\n my_db = MynewcoderDB()\n infos = my_db.getInfo(sqlstr)\n my_db.close()\n return infos\n\ndef data_single_handle(comp_list):\n comp_data = []\n comp_num = []\n comp_list = change_dic(comp_list)\n for tdata in comp_list:\n comp_data.append(tdata['name'])\n comp_num.append(int(tdata['value']))\n temp_data = comp_data\n temp_num = comp_num\n comp_data = json.dumps(comp_data)\n comp_num = json.dumps(comp_num)\n return comp_data,comp_num,temp_data,temp_num \n\ndef data_handle(sqlstr):\n data = []\n num = []\n beforeinfo = person_sql(sqlstr)\n number = int(len(beforeinfo)3.143)\n for tdata,tnum in beforeinfo:\n data.append(tdata)\n num.append(int(tnum3.143)) \n tdata = data\n tnum = num\n data = json.dumps(data)\n num = json.dumps(num)\n return data,num,tdata,tnum,number\n\ndef change_dic(List):\n alist = []\n data = Counter(List).most_common(5)\n for tdata,tnum in data:\n a = {}\n a['name'] = tdata\n a['value'] = int(tnum3.143)\n alist.append(a)\n return alist\n\ndef word_extract(str):\n wordcut = WordCut(str)\n word_dict = {}\n for word, flag in wordcut.cutWords():\n\n if flag == \"n\" or flag == \"vn\":\n if word not in word_dict:\n word_dict[word] = 1\n else:\n word_dict[word] += 1\n if flag == \"eng\":\n word = word.upper()\n if word not in word_dict:\n word_dict[word] = 10\n else:\n word_dict[word] += 10\n\n import operator\n word_dict = sorted(word_dict.items(), key=operator.itemgetter(1))\n word_list = []\n for ktemp,vtemp in word_dict:\n word = {}\n word['text'] = ktemp\n import random\n word['weight'] = int(vtemp)\n word_list.append(word)\n word_list=word_list[len(word_list)-20:-1]\n return word_list\n\ndef readexcel(keyword,filename = \"lagou.xls\"):\n book = xlrd.open_workbook(filename)#得到Excel文件的book对象，实例化对象\n sheet0 = book.sheet_by_index(0) # 通过sheet索引获得sheet对象\n nrows = sheet0.nrows # 获取行总数\n ncols = sheet0.ncols #获取列总数\n words = \"\"\n for i in range(0,nrows):\n row_data = sheet0.row_values(i) # 获得第1行的数据列表\n if row_data[0] == keyword:\n words = words+row_data[11]\n return word_extract(words)\n\n\n" }, { "alpha_fraction": 0.5702868103981018, "alphanum_fraction": 0.5961447954177856, "avg_line_length": 24.321428298950195, "blob_id": "acfa33c211a738fcda5d050578618e9278a34fad", "content_id": "8756fa3fd31805a80ac522c146c01bde1d37dd2f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2351, "license_type": "no_license", "max_line_length": 113, "num_lines": 84, "path": "/src/resume_tool/school_name_extractor.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# encoding:utf-8\nfrom major_extractor import \n\nimport re\n\ninput_encode = 'utf-8'\n\n#\|[\\u4e00-\\u9fa5]中专+\|[\\u4e00-\\u9fa5]+大专\nschool_str1 = u\"([\\u4e00-\\u9fa5]+大学\|[\\u4e00-\\u9fa5]+学院\|[\\u4e00-\\u9fa5]+分校\|[\\u4e00-\\u9fa5]+学校\|[\\u4e00-\\u9fa5]+电大)\"\n#school_str1 = u\"[\\u4e00-\\u9fa5]+(大学\|学院\|分校\|学校\|电大)\"\nschool_pattern1 = re.compile(school_str1)\n\nstop_words = {}\n\n#stop_words[u\"至今\"] = 0\n\n#stop_words = {u\"至今\": \"\", u\"月\": \"\", u\"本科\": \"\", u\"研究生\": \"\", u\"硕士\": \"\", u\"硕士研究生\": \"\"}\nstop_words[u\"至今\"] = 0\nstop_words[u\"月\"] = 0\nstop_words[u'年'] = 0\nstop_words[u'日'] = 0\n\n#消除学校前面的专业\n# # 构建专业知识库\n# major_set = set([v.strip() for v in open('major_dic', 'r')])\n\n\n# 将items（list）所有空元素，过滤掉\ndef drop_null(items):\n result = []\n for item in items:\n if len(item.strip()) == 0:\n continue\n result.append(item.strip())\n return result\n\n# items 是学校,当学校名字中含有以stop_words开头时,将stop_wrods替换成空.\ndef drop_stop_words(items):\n result = []\n for item in items:\n if len(item.strip()) == 0:\n continue\n for sw in stop_words:\n if item.startswith(sw):\n item = item.replace(sw, \"\")\n result.append(item.strip())\n return result\n\n# 抽取日期信息,并将日期按照顺序排列存入list\ndef school_name_extract(str):\n result_list = []\n school_list = school_pattern1.findall(str)\n for d in school_list:\n # print d\n result_list.append(d)\n result_list = drop_null(result_list)\n result_list = drop_stop_words(result_list)\n return result_list\n\ndef get_education_number_from_school_name(input_str):\n date_size = 0\n size = len(school_name_extract(input_str))\n return size\n\ndef process(input_file_path):\n for line in open(input_file_path, 'r'):\n line = line.strip().decode(input_encode)\n print line\n school_list = school_name_extract(line)\n\n for d in school_list:\n print d\n\n print 'education number: '\n print get_education_number_from_school_name(line)\n print '-------'\n\ndef main():\n print 'this is main'\n input_file_path = 'data/samples'\n result_dic = process(input_file_path)\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.6340425610542297, "alphanum_fraction": 0.6340425610542297, "avg_line_length": 28.5, "blob_id": "6c795c3783ac633feb2120de1fb6d409000d87d7", "content_id": "54ebbcdf369d870986072931ab33f7ccfdaa9fab", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 235, "license_type": "no_license", "max_line_length": 48, "num_lines": 8, "path": "/src/intr/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom intr import views\napp_name = 'intr'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^info/$',views.info,name = 'info'),\n url(r'^money/$',views.money,name = 'money'),\n]" }, { "alpha_fraction": 0.45805981755256653, "alphanum_fraction": 0.46243616938591003, "avg_line_length": 38.53845977783203, "blob_id": "5a5b9ab55ce4339542aaf8a726a9627b67cedf28", "content_id": "e290818fe4079b0b423d4a90f08e284afe284f25", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4335, "license_type": "no_license", "max_line_length": 155, "num_lines": 104, "path": "/src/pay/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#--coding:utf-8--\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import \nfrom online.models import User\nimport sys \nreload(sys) \nsys.setdefaultencoding('utf8') \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n \n if req.session['islogin'] == True:\n return render(req,\"payindex.html\")\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\ndef setjian(req):\n if req.method == 'POST':\n try:\n text=req.POST.get(\"text\",'')\n intr=req.POST.get(\"intr\",'')\n myinfo = text.split()\n comp = myinfo[1]\n local = myinfo[3]\n gw = myinfo[5]\n my_db = MynewcoderDB()\n sqlstr = \"SELECT * from pay where 公司 = '\"+comp+\"' and 地点 = '\"+local+\"' and 岗位 = '\"+gw+\"' and 说明 ='\"+intr+\"'\"\n myinfos = my_db.getInfo(sqlstr)\n mynum = int(myinfos[0][4])\n mynum = mynum-1\n sqlstr = \"Update pay SET 可信度 = \"+str(mynum)+\" where 公司 = '\"+comp+\"' and 地点 = '\"+local+\"' and 岗位 = '\"+gw+\"' and 说明 ='\"+intr+\"'\"\n my_db.execute(sqlstr)\n my_db.commit()\n my_db.close()\n msg = \"点评成功\"\n return HttpResponse(msg)\n except Exception,e:\n print e\n msg = \"输入错误，请联系管理员\"\n return HttpResponse(msg)\n else:\n msg = \"点评成功\"\n return HttpResponse(msg) \ndef setjia(req):\n if req.method == 'POST':\n try:\n text=req.POST.get(\"text\",'')\n intr=req.POST.get(\"intr\",'')\n myinfo = text.split()\n comp = myinfo[1]\n local = myinfo[3]\n gw = myinfo[5]\n my_db = MynewcoderDB()\n sqlstr = \"SELECT * from pay where 公司 = '\"+comp+\"' and 地点 = '\"+local+\"' and 岗位 = '\"+gw+\"' and 说明 ='\"+intr+\"'\"\n myinfos = my_db.getInfo(sqlstr)\n mynum = int(myinfos[0][4])\n mynum = mynum+1\n sqlstr = \"Update pay SET 可信度 = \"+str(mynum)+\" where 公司 = '\"+comp+\"' and 地点 = '\"+local+\"' and 岗位 = '\"+gw+\"' and 说明 ='\"+intr+\"'\"\n my_db.execute(sqlstr)\n my_db.commit()\n my_db.close()\n msg = \"点评成功\"\n return HttpResponse(msg)\n except Exception,e:\n print e\n msg = \"输入错误，请联系管理员\"\n return HttpResponse(msg)\n else:\n msg = \"点评成功\"\n return HttpResponse(msg) \n \ndef getpay(req):\n if req.method == 'POST':\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(*condition)\n if r.beans<5:\n msg = '用户豆不够，请充值'\n return render(req,'msg.html', locals())\n else:\n try:\n text=req.POST.get(\"paytext\",'') \n my_db = MynewcoderDB()\n sqlstr = \"SELECT from pay where 公司 like '%\"+text+\"%' OR 地点 like '%\"+text+\"%' OR 岗位 like '%\"+text+\"%' ORDER BY 可信度 DESC limit 10 \"\n print sqlstr\n infos = my_db.getInfo(sqlstr)\n beans = r.beans - 5\n User.objects.filter(name=user_info['name']).update(beans=beans)\n req.session['beans'] = beans\n my_db.close()\n return render(req,\"payindex.html\",locals())\n except Exception,e:\n print e\n msg = \"输入错误，请联系管理员\"\n return render(req,'msg.html', locals())\n else:\n return render(req,\"payindex.html\",{'infos':infos})\n\n" }, { "alpha_fraction": 0.6800356507301331, "alphanum_fraction": 0.7139037251472473, "avg_line_length": 42.153846740722656, "blob_id": "080d62dda117f6cd6b0bf922bbe796066099a088", "content_id": "2953fd6699f7a1333c2cd508fc13adc84fa5364b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1122, "license_type": "no_license", "max_line_length": 91, "num_lines": 26, "path": "/src/online/models.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.db import models#do not understand only key\nclass User(models.Model):\n name = models.CharField(max_length=50)\n passwd = models.CharField(max_length=50)\n email = models.CharField(max_length=50)\n graduate = models.CharField(max_length=250,blank=True, null=True)\n beans = models.IntegerField()\n vip = models.BinaryField()\n create_date = models.DateTimeField(blank=True, null=True)\n update_date = models.DateTimeField(blank=True, null=True)\n role = models.CharField(max_length=255, blank=True)\n head_image = models.ImageField(upload_to='images',max_length=255,blank=True, null=True)\n comp = models.CharField(max_length=50)\n job = models.CharField(max_length=50)\n edu = models.CharField(max_length=50)\n \n \n mycomp = models.CharField(max_length=50)\n myjob = models.CharField(max_length=50)\n mymoney = models.CharField(max_length=50)\n myintr = models.CharField(max_length=254)\n mylocal = models.CharField(max_length=50)\n mymoney = models.CharField(max_length=255)\n \n myresume = models.CharField(max_length=255,null=True)\n# Create your models here.\n" }, { "alpha_fraction": 0.5500991940498352, "alphanum_fraction": 0.5590277910232544, "avg_line_length": 36.69158935546875, "blob_id": "6f9b44b99da2f56b4db2b863edd0484bf2e29068", "content_id": "9a4dc443f070fca7d3e7359905108f09649b8107", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4252, "license_type": "no_license", "max_line_length": 76, "num_lines": 107, "path": "/src/resume/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#--coding:utf-8--\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import \nfrom online.models import User\nfrom resume_tool.run import \nfrom resume_tool.all_extractor2 import \nfrom resume_tool.main import \nfrom django.shortcuts import redirect\n\nimport json\nimport sys \nimport random \nreload(sys) \nsys.setdefaultencoding('utf8') \n\nimport os\n \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n \n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(condition)\n if r.myresume != None and len(r.myresume)!=0:\n flag = 0\n result_list = process(r.myresume)\n info_list = result_list[0]\n ship_list = result_list[1]\n skill_list = result_list[2]\n project_list = result_list[3]\n socre_list = result_list[4]\n word_list = []\n for skill in skill_list:\n word = {}\n word['text'] = skill\n word['weight'] = random.uniform(100, 1000)\n word_list.append(word)\n word_list = json.dumps(word_list)\n return render(req,\"resumeindex.html\", locals())\n else:\n flag = 1\n return render(req,\"resumeindex.html\", locals())\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n \ndef upload_file(req): \n if req.method == \"POST\": # 请求方法为POST时，进行处理 \n user_info = req.session['user_info']\n user_name = user_info['name']\n myFile =req.FILES.get(\"myfile\", None) # 获取上传的文件，如果没有文件，则默认为None \n if not myFile: \n return HttpResponse(\"no files for upload!\") \n filename = \"\"\n if cmp(get_os(),\"n\")==0:\n filename = sys.path[0]+\"\\\\\"+user_name+myFile.name\n else:\n filename = sys.path[0]+\"/\"+user_name+myFile.name\n destination = open(filename,'wb+') # 打开特定的文件进行二进制的写操作 \n for chunk in myFile.chunks(): # 分块写入文件 \n destination.write(chunk) \n destination.close()\n print filename\n result_list,myresume = runresume(filename)\n User.objects.filter(name=user_name).update(myresume=myresume)\n info_list = result_list[0]\n ship_list = result_list[1]\n skill_list = result_list[2]\n project_list = result_list[3]\n socre_list = result_list[4]\n word_list = []\n for skill in skill_list:\n word = {}\n word['text'] = skill\n word['weight'] = random.uniform(100, 1000)\n word_list.append(word)\n word_list = json.dumps(word_list)\n return render(req,\"resumeindex.html\", locals())\n\ndef upload_file_person(req):\n if req.method == \"POST\": # 请求方法为POST时，进行处理\n user_info = req.session['user_info']\n user_name = user_info['name']\n myFile =req.FILES.get(\"myfile\", None) # 获取上传的文件，如果没有文件，则默认为None\n if not myFile:\n return HttpResponse(\"no files for upload!\")\n filename = \"\"\n if cmp(get_os(),\"n\")==0:\n filename = sys.path[0]+\"\\\\\"+user_name+myFile.name\n else:\n filename = sys.path[0]+\"/\"+user_name+myFile.name\n destination = open(filename,'wb+') # 打开特定的文件进行二进制的写操作\n for chunk in myFile.chunks(): # 分块写入文件\n destination.write(chunk)\n destination.close()\n result_list,myresume = runresume(filename)\n User.objects.filter(name=user_name).update(myresume=myresume)\n return HttpResponseRedirect(\"../../person/index\")" }, { "alpha_fraction": 0.5429542064666748, "alphanum_fraction": 0.5468370914459229, "avg_line_length": 33.7303352355957, "blob_id": "9fdbbcb5af4ce067e13a8feaa33a2a7a2cbe4594", "content_id": "9ba51976592398265de28e8676f88d4d362d3a1b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6609, "license_type": "no_license", "max_line_length": 167, "num_lines": 178, "path": "/src/online/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#encoding:utf-8\nfrom django.shortcuts import render_to_response,render\nfrom django.http import HttpResponseRedirect\nfrom online.models import User\nfrom django.template import RequestContext\nimport time\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom django import forms\n \nmail_host=\"smtp.163.com\" #设置服务器\nmail_user=\"18309238981\" #用户名\nmail_pass=\"19920203\" #口令 \nmail_postfix=\"163.com\" #发件箱的后缀\n\nclass ImageUploadForm(forms.Form):\n image = forms.ImageField()\n\ndef forget(req):\n pass \n \ndef send_mail(to_list,sub,content): \n me=mail_user+\"<\"+mail_user+\"@\"+mail_postfix+\">\" \n msg = MIMEText(content,_subtype='plain',_charset='utf-8') \n msg['Subject'] = sub \n msg['From'] = me \n msg['To'] = \";\".join(to_list) \n try: \n server = smtplib.SMTP() \n server.connect(mail_host) \n server.login(mail_user,mail_pass) \n server.sendmail(me, to_list, msg.as_string()) \n server.close() \n return True \n except Exception, e: \n print str(e) \n return False\n\ndef gotologin(req):\n if req.method == 'GET':\n return render(req,'login.html', locals())\n\ndef login(req):\n if req.method == 'GET':\n msg = '请求方式错误'\n return render(req, 'msg.html', locals())\n\n name = req.POST['name']\n passwd = req.POST['passwd']\n # 获取的表单数据与数据库进行比较\n condition = {'email': name}\n try:\n r = User.objects.get(condition)\n except User.DoesNotExist:\n msg = \"邮箱不存在，请先注册\"\n return render(req,'user_register.html', locals())\n\n else:\n if r.passwd == passwd:\n req.session['islogin'] = True\n user_info = {}\n user_info['id'] = r.id\n user_info['name'] = r.name\n user_info['email'] = r.email\n user_info['role'] = r.role\n req.session['beans'] = r.beans\n req.session['head_image'] = r.head_image.name\n\n user_role = user_info['role']\n print user_role\n if user_role == '管理员':\n req.session['user_info'] = user_info\n msg = \"登录成功！\"\n return render(req, \"manager_index.html\", locals())\n elif user_role == '普通用户':\n req.session['user_info'] = user_info\n msg = \"登录成功！\"\n return HttpResponseRedirect('/school/index/')\n else: # there are only 2 roles in system for now, will add 3rd role which is \"Super user\" in the future\n # req.session['user_info'] = user_info\n # msg = '您不是管理员'\n # return render(req,'msg.html', locals())\n return HttpResponseRedirect('/') # for super user, will jump to super_user_index.html\n\n\n # if(r.role == u\"管理员\"):\n # req.session['role']=True\n # else:\n # req.session['role']=False\n # req.session['user_info'] = user_info\n # msg=\"登录成功！\"\n # return HttpResponseRedirect('/')\n else:\n msg = '密码错误！'\n return render(req, 'login.html', locals())\n \ndef logout(req):\n req.session['islogin'] = False\n req.session['user_info'] = {}\n return HttpResponseRedirect('/')\n\n\ndef register(req):\n if req.method == 'GET':\n status = False\n return render(req,'user_register.html', locals())\n else:\n status = True\n name = req.POST['aname']\n email = req.POST['email']\n passwd = req.POST['apasswd']\n graduate = req.POST['graduate']\n filterResult=User.objects.filter(name=name)\n emailfilterResult = User.objects.filter(email=email)\n if len(filterResult)>0:\n msg = '用户名被人注册过了！'\n return render(req, 'user_register.html', locals())\n if len(emailfilterResult)>0:\n msg = '邮箱被人注册过了！'\n return render(req, 'user_register.html', locals())\n User.objects.create(name= name,passwd=passwd,email=email,graduate=graduate,beans=1,role=\"普通用户\",create_date=time.strftime('%Y-%m-%d %X', time.localtime() ))\n msg = '注册完成，请直接登录！'\n return render(req, 'login.html', locals())\n\n\ndef changepasswd(req):\n if not req.session['islogin']:\n msg = '你当前还没有登录，请先登录！'\n return render(req, 'login.html', locals())\n msg='changepasswd page'\n if req.method == 'GET':\n status = False\n return render(req,'changepasswd.html', locals())\n else:\n status=True\n passwd = req.POST['passwd']\n name = req.session['user_info']['name']\n User.objects.filter(name=name).update(passwd=passwd)\n req.session['islogin']=False\n msg=\"密码修改成功，请重新登录！\"\n return render(req, 'login.html', locals())\n \ndef getpasswd(req):\n msg='getpasswd page'\n if req.method == 'GET':\n status = False\n return render(req,'getpasswd.html', locals())\n else:\n status=True\n name = req.POST['name']\n try:\n r = User.objects.get(name = name)\n except User.DoesNotExist:\n msg=\"用户名不存在，请先注册！\"\n return render(req,'msg.html', locals())\n passwd = r.passwd\n email = r.email\n sub = \"能耗管理系统密码找回\"\n content = \"您的密码是：\"+str(passwd)\n send_mail(email,sub,content)\n msg=\"密码已经发送到您的邮箱，请查收！\"\n return render(req,'msg.html', locals())\n \ndef changehead_image(req):\n if req.method == 'GET':\n status = False\n return render_to_response('changehead_image.html', locals())\n if req.method == 'POST':\n form = ImageUploadForm( req.POST, req.FILES ) # 有文件上传要传如两个字段\n if form.is_valid():\n m = User.objects.get(name = req.session['user_info']['name'])\n m.head_image = form.cleaned_data['image'] # 直接在这里使用字段名获取即可\n m.save()\n req.session['head_image'] = m.head_image.name\n msg = \"image upload success\"\n return HttpResponseRedirect('/')\n msg = \"allowed only via POST\"\n return render_to_response('msg.html', locals())" }, { "alpha_fraction": 0.47985348105430603, "alphanum_fraction": 0.47985348105430603, "avg_line_length": 27.763158798217773, "blob_id": "21c86df6ada67a6cc8f7b5efccf7c3b626444c6e", "content_id": "8032d7320b6d6d72a0a2f7b224e0e3d1632925e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 1092, "license_type": "no_license", "max_line_length": 68, "num_lines": 38, "path": "/src/jiuye/static/js/pay.js", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "$(\"#mycom\").click(function(){\n\t var comtext = $(\"#comtext\").val();\n \t $.post(\"../info/\",{'comtext':comtext}, function(ret){ \n \t\t \t$(\"#containermain\").html(ret);\n })\n\n})\n\n$(\"#mypay\").click(function(){\n\t var paytext = $(\"#paytext\").val();\n \t $.post(\"../getpay/\",{'paytext':paytext}, function(ret){ \n \t\t \t$(\"#containermain\").html(ret); \t \t\t \t\n })\n \t\n})\n\n$(\"#jiabtn\").click(function(){\n\t var text = $(\"#myinfo\").text();\n\t var intr = $(\"#myintr\").text();\n \t $.post(\"../setjia/\",{'text':text,'intr':intr}, function(ret){ \n \t\t \t$(\"#retsult\").html(ret);\n \t\t \t$(\"#jiabtn\").attr(\"disabled\", true);\n \t\t \t$(\"#jianbtn\").attr(\"disabled\", true);\n \t\t \t//$(\"#containermain\").html(ret); \t \t\t \t\n })\n \t\n})\n$(\"#jianbtn\").click(function(){\n\t var text = $(\"#myinfo\").text();\n\t var intr = $(\"#myintr\").text();\n \t $.post(\"../setjian/\",{'text':text,'intr':intr}, function(ret){ \n \t\t \t$(\"#retsult\").html(ret);\n \t\t \t$(\"#jiabtn\").attr(\"disabled\", true);\n \t\t \t$(\"#jianbtn\").attr(\"disabled\", true);\n \t\t \t//$(\"#containermain\").html(ret); \t \t\t \t\n })\n \t\n})" }, { "alpha_fraction": 0.5319634675979614, "alphanum_fraction": 0.586758017539978, "avg_line_length": 20.899999618530273, "blob_id": "0e9d7cb118b1116bc63e786574fcc86ce0de85b5", "content_id": "7859a2462cb6365380f6f8593693c6316e6a729e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 438, "license_type": "no_license", "max_line_length": 51, "num_lines": 20, "path": "/src/online/migrations/0002_auto_20180117_2209.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n# Generated by Django 1.11.2 on 2018-01-17 14:09\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('online', '0001_initial'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='user',\n name='myintr',\n field=models.CharField(max_length=254),\n ),\n ]\n" }, { "alpha_fraction": 0.5, "alphanum_fraction": 0.5466472506523132, "avg_line_length": 18.08333396911621, "blob_id": "7039d3d4b9b8f9d1d8ea3b6fbe9679edad4d644f", "content_id": "eea1f922eb8b376b12bb9098d0886de318ca8293", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 796, "license_type": "no_license", "max_line_length": 49, "num_lines": 36, "path": "/src/tools/link.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-- coding: utf-8 -- \n\n''''' \n不同平台，实现对所在内网端的ip扫描 \n \n有时候需要知道所在局域网的有效ip，但是又不想找特定的工具来扫描。 \n使用方法 python ip_scaner.py 192.168.1.1 \n(会扫描192.168.1.1-255的ip) \n'''\n \nimport platform \nimport sys \nimport os \nimport time \nimport thread\n\n\n \ndef ping_ip(ip_str): \n cmd = [\"ping\", \"-{op}\".format(op=get_os()), \n \"1\", ip_str] #only one bag\n output = os.popen(\" \".join(cmd)).readlines() \n \n flag = False\n for line in list(output): \n if not line: \n continue\n if str(line).upper().find(\"TTL\") >=0: \n flag = True\n break\n if flag: \n return True\n return False\n \nif __name__ == \"__main__\": \n print ping_ip(\"172.19.10.120\")" }, { "alpha_fraction": 0.546832799911499, "alphanum_fraction": 0.5525367856025696, "avg_line_length": 38.892215728759766, "blob_id": "d0e7581d724b67bfc63397497e78d02f85c63269", "content_id": "7ac3b78a4d51ee9fd87c2cbb80c4d2fa8b9ced09", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6982, "license_type": "no_license", "max_line_length": 286, "num_lines": 167, "path": "/src/manager/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#--coding:utf-8--\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import \nfrom online.models import User\nfrom difflib import Match\n\nimport json\nimport sys \nimport os\nimport resume_tool\nimport re\nimport time\nimport datetime\nimport xlrd\n\nreload(sys) \nsys.setdefaultencoding('utf8') \n \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n user_role = user_info['role']\n if user_role == '管理员':\n return render(req, \"manager_index.html\", locals())\n else:\n msg = '您不是管理员'\n return render(req,'msg.html', locals())\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.method == 'POST':\n msg = \"模式错误\"\n return render(req,\"msg.html\", locals())\n\ndef file_upload(req):\n t = time.time()\n time_now = time.strftime('%Y%m%d_%H%M%S', time.localtime(t))\n\n try:\n islogin = req.session['islogin']\n except Exception, e:\n msg = '请登录'\n return render(req, 'msg.html', locals())\n\n\n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n user_role = user_info['role']\n if user_role != '管理员':\n msg = '您不是管理员'\n return render(req, 'msg.html', locals())\n\n if req.method == 'POST':\n excelin = req.FILES.get(\"excelin\", None)\n print sys.path[0] #D:\\jiuye0320\\jiuye\\src\n if not excelin: \n return HttpResponse(\"no files for upload!\") \n print excelin.name\n fileName = \"\"\n \n # 判断扩展名\n if re.search(r'.xlsx$', excelin.name) is not None:\n nameUnextended = excelin.name.split(\".xlsx\")[0]\n fileName = sys.path[0] + \"\\\\\" + \"excelfiles\\\\\" + nameUnextended + \"_uploadDate_\" + time_now + \".xlsx\"\n destination = open(fileName,'wb+') # 打开特定的文件进行二进制的写操作 \n for chunk in excelin.chunks(): # 分块写入文件 \n destination.write(chunk) \n destination.close()\n print \"xlsx\"\n elif re.search(r'.xls$', excelin.name) is not None:\n nameUnextended = excelin.name.split(\".xls\")[0]\n fileName = sys.path[0] + \"\\\\\" + \"excelfiles\\\\\" + nameUnextended + \"_uploadDate_\" + time_now + \".xls\"\n destination = open(fileName,'wb+') # 打开特定的文件进行二进制的写操作 \n for chunk in excelin.chunks(): # 分块写入文件 \n destination.write(chunk) \n destination.close()\n print \"xls\"\n else:# for later .csv or other type file\n return HttpResponse(\"Wrong file extension! Please re-select file.\")\n print fileName\n \n #read and write line by line from uploaded Excel files to DB\n #file I/O test\n xlsx_data = xlrd.open_workbook(fileName)\n table = xlsx_data.sheet_by_index(0)\n nrows = table.nrows\n ncols = table.ncols\n colnames = table.row_values(0)\n sql_args = \"\"\n print nrows,ncols\n for i in range(1,nrows):\n WorkList = []\n row = table.row_values(i)\n for j in range(0, ncols):\n if type(row[j]) == float:\n row[j] = str(row[j])\n WorkList.append(row[j])\n# print WorkList[0] + \", \" + WorkList[1]\n my_db = MynewcoderDB()\n for temp in range(0,len(WorkList)):\n WorkList[temp]=WorkList[temp].replace(\"\\'\",\"-\");\n sql_query = \"select * from school where \"+\" 采集日期='\" + WorkList[0] + \"' and 公司='\" + WorkList[1] + \"'and 职位='\" + WorkList[2]+ \"'and 职位描述='\" + WorkList[3] + \"'and 投递方式= '\" + WorkList[4]+ \"'and 分类='\" + WorkList[5] + \"'and 地点= '\" + WorkList[6] + \"'and URL= '\" + WorkList[7] + \"'\"\n tempinfo = my_db.getInfo(sql_query)\n if tempinfo ==None or len(tempinfo)==0:\n sql_query = \"REPLACE INTO school VALUES ('\" + WorkList[0] + \"','\" + WorkList[1] + \"','\" + WorkList[2]+ \"','\" + WorkList[3] + \"','\" + WorkList[4] + \"','\" + WorkList[5] + \"','\" + WorkList[6]+ \"','\" + WorkList[7] + \"'\" +\",0)\"\n #the sql_query above may cause issue because the data from Excel file may contains '\"' symbol\n #the '\"' symbol may cause sql_query cannot be executed\n #print sql_query\n my_db.execute(sql_query)\n my_db.commit()\n WorkList = []#reset WorkList\n uploads = 1\n return render(req,\"manager_index.html\", locals()) \n\ndef bean_recharge(req):\n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n user_role = user_info['role']\n if user_role != '管理员':\n msg = '您不是管理员'\n return render(req, 'msg.html', locals())\n\n if req.method == 'POST':\n recharge_user_name = req.POST['recharge_user_name']\n recharge_beans = req.POST['recharge_beans']\n \n user_beans = User.objects.get(email=recharge_user_name).beans\n pre_beans = user_beans#充值前校招豆数量\n \n recharge_beans_int = int(recharge_beans)\n user_beans += recharge_beans_int\n \n User.objects.filter(email=recharge_user_name).update(beans=user_beans)\n \n aft_beans = User.objects.get(email=recharge_user_name).beans#充值后校招豆数量\n u_beans = aft_beans - pre_beans#充值数量\n \n return render(req,\"manager_index.html\", {'u_beans': u_beans})\n \ndef authorize(req):\n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n user_role = user_info['role']\n if user_role != '管理员':\n msg = '您不是管理员'\n return render(req, 'msg.html', locals())\n\n if req.method == 'POST':\n authorize_user_name = req.POST['authorize_user_name']\n pre_user_role = User.objects.get(name=authorize_user_name).role\n User.objects.filter(name=authorize_user_name).update(role='管理员')#authorize to manager\n user_role = User.objects.get(name=authorize_user_name).role\n if pre_user_role == user_role:\n auth_msg = '用户 ' + authorize_user_name + ' 已经是管理员'\n else:\n auth_msg = '用户 ' + authorize_user_name + ' 管理员权限授权成功'\n role_data = [0, auth_msg]\n return render(req,\"manager_index.html\", {'role_data': role_data})\n" }, { "alpha_fraction": 0.6397306323051453, "alphanum_fraction": 0.6397306323051453, "avg_line_length": 32.11111068725586, "blob_id": "03c7296a97b66991cf8d40e3f8026a63f0710606", "content_id": "10cd822bb173d1e142d3060cff9ce29724cd03e0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 297, "license_type": "no_license", "max_line_length": 54, "num_lines": 9, "path": "/src/pay/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom pay import views\napp_name = 'pay'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^getpay/$',views.getpay,name = 'getpay'),\n url(r'^setjia/$',views.setjia,name = 'setjia'),\n url(r'^setjian/$',views.setjian,name = 'setjian'),\n]" }, { "alpha_fraction": 0.605681836605072, "alphanum_fraction": 0.6193181872367859, "avg_line_length": 54, "blob_id": "289791ecd9217a990466f10f6a7deddbddc5c798", "content_id": "27ea1038735481153ce7ca391be2d54faa544f50", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 880, "license_type": "no_license", "max_line_length": 87, "num_lines": 16, "path": "/src/vip/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom vip import views\napp_name = 'vip'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^search/$',views.search,name = 'search'),\n url(r'^share_search/$',views.share_search,name = 'share_search'),\n url(r'^delete/(?P<res_id>[0-9]+)$', views.delete, name='delete'),\n url(r'^order_delete/(?P<res_id>[0-9]+)$', views.order_delete, name='order_delete'),\n url(r'^vip_edit/(?P<res_id>[0-9]+)$',views.vip_edit,name='vip_edit'),\n url(r'^order_edit/(?P<res_id>[0-9]+)$', views.order_edit, name='order_edit'),\n url(r'^show/(?P<res_id>[0-9]+)$', views.show, name='show'),\n url(r'^order_show/(?P<res_id>[0-9]+)$', views.order_show, name='order_show'),\n url(r'^vip_edit/action$',views.vip_edit_action,name='vip_edit_action'),\n url(r'^order_edit/action$', views.order_edit_action, name='order_edit_action')\n]\n" }, { "alpha_fraction": 0.5795167088508606, "alphanum_fraction": 0.5855005979537964, "avg_line_length": 23.828571319580078, "blob_id": "e17fd388c4b35ec1b1896c624cec3f48b18a52e7", "content_id": "950ac890ad72375c7a0c33d2a8a2c5e048a0a280", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4501, "license_type": "no_license", "max_line_length": 102, "num_lines": 175, "path": "/src/resume_tool/all_extractor2.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# encoding:utf-8\n\nfrom date_extractor import \nfrom school_name_extractor import \nfrom major_extractor import \nfrom degree_extractor import \nfrom school_ship_extractor import \nfrom skill_extractor import \nfrom project_extractor import \n\nimport chardet\n\nimport re\n\nspace_pattern = re.compile('\\s')\n\ninput_encode = 'utf-8'\n\ndef init_info_dic():\n result = {}\n result['start_date'] = ''\n result['end_date'] = ''\n result['school'] = ''\n result['major'] = ''\n result['degree'] = '' \n return result\n\n# 获取教育经历的数目\ndef get_education_number(from_date, from_school):\n if from_school == 0 or from_school == 0:\n return max(from_date, from_school)\n else:\n return min(from_date, from_school)\n\n# 替换str中所有在list中出现的字符串\ndef replace_list_from_str(str, str_list):\n for s in str_list:\n str = str.replace(s, ' ')\n return str\n\n\ndef skill_info_extract(input_str):\n skill_list = skill_extract(input_str);\n return skill_list \n\n#项目相关\ndef project_info_extract(input_str):\n project_list,project_score = project_extract(input_str);\n return project_list,project_score \n\n\n#奖学金相关\ndef ship_info_extract(input_str):\n ship_list = ship_extract(input_str);\n return ship_list\n \n \n \n# PS: 解析的格式是utf-8\ndef school_info_extract(input_str):\n result = []\n if len(input_str.strip()) == 0:\n return result\n #input_str_src = space_pattern.sub('', input_str)\n \n \n #时间\n date_list = date_extract(input_str)\n tmp_str = replace_list_from_str(input_str, date_list)\n # for d in date_list:\n # print d\n # school_list = school_name_extract(input_str)\n # for d in school_list:\n # print d\n \n #专业\n major_list = major_extract(input_str)\n tmp_str = replace_list_from_str(tmp_str, major_list)\n # for d in major_list:\n # print d\n \n #学历\n degree_list = degree_extract(input_str)\n tmp_str = replace_list_from_str(tmp_str, degree_list)\n # for d in degree_list:\n # print d\n \n #学校\n school_list = school_name_extract(tmp_str)\n\n date_number = get_education_number_from_date(input_str)\n school_number = get_education_number_from_school_name(input_str)\n education_number = get_education_number(school_number, date_number)\n\n for i in range(0, education_number):\n tmp = init_info_dic()\n j = i 2\n if j < len(date_list):\n tmp['start_date'] = date_list[j]\n j += 1\n if j < len(date_list):\n tmp['end_date'] = date_list[j]\n\n if i < len(school_list):\n tmp['school'] = school_list[i]\n\n if i < len(major_list):\n tmp['major'] = major_list[i]\n\n if i < len(degree_list):\n tmp['degree'] = degree_list[i]\n\n if tmp['end_date'] == '' and (input_str.__contains__(u\"至今\") or input_str.__contains__(u\"致今\")):\n tmp['end_date'] = '1970/01/01'\n\n result.append(tmp)\n return result\n\ndef process(input_file_path):\n result_list = []\n items = []\n items.__sizeof__()\n all_line = \"\"\n school_info_list = []\n for line in open(input_file_path, 'r'):\n try:\n line = line.strip().decode('utf-8') # 设置编码格式\n except:\n try:\n line = line.strip().decode('gb2312') \n except:\n continue \n all_line = all_line +line \n info_list = school_info_extract(line) \n for info in info_list:\n if len(info_list) != 0:\n school_info_list.append(info)\n \n result_list.append(school_info_list)\n #整体分析\n \n #奖学金分析\n ship_list = ship_info_extract(all_line)\n for ship in ship_list:\n print \"奖学金相关:\"+ship\n \n result_list.append(ship_list) \n #关键词分析\n skill_list = skill_info_extract(all_line)\n for temp in skill_list:\n print \"能力:\"+temp\n \n result_list.append(skill_list) \n \n project_list,socre_list = project_info_extract(all_line)\n \n for i in range (0,len(project_list)):\n print \"项目:\"+project_list[i],socre_list[i]\n \n result_list.append(project_list) \n result_list.append(socre_list) \n return result_list\n \n\n\ndef main():\n print 'this is main'\n input_file_path = r'12.txt'\n #input_file_path = 'data/test'\n result_dic = process(input_file_path)\n\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.5752941370010376, "alphanum_fraction": 0.5870588421821594, "avg_line_length": 21.972972869873047, "blob_id": "01178359375ded3f99d98f7180f51fd62e6fa62f", "content_id": "b258eef104cc6a2f7ea72403f37f5b7a7ddd7815", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 998, "license_type": "no_license", "max_line_length": 64, "num_lines": 37, "path": "/src/resume_tool/union_extractor.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#学生会相关\n# encoding:utf-8\nimport re\n\nunion_str1 = u\"(主席\|部长\|会长\|班长\|副主席\|副部长\|副会长\|党支书\|团支书\|学习委员\|宣传委员\|生活委员)\"\nunion_pattern1 = re.compile(union_str1)\n\n# 抽取学位信息,并将日期按照顺序排列存入list\ndef union_extract(str):\n result_list = []\n school_list = union_pattern1.findall(str)\n for d in school_list:\n # print d\n result_list.append(d)\n return result_list\n\n# 会破坏信息排布的顺序\n\n\ndef process(input_file_path):\n for line in open(input_file_path, 'r'):\n try:\n line = line.strip().decode('utf-8') # 设置编码格式\n except:\n line = line.strip().decode('gb2312') \n degree_list = union_extract(line)\n for d in degree_list:\n print d\n print '-------'\n\ndef main():\n print 'this is main'\n input_file_path = 'lcy.txt'\n result_dic = process(input_file_path)\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.59375, "alphanum_fraction": 0.78125, "avg_line_length": 15, "blob_id": "6792a60874d7959c7b678214d7ff86197852c75f", "content_id": "90bc7d2ddd5208b0a6912c0e620e98d0e9a2847b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 50, "license_type": "no_license", "max_line_length": 16, "num_lines": 2, "path": "/README.md", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# 校招项目test1.0版\n基于Django1.11.2开发\n" }, { "alpha_fraction": 0.6572890281677246, "alphanum_fraction": 0.6572890281677246, "avg_line_length": 38.20000076293945, "blob_id": "4a26f6b10dda8bc0b2bc1bd3ae6d9938f6830cf4", "content_id": "186dca0464e6f3f01559dbf8957d1bcd7c2c780b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 391, "license_type": "no_license", "max_line_length": 63, "num_lines": 10, "path": "/src/manager/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom manager import views\napp_name = 'manager'\nurlpatterns = [\n# url(r'^index/$',views.index,name = 'index'),\n url(r'^$',views.index,name = 'manager_index'),\n url(r'^upload/$',views.file_upload,name = 'manager_index'),\n url(r'^bean/$',views.bean_recharge,name = 'manager_index'),\n url(r'^authorize/$',views.authorize,name = 'manager_index')\n]" }, { "alpha_fraction": 0.5124481320381165, "alphanum_fraction": 0.5539419054985046, "avg_line_length": 19.95652198791504, "blob_id": "67920bce6a5c380b2f5e844b5f8ddb391ff3aa5b", "content_id": "470d9378bf639f668bd961714a7d57c5938ae0d6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 482, "license_type": "no_license", "max_line_length": 46, "num_lines": 23, "path": "/src/forum/migrations/0002_auto_20180125_1210.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n# Generated by Django 1.10 on 2018-01-25 04:10\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('forum', '0001_initial'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='column',\n name='manager',\n ),\n migrations.RemoveField(\n model_name='column',\n name='parent',\n ),\n ]\n" }, { "alpha_fraction": 0.4646974205970764, "alphanum_fraction": 0.5497118234634399, "avg_line_length": 28.489360809326172, "blob_id": "ed85ebc2ca95af93c9a88fa83aee2869c985edc0", "content_id": "77371f687365e23a6d3ae036216bba88fa53acb9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1388, "license_type": "no_license", "max_line_length": 81, "num_lines": 47, "path": "/src/tools/wol.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "import struct\nimport sys\nimport socket\n\ndef to_hex_int(s):\n return int(s.upper(), 16)\n\ndef wolrun(mac,ip):\n ip_prefix = '.'.join(ip.split('.')[:-1]) \n destip =ip = '%s.%s'%(ip_prefix,'255')\n print destip \n dest = (destip, 9)\n spliter = \"\"\n if mac.count(\":\") == 5: spliter = \":\"\n if mac.count(\"-\") == 5: spliter = \"-\"\n if spliter == \"\":\n print(\"MAC address should be like XX:XX:XX:XX:XX:XX / XX-XX-XX-XX-XX-XX\")\n return False\n\n parts = mac.split(spliter)\n a1 = to_hex_int(parts[0])\n a2 = to_hex_int(parts[1]) \n a3 = to_hex_int(parts[2])\n a4 = to_hex_int(parts[3])\n a5 = to_hex_int(parts[4])\n a6 = to_hex_int(parts[5])\n addr = [a1, a2, a3, a4, a5, a6]\n\n packet = chr(255) + chr(255) + chr(255) + chr(255) + chr(255) + chr(255)\n\n for n in range(0,16):\n for a in addr:\n packet = packet + chr(a)\n\n packet = packet + chr(0) + chr(0) + chr(0) + chr(0) + chr(0) + chr(0)\n\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n s.setsockopt(socket.SOL_SOCKET,socket.SO_BROADCAST,1)\n s.sendto(packet,dest)\n\n print(\"WOL packet %d bytes sent !\" % len(packet))\n return True\nif __name__ == \"__main__\": \n #wolrun('00-23-7d-c7-a5-44','10.13.28.202')\n wolrun('00-1f-c6-ac-ec-ec','10.13.28.69')\n #wolrun('00-23-7d-c7-9a-a4','10.13.28.140')\n #wolrun('2c:60:0c:52:81:ce','172.0.19.125')\n\n\n" }, { "alpha_fraction": 0.6125158071517944, "alphanum_fraction": 0.6169405579566956, "avg_line_length": 26.75438690185547, "blob_id": "79930f1f0bd868667b9867d8b62d5fdf71912f36", "content_id": "a341045a5a4a7156e6a0bc88f0d2b283a92768fe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1618, "license_type": "no_license", "max_line_length": 107, "num_lines": 57, "path": "/src/resume_tool/pdf2txt.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -- coding:utf8 --\nfrom cStringIO import StringIO\n\nfrom pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\nfrom pdfminer.converter import TextConverter\nfrom pdfminer.layout import LAParams\nfrom pdfminer.pdfpage import PDFPage\n\nimport sys\nimport logging\nimport chardet\nfrom tools import \naddsys()\n\n\ndef convert_pdf_to_txt(path):\n try:\n logging.debug('Converting pdf to txt: ' + str(path))\n rsrcmgr = PDFResourceManager()\n retstr = StringIO()\n codec = 'utf-8'\n laparams = LAParams()\n device = TextConverter(rsrcmgr, retstr, codec=codec, laparams=laparams)\n fp = file(path, 'rb')\n interpreter = PDFPageInterpreter(rsrcmgr, device)\n password = \"\"\n maxpages = 0\n caching = True\n pagenos = set()\n\n for page in PDFPage.get_pages(fp, pagenos, maxpages=maxpages, password=password, caching=caching,\n check_extractable=True):\n interpreter.process_page(page)\n\n text = retstr.getvalue()\n\n fp.close()\n device.close()\n retstr.close()\n # print chardet.detect(text)\n print text\n return text\n except Exception, e:\n logging.error('Error in file: ' + path + str(e))\n return \"\"\n\n\n# 将pdf文档转换为txt格式\ndef handle_pdffiles(pdffile):\n # print pdffile\n # pdf2t = pdffile[:-4] # 以后缀.pdf切词\n convert_pdf_to_txt(pdffile)\n\n # print os.path.exists(pdf2t + \".txt\")\n # f = open(pdf2t + '.txt', 'w+') # 以txt格式保存\n # f.write(convert_pdf_to_txt(pdffile))\n # f.close()\n" }, { "alpha_fraction": 0.6180555820465088, "alphanum_fraction": 0.6284722089767456, "avg_line_length": 31.11111068725586, "blob_id": "f50335b960e5be3f2a7303e725d0b2d9d13f8058", "content_id": "ae1452c6897ae12017f050078c6a16882c231ca6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 288, "license_type": "no_license", "max_line_length": 51, "num_lines": 9, "path": "/src/person/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom person import views\napp_name = 'person'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^index2/$', views.index2, name='index2'),\n url(r'^info/$',views.info,name = 'info'),\n url(r'^test/$',views.test,name = 'test'),\n]" }, { "alpha_fraction": 0.6824644804000854, "alphanum_fraction": 0.6872037649154663, "avg_line_length": 27.200000762939453, "blob_id": "dd346beab95bc82f78ff29ddbc9394d23c48ae17", "content_id": "fee6b04654890761827a4d9b69ec2cecc5f3d2fb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 518, "license_type": "no_license", "max_line_length": 75, "num_lines": 15, "path": "/src/resume_tool/tools.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -- coding:utf8 --\nimport platform\ndef addsys():\n import sys #这里只是一个对sys的引用，只能reload才能进行重新加载\n stdi,stdo,stde=sys.stdin,sys.stdout,sys.stderr \n reload(sys) #通过import引用进来时,setdefaultencoding函数在被系统调用后被删除了，所以必须reload一次\n sys.stdin,sys.stdout,sys.stderr=stdi,stdo,stde \n sys.setdefaultencoding('utf-8')\n \ndef get_os(): \n os = platform.system() \n if os == \"Windows\": \n return \"n\"\n else: \n return \"c\"" }, { "alpha_fraction": 0.4630792438983917, "alphanum_fraction": 0.4810523986816406, "avg_line_length": 32.463768005371094, "blob_id": "0a94d4a2444d92c542a3a16cf25ae3b2d7bdca6a", "content_id": "3772eb847ff89d0103ef2ee4ae634e2cf4f038b8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9438, "license_type": "no_license", "max_line_length": 370, "num_lines": 276, "path": "/src/bgtool/com.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "GB18030", "text": "__author__ = 'corey'\n# -- coding:utf-8 --\nimport urllib\nimport urllib2\nimport re,json\nfrom bs4 import BeautifulSoup\nimport requests\nimport MySQLdb\n\ndef changekey(key):\n return key.strip().replace(\"'\",'\"')\n\ndef get(url):\n try:\n request = urllib2.Request(url)\n #request.add_header('Pragma', 'no-cache')\n response = urllib2.urlopen(request,timeout=20)\n page = response.read()\n return page\n except:\n return \"\"\nclass Spider:\n \n def __init__(self):\n self.siteURL = 'https://www.nowcoder.com/discuss/'\n\n def getPage(self,pageIndex):\n self.Pindex = pageIndex\n url = self.siteURL + str(pageIndex)+\"?type=0&order=0&page=1\"\n return get(url)\n\n \n def getContents(self,pageIndex):\n page = self.getPage(pageIndex)\n soup = BeautifulSoup(page,\"lxml\")\n try:\n #标题\n self.title = soup.find(\"h1\",{\"class\":\"discuss-title\"})\n self.title = soup.h1.get_text().strip()\n #作者\n louzhu =soup.find(\"span\",{\"class\":\"post-name\"})\n louzhu = louzhu.find(\"a\")\n self.louzhu = louzhu.attrs['title']\n self.louzhu = changekey(self.louzhu)\n\n #时间\n lztime =soup.find(\"span\",{\"class\":\"post-time\"})\n lztime = lztime.get_text()\n lztime = lztime.split()\n self.lztime = lztime[1]+\" \"+lztime[2]\n self.lztime = changekey(self.lztime)\n\n #发表内容\n context =soup.find(\"div\",{\"class\":\"post-topic-des\"})\n context = context.get_text()\n context = context.split()\n self.context = context[0]\n for i in range(1,len(context)-2):\n self.context = self.context+context[i]\n self.context = changekey(self.context)\n #pattern = re.compile('h1 class=\"discuss-title\".')\n #items = re.findall(pattern,page)\n #print items\n \n \n #评论数\n num = soup.findAll(\"h1\")\n num = num[2].get_text()\n num = num.encode(\"utf-8\")\n num = re.findall(r'(\\w[0-9]+)\\w',num)\n self.num = int(num[0])\n return True\n except:\n return False\n\n \n def getComPage(self,page):\n #最多pagesize是100\n #https://www.nowcoder.com/comment/list-by-page-v2?pageSize=200000&page=1&order=1&entityId=62719&entityType=8\n url = \"https://www.nowcoder.com/comment/list-by-page-v2?pageSize=100&page=\"+str(page)+\"&order=1&entityId=\"+str(self.Pindex)+\"&entityType=\"+str(8);\n return get(url)\n \n def getComments(self,pageid):\n ids = []\n subcom= []\n compage = self.getComPage(pageid)\n #每次主回复的消息\n self.compage =json.loads(compage)\n #回复的子回复，每次的深度最多为1\n if self.num>100:\n num = 100\n else:\n num = self.num\n for i in range(0,num):\n ids.append(self.compage['data']['comments'][i]['id'])\n self.ids = ids\n for idtemp in ids:\n subcom.append(json.loads(self.getID(idtemp)))\n self.subcom = subcom\n \n\n def getID(self,idindex):\n #https://www.nowcoder.com/comment/list-by-page-v2?token=&pageSize=10&page=1&order=1&entityId=1091685&entityType=2&_=1511231131699\n url = \"https://www.nowcoder.com/comment/list-by-page-v2?token=&pageSize=100&page=1&order=1&entityId=\"+str(idindex)+\"&entityType=2\"\n return get(url)\n def save(self,sqlstr):\n self.db_conn= MySQLdb.connect(\n host='localhost',\n port = 3306,\n user='liu',\n passwd='123456',\n db ='newcoder',\n charset='utf8',\n )\n \n self.db_cur = self.db_conn.cursor()\n try:\n \n self.db_cur.execute(sqlstr)\n except:\n print \"error\" \n self.db_cur.close()\n self.db_conn.commit()\n self.db_conn.close()\n \n def saveall(self):\n #保存标题等\n sqlstr = \"insert into discuss values('\"+str(self.Pindex)+\"','\"+self.title+\"','\"+self.louzhu+\"','\"+self.lztime+\"','\"+self.context+\"','\"+str(self.num)+\"')\"\n self.save(sqlstr)\n \n #保存一级评论\n if self.num >100:\n num = 100\n else:\n num = self.num\n for i in range (0,num):\n sqlstr = \"insert into comment values('\"+str(self.Pindex)+\"','0','\"+str(self.compage['data']['comments'][i]['id'])+\"','\"+self.compage['data']['comments'][i]['authorName']+\"','\"+str(self.compage['data']['comments'][i]['authorId'])+\"','\"+self.compage['data']['comments'][i]['content']+\"')\"\n self.save(sqlstr)\n \n #保存二级评论\n for i in range (0,len(self.subcom)):\n for j in range (0,int(self.subcom[i][\"data\"][\"totalCnt\"])):\n print i,j\n sqlstr = \"insert into comment values('\"+str(self.Pindex)+\"','\"+str(self.subcom[i]['data']['comments'][j]['toCommentId'])+\"','\"+str(self.subcom[i]['data']['comments'][j]['id'])+\"','\"+self.subcom[i]['data']['comments'][j]['authorName']+\"','\"+str(self.subcom[i]['data']['comments'][j]['authorId'])+\"','\"+self.subcom[i]['data']['comments'][j]['content']+\"')\"\n self.save(sqlstr)\n \n \n #该网页需要登录，对比cookie之后发现存在变量t记录登录信息，postman实验成功\n def getUser(self,userindex):\n url = \"https://www.nowcoder.com/profile/\"+str(userindex)+\"/basicinfo\"\n try:\n opener = urllib2.build_opener()\n opener.addheaders.append(('Cookie', 't=C139A5266EFAE233DD9C9FC10C0A1B5C'))\n page = opener.open(url,timeout=20)\n soup = BeautifulSoup(page,\"lxml\")\n istrue = soup.find(\"div\",{\"class\":\"null-tip\"})\n except:\n istrue = \"\"\n if istrue == None:\n try:\n name = soup.find(\"dd\",{\"id\":\"nicknamePart\"})\n name = name.get_text().strip()\n except:\n name = \"\"\n try:\n city = soup.find(\"li\",{\"class\":\"profile-city\"})\n city = city.get_text().strip()\n except:\n city = \"\"\n try:\n edu = soup.find(\"dd\",{\"id\":\"schoolInfoPart\"})\n edu = edu.get_text().strip()\n except:\n edu = \"\"\n try:\n intr = soup.find(\"dd\",{\"id\":\"introductionPart\"})\n intr = intr.get_text().strip()\n except:\n intr = \"\"\n try:\n liv = soup.find(\"dd\",{\"id\":\"livePlacePart\"})\n liv = liv.get_text().strip()\n except:\n liv = \"\"\n try:\n cur = soup.find(\"dd\",{\"id\":\"curIdentityPart\"})\n cur = cur.get_text().strip()\n except:\n cur = \"\"\n try:\n wor = soup.find(\"dd\",{\"id\":\"workTimePart\"})\n wor = wor.get_text().strip()\n except:\n wor = \"\"\n try:\n el = soup.find(\"dd\",{\"id\":\"eduLevelPart\"})\n el = el.get_text().strip()\n except:\n el = \"\"\n try:\n com = soup.find(\"dd\",{\"id\":\"companyInfoPart\"})\n com = com.get_text().strip()\n except:\n com = \"\"\n try:\n job = soup.find(\"dd\",{\"id\":\"jobInfoPart\"})\n job = job.get_text().strip()\n except:\n job = \"\"\n #写入数据库 \n sqlstr = \"insert into NCuser values('\"+str(userindex)+\"','\"+name+\"','\"+city+\"','\"+edu+\"','\"+intr+\"','\"+liv+\"','\"+cur+\"','\"+wor+\"','\"+el+\"','\"+com+\"','\"+job+\"')\"\n sqlstr = sqlstr.encode(\"utf-8\")\n try:\n self.db_cur.execute(sqlstr)\n print name,city,edu,intr,liv,cur,wor,el,com,job\n\n except:\n print \"error\"\n else:\n print \"跳过\",userindex\n \n def getAllUser(self):\n\n for i in range(176046,900000):\n self.db_conn= MySQLdb.connect(\n host='localhost',\n port = 3306,\n user='liu',\n passwd='123456',\n db ='newcoder',\n charset='utf8',\n )\n \n self.db_cur = self.db_conn.cursor()\n self.getUser(i)\n self.db_cur.close()\n self.db_conn.commit()\n self.db_conn.close()\n def getAllContents(self):\n for i in range(100,10000):\n try:\n istrue = self.getContents(i)\n #大于100的必须分开\n count = 1;\n if istrue:\n print i\n while(self.num>100):\n self.getComments(count)\n self.saveall()\n count = count+1\n self.num -=100\n self.getComments(count)\n self.saveall()\n \n else:\n print \"跳过\",i\n except:\n pass\n \n \n \nimport sys\nstdout = sys.stdout\nreload(sys)\nsys.stdout = stdout\n \nspider = Spider()\nspider.getAllContents()\n\n'''\nspider.getContents(62186)\n\nspider.getComments()\n\n#spider.printall()\n'''\n" }, { "alpha_fraction": 0.5195214152336121, "alphanum_fraction": 0.5491183996200562, "avg_line_length": 39.71794891357422, "blob_id": "1003f7d8094c538f6e8fb0228e04c6294f623d7f", "content_id": "2ba2b348f616ccd416636673bc134cf0d73b42a0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1588, "license_type": "no_license", "max_line_length": 114, "num_lines": 39, "path": "/src/online/migrations/0001_initial.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n# Generated by Django 1.11.2 on 2018-01-17 13:57\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='User',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=50)),\n ('passwd', models.CharField(max_length=50)),\n ('email', models.CharField(max_length=50)),\n ('beans', models.IntegerField()),\n ('vip', models.BinaryField()),\n ('create_date', models.DateTimeField(blank=True, null=True)),\n ('update_date', models.DateTimeField(blank=True, null=True)),\n ('role', models.CharField(blank=True, max_length=255)),\n ('head_image', models.ImageField(blank=True, max_length=255, null=True, upload_to=b'images')),\n ('comp', models.CharField(max_length=50)),\n ('job', models.CharField(max_length=50)),\n ('edu', models.CharField(max_length=50)),\n ('mycomp', models.CharField(max_length=50)),\n ('myjob', models.CharField(max_length=50)),\n ('myintr', models.CharField(max_length=255)),\n ('mylocal', models.CharField(max_length=50)),\n ('mymoney', models.CharField(max_length=255)),\n ],\n ),\n ]\n" }, { "alpha_fraction": 0.5183805227279663, "alphanum_fraction": 0.5315449833869934, "avg_line_length": 33.7068977355957, "blob_id": "0460f1d9633c73fecdca3a5b011640fac0d3f49f", "content_id": "4b53cf5c214be14182f373c26c71d9d7d287a7c8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4186, "license_type": "no_license", "max_line_length": 144, "num_lines": 116, "path": "/src/school/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#--coding:utf-8--\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import \nfrom online.models import User\nimport json\nimport sys\nimport re\nfrom django.views.decorators.cache import cache_page\nreload(sys) \nsys.setdefaultencoding('utf8') \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.session['islogin'] == True:\n return render(req,\"school_index.html\", locals())\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.method == 'POST':\n msg = \"模式错误\"\n return render(req,\"msg.html\", locals())\n\n\ndef all(req):\n if req.method == 'GET':\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(condition)\n if r.beans < 1:\n msg = '用户豆不够，请充值'\n return render(req, 'msg.html', locals())\n else:\n try:\n text = req.GET.get(\"id\", '')\n my_db = MynewcoderDB()\n sqlstr = \"SELECT from school where id = \"+text\n infos = my_db.getInfo(sqlstr)[0]\n beans = r.beans - 0\n User.objects.filter(name=user_info['name']).update(beans=beans)\n req.session['beans'] = beans\n my_db.close()\n return render(req, \"school_all.html\", locals())\n except Exception, e:\n msg = '没有该信息，请重新查询'\n return render(req, \"msg.html\", locals())\n\n#@cache_page(60 * 5)\ndef info(req):\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(*condition)\n if r.beans<1:\n msg = '用户豆不够，请充值'\n return render(req,'msg.html', locals())\n else:\n try:\n if req.method == 'POST':\n text=req.POST.get(\"comtext\",'')\n zw=req.POST.get(\"zw\",'')\n fl=req.POST.get(\"fl\",'')\n else:\n text = req.GET.get(\"comtext\")\n if text == None:\n text = \"\"\n zw = req.GET.get(\"zw\")\n if zw == None:\n zw = \"\"\n fl = \"\"\n my_db = MynewcoderDB()\n sqlstr = \"SELECT from school where 公司 like '%\"+text+\"%'and 职位 like '%\"+zw+\"%' and 分类 like '%\"+fl+\"%' order by id DESC limit 10\"\n infos = my_db.getInfo(sqlstr)\n beans = r.beans - 0\n User.objects.filter(name=user_info['name']).update(beans=beans)\n req.session['beans'] = beans\n my_db.close()\n return render(req,\"school_index.html\", locals())\n except Exception,e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\", locals())\n\n\ndef test_one(req):\n my_db = MynewcoderDB()\n sqlstr0 = \"SELECT * from school where 职位 like '%测试%'\"\n infos0 = my_db.getInfo(sqlstr0)\n num0 = 0\n for maindata in range(len(infos0)):\n num0 += 1\n sqlstr1 = \"SELECT * from school where 职位 like '%数据%'\"\n infos1 = my_db.getInfo(sqlstr1)\n num1 = 0\n for maindata in range(len(infos1)):\n num1 += 1\n sqlstr2 = \"SELECT * from school where 职位 like '%java%'\"\n infos2 = my_db.getInfo(sqlstr2)\n num2 = 0\n for maindata in range(len(infos2)):\n num2 += 1\n sqlstr3 = \"SELECT * from school where 职位 like '%游戏%'\"\n infos3 = my_db.getInfo(sqlstr3)\n num3 = 0\n for maindata in range(len(infos3)):\n num3 += 1\n sqlstr4 = \"SELECT * from school where 职位 LIKE '%测试%' \"\n infos4 = my_db.getInfo(sqlstr4)\n my_db.close()\n jsdata1 = ['数据','java','游戏','测试']\n jsdata2 = [num0, num1, num2, num3]\n return render(req,'test_one.html',locals())\n" }, { "alpha_fraction": 0.6525725722312927, "alphanum_fraction": 0.6581762433052063, "avg_line_length": 33.641178131103516, "blob_id": "97d94a46eb9638cea09e3e9e0f6742dbf1cf07ce", "content_id": "5b7b0010620ace060fdf16f1f377eb28f0d33748", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6241, "license_type": "no_license", "max_line_length": 149, "num_lines": 170, "path": "/src/forum/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#--coding:utf-8--\nfrom forum.models import Nav, Post, Comment, Application, Column, Message\nfrom django.views.generic import View,TemplateView,ListView,DetailView\nfrom django.utils.timezone import now, timedelta\nfrom datetime import datetime\nfrom django.core.cache import cache\nfrom django.template import RequestContext\nfrom django.shortcuts import render_to_response,get_object_or_404\nfrom django.views.generic.edit import CreateView,UpdateView,DeleteView,FormView\nfrom django.http import HttpResponse,HttpResponseRedirect\nfrom django.core.urlresolvers import reverse_lazy\nfrom online.models import \nfrom form import MessageForm, PostForm\nfrom django.db.models import Q\n\nPAGE_NUM = 50\nclass BaseMixin(object):\n def get_context_data(self,args,kwargs):\n context = super(BaseMixin,self).get_context_data(kwargs)\n try:\n context['nav_list'] = Nav.objects.all()\n context['column_list'] = Column.objects.all()[0:5]\n context['last_comments'] = Comment.objects.all().order_by(\"-created_at\")[0:10]\n except Exception as e:\n print u'[BaseMixin]加载基本信息出错'\n\n return context\n\n#首页\nclass index(BaseMixin,ListView):\n model = Post\n queryset = Post.objects.all()\n template_name = 'forumindex.html'\n context_object_name = 'post_list'\n paginate_by = PAGE_NUM #分页--每页的数目\n def get_context_data(self,kwargs):\n kwargs['foruminfo'] =get_forum_info()\n kwargs['online_ips_count'] =get_online_ips_count()\n kwargs['hot_posts'] = self.queryset.order_by(\"-responce_times\")[0:10] \n return super(index,self).get_context_data(kwargs)\n \n \n#得到论坛信息，贴子数，用户数，昨日发帖数，今日发帖数\ndef get_forum_info():\n #请使用缓存\n oneday = timedelta(days=1)\n today = now().date()\n lastday = today - oneday\n todayend = today + oneday\n post_number = Post.objects.count()\n\n lastday_post_number = cache.get('lastday_post_number', None)\n today_post_number = cache.get('today_post_number', None)\n\n if lastday_post_number is None:\n lastday_post_number = Post.objects.filter(created_at__range=[lastday,today]).count()\n cache.set('lastday_post_number',lastday_post_number,6060)\n \n if today_post_number is None:\n today_post_number = Post.objects.filter(created_at__range=[today,todayend]).count()\n cache.set('today_post_number',today_post_number,6060)\n\n info = {\"post_number\":post_number,\n \"lastday_post_number\":lastday_post_number,\n \"today_post_number\":today_post_number}\n return info\n\ndef get_online_ips_count():\n online_ips = cache.get(\"online_ips\", [])\n if online_ips:\n online_ips = cache.get_many(online_ips).keys()\n return len(online_ips)\n return 0\n\n#帖子\ndef postdetail(request,post_pk):\n post_pk = int(post_pk)\n post = Post.objects.get(pk=post_pk)\n comment_list = post.comment_set.all() \n #统计帖子的访问访问次数\n if 'HTTP_X_FORWARDED_FOR' in request.META:\n ip = request.META['HTTP_X_FORWARDED_FOR']\n else:\n ip = request.META['REMOTE_ADDR']\n title = post.title\n visited_ips = cache.get(title, [])\n\n if ip not in visited_ips:\n post.view_times += 1\n post.save()\n visited_ips.append(ip)\n cache.set(title,visited_ips,1560)\n return render_to_response('post_detail.html',{'post':post,'comment_list':comment_list})\n#用户已发贴\nclass UserPostView(ListView):\n template_name = 'user_posts.html'\n context_object_name = 'user_posts'\n paginate_by = PAGE_NUM\n\n def get_queryset(self):\n user_posts = Post.objects.filter(author=self.request.user)\n return user_posts\n\n\n#发帖\nclass PostCreate(CreateView): \n model = Post\n template_name = 'form.html'\n form_class = PostForm\n #fields = ('title', 'column', 'type_name','content')\n #SAE django1.5中fields失效，不知原因,故使用form_class\n success_url = reverse_lazy('user_post')\n #这里我们必须使用reverse_lazy() 而不是reverse，因为在该文件导入时URL 还没有加载。\n\n def form_valid(self, form):\n #此处有待加强安全验证\n validate = self.request.POST.get('validate',None)\n formdata = form.cleaned_data\n if self.request.session.get('validate',None) != validate:\n return HttpResponse(\"验证码错误！<a href='/'>返回</a>\")\n user = User.objects.get(username=self.request.user.username)\n #form.instance.author = user\n #form.instance.last_response = user\n formdata['author'] = user\n formdata['last_response'] = user\n p = Post(formdata)\n p.save()\n user.levels += 5 #发帖一次积分加 5\n user.save()\n return HttpResponse(\"发贴成功！<a href='/'>返回</a>\")\n \n#编辑贴\nclass PostUpdate(UpdateView): \n model = Post\n template_name = 'form.html'\n success_url = reverse_lazy('user_post') \n\n\n#删贴\nclass PostDelete(DeleteView):\n model = Post\n template_name = 'delete_confirm.html'\n success_url = reverse_lazy('user_post') \n#所有板块\ndef columnall(request):\n column_list = Column.objects.all()\n return render_to_response('column_list.html',{'column_list':column_list })\n\n#每个板块\ndef columndetail(request,column_pk):\n column_obj = Column.objects.get(pk=column_pk)\n column_posts = column_obj.post_set.all()\n \n return render_to_response('column_detail.html',{'column_obj':column_obj,'column_posts':column_posts },context_instance=RequestContext(request)) \n#搜索 \nclass SearchView(ListView):\n template_name = 'search_result.html'\n context_object_name = 'post_list'\n paginate_by = PAGE_NUM\n\n def get_context_data(self,kwargs):\n kwargs['q'] = self.request.GET.get('srchtxt','')\n return super(SearchView,self).get_context_data(kwargs)\n\n def get_queryset(self):\n #获取搜索的关键字\n q = self.request.GET.get('srchtxt','')\n #在帖子的标题和内容中搜索关键字\n post_list = Post.objects.only('title','content').filter(Q(title__icontains=q)\|Q(content__icontains=q));\n return post_list\n" }, { "alpha_fraction": 0.5509138107299805, "alphanum_fraction": 0.5509138107299805, "avg_line_length": 33.818180084228516, "blob_id": "477de4a7a68bf5300cd7fdd165891043e2ba1d58", "content_id": "4ce4e26bee039baca35a81226c80fc65fbec3c3b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "HTML", "length_bytes": 427, "license_type": "no_license", "max_line_length": 88, "num_lines": 11, "path": "/src/forum/templates/column_detail.html", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "板块:{{ column_obj.name}}; 管理员:{{ column_obj.manager }}; 帖子:{{column_obj.post_number}}<br>\n\n{% for post in column_posts %}\n<a href=\"{{ post.get_absolute_url }}\" >{{ post.title }}</a>\n \n 发表于:{{ post.created_at\|date:\"Y-m-d H:i:s\" }};\n 作者:{{ post.author }}；\n 最后回复:{{ post.last_response }}\n 回复点击数:{{ post.responce_times }}/{{ post.view_times }}\n\t<br>\n{% endfor %}\n" }, { "alpha_fraction": 0.4942159354686737, "alphanum_fraction": 0.5080333948135376, "avg_line_length": 25.598291397094727, "blob_id": "51b1e42788054e161c076b90ffde269936ba666a", "content_id": "ac3f289373bcbe7b23aebea830a0528df351ca21", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3200, "license_type": "no_license", "max_line_length": 113, "num_lines": 117, "path": "/src/resume_tool/codeinit.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -- coding:utf-8 --\nimport MySQLdb\nimport jieba.posseg as pseg\nimport jieba.analyse as ana\nimport jieba\nimport re\n#数据库相关\nclass MynewcoderDB:\n def __init__(self):\n self.db_conn= MySQLdb.connect(\n host='118.24.92.135',\n port = 3306,\n user='meng',\n passwd='123456',\n db ='newcoder',\n charset='utf8',\n )\n self.db_cur = self.db_conn.cursor()\n def execute(self,sqlstr):\n self.db_cur.execute(sqlstr)\n def getInfo(self,sqlstr):\n sqlstr = sqlstr.decode(\"utf-8\")\n self.execute(sqlstr)\n return self.db_cur.fetchall()\n def commit(self):\n self.db_conn.commit()\n def close(self):\n self.db_cur.close()\n self.db_conn.close()\n\nclass WordCut:\n def __init__(self,sentence):\n self.sentence = sentence\n def cutWords(self):\n self.words = pseg.cut(self.sentence)\n return self.words\n def top(self,num=100):\n words = ana.extract_tags(self.sentence,num)\n return words\nclass Info:\n def __init__(self):\n jieba.load_userdict(\"words.txt\")\n def getAlllist(self,sqlstr):\n self.my_db = MynewcoderDB()\n infos = self.my_db.getInfo(sqlstr)\n \n for key in infos:\n self.id = key[0]\n self.findCode(key[1]+key[4])\n def findCode(self,infostr):\n pass\n \n#邀请码相关\nclass InviteCode(Info):\n def __init__(self,usestr=\"内推码\"):\n Info.__init__(self)\n self.usestr = usestr\n sqlstr = \"SELECT from discuss WHERE context like '%\"+self.usestr+\"%' OR title like '%\"+self.usestr+\"%'\"\n self.getAlllist(sqlstr)\n \n #找到分词中邀请码最近的eng属性的词汇就是邀请码or内推码的内容\n def findCode(self,infostr):\n wordcut = WordCut(infostr)\n minilen = 1000\n i = 0\n lcyflagi = 0\n code = \"\"\n mycompany = \"\"\n for word, flag in wordcut.cutWords():\n i = i + 1\n if cmp(flag,\"company\")==0:\n mycompany = word\n if cmp(flag,\"lcy\")==0:\n lcyflagi = i\n if cmp(flag,\"eng\")==0:\n if (i-lcyflagi)<minilen:\n minilen = i-lcyflagi\n code = word\n if lcyflagi == 0:\n continue\n if code!=\"\" and lcyflagi!=0 and len(code)>6:\n print self.id,mycompany,code\n \n#类型相关\nclass TypeOffer(Info):\n def __init__(self):\n Info.__init__(self)\n sqlstr = \"SELECT * from discuss\"\n self.getAlllist(sqlstr)\n \n def findCode(self,infostr):\n wordcut = WordCut(infostr)\n mycompanylist = []\n for word, flag in wordcut.cutWords():\n if cmp(flag,\"company\")==0:\n mycompanylist.append(word)\n if(len(mycompanylist)>0 and len(infostr)>2000):\n print self.id\n for mycom in mycompanylist:\n print mycom\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\nif __name__==\"__main__\":\n icode = TypeOffer()\n" } ]	57
theuscarvalho/remessa-santander-grafica	https://github.com/theuscarvalho/remessa-santander-grafica	1a9aa5f370d5c8546085b55e977db381c6d13286	1e29f6ea4ebf50973a05561796f4627e79a34586	d5151661c573f2d052d271d9da1c453d210a01e6	refs/heads/master	2020-05-05T09:43:35.611230	2017-08-19T15:06:53	2017-08-19T15:06:53	null	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.61623615026474, "alphanum_fraction": 0.6715866923332214, "avg_line_length": 26.100000381469727, "blob_id": "55dccc782a8672837c8a690b8ac3dbd08a113340", "content_id": "c7f83da916280efdbbff5b5cf6e7235e8ef51096", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 271, "license_type": "no_license", "max_line_length": 57, "num_lines": 10, "path": "/test.py", "repo_name": "theuscarvalho/remessa-santander-grafica", "src_encoding": "UTF-8", "text": "from database import connection as conn\nfrom vigo import Boletos\nfrom datetime import datetime as dt\n\nboletos = Boletos(conn, dt(2014, 1, 1), dt(2014, 12, 1), \n \"EMITIR TODO MES\", \"'L'\", \"15\")\nprint boletos.count\n\nfor boleto in boletos:\n print boleto\n" }, { "alpha_fraction": 0.7421383857727051, "alphanum_fraction": 0.7421383857727051, "avg_line_length": 14.899999618530273, "blob_id": "0dd5f05908b83a7d5b4002e4242187119f094310", "content_id": "c917b78aacf932ef602c86e8abb35f10fb8b2aec", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 159, "license_type": "no_license", "max_line_length": 38, "num_lines": 10, "path": "/adapter.wsgi", "repo_name": "theuscarvalho/remessa-santander-grafica", "src_encoding": "UTF-8", "text": "import os\nimport sys\nsys.path.append('/var/www/appremessa')\n\nos.chdir(os.path.dirname(__file__))\n\nimport bottle\nimport app\n\napplication = bottle.default_app()\n" } ]	2
abeishekeeva/python-resume	https://github.com/abeishekeeva/python-resume	4971ac23abbe58c3e2d064007fea645f7ffd0d6f	108a6bfda564c9a2333dc2b740b99238eea32416	69502348eea9faa83f0178d48fbd845152efe5d9	refs/heads/master	2020-03-20T20:15:05.922793	2019-02-06T12:01:49	2019-02-06T12:01:49	137,677,452	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.7169230580329895, "alphanum_fraction": 0.7230769395828247, "avg_line_length": 45.47618865966797, "blob_id": "c44123843eaaedbe4bd4796888ae9b9f767d4a29", "content_id": "e31d9bd28a9db28aadf028ea293f5fdeb4acd9d2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 975, "license_type": "no_license", "max_line_length": 115, "num_lines": 21, "path": "/blog/views.py", "repo_name": "abeishekeeva/python-resume", "src_encoding": "UTF-8", "text": "from django.shortcuts import render\nfrom django.utils import timezone\nfrom .models import Post\nfrom django.shortcuts import render, get_object_or_404\n\n# Create your views here.\ndef post_list(request):\n posts = Post.objects.filter(published_date__lte=timezone.now(), tag=\"programming\").order_by('published_date')\n return render(request, 'blog/post_list.html', {'posts': posts})\n\ndef post_list_other(request):\n posts_other = Post.objects.filter(published_date__lte=timezone.now(), tag=\"general\").order_by('published_date')\n return render(request, 'blog/post_list.html', {'posts_other': posts_other})\n\ndef post_list_books(request):\n posts_books = Post.objects.filter(published_date__lte=timezone.now(), tag=\"books\").order_by('published_date')\n return render(request, 'blog/post_list.html', {'posts_other': posts_books})\n\ndef post_detail(request, pk):\n post = get_object_or_404(Post, pk=pk)\n return render(request, 'blog/post_detail.html', {'post': post})" }, { "alpha_fraction": 0.7523809671401978, "alphanum_fraction": 0.7523809671401978, "avg_line_length": 25.25, "blob_id": "4972d4c258ccaaa311342c7bd54668ed1896a021", "content_id": "5faef8bb0bdf2471969b8eb3396087906b97867b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 105, "license_type": "no_license", "max_line_length": 64, "num_lines": 4, "path": "/README.md", "repo_name": "abeishekeeva/python-resume", "src_encoding": "UTF-8", "text": "# python-resume\nOnline resume using Python and Bootstrap. Here is how it looks: \n\n![Screenshot](img.png)\n" } ]	2
astroboi-SH-KWON/remove_motif_error	https://github.com/astroboi-SH-KWON/remove_motif_error	f15616ace9469a2ea3a29bb4ea1ed2b64faca700	dea93b19183f0ed7e841346c0cfd23236a848da1	2f84576c24154d7af012400b12130c802213b3e4	refs/heads/main	2023-04-23T17:51:09.460710	2021-05-12T08:01:51	2021-05-12T08:01:51	341,741,502	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6104294657707214, "alphanum_fraction": 0.6104294657707214, "avg_line_length": 35.11111068725586, "blob_id": "33eb883fef0540fc139babd8fa75b00415a5c472", "content_id": "6b0ba6af2821bec6e8c4b788dcb1f58f21c592f9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 326, "license_type": "no_license", "max_line_length": 101, "num_lines": 9, "path": "/astroboi_bio_tools/ToolLogicPrep.py", "repo_name": "astroboi-SH-KWON/remove_motif_error", "src_encoding": "UTF-8", "text": "\n\nclass ToolLogicPreps:\n def __init__(self):\n pass\n\n def sort_list_by_ele(self, data_list, ele_idx, up_down_flag=True):\n result_list = []\n for tmp_arr in sorted(data_list, key=lambda tmp_arr: tmp_arr[ele_idx], reverse=up_down_flag):\n result_list.append(tmp_arr)\n return result_list" }, { "alpha_fraction": 0.638126015663147, "alphanum_fraction": 0.638126015663147, "avg_line_length": 46.38461685180664, "blob_id": "3f62ae05902918b7a9ea24a6a3095a25298a5b53", "content_id": "cea718c8e64377effdf1154111b085ac14df6c0a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 619, "license_type": "no_license", "max_line_length": 92, "num_lines": 13, "path": "/LogicPrep.py", "repo_name": "astroboi-SH-KWON/remove_motif_error", "src_encoding": "UTF-8", "text": "\nfrom astroboi_bio_tools.ToolLogicPrep import ToolLogicPreps\nclass LogicPreps(ToolLogicPreps):\n def make_list_to_dict_by_elekey(self, input_list, key_idx):\n result_dict = {}\n for input_arr in input_list:\n if input_arr[key_idx] in result_dict:\n result_dict[input_arr[key_idx]].append(input_arr)\n else:\n result_dict.update({input_arr[key_idx]: [input_arr]})\n return result_dict\n\n def filterout_ele_w_trgt_str(self, input_list, arr_idx, trgt_str):\n return [input_arr for input_arr in input_list if trgt_str not in input_arr[arr_idx]]\n\n\n" }, { "alpha_fraction": 0.6008174419403076, "alphanum_fraction": 0.6226158142089844, "avg_line_length": 37.6315803527832, "blob_id": "da0d10019d6da5d9658181ca2007798c9ce29e1f", "content_id": "f6024081e643166011cc1f6a99dc0780d6b9f14a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4404, "license_type": "no_license", "max_line_length": 154, "num_lines": 114, "path": "/Main.py", "repo_name": "astroboi-SH-KWON/remove_motif_error", "src_encoding": "UTF-8", "text": "import time\nimport os\nimport platform\n\nimport Util\nimport Logic\nimport LogicPrep\n#################### st env ####################\nWORK_DIR = os.getcwd() + \"/\"\nPROJECT_NAME = WORK_DIR.split(\"/\")[-2]\nSYSTEM_NM = platform.system()\n\nif SYSTEM_NM == 'Linux':\n # REAL\n pass\nelse:\n # DEV\n WORK_DIR = \"D:/000_WORK/KimNahye/20210217_ML/WORK_DIR_4_remove_motif_error/\"\n\nIN = 'input/'\nOU = 'output/'\n\n# input file ONLY for recount_motif_error() must be tsv format with \".txt\" extension\nMOTIF_ERROR_FL = [\n 'merged_NG_SsAPOBEC3B_LibB_Rep1_Seq_freq_2_13.txt'\n , 'Seq_freq_all_merged_RY_ABE8e_V106W.txt'\n , 'Seq_freq_all_merged_RY_YE1_BE4max.txt'\n , 'Seq_freq_all_merged_NG_ABE8e_V106W.txt'\n ]\n\nos.makedirs(WORK_DIR + IN, exist_ok=True)\nos.makedirs(WORK_DIR + OU, exist_ok=True)\n\n#################### en env ####################\n\n\"\"\"\n for single files\n input : MOTIF_ERROR_FL \n\"\"\"\ndef recount_motif_error():\n util = Util.Utils()\n logic = Logic.Logics()\n logic_prep = LogicPrep.LogicPreps()\n\n for err_fl_path in MOTIF_ERROR_FL:\n motif_err_fl = util.read_tsv_ignore_N_line(WORK_DIR + IN + err_fl_path)\n\n # filter out missing values\n flted_1_motif_err_fl = logic_prep.filterout_ele_w_trgt_str(motif_err_fl, 2, '-')\n motif_err_fl.clear()\n # #NAME? is removed\n flted_2_motif_err_fl = logic_prep.filterout_ele_w_trgt_str(flted_1_motif_err_fl, 2, 'N')\n flted_1_motif_err_fl.clear()\n flted_3_motif_err_fl = logic_prep.filterout_ele_w_trgt_str(flted_2_motif_err_fl, 2, 'n')\n flted_2_motif_err_fl.clear()\n\n motif_err_dict = logic_prep.make_list_to_dict_by_elekey(flted_3_motif_err_fl, 0)\n\n result_list = logic.recount_total_proportion_by_dictkey(motif_err_dict, 3)\n\n # head = ['Filename', 'INDEX', 'seq', 'Motif', 'Count', 'Total_cnt', 'Proportion', 'Substitution']\n head = ['Filename', 'seq', 'Motif', 'Count', 'Total_cnt', 'Proportion', 'Substitution']\n util.make_excel(WORK_DIR + OU + 'new_' + err_fl_path.replace('.txt', ''), head, result_list)\n\n\n\"\"\"\n for paired files\n input : null\n paired files must have file name pattern like \"_Rep1_\" and \"_Rep2_\"\n this method analyzes all patterned files in \"/input/\"\n\"\"\"\ndef recount_paired_motif_error():\n deli_ch = \"^\"\n\n util = Util.Utils()\n logic = Logic.Logics()\n logic_prep = LogicPrep.LogicPreps()\n\n sources = util.get_files_from_dir(WORK_DIR + IN + \"/Rep1.txt\")\n\n for paired_rep1 in sources:\n motif_err_fl = util.read_tsv_ignore_N_line(paired_rep1)\n motif_err_fl += util.read_tsv_ignore_N_line(paired_rep1.replace(\"_Rep1_\", \"_Rep2_\"))\n\n # filter out missing values\n flted_1_motif_err_fl = logic_prep.filterout_ele_w_trgt_str(motif_err_fl, 2, '-')\n motif_err_fl.clear()\n # #NAME? is removed\n flted_2_motif_err_fl = logic_prep.filterout_ele_w_trgt_str(flted_1_motif_err_fl, 2, 'N')\n flted_1_motif_err_fl.clear()\n flted_3_motif_err_fl = logic_prep.filterout_ele_w_trgt_str(flted_2_motif_err_fl, 2, 'n')\n flted_2_motif_err_fl.clear()\n\n \"\"\"\n Filename\tSeq\tMotif\tCount\tTotal_cnt\tProportion\tSubstitution\n TTTGACACACACACAGCACTCATAGCAC\tTTTGACACACACACAGCACTCATAGCACTCTAGACCCAAGCCACGAACGAGCGCGGTAAGCTTGGCGTAACTAGATCT\tCCCAAGCC\t540\t580\t0.931\tref_from_result\n TTTGACACACACACAGCACTCATAGCAC\tTTTGACACACACACAGCACTCATAGCACTCTAGACCCGAGCCACGAACGAGCGCGGTAAGCTTGGCGTAACTAGATCT\tCCCGAGCC\t22\t580\t0.0379\talt\n TTTGACACACACACAGCACTCATAGCAC\tTTTGACACACACACAGCACTCATAGCACTCTAGACCCAAACCACGAACGAGCGCGGTAAGCTTGGCGTAACTAGATCT\tCCCAAACC\t4\t580\t0.0069\talt\n \"\"\"\n\n motif_err_dict = logic.make_merged_list_to_dict_for_motiff_err(flted_3_motif_err_fl, 0, [1, 2, -1], deli_ch, 3)\n\n result_list = logic.recount_total_proportion_merged_dict_by_dictkey(motif_err_dict, deli_ch)\n\n head = ['Filename', 'seq', 'Motif', 'Count', 'Total_cnt', 'Proportion', 'Substitution']\n util.make_excel(paired_rep1.replace(\"input\", \"output\").replace('.txt', ''), head, result_list)\n\n\nif __name__ == '__main__':\n start_time = time.perf_counter()\n print(\"start [ \" + PROJECT_NAME + \" ]>>>>>>>>>>>>>>>>>>\")\n # recount_motif_error()\n recount_paired_motif_error()\n print(\"::::::::::: %.2f seconds ::::::::::::::\" % (time.perf_counter() - start_time))\n" }, { "alpha_fraction": 0.7951807379722595, "alphanum_fraction": 0.7951807379722595, "avg_line_length": 26.66666603088379, "blob_id": "8bfd8712d317d17a0705df025f83a4f7b7a515b6", "content_id": "646600826fece2ca6f52685b506d5c9235099ba7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 83, "license_type": "no_license", "max_line_length": 49, "num_lines": 3, "path": "/Util.py", "repo_name": "astroboi-SH-KWON/remove_motif_error", "src_encoding": "UTF-8", "text": "\nfrom astroboi_bio_tools.ToolUtil import ToolUtils\nclass Utils(ToolUtils):\n pass" }, { "alpha_fraction": 0.46979591250419617, "alphanum_fraction": 0.4746938645839691, "avg_line_length": 36.10606002807617, "blob_id": "cea04826aab367c7a3ebc7e24b5a4ce78d50ca42", "content_id": "ce0f042815ac3995cbe4b0ac6bd61fcf62944e66", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2450, "license_type": "no_license", "max_line_length": 108, "num_lines": 66, "path": "/Logic.py", "repo_name": "astroboi-SH-KWON/remove_motif_error", "src_encoding": "UTF-8", "text": "\nfrom astroboi_bio_tools.ToolLogic import ToolLogics\nclass Logics(ToolLogics):\n def recount_total_proportion_by_dictkey(self, motif_err_dict, idx):\n result_list = []\n for key, val_list in motif_err_dict.items():\n tot = 0\n for val_arr in val_list:\n tot += int(val_arr[idx])\n\n for val_arr in val_list:\n tmp_arr = val_arr[:5]\n # new total\n tmp_arr.append(tot)\n # new proportion\n if tot == 0:\n tmp_arr.append(0.0)\n else:\n tmp_arr.append(int(val_arr[idx]) / tot)\n tmp_arr.append(val_arr[-1])\n result_list.append(tmp_arr)\n return result_list\n\n def make_merged_list_to_dict_for_motiff_err(self, merged_list, key_idx, scnd_key_arr, deli_ch, val_idx):\n result_dict = {}\n for val_arr in merged_list:\n key = val_arr[key_idx]\n scnd_key = \"\"\n for i in scnd_key_arr:\n tmp_val = val_arr[i] + deli_ch\n scnd_key += tmp_val\n\n if key in result_dict:\n if scnd_key in result_dict[key]:\n result_dict[key][scnd_key] += int(val_arr[val_idx])\n else:\n result_dict[key].update({scnd_key: int(val_arr[val_idx])})\n else:\n result_dict.update({key: {scnd_key: int(val_arr[val_idx])}})\n\n return result_dict\n\n def recount_total_proportion_merged_dict_by_dictkey(self, motif_err_dict, deli_ch):\n result_list = []\n for key, val_dict in motif_err_dict.items():\n tot = 0\n for cnt in val_dict.values():\n tot += int(cnt)\n\n for scnd_key, cnt_val in val_dict.items():\n tmp_arr = [key]\n seq_motif_sub_arr = scnd_key.split(deli_ch)\n #add Seq, Motif\n tmp_arr += seq_motif_sub_arr[:2]\n # cnt\n tmp_arr.append(cnt_val)\n # new total\n tmp_arr.append(tot)\n # new proportion\n if tot == 0:\n tmp_arr.append(0.0)\n else:\n tmp_arr.append(int(cnt_val) / tot)\n #add Substitution\n tmp_arr.append(seq_motif_sub_arr[2])\n result_list.append(tmp_arr)\n return result_list\n" } ]	5
ku3i/flatcat	https://github.com/ku3i/flatcat	a7ceece24c25d42285d57532cef6b83fa7a8b6ea	015784b645b63ad7d2c982467a53679b43aac615	a7c6d4c9d7ae0c84b4a5c8cdd34fed77ed9ca11c	refs/heads/master	2023-09-04T08:01:59.618666	2021-09-29T15:42:07	2021-09-29T15:42:07	337,092,707	1	0	null	null	null	null	null	[ { "alpha_fraction": 0.47789472341537476, "alphanum_fraction": 0.5052631497383118, "avg_line_length": 18, "blob_id": "f46a22746a46476ce783b5fce5758e867e3d0ea5", "content_id": "0f57bc28204665845acdc7ee55937291e8991767", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 950, "license_type": "no_license", "max_line_length": 58, "num_lines": 50, "path": "/src/common/median3.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/---------------------------------+\n \| Supreme Machines \|\n \| Sensorimotor Firmware \|\n \| Matthias Kubisch \|\n \| [email protected] \|\n \| November 2018 \|\n +---------------------------------/\n\n#ifndef SUPREME_MEDIAN3_HPP\n#define SUPREME_MEDIAN3_HPP\n\nnamespace supreme {\n\ntemplate <typename T> inline T median_of_3(T a, T b, T c);\n\n/* computes median-of-three of a given variable/\ntemplate <typename T>\nclass Median3 {\n\tT val_1, val_2;\npublic:\n\tMedian3() : val_1(), val_2() {}\n\n\tT step(T val_0) {\n\t\tconst T result = median_of_3(val_0, val_1, val_2);\n\t\tval_2 = val_1;\n\t\tval_1 = val_0;\n\t\treturn result;\n\t}\n\n\tvoid reset(void) { val_1 = {}; val_2 = {}; }\n};\n\ntemplate <typename T>\ninline T median_of_3(T a, T b, T c)\n{\n\tif (a > b)\n\t\tif (b > c)\n\t\t\treturn b;\n\t\telse\n\t\t\treturn (a > c) ? c : a;\n\telse\n\t\tif (b < c)\n\t\t\treturn b;\n\t\telse\n\t\t\treturn (a > c) ? a : c;\n}\n\n} / namespace supreme /\n\n#endif / SUPREME_MEDIAN3_HPP /\n" }, { "alpha_fraction": 0.48901546001434326, "alphanum_fraction": 0.499864399433136, "avg_line_length": 28.015748977661133, "blob_id": "18f4d1447ceef5d10e5b93c8d67272c006afadf6", "content_id": "16bf2b72b3587a4271c10295467505594f67a550", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3687, "license_type": "no_license", "max_line_length": 121, "num_lines": 127, "path": "/src/midi/midi_in.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/---------------------------------+\n \| Matthias Kubisch \|\n \| [email protected] \|\n \| July 2017 \|\n +---------------------------------/\n\n#include <vector>\n#include <cassert>\n#include <string>\n#include <memory>\n#include <common/log_messages.h>\n#include \"RtMidi.h\"\n\n/\n g++ -Wall -D__LINUX_ALSA__ -o test test.cpp RtMidi.cpp -lasound -lpthread\n/\n\n/ [0,1] -> [-1,1] /\ninline float normed(float value) { return 2 value - 1; }\n\nclass MidiIn\n{\n struct Data_t {\n Data_t() : raw(.0), unlocked(false), changed(false) {}\n float raw;\n mutable bool unlocked;\n mutable bool changed;\n };\n\n static constexpr std::size_t max_channel = 256;\n const Data_t default_val = Data_t{};\n\n std::unique_ptr<RtMidiIn> midi_ptr;\n std::vector<unsigned char> message;\n std::vector<Data_t> data;\n\n std::string interface_name;\n bool init_success;\n bool verbose;\n\npublic:\n MidiIn(unsigned port, bool verbose = true)\n : midi_ptr(nullptr)\n , message()\n , data(max_channel, default_val)\n , interface_name()\n , init_success(false)\n , verbose(verbose)\n {\n assert(data.size() == max_channel);\n try {\n midi_ptr = std::unique_ptr<RtMidiIn>(new RtMidiIn());\n }\n catch (RtMidiError &error) {\n error.printMessage();\n err_msg(__FILE__,__LINE__,\"Error while initializing MIDI system.\");\n }\n\n if (port < midi_ptr->getPortCount())\n {\n try {\n midi_ptr->openPort(port);\n }\n catch (RtMidiError &error) {\n error.printMessage();\n err_msg(__FILE__,__LINE__,\"Error while opening MIDI port: %u\", port);\n }\n } else {\n wrn_msg(\"No MIDI interface initialized. No device on MIDI port: %u\", port);\n return;\n }\n\n midi_ptr->ignoreTypes( false, false, false );\n\n init_success = true;\n interface_name = midi_ptr->getPortName(port);\n sts_msg(\"MIDI Interface '%s' successfully initialized.\", interface_name.c_str());\n }\n\n bool fetch() {\n if (not init_success) return false;\n\n try {\n std::size_t num_bytes = 0;\n do {\n const float dt = midi_ptr->getMessage( &message );\n num_bytes = message.size();\n\n if (num_bytes >= 2) {\n const unsigned char channel = message[1];\n data[channel].raw = static_cast<float>(message[2] / 127.0);\n data[channel].changed = true;\n if (verbose)\n dbg_msg(\"MIDI(%03u): ch=%03u val=%4.2f dt=%4.2f\", message[0], message[1], data[channel].raw, dt);\n }\n } while (num_bytes >= 2); /* until message queue cleared /\n\n }\n catch (RtMidiError &error) {\n error.printMessage();\n return false;\n }\n\n return true;\n }\n\n bool has_changed(std::size_t index) const {\n const Data_t& d = data.at(index);\n bool c = d.changed;\n d.changed = false;\n return c;\n }\n\n float operator[] (std::size_t index) const { return data.at(index).raw; }\n\n / normed and unlocked /\n float get(std::size_t index, float unlock_initial = 0.0) const {\n const float val = normed(data.at(index).raw);\n\n if (data.at(index).unlocked) return val;\n else if (std::abs(val - unlock_initial) <= 1/127.) { / check if we can unlock /\n data.at(index).unlocked = true;\n return val;\n }\n else return .0;\n }\n};\n\n\n" }, { "alpha_fraction": 0.5546143651008606, "alphanum_fraction": 0.5557289123535156, "avg_line_length": 27.213836669921875, "blob_id": "7290fdec0b3c33a249ee6d89707b85dc46824c52", "content_id": "c2002871ff2e6409bc8192cce445d8c3d155c03d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4486, "license_type": "no_license", "max_line_length": 133, "num_lines": 159, "path": "/src/control/behavior_state_machine.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BEHAVIOR_STATE_MACHINE_H_INCLUDED\n#define BEHAVIOR_STATE_MACHINE_H_INCLUDED\n\n#include <common/log_messages.h>\n#include <robots/simloid.h>\n#include <control/controlparameter.h>\n#include <control/jointcontrol.h>\n\n/\n \\ \| /\n -- o --\n / \| \\\n/\n\n//class State_Base {\n//public:\n// State_Base(const std::string& name)\n// : name(name)\n// { dbg_msg(\"Creating state: %\", name.c_str()); }\n//\n// virtual void entry(void) = 0;\n// virtual void step(void) = 0;\n//\n// const std::string& get_name(void) const { return name; }\n//\n//protected:\n// const std::string name;\n//};\n\nenum Behaviorial_State {\n// standing,\n// walking_forwards,\n// stop_walking_forwards,\n// walking_backwards,\n// walking_left,\n// walking_right,\n// running_forwards,\n// running_backwards,\n// running_left,\n// running_right,\n// stop_from_running,\n// roll_over_left,\n// roll_over_right,\n//\n turning_left,\n stop_turning_left,\n//\n// turning_right,\n// stop_turning_right,\n};\n\n/\nTODO: table with allowed transitions:\n/\n\n\nenum Direction { forwards, backwards, left, right };\nenum Gait { stand, walk, turn, run, stop };\n\nclass Behavioral_Statemachine {\npublic:\n Behavioral_Statemachine( const control::Control_Vector& parameter_set\n , robots::Simloid& robot\n , control::Jointcontrol& control\n , Behaviorial_State initial_state = Behaviorial_State::stop_turning_left/walking_forwards/)\n : parameter_set(parameter_set)\n , robot(robot)\n , control(control)\n , state(initial_state)\n , last_state(state)\n , command()\n {\n dbg_msg(\"Creating behavioral state machine: \\n Initial state is %u\", state);\n //TODO: how make the mapping from enum (state) to parameter_setID\n\n }\n\n void loop(void)\n {\n switch(state)\n {\n case turning_left:\n if (command.gait == Gait::stop)\n state = stop_turning_left;\n break;\n\n case stop_turning_left:\n if (command.gait == Gait::turn)\n state = turning_left;\n break;\n\n// case standing:\n// if (command.gait == Gait::walk)\n// state = walking_forwards;\n// break;\n//\n// case walking_forwards:\n// if (command.gait == Gait::stop)\n// state = stop_walking_forwards;\n//\n// //TODO control.set_control_parameter(parameter_set.get(current_behavior).get_parameter(), true);\n// break;\n//\n// case stop_walking_forwards:\n// / TODO: wait for stop : robot.motion_stopped(threshold) /\n// if (command.gait == Gait::walk) {\n// state = walking_forwards;\n// }\n// else if (robot.motion_stopped(0.01))\n// {\n// state = standing;\n// dbg_msg(\"goto state: standing\");\n// }\n// break;\n\n default:\n assert(false);\n }\n\n if (last_state != state) {\n dbg_msg(\"State has changed from %u to %u\", last_state, state);\n last_state = state;\n }\n }\n\n\n / command functions /\n void walk(void) { command.gait = Gait::walk; dbg_msg(\"walk\"); }\n void turn(void) { command.gait = Gait::turn; dbg_msg(\"turn\"); }\n void run (void) { command.gait = Gait::run; dbg_msg(\"run\" ); }\n void stop(void) { command.gait = Gait::stop; dbg_msg(\"stop\"); }\n / stand is implicit and will be activated when stopping has succeeded /\n\n void forwards (void) { command.dir = Direction::forwards; dbg_msg(\"forwards\" ); }\n void backwards(void) { command.dir = Direction::backwards; dbg_msg(\"backwards\"); }\n void left (void) { command.dir = Direction::left; dbg_msg(\"left\" ); }\n void right (void) { command.dir = Direction::right; dbg_msg(\"right\" ); }\n\n Behaviorial_State get_state(void) const { return state; }\n\nprivate:\n const control::Control_Vector& parameter_set;\n robots::Simloid& robot;\n control::Jointcontrol& control;\n Behaviorial_State state, last_state;\n\n struct control_commands {\n control_commands()\n : gait(Gait::stand)\n , dir(Direction::forwards)\n { dbg_msg(\"Creating control commands.\"); }\n\n Gait gait;\n Direction dir;\n } command;\n\n\n};\n#endif // BEHAVIOR_STATE_MACHINE_H_INCLUDED\n" }, { "alpha_fraction": 0.7117347121238708, "alphanum_fraction": 0.7193877696990967, "avg_line_length": 27, "blob_id": "d4a8eaaba04f5ab29dc44ad71ff0c5d62b0f2f8b", "content_id": "76c9fcbf372f6f5cbb95458a139d885e4950793b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 392, "license_type": "no_license", "max_line_length": 72, "num_lines": 14, "path": "/src/learning/action_module.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef ACTION_MODULE_H_INCLUDED\n#define ACTION_MODULE_H_INCLUDED\n\nclass Action_Module_Interface\n{\npublic:\n virtual std::size_t get_number_of_actions(void) const = 0;\n virtual std::size_t get_number_of_actions_available(void) const = 0;\n virtual bool exists(const std::size_t action_index) const = 0;\n virtual ~Action_Module_Interface() {}\n};\n\n\n#endif // ACTION_MODULE_H_INCLUDED\n" }, { "alpha_fraction": 0.6328938007354736, "alphanum_fraction": 0.6453851461410522, "avg_line_length": 24.280702590942383, "blob_id": "5ad7c409f35bb60e57ec746cc3856eee9e58c086", "content_id": "baf23428a5c504a5e5270307955b04ab18ad7d8c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1441, "license_type": "no_license", "max_line_length": 110, "num_lines": 57, "path": "/src/draw/network3D.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ network3D.h /\n\n#ifndef NETWORK3D_H\n#define NETWORK3D_H\n\n#include <cassert>\n#include <vector>\n\n#include \"draw.h\"\n#include \"axes3D.h\"\n#include \"../common/log_messages.h\"\n\nclass network3D\n{\npublic:\n network3D(unsigned int number_of_nodes, const axes3D& axis)\n : number_of_nodes(number_of_nodes)\n , axis(axis)\n , pointer(0)\n , special_node(0)\n , activated_node(0)\n , n_pos(number_of_nodes)\n , n_size(number_of_nodes)\n , n_edges(number_of_nodes)\n {\n for (unsigned int i = 0; i < number_of_nodes; ++i)\n n_edges.at(i).assign(number_of_nodes, 0);\n }\n\n void draw(float x_angle, float y_angle) const;\n\n void update_node(unsigned int n, float x0, float x1, float x2, float s);\n void update_node(unsigned int n, float s);\n void update_edge(unsigned int i, unsigned int j, unsigned char op);\n void update_all_edges_of(unsigned int i, unsigned char op);\n void special (unsigned int n);\n void activated (unsigned int n);\n\n const Point& get_position(unsigned int index) const { assert(index < n_pos.size()); return n_pos[index]; }\n\nprivate:\n const unsigned int number_of_nodes;\n const axes3D& axis;\n\n unsigned int pointer;\n unsigned int special_node;\n unsigned int activated_node;\n\n std::vector<Point> n_pos;\n std::vector<float> n_size;\n\n / size == 0, kein Knoten /\n std::vector<std::vector<unsigned char> > n_edges;\n\n};\n\n#endif /NETWORK3D_H/\n" }, { "alpha_fraction": 0.6324435472488403, "alphanum_fraction": 0.6324435472488403, "avg_line_length": 20.954545974731445, "blob_id": "acbfda02ac506c04f2e60b1bf2f9f7cb1ff79008", "content_id": "21430e2e474f031357cc3189998fe6541bc3f6fc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 487, "license_type": "no_license", "max_line_length": 86, "num_lines": 22, "path": "/src/control/pidcontrol.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef PIDCONTROL_H\r\n#define PIDCONTROL_H\r\n\n#include \"../main.h\"\n\nclass pidcontrol\n{\n private:\n double kp, ki, kd; // PID parameter\n double u; // output\n\n public:\n pid(void);\n void loop(const double set_point, const double process_variable);\n void reset(void);\n\n void set_control_parameter(const double kp, const double ki, const double kd);\n void set_limits(const double llower, const double lupper);\n};\n\n\n#endif // PIDCONTROL\n\n\n" }, { "alpha_fraction": 0.5765306353569031, "alphanum_fraction": 0.6030611991882324, "avg_line_length": 26.94285774230957, "blob_id": "32cad6e39b3f3eeee0d2c53c67cc3d8cf930e48f", "content_id": "991d62bf9af8f9573acdaa1ea60cee267176c37d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 980, "license_type": "no_license", "max_line_length": 100, "num_lines": 35, "path": "/src/draw/display.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <draw/display.h>\n\nnamespace draw {\n\nvoid hbar(float px, float py, float dx, float dy, float value, float max_value, Color4 const& color)\n{\n const float frac = clip(value/max_value, 0.f, 1.f);\n set_color(color, 0.3);\n fill_rect(px, py, dx, dy);\n set_color(color);\n fill_rect(px, py, dxfrac, dy);\n}\n\nvoid vbar(float px, float py, float dx, float dy, float value, float max_value, Color4 const& color)\n{\n const float frac = clip(value/max_value, 0.f, 1.f);\n set_color(color, 0.3);\n fill_rect(px, py, dx, dy);\n set_color(color);\n fill_rect(px, py, dx, dyfrac);\n}\n\nvoid block(float px, float py, float sx, float sy, float value, float max_value)\n{\n float frac = clip(fabs(value/max_value), 0.f, 1.f);\n\n if (value >= 0.f) glColor4f(.0f, .5f, 1.f, frac);\n else glColor4f(1.f, .5f, .0f, frac);\n\n draw_fill_rect(px, py, sx, sy);\n glColor4f(.9f, .9f, .9f, .5f);\n draw_rect(px, py, sx, sy);\n}\n\n} / namespace draw /\n\n\n" }, { "alpha_fraction": 0.5724981427192688, "alphanum_fraction": 0.5824806690216064, "avg_line_length": 34.14912414550781, "blob_id": "18dc26e90039618558aeb92caf0122847bf10002", "content_id": "c62538484384dc1c1705a14a32adf1ae3a5a7bbc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4007, "license_type": "no_license", "max_line_length": 135, "num_lines": 114, "path": "/src/evolution/micro_evolution.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef MICRO_EVOLUTION_H_INCLUDED\n#define MICRO_EVOLUTION_H_INCLUDED\n\n//#include <evolution/fitness.h> //TODO check for circular dependency\n#include <evolution/individual.h>\n#include <evolution/population.h>\n#include <evolution/pool_strategy.h>\n\n/* 29.09.15: Elmar hat heute: \"Papa, mehr Gurke bitte, ohne Schale\" gesagt. /\n\nclass MicroEvolution {\n\n Population population;\n Individual child; // temp individual for crossover operation\n\n std::size_t candidate_idx;\n\n double moving_rate;\n double selection_bias;\n\n enum Trial_t {\n trial_initial,\n trial_crossover,\n trial_refreshing\n };\n\n Trial_t trial;\n uint32_t trial_count;\n std::string name;\n\npublic:\n MicroEvolution( const std::string& name\n , std::size_t population_size\n , std::size_t individual_size\n , double init_mutation_rate\n , double meta_mutation_rate\n , double moving_rate\n , double selection_bias )\n : population(population_size, individual_size, init_mutation_rate, meta_mutation_rate)\n , child(population[0])\n , candidate_idx(0)\n , moving_rate(moving_rate)\n , selection_bias(selection_bias)\n , trial(trial_initial)\n , trial_count(0)\n , name(name)\n { / dbg_msg(\"Creating micro evolution.\"); / }\n\n ~MicroEvolution() { / dbg_msg(\"Destroying micro evolution.\"); / }\n\n void generate_start_population(const VectorN& seed) {\n //dbg_msg(\"Generate start population.\");\n for (std::size_t i = 0; i < seed.size(); ++i)\n dbg_msg(\"[%u]: %+1.3f\", i, seed[i]); // TODO remove\n\n population.initialize_from_seed(seed);\n }\n\n const VectorN& get_next_candidate_genome(void) {\n\n //sts_msg(\"%s Trial: %2u (%d)\", name.c_str(), trial_count, trial);\n\n if (trial_count < population.get_size())\n {\n trial = trial_initial;\n candidate_idx = trial_count;\n return population[trial_count].genome;\n }\n else if (random_value(0.0, 1.0) > moving_rate) /TODO: this is actually the inverse moving rate, rename!/\n {\n /* select parents from pool and crossover /\n std::size_t parent_1 = biased_random_index_inv(population.get_size(), selection_bias);\n std::size_t parent_2 = biased_random_index_inv(population.get_size(), selection_bias);\n //sts_msg(\" crossing %2u and %2u\", parent_1, parent_2);\n\n trial = trial_crossover;\n candidate_idx = parent_1;\n crossover( population[parent_1]\n , population[parent_2]\n , child );\n\n child.mutate();\n return child.genome;\n }\n else\n {\n trial = trial_refreshing;\n candidate_idx = random_index(population.get_size());\n return population[candidate_idx].genome;\n }\n }\n\n /* no state machine /\n void set_candidate_fitness(const double fitness) {\n\n if (trial == trial_crossover) {\n child.fitness = fitness; // assign fitness to child in crossover trial\n std::size_t replace_idx = get_replacement_candidate_for(child, population);\n if (child.fitness > population[replace_idx].fitness) {\n //dbg_msg(\"Got replacement candidate %u (%1.3f > %1.3f)\", replace_idx, child.fitness, population[replace_idx].fitness);\n population[replace_idx] = child;\n } //else dbg_msg(\"not replaced (%1.3f < %1.3f)\", child.fitness, population[replace_idx].fitness);\n }\n else {\n population[candidate_idx].fitness.set_value(fitness); //averaging\n //dbg_msg(\"refreshing id:%u (%1.2f)\", candidate_idx, population[candidate_idx].fitness.get_value());\n }\n ++trial_count;\n dbg_msg(\"fit(%1.2f)\", population[candidate_idx].fitness.get_value());\n }\n\n};\n\n#endif // MICRO_EVOLUTION_H_INCLUDED\n" }, { "alpha_fraction": 0.6268116235733032, "alphanum_fraction": 0.6280193328857422, "avg_line_length": 33.5, "blob_id": "a164a319ddb194b4b54d80db3c6170672a970611", "content_id": "6872bee112a8052f1764404dd3f91331f340cd0b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1656, "license_type": "no_license", "max_line_length": 133, "num_lines": 48, "path": "/src/learning/reward.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef REWARD_H_INCLUDED\n#define REWARD_H_INCLUDED\n\n#include <common/integrator.h>\n#include <control/sensorspace.h>\n\nclass reward_base\n{\nprotected:\n std::vector<sensor_signal> rewards;\n std::vector<Integrator> aggregated_rewards;\n\npublic:\n reward_base(std::size_t number_of_policies_to_reserve)\n : rewards()\n , aggregated_rewards(number_of_policies_to_reserve)\n {\n dbg_msg(\"Creating reward base, reserving %u items.\", number_of_policies_to_reserve);\n rewards.reserve(number_of_policies_to_reserve);\n }\n\n virtual ~reward_base() {}\n\n double get_current_reward(std::size_t index) const { assert(index < rewards.size()); return rewards[index].current; }\n double get_last_reward (std::size_t index) const { assert(index < rewards.size()); return rewards[index].last; }\n const std::string& get_reward_name (std::size_t index) const { assert(index < rewards.size()); return rewards[index].name; }\n std::size_t get_number_of_policies(void) const { return rewards.size(); }\n\n double get_aggregated_last_reward(std::size_t index) const {\n return aggregated_rewards.at(index).get_avg_value();\n }\n\n void clear_aggregations(void) {\n for (std::size_t i = 0; i < aggregated_rewards.size(); ++i)\n aggregated_rewards[i].reset();\n }\n\n void execute_cycle(void) {\n assert(aggregated_rewards.size() >= rewards.size());\n for (std::size_t i = 0; i < rewards.size(); ++i) {\n rewards[i].execute_cycle();\n aggregated_rewards[i].add(rewards[i].current);\n }\n }\n};\n\n\n#endif // REWARD_H_INCLUDED\n" }, { "alpha_fraction": 0.5574687719345093, "alphanum_fraction": 0.5619888305664062, "avg_line_length": 35.00775146484375, "blob_id": "8fad1c0cccbf3b2ed844b19776860a836ed7136f", "content_id": "0189e45d5c5722838ff006d3343c1e344aa13b78", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4646, "license_type": "no_license", "max_line_length": 121, "num_lines": 129, "path": "/src/learning/predictor.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <learning/predictor.h>\n\n double Predictor_Base::calculate_prediction_error() {\n auto const &predictions = get_prediction();\n assertion( input.size() == predictions.size()\n , \"Input and prediction vector must have same size (%u =/= %u)\"\n , input.size(), predictions.size()\n );\n\n //test_range(input, -1.0, 1.0, \"input\");\n //test_range(predictions, -1.0, 1.0, \"predictions\");\n\n / sum of squared distances to input /\n const double error = squared_distance(input, predictions);\n\n /* The prediction error is being normalized by the number\n * of weights/inputs and the max. input range [-1,+1] so\n * that it is independent of the size of input space.\n * Also it should be limited [0..1].\n /\n prediction_error = normalize_factor sqrt(error);\n assert_in_range(prediction_error, predictor_constants::error_min, predictor_constants::error_max);\n return prediction_error;\n }\n\n\n Predictor::Predictor( const sensor_vector& input\n , const double learning_rate\n , const double random_weight_range\n , const std::size_t experience_size )\n : Predictor_Base(input, learning_rate, random_weight_range, experience_size)\n , weights(input.size())\n {\n //dbg_msg(\"Initialize simple predictor.\");\n initialize_from_input();\n predict(); // initialize prediction error\n }\n\n\n /* initialize weights and experience with random values\n /\n void Predictor::initialize_randomized(void)\n {\n assert(weights.size() == input.size());\n for (std::size_t m = 0; m < input.size(); ++m)\n weights[m] = input[m] + random_value(-random_weight_range,\n +random_weight_range);\n experience.assign(experience.size(), weights);\n prediction_error = predictor_constants::error_min;\n }\n\n\n / initialize weights and experience from first input\n /\n void Predictor::initialize_from_input(void)\n {\n assert(weights.size() == input.size());\n weights = input.get();\n experience.assign(experience.size(), weights);\n prediction_error = predictor_constants::error_min;\n }\n\n\n / copy assignment to base type\n /\n void Predictor::copy(Predictor_Base const& other)\n {\n Predictor_Base::operator=(other); // copy base members\n Predictor const& rhs = dynamic_cast<Predictor const&>(other); /TODO definitely write a test for that crap :) /\n assert(weights.size() == rhs.weights.size());\n weights = rhs.weights;\n }\n\n\n /* make the prediction based on actual weights\n /\n double Predictor::predict(void) {\n //dbg_msg(\"predict\");\n / prediction is trivial for simple predictor/\n return calculate_prediction_error();\n }\n\n / adapt the weights to the current\n * input sample and learn from experience\n /\n void Predictor_Base::adapt(void)\n {\n //dbg_msg(\"adapt\");\n if (experience.size() == 1)\n learn_from_input_sample();\n else\n {\n /* create random index to skip an arbitrary\n * sample and replaced it by current input /\n std::size_t rand_idx = random_index(experience.size());\n\n learn_from_experience(rand_idx); // adapt without new sample\n predict(); // refresh prediction error\n learn_from_input_sample(); // adapt to new sample\n\n /* Insert current input into random position of experience list.\n * This must be done after adaptation to guarantee a positive learning progress /\n experience[rand_idx] = input.get();\n }\n }\n\n\n\n void Predictor::learn_from_input_sample(void) {\n for (std::size_t m = 0; m < input.size(); ++m)\n weights[m] += learning_rate (input[m] - weights[m]) / experience.size();\n }\n\n\n\n void Predictor::learn_from_experience(std::size_t skip_idx) {\n assert(experience.size() > 1);\n assert(experience[0].size() == weights.size());\n /* learn the list /\n for (std::size_t m = 0; m < weights.size(); ++m) {\n double delta = .0;\n for (std::size_t i = 0; i < experience.size(); ++i) {\n if (i != skip_idx)\n delta += (experience[i][m] - weights[m]);\n }\n delta = learning_rate / (experience.size() - 1);\n weights[m] += delta;\n }\n }\n\n" }, { "alpha_fraction": 0.6500375270843506, "alphanum_fraction": 0.6511628031730652, "avg_line_length": 29.643678665161133, "blob_id": "922e16701143f39d5b4a3b295afb708dc4144112", "content_id": "b2de66c2d4038ede4c5060402fdc98f5d5ddb644", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2666, "license_type": "no_license", "max_line_length": 107, "num_lines": 87, "path": "/src/learning/payload.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef PAYLOAD_H_INCLUDED\n#define PAYLOAD_H_INCLUDED\n\n#include <vector>\n#include <common/static_vector.h>\n#include <common/log_messages.h>\n#include <learning/q_function.h>\n#include <learning/eligibility.h>\n\nclass Empty_Payload\n{\npublic:\n Empty_Payload() {}\n Empty_Payload& operator=(const Empty_Payload& /other/) { return this; };\n};\n\nclass State_Payload\n{\npublic:\n State_Payload(const Action_Module_Interface& actions, std::size_t number_of_policies, double q_initial)\n : policies(number_of_policies, actions, q_initial)\n , eligibility_trace(actions.get_number_of_actions())\n {}\n\n State_Payload& operator=(const State_Payload& other) {\n assert(this != &other); // no self-assignment\n copy_with_flaws(other); // redirect copy-assignment\n return this;\n }\n\n /* This must be used by the motor layer to copy states associated to the copied action./\n void copy_payload(std::size_t from_idx, std::size_t to_idx) {\n for (std::size_t policy_idx = 0; policy_idx < policies.size(); ++policy_idx)\n policies[policy_idx].copy_q_value(from_idx, to_idx);\n eligibility_trace[to_idx] = eligibility_trace[from_idx];\n }\n\nprivate:\n void copy_with_flaws(const State_Payload& other)\n {\n / inherit flawed Q-values, i.e. mutate, hidden in the copy assignment [!] /\n policies = other.policies;\n\n / inherit flawless eligibility traces /\n eligibility_trace = other.eligibility_trace;\n }\npublic:\n copyable_static_vector<Policy> policies;\n copyable_static_vector<Eligibility> eligibility_trace;\n\n friend class SARSA;\n friend class Epsilon_Greedy;\n friend class Boltzmann_Softmax;\n friend class State_Payload_Graphics;\n friend class Payload_Graphics;\n};\n\n\nclass Motor_Payload\n{\n typedef static_vector<State_Payload> state_vector_t;\n\n std::size_t index = 0;\n state_vector_t states = nullptr;\n\npublic:\n Motor_Payload(){}\n Motor_Payload& operator=(const Motor_Payload& other) {\n assert(this != &other); // no self-assignment\n //copy_with_flaws(other); // redirect copy-assignment\n sts_msg(\"Copy action value from %lu to %lu.\", other.index, index);\n assert(other.index != index); /* If this was thrown, check payloads connected. /\n assert(states != nullptr);\n for (std::size_t i = 0; i < (states).size(); ++i)\n (states)[i].copy_payload(other.index, index);\n return this;\n }\n\n /* Two-phase initialization for connecting payloads./\n void connect(std::size_t idx, state_vector_t s) {\n index = idx;\n states = s;\n }\n};\n\n\n#endif // PAYLOAD_H_INCLUDED\n" }, { "alpha_fraction": 0.6885474920272827, "alphanum_fraction": 0.6885474920272827, "avg_line_length": 20.57575798034668, "blob_id": "0d79d2f95eeff9a7516975235febb8408e637390", "content_id": "b32ef3e097595649cf0e5bfa972872a1461c1e8e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 716, "license_type": "no_license", "max_line_length": 87, "num_lines": 33, "path": "/src/common/basic.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BASIC_H\r\n#define BASIC_H\r\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <stdarg.h>\n#include <errno.h>\n#include <string.h>\n#include <vector>\n#include <algorithm>\n#include <string>\n#include <sys/stat.h>\n#include <dirent.h>\n#include \"log_messages.h\"\n\n\n/* file opening, formatted yeah! /\nFILE open_file(const char mode, const char format, ...);\n\nnamespace basic { /*IDEA: Consider moving this to file_io/\n\nstd::string make_directory(const char format, ...);\n\ntypedef std::vector<std::string> Filelist;\nFilelist list_directory(const char target_dir = \"\", const char* filename_ending = \"\");\n\nstd::size_t get_file_size(FILE* fd);\n\nstd::string get_timestamp();\n\n} // namespace basic\n\r\n#endif // BASIC_H\r\n" }, { "alpha_fraction": 0.6209813952445984, "alphanum_fraction": 0.624365508556366, "avg_line_length": 18.700000762939453, "blob_id": "eda8368e26dea3948871f4404055799cccfca114", "content_id": "6f25a6fb89926fa52275e89c3018f1cc15afa4d1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 591, "license_type": "no_license", "max_line_length": 58, "num_lines": 30, "path": "/src/common/save_load.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SAVE_LOAD_H_INCLUDED\n#define SAVE_LOAD_H_INCLUDED\n\n#include <string>\n#include <common/file_io.h>\n\n\nnamespace common {\n\nclass Save_Load {\npublic:\n\n typedef file_io::CSV_File<double> csv_file_t;\n typedef std::string folder_t;\n\n\n// template<typename... Args>\n // void save(const char* folder_t, const Args&... args)\n\n // template<typename... Args>\n // void load(const char* folder_t, const Args&... args)\n\n// virtual void save(folder_t& f, ... ) = 0;\n// virtual void load(folder_t& f, ... ) = 0;\n virtual ~Save_Load() {}\n};\n\n}\n\n#endif /* SAVE_LOAD_H_INCLUDED /\n" }, { "alpha_fraction": 0.5953088998794556, "alphanum_fraction": 0.599933922290802, "avg_line_length": 32.172603607177734, "blob_id": "1955a3f7fa8fb026db5225c92e70aeccb9b63d52", "content_id": "211c20b04b427189d52b2141794d086b856fe4ce", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 12117, "license_type": "no_license", "max_line_length": 139, "num_lines": 365, "path": "/src/learning/sarsa.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/\nTo later make an easier transition from discrete tabular based value functions\nto linear function approximators or neural networks you should encapsulate the\nfunction lookup as Q(s_t,a_t) where s,a can be scalars (indices) or vectors\nand where the adaption is just taking the reward and adapts accordingly, no matter what mechanism is behind.\n\n/\n\n#ifndef SARSA_H_INCLUDED\n#define SARSA_H_INCLUDED\n\n#include <algorithm>\n\n#include <common/modules.h>\n#include <common/static_vector.h>\n\n#include <learning/reinforcement_learning.h>\n#include <learning/sarsa_constants.h>\n#include <learning/gmes.h>\n#include <learning/reward.h>\n#include <learning/payload.h>\n#include <learning/action_selection.h>\n#include <learning/eligibility.h>\n\n#include <robots/joint.h>\n\n/*\n \"There is a chance that the mouse is going to say ‘yes I see the best move, but...the hell with it’ and jump over the edge!\n All in the name of exploration.\"\n\n --Travis DeWolf--\n/\n\nnamespace RL {\n typedef std::size_t State;\n typedef std::size_t Action;\n}\n\n/** TODO: überprüfe, ob für die off-policies Q-learning bessere Ergebnisse liefert als SARSA,\n * Antwort: Möglicherweise gibt es Probleme den Max-Operator zusammen mit nichtstationären Aktionen zu verwenden. /\nclass SARSA : public learning::RL_Interface {\npublic:\n\n SARSA( static_vector<State_Payload>& states\n , const reward_base& rewards\n , /const/ Action_Selection_Base& action_selection\n , std::size_t number_of_actions\n , const std::vector<double>& learning_rates\n , float discounting = sarsa_constants::GAMMA\n , float trace_decay = sarsa_constants::LAMBDA\n , RL::State initial_state = 0\n , RL::Action initial_action = 0 )\n : states(states)\n , rewards(rewards)\n , action_selection(action_selection)\n , current_state(initial_state)\n , last_state()\n , current_action(initial_action)\n , last_action()\n , number_of_policies(states[0].policies.size())\n , number_of_actions(number_of_actions)\n , current_policy(0)\n , deltaQ(number_of_policies)\n , learning_rates(learning_rates)\n , discounting(discounting)\n , trace_decay(trace_decay)\n , learning_enabled(true)\n {\n sts_msg(\"Creating discrete Reinforcement Learner: SARSA.\\\n \\n states = %u\\n actions = %u\\n policies = %u\"\n , states.size(), number_of_actions, number_of_policies);\n assert(number_of_policies > 0);\n assert(number_of_actions > 0);\n assert_in_range(learning_rates, 0.0001, 0.5);\n promise(learning_rates.size() >= number_of_policies, __FILE__, __LINE__\n , \"Number of learning rates %u must be equal to the number of policies %u\"\n , learning_rates.size(), number_of_policies);\n assert(states[0].policies.size() == rewards.get_number_of_policies() );\n\n sts_msg(\"GAMMA=%1.3f LAMBDA = %1.3f\", discounting, trace_decay);\n }\n\n double get_current_reward (std::size_t index) const { return rewards.get_current_reward(index); }\n bool positive_current_delta(std::size_t index) const { assert(index < number_of_policies); return deltaQ[index] > 0.0; }\n std::size_t get_current_policy (void) const { return current_policy; }\n std::size_t get_current_action (void) const { return current_action; }\n std::size_t get_current_state (void) const { return current_state; }\n std::size_t get_number_of_policies(void) const { return number_of_policies; }\n std::size_t get_number_of_actions (void) const { return number_of_actions; }\n\n void select_policy(std::size_t index, bool print_status = true)\n {\n if (index < number_of_policies) {\n current_policy = index;\n if (print_status)\n sts_msg(\"Selecting policy: %s\", rewards.get_reward_name(current_policy).c_str());\n }\n else wrn_msg(\"Invalid policy\");\n }\n\n void decay_eligibility_traces(void) {\n /TODO non existing states should be reset to zero /\n for (std::size_t s = 0; s < states.size(); ++s)\n for (std::size_t a = 0; a < number_of_actions; ++a)\n states[s].eligibility_trace[a].decay(discountingtrace_decay);\n }\n\n\n\n\n void execute_cycle(RL::State new_state, RL::Action new_action)\n {\n save_prev_state_and_action();\n current_state = new_state;\n current_action = new_action;\n if (learning_enabled)\n learning_step();\n }\n\n void execute_cycle(RL::State new_state)\n {\n save_prev_state_and_action();\n\n / set new state /\n current_state = new_state;\n\n /* action selection should not be part of Sarsa module /\n / action selection (e.g.Epsilon Greedy or Boltzmann) /\n if (not random_actions)\n current_action = action_selection.select_action(current_state, current_policy);\n else\n current_action = action_selection.select_randomized();\n\n if (learning_enabled)\n learning_step();\n }\n\n void toggle_random_actions(void) {\n random_actions = not random_actions;\n sts_msg(\"Random actions: %s\", random_actions? \"ON\":\"OFF\");\n }\n\n void enable_learning(bool enable) {\n if (learning_enabled != enable) {\n sts_msg(\"SARSA learning=%s\", enable?\"ENABLED\":\"DISABLED\");\n learning_enabled = enable;\n }\n }\n\n bool is_exploring(void) const {\n return action_selection.is_exploring();\n }\n\nprivate:\n\n void learning_step(void)\n {\n / Q-learning (SARSA) /\n assert(deltaQ.size() == number_of_policies);\n assert(states[last_state].eligibility_trace.size() == number_of_actions);\n\n /* TODO rethink: use q-learning for off-policies.. and sarsa for the on-policy learning step /\n\n for (std::size_t pi = 0; pi < number_of_policies; ++pi)\n //TODO testing\n //if (pi == current_policy)\n deltaQ[pi] = rewards.get_aggregated_last_reward(pi) + discounting states[current_state].policies[pi].qvalues[current_action]\n - states[last_state ].policies[pi].qvalues[last_action ];\n\n //else deltaQ[pi] = .0;\n //TODO use get_max_q\n\n /* decay eligibility traces /\n if (current_state != last_state)\n decay_eligibility_traces();\n\n / reset trace /\n states[last_state].eligibility_trace[last_action].reset();\n\n / update all Q-Values according to their trace /\n /TODO this method is inherently slow. try to improve /\n for (std::size_t s = 0; s < states.size(); ++s)\n for (std::size_t a = 0; a < number_of_actions; ++a)\n for (std::size_t pi = 0; pi < number_of_policies; ++pi)\n states[s].policies[pi].qvalues[a] += learning_rates[pi] * deltaQ[pi] * states[s].eligibility_trace[a].get();\n }\n\n\n void save_prev_state_and_action(void) { last_state = current_state; last_action = current_action; }\n\n static_vector<State_Payload>& states;\n const reward_base& rewards;\n Action_Selection_Base& action_selection;\n\n RL::State current_state;\n RL::State last_state;\n\n RL::Action current_action;\n RL::Action last_action;\n\n const std::size_t number_of_policies;\n const std::size_t number_of_actions;\n std::size_t current_policy;\n\n std::vector<double> deltaQ;\n\n const std::vector<double> learning_rates;\n float discounting; // aka Gamma\n float trace_decay; // aka Lambda\n\n bool random_actions = false;\n bool learning_enabled;\n\n friend class SARSA_Graphics;\n friend class Policy_Selector_Graphics;\n};\n\nclass pseudo_random_order {\n\n unsigned ptr = 0;\n std::vector<unsigned> cards;\n\n void shuffle(void) { std::random_shuffle(cards.begin(), cards.end()); }\n\npublic:\n pseudo_random_order(unsigned number_of_values)\n : cards()\n {\n cards.reserve(number_of_values);\n for (unsigned i=0; i < number_of_values; ++i)\n cards.emplace_back(i);\n shuffle();\n }\n\n unsigned next(void) {\n unsigned result = cards[ptr++];\n if (ptr >= cards.size()) { shuffle(); ptr = 0; }\n return result;\n }\n};\n\nclass Policy_Selector /*TODO: move to separate file /\n{\n SARSA& sarsa;\n const std::size_t number_of_policies;\n std::size_t current_policy;\n\n std::vector<uint64_t> policy_trial_duration;\n uint64_t cycles;\n bool random_policy_mode;\n mutable bool trial_has_ended;\n\n pseudo_random_order pseudo_rnd_order;\n\n struct policy_profile {\n std::vector<std::size_t> items;\n std::size_t current;\n bool play;\n\n policy_profile() : items(), current(), play() {}\n } profile;\n\n\n\npublic:\n Policy_Selector(SARSA& sarsa, const std::size_t number_of_policies, bool random_policy_mode = true, uint64_t default_duration_s = 10)\n : sarsa(sarsa)\n , number_of_policies(number_of_policies)\n , current_policy(0)\n , policy_trial_duration(number_of_policies, default_duration_s * 100) /* make constant in setup /\n , cycles(0)\n , random_policy_mode(random_policy_mode)\n , trial_has_ended(false)\n , pseudo_rnd_order(number_of_policies)\n , profile()\n {\n dbg_msg(\"Creating Policy Selector with %u policies.\", number_of_policies);\n select_random_policy();\n }\n\n void toggle_random_policy_mode(void) {\n random_policy_mode = !random_policy_mode;\n sts_msg(\"Random Policy Mode: %s\", random_policy_mode ? \"ON\" : \"OFF\");\n }\n\n void select_random_policy(void) {\n / select a random policy which is different from the previous one /\n if (number_of_policies > 1) {\n /\n current_policy += random_int(1, number_of_policies - 1);\n current_policy %= number_of_policies;\n /\n current_policy = pseudo_rnd_order.next();\n assert(current_policy < number_of_policies);\n }\n sarsa.select_policy(current_policy, false);\n cycles = 0;\n trial_has_ended = true;\n }\n\n void select_policy(std::size_t new_policy) {\n if (new_policy >= number_of_policies) {\n dbg_msg(\"No such policy: %u\", new_policy);\n return;\n }\n current_policy = new_policy;\n sarsa.select_policy(current_policy);\n cycles = 0;\n trial_has_ended = true;\n }\n\n void execute_cycle(void) {\n\n ++cycles;\n if (cycles < policy_trial_duration[current_policy]) return;\n\n if (profile.play)\n {\n if (profile.items.size() > 0) {\n select_policy(profile.items.at(profile.current++));\n if (profile.current >= profile.items.size())\n profile.current = 0;\n }\n else profile.play = false;\n }\n else if (random_policy_mode)\n {\n select_random_policy();\n }\n else {\n cycles = 0;\n trial_has_ended = true;\n }\n }\n\n void set_profile(std::vector<std::size_t> p) { profile.items = p; profile.current = 0; }\n void toggle_playing_profile(void) {\n profile.play = not profile.play;\n sts_msg(\"Policy Profile Mode: %s\", profile.play ? \"ON\" : \"OFF\");\n }\n\n\n void set_policy_trial_duration(std::size_t index, uint64_t duration) {\n assert(index < policy_trial_duration.size());\n policy_trial_duration[index] = duration;\n }\n\n uint64_t get_trial_time_left(void) const {\n return policy_trial_duration[current_policy] - cycles;\n }\n\n std::size_t size() const { return number_of_policies; }\n\n bool has_trial_ended(void) const {\n const bool result = trial_has_ended;\n trial_has_ended = false;\n return result;\n }\n\n std::size_t get_current_policy(void) const { return current_policy; }\n\n friend class Policy_Selector_Graphics;\n};\n\n#endif // SARSA_H_INCLUDED\n" }, { "alpha_fraction": 0.5357781648635864, "alphanum_fraction": 0.5822898149490356, "avg_line_length": 24.976743698120117, "blob_id": "f365546254cd05d55b60268e85c17703651894b2", "content_id": "ff79e5fe59c65eb7704b6e3153f780b6298070e9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1118, "license_type": "no_license", "max_line_length": 83, "num_lines": 43, "path": "/src/draw/plot3D.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ plot3D.cpp /\n\n#include \"plot3D.h\"\n\nvoid plot3D::draw(float x_angle, float y_angle) const\n{\n glPushMatrix();\n glTranslatef(axis.px, axis.py, axis.pz);\n glRotatef(y_angle, 1.0, 0.0, 0.0);\n glRotatef(x_angle, 0.0, 1.0, 0.0);\n\n glBegin(GL_LINE_STRIP);\n //glColor4ubv(color);\n for (unsigned int i = number_of_samples - 1; i != 0; --i) {\n// color[3] = 255i/N; //TODO\n glColor4ubv(color);\n glVertex3f(0.5 * axis.width * signal0[(i + pointer) % number_of_samples],\n 0.5 * axis.height * signal1[(i + pointer) % number_of_samples],\n 0.5 * axis.depth * signal2[(i + pointer) % number_of_samples]);\n }\n glEnd();\n glPopMatrix();\n}\n\nvoid plot3D::add_sample(float s0, float s1, float s2)\n{\n increment_pointer();\n signal0[pointer] = s0;\n signal1[pointer] = s1;\n signal2[pointer] = s2;\n}\n\nvoid plot3D::add_sample(const std::vector<double>& sample)\n{\n assert(sample.size() >= 3);\n increment_pointer();\n signal0[pointer] = sample[0];\n signal1[pointer] = sample[1];\n signal2[pointer] = sample[2];\n}\n\n\n/* plot3D.cpp /\n\n" }, { "alpha_fraction": 0.5452603101730347, "alphanum_fraction": 0.5918803215026855, "avg_line_length": 31.582279205322266, "blob_id": "9ee7b634e2b11adf1d34c260398c846e4ac8c6eb", "content_id": "76350d8e2af5aea1ed7105786e4a9dcbdf4dafc0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5148, "license_type": "no_license", "max_line_length": 123, "num_lines": 158, "path": "/src/tests/predictor_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n\n#include <control/sensorspace.h>\n#include <learning/predictor.h>\n#include <learning/state_predictor.h>\n#include <common/log_messages.h>\n\nclass test_space : public sensor_vector {\npublic:\n test_space(const double sigma) : sensor_vector(3) {\n sensors.emplace_back(\"Foo\", [sigma](){ return 0.42 + rand_norm_zero_mean(sigma); });\n sensors.emplace_back(\"Bar\", [sigma](){ return 0.37 + rand_norm_zero_mean(sigma); });\n sensors.emplace_back(\"Baz\", [sigma](){ return -0.23 + rand_norm_zero_mean(sigma); });\n }\n};\n\nTEST_CASE( \"predictor adapts\" , \"[predictor]\")\n{\n test_space sensors{0.0};\n sensors.execute_cycle();\n Predictor pred{ sensors, 0.1, 0.01, 1 };\n pred.initialize_randomized();\n const std::vector<double>& w = pred.get_prediction();\n REQUIRE( w.size() == 3 );\n\n for (unsigned i = 0; i < 100; ++i) {\n sensors.execute_cycle();\n pred.adapt();\n }\n\n REQUIRE( close(w[0], 0.42, 0.001) );\n REQUIRE( close(w[1], 0.37, 0.001) );\n REQUIRE( close(w[2],-0.23, 0.001) );\n\n dbg_msg(\"%6.4f %6.4f %6.4f\", w[0], w[1], w[2]);\n}\n\nTEST_CASE( \"adapt with experience replay\" , \"[predictor]\")\n{\n srand((unsigned) time(0));\n test_space sensors(0.01); /* with random /\n sensors.execute_cycle();\n Predictor pred{ sensors, 0.1, 0.01, 100 };\n pred.initialize_randomized();\n\n const std::vector<double>& w = pred.get_prediction();\n REQUIRE( w.size() == 3 );\n\n for (unsigned i = 0; i < 1000; ++i) {\n sensors.execute_cycle();\n pred.adapt();\n }\n REQUIRE( close(w[0], 0.42, 0.1) );\n REQUIRE( close(w[1], 0.37, 0.1) );\n REQUIRE( close(w[2],-0.23, 0.1) );\n\n dbg_msg(\"%6.4f %6.4f %6.4f\", w[0], w[1], w[2]);\n}\n\nTEST_CASE( \"prediction error must be constant without learning step\" )\n{\n srand((unsigned) time(0));\n test_space sensors(0.0); /* without random /\n sensors.execute_cycle();\n Predictor pred{ sensors, 0.1, 0.01, 1 };\n pred.initialize_randomized();\n\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.predict();\n double pred_err_start = pred.get_prediction_error();\n dbg_msg(\"Prediction error start: %1.5f\", pred_err_start);\n REQUIRE( pred_err_start > 0.0 );\n\n for (unsigned i = 0; i < 13; ++i) {\n sensors.execute_cycle();\n double pred_err_new = pred.predict();\n REQUIRE( pred_err_new == pred.get_prediction_error() );\n dbg_msg(\"Prediction error %u: %1.5f %1.5f\", i, pred_err_start, pred_err_new);\n REQUIRE( close(pred_err_start, pred_err_new, 0.001) );\n }\n}\n\nTEST_CASE( \"prediction error must decrease after learning step\" )\n{\n srand((unsigned) time(0));\n test_space sensors(0.0); /* without random /\n sensors.execute_cycle();\n Predictor pred{ sensors, 0.1, 0.01, 1 };\n pred.initialize_randomized();\n\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.predict();\n\n double pred_err_start = pred.get_prediction_error();\n dbg_msg(\"Prediction error start: %1.3f\", pred_err_start);\n REQUIRE( pred_err_start > 0.0 );\n\n for (unsigned i = 0; i < 42; ++i) {\n sensors.execute_cycle();\n double pred_err_before = pred.predict();\n pred.adapt();\n double pred_err_after = pred.predict();\n REQUIRE( pred_err_after == pred.get_prediction_error() );\n dbg_msg(\"Prediction error %u: %1.5f %1.5f\", i, pred_err_before, pred_err_after);\n REQUIRE( pred_err_before > pred_err_after );\n }\n}\n\nTEST_CASE( \"prediction error is reset on (re-)initialization\" )\n{\n srand((unsigned) time(0));\n test_space sensors(0.1); /* with random /\n sensors.execute_cycle();\n Predictor pred{ sensors, 0.1, 0.01, 1 };\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.initialize_randomized();\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.predict();\n REQUIRE( pred.get_prediction_error() > 0.0 );\n pred.initialize_from_input();\n REQUIRE( pred.get_prediction_error() == 0.0 );\n sensors.execute_cycle();\n pred.predict();\n REQUIRE( pred.get_prediction_error() > 0.0 );\n}\n\n\nTEST_CASE( \"state predictor construction\", \"[predictor]\" )\n{\n test_space sensors(0.01);\n learning::State_Predictor pred(sensors, 0.01, 0.1, /experience buffer = /100, /hidden size = /2, /time_delay=/1);\n}\n\nTEST_CASE( \"state predictor adapts\" , \"[predictor]\")\n{\n test_space sensors{0.0};\n sensors.execute_cycle();\n learning::State_Predictor pred{ sensors, 0.05, 0.01, 1, 2, 1};\n pred.initialize_randomized();\n const std::vector<double>& w = pred.get_prediction();\n REQUIRE( w.size() == 3 );\n pred.predict();\n pred.adapt();\n double err = pred.get_prediction_error();\n\n for (unsigned i = 0; i < 1000; ++i) {\n sensors.execute_cycle();\n pred.predict();\n pred.adapt();\n REQUIRE( err > pred.get_prediction_error() );\n err = pred.get_prediction_error();\n if (i%100 == 0)\n dbg_msg(\"w0:%6.4f w1:%6.4f w2:%6.4f, error: %6.4f\", w[0], w[1], w[2], err);\n }\n REQUIRE( close(w[0], 0.42, 0.001) );\n REQUIRE( close(w[1], 0.37, 0.001) );\n REQUIRE( close(w[2],-0.23, 0.001) );\n}\n" }, { "alpha_fraction": 0.691236674785614, "alphanum_fraction": 0.6986076831817627, "avg_line_length": 30.30769157409668, "blob_id": "84b54f6ecca3e4dfd59e0c68dd53064b7854d759", "content_id": "0eb9027614077780a0a47bf4c9a0adedf5d28b13", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1221, "license_type": "no_license", "max_line_length": 90, "num_lines": 39, "path": "/src/robots/robot.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef ROBOT_H_INCLUDED\n#define ROBOT_H_INCLUDED\n\n#include <robots/joint.h>\n#include <robots/accel.h>\n\nnamespace robots {\n\n/\n This robot interface should unify all types of robots and make them available\n * to other components as a pure structure of joints, i.e. angles, velocities and torques.\n * This shall ensure the independence of the individual segment lengths, masses or other\n * construction details. All components should prefer to use this interface instead of\n * using a certain robot class directly.\n /\nclass Robot_Interface {\npublic:\n virtual std::size_t get_number_of_joints (void) const = 0;\n virtual std::size_t get_number_of_symmetric_joints (void) const = 0;\n virtual std::size_t get_number_of_accel_sensors (void) const = 0;\n\n virtual const Jointvector_t& get_joints(void) const = 0;\n virtual Jointvector_t& set_joints(void) = 0;\n\n virtual const Accelvector_t& get_accels(void) const = 0;\n virtual Accelvector_t& set_accels(void) = 0;\n\n virtual ~Robot_Interface() {}\n\n virtual bool execute_cycle(void) = 0;\n\n virtual double get_normalized_mechanical_power(void) const = 0;\n\n};\n\n} // namespace robots\n\n\n#endif // ROBOT_H_INCLUDED\n" }, { "alpha_fraction": 0.5097216367721558, "alphanum_fraction": 0.5408749580383301, "avg_line_length": 36.40495681762695, "blob_id": "cd1deb8eef93ddc45cfbc8e50a73cb560d1061d7", "content_id": "66769db110c63113e407066904373c53b3cfddf8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4526, "license_type": "no_license", "max_line_length": 156, "num_lines": 121, "path": "/src/learning/gmes_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GMES_GRAPHICS_H_INCLUDED\n#define GMES_GRAPHICS_H_INCLUDED\n\n#include <draw/draw.h>\n#include <draw/axes3D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n#include <draw/display.h>\n#include <learning/gmes.h>\n#include <learning/predictor_graphics.h>\n\nclass GMES_Graphics : public Graphics_Interface {\npublic:\n GMES_Graphics(const GMES& gmes, const sensor_vector& input, unsigned num_samples = 100)\n : gmes(gmes)\n , expert(gmes.expert)\n , input(input)\n , axis(.0, .0, .0, 1., 1., 1., 0)\n , plot(num_samples, axis, LineColorMix0[0])\n , graph(expert.size(), axis)\n , predictor_graphics()\n {\n predictor_graphics.reserve(expert.size());\n\n int dsize = (int) ceil(sqrt(expert.size()));\n for (unsigned int n = 0; n < expert.size(); ++n)\n {\n graph.update_node(n,\n -1.0 + 2.0/dsize(n%dsize) + 1.0/dsize,\n -1.0 + 2.0/dsize(n/dsize) + 1.0/dsize,\n -1.0,\n gmes_constants::initial_learning_capacity);\n\n predictor_graphics.emplace_back(expert[n].get_predictor());\n }\n sts_msg(\"Created GMES Graphics Extension\");\n }\n\n void update_on_load(void)\n {\n for (unsigned int i = 0; i < expert.size(); ++i) {\n if (!expert[i].does_exists()) continue;\n graph.update_node(i,\n expert[i].get_predictor().get_prediction()[0],\n expert[i].get_predictor().get_prediction()[1],\n expert[i].get_predictor().get_prediction()[2],\n fmin(2.0, expert[i].learning_capacity));\n for (unsigned int j = 0; j < expert.size(); ++j) {\n if (!expert[j].does_exists()) continue;\n graph.update_edge(i, j, (unsigned char) 255 expert[i].transition[j]);\n graph.update_edge(j, i, (unsigned char) 255 * expert[j].transition[i]);\n }\n }\n }\n\n void execute_cycle(uint64_t /cycle/)\n {\n assert(input.size() >= 3);\n plot.add_sample((float) input[0], (float) input[1], (float) input[2]);\n\n for (unsigned int n = 0; n < expert.size(); ++n) {\n graph.update_edge(n, gmes.get_winner(), (unsigned char) 255 * expert[n].transition[gmes.get_winner()]);\n graph.update_edge(gmes.get_winner(), n, (unsigned char) 255 * expert[gmes.get_winner()].transition[n]);\n }\n\n graph.update_node(gmes.get_recipient(),\n fmin(2.0, expert[gmes.get_recipient()].learning_capacity));\n\n graph.update_node(gmes.get_winner(),\n expert[gmes.get_winner()].get_predictor().get_prediction()[0], /*TODO: this kind of drawing is only valid for simple predictor!/\n expert[gmes.get_winner()].get_predictor().get_prediction()[1], /*TODO: maybe draw the average of all experience vectors /\n expert[gmes.get_winner()].get_predictor().get_prediction()[2],\n fmin(2.0, expert[gmes.get_winner()].learning_capacity));\n\n graph.activated(gmes.get_winner());\n graph.special(gmes.get_to_insert());\n }\n\n void draw_experience(const pref& p) const {\n glPushMatrix();\n glRotatef(p.y_angle, 1.0, 0.0, 0.0);\n glRotatef(p.x_angle, 0.0, 1.0, 0.0);\n glScalef(0.5, 0.5, 0.5);\n for (std::size_t i = 0; i < gmes.get_max_number_of_experts(); ++i)\n if (expert[i].exists)\n predictor_graphics[i].draw(p);\n glPopMatrix();\n }\n\n void draw(const pref& p) const\n {\n draw_experience(p);\n\n glColor4f(1.0, 1.0, 1.0, 1.0);\n glprintf(0.8, -0.7, 0.0, 0.03, \"%u/%u\", gmes.get_number_of_experts(), gmes.get_max_number_of_experts());\n //glprintf(0.8, -0.8, 0.0, 0.03, \"%u\" , gmes.get_to_insert());\n\n glLineWidth(2.0f);\n glColor4f(1.0, 1.0, 1.0, 0.2);\n axis.draw(p.x_angle, p.y_angle);\n\n glColor4f(1.0, 1.0, 1.0, 1.0);\n plot.draw(p.x_angle, p.y_angle);\n graph.draw(p.x_angle, p.y_angle);\n\n draw_vector2(-1.0, 1.2, 0.1, 2.0, gmes.get_activations());\n }\n\n const GMES& gmes;\n const Expert_Vector& expert;\n const sensor_vector& input;\n\n const axes3D axis;\n plot3D plot;\n network3D graph;\n\n std::vector<Predictor_Graphics> predictor_graphics;\n};\n\n#endif // GMES_GRAPHICS_H_INCLUDED\n" }, { "alpha_fraction": 0.5598926544189453, "alphanum_fraction": 0.5653818249702454, "avg_line_length": 31.661354064941406, "blob_id": "9429b1bd1f80a79c783256f13fa9e87943a5caac", "content_id": "457d5d99012eb5c0e066cb5ac6143a05a1c66387", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8198, "license_type": "no_license", "max_line_length": 158, "num_lines": 251, "path": "/src/learning/gmes.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <learning/gmes.h>\n\n GMES::GMES( Expert_Vector& expert\n , double learning_rate\n , bool one_shot_learning\n , std::size_t number_of_initial_experts\n , std::string const& name )\n : expert(expert)\n , Nmax(expert.size()) /** TODO: check usage of Nmax /\n , min_prediction_error(.0)\n , learning_progress(.0)\n , learning_rate(learning_rate)\n , one_shot_learning(one_shot_learning)\n , learning_enabled(true)\n , number_of_experts(0)\n , winner(0)\n , last_winner(0)\n , recipient(0)\n , to_insert(0)\n , activations(Nmax)\n , new_node(false)\n , name(name)\n {\n assert(in_range(number_of_initial_experts, std::size_t{1}, Nmax));\n for (std::size_t n = 0; n < number_of_initial_experts; ++n)\n expert[n].create_randomized();\n sts_msg(\"Created GMES (%s) with %u experts and learning rate %.4f\", name.c_str(), Nmax, learning_rate);\n number_of_experts = count_existing_experts();\n }\n\n GMES::~GMES() { dbg_msg(\"Destroying GMES (%s).\", name.c_str()); }\n\n / gmes main loop\n /\n /* TODO: consider changing the call pattern of private member\n * methods to functions like x = f(y) and make them\n /\n void GMES::execute_cycle(void)\n {\n / backup last winner /\n last_winner = winner;\n\n / determine new winner /\n winner = determine_winner();\n\n if (learning_enabled) {\n / if needed, insert expert before adaptation takes place /\n insert_expert_on_demand();\n\n / adapt weights of winner /\n expert[winner].adapt_weights();\n\n / estimate learning progress: L = -dE/dt /\n estimate_learning_progress();\n\n / shift learning capacity proportional to progress in learning /\n adjust_learning_capacity();\n\n / refreshes transitions according to adaptation /\n refresh_transitions();\n\n / count experts /\n number_of_experts = count_existing_experts(); /* could be a member method of experts class /\n\n / assert learning_capacity does not leak /\n check_learning_capacity();\n\n / choose next expert to insert\n * if all available experts are in use,\n * find and take the one with max. learning capacity /\n to_insert = (number_of_experts < Nmax) ? number_of_experts : arg_max_capacity();\n\n } else / learning_disabled /\n learning_progress = .0;\n\n update_activations();\n }\n\n\n / if needed, insert expert\n * before adaptation takes place\n /\n void GMES::insert_expert_on_demand(void)\n {\n new_node = false;\n\n if (expert[winner].learning_capacity_is_exhausted() && to_insert != winner)\n {\n / copy weights and payload /\n expert.copy(to_insert, winner, one_shot_learning);\n\n / clear transitions emanating from 'to_insert' /\n expert[to_insert].clear_transitions();\n clear_transitions_to(to_insert);\n\n / exchange learning capacity /\n const double share = (expert[winner].learning_capacity + expert[to_insert].learning_capacity)/2;\n expert[winner ].learning_capacity += share;\n expert[to_insert].learning_capacity -= share;\n\n / set new transition /\n expert[to_insert].reset_transition(winner);\n winner = to_insert;\n new_node = true;\n }\n }\n\n\n / estimate learning progress: L = -dE/dt\n /\n void GMES::estimate_learning_progress(void)\n {\n const double prediction_error_before_adaption = expert[winner].get_prediction_error();\n learning_progress = prediction_error_before_adaption - expert[winner].redo_prediction();\n //TODO assert_in_range(learning_progress, 0.0, 1.0);\n assert_in_range(learning_progress, -0.2, 1.0); //TODO FIXME:\n clip(learning_progress, 0., 1.);\n }\n\n\n / adjust learning capacity by shifting a fraction of learning capacity\n * away from the winner towards a randomly picked expert\n /\n void GMES::adjust_learning_capacity(void)\n {\n const double delta_capacity = expert[winner].learning_capacity\n - expert[winner].learning_capacity exp(-learning_rate * learning_progress); /** TODO: reorder eq. to: x * (1-exp) /\n\n recipient = random_index(Nmax);\n\n expert[winner ].learning_capacity -= delta_capacity;\n expert[recipient].learning_capacity += delta_capacity;\n }\n\n\n / refreshes transitions according to adaptations\n /\n void GMES::refresh_transitions(void)\n {\n / invalidate connections emanating from winner /\n for (std::size_t n = 0; n < Nmax; ++n) {\n expert[n].transition[winner] = exp(-learning_rate * learning_progress); /** TODO: this factor can be computed beforehand and used several times/\n expert[winner].transition[n] = exp(-learning_rate * learning_progress);\n }\n\n /* validate the connection from last_winner to winner /\n expert[winner].reset_transition(last_winner);\n }\n\n\n / refreshes the activation vector with the current\n * experts' activations, i.e. inverse prediction error\n /\n void GMES::update_activations(void)\n {\n / compute activations /\n for (std::size_t n = 0; n < Nmax; ++n)\n activations[n] = expert[n].update_and_get_activation();\n }\n\n\n / compute all predictions and determine\n * the expert with minimal prediction error\n /\n std::size_t GMES::determine_winner(void)\n {\n std::size_t winner = 0;\n double min_error = expert[0].make_prediction();\n\n for (std::size_t n = 1; n < Nmax; ++n)\n {\n if (expert[n].exists)\n {\n / compute prediction error and\n * find the best predicting expert /\n if (expert[n].make_prediction() < min_error)\n {\n winner = n;\n min_error = expert[n].get_prediction_error();\n }\n }\n }\n assert(winner < Nmax);\n min_prediction_error = min_error;\n return winner;\n }\n\n\n / find the expert for which the\n * learning capacity is maximal.\n /\n std::size_t GMES::arg_max_capacity(void) const\n {\n std::size_t with_max_capacity = 0;\n double max_capacity = 0.0;\n for (std::size_t n = 0; n < Nmax; ++n)\n {\n if (expert[n].exists && expert[n].learning_capacity > max_capacity) {\n max_capacity = expert[n].learning_capacity;\n with_max_capacity = n;\n }\n }\n return with_max_capacity;\n }\n\n\n / count number of experts where\n * the 'exists' flag is set to true\n /\n std::size_t GMES::count_existing_experts(void) const\n {\n std::size_t num_experts = 0;\n for (std::size_t n = 0; n < Nmax; ++n)\n if (expert[n].exists)\n ++num_experts;\n return num_experts;\n }\n\n\n / double check that the total learning\n * capacity stays constants all the time\n /\n void GMES::check_learning_capacity(void) const\n {\n double sum_capacity = 0.0;\n for (std::size_t n = 0; n < Nmax; ++n)\n sum_capacity += expert[n].learning_capacity;\n\n double leakage = std::abs(sum_capacity - Nmax gmes_constants::initial_learning_capacity);\n\n if (leakage > 1e-10) //1e-12\n wrn_msg(\"Learning capacity (%e) is leaking %e\", sum_capacity, leakage);\n }\n\n\n /* remove all transitions emanating\n * from a a certain node 'to_clear'\n /\n void GMES::clear_transitions_to(std::size_t to_clear)\n {\n for (std::size_t n = 0; n < Nmax; ++n)\n expert[n].transition[to_clear] = .0;\n }\n\n\n void GMES::enable_learning(bool enable) {\n if (learning_enabled != enable) {\n sts_msg(\"GMES (%s) learning is now %s\", name.c_str(), enable? \"ENABLED\":\"DISABLED\");\n learning_enabled = enable;\n }\n }\n" }, { "alpha_fraction": 0.4451327323913574, "alphanum_fraction": 0.49823009967803955, "avg_line_length": 18.13559341430664, "blob_id": "255e38cde0f0b998c9311dc7fb567b6d5757a166", "content_id": "d40804d3a231b2960e260e77ee1b827fdd1119eb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1130, "license_type": "no_license", "max_line_length": 102, "num_lines": 59, "path": "/src/draw/axes.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ axes.cpp /\n\n#include \"axes.h\"\n\naxes::axes(float x, float y, float z, float w, float h, int flags, std::string namestr, float def_amp)\n: px(x), py(y), pz(z)\n, width(w), height(h)\n, flag(flags)\n, countNum(0)\n, max_amplitude(+def_amp)\n, min_amplitude(-def_amp)\n, font_height(clip(0.75 height, .01, .04))\n, name(namestr)\n{\n /* init axes coordinates /\n a[0][0] = -0.5width;\n a[0][1] = 0;\n\n a[1][0] = 0.5width;\n a[1][1] = 0;\n\n a[2][0] = 0;\n a[2][1] = 0.5height;\n\n a[3][0] = 0;\n a[3][1] = -0.5height;\n}\n\nvoid axes::draw(void) const\n{\n glPushMatrix();\n glTranslatef(px, py, pz);\n\n glColor4ubv(white_trans);\n glLineWidth(1.0f);\n\n draw_rect(width, height);\n\n glColor4ubv(white_trans2);\n glprints(-0.5 width, -0.5 * height + 0.5 * font_height, 0.0,\n font_height,\n name);\n\n switch(flag)\n {\n case 0:\n draw_line2D(a[0], a[1]);\n draw_line2D(a[2], a[3]);\n break;\n case 1:\n draw_line2D(a[0], a[1]);\n break;\n default:\n break;\n }\n glPopMatrix();\n}\n\n/* axes.cpp /\n\n" }, { "alpha_fraction": 0.5352681875228882, "alphanum_fraction": 0.5470242500305176, "avg_line_length": 34.350650787353516, "blob_id": "22cbb5606c316c3ccd944dd177d33dcfdce1fb74", "content_id": "f0c16cb24c922297630792b66ae333a7a1835cf0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2722, "license_type": "no_license", "max_line_length": 132, "num_lines": 77, "path": "/src/tests/test_robot.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef TEST_ROBOT_H_INCLUDED\n#define TEST_ROBOT_H_INCLUDED\n\n#include <robots/robot.h>\n#include <common/modules.h>\n\nclass Test_Robot : public robots::Robot_Interface {\npublic:\n\n Test_Robot(std::size_t num_joints, std::size_t num_sym_joints)\n : num_joints(num_joints)\n , num_sym_joints(num_sym_joints)\n , joints()\n , accels(0)\n {\n for (unsigned i = 0; i < num_joints; ++i)\n joints.emplace_back(i, robots::Joint_Type_Normal, i, \"joint\"+std::to_string(i), -1.0, +1.0, 0.0);\n\n / assign symmetric joints /\n REQUIRE( num_joints >= num_sym_joints2 );\n for (unsigned i = 0; i < num_sym_joints2; i+=2) {\n joints[i ].type = robots::Joint_Type_Normal;\n joints[i+1].type = robots::Joint_Type_Symmetric;\n joints[i ].symmetric_joint = i + 1;\n joints[i+1].symmetric_joint = i;\n REQUIRE( i < joints.size() );\n }\n for (unsigned i = num_sym_joints2; i < num_joints; ++i) {\n joints[i].type = robots::Joint_Type_Normal;\n joints[i].symmetric_joint = i;\n REQUIRE( i < joints.size() );\n }\n\n for (unsigned i = 0; i < num_joints; ++i) {\n dbg_msg(\"creating joint jID = %u symID= %u type = %s\", joints[i].joint_id\n , joints[i].symmetric_joint\n , joints[i].type==robots::Joint_Type_Normal? \"normal\":\"symmetric\");\n }\n }\n\n std::size_t get_number_of_joints (void) const { return num_joints; }\n std::size_t get_number_of_symmetric_joints (void) const { return num_sym_joints; }\n std::size_t get_number_of_accel_sensors (void) const { return 0; }\n\n const robots::Jointvector_t& get_joints(void) const { return joints; }\n robots::Jointvector_t& set_joints(void) { return joints; }\n\n const robots::Accelvector_t& get_accels(void) const { return accels; }\n robots::Accelvector_t& set_accels(void) { return accels; }\n\n bool execute_cycle(void) {\n for (auto& j: joints) { j.motor.transfer(); j.motor = .0; }\n return true;\n }\n\n double get_normalized_mechanical_power(void) const { return .0; }\n\n void set_random_inputs(void) {\n for (auto& jx : set_joints()) {\n jx.s_ang = random_value(-1.0,1.0);\n jx.s_vel = random_value(-1.0,1.0);\n jx.motor = random_value(-1.0,1.0);\n jx.motor.transfer(); // for filling backed\n }\n }\n\n std::size_t num_joints;\n std::size_t num_sym_joints;\n robots::Jointvector_t joints;\n robots::Accelvector_t accels;\n\n};\n\n\n\n\n#endif // TEST_ROBOT_H_INCLUDED\n" }, { "alpha_fraction": 0.636970579624176, "alphanum_fraction": 0.6455247402191162, "avg_line_length": 29.291139602661133, "blob_id": "b0c11583460cce11b9bc8853bc76d35ba13cbd80", "content_id": "8702af1b2ed6f83e2daba946b085ecb487f7b912", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4793, "license_type": "no_license", "max_line_length": 110, "num_lines": 158, "path": "/src/learning/predictor.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef PREDICTOR_H\r\n#define PREDICTOR_H\r\n\n#include <stdio.h>\n#include <stdarg.h>\n#include <memory>\n#include <common/modules.h>\n#include <common/vector_n.h>\n#include <common/log_messages.h>\n#include <common/static_vector.h>\n#include <common/save_load.h>\n#include <control/sensorspace.h>\n\n/** Notes regarding normalizing the prediction error\n * N: input size\n * a: max input range [-a,+a], a =!= 1\r\n * The max prediction error e_max = sqrt(N * (2a)^2)\n * Normalize factor is therefore 1/e_max\n /\n\n/* Experience Replay.\n * Do not learn directly every sample. But put new sample\n * on random position into experience replay list and learn the list on every step.\n /\nnamespace predictor_constants {\n const double error_max = 1.0;\n const double error_min = 0.0;\n}\n\nclass Predictor_Base;\ntypedef std::unique_ptr<Predictor_Base> Predictor_ptr;\n\n\nclass Predictor_Base : public common::Save_Load {\n\n Predictor_Base(const Predictor_Base& other) = delete;\n\nprotected:\n\n double calculate_prediction_error();\n\n / constants /\n const sensor_input_interface& input;\n const double learning_rate;\n const double random_weight_range;\n const double normalize_factor;\n\n / non-const /\n double prediction_error;\n std::vector<VectorN> experience; // replay buffer\n\n Predictor_Base& operator=(const Predictor_Base& other)\n {\n prediction_error = other.prediction_error;\n assert(experience.size() == other.experience.size());\n experience = other.experience;\n return this;\n }\n\npublic:\n typedef VectorN vector_t;\n\n Predictor_Base( const sensor_input_interface& input\n , const double learning_rate\n , const double random_weight_range\n , const std::size_t experience_size )\n : input(input)\n , learning_rate(learning_rate)\n , random_weight_range(random_weight_range)\n , normalize_factor( 1.0 / (sqrt(input.size() * 4)))\n , prediction_error(predictor_constants::error_min)\n , experience(experience_size)\n {\n //dbg_msg(\"Experience Replay: %s (%ul)\", (experience_size > 1 ? \"on\" : \"off\"), experience_size);\n //dbg_msg(\"Input dimension: %u\", input.size());\n assert_in_range(input.size(), 1ul, 500ul);\n assert_in_range(experience_size, 1ul, 1000ul);\n assert_in_range(learning_rate, 0.0, +1.0);\n assert_in_range(random_weight_range, -1.0, +1.0);\n\n experience.assign(experience.size(), VectorN(input.size(), .0) ); // zero initialize experience anyhow\n }\n\n /* non-virtual /\n double get_prediction_error(void) const { return prediction_error; }\n std::vector<VectorN> const& get_experience(void) const { return experience; }\n void adapt(void);\n\n / virtual /\n virtual ~Predictor_Base() = default;\n\n virtual void copy(Predictor_Base const& other) = 0;\n\n virtual double predict(void) = 0;\n virtual double verify (void) = 0; // verification can e.g. be prediction by default\n\n virtual void initialize_randomized(void) = 0;\n virtual void initialize_from_input(void) = 0;\n\n virtual vector_t const& get_prediction(void) const = 0;\n\n //virtual void draw(void) const = 0;\n\n virtual vector_t const& get_weights(void) const = 0;\n virtual vector_t & set_weights(void) = 0;\n\nprivate:\n virtual void learn_from_input_sample(void) = 0;\n virtual void learn_from_experience(std::size_t /skip_idx/) {\n assert(false && \"Learning from experience is not implemented yet.\");\n };\n};\n\n\n/* simple predictor /\r\nclass Predictor : public Predictor_Base {\n\n Predictor(const Predictor& other) = delete;\n Predictor& operator=(const Predictor& other) = delete;\n\npublic:\n\n Predictor( const sensor_vector& input\n , const double learning_rate\n , const double random_weight_range\n , const std::size_t experience_size = 1 );\n\n\n virtual ~Predictor() = default;\n\n void copy(Predictor_Base const& other) override;\n\n Predictor_Base::vector_t const& get_prediction(void) const override { return weights; }\n\n double predict(void) override;\n double verify(void) override { return predict(); }\n\n void initialize_randomized(void) override;\n void initialize_from_input(void) override;\n\n //void draw(void) const { assert(false);/ not implemented/ }\n\n vector_t const& get_weights(void) const override { return weights; }\n vector_t & set_weights(void) override { return weights; }\n\nprivate:\n\n void learn_from_input_sample(void) override;\n void learn_from_experience(std::size_t skip_idx) override;\n\n VectorN weights;\n\n friend class Predictor_Graphics;\n};\r\n\n\n\r\n#endif // PREDICTOR_H\r\n" }, { "alpha_fraction": 0.5681225061416626, "alphanum_fraction": 0.5887691378593445, "avg_line_length": 34.11952209472656, "blob_id": "00297c0c43b333ad292bd2b1d09c912983497a02", "content_id": "5fe6b08decf31f397f2eeef9994629dde431115e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8815, "license_type": "no_license", "max_line_length": 126, "num_lines": 251, "path": "/src/learning/state_layer.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef STATE_LAYER_H_INCLUDED\n#define STATE_LAYER_H_INCLUDED\n\n#include <common/integrator.h>\n#include <robots/robot.h>\n#include <robots/joint.h>\n#include <control/jointcontrol.h>\n#include <control/control_vector.h>\n#include <control/sensorspace.h>\n#include <learning/expert.h>\n#include <learning/gmes.h>\n#include <learning/payload.h>\n#include <learning/state_predictor.h>\n#include <learning/learning_machine_interface.h>\n\n/ graphics /\n#include <draw/draw.h>\n#include <draw/axes.h>\n#include <draw/axes3D.h>\n#include <draw/plot1D.h>\n#include <draw/plot2D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n#include <draw/color_table.h>\n\n\nnamespace learning {\n\n\nnamespace state_layer_constants {\n const unsigned subspace_num_datapoints = 200; // 2s of data at 100Hz\n}\n\nclass State_Space : public sensor_vector {\npublic:\n State_Space(const robots::Jointvector_t& joints)\n : sensor_vector(joints.size())\n {\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_ang\", [&j](){ return j.s_ang; });\n\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_vel\", [&j](){ return j.s_vel; });\n\n\n / TODO: why is there no accel data included/\n\n sensors.emplace_back(\"bias\", [](){ return 0.1; });\n }\n};\n\nclass State_Layer : public control::Statemachine_Interface, public learning::Learning_Machine_Interface {\npublic:\n State_Layer( robots::Robot_Interface const& robot\n , static_vector_interface& payloads\n , std::size_t max_num_state_experts\n , double learning_rate\n , double growth_rate\n , std::size_t experience_size\n , std::size_t hidden_layer_size\n , std::size_t time_delay_size )\n : max_num_state_experts(max_num_state_experts)\n , payloads(payloads)\n , statespace(robot.get_joints())\n , experts(max_num_state_experts, payloads, statespace, learning_rate, experience_size, hidden_layer_size, time_delay_size)\n , gmes(experts, growth_rate, / one shot learning = /false)\n {\n dbg_msg(\"Creating new competitive state layer.\");\n assert(payloads.size() == max_num_state_experts); /TODO: re-factor that, move payloads<template> to state_layer/\n assert(learning_rate > 0.);\n assert(growth_rate > 0.);\n }\n\n void execute_cycle(void) {\n statespace.execute_cycle();\n gmes.execute_cycle();\n }\n\n bool has_state_changed(void) const override { return gmes.has_state_changed(); };\n std::size_t get_state(void) const override { return gmes.get_state(); }\n\n double get_learning_progress(void) const override { return gmes.get_learning_progress(); }\n\n void enable_learning(bool b) override { gmes.enable_learning(b); }\n void toggle_learning(void) { gmes.enable_learning(not gmes.is_learning_enabled()); }\n\n void save(const std::string& foldername) const {\n const std::size_t num_experts = gmes.get_number_of_experts();\n sts_msg(\"Saving %lu state expert%s to folder %s\", num_experts, (num_experts>1?\"s\":\"\"), foldername.c_str());\n const std::string folder = basic::make_directory((foldername + \"/state\").c_str());\n for (std::size_t i = 0; i < num_experts; ++i)\n {\n //auto const& ctrl = get_controller_weights(i);\n //ctrl.save_to_file(folder + \"/motor_expert_\" + std::to_string(i) + \".dat\", i);\n }\n }\n\n std::size_t max_num_state_experts;\n static_vector_interface& payloads;\n State_Space statespace;\n Expert_Vector experts;\n GMES gmes;\n\n friend class State_Layer_Graphics;\n};\n\n\n\n\nstruct sensor_subspace_graphics : public Graphics_Interface\n{\n sensor_subspace_graphics( robots::Joint_Model const& j0\n , robots::Joint_Model const& j1\n , Vector3 pos, float size\n , ColorTable const& colortable)\n : j0(j0)\n , j1(j1)\n , axis_xy(pos.x + size/2 , pos.y + size/2, pos.z, size, size, 0, std::string(\"xy\"))\n , axis_dt(pos.x + (2.0 + size)/2, pos.y + size/2, pos.z, 2.0-size, size, 1, std::string(j0.name + \"/\" + j1.name))\n , plot_xy(state_layer_constants::subspace_num_datapoints, axis_xy, colors::magenta )\n , plot_j0(state_layer_constants::subspace_num_datapoints, axis_dt, colors::light0, \"j0\")\n , plot_j1(state_layer_constants::subspace_num_datapoints, axis_dt, colors::light1, \"j1\")\n , plot_p0(state_layer_constants::subspace_num_datapoints, axis_dt, colortable , \"p0\")\n , plot_p1(state_layer_constants::subspace_num_datapoints, axis_dt, colortable , \"p1\")\n {\n dbg_msg(\"Initialize subspace for joints:\\n\\t%2u: %s\\n\\t%2u: %s\", j0.joint_id, j0.name.c_str()\n , j1.joint_id, j1.name.c_str() );\n }\n\n void draw(const pref&) const {\n axis_xy.draw();\n plot_xy.draw();\n\n axis_dt.draw();\n plot_j0.draw(); // signals\n plot_j1.draw();\n plot_p0.draw_colored(); // predictions\n plot_p1.draw_colored();\n }\n\n void execute_cycle(float s0, float s1, unsigned expert_id) {\n plot_xy.add_sample(j0.s_ang, j1.s_ang); // TODO: add velocities\n plot_j0.add_sample(j0.s_ang);\n plot_j1.add_sample(j1.s_ang);\n plot_p0.add_colored_sample(s0, expert_id);\n plot_p1.add_colored_sample(s1, expert_id);\n }\n\n const robots::Joint_Model& j0;\n const robots::Joint_Model& j1;\n\n axes axis_xy;\n axes axis_dt;\n\n plot2D plot_xy;\n plot1D plot_j0, plot_j1; // joint sensor values\n colored_plot1D plot_p0, plot_p1; // predictions\n\n};\n\nclass State_Layer_Graphics : public Graphics_Interface {\npublic:\n\n State_Layer_Graphics( State_Layer const& state_layer\n , robots::Robot_Interface const& robot )\n : state_layer(state_layer)\n , num_experts()\n , max_experts(state_layer.gmes.get_max_number_of_experts())\n , winner()\n , subspace()\n , colortable(5, /randomized/true)\n , integrator()\n , axis_dt(-0.5,-1.50, 0.0, 1.0, 1.0, 1, \"PredErr\")\n , axis_ne(-0.5,-1.75, 0.0, 1.0, 0.5, 1, \"#Experts\")\n , pred_err(2000, axis_dt, colors::gray0)\n , pred_err_avg(2000, axis_dt, colors::white)\n , num_exp(2000, axis_ne, colors::cyan)\n {\n /** TODO\n + also for the rest of the joints\n + consider grouping the graphs in legs (4x 3D) instead of 6x 2D\n /\n const unsigned N = robot.get_number_of_symmetric_joints();\n const float size = 2.0/N;\n subspace.reserve(N);\n Vector3 pos(-1.0, 1.0, 0.);\n for (auto const& j0: robot.get_joints()) {\n if (j0.is_symmetric()) {\n robots::Joint_Model const& j1 = robot.get_joints()[j0.symmetric_joint];\n pos.y -= size;\n subspace.emplace_back(j1, j0, pos, size, colortable);\n }\n }\n }\n\n void draw(const pref& p) const {\n glColor3f(1.0, 1.0, 1.0);\n glprintf(-0.95, 0.95, 0., 0.025, \"%03u (%03u/%03u)\", winner, num_experts, max_experts);\n\n for (auto& s: subspace)\n s.draw(p);\n\n axis_dt.draw();\n axis_ne.draw();\n pred_err.draw();\n pred_err_avg.draw();\n num_exp.draw();\n };\n\n void execute_cycle() {\n\n num_experts = state_layer.gmes.get_number_of_experts(); // update number of experts\n winner = state_layer.gmes.get_winner();\n auto const& predictions = state_layer.experts[winner].get_predictor().get_prediction();\n\n for (auto& s: subspace) {\n auto s0 = predictions.at(s.j0.joint_id); /TODO: how to access the velocities/\n auto s1 = predictions.at(s.j1.joint_id);\n s.execute_cycle(s0, s1, winner);\n }\n\n integrator.add(state_layer.gmes.get_min_prediction_error());\n if (integrator.get_number_of_samples()==1000) {\n const float val = 100.0integrator.get_avg_value_and_reset();\n avg_err = 0.99avg_err + 0.01val;\n pred_err.add_sample(val);\n pred_err_avg.add_sample(avg_err);\n num_exp.add_sample(state_layer.gmes.get_number_of_experts());\n }\n };\n\n State_Layer const& state_layer;\n std::size_t num_experts;\n std::size_t max_experts;\n std::size_t winner;\n\n std::vector<sensor_subspace_graphics> subspace;\n ColorTable colortable;\n\n Integrator integrator;\n float avg_err = 0.0;\n axes axis_dt;\n axes axis_ne;\n plot1D pred_err,pred_err_avg;\n plot1D num_exp;\n};\n\n} // namespace learning\n\n#endif // STATE_LAYER_H_INCLUDED\n" }, { "alpha_fraction": 0.6587436199188232, "alphanum_fraction": 0.6926994919776917, "avg_line_length": 33.64706039428711, "blob_id": "0c954b5e38fba275f8cddec1e5808b47004ea6ea", "content_id": "c67bb978059aa921197c79eff5f060eba438e79f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 589, "license_type": "no_license", "max_line_length": 82, "num_lines": 17, "path": "/src/learning/sarsa_constants.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SARSA_CONSTANTS_H_INCLUDED\n#define SARSA_CONSTANTS_H_INCLUDED\n\nnamespace sarsa_constants {\n\n / sarsa /\n const double EPSILON = 0.1; // probability for non-greedy action\n const double GAMMA = 0.99; // discount factor\n const double LAMBDA = 0.9; // eligibility trace factor (better do not touch)\n const double ALPHA = 0.1; // Reinforcement Learning Rate\n\n const unsigned int policy_change_cycle = 5000; // 10 sec. @ 100Hz\n const unsigned int number_of_policies = 3;\n const unsigned int number_of_actions = 3;\n}\n\n#endif // SARSA_CONSTANTS_H_INCLUDED\n" }, { "alpha_fraction": 0.6523109078407288, "alphanum_fraction": 0.6523109078407288, "avg_line_length": 34.70000076293945, "blob_id": "0e033d7dfa353b7c28049f9bae876c9d37b5e902", "content_id": "a3ec9da8db85a869bc1946d49794e2f743f0ed25", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2856, "license_type": "no_license", "max_line_length": 139, "num_lines": 80, "path": "/src/learning/state_predictor.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef STATE_PREDICTOR_H\n#define STATE_PREDICTOR_H\n\n#include <learning/predictor.h>\n#include <learning/autoencoder.h>\n#include <learning/time_delay_network.h>\n\nnamespace learning {\n\nclass State_Predictor : public Predictor_Base {\n\n State_Predictor(const State_Predictor& other) = delete;\n State_Predictor& operator=(const State_Predictor& other) = delete;\n\npublic:\n\n State_Predictor( const sensor_vector& inputs\n , const double learning_rate\n , const double random_weight_range\n , const std::size_t experience_size\n , const std::size_t hidden_layer_size\n , const std::size_t time_delay_size)\n : Predictor_Base(inputs, learning_rate, random_weight_range, experience_size)\n , enc(inputs.size(), inputs.size(), hidden_layer_size, time_delay_size, random_weight_range )\n {\n dbg_msg(\"Initialize State Predictor using TDNN.\");\n }\n\n virtual ~State_Predictor() = default;\n\n void copy(Predictor_Base const& other) override {\n Predictor_Base::operator=(other); // copy base members\n State_Predictor const& rhs = dynamic_cast<State_Predictor const&>(other);\n enc = rhs.enc;\n dbg_msg(\"Copying state predictor weights.\");\n };\n\n Predictor_Base::vector_t const& get_prediction(void) const override { return enc.get_outputs(); }\n\n double predict(void) override {\n enc.propagate_and_shift(input);\n return calculate_prediction_error();\n };\n\n double verify(void) override {\n enc.propagate();\n return calculate_prediction_error();\n }\n\n void initialize_randomized(void) override {\n //enc.randomize_weight_matrix(random_weight_range);\n auto initial_experience = input.get();\n for (auto& w: initial_experience)\n w += random_value(-random_weight_range, random_weight_range);\n experience.assign(experience.size(), initial_experience);\n prediction_error = predictor_constants::error_min;\n };\n\n void initialize_from_input(void) override { assert(false && \"one shot learning not supported.\"); }\n\n void draw(void) const { assert(false); /not implemented/ }\n\n vector_t const& get_weights(void) const override { assert(false); return dummy; /not implemented/ }\n vector_t & set_weights(void) override { assert(false); return dummy; /not implemented/ }\n\nprivate:\n\n void learn_from_input_sample(void) override { enc.adapt(input, learning_rate); };\n void learn_from_experience(std::size_t /skip_idx/) override { assert(false && \"Learning from experience is not implemented yet.\"); };\n\n Timedelay_Network enc;\n\n VectorN dummy = {}; // remove when implementing get_weights\n\n friend class Predictor_Graphics;\n};\n\n} / namespace learning /\n\n#endif / STATE_PREDICTOR_H /\n" }, { "alpha_fraction": 0.5758234262466431, "alphanum_fraction": 0.6127737760543823, "avg_line_length": 34.390533447265625, "blob_id": "7ed04cad456e173bd6626651bfde2d6b5b418be2", "content_id": "22491b456f34fb857b8965700446e796e0078c03", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5981, "license_type": "no_license", "max_line_length": 112, "num_lines": 169, "path": "/src/tests/motor_predictor_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n\n#include <learning/predictor.h>\n#include <learning/motor_predictor.h>\n#include <common/log_messages.h>\n#include <tests/test_robot.h>\n\nnamespace local_tests {\n\nclass Test_Motor_Space : public sensor_vector {\npublic:\n Test_Motor_Space(const robots::Jointvector_t& joints, const double sigma)\n : sensor_vector(joints.size())\n {\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name, [&j, sigma](){ return j.motor.get() + rand_norm_zero_mean(sigma); });\n }\n};\n\n\n\nTEST_CASE( \"motor predictor adapts\" , \"[motor_predictor]\")\n{\n srand(2310); // set random seed\n\n Test_Robot robot(5,2);\n Test_Motor_Space motors{robot.get_joints(), 0.01};\n control::Control_Parameter params = control::get_initial_parameter(robot,{0.,0.,0.}, false);\n\n / set constant outputs to learn from /\n auto& joints = robot.set_joints();\n REQUIRE( joints.size() == 5 );\n joints[0].motor = +.4223;\n joints[1].motor = +.3771;\n joints[2].motor = -.2342;\n joints[3].motor = -.1337;\n joints[4].motor = +.6789;\n\n / assert that motor space is correct /\n motors.execute_cycle();\n REQUIRE( motors.size() == 5 );\n for (unsigned i = 0; i < 5; ++i)\n REQUIRE( close(motors[i], joints[i].motor.get(), 0.1) );\n\n / initialize predictors /\n motors.execute_cycle();\n learning::Motor_Predictor pred{ robot, motors, 0.25, 0.01, 1, params, /noise=/.0 };\n pred.initialize_randomized();\n\n / adapt a 'few' cycles /\n for (unsigned i = 0; i < 2500; ++i) {\n motors.execute_cycle();\n pred.predict();\n pred.adapt();\n }\n\n / compare predictions with constant motor output /\n auto const& predictions = pred.get_prediction();\n print_vector(predictions);\n\n REQUIRE( predictions.size() == 5 );\n REQUIRE( close(predictions[0], 0.4223, 0.02) );\n REQUIRE( close(predictions[1], 0.3771, 0.02) );\n REQUIRE( close(predictions[2],-0.2342, 0.02) );\n REQUIRE( close(predictions[3],-0.1337, 0.02) );\n REQUIRE( close(predictions[4], 0.6789, 0.02) );\n\n}\n\n//TEST_CASE( \"motor predictor adapts with experience replay\" , \"[motor_predictor]\") /TODO EXPERIENCE REPLAY /\n//{\n// srand((unsigned) time(0));\n// Test_Robot robot(5,2);\n// Test_Motor_Space motors(robot.get_joints(), 0.01); /** with random /\n// control::Control_Parameter params = control::get_initial_parameter(robot,{0.,0.,0.}, false);\n// motors.execute_cycle();\n// learning::Motor_Predictor pred{ robot, motors, 0.1, 0.01, 1 /TODO/, params};\n// pred.initialize_randomized();\n//\n// const std::vector<double>& w = pred.get_prediction();\n// REQUIRE( w.size() == 3 );\n//\n// for (unsigned i = 0; i < 1000; ++i) {\n// motors.execute_cycle();\n// pred.adapt();\n// }\n// REQUIRE( close(w[0], 0.42, 0.1) );\n// REQUIRE( close(w[1], 0.37, 0.1) );\n// REQUIRE( close(w[2],-0.23, 0.1) );\n//\n// dbg_msg(\"%6.4f %6.4f %6.4f\", w[0], w[1], w[2]);\n//}\n\nTEST_CASE( \"motor prediction error must be constant without learning step\", \"[motor_predictor]\" )\n{\n Test_Robot robot(5,2);\n srand((unsigned) time(0));\n Test_Motor_Space motors(robot.get_joints(), 0.0); /** without random /\n control::Control_Parameter params = control::get_initial_parameter(robot,{0.,0.,0.}, false);\n motors.execute_cycle();\n learning::Motor_Predictor pred{ robot, motors, 0.1, 0.01, 1, params, /noise=/.0 };\n pred.initialize_randomized();\n\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.predict();\n double pred_err_start = pred.get_prediction_error();\n dbg_msg(\"Prediction error start: %e\", pred_err_start);\n REQUIRE( pred_err_start > 0.0 );\n\n for (unsigned i = 0; i < 13; ++i) {\n motors.execute_cycle();\n double pred_err_new = pred.predict();\n REQUIRE( pred_err_new == pred.get_prediction_error() );\n dbg_msg(\"Prediction error %u: %e %e\", i, pred_err_start, pred_err_new);\n REQUIRE( close(pred_err_start, pred_err_new, 0.001) );\n }\n}\n\nTEST_CASE( \"motor prediction error must decrease after learning step\", \"[motor_predictor]\" )\n{\n srand((unsigned) time(0));\n Test_Robot robot(5,2);\n Test_Motor_Space motors(robot.get_joints(), 0.0); /* without random /\n control::Control_Parameter params = control::get_initial_parameter(robot,{0.,0.,0.}, false);\n motors.execute_cycle();\n learning::Motor_Predictor pred{ robot, motors, 0.1, 0.01, 1, params, /noise=/.0 };\n pred.initialize_randomized();\n\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.predict();\n\n double pred_err_start = pred.get_prediction_error();\n dbg_msg(\"+++Prediction error start: %e\", pred_err_start);\n REQUIRE( pred_err_start > 0.0 );\n\n for (unsigned i = 0; i < 42; ++i) {\n motors.execute_cycle();\n double pred_err_before = pred.predict();\n pred.adapt();\n double pred_err_after = pred.verify();//predict();\n REQUIRE( pred_err_after == pred.get_prediction_error() );\n dbg_msg(\"Prediction error %u: %e %e\", i, pred_err_before, pred_err_after);\n REQUIRE( pred_err_before > pred_err_after );\n }\n}\n\nTEST_CASE( \"motor prediction error is reset on (re-)initialization\", \"[motor_predictor]\")\n{\n srand((unsigned) time(0));\n Test_Robot robot(5,2);\n Test_Motor_Space motors(robot.get_joints(), 0.0); /* without random /\n control::Control_Parameter params = control::get_initial_parameter(robot,{0.,0.,0.}, false);\n motors.execute_cycle();\n learning::Motor_Predictor pred{ robot, motors, 0.1, 0.01, 1, params, /noise=/.0 };\n pred.initialize_randomized();\n\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.predict();\n\n REQUIRE( pred.get_prediction_error() > 0.0 );\n /not supported pred.initialize_from_input();\n REQUIRE( pred.get_prediction_error() == 0.0 );\n motors.execute_cycle();\n pred.predict();\n REQUIRE( pred.get_prediction_error() > 0.0 );\n /\n}\n\n} // namespace local_tests\n" }, { "alpha_fraction": 0.4707980453968048, "alphanum_fraction": 0.5042353868484497, "avg_line_length": 30.15277862548828, "blob_id": "590b9de375ab67d7376616395f7ec60448b3fbef", "content_id": "074a77415f9b8732dea313600e8a608c895729ec", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2243, "license_type": "no_license", "max_line_length": 137, "num_lines": 72, "path": "/src/control/jointcontrol_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/---------------------------------+\n \| Matthias Kubisch \|\n \| [email protected] \|\n \| July 2017 \|\n +---------------------------------/\n\n#ifndef JOINTCONTROL_GRAPHICS_H_INCLUDED\n#define JOINTCONTROL_GRAPHICS_H_INCLUDED\n\n#include <draw/draw.h>\n\n#include <robots/robot.h>\n#include <control/jointcontrol.h>\n\nnamespace control {\n\nclass Jointcontrol_Graphics : public Graphics_Interface\n{\n const robots::Robot_Interface& robot;\n const control::Jointcontrol& control;\n\npublic:\n Jointcontrol_Graphics(const robots::Robot_Interface& robot, const control::Jointcontrol& control) : robot(robot), control(control) {}\n\n void draw(const pref& /p/) const\n {\n const double line_height = 0.02;\n const double row_width = 0.15;\n const double xstart = -1.0;\n const double ystart = +1.0;\n\n double xpos = xstart;\n double ypos = ystart;\n\n glColor3f(1.0,1.0,1.0);\n\n /print header/\n glprintc(xpos, ypos, 0.0, 0.5line_height, \"#\");\n for (std::size_t i = 0; i < robot.get_number_of_joints(); ++i) {\n if (robot.get_joints()[i].type == robots::Joint_Type_Normal) {\n xpos += row_width;\n glprintf(xpos, ypos, 0.0, 0.5line_height, \"%2lu_%s\", i, robot.get_joints()[i].name.substr(0,16).c_str());\n }\n }\n for (std::size_t k = 0; k < control.core.input.size(); ++k)\n {\n xpos = xstart;\n ypos -= line_height;\n\n glColor3f(1.0,1.0,1.0);\n glprintf(xpos, ypos, 0.0, line_height, \"%2lu: \", k);\n for (std::size_t i = 0; i < robot.get_number_of_joints(); ++i)\n {\n if (robot.get_joints()[i].type == robots::Joint_Type_Normal) {\n const double w = control.core.weights[i][k];\n\n if (w == 0.0) glColor3f(0.5, 0.5, 0.5);\n else if (w > 0.0) glColor3f(1.0, 0.5, 0.5);\n else glColor3f(0.5, 0.5, 1.0);\n\n xpos += row_width;\n glprintf(xpos, ypos, 0.0, line_height, \"%+1.3f\", control.core.weights[i][k]);\n }\n }\n\n }\n }\n};\n\n} // namespace control\n\n#endif // JOINTCONTROL_GRAPHICS_H_INCLUDED\n" }, { "alpha_fraction": 0.6202531456947327, "alphanum_fraction": 0.6445147395133972, "avg_line_length": 22.121952056884766, "blob_id": "3b3e2d008778767d1df563cab9459e3a718e3d92", "content_id": "13569398079fd62e45b8e9714d4748d03b21b88a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 948, "license_type": "no_license", "max_line_length": 82, "num_lines": 41, "path": "/src/draw/plot3D.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ plot3D.h /\n\n#ifndef plot3D_H\n#define plot3D_H\n\n#include <vector>\n#include <cassert>\n#include \"axes3D.h\"\n\nclass plot3D\n{\npublic:\n plot3D(unsigned int number_of_samples, const axes3D& axis, const GLubyte c[4])\n : number_of_samples(number_of_samples)\n , pointer(0)\n , axis(axis)\n , signal0(number_of_samples)\n , signal1(number_of_samples)\n , signal2(number_of_samples)\n {\n for (unsigned int i = 0; i < 4; ++i) color[i] = c[i];\n }\n\n void draw(float x_angle, float y_angle) const;\n\n void add_sample(float s0, float s1, float s2);\n void add_sample(const std::vector<double>& sample);\n\nprivate:\n void increment_pointer(void) { ++pointer; pointer %= number_of_samples; }\n\n const unsigned int number_of_samples;\n unsigned int pointer;\n const axes3D& axis;\n std::vector<float> signal0;\n std::vector<float> signal1;\n std::vector<float> signal2;\n GLubyte color[4];\n};\n\n#endif /plot3D_H/\n" }, { "alpha_fraction": 0.748251736164093, "alphanum_fraction": 0.748251736164093, "avg_line_length": 19.428571701049805, "blob_id": "235e5f32c2dc6dfde0185edf9a52ea6e31eb59b7", "content_id": "4075ef63ea934d6554a826c4f5dd281cb47ce335", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 143, "license_type": "no_license", "max_line_length": 36, "num_lines": 7, "path": "/src/common/vector_n.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef VECTOR_N_H_INCLUDED\n#define VECTOR_N_H_INCLUDED\n\n#include <vector>\ntypedef std::vector<double> VectorN;\n\n#endif // VECTOR_N_H_INCLUDED\n" }, { "alpha_fraction": 0.5224782824516296, "alphanum_fraction": 0.5413675904273987, "avg_line_length": 23.28440284729004, "blob_id": "61cf9a46db5f5a27885e351bcb55832083b3dd8f", "content_id": "bd2fcd51f4b2d748fdf2e5727a423e4a377e2bfe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2647, "license_type": "no_license", "max_line_length": 89, "num_lines": 109, "path": "/src/common/vector2.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef Vector2_H_INCLUDED\n#define Vector2_H_INCLUDED\n\n#include <common/modules.h>\n\nclass Vector2\n{\npublic:\n\n Vector2(const Vector2& rhs) : x(rhs.x), y(rhs.y) {}\n Vector2(void) : x(.0), y(.0) {}\n Vector2(double x, double y) : x(x), y(y) {}\n Vector2(double val) : x(val), y(val) {}\n Vector2(const double val[2]) : x(val[0]), y(val[1]) {}\n\n Vector2& operator=(const Vector2& rhs) {\n this->x = rhs.x;\n this->y = rhs.y;\n return this;\n }\n\n Vector2& operator=(const double& rhs) {\n this->x = rhs;\n this->y = rhs;\n return this;\n }\n\n Vector2& operator+=(const Vector2& rhs) {\n this->x += rhs.x;\n this->y += rhs.y;\n return this;\n }\n Vector2& operator-=(const Vector2& rhs) {\n this->x -= rhs.x;\n this->y -= rhs.y;\n return this;\n }\n Vector2& operator=(const double& rhs) {\n this->x = rhs;\n this->y = rhs;\n return this;\n }\n Vector2& operator/=(const double& rhs) {\n this->x /= rhs;\n this->y /= rhs;\n return this;\n }\n// void clip(double max_val) {\n// x = clip(x, max_val);\n// y = clip(y, max_val);\n// }\n double length() { return sqrt(xx + yy); }\n\n void normalize(void) {\n double l = 1.0 / length();\n x = l;\n y = l;\n }\n double angle_phi (void) const { return atan2(y,x); }\n\n void random(double lower, double upper)\n {\n x = random_value(lower, upper);\n y = random_value(lower, upper);\n }\n\n void zero(void) { x = .0; y = .0; }\n\n bool is_zero(void) const { return (x == .0 && y == .0); }\n\n double x, y;\n};\n\ninline Vector2 operator+(Vector2 lhs, const Vector2& rhs) {\n lhs += rhs;\n return lhs;\n}\ninline Vector2 operator-(Vector2 lhs, const Vector2& rhs) {\n lhs -= rhs;\n return lhs;\n}\ninline Vector2 operator(Vector2 lhs, const double& rhs) {\n lhs = rhs;\n return lhs;\n}\ninline Vector2 operator(const double& lhs, Vector2 rhs) {\n rhs = lhs;\n return rhs;\n}\ninline double operator(const Vector2& lhs, const Vector2& rhs) { //scalar multiplication\n return lhs.x rhs.x\n + lhs.y * rhs.y;\n}\ninline Vector2 operator/(Vector2 lhs, const double& rhs) {\n lhs /= rhs;\n return lhs;\n}\ninline double distance(const Vector2& lhs, const Vector2& rhs) {\n return sqrt((lhs.x - rhs.x) * (lhs.x - rhs.x)\n + (lhs.y - rhs.y) * (lhs.y - rhs.y));\n}\ninline Vector2 clip(const Vector2& v, double max_val) {\n Vector2 result;\n result.x = clip(v.x, max_val);\n result.y = clip(v.y, max_val);\n return result;\n}\n\n#endif // Vector2_H_INCLUDED\n" }, { "alpha_fraction": 0.6666666865348816, "alphanum_fraction": 0.675000011920929, "avg_line_length": 33.28571319580078, "blob_id": "eef576f32e7e74f58d2f94673e9c6de5b4752807", "content_id": "c71283dd61c41b715ce7bf87235b5608d2065894", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 960, "license_type": "no_license", "max_line_length": 79, "num_lines": 28, "path": "/src/learning/reinforcement_learning.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef REINFORCEMENT_LEARNING_H_INCLUDED\n#define REINFORCEMENT_LEARNING_H_INCLUDED\n\nnamespace learning {\n\nclass RL_Interface {\npublic:\n virtual ~RL_Interface() {}\n virtual std::size_t get_current_state (void) const = 0;\n virtual std::size_t get_current_action(void) const = 0;\n virtual std::size_t get_current_policy(void) const = 0;\n virtual bool is_exploring (void) const = 0;\n virtual bool positive_current_delta(std::size_t policy) const = 0;\n};\n\nclass Non_Learning : public RL_Interface {\npublic:\n Non_Learning(void) {}\n std::size_t get_current_state (void) const { return 0; }\n std::size_t get_current_action(void) const { return 0; }\n std::size_t get_current_policy(void) const { return 0; }\n bool is_exploring (void) const { return false; }\n bool positive_current_delta(std::size_t /policy/) const { return false; }\n};\n\n} // namespace learning\n\n#endif // REINFORCEMENT_LEARNING_H_INCLUDED\n" }, { "alpha_fraction": 0.5551941990852356, "alphanum_fraction": 0.5633242726325989, "avg_line_length": 30.810344696044922, "blob_id": "a4f7245d335c089d0453c5f1a96b5ccaae9c74d4", "content_id": "e93c8eeccb8acc7d1b0ab1b90a1d400db4b0fab3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5535, "license_type": "no_license", "max_line_length": 151, "num_lines": 174, "path": "/src/learning/autoencoder.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef AUTOENCODER_H_INCLUDED\n#define AUTOENCODER_H_INCLUDED\n\n#include <common/modules.h>\n#include <common/static_vector.h>\n#include <control/sensorspace.h>\n\n\nnamespace learning {\n\nclass Autoencoder {\n typedef VectorN vector_t;\n typedef copyable_static_vector<copyable_static_vector<double>> matrix_t;\n\npublic:\n Autoencoder( const std::size_t input_size\n , const std::size_t hidden_size\n , const double random_weight_range )\n : hidden(hidden_size)\n , outputs(input_size)\n , delta(outputs.size())\n , weights(hidden.size(), input_size)\n {\n dbg_msg(\"Creating Autoencoder with %u inputs and %u hidden units.\", input_size, hidden_size);\n assertion(hidden_size > 0, \"Hidden layer must have min. size of 1 (currently=%u)\", hidden_size);\n assertion(input_size > hidden_size, \"Input size (%u) must be greater than hidden layer size (%u).\", input_size, hidden_size);\n\n assert(weights.size() == hidden_size);\n assert(weights[0].size() == input_size);\n assert(outputs.size() == input_size);\n\n if (random_weight_range == 0.0)\n wrn_msg(\"Weights will be all zeros\");\n else\n randomize_weight_matrix(random_weight_range);\n }\n\n\n template <typename InputVector_t>\n void propagate(const InputVector_t& inputs) {\n /* encoder /\n propagate_forward(inputs);\n\n / decoder/\n propagate_backward(hidden);\n }\n\n\n template <typename InputVector_t>\n void propagate_forward(const InputVector_t& inputs)\n {\n assert(inputs.size() == outputs.size());\n\n / encoder /\n for (std::size_t i = 0; i < hidden.size(); ++i) {\n double act = 0.;\n for (std::size_t j = 0; j < inputs.size(); ++j)\n act += weights[i][j] inputs[j];\n hidden[i] = tanh(act);\n }\n }\n\n template <typename InputVector_t>\n void propagate_backward(const InputVector_t& hidden_input)\n {\n assert(hidden_input.size() == hidden.size());\n\n /* decoder/\n for (std::size_t j = 0; j < outputs.size(); ++j) {\n double act = 0.;\n for (std::size_t i = 0; i < hidden_input.size(); ++i)\n act += weights[i][j] hidden_input[i];\n outputs[j] = tanh(act);\n /** TODO: The decoder should not use the tanh activation function.\n This must also be considered in the weight change. /\n }\n }\n\n\n template <typename InputVector_t>\n void adapt(const InputVector_t& inputs, const double learning_rate) {\n assert(inputs.size() == outputs.size());\n assert_in_range(learning_rate, 0.0, 0.5);\n\n /* x_j : inputs[j]\n y_j : outputs[j]\n h_i : hidden[i]\n\n eta : learning_rate\n\n decoder weight change:\n error : e_j = (x_j - y_j)\n delta : d2_j = e_j * (1.0 + y_j) * (1.0 - y_j)\n weight: dw2_ji = eta * d2_j * h_i\n\n encoder weight change:\n error : eh_i = sum_j d2_j * w2_ji\n delta : d1_i = eh_i * (1.0 + h_i) * (1.0 - h_i)\n weight: dw1_ji = eta * d1_i * x_j\n\n /\n\n for (std::size_t j = 0; j < outputs.size(); ++j)\n delta[j] = (inputs[j] - outputs[j]) tanh_(outputs[j]); /** this tanh_ is not needed, when transfer function of output layer is omitted /\n\n for (std::size_t i = 0; i < hidden.size(); ++i)\n {\n double error_i = .0;\n for (std::size_t j = 0; j < outputs.size(); ++j)\n error_i += delta[j] weights[i][j];\n\n const double delta_i = error_i * tanh_(hidden[i]);\n for (std::size_t j = 0; j < outputs.size(); ++j)\n weights[i][j] += learning_rate * (delta_i * inputs[j] + delta[j] * hidden[i]);\n }\n }\n\n vector_t const& get_outputs() const { return outputs; }\n matrix_t const& get_weights() const { return weights; }\n\n\n void randomize_weight_matrix(const double random_weight_range) {\n assert_in_range(random_weight_range, 0.0, 0.5);\n const double normed_std_dev = random_weight_range / sqrt(weights[0].size());\n assert(normed_std_dev != 0.0);\n\n for (std::size_t i = 0; i < weights.size(); ++i) {\n for (std::size_t j = 0; j < weights[i].size(); ++j)\n weights[i][j] = rand_norm_zero_mean(normed_std_dev); // normalized by sqrt(N), N:#inputs\n }\n }\n\n template <typename InputVector_t, typename TargetVector_t>\n void train(const InputVector_t& inputs, const TargetVector_t& targets, const double learning_rate)\n {\n set_hidden(targets);\n adapt(inputs, learning_rate);\n }\n\n vector_t const& get_forward() const { return hidden; }\n\n template <typename InputVector_t>\n vector_t const& propagate_forward_and_get(const InputVector_t& inputs) {\n propagate_forward(inputs);\n return hidden;\n }\n\n template <typename InputVector_t>\n vector_t const& propagate_backward_and_get(const InputVector_t& hidden_input) {\n propagate_backward(hidden_input);\n return outputs;\n }\n\nprivate:\n\n template <typename InputVector_t>\n void set_hidden(const InputVector_t& x) {\n assert(x.size() == hidden.size());\n if (is_vector_zero(x))\n wrn_msg(\"Assigned hidden vector is all zeros.\");\n hidden = x;\n }\n\n vector_t hidden;\n vector_t outputs;\n vector_t delta;\n matrix_t weights;\n};\n\n\n} // learning\n\n\n#endif // AUTOENCODER_H_INCLUDED\n" }, { "alpha_fraction": 0.657505989074707, "alphanum_fraction": 0.6588801145553589, "avg_line_length": 33.2470588684082, "blob_id": "6ba53c8f0e525acf7b5a8c3a368df926881545aa", "content_id": "bf898143ca65a7e64d53acd67472766db25e1087", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2911, "license_type": "no_license", "max_line_length": 132, "num_lines": 85, "path": "/src/common/static_vector.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef STATIC_VECTOR_H_INCLUDED\n#define STATIC_VECTOR_H_INCLUDED\n\n#include <algorithm>\n#include <vector>\n#include <cassert>\n#include <common/log_messages.h>\n\nclass static_vector_interface {\npublic:\n virtual ~static_vector_interface() = default;\n virtual std::size_t size(void) const = 0;\n virtual void copy(std::size_t, std::size_t) = 0;\n};\n\n\n/** this vector wrapper class prevents direct access to the underlying vector.\n * no copying of elements allowed\n * constructed via emplace back\n * no resizing\n * save indexing\n /\n\ntemplate <typename element_t>\nclass static_vector : public static_vector_interface {\n//TODO static_vector(const static_vector& other) = delete; // non construction-copyable\n static_vector& operator=(const static_vector&) = delete; // non copyable\npublic:\n\n template<typename... Args>\n explicit static_vector(std::size_t number_of_elements, const Args&... args) : content() {\n content.reserve(number_of_elements);\n for (std::size_t index = 0; index < number_of_elements; ++index)\n content.emplace_back(/index, /args...);\n }\n\n explicit static_vector(std::vector<element_t> const& vec) : content(vec) {}\n\n static_vector& operator=(std::vector<element_t> const& vec) {\n assert(content.size() == vec.size());\n this->content = vec;\n return this;\n }\n\n virtual ~static_vector() = default;\n\n std::size_t size() const override final { return content.size(); }\n\n element_t& operator[] (std::size_t index) { return content.at(index); }\n const element_t& operator[] (std::size_t index) const { return content.at(index); }\n\n element_t get_max (void) const { return std::max_element(content.begin(), content.end()); }\n element_t get_min (void) const { return std::min_element(content.begin(), content.end()); }\n std::size_t get_argmax(void) const { return std::distance(content.begin(), std::max_element(content.begin(), content.end())); }\n std::size_t get_argmin(void) const { return std::distance(content.begin(), std::min_element(content.begin(), content.end())); }\n\n void copy(std::size_t dst, std::size_t src) override final { content.at(dst) = content.at(src); }\n\n void zero(void) { std::fill(content.begin(), content.end(), .0); }\n\n std::vector<element_t> const& get_content(void) const { return content; }\n\nprotected:\n std::vector<element_t> content;\n};\n\ntemplate <typename element_t>\nclass copyable_static_vector : public static_vector<element_t>\n{\npublic:\n template<typename... Args>\n explicit copyable_static_vector(const Args&... args)\n : static_vector<element_t>(args...)\n {}\n\n copyable_static_vector& operator=(const copyable_static_vector& other) {\n assert(this->content.size() == other.content.size());\n this->content = other.content;\n return this;\n }\n};\n\n\n\n#endif // STATIC_VECTOR_H_INCLUDED\n" }, { "alpha_fraction": 0.5780494213104248, "alphanum_fraction": 0.5902724862098694, "avg_line_length": 34.36936950683594, "blob_id": "5526d568e27f29e1eb4d5e9bee0dd6e435b03278", "content_id": "f4c5dab8be261f36b0d989d59f49cd3595fa3987", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3927, "license_type": "no_license", "max_line_length": 157, "num_lines": 111, "path": "/src/common/robot_conf.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"./robot_conf.h\"\n\nnamespace { //TODO maybe set up a file constants.h\n const unsigned int max_number_of_joints = 64;\n const unsigned int max_number_of_accels = 50;\n const unsigned int max_number_of_bodies = max_number_of_joints + 1;\n}\n\nvoid\nRobot_Configuration::read_robot_info(std::string const& server_message)\n{\n sts_msg(\"Reading for configuration.\");\n if (0 == server_message.length())\n err_msg(__FILE__, __LINE__, \"Received no more bytes. Close socket and exit. (robot conf)\");\n\n\n / response OK, parsing robot information /\n sts_msg(\"Received response, now checking configuration...\");\n int offset = 0;\n const char msg = server_message.c_str();\n\n if (sscanf(msg, \"%u %u %u\\n%n\", &number_of_bodies, &number_of_joints, &number_of_accels, &offset) == 3) {\n sts_msg(\"Robot's configuration: \\n %3u Bodies\\n %3u Joints\\n %3u Acceleration Sensors\\n\\n\", number_of_bodies, number_of_joints, number_of_accels);\n } else\n err_msg(__FILE__, __LINE__, \"Unable to read the robot's configuration. Exit.\\n Message was: \\n%s\\n\", msg);\n\n\n /* checking for consistency /\n if ((number_of_joints < 1) \|\| (number_of_joints > max_number_of_joints))\n err_msg(__FILE__, __LINE__, \"Number of joints (%u) out of range (1..%u). Exit.\", number_of_joints, max_number_of_joints);\n\n if (number_of_accels > max_number_of_accels)\n err_msg(__FILE__, __LINE__, \"Number of acceleration sensors (%u) out of range (0..%u). Exit.\", number_of_accels, max_number_of_accels);\n\n if ((number_of_bodies < 2) \|\| (number_of_bodies > max_number_of_bodies))\n err_msg(__FILE__, __LINE__, \"Number of bodies (%u) out of range (2..%u). Exit.\", number_of_bodies, max_number_of_bodies);\n\n\n\n msg = read_joints(msg, &offset);\n msg = read_bodies(msg, &offset);\n accels.assign(number_of_accels, robots::Accel_Sensor());\n}\n\nconst char\nRobot_Configuration::read_joints(const char* msg, int* offset)\n{\n char tmp_name[256];\n unsigned int tmp_id, tmp_type, tmp_sym;\n float tmp_jslo, tmp_jshi, tmp_jdef;\n\n joints.reserve(number_of_joints);\n joints.clear();\n\n\n for (unsigned int i = 0; i < number_of_joints; ++i)\n {\n msg += (offset);\n if (sscanf(msg, \"%u %u %u %e %e %e %256s\\n%n\", &tmp_id, &tmp_type, &tmp_sym, &tmp_jslo, &tmp_jshi, &tmp_jdef, tmp_name, offset) == 7) {\n sts_msg(\"Joint %02u (%s), Type %u, a.w. %u, limits(%+1.2f, %+1.2f, %+1.2f)\", tmp_id, tmp_name, tmp_type, tmp_sym, tmp_jslo, tmp_jdef, tmp_jshi);\n } else\n err_msg(__FILE__, __LINE__, \"could not parse joint %u\", i);\n\n if (tmp_type > 1)\n err_msg(__FILE__, __LINE__, \"FIXME: Invalid Joint Type.\");\n\n joints.emplace_back( i\n , tmp_type?robots::Joint_Type_Symmetric:robots::Joint_Type_Normal\n , tmp_sym, tmp_name, tmp_jslo, tmp_jshi, tmp_jdef);\n\n assert(i == joints.size()-1);\n }\n\n return msg;\n}\n\nconst char\nRobot_Configuration::read_bodies(const char* msg, int* offset)\n{\n char tmp_name[256];\n unsigned int tmp_id = 0;\n\n bodies.reserve(number_of_bodies);\n bodies.clear();\n sts_add(\"Reading bodies:\");\n for (unsigned int i = 0; i < number_of_bodies; ++i)\n {\n msg += (offset);\n if (sscanf(msg, \"%u %256s\\n%n\", &tmp_id, tmp_name, offset) == 2) {\n sts_add(\"%02u (%s)\", tmp_id, tmp_name);\n } else\n err_msg(__FILE__, __LINE__, \"could not parse body %u\", i);\n\n bodies.emplace_back(tmp_name);\n assert(i == bodies.size()-1);\n }\n sts_msg(\"Done.\");\n return msg;\n}\n\nunsigned int\nRobot_Configuration::get_number_of_symmetric_joints(void) const\n{\n unsigned int num_sym_joints = 0;\n for (auto const& j : joints)\n if (robots::Joint_Type_Symmetric == j.type)\n ++num_sym_joints;\n\n assert(num_sym_joints 2 <= number_of_joints);\n return num_sym_joints;\n}\n" }, { "alpha_fraction": 0.5319851636886597, "alphanum_fraction": 0.5369376540184021, "avg_line_length": 28.017963409423828, "blob_id": "95972f461f11c22166236a8ee3289282b60e653f", "content_id": "4bc23694fe92bc5f603f9479c933e9f8d09f4758", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4846, "license_type": "no_license", "max_line_length": 97, "num_lines": 167, "path": "/src/common/file_io.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef FILE_IO_H_INCLUDED\n#define FILE_IO_H_INCLUDED\n\n#include <cstdio>\n#include <vector>\n#include <string>\n#include <assert.h>\n#include <common/basic.h>\n#include <common/noncopyable.h>\n#include <common/log_messages.h>\n\nnamespace file_io {\n\nnamespace constants {\n const size_t format_length = 32;\n const char separator[] = \" \";\n}\n\ntemplate <typename T>\nclass CSV_File : public noncopyable\n{\n CSV_File(const CSV_File& other) = delete; // non construction-copyable\n CSV_File& operator=(const CSV_File&) = delete; // non copyable\npublic:\n CSV_File(const std::string& filename, const std::size_t rows, const std::size_t cols)\n : nbytes(cols * constants::format_length)\n , txtbuf((char ) malloc(nbytes + 1))\n , data(rows, std::vector<T>(cols))\n , max_rows(rows)\n , max_cols(cols)\n , filename(filename)\n {}\n\n ~CSV_File() { free(txtbuf); }\n\n bool read(void)\n {\n FILE csv_file = open_file(\"r\", filename.c_str());\n bool result = true;\n unsigned int row = 0;\n while (row < max_rows)\n { // read next line\n if (getline(&txtbuf, &nbytes, csv_file) < 0) {\n wrn_msg(\"Error reading csv file %s in line %u\\n\", filename.c_str(), row + 1);\n result = false;\n break;\n }\n char* token = strtok(txtbuf, constants::separator); // get first token\n unsigned int col = 0;\n while (col < max_cols) {\n if (token != NULL)\n data[row][col++] = atof(token);\n else {\n wrn_msg(\"Unexpected columns size: %u (%u expected)\", col, max_cols);\n result = false;\n break;\n }\n token = strtok(NULL, constants::separator); // get next token\n }\n ++row;\n }\n fclose(csv_file);\n return result;\n }\n\n void write(void)\n {\n FILE* csv_file = open_file(\"w\", filename.c_str());\n for (std::size_t row = 0; row < max_rows; ++row)\n {\n for (std::size_t col = 0; col < max_cols; ++col)\n fprintf(csv_file, \"%+1.8e \", data[row][col]);\n fprintf(csv_file, \"\\n\");\n }\n fclose(csv_file);\n }\n\n template <typename Vector_t>\n void get_line(std::size_t row_index, Vector_t& line) const {\n assert(row_index < data.size());\n //dbg_msg(\"reading line %2u, size: %u, line size: %u\", row_index, max_cols, line.size());\n assert(line.size() == max_cols);\n line = data[row_index];\n }\n void get_line(std::size_t row_index, T& value) const {\n assert(row_index < data.size());\n //dbg_msg(\"reading line %2u, size: 1\", row_index);\n value = data[row_index][0];\n }\n\n template <typename Vector_t>\n void set_line(std::size_t row_index, Vector_t const& line) {\n assert(row_index < data.size() && line.size() == max_cols);\n //dbg_msg(\"writing line %2u, size: %u\", row_index, max_cols);\n data[row_index] = line;\n }\n void set_line(std::size_t row_index, const T& value) {\n assert(row_index < data.size());\n //dbg_msg(\"writing line %2u, size: 1\", row_index);\n data[row_index][0] = value;\n }\n\n std::string const& get_filename(void) const { return filename; }\n\nprivate:\n std::size_t nbytes;\n char* txtbuf;\n std::vector< std::vector<T> > data;\n const std::size_t max_rows;\n const std::size_t max_cols;\n const std::string filename;\n};\n\n\nclass Logfile\n{\n Logfile(const Logfile& other) = delete; // non construction-copyable\n Logfile& operator=(const Logfile&) = delete; // non copyable\npublic:\n Logfile(const std::string& filename, bool append = false)\n : filename(filename)\n , fd(open_file(append ? \"a\":\"w\", filename.c_str()))\n {}\n\n ~Logfile() {\n std::size_t fs = basic::get_file_size(fd);\n fclose(fd);\n if (fs == 0) { // no data written\n sts_msg(\"No data written, removing file: %s\", filename.c_str());\n remove(filename.c_str());\n }\n }\n\n void append(const char* format, ...)\n {\n va_list args;\n va_start(args, format);\n vfprintf(fd, format, args);\n fprintf(fd, \"\\n\");\n va_end(args);\n }\n void append(const std::vector<double> Vector)\n {\n for (std::size_t idx = 0; idx < Vector.size(); ++idx)\n fprintf(fd, \"%+1.8e \", Vector[idx]);\n fprintf(fd, \"\\n\");\n }\n\n void flush(void) { fflush(fd); }\n\n void next(void) {\n fflush(fd);\n fclose(fd);\n auto name = filename + \"_\" + std::to_string(++index);\n fd = open_file(\"w\", name.c_str());\n }\n\n\n const std::string filename;\n FILE* fd;\n std::size_t index = 0;\n};\n\n\n} // namespace file_io\n\n#endif // FILE_IO_H_INCLUDED\n" }, { "alpha_fraction": 0.5402750372886658, "alphanum_fraction": 0.5486247539520264, "avg_line_length": 28.941177368164062, "blob_id": "db7e490de0c60fd50e3975d2a1b9a64763f8217d", "content_id": "2bd50de68ec28b0cda7ea9129bc4f039b89699b4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2036, "license_type": "no_license", "max_line_length": 125, "num_lines": 68, "path": "/src/learning/q_function.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef Q_FUNCTION_H_INCLUDED\n#define Q_FUNCTION_H_INCLUDED\n\n#include <cassert>\n#include <common/modules.h>\n#include <common/static_vector.h>\n#include <common/log_messages.h>\n#include <learning/action_module.h>\n\nclass Policy {\n\n const Action_Module_Interface& actions;\n\npublic:\n\n static_vector<double> qvalues;\n\n Policy(const Action_Module_Interface& actions, double initial)\n : actions(actions)\n , qvalues(actions.get_number_of_actions())\n {\n// dbg_msg(\"Creating Policies.\");\n assert(actions.get_number_of_actions() >= 1);\n for (std::size_t i = 0; i < qvalues.size(); ++i)\n qvalues[i] = initial + rand_norm_zero_mean(0.01);\n }\n\n double get_max_q(void) const\n {\n double max_q = qvalues[0];\n for (std::size_t i = 1; i < qvalues.size(); ++i)\n if (actions.exists(i) and qvalues[i] > max_q)\n max_q = qvalues[i];\n return max_q;\n }\n std::size_t get_argmax_q(void) const\n {\n double max_q = qvalues[0];\n std::size_t argmax = 0;\n\n for (std::size_t i = 1; i < qvalues.size(); ++i)\n if (actions.exists(i) and qvalues[i] > max_q) {\n max_q = qvalues[i];\n argmax = i;\n }\n return argmax;\n }\n\n void copy_with_flaws(const Policy& other) {\n assert(qvalues.size() == other.qvalues.size());\n for (std::size_t a = 0; a < qvalues.size(); ++a)\n if (actions.exists(a))\n qvalues[a] = other.qvalues[a]\n + random_value(-0.05 * other.qvalues[a], /*TODO this is imprecise and may cause the trouble !!!/\n +0.05 * other.qvalues[a]); /** TODO: put into method and use gaussian noise /\n }\n\n void copy_q_value(std::size_t from_idx, std::size_t to_idx) {\n qvalues[to_idx] = qvalues[from_idx];\n }\n\n Policy& operator=(const Policy& other) {\n this->copy_with_flaws(other);\n return this;\n }\n};\n\n#endif // Q_FUNCTION_H_INCLUDED\n" }, { "alpha_fraction": 0.5952380895614624, "alphanum_fraction": 0.6020408272743225, "avg_line_length": 28.399999618530273, "blob_id": "bb483b51fe644fb0e26a0a262247e7e17c1266c2", "content_id": "a7b13fe736977444dcb9f96e66edaceb001c4667", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1470, "license_type": "no_license", "max_line_length": 90, "num_lines": 50, "path": "/src/control/controlmixer.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef CONTROLMIXER_H\n#define CONTROLMIXER_H\n\n#include <control/jointcontrol.h>\n#include <robots/robot.h>\n\nnamespace control {\n\nclass Controlmixer {\n\nstd::vector<Jointcontrol> control;\n\npublic:\n Controlmixer(robots::Robot_Interface& robot, std::size_t num_controller)\n : control()\n {\n for (std::size_t i = 0; i < num_controller; ++i)\n control.emplace_back(robot);\n }\n\n std::size_t size(void) const { return control.size(); }\n\n /* 'one-hot' switching /\n void set_active(std::size_t index) {\n for (std::size_t i = 0; i < control.size(); ++i)\n control[i].set_input_gain((index == i) ? 1. : 0.);\n }\n\n void fade(std::size_t i, std::size_t j, float val, float gain = 1.f) {\n const float g = clip(val, 0.f, 1.f);\n control.at(i).set_input_gain((1.f - g) gain); // active if g -> 0\n control.at(j).set_input_gain( g * gain); // active if g -> 1\n }\n\n void set_control_parameter(std::size_t index, const Control_Parameter& controller) {\n control.at(index).set_control_parameter(controller);\n }\n\n Jointcontrol& operator[] (std::size_t index) { return control.at(index); }\n const Jointcontrol& operator[] (std::size_t index) const { return control.at(index); }\n\n void execute_cycle(void) { for (auto& c: control) c.execute_cycle(); }\n\n void reset(void) { for (auto& c: control) c.reset(); }\n\n};\n\n} /* namespace control /\n\n#endif / CONTROLMIXER_H /\n" }, { "alpha_fraction": 0.5307372212409973, "alphanum_fraction": 0.5618417859077454, "avg_line_length": 33.89743423461914, "blob_id": "cd123f176969266757b58a1519ce4a21146b8dfe", "content_id": "0d6e28f17e2b30b73a36fa406fd9ddc5e90e5cce", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4083, "license_type": "no_license", "max_line_length": 119, "num_lines": 117, "path": "/src/draw/display.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef DISPLAY_H_INCLUDED\n#define DISPLAY_H_INCLUDED\n\n#include <vector>\n#include <limits>\n#include <draw/draw.h>\n#include <common/modules.h>\n#include <basic/color.h>\n\ntemplate <typename Element_t = double, typename Vector_t = std::vector<Element_t> > inline void\ndraw_vector( const double posx\n , const double posy\n , const double height\n , const double width\n , const Vector_t& vec\n , const Element_t& max_val = 1.0 )\n{\n glColor4f(1.0, 1.0, 1.0, 1.0);\n const double wbar = width/vec.size();\n const double hbar = height/std::abs(max_val);\n for (std::size_t i = 0; i < vec.size(); ++i)\n draw_fill_rect(posx + iwbar, posy, wbar, hbarclip(vec[i], 0.0, max_val));\n}\n\ntemplate <typename Element_t = double, typename Vector_t = std::vector<Element_t> > inline void\ndraw_vector2( const double posx\n , const double posy\n , const double height\n , const double width\n , const Vector_t& vec\n , const Element_t& max_val = 1.0 )\n{\n //const double wbar = std::min(width/vec.size(), 0.05);\n const double wbar = width/vec.size();\n\n for (std::size_t i = 0; i < vec.size(); ++i) {\n\n if (vec[i] > max_val)\n glColor3f(1.0, 0.0, 0.0);\n else if (vec[i] < -max_val)\n glColor3f(0.0, 0.0, 1.0);\n else\n glColor4f((vec[i] > 0) ? 1.0 : 0.5\n , 0.5\n , (vec[i] < 0) ? 1.0 : 0.5\n , 0.1 + 0.9 fabs(clip(vec[i]/max_val , 1.0)));\n\n draw::fill_rect(posx + iwbar, posy, 0.9wbar, height);\n }\n}\n\nnamespace draw {\n\n\n\nvoid hbar(float px, float py, float dx, float dy, float value, float max_value, Color4 const& color = colors::white);\nvoid vbar(float px, float py, float dx, float dy, float value, float max_value, Color4 const& color = colors::white);\nvoid block(float px, float py, float sx, float sy, float value, float max_value);\n\ntemplate <typename Element_t = double, typename Vector_t = std::vector<Element_t> > inline void\nvector_dual( const double posx\n , const double posy\n , const double height\n , const double width\n , const Vector_t& vec1\n , const Vector_t& vec2\n , const Element_t& max_val = 1.0\n , bool with_numbers = true )\n{\n assert(vec1.size() == vec2.size());\n const double wbar = width/vec1.size();\n\n for (std::size_t i = 0; i < vec1.size(); ++i)\n {\n if (vec1[i] > 0.f) draw::vbar(posx+wbari, posy, wbar.4f, height, +vec1[i], max_val, colors::cyan);\n else draw::vbar(posx+wbari, posy, wbar.4f, height, -vec1[i], max_val, colors::magenta);\n if (with_numbers)\n glprintf(posx+wbari, posy-height0.5, .0f, 0.1wbar, \"%+3.1f\", vec1[i]);\n\n if (vec2[i] > 0.f) draw::vbar(posx+wbari+wbar.4f, posy, wbar.4f, height, +vec2[i], max_val, colors::cyan_l);\n else draw::vbar(posx+wbari+wbar.4f, posy, wbar.4f, height, -vec2[i], max_val, colors::magenta_l);\n if (with_numbers)\n glprintf(posx+wbari+wbar.4f, posy-height0.5, .0f, 0.1wbar, \"%+3.1f\", vec2[i]);\n\n }\n}\n\ntemplate <typename Vector_t = std::vector<double> > inline void\nvec3( const float posx\n , const float posy\n , const float height\n , const float width\n , const Vector_t& vec\n , std::size_t max_elements = std::numeric_limits<std::size_t>::max())\n{\n const std::size_t len = std::min(vec.size(), max_elements);\n double max_value = 0;\n for (std::size_t i = 0; i < len; ++i)\n max_value = std::max(max_value, fabs(vec[i]));\n\n const float wbar = width/vec.size();\n const float px = posx - 0.5width + 0.5wbar;\n const float py = posy;\n\n for (std::size_t i = 0; i < len; ++i) {\n block(px + iwbar, py, 0.9wbar, 0.9height, vec[i], max_value);\n }\n glColor3f(0.8,0.8,0.8);\n for (std::size_t i = 0; i < len; ++i) {\n glprintf(px + (i-0.40)wbar, py, 0, 0.008, \"%.2f\", vec[i]);\n }\n\n}\n\n} / namespace draw /\n\n#endif // DISPLAY_H_INCLUDED\n" }, { "alpha_fraction": 0.5428509712219238, "alphanum_fraction": 0.5482751131057739, "avg_line_length": 32.39855194091797, "blob_id": "01a873124223deec5a903741bb0ef469a1fa8614", "content_id": "79b3884e842b5c35a08f8142fda12f1b7e6c0044", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4609, "license_type": "no_license", "max_line_length": 124, "num_lines": 138, "path": "/src/control/control_core.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef CONTROL_CORE_H_INCLUDED\n#define CONTROL_CORE_H_INCLUDED\n\n#include <vector>\n#include <common/log_messages.h>\n#include <robots/robot.h>\n\n\nnamespace control {\n\nnamespace constants {\n const double initial_bias = 0.1;\n}\n\ninline std::size_t get_number_of_inputs(robots::Robot_Interface const& robot) {\n / angle, velocity, motor output + xyz-acceleration + bias /\n return 3 robot.get_number_of_joints() + 3 * robot.get_number_of_accel_sensors() + 1;\n}\n\nstruct sym_input {\n double x,y;\n\n sym_input& operator=(const double& gain) {\n this->x = gain;\n this->y = gain;\n return this;\n }\n};\n\nclass Fully_Connected_Symmetric_Core\n{\npublic:\n std::vector<std::vector<double> > weights;\n std::vector<sym_input> input;\n std::vector<double> activation;\n\n double gain = 1.0;\n\n Fully_Connected_Symmetric_Core(robots::Robot_Interface const& robot)\n : weights(robot.get_number_of_joints(), std::vector<double>(get_number_of_inputs(robot), 0.0))\n , input(get_number_of_inputs(robot))\n , activation(robot.get_number_of_joints())\n {\n /dbg_msg(\"Fully connected symmetric core.\\n\\t weights: %u x %u \", weights.size(), weights.at(0).size());/\n assert(get_number_of_inputs(robot) > 0);\n assert(robot.get_number_of_joints() > 0);\n }\n\n\n void prepare_inputs(const robots::Robot_Interface& robot)\n {\n std::size_t index = 0;\n for (auto const& jx : robot.get_joints())\n {\n auto const& jy = robot.get_joints()[jx.symmetric_joint];\n\n /*IDEA: consider using a virtual (integrated) angle /\n input[index++] = {jx.s_ang , jy.s_ang };\n input[index++] = {jx.s_vel , jy.s_vel };\n input[index++] = {jx.motor.get_backed(), jy.motor.get_backed()};\n }\n\n for (auto const& a : robot.get_accels())\n {\n input[index++] = {a.v.x, -a.v.x}; // mirror the x-axes\n input[index++] = {a.v.y, a.v.y};\n input[index++] = {a.v.z, a.v.z};\n }\n\n input[index++] = {constants::initial_bias, constants::initial_bias};\n assert(index == input.size());\n\n /* apply input gain /\n for (auto& i : input)\n i = gain;\n }\n\n void update_outputs(const robots::Robot_Interface& robot, bool is_symmetric, bool is_switched)\n {\n assert(input.size() == weights[0].size());\n assert(activation.size() == robot.get_number_of_joints());\n assert(!(is_switched and is_symmetric));\n for (std::size_t i = 0; i < activation.size(); ++i)\n {\n activation[i] = .0;\n bool swap_inputs = is_switched != (is_symmetric and robot.get_joints()[i].type == robots::Joint_Type_Symmetric);\n for (std::size_t k = 0; k < input.size(); ++k)\n activation[i] += weights[i][k] * (swap_inputs ? input[k].y : input[k].x);\n }\n }\n\n void write_motors(robots::Robot_Interface& robot, bool is_switched)\n {\n robots::Jointvector_t& joints = robot.set_joints();\n assert(activation.size() == joints.size());\n\n for (std::size_t i = 0; i < activation.size(); ++i)\n joints[(is_switched ? joints[i].symmetric_joint : i)].motor += clip(activation[i], 1.0);\n }\n\n void apply_weights(robots::Robot_Interface const& /robot/, std::vector<double> const& params)\n {\n assert(params.size() == weights.size() * weights.at(0).size());\n std::size_t param_index = 0;\n for (auto& w_i : weights)\n for (auto& w_ik : w_i)\n w_ik = params[param_index++];\n\n assert(param_index == params.size());\n }\n\n void apply_symmetric_weights(robots::Robot_Interface const& robot, std::vector<double> const& params)\n {\n robots::Jointvector_t const& joints = robot.get_joints();\n\n std::size_t param_index = 0;\n for (std::size_t ix = 0; ix < robot.get_number_of_joints(); ++ix)\n {\n if (joints[ix].type == robots::Joint_Type_Normal)\n {\n std::size_t iy = joints[ix].symmetric_joint; // get symmetric counter part of ix\n assert(iy < robot.get_number_of_joints());\n for (std::size_t k = 0; k < weights[ix].size(); ++k)\n {\n weights[ix][k] = params[param_index++];\n weights[iy][k] = weights[ix][k];\n }\n }\n }\n assert(param_index == params.size());\n }\n\n};\n\n\n} // namespace control\n\n#endif // CONTROL_CORE_H_INCLUDED\n" }, { "alpha_fraction": 0.6021189093589783, "alphanum_fraction": 0.614479124546051, "avg_line_length": 22.59722137451172, "blob_id": "03fa8b0d6f9841d495cfb1c21b1ce79f2eb21fbe", "content_id": "8121d06056ee52b55a8adbd52da6f2f761d7eb0a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1699, "license_type": "no_license", "max_line_length": 91, "num_lines": 72, "path": "/src/common/socket_client.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/---------------------------------+\n \| Matthias Kubisch \|\n \| [email protected] \|\n \| July 2017 \|\n +---------------------------------/\n\n#ifndef SOCKET_CLIENT_H\n#define SOCKET_CLIENT_H\n\n#include <stdio.h>\n#include <string.h>\n#include <stdlib.h>\n#include <unistd.h>\n#include <string>\n#include <fcntl.h>\n#include <netinet/in.h>\n#include <arpa/inet.h>\n#include <netdb.h>\n#include <common/globalflag.h>\n#include <common/log_messages.h>\n#include <common/lock.h>\n\nnamespace network {\n\nnamespace constants {\n const unsigned seconds_us = 10001000;\n const unsigned default_port = 7777;\n const unsigned msglen = 8192;\n}\n\nstd::string hostname_to_ip(const char hostname);\n\nclass Socket_Client\n{\n Socket_Client( const Socket_Client& other ) = delete; // non construction-copyable\n Socket_Client& operator=( const Socket_Client& ) = delete; // non copyable\n\npublic:\n Socket_Client()\n : srv_addr()\n , server()\n , sockfd(-1)\n , connection_established(false)\n , msgbuf()\n , mtx()\n { sts_msg(\"Creating client socket.\"); }\n\n ~Socket_Client(void) { sts_msg(\"Destroying client socket.\"); }\n\n bool open_connection(const char* server_addr, const unsigned short port);\n void close_connection(void);\n\n std::string recv(unsigned int time_out_us);\n void send(const char* format, ...); /* sends independent messages immediately /\n\n void append(const char format, ...);\n void flush();\n void eat(void);\n\nprivate:\n struct sockaddr_in srv_addr;\n struct hostent server;\n int sockfd;\n bool connection_established;\n\n std::string msgbuf;\n common::mutex_t mtx;\n};\n\n} / namespace network /\n\n#endif / SOCKET_CLIENT_H /\n" }, { "alpha_fraction": 0.5363878607749939, "alphanum_fraction": 0.5384615659713745, "avg_line_length": 36.36531448364258, "blob_id": "b272c67f36b1a5059b8a1904c021f0156b9f28d2", "content_id": "74d30990cf2953722539450d5d707d912077368d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 10127, "license_type": "no_license", "max_line_length": 119, "num_lines": 271, "path": "/src/evolution/evolution.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include \"evolution.h\"\n\n/TODO: --watch does not work, when current trial == 0/\n\n/* constructor for a new evolution /\nEvolution::Evolution(Evaluation_Interface &evaluation, const Setting& settings, const std::vector<double>& seed_genome)\n: evaluation(evaluation)\n, settings(settings)\n, projectname(create_project_name_and_folder(settings.project_name))\n, conffilename(FOLDER_PREFIX + projectname + \"/evolution.conf\")\n, configuration(settings.save_to_projectfile(conffilename))\n, state(Evolution_State::stopped)\n, seed(seed_genome)\n, population(settings.population_size, seed.size(), settings.init_mutation_rate, settings.meta_mutation_rate)\n, strategy()\n, evolution_log(FOLDER_PREFIX + projectname + \"/evolution.log\")\n, bestindiv_log(FOLDER_PREFIX + projectname + \"/bestindiv.log\")\n, verbose(settings.visuals)\n, playback_only(false)\n{\n common_setup();\n\n sts_msg(\"Setting up a new evolution project: %s\", projectname.c_str());\n\n if (seed.empty()) err_msg(__FILE__, __LINE__, \"Seed genome is missing.\");\n\n /TODO move to initializer list? /\n if (settings.strategy == \"GENERATION\")\n strategy = Strategy_Pointer(new Generation_Based_Evolution( population,\n evaluation,\n configuration,\n settings.max_generations,\n 0,\n settings.selection_size,\n FOLDER_PREFIX + projectname,\n settings.visuals ));\n else if (settings.strategy == \"POOL\")\n strategy = Strategy_Pointer(new Pool_Evolution( population,\n evaluation,\n configuration,\n settings.max_trials,\n 0,\n settings.moving_rate,\n settings.selection_bias,\n FOLDER_PREFIX + projectname));\n else\n err_msg(__FILE__, __LINE__, \"Unknown type of evolution strategy.\");\n\n write_config();\n\n\n assert(strategy != nullptr);\n assert(not settings.fitness_function.empty());\n\n dbg_msg(\"Init population filename: %s\", settings.initial_population.c_str());\n if (not settings.initial_population.empty())\n strategy->load_start_population(settings.initial_population);\n else\n strategy->generate_start_population(seed);\n\n state = Evolution_State::running;\n sts_msg(\"Ready to create.\");\n\n}\n\n/ constructor for resuming previous evolution /\nEvolution::Evolution(Evaluation_Interface &evaluation, const Setting& settings, bool playback_only)\n: evaluation(evaluation)\n, settings(settings)\n, projectname(settings.project_name)\n, conffilename(FOLDER_PREFIX + projectname + \"/evolution.conf\")\n, configuration(conffilename)\n, state(Evolution_State::stopped)\n, seed()\n, population(settings.population_size,\n configuration.readUINT(\"INDIVIDUAL_SIZE\"),\n settings.init_mutation_rate,\n settings.meta_mutation_rate)\n, strategy()\n, evolution_log(FOLDER_PREFIX + projectname + \"/evolution.log\", true)\n, bestindiv_log(FOLDER_PREFIX + projectname + \"/bestindiv.log\", true)\n, verbose(settings.visuals)\n, playback_only(playback_only)\n{\n common_setup();\n\n if (settings.strategy == \"GENERATION\")\n strategy = Strategy_Pointer(new Generation_Based_Evolution( population,\n evaluation,\n configuration,\n settings.max_generations,\n settings.cur_generations,\n settings.selection_size,\n FOLDER_PREFIX + projectname,\n settings.visuals ));\n else if (settings.strategy == \"POOL\")\n strategy = Strategy_Pointer(new Pool_Evolution( population,\n evaluation,\n configuration,\n settings.max_trials,\n settings.cur_trials,\n settings.moving_rate,\n settings.selection_bias,\n FOLDER_PREFIX + projectname));\n else\n err_msg(__FILE__, __LINE__, \"Unknown type of evolution strategy.\");\n\n assert(strategy != nullptr);\n assert(not settings.fitness_function.empty());\n\n switch (configuration.readINT(\"STATUS\")) {\n case 1: sts_msg(\"Former evolution has not finished.\"); break;\n case 2: sts_msg(\"Former evolution was finished. \"); break;\n default:\n wrn_msg(\"Project has invalid status. Abort.\");\n state = Evolution_State::stopped;\n return;\n }\n\n sts_msg(\"Resume existing evolution project: %s\", projectname.c_str());\n strategy->resume();\n\n if (not playback_only) {\n state = Evolution_State::running;\n sts_msg(\"Ready for resuming.\");\n } else {\n state = Evolution_State::playback;\n sts_msg(\"Ready for playback.\");\n }\n}\n\n\nvoid\nEvolution::common_setup(void)\n{\n / since evolution uses mutation,\n * we have to be for sure that random values\n * are initialized properly /\n sts_msg(\"Initializing random number generator.\");\n srand((unsigned) time(NULL));\n}\n\nvoid\nEvolution::write_config()\n{\n / test if the project already exists /\n if (configuration.readUINT(\"STATUS\") > 0)\n err_msg(__FILE__, __LINE__, \"Project already exists. Please use '--resume' or '-r' instead. Exit.\");\n else\n sts_msg(\"Safely override existing configuration file.\");\n\n configuration.writeUINT(\"STATUS\" , 0); // evolution has not started yet\n configuration.writeUINT(\"INDIVIDUAL_SIZE\", population.get_individual_size());\n\n assert(strategy != nullptr);\n strategy->save_config(configuration);\n\n configuration.finish();\n dbg_msg(\"Done writing configuration file.\");\n}\n\nbool Evolution::loop(void)\n{\n switch(state)\n {\n case running:\n state = strategy->execute_trial();\n save_best_individual();\n save_statistics();\n /* TODO update GUI status /\n break;\n\n case finished:\n sts_msg(\"Finished.\");\n configuration.load();\n configuration.writeINT(\"STATUS\", 2);\n configuration.writeUINT(\"RANDOM_INIT\", settings.rnd.init);\n strategy->save_config(configuration);\n configuration.finish();\n state = Evolution_State::stopped;\n break;\n\n case playback:\n state = strategy->playback();\n break;\n\n case aborted: / by user /\n wrn_msg(\"Aborted.\");\n prepare_quit();\n break;\n\n case stopped:\n sts_msg(\"Stopped.\");\n return false;\n\n default:\n err_msg(__FILE__, __LINE__, \"Invalid Evolution status.\");\n break;\n }\n\n return true;\n}\n\nvoid\nEvolution::prepare_quit(void)\n{\n if (not playback_only) {\n sts_msg(\"Saving data.\");\n configuration.load();\n configuration.writeINT(\"STATUS\", 1);\n configuration.writeUINT(\"RANDOM_INIT\", settings.rnd.init);\n strategy->save_config(configuration);\n configuration.finish();\n }\n sts_msg(\"Sending evolution stop signal.\");\n state = Evolution_State::stopped;\n}\n\n/ abort evolution externally before finished /\nvoid\nEvolution::finish(void)\n{\n sts_msg(\"Closing.\");\n dbg_msg(\"Current state is: %d\", state);\n\n if ((Evolution_State::running == state) \|\| (Evolution_State::aborted == state))\n prepare_quit();\n else if (Evolution_State::playback == state)\n state = Evolution_State::stopped;\n\n}\n\n/ save best individual, write/append to file /\nvoid Evolution::save_best_individual(void)\n{\n if (not (Evolution_State::running == state or Evolution_State::finished == state)) {\n wrn_msg(\"Evolution status is NOT running or finished. Saving best individual aborted.\");\n return;\n }\n\n if (strategy->is_there_a_new_best_individual()) {\n const Individual& best = strategy->get_best_individual();\n if (verbose) sts_msg(\"Saving best individual.\");\n bestindiv_log.append(best.genome);\n bestindiv_log.flush(); // flush, to save data in case of error\n } //else\n //dbg_msg(\"Saving best individual skipped.\");\n}\n\nvoid Evolution::save_statistics(void)\n{\n assert(strategy != nullptr);\n statistics_t const& fstats = strategy->get_fitness_statistics();\n statistics_t const& mstats = strategy->get_mutation_statistics();\n evolution_log.append( \"%+1.8e %+1.8e %+1.8e %+1.8e %+1.8e %+1.8e\"\n , fstats.max, fstats.avg, fstats.min\n , mstats.max, mstats.avg, mstats.min );\n evolution_log.flush();\n}\n\nstd::string\ncreate_project_name_and_folder(std::string name)\n{\n if (name.empty()) { // no name given yet? create project name as time stamp\n name = basic::get_timestamp();\n wrn_msg(\"Create project's name from current time stamp.\");\n }\n sts_msg(\"Project name is '%s'\", name.c_str());\n basic::make_directory(\"%s%s\", FOLDER_PREFIX, name.c_str());\n return name;\n}\n\n" }, { "alpha_fraction": 0.5724815726280212, "alphanum_fraction": 0.5995085835456848, "avg_line_length": 17.5, "blob_id": "d1f60613832a39385f587f445d2233fd1ee2a2b9", "content_id": "3d6fe09f1241b05ca97df4d0db7040c12b38634a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 407, "license_type": "no_license", "max_line_length": 51, "num_lines": 22, "path": "/src/robots/accel.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef ACCEL_H_INCLUDED\n#define ACCEL_H_INCLUDED\n\n#include \"../../simloidTCP/src/basic/vector3.h\"\n\nnamespace robots {\n\nstruct Accel_Sensor\n{\n Accel_Sensor() : a(), v() {}\n void integrate(void) { v = 0.1 a + 0.9 * v; }\n void reset (void) { v = 0.0; a = 0.0; }\n\n Vector3 a;\n Vector3 v;\n};\n\ntypedef std::vector<Accel_Sensor> Accelvector_t;\n\n} // namespace\n\n#endif // ACCEL_H_INCLUDED\n" }, { "alpha_fraction": 0.5091561079025269, "alphanum_fraction": 0.522937536239624, "avg_line_length": 31.29878044128418, "blob_id": "07aad3498c1a62fb01527c07b7fc21f1c50e8bb8", "content_id": "1f166930dfda1b1268faeb8c9925eaec5a0384d9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5297, "license_type": "no_license", "max_line_length": 154, "num_lines": 164, "path": "/src/learning/payload_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef PAYLOAD_GRAPHICS_H_INCLUDED\n#define PAYLOAD_GRAPHICS_H_INCLUDED\n\n#include <common/static_vector.h>\n#include <draw/color_table.h>\n#include <draw/graphics.h>\n\n#include <learning/gmes.h>\n#include <learning/gmes_graphics.h>\n#include <learning/expert.h>\n#include <learning/payload.h>\n#include <learning/sarsa.h>\n\nclass Payload_Graphics : public Graphics_Interface {\n\n const GMES& gmes;\n const GMES_Graphics& gmes_graphics;\n const static_vector<State_Payload>& payload;\n const SARSA& sarsa;\n const ColorTable table;\n\npublic:\n Payload_Graphics( const GMES& gmes\n , const GMES_Graphics& gmes_graphics\n , const static_vector<State_Payload>& payload\n , const SARSA& sarsa )\n : gmes(gmes)\n , gmes_graphics(gmes_graphics)\n , payload(payload)\n , sarsa(sarsa)\n , table(3)\n {\n dbg_msg(\"Creating payload graphics\");\n }\n\n void draw(const pref& p) const\n {\n glPushMatrix();\n glRotatef(p.y_angle, 1.f, 0.f, 0.f);\n glRotatef(p.x_angle, 0.f, 1.f, 0.f);\n\n for (std::size_t n = 0; n < gmes.get_max_number_of_experts(); ++n) {\n if (gmes.expert[n].does_exists()) {\n const Point& point = gmes_graphics.graph.get_position(n);\n\n unsigned int argmax_q = payload[n].policies[sarsa.get_current_policy()].get_argmax_q();\n const Color4& c = table.get_color(argmax_q);\n\n glColor3f(c.r, c.g, c.b);\n glprintf(point.x, point.y, point.z, 0.03, \"%u\", argmax_q);\n\n /** make that switchable /\n }\n }\n glEnd();\n glPopMatrix();\n }\n\n};\n\n#include <draw/graphics.h>\n#include <draw/display.h>\n#include <common/vector2.h>\n\nclass State_Payload_Graphics : public Graphics_Interface {\n\n typedef static_vector<State_Payload> Payload_Vector_t;\n\n const Payload_Vector_t& payloads;\n const Action_Module_Interface& actions;\n const std::size_t num_policies, num_states;\n\npublic:\n State_Payload_Graphics(const Payload_Vector_t& payloads, const Action_Module_Interface& actions)\n : payloads(payloads)\n , actions(actions)\n , num_policies(payloads[0].policies.size())\n , num_states(payloads.size())\n {\n dbg_msg(\"payload graphics for %u states and %u policies.\", num_states, num_policies);\n\n }\n void draw(const pref& /p/) const {\n\n /TODO draw user selected policy a little bigger /\n const float space = 1.8/num_policies;\n const float height = 0.6space/num_states;\n const float offy = space num_policies/2;\n for (std::size_t i = 0; i < num_policies; ++i)\n for (std::size_t s = 0; s < num_states; ++s) {\n draw::vec3( 0.0\n , -(ispace) - sheight + offy\n , height\n , 2.0\n , payloads[s].policies[i].qvalues\n , actions.get_number_of_actions_available()\n );\n }\n }\n\n void draw2(const pref& /p/, unsigned real_num_states, unsigned cur_policy, unsigned cur_state, unsigned cur_action, unsigned RL_sel_action ) const {\n\n const unsigned N = real_num_states;\n assert(real_num_states<=num_states);\n const unsigned K = actions.get_number_of_actions_available();\n\n // draw rectangles\n const float ds = 2./(float) N;\n const float da = 2./(float) K;\n const float size = 0.05;\n\n const float Ds = 1 - ds/2;\n const float Da = 1 - da/2;\n\n const unsigned n = cur_state; // current state\n const unsigned k = cur_action; // current action\n\n for (unsigned s = 0; s < N; ++s)\n draw_square(-1, Ds - sds, size);\n draw_fill_square(-1, Ds - nds, size);\n\n for (unsigned a = 0; a < K; ++a)\n draw_square(1, Da - ada, size);\n draw_fill_square(1, Da - kda, size);\n\n draw_square(1, Da - RL_sel_actionda, 1.5size);\n\n unsigned p = cur_policy;\n\n for (unsigned s = 0; s < N; ++s)\n {\n auto const& pay = payloads[s].policies[p];\n const unsigned a_maxq = pay.get_argmax_q();\n const unsigned a_minq = pay.get_argmin_q();\n\n const float qmax = pay.qvalues[a_maxq];\n const float qmin = pay.qvalues[a_minq];\n\n\n\n const float d = clip(1.f/(qmax-qmin), 0.001, 1000);\n\n for (unsigned a = 0; a < K; ++a)\n {\n glLineWidth(a==a_maxq? 1.5f: 0.5f);\n float val = clip( (pay.qvalues[a] - qmin) * d,0,1);\n //val =clip(val,-1.f,1.f);\n //sts_msg(\"val=%1.5f\", val);\n //assert(in_range(val, -0.1f,1.f));\n\n if (a == RL_sel_action and s == n)\n set_color(colors::yellow);\n else //if (val>0)\n set_color(colors::cyan, val);\n //else\n // set_color(colors::magenta, -val);\n draw_line(-1, Ds - sds, 1, Da - ada);\n }\n }\n\n }\n};\n\n#endif // PAYLOAD_GRAPHICS_H_INCLUDED\n" }, { "alpha_fraction": 0.6008968353271484, "alphanum_fraction": 0.6008968353271484, "avg_line_length": 30.85714340209961, "blob_id": "b049fcd0ff72a2b1e8c02442132a69daf09ae9e0", "content_id": "055763c89c4caf70acdbc7b476d2c466283cff13", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2007, "license_type": "no_license", "max_line_length": 96, "num_lines": 63, "path": "/src/common/config.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* config.h /\n\n#ifndef CONFIG_H\n#define CONFIG_H\n\n#include <cstdio>\n#include <cstdlib>\n#include <cstring>\n#include <climits>\n#include <fstream>\n#include <map>\n\n#include <common/log_messages.h>\n#include <common/modules.h>\n\n\nclass config\n{\n config( const config& other ) = delete; // non construction-copyable\n config& operator=( const config& ) = delete; // non copyable\n\n typedef std::pair<std::string,std::string> element_t;\n\npublic:\n config(const std::string& filename, bool quit_on_fail = false)\n : fd()\n , filename(filename)\n , configuration()\n {\n if (not filename.empty())\n sts_msg(\"Configuration filename is: %s\", filename.c_str());\n else\n err_msg(__FILE__, __LINE__, \"No file name provided for configuration.\");\n\n if (not load() and quit_on_fail)\n err_msg(__FILE__, __LINE__, \"Configuration file does not exits, but was expected.\");\n }\n\n bool load(void);\n void clear(void) { configuration.clear(); }\n void finish(void);\n unsigned get_num_entries(void) const { return configuration.size(); }\n\n int readINT (std::string const& name, int def = {});\n unsigned readUINT(std::string const& name, unsigned def = {});\n bool readBOOL(std::string const& name, bool def = {});\n double readDBL (std::string const& name, double def = {});\n std::string readSTR (std::string const& name, std::string const& def = {});\n\n void writeINT (std::string const& name, int value);\n void writeUINT(std::string const& name, unsigned int value);\n void writeBOOL(std::string const& name, bool value);\n void writeDBL (std::string const& name, double value);\n void writeSTR (std::string const& name, std::string const& value);\n\nprivate:\n FILE fd;\n const std::string filename;\n std::map<std::string,std::string> configuration;\n void parse(void);\n};\n\n#endif //CONFIG_H\n" }, { "alpha_fraction": 0.6447876691818237, "alphanum_fraction": 0.6602316498756409, "avg_line_length": 14.235294342041016, "blob_id": "9bc5448db3e8e30e776a413e03834cfd7dfc497a", "content_id": "cb8b2e94bf55b6e8db9bd3b9205f25d2a448f6b7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 259, "license_type": "no_license", "max_line_length": 37, "num_lines": 17, "path": "/src/draw/hsv.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef HSV_H\n#define HSV_H\n\n#include <basic/color.h>\n\nnamespace draw {\n\nstruct RGBColor { uint8_t r, g, b; };\nstruct HSVColor { uint8_t h, s, v; };\n\n\nRGBColor hsv2rgb(HSVColor hsv);\nHSVColor rgb2hsv(RGBColor rgb);\n\n} /* namespace draw /\n\n#endif / HSV_H /\n" }, { "alpha_fraction": 0.5412371158599854, "alphanum_fraction": 0.5541236996650696, "avg_line_length": 18.399999618530273, "blob_id": "60dcaac89cfb5c10b208dfeb91d150782e786122", "content_id": "e562b78212dfe8f44b32e257b896b8cc51772be2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 776, "license_type": "no_license", "max_line_length": 64, "num_lines": 40, "path": "/src/common/integrator.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef INTEGRATOR_H_INCLUDED\n#define INTEGRATOR_H_INCLUDED\n\nclass Integrator {\n\n double sum;\n std::size_t number;\n double last;\n\npublic:\n Integrator() : sum(0.0), number(0), last(0.0) {}\n\n void add(double sample) {\n sum += sample;\n ++number;\n }\n\n double get_avg_value(void) const {\n if (number > 0) return sum/number;\n else return .0;\n }\n\n double get_avg_value_and_reset(void) {\n double result = get_avg_value();\n reset();\n last = result;\n return result;\n }\n\n std::size_t get_number_of_samples() const { return number; }\n\n double get_last(void) const { return last; }\n\n void reset(void) {\n sum = 0.0;\n number = 0;\n }\n};\n\n#endif // INTEGRATOR_H_INCLUDED\n" }, { "alpha_fraction": 0.586705207824707, "alphanum_fraction": 0.589595377445221, "avg_line_length": 23.714284896850586, "blob_id": "1a5019c4a55ab536d408dbd61eb41b8a8167b7db", "content_id": "e72910ba9a9d9a0f2cbdb850df982733f40a84f9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 692, "license_type": "no_license", "max_line_length": 109, "num_lines": 28, "path": "/src/common/view_manager.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef VIEW_MANAGER_H_INCLUDED\n#define VIEW_MANAGER_H_INCLUDED\n\n#include <common/log_messages.h>\n\nclass View_Manager {\n\n const unsigned num_views;\n unsigned view_id;\n\npublic:\n View_Manager(const std::size_t num_views) : num_views(num_views), view_id(0) {}\n\n unsigned get() const { return view_id; }\n void set(unsigned v) { if (v < num_views) view_id = v; else wrn_msg(\"Could not set view: %u\", v); }\n\n void key_pressed(SDL_Keysym const& keysym)\n {\n switch (keysym.sym)\n {\n case SDLK_v : ++view_id; if (view_id >= num_views) view_id = 0; break;\n default : return;\n }\n }\n\n};\n\n#endif // VIEW_MANAGER_H_INCLUDED\n" }, { "alpha_fraction": 0.629687488079071, "alphanum_fraction": 0.637499988079071, "avg_line_length": 31.40506362915039, "blob_id": "d4097b2d5ea4338d4e2d181dcea027af5c15e0dc", "content_id": "5bbf2647eb7e259e965c25290a94b82355d40936", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2560, "license_type": "no_license", "max_line_length": 100, "num_lines": 79, "path": "/src/learning/action_selection.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef ACTION_SELECTION_H_INCLUDED\n#define ACTION_SELECTION_H_INCLUDED\n\n#include <common/static_vector.h>\n#include <common/log_messages.h>\n#include <learning/action_module.h>\n#include <learning/payload.h>\n\ntemplate <typename Vector_t> std::size_t select_from_distribution(Vector_t const& distribution);\n\nclass Action_Selection_Base\n{\npublic:\n typedef static_vector<double> Vector_t;\n\nprotected:\n Vector_t selection_probabilities;\n const static_vector<State_Payload>& states;\n const Action_Module_Interface& actions;\n const double exploration_rate;\n\n bool explorative_selection = false;\n\npublic:\n Action_Selection_Base( const static_vector<State_Payload>& states\n , const Action_Module_Interface& actions\n , const double exploration_rate )\n : selection_probabilities(actions.get_number_of_actions())\n , states(states)\n , actions(actions)\n , exploration_rate(exploration_rate)\n {}\n const Vector_t& get_distribution(void) const { return selection_probabilities; }\n\n virtual ~Action_Selection_Base() = default;\n virtual std::size_t select_action( std::size_t current_state, std::size_t current_policy) = 0;\n\n bool is_exploring(void) { return explorative_selection; }\n\n std::size_t select_randomized(void) {\n selection_probabilities.zero(); // set all zeros\n const double portion = 1.0 / actions.get_number_of_actions_available();\n\n for (std::size_t i = 0; i < selection_probabilities.size(); ++i)\n if (actions.exists(i))\n selection_probabilities[i] = portion;\n\n explorative_selection = true;\n\n return select_from_distribution(selection_probabilities); // uniform, only available actions\n }\n\n};\n\nvoid print_distribution(const Action_Selection_Base::Vector_t& distribution);\n\n\n/* Selects an index from given discrete probability distribution.\n * The given distribution must sum up to 1.\n /\ntemplate <typename Vector_t>\nstd::size_t\nselect_from_distribution(Vector_t const& distribution)\n{\n const double x = random_value(0.0, 1.0);\n double sum = 0.0;\n for (std::size_t i = 0; i < distribution.size(); ++i)\n {\n assert((0.0 <= distribution[i]) and (distribution[i] <= 1.0));\n sum += distribution[i];\n if (x < sum)\n return i;\n assert(sum < 1.0);\n }\n dbg_msg(\"sum: %1.4f\", sum);\n assert(false);\n}\n\n#endif // ACTION_SELECTION_H_INCLUDED\n" }, { "alpha_fraction": 0.5764706134796143, "alphanum_fraction": 0.6025210022926331, "avg_line_length": 18.19354820251465, "blob_id": "75dc1f71b2580a005762f7665b860e111d8d478a", "content_id": "d1041ef77efb497f623b3485a9a50d07ca822327", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1190, "license_type": "no_license", "max_line_length": 73, "num_lines": 62, "path": "/src/common/stopwatch.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef STOPWATCH_H\n#define STOPWATCH_H\n\n#include <sys/time.h>\n#include <unistd.h>\n\n\nclass Stopwatch\n{\npublic:\n Stopwatch()\n : last_time_us(0)\n , current_time_us(0)\n , start_time_us(0)\n , timestamp()\n {\n reset();\n }\n\n void reset(void)\n {\n get_time();\n start_time_us = current_time_us;\n }\n\n unsigned long long get_time_passed_us(void)\n {\n last_time_us = current_time_us; // save last time stamp\n get_time();\n return current_time_us - last_time_us;\n }\n\n unsigned long long get_current_time_ms(void) {\n get_time();\n return current_time_us/1000;\n }\n\nprivate:\n void get_time()\n {\n gettimeofday(&timestamp, NULL);\n current_time_us = timestamp.tv_sec10001000 + timestamp.tv_usec;\n }\n unsigned long long last_time_us; // last time\n unsigned long long current_time_us; // current time\n unsigned long long start_time_us;\n struct timeval timestamp;\n};\n\n/\nunsigned int get_hours(unsigned long long time_ms)\n{\n return time_ms / (10003600);\n}\n\nunsigned int get_minutes(unsigned long long time_ms)\n{\n return time_ms % (10003600);\n}\n/\n\n#endif // STOPWATCH_H\n" }, { "alpha_fraction": 0.6359530091285706, "alphanum_fraction": 0.6362541317939758, "avg_line_length": 32.867347717285156, "blob_id": "276919b24f375f8a8869124883c9ceebabe807c2", "content_id": "2f07cba578934d783c561373b5a233ab1485bc85", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3321, "license_type": "no_license", "max_line_length": 105, "num_lines": 98, "path": "/src/learning/homeokinetic_predictor.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef HOMEOKINETIC_PREDICTOR_H\n#define HOMEOKINETIC_PREDICTOR_H\n\n#include <control/sensorspace.h>\n#include <learning/predictor.h>\n#include <learning/homeokinesis.h>\n\nnamespace learning {\n\n/--------------------------------------------+\n \| Homeokinetic Prediction and Control Core \|\n +--------------------------------------------/\n\nclass Homeokinetic_Core : public Predictor_Base\n{\n Homeokinetic_Control core;\n\n Homeokinetic_Core(const Homeokinetic_Core& other) = delete;\n Homeokinetic_Core& operator=(const Homeokinetic_Core& other) = delete;\n\npublic:\n\n Homeokinetic_Core( sensor_input_interface const& input\n , std::size_t number_of_joints\n , double learning_rate\n , double random_weight_range\n , std::size_t context\n )\n : Predictor_Base(input, learning_rate, random_weight_range, 1)\n , core( input, number_of_joints, random_weight_range, context)\n {\n dbg_msg(\"Initialize Predictor using homeokinetic controller.\");\n }\n\n virtual ~Homeokinetic_Core() = default;\n\n void copy(Predictor_Base const& other) override {\n Predictor_Base::operator=(other); // copy base members\n Homeokinetic_Core const& rhs = dynamic_cast<Homeokinetic_Core const&>(other);\n core = rhs.core;\n dbg_msg(\"Copying homeokinetic pred/ctrl weights.\");\n };\n\n Predictor_Base::vector_t const& get_prediction(void) const override { return core.get_prediction(); }\n\n double predict(void) override {\n core.read_next_state(input); //TODO alt: consider to inject, actual motor commands\n core.predict();\n core.backup_state();\n core.control();\n return calculate_prediction_error();\n };\n\n double verify(void) override {\n core.predict();\n return calculate_prediction_error();\n }\n\n void initialize_randomized(void) override {\n core.randomize_weights(random_weight_range);\n prediction_error = predictor_constants::error_min;\n /Note: experience buffer not randomized here. not used /\n };\n\n void initialize_from_input(void) override { assert(false && \"one shot learning not supported.\"); }\n\n void draw(void) const { assert(false && \"not implemented yet.\"); }\n\n Homeokinetic_Control::Vector_t& set_motor_data(void) { return core.set_motor_data(); }\n\n void learn_motor(void) { core.reconstruct(); core.adapt_controller(); }\n\n double get_timeloop_error(void) const { return core.get_timeloop_error(); }\n double get_prediction_error(void) const { return core.get_prediction_error(); }\n\n vector_t const& get_weights(void) const override { assert(false); return dummy; /not implemented/ }\n vector_t & set_weights(void) override { assert(false); return dummy; /not implemented/ }\n\nprivate:\n\n void learn_from_input_sample(void) override {\n core.reconstruct();\n core.adapt_prediction();\n core.adapt_controller();\n }\n\n void learn_from_experience(std::size_t /skip_idx/) override {\n assert(false && \"Learning from experience is not implemented yet.\");\n };\n\n\n VectorN dummy = {}; // remove when implementing get_weights\n //friend class Predictor_Graphics;\n};\n\n} / namespace learning /\n\n#endif / HOMEOKINETIC_PREDICTOR_H /\n\n\n" }, { "alpha_fraction": 0.5500461459159851, "alphanum_fraction": 0.5548893213272095, "avg_line_length": 35.42856979370117, "blob_id": "dd484ebed47f0a249f70ad2d8558f885d076cbcd", "content_id": "ab1f1ffbd9ea84730303e3b3c3fc1cec6bab2c49", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4336, "license_type": "no_license", "max_line_length": 147, "num_lines": 119, "path": "/src/control/controlparameter.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include <control/controlparameter.h>\n\nnamespace control {\n\n\n Control_Parameter::Control_Parameter( const std::string& filename\n , std::size_t number_of_params\n , bool symmetric\n , bool mirrored\n , unsigned robot_id\n , uint64_t rnd_init )\n : parameter()\n , symmetric(symmetric)\n , mirrored (mirrored )\n , robot_id (robot_id )\n , rnd_init (rnd_init )\n {\n sts_msg(\"Loading controller weights from CSV file:\\n '%s'\", filename.c_str());\n file_io::CSV_File<double> csv_file(filename, 1, number_of_params);\n if (csv_file.read()) {\n parameter.assign(number_of_params, 0.0);\n csv_file.get_line(0, parameter);\n } else\n wrn_msg(\"Could not read from file: %s\", filename.c_str());\n }\n\n\n Control_Parameter::Control_Parameter(const std::string& filename)\n : parameter()\n , symmetric()\n , mirrored ()\n , robot_id ()\n , rnd_init ()\n {\n sts_msg(\"Loading controller file: '%s'\", filename.c_str());\n file_io::Data_Reader dat_file(filename, /verbose=/false);\n assert(dat_file.read(\"parameter\", parameter)); /* TODO: remove assert dependence /\n\n symmetric = (\"symmetric\" == dat_file.read_string(\"symmetry\" )) ? true : false;\n mirrored = (\"original\" == dat_file.read_string(\"propagation\")) ? false : true;\n robot_id = dat_file.read_unsigned(\"robot_id\", 0);\n rnd_init = dat_file.read_unsigned(\"random_init\", 0);\n }\n\n\n Control_Parameter::Control_Parameter(const std::vector<double>& parameter, bool symmetric, bool mirrored, unsigned robot_id, uint64_t rnd_init)\n : parameter(parameter)\n , symmetric(symmetric)\n , mirrored (mirrored )\n , robot_id (robot_id )\n , rnd_init (rnd_init )\n {}\n\n void Control_Parameter::set_from_matrix(matrix_t const& weights)\n {\n assert(symmetric == false and mirrored == false); /TODO accept mirrored weights /\n assert(weights.size() > 0 and weights[0].size() > 0);\n assert(parameter.size() == weights.size() * weights[0].size());\n\n std::size_t p = 0;\n for (auto const& wi : weights)\n for (auto const& wij : wi)\n parameter[p++] = wij;\n assert( p == parameter.size());\n }\n\n Control_Parameter::Control_Parameter(const Control_Parameter& other)\n : noncopyable()\n , parameter(other.parameter)\n , symmetric(other.symmetric)\n , mirrored (other.mirrored )\n , robot_id (other.robot_id )\n , rnd_init (other.rnd_init )\n { /dbg_msg(\"Copying control parameter.\");/ }\n\n\n Control_Parameter& Control_Parameter::operator=(const Control_Parameter& other)\n {\n if (this != &other) // avoid invalid self-assignment\n {\n parameter = other.parameter;\n symmetric = other.symmetric;\n mirrored = other.mirrored;\n robot_id = other.robot_id;\n rnd_init = other.rnd_init;\n }\n return this;\n }\n\n void Control_Parameter::add_gaussian_noise(double sigma) {\n if (sigma == 0.0) return;\n const double s = sigma/sqrt(parameter.size());\n for (auto &u : parameter) {\n u += rand_norm_zero_mean(s);\n }\n }\n\n void Control_Parameter::print() const {\n for ( auto const& p : parameter ) printf(\"% 5.2f \", p);\n printf(\"\\n\");\n }\n\n void Control_Parameter::save_to_file(const std::string& filename, std::size_t id) const {\n sts_msg(\"Saving control parameter to file: %s\", filename.c_str());\n\n FILE ctrl = open_file(\"w\", filename.c_str());\n\n fprintf(ctrl, \"name = \\\"motor-expert-%zu\\\"\\n\", id);\n if (robot_id > 0) fprintf(ctrl, \"robot_id = %u\\n\", robot_id);\n if (rnd_init > 0) fprintf(ctrl, \"random_init = %zu\\n\", rnd_init);\n fprintf(ctrl, \"symmetry = \\\"%s\\\"\\n\", symmetric ? \"symmetric\" : \"asymmetric\");\n fprintf(ctrl, \"propagation = \\\"%s\\\"\\n\", mirrored ? \"mirrored\" : \"original\");\n fprintf(ctrl, \"parameter = { \");\n for ( auto const& p : parameter ) fprintf(ctrl, \"%e \", p);\n fprintf(ctrl, \"}\\n\\n\");\n fclose(ctrl);\n }\n\n} /* namespace control /\n" }, { "alpha_fraction": 0.6003438830375671, "alphanum_fraction": 0.6037828326225281, "avg_line_length": 24.929935455322266, "blob_id": "aded085bc6c87633fb08a061ed7777887dfaaeb3", "content_id": "c3b4744170920b83ad0e7aa16eba85876502e7c0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4071, "license_type": "no_license", "max_line_length": 129, "num_lines": 157, "path": "/src/control/sensorspace.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SENSORSPACE_H_INCLUDED\n#define SENSORSPACE_H_INCLUDED\n\n#include <vector>\n#include <string>\n#include <queue>\n#include <cassert>\n#include <functional>\n#include <common/vector_n.h>\n\n/TODO namespace /\n\nclass sensor_signal\n{\npublic:\n sensor_signal(const std::string& name, std::function<double()> lambda)\n : name(name)\n , lambda(lambda)\n , current(.0)\n , last(current)\n {}\n\n void execute_cycle(void) {\n last = current;\n current = lambda();\n }\n\n std::string name;\n std::function<double()> lambda;\n\n double operator() (void) const { return current; }\n\n double current;\n double last;\n};\n\n\nclass sensor_input_interface {\npublic:\n virtual std::size_t size(void) const = 0;\n virtual VectorN get(void) const = 0;\n virtual ~sensor_input_interface() {}\n virtual double operator[] (std::size_t index) const = 0;\n};\n\n\nclass sensor_vector : public sensor_input_interface\n{\nprotected:\n std::vector<sensor_signal> sensors;\n\npublic:\n sensor_vector(std::size_t number_of_elements = 0)\n : sensors()\n {\n //dbg_msg(\"Creating sensor vector, reserving space for %u elements.\", number_of_elements);\n sensors.reserve(number_of_elements);\n }\n\n /* embed 'normal' vector /\n explicit sensor_vector(const VectorN& plain)\n : sensor_vector(plain.size())\n {\n for (std::size_t i = 0; i < plain.size(); ++i)\n sensors.emplace_back(std::to_string(i) , [&plain, i](){ return plain[i]; });\n }\n\n virtual ~sensor_vector() { /dbg_msg(\"Destroying sensor vector base.\");/ };\n\n void execute_cycle(void) {\n for (auto& s : sensors) s.execute_cycle();\n }\n\n std::size_t size(void) const { return sensors.size(); }\n\n double operator[] (std::size_t index) { return sensors.at(index)(); }\n double operator[] (std::size_t index) const { return sensors.at(index)(); }\n\n VectorN get(void) const {\n VectorN result(sensors.size());\n for (std::size_t i = 0; i < sensors.size(); ++i)\n result[i] = sensors[i]();\n return result;\n }\n};\n\ntemplate <std::size_t NumTaps>\nclass time_embedded_signal\n{\npublic:\n time_embedded_signal(const std::string& name, std::function<double()> lambda)\n : name(name)\n , lambda(lambda)\n , buffer()\n {\n static_assert(NumTaps > 0, \"Min. length of tapped delay line is 1.\");\n buffer.assign(NumTaps, .0);\n assert(buffer.size() == NumTaps);\n }\n\n void execute_cycle(void) {\n buffer.push_front(lambda());\n buffer.pop_back();\n }\n\n std::string name;\n std::function<double()> lambda;\n\n double operator() (void) const { return buffer.front(); }\n\n double operator[] (std::size_t index) { return buffer.at(index); }\n double operator[] (std::size_t index) const { return buffer.at(index); }\n\n std::deque<double> buffer;\n};\n\ntemplate <std::size_t NumTaps>\nclass time_embedded_sensors : public sensor_input_interface\n{\nprotected:\n std::vector<time_embedded_signal<NumTaps>> sensors;\n\npublic:\n time_embedded_sensors(std::size_t number_of_elements)\n : sensors()\n {\n //dbg_msg(\"Creating time-embedded sensors, reserving space for %u delay lines of size %u.\", number_of_elements, NumTaps);\n sensors.reserve(number_of_elements);\n }\n\n virtual ~time_embedded_sensors() {};\n\n void execute_cycle(void) {\n for (auto& s : sensors) s.execute_cycle(); // propagate delay lines\n }\n\n std::size_t size(void) const { return sensors.size()NumTaps + 1 /bias/; }\n\n\n double operator[] (std::size_t index) const {\n assert(index < size());\n const std::size_t i = index/NumTaps; // get buffer index\n const std::size_t j = index%NumTaps; // get element index\n if (i >= sensors.size()) return 0.1;\n return sensors[i][j];\n }\n\n VectorN get(void) const {\n VectorN result(size());\n for (std::size_t i = 0; i < size(); ++i)\n result[i] = (this)[i];\n return result;\n }\n\n};\n\n#endif // SENSORSPACE_H_INCLUDED\n" }, { "alpha_fraction": 0.5258910655975342, "alphanum_fraction": 0.5319435000419617, "avg_line_length": 29.95833396911621, "blob_id": "e4d83b60b7e25e8ef5e9da5e69ea24bae44464f2", "content_id": "a31ab0106a0fe99796a96ab8f76ccf18a5f7051a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1487, "license_type": "no_license", "max_line_length": 123, "num_lines": 48, "path": "/src/common/settings.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include <common/settings.h>\n\n/TODO create argument parser, similar to python, showing all arguments via --help /\n\nbool\nread_option_flag(int argc, char *argv, const char short_name, const char* ext_name)\n{\n for (int i = 1; i < argc; ++i) {\n if ((strncmp(argv[i], ext_name, strlen(ext_name)) == 0) \|\| (strncmp(argv[i], short_name, strlen(short_name)) == 0))\n return true;\n }\n return false;\n}\n\nstd::string\nread_string_option(int argc, char *argv, const char short_name, const char* ext_name, const std::string default_value)\n{\n std::string result = default_value;\n\n for (int i = 1; i < argc; ++i)\n {\n if ((strncmp(argv[i], ext_name, strlen(ext_name)) == 0) \|\| (strncmp(argv[i], short_name, strlen(short_name)) == 0))\n {\n if (argc < i+2) {\n printf(\"usage: %s %s <value>\\n\", argv[0], short_name);\n exit(EXIT_FAILURE);\n } else {\n result = argv[i+1];\n ++i;\n }\n }\n }\n return result;\n}\n\nstd::string\nread_options(int argc, char *argv, const char default_path)\n{\n if (read_option_flag(argc, argv, \"-h\", \"--help\"))\n {\n printf(\"Help, Options: \\n\");\n printf(\" -s --settings : filename of settings file\\n\");\n printf(\" -h --help : show help \\n\");\n printf(\"\\n\");\n exit(EXIT_SUCCESS);\n }\n return read_string_option(argc, argv, \"-s\", \"--settings\", default_path);\n}\n" }, { "alpha_fraction": 0.6481481194496155, "alphanum_fraction": 0.6764705777168274, "avg_line_length": 34.30769348144531, "blob_id": "b962925b14d77c5adbc397fc265dc4229404d44e", "content_id": "b42842451112010b0f442d5b98a2016101ffda04", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 918, "license_type": "no_license", "max_line_length": 85, "num_lines": 26, "path": "/src/learning/gmes_constants.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GMES_CONSTANTS_H_INCLUDED\n#define GMES_CONSTANTS_H_INCLUDED\n\nnamespace gmes_constants\n{\n const double global_learning_rate = 35.0; // used for edges\n const double local_learning_rate = 0.005; // used for predictors\n\n const double initial_learning_capacity = 1.0;\n const double learning_capacity_exhausted = 0.01;\n const double initial_transition_validation = 1.0;\n const double transition_exist_treshold = 0.01;\n const double perceptive_width = 0.1; /** TODO: derive this factor /\n\n const std::size_t experience_size = 100;\n\n / This setting could be used in combination with initial weights,\n * if we want to insert prior domain knowledge to the system.\n * Otherwise this must be 1. /\n const std::size_t number_of_initial_experts = 1;\n\n / general /\n const double random_weight_range = 0.1;\n}\n\n#endif // GMES_CONSTANTS_H_INCLUDED\n" }, { "alpha_fraction": 0.615081787109375, "alphanum_fraction": 0.623323917388916, "avg_line_length": 50.44936752319336, "blob_id": "5a8a0b52461fa0d5cbfc30a3b793b70ce26c2854", "content_id": "14d3a8c21c2d058d7673650873eafd8a322a2c5c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8129, "license_type": "no_license", "max_line_length": 259, "num_lines": 158, "path": "/src/control/spaces.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SPACES_H_INCLUDED\n#define SPACES_H_INCLUDED\n\n#include <robots/joint.h>\n#include <control/jointcontrol.h>\n#include <control/sensorspace.h>\n\n#include <robots/simloid.h>\n\n#include <learning/action_module.h>\n#include <learning/payload.h>\n#include <learning/sarsa.h>\n#include <learning/gmes.h>\n#include <learning/learning_machine_interface.h>\n\n/* TODO:\n * Think of having intermediate 'terminal' states, i.e. that switch to a specific policy and\n * giving a binary like reward of 1 when reaching.\n /\n\nnamespace control {\n\nclass pendulum_sensor_space : public sensor_vector {\npublic:\n pendulum_sensor_space(const robots::Jointvector_t& joints)\n : sensor_vector(3)\n {\n assert(joints.size() == 1);\n sensors.emplace_back(\"[1] sin phi\" , [&joints](){ return +sin(M_PI joints[0].s_ang); });\n sensors.emplace_back(\"[2] cos phi\" , [&joints](){ return -cos(M_PI * joints[0].s_ang); });\n sensors.emplace_back(\"[3] velocity\", [&joints](){ return +joints[0].s_vel; });\n }\n};\n\nclass pendulum_reward_space : public reward_base\n{\npublic:\n pendulum_reward_space( const GMES& gmes\n , const robots::Jointvector_t& joints )\n : reward_base(3)\n {\n assert(joints.size() == 1);\n\n /** Cite: >The reward for swinging up is simply given by a measure of the height of the pole. In order to incorporate exploration due\n * to optimistic value initialization, we chose to give the negative distance of the pole to the horizontal plane\n * located on the top position.<\n * Reward should be normed and have a max. value of zero, so optimistic initialization of Q-values is easily zero.\n /\n rewards.emplace_back(\"Intrinsic learning\", [&gmes ](){ return gmes.get_learning_progress(); });\n rewards.emplace_back(\"Pendulum swing-up\" , [&joints](){ return exp(-100(std::abs(joints[0].s_ang)-1.0)(std::abs(joints[0].s_ang)-1.0)); });\n rewards.emplace_back(\"Resting position\" , [&joints](){ return exp(-100(std::abs(joints[0].s_ang)-0.0)(std::abs(joints[0].s_ang)-0.0)); });\n\n /* in principle the swing-up are two tasks, swing-up and balancing. Think of splitting this up and introduce a task planner.\n * switch to balancing when swing up is finished, on the other hand this introduces the need for terminal conditions.\n /\n\n sts_msg(\"Creating %u reward signals for Pendulum.\", rewards.size());\n }\n};\n\n/ these rewards are probably all wrong!!!\n try using a distance based reward (distance to a virtual goal, e.g. which can hardly be reached)\n or which can be reached and terminates the trial.\n however distance to goal is not available on real machines... so can we accumulate positions...?\n\n Relation to HER:\n HER (hindsight experience replay)\n move the goal towards the actual reached position.\n moving the robot in different directions within the plane can be considered as moving to a specific location.\n if the action was taken, select the respective reward function of subpolicy that would maximize learning.\n but we already learn everything simultaneously. so this is a generalization of HER?\n\n try move over from discrete separate goals to a goal vector.. e.g. for the walking robot...simply the direction in 2D or 3D space to move!\n\n\n /\nclass walking_reward_space : public reward_base\n{\npublic:\n walking_reward_space(robots::Simloid const& robot)\n : reward_base(16)\n {\n / add dummies for intrinsic learning, until state and motor layers are constructed /\n rewards.emplace_back(\"intrinsic state motivation\" , [](){ return .0; } );\n rewards.emplace_back(\"intrinsic motor motivation\" , [](){ return .0; } );\n\n switch(robot.robot_ID){\n case 10: / Tadpole /\n rewards.emplace_back(\"walking forwards\" , [&robot](){ return robot.get_avg_velocity_forward();/ - std::abs(robot.get_avg_velocity_left()) - std::abs(robot.get_avg_rotational_speed()) )/(1. + robot.get_normalized_mechanical_power()); / });\n rewards.emplace_back(\"walking backwards\" , [&robot](){ return -robot.get_avg_velocity_forward()/ - std::abs(robot.get_avg_velocity_left()) - std::abs(robot.get_avg_rotational_speed()) )/(1. + robot.get_normalized_mechanical_power())/; });\n rewards.emplace_back(\"turning left\" , [&robot](){ return +0.1robot.get_avg_rotational_speed()//(1. + robot.get_normalized_mechanical_power())/; });\n rewards.emplace_back(\"turning right\" , [&robot](){ return -0.1robot.get_avg_rotational_speed()//(1. + robot.get_normalized_mechanical_power())/; });\n //rewards.emplace_back(\"stopping/resting\" , [&robot](){ return .1f/(1.f + /robot.get_motion_level() +/ robot.get_normalized_mechanical_power()); });\n break;\n case 31: / Fourlegged /\n rewards.emplace_back(\"walking forwards\" , [&robot](){ return +robot.get_avg_velocity_forward(); });\n //rewards.emplace_back(\"walking backwards\" , [&robot](){ return -robot.get_avg_velocity_forward() });\n //rewards.emplace_back(\"turning left\" , [&robot](){ return +0.1robot.get_avg_rotational_speed() });\n //rewards.emplace_back(\"turning right\" , [&robot](){ return -0.1robot.get_avg_rotational_speed() });\n //rewards.emplace_back(\"stopping\" , [&robot](){ return -1.0/(1. + robot.get_normalized_mechanical_power())/; });\n //rewards.emplace_back(\"get_up\" , [&robot](){ return +robot.get_avg_position().z - robot.get_accels()[0].v.y; });\n //rewards.emplace_back(\"walking left\" , [&robot](){ return +robot.get_avg_velocity_left(); });\n //rewards.emplace_back(\"walking right\" , [&robot](){ return -robot.get_avg_velocity_left(); });\n break;\n\n case 38: /* Hannah /\n rewards.emplace_back(\"walking forwards\" , [&robot](){ return +robot.get_avg_velocity_forward(); });\n sts_msg(\"Reward for Hannah.\");\n break;\n\n case 61: / Flatcat /\n rewards.emplace_back(\"crawling forwards\" , [&robot](){ return +robot.get_avg_velocity_forward(); });\n sts_msg(\"Reward for Flatcat.\");\n default:\n break;\n }\n /* TODO: consider a penalty for dropping in the reward function.\n * However this should only apply for the walking robots, not the crawlers.\n \n TODO: make a policy for stopping, use simloid.is_motion_stopped? and minimize ctrl output.\n /\n sts_msg(\"Creating %u reward signals for walking.\" , rewards.size());\n }\n\n\n\n void add_intrinsic_rewards( learning::Learning_Machine_Interface const& state_learner\n , learning::Learning_Machine_Interface const& motor_learner )\n {\n rewards.at(0) = { \"intrinsic state motivation\", [&state_learner]() { return state_learner.get_learning_progress(); } };\n rewards.at(1) = { \"intrinsic motor motivation\", [&motor_learner]() { return motor_learner.get_learning_progress(); } };\n }\n\n};\n\n\n\n\n/GMES spaces /\nclass GMES_joint_space : public sensor_vector\n{\npublic:\n GMES_joint_space(const robots::Joint_Model& joint)\n : sensor_vector(3)\n {\n sensors.emplace_back(\"[1] angle\" , [&joint](){ return joint.s_ang; });\n sensors.emplace_back(\"[2] velocity\" , [&joint](){ return joint.s_vel; });\n sensors.emplace_back(\"[3] torque\" , [&joint](){ return joint.motor.get(); }); //don't even think of removing that\n /*TODO: Think about: the introduction of the motor signal in the sensor space makes that inherently instable.\n The adaption can skip the world in the loop and can directly influence the sensor space without actually moving the robot.\n * Also, reducing sensor space dimension reduces the overall cost. /\n }\n};\n\n} // namespace control\n\n\n#endif // SPACES_H_INCLUDED\n" }, { "alpha_fraction": 0.5631470084190369, "alphanum_fraction": 0.5828157067298889, "avg_line_length": 28.272727966308594, "blob_id": "e2e3a8fc3343a598ce6d429a5b34ce03503f6330", "content_id": "637a0fa23c02c196b9e7d02b12f8795921fd2411", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 966, "license_type": "no_license", "max_line_length": 72, "num_lines": 33, "path": "/src/learning/action_selection.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <learning/action_selection.h>\n\n/* Prints the values of the distribution to terminal.\n /\nvoid\nprint_distribution(const Action_Selection_Base::Vector_t& distribution)\n{\n sts_msg(\"Length: %u\", distribution.size());\n for (std::size_t i = 0; i < distribution.size(); ++i)\n printf(\"%+1.3f \", distribution[i]);\n printf(\"\\n\");\n}\n\n///* Selects an index from given discrete probability distribution.\n// * The given distribution must sum up to 1.\n// /\n//template <typename Vector_t>\n//std::size_t\n//select_from_distribution(Vector_t const& distribution)\n//{\n// const double x = random_value(0.0, 1.0);\n// double sum = 0.0;\n// for (std::size_t i = 0; i < distribution.size(); ++i)\n// {\n// assert((0.0 <= distribution[i]) and (distribution[i] <= 1.0));\n// sum += distribution[i];\n// if (x < sum)\n// return i;\n// assert(sum < 1.0);\n// }\n// dbg_msg(\"sum: %1.4f\", sum);\n// assert(false);\n//}\n" }, { "alpha_fraction": 0.6137579679489136, "alphanum_fraction": 0.6147770881652832, "avg_line_length": 34.36035919189453, "blob_id": "fb80f72a2e17e926c242618cfb1e780300cc3fb4", "content_id": "b67233e6a10cbde9de7b6aa40feb0ea21e9287cf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3925, "license_type": "no_license", "max_line_length": 117, "num_lines": 111, "path": "/src/evolution/generation_based_strategy.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GENERATION_BASED_STRATEGY_H_INCLUDED\n#define GENERATION_BASED_STRATEGY_H_INCLUDED\n\n#include <evolution/evolution_strategy.h>\n#include <common/stopwatch.h>\n#include <common/modules.h>\n#include <common/log_messages.h>\n\n/* TODO:\n * create object time statistics /\n\nclass Generation_Based_Evolution: public Evolution_Strategy\n{\npublic:\n Generation_Based_Evolution(Population& population,\n Evaluation_Interface& evaluation,\n config& configuration,\n std::size_t max_generation,\n std::size_t cur_generation,\n const std::size_t selection_size,\n const std::string& project_folder_path,\n const bool verbose = true)\n : Evolution_Strategy(population, evaluation, configuration, project_folder_path, verbose)\n , max_generation(max_generation)\n , cur_generation(cur_generation)\n , selection_size(selection_size)\n , verbose(verbose)\n {\n assert(max_generation > 0);\n assert(max_generation >= cur_generation);\n assert(selection_size < population.get_size());\n assert(selection_size > 1);\n\n sts_msg(\"created generation-based policy.\");\n sts_msg(\"selection size is %u.\", selection_size);\n }\n\n ~Generation_Based_Evolution() { sts_msg(\"destroyed generation-based policy.\"); }\n\n bool evaluate_generation(void);\n\n void selection(void) {\n if (verbose) sts_msg(\"Selecting.\");\n population.sort_by_fitness();\n }\n\n void recombination_crossover(void);\n void mutation(void);\n bool show_selection(void);\n\n Evolution_State playback(void) { return show_selection()? Evolution_State::playback : Evolution_State::stopped; }\n\n void resume(void)\n {\n if (cur_generation > 0) {\n std::size_t max_gen_old = max_generation;\n max_generation += (cur_generation - (cur_generation % max_generation));\n if (max_generation > max_gen_old)\n wrn_msg(\"max. generation increased from %u to %u.\", max_gen_old, max_generation);\n\n load_state();\n recombination_crossover();\n mutation();\n sts_msg(\"Generation-based strategy is ready to resume.\");\n } else\n wrn_msg(\"Nothing to resume. Skip.\");\n }\n\n void save_config(config& configuration)\n {\n sts_msg(\"Saving generation-based strategy settings.\");\n configuration.writeUINT(\"MAX_GENERATIONS\" , max_generation);\n configuration.writeUINT(\"CURRENT_GENERATION\", cur_generation);\n configuration.writeUINT(\"SELECTION_SIZE\" , selection_size);\n }\n\n std::size_t get_max_generation(void) const { return max_generation; }\n std::size_t get_cur_generation(void) const { return cur_generation; }\n std::size_t get_selection_size(void) const { return selection_size; }\n\n std::size_t get_max_trials (void) const { return max_generation population.get_size(); }\n std::size_t get_current_trial (void) const { return cur_generation * population.get_size(); }\n\n Evolution_State execute_trial(void)\n {\n if (!evaluate_generation()) {\n return Evolution_State::aborted;\n }\n selection();\n save_state();\n\n ++cur_generation;\n if (cur_generation >= max_generation) {\n return Evolution_State::finished;\n } // else\n recombination_crossover();\n mutation();\n return Evolution_State::running;\n }\n\n const Individual& get_best_individual(void) const { return population[0]; }\n bool is_there_a_new_best_individual(void) { return true; /* TODO implement /}\n\nprivate:\n std::size_t max_generation;\n std::size_t cur_generation;\n const std::size_t selection_size;\n const bool verbose;\n};\n\n#endif // GENERATION_BASED_STRATEGY_H_INCLUDED\n" }, { "alpha_fraction": 0.56627357006073, "alphanum_fraction": 0.5761792659759521, "avg_line_length": 38.62616729736328, "blob_id": "2d5555b1ba6bb3974ae453f1d655f7d2efb2e949", "content_id": "37f0033f71cdff60c640a655454099c0aad588b6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4240, "license_type": "no_license", "max_line_length": 138, "num_lines": 107, "path": "/src/learning/self_adj_motor_space.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SELF_ADJ_MOTOR_SPACE_H_INCLUDED\n#define SELF_ADJ_MOTOR_SPACE_H_INCLUDED\n\n#include <control/control_vector.h>\n\nnamespace control {\n\nclass self_adjusting_motor_space : public Action_Module_Interface\n{\n control::Jointcontrol control;\n\n std::size_t applied_policy;\n std::size_t applied_action;\n std::size_t applied_state;\n\n const learning::RL_Interface& learner;\n const bool self_adjusting;\n CompetitiveMotorLayer motor_layer;\n\npublic:\n\n self_adjusting_motor_space( robots::Robot_Interface& robot\n , const Control_Vector& parameter_set\n , const std::size_t max_actions\n , const std::size_t num_actions_begin\n , static_vector<State_Payload>& state_payload\n , const learning::RL_Interface& learner\n , bool self_adjusting\n , const double mutation_rate\n , const double learning_rate\n , const control::Minimal_Seed_t& seed )\n : control(robot)\n , applied_policy(0)\n , applied_action(0)\n , applied_state(0)\n , learner(learner)\n , self_adjusting(self_adjusting)\n , motor_layer(robot, state_payload, parameter_set, max_actions, num_actions_begin, mutation_rate, learning_rate, self_adjusting, seed)\n {\n dbg_msg(\"Creating self adjusting motor space.\");\n control.set_control_parameter(motor_layer.get_unit(0).weights); // initialize non-mutated start controller\n control.print_parameter();\n control.reset();\n }\n\n void execute_cycle(bool state_has_changed)\n {\n if (state_has_changed) { / apply action learning on state change only /\n / adjust previously selected weights/\n motor_layer.adapt(learner.positive_current_delta(applied_policy));\n\n / update and check state + action from learner /\n applied_policy = learner.get_current_policy();\n applied_action = learner.get_current_action();\n applied_state = learner.get_current_state();\n\n if (self_adjusting)\n motor_layer.enable_adaption(applied_policy == 0); /TODO use enums for policies/\n motor_layer.create_mutated_weights(applied_action);\n\n /* apply new weights /\n control.set_control_parameter(motor_layer.get_mutated_weights());\n }\n control.execute_cycle();\n }\n\n std::size_t get_number_of_actions (void) const { return motor_layer.get_number_of_motor_units(); }\n std::size_t get_number_of_actions_available(void) const { return motor_layer.get_number_of_available_motor_units(); }\n\n bool exists(const std::size_t action_index) const { return motor_layer.exists(action_index); }\n\n friend class self_adjusting_motor_space_graphics;\n};\n\n#include <draw/draw.h>\n\n/TODO refactor and move to gmes_action_module/\nclass self_adjusting_motor_space_graphics : public Graphics_Interface {\n const self_adjusting_motor_space& space;\n CompetitiveMotorLayer_Graphics motor_layer_graphics;\npublic:\n self_adjusting_motor_space_graphics(const self_adjusting_motor_space& space)\n : space(space)\n , motor_layer_graphics(space.motor_layer, -1.0, 0.0, 0.5) {}\n\n void execute_cycle(bool state_has_changed) {\n if (state_has_changed)\n motor_layer_graphics.execute_cycle();\n }\n void draw(const pref& p) const\n {\n motor_layer_graphics.draw(p);\n\n const MotorUnit& motor = space.motor_layer.get_unit(space.applied_action);\n glColor3f(1.0, 1.0, 1.0);\n glprintf(-0.9, 0.65, 0.0, 0.03, \"%3u:%2u\"\n , space.applied_state\n , space.applied_action );\n\n draw_vector2(-0.9, 0.6, 0.05, 1.0, motor.weights.get_parameter(), 3.0);\n }\n};\n\n\n} /* namespace control /\n\n#endif / SELF_ADJ_MOTOR_SPACE_H_INCLUDED /\n" }, { "alpha_fraction": 0.568509042263031, "alphanum_fraction": 0.5880167484283447, "avg_line_length": 21.893617630004883, "blob_id": "957bf0c26c8f0d00d011b1b7aadd0086cef65692", "content_id": "4008242d0ddcc05649cb0f2562def0eca456fea9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2154, "license_type": "no_license", "max_line_length": 83, "num_lines": 94, "path": "/src/draw/plot1D.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ plot1D.cpp /\n\n#include \"plot1D.h\"\n\nvoid\nplot1D::draw(void) const\n{\n glPushMatrix();\n auto_scale();\n\n set_color(color);\n draw_line_strip();\n\n glPopMatrix();\n draw_statistics();\n}\n\nvoid\ncolored_plot1D::draw_colored(void) const\n{\n glPushMatrix();\n auto_scale();\n\n draw_colored_line_strip();\n\n glPopMatrix();\n set_color(color);\n draw_statistics();\n}\n\nvoid\nplot1D::auto_scale(void) const\n{\n const float scale = 2.0 / (axis.max_amplitude - axis.min_amplitude);\n const float offset = 0.0;// (axis.max_amplitude + axis.min_amplitude) / 2;\n\n glTranslatef(axis.px - 0.5 axis.width, axis.py - offset, axis.pz);\n glScalef( axis.width / number_of_samples\n , 0.5 * axis.height * scale /2 //TODO scale is wrong, remove 0.5?\n , 1.0);\n\n}\n\nvoid\nplot1D::draw_line_strip(void) const\n{\n glLineWidth(1.0f);\n glBegin(GL_LINE_STRIP);\n for (unsigned i = number_of_samples+1; i-- > 1; ) { // zero omitted\n const unsigned pos = (i + pointer) % number_of_samples;\n glVertex2f( i, signal[pos]);\n }\n glEnd();\n}\n\nvoid\ncolored_plot1D::draw_colored_line_strip(void) const\n{\n glLineWidth(1.0f);\n glBegin(GL_LINE_STRIP);\n for (unsigned i = number_of_samples+1; i-- > 1; ) { // zero omitted\n const unsigned pos = (i + pointer) % number_of_samples;\n set_color(colortable.get_color(colors[pos]));\n glVertex2f( i, signal[pos]);\n }\n glEnd();\n}\n\nvoid\nplot1D::draw_statistics(void) const\n{\n glprintf( axis.px - 0.5 * axis.width\n , axis.py + 0.5 * axis.height - 1.1 * axis.font_height * (axis_id + 1)\n , axis.pz\n , axis.font_height\n , \"%+.4f %s\"\n , signal[pointer], name.c_str() );\n}\n\nvoid\nplot1D::add_sample(const float s)\n{\n increment_pointer();\n //assert(pointer < number_of_samples);\n signal[pointer] = s;\n\n /* autoscale //TODO überarbeiten /\n if (pointer == 0) {\n axis.max_amplitude = decrement;\n axis.min_amplitude = decrement;\n }\n axis.max_amplitude = std::max(axis.max_amplitude, s);\n axis.min_amplitude = std::min(axis.min_amplitude, s);\n}\n\n" }, { "alpha_fraction": 0.662446141242981, "alphanum_fraction": 0.6640625, "avg_line_length": 35.75247573852539, "blob_id": "939861ed977782a33208e4237490ed9f59ac8a9a", "content_id": "2dffe8698583f5573d89f0c0f1fd294dbe63b56f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3712, "license_type": "no_license", "max_line_length": 125, "num_lines": 101, "path": "/src/evolution/individual.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef INDIVIDUAL_H_INCLUDED\n#define INDIVIDUAL_H_INCLUDED\n\n#include <vector>\n#include <cassert>\n#include <float.h>\n#include <common/modules.h>\n#include <common/log_messages.h>\n#include <common/incremental_average.h>\n\nclass Individual;\n\nclass Fitness_Value\n{\npublic:\n\n explicit Fitness_Value(void) : fitness() {}\n\n double get_value(void) const { return fitness.get(); }\n void set_value(double value) { fitness.sample(value); } // average fitness incrementally\n\n void reset(void) { fitness.reset(); }\n std::size_t get_number_of_evaluations(void) const { return fitness.get_num_samples(); }\n\n double get_value_or_default(double default_value = 0.0) const {\n return (fitness.get_num_samples()>0) ? fitness.get() : default_value;\n }\n\nprivate:\n incremental_average fitness;\n\n friend bool operator==(const Fitness_Value& lhs, const Fitness_Value& rhs);\n friend bool operator!=(const Fitness_Value& lhs, const Fitness_Value& rhs);\n friend bool operator< (const Fitness_Value& lhs, const Fitness_Value& rhs);\n friend bool operator> (const Fitness_Value& lhs, const Fitness_Value& rhs);\n friend bool operator<=(const Fitness_Value& lhs, const Fitness_Value& rhs);\n friend bool operator>=(const Fitness_Value& lhs, const Fitness_Value& rhs);\n friend void crossover (const Individual& mother, const Individual& father, Individual& child);\n};\n\ninline bool operator==(const Fitness_Value& lhs, const Fitness_Value& rhs) { return lhs.fitness.get() == rhs.fitness.get(); }\ninline bool operator!=(const Fitness_Value& lhs, const Fitness_Value& rhs) { return !operator==(lhs,rhs); }\ninline bool operator< (const Fitness_Value& lhs, const Fitness_Value& rhs) { return lhs.fitness.get() < rhs.fitness.get(); }\ninline bool operator> (const Fitness_Value& lhs, const Fitness_Value& rhs) { return operator< (rhs,lhs); }\ninline bool operator<=(const Fitness_Value& lhs, const Fitness_Value& rhs) { return !operator> (lhs,rhs); }\ninline bool operator>=(const Fitness_Value& lhs, const Fitness_Value& rhs) { return !operator< (lhs,rhs); }\n\nvoid crossover(const Individual& mother, const Individual& father, Individual& child);\n\nclass Individual\n{\npublic:\n Individual(unsigned int individual_size, double init_mutation_rate, double meta_mutation_rate)\n : genome(individual_size)\n , fitness()\n , mutation_rate(init_mutation_rate)\n , meta_mutation_rate(meta_mutation_rate)\n {\n assert(individual_size > 0);\n assert(init_mutation_rate > .0);\n assert(meta_mutation_rate > .0);\n }\n\n ~Individual() {}\n\n\n Individual(const Individual& mother, const Individual& father)\n : genome(mother.genome.size())\n , fitness()\n , mutation_rate()\n , meta_mutation_rate()\n {\n crossover(mother, father, this);\n }\n\n Individual& operator=(const Individual& other) {\n if (this != &other) // avoid invalid self-assignment\n {\n assert(other.genome.size() == genome.size());\n genome = other.genome;\n fitness = other.fitness;\n mutation_rate = other.mutation_rate;\n meta_mutation_rate = other.meta_mutation_rate;\n }\n return this;\n }\n\n void mutate(void);\n void initialize_from_seed(const std::vector<double>& seed); /* TODO make a constructor of that /\n std::size_t get_size(void) const { return genome.size(); }\n\n std::vector<double> genome;\n Fitness_Value fitness;\n\n double mutation_rate;\n double meta_mutation_rate;\n};\n\nvoid crossover(const Individual& mother, const Individual& father, Individual& child);\n\n#endif // INDIVIDUAL_H_INCLUDED\n" }, { "alpha_fraction": 0.5903158783912659, "alphanum_fraction": 0.5933421850204468, "avg_line_length": 27.88524627685547, "blob_id": "192a1437f6e44caefc8ac015701d6fd3de16539b", "content_id": "c2e1cd0617adbbb17f1ff61835e0786c856f8fe1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5287, "license_type": "no_license", "max_line_length": 108, "num_lines": 183, "path": "/src/common/config.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"./config.h\"\n\nbool\nconfig::load(void)\n{\n / try to open file for reading /\n fd = fopen(filename.c_str(), \"r\");\n if (fd == NULL) {\n wrn_msg(\"Cannot load configuration file %s\", filename.c_str());\n return false;\n }\n\n sts_msg(\"Configuration file %s is opened, parsing...\", filename.c_str());\n parse();\n fclose(fd); // and close\n sts_msg(\"Done.\");\n return true;\n}\n\nvoid\nconfig::parse(void)\n{\n unsigned number_of_entries = 0;\n\n char name[64];\n char value[1024];\n\n if (NULL == fd)\n err_msg(__FILE__, __LINE__, \"Cannot parse config file. File is not open.\");\n fseek(fd, 0, SEEK_SET);\n\n while(true) {\n if (2 == fscanf(fd, \"%64s = %1024s\\n\", name, value)) {\n sts_msg(\"\| %2u %s = %s\", ++number_of_entries, name, value);\n configuration.insert(element_t(std::string{name}, std::string{value}));\n }\n else break;\n }\n}\n\nint\nconfig::readINT(std::string const& name, int def)\n{\n int result = def;\n if (configuration.find(name) != configuration.end())\n result = std::stoi(configuration[name]);\n\n sts_msg(\"Reading %s <int> = %d %s\", name.c_str(), result, (result == def)? \"(DEFAULT)\":\"\");\n return result;\n}\n\nunsigned\nconfig::readUINT(std::string const& name, unsigned def)\n{\n unsigned result = def;\n if (configuration.find(name) != configuration.end())\n result = std::stoul(configuration[name]);\n\n sts_msg(\"Reading %s <unsigned> = %u %s\", name.c_str(), result, (result == def)? \"(DEF)\":\"\");\n return result;\n}\n\nbool\nconfig::readBOOL(std::string const& name, bool def)\n{\n bool result = def;\n\n if (configuration.find(name) != configuration.end())\n {\n if (\"YES\" == configuration[name] or \"TRUE\" == configuration[name]) result = true;\n else if (\"NO\" == configuration[name] or \"FALSE\" == configuration[name]) result = false;\n else\n wrn_msg(\"unrecognized boolean value.\");\n }\n\n sts_msg(\"Reading %s <bool> = %s %s\", name.c_str(), result? \"TRUE\":\"FALSE\", (result == def)? \"(DEF)\":\"\");\n return result;\n}\n\ndouble\nconfig::readDBL(std::string const& name, double def)\n{\n double result = def;\n if (configuration.find(name) != configuration.end())\n result = std::stod(configuration[name]);\n\n sts_msg(\"Reading %s <double> = %f %s\", name.c_str(), result, (result == def)? \"(DEF)\":\"\");\n return result;\n}\n\nstd::string\nconfig::readSTR(std::string const& name, std::string const& def)\n{\n std::string result = def;\n if (configuration.find(name) != configuration.end())\n result = configuration[name];\n\n sts_msg(\"Reading %s <string> = \\\"%s\\\" %s\", name.c_str(), result.c_str(), (result == def)? \"(DEF)\":\"\");\n return result;\n}\n\nvoid\nconfig::writeINT(std::string const& name, int value)\n{\n if (configuration.find(name) != configuration.end()) {\n sts_msg(\"Replacing entry %s with value %d\", name.c_str(), value);\n configuration[name] = std::to_string(value);\n return;\n }\n\n sts_msg(\"Adding entry %s with value %d\", name.c_str(), value);\n configuration.insert(element_t(name, std::to_string(value)));\n}\n\nvoid\nconfig::writeUINT(std::string const& name, unsigned value)\n{\n if (configuration.find(name) != configuration.end()) {\n sts_msg(\"Replacing entry %s with value %u\", name.c_str(), value);\n configuration[name] = std::to_string(value);\n return;\n }\n\n sts_msg(\"Adding entry %s with value %u\", name.c_str(), value);\n configuration.insert(element_t(name, std::to_string(value)));\n}\n\nvoid\nconfig::writeBOOL(std::string const& name, bool value)\n{\n if (configuration.find(name) != configuration.end()) {\n sts_msg(\"Replacing entry %s with value %s\", name.c_str(), value ? \"YES\" : \"NO\");\n configuration[name] = value ? \"YES\" : \"NO\";\n return;\n }\n\n sts_msg(\"Adding entry %s with value %s\", name.c_str(), value ? \"YES\" : \"NO\");\n configuration.insert(element_t(name, value ? std::string{\"YES\"} : std::string{\"NO\"}));\n}\n\nvoid\nconfig::writeDBL(std::string const& name, double value)\n{\n if (configuration.find(name) != configuration.end()) {\n sts_msg(\"Replacing entry %s with value %f\", name.c_str(), value);\n configuration[name] = std::to_string(value);\n return;\n }\n\n sts_msg(\"Adding entry %s with value %f\", name.c_str(), value);\n configuration.insert(element_t(name, std::to_string(value)));\n}\n\nvoid\nconfig::writeSTR(std::string const& name, std::string const& value)\n{\n if (configuration.find(name) != configuration.end()) {\n sts_msg(\"Replacing entry %s with value %s\", name.c_str(), value.c_str());\n configuration[name] = value;\n return;\n }\n\n sts_msg(\"Adding entry %s with value %s\", name.c_str(), value.c_str());\n configuration.insert(element_t(name, value));\n}\n\nvoid\nconfig::finish(void)\n{\n / open configuration file for writing /\n std::fstream fs;\n fs.open(filename.c_str(), std::fstream::out);\n\n if (fs.is_open())\n {\n sts_add(\"Writing configuration file...\");\n for (auto const& c: configuration)\n fs << c.first << \" = \" << c.second << std::endl;\n sts_msg(\"Done.\");\n fs.close();\n }\n else wrn_msg(\"Cannot initialize configuration file %s\", filename.c_str());\n}\n" }, { "alpha_fraction": 0.6007751822471619, "alphanum_fraction": 0.6062471270561218, "avg_line_length": 34.0880012512207, "blob_id": "6bf941c4e8bcba7de9ce991d7818b9cea65b354e", "content_id": "42e3de760130560520760f295b389e57d070ccc1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8772, "license_type": "no_license", "max_line_length": 115, "num_lines": 250, "path": "/src/evolution/pool_strategy.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef POOL_STRATEGY_H_INCLUDED\n#define POOL_STRATEGY_H_INCLUDED\n\n#include <vector>\n#include <string>\n#include <algorithm>\n\n#include <evolution/evolution_strategy.h>\n#include <evolution/evaluation_interface.h>\n#include <common/log_messages.h>\n#include <common/modules.h>\n\ninline double biased_random_value(double xrand, double bias)\n{\n return tanh(biasxrand)/tanh(bias);\n}\n\ninline std::size_t biased_random_index(std::size_t N, double bias)\n{\n return floor(biased_random_value(random_value(), bias) * N);\n}\n\ninline std::size_t biased_random_index_inv(std::size_t N, double bias)\n{\n return N - biased_random_index(N, bias) -1; //N - (0..N-1) - 1\n}\n\n/* search the population (sorted-by-fitness) bottom-up and replace\n * the best individual you can get with lower fitness than yours.\n /\nstd::size_t get_replacement_candidate_for(Individual& opponent, Population& population);\n\n/ generation free /\nclass Pool_Evolution: public Evolution_Strategy\n{\npublic:\n Pool_Evolution(Population& population,\n Evaluation_Interface& evaluation,\n config& configuration,\n std::size_t max_trials,\n std::size_t current_trial,\n double moving_rate,\n double selection_bias,\n const std::string& project_folder_path,\n const bool verbose = true )\n : Evolution_Strategy(population, evaluation, configuration, project_folder_path, verbose)\n , population(population)\n , max_trials(max_trials)\n , current_trial(current_trial)\n , moving_rate(moving_rate)\n , selection_bias(selection_bias)\n , best_individual_has_changed(false)\n , current_playback_idx()\n {\n sts_msg(\"Created pool evolution strategy.\");\n assert(current_trial <= max_trials);\n assert(max_trials > 1);\n }\n\n ~Pool_Evolution() { sts_msg(\"Destroyed pool evolution strategy.\"); }\n\n bool evaluate(Individual& individual)\n {\n evaluation.constrain(individual.genome);\n if (evaluation.evaluate(individual.fitness, individual.genome, random_value(0.0, 1.0))) {\n sts_add(\"F=%+1.3f\", individual.fitness.get_value_or_default());\n return true;\n } else {\n sts_msg(\"Evaluation aborted without result.\");\n return false;\n }\n }\n\n bool crossover_trial(void)\n {\n / select parents from pool and crossover /\n std::size_t parent_1 = biased_random_index_inv(population.get_size(), selection_bias);\n std::size_t parent_2 = biased_random_index_inv(population.get_size(), selection_bias);\n\n Individual child(population[parent_1], population[parent_2]);\n sts_add(\"[cross %2u + %2u]\", parent_1, parent_2);\n\n child.mutate();\n\n if (not evaluate(child)) return false;\n\n std::size_t replace_idx = get_replacement_candidate_for(child, population);\n\n if (child.fitness > population[replace_idx].fitness)\n {// if better than the replacement candidate, replace it.\n sts_add(\"[> %+1.3f] replace %2u\", population[replace_idx].fitness.get_value(), replace_idx);\n population[replace_idx] = child;\n\n best_individual_has_changed \|= (replace_idx == 0);\n\n if (population[replace_idx].fitness.get_number_of_evaluations() == 0)\n err_msg(__FILE__, __LINE__, \"overriding a not yet tested one: %u\", replace_idx);\n }\n else sts_add(\"[< %+1.3f] discard\", population[replace_idx].fitness.get_value_or_default());\n\n return true;\n }\n\n bool refreshing_trial(void)\n {\n std::size_t candidate_idx = random_index(population.get_size());\n Individual candidate(population[candidate_idx]); // select candidate from pool\n sts_add(\"[refr. %2u (%2u)]\", candidate_idx, candidate.fitness.get_number_of_evaluations());\n bool result = evaluate(candidate);\n\n / push_back p to population, replace the old one /\n population[candidate_idx] = candidate;\n\n return result;\n }\n\n bool initial_trial(void)\n {\n sts_add(\"[initial trial]\");\n assert(current_trial < population.get_size());\n std::size_t candidate_idx = current_trial;\n Individual& candidate(population[candidate_idx]); // select candidates successively from pool\n return evaluate(candidate);\n }\n\n Evolution_State execute_trial(void)\n {\n bool result = false;\n bool is_initial = current_trial < population.get_size();\n\n / prepare /\n if (current_trial % population.get_size() == 0)\n evaluation.prepare_evaluation(current_trial, max_trials);\n\n sts_add(\"T: %u\", current_trial);\n if (is_initial) {\n result = initial_trial();\n } else if (random_value(0.0, 1.0) > moving_rate) {\n result = crossover_trial();\n } else {\n result = refreshing_trial();\n }\n printf(\"\\n\");\n\n if (not result) return Evolution_State::aborted;\n\n if (!is_initial)\n update_population_statistics();\n\n if (++current_trial < max_trials) {\n if (current_trial > 0 and (current_trial % population.get_size() == 0))\n save_state();\n return Evolution_State::running;\n }\n else {\n save_state();\n return Evolution_State::finished;\n }\n\n }\n\n Evolution_State playback(void)\n {\n sts_msg(\"playback individual: %u\", current_playback_idx);\n if (not evaluate(population[current_playback_idx]))\n return Evolution_State::aborted;\n\n update_population_statistics();\n\n if (++current_playback_idx < population.get_size()) return Evolution_State::playback;\n else return Evolution_State::stopped;\n }\n\n void resume(void)\n {\n if (current_trial > 0) {\n std::size_t max_trials_old = max_trials;\n max_trials += (current_trial - (current_trial % max_trials));\n if (max_trials > max_trials_old)\n wrn_msg(\"Max. trials increased from %u to %u.\", max_trials_old, max_trials);\n load_state(); /TODO consider to move that out of the if clause /\n population.sort_by_fitness(); /*TODO consider to move that out of the if clause /\n sts_msg(\"Strategy ready to resume.\");\n } else\n wrn_msg(\"Nothing to resume. Skip.\");\n }\n\n void save_config(config& configuration)\n {\n sts_msg(\"Saving pool strategy settings.\");\n configuration.writeUINT(\"MAX_TRIALS\" , max_trials);\n configuration.writeUINT(\"CURRENT_TRIAL\" , current_trial);\n configuration.writeDBL (\"MOVING_RATE\" , moving_rate);\n configuration.writeDBL (\"SELECTION_BIAS\", selection_bias);\n }\n\n std::size_t get_max_trials (void) const { return max_trials; }\n std::size_t get_current_trial(void) const { return current_trial; }\n\n const Individual& get_best_individual(void) const\n {\n sts_msg(\"Get best individual (%1.4f)\", population.get_best_individual().fitness.get_value_or_default());\n return population.get_best_individual();\n }\n\n bool is_there_a_new_best_individual(void) {\n bool result = best_individual_has_changed;\n best_individual_has_changed = false;\n return result;\n }\n\n /* think about to use 'insertion sort' and keeping this list up-to-date all the time /\n void update_population_statistics(void)\n {\n double last_best_fitness = population.get_best_individual().fitness.get_value();\n population.sort_by_fitness();\n best_individual_has_changed \|= (last_best_fitness != population.get_best_individual().fitness.get_value());\n\n fitness_stats .reset();\n mutation_stats.reset();\n\n for (std::size_t i = 0; i < population.get_size(); ++i) {\n\n if (population[i].fitness.get_number_of_evaluations() > 0) {\n fitness_stats .add_sample(population[i].fitness.get_value());\n mutation_stats.add_sample(population[i].mutation_rate);\n } else dbg_msg(\"not evaluated, skipping %u\", i);\n }\n\n fitness_stats .update_average();\n mutation_stats.update_average();\n\n if ((current_trial+1) % population.get_size() == 0)\n sts_msg(\"max:%+1.4f, avg:%+1.4f, min:%+1.4f\",\n fitness_stats.max,\n fitness_stats.avg,\n fitness_stats.min);\n }\n\n Population& population;\n std::size_t max_trials;\n std::size_t current_trial;\n const double moving_rate;\n const double selection_bias;\n\n bool best_individual_has_changed;\n std::size_t current_playback_idx;\n};\n\n#endif // POOL_STRATEGY_H_INCLUDED\n" }, { "alpha_fraction": 0.6700814366340637, "alphanum_fraction": 0.671920120716095, "avg_line_length": 29.95121955871582, "blob_id": "33c1674695bcca3c036295dcbb2994d6c84012fe", "content_id": "c86e3ec61f744266461b864edc396a67813212c4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3807, "license_type": "no_license", "max_line_length": 122, "num_lines": 123, "path": "/src/common/event_manager.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EVENT_MANAGER_H\n#define EVENT_MANAGER_H\n\n#include <SDL2/SDL_events.h>\n#include <SDL2/SDL_keyboard.h>\n#include <SDL2/SDL_keycode.h>\n#include <algorithm>\n#include <functional>\n#include <common/log_messages.h>\n#include <common/globalflag.h>\n#include <common/visuals.h>\n\nstruct Mouse_Click_Event\n{\n bool clicked;\n int position_x;\n int position_y;\n};\n\nstruct Joystick\n{\n float x0, y0;\n float x1, y1;\n};\n\nclass Event_Manager\n{\n\npublic:\n Event_Manager()\n : event()\n , joystick()\n , user_callback()\n , mouse_button_left()\n , mouse_button_right()\n , mouse_button_middle()\n , mouse_position_x()\n , mouse_position_y()\n , mouse_wheel_position()\n , mouse_wheel_position_last()\n {}\n\n void process_events(void);\n\n typedef std::function<void(SDL_Keysym const& key )> Keysym_t;\n typedef std::function<void(SDL_JoyButtonEvent const& joystick)> Joybutton_t;\n typedef std::function<void(SDL_JoyAxisEvent const& joystick)> Joyaxis_t;\n typedef std::function<void(SDL_JoyHatEvent const& joystick)> Joyhat_t;\n\n\n void reg_usr_cb_key_pressed (Keysym_t cb);\n void reg_usr_cb_key_released (Keysym_t cb);\n void reg_usr_cb_joystick_button_pressed (Joybutton_t cb);\n void reg_usr_cb_joystick_button_released(Joybutton_t cb);\n void reg_usr_cb_joystick_motion_axis (Joyaxis_t cb);\n void reg_usr_cb_joystick_motion_hat (Joyhat_t cb);\n //void reg_usr_cb_mouse (callback_type callback_function);\n\n Joystick const& get_joystick(void) const { return joystick; }\n\n int get_mouse_wheel_position (void) const { return mouse_wheel_position; }\n\nprivate:\n void handle_key_pressed (SDL_Keysym const& key);\n void handle_key_released(SDL_Keysym const& key);\n\n void handle_mouse_motion (SDL_MouseMotionEvent const& motion);\n void handle_mouse_wheel (SDL_MouseWheelEvent const& wheel );\n void handle_mouse_button_pressed (SDL_MouseButtonEvent const& button);\n void handle_mouse_button_released(SDL_MouseButtonEvent const& button);\n\n void handle_joystick_motion_axis (SDL_JoyAxisEvent const& joystick);\n void handle_joystick_motion_hat (SDL_JoyHatEvent const& joystick);\n void handle_joystick_button_pressed (SDL_JoyButtonEvent const& joystick);\n void handle_joystick_button_released(SDL_JoyButtonEvent const& joystick);\n\n / private member callbacks /\n void on_left_mouse_button_pressed (void);\n void on_right_mouse_button_pressed (void);\n void on_middle_mouse_button_pressed(void);\n\n void on_left_mouse_button_released (void);\n void on_right_mouse_button_released (void);\n void on_middle_mouse_button_released(void);\n\n void on_mouse_wheel_up (void);\n void on_mouse_wheel_down(void);\n\n\n SDL_Event event;\n Joystick joystick;\n\n / callbacks /\n struct UserCallbacks {\n Keysym_t key_pressed = nullptr; // think about a general callback register function for all events\n Keysym_t key_released = nullptr;\n Joybutton_t joystick_button_pressed = nullptr;\n Joybutton_t joystick_button_released = nullptr;\n Joyaxis_t joystick_motion_axis = nullptr;\n Joyhat_t joystick_motion_hat = nullptr;\n } user_callback;\n\n //callback_type user_callback_mouse;\n\n Mouse_Click_Event mouse_button_left;\n Mouse_Click_Event mouse_button_right;\n Mouse_Click_Event mouse_button_middle;\n\n int mouse_position_x;\n int mouse_position_y;\n int mouse_wheel_position;\n int mouse_wheel_position_last;\n\n};\n\nvoid on_button_pressed_ESCAPE (void);\nvoid on_button_pressed_SPACE (void);\nvoid on_button_pressed_BACKSPACE(void);\nvoid on_button_pressed_RETURN (void);\nvoid quit(void);\n\n\n#endif / EVENT_MANAGER_H /\n" }, { "alpha_fraction": 0.5305282473564148, "alphanum_fraction": 0.567230761051178, "avg_line_length": 27.21290397644043, "blob_id": "873270635eaca42de3d871c02d66e8ccb0fc9421", "content_id": "9d393fd222a988f9458f5f0f5160445c7b3a6208", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8746, "license_type": "no_license", "max_line_length": 106, "num_lines": 310, "path": "/src/tests/main.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#define CATCH_CONFIG_MAIN\n#include <tests/catch.hpp>\n#include <common/modules.h>\n/ Framework tests\n * Di, 8.November 2016\n * Hillary oder Donald? /\n\nconst double tolerance = 0.0001; // DO NOT CHANGE!\n\n\n#include <common/incremental_average.h>\nTEST_CASE( \"Incremental Average\", \"[math]\" ) {\n incremental_average inc;\n\n REQUIRE( inc.get_num_samples() == 0 );\n\n inc.sample(-0.01337);\n\n REQUIRE( inc.get() == -0.01337 );\n REQUIRE( inc.get_num_samples() == 1 );\n\n inc.sample(-0.01337);\n\n REQUIRE( inc.get() == -0.01337 );\n REQUIRE( inc.get_num_samples() == 2 );\n\n inc.reset();\n for (unsigned i = 0; i < 10; ++i)\n inc.sample(1.0i);\n\n REQUIRE( inc.get_num_samples() == 10 );\n REQUIRE( inc.get() == 4.5 );\n\n inc.reset();\n REQUIRE( inc.get_num_samples() == 0 );\n\n double sum = .0;\n for (unsigned int i = 0; i < 10000; ++i) {\n double r = random_value(0.0, 23.42);\n sum += r;\n inc.sample(r);\n }\n sum /= 10000;\n REQUIRE( close(inc.get(), sum, tolerance) );\n}\n\n#include <common/modules.h>\nTEST_CASE( \"unwrap\", \"[math]\" ) {\n\n /* Test of wrap2 and unwrap /\n double step = M_PI_4;\n double angle = -4M_PI;\n double unwrapped = angle;\n\n for (unsigned int i = 0; i < 32; ++i)\n {\n double mod_angle = wrap2(angle);\n REQUIRE( close(mod_angle, wrap(angle), tolerance) ); // check 2nd version\n unwrapped = unwrap(mod_angle, unwrapped);\n REQUIRE( close(angle, unwrapped, tolerance) );\n angle += step;\n }\n for (unsigned int i = 0; i < 32; ++i)\n {\n double mod_angle = wrap2(angle);\n REQUIRE( close(mod_angle, wrap(angle), tolerance) ); // check 2nd version\n unwrapped = unwrap(mod_angle, unwrapped);\n REQUIRE( close(angle, unwrapped, tolerance) );\n angle -= step;\n }\n}\n\n#include <evolution/pool_strategy.h>\nTEST_CASE( \"biased_random_index\", \"[math]\") {\n srand(2342);\n /* test case for biased random index /\n double selection_bias = 1.0;\n unsigned N = 7;\n std::vector<unsigned> bins(N,0);\n std::size_t total = 50000;\n\n for (unsigned i = 0; i < total; ++i){\n unsigned k = biased_random_index_inv(N, selection_bias);\n REQUIRE( k < bins.size() );\n ++bins[k];\n }\n\n auto draw_bar = [](unsigned len) {\n std::string foo{};\n for (unsigned i = 0; i < len;++i) foo.append(\"=\");\n return foo;\n };\n\n unsigned counter = total;\n for (unsigned p = 0; p < bins.size(); ++p) {\n dbg_msg(\"%2u: %5u %5.2f %s\", p, bins[p], 100.0bins[p]/total, draw_bar(50bins[p]/total).c_str());\n REQUIRE( bins[p] < counter );\n counter = bins[p];\n }\n srand((unsigned) time(0));\n}\n\n\nTEST_CASE( \"random_index\", \"[math]\") {\n srand((unsigned) time(0));\n\n / check that random index does not overshoot range /\n REQUIRE( random_index(0) == 0 );\n unsigned counter = 0;\n for (unsigned i = 1; i < 1337; ++i) {\n unsigned rnd_idx = random_index(i);\n if (rnd_idx >= i) ++counter;\n }\n REQUIRE( counter == 0);\n\n / weakly check uniform distribution /\n const unsigned num_bins = 13;\n std::vector<unsigned> bins(num_bins);\n for (unsigned i = 0; i < 13000; ++i) {\n ++bins.at(random_index(num_bins));\n }\n\n for (auto& b : bins) {\n REQUIRE( in_range(b, 900u, 1100u));\n dbg_msg(\"%u\", b);\n }\n}\n\n\n#include <common/backed.h>\nTEST_CASE( \"backed value\", \"[common]\") {\n\n common::backed_t<int> value_unsigned;\n REQUIRE( value_unsigned.get() == 0 );\n REQUIRE( value_unsigned.get_backed() == 0 );\n value_unsigned = 44;\n value_unsigned = 50;\n value_unsigned += 5;\n REQUIRE( value_unsigned.get() == 55 );\n REQUIRE( value_unsigned.get_backed() == 0 );\n value_unsigned.transfer();\n REQUIRE( value_unsigned.get_backed() == 55 );\n value_unsigned.reset();\n REQUIRE( value_unsigned.get() == 0 );\n REQUIRE( value_unsigned.get_backed() == 0 );\n\n\n common::backed_t<bool> value_bool(true);\n REQUIRE( value_bool.get() == true );\n REQUIRE( value_bool.get_backed() == false );\n value_bool = true;\n REQUIRE( value_bool.get() == true );\n REQUIRE( value_bool.get_backed() == false );\n value_bool.transfer();\n REQUIRE( value_bool.get_backed() == true );\n value_bool.reset();\n REQUIRE( value_bool.get() == false );\n REQUIRE( value_bool.get_backed() == false );\n\n common::backed_t<double> value_double(.6, .9);\n REQUIRE( value_double.get() == .6 );\n REQUIRE( value_double.get_backed() == .9 );\n value_double = .3;\n value_double = .38;\n value_double += .02;\n REQUIRE( value_double.get() == .4 );\n REQUIRE( value_double.get_backed() == .9 );\n value_double.transfer();\n REQUIRE( value_double.get_backed() == .4 );\n value_double.reset();\n REQUIRE( value_double.get() == .0 );\n REQUIRE( value_double.get_backed() == .0 );\n\n}\n\nTEST_CASE( \"delayed values\", \"[common]\") {\n\n common::delayed_t<int> value_unsigned(0,1);\n REQUIRE( value_unsigned.get() == 0 );\n REQUIRE( value_unsigned.get_delayed() == 0 );\n value_unsigned = 44;\n value_unsigned = 50;\n value_unsigned += 5;\n REQUIRE( value_unsigned.get() == 55 );\n REQUIRE( value_unsigned.get_delayed() == 0 );\n value_unsigned.transfer();\n REQUIRE( value_unsigned.get_delayed() == 55 );\n value_unsigned.reset();\n REQUIRE( value_unsigned.get() == 0 );\n REQUIRE( value_unsigned.get_delayed() == 0 );\n\n\n common::delayed_t<bool> value_bool(true, 1);\n REQUIRE( value_bool.get() == true );\n REQUIRE( value_bool.get_delayed() == true );\n value_bool = false;\n REQUIRE( value_bool.get() == false );\n REQUIRE( value_bool.get_delayed() == true );\n value_bool.transfer();\n REQUIRE( value_bool.get_delayed() == false );\n value_bool = true;\n value_bool.transfer();\n REQUIRE( value_bool.get_delayed() == true );\n value_bool.reset();\n REQUIRE( value_bool.get() == false );\n REQUIRE( value_bool.get_delayed() == false );\n\n\n common::delayed_t<double> value_double(.001, 3);\n REQUIRE( value_double.get() == .001 );\n value_double = .654;\n REQUIRE( value_double.get() == .654 );\n REQUIRE( value_double.get_delayed() == .001 );\n value_double.transfer();\n\n value_double = .321;\n value_double += 1.0;\n REQUIRE( value_double.get() == 1.321 );\n REQUIRE( value_double.get_delayed() == .001 );\n value_double.transfer();\n\n //NO automatic reset.\n REQUIRE( value_double.get() == 1.321 );\n\n\n REQUIRE( value_double.get_delayed() == .001 );\n\n value_double = .1337;\n value_double.transfer();\n\n REQUIRE( value_double.get_delayed() == .654 );\n\n value_double = .2342;\n value_double.transfer();\n REQUIRE( value_double.get_delayed() == 1.321 );\n\n value_double.transfer();\n REQUIRE( value_double.get_delayed() == .1337 );\n\n value_double.transfer();\n REQUIRE( value_double.get_delayed() == .2342 );\n\n value_double.transfer();\n\n value_double = 3.1415;\n REQUIRE( value_double.get_delayed() != .0 );\n value_double.reset();\n REQUIRE( value_double.get() == .0 );\n REQUIRE( value_double.get_delayed() == .0 );\n}\n\nTEST_CASE( \"zeroing vectors\", \"[modules]\") {\n std::vector<double> vect_d = {1.1,2.2,3.3,4.4};\n std::vector<double> zero_d = { .0, .0, .0, .0};\n REQUIRE( vect_d.size() == 4);\n zero(vect_d);\n REQUIRE( vect_d.size() == 4);\n REQUIRE( (vect_d == zero_d) );\n\n std::vector<int> vect_i = {-1,+2,-3};\n std::vector<int> zero_i = { 0, 0, 0};\n REQUIRE( vect_i.size() == 3);\n zero(vect_i);\n REQUIRE( vect_i.size() == 3);\n REQUIRE( (vect_i == zero_i) );\n\n std::vector<bool> vect_b = { true, false};\n std::vector<bool> zero_b = {false, false};\n REQUIRE( vect_b.size() == 2);\n zero(vect_b);\n REQUIRE( vect_b.size() == 2);\n REQUIRE( (vect_b == zero_b) );\n}\n\n#include <common/integrator.h>\nTEST_CASE( \"integrator tests\", \"[integrator]\") {\n Integrator inc;\n\n REQUIRE( inc.get_number_of_samples() == 0 );\n\n inc.add(-0.01337);\n\n REQUIRE( inc.get_avg_value() == -0.01337 );\n REQUIRE( inc.get_number_of_samples() == 1 );\n\n inc.add(-0.01337);\n\n REQUIRE( inc.get_avg_value() == -0.01337 );\n REQUIRE( inc.get_number_of_samples() == 2 );\n\n inc.reset();\n for (unsigned i = 0; i < 10; ++i)\n inc.add(1.0i);\n\n REQUIRE( inc.get_number_of_samples() == 10 );\n REQUIRE( inc.get_avg_value() == 4.5 );\n\n inc.reset();\n REQUIRE( inc.get_number_of_samples() == 0 );\n REQUIRE( inc.get_avg_value() == .0 );\n\n double sum = .0;\n for (unsigned int i = 0; i < 10000; ++i) {\n double r = random_value(0.0, 23.42);\n sum += r;\n inc.add(r);\n }\n sum /= 10000;\n REQUIRE( close(inc.get_avg_value(), sum, tolerance) );\n}\n" }, { "alpha_fraction": 0.6304271221160889, "alphanum_fraction": 0.6540769338607788, "avg_line_length": 26.77450942993164, "blob_id": "217efd39be354a6c81986ff476c29e78c77a8c7d", "content_id": "3d8592923e25b0d3af255f809f3dacdbe7cd77e8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2833, "license_type": "no_license", "max_line_length": 107, "num_lines": 102, "path": "/src/common/gui.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* gui.cpp /\n#include \"./gui.h\"\n\nfloat progress_val1;\nfloat progress_val2;\n\nextern GlobalFlag do_pause;\n\n\ngboolean\nupdate_progressbar1(gpointer data)\n{\n gtk_progress_bar_set_fraction(GTK_PROGRESS_BAR(data), progress_val1);\n return 1;\n}\n\ngboolean\nupdate_progressbar2(gpointer data)\n{\n gtk_progress_bar_set_fraction(GTK_PROGRESS_BAR(data), progress_val2);\n return 1;\n}\n\nint\nthread_gtk_main(void)\n{\n gtk_main();\n return 0;\n}\n\nvoid\non_button_start_clicked(GtkObject object, gpointer /user_data/)\n{\n do_pause.toggle();\n\n if (do_pause.status()) {\n gtk_button_set_label(GTK_BUTTON(object), \"Start\");\n sts_msg(\"Paused.\");\n }\n else {\n gtk_button_set_label(GTK_BUTTON(object), \"Pause\");\n sts_msg(\"Continuing.\");\n }\n}\n\nGTK_gui::GTK_gui(void)\n: num_vscale(10)\n, init_result(gtk_init_check(0, NULL))\n, window(gtk_window_new(GTK_WINDOW_TOPLEVEL))\n, table(gtk_table_new(6, num_vscale, TRUE))\n, label(gtk_label_new(\"Progress Bar Example\"))\n, progressbar1(gtk_progress_bar_new())\n, progressbar2(gtk_progress_bar_new())\n, button_start(gtk_button_new_with_label(\"Start\"))\n, multiscale()\n, main_gtk()\n{\n\n multiscale.reserve(num_vscale);\n for (unsigned int i = 0; i < num_vscale; ++i)\n multiscale.emplace_back(0.0, 100.0, 50.0, 0.1);\n\n sts_msg(\"Starting GTK window.\");\n if (!init_result) {\n wrn_msg(\"Starting without GTK-Window.\");\n return;\n }\n\n setlocale(LC_NUMERIC, \"C\");\n\n gtk_container_add(GTK_CONTAINER(window), table);\n gtk_table_set_row_spacing(GTK_TABLE(table), 3, 100);\n //gtk_table_set_row_spacings(GTK_TABLE(table), 100);\n\n /* set positions /\n gtk_table_attach_defaults(GTK_TABLE(table), label , 0, num_vscale, 0, 1);\n gtk_table_attach_defaults(GTK_TABLE(table), progressbar1, 0, num_vscale, 1, 2);\n gtk_table_attach_defaults(GTK_TABLE(table), progressbar2, 0, num_vscale, 2, 3);\n\n for (unsigned int i = 0; i < num_vscale; ++i)\n gtk_table_attach_defaults(GTK_TABLE(table), multiscale[i].get(), 0+i, 1+i, 3, 5);\n\n gtk_table_attach_defaults(GTK_TABLE(table), button_start, 0, 1, 5, 6);\n\n / Set the timeout to handle automatic updating of the progress bar /\n g_timeout_add(1000, update_progressbar1, progressbar1);\n g_timeout_add(500, update_progressbar2, progressbar2);\n\n / signals /\n g_signal_connect(GTK_OBJECT (button_start), \"clicked\", GTK_SIGNAL_FUNC(on_button_start_clicked), NULL);\n g_signal_connect(window, \"delete_event\", G_CALLBACK(gtk_window_iconify), NULL); // minimize window\n\n / show /\n gtk_widget_show(button_start);\n gtk_widget_show(progressbar1);\n gtk_widget_show(progressbar2);\n gtk_widget_show(label);\n gtk_widget_show(table);\n gtk_widget_show(window);\n\n main_gtk = std::unique_ptr<std::thread>(new std::thread(thread_gtk_main));\n}\n" }, { "alpha_fraction": 0.5736607313156128, "alphanum_fraction": 0.5885416865348816, "avg_line_length": 29.534090042114258, "blob_id": "1835243a08e664501e4e49f4f6ec114a3f362f86", "content_id": "8d3f97df90f56c492b9984fc3ed13ea63029045f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2688, "license_type": "no_license", "max_line_length": 109, "num_lines": 88, "path": "/src/tests/homeokinesis_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <common/modules.h>\n#include <learning/homeokinesis.h>\n#include <controller/pid_control.hpp>\n\nnamespace local_tests {\n\nnamespace homeokinesis_tests {\n\nstruct Test_Sensor_Space : public sensor_vector {\n Test_Sensor_Space(const robots::Jointvector_t& joints)\n : sensor_vector(2joints.size() + 1)\n {\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_ang\", [&j](){ return j.s_ang; });\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_vel\", [&j](){ return j.s_vel; });\n\n sensors.emplace_back(\"bias\", [&](){ return 0.01; });\n assert(sensors.size() == 2joints.size() + 1);\n }\n\n};\n\n\n\nTEST_CASE( \"homeokinetic controller construction + basic stuff\" , \"[homeokinesis]\")\n{\n srand(time(0)); // set random seed\n\n Test_Robot robot(5,2);\n robot.set_random_inputs(); // random initialize sensors\n\n Test_Sensor_Space sensors{robot.get_joints()};\n sensors.execute_cycle();\n\n std::vector<supreme::pid_control> pid(robot.get_joints().size());\n\n double random_range = 0.1;\n VectorN ext = {0,0,0};\n learning::Homeokinetic_Control homeoctrl(sensors, robot.get_joints().size(), random_range, random_range);\n\n REQUIRE( homeoctrl.get_curr_state().size() == sensors.size() );\n REQUIRE( homeoctrl.get_next_state().size() == sensors.size() );\n\n// REQUIRE( homeoctrl.control_enabled == false );\n\n for (std::size_t i = 0; i < sensors.size(); ++i)\n REQUIRE ( sensors[i] != 0.0 );\n\n auto const& x0 = homeoctrl.get_curr_state();\n for (std::size_t i = 0; i < x0.size(); ++i)\n REQUIRE ( x0[i] != 0.0 );\n\n // check predictor and controller weights are random initialized\n auto const& weights = homeoctrl.pred.get_weights();\n double sum = .0;\n int diff = 0;\n for (std::size_t i = 0; i < weights.size(); ++i)\n for (std::size_t j = 0; j < weights[i].size(); ++j) {\n diff += ( weights[i][j] != .0 )? 0 : 1;\n sum += weights[i][j];\n }\n REQUIRE( diff == 0 );\n const double max_range = 0.5 random_range * weights.size()weights[0].size();\n dbg_msg(\"Max rand: %e < %e\", std::abs(sum), max_range);\n REQUIRE( std::abs(sum) <= max_range ); // check small\n REQUIRE( std::abs(sum) != 0. ); // but not zero\n\n\n // check motor outputs are NON-ZERO\n sensors.execute_cycle();\n homeoctrl.execute_cycle(sensors);\n sts_add(\"[\");\n for (auto const& e: homeoctrl.get_motor_data()) {\n sts_add(\"%+1.2f\", e);\n REQUIRE ( e != 0.0 );\n }\n sts_msg(\"]\");\n\n}\n\n\n} / homeokinesis_tests /\n\n} / local_tests /\n\n" }, { "alpha_fraction": 0.6531281471252441, "alphanum_fraction": 0.6536327004432678, "avg_line_length": 35.009090423583984, "blob_id": "53ac8724d75de54175c08ee3d28630df4c1ad8a4", "content_id": "e5e438075df5890680f0a88a000028ff0d69e5bc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3964, "license_type": "no_license", "max_line_length": 112, "num_lines": 110, "path": "/src/learning/bimodel_predictor.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BIMODEL_PREDICTOR_H\n#define BIMODEL_PREDICTOR_H\n\n#include <control/sensorspace.h>\n#include <learning/predictor.h>\n#include <learning/forward_inverse_model.hpp>\n\nnamespace learning {\n\n/* TODO implement Adam! /\n\nclass BiModel_Predictor : public Predictor_Base\n{\n typedef NeuralModel<learning::TanhTransfer<>> NeuralModel_t;\n typedef learning::BidirectionalModel<NeuralModel_t,NeuralModel_t> BidirectionalModel_t;\n\n BidirectionalModel_t mod;\n\n / inputs from external models /\n sensor_input_interface const& ctrl_context; // base to make prediction from\n model::vector_t& gradient; // vector to connect ext. back-propagation error information\n\n BiModel_Predictor(const BiModel_Predictor& other) = delete;\n BiModel_Predictor& operator=(const BiModel_Predictor& other) = delete;\n\n double regularization_rate;\n\npublic:\n\n BiModel_Predictor( sensor_input_interface const& input\n , sensor_input_interface const& ctrl_context\n , model::vector_t& gradient\n , double learning_rate\n , double random_weight_range\n , double regularization_rate\n )\n : Predictor_Base(input, learning_rate, random_weight_range, /experience replay OFF / 1)\n , mod(ctrl_context.size(), input.size(), random_weight_range)\n , ctrl_context(ctrl_context)\n , gradient(gradient)\n , regularization_rate(regularization_rate)\n {\n assert(gradient.size() == ctrl_context.size());\n }\n\n virtual ~BiModel_Predictor() = default;\n\n void copy(Predictor_Base const& other) override {\n Predictor_Base::operator=(other); // copy base members\n BiModel_Predictor const& rhs = dynamic_cast<BiModel_Predictor const&>(other);\n mod = rhs.mod;\n };\n\n Predictor_Base::vector_t const& get_prediction (void) const override { return mod.get_forward_result(); }\n Predictor_Base::vector_t const& get_reconstruction(void) const { return mod.get_inverse_result(); }\n\n double predict(void) override {\n mod.propagate_forward(ctrl_context);\n mod.propagate_inverse(input);\n\n return calculate_prediction_error();\n };\n\n double verify(void) override {\n mod.propagate_forward(ctrl_context);\n return calculate_prediction_error();\n }\n\n void initialize_randomized(void) override {\n mod.randomize_weights(random_weight_range);\n prediction_error = predictor_constants::error_min;\n /Note: experience buffer not randomized here. because it is not used /\n };\n\n void initialize_from_input(void) override { assert(false && \"one shot learning not supported.\"); }\n\n void draw(void) const { assert(false && \"not implemented yet.\"); }\n\n double get_prediction_error(void) const { return mod.get_forward_error(); } //OK\n double get_reconstruction_error(void) const { return mod.get_inverse_error(); } //OK\n\n BidirectionalModel_t& get_model(void) { return mod; }\n\n model::vector_t get_gradient(void) const { return mod.get_backprop_gradient(); }\n\n vector_t const& get_weights(void) const override { assert(false); return dummy; /not implemented/ }\n vector_t & set_weights(void) override { assert(false); return dummy; /not implemented/ }\n\nprivate:\n\n void learn_from_input_sample(void) override\n {\n assert( gradient.size() == ctrl_context.size() );\n for (unsigned i = 0; i < gradient.size(); ++i)\n gradient[i] += ctrl_context[i]; / add the predictor's back-propagated\n error information to the training target /\n mod.adapt(gradient, input, learning_rate, regularization_rate);\n zero(gradient); // clear, mark as used\n\n //TODO check if needed mod.constrain_weights();\n }\n\n\n VectorN dummy = {}; // remove when implementing get_weights\n\n};\n\n} / namespace learning /\n\n#endif / BIMODEL_PREDICTOR_H /\n\n\n\n" }, { "alpha_fraction": 0.5313807725906372, "alphanum_fraction": 0.5499103665351868, "avg_line_length": 23.970149993896484, "blob_id": "15f70d3b1cf241fff4bbc6e5c7f8f5d67969dee5", "content_id": "8e6fb97bda2dbf1f2d86e4fd0e9396eae13b3a5a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1673, "license_type": "no_license", "max_line_length": 116, "num_lines": 67, "path": "/src/robots/joint.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef JOINT_H\n#define JOINT_H\n\n#include <string>\n#include <common/log_messages.h>\n#include <common/backed.h>\n\nnamespace robots {\n\nenum Joint_Type {Joint_Type_Normal, Joint_Type_Symmetric};\n\n//TODO: template <typename Float_t = double>\nclass Joint_Model\n{\npublic:\n Joint_Model( unsigned int joint_id\n , Joint_Type type\n , unsigned int symmetric_joint\n , const std::string& name\n , double limit_lo\n , double limit_hi\n , double default_pos\n )\n : joint_id(joint_id)\n , s_ang(.0)\n , s_vel(.0)\n , s_cur(.0)\n , s_vol(.0)\n , s_tmp(.0)\n , motor(.0)\n , type(type)\n , symmetric_joint(symmetric_joint)\n , name(name)\n , limit_lo(limit_lo)\n , limit_hi(limit_hi)\n , default_pos(default_pos)\n {\n sts_add(\"J=%2u '%16s'\", joint_id, name.c_str());\n sts_add(\"L=(%+1.2f, %+1.2f, %+1.2f) (%+3.1f, %+3.1f, %+3.1f)\", limit_lo , limit_hi , default_pos\n , limit_lo180, limit_hi180, default_pos180);\n sts_msg(\"T=%u, S=%2u\\n\", type, symmetric_joint);\n }\n\n const unsigned int joint_id;\n double s_ang;\n double s_vel;\n double s_cur;\n double s_vol;\n double s_tmp;\n common::backed_t<double> motor;\n\n Joint_Type type;\n unsigned int symmetric_joint;\n const std::string name;\n\n const double limit_lo;\n const double limit_hi;\n const double default_pos;\n\n bool is_symmetric() const { return type == Joint_Type_Symmetric; }\n};\n\ntypedef std::vector<Joint_Model> Jointvector_t;\n\n} /* namespace robots /\n\n#endif / JOINT_H /\n" }, { "alpha_fraction": 0.6115702390670776, "alphanum_fraction": 0.6264463067054749, "avg_line_length": 15.80555534362793, "blob_id": "7251b0643e64cac6dd38f0bd4db5ed11048769ce", "content_id": "787f5a19c6e2d3a4fca8fe59039f30f7b880b92c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 605, "license_type": "no_license", "max_line_length": 46, "num_lines": 36, "path": "/src/draw/network2D.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ network2D.h /\n\n#ifndef NETWORK2D_H\n#define NETWORK2D_H\n\n#include \"axes.h\"\n\n\nclass network2D\n{\n private:\n float X; // position vector\n float size;\n /* size == 0, kein Knoten /\n unsigned char edges;\n\n int pointer;\n int special_node;\n int activated_node;\n int N;\n float px, py;\n float width, height;\n GLubyte color[4];\n axes A;\n\n public:\n network2D(int, axes, const GLubyte[4]);\n void draw(void);\n\n void update_node(int, float, float, float);\n void update_edge(int, int, unsigned char);\n void special(int);\n void activated(int);\n};\n\n#endif /NETWORK2D_H/\n" }, { "alpha_fraction": 0.6466346383094788, "alphanum_fraction": 0.6520432829856873, "avg_line_length": 23.441177368164062, "blob_id": "c3f672f3fb57125a63ecb7cfd12f47ce22c15d50", "content_id": "2a449fab0509325b3ee7ecf381c8665b00d4518b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1664, "license_type": "no_license", "max_line_length": 83, "num_lines": 68, "path": "/src/common/robot_conf.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef ROBOT_CONF_H\r\n#define ROBOT_CONF_H\r\n\n#include <cstdio>\n#include <cstdlib>\n#include <vector>\n#include <assert.h>\n\n#include <robots/joint.h>\n#include <robots/accel.h>\n#include <common/socket_client.h>\n#include <basic/vector3.h>\n\n\nstruct Body_Segment /TODO move to separate header /\n{\n Body_Segment(const char* name) : position(), velocity(), force(), name(name) {}\n Vector3 position;\n Vector3 velocity;\n Vector3 force;\n std::string name;\n};\ntypedef std::vector<Body_Segment> Bodyvector_t;\n\nclass Robot_Configuration\n{\n\npublic:\n unsigned number_of_joints = 0;\n unsigned number_of_accels = 0;\n unsigned number_of_bodies = 0;\n\n robots::Jointvector_t joints;\n robots::Accelvector_t accels;\n Bodyvector_t bodies;\n\n bool const interlaced;\n\n Robot_Configuration(std::string const& server_message, bool interlaced)\n : joints()\n , accels()\n , bodies()\n , interlaced(interlaced)\n {\n read_robot_info(server_message);\n }\n\n void delete_symmetry_information(void) { //TODO get rid of that method\n sts_msg(\"Deleting symmetry information.\");\n for (unsigned int i = 0; i < joints.size(); ++i) {\n joints[i].type = robots::Joint_Type_Normal;\n joints[i].symmetric_joint = i; // delete reference to other joints\n }\n assert(get_number_of_symmetric_joints() == 0);\n }\n\n unsigned int get_number_of_symmetric_joints(void) const;\n\n\n void read_robot_info(std::string const& server_message);\n\nprivate:\n const char* read_joints(const char* msg, int* offset);\n const char* read_bodies(const char* msg, int* offset);\n};\n\n\n#endif // ROBOT_CONF_H\n" }, { "alpha_fraction": 0.4806746244430542, "alphanum_fraction": 0.48629656434059143, "avg_line_length": 26.901960372924805, "blob_id": "c2889845469bfe8fe35c71947dcc0225c7e9a516", "content_id": "4793e2ceb12ce01d7a371b20796444289e267efe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1423, "license_type": "no_license", "max_line_length": 68, "num_lines": 51, "path": "/src/robots/simloid_log.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/---------------------------------+\n \| Matthias Kubisch \|\n \| [email protected] \|\n \| July 2017 \|\n +---------------------------------/\n\n#ifndef SIMLOID_LOG_H_INCLUDED\n#define SIMLOID_LOG_H_INCLUDED\n\n#include <sstream>\n#include <robots/simloid.h>\n\nnamespace robots {\n\nclass Simloid_Log : public Loggable<2048> {\n\n const Simloid& simloid;\n\npublic:\n Simloid_Log(const Simloid& simloid) : simloid(simloid) {}\n\n const char* log()\n {\n for (auto const& j : simloid.get_joints())\n append(\"%+e %+e %+e \", j.s_ang, j.s_vel, j.motor.get());\n\n for (auto const& a : simloid.get_accels())\n append(\"%+e %+e %+e \", a.v.x, a.v.y, a.v.z);\n\n append( \"%+e %+e %+e %+e %+e %+e %+e %+e %+e %+e %+e\"\n , simloid.get_avg_rotation_inf_ang()\n , simloid.get_avg_rotational_speed()\n , simloid.get_avg_velocity_forward()\n , simloid.get_avg_velocity_left()\n , simloid.get_normalized_mechanical_power()\n /** global coordinates /\n , simloid.get_avg_position().x\n , simloid.get_avg_position().y\n , simloid.get_avg_position().z\n , simloid.get_avg_velocity().x\n , simloid.get_avg_velocity().y\n , simloid.get_avg_velocity().z\n );\n\n return done();\n }\n};\n\n} // namespace robots\n\n#endif // SIMLOID_LOG_H_INCLUDED\n" }, { "alpha_fraction": 0.49117422103881836, "alphanum_fraction": 0.5495011806488037, "avg_line_length": 30.780487060546875, "blob_id": "249afbf67f048254f3da6045d78cdcdb2081b1d0", "content_id": "1d7d8996a7c303bebbd672056997100f17cdaac5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1303, "license_type": "no_license", "max_line_length": 98, "num_lines": 41, "path": "/src/common/misc.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include \"misc.h\"\n\n\nstd::string //TODO make stepsize as parameter\nget_time_from_cycle_counter(unsigned long long cycles)\n{\n char time_str[1024];\n unsigned int days = cycles / (100 3600 * 24);\n unsigned int hours = cycles / (100 * 3600) % 24;\n unsigned int minutes = (cycles / (100 * 60)) % 60;\n unsigned int seconds = (cycles / 100) % 60;\n unsigned int hsecs = (cycles % 100);\n\n if (days > 0)\n snprintf(time_str, 1024, \"%3u:%02u:%02u:%02u:%02u\", days, hours, minutes, seconds, hsecs);\n else\n snprintf(time_str, 1024, \"%02u:%02u:%02u:%02u\", hours, minutes, seconds, hsecs);\n return std::string(time_str);\n}\n\nstd::string to_str(const std::vector<double>& vect)\n{\n char buf[256] = \"\";\n int len = 0;\n if (vect.size() == 0) return buf;\n int n = snprintf(buf, sizeof(buf), \"%+1.2f\", vect[0]);\n for (std::size_t i = 1; i < vect.size(); ++i) {\n if (n >= 0 && n < ((int)sizeof(buf) - len)) {\n len += n;\n n = snprintf(buf + len, sizeof(buf) - len, \" %+1.2f\", vect[i]);\n } else break;\n }\n return buf;\n}\n\ntemplate <typename vector_t> void\nprint(const vector_t& content) {\n for (std::size_t i = 0; i < content.size(); ++i)\n std::cout << content[i] << \" \";\n std::cout << std::endl;\n}\n" }, { "alpha_fraction": 0.5112841725349426, "alphanum_fraction": 0.5225023031234741, "avg_line_length": 33.598175048828125, "blob_id": "75c820afcf4dc988b2584e9eb7f9fadacbb756d6", "content_id": "d21bf2ad10d58fa3990b6d228d762014ade7f3d9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 7577, "license_type": "no_license", "max_line_length": 121, "num_lines": 219, "path": "/src/learning/homeokinesis.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef HOMEOKINESIS_H\n#define HOMEOKINESIS_H\n\n#include <robots/robot.h>\n#include <control/sensorspace.h>\n#include <learning/forward_inverse_model.hpp>\n\nnamespace learning {\n\nnamespace homeokinetic_constants {\n const float learning_rate_pred = 0.005; //both values 0.001..0.01 is very nice;\n const float learning_rate_ctrl = 0.005;\n\n const float regularization_rate = 0.0;\n}\n\n/*TODO description\n\n + using L1 regularization_rate to prevent overfitting and encourage sparsity\n/\n\nclass Homeokinetic_Control {\npublic:\n typedef NeuralModel<TanhTransfer<>> Forward_t;\n typedef BidirectionalModel<Forward_t,Forward_t> PredictionModel_t;\n typedef BidirectionalModel<Forward_t,Forward_t> ControllerModel_t;\n typedef model::vector_t Vector_t;\n\n Vector_t x0, x1, y0, y1;\n Vector_t X0, X1, Y0;\n\n ControllerModel_t ctrl;\n PredictionModel_t pred;\n\n float learning_rate_pred;\n float learning_rate_ctrl;\n float regularization_rate;\n\n Vector_t gradient; // error gradient signal, used to transport error information between Models\n\n struct Option_t {\n bool prediction_learning = true;\n bool controller_learning = true;\n bool verbose = true;\n } option = {};\n\n\n Homeokinetic_Control( sensor_input_interface const& input\n , std::size_t number_of_joints\n , float init_weight_range\n , unsigned context = 0\n , float learning_rate_pred = homeokinetic_constants::learning_rate_pred\n , float learning_rate_ctrl = homeokinetic_constants::learning_rate_ctrl\n , float regularization_rate = homeokinetic_constants::regularization_rate\n )\n : x0(input.size())\n , x1(input.size())\n , y0(number_of_joints + context)\n , y1(number_of_joints + context)\n , X0(input.size())\n , X1(input.size())\n , Y0(number_of_joints + context)\n , ctrl(/in=/x0.size(), /out=/y0.size(), init_weight_range)\n , pred(/in=/y0.size(), /out=/x1.size(), init_weight_range)\n , learning_rate_pred(learning_rate_pred)\n , learning_rate_ctrl(learning_rate_ctrl)\n , regularization_rate(regularization_rate)\n , gradient(number_of_joints + context)\n {\n sts_add(\"creating homeokinetic controller with\\n%u joints and \\n%u context neurons.\", number_of_joints, context);\n sts_msg(\"+ PRED learning rate = %e\", learning_rate_pred);\n sts_msg(\"+ CTRL Learning rate = %e\", learning_rate_ctrl);\n sts_msg(\"+ Normalization rate = %e\", regularization_rate);\n read_sensor_data(x0, input);\n control();\n }\n\n\n void execute_cycle(sensor_input_interface const& input)\n {\n /--------------------------------------------+\n \| Notation: \|\n \| lower case real sensor/motor data x, y \|\n \| upper case prediction/reconstruction X, Y \|\n \| time indices: x0 = x(t+0) = x(t) \|\n \| x1 = x(t+1) \|\n +--------------------------------------------/\n\n /* original order changed, because robot.update must be executed between steps 1. + 2. /\n\n /* 2.) Prediction /\n read_next_state(input);\n predict();\n\n /* 3.) Reconstruction /\n reconstruct();\n\n /* 4.) Adaption/Learning /\n adapt_prediction();\n adapt_controller();\n\n /* 0.) time-step border /\n backup_state();\n\n /* 1.) Control /\n control();\n\n if (option.verbose) {\n sts_add(\"pe=%+.3f, tle=%.3f\", pred.get_forward_error(), ctrl.get_inverse_error());\n print_vector(y0,\"y\");\n }\n }\n\n\n /----------------------------------+\n \| time-step border: \|\n \| copy sensory state x(t) = x(t+1) \|\n +----------------------------------/\n void backup_state(void) {\n x0 = x1;\n }\n\n /------------------------------------+\n \| create control command y(t+1) from \|\n \| current sensory state x(t) \|\n +------------------------------------/\n void control(void) {\n y1 = ctrl.propagate_forward(x0);\n }\n\n /------------------------------------+\n \| read next state x(t+1) from inputs \|\n +------------------------------------/\n template <typename Input_t>\n void read_next_state(Input_t const& input) {\n y0 = y1;\n read_sensor_data(x1, input);\n }\n\n /--------------------------------------------+\n \| make prediction x^(t+1) from x(t) and y(t) \|\n +--------------------------------------------/\n void predict(void) {\n X1 = pred.propagate_forward(y0); // make prediction\n }\n\n /-------------------------------------------------------+\n \| make reconstruction from real next state x(t+1) \|\n \| to assumed motor commands y^(t), \|\n \| make reconstruction from assumed motor commands y^(t) \|\n \| to reconstructed sensor state x^(t) \|\n +-------------------------------------------------------/\n void reconstruct(void) {\n Y0 = pred.propagate_inverse(x1);\n / X0 = / ctrl.propagate_inverse(Y0);\n }\n\n / 4.a) ------------------------------+\n \| learn predictive model to map from \|\n \| current motor commands y(t) \|\n \| to next sensor state x(t+1) \|\n +------------------------------------/\n void adapt_prediction(void) {\n if (option.prediction_learning) {\n pred.adapt(y0, x1, learning_rate_pred, regularization_rate);\n gradient = pred.get_backprop_gradient(); // get the back-propagated error information\n }\n }\n\n / 4.b) ---------------------------------------------+\n \| learn controller to map from x(t) to y^(t), \|\n \| the INVERSE learns to reconstruct x(t) from y^(t) \|\n +---------------------------------------------------/\n void adapt_controller(void) {\n if (option.controller_learning) {\n assert( gradient.size() == Y0.size() );\n for (unsigned i = 0; i < gradient.size(); ++i)\n gradient[i] += Y0[i]; // add the predictor's back-propagated error information to the target\n ctrl.adapt(x0, gradient, learning_rate_ctrl, regularization_rate);\n\n zero(gradient);\n }\n }\n\n\n Vector_t const& get_curr_state(void) const { return x0; }\n Vector_t const& get_next_state(void) const { return x1; }\n Vector_t const& get_prediction(void) const { return X1; }\n\n Vector_t const& get_motor_data(void) const { return y1; }\n Vector_t & set_motor_data(void) { return y1; }\n\n void randomize_weights(double range) {\n pred.randomize_weights(range);\n ctrl.randomize_weights(range);\n }\n\n double get_timeloop_error (void) const { return ctrl.get_inverse_error(); }\n double get_prediction_error (void) const { return pred.get_forward_error(); }\n\n double get_control_error (void) const { return ctrl.get_forward_error(); }\n double get_reconstruction_error(void) const { return pred.get_inverse_error(); }\n\nprivate:\n\n // makes a copy of the sensor data\n template <typename Vector_t, typename Input_t>\n void read_sensor_data(Vector_t& vec, Input_t const& input) {\n assert(input.size() == vec.size());\n for (std::size_t i = 0; i < input.size(); ++i)\n vec[i] = input[i];\n }\n\n friend class Homeokinetic_Graphics;\n};\n\n} / namespace learning /\n\n#endif / HOMEOKINESIS_H /\n" }, { "alpha_fraction": 0.5760056972503662, "alphanum_fraction": 0.5792096853256226, "avg_line_length": 27.09000015258789, "blob_id": "6779f72438670e4b21617920b51acc0ce4bc8ea6", "content_id": "5509911512b306de0093cd8ff183df058740b145", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2809, "license_type": "no_license", "max_line_length": 111, "num_lines": 100, "path": "/src/common/datalog.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef DATALOG_H_INCLUDED\n#define DATALOG_H_INCLUDED\n\n#include <string>\n#include <common/basic.h>\n#include <common/file_io.h>\n#include <common/log_messages.h>\n#include <common/settings.h>\n\nnamespace constants {\n const std::string logfolder = \"./data/\";\n const std::string logfileext = \".log\";\n}\n\nclass Datalog {\npublic:\n\n Datalog(int argc, char* argv, std::string const& folder = constants::logfolder )\n : filename ( read_string_option(argc, argv, \"--outfile\" , \"-o\", get_timestamped_file_name(folder)) )\n , logfile ( filename )\n , enabled ( read_option_flag (argc, argv, \"--enable_logging\", \"-l\") )\n , incl_video( read_option_flag (argc, argv, \"--include_video\" , \"-i\") )\n {\n sts_msg(\"Created data log file: %s (%s)\", filename.c_str(), enabled ? \"Enabled\":\"Disabled\");\n sts_msg(\"Video will%s be recorded.\", incl_video ? \"\":\" NOT\");\n }\n\n template<typename... Args>\n void log(const char* format, const Args&... args)\n {\n if (!enabled) return;\n\n logfile.append(format, args...);\n }\n\n void toggle_logging(void) {\n enabled = !enabled;\n sts_msg(\"Logging: %s\", enabled ? \"ON\":\"OFF\");\n if (not enabled)\n logfile.flush();\n }\n\n void set_enable(bool en) { enabled = en; }\n\n bool is_enabled(void) const { return enabled; }\n bool is_video_included(void) const { return enabled and incl_video; }\n\n void flush(void) { logfile.flush(); }\n void next (void) { logfile.next(); }\n\nprivate:\n const std::string filename;\n file_io::Logfile logfile;\n bool enabled;\n bool incl_video;\n\n std::string get_timestamped_file_name(const std::string& folder) {\n return basic::make_directory(folder.c_str()) // folder\n + basic::get_timestamp() // name as time stamp\n + constants::logfileext; // extension\n }\n};\n\n\ntemplate <unsigned BufferSize = 1024>\nclass Loggable {\n\n char buffer[BufferSize];\n std::size_t total_bytes = 0;\n\nprotected:\n\n template<typename... Args>\n void append(const char* format, const Args&... args)\n {\n if (total_bytes + 1 < BufferSize) {\n auto n = snprintf(buffer+total_bytes, BufferSize-total_bytes, format, args...);\n if (n < 0) err_msg(__FILE__,__LINE__,\" Encoding error.\");\n if (static_cast<unsigned>(n) < BufferSize-total_bytes) {\n total_bytes += n;\n return;\n }\n }\n err_msg(__FILE__,__LINE__,\" Buffer size of %u too small.\", BufferSize);\n }\n\n const char* done(void) {\n //dbg_msg(\"Bytes written %u\", total_bytes);\n total_bytes = 0;\n return buffer;\n }\n\npublic:\n virtual ~Loggable() {}\n virtual const char* log(void) = 0;\n\n};\n\n\n#endif // DATALOG_H_INCLUDED\n" }, { "alpha_fraction": 0.5406643748283386, "alphanum_fraction": 0.5715922117233276, "avg_line_length": 25.044776916503906, "blob_id": "94f8e4cb0fd44aaf380563c84fd79af6c2f8dae8", "content_id": "e6f9820d3e1eb1255770634d9ef869ae954ecdc9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1746, "license_type": "no_license", "max_line_length": 62, "num_lines": 67, "path": "/src/learning/homeokinesis_gfx.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/---------------------------------+\n \| Matthias Kubisch \|\n \| [email protected] \|\n \| August 2020 \|\n +---------------------------------/\n\n#ifndef HOMEOKINESIS_GRAPHICS_H\n#define HOMEOKINESIS_GRAPHICS_H\n\n#include <draw/draw.h>\n#include <draw/axes.h>\n#include <draw/axes3D.h>\n#include <draw/plot1D.h>\n#include <draw/plot2D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n\n#include <learning/homeokinesis.h>\n\nnamespace learning {\n\nclass Homeokinesis_Graphics : public Graphics_Interface\n{\n Homeokinetic_Control const& ctrl;\n\npublic:\n Homeokinesis_Graphics(Homeokinetic_Control const& ctrl)\n : ctrl(ctrl)\n , axes_err(0., -0.5, 0., 2.0, 1.0, 1, \"error\" , 0.001)\n , plot_pre(1000, axes_err, colors::cyan , \"pre\")\n , plot_tle(1000, axes_err, colors::magenta , \"tle\")\n , plot_rec(1000, axes_err, colors::yellow , \"rec\")\n , plot_ctr(1000, axes_err, colors::green , \"ctr\")\n {}\n\n void draw(const pref& /p/) const {\n axes_err.draw();\n plot_pre.draw();\n plot_tle.draw();\n plot_rec.draw();\n plot_ctr.draw();\n draw_motor_context();\n }\n\n void draw_motor_context(void) const\n {\n draw::vector_dual(-1,-0.8,0.06,1.0, ctrl.y0, ctrl.Y0);\n }\n\n void execute_cycle(uint64_t /cycle/) {\n plot_pre.add_sample(ctrl.get_prediction_error ());\n plot_tle.add_sample(ctrl.get_timeloop_error ());\n plot_rec.add_sample(ctrl.get_reconstruction_error());\n plot_ctr.add_sample(ctrl.get_control_error ());\n }\n\n axes axes_err;\n plot1D plot_pre;\n plot1D plot_tle;\n plot1D plot_rec;\n plot1D plot_ctr;\n};\n\n} /* namespace learning /\n\n#endif / HOMEOKINESIS_GRAPHICS_H /\n\n" }, { "alpha_fraction": 0.6547368168830872, "alphanum_fraction": 0.6610526442527771, "avg_line_length": 19.65217399597168, "blob_id": "96e8faf0e3de5ff76af87ce9f6d3f74d14b775e9", "content_id": "572f69afa413856ca992e4c3d3e3afe03b747e31", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 475, "license_type": "no_license", "max_line_length": 77, "num_lines": 23, "path": "/src/common/change_limiter.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef CHANGE_LIMITER_H\n#define CHANGE_LIMITER_H\n\nnamespace common {\n\ntemplate <typename T>\nclass ChangeLimiter {\n\tT last_pos, last_vel;\n\tconst T max_value;\npublic:\n\tChangeLimiter(T max_value) : last_pos(), last_vel(), max_value(max_value) {}\n\n\tT step(T current) {\n\t const double vel = current - last_pos;\n\t\tlast_pos = last_pos + 0.5clip((vel + last_vel)/2, max_value);\n\t last_vel = vel;\n\t\treturn last_pos;\n\t}\n};\n\n} /* namespace common /\n\n#endif // CHANGE_LIMITER_H\n" }, { "alpha_fraction": 0.2917245626449585, "alphanum_fraction": 0.2929152548313141, "avg_line_length": 65.2368392944336, "blob_id": "2d406c1f487c03b61866e9b2a6d6b62bd32cc295", "content_id": "9a64af574cdbeb1bddb58d5ace3663d76171ef96", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5039, "license_type": "no_license", "max_line_length": 142, "num_lines": 76, "path": "/src/common/setup.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SETUPSDL_H\r\n#define SETUPSDL_H\r\n\r\n#include <sys/time.h>\n#include <unistd.h>\n#include <cmath>\n#include <chrono>\n#include <SDL2/SDL.h>\n#include <thread>\n\n#include <common/globalflag.h>\n#include <common/basic.h>\n#include <common/event_manager.h>\n#include <common/stopwatch.h>\n#include <common/application_base.h>\r\n#include <common/visuals.h>\n#include <common/misc.h>\n#include <common/gui.h>\n#include <common/modules.h>\n#include <common/timer.h>\n#include <draw/draw.h>\n#include <external/gl2ps/gl2ps.h>\n\nvoid init_SDL(const bool visuals, const std::size_t window_width, const std::size_t window_height, const std::string& name = \"working title\");\nvoid deinit_SDL(void);\nvoid init_OpenGL(const std::size_t window_width, const std::size_t window_height);\nvoid init_controls(void);\nvoid signal_terminate_handler(int signum);\nint process_application(void data);\n\nvoid ui_main_loop(GlobalFlag& do_quit, const GlobalFlag& do_drawing, Event_Manager& em, const Application_Base& app);\nvoid draw_screen(const double& fps, const Application_Base& app);\nvoid fps_controller(double& fps, const double& sp_fps);\n\n\n/* get rid of that macros, prevents gtk from linking!/\n\n#define DEFINE_GLOBALS() \\\n \\\nextern float progress_val1; \\\nextern float progress_val2; \\\n \\\nextern GlobalFlag do_pause; \\\nextern GlobalFlag do_quit; \\\nextern GlobalFlag fast_forward; \\\nextern GlobalFlag draw_grid; \\\nextern GlobalFlag do_drawing; \\\nextern GlobalFlag screenshot; \\\n\n\n#define APPLICATION_MAIN() \\\n \\\nint main(int argc, char argv[]) \\\n{ \\\n srand((unsigned) time(NULL)); \\\n signal(SIGINT, signal_terminate_handler); \\\n \\\n Event_Manager em; \\\n Application app(argc, argv, em); \\\n \\\n init_SDL(app.visuals_enabled(), app.window_width, app.window_height, app.name); \\\n \\\n /GUI_Starter gui(app.visuals_enabled());/ \\\n \\\n atexit(quit); \\\n std::thread app_thread(process_application, &app); \\\n \\\n if (app.visuals_enabled()) ui_main_loop(do_quit, do_drawing, em, app); \\\n sts_msg(\"waiting for main thread to join.\"); \\\n app_thread.join(); \\\n \\\n sts_msg(\"Bye.\"); \\\n return 0; \\\n} \\\n\r\n#endif // SETUPSDL_H\n" }, { "alpha_fraction": 0.637982189655304, "alphanum_fraction": 0.642433226108551, "avg_line_length": 31.095237731933594, "blob_id": "c528847e5dd28c910d0c649f90a0c83a0441fe4c", "content_id": "8ee4bfa369ddb4a1622a84a3301f36c2cba31e4d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 674, "license_type": "no_license", "max_line_length": 87, "num_lines": 21, "path": "/src/evolution/pool_strategy.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <evolution/pool_strategy.h>\n\n/** TODO think about to include a check of number of evaluations? /\nstd::size_t get_replacement_candidate_for(Individual& opponent, Population& population)\n{\n if (opponent.fitness < population.get_last_individual().fitness)\n return population.get_size() - 1;\n\n std::size_t i = population.get_size();\n std::size_t candidate_idx = i-1;\n\n while (i > 0) {\n --i;\n if (opponent.fitness > population[i].fitness)\n candidate_idx = i;\n else break;\n }\n //dbg_msg(\"Replacement candidate is %u\", candidate_idx);\n assert(candidate_idx < population.get_size());\n return candidate_idx;\n}\n" }, { "alpha_fraction": 0.589841365814209, "alphanum_fraction": 0.5973538160324097, "avg_line_length": 28.09787940979004, "blob_id": "be6a5d73822e2dd3ddbefd478c3d19920ea408b1", "content_id": "b050b19b2b3ea4c2d0fce9ecbd7899d547a77087", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 17838, "license_type": "no_license", "max_line_length": 171, "num_lines": 613, "path": "/src/robots/simloid.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ simloid.cpp /\n#include \"./simloid.h\"\n\nnamespace robots {\n\nSimloid::Simloid( bool interlaced_mode,\n unsigned short port,\n unsigned int robot_ID,\n unsigned int scene_ID,\n bool visuals,\n bool realtime,\n std::vector<double> modelparams,\n bool initially_fixed\n )\n : port(port)\n , robot_ID(robot_ID)\n , scene_ID(scene_ID)\n , visuals(visuals)\n , realtime(realtime)\n , child_pid()\n , mtx()\n , client()\n , connection_established(open_connection())\n , record_frame(false)\n , configuration(client.recv(5network::constants::seconds_us), interlaced_mode)\n , timestamp()\n , body_position0(configuration.number_of_bodies)\n , average_position()\n , average_position0()\n , average_velocity()\n , average_rotation()\n , avg_rot_inf_ang()\n , avg_rot_inf_ang_last()\n , avg_velocity_forward()\n , avg_velocity_left()\n , left_id(get_body_id_by_name(configuration.bodies, \"left\"))\n , rift_id(get_body_id_by_name(configuration.bodies, \"rift\"))\n , initially_fixed(initially_fixed)\n{\n if (interlaced_mode) client.send(\"INTERLACED MODE\\n\");\n else client.send(\"SEQUENTIAL MODE\\n\");\n\n sts_msg(\"Done reading robot configuration. Sending acknowledge.\");\n client.send(\"ACK\\n\");\n\n assert(configuration.number_of_bodies > 0);\n\n if (connection_established)\n {\n sts_msg(\"Connection established.\");\n\n if (0 == modelparams.size()) init_robot();\n else {\n read_sensor_data();\n reinit_robot_model(modelparams);\n }\n }\n else\n wrn_msg(\"Cannot connect to robot.\");\n}\n\nSimloid::~Simloid()\n{\n if (connection_established)\n close_connection();\n}\n\nbool\nSimloid::open_connection(void)\n{\n sts_msg(\"Forking process.\");\n\n child_pid = fork();\n if (child_pid >= 0) // fork was successful\n {\n if (child_pid == 0) // child process\n {\n sts_msg(\"Child process says \\'hello\\'.\");\n sts_msg(\"Checking if processor is available...\");\n if (system(NULL)) sts_msg(\"OK.\");\n else err_msg(__FILE__, __LINE__, \"FAILED.\");\n\n char portarg[8], robotarg[8], scenearg[8], nographics[8] = \"\", norealtime[8] = \"\"; //TODO: aufnäumen, extra funktion oder so.\n snprintf(portarg, 8, \"%d\", port);\n snprintf(robotarg, 8, \"%d\", robot_ID);\n snprintf(scenearg, 8, \"%d\", scene_ID);\n\n if (!visuals) snprintf(nographics, 8, \"-ng\");\n if (!realtime) snprintf(norealtime, 8, \"-nr\");\n\n sts_msg(\"Starting simloid on port %s with robot '%s' and scene '%s'.\", portarg, robotarg, scenearg);\n if (!visuals) sts_msg(\"Starting without graphics.\");\n if (!realtime) sts_msg(\"Starting with maximal speed (no realtime).\");\n\n int i = execl(\"../simloidTCP/bin/Release/simloid\", \"simloid\", \"--port\", portarg, \"--robot\", robotarg, \"--scene\", scenearg, nographics, norealtime, (char ) 0);\n err_msg(__FILE__, __LINE__, \"\\n +++ Could not start simloid. Status: %d +++ \\n%s\\n\", i, strerror(errno));\n }\n else //Parent process\n {\n sts_msg(\"Parent process says \\'hello\\'.\");\n }\n }\n else\n {\n wrn_msg(\"Forking process failed.\");\n return false;\n }\n\n sts_msg(\"Waiting a second for simloid to start.\");\n sleep(1);\n sts_msg(\"Continuing.\");\n\n return client.open_connection(\"127.0.0.1\", port);\n}\n\nvoid\nSimloid::simulation_idle(double sec)\n{\n sts_msg(\"Waiting for %1.1f seconds.\", sec);\n / pass 100 time steps per second /\n for (unsigned i = 0; i < round(100.0 sec); ++i)\n {\n read_sensor_data();\n client.send(\"DONE\\n\");\n }\n}\n\nvoid\nSimloid::set_robot_to_default_position(void)\n{\n if (initially_fixed) dbg_msg(\"Robot is fixed during initialization.\");\n client.send(\"GRAVITY OFF\\n%s\", initially_fixed ? \"FIXED 0\\n\":\"\");\n\n double sec = 2; // should be enough\n sts_msg(\"Setting robot to default joint position.\");\n\n char msg[network::constants::msglen];\n /* pass 100 time steps per second /\n for (unsigned i = 0; i < round(100.0 sec); ++i)\n {\n read_sensor_data();\n\n short n = snprintf(msg, network::constants::msglen, \"PX\");\n for (auto& j: configuration.joints)\n n += snprintf(msg + n, network::constants::msglen - n, \" %lf\", j.default_pos);\n\n snprintf(msg + n, network::constants::msglen - n, \"\\nDONE\\n\");\n client.send(msg);\n }\n read_sensor_data();\n\n client.send(\"%sGRAVITY ON\\nDONE\\n\", initially_fixed ? \"FIXED 0\\n\":\"\");\n read_sensor_data();\n}\n\nvoid\nSimloid::reset(void) //non-public\n{\n /* resetting simloid, resetting motor output /\n for (auto& j: configuration.joints)\n j.motor.reset();\n client.send(\"UA 0\\nRESET\\nDONE\\n\");\n read_sensor_data(); // note: a reset must be followed by an update\n}\n\nvoid\nSimloid::save_state(void)\n{\n if (!connection_established)\n {\n wrn_msg(\"Cannot save state. Not connected.\");\n return;\n }\n / saving state of simloid /\n client.send(\"UA 0\\nNEWTIME\\nSAVE\\nDONE\\n\");\n read_sensor_data();\n\n // save initial position\n for (std::size_t i = 0; i < configuration.bodies.size(); ++i)\n body_position0[i] = configuration.bodies[i].position;\n\n update_avg_position();\n update_avg_velocity();\n average_position0 = average_position;\n}\n\n\nvoid\nSimloid::restore_state(void)\n{\n common::lock_t lock(mtx);\n\n if (!connection_established) {\n wrn_msg(\"Cannot restore state. Not connected.\");\n return;\n }\n\n / restoring last snapshot of simloid, resetting motor output /\n for (auto& j: configuration.joints) j.motor.reset();\n reset_all_forces();\n client.send(\"UA 0\\nNEWTIME\\nRESTORE\\nDONE\\n\");\n read_sensor_data(); // note: a reset must be followed by an update\n\n / TODO: irgendwie ist nachm restore und sensorupdate noch die alten werte da /\n}\n\nvoid\nSimloid::finish(void)\n{\n if (connection_established) {\n sts_msg(\"Closing connection to simloid.\");\n close_connection();\n }\n else wrn_msg(\"Cannot finish simloid. Already disconnected.\");\n return;\n}\n\nbool\nSimloid::idle(void)\n{\n common::lock_t lock(mtx);\n\n if (!connection_established) {\n wrn_msg(\"Not connected.\");\n return false;\n }\n\n send_pause_command();\n return true;\n}\n\nbool\nSimloid::update(void)\n{\n common::lock_t lock(mtx);\n\n if (!connection_established) {\n wrn_msg(\"Cannot update sensor values. Not connected.\");\n return false;\n }\n\n write_motor_data();\n read_sensor_data();\n\n update_avg_position();\n update_avg_velocity();\n update_rotation_z();\n update_robot_velocity();\n return true;\n}\n\n\nvoid Simloid::init_robot(void) // non-public\n{\n sts_msg(\"Initializing robot.\");\n\n / initialize motor voltages /\n for (auto& j: configuration.joints) j.motor.reset();\n set_robot_to_default_position();\n save_state();\n}\n\nvoid\nSimloid::close_connection(void)\n{\n / close connection and socket /\n sts_msg(\"Closing connection to server.\");\n client.send(\"EXIT\\n\");\n client.close_connection();\n sts_msg(\"TCP connection terminated. Waiting for simloid to exit.\");\n\n int status;\n while (waitpid(child_pid, &status, 0) != child_pid)\n {\n sts_msg(\"Tick...\");\n sleep(1);\n }\n sts_msg(\"Simloid finished.\");\n connection_established = false;\n}\n\ndouble\nread_double(const char msg_buffer, unsigned int offset)\n{\n double value = .0;\n const char msg = msg_buffer + (offset);\n int chars_read = 0;\n\n if (1 == sscanf(msg, \" %lf%n\", &value, &chars_read))\n {\n msg += chars_read;\n offset += chars_read;\n }\n else wrn_msg(\"Cannot read expected (double) value from TCP message at offset %d.\", offset);\n\n return value;\n}\n\nVector3\nread_vector3(const char msg_buffer, unsigned int offset)\n{\n const double x = read_double(msg_buffer, offset);\n const double y = read_double(msg_buffer, offset);\n const double z = read_double(msg_buffer, offset);\n return Vector3(x, y, z);\n}\n\nvoid\nSimloid::eat_server_msg(void) {\n dbg_msg(\"eating msg\");\n client.eat();\n dbg_msg(\"eating msg done\");\n}\n\nvoid\nSimloid::read_sensor_data(void)\n{\n static std::string srv_msg;\n unsigned charcount = 0;\n\n srv_msg = client.recv(60network::constants::seconds_us);\n\n unsigned len = srv_msg.length();\n\n if (0 == len)\n {\n wrn_msg(\"Received no more bytes. Cancel reading sensory data.\");\n close_connection();\n return;\n }\n\n /* read time stamp /\n const char server_message = srv_msg.c_str();\n\n unsigned frames_read = 0;\n\n do {\n\n timestamp = read_double(server_message, &charcount);\n\n /* read angles /\n for (auto& j: configuration.joints)\n j.s_ang = clip(read_double(server_message, &charcount));\n\n / read angle rate /\n for (auto& j: configuration.joints)\n j.s_vel = clip(read_double(server_message, &charcount));\n\n / read motor current /\n for (auto& j: configuration.joints)\n j.s_cur = read_double(server_message, &charcount);\n\n / read acceleration sensors /\n for(auto& s: configuration.accels)\n s.a = read_vector3(server_message, &charcount);\n\n / read body positions + velocities /\n for (auto& b: configuration.bodies)\n {\n b.position = read_vector3(server_message, &charcount);\n b.velocity = read_vector3(server_message, &charcount);\n }\n\n ++frames_read;\n\n } while (charcount < len-1);\n\n if (len-1 != charcount)\n wrn_msg(\"Server message has different number of bytes than expected, %u != %u\", len-1, charcount);\n if (frames_read > 1)\n wrn_msg(\"%u frame%s skipped.\", frames_read-1, frames_read > 2 ? \"s\":\"\");\n}\n\n\nvoid\nSimloid::write_motor_data(void)\n{\n static char msg[network::constants::msglen];\n unsigned n = snprintf(msg, network::constants::msglen, \"UX\");\n\n for (auto& j: configuration.joints)\n n += snprintf(msg + n, network::constants::msglen - n, \" %lf\", clip(j.motor.get()));\n\n auto const& bodies = configuration.bodies;\n for (unsigned i = 0; i < bodies.size(); ++i)\n if (bodies[i].force.length() > .0)\n n += snprintf(msg + n, network::constants::msglen - n, \"\\nFI %u %lf %lf %lf\", i, bodies[i].force.x,\n bodies[i].force.y,\n bodies[i].force.z);\n snprintf(msg + n, network::constants::msglen - n, \"\\n%sDONE\\n\", record_frame ? \"RECORD\\n\" : \"\");\n client.send(msg);\n\n / transfer motor data u(t) to u(t-1) and reset value /\n for (auto& j: configuration.joints) {\n j.motor.transfer();\n j.motor = .0;\n }\n\n record_frame = false;\n return;\n}\n\nvoid\nSimloid::send_pause_command(void) { client.send(\"PAUSE\\nDONE\\n\"); }\n\nvoid\nSimloid::set_low_sensor_quality(bool low_quality)\n{\n common::lock_t lock(mtx);\n if (low_quality)\n client.send(\"SENSORS POOR\\n\");\n else\n client.send(\"SENSORS GOOD\\n\");\n}\n\nvoid\nSimloid::update_avg_position(void)\n{\n Vector3 position(.0);\n for (auto& b: configuration.bodies)\n position += b.position;\n\n average_position = position / configuration.bodies.size();\n}\n\nvoid\nSimloid::update_avg_velocity(void)\n{\n Vector3 velocity(.0);\n for (auto& b: configuration.bodies)\n velocity += b.velocity;\n\n average_velocity = velocity / configuration.bodies.size();\n}\n\nVector3\nSimloid::get_min_position(void) const\n{\n Vector3 min_position(DBL_MAX);\n for (auto& b: configuration.bodies)\n {\n min_position.x = std::min(min_position.x, b.position.x);\n min_position.y = std::min(min_position.y, b.position.y);\n min_position.z = std::min(min_position.z, b.position.z);\n }\n return min_position;\n}\n\nVector3\nSimloid::get_max_position(void) const\n{\n Vector3 max_position(-DBL_MAX);\n for (auto& b: configuration.bodies)\n {\n max_position.x = std::max(max_position.x, b.position.x);\n max_position.y = std::max(max_position.y, b.position.y);\n max_position.z = std::max(max_position.z, b.position.z);\n }\n return max_position;\n}\n\ndouble\nSimloid::get_motion_level(void) const\n{\n double sum_v = .0;\n for (auto& j: configuration.joints)\n sum_v += fabs(j.s_vel);\n return sum_v/configuration.number_of_joints;\n}\n\nbool\nSimloid::motion_stopped(double thrsh) const\n{\n return (get_motion_level() < thrsh);\n}\n\nbool\nSimloid::dropped(double level) const\n{\n assert(level >= 0 && level <= 1.0);\n return (configuration.bodies[0].position.z < level body_position0[0].z);\n}\n\ndouble\nSimloid::dx_from_origin(void) const\n{\n return configuration.bodies[0].position.x - body_position0[0].x;\n}\n\ndouble\nSimloid::dy_from_origin(void) const\n{\n return configuration.bodies[0].position.y - body_position0[0].y;\n}\n\n/* for mean calculation of circular quantities see:\n https://en.wikipedia.org/wiki/Mean_of_circular_quantities\n /\nvoid\nSimloid::update_rotation_z(void)\n{\n assert(configuration.number_of_bodies == configuration.bodies.size());\n double sum_y = 0.0;\n double sum_x = 0.0;\n\n for (std::size_t i = 0; i < configuration.number_of_bodies; ++i) {\n Vector3 relative_position = configuration.bodies[i].position - average_position; // avg free position\n relative_position.normalize();\n const double len = relative_position.length();\n\n if (len != 0) assert_close(len, 1.0, 0.001, \"relative position\");\n\n const Vector3 relpos0 = body_position0[i] - average_position0;\n const double relative_rotation = relative_position.angle_phi() - relpos0.angle_phi();\n\n sum_y += sin(relative_rotation);\n sum_x += cos(relative_rotation);\n }\n\n average_rotation = atan2( sum_y/configuration.number_of_bodies\n , sum_x/configuration.number_of_bodies );\n\n avg_rot_inf_ang_last = avg_rot_inf_ang;\n avg_rot_inf_ang = unwrap(average_rotation, avg_rot_inf_ang);\n}\n\nvoid\nSimloid::update_robot_velocity(void)\n{\n const double& a = average_rotation;\n const double& x = average_velocity.x;\n const double& y = -average_velocity.y;\n\n avg_velocity_left = cos(a)x - sin(a)y;\n avg_velocity_forward = sin(a)x + cos(a)y;\n}\n\ndouble\nSimloid::get_normalized_mechanical_power(void) const\n{\n double power = .0;\n for (auto& j: configuration.joints)\n power += square(j.motor.get());\n return power/configuration.number_of_joints;\n}\n\nunsigned\nSimloid::get_body_id_by_name(const Bodyvector_t& bodies, const std::string& name) const {\n for (std::size_t i = 0; i < bodies.size(); ++i)\n if (bodies[i].name == name) return i;\n return bodies.size();\n}\n\nVector3\nSimloid::get_min_feet_pos(void) const {\n assert(left_id < configuration.bodies.size());\n assert(rift_id < configuration.bodies.size());\n return Vector3{ std::min(configuration.bodies[left_id].position.x, configuration.bodies[rift_id].position.x)\n , std::min(configuration.bodies[left_id].position.y, configuration.bodies[rift_id].position.y)\n , std::min(configuration.bodies[left_id].position.z, configuration.bodies[rift_id].position.z) };\n}\n\nVector3\nSimloid::get_max_feet_pos(void) const {\n assert(left_id < configuration.bodies.size());\n assert(rift_id < configuration.bodies.size());\n return Vector3{ std::max(configuration.bodies[left_id].position.x, configuration.bodies[rift_id].position.x)\n , std::max(configuration.bodies[left_id].position.y, configuration.bodies[rift_id].position.y)\n , std::max(configuration.bodies[left_id].position.z, configuration.bodies[rift_id].position.z) };\n}\n\nuint64_t\nSimloid::randomize_model(double rnd_amp, double growth, double friction, uint64_t inst)\n{\n common::lock_t lock(mtx);\n if (0 == inst) {/ not initialized yet? /\n inst = time(NULL);\n sts_msg(\"Initializing random seed, instance is: %lu\", inst);\n }\n\n sts_msg(\"Req. new model for robot %u and inst. %lu, amp. %lf, grow %lf, fric %lf\", robot_ID, inst, rnd_amp, growth, friction);\n eat_server_msg();\n client.send(\"MODEL %u 4 %lu %lf %lf %lf\\nRESET\\nDONE\\n\", robot_ID, inst, rnd_amp, growth, friction);\n configuration.read_robot_info( client.recv(5network::constants::seconds_us) );\n client.send(\"ACK\\n\");\n //read_sensor_data();\n assert(configuration.number_of_bodies > 0);\n init_robot();\n return inst;\n}\n\nvoid\nSimloid::reinit_robot_model(std::vector<double> const& params)\n{\n common::lock_t lock(mtx);\n sts_msg(\"Requesting new model for robot_id %u with %u params\", robot_ID, params.size());\n client.send(\"MODEL %u %u %s\\nDONE\\n\", robot_ID, params.size(), common::to_string(params).c_str());\n configuration.read_robot_info( client.recv(5network::constants::seconds_us) );\n client.send(\"ACK\\n\");\n assert(configuration.number_of_bodies > 0);\n //read_sensor_data();\n init_robot();\n}\n\nvoid\nSimloid::reinit_motor_model(std::vector<double> const& params)\n{\n common::lock_t lock(mtx);\n sts_msg(\"Requesting new motor model with %u params\", params.size());\n client.send(\"MOTOR %u %s\\nDONE\\n\", params.size(), common::to_string(params).c_str());\n}\n\n} // namespace robots\n" }, { "alpha_fraction": 0.6436519026756287, "alphanum_fraction": 0.6487874388694763, "avg_line_length": 28.20833396911621, "blob_id": "1d002bb6e7e9d6c5b09874a60c0314f5cc1c4b44", "content_id": "e1ad76a4864f30c269567937bba6227aac1a1ddc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3505, "license_type": "no_license", "max_line_length": 163, "num_lines": 120, "path": "/src/evolution/evolution_strategy.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EVOLUTION_POLICY_H_INCLUDED\n#define EVOLUTION_POLICY_H_INCLUDED\n\n#include <common/config.h>\n#include <evolution/population.h>\n#include <evolution/evaluation_interface.h>\n\n\nenum Evolution_State\n{\n stopped = 0,\n running,\n aborted,\n finished,\n playback\n};\n\nstruct statistics_t {\n statistics_t()\n : max(-DBL_MAX)\n , min(+DBL_MAX)\n , sum(0.0)\n , avg(0.0)\n , num_samples(0)\n {}\n\n void add_sample(double val) {\n max = std::max(val, max);\n min = std::min(val, min);\n sum += val;\n ++num_samples;\n }\n\n void update_average(void) {\n avg = sum/num_samples;\n }\n\n void reset() { this = statistics_t{}; }\n\n double max;\n double min;\n double sum;\n double avg;\n uint64_t num_samples;\n};\n\n/* base class /\nclass Evolution_Strategy\n{\npublic:\n Evolution_Strategy(Population& population, Evaluation_Interface& evaluation, config& configuration, const std::string& project_folder_path, const bool verbose)\n : population(population)\n , evaluation(evaluation)\n , configuration(configuration)\n , csv_population(project_folder_path + \"/population.log\", population.get_size(), population.get_individual_size())\n , csv_mutation (project_folder_path + \"/mutation.log\" , population.get_size(), 1)\n , csv_fitness (project_folder_path + \"/fitness.log\" , population.get_size(), 1)\n , fitness_stats()\n , mutation_stats()\n , verbose(verbose)\n {}\n\n virtual ~Evolution_Strategy() { };\n\n virtual Evolution_State execute_trial(void) = 0;\n virtual Evolution_State playback(void) = 0;\n virtual void resume(void) = 0;\n\n virtual void save_config(config& configuration) = 0;\n\n statistics_t const& get_fitness_statistics (void) const { return fitness_stats; }\n statistics_t const& get_mutation_statistics(void) const { return mutation_stats; }\n\n virtual std::size_t get_max_trials (void) const = 0;\n virtual std::size_t get_current_trial(void) const = 0;\n\n virtual const Individual& get_best_individual(void) const = 0;\n virtual bool is_there_a_new_best_individual(void) = 0;\n\n void save_state(void) {\n if (verbose) sts_msg(\"Saving population state:\");\n save_population (population, csv_population);\n save_mutation_rates(population, csv_mutation);\n save_fitness_values(population, csv_fitness);\n }\n void load_state(void) {\n if (verbose) sts_msg(\"Loading population state:\");\n load_population (population, csv_population);\n load_mutation_rates(population, csv_mutation);\n load_fitness_values(population, csv_fitness);\n }\n\n void generate_start_population(const std::vector<double>& seed) {\n sts_msg(\"Generate start population.\");\n population.initialize_from_seed(seed);\n }\n\n void load_start_population(const std::string& filename) {\n dbg_msg(\"Will try to load the population from file %s\", filename.c_str());\n file_io::CSV_File<double> pop_csv(filename, population.get_size(), population.get_individual_size());\n load_population(population, pop_csv);\n }\n\n Population& population;\n Evaluation_Interface& evaluation;\n config& configuration;\n\n file_io::CSV_File<double> csv_population;\n file_io::CSV_File<double> csv_mutation;\n file_io::CSV_File<double> csv_fitness;\n\n statistics_t fitness_stats;\n statistics_t mutation_stats;\n\n\n const bool verbose;\n};\n\n\n#endif // EVOLUTION_POLICY_H_INCLUDED\n" }, { "alpha_fraction": 0.6105263233184814, "alphanum_fraction": 0.6509180068969727, "avg_line_length": 40.68367385864258, "blob_id": "e80d2f9807c5ecafb6f7184c428aa067fcbe7881", "content_id": "c3298bffff39e1a7f4b55523f724cb8e5326c8f2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4085, "license_type": "no_license", "max_line_length": 139, "num_lines": 98, "path": "/src/tests/learning.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <vector>\n\n#include <common/modules.h>\n#include <common/static_vector.h>\n#include <learning/payload.h>\n#include <learning/epsilon_greedy.h>\n\n\nclass no_actions : public Action_Module_Interface {\npublic:\n std::size_t get_number_of_actions(void) const { return 7; }\n std::size_t get_number_of_actions_available(void) const { return 6; }\n bool exists(const std::size_t action_index) const {\n if (action_index == 3) return false; // id 3 is not available\n else return true;\n }\n};\n\nTEST_CASE( \"Epsilon Greedy\" , \"[learning]\") {\n REQUIRE( true );\n\n const unsigned num_states = 10;\n const unsigned num_policies = 3;\n const no_actions actions;\n static_vector<State_Payload> states(num_states, actions, num_policies, 0.0);\n\n learning::Epsilon_Greedy greedy(states, actions, 0.01);\n\n const unsigned avail_actions = actions.get_number_of_actions_available();\n const unsigned rand_idx = random_index(avail_actions);\n\n Action_Selection_Base::Vector_t const& selection_probabilities = greedy.get_distribution();\n print_distribution(selection_probabilities);\n\n /check that probabilities are restricted to [0,1] and sum up to 1 /\n auto check_probabilities = [&](Action_Selection_Base::Vector_t const& prob) {\n double sum = .0;\n for (unsigned i = 0; i < prob.size(); ++i) {\n sum += prob[i];\n REQUIRE( in_range(prob[i], 0.0, 1.0) );\n }\n REQUIRE( in_range(sum, 0.0, 1.0) );\n };\n\n check_probabilities(selection_probabilities);\n\n / check distribution of selected actions /\n auto test_selection = [&](learning::Epsilon_Greedy g, unsigned state_id, unsigned policy_id, unsigned action_id) {\n states[state_id].policies[policy_id].qvalues[action_id] = 1.0; // set Q-value\n unsigned result = g.select_action(state_id, policy_id);\n states[state_id].policies[policy_id].qvalues[action_id] = 0.0; // clear it\n return result;\n };\n\n auto check_distribution = [&](learning::Epsilon_Greedy g, unsigned state_id, unsigned policy_id, unsigned action_id, double expected) {\n REQUIRE( state_id < states.size() );\n REQUIRE( policy_id < states[0].policies.size() );\n REQUIRE( action_id < states[0].policies[0].qvalues.size() );\n const unsigned total = 2000;\n unsigned counter = 0;\n for (unsigned i = 0; i < total; ++i)\n if (action_id == test_selection(g, state_id, policy_id, action_id)) ++counter;\n dbg_msg(\"Selection rate: %4u/%4u %4.1f ~ %4.1f\", counter, total, 100.0counter/total, expected);\n REQUIRE( close(100.0counter/total, expected, 3.0) ); // values in 3.0% tolerance\n };\n\n learning::Epsilon_Greedy greedy_10(states, actions, 0.10);\n learning::Epsilon_Greedy greedy_40(states, actions, 0.40);\n learning::Epsilon_Greedy greedy_70(states, actions, 0.70);\n\n check_distribution(greedy_10, 0,0,0, 90.0); check_probabilities(greedy_10.get_distribution());\n check_distribution(greedy_10, 1,2,3, 0.0); check_probabilities(greedy_10.get_distribution()); // action not available\n check_distribution(greedy_40, 9,1,6, 60.0); check_probabilities(greedy_40.get_distribution());\n check_distribution(greedy_70, 3,0,1, 30.0); check_probabilities(greedy_70.get_distribution());\n}\n\n\n\nTEST_CASE( \"select_from_distribution\" ,\"[eps_greedy]\")\n{\n Action_Selection_Base::Vector_t selection_probabilities{5};\n selection_probabilities[0] = 0.50; //50\n selection_probabilities[1] = 0.25; //75\n selection_probabilities[2] = 0.10; //85\n selection_probabilities[3] = 0.10; //95\n selection_probabilities[4] = 0.05; //100\n\n / Testing binning./\n std::vector<std::size_t> bins(selection_probabilities.size());\n for (unsigned i = 0; i < 1000; ++i)\n ++bins[select_from_distribution(selection_probabilities)];\n\n for (unsigned i = 0; i < bins.size(); ++i) {\n dbg_msg(\"%+1.3f ~ %+1.3f\", bins[i]/1000.0, selection_probabilities[i]);\n REQUIRE( close(bins[i]/1000.0, selection_probabilities[i], 0.05) );\n }\n}\n" }, { "alpha_fraction": 0.6266195774078369, "alphanum_fraction": 0.6277974247932434, "avg_line_length": 24.34328269958496, "blob_id": "925aa0d0e5bb943c211d9f637639a488dbcd134e", "content_id": "8e0378b3de3217238ee722416f92b4608268b5c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1698, "license_type": "no_license", "max_line_length": 109, "num_lines": 67, "path": "/src/control/behavior_switcher.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BEHAVIOR_SWITCHER_H_INCLUDED\n#define BEHAVIOR_SWITCHER_H_INCLUDED\n\n#include <control/controlparameter.h>\n#include <control/control_vector.h>\n#include <control/jointcontrol.h>\n\nnamespace control {\n\n\nclass Behavior_Switcher\n{\npublic:\n Behavior_Switcher(const Control_Vector& parameter_set, Jointcontrol& control)\n : parameter_set(parameter_set)\n , control(control)\n , current_behavior(0)\n {\n dbg_msg(\"Creating Behavior Switcher.\");\n }\n\n void random(void) {\n current_behavior = random_index(parameter_set.size());\n sts_msg(\"Switching to random behavior: %u\", current_behavior);\n switch_behavior();\n }\n\n void next(void) {\n ++current_behavior;\n if (current_behavior >= parameter_set.size())\n current_behavior = 0;\n //sts_msg(\"Current behavior: %u\", current_behavior);\n switch_behavior();\n }\n\n bool step(void) {\n if (triggered) {\n next();\n triggered = false;\n return true;\n }\n else return false;\n }\n\n void trigger(void) { triggered = true; }\n\n Control_Parameter const& get_current_behavior(void) const { return parameter_set.get(current_behavior); }\n\nprivate:\n\n void switch_behavior(void) {\n control.set_control_parameter(parameter_set.get(current_behavior));\n\n //TODO is that still needed?\n control.switch_symmetric(parameter_set.get(current_behavior).is_mirrored());\n }\n\n const Control_Vector& parameter_set;\n Jointcontrol& control;\n std::size_t current_behavior;\n bool triggered = false;\n};\n\n\n} // namespace control\n\n#endif // BEHAVIOR_SWITCHER_H_INCLUDED\n" }, { "alpha_fraction": 0.4704939126968384, "alphanum_fraction": 0.5163566470146179, "avg_line_length": 18.60377311706543, "blob_id": "c9761e39b3540c0bc75489a48adae68a44c76246", "content_id": "890e6213451cefd547649e9053652860fbb6a60d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3122, "license_type": "no_license", "max_line_length": 70, "num_lines": 159, "path": "/src/draw/network2D.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ network2D.cpp /\n\n#include \"network2D.h\"\n#include \"draw.h\"\n\n///////////////////////////////////\n////// TODO REFACTOR THAT /////////\n///////////////////////////////////\n\nnetwork2D::network2D(int Na, axes a, const GLubyte c[4])\n{\n int i;\n N = Na;\n\n special_node = 0;\n\tfor (i = 0; i < 4; i++) color[i] = c[i];\n\n /* allocate memory /\n / Speicher reservieren für Zeilen-Zeiger /\n\tX = (float ) malloc(N sizeof(float ));\n if (NULL == X) {\n\t\tfprintf(stderr, \"Fehler: Zu wenig Speicher.\\n\");\n\t\texit(-1);\n\t}\n\n\t/ Speicher reservieren für Spalten /\n\tfor (i = 0; i < N; i++) {\n\t\tX[i] = (float ) calloc(2, sizeof(float));\n\t\tif (NULL == X[i]) {\n\t\t\tfprintf(stderr, \"Fehler: Zu wenig Speicher.\\n\");\n\t\t\texit(-1);\n\t\t}\n\t}\n\n\n\tsize = (float) calloc(N, sizeof(float));\n\n for (i = 0; i < N; i++) {\n X[i][0] = 0.0f;\n X[i][1] = 0.0f;\n\t\tsize[i] = 0.0f;\n }\n pointer = 0;\n\n / link to axis /\n px = a->px;\n py = a->py;\n A = a;\n width = 0.5a->width;\n height = 0.5a->height;\n\n\n\t/ Speicher reservieren für Zeilen-Zeiger /\n\tedges = (unsigned char ) malloc(N sizeof(unsigned char ));\n if (NULL == edges) {\n\t\tfprintf(stderr, \"Fehler: Zu wenig Speicher.\\n\");\n\t\texit(-1);\n\t}\n\n\t/ Speicher reservieren für Spalten /\n\tfor (i = 0; i < N; i++) {\n\t\tedges[i] = (unsigned char ) calloc(N, sizeof(unsigned char));\n\t\tif (NULL == edges[i]) {\n\t\t\tfprintf(stderr, \"Fehler: Zu wenig Speicher.\\n\");\n\t\t\texit(-1);\n\t\t}\n\t}\n\n}\n\nvoid network2D::draw()\n{\n /* draw nodes /\n\n\tint i,j;\n\tglColor4ub(255,255,255,255);\n\n\tfor (i = 0; i < N; i++) {\n\t\tif (size[i] > 0) { //node exists\n\t\t\tdraw_fill_square(X[i][0], X[i][1], 0.005);\n\t\t}\n\t}\n\n\tglColor4ub(255,255,255,96);\n\n\tfor (i = 0; i < N; i++) {\n\t\tif (size[i] > 0) { //node exists\n\t\t\tdraw_fill_square(X[i][0], X[i][1], 0.005+0.01size[i]);\n\t\t}\n\t}\n\n\n\t/glColor4ub(255,192,0,128);\n\tif (size[special_node] > 0)\n\t\tdraw_fill_rect(X[special_node][0], X[special_node][1], 0.02);\n\t/\n\tglColor4ub(0,128,255,128);\n\tif (size[activated_node] > 0)\n\t\tdraw_fill_square(X[activated_node][0], X[activated_node][1], 0.015);\n\n\t/* draw edges /\n\tglLineWidth(1.0f);\n\n\tfor (i = 0; i < N; i++)\n\t\tfor (j = 0; j < N; j++)\n\t\t\tif (i != j)\n\t\t\t\tif (edges[j][i])\n\t\t\t\t{\n\t\t\t\t\tglColor4ub(255,255,255,edges[j][i]);\n\t\t\t\t\tdraw_line(X[i], X[j]);\n\t\t\t\t}\n\n}\n\nvoid network2D::special(int n)\n{\n\n\tif (n >= 0 && n < N)\n\t\tspecial_node = n;\n\telse {\n \tfprintf(stderr, \"Fehler: Falscher Knotenindex\");\n \texit(-1);\n }\n}\n\nvoid network2D::activated(int n)\n{\n\n\tif (n >= 0 && n < N)\n\t\tactivated_node = n;\n\telse {\n \tfprintf(stderr, \"Fehler: Falscher Knotenindex\");\n \texit(-1);\n }\n}\n\nvoid network2D::update_node(int n, float x0, float x1, float s)\n{\n if (n >= 0 && n < N) {\n \t\tX[n][0] = px + x0width;\n \t\tX[n][1] = py + x1height;\n \t\tsize[n] = s;\n } else {\n \tfprintf(stderr, \"Fehler: Falscher Knotenindex\");\n \texit(-1);\n }\n}\n\nvoid network2D::update_edge(int i, int j, unsigned char op)\n{\n if (j>=0 && j<N && i>=0 && i<N) {\n \tedges[i][j] = op;\n \t} else {\n \tfprintf(stderr, \"Fehler: Falscher Kantenindex\");\n \texit(-1);\n }\n}\n\n/ network2D.cpp /\n\n" }, { "alpha_fraction": 0.5695159435272217, "alphanum_fraction": 0.5935461521148682, "avg_line_length": 25.97222137451172, "blob_id": "c41104c586492e2b0b564913ea651da0ad504cfa", "content_id": "467ba93338a32bf04c43716520f190ef85580726", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2913, "license_type": "no_license", "max_line_length": 93, "num_lines": 108, "path": "/src/draw/plot2D.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ plot2D.cpp /\n\n#include \"plot2D.h\"\n\nvoid plot2D::autoscale(void) const\n{\n float scale = 2.0 / (axis.max_amplitude - axis.min_amplitude);\n float offset0 = 0;//(axis.max_amplitude + axis.min_amplitude) / 2;\n\n glTranslatef(axis.px + offset0, axis.py + offset0, axis.pz);\n glScalef( 0.5 axis.width * scale\n , 0.5 * axis.height * scale\n , 1.0);\n}\n\nvoid plot2D::draw(void) const\n{\n glPushMatrix();\n autoscale();\n\n glLineWidth(1.0f);\n\n glBegin(GL_LINE_STRIP);\n for (unsigned i = number_of_samples; i-- > 1; ) { // zero omitted\n set_color(color, (float) i/number_of_samples);\n const unsigned pos = (i + pointer) % number_of_samples;\n glVertex2f( signal[pos].x - offset.x, signal[pos].y - offset.y );\n }\n glEnd();\n glPopMatrix();\n}\n\n//void plot2D::draw_in_time(void) const\n//{\n// glPushMatrix();\n// glTranslatef(px,py,pz);\n//\n// glBegin(GL_LINE_STRIP);\n// for (unsigned int i = number_of_samples - 1; i != 0; --i) {\n// glVertex3f(0.5 * width + i * width / number_of_samples,\n// 0.5 * height * signal[0][(i + pointer) % number_of_samples],\n// 0.5 * depth * signal[1][(i + pointer) % number_of_samples]);\n// }\n// glEnd();\n// glPopMatrix();\n//}\n\nvoid plot2D::adjust_amplitude(float s0, float s1) const\n{\n if (pointer == 0) {\n axis.max_amplitude = decrement;\n axis.min_amplitude = decrement;\n }\n\n axis.max_amplitude = std::max(axis.max_amplitude, std::max(s0, s1));\n axis.min_amplitude = std::min(axis.min_amplitude, std::min(s0, s1));\n}\n\nvoid plot2D::add_sample(float s0, float s1)\n{\n increment_pointer();\n signal[pointer].x = s0;\n signal[pointer].y = s1;\n\n adjust_amplitude(s0,s1);\n}\n\nvoid plot2D::add_sample(const std::vector<double>& sample)\n{\n assert(sample.size() >= 2);\n increment_pointer();\n signal[pointer].x = sample[0];\n signal[pointer].y = sample[1];\n\n adjust_amplitude(sample[0],sample[1]);\n}\n\nvoid colored_plot2D::draw_colored(void) const\n{\n glPushMatrix();\n autoscale();\n\n glLineWidth(1.0f);\n\n glBegin(GL_LINE_STRIP);\n for (unsigned i = number_of_samples; i-- > 1; ) { // zero omitted\n const unsigned pos = (i + pointer) % number_of_samples;\n set_color(colortable.get_color(colors[pos]), (float) i/number_of_samples);\n glVertex2f( signal[pos].x-offset.x, signal[pos].y-offset.y );\n }\n glEnd();\n glPopMatrix();\n}\n\nvoid colored_plot2D::draw_colored_scatter(void) const\n{\n glPushMatrix();\n autoscale();\n for (unsigned i = number_of_samples; i-- > 1; ) { // zero omitted\n const unsigned pos = (i + pointer) % number_of_samples;\n set_color(colortable.get_color(colors[pos]), 0.2f(float) i/number_of_samples+0.10f);\n draw_fill_square(signal[pos].x-offset.x, signal[pos].y-offset.y, 0.02);\n }\n glPopMatrix();\n}\n\n\n/ plot2D.cpp /\n" }, { "alpha_fraction": 0.49416643381118774, "alphanum_fraction": 0.4962877333164215, "avg_line_length": 45.29910659790039, "blob_id": "8cfdc59cd21715f7df6d7aed621bbad42d18ec49", "content_id": "6588f7d6ebd1f8f417373e77da58436260ca81c3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 10371, "license_type": "no_license", "max_line_length": 209, "num_lines": 224, "path": "/src/learning/expert_vector.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EXPERT_VECTOR_H_INCLUDED\n#define EXPERT_VECTOR_H_INCLUDED\n\n#include <memory>\n#include <common/static_vector.h>\n#include <common/save_load.h>\n#include <control/sensorspace.h>\n#include <control/control_vector.h>\n#include <robots/robot.h>\n#include <learning/expert.h>\n#include <learning/predictor.h>\n#include <learning/state_predictor.h>\n#include <learning/motor_predictor.h>\n#include <learning/state_action_predictor.h>\n#include <learning/homeokinetic_predictor.h>\n#include <learning/bimodel_predictor.h>\n\n/ The Expert Vector merely work as a container\n * and should neither carry any information nor functionality\n * regarding the expert modules in it. However this is theory. :)\n /\n\nclass Expert_Vector : public common::Save_Load {\n\n std::vector<Expert> experts;\n static_vector_interface& payloads;\n\n Expert_Vector( const std::size_t max_number_of_experts\n , static_vector_interface& payloads )\n : experts()\n , payloads(payloads)\n {\n assert(payloads.size() == max_number_of_experts);\n assert(max_number_of_experts > 0);\n experts.reserve(max_number_of_experts);\n }\n\npublic:\n Expert_Vector(Expert_Vector&& other) = default;\n Expert_Vector& operator=(Expert_Vector&& other) = default;\n\n Expert& operator[] (const std::size_t index) { return experts.at(index); }\n const Expert& operator[] (const std::size_t index) const { return experts.at(index); }\n\n std::size_t size(void) const { return experts.size(); }\n\n void save(std::string f)\n {\n auto const cols = experts.at(0).get_predictor().get_weights().size();\n csv_file_t csv(f+\"experts.dat\", experts.size(), cols);\n for (std::size_t i = 0, line = 0; i < experts.size(); ++i) {\n if (experts[i].does_exists())\n csv.set_line(line++, experts[i].get_predictor().get_weights());\n }\n csv.write();\n }\n\n void load(std::string f)\n {\n auto const cols = experts.at(0).get_predictor().get_weights().size();\n csv_file_t csv(f+\"experts.dat\", experts.size(), cols);\n csv.read();\n for (std::size_t i = 0; i < experts.size(); ++i) {\n csv.get_line(i, experts[i].set_predictor().set_weights());\n experts[i].exists = !(i > 0 && is_vector_zero(experts[i].get_predictor().get_weights()));\n }\n }\n\n\n void copy(std::size_t to, std::size_t from, bool one_shot_learning) {\n\n experts.at(to).exists = true; // create\n\n if (one_shot_learning) experts.at(to).reinit_predictor_weights();\n else\n experts.at(to).predictor->copy( (experts.at(from).predictor) );\n\n payloads.copy(to, from); /* take a flawed copy of the payload /\n }\n\n / simple sensor state space constructor /\n Expert_Vector( const std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , const sensor_vector& input\n , const double local_learning_rate\n , const double random_weight_range\n , const std::size_t experience_size )\n : Expert_Vector(max_number_of_experts, payloads)\n {\n assert(local_learning_rate > 0.);\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back( Predictor_ptr( new Predictor(input, local_learning_rate, random_weight_range, experience_size) )\n , max_number_of_experts );\n }\n\n / time-delay network sensor state space constructor /\n Expert_Vector( const std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , const sensor_vector& input\n , const double local_learning_rate\n , const std::size_t experience_size\n , const std::size_t hidden_layer_size\n , const std::size_t time_delay_size )\n : Expert_Vector(max_number_of_experts, payloads)\n {\n assert(local_learning_rate > 0.);\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back( Predictor_ptr( new learning::State_Predictor(input, local_learning_rate, gmes_constants::random_weight_range, experience_size, hidden_layer_size, time_delay_size) )\n , max_number_of_experts );\n }\n\n / motor action space constructor /\n Expert_Vector( std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , sensor_vector const& motor_targets\n , double local_learning_rate\n , std::size_t experience_size\n , double noise_level\n , control::Control_Vector const& ctrl_params\n , robots::Robot_Interface const& robot )\n : Expert_Vector(max_number_of_experts, payloads)\n {\n sts_msg(\"Creating motor expert vector with %u elements in control parameter vector.\", ctrl_params.size());\n assert(local_learning_rate > 0.);\n assert(ctrl_params.size() == max_number_of_experts);\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back( Predictor_ptr( new learning::Motor_Predictor(robot, motor_targets, local_learning_rate, gmes_constants::random_weight_range, experience_size, ctrl_params.get(i), noise_level))\n , max_number_of_experts );\n }\n\n / state action space constructor /\n Expert_Vector( const std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , const time_embedded_sensors<16>& input\n , const double local_learning_rate\n , const std::size_t experience_size\n , const std::size_t hidden_layer_size // ergibt sich aus num joints\n , const double random_weight_range\n )\n : Expert_Vector(max_number_of_experts, payloads)\n {\n assert(local_learning_rate > 0.);\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back( Predictor_ptr( new learning::State_Action_Predictor( input\n , local_learning_rate\n , random_weight_range\n , experience_size\n , hidden_layer_size\n ) )\n , max_number_of_experts );\n }\n\n\n\n / homeokinetic expert constructor /\n Expert_Vector( std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , sensor_input_interface const& input\n , std::size_t number_of_motor_outputs\n , std::size_t number_of_context_units\n , double local_learning_rate\n , double random_weight_range\n )\n : Expert_Vector(max_number_of_experts, payloads)\n {\n assert(local_learning_rate > 0.);\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back( Predictor_ptr( new learning::Homeokinetic_Core( input\n , number_of_motor_outputs\n , local_learning_rate\n , random_weight_range\n , number_of_context_units\n ) )\n , max_number_of_experts );\n }\n\n\n / bimodel expert constructor /\n Expert_Vector( std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , sensor_input_interface const& input\n , sensor_input_interface const& gateway\n , learning::model::vector_t& gradient\n , double local_learning_rate\n , double random_weight_range\n , double regularization_rate\n )\n : Expert_Vector(max_number_of_experts, payloads)\n {\n assert(local_learning_rate > 0.);\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back(\n Predictor_ptr( new learning::BiModel_Predictor( input\n , gateway\n , gradient\n , local_learning_rate\n , random_weight_range\n , regularization_rate\n ) )\n , max_number_of_experts );\n }\n\n};\n\n/ possible template constructor\n\ntemplate <typename PredictorType>\nclass Expert_Vector : public Expert_Vector_Base {\npublic:\n\n template<typename... Args>\n Expert_Vector( const std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , const Args&... predictor_args)\n : Expert_Vector_Base(max_number_of_experts, payloads)\n {\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back( Predictor_ptr(new PredictorType(predictor_args...)), max_number_of_experts );\n }\n};\n\n/\n\n#endif // EXPERT_VECTOR_H_INCLUDED\n" }, { "alpha_fraction": 0.6953405141830444, "alphanum_fraction": 0.7096773982048035, "avg_line_length": 35.39130401611328, "blob_id": "3e05216a5129c27b6ddb43826e9221e862234514", "content_id": "7f52c80267773ee0a0534489b9f9bc816a524c07", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 837, "license_type": "no_license", "max_line_length": 140, "num_lines": 23, "path": "/src/tests/homeokinetic_core_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <common/log_messages.h>\n#include <control/sensorspace.h>\n#include <learning/predictor.h>\n#include <learning/time_state_space.h>\n#include <learning/homeokinetic_predictor.h>\n#include <controller/pid_control.hpp>\n\nTEST_CASE( \"homeokinetic predictor construction\", \"[homeokinetic_core]\" )\n{\n typedef learning::Homeokinetic_Control::Vector_t ExtInputVector_t;\n typedef std::vector<supreme::pid_control> PID_joint_control_vector_t;\n\n Test_Robot robot(5,2);\n learning::Time_State_Space<8> inputs{robot};\n PID_joint_control_vector_t pid;\n ExtInputVector_t ext_input;\n\n //test_space sensors(0.01);\n learning::Homeokinetic_Core core(inputs, robot.get_joints().size(), /learning_rate=/0.01, /random_weight_range=/0.1, /context=/4);\n}\n" }, { "alpha_fraction": 0.6580827832221985, "alphanum_fraction": 0.6638880372047424, "avg_line_length": 32.25742721557617, "blob_id": "ae4bbaaff41f723d5873ffa5f4477b82ae04bbae", "content_id": "4afb89c0238a64886d2739d287402af9382ef9ec", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6718, "license_type": "no_license", "max_line_length": 137, "num_lines": 202, "path": "/src/common/modules.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ modules.h\n * contains common mathematical functions and helpers /\n\n#ifndef MODULES_H\n#define MODULES_H\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <string>\n#include <math.h>\n#include <float.h>\n#include <assert.h>\n#include <vector>\n#include <algorithm>\n#include \"vector_n.h\"\n#include <common/log_messages.h>\n\n/ tanh() on a vector_t /\ntemplate <typename Vector_t>\nvoid vector_tanh(Vector_t& vec) { for (auto& v : vec) v = tanh(v); }\n\n/ TODO: find a better name, this one is actually wrong /\ninline double tanh_(double x) { return (1.0 + x) (1.0 - x); }\n\n/* sigmoid function /\ndouble sigmoid(double x);\n\n/ squares the var /\ninline double square(double x) { return x x; }\n\n/* clips values to Interval [-1,+1] /\ndouble clip(double x);\n\n/ clips values to Interval [-ul_limit,+ul_limit] /\ndouble clip(double x, double ul_limit);\n\n/ clips values to Interval [l_limit, u_limit] /\ndouble clip(double x, double l_limit, double u_limit);\n\n/ clip() on a vector_t /\ntemplate <typename Vector_t>\nvoid vector_clip(Vector_t& vec) { for (auto& v : vec) v = clip(v); }\n\n/ checks if vector is zero /\ntemplate <typename Vector_t>\nbool is_vector_zero(Vector_t const& vec) {\n for (auto& v : vec)\n if (v != 0.0) return false;\n return true;\n}\n\n/ make vector zero /\ntemplate <typename Vector_t>\nvoid zero(Vector_t& v) {\n typename Vector_t::value_type s{};\n std::fill(v.begin(), v.end(), s);\n}\n\ntemplate <typename Vector_t>\nvoid print_vector(Vector_t const& vec, const char name = \"\", const char* format = \"%+4.2f \") {\n printf(\"%s = [\", name);\n for (std::size_t i = 0; i < vec.size(); ++i)\n printf(format, vec[i]);\n printf(\"]\\n\");\n}\n\n/* min of 3 arguments /\ndouble fmin3(double x, double y, double z);\n\n/ max of 3 arguments /\ndouble fmax3(double x, double y, double z);\n\n/ computes the minimum of N arguments /\ndouble fminN(double x, unsigned int N);\n\n/* computes the maximum of N arguments /\ndouble fmaxN(double x, unsigned int N);\n\n/* computes median-of-three /\ndouble median3(double a, double b, double c);\n\n/ generates a double random value within interval [a,b] /\ndouble random_value(double a, double b);\n\n/ generates a integer random index within interval [0,N[ /\nunsigned int random_index(unsigned int N);\n\n/ generates a integer random value within interval [a,b] /\nint random_int(int a, int b);\n\n/ generates a pseudo-random double between 0.0 and 0.999... /\ndouble random_value(void);\n\n/ normally distributed random value /\ndouble random_value_norm(const double m, const double s, const double min, const double max);\n\n/ zero mean normal distributed random value with max. 3 sigma /\ninline double rand_norm_zero_mean(double sigma) { return random_value_norm(0.0, sigma, -3sigma, 3sigma); }\n\n/ random sign /\ninline int rand_sign(void) { return (random_index(2) == 0) ? -1 : 1; }\n\n/ returns a random vector of size N with values in [a,b]/\nstd::vector<double> random_vector(std::size_t N, double a, double b);\n\n/ multiplies matrix by vector /\nvoid mult_mat_by_vect(double result_vect, const double mat, const double vect, const unsigned int Zeilen, const unsigned int Spalten);\nvoid mult_mat_by_vect(VectorN& result_vect, const VectorN& mat, const VectorN& vect);\n\n/* computes the argument which minimizes the function /\nint argmin(double f, unsigned int N);\n\n/* computes the argument which maximizes the function /\nint argmax(double f, unsigned int N);\n\n/* computes the squared distance of two vectors /\ndouble squared_distance(double x , double y, unsigned int length);\n\n/ computes the squared distance of two vectors /\ntemplate <typename Vector_Type_A, typename Vector_Type_B>\ndouble squared_distance(Vector_Type_A const& x , Vector_Type_B const& y) {\n assert(x.size() == y.size());\n double d = .0;\n for (std::size_t i = 0; i < x.size(); ++i)\n d += square(x[i] - y[i]);\n return d;\n}\n\n/ true is integer is even /\ninline bool is_even(unsigned int number) { return (number%2 == 0); }\n\n/ signum /\ndouble sign(double x);\n\n/ floating point modulo, better than fmod /\ndouble modulo(double a, double b);\n\n/ maps arbitrary angles from interval -inf..+inf to -pi..+pi /\ndouble wrap(double angle);\n\n/ maps arbitrary angles from interval -inf..+inf to -pi..+pi by Martin Marmulla /\ndouble wrap2(double angle);\n\n/ unwraps angles of -pi..+pi to -inf..+inf /\ndouble unwrap(double new_angle, double last_angle);\n\n/ checks that value is close to refval by max distance of tolerance /\ninline bool close(double value, double refval, double tolerance) { return (fabs(value - refval) < tolerance); }\n\ninline bool toggle(bool& b) { b = !b; return b; }\n\n/ checks for vector that value is close to refval by max distance of tolerance /\ntemplate <typename Vector_t>\ninline bool close(Vector_t vec, Vector_t refvec, double tolerance) {\n assert(vec.size() == refvec.size());\n for (std::size_t i = 0; i < vec.size();++i)\n if (fabs(vec[i] - refvec[i]) > tolerance)\n return false;\n return true;\n}\n\n/ asserts that value is close to refval by max distance of tolerance /\ninline void assert_close(double value, double refval, double tolerance, const char msg) {\n if (not close(value, refval, tolerance))\n err_msg(__FILE__, __LINE__, \"%s: value %f not close to %f by tolerance of %f.\\n\", msg, value, refval, tolerance);\n}\n\n/* checks if variable is in the given range [lower, upper]/\ntemplate <typename T>\ninline bool in_range(T value, T lower, T upper) { return (lower <= value) and (value <= upper); }\n\ninline void test_range(double value, double lower, double upper, const char msg)\n{\n if (not in_range(value, lower, upper))\n err_msg(__FILE__, __LINE__, \"%s: value %f out of range [%f %f].\\n\", msg, value, lower, upper);\n}\ninline void test_range(std::size_t value, std::size_t lower, std::size_t upper, const char* msg)\n{\n if (not in_range(value, lower, upper))\n err_msg(__FILE__, __LINE__, \"%s: value %lu out of range [%lu %lu].\\n\", msg, value, lower, upper);\n}\n\ntemplate <typename Vector_t>\ninline void test_range(Vector_t const& values, double lower, double upper, const char* msg)\n{\n for (std::size_t i = 0; i < values.size(); ++i)\n if (not in_range(values[i], lower, upper))\n err_msg(__FILE__, __LINE__, \"%s: value %f from vector index %u out of range [%f %f].\\n\", msg, values[i], i, lower, upper);\n}\n\n#define assert_in_range(VALUE, LOWER, UPPER) \\\ntest_range(VALUE, LOWER, UPPER, #VALUE); \\\n\nstd::string random_string(size_t length);\n\n\ntemplate <typename A_t, typename B_t>\nvoid check_vectors(A_t const& a, B_t const& b) {\n assertion(a.size() == b.size(), \"Incompatible vector lengths %u =/= %u\", a.size(), b.size());\n}\n\n#endif /MODULES_H/\n" }, { "alpha_fraction": 0.600806474685669, "alphanum_fraction": 0.6270161271095276, "avg_line_length": 16.714284896850586, "blob_id": "f4470256c03e045b8190e63e1dfa9cbbf30cc301", "content_id": "5e91a176439707f5ca3f9fab10d800d1f1e1fed7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 496, "license_type": "no_license", "max_line_length": 58, "num_lines": 28, "path": "/src/draw/axes3D.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* axes3D.h /\n\n#ifndef AXES3D_H\n#define AXES3D_H\n\n#include <GL/gl.h>\n#include <GL/glu.h>\n#include <GL/glut.h>\n\nclass axes3D\n{\n friend class plot2D;\n friend class plot3D;\n friend class network3D;\n\nprivate:\n float px, py, pz;\n float width, height, depth;\n GLfloat r[8][3], a[6][3];\n int flag; / axes flag /\n\npublic:\n axes3D(float, float, float, float, float, float, int);\n void draw(float x_angle, float y_angle) const;\n void axesflag(int);\n};\n\n#endif /AXES3D_H/\n" }, { "alpha_fraction": 0.5576279759407043, "alphanum_fraction": 0.5754688382148743, "avg_line_length": 30.02201271057129, "blob_id": "d7b0854048d01b4469acb6f93f891d1102f99622", "content_id": "a3f65590b2b55ab8de2be8e3c901a61a3f6612f6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 9865, "license_type": "no_license", "max_line_length": 151, "num_lines": 318, "path": "/src/common/setup.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <common/setup.h>\n\nGlobalFlag do_pause (true );\nGlobalFlag do_quit (false);\nGlobalFlag fast_forward(false);\nGlobalFlag draw_grid (false);\nGlobalFlag do_drawing (false);\nGlobalFlag screenshot (false);\n\nstatic SDL_Window window;\nstatic SDL_GLContext glcontext;\n\n/* screen properties, consider finding a better place /\nVisuals screen;\n\nfloat t_delay_ms = 10.0f; // convert to unsigned int\ndouble speed_factor = 1.0;\n\nint\nprocess_application(void data)\n{\n auto cur_time = std::chrono::high_resolution_clock::now();\n auto lst_time = cur_time;\n\n SimpleTimer tim = SimpleTimer(101000/us/, true); // 10 ms timer\n\n Application_Base a = static_cast<Application_Base >(data);\n sts_msg(\"Setting up application.\");\n\n /TODO better place?/\n do_drawing.enable(); // enable drawing\n\n sts_msg(\"Entering application main loop.\");\n while (!do_quit.status())\n {\n if (do_pause.status()) {\n usleep(10000); // 10ms\n a->paused();\n }\n else\n {\n if (a->get_cycle_count() % 100 == 0) {\n lst_time = cur_time;\n cur_time = std::chrono::high_resolution_clock::now();\n long long elapsed_ms = std::chrono::duration_cast<std::chrono::milliseconds>(cur_time - lst_time).count();\n speed_factor = 1000 / static_cast<double>(elapsed_ms);\n }\n\n if (!a->loop())\n {\n sts_msg(\"Application main loop has terminated.\");\n break;\n }\n\n if (!fast_forward.status()) {\n while(!tim.check_if_timed_out_and_restart((unsigned)t_delay_ms1000))\n usleep(100);\n }\n }\n }\n sts_msg(\"Finishing application.\");\n a->finish();\n return 0;\n}\n\nvoid\nfps_controller(double &fps, const double &sp_fps)\n{\n static double delay_ms = 1.0;\n static double time_passed_s;\n static Stopwatch stopwatch;\n\n / controller /\n delay_ms += 0.01 (fps - sp_fps);\n if (delay_ms < 0.0) delay_ms = 0.0;\n\n usleep((int) round(1000 * delay_ms));\n time_passed_s = stopwatch.get_time_passed_us() / 1000000.0;\n\n if (time_passed_s > 0)\n fps = 0.9 * fps + 0.1 / time_passed_s; // low pass filter\n}\n\nvoid\nui_main_loop(GlobalFlag& do_quit, const GlobalFlag& do_drawing, Event_Manager& em, const Application_Base& app)\n{\n sts_msg(\"Entering UI main loop.\");\n double fps = 0.0;\n\n while (!do_quit.status())\n {\n em.process_events();\n\n if (do_drawing.status())\n draw_screen(fps, app);\n\n fps_controller(fps, visuals_defaults::frames_per_second); // delay to reach stable 25 f/s\n\n }\n sts_msg(\"Leaving UI main loop.\");\n return;\n}\n\nvoid\ninit_SDL(const bool visuals, const std::size_t window_width, const std::size_t window_height, const std::string& name)\n{\n if (visuals)\n {\n sts_msg(\"Initializing SDL.\");\n /* Dimensions of our window. /\n assert_in_range(window_width , 100ul, 2048ul);\n assert_in_range(window_height, 100ul, 1024ul);\n\n screen.window_size_x = window_width;\n screen.window_size_y = window_height;\n\n / First, initialize SDL's video subsystem. /\n sts_msg(\"Initializing SDL VIDEO.\");\n if (SDL_Init(SDL_INIT_VIDEO))\n err_msg(__FILE__, __LINE__, \"SDL Video initialization failed: %s\", SDL_GetError());\n\n sts_msg(\"Initializing SDL Window.\");\n window = SDL_CreateWindow( name.c_str() // title\n , SDL_WINDOWPOS_UNDEFINED // initial x position\n , SDL_WINDOWPOS_UNDEFINED // initial y position\n , window_width // width, in pixels\n , window_height // height, in pixels\n , SDL_WINDOW_OPENGL\n // \| SDL_WINDOW_RESIZABLE // flags\n );\n\n if (window == NULL)\n err_msg(__FILE__, __LINE__, \"SDL Window initialization failed: %s\", SDL_GetError());\n\n sts_msg(\"Creating GL context.\");\n glcontext = SDL_GL_CreateContext(window);\n\n / Color depth in bits of our window. /\n //TODO int bpp = info->vfmt->BitsPerPixel;\n\n// SDL_GL_SetAttribute(SDL_GL_RED_SIZE, 5);\n// SDL_GL_SetAttribute(SDL_GL_GREEN_SIZE, 5);\n// SDL_GL_SetAttribute(SDL_GL_BLUE_SIZE, 5);\n// SDL_GL_SetAttribute(SDL_GL_DEPTH_SIZE, 16);\n// SDL_GL_SetAttribute(SDL_GL_DOUBLEBUFFER, 1);\n\n / Flags we will pass into SDL_SetVideoMode. /\n //int flags = SDL_OPENGL;// \| SDL_FULLSCREEN;\n\n / set the video mode /\n //if (SDL_SetVideoMode(screen.window_size_x, screen.window_size_y, bpp, flags) == 0)\n // err_msg(__FILE__, __LINE__, \"Initialization of video mode failed: %s\", SDL_GetError());\n\n init_OpenGL(screen.window_size_x, screen.window_size_y);\n\n / Initialize GLUT /\n sts_msg(\"Initialize GLUT.\");\n int argc = 1; // fake arguments for glutInit()\n char argv[] = {strdup(\"sim\"), NULL};\n glutInit(&argc, argv);\n\n } // done initializing visuals\n\n /* initializing control pad /\n init_controls();\n\n atexit(deinit_SDL);\n}\n\nvoid\ndeinit_SDL(void) {\n sts_msg(\"De-initialize SDL on exit.\");\n SDL_GL_DeleteContext(glcontext);\n SDL_DestroyWindow(window);\n SDL_Quit();\n}\n\nvoid\ninit_OpenGL(const std::size_t window_width, const std::size_t window_height)\n{\n sts_msg(\"Initialize OpenGL.\");\n double ratio = (double) window_width / (double) window_height;\n\n glEnable(GL_BLEND);\n glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);\n\n //glEnable(GL_LINE_SMOOTH);\n //glHint(GL_LINE_SMOOTH_HINT, GL_DONT_CARE);\n\n glShadeModel(GL_SMOOTH); // shading model Gouraud (smooth)\n\n / Culling. /\n glCullFace(GL_BACK);\n glFrontFace(GL_CCW);\n glEnable(GL_CULL_FACE);\n\n / Set the clear color. /\n glClearColor(0.0, 0.0, 0.0, 0.0);//glClearColor(0.05, 0.0, 0.1, 0.0);\n\n / Setup our view port. /\n glViewport(0, 0, window_width, window_height);\n\n / Change to the projection matrix and set our viewing volume. /\n glMatrixMode(GL_PROJECTION);\n glLoadIdentity();\n gluPerspective(visuals_defaults::gl_fovy,\n ratio,\n visuals_defaults::gl_zNear,\n visuals_defaults::gl_zFar);\n}\n\nvoid\ninit_controls()\n{\n sts_msg(\"Initializing controls.\");\n / Initialize the SDL joystick subsystem /\n if (SDL_InitSubSystem(SDL_INIT_JOYSTICK))\n err_msg(__FILE__, __LINE__, \"SDL joystick initialization failed: %s\", SDL_GetError());\n\n int num_buttons = 0; // number of control pad buttons\n int num_axes = 0; // number of control pad axes pairs\n SDL_Joystick ctrl;\n\n /* initialize control pad /\n int JNum = SDL_NumJoysticks();\n int joystick_ID = 0;\n\n if (JNum > 0)\n {\n if (JNum > 1) sts_msg(\"Available control pads: %d\", JNum);\n\n / open control pad with jID 0 /\n ctrl = SDL_JoystickOpen(joystick_ID);\n if (NULL != ctrl)\n {\n num_buttons = SDL_JoystickNumButtons(ctrl);\n num_axes = SDL_JoystickNumAxes(ctrl);\n\n if (num_buttons < 1) wrn_msg(\"Control pad \\\"%s\\\" has no buttons available.\", SDL_JoystickName(ctrl));\n if (num_axes < 2) wrn_msg(\"Control pad \\\"%s\\\" has insufficient axes.\" , SDL_JoystickName(ctrl));\n\n sts_msg(\"Control pad \\\"%s\\\" found with %d axes and %d buttons.\", SDL_JoystickName(ctrl), num_axes, num_buttons);\n }\n else\n err_msg(__FILE__, __LINE__, \"control pad error: %s\", SDL_GetError());\n\n }\n else wrn_msg(\"There is no control pad available.\");\n\n return;\n}\n\nvoid\ndraw_screen(const double& fps, const Application_Base& app)\n{\n static FILE fp;\n const unsigned long long cycles = app.get_cycle_count();\n\n /* prepare screen shot /\n if (screenshot.status())\n {\n\n int buffsize = 10241024;\n\n fp = open_file(\"wb\", \"screenshot_%llu.svg\", cycles);\n sts_msg(\"Prepare screen shot at cycle: %llu\",cycles);\n\n gl2psBeginPage(\"screenshot\", \"gl2ps\", NULL, GL2PS_SVG, GL2PS_SIMPLE_SORT,\n GL2PS_DRAW_BACKGROUND \| GL2PS_USE_CURRENT_VIEWPORT,\n GL_RGBA, 0, NULL, 0, 0, 0, buffsize, fp, \"foo.svg\");\n }\n\n /* Clear the color and depth buffers. /\n glClear(GL_COLOR_BUFFER_BIT \| GL_DEPTH_BUFFER_BIT);\n\n / We don't want to modify the projection matrix. /\n glMatrixMode(GL_MODELVIEW);\n glLoadIdentity();\n\n glTranslatef( screen.x_position + screen.x_position_disp\n , screen.y_position + screen.y_position_disp\n ,-screen.zdist );\n\n if (screen.rotate_view && (screen.x_angle += 0.1f screen.rot_factor) > 360.0f)\n screen.x_angle = 0.0f;\n\n const pref p = {screen.x_angle + screen.x_angle_disp, screen.y_angle + screen.y_angle_disp};\n app.draw(p); // draw application dependent content\n\n /* overlay grid /\n if (draw_grid.status()) {\n glColor4f(1.0f, 1.0f, 1.0f, 0.1f);\n glLineWidth(1.0f);\n draw_grid2D(1.0, 5);\n }\n\n if (screen.show_fps) {\n glColor3f(1.0, 1.0, 1.0);\n glprintf(-1.0,-1.0, 0.0, .025, \"%s (%lu), %d fps %1.2fx\", get_time_from_cycle_counter(cycles).c_str(), cycles, (int) round(fps), speed_factor);\n }\n\n / finish screen shot /\n if (screenshot.status()) {\n if (GL2PS_OVERFLOW == gl2psEndPage())\n wrn_msg(\"Overflow while creating screen shot. Increase buffer size.\");\n fclose(fp);\n screenshot.disable();\n }\n\n SDL_GL_SwapWindow(window); // swap the buffers\n}\n\nvoid\nsignal_terminate_handler(int signum)\n{\n sts_msg(\"Got a SIGINT(%d) from user\\n\", signum);\n quit();\n}\n" }, { "alpha_fraction": 0.6905989646911621, "alphanum_fraction": 0.6905989646911621, "avg_line_length": 30.487499237060547, "blob_id": "39e92cbe80f2d489fe8e2afc106c83d1f934476c", "content_id": "f7d0730498a0d511e5e6b5e28ebbb948fa49f93c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2521, "license_type": "no_license", "max_line_length": 120, "num_lines": 80, "path": "/src/evolution/evolution.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EVOLUTION_H\r\n#define EVOLUTION_H\r\n\n#include <vector>\n#include <algorithm>\n#include <float.h>\n#include <math.h>\n#include <memory>\n\n#include <common/config.h>\n#include <common/stopwatch.h>\n#include <common/modules.h>\n#include <common/log_messages.h>\n#include <common/file_io.h>\n\n#include <robots/simloid.h>\n\n#include <evolution/individual.h>\n#include <evolution/population.h>\n#include <evolution/evaluation_interface.h>\n#include <evolution/evolution_strategy.h>\n#include <evolution/generation_based_strategy.h>\n#include <evolution/pool_strategy.h>\n#include <evolution/setting.h>\n\ntypedef std::shared_ptr<Evolution_Strategy> Strategy_Pointer;\n\nclass Evolution\n{\n Evolution( const Evolution& other ) = delete; // non construction-copyable\n Evolution& operator=( const Evolution& ) = delete; // non copyable\n\npublic:\n Evolution(Evaluation_Interface &evaluation, const Setting& settings, const std::vector<double>& seed_genome); // new\n Evolution(Evaluation_Interface &evaluation, const Setting& settings, bool playback_only); // resume or watch\n\n ~Evolution() { sts_msg(\"Evolution shut down.\"); }\n\n bool loop(void);\n void finish(void);\n\n statistics_t const& get_fitness_statistics (void) const { return strategy->get_fitness_statistics(); }\n statistics_t const& get_mutation_statistics(void) const { return strategy->get_mutation_statistics(); }\n\n std::size_t get_number_of_trials(void) const { return strategy->get_max_trials(); }\n std::size_t get_current_trial (void) const { return strategy->get_current_trial(); }\n std::size_t get_population_size (void) const { return population.get_size(); }\n\n std::vector<double> get_best_individuals_genome(void) const { return population.get_best_individual().genome; }\n\nprivate:\n Evaluation_Interface& evaluation;\n const Setting& settings;\n const std::string projectname;\n const std::string conffilename;\n config configuration;\n\n Evolution_State state;\n\n std::vector<double> seed;\n Population population;\n Strategy_Pointer strategy;\n\n file_io::Logfile evolution_log;\n file_io::Logfile bestindiv_log;\n\n const bool verbose;\n const bool playback_only;\n\n void common_setup(void);\n void save_best_individual(void);\n void save_statistics(void);\n void write_config(void);\n\n void prepare_quit();\n};\n\nstd::string create_project_name_and_folder(std::string name);\n\n#endif // EVOLUTION_H\n" }, { "alpha_fraction": 0.5971605777740479, "alphanum_fraction": 0.5989352464675903, "avg_line_length": 44.060001373291016, "blob_id": "d9171c8023ec598d422ad394fcbea1ffb832d23a", "content_id": "7b3ac275f4a74f005411a54e604ac4a0c8658d68", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2254, "license_type": "no_license", "max_line_length": 136, "num_lines": 50, "path": "/src/learning/time_state_space.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef TIME_STATE_SPACE_H\n#define TIME_STATE_SPACE_H\n\n#include <robots/robot.h>\n#include <control/sensorspace.h>\n\nnamespace learning {\n\ntemplate <std::size_t NumTaps>\nclass Time_State_Space : public time_embedded_sensors<NumTaps> {\npublic:\n Time_State_Space(const robots::Robot_Interface& robot)\n : time_embedded_sensors<NumTaps>(robot.get_joints().size())\n {\n auto const& joints = robot.get_joints();\n auto const& accels = robot.get_accels();\n\n for (robots::Joint_Model const& j : joints) {\n time_embedded_sensors<NumTaps>::sensors.emplace_back(j.name + \"_ang\", [&j](){ return j.s_ang; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(j.name + \"_vel\", [&j](){ return j.s_vel; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(j.name + \"_vol\", [&j](){ return j.motor.get_backed(); });\n }\n //for (robots::Joint_Model const& j : joints)\n // time_embedded_sensors<NumTaps>::sensors.emplace_back(j.name + \"_cur\", [&j](){ return j.motor.get_backed();/j.s_cur;/ });\n\n for (robots::Accel_Sensor const& a : accels) {\n time_embedded_sensors<NumTaps>::sensors.emplace_back(\"acc_x\", [&a](){ return a.a.x; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(\"acc_y\", [&a](){ return a.a.y; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(\"acc_z\", [&a](){ return a.a.z; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(\"vel_x\", [&a](){ return a.v.x; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(\"vel_y\", [&a](){ return a.v.y; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(\"vel_z\", [&a](){ return a.v.z; });\n }\n\n // don't know if that helps much.\n //time_embedded_sensors<NumTaps>::sensors.emplace_back(\"noise\", [](){ return 0.1 + rand_norm_zero_mean(0.1); });\n\n //IDEA: consider avg rotational speed... as the gyroscope\n\n /\n for (robots::Joint_Model const& j : joints)\n time_embedded_sensors<NumTaps>::sensors.emplace_back(j.name + \"_mot\", [&j](){ return j.motor.get(); });/\n\n /Added bias internally /\n }\n};\n\n} /* namespace learning /\n\n#endif / TIME_STATE_SPACE_H /\n\n" }, { "alpha_fraction": 0.5565939545631409, "alphanum_fraction": 0.5913810729980469, "avg_line_length": 24.342105865478516, "blob_id": "55d2c48dff3fc1311be9d6a172cf227c9ad0e5e0", "content_id": "7f69cd485352baeae8d24353cf872044d6bb5ea8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1926, "license_type": "no_license", "max_line_length": 122, "num_lines": 76, "path": "/src/common/visuals.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef VISUALS_H_INCLUDED\n#define VISUALS_H_INCLUDED\n\n#include <basic/color.h>\n#include <GL/gl.h>\n\nnamespace visuals_defaults {\n const unsigned int window_width = 512;\n const unsigned int window_height = 512;\n const unsigned int frames_per_second = 25;\n const float zdist = 1.7320508f + 0.01f; // z = sin(pi/3) / sin(pi/6) for 60 deg view angle to be 2 units width;\n const unsigned int rot_factor = 1;\n const float zoom_factor = 0.95;\n\n const float gl_fovy = 60.0;\n const float gl_zNear = 0.05;\n const float gl_zFar = 1024.0;\n}\n\n//TODO improve this\nstruct Visuals\n{\n Visuals()\n : window_size_x(visuals_defaults::window_width)\n , window_size_y(visuals_defaults::window_height)\n , mdy(0.0f)\n , mdx(0.0f)\n , x_angle(0.0f)\n , y_angle(20.0f)\n , x_angle_disp(0.0f)\n , y_angle_disp(0.0f)\n , x_position(.0f)\n , y_position(.0f)\n , x_position_disp(.0f)\n , y_position_disp(.0f)\n , zdist(visuals_defaults::zdist)\n , rotate_view(true)\n , rot_factor(visuals_defaults::rot_factor)\n , show_fps(true)\n , snap(1.0)\n {}\n\n void reset(void) {\n x_angle = .0f;\n y_angle = 20.0f;\n x_position = .0f;\n y_position = .0f;\n zdist = visuals_defaults::zdist;\n rotate_view = true;\n rot_factor = visuals_defaults::rot_factor;\n mdx = .0f;\n mdy = .0f;\n }\n\n void set_background_color(Color4 color) { glClearColor(color.r, color.g, color.b, color.a); }\n\n unsigned int window_size_x;\n unsigned int window_size_y;\n float mdy;\n float mdx;\n float x_angle;\n float y_angle;\n float x_angle_disp;\n float y_angle_disp;\n float x_position;\n float y_position;\n float x_position_disp;\n float y_position_disp;\n float zdist;\n bool rotate_view;\n int rot_factor;\n bool show_fps;\n float snap;\n};\n\n#endif // VISUALS_H_INCLUDED\n" }, { "alpha_fraction": 0.5237889289855957, "alphanum_fraction": 0.5730968713760376, "avg_line_length": 25.88372039794922, "blob_id": "018bd1f69f91a5a6aafd36cea0be056fffc0681f", "content_id": "b8167dd8d1d12b52efd100a545cb826f36d9df0c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2312, "license_type": "no_license", "max_line_length": 106, "num_lines": 86, "path": "/src/draw/network3D.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ network3D.cpp /\n\n#include \"network3D.h\"\n\nvoid network3D::draw(float x_angle, float y_angle) const\n{\n glPushMatrix();\n glTranslatef(axis.px, axis.py, axis.pz);\n glRotatef(y_angle, 1.0, 0.0, 0.0);\n glRotatef(x_angle, 0.0, 1.0, 0.0);\n\n / draw nodes /\n glColor4ub(255,255,255,160);\n\n for (unsigned int i = 0; i < number_of_nodes; ++i)\n draw_solid_cube(n_pos[i].x, n_pos[i].y, n_pos[i].z, 0.005);\n\n glColor4ub(255,255,255,96);\n\n for (unsigned int i = 0; i < number_of_nodes; ++i)\n draw_solid_cube(n_pos[i].x, n_pos[i].y, n_pos[i].z, 0.005 + 0.01 n_size[i]);\n\n\n glColor4ub(255,192,0,128);\n if (n_size[special_node] > 0)\n draw_solid_cube(n_pos[special_node].x, n_pos[special_node].y, n_pos[special_node].z, 0.02);\n\n glColor4ub(0,128,255,128);\n if (n_size[activated_node] > 0)\n draw_solid_cube(n_pos[activated_node].x, n_pos[activated_node].y, n_pos[activated_node].z, 0.015);\n\n /* draw edges /\n glLineWidth(1.0f);\n for (unsigned int i = 0; i < number_of_nodes; ++i)\n for (unsigned int j = i+1; j < number_of_nodes; ++j)\n if (n_edges[j][i] \|\| n_edges[i][j])\n {\n glColor4ub(255, 128, 32, std::max(n_edges[j][i],n_edges[i][j]));\n draw_line(n_pos[i], n_pos[j]);\n }\n glPopMatrix();\n}\n\nvoid network3D::special(unsigned int n)\n{\n assert(n < number_of_nodes);\n special_node = n;\n}\n\nvoid network3D::activated(unsigned int n)\n{\n assert(n < number_of_nodes);\n activated_node = n;\n}\n\nvoid network3D::update_node(unsigned int n, float x0, float x1, float x2, float s)\n{\n assert(n < number_of_nodes);\n n_pos[n].x = x0 axis.width / 2;\n n_pos[n].y = x1 * axis.height / 2;\n n_pos[n].z = x2 * axis.depth / 2;\n n_size[n] = s;\n}\n\nvoid network3D::update_node(unsigned int n, float s)\n{\n assert(n < number_of_nodes);\n n_size[n] = s;\n}\n\nvoid network3D::update_edge(unsigned int i, unsigned int j, unsigned char op)\n{\n assert(i < number_of_nodes && j < number_of_nodes);\n n_edges[i][j] = op;\n}\n\nvoid network3D::update_all_edges_of(unsigned int i, unsigned char op)\n{\n assert(i < number_of_nodes);\n for (unsigned int k = 0; k < number_of_nodes; ++k) {\n n_edges[i][k] = op;\n n_edges[k][i] = op;\n }\n}\n\n/* network3D.cpp /\n" }, { "alpha_fraction": 0.6401015520095825, "alphanum_fraction": 0.6472080945968628, "avg_line_length": 32.38983154296875, "blob_id": "d6ac16dabcb42fb2d1f05064b6074af84b132510", "content_id": "083b2fbb69a2397dd2df0eb55d5cb8d9cf6cad22", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1970, "license_type": "no_license", "max_line_length": 113, "num_lines": 59, "path": "/src/learning/epsilon_greedy.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EPSILON_GREEDY_H_INCLUDED\n#define EPSILON_GREEDY_H_INCLUDED\n\n#include <vector>\n#include <common/static_vector.h>\n#include <common/log_messages.h>\n#include <learning/action_selection.h>\n#include <learning/action_module.h>\n#include <learning/payload.h>\n\nnamespace learning {\n\nclass Epsilon_Greedy : public Action_Selection_Base\n{\n\npublic:\n Epsilon_Greedy( const static_vector<State_Payload>& states\n , const Action_Module_Interface& actions\n , const double exploration_rate )\n : Action_Selection_Base(states, actions, exploration_rate)\n {\n dbg_msg(\"Creating 'Epsilon Greedy' action selection.\");\n assert_in_range(exploration_rate, 0.01, 0.99);\n }\n\nprivate:\n void update_distribution(const std::size_t greedy_action)\n {\n assert(actions.exists(greedy_action));\n selection_probabilities.zero(); // set all zeros\n\n if (actions.get_number_of_actions_available() > 1) {\n const double non_greedy_portion = exploration_rate / (actions.get_number_of_actions_available() - 1);\n\n for (std::size_t i = 0; i < selection_probabilities.size(); ++i)\n if (actions.exists(i) and (i != greedy_action))\n selection_probabilities[i] = non_greedy_portion;\n\n selection_probabilities[greedy_action] = 1.0 - exploration_rate;\n }\n else if (actions.get_number_of_actions_available() == 1) {\n selection_probabilities[greedy_action] = 1.0;\n }\n else assert(false and \"no actions available\");\n }\n\npublic:\n std::size_t select_action(std::size_t current_state, std::size_t current_policy) override\n {\n update_distribution(states[current_state].policies[current_policy].get_argmax_q());\n\n / create random variable and select /\n return select_from_distribution(selection_probabilities);\n }\n};\n\n} // namespace learning\n\n#endif // EPSILON_GREEDY_H_INCLUDED\n" }, { "alpha_fraction": 0.647251307964325, "alphanum_fraction": 0.6753926873207092, "avg_line_length": 30.83333396911621, "blob_id": "32efdf36054da21d4b44d75a33167a35b70c44f1", "content_id": "14d1ba65f5ca5563a02e6660ce2671e19ccbf63c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1528, "license_type": "no_license", "max_line_length": 103, "num_lines": 48, "path": "/src/learning/eigenzeit.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef LEARNING_EIGENZEIT_H\n#define LEARNING_EIGENZEIT_H\n\n#include <common/log_messages.h>\n#include <control/statemachine.h>\n\nnamespace learning {\n\n/* Eigenzeit.h\n The concept of eigenzeit constitutes a mechanism for translating the inherent time step (cycle)\n of usually 10ms (100Hz) into a 'time' or cycle which only depends on gmes' state changes. Eigenzeit\n passes only when a new state is recognized by gmes. If a state does not change within a given time\n it is recognized as a state transition into itself and another eigenzeit cycle is executed. /\n\nclass Eigenzeit\n{\n const control::Statemachine_Interface& statemachine;\n const uint64_t timeout_10ms;\n uint64_t system_cycle;\n uint64_t eigenzeit_cycle;\n\npublic:\n Eigenzeit(const control::Statemachine_Interface& statemachine, const uint64_t timeout_10ms = 100)\n : statemachine(statemachine)\n , timeout_10ms(timeout_10ms)\n , system_cycle(0)\n , eigenzeit_cycle(0)\n {\n dbg_msg(\"Creating Eigenzeit with max. timeout of %1.2f s\", timeout_10ms/(100.0));\n assert(timeout_10ms > 0);\n }\n\n void execute_cycle(void)\n {\n ++system_cycle;\n if (statemachine.has_state_changed() or (system_cycle >= timeout_10ms)) {\n system_cycle = 0;\n ++eigenzeit_cycle;\n }\n }\n\n bool has_progressed(void) const { return system_cycle == 0; }\n uint64_t get_cycle (void) const { return eigenzeit_cycle; }\n};\n\n} / namespace learning /\n\n#endif / LEARNING_EIGENZEIT_H /\n" }, { "alpha_fraction": 0.5770773887634277, "alphanum_fraction": 0.5878223776817322, "avg_line_length": 28.70212745666504, "blob_id": "fe3f4d14d3cf4b7cb9b17976f173fccdf9c7c624", "content_id": "ff4e2d6f9a9eee6b75c2c7036f79c2897bb62e43", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6980, "license_type": "no_license", "max_line_length": 138, "num_lines": 235, "path": "/src/common/udp.hpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef UDP_HPP\n#define UDP_HPP\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <unistd.h>\n#include <string.h>\n#include <sys/types.h>\n#include <sys/socket.h>\n#include <arpa/inet.h>\n#include <netinet/in.h>\n#include <time.h>\n\n#include <common/log_messages.h>\n#include <common/lock.h>\n\n/\n This UDP class is optionally able to do a multicast connection.\n Attention: this might spam the network if datagram size and frequency is large.\n Example usage for UDP Multicast Sender and Receiver\n group: 239.255.255.252\n port: 1900\n\n Otherwise, just specify address (group) and port.\n\n/\n\nnamespace network {\n\n const uint16_t default_port = 7331;\n\n template <typename T, typename B>\n std::size_t getfrom(T& var, const B buf, std::size_t offset)\n {\n var = T{};\n memcpy(&var, buf+offset, sizeof(T));\n return offset+sizeof(T);\n }\n\n template <typename B>\n bool validate(const B* buf, std::size_t maxl)\n {\n B sum = B{0};\n for (std::size_t i = 0; i < maxl; ++i)\n sum += buf[i];\n return sum == 0;\n }\n\n\ntemplate <unsigned NBytes>\nclass UDPSender {\n\n int sockfd;\n struct sockaddr_in addr{};\n\n uint8_t msg[NBytes];\n uint8_t buf[NBytes];\n\n common::mutex_t l_buf{};\n common::mutex_t l_adr{};\n bool tx_ready = false;\n\npublic:\n UDPSender(std::string const& group, uint16_t port = default_port)\n : sockfd(socket(AF_INET, SOCK_DGRAM, 0)) /* create UDP socket /\n , msg(), buf()\n {\n / check for problems/\n if (sockfd < 0)\n err_msg(__FILE__,__LINE__,\"Cannot create UDP socket with group %s and port %u.\\n%s\", group.c_str(), port, strerror(errno));\n\n / set destination /\n common::lock_t lock(l_adr);\n memset(&addr, 0, sizeof(addr)); //TODO constructor\n addr.sin_family = AF_INET;\n addr.sin_addr.s_addr = inet_addr(group.c_str());\n addr.sin_port = htons(port);\n }\n\n void transmit(void)\n {\n common::lock_t lock(l_adr);\n int nbytes = sendto(sockfd, msg, NBytes, 0, (struct sockaddr) &addr, sizeof(addr));\n\n { common::lock_t lock(l_buf);\n std::swap(msg, buf); /* swap buffers /\n tx_ready = false; // reset flag, waiting for data\n } /end lock/\n\n if (nbytes < 0)\n wrn_msg(\"Sending failed: %s\", strerror(errno));\n\n promise(nbytes == NBytes,__FILE__,__LINE__,\"Sending failed, message too short.\");\n }\n\n void set_buffer(const uint8_t src, std::size_t size) {\n assertion(size == NBytes, \"Buffer length does not match. %u =!= %u\", size, NBytes);\n common::lock_t lock(l_buf);\n memcpy(buf, src, NBytes);\n tx_ready = true; // set flag, ready for transmission\n }\n\n bool data_ready(void) const { return tx_ready; }\n\n void change_destination(std::string const& dest, uint16_t port = default_port)\n {\n common::lock_t lock(l_adr);\n addr.sin_addr.s_addr = inet_addr(dest.c_str());\n addr.sin_port = htons(port);\n }\n};\n\n\ntemplate <unsigned NBytes>\nclass UDPReceiver {\n\n int sockfd;\n struct sockaddr_in addr{};\n\n uint8_t msg[NBytes];\n bool rx_ready = false;\n\npublic:\n UDPReceiver(const char* group, uint16_t port = default_port, bool multicast = false)\n : sockfd(socket(AF_INET, SOCK_DGRAM, 0)) /* create UDP socket /\n , msg()\n {\n / check for problems/\n if (sockfd < 0)\n err_msg(__FILE__,__LINE__,\"Cannot create UDP socket with group %s and port %d.\\n%s\", group, port, strerror(errno));\n\n / allow multiple sockets to use the same port /\n unsigned optval = 1;\n if (setsockopt(sockfd, SOL_SOCKET, SO_REUSEADDR, (char) &optval, sizeof(optval)) < 0)\n err_msg(__FILE__,__LINE__,\"Reusing address failed. Cannot allow multiple sockets to use the same port.\\n%s\", strerror(errno));\n\n /* set destination /\n memset(&addr, 0, sizeof(addr));\n addr.sin_family = AF_INET;\n addr.sin_addr.s_addr = htonl(INADDR_ANY); / <- Note: allow any, differs from sender /\n addr.sin_port = htons(port);\n\n / bind receive address /\n if (bind(sockfd, (struct sockaddr) &addr, sizeof(addr)) < 0)\n err_msg(__FILE__,__LINE__,\"Cannot bind receive address.\\n%s\", strerror(errno));\n\n if (multicast) {\n /* request the kernel to join a multicast group /\n struct ip_mreq mreq;\n mreq.imr_multiaddr.s_addr = inet_addr(group);\n mreq.imr_interface.s_addr = htonl(INADDR_ANY);\n if (setsockopt(sockfd, IPPROTO_IP, IP_ADD_MEMBERSHIP, (char) &mreq, sizeof(mreq)) < 0)\n err_msg(__FILE__,__LINE__,\"Cannot set socket options for multicast group.\\n%s\", strerror(errno));\n }\n\n struct timeval read_timeout;\n read_timeout.tv_sec = 1;\n read_timeout.tv_usec = 0;\n if (setsockopt(sockfd, SOL_SOCKET, SO_RCVTIMEO, &read_timeout, sizeof(read_timeout)) < 0)\n err_msg(__FILE__,__LINE__,\"Cannot set socket options for read timeout.\\n%s\", strerror(errno));\n }\n\n void receive_message(void)\n {\n assert(rx_ready == false && \"Received data not acknowledged yet.\");\n\n socklen_t addrlen = sizeof(addr);\n int nbytes = recvfrom(sockfd, msg, NBytes, 0, (struct sockaddr ) &addr, &addrlen);\n if (nbytes < 0) {\n wrn_msg(\"Receiving failed: %s\", strerror(errno));\n return;\n }\n\n if (nbytes == 0) return;\n\n if (nbytes != NBytes)\n wrn_msg(\"Invalid message length: %u != %u.\", nbytes, NBytes);\n\n rx_ready = true;\n }\n\n const uint8_t get_message(void) const { return msg; }\n\n bool data_received(void) const { return rx_ready; }\n\n void acknowledge(void) { rx_ready = false; /* clear flag, ready to receive / }\n\n}; / class UDP_Receiver /\n\n\ntemplate <unsigned N>\nclass Sendbuffer {\n\tstatic const unsigned NumSyncBytes = 2;\n\tstatic const uint8_t chk_init = 0xFE; / (0xff + 0xff) % 256 /\n\tuint16_t ptr = NumSyncBytes;\n\tuint8_t buffer[N];\n\tuint8_t checksum = chk_init;\npublic:\n\tSendbuffer()\n\t{\n\t\tstatic_assert(N > NumSyncBytes, \"Invalid buffer size.\");\n\t\tfor (uint8_t i = 0; i < NumSyncBytes; ++i)\n\t\t\tbuffer[i] = 0xFF; // init sync bytes once\n\t}\n\tvoid add_byte(uint8_t byte) {\n\t\tassertion(ptr < (N-1), \"ptr=%u N=%u\", ptr, N);\n\t\tbuffer[ptr++] = byte;\n\t\tchecksum += byte;\n\t}\n\n template <typename T>\n\tSendbuffer& add(T var) {\n\t const uint8_t const bytes = (const uint8_t ) &var;\n for (unsigned i = 0; i < sizeof(T); ++i)\n add_byte(bytes[i]);\n return this;\n\t}\n\n\tvoid reset(void) { ptr = NumSyncBytes; }\n\n\tvoid add_checksum() {\n\t\tassertion(ptr < N, \"ptr=%u N=%u\", ptr, N);\n\t\tbuffer[ptr++] = ~checksum + 1; /* two's complement checksum /\n\t\tchecksum = chk_init;\n\t}\n\n\tconst uint8_t get(void) const { return buffer; }\n\n\tuint16_t size(void) const { return ptr; }\n\n}; /* Sendbuffer /\n\n} / namespace network /\n\n#endif / UDP_HPP /\n" }, { "alpha_fraction": 0.6654788255691528, "alphanum_fraction": 0.6703786253929138, "avg_line_length": 22.144329071044922, "blob_id": "23c6f5599287e2808ab60bca3dd327ca55df2013", "content_id": "59319ff8415dab71c80ee484e930d8a182c883cc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2245, "license_type": "no_license", "max_line_length": 135, "num_lines": 97, "path": "/src/common/gui.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GUI_H\n#define GUI_H\n\n#include <vector>\n#include <memory>\n#include <thread>\n#include <locale.h>\n#include <gtk/gtk.h>\n#include <common/globalflag.h>\n#include <common/log_messages.h>\n\n\nextern float progress_val1;\nextern float progress_val2;\n\nextern GlobalFlag do_pause;\n\ngboolean update_progressbar1(gpointer data);\ngboolean update_progressbar2(gpointer data);\n\nint thread_gtk_main(void);\n\nvoid on_button_start_clicked(GtkObject object, gpointer user_data);\n\nclass vertical_scaler\n{\npublic:\n vertical_scaler(double value_min, double value_max, double value_default, double increment)\n : adjustment((GtkAdjustment) gtk_adjustment_new(value_default, value_min, value_max, increment, 1.0, 0.0))\n , vscale(gtk_vscale_new(adjustment))\n {\n gtk_scale_set_digits(GTK_SCALE(vscale), 1); //TODO num digits\n gtk_scale_set_value_pos(GTK_SCALE(vscale), GTK_POS_BOTTOM);\n gtk_widget_show(vscale);\n }\n\n GtkWidget get(void) { return vscale; }\n\nprivate:\n GtkAdjustment adjustment;\n GtkWidget vscale;\n};\n\nclass gui_interface\n{\npublic:\n virtual ~gui_interface() {};\n};\n\nclass no_gui : public gui_interface\n{\npublic:\n no_gui() { sts_msg(\"No GUI loaded.\"); }\n};\n\nclass GTK_gui : public gui_interface\n{\n GTK_gui(const GTK_gui& other) = delete; // non construction-copyable\n GTK_gui& operator=( const GTK_gui&) = delete; // non copyable\n\npublic:\n GTK_gui();\n\n ~GTK_gui()\n {\n dbg_msg(\"closing GTK main thread\");\n gtk_main_quit();\n main_gtk->join();\n }\n\nprivate:\n const unsigned int num_vscale;\n const bool init_result;\n\n GtkWidget window;\n GtkWidget table;\n GtkWidget label;\n GtkWidget progressbar1;\n GtkWidget progressbar2;\n GtkWidget button_start;\n\n std::vector<vertical_scaler> multiscale;\n std::unique_ptr<std::thread> main_gtk;\n};\n\nclass GUI_Starter\n{\n std::unique_ptr<gui_interface> gui;\n\n GUI_Starter(const GUI_Starter& other) = delete; // non construction-copyable\n GUI_Starter& operator=( const GUI_Starter&) = delete; // non copyable\n\npublic:\n GUI_Starter(bool visuals_enabled = true) : gui(visuals_enabled ? (gui_interface) new GTK_gui() : (gui_interface) new no_gui()) {}\n};\n\n#endif /GUI_H/\n" }, { "alpha_fraction": 0.6325392723083496, "alphanum_fraction": 0.63608717918396, "avg_line_length": 33.88495635986328, "blob_id": "c2f64f4b92e8c8bc3a48a356053f9f38b310d9b9", "content_id": "e92bdb0c62ec0fdc03db70e3a77b70fd6940d4ed", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3949, "license_type": "no_license", "max_line_length": 100, "num_lines": 113, "path": "/src/learning/gmes.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GMES_H\r\n#define GMES_H\n\r\n#include <cmath>\n#include <vector>\n#include <cassert>\n#include <common/log_messages.h>\n#include <common/modules.h>\n#include <common/vector_n.h>\n#include <control/statemachine.h>\n#include <learning/expert_vector.h>\n#include <learning/gmes_constants.h>\n#include <learning/q_function.h>\n#include <learning/payload.h>\n#include <learning/learning_machine_interface.h>\r\n/* first object oriented implementation of GMES\n * 23.02.2015 (Elmar ist heute 16 Monate alt geworden) /\n\n\n/\n Überlege Dir eine geschachtelte Gmes-Schichten-Anordnung, welche zunächst aus Sensordaten (1)\n * Fixpunkte und verschiedene Attraktoren (2) erkennt und dann in \"kombinierte Gelenke\"-Features (3)\n * bishin zu Körperbewegungen oder -posen (4) erkennt.\n /\n\n/* TODO: namespace learning /\n/* TODO: compute the activations of GMES as softmax activation function (YL)/\n\n\nclass GMES_Graphics;\nclass Payload_Graphics;\nclass Force_Field;\n\n/ Growing_Multi_Expert_Structure\n /\nclass GMES : public control::Statemachine_Interface\n , public learning::Learning_Machine_Interface\n{\n GMES(const GMES& other) = delete; // non construction-copyable\n\npublic:\n GMES(GMES&& other) = default;\n\n explicit GMES( Expert_Vector& expert\n , double learning_rate = gmes_constants::global_learning_rate\n , bool one_shot_learning = true\n , std::size_t number_of_initial_experts = gmes_constants::number_of_initial_experts\n , std::string const& name = \"...\");\n\n ~GMES();\n\n bool is_learning_enabled (void) const { return learning_enabled; }\n bool has_state_changed (void) const { return winner != last_winner; }\n bool has_new_node (void) const { return new_node; }\n\n std::size_t get_number_of_experts (void) const { return number_of_experts; }\n std::size_t get_max_number_of_experts (void) const { return expert.size(); }\n std::size_t get_winner (void) const { return winner; }\n std::size_t get_state (void) const { return winner; }\n std::size_t get_recipient (void) const { return recipient; }\n std::size_t get_to_insert (void) const { return to_insert; }\n\n double get_learning_progress (void) const { return learning_progress; }\n double get_min_prediction_error (void) const { return min_prediction_error; }\n\n VectorN const& get_activations (void) const { return activations; }\n\n\n void enable_learning(bool enable);\n void execute_cycle(void);\n void update_activations(void);\n\nprivate:\n\n std::size_t determine_winner (void);\n std::size_t arg_max_capacity (void) const;\n std::size_t count_existing_experts (void) const;\n void check_learning_capacity (void) const;\n\n void estimate_learning_progress(void);\n void adjust_learning_capacity (void);\n void refresh_transitions (void);\n void insert_expert_on_demand (void);\n\n void clear_transitions_to(std::size_t to_clear);\n\n Expert_Vector& expert;\n const std::size_t Nmax;\n\n double min_prediction_error;\n double learning_progress;\n const double learning_rate;\n\n const bool one_shot_learning;\n bool learning_enabled;\n\n std::size_t number_of_experts; // number of existing experts\n std::size_t winner; // winning expert\n std::size_t last_winner; // winning expert of last time step\n std::size_t recipient; // donee of the learning capacity consumed by the winner\n std::size_t to_insert; // expert to be inserted on demand\n\n VectorN activations;\n bool new_node;\n\n std::string name;\n\n friend class GMES_Graphics;\n friend class Payload_Graphics;\n friend class Force_Field;\n};\n\n#endif // GMES_H\r\n" }, { "alpha_fraction": 0.6044343113899231, "alphanum_fraction": 0.6073352694511414, "avg_line_length": 33.97101593017578, "blob_id": "05ec9ef9c086908d71187f2fc313c652105b7d12", "content_id": "3824b4f9799c4e5242500af17312653251e9c397", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4826, "license_type": "no_license", "max_line_length": 140, "num_lines": 138, "path": "/src/control/controlparameter.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef CONTROLPARAMETER_H_INCLUDED\n#define CONTROLPARAMETER_H_INCLUDED\n\n#include <vector>\n#include <memory>\n#include <common/basic.h>\n#include <common/modules.h>\n#include <common/log_messages.h>\n#include <common/noncopyable.h>\n#include <common/file_io.h>\n#include <common/datareader.h>\n\nnamespace control {\n\ntemplate <typename T> inline std::vector<T>& operator+=(std::vector<T>& lhs, std::vector<T>const & rhs);\ntemplate <typename T> inline std::vector<T>& operator-=(std::vector<T>& lhs, std::vector<T>const & rhs);\ntemplate <typename T> inline std::vector<T>& operator=(std::vector<T>& lhs, T const& rhs);\ntemplate <typename T> inline std::vector<T>& operator/=(std::vector<T>& lhs, T const& rhs);\n\nclass Control_Parameter : public noncopyable\n{\npublic:\n typedef std::vector<std::vector<double>> matrix_t;\n\n explicit Control_Parameter( const std::string& filename\n , std::size_t number_of_params\n , bool symmetric\n , bool mirrored = false\n , unsigned robot_id = 0\n , uint64_t rnd_init = 0 );\n\n explicit Control_Parameter(const std::string& filename);\n\n explicit Control_Parameter( const std::vector<double>& parameter\n , bool symmetric = false\n , bool mirrored = false\n , unsigned robot_id = 0\n , uint64_t rnd_init = 0 );\n\n void set_from_matrix( matrix_t const& weights );\n\n explicit Control_Parameter() : parameter(), symmetric(), mirrored(), robot_id(), rnd_init() { assert(false and \"Should not be used.\"); }\n\n Control_Parameter(const Control_Parameter& other);\n\n Control_Parameter& operator=(const Control_Parameter& other);\n\n ~Control_Parameter() { /dbg_msg(\"Destroying control parameters.\");/ }\n\n const std::vector<double>& get_parameter(void) const { return parameter; }\n std::vector<double>& set_parameter(void) { return parameter; }\n std::size_t size (void) const { return parameter.size(); }\n\n const double& operator[](std::size_t idx) const { return parameter.at(idx); }\n double& operator[](std::size_t idx) { return parameter.at(idx); }\n\n bool is_symmetric(void) const { return symmetric; }\n bool is_mirrored (void) const { return mirrored; }\n\n unsigned get_robot_id(void) const { return robot_id; }\n uint64_t get_rnd_init(void) const { return rnd_init; }\n\n void add_gaussian_noise(double sigma);\n\n void print() const;\n\n void save_to_file(const std::string& filename, std::size_t id) const;\n\n /* arithmetic overloads /\n\n Control_Parameter& operator+=(const Control_Parameter& rhs) {\n this->parameter += rhs.parameter;\n return this;\n }\n Control_Parameter& operator-=(const Control_Parameter& rhs) {\n this->parameter -= rhs.parameter;\n return this;\n }\n Control_Parameter& operator=(const double& rhs) {\n this->parameter = rhs;\n return this;\n }\n Control_Parameter& operator/=(const double& rhs) {\n this->parameter /= rhs;\n return this;\n }\n\n\nprivate:\n\n std::vector<double> parameter;\n bool symmetric;\n bool mirrored;\n unsigned robot_id;\n uint64_t rnd_init;\n\n};\n\n\ninline Control_Parameter operator+(Control_Parameter lhs, const Control_Parameter& rhs) { lhs += rhs; return lhs; }\ninline Control_Parameter operator-(Control_Parameter lhs, const Control_Parameter& rhs) { lhs -= rhs; return lhs; }\ninline Control_Parameter operator(Control_Parameter lhs, const double& rhs) { lhs = rhs; return lhs; }\ninline Control_Parameter operator(const double& lhs, Control_Parameter rhs) { rhs = lhs; return rhs; }\ninline Control_Parameter operator/(Control_Parameter lhs, const double& rhs) { lhs /= rhs; return lhs; }\n\ntemplate <typename T>\ninline std::vector<T>& operator+=(std::vector<T>& lhs, std::vector<T>const& rhs) {\n assert(lhs.size() == rhs.size());\n for (std::size_t i = 0; i < lhs.size(); ++i)\n lhs[i] += rhs[i];\n return lhs;\n}\n\ntemplate <typename T>\ninline std::vector<T>& operator-=(std::vector<T>& lhs, std::vector<T>const& rhs) {\n assert(lhs.size() == rhs.size());\n for (std::size_t i = 0; i < lhs.size(); ++i)\n lhs[i] -= rhs[i];\n return lhs;\n}\n\ntemplate <typename T>\ninline std::vector<T>& operator=(std::vector<T>& lhs, T const& rhs) {\n for (T& e: lhs) e = rhs;\n return lhs;\n}\n\ntemplate <typename T>\ninline std::vector<T>& operator/=(std::vector<T>& lhs, T const& rhs) {\n for (T& e: lhs) e /= rhs;\n return lhs;\n}\n\n\n\n} // namespace control\n\n#endif // CONTROLPARAMETER_H_INCLUDED\n" }, { "alpha_fraction": 0.534007728099823, "alphanum_fraction": 0.5768402814865112, "avg_line_length": 26.6488094329834, "blob_id": "60cdf92f8af48c6eb7de7b83e87cdaeb59c93e91", "content_id": "6836fa55b695dc7df077fca73960058967751327", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4646, "license_type": "no_license", "max_line_length": 82, "num_lines": 168, "path": "/src/tests/neural_model_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <limits>\n#include <common/modules.h>\n#include <learning/forward_inverse_model.hpp>\n\n\nnamespace local_tests {\n\nnamespace neural_model_tests {\n\ntypedef learning::NeuralModel<learning::TanhTransfer<>> NeuralModelType;\ntypedef learning::InverseNeuralModel InverseModelType;\n\n\nTEST_CASE( \"neural_model construction\" , \"[neural_model]\")\n{\n srand(time(0)); // set random seed\n\n double random_range = 0.01;\n\n NeuralModelType model(13, 7, random_range);\n\n learning::model::vector_t inputs(13);\n\n / check weights are not zero, but randomized /\n auto const& weights = model.get_weights();\n double sum = .0;\n int diff = 0;\n for (std::size_t i = 0; i < weights.size(); ++i)\n for (std::size_t j = 0; j < weights[i].size(); ++j) {\n diff += ( weights[i][j] != .0 )? 0 : 1;\n sum += weights[i][j];\n }\n\n / check randomize_weight_matrix() is executed /\n REQUIRE( diff == 0 );\n const double max_range = 0.5 random_range * weights.size()weights[0].size();\n dbg_msg(\"Max rand: %e < %e\", std::abs(sum), max_range);\n REQUIRE( std::abs(sum) <= max_range ); // check small\n REQUIRE( std::abs(sum) != 0. ); // but not zero\n\n // check matrix size\n REQUIRE( weights .size() == 7 );\n REQUIRE( weights[0].size() == 13 );\n\n //auto const& inputs = model.get_inputs ();\n auto const& outputs = model.get_outputs();\n\n // check vector size\n //REQUIRE( inputs.size() == 13 );\n REQUIRE( outputs.size() == 7 );\n\n // check in and outputs are zero on initialization\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( outputs[i] == .0 );\n\n// for (std::size_t i = 0; i < inputs.size(); ++i)\n// REQUIRE( inputs[i] == .0 );\n\n model.propagate(inputs);\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( outputs[i] == .0 );\n\n NeuralModelType model2 = model;\n}\n\n\nTEST_CASE( \"neural_model learning (non-linear)\", \"[neural_model]\")\n{\n srand(time(0)); // set random seed\n\n const double learning_rate = 0.005;\n\n std::vector<double> X = {1,0.5,1,-1,0,1,1,0,-1,-1,1,0.75,1,0,1,0.5,-1,1};\n std::vector<double> Y = {0.7,0,-0.8,0,-0.5,0.4,0,-0.5,-0.7,0.5};\n\n NeuralModelType model(X.size(), Y.size(), 0.01);\n\n / check error is decreasing (forward) /\n auto const& Y_ = model.propagate(X);\n\n double err0 = squared_distance(Y, model.get_outputs());\n double err1;\n\n for (std::size_t trials = 0; trials < 500; ++trials) {\n // adapt\n model.adapt(X, Y, learning_rate);\n\n // verify\n model.propagate(X);\n err1 = squared_distance(Y, model.get_outputs());\n REQUIRE( err0 > err1 );\n err0 = err1;\n\n }\n\n model.propagate(X);\n print_vector(Y);\n print_vector(Y_);\n\n REQUIRE( close(Y_, Y, 0.01) );\n\n}\n\nTEST_CASE( \"inverse-neural model learning (non-linear)\", \"[inverse_neural_model]\")\n{\n srand(time(0)); // set random seed\n\n const double learning_rate = 0.0025;\n\n // still a reasonable number\n double f = InverseModelType::G(0.999999999999999943);\n sts_msg(\"%f\", f);\n REQUIRE( close(f, 18.714974, 0.00001) );\n\n // infinity\n double g = InverseModelType::G(1.0);\n REQUIRE( g == std::numeric_limits<double>::infinity() );\n\n // not a number anymore\n double h = InverseModelType::G(2.0);\n REQUIRE( std::isnan(h) );\n\n std::vector<double> X = {0.9,0.5,0.9,-0.9,0,0.9,0.9,0,-0.9,-0.9};\n std::vector<double> Y = {0.7,0,-0.8,0,-0.5,0.4,0,-0.5,-0.7,0.5};\n\n InverseModelType model(X.size(), Y.size(), 0.01);\n NeuralModelType proof(Y.size(), X.size(), 0.01); // for comparison\n\n\n / check error is decreasing (forward) /\n auto const& Y_ = model.propagate(X);\n\n double err0 = squared_distance(Y, model.get_outputs());\n double err1;\n\n for (std::size_t trials = 0; trials < 800; ++trials) {\n // adapt\n model.adapt(X, Y, learning_rate);\n proof.adapt(Y, X, learning_rate20); // for comparison\n\n // verify\n model.propagate(X);\n proof.propagate(Y);\n auto const& O = model.get_outputs();\n //print_vector(O,\"o\");\n err1 = squared_distance(Y, O);\n REQUIRE( err0 > err1 );\n err0 = err1;\n }\n\n /* Y_ = / model.propagate(X);\n auto const& X_ = proof.propagate(Y_);\n\n print_vector(Y ,\"target output\");\n print_vector(Y_,\"___prediction\");\n REQUIRE( close(Y_, Y, 0.01) );\n\n print_vector(X ,\"_____original\");\n print_vector(X_,\"reconstructed\");\n REQUIRE( close(X_, X, 0.01) );\n}\n\n\n} / namespace local_tests /\n} / namespace neural_model_tests /\n\n" }, { "alpha_fraction": 0.5209237337112427, "alphanum_fraction": 0.5517668724060059, "avg_line_length": 29.861244201660156, "blob_id": "31bd5ae4b3b9eaed264f43c2d9d3eaad3aebdda9", "content_id": "03758aff8e539f01ebaffd230cc1625dc9892323", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6452, "license_type": "no_license", "max_line_length": 110, "num_lines": 209, "path": "/src/tests/time_delay_network_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <common/modules.h>\n#include <learning/time_delay_network.h>\n\n\nnamespace local_tests {\nnamespace time_delay_network_tests {\n\n\nstruct TD_Sensor_Space : public sensor_vector {\n TD_Sensor_Space(const robots::Jointvector_t& joints)\n : sensor_vector(3joints.size() + 1)\n {\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_ang\", [&j](){ return j.s_ang; });\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_vel\", [&j](){ return j.s_vel; });\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_mot\", [&j](){ return j.motor.get_backed(); });\n\n sensors.emplace_back(\"bias\", [&](){ return 0.13; });\n assert(sensors.size() == 3joints.size() + 1);\n }\n};\n\n\nTEST_CASE( \"FIR Synapse Test\" , \"[Time Delay Network]\")\n{\n\n Test_Robot robot(1,0);\n TD_Sensor_Space inputs{robot.get_joints()};\n\n const unsigned num_taps = 3;\n\n learning::FIR_type_synapse td_input(inputs.size(), num_taps);\n\n for (auto& j: robot.set_joints()) {\n j.s_ang = 1.1;\n j.s_vel = 2.2;\n j.motor = 3.3;\n j.motor.transfer();\n }\n\n inputs.execute_cycle();\n td_input.propagate(inputs);\n\n REQUIRE( td_input.get().size() == num_tapsinputs.size() );\n\n auto const& vec = td_input.get();\n\n// for (auto const& v : vec)\n// printf(\"%5.2f \", v);\n\n auto const& target = std::vector<double>{1.1, 2.2, 3.3, 0.13, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};\n REQUIRE( vec == target );\n\n for (auto& j: robot.set_joints()) {\n j.s_ang = 1.0;\n j.s_vel = 2.0;\n j.motor = 3.0;\n j.motor.transfer();\n }\n\n inputs.execute_cycle();\n td_input.propagate(inputs);\n auto const& target2 = std::vector<double>{1.0, 2.0, 3.0, 0.13, 1.1, 2.2, 3.3, 0.13, 0.0, 0.0, 0.0, 0.0};\n REQUIRE( vec == target2 );\n\n inputs.execute_cycle();\n td_input.propagate(inputs);\n auto const& target3 = std::vector<double>{1.0, 2.0, 3.0, 0.13, 1.0, 2.0, 3.0, 0.13, 1.1, 2.2, 3.3, 0.13};\n REQUIRE( vec == target3 );\n}\n\n\nTEST_CASE( \"time delay network construction\" , \"[Time Delay Network]\")\n{\n srand(time(0)); // set random seed\n\n Test_Robot robot(5,2);\n robot.set_random_inputs();\n TD_Sensor_Space inputs{robot.get_joints()};\n const double random_range = 0.1;\n learning::Timedelay_Network tdn(inputs.size(), inputs.size(), 3, 10, random_range);\n\n auto const& outputs = tdn.get_outputs();\n REQUIRE( outputs.size() == inputs.size() );\n\n for (std::size_t i = 0; i < outputs.size(); ++i) {\n REQUIRE( outputs[i] == .0 );\n REQUIRE( inputs[i] == .0 );\n }\n\n tdn.propagate_and_shift(inputs);\n\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( outputs[i] == .0 );\n\n /* check weights are not zero /\n auto const& w1 = tdn.get_weights().hi;\n double sum = .0;\n int diff = 0;\n for (std::size_t i = 0; i < w1.size(); ++i)\n for (std::size_t j = 0; j < w1[i].size(); ++j) {\n diff += ( w1[i][j] != .0 )? 0 : 1;\n sum += w1[i][j];\n }\n auto const& w2 = tdn.get_weights().oh;\n sum = .0;\n diff = 0;\n for (std::size_t i = 0; i < w2.size(); ++i)\n for (std::size_t j = 0; j < w2[i].size(); ++j) {\n diff += ( w2[i][j] != .0 )? 0 : 1;\n sum += w2[i][j];\n }\n\n / check randomize_weight_matrix() is executed /\n REQUIRE( diff == 0 );\n const double max_range = 0.5 random_range * w1.size()w1[0].size();\n dbg_msg(\"Max rand: %e < %e\", std::abs(sum), max_range);\n REQUIRE( std::abs(sum) <= max_range ); // check small\n REQUIRE( std::abs(sum) != 0. ); // but not zero\n\n\n /* check that autoencoder is copyable */\n learning::Timedelay_Network tdn2 = tdn;\n}\n\n\nTEST_CASE( \"time delay network learning\", \"[Time Delay Network]\")\n{\n srand(time(0)); // set fixed seed\n\n Test_Robot robot(5,2);\n TD_Sensor_Space inputs{robot.get_joints()};\n\n robot.set_random_inputs();\n const unsigned number_of_taps = 10;\n\n const double learning_rate = 0.02;\n learning::Timedelay_Network tdn(inputs.size(), inputs.size(), 3, number_of_taps, 0.1);\n\n SECTION( \"vector_tanh computes tanh element-wise\") {\n std::vector<double> vec = {1.1, -5.0, 10.0};\n vector_tanh(vec);\n REQUIRE( not is_vector_zero(vec) );\n REQUIRE( vec[0] == tanh( 1.1) );\n REQUIRE( vec[1] == tanh(-5.0) );\n REQUIRE( vec[2] == tanh(10.0) );\n }\n\n SECTION(\" outputs are zero at initialization time.\") {\n\n REQUIRE( is_vector_zero(tdn.get_hidden()) );\n REQUIRE( is_vector_zero(tdn.get_outputs()) );\n\n inputs.execute_cycle();\n tdn.propagate_and_shift(inputs);\n\n print_vector(tdn.get_hidden() ,\"hidden\");\n print_vector(tdn.get_outputs(),\"output\");\n\n REQUIRE( not is_vector_zero(tdn.get_hidden()) );\n REQUIRE( not is_vector_zero(tdn.get_outputs()) );\n\n inputs.execute_cycle();\n tdn.propagate_and_shift(inputs);\n\n REQUIRE( not is_vector_zero(tdn.get_hidden()) );\n REQUIRE( not is_vector_zero(tdn.get_outputs()) );\n }\n\n SECTION(\" reducing squared distance over training time.\") {\n\n / shift all inputs /\n for (std::size_t tap = 0; tap < number_of_taps; ++tap) {\n inputs.execute_cycle();\n tdn.propagate_and_shift(inputs);\n }\n\n / adapt without shifting and see if error drops /\n sts_msg(\"Error before training: %e \", squared_distance(inputs, tdn.get_outputs()));\n\n std::size_t max_trials = 10000;\n std::size_t trials = 0;\n double err1,err0;\n for (; trials < max_trials; ++trials) {\n tdn.propagate(); /* without shifting /\n err0 = squared_distance(inputs, tdn.get_outputs());\n tdn.adapt(inputs, learning_rate);\n tdn.propagate();\n err1 = squared_distance(inputs, tdn.get_outputs());\n\n if (err0 <= err1) {\n dbg_msg(\"Prediction Error before %e and %e after adaption.\", err0, err1);\n break;\n }\n }\n sts_msg(\"Error after training of %lu trials: %e \",trials,squared_distance(inputs, tdn.get_outputs()));\n if (err1 > 10e-25)\n REQUIRE( (trials == max_trials) );\n else REQUIRE( true );\n\n }\n}\n\n}} // namespace local_tests\n\n\n" }, { "alpha_fraction": 0.32941803336143494, "alphanum_fraction": 0.41644421219825745, "avg_line_length": 17.534652709960938, "blob_id": "cfb0e81cb23ba4a8e40d02ed36479afa85e2e695", "content_id": "8de4b4c1d30c59733863a8faa40a4288de1d861b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1873, "license_type": "no_license", "max_line_length": 75, "num_lines": 101, "path": "/src/draw/axes3D.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ axes3D.cpp /\n\n#include \"axes3D.h\"\n#include \"draw.h\"\n\naxes3D::axes3D(float x, float y, float z, float w, float h, float d, int f)\n: px(x), py(y), pz(z), width(w), height(h), depth(d), flag(f)\n{\n / init frame coordinates /\n r[0][0] = -.5width;\n r[0][1] = +.5height;\n r[0][2] = -.5depth;\n\n r[1][0] = +.5width;\n r[1][1] = +.5height;\n r[1][2] = -.5depth;\n\n r[2][0] = +.5width;\n r[2][1] = -.5height;\n r[2][2] = -.5depth;\n\n r[3][0] = -.5width;\n r[3][1] = -.5height;\n r[3][2] = -.5depth;\n\n r[4][0] = -.5width;\n r[4][1] = +.5height;\n r[4][2] = +.5depth;\n\n r[5][0] = +.5width;\n r[5][1] = +.5height;\n r[5][2] = +.5depth;\n\n r[6][0] = +.5width;\n r[6][1] = -.5height;\n r[6][2] = +.5depth;\n\n r[7][0] = -.5width;\n r[7][1] = -.5height;\n r[7][2] = +.5depth;\n\n\n / init axes coordinates /\n a[0][0] = -.5width;\n a[0][1] = 0;\n a[0][2] = 0;\n\n a[1][0] = +.5width;\n a[1][1] = 0;\n a[1][2] = 0;\n\n a[2][0] = 0;\n a[2][1] = -.5height;\n a[2][2] = 0;\n\n a[3][0] = 0;\n a[3][1] = +.5height;\n a[3][2] = 0;\n\n a[4][0] = 0;\n a[4][1] = 0;\n a[4][2] = -.5depth;\n\n a[5][0] = 0;\n a[5][1] = 0;\n a[5][2] = +.5depth;\n}\n\nvoid axes3D::draw(float x_angle, float y_angle) const\n{\n glColor4ubv(white_trans);\n\n glPushMatrix();\n glTranslatef(px, py, pz);\n glRotatef(y_angle, 1.0, 0.0, 0.0);\n glRotatef(x_angle, 0.0, 1.0, 0.0);\n\n draw_cube(r[0],r[1],r[2],r[3],r[4],r[5],r[6],r[7]);\n\n switch(flag)\n {\n case 0:\n draw_line(a[0],a[1]);\n draw_line(a[2],a[3]);\n draw_line(a[4],a[5]);\n break;\n case 1:\n draw_line(a[0],a[1]);\n break;\n default:\n break;\n }\n glPopMatrix();\n}\n\nvoid axes3D::axesflag(int f)\n{\n flag = f;\n}\n\n/ axes.cpp /\n\n" }, { "alpha_fraction": 0.47461751103401184, "alphanum_fraction": 0.5048678517341614, "avg_line_length": 30.25, "blob_id": "de1f7899c99cac7396ce225b6a78fd4d4e1f6432", "content_id": "134cf3221c388d28bd1fc02cbb8d5c71caa65404", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2876, "license_type": "no_license", "max_line_length": 129, "num_lines": 92, "path": "/src/robots/pole.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"pole.h\"\n\nnamespace robots {\n\nbool\npole::execute_cycle(void)\n{\n / apply action and update pole cart/\n assert(joints.size() == 1);\n\n update_dynamics(joints[0].motor.get());\n\n joints[0].s_ang = wrap2(theta) / M_PI;\n joints[0].s_vel = tanh(theta_dot / (2M_PI));\n joints[0].motor = force;\n assert(std::abs(joints[0].s_ang) <= 1.0);\n\n joints[0].motor.transfer();\n joints[0].motor = .0;\n return true;\n}\n\nvoid\npole::reset_state(bool tilted)\n{\n theta_dot = 0.0;\n if (tilted)\n theta = M_PI + rand_sign() * random_value( deg_to_rad(2.)\n , deg_to_rad(5.) );\n else\n theta = M_PI;\n}\n\n\n/-----------------------------------------------------------------------\n * Pole: Takes an action [-1,+1] and the current values of the two state \n variables theta and theta_dot and updates their values by estimating \n the state dt seconds later, using Explicit Euler's Method. \n -----------------------------------------------------------------------/\nvoid\npole::update_dynamics(const double action)\n{\n assert(std::abs(action) <= 1.0);\n force = pole_constants::force_mag clip(action, 1.0);\n\n for (std::size_t t = 0; t < 10; ++t)\n {\n double theta_dotdot = -0.2 * sign(theta_dot) /* dry friction /\n - pole_constants::friction theta_dot /* fluid friction /\n - (pole_constants::gravity / pole_constants::length) sin(theta) /* torque induced by gravity /\n + force (pole_constants::length / pole_constants::mass); /* torque induced by motor force /\n\n / Update using Euler's method /\n theta += theta_dot pole_constants::dt;\n theta_dot += theta_dotdot * pole_constants::dt;\n }\n}\n\nbool\npole::top(double top_range) const\n{\n /* [-1,+1] is top, 0 is bottom /\n assert_in_range(top_range, 0.0, 0.5);\n const double angle = wrap2(theta) / M_PI;\n\n return std::abs(angle) > (1.0 - top_range);\n}\n\nvoid\npole::draw(const float pos_x, const float pos_y, const float size) const\n{\n const double s = size/pole_constants::max_x;\n\n double length = clip(force)0.1;\n\n if (force < 0) glColor4f(1.0, .5, 0.0, 0.7);\n else glColor4f(0.5, 0.0, 1.0, 0.7);\n draw_fill_rect(pos_x+length/2, pos_y, std::abs(length), 0.01);\n\n glColor3f(1.0, 1.0, 1.0);\n glprintf(pos_x, pos_y + 0.1 * s, 0.0, 0.025, \"F=%+1.2f\", force / pole_constants::force_mag);\n\n glLineWidth(2.0f);\n draw_line( pos_x\n , pos_y\n , 0.0\n , pos_x + 2 * s * pole_constants::length * sin(theta)\n , pos_y - 2 * s * pole_constants::length * cos(theta)\n , 0.0);\n}\n\n} // namespace robots\n" }, { "alpha_fraction": 0.560606062412262, "alphanum_fraction": 0.5617715716362, "avg_line_length": 19.188236236572266, "blob_id": "9653824d0697040a1d52b5bba5c2aede60170762", "content_id": "9876a590e156ae04ff9a69fde68d615f3bd843f0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1716, "license_type": "no_license", "max_line_length": 103, "num_lines": 85, "path": "/src/common/backed.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BACKED_H_INCLUDED\n#define BACKED_H_INCLUDED\n\n#include <cassert>\n#include <deque>\n\nnamespace common {\n\ntemplate <typename T>\nclass backed_t\n{\n T actual, backup;\n\npublic:\n explicit backed_t(T const& actual = T(), T const& backup = T()) : actual(actual), backup(backup) {}\n\n void reset(void) { actual = T{}; backup = T{}; }\n\n void set_backed(T const& value) { backup = value; }\n\n void transfer() { backup = actual; }\n\n T const& get() const { return actual; }\n T const& get_backed() const { return backup; }\n\n backed_t& operator=(const T& value) {\n this->actual = value;\n return this;\n }\n\n backed_t& operator+=(const T& value) {\n this->actual += value;\n return this;\n }\n};\n\ntemplate <typename T>\nclass delayed_t\n{\n T actual;\n const unsigned steps;\n\n typedef std::deque<T> buffer_t;\n buffer_t backup;\n\npublic:\n explicit delayed_t(T const& init = T(), const unsigned steps = 1)\n : actual(init)\n , steps(steps)\n , backup()\n {\n assert(steps > 0);\n backup.assign(steps, init);\n }\n\n void reset(void) {\n backup.assign(steps, T{});\n actual = T{};\n assert(backup.size() == steps);\n }\n\n void transfer() {\n backup.push_back(actual);\n backup.pop_front();\n assert(backup.size() == steps);\n }\n\n T const& get() const { return actual; }\n T const& get_delayed() const { return backup.front(); }\n\n delayed_t& operator=(const T& value) {\n this->actual = value;\n return this;\n }\n\n delayed_t& operator+=(const T& value) {\n this->actual += value;\n return this;\n }\n};\n\n\n} // common\n\n#endif // BACKED_H_INCLUDED\n" }, { "alpha_fraction": 0.6282961368560791, "alphanum_fraction": 0.6440162062644958, "avg_line_length": 22.759037017822266, "blob_id": "4e0dbacb8f2262557a5bdde227c50a214bd6dca8", "content_id": "d10960f8ea0088cd1954db2c0dd10e67266568df", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1972, "license_type": "no_license", "max_line_length": 77, "num_lines": 83, "path": "/src/draw/plot2D.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* plot2D.h /\n\n#ifndef plot2D_H\n#define plot2D_H\n\n#include <cassert>\n#include <vector>\n#include <algorithm>\n#include <basic/color.h>\n#include <common/vector2.h>\n#include <draw/axes.h>\n#include <draw/axes3D.h>\n#include <draw/color_table.h>\n\n//TODO autoscale.\n// use gltranslate and glscale for autoscale\n// * also autoadjust the offset\n// Draw in time?\n\nclass plot2D\n{\npublic:\n plot2D(unsigned int number_of_samples, axes& axis, const Color4& color)\n : number_of_samples(number_of_samples)\n , pointer(0)\n , axis(axis)\n , signal(number_of_samples)\n , color(color)\n , decrement(0.999)\n { }\n\n void draw(void) const;\n //void draw_in_time(void) const;\n void add_sample(float s0, float s1);\n void add_sample(const std::vector<double>& sample);\n void reset(void) { std::fill(signal.begin(), signal.end(), Vector2()); }\n\nprotected:\n void adjust_amplitude(float s0, float s1) const;\n void autoscale(void) const;\n void increment_pointer(void) { ++pointer; pointer %= number_of_samples; }\n\n const unsigned int number_of_samples;\n unsigned int pointer;\n axes& axis;\n\n std::vector<Vector2> signal;\n const Color4 color;\n\n const float decrement;\n\npublic:\n Vector2 offset = {};\n};\n\n\nclass colored_plot2D : public plot2D {\npublic:\n colored_plot2D( unsigned int number_of_samples\n , axes& a\n , ColorTable const& colortable\n /, const char name = \"\"/)\n : plot2D(number_of_samples, a, colors::white0) //TODO, name)\n , colors(number_of_samples)\n , colortable(colortable)\n {}\n\n void add_colored_sample(float s0, float s1, unsigned color_index) {\n add_sample(s0, s1);\n colors.at(pointer) = color_index;\n }\n\n void draw_colored(void) const;\n void draw_colored_scatter(void) const;\nprivate:\n //void draw_colored_line_strip(void) const;\n\n std::vector<unsigned> colors;\n ColorTable const& colortable;\n\n};\n\n#endif /plot2D_H/\n" }, { "alpha_fraction": 0.5533024072647095, "alphanum_fraction": 0.5866164565086365, "avg_line_length": 36.93406677246094, "blob_id": "6890be0c799908612e01c1584a988beb94345e3e", "content_id": "71c1f71d68bf3269b3a8239f14fb5b930174060b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3452, "license_type": "no_license", "max_line_length": 159, "num_lines": 91, "path": "/src/learning/competitive_motor_layer_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef COMPETITIVE_MOTOR_LAYER_GRAPHICS_H_INCLUDED\n#define COMPETITIVE_MOTOR_LAYER_GRAPHICS_H_INCLUDED\n\n#include <draw/draw.h>\n#include <draw/axes3D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n#include <draw/display.h>\n\n#include <learning/competitive_motor_layer.h>\n\nclass CompetitiveMotorLayer_Graphics : Graphics_Interface {\npublic:\n CompetitiveMotorLayer_Graphics( const CompetitiveMotorLayer& motor_layer\n , const float posx\n , const float posy\n , const float width )\n : motor_layer(motor_layer)\n , axis(posx, posy, 0.0, width, width, width, 0)\n , graph(motor_layer.get_number_of_motor_units(), axis, white)\n {\n dbg_msg(\"Creating Graphics Module for Competitive Motor Layer.\");\n }\n\n void execute_cycle(void)\n {\n const std::vector<double>& weights = motor_layer.get_weights(motor_layer.last_selected_idx);\n assert(weights.size() >= 4); /TODO make general /\n\n graph.update_node( motor_layer.last_selected_idx\n , weights[1] // zero omitted\n , weights[2]\n , weights[3]\n , fmin(2.0, motor_layer.get_unit(motor_layer.last_selected_idx).learning_capacity / MotorLayerConstants::initial_learning_capacity) );\n\n graph.update_node( motor_layer.recipient_idx\n , fmin(2.0, motor_layer.get_unit(motor_layer.recipient_idx).learning_capacity / MotorLayerConstants::initial_learning_capacity) );\n\n graph.activated(motor_layer.last_selected_idx);\n //graph.special(gmes.get_to_insert());\n\n //TODO use for current variate weight vector\n //const std::vector<double>& variate = motor_layer.get_variate_weights();\n //plot.add_sample((float) variate[1], (float) variate[2], (float) variate[3]);\n\n /*TODO display motor weights in the 'cube' but display weights belonging to different joints with different colors. /\n }\n\n void draw(const pref& p) const\n {\n /* plots and drawings /\n glLineWidth(2.0f);\n glColor4f(1.0, 1.0, 1.0, 0.2);\n axis.draw(p.x_angle, p.y_angle);\n\n glColor4f(1.0, 1.0, 1.0, 1.0);\n graph.draw(p.x_angle, p.y_angle);\n\n / text /\n for (std::size_t i = 0; i < motor_layer.motor_units.size(); ++i) {\n\n if (motor_layer.last_selected_idx == i) glColor3f(1.0, 0.5, 1.0);\n else if (motor_layer.motor_units[i].exists)\n glColor3f(.9f, .9f, .9f);\n else\n glColor3f(.3f,.3f,.3f);\n\n glprintf(-1.1, -1.2 - 0.05i, 0.0, 0.04, \"%2u\" , i);\n draw::hbar(-1.4, -1.2 - 0.05i, 0.3, 0.02, 0.5 motor_layer.motor_units[i].learning_capacity, MotorLayerConstants::initial_learning_capacity);\n draw_vector2(-1.0, -1.2 - 0.05i, 0.045, 2.0, motor_layer.motor_units[i].weights.get_parameter(), 3.0);\n }\n\n\n if (motor_layer.is_adaption_enabled()) glColor3f(.9f,.9f,.9f);\n else glColor3f(.3f,.3f,.3f);\n\n glprintf(-1.0, -1.1, 0.0, 0.05, \"learning: %s\", motor_layer.is_adaption_enabled() ? \"enabled\" : \"disabled\");\n }\n\nprivate:\n\n const CompetitiveMotorLayer& motor_layer;\n\n /draw/\n const axes3D axis;\n network3D graph;\n};\n\n\n#endif // COMPETITIVE_MOTOR_LAYER_GRAPHICS_H_INCLUDED\n" }, { "alpha_fraction": 0.5627448558807373, "alphanum_fraction": 0.5704537034034729, "avg_line_length": 32.24369812011719, "blob_id": "2d9b278c1f41d01e18b2ad0dd7465329e81db169", "content_id": "c16c160f8b0c432cc77a38e84894891a75a13346", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 7914, "license_type": "no_license", "max_line_length": 166, "num_lines": 238, "path": "/src/control/jointcontroller.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"jointcontroller.h\"\n\nJointcontroller::Jointcontroller( Robot_Configuration& configuration\n , bool symmetric_controller\n , double param_p\n , double param_d\n , double param_m\n , const std::string& seed_filename\n )\n: robot(configuration)\n, num_inputs(3 robot.number_of_joints + 3 * robot.number_of_accels + 1) /* angle, velocity, motor output, + xyz-Accel + bias, Idea: add 1 for antisymmetric bias? /\n, total_num_params(0) // will be assigned later\n, activation(robot.number_of_joints)\n, weights(robot.number_of_joints, std::vector<double>(num_inputs, 0.0))\n, X(num_inputs)\n, Y(num_inputs)\n, seed_from_file()\n{\n sts_msg(\"Creating joint controller.\");\n if (robot.number_of_joints < 1) err_msg(__FILE__, __LINE__, \"No motor outputs.\");\n if (0 == robot.number_of_accels) wrn_msg(\"No use of acceleration sensors in controller.\");\n\n sts_msg(\"Controller type is %s\", symmetric_controller? \"symmetric\":\"asymmetric\");\n if (not symmetric_controller)\n robot.delete_symmetry_information();\n\n unsigned int num_sym_joints = robot.get_number_of_symmetric_joints();\n sts_msg(\"Number of symmetric joints is %u.\", num_sym_joints);\n\n if (num_sym_joints 2 > robot.number_of_joints) err_msg(__FILE__, __LINE__, \"Invalid number of symmetric joints.\\n\");\n\n total_num_params = num_inputs * (robot.number_of_joints - num_sym_joints);\n seed_from_file.reserve(total_num_params);\n\n assert(weights.size() == robot.number_of_joints);\n assert(weights[0].size() == num_inputs);\n\n set_initial_parameter(param_p, param_d, param_m); // load defaults\n if (seed_filename != \"\")\n load_seed(seed_filename); // load seed\n\n print_parameter();\n sts_msg(\"Created controller with: \\n %u inputs\\n %u outputs\\n %u params.\", num_inputs, robot.number_of_joints, total_num_params);\n}\n\nconst std::vector<double>\nJointcontroller::get_control_parameter(void) const\n{\n std::vector<double> transmission_params(total_num_params);\n assert(weights.size() == robot.number_of_joints);\n assert(weights[0].size() == num_inputs);\n\n unsigned int p = 0;\n for (unsigned int i = 0; i < robot.number_of_joints; ++i)\n if (robot.joints[i].type == robots::Joint_Type_Normal)\n for (unsigned int k = 0; k < num_inputs; ++k)\n {\n if ((p < total_num_params) && (weights[i][k] == weights[robot.joints[i].symmetric_joint][k]))\n transmission_params[p++] = weights[i][k];\n else\n err_msg(__FILE__, __LINE__, \"Inconsistent parameter transmission.\");\n }\n\n assert(p == total_num_params);\n return transmission_params;\n}\n\nvoid /* load parameters from file /\nJointcontroller::load_seed(const std::string& filename)\n{\n sts_msg(\"Load controller weights from csv file '%s'\", filename.c_str());\n assert(seed_from_file.size() == 0);\n assert(not filename.empty());\n\n file_io::CSV_File<double> seed_csv(filename, 1, total_num_params);\n seed_csv.read();\n seed_from_file.assign(total_num_params, 0.0);\n seed_csv.get_line(0, seed_from_file);\n\n assert(seed_from_file.size() == total_num_params);\n set_control_parameter(seed_from_file); // apply weights\n}\n\nvoid\nJointcontroller::set_seed_parameter(void)\n{\n assert(seed_from_file.size() == total_num_params);\n set_control_parameter(seed_from_file);\n}\n\nvoid\nJointcontroller::set_control_parameter(const std::vector<double>& params)\n{\n assert(params.size() == total_num_params);\n assert(weights.size() == robot.number_of_joints);\n assert(weights[0].size() == num_inputs);\n\n unsigned int p = 0;\n for (unsigned int i = 0; i < robot.number_of_joints; ++i)\n {\n if (robot.joints[i].type == robots::Joint_Type_Normal)\n {\n assert(robot.joints[i].symmetric_joint < robot.number_of_joints);\n for (unsigned int k = 0; k < num_inputs; ++k)\n {\n weights[i][k] = params[p++]; // apply weights\n weights[robot.joints[i].symmetric_joint][k] = weights[i][k];\n }\n }\n }\n assert(p == params.size());\n}\n\nvoid\nJointcontroller::set_initial_parameter(double p, double d, double m)\n{\n / set default weights for asymmetric joints /\n for (unsigned int i = 0; i < robot.number_of_joints; ++i)\n if (robot.joints[i].type == robots::Joint_Type_Normal)\n {\n weights[i][i3 + 0] = -p; // spring\n weights[i][i3 + 1] = d; // positive friction\n weights[i][i3 + 2] = m; // motor neuron's self coupling\n\n /* divide by initial bias, because INITIAL BIAS < 1 /\n weights[i][num_inputs-1] = -weights[i][i3 + 0] * robot.joints[i].default_pos * 1.0/INITIAL_BIAS;\n /* if p is zero this bias is also zero /\n }\n\n / copy weights for symmetric joints /\n for (unsigned int i = 0; i < robot.number_of_joints; ++i)\n if (robot.joints[i].type == robots::Joint_Type_Symmetric)\n for (unsigned int k = 0; k < num_inputs; ++k)\n weights[i][k] = weights[robot.joints[i].symmetric_joint][k];\n}\n\nvoid\nJointcontroller::print_parameter(void) const\n{\n sts_msg(\"Printing controller parameter:\");\n\n /print header/\n printf(\" # \|\");\n for (std::size_t i = 0; i < robot.number_of_joints; ++i)\n if (robot.joints[i].type == robots::Joint_Type_Normal)\n printf(\"%4lu \|\", i);\n printf(\"\\n\");\n\n for (std::size_t k = 0; k < num_inputs; ++k)\n {\n printf(\"%2lu: \|\", k);\n for (std::size_t i = 0; i < robot.number_of_joints; ++i)\n {\n if (robot.joints[i].type == robots::Joint_Type_Normal)\n printf(\"% 1.3f\|\", weights[i][k]);\n }\n printf(\"\\n\");\n }\n printf(\"\\n\");\n}\n\ndouble\nJointcontroller::get_normalized_mechanical_power(void) const\n{\n double power = .0;\n for (auto& j : robot.joints)\n power += square(j.motor.get());\n return power/robot.number_of_joints;\n}\n\nvoid\nJointcontroller::reset(void)\n{\n for (auto& j : robot.joints)\n j.motor = random_value(-0.01, 0.01);\n\n / reset integrated velocities from acceleration sensors /\n for (auto& a : robot.accels) a.reset();\n}\n\nvoid\nJointcontroller::loop(void)\n{\n unsigned int index = 0;\n for (auto& jx : robot.joints)\n {\n //virt_ang[i] = clip(virt_ang[i] + x[i][0]);\n auto& jy = robot.joints[jx.symmetric_joint];\n\n X[index] = jx.s_ang;\n Y[index++] = jy.s_ang;\n\n X[index] = jx.s_vel;\n Y[index++] = jy.s_vel;\n\n X[index] = jx.motor.get_backed();\n Y[index++] = jy.motor.get_backed(); //ganz wichtig!, bringt irre viel dynamic für den anfang\n }\n\n for (auto& a : robot.accels)\n {\n / integrate velocities from acceleration sensors /\n a.integrate();\n\n X[index] = a.v.x;\n Y[index++] = -a.v.x; // mirror the x-axes\n\n X[index] = a.v.y;\n Y[index++] = a.v.y;\n\n X[index] = a.v.z;\n Y[index++] = a.v.z;\n\n }\n\n X[index] = INITIAL_BIAS; // bias\n Y[index++] = INITIAL_BIAS; // bias\n\n assert(index == num_inputs);\n assert(activation.size() == robot.number_of_joints);\n\n for (unsigned int i = 0; i < robot.number_of_joints; ++i)\n {\n activation[i] = 0;\n if (robot.joints[i].type == robots::Joint_Type_Symmetric)\n {\n for (unsigned int k = 0; k < num_inputs; ++k)\n activation[i] += weights[i][k] Y[k];\n }\n else\n {\n for (unsigned int k = 0; k < num_inputs; ++k)\n activation[i] += weights[i][k] * X[k];\n }\n //TODO robot.joints[i].motor = tanh(activation[i]);\n robot.joints[i].motor = clip(activation[i], 1.0);\n }\n}\n" }, { "alpha_fraction": 0.7096478939056396, "alphanum_fraction": 0.7105624079704285, "avg_line_length": 46.543479919433594, "blob_id": "7d01c6f150b4a35e8bb3b10b757f796986607607", "content_id": "9db5a44405cbda3ea5cecf4a40367fec7de49b32", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2187, "license_type": "no_license", "max_line_length": 126, "num_lines": 46, "path": "/src/evolution/population.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef POPULATION_H_INCLUDED\n#define POPULATION_H_INCLUDED\n\n#include <vector>\n#include <algorithm>\n#include <common/file_io.h>\n#include <common/log_messages.h>\n#include <evolution/individual.h>\n\nclass Population\n{\n std::vector<Individual> individuals;\n\npublic:\n Population(std::size_t population_size, std::size_t individual_size, double init_mutation_rate, double meta_mutation_rate)\n : individuals(population_size, Individual(individual_size, init_mutation_rate, meta_mutation_rate))\n {\n assert(population_size > 0);\n sts_msg(\"[Population:] Initializing population with %u individuals of size %u\", population_size, individual_size);\n }\n\n ~Population() { dbg_msg(\"[Population:] Destroying population with %u individuals.\", get_size()); }\n\n void initialize_from_seed(const std::vector<double>& seed);\n void sort_by_fitness(void);\n\n const Individual& get_best_individual(void) const { return individuals.front(); }\n const Individual& get_last_individual(void) const { return individuals.back (); }\n\n std::size_t get_size (void) const { return individuals.size(); }\n std::size_t get_individual_size(void) const { return individuals[0].genome.size(); }\n\n Individual& operator[] (std::size_t index) { assert(index < individuals.size()); return individuals[index]; }\n const Individual& operator[] (std::size_t index) const { assert(index < individuals.size()); return individuals[index]; }\n\n friend void save_population (Population& population, file_io::CSV_File<double>& csv_population);\n friend void load_population (Population& population, file_io::CSV_File<double>& csv_population);\n friend void save_mutation_rates(Population& population, file_io::CSV_File<double>& csv_mutation);\n friend void load_mutation_rates(Population& population, file_io::CSV_File<double>& csv_mutation);\n friend void save_fitness_values(Population& population, file_io::CSV_File<double>& csv_fitness);\n friend void load_fitness_values(Population& population, file_io::CSV_File<double>& csv_fitness);\n friend void print_population (Population& population);\n\n};\n\n#endif // POPULATION_H_INCLUDED\n" }, { "alpha_fraction": 0.7098360657691956, "alphanum_fraction": 0.7098360657691956, "avg_line_length": 33.85714340209961, "blob_id": "a2140b161e72819b8a31b849174c7bece9371f0c", "content_id": "53d9b800c8de5461ffd833a616022b493732fde2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1220, "license_type": "no_license", "max_line_length": 133, "num_lines": 35, "path": "/src/learning/gmes_action_module.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GMES_ACTION_MODULE_H_INCLUDED\n#define GMES_ACTION_MODULE_H_INCLUDED\n\n#include <common/log_messages.h>\n#include <learning/action_module.h>\n#include <learning/motor_layer.h>\n#include <learning/reinforcement_learning.h>\n\nnamespace learning {\n\n/** Maybe we don't need this class at all. Consider to move that into motor layer/\n\nclass gmes_action_module : public Action_Module_Interface {\n\n Motor_Layer const& motor_layer;\n\npublic:\n gmes_action_module( Motor_Layer const& motor_layer )\n : motor_layer(motor_layer)\n {\n dbg_msg(\"Creating gmes_action_module with max. number of actions %u:\", get_number_of_actions());\n }\n\n ~gmes_action_module() = default;\n\n control::Control_Parameter const& get_controller_weights(std::size_t id) const { return motor_layer.get_controller_weights(id); }\n\n std::size_t get_number_of_actions(void) const { return motor_layer.get_max_number_of_experts(); }\n std::size_t get_number_of_actions_available(void) const { return motor_layer.get_cur_number_of_experts(); }\n bool exists(const std::size_t action_index) const { return motor_layer.has_expert(action_index); }\n};\n\n} // namespace learning\n\n#endif // GMES_ACTION_MODULE_H_INCLUDED\n" }, { "alpha_fraction": 0.5785627365112305, "alphanum_fraction": 0.5931790471076965, "avg_line_length": 19.78481101989746, "blob_id": "d6847b59b1821df5d094041905759ed708420553", "content_id": "b87e31b1095cc7b67e87eab35373506e0b22a151", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1646, "license_type": "no_license", "max_line_length": 85, "num_lines": 79, "path": "/src/common/log_messages.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ log_messages.cpp /\n#include \"log_messages.h\"\n\n#define KNRM \"\\x1B[0m\"\n#define KRED \"\\x1B[31m\"\n#define KGRN \"\\x1B[32m\"\n#define KYEL \"\\x1B[33m\"\n#define KBLU \"\\x1B[34m\"\n#define KMAG \"\\x1B[35m\"\n#define KCYN \"\\x1B[36m\"\n#define KWHT \"\\x1B[37m\"\n\nvoid\nsts_msg(const char format, ...)\n{\n va_list args;\n va_start(args, format);\n vprintf(format, args);\n printf(\"\\n\");\n va_end(args);\n}\n\nvoid\nsts_add(const char* format, ...)\n{\n va_list args;\n va_start(args, format);\n vprintf(format, args);\n printf(\" \");\n va_end(args);\n}\n\nvoid\ndbg_msg(const char* format, ...)\n{\n va_list args;\n va_start(args, format);\n printf(\"%s‹dbg›%s \", KGRN, KNRM);\n vprintf(format, args);\n printf(\"\\n\");\n va_end(args);\n}\n\nvoid\nwrn_msg(const char* format, ...)\n{\n static unsigned long wrn_msg_cnt = 0;\n va_list args;\n va_start(args, format);\n printf(\"%sWARNING %ld: %s\", KYEL, ++wrn_msg_cnt, KNRM);\n vprintf(format, args);\n printf(\"\\n\");\n va_end(args);\n}\n\nvoid\nerr_msg(const char* file, unsigned int line, const char* format, ...)\n{\n va_list args;\n va_start(args, format);\n printf(\"%sERROR:%s \", KRED, KNRM);\n vprintf(format, args);\n printf(\"%s File %s in line %d%s\\n\", KWHT, file, line, KNRM);\n va_end(args);\n exit(EXIT_FAILURE);\n}\n\nvoid\npromise(bool condition, const char* file, unsigned int line, const char* format, ...)\n{\n if (condition) return;\n va_list args;\n va_start(args, format);\n printf(\"%sASSERTION:%s \", KCYN, KNRM);\n vprintf(format, args);\n printf(\"%s\\nFile %s in line %d%s\\n\", KWHT, file, line, KNRM);\n va_end(args);\n exit(EXIT_FAILURE);\n}\n" }, { "alpha_fraction": 0.5892351269721985, "alphanum_fraction": 0.6024551391601562, "avg_line_length": 23.627906799316406, "blob_id": "4214ebfe74dc66802b3674562d49b2f80aa1045a", "content_id": "09d094bf83ea5efb8b012a8a80e33d2e24573cc9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1059, "license_type": "no_license", "max_line_length": 120, "num_lines": 43, "path": "/src/draw/graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GRAPHICS_H_INCLUDED\n#define GRAPHICS_H_INCLUDED\n\n#include <draw/draw.h>\n#include <basic/vector3.h>\n\n/*TODO namespace draw and rename to graphics_base /\n\nstruct pref {\n float x_angle;\n float y_angle;\n};\n\nclass Graphics_Interface\n{\n Vector3 pos;\n double scale;\n\npublic:\n\n Graphics_Interface()\n : pos(0.0), scale(1.0) {}\n\n Graphics_Interface(double px, double py, double pz = 0.0, double s = 1.0)\n : pos(px, py, pz), scale(s) {}\n\n virtual ~Graphics_Interface() {}\n virtual void draw(const pref&) const = 0; // consider to make this protected/private\n\n void drawing(const pref& p) const {\n glPushMatrix();\n glTranslatef(pos.x, pos.y, pos.z);\n glScalef(scale, scale, scale);\n draw(p);\n glPopMatrix();\n }\n\n Graphics_Interface& set_position(double px, double py, double pz = 0.0) { pos = Vector3(px, py, pz); return this; }\n Graphics_Interface& set_scale (double s ) { scale = s; return this; }\n\n};\n\n#endif // GRAPHICS_H_INCLUDED\n" }, { "alpha_fraction": 0.6128472089767456, "alphanum_fraction": 0.623071014881134, "avg_line_length": 34.5, "blob_id": "ad0620905dbb681580adf3ddf3d28103ba2a6a37", "content_id": "2ecc482bed0594625b670847d28ee5aec96924df", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5184, "license_type": "no_license", "max_line_length": 138, "num_lines": 146, "path": "/src/tests/jointcontroller_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <vector>\n\n#include <common/modules.h>\n#include <common/log_messages.h>\n#include <robots/robot.h>\n#include <robots/joint.h>\n#include <control/controlparameter.h>\n#include <control/jointcontrol.h>\n#include <tests/test_robot.h>\n\n\nstatic void printf_param(control::Control_Parameter const& ctrl, robots::Robot_Interface const& robot) {\n auto num_inputs = control::get_number_of_inputs(robot);\n auto p = ctrl.get_parameter();\n unsigned j = 0;\n printf(\"%s \", (robot.get_joints().at(j).type == robots::Joint_Type_Symmetric)?\"~\":\"=\");\n for (std::size_t i = 0; i < p.size(); ++i) {\n if ((i > 0) and (i % num_inputs == 0)) printf(\"\\n%s \", (robot.get_joints().at(++j).type == robots::Joint_Type_Symmetric)?\"/\":\"=\");\n printf(\"% 5.2f \", p[i]);\n }\n printf(\"\\n\");\n}\n\n\n\nTEST_CASE( \"Make Asymmetric\" , \"[jointcontrol]\") {\n\n std::vector<std::pair<unsigned, unsigned>> vec = {{3,1}, {5,2}, {4,2}, {3,0}};\n for (auto& s : vec)\n { /* LOOP OVER TESTCASE /\n\n std::size_t num_joints = s.first;\n std::size_t num_sym_joints = s.second;\n\n Test_Robot robot(num_joints, num_sym_joints);\n\n control::Control_Parameter asym_ctrl = control::get_initial_parameter(robot, {1.1, 2.2, 3.3}, false);\n\n REQUIRE( not asym_ctrl.is_symmetric() );\n REQUIRE( asym_ctrl.get_parameter().size() == control::get_number_of_inputs(robot) num_joints );\n printf_param(asym_ctrl, robot);\n\n control::Control_Parameter sym_ctrl = control::make_symmetric(robot, asym_ctrl);\n REQUIRE( sym_ctrl.is_symmetric() );\n REQUIRE( sym_ctrl.get_parameter().size() == control::get_number_of_inputs(robot) * (num_joints-num_sym_joints) );\n if (num_sym_joints > 0)\n REQUIRE( sym_ctrl.get_parameter().size() < asym_ctrl.get_parameter().size() );\n printf_param(sym_ctrl, robot);\n\n control::Control_Parameter new_asym_ctrl = control::make_asymmetric(robot, sym_ctrl);\n REQUIRE( not new_asym_ctrl.is_symmetric() );\n REQUIRE( new_asym_ctrl.get_parameter().size() == asym_ctrl.get_parameter().size() );\n REQUIRE( new_asym_ctrl.get_parameter() == asym_ctrl.get_parameter() );\n if (num_sym_joints > 0)\n REQUIRE( new_asym_ctrl.get_parameter().size() > sym_ctrl.get_parameter().size() );\n printf_param(new_asym_ctrl, robot);\n\n control::Control_Parameter new_sym_ctrl = control::make_symmetric(robot, asym_ctrl);\n REQUIRE( new_sym_ctrl.is_symmetric() );\n REQUIRE( new_sym_ctrl.get_parameter().size() == sym_ctrl.get_parameter().size() );\n REQUIRE( new_sym_ctrl.get_parameter() == sym_ctrl.get_parameter() );\n printf_param(new_sym_ctrl, robot);\n\n } /* END TEST CASE LOOP /\n}\n\nTEST_CASE( \"Making Asymmetric keeps structure\" , \"[jointcontrol]\") {\n\n printf(\"\\n____\\nASYMMETRIC\\n\");\n\n std::vector<std::pair<unsigned, unsigned>> vec = {{4,2}, {8,4}, {4,0}, {5,2}, {3,1}};\n\n for (auto& s : vec)\n { / LOOP OVER TESTCASE /\n printf(\"\\n------------------------\\n\");\n\n const std::size_t num_joints = s.first;\n const std::size_t num_sym_joints = s.second;\n\n Test_Robot robot(num_joints, num_sym_joints);\n const std::size_t num_inputs = control::get_number_of_inputs(robot);\n\n const std::size_t num_sym_params = num_inputs(num_joints-num_sym_joints);\n\n control::Fully_Connected_Symmetric_Core core(robot);\n robot.set_random_inputs();\n core.prepare_inputs(robot);\n\n printf(\"Inputs: \\n\");\n for (auto const& jx : robot.get_joints())\n printf(\"%5.2f %5.2f %5.2f\\n\", jx.s_ang, jx.s_vel, jx.motor.get_backed());\n printf(\"\\n\");\n\n\n //TODO control::Control_Parameter sym_ctrl = control::get_initial_parameter(robot, {1.1, 2.2, 3.3}, true);\n\n std::vector<double> rnd_parameter;\n\n\n for (std::size_t i = 0; i < num_sym_params; ++i)\n rnd_parameter.push_back(random_value());\n\n control::Control_Parameter sym_ctrl = control::Control_Parameter( rnd_parameter, /symmetric/true);\n\n\n\n /* randomize parameters /\n /for (auto& p : sym_ctrl.set_parameter())\n p += random_value();/\n\n REQUIRE( sym_ctrl.is_symmetric() );\n REQUIRE( sym_ctrl.get_parameter().size() == num_sym_params );\n core.apply_symmetric_weights(robot, sym_ctrl.get_parameter());\n core.update_outputs(robot, true, false);\n std::vector<double> sym_activation = core.activation;\n\n control::Control_Parameter asym_ctrl = control::make_asymmetric(robot, sym_ctrl);\n\n printf(\"\\n sym: \"); sym_ctrl.print();\n printf(\"\\n asm: \"); asym_ctrl.print();\n\n REQUIRE( not asym_ctrl.is_symmetric() );\n\n if (num_sym_joints > 0)\n REQUIRE( asym_ctrl.get_parameter().size() > sym_ctrl.get_parameter().size() );\n\n core.prepare_inputs(robot);\n core.apply_weights(robot, asym_ctrl.get_parameter());\n core.update_outputs(robot, false, false);\n\n printf(\"\\nActivations:\");\n for (auto s: sym_activation) printf(\"%e \", s);\n printf(\"\\n\");\n\n REQUIRE( close(sym_activation, core.activation, 0.000001) );\n //printf(\"%e =?= %e\", sym_activation, core.activation);\n\n printf_param( sym_ctrl, robot);\n printf(\"\\nasymmetric\\n\");\n printf_param(asym_ctrl, robot);\n\n }\n\n printf(\"\\n____\\nDONE\\n\");\n}\n\n" }, { "alpha_fraction": 0.5399810671806335, "alphanum_fraction": 0.5478284358978271, "avg_line_length": 28.098424911499023, "blob_id": "55b0ce947ca9f6aea10a947fcac3ecafbb7ef02b", "content_id": "2e65af604aef4bae786eb7e198feace60b08f08b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 7391, "license_type": "no_license", "max_line_length": 126, "num_lines": 254, "path": "/src/learning/time_delay_network.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef TIME_DELAY_NETWORK_H_INCLUDED\n#define TIME_DELAY_NETWORK_H_INCLUDED\n\n#include <deque>\n\n#include <common/modules.h>\n#include <common/static_vector.h>\n#include <control/sensorspace.h>\n\n\n/* TODO consider a time-delay autoencoder /\n\nnamespace learning {\n\n/\n + time expansion through FIR-type synapses DONE\n + non-linear expansion through tanh or ReLU neurons\n + configurable number of hidden layers\n/\n\n\n\n/ FIR-type Synapse\n Expands the input vector v(t) by its recent values v(t-1), v(t-2),... , with each\n tapped delay line having the same size of \"number of taps\" = N.\n\n v1(t) ---> [v1(t-1)] --->[v1(t-2)]---> ... --->[v1(t-N)]\n v2(t) ---> [v2(t-1)] --->[v2(t-2)]---> ... --->[v2(t-N)]\n ... ...\n vK(t) ---> [vK(t-1)] --->[vK(t-2)]---> ... --->[vK(t-N)]\n\n plus additional bias\n\n/\nclass FIR_type_synapse {\n\n typedef std::vector<double> vector_t;\n typedef std::deque<vector_t> buffer_t; // replace by something which is more efficient\n\n std::size_t number_of_taps;\n std::size_t input_size; / size of raw inputs /\n vector_t td_inputs; / time delayed inputs /\n buffer_t buffer; / structure holding all FIFOs /\n\n\n void expand(void) {\n std::size_t p = 0;\n for (auto const& vec : buffer)\n for (auto const& el : vec)\n td_inputs[p++] = el;\n assert(p == td_inputs.size());\n }\n\npublic:\n\n FIR_type_synapse(std::size_t input_size, std::size_t number_of_taps)\n : number_of_taps(number_of_taps)\n , input_size(input_size)\n , td_inputs(input_sizenumber_of_taps)\n , buffer(input_size)\n {\n /* initialize buffer with zero vectors /\n buffer.assign(number_of_taps, vector_t(input_size));\n\n assert(buffer.size() == number_of_taps);\n assert(buffer.size()buffer[0].size() == td_inputs.size());\n dbg_msg(\"Created FIR-type Synapse of size %u x %u.\", input_size, number_of_taps);\n }\n\n vector_t const& get (void) const { return td_inputs; }\n buffer_t const& get_buffer(void) const { return buffer; }\n\n\n std::size_t size(void) const { return td_inputs.size(); }\n\n template <typename InputVector_t>\n void propagate(const InputVector_t& inputs)\n {\n assert(input_size == inputs.size());\n\n /* shift /\n buffer.pop_back(); // remove oldest input vector\n buffer.push_front(inputs.get()); // fill in new values\n assert(buffer.size() == number_of_taps);\n\n /\n for (auto const& vec : buffer) {\n printf(\"[ \");\n for (auto const& el : vec)\n printf(\"%5.2f \", el);\n printf(\"]\\n\");\n }\n printf(\"\\n\");\n /\n\n expand(); // prepare vector for returning in 'get()'\n }\n\n\n\n};\n\n\n/TODO move the tapped delay line to input space, in order to have all experts share the same tapping line. /\n\n/* Time-Delay feed-forward network\n with a single tanh-type hidden layer.\n/\ntypedef std::vector<double> vector_t;\ntypedef copyable_static_vector<copyable_static_vector<double>> matrix_t;\n\nstruct TDNWeights {\n TDNWeights(std::size_t inp, std::size_t hid, std::size_t out): hi(hid, inp), oh(out, hid) /, biases_oh(output.size())/{}\n\n matrix_t hi;\n matrix_t oh;\n //TODO vector_t biases_oh;\n};\n\n\nclass Timedelay_Network\n{\n\n FIR_type_synapse td_input; / time delayed inputs /\n vector_t hidden;\n vector_t output;\n vector_t delta;\n\n TDNWeights weights;\n\n void randomize(matrix_t& mat, double std_dev) {\n assert_in_range(std_dev, 0.0, 0.1);\n const double normed_stddev = std_dev / sqrt(mat[0].size()); // normalize by sqrt(N), N:#inputs\n for (std::size_t i = 0; i < mat.size(); ++i)\n for (std::size_t j = 0; j < mat[i].size(); ++j)\n mat[i][j] = rand_norm_zero_mean(normed_stddev);\n }\n\n void propagate_forward(vector_t& out, matrix_t const& mat, vector_t const& in) {\n assert(out.size() == mat.size());\n assert( in.size() == mat[0].size());\n\n for (std::size_t i = 0; i < out.size(); ++i) {\n double act = 0.;\n for (std::size_t j = 0; j < in.size(); ++j)\n act += mat[i][j] in[j];\n out[i] = act;\n }\n }\n\npublic:\n\n Timedelay_Network( std::size_t input_size\n , std::size_t target_size\n , std::size_t hidden_size\n , std::size_t number_of_taps\n , double rnd_init_range)\n : td_input(input_size, number_of_taps)\n , hidden(hidden_size)\n , output(target_size)\n , delta (output.size())\n , weights(td_input.size(), hidden.size(), output.size())\n {\n randomize_weight_matrix(rnd_init_range);\n }\n\n\n void propagate(void) {\n /* time delayed input to hidden layer /\n propagate_forward(hidden, weights.hi, td_input.get());\n vector_tanh(hidden);\n\n / hidden to output layer /\n propagate_forward(output, weights.oh, hidden);\n vector_tanh(output);\n }\n\n template <typename InputVector_t>\n void propagate_and_shift(const InputVector_t& inputs)\n {\n / shift and time expand next inputs /\n td_input.propagate(inputs);\n propagate();\n }\n\n template <typename TargetVector_t>\n void adapt(const TargetVector_t& targets, double learning_rate)\n {\n assert_in_range(learning_rate, 0.0, 0.5);\n assert(targets.size() == output.size());\n\n / delta error /\n for (std::size_t i = 0; i < output.size(); ++i)\n delta[i] = (targets[i] - output[i]) tanh_(output[i]);\n\n\n /* estimate error for hidden units and adapt input to hidden weights /\n for (std::size_t i = 0; i < hidden.size(); ++i)\n {\n / estimate 'hidden' errors /\n double error_i = .0;\n for (std::size_t j = 0; j < output.size(); ++j)\n error_i += weights.oh[j][i] delta[j];\n\n /* adapt weights.hi /\n const double delta_i = error_i tanh_(hidden[i]);\n for (std::size_t j = 0; j < output.size(); ++j)\n weights.hi[i][j] += learning_rate * delta_i * td_input.get()[j];\n }\n\n\n /* adapt weight_oh /\n for (std::size_t i = 0; i < output.size(); ++i)\n for (std::size_t j = 0; j < hidden.size(); ++j)\n weights.oh[i][j] += learning_rate delta[i] * hidden[j];\n }\n\n /\n x_j : inputs[j]\n t_j : targets[j]\n y_j : outputs[j]\n h_i : hidden[i]\n\n eta : learning_rate\n\n second layer weight change:\n error : e_j = (t_j - y_j)\n delta : d2_j = e_j (1.0 + y_j) * (1.0 - y_j)\n weight: dw2_ji = eta * d2_j * h_i\n\n first layer weight change:\n error : eh_i = sum_j w2_ji * d2_j\n delta : d1_i = eh_i * (1.0 + h_i) * (1.0 - h_i)\n weight: dw1_ji = eta * d1_i * x_j\n\n /\n\n vector_t const& get_outputs() const { return output; }\n vector_t const& get_hidden() const { return hidden; }\n TDNWeights const& get_weights() const { return weights; }\n\n void randomize_weight_matrix(double random_weight_range)\n {\n randomize(weights.hi, random_weight_range);\n randomize(weights.oh, random_weight_range);\n }\n};\n\n\n\n\n} // namespace learning\n\n#endif // TIME_DELAY_NETWORK_H_INCLUDED\n" }, { "alpha_fraction": 0.5881384015083313, "alphanum_fraction": 0.5963756442070007, "avg_line_length": 17.90625, "blob_id": "34dd4fb384fa9620c0039bdab3eb94138b95cfbe", "content_id": "918ce1cc7e46fa0d9274afecde5c1f6fe82981ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 607, "license_type": "no_license", "max_line_length": 68, "num_lines": 32, "path": "/src/common/incremental_average.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef INCREMENTAL_AVERAGE_H\n#define INCREMENTAL_AVERAGE_H\n\nclass incremental_average {\npublic:\n\n explicit incremental_average(void) : mean{.0}, num_samples{0} {}\n\n void sample(double value) {\n ++num_samples;\n mean = mean + (value - mean) / num_samples;\n }\n\n double get(void) const {\n assert(num_samples>0);\n return mean;\n }\n\n std::size_t get_num_samples(void) const { return num_samples; }\n\n void reset(void) {\n mean = .0;\n num_samples = 0;\n }\n\nprivate:\n double mean;\n std::size_t num_samples;\n};\n\n\n#endif // INCREMENTAL_AVERAGE_H\n\n\n" }, { "alpha_fraction": 0.6181672215461731, "alphanum_fraction": 0.6302250623703003, "avg_line_length": 36.69696807861328, "blob_id": "913757b6b5c51dc9022875ed010d5ad7f5df0569", "content_id": "2baac6e1eb6b26cab82b3c8b9ee5ee555d062dff", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3732, "license_type": "no_license", "max_line_length": 140, "num_lines": 99, "path": "/src/evolution/generation_based_strategy.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include \"generation_based_strategy.h\"\n\nbool\nGeneration_Based_Evolution::show_selection(void)\n{\n fitness_stats.reset();\n double generation_rnd = random_value(0.0, 1.0); // provide a random value used by evaluation, same for every individual of a generation,\n\n sts_msg(\"Showing selection\");\n evaluation.prepare_generation(cur_generation, max_generation); // prepare generation\n for (std::size_t i = 0; i < selection_size; ++i)\n {\n sts_msg(\" showing individual no. %3u/%u \", i + 1, selection_size);\n evaluation.constrain(population[i].genome);\n if (evaluation.evaluate(population[i].fitness, population[i].genome, generation_rnd))\n {\n auto const& fitval = population[i].fitness.get_value();\n sts_msg(\" = %1.3f (% e)\", fitval, fitval);\n fitness_stats.add_sample(fitval);\n }\n else return false;\n }\n fitness_stats.update_average();\n sts_msg(\"Result: max: %1.2f avg: %1.2f min: %1.2f\\n\", fitness_stats.max, fitness_stats.avg, fitness_stats.min);\n return true;\n}\n\n\nbool\nGeneration_Based_Evolution::evaluate_generation(void)\n{\n fitness_stats .reset();\n mutation_stats.reset();\n\n double generation_rnd = random_value(0.0, 1.0); // provide a random value used by evaluation, same for every individual of a generation,\n\n if (verbose) sts_msg(\"Evaluate generation %u/%u:\", cur_generation, max_generation);\n evaluation.prepare_generation(cur_generation, max_generation); // prepare generation\n\n for (std::size_t i = 0; i < population.get_size(); ++i)\n {\n if (verbose) sts_msg(\" testing individual no.%3lu/%lu [%lu/%lu]\", i + 1, population.get_size(), cur_generation, max_generation);\n else printf(\"\\rtesting individual no.%3lu/%lu [%lu/%lu]\", i + 1, population.get_size(), cur_generation, max_generation);\n\n population[i].fitness.reset();\n evaluation.constrain(population[i].genome);\n if (evaluation.evaluate(population[i].fitness, population[i].genome, generation_rnd))\n {\n auto const& fitval = population[i].fitness.get_value();\n auto const& mutval = population[i].mutation_rate;\n\n if (verbose) sts_msg(\" fit=%+07.3f mu=%1.5f\\n\", fitval, mutval);\n\n fitness_stats .add_sample(fitval);\n mutation_stats.add_sample(mutval);\n }\n else\n {\n sts_msg(\"Stopped in generation %u, max=%+07.3ff\", cur_generation, fitness_stats.max);\n return false;\n }\n }\n fitness_stats .update_average();\n mutation_stats.update_average();\n\n sts_msg(\"\\rGeneration result: max=%+07.3f avg=%+07.3f min=%+07.3f\", fitness_stats.max, fitness_stats.avg, fitness_stats.min);\n\n return true;\n}\n\nvoid\nGeneration_Based_Evolution::recombination_crossover(void)\n{\n if (selection_size < 2) {\n wrn_msg(\"Recombination skipped, selection size is only 1.\");\n return;\n }\n if (verbose) sts_msg(\"Doing recombination with crossover.\");\n assert(selection_size > 1);\n for (std::size_t child_idx = selection_size; child_idx < population.get_size(); ++child_idx)\n {\n std::size_t mother_idx = random_index(selection_size);\n std::size_t father_idx = random_index(selection_size);\n while (mother_idx == father_idx)\n mother_idx = random_index(selection_size); // (!) this gets an infinite loop with selection_size = 1\n\n crossover(population[mother_idx],\n population[father_idx],\n population[ child_idx]);\n }\n}\n\nvoid\nGeneration_Based_Evolution::mutation(void)\n{\n if (verbose) sts_msg(\"Mutating\");\n for (std::size_t i = selection_size; i < population.get_size(); ++i)\n population[i].mutate();\n}\n" }, { "alpha_fraction": 0.5732483863830566, "alphanum_fraction": 0.5940678715705872, "avg_line_length": 36.39146041870117, "blob_id": "394299b0a378237a154b03dc38f4b5989cc5c1f0", "content_id": "a927ec42d6dea9fa5f5138a8d5d17e6ff034c8b5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 10519, "license_type": "no_license", "max_line_length": 156, "num_lines": 281, "path": "/src/learning/motor_layer.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef MOTOR_LAYER_H_INCLUDED\n#define MOTOR_LAYER_H_INCLUDED\n\n#include <robots/robot.h>\n#include <robots/joint.h>\n#include <control/jointcontrol.h>\n#include <control/control_vector.h>\n#include <control/controlparameter.h>\n#include <control/sensorspace.h>\n#include <learning/expert.h>\n#include <learning/gmes.h>\n#include <learning/payload.h>\n#include <learning/motor_predictor.h>\n\n/ graphics /\n#include <draw/draw.h>\n#include <draw/axes.h>\n#include <draw/axes3D.h>\n#include <draw/plot1D.h>\n#include <draw/plot2D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n#include <draw/display.h>\n\n\nnamespace learning {\n\nnamespace motor_layer_constants {\n const double number_of_experts = 19;\n const double learning_rate = 0.001;\n const double growth_rate = 1.0;\n const std::size_t experience_size = 100;\n const double noise_level = 0.005; // 0.01 is maybe too high\n}\n\n\nclass Motor_Space : public sensor_vector {\npublic:\n Motor_Space(const robots::Jointvector_t& joints)\n : sensor_vector(joints.size())\n {\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name, [&j](){ return clip(j.motor.get()); });\n //sensors.emplace_back(j.name, [&j](){ return clip(j.motor.get() + rand_norm_zero_mean(0.01), 1.0); });\n }\n};\n\n\nstd::size_t getnum(void){\n return random_index(5);\n}\n\nclass Motor_Layer : public learning::Learning_Machine_Interface {\npublic:\n Motor_Layer( robots::Robot_Interface const& robot\n , std::size_t max_num_motor_experts = motor_layer_constants::number_of_experts\n , const double learning_rate = motor_layer_constants::learning_rate\n , const double growth_rate = motor_layer_constants::growth_rate\n , std::size_t experience_size = motor_layer_constants::experience_size\n , double noise_level = motor_layer_constants::noise_level\n , std::string const& initial_parameter_folder = \"\"\n , control::Minimal_Seed_t seed = {0.,0.,0.}\n , std::size_t num_initial_experts = 1)\n : params(control::param_factory(robot, max_num_motor_experts, initial_parameter_folder, seed))\n , payloads(max_num_motor_experts)\n , motorspace(robot.get_joints())\n , experts(max_num_motor_experts, payloads, motorspace, learning_rate, experience_size, noise_level, params, robot )\n , gmes(experts, growth_rate, / one shot learning = /false, num_initial_experts, \"motor-layer\")\n {\n sts_msg(\"Creating motor layer with max. %u experts and growth rate: %1.4f\", max_num_motor_experts, growth_rate);\n if (initial_parameter_folder != \"\")\n sts_msg(\"Reading initial parameters from '%s'\", initial_parameter_folder.c_str());\n\n sts_msg(\"Number of initial experts: %u (loaded and created: %u)\", num_initial_experts, params.size());\n assert(params.size() == max_num_motor_experts);\n assert(get_max_number_of_experts() == max_num_motor_experts);\n assert(get_cur_number_of_experts() == num_initial_experts);\n\n assert(learning_rate > 0.);\n assert(growth_rate > 0.);\n }\n\n void execute_cycle(void) {\n motorspace.execute_cycle();\n gmes.execute_cycle();\n }\n\n void enable_learning(bool b) override { gmes.enable_learning(b); }\n void toggle_learning(void) { gmes.enable_learning(not gmes.is_learning_enabled()); }\n double get_learning_progress(void) const override { return gmes.get_learning_progress(); }\n\n std::size_t get_state(void) const { return gmes.get_state(); }\n\n bool is_learning_enabled(void) const { return gmes.is_learning_enabled(); }\n\n /* This is somewhat ugly. But the only way I have figured out to do that. /\n const control::Control_Parameter& get_controller_weights(std::size_t id) const {\n// dbg_msg(\"Fetching controller weights.\");\n Predictor_Base const& other = experts[id].get_predictor();\n Motor_Predictor const& motor_pred = dynamic_cast<Motor_Predictor const&>(other);\n return motor_pred.get_controller_weights();\n }\n\n\n std::size_t get_max_number_of_experts() const { return experts.size(); }\n std::size_t get_cur_number_of_experts() const { return gmes.get_number_of_experts(); }\n\n bool has_expert(std::size_t id) const { return experts[id].does_exists(); }\n\n void save(const std::string& foldername) const {\n const std::size_t num_experts = get_cur_number_of_experts();\n sts_msg(\"Saving %lu motor expert%s to folder %s\", num_experts, (num_experts>1?\"s\":\"\"), foldername.c_str());\n const std::string folder = basic::make_directory((foldername + \"/motor\").c_str());\n for (std::size_t i = 0; i < num_experts; ++i)\n {\n auto const& ctrl = get_controller_weights(i);\n ctrl.save_to_file(folder + \"/motor_expert_\" + std::to_string(i) + \".dat\", i);\n }\n }\n\n void connect_payloads(static_vector<State_Payload> s) {\n dbg_msg(\"Connecting payloads\");\n for (std::size_t i = 0; i < payloads.size(); ++i)\n payloads[i].connect(i, s);\n }\n\n control::Control_Vector params;\n static_vector<Motor_Payload> payloads;\n Motor_Space motorspace;\n Expert_Vector experts;\n GMES gmes;\n\n friend class Motor_Layer_Graphics;\n};\n\n\n\nnamespace constants {\n const unsigned subspace_num_datapoints = 200; // 2s of data at 100Hz\n}\n\nstruct motor_subspace_graphics : public Graphics_Interface\n{\n motor_subspace_graphics( robots::Joint_Model const& j0\n , robots::Joint_Model const& j1\n , Vector3 pos, float size\n , ColorTable const& colortable )\n : j0(j0)\n , j1(j1)\n , axis_xy(pos.x + size/2 , pos.y + size/2, pos.z, size, size, 0, std::string(\"xy\"))\n , axis_dt(pos.x + (2.0 + size)/2, pos.y + size/2, pos.z, 2.0-size, size, 1, std::string(j0.name + \"/\" + j1.name))\n , plot_xy(constants::subspace_num_datapoints, axis_xy, colors::magenta )\n , plot_j0(constants::subspace_num_datapoints, axis_dt, colors::light0 )\n , plot_j1(constants::subspace_num_datapoints, axis_dt, colors::light1 )\n , plot_p0(constants::subspace_num_datapoints, axis_dt, colortable )\n , plot_p1(constants::subspace_num_datapoints, axis_dt, colortable )\n {\n dbg_msg(\"Initialize motor subspace for joints:\\n\\t%2u: %s\\n\\t%2u: %s\", j0.joint_id, j0.name.c_str()\n , j1.joint_id, j1.name.c_str() );\n }\n\n void draw(const pref&) const {\n axis_xy.draw();\n plot_xy.draw();\n\n axis_dt.draw();\n plot_j0.draw(); // signals\n plot_j1.draw();\n plot_p0.draw_colored(); // predictions\n plot_p1.draw_colored();\n }\n\n void execute_cycle(float s0, float s1, unsigned expert_id) {\n plot_xy.add_sample(j0.motor.get(), j1.motor.get());\n plot_j0.add_sample(j0.motor.get());\n plot_j1.add_sample(j1.motor.get());\n plot_p0.add_colored_sample(s0, expert_id);\n plot_p1.add_colored_sample(s1, expert_id);\n }\n\n const robots::Joint_Model& j0;\n const robots::Joint_Model& j1;\n\n axes axis_xy;\n axes axis_dt;\n\n plot2D plot_xy;\n plot1D plot_j0, plot_j1; // joint motor commands\n colored_plot1D plot_p0, plot_p1; // predictions\n\n};\n\nclass Motor_Layer_Graphics : public Graphics_Interface {\npublic:\n\n Motor_Layer_Graphics( Motor_Layer const& motor_layer\n , robots::Robot_Interface const& robot )\n : motor_layer(motor_layer)\n , num_experts()\n , max_experts(motor_layer.gmes.get_max_number_of_experts())\n , winner()\n , subspace()\n , colortable(5, /randomized/true)\n {\n /** TODO\n + also for the rest of the joints\n + consider grouping the graphs in legs (4x 3D) instead of 6x 2D\n /\n const unsigned N = robot.get_number_of_symmetric_joints();\n const double size = 2.0/N;\n subspace.reserve(N);\n Vector3 pos(-1.0, 1.0, 0.);\n for (auto const& j0: robot.get_joints()) {\n if (j0.is_symmetric()) {\n robots::Joint_Model const& j1 = robot.get_joints()[j0.symmetric_joint];\n pos.y -= size;\n subspace.emplace_back(j1, j0, pos, size, colortable);\n }\n }\n }\n\n void draw(const pref& p) const {\n glColor3f(1.0, 1.0, 1.0);\n glprintf(1.05, 0.95, 0.0, 0.05, \"no. exp. = %03u (%03u/%03u)\", winner, num_experts, max_experts);\n\n auto const learning = motor_layer.is_learning_enabled();\n if (!learning) glColor3f(.3f,.3f,.3f);\n glprintf(1.05, 0.90, 0.0, 0.05, \"learning: %s\", learning ? \"enabled\" : \"disabled\");\n\n\n for (auto& s: subspace)\n s.draw(p);\n\n for (std::size_t i = 0; i < motor_layer.experts.size(); ++i)\n {\n if (winner == i) glColor3f(1.0, 0.5, 1.0);\n else if (motor_layer.experts[i].does_exists())\n glColor3f(.9f, .9f, .9f);\n else\n glColor3f(.3f,.3f,.3f);\n glprintf(-1.1, -1.5 - 0.05i, 0.0, 0.04, \"%2u\" , i);\n\n auto const& col = motor_layer.experts[i].does_exists() ? colortable[i] : colors::gray;\n draw::hbar(-1.4, -1.5 - 0.05i, 0.3, 0.02, 0.5 motor_layer.experts[i].get_learning_capacity(), gmes_constants::initial_learning_capacity, col);\n\n auto const& pred = motor_layer.experts[i].get_predictor();\n glPushMatrix();\n glTranslatef(0.0, -1.5 - 0.05i , 0.0);\n pred.draw();\n glPopMatrix();\n }\n\n };\n\n void execute_cycle() {\n\n num_experts = motor_layer.gmes.get_number_of_experts(); // update number of experts\n winner = motor_layer.gmes.get_winner();\n auto const& predictions = motor_layer.experts[winner].get_predictor().get_prediction();\n\n for (auto& s: subspace) {\n auto s0 = predictions.at(s.j0.joint_id);\n auto s1 = predictions.at(s.j1.joint_id);\n s.execute_cycle(s0, s1, winner);\n }\n };\n\n Motor_Layer const& motor_layer;\n std::size_t num_experts;\n std::size_t max_experts;\n std::size_t winner;\n\n std::vector<motor_subspace_graphics> subspace;\n ColorTable colortable;\n};\n\n} // namespace learning\n\n\n#endif // MOTOR_LAYER_H_INCLUDED\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5425454378128052, "alphanum_fraction": 0.5592727065086365, "avg_line_length": 25.44230842590332, "blob_id": "37098e775257a65b91525bfbbb184b723110e38a", "content_id": "23a1a41a736b0da5ed6ef9cb50c3d796811edbfc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1375, "license_type": "no_license", "max_line_length": 123, "num_lines": 52, "path": "/src/common/timer.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/\n\n +---------------------------------+\n \| Matthias Kubisch \|\n \| [email protected] \|\n \| November 2018 \|\n +---------------------------------+\n\n/\n\n#ifndef TIMER_HPP\n#define TIMER_HPP\n\n#include <chrono>\n\nclass SimpleTimer {\n uint64_t timeout_us;\n std::chrono::high_resolution_clock::time_point time_0; //what type?\n bool enabled;\n uint64_t elapsed_us;\n\npublic:\n SimpleTimer(uint64_t timeout_us, bool enabled = false)\n : timeout_us(timeout_us)\n , time_0(std::chrono::high_resolution_clock::now())\n , enabled(enabled)\n , elapsed_us()\n {}\n\n void start(void) { enabled = true; }\n void stop(void) { enabled = false; }\n\n bool check_if_timed_out_and_restart(uint64_t new_timeout_us) {\n timeout_us = new_timeout_us;\n return check_if_timed_out_and_restart();\n }\n\n bool check_if_timed_out_and_restart(void) {\n if (not enabled) return false;\n const auto time_1 = std::chrono::high_resolution_clock::now();\n elapsed_us = static_cast<uint64_t>(std::chrono::duration_cast<std::chrono::microseconds>(time_1 - time_0).count());\n bool is_timed_out = elapsed_us >= timeout_us;\n if (is_timed_out) {\n time_0 = time_1; // reset\n return true;\n } else return false;\n }\n\n uint64_t get_elapsed_us(void) const { return elapsed_us; }\n};\n\n#endif / TIMER_HPP /\n" }, { "alpha_fraction": 0.5325947403907776, "alphanum_fraction": 0.5419074892997742, "avg_line_length": 30.938461303710938, "blob_id": "9d89f5a5bf4748562ee315107d3952c2e9789515", "content_id": "40060b02ae12477f677ccabf6ce67e5ae5af3e10", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6228, "license_type": "no_license", "max_line_length": 119, "num_lines": 195, "path": "/src/common/socket_server.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SOCKET_SERVER_H\n#define SOCKET_SERVER_H\n\n#include <fcntl.h>\n#include <stdio.h>\n#include <stdlib.h>\n#include <unistd.h>\n#include <string.h>\n#include <sys/types.h>\n#include <sys/socket.h>\n#include <netinet/in.h>\n#include <netinet/tcp.h>\n#include <arpa/inet.h>\n\nnamespace network {\n\n namespace constants {\n const unsigned max_connections = 1;\n const unsigned max_tcp_msg_len = 8192;\n const unsigned timeout_ms = 500;\n }\n\nclass Socket_Server\n{\n int sockfd;\n int connectfd = -1;\n uint16_t port;\n struct sockaddr_in serv_addr = {};\n std::string recv_stream = \"\";\n std::string current_client_addr = \"\";\n\npublic:\n Socket_Server(const uint16_t port)\n : sockfd(socket(AF_INET, SOCK_STREAM, 0))\n , port(port)\n {\n if (sockfd < 0)\n err_msg(__FILE__,__LINE__,\"Cannot create TCP socket listening to port %u.\\n%s\", port, strerror(errno));\n\n memset(&serv_addr, 0, sizeof(serv_addr)); / clear struct /\n serv_addr.sin_family = AF_INET; / socket address type /\n serv_addr.sin_port = htons(port); / set port, convert unsigned to network byte order /\n serv_addr.sin_addr.s_addr = htonl(INADDR_ANY); / set IP of own host (INADDR_ANY) /\n int flag = 1; / set TCP_NODELAY flag /\n setsockopt(sockfd, IPPROTO_TCP, TCP_NODELAY, &flag, sizeof(int));\n\n / make the socket non-blockable /\n int x = fcntl(sockfd ,F_GETFL, 0);\n if (fcntl(sockfd, F_SETFL, x \| O_NONBLOCK))\n err_msg(__FILE__,__LINE__,\"could not set socket blocking\");\n\n / set timeout /\n struct timeval read_timeout = timeval{};\n read_timeout.tv_usec = constants::timeout_ms 1000;\n if (setsockopt(sockfd, SOL_SOCKET, SO_RCVTIMEO, &read_timeout, sizeof(read_timeout)) < 0)\n err_msg(__FILE__,__LINE__,\"Cannot set socket options for read timeout.\\n%s\", strerror(errno));\n\n /* bind socket to address and port /\n if (-1 == bind(sockfd, (struct sockaddr ) &serv_addr, sizeof(serv_addr)))\n {\n close(sockfd);\n err_msg(__FILE__,__LINE__,\"Failed binding the socket.\\nPlease wait until released or try another port.\\n\");\n }\n\n if (-1 == listen(sockfd, (int) constants::max_connections))\n {\n close(sockfd);\n err_msg(__FILE__,__LINE__,\"Failed listening to socket.\");\n }\n }\n\n ~Socket_Server() {\n close_connection();\n close(sockfd);\n }\n\n bool open_connection(void)\n {\n /* wait for client connection and accept, if any. /\n struct sockaddr_storage addr;\n socklen_t len = sizeof addr;\n\n\n connectfd = accept(sockfd, (struct sockaddr) &addr, &len);\n if (connectfd < 0) {\n return false;\n }\n\n set_socket_blocking(connectfd, false);\n\n char client_addr[INET6_ADDRSTRLEN];\n unsigned port;\n if (addr.ss_family == AF_INET) { /* IPv4 /\n struct sockaddr_in s = (struct sockaddr_in) &addr;\n port = ntohs(s->sin_port);\n inet_ntop(AF_INET, &s->sin_addr, client_addr, sizeof client_addr);\n } else { // AF_INET6\n struct sockaddr_in6 s = (struct sockaddr_in6) &addr;\n port = ntohs(s->sin6_port);\n inet_ntop(AF_INET6, &s->sin6_addr, client_addr, sizeof client_addr);\n }\n\n sts_msg(\"Connection opened to client: %s:%u\", client_addr, port);\n current_client_addr = client_addr;\n return true;\n }\n\n void close_connection(void)\n {\n if (connectfd < 0) return; / already closed, nothing to do /\n\n if (shutdown(connectfd, SHUT_RDWR) < 0)\n wrn_msg(\"Cannot shutdown TCP connection.\\n%s\", strerror(errno));\n else\n sts_msg(\"Connection successfully shut down.\");\n\n close(connectfd);\n sts_msg(\"Connection closed.\");\n }\n\n bool send_message(std::string const& msg) const\n {\n if (write(connectfd, msg.c_str(), msg.length()) < 0) {\n wrn_msg(\"Failed writing to TCP socket.\\n%s\", strerror(errno));\n return false;\n }\n return true;\n }\n\n std::string get_next_msg(void) const\n {\n / create and clear buffer /\n char buffer[constants::max_tcp_msg_len];\n memset(buffer, 0, constants::max_tcp_msg_len);\n\n / read from socket (blocking) /\n if (recv(connectfd, buffer, constants::max_tcp_msg_len, MSG_DONTWAIT) < 0)\n {\n if (errno != EAGAIN)\n wrn_msg(\"Failed reading from socket.\\n%s\", strerror(errno));\n return \"\";\n }\n\n /if (0 == n)\n {\n printf(\"Reading no more bytes from socket. Exiting.\\n\");\n return std::string(\"EXIT\\n\");\n }/\n\n return std::string(buffer);\n }\n\n / return part of the stream until next newline character /\n std::string get_next_line(void)\n {\n std::string::size_type pos;\n\n recv_stream = get_next_msg();\n if (recv_stream.size() == 0) return \"\";\n\n // continue reading if there was data but no complete command, timeout 10ms\n unsigned t = 0;\n while((pos = recv_stream.find(\"\\n\", 0)) == std::string::npos) {\n usleep(1000);\n recv_stream += get_next_msg();\n if (++t >= 10) {\n wrn_msg(\"Timeout reading data line.\");\n break;\n }\n }\n\n std::string retstr = recv_stream.substr(0, pos);\n recv_stream.erase(0, pos+1);\n\n return retstr;\n }\n\n\n std::string const& get_current_client_address(void) const { return current_client_addr; }\n\n\n bool set_socket_blocking(int fd, bool blocking)\n {\n if (fd < 0) return false;\n int flags = fcntl(fd, F_GETFL, 0);\n if (flags == -1) return false;\n flags = blocking ? (flags & ~O_NONBLOCK) : (flags \| O_NONBLOCK);\n return (fcntl(fd, F_SETFL, flags) == 0) ? true : false;\n }\n\n};\n\n} / namespace network /\n\n#endif / SOCKET_SERVER_H /\n" }, { "alpha_fraction": 0.6127395629882812, "alphanum_fraction": 0.6369785666465759, "avg_line_length": 36.74468231201172, "blob_id": "6e8cf63151f91ba671afbf2abd0b6f37b73240a7", "content_id": "50387d6aa4f6f29376e55d07e74dff0c669354ea", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1774, "license_type": "no_license", "max_line_length": 96, "num_lines": 47, "path": "/src/evolution/individual.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include \"individual.h\"\n\nvoid\ncrossover(const Individual& mother, const Individual& father, Individual& child)\n{\n assert(child.genome.size() == father.genome.size());\n assert(child.genome.size() == mother.genome.size());\n\n / 50% chance to inherit each feature from either mother or father /\n for (unsigned int j = 0; j < child.genome.size(); j++)\n child.genome[j] = (random_value(0, 1.0) < 0.5) ? mother.genome[j] : father.genome[j];\n\n / average the mutation rate /\n child.fitness.reset();\n child.mutation_rate = .5 mother.mutation_rate + .5 * father.mutation_rate;\n child.meta_mutation_rate = .5 * mother.meta_mutation_rate + .5 * father.meta_mutation_rate;\n}\n\nvoid\nIndividual::initialize_from_seed(const std::vector<double>& seed)\n{\n assert(genome.size() == seed.size());\n double sigma = mutation_rate / sqrt(genome.size()); // normalize\n for (unsigned int j = 0; j < genome.size(); ++j)\n genome[j] = seed[j] + rand_norm_zero_mean(sigma);\n fitness.reset();\n}\n\nvoid\nIndividual::mutate(void)\n{\n assert_in_range(mutation_rate , 0.0001, 1.0 );\n assert_in_range(meta_mutation_rate, 0.0 , M_LN2);\n assert(genome.size() > 0);\n /** TODO search for literature reference of that. /\n const double rnd_val = random_value_norm(0.0, meta_mutation_rate, -M_LN2, M_LN2);\n const double factor = exp(rnd_val); // mutate the mutation rate\n assert_in_range(factor, 0.5, 2.0);\n\n mutation_rate = factor;\n mutation_rate = clip(mutation_rate, 0.0001, 1.0);\n// sts_msg(\" mutating: mu = %1.5f, d mu = %1.3f\", mutation_rate, factor);\n const double sigma = mutation_rate / sqrt(genome.size()); // normalize\n\n for (auto& element : genome)\n element += rand_norm_zero_mean(sigma);\n}\n" }, { "alpha_fraction": 0.5536980628967285, "alphanum_fraction": 0.6137284636497498, "avg_line_length": 37.70588302612305, "blob_id": "12ca67c5f9ff78463ac1213e305c7832a13f5bce", "content_id": "b43f0384095143dbc529e62595a9d6182437707d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3948, "license_type": "no_license", "max_line_length": 128, "num_lines": 102, "path": "/src/draw/draw.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* draw.h /\n#ifndef DRAW_H\n#define DRAW_H\n\n#include <GL/gl.h>\n#include <GL/glu.h>\n#include <GL/glut.h>\n#include <stdio.h>\n#include <stdlib.h>\n#include <stdarg.h>\n\n#include <string.h>\n#include <string>\n\n#include <basic/color.h>\n\nconst GLubyte red [] = {255, 0, 0, 255};\nconst GLubyte green [] = { 0, 255, 0, 255};\nconst GLubyte blue [] = { 0, 0, 255, 255};\nconst GLubyte white [] = {255, 255, 255, 255};\nconst GLubyte white_trans [] = {255, 255, 255, 64};\nconst GLubyte white_trans2 [] = {255, 255, 255, 128};\nconst GLubyte cyan [] = { 0, 255, 255, 255};\nconst GLubyte yellow [] = {255, 255, 0, 255};\nconst GLubyte black [] = { 0, 0, 0, 255};\nconst GLubyte orange [] = {255, 128, 0, 255};\nconst GLubyte magenta [] = {255, 0, 255, 255};\n\nconst GLubyte LineColorMix0[4][4] = {{ 0, 128, 255, 255},\n { 0, 128, 255, 196},\n { 0, 128, 255, 128},\n { 0, 128, 255, 64}};\n\n\nconst GLubyte LineColorMix1[4][4] = {{255, 0, 0, 255},\n {255, 0, 0, 196},\n {255, 0, 0, 128},\n {255, 0, 0, 64}};\n\ninline void set_color(Color4 c) { glColor4d(c.r, c.g, c.b, c.a); }\ninline void set_color(Color4 c, float a) { glColor4d(c.r, c.g, c.b, a); }\n\n\nstruct Point\n{\n float x;\n float y;\n float z;\n};\n\n\nvoid draw_line(const GLfloat x[3], const GLfloat y[3]);\nvoid draw_line(const Point& p1, const Point& p2);\nvoid draw_line(const float x1, const float y1, const float z1,\n const float x2, const float y2, const float z2);\nvoid draw_line(const float x1, const float y1,\n const float x2, const float y2);\n\nvoid draw_line2D(const GLfloat x[2], const GLfloat y[2]);\n\nvoid draw_rect(const GLfloat x1[3], const GLfloat x2[3], const GLfloat x3[3], const GLfloat x4[3]) __attribute__ ((deprecated));\n\nvoid draw_rect(const float size_x, const float size_y);\nvoid draw_rect(const float x, const float y, const float size_x, const float size_y);\n\nvoid draw_square(const float size);\nvoid draw_square(const float x, const float y, const float size);\n\nvoid draw_cube(const GLfloat x1[3], const GLfloat x2[3], const GLfloat x3[3], const GLfloat x4[3],\n const GLfloat x5[3], const GLfloat x6[3], const GLfloat x7[3], const GLfloat x8[3]);\n\nvoid draw_solid_cube(const float x, const float y, const float z, const float size);\nvoid draw_wire_cube(const float x, const float y, const float z, const float size);\n\nvoid draw_fill_rect(const float size_x, const float size_y);\nvoid draw_fill_rect(const float x, const float y, const float size_x, const float size_y);\n\nvoid draw_fill_square(const float size);\nvoid draw_fill_square(const float x, const float y, const float size);\n\n\n\nvoid fill_rect(const GLfloat x1[3], const GLfloat x2[3], const GLfloat x3[3], const GLfloat x4[3]);\n\nvoid draw_grid2D(const float range, const int lines);\nvoid draw_grid3D(const float range, const int lines);\n\nvoid draw_text_small(const float x, const float y, const float z, const char str) __attribute__ ((deprecated));\nvoid draw_text_medium(const float x, const float y, const float z, const char str) __attribute__ ((deprecated));\n\nvoid output(const GLfloat x, const GLfloat y, const GLfloat z, const char text) __attribute__ ((deprecated));\n\nvoid gl_msg(const float px, const float py, const float pz, const char* format, ...) __attribute__ ((deprecated));\n\nvoid glprintf(float x, float py, float pz, float scale_factor, const char* format, ...);\nvoid glprintc(float x, float py, float pz, float scale_factor, const char* str);\nvoid glprints(float x, float py, float pz, float scale_factor, const std::string str);\n\nnamespace draw {\n void fill_rect(float px, float py, float dx, float dy);\n}\n#endif /DRAW_H/\n" }, { "alpha_fraction": 0.5519034266471863, "alphanum_fraction": 0.5644166469573975, "avg_line_length": 31.4228572845459, "blob_id": "972e385ce11ba351cf474c26cb4a2d0edde5ceb3", "content_id": "607fa1adc12eb936e0e4355fbbbc83b6e6c8d71e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 11348, "license_type": "no_license", "max_line_length": 137, "num_lines": 350, "path": "/src/learning/forward_inverse_model.hpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BIDIRECTIONAL_MODELS_H_INCLUDED\n#define BIDIRECTIONAL_MODELS_H_INCLUDED\n\n#include <vector>\n#include <common/static_vector.h>\n#include <common/modules.h>\n\n\nnamespace learning {\n\n\n\nnamespace model {\n typedef double scalar_t;\n typedef std::vector<scalar_t> vector_t;\n typedef copyable_static_vector<copyable_static_vector<scalar_t>> matrix_t;\n\n template <typename Float_t>\n struct AdamData {\n static constexpr Float_t b1 = 0.9;\n static constexpr Float_t b2 = 0.999;\n static constexpr Float_t e0 = 10e-8;\n\n Float_t m, v, bt1, bt2;\n\n AdamData(void) : m(.0), v(.0), bt1(1.), bt2(1.) { }\n\n Float_t get(Float_t grad) {\n bt1 = b1;\n bt2 = b2;\n\n m = b1 * m + (1. - b1) * grad; // 1st momentum\n v = b2 * v + (1. - b2) * grad * grad; // 2nd momentum\n\n const Float_t M = m / (1. - bt1);\n const Float_t V = v / (1. - bt2);\n\n return (M / (sqrt(V) + e0));\n\n }\n };\n\n typedef copyable_static_vector<copyable_static_vector<AdamData<scalar_t>>> gradient_t;\n\n\n\n template <typename MatrixType>\n void randomize_weights(MatrixType& mat, double random_weight_range) {\n assert_in_range(random_weight_range, 0.0, 5.0);\n const double normed_std_dev = random_weight_range / sqrt(mat[0].size());\n assert(normed_std_dev != 0.0);\n\n for (std::size_t i = 0; i < mat.size(); ++i)\n for (std::size_t j = 0; j < mat[i].size(); ++j)\n mat[i][j] = rand_norm_zero_mean(normed_std_dev); // normalized by sqrt(N), N:#inputs\n\n }\n}\n\ntemplate <typename Vector_t>\nclass twopart_vector {\npublic:\n\n Vector_t& part0;\n Vector_t& part1;\n\n twopart_vector(Vector_t& part0, Vector_t& part1) : part0(part0), part1(part1) {\n assert(part0.size() > 0);\n assert(part1.size() > 0);\n }\n\n\n twopart_vector& operator=(Vector_t const& vec) {\n assert(vec.size() == size());\n for (std::size_t i = 0; i < size(); ++i)\n this->operator[](i) = vec[i];\n return this;\n }\n\n twopart_vector& operator=(twopart_vector const& other) {\n assert(part0.size() == other.part0.size());\n assert(part1.size() == other.part1.size());\n part0 = other.part0;\n part1 = other.part1;\n return this;\n }\n\n std::size_t size(void) const { return part0.size() + part1.size(); }\n\n model::scalar_t& operator[] (std::size_t index) {\n const auto s0 = part0.size();\n if (index < s0) return part0[index];\n else if (index < s0 + part1.size()) return part1[index-s0];\n else {\n assert(false);\n return part0[0];\n }\n }\n\n const model::scalar_t& operator[] (std::size_t index) const {\n const auto s0 = part0.size();\n if (index < s0) return part0[index];\n else if (index < s0 + part1.size()) return part1[index-s0];\n else {\n assert(false);\n return part0[0];\n }\n }\n};\n\ntemplate <typename T = model::scalar_t>\nclass LinearTransfer {\npublic:\n static T transfer(T const& x) { return x; }\n static T derive(T const& /y/) { return T{1}; }\n};\n\ntemplate <typename T = model::scalar_t>\nclass TanhTransfer {\npublic:\n static T transfer(T const& x) { return tanh(x); } // normal tangens hyperbolicus\n static T derive (T const& y) { return (1.0 + y) * (1.0 - y); } // this is y' with y=tanh(x)\n static T inverse (T const& x) { return atanh(x); /log((1+x)/(1-x))/2/ } // area tangens hyperbolicus = tanh^-1\n};\n\nstruct Weight_Statistics_t {\n\n static constexpr float zero_thrsh = 0.001;\n\n unsigned num = 0, total = 0;\n float avg = 0.f, ext = 0.f, vol = 0.f;\n\n};\n\ntemplate <typename Transfer_t>\nclass NeuralModel {\n\n model::vector_t y; // output, predictions\n model::matrix_t W; // Weights\n model::scalar_t E; // prediction error\n model::vector_t d; // delta error (used for back-propagation)\n\n model::gradient_t G; // Adam's Gradient Statistics\n\npublic:\n NeuralModel(std::size_t size_in, std::size_t size_out, double random_weight_range)\n : y(size_out)\n , W(size_out, size_in)\n , E()\n , d(size_out)\n , G(size_out, size_in)\n {\n model::randomize_weights(W, random_weight_range);\n }\n\n /* calculate and get back-propagated delta error /\n model::vector_t get_backprop_err(void) const\n {\n model::vector_t r(W[0].size());\n for (std::size_t j = 0; j < r.size(); ++j)\n for (std::size_t i = 0; i < d.size(); ++i)\n r[j] += d[i] W[i][j];\n return r;\n }\n\n\n template <typename InputVector_t>\n model::vector_t const& propagate(InputVector_t const& in)\n {\n check_vectors(in, W[0]);\n\n for (std::size_t i = 0; i < y.size(); ++i) {\n model::scalar_t a = .0;\n for (std::size_t j = 0; j < in.size(); ++j)\n a += W[i][j] * in[j];\n y[i] = Transfer_t::transfer(a);\n }\n return y;\n }\n\n /* adapt should follow a propagation to fill y /\n template <typename InputVector_From_t, typename InputVector_To_t>\n void adapt(InputVector_From_t const& in, InputVector_To_t const& tar, double learning_rate, double regularization_rate)\n {\n check_vectors(tar, y);\n check_vectors(in, W[0]);\n assert_in_range(learning_rate, 0.0, 1.0);\n E = .0; // reset total sum of prediction errors\n for (std::size_t i = 0; i < y.size(); ++i) {\n const model::scalar_t e_i = tar[i] - y[i];\n E += square(e_i);\n d[i] = Transfer_t::derive(y[i]) e_i; // remember delta error for back-propagation signal\n for (std::size_t j = 0; j < in.size(); ++j)\n //W[i][j] += learning_rate * d[i] * in[j] - regularization_rate * W[i][j];//* sign(W[i][j]);\n /* target learning L1 regularization /\n W[i][j] += learning_rate G[i][j].get(d[i] * in[j]) - regularization_rate * W[i][j];\n }\n }\n\n model::matrix_t const& get_weights() const { return W; }\n model::vector_t const& get_outputs() const { return y; }\n model::scalar_t get_error () const { return E/y.size(); }\n\n void randomize_weights(double random_weight_range) { model::randomize_weights(W, random_weight_range); }\n\n\n void constrain_weights(void) {\n for (std::size_t i = 0; i < W.size(); ++i)\n for (std::size_t j = 0; j < W[i].size(); ++j)\n W[i][j] = clip(W[i][j], 5);\n }\n\n //move to w_statistics class\n Weight_Statistics_t get_weight_statistics(void) const {\n Weight_Statistics_t stat = {};\n stat.num = 0;\n stat.total = W.size() * W[0].size();\n for (std::size_t i = 0; i < W.size(); ++i)\n for (std::size_t j = 0; j < W[i].size(); ++j) {\n const float w = W[i][j];\n stat.avg += w;\n stat.vol += std::abs(w);\n if (fabs(w) > Weight_Statistics_t::zero_thrsh) ++stat.num;\n if (fabs(w) > fabs(stat.ext)) stat.ext = w;\n }\n\n stat.avg /= stat.total; // normalize by number of weights\n return stat;\n }\n\n}; /* LinearModel /\n\n\nclass InverseNeuralModel {\n\n model::vector_t y, a; // output, temp\n model::matrix_t M; // Weights\n model::scalar_t e; // prediction error\n\npublic:\n static constexpr auto& G = TanhTransfer<>::inverse;\n\n InverseNeuralModel(std::size_t size_in, std::size_t size_out, double random_weight_range)\n : y(size_out)\n , a(size_in)\n , M(size_out, size_in)\n , e()\n {\n model::randomize_weights(M, random_weight_range);\n }\n\n template <typename InputVector_t>\n model::vector_t const& propagate(InputVector_t const& in)\n {\n check_vectors(in, a);\n for (std::size_t j = 0; j < in.size(); ++j)\n a[j] = G(in[j]);\n\n for (std::size_t i = 0; i < y.size(); ++i) {\n y[i] = .0;\n for (std::size_t j = 0; j < a.size(); ++j)\n y[i] += M[i][j] a[j];\n }\n\n return y;\n }\n\n /* adapt should follow a propagation to fill y /\n template <typename InputVector_From_t, typename InputVector_To_t>\n void adapt(InputVector_From_t const& in, InputVector_To_t const& tar, double learning_rate, double normalize_rate) {\n check_vectors(tar, y);\n check_vectors(in, a);\n assert_in_range(learning_rate, 0.0, 5.0);\n\n e = .0;\n for (std::size_t j = 0; j < in.size(); ++j)\n a[j] = G(in[j]);\n\n for (std::size_t i = 0; i < y.size(); ++i) {\n model::scalar_t err_i = learning_rate (tar[i] - y[i]);\n e += square(tar[i] - y[i]);\n for (std::size_t j = 0; j < in.size(); ++j)\n M[i][j] += err_i * a[j] - normalize_rate * sign(M[i][j]);\n }\n }\n\n model::matrix_t const& get_weights() const { return M; }\n model::vector_t const& get_outputs() const { return y; }\n model::scalar_t get_error () const { return e/y.size(); }\n\n void randomize_weights(double random_weight_range) { model::randomize_weights(M, random_weight_range); }\n\n\n}; /* LinearModel /\n\n\ntemplate <typename ForwardType, typename InverseType>\nclass BidirectionalModel {\n\n ForwardType m_forward;\n InverseType m_inverse;\n\npublic:\n BidirectionalModel(std::size_t size_x, std::size_t size_y, double random_weight_range)\n : m_forward(size_x, size_y, random_weight_range)\n , m_inverse(size_y, size_x, random_weight_range)\n {}\n\n template <typename InputVector_t> model::vector_t const& propagate_forward(InputVector_t const& X) { return m_forward.propagate(X); }\n template <typename InputVector_t> model::vector_t const& propagate_inverse(InputVector_t const& Y) { return m_inverse.propagate(Y); }\n\n template <typename InputVector_X_t, typename InputVector_Y_t>\n void adapt(InputVector_X_t const& X, InputVector_Y_t const& Y, double learning_rate, double regularization_rate)\n {\n m_forward.adapt(/from/X, /to/Y, learning_rate, regularization_rate);\n m_inverse.adapt(/from/Y, /to/X, learning_rate, regularization_rate);\n }\n\n model::matrix_t const& get_weights () const { return m_forward.get_weights(); }\n model::matrix_t const& get_weights_inverse() const { return m_inverse.get_weights(); }\n\n model::vector_t const& get_forward_result() const { return m_forward.get_outputs(); }\n model::vector_t const& get_inverse_result() const { return m_inverse.get_outputs(); }\n\n model::scalar_t get_forward_error() const { return m_forward.get_error(); }\n model::scalar_t get_inverse_error() const { return m_inverse.get_error(); }\n\n\n void randomize_weights(double range) {\n m_forward.randomize_weights(range);\n m_forward.randomize_weights(range);\n }\n\n model::vector_t get_backprop_gradient(void) const { return m_forward.get_backprop_err(); }\n\n ForwardType const& get_forward_model(void) const { return m_forward; }\n InverseType const& get_inverse_model(void) const { return m_inverse; }\n\n\n void constrain_weights(void) {\n m_forward.constrain_weights();\n m_inverse.constrain_weights();\n }\n\n}; / BidirectionalModel /\n\n\n} / namespace learning /\n\n#endif / BIDIRECTIONAL_MODELS_H_INCLUDED /\n" }, { "alpha_fraction": 0.6607192754745483, "alphanum_fraction": 0.6626707315444946, "avg_line_length": 32.52336502075195, "blob_id": "134abcd5b292da8b5dc9aad6db97ec887f708b69", "content_id": "bf1ed5aafe20f51420001208ad19596b605f625a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3587, "license_type": "no_license", "max_line_length": 127, "num_lines": 107, "path": "/src/control/jointcontrol.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef JOINTCONTROL_H_INCLUDED\n#define JOINTCONTROL_H_INCLUDED\n\n#include <vector>\n#include <cassert>\n\n#include <common/modules.h>\n#include <common/config.h>\n#include <common/log_messages.h>\n\n#include <control/controlparameter.h>\n#include <control/control_vector.h>\n#include <control/control_core.h>\n\n#include <robots/robot.h>\n#include <robots/joint.h>\n\nnamespace control {\n\nclass Jointcontrol;\n\nstruct Minimal_Seed_t {\n Minimal_Seed_t(const std::vector<double>& vec)\n : pgain(), damping(), motor_self()\n {\n assert(vec.size() >= 3);\n pgain = vec[0];\n damping = vec[1];\n motor_self = vec[2];\n }\n\n Minimal_Seed_t(double pgain, double damping, double motor_self) : pgain(pgain), damping(damping), motor_self(motor_self) {}\n\n std::vector<double> get_vector() const { return {pgain, damping, motor_self}; }\n double pgain;\n double damping;\n double motor_self;\n};\n\nstd::size_t get_number_of_inputs(robots::Robot_Interface const& robot);\nControl_Parameter get_initial_parameter(robots::Robot_Interface const& robot, const Minimal_Seed_t& seed, bool symmetric);\nControl_Parameter make_symmetric (robots::Robot_Interface const& robot, const Control_Parameter& other);\nControl_Parameter make_asymmetric (robots::Robot_Interface const& robot, const Control_Parameter& other);\nControl_Parameter turn_symmetry (robots::Robot_Interface const& robot, const Control_Parameter& other);\n\nControl_Parameter initialize_anyhow ( robots::Robot_Interface const& robot, Jointcontrol const& control\n , bool is_symmetric, const Minimal_Seed_t params_pdm, const std::string& filename);\n\nControl_Vector param_factory( const robots::Robot_Interface& robot\n , std::size_t number_of_motor_units\n , const std::string& folder\n , const control::Minimal_Seed_t& seed );\n\n/* TODO: check for initialization problem in controller when using csl hold /\n\nclass Jointcontrol\n{\npublic:\n Jointcontrol(robots::Robot_Interface& robot);\n\n void execute_cycle(void);\n void reset(void);\n\n void insert_motor_command(unsigned index, double value);\n\n void switch_symmetric(bool switched);\n void switch_symmetric() { switch_symmetric(not is_switched); }\n\n void set_control_parameter(const Control_Parameter& controller);\n void set_control_parameter(const std::vector<double>& params);\n\n double get_normalized_mechanical_power(void) const;\n double get_normalized_control_change(void) const;\n\n void print_parameter(void) const;\n\n std::size_t get_number_of_parameter (void) const { return number_of_params_asym; }\n std::size_t get_number_of_symmetric_parameter(void) const { return number_of_params_sym; }\n\n bool is_symmetric(void) const { return symmetric_controller; }\n\n double get_L1_norm(void);\n\n void set_input_gain(double g) { core.gain = clip(g, 0., 1.); }\n\nprivate:\n\n void apply_symmetric_weights(const std::vector<double>& params);\n void apply_weights (const std::vector<double>& params);\n void integrate_accels (void);\n\n robots::Robot_Interface& robot;\n Fully_Connected_Symmetric_Core core;\n\n const std::size_t number_of_params_sym;\n const std::size_t number_of_params_asym;\n\n bool symmetric_controller;\n bool is_switched;\n\n friend class Jointcontrol_Graphics;\n};\n\n\n} // namespace control\n\n#endif // JOINTCONTROL_H_INCLUDED\n" }, { "alpha_fraction": 0.5627689957618713, "alphanum_fraction": 0.5649210810661316, "avg_line_length": 38.26760482788086, "blob_id": "0f1efac37a082b5159c8120d6cf00819b61aff7d", "content_id": "5a34081cb49a98f31231ce7f866fb17e4276bbd2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2788, "license_type": "no_license", "max_line_length": 168, "num_lines": 71, "path": "/src/control/control_vector.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <control/control_vector.h>\n\n\nnamespace control {\n\n void randomize_control_parameter(Control_Parameter& params, double std_dev, double max_dev)\n {\n assert_in_range(std_dev, 0.0, max_dev);\n for (std::size_t i = 0; i < params.size(); ++i)\n params[i] += random_value_norm(.0, std_dev, -max_dev, +max_dev);\n }\n\n\n Control_Vector::Control_Vector( std::size_t max_number_of_parameter_sets\n , const std::string& foldername\n , bool include_mirrored )\n : max_number_of_parameter_sets(max_number_of_parameter_sets)\n , controls()\n {\n sts_msg(\"Initialize control vector from folder: %s\", foldername.c_str());\n controls.reserve(max_number_of_parameter_sets);\n if (not foldername.empty())\n {\n basic::Filelist files = basic::list_directory(foldername.c_str(), \".dat\");\n / add files successively /\n if (files.size() > 0) {\n for (auto& f : files)\n controls.emplace_back(foldername + f);\n } else\n sts_msg(\"No files found in '%s'\", foldername.c_str());\n } else\n sts_msg(\"Initialize empty control vector.\");\n\n if (include_mirrored) {\n std::size_t len = controls.size();\n for (std::size_t i = 0; i < len; ++i)\n if (not controls[i].is_symmetric() and not controls[i].is_mirrored()) {\n controls.emplace_back(controls[i].get_parameter(), false, true);\n sts_msg(\"Adding mirrored variant of controller %lu\", i);\n }\n }\n if (controls.size() >= max_number_of_parameter_sets)\n wrn_msg(\"Maximum number of parameter sets (%lu) exceeded (%lu).\\nConsider checking the configuration file.\", max_number_of_parameter_sets, controls.size());\n }\n\n\n void Control_Vector::add( const std::string& filename\n , const std::size_t number_of_params\n , bool symmetric\n , bool mirrored )\n {\n assert(controls.size() <= max_number_of_parameter_sets);\n controls.emplace_back(filename, number_of_params, symmetric, mirrored);\n }\n\n\n void Control_Vector::add(const std::string& filename) {\n assert(controls.size() <= max_number_of_parameter_sets);\n controls.emplace_back(filename);\n }\n\n void Control_Vector::add(const Control_Parameter& params) {\n assert(controls.size() <= max_number_of_parameter_sets);\n controls.emplace_back(params);\n }\n\n void Control_Vector::reload(std::size_t index, const std::string& filename) {\n controls.at(index) = Control_Parameter(filename);\n }\n\n} / namespace control /\n" }, { "alpha_fraction": 0.5117924809455872, "alphanum_fraction": 0.5227987170219421, "avg_line_length": 23.461538314819336, "blob_id": "1515ac75c1f9a98afe4b4899d0405e06f5fa0993", "content_id": "f3b9ed964d105798e747c4633d73dc8cc86317d3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1272, "license_type": "no_license", "max_line_length": 77, "num_lines": 52, "path": "/src/control/pusher.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef PUSHER_H_INCLUDED\n#define PUSHER_H_INCLUDED\n\n#include <basic/vector3.h>\n\nclass robot_pusher {\n\n robots::Simloid& robot;\n double probability;\n double strength;\n unsigned body_index;\n Vector3 force;\n unsigned duration;\n bool active;\n\npublic:\n robot_pusher(robots::Simloid& robot, double probability, double strength)\n : robot(robot)\n , probability(probability)\n , strength(strength)\n , body_index(0)\n , force(0.)\n , duration(0)\n , active(false)\n {\n assert(probability < 1.0 and probability >= 0.);\n }\n\n void execute_cycle(void) {\n if (!active) {\n if (random_value() > 1.0 - probability)\n {\n force.random(-strength,+strength);\n body_index = random_index(robot.get_number_of_bodies());\n robot.set_force(body_index, force);\n duration = random_int(0, 25);\n active = true;\n }\n }\n else { / still active /\n if (duration == 0)\n {\n force.zero();\n robot.set_force(body_index, force); // set force to zero\n active = false;\n } else\n --duration;\n }\n }\n};\n\n#endif // PUSHER_H_INCLUDED\n" }, { "alpha_fraction": 0.6998654007911682, "alphanum_fraction": 0.7012113332748413, "avg_line_length": 24.586206436157227, "blob_id": "fef6ea478015b3d0ad8d286a05e08517d3788f59", "content_id": "750d0b49e4bb336387352a79ef783637f8800c52", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1486, "license_type": "no_license", "max_line_length": 128, "num_lines": 58, "path": "/src/control/jointcontroller.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef JOINTCONTROLLER_H\r\n#define JOINTCONTROLLER_H\r\n\n#include <vector>\n#include <cmath>\n#include <cassert>\n\n#include <common/modules.h>\n#include <common/config.h>\n#include <common/robot_conf.h>\n#include <common/file_io.h>\n\n#define INITIAL_BIAS 0.1\n\n/* WARNING: This class is outdated, do not use it anymore.\n * Use Jointcontrol instead.\n /\n\nclass Jointcontroller\n{\npublic:\n Jointcontroller( Robot_Configuration& configuration, bool symmetric_controller\n , double param_p, double param_d, double param_m, const std::string& seed_filename) __attribute_deprecated__;\n\n ~Jointcontroller() { sts_msg(\"Destroying joint controller.\"); }\n\n void loop(void);\n void reset(void);\n\n const std::vector<double> get_control_parameter(void) const;\n unsigned int get_number_of_parameter(void) const { return total_num_params; }\n\n void set_control_parameter(const std::vector<double>& params);\n void set_seed_parameter(void);\n double get_normalized_mechanical_power(void) const;\n void print_parameter(void) const;\n\nprivate:\n Robot_Configuration& robot;\n\n const unsigned int num_inputs;\n unsigned int total_num_params;\n\n std::vector<double> activation;\n std::vector<std::vector<double> > weights;\n\n std::vector<double> X;\n std::vector<double> Y;\n\n std::vector<double> seed_from_file;\n\n void set_initial_parameter(double p, double d, double m);\n void load_seed(const std::string& filename);\n\n};\n\n\n#endif // JOINTCONTROLLER\n" }, { "alpha_fraction": 0.5432921051979065, "alphanum_fraction": 0.5670789480209351, "avg_line_length": 28.19444465637207, "blob_id": "978dee03b90a64bcaa94ff39b651731c1e938c86", "content_id": "0ed9d0b438fbe37492eeb3028cf0602c548237a1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1051, "license_type": "no_license", "max_line_length": 88, "num_lines": 36, "path": "/src/learning/predictor_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef PREDICTOR_GRAPHICS_H_INCLUDED\n#define PREDICTOR_GRAPHICS_H_INCLUDED\n\n#include <common/modules.h>\n#include <draw/graphics.h>\n#include <basic/color.h>\n#include <learning/predictor.h>\n\nclass Predictor_Graphics : public Graphics_Interface\n{\n const Predictor_Base& predictor;\n const Color4 color;\n\npublic:\n Predictor_Graphics(const Predictor_Base& predictor)\n : predictor(predictor)\n , color(random_value(0.2, 1.0), random_value(0.2, 1.0), random_value(0.2, 1.0), 0.2)\n {\n assert(predictor.get_experience()[0].size() >= 3);\n }\n\n void draw(const pref& /p/) const\n {\n auto const& experience = predictor.get_experience();\n if (experience.size() > 1) {\n set_color(color);\n for (std::size_t i = 0; i < experience.size(); ++i)\n draw_solid_cube( experience[i][0]\n , experience[i][1]\n , experience[i][2]\n , 0.02 );\n }\n }\n};\n\n#endif // PREDICTOR_GRAPHICS_H_INCLUDED\n" }, { "alpha_fraction": 0.45288196206092834, "alphanum_fraction": 0.4620310962200165, "avg_line_length": 24.418603897094727, "blob_id": "7d41fe26373680567ba0ba70726420d61e77d492", "content_id": "1828ad70ed2acf246cc56d38ee971ee0891d565b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1093, "license_type": "no_license", "max_line_length": 102, "num_lines": 43, "path": "/src/SConscript", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\"\"\"\n +----------------------------------+\n \| Framework \|\n \| Build Script \|\n \| Matthias Kubisch \|\n \| [email protected] \|\n \| May 2019 \|\n +----------------------------------+\n\n\"\"\"\n\nimport os\n\nfor root, dirs, files in os.walk(\".\"):\n for file in files:\n if file.endswith(\".cpp\"):\n print(os.path.join(root, file))\n\ncpppaths = ['.','../../simloidTCP/src']\n\n\nsrc_files = [ 'common/log_messages.cpp'\n , 'common/modules.cpp'\n , 'common/settings.cpp'\n , 'common/basic.cpp'\n , 'control/jointcontrol.cpp'\n , 'control/controlparameter.cpp'\n , 'control/control_vector.cpp'\n , 'learning/gmes.cpp'\n , 'learning/predictor.cpp'\n , 'learning/action_selection.cpp'\n , 'serial/rs232.c'\n ]\n\n# common flags\ncppflags = ['-O2', '-Wall', '-Wextra', '-Wno-psabi']\n\n# c++ only flags\ncxxflags = ['-std=c++11']\n\n\n\nLibrary('../libframework', source = src_files, CPPPATH=cpppaths, CPPFLAGS=cppflags, CXXFLAGS=cxxflags)\n" }, { "alpha_fraction": 0.5910329222679138, "alphanum_fraction": 0.5962347984313965, "avg_line_length": 22.470930099487305, "blob_id": "630bdbad76af62f3d8b284e3a8ea8bde25ef71a1", "content_id": "faced50cb8c8fee65d66f7116b5258b1e5b93640", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4037, "license_type": "no_license", "max_line_length": 104, "num_lines": 172, "path": "/src/common/socket_client.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ implements a simple socket client for simloid communication /\n\n#include \"./socket_client.h\"\n\nextern GlobalFlag do_quit;\n\nnamespace network {\n\n\nstd::string hostname_to_ip(const char hostname)\n{\n struct hostent * h = gethostbyname(hostname);\n struct in_addr addr_list;\n\n std::string ipstr = \"\";\n\n if (h != nullptr)\n {\n addr_list = (struct in_addr ) h->h_addr_list;\n if (addr_list[0] != nullptr)\n ipstr = inet_ntoa(addr_list[0]);\n }\n\n sts_msg(\"resolving hostname: %s >> %s\", hostname, ipstr.c_str());\n return ipstr;\n}\n\n\nbool\nSocket_Client::open_connection(const char server_addr, const unsigned short port)\n{\n if (connection_established)\n {\n wrn_msg(\"Already connected to server.\");\n return false;\n }\n\n /* create socket /\n sockfd = socket(AF_INET, SOCK_STREAM, 0);\n if (sockfd < 0)\n {\n wrn_msg(\"Cannot create socket\");\n return false;\n }\n\n srv_addr.sin_family = AF_INET;\n srv_addr.sin_port = htons(port);\n int result = inet_pton(AF_INET, server_addr, &srv_addr.sin_addr);\n\n if (0 > result)\n {\n wrn_msg(\"First parameter is not a valid address family.\");\n close(sockfd);\n return false;\n }\n else if (0 == result)\n {\n wrn_msg(\"Char string (second parameter does not contain valid IP address)\");\n close(sockfd);\n return false;\n }\n\n if (-1 == connect(sockfd, (struct sockaddr )&srv_addr, sizeof(srv_addr)))\n {\n wrn_msg(\"Connect failed\");\n close(sockfd);\n return false;\n }\n\n /* make the socket non-blockable /\n int x = fcntl(sockfd ,F_GETFL, 0);\n fcntl(sockfd, F_SETFL, x \| O_NONBLOCK);\n\n connection_established = true;\n sts_msg(\"Socket opened.\");\n return true;\n}\n\nvoid\nSocket_Client::close_connection(void)\n{\n if (connection_established)\n {\n shutdown(sockfd, SHUT_RDWR);\n close(sockfd);\n connection_established = false;\n sts_msg(\"Socket closed.\");\n }\n else\n wrn_msg(\"No connection left to be closed.\");\n}\n\nvoid\nSocket_Client::send(const char format, ...)\n{\n char buffer[constants::msglen];\n memset(buffer, 0, constants::msglen);\n\n va_list args;\n va_start(args, format);\n vsnprintf(buffer, constants::msglen, format, args);\n va_end(args);\n\n common::lock_t lock(mtx);\n if ((unsigned) write(sockfd, buffer, strlen(buffer)) != strlen(buffer))\n err_msg(__FILE__, __LINE__, \"Send incomplete.\");\n}\n\nvoid\nSocket_Client::append(const char* format, ...)\n{\n char buffer[constants::msglen];\n memset(buffer, 0, constants::msglen);\n\n va_list args;\n va_start(args, format);\n vsnprintf(buffer, constants::msglen, format, args);\n va_end(args);\n\n msgbuf.append(buffer);\n}\n\nvoid\nSocket_Client::flush(void)\n{\n if (msgbuf.empty()) return;\n\n { common::lock_t lock(mtx);\n if ((unsigned) write(sockfd, msgbuf.c_str(), msgbuf.length()) != msgbuf.length())\n err_msg(__FILE__, __LINE__, \"Flush incomplete.\");\n } // end lock\n msgbuf.clear();\n}\n\nvoid\nSocket_Client::eat(void)\n{\n char buffer[constants::msglen];\n read(sockfd, buffer, constants::msglen);\n}\n\nstd::string\nSocket_Client::recv(unsigned int timeout_us = 0)\n{\n char buffer[constants::msglen];\n unsigned int time_spent_us = 0;\n unsigned int interval_us = 1;\n\n int len = read(sockfd, buffer, constants::msglen);\n\n while ((-1 == len) && (time_spent_us < timeout_us) && !do_quit.status())\n {\n usleep(interval_us);\n time_spent_us += interval_us; // sum up time spent\n\n if (interval_us < 4096) // double the interval for next sleep\n interval_us = 2;\n\n len = read(sockfd, buffer, constants::msglen);\n }\n\n if (0 == len) return \"\";\n else if (0 > len) {\n if (do_quit.status()) wrn_msg(\"Received signal to exit during read. Cancel reception of data.\");\n else wrn_msg(\"Connection timed out.\");\n return \"\";\n }\n\n return std::string(buffer);\n}\n\n} / namespace network /\n" }, { "alpha_fraction": 0.6228076219558716, "alphanum_fraction": 0.6276530623435974, "avg_line_length": 34.47941970825195, "blob_id": "f6213852e33a897f69c6116803443b4b4fcc1979", "content_id": "20972c3b96ce864ba6ef034cab43fa43bb5b4493", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 14653, "license_type": "no_license", "max_line_length": 171, "num_lines": 413, "path": "/src/control/jointcontrol.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include \"jointcontrol.h\"\n\nnamespace control {\n\n\nJointcontrol::Jointcontrol(robots::Robot_Interface& robot)\n: robot(robot)\n, core(robot)\n, number_of_params_sym(get_number_of_inputs(robot) (robot.get_number_of_joints() - robot.get_number_of_symmetric_joints()))\n, number_of_params_asym(get_number_of_inputs(robot) * robot.get_number_of_joints())\n, symmetric_controller(false)\n, is_switched(false)\n{\n sts_msg(\"Creating joint controller.\");\n if (robot.get_number_of_joints() < 1) err_msg(__FILE__, __LINE__, \"No motor outputs.\");\n if (0 == robot.get_number_of_accel_sensors()) wrn_msg(\"No use of acceleration sensors in controller.\");\n\n sts_msg(\"Number of symmetric joints is %u.\", robot.get_number_of_symmetric_joints());\n\n sts_msg( \"Created controller with: \\n %u inputs\\n %u outputs\\n %u symmetric params\\n %u asymmetric params.\"\n , get_number_of_inputs(robot)\n , robot.get_number_of_joints()\n , number_of_params_sym\n , number_of_params_asym);\n reset();\n}\n\n\nvoid\nJointcontrol::switch_symmetric(bool switched)\n{\n if (not symmetric_controller)\n is_switched = switched;\n else\n is_switched = false;\n /* switching symmetry does not have any effect on symmetrical controller weights /\n}\n\n\nvoid\nJointcontrol::set_control_parameter(const Control_Parameter& controller)\n{\n if (controller.is_symmetric()) apply_symmetric_weights(controller.get_parameter());\n else apply_weights(controller.get_parameter());\n symmetric_controller = controller.is_symmetric();\n\n switch_symmetric(controller.is_mirrored());\n}\n\n\nvoid\nJointcontrol::set_control_parameter(const std::vector<double>& params)\n{\n if (symmetric_controller) apply_symmetric_weights(params);\n else apply_weights(params);\n}\n\n\nvoid\nJointcontrol::reset(void)\n{\n for (auto& j : robot.set_joints()) {\n j.motor.reset();\n j.motor = random_value(-0.01, 0.01);\n }\n for (auto& a : robot.set_accels()) a.reset(); // reset integrated velocities from acceleration sensors\n}\n\n\nvoid\nJointcontrol::insert_motor_command(unsigned index, double value)\n{\n assert(index < robot.set_joints().size());\n auto& j = robot.set_joints()[index];\n j.motor += value;\n}\n\n\nvoid\nJointcontrol::integrate_accels(void)\n{\n for (auto& a : robot.set_accels()) a.integrate(); // integrate velocities from acceleration sensors\n}\n\n\nvoid\nJointcontrol::execute_cycle(void)\n{\n integrate_accels();\n core.prepare_inputs(robot);\n core.update_outputs(robot, symmetric_controller, is_switched);\n core.write_motors (robot, is_switched);\n}\n\n\nvoid\nJointcontrol::apply_symmetric_weights(const std::vector<double>& params)\n{\n assert(params.size() == number_of_params_sym);\n core.apply_symmetric_weights(robot, params);\n}\n\nvoid\nJointcontrol::apply_weights(const std::vector<double>& params)\n{\n assert(params.size() == number_of_params_asym);\n core.apply_weights(robot, params);\n}\n\nvoid\nJointcontrol::print_parameter(void) const\n{\n robots::Jointvector_t const& joints = robot.get_joints();\n\n sts_msg(\"Printing controller parameter:\");\n printf(\"joints:%zu inputs:%zu\\n\", robot.get_number_of_joints(), get_number_of_inputs(robot));\n /print header/\n printf(\" # \|\");\n for (std::size_t i = 0; i < robot.get_number_of_joints(); ++i)\n if (!symmetric_controller or joints[i].type == robots::Joint_Type_Normal)\n printf(\"%4zu \|\", i);\n printf(\"\\n\");\n\n for (std::size_t k = 0; k < get_number_of_inputs(robot); ++k)\n {\n printf(\"%2zu: \|\", k);\n for (std::size_t i = 0; i < robot.get_number_of_joints(); ++i)\n {\n if (!symmetric_controller or joints[i].type == robots::Joint_Type_Normal)\n printf(\"% 1.3f\|\", core.weights[i][k]);\n }\n printf(\"\\n\");\n }\n printf(\"\\n\");\n}\n\ndouble\nJointcontrol::get_normalized_mechanical_power(void) const\n{\n double power = .0;\n for (auto const& j : robot.get_joints())\n power += square(j.motor.get());\n return power/robot.get_number_of_joints();\n}\n\ndouble\nJointcontrol::get_normalized_control_change(void) const\n{\n double change = .0;\n for (auto const& j : robot.get_joints())\n change += square(j.motor.get() - j.motor.get_backed());\n return change/robot.get_number_of_joints();\n}\n\nControl_Parameter\nget_initial_parameter(robots::Robot_Interface const& robot, const Minimal_Seed_t& seed, bool symmetric = false)\n{\n const std::size_t number_of_joints = robot.get_number_of_joints();\n const std::size_t number_of_inputs = get_number_of_inputs(robot);\n std::vector<double> params(number_of_jointsnumber_of_inputs);\n\n /* set default weights for asymmetric joints /\n for (std::size_t i = 0; i < number_of_joints; ++i)\n {\n const std::size_t pos = inumber_of_inputs + i * 3;\n\n params[pos + 0] = -seed.pgain; // spring\n params[pos + 1] = seed.damping; // positive friction\n params[pos + 2] = seed.motor_self; // motor neuron's self coupling\n\n /** Divide by initial bias, because bias in the control input will be < 1.\n * If pgain or default_pos is zero this bias is also zero.\n /\n params[(i+1)number_of_inputs - 1] = -params[pos + 0] * robot.get_joints()[i].default_pos * 1.0 / constants::initial_bias;\n }\n\n if (symmetric)\n return make_symmetric(robot, Control_Parameter(params));\n\n return Control_Parameter(params); /* asymmetric /\n}\n\n/IDEA: instead of throwing away the not used weights, we could average the corresponding weight pairs/\nControl_Parameter\nmake_symmetric(robots::Robot_Interface const& robot, const Control_Parameter& other) {\n if (other.is_symmetric())\n return other;\n\n dbg_msg(\"Making controller symmetric.\");\n assert(robot.get_number_of_joints() > robot.get_number_of_symmetric_joints());\n const std::size_t num_joints = robot.get_number_of_joints() - robot.get_number_of_symmetric_joints();\n const std::size_t number_of_inputs = get_number_of_inputs(robot);\n const std::vector<double>& other_params = other.get_parameter();\n std::size_t p = 0;\n\n std::vector<double> params(number_of_inputs * num_joints);\n for (std::size_t i = 0; i < robot.get_number_of_joints(); ++i)\n {\n if (robot.get_joints()[i].type == robots::Joint_Type_Normal)\n for (std::size_t k = 0; k < number_of_inputs; ++k)\n params[p++] = other_params[inumber_of_inputs + k];\n }\n assert(p == params.size());\n return Control_Parameter(params, true);\n\n}\n\nbool is_sagittal_accel_sensor(robots::Robot_Interface const& robot, std::size_t input_pos) {\n return (input_pos == 3 robot.get_number_of_joints());\n}\n\nstd::size_t swap_sym_joint_pos(robots::Robot_Interface const& robot, std::size_t joint_id, std::size_t input_pos) {\n\n std::size_t sym_id = robot.get_joints()[joint_id].symmetric_joint;\n assert(sym_id < robot.get_number_of_joints());\n assert(input_pos < get_number_of_inputs(robot));\n\n if (joint_id == sym_id){ /* not symmetric /\n //dbg_msg(\"1) not changed %u\", input_pos);\n return input_pos;\n }\n\n if (input_pos >= 3 robot.get_number_of_joints()){ /* no joint pos, angle or torque /\n //dbg_msg(\"2) not changed %u\", input_pos);\n return input_pos;\n }\n\n std::size_t jid = input_pos/3;\n std::size_t rem = input_pos%3;\n assert(jid < robot.get_number_of_joints());\n\n std::size_t result = 0;\n sym_id = robot.get_joints()[jid].symmetric_joint;\n result = sym_id3 + rem;\n\n //dbg_msg(\"Transforming %u to %u\", input_pos, result);\n assert(result < get_number_of_inputs(robot));\n\n return result;\n}\n\n\nControl_Parameter\nmake_asymmetric(robots::Robot_Interface const& robot, const Control_Parameter& other) {\n if (not other.is_symmetric()) {\n //dbg_msg(\"Skipping, already asymmetric.\");\n return other;\n }\n\n dbg_msg(\"Making controller asymmetric.\");\n const std::size_t number_of_inputs = get_number_of_inputs(robot);\n\n /*TODO it must be asserted that joint IDs are unique and contiguous over the full range, different places, each constructor/\n\n std::vector<std::vector<double> > temp_weights(robot.get_number_of_joints(), std::vector<double>(number_of_inputs, 0.0));\n\n const std::vector<double>& other_params = other.get_parameter();\n const std::size_t expected_other_size = number_of_inputs * (robot.get_number_of_joints() - robot.get_number_of_symmetric_joints());\n\n /* check the correct number of input params /\n //dbg_msg(\"num other params %u (%u)\", other_params.size(), expected_other_size);\n assert(other_params.size() == expected_other_size);\n\n std::size_t param_index = 0;\n for (std::size_t ix = 0; ix < robot.get_number_of_joints(); ++ix)\n if (robot.get_joints()[ix].type == robots::Joint_Type_Normal)\n {\n std::size_t iy = robot.get_joints()[ix].symmetric_joint; // get symmetric counterpart of ix\n assert(robot.get_joints()[iy].symmetric_joint == ix);\n if (ix!=iy)\n assert(robot.get_joints()[iy].type == robots::Joint_Type_Symmetric);\n assert(iy < robot.get_number_of_joints());\n for (std::size_t k = 0; k < number_of_inputs; ++k)\n {\n temp_weights[ix][k] = other_params[param_index++];\n if (iy != ix) {\n temp_weights[iy][swap_sym_joint_pos(robot, ix, k)] = temp_weights[ix][k];\n }\n }\n }\n assert(param_index == other_params.size());\n\n // get them out\n std::vector<double> params;\n params.reserve(number_of_inputs robot.get_number_of_joints());\n for (std::size_t ix = 0; ix < robot.get_number_of_joints(); ++ix) {\n printf(\"\\n\");\n for (std::size_t k = 0; k < number_of_inputs; ++k) {\n params.push_back(temp_weights[ix][k]);\n printf(\" %+5.2f\", temp_weights[ix][k]);\n }\n }\n printf(\"\\n\");\n\n other.print();\n\n assert(params.size() == number_of_inputs * robot.get_number_of_joints());\n\n return Control_Parameter(params, false);\n}\n\n\nControl_Parameter\nturn_symmetry(robots::Robot_Interface const& robot, const Control_Parameter& other) {\n if (other.is_symmetric() or !other.is_mirrored()) {\n dbg_msg(\"Nothing to turn to original.\");\n return other; /* nothing to do/\n }\n\n dbg_msg(\"Turning controller to original.\");\n Control_Parameter orig = other; // make a safe copy\n auto& params = orig.set_parameter();\n orig.print();\n printf(\"\\n\");\n std::size_t number_of_inputs = get_number_of_inputs(robot);\n\n / copy params to temp weight matrix /\n std::vector<std::vector<double> > weights{ robot.get_number_of_joints()\n , std::vector<double>(number_of_inputs, 0.0)};\n std::size_t p = 0;\n for (auto& w_i : weights)\n for (auto& w_ik : w_i)\n w_ik = params[p++];\n assert(p == params.size());\n\n p = 0;\n for (std::size_t ix = 0; ix < robot.get_number_of_joints(); ++ix) {\n for (std::size_t k = 0; k < number_of_inputs; ++k) {\n unsigned j = swap_sym_joint_pos(robot, ix, k);\n int sign = is_sagittal_accel_sensor(robot,k)? -1:1;\n params[p++] = signweights[robot.get_joints()[ix].symmetric_joint][j];\n }\n }\n\n assert(p == params.size());\n\n orig.print();\n\n return Control_Parameter(params, false, false);\n}\n\n/** this initializer is capable of reading a symmetric file and transform it to asymmetric /\nControl_Parameter\ninitialize_anyhow(robots::Robot_Interface const& robot, Jointcontrol const& control, bool force_symmetric, const Minimal_Seed_t params_pdm, const std::string& filename ) {\n\n if (filename.empty())\n return get_initial_parameter(robot, params_pdm, force_symmetric);\n\n sts_msg(\"Reading seed from file: %s\", filename.c_str());\n sts_msg(\"Trying to fetch %s controller.\", force_symmetric? \"a symmetric\" : \"an asymmetric\");\n\n std::size_t num_params = force_symmetric ? control.get_number_of_symmetric_parameter() : control.get_number_of_parameter();\n\n Control_Parameter param0( filename, num_params, force_symmetric );\n if (param0.get_parameter().size() == num_params)\n return param0; / success /\n\n / try again with different symmetry /\n bool new_symmetry = not force_symmetric;\n\n num_params = new_symmetry ? control.get_number_of_symmetric_parameter() : control.get_number_of_parameter();\n\n Control_Parameter param1( filename, num_params, new_symmetry );\n if (param1.get_parameter().size() == num_params)\n {/ success, now force correct symmetry /\n\n if (force_symmetric) return make_symmetric(robot, param1);\n else return make_asymmetric(robot, param1);\n }\n else\n err_msg(__FILE__,__LINE__,\"Could not read controller weights from file: %s.\", filename.c_str());\n\n return Control_Parameter();\n}\n\nControl_Vector param_factory( const robots::Robot_Interface& robot\n , std::size_t number_of_motor_units\n , const std::string& folder\n , const control::Minimal_Seed_t& seed )\n{\n sts_msg(\"Launching motor control parameter vector factory.\");\n control::Control_Vector params(number_of_motor_units, folder);\n\n sts_msg(\"Number of elements loaded from files: %u\", params.size());\n assert(number_of_motor_units>=params.size());\n\n if (number_of_motor_units > params.size()) {\n sts_msg(\"Added the rest (%u) as randomized values.\", number_of_motor_units - params.size());\n for (std::size_t i = params.size(); i < number_of_motor_units; ++i) {\n sts_msg(\"Create randomized motor control parameter no.: %u\", i);\n control::Control_Parameter p = control::get_initial_parameter(robot, seed, false/symmetric?/);//(i % 2 == 0));\n control::randomize_control_parameter(p, 0.1, 1.0);\n /TODO make random parameters to settings, and constrain motor self not not go beyond zero /\n /*TODO also: make settings grouped and only give the local settings as ref /\n params.add(p);\n }\n }\n assert(params.size() == number_of_motor_units);\n sts_msg(\"Done creating %u motor control vectors.\", params.size());\n return params;\n}\n\ndouble Jointcontrol::get_L1_norm(void)\n{\n /** think about: shall we exclude bias weights/\n double sum_abs_weights = .0;\n for (auto const& wi: core.weights)\n for (auto const& wik: wi)\n sum_abs_weights += std::abs(wik);\n return sum_abs_weights/number_of_params_asym;\n}\n\n} // namespace control\n" }, { "alpha_fraction": 0.5971502661705017, "alphanum_fraction": 0.606217622756958, "avg_line_length": 22.393939971923828, "blob_id": "93f4d254af88da60a32f81711b5c2208d26130fc", "content_id": "43c5b2726eee4d40f50170a471fee4b5ea7bc85c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 772, "license_type": "no_license", "max_line_length": 149, "num_lines": 33, "path": "/src/draw/barplot.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BARPLOT_H_INCLUDED\n#define BARPLOT_H_INCLUDED\n\n#include <draw/draw.h>\n#include <draw/display.h>\n\nnamespace draw {\n\nclass BarPlot\n{\n const float x, y, w, h, maxval;\n float curval;\n Color4 const& color, max_color;\n\npublic:\n\n BarPlot(float x, float y, float w, float h, float maxval = 1.0, Color4 const& color = colors::white, Color4 const& max_color = colors::redorange)\n : x(x), y(y), w(w), h(h), maxval(maxval), curval(0.0), color(color), max_color(max_color)\n {}\n\n void add_sample(float x) { curval=x; }\n\n void draw() const {\n if (curval<maxval)\n vbar(x, y, w, h, curval, maxval, color);\n else\n vbar(x, y, w, h, curval, maxval, max_color);\n }\n};\n\n} / namespace draw /\n\n#endif // BARPLOT_H_INCLUDED\n" }, { "alpha_fraction": 0.695557177066803, "alphanum_fraction": 0.695557177066803, "avg_line_length": 40.60606002807617, "blob_id": "a200f7ceb0f12a5bd74332848b38c6623e007e4f", "content_id": "a2e16ea8d69f90dc971b034d7bf90c3c90cf852b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1373, "license_type": "no_license", "max_line_length": 153, "num_lines": 33, "path": "/src/common/settings.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SETTINGS_H_INCLUDED\n#define SETTINGS_H_INCLUDED\n\n#include <string.h>\n#include <common/datareader.h>\n\nbool read_option_flag (int argc, char argv, const char short_name, const char* ext_name);\nstd::string read_string_option(int argc, char *argv, const char short_name, const char* ext_name, const std::string default_value);\n\nstd::string read_options(int argc, char *argv, const char default_path);\n\nclass Settings_Base {\n\n const std::string filename;\n file_io::Data_Reader settings_file;\n\npublic:\n\n Settings_Base(int argc, char *argv, const char default_path)\n : filename(read_options(argc, argv, default_path))\n , settings_file(filename)\n {}\n\n file_io::uint_t read_uint (const file_io::key_t name, file_io::uint_t default_value) { return settings_file.read_unsigned(name, default_value); }\n file_io::float_t read_float(const file_io::key_t name, file_io::float_t default_value) { return settings_file.read_float (name, default_value); }\n file_io::string_t read_str (const file_io::key_t name, file_io::string_t default_value) { return settings_file.read_string (name, default_value); }\n file_io::vector_t read_vec (const file_io::key_t name, file_io::vector_t default_value) { return settings_file.read_vector (name, default_value); }\n\n virtual ~Settings_Base() {}\n};\n\n\n#endif // SETTINGS_H_INCLUDED\n" }, { "alpha_fraction": 0.5948219895362854, "alphanum_fraction": 0.6132686138153076, "avg_line_length": 38.11392593383789, "blob_id": "312f673e5eb4e2dc0918b3ed03a62cd415149420", "content_id": "e73539edbc8e498d90821a54188d448d639cd8f5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3090, "license_type": "no_license", "max_line_length": 115, "num_lines": 79, "path": "/src/learning/boltzmann_softmax.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BOLTZMANN_SOFTMAX_H_INCLUDED\n#define BOLTZMANN_SOFTMAX_H_INCLUDED\n\n#include <vector>\n#include <common/static_vector.h>\n#include <common/log_messages.h>\n#include <learning/action_selection.h>\n#include <learning/action_module.h>\n#include <learning/payload.h>\n\nclass Boltzmann_Softmax : public Action_Selection_Base\n{\n / TODO this does not work for non-existing actions,\n rethink variably sized action space, this makes EVERYTHING way too complicated /\n\n template <typename VectorType>\n static void boltzmann_layer(VectorType& output, const VectorType& input, const double inv_temp) {\n assert(output.size() == input.size());\n assert(output.size() > 0);\n\n const double maxq = input.get_max();\n double sum = .0;\n for (std::size_t i = 0; i < input.size(); ++i)\n sum += exp(inv_temp (input[i] - maxq));\n\n assert(sum > .0);\n for (std::size_t i = 0; i < input.size(); ++i)\n output[i] = exp(inv_temp * (input[i] - maxq)) / sum;\n }\n\npublic:\n Boltzmann_Softmax( const static_vector<State_Payload>& states\n , const Action_Module_Interface& actions\n , const double exploration_rate )\n : Action_Selection_Base(states, actions, exploration_rate)\n {\n dbg_msg(\"Creating 'Boltzmann/Softmax' action selection.\");\n assert_in_range(exploration_rate, 0.01, 0.99);\n\n// dbg_msg(\"Begin Testing of Boltzmann/Softmax Module\");\n// const unsigned avail_actions = actions.get_number_of_actions_available();\n//\n// std::vector<double> qval = random_vector(avail_actions, -10.0, +10.0);\n//\n// boltzmann_layer(selection_probabilities, qval, 1.0);\n//\n// print_distribution(selection_probabilities);\n// double sump = .0;\n// for (unsigned i = 0; i < selection_probabilities.size(); ++i) {\n// sump = selection_probabilities[i];\n// assert(selection_probabilities[i] > 0 and selection_probabilities[i] < 1.0);\n// }\n// assert_close(sump, 1.0, 0.0001);\n//\n// dbg_msg(\"Testing binning.\");\n// std::vector<std::size_t> bins(selection_probabilities.size());\n// for (unsigned i = 0; i < 1000; ++i)\n// ++bins[select_from_distribution(selection_probabilities)];\n//\n// for (unsigned i = 0; i < bins.size(); ++i) {\n// printf(\"%+1.3f ~ %+1.3f\\n\", bins[i]/1000.0, selection_probabilities[i]);\n// assert_close(bins[i]/1000.0, selection_probabilities[i], 0.05);\n// }\n//\n// dbg_msg(\"End Testing of Boltzmann/Softmax Module\");\n }\n\n std::size_t select_action(std::size_t current_state, std::size_t current_policy)\n {\n assert(actions.get_number_of_actions_available() > 1);\n double inv_temp = 1.0;//TODO\n boltzmann_layer(selection_probabilities, states[current_state].policies[current_policy].qvalues, inv_temp);\n\n /* create random variable and select /\n return select_from_distribution(selection_probabilities);\n }\n};\n\n#endif // BOLTZMANN_SOFTMAX_H_INCLUDED\n" }, { "alpha_fraction": 0.7198795080184937, "alphanum_fraction": 0.7259036302566528, "avg_line_length": 19.75, "blob_id": "ca778c86270c66b1ec776e16fc470034a4600a32", "content_id": "58fb0639a53c2b7e7e91f998b2d1a6cfb33bc6a2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 332, "license_type": "no_license", "max_line_length": 51, "num_lines": 16, "path": "/src/control/statemachine.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef STATEMACHINE_H_INCLUDED\n#define STATEMACHINE_H_INCLUDED\n\nnamespace control {\n\nclass Statemachine_Interface {\npublic:\n virtual bool has_state_changed(void) const = 0;\n virtual std::size_t get_state(void) const = 0;\n virtual ~Statemachine_Interface() {}\n};\n\n\n} // namespace control\n\n#endif // STATEMACHINE_H_INCLUDED\n" }, { "alpha_fraction": 0.529197096824646, "alphanum_fraction": 0.5863747000694275, "avg_line_length": 19.797468185424805, "blob_id": "75e73718658c6b28e3d637e3e019cad18ec35d3c", "content_id": "1757e255d6c7737b72d6a266ece9806c406c97ff", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1644, "license_type": "no_license", "max_line_length": 70, "num_lines": 79, "path": "/src/tests/time_state_space_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <common/modules.h>\n#include <control/sensorspace.h>\n#include <learning/time_state_space.h>\n\n\nnamespace local_tests {\nnamespace time_state_space {\n\n\nTEST_CASE( \"TES Construction with length 1\", \"[time_embedded_signal]\")\n{\n double j = 13.37;\n time_embedded_signal<1> signal(\"foo\", [&j](){ return j; });\n\n REQUIRE( signal.buffer.size() == 1 );\n\n REQUIRE( signal() == 0.0 );\n\n signal.execute_cycle();\n REQUIRE( signal() == 13.37 );\n\n j = 23.42;\n\n REQUIRE( signal() == 13.37 );\n signal.execute_cycle();\n\n REQUIRE( signal() == 23.42 );\n}\n\nTEST_CASE( \"TES Construction with length N\", \"[time_embedded_signal]\")\n{\n double j = 13.37;\n const unsigned BUFLEN = 3;\n time_embedded_signal<BUFLEN> signal(\"foo\", [&j](){ return j; });\n signal.execute_cycle();\n\n REQUIRE( signal.buffer.size() == BUFLEN );\n\n REQUIRE( signal() == 13.37 );\n\n j = 23.42;\n\n REQUIRE( signal[0] == 13.37 );\n\n signal.execute_cycle();\n\n REQUIRE( signal[0] == 23.42 );\n REQUIRE( signal[1] == 13.37 );\n REQUIRE( signal[2] == 0.00 );\n\n j = 77.99;\n signal.execute_cycle();\n\n REQUIRE( signal[0] == 77.99 );\n REQUIRE( signal[1] == 23.42 );\n REQUIRE( signal[2] == 13.37 );\n\n signal.execute_cycle();\n signal.execute_cycle();\n\n REQUIRE( signal[0] == 77.99 );\n REQUIRE( signal[1] == 77.99 );\n REQUIRE( signal[2] == 77.99 );\n}\n\nTEST_CASE( \"TSS Construction\" , \"[Time_State_Space]\")\n{\n Test_Robot robot(3,0);\n learning::Time_State_Space<7> inputs{robot};\n\n //robot.set_joint\n\n}\n\n} / time_state_space /\n} / local_tests /\n\n" }, { "alpha_fraction": 0.5616732239723206, "alphanum_fraction": 0.5806220769882202, "avg_line_length": 28.44210433959961, "blob_id": "05c6288e8c5a35e22760f24df64b948d2a1191b2", "content_id": "11f5cbad17f4e23a3b958770827a2e99823c06cd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2797, "license_type": "no_license", "max_line_length": 125, "num_lines": 95, "path": "/src/common/basic.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ basic.cpp /\n\n#include \"basic.h\"\n#include <algorithm>\n\nFILE\nopen_file(const char* mode, const char* format, ...)\n{\n char filename[1024];\n va_list args;\n va_start(args, format);\n vsnprintf(filename, 1024, format, args);\n va_end(args);\n\n FILE * fd = fopen(filename, mode);\n if (NULL == fd)\n {\n perror(\"Error\");\n err_msg(__FILE__, __LINE__, \"could not open file %s in mode %s.\", filename, mode);\n }\n\n return fd;\n}\n\nnamespace basic {\n\nstd::string\nmake_directory(const char format, ...)\n{\n char foldername[256];\n va_list args;\n va_start(args, format);\n vsnprintf(foldername, 256, format, args);\n va_end(args);\n\n int md = mkdir(foldername, S_IRWXU \| S_IRWXG \| S_IROTH \| S_IXOTH);\n if (!md) sts_msg(\"created folder %s\", foldername);\n else if (errno != EEXIST) err_msg(__FILE__, __LINE__, \"could not create folder %s.\\n %s\\n\", foldername, strerror(errno));\n\n return foldername;\n}\n\nFilelist list_directory(const char target_dir, const char* filter)\n{\n const unsigned max_files = 1024;\n std::vector<std::string> files_in_directory;\n struct dirent epdf;\n DIR dpdf;\n\n if (0 == strcmp(target_dir,\"\"))\n dpdf = opendir(\"./\");\n else\n dpdf = opendir(target_dir);\n\n if (dpdf != NULL) {\n while ((epdf = readdir(dpdf)) and (files_in_directory.size() < max_files)) {\n if (strcmp(epdf->d_name, \".\") and strcmp(epdf->d_name, \"..\") and (nullptr != strstr(epdf->d_name, filter)))\n files_in_directory.emplace_back(epdf->d_name);\n }\n if (files_in_directory.size() > 1)\n std::sort(files_in_directory.begin(), files_in_directory.end());\n sts_msg(\"Read %u files in directory %s\", files_in_directory.size(), target_dir);\n for (std::size_t i = 0; i < std::min(std::size_t{10}, files_in_directory.size()); ++i)\n sts_msg(\"\\t%s\", files_in_directory[i].c_str());\n if (files_in_directory.size()>10)\n sts_msg(\"\\t...truncated.\");\n } else err_msg(__FILE__, __LINE__, \"Could not open directory %s\", target_dir);\n\n return files_in_directory;\n}\n\nstd::size_t get_file_size(FILE* fd)\n{\n // obtain file size\n if (fd == nullptr) return 0;\n fseek(fd, 0, SEEK_END);\n long int file_size = ftell(fd);\n rewind(fd);\n return (file_size > 0) ? file_size : 0;\n}\n\nstd::string get_timestamp(void) {\n time_t t0 = time(NULL); // initialize time\n struct tm * timeinfo = localtime(&t0); // get time info\n char timestamp[256];\n snprintf(\n timestamp, 256, \"%02d%02d%02d%02d%02d%02d\",\n timeinfo->tm_year-100, timeinfo->tm_mon + 1,\n timeinfo->tm_mday, timeinfo->tm_hour,\n timeinfo->tm_min, timeinfo->tm_sec\n );\n return timestamp;\n}\n\n} /* namespace basic /\n" }, { "alpha_fraction": 0.6840336322784424, "alphanum_fraction": 0.6857143044471741, "avg_line_length": 19.517240524291992, "blob_id": "99d6583f5540cc4997f6550bcded53561ba3d9ee", "content_id": "120f94d234aab299b7a352ce4bbd4d635473aafb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 595, "license_type": "no_license", "max_line_length": 67, "num_lines": 29, "path": "/src/common/misc.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef MISC_H_INCLUDED\n#define MISC_H_INCLUDED\n\n#include <iostream>\n#include <vector>\n#include <string>\n\nstd::string get_time_from_cycle_counter(unsigned long long cycles);\n\nstd::string to_str(const std::vector<double>& vect);\n\ntemplate <typename vector_t> void print(const vector_t& content);\n\nnamespace common {\n\ntemplate <typename T>\nstd::string to_string(std::vector<T> const& vect)\n{\n if (vect.size() == 0) return \"\";\n\n std::string result;\n for (auto const& v: vect) result.append(std::to_string(v)+\" \");\n return result;\n}\n\n} / namespace common /\n\n\n#endif // MISC_H_INCLUDED\n" }, { "alpha_fraction": 0.53532874584198, "alphanum_fraction": 0.5411362051963806, "avg_line_length": 42.60389709472656, "blob_id": "d1e066948b5ba6deda3777b2fe979203229d5df0", "content_id": "6c7f159c372f60379af10799c32dbce19c270c94", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 13431, "license_type": "no_license", "max_line_length": 132, "num_lines": 308, "path": "/src/evolution/setting.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"./setting.h\"\n\n\nbool read_option_bool (int argc, char argv, const std::string long_name, const std::string short_name, bool def = false);\nunsigned int read_option_uint (int argc, char argv, const std::string long_name, const std::string short_name, unsigned int def);\nstd::string read_option_string(int argc, char argv, const std::string long_name, const std::string short_name);\n\nSetting::Setting( int argc, char argv )\n : project_name()\n , project_status(NEW)\n , visuals(not read_option_bool(argc, argv, \"--blind\", \"-b\"))\n , interlaced_mode(false)\n , tcp_port(read_option_uint(argc, argv, \"--port\", \"-p\", network::constants::default_port))\n , robot_ID(31)\n , scene_ID(0)\n , max_steps(1000)\n , max_power(100)\n , max_dctrl(100)\n , initial_steps(0)\n , efficient(true)\n , drop_penalty(true)\n , out_of_track_penalty(true)\n , stop_penalty(false)\n , symmetric_controller(true)\n , strategy(\"GENERATION\")\n , population_size(500)\n , selection_size(100)\n , max_generations(500)\n , cur_generations(0)\n , max_trials(max_generations population_size)\n , cur_trials(0)\n , init_mutation_rate(0.01)\n , meta_mutation_rate(0.5)\n , moving_rate(0.5)\n , selection_bias(1.0) // (0,...,5]\n , seed()\n , initial_population()\n , param{3.0, -1.0, 1.0}\n , push()\n , fitness_function(\"FORWARDS\")\n , rnd({\"NONE\", 0.0, 0})\n , growth({1.0, 0.0})\n , friction(1.0)\n , low_sensor_quality(false)\n , L1_normalization(false)\n , target(.0)\n , drop_level(.5)\n , stop_level(0.0005)\n , corridor(.5)\n , initially_fixed(false)\n{\n if (read_option_bool(argc, argv, \"--help\", \"-h\"))\n {\n print_options();\n exit(EXIT_SUCCESS);\n }\n else if (read_option_bool(argc, argv, \"--new\" , \"-n\"))\n {\n project_name = read_option_string(argc, argv, \"--new\" , \"-n\");\n read_setting_file(read_option_string(argc, argv, \"--settings\", \"-s\"));\n project_status = NEW;\n }\n else if (read_option_bool(argc, argv, \"--resume\" , \"-r\"))\n {\n project_name = read_option_string(argc, argv, \"--resume\", \"-r\");\n read_project_file(project_name);\n project_status = RESUME;\n }\n else if (read_option_bool(argc, argv, \"--watch\", \"-w\"))\n {\n project_name = read_option_string(argc, argv, \"--watch\" , \"-w\");\n read_project_file(project_name);\n project_status = WATCH;\n }\n\n assert(project_name != \"\");\n}\n\nvoid Setting::read_setting_file(const std::string& setting_name)\n{\n if (setting_name == \"\") {\n wrn_msg(\"No settings file provided. Using default settings.\");\n return;\n }\n read_configuration(setting_name);\n}\n\nvoid Setting::read_project_file(const std::string& project_name) {\n read_configuration(FOLDER_PREFIX + project_name + \"/evolution.conf\");\n}\n\nvoid\nSetting::read_configuration(std::string const& filename)\n{\n /* read all static settings /\n sts_msg(\"Reading configuration: %s\", filename.c_str());\n config settings_file(filename, true /quit on fail/);\n\n robot_ID = settings_file.readINT (\"ROBOT\" , robot_ID );\n scene_ID = settings_file.readINT (\"SCENE\" , scene_ID );\n\n initial_steps = settings_file.readUINT(\"INITIAL_STEPS\" , initial_steps );\n max_steps = settings_file.readUINT(\"MAX_STEPS\" , max_steps );\n\n push.mode = settings_file.readUINT(\"PUSH_MODE\" , push.mode );\n push.body = settings_file.readUINT(\"PUSH_BODY\" , push.body );\n push.cycle = settings_file.readUINT(\"PUSH_CYCLE\" , push.cycle );\n push.steps = settings_file.readUINT(\"PUSH_STEPS\" , push.steps );\n push.strength = settings_file.readDBL (\"PUSH_STRENGTH\" , push.strength );\n\n assert ( push.steps <= push.cycle );\n\n efficient = settings_file.readBOOL(\"EFFICIENT\" , efficient );\n max_power = settings_file.readUINT(\"MAX_POWER\" , max_power );\n max_dctrl = settings_file.readUINT(\"MAX_DCTRL\" , max_dctrl );\n\n drop_penalty = settings_file.readBOOL(\"DROP_PENALTY\" , drop_penalty );\n drop_level = settings_file.readDBL (\"DROP_LEVEL\" , drop_level );\n\n out_of_track_penalty = settings_file.readBOOL(\"OUT_OF_TRACK_PENALTY\", out_of_track_penalty);\n corridor = settings_file.readDBL (\"CORRIDOR\" , corridor );\n\n stop_penalty = settings_file.readBOOL(\"STOP_PENALTY\" , stop_penalty );\n stop_level = settings_file.readDBL (\"STOP_LEVEL\" , stop_level );\n\n symmetric_controller = settings_file.readBOOL(\"SYMMETRIC_CONTROLLER\", symmetric_controller);\n strategy = settings_file.readSTR (\"STRATEGY\" , strategy );\n\n max_generations = settings_file.readUINT(\"MAX_GENERATIONS\" , max_generations );\n cur_generations = settings_file.readUINT(\"CURRENT_GENERATION\" , cur_generations );\n population_size = settings_file.readUINT(\"POPULATION_SIZE\" , population_size );\n selection_size = settings_file.readUINT(\"SELECTION_SIZE\" , selection_size );\n\n max_trials = settings_file.readUINT(\"MAX_TRIALS\" , max_trials );\n cur_trials = settings_file.readUINT(\"CURRENT_TRIAL\" , cur_trials );\n moving_rate = settings_file.readDBL (\"MOVING_RATE\" , moving_rate );\n selection_bias = settings_file.readDBL (\"SELECTION_BIAS\" , selection_bias );\n\n init_mutation_rate = settings_file.readDBL (\"INIT_MUTATION_RATE\" , init_mutation_rate );\n meta_mutation_rate = settings_file.readDBL (\"META_MUTATION_RATE\" , meta_mutation_rate );\n\n param.pgain = settings_file.readDBL (\"PARAM_P\" , param.pgain );\n param.damping = settings_file.readDBL (\"PARAM_D\" , param.damping );\n param.motor_self = settings_file.readDBL (\"PARAM_M\" , param.motor_self );\n\n seed = settings_file.readSTR (\"SEED\" , seed );\n initial_population = settings_file.readSTR (\"INIT_POPULATION\" , initial_population );\n fitness_function = settings_file.readSTR (\"FITNESS_FUNCTION\" , fitness_function );\n\n rnd.mode = settings_file.readSTR (\"RANDOM_MODE\" , rnd.mode );\n rnd.value = settings_file.readDBL (\"RANDOM_VALUE\" , rnd.value );\n rnd.init = settings_file.readUINT(\"RANDOM_INIT\" , rnd.init );\n\n growth.init = settings_file.readDBL (\"GROWTH_INIT\" , growth.init );\n growth.rate = settings_file.readDBL (\"GROWTH_RATE\" , growth.rate );\n\n friction = settings_file.readDBL (\"FRICTION\" , friction );\n target = settings_file.readDBL (\"TARGET\" , target );\n\n interlaced_mode = settings_file.readBOOL(\"INTERLACED\" , interlaced_mode );\n low_sensor_quality = settings_file.readBOOL(\"LOW_SENSOR_QUALITY\" , low_sensor_quality );\n\n initially_fixed = settings_file.readBOOL(\"INITIALLY_FIXED\" , initially_fixed );\n\n L1_normalization = settings_file.readBOOL(\"L1_NORMALIZATION\" , L1_normalization );\n assert(false == L1_normalization);\n}\n\nconst std::string&\nSetting::save_to_projectfile(const std::string& filename) const\n{\n sts_msg(\"Saving to project file.\");\n config project_file(filename);\n\n if (interlaced_mode) / default is false for evolution, so only write if true /\n project_file.writeBOOL(\"INTERLACED\", true);\n\n project_file.writeUINT(\"ROBOT\" , robot_ID );\n project_file.writeUINT(\"SCENE\" , scene_ID );\n project_file.writeUINT(\"MAX_STEPS\" , max_steps );\n project_file.writeUINT(\"MAX_POWER\" , max_power );\n project_file.writeUINT(\"MAX_DCTRL\" , max_dctrl );\n project_file.writeUINT(\"INITIAL_STEPS\" , initial_steps );\n project_file.writeUINT(\"PUSH_MODE\" , push.mode );\n project_file.writeUINT(\"PUSH_BODY\" , push.body );\n project_file.writeUINT(\"PUSH_CYCLE\" , push.cycle );\n project_file.writeUINT(\"PUSH_STEPS\" , push.steps );\n project_file.writeUINT(\"PUSH_STRENGTH\" , push.strength );\n project_file.writeSTR (\"STRATEGY\" , strategy );\n\n assert(not fitness_function.empty());\n project_file.writeSTR (\"FITNESS_FUNCTION\" , fitness_function);\n\n if (\"\" != seed) project_file.writeSTR(\"SEED\" , seed);\n\n project_file.writeBOOL(\"EFFICIENT\" , efficient);\n project_file.writeBOOL(\"DROP_PENALTY\" , drop_penalty);\n project_file.writeDBL (\"DROP_LEVEL\" , drop_level);\n project_file.writeBOOL(\"OUT_OF_TRACK_PENALTY\", out_of_track_penalty);\n project_file.writeDBL (\"CORRIDOR\" , corridor);\n project_file.writeBOOL(\"STOP_PENALTY\" , stop_penalty);\n project_file.writeDBL (\"STOP_LEVEL\" , stop_level);\n project_file.writeBOOL(\"SYMMETRIC_CONTROLLER\", symmetric_controller);\n\n project_file.writeDBL (\"INIT_MUTATION_RATE\" , init_mutation_rate);\n project_file.writeDBL (\"META_MUTATION_RATE\" , meta_mutation_rate);\n\n project_file.writeUINT(\"POPULATION_SIZE\" , population_size);\n\n project_file.writeSTR (\"RANDOM_MODE\" , rnd.mode);\n project_file.writeDBL (\"RANDOM_VALUE\" , rnd.value);\n project_file.writeUINT(\"RANDOM_INIT\" , rnd.init);\n\n project_file.writeDBL (\"GROWTH_INIT\" , growth.init);\n project_file.writeDBL (\"GROWTH_RATE\" , growth.rate);\n\n project_file.writeDBL (\"FRICTION\" , friction);\n\n project_file.writeBOOL(\"LOW_SENSOR_QUALITY\" , low_sensor_quality);\n project_file.writeBOOL(\"L1_NORMALIZATION\" , L1_normalization);\n project_file.writeDBL (\"TARGET\" , target);\n\n project_file.writeBOOL(\"INITIALLY_FIXED\" , initially_fixed);\n\n /* NOTE: Strategy-specific settings are saved separately!\n e.g. selection bias, moving rate, max generations, current_trial /\n\n project_file.finish();\n return filename;\n}\n\n\nvoid\nSetting::print_options(void)\n{\n printf(\"Help, Options: \\n\");\n printf(\" -p --port change TCP port\\n\");\n printf(\" -n --new create new evolution\\n\");\n printf(\" -r --resume resume with existing evolution\\n\");\n printf(\" -w --watch watch existing evolution\\n\");\n printf(\" -s --settings settings file\\n\");\n printf(\" -b --blind no visuals\\n\");\n printf(\" -h --help display this help\\n\");\n printf(\"\\n\");\n}\n\n\nbool read_option_bool(int argc, char argv, const std::string long_name, const std::string short_name, bool def)\n{\n bool value = def;\n\n for (int i = 1; i < argc; ++i)\n {\n const std::string option_name = argv[i];\n value = (long_name == option_name \|\| short_name == option_name);\n if (value) break;\n }\n dbg_msg(\"Reading option '%s' = %s\", long_name.c_str(), value? \"true\":\"false\");\n return value;\n}\n\nunsigned int\nread_option_uint(int argc, char argv, const std::string long_name, const std::string short_name, unsigned int def = 0)\n{\n unsigned int value = def;\n\n for (int i = 1; i < argc; ++i)\n {\n const std::string option_name = argv[i];\n if (long_name == option_name \|\| short_name == option_name)\n {\n if (argc < i+2) {\n printf(\"usage: %s %s <uint>\\n\", argv[0], long_name.c_str());\n exit(0);\n } else {\n value = abs(atoi(argv[i+1]));\n ++i;\n }\n break;\n }\n }\n dbg_msg(\"Reading option '%s' = %u\", long_name.c_str(), value);\n return value;\n}\n\nstd::string\nread_option_string(int argc, char argv, const std::string long_name, const std::string short_name)\n{\n std::string value;\n\n for (int i = 1; i < argc; ++i)\n {\n const std::string option_name = argv[i];\n if (long_name == option_name \|\| short_name == option_name)\n {\n if (argc < i+2) {\n printf(\"usage: %s %s <string>\\n\", argv[0], long_name.c_str());\n exit(0);\n } else {\n value = argv[i+1];\n ++i;\n }\n break;\n }\n }\n dbg_msg(\"Reading option '%s' = '%s'\", long_name.c_str(), value.c_str());\n return value;\n}\n" }, { "alpha_fraction": 0.6996879577636719, "alphanum_fraction": 0.6996879577636719, "avg_line_length": 27.488889694213867, "blob_id": "ee6933cb462909a599bc7ba6342e2760016fcf20", "content_id": "2317dc21b4cd9cc80a63717bcc3254ac6e83e533", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1282, "license_type": "no_license", "max_line_length": 128, "num_lines": 45, "path": "/src/control/control_vector.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef CONTROL_VECTOR_H_INCLUDED\n#define CONTROL_VECTOR_H_INCLUDED\n\n#include <string>\n#include <memory>\n#include <common/basic.h>\n#include <common/modules.h>\n#include <common/static_vector.h>\n#include <control/controlparameter.h>\n\nnamespace control {\n\n\nvoid randomize_control_parameter(Control_Parameter& params, double std_dev, double max_dev);\n\n\nclass Control_Vector : public static_vector_interface\n{\npublic:\n Control_Vector(std::size_t max_number_of_parameter_sets, const std::string& foldername = \"\", bool include_mirrored = false);\n\n const Control_Parameter& get(std::size_t index) const { return controls.at(index); }\n\n void add( const std::string& filename\n , const std::size_t number_of_params\n , bool symmetric\n , bool mirrored );\n\n void add(const std::string& filename);\n void add(const Control_Parameter& params);\n\n void reload(std::size_t index, const std::string& filename);\n\n std::size_t size(void) const override { return controls.size(); }\n void copy(std::size_t dst, std::size_t src) override { controls.at(dst) = controls.at(src); }\n\nprivate:\n const std::size_t max_number_of_parameter_sets;\n std::vector<Control_Parameter> controls;\n};\n\n\n} // namespace control\n\n#endif // CONTROL_VECTOR_H_INCLUDED\n" }, { "alpha_fraction": 0.6237413883209229, "alphanum_fraction": 0.634870171546936, "avg_line_length": 23.506492614746094, "blob_id": "27c8728545d2f74d0ec3580c2ece1692980f040d", "content_id": "11240fa71e89816fba6a6d8650098d1b1785711e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1887, "license_type": "no_license", "max_line_length": 108, "num_lines": 77, "path": "/src/draw/plot1D.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ plot1D.h /\n\n#ifndef plot1D_H\n#define plot1D_H\n\n#include <vector>\n#include <algorithm>\n#include <draw/axes.h>\n#include <draw/draw.h>\n#include <draw/color_table.h>\n#include <common/modules.h>\n#include <common/log_messages.h>\n#include <basic/color.h>\n\nclass plot1D {\npublic:\n plot1D(unsigned int number_of_samples, axes& a, const Color4& c = colors::white0, const char name = \"\")\n : number_of_samples(number_of_samples)\n , pointer(number_of_samples-1)\n , axis(a)\n , axis_id(axis.countNum++)\n , signal(number_of_samples)\n , color(c)\n , decrement(0.99)\n , name(name)\n { }\n\n virtual ~plot1D() = default;\n\n void draw(void) const;\n void add_sample(const float s);\n void reset(void) { std::fill(signal.begin(), signal.end(), .0); pointer = number_of_samples-1; }\n\nprotected:\n void increment_pointer(void) { ++pointer; pointer %= number_of_samples; }\n\n void auto_scale (void) const;\n void draw_statistics(void) const;\n void draw_line_strip(void) const;\n\n const unsigned int number_of_samples;\n unsigned int pointer;\n axes& axis;\n unsigned int axis_id;\n std::vector<float> signal;\n Color4 color;\n const float decrement;\n const std::string name;\n};\n\n\nclass colored_plot1D : public plot1D {\npublic:\n colored_plot1D( unsigned int number_of_samples\n , axes& a\n , ColorTable const& colortable\n , const char* name = \"\")\n : plot1D(number_of_samples, a, colors::white0, name)\n , colors(number_of_samples)\n , colortable(colortable)\n {}\n\n void add_colored_sample(float s, unsigned color_index) {\n add_sample(s),\n colors.at(pointer) = color_index;\n }\n\n void draw_colored(void) const;\n\nprivate:\n void draw_colored_line_strip(void) const;\n\n std::vector<unsigned> colors;\n ColorTable const& colortable;\n\n};\n#endif /plot1D_H/\n" }, { "alpha_fraction": 0.6771546006202698, "alphanum_fraction": 0.6771546006202698, "avg_line_length": 68.57142639160156, "blob_id": "1c0f6797af9f911cd752e84b13f96b6d8ca590c9", "content_id": "e6d6f7072781e607ca86e13fbd77ae89e78cd224", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1462, "license_type": "no_license", "max_line_length": 130, "num_lines": 21, "path": "/src/evolution/fitness.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"./fitness.h\"\n\nFitness_ptr assign_fitness( const robots::Simloid& robot\n , const Setting& settings)\n{\n Fitness_ptr fitness_function;\n const std::string& fitness(settings.fitness_function);\n\n if (\"FORWARDS\" == fitness) fitness_function = Fitness_ptr(new Fitness_Forwards (robot, settings, /use_avg=/true ));\n else if (\"FORWARDS_MIN\" == fitness) fitness_function = Fitness_ptr(new Fitness_Forwards (robot, settings, /use_avg=/false));\n else if (\"FORWARDS_FEET\"== fitness) fitness_function = Fitness_ptr(new Fitness_Forwards_Feet(robot, settings));\n else if (\"BACKWARDS\" == fitness) fitness_function = Fitness_ptr(new Fitness_Backwards(robot, settings, /use_avg=/true ));\n else if (\"BACKWARDS_MIN\"== fitness) fitness_function = Fitness_ptr(new Fitness_Backwards(robot, settings, /use_avg=/false));\n else if (\"SIDEWARDS\" == fitness) fitness_function = Fitness_ptr(new Fitness_Sidewards(robot, settings, /use_avg=/true ));\n else if (\"SIDEWARDS_MIN\"== fitness) fitness_function = Fitness_ptr(new Fitness_Sidewards(robot, settings, /use_avg=/false));\n else if (\"TURNING\" == fitness) fitness_function = Fitness_ptr(new Fitness_Turning (robot, settings));\n else if (\"STOPPING\" == fitness) fitness_function = Fitness_ptr(new Fitness_Stopping (robot, settings));\n else err_msg(__FILE__, __LINE__, \"Wrong name of fitness function.\");\n\n return fitness_function;\n}\n" }, { "alpha_fraction": 0.5379236936569214, "alphanum_fraction": 0.5740994215011597, "avg_line_length": 26.86864471435547, "blob_id": "4463f4788bccbc6abc34c1ed609efd9749fecd77", "content_id": "8dfddd7431f07018c05e3b25ac605ddeb5ae97ac", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6579, "license_type": "no_license", "max_line_length": 102, "num_lines": 236, "path": "/src/tests/forward_inverse_model_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <common/modules.h>\n#include <learning/forward_inverse_model.hpp>\n\n\nnamespace local_tests {\n\nnamespace forward_inverse_model_tests {\n\ntypedef learning::NeuralModel<learning::LinearTransfer<>> LinearNeural_t;\ntypedef learning::NeuralModel<learning::TanhTransfer<>> NeuralForward_t;\ntypedef learning::InverseNeuralModel NeuralInverse_t;\n\ntypedef learning::BidirectionalModel<LinearNeural_t,LinearNeural_t> BidirectionalLinearModelType;\ntypedef learning::BidirectionalModel<NeuralForward_t,NeuralInverse_t> BidirectionalNonlinearModelType;\n\n\nTEST_CASE( \"twopart_vector access and manipulation\" , \"[twopart_vector]\")\n{\n typedef std::vector<double> Vector_t;\n\n Vector_t v1(3);\n Vector_t v2(5);\n\n learning::twopart_vector<Vector_t> vec(v1,v2);\n\n // check sizes\n REQUIRE( vec .size() == 8 );\n REQUIRE( vec.part0.size() == 3 );\n REQUIRE( vec.part1.size() == 5 );\n\n for (std::size_t i = 0; i < vec.size(); ++i)\n REQUIRE( vec[i] == 0 );\n\n auto& A = vec.part0;\n auto& B = vec.part1;\n\n for (std::size_t i = 0; i < A.size(); ++i)\n REQUIRE( A[i] == 0 );\n\n for (std::size_t i = 0; i < B.size(); ++i)\n REQUIRE( B[i] == 0 );\n\n A[0] = 0;\n A[1] = 1;\n A[2] = 2;\n\n B[0] = 3;\n B[1] = 4;\n B[2] = 5;\n B[3] = 6;\n B[4] = 7;\n\n\n REQUIRE( v1 == A );\n REQUIRE( v2 == B );\n\n // elements can be read\n for (std::size_t i = 0; i < vec.size(); ++i) {\n dbg_msg(\"%u = %f\", i, vec[i]);\n REQUIRE( close(vec[i], i, 0.00001) );\n }\n\n // elements can be written\n vec[4] = 23;\n REQUIRE( B[1] == 23 );\n\n vec[1] = 17;\n REQUIRE( A[1] == 17 );\n}\n\nTEST_CASE( \"forward_inverse_model construction\" , \"[forward_inverse_model]\")\n{\n srand(time(0)); // set random seed\n\n double random_range = 0.01;\n\n BidirectionalLinearModelType model(13, 7, random_range);\n\n /* check weights are not zero, but randomized /\n auto const& weights = model.get_weights();\n double sum = .0;\n int diff = 0;\n for (std::size_t i = 0; i < weights.size(); ++i)\n for (std::size_t j = 0; j < weights[i].size(); ++j) {\n diff += ( weights[i][j] != .0 )? 0 : 1;\n sum += weights[i][j];\n }\n\n / check randomize_weight_matrix() is executed /\n REQUIRE( diff == 0 );\n const double max_range = 0.5 random_range * weights.size()weights[0].size();\n dbg_msg(\"Max rand: %e < %e\", std::abs(sum), max_range);\n REQUIRE( std::abs(sum) <= max_range ); // check small\n REQUIRE( std::abs(sum) != 0. ); // but not zero\n\n // check matrix size\n REQUIRE( weights .size() == 7 );\n REQUIRE( weights[0].size() == 13 );\n\n auto const& inputs = model.get_inverse_result();\n auto const& outputs = model.get_forward_result();\n\n // check vector size\n REQUIRE( inputs.size() == 13 );\n REQUIRE( outputs.size() == 7 );\n\n // check in and outputs are zero on initialization\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( outputs[i] == .0 );\n\n for (std::size_t i = 0; i < inputs.size(); ++i)\n REQUIRE( inputs[i] == .0 );\n\n model.propagate_forward(inputs);\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( outputs[i] == .0 );\n\n model.propagate_inverse(outputs);\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( inputs[i] == .0 );\n\n BidirectionalLinearModelType model2 = model;\n}\n\n\nTEST_CASE( \"forward_inverse_model learning (linear)\", \"[forward_inverse_model]\")\n{\n dbg_msg(\"linear\");\n srand(time(0)); // set random seed\n\n const double learning_rate = 0.005;\n\n std::vector<double> X = {1,0.5,1,-1,0,1,1,0,-1,-1,1,0.75,1,0,1,0.5,-1,1};\n std::vector<double> Y = {0.9,0,-0.9,0,-0.9,0.9,0,-0.9,-0.9,0.5};\n\n BidirectionalLinearModelType model(X.size(), Y.size(), 0.01);\n\n / check error is decreasing (forward) /\n auto const& Y_ = model.propagate_forward(X);\n auto const& X_ = model.propagate_inverse(Y); // inverse direction\n\n double ery0 = squared_distance(Y, model.get_forward_result());\n double erx0 = squared_distance(X, model.get_inverse_result()); // inverse error\n\n double ery1, erx1;\n\n for (std::size_t trials = 0; trials < 500; ++trials) {\n // adapt\n model.adapt(X, Y, learning_rate);\n\n // verify\n model.propagate_forward(X);\n model.propagate_inverse(Y);\n\n ery1 = squared_distance(Y, model.get_forward_result());\n erx1 = squared_distance(X, model.get_inverse_result());\n //dbg_msg(\"E_fw: %+e \| E_bw: %+e\",ery1, erx1);\n REQUIRE( ery0 > ery1 );\n REQUIRE( erx0 > erx1 );\n ery0 = ery1;\n erx0 = erx1;\n }\n\n model.propagate_forward(X);\n print_vector(Y);\n print_vector(Y_);\n\n REQUIRE( close(Y_, Y, 0.01) );\n\n model.propagate_inverse(Y);\n print_vector(X);\n print_vector(X_);\n\n REQUIRE( close(X_, X, 0.01) );\n\n}\n\nTEST_CASE( \"forward_inverse_model learning (non-linear)\", \"[forward_inverse_model]\")\n{\n dbg_msg(\"non-linear\");\n srand(time(0)); // set random seed\n\n const double learning_rate = 0.005;\n\n std::vector<double> X = {1,0.5,1,-1,0,1,1,0,-1,-1,1,0.75,1,0,1,0.5,-1,1};\n std::vector<double> Y = {0.9,0,-0.9,0,-0.9,0.9,0,-0.9,-0.9,0.5};\n\n BidirectionalNonlinearModelType model(X.size(), Y.size(), 0.01);\n\n / check error is decreasing (forward) /\n auto const& Y_ = model.propagate_forward(X);\n auto const& X_ = model.propagate_inverse(Y); // inverse direction\n\n double ery0 = squared_distance(Y, model.get_forward_result());\n double erx0 = squared_distance(X, model.get_inverse_result()); // inverse error\n\n double ery1, erx1;\n\n for (std::size_t trials = 0; trials < 500; ++trials) {\n // adapt\n model.adapt(X, Y, learning_rate);\n\n // verify\n model.propagate_forward(X);\n model.propagate_inverse(Y);\n\n ery1 = squared_distance(Y, model.get_forward_result());\n erx1 = squared_distance(X, model.get_inverse_result());\n //dbg_msg(\"E_fw: %+e \| E_bw: %+e\",ery1, erx1);\n REQUIRE( ery0 > ery1 );\n REQUIRE( erx0 >= erx1 );\n ery0 = ery1;\n erx0 = erx1;\n }\n\n model.propagate_forward(X);\n print_vector(Y);\n print_vector(Y_);\n for (unsigned i = 0; i < Y_.size(); ++i)\n REQUIRE( close(Y_[i], Y[i], 0.03) );\n\n dbg_msg(\"---\");\n model.propagate_inverse(Y);\n print_vector(X);\n print_vector(X_);\n\n REQUIRE( close(X_, X, 0.03) );\n\n}\n\n\n\n}} // namespace local_tests::forward_inverse_model_tests\n\n\n" }, { "alpha_fraction": 0.608428418636322, "alphanum_fraction": 0.6255487203598022, "avg_line_length": 26.768293380737305, "blob_id": "f6730e7dbce15d065baf93149c3e6754188d9b95", "content_id": "1c5e7e20e0da2493bc82d7916579976bd23f659e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2278, "license_type": "no_license", "max_line_length": 116, "num_lines": 82, "path": "/src/robots/pole.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef POLE_H\n#define POLE_H\n\n#include <cmath>\n#include <vector>\n#include <cassert>\n#include <common/modules.h>\n#include <common/static_vector.h>\n#include <common/vector_n.h>\n\n#include <draw/draw.h>\n\n#include <robots/joint.h>\n#include <robots/accel.h>\n#include <robots/robot.h>\n\nnamespace robots {\n\nnamespace pole_constants\n{\n const double gravity = 9.81;\n const double mass = 0.1;\n const double length = 0.5; / actually half the pole's length /\n\n const double force_mag = 2.0;\n const double dt = 0.001; / step size /\n const double max_x = 10.0; / meters /\n const double friction = 0.5;\n}\n\ninline double deg_to_rad(double deg) { return degM_PI/180.0; }\n\nclass pole : public Robot_Interface\n{\nprivate:\n double theta; /* pole angle [rad] /\n double theta_dot; / pole angular velocity [rad/s] /\n double force; / force exerted to hinge joint /\n\n Jointvector_t joints;\n\n void reset_state(bool tilted = false);\n void update_dynamics(const double action);\n\n Accelvector_t accels;\n\npublic:\n pole(bool tilted = false)\n : theta()\n , theta_dot()\n , force()\n , joints()\n , accels() //empty\n {\n joints.reserve(1);\n joints.emplace_back(0, Joint_Type_Normal, 0, \"joint0\", -1.0, +1.0, 0.0);\n assert(joints.size() == 1);\n reset_state(tilted);\n }\n\n bool execute_cycle(void);\n\n void draw(const float pos_x, const float pos_y, const float size) const; //TODO make independent of pos and size\n bool top(double range = 0.01) const;\n double height(void) const { return -cos(theta); }\n\n / implement the robot interface /\n std::size_t get_number_of_joints (void) const { return 1; }\n std::size_t get_number_of_symmetric_joints(void) const { return 0; }\n std::size_t get_number_of_accel_sensors (void) const { return 0; }\n\n const Jointvector_t& get_joints(void) const { return joints; }\n Jointvector_t& set_joints(void) { return joints; }\n\n const Accelvector_t& get_accels(void) const { return accels; }\n Accelvector_t& set_accels(void) { return accels; }\n\n double get_normalized_mechanical_power(void) const { return force force; };\n};\n\n} // namespace robots\n#endif /* POLE_H /\n\n" }, { "alpha_fraction": 0.6151208877563477, "alphanum_fraction": 0.6199211478233337, "avg_line_length": 41.732601165771484, "blob_id": "a9f8a7f028403d2cd89b3b3afc05876df880532b", "content_id": "a8e8a5afc59431b7186186649b56ad5f2c09ba44", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 11666, "license_type": "no_license", "max_line_length": 165, "num_lines": 273, "path": "/src/learning/competitive_motor_layer.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef COMPETITIVE_MOTOR_LAYER_H_INCLUDED\n#define COMPETITIVE_MOTOR_LAYER_H_INCLUDED\n\n#include <vector>\n#include <sstream>\n#include <iostream>\n#include <iomanip>\n\n#include <common/log_messages.h>\n#include <common/misc.h>\n\n#include <control/controlparameter.h>\n\nnamespace MotorLayerConstants {\n const std::size_t initial_learning_capacity = 1000;\n const control::Minimal_Seed_t seed = {2.0, 0.0, 1.0};\n\n const double symmetry_ratio = 0.5;\n}\n\nclass MotorUnit {\npublic:\n MotorUnit(const control::Control_Parameter& seed, const bool exists = false)\n : weights(seed)\n , selection_count(0) // selections should also be counted by q for better analysis of competition for motor neurons\n , learning_capacity(MotorLayerConstants::initial_learning_capacity)\n , exists(exists)\n {\n dbg_msg(\"Creating Motor Unit with %u weights: (%s)\", seed.size(), to_str(seed.get_parameter()).c_str());\n }\n\n MotorUnit& operator=(const MotorUnit& other) {\n dbg_msg(\"Copy motor neuron.\");\n weights = other.weights; // copy with flaws?\n exists = true;\n selection_count = 0;\n learning_capacity = other.learning_capacity;\n return this;\n }\n\n control::Control_Parameter weights;\n std::size_t selection_count;\n std::uint64_t learning_capacity;\n bool exists;\n};\n\n\nclass CompetitiveMotorLayer\n{\npublic:\n CompetitiveMotorLayer( const robots::Robot_Interface& robot\n , static_vector<State_Payload>& states\n , const control::Control_Vector& parameter_sets\n , const std::size_t number_of_motor_units\n , const std::size_t number_of_motor_units_begin\n , const double mutation_rate\n , const double learning_rate\n , bool do_adaption\n , const control::Minimal_Seed_t& seed = MotorLayerConstants::seed)\n : robot(robot)\n , states(states)\n , parameter_sets(parameter_sets)\n , motor_units()\n , mutated_weights(parameter_sets.get(0))\n , last_selected_idx()\n , recipient_idx()\n , do_adaption(do_adaption)\n , mutation_rate(mutation_rate)\n , learning_rate(learning_rate)\n {\n dbg_msg(\"Creating competitive motor layer with %u motor units.\", number_of_motor_units);\n assert_in_range(number_of_motor_units, 1ul, 100ul);\n assert_in_range(learning_rate, 0.0010, 0.5);\n assert_in_range(mutation_rate, 0.0001, 0.5);\n motor_units.reserve(number_of_motor_units);\n\n if (parameter_sets.size() <= number_of_motor_units)\n sts_msg(\"Initialize %u of %u parameter sets.\", parameter_sets.size(), number_of_motor_units);\n else\n wrn_msg(\"Can not initialize all %u parameter sets. Limit is %u.\", parameter_sets.size(), number_of_motor_units);\n\n for (std::size_t i = 0; i < std::min(parameter_sets.size(), number_of_motor_units); ++i)\n {\n motor_units.emplace_back(parameter_sets.get(i), true);\n }\n assert(motor_units.size() <= number_of_motor_units);\n\n //if ?\n for (std::size_t i = motor_units.size(); i < number_of_motor_units; ++i) {\n control::Control_Parameter params = control::get_initial_parameter(robot, seed, /symmetric?/(i % 2 == 0));\n\n control::randomize_control_parameter(params, 0.1, 1.0); /*TODO make to settings, and constrain motor self not not go beyond zero /\n /*TODO also: make settings grouped and only give the local settings as ref /\n motor_units.emplace_back(params, (i < number_of_motor_units_begin));\n }\n// else\n// for (std::size_t i = motor_units.size(); i < number_of_motor_units; ++i)\n// motor_units.emplace_back(/placeholder motor units/);\n\n assert(motor_units.size() == number_of_motor_units);\n }\n\n bool exists(const std::size_t idx) const { assert(idx < motor_units.size()); return motor_units[idx].exists; }\n\n /** Only the current active motor neuron is going to be adapted. This adaption happens\n * iff we receive a positive delta from the superordinate learning layer, e.g. sarsa/RL.\n * That means that only if we notice an increase in our reward expectation we take over\n * the just evaluated changes to our controller weights.\n * By using the delta signal we make sure that different policies with different relative\n * reward levels do not affect the weight adjustment and are treated more or less equally.\n /\n void adapt(bool positive_delta)\n {\n if (not do_adaption) return;\n assert(motor_units[last_selected_idx].exists == true); // must not be executed on non-existing units\n if (positive_delta) {\n do_adapt(last_selected_idx);\n }\n }\n\n /* Create a randomized variant of the current motor neuron's controller weights.\n * This mutated weights will be evaluated by the next eigenzeit cycle and in case\n * of success, i.e. a positive learning delta, the new mutated weights will be adopted.\n /\n void create_mutated_weights(std::size_t selected_idx)\n {\n assert(exists(selected_idx)); // must not be executed on non-existing units\n\n mutated_weights = motor_units[selected_idx].weights;\n\n if (do_adaption)\n for (std::size_t i = 0; i < mutated_weights.size(); ++i)\n mutated_weights[i] += random_value(-mutation_rate, +mutation_rate);\n /TODO\n I want to use here the same mechanics as in Individual::mutate(void) (evolution)\n /\n ++(motor_units[selected_idx].selection_count);\n last_selected_idx = selected_idx;\n }\n\n const std::vector<double>& get_weights(std::size_t index) const {\n assert(index < motor_units.size());\n return motor_units[index].weights.get_parameter();\n }\n\n const control::Control_Parameter& get_mutated_weights(void) const { return mutated_weights; }\n\n const MotorUnit& get_unit(std::size_t index) const {\n assert(index < motor_units.size());\n return motor_units[index];\n }\n std::size_t get_number_of_motor_units(void) const { return motor_units.size(); }\n\n std::size_t get_number_of_available_motor_units(void) const\n {\n std::size_t available_units = 0;\n for (std::size_t i = 0; i < motor_units.size(); ++i)\n if (motor_units[i].exists)\n ++available_units;\n return available_units;\n }\n\n void enable_adaption(bool enable) { do_adaption = enable; }\n bool is_adaption_enabled(void) const { return do_adaption; }\n\nprivate:\n\n /* We're using the learning capacity here to decide which module should be replaced\n * by the cloning. We choose the candidate with highest learning capacity to be\n * replaced by the one having the lowest learning capacity.\n /\n std::size_t find_replacement_candidate_for(std::size_t min_index)\n {\n / always returns a valid index /\n std::size_t max_learning_capacity = motor_units[min_index].learning_capacity;\n std::size_t argmax = min_index;\n for (std::size_t idx = 0; idx < motor_units.size(); ++idx) {\n / if there's a free slot, take it /\n if (not motor_units[idx].exists) return idx;\n if (motor_units[idx].learning_capacity > max_learning_capacity)\n {\n max_learning_capacity = motor_units[idx].learning_capacity;\n argmax = idx;\n }\n }\n return argmax;\n }\n\n /* Replace the least adapted motor neuron by a copy of the most adapted one.\n * When cloning the motor neuron, the corresponding q-values and eligibility\n * trace which the motor neuron is connected with will also be cloned.\n * TODO: Instead of just cloning, think of using a kind of crossover operation\n * for the creation of the new unit.\n /\n void clone(std::size_t current_idx)\n {\n std::size_t replace_idx = find_replacement_candidate_for(current_idx);\n dbg_msg(\"Cloning motor unit %u to %u\", current_idx, replace_idx);\n\n unsigned int learning_capacity_remainder = (motor_units[current_idx].learning_capacity + motor_units[replace_idx].learning_capacity) % 2;\n motor_units[current_idx].learning_capacity = (motor_units[current_idx].learning_capacity + motor_units[replace_idx].learning_capacity) / 2;\n\n motor_units[replace_idx] = motor_units[current_idx]; // replace\n motor_units[replace_idx].learning_capacity += learning_capacity_remainder;\n\n /* Change symmetry to balance the ratio of symmetric and asymmetric joint count /\n if (get_number_of_symmetric_units() < (motor_units.size()/2))\n motor_units[replace_idx].weights = make_symmetric(robot, motor_units[replace_idx].weights);\n else\n motor_units[replace_idx].weights = make_asymmetric(robot, motor_units[replace_idx].weights);\n\n / copy associate Q-values and eligibility traces /\n for (std::size_t i = 0; i < states.size(); ++i)\n states[i].copy_payload(current_idx, replace_idx);\n }\n\n /* Shift the original weight vector of the selected motor neuron a little bit\n * towards the mutated weight vector. Decrease learning capacity.\n /\n void do_adapt(std::size_t current_idx) {\n --(motor_units[current_idx].learning_capacity);\n assert(mutated_weights.size() == motor_units[current_idx].weights.size()); /* TODO: this was thrown, CHECK!!! somehow large reward values will induce that/\n for (std::size_t i = 0; i < motor_units[current_idx].weights.size(); ++i) {\n double delta = learning_rate (mutated_weights[i] - motor_units[current_idx].weights[i]);\n motor_units[current_idx].weights[i] += delta;\n }\n\n /** When learning capacity is exhausted we clone the motor unit /\n if (motor_units[current_idx].learning_capacity == 0) {\n clone(current_idx);\n }\n\n /* Redistribute the learning capacity to the group of all (possible) motor units.\n * This enforces the growing up to the maximal size of the group.\n /\n recipient_idx = random_index(motor_units.size());\n ++(motor_units[recipient_idx].learning_capacity);\n\n / assert that learning capacity does not drain /\n unsigned int sum = 0;\n for (std::size_t i = 0; i < motor_units.size(); ++i)\n sum += motor_units[i].learning_capacity;\n assert(sum == motor_units.size() MotorLayerConstants::initial_learning_capacity);\n\n }\n\n std::size_t get_number_of_symmetric_units() {\n std::size_t num_sym = 0;\n for (std::size_t i = 0; i < motor_units.size(); ++i)\n if (motor_units[i].weights.is_symmetric())\n ++num_sym;\n return num_sym;\n }\n\n const robots::Robot_Interface& robot; // only needed for changing symmetry of controller weights\n static_vector<State_Payload>& states;\n const control::Control_Vector& parameter_sets;\n\n std::vector<MotorUnit> motor_units;\n control::Control_Parameter mutated_weights;\n std::size_t last_selected_idx;\n std::size_t recipient_idx;\n bool do_adaption;\n\n const double mutation_rate;\n const double learning_rate;\n\n friend class CompetitiveMotorLayer_Graphics;\n};\n\n\n\n#endif // COMPETITIVE_MOTOR_LAYER_H_INCLUDED\n" }, { "alpha_fraction": 0.5604562759399414, "alphanum_fraction": 0.5615969300270081, "avg_line_length": 24.288461685180664, "blob_id": "06a7b61ad22be89a00f75f8091f122b410bd2526", "content_id": "de175a3b0fa57fa2e4f06228e4880345f8a216e0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2630, "license_type": "no_license", "max_line_length": 82, "num_lines": 104, "path": "/src/evolution/setting.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* setting.h /\n\n#ifndef SETTING_H\n#define SETTING_H\n\n#include <stdio.h>\n\n#include <common/log_messages.h>\n#include <common/socket_client.h>\n#include <control/jointcontrol.h>\n#include <evolution/evolution_strategy.h>\n\n#define FOLDER_PREFIX \"../data/exp/\"\n\nenum PStatus {NEW, RESUME, WATCH}; /* TODO rename /\n\nclass Setting\n{\n private:\n void print_options(void);\n\n public:\n / general /\n std::string project_name;\n PStatus project_status;\n bool visuals;\n bool interlaced_mode;\n\n / simloid /\n unsigned short tcp_port;\n unsigned int robot_ID;\n unsigned int scene_ID;\n\n / evaluation /\n unsigned int max_steps;\n unsigned int max_power;\n unsigned int max_dctrl;\n unsigned int initial_steps;\n\n bool efficient;\n bool drop_penalty;\n bool out_of_track_penalty;\n bool stop_penalty;\n bool symmetric_controller;\n\n / evolution /\n std::string strategy;\n unsigned int population_size;\n unsigned int selection_size;\n unsigned int max_generations;\n unsigned int cur_generations;\n unsigned int max_trials;\n unsigned int cur_trials;\n double init_mutation_rate;\n double meta_mutation_rate;\n double moving_rate;\n double selection_bias;\n std::string seed;\n std::string initial_population;\n\n control::Minimal_Seed_t param;\n\n struct Push_Settings {\n unsigned int mode;\n unsigned int body;\n unsigned int cycle;\n unsigned int steps;\n double strength;\n } push;\n\n std::string fitness_function;\n\n struct Random_Mode_Settings {\n std::string mode;\n double value;\n mutable uint64_t init;\n } rnd;\n\n struct Growth_Settings {\n double init;\n double rate;\n } growth;\n\n double friction;\n\n bool low_sensor_quality;\n bool L1_normalization;\n\n double target;\n double drop_level;\n double stop_level;\n double corridor;\n\n bool initially_fixed;\n\n Setting(int argc, char argv);\n void read_setting_file(const std::string& setting_name);\n void read_project_file(const std::string& project_name);\n void read_configuration(const std::string& filename);\n const std::string& save_to_projectfile(const std::string& filename) const;\n\n};\n\n#endif //SETTING_H\n" }, { "alpha_fraction": 0.4878166913986206, "alphanum_fraction": 0.49569645524024963, "avg_line_length": 28.045774459838867, "blob_id": "06cc6221e6cc9271242f1375b75f3661f5f9eb08", "content_id": "e30d87d7baf9e2c1b5f9e1577e53793f748fa489", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8249, "license_type": "no_license", "max_line_length": 107, "num_lines": 284, "path": "/src/common/datareader.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef DATAREADER_H_INCLUDED\n#define DATAREADER_H_INCLUDED\n\n#include <string>\n#include <vector>\n#include <map>\n\n#include <common/noncopyable.h>\n#include <common/basic.h>\n\nnamespace file_io {\n\n/\n i = 5 int\n s = \"hallo\" string\n f = {0.1} float\n v = {0 1 1 2} vector\n # comment\n/\n\n typedef std::string key_t;\n\n typedef uint64_t uint_t;\n typedef int64_t sint_t;\n typedef double float_t;\n typedef std::vector<double> vector_t;\n typedef std::string string_t;\n\nnamespace {\n\n const std::size_t key_size = 256;\n const std::size_t val_size = 1048576; // 1 kbyte\n const char separator[] = \" \";\n\n const char pattern_vec[] = \" %256[a-z_] = { %1048576[\\n\\t eE0-9.+-] } %n\";\n const char pattern_str[] = \" %256[a-z_] = \\\"%1048576[^\\\"]\\\" %n\";\n const char pattern_int[] = \" %256[a-z_] = %ld %n\";\n}\n\ninline bool is_empty(const char msg) {\n while(msg != '\\0') {\n if (not isspace(msg))\n return false;\n msg++;\n }\n return true;\n}\n\nclass Data_Reader : public noncopyable{\n\n FILE* fd;\n const std::size_t file_size;\n char* txtbuf;\n char* refto_txtbuf;\n\n char key[key_size];\n\n char str_val[val_size];\n long int int_val;\n\n Data_Reader ( const Data_Reader& ) = delete; // non construction-copyable\n Data_Reader& operator=( const Data_Reader& ) = delete; // non copyable\n\n /* maps /\n std::map<key_t, vector_t> map_vec;\n std::map<key_t, string_t> map_str;\n std::map<key_t, sint_t> map_int;\n\n bool verbose;\n\npublic:\n Data_Reader(const std::string& filename, bool verbose = true)\n : fd(open_file(\"r\", filename.c_str()))\n , file_size(basic::get_file_size(fd))\n , txtbuf((char) malloc (sizeof(char) * file_size))\n , refto_txtbuf(txtbuf)\n , int_val(0)\n , map_vec()\n , map_str()\n , map_int()\n , verbose(verbose)\n {\n if (verbose) sts_msg(\"Reading file %s \\n with size: %zu bytes.\", filename.c_str(), file_size);\n\n if (nullptr == txtbuf)\n err_msg(__FILE__, __LINE__, \"Cannot allocate memory.\");\n\n // copy complete file into buffer\n std::size_t result = fread(txtbuf, 1, file_size, fd);\n if (result != file_size)\n err_msg(__FILE__, __LINE__, \"Cannot read full file.\");\n\n fclose(fd);\n parse();\n }\n\n ~Data_Reader() {\n if (refto_txtbuf != nullptr)\n free(refto_txtbuf);\n }\n\nprivate:\n\n vector_t decode(char value)\n {\n vector_t data;\n char token = strtok(value, separator); // get first token\n while (nullptr != token) {\n if (not is_empty(token))\n data.emplace_back(atof(token));\n token = strtok(nullptr, separator); // get next token\n }\n return data;\n }\n\n void remove_comments(void)\n {\n bool comment_found = false;\n for (std::size_t i = 0; i < file_size; ++i)\n {\n if ('#' == txtbuf[i])\n comment_found = true;\n else if (comment_found && ('\\n' == txtbuf[i]))\n comment_found = false;\n\n if (comment_found)\n txtbuf[i] = ' '; // clear comments\n }\n }\n\n void parse(void) {\n\n remove_comments();\n int offset = 0;\n while (true)\n {\n if (2 == sscanf(txtbuf, pattern_vec, key, str_val, &offset))\n {\n vector_t data = decode(str_val);\n if (verbose) {\n printf(\"\\t%s = ( \", key);\n for (std::size_t i = 0; i < std::min(std::size_t{8}, data.size()); ++i)\n printf(\"%1.2f \", data[i]);\n if (data.size() > 8)\n printf(\"... ) N=%zu\\n\", data.size());\n else\n printf(\") \\n\");\n }\n if (0 == map_vec.count(key))\n map_vec.emplace(key, data);\n else wrn_msg(\"\\tSkipping vector %s, already in list.\", key);\n }\n else if (2 == sscanf(txtbuf, pattern_str, key, str_val, &offset)) {\n if (verbose) sts_msg(\"\\t%s = \\'%s\\'\", key, str_val);\n if (0 == map_str.count(key))\n map_str.emplace(key, str_val);\n else wrn_msg(\"\\tSkipping string %s, already in list.\", key);\n }\n else if (2 == sscanf(txtbuf, pattern_int, key, &int_val, &offset)) {\n if (verbose) sts_msg(\"\\t%s = <%ld>\", key, int_val);\n if (0 == map_int.count(key))\n map_int.emplace(key, int_val);\n else wrn_msg(\"\\tSkipping integer %s, already in list.\", key);\n }\n else {\n if (verbose) sts_msg(\"Done reading\");\n break;\n }\n txtbuf += offset;\n }\n }\n\npublic:\n bool read (const key_t& key, uint_t& value)\n {\n if (map_int.count(key)) {\n value = static_cast<uint_t>(map_int[key]);\n return true;\n }\n return false;\n }\n bool read (const key_t& key, sint_t& value)\n {\n if (map_int.count(key)) {\n value = map_int[key];\n return true;\n }\n return false;\n }\n bool read (const key_t& key, float_t& value)\n {\n if (map_vec.count(key)) {\n const vector_t& vec = map_vec[key];\n if (vec.size() == 1) {\n value = vec[0];\n return true;\n }\n }\n return false;\n }\n bool read (const key_t& key, string_t& value)\n {\n if (map_str.count(key)) {\n value = map_str[key];\n return true;\n }\n return false;\n }\n bool read (const key_t& key, vector_t& value)\n {\n if (map_vec.count(key)) {\n value = map_vec[key];\n return true;\n }\n return false;\n }\n\n uint_t read_unsigned(const key_t& key, const uint_t default_value = uint_t()) {\n uint_t result;\n if (not read(key, result)) {\n wrn_msg(\"Cannot read unsigned '%s'. Taking default: %u\", key.c_str(), default_value);\n return default_value;\n }\n return result;\n }\n\n sint_t read_signed(const key_t& key, const sint_t default_value = sint_t()) {\n sint_t result;\n if (not read(key, result)) {\n wrn_msg(\"Cannot read signed '%s'. Taking default: %d\", key.c_str(), default_value);\n return default_value;\n }\n return result;\n }\n\n float_t read_float(const key_t& key, const float_t default_value = float_t()) {\n float_t result;\n if (not read(key, result)) {\n wrn_msg(\"Cannot read float '%s'. Taking default: %f\", key.c_str(), default_value);\n return default_value;\n }\n return result;\n }\n\n string_t read_string(const key_t& key, const string_t default_value = string_t()) {\n string_t result;\n if (not read(key, result)) {\n wrn_msg(\"Cannot read string '%s'. Taking default: %s\", key.c_str(), default_value.c_str());\n return default_value;\n }\n return result;\n }\n\n vector_t read_vector(const key_t& key, const vector_t default_value = vector_t()) {\n vector_t result;\n if (not read(key, result)) {\n wrn_msg(\"Cannot read vector '%s'.\", key.c_str());\n return default_value;\n }\n return result;\n }\n\n template <typename result_t>\n result_t read_type(const key_t& key, const result_t default_value = result_t()) {\n result_t result;\n if (not read(key, result)) {\n wrn_msg(\"Cannot read '%s'\", key.c_str());\n return default_value;\n }\n return result;\n }\n\n// void write(const std::string& key, const uint_t& value) { }\n// void write(const std::string& key, const sint_t& value) { }\n// void write(const std::string& key, const float_t& value) { }\n// void write(const std::string& key, const string_t& value) { }\n// void write(const std::string& key, const vector_t& value) { }\n\n};\n\n\n} // namespace file_io\n\n#endif // DATAREADER_H_INCLUDED\n" }, { "alpha_fraction": 0.6285289525985718, "alphanum_fraction": 0.6404160261154175, "avg_line_length": 20.70967674255371, "blob_id": "d0bc7decf65e6e4a590aaf3a744ccabbbb83448d", "content_id": "6594fbbae7db371dbaad64f70cf7870e4239169a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 673, "license_type": "no_license", "max_line_length": 79, "num_lines": 31, "path": "/src/learning/eligibility.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef ELIGIBILITY_H_INCLUDED\n#define ELIGIBILITY_H_INCLUDED\n\n#include <cassert>\n#include <iostream>\n\n/** TODO: visualize traces by displaying the max trace per state.\n /\nclass Eligibility\n{\n double value;\n\npublic:\n Eligibility()\n : value(0.0)\n {}\n\n void decay(float factor) { value = factor; }\n void reset(void) { value = 1.0; }\n double get(void) const { assert(in_range(value, 0.0, 1.0)); return value; }\n\n friend std::ostream& operator<< (std::ostream& os, const Eligibility& e);\n};\n\ninline std::ostream& operator<< (std::ostream& os, const Eligibility& e) {\n os << e.value;\n return os;\n}\n\n\n#endif // ELIGIBILITY_H_INCLUDED\n" }, { "alpha_fraction": 0.6454652547836304, "alphanum_fraction": 0.6643109321594238, "avg_line_length": 23.257143020629883, "blob_id": "4f9f9bed2850395069719683374e69eb3987a195", "content_id": "67b804f94f9c519ce7d23bee3c1544979fc0179a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 849, "license_type": "no_license", "max_line_length": 99, "num_lines": 35, "path": "/src/learning/motor_predictor_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef MOTOR_PREDICTOR_GRAPHICS_H_INCLUDED\n#define MOTOR_PREDICTOR_GRAPHICS_H_INCLUDED\n\n#include <common/modules.h>\n#include <draw/graphics.h>\n#include <draw/display.h>\n#include <basic/color.h>\n#include <learning/motor_predictor.h>\n\nnamespace learning {\n\nclass Motor_Predictor_Graphics : public Graphics_Interface\n{\n const Motor_Predictor& predictor;\n const Color4 color;\n\npublic:\n Motor_Predictor_Graphics(const Motor_Predictor& predictor, Color4 const& color = colors::white)\n : predictor(predictor)\n , color(color)\n {\n }\n\n void draw(const pref&) const {\n float s = 2.0/predictor.core.weights.size();\n unsigned i = 0;\n for (auto const& wi : predictor.core.weights)\n draw_vector2(0.0 + si++, 0.0, 0.045, s, wi, 3.0);\n }\n\n};\n\n}\n\n#endif / MOTOR_PREDICTOR_GRAPHICS_H_INCLUDED /\n" }, { "alpha_fraction": 0.6269165277481079, "alphanum_fraction": 0.6269165277481079, "avg_line_length": 31.61111068725586, "blob_id": "406719aa8c69da92333a0be5d2c215c619b9febe", "content_id": "0ef291677a64e3180576cbc663b8736e7b67bab4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C", "length_bytes": 587, "license_type": "no_license", "max_line_length": 91, "num_lines": 18, "path": "/src/common/log_messages.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef LOG_MESSAGES_H\n#define LOG_MESSAGES_H\n\n#include <cstdlib>\n#include <cstdio>\n#include <cstdarg>\n\nvoid sts_msg(const char format, ...);\nvoid sts_add(const char* format, ...);\nvoid dbg_msg(const char* format, ...);\nvoid wrn_msg(const char* format, ...);\nvoid err_msg(const char* file, unsigned int line, const char* format, ...);\nvoid promise(bool condition, const char* file, unsigned int line, const char* format, ...);\n\n#define assertion(COND, FMT, ...) \\\npromise(COND, __FILE__, __LINE__, #COND \" \" FMT, ##__VA_ARGS__) \\\n\n#endif // LOG_MESSAGES_H\n" }, { "alpha_fraction": 0.6624472737312317, "alphanum_fraction": 0.6624472737312317, "avg_line_length": 15.928571701049805, "blob_id": "59e0a9395b9edd70f8de14a2f0aaa180c6fa4369", "content_id": "1d5ef2b9432a1401cd55375e74301f9a7d19dcf7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 237, "license_type": "no_license", "max_line_length": 41, "num_lines": 14, "path": "/src/common/lock.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef LOCK_H_INCLUDED\n#define LOCK_H_INCLUDED\n\n#include <mutex>\n\n/** Self-unlocking LOCK /\nnamespace common {\n\ntypedef std::mutex mutex_t;\ntypedef std::lock_guard<mutex_t> lock_t;\n\n} // common\n\n#endif // LOCK_H_INCLUDED\n" }, { "alpha_fraction": 0.6533289551734924, "alphanum_fraction": 0.6539849042892456, "avg_line_length": 36.17073059082031, "blob_id": "e6446986388fa84eecdc1c2cb4256d951cde6dfd", "content_id": "4ac8967363672ad0a28a9a9194522a7385c129f6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3049, "license_type": "no_license", "max_line_length": 139, "num_lines": 82, "path": "/src/learning/state_action_predictor.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef STATE_ACTION_PREDICTOR_H\n#define STATE_ACTION_PREDICTOR_H\n\n#include <control/sensorspace.h>\n#include <learning/predictor.h>\n#include <learning/autoencoder.h>\n\nnamespace learning {\n\nclass State_Action_Predictor : public Predictor_Base {\n\n State_Action_Predictor(const State_Action_Predictor& other) = delete;\n State_Action_Predictor& operator=(const State_Action_Predictor& other) = delete;\n\npublic:\n\n State_Action_Predictor( const time_embedded_sensors<16>& inputs\n , const double learning_rate\n , const double random_weight_range\n , const std::size_t experience_size\n , const std::size_t hidden_layer_size // not needed, replace with num joints, remove\n )\n : Predictor_Base(inputs, learning_rate, random_weight_range, experience_size)\n , enc(inputs.size(), hidden_layer_size, random_weight_range )\n {\n //dbg_msg(\"Initialize State Action Predictor using Autoencoder and time-embedded inputs.\");\n }\n\n virtual ~State_Action_Predictor() = default;\n\n void copy(Predictor_Base const& other) override {\n Predictor_Base::operator=(other); // copy base members\n State_Action_Predictor const& rhs = dynamic_cast<State_Action_Predictor const&>(other);\n enc = rhs.enc;\n dbg_msg(\"Copying state predictor weights.\");\n };\n\n Predictor_Base::vector_t const& get_prediction(void) const override { return enc.get_outputs(); }\n\n double predict(void) override {\n enc.propagate(input);\n return calculate_prediction_error();\n };\n\n double verify(void) override {\n enc.propagate(input);\n return calculate_prediction_error();\n }\n\n void initialize_randomized(void) override {\n enc.randomize_weight_matrix(random_weight_range);\n auto initial_experience = input.get();\n for (auto& w: initial_experience)\n w += random_value(-random_weight_range, random_weight_range);\n experience.assign(experience.size(), initial_experience);\n prediction_error = predictor_constants::error_min;\n };\n\n void initialize_from_input(void) override { assert(false && \"one shot learning not supported.\"); }\n\n void draw(void) const { assert(false); /not implemented/ }\n\n Autoencoder const& get_encoder(void) const { return enc; }\n\n vector_t const& get_weights(void) const override { assert(false); return dummy; /not implemented/ }\n vector_t & set_weights(void) override { assert(false); return dummy; /not implemented/ }\n\nprivate:\n\n void learn_from_input_sample(void) override { enc.adapt(input, learning_rate); };\n void learn_from_experience(std::size_t /skip_idx/) override { assert(false && \"Learning from experience is not implemented yet.\"); };\n\n Autoencoder enc;\n\n VectorN dummy = {}; // remove when implementing get_weights\n\n friend class Predictor_Graphics;\n};\n\n} / namespace learning /\n\n#endif / State_Action_Predictor_H /\n\n" }, { "alpha_fraction": 0.5831242799758911, "alphanum_fraction": 0.5905189514160156, "avg_line_length": 27.152416229248047, "blob_id": "95cd662359b5c3d9976a4c7c891753003a52baa0", "content_id": "d879fcb6118e34eb607a6822ec45b070eb1275b0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 7573, "license_type": "no_license", "max_line_length": 129, "num_lines": 269, "path": "/src/evolution/fitness.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef FITNESS_H\n#define FITNESS_H\n\n#include <math.h>\n#include <memory>\n\n#include <common/modules.h>\n#include <common/config.h>\n\n#include <robots/simloid.h>\n\n#include <evolution/setting.h>\n\n/TODO: how to incorporate L1 normalization into fitness?\n How to access the weight matrix?\n we only need to access the norm value L1 = sum \|w_i\|\n start with computing this value and print it to terminal after each evaluation.\n /\n\nstruct fitness_data\n{\n fitness_data()\n : fit (.0)\n , power(.0)\n , dctrl(.0)\n , temp (.0)\n , steps( 0)\n , max_steps(0)\n , dropped(false)\n , out_of_track(false)\n , stopped(false)\n {}\n double fit;\n double power;\n double dctrl;\n double temp;\n std::size_t steps;\n std::size_t max_steps;\n bool dropped;\n bool out_of_track;\n bool stopped;\n};\n\n\ninline double step_ratio(fitness_data const& data) { return clip(static_cast<double>(data.steps)/data.max_steps, 0.0, 1.0); }\n\nclass Fitness_Base {\npublic:\n Fitness_Base(const std::string& name, const robots::Simloid& robot, const Setting& settings)\n : robot(robot)\n , s(settings)\n {\n sts_msg(\"Fitness Name = %s\", name.c_str());\n sts_msg(\"DROPPED = %s\", s.drop_penalty ? \"YES\":\"NO\");\n sts_msg(\"OUT-OF-TRACK = %s\", s.out_of_track_penalty ? \"YES\":\"NO\");\n sts_msg(\"STOPPED = %s\", s.stop_penalty ? \"YES\":\"NO\");\n sts_msg(\"TARGET = %1.2f\", s.target);\n sts_msg(\"DROP_LEVEL = %1.2f\", s.drop_level);\n sts_msg(\"STOP_LEVEL = %1.2f\", s.stop_level);\n sts_msg(\"CORRIDOR = %1.2f\", s.corridor);\n\n assert(s.target >= .0);\n assert(s.corridor >= .0);\n assert_in_range(s.drop_level, 0., 1.);\n assert_in_range(s.stop_level, 0., 1.);\n }\n\n virtual void start (fitness_data& /data/) {};\n virtual void step (fitness_data& data) = 0;\n virtual void finish(fitness_data& data) = 0;\n virtual ~Fitness_Base() {};\n\nprivate:\n const unsigned min_steps = 100;\n\nprotected:\n const robots::Simloid& robot;\n const Setting& s; //settings\n\n bool dropped(void) { return s.drop_penalty && robot.dropped(s.drop_level); }\n bool stopped(fitness_data& data) { return s.stop_penalty && (data.steps > min_steps) && robot.motion_stopped(s.stop_level); }\n\n bool out_of_track_x(void) { return s.out_of_track_penalty && (fabs(robot.dx_from_origin()) > s.corridor); }\n bool out_of_track_y(void) { return s.out_of_track_penalty && (fabs(robot.dy_from_origin()) > s.corridor); }\n\n\n};\n\ntypedef std::shared_ptr<Fitness_Base> Fitness_ptr;\n\nFitness_ptr assign_fitness(const robots::Simloid& robot, const Setting& settings);\n\nclass Fitness_Forwards : public Fitness_Base\n{\npublic:\n Fitness_Forwards(const robots::Simloid& robot, const Setting& s, bool use_avg = true)\n : Fitness_Base(\"FORWARDS\", robot, s)\n , use_avg(use_avg)\n { sts_msg(\"Evolve getting forwards.\"); }\n\n void step(fitness_data& data) override\n {\n if (dropped()) data.dropped = true;\n if (stopped(data)) data.stopped = true;\n if (out_of_track_x()) data.out_of_track = true;\n }\n\n void finish(fitness_data& data) override\n {\n /* forwards has negative sign on y axis /\n data.fit = use_avg ? -robot.get_avg_position().y : -robot.get_max_position().y;\n\n if (s.target != .0 and data.fit > s.target) {\n data.fit = clip(data.fit, std::abs(s.target));\n //dbg_msg(\"Target overshot. \\n\");\n data.fit = step_ratio(data);\n }\n\n if (data.dropped \|\| data.out_of_track \|\| data.stopped)\n data.fit -= 1.0;// - step_ratio(data);\n }\n\nprivate:\n bool use_avg;\n};\n\n\nclass Fitness_Forwards_Feet : public Fitness_Base\n{\npublic:\n Fitness_Forwards_Feet(const robots::Simloid& robot, const Setting& s)\n : Fitness_Base(\"FORWARDS_FEET\", robot, s)\n { sts_msg(\"Evolve walking forwards, taking only feet position into account.\"); }\n\n void step(fitness_data& data) override\n {\n if (dropped()) data.dropped = true;\n if (stopped(data)) data.stopped = true;\n if (out_of_track_x()) data.out_of_track = true;\n }\n\n void finish(fitness_data& data) override\n {\n /* forwards has negative sign on y axis /\n data.fit = -robot.get_max_feet_pos().y;\n\n if (data.dropped \|\| data.out_of_track \|\| data.stopped)\n data.fit -= 1.0 - step_ratio(data);\n }\n};\n\n\nclass Fitness_Backwards : public Fitness_Base\n{\npublic:\n Fitness_Backwards(const robots::Simloid& robot, const Setting& s, bool use_avg = true)\n : Fitness_Base(\"BACKWARDS\", robot, s)\n , use_avg(use_avg)\n { sts_msg(\"Evolve getting backwards.\"); }\n\n void step(fitness_data& data) override\n {\n if (dropped()) data.dropped = true;\n if (stopped(data)) data.stopped = true;\n if (out_of_track_x()) data.out_of_track = true;\n }\n\n void finish(fitness_data& data) override\n {\n / backwards has positive sign on y axis /\n data.fit = use_avg ? robot.get_avg_position().y : robot.get_min_position().y;\n\n if (data.dropped \|\| data.out_of_track \|\| data.stopped)\n data.fit -= 1.0;// - step_ratio(data);\n }\n\nprivate:\n bool use_avg;\n};\n\n\nclass Fitness_Stopping : public Fitness_Base\n{\npublic:\n Fitness_Stopping(const robots::Simloid& robot, const Setting& s)\n : Fitness_Base(\"STOPPING\", robot, s)\n { sts_msg(\"Evolve stopping.\"); }\n\n void step(fitness_data& data) override\n {\n if (dropped()) data.dropped = true;\n }\n\n void finish(fitness_data& data) override\n {\n data.fit = 10.0/(1 + data.power);\n data.fit+= 10.0/(1 + data.dctrl);\n\n if (data.dropped)\n data.fit -= 10.0 (1.0 - step_ratio(data));\n }\n};\n\n\nclass Fitness_Sidewards : public Fitness_Base\n{\npublic:\n Fitness_Sidewards(const robots::Simloid& robot, const Setting& s, bool use_avg = true)\n : Fitness_Base(\"SIDEWARDS\", robot, s)\n , use_avg(use_avg)\n { sts_msg(\"Evolve walking sidewards.\"); }\n\n void step(fitness_data& data) override\n {\n if (dropped()) data.dropped = true;\n if (stopped(data)) data.stopped = true;\n if (out_of_track_y()) data.out_of_track = true;\n\n data.temp = fmax((data.temp), fabs(robot.get_avg_position().y));\n }\n\n void finish(fitness_data& data) override\n {\n /* left has positive sign on x axis /\n data.fit = use_avg ? robot.get_avg_position().x : robot.get_min_position().x;\n data.fit -= data.temp;\n\n if (data.dropped)\n data.fit -= 1.0;// - step_ratio(data);\n\n / no penalty for out of track here /\n }\nprivate:\n bool use_avg;\n};\n\nclass Fitness_Turning : public Fitness_Base\n{\npublic:\n Fitness_Turning(const robots::Simloid& robot, const Setting& s)\n : Fitness_Base(\"TURNING\", robot, s)\n { sts_msg(\"Evolve turning around.\"); }\n\n void start(fitness_data& data) override {\n data.temp = robot.get_avg_rotation();\n }\n\n void step(fitness_data& data) override\n {\n if (dropped()) data.dropped = true;\n if (stopped(data)) data.stopped = true;\n\n if (out_of_track_y() \|\| out_of_track_x())\n data.out_of_track = true;\n\n / sum of rotation /\n data.temp = unwrap(robot.get_avg_rotation(), data.temp);\n }\n\n void finish(fitness_data& data) override\n {\n data.fit = data.temp;\n\n if (data.dropped)\n data.fit -= 2M_PI;// * (1.0 - step_ratio(data));\n }\n};\n\n#endif /* FITNESS_H /\n" }, { "alpha_fraction": 0.5431813597679138, "alphanum_fraction": 0.5604118704795837, "avg_line_length": 32.279720306396484, "blob_id": "d620f2a88e024abee9b2181b658827c1cd211b5c", "content_id": "f4f2fa4212537c1af20e9c5860e3a2e042c0b9f0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4759, "license_type": "no_license", "max_line_length": 148, "num_lines": 143, "path": "/src/robots/spinalcord_watch.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/---------------------------------+\n \| Matthias Kubisch \|\n \| [email protected] \|\n \| July 2017 \|\n +---------------------------------/\n\n#ifndef SPINALCORD_WATCH_H_INCLUDED\n#define SPINALCORD_WATCH_H_INCLUDED\n\n#include <draw/draw.h>\n#include <draw/axes.h>\n#include <draw/axes3D.h>\n#include <draw/plot1D.h>\n#include <draw/plot2D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n\n/* TODO:\n * make this class more compatible to different (simloid) robots, e.g. with different draw-setups (use new data reader for reading this setup)\n * draw acceleration sensors\n /\n\nnamespace robots {\n\nnamespace constants {\n const double vel_amp = 1.0;///3.0;\n}\n\n// consider using Motorplot from Hannah\n\nclass Spinalcord_Watch : public Graphics_Interface\n{\npublic:\n Spinalcord_Watch(const robots::Robot_Interface& robot, const std::size_t num_datapoints)\n : joints(robot.get_joints())\n , num_joints(robot.get_number_of_joints())\n , accels(robot.get_accels())\n , plot_axs()\n , plot_pos()\n , plot_vel()\n , plot_vol()\n , plot_cur()\n , plot_tmp()\n , subspace_axes()\n , subspace_portrait()\n , axes_accel(0.,-1.25, 0., 2.0, 0.25, 1, \"Accel\")\n , plot_accel_x(1000, axes_accel, colors::cyan , \"ax\")\n , plot_accel_y(1000, axes_accel, colors::magenta, \"ay\")\n , plot_accel_z(1000, axes_accel, colors::yellow , \"az\")\n {\n plot_axs.reserve(num_joints);\n plot_pos.reserve(num_joints);\n plot_vel.reserve(num_joints);\n plot_vol.reserve(num_joints);\n plot_cur.reserve(num_joints);\n plot_tmp.reserve(num_joints);\n\n subspace_axes.reserve(num_joints);\n subspace_portrait.reserve(num_joints);\n\n for (std::size_t i = 0; i < num_joints; ++i)\n {\n const double width = 1.0;\n const double height = 4.0 / num_joints;\n const double posx = (i%2==0)? -1.5 : 1.5;\n const double posy = 1.0 - height (i/2 + 0.5);\n\n plot_axs.emplace_back(posx, posy, 0., width, height, 1, std::to_string(i) + ' ' + joints[i].name);\n plot_cur.emplace_back(num_datapoints, plot_axs[i], colors::yellow, \"cur\");\n plot_tmp.emplace_back(num_datapoints, plot_axs[i], colors::orange, \"tmp\");\n plot_vol.emplace_back(num_datapoints, plot_axs[i], colors::cyan , \"vol\");\n plot_vel.emplace_back(num_datapoints, plot_axs[i], colors::pidgin, \"vel\");\n plot_pos.emplace_back(num_datapoints, plot_axs[i], colors::white , \"pos\");\n\n subspace_axes .emplace_back(posx + ((i%2==0) ? -1:1) * (width0.5+0.5height), posy, 0., height, height, 0, 'j' + std::to_string(i));\n subspace_portrait.emplace_back(num_datapoints, subspace_axes[i], colors::white);\n\n }\n //TODO: assert(robot.get_number_of_accel_sensors() >= 1); //TODO make applicable for more than one sensor\n }\n\n void draw(const pref& /p/) const {\n for (std::size_t i = 0; i < num_joints; ++i) {\n plot_axs[i].draw();\n plot_cur[i].draw();\n plot_tmp[i].draw();\n plot_pos[i].draw();\n plot_vel[i].draw();\n plot_vol[i].draw();\n\n subspace_axes[i] .draw();\n subspace_portrait[i].draw();\n }\n axes_accel .draw();\n plot_accel_x.draw();\n plot_accel_y.draw();\n plot_accel_z.draw();\n }\n\n void execute_cycle(uint64_t /cycle/) {\n for (std::size_t i = 0; i < num_joints; ++i) {\n plot_cur[i].add_sample(joints[i].s_cur);\n plot_tmp[i].add_sample(joints[i].s_tmp);\n plot_pos[i].add_sample(joints[i].s_ang);\n plot_vel[i].add_sample(joints[i].s_vel * constants::vel_amp);\n plot_vol[i].add_sample(joints[i].motor.get_backed());\n\n subspace_portrait[i].add_sample(joints[i].s_ang,\n joints[i].s_vel * constants::vel_amp);\n }\n if (accels.size() > 0) {\n plot_accel_x.add_sample(accels[0].a.x);\n plot_accel_y.add_sample(accels[0].a.y);\n plot_accel_z.add_sample(accels[0].a.z);\n }\n }\n\n const robots::Jointvector_t& joints;\n const std::size_t num_joints;\n\n const robots::Accelvector_t& accels;\n\n std::vector<axes> plot_axs;\n\n std::vector<plot1D> plot_pos;\n std::vector<plot1D> plot_vel;\n std::vector<plot1D> plot_vol;\n std::vector<plot1D> plot_cur;\n std::vector<plot1D> plot_tmp;\n\n std::vector<axes> subspace_axes;\n std::vector<plot2D> subspace_portrait;\n\n axes axes_accel;\n plot1D plot_accel_x;\n plot1D plot_accel_y;\n plot1D plot_accel_z;\n};\n\n} // namespace robots\n\n#endif // SPINALCORD_WATCH_H_INCLUDED\n" }, { "alpha_fraction": 0.5707285404205322, "alphanum_fraction": 0.5877164602279663, "avg_line_length": 29.600000381469727, "blob_id": "113bf370ca0c243cae445bf3fd81ba2b39442bd1", "content_id": "57c770fb6e1bd87b1d976cdd017480790739d1da", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3061, "license_type": "no_license", "max_line_length": 89, "num_lines": 100, "path": "/src/tests/autoencoder_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <common/modules.h>\n#include <learning/autoencoder.h>\n\n\nnamespace local_tests {\n\nnamespace autoencoder_tests {\n\nstruct Test_Sensor_Space : public sensor_vector {\n Test_Sensor_Space(const robots::Jointvector_t& joints)\n : sensor_vector(2joints.size() + 1)\n {\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_ang\", [&j](){ return j.s_ang; });\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_vel\", [&j](){ return j.s_vel; });\n\n sensors.emplace_back(\"bias\", [&](){ return 0.01; });\n assert(sensors.size() == 2joints.size() + 1);\n }\n};\n\n\n\nTEST_CASE( \"autoencoder construction\" , \"[autoencoder]\")\n{\n srand(time(0)); // set random seed\n\n Test_Robot robot(5,2);\n Test_Sensor_Space inputs{robot.get_joints()};\n const double random_range = 0.1;\n learning::Autoencoder autoenc(inputs.size(), 3, random_range);\n\n auto const& outputs = autoenc.get_outputs();\n REQUIRE( outputs.size() == inputs.size() );\n\n for (std::size_t i = 0; i < outputs.size(); ++i) {\n REQUIRE( outputs[i] == .0 );\n REQUIRE( inputs[i] == .0 );\n }\n\n autoenc.propagate(inputs);\n\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( outputs[i] == .0 );\n\n /* check weights are not zero /\n auto const& weights = autoenc.get_weights();\n double sum = .0;\n int diff = 0;\n for (std::size_t i = 0; i < weights.size(); ++i)\n for (std::size_t j = 0; j < weights[i].size(); ++j) {\n diff += ( weights[i][j] != .0 )? 0 : 1;\n sum += weights[i][j];\n }\n\n / check randomize_weight_matrix() is executed /\n REQUIRE( diff == 0 );\n const double max_range = 0.5 random_range * weights.size()weights[0].size();\n dbg_msg(\"Max rand: %e < %e\", std::abs(sum), max_range);\n REQUIRE( std::abs(sum) <= max_range ); // check small\n REQUIRE( std::abs(sum) != 0. ); // but not zero\n\n\n /* check that autoencoder is copyable */\n learning::Autoencoder autoenc2 = autoenc;\n}\n\n\nTEST_CASE( \"auto encoder learning\", \"[autoencoder]\")\n{\n srand(time(0)); // set random seed\n\n Test_Robot robot(5,2);\n Test_Sensor_Space inputs{robot.get_joints()};\n\n for (auto& j: robot.set_joints())\n j.s_ang = 1.0;\n\n const double learning_rate = 0.02;\n learning::Autoencoder autoenc(inputs.size(), 3, 0.1);\n REQUIRE ( squared_distance(inputs, autoenc.get_outputs()) == .0 );\n\n for (std::size_t trials = 0; trials < 50; ++trials) {\n inputs.execute_cycle();\n autoenc.propagate(inputs);\n double err0 = squared_distance(inputs, autoenc.get_outputs());\n autoenc.adapt(inputs, learning_rate);\n autoenc.propagate(inputs);\n double err1 = squared_distance(inputs, autoenc.get_outputs());\n\n dbg_msg(\"Pred. Error before %e and %e after adaption (%u).\", err0, err1, trials);\n REQUIRE( err0 > err1 );\n }\n}\n\n}} // namespace local_tests::autoencoder_tests\n\n" }, { "alpha_fraction": 0.5960773825645447, "alphanum_fraction": 0.6024383902549744, "avg_line_length": 39.13829803466797, "blob_id": "e535557b1a969db286f743ebbdec0c04f58a7ff8", "content_id": "20744a1ed54906a55d07baaec0d3c3aabfe455b8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3773, "license_type": "no_license", "max_line_length": 146, "num_lines": 94, "path": "/src/common/application_base.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/---------------------------------+\n \| Matthias Kubisch \|\n \| [email protected] \|\n \| July 2017 \|\n +---------------------------------/\n\n#ifndef APPLICATION_BASE_H\n#define APPLICATION_BASE_H\n\n#include <string>\n#include <common/event_manager.h>\n#include <common/datalog.h>\n#include <common/globalflag.h>\n#include <draw/graphics.h>\n\nextern GlobalFlag do_pause;\n\nnamespace constants {\n const unsigned default_window_width = 400;\n const unsigned default_window_height = 400;\n}\n\nclass Application_Base\n{\npublic:\n virtual bool loop() = 0; / for everything which has to be done repeatedly /\n virtual void finish() = 0; / for things to be done before exiting /\n virtual void draw(const pref&) const = 0; / things to draw /\n virtual bool visuals_enabled() { return true; }; / returns true if the application was started with visuals /\n uint64_t get_cycle_count(void) const { return cycles; } / returns the current application cycle /\n virtual void paused() {}\n\n\n virtual void user_callback_key_pressed (SDL_Keysym const& /key/) {}\n virtual void user_callback_key_released(SDL_Keysym const& /key/) {}\n virtual void user_callback_joystick_button_pressed (SDL_JoyButtonEvent const& /joystick/) {}\n virtual void user_callback_joystick_button_released(SDL_JoyButtonEvent const& /joystick/) {}\n virtual void user_callback_joystick_motion_axis (SDL_JoyAxisEvent const& /joystick/) {}\n virtual void user_callback_joystick_motion_hat (SDL_JoyHatEvent const& /joystick/) {}\n\n Application_Base( int argc\n , char* argv\n , Event_Manager& em\n , const std::string& name\n , unsigned width = constants::default_window_width\n , unsigned height = constants::default_window_height )\n : em(em)\n , name(name)\n , window_width(width)\n , window_height(height)\n , cycles(0)\n , logger(argc, argv)\n {\n sts_msg(\"Loading application...\");\n /* register key event /\n em.reg_usr_cb_key_pressed (std::bind(&Application_Base::base_callback_key_pressed , this, std::placeholders::_1));\n em.reg_usr_cb_key_released (std::bind(&Application_Base::base_callback_key_released , this, std::placeholders::_1));\n em.reg_usr_cb_joystick_button_pressed (std::bind(&Application_Base::user_callback_joystick_button_pressed , this, std::placeholders::_1));\n em.reg_usr_cb_joystick_button_released(std::bind(&Application_Base::user_callback_joystick_button_released, this, std::placeholders::_1));\n em.reg_usr_cb_joystick_motion_axis (std::bind(&Application_Base::user_callback_joystick_motion_axis , this, std::placeholders::_1));\n em.reg_usr_cb_joystick_motion_hat (std::bind(&Application_Base::user_callback_joystick_motion_hat , this, std::placeholders::_1));\n\n if (read_option_flag(argc, argv, \"-c\", \"--no_pause\"))\n do_pause.disable();\n }\n\n Event_Manager& em;\n const std::string name;\n const unsigned window_width;\n const unsigned window_height;\n\n uint64_t cycles;\n\n Datalog logger;\n\nprotected:\n virtual ~Application_Base() = default;\n\nprivate:\n void base_callback_key_pressed (const SDL_Keysym& keysym) {\n switch (keysym.sym) {\n case SDLK_RSHIFT: logger.toggle_logging(); break;\n default: break;\n }\n user_callback_key_pressed (keysym);\n };\n\n void base_callback_key_released(const SDL_Keysym& keysym) {\n user_callback_key_released(keysym);\n };\n};\n\n\n#endif // APPLICATION_BASE_H\n" }, { "alpha_fraction": 0.5518296360969543, "alphanum_fraction": 0.5818137526512146, "avg_line_length": 40.47208023071289, "blob_id": "5472ae96259f1d679a664a89bdc4cfda1daff5c9", "content_id": "588929c8485c1ca978ae7fe6e72e75f05f8d0414", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8171, "license_type": "no_license", "max_line_length": 146, "num_lines": 197, "path": "/src/learning/sarsa_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SARSA_GRAPHICS_H_INCLUDED\n#define SARSA_GRAPHICS_H_INCLUDED\n\n#include <common/integrator.h>\n\n#include <draw/draw.h>\n#include <draw/axes.h>\n#include <draw/axes3D.h>\n#include <draw/plot1D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/color_table.h>\n#include <draw/graphics.h>\n\n#include \"sarsa.h\"\n\n/* REWARD DISPLAY\n\n Shows the rewards on different time scales\n\n 1) Immediate reward r(t), according to system time t.\n 2) Immediate reward r(T), according to eigentime T.\n 3) Trial reward, accumulated eigenstep rewards for a full trial (Episode), before switching to another policy\n 4) Bunch reward, trial rewards over time.\n\n /\n\nclass SARSA_Graphics : public Graphics_Interface {\npublic:\n SARSA_Graphics(const SARSA& sarsa) //TODO make position on the screen configurable\n : sarsa(sarsa)\n , num_policies(sarsa.get_number_of_policies())\n , last_policy(sarsa.get_current_policy())\n , table(4)\n , axis_reward_total(-0.0, -0.5, 0.0, 1.98, 0.98, 0, \"total\", 0.1)\n , axis_reward_systemstep()\n , axis_reward_eigenstep ()\n , axis_reward_trial ()\n , axis_reward_bunch()\n , plot_reward_total(2000, axis_reward_total, Color4::set_transparency(colors::cyan_l, 0.30), \"total\")\n , plot_reward_t_avg(2000, axis_reward_total, Color4::set_transparency(colors::cyan , 0.60), \"t_avg\")\n , plot_reward_t_max(2000, axis_reward_total, Color4::set_transparency(colors::orange, 0.60), \"t_max\")\n , plot_reward_systemstep()\n , plot_reward_eigenstep ()\n , plot_reward_trial ()\n , plot_reward_bunch ()\n //, plot_reward_trial_long()\n , reward_trial(num_policies)\n , reward_bunch(num_policies)\n {\n axis_reward_eigenstep .reserve(num_policies);\n axis_reward_systemstep.reserve(num_policies);\n axis_reward_trial .reserve(num_policies);\n axis_reward_bunch .reserve(num_policies);\n plot_reward_systemstep.reserve(num_policies);\n plot_reward_eigenstep .reserve(num_policies);\n plot_reward_trial .reserve(num_policies);\n plot_reward_bunch .reserve(num_policies);\n\n unsigned N = sarsa.get_number_of_policies();\n for (std::size_t i = 0; i < N; ++i) {\n float ypos = N/2 0.2 - 0.2i + 0.5;\n axis_reward_systemstep.emplace_back(+0.75, ypos, 0.0, 0.48, 0.18, 0, \"r(t)\" , 0.001);\n axis_reward_eigenstep .emplace_back(+0.25, ypos, 0.0, 0.48, 0.18, 0, \"r(T)\" , 0.001);\n axis_reward_trial .emplace_back(-0.25, ypos, 0.0, 0.48, 0.18, 0, \"R/trial\", 0.001);\n axis_reward_bunch .emplace_back(-0.75, ypos, 0.0, 0.48, 0.18, 0, sarsa.rewards.get_reward_name(i).substr(0,14), 0.001);\n\n plot_reward_systemstep.emplace_back(400 , axis_reward_systemstep[i], Color4::set_transparency(table.get_color(i), 1.00));\n plot_reward_eigenstep .emplace_back(400 , axis_reward_eigenstep [i], Color4::set_transparency(table.get_color(i), 0.80));\n plot_reward_trial .emplace_back(100 , axis_reward_trial [i], Color4::set_transparency(table.get_color(i), 0.60));\n plot_reward_bunch .emplace_back(100 , axis_reward_bunch [i], Color4::set_transparency(table.get_color(i), 0.40));\n //plot_reward_trial_long.emplace_back(2000, axis_reward_total , Color4::set_transparency(table.get_color(i), 0.75), \"L-Trial\");\n }\n\n dbg_msg(\"Creating Sarsa Graphics for %lu policies.\", num_policies);\n }\n\n void execute_cycle(uint64_t /cycle/, bool state_changed, bool trial_ended)\n {\n\n for (std::size_t i = 0; i < num_policies; ++i)\n plot_reward_systemstep[i].add_sample(sarsa.rewards.get_current_reward(i));\n\n\n if (state_changed)\n {\n assert(sarsa.rewards.get_number_of_policies() == num_policies);\n\n for (std::size_t i = 0; i < num_policies; ++i)\n {\n if (sarsa.get_current_policy() == i) {\n const double value = sarsa.rewards.get_aggregated_last_reward(i);\n plot_reward_eigenstep[i].add_sample(value);\n reward_trial[i].add(value);\n }\n }\n }\n\n if (/last_policy != sarsa.get_current_policy() or/ trial_ended) { // policy changed, trial ended\n std::size_t i = last_policy;\n last_policy = sarsa.get_current_policy();\n\n const float trial_reward_i = reward_trial[i].get_avg_value_and_reset();\n reward_bunch[i].add(trial_reward_i);\n plot_reward_trial[i].add_sample(trial_reward_i);\n\n if (reward_bunch[i].get_number_of_samples() >= 10)\n plot_reward_bunch[i].add_sample(reward_bunch[i].get_avg_value_and_reset());\n\n //plot_reward_trial_long[i].add_sample(trial_reward_i);\n\n ++trial_count;\n if (trial_count % num_policies == 0) {\n double total = .0;\n for (auto const& r: reward_trial)\n total += r.get_last();\n reward_t_avg = 0.95reward_t_avg + 0.05total;\n reward_t_max = std::max(reward_t_max, reward_t_avg);\n plot_reward_total.add_sample(total);\n plot_reward_t_avg.add_sample(reward_t_avg);\n plot_reward_t_max.add_sample(reward_t_max);\n }\n }\n }\n\n void draw(const pref& /p/) const\n {\n axis_reward_total.draw();\n plot_reward_total.draw();\n plot_reward_t_max.draw();\n plot_reward_t_avg.draw();\n for (std::size_t i = 0; i < num_policies; ++i) {\n axis_reward_systemstep[i].draw();\n axis_reward_eigenstep [i].draw();\n axis_reward_trial [i].draw();\n axis_reward_bunch [i].draw();\n plot_reward_systemstep[i].draw();\n plot_reward_eigenstep [i].draw();\n plot_reward_trial [i].draw();\n plot_reward_bunch [i].draw();\n //plot_reward_trial_long[i].draw();\n }\n\n glColor3f(1.0, 1.0, 1.0);\n glprintf(-0.9, 1.0, 0.0, 0.03, \"cur: %+.4f\", sarsa.get_current_reward(sarsa.get_current_policy()));\n }\n\nprivate:\n\n const SARSA& sarsa;\n const std::size_t num_policies;\n std::size_t last_policy;\n\n std::size_t trial_count = 0;\n double reward_t_avg = .0;\n double reward_t_max = .0;\n\n const ColorTable table;\n axes axis_reward_total;\n std::vector<axes> axis_reward_systemstep;\n std::vector<axes> axis_reward_eigenstep;\n std::vector<axes> axis_reward_trial;\n std::vector<axes> axis_reward_bunch;\n\n plot1D plot_reward_total;\n plot1D plot_reward_t_avg;\n plot1D plot_reward_t_max;\n std::vector<plot1D> plot_reward_systemstep;\n std::vector<plot1D> plot_reward_eigenstep;\n std::vector<plot1D> plot_reward_trial;\n std::vector<plot1D> plot_reward_bunch;\n //std::vector<plot1D> plot_reward_trial_long;\n\n std::vector<Integrator> reward_trial;\n std::vector<Integrator> reward_bunch;\n\n};\n\nclass Policy_Selector_Graphics : public Graphics_Interface {\n const Policy_Selector& policy_selector;\npublic:\n Policy_Selector_Graphics(const Policy_Selector& policy_selector) : policy_selector(policy_selector) {}\n\n void draw(const pref& /p/) const {\n unsigned int time_left = policy_selector.get_trial_time_left();\n unsigned int minutes = (time_left / 6000);\n unsigned int seconds = (time_left / 100) % 60;\n unsigned int hsecs = (time_left % 100);\n glColor3f(1.0,1.0,1.0);\n glprintf(0.0,-0.05, 0.0, 0.03, \"[%u] %s\", policy_selector.current_policy\n , policy_selector.sarsa.rewards.get_reward_name(policy_selector.current_policy).c_str());\n glprintf(0.0,-0.10, 0.0, 0.03, \"left: %u:%02u:%02u [%c]\", minutes, seconds, hsecs, policy_selector.profile.play ? '+' :\n policy_selector.random_policy_mode ? '~' : '=');\n }\n};\n\n#endif // SARSA_GRAPHICS_H_INCLUDED\n\n" }, { "alpha_fraction": 0.6621403098106384, "alphanum_fraction": 0.6637568473815918, "avg_line_length": 36.719512939453125, "blob_id": "738f5f94ff01223ef32f4fc23bccf28f291cd510", "content_id": "fc1dcb3746cf604843bd2dd036ae82dca637dd19", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3093, "license_type": "no_license", "max_line_length": 129, "num_lines": 82, "path": "/src/learning/expert.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EXPERT_H_INCLUDED\n#define EXPERT_H_INCLUDED\n\n#include <memory>\n#include <common/save_load.h>\n#include <learning/gmes_constants.h>\n#include <learning/predictor.h>\n#include <control/sensorspace.h>\n\n/* TODO\n * + get 3d graphical representation, note: must provided by the underlying type\n * + in general, an expert should not care if it exists or not, this should be supervised by the expert_vector.\n * + optional: restrict transitions to have max. k connections\n /\n\nclass Expert : public common::Save_Load {\n Expert(const Expert& other) = delete; // non construction-copyable\n\npublic:\n\n Expert( Predictor_ptr predictor\n , const std::size_t max_number_of_nodes )\n : exists(false)\n , predictor(std::move(predictor))\n , learning_capacity(gmes_constants::initial_learning_capacity)\n , perceptive_width(gmes_constants::perceptive_width)\n , transition(max_number_of_nodes)\n { }\n\n Expert(Expert&& other) = default;\n Expert& operator=(Expert&& other) = default;\n\n bool learning_capacity_is_exhausted(void) const { return learning_capacity < gmes_constants::learning_capacity_exhausted; }\n double get_learning_capacity (void) const { return learning_capacity; }\n double get_prediction_error (void) const { return predictor->get_prediction_error(); }\n void adapt_weights (void) { predictor->adapt(); }\n void reinit_predictor_weights (void) { predictor->initialize_from_input(); }\n\n double update_and_get_activation (void) const {\n if (not exists) return 0.0;\n double e = predictor->get_prediction_error();\n return exp(-ee/perceptive_width);\n }\n\n /* make prediction and update prediction error /\n double make_prediction(void) { return predictor->predict(); }\n double redo_prediction(void) { return predictor->verify(); }\n\n void clear_transitions(void) { for (auto& t : transition) t = 0.0; }\n\n void create_randomized(void) {\n exists = true;\n predictor->initialize_randomized();\n }\n\n bool exists_transition(std::size_t index) const { return transition.at(index) > gmes_constants::transition_exist_treshold; }\n void reset_transition(std::size_t index) { transition.at(index) = gmes_constants::initial_transition_validation; }\n\n Predictor_Base const& get_predictor(void) const { return predictor; }\n Predictor_Base & set_predictor(void) { return predictor; }\n\n bool does_exists(void) const { return exists; }\n\n //Predictor_Base::vector_t const& get_weights(void) const { return predictor->get_weights(); }\n //Predictor_Base::vector_t & set_weights(void) { return predictor->set_weights(); }\n\n\nprivate:\n bool exists;\n Predictor_ptr predictor;\n double learning_capacity;\n const double perceptive_width;\n VectorN transition; // validity of connections\n\n friend class Expert_Vector;\n friend class GMES;\n friend class GMES_Graphics;\n friend class Force_Field;\n};\n\n\n#endif // EXPERT_H_INCLUDED\n" }, { "alpha_fraction": 0.5300957560539246, "alphanum_fraction": 0.5396716594696045, "avg_line_length": 28.806121826171875, "blob_id": "00017f2f78ee6c84b5c8cc920499a66583aaf4d8", "content_id": "50828274bd92c3ea43013bc710173cc5017fa34d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2924, "license_type": "no_license", "max_line_length": 87, "num_lines": 98, "path": "/src/control/positioncontrol.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef POSITION_CONTROL_H\n#define POSITION_CONTROL_H\n\n#include <common/log_messages.h>\n#include <robots/robot.h>\n#include <robots/joint.h>\n#include <controller/pid_control.hpp>\n\n\nnamespace control {\n\n/* A simple multi-joint PID position controller,\n based on the supreme lib PID controller /\n\nclass position_control // <- rename this... to homeostatic controller ?\n{\n robots::Jointvector_t& joints;\n\n struct JointCtrl_t {\n\n JointCtrl_t(uint8_t id) : pid(id, 0.01), enabled(true) {}\n supreme::pid_control pid;\n bool enabled;\n //TODO other?\n };\n\n std::vector<JointCtrl_t> ctrl;\n\n\n float joint_range; //TODO make a proper mapping\n // find general solution, inc. the limits of the joints\n\npublic:\n\n bool enable_unruh = false;\n\n\n position_control(robots::Robot_Interface& robot, float joint_range = 1.f)\n : joints(robot.set_joints())\n , ctrl()\n , joint_range(joint_range)\n {\n for (unsigned i = 0; i < joints.size(); ++i)\n {\n //PID setup\n ctrl.emplace_back(i);\n auto& c = ctrl.back();\n c.pid.set_pid(6.0,0,0); //tadpole\n }\n }\n\n template <typename ControlVector_t, typename ExternalControl_t>\n void execute_cycle( ControlVector_t& vec, ExternalControl_t const& ext = {}\n , const bool enable_external_control = false\n , const bool disable_internal_control = false )\n {\n assert(joints.size() <= vec.size());\n\n if (disable_internal_control)\n for (unsigned i = 0; i < vec.size(); ++i) vec[i] = 0;\n\n if (enable_external_control) { // inject external control inputs\n const std::size_t N = std::min(ext.size(), vec.size()); // match lengths\n for (unsigned i = 0; i < N; ++i)\n vec[i] += ext[i];\n }\n\n for (std::size_t i = 0; i < joints.size(); ++i) {\n ctrl[i].pid.set_target_value(joint_range vec[i]);\n joints[i].motor = ctrl[i].enabled ? ctrl[i].pid.step(joints[i].s_ang) : 0.;\n\n // simulate simple temperature behavior\n const float a = 0.9995;\n joints[i].s_tmp = joints[i].s_tmpa + (1-a)joints[i].s_cur;\n\n if (joints[i].s_tmp > 1.5 && ctrl[i].enabled) {\n ctrl[i].enabled = false;\n sts_msg(\"motor %u OFF\",i);\n }\n if (joints[i].s_tmp < 0.75 && !ctrl[i].enabled) {\n ctrl[i].enabled = true;\n sts_msg(\"motor %u REACTIVATED\",i);\n }\n\n if (enable_unruh) {\n // This is an unruh controller, use if nothing else works\n float err = vec[i]-joints[i].s_ang;\n joints[i].motor += clip(1err + joints[i].motor.get_backed(), 1);\n vec[i] = joints[i].s_ang;\n }\n }\n }\n\n};\n\n} / namespace control /\n\n#endif / POSITION_CONTROL_H /\n\n\n\n" }, { "alpha_fraction": 0.5292682647705078, "alphanum_fraction": 0.5292682647705078, "avg_line_length": 19.9743595123291, "blob_id": "c385aac63af22ef5f00e14f59fa4c20cbdd964a2", "content_id": "4aa195c4cfe9f9c882cb3aca3a62ab31f02332f8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 820, "license_type": "no_license", "max_line_length": 85, "num_lines": 39, "path": "/src/common/globalflag.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GLOBALFLAG_H\r\n#define GLOBALFLAG_H\n#include <common/lock.h>\n\nclass GlobalFlag\n{\n GlobalFlag( const GlobalFlag& other ) = delete; // non construction-copyable\n GlobalFlag& operator=( const GlobalFlag& ) = delete; // non copyable\n\n common::mutex_t mtx;\n bool flag;\n\npublic:\n GlobalFlag() : mtx(), flag(false) {}\n GlobalFlag(bool flag) : mtx(), flag(flag) {}\n\n void toggle(void) {\n common::lock_t lock(mtx);\n flag = !flag;\n }\n void enable(void) {\n if (!flag) {\n common::lock_t lock(mtx);\n flag = true;\n }\n }\n void disable(void) {\n if (flag) {\n common::lock_t lock(mtx);\n flag = false;\n }\n }\n bool status(void) const\n {\n return flag;\n }\n};\n\n#endif // GLOBALFLAG\n\n" }, { "alpha_fraction": 0.7343999743461609, "alphanum_fraction": 0.7391999959945679, "avg_line_length": 31.894737243652344, "blob_id": "dd40d4c6ec5d3aac8dc1885fe5750987ec30292c", "content_id": "85ff802dc104c4f52556da0ac22085fb9d4c1f14", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 625, "license_type": "no_license", "max_line_length": 97, "num_lines": 19, "path": "/src/evolution/evaluation_interface.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EVALUATION_INTERFACE_H_INCLUDED\n#define EVALUATION_INTERFACE_H_INCLUDED\n\n#include <vector>\n\nclass Evaluation_Interface\n{\npublic:\n typedef std::vector<double> genome_t;\n\n virtual ~Evaluation_Interface() = default;\n virtual bool evaluate(Fitness_Value &fitness, const genome_t& genome, double rand_value) = 0;\n virtual void prepare_generation(unsigned cur_generation, unsigned max_generation) = 0;\n virtual void prepare_evaluation(unsigned cur_trial, unsigned max_trial) = 0;\n\n virtual void constrain(genome_t& /genome/) { / implement optionally / };\n};\n\n#endif // EVALUATION_INTERFACE_H_INCLUDED\n" }, { "alpha_fraction": 0.6103286147117615, "alphanum_fraction": 0.6103286147117615, "avg_line_length": 23.461538314819336, "blob_id": "a588f846c590d6acef636c727362bd3fac7c3bb5", "content_id": "8663d088b9c0d8ed4fcdc262334f8f98b01667ea", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 639, "license_type": "no_license", "max_line_length": 89, "num_lines": 26, "path": "/src/control/csl.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef CSL_H\r\n#define CSL_H\r\n\n//#include \"../main.h\"\n\nclass csl\n{\n private:\n double z; // csl hidden state\n double gi; // loop gain\n double gf; // feedback gain\n double u; // output\n\n public:\n csl(void);\n csl(double motor_output, double angle_sensor);\n csl(double motor_output, double angle_sensor, double angular_velocity_sensor);\n void loop(const double mode);\n void reset(const double initial);\n\n void set_control_parameter(const double gi, const double gf);\n void set_joint_limits(const double llower, const double lupper);\n};\n\n\n#endif // CSL\n\n" }, { "alpha_fraction": 0.5076452493667603, "alphanum_fraction": 0.5188583135604858, "avg_line_length": 22.926828384399414, "blob_id": "b6a184c5f5163c8a781da4f5f1fb6773f09f43dc", "content_id": "9d1ec51e34e418f1e75e0c6d6f75120e64c500dd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 981, "license_type": "no_license", "max_line_length": 81, "num_lines": 41, "path": "/src/common/autopause.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/---------------------------------+\n \| Matthias Kubisch \|\n \| [email protected] \|\n \| July 2017 \|\n +---------------------------------/\n\n#ifndef AUTOPAUSE_H_INCLUDED\n#define AUTOPAUSE_H_INCLUDED\n\n/*\n AUTO PAUSE\n * Used to pause the application automatically after certain time steps (cycles).\n * Steps have to be provided as a list.\n /\n\nclass Auto_Pause {\n\n GlobalFlag& do_pause;\n std::vector<uint64_t> times;\n std::size_t ptr;\n\npublic:\n\n Auto_Pause(GlobalFlag& do_pause, std::vector<uint64_t> const& times)\n : do_pause(do_pause)\n , times(times)\n , ptr(0)\n {\n std::sort(this->times.begin(), this->times.end());\n }\n\n void execute_cycle(uint64_t current_cycles) {\n if (ptr < times.size() and current_cycles >= times[ptr]) {\n ++ptr;\n do_pause.enable();\n sts_msg(\"Automatically paused at step %llu\", current_cycles);\n }\n }\n};\n\n#endif // AUTOPAUSE_H_INCLUDED\n" }, { "alpha_fraction": 0.5275567173957825, "alphanum_fraction": 0.5541372299194336, "avg_line_length": 31.965347290039062, "blob_id": "de5578ca3700d3ee7bbbbbd3aa1774a85b58b7da", "content_id": "f8a8635612921d93294f3065309520d311ba2a18", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6659, "license_type": "no_license", "max_line_length": 190, "num_lines": 202, "path": "/src/learning/forcefield.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef FORCEFIELD_H_INCLUDED\n#define FORCEFIELD_H_INCLUDED\n\n#include <basic/vector3.h>\n#include <draw/axes3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n\n#include <learning/gmes.h>\n#include <learning/expert.h>\n\nnamespace ff_constants\n{\n const double mass = 0.1; // mass of one mass point 10g\n const double k_spring = 0.05;\n const double damper = 22sqrt(mass k_spring); // constant for the dampers\n const double distance_0 = 0.2;\n const double fluid_friction = 1.0 * mass;\n const double center_gravity = 0.0001;\n\n\n /* wall collision and damping /\n const double wall_damping = 0.5;\n const double wall_decay = 0.8;\n const double sticktion = 0.1;\n}\n\nclass Particle\n{\npublic:\n Particle()\n : position(random_value(-0.50,0.50),random_value(-0.50,0.50),random_value(-0.50,0.50))\n , velocity(random_value(-0.10,0.10),random_value(-0.10,0.10),random_value(-0.10,0.10))\n , force(.0)\n , mass(ff_constants::mass)\n {}\n\n void reset(void)\n {\n position = 0.0;\n velocity = 0.0;\n force = 0.0;\n }\n\n Vector3 position;\n Vector3 velocity;\n Vector3 force;\n const double mass;\n};\n\n\ninline void wall_contact(Particle& p)\n{\n\n if (p.position.x < -1.0 \|\| p.position.x > +1.0) {\n p.velocity.x = -ff_constants::wall_decay;\n p.velocity.y = (fabs(p.velocity.y) > ff_constants::sticktion) ff_constants::wall_damping;\n p.velocity.z = (fabs(p.velocity.z) > ff_constants::sticktion) ff_constants::wall_damping;\n }\n\n if (p.position.y < -1.0 \|\| p.position.y > +1.0) {\n p.velocity.x = (fabs(p.velocity.x) > ff_constants::sticktion) ff_constants::wall_damping;\n p.velocity.y = -ff_constants::wall_decay;\n p.velocity.z = (fabs(p.velocity.z) > ff_constants::sticktion) * ff_constants::wall_damping;\n }\n\n if (p.position.x < -1.0 \|\| p.position.x > +1.0) {\n p.velocity.x = (fabs(p.velocity.x) > ff_constants::sticktion) ff_constants::wall_damping;\n p.velocity.y = (fabs(p.velocity.y) > ff_constants::sticktion) ff_constants::wall_damping;\n p.velocity.z = -ff_constants::wall_decay;\n }\n p.position.clip(1.0);\n}\n\nVector3 gravity(const Vector3& vec1, const Vector3& vec2, double g_const, double min_distance)\n{\n Vector3 force(0.0);\n double r = distance(vec1, vec2);\n\n if (r < min_distance)\n force = -g_const (vec1 - vec2);\n else\n force = -g_const * (vec1 - vec2) / (r * r);\n\n return force;\n}\n\nVector3 repell(const Vector3& vec1, const Vector3& vec2, double g_const, double min_distance)\n{\n Vector3 force(0.0);\n double r = distance(vec1, vec2);\n double k = g_const * clip(2min_distance - fabs(r), 0.0, 2min_distance);// + 0.0001;\n force = k * ((vec1 - vec2)/r);\n return force;\n}\n\nclass Force_Field : public Graphics_Interface {\npublic:\n Force_Field(const GMES& gmes)\n : gmes(gmes)\n , expert(gmes.expert)\n , activations(gmes.get_activations())\n , particle(expert.size())\n , dt(0.001)\n , ff_axis(.0, .0, .0, 1., 1., 1., 0)\n , ff_graph(expert.size(), ff_axis, white)\n {\n dbg_msg(\"Creating Forcefield\");\n }\n\n void calculate_new_node_position(void) {\n std::size_t winner = gmes.get_winner();\n particle[winner].reset();\n\n std::size_t count_transitions = 0;\n Vector3 new_position = 0.0;\n new_position.random(-0.5ff_constants::distance_0, +0.5ff_constants::distance_0);\n\n for (std::size_t k = 0; k < expert[winner].transition.size(); ++k) {\n if (expert[winner].exists_transition(k)) {\n new_position += particle[k].position;\n ++count_transitions;\n }\n }\n new_position /= count_transitions;\n particle[winner].position = new_position;\n }\n\n void execute_cycle(uint64_t cycle)\n {\n if (gmes.has_new_node())\n calculate_new_node_position();\n\n for (unsigned int i = 0; i < expert.size(); ++i)\n {\n particle[i].force = .0; //clear\n particle[i].force += -ff_constants::fluid_friction * particle[i].velocity;\n\n if (expert[i].exists) //TODO check also if node is isolated\n {\n particle[i].force += gravity(particle[i].position, Vector3(.0), ff_constants::center_gravity, ff_constants::distance_0);\n\n for (unsigned int k = 0; k < expert.size(); ++k)\n {\n if (i != k && expert[k].exists)\n {\n double dx = distance(particle[i].position, particle[k].position);\n\n if (expert[i].exists_transition(k) \|\| expert[k].exists_transition(i)) {\n particle[i].force += -ff_constants::k_spring * clip(dx - ff_constants::distance_0, ff_constants::distance_0) * ((particle[i].position - particle[k].position)/dx);\n //TODO add damper forces\n }\n else\n particle[i].force += repell(particle[i].position, particle[k].position, 1.0, ff_constants::distance_0);\n\n }\n }\n }\n\n particle[i].velocity += particle[i].force / particle[i].mass * dt;\n particle[i].position += particle[i].velocity * dt;\n\n wall_contact(particle[i]);\n }\n\n /* update drawings /\n for (unsigned int i = 0; i < expert.size(); ++i)\n {\n ff_graph.update_node(i,\n particle[i].position.x,\n particle[i].position.y,\n particle[i].position.z,\n fmin(2.0, expert[i].learning_capacity));\n\n for (unsigned int k = 0; k < expert.size(); ++k)\n ff_graph.update_edge(i, k, (expert[i].exists_transition(k) ? expert[i].transition[k]192 : 0)); //TODO use color of edge to display eligibility traces\n }\n ff_graph.activated(gmes.get_winner());\n ff_graph.special(gmes.get_to_insert());\n }\n\n void draw(const pref& p) const\n {\n ff_axis .draw(p.x_angle, p.y_angle);\n ff_graph.draw(p.x_angle, p.y_angle);\n glprintf(0.8, 0.7, 0.0, 0.03, \"%u/%u\", gmes.get_number_of_experts(), gmes.get_max_number_of_experts());\n }\n\n const GMES& gmes;\n const Expert_Vector& expert;\n const VectorN& activations;\n\n std::vector<Particle> particle;\n const double dt;\n\n /* drawing /\n axes3D ff_axis;\n network3D ff_graph;\n\n};\n\n#endif // FORCEFIELD_H_INCLUDED\n" }, { "alpha_fraction": 0.6788091063499451, "alphanum_fraction": 0.6837127804756165, "avg_line_length": 34.24691390991211, "blob_id": "16e55e98f85e007cbc768b23cf2832234f2603d6", "content_id": "80525a4e4d48f1ef910d79ee4be7fee99a36ad35", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5710, "license_type": "no_license", "max_line_length": 205, "num_lines": 162, "path": "/src/robots/simloid.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SIMLOID_H\n#define SIMLOID_H\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <string.h>\n#include <math.h>\n#include <sys/wait.h>\n#include <sys/types.h>\n#include <signal.h>\n#include <unistd.h>\n#include <errno.h>\n#include <float.h>\n#include <assert.h>\n#include <algorithm>\n#include <vector>\n\n#include <common/lock.h>\n#include <common/modules.h>\n#include <common/socket_client.h>\n#include <common/basic.h>\n#include <common/misc.h>\n#include <common/log_messages.h>\n#include <common/robot_conf.h>\n#include <robots/robot.h>\n#include <robots/joint.h>\n#include <basic/vector3.h>\n\nnamespace robots {\n\n/TODO think about how to get simloid thread save:\n analyze which resources are accessed simultaneously and lock only them instead of locking every method./\n\nclass Simloid : public Robot_Interface\n{\npublic:\n const unsigned short port;\n const unsigned int robot_ID;\n const unsigned int scene_ID;\n const bool visuals;\n const bool realtime;\n\nprivate:\n pid_t child_pid;\n common::mutex_t mtx;\n\n network::Socket_Client client;\n bool connection_established;\n bool record_frame;\n Robot_Configuration configuration;\n\n double timestamp;\n std::vector<Vector3> body_position0;\n\n Vector3 average_position;\n Vector3 average_position0;\n Vector3 average_velocity;\n double average_rotation;\n double avg_rot_inf_ang;\n double avg_rot_inf_ang_last;\n\n double avg_velocity_forward;\n double avg_velocity_left;\n\n const unsigned left_id;\n const unsigned rift_id;\n\n bool initially_fixed;\n\n bool open_connection(void);\n void close_connection(void);\n void simulation_idle(double sec);\n void set_robot_to_default_position(void);\n void init_robot(void);\n void read_robot_configuration(void);\n void read_sensor_data(void);\n void write_motor_data(void);\n void send_pause_command(void);\n void reset(void);\n void eat_server_msg(void);\n void update_avg_position(void);\n void update_avg_velocity(void);\n void update_rotation_z(void);\n void update_robot_velocity(void);\n\npublic:\n Simloid(bool interlaced_mode, unsigned short port, unsigned int robot_ID, unsigned int scene_ID, bool visuals, bool realtime = true, std::vector<double> modelparams = {}, bool initially_fixed = false);\n ~Simloid(void);\n\n bool update(void); //locking\n bool idle(void); //locking\n bool is_connected(void) const { return connection_established; }\n void restore_state(void); //locking\n void save_state(void);\n void finish(void);\n\n void set_force(std::size_t body_index, const Vector3& force) { configuration.bodies.at(body_index).force = force; }\n\n void reset_all_forces(void) {\n for (std::size_t i = 0; i < configuration.bodies.size(); ++i)\n configuration.bodies[i].force.zero();\n }\n\n / implements the robot interface /\n bool execute_cycle(void) { return update(); }\n\n std::size_t get_number_of_joints (void) const { return configuration.number_of_joints; }\n std::size_t get_number_of_symmetric_joints(void) const { return configuration.get_number_of_symmetric_joints(); }\n std::size_t get_number_of_accel_sensors (void) const { return configuration.number_of_accels; }\n std::size_t get_number_of_bodies (void) const { return configuration.number_of_bodies; }\n\n const Jointvector_t& get_joints(void) const { return configuration.joints; }\n Jointvector_t& set_joints(void) { return configuration.joints; }\n\n const Accelvector_t& get_accels(void) const { return configuration.accels; }\n Accelvector_t& set_accels(void) { return configuration.accels; }\n\n const Bodyvector_t& get_bodies(void) const { return configuration.bodies; }\n Bodyvector_t& set_bodies(void) { return configuration.bodies; }\n\n Vector3 get_min_position(void) const;\n Vector3 get_max_position(void) const;\n Vector3 get_min_feet_pos(void) const; /* Currently, this is handcrafted for bipeds, only! /\n Vector3 get_max_feet_pos(void) const; /* Currently, this is handcrafted for bipeds, only! /\n\n const Vector3& get_avg_position(void) const { return average_position; }\n const Vector3& get_avg_velocity(void) const { return average_velocity; }\n double get_avg_rotation(void) const { return average_rotation; }\n\n double get_avg_rotation_inf_ang(void) const { return avg_rot_inf_ang; }\n double get_avg_rotational_speed(void) const { return (avg_rot_inf_ang - avg_rot_inf_ang_last)100; }\n\n double get_avg_velocity_forward(void) const { return avg_velocity_forward; }\n double get_avg_velocity_left (void) const { return avg_velocity_left; }\n\n double get_bodyheight0 (void) const { return body_position0[0].z; }\n\n double get_normalized_mechanical_power(void) const;\n\n double get_motion_level(void) const;\n bool motion_stopped(double thrsh) const;\n bool dropped(double level = 0.5) const;\n double dx_from_origin(void) const;\n double dy_from_origin(void) const;\n\n unsigned get_body_id_by_name(const Bodyvector_t& bodies, const std::string& name) const;\n\n void record_next_frame() { record_frame = true; }\n\n uint64_t randomize_model(double rnd_amp, double growth, double friction, uint64_t inst); //locking\n\n void reinit_robot_model(std::vector<double> const& params); //locking\n void reinit_motor_model(std::vector<double> const& params); //locking\n\n void set_low_sensor_quality(bool low_quality); //locking\n\n void toggle_body_fixed(unsigned index = 0) { common::lock_t lock(mtx); client.send(\"FIXED %u\\n\", index); } //locking\n};\n\n} // namespace robots\n\n#endif /* SIMLOID_H /\n" }, { "alpha_fraction": 0.75, "alphanum_fraction": 0.7554348111152649, "avg_line_length": 23.53333282470703, "blob_id": "65543a6709b226582e48763593b951779045b707", "content_id": "d46724ca1d1bc928c196a76bc8b5b0c3f58b16c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 368, "license_type": "no_license", "max_line_length": 57, "num_lines": 15, "path": "/src/learning/learning_machine_interface.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef LEARNING_MACHINE_INTERFACE_HPP_INCLUDED\n#define LEARNING_MACHINE_INTERFACE_HPP_INCLUDED\n\nnamespace learning {\n\nclass Learning_Machine_Interface {\npublic:\n virtual double get_learning_progress(void) const = 0;\n virtual void enable_learning(bool b) = 0;\n virtual ~Learning_Machine_Interface() {}\n};\n\n}\n\n#endif // LEARNING_MACHINE_INTERFACE_HPP_INCLUDED\n" }, { "alpha_fraction": 0.6016523241996765, "alphanum_fraction": 0.6103448271751404, "avg_line_length": 28.741453170776367, "blob_id": "557d649d156402a35be2ac14497a4cc5be43a806", "content_id": "2645164a01a53666e1f0b5ac340a85ea344f9268", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 13920, "license_type": "no_license", "max_line_length": 152, "num_lines": 468, "path": "/src/common/event_manager.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"./event_manager.h\"\n\nextern GlobalFlag do_quit;\nextern GlobalFlag do_pause;\nextern GlobalFlag fast_forward;\nextern GlobalFlag draw_grid;\nextern GlobalFlag screenshot;\n\nextern Visuals screen;\nextern float t_delay_ms;\n\nvoid\nquit()\n{\n static bool q = false;\n if (!q) // prevent second call\n {\n q = true;\n sts_msg(\"Sending signal to exit.\");\n do_quit.enable();\n }\n}\n\nvoid\non_button_pressed_ESCAPE(void)\n{\n sts_msg(\"Pressed key ESC. Quit.\");\n quit();\n}\n\nvoid\non_button_pressed_SPACE(void)\n{\n do_pause.toggle();\n if (do_pause.status()) sts_msg(\"Paused.\");\n else sts_msg(\"Continuing.\");\n}\n\nvoid\non_button_pressed_BACKSPACE(void)\n{\n fast_forward.toggle();\n if (fast_forward.status()) sts_msg(\"Fast mode enabled.\");\n else sts_msg(\"Fast mode disabled.\");\n}\n\nvoid\non_button_pressed_RETURN(void)\n{\n dbg_msg(\"Pressed key RETURN.\");\n}\n\nvoid\non_button_pressed_G(void)\n{\n sts_msg(\"Toggled drawing grid.\");\n draw_grid.toggle();\n}\n\nvoid\non_button_pressed_S(void)\n{\n screenshot.enable();\n}\n\nvoid\non_button_pressed_F(void)\n{\n screen.show_fps = not screen.show_fps;\n sts_msg(\"Statistics: %s\", screen.show_fps ? \"ON\":\"OFF\");\n}\n\nvoid\non_button_pressed_PAGEUP(void)\n{\n if (!screen.rotate_view)\n screen.rotate_view = true;\n else if (screen.rot_factor < 20)\n ++screen.rot_factor;\n sts_msg(\"Rotation factor: %d\", screen.rot_factor);\n}\n\nvoid\non_button_pressed_PAGEDOWN(void)\n{\n if (!screen.rotate_view)\n screen.rotate_view = true;\n else if (screen.rot_factor > -20)\n --screen.rot_factor;\n sts_msg(\"Rotation factor: %d\", screen.rot_factor);\n}\n\nvoid\non_button_pressed_PLUS(void)\n{\n if (t_delay_ms > 2) t_delay_ms = 0.5;\n else fast_forward.enable();\n sts_msg(\"Delay: %.2f ms (ff %d)\", t_delay_ms, fast_forward.status());\n}\n\nvoid\non_button_pressed_MINUS(void)\n{\n if (fast_forward.status())\n fast_forward.disable();\n else if (t_delay_ms < 1000) t_delay_ms = 2.0;\n sts_msg(\"Delay: %.2f ms (ff %d)\", t_delay_ms, fast_forward.status());\n}\nvoid\nEvent_Manager::on_left_mouse_button_pressed(void)\n{\n mouse_button_left.clicked = true;\n mouse_button_left.position_x = event.button.x;\n mouse_button_left.position_y = event.button.y;\n screen.rotate_view = false;\n}\n\nvoid\nEvent_Manager::on_left_mouse_button_released(void)\n{\n mouse_button_left.clicked = false;\n\n screen.x_angle += screen.x_angle_disp;\n screen.y_angle += screen.y_angle_disp;\n screen.x_angle_disp = 0.0f;\n screen.y_angle_disp = 0.0f;\n\n / snap to grid /\n screen.x_angle = screen.snapround(screen.x_angle/screen.snap);\n screen.y_angle = screen.snapround(screen.y_angle/screen.snap);\n\n sts_msg(\"new pos: % 7.2f, % 7.2f\", screen.x_angle, screen.y_angle);\n}\n\nvoid\nEvent_Manager::on_right_mouse_button_pressed(void)\n{\n mouse_button_right.clicked = true;\n mouse_button_right.position_x = event.button.x;\n mouse_button_right.position_y = event.button.y;\n}\n\nvoid\nEvent_Manager::on_right_mouse_button_released(void)\n{\n mouse_button_right.clicked = false;\n screen.x_position += screen.x_position_disp;\n screen.y_position += screen.y_position_disp;\n screen.x_position_disp = 0.0f;\n screen.y_position_disp = 0.0f;\n}\n\nvoid\nEvent_Manager::on_middle_mouse_button_pressed(void)\n{\n mouse_button_middle.clicked = true;\n mouse_button_middle.position_x = event.button.x;\n mouse_button_middle.position_y = event.button.y;\n screen.reset();\n}\n\nvoid\nEvent_Manager::on_middle_mouse_button_released(void)\n{\n mouse_button_middle.clicked = false;\n}\n\nvoid\nEvent_Manager::on_mouse_wheel_up(void)\n{\n ++mouse_wheel_position;\n if (screen.zdist visuals_defaults::zoom_factor > visuals_defaults::gl_zNear)\n screen.zdist = visuals_defaults::zoom_factor;\n}\n\nvoid\nEvent_Manager::on_mouse_wheel_down(void)\n{\n --mouse_wheel_position;\n if (screen.zdist < 8)\n screen.zdist /= visuals_defaults::zoom_factor;\n}\n\nvoid\nEvent_Manager::handle_mouse_wheel(SDL_MouseWheelEvent const& wheel)\n{\n if (wheel.y > 0)\n on_mouse_wheel_up();\n else if (wheel.y < 0)\n on_mouse_wheel_down();\n}\n\nvoid\nEvent_Manager::handle_key_pressed(SDL_Keysym const& key)\n{\n switch (key.sym)\n {\n case SDLK_ESCAPE: on_button_pressed_ESCAPE(); break;\n case SDLK_SPACE: on_button_pressed_SPACE(); break;\n case SDLK_BACKSPACE: on_button_pressed_BACKSPACE(); break;\n case SDLK_RETURN: on_button_pressed_RETURN(); break;\n /\n case SDLK_TAB: break;\n case SDLK_UP: break;\n case SDLK_F1: break; /\n case SDLK_g: on_button_pressed_G(); break;\n case SDLK_f: on_button_pressed_F(); break;\n case SDLK_s: on_button_pressed_S(); break;\n /\n case SDLK_0: break;\n case SDLK_2: break;\n /\n case SDLK_PLUS: on_button_pressed_PLUS(); break;\n case SDLK_MINUS: on_button_pressed_MINUS(); break;\n\n case SDLK_PAGEUP: on_button_pressed_PAGEUP(); break;\n case SDLK_PAGEDOWN: on_button_pressed_PAGEDOWN(); break;\n\n default: break;\n }\n\n if (nullptr != user_callback.key_pressed) user_callback.key_pressed(key);\n}\n\nvoid\nEvent_Manager::handle_key_released(SDL_Keysym const& key)\n{\n switch (key.sym)\n {\n /case SDLK_ESCAPE: break;/\n /case SDLK_SPACE: break;/\n /case SDLK_BACKSPACE: break;/\n /\n case SDLK_TAB: break;\n case SDLK_UP: break;\n case SDLK_F1: break;\n case SDLK_1: break;\n case SDLK_0: break;\n case SDLK_2: break;\n case SDLK_RETURN: break;\n case SDLK_PLUS: break;\n case SDLK_MINUS: break;\n\n case SDLK_PAGEUP: break;\n case SDLK_PAGEDOWN: break;\n /\n default: break;\n }\n if (nullptr != user_callback.key_released) user_callback.key_released(key);\n}\n\nvoid\nEvent_Manager::handle_mouse_button_pressed(SDL_MouseButtonEvent const& m)\n{\n switch (m.button)\n {\n case SDL_BUTTON_LEFT : on_left_mouse_button_pressed (); break;\n case SDL_BUTTON_RIGHT : on_right_mouse_button_pressed (); break;\n case SDL_BUTTON_MIDDLE : on_middle_mouse_button_pressed(); break;\n default: break;\n }\n}\n\nvoid\nEvent_Manager::handle_mouse_button_released(SDL_MouseButtonEvent const& m)\n{\n switch (m.button)\n {\n case SDL_BUTTON_LEFT : on_left_mouse_button_released (); break;\n case SDL_BUTTON_RIGHT : on_right_mouse_button_released (); break;\n case SDL_BUTTON_MIDDLE : on_middle_mouse_button_released(); break;\n default: break;\n }\n}\n\nvoid\nEvent_Manager::handle_mouse_motion(SDL_MouseMotionEvent const& m)\n{\n mouse_position_x = m.x;\n mouse_position_y = m.y;\n\n if (mouse_button_left.clicked) {\n screen.mdx = (float) (mouse_button_left.position_x - mouse_position_x) / screen.window_size_x;\n screen.mdy = (float) (mouse_button_left.position_y - mouse_position_y) / screen.window_size_y;\n screen.x_angle_disp = -90 screen.mdx;\n screen.y_angle_disp = -90 * screen.mdy;\n //dbg_msg(\"(%1.2f,%1.2f)\", screen.mdx, screen.mdy);\n }\n\n if (mouse_button_right.clicked) {\n screen.x_position_disp = -(float) (mouse_button_right.position_x - mouse_position_x) * 2 / std::min(screen.window_size_y, screen.window_size_x);\n screen.y_position_disp = +(float) (mouse_button_right.position_y - mouse_position_y) * 2 / std::min(screen.window_size_y, screen.window_size_x);\n }\n}\n\nvoid\nEvent_Manager::handle_joystick_motion_axis(SDL_JoyAxisEvent const& j)\n{\n switch(j.axis)\n {\n case 0: joystick.x0 = (float) (+j.value / 32767.0); /dbg_msg(\"jx0\");/ break;\n case 1: joystick.y0 = (float) (-j.value / 32767.0); /dbg_msg(\"jy0\");/ break;\n case 2: joystick.x1 = (float) (+j.value / 32767.0); /dbg_msg(\"jx1\");/ break;\n case 3: joystick.y1 = (float) (-j.value / 32767.0); /dbg_msg(\"jy1\");/ break;\n default: dbg_msg(\"unknown axis %u\", j.axis); break;\n }\n if (nullptr != user_callback.joystick_motion_axis) user_callback.joystick_motion_axis(j);\n}\n\nvoid\nEvent_Manager::handle_joystick_motion_hat(SDL_JoyHatEvent const& j)\n{\n switch(j.value)\n {\n case SDL_HAT_LEFTUP : /dbg_msg(\"L-U\");/ break;\n case SDL_HAT_UP : /dbg_msg(\" U\");/ break;\n case SDL_HAT_RIGHTUP : /dbg_msg(\"R-U\");/ break;\n case SDL_HAT_LEFT : /dbg_msg(\"L \");/ break;\n case SDL_HAT_CENTERED : /dbg_msg(\" C \");/ break;\n case SDL_HAT_RIGHT : /dbg_msg(\"R \");/ break;\n case SDL_HAT_LEFTDOWN : /dbg_msg(\"L-D\");/ break;\n case SDL_HAT_DOWN : /dbg_msg(\" D\");/ break;\n case SDL_HAT_RIGHTDOWN : /dbg_msg(\"R-U\");/ break;\n default: dbg_msg(\"unknown hat %u\", j.hat); break;\n }\n if (nullptr != user_callback.joystick_motion_hat) user_callback.joystick_motion_hat(j);\n}\n\nvoid\nEvent_Manager::handle_joystick_button_pressed(SDL_JoyButtonEvent const& joystick)\n{\n switch (joystick.button) //IDEA: consider making names for rumblepad\n {\n case 0: dbg_msg(\"Button 0 pressed\"); break;\n case 1: dbg_msg(\"Button 1 pressed\"); break;\n case 2: dbg_msg(\"Button 2 pressed\"); break;\n case 3: dbg_msg(\"Button 3 pressed\"); break;\n case 4: dbg_msg(\"Button 4 pressed\"); break;\n case 5: dbg_msg(\"Button 5 pressed\"); break;\n case 6: dbg_msg(\"Button 6 pressed\"); break;\n case 7: dbg_msg(\"Button 7 pressed\"); break;\n case 8: dbg_msg(\"Button 8 pressed. Escape.\"); quit(); break;\n case 9: dbg_msg(\"Button 9 pressed. Toggle Pause.\"); do_pause.toggle(); break;\n case 10: dbg_msg(\"Button A pressed\"); break;\n case 11: dbg_msg(\"Button B pressed\"); break;\n default: dbg_msg(\"unknown button %u\", joystick.button); break;\n }\n\n if (nullptr != user_callback.joystick_button_pressed) user_callback.joystick_button_pressed(joystick);\n}\n\nvoid\nEvent_Manager::handle_joystick_button_released(SDL_JoyButtonEvent const& joystick)\n{\n switch (joystick.button)\n {\n case 0: break;\n case 1: break;\n case 2: break;\n case 3: break;\n case 4: break;\n case 5: break;\n case 6: break;\n case 7: break;\n case 8: break;\n case 9: break;\n case 10: break;\n case 11: break;\n default: break;\n }\n if (nullptr != user_callback.joystick_button_released) user_callback.joystick_button_released(joystick);\n}\n\nvoid\nhandle_application_quit(void)\n{\n sts_msg(\"handle application quit\");\n quit();\n}\n\nvoid\nEvent_Manager::process_events(void)\n{\n /* Grab all the events off the queue. /\n while (SDL_PollEvent(&event))\n {\n switch (event.type)\n {\n / all possible events /\n case SDL_KEYDOWN : handle_key_pressed (event.key.keysym); break;\n case SDL_KEYUP : handle_key_released (event.key.keysym); break;\n case SDL_MOUSEBUTTONDOWN : handle_mouse_button_pressed (event.button ); break;\n case SDL_MOUSEBUTTONUP : handle_mouse_button_released (event.button ); break;\n case SDL_MOUSEMOTION : handle_mouse_motion (event.motion ); break;\n case SDL_MOUSEWHEEL : handle_mouse_wheel (event.wheel ); break;\n case SDL_JOYAXISMOTION : handle_joystick_motion_axis (event.jaxis ); break;\n case SDL_JOYHATMOTION : handle_joystick_motion_hat (event.jhat ); break;\n case SDL_JOYBUTTONDOWN : handle_joystick_button_pressed (event.jbutton ); break;\n case SDL_JOYBUTTONUP : handle_joystick_button_released(event.jbutton ); break;\n case SDL_QUIT : handle_application_quit(); break;\n default:\n /assertion(false, \"unhandled SDL event: %u\", event.type); / break;\n }\n } / while /\n}\n\nvoid\nEvent_Manager::reg_usr_cb_key_pressed(Keysym_t callback_function)\n{\n if (callback_function == nullptr)\n wrn_msg(\"Could not register user callback function for 'keyboard pressed'.\");\n else\n user_callback.key_pressed = callback_function;\n}\n\nvoid\nEvent_Manager::reg_usr_cb_key_released(Keysym_t func)\n{\n if (func == nullptr)\n wrn_msg(\"Could not register user callback function for 'keyboard released'.\");\n else\n user_callback.key_released = func;\n}\n\nvoid\nEvent_Manager::reg_usr_cb_joystick_button_pressed(Joybutton_t func)\n{\n if (func == nullptr)\n wrn_msg(\"Could not register user callback function for joystick button pressed.\");\n else\n user_callback.joystick_button_pressed = func;\n}\n\nvoid\nEvent_Manager::reg_usr_cb_joystick_button_released(Joybutton_t func)\n{\n if (func == nullptr)\n wrn_msg(\"Could not register user callback function for joystick button released.\");\n else\n user_callback.joystick_button_released = func;\n}\n\nvoid\nEvent_Manager::reg_usr_cb_joystick_motion_axis(Joyaxis_t func)\n{\n if (func == nullptr)\n wrn_msg(\"Could not register user callback function for joystick motion axis.\");\n else\n user_callback.joystick_motion_axis = func;\n}\n\nvoid\nEvent_Manager::reg_usr_cb_joystick_motion_hat(Joyhat_t func)\n{\n if (func == nullptr)\n wrn_msg(\"Could not register user callback function for joystick motion hat.\");\n else\n user_callback.joystick_motion_hat = func;\n}\n\n/void\nEvent_Manager::register_user_callback_mouse(callback_type func)\n{\n if (func == nullptr)\n wrn_msg(\"Could not register user callback function for mouse.\");\n else\n user_callback_mouse = func;\n}/\n" }, { "alpha_fraction": 0.49237126111984253, "alphanum_fraction": 0.5343292951583862, "avg_line_length": 27.089284896850586, "blob_id": "565e0bd7f058c316796705060a75e146d96b7e23", "content_id": "2c396b4bfab5c5c9a41763d424e515aa52efcd1f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3146, "license_type": "no_license", "max_line_length": 93, "num_lines": 112, "path": "/src/robots/simloid_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/---------------------------------+\n \| Matthias Kubisch \|\n \| [email protected] \|\n \| July 2017 \|\n +---------------------------------/\n\n#ifndef SIMLOID_GRAPHICS_H\n#define SIMLOID_GRAPHICS_H\n\n#include <robots/simloid.h>\n#include <draw/draw.h>\n#include <draw/axes.h>\n#include <draw/plot2D.h>\n\nnamespace robots {\nnamespace constants {\n const double bsize = 0.01;\n}\n\nclass Simloid_Graphics : public Graphics_Interface {\n const Simloid& simloid;\n axes axis_position;\n plot2D plot_position;\n\npublic:\n Simloid_Graphics(const Simloid& simloid)\n : simloid(simloid)\n , axis_position(.5, .5, 0.0, 1.0, 1.0, 0, \"pos xy\")\n , plot_position(1000, axis_position, colors::magenta)\n {}\n\n void draw_body_position(void) const\n {\n const Bodyvector_t& bodies = simloid.get_bodies();\n const Vector3& bodypos = simloid.get_avg_position();\n Vector3 bodyvel_absolute = simloid.get_avg_velocity();\n\n double bodyvel_forward = simloid.get_avg_velocity_forward();\n double bodyvel_left = simloid.get_avg_velocity_left();\n\n glColor3f(1.0, 1.0, 1.0);\n for (auto const& b : bodies)\n {\n draw_rect( (b.position.x - bodypos.x)\n , (b.position.y - bodypos.y)\n , constants::bsize, constants::bsize);\n }\n\n / velocity vector /\n glColor3f(1.0, 0.75, 0.0);\n bodyvel_absolute.clip(0.5);\n draw_line(0.0, 0.0, bodyvel_absolute.x, bodyvel_absolute.y);\n\n glColor3f(0.0, 1.0, 1.0);\n draw_line(0.0, 0.0, bodyvel_left, -bodyvel_forward);\n\n }\n\n void draw_body_rotation(void) const\n {\n const double rot_norm = -simloid.get_avg_rotation();\n const double rot_inf = simloid.get_avg_rotation_inf_ang();\n const double rot_speed = -clip(simloid.get_avg_rotational_speed(), 1.0)/2;\n\n const float sin_rot = sin(rot_norm)/2;\n const float cos_rot = cos(rot_norm)/2;\n\n / compass /\n glColor3f(1.0, 0.0, 0.0);\n draw_line(0.0, 0.0, sin_rot, cos_rot); // north\n glColor3f(0.0, 1.0, 0.0);\n draw_line(0.0, 0.0, -sin_rot, -cos_rot); // south\n\n / speed /\n glColor3f(1.0, 0.0, 1.0);\n draw_line( sin_rot\n , cos_rot\n , sin_rot + cos_rot rot_speed\n , cos_rot - sin_rot * rot_speed);// rot_speed\n\n glColor3f(1.0, 1.0, 1.0);\n glprintf(-.5, .5, 0.0, 0.05, \"rot: %+1.2f (%+1.2f pi)\", rot_norm/M_PI, rot_inf/M_PI);\n\n }\n\n void draw(const pref& /p/) const\n {\n glPushMatrix();\n glTranslatef(-0.5, 0.5, 0.);\n\n glColor4f(1.0, 1.0, 1.0, 0.25);\n draw_rect(0.0, 0.0, 1.0, 1.0);\n draw_body_position();\n draw_body_rotation();\n\n glPopMatrix();\n\n axis_position.draw();\n plot_position.draw();\n }\n\n void execute_cycle(void) {\n const Vector3& bodypos = simloid.get_avg_position();\n plot_position.add_sample(bodypos.x, bodypos.y);\n }\n\n void reset(void) { plot_position.reset(); }\n};\n\n} /* namespace robots /\n\n#endif / SIMLOID_GRAPHICS_H /\n" }, { "alpha_fraction": 0.7262181043624878, "alphanum_fraction": 0.7262181043624878, "avg_line_length": 24.352941513061523, "blob_id": "26a344ab901d5f878dfc9d66211508be14f9cf73", "content_id": "ecceca2758ad717e45622506b538673529cdd867", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 431, "license_type": "no_license", "max_line_length": 76, "num_lines": 17, "path": "/src/learning/eigenzeit_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef LEARNING_EIGENZEIT_GRAPHICS_H\n#define LEARNING_EIGENZEIT_GRAPHICS_H\n\n#include <learning/eigenzeit.h>\n\nnamespace learning {\n\nclass Eigenzeit_Graphics : public Graphics_Interface {\n const Eigenzeit& eigenzeit;\npublic:\n Eigenzeit_Graphics(const Eigenzeit& eigenzeit) : eigenzeit(eigenzeit) {}\n void draw(const pref& /p/) const { /TODO/ }\n};\n\n} / namespace learning /\n\n#endif / LEARNING_EIGENZEIT_GRAPHICS_H /\n" }, { "alpha_fraction": 0.6204379796981812, "alphanum_fraction": 0.6291970610618591, "avg_line_length": 18.02777862548828, "blob_id": "fa7de8608a023a2b0c87e8d68978b11a0e00447a", "content_id": "a4729a78040ebc5f19c3f88a056837a64324b069", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 685, "license_type": "no_license", "max_line_length": 105, "num_lines": 36, "path": "/src/draw/axes.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ axes.h /\n\n#ifndef AXES_H\n#define AXES_H\n\n#include <GL/gl.h>\n#include <GL/glu.h>\n#include <GL/glut.h>\n#include <string>\n\n#include \"draw.h\"\n#include \"../common/modules.h\"\n\nclass axes\n{\n friend class plot1D;\n friend class plot2D;\n\nprivate:\n float px, py, pz;\n float width, height;\n GLfloat a[4][2];\n int flag; / axes flag /\n unsigned int countNum;\n float max_amplitude;\n float min_amplitude;\n float font_height;\n std::string name;\n\npublic:\n axes(float x, float y, float z, float w, float h, int flags, std::string name, float def_amp = 1.0f);\n void set_fontheight(float f) { font_height = f; }\n void draw(void) const;\n};\n\n#endif /AXES_H/\n" }, { "alpha_fraction": 0.5374696850776672, "alphanum_fraction": 0.5733435750007629, "avg_line_length": 20.877094268798828, "blob_id": "01b048ecf6da2287ba8a781ae4ba549d1df8cb33", "content_id": "a009c8987fbb0238695b861749c35ae8cee403fe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 7833, "license_type": "no_license", "max_line_length": 98, "num_lines": 358, "path": "/src/draw/draw.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/ draw.c /\n\n#include \"draw.h\"\n\nvoid draw_line(const GLfloat x[3], const GLfloat y[3])\n{\n glBegin(GL_LINE_STRIP);\n glVertex3fv(x);\n glVertex3fv(y);\n glEnd();\n}\n\nvoid draw_line(const float x1, const float y1, const float z1,\n const float x2, const float y2, const float z2)\n{\n glBegin(GL_LINE_STRIP);\n glVertex3f(x1, y1, z1);\n glVertex3f(x2, y2, z2);\n glEnd();\n}\n\nvoid draw_line(const float x1, const float y1,\n const float x2, const float y2)\n{\n glBegin(GL_LINE_STRIP);\n glVertex2f(x1, y1);\n glVertex2f(x2, y2);\n glEnd();\n}\n\nvoid draw_line(const Point& p1, const Point& p2)\n{\n glBegin(GL_LINE_STRIP);\n glVertex3f(p1.x, p1.y, p1.z);\n glVertex3f(p2.x, p2.y, p2.z);\n glEnd();\n}\n\nvoid draw_line2D(const GLfloat x[2], const GLfloat y[2])\n{\n glBegin(GL_LINE_STRIP);\n glVertex2fv(x);\n glVertex2fv(y);\n glEnd();\n}\n\nvoid draw_rect(const GLfloat x1[3], const GLfloat x2[3], const GLfloat x3[3], const GLfloat x4[3])\n{\n glBegin(GL_LINE_STRIP);\n glVertex3fv(x1);\n glVertex3fv(x2);\n glVertex3fv(x3);\n glVertex3fv(x4);\n glVertex3fv(x1);\n glEnd();\n}\n\nvoid draw_cube(const GLfloat x1[3], const GLfloat x2[3], const GLfloat x3[3], const GLfloat x4[3],\n const GLfloat x5[3], const GLfloat x6[3], const GLfloat x7[3], const GLfloat x8[3])\n{\n glBegin(GL_LINE_STRIP);\n glVertex3fv(x1);\n glVertex3fv(x2);\n glVertex3fv(x3);\n glVertex3fv(x4);\n glVertex3fv(x1);\n glVertex3fv(x5);\n glVertex3fv(x6);\n glVertex3fv(x7);\n glVertex3fv(x8);\n glVertex3fv(x5);\n glEnd();\n glBegin(GL_LINE_STRIP);\n glVertex3fv(x2);\n glVertex3fv(x6);\n glEnd();\n glBegin(GL_LINE_STRIP);\n glVertex3fv(x3);\n glVertex3fv(x7);\n glEnd();\n glBegin(GL_LINE_STRIP);\n glVertex3fv(x4);\n glVertex3fv(x8);\n glEnd();\n\n\n}\n\nvoid draw_wire_cube(const float x, const float y, const float z, const float size)\n{\n glPushMatrix();\n glTranslatef(x, y, z);\n glutWireCube(size);\n glPopMatrix();\n}\n\nvoid draw_solid_cube(const float x, const float y, const float z, const float size)\n{\n glPushMatrix();\n glTranslatef(x, y, z);\n glutSolidCube(size);\n glPopMatrix();\n}\n\n\nvoid draw_grid2D(const float range, const int lines)\n{\n float v0[2], v1[2];\n float F = range/lines;\n\n for (int i = -lines; i <= lines; ++i)\n {\n v0[0] = iF;\n v0[1] = -range;\n v1[0] = iF;\n v1[1] = +range;\n draw_line2D(v0, v1);\n\n v0[0] = -range;\n v0[1] = iF;\n v1[0] = +range;\n v1[1] = iF;\n draw_line2D(v0, v1);\n }\n}\n\nvoid draw_grid3D(const float range, const int LINES)\n{\n float v0[3], v1[3];\n float F = range/LINES;\n\n for (int i = -LINES; i <= LINES; i++)\n for (int j = -LINES; j <= LINES; j++)\n {\n v0[0] = iF;\n v0[1] = jF;\n v0[2] = -range;\n v1[0] = iF;\n v1[1] = jF;\n v1[2] = +range;\n draw_line(v0, v1);\n\n v0[0] = jF;\n v0[1] = -range;\n v0[2] = iF;\n v1[0] = jF;\n v1[1] = +range;\n v1[2] = iF;\n draw_line(v0, v1);\n\n v0[0] = -range;\n v0[1] = iF;\n v0[2] = jF;\n v1[0] = +range;\n v1[1] = iF;\n v1[2] = jF;\n draw_line(v0, v1);\n }\n}\n\nvoid\ndraw_fill_rect(const float size_x, const float size_y)\n{\n const float sx = size_x/2;\n const float sy = size_y/2;\n\n glBegin(GL_QUADS);\n glVertex2f( sx, sy);\n glVertex2f(-sx, sy);\n glVertex2f(-sx,-sy);\n glVertex2f( sx,-sy);\n glEnd();\n}\n\nvoid\ndraw_fill_square(const float size)\n{\n draw_fill_rect(size, size);\n}\n\nvoid\ndraw_fill_square(const float x, const float y, const float size)\n{\n glPushMatrix();\n glTranslatef(x, y, .0f);\n draw_fill_rect(size, size);\n glPopMatrix();\n}\n\nvoid draw_fill_rect(const float x, const float y, const float size_x, const float size_y)\n{\n glPushMatrix();\n glTranslatef(x, y, .0f);\n draw_fill_rect(size_x, size_y);\n glPopMatrix();\n}\n\nvoid draw_rect(const float size_x, const float size_y)\n{\n const float sx = size_x/2;\n const float sy = size_y/2;\n\n glBegin(GL_LINE_STRIP);\n glVertex2f( sx, sy);\n glVertex2f(-sx, sy);\n glVertex2f(-sx,-sy);\n glVertex2f( sx,-sy);\n glVertex2f( sx, sy);\n glEnd();\n}\n\nvoid draw_square(const float size)\n{\n draw_rect(size, size);\n}\n\nvoid draw_square(const float x, const float y, const float size)\n{\n glPushMatrix();\n glTranslatef(x, y, .0f);\n draw_rect(size, size);\n glPopMatrix();\n}\n\nvoid draw_rect(const float x, const float y, const float size_x, const float size_y)\n{\n glPushMatrix();\n glTranslatef(x, y, .0f);\n draw_rect(size_x, size_y);\n glPopMatrix();\n}\n\n\nvoid fill_rect(const GLfloat x1[3], const GLfloat x2[3], const GLfloat x3[3], const GLfloat x4[3])\n{\n glBegin(GL_TRIANGLE_STRIP);\n glVertex3fv(x2);\n glVertex3fv(x1);\n glVertex3fv(x3);\n glVertex3fv(x4);\n glEnd();\n}\n\n//GLUT_BITMAP_9_BY_15\n//GLUT_BITMAP_8_BY_13\n//GLUT_BITMAP_HELVETICA_10\n//GLUT_BITMAP_HELVETICA_18\nvoid draw_text_small(const float x, const float y, const float z, const char str)\n{\n glRasterPos3f(x,y,z);\n for (unsigned int j = 0; j < strlen(str); j++)\n glutBitmapCharacter(GLUT_BITMAP_8_BY_13,str[j]);\n\n}\n\n\nvoid output(const GLfloat x, const GLfloat y, const GLfloat z, char text)\n{\n char p;\n\n glPushMatrix();\n glTranslatef(x, y, z);\n for (p = text; p; p++)\n glutStrokeCharacter(GLUT_STROKE_ROMAN, p);\n glPopMatrix();\n}\n\nvoid draw_text_medium(const float x, const float y, const float z, const char str)\n{\n glRasterPos3f(x,y,z);\n for (unsigned int j = 0; j < strlen(str); j++)\n glutBitmapCharacter(GLUT_BITMAP_9_BY_15,str[j]);\n}\n\nvoid gl_msg(const float px, const float py, const float pz, const char format, ...)\n{\n char text[256];\n va_list args;\n va_start(args, format);\n int length = vsnprintf(text, 256, format, args);\n va_end(args);\n\n if (length > 0) {\n glRasterPos3f(px, py, pz);\n for (int i = 0; i < length; ++i)\n glutBitmapCharacter(GLUT_BITMAP_HELVETICA_10, text[i]);\n }\n\n}\n\nvoid\nglprintf(float x, float y, float z, float line_height, const char* format, ...)\n{\n char text[256];\n va_list args;\n va_start(args, format);\n int length = vsnprintf(text, 256, format, args);\n va_end(args);\n\n if (length > 0)\n {\n glPushMatrix();\n glTranslatef(x, y, z);\n const float size = line_height / 128.0f;\n glScalef(size, size, size);\n glLineWidth(1.0f);\n for (int i = 0; i < length; ++i)\n glutStrokeCharacter(GLUT_STROKE_MONO_ROMAN, text[i]);\n glPopMatrix();\n }\n}\n\nvoid\nglprintc(float x, float y, float z, float line_height, const char* str)\n{\n if (strlen(str) > 0)\n {\n glPushMatrix();\n glTranslatef(x, y, z);\n const float size = line_height / 128.0;\n glScalef(size, size, size);\n glLineWidth(1.0f);\n for (unsigned int i = 0; i < strlen(str); ++i)\n glutStrokeCharacter(GLUT_STROKE_MONO_ROMAN, str[i]);\n glPopMatrix();\n }\n}\n\nvoid\nglprints(float x, float y, float z, float line_height, const std::string str)\n{\n if (str.size() > 0)\n {\n glPushMatrix();\n glTranslatef(x, y, z);\n const float size = line_height / 128.0;\n glScalef(size, size, size);\n glLineWidth(1.0f);\n for (std::size_t i = 0; i < str.size(); ++i)\n glutStrokeCharacter(GLUT_STROKE_MONO_ROMAN, str[i]);\n glPopMatrix();\n }\n}\n\nnamespace draw {\n\nvoid fill_rect(float px, float py, float dx, float dy) {\n glBegin(GL_QUADS);\n glVertex2f(px , py);\n glVertex2f(px+dx, py);\n glVertex2f(px+dx, py+dy);\n glVertex2f(px , py+dy);\n glEnd();\n}\n\n}\n\n/* draw.c /\n\n" }, { "alpha_fraction": 0.6126821041107178, "alphanum_fraction": 0.6155955195426941, "avg_line_length": 38.161075592041016, "blob_id": "40d144a378c39b53d7d25687b654564934503def", "content_id": "9b6656612cd7e337581e2febf99cf919a0a9c516", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5835, "license_type": "no_license", "max_line_length": 138, "num_lines": 149, "path": "/src/learning/motor_predictor.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef MOTOR_PREDICTOR_H_INCLUDED\n#define MOTOR_PREDICTOR_H_INCLUDED\n\n#include <common/log_messages.h>\n#include <robots/robot.h>\n#include <control/controlparameter.h>\n#include <control/jointcontrol.h>\n#include <control/control_core.h>\n#include <learning/predictor.h>\n\n//#include <draw/display.h>\n\nnamespace learning {\n\n\nclass Motor_Predictor : public Predictor_Base {\npublic:\n Motor_Predictor( robots::Robot_Interface const& robot\n , sensor_vector const& motor_targets\n , double learning_rate\n , double random_weight_range\n , std::size_t experience_size\n , control::Control_Parameter const& parameter\n , double noise_level\n )\n : Predictor_Base(motor_targets, learning_rate, random_weight_range, experience_size)\n , robot(robot)\n , core(robot)\n , motor_targets(motor_targets)\n , params(control::turn_symmetry(robot, control::make_asymmetric(robot, parameter)))\n , params_changed(false)\n , noise_level(noise_level)\n {\n core.apply_weights(robot, params.get_parameter());\n }\n\n void copy(Predictor_Base const& other) override {\n Predictor_Base::operator=(other); // copy base members\n Motor_Predictor const& rhs = dynamic_cast<Motor_Predictor const&>(other); /TODO definitely write a test for that crap :) /\n core = rhs.core;\n }\n\n double predict(void) override {\n core.prepare_inputs(robot);\n add_noise_to_inputs(core.input, noise_level);\n assert(!(params.is_mirrored() and params.is_symmetric()));\n core.update_outputs(robot, params.is_symmetric(), params.is_mirrored());\n //vector_tanh(core.activation);\n vector_clip(core.activation);\n return calculate_prediction_error();\n }\n\n double verify(void) override {\n assert(!(params.is_mirrored() and params.is_symmetric()));\n core.update_outputs(robot, params.is_symmetric(), params.is_mirrored());\n vector_clip(core.activation);\n return calculate_prediction_error();\n }\n\n void initialize_randomized(void) override {\n\n for (auto& w_k : core.weights)\n for (auto& w_ik : w_k)\n w_ik += random_value(-random_weight_range,+random_weight_range); // don't override weights initialized by params\n\n auto initial_experience = input.get(); /*TODO this code is the same in state predictor, move to base?/\n for (auto& w: initial_experience)\n w += random_value(-random_weight_range, random_weight_range);\n experience.assign(experience.size(), initial_experience);\n\n prediction_error = predictor_constants::error_min;\n\n }\n\n void initialize_from_input(void) override { assert(false && \"one shot learning not supported.\"); }\n\n Predictor_Base::vector_t const& get_prediction(void) const override { return core.activation; }\n\n control::Control_Parameter const& get_controller_weights() const {\n if (params_changed) {\n params.set_from_matrix(core.weights);\n //params.print();\n params_changed = false;\n }\n return params;\n }\n\n /\n void draw(void) const {\n float s = 2.0/core.weights.size();\n unsigned i = 0;\n for (auto const& wi : core.weights)\n draw_vector2(0.0 + si++, 0.0, 0.045, s, wi, 3.0);\n }\n /\n\n vector_t const& get_weights(void) const override { assert(false); return dummy; /not implemented/ }\n vector_t & set_weights(void) override { assert(false); return dummy; /not implemented/ }\n\nprivate:\n robots::Robot_Interface const& robot;\n control::Fully_Connected_Symmetric_Core core;\n sensor_vector const& motor_targets;\n\n mutable control::Control_Parameter params; // for loading, saving, buffering\n mutable bool params_changed;\n const double noise_level;\n\n VectorN dummy = {}; // remove when implementing get_weights\n\n void learn_from_input_sample(void) override {\n /* Regarding the gradient descent on the motor controller weights:\n * TODO: We handle the 'recurrent' motor connections as just ordinary inputs, this can get stuck,\n * since it learns to copy only the old motor value's input weights.\n /\n auto const& inputs = core.input;\n VectorN const& predictions = core.activation;\n std::vector<VectorN>& weights = core.weights; // non const ref\n assert(motor_targets.size() == predictions.size());\n assert(motor_targets.size() == weights.size());\n assert(inputs.size() == weights.at(0).size());\n\n for (std::size_t k = 0; k < motor_targets.size(); ++k) { // for num of motor outputs\n double err = motor_targets[k] - predictions[k];\n for (std::size_t i = 0; i < weights[k].size(); ++i) { // for num of inputs\n weights[k][i] += learning_rate err * tanh_(predictions[k]) * inputs[i].x;\n /** This gradient is intentionally wrong, derivative of clip transfer function would be not continuous. /\n }\n }\n params_changed = true; /TODO: move to learn_from_experience, if supported /\n }\n\n void learn_from_experience(std::size_t /skip_idx/) override { assert(false && \"Learning from experience is not implemented yet.\"); }\n\n void add_noise_to_inputs(std::vector<control::sym_input>& inputs, double sigma) {\n const double s = sigma/sqrt(inputs.size());\n for (auto &in : inputs) {\n const double rndval = rand_norm_zero_mean(s);\n in.x += rndval;\n in.y += rndval;\n }\n }\n\n friend class Motor_Predictor_Graphics;\n};\n\n} // namespace learning\n\n#endif // MOTOR_PREDICTOR_H_INCLUDED\n" }, { "alpha_fraction": 0.6461870670318604, "alphanum_fraction": 0.65297931432724, "avg_line_length": 32.391754150390625, "blob_id": "4094a5675dae87cffab2ccf4ffd7d8015b1c3d57", "content_id": "5fb8e8f8aff58f03ea474eb0f7ffb4a5df2777ad", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3239, "license_type": "no_license", "max_line_length": 149, "num_lines": 97, "path": "/src/evolution/population.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include \"population.h\"\n\n\nvoid\nPopulation::initialize_from_seed(const std::vector<double>& seed) //TODO take a vector of vectors and init the population or just use load_population\n{\n for (std::size_t i = 0; i < individuals.size(); ++i)\n individuals[i].initialize_from_seed(seed);\n}\n\nbool greater_than(const Individual& ind1, const Individual& ind2) {\n if (ind1.fitness.get_number_of_evaluations() > 0 and ind2.fitness.get_number_of_evaluations() > 0)\n return (ind1.fitness > ind2.fitness);\n else return false;\n}\n\nvoid\nPopulation::sort_by_fitness(void)\n{\n std::sort(individuals.begin(), individuals.end(), greater_than);\n}\n\nvoid\nsave_population(Population& population, file_io::CSV_File<double>& csv_population)\n{\n //sts_msg(\"Saving population.\");\n for (std::size_t idx = 0; idx < population.individuals.size(); ++idx)\n csv_population.set_line(idx, population.individuals[idx].genome);\n csv_population.write();\n}\n\nvoid\nload_population(Population& population, file_io::CSV_File<double>& csv_population)\n{\n if (csv_population.read())\n {\n sts_msg(\"Loading population.\");\n for (std::size_t idx = 0; idx < population.individuals.size(); ++idx)\n csv_population.get_line(idx, population.individuals[idx].genome);\n }\n else wrn_msg(\"Could not load population.\");\n}\n\nvoid\nsave_mutation_rates(Population& population, file_io::CSV_File<double>& csv_mutation)\n{\n //sts_msg(\"Saving mutation rates.\");\n for (std::size_t idx = 0; idx < population.individuals.size(); ++idx)\n csv_mutation.set_line(idx, population.individuals[idx].mutation_rate);\n csv_mutation.write();\n}\n\nvoid\nload_mutation_rates(Population& population, file_io::CSV_File<double>& csv_mutation)\n{\n if (csv_mutation.read()) {\n sts_msg(\"Loading mutation rates.\");\n for (std::size_t idx = 0; idx < population.individuals.size(); ++idx)\n csv_mutation.get_line(idx, population.individuals[idx].mutation_rate);\n }\n else wrn_msg(\"Could not load mutation rates.\");\n}\n\nvoid\nsave_fitness_values(Population& population, file_io::CSV_File<double>& csv_fitness)\n{\n //sts_msg(\"Saving fitness values.\");\n for (std::size_t idx = 0; idx < population.individuals.size(); ++idx)\n csv_fitness.set_line(idx, population.individuals[idx].fitness.get_value_or_default());\n csv_fitness.write();\n}\n\nvoid\nload_fitness_values(Population& population, file_io::CSV_File<double>& csv_fitness)\n{\n if (csv_fitness.read()) {\n sts_msg(\"Loading fitness values.\");\n for (std::size_t idx = 0; idx < population.individuals.size(); ++idx)\n {\n double fitness = -DBL_MIN;\n csv_fitness.get_line(idx, fitness);\n population.individuals[idx].fitness.set_value(fitness);\n }\n }\n else wrn_msg(\"Could not load fitness values.\");\n}\n\nvoid\nprint_population(Population& population)\n{\n sts_msg(\"Print population:\");\n for (std::size_t i = 0; i < population.individuals.size(); ++i) {\n sts_msg(\"%2u. %1.2f\", i, population.individuals[i].fitness);\n for (std::size_t k = 0; k < population.individuals[i].genome.size(); ++k)\n sts_msg(\"genome %u %1.3f\", k, population.individuals[i].genome[k]);\n }\n}\n" }, { "alpha_fraction": 0.5801281929016113, "alphanum_fraction": 0.5942307710647583, "avg_line_length": 31.5, "blob_id": "20b896459bc3509cf75f5d40ba1e2414468323bd", "content_id": "5165a747c9813deb03e697e9562a48cf3d6b9fee", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1560, "license_type": "no_license", "max_line_length": 95, "num_lines": 48, "path": "/src/draw/color_table.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef COLOR_TABLE_H_INCLUDED\n#define COLOR_TABLE_H_INCLUDED\n\n#include <vector>\n#include <random>\n#include <algorithm>\n#include <basic/color.h>\n\n\nclass ColorTable\n{\n std::vector<Color4> colors;\n unsigned int num_variations;\n unsigned int max_colors;\n float get(unsigned int x) { return (x * 1.0)/(num_variations-1); }\n\npublic:\n ColorTable(unsigned int num_variations, bool initialize_randomized = false)\n : colors()\n , num_variations(num_variations)\n , max_colors(num_variations * num_variations * num_variations)\n {\n assert(num_variations > 1);\n //unsigned int i=0;\n colors.reserve(max_colors);\n for (unsigned int r = num_variations; r-- > 0; )\n for (unsigned int g = num_variations; g-- > 0; )\n for (unsigned int b = num_variations; b-- > 0; ) {\n colors.emplace_back(get(r), get(g), get(b), 1.0);\n //dbg_msg(\"%2u: %1.2f %1.2f %1.2f\",i++, get(r), get(g), get(b));\n }\n if (initialize_randomized) randomize();\n }\n\n const Color4& get_color(unsigned int index) const { return colors[index % max_colors]; }\n\n // Color4& operator[] (std::size_t index) { return colors[index % max_colors]; }\n const Color4& operator[] (std::size_t index) const { return colors[index % max_colors]; }\n\n\n void randomize() {\n auto engine = std::default_random_engine{};\n engine.seed(time(0));\n std::shuffle(std::begin(colors), std::end(colors), engine);\n }\n};\n\n#endif // COLOR_TABLE_H_INCLUDED\n" }, { "alpha_fraction": 0.5542522072792053, "alphanum_fraction": 0.5923753380775452, "avg_line_length": 21.733333587646484, "blob_id": "429932ccce71079274ee46b457af50ea0c85dd45", "content_id": "58079d7435bb632d22cc9d3d55f0a94ada2f2674", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 341, "license_type": "no_license", "max_line_length": 57, "num_lines": 15, "path": "/src/tests/motor_layer_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n\n#include <learning/motor_layer.h>\n#include <tests/test_robot.h>\n\nTEST_CASE(\" motor layer \", \"[Motor_Layer]\") {\n\n Test_Robot robot(5,2);\n learning::Motor_Layer motor(robot, 10, 0.01, 1.0, 1);\n\n for (unsigned int t = 0; t < 10; ++t) {\n robot.execute_cycle();\n motor.execute_cycle();\n }\n}\n" } ]	173
Nikovit/backup	https://github.com/Nikovit/backup	7b510e1e4c2499e309d423ed4468a2402a3ef47c	fbb8402f518434672996be5f1e741750490e1862	f786c6d31d272c1155da3604f15eaa096f8d3ebc	refs/heads/master	2021-01-20T12:55:21.800413	2016-07-21T07:03:20	2016-07-21T07:03:20	63,659,713	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.5458874702453613, "alphanum_fraction": 0.550000011920929, "avg_line_length": 40.80995559692383, "blob_id": "2477fe616d8beae17c9d33b8f7cd8b05bb557293", "content_id": "390658678e6fb9246a41cc47641807c0295287d7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9240, "license_type": "no_license", "max_line_length": 122, "num_lines": 221, "path": "/ftpsync/pyftpsync.py", "repo_name": "Nikovit/backup", "src_encoding": "UTF-8", "text": "# -- coding: iso-8859-1 --\n\"\"\"\nSimple folder synchronization using FTP.\n\n(c) 2012-2015 Martin Wendt; see https://github.com/mar10/pyftpsync\nLicensed under the MIT license: http://www.opensource.org/licenses/mit-license.php\n\nUsage examples:\n > pyftpsync.py --help\n > pyftpsync.py upload . ftps://example.com/myfolder\n\"\"\"\nfrom __future__ import print_function\n\nfrom pprint import pprint\n\nfrom ftpsync import __version__\nfrom ftpsync.targets import make_target, FsTarget\n\nfrom ftpsync.synchronizers import UploadSynchronizer, \\\n DownloadSynchronizer, BiDirSynchronizer, DEFAULT_OMIT\n\n\n#def disable_stdout_buffering():\n# \"\"\"http://stackoverflow.com/questions/107705/python-output-buffering\"\"\"\n# # Appending to gc.garbage is a way to stop an object from being\n# # destroyed. If the old sys.stdout is ever collected, it will\n# # close() stdout, which is not good.\n# gc.garbage.append(sys.stdout)\n# sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 0)\n#disable_stdout_buffering()\ntry:\n import argparse\nexcept ImportError:\n print(\"argparse missing (requires 2.7+, 3.2+ or pip/easy_install)\")\n raise\n\n\n\n\ndef namespace_to_dict(o):\n \"\"\"Convert an argparse namespace object to a dictionary.\"\"\"\n d = {}\n for k, v in o.__dict__.items():\n if not callable(v):\n d[k] = v\n return d\n\n\n#===============================================================================\n# run\n#===============================================================================\ndef run():\n parser = argparse.ArgumentParser(\n description=\"Synchronize folders over FTP.\",\n epilog=\"See also https://github.com/mar10/pyftpsync\"\n )\n\n qv_group = parser.add_mutually_exclusive_group()\n qv_group.add_argument(\"--verbose\", \"-v\", action=\"count\", default=3,\n help=\"increment verbosity by one (default: %(default)s, range: 0..5)\")\n qv_group.add_argument(\"--quiet\", \"-q\", action=\"count\", default=0,\n help=\"decrement verbosity by one\")\n\n parser.add_argument(\"--version\", action=\"version\", version=\"%s\" % __version__)\n parser.add_argument(\"--progress\", \"-p\",\n action=\"store_true\",\n default=False,\n help=\"show progress info, even if redirected or verbose < 3\")\n\n subparsers = parser.add_subparsers(help=\"sub-command help\")\n\n def __add_common_sub_args(parser):\n parser.add_argument(\"local\",\n metavar=\"LOCAL\",\n# required=True,\n default=\".\",\n help=\"path to local folder (default: %(default)s)\")\n parser.add_argument(\"remote\",\n metavar=\"REMOTE\",\n help=\"path to remote folder\")\n# upload_parser.add_argument(\"--dry-run\",\n# action=\"store_true\",\n# help=\"just simulate and log results; don't change anything\")\n parser.add_argument(\"-x\", \"--execute\",\n action=\"store_false\", dest=\"dry_run\", default=True,\n help=\"turn off the dry-run mode (which is ON by default), \"\n \"that would just print status messages but does \"\n \"not change anything\")\n parser.add_argument(\"-f\", \"--include-files\",\n help=\"wildcard for file names (default: all, \"\n \"separate multiple values with ',')\")\n parser.add_argument(\"-o\", \"--omit\",\n help=\"wildcard of files and directories to exclude (applied after --include)\")\n parser.add_argument(\"--store-password\",\n action=\"store_true\",\n help=\"save password to keyring if login succeeds\")\n parser.add_argument(\"--no-prompt\",\n action=\"store_true\",\n help=\"prevent prompting for missing credentials\")\n parser.add_argument(\"--no-color\",\n action=\"store_true\",\n help=\"prevent use of ansi terminal color codes\")\n\n # Create the parser for the \"upload\" command\n upload_parser = subparsers.add_parser(\"upload\",\n help=\"copy new and modified files to remote folder\")\n __add_common_sub_args(upload_parser)\n\n upload_parser.add_argument(\"--force\",\n action=\"store_true\",\n help=\"overwrite remote files, even if the target is newer (but no conflict was detected)\")\n upload_parser.add_argument(\"--resolve\",\n default=\"skip\",\n choices=[\"local\", \"skip\", \"ask\"],\n help=\"conflict resolving strategy (default: '%(default)s')\")\n upload_parser.add_argument(\"--delete\",\n action=\"store_true\",\n help=\"remove remote files if they don't exist locally\")\n upload_parser.add_argument(\"--delete-unmatched\",\n action=\"store_true\",\n help=\"remove remote files if they don't exist locally \"\n \"or don't match the current filter (implies '--delete' option)\")\n\n upload_parser.set_defaults(command=\"upload\")\n\n\n # Create the parser for the \"download\" command\n download_parser = subparsers.add_parser(\"download\",\n help=\"copy new and modified files from remote folder to local target\")\n __add_common_sub_args(download_parser)\n\n download_parser.add_argument(\"--force\",\n action=\"store_true\",\n help=\"overwrite local files, even if the target is newer (but no conflict was detected)\")\n download_parser.add_argument(\"--resolve\",\n default=\"skip\",\n choices=[\"remote\", \"skip\", \"ask\"],\n help=\"conflict resolving strategy (default: '%(default)s')\")\n download_parser.add_argument(\"--delete\",\n action=\"store_true\",\n help=\"remove local files if they don't exist on remote target\")\n download_parser.add_argument(\"--delete-unmatched\",\n action=\"store_true\",\n help=\"remove local files if they don't exist on remote target \"\n \"or don't match the current filter (implies '--delete' option)\")\n\n download_parser.set_defaults(command=\"download\")\n\n # Create the parser for the \"sync\" command\n sync_parser = subparsers.add_parser(\"sync\",\n help=\"synchronize new and modified files between remote folder and local target\")\n __add_common_sub_args(sync_parser)\n\n sync_parser.add_argument(\"--resolve\",\n default=\"ask\",\n choices=[\"old\", \"new\", \"local\", \"remote\", \"skip\", \"ask\"],\n help=\"conflict resolving strategy (default: '%(default)s')\")\n\n sync_parser.set_defaults(command=\"synchronize\")\n\n # Parse command line\n args = parser.parse_args()\n\n if not hasattr(args, \"command\"):\n parser.error(\"missing command (choose from 'upload', 'download', 'sync')\")\n\n # Post-process and check arguments\n args.verbose -= args.quiet\n del args.quiet\n\n if hasattr(args, \"delete_unmatched\") and args.delete_unmatched:\n args.delete = True\n\n ftp_debug = 0\n if args.verbose >= 5:\n ftp_debug = 1\n\n args.local_target = make_target(args.local, {\"ftp_debug\": ftp_debug})\n\n if args.remote == \".\":\n parser.error(\"'.' is expected to be the local target (not remote)\")\n args.remote_target = make_target(args.remote, {\"ftp_debug\": ftp_debug})\n if not isinstance(args.local_target, FsTarget) and isinstance(args.remote_target, FsTarget):\n parser.error(\"a file system target is expected to be local\")\n\n # Let the command handler do its thing\n opts = namespace_to_dict(args)\n if args.command == \"upload\":\n s = UploadSynchronizer(args.local_target, args.remote_target, opts)\n elif args.command == \"download\":\n s = DownloadSynchronizer(args.local_target, args.remote_target, opts)\n elif args.command == \"synchronize\":\n s = BiDirSynchronizer(args.local_target, args.remote_target, opts)\n else:\n parser.error(\"unknown command %s\" % args.command)\n\n try:\n s.run()\n except KeyboardInterrupt:\n print(\"\\nAborted by user.\")\n return\n finally:\n # prevent sporadic exceptions in ftplib, when closing in __del__\n s.local.close()\n s.remote.close()\n\n stats = s.get_stats()\n if args.verbose >= 4:\n pprint(stats)\n elif args.verbose >= 1:\n if args.dry_run:\n print(\"(DRY-RUN) \", end=\"\")\n print(\"Wrote %s/%s files in %s dirs. Elap: %s\"\n % (stats[\"files_written\"], stats[\"local_files\"], stats[\"local_dirs\"], stats[\"elap_str\"]))\n\n return\n\n\n# Script entry point\nif __name__ == \"__main__\":\n run()\n" }, { "alpha_fraction": 0.707952618598938, "alphanum_fraction": 0.7184433341026306, "avg_line_length": 27.57281494140625, "blob_id": "430ac348893cd4dce7270cd507f568bfd63c024b", "content_id": "9c426c1daa98763c7dec31c97fd1a6b2a8d9ce3a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3430, "license_type": "no_license", "max_line_length": 139, "num_lines": 103, "path": "/PostgreSQL_bacup2.py", "repo_name": "Nikovit/backup", "src_encoding": "UTF-8", "text": "#!/usr/local/bin/python3.5\n# -- coding: utf-8 --\n\nimport os\nimport subprocess\nimport time\nimport logging\nimport sys\nimport datetime\nimport stat\nfrom os import listdir\nfrom os.path import isfile, join\nfrom datetime import timedelta, datetime\nimport os.path, time\nimport ftplib\n\n#Логирование\n# импортируем модуль\nimport logging\n# создаём объект с именем модуля\nlogger = logging.getLogger(__name__)\n# задаём уровень логгирования\nlogger.setLevel(logging.INFO)\n# создаём обрабочтик файла лога\nhandler = logging.FileHandler('backup.log')\n# задаём уровень логгирования\nhandler.setLevel(logging.INFO)\n# форматируем записи\nformatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\n# устанавливаем формат для обработчика\nhandler.setFormatter(formatter)\n# добавляем обработчик к логгеру\nlogger.addHandler(handler)\n\n\n#Создание дампов PostgreSQL\n#database = ['trade', 'buh2bd', 'salary']\ndatabase = ['template0']\nbackupdir='/home/garbage/backup/'\ndate = time.strftime('%Y-%m-%d')\n\nfor backup in database:\n subprocess.call('cd %s \| pg_dump -U postgres %s \| gzip -9 -c > %s/%s-%s.gz' % (backupdir, backup, backupdir, date, backup), shell=True)\n #filename = '%s%s-%s.gz' % (backupdir, date, backup) - не помню зачем мне это нужно\n logger.info(backup + ' дамп создан и заархивирован')\nelse:\n logger.error(backup + ' дамп не создан')\n\n\n\n# Удаление старых копий\nremoval_period=timedelta(days=30)\n\nonlyfiles = [f for f in listdir(backupdir) if isfile(join(backupdir, f))]\n\nfor filename in onlyfiles:\n how_long_ago_creation_date = datetime.now()-datetime.fromtimestamp(os.path.getctime(backupdir+filename))\n print(filename+\" created \"+str(how_long_ago_creation_date)+\" ago\")\n if (how_long_ago_creation_date>removal_period):\n print(\"Delete file: \"+filename)\n os.remove(backupdir+filename)\n\n\n#####Синхронизация с FTP######\n\n# # #Настройки FTP\n# host = '192.168.0.26'\n# ftp_user = 'buuser'\n# ftp_password = 'buuserpwd'\n# REMOTE_FOLDER = '1C8'\n# LOCAL_FOLDER = '/home/garbage/backup/'\n#\n#\n# #соединяемся с сервером\n# server = ftplib.FTP(host)\n# server.login(ftp_user, ftp_password)\n#\n# #делаем текущими папки для синхронизации\n# server.cwd(REMOTE_FOLDER)\n# os.chdir(LOCAL_FOLDER)\n#\n# #получаем список файлов с синхронизируемых папках\n# remote_files = set(server.nlst())\n# local_files = set(os.listdir(os.curdir))\n#\n# #загружаем недостающие файлы на ftp\n# for local_file in local_files - remote_files:\n# server.storbinary('STOR ' + local_file, open(local_file, 'rb'))\n#\n# #закрываем соединение с сервером\n# server.close()\n\nfrom ftpsync.targets import FsTarget\nfrom ftpsync.ftp_target import FtpTarget\nfrom ftpsync.synchronizers import BiDirSynchronizer\n\nlocal = FsTarget(\"/home/garbage/backup/\")\nuser =\"buuser\"\npasswd = \"buuserpwd\"\nremote = FtpTarget(\"1C8\", \"192.168.0.26\", user, passwd, tls=True)\nopts = {\"resolve\": \"skip\", \"verbose\": 1, \"dry_run\" : False}\ns = BiDirSynchronizer(local, remote, opts)\ns.run()\n\n\n\n\n\n\n\n\n\n\n\n\n" } ]	2
fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV	https://github.com/fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV	f3a31591963fe8cc9b9f09c88f096411af07420a	80d43859b5b0efc487629a1f683acc97835fbf3f	cb57a29dbb6a4e923eaa1fb1b7b17a77b7c9700a	refs/heads/main	2023-05-07T19:21:30.291433	2021-06-05T17:09:26	2021-06-05T17:09:26	374,027,439	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.698630154132843, "alphanum_fraction": 0.7397260069847107, "avg_line_length": 26.25, "blob_id": "52108a61c802cd3c8884dee29d13806b200aabda", "content_id": "8cd16189154e35d13e717d1e3a1d0dd3f1c39fbe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 219, "license_type": "no_license", "max_line_length": 54, "num_lines": 8, "path": "/Deteccao/teste_opencv.py", "repo_name": "fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV", "src_encoding": "UTF-8", "text": "import cv2\nprint(cv2.__version__)\n\nimagem = cv2.imread('opencv-python.jpg')\nimagem_gray = cv2.cvtColor(imagem, cv2.COLOR_BGR2GRAY)\ncv2.imshow('Original', imagem)\ncv2.imshow('Original Cinza', imagem_gray)\ncv2.waitKey()\n\n" }, { "alpha_fraction": 0.6510066986083984, "alphanum_fraction": 0.6923937201499939, "avg_line_length": 37.826087951660156, "blob_id": "05629b15b622c34312b7d43212a2e0f0f147df5b", "content_id": "0496dca786992ba48b489839147557d8cee5ffee", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 894, "license_type": "no_license", "max_line_length": 110, "num_lines": 23, "path": "/Deteccao/exemplo2.py", "repo_name": "fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV", "src_encoding": "UTF-8", "text": "import cv2\n\nclassify_face = cv2.CascadeClassifier('cascades\\\\haarcascade_frontalface_default.xml')\nclassify_eye = cv2.CascadeClassifier('cascades\\\\haarcascade_eye.xml')\n\nimage = cv2.imread('pessoas\\\\beatles.jpg')\nimage_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nfaces_detected = classify_face.detectMultiScale(image_gray, scaleFactor=1.1, minNeighbors=9, minSize=(30, 30))\n\nfor (x, y, w, h) in faces_detected:\n image = cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 2)\n\n face = image[y:y+h, x:x+w]\n face_gray = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n eye_detected = classify_eye.detectMultiScale(face_gray, scaleFactor=1.1, minNeighbors=2)\n\n for(ox, oy, ow, oh) in eye_detected:\n cv2.rectangle(face, (ox, oy), (ox + ow, oy + oh), (0, 190, 0), 2)\n\n\n# cv2.imshow('Faces e olhos detectados', face_gray)\ncv2.imshow('Faces e olhos detectados', image)\ncv2.waitKey()\n\n" }, { "alpha_fraction": 0.6567901372909546, "alphanum_fraction": 0.7061728239059448, "avg_line_length": 26, "blob_id": "bbc73764f952d97d1ac69da33115d000b5684994", "content_id": "0b9b9040597f335abd51f5d66ccfff3e0485f6a8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 405, "license_type": "no_license", "max_line_length": 88, "num_lines": 15, "path": "/Deteccao/exemplo5.py", "repo_name": "fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV", "src_encoding": "UTF-8", "text": "import cv2\nclassify_clock = cv2.CascadeClassifier('cascades\\\\relogios.xml')\n\nimage = cv2.imread('outros\\\\relogio2.jpg')\nimage_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\ndetected = classify_clock.detectMultiScale(image_gray, scaleFactor=1.01, minNeighbors=5)\n\nfor (x, y, w, h) in detected:\n cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)\n\n\n\ncv2.imshow('Clock image', image)\ncv2.waitKey()\n" }, { "alpha_fraction": 0.6611111164093018, "alphanum_fraction": 0.7037037014961243, "avg_line_length": 34.86666488647461, "blob_id": "c041e9032bcc1e1e847b98ab326eb90b9dd273df", "content_id": "e659305bde963ee20593815f4d792ccbdbbc50b7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 540, "license_type": "no_license", "max_line_length": 110, "num_lines": 15, "path": "/Deteccao/exemplo1.py", "repo_name": "fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV", "src_encoding": "UTF-8", "text": "import cv2\n\nclassify_face = cv2.CascadeClassifier('cascades\\\\haarcascade_frontalface_default.xml')\nimage = cv2.imread('pessoas\\\\pessoas2.jpg')\nimage_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nfaces_detected = classify_face.detectMultiScale(image_gray, scaleFactor=1.1, minNeighbors=9, minSize=(30, 30))\n# print(len(faces_detected))\n# print(faces_detected)\n\nfor (x, y, w, h) in faces_detected:\n # print(x, y, w, h)\n cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 2)\n\ncv2.imshow('Faces encontradas', image)\ncv2.waitKey()\n\n\n" }, { "alpha_fraction": 0.6419098377227783, "alphanum_fraction": 0.692307710647583, "avg_line_length": 24.133333206176758, "blob_id": "fc5e4a057cbad230ade8f88911b6f1b72b5975cb", "content_id": "328cf579c7f77e142b7c53e913b4178dc0470fd2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 377, "license_type": "no_license", "max_line_length": 70, "num_lines": 15, "path": "/Deteccao/exemplo6.py", "repo_name": "fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV", "src_encoding": "UTF-8", "text": "import cv2\nclassify_car = cv2.CascadeClassifier('cascades\\\\cars.xml')\n\nimage = cv2.imread('outros\\\\carro3.jpg')\nimage_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\ndetected = classify_car.detectMultiScale(image_gray, scaleFactor=1.01)\n\nfor (x, y, w, h) in detected:\n cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)\n\n\n\ncv2.imshow('Car image', image)\ncv2.waitKey()\n" }, { "alpha_fraction": 0.6659038662910461, "alphanum_fraction": 0.709382176399231, "avg_line_length": 28.133333206176758, "blob_id": "1b70a0157cc83256b22d599484094cd873b64205", "content_id": "6698e42ac13440daa8865f473bc42af1a69e9ab5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 437, "license_type": "no_license", "max_line_length": 80, "num_lines": 15, "path": "/Deteccao/detect_cats.py", "repo_name": "fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV", "src_encoding": "UTF-8", "text": "import cv2\nclassify_cat = cv2.CascadeClassifier('cascades\\\\haarcascade_frontalcatface.xml')\n\ncat_image = cv2.imread('animais\\\\gato2.jpg')\ncat_image_gray = cv2.cvtColor(cat_image, cv2.COLOR_BGR2GRAY)\n\ncat_detected = classify_cat.detectMultiScale(cat_image_gray, scaleFactor=1.08)\n\nfor (x, y, w, h) in cat_detected:\n cv2.rectangle(cat_image, (x, y), (x+w, y+h), (0, 255, 0), 2)\n\n\n\ncv2.imshow('Cat image gray', cat_image)\ncv2.waitKey()\n" } ]	6
thedongregga/wayback-machine-scraper	https://github.com/thedongregga/wayback-machine-scraper	31625666ee324750f79f7541869c3f34dfa5c064	32ba9503fa8438ee75d16909911821d6ca336e8f	2dc355ba0ef9b3bf89d6c4cc044f91b280924545	refs/heads/master	2023-03-06T23:36:53.193039	2021-02-15T19:00:15	2021-02-15T19:00:15	null	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6047928333282471, "alphanum_fraction": 0.6121039986610413, "avg_line_length": 34.17142868041992, "blob_id": "2f12aa8f4d8c4c33892c4d01d587dbae1e6dfba7", "content_id": "89624fabc8f293226285184709a91c86d1e6c1a0", "detected_licenses": [ "ISC" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2462, "license_type": "permissive", "max_line_length": 102, "num_lines": 70, "path": "/wayback_machine_scraper/mirror_spider.py", "repo_name": "thedongregga/wayback-machine-scraper", "src_encoding": "UTF-8", "text": "import os\nfrom datetime import datetime\n\ntry:\n from urllib.parse import quote_plus\nexcept ImportError:\n from urllib import quote_plus\n\nfrom scrapy.spiders import CrawlSpider, Rule\nfrom scrapy.linkextractors import LinkExtractor\n\nfrom scrapy_wayback_machine import WaybackMachineMiddleware\n\n\nclass MirrorSpider(CrawlSpider):\n name = 'mirror_spider'\n handle_httpstatus_list = [404]\n\n def __init__(self, domains, directory, allow=(), deny=(), unix=False):\n self.directory = directory\n self.unix = unix\n self.rules = (\n Rule(LinkExtractor(allow=allow, deny=deny), callback='save_page'),\n )\n\n # parse the allowed domains and start urls\n self.allowed_domains = []\n self.start_urls = []\n for domain in domains:\n url_parts = domain.split('://')\n unqualified_url = url_parts[-1]\n url_scheme = url_parts[0] if len(url_parts) > 1 else 'http'\n full_url = '{0}://{1}'.format(url_scheme, unqualified_url)\n bare_domain = unqualified_url.split('/')[0]\n self.allowed_domains.append(bare_domain)\n self.start_urls.append(full_url)\n\n super().__init__()\n\n def parse_start_url(self, response):\n # scrapy doesn't call the callbacks for the start urls by default,\n # this overrides that behavior so that any matching callbacks are called\n for rule in self._rules:\n if rule.link_extractor._link_allowed(response):\n if rule.callback:\n rule.callback(response)\n\n def save_page(self, response):\n # ignore 404s\n if response.status == 404:\n return\n\n # make the parent directory\n url_parts = response.url.split('://')[1].split('/')\n if os.name == 'nt':\n url_parts = [quote_plus(url_part) for url_part in url_parts]\n parent_directory = os.path.join(self.directory, *url_parts)\n os.makedirs(parent_directory, exist_ok=True)\n\n # construct the output filename\n time = response.meta['wayback_machine_time']\n if self.unix:\n filename = '{0}.snapshot'.format(time.timestamp())\n else:\n filename = '{0}.snapshot'.format(time.strftime(WaybackMachineMiddleware.timestamp_format))\n full_path = os.path.join(parent_directory, filename)\n\n # write out the file\n with open(full_path, 'wb') as f:\n f.write(response.body)\n" } ]	1
infknight/SILT	https://github.com/infknight/SILT	8852f9a630eb358e4a3fb33848b5f9de1797c38e	c581d111c10ba8a0c34aa77f7413974102344c69	1ec45da2851de4c285da9006d0c03127452ddabc	refs/heads/main	2023-03-03T03:33:39.566822	2021-02-12T18:07:48	2021-02-12T18:07:48	338,394,418	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.6249029636383057, "alphanum_fraction": 0.634699285030365, "avg_line_length": 37.20143127441406, "blob_id": "ff5733a33ea37982f1132ca424aba8d627bb6d99", "content_id": "122f20053b9c567a031dfc70c3518278b2eb54ee", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 37361, "license_type": "no_license", "max_line_length": 161, "num_lines": 978, "path": "/app.py", "repo_name": "infknight/SILT", "src_encoding": "UTF-8", "text": "from flask import Flask, render_template, url_for, flash, redirect, request, session, make_response\nfrom flask_wtf.file import FileField, FileAllowed\nfrom flask_sqlalchemy import SQLAlchemy\nfrom datetime import datetime\nfrom flask_bcrypt import Bcrypt\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, PasswordField, SubmitField, BooleanField, TextAreaField\nfrom wtforms.validators import DataRequired, Length, ValidationError, EqualTo, Email\nfrom flask_login import LoginManager, UserMixin, login_user, current_user, logout_user, login_required\nfrom PIL import Image\nimport re\nimport secrets\nimport os\nfrom flask import Flask, redirect, url_for\nimport time\nimport requests\nimport json\nimport pandas as pd\nimport folium\nimport urllib.parse\nfrom requests_oauthlib import OAuth1\nimport tweepy\n\napp = Flask(__name__)\n\n# this is the serects numbers\napp.config['SECRET_KEY'] = 'ea7b11f0714027a81e7f81404612d80d'\n\n# how to add the\n# DB_URL = 'postgresql+psycopg2://jasonjia:[email protected]/SILT_DB'.format(user=POSTGRES_USER,pw=POSTGRES_PW,url=POSTGRES_URL,db=POSTGRES_DB)\n# DB_URL1 = 'postgresql://jasonjia:[email protected]:5432/SILT_DB_test'\nDB_URL1 = 'postgresql://doadmin:jglyvd028l8ced6h@db-silt-db-do-user-8284135-0.b.db.ondigitalocean.com:25060/defaultdb'\napp.config['SQLALCHEMY_DATABASE_URI']=DB_URL1\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False # silence the deprecation warning\n\ndb = SQLAlchemy(app)\nbcrypt = Bcrypt(app)\nlogin_manager = LoginManager(app)\nlogin_manager.login_view = 'login'\n# bootstrap color\nlogin_manager.login_message_category = 'info'\n\n@login_manager.user_loader\ndef load_user(user_id):\n return User.query.get(int(user_id))\n\nclass User(db.Model, UserMixin):\n id = db.Column(db.Integer, primary_key = True)\n email = db.Column(db.String(180), unique = True, nullable = False)\n twitter_username = db.Column(db.String(50), unique=True, default = None)\n username = db.Column(db.String(30), unique = True, nullable = False)\n password = db.Column(db.String(), nullable = False)\n user_pic = db.Column(db.String(20), nullable = False, default='default.jpg')\n posts = db.relationship('Post', backref='author', lazy = True)\n posts_ac = db.relationship('Post_ac', backref='author', lazy = True)\n post_h = db.relationship('Post_h', backref='author', lazy = True)\n post_sp = db.relationship('Post_sp', backref='author', lazy = True)\n post_cr = db.relationship('Post_cr', backref='author', lazy = True)\n post_ev = db.relationship('Post_ev', backref='author', lazy = True)\n spotifyartist = db.relationship('SpotifyArtist', backref='author', lazy = True)\n\n def __init__(self, email, username, password):\n self.email = email\n self.username = username\n self.password = password\n\n def __repr__ (self):\n return f\"User('{self.username}', '{self.email}', '{self.user_pic}', '{self.id}')\"\n\n\nclass Post(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n title = db.Column(db.String(50), nullable = False)\n post_time = db.Column(db.DateTime, nullable = False, default=datetime.utcnow)\n content = db.Column(db.String, nullable = False)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n\n def __repr__(self):\n return f\"Post('{self.tile}', '{self.post_time}', '{self.content}')\"\n\n\nclass Post_ac(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n title = db.Column(db.String(50), nullable = False)\n post_time = db.Column(db.DateTime, nullable = False, default=datetime.utcnow)\n content = db.Column(db.String, nullable = False)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n\n def __repr__(self):\n return f\"Post('{self.tile}', '{self.post_time}', '{self.content}')\"\n\n\nclass Post_h(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n title = db.Column(db.String(50), nullable = False)\n post_time = db.Column(db.DateTime, nullable = False, default=datetime.utcnow)\n content = db.Column(db.String, nullable = False)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n\n def __repr__(self):\n return f\"Post('{self.tile}', '{self.post_time}', '{self.content}')\"\n\nclass Post_sp(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n title = db.Column(db.String(50), nullable = False)\n post_time = db.Column(db.DateTime, nullable = False, default=datetime.utcnow)\n content = db.Column(db.String, nullable = False)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n\n def __repr__(self):\n return f\"Post('{self.tile}', '{self.post_time}', '{self.content}')\"\n\n\nclass Post_cr(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n title = db.Column(db.String(50), nullable = False)\n post_time = db.Column(db.DateTime, nullable = False, default=datetime.utcnow)\n content = db.Column(db.String, nullable = False)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n\n def __repr__(self):\n return f\"Post('{self.tile}', '{self.post_time}', '{self.content}')\"\n\n\n\nclass Post_ev(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n title = db.Column(db.String(50), nullable = False)\n post_time = db.Column(db.DateTime, nullable = False, default=datetime.utcnow)\n content = db.Column(db.String, nullable = False)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n\n def __repr__(self):\n return f\"Post('{self.tile}', '{self.post_time}', '{self.content}')\"\n\n\n\n\n\nclass SpotifyArtist(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n artist_name = db.Column(db.String(2000), nullable=False)\n artist_id = db.Column(db.String(2000), nullable=False)\n # time_range = db.Column(db.String(15), nullable=False)\n\n def __repr__(self):\n return f\"SpotifyArtist('{self.artist_name}', '{self.artist_id}')\"\n\n\n# do not change this\n# from form import account, LoginForm, update_account, PostForm, spotify_profile\n\n####################\n## FORMS ##\n####################\n\nclass account(FlaskForm):\n # user name not null and not too long. Add validation\n username = StringField('Username', validators=[DataRequired(), Length(min = 2, max = 30)])\n email = StringField('Email', validators=[DataRequired(), Length(min = 6), Email()])\n\n password = PasswordField('Password', validators=[DataRequired()])\n confirmed_password = PasswordField('Confirm Password', validators=[DataRequired(), EqualTo('password')])\n submit = SubmitField('Sign Up')\n # def tamu_email_validate(self, form, field):\n # # [A-Za-z0-9] firt character to match it.\n # if not re.search(r\"^[A-Za-z0-9](\\.?[a-z0-9]){5,}@tamu\\.edu$\", field.data):\n # raise ValidationError(\"Invalid Email Address\")\n # return True\n # def validate_email(self, email):\n # # [A-Za-z0-9] firt character to match it.\n # if not re.search(r\"^[A-Za-z0-9](\\.?[a-z0-9]){5,}@tamu\\.edu$\", field.data):\n # raise ValidationError(\"Invalid Email Address\")\n # # return True\n def validate_username(self, username):\n user = User.query.filter_by(username=username.data).first()\n if user:\n raise ValidationError('Username is taken, Please choose a new one')\n\n def validate_email(self, email):\n email = User.query.filter_by(email=email.data).first()\n if email:\n raise ValidationError('Email is taken, Please choose a new one')\n # if not re.search(r\"^[A-Za-z0-9](\\.?[a-z0-9]){5,}@tamu\\.edu$\", email):\n # raise ValidationError(\"Invalid Email Address\")\n\n\n\n\nclass LoginForm(FlaskForm):\n email = StringField('Email', validators=[DataRequired(), Length(min = 6)])\n password = PasswordField('Password', validators=[DataRequired()])\n remeber = BooleanField('Remember Me')\n submit = SubmitField('Login')\n\n\n\nclass update_account(FlaskForm):\n # user name not null and not too long. Add validation\n username = StringField('Username', validators=[DataRequired(), Length(min = 2, max = 30)])\n email = StringField('Email', validators=[DataRequired(), Length(min = 6), Email()])\n picture = FileField('Update Your Picture', validators=[FileAllowed(['jpg', 'png'])])\n submit = SubmitField('Update')\n def validate_username(self, username):\n if username.data != current_user.username:\n user = User.query.filter_by(username=username.data).first()\n if user:\n raise ValidationError('Username is taken, Please choose a new one')\n\n def validate_email(self, email):\n if email.data != current_user.email:\n email = User.query.filter_by(email=email.data).first()\n if email:\n raise ValidationError('Email is taken, Please choose a new one')\n\nclass PostForm(FlaskForm):\n title = StringField('Title', validators=[DataRequired()])\n content = TextAreaField('Content', validators=[DataRequired()])\n submit = SubmitField('Post')\n tweet = BooleanField('Post On Twitter')\n\nclass spotify_profile(FlaskForm):\n artist_name = StringField('Artist', validators=[DataRequired()])\n artist_id = StringField('Artist_ID', validators=[DataRequired()])\n # time_range = StringField('time_range')\n\n########################\n## END FORMS ##\n########################\n\n\[email protected](\"/\", methods=['GET', 'POST'])\[email protected](\"/home\", methods=['GET', 'POST'])\n# in terminal:\n# debug mode in flask: export FLASK_DEBUG=1\n# run flask: flask run\ndef home():\n posts = Post.query.all()\n return render_template(\"home.html\", posts=posts)\n\n\n# @app.route(\"/funny\")\n# def funny():\n# return render_template(\"funny.html\")\n#\n#\[email protected](\"/Events\", methods=['GET', 'POST'])\ndef eve():\n posts_ev = Post_ev.query.all()\n return render_template(\"Events.html\", posts=posts_ev)\n\[email protected](\"/funny\", methods=['GET', 'POST'])\ndef fun():\n posts_h = Post_h.query.all()\n return render_template(\"funny.html\", posts= posts_h)\n\[email protected](\"/studyLounge\", methods=['GET', 'POST'])\ndef study_lounge():\n posts_ac = Post_ac.query.all()\n return render_template(\"studylounge.html\", posts = posts_ac)\n\[email protected](\"/sports\", methods=['GET', 'POST'])\ndef sports():\n posts_sp = Post_sp.query.all()\n return render_template(\"sports.html\", posts = posts_sp)\n\[email protected](\"/course\", methods=['GET', 'POST'])\ndef course():\n posts_cr = Post_cr.query.all()\n return render_template(\"course.html\", posts = posts_cr)\n\n\[email protected]('/profile/<username>')\ndef user_profile(username):\n\n # data we query\n # dbArtists = SpotifyArtist.query.filter_by(user_id = current_user.id).first()\n\n\n data = User.query.filter_by(username = username).first()\n spotify_data = SpotifyArtist.query.filter_by(user_id = data.id).first()\n print (spotify_data)\n # print ((data))\n\n artistArr = []\n if (spotify_data != None):\n if (len(spotify_data.artist_name.split(',! ')) == 31):\n artistArr = spotify_data.artist_name.split(',! ')[20:-1]\n print(artistArr)\n # return render_template(\"user_profile.html\", posts=data, art = spotify_data)\n return render_template(\"user_profile.html\", posts=data, art=artistArr, len=len(artistArr))\n\n\n return str(username)\n\n\n\[email protected](\"/resources\")\ndef resources():\n return render_template(\"resources.html\")\n\ndef save_image(form_picture):\n random_h = secrets.token_hex(8)\n _, fext = os.path.splitext(form_picture.filename)\n picture_fn = random_h + fext\n # root path attrinbute\n picture_path = os.path.join(app.root_path, 'static/image', picture_fn)\n output_size = (125,125)\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\[email protected](\"/profile\", methods = ['GET', 'POST'])\n@login_required\ndef profile(artists=[], artist_ids=[]):\n\n time_range = ['short_term', 'medium_term', 'long_term']\n leng = 0\n\n print(artists)\n\n if (len(artists) != 0):\n # going to be 3\n artists_string = \"\"\n artists_id_string = \"\"\n time_range_string = \"\"\n for i in range(len(artists)):\n for j in range(len(artists[0])):\n # artists[i][j], artist_ids[i][j]\n artists_string+=artists[i][j]\n artists_string+=\",! \"\n artists_id_string+=artist_ids[i][j]\n artists_id_string+=\", \"\n\n print(artists_string)\n print(artists_id_string)\n spo = SpotifyArtist(artist_name = artists_string, artist_id = artists_id_string, author=current_user)\n db.session.add(spo)\n db.session.commit()\n\n # how can we save it to a online drive???\n #image_file = 'https://i.pinimg.com/originals/0c/3b/3a/0c3b3adb1a7530892e55ef36d3be6cb8.png'\n form = update_account()\n if form.validate_on_submit():\n if form.picture.data:\n pic_file = save_image(form.picture.data)\n current_user.user_pic = pic_file\n current_user.username = form.username.data\n current_user.email = form.email.data\n db.session.commit()\n flash('You account is updated! ', 'success')\n return redirect(url_for('profile'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n form.email.data = current_user.email\n image_file = url_for('static', filename = 'image/' + current_user.user_pic, width=100)\n\n dbArtists = SpotifyArtist.query.filter_by(user_id = current_user.id).first()\n print(\"dbArtists:\", dbArtists)\n # return render_template(\"home.html\", posts=posts)\n\n artistArr = []\n if (dbArtists != None):\n if (len(dbArtists.artist_name.split(',! ')) == 31):\n artistArr = dbArtists.artist_name.split(',! ')[20:-1]\n print(artistArr)\n\n return render_template(\"profile.html\", title='Profile', image_file = image_file, form = form, leng=len(artistArr), posts=artistArr)\n\n\[email protected](\"/register\", methods=['GET','POST'])\ndef register():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n form = account()\n if form.validate_on_submit():\n # hash the paswword to save to our database\n hashed_password = bcrypt.generate_password_hash(form.password.data).decode('utf-8')\n\n # create a new user\n user= User(username = form.username.data, email = form.email.data, password = hashed_password)\n db.session.add(user)\n db.session.commit()\n\n flash(f'Account created! You can now log in! ','success')\n # we also need to redirect user to home page\n return redirect(url_for('login'))\n return render_template('register.html', title = 'Register', form = form)\n\n\[email protected](\"/login\", methods = ['GET', 'POST'])\ndef login():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n form = LoginForm()\n if form.validate_on_submit():\n user = User.query.filter_by(email = form.email.data).first()\n if user and bcrypt.check_password_hash(user.password, form.password.data):\n login_user(user, remember = form.remeber.data)\n next_page = request.args.get('next')\n # special python return\n return redirect(next_page) if next_page else redirect(url_for('home'))\n else:\n flash('Login not successful. Please check your password and email.', 'danger')\n return render_template('login.html', title = 'Login', form = form)\n\[email protected](\"/logout\")\ndef logout():\n logout_user()\n return redirect(url_for('home'))\n\ngloabal_true = False\[email protected](\"/post/new\", methods=['GET', 'POST'])\n@login_required\ndef new_post():\n form = PostForm()\n # and form.tweet.data == True\n if gloabal_true == True:\n\n twitter_consumer_key = \"bw5c7K2tzsceOlgenVFDRnogU\"\n twitter_consumer_secret = \"CTXbMs9vFwFCdYrM2CGkVsSsLl53LpO43FNeAwTcX5zukDg36m\"\n token_url = 'https://api.twitter.com/1.1/statuses/update.json'\n token_secret = (session[\"twitter_secret\"])\n access_token = (session[\"twitter_token\"])\n print (\"Auth: \")\n print(access_token, token_secret)\n if form.tweet.data == True:\n print (\"it is true\")\n\n auth = tweepy.OAuthHandler(twitter_consumer_key, twitter_consumer_secret)\n auth.set_access_token(access_token, token_secret)\n # Create API object\n api = tweepy.API(auth)\n # Create a tweet\n api.update_status(form.content.data)\n # post_response = requests.post(resource_url, auth=tw, data=body)\n # post_response = requests.post(request_url, auth = tw)\n # body = {'code': code, 'redirect_uri': redirect_uri, 'grant_type': 'authorization_code', 'client_id': CLI_ID, 'client_secret': CLI_SEC}\n\n\n if form.validate_on_submit():\n post = Post(title=form.title.data, content=form.content.data, author=current_user)\n db.session.add(post)\n db.session.commit()\n flash('Your post has been created', 'success')\n return redirect(url_for('home'))\n return render_template('create_post.html', title = 'Forum', form = form)\n\n\[email protected](\"/post/new/ac\", methods=['GET', 'POST'])\n@login_required\ndef new_post_ac():\n form = PostForm()\n # and form.tweet.data == True\n if form.tweet == True:\n flash(\"make a tweet\",'success')\n if form.validate_on_submit():\n post = Post_ac(title=form.title.data, content=form.content.data, author=current_user)\n print (request.form.get('mycheckbox'))\n db.session.add(post)\n db.session.commit()\n flash('Your post has been created', 'success')\n return redirect(url_for('home'))\n return render_template('create_post.html', title = 'Forum', form = form)\n\[email protected](\"/post/new/h\", methods=['GET', 'POST'])\n@login_required\ndef new_post_h():\n form = PostForm()\n # and form.tweet.data == True\n if form.tweet == True:\n flash(\"make a tweet\",'success')\n if form.validate_on_submit():\n post = Post_h(title=form.title.data, content=form.content.data, author=current_user)\n print (request.form.get('mycheckbox'))\n db.session.add(post)\n db.session.commit()\n flash('Your post has been created', 'success')\n return redirect(url_for('home'))\n return render_template('create_post.html', title = 'Forum', form = form)\n\n\n\[email protected](\"/post/new/sp\", methods=['GET', 'POST'])\n@login_required\ndef new_post_sp():\n form = PostForm()\n # and form.tweet.data == True\n if form.tweet == True:\n flash(\"make a tweet\",'success')\n if form.validate_on_submit():\n post = Post_sp(title=form.title.data, content=form.content.data, author=current_user)\n print (request.form.get('mycheckbox'))\n db.session.add(post)\n db.session.commit()\n flash('Your post has been created', 'success')\n return redirect(url_for('home'))\n return render_template('create_post.html', title = 'Forum', form = form)\n\[email protected](\"/post/new/ev\", methods=['GET', 'POST'])\n@login_required\ndef new_post_ev():\n form = PostForm()\n # and form.tweet.data == True\n if form.tweet == True:\n flash(\"make a tweet\",'success')\n if form.validate_on_submit():\n post = Post_ev(title=form.title.data, content=form.content.data, author=current_user)\n print (request.form.get('mycheckbox'))\n db.session.add(post)\n db.session.commit()\n flash('Your post has been created', 'success')\n return redirect(url_for('home'))\n return render_template('create_post.html', title = 'Forum', form = form)\n\n\n\[email protected](\"/post/new/cr\", methods=['GET', 'POST'])\n@login_required\ndef new_post_cr():\n form = PostForm()\n # and form.tweet.data == True\n if form.tweet == True:\n flash(\"make a tweet\",'success')\n if form.validate_on_submit():\n post = Post_cr(title=form.title.data, content=form.content.data, author=current_user)\n print (request.form.get('mycheckbox'))\n db.session.add(post)\n db.session.commit()\n flash('Your post has been created', 'success')\n return redirect(url_for('home'))\n return render_template('create_post.html', title = 'Forum', form = form)\n\n# oauth = OAuth(app)\n#\n# twitter = oauth.remote_app('twitter',\n# consumer_key = 'bw5c7K2tzsceOlgenVFDRnogU',\n# consumer_secret='CTXbMs9vFwFCdYrM2CGkVsSsLl53LpO43FNeAwTcX5zukDg36m',\n# base_url='https://api.twitter.com/1.1/',\n# request_token_url='https://api.twitter.com/oauth/request_token',\n# access_token_url='https://api.twitter.com/oauth/access_toke',\n# authorize_url='https://api.twitter.com/oauth/authorize'\n# )\n\n\n\n# DELETE this\n\n\n\n\n\n\[email protected]('/twitter_login')\ndef twitterPostForRequestToken():\n request_url = 'https://api.twitter.com/oauth/request_token'\n\t# authorization = app.config['AUTHORIZATION']\n twitter_redirect_url = \"http%3A%2F%2Fsilt-tamu.herokuapp.com%2Ftwitter_callback\"\n # oauth_callback=\"http%3A%2F%2Fmyapp.com%3A3005%2Ftwitter%2Fprocess_callback\"\n\n\t# headers = {'Authorization': authorization, 'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'}\n #headers = {'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'}\n twitter_consumer_key = \"bw5c7K2tzsceOlgenVFDRnogU\"\n twitter_consumer_secret = \"CTXbMs9vFwFCdYrM2CGkVsSsLl53LpO43FNeAwTcX5zukDg36m\"\n tw = OAuth1(twitter_consumer_key, twitter_consumer_secret)\n headers = {'oauth_callback': twitter_redirect_url, 'oauth_consumer_key': twitter_consumer_key}\n #body = {'code': code, 'redirect_uri': redirect_uri, 'grant_type': 'authorization_code', 'client_id': CLI_ID, 'client_secret': CLI_SEC}\n\n post_response = requests.post(request_url, auth = tw)\n # print(\"Twitter Post Response:\")\n\n attrs = vars(post_response)\n twitter_oauth = attrs.get('_content')\n\n oauth_arr = str(twitter_oauth)[2:].split('&')\n # oauth_token = oauth_arr[0].split('=')[1]\n # oauth_token_secret = oauth_arr[1].split('=')[1]\n oauth_token = oauth_arr[0]\n oauth_token_secret = oauth_arr[1]\n # print (oauth_token)\n # print (oauth_token_secret)\n authorize_url = \"https://api.twitter.com/oauth/authorize?\" + oauth_token\n return redirect(authorize_url)\n\n\n # 200 code indicates access token was properly granted\n # if post_response.status_code == 200:\n # json = post_response.json()\n # return json['access_token'], json['refresh_token'], json['expires_in']\n # else:\n # print(\"LOGGING: \" + 'getToken:' + str(post_response.status_code))\n # # logging.error('getToken:' + str(post_response.status_code))\n # return None\n\n# https://yourCallbackUrl.com?oauth_token=NPcudxy0yU5T3tBzho7iCotZ3cnetKwcTIRlX0iwRl0&oauth_verifier=uw7NjWHT6OJ1MpJOXsHfNxoAhPKpgI8BlYDhxEjIBY\[email protected]('/twitter_callback')\ndef twitter_callback():\n url_parse = request.url\n parse_arr = url_parse.split('=')[1:]\n token = parse_arr[0].split('&')[0]\n verifier = parse_arr[1]\n # print (token, verifier)\n\n request_url = 'https://api.twitter.com/oauth/access_token'\n\t# authorization = app.config['AUTHORIZATION']\n # twitter_redirect_url = \"http%3A%2F%2F127.0.0.1%3A5000%2Ftwitter_callback\"\n # oauth_callback=\"http%3A%2F%2Fmyapp.com%3A3005%2Ftwitter%2Fprocess_callback\"\n\n\t# headers = {'Authorization': authorization, 'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'}\n #headers = {'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'}\n twitter_consumer_key = \"bw5c7K2tzsceOlgenVFDRnogU\"\n twitter_consumer_secret = \"CTXbMs9vFwFCdYrM2CGkVsSsLl53LpO43FNeAwTcX5zukDg36m\"\n\n # oauth = OAuth1(client_key,\n # client_secret=client_secret,\n # resource_owner_key=resource_owner_key,\n # resource_owner_secret=resource_owner_secret,\n # verifier=verifier)\n\n tw = OAuth1(twitter_consumer_key, client_secret=twitter_consumer_secret, resource_owner_key=token, verifier=verifier)\n post_response = requests.post(request_url, auth = tw)\n attrs = vars(post_response)\n # print (attrs)\n twitter_oauth = attrs.get('_content')\n # print (\"Content: \")\n # print (twitter_oauth)\n oauth_arr = str(twitter_oauth)[2:].split('&')\n # oauth_token = oauth_arr[0].split('=')[1]\n # oauth_token_secret = oauth_arr[1].split('=')[1]\n oauth_token = oauth_arr[0].split('=')[1]\n oauth_token_secret = oauth_arr[1].split('=')[1]\n\n print (oauth_token, oauth_token_secret)\n\n print(\"tokens:\")\n print(oauth_token, oauth_token_secret)\n\n session.pop('twitter_token', None) # delete visits\n session.pop('twitter_secret', None) # delete visits\n session['twitter_token'] = oauth_token\n session['twitter_secret'] = oauth_token_secret\n session.modified = True\n # posts = {\"status\": \"test tweet\"}\n # token_url = 'https://api.twitter.com/1.1/statuses/update.json'\n # tw = OAuth1(twitter_consumer_key,\n # resource_owner_key=oauth_token,\n # resource_owner_secret=oauth_token_secret,\n # client_secret=twitter_consumer_secret)\n # a = requests.post(token_url, data=posts, auth = tw)\n # print (vars(a))\n #\n # auth = tweepy.OAuthHandler(twitter_consumer_key, twitter_consumer_secret)\n # auth.set_access_token(oauth_token, oauth_token_secret)\n # # Create API object\n # api = tweepy.API(auth)\n # # Create a tweet\n # api.update_status(\"Hello Tweepy2\")\n return redirect('/')\n\n\n##############################\n# Spotify section\n##############################\n\n# Spotify Prerequirements\nCLI_ID = \"035c861c44084c46bf08f93efed2bb4c\"\nCLI_SEC = \"18cba64539fc4c39894f8b17b4e78b6e\"\nAPI_BASE = 'https://accounts.spotify.com'\nREDIRECT_URI = \"http://silt-tamu.herokuapp.com/api_callback\"\nSCOPE = 'playlist-modify-private,playlist-modify-public,user-top-read, user-library-read'\n\n# Set this to True for testing but you probaly want it set to False in production.\nSHOW_DIALOG = True\n# Spotify pre-requirements end\n\[email protected](\"/spotify_authorize\")\ndef authorize():\n client_id = CLI_ID\n redirect_uri = REDIRECT_URI\n # TODO: change scope value\n scope = SCOPE\n\n # state_key = createStateKey(15)\n # session['state_key'] = state_key\n\n authorize_url = 'https://accounts.spotify.com/en/authorize?'\n # parameters = 'response_type=code&client_id=' + client_id + '&redirect_uri=' + redirect_uri + '&scope=' + scope + '&state=' + state_key\n parameters = 'response_type=code&client_id=' + client_id + '&redirect_uri=' + redirect_uri + '&scope=' + scope\n response = make_response(redirect(authorize_url + parameters))\n print(\"response\")\n return response\n\n\n\"\"\"\nCalled after a new user has authorized the application through the Spotift API page.\nStores user information in a session and redirects user back to the page they initally\nattempted to visit.\n\"\"\"\[email protected]('/api_callback')\ndef callback():\n # make sure the response came from Spotify\n # if request.args.get('state') != session['state_key']:\n # \t# return render_template('index.html', error='State failed.')\n # print(\"Error: State Failed\")\n # return\n if request.args.get('error'):\n \t# return render_template('index.html', error='Spotify error.')\n print(\"Error: Spotify error\")\n\n else:\n code = request.args.get('code')\n # session.pop('state_key', None)\n \t# get access token to make requests on behalf of the user\n payload = getToken(code)\n if payload != None:\n session['token'] = payload[0]\n session['refresh_token'] = payload[1]\n session['token_expiration'] = time.time() + payload[2]\n else:\n # return render_template('index.html', error='Failed to access token.')\n return \"Failed to access token\"\n\n current_user = getUserInformation(session)\n print(\"CURRENT USER:\", current_user)\n\n session['user_id'] = current_user['id']\n # logging.info('new user:' + session['user_id'])\n print(\"LOGGING: \" + 'new user:' + session['user_id'])\n\n # track_ids = getAllTopTracks(session)\n artist_names, artist_ids = getAllTopArtists(session)\n\n # if form.validate_on_submit() and form.tweet.data == True:\n # post = Post(title=form.title.data, content=form.content.data, author=current_user)\n # db.session.add(post)\n # db.session.commit()\n # flash('Your post has been created', 'success')\n # return redirect(url_for('home'))\n\n\n\n # print(\"------------------Artists---------------------\")\n time_range = ['short_term', 'medium_term', 'long_term']\n\n # for i in range(len(artist_names)):\n # term = time_range[i]\n #\n # for j in range(len(artist_names[0])):\n # print(artist_names[i][j], artist_ids[i][j])\n # SpotifyArtist = SpotifyArtist(user_id= , artist_name=artist_names[i][j], artist_id=artist_ids[i][j], time_range=term)\n\n print(\"\\nright before printing track_ids\")\n return profile(artists=artist_names, artist_ids=artist_ids)\n\n\ndef getToken(code):\n token_url = 'https://accounts.spotify.com/api/token'\n\t# authorization = app.config['AUTHORIZATION']\n redirect_uri = REDIRECT_URI\n\n\t# headers = {'Authorization': authorization, 'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'}\n headers = {'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'}\n body = {'code': code, 'redirect_uri': redirect_uri, 'grant_type': 'authorization_code', 'client_id': CLI_ID, 'client_secret': CLI_SEC}\n post_response = requests.post(token_url, headers=headers, data=body)\n\n # 200 code indicates access token was properly granted\n if post_response.status_code == 200:\n json = post_response.json()\n return json['access_token'], json['refresh_token'], json['expires_in']\n else:\n print(\"LOGGING: \" + 'getToken:' + str(post_response.status_code))\n # logging.error('getToken:' + str(post_response.status_code))\n return None\n\n\n\"\"\"\nMakes a GET request with the proper headers. If the request succeeds, the json parsed\nresponse is returned. If the request fails because the access token has expired, the\ncheck token function is called to update the access token.\nReturns: Parsed json response if request succeeds or None if request fails\n\"\"\"\ndef makeGetRequest(session, url, params={}):\n\theaders = {\"Authorization\": \"Bearer {}\".format(session['token'])}\n\tresponse = requests.get(url, headers=headers, params=params)\n\n\t# 200 code indicates request was successful\n\tif response.status_code == 200:\n\t\treturn response.json()\n\n\t# if a 401 error occurs, update the access token\n\telif response.status_code == 401 and checkTokenStatus(session) != None:\n\t\treturn makeGetRequest(session, url, params)\n\telse:\n # print(\"LOGGING: makeGetRequest\")\n # print(\"LOGGING: makeGetRequest: \" + str(response.status_code))\n\t\t# logging.error('makeGetRequest:' + str(response.status_code))\n\t\treturn None\n\n\n\ndef getUserInformation(session):\n\turl = 'https://api.spotify.com/v1/me'\n\tpayload = makeGetRequest(session, url)\n\n\tif payload == None:\n\t\treturn None\n\n\treturn payload\n\n\"\"\"\nGets the top tracks of a user for all three time intervals. Used to display the top\ntracks on the TopTracks feature page.\nReturns: A list of tracks IDs for each of the three time intervals\n\"\"\"\ndef getAllTopTracks(session, limit=10):\n url = 'https://api.spotify.com/v1/me/top/tracks'\n track_ids = []\n time_range = ['short_term', 'medium_term', 'long_term']\n\n for time in time_range:\n track_range_ids = []\n\n params = {'limit': limit, 'time_range': time}\n payload = makeGetRequest(session, url, params)\n\n # print(\"------------------PAYLOAD---------------------\")\n # print(payload)\n # print(\"------------------PAYLOAD END-------------\")\n\n if payload == None:\n return None\n\n for track in payload['items']:\n track_range_ids.append(track['id'])\n\n track_ids.append(track_range_ids)\n\n return track_ids\n\n# TODO: situation where user has no tracks\ndef getAllTopArtists(session, limit=10):\n url = 'https://api.spotify.com/v1/me/top/artists'\n artist_names = []\n artist_ids = []\n time_range = ['short_term', 'medium_term', 'long_term']\n\n for time in time_range:\n track_range_ids = []\n\n params = {'limit': limit, 'time_range': time}\n payload = makeGetRequest(session, url, params)\n\n if payload == None:\n return None\n\n artist_range_names = []\n artist_range_ids = []\n\n for artist in payload['items']:\n artist_range_names.append(artist['name'])\n artist_range_ids.append(artist['id'])\n\n artist_names.append(artist_range_names)\n artist_ids.append(artist_range_ids)\n\n return artist_names, artist_ids\n\n##############################\n# Yelp API Section #\n##############################\n\"\"\" END POINTS \"\"\"\n# Business Search URL -- 'https://api.yelp.com/v3/businesses/search'\n# Phone Search URL -- 'https://api.yelp.com/v3/businesses/search/phone'\n# Transaction Search URL -- 'https://api.yelp.com/v3/transactions/{transaction_type}/search'\n# Business Details URL -- 'https://api.yelp.com/v3/businesses/{id}'\n# Business Match URL -- 'https://api.yelp.com/v3/businesses/matches'\n# Reviews URL -- 'https://api.yelp.com/v3/businesses/{id}/reviews'\n# Autocomplete URL -- 'https://api.yelp.com/v3/autocomplete'\n\n# Define my API key, Endpoint, and Header\nAPI_KEY = 'nTM36O5k4QpcgkccZVAMhP8U4BxpO68EYzIA7KPXpRmnT31qUK49B7sfYQ2uA2_uzGRr94oA9aIxdD4PyIa0hyaXIccmnOGCVQ2tMJg4s3-a24CLE3syjaMHsqWRX3Yx'\nENDPOINT_PREFIX = 'https://api.yelp.com/v3/'\nHEADERS = {'Authorization': 'bearer %s' % API_KEY}\nEMPTY_RESPONSE = json.dumps('')\n\n# render popular locations webpage / make yelp API calls with user input for 'term' key\[email protected](\"/popular_locations\", methods=['GET'])\ndef popular_locations():\n # get user input from html form\n term = request.args.get('searchInput', None)\n\n # Check if user inputted a term\n if term == None:\n print(\"No term provided for business search, return nothing.\")\n\n # Define Business Search paramters\n parameters = {\n 'location': 'College Station, TX',\n 'radius': 15000,\n 'term': term,\n 'sort_by': 'best_match',\n 'limit': 50\n }\n\n # Make request to Yelp API\n url = ENDPOINT_PREFIX + 'businesses/search'\n response = requests.get(url, params = parameters, headers = HEADERS)\n\n # Check for good status code - if so, get JSON response and populate map\n if response.status_code == 200:\n print('Got 200 for business search')\n\n # Try/catch for invalid user input for 'term': key-value\n try:\n # Convert JSON string to dictionary\n businessSearchData = response.json()\n\n # Create dataframe from API response (businesses, list of dictionaries)\n dFrame = pd.DataFrame.from_dict(businessSearchData['businesses'])\n\n # YELP MAP - RESTAURANTS MARKED\n # Get latitude and longitude from Yelp API response\n cStatLat = 30.627977\n cStatLong = -96.334404\n\n # Generate base map of college station\n yelpMap = folium.Map(location = [cStatLat, cStatLong], zoom_start = 13)\n\n # Generate map of restaurants - Iterate through dataframe and add business markers\n for row in dFrame.index:\n latLong = dFrame['coordinates'][row]\n latitude = latLong['latitude']\n longitude = latLong['longitude']\n name = dFrame['name'][row]\n rating = dFrame['rating'][row]\n price = dFrame['price'][row]\n location = dFrame['location'][row]\n\n # Get address-1 from Location dictionary\n for loc in location.keys():\n if loc == 'address1':\n address = location[loc]\n\n # Create popup message for pin\n details = ('{}' + '<br><br>' + 'Address: {}' + '<br>' + 'Price: {}' + '<br>' + 'Rating: {}/5').format(name, address, price, rating)\n\n # Resize popup pin\n test = folium.Html(details, script = True)\n popup = folium.Popup(test, max_width = 300, min_width = 300)\n\n # Create and business marker to map\n marker = folium.Marker(location = [latitude, longitude], popup = popup, icon = folium.Icon(color = \"darkred\"))\n marker.add_to(yelpMap)\n\n # Display map on webpage\n yelpMap.save('./templates/yelpMap.html')\n except KeyError:\n print('ERROR: User input provided an invalid key-value.')\n flash(f'There was an error with your input.', 'danger')\n return redirect(url_for('popular_locations'))\n else:\n print('Received non-200 response({}) for business search, returning empty response'.format(response.status_code))\n return EMPTY_RESPONSE\n return render_template('popularLocations.html', businessData = dFrame, isBusinessDataEmpty = dFrame.empty)\n\[email protected](\"/yelp_map\")\ndef yelp_map():\n return render_template('yelpMap.html')\n\[email protected](\"/empty_yelp_map\")\ndef empty_yelp_map():\n return render_template('./templates/blank_yelpMap.html')\n\nif __name__ == '__main__':\n app.run(debug=True)\n" }, { "alpha_fraction": 0.44692736864089966, "alphanum_fraction": 0.4581005573272705, "avg_line_length": 16.899999618530273, "blob_id": "431a97f5a77c098fe9ec11076dce84cd6025616d", "content_id": "bf0b623e6a86a8ecf8f9aae03b539e6d9d27686a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "HTML", "length_bytes": 179, "license_type": "no_license", "max_line_length": 28, "num_lines": 10, "path": "/templates/spotify.html", "repo_name": "infknight/SILT", "src_encoding": "UTF-8", "text": "{% extends \"base.html\" %}\n{% block content %}\n\n {% for key in genres %}\n <h1>{{key}}</h1>\n <p>{{genres[key]}}</p>\n <hr>\n {% endfor %}\n\n{% endblock content %}\n" }, { "alpha_fraction": 0.5520150065422058, "alphanum_fraction": 0.5730240345001221, "avg_line_length": 48.627906799316406, "blob_id": "9cfc910a4f0e504d0de3e5215deb7ca9a5d1cf8b", "content_id": "c92e92107e9bf7d8b7a9753bc9d54f28568c7714", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "HTML", "length_bytes": 12820, "license_type": "no_license", "max_line_length": 398, "num_lines": 258, "path": "/templates/resources.html", "repo_name": "infknight/SILT", "src_encoding": "UTF-8", "text": "{% extends \"base.html\" %}\n\n{% block content %}\n\n <!DOCTYPE html>\n<html>\n<head>\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n<style>\n body {\n font-family: Arial, Helvetica, sans-serif;\n }\n\n .flip-card {\n background-color: transparent;\n width: 300px;\n height: 300px;\n perspective: 1000px;\n }\n\n .richard-flip-card-inside {\n position: relative;\n width: 100%;\n height: 100%;\n text-align: center;\n transition: transform 0.6s;\n transform-style: preserve-3d;\n box-shadow: 0 4px 8px 0 rgba(0,0,0,0.2);\n }\n\n .richard-flip-card-front, .flip-card-back {\n position: absolute;\n width: 100%;\n height: 100%;\n -webkit-backface-visibility: hidden;\n backface-visibility: hidden;\n }\n\n .richard-flip-card-front {\n background-color: #bbb;\n color: black;\n }\n\n .flip-card-back {\n background-color: grey;\n color: white;\n transform: rotateY(180deg);\n }\n\n .flip-card:hover .richard-flip-card-inside {\n transform: rotateY(180deg);\n }\n\n {\n box-sizing: border-box;\n }\n\n / Create two equal columns that floats next to each other /\n .column {\n float: left;\n width: 33%;\n padding: 10px;\n }\n\n / Clear floats after the columns */\n .row:after {\n content: \"\";\n display: table;\n clear: both;\n }\n .TopLeft{\n position: relative;\n display: block;\n margin-left: auto;\n margin-right: auto;\n }\n .MidLeft{\n position: relative;\n display: block;\n margin-left: auto;\n margin-right: auto;\n }\n .BotLeft{\n position: relative;\n display: block;\n margin-left: auto;\n margin-right: auto;\n }\n p{\n font-size: 14px;\n }\n\n</style>\n</head>\n<body>\n\n\n<h1 id=\"id1\",\n style=\"text-align:center;\n color: rgb(143, 142, 142);\n font-style: italic;\">Loading Inspirational Quotes Page\n </h1>\n<script src=\"https://ajax.googleapis.com/ajax/libs/jquery/3.4.1/jquery.min.js\"></script>\n<script>\nurl = \"https://api.forismatic.com/api/1.0/?method=getQuote&lang=en&format=jsonp&jsonp=?\";\n\n$.getJSON(url, function(data){\n document.getElementById(\"id1\").innerHTML = data.quoteText;\n});\n</script>\n\n <div class=\"row\">\n <div class=\"column\" style=\"background-color:#FFFFFF;\">\n <h2 style = \"color: #330000; text-align:center;\">Mental Health</h2>\n <p style = \"color: #330000; text-align:center;\">CAPS remains committed to supporting students' mental health and wellbeing. We care about you and know this can be a difficult time!</p>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n <!-- <img src=\"http://people.tamu.edu/~rwlui9/Server/TL.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\"> -->\n <img src=\"../static/image/mental_health.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Counseling and Pyschological Services</h5>\n <a style = \"color: #FFFFFF\", href=\"https://caps.tamu.edu/\", target=\"_blank\">Official TAMU Services</a>\n <p>CAPS is currently not taking walk-in appointments. To schedule an appointment to meet with a counselor, please visit our Services page. If you are in need of urgent, crisis-related services, you may call us at (979) 845-4427 during TAMU hours of operation to discuss options to assist you or visit our Emergency Resources page for additional resources.</p>\n\n </div>\n </div>\n </div>\n <br>\n <div class=\"flip-card MidLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n <!-- <img src=\"http://people.tamu.edu/~rwlui9/Server/ML.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\"> -->\n <img src=\"../static/image/mental_health_2.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>EMERGENCY RESOURCES</h5>\n <a style = \"color: #FFFFFF\", href=\"https://caps.tamu.edu/emergency-resources/\", target=\"_blank\">ONLY FOR EMERGENCIES</a>\n <p>If you are currently in a life threatening situation or your safety is at risk, call 911 or visit your nearest emergency room.</p>\n </div>\n </div>\n </div>\n <br>\n <div class=\"flip-card BotLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n <!-- <img src=\"http://people.tamu.edu/~rwlui9/Server/BL.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\"> -->\n <img src=\"../static/image/suicide_awareness_prevention.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Suicide Awareness and Prevention</h5>\n <a style = \"color: #FFFFFF\", href=\"https://caps.tamu.edu/suicide-awareness-prevention/\", target=\"_blank\">Not Another Aggie</a>\n <p>The Suicide Awareness & Prevention Office offers a variety of services for individuals, programs, departments, and student groups. Knowing the signs of suicide is important in helping someone who may be at risk. By offering your understanding, reassurance, and support, you can help your loved one or friend seek the help they need.</p>\n </div>\n </div>\n </div>\n </div>\n\n <div class=\"column\" style=\"background-color:#330000;\">\n <h2 style = \"color: #FFFFFF; text-align:center;\">Tutoring</h2>\n <p style = \"color: #FFFFFF; text-align:center;\">Our undergraduate peer tutors have performed well in the subject-areas they cover. They are trained in creating a student-centered, active learning environment.”</p>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n <!-- <img src=\"http://people.tamu.edu/~rwlui9/Server/TM.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\"> -->\n <img src=\"../static/image/tutoring.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Study Hub</h5>\n <a style = \"color: #FFFFFF\", href=\"http://studyhub.tamu.edu/\", target=\"_blank\">Welcome to StudyHUB!</a>\n <p>As a core value of Texas A&M University, Excellence can be enhanced by how we serve our students. We are committed to providing access to academic support so that Aggies can achieve academic excellence.</p>\n </div>\n </div>\n </div>\n <br>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n <img src=\"../static/image/Si_sessions.jpg\" width=\"780&height=589\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Supplemental Instruction</h5>\n <!-- <a style = \"color: #FFFFFF\", href=\"http://studyhub.tamu.edu/\", target=\"_blank\">SI Sessions</a> -->\n <a style = \"color: #FFFFFF\", href=\"http://studyhub.tamu.edu/\", target=\"_blank\">SI Sessions</a>\n <p>SI is a peer-led, academic assistance program that can significantly improve performance for students who attend regularly. Students who attend 10 or more sessions throughout the semester have statistically been shown to earn half to a full letter grade higher than those students who do not attend.</p>\n </div>\n </div>\n </div>\n <br>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n <!-- <img src=\"http://people.tamu.edu/~rwlui9/Server/BM.jpg\" width=\"780&height=520\" alt=\"Avatar\" style=\"width:300px;height:300px;\"> -->\n <img src=\"../static/image/success_initiative.jpg\" width=\"780&height=520\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Texas Success Iniative</h5>\n <a style = \"color: #FFFFFF\", href=\"http://asc.tamu.edu/TX-Success-Initiative\", target=\"_blank\">College-level reading, writing and math</a>\n <p>The Texas Success Initiative (TSI) is a statewide program created by the Texas State Legislature that helps ensure that all incoming college students are prepared for college-level reading, writing and math. </p>\n </div>\n </div>\n </div>\n </div>\n\n <div class=\"column\" style=\"background-color:#FFFFFF;\">\n <h2 style = \"color: #330000; text-align:center;\">Traditions</h2>\n <p style = \"color: #330000; text-align:center;\">Learn about the storied traditions of Texas A&M. Learn about orientation, the Aggie Culture, the Corps, Gameday, Remembrance, and many more!</p>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n <!-- <img src=\"http://people.tamu.edu/~rwlui9/Server/TR.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\"> -->\n <img src=\"../static/image/traditions.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Fish Camp</h5>\n <a style = \"color: #FFFFFF\", href=\"https://www.tamu.edu/traditions/orientation/fish-camp/\", target=\"_blank\">Incoming Freshmen</a>\n <p>Each year, Texas A&M incoming freshmen are welcomed to the university at Fish Camp, a four day orientation program that takes place at the Lakeview Methodist Conference Center in Palestine, Texas. At Fish Camp, freshmen are given opportunities to learn Aggie traditions, make friends and learn more about life at Texas A&M. The camp is led entirely by Texas A&M students.</p>\n </div>\n </div>\n </div>\n <br>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n <!-- <img src=\"http://people.tamu.edu/~rwlui9/Server/MR.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\"> -->\n <img src=\"../static/image/msc.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Memorial Student Center</h5>\n <a style = \"color: #FFFFFF\", href=\"https://www.tamu.edu/traditions/remembrance/msc/\", target=\"_blank\">Home Away From Home</a>\n <p>The Memorial Student Center — or the MSC, as it is known on campus — was built and dedicated on Muster Day (April 21) 1951 to all of the Aggies that have lost their lives in wars past, present, or future.</p>\n </div>\n </div>\n </div>\n <br>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n <!-- <img src=\"http://people.tamu.edu/~rwlui9/Server/BR.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\"> -->\n <img src=\"../static/image/muster.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Muster</h5>\n <a style = \"color: #FFFFFF\", href=\"https://www.tamu.edu/traditions/remembrance/muster/\", target=\"_blank\">A&M’s Most Solemn and Visible Tradition</a>\n <p>At each Muster ceremony around the world, a speaker addresses the crowd before the “Roll Call for the Absent.” Names of those from that area who have died in the past year will be read, and as each name is called, a family member or friend will answer “Here” to show that Aggie is present in spirit. Then, a candle will be lit.</p>\n </div>\n </div>\n </div>\n </div>\n </div>\n\n</body>\n\n\n\n\n{% endblock content %}\n" }, { "alpha_fraction": 0.48159509897232056, "alphanum_fraction": 0.691717803478241, "avg_line_length": 15.717948913574219, "blob_id": "326717ed70e86bb7e34098f554ab52a25a778632", "content_id": "676fe72d45b04ef428e729086a7b4ffdb8c23dd5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 652, "license_type": "no_license", "max_line_length": 24, "num_lines": 39, "path": "/requirements.txt", "repo_name": "infknight/SILT", "src_encoding": "UTF-8", "text": "bcrypt==3.2.0\nbranca==0.4.1\ncertifi==2020.11.8\ncffi==1.14.3\nchardet==3.0.4\nclick==7.1.2\ndnspython==2.0.0\nemail-validator==1.1.2\nFlask==1.1.2\nFlask-Bcrypt==0.7.1\nFlask-Dance==3.1.0\nFlask-Login==0.5.0\nFlask-SQLAlchemy==2.4.4\nFlask-WTF==0.14.3\nfolium==0.11.0\ngunicorn==20.0.4\nidna==2.10\nitsdangerous==1.1.0\nJinja2==2.11.2\nMarkupSafe==1.1.1\nnumpy==1.19.4\noauthlib==3.1.0\npandas==1.1.4\nPillow==8.0.1\npsycopg2-binary==2.8.6\npycparser==2.20\nPySocks==1.7.1\npython-dateutil==2.8.1\npytz==2020.4\nrequests==2.25.0\nrequests-oauthlib==1.3.0\nsix==1.15.0\nspotipy==2.16.1\nSQLAlchemy==1.3.20\ntweepy==3.9.0\nurllib3==1.26.2\nURLObject==2.4.3\nWerkzeug==1.0.1\nWTForms==2.3.3\n" }, { "alpha_fraction": 0.727544903755188, "alphanum_fraction": 0.7559880018234253, "avg_line_length": 38.29411697387695, "blob_id": "fd073924c3542c6421f620b14f7a972c8f84906e", "content_id": "d6b7927b21cf16798c8fcaf9c3f6d062b6b40f40", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 668, "license_type": "no_license", "max_line_length": 255, "num_lines": 17, "path": "/README.md", "repo_name": "infknight/SILT", "src_encoding": "UTF-8", "text": "# SILT\n\n## General info\n###### SILT is an application to help guide freshman transition to college by providing them an outlet to meet similar students utilizing Spotify and Twitter API around the campus as well as use Yelp's API to suggest popular locations around the BCS area.\n###### You could view all the commit through here: https://github.tamu.edu/CSCE315-902-B9/sprint2 and here: https://github.tamu.edu/CSCE315-902-B9/sprint1\n\n\n## Installation and Setup\n### Please read the appropriate guide for your environment of choice:\n```\n$ pip install flask\n$ pip3 install psycogn2\n$ brew install postgres\n```\n## To run this project:\n$ export DEBUG_FLASK=1\n$ flask run\n" } ]	5
onikbakht/Login-process-with-python	https://github.com/onikbakht/Login-process-with-python	b2ec7432f2a98f1b8f819c3dbdad305e17eb0ef2	c11bd8db68ca39ca7282df2714edfb538287a44c	26bd772d93f35da9fabcc6a627bb17e550c59531	refs/heads/main	2023-02-20T04:05:29.916205	2021-01-19T16:41:30	2021-01-19T16:41:30	331,041,425	1	0	null	null	null	null	null	[ { "alpha_fraction": 0.6141732335090637, "alphanum_fraction": 0.6209223866462708, "avg_line_length": 29.92173957824707, "blob_id": "97bddd94914329b7348d2ac6c1b7ce26840ce87e", "content_id": "17dcb8db47a193013eaa95ef87b22a2201469cf1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3556, "license_type": "no_license", "max_line_length": 102, "num_lines": 115, "path": "/Login-process-with-Python.py", "repo_name": "onikbakht/Login-process-with-python", "src_encoding": "UTF-8", "text": "#before you try the program you need to create an excel file\n# and enter the path of the created excel file into the ''path'' variable\nimport xlrd\nfrom xlutils.copy import copy\n\n#enter the path of your created excel file in '''here''' (its going to be used as your local database)\n#instead of '\\' use '\\\\' in the path you want to enter\npath='here'\n\n#the main menu function\ndef mainMenu():\n command=input(\"\"\"\n 1> log in\n 2> create account\n \"\"\")\n if command==\"1\":\n logIn()\n elif command ==\"2\":\n createAccount()\n else:\n print(\"Wrong entry\")\n mainMenu()\n\n# if the user chooses to create an account\ndef createAccount():\n username=input(\"enter the ''New'' user name\")\n #geting an username and checking if it already exists\n # if it already exists user can try another username or go back to main menu\n if userExCheck(username)!=0 :\n print(\"SORRY!! \\n Username already exists\")\n command=input(\"\"\"\n 1> try a new user name\n 2> go to main menu\"\"\")\n if command==\"1\":\n createAccount()\n elif command==\"2\":\n mainMenu()\n else:\n print(\"wrong entry \\n heading back to main menu\")\n mainMenu()\n #if the entered username doesnt exist we ask for password\n else:\n newpassword=input(\"enter the password please.... : \")\n #opening our excl db and dedicating the entered pasword to the entered username in excl db\n file = xlrd.open_workbook(path)\n filesheet = file.sheet_by_index(0)\n row = filesheet.nrows\n\n wb = copy(file)\n w_sheet = wb.get_sheet(0)\n\n\n w_sheet.write(row, 1, username)\n\n w_sheet.write(row, 2, newpassword)\n wb.save(path)\n print(\"account created!! successfully heading to main menu...\")\n mainMenu()\n\n\n#the function which checks if the username already exists\ndef userExCheck(username):\n file = xlrd.open_workbook(path)\n filesheet = file.sheet_by_index(0)\n row = 0\n for l in range(1, filesheet.nrows):\n\n if username == str(filesheet.cell_value(l, 1)):\n row = l\n return row\n break\n\n return row\n\n#checking if the entered password matches the username which we are checking\ndef passCheck(password,row):\n file = xlrd.open_workbook(path)\n filesheet = file.sheet_by_index(0)\n if password == str(filesheet.cell_value(row, 2)):\n passIsRight=True\n return passIsRight\n\n\n#when the user chooses to log in\ndef logIn():\n\n username=input(\"enter your Username\")\n #checking the existance of untered username\n if(userExCheck(username)==0):\n print(\"username does not exist!!\\n\\n\")\n mainMenu()\n #if the username exists we ask for the password\n else:\n row=userExCheck(username)\n loginpassword=input(\"enter yout Password\")\n if (passCheck(loginpassword,row)==True):\n print(\"LOGG IN SUCCESSFULLY ACOMPLISHED\")\n print(\"hi dude welcome to our site\\n\\n\")\n #if the password is wrong we give the user 2 more chances to enter the correct password\n else:\n for i in range(2,4):\n\n print(f\"attempt {i}th from 3\")\n loginpassword =input(\"enter the password\")\n passCheck(loginpassword,row)\n if (passCheck(loginpassword, row) == True):\n print(\"LOG IN SUCCESSFULLY ACOMPLISHED\")\n print(\"hi dude welcome to our site\\n\\n\")\n exit()\n\n print(\"Account susspended!!! \\n(too many tries)\")\n mainMenu()\n\n\nmainMenu()\n" } ]	1
SrikanthreddyR/BasicPythonPrograms	https://github.com/SrikanthreddyR/BasicPythonPrograms	6cc4334328b4e298cd1136aa74d755d3c33062c8	26fe0df46889d7596212fbd77801475b14d433d3	5082cafa4e26726f91d1fe0082d8b1fb0b09697c	refs/heads/master	2023-05-08T11:25:43.402602	2021-06-01T04:19:30	2021-06-01T04:19:30	207,070,761	0	0	null	null	null	null	null	[ { "alpha_fraction": 0.389413982629776, "alphanum_fraction": 0.4385633170604706, "avg_line_length": 10.276596069335938, "blob_id": "e333538d59010800f1f8f8c46cfe6308ad115dc9", "content_id": "db4c04f0390e6f01708b3580f5a654ef08c4b65a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 529, "license_type": "no_license", "max_line_length": 35, "num_lines": 47, "path": "/jump_numbers.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Thu Apr 11 19:34:13 2019\n\nJumps\n\n@author: Admin\n\"\"\"\n\nnum=int(input());\n\nval=0;\n\nans=0;\n\nmod=num%6;\n\nif (mod==1 or mod==3 or mod==0):\n print(\"YES\")\nelse:\n print(\"NO\")\n\n'''\nwhile(val<num):\n val=val+1;\n if (val==num):\n ans=1;\n break;\n \n val=val+2;\n if (val==num):\n ans=1;\n break;\n \n val=val+3;\n if (val==num):\n ans=1;\n break;\n \n print(val)\n\nif (ans==1):\n print(\"YES\");\nelse:\n print(\"NO\");\n \n'''" }, { "alpha_fraction": 0.4721906781196594, "alphanum_fraction": 0.4926220178604126, "avg_line_length": 20, "blob_id": "49f89b5dce2d82991cbf8352c32206ebd4295ac5", "content_id": "815dc29e38825af890dc12775601e12e46bbebde", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 881, "license_type": "no_license", "max_line_length": 67, "num_lines": 42, "path": "/GuessMovieName.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Mon Apr 1 19:27:20 2019\n\nGuess the movie name\n\n@author: Admin\n\"\"\"\nimport random\nmovies=[\"tagaru\",\"bellbottom\",\"manasare\",\"gajakesari\",\"vishvasam\"];\n\ndef play():\n name=list(random.choice(movies));\n print(name);\n user_guess=[\"_\"]len(name);\n c=0;\n while(1):\n c=c+1;\n print(user_guess,\"\\n\");\n gl=input(\"Guess and enter the charecter : \");\n g=0;\n for i in range(len(user_guess)):\n if (name[i]==gl):\n user_guess[i]=gl;\n g=1;\n \n if(g==1):\n print(\"Guessed letter is correct\\n\",)\n else:\n print(\"try again\");\n \n if(name==user_guess):\n print(\"The movie name is : \", name);\n break;\n \n return c;\n\nprint(\"The Game is ON\");\n\ncount=play();\n\nprint(\"No of guesses: \",count);" }, { "alpha_fraction": 0.5064267516136169, "alphanum_fraction": 0.5475578308105469, "avg_line_length": 17.571428298950195, "blob_id": "a1f23f92b9364ea3762789b827bad169991df79d", "content_id": "7aa8c2819ff2f4a7295d8940341bf9351a1771ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 389, "license_type": "no_license", "max_line_length": 40, "num_lines": 21, "path": "/String Sort.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Thu Apr 18 18:37:34 2019\n\nString Sort\n\n@author: Admin\n\"\"\"\ninp_list=list(map(int,input().split()));\n\nfor i in range(len(inp_list)):\n if(inp_list[i]==0):\n del inp_list[i];\n inp_list.append(0);\n\n\nfor i in range(len(inp_list)):\n if (i!=len(inp_list)-1):\n print(inp_list[i],end=\" \");\n else:\n print(inp_list[i],end=\"\");" }, { "alpha_fraction": 0.4841269850730896, "alphanum_fraction": 0.5370370149612427, "avg_line_length": 16.5238094329834, "blob_id": "408c8a1578733fb2e8161aeba10113599b8baad0", "content_id": "b2f93a46e46b243641dda54f5b7d679b96468130", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 378, "license_type": "no_license", "max_line_length": 48, "num_lines": 21, "path": "/cal_sqrt.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Thu Apr 11 20:13:04 2019\n\nCalculate Q = Square root of [(2 C * D)/H]\n\n@author: Admin\n\"\"\"\n\nimport math\n\nx=input();\n\ninp_list=list(map(int,x.split(\",\")));\n\nfor i in range(len(inp_list)):\n res=round(math.sqrt((250inp_list[i])/30));\n if(i!=len(inp_list)-1):\n print(res,end=\",\");\n else:\n print(res,end=\"\");\n \n \n" }, { "alpha_fraction": 0.3457249104976654, "alphanum_fraction": 0.3779430091381073, "avg_line_length": 13.600000381469727, "blob_id": "5f06690e6e06b3b43edad972539c5caf2891ce02", "content_id": "5620214b87fa926f475089da43c2db4e4b79ac29", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 807, "license_type": "no_license", "max_line_length": 35, "num_lines": 55, "path": "/Magic_square.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "\"\"\"\n\nMagic Square \n\n\nCreated on Tue Mar 19 17:50:14 2019\n\n@author: Admin\n\"\"\"\n\nn=int(input());\n\n#list_length=nn;\n\nmagic_square=[];\n\nfor i in range (nn):\n magic_square.append(\"-\");\n \n\ndef index(p,q,i):\n pos=pn+q;\n \n if(magic_square[pos]==\"-\"):\n magic_square[pos]=i;\n else:\n p=p+1;\n q=q-2;\n index(p,q,i);\n\nfor i in range (1,nn):\n if(i==1):\n p=int(n/2);\n q=n-1;\n index(p,q,i);\n else:\n p=p-1;\n q=q+1;\n \n if(p==-1 and q==n):\n p=0;\n q=n-1;\n index(p,q,i);\n \n elif(p==-1):\n p=n-1;\n index(p,q,i);\n \n elif(q==n):\n q=0;\n index(p,q,i);\n else:\n index(p,q,i);\n\nprint(magic_square);\n " }, { "alpha_fraction": 0.4939759075641632, "alphanum_fraction": 0.5843373537063599, "avg_line_length": 11.769230842590332, "blob_id": "a616a4c66e1dae22dccb16b0f929871991b9ca89", "content_id": "bd6b29f77b1021b4812d0452e21342e4cb60f3da", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 166, "license_type": "no_license", "max_line_length": 35, "num_lines": 13, "path": "/factorial_of_n.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Thu Feb 28 09:37:02 2019\n\n@author: Admin\n\"\"\"\n\n\nn=int(input());\nfactn=1;\nfor i in range(n):\n factn=factn(i+1);\nprint(factn)\n" }, { "alpha_fraction": 0.5096524953842163, "alphanum_fraction": 0.5444015264511108, "avg_line_length": 15.193548202514648, "blob_id": "ab327347634dfcee0610ef00805e072953d3444c", "content_id": "276d1974a2d4dff4de20c911dce8f7df40c74aa5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 518, "license_type": "no_license", "max_line_length": 35, "num_lines": 31, "path": "/Sorting_using_random.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Thu Feb 28 11:59:58 2019\n\n@author: Admin\n\"\"\"\n\n\nimport random\n\nn=int(input());\ngiven_list=[]\n\nfor i in range (n):\n given_list.append(int(input()))\n \nsorted_list=given_list.copy()\nsorted_list.sort()\n\nwhile(1):\n i=random.randint(0,n-1);\n j=random.randint(0,n-1);\n \n x=given_list[i];\n given_list[i]=given_list[j];\n given_list[j]=x;\n \n if(given_list==sorted_list):\n for i in given_list:\n print(i,end=\" \")\n break;\n \n \n\n\n" }, { "alpha_fraction": 0.4714764952659607, "alphanum_fraction": 0.49832215905189514, "avg_line_length": 15.583333015441895, "blob_id": "66429fe91a9fa50d884ce41f57352791351d9b55", "content_id": "dbaff9b1b43aa95ef27191f423ef9055918b33df", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 596, "license_type": "no_license", "max_line_length": 38, "num_lines": 36, "path": "/upper_tranglr_matrix.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\n\nUpper Traingular square matrix\n\nCreated on Thu Mar 21 18:00:55 2019\n\n@author: Admin\n\"\"\"\n\n\ndef UpTrgMatrix(matrix,num):\n for i in range(num):\n for j in range (num):\n if (i>j):\n matrix[i][j]=0;\n return matrix;\n\nnum=int(input())\nmat=[]\n\nfor i in range (num):\n x=input().split();\n mat.append(x);\n\nmat=UpTrgMatrix(mat,num);\n\nfor i in range(num):\n for j in range(num):\n if (j==num-1):\n print(mat[i][j], end=\"\")\n else:\n print(mat[i][j], end=\" \");\n \n if(i<num-1):\n print();" }, { "alpha_fraction": 0.4886499345302582, "alphanum_fraction": 0.5292711853981018, "avg_line_length": 18.85365867614746, "blob_id": "7de2f202a48b9713e90f9e70d35074b70e5b3ebf", "content_id": "cbf746d772bd11d1d063932d58d2914f12a2ab97", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 837, "license_type": "no_license", "max_line_length": 48, "num_lines": 41, "path": "/Flames.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Mon Apr 8 20:16:43 2019.\n\nFLAMES\n\n@author: Admin\n\"\"\"\n\nName1=list(input(\"Enter 1st Name: \"));\n\nName2=list(input(\"Enter 2nd Name: \"));\n\ncount=0;\ndel_count=0;\nfor i in range(len(Name1)):\n for j in range(len(Name2)):\n if (Name1[i]==Name2[j]):\n Name1[i]=\"+\"\n Name2[j]=\"\";\n del_count=del_count+1;\n break;\n\ncount=len(Name1)+len(Name2)-del_count2;\n\nprint(Name1,\" \",Name2,\" \",\"count=\",count);\n\n\nflames_list=['f','l','a','m','e','s'];\n\nfor n in range (5):\n z=[];\n flames_list[(count-1)%len(flames_list)]='-';\n i=(count-1)%len(flames_list);\n for j in range(i+1,len(flames_list)):\n z.append(flames_list[j]);\n for k in range(i):\n z.append(flames_list[k]);\n flames_list=list(z);\n\nprint(flames_list);\n \n \n\n" }, { "alpha_fraction": 0.4359672963619232, "alphanum_fraction": 0.4877384305000305, "avg_line_length": 13.720000267028809, "blob_id": "0a2879fd4836985f70fd1e34272dc957ef4b0928", "content_id": "3d5a0179a55b7287edcaca489c2c29718f1962be", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 367, "license_type": "no_license", "max_line_length": 36, "num_lines": 25, "path": "/generating_matrix.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Thu Feb 28 09:56:09 2019\n\n@author: Admin\n\"\"\"\n\nx,y=input().split();\n\nx=int(x);\ny=int(y);\nn=xy;\n\nmatrix=[]\nfor i in range(1,n+1):\n matrix.append(i)\n\nn=len(matrix)\nfor i in range(n):\n if((i+1)%y!=0):\n print(matrix[i], end=\" \")\n else :\n if((i+1)%y==0):\n print(matrix[i], end=\" \")\n print();" }, { "alpha_fraction": 0.4474708139896393, "alphanum_fraction": 0.5408560037612915, "avg_line_length": 11.2380952835083, "blob_id": "d78995e7df820e7075f9d8a752b4af116bf8a4b5", "content_id": "63032c9fc2b009889ceaa0dc8064d694846775e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 257, "license_type": "no_license", "max_line_length": 51, "num_lines": 21, "path": "/Numpy_Array.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\n\n\n\nCreated on Tue Mar 19 17:32:50 2019\n\n@author: Admin\n\"\"\"\n\n\n\nimport numpy\n\n\nmagic_square=numpy.array([[1,2,3],[4,5,6],[7,8,9]])\n\nfor i in range(3):\n for j in range(3):\n print(magic_square[i][j], end=\" \");\n print();\n" }, { "alpha_fraction": 0.5959596037864685, "alphanum_fraction": 0.6060606241226196, "avg_line_length": 14.230769157409668, "blob_id": "02c90bbbd8497f01aa443757995a35cfd7acde91", "content_id": "6f8143c7a7a022d5a87058dfdaf6d2329f5f4f89", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 198, "license_type": "no_license", "max_line_length": 49, "num_lines": 13, "path": "/SpaceCountInFile.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo=open('C:/Users/rangamma/Music/sample.txt','r')\n\ntext=fo.read()\nprint(text);\n\ncount=0;\n\n## Counting number of bank spaces\nfor i in text: \n if(i==' '): \n count=count+1\n\nprint(count)\n" }, { "alpha_fraction": 0.4318181872367859, "alphanum_fraction": 0.49793389439582825, "avg_line_length": 17.076923370361328, "blob_id": "b4eadee81ffe3845b242f88f37131f260097fe64", "content_id": "96bb9b34ad3aef3fba90b72a706b06fbfab7bcd4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 484, "license_type": "no_license", "max_line_length": 184, "num_lines": 26, "path": "/Special Characterin a string .py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Thu Apr 11 18:44:45 2019\n\nSpecial Character in a string\n\n@author: Admin\n\"\"\"\n\ns=input();\n\ns_list=list(s);\n\nc=0;\n\nfor i in range(len(s_list)):\n if (((ord(s_list[i])>=65) and (ord(s_list[i])<=90)) or ((ord(s_list[i])>=97) and (ord(s_list[i])<=122)) or ((ord(s_list[i])>=48) and (ord(s_list[i])<=57)) or (ord(s_list[i])==32)):\n c=0;\n else:\n c=1;\n break;\n\nif c==0:\n print(\"NO\");\nelse: \n print(\"YES\");\n \n \n" }, { "alpha_fraction": 0.6708860993385315, "alphanum_fraction": 0.6708860993385315, "avg_line_length": 16.55555534362793, "blob_id": "468831994d08751e632c1bfaa2bd7f69a37f2640", "content_id": "7b8a36bf0826bd8fdef119d0388ad1882e0f8c0b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 158, "license_type": "no_license", "max_line_length": 49, "num_lines": 9, "path": "/WordCountInFile.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo=open('C:/Users/rangamma/Music/sample.txt','r')\n\ntext=fo.read()\n\nl=list(text.split()) ## Splitting text into words\n\nprint('Word Count:',len(l))\n\nfo.close()\n" }, { "alpha_fraction": 0.39929741621017456, "alphanum_fraction": 0.43676814436912537, "avg_line_length": 24.909090042114258, "blob_id": "27db08cb574138f903b56815a03b0be8ab0b3433", "content_id": "231d04ad8585008af71cf0f70320f469526d93e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 854, "license_type": "no_license", "max_line_length": 197, "num_lines": 33, "path": "/Counter_Spiral.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Thu Apr 4 19:26:00 2019\n\nMaximum Numeric in a string;\n\n@author: Admin\n\"\"\"\n\n\n\"\"\" for j in range(i,len(in_str)):\n if(in_str[i]=='0' or in_str[i]=='1' or in_str[i]=='2' or in_str[i]=='3' or in_str[i]=='4' or in_str[i]=='5' or in_str[i]=='6' or in_str[i]=='7' or in_str[i]=='8' or in_str[i]=='9'):\n x.append(in_str[i]);\n else:\n break;\n st=''.join(x);\n num.append(st);\n\"\"\"\n\n\nin_str=input();\n\nnum=[];\nx=[];\n\nfor i in range(len(in_str)):\n if(in_str[i]=='0' or in_str[i]=='1' or in_str[i]=='2' or in_str[i]=='3' or in_str[i]=='4' or in_str[i]=='5' or in_str[i]=='6' or in_str[i]=='7' or in_str[i]=='8' or in_str[i]=='9'):\n x.append(in_str[i]);\n\n else:\n x.append('-'); \n \nprint(x);" }, { "alpha_fraction": 0.5995671153068542, "alphanum_fraction": 0.6320346593856812, "avg_line_length": 13.46875, "blob_id": "010805bbbd7131ffa996d4111f0e0cdc0781a281", "content_id": "71f9df199504e84a9ed8af5270a995d9c4c96ade", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 462, "license_type": "no_license", "max_line_length": 44, "num_lines": 32, "path": "/computing paradox.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Tue Mar 12 12:52:23 2019\n\n@author: Admin\n\"\"\"\n\nn=int(input())\n\nsongs_list=input();\n\nsongs_list=list(map(int,songs_list.split()))\n\nfav_song_pos=int(input())\n\nfav_song=songs_list[fav_song_pos-1]\n\n#print(fav_song,\"fav song\")\n\nsorted_list=songs_list.copy()\n\n#print(sorted_list,\"cpopied\");\n\n\nsorted_list.sort()\n\n#print(sorted_list, \"sorted\");\n\nfor i in range(n):\n if fav_song==sorted_list[i]:\n print(i+1)\n break;" }, { "alpha_fraction": 0.5337662100791931, "alphanum_fraction": 0.5532467365264893, "avg_line_length": 19.276315689086914, "blob_id": "a9ff4e7aa8d6016f6af6efe947449b00e0a7c81e", "content_id": "9f111bea08fa6192a6d25f8306ff2d571159641d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1540, "license_type": "no_license", "max_line_length": 47, "num_lines": 76, "path": "/permutations_comb.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Thu Apr 18 19:32:51 2019\n\n@author: Admin\n\"\"\"\n\n#from itertools import permutations \n\nn,m=map(int,input().split());\n\ninp_list=list(map(int,input().split()));\n\n#perm = permutations(inp_list, m) \n# \n#perm=list(perm);\n#\n#val_list=[];\n#\n#for i in range(len(perm)):\n# s=sorted(perm[i]);\n# val=s[m-1]-s[0];\n# val_list.append(val);\n# \n#val_list=sorted(val_list);\n#\n#print(val_list[0]);\n\n\nimport sys; \n \n# arr[0..n-1] represents sizes of packets \n# m is number of students. \n# Returns minimum difference between maximum \n# and minimum values of distribution. \ndef findMinDiff(arr, n, m): \n \n # if there are no chocolates or number \n # of students is 0 \n if (m==0 or n==0): \n return 0\n \n # Sort the given packets \n arr.sort() \n \n # Number of students cannot be more than \n # number of packets \n if (n < m): \n return -1\n \n # Largest number of chocolates \n min_diff = sys.maxsize \n \n # Find the subarray of size m such that \n # difference between last (maximum in case \n # of sorted) and first (minimum in case of \n # sorted) elements of subarray is minimum. \n first = 0\n last = 0\n i=0\n while(i+m-1<n ): \n \n diff = arr[i+m-1] - arr[i] \n if (diff < min_diff): \n \n min_diff = diff \n first = i \n last = i + m - 1\n \n i+=1\n \n return (arr[last] - arr[first]) \n \n# Driver Code \nif __name__ == \"__main__\": \n print(findMinDiff(inp_list, n, m))" }, { "alpha_fraction": 0.5046948194503784, "alphanum_fraction": 0.5187793374061584, "avg_line_length": 21.473684310913086, "blob_id": "ae23602d591b5145ed778e0c0b21bb85e45a72e8", "content_id": "8ef6cffba2edc7a9b5c48ebcf455e929b505a949", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 426, "license_type": "no_license", "max_line_length": 48, "num_lines": 19, "path": "/getAlphabetInFile.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo= open(\"alphabetInFile.txt\",\"w+\")\n\ndef alphabetInFile(count,n):\n for i in range(count,n+count):\n while(i>90):\n i=i-26;\n fo.write(chr(i)+\" \")\n fo.write(\"\\n\")\n\ncount=65;\nwhile(True):\n m=input('Wanna enter a value ? Enter T\\F: ')\n if(m==\"T\" or m==\"t\"):\n n=int(input('Enter a number'));\n alphabetInFile(count,n)\n count=count+n;\n else:\n fo.close()\n break" }, { "alpha_fraction": 0.5285714268684387, "alphanum_fraction": 0.5555555820465088, "avg_line_length": 18.090909957885742, "blob_id": "1a17aa572ad5b516c6068027e8dd0939550a21fe", "content_id": "b0a57c404d4789ca4024db39b7c626c3b4da67fb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 630, "license_type": "no_license", "max_line_length": 96, "num_lines": 33, "path": "/Binary_Matrix.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\n\nGiven a matrix with N rows and M columns, the task is to check if the matrix is a Binary Matrix.\nA binary matrix is a matrix in which all the elements are either 0 or 1\n\nCreated on Thu Mar 21 19:21:14 2019\n\n@author: Admin\n\"\"\"\n\nN, M=input().split();\nmat=[];\n\nN=int(N);\nM=int(M);\n\nfor i in range (N):\n x=list(map(int,input().split()));\n mat.append(x);\n \nresult=\"True\";\n\nfor i in range (N):\n for j in range (M):\n if (mat[i][j]!=0) and (mat[i][j]!=1):\n result=\"False\"\n break;\n \nif result==\"True\":\n print(\"YES\",end=\"\");\nelse :\n print(\"NO\",end=\"\"); " }, { "alpha_fraction": 0.37841352820396423, "alphanum_fraction": 0.4161248505115509, "avg_line_length": 17.774999618530273, "blob_id": "a86145cc091ee1ae5b4d72236e060db210d8db2b", "content_id": "707e3f118f758f21d803a016433cad14fb7b17b2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 769, "license_type": "no_license", "max_line_length": 189, "num_lines": 40, "path": "/Maximum Numeric in a string.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Thu Apr 4 19:26:00 2019\n\nMaximum Numeric in a string;\n\n@author: Admin\n\"\"\"\n\nin_str=input();\nx=[];\nnum=[];\nz=[];\n\nfor i in range(len(in_str)):\n if(in_str[i]=='0' or in_str[i]=='1' or in_str[i]=='2' or in_str[i]=='3' or in_str[i]=='4' or in_str[i]=='5' or in_str[i]=='6' or in_str[i]=='7' or in_str[i]=='8' or in_str[i]=='9'):\n x.append(in_str[i]);\n else:\n x.append('-');\n\nprint(x);\n\ni=0;\nwhile(i<len(x)-1):\n c=0\n while(x[i]!='-'):\n z.append(x[i]);\n if(i<len(x)):\n i=i+1;\n print(i)\n c=1\n \n if(c==1):\n st=''.join(z);\n num.append(st);\n if(x[i]=='-'):\n i=i+1;\n print(\"i=\",i);\n\nprint(\"num=\",num)\n \n \n " }, { "alpha_fraction": 0.48363634943962097, "alphanum_fraction": 0.5490909218788147, "avg_line_length": 11, "blob_id": "d7699c4218340c0cdcd15f4b1fed29629c4a1dd6", "content_id": "277fac4241c66609acd71a73ab1dfbc3cead4868", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 275, "license_type": "no_license", "max_line_length": 35, "num_lines": 23, "path": "/disct().py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Tue Mar 12 10:35:05 2019\n\n@author: Admin\n\"\"\"\n#import string\n\n#s=string.ascii_letters\n\n##print(s)\n\n#print(s[-4]);\n\n#print(int(li))...\n\n\ndef test(i,j):\n if(i==0):\n return j\n else:\n return test(i-1,i+j)\nprint(test(4,7))" }, { "alpha_fraction": 0.5442478060722351, "alphanum_fraction": 0.5486725568771362, "avg_line_length": 18.65217399597168, "blob_id": "045c675f2c81991ed022d00e2334516c2cfc3f97", "content_id": "f77d49f5c4fd5ab88c4e74de6e7af8ec7c21ee49", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 452, "license_type": "no_license", "max_line_length": 92, "num_lines": 23, "path": "/RemoveNewLineCharsInFile.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo=open('C:/Users/rangamma/Music/sample.txt','r')\n\nl=fo.readlines()\n\nf=[]\nfor i in l:\n if (i!='\\n'):\n if('\\n' in i):\n f.append(i[:len(i)-2])\n else:\n ## If \\n is not present at the end of the file, i.e. after last line in the file\n f.append(i)\nfo.close()\n\nentstr=\". \".join(f) ## To convert the list of lines to single string\n\nfo=open('sample.txt','w+')\n\nfo.write(entstr)\n\nfo.seek(0)\n\nprint(fo.read())\n" }, { "alpha_fraction": 0.8429751992225647, "alphanum_fraction": 0.8429751992225647, "avg_line_length": 59.5, "blob_id": "67db56689049dd4b43dc963e35376ac7b7a72f94", "content_id": "f5600ac95ccbeca2f7adb1368842702366724f0e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 121, "license_type": "no_license", "max_line_length": 93, "num_lines": 2, "path": "/README.md", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# Beginner Python Programs\nBasic Python Programs which one encounters in the intial stage of learning Python Programming\n" }, { "alpha_fraction": 0.6533665657043457, "alphanum_fraction": 0.6882793307304382, "avg_line_length": 19, "blob_id": "e2de0c34353c674233ec861663754163043569f1", "content_id": "5193a39445ebb7e36f46be29ebb59c18cd92f469", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 401, "license_type": "no_license", "max_line_length": 136, "num_lines": 20, "path": "/Duplicate Elements.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Mon Apr 1 18:35:34 2019\n\nDuplicate Elements\n\nWith a given list L of integers, write a program to print this list L after removing all duplicate values with original order preserved.\n\n@author: Admin\n\"\"\"\n\n\norg_list=list(map(int,input().split()));\n\nfinal_list=[];\n\nfinal_list.append(org_list[0]);\n\nfor i in range(1,len(org_list)):\n for j in range(final_list);\n\n" }, { "alpha_fraction": 0.5857142806053162, "alphanum_fraction": 0.5928571224212646, "avg_line_length": 8.399999618530273, "blob_id": "98933884348fda89d6df34f1737dc04df86c459a", "content_id": "3e0bfe521f50cc4c9fefd7855320de4c7a8fcd12", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 140, "license_type": "no_license", "max_line_length": 30, "num_lines": 15, "path": "/CopyFileContent.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo=open('sample.txt','r')\n\ns=fo.read()\n\nprint(s,'\\n')\n\nfo.close()\n\nfc=open('samplecopy.txt','w+')\n\nfc.write(s)\n\nfc.seek(0)\n\nprint(fc.read())" }, { "alpha_fraction": 0.4661654233932495, "alphanum_fraction": 0.4987468719482422, "avg_line_length": 13.107142448425293, "blob_id": "891c67f1c3192356a13aade88677be0afd5935e7", "content_id": "107c8a1f1c39a62610307cd89ad4dceca3079597", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 399, "license_type": "no_license", "max_line_length": 45, "num_lines": 28, "path": "/Equal_Transpose_Matrix.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Thu Mar 21 18:58:38 2019\n\n\n\n@author: Admin\n\"\"\"\n\n\nnum=int(input())\nmat=[]\n\nfor i in range (num):\n x=input().split();\n mat.append(x);\n\nresult=\"True\";\n\nfor i in range (num):\n for j in range (num):\n if (i!=j) and (mat[i][j]!=mat[j][i]):\n result=\"False\"\n break;\nif result==\"True\":\n print(\"YES\");\nelse :\n print(\"NO\");\n " }, { "alpha_fraction": 0.4073319733142853, "alphanum_fraction": 0.4480651617050171, "avg_line_length": 16.571428298950195, "blob_id": "7badee1547fc9813ae328c0cdc71e74ddfad8fac", "content_id": "50e4113d1975d166b3d43bc83ebbcd3a9c143b81", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 491, "license_type": "no_license", "max_line_length": 68, "num_lines": 28, "path": "/check_rect.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -- coding: utf-8 --\n\"\"\"\nCreated on Mon Apr 15 19:04:42 2019\n\n@author: Admin\n\"\"\"\n\ndef rectangle(z):\n count=0;\n a=z[0];\n b=z[1];\n c=z[2];\n d=z[3];\n if((a==b or a==c or a==d)):\n if ((a==b and c==d) or (a==c and b==d) or (a==d and b==c )):\n count=1;\n if (count==1):\n return \"YES\"\n print(\"YES\");\n else:\n return \"NO\"\n print(\"NO\");\n\nt=int(input());\n\nfor i in range(t):\n l=list(map(int,input().split()));\n rectangle(l);" }, { "alpha_fraction": 0.6296296119689941, "alphanum_fraction": 0.6296296119689941, "avg_line_length": 10, "blob_id": "65185c16bd66073700343cf556d1d434d23591bd", "content_id": "6d284aae11beb6f0b33fabb1e8969920f4118e0e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 54, "license_type": "no_license", "max_line_length": 26, "num_lines": 5, "path": "/readlinesFromFile.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo=open('sample.txt','r+')\n\nl=fo.readlines()\n\nprint(l)" }, { "alpha_fraction": 0.6000000238418579, "alphanum_fraction": 0.6095238327980042, "avg_line_length": 11.411765098571777, "blob_id": "9de613b0b74d1b6ae3e0fdd3de58f05acf3cf550", "content_id": "5e17ad3d3585f6c67e6c897059e371497a9f3cd7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 210, "license_type": "no_license", "max_line_length": 40, "num_lines": 17, "path": "/capitalizeWordsInFile.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo=open('sample.txt','r+')\n\nl=fo.read().split()\n\n## Capitalize first letter of every word\nfor i in range(len(l)):\n l[i]=l[i].capitalize()\n\ns=\" \".join(l)\n\nfo.seek(0)\n\nfo.write(s)\n\nfo.seek(0)\n\nprint(fo.read())" } ]	29

atomic-waffle/config-files

https://github.com/atomic-waffle/config-files

4bfa64071043023fda14de4b1d33f89ecaa3ee18

4a45d225155fcce7a230d8bf83e8c109e3dad7f2

dec4ca043520dd2a9c2673a5ff012f02490bff15

refs/heads/master

2023-03-11T10:55:32.463496

2021-02-17T18:42:57

337,500,651

0

null

[ { "alpha_fraction": 0.5668991804122925, "alphanum_fraction": 0.5903614163398743, "avg_line_length": 36.5476188659668, "blob_id": "fd86c9e503e38e91fe06943d830435e8a635ad92", "content_id": "2f113c669870c0a4dad516977ac0d4438400bcf1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1577, "license_type": "no_license", "max_line_length": 78, "num_lines": 42, "path": "/bin/move_window", "repo_name": "atomic-waffle/config-files", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python3\nimport subprocess\nimport sys\n# calibration\ncal = 4\n# direction, as argument from user input (l, r, u, d / h+, h-, v+, v-)\ndirection = sys.argv[1]\n# move step size \nmv = -1 if direction in [\"l\", \"d\", \"h-\", \"v-\"] else 1\n\ndef get(command):\n return subprocess.check_output([\"/bin/bash\", \"-c\", command])\\\n .decode(\"utf-8\")\n\ndef execute(command):\n subprocess.call([\"/bin/bash\", \"-c\", command])\n# find the top shift (height of the panel = resolution - working area)\nres_output = get(\"xrandr\").split(); idf = res_output.index(\"current\")\nres = (int(res_output[idf+1]), int(res_output[idf+3].replace(\",\", \"\")))[-1]\ntopshift = int(res) - int(get(\"wmctrl -d\").split()[8].split(\"x\")[-1])+cal\n# find frontmost window\ndef get_windowid():\n cmd = \"xprop -root\"\n frontmost = [l for l in get(cmd).splitlines() if\\\n \"ACTIVE_WINDOW(WINDOW)\" in l][0].split()[-1]\n return frontmost[:2]+\"0\"+frontmost[2:]\n# get window geometry, create move command\nset_w = [w.split()[0:6] for w in get(\"wmctrl -lG\").splitlines()\\\n if get_windowid() in w][0]\nset_w[0] = \"wmctrl -ir \"+set_w[0]+\" -e 0\"\nset_w.pop(1)\n\nif direction in [\"l\", \"r\"]:\n set_w[1] = str(int(set_w[1])+mv); set_w[2] = str(int(set_w[2])-topshift) \nelif direction in [\"u\", \"d\"]:\n set_w[2] = str(int(set_w[2])-topshift-mv) \nelif direction in [\"v-\", \"v+\"]:\n set_w[2] = str(int(set_w[2])-topshift); set_w[4] = str(int(set_w[4])+mv)\nelif direction in [\"h-\", \"h+\"]:\n set_w[2] = str(int(set_w[2])-topshift); set_w[3] = str(int(set_w[3])+mv)\n\nexecute((\",\").join(set_w))\n" }, { "alpha_fraction": 0.4399999976158142, "alphanum_fraction": 0.47999998927116394, "avg_line_length": 4.75, "blob_id": "7c99ef9fba143f7d157f7d8820c3679263f0200c", "content_id": "14a7f9e8bde80e28802d211b75623f7e8fbb62ff", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 25, "license_type": "no_license", "max_line_length": 8, "num_lines": 4, "path": "/bin/mpd_starter.sh", "repo_name": "atomic-waffle/config-files", "src_encoding": "UTF-8", "text": "if [ 1 ]\nthen \n\tmpd\nfi\n\n\n" }, { "alpha_fraction": 0.782608687877655, "alphanum_fraction": 0.782608687877655, "avg_line_length": 22, "blob_id": "a55420f57f6b3616b017db7870faf8584564eda5", "content_id": "3ce81c850fa4deecdb8892f496ed38bf387576e2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 23, "license_type": "no_license", "max_line_length": 22, "num_lines": 1, "path": "/README.md", "repo_name": "atomic-waffle/config-files", "src_encoding": "UTF-8", "text": "# configs and programs\n" } ]

3

arbabiha/Particles2PDEs

https://github.com/arbabiha/Particles2PDEs

a89ff21fe7eb1dd9462ef72a9b5fb62ea9e9bf15

6a0a791926cf30c16fbb017b6c5b2ceea8a312b2

d4f4b1ffbb9160f9ef1cd0aade5f0809c304c5ba

refs/heads/master

2023-01-05T13:21:07.949449

2020-11-06T17:21:06

292,632,911

8

1

null

[ { "alpha_fraction": 0.6140252351760864, "alphanum_fraction": 0.6268572807312012, "avg_line_length": 28.407285690307617, "blob_id": "5c3bd38eeb4239c0e615c143fc7a6312b8e57c56", "content_id": "85eaa04ffec4e866a957932145c8fb4c97705b7b", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8884, "license_type": "permissive", "max_line_length": 108, "num_lines": 302, "path": "/thehood/model_library.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\"\nLibrary of neural nets for learning PDEs.\n\nH. Arbabi, Aptil 2020, [email protected].\n\"\"\"\n\n\n\n\nimport numpy as np\nimport time\nimport tensorflow as tf\nimport tensorflow.keras as keras\nfrom tensorflow.keras.layers import Dense, Activation\nfrom tensorflow.keras.layers import Concatenate, Conv1D\n\n\nimport BarLegacy as BL\n\n# tf.keras.backend.set_floatx('float32')\n# tf.keras.backend.set_floatx('float64')\n\ndef Discretized_PDE_net(n_grid:int, n_stencil: int, n_conv=3, n_neurons=32):\n \"\"\"A CNN model for learning Burgers PDE.\n\n E.g. the model with n_stencil=3 learns du_j/dt=f(u_j,u_{j-1},u_{j+1}).\n\n Args:\n n_grid: size of the (periodically-padded) input grid\n n_stencil: the kernel size of the first convolutional layer, \n AND the stencil size of the finite difference\n n_conv: total number of convolutional layers\n n_neurons: size of hidden layers\n\n Returns:\n tf.keras.model that maps the field u (input) to u_t (output). \n \"\"\"\n u=tf.keras.Input(shape=(n_grid,),name=\"input_field\")\n\n u_embeded=stencil_embedding(u,n_stencil)\n\n\n\n # 1st convolution+ activation layers\n clayer1= Conv1D(n_neurons,1,padding='valid',name='convolution_1')\n u_out = clayer1(u_embeded)\n u_out = tf.keras.activations.relu(u_out)\n\n for layer_ind in range(n_conv-2):\n clayer= Conv1D(n_neurons,1,padding='valid',name='convolution_'+str(layer_ind+2))\n u_out = clayer(u_out)\n u_out = tf.keras.activations.relu(u_out)\n \n clayer= Conv1D(1,1,padding='valid',name='convolution_'+str(n_conv))\n u_out = clayer(u_out)\n\n\n return tf.keras.Model(u,u_out)\n \n\ndef Functional_PDE_net(n_grid: int, dx: float, n_stencil: int, \n n_conv=3, n_neurons=32):\n \"\"\"A functional model for learning PDEs.\n\n The model computes u_x,u_xx via finite difference and then\n at each x models u_t=f(u,u_x,u_xx) with trainable neural net.\n\n Args:\n n_grid: size of the (periodically-padded) input grid\n dx: uniform grid spacing\n n_stencil: the kernel size of the first convolutional layer, \n AND the stencil size of the finite difference\n n_conv: total number of convolutional layers\n n_neurons: size of hidden layers\n\n Returns:\n tf.keras.model that maps the field u (input) to u_t (output). \n \"\"\"\n\n u=tf.keras.Input(shape=(n_grid,),name=\"input_field\")\n\n # fixed layer for u_xx\n laplacian_layer = finite_diff_layer(dx,2,n_stencil)\n u_xx= laplacian_layer(u)\n\n # fixed layer for u_x\n laplacian_layer = finite_diff_layer(dx,1,n_stencil)\n u_x= laplacian_layer(u)\n\n # putting u,u_x and u_xx together\n us = tf.stack((u,u_x,u_xx),axis=-1,name='stack_u_ux_uxx')\n\n\n # 1st convolution+ activation layers\n clayer1= Conv1D(n_neurons,1,padding='valid',name='convolution_1')\n u_out = clayer1(us)\n u_out = tf.keras.activations.relu(u_out)\n\n for layer_ind in range(n_conv-2):\n clayer= Conv1D(n_neurons,1,padding='valid',name='convolution_'+str(layer_ind+2))\n u_out = clayer(u_out)\n u_out = tf.keras.activations.relu(u_out)\n \n clayer= Conv1D(1,1,padding='valid',name='convolution_'+str(n_conv))\n u_out = clayer(u_out)\n return tf.keras.Model(u,u_out)\n\ndef Burgers_PDE_greybox(n_grid: int, dx: float, nu: float, n_stencil: int, n_conv=3):\n \"\"\"A CNN model for learning Burgers PDE only uux part.\n\n The model computes u_x,u_xx via finite difference and then\n at each x models u_t=f(u,u_x,u_xx) with trainable neural net.\n\n Args:\n n_grid: size of the (periodically-padded) input grid\n dx: uniform grid spacing\n n_stencil: the kernel size of the first convolutional layer, \n AND the stencil size of the finite difference\n n_conv: total number of convolutional layers\n nu: viscosity\n\n Returns:\n tf.keras.model that maps the field u (input) to u_t (output). \n \"\"\"\n\n u=tf.keras.Input(shape=(n_grid,),name=\"input_field\")\n\n \n\n # fixed layer for u_xx\n laplacian_layer = finite_diff_layer(dx,2,n_stencil)\n u_xx= laplacian_layer(u)\n # print(u_xx[...,tf.newaxis].shape)\n\n # fixed layer for u_x\n ux_layer = finite_diff_layer(dx,1,n_stencil)\n u_x= ux_layer(u)\n\n # putting u u_x together\n us = tf.stack((u,u_x),axis=-1,name='stack_u_ux_uxx')\n\n\n # 1st convolution+ activation layers\n clayer1= Conv1D(32,1,padding='valid',name='convolution_1')\n u_out = clayer1(us)\n u_out = tf.keras.activations.relu(u_out)\n\n for layer_ind in range(n_conv-2):\n clayer= Conv1D(32,1,padding='valid',name='convolution_'+str(layer_ind+2))\n u_out = clayer(u_out)\n u_out = tf.keras.activations.relu(u_out)\n \n clayer= Conv1D(1,1,padding='valid',name='convolution_'+str(n_conv))\n u_out = clayer(u_out) + nu * u_xx[...,tf.newaxis]\n \n\n return tf.keras.Model(u,u_out)\n\n\n\n\n\ndef Burgers_PDE(n_grid: int, dx: float, n_stencil: int, nu: float):\n \"\"\"A standard discretization of Burgers.\n\n The model computes u_t= - u*u_x+ nu*u_xx.\n\n Args:\n n_grid: size of the (periodically-padded) input grid\n dx: uniform grid spacing\n n_stencil: the kernel size of the first convolutional layer, \n AND the stencil size of the finite difference\n ns: resolutaion rate of interpolation\n nu: viscosity\n\n Returns:\n tf.keras.model that maps the field u (input) to u_t (output). \n \"\"\"\n\n u=tf.keras.Input(shape=(n_grid,),name=\"input_field\")\n\n # fixed layer for u_xx\n laplacian_layer = finite_diff_layer(dx,2,n_stencil)\n u_xx= laplacian_layer(u)\n\n # fixed layer for u_x\n laplacian_layer = finite_diff_layer(dx,1,n_stencil)\n u_x= laplacian_layer(u)\n\n # putting u,u_x and u_xx together\n u_t =- u*u_x + nu * u_xx \n\n\n return tf.keras.Model(u,u_t)\n \n\n\n\n\n\nclass finite_diff_layer(tf.keras.layers.Layer):\n \"\"\"A layer of frozen finite difference on uniform periodic grid.\"\"\"\n\n def __init__(self, dx: float, derivative_order: int, stencil_size: int):\n \"\"\"Constructor.\n\n Args:\n dx: spacing between grid points\n derivative_order: larger than 0\n stencil_size: at this point we only accept odd numbers \n \"\"\"\n super(finite_diff_layer, self).__init__()\n assert stencil_size % 2 ==1, \"I accept only odd stencil size\"\n self.stencil_size=stencil_size\n\n int_grid= np.arange(-stencil_size//2+1,stencil_size//2+1,1)\n local_grid = int_grid* dx # local position of points\n\n damethod=BL.Method.FINITE_DIFFERENCES\n standard_coeffs= BL.coefficients(local_grid,damethod,derivative_order)\n \n # self.coeffs=tf.constant(standard_coeffs,dtype=tf.float64,name='df_coeffs_O'+str(derivative_order))\n self.coeffs=tf.constant(standard_coeffs,dtype=tf.float32,name='df_coeffs_O'+str(derivative_order))\n\n def build(self, input_shape):\n pass\n\n def call(self,u):\n u_embeded=stencil_embedding(u, self.stencil_size)\n return tf.einsum('s,bxs->bx', self.coeffs, u_embeded)\n\n\n\n\n\n\n\n\n\ndef stencil_embedding(inputs:tf.Tensor,stencil_width:int)-> tf.Tensor:\n \"\"\"Embedding the input data with the size of stencil.\n\n Args:\n inputs: values of the field on a periodic 1d grid, shape=(...,x)\n stencil_width: width of the stencil\n\n Returns:\n tensor of shape (...,x,stencil_width) the values of stencil nodes\n \"\"\"\n if stencil_width % 2 ==1:\n npad = stencil_width//2\n else:\n raise NotImplementedError('only accept odd stencil size')\n\n\n padded_inputs=tf.concat([inputs[:,-npad:],inputs,inputs[:,:npad]],axis=-1)\n\n # we add (y,depth) dimension to fake an image\n embedded=tf.image.extract_patches(padded_inputs[...,tf.newaxis,tf.newaxis],\n sizes=[1,stencil_width,1,1],\n strides=[1, 1, 1, 1],\n rates=[1, 1, 1, 1],\n padding='VALID',name='stencil_embeded')\n\n\n return tf.squeeze(embedded,axis=-2,name='squeeeze_me') # remove (y,) dimension\n\n\n\n\ndef fdm_2nd_der(nx:int,dx:float,ns:int):\n \"\"\"Computing the finite-diff ccoefficients for 2nd derivative.\n \n Args:\n nx: number of grid points\n dx: (uniform) grid spacing\n ns: (odd) stencil width\n \n Returns:\n alpha: tf.tensor of shape (nx,s) where alpha[ix,:] is the\n finite diff coefficients for estimating d2u/dx2 with O(h2) error.\n \"\"\"\n \n assert ns % 2 ==1, \"I accept only odd stencil width\"\n\n int_grid= np.arange(-ns//2+1,ns//2+1,1)\n local_grid = int_grid* dx # local position of points\n\n\n damethod=BL.Method.FINITE_DIFFERENCES\n\n a= BL.coefficients(local_grid,damethod,2)\n\n return tf.constant(a,dtype=tf.float32,name='a_xx')\n\n\n\n\n\nif __name__=='__main__':\n\n print('pass?')\n\n\n\n" }, { "alpha_fraction": 0.5705607533454895, "alphanum_fraction": 0.6042056083679199, "avg_line_length": 24.77108383178711, "blob_id": "68acd8872bbdd90d424c8075e7067df61fa2502a", "content_id": "fef18ea1f8809b73f72967282ccdfe4f801aba30", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4280, "license_type": "permissive", "max_line_length": 91, "num_lines": 166, "path": "/gap_tooth.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\" \nIllustrating gap-tooth method and data generation for Burgers model. \n\nFrom the paper\n[1]: \"Particles to PDEs parsimoniously\" by Arbabi & Kevrekidis 2020\n\nGap-tooth method is based on \n[2]: \"The Gap-Tooth Method in Particle Simulations\" by Gear, Lu & Kevrekidis 2003\n\n\nHassan Arbabi, [email protected] \nApril 4, 2020\n\"\"\"\n\n\nimport numpy as np\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport timeit\n\n\nfrom sys import path\npath.append('./thehood/')\n\nimport BurgersGapTooth as BGT\nimport CFDroutines as CR\n\n\n\ndef gaptooth_vs_truth(nu=0.05, N=128, Z=10000.0, alpha=.1):\n \"\"\"Running gap-tooth and comapre vs finite volume.\n\n Generates fig 1(b) in the paper.\n \n Args:\n nu: viscosity\n N: numbeer of teeth\n Z: resolution factor, i.e., number of particles per unit mass\n alpha: fraction of space occupied by teeth\n\n \"\"\"\n np.random.seed(41)\n\n rho0=lambda x: 1-np.sin(x)/2\n\n\n tsys = BGT.BurgersGapToothSystem(alpha=alpha,N=N,nu=nu)\n tsys.initialize(Z/alpha,rho0=rho0)\n tsys.run_gap_tooth(dt = .002, Nt = 1000,save_history=True)\n grid_g,rho_g,t_g=tsys.tooth_center_x,tsys.rho_history,tsys.rho_history_t\n rho_g=np.stack(rho_g)\n\n dx = grid_g[1]-grid_g[0]\n\n def FiniteVolume(t,y):\n dydt= - CR.WENO_FVM_convection(y,dx) + nu * CR.diffusion_term(y,dx)\n return dydt\n\n u0 = rho0(grid_g) \n Sol = solve_ivp(FiniteVolume,[0,t_g[-1]],u0,method='BDF',t_eval=t_g,max_step=0.01)\n rho_truth=Sol.y.T\n\n\n x_padded = np.concatenate((grid_g,2*grid_g[-1:]-grid_g[-2:-1]))\n periodic_pad = lambda u: np.concatenate([u,u[:,-1:]],axis=-1)\n\n plt.figure(figsize=[4,2])\n plt.subplot(1,2,1)\n plt.contourf(x_padded,t_g,periodic_pad(rho_g),30,cmap='jet')\n plt.yticks([0,2]),plt.xticks([0,2*np.pi],['0','$2\\pi$'])\n plt.title(r'$\\rho(x,t)$'+'\\n gap tooth')\n plt.colorbar()\n plt.xlabel(r'$x$'),plt.ylabel(r'$t$')\n\n er = rho_g-rho_truth\n rMSE = np.mean(er**2)/np.var(rho_truth)\n\n plt.subplot(1,2,2)\n plt.contourf(x_padded,t_g,periodic_pad(er),30,cmap='jet')\n plt.yticks([0,2]),plt.xticks([0,2*np.pi],['0','$2\\pi$'])\n plt.title('error \\n rMSE={:.1e}'.format(rMSE))\n plt.colorbar()\n plt.xlabel(r'$x$'),plt.ylabel(r'$t$')\n\n plt.tight_layout()\n plt.savefig('Burgers_gaptooth.png',dpi=450)\n\n\ndef basic_traj():\n pass\n\n\n\ndef generate_data():\n \"\"\"Generates 12 trajectories of gap-tooth simulation with random initial conditions.\"\"\"\n\n ntraj=12\n alpha = .1\n N =128\n Zk = 500\n Z = Zk*1000\n nu =.05\n Nt = 1000\n dt =.002\n\n rho_histories=[]\n teeth_histograms=[]\n rho0s=[]\n\n for k in range(ntraj):\n print(10*'--')\n print('k='+str(k))\n rho0=get_random_rho()\n tsys=BGT.BurgersGapToothSystem(alpha=alpha,N=N,nu=nu)\n tsys.initialize(Z,rho0=rho0)\n rho0s.append(rho0(tsys.tooth_center_x)) \n print('# of particles='+str(tsys.particle_count))\n tsys.run_gap_tooth(dt=dt,Nt=Nt,save_inner_histograms=True)\n\n rho_history = np.stack(tsys.rho_history,axis=1).T\n rho_histories.append(rho_history)\n\n histograms_history = np.stack(tsys.inner_histograms_history)\n teeth_histograms.append(histograms_history)\n\n np.savez('BurgersGT_Z{}k_N{}_n{}_'.format(Zk,N,ntraj)+'.npz',\n Density=rho_histories,t=tsys.t,\n Z=Z,alpha=alpha,nu=nu,x=tsys.tooth_center_x,\n rho0s=np.stack(rho0s),teeth_histograms=teeth_histograms) \n\n\ndef get_random_rho():\n \"\"\"Generates a positive random profile.\n\n Returns:\n a callable that is positive everywhere on (0,2pi).\n \"\"\"\n\n N= 20\n A = np.random.rand(N)-.5\n phi,l=np.random.rand(N)*2*np.pi,np.random.randint(1,high=7,size=N)\n\n def rho(x):\n y = 0\n for k in range(N):\n y = y + A[k]*np.sin(l[k]*x + phi[k])\n return y\n \n # make it positive\n x = np.linspace(0,2*np.pi,num=128)\n r = rho(x)\n rmin = np.min(r)\n if rmin<0.05:\n rho2= lambda x: rho(x) + np.abs(rmin) + .1\n else:\n rho2 = rho\n\n return rho2\n\nif __name__ == \"__main__\":\n ttin = timeit.default_timer()\n gaptooth_vs_truth()\n generate_data()\n\n print('whole run took {} seconds'.format(timeit.default_timer() - ttin))\n\n\n" }, { "alpha_fraction": 0.6126201152801514, "alphanum_fraction": 0.6255745887756348, "avg_line_length": 30.467105865478516, "blob_id": "9dda92d9c135d74767da310c19057a69f1005cb8", "content_id": "00d08a50d135038324bce5f12b5c4f2f60cb6fb0", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4786, "license_type": "permissive", "max_line_length": 109, "num_lines": 152, "path": "/thehood/diffusion_maps.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\"A simple implementation of diffusion maps and geometric harmonics.\n\nFrom Coifman, Ronald R., and Stéphane Lafon. 'Diffusion maps' \nApplied and computational harmonic analysis 21.1 (2006): 5-30.\nand \"Geometric harmonics: a novel tool \nfor multiscale out-of-sample extension of empirical functions.\" Applied and \nComputational Harmonic Analysis 21.1 (2006): 31-52.\n\n\nThis code is not optimized for speed, but it can handel arbitrary distance function. \nFor a more optimized implementation see\nhttps://github.com/jmbr/diffusion-maps .\n\n\n\nH. Arbabi, [email protected].\n\"\"\"\n\nimport numpy as np\nfrom scipy.spatial.distance import pdist, squareform, cdist\nimport scipy.sparse\n\n\nclass Manifold_Model:\n \"\"\"A simple object for computing diffusion maps and geometric harmonics.\"\"\"\n\n def __init__(self, X, distance_function, alpha = 1 ):\n \"\"\"Constructor.\n\n Args:\n X: data points (observaions x features)\n distance function: a callable that takes two data points and spits out their distance\n alpha: operator parameter (1:Laplace-Beltrami, 1/2:Fokker-Planck, 0: Normalized Graph Laplacian) \n \"\"\"\n self.X =X\n self.alpha=alpha\n self.distance_function= distance_function\n self.distance_matrix = squareform(pdist(self.X,metric=self.distance_function)) \n\n def compute_diffusion_maps(self, epsilon=None, num_pairs = 10):\n \"\"\"Computing the first 10 diffusion maps.\n\n Args:\n epsilon: the kernel width, if None the median of distance matrices is used\n num_pairs: number of eigen-vector -values for output\n \n Returns:\n matrix of diffusion map coordiantes and their eigenvalues\n \"\"\"\n\n if epsilon is None:\n epsilon=np.median(self.distance_matrix)\n \n self.epsilon=epsilon\n\n self.W=np.exp(-(self.distance_matrix**2)/self.epsilon)\n\n d=np.sum(self.W, axis=1)**self.alpha\n d_inv = 1.0/d\n W_bar=d_inv[:,np.newaxis] * (self.W * d_inv[np.newaxis,:])\n \n row_sums = W_bar.sum(axis=1)\n W_hat = W_bar / row_sums[:, np.newaxis]\n \n eigenvalues, v_unsrtd = np.linalg.eig(W_hat)\n id_eigvalue=np.argsort(eigenvalues)[::-1]\n\n self.phis = np.real(v_unsrtd[:,id_eigvalue][:,:num_pairs])\n self.eigenvalues = np.real(eigenvalues[id_eigvalue][:num_pairs])\n\n return self.phis,self.eigenvalues\n\n def compute_geometric_harmonics(self,dim=None,epsilon_interp=None):\n \"\"\"Computing the basis for geometric harmonic interpolation.\"\"\"\n \n if epsilon_interp is None:\n epsilon_interp= 50 * np.median(self.distance_matrix)\n print('eps interp set to {:.1e}'.format(epsilon_interp))\n self.epsilon_interp = epsilon_interp\n\n W=np.exp(-(self.distance_matrix**2)/self.epsilon_interp)\n\n eigenvalues, v_unsrtd = np.linalg.eigh(W)\n id_eigvalue=np.argsort(eigenvalues)[::-1]\n\n if dim is None:\n ratio = eigenvalues/eigenvalues[0]\n dim = ratio[ratio>1e-6].shape[0]\n print('cut-off dim is '+str(dim))\n\n self.psis = v_unsrtd[:,id_eigvalue][:,:dim]\n self.psi_eigvales = eigenvalues[id_eigvalue][:dim]\n\n\n def interpolate_geometric_harmonics(self,X, f):\n \"\"\"Using the Geometric Harmonics to interpolate the diffusion map coordinates.\n\n Args:\n X: new data points\n f: the function (values on self.X) to be interpolated\n\n Returns:\n the value of diffusion map coordinates for new data points.\n \"\"\"\n if not hasattr(self, 'psis'):\n print('computing the basis ...')\n self.compute_geometric_harmonics()\n \n new_distance = cdist(X,self.X,metric=self.distance_function)\n W=np.exp(-(new_distance**2)/self.epsilon_interp) \n\n a = self.psis.T @ f\n\n extended_psis = W @ (self.psis / self.psi_eigvales[np.newaxis,:]) \n\n extended_f = extended_psis @ a\n\n return extended_f\n\n\n\n\n\n\n\ndef BasicDiffusionMaps(W,alpha=1):\n \"\"\"Computing first 10 diffusion maps.\n \n Args:\n W: affinity matrix of data points\n alpha: operator parameter (1:Laplace-Beltrami, 1/2:Fokker-Planck, 0: Normalized Graph Laplacian)\n \n Returns:\n phis: the vector of diffusion maps\n lams: the corresponding eigenvalues\n \"\"\"\n \n d=np.sum(W, axis=1)**alpha\n d_inv = 1.0/d\n W_bar=d_inv[:,np.newaxis] * (W * d_inv[np.newaxis,:])\n \n\n row_sums = W_bar.sum(axis=1)\n W_hat = W_bar / row_sums[:, np.newaxis]\n \n\n eigenvalues, v_unsrtd = np.linalg.eig(W_hat)\n id_eigvalue=np.argsort(eigenvalues)[::-1]\n phis = v_unsrtd[:,id_eigvalue]\n eigenvalues = eigenvalues[id_eigvalue]\n\n return phis[:,:10],eigenvalues[:10]\n\n\n\n" }, { "alpha_fraction": 0.5860602855682373, "alphanum_fraction": 0.6194279789924622, "avg_line_length": 26.910072326660156, "blob_id": "e12601255b82fdfe582f685c528429fa6afab0fe", "content_id": "7422d0aa199bd7deedab8e22d58d80d6b3385ba4", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7762, "license_type": "permissive", "max_line_length": 105, "num_lines": 278, "path": "/learn_PDE_density.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\"\nLearning PDEs for density field of particles.\n\nFrom \"Particles to PDEs Parsimoniously\" by Arbabi & Kevrekidis 2020\n\nH. Arbabi, August 2020, [email protected].\n\"\"\"\n\nimport numpy as np\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport time\nimport scipy.io as sio\nimport importlib\nimport tensorflow as tf\nimport tensorflow.keras as keras\nfrom scipy.integrate import solve_ivp\nfrom sklearn.model_selection import train_test_split\nfrom scipy.ndimage import gaussian_filter\n\nfrom sys import path\npath.append('./thehood/')\n\nimport model_library as ML\nimport CFDroutines as CR\n\n\nplt.rc('text', usetex=True)\n\nfont = {'family' : 'serif',\n 'size' : 8}\n\nmatplotlib.rc('font', **font)\n\ndef prep_data(filename = 'BurgersGT_Z500k_N128_n12.npz',\n ti=[0,10],\n smoothing_sigma=1):\n \"\"\"Load and preprocess microscopic data.\n \n Args:\n filename: name of file with gap_tooth data\n ti: index of trajectories to pick up\n smoothing_sigma: the STD of teh gaussian filter\n \n Returns:\n x: space grid\n t: time grid\n v: density field\n dvdt: density field time derivative\n rho0s: initial conditions (not gap-tooth particle estimates)\n data_tag: string to distinguish\n \"\"\"\n\n Data=np.load(filename)\n x=Data['x'].astype('float32')\n Density=Data['Density'][ti[0]:ti[1]]\n rho0s=Data['rho0s'][ti[0]:ti[1]]\n t = Data['t']\n\n \n smoother= lambda u: gaussian_filter(u,smoothing_sigma,mode='wrap')\n v_temp = np.apply_along_axis(smoother,2,Density)\n\n dvdt,v = [],[]\n dt = t[1]-t[0]\n\n for batch in range(v_temp.shape[0]):\n vt_temp= (v_temp[batch,1:,:]-v_temp[batch,:-1,:])/dt\n dvdt.append(vt_temp)\n v.append(v_temp[batch,:-1,:])\n\n dvdt = np.concatenate(dvdt,axis=0)\n v = np.concatenate(v,axis=0)\n\n data_tag = '_sigma'+str(smoothing_sigma)+'_N'+str(Density.shape[-1])\n\n return x,t,v,dvdt,rho0s.squeeze(),data_tag\n\n\ndef learn_functional_model(x,t,v,dvdt,rho0s,data_tag):\n \"\"\"Learning the functional form of the PDE via neural net.\n \n Args:\n x: space grid\n t: time grid\n v: density field\n dvdt: density field time derivative\n data_tag: string to distinguish\n\n Returns:\n trained keras model mapping v to dvdt\n \"\"\"\n\n print(200*'=')\n print('learning a functional model of the PDE ...')\n model_tag='_arch1o'+data_tag\n\n x_train,x_test,y_train,y_test=train_test_split(v,dvdt,train_size=.85,random_state=42)\n\n n_grid = x.shape[0]\n dx = x[1]-x[0]\n\n nn_model = ML.Functional_PDE_net(n_grid,dx,3,n_conv=2,n_neurons=48)\n\n adam_opt=tf.keras.optimizers.Adam(learning_rate=.001)\n nn_model.compile(optimizer=adam_opt,loss='mse')\n\n PDEfit_history=nn_model.fit(x_train,y_train,\n batch_size=64,epochs=256,\n verbose=0,validation_split=.1)\n\n plt.figure(figsize=[3,2.5])\n plt.plot(PDEfit_history.history['loss']/np.var(y_test),label='training loss')\n plt.plot(PDEfit_history.history['val_loss']/np.var(y_test),label='validation loss')\n plt.yscale('log')\n plt.legend(),plt.tight_layout()\n plt.savefig('fit'+model_tag,dpi=350)\n\n eval_loss=nn_model.evaluate(x=x_test,y=y_test,verbose=0)\n eval_lossp=100*eval_loss/np.var(y_test)\n print('test loss %',eval_lossp )\n\n # nn_model.save('./models/nn'+model_tag)\n return nn_model\n\ndef learn_discretized_model(x,t,v,dvdt,rho0s,data_tag):\n \"\"\"Learning the discretized form of the PDE via neural net.\n \n Args:\n x: space grid\n t: time grid\n v: density field\n dvdt: density field time derivative\n data_tag: string to distinguish\n\n Returns:\n trained keras model mapping v to dvdt\n \"\"\"\n\n print(200*'=')\n print('learning a discretized model of the PDE ...')\n model_tag='_arch2o'+data_tag\n\n x_train,x_test,y_train,y_test=train_test_split(v,dvdt,train_size=.85,random_state=42)\n\n n_grid = x.shape[0]\n\n nn_model = ML.Discretized_PDE_net(n_grid,3,n_conv=3,n_neurons=48)\n\n adam_opt=tf.keras.optimizers.Adam(learning_rate=.001)\n nn_model.compile(optimizer=adam_opt,loss='mse')\n\n PDEfit_history=nn_model.fit(x_train,y_train,\n batch_size=64,epochs=256,\n verbose=0,validation_split=.1)\n\n plt.figure(figsize=[3,2.5])\n plt.plot(PDEfit_history.history['loss']/np.var(y_test),label='training loss')\n plt.plot(PDEfit_history.history['val_loss']/np.var(y_test),label='validation loss')\n plt.yscale('log')\n plt.legend(),plt.tight_layout()\n plt.savefig('fit'+model_tag,dpi=350)\n\n eval_loss=nn_model.evaluate(x=x_test,y=y_test,verbose=0)\n eval_lossp=100*eval_loss/np.var(y_test)\n print('test loss %',eval_lossp )\n return nn_model\n\n\ndef test_models(nn1,nn2,x,t,v,dvdt,rho0s,data_tag):\n \"\"\"Testing nn models in estimating dvdt and trajectory predictions.\n \n \n Args:\n models: list of nn models\n x: space grid\n t: time grid\n v: density field\n dvdt: density field time derivative\n data_tag: string to distinguish\n\n Returns:\n saves comparison figures\n \"\"\"\n\n RHS1=lambda t,u: nn1.predict(u[np.newaxis,:]).squeeze()\n RHS2=lambda t,u: nn2.predict(u[np.newaxis,:]).squeeze()\n\n k = 200\n\n # dvdt plots\n plt.figure(figsize=[6.75/2,1.7])\n plt.subplot(1,2,1)\n plt.plot(x,dvdt[k],'k',label='gap tooth')\n plt.plot(x,RHS1(0,v[k]))\n plt.subplot(1,2,2)\n plt.plot(x,dvdt[k],'k',label='gap tooth')\n plt.plot(x,RHS2(0,v[k]))\n\n plt.savefig('ddensity_dt.png',dpi=450)\n\n # trajectory pred\n v_gt = v[::10]\n t_eval = t[:-1:10]\n u0_truth = rho0s\n\n dx = x[1]-x[0]\n\n def Standard_FV(t,y):\n dydt= - CR.WENO_FVM_convection(y,dx) + .05 * CR.diffusion_term(y,dx)\n return dydt\n\n v_truth = solve_ivp(Standard_FV,[0,t_eval[-1]],u0_truth,method='BDF',t_eval=t_eval,max_step=0.01).y.T\n\n\n v_nn1 = solve_ivp(RHS1,[0,t_eval[-1]],u0_truth,method='BDF',t_eval=t_eval,max_step=0.01).y.T\n v_nn2 = solve_ivp(RHS2,[0,t_eval[-1]],u0_truth,method='BDF',t_eval=t_eval,max_step=0.01).y.T\n\n\n plt.figure(figsize=[6.75,2])\n\n plt.subplot(1,4,1)\n plt.contourf(x,t_eval,v_truth,30,cmap='jet')\n plt.colorbar()\n plt.yticks([0,2]),plt.xticks([0,2*np.pi],['0',r'$2\\pi$'])\n plt.title(r'$\\rho(x,t)$'+'\\n truth')\n plt.xlabel(r'$x$'),plt.ylabel(r'$t$')\n\n er0 = v_truth - v_gt\n rmse0 = np.mean(er0**2)/np.var(v_truth)\n\n plt.subplot(1,4,2)\n plt.contourf(x,t_eval,er0,30,cmap='jet')\n plt.colorbar()\n plt.yticks([0,2]),plt.xticks([0,2*np.pi],['0',r'$2\\pi$'])\n plt.title('gap-tooth error \\n rMSE={:.1e}'.format(rmse0))\n plt.xlabel(r'$x$'),plt.ylabel(r'$t$')\n\n er1 = v_truth - v_nn1\n rmse1 = np.mean(er1**2)/np.var(v_truth)\n\n plt.subplot(1,4,3)\n plt.contourf(x,t_eval,er1,30,cmap='jet')\n plt.colorbar()\n plt.yticks([0,2]),plt.xticks([0,2*np.pi],['0',r'$2\\pi$'])\n plt.title('arch. 1 error \\n rMSE={:.1e}'.format(rmse1))\n plt.xlabel(r'$x$'),plt.ylabel(r'$t$')\n\n er2 = v_truth - v_nn2\n rmse2 = np.mean(er2**2)/np.var(v_truth)\n\n plt.subplot(1,4,4)\n plt.contourf(x,t_eval,er2,30,cmap='jet')\n plt.colorbar()\n plt.yticks([0,2]),plt.xticks([0,2*np.pi],['0',r'$2\\pi$'])\n plt.title('arch. 2 error \\n rMSE={:.1e}'.format(rmse2))\n plt.xlabel(r'$x$'),plt.ylabel(r'$t$')\n\n plt.tight_layout()\n plt.savefig('u_traj.png',dpi=450)\n\n\n\n\n\n\nif __name__=='__main__':\n ttin=time.time()\n\n train_data = prep_data(smoothing_sigma=1)\n nn1=learn_functional_model(*train_data)\n nn2=learn_discretized_model(*train_data)\n\n test_data=prep_data(smoothing_sigma=1,ti=[11,12])\n test_models(nn1,nn2,*test_data)\n\n print( 'run took {} seconds'.format(time.time()-ttin) )\n\n\n\n" }, { "alpha_fraction": 0.7756806015968323, "alphanum_fraction": 0.7858439087867737, "avg_line_length": 80, "blob_id": "0e4dd4bb39140676464cfaa443b242c30e17f9f5", "content_id": "9c99172280bc8c7f9b90b5d295c0ff3cfc96b433", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2755, "license_type": "permissive", "max_line_length": 881, "num_lines": 34, "path": "/README.md", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "# Particles2PDEs\n\nSource code for \"Particles to PDEs Parsimoniously\" by Arbabi & Kevrekidis 2020\n\nUsing first prinicples typically leads to microscopic evolution laws for physical, chemical and biological systems (e.g. lattice dynamics in crystals, molecular interaction in reactions or neuron response in tissue). Yet some of these systems may also admit coarse-grained evolution laws, e.g. in the form of PDEs, which can result in huge savings in computation. We propose a frameowrk for 1) identifying the coarse-grained variable from data and 2) finding the PDE that governs that variable evolution. The example we use is a model of collective particle motion that leads to the Burgers PDE at the coarse-level description.\n\nThe below figures, taken from the above paper, shows the setup for discovering the coarse variable from particle data. We collect particle distributions (<img src=\"https://render.githubusercontent.com/render/math?math=\\mu_i, i=1,2,\\ldots,m\">) from simulations. Thinking of each distribution as a data point, we hypothesize that the data cloud lies close to a low-dimensional manifold. The coordinates of that manifold, equipped with unbalanced optimal transport distance <img src=\"https://render.githubusercontent.com/render/math?math=d_W\">, and mined via Diffusion Maps (kernel matrix <img src=\"https://render.githubusercontent.com/render/math?math=W\">), are our candidates for coarse-grained variable. In the example, the discovered coordinate is one-to-one with density field (proportional to zeroth moment <img src=\"https://render.githubusercontent.com/render/math?math=M_0\">).\n\n\n\n<img src=\"../master/thehood/sketch1.png\" width=\"750\">\n<img src=\"../master/thehood/distances_and_moments.png\" width=\"750\">\n\n## main files:\n\n**gap_tooth** illustrates the gap-tooth scheme for parsimonious simulations of particle dynamics (Fig. 1 in the paper).\n\n**learn_PDE_density** builds and trains neural nets that learn the right-hand-side of PDEs for the denisty (*a priori* known coarse-grained variable).\n\n**Variable Identification** uses unnormalized/unbalanced optimal transport distance + diffusion maps to learn the coarse-grained variable from particle data.\n\n**learn_PDE_phi** builds and trains neural nets that learn the right-hand-side of PDEs for the denisty (coarse-grained variable discovered from data).\n\n## files under 'thehood':\n\n**unbalanced_transport:** implementations of Chizat *et al.* 2018 and Gangbo *et al.* 2019 (analytical) formulations of unbalanced optimal transport.\n\n**BurgersGapTooth:** the gap-tooth implementation of the particle model leading to Burgers PDE.\n\n**model_library:** collection of neural net models for learning PDEs.\n\n## dependencies\n\n[TensorFlow >=2.0](https://www.tensorflow.org/install)\n\n" }, { "alpha_fraction": 0.6007168292999268, "alphanum_fraction": 0.613312840461731, "avg_line_length": 33.199649810791016, "blob_id": "b2bc7a26fecaeca1dc25b896df8a1223e4ec763a", "content_id": "c45357f417144b39f450a884db6312d6d565bf73", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 19530, "license_type": "permissive", "max_line_length": 147, "num_lines": 571, "path": "/thehood/BurgersGapTooth.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\" \nAn implementaion of gap-tooth scheme for Burgers equation.\n\nBased on \"The Gap-Tooth Method in Particle Simulations\" by Gear, Lu & Kevrekidis 2003\nHassan Arbabi, [email protected] \nApril 4, 2020\n\"\"\"\n\n\n\n\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport timeit\nimport os\nimport scipy.fftpack as fftpack\nfrom scipy.linalg import toeplitz\nimport scipy.integrate\nimport pickle\n\n\n\nclass BurgersGapToothSystem:\n \"\"\"Class of gap tooth modeling of Burgers system.\n\n\n Attributes:\n teeth: list of arrays; i-th array is the particle positons in tooth i\n cavities: list of arrays; i-th array is the anti-particle positons in tooth i\n\n right_outflux_particle: list of arrays; i-th array is the extrusion of particles getting out of tooth i\n right_outflux_anti: list of arrays; i-th array is the extrusion of anti_particles getting out of tooth i\n left_outflux_particle: list of arrays; i-th array is the extrusion of particles getting out of tooth i\n left_outflux_anti: list of arrays; i-th array is the extrusion of anti_particles getting out of tooth i\n\n adjacency_mat: an array; the i-th row is [index of patch to the left of patch i, i, index of right patch]\n left_boundary: list of left boundary positions\n right_boundary: list of right boundary positions\n t: list of time stamps so far \n .....\n \"\"\"\n \n def __init__(self, N=40, alpha = 1, domain_bounds=[0,2*np.pi], nu=0.05, dt = None ):\n \"\"\"Creating a uniform grid of teeth on a periodic domain.\n\n Args:\n N: number of teeth\n alpha: gap length; 1 no gap between teeth, 0 infinitely thin teeth\n domain_bounds: bounds of the domain duh\n nu: viscosity\n \"\"\"\n self.N = N\n self.dt=dt\n self.alpha=alpha\n self.nu = nu\n self.t = [0]\n self.domain_bounds=domain_bounds\n D = (domain_bounds[1]-domain_bounds[0])/N\n self.tooth_center_x = np.arange(domain_bounds[0],domain_bounds[1],D)\n self.tooth_width = alpha*D\n self.left_boundaries = self.tooth_center_x - self.tooth_width/2\n self.right_boundaries = self.tooth_center_x + self.tooth_width/2\n\n # only 2 neighbors are required\n self.adjacency_mat = np.stack((np.arange(-1,N-1),np.arange(N),np.arange(1,N+1)),axis=1)\n self.adjacency_mat[N-1,2] = 0\n\n # flux resitriution coeffs\n self.a11 = 1- self.alpha**2\n self.a10 = - self.alpha *(1-self.alpha)/2\n self.a12 = self.alpha * (1+self.alpha)/2\n \n def initialize(self, Z: float, rho0=lambda x: 1-np.sin(np.pi*x)/2, inner_histogram_size = 10):\n \"\"\"Lift density to particle positions.\n\n Args:\n Z: resolution, i.e., number of particles representing one unit of mass\n rho: initial density profile callable or numpy array\n inner_histogram_size: number of bins for tooth histograms\n\n Return:\n teeth\n \"\"\"\n self.Z=Z \n self.teeth = []\n\n if callable(rho0):\n rho_vals = rho0(self.tooth_center_x)\n else:\n rho_vals = rho0\n\n for xc,itooth in zip(self.tooth_center_x,range(self.N)):\n rho_tooth = rho_vals[itooth]\n tooth_bounds=[xc-self.tooth_width/2,xc+self.tooth_width/2]\n patch,_=lift_from_density(self.Z,rho_tooth,tooth_bounds)\n self.teeth.append(patch)\n \n self.cavities = [np.array([]) for k in range(self.N)]\n\n self.particle_history = [self.teeth]\n self.particle_history_t = [0]\n\n self.compute_density()\n self.rho_history = [self.rho]\n self.rho_history_t = [0]\n \n self.inner_histogram_size=inner_histogram_size\n self.compute_inner_histograms()\n self.inner_histograms_history = [self.inner_histograms]\n self.inner_histograms_t=[0]\n\n def compute_density(self):\n \"\"\"Computing density of each tooth.\"\"\"\n self.rho = np.array([(tooth.size/self.Z)/self.tooth_width for tooth in self.teeth])\n\n def compute_inner_histograms(self):\n \"\"\"Computes the histogram of particles within each tooth.\"\"\"\n self.inner_histograms=[]\n for tooth,lb,rb in zip(self.teeth,self.left_boundaries,self.right_boundaries):\n self.inner_histograms.append(np.histogram(tooth,bins=self.inner_histogram_size,range=[lb,rb])[0])\n\n @property \n def how_many_redistributions(self):\n \"\"\"Number of redistributions so that every (anti)-particle ends up inside teeth.\"\"\"\n self.compute_density()\n u_max = np.max(self.rho)\n jump_max = max(u_max*self.dt,4*np.sqrt(2*self.nu*self.dt))\n return int(jump_max/(self.tooth_width/2)+1)\n\n @property\n def total_mass(self):\n self.compute_density()\n return np.sum(self.rho * (self.domain_bounds[1]-self.domain_bounds[0])/self.N)\n\n @property\n def particle_count(self):\n \"\"\"No. of particles in teeth.\"\"\"\n each_tooth=[p.size for p in self.teeth]\n return sum(each_tooth)\n \n @property\n def anti_count(self):\n \"\"\"No of anti-particles in teeth.\"\"\"\n each_tooth=[p.size for p in self.cavities]\n return sum(each_tooth)\n\n @property\n def particle_count_outflux(self):\n \"\"\"No of particles in outflux.\"\"\"\n right=[p.size for p in self.right_outflux_particle]\n left=[p.size for p in self.left_outflux_particle]\n return sum(right)+sum(left)\n \n\n @property\n def anti_count_ouflux(self):\n \"\"\"No of particles in teeth.\"\"\"\n right=[p.size for p in self.right_outflux_anti]\n left=[p.size for p in self.left_outflux_anti]\n return sum(right)+sum(left)\n\n\n def update_teeth(self):\n \"\"\"Updating the state of particles within teeth via Burgers dynamics.\n \n This update takes each particle from x_t to x_{t+1} but does not \n include the implementaion of influx or computation of outflux. \n \"\"\"\n \n self.teeth = [Burgers_tooth_update(tooth,self.nu,self.tooth_width,self.dt,self.Z) for tooth in self.teeth]\n \n \n def compute_outflux(self):\n \"\"\"Computes the outgoing particles and anti-particles from all teeth.\n \n From patches, done for all patches, by the patches ;).\n It looks at teeth, identifies what particles have gone out of teeth,\n and puts their extrusion in right_outflux and left_outlfux.\"\"\"\n\n # not efficient duh \n self.right_outflux_particle = [x[x>right_bdry]-right_bdry for x,right_bdry in zip(self.teeth,self.right_boundaries)]\n self.left_outflux_particle = [x[x<left_bdry]-left_bdry for x,left_bdry in zip(self.teeth,self.left_boundaries)]\n self.teeth=[patch[(patch<right_bdry)&(patch>left_bdry)] \\\n for patch,left_bdry,right_bdry in zip(self.teeth,self.left_boundaries,self.right_boundaries)]\n \n\n\n\n self.right_outflux_anti = [x[x>right_bdry]-right_bdry for x,right_bdry in zip(self.cavities,self.right_boundaries)]\n self.left_outflux_anti = [x[x<left_bdry]-left_bdry for x,left_bdry in zip(self.cavities,self.left_boundaries)]\n self.cavities=[patch[(patch<right_bdry)&(patch>left_bdry)] \\\n for patch,left_bdry,right_bdry in zip(self.cavities,self.left_boundaries,self.right_boundaries)]\n return\n\n\n\n def _interpolate_influx(self,outflux):\n \"\"\"Interpolates one outflux into three influxes.\n \n IMPORTANT: indices show order in the flow direction: 0 upstream,\n 1 current tooth, 2 downstream\n \"\"\"\n\n n = outflux.size\n n10 = int(- self.a10 * n)\n n12 = int(self.a12 * n)\n\n # in order to preserve mass we compute n11 this way\n n11 = n - n12 + n10\n # print(str(n)+'--->'+str(n10)+','+str(n11)+','+str(n12))\n\n np.random.shuffle(outflux)\n I_upstream_anti = outflux[:n10] # this one makes particles anti-particles and vice versa\n I_self = outflux[:n11]\n I_downstream = outflux[n-n12:n] # avoiding special behavior when n12=0\n\n\n return I_upstream_anti,I_self,I_downstream\n\n def _distribute_outflux(self,O1,I0,I1,I2):\n \"\"\"Breaking outflux O1 into influxes I0,I1,I2.\"\"\"\n I_upstream_anti,I_self,I_downstream=self._interpolate_influx(O1)\n I0.append(I_upstream_anti)\n I1.append(I_self)\n I2.append(I_downstream)\n\n\n def compute_influx(self):\n \"\"\"Computes the influx of particles and anti-particles into each tooth.\n \n Uses the quadratic interpolation formula to redistribute the outflux \n into influxes.\n \"\"\"\n self.left_influx_particle = [ [] for i in range(self.N) ]\n self.left_influx_anti = [ [] for i in range(self.N) ]\n self.right_influx_particle = [ [] for i in range(self.N) ]\n self.right_influx_anti = [ [] for i in range(self.N) ]\n\n\n # right-going fluxes\n upstream,downstream = self.adjacency_mat[:,0],self.adjacency_mat[:,2]\n\n for j in range(self.N):\n # particles\n self._distribute_outflux( self.right_outflux_particle[j],\\\n self.right_influx_anti[upstream[j]],self.right_influx_particle[j],self.right_influx_particle[downstream[j]])\n\n self._distribute_outflux( self.right_outflux_anti[j],\\\n self.right_influx_particle[upstream[j]],self.right_influx_anti[j],self.right_influx_anti[downstream[j]])\n\n\n\n # left-going fluxes\n upstream,downstream = self.adjacency_mat[:,2],self.adjacency_mat[:,0]\n \n for j in range(self.N):\n # particles\n self._distribute_outflux(self.left_outflux_particle[j],\\\n self.left_influx_anti[upstream[j]],self.left_influx_particle[j], self.left_influx_particle[downstream[j]])\n\n\n\n # anti-particles\n self._distribute_outflux(self.left_outflux_anti[j],\\\n self.left_influx_particle[upstream[j]],self.left_influx_anti[j], self.left_influx_anti[downstream[j]])\n\n\n\n # TODO: consider moving this to the loop\n self.left_influx_particle=[np.concatenate(x) for x in self.left_influx_particle]\n self.left_influx_anti=[np.concatenate(x) for x in self.left_influx_anti]\n self.right_influx_particle=[np.concatenate(x) for x in self.right_influx_particle]\n self.right_influx_anti=[np.concatenate(x) for x in self.right_influx_anti]\n\n \n\n def collect_influx(self):\n \"\"\"Merge influxes with each tooth and cavity.\"\"\"\n \n # loop seems more comprehensible than list comprehension\n for j in range(self.N):\n incoming_particles_l= self.left_influx_particle[j] + self.right_boundaries[j]\n incoming_particles_r= self.right_influx_particle[j] + self.left_boundaries[j]\n \n self.teeth[j] = np.concatenate((self.teeth[j],incoming_particles_l,incoming_particles_r)) \n\n incoming_anti_l= self.left_influx_anti[j] + self.right_boundaries[j]\n incoming_anti_r= self.right_influx_anti[j] + self.left_boundaries[j]\n\n self.cavities[j] = np.concatenate((self.cavities[j],incoming_anti_l,incoming_anti_r))\n\n\n def fuse_cavity_tooth(self):\n \"\"\"Annihilate anti-particles and particles within each tooth.\"\"\"\n self.teeth=[annihilate_particles(tooth,cavity) for tooth,cavity in zip(self.teeth,self.cavities) ]\n self.cavities = [np.array([]) for k in range(self.N)]\n \n \n def redistribute_particles(self, max_iter = 5):\n \"\"\"Redistributes particles and anti-particles till they land inside teeth.\n \n Repeates outflux, influx computation and merge with tooth until all particles or anti-particle are inside the teeth or max_iter is reached.\n \"\"\"\n for j in range(max_iter):\n self.compute_outflux()\n if (self.anti_count_ouflux + self.particle_count_outflux)==0:\n break\n self.compute_influx()\n self.collect_influx()\n # self.visualize_teeth()\n\n if j==max_iter-1: \n print('max iteration reached for redistribution process')\n\n\n def run_gap_tooth(self, dt: float, Nt = 1, \n show_runtime=True, \n save_history=True, \n save_particle_history=False,\n save_inner_histograms=False):\n \"\"\"Continue simulating the system.\n \n Args:\n dt: time step\n Nt: number of time steps\n show_runtime: show how much time for whole run has elapsed\n save_history: saves the history of the rho evolution to self.rho_history\n save_particle_history: saves the history of particle position in teeth -- MEMORY EXPENSIVE\n tooth_histograms: the number of bins used for histograms within each tooth,\n if None, those histograms are not computed or saved.\n \"\"\"\n\n self.dt = dt\n ttin = timeit.default_timer()\n\n n_redist = self.how_many_redistributions * 4\n\n for _ in range(Nt):\n self.t.append(self.t[-1]+self.dt)\n self.update_teeth()\n self.redistribute_particles(max_iter=n_redist)\n self.fuse_cavity_tooth()\n \n if save_history:\n self.compute_density()\n self.rho_history.append(self.rho)\n self.rho_history_t.append(self.t[-1])\n\n if save_inner_histograms:\n self.compute_inner_histograms()\n self.inner_histograms_history.append(self.inner_histograms)\n self.inner_histograms_t.append(self.t[-1])\n\n if save_particle_history:\n self.particle_history.append(self.teeth)\n self.particle_history_t.append(self.t[-1])\n \n \n\n\n if show_runtime:\n print('run took {} seconds'.format(timeit.default_timer() - ttin))\n\n\n\ndef lift_from_density(Z: int, rho: float, bin_edges):\n \"\"\"Lifting density funcation to particle positions for a single tooth.\n\n This is Hassan's understanding of the paper; not optimized but understandable.\n\n Args:\n Z: resolution, i.e., number of particles representing one unit of mass\n rho: value of density within bin\n bin_edges: duh\n n_bin: how many bins put into the domain and used for lifting\n \n Returns:\n x_particles: array of particle positions\n Z_new: the adjusted value of Z\n \"\"\"\n\n bin_edges = np.array(bin_edges)\n bin_width=bin_edges[1]-bin_edges[0]\n x_bin = (bin_edges[:-1]+bin_edges[1:])/2 # center of the bin\n\n mass_bin = rho * bin_width\n n_particles_bin = np.floor(mass_bin * Z )\n\n\n x_particles= []\n\n r = np.random.rand(int(n_particles_bin)) -.5 # n_particles_bin random numbers in [-.5,.5]\n x_particles = x_bin+ bin_width * r\n\n\n # we have slightly changed Z so we report back the new Z\n Z_new = x_particles.size / np.sum(mass_bin)\n\n\n return x_particles,Z_new\n\n\ndef annihilate_particles(tooth,cavity):\n \"\"\"Cancel particles in tooth with anti-particles in cavity.\n \n Finds the closest particle in tooth to each anti-particle in cavity\n and deletes that particle.\n \n Args:\n tooth: array of particle positions\n cavity: array of anti-particle positions\n \n Returns:\n array of remaining particles\n \"\"\"\n\n # not efficient\n for j in range(cavity.size):\n dist = np.abs(cavity[j]-tooth)\n ind_close = np.argmin(dist)\n tooth=np.delete(tooth,ind_close)\n\n return tooth\n\ndef Burgers_tooth_update(x_particles: np.ndarray, nu: float, tooth_width: float, dt: float, Z: float):\n \"\"\"Updates particles positions according to viscous Burgers.\n \n Args:\n x_particles: array of particle positions\n nu: viscosity\n tooth_width: width of the teeth\n dt: time step\n Z: resolution factor, i.e., number of particles per unit mass\n \"\"\"\n\n n_partciles = x_particles.size\n mass_tooth = n_partciles/Z \n rho_tooth = mass_tooth/tooth_width\n u_drift = rho_tooth/2\n \n jump = u_drift*dt + np.random.randn(n_partciles)*np.sqrt(2*nu*dt)\n x_particles=x_particles + jump\n return x_particles\n\n\n\n\ndef compute_truth_Burgers(rho_0, nu: float = 0.05, t: float=0, \n ngrid = None):\n \"\"\"Computing the truth density of Burgers via Cole-Hopf transform.\n\n Be careful: sometimes lead to artificially irregular approximations.\n \n Args:\n rho_0: the density profile at t=0, either callable or array\n nu: viscosity\n t: time\n ngrid: size of the grid on [0,2pi], overwritten if rho_0 is array\n \n Returns:\n the grid and density profile\n \"\"\"\n \n if not callable(rho_0):\n density = rho_0\n ngrid=rho_0.shape[0]\n x = np.arange(0,1-.5/ngrid,1/ngrid) * 2* np.pi \n else:\n x = np.arange(0,1-.5/ngrid,1/ngrid) * 2* np.pi \n density = rho_0(x)\n\n \n\n cumdensity = np.cumsum(density) * 2 * np.pi / ngrid\n total_mass = cumdensity[-1]\n r = np.exp( total_mass/4/np.pi/nu * x - cumdensity / 2 / nu )\n a_k = fftpack.fft(r)\n\n N = a_k.size\n k = np.concatenate((np.arange(0,N/2),np.arange(-N/2,0)))\n f = np.exp(nu * (1j*k-total_mass/4/np.pi/nu)**2 * t) * a_k\n r = fftpack.ifft(f)\n rx = fftpack.ifft(1j*k*f)\n rho = total_mass / 2 / np.pi - 2 * nu * np.real(rx / r)\n\n return x,rho\n\n\n\n\n\n\n\n\ndef quick_run(rho0: callable, N = 32, Z = 1000, nu = .05, alpha = .1, dt = .002, Nt = 1000):\n \"\"\"Runing the Burgers example.\n \n This is to be used for quick data generation without the need to save the solver.\n\n Args:\n rho: the initial density profile on [0,2pi)\n N: no. of teeth\n Z: resolution factor, i.e., no. of particles representing each unit of mass\n nu: viscosity\n alpha: fraction of space covered by teeth\n dt: time step\n Nt: number of time steps\n\n Returns:\n the grid of tooth (x of center), the density evaluated at the end of simulation\n\n \"\"\"\n\n\n tsys=BurgersGapToothSystem(alpha=alpha,N=N,nu=nu)\n tsys.initialize(Z,rho0=rho0)\n print('no. of particles:'+str(tsys.particle_count))\n\n tsys.run_gap_tooth(dt=dt,Nt=Nt)\n tsys.compute_density()\n return tsys.tooth_center_x,tsys.rho\n\ndef parameter_dependence():\n \"\"\"Plots dependence of solution based on no. of teeth and resolution factor.\n\n This is fig. 2 in the paper.\n \"\"\"\n\n rho0=lambda x: 1-np.sin(x)/2\n\n # vary the grid size \n Z0 = 100000\n Ns = [16,32,64]\n\n grid_N,sol_N=[],[]\n\n\n # for N in Ns:\n # grid,sol=quick_run(rho0,N=N,Z=Z0)\n # grid_N.append(grid),sol_N.append(sol)\n\n\n # vary the resolution factors \n Zs = [1000,10000,100000]\n N0 = 128\n\n grid_Z,sol_Z=[],[]\n\n\n for Z in Zs:\n grid,sol=quick_run(rho0,N=N0,Z=Z)\n grid_Z.append(grid),sol_Z.append(sol)\n\n # # # da truth\n x,rho=compute_truth_Burgers(rho0,t=2)\n x[x>np.pi]=x[x>np.pi]-2*np.pi\n ix = np.argsort(x)\n x_truth,rho_truth = x[ix],rho[ix]\n\n np.savez('../data/Burgers_benchmark13',\\\n grid_N=grid_N, sol_N=sol_N, \n Ns=Ns,grid_truth=x_truth,sol_truth=rho_truth,\\\n grid_Z=grid_Z, sol_Z=sol_Z, Zs=Zs, N0=N0,Z0=Z0)\n\n\n\nif __name__ == \"__main__\":\n ttin = timeit.default_timer()\n parameter_dependence()\n print('whole run took {} seconds'.format(timeit.default_timer() - ttin))\n\n\n" }, { "alpha_fraction": 0.5906279683113098, "alphanum_fraction": 0.6049001216888428, "avg_line_length": 32.624000549316406, "blob_id": "5da167ed7a7c6a2c32903388d3cc1229982ea720", "content_id": "1b057db1dc2a7e44996b9441da77ab471829c6e5", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4204, "license_type": "permissive", "max_line_length": 103, "num_lines": 125, "path": "/thehood/unbalanced_transport.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\" Unbalanced optimal transport.\n\nTwo methods for calculation of optimal transport distance \nbetween two distributions with uneqaul masses.\n\nHassan Arbabi, July 2020, [email protected]\n\"\"\"\n\n#TODO: implement the UW1 explicit difference from Osher paper\n\n\nimport numpy as np\nimport scipy.linalg as linalg\n\n\n\n\ndef sinkhorn_log(mu,nu,c,epsilon, \n options={'niter':1000, 'tau':-0.5, 'rho':np.inf}):\n \"\"\"Computing stabilized sinkhorn over log domain with acceleration.\n\n Adapted from \"SCALING ALGORITHMS FOR UNBALANCED OPTIMAL TRANSPORT PROBLEMS\" by Chizat et al, 2017\n and the MATLAB code at 'https://github.com/gpeyre/2017-MCOM-unbalanced-ot' by G. Peyre.\n\n Args:\n mu: a histogram vector (marginal)\n nu: a histogram vector (marginal)\n c: cost matrix\n epsilon: the entropic regularization strength\n options: \n niter: max number of the iteration \n tau: is avering step and negative values usually lead to acceleration\n rho: the strngth of mass variation constraint. np.inf results in classical transport\n but finite values allow mass variation\n \n Returns:\n gamma: the coupling distribution\n Wprimal: the primal transport distance of iterations\n Wdual: its dual\n err: the difference with previous iterate or marginal violation\n Wdistance: the Wasserstein distance at the end (with the entropy term removed)\n \"\"\"\n\n for key,val in zip(['tau','rho','niter'],[-.5,np.inf,500]):\n options.setdefault(key, val)\n rho,tau,niter = options['rho'],options['tau'],options['niter']\n\n lam = rho/(rho+epsilon)\n if rho==np.inf:\n lam=1.0\n\n H1 = np.ones_like(mu)\n H2 = np.ones_like(nu)\n\n ave = lambda tau, u, u1: tau*u+(1-tau)*u1\n\n lse = lambda A: np.log(np.sum(np.exp(A),axis=1))\n M = lambda u,v:(-c+u[:,np.newaxis]@H2[np.newaxis,:] + H1[:,np.newaxis]@v[np.newaxis,:] )/epsilon\n\n # kullback divergence\n H = lambda p: -np.sum( p.flatten()*(np.log(p.flatten()+1e-20)-1) )\n KL = lambda h,p: np.sum( h.flatten()* np.log( h.flatten()/p.flatten() ) - h.flatten()+p.flatten())\n KLd = lambda u,p: np.sum( p.flatten()*( np.exp(-u.flatten()) -1) )\n dotp = lambda x,y: np.sum(x*y); \n\n err,Wprimal,Wdual = [],[],[]\n u = np.zeros_like(mu)\n v = np.zeros_like(nu)\n\n for _ in range(niter):\n u1=u\n u = ave(tau, u, lam*epsilon*np.log(mu) - lam*epsilon*lse( M(u,v) ) + lam*u )\n v = ave(tau, v, lam*epsilon*np.log(nu) - lam*epsilon*lse( M(u,v).T) + lam*v )\n gamma = np.exp(M(u,v))\n\n if rho==np.inf: \n Wprimal.append(dotp(c,gamma) - epsilon*H(gamma))\n Wdual.append( dotp(u,mu) + dotp(v,nu) - epsilon*np.sum(gamma) )\n err.append( np.linalg.norm( np.sum(gamma,axis=1)-mu ) )\n else:\n Wprimal.append( dotp(c,gamma) - epsilon*H(gamma) \\\n + rho*KL(np.sum(gamma,axis=1),mu) \\\n + rho*KL(np.sum(gamma,axis=0),nu) )\n\n Wdual.append( -rho*KLd(u/rho,mu) - rho*KLd(v/rho,nu) \\\n - epsilon*np.sum( gamma))\n err.append(np.linalg.norm(u-u1, ord=1) )\n \n WDistance = Wprimal[-1]+epsilon*H(gamma)\n\n return gamma,Wprimal,Wdual,err,WDistance\n\n\ndef unbalanced_Wasserstein_L1(mu,nu,x = None,alpha = 1):\n \"\"\"\n Compute the unnormalized L^1 Wasserstein metric for two distributions on the same domain.\n\n Adapted from \"Unnormalized optimal transport\" by Gangbo et al, 2019. See refs within.\n\n Args:\n mu,nu: vector of histogram values on the same (uniformly-sized) bins\n x : the grid for histogram bin centers\n alpha: the inverse of the weight of mass difference in the objective function\n larger alpha means more mass generation/destruction is allowed\n \n Returns:\n the unnormalized L^1 Wasserstein metric between mu and nu\n\n \"\"\"\n\n N = mu.size\n \n if x is None:\n x = np.linspace(0,1,N)\n\n dx = x[1]-x[0]\n\n mass_diff = np.sum((mu-nu) * dx) \n\n Integrand = np.abs( np.cumsum(mu-nu) - x * mass_diff )\n\n\n UW1 = np.sum(Integrand * dx) + (1/alpha)* np.abs( mass_diff )\n\n return UW1\n\n" }, { "alpha_fraction": 0.5042237639427185, "alphanum_fraction": 0.5526255965232849, "avg_line_length": 20.786069869995117, "blob_id": "cbc2464303434b71f848db1fce75e084f00a43ca", "content_id": "781f63b34d1540026bff84c1f3fc66b456a9cc8d", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8760, "license_type": "permissive", "max_line_length": 77, "num_lines": 402, "path": "/thehood/CFDroutines.py", "repo_name": "arbabiha/Particles2PDEs", "src_encoding": "UTF-8", "text": "\"\"\"Some routines for solving 1D PDES\"\"\"\n\n\nimport numpy as np \nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport timeit\nimport scipy.io as sio\nfrom scipy.integrate import solve_ivp\n\n\n\n\n\ndef test_flux_WENO():\n x = np.linspace(0,2*np.pi,num=200,endpoint=False)\n h = x[1]-x[0]\n b = 1.5\n u = np.sin(b*x)\n\n\n dfdx_truth=b* np.sin(b*x)*np.cos(b*x)\n dfdx_WENO=WENO_FVM_convection(u,h)\n\n plt.figure(figsize=[6.5,4])\n plt.plot(x,dfdx_truth,'x')\n plt.plot(x,dfdx_WENO,'--')\n # plt.ylim(-1,1.1)\n # plt.xlim(-.01,.05)\n\n plt.savefig('./figs/WENO_flux.png')\n\n\ndef WENO_FVM_convection(V,h,flux='Gudonov'):\n \"\"\" Finite-volume approximation of momentum flux using WENO.\n\n\n Args:\n V: solution values on 1d periodic grid\n h: (uniform) grid size\n\n Returns:\n array of same size as V holding values of d(v**2)/dx\n \"\"\"\n\n # periodify\n V = np.concatenate((V[-3:],V,V[0:3]),axis=0)\n\n # compute v+ and v- at the right boundaries of each cell \n v1 = V[0:-5]\n v2 = V[1:-4]\n v3 = V[2:-3]\n v4 = V[3:-2]\n v5 = V[4:-1]\n v6 = V[5:]\n\n v_minus = WENO_reconstruct(v1,v2,v3,v4,v5)\n v_plus = WENO_reconstruct(v6,v5,v4,v3,v2)\n\n\n if flux is 'Gudonov':\n F = Gudonov_v2(v_minus,v_plus)\n else:\n raise NotImplementedError('flux method not known or implemented yet')\n\n\n # flux balance\n\n dFdx = (1/h)*(F[1:]-F[:-1])\n\n\n\n return dFdx\n\n\ndef Gudonov_v2(v_minus,v_plus):\n \"\"\"Computing Gudonov method for flux f(v)=v^2/2.\"\"\"\n\n\n F=np.zeros(v_minus.shape)\n\n # when v- is smaller than v+\n leftD = np.where(v_minus<=v_plus)\n\n F[leftD]=np.minimum(v_minus[leftD]**2,v_plus[leftD]**2)\n\n x_cross=np.where(v_minus*v_plus<0)\n F[x_cross]=0\n\n\n\n\n # when v- is larger than v+\n rightD= np.where(v_minus>v_plus) # optimize this later\n\n F[rightD]=np.maximum(v_minus[rightD]**2,v_plus[rightD]**2)\n\n return F/2\n\ndef WENO_reconstruct(v1,v2,v3,v4,v5):\n \"\"\"Reconstructing value of field v at boundary nodes.\n\n\n Args:\n v1: array holding values at node i-2\n v2: array holding values at node i-1\n v3: array holding values at node i\n v4: array holding values at node i+1\n v5: array holding values at node i+2\n\n Returns:\n array of values at (boundary) node i+1/2\n \"\"\"\n\n\n\n # combining three stenccils\n phi1 = v1/3 - 7*v2/6 + 11*v3/6\n phi2 =-v2/6 + 5*v3/6 + v4/3\n phi3 = v3/3 + 5*v4/6 - v5/6\n\n # measures of smoothness for each stencil (larger the S --> less smooth)\n S1 = (13/12)*(v1-2*v2+v3)**2+(1/4)*(v1-4*v2+3*v3)**2\n S2 = (13/12)*(v2-2*v3+v4)**2+(1/4)*(v2-v4)**2\n S3 = (13/12)*(v3-2*v4+v5)**2+(1/4)*(3*v3-4*v4+v5)**2\n\n # deciding the weights at each point\n V = np.stack((v1,v2,v3,v4,v5),axis=1)\n EPS = np.amax(V,axis=1)**2 * 1e-6 + 1e-99\n\n # non-normalized weights\n a1 = 0.1/ (S1+EPS)**2\n a2 = 0.6/ (S2+EPS)**2\n a3 = 0.3/ (S3+EPS)**2\n\n # combine the stencils\n v = (a1*phi1 + a2*phi2 + a3*phi3)/(a1+a2+a3)\n\n \n return v\n\n\n\n\n\ndef linear_adv_WENO(C,h,U):\n \"\"\"Computing the advection term via WENO.\n \n Args:\n C: the field to be advected\n h: grid spacing\n U: velocity field\n \n Returns:\n np.array containing U* dC/dx\n \n (use this for advection by a given velocity field)\n see Osher & Fedkiw, Level Set Methods, 2003\n \n \"\"\"\n\n N = C.shape[0]\n adv_term=np.zeros(N)\n\n \n if not isinstance(U, (np.ndarray)):\n U = U * np.ones(N)\n \n\n # index of points with left and right updwind direction\n LeftD =np.where(U>=0)\n RightD=np.where(U<0)\n\n # periodify\n C = np.concatenate((C[-3:],C,C[0:3]),axis=0)\n\n\n\n # divided difference at all points\n D = (C[1:]-C[:-1])/h\n\n v1 = D[0:-5]\n v2 = D[1:-4]\n v3 = D[2:-3]\n v4 = D[3:-2]\n v5 = D[4:-1]\n v6 = D[5:]\n\n\n\n # now reconstruct the actual derivative using WENO\n vx_left = WENO_reconstruct(v1[LeftD],v2[LeftD],v3[LeftD],\\\n v4[LeftD],v5[LeftD])\n vx_right = WENO_reconstruct(v6[RightD],v5[RightD],v4[RightD],\\\n v3[RightD],v2[RightD])\n\n adv_term[LeftD] =vx_left * U[LeftD]\n adv_term[RightD]=vx_right* U[RightD]\n\n\n return adv_term\n\ndef diffusion_term(V,h,nu=1):\n # central finite-difference to approximate the diffusion term\n # periodify\n V = np.concatenate((V[-1:],V,V[0:1]),axis=0)\n D2V = (-2*V[1:-1] + V[0:-2] + V[2:]) / (h**2)\n return D2V*nu\n\ndef test_diffusion():\n x = np.linspace(0,2*np.pi,num=200,endpoint=False)\n h = x[1]-x[0]\n\n\n c0 = np.sin(x) + np.cos(x)\n\n\n\n diff_cd= diffusion_term(c0,h,nu=1)\n diff_tru = -np.sin(x) - np.cos(x)\n\n\n plt.figure(figsize=[6.5,4])\n plt.plot(x,diff_cd,'x')\n plt.plot(x,diff_tru,'--')\n # plt.ylim(-1,1.1)\n # plt.xlim(-.01,.05)\n\n plt.savefig('./figs/diff_test.png')\n\n\ndef test_WENOderivative():\n x = np.linspace(0,2*np.pi,num=200,endpoint=False)\n h = x[1]-x[0]\n c0 = np.sin(x) + 2.1*np.cos(2*x)\n\n U0 = 1\n # U = U0 *np.ones(x.shape) # constant velocity\n\n adv_WENO = linear_adv_WENO(c0,h,U0)\n adv_tru = (np.cos(x)-4.2*np.sin(2*x)) * U0\n\n plt.figure(figsize=[6.5,4])\n plt.plot(x,adv_WENO,'x')\n plt.plot(x,adv_tru,'--')\n # plt.ylim(-1,1.1)\n # plt.xlim(-.01,.05)\n\n plt.savefig('./figs/WENO_der_test.png')\n\n\n\ndef solve_ivp_TVD(F,tspan,y0,tval,max_step=.01):\n \"\"\"Solving initial value problem with a total variation method.\n \n Args:\n F: callable that takes (t,y) and returns ydot\n tsapn: time span of integration\n u0: initial condition\n tval: values of t at which the solution is recorded\n\n \n Returns:\n Y: the array nt*n where n is the dimension of y0\n \"\"\"\n nt = len(tval)\n Y = np.zeros((nt,y0.shape[0]))\n\n assert tspan[0] <= tval[0], 'tspan should contain tvals'\n assert tspan[-1] >= tval[-1], 'tspan should contain tvals'\n\n tstart = tspan[0]\n\n for j in range(nt):\n # re-compute the dt\n interval = tval[j]-tstart\n nstep = int(interval//max_step)\n\n if nstep !=0:\n dt = interval/nstep\n \n for k in range(nstep):\n y0 = TVD_RK3(F,tstart+k*dt,y0,dt)\n\n Y[j]=y0\n tstart=tval[j]\n\n return Y\n \n\n\n\ndef TVD_RK3(F,t,u,dt):\n \"\"\"Total variation Runge-Kutta for time marching hyperbolic PDEs.\n \n Args:\n F: callable that takes (t,u) and returns udot\n t: time\n dt: time step size\n u: solution at time t0\n\n Returns:\n u_next: solution at time t0+dt\n \n \"\"\"\n # Total Variation Diminishing (TVD) Runge-Kutta of order 3\n # for solving udot=F(t,u)\n # see Shu 2009, \n # or Osher-Fedkiw 2003 for more detail\n\n u1 = u + dt*F(t,u)\n u2 = (3/4)*u + (1/4)*u1 + (1/4)*dt*F(t+dt,u1)\n u_next = (1/3)*u + (2/3) *u2 + (2/3)*dt*F(t+dt/2,u2)\n\n return u_next\n\n\ndef linear_advection_test():\n # testing WENO in linear advection\n\n x = np.linspace(0,2*np.pi,num=100,endpoint=False)\n h = x[1]-x[0]\n\n t = np.linspace(0,10,301)\n\n def WaveS(x):\n s=np.zeros(x.shape)\n s[x>2]=1\n s[x>3]=.5\n return s\n\n c0 = WaveS(x)\n U0 = .1\n U = U0 *np.ones(x.shape) # constant velocity\n\n\n # ode RHS\n rhs = lambda t,y: -1.0*linear_adv_WENO(y,h,V0=U)\n\n\n Sol = solve_ivp(rhs,(np.amin(t),np.amax(t)),c0,method='RK45',\\\n t_eval=t,max_step=.1 )\n\n\n V = np.transpose(Sol.y)\n dt = t[1]-t[0]\n\n plt.figure(figsize=[6.5,4])\n plt.subplot(2,2,1)\n j = 0\n Vtruth = WaveS(x-U0*j*dt)\n plt.plot(x,V[j,:])\n plt.plot(x,Vtruth,'--')\n plt.xlabel('$X$'),plt.ylabel('$V$')\n plt.title('initial condition')\n plt.ylim(-1,1)\n\n\n plt.subplot(2,2,2)\n j = 100\n Vtruth = WaveS(x-U0*j*dt)\n plt.plot(x,V[j,:])\n plt.plot(x,Vtruth,'--')\n plt.xlabel('$X$'),plt.ylabel('$V$')\n plt.title('t='+str(t[j]))\n plt.ylim(-1,1)\n\n plt.subplot(2,2,3)\n j = 200\n Vtruth = WaveS(x-U0*j*dt)\n plt.plot(x,V[j,:])\n plt.plot(x,Vtruth,'--')\n plt.xlabel('$X$'),plt.ylabel('$V$')\n plt.title('t='+str(t[j]))\n plt.ylim(-1,1)\n\n\n plt.subplot(2,2,4)\n j = 300\n Vtruth = WaveS(x-U0*j*dt)\n plt.plot(x,V[j,:])\n plt.plot(x,Vtruth,'--')\n plt.xlabel('$X$'),plt.ylabel('$V$')\n plt.title('t='+str(t[j]))\n plt.ylim(-1,1)\n\n plt.subplots_adjust(wspace=.4,hspace=.6)\n plt.savefig('./figs/WENO_lin_avd.png')\n\nif __name__=='__main__':\n print('Jesus take the wheel')\n\n # V = np.arange(0,10)\n # V = np.concatenate((V[-3:],V,V[:3]),axis=0)\n # print(V)\n # linear_advection_test()\n # test_WENOderivative()\n # test_diffusion()\n # test_flux_WENO()\n # test_Burgers_WENO()\n test_WENOderivative()\n\n\n" } ]

8

mingtak/funlog.portlet.profile

https://github.com/mingtak/funlog.portlet.profile

daa7b5996ba1f05f163a6aa35138050ff6586ef5

c9d5a268c7bfa6e6622386e70bb859c096d2a632

e29861464f3e8079e4f2ccb93e14205e43f1902e

refs/heads/master

2021-01-21T22:29:09.425776

2015-03-23T14:37:08

32,737,509

0

null

[ { "alpha_fraction": 0.7727272510528564, "alphanum_fraction": 0.7878788113594055, "avg_line_length": 32, "blob_id": "bd9bf6ee470b1b416f585f11faa9d3dfb50d4e0e", "content_id": "7daa3993dfb84eb7e5456ed127de242783f5b63b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 198, "license_type": "no_license", "max_line_length": 64, "num_lines": 6, "path": "/funlog/portlet/profile/__init__.py", "repo_name": "mingtak/funlog.portlet.profile", "src_encoding": "UTF-8", "text": "from zope.i18nmessageid import MessageFactory\nProfileMessageFactory = MessageFactory('funlog.portlet.profile')\n\n\ndef initialize(context):\n \"\"\"Initializer called when used as a Zope 2 product.\"\"\"\n" } ]

1

mr-billyu/git_notes

https://github.com/mr-billyu/git_notes

6751f6b8f9f07d6287b8cf2745c595b01179cf2c

8974fd7a6bc874b74b175116f327cfd742936d5f

8bc932e2e78ede4d38f09d1bf58c977b41005ae4

refs/heads/master

2020-04-18T15:56:28.782148

2019-05-03T19:13:07

167,622,649

0

null

[ { "alpha_fraction": 0.6641981601715088, "alphanum_fraction": 0.6682839393615723, "avg_line_length": 18.191177368164062, "blob_id": "d8b1b5eeb25d0d0e4101677ffe2573e949b2efb0", "content_id": "ea9b59d9c256ddd92148d68dfcf8326fc9e14469", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 3916, "license_type": "no_license", "max_line_length": 187, "num_lines": 204, "path": "/single_user_git.md", "repo_name": "mr-billyu/git_notes", "src_encoding": "UTF-8", "text": "#Git Notes\n\n##Local\n\n###Create repository\n\nTo create a git repository, switch to the parent directory and run `git init project-name`. The following example creates a git_notes repository under the parent directory ~/Applications.\n\n cd ~/Applications\n git init git_notes\n\nTo create a git repository for an existing directory, switch to the directory and run `git init`. This will create the `.git` file.\n\n cd ~/Applications/Project_x\n git init\n\n###Display status of repository\n\n git status\n\n###Display files tracked by git\n\n git ls-files\n\n###Ignore files\n\nInstruct git to ignore a file, enter the file name into `.gitignore`. The .gitignore file should be committed.\n\n###Stage files to be commited\n\n git add file\n\n###Unstage file\n\nChange file back to untracted status.\n\n git reset HEAD file\n\n###Get fresh copy of file\n\nChange file back to last commit.\n\n git checkout file\n\n###Add file to git\n\n git add file\n\n###Commit files\n\nIt is wise to run `git status` before and after a commit.\n\n git commit \n git commit file\n\n###Display commit messages for current branch\n\n git log\n git log --oneline\n\n###Display commit messages for file\n\n git log file\n git log --oneline file\n\n###Display branches\n\n git branch\n\n###Create branch\n\n git branch develop\n\n###Change branch\n\n git commit -a # Save work in current branch\n git checkout branch-name\n\n###Delete branch\n\n git branch -d branch-name\n\n###Merge develop branch to master\n\n git commit -a # Save work in develop branch\n git checkout master \n git merge develop\n\n###Update branch with changes from master\n\n git checkout branch\n git commit -a # Save work in branch\n git merge master \n\n###Show file differences\n\n**Note:** `d` is an alias for `difftool`.\n\nDisplay differences between working directory and index.\n\n git d filename\n\nDisplay differences between index and last commit.\n\n git d --cached filename\n\nDisplay differences between working directory and last commit.\n\n git d HEAD filename\n\nDisplay differences between two branches.\n\n git d master develop filename\n\n###Display specified version of file\n\n git show SHA:file\n\n###Undo merge\n\n git reset --merge ORIG_HEAD\n\n###Rename file or directory\n\nChange the file name and prepare it for commit.\n\n git mv 'original file/dir' 'renamed file/dir'\n\n###Remove file\n\nRemove the file from the working directory and stages the deletion.\n\n git rm file\n\n###Git tags\n\nList all tags.\n\n git tag\n\nAdd tag to current commit.\n\n git tag -a v1.0 -m \"v1.0 of file|project|etc\"\n\nAdd tag to SHA commit.\n\n git tag -a v1.0 -m \"v1.0 of file|project|etc\" SHA\n\nDisplays detail about tag v1.0.\n \n git show v1.0\n\nDelete tag v1.0.\n\n git tag -d v1.0\n\n###List git configuration\n\n git config -l\n\n###Configure difftool\n\n git config --global diff.tool gvimdiff\n git config --global difftool.prompt false\n git config --global alias.d difftool\n\n##Remote\n\nRemote can be another server, cloud server, GitHub, etc\n\n###View existing remotes\n\n git remote -v\n\n###Rename remote\n\nRename remote origin to server.\n\n git remote rename origin server\n\n###GitHub\n\nSign on GitHub and create repository for git_notes.\n\n####Associate local repository with GitHub repository.\n\n git remote add server https://github.com/mr-billyu/git_notes.git\n\n####Push local branches to GitHub repository.\n\n git push server master\n git push server develop\n\n####Clone a repository\n\nChange to the directory that will be the parent of the clone. ie. ~/Applications\n\n git clone url\n\nThe clone directory will have been created under the parent directory. ie. ~/Applications/clone_dir\n\n##Git Reference\n\n<https://git-scm.com/docs/>\n\n" }, { "alpha_fraction": 0.7422680258750916, "alphanum_fraction": 0.75, "avg_line_length": 42, "blob_id": "b4d6558b6c4096246edc2013903530756a522957", "content_id": "41f0c950758459d2e0e63142f5755cc1a407a7ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 388, "license_type": "no_license", "max_line_length": 199, "num_lines": 9, "path": "/build_single_user_git.py", "repo_name": "mr-billyu/git_notes", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python3\n\nimport subprocess\n\n# Create HTML with Code Highlighting and Sidebar\ncmd = 'pandoc -o /home/pi/Applications/git_notes/single_user_git.html single_user_git.md -s --self-contained --toc --toc-depth=4 -c sidebar.css --template=/home/pi/Applications/Markdown/toc.template'\ncmd = cmd.split()\nresults = subprocess.check_output(cmd)\nprint(str(results, encoding='UTF-8'))\n\n" } ]

2

ahollyer/python-exercises

https://github.com/ahollyer/python-exercises

913888de761cd0282707e5a1a2cc9cd11cbac105

6b97276174dd3f3d9cb5a03608740421d51fbebd

c021a68c1d0a977d0ad8deff0e0d0ab7aaaea105

refs/heads/master

2021-01-19T21:18:51.458363

2017-06-03T19:00:11

88,637,227

0

null

[ { "alpha_fraction": 0.6459096670150757, "alphanum_fraction": 0.6581196784973145, "avg_line_length": 39.95000076293945, "blob_id": "995a2cb5ed4d4840460d32d5113c93f7787e9faa", "content_id": "a39f66736f77cfb95f2dc1cdc2cde4f049c691a6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 819, "license_type": "no_license", "max_line_length": 141, "num_lines": 20, "path": "/degree_conversion.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# degree_conversion.py\n# by Aspen\n# Asks for degrees and units, converts between Fahrenheit and Celsius, and prints the converted temperature\n\nprint(\"***\\nINSTRUCTIONS: This program converts between Celsius and Fahrenheit. First, enter your units, then enter the temperature.\\n***\\n\")\n\ndef convert():\n input_units = input(\"Starting units? Please type C or F, then press Enter: \").upper()\n input_temp = float(input(\"Please type the temperature (ex. 32), then press Enter: \"))\n\n if input_units == \"C\":\n converted_units = \"F\"\n converted_temp = input_temp * (9/5) + 32\n elif input_units == \"F\":\n converted_units = \"C\"\n converted_temp = (input_temp - 32) * (5/9)\n print(\"Your converted temperature is:\", converted_temp, converted_units)\n\nif __name__ == \"__main__\":\n convert()\n" }, { "alpha_fraction": 0.6384040117263794, "alphanum_fraction": 0.6408977508544922, "avg_line_length": 27.64285659790039, "blob_id": "a3aabf4b6d0b7dd93f538d69731557f0a9d6b07e", "content_id": "502c701036d0b987c9aebc6d4e2922b3f5c346a2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 401, "license_type": "no_license", "max_line_length": 87, "num_lines": 14, "path": "/banner.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# banner.py\n# By Aspen\n# Prints a string with a box around it. The box stretches to fit the size of the string\n\nprint(\"\\n***\\nINSTRUCTIONS: Enter text to create a banner with that message\\n***\\n\")\n\nmessage = input(\"What do you want your banner to say? \")\n\ndef print_banner(str):\n width = len(str) + 4\n print(\"*\" * width)\n print(\"* \" + str + \" *\")\n print(\"*\" * width)\nprint_banner(message)\n" }, { "alpha_fraction": 0.5126582384109497, "alphanum_fraction": 0.577531635761261, "avg_line_length": 22.407407760620117, "blob_id": "a546c5fbb21e43049f8883c65dc1fb21ea80fb04", "content_id": "86f12440423b2e20a864b3633de2213c77f4c9a5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 632, "license_type": "no_license", "max_line_length": 95, "num_lines": 27, "path": "/turtle_graphics/night_sky.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# night_sky.py\n# By Aspen\n# Draws a random night sky full of stars\n\nfrom turtle import *\nimport random\nimport shapes\n\ndef draw_sky():\n bgcolor('#001220')\n pretty_colors = ['#efe9f4', '#fff', '#e05cb2', '#5fbff9', '#1cefff', '#bd5ce0', '#9368ff' ]\n\n for i in range(60):\n speed(10)\n penup()\n x = random.randint(-250, 250)\n setx(x)\n y = random.randint(-250, 250)\n sety(y)\n pendown()\n star_color = pretty_colors[random.randint(0, len(pretty_colors)-1)]\n star_size = random.randint(5, 20)\n shapes.draw_star(star_size, star_color)\n\n mainloop()\n\ndraw_sky()\n" }, { "alpha_fraction": 0.5832321047782898, "alphanum_fraction": 0.589307427406311, "avg_line_length": 23.205883026123047, "blob_id": "c012eb0c593d988068e221f661da3518602531e3", "content_id": "7dcfbf186cb10c1ab23255e4f1db3f0a429418f3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 823, "license_type": "no_license", "max_line_length": 68, "num_lines": 34, "path": "/file-io-exercises/histograms.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# histograms.py\n# By Aspen\n# histogram module for ex_3.py\n\nimport string\n\ndef letter_histogram (word):\n histogram = {}\n for char in word:\n if char in histogram:\n histogram[char] += 1\n else:\n histogram[char] = 1\n return histogram\n\n\ndef word_histogram(text):\n stripped_text = text.translate(string.punctuation)\n print(stripped_text)\n histogram = {}\n for word in stripped_text.lower().split():\n if word in histogram:\n histogram[word] += 1\n else:\n histogram[word] = 1\n # To print histogram\n # for key, value in histogram.items():\n # print(key + \":\", value)\n return histogram\n\nif __name__ == '__main__':\n ans = input(\"Feed me a string, and I'll count the characters: \")\n ans = letter_histogram(ans)\n print(ans)\n" }, { "alpha_fraction": 0.510869562625885, "alphanum_fraction": 0.510869562625885, "avg_line_length": 25.285715103149414, "blob_id": "581152d16c5d2f6bc429e8734263525c6ffe2b3d", "content_id": "e18ddea7cc2d5193f900b334532bd3915db7c28f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 368, "license_type": "no_license", "max_line_length": 70, "num_lines": 14, "path": "/function_exercises/ex8_ex9.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# Asks the user to play again\n\ndef play_again():\n while True:\n answer = input(\"Do you want to play again? Y or N? \").upper()\n if answer == \"Y\":\n return True\n elif answer == \"N\":\n return False\n else:\n print(\"\\nThat is not a valid answer. Please type y or n.\")\n\nif __name__ == \"__main__\":\n play_again()\n" }, { "alpha_fraction": 0.6332518458366394, "alphanum_fraction": 0.6332518458366394, "avg_line_length": 30.461538314819336, "blob_id": "fc295b34567190c4ef4473178ff98dd50078fee8", "content_id": "cd9e07193d5e2395e5eb244ab0acb1896b769d12", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 818, "license_type": "no_license", "max_line_length": 230, "num_lines": 26, "path": "/madlib2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# madlib.py\n# by Aspen Hollyer\n# Asks for input as strings, generates and prints a madlib string\n\n# Get words from user\ndef get_input():\n words = {}\n words['name'] = input(\"Give me a name: \")\n words['subject'] = input(\"Give me a school subject: \")\n words['pet'] = input(\"Give me an animal: \")\n words['power'] = input(\"Give me a superpower: \")\n words['food'] = input(\"Give me a food: \")\n words['num'] = input(\"Give me a number: \")\n return words\n\n# Print madlib with user's words\ndef madlib(dict_of_words):\n print(\"%(name)s's %(pet)s ate some magical %(food)s that granted him the power of %(power)s. It also made him a genius! He went on to major in %(subject)s and win a nobel prize at the age of %(num)s. The end.\" % dict_of_words)\n\n\ndef main():\n\n words = get_input()\n madlib(words)\n\nmain()\n" }, { "alpha_fraction": 0.6690763235092163, "alphanum_fraction": 0.6730923652648926, "avg_line_length": 37.90625, "blob_id": "fc6f669f9cc87804e0af54d1108c5ae3a6200b6f", "content_id": "bcaf5b2bf3b7c0e174ceeceeb0a0e995f43be67c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1249, "license_type": "no_license", "max_line_length": 72, "num_lines": 32, "path": "/dictionary_exercises/word_histogram.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# word_histogram.py\n# By Aspen\n\n# INSTRUCTIONS: Write a word histogram function that accepts\n# a paragraph of text and returns a dictionary containing the\n# tally of how many times each word was used in the text.\n\nimport string\n\ndef word_histogram(text):\n stripped_text = text.translate(string.punctuation)\n histogram = {}\n for word in stripped_text.lower().split():\n if word in histogram:\n histogram[word] += 1\n else:\n histogram[word] = 1\n # To print histogram\n #for key, value in histogram.items():\n # print(key + \":\", value)\n return histogram\n\nif __name__ == '__main__':\n word_histogram(\"Congress returns Tuesday from its spring recess,\\\n facing yet another down-to-the-wire spate of deal-making — and a\\\n White House anxious to claim its first major legislative win. On\\\n Friday night, the funding measure lawmakers approved last year\\\n to keep the federal government running will expire. The timing\\\n leaves members of the House and Senate just four days to reach\\\n a new agreement to fund the government, or risk a partial\\\n shutdown of federal agencies on Saturday — the 100th day of\\\n Donald Trump's presidency.\")\n" }, { "alpha_fraction": 0.5090909004211426, "alphanum_fraction": 0.6340909004211426, "avg_line_length": 26.5, "blob_id": "8def96385244765c863951d521c75927b580b4d0", "content_id": "67b3db556e054449b7c8c3f59372fbc98338c711", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2200, "license_type": "no_license", "max_line_length": 70, "num_lines": 80, "path": "/turtle_graphics/dc_logo.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# dc_logo.py\n# By Aspen\n# Draws the DigitalCrafts logo\n\nfrom turtle import *\nimport random\nimport shapes\n\n\ndef draw_logo():\n hideturtle()\n penup()\n speed(9)\n setpos(-250, 100)\n pendown()\n shapes.draw_trapezoid(100, pcolor='#7EC04B', fcolor='#7EC04B')\n shapes.draw_rhombus(50, pcolor='#5D8842', fcolor='#5D8842')\n shapes.draw_triangle_down(50, pcolor='#7EC04B', fcolor='#7EC04B')\n right(60)\n forward(50)\n right(60)\n shapes.draw_triangle_up(50, pcolor='#2A94CE', fcolor='#2A94CE')\n forward(50)\n shapes.draw_rhombus(50, pcolor='#1771B5', fcolor='#1771B5')\n right(60)\n shapes.draw_trapezoid(100, pcolor='#2A94CE', fcolor='#2A94CE')\n back(150)\n right(60)\n shapes.draw_triangle_down(50, pcolor='#1B67A1', fcolor='#1B67A1')\n forward(50)\n right(60)\n shapes.draw_triangle_down(50, pcolor='#467866', fcolor='#467866')\n forward(50)\n right(60)\n shapes.draw_triangle_down(50, pcolor='#1F84B1', fcolor='#1F84B1')\n forward(50)\n right(60)\n shapes.draw_triangle_down(40, pcolor='#278DC9', fcolor='#278DC9')\n penup()\n forward(60)\n right(120)\n pendown()\n shapes.draw_triangle_up(40, pcolor='#A6AAAB', fcolor='#A6AAAB')\n right(120)\n forward(10)\n right(120)\n shapes.draw_triangle_down(50, pcolor='#646B6F', fcolor='#646B6F')\n shapes.draw_triangle_up(50, pcolor='#C08258', fcolor='#C08258')\n right(300)\n shapes.draw_triangle_up(50, pcolor='#939597', fcolor='#939597')\n forward(50)\n right(120)\n shapes.draw_triangle_up(50, pcolor='#FBB231', fcolor='#FBB231')\n forward(50)\n right(120)\n back(50)\n shapes.draw_rhombus_mirror(50, pcolor='#F27830', fcolor='#F27830')\n right(120)\n forward(50)\n right(180)\n penup()\n back(100)\n right(60)\n forward(50)\n left(60)\n pendown()\n shapes.draw_trapezoid(100, pcolor='#F79E34', fcolor='#F79E34')\n right(60)\n forward(50)\n right(60)\n shapes.draw_triangle_up(50, pcolor='#BBBDBF', fcolor='#BBBDBF')\n forward(50)\n left(120)\n shapes.draw_rhombus_mirror(50, pcolor='#A6AAAB', fcolor='#A6AAAB')\n right(180)\n shapes.draw_trapezoid(100, pcolor='#BBBDBF', fcolor='#BBBDBF')\n\n mainloop()\n\ndraw_logo()\n" }, { "alpha_fraction": 0.5604395866394043, "alphanum_fraction": 0.5686812996864319, "avg_line_length": 30.65217399597168, "blob_id": "a2baa40117cb2024b492f477a27629855afeb71d", "content_id": "635a3f6d5f0eaaedafff1614b3868e446d367318", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 728, "license_type": "no_license", "max_line_length": 108, "num_lines": 23, "path": "/day_of_week.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# day_of_week.py\n# by Aspen\n# Accepts an integer 0-6 and prints the corresponding weekday as a string\n\ndef main():\n\n def day_of_week():\n days = [\"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\"]\n\n while True:\n try:\n num = int(input(\"Enter a number from 0-6: \"))\n print(\"You chose {num}. The corresponding weekday is {day}.\".format(num=num, day=days[num]))\n break\n except ValueError:\n print(\"Your input must be a number. Try again.\")\n except IndexError:\n print(\"The number you chose has no corresponding weekday. Please choose a number from 0-6.\")\n\n\n day_of_week()\n\nmain()\n" }, { "alpha_fraction": 0.6279342770576477, "alphanum_fraction": 0.6936619877815247, "avg_line_length": 25.625, "blob_id": "670383e47ebea4a161a2abc20243c51082eb1dde", "content_id": "68a7e43fb0eebdb631380c23d1145faa5e7ef463", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 852, "license_type": "no_license", "max_line_length": 68, "num_lines": 32, "path": "/dictionary_exercises/ex_1.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# ex_1.py\n# By Aspen\n# Modifies a simple dictionary\n\nphonebook_dict = {\n 'Alice': '703-493-1834',\n 'Bob': '857-384-1234',\n 'Elizabeth': '484-584-2923'\n}\n\nprint(\"\\nDictionary Exercises: Phonebook\")\nprint(\"phonebook_dict = \", phonebook_dict)\n\nprint(\"\\n1. Print Elizabeth's phone number:\")\nprint(\"Elizabeth's Number bracket:\", phonebook_dict['Elizabeth'])\nprint(\"Elizabeth's Number .get():\", phonebook_dict.get('Elizabeth'))\n\nprint(\"\\n2. Add an entry to the dictionary.\")\nphonebook_dict['Kareem'] = '968-345-2345'\nprint(phonebook_dict)\n\nprint(\"\\n3. Delete Alice's phone entry.\")\ndel phonebook_dict['Alice']\nprint(phonebook_dict)\n\nprint(\"\\n4. Change Bob's phone number.\")\nphonebook_dict['Bob'] = '968-345-2345'\nprint(phonebook_dict)\n\nprint(\"\\n5. Print all the phonebook entries.\")\nfor key, value in phonebook_dict.items():\n print(key + \": \" + value)\n" }, { "alpha_fraction": 0.6373239159584045, "alphanum_fraction": 0.6408450603485107, "avg_line_length": 26.047618865966797, "blob_id": "949b991c7dd7d48365b022df3af6f549e4ae2d85", "content_id": "85b2a8ca9bca768940ebfb494d88e8e1d55c394c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 568, "license_type": "no_license", "max_line_length": 68, "num_lines": 21, "path": "/dictionary_exercises/letter_histogram.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# letter_histogram.py\n# By Aspen\n\n# INSTRUCTIONS: Write a histogram function that takes a word as\n# input and returns a dictionary containing the tally of how\n# many times each letter of the alphabet was used in the word.\n\ndef letter_histogram (word):\n histogram = {}\n for char in word:\n if char in histogram:\n histogram[char] += 1\n else:\n histogram[char] = 1\n return histogram\n\n\nif __name__ == '__main__':\n ans = input(\"Feed me a string, and I'll count the characters: \")\n ans = letter_histogram(ans)\n print(ans)\n" }, { "alpha_fraction": 0.5442739129066467, "alphanum_fraction": 0.5602124929428101, "avg_line_length": 26.770492553710938, "blob_id": "851f4593e603538a658b5ab18db373510c1ec7ff", "content_id": "20641926ccd15ec3131944a9f335cc626ff689e1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1694, "license_type": "no_license", "max_line_length": 137, "num_lines": 61, "path": "/guess_the_number.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# guess_the_number.py\n# By Aspen\n# Generates a random number 0-100, asks for guesses, and prints whether the guess was too high or low. Accepts 5 guesses before win/lose.\n\nimport random\n\n# assign a secret number\ndef get_secret_num():\n secret_num = random.randint(1, 100)\n return secret_num\n\n# prompt the user to guess the secret number\ndef get_guess():\n while True:\n try:\n guess_num = int(input(\"What's the number? \"))\n if 0 < guess_num < 101:\n return guess_num\n else:\n print(\"Your number must be between 1 and 100. Try again.\")\n except ValueError:\n print(\"Your guess must be an integer (ex: 5). Try again.\")\n\n# compare user's guess to secret number\ndef compare(s, g):\n tries = 4\n win = False\n while not win and tries > 0:\n if g == s:\n print(\"You win!!\")\n win = True\n elif g > s:\n print(\"Nope, too high. Guesses left:\", tries)\n tries -= 1\n g = get_guess()\n elif g < s:\n print(\"Nope, too low. Guesses left:\", tries)\n tries -=1\n g = get_guess()\n print(\"You ran out of guesses! You lose.\")\n\n# ask user to play again\ndef play_again():\n play = input(\"Do you want to play again? Yes or no: \").lower()\n if play == 'yes':\n main()\n elif play == 'no':\n print(\"Okay, bye!\")\n else:\n print(\"That answer doesn't make sense.\")\n play_again()\n\ndef main():\n print(\"I'm thinking of a number between 1 and 100. You get 5 tries to guess the number!\\n\")\n\n snum = get_secret_num()\n gnum = get_guess()\n compare(snum, gnum)\n play_again()\n\nmain()\n" }, { "alpha_fraction": 0.6430155038833618, "alphanum_fraction": 0.6452327966690063, "avg_line_length": 27.1875, "blob_id": "961683e270c7f79a0d57e538c49b7540b775b0dd", "content_id": "94564aa55998050ad20176d2bd68673e027ffe44", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 451, "license_type": "no_license", "max_line_length": 75, "num_lines": 16, "path": "/file-io-exercises/ex_1.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# ex_1.py\n# By Aspen\n# INSTRUCTIONS: Write a program that prompts the user to enter a file name,\n# reads the contents of the file, and prints it to the screen.\n\ndef read_my_file():\n print(\"\\nThis program prints the contents of your file to the screen.\")\n my_file = input('Enter the name of the file to print: ')\n\n with open(my_file, 'r') as fh:\n contents = fh.read()\n\n print(contents)\n\nif __name__ == '__main__':\n read_my_file()\n" }, { "alpha_fraction": 0.6487935781478882, "alphanum_fraction": 0.6487935781478882, "avg_line_length": 38.26315689086914, "blob_id": "38155ccbed3ab38265b50d7740b1f07655d3d95a", "content_id": "cefa862d48bceeff97b0dab5ea07fc02dd5860e5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 746, "license_type": "no_license", "max_line_length": 283, "num_lines": 19, "path": "/madlib.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# madlib.py\n# by Aspen Hollyer\n# Asks for input as strings, generates and prints a madlib string\n\ndef main():\n\n name = input(\"Give me a name: \")\n subject = input(\"Give me a school subject: \")\n pet = input(\"Give me an animal: \")\n power = input(\"Give me a superpower: \")\n food = input(\"Give me a food: \")\n num = input(\"Give me a number: \")\n\n def madlib(name, subject, pet, power, food, num):\n print(\"{name}'s {pet} ate some magical {food} that granted him the power of {power}. It also made him a genius! He went on to major in {subject} and win a nobel prize at the age of {num}. The end.\".format(name=name, pet=pet, food=food, power=power, subject=subject, num=num))\n\n madlib(name, subject, pet, power, food, num)\n\nmain()\n" }, { "alpha_fraction": 0.6494845151901245, "alphanum_fraction": 0.6529209613800049, "avg_line_length": 28.100000381469727, "blob_id": "90566b9c34bb803c9b79e2d8aaace30ef18e40d0", "content_id": "a591d20f1d8b5ba6b2341bc455807ea2e70f1931", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 291, "license_type": "no_license", "max_line_length": 106, "num_lines": 10, "path": "/hello2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# hello2.py\n# by Aspen Hollyer\n# Asks for a name as input and prints the name in all caps as well as the number of characters in the name\n\ndef main():\n name = input(\"What is your name? \").upper()\n print(\"HELLO,\", name)\n print(\"YOUR NAME HAS\", len(name), \"LETTERS IN IT! AWESOME!\")\n\nmain()\n" }, { "alpha_fraction": 0.5050504803657532, "alphanum_fraction": 0.5959596037864685, "avg_line_length": 14.230769157409668, "blob_id": "86cfd10c870369539d2ebcf02cff754a73392e39", "content_id": "9847e775729b7eaddb1deb3107c0c560eb56720c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 198, "license_type": "no_license", "max_line_length": 20, "num_lines": 13, "path": "/turtle_graphics/draw_square.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "from turtle import *\n\ndef draw_square():\n forward(100)\n right(90)\n forward(100)\n right(90)\n forward(100)\n right(90)\n forward(100)\n draw_square()\n mainloop()\ndraw_square()\n" }, { "alpha_fraction": 0.6208718419075012, "alphanum_fraction": 0.6552179455757141, "avg_line_length": 17.463415145874023, "blob_id": "2a38121cb7b1294c9539c091079cb537b46de7c7", "content_id": "49c4b716d7974c9a9450b9b8b690847714c022e0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 757, "license_type": "no_license", "max_line_length": 77, "num_lines": 41, "path": "/breakwhile.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# breakwhile.py\n# Aspen Hollyer\n\n# MITx 6.001x Practice problems for understanding while loops\n\n# Practice using break statement\nnum = 10\nwhile True:\n if num < 7:\n print('Breaking out of loop')\n break\n print(num)\n num -= 1\nprint('Outside of loop')\n\n\n# Convert the following into code that uses a while loop.\n# print 2\n# prints 4\n# prints 6\n# prints 8\n# prints 10\n# prints Goodbye!\n\nnum = 0\nwhile num < 10:\n num += 2\n print(num)\nprint('Goodbye!')\n\n# Write a while loop that sums the values 1 through end, inclusive. end is a\n# variable that we define for you. So, for example, if we define end to be 6,\n# your code should print out the result: 21.\n\nend = 6\nnum = 1\ntotal = 0\nwhile num <= end:\n total += num\n num += 1\nprint(total)\n" }, { "alpha_fraction": 0.5483871102333069, "alphanum_fraction": 0.5648592710494995, "avg_line_length": 29.35416603088379, "blob_id": "c56f6fb69c053cb0029d603eab452261f8a4f8bc", "content_id": "3134a3a6946b8caed5a4a3d6297e81ce347e3259", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1457, "license_type": "no_license", "max_line_length": 159, "num_lines": 48, "path": "/guess_game_2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# guess_game_2.py\n# By Aspen\n# A program that guesses a number from 1-100 chosen by you.\n\ndef guess():\n playing = True\n min = 1\n max = 100\n guess_count = 0\n\n while playing:\n\n # Update guess with new min/max values\n current_guess = int((min + max)/2)\n # Accumulate guesses\n guess_count += 1\n # Guess a number\n print(\"Is it {current_guess}?\".format(current_guess=current_guess))\n # Ask user if guess is correct\n answer1 = input(\"Type Y or N: \").lower()\n\n if answer1 == 'y':\n # Print win message\n print(\"I win! Your number is {current_guess}. It only took me {guess_count} guesses.\".format(current_guess=current_guess, guess_count=guess_count))\n # Stop playing\n playing = False\n\n elif answer1 == 'n':\n # Ask user if guess was too high or too low\n print(\"Was my guess too high or too low?\")\n answer2 = input(\"Type H or L: \").lower()\n # Update range of possible guesses to reflect user input\n if answer2 == 'h':\n max = current_guess - 1\n elif answer2 == 'l':\n min = current_guess + 1\n\n\ndef play():\n input(\"Ready? Don't tell me your number. Press ENTER to begin. \")\n guess()\n\ndef main():\n print(\"\\n***\\nINSTRUCTIONS: Hello. I am GuessBot. Choose a number from 1-100. I will try to guess your number.\\n***\\n\")\n\n play()\n\nmain()\n" }, { "alpha_fraction": 0.5709219574928284, "alphanum_fraction": 0.6063829660415649, "avg_line_length": 30.33333396911621, "blob_id": "c1399228e2eef73f6d23110014a8c60ddbece202", "content_id": "066d831136dd3ac651dadd18ba79d47327fa9d70", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 282, "license_type": "no_license", "max_line_length": 65, "num_lines": 9, "path": "/multiplication_table.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# multiplication_table.py\n# By Aspen\n# Uses a loop to print the multiplication table for numbers 1-10.\n\nprint(\"\\n9. Multiplication Table:\")\nfor i in range(1, 11):\n print(\"Multiples of\", str(i))\n for j in range(1, 11):\n print(str(i) + \" * \" + str(j) + \" = \" + str(i*j))\n" }, { "alpha_fraction": 0.7306064963340759, "alphanum_fraction": 0.737658679485321, "avg_line_length": 39.514286041259766, "blob_id": "71b964b789d33d52be041c4e32a076d5238d933c", "content_id": "4713477fa773444ebd230a924317171107e232a8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1418, "license_type": "no_license", "max_line_length": 180, "num_lines": 35, "path": "/README.md", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# Python Programming Exercises\n\n---------\n### What is this?\n\nThis repository contains various programs written in Python 3. These are learning exercises.\n\n-------\n### Running the Programs\n\nEach program has a specification at the beginning of the script. To run an interactive program, download the file and run it on the command line. Some interactive programs include:\n\n* madlib.py - Fill in words to generate a silly madlib.\n* guess_the_number.py - Try to determine a random number 1-100 within 5 guesses.\n* tip_calc2.py - Supply the bill amount, number of guests, and tip percentage to receive the cost per guest.\n* degree_converter.py - Supply a temperature in Celsius or Fahrenheit and have it converted.\n* caesar_cipher.py - Provide a string and shift key to encrypt or decrypt messages.\n* banner.py - Supply a message and the program will print a banner with your message.\n* guess_game_2.py - Think of a random number, and the program will try to guess your number.\n\n---------\n### To Do's\n\n- [ ] Add exception handling to degree_conversion.py\n- [x] Format tip_calc.py to display 2 decimal places\n- [x] Complete Bonus Problem in list_exercises.py\n- [x] Complete Ceasar Cipher in string_exercises.py\n- [ ] Create alternate Caesar Cipher using ord()/chr()\n- [ ] Add exception handling to guess_game_2.py\n\n----------\nIf you have questions or want to learn together, feel free to e-mail me at [email protected]\n\nCheers!\nAspen\n" }, { "alpha_fraction": 0.5774134993553162, "alphanum_fraction": 0.5828779339790344, "avg_line_length": 20.115385055541992, "blob_id": "5f83c76d8e31f2aadc0daae86a7dff8db55cc137", "content_id": "458adc53ed4d99fa4036398ed7c94854975b4336", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 549, "license_type": "no_license", "max_line_length": 74, "num_lines": 26, "path": "/file-io-exercises/ex_4.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# ex_4.py\n# By Aspen\n# INSTRUCTIONS: Write a program that takes a JSON file name as input and\n# plots the X,Y data.\n\nimport json\nimport matplotlib.pyplot as p\n\ndef plot_data(source):\n with open(source, 'r') as fh:\n obj = json.load(fh)\n\n xs = []\n ys = []\n\n for i in obj['data']:\n xs.append(i[0])\n ys.append(i[1])\n\n p.plot(xs, ys)\n p.show()\n\nif __name__ == '__main__':\n print(\"\\nThis program accepts a .JSON file and plots the X,Y data.\\n\")\n my_file = input(\"Enter the file name: \")\n plot_data(my_file)\n" }, { "alpha_fraction": 0.5775368809700012, "alphanum_fraction": 0.6064438223838806, "avg_line_length": 19.37423324584961, "blob_id": "3f442177cef9c682b8afb6b00b2d19c34eb22a24", "content_id": "3cc00d16f2a6ccf69c679757a8a391ce5d525ae7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3321, "license_type": "no_license", "max_line_length": 76, "num_lines": 163, "path": "/turtle_graphics/shapes.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# shapes.by\n# By Aspen\n# A module for drawing shapes with the built-in turtle module for Python3\n\nfrom turtle import *\n\n# 1. An equilateral triangle\ndef draw_triangle_up(size, pcolor='black', fcolor='white', wait=False):\n pencolor(pcolor)\n fillcolor(fcolor)\n begin_fill()\n for i in range(3):\n forward(size)\n left(120)\n end_fill()\n if wait:\n mainloop()\n#draw_triangle_up()\n\ndef draw_triangle_down(size, pcolor='black', fcolor='white', wait=False):\n pencolor(pcolor)\n fillcolor(fcolor)\n begin_fill()\n for i in range(3):\n forward(size)\n right(120)\n end_fill()\n if wait:\n mainloop()\n#draw_triangle_down()\n\n# 2. A square\ndef draw_square(size, color, wait=False):\n pencolor(color)\n fillcolor(color)\n for i in range(4):\n forward(size)\n left(90)\n if wait:\n mainloop()\n#draw_square()\n\n# 3. A pentagon\ndef draw_pentagon(size, color, wait=False):\n pencolor(color)\n fillcolor(color)\n for i in range(5):\n forward(size)\n left(72)\n if wait:\n mainloop()\n#draw_pentagon(100, 'black', wait=True)\n\n# 4. A hexagon\ndef draw_hexagon(size, color, wait=False):\n pencolor(color)\n fillcolor(color)\n for i in range(6):\n forward(size)\n left(60)\n if wait:\n mainloop()\n#draw_hexagon()\n\n# 5. An octagon\ndef draw_octagon(size, wait=False):\n pencolor(color)\n fillcolor(color)\n for i in range(8):\n forward(size)\n left(45)\n if wait:\n mainloop()\n#draw_octagon()\n\n# 6. A star\ndef draw_star(size, color, wait=False):\n pencolor(color)\n for i in range(5):\n forward(size)\n right(144)\n if wait:\n mainloop()\n#draw_star(200, 'black')\n\n# 7. A circle\ndef draw_circle(size, color, wait=False):\n pencolor(color)\n fillcolor(color)\n circle(size)\n mainloop()\n#draw_circle()\n\n# BONUS STUFF\ndef draw_trapezoid(size, pcolor='black', fcolor='white', wait=False):\n pencolor(pcolor)\n fillcolor(fcolor)\n begin_fill()\n forward(size)\n left(120)\n forward(size/2)\n left(60)\n forward(size/2)\n left(60)\n forward(size/2)\n left(120)\n forward(size)\n end_fill()\n if wait:\n mainloop()\n#draw_trapezoid(100, pcolor='blue', fcolor='lightblue')\n\ndef draw_trapezoid_mirror(size, pcolor='black', fcolor='white', wait=False):\n pencolor(pcolor)\n fillcolor(fcolor)\n begin_fill()\n forward(size/2)\n right(120)\n forward(size/2)\n right(60)\n forward(size/2)\n right(60)\n forward(size)\n right(120)\n forward(size)\n end_fill()\n if wait:\n mainloop()\n#draw_trapezoid(100, pcolor='blue', fcolor='lightblue')\n\ndef draw_rhombus(size, pcolor='black', fcolor='white', wait=False):\n pencolor(pcolor)\n fillcolor(fcolor)\n begin_fill()\n forward(size)\n left(120)\n forward(size)\n left(60)\n forward(size)\n left(120)\n forward(size)\n left(60)\n end_fill()\n if wait:\n mainloop()\n#draw_rhombus(100)\n\ndef draw_rhombus_mirror(size, pcolor='black', fcolor='white', wait=False):\n pencolor(pcolor)\n fillcolor(fcolor)\n begin_fill()\n forward(size)\n right(120)\n forward(size)\n right(60)\n forward(size)\n right(120)\n forward(size)\n right(60)\n end_fill()\n if wait:\n mainloop()\n#draw_rhombus(100)\n" }, { "alpha_fraction": 0.5810564756393433, "alphanum_fraction": 0.6156648397445679, "avg_line_length": 21.875, "blob_id": "b4bf007b16025238caee5987a51b202bee7c3505", "content_id": "64b40dbb5ebf14fcba3c64ee711b49b7658c9eec", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 549, "license_type": "no_license", "max_line_length": 60, "num_lines": 24, "path": "/triangle_numbers.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# triangle_numbers.py\n# By Aspen\n# A program that prints the first 100 triangle numbers.\n\n# What is a triangle number? See:\n# https://www.mathsisfun.com/algebra/triangular-numbers.html\n\n# Recursive solution\ndef print_triangle_nums(n):\n if n == 0:\n print(\"Done!\")\n else:\n num = n * (n+1) / 2\n print(num)\n print_triangle_nums(n-1)\nprint_triangle_nums(100)\n\n# For loop\ndef print_triangle_nums2(p):\n for i in range(1, p+1):\n num = i * (i+1) / 2\n print(num)\n print(\"Done!\")\nprint_triangle_nums2(100)\n" }, { "alpha_fraction": 0.6319702863693237, "alphanum_fraction": 0.6617100238800049, "avg_line_length": 15.8125, "blob_id": "12a5fd0cd3c8aff95405110d0d3d55de55386972", "content_id": "c87afc5705fb87d88005e5bb454e43611c4bd534", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 269, "license_type": "no_license", "max_line_length": 40, "num_lines": 16, "path": "/function_exercises/ex7.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# Plots Celsius to Fahrenheit conversion\n\nimport matplotlib.pyplot as plot\n\ndef t(x):\n return x * (9/5) + 32\n\nc = list(range(0, 100))\nf = []\nfor x in c:\n f.append(t(x))\n\nplot.plot(c, f)\nplot.xlabel('Degrees Celsius')\nplot.ylabel('Degrees Fahrenheit')\nplot.show()\n" }, { "alpha_fraction": 0.5088408589363098, "alphanum_fraction": 0.5206286907196045, "avg_line_length": 16.55172348022461, "blob_id": "1a08c55ed778372859f86259497a1c830c875aac", "content_id": "443f40105a05e36e4d0a355cac67dcb7da1fc787", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1018, "license_type": "no_license", "max_line_length": 58, "num_lines": 58, "path": "/phonebook.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "#!/usr/bin/env\n\n# phonebook.py\n# By Aspen\n# A command line program to manage a phone book.\n\ndef lookup_entry():\n print('lookup')\n return\n\ndef set_entry():\n print('set')\n return\n\ndef delete_entry():\n print('delete')\n return\n\ndef list_entries():\n print('list')\n return\n\ndef quit():\n print('quit')\n in_use = False\n\ndef use_phonebook():\n in_use = True\n\n functions = {\n '1': lookup_entry,\n '2': set_entry,\n '3': delete_entry,\n '4': list_entries,\n '5': quit\n }\n\n instructions = 'Electronic Phone Book\\n\\\n =====================\\n\\\n 1. Look up an entry\\n\\\n 2. Set an entry\\n\\\n 3. Delete an entry\\n\\\n 4. List all entries\\n\\\n 5. Quit\\n'\n\n\n while in_use:\n print(instructions)\n\n ans = str(input(\"What do you want to do (1-5)? \"))\n \n if ans in functions:\n functions[ans]()\n else:\n print(\"That is not a valid input. Try again.\")\n\nif __name__ == '__main__':\n use_phonebook()\n" }, { "alpha_fraction": 0.650306761264801, "alphanum_fraction": 0.6625766754150391, "avg_line_length": 27.34782600402832, "blob_id": "c873cd15ea77e3f688522d1f2ca7827d912e5533", "content_id": "e87e36ef3b717913e40def3bb385956b0a29240d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 652, "license_type": "no_license", "max_line_length": 77, "num_lines": 23, "path": "/file-io-exercises/bonus_challenge.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# bonus_challenge.py\n# By Aspen\n# INSTRUCTIONS: Write a program that writes to an in-memory file and keeps\n# track of how many characters/bytes were added and prints that information\n# to the screen. Continue adding characters until your program dies.\n\n# 1. At what count did your computer crash?\n\n# 2. What kind of error did you get?\n\n# 3. Did your program crash earlier or later than expected? Why do you think?\n\ndef crash():\n fh = open('crash.txt', 'w')\n counter = 0\n while True:\n fh.write('c' * (10 ** counter))\n counter += 10 ** counter\n print('Characters Written:', counter)\n\n\nif __name__ == '__main__':\n crash()\n" }, { "alpha_fraction": 0.6653322577476501, "alphanum_fraction": 0.6749399304389954, "avg_line_length": 39.290321350097656, "blob_id": "f4961088d896facf4471c7da638526a618919fa8", "content_id": "0bbe2a2f04480cfca41a96889ea9da165110994e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1249, "license_type": "no_license", "max_line_length": 220, "num_lines": 31, "path": "/tip_calc2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# tip_calc.py\n# by Aspen\n# Accepts a bill amount, service level, and number of patrons. Prints price per patron including appropriate tip.\n\nprint(\"\\n***\\nINSTRUCTIONS: This program accepts your bill amount, number of patrons, and level of satisfaction with your service. It splits your check and returns the price per patron including appropriate tip.\\n***\\n\")\n\ndef main():\n # ask for bill amount\n bill = float(input(\"What is the amount of your bill? $\"))\n\n # ask for number of patrons\n num_guests = int(input(\"How many people are splitting the check? \"))\n\n # ask for satisfaction level\n service = input(\"\\nHow was the service?\\ngood\\nfair\\nbad\\nPlease type one of the above options, then press Enter. \").lower()\n\n\n # calculate tip amount\n tips = {\"good\" : 0.20,\n \"fair\" : 0.15,\n \"bad\" : 0.10}\n tip_percentage = tips[service]\n tip = bill * tip_percentage\n\n # calculate cost per guest\n cost_per_guest = (bill + tip) / num_guests\n\n # print message with tip amount\n print(\"\\nYour bill was ${bill:.2f}, and your service was {service}. You should tip ${tip:.2f}.\\n\\nEach guest owes ${cost_per_guest:.2f}.\".format(bill=bill, service=service, tip=tip, cost_per_guest=cost_per_guest))\n\nmain()\n" }, { "alpha_fraction": 0.4771241843700409, "alphanum_fraction": 0.5424836874008179, "avg_line_length": 14.300000190734863, "blob_id": "84a47a2aaf3e38f313e44cd09b88266b9455b06b", "content_id": "548e2ab4d1442b298b2014e97073541113c528f1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 153, "license_type": "no_license", "max_line_length": 32, "num_lines": 10, "path": "/1_to_10.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# 1_to_10.py\n# By Aspen\n# prints 1-10 using a while loop\n\ndef main():\n count = 0\n while count < 10:\n count += 1\n print(count)\nmain()\n" }, { "alpha_fraction": 0.6775994300842285, "alphanum_fraction": 0.685275673866272, "avg_line_length": 34.82500076293945, "blob_id": "2c897667fe7fb646492e92af0d42b973ec61656f", "content_id": "4da42adf58ce5bdf5fe5bb5cf52a652cb3866bda", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1435, "license_type": "no_license", "max_line_length": 76, "num_lines": 40, "path": "/dictionary_exercises/bonus_challenge.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# bonus_challenge.py\n# By Aspen\n\n# INSTRUCTIONS: Given a histogram tally (one returned from either\n# letter_histogram or word_histogram), print the top 3 words or letters.\n\nimport string\n\ndef word_histogram(text):\n stripped_text = text.translate(string.punctuation)\n histogram = {}\n for word in stripped_text.lower().split():\n if word in histogram:\n histogram[word] += 1\n else:\n histogram[word] = 1\n # To print histogram\n # for key, value in histogram.items():\n # print(key + \":\", value)\n return histogram\n\ndef top_3(histogram):\n # Generate list of words sorted by values in descending order\n sorted_dict = sorted(histogram, key=histogram.__getitem__, reverse=True)\n # To print top 3\n # print(sorted_dict[:3])\n return sorted_dict[:3]\n\nif __name__ == '__main__':\n hist = word_histogram(\"Congress returns Tuesday from its spring\\\n recess,facing yet another down-to-the-wire spate of deal-making\\\n and a White House anxious to claim its first major legislative\\\n win. On Friday night, the funding measure lawmakers approved\\\n last year to keep the federal government running will expire.\\\n The timing leaves members of the House and Senate just four\\\n days to reach a new agreement to fund the government, or risk\\\n a partial shutdown of federal agencies on Saturday — the 100th\\\n day of Donald Trump's presidency.\")\n\n top_3(hist)\n" }, { "alpha_fraction": 0.5341365337371826, "alphanum_fraction": 0.5401606559753418, "avg_line_length": 23.899999618530273, "blob_id": "3051937ebf408ecf886a4de95fb11033626fa763", "content_id": "9d14ace56d2919fbe578666dddd96e87ea8eb6da", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 498, "license_type": "no_license", "max_line_length": 66, "num_lines": 20, "path": "/coins.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# coins.py\n# By Aspen\n# Accumulates coins until the user tells the program to stop\n\ndef main():\n want_coins = True\n coins = 0\n\n while want_coins:\n print(\"You have {0} coins.\".format(coins))\n answer = input(\"Do you want another? Yes or no: \").lower()\n if answer == \"yes\":\n coins += 1\n elif answer == \"no\":\n print(\"Okay, bye!\")\n want_coins = False\n else:\n print(\"That answer makes no sense. Try again.\")\n\nmain()\n" }, { "alpha_fraction": 0.6369958519935608, "alphanum_fraction": 0.6383866667747498, "avg_line_length": 28.95833396911621, "blob_id": "0478e8515543beb70b3634d018288c4c0d3fbfeb", "content_id": "a2b9fbd59c88b20118d245ed9d8c1a2d6911df54", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 719, "license_type": "no_license", "max_line_length": 75, "num_lines": 24, "path": "/file-io-exercises/ex_3.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# ex_3.py\n# By Aspen\n# INSTRUCTIONS: Write a program that prompts the user to enter a file name,\n# then prints the letter histogram and the word histogram of the file.\n\nimport histograms as h\n\ndef print_histograms(file):\n with open(file, 'r') as fh:\n content = fh.read()\n\n letter_hist_dict = h.letter_histogram(content)\n print(\"Histogram of Characters:\")\n for key, val in letter_hist_dict.items():\n print(\"{}: {}\".format(key, val))\n\n word_hist_dict = h.word_histogram(content)\n print(\"Histogram of Words:\")\n for key, val in word_hist_dict.items():\n print(\"{}: {}\".format(key, val))\n\nif __name__ == '__main__':\n ans = input('Enter the file name: ')\n print_histograms(ans)\n" }, { "alpha_fraction": 0.5472496747970581, "alphanum_fraction": 0.5592383742332458, "avg_line_length": 25.754716873168945, "blob_id": "9e88bbcd89d41457dcba53d52c5d0dc7437ac991", "content_id": "c81ddbb6c5ff231b32d6da549db3b3c5a0e48fe1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1418, "license_type": "no_license", "max_line_length": 69, "num_lines": 53, "path": "/string_exercises.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# string_exercises.py\n# By Aspen\n# A collection of basic exercises on string_exercises\n\nstr = \"doubt kills more dreams than failure ever will\"\nprint(\"\\nInitial string:\")\nprint(str, \"\\n\")\n\n# 1. Uppercase a string\nprint(\"\\n1. Uppercase a string:\")\nprint(str.upper(), \"\\n\")\n\n# 2. Capitalize a string\nprint(\"\\n2. Capitalize a string:\")\nprint(str.capitalize(), \"\\n\")\n\n# 3. Reverse a string\nprint(\"\\n3. Reverse a string:\")\ndef reverse_str(message):\n listified = list(message)\n listified.reverse()\n new_string = \"\".join(listified)\n return new_string\nprint(reverse_str(str))\n\n# 4. Leetspeak\nprint(\"\\n4. Translate to Leetspeak:\")\ndef leetspeak(str):\n translation_key = { 'A' : '4',\n 'E' : '3',\n 'G' : '6',\n 'I' : '1',\n 'O' : '0',\n 'S' : '5',\n 'T' : '7' }\n str = str.upper()\n translated = \"\"\n for char in str:\n if char in translation_key:\n translated += translation_key[char]\n else:\n translated += char\n return translated\nprint(leetspeak(str), \"\\n\")\n\n# 5. Long-long vowels\nprint(\"\\n5. Long-long vowels:\")\ndef long_vowels(str):\n str = str.replace('ee', 'eeeee')\n str = str.replace('oo', 'ooooo')\n return str\nprint(\"Normal: A good man feels happy.\")\nprint(long_vowels(\"Long-long vowels: A good man feels happy.\"), \"\\n\")\n" }, { "alpha_fraction": 0.5666666626930237, "alphanum_fraction": 0.621052622795105, "avg_line_length": 15.285714149475098, "blob_id": "51cfb9851f30285cb462c8e83cdecc71eeb03907", "content_id": "048ff90b8abdecc4e6e5bfb2922ab366c23de950", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 570, "license_type": "no_license", "max_line_length": 60, "num_lines": 35, "path": "/for-loops.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# for-loops.py\n# Aspen Hollyer\n\n# MITx 6.001x for loop practice.\n\n# Convert the following into code that uses a for loop.\n# prints 2\n# prints 4\n# prints 6\n# prints 8\n# prints 10\n# prints \"Goodbye!\"\n\nfor i in range(2, 11, 2):\n print(i)\nprint(\"Goodbye!\")\n\n\n# Convert the following code into code that uses a for loop.\n# prints \"Hello!\"\n# prints 10\n# prints 8\n# prints 6\n# prints 4\n# prints 2\n\nprint(\"Hello!\")\nfor i in range(10, 1, -2):\n print(i)\n\nfor variable in range(20):\n if variable % 4 == 0:\n print(variable)\n if variable % 16 == 0:\n print('Foo!')\n" }, { "alpha_fraction": 0.5545712113380432, "alphanum_fraction": 0.6009922027587891, "avg_line_length": 24.196428298950195, "blob_id": "eece44d64b472550e82d809e68ad0ba372e2e5c1", "content_id": "35e1ee28752cb74773b46ebe707d67d687390523", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2822, "license_type": "no_license", "max_line_length": 93, "num_lines": 112, "path": "/list_exercises.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# list-exercises.py\n# By Aspen\n# a collection of brief programming exercises on lists\n\nimport random\n\nnum_list = list(range(-5, 10))\nrandom.shuffle(num_list)\nprint(\"The list of numbers is:\", num_list)\n\n# 1. Sum the Numbers\n# Given a list of numbers, print their sum.\nex1_sum = 0\nfor num in num_list:\n ex1_sum += num\nprint(\"1. Sum the Numbers:\", ex1_sum)\n\n# 2. Largest Number\nex2_largest = 0\nfor num in num_list:\n if num > ex2_largest:\n ex2_largest = num\nprint(\"2. Largest Number:\", ex2_largest)\n\n# 3. Smallest Number\nex3_smallest = num_list[0]\nfor num in num_list:\n if num < ex3_smallest:\n ex3_smallest = num\nprint(\"3. Smallest Number:\", ex3_smallest)\n\n# 4. Even Numbers\nprint(\"4. Even Numbers:\")\nfor num in num_list:\n if num % 2 == 0:\n print(\"\\t\", num)\n\n# 5. Positive Numbers\nprint(\"5. Positive Numbers:\")\nfor num in num_list:\n if num > 0:\n print(\"\\t\", num)\n\n# 6. Positive Numbers II\nex6_list = []\nfor num in num_list:\n if num > 0:\n ex6_list.append(num)\nprint(\"6. List of Positive Numbers:\", ex6_list)\n\n# 7. Multiply a List\nex7_list = []\ndef multiply_list(list, factor):\n for num in list:\n ex7_list.append(num * factor)\n return ex7_list\nprint(\"7. Multiply a List:\\n\", multiply_list(num_list, 2))\n\n# 8. Multiply Vectors\nex8_list1 = [2, 4, 5]\nex8_list2 = [2, 3, 6]\nproduct = []\ndef multiply_vectors(a, b):\n for i in range(0, len(a)):\n product.append(a[i] * b[i])\n return product\nprint(\"8. Multiply Vectors:\\n\", multiply_vectors(ex8_list1, ex8_list2))\n\n# 9. Matrix Addition\nex9_list1 = [[1, 3], [2, 4]]\nex9_list2 = [[5,2], [1, 0]]\nex9_sum = []\ndef add_matrices(a, b):\n # Iterate over list\n for i in range(0, len(a)):\n # Iterate over sub-list\n temp = []\n for j in range(0, len(a[i])):\n temp.append(a[i][j] + b[i][j])\n ex9_sum.append(temp)\n return ex9_sum\nprint(\"9. Matrix Addition:\\n\", add_matrices(ex9_list1, ex9_list2))\n\n# 10. Matrix Addition II\n# see above\nprint(\"10. Matrix Addition II: (see exercise 9)\")\n\n# 11. De-dup\nex11_original = [1, 2, 2, 3, 4, 5, 6, 4, 7, 5, 5]\nex11_deduped = []\ndef dedup(list):\n for num in ex11_original:\n if not num in ex11_deduped:\n ex11_deduped.append(num)\n return ex11_deduped\nprint(\"11. De-dup:\\nOriginal List:\", ex11_original, \"\\nDe-Duped List:\", dedup(ex11_original))\n\n# Bonus: Matrix Multiplication\nbonus1 = [[2, -2], [5, 3]]\nbonus2 = [[-1, 4], [7, -6]]\ndef multiply_matrices(a, b):\n product = []\n for i in range(len(a)):\n temp = []\n for j in range(len(a[i])):\n sum = 0\n for k in range(len(b[i])):\n sum += (a[i][k] * b[k][j])\n temp.append(sum)\n product.append(temp)\n return product\nprint(\"Bonus - Matrix Multiplication:\", multiply_matrices(bonus1, bonus2))\n" }, { "alpha_fraction": 0.5857987999916077, "alphanum_fraction": 0.5986193418502808, "avg_line_length": 25.6842098236084, "blob_id": "7b7a6e0350ba316ceaff04a90c2c3069e102d075", "content_id": "f5d6602190cc59d43322a4067ea3ff90e0182abb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1014, "license_type": "no_license", "max_line_length": 68, "num_lines": 38, "path": "/dictionary_exercises/ex_2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# ex_2.py\n# By Aspen\n# Work with a nested dictionary\n\nramit = {\n 'name': 'Ramit',\n 'email': '[email protected]',\n 'interests': ['movies', 'tennis'],\n 'friends': [\n {\n 'name': 'Jasmine',\n 'email': '[email protected]',\n 'interests': ['photography', 'tennis']\n },\n {\n 'name': 'Jan',\n 'email': '[email protected]',\n 'interests': ['movies', 'tv']\n }\n ]\n}\n\nprint(\"\\nNested Dictionary Exercises: Ramit\")\nprint(\"ramit = \", ramit)\n\nprint(\"\\n1. Write an expression that gets Ramit's email address.\")\nprint(\"ramit.get('email'):\", ramit.get('email'))\nprint(\"ramit['email']:\", ramit['email'])\n\nprint(\"\\n2. Write an expression that gets Ramit's first interest.\")\nprint(\"ramit['interests'][0]:\", ramit['interests'][0])\n\nprint(\"\\n3. Write an expression that gets Jasmine's email address.\")\nprint(\"ramit['friends'][0]['email']:\", ramit['friends'][0]['email'])\n\nprint(\"\\n4. Write an expression that gets Jan's second interest.\")\nprint(\"ramit['friends'][1]['interests'][1]:\", \\\n ramit['friends'][1]['interests'][1])\n" }, { "alpha_fraction": 0.5212159752845764, "alphanum_fraction": 0.5535148978233337, "avg_line_length": 24.063491821289062, "blob_id": "d4cc7a4afaf5ff91e7543c987a539b9d44d098d1", "content_id": "e6a7775dce1cf235b0a03e9f4d2f8c32859acfda", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1579, "license_type": "no_license", "max_line_length": 72, "num_lines": 63, "path": "/loop_exercises.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# loop_exercises.py\n# By Aspen\n# A collection of brief programming exercises with loops\n\n# 1. 1 to 10\nprint(\"\\n1. 1 to 10:\")\nfor i in range(1, 11):\n print(\"\\t\", i)\n\n# 2. n to m\nprint(\"\\n2. n to m:\")\nstart = int(input(\"Start from: \"))\nend = int(input(\"End on: \"))\ndef count_loop(start, end):\n for i in range(start, end+1):\n print(\"\\t\", i)\ncount_loop(start, end)\n\n# 3. Odd Numbers\nprint(\"\\n3. Odd Numbers:\")\nfor i in range(1, 10, 2):\n print(\"\\t\", i)\n\n# 4. Print a Square\nprint(\"\\n4. Print a Square:\")\nunit = \"*\"\nside = 5\nfor i in range(0, side):\n print(unit * side)\n\n# 5. Print a Square II\nprint(\"\\n5. Print a Square II:\")\nside = int(input(\"How long is each side? Enter an integer: \"))\nfor i in range(0, side):\n print(unit * side)\n\n# 6. Print a Box\nprint(\"\\n6. Print a Box:\")\nheight = int(input(\"How tall is the box? Enter an integer: \"))\nwidth = int(input(\"How wide is the box? Enter an integer: \"))\ndef print_box(w, h):\n print(\"*\" * w)\n for i in range(0, (h-2)):\n print(\"*\" + (\" \" * (w-2)) + \"*\")\n print(\"*\" * w)\nprint_box(width, height)\n\n# 7. Print a Triangle\n(print(\"\\n7. Print a Triangle:\"))\nprint(\" \" * 3 + \"*\" * 1 + \" \" * 3)\nprint(\" \" * 2 + \"*\" * 3 + \" \" * 2)\nprint(\" \" * 1 + \"*\" * 5 + \" \" * 1)\nprint(\" \" * 0 + \"*\" * 7 + \" \" * 0)\n\n# 8. Print a Triangle II\nprint(\"\\n8. Print a Triangle II:\")\nheight = int(input(\"What is the triangle's height? Enter an integer: \"))\ndef print_triangle(h):\n num_stars = 1\n for i in range(0, h):\n print(\" \" * (h-i) + \"*\" * num_stars + \" \" * (h-i))\n num_stars += 2\nprint_triangle(height)\n" }, { "alpha_fraction": 0.5774134993553162, "alphanum_fraction": 0.5871281027793884, "avg_line_length": 27.894737243652344, "blob_id": "93ee2d0ba32e97f11da1bc8899da4911a48edff7", "content_id": "e3ec767427f7ba48d3ed737819ec6e386c309ec2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1647, "license_type": "no_license", "max_line_length": 78, "num_lines": 57, "path": "/caesar_cipher.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python3\n\n# caesar_cipher.py\n# By Aspen\n# Accepts a string and amount to shift, returns shifted string\n\n# 6. Caesar Cipher\n\n# Create a list of characters to use indices for shifting\n## NOTE: 4/24/17 can use string instead of list:\n## string.ascii_lowercase\nCHARS = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',\n'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n\n# Choose a string to translate\nstr = input(\"Enter a string to shift: \").lower()\n\n\n# Determine the amount to shift\nshift_key = int(input(\"How much do you want to shift? Enter an integer.\"))\n\n\n\n# Create a function that accepts the string & shift key\ndef c_shift(str, shift_key):\n # Create an empty string to add shifted chars\n shifted_str = \"\"\n\n # Iterate over each character in the input string\n for char in str:\n\n # Don't shift spaces or punctuation; just add to shifted_str\n try:\n i = chars.index(char)\n except ValueError:\n shifted_str += char\n continue\n\n # Apply the shift key\n try:\n c = chars[i + shift_key]\n except IndexError:\n if i + shift_key < 0:\n c = chars[26 + (i + shift_key)]\n elif i + shift_key > 25:\n c = chars[i + shift_key - 26]\n\n # Append the shifted character to shifted_str\n shifted_str += c\n return shifted_str\n\nprint(str)\nprint(\"Shifted:\", c_shift(str, shift_key))\nprint(\"Unshifted:\", c_shift(c_shift(str, shift_key), -shift_key))\n\npuzzle = \"lbh zhfg hayrnea jung lbh unir yrnearq\"\nprint(\"\\nPuzzle:\", puzzle + \"\\nDeciphered:\", c_shift(puzzle, -13))\n" }, { "alpha_fraction": 0.4808126389980316, "alphanum_fraction": 0.48758465051651, "avg_line_length": 19.136363983154297, "blob_id": "e97ef644339be919ec7f9c799478d312b654fcde", "content_id": "798fa3b10c46e1dd35fac018b00629f64d51d6b3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 443, "license_type": "no_license", "max_line_length": 55, "num_lines": 22, "path": "/error_handling/error_test.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python3\n\ndef catch_error ():\n while True:\n try:\n x = int(input(\"Please enter an integer: \"))\n\n except ValueError:\n raise ValueError(\"This is not an integer.\")\n\n except MemoryError:\n print(\"Ran out of memory.\")\n\n else:\n x += 13\n print(x)\n\n finally:\n print(\"Going around again!\")\n\nif __name__ == '__main__':\n catch_error()\n" }, { "alpha_fraction": 0.5132948160171509, "alphanum_fraction": 0.5202311873435974, "avg_line_length": 32.269229888916016, "blob_id": "d355fc4afef414d6a06ff7d4f48660ecd820ca64", "content_id": "8592844481e922a115044816f1d113addcce04fd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 865, "license_type": "no_license", "max_line_length": 110, "num_lines": 26, "path": "/work_or_sleep_in.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# work_or_sleep_in.py\n# by Aspen\n# Accepts a number, converts it to the corresponding weekday, and prints an instruction to sleep in or get up.\n\ndef main():\n\n def work_or_sleep():\n days = [\"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\"]\n\n while True:\n try:\n num = int(input(\"Enter a number from 0-6: \"))\n if 0 < num < 6:\n print(\"It's \", days[num], \". Go to work!\", sep=\"\")\n else:\n print(\"It's \", days[num], \". Go back to bed!\", sep=\"\")\n break\n except ValueError:\n print(\"Your input must be a number. Try again.\")\n except IndexError:\n print(\"The number you chose has no corresponding weekday. Please choose a number from 0-6.\")\n\n\n work_or_sleep()\n\nmain()\n" }, { "alpha_fraction": 0.6122449040412903, "alphanum_fraction": 0.6326530575752258, "avg_line_length": 28.399999618530273, "blob_id": "f007259ec30890e60488fc1b846b7e413a64966d", "content_id": "e9fbfd942eb780d5bf2b621d15f71e01cae9276a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 441, "license_type": "no_license", "max_line_length": 140, "num_lines": 15, "path": "/secs_converter.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# secs_converter.py\n# By Aspen & Sammy\n# Asks for a number of seconds and prints the corresponding number of hours, days, and years\n\ndef main():\n\n secs = int(input(\"How many seconds does it take? \"))\n mins = secs / 60\n hours = mins / 60\n days = hours / 24\n years = days / 365\n\n print(\"It takes\", secs, \"seconds. That's a lot of time.\\nThat's\", hours, \"hours.\\nThat's\", days, \"days.\\nThat's\", years, \"years.\\nWow!\")\n\nmain()\n" }, { "alpha_fraction": 0.6739130616188049, "alphanum_fraction": 0.6739130616188049, "avg_line_length": 22, "blob_id": "52425ae7a436e90d05ca25200feeec51032e47f7", "content_id": "17917dcdf000b49e001e2f9c1008105dfd771f7c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 92, "license_type": "no_license", "max_line_length": 38, "num_lines": 4, "path": "/function_exercises/ex1.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# Accepts a name and prints a greeting\n\ndef hello(name):\n print(\"Hello {}\".format(name))\n" }, { "alpha_fraction": 0.625201940536499, "alphanum_fraction": 0.631663978099823, "avg_line_length": 22.80769157409668, "blob_id": "e7d2072865122bd7884716230f6a3f5ed57346ac", "content_id": "1f8bb9c534bfd625419251a29b785c938c01690d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 619, "license_type": "no_license", "max_line_length": 56, "num_lines": 26, "path": "/caesar_cipher2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python3\n\n# caesar_cipher2.py\n\n# Accepts a rotation amount and string, returns a string\n# shifted by the rotation amount\n\nimport string\nimport sys\n\ndef caesar_cipher (rotation, text):\n answer = \"\"\n for t in text:\n t = t.lower()\n if t in string.ascii_lowercase:\n index = string.ascii_lowercase.index(t)\n index = index - rotation\n answer += string.ascii_lowercase[index]\n answer += t\n return answer\n\nif __name__ == \"__main__\":\n rotation = int(sys.argv[1])\n text = sys.argv[2]\n answer = caesar_cipher(rotation, text)\n print('Answer: {}'.format(answer))\n" }, { "alpha_fraction": 0.5692307949066162, "alphanum_fraction": 0.6000000238418579, "avg_line_length": 20.66666603088379, "blob_id": "0f77c0a4d3d7c84ccdea4507af538105b3d8f16e", "content_id": "f542eff7c377972fb30727e07736985cc82b1e8e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 195, "license_type": "no_license", "max_line_length": 62, "num_lines": 9, "path": "/factor_a_number.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# factor_a_number.py\n# By Aspen\n# Accepts an integer and prints its factors in ascending order\n\ndef factor(n):\n for i in range(1, n+1):\n if n % i == 0:\n print(i)\nfactor(120)\n" }, { "alpha_fraction": 0.6253870129585266, "alphanum_fraction": 0.6269350051879883, "avg_line_length": 27.086956024169922, "blob_id": "400d4b3dc53f0ca1f51ba06888f88c779aa53d61", "content_id": "fc808479ad40f4ef3140837b726df5494bd6699a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 646, "license_type": "no_license", "max_line_length": 75, "num_lines": 23, "path": "/file-io-exercises/ex_2.py", "repo_name": "ahollyer/python-exercises", "src_encoding": "UTF-8", "text": "# ex_2.py\n# By Aspen\n# INSTRUCTIONS: Write a program that prompts the user to enter a file name,\n# prompts the user to enter content, and saves the content to the file.\n\ndef write_my_file():\n print(\"\\nThis program accepts a file name and content, then creates\\\n the file for you.\\n\")\n\n name = input(\"What should we call the file? \")\n content = input(\"What should we put in the file? \")\n\n with open(name, 'w') as fh:\n fh.write(content)\n\n print('\\nDone! Here are the contents of', name + \":\\n\")\n with open(name, 'r') as fh:\n content = fh.read()\n\n print(content)\n\nif __name__ == '__main__':\n write_my_file()\n" } ]

44

yann-a/ASCADv2-Internship

https://github.com/yann-a/ASCADv2-Internship

86063795f6f745a849a0166a4ec066eadcf9f6e7

655518535e369bc817adc1d0c08a2a077c3ba220

5080f9be4381d56ddd0a3791bce396a8a0cdccdb

refs/heads/main

2023-07-14T15:43:57.244908

2021-08-23T22:34:24

399,164,460

0

null

[ { "alpha_fraction": 0.6159420013427734, "alphanum_fraction": 0.6231883764266968, "avg_line_length": 12.800000190734863, "blob_id": "c05a4a1fe9ad2bbac50e38125ebf4513c2aa3db3", "content_id": "aac4ad78398beb0bb35b0cf0786d65038a07223f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 138, "license_type": "no_license", "max_line_length": 30, "num_lines": 10, "path": "/scripts/snr_beta.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute SNR for beta\n\nfrom utils.snr import snr\n\nsnr(\n 'beta_mask',\n nb_files=1,\n save=False,\n output_path='snr/beta.npy'\n)\n" }, { "alpha_fraction": 0.5705289840698242, "alphanum_fraction": 0.5922544002532959, "avg_line_length": 29.24761962890625, "blob_id": "b2097ab174a1c7abaa8922253d33d533b919ab54", "content_id": "6bb0cd80e5ed4a8227e063a36bc33be1e7cde6a0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3176, "license_type": "no_license", "max_line_length": 105, "num_lines": 105, "path": "/scripts/utils/lda.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Computes LDA and PI\nimport numpy as np\nimport scalib.modeling as mdl\nimport matplotlib.pyplot as plt\nimport pickle\n\nfrom tqdm import tqdm\n\nfrom utils.multilabelize import multilabelize\n\n# Variables\ntraces_path = 'data/traces{}.npy'\nmetadata_path = 'data/metadata{}.npz'\nnb_traces_per_file = 99_999\nnb_files = 1\nlda_batch_size = 10_000\n\ndef lda(\n target_label,\n snr_path,\n nb_poi=2800,\n lda_dim=28,\n nb_bytes=1,\n nb_classes=256,\n can_be_zero=True,\n nb_files_train=1,\n gemm_mode=1,\n save=False,\n output_path=None\n):\n pois = [np.argsort(np.load(snr_path.format(index_byte)))[-nb_poi:] for index_byte in range(nb_bytes)]\n\n lda = mdl.MultiLDA(\n [nb_classes] * nb_bytes,\n [lda_dim] * nb_bytes,\n pois,\n gemm_mode\n )\n\n for index_train_file in tqdm(range(nb_files_train)):\n traces_train = np.load(traces_path.format(index_train_file + 1), mmap_mode='r')\n metadata_train = np.load(metadata_path.format(index_train_file + 1))\n multilabel_train = multilabelize(\n metadata_train['masks'][:],\n metadata_train['keys'][:],\n metadata_train['plaintext'][:]\n )\n\n l = multilabel_train[target_label][:-1].astype(np.uint16)\n if not can_be_zero:\n l -= 1\n\n for s in tqdm(range(0, nb_traces_per_file, lda_batch_size)):\n traces = traces_train[s:min(s + lda_batch_size, nb_traces_per_file), :].astype(np.int16)\n labels = l[s:min(s + lda_batch_size, nb_traces_per_file), :]\n lda.fit_u(traces, labels)\n del traces\n del traces_train\n\n lda.solve(done=True)\n\n if save:\n if output_path is None:\n print('No filename provided. Skipping saving')\n else:\n with open(output_path, 'wb') as output_file:\n pickle.dump(lda, output_file)\n\n return lda\n\ndef pi(\n lda,\n target_byte,\n nb_bytes=1,\n nb_classes=256,\n can_be_zero=True,\n null_threshold=1e-20,\n nb_files_train=2,\n nb_files_test=1\n):\n pis = [[] for _ in range(nb_bytes)]\n for index_test_file in tqdm(range(nb_files_train, nb_files_train + nb_files_test)):\n traces_test = np.load(traces_path.format(index_test_file + 1), mmap_mode='r')\n metadata_test = np.load(metadata_path.format(index_test_file + 1))\n multilabel_test = multilabelize(\n metadata_test['masks'][:20_000],\n metadata_test['keys'][:20_000],\n metadata_test['plaintext'][:20_000]\n )\n\n probas = list(lda.predict_proba(traces_test[:20_000].astype(np.int16)))\n labels = multilabel_test[target_byte][:len(probas)]\n\n if not can_be_zero:\n labels -= 1\n\n for index_byte in range(nb_bytes):\n prs = probas[index_byte]\n prs[np.where(prs < null_threshold)] = null_threshold\n pi = np.mean(np.log2(prs[np.arange(len(labels)), labels]))\n pis[index_byte].append(pi)\n\n for index_byte in range(nb_bytes):\n pi = np.log2(nb_classes) + np.mean(np.array(pis[index_byte]))\n print(f'For byte no {index_byte}, pi = {pi} with maximum {np.log2(nb_classes)}')\n" }, { "alpha_fraction": 0.6555023789405823, "alphanum_fraction": 0.6650717854499817, "avg_line_length": 18, "blob_id": "59ba97a740b0dbf3d85b84af98b857b75746e101", "content_id": "245facccbd528b1488739f6d68678b6cf8737d5d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 209, "license_type": "no_license", "max_line_length": 48, "num_lines": 11, "path": "/scripts/lda_permvalue.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute and pickle LDA for the permuted values\n\nfrom utils.lda import lda, pi\n\nlda(\n 'sbox_masked',\n 'snr/permvalue/permvalue{}.npy',\n nb_bytes=16,\n save=False,\n output_path='lda/permvalue'\n)\n" }, { "alpha_fraction": 0.6274510025978088, "alphanum_fraction": 0.6274510025978088, "avg_line_length": 14.300000190734863, "blob_id": "f2c081bf20338441e9e53f191656e7ebd2772731", "content_id": "84df78ab8d18fb2c680794dd9bfe259da49782a6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 153, "license_type": "no_license", "max_line_length": 33, "num_lines": 10, "path": "/scripts/lda_beta.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute and pickle LDA for beta\n\nfrom utils.lda import lda, pi\n\nlda(\n 'beta_mask',\n 'snr/beta.npy',\n save=False,\n output_path='lda/beta'\n)\n" }, { "alpha_fraction": 0.2617669999599457, "alphanum_fraction": 0.5793588757514954, "avg_line_length": 49.75396728515625, "blob_id": "5e7b4d2f5f91298fa99867fa21a2c40ebebe0353", "content_id": "1956ce2155beb8829604c87ccb7f01a7c195b0ea", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6395, "license_type": "no_license", "max_line_length": 149, "num_lines": 126, "path": "/scripts/utils/multilabelize.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Retrieve masks and permutations. Adapted from https://github.com/ANSSI-FR/ASCAD/blob/58c527fdd82c5c5896d61eeb019cccdd628248eb/ASCAD_generate.py\nimport numpy as np\n\nlog_table = np.array([\n 0, 0, 25, 1, 50, 2, 26, 198, 75, 199, 27, 104, 51, 238, 223, 3,\n 100, 4, 224, 14, 52, 141, 129, 239, 76, 113, 8, 200, 248, 105, 28, 193,\n 125, 194, 29, 181, 249, 185, 39, 106, 77, 228, 166, 114, 154, 201, 9, 120,\n 101, 47, 138, 5, 33, 15, 225, 36, 18, 240, 130, 69, 53, 147, 218, 142,\n 150, 143, 219, 189, 54, 208, 206, 148, 19, 92, 210, 241, 64, 70, 131, 56,\n 102, 221, 253, 48, 191, 6, 139, 98, 179, 37, 226, 152, 34, 136, 145, 16,\n 126, 110, 72, 195, 163, 182, 30, 66, 58, 107, 40, 84, 250, 133, 61, 186,\n 43, 121, 10, 21, 155, 159, 94, 202, 78, 212, 172, 229, 243, 115, 167, 87,\n 175, 88, 168, 80, 244, 234, 214, 116, 79, 174, 233, 213, 231, 230, 173, 232,\n 44, 215, 117, 122, 235, 22, 11, 245, 89, 203, 95, 176, 156, 169, 81, 160,\n 127, 12, 246, 111, 23, 196, 73, 236, 216, 67, 31, 45, 164, 118, 123, 183,\n 204, 187, 62, 90, 251, 96, 177, 134, 59, 82, 161, 108, 170, 85, 41, 157,\n 151, 178, 135, 144, 97, 190, 220, 252, 188, 149, 207, 205, 55, 63, 91, 209,\n 83, 57, 132, 60, 65, 162, 109, 71, 20, 42, 158, 93, 86, 242, 211, 171,\n 68, 17, 146, 217, 35, 32, 46, 137, 180, 124, 184, 38, 119, 153, 227, 165,\n 103, 74, 237, 222, 197, 49, 254, 24, 13, 99, 140, 128, 192, 247, 112, 7\n])\n\nalog_table = np.array([\n 1, 3, 5, 15, 17, 51, 85, 255, 26, 46, 114, 150, 161, 248, 19, 53,\n 95, 225, 56, 72, 216, 115, 149, 164, 247, 2, 6, 10, 30, 34, 102, 170,\n 229, 52, 92, 228, 55, 89, 235, 38, 106, 190, 217, 112, 144, 171, 230, 49,\n 83, 245, 4, 12, 20, 60, 68, 204, 79, 209, 104, 184, 211, 110, 178, 205,\n 76, 212, 103, 169, 224, 59, 77, 215, 98, 166, 241, 8, 24, 40, 120, 136,\n 131, 158, 185, 208, 107, 189, 220, 127, 129, 152, 179, 206, 73, 219, 118, 154,\n 181, 196, 87, 249, 16, 48, 80, 240, 11, 29, 39, 105, 187, 214, 97, 163,\n 254, 25, 43, 125, 135, 146, 173, 236, 47, 113, 147, 174, 233, 32, 96, 160,\n 251, 22, 58, 78, 210, 109, 183, 194, 93, 231, 50, 86, 250, 21, 63, 65,\n 195, 94, 226, 61, 71, 201, 64, 192, 91, 237, 44, 116, 156, 191, 218, 117,\n 159, 186, 213, 100, 172, 239, 42, 126, 130, 157, 188, 223, 122, 142, 137, 128,\n 155, 182, 193, 88, 232, 35, 101, 175, 234, 37, 111, 177, 200, 67, 197, 84,\n 252, 31, 33, 99, 165, 244, 7, 9, 27, 45, 119, 153, 176, 203, 70, 202,\n 69, 207, 74, 222, 121, 139, 134, 145, 168, 227, 62, 66, 198, 81, 243, 14,\n 18, 54, 90, 238, 41, 123, 141, 140, 143, 138, 133, 148, 167, 242, 13, 23,\n 57, 75, 221, 124, 132, 151, 162, 253, 28, 36, 108, 180, 199, 82, 246, 1\n])\n\n\n# Multiplication function in GF(2^8)\ndef multGF256(a, b):\n return alog_table[(log_table[a] + log_table[b]) % 255]\n\nAES_Sbox = np.array([\n 0x63, 0x7C, 0x77, 0x7B, 0xF2, 0x6B, 0x6F, 0xC5, 0x30, 0x01, 0x67, 0x2B, 0xFE, 0xD7, 0xAB, 0x76,\n 0xCA, 0x82, 0xC9, 0x7D, 0xFA, 0x59, 0x47, 0xF0, 0xAD, 0xD4, 0xA2, 0xAF, 0x9C, 0xA4, 0x72, 0xC0,\n 0xB7, 0xFD, 0x93, 0x26, 0x36, 0x3F, 0xF7, 0xCC, 0x34, 0xA5, 0xE5, 0xF1, 0x71, 0xD8, 0x31, 0x15,\n 0x04, 0xC7, 0x23, 0xC3, 0x18, 0x96, 0x05, 0x9A, 0x07, 0x12, 0x80, 0xE2, 0xEB, 0x27, 0xB2, 0x75,\n 0x09, 0x83, 0x2C, 0x1A, 0x1B, 0x6E, 0x5A, 0xA0, 0x52, 0x3B, 0xD6, 0xB3, 0x29, 0xE3, 0x2F, 0x84,\n 0x53, 0xD1, 0x00, 0xED, 0x20, 0xFC, 0xB1, 0x5B, 0x6A, 0xCB, 0xBE, 0x39, 0x4A, 0x4C, 0x58, 0xCF,\n 0xD0, 0xEF, 0xAA, 0xFB, 0x43, 0x4D, 0x33, 0x85, 0x45, 0xF9, 0x02, 0x7F, 0x50, 0x3C, 0x9F, 0xA8,\n 0x51, 0xA3, 0x40, 0x8F, 0x92, 0x9D, 0x38, 0xF5, 0xBC, 0xB6, 0xDA, 0x21, 0x10, 0xFF, 0xF3, 0xD2,\n 0xCD, 0x0C, 0x13, 0xEC, 0x5F, 0x97, 0x44, 0x17, 0xC4, 0xA7, 0x7E, 0x3D, 0x64, 0x5D, 0x19, 0x73,\n 0x60, 0x81, 0x4F, 0xDC, 0x22, 0x2A, 0x90, 0x88, 0x46, 0xEE, 0xB8, 0x14, 0xDE, 0x5E, 0x0B, 0xDB,\n 0xE0, 0x32, 0x3A, 0x0A, 0x49, 0x06, 0x24, 0x5C, 0xC2, 0xD3, 0xAC, 0x62, 0x91, 0x95, 0xE4, 0x79,\n 0xE7, 0xC8, 0x37, 0x6D, 0x8D, 0xD5, 0x4E, 0xA9, 0x6C, 0x56, 0xF4, 0xEA, 0x65, 0x7A, 0xAE, 0x08,\n 0xBA, 0x78, 0x25, 0x2E, 0x1C, 0xA6, 0xB4, 0xC6, 0xE8, 0xDD, 0x74, 0x1F, 0x4B, 0xBD, 0x8B, 0x8A,\n 0x70, 0x3E, 0xB5, 0x66, 0x48, 0x03, 0xF6, 0x0E, 0x61, 0x35, 0x57, 0xB9, 0x86, 0xC1, 0x1D, 0x9E,\n 0xE1, 0xF8, 0x98, 0x11, 0x69, 0xD9, 0x8E, 0x94, 0x9B, 0x1E, 0x87, 0xE9, 0xCE, 0x55, 0x28, 0xDF,\n 0x8C, 0xA1, 0x89, 0x0D, 0xBF, 0xE6, 0x42, 0x68, 0x41, 0x99, 0x2D, 0x0F, 0xB0, 0x54, 0xBB, 0x16\n])\n\nG = np.array([0x0C, 0x05, 0x06, 0x0b, 0x09, 0x00, 0x0a, 0x0d, 0x03, 0x0e, 0x0f, 0x08, 0x04, 0x07, 0x01, 0x02])\n\n\ndef permIndices(i, m0, m1, m2, m3):\n x0, x1, x2, x3 = m0 & 0x0f, m1 & 0x0f, m2 & 0x0f, m3 & 0x0f\n return G[G[G[G[(15-i) ^ x0] ^ x1] ^ x2] ^ x3]\n\n\ndef multilabelize(masks, keys, plaintexts):\n def mult_sbox_mask_f(target_byte):\n ind = permIndices(target_byte, masks[:, 0], masks[:, 1], masks[:, 2], masks[:, 3])\n alpha = masks[:, 18]\n beta = masks[:, 17]\n\n mask_size = masks.shape[0]\n S = AES_Sbox[plaintexts[np.arange(mask_size), ind] ^ keys[np.arange(mask_size), ind]]\n\n return multGF256(alpha, S) ^ beta\n\n def mult_sbox_mask_with_perm_f(target_byte):\n alpha = masks[:, 18]\n beta = masks[:, 17]\n\n S = AES_Sbox[plaintexts[:, target_byte] ^ keys[:, target_byte]]\n\n return multGF256(alpha, S) ^ beta\n\n def permind_f(target_byte):\n return permIndices(target_byte, masks[:, 0], masks[:, 1], masks[:, 2], masks[:, 3])\n\n def alpha_mask_f():\n return masks[:, 18]\n\n def beta_mask_f():\n return masks[:, 17]\n\n y_alpha = alpha_mask_f()\n y_beta = beta_mask_f()\n y_sbox = []\n y_sbox_with_perm = []\n y_permind = []\n\n for i in range(16):\n y_sbox.append(mult_sbox_mask_f(i))\n y_sbox_with_perm.append(mult_sbox_mask_with_perm_f(i))\n y_permind.append(permind_f(i))\n\n y_sbox = np.transpose(y_sbox)\n y_sbox_with_perm = np.transpose(y_sbox_with_perm)\n y_permind = np.transpose(y_permind)\n\n multilabel_type = np.dtype([\n (\"alpha_mask\", np.uint8, (1,)),\n (\"beta_mask\", np.uint8, (1,)),\n (\"sbox_masked\", np.uint8, (16,)),\n (\"sbox_masked_with_perm\", np.uint8, (16,)),\n (\"perm_index\", np.uint8, (16,))\n ])\n multilabel = np.array([(y_alpha[n], y_beta[n], y_sbox[n], y_sbox_with_perm[n], y_permind[n]) for n in range(len(masks))], dtype=multilabel_type)\n\n return multilabel\n" }, { "alpha_fraction": 0.6320754885673523, "alphanum_fraction": 0.6556603908538818, "avg_line_length": 16.66666603088379, "blob_id": "e0016956eefe85cabed35196efaa8f7e2522113e", "content_id": "deed1815b8cad73fb5f061f698de958deb4a742a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 212, "license_type": "no_license", "max_line_length": 47, "num_lines": 12, "path": "/scripts/snr_permindex.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Comopute SNR for the permutation indexes\n\nfrom utils.snr import snr\n\nsnr(\n 'perm_index',\n nb_files=1,\n nb_classes=16,\n nb_bytes=16,\n save=False,\n output_path='snr/permindex/permindex{}.npy'\n)\n" }, { "alpha_fraction": 0.60326087474823, "alphanum_fraction": 0.625, "avg_line_length": 14.333333015441895, "blob_id": "bb3481f42e4ceca314169b37fd3c7bbca19016de", "content_id": "d69d666297feb78aefb35859c4441141e5844912", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 184, "license_type": "no_license", "max_line_length": 31, "num_lines": 12, "path": "/scripts/snr_alpha.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute SNR for alpha\n\nfrom utils.snr import snr\n\nsnr(\n 'alpha_mask',\n nb_classes=255,\n can_be_zero=False,\n nb_files=1,\n save=False,\n output_path='snr/alpha.npy'\n)\n" }, { "alpha_fraction": 0.6375501751899719, "alphanum_fraction": 0.6656626462936401, "avg_line_length": 35, "blob_id": "886cf0327643fcf2499d5ff415f1a2f64a97f283", "content_id": "81cd2e975afac63016c4f6c6b8fabf136cb10a21", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2988, "license_type": "no_license", "max_line_length": 166, "num_lines": 83, "path": "/scripts/attack.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Run the attack and draw key rank accuracy by number of traces\nimport numpy as np\nimport pickle\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\n\nfrom utils.multilabelize import multilabelize as multilabelizer, multGF256, AES_Sbox\nimport scalib.postprocessing.rankestimation as rk\n\ntraces = np.load('data/traces1.npy', mmap_mode='r')\nmetadata = np.load('data/metadata1.npz')\n\nkey_ranks = []\nmin_traces = 0\nmax_traces = 3000\ntraces_step = 10\n\nfor nb_traces in tqdm(range(min_traces, max_traces + 1, traces_step)):\n multilabelize = multilabelizer(\n metadata['masks'][:nb_traces],\n metadata['keys'][:nb_traces],\n metadata['plaintext'][:nb_traces]\n )\n\n # Draw key and artificially replace the dataset's key with it\n k = np.random.randint(0, 256, 16)\n plaintexts = metadata['keys'][:nb_traces] ^ metadata['plaintext'][:nb_traces] ^ k\n\n # Get alpha\n lda_alpha = pickle.load(open('lda/alpha', 'rb'))\n prs_alpha = list(lda_alpha.predict_proba(traces[:nb_traces].astype(np.int16)))[0]\n\n max_alpha = np.argmax(prs_alpha, axis=1)+1\n true_alpha = metadata['masks'][:nb_traces, 18]\n\n # Get beta\n lda_beta = pickle.load(open('lda/beta', 'rb'))\n prs_beta = list(lda_beta.predict_proba(traces[:nb_traces].astype(np.int16)))[0]\n\n max_beta = np.argmax(prs_beta, axis=1)\n true_beta = metadata['masks'][:nb_traces, 17]\n\n # Get permindex\n lda_permindex = pickle.load(open('lda/permindex', 'rb'))\n prs_permindex = np.array(list(lda_permindex.predict_proba(traces[:nb_traces].astype(np.int16))))\n\n max_permindexes = np.argmax(prs_permindex, axis=2).T\n true_permindexes = multilabelize['perm_index']\n\n # Get permvalues\n lda_permvalue = pickle.load(open('lda/permvalue', 'rb'))\n prs_permvalue = np.array(list(lda_permvalue.predict_proba(traces[:nb_traces].astype(np.int16))))\n\n max_permvalue = np.argmax(prs_permvalue, axis=2).T\n true_permvalue = multilabelize['sbox_masked']\n\n probas = np.zeros((nb_traces, 256, 16))\n index_manip = np.argmax(prs_permindex, axis=0)\n\n # Attack\n for target_byte in range(16):\n for key_byte in range(256):\n C = multGF256(max_alpha, AES_Sbox[plaintexts[:, target_byte] ^ key_byte]) ^ max_beta\n proba_c = prs_permvalue[index_manip[:, target_byte], np.arange(nb_traces), C]\n\n probas[:, key_byte, target_byte] = proba_c\n\n probas = probas / np.sum(probas, axis=1)[:, None]\n key_probability = np.sum(np.log(probas), axis=0)\n\n # Estimate key rank and store it\n lmin, l, lmax = rk.rank_accuracy(-key_probability.T, k)\n\n key_ranks.append(l)\n\n# Plot key rank accuracy\nplt.plot(range(min_traces, max_traces + 1, traces_step), key_ranks)\nprint(key_ranks)\nplt.xlabel('Number of traces')\nplt.ylabel('Key rank estimation')\nplt.yscale('log', base=2)\nplt.yticks([np.float(2**0), np.float(2**16), np.float(2**32), np.float(2**48), np.float(2**64), np.float(2**80), np.float(2**96), np.float(2**112), np.float(2**128)])\nplt.show()\n" }, { "alpha_fraction": 0.6150000095367432, "alphanum_fraction": 0.6299999952316284, "avg_line_length": 15.666666984558105, "blob_id": "9879462917298ab02a2a7cc3b86f4df6cafe2ac6", "content_id": "7be557e6c9290cf979b2b0e97688681ce748bc3c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 200, "license_type": "no_license", "max_line_length": 34, "num_lines": 12, "path": "/scripts/lda_alpha.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute and pickle LDA for alpha\n\nfrom utils.lda import lda, pi\n\nlda(\n 'alpha_mask',\n 'snr/alpha.npy',\n nb_classes=255,\n can_be_zero=False,\n save=False,\n output_path='lda/alpha'\n)\n" }, { "alpha_fraction": 0.630081295967102, "alphanum_fraction": 0.6504064798355103, "avg_line_length": 17.923076629638672, "blob_id": "0e60347952f8372b6c922b2021736f8d7657f99b", "content_id": "ebe68ed3f6af45e3fdb1ec832a4460384e604627", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 246, "license_type": "no_license", "max_line_length": 52, "num_lines": 13, "path": "/scripts/lda_permindex.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute and pickle LDA for the permutation indexes\n\nfrom utils.lda import lda, pi\n\nlda(\n 'perm_index',\n 'snr/permindex/permindex{}.npy',\n nb_bytes=16,\n nb_classes=16,\n lda_dim=5,\n save=False,\n output_path='lda/permindex'\n)\n" }, { "alpha_fraction": 0.6130719184875488, "alphanum_fraction": 0.6287581920623779, "avg_line_length": 26.321428298950195, "blob_id": "2d007d7ea317fd0c090da64b516dbe1172149529", "content_id": "7090dfbfabb4b11755d50086917f73f0c3628178", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1530, "license_type": "no_license", "max_line_length": 130, "num_lines": 56, "path": "/scripts/utils/snr.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute SNR\nimport numpy as np\nimport scalib.metrics as mts\nimport matplotlib.pyplot as plt\n\nfrom tqdm import tqdm\n\nfrom utils.multilabelize import multilabelize\n\n# Variables\ntraces_path = 'data/traces{}.npy'\nmetadata_path = 'data/metadata{}.npz'\nnb_values_per_trace = 1_000_000\n\n# SNR function\ndef snr(\n target_label,\n nb_traces=99_999,\n nb_classes=256,\n nb_bytes=1,\n can_be_zero=True,\n nb_files=8,\n plot=True,\n save=False,\n output_path=None\n):\n # Extract SNR\n snr = mts.SNR(nc=nb_classes, ns=nb_values_per_trace, np=nb_bytes)\n\n for index_file in tqdm(range(nb_files)):\n traces = np.load(traces_path.format(index_file + 1), mmap_mode='r')\n metadata = np.load(metadata_path.format(index_file + 1))\n\n multilabel = multilabelize(metadata['masks'][:nb_traces], metadata['keys'][:nb_traces], metadata['plaintext'][:nb_traces])\n\n traces = traces[:nb_traces].astype(np.int16)\n labels = np.reshape(multilabel[target_label][:nb_traces], newshape=(nb_traces, nb_bytes)).astype(np.uint16)\n\n if not can_be_zero:\n labels -= 1\n\n snr.fit_u(traces, labels)\n\n snr_val = snr.get_snr()\n\n if save:\n if output_path is None:\n print('No filename provided. Skipping saving')\n else:\n for index_byte in range(nb_bytes):\n np.save(output_path.format(index_byte), snr_val[index_byte])\n\n if plot:\n for index_byte in range(nb_bytes):\n plt.plot(snr_val[index_byte])\n plt.show()\n" }, { "alpha_fraction": 0.7557692527770996, "alphanum_fraction": 0.8038461804389954, "avg_line_length": 56.77777862548828, "blob_id": "3bb8a8e5f986e4c9142039e481a17dbe264b918d", "content_id": "188177782a2e009ee2409c24fc3777034a206390", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 520, "license_type": "no_license", "max_line_length": 223, "num_lines": 9, "path": "/README.md", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# ASCADv2-Internship\n\nThis repository holds the code I wrote for my internship at UCLouvain on SCA against ASCADv2.\n\nIt relies on SCALib, a python library developped by Olivier Bronchain.\n\nAll the code contained in this repository is mine, except the one in `scripts/utils/multilabelize.py` which I adapted from [https://github.com/ANSSI-FR/ASCAD/blob/58c527fdd82c5c5896d61eeb019cccdd628248eb/ASCAD_generate.py].\n\nI intend to make this repository more reproducible in the future, but for now I haven't written instructions for it.\n" }, { "alpha_fraction": 0.6402116417884827, "alphanum_fraction": 0.6560846567153931, "avg_line_length": 16.18181800842285, "blob_id": "24e12d1e1a93da0d119effafcc0b7c27c5cc0a9f", "content_id": "678f962edbfa1f78720cc00592799b6cfe86181b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 189, "license_type": "no_license", "max_line_length": 47, "num_lines": 11, "path": "/scripts/snr_permvalue.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Compute SNR for the permuted values\n\nfrom utils.snr import snr\n\nsnr(\n 'sbox_masked',\n nb_files=1,\n nb_bytes=16,\n save=False,\n output_path='snr/permvalue/permvalue{}.npy'\n)\n" }, { "alpha_fraction": 0.636686384677887, "alphanum_fraction": 0.6485207080841064, "avg_line_length": 39.238094329833984, "blob_id": "031e4346ba05f9f346bebf2adeab3f15be9093f2", "content_id": "7fadd7db1fa31025a3f7ada83ff6b56771fcd230", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 845, "license_type": "no_license", "max_line_length": 93, "num_lines": 21, "path": "/scripts/h5_to_npy.py", "repo_name": "yann-a/ASCADv2-Internship", "src_encoding": "UTF-8", "text": "# Transform ANSSI's h5 files to npy/npz files, used for the attack\n\nimport numpy as np\nimport h5py as h5\nfrom tqdm import tqdm\n\nbase_filename = 'ascadv2-stm32-conso-raw-traces{}.{}'\nN = 8\n\nfor index_file in tqdm(range(N, N+1)):\n with h5.File(base_filename.format(index_file, 'h5')) as data:\n traces = np.array(data['traces'])\n masks = np.array(data['metadata'][:, 'masks'])\n keys = np.array(data['metadata'][:, 'key'])\n plaintext = np.array(data['metadata'][:, 'plaintext'])\n ciphertext = np.array(data['metadata'][:, 'ciphertext'])\n\n with open('data/traces{}.npy'.format(index_file), 'wb') as npyfile:\n np.save(npyfile, traces)\n with open('data/metadata{}.npz'.format(index_file), 'wb') as npyfile:\n np.savez(npyfile, masks=masks, keys=keys, plaintext=plaintext, ciphertext=ciphertext)\n" } ]

14

MrCodeGuy/Jerry1.2

https://github.com/MrCodeGuy/Jerry1.2

2ce5f25f7a6aef1e4cb38ecbba8f3c9f244d3bc1

b78027ba6a36de77e25bf2f5d1c17030238895ee

bf086a61a5097837ac136faf7f125d11b5406fab

refs/heads/master

2022-12-11T19:27:45.533724

2020-08-25T16:18:59

290,264,190

0

null

[ { "alpha_fraction": 0.5936739444732666, "alphanum_fraction": 0.6072297692298889, "avg_line_length": 46.3220329284668, "blob_id": "34ef9dbe30a50b51dd3719c299b0559d187af006", "content_id": "e07a9b4bf3fbcd49d1837a2ed03e859d171e5b94", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2877, "license_type": "no_license", "max_line_length": 203, "num_lines": 59, "path": "/Jerry 1.2.py", "repo_name": "MrCodeGuy/Jerry1.2", "src_encoding": "UTF-8", "text": "def favor(): # ask to define favor\r\n favor = int(input(\"Do you need any help from me? (reply with a positive number for yes and a negative number for no do not type zero).\")) # says do you need help\r\n \r\n if favor < 0: # if the favor is less than zero\r\n print(\"Bye! More upgrades will come!\") # say bye! more upgrades will come\r\n\r\n \r\n else: #if not\r\n print(\"The favor would be a fun shape\") # says the favor would be a fun shape\r\n import turtle # first step on bringing the turtle\r\n import random\r\n fred = turtle.Pen()# second step on bringing the turtle\r\n colorlist = [\"red\",\"orange\",\"yellow\",\"green\",\"blue\",\"purple\",\"pink\"] # make a list called colorlist\r\n fred.shape(\"turtle\") # Change the shape of the turtle to a turtle\r\n fred.speed(0) # sets speed to zero\r\n \r\n\r\n for i in range(456): # repeat 456 times\r\n col=random.choice (colorlist)# pick a random color from color list\r\n fred.color(col) # change color to col\r\n fred.left(78) # turns left 78 degrees\r\n fred.forward(90) # moves forward 90 pixels\r\n\r\n \r\n favor2 = int(input(\" Do you want another favor? Type a negative number for no and a positive number for yes\")) # asks if you want another favor\r\n if favor2 > 0: #if you asked for the favor\r\n print(\"Ok! The other favor is a game called Jerry Guesses\") # print the message\r\n print (\" The rules are simple. I will choose a number and all you need to do is guess a number which is greater than the number you can use the numbers from the range of 1-11\")# print the message\r\n numberlist = [ \"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\"] # create list\r\n\r\n \r\n for i in range(3): # repeat 3 times\r\n number = random.choice(numberlist) # pick a number in number list\r\n guess = int(input(\" Pick your number!\")) # say pick your number\r\n print(\"Good Job! The number was...\") # say the number was\r\n print(number) # say the number\r\n \r\n \r\n \r\n print(\"Bye! More upgrades will come!\") # says bye! more upgrades will come \r\n \r\n\r\n\r\nname = input (\"What is your name?\") # ask what is your name\r\nprint ( \"Hello\" , name) # says hello your name\r\nprint (\"My name is Jerry!\") # says his name is Jerry\r\nwelness = 0 # the variable wellness equals 0\r\nwellness = int (input (\"How are you doing? (respond as a positive number for well and a negetive number for not well do not choose zero)\")) # asks if you are doing well\r\n\r\n\r\nif wellness > 0: # if the variable wellness is greater than zero\r\n print(\"Awesome!\") # say awesome\r\n favor() # do the function favor\r\n\r\n \r\n \r\nelse: # if not\r\n print(\"Hope you get better!\") # says hope you get better\r\n favor() # do the function favor\r\n \r\n\r\n \r\n\r\n\r\n \r\n\r\n" }, { "alpha_fraction": 0.6857143044471741, "alphanum_fraction": 0.7428571581840515, "avg_line_length": 16.5, "blob_id": "115595673f472dfa84e0203634b5dd9d6b24309f", "content_id": "b72f36c5f9b8284730a6b402c5986021cd4dcc48", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 35, "license_type": "no_license", "max_line_length": 23, "num_lines": 2, "path": "/README.md", "repo_name": "MrCodeGuy/Jerry1.2", "src_encoding": "UTF-8", "text": "# Jerry1.2\nThe third of the Jerrys\n" } ]

2

vilcans/LD24

https://github.com/vilcans/LD24

87a4aefbbcceb0b33a30fcac64193de577582e14

b957acc0880f96af07dfa57a07f447f6bef962f0

74d838e11813a196a5a83fa12bc6669aede280a0

refs/heads/master

2021-01-22T16:18:21.173203

2012-09-14T08:50:39

null

0

null

[ { "alpha_fraction": 0.4834834933280945, "alphanum_fraction": 0.48848849534988403, "avg_line_length": 20.717391967773438, "blob_id": "75032a07620c25ce984fafbf13b555af8d68ea7d", "content_id": "73a32314dd6cde28f5b227b37d30d2f3e9d2583d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 999, "license_type": "no_license", "max_line_length": 97, "num_lines": 46, "path": "/bin/make-levels.py", "repo_name": "vilcans/LD24", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\nimport yaml\nimport sys\nimport json\n\nlevels = yaml.load_all(sys.stdin)\n\nprint 'levels = [];'\n\ntypes = {\n 'p': 'pawn',\n 'b': 'bishop',\n 'r': 'rook',\n 'n': 'knight',\n 'k': 'king',\n 'q': 'queen',\n}\n\nfor number, data in enumerate(levels, 1):\n\n print 'levels[%d] = function(board) {' % number\n\n board = data['board']\n rows = board.split(' ')\n assert len(rows) == 8\n assert all(len(row) == 8 for row in rows)\n rows.reverse()\n for r in rows:\n print '// ' + r\n\n for r in range(8):\n for c in range(8):\n p = rows[r][c]\n if p == '.':\n continue\n type = types[p.lower()]\n team = 'Piece.WHITE' if p.islower() else 'Piece.BLACK'\n print 'board.addPiece(new Piece({type: %r, team: %s}), board.getSquare(%d, %d));' % (\n type, team, r, c\n )\n\n print 'return %s;' % json.dumps({\n 'description': data['description']\n })\n print '}'\n" }, { "alpha_fraction": 0.7477797269821167, "alphanum_fraction": 0.75133216381073, "avg_line_length": 23.478260040283203, "blob_id": "df8659382d8c2edfd2b288cac8a79730971b7bd1", "content_id": "d65cc17da2bf61a7d32c82a498567309a8b42546", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 563, "license_type": "no_license", "max_line_length": 75, "num_lines": 23, "path": "/bin/release", "repo_name": "vilcans/LD24", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\nexport RELEASES_MASTER_REPO=filur:releases/LD24.git\nexport GIT_WORK_TREE=$PWD\nexport GIT_DIR=$PWD/releases.git\n\nif [ ! -e $GIT_DIR ]; then\n git clone --bare $RELEASES_MASTER_REPO $GIT_DIR\nfi\ngit config core.bare false\ngit config remote.origin.fetch '+refs/heads/*:refs/remotes/origin/*'\ngit fetch\ngit reset --mixed origin/master # fast-forward\n\ngit status\ngit add -fA site/ nginx.conf\ngit status\necho 'Starting a shell so you can look around. Exit the shell to continue.'\nbash\necho 'Press enter to make the release'\nread\ngit commit\ngit push origin\n" } ]

2

cookingaunt/First

https://github.com/cookingaunt/First

79c78b540e33ddb68d2d9e706edcd3514ffe4091

64420417e26b1ab8df79ba202cd526d8ecbaf86d

eb58ed67222c962c7a4abb3a117b493121b4e9a9

refs/heads/master

2022-12-06T15:12:36.127482

2020-08-26T11:06:52

290,471,091

3

1

null

[ { "alpha_fraction": 0.800000011920929, "alphanum_fraction": 0.800000011920929, "avg_line_length": 16.5, "blob_id": "8955d7e174959f993e2a6f540f916730a1675c1e", "content_id": "5f6954ccde50e5384017b1ace0de1a23d0b4292a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 35, "license_type": "no_license", "max_line_length": 26, "num_lines": 2, "path": "/README.md", "repo_name": "cookingaunt/First", "src_encoding": "UTF-8", "text": "# First\nmy first github repository\n" }, { "alpha_fraction": 0.7749999761581421, "alphanum_fraction": 0.7749999761581421, "avg_line_length": 40, "blob_id": "45191c9d00216f41c357f3cfc65e9abfaece0bb5", "content_id": "99d221b475dfe73b71281c0eaf81a5766d27af24", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 40, "license_type": "no_license", "max_line_length": 40, "num_lines": 1, "path": "/first_git.py", "repo_name": "cookingaunt/First", "src_encoding": "UTF-8", "text": "print(\"this is my first GitHub attempt\")" } ]

2

mosherbd/devcontainer-base

https://github.com/mosherbd/devcontainer-base

b2fe8fa9a8d970bc4daacb583d3663c6904aef4b

1abc31d585353d292a716bf0df7502e1d85bf0d0

0e49509197197dd729987432818ac79e45f31009

refs/heads/main

2023-02-25T05:59:57.737736

2021-10-30T11:56:09

331,085,551

0

null

[ { "alpha_fraction": 0.7698412537574768, "alphanum_fraction": 0.7936508059501648, "avg_line_length": 41, "blob_id": "d01df7b981c24cd5af3fba546fb72a87dbb88341", "content_id": "ce024a07c97321359aabf3215b4be6ae014edec0", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": true, "language": "Shell", "length_bytes": 252, "license_type": "permissive", "max_line_length": 114, "num_lines": 6, "path": "/dotnet/utils/dotnet-install.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/sh\n\ncurl https://packages.microsoft.com/config/ubuntu/21.04/packages-microsoft-prod.deb -o packages-microsoft-prod.deb\ndpkg -i packages-microsoft-prod.deb\nrm packages-microsoft-prod.deb\napt-helper --no-upgrade apt-transport-https dotnet-sdk-5.0\n" }, { "alpha_fraction": 0.7507987022399902, "alphanum_fraction": 0.7571884989738464, "avg_line_length": 19.933332443237305, "blob_id": "3b9681c878314d91f75f1fe223121cbc1972bd10", "content_id": "048deb024c7fb838877d33443aa268b7167cd3f3", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 313, "license_type": "permissive", "max_line_length": 52, "num_lines": 15, "path": "/base/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM ubuntu\nLABEL maintainer=\"[email protected]\"\nLABEL LABEL version=\"1.0\"\nLABEL description=\"base development container image\"\n\nARG USERNAME=\"docker\"\n\nENV DEVCONTAINER_UTILS_PATH=/usr/local/bin\n\nCOPY utils/* ${DEVCONTAINER_UTILS_PATH}/\n\nRUN apt-helper sudo curl && \\\n\tadduser-nopasswd-sudo.sh ${USERNAME}\n\nUSER ${USERNAME}" }, { "alpha_fraction": 0.7563451528549194, "alphanum_fraction": 0.7664974331855774, "avg_line_length": 34.90909194946289, "blob_id": "abbc21f0b41bc6603b334f95b8b13dc41a0a7f5d", "content_id": "92f1264da00df1a67f667c06a45afeea254c1a26", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 394, "license_type": "permissive", "max_line_length": 195, "num_lines": 11, "path": "/gui/README.md", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "# dev-base-gui\n\n## Overview\nThis is a ubuntu-based docker image that provides the required dependencies to forward x11 clients from a docker container. Use this Dockerfile to extend this functionality to ubuntu base images.\n\n## X11 Servers\n### Windows\n- VcXSrv: https://sourceforge.net/projects/vcxsrv/\n- Xming https://sourceforge.net/projects/xming/\n### OSX\n- XQuartz: https://www.xquartz.org/" }, { "alpha_fraction": 0.5279411673545837, "alphanum_fraction": 0.5367646813392639, "avg_line_length": 16.921052932739258, "blob_id": "1e1404e1581e153337124c4e526dcc45fc452808", "content_id": "20efaea15b0f38b3d9014e58dd382508e779f642", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 680, "license_type": "permissive", "max_line_length": 56, "num_lines": 38, "path": "/node/utils/npm-install.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\nNODE_VERSION=node\nNPM_VERSION=\nPACKAGES=()\nwhile [[ $# -gt 0 ]]; do\n key=\"$1\"\n case $key in\n --node-version)\n NODE_VERSION=$2\n shift # past argument\n shift # past value\n ;;\n --npm-version)\n NPM_VERSION=$2\n shift # past argument\n shift # past value\n ;;\n *) # unknown option\n PACKAGES+=(\"$1\") # save it in an array for later\n shift # past argument\n ;;\n esac\ndone\n\n. nvm-install.sh\n\n. nvm-activate.sh\n\nnvm install ${NODE_VERSION}\n\nif [[ ! -z ${NPM_VERSION} ]]; then\n npm install -g npm@${NPM_VERSION}\nfi\n\nif [[ ${#PACKAGES[@]} -gt 0 ]]; then\n npm install \"${PACKAGES[@]}\"\nfi" }, { "alpha_fraction": 0.8469387888908386, "alphanum_fraction": 0.8469387888908386, "avg_line_length": 48.5, "blob_id": "f08e51ee6ec0384678f99f7b4bb5c179ae221a8c", "content_id": "a01d43314cb9e80a6b7f21815ea7db55c4881744", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 98, "license_type": "permissive", "max_line_length": 61, "num_lines": 2, "path": "/cpp/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\nRUN sudo apt-helper --no-upgrade build-essential gdb valgrind" }, { "alpha_fraction": 0.642201840877533, "alphanum_fraction": 0.6513761281967163, "avg_line_length": 42.599998474121094, "blob_id": "021d00dc936873f3605653a57a1a3a86dbdd4923", "content_id": "42c85e34c99250ceedfb91f2ab42826d6d4c9346", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 218, "license_type": "permissive", "max_line_length": 118, "num_lines": 5, "path": "/node/utils/nvm-install.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/sh\n\nNVM_VERSION=$(curl -s https://api.github.com/repos/nvm-sh/nvm/tags | grep name -m 1 | xargs | cut -c7- | sed 's/,$//')\n\ncurl -o- https://raw.githubusercontent.com/nvm-sh/nvm/${NVM_VERSION}/install.sh | bash\n" }, { "alpha_fraction": 0.6783784031867981, "alphanum_fraction": 0.6932432651519775, "avg_line_length": 36.04999923706055, "blob_id": "231663117182d6a6745a906c9df6565aace269c3", "content_id": "62d1e615a531d3dc46afbc7b427916733736539e", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 740, "license_type": "permissive", "max_line_length": 146, "num_lines": 20, "path": "/azureagent/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\n\nARG TARGETARCH=amd64\nARG AGENT_VERSION=2.185.1\n\nWORKDIR /home/docker/azp\n\nCOPY utils/* ${DEVCONTAINER_UTILS_PATH}/\n\nRUN curl -LsS https://aka.ms/InstallAzureCLIDeb | sudo bash && \\\n if [ \"$TARGETARCH\" = \"amd64\" ]; then \\\n AZP_AGENTPACKAGE_URL=https://vstsagentpackage.azureedge.net/agent/${AGENT_VERSION}/vsts-agent-linux-x64-${AGENT_VERSION}.tar.gz; \\\n else \\\n AZP_AGENTPACKAGE_URL=https://vstsagentpackage.azureedge.net/agent/${AGENT_VERSION}/vsts-agent-linux-${TARGETARCH}-${AGENT_VERSION}.tar.gz; \\\n fi; \\\n curl -LsS \"$AZP_AGENTPACKAGE_URL\" | tar -xz; \\\n sudo ./bin/installdependencies.sh && \\\n sudo apt-helper --clean\n\nENTRYPOINT [ \"bash\", \"-c\", \"azureagent-start.sh\" ]" }, { "alpha_fraction": 0.7576923370361328, "alphanum_fraction": 0.7769230604171753, "avg_line_length": 26.88888931274414, "blob_id": "f5a6b9b2b830b05c97a0b904694866456ee79199", "content_id": "776dc4f060dd9e97d8e4555558413dcf70691680", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 260, "license_type": "permissive", "max_line_length": 64, "num_lines": 9, "path": "/gui/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\r\n\r\nLABEL maintainer=\"[email protected]\"\r\nLABEL LABEL version=\"1.0\"\r\nLABEL description=\"containerized gui development image\"\r\n\r\nENV DISPLAY=host.docker.internal:0.0\r\n\r\nRUN sudo apt-helper --no-install-recommends xorg libgl1-mesa-glx\r\n" }, { "alpha_fraction": 0.5, "alphanum_fraction": 0.5, "avg_line_length": 18, "blob_id": "4c1f8369ac474bbdc10eac5d7b2ea8044ed65a3f", "content_id": "62de4e9e2f66c7d6eda17fd3edecd135cf3b3e23", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 94, "license_type": "permissive", "max_line_length": 52, "num_lines": 5, "path": "/node/utils/nvm-activate.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/sh\n\nexport NVM_DIR=\"${HOME}/.nvm\"\n\n[ -s \"${NVM_DIR}/nvm.sh\" ] && \\. \"${NVM_DIR}/nvm.sh\"" }, { "alpha_fraction": 0.6292906403541565, "alphanum_fraction": 0.6384439468383789, "avg_line_length": 24.764705657958984, "blob_id": "fad5c4438daeb4bc2b9ec5f24241215c015ddc27", "content_id": "bdb6bbc6ce0eb5ba914aa350e65264514bf20314", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 437, "license_type": "permissive", "max_line_length": 70, "num_lines": 17, "path": "/base/utils/adduser-nopasswd-sudo.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/sh\n\n# Adds a sudo user that does not require a password.\n\n# Requires sudo\n\nif [ \"$#\" -ne 1 ]; then\n echo \"usage: ${0} [username]\"\n exit 1\nfi\n\nUSERNAME=\"$1\"\nadduser --disabled-password --gecos '' ${USERNAME}\nadduser ${USERNAME} sudo\necho '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers\necho 'USER=\"'${USERNAME}'\"; export USER' >> /home/${USERNAME}/.bashrc\necho 'USER=\"'${USERNAME}'\"; export USER' >> /home/${USERNAME}/.profile" }, { "alpha_fraction": 0.7304075360298157, "alphanum_fraction": 0.7304075360298157, "avg_line_length": 28.380952835083008, "blob_id": "05468ab9fc7b57dcf2659f6fa107d0cd0fc2f457", "content_id": "ca90f03d9f4a29458c0f398a3ca1b99f4d88fe0a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 638, "license_type": "permissive", "max_line_length": 197, "num_lines": 21, "path": "/java/README.md", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "# devcontainer-java\r\n\r\n## Overview\r\n\r\nThis Dockerfile extends the brandonmosher/devcontainer-base container image with sdkman, java sdk, ant, ivy, maven, and gradle. Specific versions for each can easily be specified in the Dockerfile.\r\n\r\n## Utils\r\n\r\nThe following utils are copied to DEVCONTAINER_UTILS_PATH.\r\n\r\n### ivy_install.sh \r\n\r\nAutomates installation of the specified version of ivy\r\n\r\n ivy_install.sh [ivy version] [ivy jar dest filepath]\r\n\r\n### sdk_install.sh\r\n\r\nWrapper for sdkman to install multiple sdks in a single command and clean up junk files to reduce container size.\r\n\r\n sdk_install.sh [\"[sdk [version]]\", ...]\r\n" }, { "alpha_fraction": 0.7987477779388428, "alphanum_fraction": 0.7987477779388428, "avg_line_length": 52.238094329833984, "blob_id": "44d1dbec16561b95b81f9313f8c80d86fe5a021d", "content_id": "07a29e0d301f023bd2f8db4699cc4e42f885b1f6", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1118, "license_type": "permissive", "max_line_length": 478, "num_lines": 21, "path": "/base/README.md", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "# devcontainer-base\n\n## Overview\n\nThis Dockerfile builds a ubuntu container image intended as a common base for language-specific or project-specific development images. The goal is to eliminate duplication of boilerplate configuration needed to make the container more suitable for interactive use. This includes setting the timezone and locale, and creating a non-root sudo user. No assumptions are made about derived containers i.e. no compilers, version control, build system, libraries, etc... are included.\n\n## Utils\n\nThe following utils are copied to DEVCONTAINER_UTILS_PATH. DEVCONTAINER_UTILS_PATH defaults to /usr/local/bin making the utils available from $PATH. Furhtermore, DEVCONTAINER_UTILS_PATH is set as a persistent environment variable so that derived images may use these utils and add additional utils as needed.\n\n### adduser-nopassword-sudo.sh\n\nAdds a sudo user that requires no password for quick and easy ad-hoc container changes\n\n adduser-nopassword-sudo.sh [username]\n\n### apt-install.sh\n\nCombines apt update/install/dist-upgrade/cleanup tasks\n\n apt-install.sh [options] [package names]\n" }, { "alpha_fraction": 0.5910151600837708, "alphanum_fraction": 0.5921820402145386, "avg_line_length": 26.190475463867188, "blob_id": "a235c807fa4baf21cb9005b21b3390fa2dffd7c4", "content_id": "ec7c79e6ad832bd69b9cc503933f2a2d74208a2b", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1714, "license_type": "permissive", "max_line_length": 76, "num_lines": 63, "path": "/update.py", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python3\n\nimport glob\nimport re\nfrom pathlib import Path\nimport os\nfrom subprocess import Popen, PIPE\n\nupdate_build = set(Path('update_build').read_text().splitlines())\nupdate_push = set(Path('update_push').read_text().splitlines())\n\ndockerfilepaths = [Path(dfp) for dfp in glob.glob(\"./*/Dockerfile\")]\n\nm = {}\nfor dockerfilepath in dockerfilepaths:\n dockerfiletext = dockerfilepath.open('r').read()\n f = str(dockerfilepath.parent)\n t = re.match(r'^FROM.*', dockerfiletext).group()\n if '-' not in t:\n continue\n t = t.split('-')[1]\n if f not in m:\n m[f] = set()\n m[f].add(t)\n\ncompleted = set()\ndef build(target, tag):\n if (target in completed) or (target not in update_build):\n return\n if target in m:\n for dep in m[target]:\n build(dep, tag)\n \n print(\"Building {}...\".format(target))\n p = Popen([\n 'docker',\n 'build',\n '--no-cache',\n '-t',\n 'brandonmosher/devcontainer-{}:{}'.format(target, tag),\n Path(target).resolve()\n ], stdout=PIPE, stderr=PIPE, stdin=PIPE)\n out = Path('{}.out'.format(target))\n out.open('wb').write(p.stderr.read())\n out.open('ab').write(p.stdout.read())\n \n if target not in update_push:\n return\n \n cmd = 'docker push brandonmosher/devcontainer-{}:{}'.format(target, tag)\n print(\"Pushing {}...\".format(target))\n p = Popen([\n 'docker',\n 'push',\n 'brandonmosher/devcontainer-{}:{}'.format(target, tag)\n ], stdout=PIPE, stderr=PIPE, stdin=PIPE)\n out.open('ab').write(p.stderr.read())\n out.open('ab').write(p.stdout.read())\n\n completed.add(target)\n\nfor target in m:\n build(target, 'latest')\n\n" }, { "alpha_fraction": 0.7684887647628784, "alphanum_fraction": 0.7749196290969849, "avg_line_length": 25, "blob_id": "0929813fb9d7dfeeae1863e0bb28f3e31ae108a7", "content_id": "6a46c7904544cfc06e046be5d03c2dd429eeec56", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 311, "license_type": "permissive", "max_line_length": 78, "num_lines": 12, "path": "/node/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\n\nLABEL maintainer=\"[email protected]\"\nLABEL LABEL version=\"1.0\"\nLABEL description=\"node development container image\"\n\nENV NODE_VERSION node\nENV NPM_VERSION latest\n\nCOPY utils/* ${DEVCONTAINER_UTILS_PATH}/\n\nRUN npm-install.sh --node-version ${NODE_VERSION} --npm-version ${NPM_VERSION}" }, { "alpha_fraction": 0.8627451062202454, "alphanum_fraction": 0.8627451062202454, "avg_line_length": 50, "blob_id": "c9240a1646a9a4bdad5eaeb30382fed1be30388a", "content_id": "606b44059b50c0fd84bda2cc40b491673994f9d2", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 102, "license_type": "permissive", "max_line_length": 85, "num_lines": 2, "path": "/README.md", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "# devcontainers\nDocker containers suitable for a general purpose interactive development environment.\n" }, { "alpha_fraction": 0.5431720614433289, "alphanum_fraction": 0.5523576140403748, "avg_line_length": 21.68055534362793, "blob_id": "e8e852e1732638eff6d38ce5e074cb92a51c7416", "content_id": "4ae0a873b5b7e86b6713d6dab31ceb592bd66fd1", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 1633, "license_type": "permissive", "max_line_length": 133, "num_lines": 72, "path": "/base/utils/apt-helper", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\n# Runs updates/install/upgrade/cleanup tasks for docker\n\nRUN_STAGES=()\nSKIP_STAGES=()\nPACKAGES=()\nwhile [[ $# -gt 0 ]]; do\n key=\"$1\"\n case $key in\n --update|--install|--upgrade|--clean)\n RUN_STAGES+=(\"${1:2}\")\n shift # past argument\n ;;\n --no-update|--no-install|--no-upgrade|--no-clean)\n SKIP_STAGES+=(\"${1:5}\")\n shift # past argument\n ;;\n *) # unknown option\n PACKAGES+=(\"$1\") # save it in an array for later\n shift # past argument\n ;;\n esac\ndone\n\nIMPLICIT_ALL=\n\nrun () {\n (([[ -n ${IMPLICIT_ALL} ]] || [[ \" ${RUN_STAGES[*]} \" =~ \" ${1} \" ]]) && (! [[ \" ${SKIP_STAGES[*]} \" =~ \" ${1} \" ]])) && return 0\n return -1\n}\n\nif run install && [[ ${#PACKAGES[@]} -eq 0 ]]; then\n echo \"usage: apt-helper [--update] [--install] [--upgrade] [--clean] [package names]\"\n exit 1\nfi\n\nif [[ ${#RUN_STAGES[@]} -eq 0 ]]; then\n echo \"Implicitly performing all non-specified operations...\"\n IMPLICIT_ALL=true\nfi\n\nset -- \"${PACKAGES[@]}\"\n\nif run update; then\n echo \"Updating...\"\n apt-get update\n # if [[ -n $(find /var/lib/apt/lists/ -maxdepth 0 -empty) ]]; then\n # echo \"Updating...\"\n # apt-get update\n # else\n # echo \"Already updated.\"\n # fi\nfi\n\nif run install; then\n echo \"Installing...\"\n DEBIAN_FRONTEND=noninteractive apt-get install -y \"$@\"\nfi\n\nif run upgrade; then\n echo \"Upgrading...\"\n DEBIAN_FRONTEND=noninteractive apt-get dist-upgrade -y\nfi\n\nif run clean; then\n echo \"Cleaning...\"\n apt-get clean autoclean\n apt-get autoremove -y\n rm -rf /var/lib/apt/lists/*\n rm -rf /tmp/*\nfi\n" }, { "alpha_fraction": 0.7364341020584106, "alphanum_fraction": 0.7441860437393188, "avg_line_length": 27.77777862548828, "blob_id": "2315657a4ef52c71069373a0e6f8a23ff7ebb641", "content_id": "036f6bd62b3b6bc05ad95a86b8c12ec6feab0079", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 258, "license_type": "permissive", "max_line_length": 58, "num_lines": 9, "path": "/playwright/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-node\n\nLABEL maintainer=\"[email protected]\"\nLABEL LABEL version=\"1.0\"\nLABEL description=\"playwright development container image\"\n\nRUN . nvm-activate.sh && \\\n npx playwright install-deps && \\\n apt-helper --no-update --no-upgrade git" }, { "alpha_fraction": 0.8214285969734192, "alphanum_fraction": 0.8452380895614624, "avg_line_length": 41.5, "blob_id": "17bb5add363813b57cccbdb7ced3e2087cb30f5c", "content_id": "aa0b9a6aad3aa744f40870324ab5370f68272052", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 84, "license_type": "permissive", "max_line_length": 47, "num_lines": 2, "path": "/python/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\nRUN apt-helper --no-upgrade python3 python3-pip" }, { "alpha_fraction": 0.801886796951294, "alphanum_fraction": 0.801886796951294, "avg_line_length": 20.399999618530273, "blob_id": "c4934778ecced1f96846ac7b3ecf841adbe96e21", "content_id": "724be64c1faae0bf3f27e07eec550deeae49313a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 106, "license_type": "permissive", "max_line_length": 40, "num_lines": 5, "path": "/dotnet/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\n\nCOPY utils/* ${DEVCONTAINER_UTILS_PATH}/\n\nRUN sudo dotnet-install.sh" }, { "alpha_fraction": 0.7095761299133301, "alphanum_fraction": 0.7174254059791565, "avg_line_length": 31.526315689086914, "blob_id": "50ef8676c396892187aa01bc51b35c6bc4942f61", "content_id": "f0ff0b3c4db0ef30e67c6f0758b7c85469cbc088", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 637, "license_type": "permissive", "max_line_length": 89, "num_lines": 19, "path": "/java/Dockerfile", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "FROM brandonmosher/devcontainer-base\r\n\r\nLABEL maintainer=\"[email protected]\"\r\nLABEL LABEL version=\"1.0\"\r\nLABEL description=\"java development container image\"\r\n\r\nARG JAVA_VERSION=\"java\"\r\nARG ANT_VERSION=\"ant\"\r\nARG MAVEN_VERSION=\"maven\"\r\nARG GRADLE_VERSION=\"gradle\"\r\nARG IVY_VERSION=2.5.0\r\nARG IVY_JAR_DEST_FILEPATH=~/.sdkman/candidates/ant/current/lib/\r\nARG VERSION_CONTROL=git\r\n\r\nCOPY utils/* ${DEVCONTAINER_UTILS_PATH}/\r\n\r\nRUN sudo apt-helper --no-upgrade zip unzip gpg ${VERSION_CONTROL} && \\\r\n sdk-install.sh ${JAVA_VERSION} ${ANT_VERSION} ${MAVEN_VERSION} ${GRADLE_VERSION} && \\\r\n ivy-install.sh ${IVY_VERSION} ${IVY_JAR_DEST_FILEPATH}\r\n" }, { "alpha_fraction": 0.6689059734344482, "alphanum_fraction": 0.6756237745285034, "avg_line_length": 27.16216278076172, "blob_id": "381c602c9d79f00fd164c7b015badb03ad0d2599", "content_id": "c9a56d3af1f5f205ef8b9bf2c4e01359a45215e4", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 1042, "license_type": "permissive", "max_line_length": 101, "num_lines": 37, "path": "/java/utils/ivy-install.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\n# Automates installation of the specified version of ivy\n\n# Requires curl, gpg, tar packages\n\nif [ \"$#\" -ne 2 ]; then\n echo \"usage: ${0} [ivy version] [ivy jar dest filepath]\"\n exit 1\nfi\n\ncd ~\n\nANT_HOST=https://downloads.apache.org/ant\nANT_KEYS_FILENAME=KEYS\n\nIVY_VERSION=\"${1}\"\nIVY_JAR_DEST_FILEPATH=\"${2}\"\n\nIVY_HOST=${ANT_HOST}/ivy/${IVY_VERSION}\nIVY_TAR_FILENAME=apache-ivy-${IVY_VERSION}-bin.tar.gz\nIVY_JAR_SRC_FILEPATH=${IVY_TAR_FILENAME/-bin.tar.gz/}/ivy-${IVY_VERSION}.jar\nIVY_ASC_FILENAME=${IVY_TAR_FILENAME}.asc\n\ncurl -O -J -L ${IVY_HOST}/${IVY_TAR_FILENAME}\ncurl -O -J -L ${IVY_HOST}/${IVY_ASC_FILENAME}\ncurl -O -J -L ${ANT_HOST}/${ANT_KEYS_FILENAME}\n\ngpg --import ${ANT_KEYS_FILENAME}\nif ! gpg --verify ${IVY_ASC_FILENAME} ${IVY_TAR_FILENAME}; then\n echo \"error: gpg could not be verified\"\n exit 1\nfi\n\nmkdir -p ${IVY_JAR_DEST_FILEPATH}\ntar -xvf ${IVY_TAR_FILENAME} -C ${IVY_JAR_DEST_FILEPATH} --strip-components=1 ${IVY_JAR_SRC_FILEPATH}\nrm -rf ${IVY_TAR_FILENAME} ${IVY_ASC_FILENAME} ${ANT_KEYS_FILENAME}\n" }, { "alpha_fraction": 0.6814988255500793, "alphanum_fraction": 0.6978922486305237, "avg_line_length": 22.08108139038086, "blob_id": "b60c35eaeeab38ad2da6aa88225b10cd4e2697d6", "content_id": "5d288083b04c3f3453ab1944a044b2d8cec42292", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 854, "license_type": "permissive", "max_line_length": 75, "num_lines": 37, "path": "/java/utils/sdk-install.sh", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\n# Wrapper for sdkman to install multiple sdks in a single command \n# and clean up junk files to reduce container size.\n\n# Requires packages zip, unzip, curl\n\n# Args are double-quoted strings containing sdkman candidates\nif [ \"$#\" -eq 0 ]; then\n echo \"usage: ${0} [\"[sdk [version]]\", ...]\"\n echo 'example: '${0}' \"java 11.0.9.hs-adpt\" \"gradle 6.8\" \"maven\" \"ivy\"'\n exit 1\nfi\n\ncd ~\n\nCONFIG=\"sdkman_auto_answer=false\nsdkman_auto_selfupdate=false\nsdkman_insecure_ssl=false\nsdkman_curl_connect_timeout=14\nsdkman_curl_max_time=20\nsdkman_beta_channel=false\nsdkman_debug_mode=false\nsdkman_colour_enable=true\nsdkman_auto_env=false\"\n\ncurl -s \"https://get.sdkman.io\" | bash\nsource .sdkman/bin/sdkman-init.sh\necho \"${CONFIG}\" > ~/.sdkman/etc/config\n\nfor arg in \"$@\"\ndo\n sdk install ${arg}\ndone\n\nrm -rf .sdkman/archives/*\nrm -rf .sdkman/tmp/*\n" }, { "alpha_fraction": 0.7454545497894287, "alphanum_fraction": 0.7454545497894287, "avg_line_length": 24.090909957885742, "blob_id": "fbf043d8a8a82cc1a429a0e94d508932168817ba", "content_id": "0395288ac1314038bf74ea115b6a2e1647eec043", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 275, "license_type": "permissive", "max_line_length": 151, "num_lines": 11, "path": "/azureagent/README.md", "repo_name": "mosherbd/devcontainer-base", "src_encoding": "UTF-8", "text": "# Run\n\n```\ndocker run -e AZP_URL=<Azure Instance URL> -e AZP_TOKEN=<Azure User Token> -e AZP_AGENT_NAME=mydockeragent brandonmosher/devcontainer-azureagent:latest\n```\n\n# Variants\n\n## dotnetcore\n\nDepends on dotnet/utils and node/utils, so docker build must be run with PATH=.." } ]

23

saeid-h/Traffic-Tracker

https://github.com/saeid-h/Traffic-Tracker

1f4a100b7d8c42bbb90ef3b81aa456e46f458497

f1aac40b1503b6bce971649bf9bd90689c11644b

7c6dae75d6f7c57023d0c627383815d18b301eeb

refs/heads/main

2023-04-16T20:31:18.749496

2021-04-26T23:50:16

361,922,307

0

null

[ { "alpha_fraction": 0.6914688944816589, "alphanum_fraction": 0.7126362919807434, "avg_line_length": 22.714284896850586, "blob_id": "1ae512ac1f5ddb5cbde4f9ab4e06f7137aece3ea", "content_id": "bdccc55eed43f611a391a92509edc8a725720176", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1559, "license_type": "permissive", "max_line_length": 319, "num_lines": 63, "path": "/README.md", "repo_name": "saeid-h/Traffic-Tracker", "src_encoding": "UTF-8", "text": "# Traffic Tracker\r\n\r\nThis is a rough estimation of traffic flow using OpenCV library.\r\nIt was tested by Python 3.9 and OpenCV 4.0\r\n\r\n\r\n\r\n# Requirements\r\n\r\nMake sure you have installed the following requirements:\r\n\r\n- Python3.9\r\n- OpenCV 4.0\r\n- numpy, imutils, argparse\r\n\r\n```bash\r\ngit clone https://github.com/saeid-h/Traffic-Tracker.git\r\n# If you do not have python3.9, install it. \r\n# It might be working with other version of Python, but it's not tested before.\r\n\r\n# Make virtual environment\r\nvirtualenv -p /usr/bin/python3.9 venv\r\n# Activate the environment\r\nsource venv/bin/activate\r\npip install <libraries>\r\n\r\n```\r\n\r\n# Demo\r\n\r\nTry the [main.py]() \r\n\r\n```bash\r\npython main.py --video-file $YOURVIDEO \r\n```\r\n\r\nYou may turn off the layer by adding the following switches:\r\n\r\n```bash\r\npython main.py --video-file $YOURVIDEO --no-speed-check\r\n```\r\n\r\n```bash\r\npython main.py --video-file $YOURVIDEO --no-heatmap\r\n```\r\n\r\n```bash\r\npython main.py --video-file $YOURVIDEO --no-car-detection\r\n```\r\n\r\nThere hyper paramers that you can change them:\r\n```\r\n--blur-window <int>\r\n--threshold <int>\r\n--object-area <int>\r\n--invalid-length <int>\r\n--dilation-kernel <int>\r\n--dilation-iter <int>\r\n--heatmap-threshold <int>\r\n--speed-limit <int>\r\n```\r\n\r\nYou may also download a sample video [here](https://spaceeco-my.sharepoint.com/personal/ovunc_spacee_com/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fovunc%5Fspacee%5Fcom%2FDocuments%2Ftraffic%2Emp4&parent=%2Fpersonal%2Fovunc%5Fspacee%5Fcom%2FDocuments&originalPath=aHR0cHM6Ly9zcGFjZWVjby1teS5zaGFyZXBvaW50LmNvbS86djovZy9wZXJzb25hbC9vdnVuY19zcGFjZWVfY29tL0VmNWxMci16NHpkTWtZMUQ5Tl9jYU9zQlYxTjNfUEJzZkg1WUlVT0hyVEdQb1E%5FcnRpbWU9YmE0Z0pnc0oyVWc)\r\n\r\n" }, { "alpha_fraction": 0.5406201481819153, "alphanum_fraction": 0.5631387233734131, "avg_line_length": 44.95121765136719, "blob_id": "5c9158f724ddd9a2a97d25c50e1b6f285284dff4", "content_id": "171a107fc37223d75837b91f220821df3852a07e", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5773, "license_type": "permissive", "max_line_length": 175, "num_lines": 123, "path": "/main.py", "repo_name": "saeid-h/Traffic-Tracker", "src_encoding": "UTF-8", "text": "'''\r\n#############################################################################\r\n#\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\r\n# Copyright (c) 2021 under MIT License #\r\n# Code by Saeid Hosseinipoor <https://saeid-h.github.io/>\t\t #\r\n# All rights reserved.\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\r\n#\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#\r\n############################################################################# \r\n\r\nReferences:\r\n https://www.learnpythonwithrune.org/opencv-counting-cars-a-simple-approach/\r\n https://towardsdatascience.com/build-a-motion-heatmap-videousing-opencv-with-python-fd806e8a2340\r\n https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/\r\n'''\r\n\r\nimport cv2\r\nimport imutils\r\nimport numpy as np\r\nimport argparse\r\nfrom collections import OrderedDict\r\nfrom tracker import CentroidTracker\r\n\r\nRED = (0, 0, 255)\r\nGREEN = (0, 255, 0)\r\nPPF_to_MPH = 15.0\r\n\r\nif __name__ == \"__main__\":\r\n parser = argparse.ArgumentParser(fromfile_prefix_chars='@', description=\"\")\r\n\r\n parser.add_argument('--video-file', default='traffic.mp4', help='')\r\n parser.add_argument('--blur-window', type=int, default=5, help='')\r\n parser.add_argument('--threshold', type=int, default=25, help='')\r\n parser.add_argument('--object-area', type=int, default=180, help='')\r\n parser.add_argument('--invalid-length', type=int, default=3, help='')\r\n parser.add_argument('--dilation-kernel', type=int, default=3, help='')\r\n parser.add_argument('--dilation-iter', type=int, default=3, help='')\r\n parser.add_argument('--heatmap-threshold', type=int, default=3, help='')\r\n parser.add_argument('--no-heatmap', action=\"store_true\", help='')\r\n parser.add_argument('--no-car-detection', action=\"store_true\", help='')\r\n parser.add_argument('--no-speed-check', action=\"store_true\", help='')\r\n parser.add_argument('--speed-limit', type=int, default=60, help='')\r\n parser.add_argument('--save-video', default=None, help='')\r\n \r\n args = parser.parse_args()\r\n\r\n blur_window = args.blur_window\r\n threshold = args.threshold\r\n object_area = args.object_area\r\n invalid_length = args.invalid_length\r\n dilation_kernel = np.ones((args.dilation_kernel, args.dilation_kernel), 'uint8')\r\n dilation_iter = args.dilation_iter\r\n heatmap_threshold = args.heatmap_threshold\r\n video_file = args.video_file\r\n\r\n ct = CentroidTracker(500)\r\n\r\n cap = cv2.VideoCapture(video_file)\r\n first_iteration_indicator = True\r\n while cap.isOpened():\r\n _, frame = cap.read()\r\n\r\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n gray = cv2.GaussianBlur(gray, (blur_window, blur_window), 0)\r\n\r\n if first_iteration_indicator:\r\n last_frame = gray\r\n accum_image = np.zeros_like(gray)\r\n first_iteration_indicator = False\r\n last_tracked_boxes = OrderedDict()\r\n last_tracked_centroids = OrderedDict()\r\n continue\r\n \r\n delta_frame = cv2.absdiff(last_frame, gray)\r\n last_frame = gray\r\n thresh1 = cv2.threshold(delta_frame, threshold, 255, cv2.THRESH_BINARY)[1]\r\n thresh = cv2.dilate(thresh1, dilation_kernel, iterations=dilation_iter)\r\n if thresh.max() == 0: continue\r\n\r\n if not args.no_car_detection or not args.no_speed_check:\r\n contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\n contours = imutils.grab_contours(contours)\r\n total_number_cars = 0\r\n rects = []\r\n for contour in contours:\r\n (x, y, w, h) = cv2.boundingRect(contour)\r\n if w < invalid_length or h < invalid_length:\r\n continue\r\n if not args.no_car_detection:\r\n total_number_cars += 1\r\n cv2.rectangle(frame, (x, y), (x + w, y + h), GREEN, 2)\r\n if not args.no_speed_check:\r\n rects.append([x, y, x+h, y+w])\r\n\r\n if not args.no_speed_check:\r\n ct.update(rects)\r\n tracked_boxes = ct.boxes\r\n tracked_centroids = ct.objects\r\n for objectID in tracked_centroids.keys():\r\n if not objectID in last_tracked_centroids.keys():\r\n continue\r\n speed_pixel_per_frame = np.sqrt((tracked_centroids[objectID][0]-last_tracked_centroids[objectID][0])**2 + \r\n (tracked_centroids[objectID][1]-last_tracked_centroids[objectID][1])**2)\r\n speed_MPH = speed_pixel_per_frame * PPF_to_MPH\r\n if speed_MPH > args.speed_limit:\r\n cv2.rectangle(frame, (tracked_boxes[objectID][0], tracked_boxes[objectID][1]), (tracked_boxes[objectID][2], tracked_boxes[objectID][3]), RED, 2)\r\n # cv2.putText(frame, \"{:4.0f} mph\".format(speed_MPH) , (tracked_boxes[objectID][0]-5, tracked_boxes[objectID][1]-5), cv2.FONT_HERSHEY_PLAIN, 1, RED, 2)\r\n last_tracked_boxes = tracked_boxes.copy()\r\n last_tracked_centroids = tracked_centroids.copy()\r\n \r\n if not args.no_heatmap:\r\n accum_image = cv2.addWeighted(accum_image, 0.98, thresh, 0.1, 0.0)\r\n color_image_video = cv2.applyColorMap(accum_image, cv2.COLORMAP_HOT)\r\n frame = cv2.addWeighted(color_image_video, 0.5, frame, 0.8, 0.0)\r\n if not args.no_car_detection:\r\n cv2.putText(frame, \"Total Number of Cars: {}\".format(total_number_cars) , (800, 700), cv2.FONT_HERSHEY_PLAIN, 2, GREEN, 2) \r\n\r\n cv2.imshow(\"Car counter\", frame)\r\n \r\n if cv2.waitKey(1) & 0xFF == ord('q'):\r\n break\r\n\r\n cap.release()\r\n cv2.destroyAllWindows()" } ]

2

jetamartin/flask_pet_adoption_website

https://github.com/jetamartin/flask_pet_adoption_website

ea28e218efdc512aac5c51524c11c699f96e06ba

db2e6a371229744416fba45288b9b910c6191268

d43f6aafc53ff287cc6631bd8cc0660bfcf2598e

refs/heads/master

2023-03-15T01:35:00.637177

2020-04-27T21:56:38

259,465,551

0

null

2020-04-27T21:55:13

2020-04-27T21:57:14

2021-03-20T03:37:38

Python

[ { "alpha_fraction": 0.4944134056568146, "alphanum_fraction": 0.6927374005317688, "avg_line_length": 16.047618865966797, "blob_id": "f63c0144e9720ab58c7807ce2d0ce5160f2c3ee6", "content_id": "c108dd8743431bd58cfd89418c9045742278598b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 358, "license_type": "no_license", "max_line_length": 26, "num_lines": 21, "path": "/templates/requirements.txt", "repo_name": "jetamartin/flask_pet_adoption_website", "src_encoding": "UTF-8", "text": "astroid==2.3.3\nblinker==1.4\nclick==7.1.1\ncolorama==0.4.3\nFlask==1.1.2\nFlask-DebugToolbar==0.11.0\nFlask-SQLAlchemy==2.4.1\nFlask-WTF==0.14.3\nisort==4.3.21\nitsdangerous==1.1.0\nJinja2==2.11.2\nlazy-object-proxy==1.4.3\nMarkupSafe==1.1.1\nmccabe==0.6.1\npsycopg2-binary==2.8.5\npylint==2.4.4\nsix==1.14.0\nSQLAlchemy==1.3.16\nWerkzeug==1.0.1\nwrapt==1.11.2\nWTForms==2.3.1\n" }, { "alpha_fraction": 0.5025799870491028, "alphanum_fraction": 0.5077399611473083, "avg_line_length": 22.609756469726562, "blob_id": "b73b3c2cdff18f39d8360a6a897585eef6433d89", "content_id": "4441d5c30e406a2dad46893f5e2226416edc715f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 969, "license_type": "no_license", "max_line_length": 104, "num_lines": 41, "path": "/models.py", "repo_name": "jetamartin/flask_pet_adoption_website", "src_encoding": "UTF-8", "text": "\"\"\"Demo file showing off a model for SQLAlchemy.\"\"\"\n\nfrom flask_sqlalchemy import SQLAlchemy\n\ndb = SQLAlchemy()\n\n\ndef connect_db(app):\n \"\"\"Connect to database.\"\"\"\n\n db.app = app\n db.init_app(app)\n\nclass Pet(db.Model):\n \"\"\"Pets for adoption\"\"\"\n\n __tablename__ = \"pets\"\n\n id = db.Column(db.Integer,\n primary_key = True,\n autoincrement = True)\n\n name = db.Column(db.String(20),\n nullable = False)\n\n species = db.Column(db.Text, \n nullable = False)\n\n photo_url = db.Column(\n db.String(200), \n default='https://mylostpetalert.com/wp-content/themes/mlpa-child/images/nophoto.gif'\n )\n\n age = db.Column(db.Float,\n nullable = True)\n\n notes = db.Column(db.Text,\n nullable = True)\n \n available = db.Column(db.Boolean,\n default = True)\n\n" }, { "alpha_fraction": 0.5706638097763062, "alphanum_fraction": 0.5770878195762634, "avg_line_length": 31.75438690185547, "blob_id": "425958b215dbe40563d6322e35ac9357afd6730a", "content_id": "dabcfe10edfc7c51f50902b739ed70cc58d59a75", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1868, "license_type": "no_license", "max_line_length": 135, "num_lines": 57, "path": "/forms.py", "repo_name": "jetamartin/flask_pet_adoption_website", "src_encoding": "UTF-8", "text": "# \"\"\"Forms for our demo Flask app.\"\"\"\n\nfrom flask_wtf import FlaskForm\nfrom wtforms.widgets.html5 import URLInput, Input\n# from wtforms.widgets.html5 import URLInput, Input\n\nfrom wtforms import StringField, FloatField, TextAreaField, Field, BooleanField, SelectField\nfrom wtforms.validators import InputRequired, Optional, Regexp, NumberRange, AnyOf, URL\n\n\nclass AddPetForm(FlaskForm):\n \"\"\"Form for adding a new Pet.\"\"\"\n\n name = StringField(\"Name:\", \n validators=[\n InputRequired(\"Pet name can't be blank\")],\n render_kw={\"placeholder\": \"Enter your pet's name\"} \n )\n\n\n species = SelectField(\n \"Species\",\n choices=[(\"cat\", \"Cat\"), (\"dog\", \"Dog\"), (\"snake\", \"Snake\"), (\"guinea pig\", \"Guinea Pig\"), (\"pig\", \"Pig\"), (\"mouse\", \"Mouse\")],\n render_kw={\"placeholder\": \"test\"}\n )\n\n photo_url = StringField('Photo_URL:', \n validators = [\n Optional(),\n URL('URL must be a valid link')],\n render_kw={\"placeholder\": \"Enter URL for pet's picture (e.g., https://..)\"} \n ) \n# Regexp('^(http|https):\\/\\/[\\w.\\-]+(\\.[\\w.\\-]+)+.*$', 0,\n # 'URL must be a valid link')])\n\n age = FloatField(\"Age:\", \n validators = [NumberRange(min=0, max=30, message=\"Pet's age can only be between 0 and 30\")],\n render_kw={\"placeholder\": \"Enter your pets age in years (e.g., 1 or 1.5 etc)\"}\n )\n\n notes = TextAreaField(\"Notes:\",\n render_kw={\"placeholder\": \"Enter any notes you thing are pertinent\"}\n )\n\n \n\nclass EditPetForm(FlaskForm):\n \"\"\" Form for editing subset of fields for a pet \"\"\"\n\n photo_url = StringField('Photo_URL:', \n validators = [\n Optional(),\n URL('URL must be a valid link')]) \n\n notes = TextAreaField(\"Notes:\")\n\n available = BooleanField(\"Available?\")\n\n" }, { "alpha_fraction": 0.5969681143760681, "alphanum_fraction": 0.6853110194206238, "avg_line_length": 53.657142639160156, "blob_id": "eae39fe967fe510b74c41396c3ed943fbf32d269", "content_id": "c863ddf972c98be061e9f68cbd0878ca1d9c210e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1913, "license_type": "no_license", "max_line_length": 160, "num_lines": 35, "path": "/seed.py", "repo_name": "jetamartin/flask_pet_adoption_website", "src_encoding": "UTF-8", "text": "from app import db\nfrom models import Pet\n\ndb.drop_all()\ndb.create_all()\n\nPet.query.delete()\n\npet1 = Pet(name=\"Bacon\", species = \"pig\", age=3, available = True,\n photo_url = \"https://images.unsplash.com/photo-1516467508483-a7212febe31a?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1952&q=80\", \n notes = \"Bacon is a sweet adorable pig. He loves to eat\")\n\npet2 = Pet(name=\"Monty\", species = \"snake\", age=4, available = True,\n photo_url = \"https://images.unsplash.com/photo-1585095595274-aeffce35511a?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1950&q=80\", \n notes = \"Monty is a Ball pythons. He eats two mice per week\")\n\npet3 = Pet(name=\"Templeton\", species = \"rat\", age=1, available = True,\n photo_url = \"https://images.unsplash.com/photo-1550697851-920b181d8ca8?ixlib=rb-1.2.1&auto=format&fit=crop&w=1950&q=80\", \n notes = \"Templeton loves his cheese and lots of attention\")\n\npet4 = Pet(name=\"Blanca\", species = \"cat\", age=4, available = True,\n photo_url = \"https://images.unsplash.com/photo-1573148164257-8a2b173be464?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1050&q=80\", \n notes = \"Blanca is a sweet kitty who loves chest and chin rubs\")\n\npet5 = Pet(name=\"Ruffy\", species = \"dog\", age=1.5, available = True,\n photo_url = \"https://images.unsplash.com/photo-1502673530728-f79b4cab31b1?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1950&q=80\", \n notes = \"Ruffy is well trained. He's a snuggler\")\n\npet6 = Pet(name=\"Garth\", species = \"Guinea Pig\", age=1, available = True,\n photo_url = \"https://animals.sandiegozoo.org/sites/default/files/2019-04/animals_guineapig-domestic.jpg\", \n notes = \"Garth is chubby little Guinea Pig who loves his kibbles \")\n\n\ndb.session.add_all([pet1, pet2, pet3, pet4, pet5, pet6])\ndb.session.commit()\n" }, { "alpha_fraction": 0.6430678367614746, "alphanum_fraction": 0.6445427536964417, "avg_line_length": 30.292306900024414, "blob_id": "0419d6d3e132449cc275ba9ec12902b8a11e89ae", "content_id": "43a8ae366d8fa147be2293809d0b19388f79ad47", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2034, "license_type": "no_license", "max_line_length": 92, "num_lines": 65, "path": "/app.py", "repo_name": "jetamartin/flask_pet_adoption_website", "src_encoding": "UTF-8", "text": "from flask import Flask, render_template, flash, redirect, render_template\nfrom flask_debugtoolbar import DebugToolbarExtension\nfrom models import db, connect_db, Pet\n\nfrom forms import AddPetForm\nfrom forms import EditPetForm\n\n\napp = Flask(__name__)\napp.config[\"SECRET_KEY\"] = \"my-secret-word\"\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = 'postgresql:///pet_adoption'\napp.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\n\ndebug = DebugToolbarExtension(app)\n\nconnect_db(app)\n\n\[email protected](\"/\")\ndef homepage():\n \"\"\"Show listing of all pets\"\"\"\n pets = Pet.query.all()\n return render_template(\"index.html\", pets = pets)\n\n\[email protected](\"/add\", methods=[\"GET\", \"POST\"])\ndef add_pet():\n \"\"\"Display Form to add a new pet.\"\"\"\n\n form = AddPetForm()\n\n if form.validate_on_submit():\n name = form.name.data.strip().capitalize()\n species = form.species.data.strip().capitalize()\n \n photo_url = form.photo_url.data if form.photo_url.data else None\n age = form.age.data\n notes = form.notes.data.strip()\n flash(f\"Added {name} the {species} as a new pet\", \"success-msg\")\n new_pet = Pet(name=name, species=species, photo_url=photo_url, age=age, notes=notes)\n db.session.add(new_pet)\n db.session.commit()\n return redirect(\"/\")\n\n else:\n return render_template(\"add_pet.html\", form=form)\n\[email protected](\"/<int:pet_id>/edit\", methods=[\"GET\", \"POST\"])\ndef edit_pet(pet_id):\n \"\"\"Display Pet detail page with form to edit some info on Pets.\"\"\"\n pet = Pet.query.get_or_404(pet_id)\n form = EditPetForm(obj = pet)\n\n if form.validate_on_submit():\n pet.photo_url = form.photo_url.data\n pet.notes = form.notes.data.strip()\n pet.available = form.available.data\n flash(f\"Updated '{pet.name}' the {pet.species}'s profile\", 'success-msg')\n db.session.add(pet)\n db.session.commit()\n # import pdb; pdb.set_trace()\n return redirect(\"/\")\n\n else:\n return render_template(\"edit_pet.html\", form=form, pet=pet)\n" } ]

5

algo-trading-python-airflow/udemy_algo_trading_airflow

https://github.com/algo-trading-python-airflow/udemy_algo_trading_airflow

dc42228f5efc5938b3caf33b83dbc89f5519fa1b

2fab8a52ceb968bb7bdbc0d82cdd0b550481c2dd

b915bb1dd60091d6f50e8d684d28ab974cb0e8e8

refs/heads/main

2023-03-24T10:15:09.508318

2021-02-25T10:34:40

337,430,008

4

null

[ { "alpha_fraction": 0.581892192363739, "alphanum_fraction": 0.5890132188796997, "avg_line_length": 24.86842155456543, "blob_id": "321a90cae86301f9e77ae08c7f0de073c3cbf95d", "content_id": "0c37c095ed82fc3a1cd735ce0b44e944453e3ad1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 983, "license_type": "no_license", "max_line_length": 74, "num_lines": 38, "path": "/airflow/dags/update_stock_prices.py", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "from airflow import DAG\nfrom airflow.operators.python import PythonOperator\nfrom airflow.utils.dates import days_ago\nfrom utils import db, tinkoff\nfrom datetime import timedelta\nimport os\n\nDAG_ID = os.path.basename(__file__).replace('.pyc', '').replace('.py', '')\nCONN_ID = 'postgres_stocks'\n\ndefault_args = {\n 'owner': 'airflow',\n 'depends_on_past': False,\n 'start_date': days_ago(1),\n 'retries': 2,\n 'retry_delay': timedelta(minutes=5),\n 'email_on_failure': False,\n 'email_on_retry': False,\n}\n\nwith DAG(\n dag_id=DAG_ID,\n default_args=default_args,\n schedule_interval='5 1 * * *',\n) as dag:\n\n update_stock_prices_aapl = PythonOperator(\n task_id='update_stock_prices_aapl',\n python_callable=db.load_df_to_db,\n op_kwargs={\n 'connector': CONN_ID,\n 'df': tinkoff.get_data_by_ticker_and_period(\n 'AAPL',\n 2,\n ).tail(1),\n 'table_name': 'aapl',\n }\n )\n" }, { "alpha_fraction": 0.586840808391571, "alphanum_fraction": 0.5902273654937744, "avg_line_length": 27.3150691986084, "blob_id": "a7571d96c47dfad486096a4c7df9363239c8f653", "content_id": "5f0f7538db51669c6023aa5b99e36a1d3038f3fd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2067, "license_type": "no_license", "max_line_length": 100, "num_lines": 73, "path": "/airflow/plugins/utils/db.py", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "import psycopg2\nfrom io import StringIO\nimport csv\nfrom airflow.hooks.base import BaseHook\nimport pandas as pd\n\n\ndef _get_db_url(connector: str) -> str:\n connection = BaseHook.get_connection(connector)\n\n return f'user={connection.login} password={connection.password} host={connection.host} ' \\\n f'port={connection.port} dbname={connection.schema}'\n\n\ndef load_df_to_db(connector: str, df: pd.DataFrame, table_name: str) -> None:\n buffer = StringIO()\n df.to_csv(buffer, index=False, sep='|', na_rep='NUL', quoting=csv.QUOTE_MINIMAL,\n header=False, float_format='%.8f', doublequote=False, escapechar='\\\\')\n buffer.seek(0)\n copy_query = f\"\"\"\n COPY {table_name}({','.join(df.columns)})\n FROM STDIN\n DELIMITER '|'\n NULL 'NUL'\n \"\"\"\n conn = psycopg2.connect(dsn=_get_db_url(connector))\n with conn.cursor() as cursor:\n cursor.copy_expert(copy_query, buffer)\n conn.commit()\n conn.close()\n\n\ndef get_data_from_price_table(connector: str, table_name: str, filter_: str = None) -> pd.DataFrame:\n query = f\"\"\"\n SELECT time,\n open,\n high,\n low,\n close,\n volume\n FROM {table_name}\n {filter_}\n \"\"\"\n with psycopg2.connect(dsn=_get_db_url(connector)) as conn:\n data = pd.read_sql(query, conn)\n\n return data\n\n\ndef get_data_from_signal_table(connector: str, filter_: str) -> pd.DataFrame:\n query = f\"\"\"\n SELECT time,\n position,\n strategy_type,\n ticker\n FROM signal\n {filter_}\n \"\"\"\n with psycopg2.connect(dsn=_get_db_url(connector)) as conn:\n data = pd.read_sql(query, conn)\n\n return data\n\n\ndef get_last_price_from_price_table(connector: str, table_name: str) -> float:\n data = get_data_from_price_table(\n connector,\n table_name,\n f\"WHERE time = (SELECT max(time) FROM {table_name})\"\n )\n data = data.sort_values(by='time', ascending=False)\n\n return data.iloc[0]['close']\n" }, { "alpha_fraction": 0.5896057486534119, "alphanum_fraction": 0.59617680311203, "avg_line_length": 32.47999954223633, "blob_id": "77e8414372fee0e8a79a11dbca0b0d766eb384c9", "content_id": "dbfc66a550d59c43eb53cc5e2b25a7db324dbac9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1674, "license_type": "no_license", "max_line_length": 84, "num_lines": 50, "path": "/airflow/plugins/utils/strategy.py", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport numpy as np\nfrom utils import db\nfrom typing import Callable\n\n\ndef apply_strategy(\n connector: str,\n source_table_name: str,\n ticker: str,\n strategy_func: Callable,\n op_kwargs: dict\n) -> None:\n data = db.get_data_from_price_table(connector, source_table_name)\n signal = strategy_func(data, **op_kwargs).assign(ticker=ticker)\n db.load_df_to_db(connector, signal, 'signal')\n\n\ndef cross_sma_strategy(\n data: pd.DataFrame,\n sma_short: int,\n sma_long: int,\n) -> pd.DataFrame:\n data['sma_short'] = data['close'].rolling(sma_short).mean()\n data['sma_long'] = data['close'].rolling(sma_long).mean()\n\n data['position'] = np.where(data['sma_short'] > data['sma_long'], 1, -1)\n\n return data[['time', 'position']].tail(1).assign(strategy_type='sma')\n\n\ndef bollinger_bands_strategy(\n data: pd.DataFrame,\n sma: int,\n dev: int,\n) -> pd.DataFrame:\n data['sma'] = data['close'].rolling(sma).mean()\n std = data['close'].rolling(sma).std() * dev\n data['lower'] = data['sma'] - std\n data['upper'] = data['sma'] + std\n\n data['distance'] = data['close'] - data['sma']\n data['position'] = np.where(data['close'] < data['lower'], 1, np.nan)\n data['position'] = np.where(data['close'] > data['upper'], -1, data['position'])\n data['position'] = np.where(data['distance'] * data['distance'].shift(1) < 0,\n 0, data['position'])\n data['position'] = data['position'].ffill().fillna(0)\n data['position'] = data['position'].astype('int8')\n\n return data[['time', 'position']].tail(1).assign(strategy_type='bollinger')\n" }, { "alpha_fraction": 0.5991129279136658, "alphanum_fraction": 0.6028659343719482, "avg_line_length": 28.494949340820312, "blob_id": "7da629676dd513360ff2cf8b079011dd0ac4d03d", "content_id": "dba93e05a8307aa4299cbe4ae6065a6bf9e89436", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2931, "license_type": "no_license", "max_line_length": 94, "num_lines": 99, "path": "/airflow/plugins/utils/tinkoff.py", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "import tinvest\nimport pandas as pd\nfrom configparser import ConfigParser\nfrom datetime import datetime, timedelta\nfrom typing import Optional\n\n\ndef _get_api_params_from_config() -> dict:\n config_parser = ConfigParser()\n config_parser.read('/usr/local/airflow/tinkoff.cfg')\n\n return {\n 'token': config_parser.get('core', 'TOKEN_TINKOFF'),\n 'use_sandbox': config_parser.get('core', 'USE_SANDBOX')\n }\n\n\ndef get_figi_from_ticker(ticker: str) -> str:\n client = tinvest.SyncClient(**_get_api_params_from_config())\n ticker_data = client.get_market_search_by_ticker(ticker)\n return ticker_data.payload.instruments[0].figi\n\n\ndef get_data_by_ticker_and_period(\n ticker: str,\n period_in_days: int = 365,\n freq: tinvest.CandleResolution = tinvest.CandleResolution.day\n) -> pd.DataFrame:\n client = tinvest.SyncClient(**_get_api_params_from_config())\n figi = get_figi_from_ticker(ticker)\n\n raw_data = client.get_market_candles(\n figi,\n datetime.now() - timedelta(days=period_in_days),\n datetime.now() - timedelta(days=1),\n freq,\n )\n\n return pd.DataFrame(\n data=(\n (\n candle.time,\n candle.o,\n candle.h,\n candle.l,\n candle.c,\n candle.v,\n ) for candle in raw_data.payload.candles\n ),\n columns=(\n 'time',\n 'open',\n 'high',\n 'low',\n 'close',\n 'volume',\n )\n )\n\n\ndef get_position_by_ticker(ticker: str) -> Optional[tinvest.PortfolioPosition]:\n client = tinvest.SyncClient(**_get_api_params_from_config())\n positions = client.get_portfolio().payload.positions\n\n filtered_positions = list(filter(lambda x: x.ticker.lower() == ticker.lower(), positions))\n if len(filtered_positions) == 0:\n return None\n return filtered_positions[0]\n\n\ndef create_limit_order_by_figi(\n figi: str,\n lots: int,\n price: float,\n op_type: str = 'Buy'\n) -> None:\n if op_type not in ('Buy', 'Sell'):\n raise ValueError('Operation type must be Sell or Buy with upper-case first letter')\n client = tinvest.SyncClient(**_get_api_params_from_config())\n client.post_orders_limit_order(\n figi=figi,\n body=tinvest.schemas.LimitOrderRequest(\n lots=lots,\n operation=tinvest.schemas.OperationType(value=op_type),\n price=price\n )\n )\n\n\ndef get_current_balance(currency_type: str) -> float:\n client = tinvest.SyncClient(**_get_api_params_from_config())\n currencies = client.get_portfolio_currencies().payload.currencies\n\n filtered_currencies = list(\n filter(lambda x: x.currency.lower() == currency_type.lower(), currencies)\n )\n if len(filtered_currencies) == 0:\n return 0.0\n return float(filtered_currencies[0].balance)\n\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.8142076730728149, "alphanum_fraction": 0.8142076730728149, "avg_line_length": 90.5, "blob_id": "2270bc8a923d6571ca928aac192495e68fd92e6b", "content_id": "c7e38a8a6ba3c084e06d62eacdc03169e03c6113", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 236, "license_type": "no_license", "max_line_length": 153, "num_lines": 2, "path": "/README.md", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "# udemy_algo_trading_airflow\nРепозиторий для курса [Инфраструктура для Алготрейдинга с Python и Apache Airflow](https://www.udemy.com/course/algo-trading-python-airflow) на Udemy.com\n" }, { "alpha_fraction": 0.5532389879226685, "alphanum_fraction": 0.5673864483833313, "avg_line_length": 32.349998474121094, "blob_id": "b0a3a21e12bd8d87a256bcce65f777d4e5d70546", "content_id": "4e4a5dd2c0cb42f8527d305332751d1957d8d201", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1343, "license_type": "no_license", "max_line_length": 92, "num_lines": 40, "path": "/airflow/plugins/utils/order.py", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport numpy as np\nfrom utils import tinkoff, db\nimport math\n\n\ndef create_limit_order_by_signals(\n connector: str,\n ticker: str,\n weights: dict,\n threshold: float = 0.5\n) -> None:\n signals = db.get_data_from_signal_table(\n connector,\n f\"WHERE time = (SELECT max(time) FROM signal) AND lower(ticker) = lower('{ticker}')\"\n )\n if len(signals) == 0:\n return\n signals['weights'] = signals['strategy_type'].map(weights)\n signals['weighted_position'] = signals['weights'] * signals['position']\n position = tinkoff.get_position_by_ticker(ticker)\n last_price = db.get_last_price_from_price_table(connector, ticker.lower())\n\n if signals['weighted_position'].sum() > threshold:\n if not position:\n tinkoff.create_limit_order_by_figi(\n tinkoff.get_figi_from_ticker(ticker),\n int(tinkoff.get_current_balance('USD') * 0.1 // last_price),\n math.ceil(last_price * 100) / 100.0,\n 'Buy'\n )\n\n elif signals['weighted_position'].sum() < -threshold:\n if position:\n tinkoff.create_limit_order_by_figi(\n position.figi,\n position.lots,\n math.floor(last_price * 100) / 100.0,\n 'Sell'\n )\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5743119120597839, "alphanum_fraction": 0.5844036936759949, "avg_line_length": 24.34883689880371, "blob_id": "933fde933eed2881ac36739a2e4bcfaf4731d5e6", "content_id": "0ef73c6533e10473197fb0cb8cf2ffa6cec8bfdc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1090, "license_type": "no_license", "max_line_length": 74, "num_lines": 43, "path": "/airflow/dags/strategy/cross_sma_strategy.py", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "from airflow import DAG\nfrom airflow.operators.python import PythonOperator\nfrom airflow.utils.dates import days_ago\nfrom utils import strategy\nfrom datetime import timedelta\nimport os\n\nDAG_ID = os.path.basename(__file__).replace('.pyc', '').replace('.py', '')\nCONN_ID = 'postgres_stocks'\n\nSMA_SHORT = 50\nSMA_LONG = 200\n\ndefault_args = {\n 'owner': 'airflow',\n 'depends_on_past': False,\n 'start_date': days_ago(1),\n 'retries': 2,\n 'retry_delay': timedelta(minutes=5),\n 'email_on_failure': False,\n 'email_on_retry': False,\n}\n\nwith DAG(\n dag_id=DAG_ID,\n default_args=default_args,\n schedule_interval='10 1 * * *',\n) as dag:\n\n cross_sma_aapl = PythonOperator(\n task_id='cross_sma_aapl',\n python_callable=strategy.apply_strategy,\n op_kwargs={\n 'connector': CONN_ID,\n 'source_table_name': 'aapl',\n 'ticker': 'AAPL',\n 'strategy_func': strategy.cross_sma_strategy,\n 'op_kwargs': {\n 'sma_short': SMA_SHORT,\n 'sma_long': SMA_LONG,\n }\n }\n )\n" }, { "alpha_fraction": 0.620514452457428, "alphanum_fraction": 0.6325817704200745, "avg_line_length": 28.990476608276367, "blob_id": "f7ee0c2b34f81525c68b3734424ffcfccb67cf4f", "content_id": "8db474a5834359b8b34262a51ae85f59f0a9f569", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 3149, "license_type": "no_license", "max_line_length": 134, "num_lines": 105, "path": "/airflow/Dockerfile", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "FROM python:3.8-slim\nLABEL maintainer=\"Puckel_\"\n\n# Never prompt the user for choices on installation/configuration of packages\nENV DEBIAN_FRONTEND noninteractive\nENV TERM linux\n\n# Airflow\nARG AIRFLOW_VERSION=2.0.1\nARG AIRFLOW_USER_HOME=/usr/local/airflow\nENV AIRFLOW_HOME=${AIRFLOW_USER_HOME}\nARG AIRFLOW_DEPS=\"\"\nARG PYTHON_DEPS=\"\"\nARG PYTHON_VERSION=3.8\nARG CONSTRAINT_URL=\"https://raw.githubusercontent.com/apache/airflow/constraints-${AIRFLOW_VERSION}/constraints-3.8.txt\"\n\n# Define ru_RU.\nENV LANGUAGE ru_RU.UTF-8\nENV LANG ru_RU.UTF-8\nENV LC_ALL ru_RU.UTF-8\nENV LC_CTYPE ru_RU.UTF-8\nENV LC_MESSAGES ru_RU.UTF-8\n\n# Disable noisy \"Handling signal\" log messages:\n# ENV GUNICORN_CMD_ARGS --log-level WARNING\n\nARG BUILD_DEPS=\" \\\n freetds-dev \\\n libkrb5-dev \\\n libsasl2-dev \\\n libssl-dev \\\n libffi-dev \\\n libpq-dev \\\n git \\\n unixodbc-dev \\\n\"\n\nRUN set -ex \\\n && apt-get update -yqq \\\n && apt-get upgrade -yqq \\\n && apt-get install -yqq --no-install-recommends \\\n ${BUILD_DEPS} \\\n freetds-bin \\\n build-essential \\\n default-libmysqlclient-dev \\\n apt-utils \\\n curl \\\n rsync \\\n netcat \\\n locales \\\n gnupg2 \\\n && sed -i 's/^# ru_RU.UTF-8 UTF-8$/ru_RU.UTF-8 UTF-8/g' /etc/locale.gen \\\n && locale-gen \\\n && update-locale LANG=ru_RU.UTF-8 LC_ALL=ru_RU.UTF-8 \\\n && useradd -ms /bin/bash -d ${AIRFLOW_USER_HOME} airflow \\\n && pip install -U pip setuptools wheel \\\n && pip install pytz \\\n && pip install pyOpenSSL \\\n && pip install ndg-httpsclient \\\n && pip install pyodbc \\\n && pip install tinvest \\\n && pip install psycopg2 \\\n && pip install pyasn1 \\\n && pip install pymssql \\\n && pip install mysqlclient \\\n && pip install xmltodict \\\n && pip install ldap3 \\\n && pip install apache-airflow[crypto,celery,postgres,vertica,odbc,password]==${AIRFLOW_VERSION} --constraint \"${CONSTRAINT_URL}\" \\\n && pip install 'apache-airflow-providers-papermill' \\\n && pip install 'apache-airflow-providers-postgres' \\\n && pip install 'apache-airflow-providers-vertica' \\\n && apt install gcc -y \\\n && apt install default-libmysqlclient-dev -y \\\n && pip install 'apache-airflow-providers-mysql' \\\n && pip install 'apache-airflow-providers-microsoft-mssql' \\\n && pip install redis \\\n && pip install flask-bcrypt \\\n && pip install papermill \\\n && pip install jupyter \\\n && if [ -n \"${PYTHON_DEPS}\" ]; then pip install ${PYTHON_DEPS}; fi \\\n && apt-get purge --auto-remove -yqq ${BUILD_DEPS} \\\n && apt-get autoremove -yqq --purge \\\n && apt-get clean \\\n && rm -rf \\\n /var/lib/apt/lists/* \\\n /tmp/* \\\n /var/tmp/* \\\n /usr/share/man \\\n /usr/share/doc \\\n /usr/share/doc-base\n\n\nRUN mkdir -p /var/log/airflow /var/spool/airflow \\\n && chown -R airflow: ${AIRFLOW_USER_HOME} \\\n && chown -R airflow: /var/log/airflow \\\n && chown -R airflow: /var/spool/airflow\n\nRUN mkdir -p /var/notebooks/output \\\n && chown -R airflow: /var/notebooks/output\n\nEXPOSE 8080 5555 8793\n\nUSER airflow\nWORKDIR ${AIRFLOW_USER_HOME}\nENTRYPOINT [\"./entrypoint.sh\"]\n" }, { "alpha_fraction": 0.7142857313156128, "alphanum_fraction": 0.7142857313156128, "avg_line_length": 15.333333015441895, "blob_id": "4964ee64d6da0371464cf1221f80ef6264e757d5", "content_id": "89e560c685529642e06fdc1494b158f048e9ba73", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 98, "license_type": "no_license", "max_line_length": 49, "num_lines": 6, "path": "/airflow/entrypoint.sh", "repo_name": "algo-trading-python-airflow/udemy_algo_trading_airflow", "src_encoding": "UTF-8", "text": "#!/bin/sh\n\nairflow db init\n\nairflow scheduler \\\n & exec airflow webserver --pid /tmp/airflow.pid\n" } ]

9

agalindom/Luigi-Wine-Reviews

https://github.com/agalindom/Luigi-Wine-Reviews

d450f6e0d4ae1ad80e6cb7f0ae598ffa97a4c098

99a1d439a751c21e293885002f5a27ae8eceef29

c4e1237466240e06eef5b684319162afc236b125

refs/heads/master

2020-07-29T15:24:33.713263

2019-11-13T19:58:06

209,855,809

0

null

[ { "alpha_fraction": 0.7444717288017273, "alphanum_fraction": 0.7444717288017273, "avg_line_length": 24.4375, "blob_id": "ac04513e0e3e3dc8c6f517b8b03d385725d86b18", "content_id": "46f43fc0d67c234c787443db078eafead5faf68c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 815, "license_type": "no_license", "max_line_length": 157, "num_lines": 32, "path": "/README.md", "repo_name": "agalindom/Luigi-Wine-Reviews", "src_encoding": "UTF-8", "text": "# Overview\n\n- The script contains an simple ML luigi-pipeline used to predict wine scores based on the taster's reviews\n\n# Libraries\n\n* pandas\n* numpy\n* sklearn\n* matplotlib\n* luigi\n* nltk\n\n# Get started\n\nFirst of all you need to download the dataset which is located in:\n\n* [Kaggle Wine Reviews](https://www.kaggle.com/zynicide/wine-reviews)\n\nAfter you download the file on your local machine, store it in:\n\n* `tac/data_root/raw/` \n\nSecond, in order to get the pipeline running, first you need to install the setup.py file to create the tac module, for this, go to the command line and run:\n\n* `pip install .`\n\nThen again on the command line run:\n\n* `luigi --module tac.ml-pipeline EvaluateModel --input_dir tac/data_root/raw/{FILENAME.csv}`\n\nAnd voilá, just wait until the pipeline finishes to see the results.\n" }, { "alpha_fraction": 0.5615585446357727, "alphanum_fraction": 0.5769948959350586, "avg_line_length": 33.92608642578125, "blob_id": "2b7a9fc8f0a09269b8da623e9a68e8e963b8be97", "content_id": "cc38d5d0beac8e9aed7c964b094b5232c049e43b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8033, "license_type": "no_license", "max_line_length": 106, "num_lines": 230, "path": "/tac/ml-pipeline.py", "repo_name": "agalindom/Luigi-Wine-Reviews", "src_encoding": "UTF-8", "text": "import os\nimport re\nimport luigi\nimport nltk\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Rectangle\nfrom nltk.corpus import stopwords\nfrom nltk.stem.porter import PorterStemmer\nfrom nltk.tokenize import RegexpTokenizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n\n\nclass Preprocessing(luigi.Task):\n input_dir = luigi.Parameter()\n\n def run(self):\n df = pd.read_csv(self.input_dir)\n df = df.drop('Unnamed: 0', axis = 1)\n\n # drop unnecessary columns\n df2 = df.drop(['region_2', 'designation', 'taster_twitter_handle',\n 'title', 'winery', 'variety', 'taster_name',\n 'region_1', 'country'], axis = 1)\n\n #\n mean_price = df2.price.mean()\n df2['price'] = df2['price'].fillna(mean_price)\n\n #df2 = df2.dropna()\n df2.to_csv(self.output().path)\n\n\n def output(self):\n target = luigi.LocalTarget('tac/data_root/task0.csv')\n\n return target\n\n\n\nclass Features(luigi.Task):\n input_dir = luigi.Parameter()\n\n def requires(self):\n return Preprocessing(self.input_dir)\n\n def run(self):\n #apply lowercase and get only the letters\n df = pd.read_csv(self.input().path)\n df.drop('Unnamed: 0', axis = 1, inplace = True)\n df['description'] = df['description'].str.lower()\n df['description']= df['description'].apply(\n lambda x: re.sub('[^a-zA-Z]',' ', x))\n\n #tokenize the descriptions file\n tokenizer = RegexpTokenizer(r'\\w+')\n tokens = df['description'].apply(tokenizer.tokenize)\n\n #apply stopwords and stemming\n stopw = stopwords.words('english')\n ps = PorterStemmer()\n tokens = tokens.apply(\n lambda x:[word for word in x if not word in stopw]\n )\n tokens = tokens.apply(lambda x: [ps.stem(word) for word in x])\n df = df.assign(description = tokens.apply(lambda x: ' '.join(x)))\n df2 = pd.get_dummies(df, columns = ['province'])\n #apply CountVectorizer to the descriptions to get a number matrix\n vect = CountVectorizer(\n analyzer='word', token_pattern=r'\\w+',max_features=600\n )\n xx = vect.fit_transform(df2['description']).toarray()\n xx = pd.DataFrame(xx)\n data = pd.concat([df2, xx], axis = 1)\n data = data.drop(['description'], axis = 1)\n\n #save data to file\n data.to_csv(self.output().path)\n\n def output(self):\n target = luigi.LocalTarget('tac/data_root/task1.csv')\n\n return target\n\n\nclass Split(luigi.Task):\n input_dir = luigi.Parameter()\n evaluate = luigi.BoolParameter(default=False)\n\n def requires(self):\n return Features(self.input_dir)\n\n def run(self):\n df = pd.read_csv(self.input().path)\n df.drop('Unnamed: 0', axis = 1, inplace = True)\n #split into target and data\n target = df['points'].values\n data = df.drop(['points'], axis = 1)\n\n #train-test split\n X_train, X_test, y_train, y_test = train_test_split(data, target,\n test_size = 0.2, random_state = 1234)\n if self.evaluate:\n X_train, X_test, y_train, y_test = train_test_split(data, target,\n test_size = 0.2, random_state = 1234)\n # output test/train splits\n X_test.to_csv(self.output()[2].path)\n pd.Series(y_test).to_csv(self.output()[3].path)\n\n X_train.reset_index(drop = True, inplace = True)\n X_train.to_csv(self.output()[0].path)\n pd.Series(y_train).to_csv(self.output()[1].path)\n\n def output(self):\n targets = [luigi.LocalTarget('tac/data_root/X_train.csv'),\n luigi.LocalTarget('tac/data_root/y_train.csv')]\n\n if self.evaluate:\n targets.append(luigi.LocalTarget('tac/data_root/X_test.csv'))\n targets.append(luigi.LocalTarget('tac/data_root/y_test.csv'))\n\n return targets\n\n\nclass TrainModel(luigi.Task):\n input_dir = luigi.Parameter()\n\n def requires(self):\n return Split(self.input_dir)\n\n def run(self):\n X = pd.read_csv(self.input()[0].path)\n X.drop('Unnamed: 0', axis = 1, inplace = True)\n y = pd.read_csv(self.input()[1].path, header=None)\n y = y[1].values\n\n #specify and fit model\n gb = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.1,\n max_depth=5, max_features='sqrt',\n min_samples_leaf=15, min_samples_split=10,\n loss='huber', random_state =5)\n gb.fit(X, y)\n\n f = open(self.output().path, 'wb')\n pickle.dump(gb, f)\n f.close()\n\n def output(self):\n return luigi.LocalTarget('tac/data_root/model.pickle')\n\n\nclass EvaluateModel(luigi.Task):\n input_dir = luigi.Parameter()\n name = luigi.Parameter(default= \"results_plot.png\")\n\n def requires(self):\n return [TrainModel(self.input_dir),\n Split(self.input_dir, True)]\n\n def run(self):\n GBModel = pickle.load(open(self.input()[0].path, 'rb'))\n X = pd.read_csv(self.input()[1][2].path)\n X.drop('Unnamed: 0', axis = 1, inplace = True)\n y = pd.read_csv(self.input()[1][3].path, header = None)\n y = y[1].values\n\n #define function to get grphic results of the model\n def plot_loss_rmse(train_score, test_score, path = None, title = None):\n '''\n Plot of the loss of the GBRegressor for training and test set along with the rmse of the model\n\n :train_score: loss of the model e.g. gb.train_score_\n :test_score: loss at each iteration, type np.float64\n '''\n fig = plt.figure(figsize = (8, 10))\n fig.suptitle('{} (RMSE: {:.4f})'.format(title, rmsle(y, preds)), fontsize = 15)\n ax = fig.add_subplot(111)\n fig.subplots_adjust(top = .93, bottom = 0.4)\n ax1 = ax.twinx()\n xaxis = np.arange(3000) + 1\n\n train = '#ff7f0e'\n test = '#1f77b4'\n\n labels = ['Training Loss', 'Test Loss']\n handles = [Rectangle((2,2),5,5,color=c) for c in [train, test]]\n\n ax1.plot(xaxis, train_score)\n ax.plot(xaxis, test_score, color = '#ff7f0e')\n ax.set_ylabel('Test Loss', fontsize = 15, labelpad = 20)\n ax1.set_ylabel('Train Loss', fontsize = 15, labelpad = 20)\n ax.set_xlabel('Boosting Iterations', fontsize = 15, labelpad = 20)\n ax.legend(handles, labels, loc = 'upper right', frameon = True, shadow = True,\n handlelength = 2.5, borderaxespad = 2, fontsize = 'medium');\n ax.grid()\n # fig.savefig(path)\n\n #define rmsle function for evaluation\n def rmsle(y, y_pred):\n '''\n Returns the root mean square error of the predictons.\n It expects an array containing the model predictions.\n\n :return: RMSE value of the predictions.\n '''\n return np.sqrt(mean_squared_error(y, y_pred))\n\n preds = GBModel.predict(X)\n\n #prepare test_score for plot\n test_score = np.zeros((3000,), dtype=np.float64)\n\n for i, y_pred in enumerate(GBModel.staged_predict(X)):\n test_score[i] = GBModel.loss_(y, y_pred)\n\n plot_loss_rmse(GBModel.train_score_, test_score,\n title = 'Model')\n\n return plt.savefig(\"{}/{}\".format('tac/data_root/', self.name))\n\n\n def output(self):\n return luigi.LocalTarget(self.name)\n" } ]

2

EarlPaquibol/Phyton

https://github.com/EarlPaquibol/Phyton

761a9943121816e75e4b185f4b317daa5553b0f6

3cfb0fbaefedb0487c49d7c292fbb228f4c7601f

ad8f4ccc6a4741dbff2391f3ea71c3a9eb8577b6

refs/heads/master

2020-08-26T16:09:11.677324

2019-11-08T08:05:10

217,067,619

0

null

[ { "alpha_fraction": 0.5591397881507874, "alphanum_fraction": 0.6129032373428345, "avg_line_length": 23.799999237060547, "blob_id": "4e262f04425b7a2d85a0d8a137420fbf836e6699", "content_id": "42f302af5a67b55685b7c436faee28f40d1af57b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 372, "license_type": "no_license", "max_line_length": 66, "num_lines": 15, "path": "/Exercises/6th kyu/Multiples_3_5.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "#If we list all the natural numbers below 10 that are multiples of\n#3 or 5, we get 3, 5, 6 and 9. The sum of these multiples is 23.\n\ndef solution(number):\n total = 0\n for e in list(range(1,number)):\n if e%3 == 0 or e%5 == 0:\n total += e\n return total\n### there was no need to create a list\n\nprint(solution(10))\n\nfor e in range(10):\n print(e)\n" }, { "alpha_fraction": 0.5304136276245117, "alphanum_fraction": 0.5352798104286194, "avg_line_length": 30.538461685180664, "blob_id": "444095392ea695ff159ac0c8c39966a5be2121e0", "content_id": "814b54ce8ad0849f0fdc6b4df4fa5d2bd16e7903", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 411, "license_type": "no_license", "max_line_length": 120, "num_lines": 13, "path": "/Exercises/6th kyu/Encode().py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def duplicate_encode(word):\n# hello = []\n# for e in word.lower():\n# if word.lower().count(e) > 1:\n# hello.append(')')\n# else:\n# hello.append('(')\n# return ''.join(hello)\n\ndef duplicate_encode(word):\n return ''.join([')' if word.lower().count(e) > 1 else '(' for e in word.lower()]) ###ey another one liner from me\n\nprint(duplicate_encode(\"Receder\"))\n\n" }, { "alpha_fraction": 0.47858941555023193, "alphanum_fraction": 0.501259446144104, "avg_line_length": 15.541666984558105, "blob_id": "9d23aaae3e89629bc8df74b0be9362dc7a7c7c2a", "content_id": "ee6f2fbe34f9082ff54f65b3d6b27dd59b8779c4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 397, "license_type": "no_license", "max_line_length": 46, "num_lines": 24, "path": "/Exercises/7th kyu+/SquareNum.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def is_square(n):\n if n < 0:\n return False\n else:\n an = int(n**(1/2))\n if an*an == n:\n return True\n else:\n return False\n\nprint(is_square(36))\n\n\n# import math\n# def is_square(n):\n# if n < 0:\n# return False\n# sqrt = math.sqrt(n)\n# return sqrt.is_integer()\n\n\n# import math\n# def is_square(n):\n# return n > -1 and math.sqrt(n) % 1 == 0;\n" }, { "alpha_fraction": 0.5807228684425354, "alphanum_fraction": 0.6060240864753723, "avg_line_length": 20.28205108642578, "blob_id": "fad2f089285c9f89d813ad83191909ea67e6c0bd", "content_id": "b723e2889b0d1a674ff17cad0dc83ff6026ad4ea", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 830, "license_type": "no_license", "max_line_length": 127, "num_lines": 39, "path": "/SQL/SQLite_demo.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "import sqlite3\nfrom Employee import Employee\n\nconn = sqlite3.connect('employee.db') #memory\n\nc = conn.cursor()\n\n# c.execute(\"\"\"CREATE TABLE employees('\n# first text,\n# last text,\n# pay integer\n# ) \"\"\")\n\n\nemp_1 = Employee('Lrae', 'Sexy', 60000)\nemp_2 = Employee('Rizza', 'Yang', 65000)\n\n\n# c.execute(\"INSERT INTO employees VALUES (?, ?, ?)\", (emp_1.first, emp_1.last, emp_1.pay))\n# conn.commit()\n\n# c.execute(\"INSERT INTO employees VALUES (:first, :last, :pay)\", {'first': emp_2.first, 'last': emp_2.last, 'pay': emp_2.pay})\n# conn.commit()\n\n\nc.execute(\"SELECT * FROM employees WHERE last=?\", ('Pogi', ))\nprint(c.fetchall())\n\nc.execute(\"SELECT * FROM employees WHERE last=:last\", {'last': 'Sexy'})\nprint(c.fetchall())\n\n\n\n# c.fetchmany(3)\n# c.fetchone()\n\n\nconn.commit()\nconn.close()\n" }, { "alpha_fraction": 0.43103447556495667, "alphanum_fraction": 0.5517241358757019, "avg_line_length": 22.200000762939453, "blob_id": "46b939cd9a0999b1ad390eed0dd6e2ad9a35c372", "content_id": "8c7f18ceb88c804983ce6776e0763ed76b6e8d53", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 232, "license_type": "no_license", "max_line_length": 57, "num_lines": 10, "path": "/Exercises/5th kyu/Move_zeroes.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def move_zeros(array):\n a = array.count(0)\n while array.count(0) > 1:\n array.remove(0)\n while array.count(0) != a:\n array.append(0)\n print(array)\n\n\nmove_zeros([9,0.0,0,9,1,2,0,1,0,1,0.0,3,0,1,9,0,0,0,0,9])\n" }, { "alpha_fraction": 0.41436463594436646, "alphanum_fraction": 0.45303866267204285, "avg_line_length": 21.5, "blob_id": "7fca3060d2631b5338b83575ccdc20a7d7333668", "content_id": "22858ec6673124f7f5782d9caee9f5f9ab7ba516", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 181, "license_type": "no_license", "max_line_length": 29, "num_lines": 8, "path": "/Exercises/7th kyu+/Get_middle.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def get_middle(s):\n length = len(s)\n if length % 2 == 0:\n a = int(length/2 - 1)\n return s[a:length-a]\n else:\n a = length//2 + 1\n return s[a-1]\n\n" }, { "alpha_fraction": 0.6792079210281372, "alphanum_fraction": 0.6881188154220581, "avg_line_length": 27.05555534362793, "blob_id": "993229cb23daa67c447b40fdbf4a0d5771e57a11", "content_id": "08e61a1441b11cc72340e2587bc9ab3ca437e3b4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1010, "license_type": "no_license", "max_line_length": 156, "num_lines": 36, "path": "/Tutorial/Basic/Dictionaries.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "#dictionary is like a class. you can create properties.\n\nstudent = {'name': 'Earl', 'age': 21, 'courses': 'Software Engineering'}\nprint(student)\n\n\nprint(student['name']) ##when key does not exist, it will return an error\nprint(student.get('phone', 'Key does not exist')) ##when key does not exist, it will return none. Second argument == msg displayed when key is not found\n\nstudent['phone'] = '3091209' ##adds a key to the dictionary student\nprint(student.get('phone', 'Key does not exist'))\n\nstudent['name'] = 'Pogi'\nprint(student['name'])\n\nstudent.update({'name': 'Earl Pogi', 'sex': 'Male'})\nprint(student)\n\ndel student['age']\nprint(student)\n\n\nSex = student.pop('sex')\n# print(Sex)\n# print(student)\n\nprint(len(student)) #gets the number of keys\nprint(student.keys()) #gets the keys of the dict\nprint(student.values()) #gets the values of each key\nprint(student.items()) #gets both keys and values by pair tuples\n\n\n# print(new_dict)\n\nfor key, value in student.items():\n print(key, value)\n" }, { "alpha_fraction": 0.6228765845298767, "alphanum_fraction": 0.6364665627479553, "avg_line_length": 32.96154022216797, "blob_id": "a939da33ad2f58c2f03d3db415f742b86b48ce98", "content_id": "4cd9283e0142777e7b301353395bf3c0e288e165", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 883, "license_type": "no_license", "max_line_length": 128, "num_lines": 26, "path": "/Exercises/7th kyu+/Artur.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# King Arthur and his knights are having a New Years party. Last year Lancelot was jealous of Arthur,\n# because Arthur had a date and Lancelot did not, and they started a duel.\n# To prevent this from happening again, Arthur wants to make\n# sure that there are at least as many women as men at this year's party. He gave you a list of integers of all the party goers.\n# Arthur needs you to return true if he needs to invite more women or false if he is all set.\n\ndef invite_more_women(attendees):\n men = 0\n women = 0\n for sex in attendees:\n if sex == -1:\n women += 1\n else:\n men += 1\n if men > women:\n return True\n else:\n return False\n\nprint(invite_more_women([1, 1, 1]))\n\n\ndef invite_more_women(attendees):\n return sum(attendees) > 0 #ONELINER\n\nprint(invite_more_women([1, 1, 1]))\n" }, { "alpha_fraction": 0.4728260934352875, "alphanum_fraction": 0.5271739363670349, "avg_line_length": 17.399999618530273, "blob_id": "b7c958a3ecb65a5e7b29268545a9cab4717dd08a", "content_id": "be3152366da7de602b5867bed6cc338636689025", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 184, "license_type": "no_license", "max_line_length": 41, "num_lines": 10, "path": "/Exercises/7th kyu+/Odd-Recurring.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "#find the odd number that recurs the most\n#unsolved\ndef find_it(seq):\n for e in seq:\n if e % 2 != 0:\n pass\n return e\n\n\nprint(find_it([1, 2, 3, 4, 5, 5, 5, 6]))\n" }, { "alpha_fraction": 0.4359550476074219, "alphanum_fraction": 0.47191011905670166, "avg_line_length": 14.344827651977539, "blob_id": "55a18d32ed78903a18575be108c1cba09c01c7b3", "content_id": "f652257ddcd4a78e5c3d05cf29f092f85d407efb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 445, "license_type": "no_license", "max_line_length": 60, "num_lines": 29, "path": "/Tutorial/Basic/Loops-Iterations.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "nums = [1, 2, 3, 4, 5]\n\nfor e in nums:\n if e == 3:\n print('Found it!')\n break\n print(e)\n\nfor e in nums:\n if e == 3:\n print('Found it!')\n continue #skips to the next iteration\n print(e)\n\n\nfor i in range(1, 11):\n print(i)\n\nx = 0\n\nwhile x < 10:\n print(x)\n x += 1\n\n# fruits = ['apple', 'banana', 'orange']\n# x = 0\n# for fruit in range(len(fruits)):\n# print(fruits[x])\n# x += 1\n" }, { "alpha_fraction": 0.5164835453033447, "alphanum_fraction": 0.5494505763053894, "avg_line_length": 18.157894134521484, "blob_id": "6d8a192fd7281831dce88deb6a923f5f227a7680", "content_id": "7e8beb729c3464480874d515fa620e82efb72d22", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 364, "license_type": "no_license", "max_line_length": 80, "num_lines": 19, "path": "/Exercises/6th kyu/Sum_digits.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "import functools\n\ndef digital_root(n):\n n = [int(e) for e in str(n)]\n while len(n) > 1:\n new = functools.reduce(lambda x,y: x+y, n)\n n = [int(e) for e in str(new)]\n return sum(n)\n\n\n\n\nprint(digital_root(166))\n\n\n# def digital_root(n):\n# return n%9 or n and 9\n\n# If n % 9 != 0 return n % 9. Otherwise return n (if n == 0) or 9 (if n != 0).\n" }, { "alpha_fraction": 0.39915966987609863, "alphanum_fraction": 0.462184876203537, "avg_line_length": 13, "blob_id": "fe80c3c74099ee2cceddb318bc657ee861371060", "content_id": "d463f43879b0a8fec42f19fa00eb4d219a50c62e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 238, "license_type": "no_license", "max_line_length": 27, "num_lines": 17, "path": "/Exercises/7th kyu+/Fibonacci.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def fibo(num):\n n1 = 0\n n2 = 1\n counter = 0\n fib_list = []\n while counter < num:\n nth = n1 +n2\n n1 = n2\n n2 = nth\n fib_list.append(n1)\n counter+=1\n return fib_list\n\n\n\n\nprint(fibo(100))\n" }, { "alpha_fraction": 0.39881831407546997, "alphanum_fraction": 0.41137370467185974, "avg_line_length": 27.16666603088379, "blob_id": "5126ff562af987159cb6ce49ec31a6c1ce51d652", "content_id": "37f700626121af15d1a3445a69989e430e4bcadc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1354, "license_type": "no_license", "max_line_length": 55, "num_lines": 48, "path": "/Exercises/5th kyu/Directions.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def dirReduc(arr):\n if len(arr) == 0:\n return arr\n dup = True\n i = 0\n while dup == True:\n if len(arr)-1 == i:\n dup = False\n elif len(arr) == 0:\n dup = False\n elif arr[i] == 'NORTH' and arr[i+1] == 'SOUTH':\n arr.remove('NORTH')\n arr.remove('SOUTH')\n i = 0\n elif arr[i] == 'EAST' and arr[i+1] == 'WEST':\n arr.remove('EAST')\n arr.remove('WEST')\n i = 0\n elif arr[i] == 'SOUTH' and arr[i+1] == 'NORTH':\n arr.remove('SOUTH')\n arr.remove('NORTH')\n i = 0\n elif arr[i] == 'WEST' and arr[i+1] == 'EAST':\n arr.remove('EAST')\n arr.remove('WEST')\n i = 0\n else:\n i += 1\n return arr\n\n\nprint(dirReduc([\"NORTH\", \"SOUTH\"]))\n\n\n\n # for i,e in enumerate(arr):\n # if e == 'NORTH' and arr[i+1] == 'SOUTH':\n # arr.remove('NORTH')\n # arr.remove('SOUTH')\n # elif e == 'EAST' and arr[i+1] == 'WEST':\n # arr.remove('EAST')\n # arr.remove('WEST')\n # elif e == 'SOUTH' and arr[i+1] == 'NORTH':\n # arr.remove('SOUTH')\n # arr.remove('NORTH')\n # elif e == 'WEST' and arr[i+1] == 'EAST':\n # arr.remove('EAST')\n # arr.remove('WEST')\n\n\n" }, { "alpha_fraction": 0.5144357085227966, "alphanum_fraction": 0.5275590419769287, "avg_line_length": 22.090909957885742, "blob_id": "ba7d11d40f8b680fed1fe54477710a3037366e79", "content_id": "da0aeadf7d37d3d4b771e0a3309566b41834eaea", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 762, "license_type": "no_license", "max_line_length": 88, "num_lines": 33, "path": "/Exercises/7th kyu+/Square_digits.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def square_digits(num):\n# num = str(num)\n# num = list(num)\n# new_list = []\n# for e in num:\n# new_list += [int(e)]\n# squared_list = []\n# for i in new_list:\n# squared_list += [i*i]\n# again = []\n# for x in squared_list:\n# again += [str(x)]\n# again = ''.join(again)\n# return int(again)\n\n\n\ndef square_digitss(num):\n return int(''.join(str(x) for x in [i*i for i in [int(e) for e in list(str(num))]]))\n\nprint(square_digitss(9119))\n\n#good answer in kata\n# def square_digits(num):\n# num = str(num)\n# ans = ''\n# for i in num:\n# ans += str(int(i)**2)\n# return int(ans)\n\ndef square_digits(num):\n return int(''.join(str(int(i)**2) for i in str(num)))\nprint(square_digits(9119))\n" }, { "alpha_fraction": 0.3580645024776459, "alphanum_fraction": 0.41129031777381897, "avg_line_length": 23.799999237060547, "blob_id": "320b82ff60b86166c1bf91bc91ffeabede95e998", "content_id": "0d4c96b64daffcb436f192203c646c08f09591bc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 620, "license_type": "no_license", "max_line_length": 56, "num_lines": 25, "path": "/Exercises/6th kyu/Outlier.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def find_outlier(integers):\n# odd = 0\n# even = 0\n# for e in integers:\n# if e%2 == 0:\n# even += 1\n# else:\n# odd += 1\n# if even > 1:\n# for e in integers:\n# if e%2 != 0:\n# return e\n# elif odd > 1:\n# for e in integers:\n# if e%2 == 0:\n# return e\n\ndef find_outlier(int):\n odds = [x for x in int if x%2!=0]\n evens= [x for x in int if x%2==0]\n return odds[0] if len(odds)<len(evens) else evens[0]\n\n\n\nprint(find_outlier([2, 4, 0, 100, 4, 11, 2602, 36]))\n" }, { "alpha_fraction": 0.5669811367988586, "alphanum_fraction": 0.5726414918899536, "avg_line_length": 27.594594955444336, "blob_id": "6e94571171054998ea9be02f05960bbd3f10f627", "content_id": "149090206f97238db78710710026df1ccafa04c3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1060, "license_type": "no_license", "max_line_length": 136, "num_lines": 37, "path": "/Tutorial/FileDemo/FileDemo.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "### File objects\n\n\n# f = open('test.txt', 'r') #opens file for reading. w for writing, will overwrite. r+ for read and write. a for appending, will add.\n# print(f.name)\n# f.close()\n\n\n# with open('test.txt', 'r') as f: #automatically closes the text file\n# f_contents = f.read() #read(x) wherein x is the number of characters you want to read\n # print(f_contents)\n # f_contents = f.readline() #readlines() every line\n # print(f_contents, end='')\n # f_contents = f.readline()\n # print(f_contents, end='')\n # for lines in f:\n # print(lines, end='')\n #f.seek(x) wherein x will be the place where it starts to read\n\n\n\n# with open('test2.txt', 'w') as f:\n# f.write('pogi')\n# f.seek(0)\n# f.write('earl')\n\n\nwith open('test.txt', 'r') as rf:\n with open('test_copy.txt', 'w') as wf:\n size_to_read = 100\n rf_chunk = rf.read(size_to_read)\n while len(rf_chunk) > 0:\n wf.write(rf_chunk)\n rf_chunk = rf.read(size_to_read)\n\n# for lines in rf:\n# wf.write(lines)\n\n\n" }, { "alpha_fraction": 0.48968106508255005, "alphanum_fraction": 0.48968106508255005, "avg_line_length": 21.20833396911621, "blob_id": "73a37670f6872a7249fa547ac493d5b57a0ed711", "content_id": "fdde15f43923893adffefa5030f140d4747a76fc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 533, "license_type": "no_license", "max_line_length": 73, "num_lines": 24, "path": "/Exercises/7th kyu+/NoVowels.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def disemvowel(string):\n# for e in string:\n# if ('a' == e or 'e' == e or 'i' == e or 'o' == e or 'u' == e or\n# 'A' == e or 'E' == e or 'I' == e or 'O' == e or 'U' == e):\n# string = string.replace(e, '')\n# return string\n\n\n\n\n\n# def disemvowel(string):\n# return \"\".join(c for c in string if c not in \"aeiouAEIOU\")\n\n\n\n\ndef disemvowel(string):\n c = ''\n for c in string:\n if c not in \"aeiouAEIOU\":\n c += c\n print (c)\nprint(disemvowel(\"This website is for losers LOL!\"))\n" }, { "alpha_fraction": 0.541015625, "alphanum_fraction": 0.6171875, "avg_line_length": 29.117647171020508, "blob_id": "bef665d73192268dd9f108694712df30f17064e4", "content_id": "b9f99757b7363a92cda629c71cd8a996dfff6757", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 512, "license_type": "no_license", "max_line_length": 88, "num_lines": 17, "path": "/Exercises/6th kyu/IQTest.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def iq_test(numbers):\n numbers = [int(e) for e in numbers.split(' ')]\n odd = [x for x in numbers if x%2 != 0]\n even = [x for x in numbers if x%2 == 0]\n return numbers.index(even[0])+1 if sum(odd) > sum(even) else numbers.index(odd[0])+1\n\n\n\nprint(iq_test(\"2 4 7 8 10\"))\n\n\n# def iq_test(numbers):\n# e = [int(i) % 2 == 0 for i in numbers.split()]\n\n# return e.index(True) + 1 if e.count(True) == 1 else e.index(False) + 1\n\n#https://www.codewars.com/kata/552c028c030765286c00007d/solutions/python\n" }, { "alpha_fraction": 0.5402414202690125, "alphanum_fraction": 0.5744466781616211, "avg_line_length": 23.219512939453125, "blob_id": "32ae603a97d1485446ebdfbe915c9efa0d174b57", "content_id": "0109136adebfd42c383672e3911acdcf16ed6c38", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 994, "license_type": "no_license", "max_line_length": 100, "num_lines": 41, "path": "/Exercises/7th kyu+/DescendingOrder.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def Descending_Order(num):\n# st = str(num)\n# my_total = []\n# # for num in st:\n# # my_total += [int(num)]\n# my_total = [int(num) for num in st]\n# my_total.sort()\n# my_total.reverse()\n# st = str(my_total)\n# st = st[1:-1]\n# st = st.replace(', ', '')\n# num = int(st)\n# print(num)\n### same funcs, just tried to make it cleaner\n\n\n\n\ndef Descending_Orderr(num):\n string_num = str(num)\n list_num = [int(num) for num in string_num]\n list_num.sort()\n list_num.reverse()\n st = str(list_num)\n num = int(st[1:-1].replace(', ', ''))\n return num\n\ndef Descending_Order(num):\n s = str(num)\n s = list(s) ##no need to convert to list, sorted automatically converts a string into a list\n s = sorted(s)\n s = reversed(s)\n s = ''.join(s)\n print(s)\n return int(s)\n\ndef Descending_Order(num):\n return int(''.join(sorted(str(num), reverse=True)))\n\nDescending_Orderr(134454236546645)\nDescending_Order(134454236546645)\n\n" }, { "alpha_fraction": 0.8032786846160889, "alphanum_fraction": 0.8032786846160889, "avg_line_length": 60, "blob_id": "353b82f947581b7d7f41027bf1bd502e9ded2a64", "content_id": "66b88435721df4855274ea75c24aacb480dc8f1d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 61, "license_type": "no_license", "max_line_length": 60, "num_lines": 1, "path": "/README.md", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# This repo contains all things about my knowledge on Phyton\n" }, { "alpha_fraction": 0.5670391321182251, "alphanum_fraction": 0.5782122611999512, "avg_line_length": 20.918367385864258, "blob_id": "1612b747f29b66805e3718c48aa2bff5ebddb532", "content_id": "927c82796997205e7342a9d34285c9765e0e5123", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1074, "license_type": "no_license", "max_line_length": 115, "num_lines": 49, "path": "/Exercises/6th kyu/Persistence.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# Write a function, persistence, that takes in a positive parameter num and returns its multiplicative persistence,\n# which is the number of times you must multiply the digits in num until you reach a single digit.\n\n\n\n# def persistence(num):\n# if len(str(num)) == 1:\n# return 0\n# num = [int(e) for e in str(num)]\n# digits = functools.reduce(lambda x,y:x*y, num)\n# counter = 1\n# while len(str(digits)) > 1:\n# digits = [int(e) for e in str(digits)]\n# num = functools.reduce(lambda x,y:x*y, digits)\n# digits = num\n# counter += 1\n# return counter\n\n\n\n\nimport functools\ndef persistence(n):\n nums = [int(x) for x in str(n)]\n sist = 0\n while len(nums) > 1:\n newNum = functools.reduce(lambda x, y: x * y, nums)\n nums = [int(x) for x in str(newNum)]\n sist = sist + 1\n return sist\n\n\nprint(persistence(25))\n\n\n\n\n\n# new = []\n# for e in str(total):\n# new += [int(e)]\n\n# num = 98\n# total = []\n# for e in str(num):\n# total += [int(e)]\n\n# num = [int(e) for e in str(num)]\n# print(total)\n" }, { "alpha_fraction": 0.4935988485813141, "alphanum_fraction": 0.49644380807876587, "avg_line_length": 20.303030014038086, "blob_id": "749413f84e64f35213605e3c837c3d84012769b8", "content_id": "b6a12f3611016c56885440dfa79a560642026be3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 703, "license_type": "no_license", "max_line_length": 65, "num_lines": 33, "path": "/Exercises/7th kyu+/DNA.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# DNA_strand (\"ATTGC\") # return \"TAACG\"\n\ndef DNA_strand(dna):\n dna = list(dna)\n for i,e in enumerate(dna):\n if e == 'A':\n dna[i] = 'T'\n elif e == 'T':\n dna[i] = 'A'\n elif e == 'G':\n dna[i] = 'C'\n elif e == 'C':\n dna[i] = 'G'\n return ''.join(dna)\n\n\n\nprint(DNA_strand('ATAGC'))\n\n# def DNA_strand(dna):\n# return dna.translate(string.maketrans(\"ATCG\",\"TAGC\"))\n # Python 3.4 solution || you don't need to import anything :)\n # return dna.translate(str.maketrans(\"ATCG\",\"TAGC\"))\n\npairs = {'A':'T','T':'A','C':'G','G':'C'}\ndef DNA_strands(dna):\n for x in dna:\n print(pairs[x])\n\n\n\n\nprint(DNA_strands('ATAGC'))\n" }, { "alpha_fraction": 0.4045092761516571, "alphanum_fraction": 0.4615384638309479, "avg_line_length": 15.32608699798584, "blob_id": "6d8c97fc01028b73fb5b81f80c17835149fec7ca", "content_id": "cacf397cfd5c3bb2e05937fd904db162fc9237c2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 754, "license_type": "no_license", "max_line_length": 47, "num_lines": 46, "path": "/Exercises/6th kyu/Equal_sides_array.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def find_even_index(arr):\n arr = list(arr)\n if (sum(arr) == arr[-1]):\n return arr.index(arr[-1])\n print(arr)\n num = arr\n a = 0\n i = 0\n while a != sum(num):\n if sum(num) == 0:\n return -1\n if a == sum(arr[1:]):\n return i\n a += num[0]\n num.remove(num[0])\n i += 1\n if (sum(arr) == 0):\n return 0\n return -1\n\n\ndef find_even_index(arr):\n for i in range(len(arr)):\n if sum(arr[:i]) == sum(arr[i+1:]):\n return i\n return -1\n\n\n\n\n\n\nprint(find_even_index([10,-80,10,10,15,35,20]))\n\n\n\n\n\n# num = [1,100,50,-51,1,1]\n# print(sum(num))\n# a = num[0]\n# num.remove(num[0])\n# print(a, sum(num))\n# a += num[1]\n# num.remove(num[1])\n# print(a, sum(num))\n\n\n\n" }, { "alpha_fraction": 0.6442952752113342, "alphanum_fraction": 0.6778523325920105, "avg_line_length": 17.625, "blob_id": "a64c43e8094aeb938200dec29a76351d3bd5e1aa", "content_id": "f592745c351a0cea153c320c933838c87c5ffdb2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 149, "license_type": "no_license", "max_line_length": 37, "num_lines": 8, "path": "/Tutorial/Basic/Enumerate.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "fruits = ['apple', 'banana', 'mango']\nname = 'Earl'\n\nobj1 = enumerate(fruits)\nobj2 = enumerate(name)\nprint(obj1)\nprint(list(obj1))\nprint(list(obj2))\n" }, { "alpha_fraction": 0.5321100950241089, "alphanum_fraction": 0.5779816508293152, "avg_line_length": 23.22222137451172, "blob_id": "6f068264f6e7f75670fb2f6183293bcfe2535093", "content_id": "f2c07644dd5c94b991149ba2f100ca32629a8da4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 436, "license_type": "no_license", "max_line_length": 67, "num_lines": 18, "path": "/Exercises/7th kyu+/Highest_lowest.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def high_and_low(numbers):\n# # numbers = numbers.split(' ')\n# numbers = list(map(int, numbers.split(' ')))\n# numbers.sort()\n# numbers = \"{}\".format(numbers[-1]) + \" {}\".format(numbers[0])\n# return numbers\n\n\n\n# print(high_and_low('5 3 2 221 321'))\n\n\n\ndef high_and_low(numbers):\n numbers = list(map(int, numbers.split(' ')))\n return f'{max(numbers)}' + f' {min(numbers)}'\n\nprint(high_and_low('5 3 2 221 321'))\n" }, { "alpha_fraction": 0.4324817657470703, "alphanum_fraction": 0.4562043845653534, "avg_line_length": 26.299999237060547, "blob_id": "b789ac22e94e7dfa3a34fda2875801b5df59e6d4", "content_id": "9fa0a4ba7a4a5cbc0a8c26e24bb99d48f958abe9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 548, "license_type": "no_license", "max_line_length": 60, "num_lines": 20, "path": "/Exercises/6th kyu/Find_odd.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def find_it(seq):\n# new = []\n# for index, value in enumerate(seq):\n# if seq.count(value)%2 != 0:\n# new += [seq.count(value)]\n# a = max(new)\n# for i,e in enumerate(seq):\n# if seq.count(e) == a:\n# a = seq[i]\n# print('retard')\n# return a\n\ndef find_it(seq):\n odd = [seq.count(e) for e in seq if seq.count(e)%2 != 0]\n for i,e in enumerate(seq):\n if seq.count(e) == max(odd):\n n = seq[i]\n return n\n\nprint(find_it([3,3,3,3,4,4,4,4,4]))\n\n\n" }, { "alpha_fraction": 0.5431235432624817, "alphanum_fraction": 0.5524475574493408, "avg_line_length": 24.235294342041016, "blob_id": "480bf4cebffcb88ca1f84224800d683d53d7f889", "content_id": "62d1e155a020c93dec9530fd47ce948a52186fa2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 429, "license_type": "no_license", "max_line_length": 81, "num_lines": 17, "path": "/OOP/Classes.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "class Pogi:\n\n def __init__(self, first, last, sex):\n self.first = first\n self.last = last\n self.sex = sex\n\n def display_info(self):\n if self.sex == 'M':\n print(f'Hi, {self.first} {self.last}! Is your phone number 69? Hihe')\n else:\n print(f'Hi, {self.first} {self.last}! Is your phone number beach?')\n\n\n\nperson1 = Pogi('Earl', 'Paquibol', 'M')\nperson1.display_info()\n" }, { "alpha_fraction": 0.49253731966018677, "alphanum_fraction": 0.5149253606796265, "avg_line_length": 14.764705657958984, "blob_id": "51aec9041264c72c2f8c1dc16f81b906044ce5c1", "content_id": "f79a1dc4810fcb681520d586c7380148f128aeb2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 268, "license_type": "no_license", "max_line_length": 47, "num_lines": 17, "path": "/Exercises/7th kyu+/SumBetween.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def get_sum(a,b):\n if a<b:\n return sum(range(a,b+1))\n else:\n return sum(range(b,a+1))\n\n\n\n\nprint(get_sum(1,0))\n\ndef get_sum(a,b):\n return sum(range(min(a, b), max(a, b) + 1))\n\ndef get_sum(a,b):\n if a>b : a,b = b,a\n return sum(range(a,b+1))\n" }, { "alpha_fraction": 0.45041322708129883, "alphanum_fraction": 0.4793388545513153, "avg_line_length": 22, "blob_id": "0e7a7abe4bf54d3b913c958f5c314d103025dabb", "content_id": "dedfb4809348962fa211604ef4c497cd0f404f37", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 484, "license_type": "no_license", "max_line_length": 104, "num_lines": 21, "path": "/Exercises/6th kyu/Ten_minute_walk.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "pairs = {'s': 1, 'n': -1, 'e': 2, 'w': -2}\ndef isValidWalk(walk):\n if len(walk) != 10:\n return False\n total = 0\n for e in walk:\n total += pairs[e]\n return total == 0\n\n\n\n\nprint(isValidWalk(['s', 'n', 's', 'n', 'e', 'w', 'w', 'w', 'w', 'w']))\n\n\n# pairs = {'s': 1, 'n': -1, 'e': 2, 'w': -2}\n# for x in pairs:\n# print(pairs[x])\n\ndef isValidWalk(walk):\n return len(walk) == 10 and walk.count('n') == walk.count('s') and walk.count('e') == walk.count('w')\n\n" }, { "alpha_fraction": 0.6036036014556885, "alphanum_fraction": 0.6081081032752991, "avg_line_length": 19.18181800842285, "blob_id": "a2001e0ff2896ef5f6964946bded7d56db1f2c02", "content_id": "4a950dc3aee095af7edfa674727d10908cebbdff", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 222, "license_type": "no_license", "max_line_length": 44, "num_lines": 11, "path": "/Exercises/6th kyu/Dubstep.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def song_decoder(song):\n song = song.split(\"WUB\")\n while song.count('') != 0:\n song.remove('')\n return ' '.join(song)\n\n\n\n\nprint(song_decoder(\"AWUBWUBWUBBWUBWUBWUBC\"))\n # => WE ARE THE CHAMPIONS MY FRIEND\n" }, { "alpha_fraction": 0.5587044358253479, "alphanum_fraction": 0.5587044358253479, "avg_line_length": 16.64285659790039, "blob_id": "71ff032f5748ac337229f73e4efbc269bd4cc6c9", "content_id": "80ee821b6b6b3ab0c9bfa4e1d5d7eab3b013e447", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 247, "license_type": "no_license", "max_line_length": 48, "num_lines": 14, "path": "/Exercises/7th kyu+/Shortest_word.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def find_short(s):\n# length = []\n# for word in s.split(' '):\n# length += [len(word)]\n# return min(length)\n\n\n\n\n\ndef find_short(s):\n return min(len(x) for x in s.split(' '))\n\nprint(find_short(\"hello world a askjhdaskdhas\"))\n" }, { "alpha_fraction": 0.6843373775482178, "alphanum_fraction": 0.6891566514968872, "avg_line_length": 26.66666603088379, "blob_id": "7922f07b5c8713a8f1f2009e7d0fa5fb89bdb3df", "content_id": "b53095a486b40d6976de05449bea8ad769f04ff5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 415, "license_type": "no_license", "max_line_length": 118, "num_lines": 15, "path": "/Exercises/7th kyu+/VowelCount.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def getCount(inputStr):\n return inputStr.count('a') + inputStr.count('e') + inputStr.count('i') + inputStr.count('o') + inputStr.count('u')\n\n\nprint(getCount('aeioudasjhgsajgdkjhsdahkjgadsfghkjadfs'))\n\n\ndef getCount(inputStr):\n num_vowels = 0\n for char in inputStr:\n if char in \"aeiouAEIOU\":\n num_vowels += 1\n return num_vowels\n\nprint(getCount('aeioudasjhgsajgdkjhsdahkjgadsfghkjadfs'))\n" }, { "alpha_fraction": 0.5433962345123291, "alphanum_fraction": 0.6150943636894226, "avg_line_length": 19.384614944458008, "blob_id": "f5a1339bc1f5bab13ed7e5be8dbde19b6fc45f19", "content_id": "628bec28ec06a757afe40fc7c2472235225d17cc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 265, "license_type": "no_license", "max_line_length": 52, "num_lines": 13, "path": "/Tutorial/Basic/Intergers.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "num = 3\nprint(type(num)) #prints the type of the variable\n\nprint(3/2) #prints 1.5\nprint(3//2) #prints 1\nprint(abs(-2))\nprint(round(2.35, 1)) #round off to 1 decimal place\n\n\na = \"300\"\n## error >>>>>>> print(a + 3)\na = int(a) #convert string to int\nprint(a+3)\n" }, { "alpha_fraction": 0.46028512716293335, "alphanum_fraction": 0.4684317708015442, "avg_line_length": 22.33333396911621, "blob_id": "89a5b5ba2b38e2348cb079dd3a5f0abba9fa1dc7", "content_id": "7eead3447546263e5ca0389eb2d476bd855fe72b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 491, "license_type": "no_license", "max_line_length": 94, "num_lines": 21, "path": "/Exercises/5th kyu/Pig_latin.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def pig_it(text):\n text = text.split (' ')\n new = []\n bruh = []\n for word in text:\n if word not in ('!@#$%^&*()'):\n new.append(word[1:] + word[0] + 'ay')\n else:\n bruh.append(word)\n bruh.append(' ')\n bruh = ''.join(nigga)\n new = ' '.join(new)\n text = f'{new}{nigga}'\n return text\n\n\n\n\ndef pig_it(text):\n lst = text.split()\n return ' '.join( [word[1:] + word[:1] + 'ay' if word.isalpha() else word for word in lst])\n\n" }, { "alpha_fraction": 0.492337167263031, "alphanum_fraction": 0.5268199443817139, "avg_line_length": 19, "blob_id": "ace0472ee3816e2be99b59f4f4244167a1b81129", "content_id": "a4c07fd38bd8284a4a8a6d8bfd2cb8f05655d197", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 522, "license_type": "no_license", "max_line_length": 62, "num_lines": 26, "path": "/Exercises/5th kyu/Scramblies.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def scramble(s1, s2):\n if len(s2) > len(s1):\n return False\n return len([char for char in s2 if char in s1]) == len(s2)\n\nprint(scramble('cedewaraaossoqqyt', 'codewars'))\n\n\n\n\n# def scramble(s1, s2):\n# for char in s2:\n# if char in s1:\n# s1 = s1.replace(char, '', 1)\n# else:\n# return False\n# return True\n\n\n# def scramble(s1, s2):\n# for char in s2:\n# if char not in s1:\n# return False\n# return True\n\n# print(scramble('katas', 'steak'))\n\n\n" }, { "alpha_fraction": 0.37377050518989563, "alphanum_fraction": 0.4229508340358734, "avg_line_length": 16.941177368164062, "blob_id": "855a5a0d8d85325d7f41a8f5930ba5793582923b", "content_id": "94147485f571a103d987da722d88c53e80687a0a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 305, "license_type": "no_license", "max_line_length": 30, "num_lines": 17, "path": "/Exercises/6th kyu/BinaryNum.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def countBits(n):\n binary = []\n while n > 0:\n if n%2 == 0:\n binary.append(0)\n n = n/2\n elif n%2 != 0:\n n -= 1\n n = n/2\n binary.append(1)\n return sum(binary)\n\n\n# def countBits(n):\n# return bin(n).count(\"1\")\n\ncountBits(1234)\n" }, { "alpha_fraction": 0.5929203629493713, "alphanum_fraction": 0.6283186078071594, "avg_line_length": 19.545454025268555, "blob_id": "2905365877ecdf72c10afdc48d0cf6aafbfcbb5f", "content_id": "00ab14f94a8cb1e73201b9962fbb41910bb39cf8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 226, "license_type": "no_license", "max_line_length": 46, "num_lines": 11, "path": "/Tutorial/Intermediate/Lamba.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "###filter, map, reduce\n\n\nimport functools\nsample = [1, 2, 3, 4, 5, 6]\nx = list(filter(lambda x: (x%2==0), sample))\ny = list(map(lambda x: x*x, sample))\nz = functools.reduce(lambda x, y: x+y, sample)\nprint(x)\nprint(y)\nprint(z)\n" }, { "alpha_fraction": 0.5729349851608276, "alphanum_fraction": 0.5887522101402283, "avg_line_length": 20.074073791503906, "blob_id": "c0a832314d6da0e762a07f2076c72c05ec7f2cbd", "content_id": "0c0a2eb88832aaf1b852f35a42deb8c4a32c3dcf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 569, "license_type": "no_license", "max_line_length": 58, "num_lines": 27, "path": "/Exercises/7th kyu+/PigLatin.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "#move the first letter of the word to the last and add ay\n\nadd = \"ay\"\nword = \"EarlP\"\ntemp_firstletter = (word[0])\npig_latin = word[1:] + temp_firstletter + add\nprint(pig_latin)\n\n#sentence\n#define things first\ndef pigLatin(string_):\n new_ = string_.split(\" \", -1)\n print(new_)\n x = 0\n total = \"\"\n delim = \" \"\n for e in new_:\n temp_firstletter = (new_[0+x][0])\n pig_latin = new_[0+x][1:] + temp_firstletter + add\n total += pig_latin + delim\n x += 1\n return total\n\n\n\nsentence = \"Dave is retarded\"\nprint(pigLatin(sentence))\n" }, { "alpha_fraction": 0.5683760643005371, "alphanum_fraction": 0.5712250471115112, "avg_line_length": 21.645160675048828, "blob_id": "d2d9c3ea550fb02f870cd3976e77fb37cb830d06", "content_id": "a11027ee3eeaef1933b3a4a1a70850a2c60d2316", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 702, "license_type": "no_license", "max_line_length": 56, "num_lines": 31, "path": "/Exercises/6th kyu/Alphabet.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "import string\nalphabet = dict.fromkeys(string.ascii_lowercase)\ni = 1\nfor x in alphabet:\n alphabet[x] = str(i)\n i += 1\n\n\n# sentence = \"The sunset sets at twelve o' clock.\"\n# sentence = list(sentence.lower().split(' '))\n# new = []\n# for word in sentence:\n# for char in word:\n# if not char in d:\n# break\n# new.append(d[char])\n\n# print(new)\n\ndef alphabet_position(text):\n text = list(text.lower().split(' '))\n num = []\n for word in text:\n for char in word:\n if not char in alphabet:\n break\n num.append(alphabet[char])\n num = ' '.join(num)\n return num\n\nalphabet_position(\"The sunset sets at twelve o' clock.\")\n" }, { "alpha_fraction": 0.5833333134651184, "alphanum_fraction": 0.5866666436195374, "avg_line_length": 14.789473533630371, "blob_id": "ddec8258dab710d247bfeb633abb0769a135e0f1", "content_id": "cbc043f0ab423e41aaa325a0ccc70e5f7764eaa6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 300, "license_type": "no_license", "max_line_length": 50, "num_lines": 19, "path": "/Exercises/7th kyu+/Isogram.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# def is_isogram(string):\n# for e in string.lower():\n# if string.lower().count(e) > 1:\n# return False\n# return True\n\n\ndef is_isogram(string):\n return len(string) == len(set(string.lower()))\n\n\n\nprint(is_isogram('qwerty'))\n\n\n\nstring = 'qwertyyq'\nq = set(string)\nprint(q)\n" }, { "alpha_fraction": 0.6244444251060486, "alphanum_fraction": 0.6288889050483704, "avg_line_length": 18.565217971801758, "blob_id": "18e11a88e90eb48a781408ef9ec724aa95406c7b", "content_id": "9de4796404514fdc247f71c75d01214bb97ae5ee", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 450, "license_type": "no_license", "max_line_length": 47, "num_lines": 23, "path": "/Tutorial/Basic/Functions.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def empty_function():\n pass\n\ndef hello_func(greeting):\n return f'{greeting} function!'\n\na = hello_func('Hi')\nprint(a)\n\ndef hello_func_default(greeting, name = 'You'):\n return f'{greeting} {name}!'\n\na = hello_func_default('sup')\nprint(a)\n\n\ndef student_info(*args, **kwargs):\n print(args) #prints tuples\n print(kwargs) #prints dict\n\ncourses = ['Math', 'Biology']\ninfo = {'name': 'Earl', 'age': 21}\nstudent_info(*courses, **info)\n" }, { "alpha_fraction": 0.5690423250198364, "alphanum_fraction": 0.5757238268852234, "avg_line_length": 18.91111183166504, "blob_id": "5e5b317b03a5e4cb7b629d3020684e00e99e0d83", "content_id": "1f1098fffe52c52ae8eadffd57db07865687a09e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 898, "license_type": "no_license", "max_line_length": 59, "num_lines": 45, "path": "/Exercises/7th kyu+/JadenCased.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# string = 'hello hello world'\n# string = string.split()\n# for e in string:\n# e.capitalize()\n# return ''.join(e.capitalize() for e in string.split())\n\n\n\n# def Jaden_casing(string):\n# string = string.split()\n# for e in string:\n# if e[0].islower() == True:\n# return False\n# return True\n\n\n# print(Jaden_casing('Hello World Wigga'))\n\n# def toJadenCase(string):\n# string = string.split()\n# for i, e in enumerate(string):\n# string[i] = e[0].upper() + e[1:]\n# return ' '.join(string)\n\n\n\n# print(toJadenCase('hello world wigga'))\n\n\n# def toJadenCase(string):\n# s = []\n# for e in string.split():\n# s += e[0].upper() + e[1:] + ' '\n# return ''.join(s[:-1])\n\n# print(toJadenCase('hello world wigga'))\n\n\n\n\n\ndef toJadenCase(string):\n return ' '.join(e.capitalize() for e in string.split())\n\nprint(toJadenCase('hello world wigga'))\n\n\n" }, { "alpha_fraction": 0.5555555820465088, "alphanum_fraction": 0.5679012537002563, "avg_line_length": 22.14285659790039, "blob_id": "59c1cf74951b11d7bf6853ba844ece270b16759f", "content_id": "a5af4d7aa2973312ffe38712351e3443b873a754", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 324, "license_type": "no_license", "max_line_length": 65, "num_lines": 14, "path": "/Exercises/6th kyu/Duplicate_count.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def duplicate_count(text):\n text = text.lower()\n counter = 0\n for e in text:\n if text.count(e) > 1:\n text = text.replace(e, '')\n counter += 1\n return counter\n\nprint(duplicate_count('abcbc'))\n\n\ndef duplicate_count(s):\n return len([c for c in set(s.lower()) if s.lower().count(c)>1])\n" }, { "alpha_fraction": 0.6847673058509827, "alphanum_fraction": 0.6953455805778503, "avg_line_length": 21.140625, "blob_id": "db6621af041fdb6fe8518d4cffde6908df86c948", "content_id": "f9366b423539c90fa9378398ccb391b45dc9f44d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1418, "license_type": "no_license", "max_line_length": 85, "num_lines": 64, "path": "/Tutorial/Basic/Lists.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "courses = [\"History\", \"Math\", \"Physics\", \"Compsci\"]\nprint(len(courses)) #prints length of lists\n\nprint(courses[-1]) #prints the last value in the list\nprint(courses[:2]) #prints first to second value\n\ncourses.append(\"Art\")\nprint(courses)\n\npopped = courses.pop() #removes art or last value from the list\ncourses.insert(0, \"Art\")\nprint(popped)\n\ncourses_2 = [\"Education\"]\ncourses.extend(courses_2) #extend for each item, append or insert for list to list\nprint(courses)\n\ncourses.remove('Math')\ncourses.remove('Education')\ncourses.remove('Art')\nprint(courses)\ncourses.reverse()\nprint(courses)\ncourses.sort()\nprint(courses)\nnum = [1,5,3,8,2]\nnum.sort(reverse=True) #sort can take an arguement, reverse\nprint(num)\n### use sorted when you need to keep the original value\nnum_sort = sorted(num)\n\n###\nprint(min(num))\nprint(max(num))\nprint(sum(num))\n\ncourses = [\"History\", \"Math\", \"Physics\", \"Compsci\"]\nprint(courses.index('Compsci'))\nprint('Art' in courses)\nprint('Math' in courses)\n\nfor course in courses:\n print(course)\n\nfor index, course in enumerate(courses):\n print(index, course)\n\nfor index, course in enumerate(courses, start=1):\n print(index, course)\n\ncourse_str = ', '.join(courses)\nprint(course_str)\n\n\na = list(range(1,100))\n\n\n\nfruits = ['banana', 'apple', 'cyka']\nfruits = '-'.join(fruits)\nprint(fruits)\n\ndef Descending_Order(num):\n return int(''.join(sorted(str(num), reverse=True)))\n\n" }, { "alpha_fraction": 0.5916115045547485, "alphanum_fraction": 0.6070640087127686, "avg_line_length": 14.100000381469727, "blob_id": "277a1ff47f8e87edcd4f64b43f45b0d8f54e9d52", "content_id": "24585dff26a3e3069e14ee8d515983ec0c50c66e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 453, "license_type": "no_license", "max_line_length": 51, "num_lines": 30, "path": "/Tutorial/Basic/Conditionals-IfElse.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "language = 'Phyton'\n\nif language == 'Phyton':\n print('Language is Phyton')\nelif languange == 'Java':\n print('Language is Java')\nelse:\n print('idk that language')\n\n\nlogged_in = False\nif not logged_in:\n print('login re')\nelse:\n print('welcome')\n\na = [1,2,3]\nb = [1,2,3]\nprint(a==b)\n\nprint(id(a))\nprint(id(b))\nprint(a is b) #is operator does is id(a) == id(b)\n\ncondition = 0\n\nif condition:\n print('is treu')\nelse:\n print('iz false')\n" }, { "alpha_fraction": 0.4566929042339325, "alphanum_fraction": 0.4711286127567291, "avg_line_length": 17.14285659790039, "blob_id": "75705dbc1d5ed2775e67fbd362ea3ca5b350943f", "content_id": "7b84ce61ca7b9d40ebc467c10c51486ffd88910e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 762, "license_type": "no_license", "max_line_length": 71, "num_lines": 42, "path": "/Exercises/7th kyu+/Mumble.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# accum(\"RqaEzty\") -> \"R-Qq-Aaa-Eeee-Zzzzz-Tttttt-Yyyyyyy\"\n\n# def accum(s):\n# total = \"\"\n# word = \"\"\n# num = 1\n# for e in s:\n# for i in range(0,num):\n# word += e\n# num += 1\n# total += word[0].upper() + word[0:-1].lower() + '-'\n# word = \"\"\n# return total[:-1]\n\n# print(accum('NANI'))\n\n\n##possible solutions\n\n# def accum(s):\n# total = \"\"\n# i = 0\n# for letter in s:\n# total += letter.upper() + letter.lower()*i + '-'\n# i += 1\n# print(total[:-1])\n\n\n\n\ndef accum(s):\n return '-'.join(c.upper() + c.lower() * i for i, c in enumerate(s))\n\n\nprint(accum('eaarl'))\nname = 'E'\na = name.lower()*5\nprint(a)\n\n# sample = 'abc'\n# for i, c in enumerate(sample):\n# print(i, c)\n" }, { "alpha_fraction": 0.5373665690422058, "alphanum_fraction": 0.5504151582717896, "avg_line_length": 22.27777862548828, "blob_id": "c2799bea2845a5dbfab4e35afc6e48ceb05eb1be", "content_id": "f7e861539e934c7f9b542aa1281b1da999cbc98a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 843, "license_type": "no_license", "max_line_length": 109, "num_lines": 36, "path": "/Exercises/7th kyu+/Exes_Ohes.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# Check to see if a string has the same amount of 'x's and 'o's.\n# The method must return a boolean and be case insensitive. The string can contain any char.\n\n\n# def xo(s):\n# x = 0\n# o = 0\n# for e in s:\n# if e == 'x':\n# x += 1\n# elif e == 'o':\n# o += 1\n# if x == o:\n# return True\n# else:\n# return False\n\n\n# print(xo('xoxoxoxo;lkjashddsakhjdsaox'))\n\n# def xoo(s):\n# s = list(s.lower())\n# return s.count('x') == s.count('o')\n\n\n# print(xoo('xoxoxXosakjdhsakjoOOxxkjlashdkhasdjas'))\n\n\ndef xo(s):\n return list(s.lower()).count('x') == list(s.lower()).count('o') ##Oct 26, 2019 4am. I became a onelinerr\n\n##THERE WAS NO NEED TO CONVERT TO LIST!!!\ndef xo(s):\n return s.lower().count('x') == s.lower().count('o')\n\nprint(xo('xoxoxoxo;lkjashddsakhjdsaox'))\n\n\n\n\n\n" }, { "alpha_fraction": 0.5270270109176636, "alphanum_fraction": 0.6891891956329346, "avg_line_length": 28.600000381469727, "blob_id": "db4c6d91fa1263fbf14f58b978b6aaa4d580e091", "content_id": "2522bdb95555c28ed16fde659f9deba6908df02b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 148, "license_type": "no_license", "max_line_length": 89, "num_lines": 5, "path": "/Exercises/5th kyu/Human_time.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def make_readable(seconds):\n return '{0:02}:{1:02}:{2:02}'.format(int(seconds/3600),int(seconds/60%60),seconds%60)\n\n\nprint(make_readable(86399))\n" }, { "alpha_fraction": 0.6457356810569763, "alphanum_fraction": 0.6532333493232727, "avg_line_length": 26.35897445678711, "blob_id": "7a214ca2d35b5db4a6c5257f495e6530cd7d0200", "content_id": "fbbad0b17d7e0703bcba0974ec965df7aa43d308", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1067, "license_type": "no_license", "max_line_length": 87, "num_lines": 39, "path": "/Tutorial/Basic/Strings.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "msg = \"Hello world\"\nprint(msg) #prints the message\n\nprint(msg[0]) #prints the first letter in the string\n\nprint(len(msg)) #length of message\n\nprint(msg[0:5]) #prints 0 to 4 index\nprint(msg[:5]) #automatically assumes start at index 0\nprint(msg[6:]) #prints the last word\nprint(msg.lower()) #prints msg in lowercase, upper() for uppercase\n\nprint(msg.count('o')) #prints the count of o's in msg\nprint(msg.find('world')) #prints the index where world starts\n\nnew_message = msg.replace(\"world\", \"bro\") #replace world with bro\nprint(new_message)\n\ngreeting = \"Hello\"\nname = \"Earl\"\nmessage = greeting + ', ' + name\nprint(message)\nmessage = \"{}, {}. My bro!\".format(greeting, name) #formatting strings\nprint(message)\n\nmessage = f\"{greeting}, {name}. My Fstring!\"\nprint(message)\n\n\n #to see what else you can do with a string pass it to the directory\nprint(dir(message))\n\n #to see what it does\nprint(help(str))\nprint(help(str.lower))\n\n\nhelloworld = \"Hello world\"\nprint(\"Hello\" in helloworld)\n" }, { "alpha_fraction": 0.5568862557411194, "alphanum_fraction": 0.56886225938797, "avg_line_length": 28.352941513061523, "blob_id": "c1f5a5b860f024556100af70b62feba0ec68a21d", "content_id": "fd7bb7b2bec53fee34f9ba598b5742ae2eeb1c58", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 501, "license_type": "no_license", "max_line_length": 108, "num_lines": 17, "path": "/Exercises/6th kyu/Reverse_five.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "def spin_words(sentence):\n sentence = sentence.split()\n new = []\n for e in sentence:\n if len(e) > 4:\n new.append(e[::-1])\n else:\n new.append(e)\n return(' '.join(new))\n\nprint(spin_words(\"Hello my friend\"))\n\ndef spin_words(sentence):\n return(' '.join([''.join(reversed(e)) if len(e) > 4 else e for e in sentence.split()])) ###my one lin3r\n\ndef spin_words(sentence):\n return ' '.join(word if len(word)<5 else word[::-1] for word in sentence.split())\n\n\n" }, { "alpha_fraction": 0.6697965860366821, "alphanum_fraction": 0.6744914054870605, "avg_line_length": 20.299999237060547, "blob_id": "67b1a7cfa824197907423e8ba3cc2fd9d6a045f4", "content_id": "71f32abe630be9401c331b9a5d0044fdc4be0232", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 639, "license_type": "no_license", "max_line_length": 65, "num_lines": 30, "path": "/Tutorial/Basic/Tuples_Sets.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "### tuple works by replacing list [] with ()\n### tuple meaning immutable cannot be mutated/modified\n\nlist_one = (1, 2, 3)\nprint(list_one)\n\n\n### sets\n### sets do not allow duplicates, print out at any order, uses {}\n\nset_one = {'Math', 'Physics'}\nset_two = {'Math', 'Physical Education'}\n\nprint(set_one.intersection(set_two)) #prints the same values\nprint(set_one.difference(set_two))\nnew_set = set_one.union(set_two)\nprint(new_set)\n\n\n###declaring empty list\nempty_list = []\nempty_list = list()\n\n#empty tuple\nempty_tuple = ()\nempty_tuple = tuple()\n\n#empty set\nempty_set = set()\nempty_set = {} ###>> this creates a disctionary not a setb\n" }, { "alpha_fraction": 0.4360119104385376, "alphanum_fraction": 0.5654761791229248, "avg_line_length": 25.8799991607666, "blob_id": "cc47af2f81082a4614cbda832020fd34dfdfa2b3", "content_id": "d1f65ed8bf147fe9477a6420e5044d99d7a90d1e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 689, "license_type": "no_license", "max_line_length": 87, "num_lines": 25, "path": "/Exercises/6th kyu/Playing_digits.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "# dig_pow(89, 1) should return 1 since 8¹ + 9² = 89 = 89 * 1\n# dig_pow(92, 1) should return -1 since there is no k such as 9¹ + 2² equals 92 * k\n# dig_pow(695, 2) should return 2 since 6² + 9³ + 5⁴= 1390 = 695 * 2\n# dig_pow(46288, 3) should return 51 since 4³ + 6⁴+ 2⁵ + 8⁶ + 8⁷ = 2360688 = 46288 * 51\n\n\n# def dig_pow(a, b):\n# temp = [int(e) for e in str(a)]\n# total = 0\n# for i, e in enumerate(temp, start=b):\n# total += e**i\n# return int(total/a) if total%a == 0 else -1\n\n\n# print(dig_pow(92, 1))\n\ndef dig_pow(n, p):\n s = 0\n for i,c in enumerate(str(n), start=1):\n s += pow(int(c),i)\n return s/n if s%n==0 else -1\n\n\n\nprint(dig_pow(92, 1))\n" }, { "alpha_fraction": 0.5126262903213501, "alphanum_fraction": 0.5202020406723022, "avg_line_length": 12.199999809265137, "blob_id": "fd6349475c6f45f7c80e213ad1f8098a73b50ef9", "content_id": "44063c8f2d18d37410929b7c27993e655d9beef9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 396, "license_type": "no_license", "max_line_length": 52, "num_lines": 30, "path": "/Exercises/7th kyu+/Calcu.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "#sample calcu program\na = 4\nb = 2\noperator = '+'\n\ndef add(a, b): #functions must be declared first\n print(a+b)\n\ndef mul(a, b):\n print(a*b)\n\ndef div(a, b):\n print(a/b)\n\ndef sub(a, b):\n print(a-b)\n\ndef pow(a):\n print(a**2)\n\nif operator == '+':\n add(a,b)\nelif operator == '-':\n sub(a,b)\nelif operator == '*':\n mul(a,b)\nelif operator == '/':\n div(a,b)\nelse:\n pow(a)\n" }, { "alpha_fraction": 0.7209302186965942, "alphanum_fraction": 0.7209302186965942, "avg_line_length": 20.5, "blob_id": "746afe46191258cbb42129e3e26c78f55f4a3ff0", "content_id": "9ba179ef2a0a200a06045e7ca017fa3e5acc11b8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 172, "license_type": "no_license", "max_line_length": 43, "num_lines": 8, "path": "/Tutorial/Importing libraries/Importing_Libs.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "import Sample_module as sm\n#from Sample_module import find_index, test\n\n\ncourses = ['Math', 'Education', 'History']\n\nindex = sm.find_index(courses, 'History')\nprint(index)\n" }, { "alpha_fraction": 0.5415162444114685, "alphanum_fraction": 0.5848375558853149, "avg_line_length": 23.086956024169922, "blob_id": "6440fe5586765c7e60d16ac8a51501eeeca04f01", "content_id": "beae981306d61a13fbc0969ee0f943b77e24ab77", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 554, "license_type": "no_license", "max_line_length": 83, "num_lines": 23, "path": "/OOP/Variables.py", "repo_name": "EarlPaquibol/Phyton", "src_encoding": "UTF-8", "text": "class Employee:\n\n raise_amount = 1.04\n num_emps = 0\n\n def __init__(self, first, last, pay):\n self.first = first\n self.last = last\n self.email = first + '.' + last + '@email.com'\n self.pay = pay\n Employee.num_emps += 1\n\n def fullname(self):\n return '{} {} {}'.format(self.first, self.last, self.raise_amount*self.pay)\n\nemp_1 = Employee('Pogi', 'Earl', 50000)\nemp_2 = Employee('Test', 'Employee', 60000)\n\nprint(emp_1.fullname())\nemp_1.raise_amount = 1.05\nprint(emp_1.fullname())\n\nprint(emp_1.num_emps)\n" } ]

55

mastier/net-surveyor

https://github.com/mastier/net-surveyor

a8dcca3b2b2611e0063d7907caece20c05f7d7f0

07c775e9804029ef70661168a046e10e35b75535

2e3e78c47b12db25e2a0b2fda07538f7e82cecbc

refs/heads/master

2022-04-22T07:01:29.058649

2020-04-23T08:47:12

null

0

null

[ { "alpha_fraction": 0.5564202070236206, "alphanum_fraction": 0.5581495761871338, "avg_line_length": 26.879518508911133, "blob_id": "0657714e4e7409107a0b47e2a8c038b45f103d3a", "content_id": "1f9168c0487800e2de3faeb9325a2d2607dbc91c", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2313, "license_type": "permissive", "max_line_length": 115, "num_lines": 83, "path": "/report_html.py", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "#!/usr/bin/python3\n\nimport json\nimport os\nfrom optparse import OptionParser\n\ndef get_ports(netmap):\n ports = []\n for host in get_hosts(netmap):\n for port in netmap['machines'][host]['ports']:\n if port not in ports:\n ports.append(port)\n ports.sort()\n return ports\n\ndef get_hosts(netmap):\n hosts = list(netmap['machines'])\n hosts.sort()\n return hosts\n\ndef get_port_data(netmap, host, port):\n data = {}\n keys = ['chassis', 'port', 'vlan', 'descr']\n pdata = netmap['machines'][host]['ports'][port]\n try:\n for key in keys:\n data[key] = pdata[key]\n except KeyError:\n pass\n return str(data)\n\ndef write_header(f):\n f.write(\"<html><body>\\n\")\n\ndef write_footer(f):\n f.write(\"</body></html>\")\n\ndef write_table_header(f, ports):\n f.write(\"<table border=1><tr>\")\n f.write(\"<td>Hostname</td>\")\n for port in ports:\n f.write(\"<td>\" + port + \"</td>\")\n f.write(\"</tr>\\n\")\n\ndef write_table_footer(f):\n f.write(\"</tr></table>\\n\")\n\ndef write_table_row(f, netmap, host, ports):\n f.write(\"<tr>\")\n f.write(\"<td>\" + host + \"</td>\")\n for port in ports:\n try:\n f.write(\"<td>\" + get_port_data(netmap, host, port) + \"</td>\")\n except KeyError:\n f.write(\"<td>\" + \"Not connected\" + \"</td>\")\n f.write(\"</tr>\\n\") \n\ndef render_html(netmap, outfile):\n ports = get_ports(netmap)\n hosts = get_hosts(netmap)\n with open(outfile, 'w') as f:\n write_header(f)\n write_table_header(f, ports)\n for host in hosts:\n write_table_row(f, netmap, host, ports)\n write_table_footer(f)\n write_footer(f)\n\ndef main(options):\n netmap = {}\n with open(options.infile) as f:\n netmap = json.load(f)\n render_html(netmap, options.outfile) \n\nif __name__ == \"__main__\":\n usage = \"usage: %prog [options] arg1 arg2\"\n parser = OptionParser(usage=usage)\n parser.add_option(\"-i\", \"--input\",\n action=\"store\", type=\"string\", dest=\"infile\", default='netmap.json', help=\"Input file\") \n parser.add_option(\"-o\", \"--output\",\n action=\"store\", type=\"string\", dest=\"outfile\", default='netmap.html', help=\"Output file\")\n (options, args) = parser.parse_args() \n main(options)" }, { "alpha_fraction": 0.7129735946655273, "alphanum_fraction": 0.7267508506774902, "avg_line_length": 28.03333282470703, "blob_id": "99802e0455c19b2919be1154b6da9f43a2145e56", "content_id": "f8a411f11871e0dc7e953d3fdb3a6962c32dcfac", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 871, "license_type": "permissive", "max_line_length": 126, "num_lines": 30, "path": "/collect-lldp-ssh.sh", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "#!/usr/bin/env bash\ninput_file=${1:-machines.txt}\nif [ ! -f $input_file ];then\n echo \"Missing machines list\"\n exit 1\nfi\ndownload_dir='/tmp/lldp'\n\nrm -rf $download_dir\nmkdir -p $download_dir\nscript=$(mktemp)\n\ncat > $script << \"EOF\"\n#!/bin/bash\nfor interface in `ls /sys/kernel/debug/i40e`\n do echo \"lldp stop\" > /sys/kernel/debug/i40e/${interface}/command\ndone\n\napt install lldpd -y\nsleep 10\nlldpcli show neighbors details -f json > /tmp/lldp_output.json\nEOF\n\nwhile read -r machine; do\n echo $machine\n scp -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null $script $machine:/tmp/\n ssh -n -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null $machine \"chmod 700 $script; sudo $script; rm $script\"\n scp -o StrictHostKeyChecking=no -o UserKnownHostsFile=/dev/null $machine:'/tmp/lldp_output.json' $download_dir/$machine.json\ndone < machines.txt\nexit 0\n" }, { "alpha_fraction": 0.7224478721618652, "alphanum_fraction": 0.7303005456924438, "avg_line_length": 29.024391174316406, "blob_id": "7382a6b16b3a4087d8358533bdadaae49b7808a6", "content_id": "d0aa4f878863518257083a2717894affee672454", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 3693, "license_type": "permissive", "max_line_length": 257, "num_lines": 123, "path": "/README.md", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "# NET-SURVEYOR\n\n## About Net-Surveyor\nNet-Surveyor is an LLDP based tool which purpose is to collect, agggregate and visualise network topology data from cloud environment, thus simplifying network configuration and troubleshooting.\n\nNet-Surveyor bases on [Link Layer Doscovery Protocol](https://en.wikipedia.org/wiki/Link_Layer_Discovery_Protocol) - LLDP - data about VLANs, switch port connectivity and link aggregation.\n\n## Usage\n\nNet-Surveyor works with multiple sources of LLDP data. Currently the preferred way is using [juju](https://jaas.ai/) and [magpie](https://jaas.ai/u/openstack-charmers-next/magpie).\n\nThe tool generates 3 types of reports:\n- HTML web page with a table of all machines and interfaces with LLDP data in appropriate cells\n- SVG network topology map with the same information as above\n- Python NetworkX graph\n\n### Juju \n\nNet-Surveyor can collect LLDP data from an existing juju environment. Please note that it will install LLDP on all the machines.\n\n1. Clone the Net-Surveyor repository\n2. Collect LLDP data:\n```\n./collect-lldp-juju.py -i \n```\nThis collects the data by default into `/tmp/lldp/`. `-i` installs LLDP on the machines. Note that if this is fresh LLDP install, this step may need to be repeated as LLDP data is collected over time, based on incoming LLDP PDUs.\n\n3. Build topology from the collected data:\n```\n./build_netmap.py -o netmap.json\n```\nThis step merges the collected data into single JSON file.\n\n4. Create report:\n```\n./report_html.py -i netmap.json -o netmap.html\n```\n\n### Juju and magpie\n\n1. Create a [juju](https://jaas.ai/) [magpie](https://jaas.ai/u/openstack-charmers-next/magpie) bundle for your environment. Magpie charm can be deployed on both bare metal machines and containers. Make sure to enable LLDP collection on bare metal machines:\n```\nseries: \"bionic\"\nmachines:\n '1':\n constraints: tags=foundation-nodes\n '2': \n constraints: tags=foundation-nodes\nservices:\n magpie-bare:\n charm: \"cs:~openstack-charmers-next/magpie\"\n series: \"bionic\"\n num_units: 2\n constraints: spaces=oam-space\n bindings:\n \"\": oam-space\n options:\n check_dns: true\n check_iperf: false\n check_bonds: \"bond0,bond1,bond2\"\n use_lldp: true # required to be true\n check_port_description: false\n to:\n - 1\n - 2\n```\n2. Deploy the bundle:\n```\njuju deploy ./magpie-bundle.yaml\n```\n3. Wait for the bundle to deploy\n4. Clone the Net-Surveyor repository\n5. Collect LLDP data:\n```\n./collect-lldp-juju.py\n```\nThis collects the data by default into `/tmp/lldp/`\n\n6. Build topology from the collected data:\n```\n./build_netmap.py -o netmap.json\n```\nThis step merges the collected data into single JSON file.\n\n7. Create report:\n```\n./report_html.py -i netmap.json -o netmap.html\n```\n\n### SSH\n\n1. Create txt fle conatining hostnames of all machines to connect to, eg: `machines.txt`\n2. Collect LLDP data:\n```\n./collect-lldp-ssh.sh machines.txt\n```\nThis collects the data by default into `/tmp/lldp/`\n\n3. Build topology from the collected data:\n```\n./build_netmap.py -o netmap.json\n```\nThis step merges the collected data into single JSON file.\n\n4. Create report:\n```\n./report_html.py -i netmap.json -o netmap.html\n```\n\n\n## Future work\n\n1. Update and publish plugin to collect [MAAS](maas.io) comissioning data.\n2. Improve SVG graph to group host interfaces into VLANs\n3. Add VLAN and host filtering for report generation\n4. Create Net-Surveyor SNAP\n5. Create Net-Surveyor server and integrate with Magpie for single bundle deployment\n\n## Bugs and feature requests\n\nUse issues reporting feature in this repository to report bugs and feature requests.\n\nAll contributions are heartily welcome.\n" }, { "alpha_fraction": 0.6245681643486023, "alphanum_fraction": 0.6295585632324219, "avg_line_length": 36.228572845458984, "blob_id": "dc89a84e0677cd362453aa4414d5a54989574565", "content_id": "e27c17289872f9a6e165858601490025b32bb63a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2605, "license_type": "permissive", "max_line_length": 138, "num_lines": 70, "path": "/collect-lldp-juju.py", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "#!/usr/bin/python3\n\nimport json\nimport os\nimport shutil\nimport tempfile\nimport subprocess\nfrom optparse import OptionParser\n\ndef get_model_machies():\n raw = subprocess.check_output('juju machines --format json',shell=True)\n ret = []\n json_machines = json.loads(raw)\n for id in json_machines['machines']:\n name = json_machines['machines'][id]['display-name']\n ret.append( (id,name) )\n return ret\n\ndef write_collector_script(install_lldp = True):\n ftmp, ftmpname = tempfile.mkstemp()\n header = \"#!/bin/bash\\n\"\n install = \"\"\"\n for interface in `ls /sys/kernel/debug/i40e`\n do echo \"lldp stop\" > /sys/kernel/debug/i40e/${interface}/command\n done\n apt install lldpd -y;\n \"\"\"\n collect = \"lldpcli show neighbors details -f json > /tmp/lldp_output.json\\n\"\n body = header\n if install_lldp:\n body += install\n body += \"\\n\"\n body += collect\n os.write(ftmp,body.encode('utf-8'))\n os.close(ftmp)\n return ftmpname\n\ndef copy_script(machine, script_name):\n subprocess.run(\"juju scp {script_name} {machine}:{script_name}\".format(machine = machine, script_name = script_name), shell = True)\n\ndef run_script(machine, script_name):\n subprocess.run(\"juju ssh {machine} \\\"chmod 700 {script_name}; sudo {script_name}; rm {script_name}\\\"\"\n .format(machine = machine, script_name = script_name), shell = True)\n\ndef collect_data(machine_id, hostname, work_dir):\n subprocess.run(\"juju scp {machine_id}:/tmp/lldp_output.json {work_dir}/{hostname}.json\"\n .format(machine_id = machine_id, hostname = hostname, work_dir = work_dir), shell = True)\n\ndef main(options):\n if os.path.isdir(options.work_dir):\n shutil.rmtree(options.work_dir)\n os.mkdir(options.work_dir)\n script_name = write_collector_script(options.install_lldp)\n for machine in get_model_machies():\n id = machine[0]\n hostname = machine[1]\n copy_script(id, script_name)\n run_script(id, script_name)\n collect_data(id, hostname, options.work_dir)\n os.remove(script_name)\n\nif __name__ == \"__main__\":\n usage = \"usage: %prog [options] arg1 arg2\"\n parser = OptionParser(usage=usage)\n parser.add_option(\"-d\", \"--dir\",\n action=\"store\", type=\"string\", dest=\"work_dir\", default=\"/tmp/lldp\", help=\"Output directory\")\n parser.add_option(\"-i\", \"--install\",\n action=\"store_true\", dest=\"install_lldp\", default=False, help=\"Install LLDP tools first\") \n (options, args) = parser.parse_args() \n main(options)" }, { "alpha_fraction": 0.586584210395813, "alphanum_fraction": 0.5880114436149597, "avg_line_length": 31.859375, "blob_id": "f723e99ac3b4e669e734f2511084539861f9bb78", "content_id": "77311d023c2a5d29b65b688065f849b8423b6d39", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2102, "license_type": "permissive", "max_line_length": 115, "num_lines": 64, "path": "/report_graph.py", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "#!/usr/bin/python3\n\nimport json\nimport os\nfrom optparse import OptionParser\nimport networkx as nx\nimport matplotlib.pyplot as plt\n\ndef get_hosts(netmap):\n hosts = list(netmap['machines'])\n hosts.sort()\n return hosts\n\ndef get_switches(netmap):\n hosts = list(netmap['switches'])\n hosts.sort()\n return hosts\n\ndef render_graph(netmap, outfile):\n colors = {'host': 'blue',\n 'switch': 'green',\n 'iface': 'red',\n 'port': 'red',\n }\n\n G = nx.Graph()\n color_map = []\n label_map = {}\n for host in get_hosts(netmap):\n G.add_node(host)\n color_map.append(colors['host'])\n label_map[host] = host\n for port in netmap['machines'][host]:\n G.add_node(host + port)\n color_map.append(colors['iface'])\n G.add_edge(host, host + port)\n label_map[host + port] = port\n for switch in get_switches(netmap):\n G.add_node(switch)\n color_map.append(colors['switch'])\n for link in netmap['links']:\n G.add_node(link['destination_host'] + link['destination_port'])\n color_map.append(colors['port'])\n G.add_edge(link['destination_host'], link['destination_host'] + link['destination_port'])\n G.add_edge(link['source_host'] + link['source_port'], link['destination_host'] + link['destination_port'])\n nx.draw(G, labels=label_map, node_color=color_map, with_labels=True)\n #plt.savefig(outfile)\n plt.show()\n\ndef main(options):\n netmap = {}\n with open(options.infile) as f:\n netmap = json.load(f)\n render_graph(netmap, options.outfile) \n\nif __name__ == \"__main__\":\n usage = \"usage: %prog [options] arg1 arg2\"\n parser = OptionParser(usage=usage)\n parser.add_option(\"-i\", \"--input\",\n action=\"store\", type=\"string\", dest=\"infile\", default='netmap.json', help=\"Input file\") \n parser.add_option(\"-o\", \"--output\",\n action=\"store\", type=\"string\", dest=\"outfile\", default='netmap.png', help=\"Output file\")\n (options, args) = parser.parse_args() \n main(options)" }, { "alpha_fraction": 0.5676092505455017, "alphanum_fraction": 0.5686375498771667, "avg_line_length": 37.88999938964844, "blob_id": "fd74133363231074d8e7441e4b77e8ccd65eadcf", "content_id": "ed27b955d92808f1f201ff30a6bdfe99c48de31c", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3890, "license_type": "permissive", "max_line_length": 116, "num_lines": 100, "path": "/build_netmap.py", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "#!/usr/bin/python3\n\nimport json\nimport os\nfrom optparse import OptionParser\n\ndef init_netmap(netmap):\n netmap['vlans'] = []\n netmap['machines'] = {}\n netmap['switches'] = {}\n netmap['links'] = []\n\ndef add_vlan(netmap, vlan):\n if vlan not in netmap['vlans']:\n netmap['vlans'].append(vlan)\n\ndef add_switch_port(netmap, switch, pdata):\n if switch not in netmap['switches']:\n netmap['switches'][switch] = {}\n if 'ports' not in netmap['switches'][switch]:\n netmap['switches'][switch]['ports'] = {}\n if pdata['port'] not in netmap['switches'][switch]['ports']:\n netmap['switches'][switch]['ports'][pdata['port']] = pdata\n if 'vlans' not in netmap['switches'][switch]:\n netmap['switches'][switch]['vlans'] = {}\n if 'vlan' in pdata:\n vlan_id = pdata['vlan']\n else:\n vlan_id = 'untagged'\n if vlan_id not in netmap['switches'][switch]['vlans']:\n netmap['switches'][switch]['vlans'][vlan_id] = []\n netmap['switches'][switch]['vlans'][vlan_id].append(pdata['port']) \n\ndef add_host_port(netmap, hostname, ifname, pdata):\n if hostname not in netmap['machines']:\n netmap['machines'][hostname] = {}\n if 'ports' not in netmap['machines'][hostname]:\n netmap['machines'][hostname]['ports'] = {}\n if ifname not in netmap['machines'][hostname]['ports']:\n netmap['machines'][hostname]['ports'][ifname] = pdata \n if 'vlans' not in netmap['machines'][hostname]:\n netmap['machines'][hostname]['vlans'] = {}\n if 'vlan' in pdata:\n vlan_id = pdata['vlan']\n else:\n vlan_id = 'untagged'\n if vlan_id not in netmap['machines'][hostname]['vlans']:\n netmap['machines'][hostname]['vlans'][vlan_id] = []\n netmap['machines'][hostname]['vlans'][vlan_id].append(ifname)\n\ndef add_link(netmap, hostname, host_port, switch_name, switch_port):\n link = {'source_host': hostname, \n 'source_port': host_port, \n 'destination_host': switch_name, \n 'destination_port': switch_port, \n }\n netmap['links'].append(link)\n\ndef parse_machine_file(netmap, work_dir, fname):\n hostname=fname.split('.')[0]\n with open(work_dir + \"/\" + fname) as f:\n data = json.load(f)\n for iface in data['lldp']['interface']:\n for ifname in iface.keys():\n iface_lldp = iface[ifname]\n pdata = {}\n pdata['raw'] = iface_lldp\n pdata['descr'] = iface_lldp['port']['descr']\n pdata['port'] = iface_lldp['port']['id']['value']\n if 'vlan' in iface_lldp:\n vid = iface_lldp['vlan']['vlan-id']\n pdata['vlan'] = vid\n add_vlan(netmap, vid)\n for chassis_name in iface_lldp['chassis'].keys():\n pdata['chassis'] = chassis_name\n add_switch_port(netmap, chassis_name, pdata)\n add_host_port(netmap, hostname, ifname, pdata)\n add_link(netmap, hostname, ifname, chassis_name, pdata['port'])\n\ndef populate_netmap(netmap, work_dir):\n for fname in os.listdir(work_dir):\n parse_machine_file(netmap, work_dir, fname)\n\ndef main(options):\n netmap = {}\n init_netmap(netmap)\n populate_netmap(netmap, options.work_dir)\n with open(options.outfile, 'w') as outfile:\n json.dump(netmap, outfile)\n \n\nif __name__ == \"__main__\":\n usage = \"usage: %prog [options] arg1 arg2\"\n parser = OptionParser(usage=usage)\n parser.add_option(\"-d\", \"--dir\",\n action=\"store\", type=\"string\", dest=\"work_dir\", default=\"/tmp/lldp\", help=\"Input directory\")\n parser.add_option(\"-o\", \"--output\",\n action=\"store\", type=\"string\", dest=\"outfile\", default='netmap.json', help=\"Output file\")\n (options, args) = parser.parse_args() \n main(options)\n\n" }, { "alpha_fraction": 0.4242086112499237, "alphanum_fraction": 0.4287407696247101, "avg_line_length": 44.53333282470703, "blob_id": "84e764d45c50e9d14165e5f88589c63b65d3c978", "content_id": "b3f69c6379163e93a34dfd287b3a8212ffa77bf1", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 14342, "license_type": "permissive", "max_line_length": 170, "num_lines": 315, "path": "/report_svg.py", "repo_name": "mastier/net-surveyor", "src_encoding": "UTF-8", "text": "#!/usr/bin/python3\n\nimport json\nimport os\nfrom optparse import OptionParser\n\nimport svgwrite\nfrom svgwrite import mm, px \n\ndef get_hosts(netmap):\n hosts = list(netmap['machines'])\n hosts.sort()\n return hosts\n\ndef get_switches_left(netmap):\n hosts = list(netmap['switches'])\n hosts.sort()\n return hosts[0::2]\n\ndef get_switches_right(netmap):\n hosts = list(netmap['switches'])\n hosts.sort()\n return hosts[1::2]\n\ndef init_side_pane(netmap, items):\n meta = {\n 'max_width': 0,\n 'max_height': 0,\n 'count' : len(items),\n 'items': items\n } \n for item in items:\n meta[item] = {}\n meta[item]['width'] = len(item)\n meta[item]['vlans'] = {}\n meta[item]['ports'] = []\n for vlan in netmap['switches'][item]['vlans']:\n meta[item]['vlans'][vlan] = {'ports': []}\n for port in netmap['switches'][item]['vlans'][vlan]:\n if len(item) > meta[item]['width']:\n meta[item]['width'] = len(item)\n meta[item]['vlans'][vlan]['ports'].append(port)\n meta[item]['ports'].append(port)\n meta[item][port] = {}\n meta[item]['height'] = len(netmap['switches'][item]['ports'])+len(netmap['switches'][item]['vlans'])\n if meta[item]['width'] > meta['max_width']:\n meta['max_width'] = meta[item]['width']\n if meta[item]['height'] > meta['max_height']:\n meta['max_height'] = meta[item]['height'] \n return meta\n\ndef init_center_pane(netmap, items, peers):\n meta = {\n 'max_width': 0,\n 'max_height': 0,\n 'count' : len(items),\n 'items': items\n } \n for item in items:\n meta[item] = {}\n meta[item]['width'] = len(item)\n meta[item]['ports_left'] = []\n meta[item]['ports_right'] = []\n meta[item]['vlans'] = {}\n meta[item]['ports'] = []\n port_left_max_width=0\n port_right_max_width=0\n for vlan in netmap['machines'][item]['vlans']:\n meta[item]['vlans'][vlan] = {'ports': []} \n for port in netmap['machines'][item]['vlans'][vlan]: \n if netmap['machines'][item]['ports'][port]['chassis'] in peers['left']:\n meta[item]['ports_left'].append(port)\n meta[item]['ports'].append(port)\n meta[item]['vlans'][vlan]['ports'].append(port)\n meta[item][port] = {'align': 'left'}\n if len(port) > port_left_max_width:\n port_left_max_width = len(port)\n if netmap['machines'][item]['ports'][port]['chassis'] in peers['right']:\n meta[item]['ports_right'].append(port)\n meta[item]['ports'].append(port)\n meta[item][port] = {'align': 'right'} \n if len(port) > port_right_max_width:\n port_right_max_width = len(port)\n\n if meta[item]['width'] < port_right_max_width + port_left_max_width:\n meta[item]['width'] = port_right_max_width + port_left_max_width\n\n if meta[item]['width'] > meta['max_width']:\n meta['max_width'] = meta[item]['width'] \n\n meta[item]['height'] = max(len(meta[item]['ports_left']),\n len(meta[item]['ports_right'])\n ) + len(netmap['machines'][item]['vlans'])\n if meta[item]['height'] > meta['max_height']:\n meta['max_height'] = meta[item]['height']\n return meta\n\ndef prepare_topology_metadata(netmap):\n meta = {}\n items = {}\n items['left'] = get_switches_left(netmap)\n items['right'] = get_switches_right(netmap)\n items['center'] = get_hosts(netmap)\n meta['left'] = init_side_pane( netmap, items['left'])\n meta['right'] = init_side_pane( netmap, items['right'])\n meta['center'] = init_center_pane( netmap, items['center'], items )\n return meta\n\ndef prepare_placement_metadata(meta, options):\n max_height = 0\n for side in ['left', 'center', 'right']:\n height = (meta[side]['max_height'] * options['port_height'] + options['item_label_height'] + options['item_hspace'])*meta[side]['count']\n if height > max_height:\n max_height = height\n \n meta['left']['x'] = options['img_left_padding']\n meta['center']['x'] = meta['left']['x'] + meta['left']['max_width']* options['col_width'] + options['item_vspace']\n meta['right']['x'] = meta['center']['x'] + meta['center']['max_width'] * options['col_width'] + options['item_vspace']\n for side in ['left', 'center', 'right']:\n meta[side]['box_height'] = max_height / meta[side]['count']\n y = 0\n for item in meta[side]['items']:\n box_x = meta[side]['x']\n box_y = meta[side]['box_height']*y\n meta[side][item]['width'] = meta[side]['max_width'] * options['col_width']\n meta[side][item]['height'] = meta[side][item]['height'] * options['port_height'] + options['item_label_height'] \n meta[side][item]['x'] = box_x\n meta[side][item]['y'] = box_y + (meta[side]['box_height'] - meta[side][item]['height']) / 2\n if side == 'center':\n for col in ['left', 'right']:\n dy = 0\n for port in meta[side][item]['ports_' + col]:\n meta[side][item][port]['width'] = (meta[side]['max_width'] / 2) * options['col_width']\n meta[side][item][port]['height'] = options['port_height']\n meta[side][item][port]['y'] = meta[side][item]['y'] + options['item_label_height'] + dy * meta[side][item][port]['height'] \n meta[side][item][port]['anchor_y'] = meta[side][item][port]['y'] + meta[side][item][port]['height'] / 2\n if col == 'left':\n meta[side][item][port]['x'] = meta[side][item]['x']\n meta[side][item][port]['anchor_x'] = meta[side][item]['x']\n if col == 'right':\n meta[side][item][port]['x'] = meta[side][item]['x'] + (meta[side]['max_width'] / 2) * options['col_width']\n meta[side][item][port]['anchor_x'] = meta[side][item][port]['x'] + meta[side][item][port]['width']\n dy += 1 \n else:\n dy = 0\n for port in meta[side][item]['ports']:\n meta[side][item][port]['width'] = meta[side]['max_width'] * options['col_width']\n meta[side][item][port]['height'] = options['port_height']\n meta[side][item][port]['x'] = meta[side][item]['x'] \n meta[side][item][port]['y'] = meta[side][item]['y'] + options['item_label_height'] + dy * meta[side][item][port]['height'] \n meta[side][item][port]['anchor_y'] = meta[side][item][port]['y'] + meta[side][item][port]['height'] / 2\n if side == 'left':\n meta[side][item][port]['anchor_x'] = meta[side][item]['x'] + meta[side][item][port]['width']\n if side == 'right':\n meta[side][item][port]['anchor_x'] = meta[side][item]['x']\n dy += 1 \n y += 1\n return meta\n\ndef port_color(netmap, item, port):\n if item in netmap['machines']:\n port_data = netmap['machines'][item]['ports'][port]\n if item in item in netmap['switches']:\n port_data = netmap['switches'][item]['ports'][port]\n if 'vlan' in port_data:\n vlan_id = int(port_data['vlan'])\n else:\n vlan_id = 0\n return vlan_color(vlan_id)\n\ndef vlan_color(vlan_id):\n if vlan_id == 0:\n return 'gray'\n rev = int(str(vlan_id)[::-1])\n color = {}\n color['r'] = (rev * (( rev / 10 ) % 10)) % 256\n color['g'] = (rev * (( rev / 100 ) % 10)) % 256\n color['b'] = (rev * (( rev / 1000 ) % 10)) % 256\n return svgwrite.rgb(color['r'], color['g'], color['b'])\n\ndef render_svg(netmap, outfile):\n dwg = svgwrite.Drawing(filename=outfile, debug=True)\n shapes = dwg.add(dwg.g(id='shapes'))\n options = {\n 'col_width' : 2.2,\n 'port_height' : 5,\n 'item_hspace' : 1,\n 'item_vspace' : 100,\n 'img_left_padding' : 10,\n 'item_label_height' : 5,\n 'format' : {\n 'line_width': 3,\n 'left' : {\n 'item' : {\n 'fill': 'none',\n 'stroke': 'black',\n },\n 'item_label' : {\n 'text_anchor': 'middle'\n },\n 'port' : {\n 'stroke': 'black'\n },\n 'port_label' : {\n 'text_anchor': 'middle'\n },\n },\n 'center' : {\n 'item' : {\n 'fill': 'none',\n 'stroke': 'black'\n },\n 'item_label' : {\n 'text_anchor': 'middle'\n }, \n 'port' : {\n 'stroke': 'black'\n },\n 'port_label' : {\n 'text_anchor': 'middle'\n }, \n },\n 'right' : {\n 'item' : {\n 'fill': 'none',\n 'stroke': 'green'\n },\n 'item_label' : {\n 'text_anchor': 'middle'\n },\n 'port' : {\n 'stroke': 'black'\n },\n 'port_label' : {\n 'text_anchor': 'middle'\n }, \n },\n }\n }\n topology = prepare_topology_metadata(netmap)\n meta = prepare_placement_metadata(topology, options)\n unit = mm\n for side in ['left', 'center', 'right']:\n for item in meta[side]['items']:\n rect = dwg.rect(insert = (\n meta[side][item]['x'] * unit, \n meta[side][item]['y'] * unit), \n size = (\n meta[side][item]['width'] * unit, \n meta[side][item]['height'] * unit),\n **options['format'][side]['item'])\n label = dwg.text(item, \n insert = (\n (meta[side][item]['x'] + meta[side][item]['width'] / 2) * unit, \n (meta[side][item]['y'] + options['item_label_height'])*unit),\n **options['format'][side]['item_label']\n )\n shapes.add(rect)\n shapes.add(label)\n for port in meta[side][item]['ports']:\n dwg_opts = options['format'][side]['port'].copy()\n if 'fill' not in dwg_opts:\n dwg_opts['fill'] = port_color(netmap, item, port)\n rect = dwg.rect(insert=(meta[side][item][port]['x'] * unit, meta[side][item][port]['y'] * unit), \n size=(meta[side][item][port]['width'] * unit, meta[side][item][port]['height'] * unit),\n **dwg_opts)\n label = dwg.text(port, \n insert=(\n (meta[side][item][port]['x'] + meta[side][item][port]['width'] / 2 ) * unit, \n (meta[side][item][port]['y'] + options['item_label_height']) * unit),\n **options['format'][side]['port_label']\n )\n shapes.add(rect)\n shapes.add(label)\n for link in netmap['links']:\n if link['source_host'] == item and link['source_port'] == port:\n for dst_side in ['left', 'center', 'right']:\n for dst_item in meta[dst_side]['items']:\n if dst_item == link['destination_host']:\n for dst_port in meta[dst_side][dst_item]['ports']:\n if dst_port == link['destination_port']:\n if 'line_stroke' not in options['format']:\n stroke = port_color(netmap, item, port)\n else:\n stroke = options['format']['line_stroke']\n line = dwg.line(\n start = (\n meta[side][item][port]['anchor_x'] * unit,\n meta[side][item][port]['anchor_y'] * unit,\n ),\n end = (\n meta[dst_side][dst_item][dst_port]['anchor_x'] * unit,\n meta[dst_side][dst_item][dst_port]['anchor_y'] * unit,\n ),\n stroke_width = options['format']['line_width'],\n stroke = stroke\n )\n shapes.add(line) \n dwg.save()\n\ndef main(options):\n netmap = {}\n with open(options.infile) as f:\n netmap = json.load(f)\n render_svg(netmap, options.outfile)\n\nif __name__ == \"__main__\":\n usage = \"usage: %prog [options] arg1 arg2\"\n parser = OptionParser(usage=usage)\n parser.add_option(\"-i\", \"--input\",\n action=\"store\", type=\"string\", dest=\"infile\", default='netmap.json', help=\"Input file\") \n parser.add_option(\"-o\", \"--output\",\n action=\"store\", type=\"string\", dest=\"outfile\", default='netmap.svg', help=\"Output file\")\n (options, args) = parser.parse_args() \n main(options)" } ]

7

TurcanuVlad/For-x-in-range

https://github.com/TurcanuVlad/For-x-in-range

8c75365ef53b73b875697cf1299ac7b6bfad398f

487dcfa7e1b43beff7d8fe2575c5f6078499cbe1

b80506ba1d111b2329615f94cfe56f0148741a92

refs/heads/main

2023-01-12T06:31:47.424971

2020-11-08T16:04:14

311,099,923

0

null

[ { "alpha_fraction": 0.5733333230018616, "alphanum_fraction": 0.6133333444595337, "avg_line_length": 23.66666603088379, "blob_id": "d2c4ad96987c54ff2d396aaee8431e5d71d1fe94", "content_id": "8408d356547c106fc01eb5d81b54cf070b2b629a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 75, "license_type": "no_license", "max_line_length": 35, "num_lines": 3, "path": "/Problema_5.py", "repo_name": "TurcanuVlad/For-x-in-range", "src_encoding": "UTF-8", "text": "n=int(input(\"Dati numarul dorit=\"))\r\nfor n in range(0,n+1,2):\r\n print(n)" }, { "alpha_fraction": 0.506771981716156, "alphanum_fraction": 0.568848729133606, "avg_line_length": 18.136363983154297, "blob_id": "3edfddf447b1afe507088268aa574e83f79db347", "content_id": "72495afc973d83ea3f2382819e8bfb1087565edd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 886, "license_type": "no_license", "max_line_length": 45, "num_lines": 44, "path": "/Problema_6.py", "repo_name": "TurcanuVlad/For-x-in-range", "src_encoding": "UTF-8", "text": "n=int(input(\"Dati numarul dorit= \"))\r\na=1\r\ns=0\r\ns1=0\r\ns2=0\r\ns3=0\r\ns4=0\r\ns5=0\r\np=1\r\nd=0\r\nfor a in range(1,n+1,1):\r\n s=a+s\r\nprint(\"Raspunsul la primul exemplu= \", s)\r\n\r\nn=int(input(\"Dati numarul dorit= \"))\r\nfor a in range(1,n+1,1):\r\n s1=((a-1)*a)+s1\r\nprint(\"Raspunsul la al 2 exemplu este= \", s1)\r\n\r\nn=int(input(\"Dati numarul dorit= \"))\r\nfor a in range(1,n+1,1):\r\n p*=a\r\n s2+=p\r\nprint(\"Raspunsul la al 3 exemplu este= \", s2)\r\n\r\nn=int(input(\"Dati numarul dorit= \"))\r\nfor a in range(1,n+1,1):\r\n a=str(a)\r\n a=int(a+\"2\")\r\n s3=s3+a\r\nprint(\"Raspunsul la exemplul 4 este= \", s3)\r\n\r\nn=int(input(\"Dati numarul dorit= \"))\r\nfor a in range(1,n+1,1):\r\n d=a/(a+1)\r\n s4=s4+d\r\nprint(\"Raspunsul la al 5 exemplu este= \", s4)\r\n\r\nn=int(input(\"Dati numarul dorit= \"))\r\nfor a in range(1,n+1,1):\r\n a=str(a)\r\n a=int(\"2\"+a)\r\n s5=s5+a\r\nprint(\"Raspunsul la al 6 exemplu= \", s5)\r\n" }, { "alpha_fraction": 0.6000000238418579, "alphanum_fraction": 0.6266666650772095, "avg_line_length": 28.399999618530273, "blob_id": "69ab749c26f56e2f936e9549272fc89200d78f64", "content_id": "4f87d0b292e7aebe4394a1c8f7e428dd85e85f1c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 150, "license_type": "no_license", "max_line_length": 44, "num_lines": 5, "path": "/Problema_4.py", "repo_name": "TurcanuVlad/For-x-in-range", "src_encoding": "UTF-8", "text": "a=int(input(\"Dati inceputul intervalului=\"))\r\nb=int(input(\"Dati sfirsitul intervalului=\"))\r\nfor a in range(a,b+1,1):\r\n if a%2!=0:\r\n print(a)" }, { "alpha_fraction": 0.4757281541824341, "alphanum_fraction": 0.5339806079864502, "avg_line_length": 24.25, "blob_id": "fd8cb136fc5d81e616f055292e22167afa74f499", "content_id": "d54136e2b543439d99a69f7e53b2c7380e66c8cb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 103, "license_type": "no_license", "max_line_length": 35, "num_lines": 4, "path": "/Problema_3.py", "repo_name": "TurcanuVlad/For-x-in-range", "src_encoding": "UTF-8", "text": "n=int(input(\"Dati numarul dorit=\"))\r\nfor n in range(1,n,1):\r\n if n%3==0 or n%5==0:\r\n print(n)" } ]

4

ccollado7/UNSAM_IA

https://github.com/ccollado7/UNSAM_IA

a5e8d1d605dacc612875e9bf0b595d73cbe0b06d

9cad4afbe61349f02524000cfa514032e5f17c09

294c686afaf7d4c4afedd52d09e3e2e75e3c0687

refs/heads/master

2022-11-14T05:18:47.006608

2020-07-07T20:47:23

null

0

null

[ { "alpha_fraction": 0.7319042682647705, "alphanum_fraction": 0.7897721529006958, "avg_line_length": 62.048484802246094, "blob_id": "17f48b9821b6282ffb3a4bbe515b72dacecfedb6", "content_id": "767ee876588423761415e01c10ae1865446a77f2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 10461, "license_type": "no_license", "max_line_length": 339, "num_lines": 165, "path": "/README.md", "repo_name": "ccollado7/UNSAM_IA", "src_encoding": "UTF-8", "text": "# Repositorio de la materia Aprendizaje Automático (UNSAM)\n\n## 1er cuatrimestre de 2020\n\n### Notebooks\n\nLos notebooks que forman parte del repositorio contienen las partes prácticas de las clases, tanto de los martes como de los jueves, y ejercicios. En principio, cada notebook de clase tiene asociado una serie de ejercicios.\n\n***\n\n### Diapositivas de las clases\n\nLas diapositivas están disponibles siguiendo los links que aparecen abajo.\n\n[Martes 10 de marzo](https://drive.google.com/file/d/1tWty4OfYgU3LRR1FzH358l5IKz2KQfvM/view). Introducción a la materia, aprendizaje automático y probabilidad.\n\n[Martes 17 de marzo](https://drive.google.com/file/d/1oFB76Vz5Szl6FWjRKAuAsoevDebgYIW_/view). Probabilidades bayesianas; funciones de probabilidad; uso del teorema de Bayes.\n\n[Jueves 2 de abril](https://drive.google.com/file/d/10cjqw1yuE-m17FS5lxOjDz8JL8sQra_0/view). Distribución normal y multinormal. Teorema Central del Límite.\n\n[Martes 7 de abril](https://drive.google.com/file/d/1Td6j5amKZBLiEPeTdibAC3IMPNa7mcjv/view). Modelos lineales. Regresión lineal y polinomial. Estimadores de máxima verosimilitud.\n\n[Martes 14 de abril](https://drive.google.com/file/d/1D1pUlX9E8jQl7IKTbjH7FistQyx_Myh8/view). Sobreajuste. Validación cruzada (K-fold, Leave One Out CV). Regularización (Ridge, Lasso).\n\n[Jueves 16 de abril](https://drive.google.com/open?id=1Dq9lvzY8iI9DAsmkQunq9JlubiK4SoxJ). Algunos detalles sobre cuadrados minimos, BGD y SGD.\n\n[Martes 21 de abril](https://drive.google.com/file/d/1DoWLNQUPJkrPTlS53YUdSLZumQn7bFDd/view). Visión bayesiana de la regresión con modelos lineales. Comparación de modelos. Clasificación: conceptos generales y funciones discriminativas. Discriminante lineal de Fischer. Perceptrón.\n\n[Jueves 30 de abril](https://drive.google.com/file/d/1WFnVMjwRVhUGBmAgpsUSSgRE1C-AQnI8/view). Repaso de clasificación y algunos conceptos sobre regresión logística.\n\n[Martes 5 de mayo](https://drive.google.com/file/d/1P2roqsFsORRWRjfqdLopzfzTATqS4fPA/view). Más de clasificación con modelos lineales. Modelos discriminativos. Regresión logística. IRLS.\n\n[Martes 12 de mayo](https://drive.google.com/file/d/1yFjeezVKlIXggt7DlFkBdXc-3hUeHOCN/view). Support Vector Machines (SVM). Problema dual y truco del kernel. SVM para clasificación y para regresión.\n\n[Martes 19 de mayo](https://drive.google.com/file/d/1NBmAnucQMYI6FAVaQ0dFNVc2PeJ0N8EL/view). Modelos generativos. Naive Bayes. Combinación de modelos. Árboles de decisión.\n\n[Martes 26 de mayo](https://drive.google.com/file/d/1J8m5yNWQkicV6pIap4PHjUIZOq68wHdz/view). Métodos de ensemble. Descomposición sesgo-varianza. Comités. Bagging. Boosting (AdaBoost y Gradient Boost). Stacking.\n\n[Martes 02 de junio](https://drive.google.com/file/d/1ILpXY9TUEVWXGXwM2q7r4fEMPqBGvBRB/view). Comentario sobre tratamiento de incertezas del target en Modelos Lineales.\n\n[Martes 9 de junio](https://drive.google.com/file/d/1GLH1R78PII3dr5UO6i0gaoz_A5P_Bekh/view?usp=sharing). Redes neuronales.\n\nMartes 23 de junio. [Transfer-Learning](https://drive.google.com/file/d/1FSQbNU-6WMdaTsPdWPNG-4pbNHLCF9zG/view?usp=sharing). [Redes Generativas Antagónicas (GAN)](https://drive.google.com/file/d/1miiPjds60ot3t04pCkuaGrgZ7wBNCEAP/view?usp=sharing). [RNNs](https://drive.google.com/file/d/1Gw6luuDlCCDw_7zfVMN8hXCso_vo2LHg/view?usp=sharing).\n\nJueves 2 de julio.<br/>\nYamila Barrera (Aristas), [\"Modelos generativos para fragancias\"](https://docs.google.com/presentation/d/16ZM79NhlEuN47ZeexD7ABkHQWkxzmq2kTRhbd0Cs_Z0/edit?usp=sharing).</br>\nEzequiel Álvarez (ICAS), [\"Intelligent Arxiv\"](https://drive.google.com/file/d/1HlSQgHc7rxQhBJgHu7G1Wk6ygVJg8-tb/view?usp=sharing).\n\n### Videos\n\nVideos de las clases virtuales\n\n__Martes 17 de marzo.__<br>\n[Primera parte (YouTube)](https://www.youtube.com/watch?v=WkpgXdN4gF8&feature=youtu.be)<br>\n[Segunda parte (Google Meet)](https://drive.google.com/file/d/1qCTc-uttzxjA3KY6M1vvKSxX9OAikGxN/view); [transcripción del chat](https://drive.google.com/file/d/1aOj1kF2Dd6LoP_AM4JB1XDd1mz3_m7hV/view?usp=sharing)\n\n__Jueves 19 de marzo.__<br>\n[Video](https://drive.google.com/file/d/1-Jg3EfaBzeMZcYBieG3Lo8EObOedBC3n/view);\n[transcripción del chat](https://drive.google.com/file/d/13DE3rf6X4EtExi-zTFyeixBuO91OK8Sd/view?usp=sharing)\n\n__Jueves 26 de marzo.__<br>\n[Video](https://drive.google.com/file/d/1EPb5TmGpaxdUVkPKNCBZrCX9-iLBbUws/view);\n[transcripción del chat](https://drive.google.com/file/d/1s2VuABeFMY7jj0k2BkLegCpLmxRFt8_Z/view?usp=sharing)\n\n__Jueves 2 de abril.__<br>\n[Video](https://drive.google.com/file/d/1GMCZ9RXDsDfxb1OtOCZyW7CpRs6oP_2w/view);\n[transcripción del chat](https://drive.google.com/file/d/1TkSw2TCSxXi9nhvhOK82H_O57WOkTnO3/view)\n\n__Martes 7 de abril.__<br>\n[Video](https://drive.google.com/file/d/1XIqKWD5L7F0xhPKkLQXJYZNelhy6z0Y2/view);\n[transcripción del chat](https://drive.google.com/file/d/1DQltVnPu_7F_WLqR9oYdSiACszRBPPfu/view)\n\n__Martes 14 de abril.__<br>\n[Video](https://drive.google.com/file/d/1tU4bR6EXmr3nHupAus1aXesu85TGKYJW/view);\n[transcripción del chat](https://drive.google.com/file/d/1T-ZyxwA3vTkAklgmvGUcUhf3o4msCIyE/view)\n\n__Jueves 16 de abril.__<br>\n[Video](https://drive.google.com/file/d/1XXQI7TDqMbJPkbJJOpVaLTVkV1pJYNSS/view);\n[transcripción del chat](https://drive.google.com/file/d/167PmPXCoAWFV_dERbRzLC0dBOwvEfWKN/view)\n\n__Martes 21 de abril.__<br>\n[Video](https://drive.google.com/file/d/1ge_a9ukMpl3_pRSCgeIF7rjex3gGVq4O/view);\n[transcripción del chat](https://drive.google.com/file/d/1FG_vZh6TFRtUY6K46CXflROLVW60CLmH/view)\n\n__Jueves 23 de abril.__<br>\nConsulta con Manu. [Video](https://drive.google.com/file/d/13kzi_M5hE8bKp5-fGQR60oYOf-n6VweJ/view); [chat](https://drive.google.com/file/d/1BOQoBVkqHJmhHA9_Um2p5OvAVDTHLxD3/view)<br>\nConsulta con Nacho. [Video](https://drive.google.com/file/d/1rP3tCtWnXSTKDNiAq5pRG15oAlwyTXOT/view); [chat](https://drive.google.com/file/d/18DuPbEdJNL1HVxvb5lFZ0YxrWfdVQEm-/view)<br>\nConsulta con Rodrigo. [Video](https://drive.google.com/file/d/1kYjWvwCxPJyvNxnpUHbwx0SauGylsA8y/view); [chat](https://drive.google.com/file/d/1ZxKsRW_t3LO3nX6Own5AO6_4-UJwzYkR/view)\n\n__Jueves 30 de abril.__<br>\n[Video](https://drive.google.com/file/d/1wHjec7ncIwPhos3Gg4e657Vb_GlazpWr/view);\n[transcripción del chat](https://drive.google.com/file/d/1fZsAIC27btj7JIQ-umx2s_RP54z5ttbE/view)\n\n__Martes 5 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1f56X7pXaroaDotMGv3XpDgAVlc9O2zZQ/view);\n[transcripción del chat](https://drive.google.com/file/d/1MoSPD9MDYXyMQHISQCZNK0ICEuJ_thXk/view)\n\n__Jueves 7 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1rh6r5vYv8t_mna51HN5KCQfWE-aDCaRx/view);\n[transcripción del chat](https://drive.google.com/file/d/1SJOX_3yS8IM3oG50bVEjeNRcPri71cxe/view)\n\n__Martes 12 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1esKkeyfF1X4ys_e6jz09CBQzgkJJ0HrI/view);\n[transcripción del chat](https://drive.google.com/file/d/1SzEF3nkY_gsqf7DQUgTAaraBbY3Msnxp/view)\n\n__Jueves 14 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1229GmCEnDCO-MJr02_He7-f6221l8wLA/view);\n[transcripción del chat](https://drive.google.com/file/d/1RW1x6iLgYKRVXmp4aCtRi3zMA6OLtUWp/view)\n\n__Martes 19 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1y3KsVKVWwKgLnq_jVgLKp7ZphXDjLtzC/view);\n[transcripción del chat](https://drive.google.com/file/d/1ejjo6do3S0xqjyaetLyJCCmWwZzdeV0Q/view)\n\n__Jueves 21 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1pjikEy8hdpoj4GMVZM_6uPUJxBLHb0d7/view);\n[transcripción del chat](https://drive.google.com/file/d/1YyxzcYlXGwQmzgK5kDoeFqG_xvmx2iMb/view)\n\n__Martes 26 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1fS3emr9LjCbLrk9yFxMlkL1ecJanmA0g/view);\n[transcripción del chat](https://drive.google.com/file/d/1u--_3sgp6O4ap4jgv2AdsSml33dTjtBI/view)\n\n__Jueves 28 de mayo.__<br>\n[Video](https://drive.google.com/file/d/1tWl7AhIPORWUiRW6eZprK9UCtNi33NW6/view);\n[transcripción del chat](https://drive.google.com/file/d/1Xrae7jSdTAqb7UfG5TfQSoqi2zfE9t6R/view)\n\n__Martes 02 de junio.__<br>\n[Video](https://drive.google.com/file/d/1uBxUyPuxcNBpZM3K-GgNMhWFh09I6IZU/view);\n[transcripción del chat](https://drive.google.com/file/d/1iZgQ58VI_X8RptUnvCjfZ6n_ZMEk4BCA/view)\n\n__Jueves 04 de junio.__<br>\n[Video](https://drive.google.com/file/d/19OrUvG7xXZCvjS1Fam6kwPFZMyPvRImI/view);\n[transcripción del chat](https://drive.google.com/file/d/1E59AAJSZGGjM0nrNZy-Olo_F-0ZgOgbd/view)\n\n__Martes 09 de junio.__<br>\n[Video](https://drive.google.com/file/d/1qCZUfab9mp3u2z7z02y9AIQMW2T31gcp/view);\n[transcripción del chat](https://drive.google.com/file/d/1GipnUXVCv6mh-lW3a860j8pEGv8xv7er/view)\n\n__Jueves 11 de junio.__<br>\n[Video](https://drive.google.com/file/d/1CK22nkykTYXJi9dr39_YiV67OtCDU-Pc/view);\n[transcripción del chat](https://drive.google.com/file/d/1VUODSCa8YLAqEn7yVkHXca9S94ElFRmJ/view)\n\n__Martes 16 de junio.__<br>\n[Video](https://drive.google.com/file/d/1qVWf_pdbVtZjBu_9ImoRbqx2aXlujLmL/view);\n[transcripción del chat](https://drive.google.com/file/d/1fYvQ8yc76yaSHG3f_1VENaTtCaUdy49A/view)\n\n__Jueves 18 de junio.__<br>\n[Video](https://drive.google.com/file/d/1Ah6qz_4KcdxmT0v__uMMNCr38EnRsgav/view);\n[transcripción del chat](https://drive.google.com/file/d/1ZWWN2TxhIDdZUxg0J5QLJGwDub0pJZXp/view)\n\n__Martes 23 de junio.__<br>\n[Video](https://drive.google.com/file/d/1WUF91z-9RTQ-ywcMnUgPV4Wnitu7xq5m/view);\n[transcripción del chat](https://drive.google.com/file/d/1DJ_aZJqrUzU0X-hU9S9ewz2pG8UH6_RC/view)\n\n__Jueves 25 de junio.__<br>\nConsulta con Manu. [Video](https://drive.google.com/file/d/19QtKBzqDXLi_FMvHrYvrj6iJJwzZ5WnN/view?usp=sharing); [chat](https://drive.google.com/file/d/1ipWf_CBqFRZ_A8hs4k0DAb0SA77_ESB4/view?usp=sharing)<br>\nConsulta con Nacho. [Video](https://drive.google.com/file/d/14Hh76EaFQPyv5T3DaYKXg59crKQ8RR2o/view); [chat](https://drive.google.com/file/d/1GJtKHYpX0LUU0w0BzmIu0NXPk3MYsU67/view)<br>\nConsulta con Rodrigo. [Video](https://drive.google.com/file/d/1AY1unWJhq3NwV3xei-oODnQf3Iulv5O1/view); [chat](https://drive.google.com/file/d/1q0KAEL30BAfTQ0zhz8IJjBWJAkSJ-zM9/view)\n\n__Martes 30 de junio.__<br>\n[Video](https://drive.google.com/file/d/17Nr7BJ-shrJ9d9Ncq_xPvbZ0Kz60wajv/view);\n[transcripción del chat](https://drive.google.com/file/d/1GuNd06vvCMUTR05rjedcpx-Y_tSjG9eK/view)\n\n__Jueves 02 de julio: Cierre de la materia e invitados especiales.__<br>\n[Video](https://drive.google.com/file/d/1HSj0TKx9PquFkTs-7j0mT1eS68vwK-9Q/view);\n[transcripción del chat](https://drive.google.com/file/d/1m5LXiS9JjsO7U-V7WDrd9OCbbQMI0AO_/view)\n" }, { "alpha_fraction": 0.6088825464248657, "alphanum_fraction": 0.6442216038703918, "avg_line_length": 30.727272033691406, "blob_id": "7ce97922b67a2e46dd45402d6b20d1a5d0ebd8ca", "content_id": "f9ca86379debb8f85ef69e39eb1ab8449214a6a4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2094, "license_type": "no_license", "max_line_length": 149, "num_lines": 66, "path": "/entregas/deJesusJoaquin/ej_1.py", "repo_name": "ccollado7/UNSAM_IA", "src_encoding": "UTF-8", "text": "import numpy as np\nimport pandas as pd\n\n### Ejercicio 1 - Probabilidad condicional #####\n\n## Se puede calcular todo lo que se pide sin necesidad de usar el teo de bayes\n\n\ndf=pd.read_csv('student-mat.csv')\nprint(df.head())\n\n##########################\n## Sin teorema de Bayes ##\n##########################\n\n## Primero debo saber cuantos alumnos sacaron grade mayor a 12\ngrade_mayor_12_bool = df['G3'] >= 12\n\ndf_grade_mayor_12 = df[grade_mayor_12_bool]\n\ntotal_grade_mayor_12 = len(df_grade_mayor_12) # total de casos de alumnos con grade mayor a 12\n\n# sobre df_grade_mayor_12, debo calcular calcular cuantos faltaron menos de 3 veces\ndf_menos_de_3_bool = df_grade_mayor_12['absences'] < 3\ndf_menos_de_3 = df_grade_mayor_12[df_menos_de_3_bool] ## dataframe con todos los que faltaron menos de 3 y sacaron mas de 12\n\nn_menos_de_3 = len(df_menos_de_3)\n\n\n\n# probabilidad\npp = float(n_menos_de_3)/total_grade_mayor_12\nprint(\"SIN TEOREMA DE BAYES\")\nprint(\"La probabilidad que se pide es %i/%i = %.2f\" % (n_menos_de_3, total_grade_mayor_12, pp))\nprint(\"\\n\")\n\n##########################\n## Con teorema de Bayes ##\n##########################\n\n# B = faltar menos de 3\n# A = sacar mas de 60%\n# Necesito P(B|A) = P(A|B) * pB/pA\n\nn_total = len(df) # cantidad total de alumnos\nn_grade_mayor_12 = len(df_grade_mayor_12) # cantidad de alumnos que sacaron mayor o igual a 12\n\ndf_menos_de_3_bool = df['absences'] < 3 #a diferencia de antes, ahora lo calculo sobre el conjunto total de alumnos, no sobre aquellos con mayor a 12\ndf_menos_de_3 = df[df_menos_de_3_bool]\n\nn_menos_de_3 = len(df_menos_de_3)\n\npB = float(n_menos_de_3)/n_total #proba de faltar menos de 3\npA = float(total_grade_mayor_12) / n_total #proba de sacar mas de 12\n\n# Ahora a calcular P(A|B)\n\ndf_condicionada = df_menos_de_3[df_menos_de_3['G3'] >= 12]\npA_B = float(len(df_condicionada))/ len(df_menos_de_3)\npB_A = pA_B * pB/pA # Teorema Bayes\n\nprint(\"CON TEOREMA DE BAYES\")\nprint(\"La probabilidad que se pide es P(B|A) = P(A|B) * pB/pA = %.2f * %.2f/%.2f = %.2f\" % (pA_B, pB, pA, pB_A ))\nprint(\"\\n\")\n\nprint(\"Ambas coinciden? : %s\" % (pp == pB_A))\n" } ]

2

KongL1013/datacom

https://github.com/KongL1013/datacom

e9f5650740cbf459f4b0b429d1d0af02664d4f83

d70dd5214d6f64c5d95d74434133f8c0299d3bb7

750e5ff5735d7e40d4621d141403bb2372baa235

refs/heads/master

2020-09-28T14:50:51.798043

2019-12-14T07:56:49

226,799,944

0

null

[ { "alpha_fraction": 0.4810086190700531, "alphanum_fraction": 0.5217576622962952, "avg_line_length": 32.56428527832031, "blob_id": "ef129d05c45910dd081dce1bbce22829aaa73a5c", "content_id": "ed4b2164da289c80e642791cfba9534cf37059e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9527, "license_type": "no_license", "max_line_length": 109, "num_lines": 280, "path": "/PycharmProjects/data_com/test12/nfold_xg_lg1.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n# In[2]:\n\n\npd.set_option('display.max_columns', None)\n\n\n# In[3]:\n\n\nwith open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n\n# In[4]:\n\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n\n# In[5]:\n\n\nwith open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n\nprint(len(data1),len(data))\n\n\n\ndata['topic_sim0_max']=data1['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data1['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data1['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_std']=data1['topic_sim0'].apply(lambda x:x[3])\ndata['topic_sim0_num']=data1['topic_sim0'].apply(lambda x:x[4])\n\ndata['topic_sim1_max']=data1['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data1['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data1['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_std']=data1['topic_sim1'].apply(lambda x:x[3])\ndata['topic_sim1_num']=data1['topic_sim1'].apply(lambda x:x[4])\ndata['topic_sim1_max1']=data1['topic_sim1'].apply(lambda x:x[5])\ndata['topic_sim1_min1']=data1['topic_sim1'].apply(lambda x:x[6])\n\n\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\nfill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\nfill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\nfill_null(data, 'topic_sim1_max1', -2)\nfill_null(data, 'topic_sim1_min1', -2)\n\n\ndata=data.drop(['follow_topic','inter_topic','topic','title_t1','title_t2','desc_t1','desc_t2'],axis=1)\n\n\nprint(len(data)-1141683)\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\n# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import accuracy_score\nimport gc\n\nfrom lightgbm import LGBMClassifier\n\nfrom sklearn.model_selection import StratifiedKFold\n\nn_fold = 5\nskf = StratifiedKFold(n_splits=n_fold, random_state=2020, shuffle=False)\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\ny_pred = np.zeros([X_evaluate.shape[0],2])\n\n\n\ncnt = 1\n\n\n\nloaded_model1 = pickle.load(open(\"model_xgboost1.pickle.dat\", \"rb\"))\ntotal_xg_pred1 = loaded_model1.predict_proba(X_evaluate)\nprint(\"total_xg_pred1\",total_xg_pred1[:5,:])\nloaded_model_bgm1 = pickle.load(open(\"LGBMClassifier2.pickle.dat\", \"rb\"))\ntotal_bgm_pred2 = loaded_model_bgm1.predict_proba(X_evaluate)\nprint(\"total_xg_pred1\",total_bgm_pred2[:5,:])\n\ny_pred += total_xg_pred1\ny_pred += total_bgm_pred2\n\nprint(\"y_pred\",y_pred[:5,:]/2)\n\nfor index ,(train_index,test_index) in enumerate(skf.split(X , y)): #训练数据五折\n if cnt < 3:\n print(\"pass = {}\".format(cnt))\n cnt += 1\n pass\n else:\n print(\"start train time = {cnt}\".format(cnt=cnt))\n print(\"train_index = \",train_index)\n print(\"train_index_% = \", len(train_index)/len(X))\n train_x, test_x, train_y, test_y = X[train_index], X[test_index], y[\n train_index], y[test_index]\n print(\"start train\")\n if cnt%2 != 0:\n print(\"model_xgboost\")\n model = XGBClassifier(\n max_depth=10,\n learning_rate=0.01,\n n_estimators=2000,\n min_child_weight=5, # 5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n )\n model.fit(train_x, train_y,\n eval_metric='auc',\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n print(\"save {} model!!!\".format(cnt))\n pickle.dump(model, open(\"model_xgboost{}.pickle.dat\".format(cnt), \"wb\"))\n else:\n print(\"LGBMClassifier\")\n model = LGBMClassifier(boosting_type='gbdt',\n task='train',\n num_leaves=2 ** 9 - 1,\n num_iterations=2000,\n learning_rate=0.01,\n n_estimators=2000,\n max_bin=425,\n subsample_for_bin=50000,\n objective='binary',\n min_split_gain=0,\n min_child_weight=5,\n min_child_samples=10,\n feature_fraction=0.9,\n feature_fraction_bynode=0.8,\n drop_rate=0.05,\n subsample=0.8,\n subsample_freq=1,\n colsample_bytree=1,\n reg_alpha=3,\n reg_lambda=5,\n seed=1000,\n n_jobs=-1,\n silent=True\n )\n # 建议使用CV的方式训练预测。\n model.fit(train_x,\n train_y,\n eval_names=['train'],\n eval_metric={'auc'},\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n print(\"save {} model!!!\".format(cnt))\n pickle.dump(model, open(\"LGBMClassifier{}.pickle.dat\".format(cnt), \"wb\"))\n\n gc.collect() # 垃圾清理，内存清理\n\n y_pred_test = model.predict(test_x)\n predictions = [round(value) for value in y_pred_test]\n accuracy = accuracy_score(test_y, predictions)\n print(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n y_pred += model.predict_proba(X_evaluate)\n print('len X_evaluate =',len(X_evaluate))\n print(\"len y_pred=\",len(y_pred))\n print(\"y_pred\")\n print(y_pred[:5, :]/cnt)\n cnt += 1\n\ny_pred = y_pred/n_fold\n\n\n# print(\"start xgboost\")\n# model_xgboost = XGBClassifier(\n# max_depth=10,\n# learning_rate=0.01,\n# n_estimators=2500,\n# min_child_weight=5, #5\n# max_delta_step=0,\n# subsample=0.8,\n# colsample_bytree=0.7,\n# reg_alpha=0,\n# reg_lambda=0.4,\n# scale_pos_weight=0.8,\n# silent=True,\n# objective='binary:logistic',\n# missing=None,\n# eval_metric='auc',\n# seed=1440,\n# gamma=0,\n# n_jobs=-1\n# # nthread=40\n# )\n# model_xgboost.fit(X_train,y_train,\n# eval_metric='auc',\n# eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n# #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n# early_stopping_rounds=50)\n\n\n\n# save model to file\n# print(\"save model!!!\")\n# pickle.dump(model_xgboost, open(\"model_xgboost1208.pickle.dat\", \"wb\"))\n#\n#\n# print('START TO SAVE RESULT!!!!!!!!!!!!!')\n# y_pred_test = model_xgboost.predict(X_test)\n# predictions = [round(value) for value in y_pred_test]\n# accuracy = accuracy_score(y_test, predictions)\n# print(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n#\n#\n# X_evaluate = data[2593669:].drop(['label'], axis=1).values\n# y_pred = model_xgboost.predict_proba(X_evaluate)\n# print(\"y_pred\")\n# print(y_pred[:5,:])\n#\n\n\ntest = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\ntest.columns = ['问题id', '用户id', '邀请创建时间']\nprint(len(test))\n# 用于保存提交结果\nresult_append = test[['问题id', '用户id', '邀请创建时间']]\nresult_append['Score'] = y_pred[:, 1]\nprint(result_append.head())\nresult_append.to_csv('result_xg_lg.txt', header=False, index=False, sep='\\t')\n\n" }, { "alpha_fraction": 0.5737146735191345, "alphanum_fraction": 0.6189990043640137, "avg_line_length": 27.367149353027344, "blob_id": "0edbe8d95c60f11e688794606b4411f9ff394af6", "content_id": "b444361a834e0860338b723403847a39bda576d4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5966, "license_type": "no_license", "max_line_length": 150, "num_lines": 207, "path": "/PycharmProjects/data_com/test12/xgboost_merge.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n# In[2]:\n\n\npd.set_option('display.max_columns', None)\n\n\n# In[3]:\n\n\nwith open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n\n# In[4]:\n\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n\n# In[5]:\n\n\nwith open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n\n# In[8]:\n\n\nprint(len(data1),len(data))\n\n\n# In[9]:\n\n\ndata['topic_sim0_max']=data1['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data1['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data1['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_std']=data1['topic_sim0'].apply(lambda x:x[3])\ndata['topic_sim0_num']=data1['topic_sim0'].apply(lambda x:x[4])\n\ndata['topic_sim1_max']=data1['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data1['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data1['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_std']=data1['topic_sim1'].apply(lambda x:x[3])\ndata['topic_sim1_num']=data1['topic_sim1'].apply(lambda x:x[4])\ndata['topic_sim1_max1']=data1['topic_sim1'].apply(lambda x:x[5])\ndata['topic_sim1_min1']=data1['topic_sim1'].apply(lambda x:x[6])\n\n\n# In[10]:\n\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\nfill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\nfill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\nfill_null(data, 'topic_sim1_max1', -2)\nfill_null(data, 'topic_sim1_min1', -2)\n\n\n# In[13]:\n\n\ndata=data.drop(['follow_topic','inter_topic','topic','title_t1','title_t2','desc_t1','desc_t2'],axis=1)\n\n\n# In[40]:\n\n\n#data=data.drop(['topic_sim0_max','topic_sim0_min','topic_sim0_avg','topic_sim1_max','topic_sim1_min','topic_sim1_avg'],axis=1)\n\n\n# In[41]:\n\n\n#data = data.drop(['topic_gz', 'topic_int', 't_invi', 't_quest', 'desc_quest_w', 'desc_quest_sw', 'desc_tit_w', 'desc_tit_sw', 'topic_quest'], axis=1)\n\n# 缺省值处理\n\n# ## 模型训练\n\n# In[18]:\n\n\nprint(len(data)-1141683)\n\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\n\n\nprint(\"load model from file\")\nloaded_model = pickle.load(open(\"model_xgboost.pickle.dat\", \"rb\"))\ntotal_xg_pred = loaded_model.predict_proba(X)\ntotal_xg_pred_1 = pd.DataFrame(total_xg_pred[:,1],columns=['xg_pred1'])\ntotal_xg_pred_2 = pd.DataFrame(total_xg_pred[:,1],columns=['xg_pred2'])\n\ndata = pd.concat([data,total_xg_pred_1],axis=1)\ndata = pd.concat([data,total_xg_pred_2],axis=1)\nprint(data.head())\n\nprint(\"start lgb merge\")\nX = data[:2593669].drop(['label'], axis=1).values\nloaded_model_bgm = pickle.load(open(\"model_lgb1203.pickle.dat\", \"rb\"))\ntotal_bgm_pred = loaded_model_bgm.predict_proba(X)\ntotal_bgm_pred_1 = pd.DataFrame(total_bgm_pred[:,1],columns=['bgm_pred1'])\ntotal_bgm_pred_2 = pd.DataFrame(total_bgm_pred[:,1],columns=['bgm_pred2'])\ndata = pd.concat([data,total_bgm_pred_1],axis=1)\ndata = pd.concat([data,total_bgm_pred_2],axis=1)\nprint(data.head())\n\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nprint(\"X = \",X[:5])\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\n\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import accuracy_score\nprint(\"start xgboost\")\nmodel_xgboost = XGBClassifier(\n max_depth=10,\n learning_rate=0.01,\n n_estimators=3000,\n min_child_weight=5, #5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n)\nmodel_xgboost.fit(X_train,y_train,\n eval_metric='auc',\n eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n# save model to file\npickle.dump(model_xgboost, open(\"model_xgboost_bgm_merge.pickle.dat\", \"wb\"))\n\n# # save model to file\n\nprint('START TO SAVE RESULT!!!!!!!!!!!!!')\ny_pred_test = model_xgboost.predict(X_test)\npredictions = [round(value) for value in y_pred_test]\naccuracy = accuracy_score(y_test, predictions)\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\ny_pred = model_xgboost.predict_proba(X_evaluate)\nprint(\"y_pred\")\nprint(y_pred[:5,:])\n\n\ntest = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\ntest.columns = ['问题id', '用户id', '邀请创建时间']\nprint(len(test))\n# 用于保存提交结果\nresult_append = test[['问题id', '用户id', '邀请创建时间']]\nresult_append['Score'] = y_pred[:, 1]\nprint(result_append.head())\nresult_append.to_csv('result_merge_1203.txt', header=False, index=False, sep='\\t')\n\n\n" }, { "alpha_fraction": 0.5847499370574951, "alphanum_fraction": 0.6017491817474365, "avg_line_length": 34.71806335449219, "blob_id": "21f3ee7747a396eb21189d7c0de164bc0412a128", "content_id": "2f38e0ca801f613fa3a57fb8f0dea83066fb3bf0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8164, "license_type": "no_license", "max_line_length": 156, "num_lines": 227, "path": "/PycharmProjects/data_com/data_process/process.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport numpy as np\nimport pickle\nimport gc\nfrom tqdm import tqdm_notebook\nimport os\nimport time\n\npd.set_option('display.max_columns',1000)\npd.set_option('display.width', 1000)\npd.set_option('display.max_colwidth',1000)\n\n\n\n\n\ntic = time.time()\n# 减少内存占用\ndef reduce_mem_usage(df):\n \"\"\" iterate through all the columns of a dataframe and modify the data type\n to reduce memory usage.\n \"\"\"\n start_mem = df.memory_usage().sum() / 1024 ** 2\n print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n\n for col in df.columns:\n col_type = df[col].dtype\n\n if col_type != object:\n c_min = df[col].min()\n c_max = df[col].max()\n if str(col_type)[:3] == 'int':\n if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n df[col] = df[col].astype(np.int8)\n elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n df[col] = df[col].astype(np.int16)\n elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n df[col] = df[col].astype(np.int32)\n elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n df[col] = df[col].astype(np.int64)\n\n end_mem = df.memory_usage().sum() / 1024 ** 2\n print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n\n return df\n\n# 解析列表，重编码id\ndef parse_str(d):\n return np.array(list(map(float, d.split())))\n\ndef parse_list_1(d):\n if d == '-1':\n return [0]\n return list(map(lambda x: int(x[1:]), str(d).split(',')))\n\ndef parse_list_2(d):\n if d == '-1':\n return [0]\n return list(map(lambda x: int(x[2:]), str(d).split(',')))\n\ndef parse_map(d):\n if d == '-1':\n return {}\n return dict([int(z.split(':')[0][1:]), float(z.split(':')[1])] for z in d.split(','))\n\nPATH = '../kanshanbei'\nSAVE_PATH = '../pkl'\nif not os.path.exists(SAVE_PATH):\n print('create dir: %s' % SAVE_PATH)\n os.mkdir(SAVE_PATH)\n\n######################### single_word\nsingle_word = pd.read_csv(os.path.join(PATH, 'single_word_vectors_64d.txt'),\n names=['id', 'embed'], sep='\\t')\nprint(single_word.head())\nsingle_word['embed'] = single_word['embed'].apply(parse_str)\nsingle_word['id'] = single_word['id'].apply(lambda x: int(x[2:]))\nprint(single_word.head())\nprint('single_word')\nwith open('../pkl/single_word.pkl', 'wb') as file:\n pickle.dump(single_word, file)\n\ndel single_word\ngc.collect() #gc 垃圾回收模块\n\n\n\n######################### word\nword = pd.read_csv(os.path.join(PATH,'word_vectors_64d.txt'),names=['id','embed'],sep='\\t')\nprint('word')\nprint(word.head())\nprint(word['embed'][1])\nprint(type(word['embed'][1]))\nword['embed'] = word['embed'].apply(parse_str) #把str转为float\nwith open(os.path.join(SAVE_PATH,'word.pkl'),'wb') as file:\n pickle.dump(word,file)\ndel word\ngc.collect()\n\n\n######################## topic\ntopic = pd.read_csv(os.path.join(PATH, 'topic_vectors_64d.txt'),\n names=['id', 'embed'], sep='\\t')\nprint('topic')\nprint(topic.head())\n\ntopic['embed'] = topic['embed'].apply(parse_str)\ntopic['id'] = topic['id'].apply(lambda x: int(x[1:]))\ntopic.head()\n\nwith open('../pkl/topic.pkl', 'wb') as file:\n pickle.dump(topic, file)\n\ndel topic\ngc.collect()\n\n######################## invite\n\n\ninvite_info = pd.read_csv(os.path.join(PATH, 'invite_info_0926.txt'),\n names=['question_id', 'author_id', 'invite_time', 'label'], sep='\\t')\ninvite_info_evaluate = pd.read_csv(os.path.join(PATH, 'invite_info_evaluate_1_0926.txt'),\n names=['question_id', 'author_id', 'invite_time'], sep='\\t')\nprint('invite_info')\nprint(invite_info.head())\ninvite_info['day'] = invite_info['invite_time']\\\n .apply(lambda x: int(x.split('-')[0][1:])).astype(np.int8)\ninvite_info['invite_hour'] = invite_info['invite_time'].apply(lambda x: int(x.split('-')[1][1:])).astype(np.int8)\n\ninvite_info_evaluate['invite_day'] = invite_info_evaluate['invite_time'].apply(lambda x: int(x.split('-')[0][1:])).astype(np.int16)\ninvite_info_evaluate['invite_hour'] = invite_info_evaluate['invite_time'].apply(lambda x: int(x.split('-')[1][1:])).astype(np.int8)\n\ninvite_info = reduce_mem_usage(invite_info)\n\nwith open('../pkl/invite_info.pkl', 'wb') as file:\n pickle.dump(invite_info, file)\n\nwith open('../pkl/invite_info_evaluate.pkl', 'wb') as file:\n pickle.dump(invite_info_evaluate, file)\n\ndel invite_info, invite_info_evaluate\ngc.collect()\n\n\n################################ member\n\nmember_info = pd.read_csv(os.path.join(PATH, 'member_info_0926.txt'),\n names=['author_id', 'gender', 'keyword', 'grade', 'hotness', 'reg_type','reg_plat','freq',\n 'A1', 'B1', 'C1', 'D1', 'E1', 'A2', 'B2', 'C2', 'D2', 'E2',\n 'score', 'topic_attent', 'topic_interest'], sep='\\t')\nprint('member_info')\nprint(member_info.head())\n\n\nmember_info['topic_attent'] = member_info['topic_attent'].apply(parse_list_1)\nmember_info['topic_interest'] = member_info['topic_interest'].apply(parse_map)\n\nmember_info = reduce_mem_usage(member_info)\n\nwith open('../pkl/member_info.pkl', 'wb') as file:\n pickle.dump(member_info, file)\n\ndel member_info\ngc.collect()\n\n\n##################### question_info\n\nquestion_info = pd.read_csv(os.path.join(PATH, 'question_info_0926.txt'),\n names=['question_id', 'question_time', 'title_sw_series', 'title_w_series', 'desc_sw_series', 'desc_w_series', 'topic'], sep='\\t')\nprint(question_info.head())\n\nquestion_info['title_sw_series'] = question_info['title_sw_series'].apply(parse_list_2)#.apply(sw_lbl_enc.transform).apply(list)\nquestion_info['title_w_series'] = question_info['title_w_series'].apply(parse_list_1)#.apply(w_lbl_enc.transform).apply(list)\nquestion_info['desc_sw_series'] = question_info['desc_sw_series'].apply(parse_list_2)#.apply(sw_lbl_enc.transform).apply(list)\nquestion_info['desc_w_series'] = question_info['desc_w_series'].apply(parse_list_1)#.apply(w_lbl_enc.transform).apply(list)\nquestion_info['topic'] = question_info['topic'].apply(parse_list_1)# .apply(topic_lbl_enc.transform).apply(list)\n\nquestion_info['question_day'] = question_info['question_time'].apply(lambda x: int(x.split('-')[0][1:])).astype(np.int16)\nquestion_info['question_hour'] = question_info['question_time'].apply(lambda x: int(x.split('-')[1][1:])).astype(np.int8)\ndel question_info['question_time']\ngc.collect()\n\nprint(question_info.head())\n\nquestion_info = reduce_mem_usage(question_info)\n\nwith open('../pkl/question_info.pkl', 'wb') as file:\n pickle.dump(question_info, file)\n\ndel question_info\ngc.collect()\n\n\n################################ answer\n#%%time\nprint('answer')\nanswer_info = pd.read_csv(os.path.join(PATH, 'answer_info_0926.txt'),\n names=['answer_id', 'question_id', 'author_id', 'answer_time', 'content_sw_series', 'content_w_series',\n 'excellent', 'recommend', 'round_table', 'figure', 'video',\n 'num_word', 'num_like', 'num_unlike', 'num_comment',\n 'num_favor', 'num_thank', 'num_report', 'num_nohelp', 'num_oppose'], sep='\\t')\nanswer_info.head()\n\nanswer_info['content_sw_series'] = answer_info['content_sw_series'].apply(parse_list_2)\nanswer_info['content_w_series'] = answer_info['content_w_series'].apply(parse_list_1)\nanswer_info.head()\n\n\nanswer_info['answer_day'] = answer_info['answer_time'].apply(lambda x: int(x.split('-')[0][1:])).astype(np.int16)\nanswer_info['answer_hour'] = answer_info['answer_time'].apply(lambda x: int(x.split('-')[1][1:])).astype(np.int8)\ndel answer_info['answer_time']\ngc.collect()\n\n\nanswer_info = reduce_mem_usage(answer_info)\n\n\n\nwith open('../pkl/answer_info.pkl', 'wb') as file:\n pickle.dump(answer_info, file)\n\ndel answer_info\ngc.collect()\ntoc = time.time()\nprint('Used time: %d' % int(toc-tic))\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.6845007538795471, "alphanum_fraction": 0.6916542649269104, "avg_line_length": 33.21938705444336, "blob_id": "c8455ba951fb88796f9de7d0a00819a54663e669", "content_id": "b3c21bb272d14d6bb293dbc8fdaeb05b365059e1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6949, "license_type": "no_license", "max_line_length": 267, "num_lines": 196, "path": "/PycharmProjects/data_com/data_process/gen_feat.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import warnings\nwarnings.filterwarnings('ignore')\n\nimport pandas as pd\nimport numpy as np\nimport pickle\nimport gc\nimport os\nimport time\nimport multiprocessing as mp\n\nfrom sklearn.preprocessing import LabelEncoder\n\ntic = time.time()\n\nSAVE_PATH = './feats'\nif not os.path.exists(SAVE_PATH):\n print('create dir: %s' % SAVE_PATH)\n os.mkdir(SAVE_PATH)\n\n\n################## member_info: 用户特征¶\n\nwith open('../pkl/member_info.pkl', 'rb') as file:\n member_info = pickle.load(file)\nmember_info.head(2)\n\n# 原始类别特征\nmember_cat_feats = ['gender', 'freq', 'A1', 'B1', 'C1', 'D1', 'E1', 'A2', 'B2', 'C2', 'D2', 'E2']\nfor feat in member_cat_feats:\n member_info[feat] = LabelEncoder().fit_transform(member_info[feat])\n\n# 用户关注和感兴趣的topic数\nmember_info['num_atten_topic'] = member_info['topic_attent'].apply(len)\nmember_info['num_interest_topic'] = member_info['topic_interest'].apply(len)\n\n\ndef most_interest_topic(d):\n if len(d) == 0:\n return -1\n return list(d.keys())[np.argmax(list(d.values()))]\n\n# 用户最感兴趣的topic\nmember_info['most_interest_topic'] = member_info['topic_interest'].apply(most_interest_topic)\nmember_info['most_interest_topic'] = LabelEncoder().fit_transform(member_info['most_interest_topic'])\n\n\ndef get_interest_values(d):\n if len(d) == 0:\n return [0]\n return list(d.values())\n\n\n# 用户topic兴趣值的统计特征\nmember_info['interest_values'] = member_info['topic_interest'].apply(get_interest_values)\nmember_info['min_interest_values'] = member_info['interest_values'].apply(np.min)\nmember_info['max_interest_values'] = member_info['interest_values'].apply(np.max)\nmember_info['mean_interest_values'] = member_info['interest_values'].apply(np.mean)\nmember_info['std_interest_values'] = member_info['interest_values'].apply(np.std)\n\n# 汇总\nfeats = ['author_id', 'gender', 'freq', 'A1', 'B1', 'C1', 'D1', 'E1', 'A2', 'B2', 'C2', 'D2', 'E2', 'score']\nfeats += ['num_atten_topic', 'num_interest_topic', 'most_interest_topic']\nfeats += ['min_interest_values', 'max_interest_values', 'mean_interest_values', 'std_interest_values']\nmember_feat = member_info[feats]\n\nmember_feat.head(3)\n\nmember_feat.to_hdf('./feats/member_feat.h5', key='data')\n\ndel member_feat, member_info\ngc.collect()\n\n\n################## question_info: 问题特征\n\nwith open('../pkl/question_info.pkl', 'rb') as file:\n question_info = pickle.load(file)\n\nquestion_info.head(2)\n\n\n# title、desc词计数，topic计数\nquestion_info['num_title_sw'] = question_info['title_sw_series'].apply(len)\nquestion_info['num_title_w'] = question_info['title_w_series'].apply(len)\nquestion_info['num_desc_sw'] = question_info['desc_sw_series'].apply(len)\nquestion_info['num_desc_w'] = question_info['desc_w_series'].apply(len)\nquestion_info['num_qtopic'] = question_info['topic'].apply(len)\n\nfeats = ['question_id', 'num_title_sw', 'num_title_w', 'num_desc_sw', 'num_desc_w', 'num_qtopic', 'question_hour']\nfeats += []\nquestion_feat = question_info[feats]\n\nquestion_feat.head(3)\n\nquestion_feat.to_hdf('./feats/question_feat.h5', key='data')\n\ndel question_info, question_feat\ngc.collect()\n\n################# member_info & question_info: 用户和问题的交互特征\n\nwith open('../pkl/invite_info.pkl', 'rb') as file:\n invite_info = pickle.load(file)\nwith open('../pkl/invite_info_evaluate.pkl', 'rb') as file:\n invite_info_evaluate = pickle.load(file)\nwith open('../pkl/member_info.pkl', 'rb') as file:\n member_info = pickle.load(file)\nwith open('../pkl/question_info.pkl', 'rb') as file:\n question_info = pickle.load(file)\n\n\n# 合并 author_id，question_id\ninvite = pd.concat([invite_info, invite_info_evaluate])\ninvite_id = invite[['author_id', 'question_id']]\ninvite_id['author_question_id'] = invite_id['author_id'] + invite_id['question_id']\ninvite_id.drop_duplicates(subset='author_question_id',inplace=True)\ninvite_id_qm = invite_id.merge(member_info[['author_id', 'topic_attent', 'topic_interest']], 'left', 'author_id').merge(question_info[['question_id', 'topic']], 'left', 'question_id')\ninvite_id_qm.head(2)\n\n\n# 分割 df，方便多进程跑\ndef split_df(df, n):\n chunk_size = int(np.ceil(len(df) / n))\n return [df[i*chunk_size:(i+1)*chunk_size] for i in range(n)]\n\ndef gc_mp(pool, ret, chunk_list):\n del pool\n for r in ret:\n del r\n del ret\n for cl in chunk_list:\n del cl\n del chunk_list\n gc.collect()\n\n# 用户关注topic和问题 topic的交集\ndef process(df):\n return df.apply(lambda row: list(set(row['topic_attent']) & set(row['topic'])),axis=1)\n\npool = mp.Pool()\nchunk_list = split_df(invite_id_qm, 100)\nret = pool.map(process, chunk_list)\ninvite_id_qm['topic_attent_intersection'] = pd.concat(ret)\ngc_mp(pool, ret, chunk_list)\n\n\n# 用户感兴趣topic和问题 topic的交集\ndef process(df):\n return df.apply(lambda row: list(set(row['topic_interest'].keys()) & set(row['topic'])),axis=1)\n\npool = mp.Pool()\nchunk_list = split_df(invite_id_qm, 100)\nret = pool.map(process, chunk_list)\ninvite_id_qm['topic_interest_intersection'] = pd.concat(ret)\ngc_mp(pool, ret, chunk_list)\n\n# 用户感兴趣topic和问题 topic的交集的兴趣值\ndef process(df):\n return df.apply(lambda row: [row['topic_interest'][t] for t in row['topic_interest_intersection']],axis=1)\n\npool = mp.Pool()\nchunk_list = split_df(invite_id_qm, 100)\nret = pool.map(process, chunk_list)\ninvite_id_qm['topic_interest_intersection_values'] = pd.concat(ret)\ngc_mp(pool, ret, chunk_list)\n\n\n# 交集topic计数\ninvite_id_qm['num_topic_attent_intersection'] = invite_id_qm['topic_attent_intersection'].apply(len)\ninvite_id_qm['num_topic_interest_intersection'] = invite_id_qm['topic_interest_intersection'].apply(len)\n\n\n# 交集topic兴趣值统计\ninvite_id_qm['topic_interest_intersection_values'] = invite_id_qm['topic_interest_intersection_values'].apply(lambda x: [0] if len(x) == 0 else x)\ninvite_id_qm['min_topic_interest_intersection_values'] = invite_id_qm['topic_interest_intersection_values'].apply(np.min)\ninvite_id_qm['max_topic_interest_intersection_values'] = invite_id_qm['topic_interest_intersection_values'].apply(np.max)\ninvite_id_qm['mean_topic_interest_intersection_values'] = invite_id_qm['topic_interest_intersection_values'].apply(np.mean)\ninvite_id_qm['std_topic_interest_intersection_values'] = invite_id_qm['topic_interest_intersection_values'].apply(np.std)\n\n\nfeats = ['author_question_id', 'num_topic_attent_intersection', 'num_topic_interest_intersection', 'min_topic_interest_intersection_values', 'max_topic_interest_intersection_values', 'mean_topic_interest_intersection_values', 'std_topic_interest_intersection_values']\nfeats += []\nmember_question_feat = invite_id_qm[feats]\nmember_question_feat.head(3)\n\nmember_question_feat.to_hdf('./feats/member_question_feat.h5', key='data')\n\ndel invite_id_qm, member_question_feat\ngc.collect()\n\ntoc = time.time()\nprint('Used time: %d' % int(toc-tic))\n\n\n################## \n\n" }, { "alpha_fraction": 0.4515170753002167, "alphanum_fraction": 0.4894971251487732, "avg_line_length": 34.8203125, "blob_id": "065adc5ab6d1ef3492e90a91f11eb1f8363beaee", "content_id": "2141821cd61bcad05f51c5d257fe660c6deb3110", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4767, "license_type": "no_license", "max_line_length": 105, "num_lines": 128, "path": "/PycharmProjects/data_com/test12/nfold_xgboost.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import lightgbm as lgb\r\nimport pickle\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nimport seaborn as sns\r\nfrom xgboost import XGBClassifier\r\nfrom sklearn.metrics import accuracy_score\r\nimport gc\r\nfrom lightgbm import LGBMClassifier\r\nfrom sklearn.model_selection import StratifiedKFold\r\n\r\npd.set_option('display.max_columns', None)\r\n\r\nwith open('train20.pkl', 'rb') as file:\r\n data = pickle.load(file)\r\nprint(data.head())\r\n\r\nX = data[:2593669].drop(['label'], axis=1).values\r\ny = data[:2593669]['label'].values\r\n\r\nn_fold = 5\r\nsteps = 2500\r\n\r\nskf = StratifiedKFold(n_splits=n_fold, random_state=0, shuffle=False)\r\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\r\ny_pred = np.zeros([X_evaluate.shape[0],2])\r\ncnt = 1\r\n\r\nfor index ,(train_index,test_index) in enumerate(skf.split(X , y)): #训练数据五折\r\n print(\"start train time = {cnt}\".format(cnt=cnt))\r\n print(\"train_index = \",train_index)\r\n print(\"train_index_% = \", len(train_index)/len(X))\r\n train_x, test_x, train_y, test_y = X[train_index], X[test_index], y[\r\n train_index], y[test_index]\r\n print(\"start train\")\r\n if True:\r\n print(\"model_xgboost\")\r\n model = XGBClassifier(\r\n max_depth=10,\r\n learning_rate=0.01,\r\n n_estimators=steps,\r\n min_child_weight=5, # 5\r\n max_delta_step=0,\r\n subsample=0.8,\r\n colsample_bytree=0.7,\r\n reg_alpha=0,\r\n reg_lambda=0.4,\r\n scale_pos_weight=0.8,\r\n silent=True,\r\n objective='binary:logistic',\r\n missing=None,\r\n eval_metric='auc',\r\n seed=1440,\r\n gamma=0,\r\n n_jobs=-1\r\n # nthread=40\r\n )\r\n model.fit(train_x, train_y,\r\n eval_metric='auc',\r\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\r\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\r\n early_stopping_rounds=50)\r\n print(\"save {} model!!!\".format(cnt))\r\n pickle.dump(model, open(\"all_xgboost{}.pickle.dat\".format(cnt), \"wb\"))\r\n else:\r\n print(\"LGBMClassifier\")\r\n model = LGBMClassifier(boosting_type='gbdt',\r\n task='train',\r\n num_leaves=2 ** 9 - 1,\r\n num_iterations=steps,\r\n learning_rate=0.01,\r\n n_estimators=2000,\r\n max_bin=425,\r\n subsample_for_bin=50000,\r\n objective='binary',\r\n min_split_gain=0,\r\n min_child_weight=5,\r\n min_child_samples=10,\r\n feature_fraction=0.9,\r\n feature_fraction_bynode=0.8,\r\n drop_rate=0.05,\r\n subsample=0.8,\r\n subsample_freq=1,\r\n colsample_bytree=1,\r\n reg_alpha=3,\r\n reg_lambda=5,\r\n seed=1000,\r\n silent=True\r\n )\r\n # 建议使用CV的方式训练预测。\r\n model.fit(train_x,\r\n train_y,\r\n eval_names=['train'],\r\n eval_metric={'auc'},\r\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\r\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\r\n early_stopping_rounds=50)\r\n print(\"save {} model!!!\".format(cnt))\r\n pickle.dump(model, open(\"LGBM1212Classifier{}.pickle.dat\".format(cnt), \"wb\"))\r\n\r\n gc.collect() # 垃圾清理，内存清理\r\n\r\n y_pred_test = model.predict(test_x)\r\n predictions = [round(value) for value in y_pred_test]\r\n accuracy = accuracy_score(test_y, predictions)\r\n print(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\r\n\r\n y_pred += model.predict_proba(X_evaluate)\r\n print('len X_evaluate =',len(X_evaluate))\r\n print(\"len y_pred=\",len(y_pred))\r\n print(\"y_pred\")\r\n print(y_pred[:5, :]/cnt)\r\n cnt += 1\r\n\r\ny_pred = y_pred/n_fold\r\n\r\n\r\nwith open('test1.pkl', 'rb') as file:\r\n result_append = pickle.load(file)\r\n\r\nprint(data[2593669:].head())\r\n\r\nresult_append['Score'] = y_pred[:, 1]\r\n\r\nprint(result_append.head())\r\n\r\nresult_append.to_csv('all_xg_result1_nfold.txt', header=False, index=False, sep='\\t')\r\n" }, { "alpha_fraction": 0.5418858528137207, "alphanum_fraction": 0.5813435912132263, "avg_line_length": 37.27906799316406, "blob_id": "84100a1b9acd998f92a96b287ffbca0ee71fe930", "content_id": "a1324b5802c44e94bfb447b16803696f665043df", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5016, "license_type": "no_license", "max_line_length": 116, "num_lines": 129, "path": "/PycharmProjects/data_com/test12/xgboost_thread.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\ndef do_xgboost():\n from xgboost import XGBClassifier\n global model_xgboost\n import lightgbm as lgb\n import pickle\n import numpy as np\n import pandas as pd\n from sklearn.model_selection import train_test_split\n import seaborn as sns\n\n pd.set_option('display.max_columns', None)\n\n with open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n def fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n def fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n with open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n print(len(data1), len(data))\n\n data['topic_sim0_max'] = data1['topic_sim0'].apply(lambda x: x[0])\n data['topic_sim0_avg'] = data1['topic_sim0'].apply(lambda x: x[1])\n data['topic_sim0_min'] = data1['topic_sim0'].apply(lambda x: x[2])\n data['topic_sim0_std'] = data1['topic_sim0'].apply(lambda x: x[3])\n data['topic_sim0_num'] = data1['topic_sim0'].apply(lambda x: x[4])\n\n data['topic_sim1_max'] = data1['topic_sim1'].apply(lambda x: x[0])\n data['topic_sim1_avg'] = data1['topic_sim1'].apply(lambda x: x[1])\n data['topic_sim1_min'] = data1['topic_sim1'].apply(lambda x: x[2])\n data['topic_sim1_std'] = data1['topic_sim1'].apply(lambda x: x[3])\n data['topic_sim1_num'] = data1['topic_sim1'].apply(lambda x: x[4])\n data['topic_sim1_max1'] = data1['topic_sim1'].apply(lambda x: x[5])\n data['topic_sim1_min1'] = data1['topic_sim1'].apply(lambda x: x[6])\n\n fill_null(data, 'topic_sim0_max', -2)\n fill_null(data, 'topic_sim0_avg', -2)\n fill_null(data, 'topic_sim0_min', -2)\n fill_null(data, 'topic_sim0_std', -2)\n fill_null(data, 'topic_sim0_num', -2)\n fill_null(data, 'topic_sim1_max', -2)\n fill_null(data, 'topic_sim1_avg', -2)\n fill_null(data, 'topic_sim1_min', -2)\n fill_null(data, 'topic_sim1_std', -2)\n fill_null(data, 'topic_sim1_num', -2)\n fill_null(data, 'topic_sim1_max1', -2)\n fill_null(data, 'topic_sim1_min1', -2)\n\n data = data.drop(['follow_topic', 'inter_topic', 'topic', 'title_t1', 'title_t2', 'desc_t1', 'desc_t2'], axis=1)\n\n print(len(data) - 1141683)\n\n from sklearn.model_selection import train_test_split\n from sklearn.metrics import roc_auc_score\n # 划分训练集和测试集\n # y_train = data[:train.shape[0]]['label'].values\n # X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n # X_test = data[train.shape[0]:].drop(['label'], axis=1).values\n X = data[:2593669].drop(['label'], axis=1).values\n y = data[:2593669]['label'].values\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\n print(\"start xgboost\")\n model_xgboost = XGBClassifier(\n max_depth=13,\n learning_rate=0.01,\n n_estimators=2000,\n min_child_weight=5,\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n )\n try:\n model_xgboost.fit(X_train, y_train,\n eval_metric='auc',\n eval_set=[(X_train, y_train), (X_test, y_test)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n # save model to file\n pickle.dump(model_xgboost, open(\"model_xgboost.pickle.dat\", \"wb\"))\n except KeyboardInterrupt:\n print('START TO SAVE RESULT!!!!!!!!!!!!!')\n # load model from file\n # loaded_model = pickle.load(open(\"model_xgboost.pickle.dat\", \"rb\"))\n y_pred_test = model_xgboost.predict(X_test)\n predictions = [round(value) for value in y_pred_test]\n accuracy = roc_auc_score(y_test, predictions)\n print(\"FINALL Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n X_evaluate = data[2593669:].drop(['label'], axis=1).values\n y_pred = model_xgboost.predict_proba(X_evaluate)\n # y_pred = model_lgb.predict_proba(X_evaluate)\n pd.DataFrame(y_pred[:, 1], columns=['y_pred'])\n test = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\n test.columns = ['问题id', '用户id', '邀请创建时间']\n print(len(test))\n # 用于保存提交结果\n result_append = test[['问题id', '用户id', '邀请创建时间']]\n result_append['Score'] = y_pred[:, 1]\n print(result_append.head())\n result_append.to_csv('result.txt', header=False, index=False, sep='\\t')\n raise\nif __name__ == '__main__':\n do_xgboost()\n\n\n\n\n" }, { "alpha_fraction": 0.597749650478363, "alphanum_fraction": 0.6385372877120972, "avg_line_length": 25.33333396911621, "blob_id": "b1ecd66aeb649faf0287389396399f6a32f296c6", "content_id": "a43abe5a51b95c153ad360b431264b9c79176459", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 711, "license_type": "no_license", "max_line_length": 66, "num_lines": 27, "path": "/PycharmProjects/data_com/test12/merge.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import pickle\nimport numpy as np\nimport pandas as pd\n\n\n# data1 = pd.read_csv('result_xgboost.txt', header=None, sep='\\t')\ndata2 = pd.read_csv('result1.txt', header=None, sep='\\t')\n# print(data1.head())\n# print(data2.head())\n# data1[3] = (data1[3]+data2[3])/2\n# print(data1.head())\n#\n#\n# data1.to_csv('result.txt', header=False, index=False, sep='\\t')\ntest = data2.head(5)\ntest.columns = ['q','w','e','r']\ntest =pd.concat( [test,pd.DataFrame(np.arange(8),columns=['w'])])\nprint(test)\ntest['w'] =np.arange(5)\ndata = np.arange(9.0,step = 1.2)\ntt = pd.DataFrame(data,columns=['q'])\n# print(tt)\n# print(data)\n# print(data2.iloc[:3,3])\n# print(np.c_[data,data2.iloc[:3,3]])\ncc = pd.concat([test,tt],axis=0)\nprint(cc)\n" }, { "alpha_fraction": 0.7011070251464844, "alphanum_fraction": 0.7380073666572571, "avg_line_length": 28, "blob_id": "bfda3b07ba4faf512a105beed106573f9158e80f", "content_id": "37b67978cf305e313eda160fca2c931ffbabe727", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 813, "license_type": "no_license", "max_line_length": 82, "num_lines": 28, "path": "/PycharmProjects/data_com/test12/1213merge.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import accuracy_score\nimport gc\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import StratifiedKFold\n\npd.set_option('display.max_columns', None)\n\nwith open('train20.pkl', 'rb') as file:\n data = pickle.load(file)\nprint(data.head())\n\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\ny_pred = np.zeros([X_evaluate.shape[0],2])\n\n\nfor i in range():\n loaded_model1 = pickle.load(open(\"all_xgboost{}.pickle.dat \".format(i), \"rb\"))\n total_xg_pred1 = loaded_model1.predict_proba(X_evaluate)\n\n" }, { "alpha_fraction": 0.4991398751735687, "alphanum_fraction": 0.6147942543029785, "avg_line_length": 28.97222137451172, "blob_id": "ff8c19ca6d2e0b7508456e1b9eea0a94a1bd81f9", "content_id": "c53a86644149a10cff23758dfa91eb787b49fc08", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7673, "license_type": "no_license", "max_line_length": 150, "num_lines": 252, "path": "/PycharmProjects/data_com/test12/lgbmTest4.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n# In[2]:\n\n\npd.set_option('display.max_columns', None)\n\n\n# In[3]:\n\n\nwith open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n\n# In[4]:\n\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n\n# In[5]:\n\n\nwith open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n\n# In[8]:\n\n\nprint(len(data1),len(data))\n\n\n# In[9]:\n\n\ndata['topic_sim0_max']=data1['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data1['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data1['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_std']=data1['topic_sim0'].apply(lambda x:x[3])\ndata['topic_sim0_num']=data1['topic_sim0'].apply(lambda x:x[4])\n\ndata['topic_sim1_max']=data1['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data1['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data1['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_std']=data1['topic_sim1'].apply(lambda x:x[3])\ndata['topic_sim1_num']=data1['topic_sim1'].apply(lambda x:x[4])\ndata['topic_sim1_max1']=data1['topic_sim1'].apply(lambda x:x[5])\ndata['topic_sim1_min1']=data1['topic_sim1'].apply(lambda x:x[6])\n\n\n# In[10]:\n\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\nfill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\nfill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\nfill_null(data, 'topic_sim1_max1', -2)\nfill_null(data, 'topic_sim1_min1', -2)\n\n\n# In[13]:\n\n\ndata=data.drop(['follow_topic','inter_topic','topic','title_t1','title_t2','desc_t1','desc_t2'],axis=1)\n\n\n# In[40]:\n\n\n#data=data.drop(['topic_sim0_max','topic_sim0_min','topic_sim0_avg','topic_sim1_max','topic_sim1_min','topic_sim1_avg'],axis=1)\n\n\n# In[41]:\n\n\n#data = data.drop(['topic_gz', 'topic_int', 't_invi', 't_quest', 'desc_quest_w', 'desc_quest_sw', 'desc_tit_w', 'desc_tit_sw', 'topic_quest'], axis=1)\n\n\n# In[14]:\n\n\ndata.head(2)\n\n\n# 缺省值处理\n\n# ## 模型训练\n\n# In[18]:\n\n\nprint(len(data)-1141683)\n\n\n# In[19]:\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\n\n# In[21]:\n\n\nmodel_lgb = LGBMClassifier(boosting_type='gbdt',\n task='train',\n num_leaves=2**9-1,\n num_iterations=2000,\n learning_rate=0.01,\n n_estimators=2000,\n max_bin=425,\n subsample_for_bin=50000,\n objective='binary',\n min_split_gain=0,\n min_child_weight=5,\n min_child_samples=10,\n feature_fraction=0.9,\n feature_fraction_bynode=0.8,\n drop_rate=0.05,\n subsample=0.8,\n subsample_freq=1,\n colsample_bytree=1,\n reg_alpha=3,\n reg_lambda=5,\n seed=1000,\n n_jobs=4,\n silent=True\n )\n# 建议使用CV的方式训练预测。\nmodel_lgb.fit(X_train,\n y_train,\n eval_names=['train'],\n eval_metric={'auc'},\n eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=100)\n\n\n# In[22]:\n\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\n\n\n# In[23]:\n\n\ny_pred = model_lgb.predict_proba(X_evaluate)\n\n\n# [1]\ttrain's auc: 0.763875\ttrain's binary_logloss: 0.465954\tvalid_1's auc: 0.763206\tvalid_1's binary_logloss: 0.46561\n# Training until validation scores don't improve for 50 rounds\n# [2]\ttrain's auc: 0.769659\ttrain's binary_logloss: 0.464518\tvalid_1's auc: 0.769093\tvalid_1's binary_logloss: 0.464178\n# [3]\ttrain's auc: 0.77108\ttrain's binary_logloss: 0.463213\tvalid_1's auc: 0.770482\tvalid_1's binary_logloss: 0.462878\n# [4]\ttrain's auc: 0.771961\ttrain's binary_logloss: 0.461849\tvalid_1's auc: 0.771291\tvalid_1's binary_logloss: 0.461519\n# [5]\ttrain's auc: 0.773232\ttrain's binary_logloss: 0.460513\tvalid_1's auc: 0.772525\tvalid_1's binary_logloss: 0.460188\n# [6]\ttrain's auc: 0.773901\ttrain's binary_logloss: 0.459217\tvalid_1's auc: 0.773155\tvalid_1's binary_logloss: 0.458898\n# [7]\ttrain's auc: 0.774043\ttrain's binary_logloss: 0.457969\tvalid_1's auc: 0.77331\tvalid_1's binary_logloss: 0.457653\n# [8]\ttrain's auc: 0.774356\ttrain's binary_logloss: 0.456738\tvalid_1's auc: 0.773593\tvalid_1's binary_logloss: 0.456426\n# [9]\ttrain's auc: 0.774624\ttrain's binary_logloss: 0.455526\tvalid_1's auc: 0.773802\tvalid_1's binary_logloss: 0.455221\n# [10]\ttrain's auc: 0.774838\ttrain's binary_logloss: 0.454347\tvalid_1's auc: 0.773992\tvalid_1's binary_logloss: 0.454047\n# \n# [1]\ttrain's auc: 0.763775\ttrain's binary_logloss: 0.46588\tvalid_1's auc: 0.763507\tvalid_1's binary_logloss: 0.465891\n# Training until validation scores don't improve for 50 rounds\n# [2]\ttrain's auc: 0.769223\ttrain's binary_logloss: 0.464457\tvalid_1's auc: 0.768826\tvalid_1's binary_logloss: 0.464472\n# [3]\ttrain's auc: 0.770967\ttrain's binary_logloss: 0.463157\tvalid_1's auc: 0.770461\tvalid_1's binary_logloss: 0.463174\n# [4]\ttrain's auc: 0.772809\ttrain's binary_logloss: 0.461796\tvalid_1's auc: 0.772294\tvalid_1's binary_logloss: 0.461816\n# [5]\ttrain's auc: 0.773698\ttrain's binary_logloss: 0.460459\tvalid_1's auc: 0.773271\tvalid_1's binary_logloss: 0.460482\n# [6]\ttrain's auc: 0.773922\ttrain's binary_logloss: 0.459161\tvalid_1's auc: 0.773457\tvalid_1's binary_logloss: 0.459188\n# [7]\ttrain's auc: 0.774355\ttrain's binary_logloss: 0.457907\tvalid_1's auc: 0.773902\tvalid_1's binary_logloss: 0.457936\n# [8]\ttrain's auc: 0.774441\ttrain's binary_logloss: 0.45667\tvalid_1's auc: 0.774017\tvalid_1's binary_logloss: 0.4567\n# [9]\ttrain's auc: 0.774521\ttrain's binary_logloss: 0.455468\tvalid_1's auc: 0.774074\tvalid_1's binary_logloss: 0.455502\n# [10]\ttrain's auc: 0.77456\ttrain's binary_logloss: 0.454296\tvalid_1's auc: 0.774088\tvalid_1's binary_logloss: 0.454333\n\n# In[18]:\n\n\ny_pred\n\n\n# In[103]:\n\n\npd.DataFrame(y_pred[:, 1], columns=['y_pred'])\n\n\n# In[24]:\n\n\ntest = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\ntest.columns = ['问题id', '用户id', '邀请创建时间']\nprint(len(test))\n# 用于保存提交结果\nresult_append = test[['问题id', '用户id', '邀请创建时间']]\n\n\n# In[25]:\n\n\nresult_append['Score'] = y_pred[:, 1]\n\n\n# In[26]:\n\n\nresult_append.head()\n\n\n# In[27]:\n\n\nresult_append.to_csv('result.txt', header=False, index=False, sep='\\t')\n\n\n# In[ ]:\n\n\n\n\n" }, { "alpha_fraction": 0.5716162919998169, "alphanum_fraction": 0.6177179217338562, "avg_line_length": 29.847972869873047, "blob_id": "c1550a6e69ac2eeeb87a4733982151dd641134d9", "content_id": "a659d70ae46efc73a387f3999b1550303dc85404", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9326, "license_type": "no_license", "max_line_length": 150, "num_lines": 296, "path": "/PycharmProjects/data_com/test12/xgboost4.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n# In[2]:\n\n\npd.set_option('display.max_columns', None)\n\n\n# In[3]:\n\n\nwith open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n\n# In[4]:\n\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n\n# In[5]:\n\n\nwith open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n\n# In[8]:\n\n\nprint(len(data1),len(data))\n\n\n# In[9]:\n\n\ndata['topic_sim0_max']=data1['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data1['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data1['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_std']=data1['topic_sim0'].apply(lambda x:x[3])\ndata['topic_sim0_num']=data1['topic_sim0'].apply(lambda x:x[4])\n\ndata['topic_sim1_max']=data1['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data1['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data1['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_std']=data1['topic_sim1'].apply(lambda x:x[3])\ndata['topic_sim1_num']=data1['topic_sim1'].apply(lambda x:x[4])\ndata['topic_sim1_max1']=data1['topic_sim1'].apply(lambda x:x[5])\ndata['topic_sim1_min1']=data1['topic_sim1'].apply(lambda x:x[6])\n\n\n# In[10]:\n\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\nfill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\nfill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\nfill_null(data, 'topic_sim1_max1', -2)\nfill_null(data, 'topic_sim1_min1', -2)\n\n\n# In[13]:\n\n\ndata=data.drop(['follow_topic','inter_topic','topic','title_t1','title_t2','desc_t1','desc_t2'],axis=1)\n\n\n# In[40]:\n\n\n#data=data.drop(['topic_sim0_max','topic_sim0_min','topic_sim0_avg','topic_sim1_max','topic_sim1_min','topic_sim1_avg'],axis=1)\n\n\n# In[41]:\n\n\n#data = data.drop(['topic_gz', 'topic_int', 't_invi', 't_quest', 'desc_quest_w', 'desc_quest_sw', 'desc_tit_w', 'desc_tit_sw', 'topic_quest'], axis=1)\n\n# 缺省值处理\n\n# ## 模型训练\n\n# In[18]:\n\n\nprint(\"len(data) = \",len(data)-1141683)\n\n\n# In[19]:\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\n\n'''\nparameters = {\n 'max_depth': [5, 10, 15, 20, 25],\n 'learning_rate': [0.01, 0.02, 0.05, 0.1],\n 'n_estimators': [1000,2000],\n 'min_child_weight': [0, 2, 5, 10, 20, 50],\n 'max_delta_step': [0, 0.2, 0.6, 1, 2],\n 'subsample': [0.6, 0.7, 0.8, 0.85, 0.95],\n 'colsample_bytree': [0.5, 0.6, 0.7, 0.8, 0.9],\n 'reg_alpha': [0, 0.25, 0.5, 0.75, 1],\n 'reg_lambda': [0.2, 0.4, 0.6, 0.8, 1],\n 'scale_pos_weight': [0.2, 0.4, 0.6, 0.8, 1]\n}\n\n\nimport xgboost as xgb\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import GridSearchCV\nfrom xgboost import XGBClassifier\n\n#\n# model_xgboost = XGBClassifier(\n# max_leaf_nodes=2**9-1,\n# # max_depth=15,\n# learning_rate=0.01,\n# n_estimators=2000,\n# min_child_weight=5,\n# max_delta_step=0,\n# subsample=0.8,\n# colsample_bytree=0.7,\n# reg_alpha=0,\n# reg_lambda=0.4,\n# scale_pos_weight=0.8,\n# silent=True,\n# objective='binary:logistic',\n# missing=None,\n# eval_metric='auc',\n# seed=1440,\n# gamma=0,\n# n_jobs=-1\n# # nthread=40 #auto detect\n# )\n# # model_xgboost.fit(X_train,y_train)\n# gsearch = GridSearchCV(model_xgboost, param_grid=parameters, scoring='roc_auc', cv=5,n_jobs=-1)\n# gsearch.fit(X_train, y_train)\n#\n# print(\"Best score: %0.3f\" % gsearch.best_score_)\n# print(\"Best parameters set:\")\n# best_parameters = gsearch.best_estimator_.get_params()\n# for param_name in sorted(parameters.keys()):\n# print(\"\\t%s: %r\" % (param_name, best_parameters[param_name]))\n#\n#\n# y_pred = gsearch.predict(X_test)\n# predictions = [round(value) for value in y_pred]\n# accuracy = roc_auc_score(y_test,predictions)\n# print(\"FINALL Accuracy: %.2f%%\" % (accuracy * 100.0))\n'''\n\n#start test\nimport xgboost as xgb\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import GridSearchCV\nfrom xgboost import XGBClassifier\nfrom sklearn import metrics\nfrom sklearn.preprocessing import MinMaxScaler #最大最小归一化\nfrom sklearn.preprocessing import StandardScaler #标准化\nfrom sklearn.model_selection import cross_val_score\nimport matplotlib.pyplot as plt\n\n'''\n#cvresult.shape[0]是其中我们用的树的个数，cvresult的结果是一个DataFrame.\ndef tun_parameters(train_x, train_y): # 通过这个函数，确定树的个数\n xgb1 = XGBClassifier(learning_rate=0.01, n_estimators=2000, max_depth=10, min_child_weight=1, gamma=0, subsample=0.8,\n colsample_bytree=0.8, objective='binary:logistic', scale_pos_weight=1, eval_metric='auc',seed=1440)\n modelfit(xgb1, train_x, train_y)\n\ndef modelfit(alg, X, y, useTrainCV=True, cv_folds=5, early_stopping_rounds=50):\n if useTrainCV:\n xgb_param = alg.get_xgb_params()\n xgtrain = xgb.DMatrix(X, label=y)\n cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\n metrics='auc', early_stopping_rounds=early_stopping_rounds)\n print(\"cvresult = \",cvresult)\n print('n_estimators=', cvresult.shape[0])\n alg.set_params(n_estimators=cvresult.shape[0])\n\n # Fit the algorithm on the data\n alg.fit(X, y,\n eval_metric='auc',\n eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n\n # Predict training set:\n dtrain_predictions = alg.predict(X)\n dtrain_predprob = alg.predict_proba(X)[:, 1]\n\n # Print model report:\n print(\"\\nModel Report\")\n print(\"Accuracy : %.4g\" % metrics.accuracy_score(y, dtrain_predictions))\n print(\"AUC Score (Train): %f\" % metrics.roc_auc_score(y, dtrain_predprob))\n\n # feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)\n # feat_imp.plot(kind='bar', title='Feature Importances')\n # plt.ylabel('Feature Importance Score')\n # plt.show()\n\ntun_parameters(X_train, y_train)\n'''\n#end test\n\n\n# max_depth 和 min_child_weight 参数调优\nprint(\"max_depth and min_child_weight\")\nparam_test1 = {\n 'max_depth':range(5,15,1),\n 'min_child_weight':range(1,10,1)\n}\ngsearch1 = GridSearchCV(estimator = XGBClassifier(learning_rate =0.01, n_estimators=200, max_depth=10,\n min_child_weight=1, gamma=0, subsample=0.8,colsample_bytree=0.8,\\\n objective= 'binary:logistic', scale_pos_weight=1, seed=1440),\n param_grid = param_test1,scoring='roc_auc',iid=False, cv=5)\ngsearch1.fit(X_train,y_train)\nprint(\"gsearch1.grid_scores_ = \",gsearch1.grid_scores_)\nprint(\"gsearch1.best_params_ = \", gsearch1.best_params_)\nprint(\"gsearch1.best_score_ = \", gsearch1.best_score_)\n# #gamma参数调优\n# print(\"gamma\")\n# param_test3 = {\n# 'gamma': [i / 10.0 for i in range(0, 5)]\n# }\n# gsearch3 = GridSearchCV(\n# estimator=XGBClassifier(learning_rate=0.1, n_estimators=160, max_depth=9, min_child_weight=1, gamma=0,\n# subsample=0.8, colsample_bytree=0.8, objective='binary:logistic', nthread=8,\n# scale_pos_weight=1, seed=27), param_grid=param_test3, scoring='roc_auc', n_jobs=-1,\n# iid=False, cv=5)\n# gsearch3.fit(X_train,y_train)\n# print(gsearch3.grid_scores_, gsearch3.best_params_, gsearch3.best_score_)\n\n#略\n\n\n\n\n\n\n\n\n# X_evaluate = data[2593669:].drop(['label'], axis=1).values\n#\n# # y_pred = model_lgb.predict_proba(X_evaluate)\n#\n# pd.DataFrame(y_pred[:, 1], columns=['y_pred'])\n#\n# test = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\n# test.columns = ['问题id', '用户id', '邀请创建时间']\n# print(len(test))\n# # 用于保存提交结果\n# result_append = test[['问题id', '用户id', '邀请创建时间']]\n# result_append['Score'] = y_pred[:, 1]\n# print(result_append.head())\n# result_append.to_csv('result.txt', header=False, index=False, sep='\\t')\n#\n\n" }, { "alpha_fraction": 0.5530909299850464, "alphanum_fraction": 0.6065454483032227, "avg_line_length": 25.95098114013672, "blob_id": "7c1ac9ef03b56e9451f2aa437ad04c2bd3b166f5", "content_id": "43f14545469f066deecac31c82b8235bbbf35ba7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5592, "license_type": "no_license", "max_line_length": 150, "num_lines": 204, "path": "/PycharmProjects/data_com/test12/xgboost2.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n# In[2]:\n\n\npd.set_option('display.max_columns', None)\n\n\n# In[3]:\n\n\nwith open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n\n# In[4]:\n\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n\n# In[5]:\n\n\nwith open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n\n# In[8]:\n\n\nprint(len(data1),len(data))\n\n\n# In[9]:\n\n\ndata['topic_sim0_max']=data1['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data1['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data1['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_std']=data1['topic_sim0'].apply(lambda x:x[3])\ndata['topic_sim0_num']=data1['topic_sim0'].apply(lambda x:x[4])\n\ndata['topic_sim1_max']=data1['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data1['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data1['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_std']=data1['topic_sim1'].apply(lambda x:x[3])\ndata['topic_sim1_num']=data1['topic_sim1'].apply(lambda x:x[4])\ndata['topic_sim1_max1']=data1['topic_sim1'].apply(lambda x:x[5])\ndata['topic_sim1_min1']=data1['topic_sim1'].apply(lambda x:x[6])\n\n\n# In[10]:\n\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\nfill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\nfill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\nfill_null(data, 'topic_sim1_max1', -2)\nfill_null(data, 'topic_sim1_min1', -2)\n\n\n# In[13]:\n\n\ndata=data.drop(['follow_topic','inter_topic','topic','title_t1','title_t2','desc_t1','desc_t2'],axis=1)\n\n\n# In[40]:\n\n\n#data=data.drop(['topic_sim0_max','topic_sim0_min','topic_sim0_avg','topic_sim1_max','topic_sim1_min','topic_sim1_avg'],axis=1)\n\n\n# In[41]:\n\n\n#data = data.drop(['topic_gz', 'topic_int', 't_invi', 't_quest', 'desc_quest_w', 'desc_quest_sw', 'desc_tit_w', 'desc_tit_sw', 'topic_quest'], axis=1)\n\n# 缺省值处理\n\n# ## 模型训练\n\n# In[18]:\n\n\nprint(len(data)-1141683)\n\n\n# In[19]:\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\nparameters = {\n 'max_depth': [10,15, 20, 25, 35],\n 'learning_rate': [0.01, 0.02, 0.05, 0.1],\n 'n_estimators': [1000,2000],\n 'min_child_weight': [0, 2, 5, 10, 20, 50],\n 'max_delta_step': [0, 0.2, 0.6, 1, 2],\n 'subsample': [0.6, 0.7, 0.8, 0.85, 0.95],\n 'colsample_bytree': [0.5, 0.6, 0.7, 0.8, 0.9],\n 'reg_alpha': [0, 0.25, 0.5, 0.75, 1],\n 'reg_lambda': [0.2, 0.4, 0.6, 0.8, 1],\n 'scale_pos_weight': [0.2, 0.4, 0.6, 0.8, 1]\n}\n\n\nimport xgboost as xgb\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.model_selection import GridSearchCV\nfrom xgboost import XGBClassifier\n\n\nmodel_xgboost = XGBClassifier(\n # max_leaf_nodes=2**9-1,\n max_depth=15,\n learning_rate=0.01,\n n_estimators=2000,\n min_child_weight=5,\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=False,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40 #auto detect\n)\n# model_xgboost.fit(X_train,y_train)\ngsearch = GridSearchCV(model_xgboost, param_grid=parameters, scoring='roc_auc', cv=5,n_jobs=-1)\ngsearch.fit(X_train, y_train)\n\nprint(\"Best score: %0.3f\" % gsearch.best_score_)\nprint(\"Best parameters set:\")\nbest_parameters = gsearch.best_estimator_.get_params()\nfor param_name in sorted(parameters.keys()):\n print(\"\\t%s: %r\" % (param_name, best_parameters[param_name]))\n\n\ny_pred = gsearch.predict(X_test)\npredictions = [round(value) for value in y_pred]\naccuracy = roc_auc_score(y_test,predictions)\nprint(\"FINALL Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\n\n# y_pred = model_lgb.predict_proba(X_evaluate)\n\npd.DataFrame(y_pred[:, 1], columns=['y_pred'])\n\ntest = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\ntest.columns = ['问题id', '用户id', '邀请创建时间']\nprint(len(test))\n# 用于保存提交结果\nresult_append = test[['问题id', '用户id', '邀请创建时间']]\nresult_append['Score'] = y_pred[:, 1]\nprint(result_append.head())\nresult_append.to_csv('result.txt', header=False, index=False, sep='\\t')\n\n\n" }, { "alpha_fraction": 0.5927634835243225, "alphanum_fraction": 0.6070908308029175, "avg_line_length": 35.45132827758789, "blob_id": "98d704ae340d7f93a68e38ae4e03577ba5ffd8e7", "content_id": "74aca4456339f9d5e3f5030e329b576b243a9f1b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4118, "license_type": "no_license", "max_line_length": 120, "num_lines": 113, "path": "/PycharmProjects/data_com/data_process/baseline.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import warnings\nwarnings.filterwarnings('ignore')\n\nimport pandas as pd\nimport numpy as np\nimport gc\nimport pickle\nimport time\n\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import StratifiedKFold\n\nfrom catboost import CatBoostClassifier, Pool\n\ntic = time.time()\n\nwith open('../pkl/invite_info.pkl', 'rb') as file:\n invite_info = pickle.load(file)\nwith open('../pkl/invite_info_evaluate.pkl', 'rb') as file:\n invite_info_evaluate = pickle.load(file)\n\n\nmember_feat = pd.read_hdf('./feats/member_feat.h5', key='data') # 0.689438\nquestion_feat = pd.read_hdf('./feats/question_feat.h5', key='data') # 0.706848\n\n\nmember_question_feat = pd.read_hdf('./feats/member_question_feat.h5', key='data') # 719116 d12\ninvite_info['author_question_id'] = invite_info['author_id'] + invite_info['question_id']\ninvite_info_evaluate['author_question_id'] = invite_info_evaluate['author_id'] + invite_info_evaluate['question_id']\n\n\ntrain = invite_info.merge(member_feat, 'left', 'author_id')\ntest = invite_info_evaluate.merge(member_feat, 'left', 'author_id')\n\ntrain = train.merge(question_feat, 'left', 'question_id')\ntest = test.merge(question_feat, 'left', 'question_id')\n\ntrain = train.merge(member_question_feat, 'left', 'author_question_id')\ntest = test.merge(member_question_feat, 'left', 'author_question_id')\n\ndel member_feat, question_feat, member_question_feat\ngc.collect()\n\ndrop_feats = ['question_id', 'author_id', 'author_question_id', 'invite_time', 'label', 'invite_day']\n\nused_feats = [f for f in train.columns if f not in drop_feats]\nprint(len(used_feats))\nprint(used_feats)\n\ntrain_x = train[used_feats].reset_index(drop=True)\ntrain_y = train['label'].reset_index(drop=True)\ntest_x = test[used_feats].reset_index(drop=True)\n\npreds = np.zeros((test_x.shape[0], 2))\nscores = []\nhas_saved = False\nimp = pd.DataFrame()\nimp['feat'] = used_feats\n\nkfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\nfor index, (tr_idx, va_idx) in enumerate(kfold.split(train_x, train_y)):\n print('*' * 30)\n X_train, y_train, X_valid, y_valid = train_x.iloc[tr_idx], train_y.iloc[tr_idx], train_x.iloc[va_idx], train_y.iloc[\n va_idx]\n cate_features = []\n train_pool = Pool(X_train, y_train, cat_features=cate_features)\n eval_pool = Pool(X_valid, y_valid, cat_features=cate_features)\n if not has_saved:\n cbt_model = CatBoostClassifier(iterations=10000,\n learning_rate=0.1,\n eval_metric='AUC',\n use_best_model=True,\n random_seed=42,\n logging_level='Verbose',\n task_type='GPU',\n devices='0',\n early_stopping_rounds=300,\n loss_function='Logloss',\n depth=12,\n )\n cbt_model.fit(train_pool, eval_set=eval_pool, verbose=100)\n # with open('./models/fold%d_cbt_v1.mdl' % index, 'wb') as file:\n # pickle.dump(cbt_model, file)\n else:\n with open('./models/fold%d_cbt_v1.mdl' % index, 'rb') as file:\n cbt_model = pickle.load(file)\n\n imp['score%d' % (index + 1)] = cbt_model.feature_importances_\n\n score = cbt_model.best_score_['validation']['AUC']\n scores.append(score)\n print('fold %d round %d : score: %.6f | mean score %.6f' % (\n index + 1, cbt_model.best_iteration_, score, np.mean(scores)))\n preds += cbt_model.predict_proba(test_x)\n\n del cbt_model, train_pool, eval_pool\n del X_train, y_train, X_valid, y_valid\n import gc\n\n gc.collect()\n\n# mdls.append(cbt_model)\n\nimp.sort_values(by='score1', ascending=False)\n\nresult = invite_info_evaluate[['question_id', 'author_id', 'invite_time']]\nresult['result'] = preds[:, 1] / 5\nresult.head()\n\nresult.to_csv('./result.txt', sep='\\t', index=False, header=False)\n\ntoc = time.time()\nprint('Used time: %d' % int(toc - tic))" }, { "alpha_fraction": 0.6106017231941223, "alphanum_fraction": 0.6389684677124023, "avg_line_length": 25.58730125427246, "blob_id": "0d4b87cc2556c28687b1e643e452a8bf9714a94f", "content_id": "c8b4659704a66b1fd50cef0274bae1f773d25a62", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3490, "license_type": "no_license", "max_line_length": 70, "num_lines": 126, "path": "/PycharmProjects/data_com/test12/test/1212split.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\r\n# coding: utf-8\r\n\r\n# In[1]:\r\n\r\n# import lightgbm as lgb\r\nimport pickle\r\nimport numpy as np\r\nimport pandas as pd\r\n# from sklearn.model_selection import train_test_split\r\n# import seaborn as sns\r\n# from xgboost import XGBClassifier\r\n# from sklearn.metrics import accuracy_score\r\nimport gc\r\n# from lightgbm import LGBMClassifier\r\n# from sklearn.model_selection import StratifiedKFold\r\n\r\npd.set_option('display.max_columns', None)\r\n\r\n# with open('train20.pkl', 'rb') as file:\r\n# data = pickle.load(file)\r\n# print(data.head())\r\n\r\n# testdata = data[:1000]\r\n\r\n# with open('test1213.pkl','rb') as file:\r\n# # pickle.dump(testdata,file)\r\n# data = pickle.load(file)\r\n# # print(data.head())\r\n# isnull_index = data['title_ans_t1_max'].isnull()\r\n# # print(isnull_index)\r\n# data_isnull = data[isnull_index]\r\n# data_notnull = data[~isnull_index]\r\n# print(len(data_notnull['title_ans_t1_max']))\r\n# print(len(data_notnull.index))\r\n# print(data_notnull['title_ans_t1_max'])\r\n# print(data_isnull['title_ans_t1_max'])\r\n#\r\n# data_isnull.reset_index(drop=True)\r\n# data_notnull = data_notnull.reset_index(drop=True)\r\n# print(data_notnull['title_ans_t1_max'])\r\n# print(data_isnull['title_ans_t1_max'])\r\n# print(data_isnull.columns)\r\n#\r\n# with open('index.txt','w') as file:\r\n# # pickle.dump(data_isnull.columns,file)\r\n# for i in data_isnull.columns:\r\n# file.write(str(i)+'\\t')\r\n\r\na = pd.Series(np.arange(1,2,0.1),index=None,name='str0.1')\r\nb = pd.Series(np.arange(0,10),index=np.arange(10),name='kk')\r\nc = pd.Series(np.arange(20,30),index=np.arange(10),name='big')\r\n\r\ntest = pd.DataFrame()\r\ntest = test.append(a)\r\ntest = test.append(b)\r\ntest = test.append(c)\r\ntest[2]['kk'] = np.nan\r\n# print(test)\r\n# print(test[2]['kk'])\r\n# print(test.iloc[1,2])\r\n# print(test[2])\r\n\r\ndef pp(d):\r\n print('ddddddd')\r\n print(d)\r\n # print(d[1])\r\n # print(pd.isnull(d[1]))\r\n# ee = pd.isnull(test.iloc[1,2])\r\n# test.apply(pp,axis=1)\r\n# print(ee)\r\n# dd = test.apply(lambda x:x if pd.isnull(x[1]) else x/2,axis=0)\r\n# print(dd)\r\n# index_null = test[2].isnull()\r\n# select = test[index_null]\r\n# print(select)\r\n\r\n# test = test.drop(columns=[1,2],axis=1)\r\n# print(test)\r\n\r\n# data_notnull = data.drop(index=isnull_index,axis=0)\r\n# print(len(data_notnull))\r\n# unan = data.head().apply(lambda x )\r\n\r\n\r\ntest2 = pd.DataFrame(np.random.rand(8, 4),columns=['a','b','c','d'])\r\n\r\ntest2['b'][2] = np.nan\r\ntest2['b'][3] = np.nan\r\ntest2['b'][4] = np.nan\r\ntest2['b'][5] = np.nan\r\ntest2['b'][6] = np.nan\r\nprint(test2)\r\n\r\nindex = test2['b'].isnull()\r\nee = test2[index]\r\nprint(ee)\r\nselect_index = np.random.choice(test2[index].index,3,replace=False)\r\nprint(np.sort(select_index))\r\ndata = test2.iloc[select_index,:]\r\nprint(data)\r\n# print(data.iloc[1,:])\r\n# print(data)\r\n# print(data[data['b'].isnull()])\r\n# print(select_index)\r\n# print(test2.iloc[select_index,:])\r\n# data1 = test2.drop(index=index,axis=0)\r\n# data2 = test2.drop([0,2,3],axis=0)\r\n# test2 = test2.reset_index()\r\n# data1 = data1.reset_index()\r\n# data2 = data2.reset_index()\r\n# # print(data1)\r\n\r\n# data = pd.concat([data1,data2],axis=0)\r\n# # print(data)\r\n# data = data.sort_values(by=\"index\" , ascending=True)\r\n# print(data)\r\n\r\n# data1 = pd.DataFrame(np.arange(0,))\r\n# print(test2[:2]['a'])\r\n# test_isnull_index = test2['b'].isnull()\r\n# print(test_isnull_index)\r\n# test_isnull = test2[test_isnull_index]\r\n# print(test_isnull)\r\n# test_isnull = test_isnull.reindex(index=np.arange(len(test_isnull)))\r\n# print(test_isnull)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n" }, { "alpha_fraction": 0.5125778913497925, "alphanum_fraction": 0.5521082878112793, "avg_line_length": 33.06938934326172, "blob_id": "5746fd350f91ce62f817b7bafa6112c34068bf96", "content_id": "74fa056577c616a9415d359812e94960dcb8bdb6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8420, "license_type": "no_license", "max_line_length": 105, "num_lines": 245, "path": "/PycharmProjects/data_com/test12/test/1213nfold_mydata.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import accuracy_score\nimport gc\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import StratifiedKFold\n\npd.set_option('display.max_columns', None)\n\nwith open('train20.pkl', 'rb') as file:\n data = pickle.load(file)\nprint(data.head())\n\n\n\n# X = data[:2593669].drop(['label'], axis=1).values\nX = data.drop(['label'], axis=1).values\nprint(\"load model from file\")\nloaded_model1 = pickle.load(open(\"all_xgboost1.pickle.dat\", \"rb\"))\ntotal_xg_pred1 = loaded_model1.predict_proba(X)\nloaded_model2 = pickle.load(open(\"all_xgboost2.pickle.dat\", \"rb\"))\ntotal_xg_pred2 = loaded_model2.predict_proba(X)\nloaded_model3 = pickle.load(open(\"all_xgboost3.pickle.dat\", \"rb\"))\ntotal_xg_pred3 = loaded_model3.predict_proba(X)\ntotal_xg_pred_1 = pd.DataFrame(total_xg_pred1[:,1],columns=['xg_pred1'])\ntotal_xg_pred_2 = pd.DataFrame(total_xg_pred2[:,1],columns=['xg_pred2'])\ntotal_xg_pred_3 = pd.DataFrame(total_xg_pred3[:,1],columns=['xg_pred3'])\n\ndata = pd.concat([data,total_xg_pred_1],axis=1)\ndata = pd.concat([data,total_xg_pred_2],axis=1)\ndata = pd.concat([data,total_xg_pred_3],axis=1)\nprint(data.head())\n\nprint(\"start lgb merge\")\nloaded_model_bgm1 = pickle.load(open(\"all_xgboost4.pickle.dat\", \"rb\"))\ntotal_bgm_pred1 = loaded_model_bgm1.predict_proba(X)\nloaded_model_bgm2 = pickle.load(open(\"all_xgboost5.pickle.dat\", \"rb\"))\ntotal_bgm_pred2 = loaded_model_bgm2.predict_proba(X)\ntotal_bgm_pred_1 = pd.DataFrame(total_bgm_pred1[:,1],columns=['bgm_pred1'])\ntotal_bgm_pred_2 = pd.DataFrame(total_bgm_pred2[:,1],columns=['bgm_pred2'])\ndata = pd.concat([data,total_bgm_pred_1],axis=1)\ndata = pd.concat([data,total_bgm_pred_2],axis=1)\nprint(data.head())\n\n\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\n# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import accuracy_score\nimport gc\n\nfrom lightgbm import LGBMClassifier\n\n\n\nfrom sklearn.model_selection import StratifiedKFold\n\nn_fold = 5\nsteps = 2300\ndepth = 3\n\n\nskf = StratifiedKFold(n_splits=n_fold, random_state=2020, shuffle=False)\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\ny_pred = np.zeros([X_evaluate.shape[0],2])\ncnt = 1\n\n\n'''\nfor index ,(train_index,test_index) in enumerate(skf.split(X , y)): #训练数据五折\n print(\"start train time = {cnt}\".format(cnt=cnt))\n print(\"train_index = \",train_index)\n print(\"train_index_% = \", len(train_index)/len(X))\n train_x, test_x, train_y, test_y = X[train_index], X[test_index], y[\n train_index], y[test_index]\n print(\"start training\")\n # if cnt%2 != 0:\n if True:\n print(\"model_xgboost\")\n model = XGBClassifier(\n max_depth=depth,\n learning_rate=0.001,\n n_estimators=steps,\n min_child_weight=5, # 5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n )\n model.fit(train_x, train_y,\n eval_metric='auc',\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=2000)\n print(\"save {} model!!!\".format(cnt))\n pickle.dump(model, open(\"./model/mydata_model_xgboost{}.pickle.dat\".format(cnt), \"wb\"))\n else:\n print(\"LGBMClassifier\")\n model = LGBMClassifier(boosting_type='gbdt',\n task='train',\n num_leaves=2 ** depth - 1,\n num_iterations=steps,\n learning_rate=0.01,\n n_estimators=2000,\n max_bin=425,\n subsample_for_bin=50000,\n objective='binary',\n min_split_gain=0,\n min_child_weight=5,\n min_child_samples=10,\n feature_fraction=0.9,\n feature_fraction_bynode=0.8,\n drop_rate=0.05,\n subsample=0.8,\n subsample_freq=1,\n colsample_bytree=1,\n reg_alpha=3,\n reg_lambda=5,\n seed=1000,\n # n_jobs=4,\n silent=True\n )\n # 建议使用CV的方式训练预测。\n model.fit(train_x,\n train_y,\n eval_names=['train'],\n eval_metric={'auc'},\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n print(\"save {} model!!!\".format(cnt))\n pickle.dump(model, open(\"./model/mydata_LGBMClassifier{}.pickle.dat\".format(cnt), \"wb\"))\n\n gc.collect() # 垃圾清理，内存清理\n\n y_pred_test = model.predict(test_x)\n predictions = [round(value) for value in y_pred_test]\n accuracy = accuracy_score(test_y, predictions)\n print(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n y_pred += model.predict_proba(X_evaluate)\n print('len X_evaluate =',len(X_evaluate))\n print(\"len y_pred=\",len(y_pred))\n print(\"y_pred\")\n print(y_pred[:5, :]/cnt)\n cnt += 1\n\ny_pred = y_pred/n_fold\n'''\n\n###sigle xgboost\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\n\n\nprint(\"start xgboost\")\nmodel_xgboost = XGBClassifier(\n max_depth=2,\n learning_rate=0.001,\n n_estimators=2200,\n min_child_weight=5, #5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n)\nmodel_xgboost.fit(X_train,y_train,\n eval_metric='auc',\n eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=200)\n# save model to file\npickle.dump(model_xgboost, open(\"./model/model_xgboost.pickle2.dat\", \"wb\"))\n\n\n# model_xgboost = pickle.load(open(\"./model/model_xgboost.pickle.dat\", \"rb\"))\n\n\ny_pred_test = model_xgboost.predict(X_test)\npredictions = [round(value) for value in y_pred_test]\naccuracy = accuracy_score(y_test, predictions)\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\n\ny_pred = model_xgboost.predict_proba(X_evaluate)\n\n\n###sigle xgboost\n\n\n\nwith open('test1.pkl', 'rb') as file:\n result_append = pickle.load(file)\n\nprint(data[2593669:].head())\n\nresult_append['Score'] = y_pred[:, 1]\n\nprint(result_append.head())\n\nresult_append.to_csv('./model/result1213_mydata2.txt', header=False, index=False, sep='\\t')\n\n" }, { "alpha_fraction": 0.6242665648460388, "alphanum_fraction": 0.662824809551239, "avg_line_length": 31.033557891845703, "blob_id": "8aeee94235a1d5d0974206e753fd477398e1be8b", "content_id": "86a5365d443ace36e6467d74d51d26dd333c45c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4790, "license_type": "no_license", "max_line_length": 157, "num_lines": 149, "path": "/PycharmProjects/data_com/test12/load_model.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n\npd.set_option('display.max_columns', None)\n\n\nwith open('train15.pkl', 'rb') as file:\n data = pickle.load(file)\n\nprint(data.head())\n\ndata=data.drop(['u_topic_0_c','u_topic_0_d','u_topic_0_z','u_topic_1_c','u_topic_1_d','u_topic_1_z', 'u_topic_ans_c','u_topic_ans_d','u_topic_ans_z'],axis=1)\n\ndata=data.drop(['topic','follow_topic','inter_topic','topic_n'],axis=1)\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n# def fill_null(data, col, null_value):\n# use_avg = data[data[col] != null_value][col].mean()\n# data.loc[data[col] == null_value, col] = use_avg\n# return data\n# def fill_null_nan(data, col, null_value):\n# use_avg = np.nan\n# data.loc[data[col] == null_value, col] = use_avg\n# return data\n\n\n\n#\ndata['topic_sim0_max']=data['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_num']=data['topic_sim0'].apply(lambda x:x[3])\n\ndata['topic_sim1_max']=data['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_num']=data['topic_sim1'].apply(lambda x:x[3])\n\n# data['topic_sim0_max']=data['topic_sim0'].apply(lambda x:x[0])\n# data['topic_sim0_avg']=data['topic_sim0'].apply(lambda x:x[1])\n# data['topic_sim0_min']=data['topic_sim0'].apply(lambda x:x[2])\n# data['topic_sim0_std']=data['topic_sim0'].apply(lambda x:x[3])\n# data['topic_sim0_num']=data['topic_sim0'].apply(lambda x:x[4])\n#\n# data['topic_sim1_max']=data['topic_sim1'].apply(lambda x:x[0])\n# data['topic_sim1_avg']=data['topic_sim1'].apply(lambda x:x[1])\n# data['topic_sim1_min']=data['topic_sim1'].apply(lambda x:x[2])\n# data['topic_sim1_std']=data['topic_sim1'].apply(lambda x:x[3])\n# data['topic_sim1_num']=data['topic_sim1'].apply(lambda x:x[4])\n# data['topic_sim1_max1']=data['topic_sim1'].apply(lambda x:x[5])\n# data['topic_sim1_min1']=data['topic_sim1'].apply(lambda x:x[6])\n#\n\n\ndata=data.drop(['topic_sim0','topic_sim1'],axis=1)\n\n\n# data['topic_sim0_max']=fill_null_nan()\n# data['topic_sim0_avg']=data['topic_sim0'].apply(lambda x:x[1])\n# data['topic_sim0_min']=data['topic_sim0'].apply(lambda x:x[2])\n# data['topic_sim0_num']=data['topic_sim0'].apply(lambda x:x[3])\n#\n# data['topic_sim1_max']=data['topic_sim1'].apply(lambda x:x[0])\n# data['topic_sim1_avg']=data['topic_sim1'].apply(lambda x:x[1])\n# data['topic_sim1_min']=data['topic_sim1'].apply(lambda x:x[2])\n# data['topic_sim1_num']=data['topic_sim1'].apply(lambda x:x[3])\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\n# fill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\n\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\n# fill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\n# fill_null(data, 'topic_sim1_max1', -2)\n# fill_null(data, 'topic_sim1_min1', -2)\n\nprint(data.head())\n\n\nprint(len(data)-1141683)\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\n\n\nprint(\"load model from file\")\nmodel_xgboost = pickle.load(open(\"model_xgboost1205.pickle.dat\", \"rb\"))\n\nfrom sklearn.metrics import accuracy_score\ny_pred_test = model_xgboost.predict(X_test)\npredictions = [round(value) for value in y_pred_test]\naccuracy = accuracy_score(y_test, predictions)\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n\n\nprint('START TO SAVE RESULT!!!!!!!!!!!!!')\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\n\ny_pred = model_xgboost.predict_proba(X_evaluate)\nprint(\"y_pred\")\nprint(y_pred[:5,:])\n\n\nwith open('test1.pkl', 'rb') as file:\n result_append = pickle.load(file)\n\nprint(data[2593669:].head())\n\nresult_append['Score'] = y_pred[:, 1]\n\nprint(result_append.head())\n\nresult_append.to_csv('result1205.txt', header=False, index=False, sep='\\t')" }, { "alpha_fraction": 0.5143929719924927, "alphanum_fraction": 0.5602836608886719, "avg_line_length": 29.12986946105957, "blob_id": "adfdded78f4d3652fb657dfa5043bac1eb59fed2", "content_id": "18601495e358b087d2a57375fb74b7e961c2896e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2397, "license_type": "no_license", "max_line_length": 97, "num_lines": 77, "path": "/PycharmProjects/data_com/test12/test/test1212.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\r\n# coding: utf-8\r\n\r\nimport lightgbm as lgb\r\nimport pickle\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nimport seaborn as sns\r\nfrom xgboost import XGBClassifier\r\nfrom sklearn.metrics import accuracy_score\r\nimport gc\r\n\r\npd.set_option('display.max_columns', None)\r\n\r\nwith open('train20.pkl', 'rb') as file:\r\n data = pickle.load(file)\r\nprint(data.head())\r\n\r\nX = data[:2593669].drop(['label'], axis=1).values\r\ny = data[:2593669]['label'].values\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\r\n\r\nprint(\"start xgboost\")\r\nmodel = XGBClassifier(\r\n max_depth=10,\r\n learning_rate=0.01,\r\n n_estimators=2000,\r\n min_child_weight=5, # 5\r\n max_delta_step=0,\r\n subsample=0.8,\r\n colsample_bytree=0.7,\r\n reg_alpha=0,\r\n reg_lambda=0.4,\r\n scale_pos_weight=0.8,\r\n silent=True,\r\n objective='binary:logistic',\r\n missing=None,\r\n eval_metric='auc',\r\n seed=1440,\r\n gamma=0,\r\n n_jobs=-1\r\n # nthread=40\r\n )\r\nmodel.fit(X_train, y_train,\r\n eval_metric='auc',\r\n eval_set=[(X_train, y_train), (X_test, y_test)], # , (X_test, y_test)\r\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\r\n early_stopping_rounds=50)\r\ncnt = 1212\r\nprint(\"save {} model!!!\".format(cnt))\r\npickle.dump(model, open(\"model_xgboost{}.pickle.dat\".format(cnt), \"wb\"))\r\n\r\nprint('START TO SAVE RESULT!!!!!!!!!!!!!')\r\ny_pred_test = model.predict(X_test)\r\npredictions = [round(value) for value in y_pred_test]\r\naccuracy = accuracy_score(y_test, predictions)\r\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\r\n\r\nprint('START TO SAVE RESULT!!!!!!!!!!!!!')\r\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\r\ny_pred = model.predict_proba(X_evaluate)\r\nprint(\"y_pred\")\r\nprint(y_pred[:5,:])\r\n\r\n\r\nwith open('test1.pkl', 'rb') as file:\r\n result_append = pickle.load(file)\r\n\r\nprint(data[2593669:].head())\r\n\r\nresult_append['Score'] = y_pred[:, 1]\r\n\r\nprint(result_append.head())\r\n\r\nresult_append.to_csv('result1212_xgboost.txt', header=False, index=False, sep='\\t')\r\n" }, { "alpha_fraction": 0.5354499220848083, "alphanum_fraction": 0.5663069486618042, "avg_line_length": 35.52117156982422, "blob_id": "4bbc05838f0b1ba5066a1d6a28951cdddf7891d6", "content_id": "3802b68a6dede13c1bccb858c1fd49f3d752b9ac", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 11267, "license_type": "no_license", "max_line_length": 145, "num_lines": 307, "path": "/PycharmProjects/data_com/test12/test/mydata2.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "import lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import accuracy_score\nimport gc\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import StratifiedKFold\n\npd.set_option('display.max_columns', None)\n\nwith open('train20.pkl', 'rb') as file:\n data = pickle.load(file)\n# print(data.head())\n\n\ndata_use = data[:2593669]\ndata_evaluate = data[2593669:]\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\n\nisnull_index = data_use['title_ans_t1_max'].isnull()\n# print(isnull_index)\ndata_isnull = data_use[isnull_index]\ndata_notnull = data_use[~isnull_index]\n\n# print(len(data_notnull['title_ans_t1_max']))\nprint('len(data_notnull.index) = ',len(data_notnull.index))\nprint('len(data_isnull.index) = ',len(data_isnull.index))\n\n\nevaluate_isnull_index = data_evaluate['title_ans_t1_max'].isnull()\nevaluate_isnull = data_evaluate[evaluate_isnull_index]\nevaluate_notnull = data_evaluate[~evaluate_isnull_index]\n\n\nprint(\"evaluate_isnull = \",len(evaluate_isnull))\nprint(\"evaluate_notnull = \",len(evaluate_notnull))\n\nchoice_scale_drop = round((len(evaluate_isnull)/len(data_evaluate) *len(data_use)-len(data_isnull))/(-1+len(evaluate_isnull)/len(data_evaluate)))\nchoice_scale_isnull = len(data_isnull) - choice_scale_drop\nprint(\"scale = {}%\".format(len(evaluate_isnull)/len(data_evaluate)))\nprint(\"choice_scale_isnull\",choice_scale_isnull)\nprint(\"selct scale = \",choice_scale_isnull/(len(data_use)-choice_scale_drop))\n\nselect_index_isnull = np.random.choice(data_use[data_use['title_ans_t1_max'].isnull()].index,choice_scale_isnull,replace=False)\n# print(np.sort(select_index_isnull))\nprint(len(select_index_isnull))\n\nselect_data_isnull = data_use.iloc[select_index_isnull,:]\nprint(\"select_data_isnull = \",len(select_data_isnull))\n# print(select_data_isnull['title_ans_t1_max'][:1000])\nprint(\"select_data_isnull() = \",select_data_isnull['title_ans_t1_max'].isnull().sum())\n\ndata_notnull = data_notnull.reset_index()\nselect_data_isnull = select_data_isnull.reset_index()\n# print(select_data_isnull.head(20))\n\n\n\n# print(\"data_notnull\",len(data_notnull[data_notnull['title_ans_t1_max'].isnull()]))\n# print(\"select_data_isnull\",len(select_data_isnull['title_ans_t1_max'].isnull().index))\ntrain_data = pd.concat([data_notnull,select_data_isnull],axis=0)\nprint(\"train_data\",len(train_data))\n# print('before sort\\n',train_data.head(40))\ntrain_data = train_data.sort_values(by=\"index\" , ascending=True)\n# print('after sort\\n',train_data.head(40))\n\n\nprint(\"len(train_data) =\",len(train_data))\nprint(\"isnull() = \",len(train_data[train_data['title_ans_t1_max'].isnull()]))\nafter_scale = len(train_data[train_data['title_ans_t1_max'].isnull()])/len(train_data)\nprint(\"scale = {}\".format(len(evaluate_isnull)/len(data_evaluate)))\nprint(\"after selct train data = {}\".format(after_scale))\n#train\n\nX = train_data.drop(['label','index'],axis=1).values\ny = train_data['label'].values\n\n'''\nX_train, X_test, y_train, y_test = train_test_split(X_isnull, y_isnull, test_size=0.1)\n\n\nprint(\"start isnull_model_xgboost\")\nisnull_model_xgboost = XGBClassifier(\n max_depth=depth,\n learning_rate=0.01,\n n_estimators=steps,\n min_child_weight=5, #5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n)\nisnull_model_xgboost.fit(X_train,y_train,\n eval_metric='auc',\n eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=200)\n# save model to file\npickle.dump(isnull_model_xgboost, open(\"./model/mydata1_isnull_xgboost.pickle.dat\", \"wb\"))\n\n\n# model_xgboost = pickle.load(open(\"./model/model_xgboost.pickle.dat\", \"rb\"))\n\n\ny_pred_test = isnull_model_xgboost.predict(X_test)\npredictions = [round(value) for value in y_pred_test]\naccuracy = accuracy_score(y_test, predictions)\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\nX_train, X_test, y_train, y_test = train_test_split(X_notnull, y_notnull, test_size=0.1)\nprint(\"start notnull_model_xgboost\")\nnotnull_model_xgboost = XGBClassifier(\n max_depth=depth,\n learning_rate=0.01,\n n_estimators=steps,\n min_child_weight=5, #5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n)\nnotnull_model_xgboost.fit(X_train,y_train,\n eval_metric='auc',\n eval_set=[(X_train, y_train),(X_test, y_test)],#, (X_test, y_test)\n #categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=200)\n# save model to file\npickle.dump(notnull_model_xgboost, open(\"./model/mydata1_notnull_xgboost.pickle.dat\", \"wb\"))\n\n\n# model_xgboost = pickle.load(open(\"./model/model_xgboost.pickle.dat\", \"rb\"))\n\n\ny_pred_test = notnull_model_xgboost.predict(X_test)\npredictions = [round(value) for value in y_pred_test]\naccuracy = accuracy_score(y_test, predictions)\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n'''\n\n\nfrom sklearn.model_selection import StratifiedKFold\n\nn_fold = 5\nsteps = 2000\ndepth = 10\n\n\nskf = StratifiedKFold(n_splits=n_fold, random_state=2020, shuffle=False)\n\n# isnul 5 fold\n# X_evaluate = data[2593669:].drop(['label'], axis=1).values\npred_y = np.zeros([data_evaluate.shape[0],2])\ncnt = 1\n\nfor index ,(train_index,test_index) in enumerate(skf.split(X , y)): #训练数据五折\n print(\"start train time = {cnt}\".format(cnt=cnt))\n print(\"train_index = \",train_index)\n print(\"train_index_% = \", len(train_index)/len(X))\n train_x, test_x, train_y, test_y = X[train_index], X[test_index], y[\n train_index], y[test_index]\n print(\"start training\")\n # if cnt%2 != 0:\n if True:\n print(\"model_xgboost\")\n model = XGBClassifier(\n max_depth=10,\n learning_rate=0.01,\n n_estimators=steps,\n min_child_weight=5, # 5\n max_delta_step=0,\n subsample=0.8,\n colsample_bytree=0.7,\n reg_alpha=0,\n reg_lambda=0.4,\n scale_pos_weight=0.8,\n silent=True,\n objective='binary:logistic',\n missing=None,\n eval_metric='auc',\n seed=1440,\n gamma=0,\n n_jobs=-1\n # nthread=40\n )\n model.fit(train_x, train_y,\n eval_metric='auc',\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=200)\n print(\"save {} model!!!\".format(cnt))\n pickle.dump(model, open(\"./model2/sample_mydata_model_xgboost{}.pickle.dat\".format(cnt), \"wb\"))\n else:\n print(\"LGBMClassifier\")\n model = LGBMClassifier(boosting_type='gbdt',\n task='train',\n num_leaves=2 ** depth - 1,\n num_iterations=steps,\n learning_rate=0.01,\n n_estimators=2000,\n max_bin=425,\n subsample_for_bin=50000,\n objective='binary',\n min_split_gain=0,\n min_child_weight=5,\n min_child_samples=10,\n feature_fraction=0.9,\n feature_fraction_bynode=0.8,\n drop_rate=0.05,\n subsample=0.8,\n subsample_freq=1,\n colsample_bytree=1,\n reg_alpha=3,\n reg_lambda=5,\n seed=1000,\n # n_jobs=4,\n silent=True\n )\n # 建议使用CV的方式训练预测。\n model.fit(train_x,\n train_y,\n eval_names=['train'],\n eval_metric={'auc'},\n eval_set=[(train_x, train_y), (test_x, test_y)], # , (X_test, y_test)\n # categorical_feature=[15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29],\n early_stopping_rounds=50)\n print(\"save {} model!!!\".format(cnt))\n pickle.dump(model, open(\"./model/mydata_LGBMClassifier{}.pickle.dat\".format(cnt), \"wb\"))\n\n gc.collect() # 垃圾清理，内存清理\n\n y_pred_test = model.predict(test_x)\n predictions = [round(value) for value in y_pred_test]\n accuracy = accuracy_score(test_y, predictions)\n print(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n pred_y += model.predict_proba(X_evaluate)\n print('len X_evaluate =',len(X_evaluate))\n print(\"len y_pred=\",len(pred_y))\n print(\"y_pred\")\n print(pred_y[:5, :]/cnt)\n cnt += 1\n\npred_y = (pred_y/n_fold)[:,1]\n\n#save data\n\n\n# isnull_pred = isnull_model_xgboost.predict_proba(evaluate_X_isnull)[:,1]\n# print(\"isnull_pred\\n\",isnull_pred)\n# notnull_pred = notnull_model_xgboost.predict_proba(evaluate_X_notnull)[:,1]\n# print(\"notnull_pred\\n\",notnull_pred)\n\n# store_isnull = pd.DataFrame()\n# store_notnull = pd.DataFrame()\n# store_isnull['index'] = evaluate_isnull['index']\n# store_isnull['y_pred'] = isnull_pred\n# print(store_isnull.head())\n#\n# store_notnull['index'] = evaluate_notnull['index']\n# store_notnull['y_pred'] = notnull_pred\n# print(store_notnull.head())\n#\n#\n# store = pd.concat([store_isnull,store_notnull],axis=0)\n# print('before sort',store.head(20))\n# store = store.sort_values(by=\"index\" , ascending=True)\n# print('after sort',store.head(40))\n#\n# y_pred = store['y_pred'].values\n\n########store data\nwith open('test1.pkl', 'rb') as file:\n result_append = pickle.load(file)\n\n# print(data[2593669:].head())\n\nresult_append['Score'] = pred_y\n\nprint(result_append.head())\n\nresult_append.to_csv('./model2/mydata_sample.txt', header=False, index=False, sep='\\t')\n\n" }, { "alpha_fraction": 0.5969184041023254, "alphanum_fraction": 0.6368651390075684, "avg_line_length": 25.540403366088867, "blob_id": "5b0f6f69a5f1727a9b8fc8bbf6735baae5f107a6", "content_id": "7c39ecc284a79ddefb327ec9fc069fabdfd5019d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5349, "license_type": "no_license", "max_line_length": 150, "num_lines": 198, "path": "/PycharmProjects/data_com/test12/xgboost_fix.py", "repo_name": "KongL1013/datacom", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport lightgbm as lgb\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\n# In[2]:\n\n\npd.set_option('display.max_columns', None)\n\n\n# In[3]:\n\n\nwith open('train11.pkl', 'rb') as file:\n data = pickle.load(file)\n\n\n# In[4]:\n\n\ndef fill_null(data, col, null_value):\n use_avg = data[data[col] != null_value][col].mean()\n data.loc[data[col] == null_value, col] = use_avg\n return data\ndef fill_null_nan(data, col, null_value):\n use_avg = np.nan\n data.loc[data[col] == null_value, col] = use_avg\n return data\n\n\n# In[5]:\n\n\nwith open('train10.pkl', 'rb') as file:\n data1 = pickle.load(file)\n\n\n# In[8]:\n\n\nprint(len(data1),len(data))\n\n\n# In[9]:\n\n\ndata['topic_sim0_max']=data1['topic_sim0'].apply(lambda x:x[0])\ndata['topic_sim0_avg']=data1['topic_sim0'].apply(lambda x:x[1])\ndata['topic_sim0_min']=data1['topic_sim0'].apply(lambda x:x[2])\ndata['topic_sim0_std']=data1['topic_sim0'].apply(lambda x:x[3])\ndata['topic_sim0_num']=data1['topic_sim0'].apply(lambda x:x[4])\n\ndata['topic_sim1_max']=data1['topic_sim1'].apply(lambda x:x[0])\ndata['topic_sim1_avg']=data1['topic_sim1'].apply(lambda x:x[1])\ndata['topic_sim1_min']=data1['topic_sim1'].apply(lambda x:x[2])\ndata['topic_sim1_std']=data1['topic_sim1'].apply(lambda x:x[3])\ndata['topic_sim1_num']=data1['topic_sim1'].apply(lambda x:x[4])\ndata['topic_sim1_max1']=data1['topic_sim1'].apply(lambda x:x[5])\ndata['topic_sim1_min1']=data1['topic_sim1'].apply(lambda x:x[6])\n\n\n# In[10]:\n\n\nfill_null(data, 'topic_sim0_max', -2)\nfill_null(data, 'topic_sim0_avg', -2)\nfill_null(data, 'topic_sim0_min', -2)\nfill_null(data, 'topic_sim0_std', -2)\nfill_null(data, 'topic_sim0_num', -2)\nfill_null(data, 'topic_sim1_max', -2)\nfill_null(data, 'topic_sim1_avg', -2)\nfill_null(data, 'topic_sim1_min', -2)\nfill_null(data, 'topic_sim1_std', -2)\nfill_null(data, 'topic_sim1_num', -2)\nfill_null(data, 'topic_sim1_max1', -2)\nfill_null(data, 'topic_sim1_min1', -2)\n\n\n# In[13]:\n\n\ndata=data.drop(['follow_topic','inter_topic','topic','title_t1','title_t2','desc_t1','desc_t2'],axis=1)\n\n\n# In[40]:\n\n\n#data=data.drop(['topic_sim0_max','topic_sim0_min','topic_sim0_avg','topic_sim1_max','topic_sim1_min','topic_sim1_avg'],axis=1)\n\n\n# In[41]:\n\n\n#data = data.drop(['topic_gz', 'topic_int', 't_invi', 't_quest', 'desc_quest_w', 'desc_quest_sw', 'desc_tit_w', 'desc_tit_sw', 'topic_quest'], axis=1)\n\n# 缺省值处理\n\n# ## 模型训练\n\n# In[18]:\n\n\nprint(len(data)-1141683)\n\n\n\nfrom lightgbm import LGBMClassifier\nfrom sklearn.model_selection import train_test_split\n# 划分训练集和测试集\n# y_train = data[:train.shape[0]]['label'].values\n# X_train = data[:train.shape[0]].drop(['label'], axis=1).values\n\n# X_test = data[train.shape[0]:].drop(['label'], axis=1).values\nX = data[:2593669].drop(['label'], axis=1).values\nX_xg_evaluate = data[2593669:].drop(['label'], axis=1).values\n\nprint(\"load model from file\")\nloaded_model = pickle.load(open(\"model_xgboost.pickle.dat\", \"rb\"))\ntotal_xg_pred = loaded_model.predict_proba(X)\n\n##start\ny_xg_pred = loaded_model.predict_proba(X_xg_evaluate)\n\nget = np.r_[total_xg_pred,y_xg_pred]\nprint(get)\nprint(\"len get = \",len(get))\nprint(\"len data =\",len(data))\n# y_xg_pred1 = pd.DataFrame(get,columns=['pred1'])\n# y_xg_pred2 = pd.DataFrame(get,columns=['pred2'])\n\n##end\n\n\ntotal_xg_pred_1 = pd.DataFrame(get[:,1],columns=['pred1'])\ntotal_xg_pred_2 = pd.DataFrame(get[:,1],columns=['pred2'])\n\nprint(total_xg_pred[:5])\ndata = pd.concat([data,total_xg_pred_1],axis=1)\ndata = pd.concat([data,total_xg_pred_2],axis=1)\nprint(data.head())\n\n################################################################################\n\n# print(\"load model from file\")\n# loaded_model = pickle.load(open(\"model_xgboost.pickle.dat\", \"rb\"))\n# total_xg_pred = loaded_model.predict_proba(X)\n# total_xg_pred_1 = pd.DataFrame(total_xg_pred[:,1],columns=['pred1'])\n# print(total_xg_pred[:5])\n\n\n# data['pred1'] = pd.concat([data['pred1'],y_xg_pred1],axis=0)\n# data['pred2'] = pd.concat([data['pred2'],y_xg_pred2],axis=0)\n# print(\"after merge\")\n# print(data.head())\n# print(data.tail())\n###################################################################################################\nX = data[:2593669].drop(['label'], axis=1).values\ny = data[:2593669]['label'].values\n\nprint(\"X = \",X[:5])\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n\nprint(\"start lgb merge\")\nmodel_lgb = pickle.load(open(\"model_lgb1203.pickle.dat\", \"rb\"))\n\nfrom sklearn.metrics import accuracy_score\n\nprint('START TO SAVE RESULT!!!!!!!!!!!!!')\ny_pred_test = model_lgb.predict(X_test)\npredictions = [round(value) for value in y_pred_test]\naccuracy = accuracy_score(y_test, predictions)\nprint(\"FINALL TEST Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n\nX_evaluate = data[2593669:].drop(['label'], axis=1).values\ny_pred = model_lgb.predict_proba(X_evaluate)\nprint(\"y_pred\")\nprint(y_pred[:5,:])\n\n\ntest = pd.read_csv('./invite_info_evaluate_1_0926.txt', header=None, sep='\\t')\ntest.columns = ['问题id', '用户id', '邀请创建时间']\nprint(len(test))\n# 用于保存提交结果\nresult_append = test[['问题id', '用户id', '邀请创建时间']]\nresult_append['Score'] = y_pred[:, 1]\nprint(result_append.head())\nresult_append.to_csv('result_fix.txt', header=False, index=False, sep='\\t')\n\n\n" } ]

18

linylgithub/data_structure

https://github.com/linylgithub/data_structure

0367a44474a5654ecd8d199d2d5925fecbb48ab1

1e87a8229df03a8682b430956189518bf5ddc2dd

db4b46e25b66bb46e3c23c8817bf01527497d5d7

refs/heads/master

2020-03-26T07:32:20.817770

2018-08-14T02:50:38

144,659,238

0

null

[ { "alpha_fraction": 0.4725162088871002, "alphanum_fraction": 0.47558894753456116, "avg_line_length": 22.983606338500977, "blob_id": "f886d95086cbfa93a69c2357b12601d6012dada8", "content_id": "26748203ee01f032b46cdb0e13408c53cfa5822c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3053, "license_type": "no_license", "max_line_length": 55, "num_lines": 122, "path": "/link_list/cycle_double_link_list.py", "repo_name": "linylgithub/data_structure", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\nfrom __future__ import absolute_import\n\n# 双向循环链表\n\n# 节点\nclass Node:\n def __init__(self, value):\n self.data = value\n self.next = None\n self.pre = None\n\n# 双向循环链表\nclass CycleDoubleLinkList:\n def __init__(self):\n self._head = Node(None)\n self._head.next = self._head\n self._head.prev = self._head\n self._rear = self._head\n\n # 插入\n def insert(self, index, value):\n if index < 0:\n print '插入位置有误'\n return\n n = Node(value)\n cur = self._head\n for i in range(index - 1):\n cur = cur.next\n if cur == self._head:\n print '插入位置有误'\n return\n n.next = cur.next\n cur.next.prev = n\n cur.next = n\n n.prev = cur\n if n.next = self.head:\n self._rear = n\n\n # 删除\n def remove(self,index):\n if self.empty():\n print '链表是空的'\n if index <= 0 and index > self.length():\n print '删除的位置有误'\n return \n cur = self._head\n for i in range(index - 1):\n cur = cur.next\n n = cur.next\n cur.next.next.prev = cur\n cur.next = cur.next.next\n if cur.next == self._head:\n self._rear = cur\n del n \n\n # 判断是否空链表\n def empty(self):\n return self._head.next == self._head\n\n def travel(self):\n cur = self._head.next\n print '正向输出'\n while cur != self._head:\n print(cur.data)\n cur = cur.next\n print '逆向输出'\n while cur.prev != self._head:\n cur = cur.prev\n print(cur.data)\n\n def search(self, value):\n cur = self._head.next\n index = 1\n while cur != self._head:\n if cur.data == value:\n print 'index:%d,value:%d'%(index,value)\n return\n cur = cur.next\n index += 1\n\n print '没有该元素'\n\n def clear(self):\n cur = self._head.next\n while cur != self._head:\n temp = cur\n cur = cur.next\n del temp\n self._head.next = self._head\n self._head.prev = self._head\n\n def appendleft(self,value):\n n = Node(value)\n self._head.next.prev = n\n n.next = self._head.next\n self._head.next = n\n n.prev = self._head\n if n.next == self._head:\n self._rear = n\n\n def appendright(self,value):\n n = Node(value)\n self._rear.next.prev = n\n n.next = self._rear.next\n self._rear.next = n\n n.prev = self._rear\n self._rear = n\n\n def length(self):\n cur = self._head.next\n count = 0 \n while cur != self._head:\n cur = cur.next\n count += 1\n return count\n\nif __name__ == '__main__':\n link = CycleDoubleLinkList()\n\n\n\n" } ]

1

FelicityPictures/Pokemon_GO

https://github.com/FelicityPictures/Pokemon_GO

3a45e8b7d602fd84ab802c248d39afb9e9908011

d93d1a934179a472c248e2c6514dd8b17d7201f1

68393cfbaabc84e07674e0e74308fe50cf64d2e3

refs/heads/master

2018-12-31T09:02:55.978544

2016-07-21T22:13:34

63,491,030

0

null

[ { "alpha_fraction": 0.5802469253540039, "alphanum_fraction": 0.5873944163322449, "avg_line_length": 25.534482955932617, "blob_id": "fd422f1fe37dff55ca793e2c86bc79cb1355b69f", "content_id": "450ae45d6cfc97ab46abaf07e98d8f332065a13e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1539, "license_type": "no_license", "max_line_length": 61, "num_lines": 58, "path": "/db.py", "repo_name": "FelicityPictures/Pokemon_GO", "src_encoding": "UTF-8", "text": "from pymongo import MongoClient\n\nconnection = MongoClient()\ndb = connection[\"pokemon\"]\n\n#for restarting database. Delete after testing\n#db.pokemon.drop()\n\n\"\"\"\nCOLLECTIONS\n(assuming all locations fit into a rectangle\npokemon: name, angle, startx, starty, endx, endy\n\"\"\"\n\ndef add_pokemon(name,lat,lng):\n \"\"\"\n Adds new location of a Pokemon\n Params: name - name of Pokemon\n lat - latitude of point where found\n lng - longitude of point where found\n Returns: True if insertion successful\n False if otherwise\n \"\"\"\n p = {\"name\":name,\n \"lat\":lat,\n \"lng\":lng}\n db.pokemon.insert(p)\n return True\n\ndef add_by_array(name,array):\n array = string_to_array(array)\n for point in array:\n add_pokemon(name,point[0],point[1])\n\ndef string_to_array(string):\n arr = []\n temp = \"\"\n string = string[1:(len(string)-1)]\n while len(string)>0:\n if string.index(',') == 0:\n string = string[1:]\n if string.index('[') == 0:\n temp = string[1:string.index(']')]\n add = [float(temp[:temp.index(',')]),\n float(temp[temp.index(',')+1:])]\n arr.append(add)\n string = string[string.index(']')+1:]\n return arr\n\ndef get_pokemon(name):\n \"\"\"\n Params: name - name of the Pokemon\n Returns: all the locations of where a Pokemon was found\n \"\"\"\n ret = list(db.pokemon.find({\"name\":name}))\n if not ret:\n return \"Pokemon has no current locations to be found\"\n return ret\n" }, { "alpha_fraction": 0.5229591727256775, "alphanum_fraction": 0.581632673740387, "avg_line_length": 19.63157844543457, "blob_id": "35acaac74cca3a8a4ce9c6dd1c41f7cc321964c0", "content_id": "24142b6669c8586d0c651972ea1df20182d47de7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 392, "license_type": "no_license", "max_line_length": 49, "num_lines": 19, "path": "/dbtest.py", "repo_name": "FelicityPictures/Pokemon_GO", "src_encoding": "UTF-8", "text": "import db\n\n\"\"\"\ndb.add_pokemon(\"Dragonite\",\"2\",'3')\ndb.add_pokemon(\"Dragonite\",\"5\",'2')\ndb.add_pokemon(\"Bulbasaur\",'5','3')\n\nprint db.get_pokemon(\"Dragonite\")\nprint db.get_pokemon(\"Meowth\")\n\nDitto = [['1','2'],['3','4'],['6','7'],['2','0']]\ndb.add_by_array(\"Ditto\",Ditto)\n\nprint db.get_pokemon(\"Ditto\")\n\"\"\"\n\ns = \"[[1.2,2],[3,4],[6,7],[2,0]]\"\nprint \"original: \" + s\nprint db.string_to_array(s)\n" }, { "alpha_fraction": 0.5799999833106995, "alphanum_fraction": 0.5928571224212646, "avg_line_length": 24, "blob_id": "8734cad7582af63c464ea3202a69838585f58f4a", "content_id": "5e0cc2d2c9964e8751607ef265718436d20178aa", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 700, "license_type": "no_license", "max_line_length": 49, "num_lines": 28, "path": "/app.py", "repo_name": "FelicityPictures/Pokemon_GO", "src_encoding": "UTF-8", "text": "from flask import Flask, render_template, request\nimport json\n#imports database\nimport db\n\napp=Flask(__name__)\n\[email protected]('/', methods=[\"POST\"])\[email protected]('/home', methods=[\"POST\"])\ndef home():\n if request.method == \"POST\":\n find = request.form.get('find')\n \n return render_template('index.html')\n\[email protected]('/add', methods=[\"POST\"])\ndef add():\n if request.method == \"POST\":\n data = request.form.get('locations')\n sep = data.index('|')\n name = data[:sep]\n locations = data[(sep+1):]\n db.add_by_array(name,locations)\n return render_template('add.html')\n\nif __name__=='__main__':\n app.debug=True\n app.run(host='0.0.0.0', port=5111)\n" } ]

3

rungsiman/dorest

https://github.com/rungsiman/dorest

668ee791879b76d825114eed465e8949db0e2697

480f0cf6369777beb6209e67e6515343ce826281

bbb0bea88da01da3db8fb4d657e6a640e16915a5

refs/heads/main

2023-01-27T18:51:52.654408

2020-12-06T13:39:36

316,101,787

0

null

[ { "alpha_fraction": 0.6197856068611145, "alphanum_fraction": 0.6261943578720093, "avg_line_length": 39.10280227661133, "blob_id": "2208bc9728dec58e24bf8c063a1da06b9335aaab", "content_id": "0ca3079f7ce87dac8cb3848f6a67b5bbeebee714", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8582, "license_type": "permissive", "max_line_length": 142, "num_lines": 214, "path": "/dorest/decorators.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Dorest's endpoint and Django-related decorators\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nimport importlib\nfrom functools import wraps\nfrom typing import Any, Callable, List, Union\n\nfrom django.conf import settings\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom rest_framework import status\nfrom rest_framework.request import Request\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom dorest.glossary import Glossary\n\n\ndef endpoint(methods: List[str], default: bool = False, throttle: str = 'base', requires: Union[tuple, list] = None,\n include_request: str = None) -> Callable[..., Any]:\n \"\"\"Intercept requests and transform them into function calls with arguments\n\n :param methods: HTTP request method\n :param default: Set as module's default endpoint in case no specific function or class name is specified in the request\n :param throttle: Throttle type\n :param requires: A list of required access permissions\n :param include_request: Include 'request' parameter in the function call\n :return: An output of the function as Django REST Framework's Response object,\n or the target function if called directly (not as an endpoint)\n \"\"\"\n\n endpoint.meta = locals()\n\n def decorator(func: Callable[..., Any]) -> Callable[..., Any]:\n @wraps(func)\n def wrapper(*args, **kwargs) -> Any:\n if len(args) == 1 and isinstance(args[0], Request):\n try:\n request = args[0]\n\n # Extract request's body from requests using methods other than GET\n body = {key: value for key, value in request.data.items()}\n parameters = request.META[Glossary.META_ENDPOINT.value].parse(**{**dict(request.GET), **body})\n\n if include_request is not None:\n parameters[include_request] = request\n\n # A request is handled differently based on how the endpoint is defined (as a class or a function)\n if Glossary.META_CLASS.value in request.META and request.META[Glossary.META_CLASS.value] is not None:\n return Response({'data': func(request.META[Glossary.META_CLASS.value][1], **parameters)}, status=status.HTTP_200_OK)\n else:\n return Response({'data': func(**parameters)}, status=status.HTTP_200_OK)\n\n except TypeError as error:\n return Response({'detail': str(error)}, status=status.HTTP_400_BAD_REQUEST)\n\n else:\n return func(*args, **kwargs)\n\n if hasattr(settings, 'DOREST'):\n throttle_class = settings.DOREST.get('DEFAULT_THROTTLE_CLASSES', None)\n\n if throttle_class is not None and len(throttle_class) > 0:\n throttle_class_branch = [node.split('.') for node in throttle_class]\n wrapper.throttle_classes = [getattr(importlib.import_module('.'.join(node[:-1])), node[-1]) for node in throttle_class_branch]\n\n wrapper.meta = endpoint.meta\n\n if requires is not None:\n wrapper.permission_classes = requires\n\n return wrapper\n\n return decorator\n\n\ndef require(validators: Union[tuple, list]):\n \"\"\"Django's standard permission_required decorator does not recognize 'user' in 'request'\n received from Django rest_framework's APIView, resulting in the user being treated as anonymous.\n\n This custom implementation solves the issue, as well as removes a redirect URL since\n this channel of communication does not provide a user interface.\n\n :param: validators: A tuple of permission validators.\n By default, a user must have all required permissions to perform an action.\n However, if only one permission was needed, set 'or' as the first member of the tuple.\n\n For example:\n (p1, p2, ('or', p3, p4))\n In this case, a user must have permissions p1 and p2, and she must also have p3 or p4\n in order to perform an action.\n \"\"\"\n\n def decorator(func):\n def wrapper(*args, **kwargs):\n \"\"\"Receive HTTP request handler (function) of Django rest_framework's APIView class.\n ---\n\n args: [0] an object of a class inherited from Django's view\n [1] an object of rest_framework.request.Request\n \"\"\"\n\n if 'request' in kwargs and _require_operator(validators, **kwargs):\n return func(*args, **kwargs)\n elif _require_operator(validators, args[1], args[0], **kwargs):\n return func(*args, **kwargs)\n else:\n return Response({'detail': _('Permission required')}, status=403)\n\n return wrapper\n\n return decorator\n\n\ndef _require_operator(validators: Union[tuple, list], request: Request, view: APIView, **kwargs) -> bool:\n \"\"\"Validate and applie AND operator on the results produced by the validators\n\n :param validators: A tuple of validators\n :param request: A request sent from Django REST framework\n :param view: Django REST framework API view\n :param kwargs: Validator's argument\n :return: Validation result\n \"\"\"\n\n def validate(validator):\n if type(validator) is tuple or type(validator) is list:\n return _require_operator(validator, request, view)\n\n elif type(validator) is str:\n return request.user.has_perm(validator)\n\n else:\n validator_instance = validator()\n\n try:\n return validator_instance.has_permission(request, view, **kwargs)\n except TypeError:\n return validator_instance.has_permission(request, view)\n\n # 'operator_or()' returns a list with 'or' as its first member\n if type(validators) is not tuple and type(validators) is not list:\n return validate(validators)\n\n elif validators[0] == 'or':\n return any([validate(v) for v in validators[1:]])\n\n elif validators[0] == 'not':\n return not validate(validators[1])\n\n else:\n return all([validate(v) for v in validators])\n\n\ndef operator_or(*args):\n \"\"\"Another form of 'or' operator in 'permissions._require_operator'.\n Instead of setting the first member of the tuple as 'or', e.g., 'permissions.require(p1, p2, (\"or\", p3, p4))',\n this function offers a different format that does not include an operator as a member.\n\n For convenience, import this function as '_or'.\n The above example can then be written as 'permissions.require(p1, p2, _or(p3, p4))'.\n \"\"\"\n\n return ('or',) + args\n\n\ndef operator_not(validator):\n \"\"\"Another form of 'not' operator in 'permissions._require_operator'.\n\n Warning: While 'permissions.operator_or' accepts multiple arguments, this operator accepts only one validator\n \"\"\"\n\n return 'not', validator\n\n\ndef mandate(**params):\n \"\"\"Check whether all required request parameters are included in an HTTP request to an APIView\n :param params: parameter 'fields' contains required request parameters\n parameter 'strict_fields' are 'fields' that cannot be blank\n \"\"\"\n\n def decorator(func):\n def wrapper(*args, **kwargs):\n \"\"\"Receive HTTP request handler (function) of Django rest_framework's APIView class.\"\"\"\n\n # Assign rest_framework.request.Request object to 'request' variable, which is to be validated\n request = args[0] if isinstance(args[0], Request) else args[1]\n\n missings = []\n blanks = []\n fields = params['fields'] if 'fields' in params else [] + params['strict_fields'] if 'strict_fields' in params else []\n strict_fields = params['strict_fields'] if 'strict_fields' in params else []\n\n for kwarg in fields:\n if kwarg not in request.data and kwarg not in request.GET:\n missings.append(kwarg)\n\n for kwarg in strict_fields:\n if kwarg in request.data and len(request.data[kwarg]) == 0:\n blanks.append(kwarg)\n\n if len(missings) > 0 or len(blanks):\n return Response({'detail': _('Parameters missing or containing blank value'),\n 'missing': missings,\n 'blank': blanks}, status=400)\n\n return func(*args, **kwargs)\n\n return wrapper\n\n return decorator\n" }, { "alpha_fraction": 0.6387904286384583, "alphanum_fraction": 0.6418615579605103, "avg_line_length": 34.87288284301758, "blob_id": "7bc4224cd6321c588f969cef8f90254c9a6ad271", "content_id": "2a8e06b7a52d445d3ad3f04ba4afb22f5351dc64", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4233, "license_type": "permissive", "max_line_length": 128, "num_lines": 118, "path": "/dorest/interfaces.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"The generic architecture\n\nThe generic mechanism forwards API calls to a preconfigured set of backend functions.\nAn basic use case is when an API should support multiple database backends.\nFor example, the following packages provides connectors for databases 'dba' and 'dbb':\n~~~~~~~~~~~~~~~~~~~~~~~\n + gen\n |- __init__.py\n |- connector.py\n + db\n | + dba\n | |- __init__.py\n | |- connector.py\n | + dbb\n | |- __init__.py\n | |- connector.py\n ...\n~~~~~~~~~~~~~~~~~~~~~~~\n\nTo make package 'gen' generic, import 'register' function from this module and implement it in 'gen/__init__.py':\n---\n generic.register('db', to=__name__, using='dba')\n---\nThe first parameter indicates the root package of the backends, and the 'using' parameter indicates which backend is being used.\n\nSuppose there is a function defined in 'gen/connector.py' that inserts data to a database and returns record ID:\n---\n def insert(title: str, article: str, author: str) -> str:\n return generic.resolve(insert)(title, article, author, datetime.now())\n---\nOnce received an API call, the 'generic.resolve' function will look for the registered configuration ('generic.register')\nin the same and parent packages, then match the caller ('gen.connector.insert') to the backend ('db.dba.connector.insert').\n\nThus, the 'insert' function in 'db/dba/connector.py' should be:\n---\n def insert(title: str, article: str, author: str, created: datetime) -> str:\n return dba_client.insert({'title': title, 'article': article, 'author': author, 'created': created})\n---\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\n\nimport importlib\nimport sys\nfrom typing import Union\nfrom types import ModuleType\n\n\ndef _get_module(module: Union[str, ModuleType]) -> ModuleType:\n return getattr(sys.modules, module, importlib.import_module(module)) if isinstance(module, str) else module\n\n\ndef _locate_package(branch: str) -> Union[str, None]:\n path = branch.split('.')\n\n for i in range(len(path) - 1, 0, -1):\n active_path = '.'.join(path[:i])\n\n try:\n package = importlib.import_module('%s.package' % active_path)\n\n if hasattr(package, '__package'):\n return package.__name__[:-8]\n\n except ModuleNotFoundError:\n continue\n\n return None\n\n\ndef bind(backend: Union[str, ModuleType], *, to: Union[str, ModuleType], using: str,\n bypass_package: bool = False) -> None:\n \"\"\"Register a backends' package by setting the attribute '__interface' to the module\n\n :param backend: The backends' package\n :param to: The root of the interface package (usually __name__)\n :param using: The selected backend\n :param bypass_package: Only locate backends in PYTHONPATH\n :return: None\n \"\"\"\n\n package = _locate_package(_get_module(to).__name__)\n\n if isinstance(backend, str) and package is not None and not bypass_package:\n backend = '%s.%s' % (package, backend)\n\n setattr(_get_module(to), '__interface', {'package': _get_module(backend), 'driver': using})\n\n\ndef resolve(caller: Union[callable, str, ModuleType]) -> callable:\n \"\"\"Call the preconfigured backend that matches the caller\n\n :param caller: The caller function or a branch indicating the function\n :return: The backend function\n \"\"\"\n\n if callable(caller):\n branch = '%s.%s' % (caller.__module__, caller.__name__)\n else:\n branch = getattr(sys.modules, caller, importlib.import_module(caller)).__name__ \\\n if isinstance(caller, str) else caller.__name__\n\n path = branch.split('.')\n depth, target = None, None\n\n # Locate the registered configuration ('interfaces.bind') in the same and parent packages\n for i in range(len(path) - 1, 0, -1):\n active_path = '.'.join(path[:i])\n module = getattr(sys.modules, active_path, importlib.import_module(active_path))\n\n if hasattr(module, '__interface'):\n depth, target = i, getattr(module, '__interface')\n\n return getattr(importlib.import_module('%s.%s.%s' % (target['package'].__name__, target['driver'],\n '.'.join(path[depth:-1]))), path[-1])\n" }, { "alpha_fraction": 0.719298243522644, "alphanum_fraction": 0.7368420958518982, "avg_line_length": 21.799999237060547, "blob_id": "6eab4aaf23f251fca32ce05e16511f54a0824486", "content_id": "3e5741a1f41c91c9873bde30987541ac222618d8", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 228, "license_type": "permissive", "max_line_length": 56, "num_lines": 10, "path": "/dist.sh", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\nrm -r build\nrm -r dist\nrm -r dorest.egg-info\n\npython3 -m pip install --user --upgrade setuptools wheel\npython3 setup.py sdist bdist_wheel\npython3 -m pip install --user --upgrade twine\npython3 -m twine upload dist/*\n" }, { "alpha_fraction": 0.7727272510528564, "alphanum_fraction": 0.7727272510528564, "avg_line_length": 21, "blob_id": "acc21a461360a95ee8c807cf74ed6239a1af6610", "content_id": "bb31b1868b2123b69ea0a3716d622e7c5ed1ebc7", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 22, "license_type": "permissive", "max_line_length": 21, "num_lines": 1, "path": "/dorest/__init__.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "from . import verbose\n" }, { "alpha_fraction": 0.8055555820465088, "alphanum_fraction": 0.8055555820465088, "avg_line_length": 17, "blob_id": "1eac1a5e35ccecbb27a86a10dafc239fc586c15e", "content_id": "157fa43483489b9c6c07c5b065997cacbd64cd17", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 36, "license_type": "permissive", "max_line_length": 26, "num_lines": 2, "path": "/README.md", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "# dorest\nDjango Open REST Framework\n" }, { "alpha_fraction": 0.6558896899223328, "alphanum_fraction": 0.6573969125747681, "avg_line_length": 44.132652282714844, "blob_id": "231edea1fcf05cdf38f7a993eda50e394d0a3f8f", "content_id": "3bfde5ed5e7e90c9429dbcbbb8fffd24b5c12175", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 13269, "license_type": "permissive", "max_line_length": 151, "num_lines": 294, "path": "/dorest/struct.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Structured-endpoint manager\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nimport importlib\nimport inspect\nimport re\nimport os\nimport sys\nfrom functools import partial\nfrom typing import Any, Callable, List, Tuple, Type, Union\nfrom types import ModuleType\n\nfrom django.conf import settings\nfrom django.core.handlers.wsgi import WSGIRequest\nfrom django.urls import include, path, re_path, resolve\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.exceptions import MethodNotAllowed\nfrom rest_framework.permissions import AllowAny\nfrom rest_framework.response import Response\n\nfrom dorest.glossary import Glossary\nfrom dorest.meta import Endpoint\nfrom dorest import meta\n\n\ndef _get_module(module: Union[str, ModuleType]) -> ModuleType:\n return getattr(sys.modules, module, importlib.import_module(module)) if isinstance(module, str) else module\n\n\ndef _get_endpoint(method: str, branch: str, module: ModuleType = None) -> Tuple[Callable[..., Any], Union[Type, None]]:\n \"\"\"Resolve the target endpoint from an API request\n\n A branch, within tree-structure packages, may either include a module or an endpoint function as a leaf.\n\n This function first assumes that the branch only include hierarchical packages as its nodes and the target module as its leaf.\n In the 'try' clause, the function will try to look for any default endpoints that matches the request method;\n if not found, it will try to find redirect operators that matches the request method, then look for the endpoint within the redirected module.\n\n If the branch contains an endpoint function as a leaf, the 'importlib.import_module' will fail to import and skip to the 'except' clause.\n\n :param method: An endpoint may accept multiple HTTP methods, the 'method' parameter specifies which method to be looking for\n :param branch: A string that indicates a branch containing packages as nodes and a module, function, or class as a leaf (e.g. 'pkg_a.pkg_b.leaf')\n :param module: A direct reference to the module which contains the endpoint. If set, the function will skip 'importlib.import_module'\n :return: A tuple containing the target endpoint function and, if applicable, its class (current version only support endpoint functions)\n \"\"\"\n\n try:\n imported_module, endpoint_function = importlib.import_module(branch), None\n\n for func in [obj for obj in [getattr(imported_module, attr) for attr in dir(imported_module)]\n if hasattr(obj, 'meta') and getattr(obj, '__module__', None) == imported_module.__name__]:\n if func.meta['default']:\n return func, None\n endpoint_function = func\n\n if endpoint_function is not None:\n return endpoint_function, None\n\n else:\n obj = getattr(imported_module, Glossary.REDIRECT.value)[method.lower()]\n\n if inspect.isfunction(obj):\n return obj, None\n else:\n return _get_endpoint(method, getattr(imported_module, Glossary.REDIRECT.value)[method.lower()].__name__)\n\n except ModuleNotFoundError:\n path = branch.split('.')\n target_function = path[-1]\n imported_module = importlib.import_module('.'.join(path[:-1])) if module is None else module\n obj = getattr(imported_module, target_function, None)\n\n if inspect.isfunction(obj) and hasattr(obj, 'meta'):\n if method in obj.meta['methods']:\n return obj, None\n else:\n raise MethodNotAllowed(method)\n\n else:\n raise AttributeError(\"Could not find endpoint '%s' with HTTP request method '%s' in '%s'\" % (target_function, method, module))\n\n\ndef walk_endpoints(branch: str, reduce: bool = False) -> dict:\n \"\"\"Walk and generate descriptions of structured API endpoints\n\n The structure of the returned dictionary is as followed:\n ---\n {\n 'package': {\n 'subpackage': {\n 'module': {\n '*': [ ... function descriptions ... ]\n }\n }\n }\n }\n ---\n\n In case the structural organization of an API results in some packages containing only one subpackage or module (other than the package's\n __init__.py), with the 'reduce' parameter true, all sequences of these packages will be merged in the returned dictionary.\n When reduced, the above example will become:\n ---\n {\n 'package/subpackage/module': {\n '*': [ ... function descriptions ... ]\n }\n }\n ---\n\n :param branch: The root of structured API endpoints (or a part of interest within the structure)\n :param reduce: Reduce the returned dictionary\n :return: A description of the tree\n \"\"\"\n\n def attach_endpoint(sub_api_tree: dict, sub_branch: List[str], endpoint: callable) -> None:\n if len(sub_branch):\n sub_branch_head = sub_branch.pop(0)\n\n if sub_branch_head not in sub_api_tree:\n sub_api_tree[sub_branch_head] = dict()\n\n attach_endpoint(sub_api_tree[sub_branch_head], sub_branch, endpoint)\n\n else:\n if '*' not in sub_api_tree:\n sub_api_tree['*'] = [Endpoint(endpoint).rest(brief=True)]\n else:\n sub_api_tree['*'].append(Endpoint(endpoint).rest(brief=True))\n\n root_module = importlib.import_module(branch)\n root_path = os.path.dirname(root_module.__file__)\n\n # Generate a list of all Python scripts\n py_files = [os.path.join(dirpath, filename) for dirpath, dirnames, filenames in os.walk(root_path) for filename in filenames\n if os.path.splitext(filename)[1] == '.py']\n\n # Generate a list of all modules for import from the list of Python scripts\n modules = [('%s.%s' % (branch, py_file.replace(os.path.dirname(root_module.__file__), '').replace('__init__', '').replace('.py', '')\n .replace('/', '.').strip('.'))).strip('.') for py_file in py_files]\n\n # Remove the root endpoint\n modules = [module for module in modules if module != branch]\n api_tree = dict()\n\n # Fill the API tree with descriptions of the functions\n for module in modules:\n sub_module = importlib.import_module(module)\n funcs = [obj for obj in [getattr(sub_module, attr) for attr in dir(sub_module)]\n if hasattr(obj, 'meta') and hasattr(obj, '__module__') and obj.__module__ == sub_module.__name__]\n sub_branch = module.replace(branch, '').strip('.')\n\n for func in funcs:\n attach_endpoint(api_tree, sub_branch.split('.'), func)\n\n return api_tree if not reduce else Endpoint.reduce(api_tree)\n\n\n@api_view(['DELETE', 'GET', 'PATCH', 'POST', 'PUT'])\n@permission_classes([AllowAny])\ndef _reply(request: WSGIRequest, topic: str, message: Union[dict, str], status: int) -> Response:\n \"\"\"Create Django REST Framework's Response object from a text message\n\n :param request: A request sent from Django REST Framework (despite not being used, this parameter is mandatory)\n :param topic: The topic of the message\n :param message: The message\n :param status: HTTP status code\n :return: Django REST Framework's Response object\n \"\"\"\n\n return Response({topic: message}, status=status)\n\n\ndef handle(request: WSGIRequest, root: Union[str, ModuleType]) -> Response:\n \"\"\"Handle a redirected request from Django REST Framework, call the target endpoint and gets the result, then create a wrapped response\n\n This function also provides special responses, which are either an API structure or a detailed description of specific endpoint function.\n To get an API structure, append '?**' after a parent endpoint.\n\n For example, suppose an API is structured as followed:\n ~~~~~~~~~~~~~~~~~~~~~~~\n + parent_pkg\n | + pkg_a\n | |- module_a.py\n | |- module_b.py\n | + pkg_b\n | | + pkg_c\n | | |- module_c.py\n ~~~~~~~~~~~~~~~~~~~~~~~\n and the root of the structured endpoints is 'http://domain/api/parent_pkg', which points to the 'parent_pkg' package,\n a GET request with URI 'http://domain/api/parent_pkg?**' will return the description of the 'parent_pkg' package, including its\n subpackages, modules, and endpoint functions. 'http://domain/api/parent_pkg/pkg_a?**' will return only those within the 'pkg_a' package.\n\n In case the structural organization of an API results in some packages containing only one subpackage or module (other than the package's\n __init__.py), appending the 'reduce' key to the request URI will merge all sequences of these packages. In the above example,\n 'http://domain/api/parent_pkg?**&reduce' will return the API structure with 'pkg_b/pkg_c/module_c' keyword.\n\n To get a description of a particular endpoint function, append '?*' to the URI, e.g., 'http://domain/api/parent_pkg/pkg_a/module_a/func?*'\n\n :param request: A request sent from Django REST Framework\n :param root: The root package of the structured endpoints\n :return: Django REST Framework's Response object\n \"\"\"\n\n # 'request.GET' is a QueryDict. When appending '?**' to the URI, '**' becomes a dictionary key\n if '**' in request.GET:\n route = re.sub(r'\\.\\*$', '', resolve(request.path_info).route)\n branch = ('%s.%s' % (root if isinstance(root, str) else root.__name__, re.sub(r'^/%s' % route, '', request.path).replace('/', '.'))).strip('.')\n return _reply(request, 'api', walk_endpoints(branch, 'reduce' in request.GET), status.HTTP_200_OK)\n\n else:\n route = re.sub(r'\\.\\*$', '', resolve(request.path_info).route)\n branch = '%s.%s' % (root if isinstance(root, str) else root.__name__, re.sub(r'^/%s' % route, '', request.path).replace('/', '.'))\n\n # Given the request URI and method, try to find the target endpoint\n try:\n endpoint, request.META[Glossary.META_CLASS.value] = _get_endpoint(request.method, branch, root if isinstance(root, str) else None)\n request.META[Glossary.META_ENDPOINT.value] = meta.Endpoint(endpoint)\n except MethodNotAllowed as error:\n return _reply(request, 'detail', error.detail, status.HTTP_403_FORBIDDEN)\n except AttributeError as error:\n return _reply(request, 'detail', str(error), status.HTTP_403_FORBIDDEN)\n\n if '*' in request.GET:\n return _reply(request, 'help', request.META[Glossary.META_ENDPOINT.value].rest(brief='brief' in request.GET), status.HTTP_200_OK)\n else:\n view = api_view([request.method])(endpoint)\n\n if hasattr(settings, 'REST_FRAMEWORK'):\n throttle_rates = settings.REST_FRAMEWORK.get('DEFAULT_THROTTLE_RATES', None)\n\n if throttle_rates is not None and getattr(endpoint, 'meta')['throttle'] in throttle_rates:\n view.view_class.throttle_custom_scope = getattr(endpoint, 'meta')['throttle']\n\n return view(request)\n\n\ndef redirect(*, methods: List[str], at: Union[str, ModuleType], to: [str, ModuleType]) -> None:\n \"\"\"Redirect an API request to the target module containing endpoints\n\n To redirect an API request, call this function in the redirecting module\n Since the call is in the redirecting module, the 'at' parameter should normally be '__name__', e.g.:\n ---\n redirect(methods=['GET'], at=__name__, to='api.target.func')\n ---\n\n This function sets the redirect attribute of the module in which it is called.\n The attribute will later be evaluated and acted upon by the 'handle' function as it processes API requests.\n\n :param methods: HTTP methods that this redirect rule applies\n :param at: The redirecting module\n :param to: The target module\n :return: None\n \"\"\"\n\n caller, target = _get_module(at), _get_module(to)\n [setattr(caller, Glossary.REDIRECT.value, {**getattr(caller, Glossary.REDIRECT.value, {}), **{method.lower(): target}}) for method in methods]\n\n\ndef bind(package: Union[str, ModuleType], *, to: Union[str, ModuleType], url: str = r'.*') -> None:\n \"\"\"Bind a package of structured endpoints to a manager\n\n ...\n\n :param package: The package of structured endpoints\n :param to: The target module\n :param url: URL path to the target module\n :return: None\n \"\"\"\n\n pkg, anchor = _get_module(package), _get_module(to)\n setattr(anchor, 'urlpatterns',\n getattr(anchor, 'urlpatterns', []) + [re_path(url, csrf_exempt(partial(handle, root=pkg)))])\n\n\ndef extend(pattern: str, *, at: Union[str, ModuleType], to: Union[str, ModuleType]) -> None:\n \"\"\"Extend 'urlpatterns' to handle requests other than those handled by Dorest managers\n\n The extended patterns are defined in a separate file indicated in the 'to' parameter.\n\n :param pattern: URL pattern according to Django URL handling specification\n :param at: The source module\n :param to: The extension module\n :return: None\n \"\"\"\n\n caller = _get_module(at)\n setattr(caller, 'urlpatterns', getattr(caller, 'urlpatterns', []) + [path(pattern, include(to))])\n" }, { "alpha_fraction": 0.5575234293937683, "alphanum_fraction": 0.5594582557678223, "avg_line_length": 40.73291778564453, "blob_id": "e6f7c11107f5a42cb067b5514d0c6ce5f35aeb58", "content_id": "47663fd3563c652c2185a45e8440e066e7d61954", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6719, "license_type": "permissive", "max_line_length": 120, "num_lines": 161, "path": "/dorest/packages.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Package manager\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nimport importlib\nimport sys\nimport os\nfrom functools import partial\nfrom typing import Union\nfrom types import ModuleType\n\nfrom django.core.handlers.wsgi import WSGIRequest\nfrom django.urls import re_path\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.permissions import AllowAny\nfrom rest_framework.response import Response\n\nfrom . import conf as _conf, struct as _struct, resources as dr_resources\n\nDEFAULT_STRUCTURE = {'struct': 'endpoints', 'resources': 'resources',\n 'conf': 'conf', 'private': 'private', 'templates': 'templates'}\n\n\ndef _get_module(module: Union[str, ModuleType]) -> ModuleType:\n return getattr(sys.modules, module, importlib.import_module(module)) if isinstance(module, str) else module\n\n\ndef bind(site: Union[str, ModuleType], *, to: Union[str, ModuleType], url: str = None) -> None:\n def resolve_module(key: str, partial_path: Union[bool, str]) -> str:\n if partial_path is None:\n return '%s.%s' % (package.__name__.replace('.package', ''), DEFAULT_STRUCTURE[key].replace('/', '.'))\n else:\n return '%s.%s' % (package.__name__.replace('.package', ''), partial_path.replace('/', '.'))\n\n def resolve_path(key: str, package_patch: str, partial_path: Union[bool, str]) -> str:\n if partial_path is None:\n return '%s%s' % (package_patch, DEFAULT_STRUCTURE[key])\n else:\n return '%s%s' % (package_patch, partial_path)\n\n site, caller = _get_module(site), _get_module(to)\n target_dir = site.__file__.replace('__init__.py', '')\n\n packages = []\n package_root_dirs = []\n\n for root, dirs, files in os.walk(target_dir):\n if all(root not in prd for prd in package_root_dirs):\n if 'package.py' in files:\n package_root_dirs.append(root)\n\n for prd in package_root_dirs:\n root = prd.replace(target_dir, '').replace('/', '.')\n package = importlib.import_module('%s.%s.package' % (site.__name__, root))\n pkg_attr = getattr(package, '__package')\n packages.append(package)\n\n if not pkg_attr['bound']:\n for key in ('struct', 'resources'):\n pkg_attr[key] = resolve_module(key, pkg_attr[key])\n\n for key in ('conf', 'private', 'templates'):\n pkg_attr[key] = resolve_path(key, package.__file__.replace('package.py', ''), pkg_attr[key])\n\n pkg_attr['bound'] = True\n setattr(package, '__package', pkg_attr)\n setattr(package, '__conf', _conf.load_dir(pkg_attr['conf']))\n pkg_conf = getattr(package, '__conf')\n\n try:\n pkg_struct = importlib.import_module(pkg_attr['struct'])\n\n if pkg_conf['urls']['struct'] is None:\n _struct.bind(pkg_struct, to=caller, url='%s%s/' % ('' if url is None else '%s/' % url.strip('/'),\n pkg_conf['urls']['root'].strip('/')))\n else:\n _struct.bind(pkg_struct, to=caller, url='%s%s/%s/' % ('' if url is None else '%s/' % url.strip('/'),\n pkg_conf['urls']['root'].strip('/'),\n pkg_conf['urls']['struct'].strip('/')))\n except ModuleNotFoundError:\n pkg_struct = None\n\n if pkg_struct is not None:\n try:\n pkg_res = importlib.import_module(pkg_attr['resources'])\n dr_resources.bind(pkg_res, to=pkg_struct)\n except ModuleNotFoundError:\n pass\n \n key = pkg_conf['urls']['root'].strip('/') if 'urls' in pkg_conf and 'root' in pkg_conf['urls'] else root\n setattr(package, '__struct', {key: pkg_struct})\n \n setattr(caller, 'urlpatterns',\n getattr(caller, 'urlpatterns', []) + [re_path('%s$' % ('' if url is None else '%s/' % url.strip('/')),\n csrf_exempt(partial(walk, root=site)))])\n setattr(site, '__packages', packages)\n\n\ndef link(module: Union[str, ModuleType], *, path: str = None, struct: str = None, resources: str = None,\n conf: str = None, private: str = None,\n templates: str = None) -> None:\n package = _get_module(module)\n\n if not hasattr(package, '__package'):\n setattr(package, '__package', {'bound': False,\n 'path': path, 'struct': struct, 'resources': resources,\n 'conf': conf, 'private': private, 'templates': templates})\n\n\ndef requires(module: Union[str, ModuleType], *, permissions: Union[list, tuple] = None) -> None:\n package = _get_module(module)\n setattr(package, '__permissions', permissions)\n\n\ndef call(branch: str, *, by: Union[str, ModuleType]) -> callable:\n caller = _get_module(by).__name__\n path = caller.split('.')\n root = None\n\n for i in range(len(path), 0, -1):\n try:\n package = importlib.import_module('%s.package' % '.'.join(path[:i]))\n \n if hasattr(package, '__package'):\n root = '.'.join(package.__name__.split('.')[:-2])\n\n except ModuleNotFoundError:\n continue\n \n if root is None:\n raise Exception(\"Function 'packages.call' must be called from within a package\")\n\n branch = branch.split('.')\n return getattr(importlib.import_module('%s.%s' % (root, '.'.join(branch[:-1]))), branch[-1])\n\n\n@api_view(['GET'])\n@permission_classes([AllowAny])\ndef walk(request: WSGIRequest, root: Union[str, ModuleType]) -> Response:\n site = _get_module(root)\n package_endpoints = {}\n\n for package in getattr(site, '__packages', []):\n permissions = getattr(package, '__permissions', [])\n\n if all(permission().has_permission(request, api_view()(walk)) for permission in permissions):\n for key, pkg_struct in getattr(package, '__struct', {}).items():\n package_endpoints[key] = _struct.walk_endpoints(pkg_struct.__name__, 'reduce' in request.GET)\n\n # print(request.user)\n # print(getattr(package, '__permissions'))\n # print(getattr(package, '__permissions')[0]().has_permission(request, api_view()(walk)))\n # print(api_view()(walk))\n\n return Response({'packages': package_endpoints}, status=status.HTTP_200_OK)\n" }, { "alpha_fraction": 0.6908817887306213, "alphanum_fraction": 0.6943888068199158, "avg_line_length": 28.791044235229492, "blob_id": "ef884dc3769528451c4e4081704b816d9bfd6271", "content_id": "dafe3f974df598639a6e425f816db2c276d658c8", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1996, "license_type": "permissive", "max_line_length": 105, "num_lines": 67, "path": "/dorest/permissions.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Implementations of additional Django REST Framework permission validators\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nfrom django.core.exceptions import ObjectDoesNotExist\n\nfrom rest_framework.permissions import BasePermission\n\n\nclass OR(BasePermission):\n \"\"\"'Or' operator for rest_framework's permission_classes\"\"\"\n\n def __init__(self, *args):\n self.validators = args\n\n def __call__(self, *args, **kwargs):\n return self\n\n def has_permission(self, request, view):\n return any(validator().has_permission(request, view) for validator in self.validators)\n\n\nclass NOT(BasePermission):\n \"\"\"'Not' operator for rest_framework's permission_classes\"\"\"\n\n def __init__(self, validator):\n self.validator = validator\n\n def __call__(self, *args, **kwargs):\n return self\n\n def has_permission(self, request, view):\n return not self.validator().has_permission(request, view)\n\n\nclass IsAccountOwner(BasePermission):\n \"\"\"Validate whether the target account in the request URL is owned by the user sending the request\"\"\"\n\n def has_permission(self, request, view, **kwargs):\n try:\n return kwargs['username'] == request.user.username\n except ObjectDoesNotExist:\n return False\n\n\nclass IsAccountManager(BasePermission):\n \"\"\"Validate whether the account sending the request has the permission to manage other accounts\"\"\"\n\n def has_permission(self, request, view):\n return len(request.user.groups.filter(name='user_manager')) > 0\n\n\nclass IsDemoUser(BasePermission):\n \"\"\"Validate whether the account is for demonstration\"\"\"\n\n def has_permission(self, request, view):\n return len(request.user.groups.filter(name='user_demo')) > 0\n\n\nclass IsTrustedUser(BasePermission):\n \"\"\"Validate whether the account is trusted\"\"\"\n\n def has_permission(self, request, view):\n return len(request.user.groups.filter(name='user_trusted')) > 0\n" }, { "alpha_fraction": 0.6939759254455566, "alphanum_fraction": 0.7024096250534058, "avg_line_length": 26.66666603088379, "blob_id": "8fe6b8be0f17c96de8a0056c439286af09cfaa5e", "content_id": "eecc670f2b2ac4eae7f74337e8b5e55fefb1a78e", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 830, "license_type": "permissive", "max_line_length": 106, "num_lines": 30, "path": "/dorest/exceptions.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Exceptions for Dorest\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nfrom typing import List\n\nfrom django.utils.translation import gettext_lazy as _\n\nfrom rest_framework import status\nfrom rest_framework.exceptions import APIException\n\n\nclass ObjectNotFound(APIException):\n status_code = status.HTTP_404_NOT_FOUND\n default_detail = _('Object not found')\n default_code = 'object_not_found'\n\n\nclass UnsupportedFileTypeError(Exception):\n \"\"\"A configuration file must be in either YAML or JSON formats\"\"\"\n\n def __init__(self, path: str, supported_types: List[str]):\n self.path = path\n self.supported_types = supported_types\n\n def __str__(self):\n return \"Unsupported file type for '%s'. Accept: %s\" % (self.path, ', '.join(self.supported_types))\n" }, { "alpha_fraction": 0.6830247044563293, "alphanum_fraction": 0.6851851940155029, "avg_line_length": 41.07792282104492, "blob_id": "120bab6f047f63e095d375673c835868dc445c21", "content_id": "e5c8d6913512d1410cbb98b63ed26d8e6e2825f5", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3240, "license_type": "permissive", "max_line_length": 141, "num_lines": 77, "path": "/dorest/resources.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Structured API endpoints resource manager\n\nThis manager associates structured API endpoints with their resources, which must also be structured according to the API structure.\n\nFor example, suppose there is a function 'bar' in a module 'foo', which is located in 'api_root_pkg.subpkg' (the actual path would be\n'.../api_root_pkg/subpkg/foo.py').\n\nFunction 'bar' needs to access an input file 'input.txt'. To use this resource manager, the 'input.txt' must be stored as\n'.../res_root/subpkg/foo/bar/input.txt', with '.../res_root' registered as the root directory of the structured API endpoints.\n\nOnce 'input.txt' is stored in the directory, calling 'resolve' with 'bar' and 'input.txt' as its parameters will return the path to the file:\n---\n def bar(...):\n with open(resources.resolve(bar, 'input.txt), 'r') as file:\n ...\n---\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nimport importlib\nimport os\nimport sys\nfrom typing import Union\nfrom types import ModuleType\n\n\ndef _get_module(module: Union[str, ModuleType]) -> ModuleType:\n return getattr(sys.modules, module, importlib.import_module(module)) if isinstance(module, str) else module\n\n\ndef bind(package: Union[str, ModuleType], *, to: Union[str, ModuleType]) -> None:\n \"\"\"Bind a root resource directory to structured API endpoints\n\n In general, this function should be called in '__init__.py' of the root package of the structured API endpoints.\n The functions will then add '__resources' attribute to the module for the 'resolve' function to use as reference.\n\n :param package: The root package of the designated resources in Python's import format\n :param to: The root package of the structured API endpoints\n :return: None\n \"\"\"\n\n setattr(_get_module(to), '__resources', _get_module(package))\n\n\ndef resolve(obj: Union[callable, str, ModuleType], path: str = None) -> str:\n \"\"\"Resolve the path where resources corresponding to the given function or module are stored\n\n :param obj: The caller function or module\n :param path: A sub-path to the target resource (usually filename) within the resolved path\n :return: A path to the target resource\n \"\"\"\n\n if callable(obj):\n branch = '%s.%s' % (obj.__module__, obj.__name__)\n else:\n branch = getattr(sys.modules, obj, importlib.import_module(obj)).__name__ if isinstance(obj, str) else obj.__name__\n\n levels = branch.split('.')\n res_level, res_module = None, None\n\n # Find a reference to the root path of the resources registered using the 'register' function\n # starting from the lowest-level package of the 'obj' (function or module)\n for i in range(len(levels) - 1, 0, -1):\n sub_branch = '.'.join(levels[:i])\n attr = getattr(sys.modules, sub_branch, importlib.import_module(sub_branch))\n\n if hasattr(attr, '__resources'):\n res_level, res_module = i, getattr(attr, '__resources')\n\n if res_module is None:\n raise ModuleNotFoundError(\"Cannot locate resource directory of '%s'\" % str(obj))\n\n return '%s/%s%s' % (os.path.dirname(res_module.__file__), '/'.join(levels[res_level:]),\n '/%s' % path if path is not None else '')\n" }, { "alpha_fraction": 0.6778810024261475, "alphanum_fraction": 0.6806427240371704, "avg_line_length": 32.75423812866211, "blob_id": "c82099c24ffc126e0b0f9349cb2c9eb3d96e7b8a", "content_id": "8cdfa415c3f1296ab886b38241720305ebbf491c", "detected_licenses": [ "BSD-3-Clause" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3983, "license_type": "permissive", "max_line_length": 124, "num_lines": 118, "path": "/dorest/conf.py", "repo_name": "rungsiman/dorest", "src_encoding": "UTF-8", "text": "\"\"\"Configuration manager\n\nThe 'resolve' function reads all YAML and JSON configurations in the directory specified in a Django project's setting file.\nThe function returns a dictionary of a tree with configuration file names as the first level.\n\nFor example, suppose there is a file named 'foo.yaml' with the following content:\n---\ngreet:\n message: Hello!\n---\nCalling 'configs.resolve(\"foo.greet\")' would return a dictionary '{message: \"Hello!\"}',\nand calling 'configs.resolve(\"foo.greet.message\")' would return a string \"Hello!\".\n\nThe path to the configuration directory is stored in 'DOREST[\"CONFIGS\"][\"PATH\"]' in Django project's setting file.\n\nThe Dorest project\n:copyright: (c) 2020 Ichise Laboratory at NII & AIST\n:author: Rungsiman Nararatwong\n\"\"\"\n\nimport importlib\nimport json\nimport re\nimport sys\nfrom os import listdir\nfrom os.path import isfile, join\nfrom typing import Any, Dict, List, Union\nfrom types import ModuleType\n\nimport yaml\nfrom django.conf import settings\n\nfrom .exceptions import UnsupportedFileTypeError\n\n\nSUPPORTED_FILE_TYPES = ['yml', 'yaml', 'json']\n\n\ndef _get_module(module: Union[str, ModuleType]) -> ModuleType:\n return getattr(sys.modules, module, importlib.import_module(module)) if isinstance(module, str) else module\n\n\ndef _load(path: str) -> dict:\n \"\"\"Select appropriate loader (YAML or JSON) based on file type\n\n :param path: Path to the configuration file\n :return: An unprocessed configuration dictionary\n \"\"\"\n\n if re.search(r'\\.(yml|yaml)$', path):\n return yaml.safe_load(open(path, 'r'))\n\n elif re.search(r'\\.json$', path):\n return json.load(path)\n\n else:\n raise UnsupportedFileTypeError(path, SUPPORTED_FILE_TYPES)\n\n\ndef _traverse(tree: Dict[str, Any], active_branch: List[str]) -> Any:\n \"\"\"Traverse the tree-structure configuration dictionary\n\n :param tree: The configuration dictionary\n :param active_branch: A list of node names in the branch of interest\n :return: A subtree or value of a leaf\n \"\"\"\n if len(active_branch) > 1:\n return _traverse(tree[active_branch[0]], active_branch[1:])\n else:\n return tree[active_branch[0]]\n\n\ndef _resolve_package(module: Union[str, ModuleType]) -> Any:\n package_branch = _get_module(module).__name__.split('.')\n pkg = None\n\n for i in range(len(package_branch) - 1, 0, -1):\n sub_branch = '.'.join(package_branch[:i])\n\n try:\n module = importlib.import_module('%s.package' % sub_branch)\n\n if hasattr(module, '__package'):\n pkg = module\n except ModuleNotFoundError:\n continue\n\n return getattr(pkg, '__conf')\n\n\ndef load_dir(path: str) -> dict:\n \"\"\"Load configurations from YAML or JSON files in a directory\n\n :param path: Path to the configuration directory\n :return: An unprocessed configuration dictionary\n \"\"\"\n return {re.sub(r'\\.(%s)$' % '|'.join(SUPPORTED_FILE_TYPES), '', file): _load(join(path, file)) for file in listdir(path)\n if isfile(join(path, file)) and re.search(r'\\.(%s)$' % '|'.join(SUPPORTED_FILE_TYPES), file)}\n\n\ndef resolve(branch: str = None, at: Union[str, ModuleType] = None) -> Any:\n \"\"\"Retrieve a subtree or value of specified node or leaf within the tree-structure configuration dictionary\n\n If 'package' is None then return the API's common configuration; otherwise, return the package's configuration\n\n :param at: A module (usually where the caller function belongs) which is part of a package\n In case the module contains the caller function, pass '__name__' to this argument\n :param branch: A branch to node or leaf of interest\n :return: A subtree or value of a leaf\n \"\"\"\n if at is None:\n return _traverse(_conf, branch.split('.')) if branch is not None else _conf\n else:\n pkg_conf = _resolve_package(at)\n return _traverse(pkg_conf, branch.split('.')) if branch is not None else pkg_conf\n\n\n_conf = load_dir(settings.DOREST['CONFIGS']['PATH'])\n" } ]

11

kpenar/LBBT-calculator

https://github.com/kpenar/LBBT-calculator

f08879e7990fb106f21ae43238d55b4b3287d603

38dde2e590b5484ff169919a86f9504ed87ea80b

f2b8f27a48efdcbb4a8b7415434e925e48b36b0a

refs/heads/main

2023-03-10T07:41:12.523969

2021-02-22T22:00:47

341,349,045

0

null

[ { "alpha_fraction": 0.5413306355476379, "alphanum_fraction": 0.6381048560142517, "avg_line_length": 27.235294342041016, "blob_id": "cbde2ad7a95a5e2536217ee5f973bb9096e39d51", "content_id": "4f3d48b4e0762e6094174ad641b0156b2d941342", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 999, "license_type": "no_license", "max_line_length": 155, "num_lines": 34, "path": "/test.py", "repo_name": "kpenar/LBBT-calculator", "src_encoding": "UTF-8", "text": "import unittest\r\nimport sys\r\nfrom calculator import Calculator\r\n\r\n# Requirements\r\n# Minimum property purchase price\tMaximum property purchase price\tStamp Duty rate (only applies to the part of the property price falling within each band)\r\n# £0\t£250,000\t0%\r\n# £250,001\t£325,000\t5%\r\n# £325,001\t£750,000\t10%\r\n# Over £750,000\t \t12%\r\n\r\nclass TestCalc(unittest.TestCase):\r\n\r\n def testFirstBand(self):\r\n\r\n calcObj = Calculator(200000)\r\n self.assertEqual(calcObj.Calc(),0,\"Should be Zero\")\r\n\r\n def testSecondBand(self):\r\n \r\n calcObj = Calculator(275000)\r\n self.assertEqual(calcObj.Calc(),1250,\"Should be 1250\")\r\n \r\n def testThirdBand(self):\r\n \r\n calcObj = Calculator(500000)\r\n self.assertEqual(calcObj.Calc(),21250,\"Should be 21250\")\r\n \r\n def testForthBand(self):\r\n \r\n calcObj = Calculator(900000)\r\n self.assertEqual(calcObj.Calc(),64250,\"Should be 64250\")\r\nif __name__ == '__main__':\r\n unittest.main()" }, { "alpha_fraction": 0.5269121527671814, "alphanum_fraction": 0.5382436513900757, "avg_line_length": 17.61111068725586, "blob_id": "c68dd89c8a441c9557fe0d23e9078ad8b7e4c114", "content_id": "662fa280420e82b737572b3955b0cb146b3dd6a9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 353, "license_type": "no_license", "max_line_length": 59, "num_lines": 18, "path": "/main.py", "repo_name": "kpenar/LBBT-calculator", "src_encoding": "UTF-8", "text": "\r\n__author__ = \"Krzysztof Penar\"\r\n__version__ = \"0.1.0\"\r\n\r\n\r\nimport sys\r\nfrom calculator import Calculator\r\n\r\ndef main():\r\n for arg in sys.argv[1:]:\r\n priceArg = int(arg)\r\n calcObj = Calculator(priceArg)\r\n print(calcObj.Calc())\r\n \r\n\r\n\r\nif __name__ == \"__main__\":\r\n \"\"\" This is executed when run from the command line \"\"\"\r\n main()" }, { "alpha_fraction": 0.4039370119571686, "alphanum_fraction": 0.5110236406326294, "avg_line_length": 28.975608825683594, "blob_id": "5e6eff1ec297e55553c3607e8e95fca0f8459f7d", "content_id": "b19de549547d25c6c6d742d504a191e41d4ced6f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1277, "license_type": "no_license", "max_line_length": 155, "num_lines": 41, "path": "/calculator.py", "repo_name": "kpenar/LBBT-calculator", "src_encoding": "UTF-8", "text": "# Requirements\r\n# Minimum property purchase price\tMaximum property purchase price\tStamp Duty rate (only applies to the part of the property price falling within each band)\r\n# £0\t£250,000\t0%\r\n# £250,001\t£325,000\t5%\r\n# £325,001\t£750,000\t10%\r\n# Over £750,000\t \t12%\r\n\r\nclass Calculator:\r\n price = ()\r\n def __init__(self, price):\r\n self.price = price\r\n \r\n def Calc(self):\r\n firstBand = 0 \r\n secondBand = 0 \r\n thirdBand = 0 \r\n forthband = 0 \r\n #This is first band ciurrently zero \r\n if(self.price <= 250000):\r\n firstBand = 0\r\n\r\n if(self.price > 250000 ): \r\n if(self.price > 325000):\r\n secondBand = 75000 * (5/100)\r\n else:\r\n taxable = self.price - 250000\r\n secondBand = taxable * (5/100)\r\n\r\n if(self.price > 325000):\r\n if(self.price > 750000):\r\n thirdBand = 425000 * (10/100)\r\n else:\r\n taxable = self.price - 325000\r\n thirdBand = taxable * (10/100)\r\n\r\n if(self.price > 750000): \r\n taxable = self.price - 750000\r\n forthband = taxable * (12/100)\r\n\r\n \r\n return (firstBand + secondBand + thirdBand +forthband)\r\n" } ]

3

Brady31027/fb_chatbot_lyla_gallery

https://github.com/Brady31027/fb_chatbot_lyla_gallery

a5f3dffd0bcc698cffb5d51070896640fd36139d

d0029322a76e8ebd935cfb34ebd00265d15b48f5

426ed6e96e7520aec0704e94b79cc188855bde4b

refs/heads/master

2021-01-12T03:52:47.701604

2017-01-08T11:06:45

78,279,934

0

null

[ { "alpha_fraction": 0.6428571343421936, "alphanum_fraction": 0.6428571343421936, "avg_line_length": 29.33333396911621, "blob_id": "a4f9e5588c1b94019ce37c589ad88c5c7e4bbbe2", "content_id": "80f98afa2b5ea497fe7d2c13d32a96c4af2140a9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 182, "license_type": "no_license", "max_line_length": 48, "num_lines": 6, "path": "/firebase/README.md", "repo_name": "Brady31027/fb_chatbot_lyla_gallery", "src_encoding": "UTF-8", "text": "**[Frameworks]** </br> \nhttps://github.com/ozgur/python-firebase </br> \n<br/> \n**[Installation]** </br> \nsudo pip install requests </br> \nsudo pip install python-firebase </br>\n" }, { "alpha_fraction": 0.7238636612892151, "alphanum_fraction": 0.730681836605072, "avg_line_length": 32.88461685180664, "blob_id": "4a2002634de5f47da7c732b32e99d2e5fdf3e167", "content_id": "046badd1c437981fecc6077d5824077f5fa9266d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 880, "license_type": "no_license", "max_line_length": 88, "num_lines": 26, "path": "/firebase/get.py", "repo_name": "Brady31027/fb_chatbot_lyla_gallery", "src_encoding": "UTF-8", "text": "from firebase import firebase\nfirebase = firebase.FirebaseApplication('https://lyla-gallery-db.firebaseio.com/', None)\n\n# followings are query samples\n\ntable_result = firebase.get('/gallery', None)\nfirst_data_result = firebase.get('/gallery/1', None)\nfirst_title_result = firebase.get('/gallery/1/title', None)\nfirst_subtitle_result = firebase.get('/gallery/1/subtitle', None)\nfirst_img_url_result = firebase.get('/gallery/1/img_url', None)\nfirst_story_result = firebase.get('/gallery/1/story', None)\n\n'''\nprint \"title: %s\"%(first_title_result)\nprint \"subtitle: %s\"%(first_subtitle_result)\nprint \"img url: %s\"%(first_img_url_result)\nprint \"story: %s\"%(first_story_result)\n'''\n\n# actually we don't have to query data level by level\n# use hirachical query by applying the second parameter\n# samples\nmultilevel_data = firebase.get('/gallery/1', 'title')\n'''\nprint multilevel_data\n'''" } ]

2

peterchen94/test_repo

https://github.com/peterchen94/test_repo

fc2554ace0ba4600eda20821c5aeaccc20ff10b8

76f9e6e8e7f13c41a294d996884a436f6e9c5175

01dcc5f8fae03963d8ad9133dd512f69381e9f4d

refs/heads/master

2018-11-01T02:20:23.179773

2018-07-28T17:59:08

142,698,241

0

null

[ { "alpha_fraction": 0.5761957764625549, "alphanum_fraction": 0.6462736129760742, "avg_line_length": 19.428571701049805, "blob_id": "1350fd52ad0fae5f0299927b41dd1054ba047cf0", "content_id": "27cb5fcacf4948ef196cf63cd4836ad16c523328", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 899, "license_type": "no_license", "max_line_length": 76, "num_lines": 42, "path": "/breakout_peter.py", "repo_name": "peterchen94/test_repo", "src_encoding": "UTF-8", "text": "import pygame\nimport time\nSCREEN_SIZE = [640,480]\n\nBRICK_WIDTH = 60\nBRICK_HEIGHT = 15\nPADDLE_WIDTH = 60\nPADDLE_HEIGHT = 15\nBALL_DIAMETER = 12\nBALL_RADIUS = BALL_DIAMETER/2\n\nMAX_PADDLE_X = SCREEN_SIZE[0] - PADDLE_WIDTH\nMAX_BALL_X = SCREEN_SIZE[0] - BALL_DIAMETER\nMAX_BALL_Y = SCREEN_SIZE[1] - BALL_DIAMETER\n\nPADDLE_Y = SCREEN_SIZE[1] - PADDLE_HEIGHT - 8\n\nBLACK = (0,0,0)\nWHITE = (255,255,255)\nBLUE = (0,0,255)\nBRICK_COLOR = (200,200,0)\n\nSTATE_HIT_BALL= 0\nSTATE_PLAYING = 1\nSTATE_WON = 2\nSTATE_GAME_OVER = 3\n\n\n \nscreen = pygame.display.set_mode(SCREEN_SIZE)\npygame.display.set_caption('Breakout')\n\nclock = pygame.time.Clock()\n \nlives = 3\nscore = 0\nstate = 0\n \npaddle = pygame.Rect(300,PADDLE_Y,PADDLE_WIDTH,PADDLE_HEIGHT)\nball = pygame.Rect(300,PADDLE_Y - BALL_DIAMETER,BALL_DIAMETER,BALL_DIAMETER)\ntime.sleep(10)\nball_V = [5,-5] \n \n \n \n " }, { "alpha_fraction": 0.5218446850776672, "alphanum_fraction": 0.5740291476249695, "avg_line_length": 30.440000534057617, "blob_id": "ff3fda3a6072596a61a10a8a99b5591fa6ce26e9", "content_id": "35bead1521865af8653ad6b5dd3f3e225a4b0b30", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 824, "license_type": "no_license", "max_line_length": 101, "num_lines": 25, "path": "/breakout.py", "repo_name": "peterchen94/test_repo", "src_encoding": "UTF-8", "text": "from visual import *\nimport math \nscene.range=10\nbricks = [box(pos=vector(i-5,9,0),lenght=1,height=1, width=0, color=color.white)for i in range(0,11)]\nball=sphere(color=color.cyan, pos=vector(0,0,0), radius=0.2)\nball.velocity = vector(0,-1,0)\ndelta_t= 0.01\npanel=box(pos=(0,-9,0), length=2, height=0.5, width=0, color=color.white)\npanel.velocity = vector(1,0,0)\n \n \nwhile True:\n rate(600)\n \n ball.pos= ball.pos + ball.velocity*delta_t\n if ball.y<10:\n ball.velocity.y=-ball.velocity.y\n if distance(ball.pos, panel.pos) <= 0.5:\n ball.velocity.y= -ball.velocity.y\n if scene.kb.keys:\n s=scene.kb.getkey()\n if s == 'right':\n panel.pos.x = panel.pos.x + 0.2\n if s == 'left':\n panel.pos.x = panel.pos.x - 0.2\n \n \n " }, { "alpha_fraction": 0.7586206793785095, "alphanum_fraction": 0.7586206793785095, "avg_line_length": 13.5, "blob_id": "28aa7e6adf867846dd045cc3df8db41ea2692bb9", "content_id": "97f22685c917963985df67f0f3e7ce012e45f068", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 29, "license_type": "no_license", "max_line_length": 16, "num_lines": 2, "path": "/README.md", "repo_name": "peterchen94/test_repo", "src_encoding": "UTF-8", "text": "# test_repo\nfor random stuff\n" } ]

3

wzx1997/ao_lai_shangcheng

https://github.com/wzx1997/ao_lai_shangcheng

136030fdad54dc545cbf421381a27f978f669971

c1224c2ad3ff86846abd86c5224dcf18d24782dd

25bfe13293f7eb839f54a5984be0c6950dd02211

refs/heads/master

2022-11-17T22:08:59.979799

2020-06-30T09:06:06

275,747,512

0

null

[ { "alpha_fraction": 0.7174887657165527, "alphanum_fraction": 0.7174887657165527, "avg_line_length": 19.272727966308594, "blob_id": "0667aa11926e0dd3a3566d92ca64dd2ee7272dd5", "content_id": "ba68dfe1044c48c3eefd36066ba71a36924763b6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 223, "license_type": "no_license", "max_line_length": 46, "num_lines": 11, "path": "/page/main_page.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from selenium.webdriver.common.by import By\n\nfrom base.base_action import BaseAction\n\n\nclass MainPage(BaseAction):\n\n me_button=By.ID,\"com.yunmall.lc:id/tab_me\"\n\n def click_me(self):\n self.click(self.me_button)\n" }, { "alpha_fraction": 0.6738544702529907, "alphanum_fraction": 0.6738544702529907, "avg_line_length": 19.63888931274414, "blob_id": "9538e3d3f78a98d6161cda09242c10ea0b747684", "content_id": "775485c08af40beb9139768beb4c55f50d8c1241", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 742, "license_type": "no_license", "max_line_length": 41, "num_lines": 36, "path": "/page/page.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from page.about_page import AboutPage\nfrom page.login_page import LoginPage\nfrom page.main_page import MainPage\nfrom page.my_page import MyPage\nfrom page.setting_page import SettingPage\nfrom page.sing_in_page import SingInPage\n\n\nclass Page:\n\n def __init__(self,driver):\n self.driver=driver\n\n @property\n def main(self):\n return MainPage(self.driver)\n\n @property\n def login(self):\n return LoginPage(self.driver)\n\n @property\n def my(self):\n return MyPage(self.driver)\n\n @property\n def sing_in(self):\n return SingInPage(self.driver)\n\n @property\n def setting(self):\n return SettingPage(self.driver)\n\n @property\n def about(self):\n return AboutPage(self.driver)" }, { "alpha_fraction": 0.7029520273208618, "alphanum_fraction": 0.7029520273208618, "avg_line_length": 30.941177368164062, "blob_id": "3fe61c402162d38031bf30657f179a8435e0e17d", "content_id": "210c066b84d9f9dad833be161fdeb0ee4c1d6c47", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 542, "license_type": "no_license", "max_line_length": 70, "num_lines": 17, "path": "/page/sing_in_page.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from selenium.webdriver.common.by import By\n\nfrom base.base_action import BaseAction\n\nclass SingInPage(BaseAction):\n account_text = By.ID, \"com.yunmall.lc:id/logon_account_textview\"\n password_text = By.ID, \"com.yunmall.lc:id/logon_password_textview\"\n login_button = By.ID, \"com.yunmall.lc:id/logon_button\"\n\n def input_account(self, text):\n self.input(self.account_text, text)\n\n def input_password(self, text):\n self.input(self.password_text, text)\n\n def click_login(self):\n self.click(self.login_button)" }, { "alpha_fraction": 0.725321888923645, "alphanum_fraction": 0.729613721370697, "avg_line_length": 24.55555534362793, "blob_id": "d51173a50ffc0e66a411b71a1ff125508530b7ad", "content_id": "711883daf4e5c98cd8d7564317742a1ab2af1b43", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 233, "license_type": "no_license", "max_line_length": 50, "num_lines": 9, "path": "/page/login_page.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from selenium.webdriver.common.by import By\nfrom base.base_action import BaseAction\n\nclass LoginPage(BaseAction):\n\n login_text=By.ID,\"com.yunmall.lc:id/textView1\"\n\n def click_login(self):\n self.click(self.login_text) " }, { "alpha_fraction": 0.7647058963775635, "alphanum_fraction": 0.7647058963775635, "avg_line_length": 38, "blob_id": "a62220e5577010b72ab12b42dd301769f89f4bf7", "content_id": "ed975e2338152ad3a3d3b6494c076d712f3c5796", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 272, "license_type": "no_license", "max_line_length": 66, "num_lines": 7, "path": "/page/setting_page.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from selenium.webdriver.common.by import By\nfrom base.base_action import BaseAction\n\nclass SettingPage(BaseAction):\n version_button=By.ID,\"com.yunmall.lc:id/setting_about_yunmall\"\n def version(self):\n self.find_element_with_scroll(self.version_button).click()" }, { "alpha_fraction": 0.7227979302406311, "alphanum_fraction": 0.7227979302406311, "avg_line_length": 31.25, "blob_id": "a1dc87238bd56b7d0c487f55d92f119c68ca2f86", "content_id": "e5a9e84a7d81befaf0c5d898912f8e64ffa0b7dd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 386, "license_type": "no_license", "max_line_length": 70, "num_lines": 12, "path": "/page/my_page.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from selenium.webdriver.common.by import By\nfrom base.base_action import BaseAction\n\nclass MyPage(BaseAction):\n\n my_name_test=By.ID,\"com.yunmall.lc:id/tv_user_nikename\"\n setting_button=By.ID,\"com.yunmall.lc:id/ymtitlebar_left_btn_image\"\n\n def my_name(self):\n return self.find_element(self.my_name_test).text\n def setting(self):\n self.click(self.setting_button)" }, { "alpha_fraction": 0.6334792375564575, "alphanum_fraction": 0.6345732808113098, "avg_line_length": 28.516128540039062, "blob_id": "1f21f9a6888868761f57c60fd31d8309a6e84af7", "content_id": "8eab5560d67dab5571001bb93a242ae71a41417c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 914, "license_type": "no_license", "max_line_length": 82, "num_lines": 31, "path": "/scripts/test_singin.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "import time\nimport pytest\n\nfrom base.base_analyze import analyze_file\nfrom base.base_driver import init_driver\nfrom page.page import Page\n\n\nclass TestLogin:\n\n def setup(self):\n self.driver = init_driver(no_reset=False)\n self.page=Page(self.driver)\n def teardown(self):\n time.sleep(5)\n self.driver.quit()\n\n @pytest.mark.parametrize(\"args\", analyze_file(\"test_login.yaml\",\"test_login\"))\n def test_login(self,args):\n account=args[\"account\"]\n password=args[\"password\"]\n toast=args[\"toast\"]\n self.page.main.click_me()\n self.page.login.click_login()\n self.page.sing_in.input_account(account)\n self.page.sing_in.input_password(password)\n self.page.sing_in.click_login()\n if toast is None:\n assert self.page.my.my_name() == account\n else:\n assert self.page.sing_in.is_toast_exist(toast)" }, { "alpha_fraction": 0.6657754182815552, "alphanum_fraction": 0.6657754182815552, "avg_line_length": 27.846153259277344, "blob_id": "5890054ab12789304d64c0e90fe7171af97597ca", "content_id": "0427590731f6b33a306e0937db787a80e50d72c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 390, "license_type": "no_license", "max_line_length": 57, "num_lines": 13, "path": "/scripts/test_version.py", "repo_name": "wzx1997/ao_lai_shangcheng", "src_encoding": "UTF-8", "text": "from base.base_driver import init_driver\nfrom page.page import Page\n\n\nclass TestVersion:\n def teardown(self):\n self.driver=init_driver()\n self.page=Page(self.driver)\n def test_version(self):\n self.page.my.setting()\n self.page.setting.version()\n self.page.about.click_update()\n assert self.page.about.is_toast_exist(\"当前已是最新版本\")" } ]

8

markedwinharvey/file_walker

https://github.com/markedwinharvey/file_walker

4f31bd73695c47fa8e6ad32ba32564c2b3d79101

b32bd1f2fcfcc04bbc942c82e58848b39a820dcb

556064111c6ca638824463baddb2a6740e1f091d

refs/heads/master

2021-01-20T12:45:01.379182

2018-02-01T23:53:57

62,860,549

0

null

[ { "alpha_fraction": 0.5469619035720825, "alphanum_fraction": 0.5566166639328003, "avg_line_length": 24.82655906677246, "blob_id": "fdfee903643c2206e939f6d71c441353c6c39c4f", "content_id": "865c00aaf3f18235f721968e29f27f98e076df8f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9529, "license_type": "no_license", "max_line_length": 139, "num_lines": 369, "path": "/filewalker/filewalker.py", "repo_name": "markedwinharvey/file_walker", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n'''\nfilewalker.py\n\nDepth-first filesystem traversal and logging\n\nfilewalker.py returns tuple of objects: \n\tfiles \t(node list)\n\tdirs \t(node list)\n\tftree \t(node tree)\n\n\nInstallation:\n \n\tpython setup.py install\n\n\nUsage:\n\n\t#!/usr/bin/env python\n\timport filewalker as fw\n\t\n\troot='/Users/CountChocula'\t\t#optional kwarg (default is current directory)\n\tmax_depth=None\t\t\t\t\t#optional kwarg (accepts integers; default is None; max_depth=0 scans only root)\n\tprint_all=False\t\t\t\t\t#optional kwarg; print files and messages (default is True)\n\n\tfiles, dirs, ftree = fw.walk( root=root, max_depth=max_depth, print_all=print_all )\n\n\nUse the returned data (output as tuple): \n\n\tfor f in files:\t\t\t\t\t\t\t\t#files is list of file nodes (in order of assessment)\n\t\tprint f.name, f.rel, f.abs, f.size\t\t#rel=relative path, abs=absolute path\n\t\t\n\tfor d in dirs:\t\t\t\t\t\t\t\t#dirs is list of dir nodes\n\t\tprint d.name, d.rel, d.abs, d.size\n\n\nInteractive Usage:\n\n\t$ python\n\t>>> import filewalker\n\t>>> filewalker.walk()\n\t\n\t>>> files, dirs, ftree = filewalker.walk()\n\t>>> ftree\n\t<filewalker.filewalker.file_tree instance at 0x10cf74c20>\n\t>>> dir(ftree.root)\n\t['__doc__', '__init__', '__module__', 'abs', 'children', 'depth', 'fsize', 'name', 'parent', 'rel', 'size', 'type']\n\t\n'''\n\nimport subprocess as sp\nimport os\nimport sys\nimport operator\n\n#--------------------------#\n#--- class declarations ---#\n#--------------------------#\n\nclass file_tree():\n\tdef __init__(self,root):\n\t\tself.root=root\n\nclass file():\n\tdef __init__(self, name, rel, abs, size, parent, depth):\n\t\tself.name\t= name\n\t\tself.rel\t= rel\n\t\tself.abs\t= abs\n\t\tself.size\t= size\n\t\tself.parent\t= parent\n\t\tself.depth\t= depth\n\t\tself.type\t= 'f'\n\t\tself.fsize\t= format_size(size)\n\t\tself.fsize0\t= self.fsize.split(' ')[0]\n\t\tself.fsize1 = self.fsize.split(' ')[1]\n\nclass dir():\n\tdef __init__(self, name, rel, abs, size, parent, depth):\n\t\tself.name\t\t= name\n\t\tself.rel\t\t= rel\n\t\tself.abs\t\t= abs\n\t\tself.children\t= []\n\t\tself.size \t\t= size\n\t\tself.parent\t\t= parent\n\t\tself.depth\t\t= depth\n\t\tself.type\t\t= 'd'\n\t\tself.fsize\t\t= '0 B'\n\t\tself.fsize0 \t= self.fsize.split(' ')[0]\n\t\tself.fsize1 \t= self.fsize.split(' ')[1]\n\nclass _error():\n\tdef __init__(self, node_path, error):\n\t\tself.node_path = node_path\n\t\tself.error = error\n\n\n#-------------------------#\n#---- misc. functions ----#\n#-------------------------#\n\ndef exit():\n\tprint; print 'Exiting...'; print\n\tsys.exit()\n\n\ndef ps(msg):\n\t''' print messages if print_all == True '''\n\tif print_all:\n\t\tprint msg\n\n\ndef get_root(**kwargs):\n\t'''confirm valid path for **kwarg 'root'; if unspecified, use root = cwd '''\n\t\n\troot = kwargs.get('root')\n\tif not root:\n\t\troot = sp.Popen(['pwd'],stdout=sp.PIPE).communicate()[0].replace('\\n','')\n\telse:\n\t\tif not os.path.exists(root):\n\t\t\tps('not a viable path')\n\t\t\texit()\n\treturn os.path.abspath(root)\n\n\ndef get_max_depth(**kwargs):\n\tmax_depth = kwargs.get('max_depth')\n\tif max_depth is not None:\n\t\ttry:\n\t\t\tmax_depth = int(max_depth)\n\t\texcept:\n\t\t\tps('invalid depth')\n\t\t\texit()\n\t\tif max_depth < 0:\n\t\t\tmax_depth = 0\n\treturn max_depth\n\n\ndef format_size(num):\n\t''' Return formatted file size of input num as string; e.g., 14156348 --> 14.1 MB'''\n\trnum = str(int(num))[::-1]\n\tslist = zip( [rnum[x:x+3][::-1] for x in range(0,len(rnum), 3) ] , 'B KB MB GB TB'.split(' ') )\n\tsize = slist[-1][0]\n\tif len(slist) > 1:\n\t\tsize += '.' + slist[-2][0][0]\n\treturn '%s %s' % (size, slist[-1][1])\n\n\n#------------------------#\n#------ FILEWALKER ------#\n#------------------------#\ndef walk(**kwargs):\n\t'''accept **kwargs: \n\t\t'root'\t\t\t(default: current dir)\n\t\t'max_depth' \t(default: None [i.e., traverse entire tree]; accepts integers)\n\t\t'print_all' \t(default: True; print all messages to stdout)\n\t\n\tRecursively walk a file hierarchy, depth-first. \n\tEach file/dir encountered becomes a node added to the file_tree, \n\t\tand appended to the respective `files` list or `dirs` list\n\tAll nodes are accessible from successive `children` lists starting at the root node. \n\t\n\t'''\t\t\n\tglobal print_all\n\tprint_all = False if kwargs.get('print_all') == False else True\n\t\n\n\tps('#--------------------------------#\\n#-------- filewalker.py ---------#\\n#--------------------------------#')\n\t\n\t#----------------------------#\n\t#---initialize some things---#\n\t#----------------------------#\n\tcurr_depth = 0\n\tall_file_list = []\n\tall_dir_list = []\n\tall_error_list = []\n\tcurr_node_list = []\n\tlength_dict = {}\n\t\n\t\n\t\n\troot = get_root(**kwargs)\n\tmax_depth = get_max_depth(**kwargs)\n\t\n\tnew_node = dir(\t\t\t#make root node\n\t\tname\t= root.split('/')[-1],\n\t\trel\t\t= '',\n\t\tabs\t\t= root,\n\t\tsize\t= 0,\n\t\tparent \t= '',\n\t\tdepth\t= -1\n\t)\n\t\n\tf_tree = file_tree(new_node)\n\tcurr_node_list.append(new_node)\t\n\t\n\tps('Scanning: %s' % root)\n\tif (kwargs):\n\t\tps(' keyword args: %s' % ', '.join([x+': '+str(kwargs[x]) for x in kwargs]) )\n\tps(root.split('/')[-1]+' (root dir)')\n\t\n\t\n\t#----------------------------#\n\t#------ begin recursion -----#\n\t#----------------------------#\n\t\n\tdef do_walk(curr_depth, curr_node_list):\n\t\t\n\t\tthis_dir_path = curr_node_list[-1].abs\n\t\t\n\t\tcmd = 'ls \"%s\"\t' % str(this_dir_path)\n\t\tfolder_contents = [x for x in sp.Popen([cmd],stdout=sp.PIPE,shell=True).communicate()[0].split('\\n') if x]\n\t\t\n\t\tif not folder_contents:\n\t\t\tcurr_node_list.pop()\n\t\t\treturn curr_depth - 1, curr_node_list\n\t\t\n\t\tfile_size_sum = 0\n\t\t\n\t\tfor this_node in folder_contents:\n\t\t\t\n\t\t\tthis_node_path = os.path.abspath(os.path.join(this_dir_path,this_node))\n\t\t\t\n\t\t\tthis_node_rel_path = this_node_path[len(root)+1:]\t#convert to rel path\n\t\t\t\t\n\t\t\tif not os.path.isdir(this_node_path):\t\t\n\t\t\t\t#------------------------#\n\t\t\t\t#----- node is file -----#\n\t\t\t\t#------------------------#\n\t\t\t\t\n\t\t\t\ttry:\n\t\t\t\t\tsize = os.path.getsize(this_node_path)\n\t\t\t\texcept Exception as e:\n\t\t\t\t\tprint 'Warning: found error\\n\\t%s' % e\n\t\t\t\t\tnew_error = _error(this_node_path, e)\n\t\t\t\t\tall_error_list.append(new_error)\n\t\t\t\t\tcontinue\n\t\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\tnew_node = file(\n\t\t\t\t\tname\t= this_node,\n\t\t\t\t\trel\t\t= this_node_rel_path,\n\t\t\t\t\tabs\t\t= this_node_path,\n\t\t\t\t\tsize\t= size,\n\t\t\t\t\tparent \t= curr_node_list[-1],\n\t\t\t\t\tdepth \t= curr_depth\n\t\t\t\t)\n\t\t\t\tall_file_list.append(new_node)\n\t\t\n\t\t\t\tcurr_node_list[-1].children.append(new_node)\n\t\t\t\t\n\t\t\t\t#msg = ' '*curr_depth+'|'+this_node\n\t\t\t\t#ps( msg + (30-len(msg))*' '+ ' f_ (%s %s)' % ( new_node.fsize0+' '*(5-len(new_node.fsize0)) , new_node.fsize1) )\n\t\t\t\t\n\t\t\t\tfile_size_sum += size\n\t\t\t\t\n\t\t\telse:\t\t\t\t\t\t\t\t\t\n\t\t\t\t#------------------------#\n\t\t\t\t#----- node is dir ------#\n\t\t\t\t#------------------------#\n\t\t\t\t\t\t\t\n\t\t\t\tnew_node = dir(\n\t\t\t\t\tname\t= this_node,\n\t\t\t\t\trel\t\t= this_node_rel_path,\n\t\t\t\t\tabs\t\t= this_node_path,\n\t\t\t\t\tsize\t= 0,\n\t\t\t\t\tparent\t= curr_node_list[-1],\n\t\t\t\t\tdepth \t= curr_depth\n\t\t\t\t)\n\t\t\t\tall_dir_list.append(new_node)\n\t\t\t\t\n\t\t\t\tcurr_node_list[-1].children.append(new_node)\n\t\t\t\t\n\t\t\t\tmsg = ' '*curr_depth+'|'+this_node\n\t\t\t\tps(msg+(30-len(msg))*' '+'_d')\n\t\t\t\t\n\t\t\t\tif max_depth is None or curr_depth < max_depth:\n\t\t\t\t\t'''recurse through directories'''\n\t\t\t\t\tcurr_depth+=1\n\t\t\t\t\tcurr_node_list.append(new_node)\n\t\t\t\t\tcurr_depth, curr_node_list = do_walk(curr_depth, curr_node_list)\t\t\t\t\t\n\t\t\t\telse:\n\t\t\t\t\n\t\t\t\t\treturn curr_depth -1, curr_node_list\n\t\telse:\t\n\t\t\t'''contents of folder explored; calculate size of total contents'''\n\t\t\tcontents_size = 0\t\t\t\n\t\t\tfor child in curr_node_list[-1].children:\n\t\t\t\tcontents_size += child.size\n\t\t\t\tnlength = len(child.name)\n\t\t\t\tif nlength in length_dict.keys():\n\t\t\t\t\tlength_dict[nlength] += 1\t\t\t\t\n\t\t\t\telse:\n\t\t\t\t\tlength_dict[nlength] = 1\n\t\t\tcurr_node_list[-1].size = contents_size\n\t\t\tfsize = format_size(contents_size)\n\t\t\tcurr_node_list[-1].fsize = fsize\n\t\t\tcurr_node_list[-1].fsize0 = fsize.split(' ')[0]\n\t\t\tcurr_node_list[-1].fsize1 = fsize.split(' ')[1]\n\t\t\tcurr_node_list.pop()\n\t\t\n\t\treturn curr_depth - 1, curr_node_list\n\tcurr_depth, curr_node_list = do_walk(curr_depth, curr_node_list)\n\t\n\t\n\tmaxlength = max(length_dict.keys())\n\t\n\t\n\tif print_all:\n\t\tprint\n\t\tprint 'File tree with contents sizes:'\n\t\tprint '------------------------------'\n\t\tprint f_tree.root.name + ' (root)'\n\t\tdef post_walk(node):\n\t\t\tfor child in node.children:\n\t\t\t\tspacer = (' ' if child.type == 'f' else '-')*(maxlength - len(child.name))\n\t\t\t\tprint '%s|%s %s |%s %s%s %s' % (' '*child.depth, child.name, spacer, child.type, ' '*(5-len(child.fsize0)),child.fsize0, child.fsize1 )\n\t\t\t\tif child.type == 'd':\n\t\t\t\t\tpost_walk(child)\n\t\t\n\t\tpost_walk(f_tree.root)\n\t\n\t\n\t#-----------------------------------------#\n\t#----- print largest dirs and files ------#\n\t#-----------------------------------------#\n\t\n\tprint '#------ Error list: ------#'\n\tfor error in all_error_list:\n\t\tprint 'Error on \\\"%s\\\": %s' % (error.node_path, error.error)\n\tprint '#-------------------------#'\n\tprint\n\t\n\tprint '#----- Largest dirs: -----#'\n\tlarge_dirs = [x for x in sorted(all_dir_list,key=operator.attrgetter('size'))[::-1]][:10]\n\tfor d in large_dirs:\n\t\tprint d.abs\n\t\tprint ' '+d.fsize\n\tprint '#-------------------------#'\t\n\tprint\n\t\n\tprint '#----- Largest files: -----#'\n\tlarge_files = [x for x in sorted(all_file_list,key=operator.attrgetter('size'))[::-1]][:10]\n\tfor f in large_files:\n\t\tprint f.abs\n\t\tprint ' '+f.fsize\n\tprint '#-------------------------#'\n\t\n\tprint 'Total directories: %d' \t% len( all_dir_list )\n\tprint 'Total files: %d' \t\t% len( all_file_list ) \n\t\n\ttfs = sum([ x.size for x in all_file_list ])\t# tfs = total file(s) size\n\tfsize = format_size(tfs)\n\t\n\tprint 'Total file(s) size: %s' % fsize\n\t\n\t#------------------------------#\n\t#--- output from filewalker\t---#\n\t#------------------------------#\n\treturn all_file_list, all_dir_list, f_tree\n\t\n\n\ndef main():\n\twalk()\nif __name__ == '__main__':\n\tmain()" }, { "alpha_fraction": 0.666132926940918, "alphanum_fraction": 0.6757405996322632, "avg_line_length": 24.510204315185547, "blob_id": "83b67b27526bbb71c202c2380b86eed26207710a", "content_id": "2ec6c8820ac1f7f9f7af2bb6de20985a748ba22c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1249, "license_type": "no_license", "max_line_length": 117, "num_lines": 49, "path": "/readme.md", "repo_name": "markedwinharvey/file_walker", "src_encoding": "UTF-8", "text": "<h4>filewalker</h4>\n\n<h5>filewalker.py</h5>\n\nDepth-first filesystem traversal and logging\n\nfilewalker.py returns tuple of objects: \n\tfiles \t(node list)\n\tdirs \t(node list)\n\tftree \t(node tree)\n\n\nInstallation:\n \n\tpython setup.py install\n\n\nUsage:\n\n\t#!/usr/bin/env python\n\timport filewalker as fw\n\t\n\troot='/Users/CountChocula'\t\t#optional kwarg (default is current directory)\n\tmax_depth=None\t\t\t\t\t#optional kwarg (accepts integers; default is None; max_depth=0 scans only root)\n\tprint_all=False\t\t\t\t\t#optional kwarg; print files and messages (default is True)\n\n\tfiles, dirs, ftree = fw.walk( root=root, max_depth=max_depth, print_all=print_all )\n\n\nUse the returned data (output as tuple): \n\n\tfor f in files:\t\t\t\t\t\t\t\t#files is list of file nodes (in order of assessment)\n\t\tprint f.name, f.rel, f.abs, f.size\t\t#rel=relative path, abs=absolute path\n\t\t\n\tfor d in dirs:\t\t\t\t\t\t\t\t#dirs is list of dir nodes\n\t\tprint d.name, d.rel, d.abs, d.size\n\n\nInteractive Usage:\n\n\t$ python\n\t>>> import filewalker as fw\n\t>>> fw.walk()\n\t\n\t>>> files, dirs, ftree = fw.walk()\n\t>>> ftree\n\t<filewalker.filewalker.file_tree instance at 0x10cf74c20>\n\t>>> dir(ftree.root)\n\t['__doc__', '__init__', '__module__', 'abs', 'children', 'depth', 'fsize', 'name', 'parent', 'rel', 'size', 'type']`" }, { "alpha_fraction": 0.6234309673309326, "alphanum_fraction": 0.6317991614341736, "avg_line_length": 16.14285659790039, "blob_id": "dd6432e97d2a235b84d2e9c17d5f503823873b02", "content_id": "1624719152669be2d5e500cc055e72d15f071fe6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 239, "license_type": "no_license", "max_line_length": 46, "num_lines": 14, "path": "/setup.py", "repo_name": "markedwinharvey/file_walker", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\nfrom setuptools import setup\ndef main():\n\n\tsetup(\n\t\tname='filewalker',\n\t\tversion='0.1',\n\t\tdescription='walk and log a file hierarchy',\n\t\tpackages=['filewalker'],\n\t\tauthor='meh',\t\n\t)\n\nif __name__ == '__main__':\n\tmain()" } ]

3

chandrimahere/KNearestNeighbour-Classifier

https://github.com/chandrimahere/KNearestNeighbour-Classifier

eae9f4abc25497f040f7d2f487f68bb1abcaf5b0

b1b68bf24d72ecb349f237583e43c6fbe1433888

e05fbb52ec276998c4144f4fb9ed91c316f918d9

refs/heads/master

2020-08-22T10:34:57.255241

2019-11-03T19:25:48

216,375,555

0

null

[ { "alpha_fraction": 0.48393890261650085, "alphanum_fraction": 0.49289098381996155, "avg_line_length": 28.10769271850586, "blob_id": "e079fa62c8a658806b82122e997aaa8457728387", "content_id": "22d87d217bd08b25357eac7e1fdf5dee6104a31c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1899, "license_type": "no_license", "max_line_length": 95, "num_lines": 65, "path": "/Weightedknn.py", "repo_name": "chandrimahere/KNearestNeighbour-Classifier", "src_encoding": "UTF-8", "text": "import operator\nfrom collections import Counter\n\nimport numpy as np\n\n\nclass KNearestNeighbors:\n def __init__(self,k,weights):\n self.k=k\n self.result=[]\n self.result_new=[]\n self.weights=weights\n\n\n def fit(self,X_train,y_train):\n self.X_train=X_train\n self.y_train=y_train\n print(\"Training Done\")\n\n def predict(self,X_test):\n for j in X_test:\n distance={}\n new_dist={}\n counter=1\n for i in self.X_train:\n c=0\n for k in range(len(j)):\n c=c+(j[k]-i[k])**2\n if self.weights == \"uniform\":\n distance[counter] = c** 1 / 2\n elif self.weights == \"distance\":\n new_dist[counter] = 1 /(c ** 1 / 2)\n counter=counter+1\n if self.weights == \"uniform\":\n distance = sorted(distance.items(), key=operator.itemgetter(1))\n self.result.append(self.classify(distance[:self.k]))\n\n\n\n elif self.weights == \"distance\":\n new_dist = sorted(new_dist.items(), key=operator.itemgetter(1), reverse=True)\n self.result_new.append(self.classify_new(new_dist[:self.k]))\n\n\n if self.weights == \"uniform\":\n return self.result\n elif self.weights == \"distance\":\n return self.result_new\n def classify(self,distance):\n label=[]\n for i in distance:\n label.append(self.y_train[i[0]])\n\n return Counter(label).most_common()[0][0]\n def classify_new(self,distance):\n y = np.unique(self.y_train)\n [s]=len(y)\n for i in range(len(y)):\n sum = 0\n for j in distance:\n if y[i] == self.y_train[i[0]]:\n sum=sum+i[1]\n s[i] = sum\n\n return y[max(s)]\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.7468926310539246, "alphanum_fraction": 0.7638418078422546, "avg_line_length": 28.16666603088379, "blob_id": "1e50b075b9f488d855f55454ba3d90c33afeb650", "content_id": "4ff0c90776ffe8b072f7c36abe99e6f6ad611376", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 885, "license_type": "no_license", "max_line_length": 66, "num_lines": 30, "path": "/knn_test2.py", "repo_name": "chandrimahere/KNearestNeighbour-Classifier", "src_encoding": "UTF-8", "text": "import numpy as np\nimport pandas as pd\nfrom KNearestRegressor import KNearestRegressors\nfrom sklearn.metrics import r2_score\nfrom sklearn.metrics import accuracy_score\n\n\n#taking inputs\ndata=pd.read_csv('Social_Network_Ads.csv')\nX=data.iloc[:,2:4].values\ny=data.iloc[:,-1].values\n#using train_test_split function\nfrom sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n\n#using StandardScaler function\nfrom sklearn.preprocessing import StandardScaler\nscaler=StandardScaler()\nX_train=scaler.fit_transform(X_train)\nX_test=scaler.transform(X_test)\n\n#an object of knn\nknn=KNearestRegressors(k=5)\nknn.fit(X_train,y_train)\n\n#using the predict() function\nknn.predict(np.array(X_test).reshape(len(X_test), len(X_test[0])))\n\nprint(round((r2_score(y_test, y_pred) * 100)))\nprint(round((accuracy_score(y_test,y_pred)*100)))\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.6991221308708191, "alphanum_fraction": 0.7102953195571899, "avg_line_length": 26.15217399597168, "blob_id": "d4f3be2dcd4119250de9d3babde200b296f829ab", "content_id": "36f9df3dacb136ffe497cc3f1967b10fa19f53c8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1253, "license_type": "no_license", "max_line_length": 84, "num_lines": 46, "path": "/knn_test.py", "repo_name": "chandrimahere/KNearestNeighbour-Classifier", "src_encoding": "UTF-8", "text": "import numpy as np\nimport pandas as pd\nfrom KNearestNeighbor import KNearestNeighbors\n\n#taking inputs\ndata=pd.read_csv('Social_Network_Ads.csv')\ndata['Gender'] = data['Gender'].replace({'Male': 0, 'Female': 1})\nX=data.iloc[:,1:4].values\ny=data.iloc[:,-1].values\n\nprint(X.shape)\nprint(y.shape)\n\n#using train_test_split function\nfrom sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)\n\n#using StandardScaler function\nfrom sklearn.preprocessing import StandardScaler\nscaler=StandardScaler()\nX_train=scaler.fit_transform(X_train)\nX_test=scaler.transform(X_test)\n\n#an object of knn\nknn=KNearestNeighbors(k=5)\nknn.fit(X_train,y_train)\n\n#using the predict() function\nknn.predict(np.array(X_test).reshape(len(X_test), len(X_test[0])))\n\n#defining a function to check the output\ndef predict_new():\n age=int(input(\"Enter the age\"))\n salary=int(input(\"Enter the salary\"))\n gender = int(input(\"Enter the gender,type '0' for Male or type '1' for female\"))\n X_new=np.array([[age],[gender],[salary]]).reshape(1,3)\n X_new=scaler.transform(X_new)\n result=knn.predict(X_new)\n if result==0:\n print(\"Will not purchase\")\n else:\n print(\"Will purchase\")\n\n\n\npredict_new()\n\n\n\n\n" }, { "alpha_fraction": 0.5216919779777527, "alphanum_fraction": 0.5325379371643066, "avg_line_length": 25.81818199157715, "blob_id": "93de65550af6d09caca981977cb685a06cc385ce", "content_id": "f0be2cc8351bee396d9fc8293c7d1b94aac7dad8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 922, "license_type": "no_license", "max_line_length": 69, "num_lines": 33, "path": "/KNearestNeighbor.py", "repo_name": "chandrimahere/KNearestNeighbour-Classifier", "src_encoding": "UTF-8", "text": "import operator\r\nfrom collections import Counter\r\n\r\nclass KNearestNeighbors:\r\n def __init__(self,k):\r\n self.k=k\r\n self.result=[]\r\n\r\n\r\n def fit(self,X_train,y_train):\r\n self.X_train=X_train\r\n self.y_train=y_train\r\n print(\"Training Done\")\r\n\r\n def predict(self,X_test):\r\n for j in X_test:\r\n distance={}\r\n counter=1\r\n for i in self.X_train:\r\n c=0\r\n for k in range(len(j)):\r\n c=c+(j[k]-i[k])**2\r\n distance[counter]=c**1/2\r\n counter=counter+1\r\n distance=sorted(distance.items(),key=operator.itemgetter(1))\r\n self.result.append(self.classify(distance=distance[:self.k]))\r\n return self.result\r\n def classify(self,distance):\r\n label=[]\r\n for i in distance:\r\n label.append(self.y_train[i[0]])\r\n\r\n return Counter(label).most_common()[0][0]\r\n\r\n\r\n" }, { "alpha_fraction": 0.8399999737739563, "alphanum_fraction": 0.8399999737739563, "avg_line_length": 61.5, "blob_id": "fe2c0078c93ad36fec31a0c4bb0f396b246077b0", "content_id": "410bc40ed2bf2c509958453992eb6bb8e764a7ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 125, "license_type": "no_license", "max_line_length": 93, "num_lines": 2, "path": "/README.md", "repo_name": "chandrimahere/KNearestNeighbour-Classifier", "src_encoding": "UTF-8", "text": "# KNearestNeighbour-Classifier\nThis code depicts how the knn algorithm works and also shows the decision boundary condition.\n" } ]

5

declarativesystems/pipelines_example_pythonenv

https://github.com/declarativesystems/pipelines_example_pythonenv

87f0f80774e3c43256ceca2c0e6dff1a9c6ffc34

968e7b946b14d56a75e8d604d5fc737c2b466468

ff8679f7a0b8dafdc160f4fc341280859ec00297

refs/heads/master

2023-03-28T16:38:31.835973

2021-04-08T01:49:31

340,528,955

0

null

[ { "alpha_fraction": 0.6988322138786316, "alphanum_fraction": 0.7062077522277832, "avg_line_length": 35.155555725097656, "blob_id": "d09cf438e3bbd473bf306a65b3b85355f19921e4", "content_id": "da2934a9cfacb71a0f37083f49a5b7ed6baaea63", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1627, "license_type": "no_license", "max_line_length": 94, "num_lines": 45, "path": "/setup.py", "repo_name": "declarativesystems/pipelines_example_pythonenv", "src_encoding": "UTF-8", "text": "# Copyright 2021 Declarative Systems Pty Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport setuptools\nimport pipelines_example_pythonenv.version\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"pipelines_example_pythonenv\",\n version=pipelines_example_pythonenv.version.get_version(),\n author=\"Geoff Williams\",\n author_email=\"[email protected]\",\n description=\"example pip packaging\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/declarativesystems/pipelines_example_pythonenv\",\n packages=setuptools.find_packages(),\n # pick from https://pypi.org/classifiers/\n classifiers=[\n \"Development Status :: 2 - Pre-Alpha\",\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Operating System :: OS Independent\",\n ],\n python_requires='>=3.6',\n entry_points={\n \"console_scripts\": (['pipelines_example_pythonenv=pipelines_example_pythonenv:main'],)\n },\n include_package_data=True,\n install_requires=[\n ]\n)\n" }, { "alpha_fraction": 0.4803921580314636, "alphanum_fraction": 0.7009803652763367, "avg_line_length": 15.319999694824219, "blob_id": "a3160d9c23f5b90cde981d901273c02777cb4709", "content_id": "47d599d0bd843181eb892dc1b71cb26ee33692e4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 408, "license_type": "no_license", "max_line_length": 24, "num_lines": 25, "path": "/requirements.txt", "repo_name": "declarativesystems/pipelines_example_pythonenv", "src_encoding": "UTF-8", "text": "bleach==3.3.0\ncertifi==2020.12.5\ncffi==1.14.5\nchardet==4.0.0\ncolorama==0.4.4\ncryptography==3.4.6\ndocutils==0.16\nidna==2.10\njeepney==0.6.0\nkeyring==22.0.1\npackaging==20.9\npkginfo==1.7.0\npycparser==2.20\nPygments==2.8.0\npyparsing==2.4.7\nreadme-renderer==28.0\nrequests==2.25.1\nrequests-toolbelt==0.9.1\nrfc3986==1.4.0\nSecretStorage==3.3.1\nsix==1.15.0\ntqdm==4.57.0\ntwine==3.3.0\nurllib3==1.26.3\nwebencodings==0.5.1\n" }, { "alpha_fraction": 0.6933333277702332, "alphanum_fraction": 0.6933333277702332, "avg_line_length": 14.050000190734863, "blob_id": "1cdfcbb788d1ba8e938a57ce7ead24d540868781", "content_id": "22f01eaeeb51c8eadbd66d63d87a0d50165ea549", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Makefile", "length_bytes": 300, "license_type": "no_license", "max_line_length": 44, "num_lines": 20, "path": "/Makefile", "repo_name": "declarativesystems/pipelines_example_pythonenv", "src_encoding": "UTF-8", "text": "dist: env\n\t. env/bin/activate; \\\n\tpython setup.py sdist bdist_wheel\n\nenv:\n\tvirtualenv env ; \\\n\t. env/bin/activate; \\\n\tpip install -r requirements.txt\n\n\npublish: dist\n\t. env/bin/activate; \\\n\tpython setup.py bdist_wheel upload -r local\n\nclean:\n\trm -rf env\n\trm -rf build\n\trm -rf dist\n\n.PHONY: clean dist" }, { "alpha_fraction": 0.675000011920929, "alphanum_fraction": 0.6968749761581421, "avg_line_length": 28.18181800842285, "blob_id": "1048e18019485e41b8603293bc486d67ba6fbd2b", "content_id": "b9770eacf565a9db1c3e36b64c8357fd24fdfa46", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 320, "license_type": "no_license", "max_line_length": 67, "num_lines": 11, "path": "/pipelines_example_pythonenv/version.py", "repo_name": "declarativesystems/pipelines_example_pythonenv", "src_encoding": "UTF-8", "text": "import os\n\nversion = \"0.0.0\"\n\ndef get_version():\n # set in pipelines environment\n build_number = os.environ.get(\"build_number\", False)\n\n # python is picky about how version numbers are handled\n # see https://www.python.org/dev/peps/pep-0440\n return f\"{version}+{build_number}\" if build_number else version" } ]

4

sidneijp/luizalabs-employee-manager

https://github.com/sidneijp/luizalabs-employee-manager

dfb234b9c70061ca61d0685a2cd07b3762dbb46f

98783a185ffa1f3f4180eeb34e46cf6d2c3cae1f

07f7613a41a9fc0237965814395df888271dbecd

refs/heads/master

2023-01-27T16:40:24.635909

2018-04-13T15:34:54

317,712,554

0

null

[ { "alpha_fraction": 0.727053165435791, "alphanum_fraction": 0.727053165435791, "avg_line_length": 23.352941513061523, "blob_id": "575ef1c4ecc7a86bcb77edecfe3a266b9f366f8b", "content_id": "103db72dffde0eef23b0f6b77ea702a000014d2d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 414, "license_type": "no_license", "max_line_length": 51, "num_lines": 17, "path": "/core/admin.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "from django.contrib import admin\n\nfrom core.models import Department, Employee\n\n\nclass DepartmentAdmin(admin.ModelAdmin):\n search_fields = ('name',)\n\n\nclass EmployeeAdmin(admin.ModelAdmin):\n list_display = ('email', 'name', 'department',)\n search_fields = ('email', 'name',)\n list_filter = ('department',)\n\n\nadmin.site.register(Department, DepartmentAdmin)\nadmin.site.register(Employee, EmployeeAdmin)\n" }, { "alpha_fraction": 0.5342960357666016, "alphanum_fraction": 0.5516245365142822, "avg_line_length": 34.512821197509766, "blob_id": "78e88cb68895cb21d834c8ce2e0838f97fcd5c9b", "content_id": "956b33615ee914ee8ee07d53a3b4f552c539cf6d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1385, "license_type": "no_license", "max_line_length": 168, "num_lines": 39, "path": "/core/migrations/0001_initial.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "# Generated by Django 2.0.4 on 2018-04-12 19:13\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Department',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=160, verbose_name='Name')),\n ],\n options={\n 'verbose_name': 'Department',\n 'verbose_name_plural': 'Departments',\n },\n ),\n migrations.CreateModel(\n name='Employee',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=160, verbose_name='Name')),\n ('email', models.EmailField(max_length=254, unique=True, verbose_name='E-mail')),\n ('department', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='core.Department', verbose_name='Department')),\n ],\n options={\n 'verbose_name': 'Employee',\n 'verbose_name_plural': 'Employees',\n },\n ),\n ]\n" }, { "alpha_fraction": 0.6486761569976807, "alphanum_fraction": 0.6737949848175049, "avg_line_length": 24.188034057617188, "blob_id": "543508be5c7dda8f3f1402d72dbdc1ad1a4013dd", "content_id": "2ae71aec2f5d61df568fead44567dc519bd70436", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2946, "license_type": "no_license", "max_line_length": 178, "num_lines": 117, "path": "/README.md", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "# employee-manager\n___\n[![CircleCI](https://circleci.com/bb/sidnei/employee-manager/tree/develop.svg?style=svg&circle-token=a05a6fd2a24f3465225aba2e23a648f9a7ee1ea8)](https://circleci.com/bb/sidnei/employee-manager/tree/develop)\n\nA Django Admin panel to manage employees' data and an API to list, add and remove employees.\n\n## Installation\n\n### Dependecies\n\n- Python 3.6\n- Django 2.0\n- pipenv 11.10.0\n\nThe project uses `pipenv` instead of `pip` to manage python - so there is no `requirements.txt`. To install, run as root:\n\n```# pip install -U pipenv```\n\nNow to install python packages dependencies:\n\n```$ make install```\n\nTo the application and the API will be available at http://localhost:8000 and the admin interface at http://localhost:8000/admin.\n\n```$ make run```\n\nA initial superuser is created:\n\n```\nusername:admin\n\npassword:employee123\n\n```\n\n### Development\n\nInstall the `dev-packages` to be able to run tests, generate coverage report, run linter, etc. \n\n```$ make dev-install```\n\nTo run automated tests:\n\n```$ make test```\n\nor run in \"watcher mode\":\n\n```$ make testd```\n\nto generate tests coverage:\n\n```$ make coverage```\n\nthen to see the report result:\n\n```make report```\n\nor for \"HTML fashion\" report:\n\n```make html```\n\n*The HTML report will be available at `./htmlcov/index.html` - open it with a web browser.\n\n## API example (list)\n\nURL schema:\n\n```http://localhost:8000/employee/[employee_email/][?department=department_name]```\n\nwith curl\n```curl -u username:password -H \"Content-Type: application/json\" http://localhost:8000/employee/[employee_email/][?department=department_name]```\n\n\n### Examples\n\nTo create some employees:\n\n```curl -u admin:employee123 -X POST -d '{\"name\": \"Jack\", \"email\": \"[email protected]\", \"department\":\"IT\"}' -H \"Content-Type: application/json\" http://localhost:8000/employee/'```\n\n```curl -u admin:employee123 -X POST -d '{\"name\": \"John\", \"email\": \"[email protected]\", \"department\":\"IT\"}' -H \"Content-Type: application/json\" http://localhost:8000/employee/'```\n\nTo edit a employee:\n\n```curl -u admin:employee123 -X PUT -d '{\"name\": \"Jackson\", \"email\": \"[email protected]\", \"department\":\"RH\"}' -H \"Content-Type: application/json\" http://localhost:8000/employee/[email protected]/```\n\nTo get a employee:\n\n```curl -u admin:employee123 -X GET -H \"Content-Type: application/json\" http://localhost:8000/employee/[email protected]/```\n\nTo list all employees:\n\n```curl -u admin:employee123 -X GET -H \"Content-Type: application/json\" http://localhost:8000/employee/```\n\n*Response sample: \n\n```\n[\n {\n \"name\": \"Jackson\",\n \"email\": \"[email protected]\",\n \"department\": \"RH\"\n },\n {\n \"name\": \"John\",\n \"email\": \"[email protected]\",\n \"department\": \"IT\"\n }\n]\n```\n\nTo list all employees by department:\n\n```curl -u admin:employee123 -X GET -H \"Content-Type: application/json\" http://localhost:8000/employee/?department=IT```\n\nTo delete a employee:\n\n```curl -u admin:employee123 -X DELETE -H \"Content-Type: application/json\" http://localhost:8000/employee/[email protected]/```" }, { "alpha_fraction": 0.7396313548088074, "alphanum_fraction": 0.7396313548088074, "avg_line_length": 21.842105865478516, "blob_id": "dfff8d4830dcb52ebfd3633e9164f4dac7d86403", "content_id": "979a0a72440ac736db4686cd2ace31159c07183f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 434, "license_type": "no_license", "max_line_length": 59, "num_lines": 19, "path": "/conftest.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "import pytest\n\nfrom employee_manager.tests.factories import UserFactory\n\n\[email protected]\ndef user_credentials():\n user = UserFactory.create()\n password = 'test_password'\n user.set_password(password)\n user.save()\n return user, password\n\n\[email protected]\ndef authenticated_client(client, user_credentials):\n user, password = user_credentials\n client.login(username=user.username, password=password)\n return client\n" }, { "alpha_fraction": 0.7394594550132751, "alphanum_fraction": 0.7481080889701843, "avg_line_length": 14.677966117858887, "blob_id": "829134ec6d0e3f2cc7687dbe9c618d62723ff69c", "content_id": "bd182bb3b0a6377171f532fa377c12564e3afb99", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Makefile", "length_bytes": 925, "license_type": "no_license", "max_line_length": 55, "num_lines": 59, "path": "/Makefile", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "SHELL:=/bin/bash\nPORT:=8000\nMANAGE:=`pipenv --venv`/bin/python manage.py\n\nrequirements:\n\tpipenv install\n\ndev-requirements:\n\tpipenv install --dev\n\nload-initial-data:\n\t$(MANAGE) loaddata employee_manager/fixtures/auth.json\n\t$(MANAGE) loaddata core/fixtures/department.json\n\nrun:\n\t$(MANAGE) runserver 0.0.0.0:$(PORT)\n\nmigrate:\n\t$(MANAGE) migrate\n\ncollectstatic:\n\t$(MANAGE) collectstatic --noinput\n\nshell:\n\t$(MANAGE) shell\n\ntest:\n\tpy.test\n\ntestd:\n\tptw\n\ncoverage:\n\tcoverage run -m py.test\n\nreport:\n\tcoverage report\n\nhtml:\n\t@coverage html\n\t@echo \"Generated coverage HTML report at ./htmlcov\"\n\nclean:\n\t@rm -f .coverage\n\t@rm -rf htmlcov/\n\t@echo \"Cleaned coverage report files\"\n\npull:\n\tgit pull origin\n\ninstall: requirements migrate load-initial-data\n\tcp settings.ini.sample settings.ini\n\ndev-install: dev-requirements migrate load-initial-data\n\tcp settings.ini.sample settings.ini\n\nupdate:\tpull install\n\ndev-update:\tpull dev-install\n" }, { "alpha_fraction": 0.7045454382896423, "alphanum_fraction": 0.7134740352630615, "avg_line_length": 34.71014404296875, "blob_id": "c7327bbcdf47935c66ae6d006e11923e26e278b2", "content_id": "af378e63560ae88f3d08ef4df19eec4c37243ba7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2464, "license_type": "no_license", "max_line_length": 76, "num_lines": 69, "path": "/core/api/tests/test_views.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "import pytest\n\nfrom core.models import Employee\nfrom . import factories\n\n\[email protected]_db\ndef test_create_employee(authenticated_client, department):\n employee = factories.EmployeeFactory.build(department=department)\n data = {\n 'name': employee.name,\n 'email': employee.email,\n 'department': employee.department.name,\n }\n response = authenticated_client.post('/employee/', data)\n assert response.status_code == 201\n last_inserted_employee = Employee.objects.last()\n assert last_inserted_employee.email == employee.email\n\n\[email protected]_db\ndef test_list_employee(authenticated_client, employees):\n expected_amount = Employee.objects.all().count()\n response = authenticated_client.get('/employee/')\n assert response.status_code == 200\n amount = len(response.json())\n assert expected_amount == amount\n\n\[email protected]_db\ndef test_list_filter_employee(authenticated_client, departments, employees):\n department = departments.pop()\n expected_amount = Employee.objects.filter(department=department).count()\n data = {'department': department.name}\n response = authenticated_client.get('/employee/', data)\n assert response.status_code == 200\n json_response = response.json()\n amount = len(json_response)\n assert expected_amount == amount\n assert department.name in [_.get('department') for _ in json_response]\n\n\[email protected]_db\ndef test_get_employee(authenticated_client, employee):\n response = authenticated_client.get('/employee/%s/' % employee.email)\n assert response.status_code == 200\n json_response = response.json()\n assert employee.email == json_response.get('email')\n assert employee.department.name == json_response.get('department')\n expected_fields = ('name', 'email', 'department')\n fields = json_response.keys()\n for expected_field in expected_fields:\n assert expected_field in fields\n assert len(expected_fields) == len(fields)\n\n\[email protected]_db\ndef test_delete_employee(authenticated_client, employee):\n response = authenticated_client.delete('/employee/%s/' % employee.email)\n assert response.status_code == 204\n invalid_id = -1\n response = authenticated_client.delete('/employee/%s/' % invalid_id)\n assert response.status_code == 404\n\n\[email protected]_db\ndef test_unauthorized_api_call(client, employees):\n response = client.get('/employee/')\n assert response.status_code == 401\n" }, { "alpha_fraction": 0.6090047359466553, "alphanum_fraction": 0.6113743782043457, "avg_line_length": 37.3636360168457, "blob_id": "dfd2682051f9ec9f44189cb3bd78d96f42f9d566", "content_id": "8f75356eaf131f7d448fa1cf13968c8ed7a85366", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 422, "license_type": "no_license", "max_line_length": 61, "num_lines": 11, "path": "/core/api/utils.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "def use_serializer(serializer_class):\n def decorator(view):\n def wrapper(*args, **kwargs):\n self = args[0]\n original_serializer_class = self.serializer_class\n self.serializer_class = serializer_class\n response = view(*args, **kwargs)\n self.serializer_class = original_serializer_class\n return response\n return wrapper\n return decorator\n" }, { "alpha_fraction": 0.7346072196960449, "alphanum_fraction": 0.747346043586731, "avg_line_length": 32.64285659790039, "blob_id": "33fc6535edd219dfdcb0b740d3adef4e4409b364", "content_id": "307b8e95a133b0ec653ed277176bd38864be97cd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 471, "license_type": "no_license", "max_line_length": 73, "num_lines": 14, "path": "/core/api/viewsets.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "from rest_framework import viewsets\n\nfrom core.api.serializers import EmployeeSerializer\nfrom core.api.filters import EmployeeFilter\nfrom core.api.utils import use_serializer\nfrom core.models import Employee\n\n\nclass EmployeeViewSet(viewsets.ModelViewSet):\n queryset = Employee.objects.all()\n serializer_class = EmployeeSerializer\n filter_class = EmployeeFilter\n lookup_field = 'email'\n lookup_value_regex = '[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+'\n" }, { "alpha_fraction": 0.8157894611358643, "alphanum_fraction": 0.8157894611358643, "avg_line_length": 21.799999237060547, "blob_id": "ba93d5eec2f10e9c2dee67c3f37f64e789257f10", "content_id": "23d1fecb1fe98a2a95705baaeb5db30fd22c143e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 228, "license_type": "no_license", "max_line_length": 45, "num_lines": 10, "path": "/core/api/urls.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "from django.urls import include, path\nfrom rest_framework import routers\n\nfrom core.api.viewsets import EmployeeViewSet\n\n\nrouter = routers.DefaultRouter()\nrouter.register(r'employee', EmployeeViewSet)\n\nurlpatterns = router.urls\n" }, { "alpha_fraction": 0.5378972887992859, "alphanum_fraction": 0.591687023639679, "avg_line_length": 21.72222137451172, "blob_id": "04d7e0944407004b1a6cdbe5950d6848f7ef7aa5", "content_id": "e609802dbfc1e153c5731352152d124dbe6f53ce", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 409, "license_type": "no_license", "max_line_length": 85, "num_lines": 18, "path": "/core/migrations/0002_auto_20180412_2251.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "# Generated by Django 2.0.4 on 2018-04-12 22:51\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('core', '0001_initial'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='department',\n name='name',\n field=models.CharField(max_length=160, unique=True, verbose_name='Name'),\n ),\n ]\n" }, { "alpha_fraction": 0.722352921962738, "alphanum_fraction": 0.722352921962738, "avg_line_length": 21.36842155456543, "blob_id": "3112a45e7810b475c14f24e5ae26737cfc221fbf", "content_id": "498522a9b5967f4a727b761a95879110b8579c9c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 425, "license_type": "no_license", "max_line_length": 59, "num_lines": 19, "path": "/core/api/tests/factories.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "import factory\n\nfrom core.models import Department, Employee\n\n\nclass DepartmentFactory(factory.django.DjangoModelFactory):\n name = factory.Faker('name')\n\n class Meta:\n model = Department\n\n\nclass EmployeeFactory(factory.django.DjangoModelFactory):\n name = factory.Faker('name')\n email = factory.Faker('email')\n department = factory.SubFactory(DepartmentFactory)\n\n class Meta:\n model = Employee\n" }, { "alpha_fraction": 0.8090909123420715, "alphanum_fraction": 0.8090909123420715, "avg_line_length": 26.5, "blob_id": "0eef295f1e584f0f0033b22582120dc248a0b97a", "content_id": "aee135c24da7b35ac3a607ede0ee81e07d664135", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "INI", "length_bytes": 110, "license_type": "no_license", "max_line_length": 48, "num_lines": 4, "path": "/pytest.ini", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "[pytest]\nDJANGO_SETTINGS_MODULE=employee_manager.settings\npython_files=tests*.py test*.py\ndata_file=.coverage\n" }, { "alpha_fraction": 0.6225930452346802, "alphanum_fraction": 0.6302952766418457, "avg_line_length": 25.86206817626953, "blob_id": "c826ccc20dd8199db971f25b84b3cccc2f2e1d81", "content_id": "c403a12c23630250d66f6caa429fd1a6e8981d7e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 779, "license_type": "no_license", "max_line_length": 69, "num_lines": 29, "path": "/core/models.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "from django.db import models\nfrom django.utils.translation import ugettext_lazy as _\n\n\nclass Department(models.Model):\n name = models.CharField(_('Name'), max_length=160, unique=True)\n\n class Meta:\n verbose_name = _('Department')\n verbose_name_plural = _('Departments')\n\n def __str__(self):\n return self.name\n\n\nclass Employee(models.Model):\n name = models.CharField(_('Name'), max_length=160)\n email = models.EmailField(_('E-mail'), unique=True)\n department = models.ForeignKey(\n Department, on_delete=models.SET_NULL, null=True, blank=True,\n verbose_name=_('Department')\n )\n\n class Meta:\n verbose_name = _('Employee')\n verbose_name_plural = _('Employees')\n\n def __str__(self):\n return self.name\n" }, { "alpha_fraction": 0.7727272510528564, "alphanum_fraction": 0.7854545712471008, "avg_line_length": 21, "blob_id": "8b55ccb89d8d2ad889d574fcb577a6b84e332752", "content_id": "be8ded8109415dd3aebf2e4ef0d89dd980ad3cbd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 550, "license_type": "no_license", "max_line_length": 67, "num_lines": 25, "path": "/core/api/tests/conftest.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "import pytest\n\nfrom .factories import DepartmentFactory, EmployeeFactory\n\n\[email protected]\ndef department():\n return DepartmentFactory.create()\n\n\[email protected]\ndef departments():\n return DepartmentFactory.create_batch(2)\n\n\[email protected]\ndef employee(department):\n return EmployeeFactory.create(department=department)\n\n\[email protected]\ndef employees(departments):\n department_1, department_2 = departments\n EmployeeFactory.create_batch(2, department=department_1)\n return EmployeeFactory.create_batch(2, department=department_2)\n" }, { "alpha_fraction": 0.70703125, "alphanum_fraction": 0.70703125, "avg_line_length": 22.272727966308594, "blob_id": "f18cd8e63bbe8c5574f9a856485657740446bfb8", "content_id": "a85f638346eacad605deb562662ebebaad1375bb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 256, "license_type": "no_license", "max_line_length": 67, "num_lines": 11, "path": "/core/api/filters.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "import django_filters\n\nfrom core.models import Employee\n\n\nclass EmployeeFilter(django_filters.FilterSet):\n department = django_filters.CharFilter(name=\"department__name\")\n\n class Meta:\n model = Employee\n fields = ('name', 'department')\n" }, { "alpha_fraction": 0.7002725005149841, "alphanum_fraction": 0.7002725005149841, "avg_line_length": 25.214284896850586, "blob_id": "561d5440835dc1abae4faf0f722b98c4700d73d9", "content_id": "2e7f0d2d8d38dfc47636a9aedbf751e69887c2c5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 367, "license_type": "no_license", "max_line_length": 65, "num_lines": 14, "path": "/core/api/serializers.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "from rest_framework import serializers\n\nfrom core.models import Department, Employee\n\n\nclass EmployeeSerializer(serializers.HyperlinkedModelSerializer):\n department = serializers.SlugRelatedField(\n slug_field='name',\n queryset=Department.objects.all(),\n )\n\n class Meta:\n model = Employee\n fields = ('name', 'email', 'department')\n" }, { "alpha_fraction": 0.639418363571167, "alphanum_fraction": 0.6476714611053467, "avg_line_length": 25.925926208496094, "blob_id": "23a66b1d11e1c8f775b56dbcf2dfbf71b2f9a00d", "content_id": "6b2f245acbfab4039a0efa4c6445817195a93f5c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5089, "license_type": "no_license", "max_line_length": 91, "num_lines": 189, "path": "/employee_manager/settings.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "\"\"\"\nDjango settings for employee_manager project.\n\nGenerated by 'django-admin startproject' using Django 2.0.4.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/2.0/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/2.0/ref/settings/\n\"\"\"\n\nfrom decouple import config, Csv\nimport os\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = config(\n 'SECRET_KEY', default='wnyyx5y0q)0m(zh221a3r4&&=xlz=xzt$s#_s+6i9k3x)#i1gd'\n)\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = config('DEBUG', default=False, cast=bool)\n\nALLOWED_HOSTS = config(\n 'ALLOWED_HOSTS', default='localhost, 127.0.0.1', cast=Csv()\n)\n\n\n# Application definition\n\nINSTALLED_APPS = [\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n\n 'rest_framework',\n 'django_filters',\n\n 'core',\n]\n\nMIDDLEWARE = [\n 'django.middleware.security.SecurityMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.locale.LocaleMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n]\n\nROOT_URLCONF = 'employee_manager.urls'\n\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n 'DIRS': [],\n 'APP_DIRS': True,\n 'OPTIONS': {\n 'context_processors': [\n 'django.template.context_processors.debug',\n 'django.template.context_processors.request',\n 'django.contrib.auth.context_processors.auth',\n 'django.contrib.messages.context_processors.messages',\n ],\n },\n },\n]\n\nWSGI_APPLICATION = 'employee_manager.wsgi.application'\n\n\n# Database\n# https://docs.djangoproject.com/en/2.0/ref/settings/#databases\n\n_DB_DEFAULTS = {\n 'django.db.backends.sqlite3': {\n 'HOST': '',\n 'PORT': '',\n 'NAME': 'db.sqlite3',\n 'USER': '',\n 'PASSWORD': '',\n },\n 'django.db.backends.postgresql': {\n 'HOST': 'localhost',\n 'PORT': '5432',\n 'NAME': 'db.sqlite3',\n 'USER': 'postgres',\n 'PASSWORD': '',\n },\n 'django.db.backends.mysql': {\n 'HOST': 'localhost',\n 'PORT': '3306',\n 'NAME': 'db.sqlite3',\n 'USER': 'root',\n 'PASSWORD': '',\n },\n}\n\n_DB_ENGINE = 'django.db.backends.%s' % config('DB_ENGINE', default='sqlite3')\n\nDATABASES = {\n 'default': {\n 'ENGINE': _DB_ENGINE,\n 'HOST': config('DB_HOST', default=_DB_DEFAULTS[_DB_ENGINE]['HOST']),\n 'PORT': config('DB_PORT', default=_DB_DEFAULTS[_DB_ENGINE]['PORT']),\n 'NAME': config('DB_NAME', default=_DB_DEFAULTS[_DB_ENGINE]['NAME']),\n 'USER': config('DB_USER', default=_DB_DEFAULTS[_DB_ENGINE]['USER']),\n 'PASSWORD': config(\n 'DB_PASSWORD', default=_DB_DEFAULTS[_DB_ENGINE]['PASSWORD']\n ),\n }\n}\n\n\n# Password validation\n# https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators\n\nAUTH_PASSWORD_VALIDATORS = [\n {\n 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n },\n]\n\n\n# Internationalization\n# https://docs.djangoproject.com/en/2.0/topics/i18n/\n\nLANGUAGE_CODE = config('LANGUAGE_CODE', default='en-us')\n\nTIME_ZONE = config('TIME_ZONE', default='UTC')\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/2.0/howto/static-files/\n\nSTATIC_URL = '/static/'\n\n\n# Django REST Framework settings\n# http://www.django-rest-framework.org\n\nREST_FRAMEWORK = {\n 'DEFAULT_AUTHENTICATION_CLASSES': (\n 'rest_framework.authentication.BasicAuthentication',\n 'rest_framework.authentication.SessionAuthentication',\n ),\n 'DEFAULT_PERMISSION_CLASSES': (\n 'rest_framework.permissions.IsAuthenticated',\n ),\n 'DEFAULT_FILTER_BACKENDS': (\n 'django_filters.rest_framework.DjangoFilterBackend',\n ),\n 'DEFAULT_RENDERER_CLASSES': (\n 'rest_framework.renderers.JSONRenderer',\n ),\n}\n\n# Custom settings for DEBUG=True\nif DEBUG:\n REST_FRAMEWORK['DEFAULT_RENDERER_CLASSES'] += (\n 'rest_framework.renderers.BrowsableAPIRenderer',\n )\n" }, { "alpha_fraction": 0.675000011920929, "alphanum_fraction": 0.675000011920929, "avg_line_length": 19, "blob_id": "f038a455f057cb9ae3e7e136eb482eb01ada44b7", "content_id": "9d2774e0055d6e563f4bf3b6515d29135a317a8e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 40, "license_type": "no_license", "max_line_length": 30, "num_lines": 2, "path": "/core/tests.py", "repo_name": "sidneijp/luizalabs-employee-manager", "src_encoding": "UTF-8", "text": "def test_pytest_sanity_test():\n pass\n" } ]

18

ranta09/spelling-correction

https://github.com/ranta09/spelling-correction

5a873916d455bfb736e8cdce3f4d4504dae2421b

b42713da13e41b63e9a90609be0a4a79611e75ae

099076fd1e1757b76db76b06b8670ce791b5b732

refs/heads/master

2022-11-24T23:31:57.200775

2020-07-31T10:25:42

null

0

null

[ { "alpha_fraction": 0.44498777389526367, "alphanum_fraction": 0.4498777389526367, "avg_line_length": 41.05263137817383, "blob_id": "524d0f99cbdc0518eb8f2e8e6f62de7d2d9cbc47", "content_id": "ac0cf8d508c46fa68d6e504cde241da5b5d0cb23", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 818, "license_type": "no_license", "max_line_length": 84, "num_lines": 19, "path": "/correct.py", "repo_name": "ranta09/spelling-correction", "src_encoding": "UTF-8", "text": "\r\n################ follow @09programmer on facebook ################\r\n\r\nfrom textblob import TextBlob # install libraries\r\n\r\nfile=open(\"programmer.txt\", \"r+\") # open file in read mode \r\na=file.read() \r\n\r\nprint(\"original text : \"+str(a)) #print original text\r\n\r\nb=TextBlob(a) #convert the data type of textblob \r\nprint(\"corrected text : \"+str(b.correct())) # print corrected text\r\n\r\nfile.close() #close file\r\n\r\nd=open(\"programmer.txt\",\"w\") # open file in write mode\r\nd.write(str(b.correct())) # update file\r\nd.close() #close file\r\n\r\n################ follow @09programmer on facebook ################" }, { "alpha_fraction": 0.8237410187721252, "alphanum_fraction": 0.8237410187721252, "avg_line_length": 138, "blob_id": "db77cd81539d5a24a42ddbd7c654d447960cfac5", "content_id": "c3c7766cd58fbbb662f4dcd3181a1d372075b955", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 278, "license_type": "no_license", "max_line_length": 255, "num_lines": 2, "path": "/README.md", "repo_name": "ranta09/spelling-correction", "src_encoding": "UTF-8", "text": "# spelling-correction\nPython - Spelling Check. Checking of spelling is a basic requirement in any text processing or analysis. The python package pyspellchecker provides us this feature to find the words that may have been mis-spelled and also suggest the possible corrections.\n" } ]

2

danielseow234/flask-taskmaster

https://github.com/danielseow234/flask-taskmaster

c8fa53de6736f2491ce9e11c3f206c5f7603f857

564a8094229ab2938fe345d825d597cc122a8a10

bb8e10d2600aef6b0699c52b1aafccf77c6ed6ca

refs/heads/main

2022-12-27T22:36:05.822990

2020-10-08T06:26:17

302,059,289

0

null

[ { "alpha_fraction": 0.6063264012336731, "alphanum_fraction": 0.612174391746521, "avg_line_length": 36.25742721557617, "blob_id": "312d63551fd4c0b214fcafe5b3dd68c60f9ae6a0", "content_id": "9185f808b9fa2e5f663bdd3458b19478527b8317", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3762, "license_type": "no_license", "max_line_length": 77, "num_lines": 101, "path": "/app.py", "repo_name": "danielseow234/flask-taskmaster", "src_encoding": "UTF-8", "text": "from flask import Flask, flash, render_template, request, redirect, url_for\nimport bcrypt, uuid\nfrom datetime import datetime\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///data.db'\ndb = SQLAlchemy(app)\napp.secret_key = b'_5#y2L\"F4Q8z\\n\\xec]/'\n\nclass User(db.Model):\n id = db.Column(db.String, nullable=False, primary_key=True)\n username = db.Column(db.String(40), nullable=False, unique=True)\n password = db.Column(db.String(200), nullable=False)\n\nclass Todo(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(40), nullable=False, unique=False)\n task = db.Column(db.String(200), nullable=False)\n completed = db.Column(db.Integer, default=0)\n date_created = db.Column(db.DateTime, default=datetime.utcnow)\n\[email protected]('/', methods=['POST', 'GET'])\ndef login():\n if request.method == 'POST':\n form_username = request.form['username']\n form_password = request.form['password']\n bytes_password = form_password.encode('utf-8')\n user_search = User.query.filter_by(username=form_username).first()\n if user_search != None:\n if bcrypt.checkpw(bytes_password, user_search.password):\n return redirect(url_for('home', id=user_search.id))\n else:\n flash('Password was incorrect.')\n else:\n flash('User not found.')\n return render_template('login.html')\n\[email protected]('/signup', methods=['POST', 'GET'])\ndef signup():\n if request.method == 'POST':\n new_username = request.form['username']\n new_password = request.form['password']\n bytes_password = new_password.encode('utf-8')\n hashed_password = bcrypt.hashpw(bytes_password, bcrypt.gensalt())\n user_check = User.query.filter_by(username=new_username).first()\n u = uuid.uuid4()\n new_user = User(id=u.hex, username=new_username, password=hashed_password)\n print(u.hex)\n if user_check == None:\n try:\n db.session.add(new_user)\n db.session.commit()\n return redirect('/')\n except:\n return 'There was an issue adding your account.'\n else:\n flash('Username is taken. Try again.')\n return render_template('signup.html')\n\[email protected]('/home/<id>', methods=['POST', 'GET'])\ndef home(id):\n user = User.query.filter_by(id=id).first()\n tasks = Todo.query.filter_by(username=user.username).all()\n if request.method == 'POST':\n task = request.form['task']\n task_username = user.username\n new_task = Todo(task=task, username=task_username)\n try:\n db.session.add(new_task)\n db.session.commit()\n return redirect(url_for('home', id=user.id))\n except:\n return 'There was an issue adding your task'\n return render_template('home.html', user=user, tasks=tasks, id=id)\n\[email protected]('/home/<id>/delete/<idt>')\ndef delete(id, idt):\n task_to_delete = Todo.query.get_or_404(idt)\n try:\n db.session.delete(task_to_delete)\n db.session.commit()\n return redirect(url_for('home', id=id))\n except:\n return 'There was a problem deleting that task.'\n\[email protected]('/home/<id>/update/<idt>', methods=['GET', 'POST'])\ndef update(id, idt):\n item = Todo.query.get_or_404(idt)\n if request.method == 'POST':\n item.task = request.form['task']\n try:\n db.session.commit()\n return redirect(url_for('home', id=id))\n except:\n return \"There was an issue updating your task\"\n else:\n return render_template('update.html', item=item, id=id)\n\nif __name__ == \"__main__\":\n app.run(debug=True)" }, { "alpha_fraction": 0.7798690795898438, "alphanum_fraction": 0.7798690795898438, "avg_line_length": 151.75, "blob_id": "fac825804b1fb4ebdaaf697889bd9edcefc87e74", "content_id": "b4748887e5a88d50bffdf6c179c061750b14c340", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1222, "license_type": "no_license", "max_line_length": 487, "num_lines": 8, "path": "/README.md", "repo_name": "danielseow234/flask-taskmaster", "src_encoding": "UTF-8", "text": "# flask-taskmaster\nSimple flask website that allows user to create and remove tasks on a todo list.\n\nI used the Flask python package as the backend of the website. I didn't use any CSS as I was kinda lazy so the website looks really blank. I bodged a simple login and sign up system that stores the user details and tasks on an SQLite database, and I used the SQLAlchemy module in python. Flask has a module named Flask-login, which adds user authentication to pages, and adds many more features. Since I didn't use that I had to come up with a way to secure the website for users. \n\nAt first, the link to a specific user's tasks were linked using \"/home/'username'\". This would be terrible, as other users could simply find out another user's username, and access their home page. To counter this I implemented a randomly generated user ID, that could be used for the link instead like \"/home/'user ID'\". This isn't the most secure method, as a user's ID could be copied from their web browser, or it could be pulled from the database if there were a breach in security.\n\nIf I were to re-do this project, I would definitely implement the flask-login module, and I would use the login-authenticator to secure users' data.\n" } ]

2

MasterOfTheMojave/JetBrains-Academy

https://github.com/MasterOfTheMojave/JetBrains-Academy

f164f32b88b8efbc2e43a64577c06d6937e4d3ca

318c5506a99eb79c25446fb258c9b9f6615ea4d0

98e6e2edf7133c72d400bb0bc4666db748bd2b90

refs/heads/master

2023-07-14T17:53:32.473142

2021-08-19T19:01:39

288,516,488

0

null

[ { "alpha_fraction": 0.3663688004016876, "alphanum_fraction": 0.3663688004016876, "avg_line_length": 35.20588302612305, "blob_id": "55390b18b3c62613d82a0041bd79d32c39aa9633", "content_id": "7f1f677161dc81d9b008aa2475af5161f6ef4069", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1247, "license_type": "no_license", "max_line_length": 48, "num_lines": 34, "path": "/Zookeeper/Stage_2_4.py", "repo_name": "MasterOfTheMojave/JetBrains-Academy", "src_encoding": "UTF-8", "text": "# For the second stage, you will need to develop\n# an animal printer. Your program should display\n# the animal identified in the code field.\n#\n# Please, don't remove the r character at the\n# start of the code template. It's a part of the\n# string and it's important. So, the string\n# should start with r\"\"\" sequence. This “r” at\n# the beginning stands for “raw” and allows\n# various characters to be used in a string\n# without escaping. For instance, “\\” in a\n# non-raw string should be escaped as\n# follows: “\\\\”.\n\nprint(\"\"\"\nSwitching on camera from habitat with camels...\n ___.-''''-.\n/___ @ |\n',,,,. | _.'''''''._\n ' | / \\\\\n | \\ _.-' \\\\\n | '.-' '-.\n | ',\n | '',\n ',,-, ':;\n ',,| ;,, ,' ;;\n ! ; !'',,,',',,,,'! ; ;:\n : ; ! ! ! ! ; ; :;\n ; ; ! ! ! ! ; ; ;,\n ; ; ! ! ! ! ; ; \n ; ; ! ! ! ! ; ;\n ;,, !,! !,! ;,;\n /_I L_I L_I /_I\nYey, our little camel is sunbathing!\"\"\")\n" }, { "alpha_fraction": 0.7358490824699402, "alphanum_fraction": 0.7358490824699402, "avg_line_length": 32.25, "blob_id": "6a060d931d32d650f92f5b9050ef9eec0ab6ac22", "content_id": "1b3f7eaada160be450b0f452ab5a377ab27bc045", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 265, "license_type": "no_license", "max_line_length": 63, "num_lines": 8, "path": "/Zookeeper/Stage_1_4.py", "repo_name": "MasterOfTheMojave/JetBrains-Academy", "src_encoding": "UTF-8", "text": "# To begin with, you will develop a simple printer.\n# Your program should display the text from the output example.\n\nprint(\"I do love animals!\")\nprint(\"Start looking after animals...\")\nprint(\"Deer looks fine.\")\nprint(\"Bat looks happy.\")\nprint(\"Lion looks healthy.\")" } ]

2

IBM/MAX-Inception-ResNet-v2

https://github.com/IBM/MAX-Inception-ResNet-v2

995356bcfdac5667e212ae8bfa61551fd14c81d5

a23c1f6ee543feefc0df1959c02f07cc44fd0999

e75f56c8c5c81750303373d5e9c8cf4861c19246

refs/heads/master

2023-05-27T15:44:50.038755

2021-06-11T04:56:06

124,603,311

28

24

Apache-2.0

2018-03-09T23:15:53

2023-04-12T12:00:36

2023-05-23T00:40:57

Python

[ { "alpha_fraction": 0.6740947365760803, "alphanum_fraction": 0.7013927698135376, "avg_line_length": 34.900001525878906, "blob_id": "e5c5d5efc8510000987838d0ef71a33d281d4b8e", "content_id": "abfcef3c32e614e2b146f9dadd3c8ca36153d808", "detected_licenses": [ "Apache-2.0", "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1795, "license_type": "permissive", "max_line_length": 87, "num_lines": 50, "path": "/config.py", "repo_name": "IBM/MAX-Inception-ResNet-v2", "src_encoding": "UTF-8", "text": "#\n# Copyright 2018-2019 IBM Corp. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n# Flask settings\nDEBUG = False\n\n# Flask-restplus settings\nRESTPLUS_MASK_SWAGGER = False\nSWAGGER_UI_DOC_EXPANSION = 'none'\n\n# API Metadata\nAPI_TITLE = 'MAX Image Classifier - Inception ResNet v2'\nAPI_DESC = 'Identify objects in images using a third-generation deep residual network.'\nAPI_VERSION = '1.2.0'\n\n# Model settings\nkeras_builtin_models = {\n 'inception_v3': {'size': (299, 299), 'license': 'Apache v2'},\n 'inception_resnet_v2': {'size': (299, 299), 'license': 'Apache v2'},\n 'xception': {'size': (299, 299), 'license': 'MIT'},\n 'resnet50': {'size': (224, 224), 'license': 'MIT'}\n}\n\n# default model\nMODEL_NAME = 'inception_resnet_v2'\nDEFAULT_MODEL_PATH = 'assets/{}.h5'.format(MODEL_NAME)\nMODEL_INPUT_IMG_SIZE = keras_builtin_models[MODEL_NAME]['size']\nMODEL_LICENSE = keras_builtin_models[MODEL_NAME]['license']\n\nMODEL_META_DATA = {\n 'id': '{}-keras-imagenet'.format(MODEL_NAME.lower()),\n 'name': '{} Keras Model'.format(MODEL_NAME),\n 'description': '{} Keras model trained on ImageNet'.format(MODEL_NAME),\n 'type': 'image_classification',\n 'license': MODEL_LICENSE,\n 'source': 'https://developer.ibm.com/exchanges/models/all/max-inception-resnet-v2/'\n}\n" }, { "alpha_fraction": 0.42465752363204956, "alphanum_fraction": 0.6575342416763306, "avg_line_length": 13.600000381469727, "blob_id": "8b07ef70e384b19a8d7136da1ac0fc1cdd2880c7", "content_id": "7080eacc4dbd4741f766da22b22f9bd51f78d9d6", "detected_licenses": [ "Apache-2.0", "MIT" ], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 73, "license_type": "permissive", "max_line_length": 16, "num_lines": 5, "path": "/requirements-test.txt", "repo_name": "IBM/MAX-Inception-ResNet-v2", "src_encoding": "UTF-8", "text": "pytest==6.2.0\nrequests==2.25.0\nflake8==3.8.4\nPillow==8.2.0\nbandit==1.6.2\n" }, { "alpha_fraction": 0.6827309131622314, "alphanum_fraction": 0.7309237122535706, "avg_line_length": 48.79999923706055, "blob_id": "2f847136e96f48018024c289394b2f115ef6b015", "content_id": "677ce3b5d460d652889b1d59c3089b708484a0dd", "detected_licenses": [ "Apache-2.0", "MIT", "CC0-1.0" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 498, "license_type": "permissive", "max_line_length": 145, "num_lines": 10, "path": "/samples/README.md", "repo_name": "IBM/MAX-Inception-ResNet-v2", "src_encoding": "UTF-8", "text": "# Sample Assets\n\n## Images\n\nAll test images are from [Pexels](https://www.pexels.com) and licensed under a [CC0 License](https://creativecommons.org/publicdomain/zero/1.0/).\n\n* [`dog.jpg`](https://www.pexels.com/photo/adorable-animal-animal-photography-beagle-452772/)\n* [`cat.jpg`](https://www.pexels.com/photo/cat-whiskers-kitty-tabby-20787/)\n* [`shuttle.jpg`](https://www.pexels.com/photo/flight-sky-earth-space-2166/)\n* [`pizza.jpg`](https://www.pexels.com/photo/baked-pepperoni-pizza-774487/)\n" }, { "alpha_fraction": 0.6808567643165588, "alphanum_fraction": 0.7212851643562317, "avg_line_length": 42.94117736816406, "blob_id": "2d3bf31997bda1afb9a5c0c216ec4057370fddc0", "content_id": "c5c31a6a6b24471ee10cfecfee97e2c7c2c6cd75", "detected_licenses": [ "Apache-2.0", "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 7470, "license_type": "permissive", "max_line_length": 543, "num_lines": 170, "path": "/README.md", "repo_name": "IBM/MAX-Inception-ResNet-v2", "src_encoding": "UTF-8", "text": "[![Build Status](https://travis-ci.org/IBM/MAX-Inception-ResNet-v2.svg?branch=master)](https://travis-ci.org/IBM/MAX-Inception-ResNet-v2) [![Website Status](https://img.shields.io/website/http/max-inception-resnet-v2.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud/swagger.json.svg?label=api+demo)](http://max-inception-resnet-v2.codait-prod-41208c73af8fca213512856c7a09db52-0000.us-east.containers.appdomain.cloud)\n\n[<img src=\"docs/deploy-max-to-ibm-cloud-with-kubernetes-button.png\" width=\"400px\">](http://ibm.biz/max-to-ibm-cloud-tutorial)\n\n# IBM Code Model Asset Exchange: Inception-ResNet-v2 Image Classifier\n\nThis repository contains code to instantiate and deploy an image classification model. This model recognizes the 1000 different classes of objects in the [ImageNet 2012 Large Scale Visual Recognition Challenge](http://www.image-net.org/challenges/LSVRC/2012/). The model consists of a deep convolutional net using the Inception-ResNet-v2 architecture that was trained on the ImageNet-2012 data set. The input to the model is a 299x299 image, and the output is a list of estimated class probabilities.\n\nThe model is based on the [Keras built-in model for Inception-ResNet-v2](https://keras.io/applications/#inceptionresnetv2). The model files are hosted on [IBM Cloud Object Storage](https://max-cdn.cdn.appdomain.cloud/max-inception-resnet-v2/1.0/assets.tar.gz). The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the [IBM Code Model Asset Exchange](https://developer.ibm.com/code/exchanges/models/) and the public API is powered by [IBM Cloud](https://ibm.biz/Bdz2XM).\n\n## Model Metadata\n| Domain | Application | Industry | Framework | Training Data | Input Data Format |\n| ------------- | -------- | -------- | --------- | --------- | -------------- | \n| Vision | Image Classification | General | Keras | [ImageNet](http://www.image-net.org/) | Image (RGB/HWC)| \n\n## References\n\n* _C. Szegedy, S. Ioffe, V. Vanhoucke, A. Alemi_, [\"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning\"](https://arxiv.org/abs/1602.07261), CoRR (abs/1602.07261), 2016.\n* [Keras Applications](https://keras.io/applications/#inceptionresnetv2)\n\n## Licenses\n\n| Component | License | Link |\n| ------------- | -------- | -------- |\n| This repository | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [LICENSE](LICENSE) |\n| Model Weights | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [Keras Inception-ResNet-v2](https://keras.io/applications/#inceptionresnetv2)|\n| Model Code (3rd party) | [MIT](https://opensource.org/licenses/MIT) | [Keras LICENSE](https://github.com/keras-team/keras/blob/master/LICENSE)|\n| Test assets | Various | [Samples README](samples/README.md) |\n\n## Pre-requisites:\n\n* `docker`: The [Docker](https://www.docker.com/) command-line interface. Follow the [installation instructions](https://docs.docker.com/install/) for your system.\n* The minimum recommended resources for this model is 2GB Memory and 2 CPUs.\n* If you are on x86-64/AMD64, your CPU must support [AVX](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) at the minimum.\n\n# Deployment options\n\n* [Deploy from Quay](#deploy-from-quay)\n* [Deploy on Red Hat OpenShift](#deploy-on-red-hat-openshift)\n* [Deploy on Kubernetes](#deploy-on-kubernetes)\n* [Run Locally](#run-locally)\n\n## Deploy from Quay\n\nTo run the docker image, which automatically starts the model serving API, run:\n\n```\n$ docker run -it -p 5000:5000 quay.io/codait/max-inception-resnet-v2\n```\n\nThis will pull a pre-built image from the Quay.io container registry (or use an existing image if already cached locally) and run it.\nIf you'd rather checkout and build the model locally you can follow the [run locally](#run-locally) steps below.\n\n## Deploy on Red Hat OpenShift\n\nYou can deploy the model-serving microservice on Red Hat OpenShift by following the instructions for the OpenShift web console or the OpenShift Container Platform CLI [in this tutorial](https://developer.ibm.com/tutorials/deploy-a-model-asset-exchange-microservice-on-red-hat-openshift/), specifying `quay.io/codait/max-inception-resnet-v2` as the image name.\n\n## Deploy on Kubernetes\n\nYou can also deploy the model on Kubernetes using the latest docker image on Quay.\n\nOn your Kubernetes cluster, run the following commands:\n\n```\n$ kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Inception-ResNet-v2/master/max-inception-resnet-v2.yaml\n```\n\nThe model will be available internally at port `5000`, but can also be accessed externally through the `NodePort`.\n\nA more elaborate tutorial on how to deploy this MAX model to production on [IBM Cloud](https://ibm.biz/Bdz2XM) can be found [here](http://ibm.biz/max-to-ibm-cloud-tutorial).\n\n## Run Locally\n\n1. [Build the Model](#1-build-the-model)\n2. [Deploy the Model](#2-deploy-the-model)\n3. [Use the Model](#3-use-the-model)\n4. [Development](#4-development)\n5. [Cleanup](#5-cleanup)\n\n### 1. Build the Model\n\nClone this repository locally. In a terminal, run the following command:\n\n```\n$ git clone https://github.com/IBM/MAX-Inception-ResNet-v2.git\n```\n\nChange directory into the repository base folder:\n\n```\n$ cd MAX-Inception-ResNet-v2\n```\n\nTo build the docker image locally, run: \n\n```\n$ docker build -t max-inception-resnet-v2 .\n```\n\nAll required model assets will be downloaded during the build process. _Note_ that currently this docker image is CPU only (we will add support for GPU images later).\n\n\n### 2. Deploy the Model\n\nTo run the docker image, which automatically starts the model serving API, run:\n\n```\n$ docker run -it -p 5000:5000 max-inception-resnet-v2\n```\n\n### 3. Use the Model\n\nThe API server automatically generates an interactive Swagger documentation page. Go to `http://localhost:5000` to load it. From there you can explore the API and also create test requests.\n\nUse the `model/predict` endpoint to load a test image (you can use one of the test images from the `samples` folder) and get predicted labels for the image from the API.\n\n![Swagger Doc Screenshot](docs/swagger-screenshot.png)\n\nYou can also test it on the command line, for example:\n\n```\n$ curl -F \"image=@samples/dog.jpg\" -X POST http://localhost:5000/model/predict\n```\n\nYou should see a JSON response like that below:\n\n```json\n{\n \"status\": \"ok\",\n \"predictions\": [\n {\n \"label_id\": \"n02088364\",\n \"label\": \"beagle\",\n \"probability\": 0.44505545496941\n },\n {\n \"label_id\": \"n02089867\",\n \"label\": \"Walker_hound\",\n \"probability\": 0.3902231156826\n },\n {\n \"label_id\": \"n02089973\",\n \"label\": \"English_foxhound\",\n \"probability\": 0.02027696929872\n },\n {\n \"label_id\": \"n02088632\",\n \"label\": \"bluetick\",\n \"probability\": 0.010103852488101\n },\n {\n \"label_id\": \"n02088238\",\n \"label\": \"basset\",\n \"probability\": 0.001649746671319\n }\n ]\n}\n```\n\n### 4. Development\n\nTo run the Flask API app in debug mode, edit `config.py` to set `DEBUG = True` under the application settings. You will then need to rebuild the docker image (see [step 1](#1-build-the-model)).\n\n### 5. Cleanup\n\nTo stop the Docker container, type `CTRL` + `C` in your terminal.\n\n## Resources and Contributions\n \nIf you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions [here](https://github.com/CODAIT/max-central-repo).\n" }, { "alpha_fraction": 0.684302568435669, "alphanum_fraction": 0.6917359232902527, "avg_line_length": 33.65151596069336, "blob_id": "09cbc41af0bca22d815768f9159458e1a4b7a33e", "content_id": "fc10a8f8ec3fbd88eec84a8f569c1d16dbabe3bf", "detected_licenses": [ "Apache-2.0", "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2287, "license_type": "permissive", "max_line_length": 90, "num_lines": 66, "path": "/core/model.py", "repo_name": "IBM/MAX-Inception-ResNet-v2", "src_encoding": "UTF-8", "text": "#\n# Copyright 2018-2019 IBM Corp. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nfrom PIL import Image\nfrom keras.backend import clear_session\nfrom keras import models\nfrom keras.preprocessing.image import img_to_array\nfrom keras.applications import imagenet_utils\nimport io\nimport numpy as np\nimport logging\nfrom flask import abort\nfrom config import DEFAULT_MODEL_PATH, MODEL_INPUT_IMG_SIZE, MODEL_META_DATA as model_meta\nfrom maxfw.model import MAXModelWrapper\n\nlogger = logging.getLogger()\n\n\nclass ModelWrapper(MAXModelWrapper):\n\n MODEL_META_DATA = model_meta\n\n def __init__(self, path=DEFAULT_MODEL_PATH):\n logger.info('Loading model from: {}...'.format(path))\n clear_session()\n\n self.model = models.load_model(path)\n # this seems to be required to make Keras models play nicely with threads\n self.model._make_predict_function()\n logger.info('Loaded model: {}'.format(self.model.name))\n\n def _read_image(self, image_data):\n try:\n image = Image.open(io.BytesIO(image_data)).convert('RGB')\n return image\n except IOError as e:\n logger.error(str(e))\n abort(400, \"The provided input is not a valid image (PNG or JPG required).\")\n\n def _pre_process(self, image, target, mode='tf'):\n image = image.resize(target)\n image = img_to_array(image)\n image = np.expand_dims(image, axis=0)\n image = imagenet_utils.preprocess_input(image, mode=mode)\n return image\n\n def _post_process(self, preds):\n return imagenet_utils.decode_predictions(preds)[0]\n\n def _predict(self, x):\n x = self._pre_process(x, target=MODEL_INPUT_IMG_SIZE)\n preds = self.model.predict(x)\n return self._post_process(preds)\n" }, { "alpha_fraction": 0.4027777910232544, "alphanum_fraction": 0.6527777910232544, "avg_line_length": 13.399999618530273, "blob_id": "1db4d02824862ce63fa4e20cb5ca160c9348f500", "content_id": "32140a0ec8bc81be169cca83b6c021202819a15b", "detected_licenses": [ "Apache-2.0", "MIT" ], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 72, "license_type": "permissive", "max_line_length": 18, "num_lines": 5, "path": "/requirements.txt", "repo_name": "IBM/MAX-Inception-ResNet-v2", "src_encoding": "UTF-8", "text": "numpy==1.17.5\ntensorflow==1.15.4\nPillow==8.2.0\nh5py==2.9.0\nkeras==2.1.4\n" } ]

6

JunshuTedLiu/KnowledgeGlobe

https://github.com/JunshuTedLiu/KnowledgeGlobe

0699a74ba52b4b1f847ebfe64812e7f4843eab08

55f6ecaea3fca6af06ec834b79552597b40b705a

4182803b97a507dbd0596bceaa3c692c720a44c8

refs/heads/master

2022-08-13T17:40:10.988403

2022-08-03T09:28:35

161,742,498

0

null

[ { "alpha_fraction": 0.5712318420410156, "alphanum_fraction": 0.5947972536087036, "avg_line_length": 64.36000061035156, "blob_id": "b18291a31b16a51d6125bc8a32cdf55f2d358aa6", "content_id": "2940d9cfffc9da4ccd66554bf2977d9c1997701a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "HTML", "length_bytes": 6537, "license_type": "no_license", "max_line_length": 450, "num_lines": 100, "path": "/old-version/contact-us.html", "repo_name": "JunshuTedLiu/KnowledgeGlobe", "src_encoding": "UTF-8", "text": "<!DOCTYPE html>\n<html>\n <head>\n <title>Contact Us - Knowledge Globe</title>\n <link rel=\"stylesheet\" href=\"style.css\">\n <link href=\"https://fonts.googleapis.com/css?family=Archivo+Black|Archivo+Narrow:400,400i,500,500i,600,600i,700,700i|Archivo:400,400i,500,500i,600,600i,700,700i&subset=latin-ext,vietnamese\" rel=\"stylesheet\">\n <link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/[email protected]/animate.min.css\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n \n <meta name=\"description\" content=\"Knowledge Globe, a.k.a. KnoGlo, is a powerful tool to analyze and visualize the distribution of research interests for the selected topic and year. You can see how these research interests distributed by subjects, keywords, countries, publishers, and publication types.\">\n </head>\n\n <body>\n <nav>\n\n <ul>\n <li><a href=\"index.html\"><img src=\"assets/KnoGlo.png\" class=\"icon\" width=100></a></li>\n <li><a href=\"visualizations.html\">visualizations</a></li>\n <li><a href=\"how-this-works.html\">how this works?</a></li>\n <li><a href=\"dataset-examples.html\">dataset examples</a></li>\n <li><a href=\"source-codes.html\">source codes</a></li>\n <li><a href=\"our-vision.html\">our vision</a></li>\n <li><strong><a href=\"contact-us.html\">contact us</a></strong></li>\n </ul>\n </nav>\n <div class=\"page\">\n <h1 class=\"large-title\">\n About the KnoGlo team.\n </h1>\n <p>\n And how to reach us out.\n </p>\n <hr>\n <p class=\"paragraph-head\">\n Knowledge Globe is a final project for the Data Management and Data Analysis course offered by the <strong>University of Chicago.</strong> Who made and participated in this project are students and faculties from UChicago's brand-new graduate program, <strong class=\"extreme-highlight\">Digital Studies of Language, Culture, and History.</strong>\n </p>\n \n <p>\n Learn more about UChicago's <a href=\"https://digitalstudies.uchicago.edu/\">Digital Studies</a> program.\n </p>\n\n <h2>\n Who are we?\n </h2>\n \n <p>\n <strong>Junshu Liu</strong> is the founder and director of this project. He is a graduate student in UChicago majoring Digital Studies of Language, Culture, and History. Learn more about Junshu and his past projects at <a href=\"https://junshuliu.com\">junshuliu.com</a>.\n </p>\n <p>\n Professor <strong>Jeffrey R. Tharsen</strong> is the instructor of the Data Analysis course. He is one of the advisors for this project. He is an excellent person who masters not only data science but also East Asian language and culture. With the help from Dr. Tharsen, I learned a lot of R programming skills, and he helped me a lot Learn more about Dr. Tharsen at his website, <a href=\"http://tharsen.net\">tharsen.net</a>.\n </p>\n <p>\n Professor <strong>Miller Prosser</strong> is the instructor of the Data Management course. He is also the advisors for Knowledge Globe. Dr. Prosser is also a professional data analyst that proficient in a lot of data management software and tools. As a beginner for data science, I learned a lot with the help of Dr. Prosser in the past 2 months. <a href=\"https://ochre.uchicago.edu/directory/miller-prosser\">Learn more</a> about Dr. Prosser.\n </p>\n <h2>Contact Us</h2>\n <p>We treasure your advice and feedback. You're welcomed to contact us with your valuable feedback to make our product better.</p>\n <dir class=\"numbers\">\n <dir class=\"number-and-caption centered-numbers\">\n <p class=\"number-small\"><strong>Junshu Liu</strong></p>\n <p class=\"number-caption\"><a href=\"mailto:[email protected]?Subject=Hi,%20I%20have%20some%20questions&#47advices%20in%20regard%20to%20Knowledge%20Globe\" target=\"_top\">[email protected]</a></p>\n <p class=\"number-caption\">Graduate student, software developer, graphic and UI/UX designer.</p>\n </dir>\n <dir class=\"number-and-caption centered-numbers\">\n <p class=\"number-small\"><strong>Jeffrey Tharsen</strong></p>\n <p class=\"number-caption\"><a href=\"mailto:[email protected]?Subject=Hi,%20I%20have%20some%20questions&#47advices%20in%20regard%20to%20Knowledge%20Globe\" target=\"_top\">[email protected]</a></p>\n <p class=\"number-caption\">Computational Scientist for the Digital Humanities in UChicago.</p>\n </dir>\n <dir class=\"number-and-caption centered-numbers\">\n <p class=\"number-small\"><strong>Miller Prosser</strong></p>\n <p class=\"number-caption\"><a href=\"mailto:[email protected]?Subject=Hi,%20I%20have%20some%20questions&#47advices%20in%20regard%20to%20Knowledge%20Globe\" target=\"_top\">[email protected]</a></p>\n <p class=\"number-caption\">Research Database Specialist in UChicago.</p>\n </dir>\n </dir>\n <hr>\n </div>\n </body>\n \n <footer>\n © 2018-2019 Knowledge Globe. All rights reserved.\n <br>\n <a href=\"https://www.junshuliu.com\">Junshu Liu</a>,\n <a href=\"http://tharsen.net\">Jeffrey R. Tharsen</a>, <a href=\"https://ochre.uchicago.edu/directory/miller-prosser\">Miller Prosser</a>.\n <hr>\n <a href=\"contact-us.html\">Contact Us</a>\n </footer>\n</html>" }, { "alpha_fraction": 0.5993953943252563, "alphanum_fraction": 0.6139893531799316, "avg_line_length": 65.625, "blob_id": "5b280075859537c0ddacf55434499cf403314862", "content_id": "3a35afd099b8605792e3926c15033cd1e6e28274", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "HTML", "length_bytes": 9605, "license_type": "no_license", "max_line_length": 669, "num_lines": 144, "path": "/corpus-and-NLP/case-studies.html", "repo_name": "JunshuTedLiu/KnowledgeGlobe", "src_encoding": "UTF-8", "text": "<!DOCTYPE html>\n<html lang=\"en\">\n\n<head>\n <meta charset=\"UTF-8\">\n <meta http-equiv=\"X-UA-Compatible\" content=\"IE=edge\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1, shrink-to-fit=no\">\n <meta http-equiv=\"x-ua-compatible\" content=\"ie=edge\">\n \n <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css\" integrity=\"sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T\" crossorigin=\"anonymous\">\n \n <link rel=\"stylesheet\" type=\"text/css\" href=\"../css/boot.css\">\n <title>Case Studies - Corpus & NLP - Knowledge Globe</title>\n\n <script>\n $(document).ready(function() {\n $(\".dropdown-toggle\").dropdown();\n });\n </script>\n</head>\n\n<body>\n <nav class=\"navbar fixed-top navbar-expand-lg navbar-custom\">\n <a class=\"navbar-brand\" href=\"../index.html\">\n \n <img src=\"../assets/KnoGlo2white.png\" class=\"align-top\" style=\"width: 70px;\">\n </a>\n <button class=\"navbar-toggler\" type=\"button\" data-toggle=\"collapse\" data-target=\"#navbarNav\">\n <span class=\"navbar-toggler-icon\"></span>\n </button>\n <div class=\"collapse navbar-collapse\" id=\"navbarNav\">\n <ul class=\"navbar-nav\">\n <li class=\"nav-item active\">\n <a class=\"nav-link\" href=\"../index.html\">Home</a>\n \n </li>\n <li class=\"nav-item dropdown\">\n <a class=\"nav-link dropdown-toggle\" href=\"#schedule\" id=\"navbarDropdown\" role=\"button\" data-toggle=\"dropdown\" aria-haspopup=\"true\" aria-expanded=\"false\">Statistical Metadata Visualization</a>\n <div class=\"dropdown-menu\">\n <a class=\"dropdown-item\" href=\"../stat-metadata-visual/how-to-use.html\">How to Use?</a>\n <a class=\"dropdown-item\" href=\"../stat-metadata-visual/case-studies.html\">Visualization Case Studies</a>\n <a class=\"dropdown-item\" href=\"../stat-metadata-visual/implementation.html\">Implementation & Source Code</a>\n <a class=\"dropdown-item\" href=\"../source-code/Statistical-Metadata-Visualization/Web/knoglo2-statistics-list.html\">Web Version</a>\n </div>\n </li>\n <li class=\"nav-item dropdown\">\n <a class=\"nav-link dropdown-toggle\" href=\"#schedule\" id=\"navbarDropdown\" role=\"button\" data-toggle=\"dropdown\" aria-haspopup=\"true\" aria-expanded=\"false\">Corpus Generator & NLP Analysis</a>\n <div class=\"dropdown-menu\">\n <a class=\"dropdown-item\" href=\"../corpus-and-NLP/how-to-use.html\">How to Use?</a>\n <a class=\"dropdown-item\" href=\"../corpus-and-NLP/case-studies.html\">Case Studies</a>\n <a class=\"dropdown-item\" href=\"../corpus-and-NLP/implementation.html\">Implementation & Source Code</a>\n </div>\n </li>\n <li class=\"nav-item\">\n <a class=\"nav-link\" href=\"../who-cares.html\">Who Cares?</a>\n </li>\n <li class=\"nav-item\">\n <a class=\"nav-link\" href=\"../about-knoglo.html\">About KnoGlo</a>\n </li>\n </ul>\n </div>\n </nav>\n \n\n \n <div style=\"margin-top: 140px;\">\n <h4 class=\"container-fluid\">Corpus Generator & NLP Analysis</h4>\n <hr>\n </div>\n \n <div class=\"jumbotron jumbotron-fluid\" style=\"background-color: #ffffff\">\n <div class=\"container\" style=\"\">\n <h2 class=\"display-2\">Case Studies.</h2>\n\n <p class=\"lead\">See KnoGlo’s corpus generator and NLP analysis tool in action and what those results mean to us.</p>\n\n </div>\n </div>\n \n\n \n \n\n <div class=\"jumbotron jumbotron-fluid\" style=\"background-color: #f2f2f2\">\n <div class=\"container\">\n \n \n <h2>Example 1. See how the Internet has evolved in the past 50 years.</h2>\n <p class=\"lead\">Keywords: <span class=\"text-monospace\">Internet</span>, <span class=\"text-monospace\">ARPANET</span>, and other related. Year: from <span class=\"text-monospace\">1969</span> to <span class=\"text-monospace\">2019</span>. 5 years as a gap.</p>\n <hr>\n <h5>Generating Corpus</h5>\n <p>There are 55 text documents inside the <a href=\"https://github.com/JunshuTedLiu/KnowledgeGlobe/tree/master/source-code/Corpus-Generator-and-NLP/Corpus%20Examples/Corpus%20Example%201%20-%20Computer%20and%20Internet\">corpus</a>. For each document, there are up to 50 titles and abstracts about a selected topic published in a specific year.</p>\n <p>For this research, I picked several keywords and a time range of 1969-2019 (5 years as a gap). Selected keywords include <span class=\"text-monospace\">Internet</span>, <span class=\"text-monospace\">ARPANET</span>, <span class=\"text-monospace\">computer network</span>, <span class=\"text-monospace\">computer</span>, and <span class=\"text-monospace\">World Wide Web</span>.</p>\n <p>When I was making the corpus, I found something interesting - there were NO results for the “World Wide Web” until 1994.</p>\n <h5>NLP Topic Modeling</h5>\n <p>I made a few attempts on different numbers of topics. When I increased the number of topics, while the word cloud seemed like it could provide more results of the top <span class=\"font-italic\">n</span> words, some of them are constantly repeating between topics. 4 topics return a fairly satisfying result in this case.</p>\n <p>View the complete topic modeling result with 4 topics by running <a href=\"https://github.com/JunshuTedLiu/KnowledgeGlobe/blob/master/source-code/Corpus-Generator-and-NLP/NLP%20Topic%20Modeling%20Example%201.ipynb\">this IPYNB file</a> in Jupyter Notebook.</p>\n <p class=\"small\">This file has the output results for Example 1. Do not re-run the code, or the old results will be overwritten.</p>\n <p>Note: after changing the topic number, be sure to change the row and columns for the distribution of word counts by dominant topic, word cloud, and word count & importance of topic keywords to match your topic numbers (i.e., 9 topics with a 3x3 visualization).</p>\n <h5>Compare with Statistical Metadata Results</h5>\n <p>We can compare the result of topic modeling from our corpus and the original subject and keyword tags from Springer. Although the topic modeling results might not contain everything that is included in Springer’s tags since my free API plan only allows me to get 50 titles and abstracts for each text file, we can find some topics share in common, like the “Medicine & Health” subject tag correlated to the top <span class=\"font-italic\">n</span> words, such as <span class=\"text-monospace\">protein</span>, <span class=\"text-monospace\">gene</span>, and <span class=\"text-monospace\">cell</span>, in one of the topics from the topic modeling result.</p>\n </div>\n </div>\n\n <div class=\"jumbotron jumbotron-fluid\" style=\"background-color: #f2f2f2\">\n <div class=\"container\">\n \n \n <h2>Example 2. Medicine in Japan.</h2>\n <p class=\"lead\">Keyword: <span class=\"text-monospace\">medicine</span>. Country: <span class=\"text-monospace\">Japan</span>. Year: from <span class=\"text-monospace\">1989</span> to <span class=\"text-monospace\">2019</span>. 5 years as a gap.</p>\n <hr>\n\n <p>Coming soon. Please check back later.</p>\n </div>\n </div>\n\n <footer class=\"bg-footer text-white d-none d-lg-block p-2\">\n <div class=\"container\">\n <div class=\"row\">\n <div class=\"col small\">\n\n\n \n <span>© 2018-2019 Knowledge Globe. All rights reserved.</span>\n <br>\n <span>Created by <a href=\"https://junshuliu.com\">Junshu Liu</a>. Advised by <a href=\"https://ochre.uchicago.edu/directory/david-schloen\">David Schloen</a>, <a href=\"http://tharsen.net/\">Jeffrey Tharsen</a>, and <a href=\"https://ochre.uchicago.edu/directory/miller-prosser\">Miller Prosser</a>.</span>\n </div>\n\n\n </div>\n </div>\n </footer>\n\n <script src=\"https://code.jquery.com/jquery-3.3.1.slim.min.js\" integrity=\"sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo\" crossorigin=\"anonymous\"></script>\n <script src=\"https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js\" integrity=\"sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1\" crossorigin=\"anonymous\"></script>\n <script src=\"https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js\" integrity=\"sha384-JjSmVgyd0p3pXB1rRibZUAYoIIy6OrQ6VrjIEaFf/nJGzIxFDsf4x0xIM+B07jRM\" crossorigin=\"anonymous\"></script>\n \n</body>\n\n</html>" }, { "alpha_fraction": 0.6720043420791626, "alphanum_fraction": 0.6957538723945618, "avg_line_length": 43.119049072265625, "blob_id": "23ab7596c475dfe02dbf43b6a96e69f5f8e1c398", "content_id": "e1c6cf7fd63d1057197a7f61419f01a6f9ca23ad", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "R", "length_bytes": 5558, "license_type": "no_license", "max_line_length": 183, "num_lines": 126, "path": "/source-code/Statistical-Metadata-Visualization/Python-and-R/KnoGlo-Visualization.R", "repo_name": "JunshuTedLiu/KnowledgeGlobe", "src_encoding": "UTF-8", "text": "# setwd(\"~/Documents/GitHub/KnowledgeGlobe/source-code/Analysis-and-Visualization\")\n\n# Every time before you switch to a new folder, you need to go back to the upper menu.\nsetwd(\"../\") # NOT FOR THE FIRST TIME OF USE!\n\n# Set your work directory, which should be the folder that contains the datasets that you want to visualize.\nsetwd(\"keyword_computer_year_1988\")\n\n# Save CSV tables\n\nsubject <- read.csv('statistics_subject.csv', stringsAsFactors = FALSE)\nkeyword <- read.csv('statistics_keyword.csv', stringsAsFactors = FALSE)\npub <- read.csv('statistics_pub.csv', stringsAsFactors = FALSE)\nyear <- read.csv('statistics_year.csv', stringsAsFactors = FALSE)\ncountry <- read.csv('statistics_country.csv', stringsAsFactors = FALSE)\ntype <- read.csv('statistics_type.csv', stringsAsFactors = FALSE)\n\nkeywordInput <- readline(prompt=\"Enter the topic (keyword) name that you've used in KnowledgeGlobe.py: \")\nkeywordInput\n\nyearInput <- readline(prompt=\"Enter the year that you've used in KnowledgeGlobe.py: \")\nyearInput\n\n# Visualizations\n\n# 1. subject\n\n# Bar Plot\npar(mar=c(12,4,4,1)) # apply margins since the name will be so long\nxcor <- barplot(subject$count, main = paste(\"Subjects of publications about\", keywordInput, \"in\", yearInput), names.arg=c(subject$value), las = 2, cex.names = 0.6)\ntext(x = xcor, y = subject$count, label = subject$count, pos = 3, cex = 0.8, col = \"red\")\ndev.off()\n# The 'mar' argument of 'par' sets the width of the margins in the order: 'bottom', 'left', 'top', 'right'. The default is to set 'left' to 4,\n# https://stackoverflow.com/questions/2807060/r-is-plotting-labels-off-the-page\n\n# Pie Chart\n# Not recommended! Unless you have paid API license with full access\n# The Basic plan for Springer API will only return the top 20 results for the statistics for each attribute\nslices <- c(subject$count)\nlbls <- c(subject$value)\npct <- round(slices/sum(slices)*100)\nlbls <- paste(lbls, pct) # add percents to labels \nlbls <- paste(lbls,\"%\",sep=\"\") # ad % to labels \npie(slices,labels = lbls, col=rainbow(length(lbls)), main=c(\"Subjects of publications about \", keywordInput, \" in \", yearInput))\n\n# 2. keyword\n\n# Bar Plot\nxcor1 <- barplot(keyword$count, main = c(\"Keywords of publications about \", keywordInput, \" in \", yearInput), names.arg=c(keyword$value), las = 2)\ntext(x = xcor1, y = keyword$count, label = keyword$count, pos = 3, cex = 0.8, col = \"red\")\n\n# Pie Chart\n# Not recommended! Unless you have paid API license with full access\n# The Basic plan for Springer API will only return the top 20 results for the statistics for each attribute\nslices1 <- c(keyword$count)\nlbls1 <- c(keyword$value)\npct1 <- round(slices1/sum(slices1)*100)\nlbls1 <- paste(lbls1, pct1) # add percents to labels\nlbls1 <- paste(lbls1,\"%\",sep=\"\") # ad % to labels\npie(slices1,labels = lbls1, col=rainbow(length(lbls1)), main=c(\"Keywords of publications about \", keywordInput, \" in \", yearInput))\n\n# 3. pub\nbarplot(pub$count, main = c(\"Publishers of the journals about \", keywordInput, \" in \", yearInput), names.arg=c(pub$value), col=rainbow(20, start=.7, end=.1), las = 2, cex.names = 0.9)\n\n# 4. year (total)\nyear$count\nbarplot(year$count, main = c(\"Quantities of publications about \", keywordInput, \" for each year\"), names.arg=c(year$value), las = 2)\n\n# 5. country\nbarplot(country$count, main = c(\"Quantities of publications about \", keywordInput, \" for each countries in \", yearInput), names.arg=c(country$value), las = 2)\n\n# 6. type\nslices2 <- c(type$count)\nlbls2 <- c(type$value)\npct2 <- round(slices2/sum(slices2)*100)\nlbls2 <- paste(lbls2, pct2) # add percents to labels\nlbls2 <- paste(lbls2,\"%\",sep=\"\") # ad % to labels\npie(slices2,labels = lbls2, col=rainbow(length(lbls2)), main=\"Types of publications about \", keywordInput, \" in \", yearInput)\n\ndev.off()\n\n# put everything together in one place.\n# (Need to fix the margin problem)\npar(mfrow = c(2,2))\n# be sure to run the code above.\n# 1. subject\nxcor <- barplot(subject$count, main = paste(\"Subjects of publications about\", keywordInput, \"in\", yearInput), names.arg=c(subject$value), las = 2, cex.names = 0.6)\ntext(x = xcor, y = subject$count, label = subject$count, pos = 3, cex = 0.8, col = \"red\")\n# 2. keyword\nxcor1 <- barplot(keyword$count, main = c(\"Keywords of publications about \", keywordInput, \" in \", yearInput), names.arg=c(keyword$value), las = 2)\ntext(x = xcor1, y = keyword$count, label = keyword$count, pos = 3, cex = 0.8, col = \"red\")\n# 3. pub\nbarplot(pub$count, main = c(\"Publishers of the journals about \", keywordInput, \" in \", yearInput), names.arg=c(pub$value), col=rainbow(20, start=.7, end=.1), las = 2, cex.names = 0.9)\n# 5. country\nbarplot(country$count, main = c(\"Quantities of publications about \", keywordInput, \" for each countries in \", yearInput), names.arg=c(country$value), las = 2)\n\ndev.off()\n\n# now, let's make something fancy!\n\n# R bokeh\n# Learn more at http://hafen.github.io/rbokeh\n# install.packages(\"rbokeh\")\nlibrary(rbokeh)\n\n# country\n# p <- figure(width = 1000, height = 600) %>%\n# ly_points(value, count, data = country,\n# color = value, glyph = value,\n# hover = list(value, count)) %>%\n# x_axis(angle = 45)\n# p\n\n# this looks better. I swapped the x and y axis.\n\n# country\np1 <- figure(width = 1000, height = 600) %>%\n ly_points(count, value, data = country,\n color = value, glyph = value,\n hover = list(count, value)) %>%\n theme_legend(background_fill_alpha = 0.25)\np1\n\n# Feel free to use any other visualization frameworks like ggplot.\n# install.packages(\"ggplot2\")\nlibrary(ggplot2)" }, { "alpha_fraction": 0.6923938989639282, "alphanum_fraction": 0.7013587355613708, "avg_line_length": 39.5625, "blob_id": "7de7878bdae4a5c8500d83eb9a6c2303361e2c59", "content_id": "0e642ee78ec28cd7cca5184e3751a7247fa41874", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7139, "license_type": "no_license", "max_line_length": 324, "num_lines": 176, "path": "/source-code/Statistical-Metadata-Visualization/Python-and-R/KnoGlo-Dataset-Generator-keyword-and-year.py", "repo_name": "JunshuTedLiu/KnowledgeGlobe", "src_encoding": "UTF-8", "text": "# Big thanks to Lanfei Liu, who built a tool in Python using Springer Metadata API and other APIs in order to generate literature review data.\n# Lanfei's code: github.com/lanfeiliu/SpringerAPI-ElsevierAPI_LiteratureReviewTable\n\n# By learning her code, I figured out how to gather data from the Springer metadata JSON output using Python with filters (query), manage these data, and create & write CSV files by columns.\n\n# I rewrote mostly everything for our own research for the Knowledge Globe project, while I kept her part of the code for querying, which is filtering by topic and year by user input. Once I figured out how she implements the query, I could add a new feature that could allow the user to filter the data by other conditions.\n\n# In the JSON metadata, \"records\" and \"facets\" has the same structure. Lanfei's code helped me to understand how to gather and organize the data. However, I rewrote these part of the code because we need to use the \"facets\" data for statistics (\"counts\"), instead of \"records\" that she was using.\n# The method of getting these data is the same as Lanfei's, but since we are making completely different things, I made some changes in the Springer_Article class - I made several lists directly for the columns in all datasets, instead of combining data in a string.\n\n### KNOWLEDGE GLOBE\n\n# KnowledgeGlobe.py\n# Junshu Liu\n\nimport unittest\nimport requests\nimport json\nimport csv\nimport os\n\nprint \"\\nKNOWLEDGE GLOBE\\n===============\\nSee how scholars around the world leading the evolution of knowledge.\\n\"\n\nprint \"Statistical Metadata Visualization. Filtered by keyword and year.\\n\"\n\nkeyword = raw_input(\"Say a topic. Could be a single word or phase.\\nTOPIC: \")\n\ndate_int = int(raw_input(\"\\nWhat range of time would you like to see? Name that year.\\nYEAR: \"))\n\n# Create target Directory if don't exist\nif not os.path.exists(\"keyword_%s_year_%i\" % (keyword,int(date_int))):\n os.mkdir(\"keyword_%s_year_%i\" % (keyword,int(date_int)))\n print 'Created directory \"keyword_%s_year_%i\"' % (keyword,int(date_int))\nelse:\n print 'A directory with the keyword \"%s\" and year \"%i\" already exists.\\nPlease try another keyword and year.\\nOr, delete the one that you had, and make a new one to get latest results.' % (keyword,int(date_int))\n\nspringer_keyword = \"?q=(\"+ \"%22\" + keyword.replace(\" \", \"%20\") + \"%22\" + \"%20AND%20year:\" + str(date_int) + \")\"\n\n\nspringer_api_key = raw_input(\"\\nEnter your Springer API key.\\nNeed help? Visit https://dev.springernature.com/ for more details.\\nAPI key: \")\n\nbase_url_springer = 'http://api.springer.com/metadata/json'\n\nurl_params_springer = {}\nurl_params_springer[\"api_key\"] = springer_api_key\nurl_params_springer[\"p\"] = 50 # June 2019: Looks like the maximum is 50 for the free plan. | December 2018: If put 200: 10 results will be returned. This doesn't affect for counts. I've tested this line of code and changed this value to 300 and 400, and the outputs are the same.\n\n# try:\n# \tfor_springer = open('statistics.txt').read()\n# \td_springer = json.loads(for_springer)\n# \tprint \"\\n=== ERROR: You have already have a saved results of Springer Metadata API in your directly. ===\\n\"\n# \tprint \"\\nIn order to not mess up your previously saved output result and dataset files, we are asking you to\"\n# \tprint \"\\ndelete or move your previous saved raw output and dataset files to the other place first,\"\n# \tprint \"\\nthen you can make a new request.\"\n# \tprint \"\\nYour API Key hasn't been used at this time, so don't worry. That doesn't count.\"\n\n# except:\n\t# d_springer = requests.get(base_url_springer + springer_keyword\n\t# \t\t\t\t\t\t\t,params=url_params_springer).json()\n\t#\n\t# print \"\\n=== See the result of Springer Metadata API in your directory. ===\\n\"\n\t#\n\t# fr_springer = open(\"statistics.txt\",\"w\")\n\t# fr_springer.write(json.dumps(d_springer))\n\t# fr_springer.close()\n\nd_springer = requests.get(base_url_springer + springer_keyword\n\t\t\t\t\t\t\t,params=url_params_springer).json()\n\nprint \"\\n=== See the result of Springer Metadata API in your directory. ===\\n\"\n\nfr_springer = open(\"keyword_%s_year_%i/statistics.txt\" % (keyword,int(date_int)),\"w\")\nfr_springer.write(json.dumps(d_springer)) # write the json output into the text file.\nfr_springer.close()\n\nclass Springer_Article():\n def __init__(self, facets={}):\n self.facets = facets\n\n def count(self):\n count_lst = [i['count'].encode('utf-8') for i in self.facets['values']]\n # combine_name = ', '.join(count_lst)\n return count_lst\n # return combine_name\n\n def value(self):\n value_lst = [i['value'].encode('utf-8') for i in self.facets['values']]\n # combine_name = ', '.join(value_lst)\n return value_lst\n # return combine_name\n\n\nfacet_cat_insts = [Springer_Article(facets) for facets in d_springer['facets']] # 6 items, means 6 attributes in \"facets\".\n\ncount = 0\n\nfor facets in facet_cat_insts:\n count = count + 1 # is 6\n\ncountlst = [i.count() for i in facet_cat_insts]\ncountlst_subject = countlst[0]\ncountlst_keyword = countlst[1]\ncountlst_pub = countlst[2]\ncountlst_year = countlst[3]\ncountlst_country = countlst[4]\ncountlst_type = countlst[5]\nvaluelst = [i.value() for i in facet_cat_insts]\nvaluelst_subject = valuelst[0]\nvaluelst_keyword = valuelst[1]\nvaluelst_pub = valuelst[2]\nvaluelst_year = valuelst[3]\nvaluelst_country = valuelst[4]\nvaluelst_type = valuelst[5]\n\nfile0=open('keyword_%s_year_%i/statistics_subject.csv' % (keyword,int(date_int)),'wb')\nwriter=csv.writer(file0)\n\nwriter.writerow(['count','value'])\nwriter.writerows(zip(countlst_subject, valuelst_subject))\n\nprint '-------- Created dataset file \"statistics_subject.csv\" -----------'\n\n# ---\n\nfile1=open('keyword_%s_year_%i/statistics_keyword.csv' % (keyword,int(date_int)),'wb')\nwriter=csv.writer(file1)\n\nwriter.writerow(['count','value'])\nwriter.writerows(zip(countlst_keyword, valuelst_keyword))\n\nprint '-------- Created dataset file \"statistics_keyword.csv\" -----------'\n\n# ---\n\nfile2=open('keyword_%s_year_%i/statistics_pub.csv' % (keyword,int(date_int)),'wb')\nwriter=csv.writer(file2)\n\nwriter.writerow(['count','value'])\nwriter.writerows(zip(countlst_pub, valuelst_pub))\n\nprint '-------- Created dataset file \"statistics_pub.csv\" -----------'\n\n# ---\n\nfile3=open('keyword_%s_year_%i/statistics_year.csv' % (keyword,int(date_int)),'wb')\nwriter=csv.writer(file3)\n\nwriter.writerow(['count','value'])\nwriter.writerows(zip(countlst_year, valuelst_year))\n\nprint '-------- Created dataset file \"statistics_year.csv\" -----------'\n\n# ---\n\nfile4=open('keyword_%s_year_%i/statistics_country.csv' % (keyword,int(date_int)),'wb')\nwriter=csv.writer(file4)\n\nwriter.writerow(['count','value'])\nwriter.writerows(zip(countlst_country, valuelst_country))\n\nprint '-------- Created dataset file \"statistics_country.csv\" -----------'\n\n# ---\n\nfile5=open('keyword_%s_year_%i/statistics_type.csv' % (keyword,int(date_int)),'wb')\nwriter=csv.writer(file5)\n\nwriter.writerow(['count','value'])\nwriter.writerows(zip(countlst_type, valuelst_type))\n\nprint '-------- Created dataset file \"statistics_type.csv\" -----------\\n'\n\n### For test purpose.\n# print subject_count\n# print count\n# print countlst_year\n" }, { "alpha_fraction": 0.7124773859977722, "alphanum_fraction": 0.7124773859977722, "avg_line_length": 60.5, "blob_id": "0e5dab563db7b757a0ef9966a263889f2e628fb2", "content_id": "f702ff7480dac17fa84bdf5d49628547fcb8188d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1106, "license_type": "no_license", "max_line_length": 382, "num_lines": 18, "path": "/README.md", "repo_name": "JunshuTedLiu/KnowledgeGlobe", "src_encoding": "UTF-8", "text": "# Knowledge Globe\n\n<p>See how researchers around the world leading the evolution of knowledge. No matter when and where.<p>\n\n<hr>\n\n<p>\n We present a new way to see how knowledge evolves by looking at what researchers around the world put their effort on specific industries in a range of time.\n </p>\n <p>\n By looking for what people were thinking and doing academically and professionally, we could observe the evolution in specific areas of study, and even how this world changes from a global and historical perspective.\n </p>\n\n<p>\n <strong>Introducing <strong class=\"extreme-highlight\">Knowledge Globe </strong>(KnoGlo),</strong> a powerful tool to <strong>analyze and visualize</strong> the distribution of research interests for the selected topic and year. You can see how these research interests distributed by <strong>subjects, keywords, countries, publishers, and publication types.</strong>\n </p>\n\n<p>Learn more at <a href=\"https://junshutedliu.github.io/KnowledgeGlobe/\">KnoGlo's official website.</a></p>" }, { "alpha_fraction": 0.7088035941123962, "alphanum_fraction": 0.7151994109153748, "avg_line_length": 42.573768615722656, "blob_id": "f345fd05614b32f8283e834a42e71dccb6b33eb7", "content_id": "9518ab0d1606f8e057a2382b82791f459667a5f1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5316, "license_type": "no_license", "max_line_length": 324, "num_lines": 122, "path": "/source-code/Corpus-Generator-and-NLP/KnoGlo-Corpus-Generator-keyword-and-year.py", "repo_name": "JunshuTedLiu/KnowledgeGlobe", "src_encoding": "UTF-8", "text": "# Big thanks to Lanfei Liu, who built a tool in Python using Springer Metadata API and other APIs in order to generate literature review data.\n# Lanfei's code: github.com/lanfeiliu/SpringerAPI-ElsevierAPI_LiteratureReviewTable\n\n# By learning her code, I figured out how to gather data from the Springer metadata JSON output using Python with filters (query), manage these data, and create & write CSV files by columns.\n\n# I rewrote mostly everything for our own research for the Knowledge Globe project, while I kept her part of the code for querying, which is filtering by topic and year by user input. Once I figured out how she implements the query, I could add a new feature that could allow the user to filter the data by other conditions.\n\n# In the JSON metadata, \"records\" and \"facets\" has the same structure. Lanfei's code helped me to understand how to gather and organize the data. However, I rewrote these part of the code because we need to use the \"facets\" data for statistics (\"counts\"), instead of \"records\" that she was using.\n# The method of getting these data is the same as Lanfei's, but since we are making completely different things, I made some changes in the Springer_Article class - I made several lists directly for the columns in all datasets, instead of combining data in a string.\n\n### KNOWLEDGE GLOBE\n\n# KnowledgeGlobe.py\n# Junshu Liu\n\nimport unittest\nimport requests\nimport json\nimport csv\nimport os\n\nprint \"\\nKNOWLEDGE GLOBE\\n===============\\nSee how researchers around the world leading the evolution of knowledge. No matter when and where.\\n\"\n\nprint \"Corpus Generator. Filtered by keyword and year.\\n\"\n\ncorpus_path = raw_input(\"Where do you want to store your content for this time?\\nEnter your corpus name. Make a new one, or enter an existing one.\\nCORPUS NAME: \")\nif not os.path.exists(\"Corpus_%s\" % corpus_path):\n os.mkdir(\"corpus_%s\" % corpus_path)\n print 'Created a new directory \"corpus_%s\" as your corpus.' % corpus_path\nelse:\n print 'A corpus directory \"corpus_%s\" already exists.\\nYour document will now be stored in this corpus,' % corpus_path\n\nif not os.path.exists(\"raw_JSON_for_corpus_%s\" % corpus_path):\n os.mkdir(\"raw_JSON_for_corpus_%s\" % corpus_path)\n print 'We also made another directory, \"Raw_JSON_%s\", to store the raw JSON output.' % corpus_path\nelse:\n print 'as well as the raw JSON output in \"raw_JSON_for_corpus_%s\".' % corpus_path\n\n\nkeyword = raw_input(\"\\nSay a topic. Could be a single word or phase.\\nTOPIC: \")\n\ndate_int = int(raw_input(\"\\nWhat range of time would you like to see? Name that year.\\nYEAR: \"))\n\nspringer_keyword = \"?q=(\"+ \"%22\" + keyword.replace(\" \", \"%20\") + \"%22\" + \"%20AND%20year:\" + str(date_int) + \")\"\n\n\nspringer_api_key = raw_input(\"\\nEnter your Springer API key.\\nNeed help? Visit https://dev.springernature.com/ for more details.\\nAPI key: \")\n\nbase_url_springer = 'http://api.springer.com/metadata/json'\n\nurl_params_springer = {}\nurl_params_springer[\"api_key\"] = springer_api_key\nurl_params_springer[\"p\"] = 50 # If put 200: 10 results will be returned. This doesn't affect for counts. I've tested this line of code and changed this value to 300 and 400, and the outputs are the same.\n\n# try:\n# \tfor_springer = open('raw_JSON_%s_%i.txt' % (keyword,int(date_int))).read()\n# \td_springer = json.loads(for_springer)\n#\n# except:\n# \td_springer = requests.get(base_url_springer + springer_keyword\n# \t\t\t\t\t\t\t\t,params=url_params_springer).json()\n#\n# \tprint \"\\n=== See the result of Springer Metadata API in your directory. ===\\n\"\n#\n# \tfr_springer = open('raw_JSON_%s_%i.txt' % (keyword,int(date_int)),\"w\")\n# \tfr_springer.write(json.dumps(d_springer))\n# \tfr_springer.close()\n\nd_springer = requests.get(base_url_springer + springer_keyword\n\t\t\t\t\t\t\t,params=url_params_springer).json()\n\nprint \"\\n=== See the result of Springer Metadata API in your directory. ===\\n\"\n\nfr_springer = open('raw_JSON_for_corpus_%s/raw_JSON_%s_%i.txt' % (corpus_path,keyword,int(date_int)),\"w\")\nfr_springer.write(json.dumps(d_springer))\nfr_springer.close()\n\nclass Springer_Article():\n def __init__(self, records={}):\n\t\tself.records = records\n\t\tself.title = records['title'].encode('utf-8')\n\t\tself.abstract = records['abstract'].encode('utf-8')[8:]# get rid of the word \"Abstract\"\n\n# facet_cat_insts = [Springer_Article(facets) for facets in d_springer['facets']] # 6 items, means 6 attributes in \"facets\".\n\narticle_insts = [Springer_Article(records) for records in d_springer['records']]\n\n# for i in article_insts:\n# \tif i.title in titlelst:\n# \t\tarticle_insts.remove(i)\n\n# count = 0\n#\n# for records in article_insts:\n# \tcount = count + 1\n\n# #Create Springer csv file\ntitlelst = [i.title for i in article_insts]\nabstractlst = [i.abstract for i in article_insts]\n\n# file1=open('abstract_full.csv','wb')\n# writer=csv.writer(file1)\n#\n# writer.writerow(['title','abstract'])\n# writer.writerow(zip(titlelst,abstractlst))\n\n\n\nalllistwithtuples = zip(titlelst,abstractlst)\n\nallinonestring = ' '.join([i for sub in alllistwithtuples for i in sub])\n\ntitlestr = \"\".join(allinonestring)\n\n# print zip(titlelst,abstractlst)\n\n# print titlestr\n\nfile1=open('Corpus_%s/title_and_abstract_%s_%i.txt' % (corpus_path,keyword,int(date_int)),'wb')\nfile1.write(titlestr)\n\nprint '--------Created text file \"title_and_abstract_%s_%i.txt\"-----------\\n' % (keyword,int(date_int))\n" } ]

6

lemeryb/Car-Class

https://github.com/lemeryb/Car-Class

5593d4760e617add482f96735e6e68f529ffe7ed

05b6edaa4cb4f20d8f04f986eb601f20b22d080e

4288c6be3d29aa479b7820fd7ea5f6053f768ed2

refs/heads/master

2020-09-08T06:49:17.027096

2019-11-12T02:42:46

221,050,596

0

null

[ { "alpha_fraction": 0.6166666746139526, "alphanum_fraction": 0.6722221970558167, "avg_line_length": 34.599998474121094, "blob_id": "4794210756fe817fa6cd2661f63733f8876f7fd7", "content_id": "d0ed3087c98cee01438b609db1ad7d248f8edb86", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 180, "license_type": "no_license", "max_line_length": 77, "num_lines": 5, "path": "/change-color.py", "repo_name": "lemeryb/Car-Class", "src_encoding": "UTF-8", "text": "from main import Car\n\npaint_car = input(\"Enter the color you want the car to be painted: \").title()\ncar = Car(\"Tesla\",\"S\",paint_car,\"2019\",\"$60,000\",\"0\")\ncar.print_description()\n\n\n" }, { "alpha_fraction": 0.5764706134796143, "alphanum_fraction": 0.5973856449127197, "avg_line_length": 27.185184478759766, "blob_id": "1ee0633e36abc9999e8bb035304b3f0795cf34f0", "content_id": "9b093af4c2db8fadfecca04b4a1a8397d641b6d5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 765, "license_type": "no_license", "max_line_length": 77, "num_lines": 27, "path": "/main.py", "repo_name": "lemeryb/Car-Class", "src_encoding": "UTF-8", "text": "# Title Car Class\n# Author: Benjamin Lemery\n# Date: 11/11/19\n# This program demonstrates how to use classes.\n# README: Start the program from change-color.py and/or odometer_increment.py\n\n\nclass Car:\n\n def __init__(self,make,model,color,year,price,mileage):\n self.make = make\n self.model = model\n self.color = color\n self.year = year\n self.price = price\n self.mileage = mileage\n\n def print_description(self):\n print(\"Manufacturer: \" + self.make)\n print(\"Model: \" + self.model)\n print(\"Color: \" + self.color)\n print(\"Year: \" + self.year)\n print(\"Price: \" + self.price)\n print(\"Mileage: \" + self.mileage)\n\ncar = Car(\"Tesla\",\"S\",\"Green\",\"2019\",\"$60,000\",\"0\")\ncar.print_description()\n\n\n\n\n" }, { "alpha_fraction": 0.5864332318305969, "alphanum_fraction": 0.6236323714256287, "avg_line_length": 27.5625, "blob_id": "11e9f968543b4b70444a2a9402949c2574d232aa", "content_id": "9fd4d73433394594f55fb916a10124baed5aa1ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 457, "license_type": "no_license", "max_line_length": 112, "num_lines": 16, "path": "/odometer_increment.py", "repo_name": "lemeryb/Car-Class", "src_encoding": "UTF-8", "text": "from main import Car\nimport time\nmileage = 0\n\nbegin_driving = int(input(\"Would you like to begin driving your new car?\\nPress 1 to drive.\\nPress 2 to exit.\"))\n\nif begin_driving == 1:\n print(\"Starting the car..\")\n while mileage < 3:\n time.sleep(1)\n mileage += 1\n print(\"Miles Driven: \" + str(mileage))\n car = Car(\"Tesla\",\"S\",\"Green\",\"2019\",\"$60,000\",str(mileage))\n car.print_description()\nelif begin_driving == 2:\n exit()\n" } ]

3

tjtgzxs/showCode

https://github.com/tjtgzxs/showCode

6ff4843a18789f18db93d876806613adddf10afa

e72d1691c386f06d9cd9658ffc8ca6cde9083790

a770848f2079bab6ef828d78659d8e56a5945986

refs/heads/master

2020-06-24T12:30:54.264099

2019-08-14T01:51:07

198,963,370

0

null

[ { "alpha_fraction": 0.5524017214775085, "alphanum_fraction": 0.5829694271087646, "avg_line_length": 24.41666603088379, "blob_id": "c03b4c3782a20cf523c5681b3311b0da879fafd2", "content_id": "97c44eb6660b5c5068f574aaef9bc288fa1ab2ae", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 916, "license_type": "no_license", "max_line_length": 85, "num_lines": 36, "path": "/addNumber/addNum.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "\"\"\"\nTalk is Cheap show me the code. --Linus Torvalds\nadd red number at a pic right head\n\"\"\"\nfrom PIL import Image,PSDraw,ImageDraw,ImageFont\nimport sys\nclass handle_pic(object):\n \"\"\"\n use pillow to handle picture\n \"\"\"\n def __init__(self,pic_path):\n self._pic=self.get_pic(pic_path)\n self._num=self.get_num()\n\n @classmethod\n def get_pic(cls,pic_path):\n im = Image.open(pic_path)\n return im\n\n @classmethod\n def get_num(cls):\n return 4\n\n def draw(self):\n base=self._pic.convert('RGBA')\n txt=Image.new(\"RGBA\",base.size,(255,255,255,0))\n fnt=ImageFont.truetype(\"./DroidSans.ttf\",size=100)\n d=ImageDraw.Draw(txt)\n d.text((base.size[0]*0.9, 10), str(self._num),fill=(255, 0, 0, 255),font=fnt)\n out = Image.alpha_composite(base, txt)\n\n out.show()\n\nif __name__==\"__main__\":\n pic=handle_pic('./4.jpeg')\n pic.draw()\n\n" }, { "alpha_fraction": 0.6260504126548767, "alphanum_fraction": 0.6260504126548767, "avg_line_length": 20.636363983154297, "blob_id": "5688651deabeee5cc98cae7e4acf4aef40ced76b", "content_id": "267a755b4f7b09dcfc0c4750bf86f87f4c04f222", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 238, "license_type": "no_license", "max_line_length": 81, "num_lines": 11, "path": "/predis.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "import redis\nimport settings\n\n\nclass predis():\n def __init__(self):\n self._r = redis.Redis(host=settings.REDIS_HOST, port=settings.REDIS_PORT)\n\n def rpush(self, code):\n self._r.rpush('codes', code)\n return True\n" }, { "alpha_fraction": 0.5316140055656433, "alphanum_fraction": 0.5465890169143677, "avg_line_length": 24.5744686126709, "blob_id": "657e8b9d1910f557b4cfaf09fca39b57f5d6dbab", "content_id": "82ce10f272539ab5baa61430465e38d14069f460", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1202, "license_type": "no_license", "max_line_length": 106, "num_lines": 47, "path": "/bargin_code/bargin.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "\"\"\"\nTalk is Cheap show me the code. --Linus Torvalds\n# task 01 generate 200 bargin codes\n# task 02 save code to mysql\n# task 03 save code to redis\n\"\"\"\nimport sys\nimport random\nimport string\nsys.path.append(\"..\")\nfrom sql import Sql\nfrom predis import predis\n\nclass bargin(object):\n def __init__(self):\n self._code = string.ascii_letters\n self._code += (\"\".join([str(x) for x in range(0, 10)]))\n\n def gene_code(self):\n code = \"\"\n for i in range(4):\n for j in range(4):\n code += random.choice(self._code) # random.choice return a random item in list/set/string\n if (i <= 2): code += \"-\"\n return code\n\n def generateList(self, num):\n list = []\n for i in range(num):\n code = self.gene_code()\n while code in list:\n code = self.gene_code()\n list.append(self.gene_code())\n return list\n\n def insert_code(self, codes):\n for code in codes:\n Sql.insert('codes', code=code)\n r=predis()\n r.rpush(code=code)\n return True\n\n\nif __name__ == \"__main__\":\n b = bargin()\n codes = b.generateList(200)\n b.insert_code(codes)\n" }, { "alpha_fraction": 0.5115044116973877, "alphanum_fraction": 0.5150442719459534, "avg_line_length": 23.565217971801758, "blob_id": "710b5fe34e48bded9c3c99619e63982322ebfe5f", "content_id": "a1ec41d263d270880397c0d82f0ecfdf52e11cf7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 565, "license_type": "no_license", "max_line_length": 55, "num_lines": 23, "path": "/statistic_word/english_text.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "\"\"\"\nTalk is Cheap show me the code. --Linus Torvalds\n# statistic words of an English text\n\"\"\"\nclass statistic():\n def statistic(self,path):\n with open(path,'r') as o:\n lines=o.read().replace(\"\\n\",\"\")\n statistic_count={}\n words=lines.split(\" \")\n for word in words:\n if(not word in statistic_count.keys()):\n statistic_count[word] =0\n statistic_count[word]+=1\n print(statistic_count)\n\n\n\n\n\nif __name__==\"__main__\":\n s=statistic()\n s.statistic(\"./test.txt\")\n" }, { "alpha_fraction": 0.5134615302085876, "alphanum_fraction": 0.517307698726654, "avg_line_length": 27.88888931274414, "blob_id": "a742cfe2dea8d4047c69e34baba19cb6a236152e", "content_id": "a6ea286d5bca4e5f4e623b41fa772134e94eadf6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 520, "license_type": "no_license", "max_line_length": 49, "num_lines": 18, "path": "/statistic_word_v2/statistic.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "\"\"\"\nTalk is Cheap show me the code. --Linus Torvalds\n# statistic words of an English text in directory\n\"\"\"\nimport glob\nimport os\nif __name__==\"__main__\":\n txts=glob.glob(os.path.join(\"test\",\"*.txt\"))\n word_dict={}\n for txt in txts:\n with open(txt,'r') as o:\n lines=o.read().replace(\"\\n\",\" \")\n words=lines.split(\" \")\n for word in words:\n if not word in word_dict.keys():\n word_dict[word]=0\n word_dict[word]+=1\n print(word_dict)\n" }, { "alpha_fraction": 0.5720930099487305, "alphanum_fraction": 0.5906976461410522, "avg_line_length": 25.9375, "blob_id": "0c460c853bf54dde15db46822ba98489e6082c4f", "content_id": "7ab332ec9ab82f3b504b305314e27d55173e487c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 430, "license_type": "no_license", "max_line_length": 55, "num_lines": 16, "path": "/handle_pic/handle.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "import glob\nimport os\nfrom PIL import Image\n\nclass handle_pic(object):\n def getPicPath(self,path):\n images=glob.glob(os.path.join(path,'*.jpg'))\n for image in images:\n im=Image.open(image)\n im.thumbnail((1920, 1280))#Resolution of xs\n print(im.format, im.size, im.mode)\n im.save(image,\"JPEG\")\n\nif __name__==\"__main__\":\n handle=handle_pic()\n handle.getPicPath(\".\")" }, { "alpha_fraction": 0.5957179069519043, "alphanum_fraction": 0.5969773530960083, "avg_line_length": 29.538461685180664, "blob_id": "782f2205670c526c5a6807f7b725e79cc77e6dfd", "content_id": "ab24de1635ee472ed38c1f7b44b92824e5f02666", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 794, "license_type": "no_license", "max_line_length": 140, "num_lines": 26, "path": "/sql.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "import mysql.connector\nimport settings\n\nMYSQL_HOSTS = settings.MYSQL_HOSTS\nMYSQL_USER = settings.MYSQL_USER\nMYSQL_PASSWORD = settings.MYSQL_PASSWORD\nMYSQL_PORT = settings.MYSQL_PORT\nMYSQL_DB = settings.MYSQL_DB\n\ncnx = mysql.connector.connect(user=MYSQL_USER, password=MYSQL_PASSWORD, host=MYSQL_HOSTS, database=MYSQL_DB,auth_plugin='mysql_native_password')\ncur = cnx.cursor(buffered=True)\n\n\nclass Sql:\n @classmethod\n def insert(cls, table, **kw):\n sql_key = \"\"\n sql_value = \"\"\n for key, value in kw.items():\n sql_key += \"`\" + str(key) + \"` \"\n sql_value += str(value)\n sql = \"INSERT INTO \" + str(table) + \" (\" + sql_key + \") \" \\\n + \" VALUES ('\" + str(sql_value) + \"') \"\n dd=1\n cur.execute(sql)\n cnx.commit()\n" }, { "alpha_fraction": 0.578199028968811, "alphanum_fraction": 0.7203791737556458, "avg_line_length": 22.55555534362793, "blob_id": "3998ac6db62ac1b6cc5e3048c265c393ff2c2416", "content_id": "af97e594d6bfb38e8b25c34d3417f3972fad7461", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 211, "license_type": "no_license", "max_line_length": 30, "num_lines": 9, "path": "/settings.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "# mysql configuration\nMYSQL_HOSTS = '192.168.99.100'\nMYSQL_USER = \"root\"\nMYSQL_PASSWORD = '123456'\nMYSQL_PORT = 3306\nMYSQL_DB = 'discount'\n# redis configuration\nREDIS_HOST='192.168.99.100'\nREDIS_PORT = 6379" }, { "alpha_fraction": 0.49940547347068787, "alphanum_fraction": 0.5136741995811462, "avg_line_length": 28.034482955932617, "blob_id": "c03377adb1766a5a31c0d8315fbf92feb1682e3d", "content_id": "a47a2328c3497d8625d30b89e486f474e85825e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 841, "license_type": "no_license", "max_line_length": 94, "num_lines": 29, "path": "/check_code/check.py", "repo_name": "tjtgzxs/showCode", "src_encoding": "UTF-8", "text": "import glob\nimport os\nimport re\ndef get_codes_in_dir(path):\n #test\n\n\n files=glob.glob(os.path.join(path,\"*.py\")) # Get all py files in directory\n total_count=0\n comment_count=0\n space_count=0\n code_count=0\n for file in files:\n with open(file,'r') as f:\n lines=f.readlines()\n for line in lines:\n total_count+=1\n regex1=re.compile(r\"(\\s*)#\")\n regex2=re.compile(r\"(\\s*)$\")\n if regex1.match(line):\n comment_count+=1\n elif regex2.match(line):\n space_count+=1\n else:\n code_count+=1\n return {\"code\":code_count,\"comment\":comment_count,\"space\":space_count,'total':total_count}\nif __name__==\"__main__\":\n count=get_codes_in_dir(\".\")\n print(count)" } ]

9

oliveira086/Liga

https://github.com/oliveira086/Liga

e0fc855603ce76131d96651978f7308aa41c4f82

145bf08f753ea880ddb69edda88b2680b1ebe3a7

362110824176245aef542782d9a2f4ac78f693d1

refs/heads/master

2020-12-27T20:35:27.798369

2020-02-14T16:16:06

238,044,307

0

null

[ { "alpha_fraction": 0.41754597425460815, "alphanum_fraction": 0.48748868703842163, "avg_line_length": 28.83783721923828, "blob_id": "834ab9df0adaa262ef298d5797477a0b7ef9cd21", "content_id": "9afa81691f44f167c681baf59a8e81805c40ea73", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3317, "license_type": "no_license", "max_line_length": 158, "num_lines": 111, "path": "/objeto.py", "repo_name": "oliveira086/Liga", "src_encoding": "UTF-8", "text": "from PIL import Image\nfrom PIL import ImageFont\nfrom PIL import ImageDraw\nimport telepot\nfrom telepot.loop import MessageLoop\nimport sys\nimport time\n\nbot = telepot.Bot('552538744:AAE5r1s7wRAHrdxHq6xYWUcRLnfVgar3xQo')\ndef handle(msg):\n content_type, chat_type, chat_id = telepot.glance(msg)\n\n if '/' in msg['text']:\n mensagemRecebida = msg['text'][1:]\n mensagemTratada = mensagemRecebida.split('/')\n\n lista = [\n {\n 'ativo': mensagemTratada[0],\n 'posicao': [90, 350],\n 'porcetagem': mensagemTratada[10],\n 'posicaoPorcentagem': [90, 405],\n 'cor':(0,255,0)\n },\n {\n 'ativo': mensagemTratada[1],\n 'posicao': [310, 350],\n 'porcetagem': mensagemTratada[11],\n 'posicaoPorcentagem': [310, 405],\n 'cor':(0,255,0)\n },\n\n {\n 'ativo': mensagemTratada[2],\n 'posicao': [535, 350],\n 'porcetagem': mensagemTratada[12],\n 'posicaoPorcentagem': [535, 405],\n 'cor':(0,255,0)\n },\n {\n 'ativo': mensagemTratada[3],\n 'posicao': [200, 520],\n 'porcetagem': mensagemTratada[13],\n 'posicaoPorcentagem': [200, 575],\n 'cor':(0,255,0)\n },\n {\n 'ativo': mensagemTratada[4],\n 'posicao': [430, 520],\n 'porcetagem': mensagemTratada[14],\n 'posicaoPorcentagem': [420, 575],\n 'cor':(0,255,0)\n },\n {\n 'ativo': mensagemTratada[5],\n 'posicao': [95, 840],\n 'porcetagem': mensagemTratada[15],\n 'posicaoPorcentagem': [80, 900],\n 'cor':(255,0,0)\n },\n {\n 'ativo': mensagemTratada[6],\n 'posicao': [310, 840],\n 'porcetagem': mensagemTratada[16],\n 'posicaoPorcentagem': [310, 900],\n 'cor':(255,0,0)\n },\n {\n 'ativo': mensagemTratada[7],\n 'posicao': [535, 840],\n 'porcetagem': mensagemTratada[17],\n 'posicaoPorcentagem': [530, 900],\n 'cor':(255,0,0)\n },\n {\n 'ativo': mensagemTratada[8],\n 'posicao': [200, 1010],\n 'porcetagem': mensagemTratada[18],\n 'posicaoPorcentagem': [200, 1070],\n 'cor':(255,0,0)\n },\n {\n 'ativo': mensagemTratada[9],\n 'posicao': [430, 1010],\n 'porcetagem': mensagemTratada[19],\n 'posicaoPorcentagem': [420, 1070],\n 'cor':(255,0,0)\n },\n ]\n\n \n imagem = Image.open('fundo.jpg')\n imagem.save('pronto.jpg')\n\n for i in range(10):\n imagem = Image.open('pronto.jpg')\n draw = ImageDraw.Draw(imagem)\n font = ImageFont.truetype(\"arial.ttf\", 30)\n\n draw.text((lista[i]['posicao'][0],lista[i]['posicao'][1]), '{}'.format(lista[i]['ativo']), (12, 12, 12), font=font)\n draw.text((lista[i]['posicaoPorcentagem'][0], lista[i]['posicaoPorcentagem'][1]), '{}'.format(lista[i]['porcetagem']), lista[i]['cor'], font=font)\n\n imagem.save('pronto.jpg')\n \n\n\nMessageLoop(bot, handle).run_as_thread()\nprint ('Online')\n\nwhile 1:\n time.sleep(10)\n\t\n\n\n\n" }, { "alpha_fraction": 0.5313059091567993, "alphanum_fraction": 0.6141068339347839, "avg_line_length": 32.681034088134766, "blob_id": "ce7a9490a9e0e980c2b6b76228fc38537e74d9d2", "content_id": "d5a293cea305515b86f2b13219fff8f40764b2c3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3918, "license_type": "no_license", "max_line_length": 114, "num_lines": 116, "path": "/index.py", "repo_name": "oliveira086/Liga", "src_encoding": "UTF-8", "text": "from PIL import Image\nfrom PIL import ImageFont\nfrom PIL import ImageDraw\nimport sys\nimport time\nimport telepot\nfrom telepot.loop import MessageLoop\nimport requests\n\nbot = telepot.Bot('552538744:AAE5r1s7wRAHrdxHq6xYWUcRLnfVgar3xQo')\ndef handle(msg):\n content_type, chat_type, chat_id = telepot.glance(msg)\n\n if msg['text'] == 'start':\n\t bot.sendMessage(chat_id,'''Digite a lista de ativos que desejas adicionar, contendo as seguintes informações:\n\t\t ID do ativo, porcetagem (alta ou baixa) e a sua cotação atual, exemplo:\nPETRA3 / +1,41% / R$ 12,99\nJBS3 / -0,41% / R$ 2,99''')\n if '/' in msg['text']:\n\t mensagem = msg['text']\n\t #print(mensagem)\n\t\t#=== Recortar a string e armazenar valores =====\n\t ativoUm = mensagem[1:6]\n\t ativoDois = mensagem[7:12]\n\t ativoTres = mensagem[13:18]\n\t ativoQuatro = mensagem[19:24]\n\t ativoCinco = mensagem[25:30]\n\t ativoSeis = mensagem[31:36]\n\t ativoSete = mensagem[37:42]\n\t ativoOito = mensagem[43:48]\n\t ativoNove = mensagem[49:54]\n\t ativoDez = mensagem[55:60]\n\t \n\t IMAGEM = Image.open(\"fundo.jpg\")\n\t draw = ImageDraw.Draw(IMAGEM)\n\t\t# font = ImageFont.truetype(<font-file>, <font-size>)\n\t font = ImageFont.truetype(\"arial.ttf\", 30)\n\t\t\n\t draw.text((95, 350), '{}'.format(ativoUm), (0, 0, 0), font=font)\n\t draw.text((310, 350), '{}'.format(ativoDois), (0, 0, 0), font=font)\n\t draw.text((535, 350), '{}'.format(ativoTres), (0, 0, 0), font=font)\n\t draw.text((200, 520), '{}'.format(ativoQuatro), (0, 0, 0), font=font)\n\t draw.text((430, 520), '{}'.format(ativoCinco), (0, 0, 0), font=font)\n\t\t\n\t draw.text((95, 840), '{}'.format(ativoSeis), (0, 0, 0), font=font)\n\t draw.text((310, 840), '{}'.format(ativoSete), (0, 0, 0), font=font)\n\t draw.text((535, 840), '{}'.format(ativoOito), (0, 0, 0), font=font)\n\t draw.text((200, 1010), '{}'.format(ativoNove), (0, 0, 0), font=font)\n\t draw.text((430, 1010), '{}'.format(ativoDez), (0, 0, 0), font=font)\n\t\t\n\t img.save('pronto.jpg')\n\t\n if '>' in msg['text']:\n\t mensagem = msg['text']\n\t porcentagemUm = mensagem[1:7]\n\t porcentagemDois = mensagem[8:14]\n\t porcentagemTres = mensagem[15:21]\n\t porcentagemQuatro = mensagem[22:28]\n\t porcentagemCinco = mensagem[29:35]\n\t porcentagemSeis = mensagem[36:42]\n\t porcentagemSete = mensagem[43:49]\n\t porcentagemOito = mensagem[50:56]\n\t porcentagemNove = mensagem[57:63]\n\t porcentagemDez = mensagem[64:70]\n\t img = Image.open(\"balanço.jpg\")\n\t draw = ImageDraw.Draw(img)\n\t\t# font = ImageFont.truetype(<font-file>, <font-size>)\n\t font = ImageFont.truetype(\"arial.ttf\", 30)\n\t\t\n\t draw.text((80, 405), '{}'.format(porcentagemUm), (0, 255, 0), font=font)\n\t draw.text((310, 405), '{}'.format(porcentagemDois), (0, 255, 0), font=font)\n\t draw.text((535, 405), '{}'.format(porcentagemTres), (0, 255, 0), font=font)\n\t draw.text((200, 575), '{}'.format(porcentagemQuatro), (0, 255, 0), font=font)\n\t draw.text((420, 575), '{}'.format(porcentagemCinco), (0, 255, 0), font=font)\n\t\t\n\t draw.text((80, 900), '{}'.format(porcentagemSeis), (255,0,0), font=font)\n\t draw.text((310, 900), '{}'.format(porcentagemSete), (255,0,0), font=font)\n\t draw.text((530, 900), '{}'.format(porcentagemOito), (255,0,0), font=font)\n\t draw.text((200, 1070), '{}'.format(porcentagemNove), (255,0,0), font=font)\n\t draw.text((420, 1070), '{}'.format(porcentagemDez), (255,0,0), font=font)\n\t \n\t img.save('pronto.jpg')\n\t\t\n\t\t\n\t url = \"https://api.telegram.org/bot552538744:AAE5r1s7wRAHrdxHq6xYWUcRLnfVgar3xQo/sendPhoto\";\n\t files = {'photo': open('pronto.jpg', 'rb')}\n\t data = {'chat_id' : chat_id}\n\t r= requests.post(url, files=files, data=data)\n\t print(r.status_code, r.reason, r.content)\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\nMessageLoop(bot, handle).run_as_thread()\nprint ('Online')\n\n# Keep the program running.\nwhile 1:\n time.sleep(10)\n\t\n\t\n\t\n" } ]

2

harkiratdhanoa/mammograms

https://github.com/harkiratdhanoa/mammograms

38b9f982c57438d72ed76b81d1dc46fb0dc49839

7bda7ec43cf610021c7408f61dd0b73d1b6dab1e

954fb092fcd8809e9f50e8f19bef57faa037bf70

refs/heads/master

2021-10-20T07:33:36.536496

2019-02-26T15:31:31

172,738,252

0

null

[ { "alpha_fraction": 0.6795398592948914, "alphanum_fraction": 0.6894001364707947, "avg_line_length": 21.943395614624023, "blob_id": "518cb136cc7f8eba4afbff39d74ca51a15640255", "content_id": "24937f1a9790e6b9e6f4ed73d43da921fcc4830b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1217, "license_type": "no_license", "max_line_length": 73, "num_lines": 53, "path": "/embeddings.py", "repo_name": "harkiratdhanoa/mammograms", "src_encoding": "UTF-8", "text": "from gensim.models import Word2Vec\nfrom sklearn.decomposition import PCA\nfrom matplotlib import pyplot\n\nimport nltk\n\nfrom nltk.corpus import stopwords\n\nstopw = stopwords.words('english')\nstopw.append('also')\n\ndef readFile(file):\n f=open(file,'r',encoding='utf-8')\n text=f.read()\n \n sentences=nltk.sent_tokenize(text)\n \n data = []\n \n for sent in sentences:\n words = nltk.word_tokenize(sent)\n words = [w.lower() for w in words if len(w)>2 and w not in stopw]\n data.append(words)\n \n return data\n\nsentences = readFile(\"bollywood.txt\")\n\n# train model\nmodel = Word2Vec(sentences, min_count=1)\nprint(model)\n# summarize vocabulary\nwords = list(model.wv.vocab)\nprint(words)\n# access vector for one word\nprint(model['actress'])\n# save model\nmodel.save('model.bin')\n# load model\nnew_model = Word2Vec.load('model.bin')\nprint(new_model)\n\n\n# fit a 2d PCA model to the vectors\nX = model[model.wv.vocab]\npca = PCA(n_components=2)\nresult = pca.fit_transform(X)\n# create a scatter plot of the projection\npyplot.scatter(result[:, 0], result[:, 1])\nwords = list(model.wv.vocab)\nfor i, word in enumerate(words):\n\tpyplot.annotate(word, xy=(result[i, 0], result[i, 1]))\npyplot.show()\n\n" } ]

1

arizuk/misc

https://github.com/arizuk/misc

53b1268cdf9a85dbcf8ad8c7d7bc06dd171a2f35

a0d92a0e0f1263b4456a2c8e49fdba1816f1113f

1924be662cc2d728254030d2bbc4773bab6c4899

refs/heads/master

2020-01-18T22:45:03.077753

2020-01-18T13:41:41

82,464,098

0

null

[ { "alpha_fraction": 0.7368420958518982, "alphanum_fraction": 0.7368420958518982, "avg_line_length": 18, "blob_id": "47c056d690dee7ad08876401d45fa8cea58b7c3c", "content_id": "f70ba1b0845c1efacb07ba12b20c39ee8b98b180", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 19, "license_type": "no_license", "max_line_length": 18, "num_lines": 1, "path": "/python/namespace_packages/hogehoge/__init__.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "from . import fuga\n" }, { "alpha_fraction": 0.5461847186088562, "alphanum_fraction": 0.5542168617248535, "avg_line_length": 17.2439022064209, "blob_id": "c11c6b15017ab03ff155eff3655a48fe153ef48f", "content_id": "02d6b2043afef4e06aba8721efca2a0ad7283852", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 747, "license_type": "no_license", "max_line_length": 51, "num_lines": 41, "path": "/python/queue/main.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "from queue import Queue, Empty\nfrom threading import Thread\nfrom time import sleep\n\n\nTHREAD_POOL_SIZE = 4\n\ndef worker(worker_queue):\n while not worker_queue.empty():\n try:\n item = worker_queue.get(block=False)\n except Empty:\n break\n else:\n sleep(1)\n print(item)\n worker_queue.task_done()\n\n\ndef main():\n worker_queue = Queue()\n\n for i in range(1, 20, 2):\n worker_queue.put(i)\n\n threads = [\n Thread(target=worker, args=(worker_queue,))\n for _ in range(THREAD_POOL_SIZE)\n ]\n\n for thread in threads:\n thread.start()\n\n worker_queue.join()\n\n while threads:\n threads.pop().join()\n\n\nif __name__ == \"__main__\":\n main()" }, { "alpha_fraction": 0.5782155394554138, "alphanum_fraction": 0.5816918015480042, "avg_line_length": 11, "blob_id": "392ed9305b1ec8a898d00d82114cd8767fda996c", "content_id": "f32820dbb2999427455b37619f5240e2384e5ed5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 863, "license_type": "no_license", "max_line_length": 45, "num_lines": 72, "path": "/book/3_4_queue_via_stack.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "class Stack\n attr_accessor :min, :top\n\n def push(val)\n min = top&.min || val\n node = StackNode.new(val, [val, min].min)\n node.next = top\n self.top = node\n end\n\n def pop\n return unless top\n val = top.val\n self.top = top.next\n return val\n end\n\n def min\n top&.min\n end\n\n def peek\n top.val\n end\nend\n\n\nclass StackNode\n attr_accessor :val, :next, :min\n\n def initialize(val, min)\n @val = val\n @min = min\n end\nend\n\n\nclass Queue\n def initialize\n @newest = Stack.new\n @oldest = Stack.new\n end\n\n def add(val)\n @newest.push(val)\n end\n\n def remove\n val = @oldest.pop\n return val if val\n\n while val = @newest.pop\n @oldest.push(val)\n end\n @oldest.pop\n end\nend\n\n\nq = Queue.new\n\nn = 5\n\nn.times do ||\n val = rand(20)\n puts \"push #{val}\"\n q.add(val)\nend\n\nn.times do\n puts \"pop #{q.remove()}\"\nend" }, { "alpha_fraction": 0.4559585452079773, "alphanum_fraction": 0.46891191601753235, "avg_line_length": 16.590909957885742, "blob_id": "056254b21b4772ffa13178355bb5f771612fb371", "content_id": "06e5c9209a1387ef82f447a7b60d5e2be70648ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 386, "license_type": "no_license", "max_line_length": 36, "num_lines": 22, "path": "/python/__get__/main.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "class A:\n def __init__(self, val):\n self.val = val\n\n def __get__(self, obj, objtype):\n print(\"__get__ called\")\n return self.val\n\n def __set__(self, obj, val):\n print(\"__set__ called\")\n self.val = val\n return self.val\n\nclass Klass:\n a = A(10)\n\n\nif __name__ == \"__main__\":\n a = A(1)\n klass = Klass()\n klass.a\n klass.a = 20" }, { "alpha_fraction": 0.5245901346206665, "alphanum_fraction": 0.5245901346206665, "avg_line_length": 14.25, "blob_id": "bf060ae4d2b02e4ca82eceb0cb2a67c9851167ed", "content_id": "febf3c1e46890c9867536063ac7db0c8467d6968", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 61, "license_type": "no_license", "max_line_length": 26, "num_lines": 4, "path": "/python/namespace_packages/ng.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "import hoge\n\nif __name__ == \"__main__\":\n hoge.fuga.hoge()\n" }, { "alpha_fraction": 0.6129032373428345, "alphanum_fraction": 0.6303763389587402, "avg_line_length": 18.578947067260742, "blob_id": "7ee40bef653f49289bda89f2b5ce3c77f7e96352", "content_id": "6eb023a5bd104bd43f94086b9d3c9e9affef93b8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 744, "license_type": "no_license", "max_line_length": 43, "num_lines": 38, "path": "/ruby/quicksort.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "# Runtime: O(N+logN) # average\ndef quicksort(array)\n quicksort_inner(array, 0, array.length-1)\nend\n\ndef quicksort_inner(array, left, right)\n index = partition(array, left, right)\n if left < index-1\n quicksort_inner(array, left, index-1)\n end\n if index < right\n quicksort_inner(array, index, right)\n end\nend\n\ndef partition(array, left, right)\n lr = left..right\n mid = (left+right)/2\n pivot = array[mid]\n\n while left <= right\n left += 1 while array[left] < pivot\n right -= 1 while array[right] > pivot\n\n if left <= right\n tmp = array[left]\n array[left] = array[right]\n array[right] = tmp\n left += 1\n right -= 1\n end\n end\n left\nend\n\narray = 10.times.map { rand(30) }\nquicksort(array)\np array\n" }, { "alpha_fraction": 0.45072993636131287, "alphanum_fraction": 0.48905110359191895, "avg_line_length": 11.767441749572754, "blob_id": "5c50a5492b2f3473a4f87693f9e5b8571373391d", "content_id": "99334a3e4dc1c4d543ecd562da47a489db6d1151", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 548, "license_type": "no_license", "max_line_length": 39, "num_lines": 43, "path": "/book/4_7_build_order.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "edges = [\n [1, 4],\n [6, 2],\n [2, 4],\n [6, 1],\n [4, 3],\n # [4, 6]\n]\n\nh = {}\ne = {}\n(1..6).each do |n|\n h[n] = 0\n e[n] = []\nend\nedges.each do |a, b|\n h[a] += 1\n e[b] << a\nend\n\n# toporigical sort\nqueue = []\nh.each do |k, v|\n queue << k if v == 0\nend\n\nans = []\nwhile queue.size > 0\n node = queue.shift\n ans << node\n e[node].each do |parent|\n h[parent] -= 1\n if h[parent] == 0\n queue << parent\n end\n end\nend\n\nif ans.size == h.values.size\n p ans.map { |n| ('a'.ord + n-1).chr }\nelse\n puts \"Circular dependency detected\"\nend" }, { "alpha_fraction": 0.5882353186607361, "alphanum_fraction": 0.5882353186607361, "avg_line_length": 16, "blob_id": "d76102b7dba7ba9e5c542bb5d743a278a7f861d8", "content_id": "135885955b803869a2ecad63607727fb7c05795d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 34, "license_type": "no_license", "max_line_length": 21, "num_lines": 2, "path": "/python/namespace_packages/hogehoge/fuga.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "def hoge():\n print(\"hogehoge\")\n" }, { "alpha_fraction": 0.6434316635131836, "alphanum_fraction": 0.6621983647346497, "avg_line_length": 17.700000762939453, "blob_id": "ceb2bcf1abb80401509a097fdb1f79afdad1ab03", "content_id": "dc8dd1f6bef1b68a61463ec46d083cff9f8b40c3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 373, "license_type": "no_license", "max_line_length": 43, "num_lines": 20, "path": "/python/pytest/test_main.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "import pytest\n\nfrom primes import is_prime\n\[email protected]()\ndef prime_numbers():\n return [1, 3, 11]\n\[email protected]()\ndef non_prime_numbers():\n return [4, 8, 9]\n\ndef test_is_prime_true(prime_numbers):\n for n in prime_numbers:\n assert is_prime(n)\n\n\ndef test_is_prime_false(non_prime_numbers):\n for n in non_prime_numbers:\n assert not is_prime(n)" }, { "alpha_fraction": 0.5494853258132935, "alphanum_fraction": 0.5724465847015381, "avg_line_length": 16.54166603088379, "blob_id": "18f2284eaf987a87260734777da3607baa3aabaa", "content_id": "945d031cb739a50ecac36d722edcaf959cd26e99", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 1263, "license_type": "no_license", "max_line_length": 63, "num_lines": 72, "path": "/book/2_5.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "require './lib/linked_list'\n\nmodule Backward\n def self.to_int(a)\n ret = 0\n d = 1\n while a\n ret += a.data * d\n d *= 10\n a = a.next\n end\n ret\n end\n\n\n def self.to_linked_list(a)\n node = nil\n while a > 0\n val = a % 10\n if node\n node.append_to_tail(val)\n else\n node = LinkedList::Node.new(val)\n end\n a /= 10\n end\n node\n end\nend\n\nmodule Forward\n def self.to_int(a)\n ret = 0\n while a\n ret *= 10\n ret += a.data\n a = a.next\n end\n ret\n end\n\n\n def self.to_linked_list(a)\n head = nil\n while a > 0\n val = a % 10\n node = LinkedList::Node.new(val)\n node.next = head\n head = node\n a /= 10\n end\n head\n end\nend\n\n\na = LinkedList::Node.new(7).append_to_tail(1).append_to_tail(6)\nb = LinkedList::Node.new(5).append_to_tail(9).append_to_tail(2)\np a\np b\nputs(Backward.to_int(a))\nputs(Backward.to_int(b))\n\nans = Backward.to_int(a) + Backward.to_int(b)\np Backward.to_linked_list(ans)\n\na = LinkedList::Node.new(6).append_to_tail(1).append_to_tail(7)\nb = LinkedList::Node.new(2).append_to_tail(9).append_to_tail(5)\nputs(Forward.to_int(a))\nputs(Forward.to_int(b))\nans = Forward.to_int(a) + Forward.to_int(b)\np Forward.to_linked_list(ans)\n" }, { "alpha_fraction": 0.46952009201049805, "alphanum_fraction": 0.4915693998336792, "avg_line_length": 24.700000762939453, "blob_id": "e8c085369496556cec744215064346c83cb1c947", "content_id": "0681d2605236bf2053dc04273380524ffc8d7028", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Java", "length_bytes": 771, "license_type": "no_license", "max_line_length": 64, "num_lines": 30, "path": "/book/1_3/URLify.java", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "public class URLify {\n public static void replaceSpaces(char[] str, int trueLength) {\n int spaceCount = 0, index, i = 0;\n for (i=0; i<trueLength; i++) {\n if (str[i] == ' ') {\n spaceCount += 1;\n }\n }\n index = trueLength + spaceCount * 2;\n if (trueLength < str.length) str[trueLength] = '\\0';\n for (i = trueLength-1; i>=0; i--) {\n if (str[i] == ' ') {\n str[index-1] = '0';\n str[index-2] = '2';\n str[index-3] = '%';\n index = index-3;\n } else {\n str[index-1] = str[i];\n index--;\n }\n }\n }\n\n public static void main(String args[]) {\n char[] str = \"aiu eo test \".toCharArray();\n replaceSpaces(str, 11);\n String s = new String(str);\n System.out.println(s);\n }\n}\n" }, { "alpha_fraction": 0.5454545617103577, "alphanum_fraction": 0.5454545617103577, "avg_line_length": 15.5, "blob_id": "39dc10a0f1e6c4bc9cee4d7c6ecbe13f2e2913f3", "content_id": "b6fdbfdf3b5d76a120bed09d02cc019d9354f6fe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 66, "license_type": "no_license", "max_line_length": 26, "num_lines": 4, "path": "/python/namespace_packages/ok.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "from hoge import fuga\n\nif __name__ == \"__main__\":\n fuga.hoge()\n" }, { "alpha_fraction": 0.5446428656578064, "alphanum_fraction": 0.555059552192688, "avg_line_length": 15.390243530273438, "blob_id": "ea491a0e592288c681c8312cb5273051e228c7d9", "content_id": "a62c530b67974a86eea110c80a704f8513da7c15", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 672, "license_type": "no_license", "max_line_length": 30, "num_lines": 41, "path": "/book/1_4.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "def one_repalce(a, b)\n cnt = 0\n a.length.times do |i|\n cnt += 1 if a[i] != b[i]\n return false if cnt > 1\n end\n true\nend\n\ndef one_remove(a, b)\n j = 0\n a.length.times do |i|\n if a[i] != b[j]\n return false if i != j\n else\n j += 1\n end\n end\n true\nend\n\ndef one_away(a, b)\n if a.length == b.length\n one_repalce(a, b)\n elsif a.length == b.length+1\n one_remove(a, b)\n elsif a.length == b.length-1\n one_remove(b, a)\n else\n false\n end\nend\n\n\np one_away(\"pale\", \"ple\")\np one_away(\"pales\", \"pale\")\np one_away(\"pale\", \"bale\")\np one_away(\"pale\", \"pales\")\np one_away(\"pale\", \"pale\")\np one_away(\"pale\", \"bae\")\np one_away(\"pale\", \"paless\")\n" }, { "alpha_fraction": 0.7625899314880371, "alphanum_fraction": 0.7625899314880371, "avg_line_length": 26.799999237060547, "blob_id": "03d2f3aa6939341b22417a69eedcef150aac2161", "content_id": "a939b6d50e6fdb342bc6b40d64f268866f7858a2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 278, "license_type": "no_license", "max_line_length": 154, "num_lines": 10, "path": "/python/flake8/main.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "TOO_LONG_LINE = \"aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\"\n\n\ndef greeting(name):\n print(\"Hello {}\".format(name))\n\n\nif __name__ == \"__main__\":\n name = \"Taro\"\n greeting(name)\n" }, { "alpha_fraction": 0.5826377272605896, "alphanum_fraction": 0.5876460671424866, "avg_line_length": 10.764705657958984, "blob_id": "f10adea770058915ade9069a2d9da2713a4ab12a", "content_id": "cdaff6262a2b5f1b43a5731b213ed3ead3afe294", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 599, "license_type": "no_license", "max_line_length": 45, "num_lines": 51, "path": "/book/3_2.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "class Stack\n attr_accessor :min, :top\n\n def push(val)\n min = top&.min || val\n node = StackNode.new(val, [val, min].min)\n node.next = top\n self.top = node\n end\n\n def pop\n return unless top\n val = top.val\n self.top = top.next\n return val\n end\n\n def min\n top&.min\n end\n\n def peek\n top.val\n end\nend\n\n\nclass StackNode\n attr_accessor :val, :next, :min\n\n def initialize(val, min)\n @val = val\n @min = min\n end\nend\n\n\n\nstack = Stack.new\n\nn = 5\n\nn.times do ||\n val = rand(20)\n puts \"push #{val}\"\n stack.push(val)\nend\n\nn.times do\n p [stack.min, stack.pop]\nend" }, { "alpha_fraction": 0.6656891703605652, "alphanum_fraction": 0.6656891703605652, "avg_line_length": 17, "blob_id": "8aa29977ba48e98412bfe09fe2e9a9c00244e9b6", "content_id": "874b8ca064bb5c16e44c3ca8ee5f5c1c2c3ccb8d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 341, "license_type": "no_license", "max_line_length": 34, "num_lines": 19, "path": "/book/2_3.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "require_relative 'lib/linked_list'\n\nhead = LinkedList::Node.new('a')\nd_node = nil\n%w(b c d e f).each do |c|\n node = head.append_to_tail(c)\n d_node = node if c == 'd'\nend\n\ndef delete_node(node)\n return false unless node.next\n next_nd = node.next\n node.data = next_nd.data\n node.next = next_nd.next\n true\nend\n\ndelete_node(d_node)\np head" }, { "alpha_fraction": 0.5720469951629639, "alphanum_fraction": 0.5769944190979004, "avg_line_length": 15.680412292480469, "blob_id": "7361af54139685255445c0be1022cf883a806d38", "content_id": "a9a30b46459004ef93e18f2d0009d5fe6ace0063", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 1617, "license_type": "no_license", "max_line_length": 69, "num_lines": 97, "path": "/book/4_11_random_node.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "require './lib/print_binary_tree'\n\nIndex = Struct.new(:val)\ndef find_by_index(node, index, curr_index = Index.new(-1))\n curr_index.val += 1\n # p \"Search #{index} node=#{node.val} curr_index=#{curr_index.val}\"\n return node if index == curr_index.val\n if node.left\n found = find_by_index(node.left, index, curr_index)\n return found if found\n end\n if node.right\n found = find_by_index(node.right, index, curr_index)\n return found if found\n end\nend\n\nclass BST\n attr_accessor :root\n\n def initialize\n @root = nil\n @size = 0\n end\n\n def add(val)\n @size += 1\n if @root\n @root.add(val)\n else\n @root = TreeNode.new(val)\n end\n end\n\n def find(val)\n @root&.find(val)\n end\n\n def random_node\n idx = rand(@size)\n find_by_index(@root, idx)\n end\nend\n\nclass TreeNode\n attr_accessor :left, :right, :val\n def initialize(val)\n @val = val\n end\n\n def add(val)\n if @val < val\n if @right\n @right.add(val)\n else\n @right = TreeNode.new(val)\n end\n else\n if @left\n @left.add(val)\n else\n @left = TreeNode.new(val)\n end\n end\n end\n\n def find(val)\n return self if @val == val\n if val <= @val\n @left&.find(val)\n else\n @right&.find(val)\n end\n end\n\n def inspect\n left = @left ? \" left=#{@left}\" : ''\n right = @right ? \" right=#{@right}\" : ''\n \"#<val=#{@val}#{left}#{right}>\"\n end\n\n def to_s\n inspect\n end\nend\n\ntree = BST.new\n5.times do |v|\n val = rand(20)\n tree.add(val)\nend\nprint_binary_tree(tree.root)\n\nputs \"#Get a node randomly\"\n5.times do\n p tree.random_node&.val\nend" }, { "alpha_fraction": 0.5947775840759277, "alphanum_fraction": 0.603481650352478, "avg_line_length": 12.269230842590332, "blob_id": "e50e174ff2f4798c7dbe552dfddbb12fe1097253", "content_id": "17af07523faa63576614cccd7d4b59b424c54742", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 1034, "license_type": "no_license", "max_line_length": 47, "num_lines": 78, "path": "/book/3_3_stack_of_plates.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "class Stack\n attr_accessor :size, :top\n\n def initialize\n @size = 0\n end\n\n\n def push(val)\n self.size += 1\n node = StackNode.new(val)\n node.next = top\n self.top = node\n end\n\n def pop\n return unless top\n self.size -= 1\n val = top.val\n self.top = top.next\n return val\n end\n\n def peek\n top.val\n end\nend\n\n\nclass StackNode\n attr_accessor :val, :next\n\n def initialize(val)\n @val = val\n end\nend\n\nclass SetOfStack\n attr_reader :capacity, :stacks\n def initialize(capacity)\n @capacity = capacity\n @stacks = []\n end\n\n def push(val)\n stack = @stacks.first\n if stack.nil? || stack.size >= capacity\n stack = Stack.new\n @stacks.unshift(stack)\n end\n stack.push(val)\n end\n\n def pop\n stack = @stacks[0]\n val = stack.pop()\n @stacks.shift if stack.size == 0\n val\n end\nend\n\n\n\nstack = SetOfStack.new(3)\n\nn = 5\n\nn.times do ||\n val = rand(20)\n puts \"push #{val}\"\n stack.push(val)\nend\n\np \"The number of stack is #{stack.stacks.size}\"\n\nn.times do\n p stack.pop\nend" }, { "alpha_fraction": 0.501075267791748, "alphanum_fraction": 0.5311827659606934, "avg_line_length": 11.94444465637207, "blob_id": "f273084136004ca3ead5e398c2170a712fb4032d", "content_id": "361bcea4fa0a2864315510214280be5c90442c58", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 465, "license_type": "no_license", "max_line_length": 38, "num_lines": 36, "path": "/book/4_7_build_order2.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "# a depends b\nedges = [\n [1, 4],\n [6, 2],\n [2, 4],\n [6, 1],\n [4, 3],\n # [4, 6]\n]\n\nnodes = (1..6).to_a\n\ne = {}\nnodes.each do |n|\n e[n] = []\nend\nedges.each do |a, b|\n e[a] << b\nend\n\ndef dfs(n, edges, processed, ans)\n processed[n] = true\n edges[n].each do |next_n|\n next if processed[next_n]\n dfs(next_n, edges, processed, ans)\n end\n ans << n\nend\n\nans = []\nprocessed = {}\nnodes.each do |n|\n next if processed[n]\n dfs(n, e, processed, ans)\nend\np ans" }, { "alpha_fraction": 0.5564388036727905, "alphanum_fraction": 0.5643879175186157, "avg_line_length": 16.24657440185547, "blob_id": "ce2584d1ca9d3b4e5d08631b991ad529a7a7f4ac", "content_id": "e075d9570a981513102b6f1a503f1bca9b1ffb27", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 1258, "license_type": "no_license", "max_line_length": 54, "num_lines": 73, "path": "/book/4_4_check_balanced.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "def printer(root)\n cur_nodes = []\n cur_nodes.push(root)\n\n while cur_nodes.size > 0\n puts cur_nodes.map { |n| n&.val || '-' }.join(\" \")\n\n next_nodes = []\n cur_nodes.each do |node|\n next_nodes.push(node&.left)\n next_nodes.push(node&.right)\n end\n break if next_nodes.all?(&:nil?)\n cur_nodes = next_nodes\n end\nend\n\nMIN = -2\n\ndef balanced?(node)\n check_height(node) != MIN\nend\n\ndef check_height(node)\n return -1 unless node\n l = check_height(node.left)\n return MIN if l==MIN\n r = check_height(node.left)\n return MIN if r==MIN\n return MIN if (l-r).abs() > 1\n [l, r].max + 1\nend\n\nclass TreeNode\n attr_reader :left, :right, :val\n def initialize(val)\n @val = val\n end\n\n def add(val)\n if @val < val\n if @right\n @right.add(val)\n else\n @right = TreeNode.new(val)\n end\n else\n if @left\n @left.add(val)\n else\n @left = TreeNode.new(val)\n end\n end\n end\n\n def inspect\n left = @left ? \" left=#{@left}\" : ''\n right = @right ? \" right=#{@right}\" : ''\n \"#<val=#{@val}#{left}#{right}>\"\n end\n\n def to_s\n inspect\n end\nend\n\nnode = TreeNode.new(rand(20))\n5.times do |v|\n val = rand(20)\n node.add(val)\nend\nprinter(node)\nputs \"blanced #{balanced?(node)}\"" }, { "alpha_fraction": 0.621730387210846, "alphanum_fraction": 0.6297786831855774, "avg_line_length": 11.146341323852539, "blob_id": "aaa0ab653bd7c0dc748b696bedfb92d32fdd080c", "content_id": "59dabbc4710c4bf8b34af7a996e93bc68c3172ae", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 497, "license_type": "no_license", "max_line_length": 30, "num_lines": 41, "path": "/ruby/stack.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "class StackNode\n attr_reader :data\n attr_accessor :next\n def initialize(data)\n @data = data\n end\nend\n\nclass Stack\n attr_accessor :top\n def initialize\n @top = nil\n end\n\n def push(data)\n node = StackNode.new(data)\n node.next = top\n self.top = node\n end\n\n def pop\n node = top\n return unless node\n self.top = node.next\n node.data\n end\nend\n\n\ndef main\n stack = Stack.new\n stack.push(1)\n stack.push(2)\n stack.push(3)\n\n 4.times do\n p stack.pop\n end\nend\n\nmain" }, { "alpha_fraction": 0.7272727489471436, "alphanum_fraction": 0.7272727489471436, "avg_line_length": 33, "blob_id": "e6e1b3ec1703976ea6f035e7ad75807774b643db", "content_id": "d095fba10a84339c7ed290b2351ac4452ef90c04", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 33, "license_type": "no_license", "max_line_length": 33, "num_lines": 1, "path": "/python/pytest/run_test.sh", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "coverage run --source . -m pytest" }, { "alpha_fraction": 0.5211678743362427, "alphanum_fraction": 0.5211678743362427, "avg_line_length": 16.564102172851562, "blob_id": "9dcb92c071bdf91482d0a776969975a18369d5f7", "content_id": "0ecb1d6d6ce1b4545e6327e6676cc089eefbc16f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 685, "license_type": "no_license", "max_line_length": 43, "num_lines": 39, "path": "/book/lib/linked_list.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "module LinkedList\n class Node\n attr_accessor :next, :data\n def initialize(data)\n @data = data\n end\n\n def append_to_tail(data)\n if self.next == nil\n self.next = Node.new(data)\n else\n self.next.append_to_tail(data)\n end\n self\n end\n\n def self.delete_node(head, data)\n node = head\n prev = nil\n\n return node.next if node.data == data\n\n while node.next\n prev = node\n node = node.next\n if node.data == data\n prev.next = node.next\n break\n end\n end\n head\n end\n\n def to_s\n \"<Node #{@data}> => #{self.next}\"\n end\n alias :inspect :to_s\n end\nend\n" }, { "alpha_fraction": 0.6847826242446899, "alphanum_fraction": 0.6847826242446899, "avg_line_length": 45, "blob_id": "6a598cf5f6b7ff6d7b3cd4471fc9d98aac8e3e33", "content_id": "173e0588005a317897ff44fc9769a0c1fd491d26", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Makefile", "length_bytes": 92, "license_type": "no_license", "max_line_length": 76, "num_lines": 2, "path": "/mruby-link-test/Makefile", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "hello: hello.c\n\tgcc -I ./mruby/include -L ./mruby/build/host/lib -lmruby ./hello.c -o hello\n" }, { "alpha_fraction": 0.5583832263946533, "alphanum_fraction": 0.5658682584762573, "avg_line_length": 16.363636016845703, "blob_id": "2a5fbefc73bf7f528582016ba34506fb5c9d9fe1", "content_id": "f1d3321307c8f5a36ed7e5f6136421d20c151e68", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 1336, "license_type": "no_license", "max_line_length": 54, "num_lines": 77, "path": "/book/4_5_bst.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "def printer(root)\n cur_nodes = []\n cur_nodes.push(root)\n\n while cur_nodes.size > 0\n puts cur_nodes.map { |n| n&.val || '-' }.join(\" \")\n\n next_nodes = []\n cur_nodes.each do |node|\n next_nodes.push(node&.left)\n next_nodes.push(node&.right)\n end\n break if next_nodes.all?(&:nil?)\n cur_nodes = next_nodes\n end\nend\n\nMIN = -2\n\ndef bst?(node)\n check_max(node) != MIN\nend\n\ndef check_max(node)\n return -1 unless node\n l = check_max(node.left)\n r = check_max(node.right)\n return MIN if l==MIN\n return MIN if r==MIN\n return MIN unless l <= node.val\n return MIN unless r == -1 || node.val < r\n [r, node.val].max\nend\n\nclass TreeNode\n attr_accessor :left, :right, :val\n def initialize(val)\n @val = val\n end\n\n def add(val)\n if @val < val\n if @right\n @right.add(val)\n else\n @right = TreeNode.new(val)\n end\n else\n if @left\n @left.add(val)\n else\n @left = TreeNode.new(val)\n end\n end\n end\n\n def inspect\n left = @left ? \" left=#{@left}\" : ''\n right = @right ? \" right=#{@right}\" : ''\n \"#<val=#{@val}#{left}#{right}>\"\n end\n\n def to_s\n inspect\n end\nend\n\nnode = TreeNode.new(rand(20))\n5.times do |v|\n val = rand(20)\n node.add(val)\nend\nprinter(node)\nputs \"bst #{bst?(node)}\"\nnode.val = 2\nprinter(node)\nputs \"bst #{bst?(node)}\"" }, { "alpha_fraction": 0.6167664527893066, "alphanum_fraction": 0.6167664527893066, "avg_line_length": 17.55555534362793, "blob_id": "d6230c91f75f1b7ca24af275f39910cc5cbcd98f", "content_id": "55b00cd1bb7a892f788b73b107c9a76a88e3224a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C", "length_bytes": 167, "license_type": "no_license", "max_line_length": 47, "num_lines": 9, "path": "/mruby-link-test/hello.c", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "#include \"mruby.h\"\n#include \"mruby/compile.h\"\n\nint main(void)\n{\n mrb_state *mrb = mrb_open();\n mrb_load_string(mrb, \"puts 'hello world'\");\n mrb_close(mrb);\n}\n" }, { "alpha_fraction": 0.5797101259231567, "alphanum_fraction": 0.5797101259231567, "avg_line_length": 16.25, "blob_id": "477fc7efb32193236fd81c415ff07becdc1a7579", "content_id": "46fceb842dc36158d07465ec19af8e58631eb177", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 69, "license_type": "no_license", "max_line_length": 26, "num_lines": 4, "path": "/python/namespace_packages/ok2.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "import hogehoge\n\nif __name__ == \"__main__\":\n hogehoge.fuga.hoge()\n" }, { "alpha_fraction": 0.5418719053268433, "alphanum_fraction": 0.5862069129943848, "avg_line_length": 15.052631378173828, "blob_id": "089f19925c3cbe7dc84c8f38aa39dd91fb1927e3", "content_id": "30fa89eeea79dc2d24cf8da56699209853774cd1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 609, "license_type": "no_license", "max_line_length": 48, "num_lines": 38, "path": "/book/2_4.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "require_relative 'lib/linked_list'\n\nhead = LinkedList::Node.new(1)\n[11, 12, 11, 1, 1, 8, 3, 16, 24, 10].each do |v|\n head.append_to_tail(v)\nend\n\ndef parition(head, pivot)\n left = nil\n left_end = nil\n right = nil\n\n node = head\n while node\n next_node = node.next\n\n if node.data < pivot\n node.next = left\n left = node\n left_end ||= left\n else\n node.next = right\n right = node\n end\n\n node = next_node\n end\n\n return right unless left\n left_end.next = right\n left\nend\n\n[1, 3, 5, 8, 11, 13, 30].each do |p|\n head = parition(head, p)\n puts \"Pivot #{p}\"\n p head\nend" }, { "alpha_fraction": 0.5451613068580627, "alphanum_fraction": 0.5526881814002991, "avg_line_length": 15.333333015441895, "blob_id": "a4d4ec2df336573792d1c2477d22e724857f412d", "content_id": "8ca0ea09e22e1891da82c910c23b5e0dfe6735bf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 930, "license_type": "no_license", "max_line_length": 47, "num_lines": 57, "path": "/book/4_3_list_of_depths.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "def printer(root)\n cur_nodes = []\n cur_nodes.push(root)\n\n while cur_nodes.size > 0\n puts cur_nodes.map(&:val).join(\" \")\n\n next_nodes = []\n cur_nodes.each do |node|\n next_nodes.push(node.left) if node.left\n next_nodes.push(node.right) if node.right\n end\n cur_nodes = next_nodes\n end\nend\n\n\nclass TreeNode\n attr_reader :left, :right, :val\n def initialize(val)\n @val = val\n end\n\n def add(val)\n if @val < val\n if @right\n @right.add(val)\n else\n @right = TreeNode.new(val)\n end\n else\n if @left\n @left.add(val)\n else\n @left = TreeNode.new(val)\n end\n end\n end\n\n def inspect\n left = @left ? \" left=#{@left}\" : ''\n right = @right ? \" right=#{@right}\" : ''\n \"#<val=#{@val}#{left}#{right}>\"\n end\n\n def to_s\n inspect\n end\nend\n\nnode = TreeNode.new(rand(20))\n10.times do |v|\n val = rand(20)\n node.add(val)\nend\n\nprinter(node)" }, { "alpha_fraction": 0.382536381483078, "alphanum_fraction": 0.382536381483078, "avg_line_length": 17.538461685180664, "blob_id": "1dcf2f3f722254442fc5d50d5b8b8b68f6a2199d", "content_id": "3843872cd159a8cc0b9d47b926b92e4e275f2a21", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 481, "license_type": "no_license", "max_line_length": 51, "num_lines": 26, "path": "/python/super/main.py", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "class A:\n def __init__(self):\n print(\"A __init__\")\n super().__init__()\n\n\nclass B:\n def __init__(self):\n print(\"B __init__\")\n super().__init__()\n\nclass C(A, B):\n def __init__(self):\n print(\"C __init__\")\n super().__init__()\n\n\nif __name__ == \"__main__\":\n c = C()\n print(\"MRO: \", [x.__name__ for x in C.__mro__])\n\n # -> % python main.py\n # C __init__\n # A __init__\n # B __init__\n # MRO: ['C', 'A', 'B', 'object']" }, { "alpha_fraction": 0.6048564910888672, "alphanum_fraction": 0.620309054851532, "avg_line_length": 18.29787254333496, "blob_id": "db16082bababdb268d695a549ec7ad9203fddbee", "content_id": "c680740e6f26416d5c81074f7ae37c2155bd3430", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 906, "license_type": "no_license", "max_line_length": 51, "num_lines": 47, "path": "/ruby/mergesort.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "# Runtime: O(N+logN)\n# Memory: O(N)\ndef mergesort(array)\n helper = Array.new(array.length)\n mergesort_inner(array, helper, 0, array.length-1)\nend\n\ndef mergesort_inner(array, helper, low, high)\n return if low == high\n\n mid = (low+high) / 2\n mergesort_inner(array, helper, low, mid)\n mergesort_inner(array, helper, mid+1, high)\n merge(array, helper, low, mid, high)\nend\n\ndef merge(array, helper, low, mid, high)\n for i in low..high\n helper[i] = array[i]\n end\n\n left = low\n right = mid+1\n current = low\n\n while left <= mid && right <= high do\n if helper[left] <= array[right]\n array[current] = helper[left]\n left += 1\n current += 1\n else\n array[current] = helper[right]\n right += 1\n current += 1\n end\n end\n\n for i in left..mid\n array[current] = helper[i]\n current += 1\n end\nend\n\narray = 10.times.map { rand(30) }\np array\nmergesort(array)\np array" }, { "alpha_fraction": 0.5922208428382874, "alphanum_fraction": 0.6198243498802185, "avg_line_length": 14.326923370361328, "blob_id": "19af16b7d2dcb73e86582c4dbba87fb488f13037", "content_id": "decf98dbc8181481a8c7b89d53817f0d34085665", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 797, "license_type": "no_license", "max_line_length": 51, "num_lines": 52, "path": "/book/2_2.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "require_relative 'lib/linked_list'\n\nhead = LinkedList::Node.new(1)\nhead.append_to_tail(2)\nhead.append_to_tail(3)\nhead.append_to_tail(4)\nhead.append_to_tail(5)\nhead.append_to_tail(6)\n# p list\n# list = Node.delete_node(list, 2)\n# p list\n\n\n# recusirve\ndef rec(node, k, &block)\n if node.next\n nth = rec(node.next, k, &block) + 1\n else\n nth = 0\n end\n\n if nth == k\n yield node\n end\n nth\nend\n\n3.times do |i|\n rec(head, i) do |n|\n puts \"the #{i}th to last element is #{n.data}\"\n end\nend\n\ndef find_kth_element(head, k)\n p1 = head\n p2 = head\n # move p1 k nodes into the list\n k.times do\n p2 = p2.next\n end\n\n while p2.next\n p2 = p2.next\n p1 = p1.next\n end\n p1\nend\n\n4.times do |i|\n node = find_kth_element(head, i)\n puts \"the #{i}th to last element is #{node.data}\"\nend\n" }, { "alpha_fraction": 0.5840455889701843, "alphanum_fraction": 0.5868945717811584, "avg_line_length": 21, "blob_id": "cb41307a0686223e62d7e6e1e3a3882c8c5091f1", "content_id": "3f8e90b30e50311cdeb3a53e6eded4b23a269932", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Ruby", "length_bytes": 351, "license_type": "no_license", "max_line_length": 54, "num_lines": 16, "path": "/book/lib/print_binary_tree.rb", "repo_name": "arizuk/misc", "src_encoding": "UTF-8", "text": "def print_binary_tree(root)\n cur_nodes = []\n cur_nodes.push(root)\n\n while cur_nodes.size > 0\n puts cur_nodes.map { |n| n&.val || '-' }.join(\" \")\n\n next_nodes = []\n cur_nodes.each do |node|\n next_nodes.push(node&.left)\n next_nodes.push(node&.right)\n end\n break if next_nodes.all?(&:nil?)\n cur_nodes = next_nodes\n end\nend" } ]

33

sochis/send-gmail-to-slack

https://github.com/sochis/send-gmail-to-slack

511da0627e01a4dc53548207b729cfa72e94257d

895ed51c9df318da8ef075de0c3d886823c942c1

03b069e4cff6aee6568505b10503f9bd0c700e9e

refs/heads/master

2020-03-19T23:30:28.324306

2018-06-15T06:26:18

137,008,786

2

0

null

[ { "alpha_fraction": 0.5810147523880005, "alphanum_fraction": 0.5810147523880005, "avg_line_length": 32.94444274902344, "blob_id": "bc0c1a4c029b0505efe7c80e0d97aa3d5ee0bce4", "content_id": "993560f21f4d1a590766d20b11673490d18a97d1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 611, "license_type": "no_license", "max_line_length": 105, "num_lines": 18, "path": "/execute_test.py", "repo_name": "sochis/send-gmail-to-slack", "src_encoding": "UTF-8", "text": "from gmail.getmail import GetMailContent\nfrom slack.send_slack import SendSlack\n\n\nclass Execute():\n def send_gmail_to_slack(self):\n get_mail = GetMailContent()\n mails = get_mail.get_mail_list()['messages']\n for mail in mails:\n content = get_mail.get_mail_content(mail['id'])\n parse = get_mail.parse_mail(content)\n text = 'From : ' + parse['from'] + '\\n' + 'date : ' + parse['date'] + '\\n \\n' + parse['body']\n SendSlack().send_slack(parse['subject'], text)\n\n\nif __name__ == '__main__':\n execute = Execute()\n execute.send_gmail_to_slack()\n" }, { "alpha_fraction": 0.5973348617553711, "alphanum_fraction": 0.6031286120414734, "avg_line_length": 35.74468231201172, "blob_id": "f3dde8dc3470384518794fc67a10dfffbfa6e976", "content_id": "73cb50ebfdb36869dda910537e72fe5a6eae80df", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1726, "license_type": "no_license", "max_line_length": 77, "num_lines": 47, "path": "/gmail/authorization.py", "repo_name": "sochis/send-gmail-to-slack", "src_encoding": "UTF-8", "text": "import json\nimport webbrowser\nfrom googleapiclient.discovery import build\nfrom httplib2 import Http\nfrom oauth2client.file import Storage\nfrom oauth2client.client import OAuth2WebServerFlow\n\n\nclass Credential():\n\n def readonly_credential(self):\n return Credential.pass_credential(self, 'readonly')\n\n def modify_credential(self):\n return Credential.pass_credential(self, 'modify')\n\n def send_credential(self):\n return Credential.pass_credential(self, 'send')\n\n def full_credential(self):\n return Credential.pass_credential(self, 'full')\n\n def pass_credential(self, scope_type):\n SCOPES = 'https://www.googleapis.com/auth/gmail.' + scope_type\n if (scope_type == 'full'):\n SCOPES = 'https://mail.google.com/'\n storage = Storage('gmail/credentials.json')\n credential = storage.get()\n if credential is None or credential.invalid:\n auth_info = json.load(open('gmail/client_secret.json'))\n info = auth_info['installed']\n flow = OAuth2WebServerFlow(client_id=info['client_id'],\n client_secret=info['client_secret'],\n scope=SCOPES,\n redirect_uri=info['redirect_uris'][0])\n auth_url = flow.step1_get_authorize_url()\n webbrowser.open(auth_url)\n code = input(\"input code : \")\n credential = flow.step2_exchange(code)\n storage = storage.put(credential)\n http = credential.authorize(Http())\n\n gmail_service = build('gmail', 'v1', http=http)\n return gmail_service\n\n if __name__ == '__main__' :\n pass_credential(1,\"full\")" }, { "alpha_fraction": 0.5338208675384521, "alphanum_fraction": 0.5338208675384521, "avg_line_length": 19.296297073364258, "blob_id": "ddc073a147ea6d292f2fc9079ffe74156a597c0e", "content_id": "fbb920489c630e01e0a0ad431960d8d123498f03", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 547, "license_type": "no_license", "max_line_length": 58, "num_lines": 27, "path": "/slack/send_slack.py", "repo_name": "sochis/send-gmail-to-slack", "src_encoding": "UTF-8", "text": "from slackclient import SlackClient\n\nfrom slack.slack_info import (\n SLACK_TOKEN,\n CHANNEL,\n USERNAME\n)\n\n\nclass SendSlack():\n\n def send_slack(self, title, text):\n sc = SlackClient(SLACK_TOKEN)\n\n attachments = [{\n 'title': title,\n 'text': text,\n 'fallback': text\n }]\n\n sc.api_call(\n \"chat.postMessage\",\n channel=CHANNEL,\n user=USERNAME,\n attachments=attachments,\n text=\"you have unread mails in your gmail box\"\n )" }, { "alpha_fraction": 0.5572519302368164, "alphanum_fraction": 0.7251908183097839, "avg_line_length": 20.83333396911621, "blob_id": "5422745b6045f10288ab5855245302609b124c37", "content_id": "d375bd917afe026357235134d606bf7342b304e3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 131, "license_type": "no_license", "max_line_length": 31, "num_lines": 6, "path": "/requirements.txt", "repo_name": "sochis/send-gmail-to-slack", "src_encoding": "UTF-8", "text": "google-api-python-client==1.6.2\ngoogle-auth==1.5.0\ngoogle-auth-httplib2==0.0.3\nhtml5lib==1.0.1\nhttplib2==0.11.3\nslackclient==1.0.5\n" }, { "alpha_fraction": 0.5374592542648315, "alphanum_fraction": 0.5439739227294922, "avg_line_length": 33.13888931274414, "blob_id": "682205d42fc168b42d2b6ae931251295836a196d", "content_id": "00c293c87fef49ef558c7762f860c0b1701cf34c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1228, "license_type": "no_license", "max_line_length": 89, "num_lines": 36, "path": "/gmail/getmail.py", "repo_name": "sochis/send-gmail-to-slack", "src_encoding": "UTF-8", "text": "from gmail.authorization import Credential\nimport base64\n\n\nclass GetMailContent():\n\n def get_mail_list(self):\n return self.service.users().messages().list(userId='me', q=\"is:unread\").execute()\n\n def get_mail_content(self, id):\n return self.service.users().messages().get(userId='me', id=id).execute()\n\n def parse_mail(self, content):\n mail = {}\n\n if 'parts' in content['payload'].keys():\n raw_body = content['payload']['parts'][0]['body']['data']\n else:\n raw_body = content['payload']['body']['data']\n mail['body'] = base64.urlsafe_b64decode(raw_body).decode('utf-8')\n mail['snippet'] = content['snippet']\n\n headers = content['payload']['headers']\n for header in headers:\n if header['name'] == 'From':\n mail['from'] = header['value']\n elif header['name'] == 'To':\n mail['to'] = header['value']\n elif header['name'] == 'Subject':\n mail['subject'] = header['value']\n elif header['name'] == 'Date':\n mail['date'] = header['value']\n return mail\n\n def __init__(self):\n self.service = Credential().readonly_credential()" } ]

5

snirav/FragranticaNotes

https://github.com/snirav/FragranticaNotes

2aaafd870294e60ff546c8340242ff5f380b22b9

b5443e4efa41fc3e080371bf3bd8eb5d14ca1308

aebef1793fbf5757e3aeb478c9f96ed591ff0e2b

refs/heads/master

2021-01-13T01:54:05.670490

2015-08-08T19:52:26

39,312,169

0

null

[ { "alpha_fraction": 0.6367098093032837, "alphanum_fraction": 0.6610814929008484, "avg_line_length": 47.62963104248047, "blob_id": "70de4a0f9d05a004c8fde8c1a0505e67a6c75eaa", "content_id": "a1190286efd36cc991e1f5ddfc62aacd313a5d73", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1313, "license_type": "no_license", "max_line_length": 131, "num_lines": 27, "path": "/UserFragCrawl.py", "repo_name": "snirav/FragranticaNotes", "src_encoding": "UTF-8", "text": "__author__ = 'Nirav'\n\nimport urllib2\nfrom bs4 import BeautifulSoup\n# import numpy as np\n\nuserList = [614510, 581227]\nfor userID in userList:\n userUrl = 'http://www.fragrantica.com/member/' + str(userID)\n\n userPerfumes = urllib2.urlopen(userUrl, timeout=10)\n userPerfumeSoup = BeautifulSoup(userPerfumes)\n perfumeHas = [int(a.attrs['id'][2:]) for a in userPerfumeSoup.findAll('ul', attrs={'id': 'imam-sortable'})[0].findAll('li')]\n perfumeWants = [int(a.attrs['id'][2:]) for a in userPerfumeSoup.findAll('ul', attrs={'id' :'zelim-sortable'})[0].findAll('li')]\n perfumeHad = [int(a.attrs['id'][2:]) for a in userPerfumeSoup.findAll('ul', attrs={'id': 'imao-sortable'})[0].findAll('li')]\n\n userLoveHateUrl = 'http://www.fragrantica.com/ajax.php?view=emotion&id=' + str(userID)\n userLoveHatePerfumes = urllib2.urlopen(userLoveHateUrl, timeout=10)\n userLoveHateSoup = BeautifulSoup(userLoveHatePerfumes)\n loveHateList = [[],[],[]]\n lovelikedislikeCount = -1\n for loveHate in userLoveHateSoup.findAll('body')[0]:\n if loveHate.name == 'h3':\n lovelikedislikeCount += 1\n elif loveHate.name == 'a':\n if loveHate.attrs['href'] != None:\n loveHateList[lovelikedislikeCount].append(int(loveHate.attrs['href'].split('.')[0].split('-')[-1]))\n" }, { "alpha_fraction": 0.41322314739227295, "alphanum_fraction": 0.41746705770492554, "avg_line_length": 39.70000076293945, "blob_id": "d644a1cfd9a5e07056b623a1f24c99df2ad20dde", "content_id": "23b930b14569d72821562cc09b6d97108f573fa9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4477, "license_type": "no_license", "max_line_length": 114, "num_lines": 110, "path": "/FragranceCrawlerApp.py", "repo_name": "snirav/FragranticaNotes", "src_encoding": "UTF-8", "text": "import Tkinter as tk\nimport tkMessageBox\nfrom FragCrawlThreadCreation import *\n\nclass fragranceCrawler(tk.Frame):\n def __init__(self, master):\n # Initialize window using the parent's constructor\n tk.Frame.__init__(self,\n master,\n width=400,\n height=300)\n # Set the title\n self.master.title('Perfume Crawler')\n \n # This allows the size specification to take effect\n self.pack_propagate(0)\n \n # We'll use the flexible pack layout manager\n self.pack()\n \n # Fill the Website List\n self.webSiteList=['http://www.fragrantica.com']\n # Fill the designers List\n self.designerEntries=['Azzaro',\n 'Bond-No-9',\n 'Boucheron',\n 'Britney-Spears',\n 'Burberry',\n 'Bvlgari',\n 'Cacharel',\n 'Calvin-Klein',\n 'Carolina-Herrera',\n 'Cartier',\n 'Chanel',\n 'Creed',\n 'Davidoff',\n 'Dior',\n 'Dolce%26Gabbana',\n 'Donna-Karan',\n 'Elizabeth-Arden',\n 'Escada',\n 'Estee-Lauder',\n 'Giorgio-Armani',\n 'Givenchy',\n 'Gucci',\n 'Guerlain',\n 'Guy-Laroche',\n 'Gwen-Stefani',\n 'Hermes',\n 'Hugo-Boss',\n 'Jean-Paul-Gaultier',\n 'Jennifer-Lopez',\n 'Kenzo',\n 'Lacoste',\n 'Lancome',\n 'Liz-Claiborne',\n 'Mariah-Carey',\n 'Moschino',\n 'Nina-Ricci',\n 'Paco-Rabanne',\n 'Paris-Hilton',\n 'Perry-Ellis',\n 'Prada',\n 'Ralph-Lauren',\n 'Rochas',\n 'Thierry-Mugler',\n 'Tommy-Hilfiger',\n 'Vera-Wang',\n 'Versace',\n 'Victoria%60s-Secret',\n 'Yves-Saint-Laurent']\n # Website Selection Menu\n self.defaultWebsite = tk.StringVar()\n self.defaultWebsite.set('Please Select the Websites to crawl')\n self.websiteDropdown = tk.OptionMenu(self,\n self.defaultWebsite, *self.webSiteList)\n \n # Designer Selection Menu\n self.defaultDesigner = tk.StringVar()\n self.defaultDesigner.set('Please Select the sites to crawl')\n self.designerList = tk.OptionMenu(self,\n self.defaultDesigner, \"All\",\n *self.designerEntries)\n \n # Declaring the buttons and linking the functions\n self.textBox=tk.Text(xscrollcommand=set(), yscrollcommand=set(), height=5,width=5)\n \n self.crawl_button = tk.Button(self,\n text='Crawl Designer',\n command=self.initiateCrawl, height=2, width=15)\n\n # Put the controls on the form\n \n self.websiteDropdown.pack(fill=tk.X, side=tk.TOP)\n self.designerList.pack(fill=tk.X, side=tk.TOP)\n self.crawl_button.pack(fill=tk.X, side=tk.TOP)\n self.textBox.pack(fill=tk.X, side=tk.TOP)\n \n # Crawl handling routine\n def initiateCrawl(self): \n crawl(self.defaultWebsite.get().title().lower(), self.defaultDesigner.get().title(), self.designerEntries)\n tkMessageBox.showinfo(\"Crawling Status\", \"Crawling Completed Successfully!!\")\n self.textBox.insert('1.0', \"Crawling Completed Successfully!!\",\"a\")\n \n def run(self):\n ''' Run the app ''' \n self.mainloop()\n \napp = fragranceCrawler(tk.Tk())\napp.run()\n" }, { "alpha_fraction": 0.5731329917907715, "alphanum_fraction": 0.5802032947540283, "avg_line_length": 42.50961685180664, "blob_id": "39d6f515ebe9ba5502601b679dcd9029c4af9416", "content_id": "b2db346172a02fe6dd2a16b6d61b97df041cb666", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4526, "license_type": "no_license", "max_line_length": 129, "num_lines": 104, "path": "/FragCrawlThreadCreation.py", "repo_name": "snirav/FragranticaNotes", "src_encoding": "UTF-8", "text": "__author__ = 'Nirav'\n\nimport threading\nimport urllib2\nfrom bs4 import BeautifulSoup\nimport numpy as np\n# import pandas as pd\n\n\n# Spider class for running the threads for all the designers\nclass mySpider(threading.Thread):\n\n def __init__(self,siteUrl,designerName):\n threading.Thread.__init__(self)\n self.siteUrl = siteUrl\n self.designerName = designerName\n\n def run(self):\n print \"Starting \" + self.designerName\n getNextlevelUrls(self.siteUrl, self.designerName)\n print \"Exiting \" + self.designerName\n\n\ndef getNextlevelUrls(siteUrl, designerName):\n # f = codecs.open('C:\\\\Perfumes\\\\'+designerName+'.txt','w',encoding='utf8')\n notesDictionary = {}\n try:\n designerResponse = urllib2.urlopen(siteUrl + '/designers/' + designerName + '.html', timeout=10)\n except urllib2.URLError, e:\n print \"Oops, timed out?\", e\n designerSoup = BeautifulSoup(designerResponse)\n links = designerSoup.findAll('a')\n perfumeCount = 0\n print designerName\n # f.write(designerName +'\\n')\n for link in links:\n if link.get('href') != None:\n if link['href'].find('/perfume/'+designerName) != -1:\n\n perfumeCount += 1\n\n perfumeID = str(link['href'].split('-')[-1].split('.')[0])\n # print(perfumeID)\n print(link.img.get('alt').split(' ', 1)[1])\n userListUrl = siteUrl+'/' + '/ajax.php?view=whoHasHadWant&perfume_id=' + perfumeID\n try:\n userListResponse = urllib2.urlopen(userListUrl,timeout=10)\n except urllib2.URLError, e:\n print \"URL Fetch time-out\",e\n userListSoup = BeautifulSoup(userListResponse)\n perfumeWhoHasHadSigWantCurr = [int(tag.string) for tag in (userListSoup.find('p').findAll('b'))]\n # print perfumeWhoHasHadSigWantCurr\n\n perfumeUrl = siteUrl+'/'+link['href']\n try:\n perfumeResponse = urllib2.urlopen(perfumeUrl,timeout=10)\n except urllib2.URLError, e:\n print \"URL Fetch time-out\",e\n perfumeSoup = BeautifulSoup(perfumeResponse)\n\n perfumeNotesTitle = perfumeSoup.findAll('span',attrs={'class':'rtgNote'})\n noteList = [perfumeTitle.img.get('alt') for perfumeTitle in perfumeNotesTitle]\n noteID = [int(perfumeTitle.attrs['title']) for perfumeTitle in perfumeNotesTitle]\n # print(noteList)\n # print(noteID)\n idx = 0\n for ith in noteID:\n if not(ith in notesDictionary):\n notesDictionary[ith] = noteList[idx]\n idx += 1\n perfumeMenWomen = np.sign(np.array([perfumeSoup.title.text.find(' men'), perfumeSoup.title.text.find(' women')]))\n # print(perfumeMenWomen)\n perfumeVotes = perfumeSoup.findAll('table',attrs={'class':'voteLS'})\n perfumeLongevityVotes = np.array([int(a.string) for a in perfumeVotes[0].findAll('td', attrs={'class':'ndSum'})])\n # print(perfumeLongevityVotes)\n perfumeSillageVotes = np.array([int(a.string) for a in perfumeVotes[1].findAll('td', attrs={'class':'ndSum'})])\n # print(perfumeSillageVotes)\n perfumeAttrs = np.append(perfumeMenWomen, np.append(perfumeLongevityVotes, perfumeSillageVotes))\n perfumeNotes = perfumeSoup.findAll('div',attrs={'id':'userMainNotes'})\n\n # perfumeNotesStr = str(perfumeNotes[0].attrs[u'title'])\n # notesVec = np.zeros(400, dtype = int)\n # perfumeNotesStrSplit = perfumeNotesStr.split(';')\n # for notes in perfumeNotesStrSplit :\n # individualNotes = notes.split(':')\n # individualNotesInt = np.array([int(str_tmp) for str_tmp in individualNotes])\n # notesVec[individualNotesInt[0]] = individualNotesInt[1]\n # print(notesVec)\n\n\ndef crawl(url, designer, designerList):\n threads = []\n if designer == 'All':\n for designer in designerList:\n try:\n t = mySpider(url, designer)\n threads.append(t)\n except Exception as errtxt:\n print errtxt\n else:\n getNextlevelUrls(url, designer)\n [t.start() for t in threads]\n [t.join() for t in threads]\n print \"Main Thread Completed!!\" \n" } ]

3

jorendorff/dht

https://github.com/jorendorff/dht

012546ff00a336cbe7c988b12ccab20315d2eb3d

3c893214dfe7f9fb26c9e59a565c3ad651784aa2

8cea6304cbbdae079cf1d762bc0140b473072cf8

refs/heads/master

2020-05-31T20:55:09.255169

2016-02-05T07:14:20

3,507,865

19

5

null

[ { "alpha_fraction": 0.5639333724975586, "alphanum_fraction": 0.5703738331794739, "avg_line_length": 19.60194206237793, "blob_id": "b5103538c53ae87ee2d4407f0e5471360ecf3902", "content_id": "0ccd1386115e6734b303a2c6a91458e516d324be", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6366, "license_type": "permissive", "max_line_length": 90, "num_lines": 309, "path": "/tables.cpp", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "#include \"tables.h\"\n#include <cstring>\n\nusing namespace std;\n\n\n// === OpenTable\n\nOpenTable::OpenTable() {\n table = new Entry[8];\n mask = 7;\n live_count = 0;\n nonempty_count = 0;\n}\n\nOpenTable::~OpenTable() {\n delete[] table;\n}\n\nOpenTable::Entry *\nOpenTable::lookup(KeyArg key)\n{\n hashcode_t h = hash(key);\n size_t i = h & mask;\n h >>= 3;\n while (!isEmpty(table[i].key)) {\n if (table[i].key == key)\n return &table[i];\n i = (i + (h | 1)) & mask;\n }\n return NULL;\n}\n\nconst OpenTable::Entry *\nOpenTable::lookup(KeyArg key) const\n{\n return const_cast<OpenTable *>(this)->lookup(key);\n}\n\nvoid\nOpenTable::rehash(size_t new_capacity)\n{\n Entry *old_table = table;\n Entry *old_table_end = table + mask + 1;\n table = new Entry[new_capacity];\n mask = new_capacity - 1;\n live_count = 0;\n nonempty_count = 0;\n for (Entry *p = old_table; p != old_table_end; ++p) {\n if (isLive(p->key))\n set(p->key, p->value);\n }\n delete[] old_table;\n}\n\nsize_t\nOpenTable::byte_size(ByteSizeOption) const\n{\n return sizeof(*this) + (mask + 1) * sizeof(Entry);\n}\n\nsize_t\nOpenTable::size() const\n{\n return live_count;\n}\n\nbool\nOpenTable::has(KeyArg key) const\n{\n return lookup(key) != NULL;\n}\n\nValue\nOpenTable::get(KeyArg key) const\n{\n const Entry *e = lookup(key);\n return e ? e->value : Value();\n}\n\nvoid\nOpenTable::set(KeyArg key, ValueArg value)\n{\n hashcode_t h = hash(key);\n size_t i = h & mask;\n h >>= 3;\n while (isLive(table[i].key)) {\n if (table[i].key == key) {\n table[i].value = value;\n return;\n }\n i = (i + (h | 1)) & mask;\n }\n\n bool tomb = isTombstone(table[i].key);\n table[i].key = key;\n table[i].value = value;\n live_count++;\n if (!tomb)\n nonempty_count++;\n if (nonempty_count > (mask + 1) * max_fill_ratio())\n rehash((mask + 1) << 1);\n}\n\nbool\nOpenTable::remove(KeyArg key)\n{\n Entry *e = lookup(key);\n if (!e)\n return false;\n makeTombstone(e->key);\n live_count--;\n if (mask > 7 && live_count < (mask + 1) * min_fill_ratio())\n rehash((mask + 1) >> 1);\n return true;\n}\n\n\n// === DenseTable\n\n#ifdef HAVE_SPARSEHASH\n\nDenseTable::DenseTable()\n{\n Key k;\n makeEmpty(k);\n map.set_empty_key(k);\n makeTombstone(k);\n map.set_deleted_key(k);\n}\n\nsize_t\nDenseTable::byte_size(ByteSizeOption) const\n{\n return sizeof(*this) + sizeof(std::pair<const Key, Value>) * map.bucket_count();\n}\n\nsize_t\nDenseTable::size() const\n{\n return map.size();\n}\n\nbool\nDenseTable::has(KeyArg key) const\n{\n return map.find(key) != map.end();\n}\n\nValue\nDenseTable::get(KeyArg key) const\n{\n Map::const_iterator it = map.find(key);\n return (it == map.end() ? Value() : it->second);\n}\n\nvoid\nDenseTable::set(KeyArg key, ValueArg value)\n{\n map[key] = value;\n}\n\nbool\nDenseTable::remove(KeyArg key)\n{\n Map::iterator it = map.find(key);\n if (it == map.end())\n return false;\n map.erase(it);\n size_t n = map.bucket_count();\n if (n > 32 && map.size() <= n / 8)\n map.resize(0);\n return true;\n}\n\n#endif // HAVE_SPARSEHASH\n\n\n// === CloseTable\n\nCloseTable::CloseTable()\n{\n size_t buckets = initial_buckets();\n table = new EntryPtr[buckets];\n memset(table, 0, buckets * sizeof(EntryPtr));\n table_mask = buckets - 1;\n entries_capacity = size_t(buckets * fill_factor());\n entries = new Entry[entries_capacity];\n entries_length = 0;\n live_count = 0;\n}\n\nCloseTable::~CloseTable()\n{\n delete[] table;\n delete[] entries;\n}\n\nCloseTable::Entry *\nCloseTable::lookup(KeyArg key, hashcode_t h)\n{\n for (Entry *e = table[h & table_mask]; e; e = e->chain) {\n if (e->key == key)\n return e;\n }\n return NULL;\n}\n\nconst CloseTable::Entry *\nCloseTable::lookup(KeyArg key) const {\n return const_cast<CloseTable *>(this)->lookup(key, hash(key));\n}\n\nvoid\nCloseTable::rehash(size_t new_table_mask)\n{\n size_t new_capacity = size_t((new_table_mask + 1) * fill_factor());\n EntryPtr *new_table = new EntryPtr[new_table_mask + 1];\n memset(new_table, 0, (new_table_mask + 1) * sizeof(EntryPtr));\n Entry *new_entries = new Entry[new_capacity];\n\n Entry *q = new_entries;\n for (Entry *p = entries, *end = entries + entries_length; p != end; p++) {\n if (!isEmpty(p->key)) {\n hashcode_t h = hash(p->key) & new_table_mask;\n q->key = p->key;\n q->value = p->value;\n q->chain = new_table[h];\n new_table[h] = q;\n q++;\n }\n }\n\n delete[] table;\n delete[] entries;\n table = new_table;\n table_mask = new_table_mask;\n entries = new_entries;\n entries_capacity = new_capacity;\n entries_length = live_count;\n}\n\nsize_t\nCloseTable::byte_size(ByteSizeOption option) const\n{\n return sizeof(*this)\n + (table_mask + 1) * sizeof(EntryPtr)\n + (option == BytesAllocated ? entries_capacity : entries_length) * sizeof(Entry);\n}\n\nsize_t\nCloseTable::size() const\n{\n return live_count;\n}\n\nbool\nCloseTable::has(KeyArg key) const\n{\n return lookup(key) != NULL;\n}\n\nValue\nCloseTable::get(KeyArg key) const\n{\n const Entry *e = lookup(key);\n return e ? e->value : Value();\n}\n\nvoid\nCloseTable::set(KeyArg key, ValueArg value)\n{\n hashcode_t h = hash(key);\n Entry *e = lookup(key, h);\n if (e) {\n e->value = value;\n } else {\n if (entries_length == entries_capacity) {\n // If the table is more than 1/4 deleted entries, simply rehash in\n // place to free up some space. Otherwise, grow the table.\n rehash(live_count >= entries_capacity * 0.75\n ? (table_mask << 1) | 1\n : table_mask);\n }\n h &= table_mask;\n live_count++;\n e = &entries[entries_length++];\n e->key = key;\n e->value = value;\n e->chain = table[h];\n table[h] = e;\n }\n}\n\nbool\nCloseTable::remove(KeyArg key)\n{\n // If an entry exists for the given key, empty it.\n Entry *e = lookup(key, hash(key));\n if (e == NULL)\n return false;\n live_count--;\n makeEmpty(e->key);\n\n // If many entries have been removed, shrink the table.\n if (table_mask > initial_buckets() && live_count < entries_length * min_vector_fill())\n rehash(table_mask >> 1);\n return true;\n}\n" }, { "alpha_fraction": 0.4968442916870117, "alphanum_fraction": 0.5218830704689026, "avg_line_length": 23.65561294555664, "blob_id": "94710112a0994678110b108fbba18f9ce9693f37", "content_id": "eb93b645a3c622f368436fd16f91708d9da77ab4", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 9665, "license_type": "permissive", "max_line_length": 108, "num_lines": 392, "path": "/hashbench.cpp", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "#include <cstring>\n#include <cmath>\n#include <stdint.h>\n#include <iostream>\n#include <iomanip>\n#ifdef HAVE_GETTIMEOFDAY\n#include <sys/time.h>\n#else\n#include <windows.h>\n#endif\n#include \"tables.h\"\n\nusing namespace std;\n\n// === Code for measuring speed\n//\n// Instead of producing a single number, we want to produce several data\n// points. Then we'll plot them, and we'll be able to see noise, nonlinearity,\n// and any other nonobvious weirdness.\n\n// Run a Test of size n once. Return the elapsed time in seconds.\ntemplate <class Test>\ndouble measure_single_run(size_t n)\n{\n Test test;\n test.setup(n);\n\n#ifdef HAVE_GETTIMEOFDAY\n struct timeval t0, t1;\n gettimeofday(&t0, NULL);\n#else\n LARGE_INTEGER f, t0, t1;\n if (!QueryPerformanceFrequency(&f))\n abort();\n if (!QueryPerformanceCounter(&t0))\n abort();\n#endif\n\n test.run(n);\n\n#ifdef HAVE_GETTIMEOFDAY\n gettimeofday(&t1, NULL);\n return t1.tv_sec - t0.tv_sec + 1e-6 * (t1.tv_usec - t0.tv_usec);\n#else\n if (!QueryPerformanceCounter(&t1))\n abort();\n return double(t1.QuadPart - t0.QuadPart) / double(f.QuadPart);\n#endif\n}\n\nconst double min_run_seconds = 0.1;\nconst double max_run_seconds = 1.0;\n\n// Run several Tests of different sizes. Write results to stdout.\n//\n// We intentionally don't scale the test size exponentially, because hash\n// tables can have nonlinear performance-falls-off-a-cliff points (table\n// resizes) that occur at exponentially spaced intervals. We want to make sure\n// we don't miss those.\n//\ntemplate <class Test>\nvoid run_time_trials()\n{\n cout << \"[\\n\";\n\n // Estimate how many iterations per second we can do.\n double estimated_speed;\n for (size_t n = 1; ; n *= 2) {\n double dt = measure_single_run<Test>(n);\n if (dt >= min_run_seconds) {\n estimated_speed = n / dt;\n break;\n }\n }\n\n // Now run trials of increasing size and print the results.\n double total = 0;\n const int trials = Test::trials();\n for (int i = 0; i < trials; i++) {\n double target_dt = min_run_seconds + double(i) / (trials - 1) * (max_run_seconds - min_run_seconds);\n size_t n = size_t(ceil(estimated_speed * target_dt));\n double dt = measure_single_run<Test>(n);\n cout << \"\\t\\t[\" << n << \", \" << dt << (i < trials - 1 ? \"],\" : \"]\") << endl;\n }\n\n cout << \"\\t]\";\n}\n\n\n// === Tests\n\nstruct GoodTest {\n static int trials() { return 10; }\n};\n\nstruct SquirrelyTest {\n static int trials() { return 25; }\n};\n\ntemplate <class Table>\nstruct InsertLargeTest : SquirrelyTest {\n Table table;\n void setup(size_t) {}\n void run(size_t n) {\n Key k = 1;\n for (size_t i = 0; i < n; i++) {\n table.set(k, k);\n k = k * 1103515245 + 12345;\n }\n }\n};\n\n// This test repeatedly builds a table of pseudorandom size (an exponential\n// distribution with median size 100), then discards the table and starts over.\n// It stops when it has done n total inserts.\n//\n// For a given n, the workload is deterministic.\n//\n// It would be simpler to repeatedly build tables of a particular\n// size. However, all the implementations have particular sizes at which they\n// rehash, an expensive operation that is *meant* to be amortized across all\n// the other inserts. The benchmark should not reward implementations for\n// having any particular rehashing threshold; so we build tables of a variety\n// of sizes.\n//\ntemplate <class Table>\nstruct InsertSmallTest : GoodTest {\n void setup(size_t) {}\n void run(size_t n) {\n Key k = 1;\n while (n) {\n Table table;\n do {\n table.set(k, k);\n k = k * 1103515245 + 12345;\n } while (--n && k % 145 != 0);\n }\n }\n};\n\ntemplate <class Table>\nstruct LookupHitTest : GoodTest {\n enum { M = 8675309 + 1 }; // jenny's number, a prime, plus 1\n Table table;\n size_t errors;\n\n void setup(size_t n) {\n Key k = 1;\n for (size_t i = 0; i < n; i++) {\n table.set(k, k);\n k = k * 31 % M;\n if (k == 1)\n break;\n }\n errors = 0;\n }\n\n void run(size_t n) {\n Key k = 1;\n for (size_t i = 0; i < n; i++) {\n if (table.get(k) != k)\n abort();\n k = k * 31 % M;\n }\n }\n};\n\ntemplate <class Table>\nstruct LookupMissTest : GoodTest {\n enum { M = 8675309 + 1 }; // jenny's number, a prime, plus 1\n Table table;\n size_t errors;\n\n void setup(size_t n) {\n Key k = 1;\n for (size_t i = 0; i < n; i++) {\n table.set(k, k);\n k = k * 31 % M;\n if (k == 1)\n break;\n }\n errors = 0;\n }\n\n void run(size_t n) {\n Key k = 1;\n for (size_t i = 0; i < n; i++) {\n if (table.get(k + M) != 0)\n abort();\n k = k * 31 % M;\n }\n }\n};\n\n// This test adds and removes entries from a table in FIFO order.\ntemplate <class Table>\nstruct WorklistTest : GoodTest {\n Table table;\n Key r, w;\n\n void setup(size_t) {\n r = 1;\n w = 1;\n for (int i = 0; i < 700; i++) {\n table.set(w, w);\n w = w * 1103515245 + 12345;\n }\n }\n\n void run(size_t n) {\n for (size_t i = 0; i < n; i++) {\n table.set(w, w);\n w = w * 1103515245 + 12345;\n\n if (!table.remove(r))\n abort();\n r = r * 1103515245 + 12345;\n }\n }\n};\n\ntemplate <class Table>\nstruct DeleteTest : SquirrelyTest {\n Table table;\n\n void setup(size_t n) {\n while (n % 7 == 0 || n % 11 == 0)\n n++;\n\n Key k = 0;\n for (size_t i = 0; i < n; i++) {\n table.set(k + 1, 0);\n k = (k + 7) % n;\n }\n }\n\n void run(size_t n) {\n while (n % 7 == 0 || n % 11 == 0)\n n++;\n\n Key k = 0;\n for (size_t i = 0; i < n; i++) {\n if (!table.remove(k + 1))\n abort();\n k = (k + 11) % n;\n }\n }\n};\n\ntemplate <class Table>\nstruct LookupAfterDeleteTest : GoodTest {\n Table table;\n\n enum { Size = 50000 };\n\n void setup(size_t n) {\n for (size_t i = 1; i <= Size; i++)\n table.set(i, i);\n for (size_t i = 1; i <= Size; i++) {\n if ((i & 0xff) != 0)\n table.remove(i);\n }\n }\n\n void run(size_t n) {\n for (size_t i = 1; i <= n; i++) {\n Key k = i % Size;\n if (table.get(k) != ((k & 0xff) == 0 ? k : Value()))\n abort();\n }\n }\n};\n\ntemplate <template <class> class Test>\nvoid run_speed_test()\n{\n cout << '{' << endl;\n\n#ifdef HAVE_SPARSEHASH\n cout << \"\\t\\\"DenseTable\\\": \";\n run_time_trials<Test<DenseTable> >();\n cout << ',' << endl;\n#endif\n\n cout << \"\\t\\\"OpenTable\\\": \";\n run_time_trials<Test<OpenTable> >();\n cout << ',' << endl;\n\n cout << \"\\t\\\"CloseTable\\\": \";\n run_time_trials<Test<CloseTable> >();\n cout << endl;\n\n cout << \"}\";\n}\n\nvoid run_one_speed_test(const char *name)\n{\n if (strcmp(name, \"InsertLargeTest\") == 0)\n run_speed_test<InsertLargeTest>();\n else if (strcmp(name, \"InsertSmallTest\") == 0)\n run_speed_test<InsertSmallTest>();\n else if (strcmp(name, \"LookupHitTest\") == 0)\n run_speed_test<LookupHitTest>();\n else if (strcmp(name, \"LookupMissTest\") == 0)\n run_speed_test<LookupMissTest>();\n else if (strcmp(name, \"WorklistTest\") == 0)\n run_speed_test<WorklistTest>();\n else if (strcmp(name, \"DeleteTest\") == 0)\n run_speed_test<DeleteTest>();\n else if (strcmp(name, \"LookupAfterDeleteTest\") == 0)\n run_speed_test<LookupAfterDeleteTest>();\n else {\n cerr << \"No such test: \" << name << endl;\n return;\n }\n cout << endl;\n}\n\nvoid run_all_speed_tests()\n{\n cout << \"{\" << endl;\n\n cout << \"\\\"InsertLargeTest\\\": \";\n run_speed_test<InsertLargeTest>();\n cout << \",\" << endl;\n\n cout << \"\\\"InsertSmallTest\\\": \";\n run_speed_test<InsertSmallTest>();\n cout << \",\" << endl;\n\n cout << \"\\\"LookupHitTest\\\": \";\n run_speed_test<LookupHitTest>();\n cout << \",\" << endl;\n\n cout << \"\\\"LookupMissTest\\\": \";\n run_speed_test<LookupMissTest>();\n cout << \",\" << endl;\n\n cout << \"\\\"WorklistTest\\\": \";\n run_speed_test<WorklistTest>();\n cout << \",\" << endl;\n\n cout << \"\\\"DeleteTest\\\": \";\n run_speed_test<DeleteTest>();\n cout << \",\" << endl;\n\n cout << \"\\\"LookupAfterDeleteTest\\\": \";\n run_speed_test<LookupAfterDeleteTest>();\n\n cout << \"}\" << endl;\n}\n\nvoid measure_space(ByteSizeOption opt)\n{\n#ifdef HAVE_SPARSEHASH\n DenseTable ht0;\n#endif\n OpenTable ht1;\n CloseTable ht2;\n\n for (int i = 0; i < 100000; i++) {\n cout << i << '\\t'\n#ifdef HAVE_SPARSEHASH\n << ht0.byte_size(opt) << '\\t'\n#else\n << 1 << '\\t'\n#endif\n << ht1.byte_size(opt) << '\\t' << ht2.byte_size(opt) << endl;\n\n#ifdef HAVE_SPARSEHASH\n ht0.set(i + 1, i);\n#endif\n ht1.set(i + 1, i);\n ht2.set(i + 1, i);\n }\n}\n\nint main(int argc, const char **argv) {\n if (argc == 2 && (strcmp(argv[1], \"-m\") == 0 || strcmp(argv[1], \"-w\") == 0)) {\n measure_space(argv[1][1] == 'm' ? BytesAllocated : BytesWritten);\n } else if (argc == 1) {\n //cout << measure_single_run<LookupHitTest<OpenTable> >(1000000) << endl;\n run_all_speed_tests();\n } else if (argc == 2) {\n run_one_speed_test(argv[1]);\n } else {\n cerr << \"usage:\\n \" << argv[0] << \"\\n \" << argv[0] << \" -m\\n \" << argv[0] << \" -w\\n\";\n return 1;\n }\n\n return 0;\n}\n" }, { "alpha_fraction": 0.5738714337348938, "alphanum_fraction": 0.6094391345977783, "avg_line_length": 32.227272033691406, "blob_id": "1318dbf869864b5e4d53404b30a0e9d1ab8dd309", "content_id": "b1122da79a4e857bf89ae5b57768b0d16287141a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1462, "license_type": "permissive", "max_line_length": 98, "num_lines": 44, "path": "/plot.py", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "from __future__ import division\nimport sys\nfrom matplotlib.pyplot import *\nimport numpy\n\ndef main(filename, outfilename):\n data = numpy.genfromtxt(filename)\n\n # plot the graph and save it\n if filename == 'figure-1-data.txt':\n suptitle('Memory allocated (log/log plot)')\n ylabel('bytes of memory allocated')\n elif filename == 'figure-2-data.txt':\n suptitle('Memory written (log/log plot)')\n ylabel('bytes of memory written')\n xlabel('number of entries')\n\n index = data[:,0]\n series1 = data[:,1]\n series2 = data[:,2]\n series3 = data[:,3]\n loglog(index, series1, '-', color='#cccccc', label='dense_hash_map (open addressing)')\n loglog(index, series2, 'b-', label='open addressing')\n loglog(index, series3, 'r-', label='Close table')\n legend(loc='upper left')\n savefig(outfilename, format='png')\n\n # compute and print summary information about which is bigger\n r1 = []\n r2 = []\n for i, _, s1, s2 in data:\n if s1 > s2:\n r1.append(s1/s2)\n else:\n r2.append(s2/s1)\n\n r1avg = sum(r1)/len(r1) - 1 if len(r1) else 0\n r2avg = sum(r2)/len(r2) - 1 if len(r2) else 0\n r1f = len(r1)/len(data)\n r2f = len(r2)/len(data)\n print(\"Implementation 1 takes up more space {:.1%} of the time, by {:.1%}\".format(r1f, r1avg))\n print(\"Implementation 2 takes up more space {:.1%} of the time, by {:.1%}\".format(r2f, r2avg))\n\nmain(sys.argv[1], sys.argv[2])\n" }, { "alpha_fraction": 0.5858041048049927, "alphanum_fraction": 0.5893980264663696, "avg_line_length": 32.727272033691406, "blob_id": "e11c82aa41644e255dae35b8222480e6a3d0755a", "content_id": "36c0c7e1cb781b678c24a5950f2d630994ed0c2a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1113, "license_type": "permissive", "max_line_length": 104, "num_lines": 33, "path": "/plot_speed.py", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "from __future__ import division\nimport sys\nimport matplotlib.pyplot as plt\nimport numpy\nimport json\n\ndef main(filename):\n with open(filename) as f:\n data = json.load(f)\n\n # plot the graph and save it\n for testname, results in data.items():\n fig = plt.figure()\n fig.suptitle(testname)\n axes = fig.gca()\n axes.set_ylabel('speed (operations/second)')\n hi = max(max(x/y for x, y in series) for series in results.values())\n axes.set_ylim(bottom=0, top=hi * 1.2)\n axes.set_xlabel('number of operations')\n\n def show(data, *args, **kwargs):\n xs = [x for x, y in data]\n ys = [x/y for x, y in data]\n axes.plot(xs, ys, *args, **kwargs)\n\n if 'DenseTable' in results:\n show(results['DenseTable'], '-o', color='#cccccc', label='dense_hash_map (open addressing)')\n show(results['OpenTable'], 'b-o', label='open addressing')\n show(results['CloseTable'], 'r-o', label='Close table')\n axes.legend(loc='best')\n fig.savefig(testname + \"-speed.png\", format='png')\n\nmain(sys.argv[1])\n" }, { "alpha_fraction": 0.7191780805587769, "alphanum_fraction": 0.7277397513389587, "avg_line_length": 28.183332443237305, "blob_id": "00158063ed08dcb545b4f3fbab8d1aae8647c467", "content_id": "8ce73bd000b341286794ab6897bc2eaf1b11249f", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Makefile", "length_bytes": 1752, "license_type": "permissive", "max_line_length": 91, "num_lines": 60, "path": "/Makefile", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "CXX=g++-apple-4.2\nCXXFLAGS=-O3 -g -Isparsehash-install/include -DNDEBUG -DHAVE_GETTIMEOFDAY -DHAVE_SPARSEHASH\n\n# To run plot.py, you need Python with matplotlib. Set the python executable to\n# use below.\n#\n# If you have MacPorts, you can:\n# sudo port install py27-matplotlib\n# This doesn't install matplotlib in your Mac's system python installation.\n# Instead it installs a copy of python in /opt/local/bin (or wherever you've\n# configured ports to install stuff) and that copy has matplotlib.\n#\nPYTHON=/opt/local/bin/python2.7\n\nSPEED_IMAGES=\\\n InsertSmallTest-speed.png \\\n InsertLargeTest-speed.png \\\n LookupHitTest-speed.png \\\n LookupMissTest-speed.png \\\n WorklistTest-speed.png \\\n DeleteTest-speed.png \\\n LookupAfterDeleteTest-speed.png\n\nall: figure-1.png figure-2.png $(SPEED_IMAGES)\n\nfigure-1.png: figure-1-data.txt plot.py\n\t$(PYTHON) plot.py $< $@\n\nfigure-2.png: figure-2-data.txt plot.py\n\t$(PYTHON) plot.py $< $@\n\nfigure-1-data.txt: hashbench\n\t./hashbench -m > $@\n\nfigure-2-data.txt: hashbench\n\t./hashbench -w > $@\n\n$(SPEED_IMAGES): hashbench-data.txt plot_speed.py\n\t$(PYTHON) plot_speed.py $<\n\nhashbench-data.txt: hashbench\n\t./hashbench > $@\n\nhashbench: hashbench.o tables.o\n\t$(CXX) -o $@ $^\n\nhashbench.o: hashbench.cpp tables.h sparsehash-install/include/sparsehash/dense_hash_map\n\t$(CXX) $(CXXFLAGS) -o $@ -c $<\n\ntables.o: tables.cpp tables.h sparsehash-install/include/sparsehash/dense_hash_map\n\t$(CXX) $(CXXFLAGS) -o $@ -c $<\n\nsparsehash-sources/configure:\n\tsvn checkout http://sparsehash.googlecode.com/svn/trunk/ sparsehash-sources\n\nsparsehash-install/include/sparsehash/dense_hash_map: sparsehash-sources/configure\n\tcd sparsehash-sources && \\\n\t\t./configure --prefix=$(PWD)/sparsehash-install && \\\n\t\tmake && \\\n\t\tmake install\n\n" }, { "alpha_fraction": 0.6471967697143555, "alphanum_fraction": 0.6559368968009949, "avg_line_length": 28.503145217895508, "blob_id": "25072b5728aa54f34fdd694e16a80e3f0987276a", "content_id": "48c8b14710c6340134c57349990a17d2d398a59d", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4691, "license_type": "permissive", "max_line_length": 83, "num_lines": 159, "path": "/tables.h", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "#ifndef tables_h_\n#define tables_h_\n\n#include <stdint.h>\n#include <cstdlib>\n#ifdef HAVE_SPARSEHASH\n#include <sparsehash/dense_hash_map>\n#endif\n\n// === Keys and values (common definitions used by both hash table implementations)\n\n// The keys to be stored in our hash tables are 64-bit values. However two keys\n// are set aside: Key(0) indicates that a record is empty, and Key(-1) indicates\n// that the record has been deleted.\n\ntypedef uint64_t Key;\ntypedef Key KeyArg;\ntypedef uint64_t Value;\ntypedef Value ValueArg;\ntypedef uint32_t hashcode_t;\n\ninline hashcode_t hash(KeyArg k) { return k; }\n\n// A key is either \"live\" (that is, an actual value), empty, or a tombstone.\n// For a given key, exactly one of the three predicates\n// isLive/isEmpty/isTombstone is true. The implementation of isLive below is\n// equivalent to !isEmpty(k) && !isTombstone(k) but *much* faster; it is the\n// only fancy thing in this program.\n\ninline bool isEmpty(KeyArg k) { return k == 0; }\ninline void makeEmpty(Key &k) { k = 0; }\ninline bool isTombstone(KeyArg k) { return k == Key(-1); }\ninline void makeTombstone(Key &k) { k = Key(-1); }\ninline bool isLive(KeyArg k) { return ((k + 1) & ~1) != 0; }\n\nenum ByteSizeOption { BytesAllocated, BytesWritten };\n\n\n#ifdef HAVE_SPARSEHASH\n// === DenseTable\n// The dense_hash_map type from Google sparsehash, included to give a baseline.\n\nclass DenseTable {\nprivate:\n struct Hasher {\n size_t operator()(KeyArg key) const { return hash(key); }\n };\n\n typedef google::dense_hash_map<Key, Value, Hasher> Map;\n Map map;\n\npublic:\n DenseTable();\n\n size_t byte_size(ByteSizeOption option) const;\n size_t size() const;\n bool has(KeyArg key) const;\n Value get(KeyArg key) const;\n void set(KeyArg key, ValueArg value);\n bool remove(KeyArg key);\n};\n#endif // HAVE_SPARSEHASH\n\n\n// === OpenTable\n// A simple hash table with open addressing.\n// See <https://en.wikipedia.org/wiki/Hash_table#Open_addressing>.\n//\nclass OpenTable {\n struct Entry {\n Key key;\n Value value;\n\n Entry() { makeEmpty(key); }\n };\n\n Entry *table; // power-of-2-sized flat hash table\n size_t live_count; // number of live entries\n size_t nonempty_count; // number of live and tombstone entries\n size_t mask; // size of table, in elements, minus 1\n\n static double min_fill_ratio() { return 0.25; }\n static double max_fill_ratio() { return 0.75; }\n\n inline Entry * lookup(KeyArg key);\n inline const Entry * lookup(KeyArg key) const;\n\n void rehash(size_t new_capacity);\n\npublic:\n OpenTable();\n ~OpenTable();\n\n size_t byte_size(ByteSizeOption option) const;\n size_t size() const;\n bool has(KeyArg key) const;\n Value get(KeyArg key) const;\n void set(KeyArg key, ValueArg value);\n bool remove(KeyArg key);\n};\n\n\n// === CloseTable\n// A vector combined with a very simple hash table for fast lookup.\n// Tyler Close proposed this.\n//\nclass CloseTable {\nprivate:\n // The number of buckets in the table initially.\n // This must be a power of two.\n static size_t initial_buckets() { return 4; }\n\n // The maximum load factor (mean number of entries per bucket).\n // It is an invariant that\n // entries_capacity == floor((table_mask + 1) * fill_factor()).\n //\n // This fill factor was chosen to make the size of the entries\n // array, in bytes, close to a power of two. (sizeof(Entry)\n // is 24 on both 32-bit and 64-bit systems.)\n //\n static double fill_factor() { return 8.0 / 3.0; }\n\n // The minimum permitted value of (live_count / entries_length).\n // If that ratio drops below this value, we shrink the table.\n static double min_vector_fill() { return 0.25; }\n\n struct Entry {\n Key key;\n Value value;\n Entry *chain;\n };\n\n typedef Entry *EntryPtr;\n\n EntryPtr *table; // power-of-2-sized hash table\n size_t table_mask; // size of table, in elements, minus one\n Entry *entries; // data vector, an array of Entry objects\n size_t entries_capacity; // size of entries, in elements\n size_t entries_length; // number of initialized entries\n size_t live_count; // entries_length less empty (removed) entries\n\n inline Entry * lookup(KeyArg key, hashcode_t h);\n inline const Entry * lookup(KeyArg key) const;\n void rehash(size_t new_table_mask);\n\npublic:\n CloseTable();\n ~CloseTable();\n\n size_t byte_size(ByteSizeOption option) const;\n size_t size() const;\n bool has(KeyArg key) const;\n Value get(KeyArg key) const;\n void set(KeyArg key, ValueArg value);\n bool remove(KeyArg key);\n};\n\n\n#endif // tables_h_\n" }, { "alpha_fraction": 0.754811704158783, "alphanum_fraction": 0.7623431086540222, "avg_line_length": 55.904762268066406, "blob_id": "4c85a604d8f3df41db268edde52969e628b6d6c9", "content_id": "fdaf16f7c367426f45476b45e01e7d8bad0acf25", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2390, "license_type": "permissive", "max_line_length": 318, "num_lines": 42, "path": "/README.md", "repo_name": "jorendorff/dht", "src_encoding": "UTF-8", "text": "dht - Deterministic hash tables\n===============================\n\nThis repo contains the source code I used to evaluate deterministic hash table performance in the specific context of implementing the ECMAScript Map object for modern JS engines.\n\nUpdates will be posted to [the deterministic hash tables page](https://wiki.mozilla.org/User:Jorend/Deterministic_hash_tables).\n\n**To run benchmarks on Mac/Linux:**\n\n* Install matplotlib. If you have MacPorts, you can do `sudo port install py27-matplotlib` but note that this doesn't install matplotlib in your Mac's system python installation. Instead it installs a copy of python in /opt/local/bin (or wherever you've configured ports to install stuff) and that copy has matplotlib.\n* Install Subversion. You need it for Sparsehash.\n* Edit the Makefile to set CXX, CXXFLAGS, and PYTHON to values that will work on your system.\n* `make`\n* Wait. The benchmarks take a while to run.\n\n**To run benchmarks on Windows:**\n\nOn Windows, you're going to use a batch file, because Windows is awesome.\n\n* You have to have Visual Studio.\n* Install Python(x,y) from [http://code.google.com/p/pythonxy/](http://code.google.com/p/pythonxy/). I used Python(x,y) version 2.7.2.1.\n* Open a Visual Studio Command Line (All Programs → Microsoft Visual Studio 2010 → Visual Studio Tools → Visual Studio Command Prompt (2010).\n* cd to the dht directory.\n* Run build.bat.\n* Wait. The benchmarks take a while to run.\n\n**What you get**\n\n* figure-1.png shows how much memory each implementation allocates. figure-1-data.txt is the raw data.\n* figure-2.png shows how much memory each implementation uses (that is, how much of the allocated memory is actually accessed). figure-2-data.txt is the raw data.\n* The images InsertSmallTest-speed.png and friends show how fast each implementation is at each test. Higher is better. The file hashbench-data.txt contains the raw data for all these graphs. It's JSON.\n\n\n## License\n\nThe code in this repository was written for the sole purpose of evaluating the data structures.\n[The results page](https://wiki.mozilla.org/User:Jorend/Deterministic_hash_tables)\ntells how this specific goal affected the code.\nThis is not a general-purpose hash table library. Don't expect too much!\n\nThat said, feel free to use everything in this repository under the terms of\n[the MIT license](http://opensource.org/licenses/MIT).\n" } ]

7

mohammad9975/Fish-Recognition-androidApp

https://github.com/mohammad9975/Fish-Recognition-androidApp

07cc07e08a2c5df54ef5aa392774c8694009b271

c604dc3da64877d2484286d8e2e92d15174d1c2e

472dfd1978ec1911184a90a0b5862b37bdbc4294

refs/heads/master

2022-12-25T03:36:17.395099

2020-09-11T01:46:09

294,244,519

2

0

null

[ { "alpha_fraction": 0.5732254981994629, "alphanum_fraction": 0.5991015434265137, "avg_line_length": 32.35185241699219, "blob_id": "3404d651958d1081d4c830cb48e288c36fba8349", "content_id": "662b15f28327be1c36bce3ab7e8815836ec1edb0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5565, "license_type": "no_license", "max_line_length": 132, "num_lines": 162, "path": "/fishClassification.py", "repo_name": "mohammad9975/Fish-Recognition-androidApp", "src_encoding": "UTF-8", "text": "import glob\r\nimport cv2\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.model_selection import train_test_split\r\nfrom tensorflow.keras.utils import to_categorical\r\nfrom tensorflow.keras import models\r\nfrom tensorflow.keras import layers\r\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\r\nfrom tensorflow.keras.optimizers import SGD,Adam\r\n# from tensorflow.keras.applications import VGG16\r\n# import pickle\r\nfrom tensorflow.keras.losses import CosineSimilarity\r\nfrom random_eraser import get_random_eraser\r\nimport datetime\r\n\r\n## show results by tensorboard\r\nlog_dir = 'logs\\\\fit\\\\' + datetime.datetime.now().strftime('%Y%m%d-%H%M%S')\r\ntensorboar_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir)\r\n\r\nepoches = 100\r\n\r\naug= ImageDataGenerator(rotation_range=30,\r\n width_shift_range=.1,\r\n height_shift_range=.1,\r\n shear_range=.2,\r\n zoom_range=.1,\r\n horizontal_flip=True,\r\n fill_mode='nearest'\r\n # preprocessing_function=get_random_eraser(v_l=0, v_h=1)\r\n )\r\n\r\ndata=[]\r\nlabels=[]\r\nfor i,item in enumerate(glob.glob(r'C:\\Users\\mohammad\\Desktop\\preprocessod images\\*\\*')):\r\n \r\n img=cv2.imread(item)\r\n r_img=cv2.resize(img,(128,128))\r\n\r\n data.append(r_img)\r\n\r\n label=item.split('\\\\')[-2].split('.')[0]\r\n # print(label)\r\n labels.append(label)\r\n\r\n if i%100==0:\r\n print('Notice {} of data processod'.format(i))\r\n # if i==200:\r\n # break\r\n \r\ndata=np.array(data)/255\r\n\r\n## onehot encoding\r\nle= LabelEncoder()\r\nlabels=le.fit_transform(labels)\r\n\r\nlabels=to_categorical(labels,13)\r\n\r\n## using class weight because of unbalancing\r\nclassTotals=labels.sum(axis=0)\r\nclassWeight=classTotals.max()/classTotals\r\nclassWeight={0:classWeight[0],1:classWeight[1],2:classWeight[2],3:classWeight[3],4:classWeight[4],5:classWeight[5],6:classWeight[6],\r\n 7:classWeight[7],8:classWeight[8],9:classWeight[9],10:classWeight[10],11:classWeight[11],12:classWeight[12]}\r\n\r\n## split data to train and test data\r\nX_train,X_test,y_train,y_test=train_test_split(data,labels,test_size=.2)\r\n\r\n\r\n\r\n# with open(r'C:\\Users\\mohammad\\Documents\\python code\\practise deeplearning\\dog_cat.pkl','rb') as f:\r\n# X_train,X_test,y_train,y_test=pickle.load(f)\r\n\r\n\r\n# baseModel= VGG16(\r\n# weights='imagenet',\r\n# include_top=False,\r\n# input_tensor=layers.Input(shape=(32,32,3))\r\n# )\r\n\r\n# for layer in baseModel.layers:\r\n# layer.trainable=False\r\n\r\n## bulid CNN from scratch\r\n\r\n# Network= models.Sequential([\r\n# layers.Conv2D(32,(8,8),activation='relu',input_shape=(32,32,3)),\r\n# layers.BatchNormalization(),\r\n# layers.MaxPool2D((2,2)),\r\n\r\n# # layers.Conv2D(64,(3,3),activation='relu'),\r\n# # layers.BatchNormalization(),\r\n# # layers.MaxPool2D((2,2)),\r\n\r\n# layers.Conv2D(64,(5,5),activation='relu'),\r\n# # baseModel,\r\n# layers.BatchNormalization(),\r\n# layers.MaxPool2D((4,4)),\r\n# layers.Flatten(),\r\n\r\n# layers.Dense(128,activation='relu'),\r\n# layers.Dropout(.5),\r\n# layers.Dense(64,activation='relu'),\r\n# layers.Dense(13,activation='softmax')\r\n# ])\r\n \r\n## transfer learning\r\nbase_model = tf.keras.applications.MobileNet(input_tensor=layers.Input(shape=(128,128,3)),include_top=False)\r\nbase_model.trainable = False\r\n\r\nNetwork = tf.keras.Sequential([\r\n base_model,\r\n# using BatchNormalization\r\n# layers.BatchNormalization(),\r\n tf.keras.layers.GlobalAveragePooling2D(),\r\n layers.Dense(64,activation='relu'),\r\n # using dropOut\r\n layers.Dropout(.2),\r\n layers.Dense(32,activation='relu'),\r\n # using dropOut\r\n layers.Dropout(.2),\r\n tf.keras.layers.Dense(13, activation='softmax')\r\n])\r\n\r\n## using learning rae decay\r\nopt= SGD(lr=0.01,decay=0.001/epoches)\r\n\r\nNetwork.compile(optimizer=opt,\r\n # loss='binary_crossentropy',\r\n loss=CosineSimilarity(axis=1),\r\n metrics=['accuracy'])\r\n\r\n\r\nH = Network.fit_generator(aug.flow(X_train,y_train,batch_size=32),\r\n epochs=epoches,validation_data=(X_test,y_test),\r\n steps_per_epoch=len(X_train)//32,class_weight=classWeight,callbacks=[tensorboar_callback])\r\n\r\nloss,acc=Network.evaluate(X_test,y_test)\r\nprint('accuracy : {:.2f}'.format(acc))\r\n\r\n\r\nprint(Network.summary())\r\n# Network.save('out_CNN.h5')\r\n\r\n## Convert the model.\r\n# converter = tf.lite.TFLiteConverter.from_keras_model(model)\r\n# tflite_model = converter.convert()\r\n\r\n## Save the TF Lite model.\r\n# with tf.io.gfile.GFile('model.tflite', 'wb') as f:\r\n# f.write(tflite_model)\r\n\r\nplt.plot(np.arange(epoches),H.history['accuracy'],label='train_accuracy')\r\nplt.plot(np.arange(epoches),H.history['val_accuracy'],label='test_accuracy')\r\nplt.plot(np.arange(epoches),H.history['loss'],label='loss')\r\nplt.plot(np.arange(epoches),H.history['val_loss'],label='val_loss')\r\n\r\nplt.legend()\r\nplt.xlabel='epoches'\r\nplt.ylabel='accuracy/loss'\r\nplt.show()\r\n" }, { "alpha_fraction": 0.7638819217681885, "alphanum_fraction": 0.7943971753120422, "avg_line_length": 63.32258224487305, "blob_id": "2f210ab3ce357e60aeeef8e34eff9cc624f43937", "content_id": "e702e4e423e5a33ecc9452e652ebb3238096a99b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2011, "license_type": "no_license", "max_line_length": 268, "num_lines": 31, "path": "/README.md", "repo_name": "mohammad9975/Fish-Recognition-androidApp", "src_encoding": "UTF-8", "text": "# Fish-Recognition-androidApp\nFish Image Recognition in Android App \n\nIn this project, we are going to develop an android app that uses mobile phone camera to classify 15 different types of fishes realtime.\n\n## Data Collection and Machine Learning Pipeline\n\nThere isn’t a comprehensive labeled dataset available for fish species so I built my own dataset of 120+ images from scratch. \nI collected images using a few different methods outlined below.\n\n### Data Sources:\n\n1) Bing image search\n2) instagram by hashtag (i.e. #browntrout)\n\nAfter collecting the images, I manually reviewed all of the photos to ensure that the images matched their respective species label and removed any miscellaneous images that didn’t belong (fishing equipment, a river, tackle box, incorrectly labeled fish species, etc).\nThe data collection and cleaning phase took about one weeks. Now equipped with 120+ images across 15 fish species, I had an initial image dataset to start building a deep learning fish identification model. \nAlthough there were so many images that I ignore them because data collection is a time-consuming process.\n\n### Building the Deep Learning Model\nRather than creating a CNN from scratch, we’ll use a pre-trained model and perform transfer learning to customize this model with our new dataset. The pre-trained model we’re going to use is MobileNet, and we'll fine tune on our images.\n\n### Develop Android App\nAfter we’ve built our TensorFlow model with transfer learning, we’ll use the TFLite converter to create a mobile-ready model variant. The model will then be used in an Android application that recognizes images captured by the camera realtime.\n\n## Results\nAfter 100 epochs, the test accuracy is about 71 percent for 15 specious. to reach a better result, we need to increase our data number to 500+. lack of data is a bottleneck in deep learning!\n\n\n\n<img src=\"https://user-images.githubusercontent.com/51213071/92834467-4f379f00-f3ef-11ea-9b42-971cda9a2715.png\" width=\"300\">\n\n\n\n\n\n" } ]

2

Scienceseb/Pairwise-data-augmentation-PyTorch

https://github.com/Scienceseb/Pairwise-data-augmentation-PyTorch

d74cc85cdb884b2dd176f3fb0795a5cbce00edd8

2438a619bcd3b03dbcd330a15b96a71d2019a894

f6f7a07aa561f0b53e5dc457688a270602339ff5

refs/heads/master

2020-08-29T00:27:11.090567

2019-10-27T14:25:06

217,865,017

0

null

[ { "alpha_fraction": 0.7884615659713745, "alphanum_fraction": 0.7884615659713745, "avg_line_length": 50.900001525878906, "blob_id": "53c77cf460a609b1d7f3a043fbc6ec02a4072a38", "content_id": "5d68c6c44e5feba17f844c5435c6aaec95237a6a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 520, "license_type": "no_license", "max_line_length": 202, "num_lines": 10, "path": "/README.md", "repo_name": "Scienceseb/Pairwise-data-augmentation-PyTorch", "src_encoding": "UTF-8", "text": "# Pair-wise-data-augmentation\nTransform class for pairwise data augmentation, flip, random crop and random rotation, ColorJitter (possibly useful for denoising, e.g., dehazing, deraining, etc.), you can add your transformation also.\n\nThis code is very useful if you work with pair of images as input ou output, you can make the same data augmentation\non both images, if you have three images you can easily extend the code for that situation. \n\nCode was made for: deep learning using PyTorch\n\nYou need:\nPyTorch and PIL\n\n" }, { "alpha_fraction": 0.6149703860282898, "alphanum_fraction": 0.625201940536499, "avg_line_length": 31, "blob_id": "791e225713c0ab9eed62925e6ad91883ea2aae0a", "content_id": "a4b5a7437f17c6700a37c6aa4f41574bcce8b607", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1857, "license_type": "no_license", "max_line_length": 167, "num_lines": 58, "path": "/code.py", "repo_name": "Scienceseb/Pairwise-data-augmentation-PyTorch", "src_encoding": "UTF-8", "text": "import random\nimport torchvision.transforms.functional as TF\nimport torchvision.transforms as transforms\nimport PIL\nfrom PIL import Image\n\n\n#Transform class for pairwise data augmentation, flip, random crop, random rotation, ColorJitter (possibly useful for denoising), you can add your transformation also.\nclass PAIR_TRANFORMATIONS(object, h_flip=False, v_flip=False, Random_crop=False, Random_rotation_degree=0, ColorJitter=[0,0,0,0]):\n\n def __call__(self, input, target):\n\n\n #Horizontal flip\n if h_flip:\n flip = random.random() < 0.5\n if flip:\n input=input.transpose(Image.FLIP_LEFT_RIGHT)\n target = target.transpose(Image.FLIP_LEFT_RIGHT)\n\n\n #Vertical flip\n if v_flip:\n flip = random.random() < 0.5\n if flip:\n input = input.transpose(Image.FLIP_TOP_BOTTOM)\n target = target.transpose(Image.FLIP_TOP_BOTTOM)\n\n\n #RandomCrop\n if Random_crop:\n i, j, h, w = transforms.RandomCrop.get_params(input, output_size=(224, 224))\n input = TF.crop(input, i, j, h, w)\n target = TF.crop(target, i, j, h, w)\n\n\n #RandomRotation\n i = transforms.RandomRotation.get_params(angle)\n input = TF.rotate(input, i)\n target = TF.rotate(target, i)\n\n\n\n #ColorJitter\n b,c,s,h=transforms.ColorJitter.get_params(ColorJitter[0],ColorJitter[1],ColorJitter[2],ColorJitter[3])\n\n input=TF.adjust_brightness(input,b)\n input=TF.adjust_contrast(input,c)\n input = TF.adjust_saturation(input, s)\n input = TF.adjust_hue(input, h)\n\n target=TF.adjust_brightness(target,b)\n target=TF.adjust_contrast(target,c)\n target = TF.adjust_saturation(target, s)\n target = TF.adjust_hue(target, h)\n\n\n return input, target\n\n" } ]

2

SaurabhG02/Algo-Problems

https://github.com/SaurabhG02/Algo-Problems

fcf204cd7694fca2a8f30b576e59cabbe2ef68da

5ea2ad6befdcf303cc68db0547612426ad5433da

2936c1d8183a3130eb4d7e6e7c7ac6d85eae947c

refs/heads/master

2022-12-02T14:09:23.611866

2020-08-19T16:15:10

288,772,434

0

null

[ { "alpha_fraction": 0.5278884172439575, "alphanum_fraction": 0.5537848472595215, "avg_line_length": 12.184210777282715, "blob_id": "9dd870c5331174c58d26ac6a9e71b8d43465ac02", "content_id": "1ec3599664491c9d4a3f3d3d67fb2034d27f0f40", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 502, "license_type": "no_license", "max_line_length": 39, "num_lines": 38, "path": "/2ndProb.py", "repo_name": "SaurabhG02/Algo-Problems", "src_encoding": "UTF-8", "text": "def fillheight(input, i , height): \n\t\n\t\n\tif height[i] != 0: \n\t\treturn\n\t\n\t\n\tif input[i] == -1: \n\t\theight[i] = 1\n\t\treturn\n\n\t \n\tif height[input[i]] == 0: \n\t\tfillheight(input, input[i] , height) \n\n\t\n\theight[i] = height[input[i]] + 1\n\n \ndef findNodes(input): \n\tn = len(input) \n\t \n\theight = [0 for i in range(n)] \n\n\t\n\tfor i in range(n): \n\t\tfillheight(input, i, height) \n\n\t \n\tht = height[0] \n\tfor i in range(1,n): \n\t\tht = max(ht, height[i]) \n\n\treturn ht \n\n\ninput = [-1 , 0, 4, 0, 3] \nprint(findNodes(input))\n\n" } ]

1

angel-penchev/portfolio

https://github.com/angel-penchev/portfolio

9ba380987d4ae3b5c5da01b255760b438f3771dc

1fa9476d74e8c49a73d7ea022f9cd9d1ecfa5cae

54eedc2c4427616622f819d5b0b797163843b3a4

refs/heads/master

2022-12-27T06:45:01.013390

2020-06-14T15:28:11

202,220,897

1

0

MIT

2019-08-13T20:42:15

2020-06-14T15:33:29

2022-12-09T23:05:46

CSS

[ { "alpha_fraction": 0.5392199754714966, "alphanum_fraction": 0.5503944158554077, "avg_line_length": 32.07246398925781, "blob_id": "d4e23507bac7e93087c41451993fcb332b7bbfb6", "content_id": "44370a484dc6289700f8eaa11a0697b2dc9aaddf", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 4564, "license_type": "permissive", "max_line_length": 121, "num_lines": 138, "path": "/scripts/navigation.js", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "function implementNavigation() {\n let current_section_slide = 0;\n let current_project_slide = 0;\n\n const section_slider = document.getElementsByClassName(\"section-slider\")[0].getElementsByClassName(\"slide\");\n const project_slider = section_slider[1].getElementsByClassName(\"slider\")[0].getElementsByClassName(\"project-slide\");\n const section_nav = document.getElementsByClassName(\"navigation\")[0];\n const nav_arrow_sections = document.getElementsByClassName(\"navigation-arrows-sections\")[0];\n const nav_arrow_projects = document.getElementsByClassName(\"navigation-arrows-project\")[0];\n\n function refreshView() {\n for (let i = 0; i < section_slider.length; i++) {\n section_slider[i].classList.remove(\"is-active\");\n }\n\n for (let i = 0; i < project_slider.length; i++) {\n project_slider[i].classList.remove(\"is-active\");\n }\n\n section_slider[current_section_slide].classList.add(\"is-active\");\n project_slider[current_project_slide].classList.add(\"is-active\");\n }\n\n section_nav.getElementsByClassName(\"nav-technologies\")[0].onclick = () => {\n current_section_slide = 0;\n refreshView();\n };\n\n section_nav.getElementsByClassName(\"nav-projects\")[0].onclick = () => {\n current_section_slide = 1;\n refreshView();\n };\n\n section_nav.getElementsByClassName(\"nav-contact\")[0].onclick = () => {\n current_section_slide = 2;\n refreshView();\n };\n\n nav_arrow_sections.getElementsByClassName(\"up\")[0].onclick = () => {\n current_section_slide -= 1;\n if (current_section_slide < 0) {\n current_section_slide = section_slider.length - 1;\n }\n refreshView();\n };\n\n nav_arrow_sections.getElementsByClassName(\"down\")[0].onclick = () => {\n current_section_slide += 1;\n if (current_section_slide > section_slider.length - 1) {\n current_section_slide = 0;\n }\n refreshView();\n };\n\n nav_arrow_projects.getElementsByClassName(\"left\")[0].onclick = () => {\n current_project_slide -= 1;\n if (current_project_slide < 0) {\n current_project_slide = project_slider.length - 1;\n }\n refreshView();\n };\n\n nav_arrow_projects.getElementsByClassName(\"right\")[0].onclick = () => {\n current_project_slide += 1;\n if (current_project_slide > project_slider.length - 1) {\n current_project_slide = 0;\n }\n refreshView();\n };\n\n document.getElementsByClassName(\"section-slider\")[0].addEventListener('touchstart', handleTouchStart, false);\n document.getElementsByClassName(\"section-slider\")[0].addEventListener('touchmove', handleTouchMove, false);\n\n var xDown = null;\n var yDown = null;\n\n function getTouches(evt) {\n return evt.touches;\n }\n\n function handleTouchStart(evt) {\n const firstTouch = getTouches(evt)[0];\n xDown = firstTouch.clientX;\n yDown = firstTouch.clientY;\n };\n\n function handleTouchMove(evt) {\n if (!xDown || !yDown) {\n return;\n }\n\n var xUp = evt.touches[0].clientX;\n var yUp = evt.touches[0].clientY;\n\n var xDiff = xDown - xUp;\n var yDiff = yDown - yUp;\n\n if (Math.abs(xDiff) > Math.abs(yDiff)) {/*most significant*/\n if (xDiff > 0) {\n /* left swipe */\n current_project_slide -= 1;\n if (current_project_slide < 0) {\n current_project_slide = project_slider.length - 1;\n }\n refreshView();\n } else {\n /* right swipe */\n current_project_slide += 1;\n if (current_project_slide > project_slider.length - 1) {\n current_project_slide = 0;\n }\n refreshView();\n }\n } else {\n if (yDiff > 0) {\n /* up swipe */\n current_section_slide -= 1;\n if (current_section_slide < 0) {\n current_section_slide = section_slider.length - 1;\n }\n refreshView();\n } else {\n /* down swipe */\n current_section_slide += 1;\n if (current_section_slide > section_slider.length - 1) {\n current_section_slide = 0;\n }\n refreshView();\n }\n }\n\n /* reset values */\n xDown = null;\n yDown = null;\n };\n\n refreshView();\n}\n" }, { "alpha_fraction": 0.772990882396698, "alphanum_fraction": 0.7771334052085876, "avg_line_length": 62.578948974609375, "blob_id": "2a74f5d97796b14178118e68d752ec6e3109b6a6", "content_id": "ec96898a686694c00a2a147294a2ee8db1a2e77e", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1208, "license_type": "permissive", "max_line_length": 472, "num_lines": 19, "path": "/data/README.md", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "# Portfolio Profile Generator\nA simple script which uses your Github profile info to quickly generate a .json file with all the portfolio information required.\n\n## How to use\n```bash\n# Install the required python modules\n$ pip install -r requirements.txt\n\n# Execute the generation script\n$ python3 ./generate_profile.py\n```\nYou will be prompted to input your Github and LinkedIn usernames, as well as your contact email address. Upon complition a [profile.json](./profile.json) file will be created, containing all the information needed for the portfolio.\n\n## Imprtant notice\nBe sure to double check all the technology/language arrays outputed in order to resolve any descrepencies. The script will look for all the languages used in all repos and match this list against [a language reference](./language-reference/language-reference.json) to convert them to valid language objects (containing \"data\" and \"image\" to be displayed in the page). If the languge is missing from the reference, it will not be outputed in [profile.json](./profile.json).\n\n## License\n[![License](http://img.shields.io/:license-mit-blue.svg?style=flat-square)](http://angel-penchev.mit-license.org)<br>\n**Copyright 2019 © Angel Penchev**" }, { "alpha_fraction": 0.54334557056427, "alphanum_fraction": 0.5457875728607178, "avg_line_length": 27.241378784179688, "blob_id": "2b8d54cc888267d99b0f92a24a58de1e278453aa", "content_id": "d7e80659bc9a18f3f029585aed1ef1c9f2774401", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 819, "license_type": "permissive", "max_line_length": 91, "num_lines": 29, "path": "/data/language_reference/generate_language_reference.py", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "import os\nimport json\n\n\ndef main():\n save_file(\"language_reference.json\", generate_data(\"../../img/icons/technologies\"))\n\ndef generate_data(directory):\n directory_contents = [x[0] for x in os.walk(directory)]\n directory_contents.pop(0)\n languages = []\n for language in directory_contents:\n title = language.replace(\"../../img/icons/technologies/\", \"\")\n languages.append({\n \"title\": title,\n \"img\": language.replace(\"../.\", \"\") + \"/\" + title + \"-original.svg\"\n })\n\n return languages\n\n\ndef save_file(filename, data):\n with open(filename, \"w\") as outfile:\n json.dump(data, outfile)\n print(\"Language catalog generated successfully!\")\n\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.5658981204032898, "alphanum_fraction": 0.5681753754615784, "avg_line_length": 30.09734535217285, "blob_id": "ec1329ec54bc2b2b49abb531e7c80af085311694", "content_id": "88b3cbdeaa78258edc70a3a0b895eee42d045a9a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3513, "license_type": "permissive", "max_line_length": 100, "num_lines": 113, "path": "/data/generate_profile.py", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "# For \"AttributeError: 'dict' object has no attribute 'iteritems'\" check the solution provided here:\n# https://stackoverflow.com/questions/30418481/error-dict-object-has-no-attribute-iteritems\n\nimport json\nfrom github import Github\nfrom bunch import bunchify\n\ndef main():\n github_username = input(\"What is your Github username? : \")\n linkedin_username = input(\"What is your LinkedIn username? (skip if the same) : \")\n if (not linkedin_username):\n linkedin_username = github_username\n \n save_file(\"profile.json\", generate_data(github_username, linkedin_username))\n\ndef generate_data(github_username, linkedin_username):\n g = Github()\n user = g.get_user(github_username)\n email = user.email or input(\"What is your Email address? : \")\n repositories = g.search_repositories(query=\"user:%s\" % github_username, sort=\"updated\")\n language_reference_dict = json.load(open(\"language_reference/language_reference.json\", \"r\"))\n language_reference = []\n for language in language_reference_dict:\n language_reference.append(bunchify(language))\n data = {}\n\n data[\"user\"] = {\n \"name\": user.name,\n \"description\": user.bio,\n \"picture\": user.avatar_url,\n \"location\": user.location,\n \"email\": email,\n \"links\": [\n {\n \"title\": \"Github\",\n \"img\": \"./img/icons/technologies/github/github-original.svg\",\n \"href\": \"https://github.com/%s\" % github_username\n },\n {\n \"title\": \"LinkedIn\",\n \"img\": \"./img/icons/technologies/linkedin/linkedin-original.svg\",\n \"href\": \"https://linkedin.com/in/%s\" % linkedin_username\n },\n {\n \"title\": \"Resume\",\n \"img\": \"./img/icons/misc/resume/resume.png\",\n \"href\": \"\"\n },\n {\n \"title\": \"Email\",\n \"img\": \"./img/icons/misc/email/email.png\",\n \"href\": \"mailto:%s\" % email\n }\n ]\n }\n\n content = [];\n for repo in repositories:\n languages = repo.get_languages()\n for language in languages:\n if not(language_catalog(language_reference, language) in content):\n content.append(language_catalog(language_reference, language))\n \n data[\"technologies\"] = [\n {\n \"title\": \"Uncategorised\",\n \"content\": content\n }\n ]\n \n\n\n data[\"projects\"] = []\n for repo in repositories:\n languages = []\n for language in repo.get_languages():\n languages.append(language_catalog(language_reference, language))\n\n data[\"projects\"].append({\n \"title\": repo.name.capitalize(),\n \"description\": repo.description,\n \"image\": \"./img/profile.jpg\",\n \"languages\": languages,\n \"href_repository\": repo.html_url,\n \"href_preview\": repo.homepage\n })\n\n \n return data\n\n\ndef get_repo_languages(repo):\n languages = []\n for language in repo.get_languages():\n languages.append(language)\n return languages\n\n\ndef language_catalog(reference, language):\n for i in reference:\n if i.title.lower() == language.lower():\n i.title = language\n return i\n\n\ndef save_file(filename, data):\n with open(filename, \"w\") as outfile:\n json.dump(data, outfile)\n print(\"Profile generated successfully!\")\n\n\nif __name__ == '__main__':\n main()" }, { "alpha_fraction": 0.7546717524528503, "alphanum_fraction": 0.7570675611495972, "avg_line_length": 68.5999984741211, "blob_id": "ab48cfda80b790cdbbb037f1d634cc48aa5b461d", "content_id": "ce8e2e24bbb50cc102f84621c165c7805de98ce8", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2088, "license_type": "permissive", "max_line_length": 359, "num_lines": 30, "path": "/README.md", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "# Portfolio\nWebpage containing all a list of all the developer technologies I am familiar with and all the significant project I have contributed to. The hosted version of the portfolio could be found [here](https://angel-penchev.github.io/portfolio/).<br>\n![AppVeyor](https://img.shields.io/appveyor/ci/angel-penchev/portfolio?style=flat-square)\n![Libraries.io dependency status for GitHub repo](https://img.shields.io/librariesio/github/angel-penchev/portfolio?style=flat-square)\n[![License](http://img.shields.io/:license-mit-blue.svg?style=flat-square)](http://angel-penchev.mit-license.org)\n\n## Portfolio Contents\nSummary of all the contents included in the portfolio website. The hosted version of the portfolio could be found [here](https://angel-penchev.github.io/portfolio/).\n### Technologies\n**Front-End:** HTML5, CSS, SASS, JavaScript, TypeScript, AngularJS<br>\n**Back-End:** Python, NodeJS, Dart, C/C++<br>\n**Database:** MySQL<br>\n**Deployment:** Git, Docker<br>\n\n### Projects\n* **Librarity**<br>\n Web on-demand library service implementing a database of books, as well as an user system for a personalized experience.\n\n* **Readability**<br>\n Mobile application (Android/iOS) which captures text using the device's camera, recognizes it using Firebase ML Kit and sends it to an Bluetooth connected embedded Arduino device which converts it letter by letter to braille alphabet.\n\n* **Text Art Studio**<br>\n Simple web-hosted tool for creation of art using ASCII characters and exporting it to clipboard for later use.\n \n## How to use the template\nAll the personal data shown in the webpage is taken from [data/profile.json](./data/profile.json), so it is easy to change the information to match your profile. The [data/generate-profile.py](./data/generate-profile.py) script could be used for the automation of this process. **How to use** information is included in the [data/README.md](./data/README.md).\n\n## License\n[![License](http://img.shields.io/:license-mit-blue.svg?style=flat-square)](http://angel-penchev.mit-license.org)<br>\n**Copyright 2019 © Angel Penchev**" }, { "alpha_fraction": 0.5649271607398987, "alphanum_fraction": 0.5719053149223328, "avg_line_length": 40.721519470214844, "blob_id": "4c9254cda713fec8ac0d6ef36d3b4c1601f32068", "content_id": "9ccdb2f942695ff5efb71908a2484edde347cec9", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 3296, "license_type": "permissive", "max_line_length": 135, "num_lines": 79, "path": "/scripts/information.js", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "function loadJSON(callback) {\n var xobj = new XMLHttpRequest();\n xobj.overrideMimeType(\"application/json\");\n xobj.open('GET', './data/profile.json', true);\n xobj.onreadystatechange = function () {\n if (xobj.readyState == 4 && xobj.status == \"200\") {\n // Required use of an anonymous callback as .open will NOT return a value but simply returns undefined in asynchronous mode\n callback(JSON.parse(xobj.responseText));\n }\n };\n xobj.send(null);\n}\n\nfunction injectDataInDocument(data) {\n injectProfile();\n injectTechnologies();\n injectProjects();\n injectContact();\n\n function injectProfile() {\n profile = document.getElementsByClassName(\"profile\")[0];\n profile.getElementsByClassName(\"picture\")[0].src = data.user.picture;\n profile.getElementsByClassName(\"name\")[0].innerHTML = data.user.name;\n profile.getElementsByClassName(\"description\")[0].innerHTML = data.user.description;\n profile.getElementsByClassName(\"location\")[0].innerHTML = data.user.location;\n\n data.user.links.forEach((link) => {\n profile.getElementsByClassName(\"links\")[0].innerHTML +=\n `<a href=\"${ link.href }\" target=\"_blank\" class=\"profile-link\">\n <img src=\"${ link.img }\" alt=\"${link.title}\">\n </a>`\n })\n }\n\n function injectTechnologies() {\n let slide = document.getElementsByClassName(\"technologies\")[0].getElementsByClassName(\"content\")[0];\n\n for (let i = 0; i < data.technologies.length; i++) {\n section = data.technologies[i];\n slide.innerHTML +=\n `<div class=\"subgroup\"> \n <h2 class=\"title\">${ section.title }</h2>\n <div class=\"languages\"></div>\n </div>`\n\n languages = slide.getElementsByClassName(\"subgroup\")[i].getElementsByClassName(\"languages\")[0];;\n section.languages.forEach((language) => {\n languages.innerHTML +=\n `<img src=\"${ language.img }\" alt=\"${ language.title }\">`\n })\n }\n \n }\n\n function injectProjects() {\n let slider = document.getElementsByClassName(\"projects\")[0].getElementsByClassName(\"slider\")[0];\n \n for (let i = 0; i < data.projects.length; i++) {\n project = data.projects[i]\n slider.innerHTML +=\n `<div class=\"project-slide\">\n <img src=\"${ project.image }\" alt=\"${ project.title }\" class=\"project-image\">\n <h1 class=\"project-title\">${ project.title }</h1>\n <div class=\"project-languages\"></div>\n <div class=\"project-description\"><p>${ project.description }</p></div>\n </div>`\n\n project_languages = slider.getElementsByClassName(\"project-slide\")[i].getElementsByClassName(\"project-languages\")[0];\n project.languages.forEach((language) => {\n project_languages.innerHTML +=\n `<img src=\"${ language.img }\" alt=\"${ language.title }\">`\n })\n }\n }\n\n function injectContact() {\n document.getElementsByName(\"contact-form\")[0].setAttribute(\"action\", \"https://formspree.io/\" + data.user.email);\n }\n}\n" }, { "alpha_fraction": 0.5808823704719543, "alphanum_fraction": 0.5808823704719543, "avg_line_length": 16.125, "blob_id": "e536166c39f476f9f22e7b574cc8d5bc9bccc988", "content_id": "ffd5f016e0c94b1e2bc63d51c10ed5885722f29f", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 136, "license_type": "permissive", "max_line_length": 39, "num_lines": 8, "path": "/scripts/main.js", "repo_name": "angel-penchev/portfolio", "src_encoding": "UTF-8", "text": "function main() {\n loadJSON((response) => {\n injectDataInDocument(response);\n implementNavigation();\n });\n}\n\nmain();" } ]

7

httaotao/kevintao-for-python

https://github.com/httaotao/kevintao-for-python

ba91c1901dc23b3807f17dfea26559494847af48

f661c5930e2df346d470b8a1a77acb9672638583

d6e21a3622c482d7637d2cd5ea815ea04f7c7c20

refs/heads/master

2017-12-21T05:43:26.809563

2016-12-14T05:57:36

76,429,280

0

null

[ { "alpha_fraction": 0.6353413462638855, "alphanum_fraction": 0.6538152694702148, "avg_line_length": 13.658227920532227, "blob_id": "466596233eec7ae65cb2b7bfb8a31a7277601e0f", "content_id": "aab15ca9d2124598e4bdbfa3de108bc306beb980", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1599, "license_type": "no_license", "max_line_length": 73, "num_lines": 79, "path": "/python基础编程/标准输入输出.txt", "repo_name": "httaotao/kevintao-for-python", "src_encoding": "GB18030", "text": "#!/usr/bin/python\r\n#enconding:utf8\r\n\r\nimport sys\r\nfd=sys.stdin\r\ndata=fd.read()\r\nsys.stdout.write(data+'\\n')\r\n#print data,\r\n\r\n这是python 标准输入输出，crtl+D结束\r\nsys.stdout.write 加'\\n'是最后换行，默认最后没有换行的\r\n而print默认最后会有\\n，而加了逗号就不会\r\n\r\n\r\n#!/usr/bin/python\r\n#encoding:utf8\r\n\r\nimport sys\r\ninput=sys.stdin\r\n\r\ndef lineCount(f):\r\n n=0\r\n for i in f:\r\n n=n+1\r\n return n\r\nprint lineCount(input)\r\n\r\n这是模拟linux的wc功能\r\ncat /etc/passwd |python 2_stdin.py\r\n输出结果就是文件的行数\r\n\r\n也可以直接执行文件，输出标准输入的行数，ctrl+D结束 \r\n\r\nsys.stderr.write:\r\nHelp on built-in function write:\r\nwrite(...)\r\n write(str) -> None. Write string str to file.\r\n \r\n Note that due to buffering, flush() or close() may be needed beforefs\r\n the file on disk reflects the data written.\r\n\r\n标准输出：\r\n在ipython进行\r\n\r\n\r\nimport sys\r\nf=open('/tmp/3.txt','w')\r\nf.write('sdffsd')\r\nf.close()\r\ncat /tmp/3.txt\r\n注意，这里用write不能写Int，只能用字符串\r\n\r\n但是如果用\r\nprint >>f,123\r\n就可以把数值123输入进文件\r\n\r\n\r\n\r\n\r\nfor i in xrange(1,11):\r\n sys.stdout.write(\"%d\\n\" %i)\r\n这里stdout采用格式化输出\r\n\r\n\r\n\r\n#!/usr/bin/python\r\n\r\nimport sys\r\nimport time\r\n\r\nfor i in xrange(1,11):\r\n sys.stdout.write(\"%d\" %i)\r\n sys.stdout.flush()\r\n time.sleep(1)\r\n\r\n这里是每隔1秒输出\r\n这里采用了sys.stdoout.flush()来清空输出buffer\r\n也可以采用python -u 来输出\r\n如果都不加，那么则会停留10秒后才全部输出\r\n\r\n\r\n\r\n\r\n" }, { "alpha_fraction": 0.539638876914978, "alphanum_fraction": 0.5503009557723999, "avg_line_length": 14.806358337402344, "blob_id": "a7c3da769f3ad554816402c27206ba0fba526ab1", "content_id": "14913ce0ce13e498914001f4aa8b14d6c3dfbdc2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6985, "license_type": "no_license", "max_line_length": 94, "num_lines": 346, "path": "/python基础编程/类和对象.txt", "repo_name": "httaotao/kevintao-for-python", "src_encoding": "GB18030", "text": "#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n\r\n def way(parameter):\r\n parameter.color='black'\r\n print parameter.__age\r\n print \"parameter is %s\" %parameter.color\r\n\r\ntom=People()\r\nprint tom._People__age\r\n\r\n这是调用类的私有属性\r\n注意，这里的tom._People 是一个下划线，而私有属性是两个下划线\r\n----------------------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n\r\n def way(self):\r\n parameter.color='black'\r\n print parameter.__age\r\n print \"parameter is %s\" %parameter.color\r\n\r\ntom=People()\r\ntom.way()\r\n\r\n这是调用类的公有方法\r\n\r\n\r\n-------------------------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n\r\n def way(parameter):\r\n parameter.color='black'\r\n print parameter.__age\r\n print \"parameter is %s\" %parameter.color\r\n \r\nren=People()\r\nprint ren.color\r\nren.way()\r\nprint ren._People__age\r\nren.color='白人'\r\nprint ren.color\r\nprint People.color\r\n\r\n这里的class 就是定义类，而 __双下划线开头的就是类的私有属性，私有属性不能在类的外面调用\r\n下面的def 是类的方法，但是要调用才可以\r\n\r\nren._People__age 这个方法是调用类的私有属性，但一般不建议这样写\r\n\r\n#coding:utf8 是为了支持中文\r\n类的命名，默认规则是一个单词，第一个字母大写\r\n\r\n注意，这里ren.color='白人'不会改变原来类的属性，因为ren已经是一个对象了，具体化了，类似于临时更改而已，但再次调用，后面的print People.color还是yellow\r\n\r\n\r\n--------------------------------------\r\n\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def way(parameter):\r\n parameter.color='black'\r\n print parameter.__age\r\n print \"parameter is %s\" %parameter.color\r\n\r\n def __talk(self):\r\n print \"ABC\"\r\n\r\n def test(self):\r\n self.__talk()\r\n\r\n def test2(self):\r\n print \"Test is ......\"\r\n\r\n cm=classmethod(test2)\r\n\r\njack=People()\r\njack.test()\r\nPeople.cm()\r\n\r\n这里的 __talk 是私有方法\r\n\r\n\r\n这里classmethod 是类方法，就是可以直接在类的外面调用方法，而不需要具体的实例，因为如果没有classmethod,\r\nPeople.test2()是不行的\r\n\r\n------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def way(parameter):\r\n parameter.color='black'\r\n print parameter.__age\r\n print \"parameter is %s\" %parameter.color\r\n\r\n def __talk(self):\r\n print \"ABC\"\r\n\r\n def test(self):\r\n self.__talk()\r\n\r\n def test2(self):\r\n print \"Test is ......\"\r\n # self.test3()\r\n\r\n def test3():\r\n print \"This is test3\"\r\n print People.color\r\n\r\n cm=classmethod(test2)\r\n sm=staticmethod(test3)\r\n\r\njack=People()\r\n#People.test3()\r\nPeople.sm()\r\n\r\n\r\nPeople.sm()这是调用静态方法，类似于‘全局函数’，因为没有参数的，默认无法调用\r\n而使用self.test3()，则会提示test3没有参数\r\n\r\n------------------------------------------------\r\n @classmethod\r\n def test2(self):\r\n print \"Test is ......\"\r\n # self.test3()\r\n\r\n @staticmethod\r\n def test3():\r\n print \"This is test3\"\r\n print People.color\r\n\r\n\r\njack=People()\r\nPeople.test2()\r\nPeople.test3()\r\n\r\n这是使用装饰器来调用，注意，@classmethod和@staticmethod只对下面的方法有效\r\n\r\n-------------------------------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n class chinese(object):\r\n print \"I am chinese\"\r\n\r\njack=People.chinese()\r\n\r\n这是外部类调用内部类\r\n\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n class chinese(object):\r\n #print \"I am chinese\"\r\n name=\"I am a chinese\"\r\n\r\njack=People.chinese()\r\nprint jack.name\r\n\r\n这是调用内部类的属性\r\n\r\n这里还可以这样写：\r\njack=People()\r\njack_name=People.chinese()\r\nprint jack_name.name\r\n\r\n这样写也可以：\r\nprint People.chinese.name\r\n\r\n或者 print People.chinese().name\r\n\r\n\r\n---------------------------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def __str__(self):\r\n return \"I am a chinese\"\r\n def think(self):\r\n print \"abc\"\r\n def __talk(self):\r\n print \"sdf\"\r\n\r\njack=People()\r\nprint jack\r\n\r\n这是调用 __str__ ，这是内置方法，不需要调用函数名，print就会默认执行\r\n注意，__str__ 不能用print ，要使用return\r\n\r\n注意，方法和函数一样，但方法需要self做为第一个参数\r\nthink是公有方法\r\n__talk是私有方法\r\n注意，私有方法是双下划线\r\n--------------------------------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def __str__(self):\r\n return \"I am a chinese\"\r\n\r\n def __init__(self):\r\n self.color='black'\r\n\r\n\r\njack=People()\r\nprint jack.color\r\nprint People.color\r\n\r\n这里的结果是 \r\nblack\r\nyellow\r\n\r\n也就是说，如果类实例化了，会自动执行__init__ 初始化函数\r\n也叫构造函数\r\n\r\n\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def __str__(self):\r\n return \"I am a chinese\"\r\n\r\n def __init__(self,c='white'):\r\n self.color=c\r\n\r\n\r\njack=People()\r\nprint jack.color\r\nprint People.color\r\n\r\n结果是：\r\nwhite\r\nyellow\r\n这里通过类来调用的属性没改变\r\n\r\n\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def __str__(self):\r\n return \"I am a chinese\"\r\n\r\n def __init__(self,c='white'):\r\n self.color=c\r\n\r\n\r\njack=People('green')\r\nprint jack.color\r\nprint People.color\r\n\r\n结果是\r\ngreen\r\nyellow\r\n\r\n-------------------------------------------\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass People(object):\r\n color='yellow'\r\n __age=10\r\n\r\n def __str__(self):\r\n return \"I am a chinese\"\r\n\r\n def __init__(self,c='white'):\r\n self.color=c\r\n self.fd.open('/etc/hosts')\r\n \r\n def __del__(self):\r\n self.fd.close()\r\n\r\njack=People('green')\r\nprint jack.color\r\nprint People.color\r\n\r\n析构函数，在脚本最后执行，一般用于释放对象空间\r\n\r\n---\r\nimport gc\r\n\r\ngc.collect()\r\n\r\npython的默认回收机制\r\n__init__是构造函数，用于初始化类的内部状态\r\n-------------------------------------------\r\n\r\n#! /usr/bin/python\r\n#coding:utf8\r\n\r\nclass Myclass(object):\r\n var1='类的公有属性'\r\n __var2='类的私有属性'\r\n\r\n def fun1(self):\r\n self.var3='对象的公有属性'\r\n self.__var4='对象的私有属性'\r\n var5='函数的局部变量'\r\n" }, { "alpha_fraction": 0.7727272510528564, "alphanum_fraction": 0.7727272510528564, "avg_line_length": 21, "blob_id": "4ac38d3f731f239cafad08b7cd86bebf28fc28b2", "content_id": "a1f3cd5eeb1b9dda51bbd6faf95cfb5e54968c9f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 22, "license_type": "no_license", "max_line_length": 21, "num_lines": 1, "path": "/README.md", "repo_name": "httaotao/kevintao-for-python", "src_encoding": "UTF-8", "text": "# kevintao-for-python\n" }, { "alpha_fraction": 0.5582557916641235, "alphanum_fraction": 0.603110671043396, "avg_line_length": 16.639896392822266, "blob_id": "3f533ffc92e6214b4dccb5b8f2c5a50a94f96ce3", "content_id": "57d4c8a37c6a8267ddfdff5f275840d61435a9d8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9693, "license_type": "no_license", "max_line_length": 87, "num_lines": 386, "path": "/python基础编程/函数.txt", "repo_name": "httaotao/kevintao-for-python", "src_encoding": "GB18030", "text": "#! /usr/bin/python\r\n\r\ndef fun():\r\n sth=raw_input('please input something:')\r\n try:\r\n if type(int(sth))==type(1):\r\n print\"%s is a number\" %sth\r\n except:\r\n print \"%s is not a number\" %sth\r\n\r\nfun()\r\n\r\n\r\n这里的try是异常处理，就是如果type(int(sth))是int，那就输出下面的，否则就输出不是number\r\n\r\ndef 是函数定义，注意函数定义后面一样需要冒号\r\nfun()是函数调用\r\n\r\n这个函数的作用是判断输入的是否是数字\r\n\r\n\r\n#! /usr/bin/python\r\nimport sys\r\ndef isNum(s):\r\n for i in s:\r\n if i in '0123456789':\r\n pass\r\n else:\r\n print\"%s is not a number\" %s\r\n break\r\n else:\r\n print\"%s is a number\" %s\r\n\r\nisNum(sys.argv[1])\r\n\r\n这个函数是通过外部输入参数来判断的，就是调用程序的时候给出参数来判断，而sys.argv[1]是输入的第一个参数\r\n\r\npython 11.py aaa \r\n这里就是判断输入的参数aaa是否是数字\r\n\r\n注意，这里一定要给出参数，否则会出现索引越界的现象\r\nIndexError: list index out of range\r\n\r\n另外这里sys是模块，import是插入模块\r\n\r\n另外这里只能判断输入的第一个参数，如果调用的时候是python 11.py 123 aaa 后面的参数aaa就没法判断了\r\n\r\n\r\n\r\n#! /usr/bin/python\r\n\r\nimport sys\r\nimport os\r\n\r\ndef isNUm(s):\r\n for i in s:\r\n if i in '0123456789':\r\n pass\r\n else:\r\n break\r\n else:\r\n print s\r\n\r\nfor i in os.listdir('/proc'):\r\n isNUm(i)\r\n这里是查看系统所有的pid，是通过查看文件/proc来查看的\r\n注意这里的os.listdir的作用是把所有的文件名称变为列表的元素，这里通过查看列表的每个元素来判断\r\n\r\n\r\n\r\n\r\ndef fun(x,y=100)\r\n print x+y\r\n\r\nfun(2,3)\r\n这里的是定义默认参数，注意这里的结果是5，如果不给3，则结果是102\r\n另外，这里定义参数，不能def fun(x=1,y)：因为定义默认参数，一定要从右往左定义\r\n\r\n\r\n#! /usr/bin/python\r\n\r\nimport sys\r\nimport os\r\n\r\ndef isNUm(s):\r\n for i in s:\r\n if i not in '0123456789':\r\n return True\r\n return False\r\nfor i in os.listdir('/proc'):\r\n if isNUm(i):\r\n print i\r\n\r\n\r\n#------------------------------------------------------------\r\n\r\n#! /usr/bin/python\r\nimport sys\r\nimport os\r\n\r\ndef isNUm(s):\r\n if s.isdigit():\r\n return True\r\n return False\r\n\r\nfor i in os.listdir('/proc'):\r\n if isNUm(i):\r\n print i\r\n\r\n这里两种写法都是对前面的优化\r\n\r\n\r\n多类型传值\r\n\r\n\r\ndef fun(x,y,z):\r\n return x+y+z\r\n\r\nt=(1,2,3)\r\nfun(*t)\r\na=[1,2,3]\r\nfun(*a)\r\n\r\n这两个就是多类型传值，如果没有*，则认为是一个参数\r\n\r\n也可以这样写：\r\nt1=(1,2)\r\nfun(1,*t1)\r\n\r\n但注意，不能写成这样：\r\nfun(*t1,1)，会显示SyntaxError: only named arguments may follow *expression\r\n\r\n如果直接赋值，可以这样写：\r\nfun(x=1,y=2,z=3),x,y,z的顺序可以随意，但是不能写成fun(a=1,b=2,c=3),要和形参的名称一致\r\n\r\n另外还可以使用字典赋值：\r\ndic={'x':1,'y':2,'z':3}\r\nfun(**dic) ,这里要两个**\r\n\r\n\r\n\r\n冗余参数\r\ndef fun(x,*argv,**kwargv):\r\n a=(1,2,3)\r\n print x\r\n print argv\r\n print kwargv\r\n\r\n\r\n后面的*argv和**kwargv是冗余参数，也就是说，函数fun一定要给出形参x的值，而后面两个不一样\r\n另外，*argv是一个元组，而**是字典，另外argv和kwargv名称可以改变\r\n\r\n因此调用的时候\r\nfun(a,1,32,'asd',(123,23),{'k':1},*('aa','bb'))\r\n结果就是：\r\n(1,2,3)\r\n(1, 32, 'asd', (123, 23), {'k': 1}, 'aa', 'bb')\r\n{}\r\n\r\n\r\n\r\n而fun(a,1,32,'asd',(123,23),{'k':1},**('aa','bb'))会出错，因此**一定要是字典，而如果字典没有**，则会默认为元组的元素，例如\r\nfun(a,1,32,'asd',(123,23),{'k':1})，结果是：\r\n(1, 2, 3)\r\n(1, 32, 'asd', (123, 23), {'k': 1})\r\n{}\r\n\r\n\r\n而fun(a,1,32,'asd',(123,23),**{'k':1})，结果是：\r\n(1, 2, 3)\r\n(1, 32, 'asd', (123, 23))\r\n{'k': 1}\r\n\r\n\r\n\r\n函数递归：\r\ndef factorial(n):\r\n if n==0\r\n return 1\r\n else:\r\n return n*factorial(n-1)\r\nprint factorial(100)\r\n\r\n\r\nimport os\r\nos.path.isdir('/tmp') 判断/tmp是不是目录，如果是就返回True，否则就返回False。但是注意这里不能判断目录是否存在，不存在也是返回False\r\n\r\nos.path.isfile()\r\n与os.path.isdir类似，但是这里是判断文件\r\n\r\n\r\n#! /usr/bin/python\r\n\r\nimport os\r\nimport sys\r\n\r\ndef print_file(path):\r\n lsdir=os.listdir(path)\r\n dirs=[i for i in lsdir if os.path.isdir(os.path.join(path,i))]\r\n files=[i for i in lsdir if os.path.isfile(os.path.join(path,i))]\r\n if files:\r\n for f in files:\r\n print os.path.join(path,f)\r\n if dirs:\r\n for d in dirs:\r\n print_file(os.path.join(path,d))\r\n\r\nprint_file(sys.argv[1])\r\n\r\n\r\n这个函数的作用是遍历目录中的所有文件\r\n\r\n这里的os.listdir是把指定目录下的名称全部变成列表\r\n然后用lsdir和dirs找出文件和目录\r\n\r\n[i for i in lsdir if os.path.isdir(os.path.join(path,i))] 判断是否是文件，用for遍历，用if来判断\r\n\r\nif files就是如果文件不为空，则遍历files里面的文件的名称，然后用print输出文件所在路径，注意，这里要连接参数path才是完成路径\r\n\r\n如果是目录，就继续调用函数来递归找到文件\r\n\r\n执行如下：\r\npython 4.py .\r\n这里的 “.” 就是当前目录，也可以python 4.py /etc/\r\n\r\n\r\n\r\n函数 reduce:\r\nreduce(function, sequence[, initial]) -> value\r\n例子：\r\nreduce(lambda x, y: x+y, [1, 2, 3, 4, 5])\r\n函数reduce的作用是调用函数来处理后面的序列，这里的函数可以是匿名函数，也可以是function\r\n\r\n匿名函数：\r\nlamba\r\n\r\nr=lamba x,y:x+y\r\nr(3,5) 结果是15\r\n\r\n\r\n内置函数：\r\ndivmod:\r\n divmod(x, y) -> (div, mod)\r\n Return the tuple ((x-x%y)/y, x%y). Invariant: div*y + mod == x.\r\n\r\n返回值，前面是商，后面是余数\r\n\r\ndivmod(5,2) 结果是2，1\r\ndivmod(10,3) 结果是3，1\r\n\r\npow：\r\npow(x, y[, z]) -> number\r\n \r\n With two arguments, equivalent to x**y. With three arguments,\r\n equivalent to (x**y) % z, but may be more efficient (e.g. for longs).\r\n\r\n\r\n如果是两个参数，则是x^y,如果是三个参数，则是x^y%z\r\n例如pow(2,3) 结果是8，而pow(2,3,4) 结果是0\r\n\r\n\r\nround:\r\n四舍五入\r\nprint round(12,3256,3) 保留三位有效数字\r\n\r\n注意，round函数是先把数值转换为float,然后再取舍，因此容易有精度不准的问题\r\n\r\n\r\ncallable:\r\n\r\ncallable(object) -> bool\r\n \r\n Return whether the object is callable (i.e., some kind of function).\r\n Note that classes are callable, as are instances with a __call__() method.\r\n\r\n判断是否可被调用，返回bool值\r\n\r\nisinstance:\r\nisinstance(object, class-or-type-or-tuple) -> bool\r\n isinstance(x, (A, B, ...))\r\n\r\n判断是否是给定的类型，与type用法类似\r\n\r\nisinstance(123,list) 判断数值123是否是list,返回的是bool\r\nisinstance主要用于判断是否是同一个类，比type方便\r\n\r\n\r\ncmp:\r\ncmp(x, y) -> integer\r\n \r\n Return negative if x<y, zero if x==y, positive if x>y.\r\n\r\n比较两个对象，返回数值\r\n可以用来比较数值，也可以比较字符串\r\n\r\ncomplex:\r\n转换为复数\r\n例如 complex('12') 结果是 12+0j\r\n注意，不能是字母\r\n\r\n\r\nhex:\r\nhex(number) -> string\r\n \r\n Return the hexadecimal representation of an integer or long integer.\r\n\r\n把数值转换成16进制的数\r\n例如hex(123) 结果是'0x7b'\r\neval(hex(10)) 把十六进制重新转换为十进制，这里不能用int，因为用hex的是字符串，而int不能是字符串\r\n\r\noct:\r\n把数值转换成八进制，用法和hex一样\r\n\r\neval:\r\neval(source[, globals[, locals]]) -> value\r\n\r\neval(\"['a','b',1]\") 结果是列表['a','b',1]\r\n\r\nchr:\r\n chr(i) -> character\r\n \r\n Return a string of one character with ordinal i; 0 <= i < 256.\r\n\r\n返回数值对应的ASCII的字符，例如chr(123) 结果是‘{’\r\n\r\nord：\r\nord(c) -> integer\r\n \r\n Return the integer ordinal of a one-character string.\r\n\r\n和chr相反，返回字符对应的ASCII，返回的是整数\r\n\r\n\r\nfilter:\r\nfilter(function or None, sequence) -> list, tuple, or string\r\n \r\n Return those items of sequence for which function(item) is true. If\r\n function is None, return the items that are true. If sequence is a tuple\r\n or string, return the same type, else return a list.\r\n\r\n用function来处理后面的sequence序列，function可以为空，也就是None\r\n\r\ndef f(x):\r\n if x%2==0:\r\n return True\r\n\r\nfilter(f,range(10))\r\n\r\n这个是过滤出偶数，结果是[0, 2, 4, 6, 8]\r\n\r\n\r\nzip:\r\nzip(seq1 [, seq2 [...]]) -> [(seq1[0], seq2[0] ...), (...)]\r\n Return a list of tuples, where each tuple contains the i-th element\r\n from each of the argument sequences. The returned list is truncated\r\n in length to the length of the shortest argument sequence.\r\n\r\n\r\na1=['1','2','3']\r\nb1=['a','b','c']\r\nc1=['I','II','III']\r\nzip(a1,b1,c1)结果是[('1', 'a', 'I'), ('2', 'b', 'II'), ('3', 'c', 'III')]\r\n\r\n如果c1变成['I','II']，那么结果就是[('1', 'a', 'I'), ('2', 'b', 'II')]\r\n结果取最短完整的\r\n\r\n\r\n\r\nmap:\r\n map(function, sequence[, sequence, ...]) -> list\r\n这个函数的作用是zip和filter的综合，如果function为None，则和zip一样，如果有function，则和filter一样\r\n\r\ndef f(x,y):\r\n return x*y\r\n\r\na1=[1,2,3]\r\nb1=[4,5,6]\r\nmap(a1,b1) 结果是[4,10,18]\r\n\r\nmap(lambda x,y:x*y,range(10),range(10)) 结果是[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\r\n\r\n\r\n\r\n列表表达式：\r\n[i*2+10 for i in range(10)] 结果是[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]\r\n[i*2+10 for i in range(10) if i%3==0] 结果是[10, 16, 22, 28]\r\n\r\n\r\n\r\n" }, { "alpha_fraction": 0.5095477104187012, "alphanum_fraction": 0.5095477104187012, "avg_line_length": 25.52777862548828, "blob_id": "9ac13e742b59dc3685e310134363a1e3a0fbfe38", "content_id": "af4718fd1ceccc1184ccfc3dddf0d93000488f4f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1095, "license_type": "no_license", "max_line_length": 56, "num_lines": 36, "path": "/python基础编程/Optparse.txt", "repo_name": "httaotao/kevintao-for-python", "src_encoding": "GB18030", "text": "#!/usr/bin/python\r\n\r\nfrom optparse import OptionParser\r\n\r\nparser=OptionParser()\r\nparser.add_option('-c','--char','--chars',\r\n dest=\"chars\",\r\n action=\"store_true\",\r\n default=False,\r\n help=\"only conut chars\")\r\n\r\nparser.add_option('-w','--word','--words',\r\n dest=\"words\",\r\n action=\"store_true\",\r\n default=False,\r\n help=\"only conut words\")\r\nparser.add_option('-l','--line','--lines',\r\n dest=\"lines\",\r\n action=\"store_true\",\r\n default=False,\r\n help=\"only conut lines\")\r\noptions,args=parser.parse_args()\r\nprint options,args\r\n\r\n这是专门用来做参数选项模块的\r\n'-c','--char'这些是参数选项\r\n\r\noptions是存放参数信息，args是存放文件信息\r\n\r\npython OptionParser.py -l -cw \r\n结果是：\r\n{'chars': True, 'lines': True, 'words': True} []\r\n\r\npython OptionParser.py -l -cw a b\r\n{'chars': True, 'lines': True, 'words': True} ['a', 'b']\r\n文件名称放到列表里面\r\n\r\n\r\n" }, { "alpha_fraction": 0.5515303015708923, "alphanum_fraction": 0.5515303015708923, "avg_line_length": 15.662983894348145, "blob_id": "c65cf62564f1f0a60775425386fa21845cdf3231", "content_id": "65f699c0eb4018ecab7db53ce2f56111ef9e30da", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3720, "license_type": "no_license", "max_line_length": 74, "num_lines": 181, "path": "/python基础编程/继承.txt", "repo_name": "httaotao/kevintao-for-python", "src_encoding": "GB18030", "text": "#! /usr/bin/python\r\n\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"I am a People\"\r\n\r\nclass chinese(People):\r\n pass\r\n\r\ncn=chinese()\r\nprint cn.color\r\ncn.think()\r\n\r\n这是类的继承，cn是类chinese的实例化，但可以继承类People的属性和方法\r\n\r\n\r\n#! /usr/bin/python\r\n\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"I am a People\"\r\n\r\n def __init__(self):\r\n self.dwell='Earth'\r\n\r\nclass chinese(People):\r\n pass\r\n\r\ncn=chinese()\r\nprint cn.dwell\r\ncn.think()\r\n\r\n\r\n这是调用继承的初始化函数\r\n\r\n------------------------------------\r\n#! /usr/bin/python\r\n\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"I am a People\"\r\n\r\n def __init__(self,c):\r\n self.dwell='Earth'\r\n print self.dwell\r\n\r\nclass chinese(People):\r\n def __init__(self):\r\n People.__init__(self,'c')\r\n\r\n\r\ncn=chinese()\r\n\r\n这是调用父类的初始化函数，这是多参数的\r\n----------------------------------------\r\n#! /usr/bin/python\r\n\r\nclass people:\r\n color='yellow'\r\n\r\na=people()\r\nprint a.color\r\n\r\n这是传统的类的命名方法，之前的都是new style\r\n-----------------------------------------------\r\n#! /usr/bin/python\r\n\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"I am a People\"\r\n\r\n def __init__(self,c):\r\n self.dwell='Earth'\r\n print self.dwell\r\n\r\nclass chinese(People):\r\n def __init__(self):\r\n super(chinese,self).__init__('red')\r\n\r\ncn=chinese()\r\n\r\n这里也可以使用super调用父类\r\n注意，这里的类的命名不能使用传统的方式，就是没有object\r\n另外，这里的参数，也不需要写成super(chinese,self).__init__(self,'red')\r\n\r\n继承里面，如果父类和子类有相同的名称，那么实际上就是在修改父类的方法\r\n\r\n------------------------------------------------------\r\n#! /usr/bin/python\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"I am a %s People\" %self.dwell\r\n\r\n def __init__(self):\r\n self.dwell='Earth'\r\n print self.dwell\r\n\r\nclass Martin(object):\r\n color='red'\r\n\r\n def __init__(self):\r\n self.dwell='Martin'\r\n\r\n\r\nclass chinese(People,Martin):\r\n def __init__(self):\r\n People.__init__(self)\r\n\r\ncn=chinese()\r\ncn.think()\r\n\r\n这是多重继承\r\n\r\n\r\n#! /usr/bin/python\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"I am a %s People\" %self.dwell\r\n\r\n def __init__(self):\r\n self.dwell='Earth'\r\n print self.dwell\r\n\r\nclass Martin(object):\r\n color='red'\r\n\r\n def __init__(self):\r\n self.dwell='Martin'\r\n print self.dwell\r\n\r\n def think(self):\r\n print \"I am a %s People\" %self.dwell\r\n\r\nclass chinese(Martin,People):\r\n pass\r\n\r\ncn=chinese()\r\ncn.think()\r\n\r\n这里如果Martin没有think方法，那么会调用People的think，但是如果有，那就调用Martin的，根据类chinese的参数的先后循序\r\n\r\n当父类中出现多个自定义的__init__方法时，多重继承只执行第一个类的__init__方法\r\n\r\n#! /usr/bin/python\r\nclass People(object):\r\n color='yellow'\r\n\r\n def think(self):\r\n print \"color is %s\" %self.color\r\n print \"I am a %s People\" %self.dwell\r\n\r\n def __init__(self):\r\n self.dwell='Earth'\r\n print self.dwell\r\n\r\nclass Martin(object):\r\n color='red'\r\n\r\n def __init__(self):\r\n self.dwell='Martin'\r\n print self.dwell\r\n\r\nclass chinese(Martin,People):\r\n pass\r\n\r\ncn=chinese()\r\ncn.think()\r\nprint cn.color\r\n这里因为Martin没有think的方法，因为调用的是People的think的方法，但参数是Martin的\r\n\n\r\n\r\n" } ]

6

GhalyahF/django_cart1

https://github.com/GhalyahF/django_cart1

919f00753230a0a72a32c2e4b470710a6026aa8f

af1b41d05abf819952ae09c52ecfa7c8c3c1934b

a70b4c1ef13f15675ff6cdb28a8e91e6607243d1

refs/heads/master

2020-04-11T04:35:33.224729

2019-10-22T22:47:29

161,517,494

0

null

[ { "alpha_fraction": 0.7333333492279053, "alphanum_fraction": 0.7333333492279053, "avg_line_length": 25.25, "blob_id": "2e523c30abca47acdd2e13c6174be097fb527efd", "content_id": "3df4945d2731115bb46e27da2819143b306010d6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 105, "license_type": "no_license", "max_line_length": 49, "num_lines": 4, "path": "/art_project/art/context_processors.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from .models import Category\n\ndef get_navbar(request):\n return {\"categories\": Category.objects.all()}\n" }, { "alpha_fraction": 0.6985592842102051, "alphanum_fraction": 0.7037310600280762, "avg_line_length": 35.09333419799805, "blob_id": "4ef592efd02b95e2b2a83f81f62daf2d77698dbf", "content_id": "530c034a86417868233f706d4940db224d0594e9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2707, "license_type": "no_license", "max_line_length": 89, "num_lines": 75, "path": "/art_project/cart/views.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from django.shortcuts import render, redirect, get_object_or_404\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib import messages\nfrom django.urls import reverse\nimport datetime\nfrom cart.extras import generate_order_id\nfrom art.models import Product, Profile\nfrom cart.models import OrderItem, Order\n\n\ndef get_pending_order(request):\n user_profile = get_object_or_404(Profile, user=request.user)\n order= Order.objects.filter(owner=user_profile, is_ordered=False)\n if order:\n return order[0]\n else:\n messages.info(request, \"Your cart is empty!\")\n\n@login_required\ndef add_to_cart(request, **kwargs):\n user_profile= get_object_or_404(Profile, user=request.user)\n product= Product.objects.filter(id=kwargs.get('item_id', \"\")).first()\n if product in request.user.profile.merchandise.all():\n messages.info(request, 'You already own this product')\n order_item, status = OrderItem.objects.get_or_create(product=product)\n user_order, status = Order.objects.get_or_create(owner=user_profile,is_ordered=False)\n user_order.items.add(order_item)\n if status:\n user_order.ref_code = generate_order_id()\n user_order.save()\n messages.info(request, 'item added to cart')\n return redirect (reverse('cart:order_summary'))\n\n@login_required\ndef delete_from_cart(request, item_id):\n item_to_delete= OrderItem.objects.filter(pk=item_id)\n if item_to_delete.exists():\n item_to_delete[0].delete()\n messages.info(request, 'Item has been deleted successfully')\n return redirect(reverse('cart:order_summary'))\n\n@login_required\ndef order_details(request, **kwargs):\n existing_order = get_pending_order(request)\n context = {\n 'order': existing_order\n }\n return render(request, 'order_summary.html', context)\n\n@login_required\ndef checkout(request, **kwargs):\n\n order_to_purchase = get_pending_order(request)\n order_to_purchase.is_ordered= True\n order_to_purchase.save()\n order_to_purchase.date_ordered=datetime.datetime.now()\n\n order_items= order_to_purchase.items.all()\n order_items.update(is_ordered= True, date_ordered= datetime.datetime.now())\n\n user_profile= get_object_or_404(Profile, user=request.user)\n order_products = [item.product for item in order_items]\n user_profile.merchandise.add(*order_products)\n user_profile.save()\n messages.info(request, \"Thank you for your purhcase!\")\n # order_to_purchase.exclude(?????)\n return redirect(reverse('profile'))\n\n context = {\n 'order': order_to_purchase,\n 'order_items': order_items,\n 'user_profile': user_profile,\n }\n\n return render(request, 'checkout.html', context)\n" }, { "alpha_fraction": 0.5452322959899902, "alphanum_fraction": 0.5579462051391602, "avg_line_length": 38.32692337036133, "blob_id": "69f273f7215dd3fe54e00e5a872032c25beee9f0", "content_id": "10e89fbe0188b0a3fa1ada0048bf10e27892b8a6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2045, "license_type": "no_license", "max_line_length": 137, "num_lines": 52, "path": "/art_project/art/migrations/0001_initial.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "# Generated by Django 2.1.4 on 2018-12-08 02:14\n\nfrom django.conf import settings\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Category',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('category', models.CharField(max_length=150)),\n ('slug', models.SlugField(max_length=200, unique=True)),\n ('description', models.TextField(default='art')),\n ],\n options={\n 'verbose_name': 'category',\n 'verbose_name_plural': 'categories',\n },\n ),\n migrations.CreateModel(\n name='Product',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=100)),\n ('photo', models.ImageField(blank=True, null=True, upload_to='')),\n ('description', models.TextField()),\n ('price', models.DecimalField(decimal_places=2, max_digits=5)),\n ('category', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='products', to='art.Category')),\n ],\n options={\n 'ordering': ('category',),\n },\n ),\n migrations.CreateModel(\n name='Profile',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('merchandise', models.ManyToManyField(blank=True, to='art.Product')),\n ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),\n ],\n ),\n ]\n" }, { "alpha_fraction": 0.7065989971160889, "alphanum_fraction": 0.710659921169281, "avg_line_length": 32.965518951416016, "blob_id": "d68d066b989934a9a06a9f235f5bb392f87d9338", "content_id": "3cf84863a9658f041e59edd9f64e352f756d02fc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 985, "license_type": "no_license", "max_line_length": 80, "num_lines": 29, "path": "/art_project/cart/models.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from django.db import models\nfrom django.contrib.auth.models import User\nfrom art.models import Product, Profile\n\n\nclass OrderItem(models.Model):\n product= models.OneToOneField(Product, on_delete=models.SET_NULL, null=True)\n is_ordered= models.BooleanField(default=False)\n date_added= models.DateTimeField(auto_now=True)\n date_ordered= models.DateTimeField(null=True)\n\n def __str__(self):\n return self.product.name\n\nclass Order(models.Model):\n ref_code= models.CharField(max_length=15)\n owner= models.ForeignKey(Profile, on_delete= models.SET_NULL, null=True)\n is_ordered= models.BooleanField(default=False)\n items= models.ManyToManyField(OrderItem)\n date_ordered= models.DateTimeField(auto_now=True)\n\n def get_cart_items(self):\n return self.items.all()\n\n def get_cart_total(self):\n return sum([item.product.price for item in self.items.all()])\n\n def __str__(self):\n return '{0}-{1}'.format(self.owner, self.ref_code)\n" }, { "alpha_fraction": 0.652814507484436, "alphanum_fraction": 0.6543256640434265, "avg_line_length": 29.4252872467041, "blob_id": "44b9daf90fe07f1cd48b3e0d575a3c63a7bdc4f0", "content_id": "10efea0e2864ad4ae36d2f88533173fe166cc913", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2647, "license_type": "no_license", "max_line_length": 87, "num_lines": 87, "path": "/art_project/art/views.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from django.shortcuts import render, redirect, get_object_or_404\nfrom django.contrib.auth import login,logout, authenticate\nfrom django.contrib import messages\nfrom .models import *\nfrom cart.models import *\nfrom .forms import *\n\ndef home(request):\n categories= Category.objects.all()\n products=Product.objects.all().order_by('price')\n\n context={\n 'categories': categories,\n 'products': products\n }\n return render (request, 'home.html', context)\n\ndef product_list(request):\n product_list= Product.objects.all()\n filtered_orders= Order.objects.filter(owner=request.user.profile, is_ordered=False)\n current_order_products= []\n if filtered_orders.exists():\n user_order= filtered_orders[0]\n user_order_items= user_order.items.all()\n current_order_products= [product.product for product in user_order_items]\n context={\n 'product_list': product_list,\n 'current_order_products': current_order_products,\n }\n return render(request, \"product_list.html\", context)\n\ndef product_category(request, slug):\n category= Category.objects.get(slug=slug)\n products=Product.objects.all()\n context={\n 'category': category,\n 'products': products\n }\n return render(request, 'cat_details.html', context)\n\ndef user_register(request):\n form=UserRegister()\n if request.method == 'POST':\n form= UserRegister(request.POST)\n if form.is_valid():\n user= form.save(commit=False)\n\n user.set_password(user.password)\n user.save()\n\n login(request, user)\n return redirect ('home' )\n context={\n \"form\": form,\n }\n\n return render(request, 'register.html', context)\n\ndef user_login(request):\n form= UserLogin()\n if request.method == 'POST':\n form = UserLogin(request.POST)\n if form.is_valid():\n username= form.cleaned_data['username']\n password= form.cleaned_data['password']\n auth_user= authenticate(username=username, password=password)\n if auth_user is not None:\n login(request, auth_user)\n messages.success(request, \"Welcome Back!\")\n return redirect('home')\n\n context={\n \"form\": form,\n }\n return render(request, 'login.html', context)\n\ndef user_logout(request):\n logout(request)\n return render (request, \"logout.html\")\n\ndef user_profile(request):\n user_profile= Profile.objects.filter(user=request.user).first()\n user_orders= Order.objects.filter(is_ordered=True, owner= user_profile)\n context={\n 'user_orders': user_orders\n }\n return render (request, 'profile.html', context)\n" }, { "alpha_fraction": 0.5272727012634277, "alphanum_fraction": 0.5848484635353088, "avg_line_length": 18.41176414489746, "blob_id": "a02d83b0f7e9339ee8eece6807a982a24221e35c", "content_id": "8f434a19bc67fedcac61335a6896c6b82e5fcae6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 330, "license_type": "no_license", "max_line_length": 47, "num_lines": 17, "path": "/art_project/cart/migrations/0003_remove_orderitem_quantity.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "# Generated by Django 2.1.4 on 2018-12-11 14:37\n\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('cart', '0002_orderitem_quantity'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='orderitem',\n name='quantity',\n ),\n ]\n" }, { "alpha_fraction": 0.6981664299964905, "alphanum_fraction": 0.6981664299964905, "avg_line_length": 36.31578826904297, "blob_id": "e6250c7e33963cae28b8bb573b63d6f854fbb669", "content_id": "7f6d31171554235ce36a26d4b4b9ce952f9ff2c6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 709, "license_type": "no_license", "max_line_length": 75, "num_lines": 19, "path": "/art_project/art_project/urls.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from django.contrib import admin\nfrom django.urls import path, include\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\nfrom art import views\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('register/', views.user_register, name='register'),\n path('login/', views.user_login, name='user_login'),\n path('logout/', views.user_logout, name='user_logout'),\n path('profile/', views.user_profile, name= 'profile'),\n path('', views.home, name= 'home'),\n path('category/<slug:slug>/', views.product_category, name='category'),\n path('', include('cart.urls', namespace='cart')),\n]\n\nurlpatterns+=static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n" }, { "alpha_fraction": 0.708737850189209, "alphanum_fraction": 0.708737850189209, "avg_line_length": 33.33333206176758, "blob_id": "295e8eeb4a642e379be8d2d1b95fe992fe7e6a55", "content_id": "c1853738bf17fe3a131ceae3cda82291a18b1dde", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 412, "license_type": "no_license", "max_line_length": 75, "num_lines": 12, "path": "/art_project/cart/urls.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from django.urls import path\nfrom cart import views\n\napp_name= 'cart'\n\nurlpatterns=[\npath('add-to-cart/<item_id>', views.add_to_cart, name=\"add_to_cart\"),\npath('item/delete/<item_id>', views. delete_from_cart, name='delete_item'),\n# path('success/', views.success, name='purchase_success'),\npath('order_summary/', views.order_details, name='order_summary'),\npath('checkout/', views.checkout, name='checkout'),\n]\n" }, { "alpha_fraction": 0.380952388048172, "alphanum_fraction": 0.6666666865348816, "avg_line_length": 13, "blob_id": "7724ea1a0e4d249d435176554311d2f5a3b60589", "content_id": "4a643892fd0b2c346a824c9152c64009804f9dce", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 42, "license_type": "no_license", "max_line_length": 14, "num_lines": 3, "path": "/art_project/requirements.txt", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "Django==2.1.11\nPillow==5.3.0\npytz==2018.7\n" }, { "alpha_fraction": 0.7239263653755188, "alphanum_fraction": 0.7300613522529602, "avg_line_length": 31.600000381469727, "blob_id": "586a12b5d1699cbf3ecb6a6ae9376bec23aff235", "content_id": "85ab8f7d32f2348d1b4ff61ceccf4104bf35bf6e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 163, "license_type": "no_license", "max_line_length": 56, "num_lines": 5, "path": "/bin/django-admin.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "#!/Users/ghalyahalansari/Development/art/art/bin/python3\nfrom django.core import management\n\nif __name__ == \"__main__\":\n management.execute_from_command_line()\n" }, { "alpha_fraction": 0.6970474720001221, "alphanum_fraction": 0.7041078209877014, "avg_line_length": 30.15999984741211, "blob_id": "e87b2deeb4420ef5955f4b468db78cd3006bfe6c", "content_id": "3ea3755b3595600cc4c897cf789308b498f8b690", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1558, "license_type": "no_license", "max_line_length": 93, "num_lines": 50, "path": "/art_project/art/models.py", "repo_name": "GhalyahF/django_cart1", "src_encoding": "UTF-8", "text": "from django.db import models\nfrom django.contrib.auth.models import User\nfrom django.conf import settings\nfrom django.utils.text import slugify\nfrom django.db.models.signals import post_save\n\nclass Category(models.Model):\n category= models.CharField(max_length=150)\n slug= models.SlugField(max_length= 200, unique= True)\n description= models.TextField(default=\"art\")\n\n class Meta:\n verbose_name= 'category'\n verbose_name_plural= 'categories'\n\n def __str__(self):\n return self.category\n\n def save(self, *args, **kwargs):\n self.slug= slugify(self.category)\n super(Category, self).save(*args, **kwargs)\n\n\n\nclass Product(models.Model):\n name= models.CharField(max_length= 100)\n category= models.ForeignKey(Category, on_delete= models.PROTECT, related_name='products')\n photo= models.ImageField(null=True, blank=True)\n description= models.TextField()\n price= models.DecimalField(max_digits=5, decimal_places=2)\n\n class Meta:\n ordering=('category',)\n\n def __str__(self):\n return self.name\n\nclass Profile(models.Model):\n user= models.OneToOneField(User, on_delete= models.CASCADE)\n merchandise= models.ManyToManyField(Product, blank=True)\n\n def __str__(self):\n return self.user.username\n\ndef post_save_profile_create(sender, instance, created, *args, **kwargs):\n if created:\n Profile.objects.get_or_create(user=instance)\n user_profile, created= Profile.objects.get_or_create(user=instance)\n\npost_save.connect(post_save_profile_create, sender=User)\n" } ]

11

dbsousa01/AI-challenge

https://github.com/dbsousa01/AI-challenge

a09da283012c3c97a684dad010a7fe4b8c9d2c2c

0fa150d3d3c1abafe4243dba4550ba1a35300469

d70fbec03a5972c66fa03d2a029ed30cbaff7cae

refs/heads/master

2020-04-13T02:48:59.364421

2019-01-19T12:42:29

162,912,947

0

null

[ { "alpha_fraction": 0.6903262734413147, "alphanum_fraction": 0.7009158730506897, "avg_line_length": 30.370370864868164, "blob_id": "ec90f804dcb3facdfa07241aabf95b8c800eed76", "content_id": "bac050e193d878342601b2664dfe5dd3335c7b48", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3494, "license_type": "no_license", "max_line_length": 118, "num_lines": 108, "path": "/langid/task1.py", "repo_name": "dbsousa01/AI-challenge", "src_encoding": "UTF-8", "text": "# Task 1 is to train an algorithm in order to distinguish en, es, pt\r\n\r\n## TF-IDF Score at Word level implementation\r\n## TF-IDF score represents the relative importance of a term in the document and the entire corpus. \r\n#TF-IDF score is composed by two terms: the first computes the normalized Term Frequency (TF), the second term is the \r\n#Inverse Document Frequency (IDF), computed as the logarithm of the number of the documents in the corpus divided by \r\n#the number of documents where the specific term appears.\r\n\r\n#TF(t) = (Number of times term t appears in a document) / (Total number of terms in the document)\r\n#IDF(t) = log_e(Total number of documents / Number of documents with term t in it)\r\n\r\nfrom sklearn import model_selection, preprocessing, metrics\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nfrom sklearn.naive_bayes import MultinomialNB\r\n\r\nimport pandas as pd \r\nimport numpy as np \r\nimport os\r\n\r\nPATH = os.getcwd()\r\ntexts_pt = []\r\nlabels = []\r\ntexts_en = []\r\ntexts_es = []\r\nX_test = []\r\n#Open file and populate arrays to create dataset - pt\r\nwith open(PATH + '/langid-data/task1/data.pt') as fp:\r\n\tfor i, line in enumerate(fp):\r\n\t\ttexts_pt.append(line.split(\"\\n\")[0])\r\n\t\tif i == 100000: #might be enough for a dataset to this problem - ? Should introduce a shuffle read in the future\r\n\t\t\tbreak\r\n\r\nfp.close()\r\n\r\n#Open file and populate arrays to create dataset - en\r\nwith open(PATH + '/langid-data/task1/data.en') as fp:\r\n\tfor i, line in enumerate(fp):\r\n\t\ttexts_en.append(line.split(\"\\n\")[0])\r\n\t\tif i == 100000: #might be enough for a dataset to this problem - ?\r\n\t\t\tbreak\r\n\r\nfp.close()\r\n\r\n#Open file and populate arrays to create dataset - es\r\nwith open(PATH + '/langid-data/task1/data.es') as fp:\r\n\tfor i, line in enumerate(fp):\r\n\t\ttexts_es.append(line.split(\"\\n\")[0])\r\n\t\tif i == 100000: #might be enough for a dataset to this problem - ?\r\n\t\t\tbreak\r\n\r\nfp.close()\r\n\r\n## Test set\r\nwith open(PATH + \"/langid.test\") as fp:\r\n\tfor i, line in enumerate(fp):\r\n\t\t\tX_test.append(line.split(\"\\n\")[0])\r\n\t\t\t\r\nfp.close()\r\n\r\nlabel_pt = ['pt'] * len(texts_pt)\r\nlabel_en = ['en'] * len(texts_en)\r\nlabel_es = ['es'] * len(texts_es)\r\n\r\ntexts = texts_pt\r\ntexts.extend(texts_en)\r\ntexts.extend(texts_es)\r\nlabels = label_pt\r\nlabels.extend(label_en)\r\nlabels.extend(label_es)\r\n\r\n#print(len(texts))\r\n#print(len(labels))\r\n\r\ntrainDF = pd.DataFrame()\r\ntrainDF['text'] = texts\r\ntrainDF['label'] = labels\r\n#print(trainDF)\r\n\r\n\r\n# split the dataset into training and validation datasets \r\nX_train, X_valid, y_train, y_valid = model_selection.train_test_split(trainDF['text'], trainDF['label'])\r\n\r\n# label encode the target variable \r\nencoder = preprocessing.LabelEncoder()\r\ny_train = encoder.fit_transform(y_train)\r\ny_valid = encoder.fit_transform(y_valid)\r\n\r\n#Calculate the TF-IDF words vector\r\ntfidf_vect = TfidfVectorizer(analyzer='word', token_pattern=r'\\w{1,}', max_features=1000000)\r\ntfidf_vect.fit(trainDF['text'])\r\nxtrain_tfidf = tfidf_vect.transform(X_train)\r\nxvalid_tfidf = tfidf_vect.transform(X_valid)\r\nxtest_tfidf = tfidf_vect.transform(X_test)\r\n\r\n# Naive Bayes on Word Level TF IDF Vectors\r\nclf = MultinomialNB()\r\nclf.fit(xtrain_tfidf, y_train)\r\n\r\npredictions = clf.predict(xvalid_tfidf)\r\n\r\nacc = metrics.accuracy_score(predictions, y_valid)\r\nprint(\"Accuracy of the model:\", acc) # It's around 86%\r\n\r\n#Run with the test set\r\npredictions = clf.predict(xtest_tfidf)\r\npredictions = encoder.inverse_transform(predictions)\r\n\r\nnp.savetxt('task1-result.txt', predictions, fmt='%s')" }, { "alpha_fraction": 0.7777777910232544, "alphanum_fraction": 0.782282292842865, "avg_line_length": 52.279998779296875, "blob_id": "bc0cda11ae977f1d53a4e4273b6c28fc29d1cfb8", "content_id": "41b950e143e5c13f69119ef4e971679e5ed4c365", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1332, "license_type": "no_license", "max_line_length": 204, "num_lines": 25, "path": "/README.md", "repo_name": "dbsousa01/AI-challenge", "src_encoding": "UTF-8", "text": "# AI coding challenge - NLP\n\n## Description\n\nThe first thing you need to be able to do is to identify these languages properly.\n\nWe need you to build a service that provided with a text, identifies the language in which it is \nwritten, and provides that answer.\nWe provide you with the initial repo to implement this, and some requirements that the service \nshould satisfy. \n\nWhether you choose to implement an existing approach or compile one, make sure you document it\nand explain your reasoning.\n\n### Tasks\n\n1 - Implement a Language Identification service that returns the language code of the language in which the text is written. The provided data and test will\ntarget Spanish (ES), Portuguese (PT-PT) and English (EN)\n\n2 - Train the system to distinguish between language variants. In this case we wish to distinguish between European Portuguese (PT-PT) and Brazilian Portuguese (PT-BR)\n\n3(not completed) - Implement a deep learning model (recommended: a BILSTM tagger) to detect code switching (language mixture) and return both a list of tokens and a list with one language label per token.\nTo simplify we are going to focus on English and Spanish, so you only need to return for each token either 'en', 'es' or 'other'\n\n*See more information about tasks 1 and 2 in langid folder, and about task3 in code_switching folder*\n" } ]

2

okest12/leetCode

https://github.com/okest12/leetCode

084707f27b6e296242417cc973ddd120213f8044

7baac8041385ea34cd0a48ac601274c4b339cda9

7e1f80bb213bf630c68d605aee134f016c58c4da

refs/heads/master

2020-09-12T02:22:54.563549

2020-01-20T02:36:01

222,269,820

0

null

[ { "alpha_fraction": 0.4282907545566559, "alphanum_fraction": 0.4675835072994232, "avg_line_length": 19.659574508666992, "blob_id": "74f82a375dc900e923a3e344db809125d5a1cc98", "content_id": "b3e0d27d8db85517c38bdd80e10d342d3dd8f88e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1018, "license_type": "no_license", "max_line_length": 44, "num_lines": 47, "path": "/add_two_number.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "class listNode(object):\r\n def __init__(self, x):\r\n self.value = x\r\n self.next = None\r\n\r\n\r\ndef add_two_number(s1, s2):\r\n l1 = create_list_node(s1)\r\n l2 = create_list_node(s2)\r\n result = dummy = listNode(-1)\r\n carry = 0\r\n while l1 or l2 or carry:\r\n sum = carry\r\n if l1:\r\n sum += l1.value\r\n l1 = l1.next\r\n if l2:\r\n sum += l2.value\r\n l2 = l2.next\r\n if sum >= 10:\r\n carry = 1\r\n sum -= 10\r\n else:\r\n carry = 0\r\n dummy.next = listNode(sum)\r\n dummy = dummy.next\r\n return result.next\r\n\r\n\r\ndef print_list_node(l):\r\n s = ''\r\n while l:\r\n s = str(l.value) + s\r\n l = l.next\r\n print(s)\r\n\r\n\r\ndef create_list_node(s):\r\n result = dummy = listNode(-1)\r\n for i in range(len(s), 0, -1):\r\n dummy.next = listNode(int(s[i - 1]))\r\n dummy = dummy.next\r\n return result.next\r\n\r\n\r\nl3 = add_two_number(\"12345\", \"67890\")\r\nprint_list_node(l3)\r\n" }, { "alpha_fraction": 0.49813199043273926, "alphanum_fraction": 0.5205479264259338, "avg_line_length": 27.740739822387695, "blob_id": "e4f013dd9c50a58bb5c191d341175f1e541de1d4", "content_id": "b99ac6f802de96a50bb01ac001125487a3da4de3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 803, "license_type": "no_license", "max_line_length": 60, "num_lines": 27, "path": "/median_after_each_input.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "import heapq\r\ndef median_after_each_input(datas):\r\n minHeap = []\r\n maxHeap = []\r\n median = 0\r\n for data in datas:\r\n if data >= median:\r\n heapq.heappush(minHeap, data)\r\n else:\r\n heapq.heappush(maxHeap, -data)\r\n\r\n if len(maxHeap) >= len(minHeap) + 2:\r\n heapq.heappush(minHeap, -heapq.heappop(maxHeap))\r\n elif len(minHeap) >= len(maxHeap) + 2:\r\n heapq.heappush(maxHeap, -heapq.heappop(minHeap))\r\n\r\n if len(maxHeap) == len(minHeap):\r\n median = (-maxHeap[0] + minHeap[0])/2\r\n elif len(maxHeap) > len(minHeap):\r\n median = -maxHeap[0]\r\n else:\r\n median = minHeap[0]\r\n print(data, end=':')\r\n print(median)\r\n\r\n\r\nmedian_after_each_input([5,4,2,-3,7,9,0,1,3,8])\r\n" }, { "alpha_fraction": 0.4866071343421936, "alphanum_fraction": 0.5133928656578064, "avg_line_length": 23.11111068725586, "blob_id": "ba665b6346cd906e1c9a1a971e60fd890e755e99", "content_id": "c23f1e9a204620f2e7681effb84901f43dc2bf3c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 224, "license_type": "no_license", "max_line_length": 43, "num_lines": 9, "path": "/find_addend.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "def find_addend(nums, target):\r\n c_index = {}\r\n for i, c in enumerate(nums):\r\n if target - c in c_index:\r\n return [c_index[target - c], i]\r\n c_index[c] = i\r\n\r\n\r\nprint(find_addend([1,3,5,7],10))" }, { "alpha_fraction": 0.5223745107650757, "alphanum_fraction": 0.5403467416763306, "avg_line_length": 43.71369171142578, "blob_id": "b8989bbd1ab4106f4c9cbad17514660f8fca02d7", "content_id": "b1f31d6b7878377ae4ae67e6e248a1781c14e7ba", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 22034, "license_type": "no_license", "max_line_length": 116, "num_lines": 482, "path": "/make_tools_package.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\n2016/12/21 first version by @author: [email protected]\r\n This is the file to make tools package automatically\r\n2017/01/13 update to add new tools, and support selecting multiple files with pattern, by Ye Sheng\r\n2017/02/09 add link.txt and notes.txt for auto_helper tool, by Ye Sheng\r\n2017/02/24 add text_styles.py for edit_dialog highlight text, by Ye Sheng\r\n2017/05/15 add new mmt_check and remove old mmt_check_tool, by Ye Sheng\r\n2017/05/18 update template and example list, by Ye Sheng\r\n2017/06/12 add tools/lib/advanced_listbox.py, by Ye Sheng\r\n2017/06/16 add tools/lib/log_info_dialog.py, by Ye Sheng\r\n2017/07/14 add test_base/version.py, by Ye Sheng\r\n2017/07/14 add tools/log_view/graphic_dialog.py, by Ye Sheng\r\n2017/07/24 add log_view.ico, by Ye Sheng\r\n2017/07/25 add auto_helper.ico, mmt_check.ico, by Ye Sheng\r\n2017/08/23 update to display code number of lines, by Ye Sheng\r\n2017/08/31 add robot_runner, by Ye Sheng\r\n2017/09/19 add function get_version() for tools, and move this file from tools to tools/lib, by Ye Sheng\r\n2017/11/09 add tool log_processor and script_runner, by Ye Sheng\r\n2017/11/23 add tool file_converter, remove tool log_processor, by Ye Sheng\r\n2017/11/06 add tti_tracer_getter, by Ye Sheng\r\n2018/01/12 update to add up_main, and move target files to ./bin, by Ye Sheng\r\n2018/01/16 update to support auto upgrade, by Ye Sheng\r\n2018/01/19 make_package support source_code=False parameter, by Ye Sheng\r\n2018/03/02 improve usability by tool_main_file, tool_depend_file and released_tool_list, by Ye Sheng\r\n2018/03/05 add tk_app_template, by Ye Sheng\r\n2018/03/05 add extract_logs, by Wang Yixiang\r\n2018/03/06 add tool_name.txt in package, by Ye Sheng\r\n2018/03/06 use tool_name as tools subfolder, by Ye Sheng\r\n2018/03/14 avoid updating SHARED_FOLDER if local version is older, by Ye Sheng\r\n2018/09/14 add file auto_helper_item.py, by Ye Sheng\r\n2019/03/29 change for python2 to python3 migration phase I, by Ye Sheng\r\n2019/04/04 change for python2 to python3 migration phase II, by Ye Sheng\r\n2019/04/24 add new tool log_collector, by Ye Sheng\r\n2019/05/15 update xlrd, xlwt file list, by Ye Sheng\r\n2019/07/05 add new tool code_tree, add calculator as demo, by Ye Sheng\r\n\"\"\"\r\n\r\nimport os\r\nimport zipfile\r\nimport sys\r\nimport hashlib\r\nimport shutil\r\n\r\n_md5 = hashlib.md5 if sys.version_info.major == 2 else lambda x: hashlib.md5(x.encode('utf8'))\r\n\r\nsuite_root = os.path.abspath(os.path.dirname(__file__)) + '/..' * 6\r\nsys.path.append(suite_root)\r\nroot = os.path.abspath(os.path.dirname(__file__) + '/../..')\r\nroot in sys.path and sys.path.remove(root)\r\nroot in sys.path or sys.path.insert(0, root)\r\n\r\nif sys.version_info.major == 2:\r\n DEFAULT_SHARED_FOLDER = r'\\\\beeefsn01.china.nsn-net.net\\DCM_project\\qch2043\\up_tools'\r\nelse:\r\n DEFAULT_SHARED_FOLDER = r'\\\\beeefsn01.china.nsn-net.net\\DCM_project\\qch2043\\up_tools_3'\r\n\r\ntestsuite_file_list = (\r\n ('__init__.py', ''),\r\n ('../../../../tool_name.txt', ''),\r\n ('../../../../testsuite/__init__.py', ''),\r\n ('../../../../testsuite/DCM/__init__.py', ''),\r\n ('../../../../testsuite/DCM/libraries/__init__.py', ''),\r\n)\r\n\r\nup_lib_file_list = ('up_lib_file_list_place_holder',)\r\n\r\nxlwt_lib_file_list = (\r\n (('xlwt/', '.*py$', 1), ''),\r\n ('xlwt/excel-formula.g', ''),\r\n)\r\n\r\nxlrd_lib_file_list = (\r\n (('xlrd/', '.*py$', 1), ''),\r\n)\r\n\r\nexample_file_list = (\r\n ('examples/example1_001.py', ''),\r\n ('examples/example2_001.py', ''),\r\n ('examples/example3_001.py', ''),\r\n ('examples/example5_pmi_ri_cqi.py', ''),\r\n ('examples/example5_pmi_ri_cqi_001.py', ''),\r\n ('examples/example_set.py', ''),\r\n ('examples/example_set_1_test_base.py', ''),\r\n ('examples/example_set_2_test_log.py', ''),\r\n ('examples/example_set_5_sdata.py', ''),\r\n ('examples/example_set_6_mmt.py', ''),\r\n ('examples/example_set_7_common.py', ''),\r\n ('examples/__init__.py', ''),\r\n)\r\n\r\ntemplate_file_list = (\r\n ('template/__init__.py', ''),\r\n ('template/FeatureID.py', ''),\r\n ('template/FeatureID_CaseID.py', ''),\r\n)\r\n\r\n\r\ntools_lib_file_list = (\r\n ('tools/__init__.py', ''),\r\n ('tools/lib/__init__.py', ''),\r\n (('tools/lib/tkdnd2.8/', '.*.dll', 2), ''),\r\n (('tools/lib/tkdnd2.8/', '.*.tcl', 2), ''),\r\n ('tools/lib/TkDND.py', ''),\r\n ('tools/lib/TwoSideScrolledText.py', ''),\r\n ('tools/lib/wait_dialog.py', ''),\r\n ('tools/lib/edit_dialog.py', ''),\r\n ('tools/lib/text_styles.py', ''),\r\n ('tools/lib/get_strings_dialog.py', ''),\r\n ('tools/lib/advanced_listbox.py', ''),\r\n ('tools/lib/super_extractor.py', ''),\r\n ('tools/lib/log_info_dialog.py', ''),\r\n ('tools/lib/select_log_dialog.py', ''),\r\n ('tools/lib/log_summary_dialog.py', ''),\r\n ('tools/lib/tk_app.py', ''),\r\n ('tools/lib/make_tools_package.py', ''),\r\n ('tools/lib/check.gif', ''),\r\n ('tools/lib/uncheck.gif', ''),\r\n (('tools/lib/robot/', '.*py$', 3), ''),\r\n (('tools/lib/robot/', '.*html$', 3), ''),\r\n (('tools/lib/robot/', '.*js$', 3), ''),\r\n (('tools/lib/robot/', '.*css$', 3), ''),\r\n (('tools/lib/robot_3/', '.*py$', 3), ''),\r\n (('tools/lib/robot_3/', '.*html$', 3), ''),\r\n (('tools/lib/robot_3/', '.*js$', 3), ''),\r\n (('tools/lib/robot_3/', '.*css$', 3), ''),\r\n (('tools/lib/psutil/', '.*py$', 1), ''),\r\n (('tools/lib/psutil/', '.*pyd$', 1), ''),\r\n (('tools/lib/psutil_3/', '.*py$', 1), ''),\r\n (('tools/lib/psutil_3/', '.*pyd$', 1), ''),\r\n)\r\n\r\n# if sys.version_info.major == 2:\r\n# tools_lib_file_list += (\r\n# (('tools/lib/psutil/', '.*py$', 1), ''),\r\n# (('tools/lib/psutil/', '.*pyd$', 1), ''),\r\n# )\r\n# else:\r\n# tools_lib_file_list += (\r\n# (('tools/lib/psutil_3/', '.*py$', 1), ''),\r\n# (('tools/lib/psutil_3/', '.*pyd$', 1), ''),\r\n# )\r\n\r\nsys_info_file_list = (\r\n # ('bin/sys_info.bat', '../../../../sys_info.bat'),\r\n ('test_base/sys_info.ico', ''),\r\n ('test_base/sys_info.gif', ''),\r\n ('test_base/sys_info.py', '')\r\n)\r\n\r\nlog_runner_file_list = (\r\n # ('bin/log_runner.bat', '../../../../log_runner.bat'),\r\n ('tools/log_runner/__init__.py', ''),\r\n ('tools/log_runner/log_runner.py', ''),\r\n ('tools/log_runner/log_runner.ico', ''),\r\n ('tools/log_runner/log_runner.gif', ''),\r\n)\r\n\r\nlog_view_file_list = (\r\n # ('bin/log_view.bat', '../../../../log_view.bat'),\r\n ('tools/log_view/__init__.py', ''),\r\n ('tools/log_view/log_view.py', ''),\r\n ('tools/log_view/graphic_dialog.py', ''),\r\n ('tools/log_view/log_view.ico', ''),\r\n ('tools/log_view/log_view.gif', '')\r\n)\r\n\r\nmmt_check_file_list = (\r\n # ('bin/mmt_check.bat', '../../../../mmt_check.bat'),\r\n ('tools/mmt_check/__init__.py', ''),\r\n ('tools/mmt_check/mmt_check.py', ''),\r\n ('tools/mmt_check/mmt_check.ico', ''),\r\n ('tools/mmt_check/mmt_check.gif', ''),\r\n ('tools/mmt_check/Tshark_MMT_sample.txt', ''),\r\n)\r\n\r\nauto_helper_file_list = (\r\n # ('bin/auto_helper.bat', '../../../../auto_helper.bat'),\r\n ('tools/auto_helper/__init__.py', ''),\r\n ('tools/auto_helper/auto_helper.py', ''),\r\n ('tools/auto_helper/auto_helper_item.py', ''),\r\n ('tools/auto_helper/auto_helper.ini', ''),\r\n ('tools/auto_helper/log_column_tool.py', ''),\r\n ('tools/auto_helper/time_tool.py', ''),\r\n ('tools/auto_helper/sdata_tool.py', ''),\r\n ('tools/auto_helper/link.txt', ''),\r\n ('tools/auto_helper/notes.txt', ''),\r\n ('tools/auto_helper/auto_helper.ico', ''),\r\n ('tools/auto_helper/auto_helper.gif', ''),\r\n ('tools/auto_helper/Tshark_MMT_sample.txt', ''),\r\n (('../../../../testsuite/DCM/user_plane/template/', '.*robot$', 0), ''),\r\n (('../../../../testsuite/DCM/user_plane/resources/', '.*robot$', 0), ''),\r\n (('../../../../testsuite/DCM/user_plane/17A/sdata_lrc_edit/', 'SDATA_edit_tool_LTEAPI1220HD_1.0.0.xls', 0), ''),\r\n (('../../../../resources/DCM/', '.*robot$', 0), ''),\r\n)\r\n\r\nrobot_runner_file_list = (\r\n # ('bin/robot_runner.bat', '../../../../robot_runner.bat'),\r\n ('tools/robot_runner/__init__.py', ''),\r\n ('tools/robot_runner/robot_runner.py', ''),\r\n ('tools/robot_runner/robot_runner.ico', ''),\r\n ('tools/robot_runner/robot_runner.gif', ''),\r\n ('tools/robot_runner/sample.robot', ''),\r\n)\r\n\r\nscript_runner_file_list = (\r\n # ('bin/script_runner.bat', '../../../../script_runner.bat'),\r\n ('tools/script_runner/__init__.py', ''),\r\n ('tools/script_runner/script_runner.py', ''),\r\n ('tools/script_runner/script_runner.ico', ''),\r\n ('tools/script_runner/script_runner.gif', ''),\r\n ('tools/script_runner/scripts/__init__.py', ''),\r\n ('tools/script_runner/scripts/sample.py', ''),\r\n ('tools/script_runner/scripts/lib/__init__.py', ''),\r\n ('tools/script_runner/scripts/lib/common.py', ''),\r\n)\r\n\r\nfile_converter_file_list = (\r\n # ('bin/file_converter.bat', '../../../../file_converter.bat'),\r\n ('tools/file_converter/__init__.py', ''),\r\n ('tools/file_converter/file_converter.py', ''),\r\n ('tools/file_converter/file_converter.ico', ''),\r\n ('tools/file_converter/file_converter.gif', ''),\r\n ('tools/file_converter/tti_tracer_getter.py', ''),\r\n)\r\n\r\nup_main_file_list = (\r\n # ('bin/up_main.bat', '../../../../up_main.bat'),\r\n ('tools/up_main/__init__.py', ''),\r\n ('tools/up_main/up_main.py', ''),\r\n ('tools/up_main/up_tools.py', ''),\r\n ('tools/up_main/up_main.ico', ''),\r\n)\r\n\r\ntk_app_template_file_list = (\r\n # ('bin/tk_app_template.bat', '../../../../tk_app_template.bat'),\r\n ('tools/tk_app_template/tk_app_template.py', ''),\r\n ('tools/tk_app_template/tk_app_template.ico', ''),\r\n ('tools/tk_app_template/tk_app_template.gif', ''),\r\n)\r\n\r\ncode_tree_file_list = (\r\n ('tools/code_tree/code_tree.py', ''),\r\n ('tools/code_tree/code_tree.ico', ''),\r\n ('tools/code_tree/code_tree.gif', ''),\r\n)\r\n\r\ncalculator_file_list = (\r\n # ('bin/tk_app_template.bat', '../../../../tk_app_template.bat'),\r\n ('tools/calculator/calculator.py', ''),\r\n ('tools/calculator/calculator.ico', ''),\r\n ('tools/calculator/calculator.gif', ''),\r\n)\r\n\r\nlog_collector_file_list = (\r\n ('tools/log_collector/log_collector.py', ''),\r\n ('tools/log_collector/log_collector.ini', ''),\r\n ('tools/log_collector/log_collector.ico', ''),\r\n ('tools/log_collector/log_collector.gif', ''),\r\n (('tools/log_collector/scripts/', '.*\\.py', 0), ''),\r\n (('tools/log_collector/scripts/', '.*\\.exe', 0), ''),\r\n)\r\n\r\nextract_logs_file_list = (\r\n # ('bin/extract_logs.bat', '../../../../extract_logs.bat'),\r\n ('tools/extract_logs/get_logs.py', ''),\r\n ('tools/extract_logs/extract_logs.py', ''),\r\n ('tools/extract_logs/extract_logs.ico', ''),\r\n ('tools/extract_logs/extract_logs.gif', ''),\r\n)\r\n\r\ntool_main_file = {\r\n 'auto_helper': (up_lib_file_list, auto_helper_file_list),\r\n 'mmt_check': (up_lib_file_list, mmt_check_file_list),\r\n 'log_view': (up_lib_file_list, log_view_file_list),\r\n 'log_runner': (up_lib_file_list, log_runner_file_list),\r\n 'robot_runner': (robot_runner_file_list,),\r\n 'script_runner': (up_lib_file_list, script_runner_file_list,),\r\n 'file_converter': (up_lib_file_list, file_converter_file_list,),\r\n 'up_main': (up_lib_file_list, up_main_file_list),\r\n 'sys_info': (sys_info_file_list,),\r\n 'extract_logs': (extract_logs_file_list,),\r\n 'log_collector': (log_collector_file_list,),\r\n 'code_tree': (code_tree_file_list,),\r\n 'tk_app_template': (tk_app_template_file_list,),\r\n 'calculator': (calculator_file_list,),\r\n\r\n}\r\n\r\ntool_depend_file = {\r\n 'auto_helper': (testsuite_file_list, tools_lib_file_list, example_file_list, template_file_list,\r\n xlrd_lib_file_list, xlwt_lib_file_list),\r\n 'mmt_check': (testsuite_file_list, tools_lib_file_list,),\r\n 'log_view': (testsuite_file_list, tools_lib_file_list, xlrd_lib_file_list, xlwt_lib_file_list),\r\n 'log_runner': (testsuite_file_list, tools_lib_file_list, xlrd_lib_file_list, xlwt_lib_file_list),\r\n 'robot_runner': (testsuite_file_list, tools_lib_file_list,),\r\n 'script_runner': (testsuite_file_list, tools_lib_file_list,),\r\n 'file_converter': (testsuite_file_list, tools_lib_file_list,),\r\n 'up_main': (testsuite_file_list, tools_lib_file_list),\r\n 'sys_info': (testsuite_file_list, ),\r\n 'extract_logs': (testsuite_file_list, tools_lib_file_list),\r\n 'log_collector': (testsuite_file_list, tools_lib_file_list,),\r\n 'code_tree': (testsuite_file_list, tools_lib_file_list,),\r\n 'tk_app_template': (testsuite_file_list, tools_lib_file_list),\r\n 'calculator': (testsuite_file_list, tools_lib_file_list),\r\n}\r\n\r\nreleased_tool_list = (\r\n ('up_tools', ('auto_helper', 'mmt_check', 'log_view', 'log_runner', 'robot_runner', 'script_runner',\r\n 'file_converter', 'up_main')),\r\n ('log_processor', ('extract_logs', 'file_converter', 'script_runner', 'log_view', 'log_collector',\r\n 'up_main')),\r\n ('code_tools', ('auto_helper', 'mmt_check', 'robot_runner', 'code_tree', 'up_main')),\r\n ('robot_runner', ('robot_runner', 'up_main')),\r\n ('calculator', ('calculator', 'robot_runner', 'up_main')),\r\n ('tk_app_template', ('tk_app_template', 'up_main')),\r\n)\r\n\r\n\r\ndef get_version(tool_name=None):\r\n import re\r\n import inspect\r\n base_version, sub_version, sub_version_count = '', '', 0\r\n up_root = os.path.abspath(os.path.dirname(__file__)) + '/../..'\r\n version_file_list = []\r\n if tool_name not in tool_main_file:\r\n tool_name = 'up_main'\r\n tool_name = tool_name or os.path.basename(inspect.stack()[1][1]).replace('.pyc', '').replace('.py', '')\r\n for file_list in tool_main_file[tool_name]:\r\n if file_list == ('up_lib_file_list_place_holder',):\r\n from test_base.version import Version\r\n base_version = Version().get_version()[0] + '.'\r\n else:\r\n for item in file_list:\r\n if type(item[0]) is str:\r\n version_file_list.append(item[0])\r\n for filename in version_file_list:\r\n if filename.endswith('.py'):\r\n filename = os.path.join(up_root, filename)\r\n if not os.path.exists(filename):\r\n filename += 'c'\r\n with open(filename, 'rb' if filename.endswith('pyc') else 'r') as rf:\r\n for line in rf:\r\n if filename.endswith('pyc') and sys.version_info.major != 2:\r\n line = str(line)\r\n m = re.match(r\"(..)?(\\d\\d\\d\\d)/(\\d\\d)/(\\d\\d)\", line)\r\n if m:\r\n version = m.group(2) + m.group(3) + m.group(4)\r\n if version > sub_version:\r\n sub_version = version\r\n sub_version_count = 1\r\n elif version == sub_version:\r\n sub_version_count += 1\r\n m = re.match(r\"\\s*from tools\\.lib\\.(\\w+)\", line)\r\n if m and m.group(1) != 'make_tools_package':\r\n add_file = os.path.join(up_root, \"tools/lib/\" + m.group(1) + \".py\")\r\n if add_file not in version_file_list:\r\n version_file_list.append(add_file)\r\n if filename.endswith('pyc'):\r\n for module in re.findall(r\"tools\\.lib\\.([a-zA-Z_]+)[Rr]\", line):\r\n if module != 'make_tools_package':\r\n add_file = os.path.join(up_root, \"tools/lib/\" + module + \".py\")\r\n if add_file not in version_file_list:\r\n version_file_list.append(add_file)\r\n return base_version + sub_version + \".%d\" % sub_version_count\r\n\r\n\r\ndef update_up_lib_file_list(file_list):\r\n if 'up_lib_file_list_place_holder' in file_list:\r\n from test_base.version import file_list as up_lib_files\r\n file_list = [i for i in file_list if i != 'up_lib_file_list_place_holder']\r\n for filename, _ in up_lib_files + (('test_base/version.py', ''),):\r\n add_item = filename, ''\r\n if add_item not in file_list:\r\n file_list.append(add_item)\r\n return file_list\r\n\r\n\r\ndef make_package(tool_name=None, source_code=False):\r\n from test_base.common import find_all_files\r\n up_root = os.path.abspath(os.path.dirname(__file__)) + '/../..'\r\n robot_root = os.path.abspath(os.path.dirname(__file__)) + '/..' * 6\r\n\r\n for release_name, tool_list in released_tool_list:\r\n if tool_name and release_name not in tool_name and release_name != tool_name:\r\n continue\r\n with open(os.path.join(suite_root, 'tool_name.txt'), 'w') as wf:\r\n wf.write(release_name)\r\n # for tool, file_list, tool_list in up_tool_list:\r\n all_file_list = []\r\n for tool in tool_list:\r\n for file_list in tool_main_file[tool] + tool_depend_file[tool]:\r\n for item in file_list:\r\n if item not in all_file_list:\r\n all_file_list.append(item)\r\n num_of_lines = 0\r\n num_of_lines_detail = {}\r\n tool_zip_name = os.path.join(up_root, 'bin/' + release_name + '.zip')\r\n zip_obj = zipfile.ZipFile(tool_zip_name, 'w', zipfile.ZIP_DEFLATED)\r\n all_file_list = update_up_lib_file_list(all_file_list)\r\n for name, target_name in dict(all_file_list).items():\r\n # up_root = C:\\robotlte_trunk\\testsuite\\DCM\\libraries\\UP\\\r\n # case1: name = 'tools/auto_helper/auto_helper.py', arcname = ''\r\n # case2: name = ('../../../../resources/DCM/', '.*robot$', 0), arcname = ''\r\n # case3: name = 'mmt_check.bat', arcname = '../../../../mmt_check.bat'\r\n if type(name) is str:\r\n name = (os.path.join(up_root, name),)\r\n else:\r\n name = find_all_files(os.path.join(up_root, name[0]), pattern=name[1], recurse=True, depth=name[2])\r\n for source_name in name:\r\n source_name = os.path.abspath(source_name)\r\n arc_name = target_name or source_name\r\n arc_name = release_name + '/' + os.path.relpath(os.path.join(up_root, arc_name), robot_root)\r\n if source_name.endswith('.py'):\r\n path = os.path.relpath(os.path.dirname(source_name), robot_root)\r\n if 'xlrd' not in path and 'xlwt' not in path:\r\n if sys.version_info.major == 2:\r\n with open(source_name) as rf:\r\n lines = len(rf.readlines())\r\n else:\r\n with open(source_name, encoding='utf8') as rf:\r\n lines = len(rf.readlines())\r\n num_of_lines_detail.setdefault(path, 0)\r\n num_of_lines_detail[path] += lines\r\n num_of_lines += lines\r\n if (not source_code and source_name.endswith('.py') and 'sample' not in source_name\r\n and '\\\\scripts\\\\' not in source_name\r\n and 'UP\\\\tools\\\\' in source_name):\r\n import compileall\r\n if sys.version_info.major != 2:\r\n compileall.compile_file(source_name, force=True, legacy=True)\r\n else:\r\n compileall.compile_file(source_name, force=True)\r\n source_name += 'c'\r\n arc_name += 'c'\r\n if 'psutil' in source_name:\r\n print(source_name, arc_name)\r\n zip_obj.write(source_name, arc_name)\r\n\r\n print(\"\\nTool: %s, code number of lines: %d\" % (release_name, num_of_lines))\r\n for folder, lines in sorted(num_of_lines_detail.items()):\r\n if lines > 0:\r\n print(\"\\t%s: %s\" % (folder, lines))\r\n zip_obj.close()\r\n with open(tool_zip_name + '.ver.txt', 'w') as wf, open(tool_zip_name, 'rb') as rf:\r\n for t in tool_list:\r\n wf.write(\"%s: %s\\n\" % (t, get_version(t)))\r\n wf.write(\"md5: %s\\n\" % hashlib.md5(rf.read()).hexdigest())\r\n if os.path.exists(DEFAULT_SHARED_FOLDER):\r\n bin_path = os.path.join(DEFAULT_SHARED_FOLDER, 'bin')\r\n if not os.path.exists(bin_path):\r\n os.mkdir(bin_path, 0o777)\r\n old_version_file = os.path.join(bin_path, release_name + '.zip.ver.txt')\r\n if os.path.exists(old_version_file):\r\n with open(old_version_file) as rf:\r\n for line in rf:\r\n if ':' in line:\r\n tool, old_version = line.rstrip(\"\\n\").split(\": \")\r\n if tool == 'md5':\r\n continue\r\n new_version = get_version(tool)\r\n print(new_version)\r\n old_version = [int(i) for i in old_version.split('.')]\r\n new_version = [int(i) for i in new_version.split('.')]\r\n if sorted([old_version, new_version]) != [old_version, new_version]:\r\n info = (\"The version of %s on shared_folder is newer!\\n%s\\n\"\r\n \"Update anyway?(y/n)\"\r\n % (tool, old_version_file))\r\n input_check = raw_input(info) if sys.version_info.major == 2 else input(info)\r\n if input_check not in ('y', 'Y'):\r\n raise Exception(info)\r\n for filename in (tool_zip_name, tool_zip_name + '.ver.txt'):\r\n shutil.copy(filename, os.path.join(DEFAULT_SHARED_FOLDER, 'bin'))\r\n print(\"\\nCopy %s files to shared_folder done.\\n\" % release_name)\r\n\r\n\r\nif __name__ == '__main__':\r\n # make_package(tool_name='log_processor')\r\n # make_package(tool_name='calculator')\r\n make_package()\r\n for t_name in tool_main_file:\r\n print(\"%s version: %s\" % (t_name, get_version(t_name)))\r\n" }, { "alpha_fraction": 0.4954545497894287, "alphanum_fraction": 0.5090909004211426, "avg_line_length": 25.5, "blob_id": "cf2576e6d85d626176eb7f79076b5ddadefcafd0", "content_id": "55afe31ad1498d70aac34a617c421254372568f9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 440, "license_type": "no_license", "max_line_length": 50, "num_lines": 16, "path": "/longest_sub_string.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "def length_of_longest_substring(s):\r\n start = 0\r\n max_len = 0\r\n max_start = 0\r\n c_index = {}\r\n for i, c in enumerate(s):\r\n if c in c_index:\r\n start = max(start, c_index[c] + 1)\r\n c_index[c] = i\r\n if i - start + 1 >= max_len:\r\n max_len = i - start + 1\r\n max_start = start\r\n return s[max_start:max_start+max_len]\r\n\r\n\r\nprint(length_of_longest_substring(\"aefbacbcedec\"))\r\n" }, { "alpha_fraction": 0.5473933815956116, "alphanum_fraction": 0.564150333404541, "avg_line_length": 42.75757598876953, "blob_id": "0e89522dca3a862d74f2208dd574a32520f3de24", "content_id": "06146b2a11731f9f15918b5aa701caf3a140eb50", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5908, "license_type": "no_license", "max_line_length": 120, "num_lines": 132, "path": "/make_tools_exe.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\n2018/01/10 first version by @author: [email protected]\r\n This is the file to make tools exe package automatically\r\n2018/01/16 update to support auto upgrade, by Ye Sheng\r\n2018/03/05 add tk_app_template, by Ye Sheng\r\n2018/03/05 add extract_logs, by Wang Yixiang\r\n2018/03/06 include pythonw.exe and python.exe into package, by Ye Sheng\r\n2018/03/06 use tool_name as tools subfolder, use up_main.exe as application name, by Ye Sheng\r\n2018/03/15 fix issue of packing additional files into target exe file, by Ye Sheng\r\n2019/03/29 change for python2 to python3 migration phase I, by Ye Sheng\r\n2019/04/24 add debug_flag, by Ye Sheng\r\n2019/07/05 add calculator as demo, by Ye Sheng\r\n\"\"\"\r\n\r\nimport os\r\nimport zipfile\r\nimport re\r\nimport sys\r\nimport shutil\r\nfrom make_tools_package import DEFAULT_SHARED_FOLDER\r\n\r\nroot = os.path.abspath(os.path.dirname(__file__) + '/../..')\r\nroot in sys.path and sys.path.remove(root)\r\nroot in sys.path or sys.path.insert(0, root)\r\nfrom test_base.common import find_all_files\r\n\r\n# input_file_pattern = r'C:\\robotlte_trunk\\testsuite\\DCM\\libraries\\UP\\{name}.zip'\r\n# output_file_pattern = r'C:\\robotlte_trunk\\testsuite\\DCM\\libraries\\UP\\{name}_exe.zip'\r\n\r\nPYTHON_PATH = r'c:\\python27' if sys.version_info.major == 2 else r'c:\\python36'\r\n\r\n\r\nmake_exe_cmd = (PYTHON_PATH + r'\\scripts\\pyinstaller ..\\up_main\\up_tools.py {console} -y '\r\n r'-n {app_name} '\r\n r'-i {lib_path}\\..\\up_main\\up_main.ico '\r\n r'--distpath {folder}\\dist '\r\n r'--workpath {folder}\\build '\r\n r'--specpath {folder}\\build')\r\n\r\ntools_list = [{'name': 'up_tools',\r\n 'input':\r\n ((r'.\\bin\\up_tools.zip', '..'),\r\n (PYTHON_PATH + r'\\lib\\site-packages\\matplotlib_one_folder.zip', '')),\r\n 'output': r'.\\bin\\up_tools_exe.zip'},\r\n\r\n {'name': 'robot_runner',\r\n 'input': ((r'.\\bin\\robot_runner.zip', '..'),),\r\n 'output': r'.\\bin\\robot_runner_exe.zip'},\r\n\r\n {'name': 'log_processor',\r\n 'input': ((r'.\\bin\\log_processor.zip', '..'),\r\n (PYTHON_PATH + r'\\lib\\site-packages\\matplotlib_one_folder.zip', '')),\r\n 'output': r'.\\bin\\log_processor_exe.zip'},\r\n\r\n {'name': 'code_tools',\r\n 'input': ((r'.\\bin\\code_tools.zip', '..'),),\r\n 'output': r'.\\bin\\code_tools_exe.zip'},\r\n\r\n {'name': 'tk_app_template',\r\n 'input': ((r'.\\bin\\tk_app_template.zip', '..'),),\r\n 'output': r'.\\bin\\tk_app_template_exe.zip'},\r\n\r\n {'name': 'calculator',\r\n 'input': ((r'.\\bin\\calculator.zip', '..'),),\r\n 'output': r'.\\bin\\calculator_exe.zip'},\r\n\r\n ]\r\n\r\n\r\ndef unzip_file(zipfilename, unziptodir, strip_level=0):\r\n if not os.path.exists(unziptodir):\r\n os.mkdir(unziptodir, 0o777)\r\n zfobj = zipfile.ZipFile(zipfilename)\r\n for name in zfobj.namelist():\r\n name = name.replace('\\\\', '/')\r\n new_name = name\r\n for i in range(strip_level):\r\n new_name = re.sub('^.*?/', '', new_name)\r\n if new_name.endswith('/'):\r\n os.mkdir(os.path.join(unziptodir, new_name))\r\n elif new_name:\r\n ext_filename = os.path.join(unziptodir, new_name)\r\n ext_dir = os.path.dirname(ext_filename)\r\n if not os.path.exists(ext_dir):\r\n os.makedirs(ext_dir, 0o777)\r\n outfile = open(ext_filename, 'wb')\r\n outfile.write(zfobj.read(name))\r\n outfile.close()\r\n\r\n\r\ndef make_exe(tools=None, debug_flag=False):\r\n\r\n if type(tools) is not tuple:\r\n tools = (tools,)\r\n tmp_folder = os.environ[\"TMP\"]\r\n for tool in tools_list:\r\n tool_name = tool['name']\r\n if not tools or tool_name in tools:\r\n working_folder = os.path.join(tmp_folder, 'up_tools')\r\n tools_folder = os.path.join(tmp_folder, 'up_tools/dist/%s' % tool_name)\r\n print(\"Use pyinstaller to generate up_main.exe in folder: %s\" % tools_folder)\r\n console = '-c' if debug_flag else '-w'\r\n os.system(make_exe_cmd.format(folder=working_folder, app_name=tool_name, console=console,\r\n lib_path=os.path.dirname(__file__)))\r\n for input_file, sub_folder in tool.get('input', []):\r\n input_file = os.path.join(root, input_file)\r\n folder = os.path.join(tools_folder, sub_folder)\r\n print(\"Unzip %s to folder: %s\" % (input_file, folder))\r\n unzip_file(input_file, folder)\r\n # shutil.copy(PYTHON_PATH + r\"\\pythonw.exe\", tools_folder)\r\n # shutil.copy(PYTHON_PATH + r\"\\python.exe\", tools_folder)\r\n shutil.move(os.path.join(tools_folder, \"%s.exe\" % tool_name),\r\n os.path.join(tools_folder, \"up_main.exe\"))\r\n output_file = os.path.join(root, tool['output'])\r\n print(\"Create output file %s from %s\" % (output_file, tools_folder))\r\n target_zip_obj = zipfile.ZipFile(output_file, 'w', zipfile.ZIP_DEFLATED)\r\n for file_name in find_all_files(tools_folder, depth=-1):\r\n target_zip_obj.write(file_name, arcname=os.path.relpath(file_name, start=os.path.dirname(tools_folder)))\r\n target_zip_obj.close()\r\n if os.path.exists(DEFAULT_SHARED_FOLDER):\r\n shutil.copy(output_file, os.path.join(DEFAULT_SHARED_FOLDER, 'bin'))\r\n\r\nif __name__ == '__main__':\r\n make_exe('up_tools')\r\n # make_exe('robot_runner')\r\n # make_exe('tk_app_template')\r\n make_exe('log_processor', debug_flag=False)\r\n make_exe('code_tools', debug_flag=False)\r\n # make_exe('calculator', debug_flag=False)\r\n make_exe(debug_flag=False)\r\n print(\"out folder: %s\" % os.path.join(DEFAULT_SHARED_FOLDER, 'bin'))\r\n" }, { "alpha_fraction": 0.5515506863594055, "alphanum_fraction": 0.5616093873977661, "avg_line_length": 25.744186401367188, "blob_id": "ecf526ffb276686162dc4bb59ebc0c25204e7c92", "content_id": "02f3829a9741a09fabeab3034defee4f7e99ff5b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1193, "license_type": "no_license", "max_line_length": 68, "num_lines": 43, "path": "/longest_palindrome.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "def longest_palindrome(s):\r\n result = ''\r\n for i in range(len(s)):\r\n for j in range(i, len(s)):\r\n sub_str = s[i:j + 1]\r\n if is_palindrom(sub_str) and len(sub_str) > len(result):\r\n result = sub_str\r\n return result\r\n\r\n\r\ndef is_palindrom(s):\r\n if s == s[::-1]:\r\n return True\r\n return False\r\n\r\n\r\ndef find_palindrome(s, l, r):\r\n while l >= 0 and r < len(s) and s[l] == s[r]:\r\n l -= 1\r\n r += 1\r\n return s[l+1:r]\r\n\r\n\r\ndef longest_palindrome_optimize(s):\r\n result = ''\r\n for i in range(len(s)-1):\r\n if 2 * (len(s) - i) - 1 <= len(result):break\r\n temp_s = find_palindrome(s, i - 1, i + 1)\r\n if len(temp_s) > len(result): result = temp_s\r\n temp_s = find_palindrome(s, i, i + 1)\r\n if len(temp_s) > len(result): result = temp_s\r\n return result\r\n\r\n\r\nprint(longest_palindrome(\"abcbab\"))\r\nprint(longest_palindrome(\"aaaa\"))\r\nprint(longest_palindrome(\"aaaacc\"))\r\nprint(longest_palindrome(\"abvd\"))\r\n\r\nprint(longest_palindrome_optimize(\"abcbab\"))\r\nprint(longest_palindrome_optimize(\"aaaa\"))\r\nprint(longest_palindrome_optimize(\"aaaacc\"))\r\nprint(longest_palindrome_optimize(\"abcd\"))\r\n" }, { "alpha_fraction": 0.4127569794654846, "alphanum_fraction": 0.49393779039382935, "avg_line_length": 33.16666793823242, "blob_id": "1022fb76ac20587f7c74c1a98fa36b797b2146dd", "content_id": "d591c980a717f5d46d4990e85ce44c5c11923375", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1897, "license_type": "no_license", "max_line_length": 89, "num_lines": 54, "path": "/middle_number_of_two_list.py", "repo_name": "okest12/leetCode", "src_encoding": "UTF-8", "text": "def middle_number(s1, s2):\r\n a, b = sorted((s1, s2), key=len)\r\n len_a, len_b = len(a), len(b)\r\n len_half = int((len_a + len_b - 1) / 2)\r\n lo, hi = 0, len_a - 1\r\n while lo < hi:\r\n i = int((lo + hi) / 2)\r\n j = len_half - i - 1\r\n if a[i] >= b[j]:\r\n hi = i\r\n else:\r\n lo = i + 1\r\n second_middle_offset = (len_a + len_b + 1) % 2\r\n i = lo\r\n #if i == 0 and (len_half + 1 < len_b) and (a[i] > b[len_half + 1]):\r\n #result = b[len_half:len_half+2]\r\n if i == (len_a - 1) and (len_half - len_a >= 0) and (a[i] < b[len_half - len_a]):\r\n result = b[len_half - len_a: len_half - len_a + 2]\r\n else:\r\n result = sorted(a[i:i + 2] + b[len_half - i: len_half - i + 2])\r\n return (result[0] + result[second_middle_offset]) / 2\r\n\r\n\r\ndef generate_sequence(mask, size):\r\n s1 = []\r\n s2 = []\r\n for index in range(size):\r\n if mask & (0x1 << index):\r\n s1.append(index)\r\n else:\r\n s2.append(index)\r\n return s1, s2\r\n\r\n\r\ndef test(size):\r\n for mask in range(1, (0x1 << size) - 1):\r\n s1, s2 = generate_sequence(mask, size)\r\n expected = (size - 1) / 2\r\n actual = middle_number(s1, s2)\r\n if expected != actual:\r\n print(s1)\r\n print(s2)\r\n print(\"Expected:%.1f, Actual:%.1f\" % (expected, actual))\r\n return\r\n\r\n\r\ntest(10)\r\ntest(9)\r\nprint(\"Expected:2, Actual:\", middle_number([2], [1,3])) #2\r\nprint(\"Expected:2.5, Actual:\", middle_number([1,2], [3,4])) #2, 3\r\nprint(\"Expected:16, Actual:\", middle_number([1,12,15,26,38], [2,13,17,30,45])) #15, 17\r\nprint(\"Expected:17, Actual:\", middle_number([1,12,15,26,38], [2,13,17,30,45,50])) #17\r\nprint(\"Expected:10.5, Actual:\", middle_number([1,2,5,6,8], [13,17,30,45,50])) #8, 13\r\nprint(\"Expected:9.5, Actual:\", middle_number([1,2,5,6,8,9,10], [13,17,30,45,50])) #9, 10" } ]

8

leoneckert/people-also-bought

https://github.com/leoneckert/people-also-bought

7bd8bdb89ab70ccb5b090e28ac981b223e0a1342

611b30ae215936efb6c46008d6019a059cdc3e71

5b00540dd6f524659daa7a82ec59282d8cf2b1cd

refs/heads/master

2021-01-19T09:51:41.010205

2017-02-16T06:33:18

82,144,203

0

null

[ { "alpha_fraction": 0.5988826751708984, "alphanum_fraction": 0.6067039370536804, "avg_line_length": 33.71844482421875, "blob_id": "659e94f3eed294d7a3337bf8322a040da0fb80ba", "content_id": "d12c84fa4750f9c5d8137be9edfb937360f75c00", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3580, "license_type": "no_license", "max_line_length": 96, "num_lines": 103, "path": "/amazonImageChain.py", "repo_name": "leoneckert/people-also-bought", "src_encoding": "UTF-8", "text": "from selenium import webdriver\nimport time, os, sys, random, urllib\n# import random\n# import ConfigParser\n# from bs4 import BeautifulSoup\n# import urllib\n# from selenium.webdriver.support.ui import WebDriverWait # available since 2.4.0\n\ndriver = None\noutput_dir = None\n\ndef type_word(input_elem, word):\n for letter in word:\n input_elem.send_keys(letter)\n # time.sleep(0.1)\n\ndef open_amazon():\n global driver\n driver = webdriver.Firefox()\n url = \"https://amazon.com\"\n driver.get(url)\n\ndef make_output_dir(word):\n global output_dir\n ts = int(time.time())\n output_dir = str(ts) + \"_\" + word\n if not os.path.isdir(output_dir):\n os.mkdir(output_dir)\n\ndef navigate_to_first_item(word):\n global driver\n search_element = driver.find_element_by_css_selector(\"#twotabsearchtextbox\")\n type_word(search_element, word) \n search_button = driver.find_element_by_css_selector(\"div.nav-search-submit input.nav-input\")\n search_button.click()\n time.sleep(2)\n results = driver.find_elements_by_css_selector(\"a.s-access-detail-page\")\n random_result = random.choice(results)\n random_result.click()\n\ndef get_main_image(count):\n global driver\n global output_dir\n # image = None\n # image = driver.find_element_by_css_selector(\"img#landingImage\")\n # url = image.get_attribute(\"src\")\n # urllib.urlretrieve(url, output_dir + \"/\" + \"{:06d}\".format(count) + \".jpg\")\n try:\n image = driver.find_element_by_css_selector(\"img#landingImage\")\n url = image.get_attribute(\"src\")\n urllib.urlretrieve(url, output_dir + \"/\" + \"{:06d}\".format(count) + \".jpg\")\n except:\n print \"landingImage didnt work\"\n try:\n image = driver.find_element_by_css_selector(\"img#landingimage\")\n url = image.get_attribute(\"src\")\n urllib.urlretrieve(url, output_dir + \"/\" + \"{:06d}\".format(count) + \".jpg\")\n except:\n print \"landingimage didnt work\"\n try:\n image = driver.find_element_by_css_selector(\"img#imgBlkFront\")\n url = image.get_attribute(\"src\")\n urllib.urlretrieve(url, output_dir + \"/\" + \"{:06d}\".format(count) + \".jpg\")\n except:\n print \"imgBlkFront didnt work\"\n\ndef open_next_item():\n global driver\n the_link = None\n headlines = driver.find_elements_by_css_selector(\"h2.a-carousel-heading\")\n for headline in headlines:\n if headline.text == \"Customers Who Bought This Item Also Bought\":\n # if headline.text == \"Sponsored Products Related To This Item\":\n parent = headline.find_element_by_xpath('./..')\n items = parent.find_elements_by_css_selector(\"li.a-carousel-card\")\n while len(items) == 0:\n parent = parent.find_element_by_xpath('./..')\n items = parent.find_elements_by_css_selector(\"li.a-carousel-card\")\n print \"in while loop\"\n random_item = random.choice(items)\n the_link = random_item.find_element_by_css_selector(\"a.a-link-normal\")\n break\n print the_link\n driver.get(the_link.get_attribute(\"href\"))\n\nif __name__ == \"__main__\":\n open_amazon()\n time.sleep(2)\n query = sys.argv[1]\n make_output_dir(query)\n navigate_to_first_item(query)\n time.sleep(5)\n get_main_image(0)\n time.sleep(2)\n count = 1\n while True:\n open_next_item()\n time.sleep(2)\n get_main_image(count)\n time.sleep(2)\n count += 1\n # if count > 10:\n # break\n\n\n\n\n" }, { "alpha_fraction": 0.761904776096344, "alphanum_fraction": 0.761904776096344, "avg_line_length": 20, "blob_id": "4b6bec773e3ed97a8370e495678e4b5865047d50", "content_id": "6993d9e051d276fce51bc45b775e43812f544880", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 21, "license_type": "no_license", "max_line_length": 20, "num_lines": 1, "path": "/README.md", "repo_name": "leoneckert/people-also-bought", "src_encoding": "UTF-8", "text": "# people-also-bought\n" } ]

2

kaiwei12014/git-test

https://github.com/kaiwei12014/git-test

9577d23fcaa6f4949794b6433df88c0612e15035

2045409b639c9d72e12ce72b7d24268104d5032e

36d0020eca917acbf02d064aaabc019c7f559a61

refs/heads/master

2020-03-14T22:44:12.046763

2018-05-09T08:37:40

131,827,810

0

null

[ { "alpha_fraction": 0.7419354915618896, "alphanum_fraction": 0.7419354915618896, "avg_line_length": 14.5, "blob_id": "1ce18459e9f0dc987554af9674cef230f1506c02", "content_id": "62f4efc48679cfa071f10382bef3acaa73e1e0bc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 31, "license_type": "no_license", "max_line_length": 20, "num_lines": 2, "path": "/hello.py", "repo_name": "kaiwei12014/git-test", "src_encoding": "UTF-8", "text": "print('hello world')\nhi python\n" } ]

1

wenyaxinluoyang/statistical-learning-methods

https://github.com/wenyaxinluoyang/statistical-learning-methods

9864a33ff6edcbe094a47998174da2d9623e86d2

a417921d0ea3e9596b58092b0cea270de5dfee78

0f119f7189538ddf8434311047d00021d252561c

refs/heads/master

2020-09-07T01:08:54.989708

2020-03-06T10:25:35

220,611,134

2

0

null

[ { "alpha_fraction": 0.5364238619804382, "alphanum_fraction": 0.5492173433303833, "avg_line_length": 33.78010559082031, "blob_id": "2c832a8bfbcf3df01aeac68d73133adfdadbea9a", "content_id": "fefb1edfe794b5738731e6c8e8732aededce9281", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7398, "license_type": "no_license", "max_line_length": 108, "num_lines": 191, "path": "/chapter_five/CART.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\nimport pickle\n\n'''\n分类与回归树 (classification and regression tree)\n是二叉树\n回归树：用平方误差最小化准则\n分类树：用基尼指数最小化准则\n基尼指数：\nsum(pk * (1-pk) = 1 - sum(pk*pk)\n某个样本点被错误分类的概率.\n样本点属于第k类的概率为pk，它被分错的概率为(1-pk)\n对于二类分类问题，若样本点属于第1个类的概率为p\nGini = p*(1-p) + (1-p)*[1-(1-p)] = p*(1-p) + p*(1-p) = 2*p*(1-p)\n'''\n\nimport math\nimport pandas as pd\nimport numpy as np\n\nINF = float('inf')\n\nclass Node:\n def __init__(self, fea_name, split_value):\n self.fea_name = fea_name\n self.split_value = split_value\n self.left = None\n self.right = None\n self.output = None\n\n# 平方误差\ndef square(value1, value2):\n return math.pow((value1-value2),2 )\n\n# 构建回归树, 特征是连续型变量，输出也是连续型变量\ndef build_regression_tree(df, target, thresold):\n if df is None: return None\n if df.empty: return None\n # 样本量小于某个数的时候停止分裂\n if df.shape[0]<=thresold: return None\n columns = df.columns.values.tolist()\n columns.remove(target)\n if columns is None: return None\n min_square_loss = INF # 最小平方损失\n split_col = None # 切分变量\n split_value = None # 切分值\n left_data = None # 左训练集\n right_data = None # 右训练集\n for col in columns:\n values = df[col].values.tolist()\n length = len(values)\n target_values = df[target].values.tolist()\n if len(list(set(values)))==1: continue\n for value in list(set(values)):\n left_target = [target_values[i] for i in range(length) if values[i]<=value]\n right_target = [target_values[i] for i in range(length) if values[i]>value]\n if len(left_target)==0 or len(right_target)==0: continue\n aver1 = np.mean(left_target)\n aver2 = np.mean(right_target)\n sum1 = sum2 = 0\n for y in left_target: sum1 += square(y, aver1)\n for y in right_target: sum2 += square(y, aver2)\n if sum1 + sum2 < min_square_loss:\n split_col = col\n split_value = value\n left_data = df[df[col] <= value]\n right_data = df[df[col] > value]\n if split_col is None: return None\n tree = Node(split_col, split_value)\n print(split_col, split_value)\n # 递归构建左右子树\n tree.output = np.average(df[target].values.tolist())\n tree.left = build_regression_tree(left_data, target, thresold)\n tree.right = build_regression_tree(right_data, target, thresold)\n return tree\n\n\n# 构建分类树, 特征是离散的\ndef build_class_tree(df, set_x, target):\n if df is None: return None\n if df.empty: return None\n # 如果某个数据集中，所有分类都为同一分类，则不需再划分,因为对于任何特征，其基尼系数都是0\n target_values = df[target].values.tolist()\n if len(set(target_values)) == 1:\n node = Node('', '')\n node.output = target_values[0]\n return node\n columns = df.columns.values.tolist()\n columns.remove(target)\n min_gini = INF\n fea_name = None\n split_value = None\n left_df = None\n right_df = None\n for col in columns:\n value_of_col = set_x[col]\n values = df[col].values.tolist()\n if len(list(set(values))) == 1:\n continue\n # 枚举特征col可取的每一个值\n for value in value_of_col:\n df1 = df[df[col] == value]\n df2 = df[df[col] != value]\n fm1 = df1.shape[0]*df1.shape[0]\n fm2 = df2.shape[0]*df2.shape[0]\n temp1 = df1[target].value_counts().reset_index()\n temp2 = df2[target].value_counts().reset_index()\n sum = 0\n for _, count in zip(temp1['index'].values.tolist(), temp1[target].values.tolist()):\n sum += (count*count)/fm1\n gini1 = 1 - sum\n sum = 0\n for _, count in zip(temp2['index'].values.tolist(), temp2[target].values.tolist()):\n sum += (count*count)/fm2\n gini2 = 1 - sum\n gini = df1.shape[0]/df.shape[0]*gini1 + df2.shape[0]/df.shape[0]*gini2\n #print(f'特征值={col}; 取值={value}; 基尼指数={gini_df_col}')\n if gini < min_gini:\n min_gini = gini\n fea_name = col\n split_value = value\n left_df = df1\n right_df = df2\n if fea_name is None: return None\n tree = Node(fea_name, split_value)\n tree.output = target_counts = df[target].value_counts()[0]\n tree.left = build_class_tree(left_df, set_x, target)\n tree.right = build_class_tree(right_df, set_x, target)\n return tree\n\ndef get_data():\n set_x = {\n \"age\":('青年','中年', '老年'),\n \"have_job\":('是', '否'),\n \"have_house\":('是', '否'),\n \"credit_detail\":('一般', '好', '非常好')}\n set_y = ['是', '否']\n age = ['青年','青年','青年','青年','青年', '中年', '中年', '中年', '中年', '中年', '老年', '老年', '老年', '老年', '老年']\n have_job = ['否', '否', '是', '是', '否', '否', '否', '是', '否', '否', '否', '否', '是', '是', '否']\n have_house = ['否', '否', '否', '是', '否', '否', '否', '是', '是', '是', '是', '是', '否', '否', '否']\n credit_detail = ['一般', '好', '好', '一般', '一般', '一般', '好', '好', '非常好', '非常好', '非常好', '好', '好', '非常好', '一般']\n y = ['否', '否', '是', '是', '否', '否', '否', '是', '是', '是', '是', '是', '是', '是', '否']\n df = pd.DataFrame()\n df['age'] = age\n df['have_job'] = have_job\n df['have_house'] = have_house\n df['credit_detail'] = credit_detail\n df['Y'] = y\n return df, set_x, set_y\n\ndef display(tree, level=1):\n if tree is not None:\n print(f'特征名:{tree.fea_name} 特征划分值:{tree.split_value} 最大分类:{tree.output} 层次:{level}')\n if tree.left is not None:\n display(tree.left, level+1)\n if tree.right is not None:\n display(tree.right, level+1)\n\ndef regression_predict(test_data, root):\n predict_target = []\n for index, row in test_data.iterrows():\n temp = root\n output = 0\n while temp is not None:\n value = row[temp.fea_name]\n output = temp.output\n if value <= temp.split_value: temp = temp.left\n else: temp = temp.right\n predict_target.append(output)\n test_data['predict_target'] = predict_target\n return test_data\n\n\nif __name__ == '__main__':\n df, set_x, set_y = get_data()\n # train_data, test_data = get_data2()\n # tree = build_regression_tree(train_data, target='pm2.5', thresold=50)\n # print('build tree success')\n # f = open('./data/model.pkl', 'wb')\n # pickle.dump(tree, f)\n # display(tree)\n # f = open('./data/model.pkl', 'rb')\n # tree = pickle.load(f)\n # result = regression_predict(test_data, tree)\n # result = regression_predict(test_data, tree)\n # result.to_csv('result.csv', index=False)\n # result.to_csv('./data/result2.csv')\n #df, set_x, set_y = get_data()\n tree = build_class_tree(df, set_x, 'Y')\n display(tree)\n\n" }, { "alpha_fraction": 0.5418760180473328, "alphanum_fraction": 0.5690954923629761, "avg_line_length": 21.349056243896484, "blob_id": "02227cf129cd10d136e6f38c1542f31ba264e01f", "content_id": "ef1053e06090d888d91daebd8016d1561814ca2a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3136, "license_type": "no_license", "max_line_length": 70, "num_lines": 106, "path": "/chapter_two/Perceptron.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\nimport numpy as np\nimport random\n'''\n李航：《统计学习方法》第二章数据理论的代码实现\n感知机学习算法的原始形式\n\n模型模型:\nyi = sign(w*xi + b)\nsign(t) t>0, sign(t)=1; t<0, sign(t)=-1\n解决方案：\nw*x + b = 0\n找到一个超平面，把正实例点和负实例点划分到超平面两侧使得\ny(i)=+1 w*x(i)+b>0\ny(i)=-1 w*x(i)+b<0\n\n损失函数，误分类点到超平面的距离\n对误分类的点来说，-y(i)(w*x(i)+b) > 0 \n因此误分类点xi到超平面的距离是\n-1/|w|*y(i)*(w*x(i))+b)\n\nM是误分类点的集合\nsign(w*x + b)的损失函数为\nxi属于M\nyi取值-1,1 \nL(w,b) = -SUM(yi* (w*xi + b)) \n\n损失函数中w,b未知，我们要找到使损失函数最小的模型参数w,b，即感知机的模型\n\n使用梯度下降不断地极小化目标函数，极小话过程中不是一次使M中所有误分类点的梯度下降，\n而是一次随机选取一个误分类点使其梯度下降：\n对w求偏导： -SUM(yixi)\n对b求偏导： -SUM(yi)\n\n设学习速率为learning_rate（0，1], 随机选择\nw + learning*yi*xi) -> w\nb + learning*yi) -> b\n'''\n\n# f(x) = sign(x) x>0返回1，x<0返回-1\ndef sign(value):\n if value>0: return 1\n elif value<0: return -1\n\n# 确定参数后，就有模型函数\ndef func(x, w, b):\n return sign(np.dot(w.T,x) + b)\n\n# 求解最佳模型参数\ndef perceptron(x_list, y_list, learning_rate, w, b):\n # w, b = init_params()\n # 前提是训练集线性可分，否则感知机算法不收敛\n count = 0\n while True:\n error_x_list, error_y_list = error_point(x_list, y_list, w, b)\n if len(error_x_list) == 0:\n break\n # 进行梯度下降算法\n w, b = SGD(learning_rate, error_x_list, error_y_list, w, b)\n count = count + 1\n print('第', count , '次梯度下降: w =', w, ', b =', b)\n return w,b\n\n# 获取当前模型的误分类点\ndef error_point(x_list, y_list, w, b):\n y_predict_list = []\n for x in x_list:\n y_predict_list.append(func(x, w, b))\n error_x_list = []\n error_y_list = []\n for x, y, y_predict in zip(x_list, y_list, y_predict_list):\n if y != y_predict:\n error_x_list.append(x)\n error_y_list.append(y)\n return error_x_list, error_y_list\n\n# 梯度下降, 随机选取一个误判点进行梯度下降\ndef SGD(learning_rate, error_x_list, error_y_list, w, b):\n index = random.randint(0, len(error_x_list)-1)\n x = error_x_list[index]\n y = error_y_list[index]\n w = w + learning_rate*y*x\n b = b + learning_rate*y\n return w,b\n\nif __name__ == '__main__':\n x1 = np.array([3,3]).reshape(2,1)\n x2 = np.array([4,3]).reshape(2,1)\n x3 = np.array([1,1]).reshape(2,1)\n x_list = [x1, x2, x3]\n y_list = [1, 1, -1]\n w = np.zeros((2,1))\n b = 0\n learning_rate = 1\n w, b = perceptron(x_list, y_list, learning_rate, w, b)\n print(w, b)\n\n\n'''\n算法的解有： \nw = (1,1) b = -3\nw = (2,1) b = -5\nw = (1,0) b = -2\nw = (3,1) b = -5\n感知机学习算法由于采用不同的初值或选择不同的误分类点，解可以不同\n'''\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.6157380938529968, "alphanum_fraction": 0.6214818954467773, "avg_line_length": 27.557376861572266, "blob_id": "1076499703e3d25bcf85f6bb02177eb0e1e4a57c", "content_id": "f8b717648a9922a6501e5610646feafa45a317fc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1861, "license_type": "no_license", "max_line_length": 114, "num_lines": 61, "path": "/chapter_eight/AdaBoost.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "import math\nimport numpy as np\n\n\n# 基础分类器\ndef basic_classify_pred(am_list, feature):\n pass\n\n\n# 分类误差率\ndef classify_missing_rate(classifier, weights, fea_mat, label_mat, am_list):\n summary = 0\n row_num, col_num = np.shape(fea_mat)\n for i in range(row_num):\n is_equal = 1 if basic_classify_pred(am_list, fea_mat[i]) != label_mat[i] else 0\n summary += weights[i] * is_equal\n return summary\n\n# 计算基础分类器的系数\ndef get_classify_coefficient(em):\n am = 0.5*math.log((1-em)/em)\n return am\n\n# 计算规范化因子\ndef get_normal_factor(classifier, weights, am, fea_mat, label_mat, am_list):\n zm = 0\n row_num, col_num = np.shape(fea_mat)\n for i in range(row_num):\n zm += weights[i] * math.exp(-am * label_mat[i] * basic_classify_pred(am_list, fea_mat[i]))\n return zm\n\n# 更新权重\ndef update_weight(classifier, weights, em, am, zm, fea_mat, label_mat, am_list):\n length = len(weights)\n new_weights = []\n for i in range(length):\n new_weights[i] = (weights[i]/zm) * math.exp(-am * label_mat[i] * basic_classify_pred(am_list, fea_mat[i]))\n weights = new_weights\n return new_weights,\n\n\n# AdaBoost算法分类\ndef AdaBoost_Classify(fea_mat, label_mat, m):\n row_num, col_num = np.shape(fea_mat)\n # 初始化训练数据的权重分布，开始每个样本的权重都一样\n weights = np.array([1/row_num]*row_num)\n cnt = 0\n am_list = []\n while cnt < m:\n em = classify_missing_rate(None, weights, fea_mat, label_mat, am_list)\n am = get_classify_coefficient(em)\n am_list.append(am)\n zm = get_normal_factor(None, weights, am, fea_mat, label_mat, am_list)\n weights = update_weight(None, weights, em, am, zm, fea_mat, label_mat, am_list)\n cnt += 1\n return am_list\n\n\n\nif __name__ == '__main__':\n pass" }, { "alpha_fraction": 0.5523401498794556, "alphanum_fraction": 0.5707395672798157, "avg_line_length": 31.160919189453125, "blob_id": "125e7e04e2b9139023de6d011b8cd56c06aabaf6", "content_id": "22b07ed35dd0b991043e486013cbc2dbf33bf296", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6200, "license_type": "no_license", "max_line_length": 75, "num_lines": 174, "path": "/chapter_three/KdTree.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\n'''\n实现kd树算法\n'''\n\nimport numpy as np\nimport math\nimport copy\n\nINF = float('inf') # 无穷大\n\n# 节点，里面应该有属于它的样本点，左孩子，右孩子\nclass Node:\n def __init__(self, point, split_value, dimension_index):\n self.point = point\n self.split_value = split_value\n self.dimension_index = dimension_index\n self.left = None\n self.right = None\n\n# 递归构建kd树\ndef build_kd_tree(x_list, k, depth=0):\n if len(x_list)==0: return None\n dimension_index = depth%k # 维度索引\n x_list.sort(key=lambda x: x[dimension_index][0])\n split_index = len(x_list)//2 # 切分点索引\n point = x_list[split_index] # 切分点\n split_value = point[dimension_index][0] # 切分值\n left_list = x_list[0: split_index]\n right_list = x_list[split_index+1: len(x_list)]\n root = Node(point, split_value, dimension_index) # 创建根节点, 随后递归构建左右子树\n root.left = build_kd_tree(left_list, k, depth+1)\n root.right = build_kd_tree(right_list, k, depth+1)\n return root # 返回当前根节点\n\n# 打印二叉树\ndef display(root, level=0, note='root'):\n if root is None: return\n print('level =', level, 'note =', note)\n print('对第', root.dimension_index, '维，以该坐标轴的中位数为分点，以垂直与该维，并过切分点的超平面做划分')\n print('切分值为:', root.split_value)\n print('落在该超平面上的点有:')\n point = root.point\n print(point)\n print('*'*50)\n display(root.left, level+1, note='left') # 打印左子树\n display(root.right, level+1, note='right') # 打印右子树\n\n# p>=1，当p=2，称为欧氏距离\n# p=1, 称为曼哈顿距离\n# p趋于正无穷, 它氏各个坐标距离的最大值\ndef distance(x1, x2, p):\n sum = 0\n for vector1, vector2 in zip(x1, x2):\n v1 = vector1[0]\n v2 = vector2[0]\n sum += math.pow(abs(v1-v2), p)\n return math.pow(sum, 1/p) # 开p次方根\n\n\n# 寻找目标叶子节点\ndef find_target_leaf_node(root, target):\n nearest_dis, nearest_node = INF, None\n node = copy.deepcopy(root)\n path = []\n while node is not None:\n path.append(node)\n point = node.point # 切分空间的超平面上的点\n dis = distance(point, target, 2)\n if dis < nearest_dis:\n nearest_dis = dis\n nearest_node = node\n dimension_index = node.dimension_index\n split_value = node.split_value\n if target[dimension_index][0] <= split_value:\n node = node.left\n else:\n node = node.right\n return nearest_dis, nearest_node, path\n\n\ndef find_target_leaf_node_two(root, target, k=1):\n k_nearest = [(distance(root.point, target, 2), root)]\n node = copy.deepcopy(root)\n path = []\n while node is not None:\n path.append(node)\n k_nearest.sort(key=lambda dis: dis[0]) # 按照距离从小到大排序\n point = node.point\n dis = distance(point, target, 2)\n if dis < k_nearest[-1][0]:\n if len(k_nearest)<k: k_nearest.append((dis, node))\n else:\n k_nearest.pop()\n k_nearest.append((dis, node))\n dimension_index = node.dimension_index\n split_value = node.split_value\n if target[dimension_index][0]<= split_value:\n node = node.left\n else:\n node = node.right\n return k_nearest, path\n\n# kd树搜索，搜索最近邻\ndef kd_tree_search_nearest(root, target):\n # 先找到目标叶子节点\n nearest_dis, nearest_node, path = find_target_leaf_node(root, target)\n # 回溯\n while len(path)!=0:\n node = path.pop()\n split_value = node.split_value\n dimension_index = node.dimension_index\n # 目标点与当前最近点距离的半径的圆与其另一边的子空间有交点\n if abs(target[dimension_index][0]-split_value) < nearest_dis:\n # 原来进入的是左子树，则需要搜索右子树\n if target[dimension_index][0]<=split_value:\n temp_node = node.right\n else:\n temp_node = node.left\n if temp_node is not None:\n path.append(temp_node)\n dis = distance(target, temp_node.point, 2)\n if dis < nearest_dis:\n nearest_dis = dis\n nearest_node = temp_node\n return nearest_dis, nearest_node\n\n# 寻找target的k近邻, 比最近邻多了一个排序数组\ndef kd_tree_search_knearest(root, target, k):\n k_nearest, path = find_target_leaf_node_two(root, target, k)\n while(len(path)) != 0:\n node = path.pop()\n split_value = node.split_value\n dimension_index = node.dimension_index\n k_nearest.sort(key=lambda dis: dis[0])\n if abs(target[dimension_index][0]-split_value) < k_nearest[-1][0]:\n if target[dimension_index][0]<=split_value:\n temp_node = node.right\n else:\n temp_node = node.left\n if temp_node is not None:\n path.append(temp_node)\n dis = distance(target, temp_node.point, 2)\n if dis < k_nearest[-1][0]:\n if len(k_nearest)<k: k_nearest.append((dis, temp_node))\n else:\n k_nearest.pop()\n k_nearest.append((dis, temp_node))\n return k_nearest\n\n# 获取例3.2数据\ndef example_three_two():\n x1 = np.array([2,3]).reshape((2,1))\n x2 = np.array([5,4]).reshape((2,1))\n x3 = np.array([9,6]).reshape((2,1))\n x4 = np.array([4,7]).reshape((2,1))\n x5 = np.array([8,1]).reshape((2,1))\n x6 = np.array([7,2]).reshape((2,1))\n x_list = [x1, x2, x3, x4, x5, x6]\n return x_list\n\n\nif __name__ == '__main__':\n x_list = example_three_two()\n root = build_kd_tree(x_list, k=2)\n # display(root)\n target = np.array([1,5]).reshape((2,1))\n k_nearest = kd_tree_search_knearest(root, target, k=3)\n # 求距离target最近的前k个点\n for item in k_nearest:\n print('distance:', item[0])\n print('point:', item[1].point)\n print('-'*50)\n\n\n" }, { "alpha_fraction": 0.5549180507659912, "alphanum_fraction": 0.5699453353881836, "avg_line_length": 33.00934600830078, "blob_id": "7dbb40c90b995b0592e7d73dd27be66256cf5050", "content_id": "f2a98e4569c85013f98ad4b3f25f22e6caed53e2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4144, "license_type": "no_license", "max_line_length": 103, "num_lines": 107, "path": "/chapter_four/naive_bayes.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\nimport pandas as pd\nimport numpy as np\n'''\n朴素贝叶斯算法\n\nP(A)是 A 的先验概率，之所以称为“先验”是因为它不考虑任何 B 方面的因素。\nP(A|B)是已知 B 发生后 A 的条件概率，也由于得自 B 的取值而被称作 A 的后验概率。\nP(B|A)是已知 A 发生后 B 的条件概率，也由于得自 A 的取值而被称作 B 的后验概率。\nP(B)是 B 的先验概率，也作标淮化常量（normalizing constant）\n'''\n\n# probability\n# 计算先验概率\n# lambda_value 为 0的时候是极大似然估计，为1时是拉普拉斯平滑\ndef proba_of_class(y_list, set_y, lambda_value):\n total = len(y_list)\n count_of_class = {key:0 for key in set_y}\n for y in y_list:\n count_of_class[y] = count_of_class[y] + 1\n probability_of_class = {}\n for value, count in count_of_class.items():\n probability_of_class[value] = (count+lambda_value)/(total+len(set_y)*lambda_value)\n return count_of_class, probability_of_class\n\n\n# 计算条件概率, 在y=ck时，特征向量的第i个特征有j个取值\n# 计算先验概率和条件概率\n# set_x 每维特征的取值集合\ndef condition_proba(df, set_x, set_y, lambda_value):\n # 在分类为c的条件下，计算第j个特征取每个值的概率\n count_of_class, probability_of_class = proba_of_class(df['Y'].values.tolist(), set_y, lambda_value)\n columns = df.columns.values.tolist()\n columns.remove('Y')\n condition_proba_dict = {y: [] for y in set_y}\n for class_name in set_y:\n # 把分类为class_name的特征向量聚合在一起\n data = df[df.Y == class_name]\n array = []\n for col_id, column in enumerate(columns):\n proba = dict()\n value_count = {value:0 for value in set_x[col_id]}\n values = data[column].value_counts()\n values = values.reset_index()\n values['index'] = values['index'].astype(type(set_x[col_id][0]))\n total = 0\n for _, row in values.iterrows():\n value = row['index']\n count = row[column]\n total = total + count\n value_count[value] = count\n for value, count in value_count.items():\n proba[value] = (count + lambda_value)/(total+len(set_x[col_id])*lambda_value)\n array.append(proba)\n condition_proba_dict[class_name] = array\n return probability_of_class, condition_proba_dict\n\n\n# 预测\ndef predict(x, set_x, probability_of_class, condition_proba_dict):\n class_list = list(probability_of_class.keys())\n target = None\n max_proba = 0\n for c in class_list:\n mul = 1\n for i, value in enumerate(x):\n proba = condition_proba_dict[c][i]\n mul = mul * proba[value]\n mul = mul * probability_of_class[c]\n print(mul)\n if mul > max_proba:\n max_proba = mul\n target = c\n return max_proba, target\n\n\n# 书本例4.1数据\ndef get_data():\n df = pd.DataFrame()\n df['X1'] = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3]\n df['X2'] = ['S', 'M', 'M', 'S', 'S', 'S', 'M', 'M', 'L', 'L', 'L', 'M', 'M', 'L', 'L']\n df['Y'] = [-1,-1,1,1,-1,-1,-1,1,1,1,1,1,1,1,-1]\n return df\n\n\n\nif __name__ == '__main__':\n df = get_data()\n set_x = [(1,2,3), ('S', 'M', 'L')]\n set_y = [-1, 1]\n lambda_value = 1\n probability_of_class, condition_proba_dict = condition_proba(df, set_x, set_y, lambda_value)\n for c in set_y:\n print(f'P(Y={c}) =', probability_of_class[c])\n for c in set_y:\n proba_list = condition_proba_dict[c]\n for index,proba in enumerate(proba_list):\n for key,value in proba.items():\n print(f'在分类{c}下, 第{index+1}个特征，取值为{key}的概率为{value}')\n print('*'*20)\n print('-'*20)\n test_x = [2, 'S']\n print(predict(test_x, set_x, probability_of_class, condition_proba_dict))\n #condition_proba(x_list, y_list)\n # count_of_class, probability_of_class = proba_of_class(y_list)\n # print(count_of_class, probability_of_class)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5602409839630127, "alphanum_fraction": 0.5674698948860168, "avg_line_length": 19.268293380737305, "blob_id": "b2fdaa8db2d5b48af12ba9d284d63dbce1267284", "content_id": "09d1a05647a9bdb645f494e118eea9f9331cbb4f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 880, "license_type": "no_license", "max_line_length": 80, "num_lines": 41, "path": "/chapter_seven/Kernel.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "import numpy as np\nimport math\n\n# 核函数\nclass Kernel_Func():\n\n # 计算\n def calculate(self, x, z):\n pass\n\n # p代表范数\n def normalForm(x, y, p):\n dis_value = 0\n for x_value, y_value in zip(x, y):\n dis_value += math.pow(y_value - x_value, p)\n return math.pow(dis_value, 1 / p)\n\n# 多项式核函数\nclass Polynomial_Kernel_Func(Kernel_Func):\n\n def __init(self, p):\n self.__p = p\n\n def calculate(self, x, z,):\n return math.pow(x * z + 1, self.__p)\n\n\n# 高斯核函数\nclass Gaussian_Kernel_Func(Kernel_Func):\n\n def __init__(self, sigma):\n self.__sigma = sigma\n\n def calculate(self, x, z):\n return math.exp(-self.normalForm(x, z, 2)/(2*self.__sigma*self.__sigma))\n\n# 线性核函数\nclass Linear_Kernel_Func(Kernel_Func):\n\n def calculate(self, x, z):\n return np.dot(x,z)[0]" }, { "alpha_fraction": 0.503125011920929, "alphanum_fraction": 0.5625, "avg_line_length": 16.66666603088379, "blob_id": "bdd415b0805bea7b784047d82f982b5daf1a084c", "content_id": "eb2ce8f4c099fa5cae6662f47b17a1712a163ebf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 402, "license_type": "no_license", "max_line_length": 40, "num_lines": 18, "path": "/chapter_three/KNN.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\n'''\nk近邻算法\n'''\n\nimport math\n\n# p>=1，当p=2，称为欧氏距离\n# p=1, 称为曼哈顿距离\n# p趋于正无穷, 它氏各个坐标距离的最大值\ndef distrance(self, x1, x2, p):\n sum = 0\n for vector1, vector2 in zip(x1, x2):\n v1 = vector1[0]\n v2 = vector2[0]\n sum += math.pow(abs(v1-v2), p)\n return math.pow(sum, 1/p) # 开p次方根\n\n\n" }, { "alpha_fraction": 0.5119877457618713, "alphanum_fraction": 0.5383438467979431, "avg_line_length": 35.53416061401367, "blob_id": "693d7838928ecc8efb95969cc6c4975625961310", "content_id": "8fcdcd2d2298c44b6486dbbaa5c200be5f4cf352", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6471, "license_type": "no_license", "max_line_length": 124, "num_lines": 161, "path": "/chapter_seven/SVM.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "import random\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport copy\nfrom chapter_seven.Kernel import *\n\n\n# 随机获取一个不等i的索引号\ndef get_index_random(i, size):\n j = i\n while i == j:\n j = random.randint(0, size-1)\n return j\n\n# 获取a2_new的上下界\ndef get_alpha_limit(a1_old, a2_old, y1, y2, c):\n if y1 != y2:\n low, high = max(0, a2_old - a1_old), min(c, c + a2_old - a1_old)\n else:\n low, high = max(0, a2_old + a1_old - c), min(c, a2_old + a1_old)\n return low, high\n\n# 其中g(x) = sum(ai*yi*K(xi,x)) + b\ndef func_g(alphas, b, fea_mat, label_list, index, kernel_func):\n label_mat = np.array(label_list).reshape(len(label_list), 1)\n alphas_mat = np.array(alphas).reshape(len(alphas), 1)\n temp = alphas_mat * label_mat # alphas_mat 和 label_mat均是列向量，此处向量temp为两向量对应位置想成\n array = []\n current_sample = fea_mat[index]\n for sample in fea_mat:\n t = kernel_func.calculate(current_sample, sample.reshape((-1, 1)))\n array.append(t)\n result = np.dot(np.array(array), temp)[0] + b\n return result\n\n# Ei = g(xi) - yi,\ndef get_e(alphas, b, fea_mat, label_list, index, kernel_func):\n result = func_g(alphas, b, fea_mat, label_list, index, kernel_func)\n return result - label_list[index]\n\n\n# 序列最小化算法(sequential minimal optimization, SMO)\ndef smo(fea_mat, label_mat, c=1, epsilon=1e-5, max_iter=20, kernel_func=Linear_Kernel_Func()):\n label_list = [label[0] for label in label_mat]\n row_num, col_num = fea_mat.shape\n # 初始化拉格朗日乘子\n alphas = np.zeros((row_num, 1)) # row_num 行，1列的数据\n # 初始化偏置\n b = 0\n iter_cnt = 0\n while iter_cnt < max_iter:\n # 外层循环代表选择第一个变量\n for i in range(row_num):\n gi = func_g(alphas, b, fea_mat, label_list, i, kernel_func)\n ei = gi - label_list[i]\n '''\n 选取的a1, 应该违反KKT条件（李航统计学习方法，说挑选违反KKT条件最严重的一个）\n 停机条件，停机：所有拉格朗日乘子满足KKT条件\n yi*g(xi) >= 1 , ai = 0\n yi*g(xi) = 1 , 0 < ai < c\n yi*g(xi) <= 1, ai = c\n 若ai < c, 则 0 <= ai < c, 若满足KKT条件则 yi*g(xi) >= 1, 考虑精度, yi*g(xi)>=1-epsilon\n 则需要违反KKT条件，则yi*g(xi)<1-epsilon, 则 yi*g(xi)-1 < -epsilon\n 若ai > 0, 则 0 < ai <=c, 若满足KKT条件则 yi*g(xi) <= 1, 考虑精度，yi*g(xi)<=1+epsilon\n 则需要违反KKT条件，则yi*g(xi)>1+epsilon, 则 yi*g(xi)-1 > epsilon\n '''\n if (label_list[i]*ei<-epsilon and alphas[i]<c) or (label_list[i]*ei>epsilon and alphas[i]>0):\n '''\n 挑选a2, 李航统计学习方法，说挑选 abs(e1 - e2)最大的，在这里是随机挑选的，\n 当然也可以计算所有的e, 然后选abs(e1 - e2)最大的作为a2\n '''\n j = get_index_random(i, row_num)\n gj = func_g(alphas, b, fea_mat, label_list, j, kernel_func)\n ej = gj - label_list[j]\n\n alpha_i_old = alphas[i]\n alpha_j_old = alphas[j]\n\n # 计算alpha_j_new的取值范围\n low, high = get_alpha_limit(alpha_i_old, alpha_j_old, label_list[i], label_list[j], c)\n\n kii = kernel_func.calculate(fea_mat[i], fea_mat[i].reshape((-1,1)))\n kij = kernel_func.calculate(fea_mat[i], fea_mat[j].reshape((-1,1)))\n kjj = kernel_func.calculate(fea_mat[j], fea_mat[j].reshape((-1,1)))\n\n alpha_j_new = alpha_j_old + label_list[j]*(ei - ej)/(kii + kjj + 2*kij)\n if alpha_j_new < low: alpha_j_new = low\n if alpha_j_new > high: alpha_j_new = high\n\n if abs(alpha_j_new - alpha_j_old) < 1e-6: continue\n\n alpha_i_new = alpha_i_old + label_list[i]*label_list[j]*(alpha_j_old - alpha_j_new)\n\n\n # 更新两个拉格朗日乘子\n alphas[i] = alpha_i_new\n alphas[j] = alpha_j_new\n\n # 更新b的值\n bi_new = -ei - label_list[i]*kii*(alpha_i_new-alpha_i_old) - label_list[j]*kij*(alpha_j_new-alpha_j_old) + b\n bj_new = -ej - label_list[i]*kij*(alpha_i_new-alpha_i_old) - label_list[j]*kjj*(alpha_j_new-alpha_j_old) + b\n if 0<alpha_i_new<c and 0<alpha_j_new<c:\n b = bi_new\n else:\n b = (bi_new + bj_new)/2\n iter_cnt += 1\n # 由w* = sum(ai*yi*xi)\n w = np.zeros(fea_mat[0].shape) # 行向量\n for i in range(row_num):\n w += alphas[i]*label_list[i]*fea_mat[i]\n # 转为列向量\n w = w.reshape((-1,1))\n return alphas, w, b\n\n\n# 数据集1，线性可分\ndef get_data1():\n # 创建40个点\n fea_mat = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]\n label_mat = np.array([-1]*20 + [1]*20).reshape((40, 1))\n # plt.scatter(fea_mat[:, 0], fea_mat[:, 1], c = [label[0] for label in label_mat])\n # plt.show()\n return fea_mat, label_mat\n\n# 显示数据集1的效果\ndef show_effect1():\n fea_mat, label_mat = get_data1()\n alphas, w, b = smo(fea_mat, label_mat, max_iter=200)\n print(w)\n print(b)\n plt.scatter(fea_mat[:, 0], fea_mat[:, 1], c=[label[0] for label in label_mat])\n x_list = list(range(-5, 5, 1))\n y_list = [(-w[0][0] * x - b) / w[1][0] for x in x_list]\n plt.plot(x_list, y_list, label='分隔超平面', color='red')\n plt.show()\n\n# 数据集2，近似线性可分\ndef get_data2():\n fea_mat = np.r_[np.random.randn(20,2), np.random.randn(20,2) + [2,2]]\n label_mat = np.array([-1]*20 + [1]*20).reshape((-1, 1))\n # plt.scatter(fea_mat[:, 0], fea_mat[:, 1], c = [label[0] for label in label_mat])\n # plt.show()\n return fea_mat, label_mat\n\ndef show_effect2():\n fea_mat, label_mat = get_data2()\n alphas, w, b = smo(fea_mat, label_mat, max_iter=200)\n print(w)\n print(b)\n plt.scatter(fea_mat[:, 0], fea_mat[:, 1], c=[label[0] for label in label_mat])\n x_list = list(range(-5, 5, 1))\n y_list = [(-w[0][0] * x - b) / w[1][0] for x in x_list]\n plt.plot(x_list, y_list, label='分隔超平面', color='red')\n plt.show()\n\n\nif __name__ == '__main__':\n # show_effect1()\n # get_data2()\n show_effect2()" }, { "alpha_fraction": 0.5426052808761597, "alphanum_fraction": 0.5514201521873474, "avg_line_length": 21.173913955688477, "blob_id": "33566cca9f6474f44fdddfffdc005ec4b05b2b63", "content_id": "f6e448e5536641a88526a5676aaefd6725151132", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1113, "license_type": "no_license", "max_line_length": 56, "num_lines": 46, "path": "/chapter_one/loss_function.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\n'统计学常用的损失函数'\n\n\nclass ZeroOne:\n '''\n 0-1，实际值和预测值相等，返回1，否则返回0\n '''\n # 计算代价函数\n def cal_loss(self, y_actual, y_predict):\n cost = 0\n for actual, predict in zip(y_actual, y_predict):\n cost = self.cal(actual, predict)\n return cost\n\n def cal(self, actual, predict):\n if actual == predict:\n return 0\n else:\n return 1\n\nclass Quadratic:\n '''\n 平方损失函数\n '''\n def cal_loss(self, y_actual, y_predict):\n cost = 0\n for actual, predict in zip(y_actual, y_predict):\n cost += self.cal(actual, predict)\n return cost\n\n def cal(self, actual, predict):\n return (actual-predict)*(actual-predict)\n\nclass Absolute:\n '''\n 绝对损失函数\n '''\n def cal_loss(self, y_actual, y_predict):\n cost = 0\n for actual, predict in zip(y_actual, y_predict):\n cost = cost + self.cal(actual, predict)\n\n def cal(self, actual, predict):\n return abs(actual-predict)\n\n" }, { "alpha_fraction": 0.5271453857421875, "alphanum_fraction": 0.5487449169158936, "avg_line_length": 26.612903594970703, "blob_id": "b7cf50261284c961159311af5aefd943be3a8034", "content_id": "17f9a4c64c1f68cbce358e712ccb04ac492649e3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1853, "license_type": "no_license", "max_line_length": 85, "num_lines": 62, "path": "/chapter_two/DualPerceptron.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\nimport numpy as np\nimport random\n'''\n感知机学习算法的对偶形式\n考虑每个样本点更新的次数，那么现在次数变成我们的参数\n'''\n\ndef sign(value):\n if value>0: return 1\n elif value<0: return -1\n\n# 计算xj * xi 备用\ndef cal_gram_matrix(x_list):\n size = len(x_list)\n matrix = np.zeros((size, size))\n for i in range(size):\n for j in range(size):\n matrix[i][j] = np.dot(x_list[i].T, x_list[j])\n return matrix\n\ndef func(x_list, x_index, y_list, n_list, learning_rate, matrix):\n result = 0\n temp = 0\n size = len(x_list)\n for j in range(size):\n result = result + n_list[j]*learning_rate*y_list[j]*matrix[j][x_index]\n temp = temp + n_list[j]*learning_rate*y_list[j] # b的值\n result = result + temp\n return result\n\n# 感知机学习算法的对偶形式\ndef dual_perceptron(x_list, y_list, n_list, learning_rate, matrix):\n count = 0\n while True:\n size = len(x_list)\n flag = False # 标记是否存在误分类点\n for i in range(size):\n if y_list[i]*func(x_list, i, y_list, n_list, learning_rate, matrix) <= 0:\n n_list[i] += 1\n flag = True\n break\n if flag==False:\n break\n count = count + 1\n print('第', count, '次迭代:')\n print(n_list)\n print('-'*50)\n return n_list\n\nif __name__ == '__main__':\n x1 = np.array([3, 3]).reshape(2, 1)\n x2 = np.array([4, 3]).reshape(2, 1)\n x3 = np.array([1, 1]).reshape(2, 1)\n x_list = [x1, x2, x3]\n y_list = [1, 1, -1]\n n_list = [0, 0, 0]\n learning_rate = 1\n matrix = cal_gram_matrix(x_list)\n n_list = dual_perceptron(x_list, y_list, n_list, learning_rate, matrix)\n print('params (n_list:', n_list, ', learning_rate: ', learning_rate, ')')\n\n" }, { "alpha_fraction": 0.5398218035697937, "alphanum_fraction": 0.5476190447807312, "avg_line_length": 30.920000076293945, "blob_id": "8d963b74530b91d11d953b42a0d400b001348533", "content_id": "fc6661b71e6f47759a54f477c603ff969243b871", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7904, "license_type": "no_license", "max_line_length": 108, "num_lines": 225, "path": "/chapter_five/decision_tree.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\n'决策树'\nimport pandas as pd\nimport math\nimport copy\n\n\n# 树的节点\nclass Node():\n def __init__(self, sign, fea_name, fea_value, data):\n self.fea_name = fea_name\n self.sign = sign\n self.fea_value = fea_value\n self.child_list = []\n self.data = data\n\n\n\n# 信息增益\ndef igain(x_list, value_of_x, y_list, value_of_y, hy):\n return hy - condition_entropy(x_list, value_of_x, y_list, value_of_y)\n\n# 信息增益比\ndef igain_ratio(x_list, value_of_x, y_list, value_of_y, hy):\n h = condition_entropy(x_list, value_of_x, y_list, value_of_y)\n hx, value_count = entropy(x_list, value_of_x)\n return (hy-h)/hx\n\n# 计算条件熵\ndef condition_entropy(x_list, value_of_x, y_list, value_of_y):\n hx, value_count = entropy(x_list, value_of_x) # x的经验熵，以及x取各种值的计数\n temp = {x: [] for x in value_of_x} # 每种x的取值下，对应的y值\n for x,y in zip(x_list, y_list):\n temp[x].append(y)\n total_x = len(x_list)\n h = 0\n for x, y_belong_x in temp.items():\n # 计算X=x的条件下, y的经验熵\n hyx, value_count_y = entropy(y_belong_x, value_of_y)\n h = h + (value_count[x]/total_x)*hyx\n return h\n\n# 计算经验熵\ndef entropy(value_list, value_of_x):\n value_count = {value:0 for value in value_of_x}\n total = len(value_list)\n for value in value_list:\n value_count[value] = value_count[value] + 1\n h = 0\n for value in value_of_x:\n count = value_count[value]\n p = count/total\n if count == 0: continue\n else: h = h + p*math.log(p, 2)\n h = -h\n return h, value_count\n\n\n# 特征的信息增益比\ndef igain_of_fea(df, set_x, set_y):\n columns = df.columns.values.tolist()\n columns.remove('Y')\n # 每种特征的信息增益\n fea_infor_gain = {col:0 for col in columns}\n y_list = df['Y'].values.tolist()\n hy, value_count_y = entropy(y_list, set_y)\n for col in columns:\n fea_infor_gain[col] = igain(df[col].values.tolist(), set_x[col], y_list, set_y, hy)\n print(fea_infor_gain)\n print('Y的熵', hy)\n return fea_infor_gain\n\ndef igain_ratio_of_fea(df, set_x, set_y):\n columns = df.columns.values.tolist()\n columns.remove('Y')\n # 每种特征的信息增益\n fea_infor_ratio_gain = {col: 0 for col in columns}\n y_list = df['Y'].values.tolist()\n hy, value_count_y = entropy(y_list, set_y)\n for col in columns:\n fea_infor_ratio_gain[col] = igain_ratio(df[col].values.tolist(), set_x[col], y_list, set_y, hy)\n print(fea_infor_ratio_gain)\n print('Y的熵', hy)\n return fea_infor_ratio_gain\n\ndef get_data():\n set_x = {\n \"age\":('青年','中年', '老年'),\n \"have_job\":('是', '否'),\n \"have_house\":('是', '否'),\n \"credit_detail\":('一般', '好', '非常好')}\n set_y = ['是', '否']\n age = ['青年','青年','青年','青年','青年', '中年', '中年', '中年', '中年', '中年', '老年', '老年', '老年', '老年', '老年']\n have_job = ['否', '否', '是', '是', '否', '否', '否', '是', '否', '否', '否', '否', '是', '是', '否']\n have_house = ['否', '否', '否', '是', '否', '否', '否', '是', '是', '是', '是', '是', '否', '否', '否']\n credit_detail = ['一般', '好', '好', '一般', '一般', '一般', '好', '好', '非常好', '非常好', '非常好', '好', '好', '非常好', '一般']\n y = ['否', '否', '是', '是', '否', '否', '否', '是', '是', '是', '是', '是', '是', '是', '否']\n df = pd.DataFrame()\n df['age'] = age\n df['have_job'] = have_job\n df['have_house'] = have_house\n df['credit_detail'] = credit_detail\n df['Y'] = y\n return df, set_x, set_y\n\n\n# 使用ID3构建决策树\ndef ID3(df, set_x, set_y, threshold):\n if df.empty: return None\n y_unique = df['Y'].values.tolist()\n # 训练数据集所有实例都属于同一类，则为单节点树\n if len(set(y_unique)) == 1:\n tree = Node(y_unique[0], None, None, df)\n return tree\n # 特征集是空集\n feature_names = df.columns.values.tolist()\n feature_names.remove('Y')\n temp = df['Y'].value_counts()\n temp = temp.reset_index()\n max_count_y = temp.loc[0]['index']\n if len(feature_names) == 0:\n tree = Node(max_count_y, None, None, df)\n return tree\n fea_infor_gain = igain_of_fea(df, set_x, set_y)\n max_infor_gain = 0\n chose_fea = None\n for key,value in fea_infor_gain.items():\n if value > max_infor_gain:\n max_infor_gain = value\n chose_fea = key\n # 最大信息增益小于阈值\n if max_infor_gain < threshold:\n tree = Node(max_count_y, None, None, df)\n return tree\n tree = Node(max_count_y, chose_fea, None, df)\n for index, data in df.groupby([chose_fea]):\n sub_df = data.drop([chose_fea], axis=1)\n child = ID3(sub_df, set_x, set_y, threshold)\n child.fea_value = index\n tree.child_list.append(child)\n return tree\n\n\n# 使用C4.5算法构建生成树, 用信息增益比来选择特征\ndef C45(df, set_x, set_y, threshold):\n if df.empty: return None\n y_unique = df['Y'].values.tolist()\n # 训练数据集所有实例都属于同一类\n if len(set(y_unique)) == 1:\n tree = Node(y_unique[0], None, None, df)\n return tree\n feature_names = df.columns.values.tolist()\n feature_names.remove('Y')\n temp = df['Y'].value_counts()\n temp = temp.reset_index()\n max_count_y = temp.loc[0]['index']\n # 特征集是空集\n if len(feature_names) == 0:\n tree = Node(max_count_y, None, None, df)\n return tree\n fea_infor_ratio_gain = igain_ratio_of_fea(df, set_x, set_y)\n max_infor_gain = 0\n chose_fea = None\n for key, value in fea_infor_ratio_gain.items():\n if value > max_infor_gain:\n max_infor_gain = value\n chose_fea = key\n if max_infor_gain < threshold:\n tree = Node(max_count_y, None, None, df)\n return tree\n tree = Node(max_count_y, chose_fea, None, df)\n for index, data in df.groupby([chose_fea]):\n sub_df = data.drop([chose_fea], axis=1)\n child = C45(sub_df, set_x, set_y, threshold)\n child.fea_value = index\n tree.child_list.append(child)\n return tree\n\n\ndef display(tree, level=1):\n if tree is not None:\n print('第', level, '层')\n print('选取的特征是:', tree.fea_name)\n print('上一层特征值:', tree.fea_value)\n print('该特征下，最大的分类:', tree.sign)\n print('='*50)\n for child in tree.child_list:\n display(child, level+1)\n\n# 剪枝\ndef cut_branch(tree, alfa, set_y):\n if tree is not None:\n # 计算该节点的经验熵\n data = tree.data\n h, value_count = entropy(data['Y'].values.tolist(), set_y)\n result = data.shape[0]*h\n if len(tree.child_list) != 0:\n sum = 0\n leaf_node_num = 0\n for child in tree.child_list:\n value, num, node = cut_branch(child, alfa, set_y)\n sum += value\n leaf_node_num += num\n # sum为当前节点统领下的叶子节点的\n if result+alfa < sum+alfa*leaf_node_num:\n tree.child_list = [] # 进行减枝\n return result, 1, tree\n else:\n return sum, leaf_node_num, tree\n else: # 是叶子节点\n return result, 1, tree\n\n\nif __name__ == '__main__':\n df, set_x, set_y = get_data()\n igain_of_fea(df, set_x, set_y)\n igain_ratio_of_fea(df, set_x, set_y)\n tree = ID3(df, set_x, set_y, 0.001)\n #tree = C45(df, set_x, set_y, 0.001)\n display(tree)\n #cost, is_leaf, tree = cut_branch(tree, 0.5, set_y)\n #print('-'*10, '减枝后的树', '-'*10)\n #display(tree)\n # infor_gain_ratio_of_fea(df, set_x, set_y)\n" }, { "alpha_fraction": 0.5555555820465088, "alphanum_fraction": 0.5944444537162781, "avg_line_length": 19.05555534362793, "blob_id": "194179e1042aaadb4ab3e8ba8e949abf769211bc", "content_id": "dee2375fdeed78022ff49ac454e20529d1e66856", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 370, "license_type": "no_license", "max_line_length": 56, "num_lines": 18, "path": "/chapter_five/temp.py", "repo_name": "wenyaxinluoyang/statistical-learning-methods", "src_encoding": "UTF-8", "text": "import math\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# 绘制 f(x) = x*log(x)的曲线\n\ndef func(x):\n return -x*math.log(x, 2)\n\ndef draw_func():\n x_list = np.linspace(0.00001, 1, 200, endpoint=True)\n y_list = [func(x) for x in x_list]\n plt.plot(x_list, y_list, lw=2.0, label='xlog(x,2)')\n plt.show()\n\n\nif __name__ == '__main__':\n draw_func()" } ]

12

shan-mathi/InterviewBit

https://github.com/shan-mathi/InterviewBit

c94e091f728b9d18d55e86130756824a3637a744

6688e4ff54d56cf75297bb72ce67926b40e45127

f85cc3fb482f1b71e7a749e1bcdbe90ba78fd059

refs/heads/main

2023-06-29T10:43:29.712472

2021-08-05T19:06:53

364,321,855

0

null

[ { "alpha_fraction": 0.4197530746459961, "alphanum_fraction": 0.4444444477558136, "avg_line_length": 26.7391300201416, "blob_id": "9e9e8f259c284dc8b2e64d1212e494dbfe389e93", "content_id": "45dd20d6e5c8044a6c4c7606cd74e304f3da72bb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 648, "license_type": "no_license", "max_line_length": 62, "num_lines": 23, "path": "/array_peak.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @return an integer\n def perfectPeak(self, A):\n n = len(A)\n\n prefix = [0]*n\n suffix = [0]*n\n prefix[0] = A[0]\n suffix[-1]=A[-1]\n\n for i in range(1,n):\n prefix[i] = max(prefix[i-1], A[i])\n #print(prefix)\n \n for i in reversed(range(n-1)):\n suffix[i] = min(suffix[i+1], A[i])\n #print(suffix)\n for i in range(1,n-1):\n if prefix[i]==A[i] and prefix[i-1]!=prefix[i]:\n if A[i]==suffix[i] and suffix[i+1]!=suffix[i]:\n return 1\n return 0\n\n \n" }, { "alpha_fraction": 0.3187499940395355, "alphanum_fraction": 0.36666667461395264, "avg_line_length": 15.821428298950195, "blob_id": "597ce47377f8f3f29dc961d0f6a143f0584d63e1", "content_id": "8e5bb21fbf15606341d500e683753437e3b31cf8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 480, "license_type": "no_license", "max_line_length": 51, "num_lines": 28, "path": "/roman_to_integer.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\n\n#brute force\ndef roman_to_integer(n):\n\n ans=0\n\n hash = {'M':1000,\n 'D':500,\n 'C':100,\n 'L':50,\n 'X':10,\n 'V':5,\n 'I':1}\n i=0\n while( i< len(n)):\n if i+1<len(n) and hash[n[i+1]]> hash[n[i]]:\n ans+= hash[n[i+1]] - hash[n[i]]\n i+=2\n else:\n ans+= hash[n[i]]\n i+=1\n return ans\n\n\n\nn = str(input())\nprint(roman_to_integer(n))\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.430402934551239, "alphanum_fraction": 0.430402934551239, "avg_line_length": 27.526315689086914, "blob_id": "0bf5d528f32e389e9a4ab53e919eb7af88abb08b", "content_id": "2e412934e3f72da1a04c83e714129b675529d427", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 546, "license_type": "no_license", "max_line_length": 49, "num_lines": 19, "path": "/level_order tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": " def level_order(self):\n if self is None:\n return []\n queue = [root]\n next_queue=[]\n level=[]\n result = []\n while not queue:\n for root in queue:\n level.append(root.data)\n if root.left:\n next_queue.append(root.left)\n if root.right:\n next_queue.append(root.right)\n result.append(level)\n level=[]\n queue=next_queue[:]\n next_queue=[]\n return result\n" }, { "alpha_fraction": 0.4240986704826355, "alphanum_fraction": 0.42979127168655396, "avg_line_length": 22.863636016845703, "blob_id": "70fe8d48dde64e8cf5f44069e63a722625ff0141", "content_id": "7a2d4da4481d0decc3cb3cb6a11e521409fb8054", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1054, "license_type": "no_license", "max_line_length": 52, "num_lines": 44, "path": "/palindrome_partitions_BT.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def palindrom_part(s):\n ans=[]\n\n def all_pallindromes(A):\n \"\"\"\n using the most optimal method i.e O(sqrt(n))\n\n :param A: a number\n :return: list of all its factors\n \"\"\"\n ans = []\n for i in range(len(A)):\n for j in range(i + 1, len(A) + 1):\n if A[i:j] == A[i:j][::-1]:\n ans.append(A[i:j])\n return ans\n\n all = all_pallindromes(s)\n\n def back_track( subset=[], booll=[0]*len(all)):\n ss = \"\".join(subset)\n\n if ss ==s:\n if subset not in ans:\n ans.append(subset[:])\n return\n\n for i in range(len(all)):\n if not booll[i]:\n subset.append(all[i])\n booll[i]=1\n else:\n continue\n if len(\"\".join(subset))<=len(s):\n back_track(subset, booll)\n\n booll[i]=0\n subset.pop()\n back_track()\n return (ans)\n\nif __name__ ==\"__main__\":\n s = str(input())\n print(palindrom_part(s))\n\n\n\n\n" }, { "alpha_fraction": 0.40445268154144287, "alphanum_fraction": 0.41372913122177124, "avg_line_length": 24.66666603088379, "blob_id": "8ec66dd724af007b9a99d0e4725cf25f9401391b", "content_id": "10e47de734edfc5bac11f4c8334c532bfb639c72", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 539, "license_type": "no_license", "max_line_length": 36, "num_lines": 21, "path": "/version_master.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "#trick is to use split function\n\nclass Solution:\n # @param A : string\n # @param B : string\n # @return an integer\n def compareVersion(self, A, B):\n a = A.split('.')\n b = B.split('.')\n if len(a)>len(b):\n b+=['0']*(len(a)-len(b))\n else:\n a+=['0']*(len(b)-len(a))\n for i,j in zip(a,b):\n if int(i)==int(j):\n continue\n elif int(i)>int(j):\n return 1\n elif int(i)<int(j):\n return -1\n return 0\n" }, { "alpha_fraction": 0.5515947341918945, "alphanum_fraction": 0.5628517866134644, "avg_line_length": 18, "blob_id": "20c9ebce3238540676519c3a234bc5304b9692a9", "content_id": "21560e728058160a5b9ae872ce74d67e9e99132e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1066, "license_type": "no_license", "max_line_length": 61, "num_lines": 56, "path": "/threading_queue.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import deque\nimport time\nimport threading\n\nclass Queue():\n def __init__(self):\n self.Q = deque()\n def dequeue(self):\n return self.Q.pop()\n def enqueue(self,val):\n self.Q.appendleft(val)\n def size(self):\n return len(self.Q)\n def peek(self):\n return self.Q[-1]\n def is_empty(self):\n return len(self.Q)==0\n\nQ = Queue()\ndef Place_order(order):\n for order in orders:\n print(\"Placing order for:\",order)\n\n Q.enqueue(order)\n time.sleep(0.5)\n\ndef Serve_order():\n time.sleep(1)\n while not Q.is_empty():\n order = Q.dequeue()\n print(\"Now serving \",order)\n time.sleep(1)\n\n\n\n\n\n\nif __name__ == \"__main__\":\n t = time.time()\n orders = ['pizza','samosa','pasta','biryani','burger']\n\n T1 = threading.Thread(target=Place_order, args=(orders,))\n T2 = threading.Thread(target=Serve_order)\n\n\n\n T1.start()\n\n T2.start()\n\n T1.join()\n T2.join()\n\n print(\"done in : \", time.time() - t)\n print(\"Hah... I am done with all my work now!\")\n\n\n" }, { "alpha_fraction": 0.5994194746017456, "alphanum_fraction": 0.6037735939025879, "avg_line_length": 30.31818199157715, "blob_id": "140b91e6e41aa0ea2f1235651b30107fba87a9ce", "content_id": "08c9466ceef13c36e14381707a7f5f2aefb6e1bd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 689, "license_type": "no_license", "max_line_length": 70, "num_lines": 22, "path": "/coverPoints.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def coverPoints(A, B):\n \"\"\"\n my approach. since it can go diagonal, we can shift the point\n in such a way it needs to travel only the longest side of the \n rectange. hence we keep adding the longest side values.\n \n :param A: x coordinates of all the points\n :param B: y coordinates of all the y points\n :return: minimum distance to reach to all the diestination points\n \"\"\"\n l = len(A)\n dist = []\n for i in range(1, l):\n x_dis = abs(A[i] - A[i - 1])\n y_dis = abs(B[i] - B[i - 1])\n dist.append(max(x_dis, y_dis))\n return sum(dist)\n\n\nA = list(map(int,input().split()))\nB = list(map(int,input().split()))\nprint(coverPoints(A,B))\n" }, { "alpha_fraction": 0.46038252115249634, "alphanum_fraction": 0.4617486298084259, "avg_line_length": 23.518518447875977, "blob_id": "3fe21116e7c837af99dbde835a36f04d014423b1", "content_id": "eae33eedbcbfe307f1fd45a879b61a5dfe5b68b4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 732, "license_type": "no_license", "max_line_length": 55, "num_lines": 27, "path": "/Construct Binary Tree From Inorder And Preorder.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n#\tdef __init__(self, x):\n#\t\tself.val = x\n#\t\tself.left = None\n#\t\tself.right = None\n\nclass Solution:\n\t# @param A : list of integers\n\t# @param B : list of integers\n\t# @return the root node in the tree\n\tdef buildTree(self, pre_o, in_o):\n\n if not in_o:\n return None\n \n for i in pre_o:\n if i in in_o:\n v = i\n pre_o.remove(i)\n break\n \n root = TreeNode(v)\n id = in_o.index(v)\n root.left = self.buildTree(pre_o, in_o[:id])\n root.right = self.buildTree(pre_o, in_o[id+1:])\n return root\n \n \n \n \n \n \n\t \n\t \n" }, { "alpha_fraction": 0.44800883531570435, "alphanum_fraction": 0.4502212405204773, "avg_line_length": 22.421052932739258, "blob_id": "700d4f5178163574c39219a6bfd002069e60ec14", "content_id": "971ee7ced7d1b31437e35e32a3fb281f2a700584", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 904, "license_type": "no_license", "max_line_length": 43, "num_lines": 38, "path": "/partition_linked_lists.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n#\tdef __init__(self, x):\n#\t\tself.val = x\n#\t\tself.next = None\n\nclass Solution:\n\t# @param A : head node of linked list\n\t# @param B : integer\n\t# @return the head node in the linked list\n\tdef partition(self, A, B):\n\n\t \n\t if A.val>= B:\n\t prev = ListNode(None)\n\t prev.next = A\n\n else:\n prev = A\n\t itr = A\n\t set =0\n while itr.next:\n if itr.next.val<B and set:\n \n temp = itr.next.next\n itr.next.next= prev.next\n prev.next = itr.next\n itr.next =temp\n if itr.next.val<B and not set:\n itr = itr.next\n \n else:\n set=1\n itr = itr.next\n if prev.val ==None:\n return prev.next\n else:\n return prev\n\t \n" }, { "alpha_fraction": 0.43167027831077576, "alphanum_fraction": 0.4403470754623413, "avg_line_length": 27.8125, "blob_id": "36ef93b8902b0e7b0ffe19ce5fa93104ad72f6df", "content_id": "d1e32478c82e5aae41449930868d49369205b80d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 461, "license_type": "no_license", "max_line_length": 53, "num_lines": 16, "path": "/prefix_equation_stacks.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import deque\nclass Solution:\n # @param A : list of strings\n # @return an integer\n def evalRPN(self, A):\n \n stack = deque()\n for i in A:\n if i not in [\"+\",\"-\",\"*\",\"/\"]:\n stack.append(i)\n else:\n val1 = stack.pop()\n val2 = stack.pop()\n sol = str(int(eval(val2 + i + val1)))\n stack.append(sol)\n return stack.pop()\n" }, { "alpha_fraction": 0.29588431119918823, "alphanum_fraction": 0.3270300328731537, "avg_line_length": 22.657894134521484, "blob_id": "1775291c316c06d91b5e5e95155e39e3523d179b", "content_id": "53d9bc02aab193da0705d64efb7a2d5f7cc84362", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 899, "license_type": "no_license", "max_line_length": 64, "num_lines": 38, "path": "/three_arrays.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "INT_MAX = 2147483647\nclass Solution:\n # @param A : tuple of integers\n # @param B : tuple of integers\n # @param C : tuple of integers\n # @return an integer\n def minimize(self, A,B,C):\n i,j,k=0,0,0\n #a,b,c=0,0,0\n res= INT_MAX\n while( i<len(A) and j<len(B) and k<len(C)):\n if min(A[i],B[j],C[k]) == A[i]:\n a=1\n b=0\n c=0\n elif min(A[i],B[j],C[k]) == B[j]:\n b=1\n a=0\n c=0\n else:\n c=1\n a=0\n b=0\n \n if max(A[i],B[j],C[k]) - min(A[i],B[j],C[k]) <= res:\n res = max(A[i],B[j],C[k]) - min(A[i],B[j],C[k])\n \n if a:\n i+=1\n elif b:\n j+=1\n elif c:\n k+=1\n \n \n \n \n return res\n" }, { "alpha_fraction": 0.4378698170185089, "alphanum_fraction": 0.44378697872161865, "avg_line_length": 21.86206817626953, "blob_id": "8f74c626b43e3ecf58f9845b74cf4ace44f3a332", "content_id": "e6b381ead69c995fd6098a1821966606b56ef3c1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 676, "license_type": "no_license", "max_line_length": 46, "num_lines": 29, "path": "/remove_kthnode_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @param B : integer\n # @return the head node in the linked list\n def removeNthFromEnd(self, A, B):\n \n itr = A\n len=0\n while itr:\n len+=1\n itr = itr.next\n \n if B>=len:\n return A.next\n \n itr=A\n count=0\n while itr:\n count+=1\n if count == len - B:\n itr.next = itr.next.next\n return A\n itr = itr.next\n \n" }, { "alpha_fraction": 0.26722338795661926, "alphanum_fraction": 0.30062630772590637, "avg_line_length": 13.9375, "blob_id": "71a463428039a0f0838124e4dc994bb48a5e4461", "content_id": "f716292b2268b3a6c5b94e8b7451ff990c642bf8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 479, "license_type": "no_license", "max_line_length": 30, "num_lines": 32, "path": "/set_zeroes.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "\ndef set_zeros(A):\n R = len(A)\n C = len(A[0])\n r = []\n c = [1]*C\n for i in range(R):\n if 0 in A[i]:\n\n for j in range(C):\n if A[i][j]==0:\n c[j]=0\n A[i]=[0]*C\n else:\n r.append(i)\n\n for i in r:\n for j in range(C):\n if not c[j]:\n A[i][j]=0\n return A\n\n\n\n\n\n\nA= [ [1, 0, 1],\n [1, 1, 1],\n [1, 0, 1] ]\n\n\nprint(set_zeros(A))\n" }, { "alpha_fraction": 0.45537757873535156, "alphanum_fraction": 0.4965675175189972, "avg_line_length": 25.75, "blob_id": "2dace252b42aaee4bd3cd79a8337c8ff9da76b19", "content_id": "721b609d3d55ca503a3be8a743a89236bea76fa5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 437, "license_type": "no_license", "max_line_length": 89, "num_lines": 16, "path": "/integer_to_roman.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\n\n#brute force\ndef integer_to_roman(n):\n\n ones=[\"\",\"I\",\"II\",\"III\",\"IV\",\"V\",\"VI\",\"VII\",\"VIII\",\"IX\"]\n tens=[\"\",\"X\",\"XX\",\"XXX\",\"XL\",\"L\",\"LX\",\"LXX\",\"LXXX\",\"XC\"]\n hundreds = [\"\",\"C\",\"CC\",\"CCC\",\"CD\",\"D\",\"DC\",\"DCC\",\"DCCC\",\"CM\"]\n thousands =[\"\",\"M\",\"MM\",\"MMM\"]\n\n ans = thousands[n//1000] + hundreds[(n%1000)//100] + tens[(n%100)//10] + ones[(n%10)]\n\n return ans\n\nn = int(input())\nprint(integer_to_roman(n))\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.43048128485679626, "alphanum_fraction": 0.4385026693344116, "avg_line_length": 22.399999618530273, "blob_id": "e7d5a46c41748c13ebea240163d560b0f017bb0b", "content_id": "b7b12c235056c312e5be9362792b103c1d25fca5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 374, "license_type": "no_license", "max_line_length": 34, "num_lines": 15, "path": "/diff_K_Hmaps.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import Counter\nclass Solution:\n # @param A : tuple of integers\n # @param B : integer\n # @return an integer\n def diffPossible(self, A, B):\n a = list(A)\n a.sort()\n ans={}\n for i in a:\n if i in ans:\n return 1\n else:\n ans[B+i]=1\n return 0\n \n\n \n" }, { "alpha_fraction": 0.37457045912742615, "alphanum_fraction": 0.41237112879753113, "avg_line_length": 25.454545974731445, "blob_id": "434e9f9e28f65f0b9cb12783fb58eb4f1b161c44", "content_id": "ce06e36f898a53f1bfbf569d737b3e20cd9423a3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 291, "license_type": "no_license", "max_line_length": 40, "num_lines": 11, "path": "/number of binary search tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : integer\n # @return an integer\n def numTrees(self, A):\n n=A\n dp=[0] * (n + 1)\n dp[0],dp[1] = 1, 1\n for i in range(2, n + 1):\n for j in range(1, i + 1):\n dp[i] += dp[i-j]*dp[j-1]\n return dp[n]\n" }, { "alpha_fraction": 0.3464399576187134, "alphanum_fraction": 0.3613177537918091, "avg_line_length": 24.432432174682617, "blob_id": "74a27365e0b183d504096b77e5cc3ef6ff1682e0", "content_id": "8098ede2185334ffca7c672fb1a3539fe8996403", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 941, "license_type": "no_license", "max_line_length": 35, "num_lines": 37, "path": "/first&last_occurence.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : tuple of integers\n # @param B : integer\n # @return a list of integers\n def searchRange(self, A, B):\n ans = []\n \n low = 0\n high = len(A) - 1\n res =-1\n # first occurence\n while (low <= high):\n mid = (low + high) // 2\n if A[mid] > B:\n high = mid - 1\n elif A[mid] < B:\n low = mid + 1\n elif A[mid] == B:\n res = mid\n high = mid - 1\n ans.append(res)\n \n low = 0\n high = len(A) - 1\n res = -1\n # second occurence\n while (low <= high):\n mid = (low + high) // 2\n if A[mid] > B:\n high = mid - 1\n elif A[mid] < B:\n low = mid + 1\n elif A[mid] == B:\n res = mid\n low = mid+1\n ans.append(res)\n return ans\n" }, { "alpha_fraction": 0.5079365372657776, "alphanum_fraction": 0.5079365372657776, "avg_line_length": 24.200000762939453, "blob_id": "262ece7d63e4937756afd0be8819899118dd2a55", "content_id": "62710709a5cc4f958fe56f45dc2e7d6907d8930f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 756, "license_type": "no_license", "max_line_length": 64, "num_lines": 30, "path": "/Remove Half Nodes.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @return the root node in the tree\n def solve(self, A):\n if not A:\n return\n \n if (not A.left and A.right) or (not A.right and A.left):\n\n \n return self.solve(self.delNode(A))\n \n A.left = self.solve(A.left)\n A.right = self.solve(A.right)\n \n return A\n \n def delNode(self, A):\n #for sure the deleted node will have a single child\n if A.left and not A.right:\n return A.left\n else:\n return A.right\n" }, { "alpha_fraction": 0.36681222915649414, "alphanum_fraction": 0.42794761061668396, "avg_line_length": 6.161290168762207, "blob_id": "52ce2d532a472698aca0aa7e828ace4e5d1200d9", "content_id": "5eb8dcf5d3b881cece67b43d0a4d2f4c3576df4a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 229, "license_type": "no_license", "max_line_length": 32, "num_lines": 31, "path": "/GCD.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "\ndef gcd(A,B):\n A,B = max(A,B), min(A,B)\n if B==0:\n return 1\n\n while(A%B!=0):\n temp=B\n\n B= A%B\n A=temp\n\n\n\n return B\nprint(gcd(56,120))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprint(excel_colimn_base(468096))\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.2648221254348755, "alphanum_fraction": 0.2905138432979584, "avg_line_length": 21, "blob_id": "a5f3100257cdbed1aa9c4ef63ce0c9b51d536262", "content_id": "9c1f8d95e41ea1f708f40fdf0866f8a108f379f6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 506, "license_type": "no_license", "max_line_length": 34, "num_lines": 23, "path": "/remove_duplicates_atmost_twice.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @return an integer\n def removeDuplicates(self, A):\n i=1\n t=0\n f=1\n \n while i<len(A):\n if A[i]==A[t] and f<2:\n t+=1\n A[t]=A[i]\n f+=1\n i+=1\n elif A[i]==A[t]:\n i+=1\n f+=1\n elif A[i]!=A[t]:\n t+=1\n A[t]=A[i]\n i+=1\n f=1\n return t+1\n" }, { "alpha_fraction": 0.44893112778663635, "alphanum_fraction": 0.45130640268325806, "avg_line_length": 22.38888931274414, "blob_id": "538665388c9313561a78733b2854997da2a4ce3f", "content_id": "7448481c96e366e47c3be1b8aca87b75fec368b1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 421, "license_type": "no_license", "max_line_length": 51, "num_lines": 18, "path": "/plusOne.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @return a list of integers\n def plusOne(self, A):\n \"\"\"\n \n :param A: an array that represntes a number\n :return: a list\n \"\"\"\n l = len(A)\n n_str=''\n for i in A:\n n_str+=str(i)\n n_incr = str(int(n_str)+1)\n ans=[]\n for i in n_incr:\n ans.append(int(i))\n return ans\n" }, { "alpha_fraction": 0.49364790320396423, "alphanum_fraction": 0.503629744052887, "avg_line_length": 23.46666717529297, "blob_id": "180a970c12620939202e19b49f3eb5f065fd5db8", "content_id": "7b6ac2d5f24b13071a301fee77e5f19c90fc5dd7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1102, "license_type": "no_license", "max_line_length": 63, "num_lines": 45, "path": "/rat_maze_BT.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def isValid(x, y, n, arr):\n if x<n and y<n:\n if arr[x][y]==1:\n return True\n else:\n return False\n else:\n return False\n\ndef RatinMaze(x, y, arr, solarr, n):\n #base condition\n if x==n-1 and y==n-1:\n solarr[x][y]='1'\n return True\n\n\n if isValid(x, y, n, arr):\n solarr[x][y]='1'\n #checking right path\n if RatinMaze(x, y+1, arr, solarr, n):\n return True\n #checking if down path is valid\n if RatinMaze(x+1, y, arr, solarr, n):\n return True\n\n #if neither is true then we backtrack and change solarr\n solarr[x][y]='0'\n return False\n return False\n\nif __name__ == \"__main__\":\n n = int(input(\"enter the size of maze\"))\n print(\"enter values\")\n arr=[]\n for i in range(n):\n arr.append(list(map(int,input().split())))\n solarr=[]\n for i in range(n):\n s = '0'*n\n solarr.append(list(s))\n if RatinMaze(0,0, arr,solarr,n):\n for i in range(n):\n print(\" \".join(solarr[i]))\n else:\n print(\"no solution\")\n\n" }, { "alpha_fraction": 0.42598187923431396, "alphanum_fraction": 0.44108760356903076, "avg_line_length": 21.066667556762695, "blob_id": "2cea947273ac03e10181188665955e52521a5d5c", "content_id": "47a4027b7142aeecbcf8ccfef938690a393cde9b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 331, "license_type": "no_license", "max_line_length": 41, "num_lines": 15, "path": "/largest_number_formed.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def arrange(A):\n li = list(map(str,A))\n li.sort()\n l = len(A)\n for i in range(l):\n for j in range(i,l):\n first = li[i]+li[j]\n second = li[j]+li[i]\n if second>first:\n li[j],li[i]= li[i], li[j]\n ans = \"\".join(li)\n return int(ans)\n\n\nprint(arrange([0,0,0,0,0]))\n" }, { "alpha_fraction": 0.46974697709083557, "alphanum_fraction": 0.4774477481842041, "avg_line_length": 23.567567825317383, "blob_id": "9bc3ea8f222b2c541f0adac9d9147bbac9b7d5ef", "content_id": "ada8743ee98a6d47f0903b2494ec6148fb53ab6c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 909, "license_type": "no_license", "max_line_length": 143, "num_lines": 37, "path": "/Kth_smallest_BST.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n#\tdef __init__(self, x):\n#\t\tself.val = x\n#\t\tself.left = None\n#\t\tself.right = None\n\nclass Solution:\n\t# @param A : root node of tree\n\t# @param B : integer\n\t# @return an integer\n\tdef kthsmallest(self, A, B):\n\t k=[0]\n\t ans=[0]\n def in_order(A):\n elements=[]\n \n #print left of subtree go all the way to leaf node\n if A.left:\n elements += in_order(A.left)\n \n k[0]+=1\n if k[0]==B:\n ans[0]= A.val\n \n \n \n v = A.val\n elements.append(v)\n \n if A.right:\n elements += in_order(A.right) #we are returning elements so we are basically appeningn the returned value of those sub nodes\n \n return elements\n \n l = in_order(A)\n return ans[0]\n" }, { "alpha_fraction": 0.5549450516700745, "alphanum_fraction": 0.5563187003135681, "avg_line_length": 29.33333396911621, "blob_id": "54eef6b92d0dea189cce79be2163407619b9dcff", "content_id": "382cd105924360af622ba95190ca1d4012b07495", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 728, "license_type": "no_license", "max_line_length": 73, "num_lines": 24, "path": "/swap_every_two_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @return the head node in the linked list\n def swapPairs(self, A):\n if A is None or A.next is None:\n return A\n temp = ListNode(-1)\n temp.next = A\n \n current = temp\n while current.next is not None and current.next.next is not None:\n first = current.next\n second = current.next.next\n first.next = second.next\n current.next = second\n current.next.next = first\n current = current.next.next\n return temp.next\n" }, { "alpha_fraction": 0.29472774267196655, "alphanum_fraction": 0.33707866072654724, "avg_line_length": 22.02083396911621, "blob_id": "046ff4f6bea4643e3ffb990c604e1d6f59819619", "content_id": "4e25b6a7156cb8b8f322c1c5c4be311d10415993", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1157, "license_type": "no_license", "max_line_length": 37, "num_lines": 48, "path": "/flip bits.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : string\n # @return a list of integers\n def flip(self, A):\n #naive approach\n #-1 2 9 -2 4 5 -10 11\n # l r\n #1 0 0 1 1 0 0 0 \n #1 1 1 0 0 1 1 1\n\n #edge case = 0 (done)\n # edge case = 10101010\n\n\n if A.count('1')==len(A):\n return []\n \n\n left,right = -1,-1\n count, max_count = 0,0\n reset = True\n currleft = 0\n\n for i in range(len(A)):\n if A[i]=='0':\n if reset:\n currleft = i\n reset = False\n count+=1\n \n else:\n if count>max_count:\n max_count = count\n left = currleft+1\n right = i\n \n if count>0:\n count-=1\n else:\n count=0\n reset = True\n\n if count>max_count:\n max_count = count\n left = currleft+1\n right = i+1\n\n return [left,right]\n \n\n \n\n\n \n \n\n" }, { "alpha_fraction": 0.4344472885131836, "alphanum_fraction": 0.47814908623695374, "avg_line_length": 15.913043022155762, "blob_id": "b20f896b7304dfd9b528c0341b8386a2a1bcf20c", "content_id": "918b95a88226d3a30a4fa199e9c22322e2ad39e9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 389, "license_type": "no_license", "max_line_length": 50, "num_lines": 23, "path": "/power_function.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def pow(x, n, d):\n if n==0 and x!=0:\n return 1%d\n elif n==0 and x==0:\n return 0\n ans = power(x, n)\n return ans % d\n\n#main power function\ndef power(x, n):\n if n==1:\n return x\n elif n%2==0:\n return power(x, n // 2) ** 2\n elif n%2==1:\n return power(x,(n+1)//2)*power(x,(n-1)//2)\n\n\n\n\nx,n,d = map(long,input().split())\n\nprint(pow(x,n,d))\n" }, { "alpha_fraction": 0.42741936445236206, "alphanum_fraction": 0.4375, "avg_line_length": 22.619047164916992, "blob_id": "68a91ac1302c0dcc171c876fdc0ad8c3b3a9a739", "content_id": "fe2a837939d451cac22391fbcdb4a141f4c5bcf1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 496, "license_type": "no_license", "max_line_length": 52, "num_lines": 21, "path": "/find_all_factors.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : integer\n # @return a list of integers\n def allFactors(self, A):\n \"\"\"\n using the most optimal method i.e O(sqrt(n))\n \n :param A: a number\n :return: list of all its factors\n \"\"\"\n \n s = int(A**0.5)\n ans=[]\n\n for i in range(1,s+1):\n if A%i==0:\n ans.append(i)\n if A/i not in ans:\n ans.append(int(A/i))\n ans.sort()\n return ans\n" }, { "alpha_fraction": 0.4223363399505615, "alphanum_fraction": 0.44672656059265137, "avg_line_length": 23.838708877563477, "blob_id": "892074d6382115b7d8a2ad11bd84cc0e59d23d14", "content_id": "763e6e116fbce1a75742dc947cf065eabce01fce", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 779, "license_type": "no_license", "max_line_length": 63, "num_lines": 31, "path": "/longest increasing subseq.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n\t# @param A : tuple of integers\n\t# @return an integer\n\tdef lis(self, A):\n\t\t#dayummm this solution is damn smart!\n\t\t#so basically sort the list and find the lcs of this and that\n\n\t\tB = sorted(set(A))\n\t\tn = len(A)\n\t\tm = len(B)\n\t\tdp = [[0 for i in range(n+1)] for j in range(m+1)]\n\n\t\tfor j in range(1,m+1):\n\t\t\tfor i in range(1,n+1):\n\t\t\t\tif A[i-1]==B[j-1]:\n\t\t\t\t\tdp[j][i] = 1 + dp[j-1][i-1]\n\t\t\t\telse:\n\t\t\t\t\tdp[j][i] = max(dp[j-1][i],dp[j][i-1])\n\t\treturn dp[m][n]\n\t\n\tdef solve_method2(self, A):\n\t\t\n n = len(A)\n dp = [1]*n\n \n for i in range(1,n):\n for j in range(0,i):\n if A[j] < A[i]:\n dp[i] = max(dp[i] , dp[j] + 1)\n \n return dp\n \n" }, { "alpha_fraction": 0.4718446731567383, "alphanum_fraction": 0.48155340552330017, "avg_line_length": 15.433333396911621, "blob_id": "34b83bd825c6cc61b2a4b20bb957664d239d8904", "content_id": "81ad696431e15e430fd552fa7b7f7427ddf21549", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 515, "license_type": "no_license", "max_line_length": 57, "num_lines": 30, "path": "/word_rank_without_rep.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\nimport string\ndef lex_rank(A):\n \"\"\"\n approach: interesting\n \n :param A: a word\n :return: the rank of the word when arranged lexically\n \"\"\"\n\n p = 0\n alp = [i for i in A]\n\n alp.sort()\n print(alp)\n ans = 0\n l = len(A)\n while p < l:\n search = A[p]\n p += 1\n i = alp.index(search)\n alp.remove(search)\n if i!=0:\n\n ans+= i*math.factorial(l-p)\n \n return ans+1\n\n\nprint(lex_rank('DTNGJPURFHYEW'))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.43807339668273926, "alphanum_fraction": 0.45183485746383667, "avg_line_length": 27.399999618530273, "blob_id": "bfef147d96e420c8efdfa8c0ebc7d3755cfd153a", "content_id": "5c5d913818b178a0decf04d25589b05a2e0a342e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 872, "license_type": "no_license", "max_line_length": 94, "num_lines": 30, "path": "/max distance.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : tuple of integers\n # @return an integer\n def maximumGap(self, A):\n \"\"\"\n suffix array= it collects the maximum elements like a filter dine during zone refining\n and we compare this to our origianl array(A).\n if A[i]<S[j].. then a bigger number is waiting for us in the end. j+=1\n else:\n we crossed a big element therefore i+=1\n\n also \n O(N) yay!\n \"\"\"\n n = len(A)\n S = [0]*len(A)\n S[-1] = A[-1]\n for i in reversed(range(n-1)):\n S[i] = max(S[i+1],A[i])\n \n #S is the suffix array\n i,j=0,0\n ans_max = 0\n while i<n and j<n:\n if A[i]<=S[j]:\n ans_max = max(ans_max, j-i)\n j+=1\n else:\n i+=1\n return ans_max\n\n\n \n\n" }, { "alpha_fraction": 0.43393146991729736, "alphanum_fraction": 0.43882545828819275, "avg_line_length": 27.049999237060547, "blob_id": "02c93263f1931d6a17c4cb47b75935475d2d03ab", "content_id": "b6fdfe66a3b9aeab91bf066aea79e531612c9532", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 613, "license_type": "no_license", "max_line_length": 50, "num_lines": 20, "path": "/largest_area_histogram.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import deque\nclass Solution:\n # @param A : list of integers\n # @return an integer\n def largestRectangleArea(self, A):\n area=0\n stack = deque()\n \n for i, h in enumerate(A):\n start = i\n while stack and stack[-1][1]>h:\n index, height = stack.pop()\n area = max(area, height*(i-index))\n start = index\n stack.append((start,h))\n \n for i, h in stack:\n area = max(area, h*(len(A)-i))\n \n return area\n \n \n \n \n" }, { "alpha_fraction": 0.5183066129684448, "alphanum_fraction": 0.5480549335479736, "avg_line_length": 15.39622688293457, "blob_id": "b405317e0d348f9263373f49216fee5a67ff17c1", "content_id": "a227b01e42dadd2d27c442db5c1e2df8d580379c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 874, "license_type": "no_license", "max_line_length": 91, "num_lines": 53, "path": "/Max_non_negative.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def Max_non_negative(A):\n \"\"\"\n aim is to travesre the list and flag the points that are negative, ans clculate the sum\n that exceeds the previous sum and change the indices accordingly\n to print the reqd. sunset.\n\n :param A:\n :return: returns a continous list of maximum non negative number\n \"\"\"\n \n \"\"\"\n Example Input\nInput 1:\n\n A = [1, 2, 5, -7, 2, 3]\nInput 2:\n\n A = [10, -1, 2, 3, -4, 100]\n\n\nExample Output\nOutput 1:\n\n [1, 2, 5]\nOutput 2:\n\n [100]\n \"\"\"\n\n\n set=[]\n summ=[]\n all_set=[]\n for j in A:\n if j<0:\n\n summ.append(sum(set))\n all_set.append(set)\n set=[]\n else:\n set.append(j)\n summ.append(sum(set))\n all_set.append(set)\n\n i = summ.index(max(summ))\n return all_set[i]\n\n\n\n return ans\n\nA = list(map(int,input().split()))\nprint(Max_non_negative(A))\n\n\n\n\n\n" }, { "alpha_fraction": 0.47187501192092896, "alphanum_fraction": 0.5062500238418579, "avg_line_length": 15.8421049118042, "blob_id": "2bd7c2ff292a3374540446dcbd1e662a49482b8d", "content_id": "b83774d35fd24362c5dff0d1f71c255f7f899d6d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 320, "license_type": "no_license", "max_line_length": 57, "num_lines": 19, "path": "/find_all_primes_tillA.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# seive of Eratosthenes--- more optimised O(sqrt(n)*logn)\ndef seive(A):\n\n sei = [True]*(A+1)\n for i in range(2,int(A**0.5)+1):\n\n\n for j in range(i**2,A+1,i):\n sei[j]=False\n ans=[]\n\n\n for i,v in enumerate(sei[2:]):\n if v:\n ans.append(i+2)\n return ans\n\n\nprint(seive(25))\n" }, { "alpha_fraction": 0.4480712115764618, "alphanum_fraction": 0.45103856921195984, "avg_line_length": 21.46666717529297, "blob_id": "e1c983594d584a738efd1b8196ad56b2435c66cd", "content_id": "c74a1d4bbbe7faa530c28b265ffeea8f6bb67ee3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 674, "license_type": "no_license", "max_line_length": 60, "num_lines": 30, "path": "/detect_loop_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\n#using Floyd's technique\n\nclass Solution:\n # @param A : head node of linked list\n # @return the first node in the cycle in the linked list\n def detectCycle(self, A):\n fp,sp = A,A\n s=0\n while fp and fp.next:\n fp = fp.next.next\n sp = sp.next\n \n if fp==sp:\n s=1\n break\n if not s:\n return None\n \n fp = A\n while fp!=sp:\n fp = fp.next\n sp = sp.next\n \n return fp\n" }, { "alpha_fraction": 0.4365781843662262, "alphanum_fraction": 0.4375614523887634, "avg_line_length": 29.96875, "blob_id": "40640a2477dc41a042621ade6c53c4bfa02d5c94", "content_id": "58bb6413e1752c5bf06ae9977fbcaae85d389599", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2034, "license_type": "no_license", "max_line_length": 105, "num_lines": 64, "path": "/cousins_binary_tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @param B : integer\n # @return a list of integers\n def solve(self, A, B):\n \"\"\"\n logic: im setting a queue that contains all the nodes of the same level\n i.e both siblings + cousins\n so im colecting all the parents of siblings and cousins\n if a particular parent's child is B then the brothers kids will be the cousins\n \n \"\"\"\n if not A:\n return []\n \n queue=[A]\n #level = [] #will store values in the same level #2d array, siblings will also be grouped togther\n next_queue=[]\n #result=[]\n #l=0\n parent_node = []\n ans=[]\n \n while queue: #atleast one node (or one child)\n for node in queue:\n #level.append(node)\n \n if node.left:\n if node.left.val==B:\n parent_node = queue\n parent_node.remove(node)\n next_queue=[]\n break\n next_queue.append(node.left)\n if node.right:\n if node.right.val==B:\n parent_node = queue\n parent_node.remove(node)\n next_queue=[]\n break\n next_queue.append(node.right)\n #print(next_queue)\n \n queue = next_queue\n next_queue=[]\n #result.append(level)\n #level=[]\n #print(parent_node)\n \n for i in parent_node:\n if i.left:\n ans.append(i.left.val)\n if i.right:\n ans.append(i.right.val)\n #print(ans)\n \n return ans\n \n \n \n \n" }, { "alpha_fraction": 0.4043927788734436, "alphanum_fraction": 0.44702842831611633, "avg_line_length": 18.820512771606445, "blob_id": "9d39e118116b9b6b2ec54668ae818eb45a50948d", "content_id": "4bb03dd3d7bb6a2f0f4d928dfecaf0dcf6c6d9ad", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 774, "license_type": "no_license", "max_line_length": 47, "num_lines": 39, "path": "/merge_intervals.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "\ndef insert(intervals, new_interval):\n ans=[]\n p=0\n for j,i in enumerate(intervals):\n if new_interval[p]<i[1]:\n if p!=1:\n sub_ans=[i[0]]\n p+=1\n elif new_interval[p]<i[0]:\n sub_ans.append(new_interval[p])\n ans.append(sub_ans)\n ans.extend(intervals[j:])\n break\n elif new_interval[p]<i[1]:\n sub_ans.append(i[1])\n ans.append(sub_ans)\n ans.extend(intervals[j+1:])\n break\n\n elif not p:\n ans.append(i)\n return ans\n\n\n\n\n\n\n\n\n\n\n\nintervals =[[1,2],[3,5],[6,7],[8,10],[12,16]]\nnew_intervals=[4,9]\n#[[1, 2], [3, 10], [12, 16]]\n\n\nprint(insert(intervals,new_intervals))\n" }, { "alpha_fraction": 0.4647887349128723, "alphanum_fraction": 0.49295774102211, "avg_line_length": 34.5, "blob_id": "4c6a30886e4e12404af8ce55b7425025d75c4a22", "content_id": "75050e77309ef63f999308f152f099641460e0b7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 213, "license_type": "no_license", "max_line_length": 88, "num_lines": 6, "path": "/pascal triangle.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n def generate(self, A: int) -> List[List[int]]:\n l=[]\n for i in range(A):\n l.append([1 if j==0 or j==i else l[i-1][j]+l[i-1][j-1] for j in range(i+1)])\n return l\n" }, { "alpha_fraction": 0.4404057562351227, "alphanum_fraction": 0.45224007964134216, "avg_line_length": 20.363636016845703, "blob_id": "3c42071ceeddb47e09e42c8ba015897fa013726c", "content_id": "03808720a8eed9a910feee021fcf1cf532426198", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1183, "license_type": "no_license", "max_line_length": 74, "num_lines": 55, "path": "/justified_allignment_text.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\n\n#brute force\ndef justified_text_alignment(A,B):\n \"\"\"\n\n :param A: a list of words from a senctence\n :param B: L number of characters regquired per line\n :return: n lines that are aligned and justifed equally spaced\n \"\"\"\n\n main=[]\n sub=[]\n ans=[]\n for i in A:\n if sub==[]:\n sub.append(i)\n l = len(i)\n elif l+len(i)+1<=B:\n sub.append(i)\n l +=len(i)+1\n elif l+len(i)+1>B:\n main.append(sub)\n sub=[i]\n l=len(i)\n main.append(sub)\n\n for line in main:\n words = len(line)\n c_count = len(\"\".join(line))\n extras = B - c_count\n if words==1:\n a = line[0] + ' '*(B-len(line[0]))\n else:\n space = extras//(words-1)\n rem = extras%(words-1)\n k=0\n while (rem>0):\n line[k] = line[k]+ ' '\n rem-=1\n k+=1\n a = (\" \"*space).join(line)\n\n\n ans.append(a)\n\n\n\n\n return ans #cooooooooool\n\n\nA = [\"the\", \"quick\", \"brown\", \"fox\", \"jumped\", \"over\", \"the\",\"lazy\",\"fox\"]\nB=24\nprint(justified_text_alignment(A,B))\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.35083797574043274, "alphanum_fraction": 0.3631284832954407, "avg_line_length": 25.322580337524414, "blob_id": "ea3b90e0c3fa0722c8c7a10011fe8f777feeaf31", "content_id": "4f15150746444241b5fb4b1bc18e45ae3fa3dcc7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 895, "license_type": "no_license", "max_line_length": 80, "num_lines": 31, "path": "/Subarray with given XOR.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @param B : integer\n # @return an integer\n def solve(self, A, B):\n count = 0\n xor =0\n freq = {}\n \"\"\"\n a = [4,2,2,6,4] k =6\n b = [ ] b is overall xor value a is the prefix xor value..... \n k will be the sufix xor value a = b^k we have calculated the freq of all\n prefix values.. k is the sufix value.\n freq = {(xor val): freq}\n \"\"\"\n \n for i in A:\n xor = xor^i\n \n if xor == B:\n count+=1\n \n if xor^B in freq:\n count+=freq[xor^B]\n \n if xor in freq:\n freq[xor]+=1\n else:\n freq[xor]=1\n \n return count\n \n \n \n \n \n \n\n" }, { "alpha_fraction": 0.4444444477558136, "alphanum_fraction": 0.469696968793869, "avg_line_length": 12.310344696044922, "blob_id": "8b91ca00276d2826b2d40a8326c3dbc2eb746b12", "content_id": "9a7cd0ed644dff62b7cd18f1d8f1be0ebfabd717", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 396, "license_type": "no_license", "max_line_length": 34, "num_lines": 29, "path": "/all_thrice_excpet_one.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "\n\n#brute force\ndef all_thrice_excpet_one(A):\n ans=0\n\n for i in reversed(range(32)):\n count=0\n for j in range(len(A)):\n if A[j]&(1<<i):\n count+=1\n if count%3!=0:\n ans += 1<<i\n return ans\n\n\n\n\n\n\n #id = f.values().index(1)\n #return f.keys()[id]\n\n\n\n\n\n\n\nA = list(map(int,input().split()))\nprint(all_thrice_excpet_one(A))\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5163204669952393, "alphanum_fraction": 0.5222551822662354, "avg_line_length": 31.799999237060547, "blob_id": "758ff3038399eb61f34e77dae61ba98a87d7bccf", "content_id": "97f27cd34afb24fcc175d593ca571699a4d04886", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 337, "license_type": "no_license", "max_line_length": 98, "num_lines": 10, "path": "/gray_code.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : integer\n # @return a list of integers\n def grayCode(self, A):\n ans=[]\n x = 2**A\n for i in range(x): #divide word into twos...\n j = i//2\n ans.append(i^j) #gray code will be the XOR of the number with its integer half value\n return ans\n \n" }, { "alpha_fraction": 0.30102789402008057, "alphanum_fraction": 0.37298092246055603, "avg_line_length": 23.321428298950195, "blob_id": "1151be7c4bcfb168897525a70b9e3228334b61cb", "content_id": "7e3265c27d70bb2a533e8f22778438ab62e97ef6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 681, "license_type": "no_license", "max_line_length": 58, "num_lines": 28, "path": "/anti diagonals.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of list of integers\n # @return a list of list of integers\n def diagonal(self, A):\n \"\"\"\n 1 2 3 5\n 4 5 6 7\n 7 8 9 8\n 9 1 2 3\n [[0,0], sum = 0\n [0,1],[1,0], sum = 1\n [0,2],[1,1],[2,0], sum = 2\n [1,2],[2,1], sum = 3\n [2,2]] sum = 4\n\n table of size N will have 2n - 1 i.e 0,1,2,...2n-1\n\n \n \"\"\"\n ans=[]\n n=len(A)\n for i in range(2*n-1):\n sub=[]\n for j in range(n):\n if j<n and i-j>=0 and i-j<n:\n sub.append(A[j][i-j])\n ans.append(sub)\n return ans\n" }, { "alpha_fraction": 0.34419551491737366, "alphanum_fraction": 0.36659878492355347, "avg_line_length": 23.549999237060547, "blob_id": "d6f7da1f5606f7a550ed6fab2b66d94bebb3a0ae", "content_id": "5b15eaced5c2613945eabb6f168c1f70cb75b751", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 491, "license_type": "no_license", "max_line_length": 36, "num_lines": 20, "path": "/sliding_window_atmost_ones.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @param B : integer\n # @return an integer\n def solve(self, A, k):\n start,good,bad,res = 0,0,0,0\n \n for i in A:\n if i==1:\n good+=1\n else:\n bad+=1\n while(bad>k):\n if A[start]==1:\n good-=1\n else:\n bad-=1\n start+=1\n res = max(res, good+bad)\n return res\n" }, { "alpha_fraction": 0.3237597942352295, "alphanum_fraction": 0.33681461215019226, "avg_line_length": 16.227272033691406, "blob_id": "46b173054ea13cf7d1e4b56490bab8f4ab9dcd5c", "content_id": "5bb102576a34ddfa91d5c9abc16b1d1bf0e27f18", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 383, "license_type": "no_license", "max_line_length": 38, "num_lines": 22, "path": "/2-Sum Binary Tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": " def t2Sum(self,B):\n\n ans = [0]\n h={}\n\n def in_order(A, B):\n if A == None:\n return\n\n in_order(A.left, B)\n\n if A.data in h:\n ans[0] += 1\n else:\n h[B - A.data] = A.data\n\n in_order(A.right, B)\n\n return\n\n in_order(self, B)\n return ans[0]\n" }, { "alpha_fraction": 0.41734617948532104, "alphanum_fraction": 0.4188287556171417, "avg_line_length": 23.14285659790039, "blob_id": "331d2b89dcce8a62da1b2c05079201208076b534", "content_id": "712f586f0f4a222fd09e375ea71e692e456a212a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1349, "license_type": "no_license", "max_line_length": 58, "num_lines": 49, "path": "/deep_copy_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list with a random pointer.\n# class RandomListNode:\n# def __init__(self, x):\n# self.label = x\n# self.next = None\n# self.random = None\n\nclass Solution:\n # @param head, a RandomListNode\n # @return a RandomListNode\n def copyRandomList(self, head):\n \n #return head\n #copy list\n itr = head\n while itr:\n copy = RandomListNode(itr.label)\n temp = itr.next\n itr.next = copy\n copy.next = temp\n itr = temp\n \n itr = head\n while itr:\n itr.next.random = itr.random.next\n itr = itr.next.next\n #create copy list\n ans = head.next\n itr = ans\n while itr or itr.next.next:\n itr.next = itr.next.next\n itr = itr.next\n \n return ans\n\n \n \n \n\"\"\" def copy_list(self, head):\n itr = head\n c = RandomListNode(0)\n copy = c\n \n while itr:\n copy.label = itr.label\n copy.next = RandomListNode(0)\n itr = itr.next\n copy = copy.next\n return c\"\"\"\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "alpha_fraction": 0.4969325065612793, "alphanum_fraction": 0.49897751212120056, "avg_line_length": 31.600000381469727, "blob_id": "5fb745db742d949151a4b3eb7340b008bd643a1e", "content_id": "dec461c04db1d42126222df3d59fc0514aa5325c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 489, "license_type": "no_license", "max_line_length": 41, "num_lines": 15, "path": "/order of people height.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @param B : list of integers\n # @return a list of integers\n def order(self, A, B):\n line = dict(zip(A,B))\n correct_order= ['x']*len(A)\n index = list(range(len(A)))\n while correct_order.count('x')>0:\n for i in sorted(line):\n count = line[i]\n id = index[count]\n index.pop(count)\n correct_order[id] = i\n return correct_order\n" }, { "alpha_fraction": 0.3450789749622345, "alphanum_fraction": 0.34750911593437195, "avg_line_length": 23.33333396911621, "blob_id": "b7b973be5cd111f86e8b2800e1fa4e183cc35f47", "content_id": "6872726f1452b604d60d5a8adb090c06d427d201", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 823, "license_type": "no_license", "max_line_length": 47, "num_lines": 33, "path": "/shortest_unique_prefix.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n\t# @param A : list of strings\n\t# @return a list of strings\n\tdef prefix(self, Input):\n\t \"\"\"\n\t Input: [zebra, dog, duck, dove]\n Output: {z, dog, du, dov}\n where we can see that\n zebra = z\n dog = dog\n duck = du\n dove = dov\n\t \"\"\"\n\t ans=[]\n\t \n\t for word in Input:\n i=0\n while i<len(word):\n prev=i\n for other in Input:\n if other==word:\n continue\n else:\n if word[:i]==other[:i]:\n i+=1\n break\n if i==prev:\n break\n\t \n ans.append(word[:i])\n \n #print(ans)\n return ans\n\t \n\t \n" }, { "alpha_fraction": 0.35172414779663086, "alphanum_fraction": 0.3751724064350128, "avg_line_length": 15.860465049743652, "blob_id": "bdf921c424d1d5530e16498b73dc37df38cc848c", "content_id": "193ca93c3659171431c7e7d776edd4656dd6a358", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 725, "license_type": "no_license", "max_line_length": 45, "num_lines": 43, "path": "/smalles_next_largest_number.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def next_largest_number(A):\n \"\"\"\n \n :param A: a string numner\n \n :return: the smallest next largest number\n \"\"\"\n\n if len(A)==1:\n return -1\n l=len(A)-2\n ans=\"\"\n while(l>=0):\n l1 = [int(i) for i in A[l:]]\n if max(l1)==l1[0]:\n l-=1\n continue\n else:\n ctrl = l1[0]\n l1.sort()\n ans+=A[:l]\n for i in l1:\n if i>ctrl:\n ans+=str(i)\n l1.remove(i)\n break\n for j in l1:\n ans+=str(j)\n break\n if l>=0:\n return ans\n else:\n return -1\n\n\n\n\n\n\n\n\nA = str(input())\nprint(next_largest_number(A))\n" }, { "alpha_fraction": 0.4031413495540619, "alphanum_fraction": 0.4267015755176544, "avg_line_length": 21.47058868408203, "blob_id": "d4e95acabc282cf496b503f05739c6c008cb10aa", "content_id": "9e52a0f05937f7fc595f8cee00b879009e5ffbfc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 382, "license_type": "no_license", "max_line_length": 68, "num_lines": 17, "path": "/hotel.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n def hotel(self, arrive, depart, K):\n events = [(t, 1) for t in arrive] + [(t, 0) for t in depart]\n events = sorted(events)\n\n guests = 0\n\n for event in events:\n if event[1] == 1:\n guests += 1\n else:\n guests -= 1\n\n if guests > K:\n return 0\n\n return 1\n" }, { "alpha_fraction": 0.4238845109939575, "alphanum_fraction": 0.44750654697418213, "avg_line_length": 24.214284896850586, "blob_id": "2ced885f13d24749b80564af1cfd645823fe34f6", "content_id": "4879ec397770e357337a4e665e3fb5d7491c596e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 762, "license_type": "no_license", "max_line_length": 46, "num_lines": 28, "path": "/add_2_linked_lists.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @param B : head node of linked list\n # @return the head node in the linked list\n def addTwoNumbers(self, A, B):\n l1=A\n l2=B\n carry=0\n cur=temp=ListNode(0)\n \n while l1 or l2 or carry:\n if l1:\n carry+=l1.val\n l1=l1.next\n if l2:\n carry+=l2.val\n l2=l2.next\n cur.next=ListNode(carry%10)\n cur=cur.next\n \n carry=carry//10\n return temp.next\n \n \n \n \n" }, { "alpha_fraction": 0.4678899049758911, "alphanum_fraction": 0.4694189727306366, "avg_line_length": 23.22222137451172, "blob_id": "b8f32211d289a783f4db9115c285f5cf0f324808", "content_id": "95b7e87d2c858da8105f93ff11a89b9c1c95ea2e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 654, "license_type": "no_license", "max_line_length": 110, "num_lines": 27, "path": "/maximum sum path(binary tree).py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : root node of tree\n # @return an integer\n def __init__(self):\n self.maX = float('-inf')\n \n \n def maxPathSum(self, A):\n #using DFS\n #global maX\n\n \n\n def dfs(node):\n \n if not node:\n return 0\n \n left_val = dfs(node.left)\n right_val = dfs(node.right)\n cur_val = node.val\n\n self.maX = max(left_val+cur_val , right_val+cur_val, cur_val,left_val+right_val+cur_val, self.maX)\n return max(left_val, right_val) + cur_val\n \n ans = dfs(A)\n return self.maX\n" }, { "alpha_fraction": 0.5709571242332458, "alphanum_fraction": 0.5742574334144592, "avg_line_length": 26.545454025268555, "blob_id": "64fddffa531b06d198f9f0404417f84f912cf6b3", "content_id": "94fc483757b0cab8b6e30a30b920c1017ab6941f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 303, "license_type": "no_license", "max_line_length": 88, "num_lines": 11, "path": "/largest_coprime_divisor.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\nclass Solution:\n # @param A : integer\n # @param B : integer\n # @return an integer\n def cpFact(self, A, B):\n #largets factor of A is A, if A is divisdible by A then remove the common factor\n while(math.gcd(A,B)!=1):\n A= A//math.gcd(A,B)\n\n return A\n" }, { "alpha_fraction": 0.4926387369632721, "alphanum_fraction": 0.4994337558746338, "avg_line_length": 30.5, "blob_id": "a97607fedd9e01894af057dfa34418c61c19b745", "content_id": "73e4e1019f590c6aec19bd5c5b7d0a13f7281a1e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 883, "license_type": "no_license", "max_line_length": 76, "num_lines": 28, "path": "/overlapping intervals.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for an interval.\n# class Interval:\n# def __init__(self, s=0, e=0):\n# self.start = s\n# self.end = e\n\nclass Solution:\n # @param intervals, a list of Intervals\n # @return a list of Interval\n def merge(self, intervals):\n ans=[]\n intervals.sort(key = lambda x: x.start)\n \n ans.append(intervals[0])\n #print(intervals[0])\n for i in intervals[1:]:\n prev_interval = ans[-1]\n if prev_interval.end >= i.start:\n #cool overlapping, but partially or fully overlapping? hmm..\n if prev_interval.end>=i.end:\n #fully overlapping no changes\n continue\n else:\n #partial overlapping\n prev_interval.end = i.end\n else:\n ans.append(i)\n return ans\n\n" }, { "alpha_fraction": 0.4342857003211975, "alphanum_fraction": 0.4628571569919586, "avg_line_length": 23.928571701049805, "blob_id": "b2ebd9e422372b4d2e08af5c5645f32b80d924fd", "content_id": "4dca0ea824e36df76c286d3af32eef21e0ad2200", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 350, "license_type": "no_license", "max_line_length": 48, "num_lines": 14, "path": "/min jumps array dp.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import sys\nsys.setrecursionlimit(10**6)\nclass Solution:\n # @param A : list of integers\n # @return an integer\n def jump(self, A):\n if A==[]:\n return -1\n\n if A[0]>len(A):\n return 1+self.jump(A[1:])\n else:\n return 1 + min(self.jump(A[1:]),\n self.jump(A[A[0]:]))\n\n" }, { "alpha_fraction": 0.4664764702320099, "alphanum_fraction": 0.4907275438308716, "avg_line_length": 18.799999237060547, "blob_id": "516326a048d69d0af5b156e63746b07eb755824a", "content_id": "34622e566f0534e5c0f0a73f3a780ed9f6937868", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 701, "license_type": "no_license", "max_line_length": 63, "num_lines": 35, "path": "/kth_smallest.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import Counter\nINT_MAX = 2147483647\n#brute force\ndef kth_smallest(a,k):\n l = len(a)\n low =min(a)\n high = max(a)\n\n while(low<=high):\n mid = low + (high - low)//2\n count_less, count_equal =0,0\n\n for i in range(l):\n if a[i]< mid:\n count_less+=1\n elif a[i]==mid:\n count_equal+=1\n\n if count_less<k and (count_less + count_equal) >=k:\n return mid\n\n elif count_less>= k:\n high = mid-1\n\n elif (count_less < k and count_less + count_equal < k):\n low = mid+1\n\n\n\n\n\na = list(map(int,input().split(', ')))\nk = int(input())\n\nprint(kth_smallest(a, k))\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.48816028237342834, "alphanum_fraction": 0.49544626474380493, "avg_line_length": 23.136363983154297, "blob_id": "a1de8532147028ef0bdbee69940735bfaeb4ef28", "content_id": "66743528250b2611c6d5aa8d425eec5a856a1811", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 549, "license_type": "no_license", "max_line_length": 40, "num_lines": 22, "path": "/min length tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @return an integer\n lenn=float('inf')\n def minDepth(self, A, l=1):\n if not A:\n return 0\n \n if not A.left and not A.right:\n self.lenn = min(self.lenn,l)\n \n self.minDepth(A.left, l+1)\n self.minDepth(A.right, l+1)\n \n return self.lenn\n \n \n" }, { "alpha_fraction": 0.29742613434791565, "alphanum_fraction": 0.3012392818927765, "avg_line_length": 21.542856216430664, "blob_id": "37dc910baaf6f1551fb8e143c652a24dc55af56a", "content_id": "ac2a28b2d77c9151c5f4791cf384a2d5a5f9f7e8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1049, "license_type": "no_license", "max_line_length": 38, "num_lines": 35, "path": "/right view tree bfs.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @return a list of integers\n def solve(self, A):\n depth={}\n def traversal(A,l=0):\n if not A:\n return\n \n if l not in depth:\n depth[l]= [A.val]\n \n else:\n depth[l].append(A.val)\n \n traversal(A.left,l+1)\n traversal(A.right,l+1)\n \n return\n \n traversal(A)\n ans=[]\n for i in depth:\n ans.append(depth[i][-1])\n \n \n \n return ans\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "alpha_fraction": 0.38732394576072693, "alphanum_fraction": 0.38967135548591614, "avg_line_length": 22.66666603088379, "blob_id": "775219b844ed4547a95e3248316e6921eb9ad7cd", "content_id": "8c334b99210aa1c286eff40f3a35053f72625329", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 426, "license_type": "no_license", "max_line_length": 55, "num_lines": 18, "path": "/check_parenthesis_stack.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "s = Stack()\nstrr = str(input())\ndef parant_check(strr,s):\n map = {']':'[','}':'{',')':'('}\n for i in strr:\n if i in ['[','{','(']:\n s.push(i)\n elif i in [']','}',')']:\n if not s.is_empty() and s.peek() == map[i]:\n s.pop()\n else:\n return False\n if s.size()==0:\n return True\n else:\n return False\n\nprint(parant_check(strr,s))\n" }, { "alpha_fraction": 0.4400978088378906, "alphanum_fraction": 0.44498777389526367, "avg_line_length": 29.769229888916016, "blob_id": "754ba8e7ce2cfdde3c8f4ab4b1df82da8e9dff97", "content_id": "5e3c90037fe28d6f17c983c5889e15d965492046", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 409, "license_type": "no_license", "max_line_length": 56, "num_lines": 13, "path": "/anagram_hashing.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : tuple of strings\n # @return a list of list of integers\n def anagrams(self, A):\n ref = {}\n #A = list(A)\n for i in range(len(A)):\n if \"\".join(sorted(A[i])) in ref:\n ref[\"\".join(sorted(A[i]))].append(i + 1)\n else:\n ref[\"\".join(sorted(A[i]))] = [i + 1]\n \n return list(ref.values())\n \n" }, { "alpha_fraction": 0.40391677618026733, "alphanum_fraction": 0.40391677618026733, "avg_line_length": 24.576923370361328, "blob_id": "fcd4ec0f528d766375d5021bec79e6b8647d8689", "content_id": "b1d59e82e3e4e0c4a5b2b4332ff61539dcbfc479", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 817, "license_type": "no_license", "max_line_length": 46, "num_lines": 26, "path": "/merge tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @param B : root node of tree\n # @return the root node in the tree\n def solve(self, A, B):\n if A is None and B is None:\n return None\n if A and not B:\n return A\n if not A and B:\n return B\n \n #print(A.val)\n A.val = A.val + B.val\n #print(A.val)\n A.left = self.solve(A.left, B.left)\n A.right = self.solve(A.right, B.right)\n \n return A\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "alpha_fraction": 0.4289044141769409, "alphanum_fraction": 0.4312354326248169, "avg_line_length": 23.787878036499023, "blob_id": "5bbee2e9db426a8844e40af2f1ebc8c5e85fdb01", "content_id": "3c722c338f7aba8470933fd63a0612eb72756815", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 858, "license_type": "no_license", "max_line_length": 43, "num_lines": 33, "path": "/Path to Given Node.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @param B : integer\n # @return a list of integers\n def __init__(self):\n self.path = []\n def solve(self, A, B):\n\n #path.append(A.val)\n def findPath(node, key):\n if not node: #or path[-1]==key:\n return\n \n self.path.append(node.val)\n if node.val != key:\n findPath(node.left,key)\n findPath(node.right, key)\n if self.path[-1]!=key:\n self.path.pop()\n \n \n\n return\n \n findPath(A,B)\n return self.path\n\n \n \n \n" }, { "alpha_fraction": 0.39620253443717957, "alphanum_fraction": 0.41012659668922424, "avg_line_length": 27.730770111083984, "blob_id": "245eab575e60a72ccad32b323387b1254d900ea9", "content_id": "f3c649556a4f732dc696f0401347da2b546eff5a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 790, "license_type": "no_license", "max_line_length": 50, "num_lines": 26, "path": "/next_greater_stack.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import collections\nclass Solution:\n # @param A : list of integers\n # @return a list of integers\n def nextGreater(self, A):\n stack = collections.deque()\n ans = collections.deque()\n \n l = len(A) - 1\n while l >= 0:\n if stack and A[l] < stack[-1]:\n ans.appendleft(stack[-1])\n \n elif stack and A[l] >= stack[-1]:\n while stack and stack[-1] <= A[l]:\n stack.pop()\n if stack:\n ans.appendleft(stack[-1])\n else:\n ans.appendleft(-1)\n \n elif len(stack) == 0:\n ans.appendleft(-1)\n stack.append(A[l])\n l -= 1\n return ans\n \n \n \n" }, { "alpha_fraction": 0.48049280047416687, "alphanum_fraction": 0.48049280047416687, "avg_line_length": 23.210525512695312, "blob_id": "4079ddbcae708311e1681610ddca9806966fe68b", "content_id": "f9cf11cb1246506c2010c0cce0ae3016b23f0da8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 487, "license_type": "no_license", "max_line_length": 46, "num_lines": 19, "path": "/reverse_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @return the head node in the linked list\n def reverseList(self, A):\n prev = None\n itr = A\n while itr:\n nextt = itr.next\n itr.next = prev\n prev = itr\n itr = nextt\n A = prev\n return A\n\n \n \n" }, { "alpha_fraction": 0.5963541865348816, "alphanum_fraction": 0.6223958134651184, "avg_line_length": 11.600000381469727, "blob_id": "6045cd3f5d15ad5ae8f6cdefaac086a071ecf0ab", "content_id": "9dc61f040065f20abc35ce3c9ecbaa18931abf0d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 384, "license_type": "no_license", "max_line_length": 67, "num_lines": 30, "path": "/number_of_unique_paths.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\nimport string\ndef number_of_unique_paths(A,B):\n \"\"\"\n\n :param A: grid length\n :param B: grid breadth\n :return: number of unique ways to reach final destination\n \"\"\"\n a= A-1\n b=B-1\n ans = math.factorial(a+b)/(math.factorial(b)*math.factorial(a))\n return ans\nprint(number_of_unique_paths(3,3))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprint(excel_colimn_base(468096))\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.40105077624320984, "alphanum_fraction": 0.4115586578845978, "avg_line_length": 17.766666412353516, "blob_id": "b978636adabf775ddd778010ad37aa8a0a3cfa92", "content_id": "923bb4020877caa416139968746cdb42c01cdc7d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 571, "license_type": "no_license", "max_line_length": 61, "num_lines": 30, "path": "/JSON_indendation.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\n\n#brute force\ndef json_indendation(S):\n \"\"\"\n\n :param S: a string\n :return: an indendation answer to prettify the string\n \"\"\"\n ans= S[0] + '\\n\\t'\n count=1\n for i in S[1:]:\n if i =='{' or i =='[':\n\n ans+= '\\n'+ '\\t'*count + i+ '\\n' + '\\t'*(count+1)\n count+=1\n elif i==',':\n ans+=',\\n' + '\\t'*count\n elif i=='}' or i ==']':\n count-=1\n ans+= '\\n'+ '\\t'*count + i\n else:\n ans+=i\n return ans\n\n\n\nS= str(input())\n\nprint(json_indendation(S))\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.3915211856365204, "alphanum_fraction": 0.4164588451385498, "avg_line_length": 24.0625, "blob_id": "fbf06ecd1731d4eaa6ea6cd7d4b71dc08eb07866", "content_id": "0d4afeb1854d76b2b2c50bd0d82082ef41b54f50", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 401, "license_type": "no_license", "max_line_length": 38, "num_lines": 16, "path": "/modular_expressions_recursive.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : integer\n # @param B : integer\n # @param C : integer\n # @return an integer\n def Mod(self, A, B, C):\n if B==0 and A==0:\n return 0\n if B==0:\n return 1\n if B%2==0:\n res = self.Mod(A, B//2, C)\n return (res**2)%C\n else:\n res = self.Mod(A,B-1, C)\n return ((A%C)*res)%C\n" }, { "alpha_fraction": 0.37945792078971863, "alphanum_fraction": 0.4022824466228485, "avg_line_length": 28.20833396911621, "blob_id": "833a297e9c174e4b245e9c6dd1fb6b872fe84b49", "content_id": "6b11ac4ea77f9decd6b371d2d8be60f8cc4fc1cf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 701, "license_type": "no_license", "max_line_length": 69, "num_lines": 24, "path": "/Tushar's bday Dp.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : tuple of integers\n # @param B : tuple of integers\n # @param C : tuple of integers\n # @return an integer\n def solve(self, A, B, C):\n cap= max(A)\n n = len(B)\n dp = [[0 for i in range(cap+1)] for j in range(n+1)]\n \n for i in range(cap+1):\n dp[0][i] = float('inf')\n #dp[1][i] = i\n \n for i in range(1, n+1):\n for j in range(1,cap+1):\n if j>=B[i-1]:\n dp[i][j]= min(dp[i-1][j], C[i-1]+dp[i][j-B[i-1]])\n else:\n dp[i][j]= dp[i-1][j]\n summ=0\n for c in A:\n summ+=dp[n][c]\n return summ\n" }, { "alpha_fraction": 0.5590476393699646, "alphanum_fraction": 0.569523811340332, "avg_line_length": 19.173076629638672, "blob_id": "136e3b725f5b796cebd2817c6be7aa6ca8d3aba1", "content_id": "44cd8200d1abe668d186bac977c446f4f87f5f28", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1050, "license_type": "no_license", "max_line_length": 42, "num_lines": 52, "path": "/print_binary_queue.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import deque\nimport time\nimport threading\n\nclass Queue():\n def __init__(self):\n self.Q = deque()\n def dequeue(self):\n return self.Q.pop()\n def enqueue(self,val):\n self.Q.appendleft(val)\n def size(self):\n return len(self.Q)\n def peek(self):\n return self.Q[-1]\n def is_empty(self):\n return len(self.Q)==0\n\nQ = Queue()\ndef Place_order(order):\n for order in orders:\n print(\"Placing order for:\",order)\n\n Q.enqueue(order)\n time.sleep(0.5)\n\ndef Serve_order():\n time.sleep(1)\n while not Q.is_empty():\n order = Q.dequeue()\n print(\"Now serving \",order)\n time.sleep(1)\n\n\n\n\n\ndef produce_binary_numbers(n):\n numbers_queue = Queue()\n numbers_queue.enqueue(\"1\")\n\n for i in range(n):\n front = numbers_queue.peek()\n print(\" \", front)\n numbers_queue.enqueue(front + \"0\")\n numbers_queue.enqueue(front + \"1\")\n\n numbers_queue.dequeue()\n\n\nif __name__ == '__main__':\n produce_binary_numbers(20)\n\n" }, { "alpha_fraction": 0.5273159146308899, "alphanum_fraction": 0.5558194518089294, "avg_line_length": 35.5217399597168, "blob_id": "9c4f97c850148650264d8a78688f60e9b5dd2c2e", "content_id": "a252b03c5b74ce0c65113cb87d0be3b794216e2f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 842, "license_type": "no_license", "max_line_length": 200, "num_lines": 23, "path": "/square_root.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# this is using the famous babylon method to find the square root\n\"\"\" \nMake an initial guess. Guess any positive number x0.\nImprove the guess. Apply the formula x1 = (x0 + S / x0) / 2. The number x1 is a better approximation to sqrt(S).\nIterate until convergence. Apply the formula xn+1 = (xn + S / xn) / 2 until the process converges. Convergence is achieved when the digits of xn+1 and xn agree to as many decimal places as you desire.\n\"\"\"\n\nclass Solution:\n # @param A : integer\n # @return an integer\n def sqrt(self, A):\n if A==0:\n return 0\n elif A in range(1,4):\n return 1\n else:\n x = A//2\n x1 = (x + A/x)/2\n while(int(x)>int(x1)):\n x = x1\n x1 = (x1 + A/x1)/2\n if int(x)==int(x1):\n return int(x)\n\n\n" }, { "alpha_fraction": 0.30315789580345154, "alphanum_fraction": 0.35789474844932556, "avg_line_length": 20.11111068725586, "blob_id": "8262e43a5ac521e9356501a5462eb5a0d159e15a", "content_id": "cef0063536f1f16c800341c73d586b839009a814", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 950, "license_type": "no_license", "max_line_length": 47, "num_lines": 45, "path": "/atoi.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : string\n # @return ascii to integer\n def atoi(self, a):\n \"\"\"\n with overflow conditons,\n \n \n :param S: a string numerical\n :return: converted integer\n \"\"\"\n pos=True\n if a[0]=='+':\n pos=True\n a=a[1:]\n elif a[0]=='-':\n pos = False\n a=a[1:]\n \n \n ref = '0123456789'\n l = 0\n ans=0\n c=0\n while(l<len(a)):\n if a[l] in ref:\n c+=1\n l += 1\n elif a[l] not in ref:\n break\n for i in range(c):\n ans+= ref.index(a[i])*(10**(c-i-1))\n \n \n \n \n if not pos:\n ans*=-1\n if (abs(ans) > ((1 << 31) - 1)):\n return -1*2147483648\n return ans\n if (abs(ans) > ((1 << 31) - 1)):\n return 2147483647\n \n return ans\n" }, { "alpha_fraction": 0.5085877776145935, "alphanum_fraction": 0.5114504098892212, "avg_line_length": 28.11111068725586, "blob_id": "162ad9599a7392f05bf415643c20769299fe9124", "content_id": "2128c677b752877111ff691417be3768f53bd882", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1048, "license_type": "no_license", "max_line_length": 142, "num_lines": 36, "path": "/N_queens.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def N_queens(n):\n ans=[]\n\n def back_track(col_array=[]):\n if len(col_array) == n: #GOAL ===> if our column length reaches n\n ans.append(col_array[:])\n return\n\n for i in range(n): #CHOICES ===> here the choice are all the column values\n col_array.append(i)\n if isValid(col_array): #CONSTRAINTS ====> sub function; ensure that no thing is right below, and nothing is diagonally opposite\n back_track(col_array)\n\n col_array.pop()\n\n def isValid(col_array):\n row_index = len(col_array)-1\n for i in range(row_index):\n dif = abs(col_array[i] - col_array[row_index])\n if dif==0 or dif== row_index - i:\n return False\n return True\n\n back_track()\n return ans\n\nif __name__ == \"__main__\":\n n = int(input())\n main=[]\n queens = N_queens(n)\n for i in queens:\n sub=[]\n for j in i:\n sub.append(\".\"*j+ 'Q'+\".\"*(n-1-j))\n main.append(sub)\n print(main)\n" }, { "alpha_fraction": 0.5146805047988892, "alphanum_fraction": 0.5354058742523193, "avg_line_length": 16.90625, "blob_id": "91f1b7eaea0789a70839799ad691f1e607217958", "content_id": "67ae90dcd30cbe0651dddbb2cea94e6b01500cee", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 579, "license_type": "no_license", "max_line_length": 92, "num_lines": 32, "path": "/primesum.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def primesum(A):\n \"\"\"\n every even number can be represented by sum of 2 prime numbers.\n here we create a list of all primes less than A. and check if i and A-i are in the list.\n :param A: even postive number\n :return: a pair of prime numbers\n \"\"\"\n if A == 4:\n return [2, 2]\n\n sei = [False]*2+ [True]*(A-1)\n for i in range(2,int(A**0.5)+1):\n\n\n for j in range(i**2,A+1,i):\n sei[j]=False\n\n\n for i in range(len(ans)):\n if ans[A-i] and ans[i]:\n return [i,A-i]\n\n\n\n\n\n\n\n\n\nA = int(input())\nprint(primesum(A))\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.4445350766181946, "alphanum_fraction": 0.46655791997909546, "avg_line_length": 34.30303192138672, "blob_id": "0c43eb4515126767684d343077b96988fa68baaf", "content_id": "06274f1353badc26fcd9077868eadf84b136d11f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1226, "license_type": "no_license", "max_line_length": 82, "num_lines": 33, "path": "/median 2 sorted array.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:\n if len(nums1)>len(nums2):\n nums1,nums2 = nums2, nums1\n \n \n x = len(nums1)\n y = len(nums2)\n \n flag = (x+y)%2==0\n \n high = x\n low=0\n while low<=high:\n xPartition = (low+high)//2\n yPartition = (x+y+1)//2 - xPartition\n \n maxLeftX = float('-inf') if xPartition==0 else nums1[xPartition-1]\n minRightX = float('inf') if xPartition==x else nums1[xPartition]\n \n maxLeftY = float('-inf') if yPartition==0 else nums2[yPartition-1]\n minRightY = float('inf') if yPartition==y else nums2[yPartition]\n \n if maxLeftX<=minRightY and maxLeftY<=minRightX:\n if not flag:\n return max(maxLeftX,maxLeftY )\n else:\n return (max(maxLeftX,maxLeftY) + min(minRightX,minRightY))/2\n \n elif maxLeftX>minRightY:\n high = xPartition-1\n else:\n low = xPartition+1\n \n \n \n \n \n" }, { "alpha_fraction": 0.3919716775417328, "alphanum_fraction": 0.3943329453468323, "avg_line_length": 25, "blob_id": "0012ae73d476cbfb996fd0a4233e96251f70e1ed", "content_id": "11dec40f14ea2ca6b000e523ffff826d20b1702d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 847, "license_type": "no_license", "max_line_length": 93, "num_lines": 27, "path": "/validate_BST.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @return an integer\n def isValidBST(self, A):\n \n def sub_validate(A, upper=float('-inf'), lower=float('inf')):\n if not A:\n return True\n \n if A.val>=upper or A.val<=lower:\n return False\n \n return sub_validate(A.left, A.val, lower) and sub_validate(A.right, upper, a.val)\n \n \n if sub_validate(A):\n return 1\n \n else:\n return 0\n \n \n \n\n \n \n \n \n \n \n \n \n\n \n\n \n" }, { "alpha_fraction": 0.397550106048584, "alphanum_fraction": 0.40089085698127747, "avg_line_length": 22.405405044555664, "blob_id": "b34d54deef766a0df8cc41f5edaf04f20f9efd04", "content_id": "4dba1d5bedee87716ca2a3adb9e944d22921f841", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 898, "license_type": "no_license", "max_line_length": 40, "num_lines": 37, "path": "/verticalOrderTraversalTre.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @return a list of list of integers\n \n def verticalOrderTraversal(self, A):\n vr={}\n def vertical(A,c=0):\n if not A:\n return\n if c in vr:\n \n vr[c].append(A.val)\n #print(self.vr)\n else:\n vr[c]=[A.val]\n #print(self.vr)\n \n vertical(A.left, c+1)\n vertical(A.right, c-1)\n \n vertical(A)\n c = list(vr.keys())\n ans=[]\n c.sort()\n for i in reversed(c):\n \n ans.append(vr[i])\n #print(ans)\n \n return ans\n \n \n \n \n" }, { "alpha_fraction": 0.37605804204940796, "alphanum_fraction": 0.38210397958755493, "avg_line_length": 22.558822631835938, "blob_id": "66d5202b1c0522d69d2815723e797a1a53f354bc", "content_id": "ded19a1e699d54d6f6eccc5c5bb9a656e0a6fe6c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 827, "license_type": "no_license", "max_line_length": 44, "num_lines": 34, "path": "/sum root to leaf.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : root node of tree\n # @return an integer\n ans=[]\n def sumNumbers(self, A):\n \n def summer(A,s=''):\n if not A:\n return\n \n s += str(A.val)\n \n if not A.right and not A.left:\n print(s)\n self.ans.append(s)\n #return\n \n if A.left:\n summer(A.left,s)\n if A.right:\n summer(A.right,s)\n \n s = s[:-1]\n \n #return\n summer(A)\n return (sum(map(int,self.ans)))%1003\n \n \n" }, { "alpha_fraction": 0.413943350315094, "alphanum_fraction": 0.47058823704719543, "avg_line_length": 14.551724433898926, "blob_id": "acb0851829b54383e1a5564c22edc3d9d77c66a7", "content_id": "bcb9e3e235e9a2f003bcec69aa5b0c9ea4f95cf0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 459, "license_type": "no_license", "max_line_length": 34, "num_lines": 29, "path": "/counting_triangles.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import Counter\nINT_MAX = 2147483647\n#brute force\ndef counting_triangles(a):\n a.sort()\n l = len(a)\n count=0\n for i in range(l-2):\n s0 = i+1\n s1 = l-1\n while(s0<s1):\n if a[i]+a[s0]> a[s1]:\n count+= s1 - s0\n s1-=1\n\n else:\n s0 += 1\n return count\n\n\n return count\n\n\n\n\n\n\na = list(map(int,input().split()))\nprint(counting_triangles(a))\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.4719334840774536, "alphanum_fraction": 0.4844074845314026, "avg_line_length": 19.04166603088379, "blob_id": "ce5b12b5df7b76318d1bd4e15701037343d6da45", "content_id": "0144ea25390d06546e12807ebebf54cef17dacbf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 481, "license_type": "no_license", "max_line_length": 38, "num_lines": 24, "path": "/subsets_duplicates.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def subset_dup(arr=[1,2,2]):\n ans = []\n arr.sort()\n\n def back_tracking(i=0, subset=[]):\n # goal\n if i == len(arr):\n if subset not in ans:\n ans.append(subset[:])\n return\n\n back_tracking(i + 1, subset)\n subset.append(arr[i])\n back_tracking(i + 1, subset)\n subset.pop()\n\n # function calling\n back_tracking()\n\n ans.sort()\n return ans\n\nif __name__ ==\"__main__\":\n print(subset_dup())\n" }, { "alpha_fraction": 0.38357487320899963, "alphanum_fraction": 0.3874396085739136, "avg_line_length": 22.35714340209961, "blob_id": "0b26a46afcf4d7518006447ea938d8ef89eb3b05", "content_id": "4668876dbb4ae569169f608d295c3038c702508b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1035, "license_type": "no_license", "max_line_length": 46, "num_lines": 42, "path": "/reorder_linked_lists.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @return the head node in the linked list\n def reorderList(self, A):\n count =0\n mid = A\n itr = A\n while itr:\n if count%2==1:\n mid = mid.next\n itr = itr.next\n count+=1\n \n prev = None\n itr = mid\n while itr:\n temp = itr.next\n itr.next = prev\n prev = itr\n itr = temp\n rev = prev\n ib = rev\n ia = A\n while ib:\n tempa = ia.next\n if tempa == None:\n ia = ib\n return A\n if tempa ==ib:\n return A\n ia.next = ib\n tempb = ib.next\n ib.next = tempa\n ia = tempa\n ib = tempb\n return A\n \n \n \n \n \n \n" }, { "alpha_fraction": 0.4867549538612366, "alphanum_fraction": 0.5, "avg_line_length": 32.55555725097656, "blob_id": "0a95b70821c7d5bffd40d81d20ccbb7f0f12a00e", "content_id": "85fbc912b23c6748e32db60d05180cb1bb66ef54", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 302, "license_type": "no_license", "max_line_length": 69, "num_lines": 9, "path": "/rotate_matrix.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of list of integers\n # @return the same list modified\n def rotate(self, A):\n l = len(A)\n for i in range(l//2):\n A[i], A[l-i-1] = A[l-i-1], A[i]\n ans = [[A[j][i] for j in range(l)] for i in range(len(A[0]))]\n return ans\n" }, { "alpha_fraction": 0.3896551728248596, "alphanum_fraction": 0.40344828367233276, "avg_line_length": 21.30769157409668, "blob_id": "e1f5baac0a84144430be185f1d25d5cf9d9d744d", "content_id": "0335800f0b76e1edb2146d4a93ba41c1fc58f58e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 290, "license_type": "no_license", "max_line_length": 35, "num_lines": 13, "path": "/remove_duplicates.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @return an integer\n def removeDuplicates(self, a):\n\n q=0\n t=1\n for i in range(1,len(a)):\n if a[i]!=a[q]:\n q=t\n a[i],a[t]=a[t],a[i]\n t+=1\n return t\n" }, { "alpha_fraction": 0.3741573095321655, "alphanum_fraction": 0.3797752857208252, "avg_line_length": 21.243244171142578, "blob_id": "2e2aca7d542218abb37bc0a3d73ba184a857e657", "content_id": "f92399f3ac3c2ed712850639bab20bd9307ea2ef", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 890, "license_type": "no_license", "max_line_length": 46, "num_lines": 37, "path": "/rotate_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @param B : integer\n # @return the head node in the linked list\n def rotateRight(self, A, B):\n itr = A\n len = 0\n while itr:\n len += 1\n itr = itr.next\n n = B%len\n if n==0:\n return A\n n = len - n\n\n\n \n count =0\n itr = A\n while itr:\n count+=1\n if count ==n:\n var = itr.next\n itr.next = None\n itr = var\n continue\n elif itr.next==None:\n itr.next = A\n return var\n \n itr = itr.next\n\n \n \n \n \n \n \n" }, { "alpha_fraction": 0.40257880091667175, "alphanum_fraction": 0.47277936339378357, "avg_line_length": 21.516128540039062, "blob_id": "efac006d4b0a17e97eb291246fcada94b1ad850b", "content_id": "5e2b611d54243e7c39dea0c60b015f4d46a9d63c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 698, "license_type": "no_license", "max_line_length": 83, "num_lines": 31, "path": "/integer_division_bit_manipulation.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "\"\"\"\napproach is we are trying to represnt the dividend in a binary format. say 36/3 =12\n12 is 1100 in binary\nso we cal also say that 3*(8 + 4 + 0 + 0) = 36\nso we keep removing the biggest power of 2 *b that is less than A.\n\"\"\"\n\nclass Solution:\n # @param A : integer\n # @param B : integer\n # @return an integer\n def divide(self, A, B):\n if A==-2147483648 and B==-1:\n return 2147483647\n\n sign=1\n \n if (A<0)^(B<0):\n sign =-1\n \n a,b = abs(A),abs(B)\n\n \n res=0\n while( a>=b):\n x=0\n while(a >= b<<1<<x):\n x+=1\n res += 1<<x\n a-= b<<x\n return res*sign\n" }, { "alpha_fraction": 0.4285714328289032, "alphanum_fraction": 0.43877550959587097, "avg_line_length": 23.55555534362793, "blob_id": "1991cfa47171043dbb934714edb7a4257730241e", "content_id": "ca6245d89a5d7c1ae471f8b1ad70c07493798022", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 686, "license_type": "no_license", "max_line_length": 59, "num_lines": 27, "path": "/binary_sort_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @return the head node in the linked list\n def solve(self, A):\n #tryin to over write instead of new memory location\n itr = A\n count=[0,0]\n while itr:\n count[itr.val]+=1\n itr = itr.next\n \n itr = A\n i=0\n while itr:\n if count[i]==0:\n i+=1\n else:\n itr.val = i\n count[i]-=1\n itr = itr.next\n return A\n \n \n\n" }, { "alpha_fraction": 0.49840256571769714, "alphanum_fraction": 0.5175718665122986, "avg_line_length": 31.947368621826172, "blob_id": "717029776038e37509b0a74dd01fd548fdbc5c94", "content_id": "3903903d934e0687d3b9a7f171985143e936b848", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 626, "license_type": "no_license", "max_line_length": 89, "num_lines": 19, "path": "/Maximum Absolute Difference.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : list of integers\n # @return an integer\n def maxArr(self, A):\n #Maximum Absolute Difference\n \"\"\"\n trick open the absolute and try to see how to optimise keeping the compleity low.\n f(i,j) = | A[i] - A[j] | + | i - j |\n :param A: a list og numbers both pos and negative\n :return: using the above function generate the max value\n \"\"\"\n l1 =[]\n l2 = []\n for i,v in enumerate(A):\n l1.append(v+i)\n l2.append(v-i)\n m1 = max(l1) - min(l1)\n m2 = max(l2)- min(l2)\n return (max(m1,m2))\n" }, { "alpha_fraction": 0.38793104887008667, "alphanum_fraction": 0.4166666567325592, "avg_line_length": 26.799999237060547, "blob_id": "f1f2977a0f17130b8c51e8d64e9ef3afc8b3e736", "content_id": "d6947142a98dfd7126263528f06b7711b466caef", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 696, "license_type": "no_license", "max_line_length": 58, "num_lines": 25, "path": "/palindrome_without_alphaN.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import string\nclass Solution:\n # @param A : string\n # @return an integer\n def isPalindrome(self, s):\n s = s.lower()\n l = len(s)\n low=0\n high = l-1\n ref=\"abcdefghijklmnopqrstuvwxyz0123456789\"\n while(low<=high):\n if s[low] in ref and s[high] in ref:\n if s[low]==s[high]:\n low+=1\n high-=1\n else:\n return 0\n elif s[low] not in ref and s[high] not in ref:\n low+=1\n high-=1\n elif s[low] not in ref:\n low+=1\n elif s[high] not in ref:\n high-=1\n return 1\n\n" }, { "alpha_fraction": 0.4577586352825165, "alphanum_fraction": 0.48362070322036743, "avg_line_length": 24.217391967773438, "blob_id": "0d23204eb54f9beac22bf46f189a759ceafbe71d", "content_id": "87c6d0b6f709958a6b26fcc0bf7409d67d099793", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1160, "license_type": "no_license", "max_line_length": 79, "num_lines": 46, "path": "/Spiral.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def spiral(A):\n \"\"\"\n there will be 4 pointers called top,bottom,left, right\n keeping one fixed the others will travers all following a single while loop\n doing the same way as following a aprial staircase\n ther will also be 4 directoion\n 0: l to r\n 1: u to d\n 2: r to l\n 3: d to u\n\n :param A: a square matrix\n :return: a linear array with the spiral elements\n \"\"\"\n l=len(A)\n top,left=0,0\n right = len(A[0])-1\n bottom = l-1\n dir=0\n ans=[]\n\n while(top<=bottom and left<=right):\n\n if dir==0:\n for i in range(left,right+1):\n ans.append(A[top][i])\n top+=1\n dir+=1\n elif dir==1:\n for i in range(top,bottom+1):\n ans.append(A[i][right])\n right-=1\n dir+=1\n elif dir==2:\n #r to l\n for i in range(right,left-1,-1):\n ans.append(A[bottom][i])\n bottom-=1\n dir+=1\n elif dir==3:\n #d to u\n for i in range(bottom,top-1,-1):\n ans.append(A[i][left])\n left+=1\n dir=0\n return ans\n" }, { "alpha_fraction": 0.31661441922187805, "alphanum_fraction": 0.34639498591423035, "avg_line_length": 29.380952835083008, "blob_id": "383ca32b2a69f3affd370bd5637ce7365747b5c3", "content_id": "d77f9f0d60a108473cc3fd99cab3c0d83d82c95f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 638, "license_type": "no_license", "max_line_length": 62, "num_lines": 21, "path": "/three_sum_closest.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "INT_MAX = 2147483647\nclass Solution:\n # @param A : list of integers\n # @param B : integer\n # @return an integer\n def threeSumClosest(self, A,B):\n i = 0,\n ans = A[0] + A[1] + A[2]\n A.sort()\n for i in range(len(A) - 2):\n l, r = i+1, len(A) - 1\n while l < r:\n if abs(ans - B) > abs(A[i] + A[l] + A[r] - B):\n ans = A[i] + A[l] + A[r]\n if A[i] + A[l] + A[r] > B:\n r -= 1\n elif A[i] + A[l] + A[r] < B:\n l += 1\n else:\n return B\n return ans\n" }, { "alpha_fraction": 0.45719844102859497, "alphanum_fraction": 0.45719844102859497, "avg_line_length": 25.912281036376953, "blob_id": "860aa47c93ddcfa2457b124d1056a44b9f325725", "content_id": "57feae955a67e59996783b02e0d7d3acab4fb8bd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1542, "license_type": "no_license", "max_line_length": 62, "num_lines": 57, "path": "/delete_duplicates_linked_list.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @param A : head node of linked list\n # @return the head node in the linked list\n def deleteDuplicates(self, A):\n prev_prev = None\n prev = A\n current = A.next\n \n while current:\n if current.val != prev.val:\n prev_prev = prev\n prev = current\n current = current.next\n else:\n while current and current.val == prev.val:\n prev = current\n current = current.next\n if prev_prev != None: prev_prev.next = current\n if prev_prev == None: A = current\n prev = current\n if current: current = current.next\n return A\n \ndef delete_duplicate_my_solution(A):\n clock = None\n itr = A\n prev = None\n while itr:\n if clock and itr.val == clock:\n if prev:\n prev.next = itr.next\n itr = itr.next\n if itr == None:\n return A\n\n elif itr.next == None:\n return A\n\n\n elif itr.val != itr.next.val:\n if prev == None:\n A = itr\n prev = itr\n clock = None\n itr = itr.next\n elif itr.val == itr.next.val:\n\n clock = itr.val\n if prev:\n prev.next = itr.next\n itr = itr.next \n" }, { "alpha_fraction": 0.36666667461395264, "alphanum_fraction": 0.36851853132247925, "avg_line_length": 20.360000610351562, "blob_id": "42a59fc61eee96cb6790b65f730a0805eaeec918", "content_id": "5b104e9088aebd4a0cbd359cb809f73c1a591e6b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 540, "license_type": "no_license", "max_line_length": 37, "num_lines": 25, "path": "/path_directory_stacks.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import deque\nclass Solution:\n # @param A : string\n # @return a strings\n def simplifyPath(self, A):\n a = A.split('/')\n \n stack = deque()\n for i in a:\n if i in [\"\",\" \",\".\"]:\n continue\n elif i==\"..\":\n if len(stack)>0:\n stack.pop()\n else:\n continue\n else:\n stack.append(i)\n \n \n final = \"/\" + '/'.join(stack)\n \n \n \n return final\n\n \n" }, { "alpha_fraction": 0.3057607114315033, "alphanum_fraction": 0.3264401853084564, "avg_line_length": 23.178571701049805, "blob_id": "825bad3ff57957a61e4b051fca02637cf364ee84", "content_id": "ea19a5e2cc5e5f5532a83c3db838265fbf058022", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 677, "license_type": "no_license", "max_line_length": 47, "num_lines": 28, "path": "/binary_addition.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "class Solution:\n # @param A : string\n # @param B : string\n # @return a strings\n def addBinary(self, A, B):\n cy=0\n ans=''\n if len(B)>len(A):\n A,B = B,A\n \n B = '0'*(len(A)-len(B)) + B\n \n A = A[::-1]\n B= B[::-1]\n for a,b in zip(A,B):\n if int(a) + int(b) +cy >2:\n ans+= '1'\n cy=1\n elif int(a) + int(b) +cy ==2:\n ans +='0'\n cy=1\n elif int(a) + int(b) +cy <2:\n ans+= str(int(a) + int(b) +cy )\n cy=0\n if cy:\n ans+='1'\n ans= ans[::-1]\n return int(ans)\n" }, { "alpha_fraction": 0.4359712302684784, "alphanum_fraction": 0.4532374143600464, "avg_line_length": 22.925926208496094, "blob_id": "b175f7325ce8f5a66fd5807fb862975069902bd3", "content_id": "b97ed9e7fb6c0eb82e1f5fa09dbe10e939d58324", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 695, "license_type": "no_license", "max_line_length": 53, "num_lines": 27, "path": "/sorted_Array_to_BST.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n # @param A : tuple of integers\n # @return the root node in the tree\n\n \n def sortedArrayToBST(self, A):\n #2 pointer method\n # ^ ^\n # [1,2,3,4,5,6,7,8,9]\n if not A:\n return None\n \n A = list(A)\n \n mid = len(A)//2\n root = TreeNode(A[mid])\n root.right = self.sortedArrayToBST(A[mid+1:])\n root.left = self.sortedArrayToBST(A[:mid])\n \n return root\n \n\n \n \n \n" }, { "alpha_fraction": 0.35561877489089966, "alphanum_fraction": 0.37837839126586914, "avg_line_length": 20.96875, "blob_id": "eb6ee2195914418feb783c4b4ed7ba7821f66512", "content_id": "db42bff15fad8807637ee1ad18d9b5fd37ff327d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 703, "license_type": "no_license", "max_line_length": 46, "num_lines": 32, "path": "/letter_phone_BT.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "def letter_phone(s):\n ans=[]\n dic= {'1':'1',\n '2':'abc',\n '3':'def',\n '4':'ghi',\n '5':'jkl',\n '6':'mno',\n '7':'pqrs',\n '8':'tuv',\n '9':'wxyz',\n '0':'0'}\n def back_tracking(pos=0,sub_Set=''):\n\n if len(sub_Set)==len(s): #GOAL\n ans.append(sub_Set)\n return\n\n for i in dic[s[pos]]: #CHOICES\n sub_Set+=i\n if pos<len(s): #CONSTRAINT\n back_tracking(pos+1,sub_Set)\n\n #pos-=1\n sub_Set = sub_Set[:-1]\n\n back_tracking()\n return ans\n\nif __name__ ==\"__main__\":\n s = str(input())\n print(letter_phone(s))\n" }, { "alpha_fraction": 0.608433723449707, "alphanum_fraction": 0.6120482087135315, "avg_line_length": 21.432432174682617, "blob_id": "7df4c3c709b5e2fee27cd182a3170d4cb87c5d80", "content_id": "9a197934eae8e7a682fd06ea4ab1bce7a765cbd1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 830, "license_type": "no_license", "max_line_length": 71, "num_lines": 37, "path": "/longest path nary tree.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "from collections import deque\nclass Solution:\n\t# @param A : list of integers\n\t# @return an integer\n\tdef solve(self, A):\n\t\n\t\tdef adjEdges(A):\n\t\t\tedges = {v: [] for v in range(len(A))}\n\n\t\t\tfor i, n in enumerate(A):\n\t\t\t\tif n == -1:\n\t\t\t\t\tstart = i\n\t\t\t\t\tcontinue\n\t\t\t\tedges[i].append(n)\n\t\t\t\tedges[n].append(i)\n\t\t\treturn start, edges\n\n\t\tdef BFS(edges, starting, flag):\n\t\t\tQ = deque()\n\t\t\tQ.appendleft((starting,0))\n\t\t\tvisited = [False]*len(edges)\n\t\t\tvisited[starting]=True\n\t\t\twhile Q:\n\t\t\t\tnode, d = Q.pop()\n\t\t\t\tneighb = edges[node]\n\t\t\t\tfor n in neighb:\n\t\t\t\t\tif not visited[n]:\n\t\t\t\t\t\tQ.appendleft((n,d+1))\n\t\t\t\t\t\tvisited[n]=True\n\t\t\t#last node will be the node with the largest distance from root node\n\t\t\t#i.e node\n\t\t\tif not flag:\n\t\t\t\treturn node\n\t\t\treturn d\n\t\t\n\t\tstart, edges = adjEdges(A)\n\t\treturn BFS(edges,BFS(edges, start,False), True)\n" }, { "alpha_fraction": 0.5261437892913818, "alphanum_fraction": 0.5555555820465088, "avg_line_length": 16, "blob_id": "ccc801f0ccc2d3d3c08335ca80c2db3bc7c953d5", "content_id": "7516c7b6596a3dd73fc327ed1054778d84ab5fb2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 306, "license_type": "no_license", "max_line_length": 43, "num_lines": 18, "path": "/number_of_trailing_zeros.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\nimport string\ndef number_of_trailing_zeros(A):\n \"\"\"\n \n :param A: integer A>0\n :return: number of trailing zeros in A!\n \"\"\"\n \n pow = int(math.log(A,5))\n ans=0\n while(pow):\n ans+= A//(5**pow)\n pow-=1\n\n return ans\n\nprint(number_of_trailing_zeros(9247))\n" }, { "alpha_fraction": 0.39387309551239014, "alphanum_fraction": 0.4018964171409607, "avg_line_length": 20.421875, "blob_id": "58db5f048db1d3e759d0b2385fe173c7fe61e47a", "content_id": "8d6caa0f85d648bd525185cf68a333655698738e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1371, "license_type": "no_license", "max_line_length": 61, "num_lines": 64, "path": "/word_rank_with_rep.py", "repo_name": "shan-mathi/InterviewBit", "src_encoding": "UTF-8", "text": "import math\nclass Solution:\n # @param A : string\n # @return an integer\n \n def fac(self,li):\n p=1\n for i in li:\n if i>1:\n p*= math.factorial(i)\n return p\n\n def duplicate(self,i,alp):\n \n d=alp.copy()\n s = list(alp.keys())[i]\n if d[s]>1:\n d[s]-=1\n else:\n d.pop(s)\n v = list(d.values())\n ans = math.factorial(sum(v)) / self.fac(v)\n \n return ans\n \n def findRank(self, A):\n \"\"\"\n approach: interesting\n \n :param A: a word\n :return: the rank of the word when arranged lexically\n \"\"\"\n \n p = 0\n alpha = [i for i in A]\n alpha.sort()\n keys=list(set(alpha))\n keys.sort()\n v= [alpha.count(i) for i in keys]\n alp = dict(zip(keys,v))\n \n\n \n ans = 0\n l = len(A)\n while p < l:\n search = A[p]\n p += 1\n i = list(alp.keys()).index(search)\n \n \n if i!=0:\n v= list(alp.values())\n #ans+= i* math.factorial(sum(v))/fac(v)\n for j in range(i):\n ans+= self.duplicate(j,alp)\n \n if alp[search]>1:\n alp[search]-=1\n else:\n alp.pop(search)\n \n \n return ans+1\n" } ]

97

xtracthub/temp-xtract-maps

https://github.com/xtracthub/temp-xtract-maps

0e2e5b812eba0f8c4ad47bfd69276b93e7f7c4f6

f4343bfa09a086f22ca2c19708ca76bb8e1cd872

51f3234ac21d0d6dd1e75b1fdab631735b195b7f

refs/heads/master

2020-06-18T08:28:16.247881

2019-07-10T15:41:11

196,232,594

0

null

[ { "alpha_fraction": 0.627289354801178, "alphanum_fraction": 0.666208803653717, "avg_line_length": 30.65217399597168, "blob_id": "11ae4a4b55404c619fed6b079a75b109f331e461", "content_id": "4eb93c8e9cdeb266eb41f581331cdccd003e3e33", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2184, "license_type": "no_license", "max_line_length": 79, "num_lines": 69, "path": "/tests/xtract_maps_tests.py", "repo_name": "xtracthub/temp-xtract-maps", "src_encoding": "UTF-8", "text": "import unittest\nimport os\nimport sys\n\nos.chdir(\"..\")\ncurrent_directory = os.getcwd()\nsys.path.append(current_directory + '/code')\n\nfrom xtract_maps_main import extract_map_metadata\n\n\nimg1 = current_directory + '/tests/test_imgs/CAIBOX_2009_map.jpg'\nimg2 = current_directory + '/tests/test_imgs/Bigelow2015_map.jpg'\nimg3 = current_directory + '/tests/test_imgs/GOMECC2_map.jpg'\nimg4 = current_directory + '/tests/test_imgs/Marion_Dufresne_map_1991_1993.jpg'\nimg5 = current_directory + '/tests/test_imgs/Oscar_Dyson_map.jpg'\nimg6 = current_directory + '/tests/test_imgs/P16S_2014_map.jpg'\nimg7 = current_directory + '/tests/test_imgs/us_states.png'\n\n\n# Test cases for xtract-maps. Very naively only checks whether it outputs\n# something or not.\nclass MapTests(unittest.TestCase):\n def setUp(self):\n pass\n\n def test_img1(self):\n img1_metadata = extract_map_metadata(img1)\n for i in range(3):\n self.assertTrue(img1_metadata[i])\n\n def test_img2(self):\n img2_metadata = extract_map_metadata(img2)\n self.assertTrue(img2_metadata[0])\n self.assertTrue(img2_metadata[1])\n self.assertFalse(img2_metadata[2])\n\n def test_img3(self):\n img3_metadata = extract_map_metadata(img3)\n self.assertTrue(img3_metadata[0])\n self.assertTrue(img3_metadata[1])\n self.assertFalse(img3_metadata[2])\n\n def test_img4(self):\n img4_metadata = extract_map_metadata(img4)\n self.assertTrue(img4_metadata[0])\n self.assertTrue(img4_metadata[1])\n self.assertFalse(img4_metadata[2])\n\n def test_img5(self):\n img5_metadata = extract_map_metadata(img5)\n self.assertTrue(img5_metadata[0])\n self.assertTrue(img5_metadata[1])\n self.assertFalse(img5_metadata[2])\n\n def test_img6(self):\n img6_metadata = extract_map_metadata(img6)\n for i in range(3):\n self.assertTrue((img6_metadata[i]))\n\n def test_img7(self):\n img7_metadata = extract_map_metadata(img7)\n self.assertFalse(img7_metadata[0])\n self.assertFalse(img7_metadata[1])\n self.assertTrue(img7_metadata[2])\n\n\nif __name__ == \"__main__\":\n unittest.main()\n" }, { "alpha_fraction": 0.7773019075393677, "alphanum_fraction": 0.7773019075393677, "avg_line_length": 20.227272033691406, "blob_id": "cc8c0ca0a4ed4a2e4a50176e1f248b8805f2a1d1", "content_id": "0807299aa8a9ad5ad12d61e54bd5eba959fd0f6a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 467, "license_type": "no_license", "max_line_length": 50, "num_lines": 22, "path": "/code/Dockerfile", "repo_name": "xtracthub/temp-xtract-maps", "src_encoding": "UTF-8", "text": "FROM python:latest\n\nMAINTAINER Ryan Wong\n\nRUN apt-get update\n\n# Copy files\nCOPY unionfind.py /\nCOPY contouring.py /\nCOPY text_extraction.py /\nCOPY coordinate_extraction.py /\nCOPY border_extraction.py /\nCOPY location_extraction.py /\nCOPY xtract_maps_main.py /\n\n# Install dependencies\nRUN pip install numpy opencv-python Pillow shapely\nRUN apt install tesseract-ocr -y\nRUN apt install libtesseract-dev -y\nRUN pip install pytesseract\n\nCMD [\"python\", \"xtract_maps_main.py\"]\n" } ]

2

hedgeneck/HedgePanel

https://github.com/hedgeneck/HedgePanel

4d60651d1316d4cc764b43b1f927ef272631b566

e47fcc6bde4682e3282b633de6775b6dd3cb74e6

f99816252511df0a74a42667ba67b8ee3bc66b12

refs/heads/master

2020-07-26T03:20:49.462430

2019-09-21T23:39:22

208,517,280

0

null

[ { "alpha_fraction": 0.7469879388809204, "alphanum_fraction": 0.7469879388809204, "avg_line_length": 29.272727966308594, "blob_id": "8db88b8d96fee15981da723d8c17a0fa70501fb0", "content_id": "0411c15fcbdcb066d1c1228942af3addefd9bab1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 332, "license_type": "no_license", "max_line_length": 62, "num_lines": 11, "path": "/HedgePanel/__init__.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "from flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'dbfd46992a739efa9b8f10ebc97338b4'\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///storage.db'\napp.config['TEMPLATES_AUTO_RELOAD'] = True\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb = SQLAlchemy(app)\n\nfrom HedgePanel import routes" }, { "alpha_fraction": 0.5884605050086975, "alphanum_fraction": 0.5955701470375061, "avg_line_length": 41.52325439453125, "blob_id": "3754a365766039fc18c507cf55909faf81e7a2eb", "content_id": "7c23a264bd04da3a1e0baba73bbbc28676dd2393", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "R", "length_bytes": 3683, "license_type": "no_license", "max_line_length": 91, "num_lines": 86, "path": "/R/teste2.R", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "ISO-8859-1", "text": "library(httr)\nlibrary(stringr)\n\nurl = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\n\nresponse = POST(url, \n body = list(\n dData1 = \"09/09/2019\" ),\n encode = \"form\")\npage = content(response, \"text\", encoding = \"iso-8859-1\")\n\n# esse pattern pega toda a parte da tabela referente a DI\npattern = paste0(\n \"<tr>\\\\s*<td>DI1[^<]*</td>\\\\s*\", #primeiro TD com nome do ativo sendo DI\n \"<td[^>]*>[A-Z]\\\\d{2}[^<]</td>\\\\s*\", # segundo TD contendo Vencimento\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # terceiro TD com Preço de Ajuste Anterior\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # quarto TD contendo Preço de Ajuste Atual\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # quinto TD contendo Variação\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*</tr>\", # Valor do Ajuste por Contrato\n \"(?:\\\\s*<tr>\\\\s*<td></td>\\\\s*\", #Começa segunda série de TD, sem nada\n \"<td[^>]*>[A-Z]\\\\d{2}[^<]</td>\\\\s*\", # Vencimento\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # TD com Preço de Ajuste Anterior\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # TD contendo Preço de Ajuste Atual\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # TD contendo Variação\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*</tr>)*\" # Valor do Ajuste por Contrato\n)\n\n# quando não for extrair nenhum grupo usa str_extract() e str_extract_all()\n# quando for extrair grupos, melhor usar str_match() e str_match_all()\ntotal_exp = str_extract(string = page, pattern = pattern)[[1]]\n\n# esse padrão serve para dividir cada table row em parte de um vetor\npattern = paste0(\n \"<tr>\\\\s*<td>[^<]*</td>\\\\s*\", #primeiro TD com nome do ativo sendo DI\n \"<td[^>]*>[A-Z]\\\\d{2}[^<]</td>\\\\s*\", # segundo TD contendo Vencimento\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # terceiro TD com Preço de Ajuste Anterior\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # quarto TD contendo Preço de Ajuste Atual\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # quinto TD contendo Variação\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*</tr>*\" # Valor do Ajuste por Contrato\n)\n\nparts = as.vector(str_extract_all(string = total_exp, pattern = pattern)[[1]])\n\n# esse padrão serve para extrair os groupings do vetor de table rows\npattern = paste0(\n \"<tr>\\\\s*<td>[^<]*</td>\\\\s*\", #primeiro TD com nome do ativo sendo DI\n \"<td[^>]*>([A-Z]\\\\d{2}[^<])</td>\\\\s*\", # segundo TD contendo Vencimento\n \"<td[^>]*>([\\\\d.,-]*)</td>\\\\s*\", # terceiro TD com Preço de Ajuste Anterior\n \"<td[^>]*>([\\\\d.,-]*)</td>\\\\s*\", # quarto TD contendo Preço de Ajuste Atual\n \"<td[^>]*>([\\\\d.,-]*)</td>\\\\s*\", # quinto TD contendo Variação\n \"<td[^>]*>([\\\\d.,-]*)</td>\\\\s*</tr>*\" # Valor do Ajuste por Contrato\n)\n\nmatches = str_match_all(string = parts, pattern = pattern)\n\nmatriz = matrix(unlist(matches),nrow=length(matches),byrow=T)\n\ndados = data.frame(matriz[,-1], stringsAsFactors = F)\n\n# essa solução para adicionar data adiciona no fim, portanto dá para melhorar\ndados['Data'] = dt\n\n# apenas para dar uma olhada e ver que ficou tudo factor\n# str(dados)\n\ncolnames(dados) = c(\"Vencimento\", \"PA_Anterior\", \"PA_Atual\", \"Variacao\", \"VAPC\")\n\ndados$Vencimento = gsub(\" \",\"\",dados$Vencimento)\n# se não escapar o . dá pau. Pode usar [.] tambem\ndados$PA_Anterior = gsub(\"\\\\.\",\"\",dados$PA_Anterior)\ndados$PA_Anterior = gsub(\",\",\".\",dados$PA_Anterior)\ndados$PA_Anterior = as.numeric(dados$PA_Anterior)\n\ndados$PA_Atual = gsub(\"\\\\.\",\"\",dados$PA_Atual)\ndados$PA_Atual = gsub(\",\",\".\",dados$PA_Atual)\ndados$PA_Atual = as.numeric(dados$PA_Atual)\n\ndados$Variacao = gsub(\"\\\\.\",\"\",dados$Variacao)\ndados$Variacao = gsub(\",\",\".\",dados$Variacao)\ndados$Variacao = as.numeric(dados$Variacao)\n\ndados$VAPC = gsub(\"\\\\.\",\"\",dados$VAPC)\ndados$VAPC = gsub(\",\",\".\",dados$VAPC)\ndados$VAPC = as.numeric(dados$VAPC)\n\n# writeLines(total_exp, \"outfile.txt\")\n" }, { "alpha_fraction": 0.640070915222168, "alphanum_fraction": 0.6835106611251831, "avg_line_length": 27.149999618530273, "blob_id": "bac876d22ae9cb5dbfe94a21c9f2d8b8683da326", "content_id": "b662b99ac56b4f25ee22539c8fe74911cb0ca315", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1137, "license_type": "no_license", "max_line_length": 90, "num_lines": 40, "path": "/testes/01_numeros_aleatorios.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "import random\nimport pylab\nimport numpy as np \nimport matplotlib.pyplot as plt \n\nx = random.sample([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 4)\n\nprint(x)\n\n\n\n# Tirei as informações desse site\n# https://www.geeksforgeeks.org/random-walk-implementation-python/\n# Para instalar o numpy e o matplotlib tem que primeiro sair do enviroment\n# >>> env\\Scripts\\deactivate\n# agora temos que primeiramente atualizar o pip\n# > python -m pip install --upgrade pip\n# Agora instala o numpy\n# > pip install numpy\n# Agora instala o matplotlib\n# Quando estamos dentro do interpretador e queremos chamar um script externo python usamos\n# exec(open(\"03_random_walk_2d.py\").read())\n\n### NUMPY\n# permite escrever matrizes, transpor, etc\n\n\n### tipos básicos de python\n# x = [1,2,5,4] LIST\n\t# [41, 'aaa', 32, ['a', 'b']] # listas podem ser heterogeneas, e aninhadas \n# t = (42, 1, 5) TUPLE - igual a lista, mas imutavel\n\t# x = 41, 37, 31 - outra maneira de inicializar\n# s = {1, 2, 4, 7} SET - não permite valores repetidos\n# d = {'a': 42, 'b': 717} DICTIONARY\n\n### referenciando tipos básicos de python\n# x[0] é 1\n# t[0] é 42\n# s[0] não funciona\n# d['a'] é 42\n\n\n" }, { "alpha_fraction": 0.6558139324188232, "alphanum_fraction": 0.6883720755577087, "avg_line_length": 18.636363983154297, "blob_id": "611c5f08dc4dc3c9faeea9e07013f782ed72474f", "content_id": "f3becef0e4f1c8d362d99119d1ddf37c1da49108", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 219, "license_type": "no_license", "max_line_length": 57, "num_lines": 11, "path": "/testes/06_function_random_walk.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "import numpy as np\n\n# vamos fazer a função random walk como uma função python\n\ndef random_walk(start=0, n=10, sigma=1):\n\trw = [start]\n\n\tfor i in range(1,n):\n\t\trw.append(rw[-1]+np.random.normal(0, sigma))\n\n\treturn rw" }, { "alpha_fraction": 0.6478873491287231, "alphanum_fraction": 0.7065727710723877, "avg_line_length": 25.6875, "blob_id": "c7140291eb5cb6306946cfa0c5603898521f6144", "content_id": "6fd8a7ace63a992f9ef04ad7f0e61e667171adc3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 432, "license_type": "no_license", "max_line_length": 93, "num_lines": 16, "path": "/testes/05_random_wak_caseiro.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "import numpy as np\n\n# uma maneira de iniciar um array com 1 elemento igual a 100 é np.array(100)\nrw = [np.array(100)]\n# np.array([1, 2, 3]) se quiser array com mais de 1\n# eu pensava em iniciar rw como um numpy array por causa do output da função normal mas acabo\n# de descobrir que a função normal com dimensão 1 retorna um float\n\nrw = [100]\n\nn = 100\n\nfor i in range(1,n):\n\trw.append(rw[-1]+np.random.normal(0, 1))\n\nprint(rw)" }, { "alpha_fraction": 0.8524590134620667, "alphanum_fraction": 0.8524590134620667, "avg_line_length": 29.5, "blob_id": "2a6a7f0f2eb43775a4310206b2f2e51a3e712ac1", "content_id": "05406e4882e825ebe8ac17fa382bed27854351c0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 62, "license_type": "no_license", "max_line_length": 47, "num_lines": 2, "path": "/README.md", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "# HedgePanel\nPainel com indicadores financeiros e econômicos\n" }, { "alpha_fraction": 0.5869289636611938, "alphanum_fraction": 0.6085025668144226, "avg_line_length": 26.578947067260742, "blob_id": "ba5c42aadd295ab103c478c9e7c6163bdd367870", "content_id": "a56c6b749421d0f262134d5fb2efcdc7a633aaaf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "R", "length_bytes": 1576, "license_type": "no_license", "max_line_length": 83, "num_lines": 57, "path": "/R/CETIP_CDI.R", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "\nler.CETIP.CDI = function(dt){\n stopifnot(is(dt, \"Date\"), length(dt) == 1)\n url = format(dt, \"ftp://ftp.cetip.com.br/MediaCDI/%Y%m%d.txt\")\n txt = try(readLines(url), silent = T)\n if(is(txt, \"try-error\")){\n stop(paste0(\"erro ao ler o CDI para a data \", dt))\n }\n txt = gsub(\" \", \"\", txt)\n cdi = as.numeric(txt)/100\n return(cdi)\n}\n\ncarregar.CETIP.CDI = function(dt){\n cdi = ler.CETIP.CDI(dt)\n dados = data.frame(data = dt, valor = cdi, stringsAsFactors = F)\n \n sql = \"INSERT INTO cdi_cetip (data, valor) VALUES (:data, :valor)\"\n r = try(dbSendQuery(conn, sql, params=dates.to.string(dados)))\n if(!is(r, \"try-error\")){\n message(\"CDI CETIP carregado com sucesso para a data \", format(dt, \"%Y-%m-%d\"))\n dbClearResult(r)\n } else {\n stop(\"Erro ao carregar CDI CETIP para a data \", format(dt, \"%Y-%m-%d\"))\n }\n}\n\nultima.data.CETIP.CDI = function(){\n dbGetQuery(conn, \"SELECT max(data) FROM CDI_CETIP\")[1,1]\n}\n\natualizar.CETIP.CDI = function(){\n message(\"Atualizando CDI CETIP...\")\n datas = dias.uteis.desde(ultima.data.CETIP.CDI())\n if(length(datas)==0){\n message(\"Nada a ser feito.\")\n } else {\n for(i in 1:length(datas)){\n try(carregar.CETIP.CDI(datas[i]))\n }\n }\n \n}\n# dt = as.Date(\"2018-07-17\", \"%Y-%m-%d\")\n# \n# ler.CETIP.CDI(dt)\n# \n# library(bizdays)\n# cal = create.calendar(\"ANBIMA\", holidaysANBIMA, weekdays=c(\"saturday\", \"sunday\"))\n# \n# datas = bizdays::bizseq(as.Date(\"2018-07-01\"), as.Date(\"2018-07-19\"), \"ANBIMA\")\n# \n# for(i in 1:length(datas)){\n# dt = datas[i]\n# cdi = ler.CETIP.CDI(dt)\n# print(dt)\n# print(cdi)\n# }\n\n\n\n" }, { "alpha_fraction": 0.5506808161735535, "alphanum_fraction": 0.6853252649307251, "avg_line_length": 27.782608032226562, "blob_id": "a5394d3140b4f919f87cd0ce8f7b06bafe4ed9ed", "content_id": "6c798705a0d24b17a192de9b6214d03ccb5dc358", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 670, "license_type": "no_license", "max_line_length": 86, "num_lines": 23, "path": "/testes/04_algumas_coisas_numericas.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "import numpy as np \n\nx = np.random.normal(1.01, 0.03, 5)\n# array([0.99444773, 0.94140399, 1.05218897, 1.00534229, 1.01679733])\n# notar como o resultado é um array, explorando melhor\n\ntype(x)\n# <class 'numpy.ndarray'>\n\ny = list(range(11, 17))\n# em python 3 range é um objeto iterator, então é necessário convertê-lo para lista\n# [11, 12, 13, 14, 15, 16] - ele só vai até 16\n\n\nz = [1] * 5\n# para criar uma lista com o elemento repetidas vezes\n# z é [1, 1, 1, 1, 1]\n\ns = np.random.random(10)\n\nmu, sigma = 0, 0.1 # mean and standard deviation\ns2 = np.random.normal(mu, sigma, 5)\n# https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.random.normal.html" }, { "alpha_fraction": 0.5707497596740723, "alphanum_fraction": 0.6383315920829773, "avg_line_length": 22.674999237060547, "blob_id": "22bff0d2f3295599b4c0e3114a9da3f66546bf4b", "content_id": "3d637df5699f86c362f3324a738f47be0f419909", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "R", "length_bytes": 1894, "license_type": "no_license", "max_line_length": 93, "num_lines": 80, "path": "/R/teste.R", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "install.packages(\"httr\")\nlibrary(httr)\nlibrary(stringr)\n\nurl = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\n\nr = POST(\n url = url,\n body = list(\n dData1 = \"opa\"\n )\n)\n\nr = POST(\n url = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\n)\n\n\ntxt = content(r, as = \"text\", encoding = \"UTF-8\")\n\n\n\nr <- GET(url)\ncontent(r, as = \"text\", encoding = \"UTF-8\")\ncontent(r)\n\n\n\nurl = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\nquery = list('form[dData1]'=\"2014-11-01\")\nresponse = POST(url, body = query)\npage = content(response, \"text\", encoding = \"iso-8859-1\")\ncat(page, \"outfile.txt\")\nwriteLines(page, \"outfile.txt\")\n\n\n\n\n\n\nurl = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\nresponse = POST(url, body = list(\n dData1 = \"09/09/2019\"\n))\nresponse = POST(url, body = list(\n dData1 = 11/09/2019\n))\npage = content(response, \"text\", encoding = \"iso-8859-1\")\nwriteLines(page, \"outfile.html\")\n\n\n\n\n\n\nurl = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\nquery = list('form[dData1]'=\"dData1=11%2F09%2F2019\")\nresponse = POST(url, body = query)\npage = content(response, \"text\", encoding = \"iso-8859-1\")\nwriteLines(page, \"outfile.html\")\n\n\n\n\n\nurl = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\nresponse = POST(url, \n body = list(\n dData1 = \"09/09/2019\" ),\n encode = \"form\")\npage = content(response, \"text\", encoding = \"iso-8859-1\")\nwriteLines(page, \"outfile.html\")\n\n\nstrings <- c(\"Home: 219 733 8965. Work: 229-293-8753 \",\n \"banana pear apple\", \"595 794 7569 / 387 287 6718\")\nphone <- \"([2-9][0-9]{2})[- .]([0-9]{3})[- .]([0-9]{4})\"\n\na = str_extract_all(strings, phone)\nb = str_match_all(strings, phone)\n" }, { "alpha_fraction": 0.6530232429504395, "alphanum_fraction": 0.6632558107376099, "avg_line_length": 29.742856979370117, "blob_id": "e86e87a1825e4367f9f985d24af37464251a6091", "content_id": "dd59dcf01971b2000ebf307467f804182e88b3c2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1079, "license_type": "no_license", "max_line_length": 131, "num_lines": 35, "path": "/HedgePanel/routes.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "from flask import render_template\nfrom HedgePanel import app\nfrom HedgePanel.models import CDI_CETIP\nimport numpy as np\nfrom datetime import datetime, date\n\[email protected](\"/\")\[email protected](\"/home\")\ndef home():\n\treturn render_template('home.html')\n\[email protected](\"/about\")\ndef about():\n\treturn render_template('about.html')\n\n\[email protected](\"/cdi_media_cetip\")\ndef cdi_media_cetip(chartID = 'char_id', chart_type = 'line', chart_height = 400):\n\tz = CDI_CETIP.query.all()\n\tn = len(z)\n\n\tdados = [[0,0] for i in range(n)]\n\n\tfor i in range(n):\n\t\tdt = datetime.strptime(z[i].data, \"%Y-%m-%d\").date()\n\t\tdt = int(datetime.combine(dt, datetime.min.time()).timestamp())*1000\n\t\tdados[i][0] = dt\n\t\tdados[i][1] = z[i].valor\n\n\tchart = {\"renderTo\": chartID, \"type\": chart_type, \"height\": chart_height,}\n\tseries = [{\"name\": 'CDI', \"data\": dados}]\n\ttitle = {\"text\": 'CDI média diária (%)'}\n\txAxis = {\"type\": 'datetime'}\n\tyAxis = {\"title\": {\"text\": 'CDI média diária (%)'}}\n\treturn render_template('cdi_media_cetip.html', chartID=chartID, chart=chart, series=series, title=title, xAxis=xAxis, yAxis=yAxis)" }, { "alpha_fraction": 0.6309148073196411, "alphanum_fraction": 0.64826500415802, "avg_line_length": 17.647058486938477, "blob_id": "5adaae61c1776b5ca0e2c5da94f6695d694d57f4", "content_id": "47e78637456934a04a36ab53024ad5f3cffb9464", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "R", "length_bytes": 634, "license_type": "no_license", "max_line_length": 67, "num_lines": 34, "path": "/R/CARGA_BDM.R", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "library(RSQLite)\nlibrary(bizdays)\ncal = bizdays::calendars()\n\n\nconectar.bdm = function(){ \n dbConnect(dbDriver(\"SQLite\"), \"../HedgePanel/storage.db\")\n}\n\ndesconectar.bdm = function(conn){\n dbDisconnect(conn)\n}\n\ndias.uteis.desde = function(date){\n if(is.na(date)) date = as.Date(\"2012-08-20\")\n datas = bizseq(date, Sys.Date()-1, \"Brazil/ANBIMA\")\n datas[datas != date]\n}\n\ndates.to.string = function(df){\n for(i in 1:ncol(df)){\n if(class(df[,i]) == \"Date\") df[,i] = format(df[,i], \"%Y-%m-%d\")\n }\n return(df)\n}\n\n\nsource(\"CETIP_CDI.R\")\n\n\nconn = conectar.bdm()\natualizar.CETIP.CDI()\natualizar.B3.CONTRATOS.DI()\ndbDisconnect(conn)\n" }, { "alpha_fraction": 0.5633187890052795, "alphanum_fraction": 0.5715927481651306, "avg_line_length": 41.24271774291992, "blob_id": "d028448169eb529158d2d0fd7a5ec18d0ae94554", "content_id": "e2cb570ab612d6f828678ba652f36461ae31677a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "R", "length_bytes": 4369, "license_type": "no_license", "max_line_length": 166, "num_lines": 103, "path": "/R/B3_CONTRATOS_DI.R", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "ISO-8859-1", "text": "library(stringr)\nlibrary(httr)\n\nler.B3.CONTRATOS.DI = function(dt){\n stopifnot(is(dt, \"Date\"), length(dt) == 1)\n url = \"http://www2.bmf.com.br/pages/portal/bmfbovespa/lumis/lum-ajustes-do-pregao-ptBR.asp\"\n response = POST(url, \n body = list(\n dData1 = format(dt,\"%d/%m/%y\") ),\n encode = \"form\")\n page = content(response, \"text\", encoding = \"iso-8859-1\")\n pattern = paste0(\n \"<tr>\\\\s*<td>DI1[^<]*</td>\\\\s*\", #primeiro TD com nome do ativo sendo DI\n \"<td[^>]*>[A-Z]\\\\d{2}[^<]</td>\\\\s*\", # segundo TD contendo Vencimento\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # terceiro TD com Preço de Ajuste Anterior\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # quarto TD contendo Preço de Ajuste Atual\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # quinto TD contendo Variação\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*</tr>\", # Valor do Ajuste por Contrato\n \"(?:\\\\s*<tr>\\\\s*<td></td>\\\\s*\", #Começa segunda série de TD, sem nada\n \"<td[^>]*>[A-Z]\\\\d{2}[^<]</td>\\\\s*\", # Vencimento\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # TD com Preço de Ajuste Anterior\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # TD contendo Preço de Ajuste Atual\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # TD contendo Variação\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*</tr>)*\" # Valor do Ajuste por Contrato\n )\n total_exp = str_extract(string = page, pattern = pattern)[[1]]\n if(is.na(total_exp)){\n stop(paste0(\"erro ao ler contratos DI para a data \", dt))\n }\n pattern = paste0(\n \"<tr>\\\\s*<td>[^<]*</td>\\\\s*\", #primeiro TD com nome do ativo sendo DI\n \"<td[^>]*>[A-Z]\\\\d{2}[^<]</td>\\\\s*\", # segundo TD contendo Vencimento\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # terceiro TD com Preço de Ajuste Anterior\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # quarto TD contendo Preço de Ajuste Atual\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*\", # quinto TD contendo Variação\n \"<td[^>]*>[\\\\d.,-]*</td>\\\\s*</tr>*\" # Valor do Ajuste por Contrato\n )\n parts = as.vector(str_extract_all(string = total_exp, pattern = pattern)[[1]])\n pattern = paste0(\n \"<tr>\\\\s*<td>[^<]*</td>\\\\s*\", #primeiro TD com nome do ativo sendo DI\n \"<td[^>]*>([A-Z]\\\\d{2}[^<])</td>\\\\s*\", # segundo TD contendo Vencimento\n \"<td[^>]*>([\\\\d.,-]*)</td>\\\\s*\", # terceiro TD com Preço de Ajuste Anterior\n \"<td[^>]*>([\\\\d.,-]*)</td>\\\\s*\", # quarto TD contendo Preço de Ajuste Atual\n \"<td[^>]*>([\\\\d.,-]*)</td>\\\\s*\", # quinto TD contendo Variação\n \"<td[^>]*>([\\\\d.,-]*)</td>\\\\s*</tr>*\" # Valor do Ajuste por Contrato\n )\n matches = str_match_all(string = parts, pattern = pattern)\n matriz = matrix(unlist(matches),nrow=length(matches),byrow=T)\n dados = data.frame(matriz[,-1], stringsAsFactors = F)\n \n colnames(dados) = c(\"Vencimento\", \"PA_Anterior\", \"PA_Atual\", \"Variacao\", \"VAPC\")\n \n dados['Data'] = dt\n \n dados$Vencimento = gsub(\" \",\"\",dados$Vencimento)\n\n dados$PA_Anterior = gsub(\"\\\\.\",\"\",dados$PA_Anterior)\n dados$PA_Anterior = gsub(\",\",\".\",dados$PA_Anterior)\n dados$PA_Anterior = as.numeric(dados$PA_Anterior)\n \n dados$PA_Atual = gsub(\"\\\\.\",\"\",dados$PA_Atual)\n dados$PA_Atual = gsub(\",\",\".\",dados$PA_Atual)\n dados$PA_Atual = as.numeric(dados$PA_Atual)\n \n dados$Variacao = gsub(\"\\\\.\",\"\",dados$Variacao)\n dados$Variacao = gsub(\",\",\".\",dados$Variacao)\n dados$Variacao = as.numeric(dados$Variacao)\n \n dados$VAPC = gsub(\"\\\\.\",\"\",dados$VAPC)\n dados$VAPC = gsub(\",\",\".\",dados$VAPC)\n dados$VAPC = as.numeric(dados$VAPC)\n \n return(dados)\n}\n\ncarregar.B3.CONTRATOS.DI = function(dt){\n dados = ler.B3.CONTRATOS.DI(dt)\n \n sql = \"INSERT INTO b3_contratos_di (vencimento, pa_anterior, pa_atual, variacao, vapc, data) VALUES (:Vencimento, :PA_Anterior, :PA_Atual, :Variacao, :VAPC, :Data)\"\n r = try(dbSendQuery(conn, sql, params=dates.to.string(dados)))\n if(!is(r, \"try-error\")){\n message(\"B3_CONTRATOS_DI carregadoS com sucesso para a data \", format(dt, \"%Y-%m-%d\"))\n dbClearResult(r)\n } else {\n stop(\"Erro ao carregar B3_CONTRATOS_DI para a data \", format(dt, \"%Y-%m-%d\"))\n }\n}\n\nultima.data.B3.CONTRATOS.DI = function(){\n dbGetQuery(conn, \"SELECT max(data) FROM B3_CONTRATOS_DI\")[1,1]\n}\n\natualizar.B3.CONTRATOS.DI = function(){\n message(\"Atualizando B3 CONTRATOS DI...\")\n datas = dias.uteis.desde(ultima.data.B3.CONTRATOS.DI())\n if(length(datas)==0){\n message(\"Nada a ser feito.\")\n } else {\n for(i in 1:length(datas)){\n try(carregar.B3.CONTRATOS.DI(datas[i]))\n }\n }\n}\n" }, { "alpha_fraction": 0.6883116960525513, "alphanum_fraction": 0.6883116960525513, "avg_line_length": 27.090909957885742, "blob_id": "efaa7e3be358e2f0c4557b7a7f868caa044707f0", "content_id": "b255147adefbe32493c3445c416663ff1cd4acad", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 308, "license_type": "no_license", "max_line_length": 57, "num_lines": 11, "path": "/HedgePanel/models.py", "repo_name": "hedgeneck/HedgePanel", "src_encoding": "UTF-8", "text": "from HedgePanel import db\n\nclass CDI_CETIP(db.Model):\n\t__tablename__ = \"CDI_CETIP\"\n\n\tid = db.Column(db.Integer, primary_key=True)\n\tdata = db.Column(db.String, unique=True, nullable=False)\n\tvalor = db.Column(db.String, nullable=False)\n\n\tdef __repr__(self):\n\t\treturn f\"CDI_CETIP('{self.data}': '{self.valor}')\"" } ]

13

skmatz/pytorch-lightning

https://github.com/skmatz/pytorch-lightning

336b83e74a10999f38629be966208f39a4b780e1

fc6d4027334b8869f02a3bdca0a0846f1cf79928

33bfdf36cc7d16c3c70c2ef8fa98d5c178c10fa2

refs/heads/master

2021-04-21T04:51:40.213877

2021-03-09T09:49:59

345,974,442

0

Apache-2.0

2021-03-09T10:53:44

2021-03-09T10:25:26

2021-03-09T10:27:39

null

[ { "alpha_fraction": 0.6492230892181396, "alphanum_fraction": 0.6617252826690674, "avg_line_length": 29.42934799194336, "blob_id": "ddcaf0de59f07cefca938db5bbc883c0a9cd7679", "content_id": "52f585409e8650262b054a0944ac2300282b04ea", "detected_licenses": [ "Apache-2.0", "LicenseRef-scancode-proprietary-license" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5599, "license_type": "permissive", "max_line_length": 89, "num_lines": 184, "path": "/tests/accelerators/test_dp.py", "repo_name": "skmatz/pytorch-lightning", "src_encoding": "UTF-8", "text": "# Copyright The PyTorch Lightning team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport torch\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\n\nimport pytorch_lightning as pl\nimport tests.helpers.pipelines as tpipes\nimport tests.helpers.utils as tutils\nfrom pytorch_lightning.callbacks import EarlyStopping\nfrom pytorch_lightning.core import memory\nfrom tests.helpers import BoringModel, RandomDataset\nfrom tests.helpers.datamodules import ClassifDataModule\nfrom tests.helpers.runif import RunIf\nfrom tests.helpers.simple_models import ClassificationModel\n\nPRETEND_N_OF_GPUS = 16\n\n\nclass CustomClassificationModelDP(ClassificationModel):\n\n def _step(self, batch, batch_idx):\n x, y = batch\n logits = self(x)\n return {'logits': logits, 'y': y}\n\n def training_step(self, batch, batch_idx):\n out = self._step(batch, batch_idx)\n loss = F.cross_entropy(out['logits'], out['y'])\n return loss\n\n def validation_step(self, batch, batch_idx):\n return self._step(batch, batch_idx)\n\n def test_step(self, batch, batch_idx):\n return self._step(batch, batch_idx)\n\n def validation_step_end(self, outputs):\n self.log('val_acc', self.valid_acc(outputs['logits'], outputs['y']))\n\n def test_step_end(self, outputs):\n self.log('test_acc', self.test_acc(outputs['logits'], outputs['y']))\n\n\n@RunIf(min_gpus=2)\ndef test_multi_gpu_early_stop_dp(tmpdir):\n \"\"\"Make sure DDP works. with early stopping\"\"\"\n tutils.set_random_master_port()\n\n dm = ClassifDataModule()\n model = CustomClassificationModelDP()\n\n trainer_options = dict(\n default_root_dir=tmpdir,\n callbacks=[EarlyStopping(monitor='val_acc')],\n max_epochs=50,\n limit_train_batches=10,\n limit_val_batches=10,\n gpus=[0, 1],\n accelerator='dp',\n )\n\n tpipes.run_model_test(trainer_options, model, dm)\n\n\n@RunIf(min_gpus=2)\ndef test_multi_gpu_model_dp(tmpdir):\n tutils.set_random_master_port()\n\n trainer_options = dict(\n default_root_dir=tmpdir,\n max_epochs=1,\n limit_train_batches=10,\n limit_val_batches=10,\n gpus=[0, 1],\n accelerator='dp',\n progress_bar_refresh_rate=0,\n )\n\n model = BoringModel()\n\n tpipes.run_model_test(trainer_options, model)\n\n # test memory helper functions\n memory.get_memory_profile('min_max')\n\n\n@RunIf(min_gpus=2)\ndef test_dp_test(tmpdir):\n tutils.set_random_master_port()\n\n dm = ClassifDataModule()\n model = CustomClassificationModelDP()\n trainer = pl.Trainer(\n default_root_dir=tmpdir,\n max_epochs=2,\n limit_train_batches=10,\n limit_val_batches=10,\n gpus=[0, 1],\n accelerator='dp',\n )\n trainer.fit(model, datamodule=dm)\n assert 'ckpt' in trainer.checkpoint_callback.best_model_path\n results = trainer.test(datamodule=dm)\n assert 'test_acc' in results[0]\n\n old_weights = model.layer_0.weight.clone().detach().cpu()\n\n results = trainer.test(model, datamodule=dm)\n assert 'test_acc' in results[0]\n\n # make sure weights didn't change\n new_weights = model.layer_0.weight.clone().detach().cpu()\n\n assert torch.all(torch.eq(old_weights, new_weights))\n\n\nclass ReductionTestModel(BoringModel):\n\n def train_dataloader(self):\n return DataLoader(RandomDataset(32, 64), batch_size=2)\n\n def val_dataloader(self):\n return DataLoader(RandomDataset(32, 64), batch_size=2)\n\n def test_dataloader(self):\n return DataLoader(RandomDataset(32, 64), batch_size=2)\n\n def add_outputs(self, output, device):\n output.update({\n \"reduce_int\": torch.tensor(device.index, dtype=torch.int, device=device),\n \"reduce_float\": torch.tensor(device.index, dtype=torch.float, device=device),\n })\n\n def training_step(self, batch, batch_idx):\n output = super().training_step(batch, batch_idx)\n self.add_outputs(output, batch.device)\n return output\n\n def validation_step(self, batch, batch_idx):\n output = super().validation_step(batch, batch_idx)\n self.add_outputs(output, batch.device)\n return output\n\n def test_step(self, batch, batch_idx):\n output = super().test_step(batch, batch_idx)\n self.add_outputs(output, batch.device)\n return output\n\n def training_epoch_end(self, outputs):\n assert outputs[0][\"loss\"].shape == torch.Size([])\n assert outputs[0][\"reduce_int\"].item() == 0 # mean([0, 1]) = 0\n assert outputs[0][\"reduce_float\"].item() == 0.5 # mean([0., 1.]) = 0.5\n\n\n@RunIf(min_gpus=2)\ndef test_dp_training_step_dict(tmpdir):\n \"\"\" This test verifies that dp properly reduces dictionaries \"\"\"\n model = ReductionTestModel()\n model.training_step_end = None\n model.validation_step_end = None\n model.test_step_end = None\n\n trainer = pl.Trainer(\n default_root_dir=tmpdir,\n max_epochs=1,\n limit_train_batches=1,\n limit_val_batches=1,\n limit_test_batches=1,\n gpus=2,\n accelerator='dp',\n )\n trainer.fit(model)\n" } ]

1

n04hk/Python_Zusammenfassung

https://github.com/n04hk/Python_Zusammenfassung

b118e967d5d5547ad3eb88f9570cb7c9de45d443

923fadb28ab4609450e532f08de41dc4bf4913d1

315788ed9c3727acca394ad107b0a55285a7ddc4

refs/heads/master

2020-04-24T20:28:43.656148

2019-04-28T13:52:31

172,245,211

1

0

null

[ { "alpha_fraction": 0.5850340127944946, "alphanum_fraction": 0.646258533000946, "avg_line_length": 28.399999618530273, "blob_id": "91df6266200493a820008f420489c5e09004ac32", "content_id": "2444c49e05e3c481a09463a7e086e320b40ce757", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 147, "license_type": "no_license", "max_line_length": 51, "num_lines": 5, "path": "/listings/v6_klassen5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n speed_of_light = 299792458 # Klassen-Variable\n\n def __init__(self):\n self.name = 'unbekannt' # Instanz-Variable\n" }, { "alpha_fraction": 0.5748031735420227, "alphanum_fraction": 0.5905511975288391, "avg_line_length": 24.399999618530273, "blob_id": "bf47ec355f87c8a71d7fa1bc8cdb2daeb1da0608", "content_id": "202b4e336337fede980e59df682a35a22682b505", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 127, "license_type": "no_license", "max_line_length": 33, "num_lines": 5, "path": "/listings/v9_matplotlib8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "with plt.xkcd():\n plt.figure()\n plt.plot(np.random.randn(10))\n plt.xlabel('Zeit (s)')\n plt.ylabel('Amplitude (V)')\n" }, { "alpha_fraction": 0.6567164063453674, "alphanum_fraction": 0.6567164063453674, "avg_line_length": 15.75, "blob_id": "7d5c65a653e63eb8ff4692afcc1d9c5d30c5a1f7", "content_id": "c87cd8b89b2a798bfebc22e68ea32b91820d4df4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 67, "license_type": "no_license", "max_line_length": 28, "num_lines": 4, "path": "/listings/v3_datei6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "f = open('mailaenderli.txt')\ntext = f.read()\nf.close()\nprint(text)\n" }, { "alpha_fraction": 0.5390008687973022, "alphanum_fraction": 0.5635407567024231, "avg_line_length": 29.83783721923828, "blob_id": "62faee12bf3217ec1f1e056ffc3af4e0636c6087", "content_id": "0a4867aeb51ecba0aff99174c48e82078a791c32", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1141, "license_type": "no_license", "max_line_length": 100, "num_lines": 37, "path": "/listings/v7_vererbung10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class SuperKlasse:\n def __init__(self):\n self.pub = 'public Variable'\n self._prot = 'protected Variable'\n self.__priv = 'private Variable'\n\n def pub_func(self):\n print('public Methode')\n\n def _prot_func(self):\n print('protected Methode')\n\n def __priv_func(self):\n print('private Methode')\n\nclass SubKlasse(SuperKlasse):\n def __init__(self):\n self.pub_func()\n self._prot_func()\n self.__priv_func() # nicht erreichbar, kann in der Subklasse wiederbenutzt werden\n\n\nsub = SubKlasse() # Ausgabe:\n# public Methode\n# protected Methode\n\n# ---------------------------------------------------------------------------\n# AttributeError Traceback (most recent call last)\n# <ipython-input-12-479270c9858a> in <module>\n# ----> 1 sub = SubKlasse()\n\n# <ipython-input-11-1794f0b16121> in __init__(self)\n# 3 self.pub_func()\n# 4 self._prot_func()\n# ----> 5 self.__priv_func() # nicht erreichbar, kann in der Subklasse wiederbenutzt werden\n\n# AttributeError: 'SubKlasse' object has no attribute '_SubKlasse__priv_func'\n" }, { "alpha_fraction": 0.7714285850524902, "alphanum_fraction": 0.7714285850524902, "avg_line_length": 34, "blob_id": "2d6f02dba3533e8d095787b536474783bf83e818", "content_id": "7c63bcebf48fff499f53edd73a86139073761d5d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 35, "license_type": "no_license", "max_line_length": 34, "num_lines": 1, "path": "/listings/v3_exception8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "raise ValueError('Falscher Wert.')\n" }, { "alpha_fraction": 0.31147539615631104, "alphanum_fraction": 0.44262295961380005, "avg_line_length": 29.5, "blob_id": "1af526a72ad77fab35d831adb1803435e3720b28", "content_id": "1a8f507d785a57767f633c6e2373f0452ca5a949", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 61, "license_type": "no_license", "max_line_length": 42, "num_lines": 2, "path": "/listings/v5_ra24.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.findall(r'\\b(?<!#)\\d+', '#10, #25, 66')\n# Ausgabe: ['66']\n" }, { "alpha_fraction": 0.7058823704719543, "alphanum_fraction": 0.7058823704719543, "avg_line_length": 16, "blob_id": "41dc54aeb38d3fff9dd4ca1e4a629aac1a5ee968", "content_id": "ac9a3140f77a189f55b9785cfad1711e1761f6c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 17, "license_type": "no_license", "max_line_length": 16, "num_lines": 1, "path": "/listings/v8_numpy12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "np.linalg.inv(M)\n" }, { "alpha_fraction": 0.4084506928920746, "alphanum_fraction": 0.4084506928920746, "avg_line_length": 13.199999809265137, "blob_id": "f0e193513710aad8fe548c8f92d4823483a56dae", "content_id": "b2f2712f500b7b496f3371570bdefdaf201edd27", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 71, "license_type": "no_license", "max_line_length": 18, "num_lines": 5, "path": "/listings/v7_vererbung14.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "d = D() # Ausgabe:\n# D.__init__\n# B.__init__\n# C.__init__\n# A.__init__\n" }, { "alpha_fraction": 0.48653846979141235, "alphanum_fraction": 0.49038460850715637, "avg_line_length": 26.36842155456543, "blob_id": "bc6033966ccc1bdcc72defd69407ebc77a3f7639", "content_id": "9949beedbc0025b87583d58c51054ffc4c803a16", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 520, "license_type": "no_license", "max_line_length": 90, "num_lines": 19, "path": "/listings/v6_my_module.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\nprint('Dies ist {}:\\n__name__ = {}'.format(__file__, __name__))\n\nclass MeineKlasse:\n def __init__(self, name):\n self.name = name\n\n def gruss(self):\n print('Hallo', self.name)\n\n# -----------------------------------------------------------------------------\nif __name__ == '__main__':\n k = MeineKlasse('Python')\n k.gruss()\n\n# Konsolen-Ausgabe:\n# Dies ist C:/Users/Noah/Documents/GitHub/Python_Zusammenfassung/listings/v6_my_module.py:\n# __name__ = __main__\n# Hallo Python\n" }, { "alpha_fraction": 0.3513513505458832, "alphanum_fraction": 0.45945945382118225, "avg_line_length": 17.5, "blob_id": "5388f4f87046a97d2329eb948436106ec4175f16", "content_id": "f08efa648694b626f29cebb65347fe73346f093a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 37, "license_type": "no_license", "max_line_length": 20, "num_lines": 2, "path": "/listings/v8_numpy15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "v = np.array([5, 6])\n# array([5, 6])\n" }, { "alpha_fraction": 0.4035087823867798, "alphanum_fraction": 0.5087719559669495, "avg_line_length": 27.5, "blob_id": "80c83298d81d5bc225b7fac494fc04a8a49f5d68", "content_id": "69dc28ba8ce410a451d310f0f3df6efaf4c6855a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 57, "license_type": "no_license", "max_line_length": 35, "num_lines": 2, "path": "/listings/v4_tupel20.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "list(map(lambda x: x*x, [1, 2, 3]))\n# Ausgabe: [1, 4, 9]\n" }, { "alpha_fraction": 0.4030612111091614, "alphanum_fraction": 0.5408163070678711, "avg_line_length": 23.5, "blob_id": "1a7240ddeed6a4b20380c7bd28612d67761d2816", "content_id": "eff4fbece4848ba466dcba5cb61304c6e6e4d54f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 196, "license_type": "no_license", "max_line_length": 39, "num_lines": 8, "path": "/listings/v8_numpy1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr1 = np.array([1, 2, 3])\nprint(arr1) # Ausgabe: [1 2 3]\narr2 = np.array([[1, 2, 3], [4, 5, 6]])\nprint(arr2) # Ausgabe:\n# [[1 2 3]\n# [4 5 6]]\narr2.ndim # Ausgabe: 2\narr2.shape # Ausgabe: (2, 3)\n" }, { "alpha_fraction": 0.4029304087162018, "alphanum_fraction": 0.43223443627357483, "avg_line_length": 33.125, "blob_id": "abde9d51b6a8488b4e75e460c7ae871c51208de2", "content_id": "d5f19b04b88e59d59a6a622e4bb5b39d1771f451", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 273, "license_type": "no_license", "max_line_length": 77, "num_lines": 8, "path": "/listings/v4_iter6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "next(iterator)\n# Ausgabe:\n# ---------------------------------------------------------------------------\n# StopIteration Traceback (most recent call last)\n# <ipython-input-8-4ce711c44abc> in <module>()\n# ----> 1 next(iterator)\n#\n# StopIteration:\n" }, { "alpha_fraction": 0.5755102038383484, "alphanum_fraction": 0.6693877577781677, "avg_line_length": 23.5, "blob_id": "5ea2d4c74000d7365aa86e52c75db35fb5158d04", "content_id": "4538cfb1c88ff5ba345ed57d7a2db6206aec8667", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 245, "license_type": "no_license", "max_line_length": 38, "num_lines": 10, "path": "/listings/v9_matplotlib2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "plt.figure()\nax1 = plt.subplot(2, 1, 1)\nax1.plot(np.random.randn(10));\nax1.set_xlabel('X1')\nax1.set_ylabel('Y1')\nax2 = plt.subplot(2, 1, 2, sharex=ax1)\nax2.plot(np.random.randn(10));\nax2.set_xlabel('X2')\nax2.set_ylabel('Y2')\nplt.tight_layout();\n" }, { "alpha_fraction": 0.758865237236023, "alphanum_fraction": 0.758865237236023, "avg_line_length": 69.5, "blob_id": "ad55936498f7e68d7359a63676002b7850dbb636", "content_id": "db74d7016ed513639ffb104836c8ce1c9c55a306", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 141, "license_type": "no_license", "max_line_length": 102, "num_lines": 2, "path": "/listings/v3_strings19.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "spruch.replace('sollten', 'muessten')\n# Ausgabe: 'Wir muessten heute das tun,\\nvon dem wir uns morgen wuenschen\\nes gestern getan zu haben.'\n" }, { "alpha_fraction": 0.6590909361839294, "alphanum_fraction": 0.7045454382896423, "avg_line_length": 13.666666984558105, "blob_id": "8faa18d2f98cbbf6668746f3eb14d1682f9893f9", "content_id": "4a8f897a6da2a7ff89ab107357ff3f9203cc1552", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 44, "license_type": "no_license", "max_line_length": 14, "num_lines": 3, "path": "/listings/v2_if1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "if Bedingung:\n Anweisung1\n Anweisung2\n" }, { "alpha_fraction": 0.409326434135437, "alphanum_fraction": 0.5440414547920227, "avg_line_length": 37.599998474121094, "blob_id": "ef1cf619721283778bb95928253be13523b90b5a", "content_id": "1a1032af99bb89fc8da1d6adb2937fcee14114d7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 193, "license_type": "no_license", "max_line_length": 54, "num_lines": 5, "path": "/listings/v8_numpy5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr = np.arange(8)\nprint(arr) # Ausgabe: array([0, 1, 2, 3, 4, 5, 6, 7])\ns = arr[2:5] # array([2, 3, 4])\ns[0] = 13 # modifiziert auch arr\nprint(arr) # Ausgabe: array([0, 1, 13, 3, 4, 5, 6, 7])\n" }, { "alpha_fraction": 0.3712121248245239, "alphanum_fraction": 0.5454545617103577, "avg_line_length": 32, "blob_id": "10fbd33c26fb5030d158f4e5f1197642f2664dd3", "content_id": "a476e7e5cecd6e42b26390b1273d8d56e44dc2d8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 132, "license_type": "no_license", "max_line_length": 67, "num_lines": 4, "path": "/listings/v4_list11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "menge = {x*y for x in range(6) for y in range(6)}\n\nprint(menge)\n# Ausgabe: set([0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 20, 25])\n" }, { "alpha_fraction": 0.2982456088066101, "alphanum_fraction": 0.7368420958518982, "avg_line_length": 10.399999618530273, "blob_id": "60adf7ab0f5c2e6974a346356125b2b46e910954", "content_id": "4a55b5b8feca06ffb3c224e9e510a2614ab626cf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 57, "license_type": "no_license", "max_line_length": 15, "num_lines": 5, "path": "/listings/v3_strings2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "Menge,Name,Wert\n3,R1,1500\n7,R2,100\n2,R3,22000\n5,R4,47000\n" }, { "alpha_fraction": 0.7307692170143127, "alphanum_fraction": 0.7307692170143127, "avg_line_length": 25, "blob_id": "9eaea0ab7eb30fb6c40326396a57a29a0a186f4f", "content_id": "9e91929d5c9d4b222ad385cfad9b265851f7086e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 78, "license_type": "no_license", "max_line_length": 32, "num_lines": 3, "path": "/listings/v4_iter3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "iterator = iter(liste)\nprint(type(iterator))\n# Ausgabe: <type 'listiterator'>\n" }, { "alpha_fraction": 0.4895833432674408, "alphanum_fraction": 0.5208333134651184, "avg_line_length": 17, "blob_id": "1e2108a281c6eb3fb656cf7d08e49126f4136877", "content_id": "f5aea2e8b55b94ae43dc0ed5b86459a8422d7e81", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 288, "license_type": "no_license", "max_line_length": 80, "num_lines": 16, "path": "/listings/v4_iter10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def counter():\n n = 0\n while True:\n wert = yield n # next() liefert None zurueck, send(x) liefert x zurueck\n if wert is not None:\n n = wert\n else:\n n += 1\n\nc = counter()\nnext(c)\n# Ausgabe: 0\nc.send(50)\n# Ausgabe: 50\nnext(c)\n# Ausgabe: 51\n" }, { "alpha_fraction": 0.3726707994937897, "alphanum_fraction": 0.5155279636383057, "avg_line_length": 22, "blob_id": "1287a2cad82f660c9f48b803320c7f16515f6601", "content_id": "23abcc736ed86f6e142b503c7a093d1fd654530d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 161, "license_type": "no_license", "max_line_length": 67, "num_lines": 7, "path": "/listings/v4_list10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "menge = set()\nfor x in range(6):\n for y in range(6):\n menge.add(x*y)\n\nprint(menge)\n# Ausgabe: set([0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 20, 25])\n" }, { "alpha_fraction": 0.7291666865348816, "alphanum_fraction": 0.7291666865348816, "avg_line_length": 18.200000762939453, "blob_id": "5da2afccdd0fcb5ac0f8a30b9682cf18b78ce524", "content_id": "fb9aef4180cfe3c4bd44973afb4721e460de196c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 96, "license_type": "no_license", "max_line_length": 41, "num_lines": 5, "path": "/listings/v6_klassen15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def quadrieren(x):\n return x*x\n\nclass MeineKlasse:\n quadrieren = staticmethod(quadrieren)\n" }, { "alpha_fraction": 0.3448275923728943, "alphanum_fraction": 0.4482758641242981, "avg_line_length": 28, "blob_id": "1a6781a5900b18642c912e37d11449e7cbc5c4af", "content_id": "75d05cfb41d43d227040a942bca7f1326ed828e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 29, "license_type": "no_license", "max_line_length": 28, "num_lines": 1, "path": "/listings/v8_numpy2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr = np.array([1., 2., 3.])\n" }, { "alpha_fraction": 0.6352941393852234, "alphanum_fraction": 0.7215686440467834, "avg_line_length": 62.75, "blob_id": "940f8376c282e42f604e2142f51be74883c07ae5", "content_id": "04727575cf866db73e06a20729521ab1054d390d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 255, "license_type": "no_license", "max_line_length": 83, "num_lines": 4, "path": "/listings/v6_klassen8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x.speed_of_light = 10 # hier wird eine neue Instanz-Variable erzeugt\nprint('x:', x.speed_of_light) # Ausgabe: x: 10\nprint('y:', y.speed_of_light) # Ausgabe: y: 299792458\nprint('MeineKlasse:', MeineKlasse.speed_of_light) # Ausgabe: MeineKlasse: 299792458\n" }, { "alpha_fraction": 0.4701492488384247, "alphanum_fraction": 0.5373134613037109, "avg_line_length": 25.799999237060547, "blob_id": "36d98074c151ca4aba373a55e252074d06cf7db1", "content_id": "07cbce2a48fa923b89c221b648e45a1a368c40af", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 268, "license_type": "no_license", "max_line_length": 52, "num_lines": 10, "path": "/listings/v9_matplotlib5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x = y = np.linspace(-3, 3, 100)\nX, Y = np.meshgrid(x, y)\nZ1 = np.exp(-X**2 - Y**2)\nZ2 = np.exp(-(X - 1)**2 - (Y - 1)**2)\nZ = (Z1 - Z2) * 2\n\nfig, ax = plt.subplots()\nCS = ax.contourf(X, Y, Z, 20, cmap=plt.cm.coolwarm);\ncbar = fig.colorbar(CS);\ncbar.ax.set_ylabel('C');\n" }, { "alpha_fraction": 0.6984127163887024, "alphanum_fraction": 0.7301587462425232, "avg_line_length": 20, "blob_id": "e5e29fec2be52c44f7d7e3ea12fca464bc368cb6", "content_id": "58c7a027ae4296c81ac09fcfd85eb2869e6be61d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 63, "license_type": "no_license", "max_line_length": 40, "num_lines": 3, "path": "/listings/v4_list2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "quadratzahlen = [n*n for n in range(11)]\n\nprint(quadratzahlen)\n" }, { "alpha_fraction": 0.4830508530139923, "alphanum_fraction": 0.5508474707603455, "avg_line_length": 38.33333206176758, "blob_id": "0bd9ebb05b1d97297ee00d89990a69dba3e18a66", "content_id": "49cb3c3562c1661a36c5afacc29591568d315321", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 118, "license_type": "no_license", "max_line_length": 47, "num_lines": 3, "path": "/listings/v3_strings9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "messung = {'spannung': 24, 'strom': 2.5}\n'U = {spannung}, I = {strom}'.format(**messung)\n# Ausgabe: 'U = 24, I = 2.5'\n" }, { "alpha_fraction": 0.6554622054100037, "alphanum_fraction": 0.6722689270973206, "avg_line_length": 18.83333396911621, "blob_id": "9013e5408ca6140fe5f3ed2da5a49ccc7fbb48ee", "content_id": "dce39cf5a68fc8ee388ea62b60ed3893b0ab56e7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 119, "license_type": "no_license", "max_line_length": 38, "num_lines": 6, "path": "/listings/v6_klassen16.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n @staticmethod\n def quadrieren(x):\n return x*x\n\nMeineKlasse.quadrieren(3) # Ausgabe: 9\n" }, { "alpha_fraction": 0.6602451801300049, "alphanum_fraction": 0.6602451801300049, "avg_line_length": 26.190475463867188, "blob_id": "45ee7c96ba5cbcd89731cbb37480c11e61521117", "content_id": "cfb973e0267d2813b7e1f9552641721dd1fd913b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 571, "license_type": "no_license", "max_line_length": 72, "num_lines": 21, "path": "/listings/v2_func13.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "modul = 'Python' # globale Variable\n\ndef anmeldung():\n print(modul) # Variable existiert bereits ausserhalb der Funktion\n\nanmeldung() # Ausgabe: Python\n\ndef wechseln():\n modul = 'C++' # erstellt eine neue lokale Variable\n print('lokal:', modul)\n\nwechseln() # Ausgabe: lokal: C++\nprint('global:', modul) # Ausgabe: global: Python\n\ndef wirklich_wechseln():\n global modul #referenzieren auf die globale Variable\n modul = 'C++'\n print('lokal:', modul)\n\nwirklich_wechseln() # Ausgabe: lokal: C++\nprint('global:', modul) # Ausgabe: global: C++\n" }, { "alpha_fraction": 0.6320754885673523, "alphanum_fraction": 0.650943398475647, "avg_line_length": 20.200000762939453, "blob_id": "daf1109b8d3a637441f366a660d750831e65d9f7", "content_id": "02370c935a7967ed21c38e5021d94418ba5b98b5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 106, "license_type": "no_license", "max_line_length": 52, "num_lines": 5, "path": "/listings/v3_exception3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "eingabe = '10 Fr.'\ntry:\n x = int(eingabe)\nexcept:\n print('Oops! Irgendein Fehler ist aufgetreten.')\n" }, { "alpha_fraction": 0.6623376607894897, "alphanum_fraction": 0.6623376607894897, "avg_line_length": 37.5, "blob_id": "aa972f2d0e4fdfc3c12abc48c32a040c6c53d8da", "content_id": "c610578d418b3124302a4604f1941b2ca08fe537", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 77, "license_type": "no_license", "max_line_length": 42, "num_lines": 2, "path": "/listings/v6_klassen10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = MeineKlasse('Wall-E') # name='Wall-E'\n# Ausgabe: Wall-E wurde erstellt.\n" }, { "alpha_fraction": 0.48022598028182983, "alphanum_fraction": 0.6384180784225464, "avg_line_length": 43.25, "blob_id": "fbf9a4425f7d6d60a39653c3ffaba4a10ca7de3c", "content_id": "3c2c5553e38c24a110e07771207b6b57ff54407f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 177, "license_type": "no_license", "max_line_length": 88, "num_lines": 4, "path": "/listings/v4_list7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = ', '.join(['{:.0f}km={:.0f}mi'.format(km, mi) for km, mi in zip(kilometer, meilen)])\n\nprint(s)\n# Ausgabe: 30km=19mi, 50km=31mi, 60km=37mi, 80km=50mi, 100km=62mi, 120km=75mi\n" }, { "alpha_fraction": 0.6829268336296082, "alphanum_fraction": 0.6829268336296082, "avg_line_length": 23.600000381469727, "blob_id": "7d01ab9255660c1124be8592e1c5e6841fec9fde", "content_id": "3d8ead0a69c0fad809cd0caaabcb526748106040", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 123, "license_type": "no_license", "max_line_length": 35, "num_lines": 5, "path": "/listings/v3_datei8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "with open('mailaenderli.txt') as f:\n zeilen = f.readlines()\nprint(zeilen)\nfor zeile in zeilen:\n print(zeile.strip())\n" }, { "alpha_fraction": 0.739130437374115, "alphanum_fraction": 0.739130437374115, "avg_line_length": 22, "blob_id": "22e51b014891e36197006ab64564262e046f45ae", "content_id": "c82ba800295a701f1fd65581cead0f09802e7779", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 23, "license_type": "no_license", "max_line_length": 22, "num_lines": 1, "path": "/listings/v6_klassen4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "objekt = MeineKlasse()\n" }, { "alpha_fraction": 0.375, "alphanum_fraction": 0.4642857015132904, "avg_line_length": 27, "blob_id": "71ce0e63aec76e57d893bdbd418049897f7807b4", "content_id": "aa83b2c0e29c78fb7b17e33e4a898be2cc16b030", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 56, "license_type": "no_license", "max_line_length": 42, "num_lines": 2, "path": "/listings/v4_tupel15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste.sort()\nliste # Ausgabe: [1, 2, 3, 4, 5]\n" }, { "alpha_fraction": 0.6138613820075989, "alphanum_fraction": 0.6336633563041687, "avg_line_length": 19.200000762939453, "blob_id": "8d7e70e2478bd8697d22633d6c809184c26edf17", "content_id": "46b86e0b224e95364692802a3d9583fc1422698b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 101, "license_type": "no_license", "max_line_length": 28, "num_lines": 5, "path": "/listings/v3_strings14.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "bin_data = 'A'.encode(utf-8)\nprint(bin_data)\n# Ausgabe: b'A'\nbin_data.decode('utf-8')\n# Ausgabe: 'A'\n" }, { "alpha_fraction": 0.6327683329582214, "alphanum_fraction": 0.6723163723945618, "avg_line_length": 21.125, "blob_id": "c09af4ba30ed7f40f2903d3c96f00e847e0d4169", "content_id": "29fd805f319b6b7c1cc49dae42648b06d285d13e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 354, "license_type": "no_license", "max_line_length": 41, "num_lines": 16, "path": "/listings/v8_numpy13.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\nt = np.arange(100)/100\ns1 = np.sin(2*np.pi*t)\ns2 = s1 + np.random.randn(*s1.shape)/4\n\nplt.figure()\nplt.plot(t, s1, '.-', label='Simulation')\nplt.plot(t, s2, '.-', label='Messung')\nplt.xlabel('Zeit (s)')\nplt.ylabel('Amplitude')\nplt.grid(True)\nplt.legend()\nplt.tight_layout()\nplt.savefig('diagramm.pdf')\n" }, { "alpha_fraction": 0.5478261113166809, "alphanum_fraction": 0.5652173757553101, "avg_line_length": 20.5625, "blob_id": "ca6c9836e1278d09217f21ea28287ab11a7fd79f", "content_id": "e31b019af31b23688b5c64cc088a2470c6e6ed5e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 345, "license_type": "no_license", "max_line_length": 56, "num_lines": 16, "path": "/listings/v5_ra2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "m = re.match(r'[a-z]+', 'hallo welt!')\nprint(m)\n# Ausgabe: <re.Match object; span=(0, 5), match='hallo'>\n\nif m is not None:\n print('group:', m.group())\n print('start:', m.start())\n print('end:', m.end())\n print('span:', m.span())\nelse:\n print('keine Uebereinstimmung')\n# Ausgabe:\n# group: hallo\n# start: 0\n# end: 5\n# span: (0, 5)\n" }, { "alpha_fraction": 0.4557721018791199, "alphanum_fraction": 0.49025487899780273, "avg_line_length": 43.46666717529297, "blob_id": "704ed362b5de870e3ec851cf2ccd2b00470c3f6e", "content_id": "cbb9c9bb56963c9cb82ef6ceea78bdedcf5edcb9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 667, "license_type": "no_license", "max_line_length": 77, "num_lines": 15, "path": "/listings/v6_klassen21.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "objekt.__priv # Ausgabe:\n# ---------------------------------------------------------------------------\n# AttributeError Traceback (most recent call last)\n# <ipython-input-5-9bcc05561362> in <module>\n# ----> 1 objekt.__priv\n#\n# AttributeError: 'MeineKlasse' object has no attribute '__priv'\n\nobjekt.__priv_funktion() # Ausgabe:\n# ---------------------------------------------------------------------------\n# AttributeError Traceback (most recent call last)\n# <ipython-input-16-6e6c693108e6> in <module>\n# ----> 1 objekt.__priv_funktion()\n#\n# AttributeError: 'MeineKlasse' object has no attribute '__priv_funktion'\n" }, { "alpha_fraction": 0.6422287225723267, "alphanum_fraction": 0.6598240733146667, "avg_line_length": 36.88888931274414, "blob_id": "bc88a27a6de234373dd4c25037e67d72ed9d7487", "content_id": "b27bb1214ae6feef24c71ad90ad786e2a5d8451b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 341, "license_type": "no_license", "max_line_length": 64, "num_lines": 9, "path": "/listings/v9_interpolate3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "f_lin = interpld(x, y) # lineare Interpolation\nf_cub = interpld(x, y, kind='cubic') # kubische Interpolation\nxnew = np.linspace(0, 10, num=101, endpoint=True)\nplt.figure()\nplt.plot(x, y, 'o', label='Stuetzwerte')\nplt.plot(xnew, f_lin(xnew), '-', label='linear')\nplt.plot(xnew, f_cub(xnew), '--', label='kubisch')\nplt.legend()\nplt.show()\n" }, { "alpha_fraction": 0.4399999976158142, "alphanum_fraction": 0.5199999809265137, "avg_line_length": 11.5, "blob_id": "b0fec7740d813df298f501d406e7166434956520", "content_id": "d0f473356314e5cddc632cc14fd9a631920c37c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 25, "license_type": "no_license", "max_line_length": 15, "num_lines": 2, "path": "/listings/v3_strings13.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "ord('A')\n# Ausgabe: (65)\n" }, { "alpha_fraction": 0.6271186470985413, "alphanum_fraction": 0.694915235042572, "avg_line_length": 28.5, "blob_id": "6dac7438f8d97cea7fafe37c7beb4bf80e7c3eb2", "content_id": "fb1f2ed877f7ba596b6e3fdbc1d06929f6340467", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 59, "license_type": "no_license", "max_line_length": 45, "num_lines": 2, "path": "/listings/v9_matplotlib4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "plt.figure()\nplt.step(np.arange(15), np.random.randn(15));\n" }, { "alpha_fraction": 0.5, "alphanum_fraction": 0.5727272629737854, "avg_line_length": 26.5, "blob_id": "098c5be830bc7ba19056694de23068c1869889dc", "content_id": "0345fc9d94c0e11062f564e2ccc094bb05ac5ee2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 110, "license_type": "no_license", "max_line_length": 32, "num_lines": 4, "path": "/listings/v3_exception1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "int('bla') => ValueError\n5/0 => ZeroDivisionError\na[1000] => IndexError\n10 + 'Fr.' => TypeError\n" }, { "alpha_fraction": 0.6041666865348816, "alphanum_fraction": 0.6822916865348816, "avg_line_length": 47, "blob_id": "e0d6ee304da4b1f876276a2439f966954ea16581", "content_id": "144bdfb558049dd3e09561509e12cbaf2f17c195", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 192, "license_type": "no_license", "max_line_length": 57, "num_lines": 4, "path": "/listings/v2_func6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "resultat1 = summe(2, 3)\nresultat2 = summe(a=10, b=2) # Schluesselwortparameter\nresultat3 = summe(b=2, a=10) # Reihenfolge ist egal\nresultat4 = summe(20, b=4) # zuerst die namelosen\n" }, { "alpha_fraction": 0.4761904776096344, "alphanum_fraction": 0.5595238208770752, "avg_line_length": 41, "blob_id": "854ba984e45c20d9b9d08e2170bf7987f85f637f", "content_id": "c7dfc12d32c6626cf92e00ef6f7bd4d95cce9490", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 84, "license_type": "no_license", "max_line_length": 65, "num_lines": 2, "path": "/listings/v4_tupel22.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "list(filter(lambda x: True if x >= 0 else False, [5, -8, 3, -1]))\n# Ausgabe: [5, 3]\n" }, { "alpha_fraction": 0.4000000059604645, "alphanum_fraction": 0.5066666603088379, "avg_line_length": 36.5, "blob_id": "e2ac30c5f7e48b2f0eca1902cff12d28c2b5d962", "content_id": "7cfe97b0ecd7f843f79f2a5c5cae5890f7da32a7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 75, "license_type": "no_license", "max_line_length": 42, "num_lines": 2, "path": "/listings/v3_strings5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'U = {0}, I = {1}'.format(spannung, strom)\n# Ausgabe: 'U = 12.56, I = 0.5'\n" }, { "alpha_fraction": 0.6470588445663452, "alphanum_fraction": 0.6470588445663452, "avg_line_length": 21.66666603088379, "blob_id": "e11d597d1c7022b3f6694558ec1b3640beaa58aa", "content_id": "fb1ade3725caa5cd8a6481d05ede77273c5e927f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 68, "license_type": "no_license", "max_line_length": 35, "num_lines": 3, "path": "/listings/v3_datei7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "with open('mailaenderli.txt') as f:\n text = f.read()\nprint(text)\n" }, { "alpha_fraction": 0.7222222089767456, "alphanum_fraction": 0.7222222089767456, "avg_line_length": 62, "blob_id": "ff5e1cdf9ebcf34e1c53706a3a14db1fbdaffeea", "content_id": "55298814c0fd5a0bd588c93ad139967a649df4ee", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 126, "license_type": "no_license", "max_line_length": 73, "num_lines": 2, "path": "/listings/v5_ra20.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.sub(r'\\bschoen\\b', 'herrlich', 'Das Wetter ist schoen oder unschoen.')\n# Ausgabe: 'Das Wetter ist herrlich oder unschoen.'\n" }, { "alpha_fraction": 0.4516128897666931, "alphanum_fraction": 0.5161290168762207, "avg_line_length": 22.25, "blob_id": "88472dd25dc9d8f726058cf6417589cb6203d465", "content_id": "73117959b8e53cdb3fc176a1f074520434777998", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 93, "license_type": "no_license", "max_line_length": 24, "num_lines": 4, "path": "/listings/v4_tupel3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x, y, z = 1, 2, 3\nprint(x) # Ausgabe: 1\nprint(y) # Ausgabe: 2\nprint(z) # Ausgabe: 3\n" }, { "alpha_fraction": 0.6969696879386902, "alphanum_fraction": 0.6969696879386902, "avg_line_length": 15.5, "blob_id": "158ab0fb0c676540dbc9db7aa5662bd05fd815c7", "content_id": "f5c22d2677793684afcb4c3280b06e1b2457a6c2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 33, "license_type": "no_license", "max_line_length": 20, "num_lines": 2, "path": "/listings/v3_datei10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "import glob\nglob.glob('*.ipynb')\n" }, { "alpha_fraction": 0.7444444298744202, "alphanum_fraction": 0.7444444298744202, "avg_line_length": 29, "blob_id": "52a07db43066c4044ac51b50f69717553d481755", "content_id": "67b4df36803ea7d2ff2283643e5057677979b704", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 90, "license_type": "no_license", "max_line_length": 34, "num_lines": 3, "path": "/listings/v4_tupel5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "vorname, nachname = t\nprint(vorname) # Ausgabe: Peter\nprint(nachname) # Ausgabe: Mueller\n" }, { "alpha_fraction": 0.406047523021698, "alphanum_fraction": 0.53995680809021, "avg_line_length": 29.866666793823242, "blob_id": "59b3ec78c8106fdf7d5c711f7cf627c39f376f67", "content_id": "e591ab6af37742317041fccdb77b1c9859a92b2f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 463, "license_type": "no_license", "max_line_length": 66, "num_lines": 15, "path": "/listings/v3_strings15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'Python ist eine Schlange.'.split()\n# Ausgabe: ['Python', 'ist', 'eine', 'Schlange.']\n\ncsv = '1;2000;30.3;44;505'\ncsv.split(';')\n# Ausgabe: ['1', '2000', '30.3', '44', '505']\n\ncsv.split(';', maxsplit=2) # max. zwei Trennungen von links her\n# Ausgabe: ['1', '2000', '30.3;44;505']\n\ncsv.rsplit(';', maxsplit=2) # max. zwei Trennungen von rechts her\n# Ausgabe: ['1;2000;30.3', '44', '505']\n\n'1;2;;;;3;4'.split(';')\n# Ausgabe: ['1', '2', '', '', '', '3', '4']\n" }, { "alpha_fraction": 0.5359116196632385, "alphanum_fraction": 0.5425414443016052, "avg_line_length": 31.321428298950195, "blob_id": "3809aa12a4611d40826060fe3a8052bbd855d479", "content_id": "056795e9a2ca13ab4a4a86876783ee5c51586b5c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 905, "license_type": "no_license", "max_line_length": 89, "num_lines": 28, "path": "/listings/v7_vererbung12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Fahrzeug:\n def __init__(self, antrieb, **kwargs):\n print('Fahrzeug.__init__(),', 'kwargs =', kwargs)\n super().__init__(**kwargs)\n self.antrieb = antrieb\n\nclass Computer:\n def __init__(self, display, **kwargs):\n print('Computer.__init__(),', 'kwargs =', kwargs)\n super().__init__(**kwargs)\n self.display = display\n\nclass Tesla(Fahrzeug, Computer):\n def __init__(self, display, dual_motor, **kwargs):\n print('Tesla.__init__()')\n super().__init__(\n antrieb='elektrisch',\n display=display,\n **kwargs\n )\n self.dual_motor = dual_motor\n\n\nt = Tesla(display='17 Zoll', dual_motor=True) # Ausgabe:\n# Tesla.__init__()\n# Fahrzeug.__init__(), kwargs = {'display': '17 Zoll'}\n# Computer.__init__(), kwargs = {}\nt.__dict__ # Ausgabe: {'display': '17 Zoll', 'antrieb': 'elektrisch', 'dual_motor': True}\n" }, { "alpha_fraction": 0.6219512224197388, "alphanum_fraction": 0.6707317233085632, "avg_line_length": 12.333333015441895, "blob_id": "f05f3a5db5f10608919552686935c20138639f4a", "content_id": "cce414860496912635e36d354b76ea13d8837184", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 82, "license_type": "no_license", "max_line_length": 14, "num_lines": 6, "path": "/listings/v2_if2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "if Bedingung:\n Anweisung1\n Anweisung2\nelse:\n Anweisung3\n Anweisung4 \n" }, { "alpha_fraction": 0.4878048896789551, "alphanum_fraction": 0.4878048896789551, "avg_line_length": 40, "blob_id": "91728793015e069f24c08cb5daa5283b34865b20", "content_id": "6fb9c92b8b75b229eca5dba491dac2da2c878a04", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 82, "license_type": "no_license", "max_line_length": 48, "num_lines": 2, "path": "/listings/v4_iter2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "print('__iter__():', hasattr(liste, '__iter__'))\n# Ausgabe: ('__iter__():', True)\n" }, { "alpha_fraction": 0.44368600845336914, "alphanum_fraction": 0.511945366859436, "avg_line_length": 40.85714340209961, "blob_id": "2a9b73f1348abdf38defd8041ed551546d496668", "content_id": "c9914de7c14010f8b40073a4cf5145f7de2e5384", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 293, "license_type": "no_license", "max_line_length": 73, "num_lines": 7, "path": "/listings/v4_tupel16.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = [2, 5, 3, 4, 1]\nsortiert = sorted(liste, reverse=True)\nprint('Liste:', liste) # Ausgabe: ('Liste:', [2, 5, 3, 4, 1])\nprint('sortiert:', sortiert) # Ausgabe: ('sortiert:', [5, 4, 3, 2, 1])\n\nliste.sort(reverse=True)\nliste # Ausgabe: [5, 4, 3, 2, 1]\n" }, { "alpha_fraction": 0.4951923191547394, "alphanum_fraction": 0.629807710647583, "avg_line_length": 28.714284896850586, "blob_id": "1c9326e10cc7f24f626df184d8c8dc3f8b46782c", "content_id": "c73e2cf871138dd3c06f84273d169acfccce1cd7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 208, "license_type": "no_license", "max_line_length": 77, "num_lines": 7, "path": "/listings/v4_list6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "temp = []\nfor km, mi in zip(kilometer, meilen):\n temp.append('{:.0f}km={:.0f}mi'.format(km, mi))\ns = ', '.join(temp)\n\nprint(s)\n# Ausgabe: 30km=19mi, 50km=31mi, 60km=37mi, 80km=50mi, 100km=62mi, 120km=75mi\n" }, { "alpha_fraction": 0.3738937973976135, "alphanum_fraction": 0.491150438785553, "avg_line_length": 21.600000381469727, "blob_id": "ba83a1c41a4779baf8a3fb722ed12b3c4f274086", "content_id": "c8523d9ece321a80b9b72d18530797441fa91a4b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 452, "license_type": "no_license", "max_line_length": 55, "num_lines": 20, "path": "/listings/v4_tupel18.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "from collections import deque\nliste = deque([1, 2, 3])\nprint(liste) # Ausgabe: deque([1, 2, 3])\nliste.rotate(1)\nprint(liste) # Ausgabe: deque([3, 1, 2])\nendlich_lang = deque(maxlen=5)\nfor n in range(10):\n endlich_lang.append(n)\n print(list(endlich_lang))\n# Ausgabe:\n# [0]\n# [0, 1]\n# [0, 1, 2]\n# [0, 1, 2, 3]\n# [0, 1, 2, 3, 4]\n# [1, 2, 3, 4, 5]\n# [2, 3, 4, 5, 6]\n# [3, 4, 5, 6, 7]\n# [4, 5, 6, 7, 8]\n# [5, 6, 7, 8, 9]\n" }, { "alpha_fraction": 0.6308724880218506, "alphanum_fraction": 0.6308724880218506, "avg_line_length": 17.625, "blob_id": "6589f4280e84ed63100bcc0cd5d32c906f564a29", "content_id": "ce30143620e924c0092c56e1c042e9955265f417", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 149, "license_type": "no_license", "max_line_length": 38, "num_lines": 8, "path": "/listings/v3_exception6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "try:\n f = open('datei.txt')\nexcept IOError:\n print('Kann Datei nicht oeffnen.')\nelse:\n print('Datei schliessen.')\n f.close()\nprint('Ende')\n" }, { "alpha_fraction": 0.6480000019073486, "alphanum_fraction": 0.6480000019073486, "avg_line_length": 24, "blob_id": "218f78ee5a5f4809c903e6fd2c0f176b7520a09a", "content_id": "e8338fe87f5cef7d1be07b22febd8cf4683cb404", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 500, "license_type": "no_license", "max_line_length": 60, "num_lines": 20, "path": "/listings/v6_klassen3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n '''Diese Klasse hat nicht viel drin.'''\n pass\n\nMeineKlasse.__doc__\n# Ausgabe: 'Diese Klasse hat nicht viel drin.'\nhelp(MeineKlasse)\n# Ausgabe:\n# Help on class MeineKlasse in module __main__:\n# \n# class MeineKlasse(builtins.object)\n# | Diese Klasse hat nicht viel drin.\n# |\n# | Data descriptors defined here:\n# |\n# | __dict__\n# | dictionary for instance variables (if defined)\n# |\n# | __weakref__\n# | list of weak references to the object (if defined)\n" }, { "alpha_fraction": 0.45928338170051575, "alphanum_fraction": 0.4918566644191742, "avg_line_length": 17.058822631835938, "blob_id": "2c10f054294959e6cf5d4d30239d8f2bf913ac6b", "content_id": "d40c5389eb40be9dfdfc8857be572c711d269893", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 307, "license_type": "no_license", "max_line_length": 47, "num_lines": 17, "path": "/listings/v5_ra5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "for m in re.finditer(r'[a-z]+', 'hallo welt!'):\n print('---')\n print('group:', m.group())\n print('start:', m.start())\n print('end:', m.end())\n print('span:', m.span())\n# Ausgabe:\n# ---\n# group: hallo\n# start: 0\n# end: 5\n# span: (0, 5)\n# ---\n# group: welt\n# start: 6\n# end: 10\n# span: (6, 10)\n" }, { "alpha_fraction": 0.5089552402496338, "alphanum_fraction": 0.5223880410194397, "avg_line_length": 25.799999237060547, "blob_id": "bae7b6a3a28454a0967d266a156cb07196c2964f", "content_id": "296d8f9f535aa8f4b7c119760079e9c8b447725b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1340, "license_type": "no_license", "max_line_length": 76, "num_lines": 50, "path": "/listings/v6_klassen9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n '''Beschreibung der Klasse.'''\n speed_of_light = 299792458\n\n def __init__(self, name):\n '''Diese Methode initialisiert die Variablen.'''\n self.name = name\n print(self.name, 'wurde erstellt.')\n\n def __del__(self):\n '''Diese Methode raeumt alles auf bevor es zerstoert wird.'''\n print(self.name, 'wurde zerstoert.')\n\n def hallo(self):\n '''Sagt Hallo.'''\n print('Hallo', self.name)\n\nhelp(MeineKlasse)\n# Ausgabe:\n# Help on class MeineKlasse in module __main__:\n#\n# class MeineKlasse(builtins.object)\n# | MeineKlasse(name)\n# |\n# | Beschreibung der Klasse.\n# |\n# | Methods defined here:\n# |\n# | __del__(self)\n# | Diese Methode raeumt alles auf bevor es zerstoert wird.\n# |\n# | __init__(self, name)\n# | Diese Methode initialisiert die Variablen.\n# |\n# | hallo(self)\n# | Sagt Hallo.\n# |\n# | ----------------------------------------------------------------------\n# | Data descriptors defined here:\n# |\n# | __dict__\n# | dictionary for instance variables (if defined)\n# |\n# | __weakref__\n# | list of weak references to the object (if defined)\n# |\n# | ----------------------------------------------------------------------\n# | Data and other attributes defined here:\n# |\n# | speed_of_light = 299792458\n" }, { "alpha_fraction": 0.4735202491283417, "alphanum_fraction": 0.585669755935669, "avg_line_length": 31.100000381469727, "blob_id": "1daa07bb0784aeb208d3f094f9cd01ca5bea16fc", "content_id": "9b27de117f76ef8ff4b3de64e6582ccb4501f57b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 642, "license_type": "no_license", "max_line_length": 59, "num_lines": 20, "path": "/listings/v6_klassen27.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Konto:\n def __init__(self, guthaben, iban):\n self.guthaben = guthaben\n self.iban = iban\n\n def __float__(self):\n return float(self.guthaben)\n\n def __add__(self, other):\n return self.guthaben + other.guthaben\n\n def __sub__(self, other):\n return self.guthaben - other.guthaben\n\nk1 = Konto(50, 'CH42 4738 2934 9267 0878 5')\nk2 = Konto(23, 'CH27 1036 5802 2994 9234 3')\nprint('float(k1) =', float(k1)) # Ausgabe: float(k1) = 50.0\nprint('float(k2) =', float(k2)) # Ausgabe: float(k2) = 23.0\nprint('k1 + k2 =', k1 + k2) # Ausgabe: k1 + k2 = 73\nprint('k1 - k2 =', k1 - k2) # Ausgabe: k1 - k2 = 27\n" }, { "alpha_fraction": 0.4191419184207916, "alphanum_fraction": 0.4983498454093933, "avg_line_length": 29.299999237060547, "blob_id": "e5139744ffd741def4615dd7c91cd27d4050d218", "content_id": "bd4018ace44a1781247ab2fe306cf8608209160f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 303, "license_type": "no_license", "max_line_length": 56, "num_lines": 10, "path": "/listings/v4_tupel8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = ['a', 'b', 'c']\nliste = liste + [1, 2] # zu vermeiden, sehr langsam\nliste\n# Ausgabe: ['a', 'b', 'c', 1, 2]\nliste += [3, 4] # viel schneller\nliste\n# Ausgabe: ['a', 'b', 'c', 1, 2, 1, 2, 3, 4]\nliste.extend([5, 6]) # noch schneller\nliste\n# Ausgabe: ['a', 'b', 'c', 1, 2, 1, 2, 3, 4, 3, 4, 5, 6]\n" }, { "alpha_fraction": 0.7333333492279053, "alphanum_fraction": 0.7333333492279053, "avg_line_length": 51.5, "blob_id": "5e62b7b0134f440781d6f57e68696096d61129b8", "content_id": "d07669662aaf39029989368d936220e143812ab6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 105, "license_type": "no_license", "max_line_length": 53, "num_lines": 2, "path": "/listings/v6_klassen20.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "print(objekt._prot) # Ausgabe: Ich bin protected.\nobjekt._prot_funktion() # Ausgabe: Ich bin protected.\n" }, { "alpha_fraction": 0.3333333432674408, "alphanum_fraction": 0.5185185074806213, "avg_line_length": 39.5, "blob_id": "f37bc08702f219ed05b1d793fb30b07d70d037b0", "content_id": "de57744c401a49ed46286333b88978ca70a66d04", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 81, "license_type": "no_license", "max_line_length": 42, "num_lines": 2, "path": "/listings/v3_strings6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'U = {0:.2f}, U = {0:.f}'.format(spannung)\n# Ausgabe: 'U = 12.56, U = 12.560000'\n" }, { "alpha_fraction": 0.6390977501869202, "alphanum_fraction": 0.6390977501869202, "avg_line_length": 43.33333206176758, "blob_id": "c5a71e64fd99494075bf2b20b7f5d3be9b59ab73", "content_id": "5fcaf46598d4a627a5faece6fe9fcb0df25e3b41", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 133, "license_type": "no_license", "max_line_length": 45, "num_lines": 3, "path": "/listings/v3_datei3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "inhalt = f.read() # gesamte Datei lesen\ninhalt = f.read(n) # n Zeichen lesen\nzeilen = f.readlines() # Liste aller Zeilen\n" }, { "alpha_fraction": 0.5625, "alphanum_fraction": 0.6625000238418579, "avg_line_length": 25.66666603088379, "blob_id": "fadabdf6ca3e11a64c6fb2b888027de2519a168d", "content_id": "2960d0842f02256b6455168cf1c6b9d7aa7e3cb0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 80, "license_type": "no_license", "max_line_length": 31, "num_lines": 3, "path": "/listings/v3_strings10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "lokale_variable = 13\nf'Wert = {lokale_variable:.3f}'\n# Ausgabe: 'Wert = 13.000'\n" }, { "alpha_fraction": 0.5545454621315002, "alphanum_fraction": 0.5727272629737854, "avg_line_length": 21, "blob_id": "aa2fe895c394dcd7a5d160514e34466783314188", "content_id": "5518027c8dd9a6a12a45d214275c2ee5eb3009f9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 110, "license_type": "no_license", "max_line_length": 38, "num_lines": 5, "path": "/listings/v2_func7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def rosen(farbe='rot'):\n print('Rosen sind ' + farbe + '.')\n\nrosen() # Aufruf 1\nrosen('gelb') # Aufruf 2\n" }, { "alpha_fraction": 0.4479166567325592, "alphanum_fraction": 0.6666666865348816, "avg_line_length": 23, "blob_id": "5d30aeb28413741929ff9e1ee51a64d92b53956c", "content_id": "d16aac42d7f8ee50e1eeed1b8192a88d283d00ba", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 96, "license_type": "no_license", "max_line_length": 41, "num_lines": 4, "path": "/listings/v4_list4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "kilometer = [30, 50, 60, 80, 100, 120]\nmeilen = [km*0.621371 for n in kilometer]\n\nprint(meilen)\n" }, { "alpha_fraction": 0.4852941036224365, "alphanum_fraction": 0.6029411554336548, "avg_line_length": 33, "blob_id": "a88c40268da85870f6964e63ad96a8cfe710cd0d", "content_id": "89580c184522c59ac822463d1b5402ad95d15728", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 68, "license_type": "no_license", "max_line_length": 45, "num_lines": 2, "path": "/listings/v9_interpolate2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x = np.linspace(0, 10, num=11, endpoint=True)\ny = np.cos(-x**2/9.0)\n" }, { "alpha_fraction": 0.5680473446846008, "alphanum_fraction": 0.5857987999916077, "avg_line_length": 20.125, "blob_id": "07351ab64a87f4aaa2a4ca61c7be7f3af9448ef7", "content_id": "18eff35508796804cc5415651136f98ed0127c7e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 169, "license_type": "no_license", "max_line_length": 30, "num_lines": 8, "path": "/listings/v3_exception4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "eingabe = '10Fr.'\ntry:\n x = int(eingabe)\n y = 1/x\nexcept ValueError as e:\n print('Oops! ' + str(e))\nexcept ZeroDivisionError as e:\n print('Oops! ' + str(e))\n" }, { "alpha_fraction": 0.6666666865348816, "alphanum_fraction": 0.6666666865348816, "avg_line_length": 22.25, "blob_id": "d65f9eef7af69550b119bdaf23e34c7211ca724f", "content_id": "8e14f0cb6127cd84147caf1c9a27c594be734536", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 93, "license_type": "no_license", "max_line_length": 29, "num_lines": 4, "path": "/listings/v6_klassen7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x.name = 'Hans'\ny.name = 'Peter'\nprint(x.name) # Ausgabe: Hans\nprint(y.name) #Ausgabe: Peter\n" }, { "alpha_fraction": 0.5248227119445801, "alphanum_fraction": 0.5319148898124695, "avg_line_length": 16.625, "blob_id": "1f4c4d4b4feb84f16922bdb30480e2b23b0f954c", "content_id": "1ea50cffba4c5c8d2fd426fdc84a72cc06eab097", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 141, "license_type": "no_license", "max_line_length": 33, "num_lines": 8, "path": "/listings/v6_klassen2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n i = 0\n\n def __init__(self, name):\n self.name = name\n\n def gruss(self):\n print('Hallo', self.name)\n" }, { "alpha_fraction": 0.5977011322975159, "alphanum_fraction": 0.5977011322975159, "avg_line_length": 28, "blob_id": "a98205245b512a60a66bd663c01ad7388c1fef32", "content_id": "31c86e60f7164cc1ff6c6bbde4a8a2d0f04e146d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 87, "license_type": "no_license", "max_line_length": 44, "num_lines": 3, "path": "/listings/v5_ra4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = re.findall(r'[a-z]+', 'hallo welt!')\nprint(liste)\n# Ausgabe: ['hallo', 'welt']\n" }, { "alpha_fraction": 0.5901639461517334, "alphanum_fraction": 0.6065573692321777, "avg_line_length": 14.25, "blob_id": "a8a0cd1f28dbd7e3aa0dd8326d71669e2b253e11", "content_id": "3e10cca74584dbe6955e53ceb0d0a81126bf1296", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 61, "license_type": "no_license", "max_line_length": 16, "num_lines": 4, "path": "/listings/v2_while3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "while Bedingung:\n Anweisung1\n if Fehler:\n break\n" }, { "alpha_fraction": 0.5657894611358643, "alphanum_fraction": 0.5745614171028137, "avg_line_length": 18, "blob_id": "c5e30c3d9ebc6ee5055e493b8ef620143c0b93cb", "content_id": "4f41d589ce1d921cce6b7dabeb0c2af153585984", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 228, "license_type": "no_license", "max_line_length": 29, "num_lines": 12, "path": "/listings/v4_iter7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def fibonacci_zahlen():\n a = 0\n b = 1\n while True:\n yield b\n a, b = b, a + b\n\nprint(type(fibonacci_zahlen))\n# Ausgabe: <type 'function'>\nf = fibonacci_zahlen()\nprint(type(f))\n# Ausgabe: <type 'generator'>\n" }, { "alpha_fraction": 0.3448275923728943, "alphanum_fraction": 0.5, "avg_line_length": 6.733333110809326, "blob_id": "fad251071acc7999acffdca978069a8a80926960", "content_id": "5baf8a748abf500cc88a4360e4c6b8a92392dd0b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 116, "license_type": "no_license", "max_line_length": 19, "num_lines": 15, "path": "/listings/v4_iter8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "next(f)\n# Ausgabe: 1\nfor n in range(10):\n print(next(f))\n# Ausgabe:\n# 1\n# 2\n# 3\n# 5\n# 8\n# 13\n# 21\n# 34\n# 55\n# 89\n" }, { "alpha_fraction": 0.4399999976158142, "alphanum_fraction": 0.5600000023841858, "avg_line_length": 24, "blob_id": "541a992c20887cf47171c23905216c1adfd7a118", "content_id": "15467a4232627dab01d6363c4cfb60655b94ce94", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 150, "license_type": "no_license", "max_line_length": 27, "num_lines": 6, "path": "/listings/v3_strings7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'{:>8.2f}'.format(sapnnung)\n# Ausgabe: ' 12.56'\n'{:<8.2f}'.format(spannung)\n# Ausgabe: '12.56 '\n'{:^8.2f}'.format(spannung)\n# Ausgabe: ' 12.56 '\n" }, { "alpha_fraction": 0.5979999899864197, "alphanum_fraction": 0.6420000195503235, "avg_line_length": 26.77777862548828, "blob_id": "ddffff87d486791e59a15254b9cae11201b658bc", "content_id": "36bb634a69262cecdf78222744e03d1fdbf9da70", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 500, "license_type": "no_license", "max_line_length": 77, "num_lines": 18, "path": "/listings/v6_klassen25.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Bank:\n def __init__(self):\n self.__guthaben = 0\n\n @property\n def guthaben(self):\n print('Das Guthaben wurde abgefragt.')\n return self.__guthaben\n\n @guthaben.setter\n def guthaben(self, n):\n self.__guthaben = n\n print('Das Guthaben wurde auf {} geaendert.'.format(self.__guthaben))\n\nk = Bank()\nk.guthaben = 1000000 # Ausgabe: Das Guthaben wurde auf 1000000 geaendert.\nprint(k.guthaben) # Ausgabe: Das Guthaben wurde abgefragt.\n# Ausgabe: 1000000\n" }, { "alpha_fraction": 0.5175438523292542, "alphanum_fraction": 0.5526315569877625, "avg_line_length": 18, "blob_id": "b5b1d4f68d69af6cba2a68669d422f1702318410", "content_id": "6799da5552f15234626f8acd529b2067218da2a8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 114, "license_type": "no_license", "max_line_length": 26, "num_lines": 6, "path": "/listings/v9_matplotlib7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x = np.logspace(-2, 1, 20)\ns = np.exp(-x)\n\nfig, ax = plt.subplots()\nax.loglog(x, s, '.-');\nax.grid(which='both');\n" }, { "alpha_fraction": 0.3636363744735718, "alphanum_fraction": 0.3787878751754761, "avg_line_length": 21, "blob_id": "01e00587f34f23a21611fe389455bf34c0e3fcf5", "content_id": "533abe01bf5e566e1b48f643dba64a33b6f365ff", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 66, "license_type": "no_license", "max_line_length": 36, "num_lines": 3, "path": "/listings/v4_tupel12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste[3:] = ['D', 'E']\nliste\n# Ausgabe: ['a', 'B', 'c', 'D', 'E']\n" }, { "alpha_fraction": 0.473053902387619, "alphanum_fraction": 0.4790419042110443, "avg_line_length": 22.85714340209961, "blob_id": "11f278981ef73c4f3c4d8b2dd20c1f0458f2e773", "content_id": "db7a78edd97c5a9129fdb4fc9f4d43c092bd8ec7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 167, "license_type": "no_license", "max_line_length": 36, "num_lines": 7, "path": "/listings/v4_tupel7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = ['a', 'b', 'c']\nliste.append('X') # rechts\nliste\n# Ausgabe: ['a', 'b', 'c', 'X']\nliste.insert(2, 'Y') # mit Index\nliste\n# Ausgabe: ['a', 'b', 'Y', 'c', 'X']\n" }, { "alpha_fraction": 0.5400000214576721, "alphanum_fraction": 0.5600000023841858, "avg_line_length": 15.666666984558105, "blob_id": "78ed9a3d38d80fa16ff8f4e15b340be3b6b417e5", "content_id": "ba926e72a7aa7033347db629e68f9e6b72279b97", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 50, "license_type": "no_license", "max_line_length": 25, "num_lines": 3, "path": "/listings/v4_tupel2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "t = (5,)\nprint(type(t))\n# Ausgabe: <type 'tuple'>\n" }, { "alpha_fraction": 0.6106870174407959, "alphanum_fraction": 0.6870229244232178, "avg_line_length": 31.75, "blob_id": "2206bc56f850fffad0f835cc7e69efc3eca5dda7", "content_id": "83a0527b0b38664722e779a0167e210efdedb757", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 131, "license_type": "no_license", "max_line_length": 49, "num_lines": 4, "path": "/listings/v9_matplotlib3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\nax1.plot(np.random.randn(10));\nax2.plot(np.random.randn(10));\nfig.tight_layout()\n" }, { "alpha_fraction": 0.6026490330696106, "alphanum_fraction": 0.6026490330696106, "avg_line_length": 49.33333206176758, "blob_id": "af4968696d276dbe43f310b6df22d941c363c675", "content_id": "48370b9876355fcb1867179ecd9041a4ce0fd69f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 151, "license_type": "no_license", "max_line_length": 69, "num_lines": 3, "path": "/listings/v5_ra6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = re.split(r'\\W+', 'Nun, dies ist ein (einfaches) Beispiel.')\nprint(liste)\n# Ausgabe: ['Nun', 'dies', 'ist', 'ein', 'einfaches', 'Beispiel', '']\n" }, { "alpha_fraction": 0.6785714030265808, "alphanum_fraction": 0.7142857313156128, "avg_line_length": 13, "blob_id": "7edce0a09f0c4daf5c110e0bce19f1c24cab1368", "content_id": "ba810afc1e6849df6de522720e55c9835b845eb4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 84, "license_type": "no_license", "max_line_length": 14, "num_lines": 6, "path": "/listings/v4_iter5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "next(iterator)\n# Ausgabe: 1\nnext(iterator)\n# Ausgabe: 2\nnext(iterator)\n# Ausgabe: 3\n" }, { "alpha_fraction": 0.6957831382751465, "alphanum_fraction": 0.7198795080184937, "avg_line_length": 32.20000076293945, "blob_id": "4c5337ec47471b7e4bc03f689bfa43be6f4679a1", "content_id": "a2109d2f1da4a49ee803c22a8adb37440c387588", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 332, "license_type": "no_license", "max_line_length": 69, "num_lines": 10, "path": "/listings/v6_klassen30.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "import sys\nsys.path.append('scripts')\nprint('\\n'.join(sys.path)) # Liste der Suchorte\n# Ausgabe: *alle in Frage kommenden Verzeichnisse*\n\nfrom my_other_module import Bank # Ausgabe:\n# Dies ist scripts\\my_other_module.py:\n# __name__ = my_other_module\nb = Bank()\nb.guthaben = 500.0 # Ausgabe: Das Guthaben wurde auf 500.0 geaendert.\n" }, { "alpha_fraction": 0.4868420958518982, "alphanum_fraction": 0.4868420958518982, "avg_line_length": 75, "blob_id": "04c0516d6dcd9fbbd518fd32ddad92a761508874", "content_id": "d7514e7d227de5e0f350704032538ed6304a1e00", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 76, "license_type": "no_license", "max_line_length": 75, "num_lines": 1, "path": "/listings/v7_vererbung15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "D.mro() # Ausgabe: [__main__.D, __main__.B, __main__.C, __main__.A, object]\n" }, { "alpha_fraction": 0.3076923191547394, "alphanum_fraction": 0.5, "avg_line_length": 38, "blob_id": "ad0941c051a2f8635fdb9635927163086cd930d3", "content_id": "d1c9e85eb722c05e31bfa9e97ef0317d6f181e63", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 78, "license_type": "no_license", "max_line_length": 53, "num_lines": 2, "path": "/listings/v4_tupel21.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "list(map(lambda x,y: x + y, [1, 2, 3], [10, 20, 30]))\n# Ausgabe: [11, 22, 33]\n" }, { "alpha_fraction": 0.688524603843689, "alphanum_fraction": 0.7213114500045776, "avg_line_length": 14.25, "blob_id": "c2ced5db12ef3f3c27143fb1c6861525958c4f04", "content_id": "5331c6a8d63c9af68b72b4514628d2da59c52207", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 61, "license_type": "no_license", "max_line_length": 24, "num_lines": 4, "path": "/listings/v2_for2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "for Variable in Sequenz:\n Anweisung1\nelse:\n Anweisung2\n" }, { "alpha_fraction": 0.534246563911438, "alphanum_fraction": 0.5753424763679504, "avg_line_length": 35.5, "blob_id": "c478dba7628bdcfa557fdb72e5fff12c994006b7", "content_id": "8cb319e2da404737b2fece3819aa77d259dcd83c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 73, "license_type": "no_license", "max_line_length": 42, "num_lines": 2, "path": "/listings/v2_func12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "punkt = {'x':1, 'y':2, 'z':2}\ndistanz(**punkt) # Dictionary entpacken\n" }, { "alpha_fraction": 0.3563218414783478, "alphanum_fraction": 0.5862069129943848, "avg_line_length": 42.5, "blob_id": "fae96e9d7051f3f5a75a793412a39914adbfe1bd", "content_id": "db8f227d39fad9aeb9334d723e60e257a496de1f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 174, "license_type": "no_license", "max_line_length": 70, "num_lines": 4, "path": "/listings/v8_numpy9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "t = linspace(1, 3, 5)\nnp.log10(t) # Ausgabe: array([0., 0.17609, 0.30103, 0.39794, 0.47712])\nnp.cumsum(t) # Ausgabe: array([1., 2.5, 4.5, 7., 10.])\nnp.mean(t) # Ausgabe: 2.0\n" }, { "alpha_fraction": 0.47987616062164307, "alphanum_fraction": 0.6099071502685547, "avg_line_length": 25.91666603088379, "blob_id": "5de86b651dff496f1844b813d663c09762752867", "content_id": "da38e27dba86ef34f3425865eeb3ffc17eb16df0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 323, "license_type": "no_license", "max_line_length": 72, "num_lines": 12, "path": "/listings/v6_klassen26.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Konto:\n def __init__(self, guthaben, iban):\n self.guthaben = guthaben\n self.iban = iban\n\n def __str__(self):\n return 'IBAN: {}\\nGuthaben: {}'.format(self.iban, self.guthaben)\n\nk = Konto(50, 'CH42 4738 2934 9267 0878 5')\nprint(k) # Ausgabe:\n# IBAN: CH42 4738 2934 9267 0878 5\n# Guthaben: 50\n" }, { "alpha_fraction": 0.692307710647583, "alphanum_fraction": 0.7142857313156128, "avg_line_length": 17.200000762939453, "blob_id": "5c2609434ac8bb9d1613e514699caa2684a0fd7d", "content_id": "ac68cf6a4eea731e9de9f6d56c1a4379ee1ecf1f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 91, "license_type": "no_license", "max_line_length": 29, "num_lines": 5, "path": "/listings/v4_list1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "quadratzahlen = []\nfor n in range(11):\n quadratzahlen.append(n*n)\n\nprint(quadratzahlen)\n" }, { "alpha_fraction": 0.7323943376541138, "alphanum_fraction": 0.7417840361595154, "avg_line_length": 41.599998474121094, "blob_id": "0a3b64d28f8627f1dc3a18f0f1389aa1baf67919", "content_id": "0e99c86589f8384179ea130ebb46a0de7f48ced2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 213, "license_type": "no_license", "max_line_length": 85, "num_lines": 5, "path": "/listings/v6_klassen29.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "from my_module import MeineKlasse\n# Ausgabe: Dies ist C:\\Users\\Noah\\switchdrive\\Python\\vorlesung\\w06\\code\\my_module.py:\n# __name__ = my_module\nm = MeineKlasse('Python User')\nm.gruss() # Ausgabe: Hallo Python User\n" }, { "alpha_fraction": 0.39423078298568726, "alphanum_fraction": 0.45192307233810425, "avg_line_length": 16.33333396911621, "blob_id": "19c373d4ea19b50324ec6027de8184e9ed274a15", "content_id": "7ff54998b78228a2f72f76c9e31a96b9a5d5bea0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 104, "license_type": "no_license", "max_line_length": 21, "num_lines": 6, "path": "/listings/v4_iter1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = [1, 2, 3]\nfor element in liste:\n print(element)\n# Ausgabe: 1\n# 2\n# 3\n" }, { "alpha_fraction": 0.6335078477859497, "alphanum_fraction": 0.6335078477859497, "avg_line_length": 16.363636016845703, "blob_id": "a9917c8217df7e3a0032853ee92ef542fd15c754", "content_id": "880f2d749166e408bc4544fdad07ae6dc247f001", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 191, "license_type": "no_license", "max_line_length": 32, "num_lines": 11, "path": "/listings/v7_vererbung4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Person:\n def func(self):\n print('Person')\n\nclass Angestellter(Person):\n def func(self):\n print('Angestellter')\n\n\na = Angestellter()\na.func() # Ausgabe: Angestellter\n" }, { "alpha_fraction": 0.6436781883239746, "alphanum_fraction": 0.6436781883239746, "avg_line_length": 16.399999618530273, "blob_id": "05ae2bc88c0037c36665af946e9c710856c1b4da", "content_id": "ec89d4d6c58eec16cd906d0983329f727bc1f9c5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 87, "license_type": "no_license", "max_line_length": 23, "num_lines": 5, "path": "/listings/v3_strings21.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'Passwort'.lower()\n# Ausgabe: 'passwort'\n\n'Passwort'.upper()\n# Ausgabe: 'PASSWORT'\n" }, { "alpha_fraction": 0.6044444441795349, "alphanum_fraction": 0.6044444441795349, "avg_line_length": 15.071428298950195, "blob_id": "4e64a46252d349803acaaa02f2daf86517bbda3c", "content_id": "d0f186fb61d13d9c77e8bc62c27e7b06e8c0a40b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 225, "license_type": "no_license", "max_line_length": 29, "num_lines": 14, "path": "/listings/v7_vererbung5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Person:\n def func(self):\n print('Person')\n\nclass Angestellter(Person):\n def func(self):\n super().func()\n print('Angestellter')\n\n\na = Angestellter()\na.func() # Ausgabe:\n# Person\n# Angestellter\n" }, { "alpha_fraction": 0.6197183132171631, "alphanum_fraction": 0.6549295783042908, "avg_line_length": 46.33333206176758, "blob_id": "664fa3257ccaf1c75d3ffb7e6e2f5ee21aab7f4b", "content_id": "f12270138be532352276ff047beb9b39c46140a2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 142, "license_type": "no_license", "max_line_length": 69, "num_lines": 3, "path": "/listings/v5_ra10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "resultat = re.subn(r'\\d+', '<Zahl>', '3 Stuecke kosten 250 Franken.')\nprint(resultat)\n# Ausgabe: ('<Zahl> Stuecke kosten <Zahl> Franken.', 2)\n" }, { "alpha_fraction": 0.5995850563049316, "alphanum_fraction": 0.6452282071113586, "avg_line_length": 29.125, "blob_id": "fb44595b8a1bc0a7c8fa7311783b7d9e92e03d3d", "content_id": "c25343ac9b0ea306b9625870686e61bacf3b4dd0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 482, "license_type": "no_license", "max_line_length": 77, "num_lines": 16, "path": "/listings/v6_klassen23.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Bank:\n def __init__(self):\n self.__guthaben = 0\n\n def get_guthaben(self):\n print('Das Guthaben wurde abgefragt.')\n return self.__guthaben\n\n def set_guthaben(self, n):\n self.__guthaben = n\n print('Das Guthaben wurde auf {} geaendert.'.format(self.__guthaben))\n\nk = Bank()\nk.set_guthaben(1000000) # Ausgabe: Das Guthaben wurde auf 1000000 geaendert.\nprint(k.get_guthaben()) # Ausgabe: Das Guthaben wurde abgefragt.\n# Ausgabe: 1000000\n" }, { "alpha_fraction": 0.604938268661499, "alphanum_fraction": 0.604938268661499, "avg_line_length": 19.25, "blob_id": "fa52f620cc7a06adc96eb31f504fcc3ea15918a1", "content_id": "9872e9a8a0760e47cb9400dbe254f1d36e2aa305", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 81, "license_type": "no_license", "max_line_length": 37, "num_lines": 4, "path": "/listings/v3_exception2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "try:\n x = int(input('Zahl eingeben: '))\nexcept:\n print('Falsche Eingabe!')\n" }, { "alpha_fraction": 0.6000000238418579, "alphanum_fraction": 0.6000000238418579, "avg_line_length": 9, "blob_id": "b5edb7b78a9afb4090c5c7e2af15be876bfbee90", "content_id": "3e597aa499f22d3807c1f7dc7f32011520ed1b2f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 10, "license_type": "no_license", "max_line_length": 9, "num_lines": 1, "path": "/listings/v3_datei5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "f.close()\n" }, { "alpha_fraction": 0.5207956433296204, "alphanum_fraction": 0.5551537275314331, "avg_line_length": 41.53845977783203, "blob_id": "d7198355c5a687cf7d91feb87ebdaf03ff832d71", "content_id": "0e34701b50791901804d19c68ce567984f5f48e7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 553, "license_type": "no_license", "max_line_length": 90, "num_lines": 13, "path": "/listings/v4_tupel17.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = ['laenger', 'lang', 'am laengsten']\nsorted(liste, key=len)\n# Ausgabe: ['lang', 'laenger', 'am laengsten']\n\n# nur [1]-tes Element (stabile Sortierung)\nliste = [('a', 3), ('a', 2), ('c', 1), ('b', 1)]\nfrom operator import itemgetter\nsorted(liste, key=itemgetter(1))\n# Ausgabe: [('c', 1), ('b', 1), ('a', 2), ('a', 3)]\nsorted(liste, key=lambda x: x[1])\n# Ausgabe: [('c', 1), ('b', 1), ('a', 2), ('a', 3)]\nsorted(liste) # zuerst nach dem ersten Unterelement sortieren, dann nach dem zweiten, ...\n# Ausgabe: [('a', 2), ('a', 3), ('b', 1), ('c', 1)]\n" }, { "alpha_fraction": 0.4776119291782379, "alphanum_fraction": 0.5223880410194397, "avg_line_length": 32.5, "blob_id": "94f20a1bb6dcab32a4db03cabc0e998e50d6ad20", "content_id": "8ebf79026408790d23551560ab8d92629909bcc1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 67, "license_type": "no_license", "max_line_length": 47, "num_lines": 2, "path": "/listings/v5_ra22.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.findall(r'[A-Za-z]+(?!\\d+)\\b', 'abc123 cde')\n# Ausgabe: ['cde']\n" }, { "alpha_fraction": 0.4780219793319702, "alphanum_fraction": 0.4780219793319702, "avg_line_length": 90, "blob_id": "e4a4fe15eb9bf156c24ecf20757514ef54b588fd", "content_id": "7217b624822cfdc7e1b5ed9e4da120ce46f858cb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 182, "license_type": "no_license", "max_line_length": 113, "num_lines": 2, "path": "/listings/v5_ra15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = re.split(r'(\\W+)', 'Nun, dies ist ein (simples) Beispiel.')\nprint(liste) # Ausgabe: ['Nun', ', ', 'dies', ' ', 'ist', ' ', 'ein', ' (', 'simples', ') ', 'Beispiel', '.', '']\n" }, { "alpha_fraction": 0.4624277353286743, "alphanum_fraction": 0.4624277353286743, "avg_line_length": 42.25, "blob_id": "cb6dd17df2c0bc1f096f37666a57f1542fda7af5", "content_id": "d56c0a8d38c7a62e9fd9c6fa04d7fe0865090485", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 173, "license_type": "no_license", "max_line_length": 51, "num_lines": 4, "path": "/listings/v4_iter4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "print('__iter__():', hasattr(iterator, '__iter__'))\nprint('__next__():', hasattr(iterator, '__next__'))\n# Ausgabe: ('__iter__():', True)\n# ('__next__():', False)\n" }, { "alpha_fraction": 0.699999988079071, "alphanum_fraction": 0.699999988079071, "avg_line_length": 9, "blob_id": "0b73d219c0b15d83c058bce1a16d0a1d5209048a", "content_id": "7bf9b7856bfa24a99e0776ca06bb4f552270bf97", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 20, "license_type": "no_license", "max_line_length": 15, "num_lines": 2, "path": "/listings/v8_numpy11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "np.transpose(M)\nM.T\n" }, { "alpha_fraction": 0.6695652008056641, "alphanum_fraction": 0.730434775352478, "avg_line_length": 13.375, "blob_id": "f2bfa581f2f4b332765afa3d79010a2c0bdc9991", "content_id": "4fdd40205206b73d1239dcf9cd23ce33a6635da0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 115, "license_type": "no_license", "max_line_length": 16, "num_lines": 8, "path": "/listings/v2_if3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "if Bedingung1:\n Anweisung1\nelif Bedingung2:\n Anweisung2\nelif Bedingung3:\n Anweisung3\nelse:\n Anweisung4\n" }, { "alpha_fraction": 0.4128205180168152, "alphanum_fraction": 0.46666666865348816, "avg_line_length": 31.5, "blob_id": "a4b70458fc4304cf47002a3e6ed8f6aadcfc7ec7", "content_id": "fd0649e64a0abbd1e78b336bb40b67b538cdbfbe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 390, "license_type": "no_license", "max_line_length": 73, "num_lines": 12, "path": "/listings/v4_tupel14.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = [2, 5, 3, 4, 1]\nsortiert = sorted(liste)\nprint('Liste:', liste) # Ausgabe: ('Liste:', [2, 5, 3, 4, 1])\nprint('sortiert:', sortiert) # Ausgabe: ('sortiert:', [1, 2, 3, 4, 5])\n\nt = (5,4,3)\nsortiert = sorted(t)\nsortiert # Ausgabe: [3, 4, 5]\n\ns = 'python'\nsortiert = sorted(s)\nsortiert # Ausgabe: ['h', 'n', 'o', 'p', 't', 'y']\n" }, { "alpha_fraction": 0.6035503149032593, "alphanum_fraction": 0.6745561957359314, "avg_line_length": 27.16666603088379, "blob_id": "566e1e1a9b70c6a3748fc0555615418d8229398c", "content_id": "04e0bb0139141f0cfea0c598ce9c57ae47523e40", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 169, "license_type": "no_license", "max_line_length": 42, "num_lines": 6, "path": "/listings/v7_vererbung9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "a = Angestellter('Max', 123456) # Ausgabe:\n# __init__() von Person\n# __init__() von Angestellter\n\nprint(a.name) # Ausgabe: Max\nprint(a.personalnummer) # Ausgabe: 123456\n" }, { "alpha_fraction": 0.5473684072494507, "alphanum_fraction": 0.5684210658073425, "avg_line_length": 46.5, "blob_id": "e8d46a7236ad303307c1223f4071f91825a2fe7e", "content_id": "e7a4f3454c31334c443737d6269513141097b52c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 95, "license_type": "no_license", "max_line_length": 47, "num_lines": 2, "path": "/listings/v3_datei4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "f.write('hello') # String schreiben\nf.writelines(['1', '2']) # Liste von Strings\n" }, { "alpha_fraction": 0.5458996295928955, "alphanum_fraction": 0.5630354881286621, "avg_line_length": 27.172412872314453, "blob_id": "f703814b56342371eaef2b988877e256488b1001", "content_id": "5fe6be2d75013f0db405aa072bd4b69400282820", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 817, "license_type": "no_license", "max_line_length": 101, "num_lines": 29, "path": "/listings/scripts/my_other_module.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\nprint('Dies ist {}:\\n__name__ = {}'.format(__file__, __name__))\n\nclass Bank:\n def __init__(self):\n self.__guthaben = 0\n\n @property\n def guthaben(self):\n print('Das Guthaben wurde abgefragt.')\n return self.__guthaben\n\n @guthaben.setter\n def guthaben(self, n):\n self.__guthaben = n\n print('Das Guthaben wurde auf {} geaendert.'.format(self.__guthaben))\n\n# --- Klasse testen -----------------------------------------------------------\nif __name__ == '__main__':\n b = Bank()\n b.guthaben = 1000\n print(b.guthaben)\n\n# Konsolen-Ausgabe:\n# Dies ist C:/Users/Noah/Documents/GitHub/Python_Zusammenfassung/listings/scripts/my_other_module.py:\n# __name__ = __main__\n# Das Guthaben wurde auf 1000 geaendert.\n# Das Guthaben wurde abgefragt.\n# 1000\n" }, { "alpha_fraction": 0.49712643027305603, "alphanum_fraction": 0.5574712753295898, "avg_line_length": 25.769229888916016, "blob_id": "b2994baf6bd3838c7c19447a86c38dfd150a3f38", "content_id": "4713bb050056121325d3088b2d4134d59cf09484", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 348, "license_type": "no_license", "max_line_length": 51, "num_lines": 13, "path": "/listings/v5_ra11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "m = re.search(r'(\\d+) ([a-z]+)', '123 hallo welt!')\nif m is not None:\n print('groups():', m.groups())\n print('group(0):', m.group(0))\n print('group(1):', m.group(1))\n print('group(2):', m.group(2))\nelse:\n print('keine Uebereinstimmung')\n# Ausgabe:\n# groups(): ('123', 'hallo')\n# group(0): 123 hallo\n# group(1): 123\n# group(2): hallo\n" }, { "alpha_fraction": 0.4811320900917053, "alphanum_fraction": 0.5943396091461182, "avg_line_length": 20.200000762939453, "blob_id": "453db88ddb50330212c1f236d38627192ca71bb7", "content_id": "58e077f8f49d52922b156e25361fa46c1320a30f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 106, "license_type": "no_license", "max_line_length": 60, "num_lines": 5, "path": "/listings/v5_ra17.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "for m in re.finditer(r'\\d+(V|A)', '230V und 10A bei 23Ohm'):\n print(m.group())\n# Ausgabe:\n# 230V\n# 10A\n" }, { "alpha_fraction": 0.5873016119003296, "alphanum_fraction": 0.6507936716079712, "avg_line_length": 41, "blob_id": "67cf80c8e9ac7905fb08d83af811f5f5986ba0dc", "content_id": "725169ab07013a4d2b986c34d8f1031305b88456", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 126, "license_type": "no_license", "max_line_length": 70, "num_lines": 3, "path": "/listings/v5_ra8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = re.sub(r'\\d+', '<Zahl>', '3 Stuecke kosten 250 Franken.', count=1)\nprint(s)\n# Ausgabe: <Zahl> Stuecke kosten 250 Franken.\n" }, { "alpha_fraction": 0.7356321811676025, "alphanum_fraction": 0.7356321811676025, "avg_line_length": 42.5, "blob_id": "fe887749f105bd351bda43ebc993035162731ce3", "content_id": "56c3d9e5c6593e08a68871731f797f47672e8e3c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 87, "license_type": "no_license", "max_line_length": 51, "num_lines": 2, "path": "/listings/v6_klassen11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "del s # loescht nur die Referenz s auf das Objekt.\n# Ausgabe: Wall-E wurde zerstoert.\n" }, { "alpha_fraction": 0.5197368264198303, "alphanum_fraction": 0.5197368264198303, "avg_line_length": 46.5, "blob_id": "7cebbb83aad829859962ff657baa95cb519845e5", "content_id": "9090c5f874583349566c2d606ef6b45da3f1296a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 760, "license_type": "no_license", "max_line_length": 75, "num_lines": 16, "path": "/listings/v6_klassen22.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "objekt.__dict__ # Ausgabe:\n# {'pub': 'Hier macht jeder was er will.',\n# '_prot': 'Ich bin protected.',\n# '_MeineKlasse__priv': 'Ich bin privat.'}\n\ndir(objekt) # Ausgabe:\n# ['_MeineKlasse__priv', '_MeineKlasse__priv_funktion', '__class__',\n# '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__',\n# '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__',\n# '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__',\n# '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__',\n# '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_prot',\n# '_prot_funktion', 'pub', 'pub_funktion']\n\nobjekt._MeineKlasse__priv # Ausgabe: 'Ich bin privat.'\nobjekt._MeineKlasse__priv_funktion() # Ausgabe: Ich bin privat.\n" }, { "alpha_fraction": 0.7599999904632568, "alphanum_fraction": 0.7599999904632568, "avg_line_length": 49, "blob_id": "313cbd159dd7a918e47464c4ae9373944c10d143", "content_id": "f0897129d576c204b9234bd747eb49acad34ca62", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 50, "license_type": "no_license", "max_line_length": 49, "num_lines": 1, "path": "/listings/v6_klassen14.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "MeineKlasse.hallo(self=s) # Ausgabe: Hallo Wall-E\n" }, { "alpha_fraction": 0.7278106212615967, "alphanum_fraction": 0.7396449446678162, "avg_line_length": 41.25, "blob_id": "99b3bb820b3ba2dd02cd2811ddaebf8763a04320", "content_id": "c49311c9fc1d91e5c7277d6a0f32d31ad8b8ecd3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 169, "license_type": "no_license", "max_line_length": 47, "num_lines": 4, "path": "/listings/v2_func1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "import time # time.time(), time.sleep()\nimport math # math.pi, math,cos(), math.log10()\nimport zipfile # ZIP-Dateien manipulieren\nimport socket # UDP-/TCP-Kommunikation\n" }, { "alpha_fraction": 0.8039215803146362, "alphanum_fraction": 0.8039215803146362, "avg_line_length": 24.5, "blob_id": "43bbebb5dfa9a05a8fcebd12314b684a14973168", "content_id": "54d928d89e5bf02d4bcb56ed3c02d2bae0462421", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 51, "license_type": "no_license", "max_line_length": 34, "num_lines": 2, "path": "/listings/v2_func2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def Funktionsname(Parameterliste):\n Anweisungen\n" }, { "alpha_fraction": 0.3760683834552765, "alphanum_fraction": 0.5384615659713745, "avg_line_length": 57.5, "blob_id": "5912393532ca3d5b780e6d691ab54f612f67105a", "content_id": "ddc5139a04496f762dcb6e76c2ecb2eacb1872f5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 117, "license_type": "no_license", "max_line_length": 85, "num_lines": 2, "path": "/listings/v5_ra16.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = re.sub(r'(\\d+)/(\\d+)/(\\d+)', r'\\2.\\1.\\3', '03/20/2019') # mit Gruppen-Referenzen\nprint(s) # Ausgabe: 20.03.2019\n" }, { "alpha_fraction": 0.6691729426383972, "alphanum_fraction": 0.6917293071746826, "avg_line_length": 43.33333206176758, "blob_id": "92d6dd0349b43f430eeb37a142110d1329c8ad6e", "content_id": "a15f1b2aa587ee7229c6b1894dd90c85b59d7bf4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 133, "license_type": "no_license", "max_line_length": 85, "num_lines": 3, "path": "/listings/v4_iter9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "gen = (i*i for i in range(1, 10)) # wie List Comprehension, aber mit runden Klammern\nprint(type(gen))\n# Ausgabe: <type 'generator'>\n" }, { "alpha_fraction": 0.6194030046463013, "alphanum_fraction": 0.641791045665741, "avg_line_length": 21.33333396911621, "blob_id": "9400136710e71bd9569e25bf0c218a9f99159aad", "content_id": "5fa95ca3fc636ed9f2634ecbd9c2f49ee9ac8233", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 134, "license_type": "no_license", "max_line_length": 39, "num_lines": 6, "path": "/listings/v3_exception5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "eingabe = '10Fr.'\ntry:\n x = int(eingabe)\n y = 1/x\nexcept (ValueError, ZeroDivisionError):\n print('Oops! Bitte wiederholen.')\n" }, { "alpha_fraction": 0.540772557258606, "alphanum_fraction": 0.6008583903312683, "avg_line_length": 18.41666603088379, "blob_id": "a90378ee7e0165a4fed1d3ae34c02b35e387b405", "content_id": "85fde0933ffdbf49a5435327fbe9a3f8f26882c9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 233, "license_type": "no_license", "max_line_length": 42, "num_lines": 12, "path": "/listings/v9_integrate2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "from scipy.integrate import trapz\n\nx = np.array([0, 1, 1.5, 1.8, 2, 3, 4, 5])\ny = np.cos(x) + 2\n\nplt.figure()\nplt.plot(x, y, 'o-', label='Samples')\nplt.ylim(0, 1.1*np.max(y))\nplt.grid(True)\nplt.xlabel('x')\nplt.ylabel('y')\nplt.show()\n" }, { "alpha_fraction": 0.682692289352417, "alphanum_fraction": 0.682692289352417, "avg_line_length": 33.66666793823242, "blob_id": "6b5c1298cf99f0ec6a0d70e2b0dd914178e3190c", "content_id": "f042f3f8ba101ddccf0c44c4a8d56f7f49d2513c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 208, "license_type": "no_license", "max_line_length": 82, "num_lines": 6, "path": "/listings/v2_func14.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def meine_funktion(a, b):\n '''Gibt die Argumente a und b in umgekehrter Reihenfolge als Tupel zurueck.'''\n return(b, a)\n\nmeine_funktion.__doc__ # Ausgabe: 'Gibt die Arguemnte ...'\nhelp(meine_funktion)\n" }, { "alpha_fraction": 0.6803519129753113, "alphanum_fraction": 0.6862170100212097, "avg_line_length": 23.35714340209961, "blob_id": "833292b70e6c8bfef4b0df353507271b7ce67cb7", "content_id": "8a324a47856e4ec95bb87f3cc1699210cb1d02b2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 341, "license_type": "no_license", "max_line_length": 79, "num_lines": 14, "path": "/listings/v4_list5.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "fruechte = ['Apfel', 'Erdbeer', 'Clementine', 'Kokosnuss', 'Birne', 'Himbeere']\n\n# konventionell:\nfruechte_abc = []\nfor frucht in fruechte:\n if frucht[0] in 'ABC':\n fruechte_abc.append(frucht)\n\nprint(fruechte_abc)\n\n# mit Listen-Abstraktion:\nfruechte_abc = [frucht for frucht in fruechte if frucht[0] in 'ABC']\n\nprint(fruechte_abc)\n" }, { "alpha_fraction": 0.6000000238418579, "alphanum_fraction": 0.6342105269432068, "avg_line_length": 62.33333206176758, "blob_id": "942cd9e0e5c14811b818cb7f7b1129dc7c1bb295", "content_id": "d1111e75117e51c3801408f0f4b9d0daf2262d79", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 380, "license_type": "no_license", "max_line_length": 82, "num_lines": 6, "path": "/listings/v5_ra13.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = re.findall(r'(\\w+)=(\\w+)', 'Jahrgang=1930, Name=Hans und Ort=Rappi')\nprint(liste) # Ausgabe: [('Jahrgang', '1930'), ('Name', 'Hans'), ('Ort', 'Rappi')]\nliste = re.findall(r'Ort=(\\w+)', 'Jahrgang=1930, Name=Hans und Ort=Rappi')\nprint(liste) # Ausgabe: ['Rappi']\nliste = re.findall(r'(dum)\\1', 'dumdum') # mit Rueckwaertsreferenz der Gruppe\nprint(liste) # Ausgabe: ['dum']\n" }, { "alpha_fraction": 0.681614339351654, "alphanum_fraction": 0.7354260087013245, "avg_line_length": 54.75, "blob_id": "e68de7e9a9a985bf15bc12a3db7942191b8c78f7", "content_id": "ede84a1d5544c632d68ca8174bc59b2845b96ffd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 223, "license_type": "no_license", "max_line_length": 91, "num_lines": 4, "path": "/listings/v4_tupel6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "vorname, nachname, *adresse = ('Peter', 'Mueller', 'Oberseestrasse 10', 8640, 'Rapperswil')\nprint(vorname) # Ausgabe: Peter\nprint(nachname) # Ausgabe: Mueller\nprint(adresse) # Ausgabe: Oberseestrasse 10, 8640, Rapperswil\n" }, { "alpha_fraction": 0.44692736864089966, "alphanum_fraction": 0.49162012338638306, "avg_line_length": 21.375, "blob_id": "9cac975392920abfdbb56df5bce5b5069700fb2e", "content_id": "1bd66d007b388af586c8c5cdab4ec19544b4c75d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 179, "license_type": "no_license", "max_line_length": 37, "num_lines": 8, "path": "/listings/v2_func10.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def distanz(x, y, z):\n print('x =', x)\n print('y =', y)\n print('z =', z)\n return (x**2 + y**2 + z**2)**0.5\n\nposition = (2, 3, 6)\ndistanz(*position) # Tupel entpacken\n" }, { "alpha_fraction": 0.8444444537162781, "alphanum_fraction": 0.8444444537162781, "avg_line_length": 29, "blob_id": "1e7d4dfdd083e2cbbe2b51853e000de520d44b97", "content_id": "cd54a8d4d2c638a1a0621ceff2985ea6419b869b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 90, "license_type": "no_license", "max_line_length": 38, "num_lines": 3, "path": "/listings/v9_interpolate1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.interpolate import interpld\n" }, { "alpha_fraction": 0.3581395447254181, "alphanum_fraction": 0.4976744055747986, "avg_line_length": 22.88888931274414, "blob_id": "1643bbcf4248892888847a78ec5a127acfbc21ce", "content_id": "51f84d43b0b8d58da6a21b64fd6f86d4cf2737a4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 215, "license_type": "no_license", "max_line_length": 33, "num_lines": 9, "path": "/listings/v3_strings11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = 'Python'\ns.center(20, '=')\n# Ausgabe: '=======Python======'\ns.ljust(20, '-')\n# Ausgabe: 'Python--------------'\ns.rjust(20, '*')\n# Ausgabe: '**************Python'\n'1234'.zfill(20)\n# Ausgabe: '000000000000001234'\n" }, { "alpha_fraction": 0.7250000238418579, "alphanum_fraction": 0.75, "avg_line_length": 19, "blob_id": "a5061559ba9a3c62c7cb0a0ed80af9147e85c2dc", "content_id": "606915220267f12a03dcee0cd2f603b4b447eeb7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 40, "license_type": "no_license", "max_line_length": 24, "num_lines": 2, "path": "/listings/v2_for1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "for Variable in Sequenz:\n Anweisung1\n" }, { "alpha_fraction": 0.7314814925193787, "alphanum_fraction": 0.7314814925193787, "avg_line_length": 53, "blob_id": "ad2509b9ce79f60fd1b136484a66b56381edb1ea", "content_id": "82c246ef81f4cd3efe8ce8667fa425ac13a40025", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 108, "license_type": "no_license", "max_line_length": 62, "num_lines": 2, "path": "/listings/v6_klassen19.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "objekt.pub = 'Hier macht jeder was er will.'\nobjekt.pub_funktion() # Ausgabe: Hier macht jeder was er will.\n" }, { "alpha_fraction": 0.6578947305679321, "alphanum_fraction": 0.6578947305679321, "avg_line_length": 21.799999237060547, "blob_id": "67f4335ada128f48b4d637ee61d79b917a65ca50", "content_id": "b37d1d4cef305f533a2646e66e2cde472d5441d6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 114, "license_type": "no_license", "max_line_length": 52, "num_lines": 5, "path": "/listings/v3_strings16.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "csv = '''Dies ist\nein mehrzeiliger\nText.'''\ncsv.splitlines()\n# Ausgabe: ['Dies ist', 'ein mehrzeiliger', 'Text.']\n" }, { "alpha_fraction": 0.5841584205627441, "alphanum_fraction": 0.6188119053840637, "avg_line_length": 66.33333587646484, "blob_id": "014ba7013f6922b919217abd549825b754faab48", "content_id": "3602a2ff15ff27882dfa238a458d15826de9f537", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 202, "license_type": "no_license", "max_line_length": 99, "num_lines": 3, "path": "/listings/v5_ra18.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.findall(r'^\\w+', 'Hallo Welt') # Ausgabe: ['Hallo']\nre.findall(r'^\\w+', 'Erste Zeile\\nZweite Zeile', flags=re.MULTILINE) # Ausgabe: ['Erste', 'Zweite']\nre.findall(r'\\A\\d', '123456') # Ausgabe: ['1']\n" }, { "alpha_fraction": 0.35555556416511536, "alphanum_fraction": 0.7111111283302307, "avg_line_length": 44, "blob_id": "faee3a90d64f196c97f1888503b745abc97a6994", "content_id": "3ce04db3de7b5d98d0a3cc174101ee524cb5294a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 45, "license_type": "no_license", "max_line_length": 44, "num_lines": 1, "path": "/listings/v9_integrate3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "trapz(x=x, y=y) # Ausgabe: 9.125227959734184\n" }, { "alpha_fraction": 0.7297297120094299, "alphanum_fraction": 0.7297297120094299, "avg_line_length": 17.5, "blob_id": "c2921a34b0c2d576fc1196f0661c888c8277e51d", "content_id": "aecaf7ebbcf506e41559913af8546731b363a251", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 37, "license_type": "no_license", "max_line_length": 27, "num_lines": 2, "path": "/listings/v7_vererbung2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Angestellter(Person):\n pass\n" }, { "alpha_fraction": 0.6785714030265808, "alphanum_fraction": 0.6785714030265808, "avg_line_length": 36.33333206176758, "blob_id": "d3f71f1fd2aecd4b64320575c695512719e368f1", "content_id": "4ade1f3f3068919558d2b247ce17938840285183", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 112, "license_type": "no_license", "max_line_length": 47, "num_lines": 3, "path": "/listings/v3_datei2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "open(file, mode='r', buffering=, encoding=None,\n errors=None, newline=None, closefd=True,\n opener=None)\n" }, { "alpha_fraction": 0.6333333253860474, "alphanum_fraction": 0.6333333253860474, "avg_line_length": 44, "blob_id": "c2d316d05859d081506a3ca00bd2d7a2e986e4b1", "content_id": "6d859da729a81902c7031185fb79053b90369128", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 90, "license_type": "no_license", "max_line_length": 54, "num_lines": 2, "path": "/listings/v5_ra21.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.findall(r'\\w+(?=.doc)', 'bericht.doc dokument.doc')\n# Ausgabe: ['bericht', 'dokument']\n" }, { "alpha_fraction": 0.5589743852615356, "alphanum_fraction": 0.6615384817123413, "avg_line_length": 23.375, "blob_id": "ec6287d1ecc0b57abd1691d2d43a2bfec11aa06f", "content_id": "3fff2032cb19df64d7aa2a9ee792d9028cd7b595", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 195, "license_type": "no_license", "max_line_length": 54, "num_lines": 8, "path": "/listings/v6_klassen17.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n speed_of_light = 299792458\n\n @classmethod\n def c0(cls):\n print('Speed of light =', cls.speed_of_light)\n\nMeineKlasse.c0() # Ausgabe: Speed of light = 299792458\n" }, { "alpha_fraction": 0.6428571343421936, "alphanum_fraction": 0.6428571343421936, "avg_line_length": 41, "blob_id": "14cb9644c9a7655d252e8061b9f6fe65bd29e0e5", "content_id": "1a0a12a24b3cb32058fa03814be0febb08f58f82", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 84, "license_type": "no_license", "max_line_length": 52, "num_lines": 2, "path": "/listings/v7_vererbung7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "p = Person('Laura') # Ausgabe: __init__() von Person\nprint(p.name) # Ausgabe: Laura\n" }, { "alpha_fraction": 0.4399999976158142, "alphanum_fraction": 0.5199999809265137, "avg_line_length": 11.5, "blob_id": "467dc2456db8ff3b8abf0c99cb7b15f93c21faea", "content_id": "cc2364e96e3487b10cc99ec413fa47a18144fa6d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 25, "license_type": "no_license", "max_line_length": 16, "num_lines": 2, "path": "/listings/v3_strings12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "chr(65)\n# Ausgabe: ('A')\n" }, { "alpha_fraction": 0.4215686321258545, "alphanum_fraction": 0.5392156839370728, "avg_line_length": 24.5, "blob_id": "156bf6f411c5c72902ac66f0991e6244c8f7c372", "content_id": "3fb0b32f3fffd91f3b33aadabad5604ee6ae9e63", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 102, "license_type": "no_license", "max_line_length": 40, "num_lines": 4, "path": "/listings/v3_strings4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "spannung = 12.56\nstrom = 0.5\n'U = {}, I = {}'.format(spannung, strom)\n# Ausgabe: 'U = 12.56, I = 0.5'\n" }, { "alpha_fraction": 0.6190476417541504, "alphanum_fraction": 0.6190476417541504, "avg_line_length": 20, "blob_id": "0cf32d1d1f78ee7d9670f98c3af4693d1c655d87", "content_id": "d467b99cc16d9b181cb61db3ab33fcb6692933b0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 42, "license_type": "no_license", "max_line_length": 24, "num_lines": 2, "path": "/listings/v2_func4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def gruss(name):\n print('Hallo', name)\n" }, { "alpha_fraction": 0.30128204822540283, "alphanum_fraction": 0.41025641560554504, "avg_line_length": 25, "blob_id": "b01a47ceacde761b0cedfcba9e785efe064a9a92", "content_id": "9bbc32c77507662ab386112318480434d5b7953e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 156, "license_type": "no_license", "max_line_length": 37, "num_lines": 6, "path": "/listings/v8_numpy8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr1 = np.ones((3, 2)) # shape=(3, 2)\narr2 = np.array([1, 2]) # shape=(2,)\narr1 + arr2 # Ausgabe:\n# array([[2., 3.],\n# [2., 3.],\n# [2., 3.]])\n" }, { "alpha_fraction": 0.5975610017776489, "alphanum_fraction": 0.6219512224197388, "avg_line_length": 12.666666984558105, "blob_id": "eafc14d35e11085e88844d365da9c426fd78b51f", "content_id": "95bc4fcb68fad07376125581d66da8722f59d7b5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 82, "license_type": "no_license", "max_line_length": 16, "num_lines": 6, "path": "/listings/v2_while4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "while Bedingung:\n Anweisung1\n if Fehler:\n break\nelse:\n Anweisung2\n" }, { "alpha_fraction": 0.7147766351699829, "alphanum_fraction": 0.7147766351699829, "avg_line_length": 31.33333396911621, "blob_id": "048db728737b77db98f46d12c1b95e00fa5abf6f", "content_id": "c63ef193fdd365d96566052feadf3b7df164531a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 291, "license_type": "no_license", "max_line_length": 55, "num_lines": 9, "path": "/listings/v3_strings20.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = ' Dieser String sollte saubere Enden haben. \\n'\nprint(s)\n# Ausgabe: Dieser String sollte saubere Enden haben.\n\ns.strip()\n# Ausgabe: 'Dieser String sollte saubere Enden haben.'\n\n'Ein Satz ohne Satzzeichen am Schluss?'.rstrip('.!?')\n# Ausgabe: 'Ein Satz ohne Satzzeichen am Schluss'\n" }, { "alpha_fraction": 0.6893203854560852, "alphanum_fraction": 0.7038834691047668, "avg_line_length": 16.16666603088379, "blob_id": "75f00c0a859ecdf1fffd6db45f6aa3859563edc5", "content_id": "f2ddfc64c77c4e2a28c06b8aa568ca88ae09e7c6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 206, "license_type": "no_license", "max_line_length": 38, "num_lines": 12, "path": "/listings/v3_strings18.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "spruch = '''Wir sollten heute das tun,\nvon dem wir uns morgen wuenschen\nes gestern getan zu haben.'''\n\n'morgen' in spruch\n# Ausgabe: True\n\nspruch.find('heute')\n# Ausgabe: 12\n\nspruch.count('en')\n#Ausgabe: 4\n" }, { "alpha_fraction": 0.6703296899795532, "alphanum_fraction": 0.6703296899795532, "avg_line_length": 21.75, "blob_id": "404e502b67a7dc83bd61fca990140715ee7c6f40", "content_id": "e86fb9cd8b2b57068b57b9fc13fc527b460fcc7a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 91, "license_type": "no_license", "max_line_length": 58, "num_lines": 4, "path": "/listings/v3_exception7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "try:\n welt_retten()\nfinally:\n print('Dinge, die so oder so gemacht werden muessen.')\n" }, { "alpha_fraction": 0.5882353186607361, "alphanum_fraction": 0.6203208565711975, "avg_line_length": 13.384614944458008, "blob_id": "1fef8d5889f679dd41e0e9933654384e23708211", "content_id": "8481ed57eee64a02587c3faa294fac115cf172ba", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 187, "license_type": "no_license", "max_line_length": 27, "num_lines": 13, "path": "/listings/v7_vererbung3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Person:\n var = 123\n\n def func(self):\n print('Person')\n\nclass Angestellter(Person):\n pass\n\n\na = Angestellter()\nprint(a.var) # Ausgabe: 123\na.func() # Ausgabe: Person\n" }, { "alpha_fraction": 0.5345911979675293, "alphanum_fraction": 0.5849056839942932, "avg_line_length": 25.5, "blob_id": "6e772685c7255e17b1203cec46dc6e0be930ed40", "content_id": "d0b7aacba374b0a4b256ffe2a6c38a0b987bb53a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 159, "license_type": "no_license", "max_line_length": 57, "num_lines": 6, "path": "/listings/v5_ra9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def func(m):\n return '(' + m.group() + ')'\n\ns = re.sub(r'\\d+', func, '3 Stuecke kosten 250 Franken.')\nprint(s)\n# Ausgabe: (3) Stuecke kosten (250) Franken.\n" }, { "alpha_fraction": 0.4000000059604645, "alphanum_fraction": 0.5600000023841858, "avg_line_length": 24, "blob_id": "53837051d338052cb55521cba79791c222b5c66f", "content_id": "7af1fdfe524696491281316f52e459047791733a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 75, "license_type": "no_license", "max_line_length": 24, "num_lines": 3, "path": "/listings/v8_numpy4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr[0, 0] # Ausgabe: 1.0\narr[2, 0] # Ausgabe: 7.0\narr[0, 2] # Ausgabe: 3.0\n" }, { "alpha_fraction": 0.7297297120094299, "alphanum_fraction": 0.7297297120094299, "avg_line_length": 36, "blob_id": "edbf0039f75a9a461f6967f7bb01680484eb8958", "content_id": "b8d2b5318dbf0a8e6e72f85442bfb3a5bd0ba41f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 74, "license_type": "no_license", "max_line_length": 37, "num_lines": 2, "path": "/listings/v2_func3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def begruessung(vorname, nachname):\n print('Hallo', vorname, nachname)\n" }, { "alpha_fraction": 0.7142857313156128, "alphanum_fraction": 0.7142857313156128, "avg_line_length": 13, "blob_id": "2a74ebd17762a8823ef2b170fd46056bbcd1ec52", "content_id": "f54e4b1f230a9a9c1e372a87ccb16188acf4de80", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 28, "license_type": "no_license", "max_line_length": 18, "num_lines": 2, "path": "/listings/v6_klassen1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class MeineKlasse:\n pass\n" }, { "alpha_fraction": 0.3863636255264282, "alphanum_fraction": 0.3863636255264282, "avg_line_length": 16.600000381469727, "blob_id": "b911decf4491a84872960a77c7de7caf4f742b15", "content_id": "fd07f2d1dc33bc3653542e04843fa52042c94057", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 88, "license_type": "no_license", "max_line_length": 25, "num_lines": 5, "path": "/listings/v3_strings17.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "''.join(['a', 'b', 'c'])\n# Ausgabe: 'abc'\n\n','.join(['a', 'b', 'c'])\n# Ausgabe: 'a,b,c'\n" }, { "alpha_fraction": 0.4838709533214569, "alphanum_fraction": 0.5080645084381104, "avg_line_length": 40.33333206176758, "blob_id": "50c258841eb1c33c21fd8e077687b62bfec7799d", "content_id": "a959cb51f8c33250bc7fafc237cf292a9f1436a1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 124, "license_type": "no_license", "max_line_length": 56, "num_lines": 3, "path": "/listings/v4_tupel19.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "summe = lambda x,y: x + y\nprint(type(summe)) # Ausgabe: <type 'function'>\nsumme(2, 3) # Ausgabe: 5\n" }, { "alpha_fraction": 0.5185185074806213, "alphanum_fraction": 0.5185185074806213, "avg_line_length": 26, "blob_id": "e02753edc39bb961579b2e8c04b46a20fa1ba92c", "content_id": "206b60cdfc4bd1861d66a748cacb662af89c2711", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 108, "license_type": "no_license", "max_line_length": 38, "num_lines": 4, "path": "/listings/v7_vererbung6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Person:\n def __init__(self, name):\n self.name = name\n print('__init__() von Person')\n" }, { "alpha_fraction": 0.375, "alphanum_fraction": 0.546875, "avg_line_length": 31, "blob_id": "dadbabf4e6849991a6b68362c6064f410e7cf821", "content_id": "5d178dee2fbedd30dc0d37c29370fc7b8c94063d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 128, "license_type": "no_license", "max_line_length": 65, "num_lines": 4, "path": "/listings/v4_tupel23.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "from functools import reduce\n\n# (((10 + 20)/2 + 30)/2 + 40)/2\nreduce(lambda x, y: (x + y)/2, [10, 20, 30, 40]) # Ausgabe: 31\n" }, { "alpha_fraction": 0.5093333125114441, "alphanum_fraction": 0.5120000243186951, "avg_line_length": 30.25, "blob_id": "b05a55213cf11e0812268873b3c555f923d71e83", "content_id": "87f61d9dcea18d474c580d52534e7b20583e4973", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 375, "license_type": "no_license", "max_line_length": 52, "num_lines": 12, "path": "/listings/v4_tupel13.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = ['a', 'b', 'c', 'd', 'e', 'f']\n\nelement = liste.pop() # letztes Element rechts\nprint(element) # Ausgabe: f\nliste # Ausgabe: ['a', 'b', 'c', 'd', 'e']\n\nelement = liste.pop(0) # mit Index\nprint(element) # Ausgabe: a\nliste # Ausgabe: ['b', 'c', 'd', 'e']\n\nliste.remove('c') # mit einem bestimmten Wert\nliste # Ausgabe: ['b', 'd', 'e']\n" }, { "alpha_fraction": 0.5728155374526978, "alphanum_fraction": 0.5970873832702637, "avg_line_length": 28.428571701049805, "blob_id": "952b960669058297d18e0f7d44a099dc2735e0a1", "content_id": "743473da0c39ed94664a7cd3c3c9869565747a86", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 206, "license_type": "no_license", "max_line_length": 73, "num_lines": 7, "path": "/listings/v2_func9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def mittelwert(a, *args): # a ist zwingend\n print('a =', 1)\n print('args =', args) # die restlichen Argumente sind im Tupel args\n a += sum(args)\n return a/len(args) + 1\n\nmittelwert(2, 3, 7)\n" }, { "alpha_fraction": 0.5428571701049805, "alphanum_fraction": 0.5685714483261108, "avg_line_length": 20.875, "blob_id": "ce751f8b2140c893646247bbb58942f94f0b6801", "content_id": "46ff3eaffae216bd5d137ccc6634cfe1f1c41a3c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 350, "license_type": "no_license", "max_line_length": 56, "num_lines": 16, "path": "/listings/v5_ra3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "m = re.search(r'[a-z]+', '123 hallo welt!')\nprint(m)\n# Ausgabe: <re.Match object; span=(4, 9), match='hallo'>\n\nif m is not None:\n print('group:', m.group())\n print('start:', m.start())\n print('end:', m.end())\n print('span:', m.span())\nelse:\n print('keine Uebereinstimmung')\n# Ausgabe:\n# group: hallo\n# start: 4\n# end: 9\n# span: (4, 9)\n" }, { "alpha_fraction": 0.601123571395874, "alphanum_fraction": 0.6423221230506897, "avg_line_length": 28.66666603088379, "blob_id": "74ab525ca2bbfd266db9be2d437412b4d7990866", "content_id": "959af6075a047238b9aabe77041123bf7d6db208", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 534, "license_type": "no_license", "max_line_length": 77, "num_lines": 18, "path": "/listings/v6_klassen24.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Bank:\n def __init__(self):\n self.__guthaben = 0\n\n def __get_guthaben(self):\n print('Das Guthaben wurde abgefragt.')\n return self.__guthaben\n\n def __set_guthaben(self, n):\n self.__guthaben = n\n print('Das Guthaben wurde auf {} geaendert.'.format(self.__guthaben))\n\n guthaben = property(__get_guthaben, __set_guthaben)\n\nk = Bank()\nk.guthaben = 1000000 # Ausgabe: Das Guthaben wurde auf 1000000 geaendert.\nprint(k.guthaben) # Ausgabe: Das Guthaben wurde abgefragt.\n# Ausgabe: 1000000\n" }, { "alpha_fraction": 0.71875, "alphanum_fraction": 0.75, "avg_line_length": 15, "blob_id": "12c85537c4ab3a22d4fbdb97b452ea2d802bceeb", "content_id": "2e2a3b18d94abb13865aa06d7673c412a6dc5733", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 32, "license_type": "no_license", "max_line_length": 16, "num_lines": 2, "path": "/listings/v2_while1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "while Bedingung:\n Anweisung1\n" }, { "alpha_fraction": 0.43421053886413574, "alphanum_fraction": 0.5131579041481018, "avg_line_length": 24.33333396911621, "blob_id": "0e7d3936521e2bcc8210b9f6410d4a6086da7b28", "content_id": "2a8800e7b2982c2ce9f58636c560bf50cb5c08e9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 228, "license_type": "no_license", "max_line_length": 37, "num_lines": 9, "path": "/listings/v9_matplotlib6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x = y = np.linspace(-3, 3, 100)\nX, Y = np.meshgrid(x, y)\nZ1 = np.exp(-X**2 - Y**2)\nZ2 = np.exp(-(X - 1)**2 - (Y - 1)**2)\nZ = (Z1 - Z2) * 2\n\nfig, ax = plt.subplots()\nCS = ax.contour(X, Y, Z);\nax.clabel(CS, inline=1, fontsize=8);\n" }, { "alpha_fraction": 0.6376811861991882, "alphanum_fraction": 0.6570048332214355, "avg_line_length": 11.176470756530762, "blob_id": "f36a3188508f041a58513c5430626cb4a8718799", "content_id": "7b47474408a7ea3bb382c5e2b34f46f116e8223d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 207, "license_type": "no_license", "max_line_length": 18, "num_lines": 17, "path": "/listings/v3_strings22.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'255'.isdigit()\n# Ausgabe: True\n\n'hallo'.isalpha()\n# Ausgabe: True\n\n'Gleis7'.isalnum()\n# Ausgabe: True\n\n'klein'.islower()\n# Ausgabe: True\n\n'GROSS'.isupper()\n# Ausgabe: True\n\n'Haus'.istitle()\n# Ausgabe: True\n" }, { "alpha_fraction": 0.5616438388824463, "alphanum_fraction": 0.6027397513389587, "avg_line_length": 42.79999923706055, "blob_id": "72c50a12f498ea96204d692cdcc0e354905f1f20", "content_id": "9aa94236ec545d25e89809d69a4338b0e59720bf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 219, "license_type": "no_license", "max_line_length": 64, "num_lines": 5, "path": "/listings/v5_ra12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "m = re.search(r'(?P<zahl>\\d+) (?P<wort>\\w+)', '123 hallo welt!')\nprint(m.group('zahl')) # Ausgabe: 123\nprint(m.group('wort')) # Ausgabe: hallo\nm.groupdict() # als Dictionary\n# Ausgabe: {'zahl': '123', 'wort': 'hallo'}\n" }, { "alpha_fraction": 0.3055555522441864, "alphanum_fraction": 0.48148149251937866, "avg_line_length": 35, "blob_id": "3787b6bafedfec58d90e02fbbb49bd55b433db5c", "content_id": "18992dab511d6cfea4494d6fc179df29ed4a1a68", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 108, "license_type": "no_license", "max_line_length": 53, "num_lines": 3, "path": "/listings/v8_numpy7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])\narr[2:5] = 9\nprint(arr) # Ausgabe: array([1, 2, 9, 9, 9, 6, 7, 8])\n" }, { "alpha_fraction": 0.5888158082962036, "alphanum_fraction": 0.6019737124443054, "avg_line_length": 24.33333396911621, "blob_id": "545ba731632ad7b93fbb67653e3a9ef8eff012c0", "content_id": "cc2bed20aa10d4b417a867124616a5d1247b65e8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 304, "license_type": "no_license", "max_line_length": 50, "num_lines": 12, "path": "/listings/v3_datei9.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "personen = ['Alice', 'Bob', 'Charlie']\nwith open('rangliste.txt', 'w') as f:\n for n, person in enumerate(personen, start=1):\n f.write(str(n) + '. ' + person + '\\n')\n\n# Ueberpruefen\nwith open('rangliste.txt') as f:\n print(f.read())\n\n# Ausgabe: 1. Alice\n# Ausgabe: 2. Bob\n# Ausgabe: 3.Charlie\n" }, { "alpha_fraction": 0.2769230902194977, "alphanum_fraction": 0.4307692348957062, "avg_line_length": 31.5, "blob_id": "de92932fd36ce8b8d9125411486cf46ad29077b0", "content_id": "4ed25e39ded7ad0e9f3aad45161b78371d05229f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 65, "license_type": "no_license", "max_line_length": 40, "num_lines": 2, "path": "/listings/v5_ra23.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.findall(r'(?<=#)\\d+', '#10, #25, 66')\n# Ausgabe: ['10', '25']\n" }, { "alpha_fraction": 0.6329966187477112, "alphanum_fraction": 0.6329966187477112, "avg_line_length": 41.42856979370117, "blob_id": "ace57ee61ae0d46f28b9771d28bd0bb1558721ea", "content_id": "6660ba92ad8c200bc33676d949cb9574900b431d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 297, "license_type": "no_license", "max_line_length": 58, "num_lines": 7, "path": "/listings/v7_vererbung8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class Angestellter(Person):\n def __init__(self, name, personalnummer):\n # Initialisierungsmethode der Superklasse aufrufen\n super().__init__(name)\n # oder Person.__init__(self, name)\n self.personalnummer = personalnummer\n print('__init__() von Angestellter')\n" }, { "alpha_fraction": 0.6111111044883728, "alphanum_fraction": 0.7222222089767456, "avg_line_length": 17, "blob_id": "a9ecfee6da8efcede52629a97bbebf6fe8a73ef5", "content_id": "c4c572c3a46642460727ed3a74485ced09683fa0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 18, "license_type": "no_license", "max_line_length": 17, "num_lines": 1, "path": "/listings/v8_numpy3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "arr[axis0, axis1]\n" }, { "alpha_fraction": 0.5409836173057556, "alphanum_fraction": 0.5792349576950073, "avg_line_length": 60, "blob_id": "4c1e4de31959c19a8b2de09c9fc2b82deaf0f22e", "content_id": "c772679990a966567882c51d1af76b5f9603df2e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 183, "license_type": "no_license", "max_line_length": 81, "num_lines": 3, "path": "/listings/v5_ra19.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "re.findall(r'\\w+$', 'Hallo Welt') # Ausgabe: ['Welt']\nre.findall(r'\\w+$', 'Punkt A\\nPunkt B', flags=re.MULTILINE) # Ausgabe: ['A', 'B']\nre.findall(r'\\d\\Z', '123456') # Ausgabe: ['6']\n" }, { "alpha_fraction": 0.47457626461982727, "alphanum_fraction": 0.6525423526763916, "avg_line_length": 18.66666603088379, "blob_id": "0f2746d6d4c6647003a14fe343fb3e34d1e56d56", "content_id": "1512cc475b726fc57254757423e9c25bc844c7a8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 118, "license_type": "no_license", "max_line_length": 38, "num_lines": 6, "path": "/listings/v4_list3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "kilometer = [30, 50, 60, 80, 100, 120]\nmeilen = []\nfor km in kilometer:\n meilen.append(km*0.621371)\n\nprint(meilen)\n" }, { "alpha_fraction": 0.6083333492279053, "alphanum_fraction": 0.6416666507720947, "avg_line_length": 39, "blob_id": "98bcbd2bd347cacb4df3d0797d05e3ba6c6a95db", "content_id": "846d1eaa72199a489717e864748e474215c187a8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 120, "license_type": "no_license", "max_line_length": 61, "num_lines": 3, "path": "/listings/v5_ra7.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = re.sub(r'\\d+', '<Zahl>', '3 Stuecke kosten 250 Franken.')\nprint(s)\n# Ausgabe: <Zahl> Stuecke kosten <Zahl> Franken.\n" }, { "alpha_fraction": 0.6172839403152466, "alphanum_fraction": 0.6419752836227417, "avg_line_length": 15.199999809265137, "blob_id": "c4a5c6ff702e19e92bd00a81db7acbc84fe42b5b", "content_id": "d7dd61554d90f25dc3795c47ae32a70a653bdf21", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 81, "license_type": "no_license", "max_line_length": 16, "num_lines": 5, "path": "/listings/v2_while2.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "while Bedingung:\n Anweisung1\n if Ausnahme:\n continue\n Anweisung2\n" }, { "alpha_fraction": 0.5161290168762207, "alphanum_fraction": 0.5161290168762207, "avg_line_length": 6.75, "blob_id": "e8a4116979f8d83afe7076b96129c55b940917d4", "content_id": "5d79551bcc4d50a1872801d2623221610dacead8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 62, "license_type": "no_license", "max_line_length": 14, "num_lines": 8, "path": "/listings/v7_vererbung11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "class A:\n pass\n\nclass B:\n pass\n\nclass C(A, B):\n pass\n" }, { "alpha_fraction": 0.6129032373428345, "alphanum_fraction": 0.6209677457809448, "avg_line_length": 61, "blob_id": "c219782e5296e29e9158682d54c4a0d1a2e0fbee", "content_id": "c4812835eb9423d18fc4a5c038f4a31d24a9435d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 124, "license_type": "no_license", "max_line_length": 79, "num_lines": 2, "path": "/listings/v5_ra14.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "liste = re.findall(r'((dum)\\2)', 'dumdum') # (dum) ist jetzt die zweite Gruppe\nprint(liste) # Ausgabe: [('dumdum', 'dum')]\n" }, { "alpha_fraction": 0.4527363181114197, "alphanum_fraction": 0.5174129605293274, "avg_line_length": 19.100000381469727, "blob_id": "433c6e1194c8b2678532c71d6aceea82745e1806", "content_id": "d480c3d6f9a9c4527d23003122d685e6817b2dc9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 201, "license_type": "no_license", "max_line_length": 69, "num_lines": 10, "path": "/listings/v2_func15.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x = [1, 2, 3]\ny = [7, 8, 9]\n\ndef foo(a, b):\n a.append(4) # Objekt veraendern\n b = [10, 11, 12] # lokale Variable b referenziert neues Objekt\n\nfoo(x, y)\nprint('x =', x)\nprint('y =', y)\n" }, { "alpha_fraction": 0.4000000059604645, "alphanum_fraction": 0.5, "avg_line_length": 19, "blob_id": "d0cc2d062f1ab052994e1d2bdf56602dc49cff6d", "content_id": "0187233f3bafb98984e6f428f8587437d3041161", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 20, "license_type": "no_license", "max_line_length": 19, "num_lines": 1, "path": "/listings/v8_numpy6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = arr[2:5].copy()\n" }, { "alpha_fraction": 0.4303797483444214, "alphanum_fraction": 0.5063291192054749, "avg_line_length": 38.5, "blob_id": "42273c741af067e97672a8d056b8ab75acf8b4da", "content_id": "e647c7251fc3fe3e6e765adc7270b82a0e408353", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 79, "license_type": "no_license", "max_line_length": 46, "num_lines": 2, "path": "/listings/v3_strings8.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "'U = {u}, I = {i}'.format(u=spannung, i=strom)\n# Ausgabe: 'U = 12.56, I = 0.5'\n" }, { "alpha_fraction": 0.5, "alphanum_fraction": 0.6708074808120728, "avg_line_length": 25.83333396911621, "blob_id": "7fc44ad278dea620ba70ba57b160f7d5e77b9d64", "content_id": "20aea04806327285b0d23dc7dc844f5e047d28bc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 322, "license_type": "no_license", "max_line_length": 53, "num_lines": 12, "path": "/listings/v9_integrate4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "from scipy.integrate import quad\n\ndef func(x):\n return np.sin(x)**2\n\n# Integration mit Gauss-Quadratur\ny, abserr = quad(func, a=0, b=np.pi)\nprint('y =', y, '; err=', abserr) # Ausgabe:\n# y = 1.5707963267948966 ; err= 1.743934249004316e-14\n# Analytische Loesung\nprint('y =', np.pi/2) # Ausgabe:\n# y = 1.5707963267948966\n" }, { "alpha_fraction": 0.5416666865348816, "alphanum_fraction": 0.6666666865348816, "avg_line_length": 35, "blob_id": "8b98660ee7687940b82414b3894f3a1c4df31b36", "content_id": "8c9f7a5d25e91b4a87dfa0968ddb20dae7adff8c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 144, "license_type": "no_license", "max_line_length": 53, "num_lines": 4, "path": "/listings/v6_klassen6.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "x = MeineKlasse()\ny = MeineKlasse()\nprint('x:', x.speed_of_light) # Ausgabe: x: 299792458\nprint('y:', y.speed_of_light) # Ausgabe: y: 299792458\n" }, { "alpha_fraction": 0.6442307829856873, "alphanum_fraction": 0.6442307829856873, "avg_line_length": 19.799999237060547, "blob_id": "1713d70a493ae9cbe51a346fd76b50d8c34e7b2c", "content_id": "bf7412a2b6c199ab8bb557a35088db4d9f05a865", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 104, "license_type": "no_license", "max_line_length": 42, "num_lines": 5, "path": "/listings/v6_klassen12.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "s = MeineKlasse('Wall-E') # name='Wall-E'\ns.hallo()\n# Ausgabe:\n# Wall-E wurde erstellt.\n# Hallo Wall-E\n" }, { "alpha_fraction": 0.5625, "alphanum_fraction": 0.5625, "avg_line_length": 15, "blob_id": "2ac1b207808101d3adeadbfbe01ed13ab128333c", "content_id": "d9808200a3197c6e145f0db4ba6b5af54d3d16f4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 48, "license_type": "no_license", "max_line_length": 25, "num_lines": 3, "path": "/listings/v4_tupel1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "t = ()\nprint(type(t))\n# Ausgabe: <type 'tuple'>\n" }, { "alpha_fraction": 0.3931034505367279, "alphanum_fraction": 0.548275887966156, "avg_line_length": 31.22222137451172, "blob_id": "60e25d8109b3bcf1eb6c47b9e9a778d7b0554051", "content_id": "3a07e313320e6cb0b00ec8c06cb56f4b08c69915", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 290, "license_type": "no_license", "max_line_length": 56, "num_lines": 9, "path": "/listings/v3_strings3.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "spannung = 12.56\nstrom = 0.5\nN = 10\nprint('N = %d, U = %f, I = %.3f' % (N, spannung, strom))\n# Ausgabe: N = 10, U = 12.560000, I = 0.500\nprint('U = %g' % spannung) # generelles Format\n# Ausgabe: U = 12.56\nprint('X = 0x%04X, Y = 0x%04X' % (7, 15)) # hex\n# Ausgabe: X = 0x0007, Y = 0x000F\n" }, { "alpha_fraction": 0.675000011920929, "alphanum_fraction": 0.6850000023841858, "avg_line_length": 39, "blob_id": "562b2f61f1bd3868c01a57f55fca90d7d84c6998", "content_id": "6f4d80e13ff991133ef020dd041a344e168e23b1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 200, "license_type": "no_license", "max_line_length": 84, "num_lines": 5, "path": "/listings/v2_func11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "def einfache_funktion(x, **kwargs):\n print('x =', x)\n print('kwargs =', kwargs) # die restlichen Argumente sind im Dictionary kwargs\n\neinfache_funktion(x='Hallo', farbe='rot', durchmesser=10)\n" }, { "alpha_fraction": 0.7428571581840515, "alphanum_fraction": 0.7428571581840515, "avg_line_length": 20, "blob_id": "82f94af7042ba17628760126c71bee16f39fa228", "content_id": "ee7e84705eee18be52ff3e057c8e8b1b1c0757de", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 315, "license_type": "no_license", "max_line_length": 47, "num_lines": 15, "path": "/listings/v3_datei11.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "import os\nfull_path = os.path.abspath('mailaenderli.txt')\nprint(full_path)\n# Ausgabe: kompletter Pfad der datei\n\nos.path.isfile(full_path)\n# Ausgabe: True\n\nos.path.isdir(full_path)\n# Ausgabe: False\n\nos.path.getsize(full_path)\nos.path.split(full_path)\nos.path.splitext(full_path)\nos.path.join('ordner', 'datei.txt')\n" }, { "alpha_fraction": 0.5789473652839661, "alphanum_fraction": 0.5789473652839661, "avg_line_length": 18, "blob_id": "92c197a69794ddf27dbdeaa2730087b38efd7e4f", "content_id": "db040e53eee580b0771c55d186d73b8d540b65d7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 57, "license_type": "no_license", "max_line_length": 31, "num_lines": 3, "path": "/listings/v4_tupel4.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "t = 'Peter', 'Mueller'\nt\n# Ausgabe: ('Peter', 'Mueller')\n" }, { "alpha_fraction": 0.5720000267028809, "alphanum_fraction": 0.5720000267028809, "avg_line_length": 40.66666793823242, "blob_id": "c4e5ffdee5df42d4dbba7fd4ad7160d5236bb054", "content_id": "d5280de70301a4dadd0fcdd15d11c5f203921cfd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 250, "license_type": "no_license", "max_line_length": 43, "num_lines": 6, "path": "/listings/v3_datei1.py", "repo_name": "n04hk/Python_Zusammenfassung", "src_encoding": "UTF-8", "text": "f = open('dokument.txt') # lesen\nf = open('dokument.txt', 'r') # lesen\nf = open('dokument.txt', 'w') # schreiben\nf = open('dokument.txt', 'a') # anhaengen\nf = open('dokument.txt', 'rb') # binaer\nf = open('dokument.txt', 'wb') # binaer\n" } ]

192

kaktojosko/BIG_rock_paper_scissors

https://github.com/kaktojosko/BIG_rock_paper_scissors

d19e930a0786a69cb757d205507ea0b33593ccdc

57474616ce7fadf470fc66188cad6f2430f9c09f

f2b2eb92d4737268c634696e46c1ee2fac5fe50a

refs/heads/main

2023-01-19T20:51:32.675719

2020-11-28T13:16:07

316,729,907

0

null

[ { "alpha_fraction": 0.5108920931816101, "alphanum_fraction": 0.5176348686218262, "avg_line_length": 34.60759353637695, "blob_id": "19a18b73541eacbf505126f1add735c0fa5f77fd", "content_id": "d9c1f0ab84b4824939926050f0e80a479ca4a881", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5964, "license_type": "no_license", "max_line_length": 106, "num_lines": 158, "path": "/my_test.py", "repo_name": "kaktojosko/BIG_rock_paper_scissors", "src_encoding": "UTF-8", "text": "# РАБОТАЕТ ОКЕЙ\r\nimport random\r\nex = False\r\nname = input('Enter your name: ')\r\nprint('Hello,', name)\r\nscore1 = 0\r\n# Пустой ввод = камень, ножницы и бумага в игре\r\n# Ввод из rock, paper даст все 15 игровых фигур\r\nmoves = [x for x in input().split(',')]\r\n\r\nprint(\"Okay, let's start\")\r\nwinning_cases = {\r\n 'water': ['snake', 'human', 'tree', 'wolf', 'sponge', 'paper', 'air'],\r\n 'dragon': ['human', 'tree', 'wolf', 'sponge', 'paper', 'air', 'water'],\r\n 'devil': ['tree', 'wolf', 'sponge', 'paper', 'air', 'water', 'dragon'],\r\n 'lightning': ['wolf', 'sponge', 'paper', 'air', 'water', 'dragon', 'devil'],\r\n 'gun': ['sponge', 'paper', 'air', 'water', 'dragon', 'devil', 'lightning'],\r\n 'rock': ['paper', 'air', 'water', 'dragon', 'devil', 'lightning', 'gun'],\r\n 'fire': ['air', 'water', 'dragon', 'devil', 'lightning', 'gun', 'rock'],\r\n 'scissors': ['water', 'dragon', 'devil', 'lightning', 'gun', 'rock', 'fire'],\r\n 'snake': ['dragon', 'devil', 'lightning', 'gun', 'rock', 'fire', 'scissors'],\r\n 'human': ['devil', 'lightning', 'gun', 'rock', 'fire', 'scissors', 'snake'],\r\n 'tree': ['lightning', 'gun', 'rock', 'fire', 'scissors', 'snake', 'human'],\r\n 'wolf': ['gun', 'rock', 'fire', 'scissors', 'snake', 'human', 'tree'],\r\n 'sponge': ['rock', 'fire', 'scissors', 'snake', 'human', 'tree', 'wolf'],\r\n 'paper': ['rock', 'fire', 'scissors', 'snake', 'human', 'tree', 'wolf', 'sponge'],\r\n 'air': ['fire', 'scissors', 'snake', 'human', 'tree', 'wolf', 'sponge', 'paper']\r\n}\r\n\r\nlosing_cases = {\r\n 'water': ['scissors', 'fire', 'rock', 'hun', 'lightning', 'devil', 'dragon'],\r\n 'dragon': ['snake', 'scissors', 'fire', 'rock', 'gun', 'lightning', 'devil'],\r\n 'devil': ['tree', 'human', 'snake', 'scissors', 'fire', 'rock', 'gun'],\r\n 'gun': ['wolf', 'tree', 'human', 'snake', 'scissors', 'fire', 'rock'],\r\n 'rock': ['sponge', 'wolf', 'tree', 'human', 'snake', 'scissors', 'fire'],\r\n 'fire': ['paper', 'sponge', 'wolf', 'tree', 'human', 'snake', 'scissors'],\r\n 'scissors': ['air', 'paper', 'sponge', 'wolf', 'tree', 'human', 'snake'],\r\n 'snake': ['water', 'air', 'paper', 'sponge', 'wolf', 'tree', 'human'],\r\n 'human': ['dragon', 'water', 'air', 'paper', 'sponge', 'wolf', 'tree'],\r\n 'tree': ['devil', 'dragon', 'water', 'air', 'paper', 'sponge', 'wolf'],\r\n 'wolf': ['lightning', 'devil', 'dragon', 'water', 'air', 'paper', 'sponge'],\r\n 'sponge': ['gun', 'lightning', 'devil', 'dragon', 'water', 'air', 'paper'],\r\n 'paper': ['rock', 'gun', 'lightning', 'devil', 'dragon', 'water', 'air'],\r\n 'air': ['fire', 'rock', 'gun', 'lightning', 'devil', 'dragon', 'water'],\r\n 'lightning': ['tree', 'human', 'snake', 'scissors', 'fire', 'rock', 'gun']\r\n}\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\nwith open('rating.txt', 'a+') as file:\r\n file.seek(0)\r\n lines = file.readlines()\r\n for line in lines:\r\n if line.startswith(name):\r\n score1 = int(line[len(name) + 1:])\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef check_name(name):\r\n exists = False\r\n with open('rating.txt', 'a+') as file:\r\n file.seek(0)\r\n lines = file.readlines()\r\n for line in lines:\r\n if name in line:\r\n exists = True\r\n\r\n if not exists:\r\n file.seek(0)\r\n file.write(name + ' ' + str(score1) + '\\n')\r\n\r\n\r\ncheck_name(name)\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef rating(z):\r\n with open('rating.txt', 'a+') as file:\r\n file.seek(0)\r\n lines = file.readlines()\r\n for line in lines:\r\n if line.startswith(z):\r\n print('Your rating:', score1)\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef ez_win(x):\r\n if len(moves) == 1:\r\n win = {'scissors': 'rock', 'rock': 'paper', 'paper': 'scissors'}\r\n print('Sorry, but the computer chose ' + win[x])\r\n else:\r\n print('Sorry, but the computer chose ' + random.choice(losing_cases[x]))\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef ez_lose(x):\r\n global score1\r\n if len(moves) == 1:\r\n lose = {'rock': 'scissors', 'scissors': 'paper', 'paper': 'rock'}\r\n loser: str = lose[x]\r\n print('Well done. The computer chose {} and failed'.format(loser))\r\n else:\r\n print('Well done. The computer chose {} and failed'.format(random.choice(winning_cases[x])))\r\n score1 += 100\r\n change_score()\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef draw(x):\r\n global score1\r\n tie = {'rock': 'rock', 'scissors': 'scissors', 'paper': 'paper', 'water': 'water', 'dragon': 'dragon',\r\n 'devil': 'devil',\r\n 'gun': 'gun', 'fire': 'fire', 'snake': 'snake',\r\n 'human': 'human', 'tree': 'tree', 'wolf': 'wolf', 'sponge': 'sponge',\r\n 'air': 'air', 'lightning': 'lightning'\r\n }\r\n print('There is a draw ({})'.format(tie[x]))\r\n score1 += 50\r\n change_score()\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef mainloop(y):\r\n global ex, name\r\n r_int = random.randint(1, 3)\r\n if r_int == 1 and y in winning_cases and y != '!exit':\r\n ez_win(y)\r\n elif r_int == 2 and y in winning_cases and y != '!exit':\r\n ez_lose(y)\r\n elif r_int == 3 and y in winning_cases and y != '!exit':\r\n draw(y)\r\n elif y == '!exit':\r\n print('Bye!')\r\n ex = True\r\n elif y == '!rating':\r\n rating(name)\r\n else:\r\n print('Invalid input')\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\ndef change_score():\r\n global name, score1\r\n with open('rating.txt', 'a+') as f2:\r\n f2.write(name + ' ' + str(score1))\r\n with open('rating.txt', 'w+') as f2:\r\n counter = 0\r\n lines = f2.readlines()\r\n for line in lines:\r\n if line.startswith(name) and counter < 1:\r\n line = ''\r\n counter += 1\r\n f2.write(name + ' ' + str(score1))\r\n\r\n\r\n# РАБОТАЕТ ОКЕЙ\r\nwhile ex is False:\r\n player_move = input().lower()\r\n mainloop(player_move)\r\n" } ]

1

reporter-law/automated_writing

https://github.com/reporter-law/automated_writing

c796afa949800f4907309be437698105613a32a7

9f134b15d5e994166bd350c769ed23ccffc7c5af

c531b30bea67bd7abb0e56aa56b733fd2ba73192

refs/heads/master

2022-11-28T22:58:14.377736

2020-08-17T09:06:08

288,129,801

1

0

null

[ { "alpha_fraction": 0.5846500992774963, "alphanum_fraction": 0.5959367752075195, "avg_line_length": 21.200000762939453, "blob_id": "7e2035d527e7afa76bbe831b5cbf235e96066d32", "content_id": "b2d80e5564046959e358304aa953a2fb479711f5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 547, "license_type": "no_license", "max_line_length": 65, "num_lines": 20, "path": "/中国知网文献检索分析/__init__.py", "repo_name": "reporter-law/automated_writing", "src_encoding": "UTF-8", "text": "\"\"\"程序说明\"\"\"\n# -*- coding: utf-8 -*-\n# Author: cao wang\n# Datetime : 2020\n# software: PyCharm\n# 收获:\nimport time\nimport logging\n\n\ndef start_logger():\n \"\"\"日志初始化设置、文件名（时间）、DEBUG为调试级别(级别导致输出内容的不同）、日志的记录格式、日期格式\"\"\"\n\n logging.basicConfig( #filename='daily_report_error_%s.log' %\n\n #datetime.strftime(datetime.now(), '%m%d%Y_%H%M%S'),\n\n level=logging.DEBUG,\n format='%(asctime)s %(message)s',\n datefmt='%m-%d %H:%M:%S')" }, { "alpha_fraction": 0.5138194561004639, "alphanum_fraction": 0.5276389122009277, "avg_line_length": 33.0099983215332, "blob_id": "ff7e6383e44626cb3ea0e6dd250276f1a754a961", "content_id": "2574c1ff37295cd0d8be9f0f960d6ba1c7b289fe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 8158, "license_type": "no_license", "max_line_length": 120, "num_lines": 200, "path": "/中国知网文献检索分析/文章深入检索.py", "repo_name": "reporter-law/automated_writing", "src_encoding": "UTF-8", "text": "\"\"\"程序说明\"\"\"\n# -*- coding: utf-8 -*-\n# Author: cao wang\n# Datetime : 2020\n# software: PyCharm\n# 收获:\nimport time\nimport logging\n\n\ndef start_logger():\n\n \"\"\"日志初始化设置、文件名（时间）、DEBUG为调试级别(级别导致输出内容的不同）、日志的记录格式、日期格式\"\"\"\n time.sleep(1)\n\n logging.basicConfig( #filename='daily_report_error_%s.log' %\n\n #datetime.strftime(datetime.now(), '%m%d%Y_%H%M%S'),\n\n level=logging.DEBUG,\n format='%(asctime)s %(message)s',\n datefmt='%m-%d %H:%M:%S')\n\n\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom urllib.parse import urljoin\nimport time\nimport random\nimport json\n#import csv\n\n# 设置谷歌驱动器的环境\noptions = webdriver.ChromeOptions()\n# 设置chrome不加载图片，提高速度\noptions.add_experimental_option(\"prefs\", {\"profile.managed_default_content_settings.images\": 2})\n# 创建一个谷歌驱动器\noptions.add_argument('--headless')\nbrowser = webdriver.Chrome(options=options)\nurl = 'http://wap.cnki.net/touch/web/guide'\nwait = WebDriverWait(browser, 20)\n\n\n# 声明一个全局列表，用来存储字典\ndata_list = []\n\n\ndef start_spider(page,keyword):\n \"\"\"请求url\"\"\"\n browser.get(url)\n # 显示等待输入框是否加载完成\n WebDriverWait(browser, 1000).until(\n EC.presence_of_all_elements_located(\n (By.ID, 'keyword')\n )\n )\n # 找到输入框的id，并输入python关键字\n browser.find_element_by_id('keyword').click()\n browser.find_element_by_id('keyword_ordinary').send_keys(keyword)\n # 输入关键字之后点击搜索\n browser.find_element_by_class_name('btn-search ').click()\n\n\n\n # print(browser.page_source)\n # 显示等待文献是否加载完成\n WebDriverWait(browser, 1000).until(EC.presence_of_all_elements_located((By.CLASS_NAME, 'g-search-body')))\n\n # 声明一个标记，用来标记翻页几页\n try:\n count = 1\n while True:\n # 显示等待加载更多按钮加载完成\n WebDriverWait(browser, 1000).until(\n EC.presence_of_all_elements_located(\n (By.CLASS_NAME, 'c-company__body-item-more')\n )\n )\n # 获取加载更多按钮\n Btn = browser.find_element_by_class_name('c-company__body-item-more')\n # 显示等待该信息加载完成\n WebDriverWait(browser, 1000).until(\n EC.presence_of_all_elements_located(\n (By.XPATH,\n '//div[@id=\"searchlist_div\"]/div[{}]/div[@class=\"c-company__body-item\"]'.format(2 * count - 1))\n )\n )\n # 获取在div标签的信息，其中format(2*count-1)是因为加载的时候有显示多少条\n # 简单的说就是这些div的信息都是奇数\n divs = browser.find_elements_by_xpath(\n '//div[@id=\"searchlist_div\"]/div[{}]/div[@class=\"c-company__body-item\"]'.format(2 * count - 1))\n # 遍历循环\n for div in divs:\n # 获取文献的题目\n name = div.find_element_by_class_name('c-company__body-title').text\n # 获取文献的作者\n author = div.find_element_by_class_name('c-company__body-author').text\n # # 获取文献的摘要\n # content = div.find_element_by_class_name('c-company__body-content').text\n # 获取文献的来源和日期、文献类型等\n text = div.find_element_by_class_name('c-company__body-name').text.split()\n if (len(text) == 3 and text[-1] == '优先') or len(text) == 2:\n # 来源\n source = text[0]\n # 日期\n datetime = text[1]\n # 文献类型\n literature_type = None\n else:\n source = text[0]\n datetime = text[2]\n literature_type = text[1]\n # 获取下载数和被引数\n temp = div.find_element_by_class_name('c-company__body-info').text.split()\n # 下载数\n download = temp[0].split('：')[-1]\n # 被引数\n cite = temp[1].split('：')[-1]\n\n # ----------2020-3-18修改----------#\n # 文献链接\n link = div.find_element_by_class_name('c-company-top-link').get_attribute('href')\n # 拼接\n link = urljoin(browser.current_url, link)\n # 获取关键字（需要访问该文献，url就是上面获取到的link）\n # browser.get(link) # 这行和下面那行不推荐使用，因为句柄的问题，会报错\n # browser.back()\n # 打印查看下未访问新窗口时的句柄\n # print(browser.current_window_handle)\n js = 'window.open(\"%s\");' % link\n # 每次访问链接的时候适当延迟\n time.sleep(random.uniform(1, 2))\n browser.execute_script(js)\n # 打印查看窗口的句柄，对比看下当前的句柄是哪个\n # 结果是原先窗口的句柄，而不是新打开窗口的句柄，因为和上面打印的句柄一样\n # print(browser.current_window_handle)\n # 切换句柄到新打开的窗口，browser.window_handles是查看全部的句柄\n # browser.switch_to_window是切换句柄\n browser.switch_to_window(browser.window_handles[1])\n # 获取关键字（使用xpath）\n key_worlds = browser.find_elements_by_xpath(\n '//div[@class=\"c-card__paper-name\"][contains(text(), \"关键词\")]/following-sibling::div[1]/a')\n key_worlds = ','.join(map(lambda x: x.text, key_worlds))\n # ----------2020-3-19修改----------#\n # 获取文献的摘要\n content = browser.find_element_by_class_name('c-card__aritcle').text\n # ----------2020-3-19修改----------#\n # 获取信息完之后先关闭当前窗口再切换句柄到原先的窗口\n browser.close()\n browser.switch_to_window(browser.window_handles[0])\n # 注：切换句柄参考该文章，感谢该博主：https://blog.csdn.net/DongGeGe214/article/details/52169761\n # ----------2020-3-18修改----------#\n\n # 声明一个字典存储数据\n data_dict = {'name': name, 'author': author, 'content': content, 'source': source, 'datetime': datetime,\n 'literature_type': literature_type, 'download': download, 'cite': cite, 'link': link,\n 'key_worlds': key_worlds}\n print(data_dict)\n data_list.append(data_dict)\n\n # 如果Btn按钮（就是加载更多这个按钮）没有找到（就是已经到底了），就退出\n if not Btn:\n break\n else:\n Btn.click()\n # 如果到了爬取的页数就退出\n if count == page: # 0的作用就是到底，当btn不存在\n break\n\n count += 1\n\n # 延迟两秒，我们不是在攻击服务器\n time.sleep(2)\n except:\n pass\n # 全部爬取结束后退出浏览器\n browser.quit()\n\n\n\n\ndef main(keyword):\n \"\"\"主函数\"\"\"\n start_spider(eval(input('请输入要爬取的页数（如果需要全部爬取请输入0）：')),keyword)\n\n # 将数据写入json文件中\n\n with open('data_json.json', 'a+', encoding='utf-8') as f:\n json.dump(data_list, f, ensure_ascii=False, indent=4)\n print('json文件写入完成')\n\n\n\n\nif __name__ == '__main__':\n list_ = ['侦查讯问告知']\n for i in list_:\n main(i)\n" }, { "alpha_fraction": 0.8805969953536987, "alphanum_fraction": 0.8805969953536987, "avg_line_length": 32.5, "blob_id": "cd9dac46f58fa45169c61f67711f078797b5bc7a", "content_id": "198aedee2a9f2790495b0cfbf60728ff1b6a384f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 151, "license_type": "no_license", "max_line_length": 46, "num_lines": 2, "path": "/README.md", "repo_name": "reporter-law/automated_writing", "src_encoding": "UTF-8", "text": "# automated_writing\n第一个cnki的检索程序并不是我写的，只是搬过来用的，具有出自哪里暂时不记得了，等记得了加上\n" }, { "alpha_fraction": 0.5469270944595337, "alphanum_fraction": 0.5526441335678101, "avg_line_length": 27.71232795715332, "blob_id": "b964405b340cffc75449fe7686d920d17ea2d350", "content_id": "aecfe5728ea7591e475e78fa80e59a5f7d72443b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2305, "license_type": "no_license", "max_line_length": 91, "num_lines": 73, "path": "/参考文献写入/参考文献写入.py", "repo_name": "reporter-law/automated_writing", "src_encoding": "UTF-8", "text": "\"\"\"程序说明\"\"\"\n# -*- coding: utf-8 -*-\n# Author: cao wang\n# Datetime : 2020\n# software: PyCharm\n# 收获:\n\nimport logging\nimport time\n#from 常用设置.文字处理.文件格式转化.读取ddp import check_file_types as check\n#from 常用设置.文字处理.docx读取.docx内容 import read_docx as read\nimport os\nimport glob\nimport docx\nimport re\nimport pprint\nimport mammoth\nfrom lxml import etree\n\n\nclass Reference_Find():\n def __init__(self,input):\n self.input = input\n\n\n\n def docx_html(self):\n \"\"\"这是由于脚注无法读取\"\"\"\n \"\"\"转为html\"\"\"\n with open(self.input, \"rb\") as docx_file:\n result = mammoth.convert_to_html(docx_file)\n html = result.value # The generated HTML\n #messages = result.messages # Any messages, such as warnings during conversion\n temp = self.input.split(\".\")[0]+\".html\"\n with open(temp, \"a\")as f:\n f.write(html)\n return temp\n\n\n def references_write_to_docx(self):\n \"\"\"首先提取脚注，然后写入\"\"\"\n temp = self.docx_html()\n print(temp)\n parser = etree.HTMLParser(encoding=\"gbk\")\n text = etree.parse(temp, parser=parser)\n content = text.xpath(\"/html/body/ol[10]//text()\")\n #print(content)\n references = []\n for i in content:\n if i == \"↑\" or \"说明：\" in i:\n pass\n else:\n try:\n text = i.split(\"；\")\n for i in text:\n references.append(i.replace(\"参见\",\"\"))\n except:\n references.append(i.replace(\"参见\", \"\"))\n #写入docx\n document = docx.Document(self.input)\n docText2 = [paragraph.text for paragraph in document.paragraphs]\n p = document.paragraphs[len(docText2)-1]\n #document.add_page_break() # 另起一页\n p.add_run(\"\\n\")\n p.add_run(\"参考文献\\n\")\n for i,text in enumerate(references):\n i = i+1\n p.add_run(\"[%d]\"%i+text+\"\\n\")\n document.save(self.input)\n\n\ninput=r\"C:\\Users\\lenovo\\Desktop\\法官量刑的限度：从危险驾驶罪的量刑情节影响因子谈起 - 副本.docx\"\nReference_Find(input).references_write_to_docx()\n\n\n\n" }, { "alpha_fraction": 0.533281147480011, "alphanum_fraction": 0.5489428639411926, "avg_line_length": 24.85714340209961, "blob_id": "57e702d668781de70c1eb3df6518c901ddc8d56d", "content_id": "8714e290d090e750a8755f08029eeb987140cf5c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1419, "license_type": "no_license", "max_line_length": 92, "num_lines": 49, "path": "/中国知网文献检索分析/数据清洗.py", "repo_name": "reporter-law/automated_writing", "src_encoding": "UTF-8", "text": "\"\"\"程序说明\"\"\"\n# -*- coding: utf-8 -*-\n# Author: cao wang\n# Datetime : 2020\n# software: PyCharm\n# 收获:\nimport time\nimport logging\nimport json\nimport re\n\n\ndef start_logger():\n \"\"\"日志初始化设置、文件名（时间）、DEBUG为调试级别(级别导致输出内容的不同）、日志的记录格式、日期格式\"\"\"\n\n logging.basicConfig( #filename='daily_report_error_%s.log' %\n\n #datetime.strftime(datetime.now(), '%m%d%Y_%H%M%S'),\n\n level=logging.DEBUG,\n format='%(asctime)s %(message)s',\n datefmt='%m-%d %H:%M:%S')\nstart_logger()\nset_ =[]\n\"\"\"\nwith open('titles.txt', 'r+', encoding='utf-8') as f:\n words = f.readlines()\n for word in words:\n word.replace('[','').replace(']','').replace(\"'\",'').replace('[','').replace(']','')\n #print(word)\n\n print(type(word))\n words = word.strip().replace('[','').replace(']','').replace(\"'\",'')\n abs = words.split(',')\n for i in abs:\n set_.append(i)\n print(len(set_))\n set_q = set(set_)\n print(set_q)\n print(len(set_q))#257,215\\208\\95\\62\n\"\"\"\nwith open('/项目/论文写作自动化/中国知网文献检索分析/data_json.json', 'r+', encoding='utf-8') as f:\n words = f.read()\n pattern = re.compile('\"name\":(.*?),')\n word = re.findall(pattern,words)\n print(word)\n print(len(word))\n set_word = set(word)\n print(len(set_word))\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5539793372154236, "alphanum_fraction": 0.5614330172538757, "avg_line_length": 27.86805534362793, "blob_id": "3ef1fed7cd7691a31e58f80d3e2757ee8299e105", "content_id": "316df7716123408d88f042d565bdb8a5f0ce90d1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4435, "license_type": "no_license", "max_line_length": 128, "num_lines": 144, "path": "/中国知网文献检索分析/(未完成）检索结果分析.py", "repo_name": "reporter-law/automated_writing", "src_encoding": "UTF-8", "text": "\"\"\"程序说明\"\"\"\n# -*- coding: utf-8 -*-\n# Author: cao wang\n# Datetime : 2020\n# software: PyCharm\n# 收获:电脑端知网无法检索，只能手机端\n\nimport logging\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom lxml import etree\nimport time\n\n\nlogging.disable(logging.DEBUG)\ndef start_logger():\n \"\"\"日志初始化设置、文件名（时间）、DEBUG为调试级别(级别导致输出内容的不同）、日志的记录格式、日期格式\"\"\"\n\n logging.basicConfig( #filename='daily_report_error_%s.log' %\n\n #datetime.strftime(datetime.now(), '%m%d%Y_%H%M%S'),\n\n level=logging.DEBUG,\n format='%(asctime)s %(message)s',\n datefmt='%m-%d %H:%M:%S')\nstart_logger()\ndef search_result(keyword):\n \"\"\"返回检索初步结果\"\"\"\n browser = webdriver.Chrome()\n wait = WebDriverWait(browser, 20)\n\n \"\"\"网页获取\"\"\"\n browser.get('http://wap.cnki.net/touch/web/guide')\n click = wait.until(EC.presence_of_element_located((By.XPATH, '//*[@id=\"keyword\"]')))\n click.click()\n input_wprd = wait.until(EC.presence_of_element_located((By.XPATH, '//*[@id=\"keyword_ordinary\"]')))\n input_wprd.clear()\n input_wprd.send_keys(keyword)\n button = wait.until(EC.presence_of_element_located((By.XPATH, '//*[@id=\"searchbody_ordinary\"]/div/div[1]/div/div[1]/a[2]')))\n button.click()\n #time.sleep(1111)\n time.sleep(11111)\n try:\n while True:\n Btn = wait.until(EC.element_to_be_clickable((By.CLASS_NAME, 'c-company__body-item-more')))\n\n if Btn:\n browser.execute_script('window.scrollTo(0,document.body.scrollHeight)')\n Btn.click()\n\n else:\n with open('xxxxxxxxx.txt', \"a+\", encoding='utf-8')as f:\n f.write(browser.page_source)\n return browser.page_source\n except:\n with open('xxxxxxxxx.txt', \"a+\", encoding='utf-8')as f:\n f.write(browser.page_source)\n return browser.page_source\n\n\n\n\n\n\ndef get_page(html):\n \"\"\"解析函数：首先要进行返回html\"\"\"\n\n html_ = etree.HTML(html)\n contents = html_.xpath('//*[@id=\"searchlist_div\"]/div')\n # 内容遍历每一页\n print(contents)\n\n # logging.info(contents.xpath('/div//text()'))\n data = []\n for texts in contents:\n # logging.info(texts)\n \"\"\"对每一页进行内容提取\"\"\"\n titles = ''.join(\n texts.xpath('//div[@class=\"c-company__body-title c-company__body-title-blue\"]//text()')).strip()\n # print(titles)\n title = [i.strip() for i in titles.split('\\n')]\n print(len(title))\n print(title[0] + '\\n')\n with open('titles.txt', \"a+\", encoding='utf-8')as f:\n f.write(str(title) + \"\\n\")\n\n\n\n\n \"\"\"\n 其余内容\n authors = ''.join(texts.xpath('//div[@class=\"c-company__body-author\"]//text()')).replace(' ','')\n author = [i.strip() for i in authors.split('\\n') if i !='']\n print(author)\n\n\n contents = ''.join(texts.xpath('//div[@class=\"c-company__body-content\"]//text()'))\n content = [i.strip().replace(' ', '') for i in contents.split('\\n') if i !='']\n #print(content)\n\n\n times = ''.join(texts.xpath('//span[@class=\"color-green\"]//text()'))\n time_ = [i.strip().replace(' ', '').replace('\\xa0',',') for i in times.split('\\n') if i !='']\n #print(time_)\n\n downloads = ''.join(texts.xpath('//a[@class=\"c-company__body-info\"]//text()'))\n download = [i.replace('\\xa0','-').replace(' ','') for i in downloads.split('\\n') if i !='']\n downloads = []\n for i in download:\n if i == '':\n pass\n else:\n downloads.append(i)\n print(downloads)\n\n\n\n\n data_list = []\n for t,a,c,ti,d in zip(title,author,content,time_,download):\n dict_datas={'title':t,'author':a,'content':c,'time':ti,'download':d}\n data_list.append(dict_datas)\n print(data_list)\n \"\"\"\n # time.sleep(11111)\n\n\n\n\ndef main(keyword):\n \"\"\"主函数\"\"\"\n html = search_result(keyword)\n get_page(html)\n\n\n\n\n\nif __name__ == \"__main__\":\n list_ = ['权利告知','米兰达规则','犯罪嫌疑人知情权','侦查讯问告知']\n for i in list_:\n main(i)\n\n\n" } ]

6

wolfmib/ja_pandas

https://github.com/wolfmib/ja_pandas

0988a026199843f4a28f0e73d9d98d93973954ed

1454746f4e999efdae6b9597d49b61090c392f1a

9099853e43e87acd78abd22ded16a5cf27f7b70a

refs/heads/master

2022-12-11T09:48:14.778989

2020-01-10T03:33:19

232,946,063

0

null

2020-01-10T02:10:32

2020-01-10T03:33:27

2021-06-02T00:54:40

Python

[ { "alpha_fraction": 0.5278500914573669, "alphanum_fraction": 0.5325351357460022, "avg_line_length": 28.015151977539062, "blob_id": "49d100f306284824c1b073ee8e2f2c80084b0c09", "content_id": "7bd11fad85bf49f23afafb49c76a7b551b682206", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1921, "license_type": "no_license", "max_line_length": 82, "num_lines": 66, "path": "/ja_pandas.py", "repo_name": "wolfmib/ja_pandas", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport ja_language as ja_lan \n\n\n\n\nclass ja_pandas:\n def __init__(self):\n self.name = \"ja_pandas\"\n self.description = \"C'est utili pour moi-meme development purpose\"\n \n def set_df(self,input_df):\n self.df = input_df.copy()\n \n def _obtenir_df(self):\n return self.df\n\n def rename_col(self,origin_col_norm,after_col_norm,option_df=_obtenir_df()):\n\n # __cnanged_dict Format:\n # {origin_name : after_name}\n __changed_dict = {origin_col_norm:after_col_norm}\n option_df.rename(columns=__changed_dict,inplace=True)\n return option_df.copy\n \n\n\nif __name__ == \"__main__\":\n\n try:\n ja_lan_df = pd.read_pickle('ja_lan_env.pkl')\n apply_lan = ja_lan_df['ja_lan'][0]\n print(\"[INFO]: Your apply language is {%s}\"%apply_lan)\n except:\n print(\"[INFO]: No ja_lan_env.pkl found !\")\n print(\"Set language as default 'English' \")\n\n ja_lan = ja_lan.language_translator()\n ja_lan.set_language_code(apply_lan)\n\n\n print(ja_lan.print(\"Test the class: ja_pandas\"))\n # Changer le norm ###########################\n name_data_list = [\"Johnny\",\"Jean\",\"Jason\",\"Douge\"]\n age_data_list = [32,25,27,45]\n # Creer la DataFrame\n test_df = pd.DataFrame({'norm_de_familiy':name_data_list,'age':age_data_list})\n print(test_df)\n \n # Change le norm de col\n test_df.rename(columns={'age': 'col_age_changed'},inplace=True)\n print(test_df)\n\n\n # test avec agent\n print(\"---------------------------------------------------------\")\n ja_pd = ja_pandas()\n test_df = pd.DataFrame({'norm_de_familiy':name_data_list,'age':age_data_list})\n print(test_df)\n print(\"-----------\\n\\n\")\n test_df = ja_pd.rename_col('age','after_age',test_df)\n print(test_df)\n print(\"-----------\\n\\n\")\n\n\n #######################################################################\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.6721311211585999, "alphanum_fraction": 0.6721311211585999, "avg_line_length": 13.75, "blob_id": "ed16570521fa9191eee12bb87808cb0540c92e44", "content_id": "c7c3a4a4f8b8433c5a986804297726332b8fd777", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 61, "license_type": "no_license", "max_line_length": 41, "num_lines": 4, "path": "/README.md", "repo_name": "wolfmib/ja_pandas", "src_encoding": "UTF-8", "text": "# ja_pandas\nC'est un peu example avec Pandas package.\n\n---\n\n\n" }, { "alpha_fraction": 0.5270270109176636, "alphanum_fraction": 0.5337837934494019, "avg_line_length": 41.14285659790039, "blob_id": "524d850a43a7f4e37fad7738ef5b68f01949e820", "content_id": "5d137f6ccfce9c7c713deda1419355e8c8a58878", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 296, "license_type": "no_license", "max_line_length": 71, "num_lines": 7, "path": "/ja_freeze.sh", "repo_name": "wolfmib/ja_pandas", "src_encoding": "UTF-8", "text": "\necho \"[Jason]: Start to crate requirements.txt\"\npip3 freeze > requirements.txt\n\necho \"[Jason]: Finish.. you can install this in other device by typing\"\necho \"----------------------------------------\"\necho \"pip or pip3 install -r requirements.txt\"\necho \"----------------------------------------\"\n" } ]

3

vmurashko322/dinamo-03-07-2021

https://github.com/vmurashko322/dinamo-03-07-2021

d6e525c0b741a2d5cc29693a2755d78af4e16091

cfb236506ed8b2100762a5ca5dd816983a3eda8c

c8813539dab930b2a7d7230e183704aee16a6709

refs/heads/main

2023-06-10T13:51:50.425342

2021-07-05T14:03:50

382,679,860

0

null

[ { "alpha_fraction": 0.6570320129394531, "alphanum_fraction": 0.6653734445571899, "avg_line_length": 31.018632888793945, "blob_id": "a3902e2c3ebb799fee3883495f62f5297062c50c", "content_id": "8a3301a532795b659c9fdb5da983484de06ed248", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5160, "license_type": "no_license", "max_line_length": 120, "num_lines": 161, "path": "/main/views.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "import json\nimport io\nimport requests\nfrom django.http import JsonResponse, Http404\nfrom django.shortcuts import get_object_or_404, redirect\n\nfrom django.views.decorators.csrf import csrf_exempt\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.renderers import JSONRenderer\nfrom rest_framework.response import Response\nfrom rest_framework.reverse import reverse_lazy\nfrom rest_framework.views import APIView\n\nfrom main.models import ProductModel, Car, User, City, Country, Material\nfrom main.serializers import ProductSerializerModel, ProductSerializer, CarSerializer, UserSerializer, CitySerializer, \\\n CountrySerializer, MaterialSerializer\n\n\nclass A:\n def __init__(self, name, username, email):\n self.name = name\n self.username = username\n self.email = email\n\n def get_name(self):\n return self.name\n\n\nuser = requests.get('https://jsonplaceholder.typicode.com/users/1')\na1 = user.json()\na = A(a1['name'], a1['username'], a1['email'])\n\nres = json.dumps(a.__dict__)\ncontent = JSONRenderer().render(a.__dict__)\n\nd = {'id': 1, 'name': 'Johan'}\n_json = json.dumps(d)\n\n\n@api_view(['GET', 'POST'])\ndef product(request):\n confeti = ProductModel.objects.all()\n data = ProductSerializer(confeti, many=True)\n if request.method == \"POST\":\n serializer = ProductSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(data.data)\n # if request.method == 'POST':\n # serializer = ProductSerializerModel(data=request.data)\n # if serializer.is_valid():\n # serializer.save()\n # return Response(serializer.data, status=201)\n # confeti = ProductModel.objects.all()\n # serializer = ProductSerializerModel(confeti, many=True)\n # return Response(serializer.data)\n\n\n@csrf_exempt\n@api_view(['GET', 'POST', 'PUT'])\ndef product_detail(request, pk):\n prod = get_object_or_404(ProductModel, pk=pk)\n if request.method == \"GET\":\n serializer = ProductSerializerModel(prod)\n return Response(serializer.data, status=status.HTTP_200_OK)\n elif request.method == 'PUT':\n serializer = ProductSerializerModel(instance=prod, data=request.data, partial=True)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=201)\n else:\n return Response(serializer.errors, status=201)\n\n\n@api_view(['GET', 'POST', 'PUT'])\ndef product2_detail(request, pk):\n prod = get_object_or_404(Car, pk=pk)\n serializer = CarSerializer(prod)\n print(serializer.data)\n if request.method == 'PUT':\n serializer = CarSerializer(instance=prod, partial=True, data=request.data)\n ######\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n else:\n return Response(serializer.errors)\n return Response(serializer.data)\n\n\ndef get_object(request):\n try:\n return ProductModel.objects.all()\n except:\n return Http404\n\n\nclass ProductNew(APIView):\n\n def get(self, request, format=None):\n obj = get_object(request)\n serializer = ProductSerializer(obj, many=True)\n return Response(serializer.data)\n\n def post(self, request, format=None):\n serializer = ProductSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return redirect(reverse_lazy('main:product'))\n return Response(serializer.errors, status=status.HTTP_404_NOT_FOUND)\n\n\nclass UserView(APIView):\n def get(self, request, format=None):\n model = User.objects.all()\n serializer = UserSerializer(model, many=True)\n return Response(serializer.data)\n\n def post(self, request, format=None):\n serializer = UserSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return redirect(reverse_lazy('main:product'))\n return Response(serializer.errors, status=status.HTTP_404_NOT_FOUND)\n\n\nclass CityView(APIView):\n def get(self, request):\n obj = City.objects.all()\n serializer = CitySerializer(obj, many=True)\n return Response(serializer.data)\n\n\nclass CountryView(APIView):\n def get(self, request):\n obj = Country.objects.all()\n serializer = CountrySerializer(obj, many=True)\n return Response(serializer.data)\n\n def post(self, request):\n serializer = CountrySerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n\n\nclass ShowMaterial(APIView):\n def get(self, request):\n obj = Material.objects.all()\n serializer = MaterialSerializer(obj, many=True)\n return Response(serializer.data)\n\n def post(self, request):\n serializer = MaterialSerializer(data=request.data)\n print(request.data)\n if serializer.is_valid():\n print(serializer.validated_data, ' из вью ')\n serializer.save()\n return Response(status=200)\n return Response(serializer.errors)\n" }, { "alpha_fraction": 0.6672598123550415, "alphanum_fraction": 0.6841636896133423, "avg_line_length": 25.761905670166016, "blob_id": "2386f6bfae10e5d052d1f4e96795ac98c71088a9", "content_id": "c890a20fa2ae3d24ad99f596a9692b15984e7314", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1162, "license_type": "no_license", "max_line_length": 73, "num_lines": 42, "path": "/main/models.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.db import models\n\n\n# Create your models here.\nclass ProductModel(models.Model):\n title = models.CharField(max_length=150)\n price = models.DecimalField(max_digits=6, decimal_places=2)\n\n\nclass Car(models.Model):\n name = models.CharField(max_length=10)\n product = models.ForeignKey('ProductModel', on_delete=models.CASCADE)\n\n\nclass User(models.Model):\n name = models.CharField(max_length=150)\n product = models.ForeignKey('ProductModel', on_delete=models.CASCADE)\n\n def __str__(self):\n return self.name\n\n\nclass Material(models.Model):\n A = (\n ('железо', 'железо'),\n ('ртуть', 'ртуть'),\n ('что-то', 'что-то')\n\n )\n title = models.ManyToManyField('ProductModel')\n model = models.CharField(max_length=150, choices=A, default='железо')\n user = models.OneToOneField('User', on_delete=models.CASCADE)\n\n\nclass City(models.Model):\n name = models.CharField(max_length=150)\n population = models.PositiveIntegerField()\n\n\nclass Country(models.Model):\n name = models.CharField(max_length=150)\n city = models.ForeignKey(\"City\", on_delete=models.CASCADE)\n" }, { "alpha_fraction": 0.741410493850708, "alphanum_fraction": 0.741410493850708, "avg_line_length": 41.61538314819336, "blob_id": "55b9262a8ace0000985ae2a05ec0e924f51beb64", "content_id": "8f7149b101e3c59d98bc61768396d0323403252f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 553, "license_type": "no_license", "max_line_length": 79, "num_lines": 13, "path": "/dinamo/urls.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.contrib import admin\nfrom django.urls import path\nfrom rest_framework.urlpatterns import format_suffix_patterns\nfrom dinamo.views import ShowPlayer, ShowPlayers, PlayerView\n\napp_name = 'dinamo'\nurlpatterns = [\n path('all_players/', ShowPlayers.as_view(), name='show_players'),\n path('player/', ShowPlayer.as_view(), name='ShowPlayers'),\n path('create_player/', PlayerView.as_view(), name='PlayerView'),\n path('create_player/<int:pk>/', PlayerView.as_view(), name='UpdatePlayer'),\n]\nurlpatterns=format_suffix_patterns(urlpatterns)" }, { "alpha_fraction": 0.5425295829772949, "alphanum_fraction": 0.5551035404205322, "avg_line_length": 41.25, "blob_id": "2a8e7f41627ca47e19ffd9d898bb1a178a063018", "content_id": "d2c629b40f5eb0009e3c020d774b1e1b3b45a3b2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2742, "license_type": "no_license", "max_line_length": 152, "num_lines": 64, "path": "/main/migrations/0001_initial.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "# Generated by Django 3.2.4 on 2021-07-04 14:02\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='City',\n fields=[\n ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=150)),\n ('population', models.PositiveIntegerField()),\n ],\n ),\n migrations.CreateModel(\n name='ProductModel',\n fields=[\n ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('title', models.CharField(max_length=150)),\n ('price', models.DecimalField(decimal_places=2, max_digits=6)),\n ],\n ),\n migrations.CreateModel(\n name='User',\n fields=[\n ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=150)),\n ('product', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.productmodel')),\n ],\n ),\n migrations.CreateModel(\n name='Material',\n fields=[\n ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('model', models.CharField(choices=[('железо', 'железо'), ('ртуть', 'ртуть'), ('что-то', 'что-то')], default='железо', max_length=150)),\n ('title', models.ManyToManyField(to='main.ProductModel')),\n ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='main.user')),\n ],\n ),\n migrations.CreateModel(\n name='Country',\n fields=[\n ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=150)),\n ('city', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.city')),\n ],\n ),\n migrations.CreateModel(\n name='Car',\n fields=[\n ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=10)),\n ('product', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.productmodel')),\n ],\n ),\n ]\n" }, { "alpha_fraction": 0.653923511505127, "alphanum_fraction": 0.6780683994293213, "avg_line_length": 25.891891479492188, "blob_id": "b136f3feaf800c8605bdeee89110b93c7d0ac263", "content_id": "398182eb05b6e6163bfc289d29488852c98c44f9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 994, "license_type": "no_license", "max_line_length": 67, "num_lines": 37, "path": "/dinamo/models.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.db import models\n\n\n# Create your models here.\n\nclass Stadium(models.Model):\n title = models.CharField(max_length=100)\n country = models.CharField(max_length=100)\n city = models.CharField(max_length=100)\n street = models.CharField(max_length=100)\n description = models.TextField()\n team = models.OneToOneField('Team', on_delete=models.CASCADE)\n\n def __str__(self):\n return self.title\n\nclass Team(models.Model):\n title = models.CharField(max_length=100)\n player = models.ForeignKey('Players', on_delete=models.CASCADE)\n\n def __str__(self):\n return self.title\n\nclass Players(models.Model):\n name = models.CharField(max_length=100)\n surname = models.CharField(max_length=100)\n # team = models.ManyToManyField('Team')\n\n def __str__(self):\n return self.name\n\nclass Sponcor(models.Model):\n title = models.CharField(max_length=100)\n teams = models.ManyToManyField('Team')\n\n def __str__(self):\n return self.title" }, { "alpha_fraction": 0.6503928303718567, "alphanum_fraction": 0.6574074029922485, "avg_line_length": 34.28712844848633, "blob_id": "b7f0b37a33bb0bc6f77cb067887a1cd6fb006f96", "content_id": "f174985fb1806e8e6de798fb71a2cdc2a6705b27", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3564, "license_type": "no_license", "max_line_length": 98, "num_lines": 101, "path": "/main/serializers.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from rest_framework import serializers\n\nfrom main.models import ProductModel, User, City, Country, Material\n\n\nclass ProductSerializer(serializers.Serializer):\n title = serializers.CharField(max_length=150)\n price = serializers.DecimalField(max_digits=6, decimal_places=2)\n\n def create(self, validated_data):\n \"\"\"\n Create and return a new `Snippet` instance, given the validated data.\n \"\"\"\n return ProductModel.objects.create(**validated_data)\n\n def update(self, instance, validated_data):\n instance.title = validated_data.get('title', instance.title)\n instance.price = validated_data.get('price', instance.price)\n instance.save()\n return instance\n\n\nclass ProductSerializerModel(serializers.ModelSerializer):\n class Meta:\n model = ProductModel\n fields = '__all__'\n\n def create(self, validated_data):\n return ProductModel.objects.create(**validated_data)\n\n\nclass CarSerializer(serializers.Serializer):\n name = serializers.CharField(max_length=15, read_only=True)\n product = ProductSerializer()\n\n def update(self, instance, validated_data):\n instance.name = validated_data.get('name', instance.name)\n instance.save()\n return instance\n\n\nclass UserSerializer(serializers.Serializer):\n name = serializers.CharField(max_length=150)\n product = ProductSerializer()\n\n def create(self, validated_data):\n product = ProductModel(**dict(validated_data['product']))\n test = ProductModel.objects.filter(title=product.title, price=product.price)\n if test:\n validated_data['product'] = test[0]\n else:\n validated_data['product'] = product\n product.save()\n return User.objects.create(**validated_data)\n\n # product=ProductModel.objects.get_or_create(**dict(validated_data['product']))\n # validated_data['product']=product[0]\n # return User.objects.create(**validated_data)\n\n\nclass CitySerializer(serializers.Serializer):\n name = serializers.CharField(max_length=150)\n population = serializers.IntegerField()\n\n\nclass CountrySerializer(serializers.Serializer):\n name = serializers.CharField(max_length=150)\n city = CitySerializer()\n\n def create(self, validated_data):\n city = City.objects.create(**dict(validated_data['city']))\n base = City.objects.filter(name=city.name, population=city.population)\n print(base)\n print(base.values())\n print(base[0])\n if base:\n validated_data['city'] = base[0]\n else:\n validated_data['city'] = city\n city.save()\n return Country.objects.create(**validated_data)\n\n\nclass MaterialSerializer(serializers.Serializer):\n title = ProductSerializer(many=True)\n model = serializers.CharField(max_length=150)\n user = UserSerializer()\n\n def create(self, validated_data):\n product_list = []\n if validated_data.get('title'):\n for i in validated_data['title']:\n prod = ProductModel.objects.get_or_create(**i)\n product_list.append(prod[0])\n if validated_data.get('user'):\n prod_user = ProductModel.objects.get_or_create(**validated_data['user']['product'])[0]\n user = User.objects.create(name=validated_data[\"user\"]['name'], product=prod_user)\n if validated_data.get('model'):\n material = Material.objects.create(model=validated_data['model'], user=user)\n material.title.set(product_list)\n return material\n" }, { "alpha_fraction": 0.5781126022338867, "alphanum_fraction": 0.5844568014144897, "avg_line_length": 20.372880935668945, "blob_id": "f4535b5cf07b36ef690e0229c4be30c56d82ae53", "content_id": "afeab8857c92702194fa9858fe84e8a0b66f598e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1261, "license_type": "no_license", "max_line_length": 69, "num_lines": 59, "path": "/less_drf.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "import requests\nimport json\n\n#\n# user = requests.get('https://jsonplaceholder.typicode.com/users/5')\n# print(type(user))\n\n\n# name = {'name': 'Vader'}\n#\n# n_json=json.dumps(name)\n# print(type(n_json))\n# l_json=json.loads(n_json)\n# print(type(l_json))\n#\n# _json = json.dumps(name)\n# print(type(_json))\n# print(_json)\n# _dict = json.loads(_json.encode())\n# print(_dict)\n\n# with open(\"first_json.json\", 'w') as file:\n# name=json.dumps(name, indent=4)\n# file.write(name)\n# d=''\n# with open(\"first_json.json\", 'r') as file:\n# d=json.load(file)\n# print(d)\n# from rest_framework.renderers import JSONRenderer\n\n# class A:\n# def __init__(self, name, username, email):\n# self.name = name\n# self.username = username\n# self.email = email\n#\n# def get_name(self):\n# return self.name\n#\n#\n# user = requests.get('https://jsonplaceholder.typicode.com/users/1')\n# a1 = user.json()\n# a = A(a1['name'], a1['username'], a1['email'])\n#\n# res=json.dumps(a.__dict__)\n# content = JSONRenderer().render(res.__dict__)\n# print(content)\n\n# d = {'id': 1, 'name': 'Johan'}\n# _json=json.dumps(d)\n# print(type(_json))\n\n# a = {\"A\", \"B\", \"C\"}\n# b = {\"C\", \"D\", \"E\"}\n# print(type(a))\n# print(a | b)\n# print(a & b)\n# print(a ^ b)\n# print(a - b)\n" }, { "alpha_fraction": 0.7158176898956299, "alphanum_fraction": 0.737265408039093, "avg_line_length": 30.08333396911621, "blob_id": "534e291a35792b5bb95a003cc5600af93ca1fed9", "content_id": "ed12dc0a4b97bca145e9753e6c9383138dcf6cbd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1119, "license_type": "no_license", "max_line_length": 56, "num_lines": 36, "path": "/dinamo/serializers.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from rest_framework import serializers\n\nfrom dinamo.models import Players\n\n\nclass PlayersSerializer(serializers.Serializer):\n name = serializers.CharField(max_length=100)\n surname = serializers.CharField(max_length=100)\n\n def create(self, validated_data):\n return Players.objects.create(**validated_data)\n\n def update(self, instance, validated_data):\n instance.name = validated_data.pop('name')\n instance.surname = validated_data.pop('surname')\n instance.save()\n return instance\n\n\nclass TeamSerializer(serializers.Serializer):\n title = serializers.CharField(max_length=100)\n player = PlayersSerializer()\n\n\nclass StadiumSerializer(serializers.Serializer):\n title = serializers.CharField(max_length=100)\n country = serializers.CharField(max_length=100)\n city = serializers.CharField(max_length=100)\n street = serializers.CharField(max_length=100)\n description = serializers.CharField()\n team = TeamSerializer()\n\n\nclass SponcorSerializer(serializers.Serializer):\n title = serializers.CharField(max_length=100)\n teams = TeamSerializer(many=True)\n" }, { "alpha_fraction": 0.6335549354553223, "alphanum_fraction": 0.6339460015296936, "avg_line_length": 34.51388931274414, "blob_id": "e741d17398c0a0c5edf9aaadfef2569a28329588", "content_id": "7cdb254461de8db3043035611ef206bd3737c293", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2557, "license_type": "no_license", "max_line_length": 115, "num_lines": 72, "path": "/dinamo/views.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.shortcuts import render, redirect\n\n# Create your views here.\nfrom django.views import generic\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom dinamo.models import Team, Players, Stadium, Sponcor\nfrom dinamo.serializers import PlayersSerializer, StadiumSerializer, TeamSerializer, SponcorSerializer\n\n\nclass ShowPlayers(generic.ListView):\n model = Team\n template_name = 'dinamo/all_players.html'\n\n def post(self, request):\n player = Players.objects.filter(name=request.POST['name_players'])\n try:\n t = player.values('id')\n print(player[0].__dict__)\n print(t)\n result = []\n for i in t:\n teams = Team.objects.get(pk=i['id'])\n print(teams)\n result.append(teams)\n context = {\"teams\": result}\n print(result)\n return render(request, 'dinamo/all_players.html', context)\n except:\n return render(request, 'dinamo/all_players.html')\n\n\nclass ShowPlayer(APIView):\n def get(self, request):\n stadium = Stadium.objects.all()\n players = Players.objects.all()\n team = Team.objects.all()\n sponcor = Sponcor.objects.all()\n serializer_player = PlayersSerializer(players, many=True)\n serializer_stadium = StadiumSerializer(stadium, many=True)\n serializer_team = TeamSerializer(team, many=True)\n serializer_sponcor = SponcorSerializer(sponcor, many=True)\n\n res = {\"players\": serializer_player.data, 'stadium': serializer_stadium.data, 'team': serializer_team.data,\n \"sponcor\": serializer_sponcor.data}\n return Response(res)\n\n\nclass PlayerView(APIView):\n def get(self, request, pk=None):\n if not pk:\n player = Players.objects.all()\n serializer = PlayersSerializer(player, many=True)\n else:\n player = Players.objects.get(pk=pk)\n serializer = PlayersSerializer(player)\n return Response(serializer.data)\n\n def post(self, request, pk):\n serializer = PlayersSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors)\n\n def put(self, request, pk):\n player = Players.objects.get(pk=pk)\n serializer = PlayersSerializer(player, data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n" }, { "alpha_fraction": 0.6895161271095276, "alphanum_fraction": 0.6948924660682678, "avg_line_length": 48.599998474121094, "blob_id": "8ad038e4c97afac0e335648387d6ac438e0bc5e1", "content_id": "f414cee3cc73b57204a676376ee425c911729f09", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 744, "license_type": "no_license", "max_line_length": 118, "num_lines": 15, "path": "/main/urls.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.contrib import admin\nfrom django.urls import path, include\nfrom .views import product, product_detail, product2_detail, ProductNew, UserView, CityView, CountryView, ShowMaterial\n\napp_name = 'main'\nurlpatterns = [\n path('product/', product, name='product'),\n path('product_detail/<int:pk>/', product_detail, name='product_detail'),\n path('product2/<int:pk>/', product2_detail, name='product2_detail'),\n path('product_class/', ProductNew.as_view(), name='ProductNew'),\n path('user_class/', UserView.as_view(), name='UserView'),\n path('city/', CityView.as_view(), name='CityView'),\n path('country/', CountryView.as_view(), name='CountryView'),\n path('material/', ShowMaterial.as_view(), name='MaterialView'),\n]\n" }, { "alpha_fraction": 0.8115941882133484, "alphanum_fraction": 0.8115941882133484, "avg_line_length": 24.75, "blob_id": "822b0773d8f4725eb91e66b356b2013c4ffb92ed", "content_id": "cf0b813a00df45e789c67d91254435ee4ede7ab2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 207, "license_type": "no_license", "max_line_length": 57, "num_lines": 8, "path": "/dinamo/admin.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.contrib import admin\n\nfrom dinamo.models import Stadium, Team, Players, Sponcor\n\nadmin.site.register(Stadium)\nadmin.site.register(Team)\nadmin.site.register(Players)\nadmin.site.register(Sponcor)\n\n" }, { "alpha_fraction": 0.8135592937469482, "alphanum_fraction": 0.8135592937469482, "avg_line_length": 25.33333396911621, "blob_id": "2f51c3bcd1b627de01b24e82d21766e45533f831", "content_id": "f85a6de8f4437be2c84db015949f12e6491dc336", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 236, "license_type": "no_license", "max_line_length": 57, "num_lines": 9, "path": "/main/admin.py", "repo_name": "vmurashko322/dinamo-03-07-2021", "src_encoding": "UTF-8", "text": "from django.contrib import admin\n\n# Register your models here.\nfrom main.models import ProductModel, Car, User, Material\n\nadmin.site.register(ProductModel)\nadmin.site.register(Car)\nadmin.site.register(User)\nadmin.site.register(Material)" } ]

12

jconnolly814/ThinkFull

https://github.com/jconnolly814/ThinkFull

ed66430395d1d50c6aa9674e865266f335595903

2f06b09f3ff61f6b9ee2d6bd514769f6ca885987

8bf7d6a5da118c24c6ae36d0264c1f3af6a1fea3

refs/heads/master

2021-01-10T09:00:24.073120

2015-10-25T23:25:00

43,975,056

0

null

[ { "alpha_fraction": 0.6678321957588196, "alphanum_fraction": 0.6968032121658325, "avg_line_length": 27.18309783935547, "blob_id": "244cf45aeb78d442e9134387c7042453abb1ec89", "content_id": "774cf2b61431c414c92e9fc589917f33bc2e2f86", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2002, "license_type": "no_license", "max_line_length": 119, "num_lines": 71, "path": "/multivariate.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\n\nloansData = pd.read_csv(\"/Users/jenniferconnolly/Documents/ThinkFul/projects/multivar/LoanStats3d.csv\")\n\nloansData['income'] = loansData['annual_inc'].astype(int)[1]\n\n\n\nloansData['interest']= loansData['int_rate'].map(lambda a: round(float(a.rstrip('%')) / 100, 4))[1]\ny = loansData['interest']\n\n\nX= loansData['income']\nX =sm.add_constant(X)\n\n\nprint \"\\n model #1\"\n\nmodel = sm.OLS(y,X).fit()\nprint model.summary()\n\nprint '\\n model #2'\n\nloansData['homeOwn'] = pd.Categorical(loansData['home_ownership']).labels\n\nX= loansData[['income','homeOwn']]\nX =sm.add_constant(X)\n\nmodel2 = sm.OLS(y,X).fit()\nprint model2.summary()\n\n\nprint '\\nmodel #2 interaction homeownership'\n\nincome = loansData['income']\nhomeOwn= loansData['homeOwn']\n\nloansData['interaction'] = income * homeOwn\n\nX= loansData[['income','homeOwn','interaction']]\n#X= loansData[['income','interaction']]\nX =sm.add_constant(X)\nmodel2a = sm.OLS(y,X).fit()\nprint model2a.summary()\n\nxx1, xx2 = np.meshgrid(np.linspace(X.income.min(), X.income.max(), 100),\n np.linspace(X.interaction.min(), X.interaction.max(), 100))\n# plot the hyperplane by evaluating the parameters on the grid\nZ = model2a.params[0] + model2a.params[1] * xx1 + model2a.params[2] * xx2\n\n# create matplotlib 3d axes\nfig = plt.figure(figsize=(12, 8))\nax = Axes3D(fig, azim=-115, elev=15)\n\n# plot hyperplane\nsurf = ax.plot_surface(xx1, xx2, Z, cmap=plt.cm.RdBu_r, alpha=0.6, linewidth=0)\n\n# plot data points - points over the HP are white, points below are black\nresid = y - model2a.predict(X)\nax.scatter(X[resid >= 0].income, X[resid >= 0].interaction, y[resid >= 0], color='black', alpha=1.0, facecolor='white')\nax.scatter(X[resid < 0].income, X[resid < 0].interaction, y[resid < 0], color='black', alpha=1.0)\n\n# set axis labels\nax.set_xlabel('income')\nax.set_ylabel('interest')\nax.set_zlabel('interaction')\nplt.show()\n\n" }, { "alpha_fraction": 0.6013767123222351, "alphanum_fraction": 0.6514393091201782, "avg_line_length": 23.538461685180664, "blob_id": "11df9a4f9e34632db090db6d320fba9ad4ac49a9", "content_id": "a35b7dbd9eaefde7dffda6da653331e0f5aa2672", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1598, "license_type": "no_license", "max_line_length": 50, "num_lines": 65, "path": "/stats.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import pandas as pd\nfrom scipy import stats\n\n\ndata = '''Region, Alcohol, Tobacco\nNorth, 6.47, 4.03\nYorkshire, 6.13, 3.76\nNortheast, 6.19, 3.77\nEast Midlands, 4.89, 3.34\nWest Midlands, 5.63, 3.47\nEast Anglia, 4.52, 2.92\nSoutheast, 5.89, 3.20\nSouthwest, 4.79, 2.71\nWales, 5.27, 3.53\nScotland, 6.08, 4.51\nNorthern Ireland, 4.02, 4.56'''\n\ndata = data.splitlines()\ndata = [i.split(', ') for i in data]\n\ncolumn_names = data[0] # this is the first row\ndata_rows = data[1::] # all of the folling rows\n\n\ndf = pd.DataFrame(data_rows, columns=column_names)\n\ndf['Alcohol'] = df['Alcohol'].astype(float)\ndf['Tobacco'] = df['Tobacco'].astype(float)\n\nmean_alc = df['Alcohol'].mean()\nmedian_alc = df['Alcohol'].median()\nmode_alc = stats.mode(df['Alcohol'])\n\nrange_alc =max(df['Alcohol']) - min(df['Alcohol'])\nstd_alc = df['Alcohol'].std()\nvar_alc = df['Alcohol'].var()\n\n\nprint '\\n'\n\nprint \"Alcohol results are\"\nprint \"Mean = {0}\".format(mean_alc)\nprint \"Median = {0}\".format(median_alc)\nprint \"Mode = {0}\".format(mode_alc)\nprint \"Range = {0}\".format(range_alc)\nprint \"STD = {0}\".format(std_alc)\nprint \"Var = {0}\".format(var_alc)\n\n\nmean_tob = df['Tobacco'].mean()\nmeadian_tob = df['Tobacco'].median()\nmode_tob = stats.mode(df['Tobacco'])\n\nrange_tob =max(df['Tobacco']) - min(df['Tobacco'])\nstd_tob = df['Tobacco'].std()\nvar_tob = df['Tobacco'].var()\n\nprint ' \\n'\nprint \"Tobacco results are\"\nprint \"Mean = {0}\".format(mean_tob)\nprint \"Median = {0}\".format(meadian_tob)\nprint \"Mode = {0}\".format(mode_tob)\nprint \"Range = {0}\".format(range_tob)\nprint \"STD = {0}\".format(std_tob)\nprint \"Var = {0}\".format(var_tob)\n\n\n\n" }, { "alpha_fraction": 0.7257346510887146, "alphanum_fraction": 0.7266250848770142, "avg_line_length": 21.93877601623535, "blob_id": "c496542a5eae4f75d8845146116cc4c45d9eea05", "content_id": "db5a0372f26d923888975aaf454b14d9f4cd7a54", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1123, "license_type": "no_license", "max_line_length": 91, "num_lines": 49, "path": "/prob_lending_club.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import matplotlib.pyplot as plt\nimport pandas as pd\nimport scipy.stats as stats\nimport os\n\nloansData = pd.read_csv('https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')\n\n\n\nloansData.dropna(inplace=True)\nloansData.boxplot(column='Amount.Funded.By.Investors')\nplt.show()\n\nloansData.dropna(inplace=True)\nloansData.boxplot(column='Amount.Requested')\nplt.show()\n\n\ndf = pd.DataFrame(loansData)\nAFBI= df.loc[:, ['Amount.Funded.By.Investors']]\nAR= df.loc[:, ['Amount.Requested']]\n\n\nbox = AFBI.plot(kind ='box')\nplt.savefig(str('box_AFBI_loansData.jpeg'))\nplt.show()\n\nhist = AFBI.plot(kind ='hist')\nplt.savefig(str('hist_AFBI_loansData.jpeg'))\nplt.show()\n\nplt.figure()\ngraph = stats.probplot(loansData['Amount.Funded.By.Investors'], dist=\"norm\", plot=plt)\nplt.savefig(str('qq_AFBI_loansData.jpeg'))\nplt.show()\n\n\nbox = AR.plot(kind ='box')\nplt.savefig(str('box_AR_loansData.jpeg'))\nplt.show()\n\nhist = AR.plot(kind ='hist')\nplt.savefig(str('hist_AR_loansData.jpeg'))\nplt.show()\n\nplt.figure()\ngraph = stats.probplot(loansData['Amount.Requested'], dist=\"norm\", plot=plt)\nplt.savefig(str('qq_AR_loansData.jpeg'))\nplt.show()" }, { "alpha_fraction": 0.6118780374526978, "alphanum_fraction": 0.6362760663032532, "avg_line_length": 42.87323760986328, "blob_id": "0a7300136266b736971c79766126e0ad69495fc6", "content_id": "8eaa33a373f265646da3868eb7f056f2e4e9075d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3115, "license_type": "no_license", "max_line_length": 116, "num_lines": 71, "path": "/database.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import sqlite3 as lite\nimport pandas as pd\n\n# Here you connect to the database. The `connect()` method returns a connection object.\ncon = lite.connect('C:\\Users\\JConno02\\projects\\sqlite\\getting_started.db')\n# Select all rows and print the result set one row at a time\nwith con:\n cur = con.cursor()\n cur.execute(\"DROP TABLE IF EXISTS cities \")\n cur.execute(\"DROP TABLE IF EXISTS weather \")\n\n cur.execute(\"CREATE TABLE cities (name TEXT, state TEXT)\")\n cur.execute(\n \"CREATE TABLE weather (city TEXT, year INTEGER, warm_month TEXT, cold_month TEXT, average_high INTEGER)\")\n\n cur.execute(\"INSERT INTO cities VALUES('Houston', 'TX')\")\n cur.execute(\"INSERT INTO cities VALUES('New York City','NY')\")\n cur.execute(\"INSERT INTO cities VALUES('Boston', 'MA')\")\n cur.execute(\"INSERT INTO cities VALUES('Chicago', 'IL')\")\n cur.execute(\"INSERT INTO cities VALUES('Miami', 'FL')\")\n cur.execute(\"INSERT INTO cities VALUES('Dallas', 'TX')\")\n cur.execute(\"INSERT INTO cities VALUES('Seattle', 'WA')\")\n cur.execute(\"INSERT INTO cities VALUES('Portland', 'OR')\")\n cur.execute(\"INSERT INTO cities VALUES('San Francisco', 'CA')\")\n cur.execute(\"INSERT INTO cities VALUES('Los Angeles', 'CA')\")\n\n cur.execute(\"INSERT INTO weather VALUES('New York City', 2013 ,'July', 'January', 62)\")\n cur.execute(\"INSERT INTO weather VALUES('Boston', 2013, 'July','January',59)\")\n cur.execute(\"INSERT INTO weather VALUES('Chicago', 2013, 'July','January',59)\")\n cur.execute(\"INSERT INTO weather VALUES('Miami', 2013, 'August','January',84)\")\n cur.execute(\"INSERT INTO weather VALUES('Dallas', 2013, 'July','January',77)\")\n cur.execute(\"INSERT INTO weather VALUES('Seattle', 2013, 'July','January',61)\")\n cur.execute(\"INSERT INTO weather VALUES('Portland', 2013, 'July','December',63)\")\n cur.execute(\"INSERT INTO weather VALUES('San Francisco', 2013,'September','December',64)\")\n cur.execute(\"INSERT INTO weather VALUES('Los Angeles', 2013,'September','December', 75)\")\n cur.execute(\"SELECT name, state, warm_month FROM cities INNER JOIN weather ON name = city ORDER BY warm_month\")\n # cur.execute(\"SELECT city, state,warm_month FROM weather ORDER BY warm_month\")\n rows = cur.fetchall()\n cols = [desc[0] for desc in cur.description]\n df = pd.DataFrame(rows, columns=cols)\n\njuly_df = df[df['warm_month'].isin(['July'])]\nnm_state = july_df.loc[:, ['name', 'state']]\n\ncount = len(nm_state)\nindex = count - 1\ntracker = index\npartb = ''\n\nwhile tracker > -1:\n if tracker == index:\n b = (nm_state.iloc[int(tracker)][:2])\n bb = b.values\n for value in bb:\n y = bb[0]\n z = bb[1]\n start = \"The cities that are warmest in July are: {0} {1}\".format(y, z)\n tracker -= 1\n else:\n b = (nm_state.iloc[int(tracker)][:2])\n bb = b.values\n partc = partb.replace(\"and \", \",\")\n for value in bb:\n y = bb[0]\n z = bb[1]\n\n partb = \"{0} and {1} {2}\".format(partc, y, z)\n final = \"{0}{1}\".format(start, partb)\n\n tracker -= 1\nprint \"{0}.\".format(final)\n" }, { "alpha_fraction": 0.5952476859092712, "alphanum_fraction": 0.6335078477859497, "avg_line_length": 26.430938720703125, "blob_id": "10eb6d175c7b88cf09a33d0618f2c3a18bc3aea7", "content_id": "3647434d843d8baf834bfd7836889732245a041b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4966, "license_type": "no_license", "max_line_length": 228, "num_lines": 181, "path": "/education.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "# Import libraries\nfrom bs4 import BeautifulSoup\nimport requests\nimport pandas as pd\nimport sqlite3 as lite\nimport matplotlib.pyplot as plt\nimport csv\nimport numpy as np\n#import statsmodels.api as sm\n\nurl = \"http://web.archive.org/web/20110514112442/http://unstats.un.org/unsd/demographic/products/socind/education.htm\"\n\nr = requests.get(url)\n\n# Use Beautiful Soup to Parse the HTML\nsoup_data = BeautifulSoup(r.content)\n\n\n\n# Select tables we are interested in\n# pull all data with class tcont\n\n\nsoup_tag = soup_data('table')[6].tr.td\n#for row in soup_tag:\n #print row\nsoup_table = soup_tag('table')[1].tr.td.div\n#for row in soup_table:\n #print row\nraw_table = soup_table('table')[0]\n#for row in raw_table:\n #print row\n\n\n\ncol_name = []\n\nfor j in raw_table('tr'):\n if j.get('class', ['Null'])[0] == 'lheader':\n for td in j.find_all('td'):\n if td.get_text() != '':\n col_name.append(td.get_text())\n break\n\n\ncountry_table = pd.DataFrame(columns=col_name)\n#print country_table\n\nedu_counter = 0\nfor j in raw_table('tr'):\n row_data = []\n if j.get('class', ['Null']) == 'tcont':\n row_data.append(j.find('td').get_text())\n for td in j.find_all('td')[1:]:\n if td.get('align'):\n row_data.append(td.get_text())\n else:\n row_data.append(j.find('td').get_text())\n for td in j.find_all('td')[1:]:\n if td.get('align'):\n row_data.append(td.get_text())\n if len(row_data) == len(col_name):\n country_table.loc[edu_counter] = row_data\n edu_counter += 1\n\n\n\nsublistdf =country_table[['Total', 'Men', 'Women']]\nnumeric_country_table= pd.to_numeric(sublistdf , errors='coerce')\ncountry_table[['Total', 'Men', 'Women']]= numeric_country_table[['Total', 'Men', 'Women']]\n\n#print numeric_country_table\n\n\ncountry_gdp = pd.DataFrame(\n columns=['country_name', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009',\n '2010'])\n\n\n# convert school years to integers\n\ncon = lite.connect('education.db')\ncur = con.cursor()\n\n\n\n# create table to hold school data\nwith con:\n cur.execute(\"DROP TABLE IF EXISTS school_years\")\n cur.execute('CREATE TABLE school_years (country_name, _Year, _Total, _Men, _Women)')\n\nwith con:\n cur.execute(\"DROP TABLE IF EXISTS gdp\")\n cur.execute(\n 'CREATE TABLE gdp (country_name text, _1999 numeric, _2000 numeric, _2001 numeric, _2002 numeric, _2003 numeric, _2004 numeric, _2005 numeric, _2006 numeric, _2007 numeric, _2008 numeric, _2009 numeric, _2010 numeric)')\n\nwith open('E:\\\\ThinkFul\\\\ny\\\\ny.gdp.mktp.cd_Indicator_en_csv_v2.csv', 'rU') as inputFile:\n next(inputFile)\n next(inputFile)\n header = next(inputFile)\n #print header\n inputReader = csv.reader(inputFile)\n gdp_row_num = 0\n for line in inputReader:\n row_data = [line[0]]\n row_data.extend(line[43:-5])\n country_gdp.loc[gdp_row_num] = row_data\n gdp_row_num += 1\n with con:\n cur.execute(\n 'INSERT INTO gdp (country_name, _1999, _2000, _2001, _2002, _2003, _2004, _2005, _2006, _2007, _2008, _2009, _2010) VALUES (\"' +\n line[0] + '\",\"' + '\",\"'.join(line[43:-5]) + '\");')\n\n\n\nsublist_gdp_df = country_gdp[country_gdp.columns[1:-1]]\nnumeric_country_table= pd.to_numeric(sublist_gdp_df, errors='coerce')\ncountry_gdp[country_gdp.columns[1:-1]]=numeric_country_table\n\ncountry_table['GDP']=0\n\n\n#print country_gdp['GDP']\n#print country_gdp\n#print country_table\n\ngdplist =[]\nfor i in range(edu_counter):\n rowid = country_gdp[country_gdp['country_name'] == country_table['Country or area'][i]].index\n if len(rowid) > 0:\n row_index = rowid.tolist()[0]\n try:\n gdp = float(country_gdp[country_table['Year'][i]][row_index]) ##extracts year from country table they pull the year, row index from gdp table and assigns that value back to GDP col of the country tab;e\n gdplist.append(gdp)\n except ValueError:\n gdp ='nan'\n gdplist.append(gdp)\n\n else:\n gdp ='nan'\n gdplist.append(gdp)\n\n#print gdplist\n#print len(gdplist)\n#print len(country_table['GDP'])\n\ncountry_table['GDP']= gdplist\n\n\ngdp = country_table['GDP'].map(lambda x: 0 if x =='nan'else float(x))\nschool_years = country_table['Year'].map(lambda x: int(x))\nlog_gdp = gdp.map(lambda x: np.log(x))\n\nprint len (gdp)\nprint len(log_gdp)\nprint len(school_years)\n\ncolors = np.random.rand(len(gdp))\nplt.scatter(log_gdp, school_years, c=colors)\nplt.show()\n\n\ny = np.matrix(gdp).transpose()\nx = np.matrix(school_years).transpose()\n\nX = sm.add_constant(x)\nmodel = sm.OLS(y,X)\nresults = model.fit()\n\ny = np.matrix(log_gdp).transpose()\nx = np.matrix(school_years).transpose()\n\nX = sm.add_constant(x)\nmodel2 = sm.OLS(y,X)\nresults2 = model2.fit()\n\nsm.graphics.tsa.plot_acf(log_gdp) #Autocorrelation\nplt.show()\n\nsm.graphics.tsa.plot_pacf(log_gdp) #Partial Autocorrelation\nplt.show()\n\n" }, { "alpha_fraction": 0.7641857266426086, "alphanum_fraction": 0.767870306968689, "avg_line_length": 27.893617630004883, "blob_id": "ad068a76d1b018d08e3e89c5ca27d25e724f56af", "content_id": "3c32d8e31d8e56ed8459c4ef9d91ff7143aa1854", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1357, "license_type": "no_license", "max_line_length": 123, "num_lines": 47, "path": "/time_series.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm\n\n\ndf = pd.read_csv(\"/Users/jenniferconnolly/Documents/ThinkFul/projects/multivar/LoanStats3d.csv\",header=0, low_memory=False)\n\n# converts string to datetime object in pandas:\ndf['issue_d_format'] = pd.to_datetime(df['issue_d'])\ndfts = df.set_index('issue_d_format')\nyear_month_summary = dfts.groupby(lambda x : x.year * 100 + x.month).count()\nloan_count_summary = year_month_summary['issue_d']\n\nprint loan_count_summary\n\n\nplt.plot(loan_count_summary)\nplt.ylabel(\"Number of Loans\")\nplt.xlabel(\"Month\")\nplt.show()\n\n### data is not stationary; transform by getting the difference\nloan_count_sum_diff = loan_count_summary.diff()\nplt.plot(loan_count_sum_diff)\nplt.ylabel(\"Number of Loans\")\nplt.xlabel(\"Difference\")\nplt.show()\n\nsm.graphics.tsa.plot_acf(loan_count_summary) #Autocorrelation\n\nplt.show()\n\nsm.graphics.tsa.plot_pacf(loan_count_summary) #Partial Autocorrelation\n\nplt.show()\n\nsm.graphics.tsa.plot_acf(loan_count_sum_diff) #Autocorrelation\n\nplt.show()\n\nsm.graphics.tsa.plot_pacf(loan_count_sum_diff) #Partial Autocorrelation\n\nplt.show()\n\n### Yes there are autocorrelated structures\n# The autocorrelations is quickly decaying but with persistent partial autocorrelations therefore the model is best\n# served by an MA terms to match the lags of significant autocorrelations." }, { "alpha_fraction": 0.6467532515525818, "alphanum_fraction": 0.6893506646156311, "avg_line_length": 28.15151596069336, "blob_id": "1f4fff5746dff86b8e88efc7ab02671417734b42", "content_id": "57f924b41c1e4c01123f1e498a2c8ea84ca1d346", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1925, "license_type": "no_license", "max_line_length": 131, "num_lines": 66, "path": "/logistic_regression.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport statsmodels.api as sm\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom numpy import loadtxt, where\nfrom pylab import scatter, show, legend, xlabel, ylabel\n\n# Function for log regression: equations : p(x) = 1/(1 + e^(intercept + 0.087423(FicoScore) ? 0.000174(LoanAmount))\ndef logistic_function(FicoScore, LoanAmount, threshold, coeff, ):\n interest_rate = -(coeff[2]) - (coeff[1]*FicoScore) -(coeff[0]*LoanAmount)\n prob = (1 / (1+ interest_rate**(coeff[2] + coeff[1] * FicoScore + coeff[0] * LoanAmount)))\n lines=plt.plot()\n if prob > threshold:\n p = 1\n else:\n p = 0\n return prob, p\n\n\nloansData = pd.read_csv('/Users/jenniferconnolly/Documents/ThinkFul/projects/graphs/loansData_clean.csv')\n\nCleanIR = loansData['Interest.Rate'].map(lambda a: round(float(a.rstrip('%')) / 100, 4))\nt = CleanIR[0:5]\n# print t\n\nCleanLL = loansData['Loan.Length'].map(lambda a: round(int(a.rstrip('months'))))\nu = CleanLL[0:5]\n# print u\n\nCleanFR_a = loansData['FICO.Range'].map(lambda a: a.split('-')[0])\nv = CleanFR_a[0:5]\n# print v\n\nloansData['IR_TF'] = (CleanIR.map(lambda x: 0 if x < .12 else 1))\n\nloansData['intercept'] = 1.0\n\ndf = pd.DataFrame(loansData)\n\ndf['FICO.SCORE'] = (CleanFR_a.astype(int))\n\nind_vars = ['Amount.Funded.By.Investors', 'FICO.SCORE', 'intercept']\nprint ind_vars\n\nlogit = sm.Logit(loansData['IR_TF'], loansData[ind_vars])\nresult = logit.fit()\nprint result\ncoeff = result.params\nprint coeff\nthreshold = 0.7\n\nprob = logistic_function(720, 10000,threshold ,coeff)[0]\npredict =logistic_function(720, 10000,threshold ,coeff)[1]\n\n\nprint prob\nprint predict\n\n\n\nprint \"The probability of getting a 12% or less interest loan of $10,000 with a 720 FICO score is: {0}% \".format(float(prob * 100))\n\nif predict ==1:\n print \"You will likely get your loan funded if the threshold is 70%\"\nelse:\n print \"You loan will likely not be funded if the threshold is 70%\"\n\n" }, { "alpha_fraction": 0.613043487071991, "alphanum_fraction": 0.6486956477165222, "avg_line_length": 19.872726440429688, "blob_id": "c63c90058586f81c57b850ad48d584306e8d5d06", "content_id": "1611ab5be9261dbfcbd043fa6ec0984a0a10c563", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1150, "license_type": "no_license", "max_line_length": 100, "num_lines": 55, "path": "/prob.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import collections\nimport scipy\nimport scipy.stats\nimport matplotlib.pyplot as plt\nimport os\n\ntestlist = [1, 4, 5, 6, 9, 9, 9]\nx = [1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4, 4, 4, 4, 5, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 9, 9]\n\nprint \"start testlist summary\"\nc = collections.Counter(testlist)\ncount_sum = sum(c.values())\n\nfor k, v in c.iteritems():\n print \"The frequency of number \" + str(k) + \" is \" + str(float(v) / count_sum)\n\nplt.boxplot(testlist)\n\n\nplt.savefig(str('box_testlist.jpeg'))\nplt.show()\n\nplt.hist(testlist, histtype='bar')\nplt.savefig(str('hist_testlist.jpeg'))\nplt.show()\n\nplt.figure()\n\ngraph1 = scipy.stats.probplot(testlist, dist=\"norm\", plot=plt)\n\nplt.savefig(str('qq_testlist.jpeg'))\nplt.show()\n\n\nprint \"start x data summary\"\nz = collections.Counter(x)\ncount_sum = sum(z.values())\n\nfor k, v in z.iteritems():\n print \"The frequency of number \" + str(k) + \" is \" + str(float(v) / count_sum)\n\nplt.boxplot(x)\n\nplt.savefig(str('box_x.jpeg'))\nplt.show()\n\nplt.hist(x, histtype='bar')\n\nplt.savefig(str('hist_x.jpeg'))\nplt.show()\n\ngraph2 = scipy.stats.probplot(x, dist=\"norm\", plot=plt)\n\nplt.savefig(str('qq_x.jpeg'))\nplt.show()\n\n\n" }, { "alpha_fraction": 0.6761904954910278, "alphanum_fraction": 0.7020407915115356, "avg_line_length": 20.58823585510254, "blob_id": "c4a55aa824b1fcedf32b9412b5a18bd068ac5f40", "content_id": "4a976dd40a51ffc9fc4296e05b5bdd4135b89109", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1470, "license_type": "no_license", "max_line_length": 93, "num_lines": 68, "path": "/linear_regression.py", "repo_name": "jconnolly814/ThinkFull", "src_encoding": "UTF-8", "text": "import numpy as np\nimport pandas as pd\nfrom pandas.tools.plotting import scatter_matrix\n\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm\n\nloansData = pd.read_csv('https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')\n# Drop null rows\nloansData.dropna(inplace=True)\n\n\na=loansData['Interest.Rate'][0:5]\nb =loansData['Loan.Length'][0:5]\nc = loansData['FICO.Range'][0:5]\n#print c\n\nCleanIR = loansData['Interest.Rate'].map(lambda a: round(float(a.rstrip('%')) / 100, 4))\nt=CleanIR[0:5]\n#print t\n\nCleanLL = loansData['Loan.Length'].map(lambda a: round(int(a.rstrip('months'))))\nu= CleanLL[0:5]\n#print u\n\nCleanFR_a = loansData['FICO.Range'].map(lambda a: a.split('-')[0])\nv= CleanFR_a[0:5]\n#print v\n\ndf = pd.DataFrame(loansData)\ndf['FICO.SCORE']= (CleanFR_a.astype(int))\n\n#print df\n\nhist = df['FICO.SCORE'].plot(kind ='hist')\nplt.show()\n\n\nj=scatter_matrix(df, alpha=0.05, figsize=(10,10))\nplt.show()\nk= pd.scatter_matrix(df, alpha=0.05, figsize=(10,10), diagonal='hist')\nplt.show()\n\n\n#df.to_csv('C:\\Users\\JConno02\\projects\\graphs\\loansData_clean.csv', header=True, index=False)\n\nintrate = CleanIR\nloanamt = df['Amount.Requested']\nfico = df['FICO.SCORE']\n\n\n\ny = np.matrix(intrate).transpose()\nprint y\n# The independent variables shaped as columns\nx1 = np.matrix(fico).transpose()\nx2 = np.matrix(loanamt).transpose()\n\nx = np.column_stack([x1,x2])\nprint x\n\nX = sm.add_constant(x)\nmodel = sm.OLS(y,X)\nf = model.fit()\n\noutput = f.summary()\n\nprint output\n\n\n" } ]

9

archius11/checkio-mission-four-to-the-floor

https://github.com/archius11/checkio-mission-four-to-the-floor

98595b94350a5b403ba478ad114a53b5acdf9d1b

7c1140eb891751dbba3a88c99a74054edb13ea1d

d39c1f94a2674bb993a77dad200219be577feccd

refs/heads/master

2020-12-11T14:12:44.834106

2019-12-12T09:44:57

233,870,226

1

0

null

2020-01-14T15:17:43

2019-12-12T09:45:03

2019-12-12T09:44:59

null

[ { "alpha_fraction": 0.2836935222148895, "alphanum_fraction": 0.29404059052467346, "avg_line_length": 33.39189147949219, "blob_id": "a5f02e7bd1503d999a1bfe302d13fb3e59b829ac", "content_id": "c13fe063c12c7d0d9a2b19c1a0b3103de5131798", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 7635, "license_type": "no_license", "max_line_length": 74, "num_lines": 222, "path": "/editor/animation/init.js", "repo_name": "archius11/checkio-mission-four-to-the-floor", "src_encoding": "UTF-8", "text": "//Dont change it\n//Dont change it\nrequirejs(['ext_editor_io', 'jquery_190', 'raphael_210'],\n function (extIO, $) {\n function fourToTheFloorCanvas(dom, data) {\n\n if (! data) {\n return\n }\n\n const input = data.in\n\n /*----------------------------------------------*\n *\n * attr\n *\n *----------------------------------------------*/\n const attr = {\n rect: {\n background: {\n 'fill': 'black',\n },\n },\n axis: {\n 'stroke-width': '0.7px',\n 'stroke': 'white',\n },\n circle: {\n center: {\n 'stroke-width': '1px',\n 'stroke': 'white',\n 'fill': 'white',\n },\n surface: {\n 'stroke-width': 0,\n 'fill': '#545454',\n 'opacity': '0.7',\n },\n },\n rectangle: {\n 'stroke-width': 1.5,\n 'stroke': 'white',\n },\n scale: {\n figure: {\n h: {\n 'fill': 'white',\n 'stroke': 'white',\n 'stroke-width': 0,\n },\n v: {\n 'fill': 'white',\n 'stroke': 'white',\n 'stroke-width': 0,\n 'text-anchor': 'end',\n },\n },\n line: {\n 'stroke-width': '0.7px',\n 'stroke': 'white',\n },\n },\n }\n\n /*----------------------------------------------*\n *\n * paper\n *\n *----------------------------------------------*/\n const [os_l, os_r, os_t, os_b] = [25, 15, 10, 15]\n const [os_w, os_h] = [os_l+os_r, os_t+os_b]\n const graph_length = 300\n const paper = Raphael(dom, graph_length+os_w, \n graph_length+os_h, 0, 0);\n\n /*----------------------------------------------*\n *\n * calculate values\n *\n *----------------------------------------------*/\n const [rectangle, circles] = input\n const [w, h] = rectangle\n let [max_x, max_y, min_x, min_y] = [0, 0, 0, 0]\n\n for (let [cx, cy, r] of circles) {\n max_x = Math.max(cx+r, max_x)\n max_y = Math.max(cy+r, max_y)\n min_x = Math.min(cx-r, min_x)\n min_y = Math.min(cy-r, min_y)\n }\n\n const right = Math.max(max_x, w)\n const left = Math.min(min_x, 0)\n const top = Math.max(max_y, h)\n const bottom = Math.min(min_y, 0)\n const pos_max = Math.max(right, top)\n const neg_min = Math.min(0, left, bottom) * -1\n\n // scale\n let grain = 0\n\n if (pos_max >= 1000) {\n grain = 500\n } else if (pos_max >= 100) {\n grain = 50\n } else if (pos_max >= 10) {\n grain = 5\n } else {\n grain = 1\n }\n\n const scale_max = Math.ceil(pos_max / (grain*2)) * grain*2\n const scales = [scale_max / 2, scale_max]\n\n const margin = grain * 0.3\n const graph_neg_size\n = Math.ceil(neg_min / (grain)) * grain + margin\n const graph_pos_size\n = Math.ceil(pos_max / (grain)) * grain + margin\n\n // ratio\n const ratio = graph_length / (graph_neg_size + graph_pos_size)\n\n /*----------------------------------------------*\n *\n * background rect\n *\n *----------------------------------------------*/\n paper.rect(os_l, os_t, graph_length, graph_length).attr(\n attr.rect.background)\n\n /*----------------------------------------------*\n *\n * circles (surface)\n *\n *----------------------------------------------*/\n for (const [cx, cy, r] of circles) {\n paper.circle(\n os_l+(graph_neg_size+cx)*ratio,\n os_t+(graph_pos_size-cy)*ratio, r*ratio).attr(\n attr.circle.surface)\n }\n\n /*----------------------------------------------*\n *\n * axis\n *\n *----------------------------------------------*/\n paper.path(['M', os_l, os_t+graph_pos_size*ratio,\n 'h', graph_length]).attr(attr.axis)\n paper.path(['M', os_l+graph_neg_size*ratio, os_t,\n 'v', graph_length]).attr(attr.axis)\n\n /*----------------------------------------------*\n *\n * rectangle\n *\n *----------------------------------------------*/\n paper.rect(\n os_l+(graph_neg_size*ratio),\n os_t+(graph_pos_size-h)*ratio, w*ratio, h*ratio).attr(\n attr.rectangle)\n\n /*----------------------------------------------*\n *\n * circles (center)\n *\n *----------------------------------------------*/\n for (const [cx, cy, r] of circles) {\n paper.circle(\n os_l+(graph_neg_size+cx)*ratio,\n os_t+(graph_pos_size-cy)*ratio, 1).attr(\n attr.circle.center)\n }\n\n /*----------------------------------------------*\n *\n * scale figures\n *\n *----------------------------------------------*/\n // 0\n paper.text(os_l+(graph_neg_size)*ratio-5,\n os_t+graph_pos_size*ratio+10, 0).attr(\n attr.scale.figure.v)\n\n for (const s of scales) {\n // horizontal\n paper.text(os_l+(graph_neg_size+s)*ratio,\n os_t+graph_pos_size*ratio+10, s).attr(\n attr.scale.figure.h)\n paper.path(['M', os_l+(graph_neg_size+s)*ratio,\n os_t+graph_pos_size*ratio, 'v', 5]).attr(\n attr.scale.line)\n\n // vertical\n paper.text(os_l+(graph_neg_size)*ratio-5,\n os_t+(graph_pos_size-s)*ratio, s).attr(\n attr.scale.figure.v)\n paper.path(['M', os_l+(graph_neg_size)*ratio,\n os_t+(graph_pos_size-s)*ratio, 'h', -4]).attr(\n attr.scale.line)\n }\n }\n\n var $tryit;\n\n var io = new extIO({\n multipleArguments: true,\n functions: {\n python: 'is_covered',\n js: 'isCovered'\n },\n animation: function($expl, data){\n fourToTheFloorCanvas(\n $expl[0],\n data\n )\n }\n });\n io.start();\n }\n);\n" }, { "alpha_fraction": 0.2821376323699951, "alphanum_fraction": 0.4153733551502228, "avg_line_length": 31.836538314819336, "blob_id": "5feb691f222ed93b1f673703f9093b9c17c104cf", "content_id": "fe691deae3c723750b243cde8c90b88dc2847707", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6830, "license_type": "no_license", "max_line_length": 111, "num_lines": 208, "path": "/verification/tests.py", "repo_name": "archius11/checkio-mission-four-to-the-floor", "src_encoding": "UTF-8", "text": "from math import sqrt\nfrom itertools import combinations\nfrom collections import defaultdict\nfrom random import choice\n\n\ndef judge(room, sensors):\n corners = (0, 0), (0, room[1]), (room[0], 0), (room[0], room[1])\n uncovered_dots = defaultdict(int)\n [uncovered_dots.update({d: 0}) for d in corners]\n\n if len(sensors) == 1: return all([is_in_range(d, sensors[0]) for d in uncovered_dots])\n\n for s1, s2 in combinations(sensors, 2):\n dots = sensors_intersections(s1, s2)\n if dots:\n for x, y in dots:\n if 0 <= x <= room[0] and 0 <= y <= room[1]: uncovered_dots[(x, y)] = 0\n\n for sensor in sensors:\n intersections = borders_intersections(room, sensor)\n for dot in intersections:\n uncovered_dots[dot] = 0\n\n for sensor in sensors:\n for dot in uncovered_dots:\n uncovered_dots[dot] += 1 if is_in_range(dot, sensor) else 0\n\n [print(k, v) for k, v in uncovered_dots.items()]\n for (x, y), n in uncovered_dots.items():\n if n < 1: return False\n if n == 1 and (x, y) not in corners: return False\n if n == 2 and 0 < x < room[0] and 0 < y < room[1]: return False\n return True\n\n\ndef is_in_range(dot, sensor):\n x, y, r = sensor\n x0, y0 = dot\n return sqrt((x-x0)**2 + (y-y0)**2) < r + 10e-6\n\n\ndef borders_intersections(room, sensor):\n dots = []\n xr, yr = room\n x, y, r = sensor\n if x <= r:\n dy = round(sqrt(r**2 - x**2),6)\n if y + dy <= yr: dots.append((0, y+dy))\n if y - dy >= 0: dots.append((0, y-dy))\n if x + r >= xr:\n dy = round(sqrt(r**2 - (xr-x)**2), 6)\n if y + dy <= yr: dots.append((xr, y+dy))\n if y - dy >= 0: dots.append((xr, y-dy))\n if y <= r:\n dx = round(sqrt(r**2 - y**2), 6)\n if x + dx <= xr: dots.append((x+dx, 0))\n if x - dx >= 0: dots.append((x-dx, 0))\n if y + r >= yr:\n dx = round(sqrt(r**2 - (yr-y)**2), 6)\n if x + dx <= xr: dots.append((x+dx, yr))\n if x - dx >= 0: dots.append((x-dx, yr))\n return dots\n\n\ndef sensors_intersections(sensor1, sensor2):\n dots = []\n x1, y1, r1 = sensor1\n x2, y2, r2 = sensor2\n d = round(sqrt((x2-x1)**2 + (y2-y1)**2), 6)\n if r1 + r2 >= d > abs(r1-r2):\n\n a = (r1**2 - r2**2 + d**2)/(2*d)\n h = sqrt(r1**2 - a**2)\n\n x_d = x1 + a*(x2 - x1)/d\n y_d = y1 + a*(y2 - y1)/d\n\n x_int_1 = round(x_d + h*(y2 - y1)/d, 6)\n y_int_1 = round(y_d - h*(x2 - x1)/d, 6)\n\n x_int_2 = round(x_d - h*(y2 - y1)/d, 6)\n y_int_2 = round(y_d + h*(x2 - x1)/d, 6)\n\n dots.extend([(x_int_1, y_int_1), (x_int_2, y_int_2)])\n return dots\n\n\ndef make_random_test_test(n):\n tests = []\n for _ in range(n):\n h = choice(range(100, 1100, 50))\n w = h * choice(range(1, 5))\n x_interval = list(range(0, w+5, w//20))\n y_interval = list(range(0, h+5, h//20))\n r_interval = list(range(h//10, h, h//20))\n sensors_num = choice(range(5, 11))\n sensors = []\n for _ in range(sensors_num):\n x = choice(x_interval)\n x_interval.remove(x)\n y = choice(y_interval)\n y_interval.remove(y)\n r = choice(r_interval)\n r_interval.remove(r)\n sensors.append([x, y, r])\n tests.append(\n {\n \"input\": [[w, h], sensors],\n \"answer\": judge([w, h], sensors)\n }\n )\n return tests\n\n\nTESTS = {\n \"Basics\": [\n {\n \"input\": [[200, 150], [[100, 75, 130]]],\n \"answer\": True\n },\n {\n \"input\": [[200, 150], [[50, 75, 100], [150, 75, 100]]],\n \"answer\": True\n },\n {\n \"input\": [[200, 150], [[50, 75, 100], [150, 25, 50], [150, 125, 50]]],\n \"answer\": False,\n },\n {\n \"input\": [[200, 150], [[100, 75, 100], [0, 40, 60], [0, 110, 60], [200, 40, 60], [200, 110, 60]]],\n \"answer\": True\n },\n {\n \"input\": [[200, 150], [[100, 75, 100], [0, 40, 50], [0, 110, 50], [200, 40, 50], [200, 110, 50]]],\n \"answer\": False\n },\n {\n \"input\": [[200, 150], [[100, 75, 110], [105, 75, 110]]],\n \"answer\": False\n },\n {\n \"input\": [[200, 150], [[100, 75, 110], [105, 75, 20]]],\n \"answer\": False\n },\n {\n \"input\": [[3, 1], [[1, 0, 2], [2, 1, 2]]],\n \"answer\": True\n },\n {\n \"input\": [[30, 10], [[0, 10, 10], [10, 0, 10], [20, 10, 10], [30, 0, 10]]],\n \"answer\": True\n },\n {\n \"input\": [[30, 10], [[0, 10, 8], [10, 0, 7], [20, 10, 9], [30, 0, 10]]],\n \"answer\": False\n }\n ],\n \"Extra\": [\n {\n \"input\": [[8, 6], [[4, 3, 5]]],\n \"answer\": True\n },\n {\n \"input\": [[2000, 1000], [[0, 0, 500], [500, 0, 500], [1000, 0, 500], [1500, 0, 500],\n [2000, 0, 500], [0, 500, 500], [500, 500, 500], [1000, 500, 500],\n [1500, 500, 500], [2000, 500, 500]]],\n \"answer\": False\n },\n {\n \"input\": [[4000, 1000], [[0, 500, 1600], [2000, 100, 500], [2100, 900, 500],\n [2500, 200, 500], [2600, 800, 500], [4000, 0, 1200]]],\n \"answer\": False\n },\n {\n \"input\": [[4000, 1000], [[0, 500, 1600], [2000, 100, 500], [2100, 900, 500],\n [2500, 200, 500], [2600, 800, 500], [4000, 0, 1200], [4000, 500, 200]]],\n \"answer\": False\n },\n {\n \"input\": [[4000, 1000], [[0, 500, 1600], [2000, 100, 500], [2100, 900, 500],\n [2500, 200, 500], [2600, 800, 500], [4000, 500, 1200], [1600, 500, 600]]],\n \"answer\": True\n },\n {\n \"input\": [[4000, 1000], [[0, 500, 1600], [2000, 100, 500], [2100, 900, 500],\n [2500, 200, 500], [2600, 800, 500], [4000, 600, 1200], [1600, 500, 600]]],\n \"answer\": False\n },\n {\n \"input\": [[100, 100], [[50, 50, 65], [25, 25, 25], [25, 75, 25], [75, 25, 25], [75, 75, 25]]],\n \"answer\": False\n },\n {\n \"input\": [[100, 100], [[50, 50, 65], [5, 5, 25], [5, 95, 25], [95, 5, 25], [95, 95, 25]]],\n \"answer\": True\n },\n {\n \"input\": [[800, 800], [[0, 0, 500], [0, 800, 500], [800, 0, 500], [800, 800, 500]]],\n \"answer\": False\n },\n {\n \"input\": [[800, 800], [[0, 0, 570], [0, 800, 500], [800, 0, 500], [800, 800, 570]]],\n \"answer\": True\n }\n ],\n \"Randoms\": make_random_test_test(10)\n}\n" }, { "alpha_fraction": 0.7777777910232544, "alphanum_fraction": 0.7777777910232544, "avg_line_length": 35, "blob_id": "8cc379f595f51499cf3c3415c32f34af6eeb86d7", "content_id": "1433d7d26ad9715c662997ee16afb37e0c8ea4a7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 36, "license_type": "no_license", "max_line_length": 35, "num_lines": 1, "path": "/README.md", "repo_name": "archius11/checkio-mission-four-to-the-floor", "src_encoding": "UTF-8", "text": "# checkio-mission-four-to-the-floor\n" } ]

3

AndrzejStecyk/Jars

https://github.com/AndrzejStecyk/Jars

6cdc99e939577e154c4357fa362b83002fa7fa6a

d2a5f2f6d64355a8d56ec6194e78c5ad5a68fd57

748e322fc416e28d3aa403db04116e17787ec7d4

refs/heads/master

2023-04-04T20:54:15.834325

2021-04-25T17:38:41

361,395,374

0

null

[ { "alpha_fraction": 0.8017241358757019, "alphanum_fraction": 0.8017241358757019, "avg_line_length": 57, "blob_id": "5b937dda2ceb9cde66b29aec58cd2709b274a5cf", "content_id": "09a235084f47551d19101636fba00f6df75d7848", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 241, "license_type": "no_license", "max_line_length": 138, "num_lines": 4, "path": "/README.md", "repo_name": "AndrzejStecyk/Jars", "src_encoding": "UTF-8", "text": "# Jars\n\nŻeby skrypt działał poprawnie, na maszynie lokalniej musi istnieć instancja mongoDb.\nZałożenie jest takie, że przy użyciu opcji TRANSFER używamy opcji --source i --destination a do DEPOSIT|WITHDRAW|LIST|HISTORY opcji --jar.\n" }, { "alpha_fraction": 0.5769087672233582, "alphanum_fraction": 0.580260694026947, "avg_line_length": 26.397958755493164, "blob_id": "a06c095bb6e0525c21b5050eccbe9ab4c0603b7f", "content_id": "97b919ba98d6058037691af1bbf99487fa8294c5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2685, "license_type": "no_license", "max_line_length": 102, "num_lines": 98, "path": "/db.py", "repo_name": "AndrzejStecyk/Jars", "src_encoding": "UTF-8", "text": "import pymongo\nimport datetime\nfrom bson.objectid import ObjectId\nfrom bson import errors as bson_errors\n\nmyclient = pymongo.MongoClient(\"mongodb://localhost:27017/\")\nmydb = myclient['TestTask']\n\n\ndef add_hitory(source, dest, operation, amount):\n mycol = mydb[\"history\"]\n data = {'source': source,\n 'destination': dest,\n 'operation': operation,\n 'amount': amount,\n 'date': datetime.datetime.now()}\n mycol.insert_one(data)\n\n\ndef create_jar(currency=None):\n mycol = mydb[\"jars\"]\n id = mycol.insert_one({'balance': 0,\n 'currency': currency})\n add_hitory(None, str(id.inserted_id), \"Creation\", 0)\n return id.inserted_id\n\n\ndef deposit(jar_id, amount, transfer=False):\n mycol = mydb[\"jars\"]\n jar = mycol.find_one(ObjectId(jar_id))\n final_amount = amount + jar['balance']\n mycol.find_one_and_update(\n {\"_id\": ObjectId(jar_id)},\n {\"$set\": {\"balance\": final_amount}\n }, upsert=True\n )\n if not transfer:\n add_hitory(None, jar_id, 'Deposit', amount)\n\n\ndef withdraw(jar_id, amount, transfer=False):\n mycol = mydb[\"jars\"]\n try:\n jar = mycol.find_one(ObjectId(jar_id))\n except bson_errors.InvalidId:\n print(\"Invalid Jar ID\")\n exit(-1)\n\n if jar['balance'] >= amount:\n final_amount = jar['balance'] - amount\n mycol.find_one_and_update(\n {\"_id\": ObjectId(jar_id)},\n {\"$set\": {\"balance\": final_amount}\n }, upsert=True\n )\n if not transfer:\n add_hitory(None, jar_id, 'Withdraw', amount)\n else:\n return \"Jar has not enough funds. Operation is canceled\"\n\n\ndef transfer(source, destination, amount):\n withdraw(source, amount, transfer=True)\n deposit(destination, amount, transfer=True)\n add_hitory(source, destination, \"Transfer\", amount)\n\n\ndef get_jar(jar_id=None):\n query = None\n mycol = mydb[\"jars\"]\n list_of_jars = list()\n try:\n if jar_id:\n query = {'_id': ObjectId(jar_id)}\n except bson_errors.InvalidId:\n print(\"Invalid Jar ID\")\n exit(-1)\n\n results = mycol.find(query)\n for x in results:\n list_of_jars.append({'id': str(x['_id']), 'balance': x['balance'], 'currency': x['currency']})\n return list_of_jars\n\n\ndef get_history(jar_id=None):\n query = None\n mycol = mydb[\"history\"]\n history = list()\n if jar_id:\n query = {'$or': [{'destination': jar_id}, {'source': jar_id}]}\n\n results = mycol.find(query)\n\n for x in results:\n del x['_id']\n x['date'] = x['date'].strftime(\"%d-%b-%Y (%H:%M:%S.%f)\")\n history.append(x)\n return history\n" }, { "alpha_fraction": 0.5785991549491882, "alphanum_fraction": 0.5817933082580566, "avg_line_length": 32.45801544189453, "blob_id": "5f1ecf40346c27223422f1afe1590d443b84ae0e", "content_id": "27423aa5c4dccdc1d83d5839d53cdb6c3c8b842a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4383, "license_type": "no_license", "max_line_length": 93, "num_lines": 131, "path": "/jars.py", "repo_name": "AndrzejStecyk/Jars", "src_encoding": "UTF-8", "text": "import db\nfrom optparse import OptionParser\nimport json\n\n\ndef create_a_jar(currency):\n if currency:\n db.create_jar(currency.upper())\n else:\n db.create_jar()\n\n\ndef make_a_deposit(jar_id, amount, currency):\n if amount and jar_id and len(jar_id) == 24:\n jar = db.get_jar(jar_id)\n try:\n if currency:\n if jar[0]['currency'] == currency.upper():\n db.deposit(jar_id, abs(amount))\n print(\"Deposit successful\")\n else:\n print(\"Currency do not match currency of destination account\")\n elif not jar[0]['currency']:\n db.deposit(jar_id, abs(amount))\n print(\"Deposit successful\")\n else:\n print(\"Currency do not match currency of destination account\")\n except IndexError:\n print(\"Jar ID does not exist\")\n else:\n parser.print_help()\n\n\ndef make_a_withdraw(jar_id, amount):\n if amount and jar_id and len(jar_id) == 24:\n db.withdraw(jar_id, abs(amount))\n print(\"Withdraw successful\")\n else:\n parser.print_help()\n\n\ndef make_a_transfer(source, destination, amount):\n if amount and source and destination and len(source) == 24 and len(\n destination) == 24:\n source_jar = db.get_jar(source)\n dest_jar = db.get_jar(destination)\n try:\n if source_jar[0]['currency'] == dest_jar[0]['currency']:\n db.transfer(source, destination, abs(amount))\n print(\"Transfer successful\")\n else:\n print(\"Accounts have different currencies. Operation Canceled\")\n except IndexError:\n print(\"Jar ID does not exist\")\n\n\ndef show_history(jar_id, order_by, desc):\n if jar_id and len(jar_id) == 24:\n hist_data = db.get_history(jar_id)\n else:\n hist_data = db.get_history(jar_id)\n\n if order_by:\n sorted_hist_data = sorted(hist_data, key=lambda i: i[order_by.lower()], reverse=desc)\n print(json.dumps(sorted_hist_data))\n else:\n print(json.dumps(hist_data))\n\n\nif __name__ == '__main__':\n parser = OptionParser()\n\n parser.add_option(\"-o\", \"--operation\", dest=\"operation\", type=str,\n help=\"[CREATE|DEPOSIT|WITHDRAW|TRANSFER|LIST|HISTORY]\")\n\n parser.add_option(\"-j\", \"--jar\", dest=\"jar_id\", type=str,\n help=\"id of the jar for\", default=False)\n\n parser.add_option(\"-a\", \"--amount\", dest=\"amount\", type=int,\n help=\"Amount of funds for specified operation\")\n\n parser.add_option(\"-s\", \"--source\", dest=\"source\", type=str,\n help=\"Source jar for funds transfer\")\n\n parser.add_option(\"-d\", \"--destination\", dest=\"destination\", type=str,\n help=\"Destination jar for funds transfer\")\n\n parser.add_option(\"-c\", \"--currency\", dest=\"currency\", type=str,\n help=\"PLN|USD|EUR\")\n\n parser.add_option(\"--order_by\", dest=\"order_by\", type=str,\n help=\"|ID|OPERATION|AMOUNT\")\n\n parser.add_option(\"--desc\", dest=\"desc\", action=\"store_true\", default=False,\n help=\"Flag enables descending sorting\")\n\n (options, args) = parser.parse_args()\n\n if not options.operation:\n parser.print_help()\n exit()\n\n allowed_currencies = ['PLN', 'USD', 'EUR']\n allowed_order_by = ['ID', 'OPERATION', 'AMOUNT']\n\n if options.currency and options.currency.upper() not in allowed_currencies:\n parser.print_help()\n exit()\n if options.order_by and options.order_by.upper() not in allowed_order_by:\n parser.print_help()\n exit()\n\n if options.operation.upper() == \"CREATE\":\n create_a_jar(options.currency)\n\n elif options.operation.upper() == \"DEPOSIT\":\n make_a_deposit(options.jar_id, options.amount, options.currency)\n\n elif options.operation.upper() == \"WITHDRAW\":\n make_a_withdraw(options.jar_id, options.amount)\n\n elif options.operation.upper() == \"TRANSFER\":\n make_a_transfer(options.source, options.destination, options.amount)\n\n elif options.operation.upper() == \"LIST\":\n print(json.dumps(db.get_jar()))\n\n elif options.operation.upper() == \"HISTORY\":\n show_history(options.jar_id, options.order_by, options.desc)\n else:\n parser.print_help()\n" }, { "alpha_fraction": 0.6304849982261658, "alphanum_fraction": 0.6304849982261658, "avg_line_length": 15.037036895751953, "blob_id": "5f2ac997567d306be1041e02c02271b95d5ab697", "content_id": "f5cefab596175e6e64affcadc6f44aa5595b526c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 433, "license_type": "no_license", "max_line_length": 35, "num_lines": 27, "path": "/test_jars.py", "repo_name": "AndrzejStecyk/Jars", "src_encoding": "UTF-8", "text": "from unittest import TestCase\nimport db\n\n\nclass Test(TestCase):\n def test_create_a_jar(self):\n self.fail()\n\n\nclass Test(TestCase):\n def test_make_a_deposit(self):\n self.fail()\n\n\nclass Test(TestCase):\n def test_make_a_withdraw(self):\n self.fail()\n\n\nclass Test(TestCase):\n def test_make_a_transfer(self):\n self.fail()\n\n\nclass Test(TestCase):\n def test_show_history(self):\n self.fail()\n" } ]

4

Nabarun21/CMS-ECAL_TPGAnalysis

https://github.com/Nabarun21/CMS-ECAL_TPGAnalysis

5e72de38a2a13e3ee80fdc3ccda87b3abda62251

4de52926331d6162e3b2eba3bf9a723f6975e12e

e1587e4483afcaf3b72a36be9f2d4df6c361e7fe

refs/heads/master

2021-05-31T16:25:53.350687

2016-03-31T16:10:40

null

0

null

[ { "alpha_fraction": 0.7222222089767456, "alphanum_fraction": 0.7626262903213501, "avg_line_length": 65, "blob_id": "0120e34a13ec4a764ed2445e04b61e7bf07f14cc", "content_id": "9dd6e6d3739badefba1144ad224531ea6946be6a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 198, "license_type": "no_license", "max_line_length": 155, "num_lines": 3, "path": "/TriggerAnalysis/python/__init__.py", "repo_name": "Nabarun21/CMS-ECAL_TPGAnalysis", "src_encoding": "UTF-8", "text": "#Automatically created by SCRAM\nimport os\n__path__.append(os.path.dirname(os.path.abspath(__file__).rsplit('/EcalPFG/TriggerAnalysis/',1)[0])+'/cfipython/slc6_amd64_gcc491/EcalPFG/TriggerAnalysis')\n" }, { "alpha_fraction": 0.6931216716766357, "alphanum_fraction": 0.7460317611694336, "avg_line_length": 17.899999618530273, "blob_id": "31ec58170ea623256c0897cd2d2e7ef4f672433b", "content_id": "753c18a62006ccf72505b4c1f09980f6c579bce4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Shell", "length_bytes": 189, "license_type": "no_license", "max_line_length": 88, "num_lines": 10, "path": "/Scripts/TriggerAnalysis/produceplotonCAFqueues.sh", "repo_name": "Nabarun21/CMS-ECAL_TPGAnalysis", "src_encoding": "UTF-8", "text": "#!/bin/bash\n\ncd /afs/cern.ch/work/n/ndev/CMSSW_7_5_6/src/CMS-ECAL_TPGAnalysis/Scripts/TriggerAnalysis\n\n\neval `scramv1 runtime -csh`\n\n./makeTrigPrimPlots.sh -r 254833 -a _test_package\n\nexit\n" }, { "alpha_fraction": 0.6881579160690308, "alphanum_fraction": 0.7197368144989014, "avg_line_length": 35.17460250854492, "blob_id": "057128b75b4cdf6303a4e12ac7aea7f4d4972b1c", "content_id": "a930e485b863a8d056897905a3fc08171e73a8fa", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2280, "license_type": "no_license", "max_line_length": 169, "num_lines": 63, "path": "/README.md", "repo_name": "Nabarun21/CMS-ECAL_TPGAnalysis", "src_encoding": "UTF-8", "text": "# CMS-ECAL_TPGAnalysis (8_0_2)\n\n##This houses the latest version of the Level 1 Ecal TPG analysis of CMS\n======================================================================\nHow to run:\n\nGet the CMSSW release area\n```bash\ncmsrel CMSSW_8_0_2\ncd CMSSW_8_0_2/src\ncmsenv\n```\n\nCheckout the repository\n```\ngit cms-init\ngit clone https://github.com/cms-ecal-L1TriggerTeam/CMS-ECAL_TPGAnalysis.git\nscram b\n``` \n\n\nBEFORE YOU RUN, you need to make sure you change the directory/path names to your directories or directories you want results in.\n\n `cd CMS-ECAL_TPGAnalysis/Scripts/TriggerAnalysis`\n\nIn particular the files you need to modify are : `mergeTPGAnalysis.sh; makeTrigPrimPlots.sh ; convertAllPlots.py ;produceplotonCAFqueues.sh `\n\n `cd ../../TPGPlotting/plots` and also modify path in `makeTPGPlots.sh`\n \n \n ###Running the analysis\n This has been tested on lxplus. After you have changed all the pathnames above:\n \n To run, e.g. on 266423 ZeroBias dataset\n ```./runTPGbatch_AAA.sh lxbatch 266423 /Cosmics/Commissioning2016-v1/RAW 80X_dataRun2_HLT_v6 -1 False```\n \n i.e.```./runTPGbatch_AAA.sh lxbatch run_number das_dataset_path Global_Tag Num_of_events_to_be_processed(-1 defaults to all events) MC_or_data(False for data)```\n \n This will run the jobs on the queue \"cmscaf1nd\", If you do not have permissions to run on the queue modify runTPGbatch_AAA.sh and change `queue=cmscaf1nd` appropiately\n \n To locally test if jobs are running:\n `cd log_and_results\n cmsRun runTPG_cfg_2.py`\n \n Once jobs are done, merge the output files.\n\n ```./mergeTPGAnalysis.sh -r 266423 -m log_and_results/266423-_ZeroBias_Run2015C-v1_RAW-batch/results/ -a testing```\n \n `-a` option is optional if u want run on the same set of data several times and want to call each merged output a different name\n\n To make plots:`./makeTrigPrimPlots.sh -r 266423 -a testing`\n\n The plots will be created in the folder: `Scripts/TriggerAnalysis/Commisioning2016`\n \n To make thumbnails: `python convertAllPlots.py -r 266423 -a testing_eg12`\n\n Copy it to your html_folder to look at it on the web.\n \n\n \nSomemore plotting options and instructions on this (partially outdated) twiki here:\n\nhttps://twiki.cern.ch/twiki/bin/viewauth/CMS/EcalPFGCode#Trigger_Primitive_Analysis\n\n" } ]

3

lunerip/Tarea-07

https://github.com/lunerip/Tarea-07

30c05fb2912146a938a8d4546b76020466e37485

f5eed57d87e9fad6f7fc4c37a573652eed39f9cd

bbf2eaef71f65f0e7ddc0316e67fbcd00d9adf84

refs/heads/master

2021-07-18T19:18:05.657860

2017-10-26T00:39:36

null

0

null

[ { "alpha_fraction": 0.4589524567127228, "alphanum_fraction": 0.4920133650302887, "avg_line_length": 31.835365295410156, "blob_id": "b44b0d8d91e39adcb2090bfb4d204299b9a92f68", "content_id": "4791835066328138571100cf265dd72bc8bc50d7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5402, "license_type": "no_license", "max_line_length": 118, "num_lines": 164, "path": "/Listas.py", "repo_name": "lunerip/Tarea-07", "src_encoding": "UTF-8", "text": "# encoding UTF-8\n# AUTOR: Luis Enrique Neri Pérez\n# DESCRIPCIÓN: Programa que muestra varios ejemplos donde se refleje que cada función funciona adecuadamente\n\n#PRIMER EJERCICIO ---------------------------------------------------------------------------------------------------\n\ndef ejercicio1(original):\n nueva = []\n acumulador = 0\n comprobador = sum(original)\n while not acumulador == comprobador:\n for dato in original:\n acumulador = acumulador + dato\n nueva.append(acumulador)\n print(\" -La lista\",original,\"devuelve\",nueva)\n\n\ndef funcion1():\n print(\"EJERCICIO #1: Regresa la suma acumulada de los datos\")\n print(\"--------------------------------------------------------------\")\n prueba1 = [1, 2, 3]\n ejercicio1(prueba1)\n prueba2 = [1, 2, 3, 4]\n ejercicio1(prueba2)\n prueba3 = []\n ejercicio1(prueba3)\n prueba4 = [5]\n ejercicio1(prueba4)\n print(\"\\n\")\n\n#SEGUNDO EJERCICIO ---------------------------------------------------------------------------------------------------\ndef ejercicio2(original):\n nueva = []\n nueva = nueva + original\n nueva.remove(nueva[0])\n nueva.remove(nueva[-1])\n print(\" -La lista\", original, \"devuelve\", nueva)\n\n\ndef funcion2():\n print(\"EJERCICIO #2: Regresa la lista sin el primer ni el último elemento \")\n print(\"--------------------------------------------------------------\")\n prueba1 = [1, 2, 3]\n ejercicio2(prueba1)\n prueba2 = [1, 2, 3, 4]\n ejercicio2(prueba2)\n prueba3 = [\"Primer\", \"Hola\", \"Mundo\", \"Último\"]\n ejercicio2(prueba3)\n prueba4 = [0,4,4,4,0]\n ejercicio2(prueba4)\n print(\"\\n\")\n\n#TERCER EJERCICIO ---------------------------------------------------------------------------------------------------\n\ndef ejercicio3(original):\n nueva = sorted(original)\n if original == nueva:\n print(\" -¿La lista\", original, \"está ordenada?:\", True)\n else:\n print(\" -¿La lista\", original, \"está ordenada?:\", False)\n\ndef funcion3():\n print(\"EJERCICIO #3: Regresa True si la lista está ordenenada o False si no lo está \")\n print(\"--------------------------------------------------------------------------------\")\n prueba1 = [1, 2, 3, 4]\n ejercicio3(prueba1)\n prueba2 = [\"A\", \"B\", \"C\", \"D\"]\n ejercicio3(prueba2)\n prueba3 = [2, 3, 1 ,5]\n ejercicio3(prueba3)\n prueba4 = [\"X\", \"B\", \"Y\", \"D\"]\n ejercicio3(prueba4)\n print(\"\\n\")\n\n#CUARTO EJERCICIO ---------------------------------------------------------------------------------------------------\n\ndef ejercicio4(prueba1, prueba2):\n original = list(prueba1.upper())\n nueva= list(prueba2.upper())\n nueva.reverse()\n if original == nueva:\n print(\" -¿La cadena\", prueba1, \"y\", prueba2, \"son anagramas?:\", True)\n else:\n print(\" -¿La cadena\" , prueba1, \"y\", prueba2 , \"son anagramas?:\", False)\n\ndef funcion4():\n print(\"EJERCICIO #4: Regresa True si las dos palabras son anagramas o False si no lo son \")\n print(\"--------------------------------------------------------------------------------\")\n prueba1 = \"Roma\"\n prueba2 = \"Amor\"\n ejercicio4(prueba1,prueba2)\n prueba1 = \"Hola\"\n prueba2 = \"Hello\"\n ejercicio4(prueba1, prueba2)\n prueba1 = \"Reto\"\n prueba2 = \"Oter\"\n ejercicio4(prueba1, prueba2)\n prueba1 = \"Pedro\"\n prueba2 = \"Peter\"\n ejercicio4(prueba1, prueba2)\n print(\"\\n\")\n\n#QUINTO EJERCICIO ---------------------------------------------------------------------------------------------------\n\ndef ejercicio5(original):\n for k in range(len(original)-1):\n if original.count(original[k]) >1:\n print(\" -¿La lista\" , original , \"repite elementos?:\", True)\n return True\n print(\" -¿La lista\", original, \"repite elementos?:\", False)\n return False\n\ndef funcion5():\n print(\"EJERCICIO #5: Regresa True si alguno de los datos están repetidos o False si no lo están \")\n print(\"------------------------------------------------------------------------------------------\")\n prueba1 = [1,2,3,4]\n ejercicio5(prueba1)\n prueba2 = [1,2,3,2]\n ejercicio5(prueba2)\n prueba3 = [\"a\", \"b\", \"c\", \"d\"]\n ejercicio5(prueba3)\n prueba4 = []\n ejercicio5(prueba4)\n print(\"\\n\")\n\n#SEXTO EJERCICIO ---------------------------------------------------------------------------------------------------\n\ndef ejercicio6(original):\n duplicado = []\n duplicado = duplicado + original\n for indice in range(len(original)-1):\n datoRep = original[indice]\n if original.count(datoRep) >1:\n while not original.count(datoRep) == 0:\n original.remove(datoRep)\n original.insert(indice,datoRep)\n print(\"Para la lista\", duplicado, \"se regresa\", original)\n\ndef funcion6():\n print(\"EJERCICIO #6: Si un elemento en la lista se repite, este de quitara dejando solo uno\")\n print(\"------------------------------------------------------------------------------------------\")\n prueba1 = [10, 30, 20, 30]\n ejercicio6(prueba1)\n prueba2 = [1,8,3,4,3]\n ejercicio6(prueba2)\n prueba2 = [1,2,3,4]\n ejercicio6(prueba2)\n prueba2 = [1,1]\n ejercicio6(prueba2)\n\n\n#Función Main que despliega todos los ejemplos del código\ndef main():\n print(\"TAREA 07: EJEMPLOS DE USO DE LISTAS\")\n print(\"\\n\")\n funcion1()\n funcion2()\n funcion3()\n funcion4()\n funcion5()\n funcion6()\n\n\nmain()" } ]

1

JeremySMorgan/2DGraphSearch

https://github.com/JeremySMorgan/2DGraphSearch

b5725a41a5f11a5fbd7f7a3c00c437df02808c70

4cea66bbd925bbcea1bca0e1ae15679947cc2788

99270a45001614d8db5a5aae3f52555b765fd1de

refs/heads/master

2018-04-29T06:03:42.344040

2017-11-28T23:05:09

92,177,718

11

0

null

[ { "alpha_fraction": 0.5574135780334473, "alphanum_fraction": 0.5672240853309631, "avg_line_length": 34.595237731933594, "blob_id": "516337888c3d9a91248660ec873c98bbf485c963", "content_id": "a0e03379a94c0eeae3d4cc6c803cb950376db4b1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4485, "license_type": "no_license", "max_line_length": 136, "num_lines": 126, "path": "/Shapes.py", "repo_name": "JeremySMorgan/2DGraphSearch", "src_encoding": "UTF-8", "text": "import pygame\nimport math\n\nclass RectangleSpace(object):\n\n def __init__(self,screen,x,y,x_width,y_width,passable,occupied,border_color,high_granularity):\n\n ''' x,y are absolute '''\n\n self.screen = screen\n\n self.x = x\n self.y = y\n\n self.x_center = int(x + (x_width/2))\n self.y_center = int(y + (y_width/2))\n\n self.x_width = x_width\n self.y_width = y_width\n\n self.border_color = border_color\n self.occupied = occupied\n self.passable = passable\n self.color = None\n\n self.high_granularity = high_granularity\n if not high_granularity:\n self.myfont = pygame.font.SysFont(\"monospace\", 15)\n\n self.label = None\n\n def update_state(self,occupied):\n if self.passable:\n self.occupied = occupied\n else:\n raise ValueError(\"Cannot call update_state on unpassable Rectangle\")\n\n def setColor(self,color):\n self.color = color\n\n def draw(self):\n if self.color is None:\n raise ValueError(\"Cannot draw Rectangle, color has not been set\")\n pygame.draw.rect(self.screen, self.color, (self.x,self.y,self.x_width,self.y_width))\n if not self.high_granularity:\n pygame.draw.rect(self.screen, self.border_color, (self.x,self.y,self.x_width,self.y_width),1)\n\n if not self.label is None:\n if not self.high_granularity:\n self.screen.blit(self.label, (self.x_center-self.x_width/5, self.y_center-self.y_width/5))\n\n if self.occupied:\n circle_x = int(self.x + (self.x_width/2) )\n circle_y = int(self.y + (self.y_width/2) )\n pygame.draw.circle(self.screen, (0,0,0), (circle_x,circle_y), int(self.x_width/10), 0)\n\n def set_text(self,text):\n if not self.high_granularity:\n self.label = self.myfont.render(str(text), 1, (5,5,0))\n\n\n def __str__(self):\n ''' returns the index position of rectangle '''\n returnStr = \"<Rect object> ( XY -> (\"+str(int(self.x/self.x_width))+\",\"+str(int(self.y/self.y_width))+\"))\"\n return returnStr\n\n\nclass Arrow(object):\n\n def __init__(self,screen,srcRect,dstRect,color):\n\n # Pygame vars\n self.screen = screen\n\n self.xSrc = srcRect.x_center\n self.ySrc = srcRect.y_center\n self.xDst = dstRect.x_center\n self.yDst = dstRect.y_center\n\n self.color = color\n self.alive = True\n\n def update_state(self,alive):\n self.alive = alive\n\n def setColor(self,color):\n self.color = color\n\n # BUG: arrow oriented incorrectly when going left\n def draw(self):\n if self.alive:\n\n # line_deg is angle(deg) between slope of line and x axis (poited to the right)\n # arr_deg is the angle between the line and the arrow heads\n # A is length of one arrow 'head'\n # B is the proportion between length of A and length of line\n arr_deg = 20\n line_length = math.sqrt( (self.yDst - self.ySrc)**2 + (self.xDst - self.xSrc)**2 )\n line_deg = 0\n if (self.xDst-self.xSrc) == 0:\n if (self.yDst - self.ySrc) < 0:\n line_deg = -90\n else:\n line_deg = 90\n else:\n line_deg = 180 - math.degrees(math.atan((self.yDst - self.ySrc)/(self.xDst-self.xSrc)))\n\n B = 5\n A = line_length/B\n\n # for left arr head, (x-Acos(line_deg - arr_deg), y-Asin(line_deg - arr_deg))\n # for right arr head, (x-Asin((90-arr_deg) - line_deg), y-Acos((90-arr_deg) - line_deg)\n left_x = self.xDst - (A*math.cos(math.radians(line_deg - arr_deg )))\n left_y = self.yDst - (A*math.sin(math.radians(line_deg - arr_deg )))\n right_x = self.xDst - (A*math.sin(math.radians((90-arr_deg) - line_deg)))\n right_y = self.yDst - (A*math.cos(math.radians((90-arr_deg) - line_deg)))\n\n arr_head_lines = [(self.xDst,self.yDst),(left_x,left_y),(right_x,right_y),(self.xDst,self.yDst)]\n pygame.draw.polygon(self.screen,self.color,arr_head_lines,0)\n\n line = [(self.xSrc,self.ySrc), (self.xDst,self.yDst)]\n pygame.draw.lines(self.screen, self.color, False, line, 1)\n\n def __str__(self):\n returnStr = \"Arrow from (\"+str(int(self.xSrc))+\",\"+str(int(self.ySrc))+\") to ( \"+str(int(self.xDst))+\",\"+str(int(self.yDst))+\")\"\n return returnStr\n" }, { "alpha_fraction": 0.5458471775054932, "alphanum_fraction": 0.5511627793312073, "avg_line_length": 34, "blob_id": "a6f6528c56be3a13d799465a7d6f23dbee3675b4", "content_id": "4100b420e4a961a3cf8920d795ae1126a42b04ef", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 18060, "license_type": "no_license", "max_line_length": 248, "num_lines": 516, "path": "/ScreenUtils.py", "repo_name": "JeremySMorgan/2DGraphSearch", "src_encoding": "UTF-8", "text": "import pygame\nimport math\nimport sys\nimport time\nfrom PIL import Image\nfrom Shapes import RectangleSpace\nfrom Shapes import Arrow\nfrom Graph import Graph\nfrom Graph import Node\n\nclass ScreenUtils(object):\n\n def __init__(self,screen,clock,fps,rectangle_spacing,colors,high_granularity):\n\n # Pygame Vars\n self.screen = screen\n self.clock = clock\n self.fps = fps\n\n # Colors\n self.background_color = colors[0]\n self.un_passable_color = colors[1]\n self.occupied_color = colors[2]\n self.un_occupied_color = colors[3]\n self.rectangle_border_color = colors[4]\n self.start_color = colors[5]\n self.end_color = colors[6]\n\n # Rectangle Geometry\n self.num_horiz_rectangles = rectangle_spacing[0]\n self.num_vert_rectangles = rectangle_spacing[1]\n self.rectangle_width = rectangle_spacing[2]\n self.rectangle_height = rectangle_spacing[3]\n\n # Shape Objects\n self.Rectangles = []\n self.Arrows = []\n self.Nodes = []\n\n # Graph\n self.graph = Graph()\n self.startRect = None\n self.startNode = None\n self.endRect = None\n self.endNode = None\n self.startXYIndex = None\n self.endXYIndex = None\n self.obstacles = []\n\n # high_granularity of rectangles results in intensive cpu demands, which\n # causes font rendering failures\n self.high_granularity = high_granularity\n\n\n def image_initialization(self,image_src):\n\n ''' Takes image file name as param.\n - gets the number of horizantal, vertical rectangles from VariableUtils\n - analyzes the image, if an area on the image (defined by granularity dictated from\n rectangle width/height) has an average darkness greater than\n (max_darkness)/(min darkness threshold), ((min darkness threshold) ~ .5),\n then the rectangle representing that area is considered impassable,\n this is reflected in node aswell\n - Green is represents the start rectangle. the start rectangle is\n the closest rectangle to the centroid of all sufficiently green pixels\n - Red is represents the end rectangle. the end rectangle is\n the closest rectangle to the centroid of all sufficiently green pixels\n - Input image is translated to size of desired screen (VariableUtils.SCREEN_WIDTH/HEIGHT)\n '''\n\n # Image variables\n image = Image.open(image_src)\n image_rgb = image.convert('RGB')\n\n # image dimensions\n image_width, image_height = image.size\n image_x_slice_width = image_width / self.num_horiz_rectangles\n image_y_slice_height = image_height / self.num_vert_rectangles\n\n # if r_ave,g_ave,b_ave are all less than darkness_threshold, area is considered obstacle\n darkness_threshold = 50\n\n # if ave value is above this threshold, then value is considred live\n light_threshold = 150\n\n # Red, Green pixel locations for start and end locations\n g_pixel_locations = []\n r_pixel_locations = []\n\n for n_x in range(self.num_horiz_rectangles):\n\n row = []\n\n for n_y in range(self.num_vert_rectangles):\n\n image_x0 = int(n_x * image_x_slice_width)\n image_x = int((n_x * (image_x_slice_width)) + image_x_slice_width)\n\n image_y0 = int(n_y * image_y_slice_height)\n image_y = int((n_y * (image_y_slice_height )) + image_y_slice_height)\n\n pixel_count = 0\n r_ave = 0\n g_ave = 0\n b_ave = 0\n\n # x,y give us absolute pixel location of image\n for x in range(image_x0,image_x,1):\n for y in range(image_y0,image_y,1):\n\n r,g,b = image_rgb.getpixel((x,y))\n pixel_count += 1\n\n r_ave += r\n g_ave += g\n b_ave += b\n\n #if r > light_threshold and g<100:\n # print(\"Red found at: \",x,\",\",y,\", \\t|\\t r:\",r,\"g:\",g,\"b:\",b, \"\\t|\\t(r > light_threshold):\",(r > light_threshold),\"(g < darkness_threshold):\",(g < darkness_threshold),\"(b < darkness_threshold):\",(b < darkness_threshold))\n\n # Test for red\n if (r > light_threshold) and (g < darkness_threshold) and (b < darkness_threshold):\n r_pixel_locations.append([x,y])\n\n # Test for green\n if (r<darkness_threshold) and (g>light_threshold) and (b<darkness_threshold):\n g_pixel_locations.append([x,y])\n\n r_ave /= pixel_count\n g_ave /= pixel_count\n b_ave /= pixel_count\n\n passable = True\n occupied = False\n\n # test to see if area is occupied\n if (r_ave < darkness_threshold) and (g_ave < darkness_threshold) and (b_ave < darkness_threshold):\n passable = False\n\n # Rectangle x,y location\n rect_x = n_x * self.rectangle_width\n rect_y = n_y * self.rectangle_height\n\n # Create Rectangle\n rect = RectangleSpace(self.screen,rect_x,rect_y,self.rectangle_width, \\\n self.rectangle_height,passable,occupied,\n self.rectangle_border_color,self.high_granularity)\n\n rect.setColor(self.background_color)\n row.append(rect)\n\n # Rectangle is not passable, thus no node is created\n if not passable:\n rect.setColor(self.un_passable_color)\n self.obstacles.append((n_x,n_y))\n\n # Rectangle is passable, thus we create a node\n else:\n xyIndex = [n_x,n_y]\n\n write_out = \"Node at: \"+str(n_x)+\",\"+str(n_y)+\"\\t\"\n #sys.stdout.write(write_out)\n\n self.Nodes.append(Node(\"-\",xyIndex))\n\n self.Rectangles.append(row)\n\n start_ave = [0,0]\n target_ave = [0,0]\n\n for xy in g_pixel_locations:\n start_ave[0] += xy[0]\n start_ave[1] += xy[1]\n start_ave[0] /= len(g_pixel_locations)\n start_ave[1] /= len(g_pixel_locations)\n\n for xy in r_pixel_locations:\n target_ave[0] += xy[0]\n target_ave[1] += xy[1]\n target_ave[0] /= len(r_pixel_locations)\n target_ave[1] /= len(r_pixel_locations)\n\n print( (\"end xy: \",target_ave) )\n\n start_index = self.getIndexXYFromAbsXY(start_ave[0],start_ave[1])\n end_index = self.getIndexXYFromAbsXY(target_ave[0],target_ave[1])\n\n self.startXYIndex = start_index\n self.endXYIndex = end_index\n\n self.makeGraph()\n\n\n def manual_initialization(self,obstacles=[],start_index=None,end_index=None):\n\n for (x,y) in obstacles:\n if self.isValidLocation(x,y):\n self.obstacles.append((x,y))\n else:\n pass\n\n ''' Set obstacle rectangles using an array of tuples of the form [(X1,Y1),(X2,Y2), ...]\n where Xn and Ym (n,m are arbitrary numbers) describing the index of a impassable rectangle,\n then creates rectangle objects, ginally creat a graph using these rectangles '''\n\n for x in range(self.num_horiz_rectangles):\n\n row = []\n\n for y in range(self.num_vert_rectangles):\n\n rect_x = x * self.rectangle_width\n rect_y = y * self.rectangle_height\n\n occupied = False\n passable = True\n if (x,y) in self.obstacles:\n passable = False\n\n rect = RectangleSpace(self.screen,rect_x,rect_y,self.rectangle_width,self.rectangle_height,passable,occupied,self.rectangle_border_color,self.high_granularity)\n rect.setColor(self.background_color)\n row.append(rect)\n\n # Rectangle is not passable, thus no node is created\n if not passable:\n rect.setColor(self.un_passable_color)\n\n # Rectangle is passable, thus we create a node\n else:\n value = Node.XYIndexToNodeValue(x,y)\n xyIndex = [x,y]\n self.Nodes.append(Node(value,xyIndex))\n\n self.Rectangles.append(row)\n\n if not type(start_index) == type(None):\n self.startXYIndex = start_index\n else:\n raise ValueError(\"ScreenUtils.manual_initialization() parameter error: start index must be provided\")\n\n if not type(end_index) == type(None):\n self.endXYIndex = end_index\n else:\n raise ValueError(\"ScreenUtils.manual_initialization() parameter error: end index must be provided\")\n\n self.makeGraph()\n\n\n\n def makeGraph(self):\n\n ''' Makes graph. The rectangles must have all been created first '''\n\n for x in range(self.num_horiz_rectangles):\n for y in range(self.num_vert_rectangles):\n\n # origin node must be at a valid spot\n if self.isValidLocation(x,y):\n\n # Add rectangles neighbors to graph\n # offset - [leftright,updown]\n offsets = [ [1,0],[-1,0],[0,1],[0,-1] ]\n\n for offset in offsets:\n\n up_down = offset[1]\n right_left = offset[0]\n\n target_x = x + right_left\n target_y = y + up_down\n\n if self.isValidLocation(target_x,target_y):\n\n srcNode = self.getNodeFromXYIndex(x,y)\n trgtNode = self.getNodeFromXYIndex(target_x,target_y)\n\n srcNode.add_neighbor(trgtNode)\n self.graph.add_connection( srcNode, trgtNode )\n\n # Start and End nodes are set here if graph is created from image_initialization()\n\n # Set start node if it has not been set already\n if type(self.startNode) == type(None):\n if type(self.startXYIndex) == type(None):\n raise ValueError(\"ScreenUtil.makeGraph(): startNode has not been set\")\n else:\n self.setStartWithXYIndex(self.startXYIndex[0],self.startXYIndex[1])\n\n # Set end node if it has not been set already\n if type(self.endNode) == type(None):\n if type(self.endXYIndex) == type(None):\n raise ValueError(\"ScreenUtil.makeGraph(): endNode has not been set\")\n else:\n self.setEndWithXYIndex(self.endXYIndex[0],self.endXYIndex[1])\n\n\n def nodeExistsFromXYIndex(self,x,y):\n\n ''' returns whether a node exists a specified xy index '''\n\n for node in self.Nodes:\n if node.x == x and node.y == y:\n return True\n return False\n\n def getNodeFromXYIndex(self,x,y):\n\n ''' returns node with specified xy index '''\n\n for node in self.Nodes:\n if node.x == x and node.y == y:\n return node\n print(\"Fatal Error:\",x,\",\",y,\"has no associated node\")\n return None\n\n\n def setRectangleColorAndTextFromGraphResults(self,tentative_values):\n max_weight = -1\n min_weight = 0\n\n # Get max_weight\n for node in tentative_values:\n if not type(node) == None:\n # Exclude nodes with infinite value\n if tentative_values[node] < math.inf:\n if tentative_values[node] > max_weight:\n max_weight = tentative_values[node]\n else:\n raise ValueError(\"Invalid return from Graph.Dijkstra, node not returned: \",str(node))\n\n for node in tentative_values:\n rect = self.getRectFromNode(node)\n\n # make sure we have a valid rectangle\n if not type(rect) == None:\n\n if not self.high_granularity:\n rect.set_text(tentative_values[node])\n\n # dont change color of start and end nodes\n if not (node.amStart or node.amEnd):\n\n # dont change color of unvisited nodes\n if tentative_values[node] < math.inf:\n\n node_weight = tentative_values[node]\n\n # Scale weight between 0-255\n min_color = 250\n max_color = 0\n colorValue = (((node_weight - min_weight) * (max_color - min_color)) / (max_weight - min_weight)) + min_color\n\n color = (min_color-int(colorValue),int(colorValue),0)\n rect.setColor(color)\n else:\n if node.amStart:\n rect.setColor(self.start_color)\n\n if not self.high_granularity:\n rect.set_text(\"Start\")\n else:\n rect.setColor(self.end_color)\n if not self.high_granularity:\n rect.set_text(\"End\")\n else:\n raise ValueError(\"Invalid return from Graph.Dijkstra, rect not created from: \",str(node))\n\n\n\n def solveShortestPath(self,algo):\n\n ''' Find shortest path from beggining rectangle to end rectangle using paramaterized algorithm '''\n\n if self.startNode == None or self.endNode == None or self.startRect == None or self.endRect == None or self.current_rect == None:\n raise ValueError(\"Unable to solveDijkstra(), start, end or current_rect have not been set\")\n\n if not str(algo).lower() in self.graph.path_algorithms:\n raise ValueError(algo,\"is either not implemented or not a valid algorithm\")\n\n tentative_values, path = self.graph.shortestPath(algo)\n\n path = list(reversed(path))\n\n print(\"Length of path\",len(path))\n\n path_iterator = 1\n done_drawing_arrows = False\n\n self.setRectangleColorAndTextFromGraphResults(tentative_values)\n\n while True:\n\n # check for quit events\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit(); sys.exit();\n\n # erase the screen\n self.screen.fill(self.background_color)\n\n if not done_drawing_arrows:\n\n fromNode = path[path_iterator-1]\n toNode = path[path_iterator]\n\n fromRect = self.getRectFromNode(fromNode)\n toRect = self.getRectFromNode(toNode)\n\n self.changeCurrent(toRect)\n\n arrow_color = (255,255,255)\n tempArrow = Arrow(self.screen,fromRect,toRect,arrow_color)\n self.Arrows.append(tempArrow)\n self.draw()\n # update the screen\n pygame.display.update()\n\n path_iterator += 1\n if (path_iterator == len(path)):\n done_drawing_arrows = True\n\n self.clock.tick(self.fps)\n\n\n\n def changeCurrent(self,newCurrentRect):\n self.current_rect.update_state(False)\n self.current_rect = newCurrentRect\n self.current_rect.update_state(True)\n\n\n def getIndexXYFromAbsXY(self,x,y):\n\n ''' returns xy index of rectangle with xy absoulute coordinates on the screen '''\n\n x_index = int(x / self.rectangle_width)\n y_index = int(y / self.rectangle_height)\n return (x_index,y_index)\n\n def getRectFromNode(self,node):\n\n ''' Returns a rectangle object given a node '''\n\n return self.Rectangles[node.x][node.y]\n\n\n def setStartWithXYIndex(self,x,y):\n\n ''' Set the start location for the graph search. X, Y must be indexes '''\n\n if self.isValidLocation(x,y):\n self.startRect = self.getRectFromXYIndex(x,y)\n self.startRect.setColor(self.start_color)\n self.startRect.update_state(True)\n self.current_rect = self.startRect\n self.startNode = self.getNodeFromXYIndex(x,y)\n self.startNode.setAmStart(True)\n else:\n raise ValueError(\"Invalid input to ScreenUtils.setStart()\")\n\n def setEndWithXYIndex(self,x,y):\n\n ''' set end point of graph '''\n\n print( (\"setting end node with xy index x:\",x,\",y:\",y) )\n\n if self.isValidLocation(x,y):\n self.endRect = self.getRectFromXYIndex(x,y)\n self.endRect.setColor(self.end_color)\n self.endNode = self.getNodeFromXYIndex(x,y)\n self.endNode.setAmEnd(True)\n for node in self.Nodes:\n node.setEnd(x,y)\n\n else:\n raise ValueError(\"Invalid input to ScreenUtils.setEnd()\")\n\n def getRectFromXYIndex(self,x,y):\n\n ''' return Rectangle object given its xy index '''\n\n return self.Rectangles[x][y]\n\n def isValidLocation(self,x_rect,y_rect):\n\n ''' Returns whether a rectangle's indexed location is valid '''\n\n # location cannot be impassable\n if (x_rect,y_rect) in self.obstacles:\n return False\n\n # xRect must be 0 or greater\n if x_rect < 0:\n return False\n\n # x rect must be less than num_horiz_rectangles, to account for zero indexing\n if x_rect >= self.num_horiz_rectangles:\n return False\n\n # xRect must be 0 or greater\n if y_rect < 0:\n return False\n\n # x rect must be less than num_horiz_rectangles, to account for zero indexing\n if y_rect >= self.num_vert_rectangles:\n return False\n\n return True\n\n def draw(self):\n for i in self.Rectangles:\n for j in i:\n j.draw()\n\n for a in self.Arrows:\n a.draw()\n" }, { "alpha_fraction": 0.5533245801925659, "alphanum_fraction": 0.5579328536987305, "avg_line_length": 28.590909957885742, "blob_id": "735b04ec6036d17308619e14b09e5b8f1732d377", "content_id": "b458bc0d6517c85ccf93f005f18a7aeaf1d3e829", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9114, "license_type": "no_license", "max_line_length": 124, "num_lines": 308, "path": "/Graph.py", "repo_name": "JeremySMorgan/2DGraphSearch", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\nfrom collections import defaultdict\nimport copy\nimport math\n\nclass Node(object):\n\n def __init__(self, value,xyIndex, xyEndIndex=[-1,-1]):\n self.neighbors = []\n self.value = value\n self.x = xyIndex[0]\n self.y = xyIndex[1]\n self.xEnd = xyEndIndex[0]\n self.yEnd = xyEndIndex[1]\n self.amStart = False\n self.amEnd = False\n self.parent = None\n\n def setAmStart(self,amStart):\n ''' Designates the node as the start node '''\n self.amStart = amStart\n\n def setAmEnd(self,amEnd):\n ''' Designates the node as the end node '''\n self.amEnd = amEnd\n\n def setEnd(self,xIndex,yIndex):\n self.xEnd = xIndex\n self.yEnd = yIndex\n\n def add_neighbor(self,node):\n self.neighbors.append(node)\n\n def getNeighbors(self):\n return self.neighbors\n\n\n def getDijkstraVerticeWeight(self, node):\n\n ''' Dijkstra Search has equal weights for all edges '''\n return 1\n\n def nodeXY(self):\n ''' debugging tool. returns nodes XY coordinates '''\n returnStr = \"(\" +str(self.x)+\",\"+str(self.y)+\")\"\n return returnStr\n\n def __str__(self):\n ''' returns the index position of node in the '(x,y)', where x and y are the indexes of the node'''\n returnStr = \"<Node object> ( XY -> (\"+str(self.x)+\",\"+str(self.y)+ \")\"\n if self.amStart:\n returnStr += \" | start -> \"+str(self.amStart)\n if self.amEnd:\n returnStr += \" | end -> \"+ str(self.amEnd)\n returnStr += \")\"\n return returnStr\n\n def getAStarHeuristicWeight(self, trgtNode):\n\n if not type(trgtNode) == type(self):\n raise ValueError(\"TargetNode is not a Node\")\n\n if self.yEnd < 0 or self.xEnd < 0:\n raise ValueError(\"End Node has not been set\")\n\n dx = math.fabs(trgtNode.x - self.xEnd)\n dy = math.fabs(trgtNode.y - self.yEnd)\n D = 1\n h = D * (dx + dy)\n\n return h\n\n @staticmethod\n def XYIndexToNodeValue(x,y):\n returnStr = \"(\"+str(x)+\",\"+str(y)+\")\"\n return returnStr\n\n\nclass Graph(object):\n\n def __init__(self, connections=[],directed=False):\n self._graph = defaultdict(set)\n self._directed = directed\n self.add_connections(connections)\n self.path_algorithms = [\"dijkstra\",\"astar\"]\n\n\n def add_connections(self, connections):\n \"\"\" Add connections (list of tuple pairs) to graph \"\"\"\n\n for node1, node2 in connections:\n self.add(node1, node2)\n\n def add_connection(self, node1, node2):\n \"\"\" Add connection between node1 and node2 \"\"\"\n\n self._graph[node1].add(node2)\n if not self._directed:\n self._graph[node2].add(node1)\n\n def get_connections(self,node):\n \"\"\" Return a Nodes connections \"\"\"\n\n return self._graph[node]\n\n def remove(self, node):\n \"\"\" Remove all references to node \"\"\"\n\n for n, cxns in self._graph.iteritems():\n try:\n cxns.remove(node)\n except KeyError:\n pass\n try:\n del self._graph[node]\n except KeyError:\n pass\n\n def shortestPath(self,algo):\n\n # 1) Assign to each node a tentative distance value (0 for initial, inf for all others)\n # 2) Create a set of visited nodes. Starts with initial node\n # 3) For the current node, consider all of its unvisited neighbors and calulate\n # (distance to the current node) + (dustance from current node to neighbor)\n # if this calculated value is less than their tentative value, replace the tentative value with this new value\n # 4) Mark the current node visited\n # 5) if the destination node is marked visited, the search is done\n # 6) set the unvisited node marked with the smallest tentative distance as the next 'current node' and repeat from 3\n\n\n if algo == \"dijkstra\":\n return self.dijkstra_search()\n\n elif algo == \"astar\":\n return self.astar_search()\n\n else:\n print(\"unknown search algorithm.\")\n\n def astar_search(self):\n\n print(\"in a star search\")\n\n graph = self\n nodes = set(graph._graph)\n initial_node = None\n target_node = None\n\n # Create 'visited' dictanary <- thats spelled wrong\n visited = set()\n tentative_values = {}\n\n # initialize node tentative values\n for node in nodes:\n\n # Set tentative value of node to inf\n tentative_values[node] = math.inf\n\n if node.amStart:\n # Initial node has tentative value of 0\n tentative_values[node] = 0\n initial_node = node\n initial_node.parent = None\n\n if node.amEnd:\n target_node = node\n\n current_node = initial_node\n not_done = True\n\n while not_done:\n\n # Mark the current node visited\n visited.add(current_node)\n\n # Set new tentative values for current node's neighbors\n for neighbor in current_node.getNeighbors():\n\n # h = heuristic\n # g = cost of movement\n\n h = current_node.getAStarHeuristicWeight(neighbor)\n g = 1\n\n f = g + h\n # total weight from init to neighbor. if this value is less than\n # the current tentative weight of the neighbor, then the tentative weight is\n # updated with this value\n newTentativeValue = f\n\n if newTentativeValue < tentative_values[neighbor]:\n tentative_values[neighbor] = newTentativeValue\n neighbor.parent = current_node\n\n min_distance = math.inf\n nextNode = None\n\n for node in nodes:\n if node not in visited:\n if tentative_values[node] < min_distance:\n nextNode = node\n min_distance = tentative_values[node]\n\n current_node = nextNode\n if current_node == None:\n raise ValueError(\"Next Node Not Found\")\n\n if current_node == target_node:\n not_done = False\n\n path = self.getPath(target_node, [])\n\n return tentative_values, path\n\n\n def dijkstra_search(self):\n\n graph = self\n nodes = set(graph._graph)\n initial_node = None\n target_node = None\n\n # Create 'visited' dictanary <- thats spelled wrong\n visited = set()\n tentative_values = {}\n\n # initialize node tentative values\n for node in nodes:\n\n # Set tentative value of node to inf\n tentative_values[node] = math.inf\n\n if node.amStart:\n\n # Initial node has tentative value of 0\n tentative_values[node] = 0\n initial_node = node\n initial_node.parent = None\n\n if node.amEnd:\n target_node = node\n\n current_node = initial_node\n not_done = True\n\n while not_done:\n\n # Mark the current node visited\n visited.add(current_node)\n\n # Set new tentative values for current node's neighbors\n for neightbor in current_node.getNeighbors():\n\n # vertice weight between current_node and neighbor\n distanceToNeighbor = current_node.getDijkstraVerticeWeight(neightbor)\n\n # total weight from init to neighbor. if this value is less than\n # the current tentative weight of the neighbor, then the tentative weight is\n # updated with this value\n newTentativeValue = tentative_values[current_node] + distanceToNeighbor\n\n if newTentativeValue < tentative_values[neightbor]:\n tentative_values[neightbor] = newTentativeValue\n neightbor.parent = current_node\n\n min_distance = math.inf\n nextNode = None\n\n for node in nodes:\n if node not in visited:\n if tentative_values[node] < min_distance:\n\n nextNode = node\n min_distance = tentative_values[node]\n\n current_node = nextNode\n if current_node == None:\n raise ValueError(\"Next Node Not Found\")\n\n if current_node == target_node:\n not_done = False\n\n\n path = self.getPath(target_node,[])\n\n return tentative_values,path\n\n\n def getPath(self,node,path):\n if node.parent == None:\n return path\n\n else:\n path.append(node)\n return self.getPath(node.parent,path)\n\n\n def is_connected(self, node1, node2):\n \"\"\" Is node1 directly connected to node2 \"\"\"\n\n return node1 in self._graph and node2 in self._graph[node1]\n\n def tester(self):\n pass\n\n def __str__(self):\n return '{}({})'.format(self.__class__.__name__, dict(self._graph))\n" }, { "alpha_fraction": 0.5010225176811218, "alphanum_fraction": 0.6134969592094421, "avg_line_length": 22.238094329833984, "blob_id": "a2b29805909462443169e96a4aabe599f2b7453e", "content_id": "303ac8edd2d3016b9fcca71e9ade1c42b79167c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 489, "license_type": "no_license", "max_line_length": 44, "num_lines": 21, "path": "/VariableUtils.py", "repo_name": "JeremySMorgan/2DGraphSearch", "src_encoding": "UTF-8", "text": "\nclass VariableUtils(object):\n FPS = 90\n\n SCREEN_WIDTH = 900\n SCREEN_HEIGHT = 900\n\n HIGH_GRANULARITY = True\n\n HORIZ_RECTS = 60\n VERT_RECTS = 60\n\n RECT_WIDTH = SCREEN_WIDTH / HORIZ_RECTS\n RECT_HEIGHT = SCREEN_HEIGHT / VERT_RECTS\n\n BACKGROUND_COLOR = (255,255,255)\n UN_PASSABLE_COLOR = (0,0,0)\n OCCUPIED_COLOR = (0,255,0)\n UN_OCCUPIED_COLOR = (190,190,190)\n RECTANGLE_BORDER_COLOR = (0,0,0)\n START_COLOR = (0, 255, 255)\n END_COLOR = (0, 255,255)\n" }, { "alpha_fraction": 0.7621178030967712, "alphanum_fraction": 0.7710663676261902, "avg_line_length": 43.70000076293945, "blob_id": "15630f80fe66b62f9fc5f9634f7dcbcc73c1efde", "content_id": "293c882a86b7852433c1b03287eeb89795ad8af7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1341, "license_type": "no_license", "max_line_length": 170, "num_lines": 30, "path": "/README.md", "repo_name": "JeremySMorgan/2DGraphSearch", "src_encoding": "UTF-8", "text": "# 2DGraphSearch\nDijkstra, A* Search Algorithms displayed using pygame3 and PIL image analysis.\nThis program is either analyzes images to create a corresponding graph or manually read obstacle locations from preprogrammed xy coordinates\n\n### A*\n- A* search from image <br>\n![A* Search](https://github.com/JeremySMorgan/2DGraphSearch/blob/master/gifs/astar.gif)\n\n### Dijkstra\n- Dijkstra search from image<br> \n![Dijkstra](https://github.com/JeremySMorgan/2DGraphSearch/blob/master/gifs/dijkstra_image_read.gif)\n- Dijkstra search from manually added obstacles<br> \n![Dijkstra](https://github.com/JeremySMorgan/2DGraphSearch/blob/master/gifs/dijkstra.gif)\n\n### Notes\n- This project is written in python3\n- Low granularity image reads result in intensive cpu processing demands. Pygame's\n image rendering fails when creating a graph with more than ~3500 nodes, if this happens\n `HIGH_GRANULARITY` must be set to `True` to prevent crashes.\n- The image analysis program sets the start position to the centroid of all sufficiently green pixels, and the end location to the centroid of all sufficiently red pixels\n\n### Dependencies\n- *pygame:* `pip3 install pygame`\n- *pillow:* `pip3 install pillow`\n\n### To Do\n- ~~Image input (PIL) for graph construction~~\n- ~~A* heuristic weight algorithm revision~~\n- ~~Fix arrow head orientation~~\n- D* Search\n" }, { "alpha_fraction": 0.5140284299850464, "alphanum_fraction": 0.5978524684906006, "avg_line_length": 36.493507385253906, "blob_id": "9767f2abfee3d6ec9a0ba0b63d9bd9a00627eb06", "content_id": "3a3c934859512a9e81c8e47e5c7b3705fd585700", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2887, "license_type": "no_license", "max_line_length": 131, "num_lines": 77, "path": "/main.py", "repo_name": "JeremySMorgan/2DGraphSearch", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n\n\"\"\" main.py: \"\"\"\n\n__author__ = \"Jeremy Morgan\"\n__date__ = \"May 17, 2017\"\n\nimport pygame\nimport sys\nimport time\nfrom Shapes import RectangleSpace\nfrom VariableUtils import VariableUtils\nfrom ScreenUtils import ScreenUtils\n\ndef main():\n\n # Pygame Constants\n fps = VariableUtils.FPS\n screen_dimensions = (VariableUtils.SCREEN_WIDTH,VariableUtils.SCREEN_HEIGHT)\n\n # Pygame Objects and Creation\n pygame.init()\n screen = pygame.display.set_mode(screen_dimensions)\n clock = pygame.time.Clock()\n high_granularity = VariableUtils.HIGH_GRANULARITY\n\n # Rectangle and Screen Colors\n background_color = VariableUtils.BACKGROUND_COLOR\n un_passable_color = VariableUtils.UN_PASSABLE_COLOR\n occupied_color = VariableUtils.OCCUPIED_COLOR\n un_occupied_color = VariableUtils.UN_OCCUPIED_COLOR\n rectangle_border_color = VariableUtils.RECTANGLE_BORDER_COLOR\n start_color = VariableUtils.START_COLOR\n end_color = VariableUtils.END_COLOR\n colors = [background_color, un_passable_color, occupied_color, un_occupied_color, rectangle_border_color,start_color,end_color]\n\n # Rectangle Spacing Constants\n num_horiz_rectangles = VariableUtils.HORIZ_RECTS\n num_vert_rectangles = VariableUtils.VERT_RECTS\n rectangle_width = VariableUtils.RECT_WIDTH\n rectangle_height = VariableUtils.RECT_HEIGHT\n rectangle_spacing = [num_horiz_rectangles,num_vert_rectangles,rectangle_width,rectangle_height]\n\n # ScreenUtil to modify screen\n screen_modifier = ScreenUtils(screen,clock,fps,rectangle_spacing,colors,high_granularity)\n\n\n # To initialize manually:\n '''\n obstacles = [(5, 8), (1, 1), (2, 2), (2, 3), (2, 4), (2, 5), (2, 6), (3, 1), (4, 2), (5, 5), \\\n (6, 1), (7, 1), (8, 1), (9, 1), (6, 7), (3, 24), (30, 12), (40, 22), (35, 19), (25, 16), (17, 41), \\\n (15, 2), (15, 3), (15, 4), (15, 5), (15, 6), (15, 7), (15, 8), (15, 9), \\\n (15, 10), (15, 11), (15, 12), (15, 14), (15, 15), (15, 16), (15, 17), (15, 18), \\\n (16, 20), (16, 21), (16, 22), (16, 24), (16, 25), (16, 26), (16, 27), (16, 28), \\\n (17, 4), (17, 13), (17, 5), (17, 6), (17, 7), (17, 8), (17, 9), (17, 10), \\\n (17, 11), (17, 12), (17, 14), (17, 15), (17, 16), (17, 17), (17, 18), \\\n (18, 21), (18, 22), (18, 24), (18, 25), (18, 26), (18, 27), (18, 28), (18, 29), (5, 2)\n ]\n obstacles = []\n start = (3, 15)\n end = (38, 17)\n screen_modifier.manual_initialization(obstacles, start, end)\n '''\n # To initialize using image\n image_src = \"map.png\"\n screen_modifier.image_initialization(image_src)\n\n\n # Available: 'dijkstra', 'astar'\n algorithm = \"astar\"\n screen_modifier.solveShortestPath(algorithm)\n\n\nif __name__ == '__main__':\n print(\"Beggning Program\")\n main()\n print(\"Program finished\")\n" } ]

6

SangminOut/DMS-Sanic

https://github.com/SangminOut/DMS-Sanic

7fb877b2a772de808b7391428e151b2a2645c59d

bbb65c584711fa23dbf0455300307c2acceba013

dafafb8f65cd93dd1f6567d9b8e431e31f19ae68

refs/heads/master

2020-06-15T06:11:33.116199

2019-07-14T12:57:11

2019-07-14T12:57:58

null

0

null

[ { "alpha_fraction": 0.5738045573234558, "alphanum_fraction": 0.5738045573234558, "avg_line_length": 20.863636016845703, "blob_id": "7bb83d0baa185fe756143ee095e6f0fecca1c70b", "content_id": "de272d9f96fe77147c3b2527b8dad48277fe174a", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 481, "license_type": "permissive", "max_line_length": 53, "num_lines": 22, "path": "/dms/views/apply/music.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass MusicApplyView(HTTPMethodView):\n def get(self, request: Request, weekday: int):\n \"\"\"\n Response Music Apply Status\n \"\"\"\n pass\n\n def post(self, request: Request, weekday: int):\n \"\"\"\n Apply Music\n \"\"\"\n pass\n\n def delete(self, request: Request, weekday: int):\n \"\"\"\n Delete Music apply on the weekday\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.5927051901817322, "alphanum_fraction": 0.5927051901817322, "avg_line_length": 19.5625, "blob_id": "8d582cb2deab86af854805d997cdcf7af3b8c7d2", "content_id": "ddaaf99a5e56b70858abeca497fd50bc5e96b274", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 329, "license_type": "permissive", "max_line_length": 44, "num_lines": 16, "path": "/dms/views/apply/stay.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass StayApplyView(HTTPMethodView):\n async def get(self, request: Request):\n \"\"\"\n Response Stay Apply Status\n \"\"\"\n pass\n\n async def post(self, response: Request):\n \"\"\"\n Apply Stay\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.6713747382164001, "alphanum_fraction": 0.6713747382164001, "avg_line_length": 33.25806427001953, "blob_id": "08740858498e033252f21c339e8654727c9606f2", "content_id": "6a6497bc38fed7d40c9bcc0992f03ead5ed27fb2", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1062, "license_type": "permissive", "max_line_length": 117, "num_lines": 31, "path": "/dms/models/point.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from datetime import datetime\n\nfrom extension import db\n\n\nclass PointItemModel(db.Model):\n __tablename__ = 'point_item'\n\n id: int = db.Column(db.Integer(), primary_key=True)\n reason: str = db.Column(db.String())\n point: int = db.Column(db.Integer())\n type: bool = db.Column(db.Boolean())\n\n\nclass PointHistoryModel(db.Model):\n __tablename__ = 'point_history'\n\n id: int = db.Column(db.Integer(), primary_key=True)\n student_id: str = db.Column(db.String(), db.ForeignKey('student.username', ondelete='CASCADE'))\n point_id: int = db.Column(db.Integer(), db.ForeignKey('point_item.id', ondelete='CASCADE'))\n point_date: datetime = db.Column(db.DateTime())\n\n\nclass PointStatusModel(db.Model):\n __tablename__ = 'point_status'\n\n student_id: str = db.Column(db.String(), db.ForeignKey('student.username', ondelete='CASCADE'), primary_key=True)\n good_point: int = db.Column(db.Integer())\n bad_point: int = db.Column(db.Integer())\n penalty_level: int = db.Column(db.Integer())\n penalty_status: bool = db.Column(db.Boolean())\n" }, { "alpha_fraction": 0.6681614518165588, "alphanum_fraction": 0.6681614518165588, "avg_line_length": 21.299999237060547, "blob_id": "b1a24bfd904ad0e3339b1a06c17351da4765ce1d", "content_id": "5e71bef5550c61a2a23bd037c596ecd90a7fe0cd", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 223, "license_type": "permissive", "max_line_length": 41, "num_lines": 10, "path": "/dms/views/report/facility_report.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass FacilityReportView(HTTPMethodView):\n def post(self, request: Request):\n \"\"\"\n Report Broken Facility\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.7802503705024719, "alphanum_fraction": 0.7802503705024719, "avg_line_length": 36.842105865478516, "blob_id": "bd76b6c4a68df20714821a60b538229aca6e6425", "content_id": "c52599731a75b7571caa952bc64c124ceb20b227", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 719, "license_type": "permissive", "max_line_length": 95, "num_lines": 19, "path": "/dms/views/apply/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.blueprints import Blueprint\n\napply_blueprint = Blueprint(\n name='apply_blueprint',\n url_prefix='/apply',\n)\n\nfrom views.apply.extension import ExtensionApplyView, ExtensionMapView\napply_blueprint.add_route(ExtensionApplyView.as_view(), '/extension/<time:int>')\napply_blueprint.add_route(ExtensionMapView.as_view(), '/extension/map/<time:int>/<class_:int>')\n\nfrom views.apply.goingout import GoingoutApplyView\napply_blueprint.add_route(GoingoutApplyView.as_view(), '/goingout')\n\nfrom views.apply.music import MusicApplyView\napply_blueprint.add_route(MusicApplyView.as_view(), '/music/<weekday:int>')\n\nfrom views.apply.stay import StayApplyView\napply_blueprint.add_route(StayApplyView.as_view(), '/stay')\n" }, { "alpha_fraction": 0.7156862616539001, "alphanum_fraction": 0.7156862616539001, "avg_line_length": 21.66666603088379, "blob_id": "cd24acd5b0131c6f63704a08591e8c14de17e1b1", "content_id": "81f6cf5475315bafa196ed538da4e903f4cb09b0", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 204, "license_type": "permissive", "max_line_length": 55, "num_lines": 9, "path": "/dms/views/meal/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic import Blueprint\n\nmeal_blueprint = Blueprint(\n name='meal_blueprint',\n url_prefix='/meal',\n)\n\nfrom views.meal.meal import MealView\nmeal_blueprint.add_route(MealView.as_view(), '/<date>')\n" }, { "alpha_fraction": 0.8280922174453735, "alphanum_fraction": 0.8280922174453735, "avg_line_length": 38.75, "blob_id": "13f39716dc1f26e3d04a67777993d856a8279810", "content_id": "2ef9b9ef02a4d0b508aa69524ea8f2bac7264aa1", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 477, "license_type": "permissive", "max_line_length": 97, "num_lines": 12, "path": "/dms/models/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from extension import db\n\nfrom models.account import StudentModel, UnsignedStudentModel\nfrom models.apply import ExtensionApplyModel, GoingoutApplyModel, MusicApplyModel, StayApplyModel\nfrom models.notice import NoticeModel, RuleModel\nfrom models.point import PointItemModel, PointHistoryModel, PointStatusModel\nfrom models.report import FacilityReportModel\n\n\nasync def init_db():\n await db.set_bind('postgres://postgres@localhost/dms-sanic')\n await db.gino.create_all()\n" }, { "alpha_fraction": 0.6884328126907349, "alphanum_fraction": 0.6884328126907349, "avg_line_length": 29.339622497558594, "blob_id": "16401d83ddf342d59e153db18fb88210785f3db9", "content_id": "e2cd9c954a3532d94193b8c26eabede32192d76e", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1608, "license_type": "permissive", "max_line_length": 100, "num_lines": 53, "path": "/dms/models/apply.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from datetime import datetime\nfrom typing import Any\n\nfrom const import EXTENSION_CLASS, EXTENSION_TIME, GOINGOUT_STATUS, MUSIC_APPLY_WEEKDAY, STAY_STATUS\nfrom extension import db\nfrom utils import kst_now\n\n\nclass BaseApplyModel(db.Model):\n __abstract__ = True\n\n id: int = db.Column(db.Integer(), primary_key=True)\n student_id: str = db.Column(db.String, db.ForeignKey('student.username', ondelete='CASCADE'))\n apply_date = db.Column(db.DateTime(), default=kst_now)\n\n @classmethod\n async def query_by_id(cls, id: Any):\n return await cls.query.where(cls.id == id).gino.first()\n\n @classmethod\n async def query_by_student(cls, student_id: str):\n return await cls.query.where(cls.student_id == student_id).gino.first()\n\n\nclass ExtensionApplyModel(BaseApplyModel):\n __tablename__ = 'apply_extension'\n\n time: int = db.Column(db.Enum(EXTENSION_TIME))\n class_: int = db.Column(db.Enum(EXTENSION_CLASS))\n seat: int = db.Column(db.Integer())\n\n\nclass GoingoutApplyModel(BaseApplyModel):\n __tablename__ = 'apply_goingout'\n\n go_out_date: datetime = db.Column(db.DateTime())\n come_back_date: datetime = db.Column(db.DateTime())\n reason: str = db.Column(db.String())\n status: str = db.Column(db.Enum(GOINGOUT_STATUS))\n\n\nclass MusicApplyModel(BaseApplyModel):\n __tablename__ = 'apply_music'\n\n day: int = db.Column(db.Enum(MUSIC_APPLY_WEEKDAY))\n singer: str = db.Column(db.String())\n song_name: str = db.Column(db.String())\n\n\nclass StayApplyModel(BaseApplyModel):\n __tablename__ = 'apply_stay'\n\n status: int = db.Column(db.Enum(STAY_STATUS))\n" }, { "alpha_fraction": 0.6544821858406067, "alphanum_fraction": 0.6570931077003479, "avg_line_length": 25.720930099487305, "blob_id": "5a43454b27306755f34ba2d424db64ca9f1a2816", "content_id": "6f424624527187146eb3a114eb9c9dbbafc8b6c3", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1149, "license_type": "permissive", "max_line_length": 75, "num_lines": 43, "path": "/dms/models/account.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from uuid import uuid4\n\nfrom extension import db\n\n\nclass BaseStudent(db.Model):\n __abstract__ = True\n\n name: str = db.Column(db.String())\n number: int = db.Column(db.Integer(), unique=True)\n email: str = db.Column(db.String(), unique=True)\n\n @classmethod\n async def query_by_number(cls, number: int):\n return await cls.query.where(cls.number == number).gino.first()\n\n\nclass StudentModel(BaseStudent):\n __tablename__ = 'student'\n\n username: str = db.Column(db.String(), primary_key=True)\n password: str = db.Column(db.String())\n\n @classmethod\n async def query_by_username(cls, username: str):\n return await cls.query.where(cls.username == username).gino.first()\n\n\ndef generate_uuid():\n while True:\n uuid = str(uuid4())[:5]\n if not UnsignedStudentModel.query_by_uuid(uuid):\n return uuid\n\n\nclass UnsignedStudentModel(BaseStudent):\n __tablename__ = 'unsigned_student'\n\n uuid: str = db.Column(db.String(), default=generate_uuid, unique=True)\n\n @classmethod\n async def query_by_uuid(cls, uuid: str):\n return await cls.query.where(cls.uuid == uuid).gino.first()\n" }, { "alpha_fraction": 0.6456310749053955, "alphanum_fraction": 0.6456310749053955, "avg_line_length": 19.600000381469727, "blob_id": "a54a06b1000bdccf0fc543a47b772713c34ba19c", "content_id": "0e18fba334ea2bfda410814087f855d621cef680", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 206, "license_type": "permissive", "max_line_length": 38, "num_lines": 10, "path": "/dms/views/report/bug_report.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass BugReportView(HTTPMethodView):\n def post(self, request: Request):\n \"\"\"\n Report Bug\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.681034505367279, "alphanum_fraction": 0.681034505367279, "avg_line_length": 33.79999923706055, "blob_id": "e0b87728bb73e39a85e249fbb32d532af906a97d", "content_id": "3106eb8de6dc0652892f0fab0dbd6e90b8d86611", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 348, "license_type": "permissive", "max_line_length": 99, "num_lines": 10, "path": "/dms/models/report.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from extension import db\n\n\nclass FacilityReportModel(db.Model):\n __tablename__ = 'facility_report'\n\n id: int = db.Column(db.Integer(), primary_key=True)\n student_id: str = db.Column(db.String(), db.ForeignKey('student.username', ondelete='CASCADE'))\n room_number: int = db.Column(db.Integer())\n content: str = db.Column(db.String())\n" }, { "alpha_fraction": 0.5952380895614624, "alphanum_fraction": 0.5952380895614624, "avg_line_length": 20, "blob_id": "1bf5b92f5b3614b7dde079cf628368b24be79a92", "content_id": "83db326b6128024d947b39614a86d7a447238087", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 336, "license_type": "permissive", "max_line_length": 44, "num_lines": 16, "path": "/dms/views/account/auth.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass AuthView(HTTPMethodView):\n async def post(self, request: Request):\n \"\"\"\n Auth and Generate JWT Token\n \"\"\"\n pass\n\n async def patch(self, request: Request):\n \"\"\"\n Refresh Access Token\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.6551724076271057, "alphanum_fraction": 0.6551724076271057, "avg_line_length": 22.200000762939453, "blob_id": "9aa7af4837dcfdc7d758b1b3f4a09df517e82f03", "content_id": "eef0f0ebc1210f1fe090d6a960ad8e5376c60b61", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 232, "license_type": "permissive", "max_line_length": 47, "num_lines": 10, "path": "/dms/views/meal/meal.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass MealView(HTTPMethodView):\n def get(self, request: Request, date: str):\n \"\"\"\n Response meal on parameter date\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.5848214030265808, "alphanum_fraction": 0.5848214030265808, "avg_line_length": 21.399999618530273, "blob_id": "41588ed4dd87df9e5d0bc4271d7f5602d1081dd1", "content_id": "273ee086a01eaca07d931eef2add7a57aca16cbb", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 672, "license_type": "permissive", "max_line_length": 66, "num_lines": 30, "path": "/dms/views/apply/extension.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass ExtensionApplyView(HTTPMethodView):\n async def get(self, request: Request, time: int):\n \"\"\"\n Response Extension Position Which Applied\n \"\"\"\n pass\n\n async def post(self, request: Request, time: int):\n \"\"\"\n Apply Extension\n \"\"\"\n pass\n\n async def delete(self, request: Request, time: int):\n \"\"\"\n Delete Extension\n \"\"\"\n pass\n\n\nclass ExtensionMapView(HTTPMethodView):\n async def get(self, request: Request, time: int, class_: int):\n \"\"\"\n Response Extension Map\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.601123571395874, "alphanum_fraction": 0.601123571395874, "avg_line_length": 18.77777862548828, "blob_id": "16da2d091ff29f93c8fc66bfee2c4eb04bae1d69", "content_id": "585d3d4f44bd948f52b06be32ab06f358b37c9e4", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 356, "license_type": "permissive", "max_line_length": 50, "num_lines": 18, "path": "/dms/views/notice/rule.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass RuleListView(HTTPMethodView):\n def get(self, request: Request):\n \"\"\"\n Response Rule List\n \"\"\"\n pass\n\n\nclass RuleView(HTTPMethodView):\n def get(self, request: Request, rule_id: int):\n \"\"\"\n Response Rule\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.6521739363670349, "alphanum_fraction": 0.6521739363670349, "avg_line_length": 19.700000762939453, "blob_id": "5e982a5c24eb2e16f3b21faf3687a957ee661d08", "content_id": "52f084ae9654388a63e5dc507081ea6708fd73ed", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 207, "license_type": "permissive", "max_line_length": 50, "num_lines": 10, "path": "/dms/__main__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from asyncio import get_event_loop\n\nfrom app import create_app\nfrom models import init_db\n\nif __name__ == '__main__':\n get_event_loop().run_until_complete(init_db())\n app = create_app()\n\n app.run()\n" }, { "alpha_fraction": 0.7222222089767456, "alphanum_fraction": 0.7314814925193787, "avg_line_length": 20.600000381469727, "blob_id": "7ce6f77e0317d7c1aa448710f29244281fd45a18", "content_id": "58c32dabc844eb9adb0d32e01a1b861f9a70b84e", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 108, "license_type": "permissive", "max_line_length": 49, "num_lines": 5, "path": "/dms/utils.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from datetime import datetime, timedelta\n\n\ndef kst_now():\n return datetime.utcnow() + timedelta(hours=9)\n" }, { "alpha_fraction": 0.5764331221580505, "alphanum_fraction": 0.5764331221580505, "avg_line_length": 18.625, "blob_id": "acca34f0785f251650c81a26f263a2620fdfdceb", "content_id": "3e81ac7c2efb649e9afafe69ac114a51f162fc39", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 314, "license_type": "permissive", "max_line_length": 43, "num_lines": 16, "path": "/dms/views/account/signup.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass SignupView(HTTPMethodView):\n async def get(self, request: Request):\n \"\"\"\n Check Is UUID valid\n \"\"\"\n pass\n\n async def post(self, request: Request):\n \"\"\"\n Signup\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.7559999823570251, "alphanum_fraction": 0.7559999823570251, "avg_line_length": 34.71428680419922, "blob_id": "082842f7eaa8e90502d3d16f783d3c374aed44b9", "content_id": "daa13c509809669d6be88e05f2940430af026a3f", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 500, "license_type": "permissive", "max_line_length": 75, "num_lines": 14, "path": "/dms/views/notice/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic import Blueprint\n\nnotice_blueprint = Blueprint(\n name='notice_blueprint',\n url_prefix='/notice',\n)\n\nfrom views.notice.notice import NoticeListView, NoticeView\nnotice_blueprint.add_route(NoticeListView.as_view(), '/notice')\nnotice_blueprint.add_route(NoticeView.as_view(), '/notice/<notice_id:int>')\n\nfrom views.notice.rule import RuleListView, RuleView\nnotice_blueprint.add_route(RuleListView.as_view(), '/rule')\nnotice_blueprint.add_route(RuleView.as_view(), '/rule/<rule_id:int>')\n" }, { "alpha_fraction": 0.48032787442207336, "alphanum_fraction": 0.5409836173057556, "avg_line_length": 13.186046600341797, "blob_id": "a2b29ea6caaa5c925dffe6f5f29eb19d24be6261", "content_id": "5b0b4753f57f9b09d68a74e8ad7218b8ff8ce898", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 610, "license_type": "permissive", "max_line_length": 32, "num_lines": 43, "path": "/dms/const.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from enum import Enum\n\n\nclass EXTENSION_TIME(Enum):\n ELEVEN = 11\n TWELVE = 12\n\n\nclass EXTENSION_CLASS(Enum):\n GA = 0\n NA = 1\n DA = 2\n RA = 3\n FLOOR_2 = 4\n FLOOR_3_SCHOOL_SIDE = 5\n FLOOR_3_DORMITORY_SIDE = 6\n FLOOR_4_SCHOOL_SIDE = 7\n FLOOR_4_DORMITORY_SIDE = 8\n FLOOR_5 = 9\n FLOOR_3_WATER = 10\n\n\nclass GOINGOUT_STATUS(Enum):\n BEFORE = 0\n ING = 1\n AFTER = 2\n\n\nclass MUSIC_APPLY_WEEKDAY(Enum):\n MON = 0\n TUE = 1\n WED = 2\n THU = 3\n FRI = 4\n SAT = 5\n SUN = 6\n\n\nclass STAY_STATUS(Enum):\n FRI_OUT = 0\n SAT_OUT = 1\n SAT_IN = 2\n STAY = 3\n" }, { "alpha_fraction": 0.7765362858772278, "alphanum_fraction": 0.7765362858772278, "avg_line_length": 28.83333396911621, "blob_id": "544436b2f531a93a4aa803c633910e2b1e3c03b6", "content_id": "eee518bc65440328d66fe11515cd5ad879cd59c6", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 358, "license_type": "permissive", "max_line_length": 69, "num_lines": 12, "path": "/dms/views/report/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic import Blueprint\n\nreport_blueprint = Blueprint(\n name='report_blueprint',\n url_prefix='/report',\n)\n\nfrom views.report.bug_report import BugReportView\nreport_blueprint.add_route(BugReportView.as_view(), '/bug')\n\nfrom views.report.facility_report import FacilityReportView\nreport_blueprint.add_route(FacilityReportView.as_view(), '/facility')\n" }, { "alpha_fraction": 0.6120218634605408, "alphanum_fraction": 0.6120218634605408, "avg_line_length": 19.33333396911621, "blob_id": "9d96e408c4244ea27363e64d8d39f1d4e125616c", "content_id": "188388f4524f2861b5a5f2d5196ac1c34a8a76ca", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 366, "license_type": "permissive", "max_line_length": 52, "num_lines": 18, "path": "/dms/views/notice/notice.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass NoticeListView(HTTPMethodView):\n def get(self, request: Request):\n \"\"\"\n Response Notice List\n \"\"\"\n pass\n\n\nclass NoticeView(HTTPMethodView):\n def get(self, request: Request, notice_id: int):\n \"\"\"\n Response Notice\n \"\"\"\n pass\n" }, { "alpha_fraction": 0.7858508825302124, "alphanum_fraction": 0.7858508825302124, "avg_line_length": 23.904762268066406, "blob_id": "0a70f4b3c749a404ce710b2459d3524e4699a476", "content_id": "61c65cb9aedec0f5cbfc3012abcc7c9d9bf32a1f", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 523, "license_type": "permissive", "max_line_length": 43, "num_lines": 21, "path": "/dms/views/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic import response\nfrom sanic.blueprints import Blueprint\nfrom sanic.request import Request\n\nfrom views.account import account_blueprint\nfrom views.apply import apply_blueprint\nfrom views.meal import meal_blueprint\nfrom views.notice import notice_blueprint\nfrom views.report import report_blueprint\n\nblueprint_group = Blueprint.group(\n account_blueprint,\n apply_blueprint,\n meal_blueprint,\n notice_blueprint,\n report_blueprint,\n)\n\n\nasync def ping(request: Request):\n return response.text('pong!')\n" }, { "alpha_fraction": 0.760869562625885, "alphanum_fraction": 0.760869562625885, "avg_line_length": 28.272727966308594, "blob_id": "edaa2411e2c41f88d7bbc3e532c19ee79e8c8f7f", "content_id": "7efa65f015e92b6db5356f17c532784204e1ed02", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 322, "license_type": "permissive", "max_line_length": 60, "num_lines": 11, "path": "/dms/views/account/__init__.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic import Blueprint\n\naccount_blueprint = Blueprint(\n name='account_blueprint',\n url_prefix='/account',\n)\n\nfrom views.account.auth import AuthView\nfrom views.account.signup import SignupView\naccount_blueprint.add_route(AuthView.as_view(), '/auth')\naccount_blueprint.add_route(SignupView.as_view(), '/signup')\n" }, { "alpha_fraction": 0.68544602394104, "alphanum_fraction": 0.68544602394104, "avg_line_length": 19.285715103149414, "blob_id": "435a8037d87b673b9f691b8e011a14abb6f83e67", "content_id": "0484bc7c8f595ff09afde55a5d1d128c31b987e9", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 426, "license_type": "permissive", "max_line_length": 67, "num_lines": 21, "path": "/dms/models/notice.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from datetime import datetime\n\nfrom extension import db\nfrom utils import kst_now\n\n\nclass BaseNoticeModel(db.Model):\n __abstract__ = True\n\n id: int = db.Column(db.Integer(), primary_key=True)\n post_date: datetime = db.Column(db.DateTime(), default=kst_now)\n title: str = db.Column(db.String())\n content: str = db.Column(db.String())\n\n\nclass NoticeModel(db.Model):\n pass\n\n\nclass RuleModel(db.Model):\n pass\n" }, { "alpha_fraction": 0.6569037437438965, "alphanum_fraction": 0.6569037437438965, "avg_line_length": 17.384614944458008, "blob_id": "0a50120de09e41534bde60868d814dba718bf428", "content_id": "ce5402a2f2b791cf228911612b2798be2d7e2cd3", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 239, "license_type": "permissive", "max_line_length": 50, "num_lines": 13, "path": "/dms/app.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic import Sanic\n\nfrom views import blueprint_group\n\n\ndef create_app() -> Sanic:\n app_ = Sanic()\n\n from views import ping\n app_.add_route(ping, '/ping', methods=['GET'])\n app_.blueprint(blueprint_group)\n\n return app_\n" }, { "alpha_fraction": 0.5837104320526123, "alphanum_fraction": 0.5837104320526123, "avg_line_length": 20.047618865966797, "blob_id": "a9c435b71e061bb242774f13f193907c0837567e", "content_id": "d1eceaa1670102ba202759cca6b91880c4612722", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 442, "license_type": "permissive", "max_line_length": 46, "num_lines": 21, "path": "/dms/views/apply/goingout.py", "repo_name": "SangminOut/DMS-Sanic", "src_encoding": "UTF-8", "text": "from sanic.request import Request\nfrom sanic.views import HTTPMethodView\n\n\nclass GoingoutApplyView(HTTPMethodView):\n async def get(self, request: Request):\n \"\"\"\n Response Goingout Apply Status\n \"\"\"\n pass\n\n async def post(self, request: Request):\n \"\"\"\n Apply Goingout\n \"\"\"\n pass\n\n async def delete(self, reqeuest: Request):\n \"\"\"\n Delete Goingout Apply\n \"\"\"\n" } ]

27

zM4loy/mp3-to-wav

https://github.com/zM4loy/mp3-to-wav

371cbb6ce672a9f6f7633282203168d34ac16f24

94cdbb3e1516dce3e8acb64729361fb4305a3dd8

4cb806a419b66b047f805b8a3cfd20cf559d41ad

refs/heads/main

2023-08-17T14:31:35.224671

2021-09-29T22:57:37

411,852,446

0

null

[ { "alpha_fraction": 0.7388535141944885, "alphanum_fraction": 0.7643312215805054, "avg_line_length": 32.105262756347656, "blob_id": "fb501f10fdb5c783d71eda46880d4da6a053b73c", "content_id": "a72524c5939e3207e2ae687e05e1d881ad95e16b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 637, "license_type": "no_license", "max_line_length": 66, "num_lines": 19, "path": "/mp3-to-wav.py", "repo_name": "zM4loy/mp3-to-wav", "src_encoding": "UTF-8", "text": "#import da lib pydub para manipular áudios\nfrom pydub import AudioSegment\n\n#definindo a função que converte o mp3 para wav\n#a função recebe como parâmetro o path do mp3\n#o path do output wav\n#e o volume em decibéis\ndef convert(path_mp3, path_wav, volume):\n #criando variavel audio que recebe o arquivo mp3\n audio = AudioSegment.from_mp3(path_mp3)\n\n #definindo a taxa de amostragem para 16000\n audio = audio.set_frame_rate(16000)\n\n #definindo o volume a partir do número de decibéis\n audio = audio.apply_gain(volume)\n\n #exportando o arquivo convertido em wav para o path especificado\n audio.export(path_wav, format=\"wav\")" }, { "alpha_fraction": 0.7777777910232544, "alphanum_fraction": 0.7925925850868225, "avg_line_length": 66.5, "blob_id": "2ae3bc6e370f559c5d91c1d9452feb03fa1b655d", "content_id": "80947c007e11e43325e07a155769290c2df63852", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 135, "license_type": "no_license", "max_line_length": 121, "num_lines": 2, "path": "/README.md", "repo_name": "zM4loy/mp3-to-wav", "src_encoding": "UTF-8", "text": "# mp3-to-wav\nA music format converter from mp3 to wav, where various music characteristics such as volume and bit rate can be changed.\n" } ]

2

laohixdxm/jan30

https://github.com/laohixdxm/jan30

da20a7ca6e5de112aa92bce995cb839570c5ca62

c53c55900cb55380ab5e8cfb31430270a04be629

df5e03735d771a881ef447ab9e40c139f887b86d

refs/heads/master

2020-04-23T23:28:30.506282

2014-02-01T18:27:03

null

0

null

[ { "alpha_fraction": 0.703180193901062, "alphanum_fraction": 0.7208480834960938, "avg_line_length": 34.375, "blob_id": "88dc1eff5410be661369bc472f3b061a9dc9786e", "content_id": "cd41e5c505dba094ed1d4b0c0c3f9c12de19deaa", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 283, "license_type": "no_license", "max_line_length": 120, "num_lines": 8, "path": "/gmail_draft.py", "repo_name": "laohixdxm/jan30", "src_encoding": "UTF-8", "text": "import imaplib\nimport time\nimport email\n\nconn = imaplib.IMAP4_SSL('imap.gmail.com', port = 993)\nconn.login('[email protected]', 'abcd86237232')\nconn.select('[Gmail]/Drafts')\nconn.append(\"[Gmail]/Drafts\", 'subject', imaplib.Time2Internaldate(time.time()), str(email.message_from_string('TEST')))\n" }, { "alpha_fraction": 0.6132075190544128, "alphanum_fraction": 0.6179245114326477, "avg_line_length": 18.272727966308594, "blob_id": "318726fa60bb6ff0fa15fc58673c5c64d722784d", "content_id": "85cd9b2637f2ef286c465b2586a7c4cff119ac0f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 212, "license_type": "no_license", "max_line_length": 49, "num_lines": 11, "path": "/cpp_list.py", "repo_name": "laohixdxm/jan30", "src_encoding": "UTF-8", "text": "import sys\n\nfp = open(\"/home/min/tmp/git_info/cpp_list\", \"r\")\nfor line in fp.readlines():\n#\tline.strip()\n#\tprint \"hello %s\" % line\n\tsys.stdout.write(\"%s\" % line)\nfp.close()\n\n#line = \"hello1\\n\"\n#print \"%s\" % line\n" }, { "alpha_fraction": 0.6967792510986328, "alphanum_fraction": 0.6999214291572571, "avg_line_length": 25.33333396911621, "blob_id": "f0ecd19c65d5c34b55b9f06a5a4da943212f8075", "content_id": "8b3044eed990f8be1bc1d18aa64918f2fe373fa3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1273, "license_type": "no_license", "max_line_length": 68, "num_lines": 48, "path": "/gmail_sender.py", "repo_name": "laohixdxm/jan30", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\nfrom smtplib import SMTP\nfrom smtplib import SMTPException\nfrom email.mime.text import MIMEText\nimport sys\n\n#Global variables\nEMAIL_SUBJECT = \"Email from Python script\"\nEMAIL_RECEIVERS = ['[email protected]']\nEMAIL_SENDER = '[email protected]'\nGMAIL_SMTP = \"smtp.gmail.com\"\nGMAIL_SMTP_PORT = 587\nTEXT_SUBTYPE = \"plain\"\n\ndef listToStr(lst):\n\t\"\"\"This method makes comma seperated list item string\"\"\"\n\treturn ','.join(lst)\n\ndef send_email(content, pswd):\n\t\"\"\"This method sends an email\"\"\"\n\n\t#Create the message\n\tmsg = MIMEText(content, TEXT_SUBTYPE)\n\tmsg[\"Subject\"] = EMAIL_SUBJECT\n\tmsg[\"From\"] = EMAIL_SENDER\n\tmsg[\"To\"] = listToStr(EMAIL_RECEIVERS)\n\n\ttry:\n\t smtpObj = SMTP(GMAIL_SMTP, GMAIL_SMTP_PORT)\n\t #Identify yourself to GMAIL EMSMTP server.\n \t smtpObj.ehlo()\n\t #Put SMTP connection in TLS mode all ehlo again\n\t smtpObj.starttls()\n\t smtpObj.ehlo()\n \t #Login to service\n\t smtpObj.login(user=EMAIL_SENDER, password=pswd)\n\t #send email\n \t smtpObj.sendmail(EMAIL_SENDER, EMAIL_RECEIVERS, msg.as_string())\n\t #close connection and session\n\t smtpObj.quit()\n\texcept SMTPException as error:\n\t print \"Errro: unable to send email : {err}\".format(err=error)\n\t\ndef main(pswd):\n\tsend_email(\"hello\", pswd)\n\nif __name__ == \"__main__\":\n\tmain(sys.argv[1])\n\t\n\t \n\n\n\n" } ]

3

sashadixie/ultimate-trash

https://github.com/sashadixie/ultimate-trash

a488d7a41065965088d87695df48cb407f66780c

82cc7eb0fbacdbc1cc807cd91778dce513aa4da7

5b432c60165dafaadbfd64f23c49c199d675bd07

refs/heads/main

2023-07-02T00:33:30.672853

2021-08-11T18:22:14

395,080,797

0

null

[ { "alpha_fraction": 0.6614906787872314, "alphanum_fraction": 0.6677018404006958, "avg_line_length": 34.77777862548828, "blob_id": "dedb71bc00c65a64bce73ccfcc0689e61d5fcf32", "content_id": "e4e78a5f9d4dda08e0951593fe3a75fb08faa26d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 322, "license_type": "no_license", "max_line_length": 112, "num_lines": 9, "path": "/test.py", "repo_name": "sashadixie/ultimate-trash", "src_encoding": "UTF-8", "text": "import pandas as pd\nimport os\n\ndf = pd.read_excel(\"empty.xlsx\", index_col=None, na_values=['NA'], usecols=None, names=None, header=None).values\nfor idx, val in enumerate(df):\n filetochange = str(idx + 1) + \".png\"\n newname = val[0] + \".png\"\n if os.path.exists(filetochange):\n os.rename(filetochange, newname)\n" }, { "alpha_fraction": 0.6795580387115479, "alphanum_fraction": 0.6850828528404236, "avg_line_length": 37.85714340209961, "blob_id": "5445de3c4e53abcc917e0727d0a7abddeb455a32", "content_id": "71a770678eb240ed0fdf5d8dfc7a62f1c7b12f11", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 543, "license_type": "no_license", "max_line_length": 92, "num_lines": 14, "path": "/js/index.js", "repo_name": "sashadixie/ultimate-trash", "src_encoding": "UTF-8", "text": "const XLSX = require('xlsx');\nconst fs = require('fs')\nconst workbook = XLSX.readFile('empty.xlsx');\nconst sheet_name_list = workbook.SheetNames;\nconst worksheet = workbook.Sheets[workbook.SheetNames[0]];\nconst columnA = Object.keys(worksheet).filter(x => /^A\\d+/.test(x)).map(x => worksheet[x].v)\nconsole.log(columnA)\ncolumnA.forEach((element, idx) => {\n const path = `${idx + 1}.png`\n if (fs.existsSync(path)) {\n fs.renameSync(path, `${element}.png`)\n }\n});\n// console.log(XLSX.utils.sheet_to_json(workbook.Sheets[sheet_name_list[0]]))" } ]

2

nyibelunger/helloapp

https://github.com/nyibelunger/helloapp

2acd3757430e0372285c776e3dfa5aff026b9b81

3532bf71655f96e872e3ad269238b13a7fa6ec67

62240174e208d10ab54eede59364ccac507e3cc4

refs/heads/master

2017-12-05T02:23:14.252615

2017-01-31T15:07:56

80,531,192

0

null

[ { "alpha_fraction": 0.6764934062957764, "alphanum_fraction": 0.687354564666748, "avg_line_length": 39.3125, "blob_id": "abf91f1a6bd4d8020677292632d622f6343335a3", "content_id": "49fda9dcba6a3d7abd5d913512a1920c40055c8d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1306, "license_type": "no_license", "max_line_length": 85, "num_lines": 32, "path": "/models.py", "repo_name": "nyibelunger/helloapp", "src_encoding": "UTF-8", "text": "from django.db import models\nfrom django.utils.encoding import smart_text as smart_unicode\nfrom datetime import datetime\n\nclass Pacient(models.Model):\n ##Základní údaje\n frst_name = models.CharField(max_length=256,verbose_name=\"Jméno\")\n scnd_name = models.CharField(max_length=256)\n email = models.EmailField(max_length=120, default='')\n #Datum přidání do databáze\n timestamp = models.DateTimeField(auto_now_add=True, auto_now=False, null=True)\n #Datum poslední úpravy\n updated = models.DateTimeField(auto_now_add=False, auto_now=True, null=True)\n is_healthy = models.BooleanField(default=True,verbose_name=\"Je plně zdráv.\")\n date_born = models.DateTimeField(auto_now_add=True, blank=True, null=True)\n\n CHOICES_POHLAVI = (\n (\"1\" , 'Muž'),\n (\"2\", 'Žena'),\n (None , 'Nevybráno'),\n )\n pohlavi = models.CharField(max_length=120, choices=CHOICES_POHLAVI, default=None)\n\n def __str__(self):\n return smart_unicode(\"Pacient: %s %s\" %(self.frst_name, self.scnd_name))\n\nclass RodAnam(models.Model):\n pacient = models.ForeignKey(Pacient, on_delete=models.CASCADE, default=None)\n ma_ra = models.BooleanField(default=False, verbose_name=\"Má RA:\")\n\n def __str__(self):\n return smart_unicode(\"%s má RA\" % self.pacient)" }, { "alpha_fraction": 0.6721527576446533, "alphanum_fraction": 0.6774193644523621, "avg_line_length": 32.043479919433594, "blob_id": "fa0e841776a2b87af809ebd1e041c521ea0f3a9d", "content_id": "db69a38d43aab37a727c30697013ae78751cc301", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1521, "license_type": "no_license", "max_line_length": 112, "num_lines": 46, "path": "/views.py", "repo_name": "nyibelunger/helloapp", "src_encoding": "UTF-8", "text": "from django.shortcuts import render, render_to_response, RequestContext, loader, HttpResponse, get_object_or_404\nfrom django.views.generic import TemplateView\nfrom .forms import PacientForm\nfrom.models import Pacient\n\n\nclass HomePageView(TemplateView):\n #aka pacient_list\n def get(self, request,**kwargs):\n return render(request, 'howdy/index.html', context=None)\n\n\nclass AboutPageView(TemplateView):\n template_name = 'howdy/about.html'\n\n#class PacientPageView(TemplateView):\n# form = PacientForm\n# template_name = 'howdy/pacient.html'\n\n#def PacientPageView(request):\n# form = PacientForm()\n# return render(request, 'howdy/pacient.html', {'form': form})\n\ndef pacient_new(request):\n form = PacientForm(request.POST or None)\n if form.is_valid():\n save_it = form.save(commit=False)\n save_it.save()\n\n return render_to_response(\"howdy/pacient.html\",\n locals(),\n context_instance=RequestContext(request))\n\ndef pacient_list(request):\n pacient_all = Pacient.objects.all()\n template = loader.get_template('howdy/index.html')\n context = {\n 'pacient_all': pacient_all,\n }\n return HttpResponse(template.render(context, request))\n\ndef pacient_detail(request, pacient_id):\n pacient = get_object_or_404(Pacient, pk = pacient_id)\n context = {'pacient': pacient}\n return render(request, 'howdy/pacient_detail.html', context)\n #return HttpResponse(\"<h3>Pacient číslo:\"+ pacient_id + \"</h3>\")" }, { "alpha_fraction": 0.5588055849075317, "alphanum_fraction": 0.5770871639251709, "avg_line_length": 41.07692337036133, "blob_id": "336262c42675cba52c210f71994230b7f81accdd", "content_id": "88e1a0a98a8410d27f989069727c49fd98f264f0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1647, "license_type": "no_license", "max_line_length": 136, "num_lines": 39, "path": "/migrations/0001_initial.py", "repo_name": "nyibelunger/helloapp", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n# Generated by Django 1.9.5 on 2017-01-30 18:06\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Pacient',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('frst_name', models.CharField(max_length=256)),\n ('scnd_name', models.CharField(max_length=256)),\n ('email', models.EmailField(default='', max_length=120)),\n ('timestamp', models.DateTimeField(auto_now_add=True, null=True)),\n ('updated', models.DateTimeField(auto_now=True, null=True)),\n ('is_healthy', models.BooleanField(default=True, verbose_name='Je plně zdráv.')),\n ('date_born', models.DateTimeField(auto_now_add=True, null=True)),\n ('pohlavi', models.CharField(choices=[('1', 'Muž'), ('2', 'Žena'), (None, 'Nevybráno')], default=None, max_length=120)),\n ],\n ),\n migrations.CreateModel(\n name='RodAnam',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('ma_ra', models.BooleanField(default=False, verbose_name='Má RA:')),\n ('pacient', models.ForeignKey(default=None, on_delete=django.db.models.deletion.CASCADE, to='howdy.Pacient')),\n ],\n ),\n ]\n" }, { "alpha_fraction": 0.6354343891143799, "alphanum_fraction": 0.6439523100852966, "avg_line_length": 31.66666603088379, "blob_id": "986f88079161d11cc07a5894b0373a4d0d97ee4d", "content_id": "8699284fdb754f4fb26aadd3cf75e08b4c7718c5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 587, "license_type": "no_license", "max_line_length": 84, "num_lines": 18, "path": "/urls.py", "repo_name": "nyibelunger/helloapp", "src_encoding": "UTF-8", "text": "from howdy import views\nfrom django.conf.urls import url, patterns, include\n\napp_name = 'howdy'\n\nurlpatterns = [\n #url(r'^$', views.HomePageView.as_view()),\n url(r'^$', views.pacient_list, name='pacient_list'),\n url(r'^/about/$', views.AboutPageView.as_view(), name= 'about_me'),\n\n # pacient_list/new_pacient\n url(r'^/pacient_new/$', views.pacient_new, name='pacient_new'),\n #url(r'^pacient/$', views.PacientPageView.as_view(), name= 'pacient'),\n\n #pacient_list/pacient_new/123/\n url(r'^/(?P<pacient_id>[0-9]+)/$', views.pacient_detail, name='pacient_detail'),\n\n]" }, { "alpha_fraction": 0.7102272510528564, "alphanum_fraction": 0.7113636136054993, "avg_line_length": 35.70833206176758, "blob_id": "50be420ae0bb34437e3237688489c283fd7dbe93", "content_id": "001782814b95770619c401c451727b141f69c1eb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 880, "license_type": "no_license", "max_line_length": 123, "num_lines": 24, "path": "/forms.py", "repo_name": "nyibelunger/helloapp", "src_encoding": "UTF-8", "text": "from django.forms import ModelForm, RadioSelect\nfrom howdy.models import Pacient, RodAnam\nfrom django import forms\nfrom django.contrib.admin import widgets\nfrom django.forms import DateTimeField, DateTimeInput\n\n\nclass PacientForm(ModelForm):\n type = forms.ChoiceField(choices=Pacient.CHOICES_POHLAVI, widget=RadioSelect)\n type2 = forms.MultipleChoiceField(required=False, widget=forms.CheckboxSelectMultiple, choices=Pacient.CHOICES_POHLAVI)\n event_date = forms.DateTimeField(widget=forms.widgets.DateTimeInput())\n\n class Meta:\n model = Pacient\n fields = ['frst_name','scnd_name', 'email','pohlavi','is_healthy']\n\n def __init__(self):\n super(PacientForm, self).__init__()\n self.fields['date_born'].widget = widgets.AdminDateWidget()\n\nclass RaForm(ModelForm):\n class Meta:\n model = RodAnam\n fields = ['ma_ra']" }, { "alpha_fraction": 0.7777777910232544, "alphanum_fraction": 0.7777777910232544, "avg_line_length": 23.08333396911621, "blob_id": "fbb0e65e161ed0ff2a86dc724347da50f3aeedec", "content_id": "12c81e51e69a07f9d5d16b3edf84dcaedfcc947b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 289, "license_type": "no_license", "max_line_length": 42, "num_lines": 12, "path": "/admin.py", "repo_name": "nyibelunger/helloapp", "src_encoding": "UTF-8", "text": "from django.contrib import admin\n\n# Register your models here.\nfrom .models import Pacient\nfrom howdy.forms import PacientForm\n\nclass PacientAdmin(admin.ModelAdmin):\n class Meta:\n model = Pacient\n\nadmin.site.register(Pacient, PacientAdmin)\n#třeba registrovat RA, ale DO pacienta" } ]

6

marquavious/pinterest_challenge

https://github.com/marquavious/pinterest_challenge

c39b7b276ac473abeefe4f42ca6d11fe413a6cca

b111150a4bf45a7f500ef9e156f38f6900195b8d

0877e48dcb5e882791b6e5133d2222cb7680c6a5

refs/heads/master

2021-03-24T11:55:43.618534

2017-10-06T23:16:24

105,960,741

0

null

[ { "alpha_fraction": 0.6424526572227478, "alphanum_fraction": 0.6516305208206177, "avg_line_length": 35.57857131958008, "blob_id": "4e412d3536b2af134824224d59735dda77b6eac3", "content_id": "fb3d116f89dd0a40199af712307dbf059f409e21", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5121, "license_type": "no_license", "max_line_length": 145, "num_lines": 140, "path": "/app.py", "repo_name": "marquavious/pinterest_challenge", "src_encoding": "UTF-8", "text": "from flask import Flask,request\nimport time\nimport datetime\n\napp = Flask(__name__)\n\n# Home page\[email protected]('/')\ndef home():\n return \"\"\" Hello, Pinterest! All the avalible routes are here:\\n\n /hello\\n\n /date\\n\n /compute\\n\\n\n Here are preformatted urls based off the challenge\\n\n /hello?firstname=tien&lastname=nguyen&gender=m\\n\n /compute?num1=5&num2=3&operation=subtract\\n\n /date\\n\\n\n Thank you for sending me this challenge. Enjoy!!\n \"\"\"\n\n# /hello route, GET\[email protected]('/hello', methods = ['GET'])\ndef hello():\n\n # Grabs parameters from the URL\n firstName = request.args.get('firstname')\n lastName = request.args.get('lastname')\n gender = request.args.get('gender')\n\n # Checks to make sure all parameters are filled out, if not return error\n if urlHasAllParameters([firstName,lastName,gender]) is False:\n return \"Please make sure the url is correctly formatted and all parameters are filled.\"\n\n # Created this check because there have been errors\n try:\n # If this goes through sucessfuly, the variables will be converted\n firstName = str(firstName)\n lastName = str(lastName)\n gender = str(gender)\n # If not, return error\n except AttributeError:\n return \"Unable to process greeting. Please check the format of the url.\"\n\n # Trim the strings and give them proper formatting\n firstName = firstName.replace(\" \", \"\").title()\n lastName = lastName.replace(\" \", \"\").title()\n gender = gender.replace(\" \", \"\").lower()\n\n # Length check to make sure the user enterd the correct things even after the formatting\n if len(firstName) <= 0 or len(lastName) <= 0 or len(gender) <= 0:\n return \"Please make sure the url is correctly formatted and all parameters are filled.\"\n\n # Returns the correct prefix based on the gender parameter\n # Check the 'returnGenderPrefix' fucntion\n prefix = returnGenderPrefix(gender)\n\n # Formats the above variables and returns\n return \"Hello {}{} {}!\".format(prefix,firstName,lastName)\n\n# /compute route, GET\[email protected]('/compute', methods = ['GET'])\ndef compute():\n\n # Grabs parameters from the URL\n num1 = request.args.get('num1')\n num2 = request.args.get('num2')\n operation = request.args.get('operation')\n\n # Checks to make sure all parameters are filled out if not, return error\n if urlHasAllParameters([num1,num2,operation]) is False:\n return \"Please make sure the url is correctly formatted and all parameters are filled.\"\n\n # Trims the strings\n num1 = num1.replace(\" \", \"\")\n num2 = num2.replace(\" \", \"\")\n operation = operation.replace(\" \", \"\")\n\n # Error check that the variables can be converted\n try:\n # If this goes through sucessfuly, the variables will be converted\n num1 = float(num1)\n num2 = float(num2)\n # If not, return error\n except ValueError:\n return \"Recieved an uncomputable value. Please make sure the url is correctly formatted and all parameters are filled.\"\n\n # Here we create the result if the 'computeValues'function and cast it to a string.\n computedValue = str(computeValues(num1,num2,operation))\n\n # Here we return the result if the 'computeValues'function in a nice to read format.\n return \"Here is the computed value result: {}\".format(computedValue)\n\n# /date route, GET\[email protected]('/date', methods = ['GET'])\ndef date():\n\n # Use datetime to retrive the current date and then format it\n currentDate = datetime.datetime.now().strftime(\"%Y-%m-%d\")\n\n # Return date\n return \"Current date: {}\".format(currentDate)\n\n# This function checks to make sure all parameters are filled out\ndef urlHasAllParameters(parameters):\n for param in parameters:\n if param == None :\n return False\n return True\n\n# This fucntion takes in a string and returns a name prefix\ndef returnGenderPrefix(gender):\n if gender == \"m\" or gender == \"male\":\n return \"Mr.\"\n elif gender == \"f\" or gender == \"female\":\n return \"Mrs.\"\n elif gender == \"n\" or gender == \"none\":\n return \"non-binary human named \"\n else:\n return \"human being named \"\n\n# This function takes in two integers and a operation string then returns the result\ndef computeValues(num1,num2,operation):\n if operation == \"add\":\n return round(num1 + num2, 2)\n elif operation == \"subtract\" or operation == \"-\":\n return round(num1 - num2, 2)\n elif operation == \"multiply\" or operation == \"*\":\n return round(num1 * num2, 2)\n elif operation == \"divide\" or operation == \"/\":\n # Edge case check, no division my 0 is allowed\n # The answer would be the first number\n if num2 == 0:\n return num1\n # Else return the result\n return round(num1 / num2, 2)\n # Edge case check\n return \"ERROR. Your number could not be computed. Please check the URL is in this format:\\n /hello?num1=INT&num2=INT&operation=OPERATION\"\n\nif __name__ == '__main__':\n app.run(port=5000, debug=True)\n" }, { "alpha_fraction": 0.7188577055931091, "alphanum_fraction": 0.7341211438179016, "avg_line_length": 53.89189147949219, "blob_id": "67bc07262d6507b1d8540486e08267c0a17d9cc3", "content_id": "771f1635872db459d8f8ef1709e694b4b92bfe55", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 2079, "license_type": "no_license", "max_line_length": 213, "num_lines": 37, "path": "/readme.md", "repo_name": "marquavious/pinterest_challenge", "src_encoding": "UTF-8", "text": "![N|jjkn](https://business.pinterest.com/profiles/contrib/pin/badge-wordmark.svg)\n# Pinterest take-home assignment\n\n#### Thank you so much for giving me this fun challenge! It challenged my googling skills and the ability to utilize my resources. I very much enjoyed this assignment!\n# Steps to get it running\n ##### 1. Unzip and navigate to the folder in the terminal\n ##### 1. If you don't have Flask installed, while in the folder run ```pip3.5 install flask```\n ##### 3. Once that's installed, run the application with ```python3.5 app.py```\n ##### 4. On the home screen, you'll see the available options that will allow you to use and test out the application\n\n### Now, if you don't feel like doing all the above, I hosted it on a server for you! :D\n### Here is the link: [https://pintrest-challenge.herokuapp.com/](https://pinterest-challenge.herokuapp.com/)\n\n\n# Challenge\n\n```\nBuild a simple REST-based web server in Scala or Python that supports the following features:\n#1\nRespond to requests of the form “/hello?firstname={first name}&lastname={last name}&gender={m/f}” and respond with “Hello Mr {First Name} {Last Name}” or “Hello Ms {First Name} {Last Name}” depending on the gender\nExample: the request “/hello?firstname=tien&lastname=nguyen&gender=m” returns “Hello Mr Tien Nguyen”\n#3\nRespond to requests of the form “/compute?num1={num1}&num2={num2}&operator={add/subtract/multiply/divide}” and respond with the result\nExample: the request “/hello?num1=5&num2=3&operation=subtract” returns “2” (5-3=2)\n#4\nRespond to requests of the form “/date” with the current date in the form “yyyy-mm-dd”\nExample: “/date” returns “2017-09-20”\n```\n\n# Edge cases considered\n - Bad Url formatting\n - Missing parameters\n - If user is non-binary\n \n# Assumptions\n- All numbers are accepted, not just integers. \n- In the challenge description, the URL for computing is stated with the `/compute` path, however, the example given uses the `/hello` path. I just went with the `/compute` pathway as this makes for a cleaner API.\n" } ]

2

nnodyy/obrazky

https://github.com/nnodyy/obrazky

97714adebe6b73155ee8fd45e44c3acec4dee65a

7a9bcd481f929ade23fed4f161535994af917e79

84e8a0f8408172ce4ad3061c618caafd0fd39b02

refs/heads/master

2021-01-24T00:04:23.698564

2018-02-25T18:03:10

122,752,615

0

null

[ { "alpha_fraction": 0.7777777910232544, "alphanum_fraction": 0.7777777910232544, "avg_line_length": 12, "blob_id": "8f43ed1dc01b64fe025175bc00f3eaebfa6ce043", "content_id": "8ad5c93bae317f403dbd698395046163fcec94da", "detected_licenses": [ "Apache-2.0" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 27, "license_type": "permissive", "max_line_length": 15, "num_lines": 2, "path": "/README.md", "repo_name": "nnodyy/obrazky", "src_encoding": "UTF-8", "text": "# obrazky\nignore pictures \n" }, { "alpha_fraction": 0.6477987170219421, "alphanum_fraction": 0.7106918096542358, "avg_line_length": 15.666666984558105, "blob_id": "1ff2d017d1db912c549d36bf9ce8c1969983ce4c", "content_id": "7d96b0976d46dcf17751835d17ce1a5bf4a8cc37", "detected_licenses": [ "Apache-2.0" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 159, "license_type": "permissive", "max_line_length": 50, "num_lines": 9, "path": "/obrazky.py", "repo_name": "nnodyy/obrazky", "src_encoding": "UTF-8", "text": "from turtle import forward, left, right, getcanvas\r\n\r\nforward(50)\r\nleft(60)\r\nforward(50)\r\nright(60)\r\nforward(50)\r\n\r\ngetcanvas().postscript(file='obrazky.ps')\r\n" } ]

2

shadowteias/Mighty

https://github.com/shadowteias/Mighty

f5cf78865aacbf765ec4b72a7b71b36c69c33ec2

7305b0531c2cd0297c3ea2927068256dbac19dc6

747781704150f718d6f7d10a2fda813b07761eb9

refs/heads/master

2020-04-10T17:42:03.093791

2019-01-10T01:49:36

161,181,407

0

null

[ { "alpha_fraction": 0.45750582218170166, "alphanum_fraction": 0.4789765477180481, "avg_line_length": 34.94533920288086, "blob_id": "9896abe9574dc75554aa0a652c2dd0fc700c0576", "content_id": "49a819e47d182a7e4714a6075e404f5fedbb4e5f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 13306, "license_type": "no_license", "max_line_length": 107, "num_lines": 311, "path": "/player.py", "repo_name": "shadowteias/Mighty", "src_encoding": "UTF-8", "text": "# import \nimport re, random\nimport gamefunctions as gf\n\n# 변수 정의\nmighty = ['S', 14]\njoker = ['joker', 0]\n# giruda\n# grave\nshapes = ['C', 'D', 'H', 'S']\n\n#게임 플레이 과정에서 정해진 기루다, 마이티 같은거 어케 자동으로 못 가져오나... 인풋으로 안넣고도.\n\nclass Player(object):\n \"\"\"\n 나는\n 핸드가 있어야 하고\n 내 점수카드를 인식해야 하고\n 남의 카드 낸걸 카운팅 해야 하고\n 누가 점수카드를 몇 장 가졌는지 알아야 하고\n 지금이 몇 라운드인지 알아야 하고\n 기루나 조커콜이나 마이티 카드를 알아야 하고\n 조커나 마이티가 나왔는지 알아야 하고\n 카드를 선택해서 낼 줄 알아야 하고\n 남이 어떤 카드가 소진됬는지 알아야 하고\n 자기 자리를 기준으로 주공이 어딨는지, 프랜드가 어딨는지 포지션(자리)를 알아야 하고\n \n 공약을 할 줄 알아야 하고\n 프랜드를 선택할줄 알아야 하고\n \n 야당/주공/프랜드일때 플레이하는법을 알아야 하고\n 이에 따라 카드를 선택해서 낼 줄 알아야 하고\n 내 정체가 드러났는지 알아야 하고\n \n 어쩌면\n 내 누적 점수를 기억해야 할 수도 있고\n 세이브/로드로 불러오기가 가능해야 할지도\n \n 나는 아래 정보로 공약을 하고,\n 내 핸드, 남의 공약\n \n 나는 아래 정보로 카드를 내요.\n 역할(주공,프랜드,야당), 핸드, 자리(누가누가옆에앉았나)/차례, 라운드카드, \n 몇번째턴인지, 프랜드가 밝혀졌는지, 내가 프랜드인데 그게 드러났는지(혹은 의심받는지...이건 어케 구현하냐;;),\n 그동안 나온 히스토리(grave를 기억하면 되겠군), 누가 무슨 카드가 떨어졌는지(남의 카드를 의심할 줄 알아야 하나?)\n \n \"\"\"\n \n def __init__(self):\n \"\"\"\n 이건 AI플레이어잆니다\n \"\"\"\n self.name = \"\" #이름. 예를 들어 윤희\n self.hand = [] #내가 가진 카드\n self.field = [] #내 현재 점수카드\n self.score = 0 #내 현재 점수\n self.role = [0,0] #00:야당, 01:프랜드, 10: 주공\n self.grave = []\n# self. #의심도(?) 누가누가 프랜드같은지... 추론은 어케 해야 하냐\n# 피하고 싶은 상황: 눈앞에서 뻔히 프랜드같은놈이 주공한테 점수 퍼주는데 프랜드인지 모르고 걔한테 점수 주는 짓.\n# 혹은 야당끼리 협력 못하고 모두 서로를 프랜드로 보고 자기 점수만 늘리려고 하는 전략.\n# 피하기 위한 정보: 누가 무슨 카드를 냈나. 그 때 상황이 (필드가) 어땠나. \n \n def setMighty(self,giruda):\n if giruda == 'S':\n mighty = ['D', 14]\n else: mighty = ['S', 14]\n return mighty\n\n \n def pledge(self, others): #공약... 쉽지 않겠는걸\n oath = [\"\",1]\n #핸드에서 한 카드 종류가 몇개인지\n #핸드에 점카는 몇개인지\n #핸드에 특수카드(마이티(빨마), 조커, 조커콜)는 몇갠지, 어떤 조합인지...\n a = gf.shapeCounter(self.hand)\n b = max(a)\n maxIndex1 = a.index(b)\n if a.count(b) == 3 and gf.dealCount(self.hand) >5 and others[1] <17:\n oath[0] = \"noGiru\"\n if others[1] < 5: oath[1] = 12\n elif others[0] != 'noGiru':\n oath[1] = others[1]\n else:\n oath[1] = others[1]+1\n elif a.count(b) == 2: # 33 또는 44 또는 55다, \n maxIndex2 = a[maxIndex1+1:].index(b) + maxIndex1 + 1\n shapeCards1 = gf.shapeCollector(self.hand, shapes[maxIndex1])\n shapeCards2 = gf.shapeCollector(self.hand, shapes[maxIndex2])\n # print(shapeCards1)\n # print(shapeCards2)\n # print(b)\n if b in [3,4] and gf.dealCount(self.hand) > 6:\n oath[0] = \"noGiru\"\n if others[1] < 5: oath[1] = 12\n elif others[0] != 'noGiru':\n oath[1] = others[1]\n else:\n oath[1] = others[1]+1\n else: #b = 5임, 두 모양으로 5개씩 <Error!!!!> b==4인 경우도 있구나! \n sum1 = 0\n sum2 = 0\n for i in range(b):\n sum1 += shapeCards1[i][1]\n sum2 += shapeCards2[i][1]\n if sum1 >= sum2 and int((sum1+6)/4) > others[1]:\n oath[0] = shapes[maxIndex1]\n if others[0] != 'noGiru': oath[1] = others[1] + 1\n else: oath[1] = others[1] + 2\n elif int((sum2+6)/4) > others[1]:\n oath[0] = shapes[maxIndex2]\n if others[0] != 'noGiru': oath[1] = others[1] + 1\n else: oath[1] = others[1] + 2\n else: pass\n elif b >= 5: # 최고 많은 카드가 한 모양일때. 아마 대다수의 경우. 6,7,8,9,10이 가능하다. 졸라많네.\n sum1 = 0\n shapeCards1 = gf.shapeCollector(self.hand, shapes[maxIndex1])\n for i in range(b):\n sum1 += shapeCards1[i][1]\n if int((sum1+6)/4) > others[1]:\n oath[0] = shapes[maxIndex1]\n if others[0] != 'noGiru': oath[1] = others[1] + 1\n else: oath[1] = others[1] + 2\n else: pass\n else: pass\n return oath\n \n# if max(shapeCounter(self.hand)) >=4:\n# if shapeCounter(self.hand).index(max(shapeCounter(self.hand)))\n# oath[0] = random.choice([\"H\",\"S\",\"C\",\"D\",\"noGiru\"])\n# oath[1] = int(8*random.random()*random.random()) + 13\n #random.randrange(13,21)\n\n def kingGrave(self,grave, giruda):\n \"\"\" 여기 tf 로 대체해야 할 부분임. 룰 베이스로 할까... \"\"\"\n graveCandidate = self.hand + grave\n mighty = self.setMighty(giruda)\n if mighty in graveCandidate:\n graveCandidate.remove(mighty)\n if joker in graveCandidate:\n graveCandidate.remove(joker)\n \n #아래에 노기루가 고려 안되어있다 젠장..... shapeCounter에도 그렇고 아래 로직에도 그렇고....\n if giruda == 'noGiru':\n while len(graveCandidate) > len(grave):\n giruMax = 0\n findMaxIndex = 0\n for i in graveCandidate:\n if i[1] > giruMax:\n giruMax = i[1]\n findMaxIndex = graveCandidate.index(i)\n graveCandidate.pop(findMaxIndex)\n elif (len(graveCandidate) - gf.shapeCounter(graveCandidate, giruda)) > len(grave): #기루 아닌게 버리기 충분할때\n tempList = []\n print('giru delete: '+ str(giruda))\n for i in graveCandidate:\n if i[0] == giruda: \n tempList.append(i)\n for i in tempList:\n graveCandidate.remove(i)#논기루만 남긴다\n print(i)\n\n while len(graveCandidate) > len(grave): #버릴만큼 남을때까지\n findMax = 0\n findMaxIndex = 0\n for i in graveCandidate: #최대값 카드를 찾는 loop\n if i[1] > findMax:\n findMax = i[1]\n findMaxIndex = graveCandidate.index(i)\n graveCandidate.pop(findMaxIndex) #여기 버릴만큼만 남았다\n \n else: #기루 아닌게 버리기 부족할때. 논기루 1개 놔두고 나머지는 기루에서 버린다. \n giruset = gf.shapeCollector(graveCandidate, giruda)\n nonGiruset = graveCandidate +[]\n if len(giruset) != len(graveCandidate): #기루 아닌것도 들고있을때 있을 때\n for k in range(len(graveCandidate) - len(giruset) - 1): #기루중에서 최종적으로 남길 걸 없앤다\n giruMax = 0\n findMaxIndex = 0\n for i in giruset:\n if i[1] > giruMax:\n giruMax = i[1]\n findMaxIndex = giruset.index(i)\n if i in nonGiruset: nonGiruset.remove(i) #하는김에 논기루셋 만들어놓기\n giruset.pop(findMaxIndex)\n for k in range(len(nonGiruset)-1): #논기루 최대 하나만 남겨야 해\n findMin = 15\n findMinIndex = 0\n for i in nonGiruset:\n if i[1] < findMin:\n findMin = i[1]\n findMinIndex = nonGiruset.index(i)\n nonGiruset.pop(findMinIndex) #논기루 min 제거한다.\n graveCandidate = giruset + nonGiruset #여기 버릴만큼만 남았다\n else: #논기루 없을때 (다 기루임 ㄷㄷ)\n for k in range(len(graveCandidate) - len(grave)): #무덤 갯수만큼 남겨야 함, 맥스를 쓰레기 후보에서 제거\n giruMax = 0\n findMaxIndex = 0\n for i in graveCandidate:\n if i[1] > giruMax:\n giruMax = i[1]\n findMaxIndex = graveCandidate.index(i)\n graveCandidate.pop(findMaxIndex)#여기 버릴만큼만 남았다\n \n self.hand = self.hand + grave\n for i in graveCandidate:\n self.hand.remove(i)\n graveAgain = graveCandidate\n return graveAgain\n\n \n def callFriend(self, giruda):\n mighty, jokerCall = gf.setMightyJokercall(giruda)\n if mighty not in self.hand:\n return mighty\n elif joker not in self.hand:\n return joker\n elif giruda == 'noGiru':\n if ['S', 14] not in self.hand:\n return ['S', 14]\n elif ['H', 14] not in self.hand:\n return ['H', 14]\n elif ['C', 14] not in self.hand:\n return ['C', 14]\n elif ['D', 14] not in self.hand:\n return ['D', 14]\n elif ['C', 13] not in self.hand:\n return ['C', 13]\n elif ['D', 13] not in self.hand:\n return ['D', 13]\n elif ['H', 13] not in self.hand:\n return ['H', 13]\n elif ['S', 13] not in self.hand:\n return ['S', 13]\n elif ['C', 12] not in self.hand:\n return ['S', 12]\n elif [giruda, 14] not in self.hand:\n return [giruda, 14]\n elif [giruda, 13] not in self.hand:\n return [giruda, 13]\n elif [giruda, 12] not in self.hand:\n return [giruda, 12]\n elif [giruda, 11] not in self.hand:\n return [giruda, 11]\n elif [giruda, 10] not in self.hand:\n return [giruda, 10]\n elif [giruda, 9] not in self.hand:\n return [giruda, 9]\n elif [giruda, 8] not in self.hand:\n return [giruda, 8]\n elif [giruda, 7] not in self.hand:\n return [giruda, 7]\n elif [giruda, 6] not in self.hand:\n return [giruda, 6]\n else: return \"noFriend\"\n print(\"친구고르기\")\n \n def possibleOptions(self, turnShape, mighty): \n if turnShape == 'all':\n count = 0\n else:\n count = gf.shapeCounter(self.hand, turnShape)\n if count > 0:\n options = gf.shapeCollector(self.hand, turnShape)\n if mighty in self.hand:\n options.append(mighty)\n if joker in self.hand:\n options.append(joker)\n else:\n options = self.hand.copy()\n return options\n\n def sayShape(self):\n # when startCard is 'joker', then first player needs to say shape.\n return 'S'\n\n\n def pickCard(self, bits, turnShape, mighty): #젤어렵겠다... AI 구현 with TF\n# print('카드 고르기')\n# self.hand[random.randrange(0,len(self.hand))]\n myOptions = self.possibleOptions(turnShape, mighty)\n myPick = myOptions[random.randrange(0,len(myOptions))]\n self.hand.remove(myPick)\n return myPick\n\n def jokerCallPick(self, bits, turnShape, mighty):\n if joker in self.hand:\n self.hand.remove(joker)\n return joker\n else:\n return self.pickCard(bits, turnShape, mighty)\n# # 인풋은 뭐뭐여야 할까\n# # 내가 선일때, 선 아닐때를 구분해야 할까...\n# if len(self.hand) > 0:\n# return self.hand.pop()\n# else: \n# print('noCardError')\n# return False\n# # list.count(s) #리스트 내 s를 세는 함수\n\n\n\n\n\"\"\"\n용어 수정할까나\n 주공 President, Declarer\n 공약 bidding, bid\n 선 lead\n 야당 defender\n\n선을 뭐라고 부르지...roundShape?\n\"\"\"" }, { "alpha_fraction": 0.48272016644477844, "alphanum_fraction": 0.5104050636291504, "avg_line_length": 39.443607330322266, "blob_id": "1cb76d6ffbbf973b783b34e5aee0569537304412", "content_id": "d52e3894d7a8408d6ea1970467b5d81f75a49a3c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5882, "license_type": "no_license", "max_line_length": 111, "num_lines": 133, "path": "/user.py", "repo_name": "shadowteias/Mighty", "src_encoding": "UTF-8", "text": "# import\nimport gamefunctions as gf\n\nmighty = ['S', 14]\njoker = ['joker', 0]\nshapes = ['C', 'D', 'H', 'S']\n\nclass User(object):\n \n def __init__(self):\n \"\"\"\n 이건 프로그램을 사용하는 게이머(닝겐)입니답.\n \"\"\"\n self.name = \"ningen\"\n self.hand = []\n self.field = []\n self.score = 0 \n self.role = [0,0] #00:야당, 01:프랜드, 10: 주공\n self.grave = []\n# 함수명은 동일하게 가자, 내부 구현만 다르면 될것 같아\n\n### 이하 복붙 영역 ###\n def setMighty(self,giruda):\n if giruda == 'S':\n mighty = ['D', 14]\n else: mighty = ['S', 14]\n return mighty\n \n def pledge(self, others): #공약... 쉽지 않겠는걸\n biddingYesNo = \"\"\n myBidding = [\"\",0]\n biddingYesNo = input(self.name + ', will you pledge? (y/n) ')\n while not(biddingYesNo in ['y', 'n']):\n biddingYesNo = input('try again, will you pledge? (y/n) ')\n if biddingYesNo == \"n\":\n return myBidding\n else:\n if others[0] != 'noGiru':\n minBidding = others[1] + 1\n else:\n minBidding = others[1] + 2\n myBidding[0] = input('choose shape, one of ' + str(shapes) + \" or 'noGiru' :\")\n while not(myBidding[0] in shapes) and not(myBidding[0] == 'noGiru'):\n myBidding[0] = input('try again, one of ' + str(shapes) + \" or 'noGiru' :\")\n\n\n if myBidding[0] != 'noGiru':\n myBidding[1] = int(input('Choose number 비트윈 ' + str(minBidding) + '~20 : '))\n while myBidding[1] < minBidding or myBidding[1] > 20:\n myBidding[1] = int(input('try again, Choose number 비트윈 ' + str(minBidding) + '~20 : '))\n else:\n myBidding[1] = int(input('Choose number 비트윈 ' + str(minBidding - 1) + '~20 : '))\n while myBidding[1] < minBidding-1 or myBidding[1] > 20:\n myBidding[1] = int(input('try again, Choose number 비트윈 ' + str(minBidding - 1) + '~20 : '))\n print(self.name + ', your bidding is ')\n return myBidding\n\n #핸드에서 한 카드 종류가 몇개인지\n #핸드에 점카는 몇개인지\n #핸드에 특수카드(마이티(빨마), 조커, 조커콜)는 몇갠지, 어떤 조합인지...\n # print('this is your hand: ' + str(self.hand))\n # while not(oath[0] in [\"H\",\"S\",\"C\",\"D\",\"noKiru\"]):\n # oath[0] = input(self.name + ', Pick one of those \"H\",\"S\",\"C\",\"D\",\"noKiru\" : ')\n # if not(oath[0] in [\"H\",\"S\",\"C\",\"D\",\"noKiru\"]): print('Plz choice again')\n # while not(oath[1] in [1,13,14,15,16,17,18,19,20]): #21은 패스\n # oath[1] = int(input('How many do you pledge? (1 for pass, 13~20 to pledge)'))\n # if not(oath[1] in [1,13,14,15,16,17,18,19,20]): print('Plz say again')\n # #random.randrange(13,21)\n \n def friendCall(self):\n print(\" << 친구고르기 >> \")\n print(self.name + ', This is your hand: ' +str(self.hand))\n userFriend = [\" \", 0]\n userPick[0] = input('Choose your friend! say one of ' + str(shapes) + ', or \"joker\"')\n while not(userFriend[0] in shapes) and not(userFriend[0] == 'joker'):\n userPick[0] = input('try gain. Choose one of ' + str(shapes) + ', or \"joker\"')\n if userPick[0] == \"joker\":\n return joker\n else:\n userPick[1] = int(input('Choose number of 2 ~ 14 (14 for A, 13 for K, ...)'))\n while not(userFriend[1] in [2,3,4,5,6,7,8,9,10,11,12,13,14]):\n userPick[1] = int(input('try again. one of 2 ~ 14 (14 for A, 13 for K, ...)'))\n return userPick\n\n def possibleOptions(self, turnShape, mighty): \n if turnShape == 'all':\n count = 0\n else:\n count = gf.shapeCounter(self.hand, turnShape)\n if count > 0:\n options = gf.shapeCollector(self.hand, turnShape)\n if mighty in self.hand:\n options += mighty\n if joker in self.hand:\n options += joker\n else:\n options = self.hand.copy()\n return options\n\n def sayShape(self):\n # when startCard is 'joker', then first player needs to say shape.\n return 'S'\n\n def pickCard(self, bits, turnShape, mighty): #젤어렵겠다... AI 구현 with TF\n# print('카드 고르기')\n# self.hand[random.randrange(0,len(self.hand))]\n#상황에 따라 보기를 주고 그 중에 고르라고 하는게 맞을것 같다. pickable card.\n if len(self.hand) <= 0:\n print('noCardError')\n return False\n \n #선 이 뭔지 파악(선이 뭔지 인자로 받아야) (혹은 내가 선인지)\n #선 과 같은 카드 있는지 확인 (내 핸드는 내가 알지 self.hand 임)\n #선 과 같은 카드 있으면 그 중에 선택(선, 마이티, 조커)\n #선 과 같은 카드 없으면 모든 핸드중에 선택\n \n print(self.name + ', This is your hand: ' +str(self.hand))\n userPick = \" \"\n while not(userPick in self.hand):\n print(\"your hand is 0 ~ \" + str(len(self.hand) - 1))\n userPick = int(input('Witch card will you choose? Tell me index '))\n # if not(userPick in self.hand): print('Plz choice again')\n if userPick > len(self.hand) - 1: print('Plz choice again')\n else: userPick = self.hand[userPick]\n self.hand.remove(userPick)\n return userPick\n \n def jokerCallPick(self, bits, turnShape, mighty):\n if joker in self.hand:\n self.hand.remove(joker)\n return joker\n else:\n return self.pickCard(bits, turnShape, mighty)\n\n\n\n" }, { "alpha_fraction": 0.43467336893081665, "alphanum_fraction": 0.46510329842567444, "avg_line_length": 28.368852615356445, "blob_id": "a0cd788f83c51a836e228dd759ac0608ff48c3ae", "content_id": "9aee93b0e3cf7ad83db12ee775e0e4f26ef678cd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4452, "license_type": "no_license", "max_line_length": 102, "num_lines": 122, "path": "/environment.py", "repo_name": "shadowteias/Mighty", "src_encoding": "UTF-8", "text": "# import \nimport re, random\nimport gamefunctions as gf\n\njoker = ['joker', 0]\nshapes = ['C', 'D', 'H', 'S']\n\nclass Environment(object):\n \"\"\"\n 나는\n 모든 플레이어에게 보이는 상황이다.\n - 그래서 grave 처럼 Declarer와 Defender에게 보이는 것이 다른 정보는 저장하지 않는다.\n 이 상황은 Player의 pickCard method에 불려가게 될 것이므로 상황을 card 이름과 bit로 둘 다 저장하고 있는다.\n\n 모든 플레이어에게 같은 것\n - score\n - 어느 자리가 여당인지\n - roundRecord\n - roundNumber\n - roundLead (선)\n - 몇 번 플레이어가 어디인지\n - 어느 자리가 선인지\n - giruda\n - \n\n 플레이어마다 다른 것\n - hand\n - grave(king과 나머지가 다르다)\n - \n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n\t\tfor the tf input, it is needed to fix input data form.\n\n\t\tself.role [53bit]\n\t\tgrave [53bit]\n\t\troundRecord [ 53bit * 4 ]\n\t\troundNumber [ 10bit ]\n\t\thand [53bit]\n\t\tother's shape [4bit * 4] (C,D,H,S)\n\n\t\tpledge [4bit(shape) + int(num) ]\n\t\tlocation [4bit] (내 기준 자리임. 내 다음에 여당 있으면 [1,0,0,0]. 여당 둘이면 [1,0,1,0])\n\t\t (0끼리는 구분 안되어도 됨. 근데 의심을 하려면 뭔가 구분자가 필요할까? 모르겠다..)\n\t\t + [4bit] (start of each round, 시작사람이 1, 내가 시작이면 [0,0,0,0])\n\t\tscore [4 ints]\n\t\t\"\"\"\n self.rule = \"defalt\"\n self.playerNumber = 5\n\n self.role = [0,0,0,0,0] # 밝혀진 것만, 처음에 Declarer, 게임중에 Friend\n self.scores = [0,0,0,0,0] #턴 넘버에 매칭\n self.locations = [0,1,2,3,4] #players[?]에 넣을 숫자.\n self.field = [] #필드에 나와있는 숫자\n\n self.roundShape = 'all'\n self.roundTrun = []\n\n self.giruda = \"\"\n self.bidding = 0\n\n self.mighty = ['S', 14]\n self.jokerCall = ['C',3]\n\n self.roundCards = list(range(0,self.playerNumber))\n\n self.bits = list(range(0,478)) # 비트화된 정보 for tf\n \"\"\"\n bits 0~4 role\n 5~9 giruda [c,d,h,s,nogiru]\n 10~14 scores\n 15~19 locations # 이건 아닌가... 각자가 다른 입장일텐데 위치에 대해선.\n 20~24 roundStart\n ...\n \"\"\"\n\n# self. #의심도(?) 누가누가 프랜드같은지... 추론은 어케 해야 하냐\n# 피하고 싶은 상황: 눈앞에서 뻔히 프랜드같은놈이 주공한테 점수 퍼주는데 프랜드인지 모르고 걔한테 점수 주는 짓.\n# 혹은 야당끼리 협력 못하고 모두 서로를 프랜드로 보고 자기 점수만 늘리려고 하는 전략.\n# 피하기 위한 정보: 누가 무슨 카드를 냈나. 그 때 상황이 (필드가) 어땠나. \n \n def setGiruda(self,giruda):\n self.giruda = giruda\n if giruda == 'S':\n self.mighty = ['D', 14]\n elif giruda == 'C':\n self.jokerCall = ['H',3]\n if giruda in shapes:\n giruIndex = shapes.index(giruda)\n bits[5+giruIndex] = 1 # 5~9: giruda\n else:\n bits[9] = 1\n\n def setDeclarer(self,kingNo):\n self.role[kingNo] = 1\n\n def setFriend(self,friendNo):\n self.role[friendNo] = 1\n\n def initialize(self): #단순히 모든 변수 다 초기처럼 똑같이 맞추면 되겠군.\n self.rule = \"defalt\"\n self.playerNumber = 5\n\n self.role = [0,0,0,0,0] # 밝혀진 것만, 처음에 Declarer, 게임중에 Friend\n self.scores = [0,0,0,0,0] #턴 넘버에 매칭\n self.locations = [0,1,2,3,4] #players[?]에 넣을 숫자.\n self.field = [] #필드에 나와있는 숫자\n\n self.roundShape = 'all'\n self.roundTrun = []\n\n self.giruda = \"\"\n self.bidding = 0\n\n self.mighty = ['S', 14]\n self.jokerCall = ['C',3]\n\n self.roundCards = list(range(0,self.playerNumber))\n\n self.bits = list(range(0,478))" }, { "alpha_fraction": 0.5183362364768982, "alphanum_fraction": 0.5397682189941406, "avg_line_length": 27.636363983154297, "blob_id": "4a4478fe7d99fcfe066369671fedd1bbba29b4fc", "content_id": "6d81f6ec1093dd23664d31a2228cf3ebc6b208c1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6829, "license_type": "no_license", "max_line_length": 93, "num_lines": 220, "path": "/gamefunctions.py", "repo_name": "shadowteias/Mighty", "src_encoding": "UTF-8", "text": "# 다양한 functions for handling cards and game.\n\n\"\"\"\n\n\"\"\"\n\nimport re, random\n\nshapes = ['C', 'D', 'H', 'S']\n\n\ndef setMightyJokercall(giruda = 'H'):\n\tmighty = ['S', 14]\n\tjokerCall = ['C', 3]\n\tif giruda == 'S':\n\t\tmighty = ['D', 14]\n\telif giruda == 'C':\n\t\tjokerCall = ['H', 3]\n\telse:\n\t\tpass\n\treturn mighty, jokerCall\nmighty, jokerCall = setMightyJokercall()\njoker = ['joker', 0]\n\n#test function run\ndef printA():\n\tprint('print A, to test')\n\ndef ruleSetting(playerNum = 5, ruleName = 'defalt'):\n \"\"\"\n 플레이어 수와 룰을 묻는다.\n AI끼리 대전하는 룰인지, 사람이 같이 하는 룰인지 확인.\n \"\"\"\n playerNumber = playerNum\n if ruleName == 'defalt':\n sets = [playerNumber, ruleName]\n return sets\n elif ruleName == \"AIs\":\n sets = [playerNumber, ruleName]\n return sets\n else:\n print(str(ruleName) + ', this rule is not ready')\n\ndef deckGenerator(ruleName = 'defalt'):\n\tif ruleName == 'defalt':\n\t\tdeck = []\n\t\tfor i in 'C', 'D', 'H', 'S':\n\t\t for k in list(range(2, 15)):\n\t\t deck.append([i,k])\n\t\tdeck.append(['joker', 0])\n\t\treturn deck\n\t\t# random.shuffle(deck)\n\telse:\n\t\tprint(str(ruleName) + ', this rule is not ready')\t\n\ndef shapeCounter(hand, shape = \"all\"):\n \"\"\"\n 'hand' is a list of cards, card is something like [\"S\", 2].\n when 'shape' is \"all\", return is ['C', 'D', 'H', 'S', mighty, joker]\n if 'shape' is one of ['C', 'D', 'H', 'S'], it return number of that shape.\n \"\"\"\n # 딸마가 있다는건 마이티랑 같은 모양이 시작카드일 때 그모양이 마이티뿐이면 그거 낸다는거지?\n # 근데 선카가 다른 카드여도 마이티는 낼 수 있는건가? 그치?\n # 낼 수 있는 카드는 [0:3] 중에 1 이상인것, 그리고 마이티, 그리고 조커.\n shapes = [0,0,0,0,0,0]\n for i in range(len(hand)):\n if hand[i][0] == 'C':\n shapes[0] += 1\n elif hand[i][0] == 'D':\n shapes[1] += 1\n elif hand[i][0] == 'H':\n shapes[2] += 1\n elif hand[i][0] == 'S':\n shapes[3] += 1\n if hand[i] == mighty: \n shapes[4] += 1\n if hand[i] == joker:\n shapes[5] += 1\n if shape == 'all':\n return shapes\n elif shape == 'C':\n return shapes[0]\n elif shape == 'D':\n return shapes[1]\n elif shape == 'H':\n return shapes[2]\n elif shape == 'S':\n return shapes[3]\n else: print('input error!!!')\n\ndef shapeCollector(hand, shape):\n result = []\n for i in hand:\n if i[0] == shape: result += [i]\n# print(result)\n return result\n\ndef numCounter(hand, num):\n result = 0\n for i in hand:\n if i[1] == num: result += 1\n return result\n\n\n#set mighty and jokercall from giruda\n\n\ndef handout(players, deck, grave): #hand out cards to AI player\n random.shuffle(deck)\n for i in range(len(players)):\n \t# global players[i].hand\n players[i].hand = deck[i*10 + 0:i*10 + 10]\n# print(players[i].hand)\n grave = deck[len(players)*10:]\n return grave\n \ndef dealCount(playersHand):\n count = 0\n for j in range(10):\n if int(playersHand[j][1]) >=10: \n count += 1\n if playersHand[j] == ['S', 14]: count -= 2 # mighty 면 점수 -1\n if playersHand[j] == ['joker', 0]: count -= 1 # joker는 -1\n# print(count)\n return count\n\ndef roundTrun(turn, startPosition): #이 함수는 아래 함수로 대체될것 같다... \n a = list(range(len(turn)))\n for i in range(len(turn)):\n a[i] = turn[startPosition]\n startPosition += 1\n if startPosition == len(turn):\n startPosition = 0\n return a\n\ndef turnSetting(position, turnStart): #턴스타트는 int, turn은 list\n roundTurn = position.copy()\n while turnStart != roundTurn[0]:\n roundTurn.append(roundTurn[0])\n roundTurn.pop(0)\n return roundTurn\n\ndef card2bits(cardList):\n bits = ['0'] *53\n bitIndex = 0\n for i in cardList:\n if i[0] == shapes[0]:\n bitIndex += 0\n bitIndex += i[1]-2\n elif i[0] == shapes[1]:\n bitIndex += 13\n bitIndex += i[1]-2\n elif i[0] == shapes[2]:\n bitIndex += 26\n bitIndex += i[1]-2\n elif i[0] == shapes[3]:\n bitIndex += 39\n bitIndex += i[1]-2\n elif i[0] == 'joker':\n bitIndex = 52\n else: \n print('!! Error card is in this list !!')\n break\n bits[bitIndex] += 1\n return bits\n\n\ndef roundWin(roundCards, mighty, giruda, jokerCall, validJoker): # validJoker: 첫턴0, 중간턴1, 끝턴0\n calculateScore = 0\n for i in range(len(roundCards)):\n if roundCards[i][1] >= 10:\n calculateScore += 1 #해당라운드의 점수지롱.\n if mighty in roundCards:\n return roundCards.index(mighty), calculateScore\n elif (joker in roundCards) and (roundCards[0] != jokerCall) and (validJoker == 1) :\n return roundCards.index(joker), calculateScore\n elif giruda != 'noGiru' : \n #이제부턴 마이티도 조커도 없으니 기루다 싸움이다. 근데 기루다가 있는지 먼저 물어보는게 예의지.\n if shapeCounter(roundCards, giruda) > 0: # 기루다가 라운드 카드중에 나왔을 때\n giruSet = shapeCollector(roundCards, giruda)\n maxCount = 0\n winCard = []\n for i in giruSet:\n if i[1] > maxCount:\n maxCount = i[1]\n winCard = i\n return roundCards.index(winCard), calculateScore\n else: #기루다 없어\n sunSet = shapeCollector(roundCards, roundCards[0][0]) #선 카드 모음\n maxCount = 0\n winCard = []\n for i in sunSet:\n if i[1] > maxCount:\n maxCount = i[1]\n winCard = i\n return roundCards.index(winCard), calculateScore\n else: # 노기루여?\n sunSet = shapeCollector(roundCards, roundCards[0][0]) #선 카드 모음\n maxCount = 0\n winCard = []\n for i in sunSet:\n if i[1] > maxCount:\n maxCount = i[1]\n winCard = i\n return roundCards.index(winCard), calculateScore\n\ndef finalWin(scores, roles):\n \"\"\"\n input: scores(list), roles(list)\n output: declarerScore(int), defenderScore(int)\n \"\"\"\n declarerScore = 0 #여당점수\n defenderScore = 0 #야당점수\n if len(scores) != len(roles):\n print('error Input in finalWin')\n for i in range(len(roles)):\n if roles[i] == 0:\n defenderScore += scores[i]\n declarerScore = 20 - defenderScore # 처음에 무덤에 들어간 점카도 여당이 먹은거니까\n return declarerScore, defenderScore" }, { "alpha_fraction": 0.6827957034111023, "alphanum_fraction": 0.725806474685669, "avg_line_length": 25.571428298950195, "blob_id": "dd16347fd1d76bad4520f014acfaf5387829c593", "content_id": "3e22e6ff500a1849d78add3e525cb3e48cbc2832", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 186, "license_type": "no_license", "max_line_length": 94, "num_lines": 7, "path": "/README.md", "repo_name": "shadowteias/Mighty", "src_encoding": "UTF-8", "text": "\"# Mighty\" \n\n# perpose\n\nTo teach myself with personal project to make card game 'Mighty'.\n\nI'll use python with tensorflow. Due is the end of Feb. 2019 (started on middle of Dec. 2018). " } ]

5

RKelley1/MovieReviewsBackend

https://github.com/RKelley1/MovieReviewsBackend

3c3884a5d4b699deeff3feb1f27e785e7c7382f3

c3dc5b7c45c3ea0c76e538c8cb8a42e2b7b668ba

bfd6ddb091f8ac633bb5b25f8addad7bd2af8ef8

refs/heads/master

2022-12-05T08:46:51.265344

2020-08-14T18:26:07

287,597,636

0

null

[ { "alpha_fraction": 0.7388724088668823, "alphanum_fraction": 0.7388724088668823, "avg_line_length": 38.70588302612305, "blob_id": "921e3c311e8ed85fd83709abe5cf5ee3e347c04a", "content_id": "35edb06cd39ab958577a04026afb952732196ece", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 674, "license_type": "no_license", "max_line_length": 69, "num_lines": 17, "path": "/moviereviews/app/urls.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from django.urls import path\n\nfrom moviereviews.app.api import userView\nfrom moviereviews.app.api import commentView\nfrom moviereviews.app.api import movieView\n\nurlpatterns = [\n path('users/home', userView.get_home),\n path('users/get', userView.get_all_users),\n path('users/get/user', userView.get_user),\n path('user/create', userView.create_user),\n path('comments/get/movie', commentView.get_comments_for_movie),\n path('comments/get/user', commentView.get_comments_for_user),\n path('comments/update/upvotes', commentView.update_upvote_count),\n path('comments/create', commentView.create_comment),\n path('movies/create', movieView.create_movie) \n]" }, { "alpha_fraction": 0.6895999908447266, "alphanum_fraction": 0.7024000287055969, "avg_line_length": 31.894737243652344, "blob_id": "b7394a41be590b782d45642e5a905df5c5b0ad05", "content_id": "07e6c35eec6a645e810d5601fa3c2c2e8babd292", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 625, "license_type": "no_license", "max_line_length": 62, "num_lines": 19, "path": "/moviereviews/app/models.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from django.db import models\nfrom django.contrib.auth.models import User\n\nclass Movie(models.Model):\n title = models.CharField(max_length=150, unique=True)\n \n def __str__(self):\n return self.title\n\nclass Comment(models.Model):\n #each comment has a relationship to 1 movie and 1 user \n movie = models.ForeignKey(Movie, on_delete=models.CASCADE)\n user = models.ForeignKey(User, on_delete=models.CASCADE)\n content = models.TextField(max_length=350)\n upvotes = models.IntegerField()\n date = models.DateTimeField(auto_now_add=True, blank=True)\n\n def __str__(self):\n return self.content\n" }, { "alpha_fraction": 0.6909986734390259, "alphanum_fraction": 0.7026422023773193, "avg_line_length": 36.233333587646484, "blob_id": "28d91f78bceb6feb7dc0451ad9386e1a8b22f1be", "content_id": "e3af705cbb171ee4ccb479cd116ae731a1a3f9f9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2233, "license_type": "no_license", "max_line_length": 98, "num_lines": 60, "path": "/moviereviews/app/api/commentView.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated\nfrom moviereviews.app.serializers import CommentSerializer\nfrom django.shortcuts import get_list_or_404, get_object_or_404\nfrom django.contrib.auth.models import User\nfrom moviereviews.app.models import Comment, Movie\n\n\n@api_view(['POST'])\n@permission_classes([IsAuthenticated])\ndef create_comment(request, format=None):\n userID = int(request.data['userID'])\n movieID = int(request.data['movieID'])\n content = request.data['content']\n upvotes = int(request.data['upvotes'])\n user = get_object_or_404(User, pk=userID)\n movie = get_object_or_404(Movie, pk=movieID)\n try:\n comment = Comment.objects.create(movie=movie, user=user, content=content, upvotes=upvotes)\n serializer = CommentSerializer(comment, many=False)\n return Response({\n 'status': 201,\n 'payload': serializer.data\n })\n except Exception as e:\n raise(e)\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef get_comments_for_movie(request, format=None):\n movieID = int(request.GET.get('movie_id', None))\n comments = get_list_or_404(Comment, movie_id=movieID)\n serializer = CommentSerializer(comments, many=True)\n return Response(serializer.data) \n\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef get_comments_for_user(request, format=None):\n userID = int(request.GET.get('user_id', None))\n comments = get_list_or_404(Comment, user_id=userID)\n serializer = CommentSerializer(comments, many=True)\n return Response(serializer.data)\n\n@api_view(['PUT'])\n@permission_classes([IsAuthenticated])\ndef update_upvote_count(request, format=None):\n commentID = int(request.data['commentID'])\n update = request.data['update']\n comment = get_object_or_404(Comment, pk=commentID)\n serializer = CommentSerializer(comment, many=False)\n if update == 'INCREMENT':\n comment.upvotes += 1\n comment.save()\n return Response(serializer.data)\n if update == 'DECREMENT':\n comment.upvotes -= 1\n comment.save()\n return Response(serializer.data)" }, { "alpha_fraction": 0.6727272868156433, "alphanum_fraction": 0.6777272820472717, "avg_line_length": 35.081966400146484, "blob_id": "b1f5e3ee283a8002fd6e3bc6ad22c7e18106095e", "content_id": "a6b8ccc6899e5612405ca32d75290b45196bfdba", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2200, "license_type": "no_license", "max_line_length": 96, "num_lines": 61, "path": "/moviereviews/app/api/userView.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated\nfrom moviereviews.app.serializers import UserSerializer\nfrom django.shortcuts import get_object_or_404\nfrom django.contrib.auth.models import User\nfrom django.core.exceptions import ValidationError\nfrom moviereviews.app.api.helpers.validators import validateEmail\n# model imports\n# from moviereviews.app.models import User\n\nfrom moviereviews.app.api.helpers.validators import validateEmail\n\n\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef get_all_users(request, format=None):\n \n if request.method == 'GET':\n users = User.objects.all()\n serializer = UserSerializer(users, many=True)\n return Response(serializer.data)\n else:\n raise('Only a get request may be performed on this endpoint')\n\n@api_view(['GET'])\ndef get_home(request, format=None):\n return Response(\"Hello this is the api home\")\n\n@api_view(['POST'])\ndef create_user(request, format=None):\n ''' TODO: \n 1.) check if email is unique\n 2.) check if email fits requirments (client side)\n\n '''\n if request.method == 'POST':\n username = request.data['username']\n email = request.data['email']\n password = request.data['password']\n if (validateEmail(email)):\n user = User.objects.create_user(username=username, email=email, password=password)\n user.save()\n serializer = UserSerializer(user, many=False)\n return Response({'status': '201',\n 'payload': serializer.data})\n else:\n raise(ValidationError('Email is not valid'))\n\n# Need to create user_login, user_logout etc...\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef get_user(request, format=None):\n if request.method == 'GET':\n userID = int(request.GET.get('user_id', None))\n user = get_object_or_404(User, pk=userID)\n serializer = UserSerializer(user, many=False)\n return Response(serializer.data)\n else:\n raise('Only a GET request may be performed on this endpoint')" }, { "alpha_fraction": 0.7864077687263489, "alphanum_fraction": 0.7864077687263489, "avg_line_length": 24.75, "blob_id": "a95e4be0ceff06f68918d37f2c010cac7f84bd32", "content_id": "96d198768a3b46ba962a78eba6b66d8330c7b4e2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 206, "license_type": "no_license", "max_line_length": 46, "num_lines": 8, "path": "/moviereviews/app/api/authView.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom moviereviews.app.models import User\n\n\n@api_view(['POST'])\ndef generate_token(request, format=None):\n pass\n" }, { "alpha_fraction": 0.7808219194412231, "alphanum_fraction": 0.7808219194412231, "avg_line_length": 35.75, "blob_id": "1b798b17baf8fa0492cfc171079ba0ebe1f56e22", "content_id": "34725d677cf3ec616883a6fc9b32f5ef94cf5fde", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 146, "license_type": "no_license", "max_line_length": 56, "num_lines": 4, "path": "/moviereviews/instructions.md", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "for deployment:\n create postgresql user in psql cli\n create database moviereviewsdb\n create superuser // python manage.py createsuperuser" }, { "alpha_fraction": 0.6984785795211792, "alphanum_fraction": 0.702627956867218, "avg_line_length": 29.16666603088379, "blob_id": "1b7dd6ea2a577911b01c0d0516cd91a69ce8e4e9", "content_id": "84e13ecfbe1154d89e8f8e90af1907e3874c4d88", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 723, "license_type": "no_license", "max_line_length": 66, "num_lines": 24, "path": "/moviereviews/app/api/movieView.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated\nfrom moviereviews.app.serializers import MovieSerializer\n\nfrom moviereviews.app.models import Movie\n\n@api_view(['POST'])\n@permission_classes([IsAuthenticated])\ndef create_movie(request, format=None):\n title = request.data['title']\n\n movie, created = Movie.objects.get_or_create(title=title)\n\n if(created):\n movie.save()\n serializer = MovieSerializer(movie)\n return Response({\n 'status': 201,\n 'payload': serializer.data\n })\n \n else:\n raise(Exception('Movie has already been created'))" }, { "alpha_fraction": 0.6827371716499329, "alphanum_fraction": 0.6827371716499329, "avg_line_length": 30.75, "blob_id": "eab1ec328cda1c46f1ec045e7512ad394ceed42c", "content_id": "ccdc0566c92ed2982cea6687cddcf27744191ec7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 643, "license_type": "no_license", "max_line_length": 59, "num_lines": 20, "path": "/moviereviews/app/serializers.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from rest_framework import serializers\nfrom .models import Comment, Movie\nfrom django.contrib.auth.models import User\n\nclass UserSerializer(serializers.ModelSerializer):\n class Meta:\n model = User\n fields = ['username', 'email', 'id']\n\nclass MovieSerializer(serializers.ModelSerializer):\n class Meta:\n model = Movie\n fields = ['title']\n\nclass CommentSerializer(serializers.ModelSerializer):\n movie = MovieSerializer(many=False, read_only=True)\n user = UserSerializer(many=False, read_only=True)\n class Meta:\n model = Comment\n fields = ['id','movie','user', 'content','upvotes']\n " }, { "alpha_fraction": 0.6760563254356384, "alphanum_fraction": 0.6760563254356384, "avg_line_length": 27.91666603088379, "blob_id": "70dff3bc7b8e7fd743a54b27c9d2141cb11b4fe3", "content_id": "315e6e07e9a61d836245f0b26ecc87ccc00bff49", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 355, "license_type": "no_license", "max_line_length": 46, "num_lines": 12, "path": "/moviereviews/app/views.py", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "from django.shortcuts import render\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\n# Create your views here.\n\n@api_view(['GET'])\ndef home(request, format=None):\n routes_list = {\n \"get_all_users\": \"/api/get_all_users\"\n }\n if request.method == 'GET':\n return Response(routes_list)\n " }, { "alpha_fraction": 0.5098901391029358, "alphanum_fraction": 0.5340659618377686, "avg_line_length": 24.33333396911621, "blob_id": "272ebeab455d7e260ae856835e950d596b643eb0", "content_id": "4333a4950bc3c51dfd351be60c0df82c5b61a1d1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 455, "license_type": "no_license", "max_line_length": 54, "num_lines": 18, "path": "/moviereviews/todo.md", "repo_name": "RKelley1/MovieReviewsBackend", "src_encoding": "UTF-8", "text": "***TODO***\n\n ***Routes***\n A.) User\n 1.) create user\n 2.) delete a user\n 3.) get a list of users\n 4.) get a user's specific information\n B.) Movie\n 1.) create movie\n 2.) get movie details\n 3.) return a list of movies\n\n C.) Comment\n 1.) create comment\n 2.) get a list of comments for a certain movie\n 3.) get a list of comments from a certain user\n 4.) delete comment" } ]

10

Kota-Yamaguchi/MD_bandit

https://github.com/Kota-Yamaguchi/MD_bandit

bffc128b17ef577cb653111d57a07ec5fdc93677

b2390c6fa3551a1250dcf5909fce070a4444301f

60e196f2b7032a137c75b160acc45c8702461d90

refs/heads/master

2022-12-01T06:25:44.250428

2020-08-12T11:24:08

286,666,653

0

null

[ { "alpha_fraction": 0.7655986547470093, "alphanum_fraction": 0.7723439931869507, "avg_line_length": 16.969696044921875, "blob_id": "28a384a6f22314739b94e1ee9da77eb4ac9b3bd9", "content_id": "ac2fec01949384915996203ac6b8376a79b7b693", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1055, "license_type": "no_license", "max_line_length": 75, "num_lines": 33, "path": "/README.md", "repo_name": "Kota-Yamaguchi/MD_bandit", "src_encoding": "UTF-8", "text": "# MD_bandit\n\n[環境構築]\nconda create -n [仮想環境名] python=3.6 \nsource activate [仮想環境名]\n\nconda install -c conda-forge mdanalysis\n\n仮想環境のMDAnalysisのanalysisの中にpca_mod.pyを設置する。\n\n・MDAnalysisの場所の確認方法\n import MDAnalysis\n \n print(MDAnalysis.__file__)\n \n\n\n[使用方法]\n\npython bandit.py -s {gro} -t {xtc} -r {gro}　-s {bandit reaction coordinate}\n-s トポロジーファイルにあたるもの,GRO形式で\n-t 解析したいトラジェクトリ\n-r ターゲットとなるGROファイル\n-s バンディットスコアにしたい反応座標を指定する\n\n\n[説明]\n\nPCA,RMSD、ContactMapの３つの反応座標のどれが一番良いかをバンディットアルゴリズムで探索する\n\nPaCSスコアを計算　→ preバンディットスコアを計算　→ バンディットスコアを計算\n\nバンディットスコア　＝ preバンディットスコア - 1サイクル前のpreバンディットスコア　+ 忘却率 * 今までのバンディットスコア\n" }, { "alpha_fraction": 0.5760576128959656, "alphanum_fraction": 0.5805580615997314, "avg_line_length": 36.533782958984375, "blob_id": "31386bd7da36ba446c62ccd7d6fb911b270ec3a1", "content_id": "5f43e5114ed1c6b4149d98b3f2e83290bdc6c9f1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5555, "license_type": "no_license", "max_line_length": 147, "num_lines": 148, "path": "/bandit/analysis_impl.py", "repo_name": "Kota-Yamaguchi/MD_bandit", "src_encoding": "UTF-8", "text": "import numpy as np\nfrom MDAnalysis.analysis import align\nfrom MDAnalysis.analysis.rms import rmsd\nimport MDAnalysis as mda\nfrom MDAnalysis import Universe\nfrom MDAnalysis.lib.util import get_weights, deprecate\nimport matplotlib.pyplot as plt\n#plt.switch_backend(\"tkagg\")\nimport os\nfrom MDAnalysis.analysis.pca import PCA\nfrom argparse import ArgumentParser\nimport sys\nfrom .analysis import Analysis\n\ndef get_option():\n argparser = ArgumentParser()\n argparser.add_argument(\"-o\",\"--option\",action='store_true')\n argparser.add_argument(\"-s\",\"--setting\", help=\"gro file.\")\n argparser.add_argument(\"-f\",\"--force\", nargs=\"*\", help=\"trajectory.\")\n argparser.add_argument(\"-r\",\"--reference\", help=\"referense .\")\n argparser.add_argument(\"-fi\",\"--fitting\", help=\"fitting trajectory.\")\n argparser.add_argument(\"--rmsd\",help=\"what atoms you want to calculate ??\")\n argparser.add_argument(\"--pca\",help=\"what atoms you want to calculate ??\")\n argparser.add_argument(\"--path\", default=\".\")\n args = argparser.parse_args()\n return args\n\n#cudir = os.getcwd()\n\nclass Analysis_impl2(Analysis):\n def __init__(self): \n self.option = get_option()\n path = self.option.path\n self.cudir = path\n if self.option.option == True:\n self.top = self.option.setting\n obje = self.option.reference\n\n if len(self.option.force)==1:\n traj = self.option.force\n elif len(self.option.force) != 1:\n traj = self.option.force\n\n print(np.array(traj).shape)\n self.ref = Universe(self.top, obje)\n #traj = traj[0]\n print(traj)\n trj = Universe(self.top, traj)\n print(\"fitting\")\n align.AlignTraj(trj, self.ref, select=\"{}\".format(self.option.fitting),filename=\"alin.xtc\").run()\n print(\"fited\")\n self.trj = Universe(self.top, \"alin.xtc\")\n print(\"align\")\n print(self.trj.trajectory) \n else:\n self.top = None\n self.trj = None\n self.ref = None \n self.pca_eigen_vec = None\n # a=map(lambda i : np.array(i), self.trj.select_atoms(\"name CA\").trajectory)\n # trj = np.array(list(a))\n # b=map(lambda i : trj[i].reshape(trj.shape[1]*3),range(trj.shape[0]))\n # self.traj_row = np.array(list(b))\n # print(self.traj_row.shape)\n \n def root_mean_square_deviation(self):\n rmsd1=np.array([])\n print(\"calculating rmsd1\")\n for ts in self.trj.trajectory:\n a =rmsd(self.trj.select_atoms(\"{}\".format(self.option.rmsd)).positions, self.ref.select_atoms(\"{}\".format(self.option.rmsd)).positions)\n rmsd1 = np.append(rmsd1, a)\n print(rmsd1)\n return rmsd1\n\n\n def PCA_(self, n_components=30, select_=\"name CA\"):\n #if self.option.option != True: \n #pc_space = np.array([])\n if self.option.option == True:\n if os.path.exists(self.cudir+\"/eigvec.npy\") ==True:\n pca = PCA(self.trj ,select=\"{}\".format(select_)).run()\n eig_vec = np.load(self.cudir+\"/eigvec.npy\")\n #eig_vec = pre_eigen_vec\n PC=pca.transform(self.trj.select_atoms(\"{}\".format(select_)), n_components=n_components,eigen_vec = eig_vec)\n print(\"axis\",PC.shape)\n #pc_space = np.append(pc_space, PC, axis =0)\n #PC = np.dot(self.traj_row, pre_eigen_vec)\n \n else:\n print(\"You don't have eigen vector, make eigvec \")\n pca = PCA(self.trj ,select=\"{}\".format(select_)).run()\n PC=pca.transform(self.trj.select_atoms(\"{}\".format(select_)), n_components=n_components)\n eig_vec = pca.p_components\n np.save(self.cudir+\"/eigvec.npy\", eig_vec)\n #pc_space = np.append(pc_space, PC, axis =1)\n \n print(\"tar\",PC.shape) \n PC_ref=pca.transform(self.ref.select_atoms(\"{}\".format(select_)), n_components=n_components, eigen_vec = eig_vec)\n print(\"ref\",PC_ref.shape)\n PC_ref=PC_ref.reshape(len(PC_ref[0]))\n print(\"ref reshape\",PC_ref.shape)\n \n pcnorm = np.linalg.norm(PC, axis =1)\n PCnormalize = [PC[i]/pcnorm[i] for i in range(len(pcnorm))]\n PCnormalize = np.array(PCnormalize)\n\n pcrefnorm = np.linalg.norm(PC_ref) \n PCnormalize_ref = PC_ref/pcrefnorm\n PC_dif = np.dot(PCnormalize,PCnormalize_ref)\n \n PC_dif = np.abs(PC_dif-1)\n print(\"dif\",PC_dif)\n return PC, PC_dif, eig_vec \n\n\n\n def ranking(self, RC, rank = 10, reverse = True):\n hist = RC\n rank_argv = []\n top_rank = sorted(hist, reverse = reverse)[:int(rank)]\n for n in range(len(Series(top_rank))):\n for i in range(len(hist)):\n if hist[i] == Series(top_rank)[n]:\n rank_argv.append(i)\n score = np.array([rank_argv, top_rank])\n return score\n\n def translater(self, top, xtc, frame, number):\n traj = md.load_xtc(xtc, top=top)\n traj = traj[frame]\n traj.save_gro(\"candi{0}_{1}.gro\".format(number,frame))\n\n def main(self):\n if self.option.rmsd != None:\n rmsd=self.root_mean_square_deviation()\n return rmsd\n if self.option.pca != None:\n pca ,pca_dif, eigvec = self.PCA_(select_=self.option.pca)\n return pca_dif\nif __name__ == \"__main__\":\n a = analysis()\n RC=a.main()\n np.save(\"RC.npy\",RC)\n #pca,eigvec = a.PCA_()\n \n #plt.plot(rmsd,color=\"r\")\n #plt.plot(pca.T[0],color=\"b\")\n #plt.show()\n" }, { "alpha_fraction": 0.6592572927474976, "alphanum_fraction": 0.6655334830284119, "avg_line_length": 31.965517044067383, "blob_id": "bbbc905cdd8fed6f8a9d7c97eb74ab7bcf00145e", "content_id": "f843433bb1e3283651e675994167e84dc2d80da5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3824, "license_type": "no_license", "max_line_length": 89, "num_lines": 116, "path": "/bandit/bandit.py", "repo_name": "Kota-Yamaguchi/MD_bandit", "src_encoding": "UTF-8", "text": "import numpy as np\nimport os\nfrom analysis_impl2 import Analysis_impl2 \nimport sys\nclass Bandit:\n\tdef __init__(self, trj, top, ref):\n\t\tself.cudir = os.getcwd()\n\t\tself.forgettingRate = 0.8\n\t\tself.rankNumber = 100\n\t\tself.banditScoreName = \"ContactMap\"\n\t\tself.RC_list =np.array( [\"RMSD\", \"ContactMap\", \"PCA\"])\n\t\t\n\t\tif os.path.exists(self.cudir+\"/prob.npy\") == False:\n\t\t\tself.RC_prob = np.zeros(self.RC_list.shape)\n\t\t\tself.RC_prob += 1/self.RC_list.shape[0]\n\t\t\n\t\telse:\n\t\t\tself.RC_prob = np.load(self.cudir+\"/prob.npy\",allow_pickle=True)\n\t\t\t\n\t\tif os.path.exists(self.cudir+\"/Score.npy\") == True:\n\t\t\tself.score = np.load(self.cudir+\"/Score.npy\",allow_pickle=True)[0]\n\t\t\tself.choice = max(self.score)\n\t\telse:\n\t\t\tself.score = {\"RMSD\": 0,\n\t\t\t\t\"ContactMap\":0,\n\t\t\t\t\"PCA\":0}\n\t\t\tchoice = np.random.choice(self.RC_list, p = self.RC_prob) \n\t\n\t\tself.RC = Analysis_impl2(trj, top, ref)\t\t\n\n\t\tself.calcRC = {\"RMSD\" : (lambda i : self.RC.rootMeanSquareDeviation(i)) } \n\t\tself.calcRC[\"ContactMap\"] = lambda i : self.RC.contactMap(i)\n\t\tself.calcRC[\"PCA\"] = lambda i : self.RC.pca(i)\n\t\t\n\n\n\tdef run(self,banditScoreName):\n\t\tprint(\"Probability of one reaction coordinate {}\".format(self.RC_prob))\t\t\n\t\tchoice = np.random.choice(self.RC_list, p = self.RC_prob) \n\t\tresult = self.calcRC[choice](None)\n\t\tprint(result.shape)\n\t\trank = self.RC.ranking(result,rank=self.rankNumber)\n\n\t\twith open(self.cudir+\"/rank.txt\", \"w\") as f:\n\t\t\t\tf.write(str(rank.T))\t\n\n\n\n\t\trank_argv = rank[0]\n\t\trank_argv = [int(n) for n in rank_argv ]\n\t\trankerValue = self.calcRC[banditScoreName](rank_argv)\n\t\ttopRanker = np.max(rankerValue)\n\n\t\tself._score(topRanker, choice)\n\t\t#if os.path.exists(self.cudir+\"/preBanditScore.npy\") == False:\n\t\t#\t\n\t\t#\tnp.save(self.cudir+\"/preBanditScore.npy\", topRanker)\n\t\t#\tdelta = topRanker\n\t\t#\tself.score[choice] = delta\n\t\t#\tnp.save(self.cudir+\"/Score.npy\", [self.score])\n\t\t#else:\n\t\t#\tpreBanditScore = np.load(self.cudir+\"/preBanditScore.npy\",allow_pickle=True)\n\t\t#\tdelta = topRanker - preBanditScore\n\t\t#\tpreScore = np.load(self.cudir+\"/Score.npy\",allow_pickle=True)[0]\n\t\t#\tself.score[choice] = delta\n\t\t#\tfor i in preScore:\n\t\t#\t\tself.score[i]+=(self.learningRate * preScore[i])\t\n\t\t#\tnp.save(self.cudir+\"/Score.npy\", [self.score])\n\t\t\n\t\tself._updateProbabilitySoftmax(self.score, tau=10)\n\t\t\t\n\tdef _updateProbabilitySoftmax(self, score, tau = 10):\n\t\tprint(\"Update Probability \")\n\t\tn = 0\n\t\tsigma = 0\n\t\tfor i in self.RC_list:\n\t\t\tsigma += np.exp(score[i]/tau)\n\t\tfor i in self.RC_list:\n\t\t\tprint(\"{} : {}\".format(i ,np.exp(score[i]/tau)/sigma))\n\t\t\tself.RC_prob[n] = np.exp(score[i]/tau)/sigma\n\t\t\tn+=1\n\t\tnp.save(self.cudir+\"/prob.npy\", self.RC_prob)\n\t\treturn self.RC_prob\n\t\t\n\n\tdef _score(self, Ranker, choiceRC):\n\t\tif os.path.exists(self.cudir+\"/preBanditScore.npy\") == False:\n\t\t\t\n\t\t\tnp.save(self.cudir+\"/preBanditScore.npy\", Ranker)\n\t\t\tdelta = Ranker\n\t\t\tself.score[choiceRC] = delta\n\t\t\tnp.save(self.cudir+\"/Score.npy\", [self.score])\n\t\telse:\n\t\t\tpreBanditScore = np.load(self.cudir+\"/preBanditScore.npy\",allow_pickle=True)\n\t\t\tdelta = Ranker - preBanditScore\n\t\t\tpreScore = np.load(self.cudir+\"/Score.npy\",allow_pickle=True)[0]\n\t\t\tself.score[choiceRC] = delta\n\t\t\tfor i in preScore:\n\t\t\t\tself.score[i]+=(self.forgettingRate * preScore[i])\t\n\t\t\tnp.save(self.cudir+\"/Score.npy\", [self.score])\n\n\t\t\n\t\t\nif __name__==\"__main__\":\n\tfrom argparse import ArgumentParser\n\tdef get_option():\n\t\targparser = ArgumentParser()\n\t\targparser.add_argument(\"-s\",\"--setting\", help=\"gro file.\")\n\t\targparser.add_argument(\"-t\",\"--traj\", nargs=\"*\", help=\"trajectory.\")\n\t\targparser.add_argument(\"-r\",\"--reference\", help=\"referense .\")\n\t\targparser.add_argument(\"-b\",\"--banditscore\",default=\"ContactMap\", help=\"BanditScore .\")\n\t\targs = argparser.parse_args()\n\t\treturn args\n\toption = get_option()\n\tbandit = Bandit(option.traj, option.setting ,option.reference)\n\tbandit.run(option.banditscore)\n" }, { "alpha_fraction": 0.7213482856750488, "alphanum_fraction": 0.7258427143096924, "avg_line_length": 25.147058486938477, "blob_id": "8c38381f9fb91a2999fc99cb4df91f3b200f423a", "content_id": "ad164ff33107f2c6b8e72f60376ca0732e60ccd7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 890, "license_type": "no_license", "max_line_length": 56, "num_lines": 34, "path": "/bandit/analysis.py", "repo_name": "Kota-Yamaguchi/MD_bandit", "src_encoding": "UTF-8", "text": "import numpy as np\nfrom MDAnalysis.analysis import align\nfrom MDAnalysis.analysis.rms import rmsd\nimport MDAnalysis as mda\nfrom MDAnalysis import Universe\n#from MDAnalysis.lib.util import get_weights, deprecate\nimport matplotlib.pyplot as plt\n#plt.switch_backend(\"tkagg\")\nimport os\nfrom MDAnalysis.analysis.pca import PCA\n#from argparse import ArgumentParser\n#import sys\n\nclass Analysis():\n\tdef rootMeanSquareDeviation(self):\n\t\treturn null\n\n\n\tdef pca(self, n_components=30, select_=\"name CA\"):\n\t\treturn null, null, null \n\n\tdef contactMap(self):\n\t\treturn null\n\n\tdef ranking(self, RC, rank = 10, reverse = True):\n\t\thist = RC\n\t\trank_argv = []\n\t\ttop_rank = sorted(hist, reverse = reverse)[:int(rank)]\n\t\tfor n in range(len(Series(top_rank))):\n\t\t\tfor i in range(len(hist)):\n\t\t\t\tif hist[i] == Series(top_rank)[n]:\n\t\t\t\t\trank_argv.append(i)\n\t\tscore = np.array([rank_argv, top_rank])\n\t\treturn score\n\n" }, { "alpha_fraction": 0.6695603728294373, "alphanum_fraction": 0.682581901550293, "avg_line_length": 38.888267517089844, "blob_id": "d0475135b16e08486ab2ba0de472292c717fbf38", "content_id": "f5334758979a5942ea156c4dbe5627e219a24706", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7142, "license_type": "no_license", "max_line_length": 79, "num_lines": 179, "path": "/bandit/pca_mod.py", "repo_name": "Kota-Yamaguchi/MD_bandit", "src_encoding": "UTF-8", "text": "# -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding:utf-8 -*-\n# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4\n#\n# MDAnalysis --- https://www.mdanalysis.org\n# Copyright (c) 2006-2017 The MDAnalysis Development Team and contributors\n# (see the file AUTHORS for the full list of names)\n#\n# Released under the GNU Public Licence, v2 or any higher version\n#\n# Please cite your use of MDAnalysis in published work:\n#\n# R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler,\n# D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein.\n# MDAnalysis: A Python package for the rapid analysis of molecular dynamics\n# simulations. In S. Benthall and S. Rostrup editors, Proceedings of the 15th\n# Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy.\n# doi: 10.25080/majora-629e541a-00e\n#\n# N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein.\n# MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations.\n# J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787\n#\n\nr\"\"\"Principal Component Analysis (PCA) --- :mod:`MDAnalysis.analysis.pca`\n=====================================================================\n\n:Authors: John Detlefs\n:Year: 2016\n:Copyright: GNU Public License v3\n\n.. versionadded:: 0.16.0\n\nThis module contains the linear dimensions reduction method Principal Component\nAnalysis (PCA). PCA sorts a simulation into 3N directions of descending\nvariance, with N being the number of atoms. These directions are called\nthe principal components. The dimensions to be analyzed are reduced by only\nlooking at a few projections of the first principal components. To learn how to\nrun a Principal Component Analysis, please refer to the :ref:`PCA-tutorial`.\n\nThe PCA problem is solved by solving the eigenvalue problem of the covariance\nmatrix, a :math:`3N \\times 3N` matrix where the element :math:`(i, j)` is the\ncovariance between coordinates :math:`i` and :math:`j`. The principal\ncomponents are the eigenvectors of this matrix.\n\nFor each eigenvector, its eigenvalue is the variance that the eigenvector\nexplains. Stored in :attr:`PCA.cumulated_variance`, a ratio for each number of\neigenvectors up to index :math:`i` is provided to quickly find out how many\nprincipal components are needed to explain the amount of variance reflected by\nthose :math:`i` eigenvectors. For most data, :attr:`PCA.cumulated_variance`\nwill be approximately equal to one for some :math:`n` that is significantly\nsmaller than the total number of components, these are the components of\ninterest given by Principal Component Analysis.\n\nFrom here, we can project a trajectory onto these principal components and\nattempt to retrieve some structure from our high dimensional data.\n\nFor a basic introduction to the module, the :ref:`PCA-tutorial` shows how\nto perform Principal Component Analysis.\n\n.. _PCA-tutorial:\n\nPCA Tutorial\n------------\n\nThe example uses files provided as part of the MDAnalysis test suite\n(in the variables :data:`~MDAnalysis.tests.datafiles.PSF` and\n:data:`~MDAnalysis.tests.datafiles.DCD`). This tutorial shows how to use the\nPCA class.\n\nFirst load all modules and test data\n\n >>> import MDAnalysis as mda\n >>> import MDAnalysis.analysis.pca as pca\n >>> from MDAnalysis.tests.datafiles import PSF, DCD\n\nGiven a universe containing trajectory data we can perform Principal Component\nAnalyis by using the class :class:`PCA` and retrieving the principal\ncomponents.\n\n >>> u = mda.Universe(PSF, DCD)\n >>> PSF_pca = pca.PCA(u, select='backbone')\n >>> PSF_pca.run()\n\nInspect the components to determine the principal components you would like\nto retain. The choice is arbitrary, but I will stop when 95 percent of the\nvariance is explained by the components. This cumulated variance by the\ncomponents is conveniently stored in the one-dimensional array attribute\n``cumulated_variance``. The value at the ith index of `cumulated_variance`\nis the sum of the variances from 0 to i.\n\n >>> n_pcs = np.where(PSF_pca.cumulated_variance > 0.95)[0][0]\n >>> atomgroup = u.select_atoms('backbone')\n >>> pca_space = PSF_pca.transform(atomgroup, n_components=n_pcs)\n\nFrom here, inspection of the ``pca_space`` and conclusions to be drawn from the\ndata are left to the user.\n\nClasses and Functions\n---------------------\n\n.. autoclass:: PCA\n.. autofunction:: cosine_content\n\n\"\"\"\nfrom __future__ import division, absolute_import\nfrom six.moves import range\nimport warnings\n\nimport numpy as np\nimport scipy.integrate\n\nfrom MDAnalysis import Universe\nfrom MDAnalysis.analysis.align import _fit_to\nfrom MDAnalysis.lib.log import ProgressMeter\n\nfrom .base import AnalysisBase\nfrom .pca import PCA\n\nclass PCA_MOD(PCA):\n def transform(self, atomgroup, n_components=None, start=None, stop=None,\n step=None,eigen_vec=None):\n \"\"\"Apply the dimensionality reduction on a trajectory\n\n Parameters\n ----------\n atomgroup: MDAnalysis atomgroup/ Universe\n The atomgroup or universe containing atoms to be PCA transformed.\n n_components: int, optional\n The number of components to be projected onto, Default none: maps\n onto all components.\n start: int, optional\n The frame to start on for the PCA transform. Default: None becomes\n 0, the first frame index.\n stop: int, optional\n Frame index to stop PCA transform. Default: None becomes n_frames.\n Iteration stops *before* this frame number, which means that the\n trajectory would be read until the end.\n step: int, optional\n Number of frames to skip over for PCA transform. Default: None\n becomes 1.\n\n Returns\n -------\n pca_space : array, shape (number of frames, number of components)\n\n .. versionchanged:: 0.19.0\n Transform now requires that :meth:`run` has been called before,\n otherwise a :exc:`ValueError` is raised.\n \"\"\"\n if np.any(eigen_vec) != None:\n self.p_components = eigen_vec\n if not self._calculated:\n raise ValueError('Call run() on the PCA before using transform')\n\n if isinstance(atomgroup, Universe):\n atomgroup = atomgroup.atoms\n\n if(self._n_atoms != atomgroup.n_atoms):\n raise ValueError('PCA has been fit for'\n '{} atoms. Your atomgroup'\n 'has {} atoms'.format(self._n_atoms,\n atomgroup.n_atoms))\n if not (self._atoms.types == atomgroup.types).all():\n warnings.warn('Atom types do not match with types used to fit PCA')\n\n traj = atomgroup.universe.trajectory\n start, stop, step = traj.check_slice_indices(start, stop, step)\n n_frames = len(range(start, stop, step))\n\n dim = (n_components if n_components is not None else\n self.p_components.shape[1])\n\n dot = np.zeros((n_frames, dim))\n\n for i, ts in enumerate(traj[start:stop:step]):\n xyz = atomgroup.positions.ravel() - self.mean\n dot[i] = np.dot(xyz, self.p_components[:, :n_components])\n\n return dot\n\n\n" }, { "alpha_fraction": 0.6403831839561462, "alphanum_fraction": 0.6462785601615906, "avg_line_length": 31.830644607543945, "blob_id": "0fb51e4e494045ed54b43ccc95218f428fe195d8", "content_id": "ceb9fbeb9292208e7d515b948a6e4aa774581eb1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4071, "license_type": "no_license", "max_line_length": 114, "num_lines": 124, "path": "/bandit/analysis_impl2.py", "repo_name": "Kota-Yamaguchi/MD_bandit", "src_encoding": "UTF-8", "text": "import numpy as np\n#from MDAnalysis.analysis import align\nfrom MDAnalysis.analysis.rms import rmsd\nimport MDAnalysis as mda\nfrom MDAnalysis import Universe\nfrom pandas import Series\n#from MDAnalysis.lib.util import get_weights, deprecate\n#import matplotlib.pyplot as plt\n#plt.switch_backend(\"tkagg\")\nimport os\nfrom MDAnalysis.analysis.pca_mod import PCA_MOD as PCA\n#from argparse import ArgumentParser\nimport sys\nfrom analysis import Analysis\nfrom scipy.spatial import distance\n\nclass Analysis_impl2(Analysis):\n\tdef __init__(self , trj, top, ref): \n\t\tself.cudir = os.getcwd()\n\t\tself.ref = Universe(top, ref)\n\t\tself.trj = Universe(top, trj)\n\n\n\tdef rootMeanSquareDeviation(self, rank_argv=None ,part = \"name CA\"):\n\t\trmsd1=np.array([])\n\t\tprint(\"calculating rmsd1\")\n\t\tfor ts in self.trj.trajectory:\n\t\t\ta =rmsd(self.trj.select_atoms(\"{}\".format(part)).positions, self.ref.select_atoms(\"{}\".format(part)).positions)\n\t\t\trmsd1 = np.append(rmsd1, a)\n\t\tprint(rmsd1)\n\n\t\tif rank_argv != None:\n\t\t\treturn rmsd1[rank_argv]\n\t\t\n\t\treturn rmsd1\n\t\n\tdef contactMap(self, rank_argv=None):\n\t\tprint(\"calculate ContactMap \")\n\t\td = map(lambda i : np.array(i), self.trj.trajectory)\n\t\td = np.array(list(d))\n\t\t\n\t\tdist_M = map(lambda i : distance.cdist(d[i], d[i] ,metric=\"euclidean\"), range(d.shape[0]))\n\t\tdist_M = np.array(list(dist_M))\n\t\t\n\t\td_ref = map(lambda i : np.array(i), self.ref.trajectory)\n\t\td_ref = np.array(list(d_ref))\n\t\tref_dist_M = map(lambda i : distance.cdist(d_ref[i], d_ref[i] ,metric=\"euclidean\"), range(d_ref.shape[0]))\n\t\tref_dist_M = np.array(list(ref_dist_M))\n\t\t\n\t\thist_dif = []\n\t\tfor i in range(dist_M.shape[0]):\n\t\t\tdist_dif = np.sum((dist_M[i] -ref_dist_M)**2)\n\t\t\thist_dif.append(dist_dif)\n\t\thist_dif = np.array(hist_dif)\n\t\thist_dif = self.min_max(hist_dif)\t\n\t\tif np.all(rank_argv) != None:\n\t\t\treturn hist_dif[rank_argv]\t\t\t\t\n\t\n\t\treturn hist_dif\n\n\tdef pca(self, comp=30, select_=\"name CA\",rank_argv=None):\n #if self.option.option != True: \n #pc_space = np.array([])\n\n\t\tn_comp = 30\n\t\tprint(\"calculate Principle Component\")\n\t\tif os.path.exists(self.cudir+\"/eigvec.npy\") ==True:\n\t\t\tpca = PCA(self.trj ,select=\"{}\".format(select_)).run()\n\t\t\teig_vec = np.load(self.cudir+\"/eigvec.npy\")\n #eig_vec = pre_eigen_vec\n\t\t\tPC=pca.transform(self.trj.select_atoms(\"{}\".format(select_)), n_components=n_comp,eigen_vec = eig_vec)\n\t\t\tprint(\"PC:{}\".format(PC.shape))\n\t#pc_space = np.append(pc_space, PC, axis =0)\n #PC = np.dot(self.traj_row, pre_eigen_vec)\n \n\t\telse:\n\t\t\tprint(\"You don't have eigen vector, make eigvec \")\n\t\t\tpca = PCA(self.trj ,select=\"{}\".format(select_)).run()\n\t\t\tPC=pca.transform(self.trj.select_atoms(\"{}\".format(select_)), n_components=n_comp)\n\t\t\teig_vec = pca.p_components\n\t\t\tprint(\"PC:{}\".format(PC.shape))\n\t\t\tnp.save(self.cudir+\"/eigvec.npy\", eig_vec)\n #pc_space = np.append(pc_space, PC, axis =1)\n \n\t\tPC_ref=pca.transform(self.ref.select_atoms(\"{}\".format(select_)), n_components=n_comp, eigen_vec = eig_vec)\n\t\tPC_ref=PC_ref.reshape(len(PC_ref[0]))\n\t\tdot_dif = PC - PC_ref\n\t\tscore = np.sum(np.abs(dot_dif),axis =1)\n\t\tprint(\"PC score {}\".format(score))\n\t\treturn score\n # \n\t#\tpcnorm = np.linalg.norm(PC, axis =1)\n\t#\tPCnormalize = [PC[i]/pcnorm[i] for i in range(len(pcnorm))]\n\t#\tPCnormalize = np.array(PCnormalize)\n#\n#\t\tpcrefnorm = np.linalg.norm(PC_ref) \n#\t\tPCnormalize_ref = PC_ref/pcrefnorm\n#\t\tPC_dif = np.dot(PCnormalize,PCnormalize_ref)\n # \n#\t\tPC_dif = np.abs(PC_dif-1)\n#\t\tif rank_argv != None:\n # return PC_dif[rank_argv]\n#\t\treturn PC_dif #eig_vec \n\t\t\n\t\t\t\t\n\n\n\tdef min_max(self, x, axis=None):\n\t\tmin = x.min(axis=axis, keepdims=True)\n\t\tmax = x.max(axis=axis, keepdims=True)\n\t\tresult = (x-min)/(max-min)\n\t\treturn result\n\n\n\tdef ranking(self, RC, rank = 10, reverse = True):\n\t\thist = RC\n\t\trank_argv = []\n\t\ttop_rank = sorted(hist, reverse = reverse)[:int(rank)]\n\t\tfor n in range(len(Series(top_rank))):\n\t\t\tfor i in range(len(hist)):\n\t\t\t\tif hist[i] == Series(top_rank)[n]:\n\t\t\t\t\trank_argv.append(int(i))\n\t\tscore = np.array([rank_argv, top_rank])\n\t\treturn score\n" } ]

6

alexletu/traditional_ml_models

https://github.com/alexletu/traditional_ml_models

6abca5e339f74b02aae0fefd4f88fd971c622148

5c7c0d6c8a7ba31f4423843235ac21eb1ef6a97b

fcb6777c2aafb8590ad398492a3317c1ac74f288

refs/heads/main

2023-01-20T12:23:58.889113

2020-11-24T09:28:45

315,579,542

0

null

[ { "alpha_fraction": 0.5733481049537659, "alphanum_fraction": 0.5769838690757751, "avg_line_length": 39.42172622680664, "blob_id": "02fc98baea9410b90ab95723f6d12dae53cb4bdf", "content_id": "291f6ab4b4c5083d8a2d288afb1071b2c2e4a6b4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 12652, "license_type": "no_license", "max_line_length": 139, "num_lines": 313, "path": "/decision_trees/decision_tree_starter.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "from collections import Counter\nimport numpy as np\nimport random\nfrom utils import extract_column, majority, find_index\n\ndef gen_clustered_thresholds(data, idx):\n sorted_data = data[data[:, idx].argsort()]\n idy = data.shape[1] - 1\n thresholds = []\n if len(data) == 1:\n return [data[0][idx]]\n for i in range(1, len(sorted_data)):\n prev = data[i - 1]\n curr = data[i]\n prev_y = prev[idy]\n curr_y = curr[idy]\n curr_x = curr[idx]\n prev_x = prev[idx]\n if prev_y != curr_y:\n thresholds.append((curr_x + prev_x) / 2)\n return thresholds\n\n\ndef gen_quantitative_thresholds(data, idx):\n xs = extract_column(data, idx)\n return [np.mean(xs)]\n\n\ndef gen_quantitative_thresholds_exact(data, idx):\n thresholds = gen_clustered_thresholds(data, idx)\n\n best_purification = -1\n best_threshold = None\n for threshold in thresholds:\n purification = DecisionTree.gini_purification(data, idx, False, [threshold])\n if purification > best_purification:\n best_purification = purification\n best_threshold = threshold\n return [best_threshold] if best_threshold is not None else gen_quantitative_thresholds(data, idx)\n\n\nclass Node:\n \"\"\"\n depth is an integer\n split_idx denotes the feature being split on 0 - d\n IF leaf node, then split_idx is None\n split_thresholds denotes:\n categories to split on -> len(children) = len(thresholds)\n quantitative data to split on -> len(children) = len(thresholds) + 1\n IF leaf node, then split_thresholds is None\n split_categorical is a boolean, denotes if this node follows categorical or quantitative splitting\n IF leaf node, then split_categorical is None\n is_leaf is a boolean denoting if this is leaf node or not\n children is a list of children\n IF leaf, children will be the empty list\n label denotes the class which this node represents\n IF not leaf, label will be None\n \"\"\"\n def __init__(self, depth, feature_names = None, class_names = None):\n self.depth = depth\n self.split_idx = None\n self.split_thresholds = None\n self.split_categorical = None\n self.is_leaf = None\n self.children = []\n self.label = None\n self.feature_names = feature_names\n self.class_names = class_names\n\n def set_leaf(self, label):\n self.is_leaf = True\n self.label = label\n\n def set_internal(self, idx, categorical, thresholds, children, label):\n self.split_idx = idx\n self.split_categorical = categorical\n self.split_thresholds = thresholds\n self.is_leaf = False\n self.children = children\n self.label = label\n\n def __repr__(self):\n offset = \"\\n\" + self.depth * \" \"\n s = \"\"\n if self.is_leaf:\n s += offset + \"Leaf Node\"\n s += offset + \" Depth = \" + str(self.depth)\n s += offset + \" Label = \" + (str(self.label) if self.class_names is None else self.class_names[int(self.label)])\n return s\n else:\n s += offset + \"Internal Node\"\n s += offset + \" Depth = \" + str(self.depth)\n s += offset + \" Split on feat = \" + (str(self.split_idx) if self.feature_names is None else self.feature_names[self.split_idx])\n s += offset + \" Categorical split = \" + str(self.split_categorical)\n s += offset + \" Categories/Thresholds = \" + str(self.split_thresholds)\n for child in self.children:\n s += child.__repr__()\n return s\n def string(self):\n offset = \"\\n\" + self.depth * \" \"\n s = \"\"\n if self.is_leaf:\n s += offset + \"Leaf Node\"\n s += offset + \" Depth = \" + str(self.depth)\n s += offset + \" Label = \" + (str(self.label) if self.class_names is None else self.class_names[int(self.label)])\n return s\n else:\n s += offset + \"Internal Node\"\n s += offset + \" Depth = \" + str(self.depth)\n s += offset + \" Split on feat = \" + (str(self.split_idx) if self.feature_names is None else self.feature_names[self.split_idx])\n s += offset + \" Categorical split = \" + str(self.split_categorical)\n s += offset + \" Categories/Thresholds = \" + str(self.split_thresholds)\n return s\n\nclass DecisionTree:\n def __init__(self, type_map, categories_map, feature_names = None, class_names = None):\n self.type_map = type_map\n self.categories_map = categories_map\n self.root = None\n self.bag_size = -1\n self.feature_names = feature_names\n self.class_names = class_names\n\n @staticmethod\n def gini_impurity(y):\n \"\"\"\n Input: 2d array of class labels (column vector)\n Output float number that represents the gini impurity. Low is good.\n \"\"\"\n cnt = Counter(y.flatten())\n gini = 0\n total = len(y)\n for elem in list(cnt): # elements of cnt are classes\n gini += (cnt[elem] / total) ** 2\n return 1 - gini\n\n\n @staticmethod\n def gini_purification(data, idx, categorical, thresholds):\n \"\"\"\n Input: data (last column is label column), feature to split on, whether or not the split is categorical,\n list of thresholds or categories\n Output: the change in gini-impurity, lower (negative) is good.\n \"\"\"\n idy = data.shape[1] -1\n y = extract_column(data, idy)\n x = extract_column(data, idx)\n gini_before = DecisionTree.gini_impurity(y)\n gini_after = 0\n\n # joined represents two columns, left is idx feature, right is the label\n joined = np.hstack((x,y))\n total = len(joined)\n\n if categorical:\n for category in thresholds:\n split_data = joined[joined[:, 0] == category]\n new_y = extract_column(split_data, 1)\n gini_after += DecisionTree.gini_impurity(new_y) * len(new_y)\n else:\n for thresh in thresholds:\n split_data_below = joined[joined[:, 0] < thresh]\n joined = joined[joined[:, 0] >= thresh]\n new_y = extract_column(split_data_below, 1)\n gini_after += DecisionTree.gini_impurity(new_y) * len(new_y)\n new_y = extract_column(joined, 1)\n gini_after += DecisionTree.gini_impurity(new_y) * len(new_y)\n gini_after /= total\n\n return gini_before - gini_after\n\n def split(self, data, idx, categorical, thresholds):\n \"\"\"\n Input: data (last column is label column), feature to split on, whether or not the split is categorical,\n list of thresholds or categories\n Output: a python list of numpy 2-d arrays representing the split data based on idx and thresholds\n \"\"\"\n total_data = []\n if categorical:\n for category in thresholds:\n split_data = data[data[:, idx] == category]\n total_data.append(split_data)\n else:\n for thresh in thresholds:\n split_data_below = data[data[:, idx] < thresh]\n data = data[data[:, idx] >= thresh]\n total_data.append(split_data_below)\n total_data.append(data)\n return total_data\n \n def segmenter(self, data):\n \"\"\"\n Input: data matrix, last column are labels\n Output returns the feature to split on (idx), categorical or not, the categories or thresholds to split on,\n the best purification\n \"\"\"\n best_idx = -1\n best_categorical = None\n best_thresholds = []\n best_purification = -1\n # for each feature\n sampled_features = np.random.choice(a=list(range(0, data.shape[1] - 1)), size=self.bag_size ,replace=False)\n for idx in sampled_features:\n feature_type = self.type_map[idx]\n if feature_type == 'categorical':\n thresholds = self.categories_map[idx] # these are really categories\n purification = DecisionTree.gini_purification(data, idx, True, thresholds)\n categorical = True\n elif feature_type == 'clustered':\n thresholds = gen_clustered_thresholds(data, idx)\n purification = DecisionTree.gini_purification(data, idx, False, thresholds)\n categorical = False\n else:\n #thresholds = gen_quantitative_thresholds(data, idx) #use with spam\n thresholds = gen_quantitative_thresholds_exact(data, idx) #use with titanic\n purification = DecisionTree.gini_purification(data, idx, False, thresholds)\n categorical = False\n if purification > best_purification:\n best_idx = idx\n best_categorical = categorical\n best_thresholds = thresholds\n best_purification = purification\n return best_idx, best_categorical, best_thresholds, best_purification\n\n def fit_helper(self, data, current_depth, max_depth, min_samples, node):\n if data.shape[0] == 0:\n node.is_leaf = True\n return\n if current_depth >= max_depth or len(data) < min_samples:\n label = majority(data)\n node.set_leaf(label)\n return\n else:\n idx, categorical, thresholds, purification = self.segmenter(data)\n if purification <= .0001 or thresholds is None:\n label = majority(data)\n node.set_leaf(label)\n return\n split_data = self.split(data, idx, categorical, thresholds)\n new_children = [Node(current_depth + 1, self.feature_names, self.class_names) for _ in range(len(split_data))]\n label = majority(data)\n node.set_internal(idx, categorical, thresholds, new_children, label)\n for i in range(len(split_data)):\n node.children[i].label = label\n self.fit_helper(split_data[i], current_depth + 1, max_depth, min_samples, node.children[i])\n\n def fit(self, data, max_depth, min_samples, bag_size = None):\n if bag_size is None:\n self.bag_size = data.shape[1] - 1\n else:\n self.bag_size = bag_size\n self.root = Node(0, self.feature_names, self.class_names)\n self.fit_helper(data, 0, max_depth, min_samples, self.root)\n return\n\n def predict_helper(self, x, node, verbose):\n offset = node.depth * \" \"\n if verbose:\n print(node.string())\n\n if node.is_leaf:\n return node.label\n\n idx = node.split_idx\n categorical = node.split_categorical\n thresholds = node.split_thresholds\n\n if categorical:\n categories = thresholds\n x_val = x[idx]\n child = categories.index(x_val)\n if verbose:\n print(offset + \" Observed value: \", x_val)\n return self.predict_helper(x, node.children[child], verbose)\n else:\n x_val = x[idx]\n child = find_index(x_val, thresholds)\n if verbose:\n print(offset + \" Observed value: \", x_val)\n return self.predict_helper(x, node.children[child], verbose)\n\n def predict(self, X, verbose = False):\n predictions = []\n for x in X:\n prediction = self.predict_helper(x, self.root, verbose)\n predictions.append(prediction)\n return np.array([predictions]).T\n\n def __repr__(self):\n return self.root.__repr__()\n\n\nclass RandomForest:\n def __init__(self, trees, sample_size, bag_size, type_map, categories_map, seed):\n self.trees = [DecisionTree(type_map, categories_map) for _ in range(trees)]\n self.sample_size = sample_size\n self.seed = seed\n self.bag_size = bag_size\n\n def fit(self, data, max_depth, min_samples):\n random.seed(self.seed)\n for tree in self.trees:\n sample = data[np.random.randint(data.shape[0], size=self.sample_size), :]\n tree.fit(sample, max_depth, min_samples, self.bag_size)\n return\n \n def predict(self, X):\n tree_predictions = []\n for tree in self.trees:\n tree_predictions.append(tree.predict(X))\n tree_predictions = np.hstack(tree_predictions)\n final_predictions = [Counter(ensemble).most_common(1)[0][0] for ensemble in tree_predictions]\n return np.array(final_predictions).T\n" }, { "alpha_fraction": 0.5748559236526489, "alphanum_fraction": 0.5989813804626465, "avg_line_length": 36.49748611450195, "blob_id": "7887dc114d128529856236506a3615492613e620", "content_id": "cc5054f3e1d99c392ee5a82571ff2e784fd4c305", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7461, "license_type": "no_license", "max_line_length": 210, "num_lines": 199, "path": "/logistic_regression/wine.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import io\nfrom save_csv import results_to_csv\nfrom sklearn.preprocessing import StandardScaler\nimport time\n\n\ndef permute_dictionaries(data, labels, rand=0):\n perm = np.random.RandomState(seed=rand).permutation(data.shape[0])\n return data[perm], labels[perm]\n\ntemp = io.loadmat(\"data.mat\")\ndata = temp[\"X\"]\nlabels = temp[\"y\"]\ndata, labels = permute_dictionaries(data, labels)\nprint(\"Loaded wine data.\")\n\ntrain_data, valid_data = data[:5500], data[5500:]\ntrain_labels, valid_labels = labels[:5500], labels[5500:]\nscaler = StandardScaler()\ntrain_data = scaler.fit_transform(train_data)\nvalid_data = scaler.transform(valid_data)\ntrain_data = np.hstack((train_data, np.ones((train_data.shape[0], 1))))\nvalid_data = np.hstack((valid_data, np.ones((valid_data.shape[0], 1))))\n\ndef gen_c_values(low_exp, high_exp):\n return [10**i for i in range(low_exp, high_exp + 1)]\n\ndef error_rate(prediction, actual):\n return np.count_nonzero(prediction - actual) / prediction.shape[0]\n\ndef print_info(title, time, alpha, c, epsilon, n_samples, n_test, n_iterations, final_loss, train_error, test_error, decay = None):\n print(\"********** - %s - **********\" % title)\n print(\" Time elapsed: %f\" % time)\n print(\" Learning rate: %f\" % alpha)\n print(\" Regularization value: %f\" % c)\n if decay is not None:\n print(\" Decay Rate: %.10f\" % decay)\n print(\" Epsilon value: %f\" % epsilon)\n print(\" Training Samples: %d\" % n_samples)\n print(\" Test samples: %d\" % n_test)\n print(\" Total iterations: %d\" % n_iterations)\n print(\" Final Loss: %f\" % final_loss)\n print(\" Final train error %f\" % train_error)\n print(\" Final error test error %f\" % test_error)\n return\n\ndef plot_data(iterations, losses, clr, title):\n fig = plt.figure()\n fig.set_size_inches(10, 5)\n fig.set_dpi(100)\n plt.subplot(1, 1, 1)\n plt.plot(iterations, losses, label=\"Loss\", color=clr, marker='.', linestyle='dashed',linewidth=1, markersize=1)\n plt.legend()\n plt.title(title + \" vs Iterations\")\n plt.xlabel(\"Iterations\")\n plt.ylabel(\"Loss\")\n plt.show()\n\n\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\n\ndef sigmoid(gamma):\n gamma[gamma <= -709] = -700\n gamma[gamma >= 709] = 700\n return 1/(1 + np.e**(-gamma))\n\ndef log_loss(z, y):\n return -y * np.log(z + 10**-300) - (1 - y) * np.log(1 - z + 10**-300)\n\ndef obj(act, pred, w, c):\n return np.mean(log_loss(pred, act)) + (c / 2) * np.linalg.norm(w)\n\ndef calc_gradient(X, y, predictions, w, c):\n n = X.shape[0]\n dz = predictions - y\n dw = (1 / n) * np.matmul(X.T, dz) + c * w\n return dw\n\ndef classify(data, w):\n probabilities = np.where(sigmoid(np.matmul(data, w)) > .5, 1, 0)\n return probabilities\n\ndef train_batch(X, x_labels, alpha, c, epsilon):\n w = np.zeros((13, 1))\n iteration = 0\n loss = np.inf\n iterations = []\n losses = []\n w_prev = np.ones((13,1))\n while not np.allclose(w_prev, w) and loss >= epsilon:\n x_predictions = sigmoid(np.matmul(X, w))\n dw = calc_gradient(X, x_labels, x_predictions, w, c)\n w_prev = w\n w = w - alpha * dw\n loss = obj(x_labels, x_predictions, w, c)\n iteration += 1\n if iteration % 10 == 0:\n iterations.append(iteration)\n losses.append(loss)\n print(loss)\n iterations.append(iteration)\n losses.append(loss)\n return w, iterations, losses\n\ndef train_sgd(X, x_labels, alpha, c, epsilon, decay=0):\n w = np.zeros((13, 1))\n iteration = 0\n loss = np.inf\n iterations = []\n losses = []\n i = 0\n w_prev = np.ones((13, 1))\n while not np.allclose(w_prev, w) and loss >= epsilon:\n #out of all x choose an index i, convert it to matrix\n x_i = np.array([X[i]])\n x_i_prediction = sigmoid(np.matmul(x_i, w))\n #convert the label to the single index i, convert to matrix\n l_i = x_labels[i]\n\n dw = calc_gradient(x_i, l_i, x_i_prediction, w, c)\n w = w - alpha * dw\n all_predictions = sigmoid(np.matmul(X, w))\n loss = obj(x_labels, all_predictions, w, c)\n alpha *= 1 / (1 + decay * iteration)\n if iteration % 10 == 0:\n iterations.append(iteration)\n losses.append(loss)\n print(loss)\n iteration += 1\n i += 1\n if i == X.shape[0]:\n X, x_labels = permute_dictionaries(X, x_labels, rand=np.random.randint(0, 100))\n i = 0\n\n iterations.append(iteration)\n losses.append(loss)\n return w, iterations, losses\n\ndef part_a(alpha, c, epsilon):\n start = time.time()\n w, iterations, losses = train_batch(train_data, train_labels, alpha, c, epsilon)\n time_elapsed = time.time() - start\n error = error_rate(classify(valid_data, w), valid_labels)\n train_error = error_rate(classify(train_data, w), train_labels)\n print_info(\"Summary Batch Gradient Decent\", time_elapsed, alpha, c, epsilon, train_data.shape[0], valid_data.shape[0], iterations[len(iterations) - 1],losses[len(losses) - 1] ,train_error, error)\n plot_data(iterations, losses, 'red', \"Batch Gradient Decent\")\n\ndef part_b(alpha, c, epsilon):\n start = time.time()\n w, iterations, losses = train_sgd(train_data, train_labels, alpha, c, epsilon)\n time_elapsed = time.time() - start\n error = error_rate(classify(valid_data, w), valid_labels)\n train_error = error_rate(classify(train_data, w), train_labels)\n print_info(\"Summary Stochastic Gradient Decent\", time_elapsed, alpha, c, epsilon, train_data.shape[0], valid_data.shape[0], iterations[len(iterations) - 1], losses[len(losses) - 1],train_error, error)\n plot_data(iterations, losses, 'blue', 'Stochastic Gradient Descent Loss')\n\ndef part_c(alpha, c, epsilon, decay):\n start = time.time()\n w, iterations, losses = train_sgd(train_data, train_labels, alpha, c, epsilon, decay)\n time_elapsed = time.time() - start\n error = error_rate(classify(valid_data, w), valid_labels)\n train_error = error_rate(classify(train_data, w), train_labels)\n print_info(\"SGD w/ decreasing learning rate\", time_elapsed, alpha, c, epsilon, train_data.shape[0], valid_data.shape[0], iterations[len(iterations) - 1], losses[len(losses) - 1], train_error, error, decay)\n plot_data(iterations, losses, 'green', 'SGD w/ Decreasing Dearning Date Loss')\n\ndef kaggle(alpha, c, epsilon):\n X = temp[\"X\"]\n test = temp[\"X_test\"]\n x_labels = temp[\"y\"]\n X, x_labels = permute_dictionaries(X, x_labels)\n X = scaler.transform(X)\n test = scaler.transform(test)\n X = np.hstack((X, np.ones((X.shape[0], 1))))\n test = np.hstack((test, np.ones((test.shape[0], 1))))\n w = np.zeros((13, 1))\n\n epoch = 0\n loss = np.inf\n while loss >= epsilon:\n x_predictions = sigmoid(np.matmul(X, w))\n\n dw = calc_gradient(X, x_labels, x_predictions, w, c)\n w = w - alpha * dw\n loss = obj(x_labels, x_predictions, w, c)\n epoch += 1\n if epoch % 1000 == 0:\n print(loss)\n test_predictions = classify(test, w)\n print(test_predictions)\n results_to_csv(test_predictions.flatten())\n\npart_a(.02, 0, .0356)\npart_b(.02, 0, .0356)\npart_c(.02, 0, .0356, .0000000001)\n\n#kaggle(1, 0, .03465751782558396)" }, { "alpha_fraction": 0.7456730604171753, "alphanum_fraction": 0.7591345906257629, "avg_line_length": 39.80392074584961, "blob_id": "4363b8918b1ca3bca4f244672d87d7029f3396a1", "content_id": "0b53d2cddd891f92e734388c691f256618b4d74d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2080, "license_type": "no_license", "max_line_length": 119, "num_lines": 51, "path": "/svms/mnist.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import sys\nif sys.version_info[0] < 3:\n raise Exception(\"Python 3 not detected.\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm\nfrom scipy import io\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.kernel_approximation import Nystroem\nfrom save_csv import results_to_csv\n\nfor data_name in [\"mnist\"]:\n data = io.loadmat(\"data/%s_data.mat\" % data_name)\n print(\"\\nloaded %s data!\" % data_name)\n fields = \"test_data\", \"training_data\", \"training_labels\"\n for field in fields:\n print(field, data[field].shape)\n\ndef permute_dictionaries(training_data, training_labels):\n\t#takes two dictionaries and permutes both while keeping consistency\n\tperm = np.random.RandomState(seed=70).permutation(len(training_data))\n\treturn (training_data[perm], training_labels[perm])\n\ntotal_data = io.loadmat(\"data/%s_data.mat\" % \"mnist\")\n\nindex = 60000\ntotal_training_data = total_data[\"training_data\"] / float(255)\ntotal_training_data_labels = total_data[\"training_labels\"]\ntotal_training_data, total_training_data_labels = permute_dictionaries(total_training_data, total_training_data_labels)\ntest_data = total_data[\"test_data\"] / float(255)\n\nfeature_map_nystroem = Nystroem(gamma = .05, n_components = 25000)\nfeatures_training = feature_map_nystroem.fit_transform(total_training_data)\nfeatures_test = feature_map_nystroem.transform(test_data)\n\nprint(\"mnist_training_data\", features_training.shape)\nprint(\"mnist_training_data_labels\", total_training_data_labels.shape)\nprint(\"mnist_test_data\", features_test.shape)\n\ndef problem5(training_data, training_data_labels, test_data, C_value):\t\n\tclassifier = svm.LinearSVC(dual = False, random_state = 10, C = C_value)\n\n\tclassifier.fit(training_data, np.ravel(training_data_labels))\n\n\tpredict_training_results = classifier.predict(training_data)\n\tprint(accuracy_score(np.ravel(training_data_labels), np.ravel(predict_training_results)))\n\tpredict_test_results = classifier.predict(test_data)\n\tresults_to_csv(predict_test_results)\n\n\nproblem5(features_training, total_training_data_labels, features_test, 5)" }, { "alpha_fraction": 0.5962145328521729, "alphanum_fraction": 0.6100946664810181, "avg_line_length": 39.19480514526367, "blob_id": "84c7ecde711a38f847585d1e612b524ebbf269a2", "content_id": "a38aecfed53af8a7209b9a789fbd7dd1f70f7c47", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3170, "license_type": "no_license", "max_line_length": 115, "num_lines": 77, "path": "/decision_trees/titanic_utils.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "from utils import extract_column\r\nfrom sklearn.impute import SimpleImputer\r\nimport numpy as np\r\nimport re\r\nimport pandas\r\n\r\ndef preprocess_titanic(data, include_labels):\r\n imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\r\n imp_mode = SimpleImputer(missing_values=np.nan, strategy='most_frequent')\r\n\r\n columns = [0 for _ in range(9)]\r\n\r\n current_col = extract_column(data, 5).flatten()\r\n current_col = [re.sub('[^0-9]', '', ticket) for ticket in current_col] #remove all letters\r\n current_col = [float(ticket) if ticket != '' else float(np.nan) for ticket in current_col] #convert to float\r\n current_col = np.array([current_col]).T\r\n current_col = imp_mean.fit_transform(current_col)\r\n columns[5] = current_col\r\n\r\n for col in [0, 1, 8]:\r\n current_col = extract_column(data, col)\r\n current_col =imp_mode.fit_transform(current_col)\r\n columns[col] = (current_col)\r\n for col in [2, 3, 4, 6]:\r\n current_col = extract_column(data, col)\r\n current_col = imp_mean.fit_transform(current_col)\r\n columns[col] = (current_col)\r\n\r\n current_col = extract_column(data, 7).flatten()\r\n current_col = [re.sub('[^A-z]', '', cabin)[0] if isinstance(cabin, str) else cabin for cabin in current_col]\r\n class_col = extract_column(data, 0).flatten()\r\n current_col = ['B' if not isinstance(current_col[i], str) and class_col[i] == 1 else current_col[i] for i in\r\n range(len(current_col))]\r\n current_col = ['D' if not isinstance(current_col[i], str) and class_col[i] == 2 else current_col[i] for i in\r\n range(len(current_col))]\r\n current_col = ['F' if not isinstance(current_col[i], str) and class_col[i] == 3 else current_col[i] for i in\r\n range(len(current_col))]\r\n current_col = np.array([current_col]).T\r\n columns[7] = current_col\r\n\r\n if include_labels:\r\n columns.append(extract_column(data, 9))\r\n return np.hstack(tuple(columns))\r\n\r\ndef load_titanic_data():\r\n path_train = 'datasets/titanic/titanic_training.csv'\r\n data = pandas.read_csv(path_train, delimiter=',', dtype=None)\r\n path_test = 'datasets/titanic/titanic_testing_data.csv'\r\n test_data = pandas.read_csv(path_test, delimiter=',', dtype=None)\r\n data = data[data.survived >= 0]\r\n y = data.values[:, [0]] # label = survived\r\n X = data.values[:, range(1, 10)]\r\n header = list(data.columns)\r\n header.append(header.pop(0))\r\n #print(header)\r\n data = np.hstack((X, y))\r\n #print(data.shape)\r\n #print(test_data.shape)\r\n test_data = test_data.values\r\n class_names =['died', 'survived']\r\n return data, test_data, header, class_names\r\n\r\ndef gen_maps(data):\r\n type_map = {}\r\n categories_map = {}\r\n\r\n for i in [0, 1, 7, 8]:\r\n type_map[i] = 'categorical'\r\n for i in [2, 3, 4, 6]:\r\n type_map[i] = 'quantitative'\r\n type_map[5] = 'clustered'\r\n\r\n categories_map[0] = [1, 2, 3]\r\n categories_map[1] = ['male', 'female']\r\n categories_map[7] = list(set(extract_column(data, 7).flatten()))\r\n categories_map[8] = ['S', 'C', 'Q']\r\n return type_map, categories_map" }, { "alpha_fraction": 0.7242571115493774, "alphanum_fraction": 0.7598479390144348, "avg_line_length": 42.83333206176758, "blob_id": "be74aab8e602bd738b49a413a5dc9c327f6f6c3f", "content_id": "9c040b984746db35cff002123f428b4c1ed18b17", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2894, "license_type": "no_license", "max_line_length": 127, "num_lines": 66, "path": "/svms/cifar10.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import sys\nif sys.version_info[0] < 3:\n raise Exception(\"Python 3 not detected.\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm\nfrom scipy import io\nfrom sklearn.metrics import accuracy_score\nfrom skimage.color import rgb2gray\nfrom skimage.feature import hog\nfrom sklearn.preprocessing import StandardScaler\nfrom save_csv import results_to_csv\n\nfor data_name in [\"cifar10\"]:\n data = io.loadmat(\"data/%s_data.mat\" % data_name)\n print(\"\\nloaded %s data!\" % data_name)\n fields = \"test_data\", \"training_data\", \"training_labels\"\n for field in fields:\n print(field, data[field].shape)\n\ndef permute_dictionaries(training_data, training_labels):\n\t#takes two dictionaries and permutes both while keeping consistency\n\tperm = np.random.RandomState(seed=70).permutation(len(training_data))\n\treturn (training_data[perm], training_labels[perm])\n\ncifar10_total_data = io.loadmat(\"data/%s_data.mat\" % \"cifar10\")\n\ncifar10_training_data = cifar10_total_data[\"training_data\"]\ncifar10_training_data_labels = cifar10_total_data[\"training_labels\"]\ncifar10_training_data, cifar10_training_data_labels = permute_dictionaries(cifar10_training_data, cifar10_training_data_labels)\ncifar10_test_data = cifar10_total_data[\"test_data\"]\n\n\ncifar10_uncompressed = np.array([np.transpose(pic.reshape(3,32,32), (1,2,0)) for pic in cifar10_training_data])\ncifar10_test_uncompressed = np.array([np.transpose(pic.reshape(3,32,32), (1,2,0)) for pic in cifar10_test_data])\n#print(cifar10_uncompressed[0].shape)\ncifar10_uncompressed_grey = np.array([rgb2gray(pic) for pic in cifar10_uncompressed])\ncifar10_test_uncompressed_grey = np.array([rgb2gray(pic) for pic in cifar10_test_uncompressed])\n#print(cifar10_uncompressed_grey[0].shape)\n\nhog_features = np.array([hog(image = pic) for pic in cifar10_uncompressed_grey])\nhog_test_features =np.array([hog(image = pic) for pic in cifar10_test_uncompressed_grey])\n\nscaler = StandardScaler()\nhog_features = scaler.fit_transform(hog_features)\nhog_test_features = scaler.transform(hog_test_features)\n\nprint(\"cifar10_training_data\", hog_features.shape)\nprint(\"cifar10_training_data_labels\", cifar10_training_data_labels.shape)\nprint(\"cifar10_test_data\", hog_test_features.shape)\n\ndef problem6(training_data, training_data_labels, test_data, linear, C_Value = 0):\n\n\tclassifier = svm.LinearSVC(dual = False, random_state = 10, verbose = 1, max_iter = 1000000)\n\n\tif(not linear):\n\t\tclassifier = svm.SVC(kernel = \"linear\", random_state = 10, verbose = 0)\n\n\tclassifier.fit(training_data, np.ravel(training_data_labels))\n\n\tpredict_training_results = classifier.predict(training_data)\n\tprint(accuracy_score(np.ravel(training_data_labels), np.ravel(predict_training_results)))\n\tpredict_test_results = classifier.predict(test_data)\n\tresults_to_csv(predict_test_results)\n\nproblem6(hog_features, cifar10_training_data_labels, hog_test_features, True, 0)\n\n" }, { "alpha_fraction": 0.644722044467926, "alphanum_fraction": 0.6596013307571411, "avg_line_length": 42.42683029174805, "blob_id": "1cfc284935b1e1f58fdf062558cae1ff70437526", "content_id": "928e2d2191d88e7c04856b393d4ae5d36a1cd08f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7124, "license_type": "no_license", "max_line_length": 120, "num_lines": 164, "path": "/discriminant_analysis/spam.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import numpy as np\nimport gc\nfrom scipy import io\nfrom scipy.stats import multivariate_normal\nfrom save_csv import results_to_csv\n\ndef sparse_to_np(sparse):\n temp = []\n for samp in range(sparse.shape[0]):\n row = sparse[samp].toarray()[0]\n temp.append(row)\n return np.asarray(temp)\ndef permute_dictionaries(data, labels, rand=25):\n perm = np.random.RandomState(seed=rand).permutation(training_data.shape[0])\n return data[perm], labels[perm]\n\n\ngc.enable()\n\nspam_data = io.loadmat(\"spam-data/spam_data.mat\")\nprint(\"Loaded spam data.\")\ntraining_data = sparse_to_np(spam_data[\"training_data\"])\ntraining_labels = spam_data[\"training_labels\"]\n\ntraining_data, training_labels = permute_dictionaries(training_data, training_labels)\ntraining_data, validation_data = training_data[:4138], training_data[4138:]\ntraining_labels, validation_labels = training_labels[:4138], training_labels[4138:]\n\nclasses = [0, 1]\nn, features = training_data.shape\n\nprint(\"\\nTraining data: \", training_data.shape)\nprint(\"Training data labels: \", training_labels.shape)\nprint(\"Validation data: \", validation_data.shape)\nprint(\"Validation labels: \", validation_labels.shape)\n\ndef empirical_mean(partitioned_data):\n return {k : np.sum(partitioned_data[k], 0, keepdims=True).transpose() / len(partitioned_data[k]) for k in classes}\n\ndef empirical_cov(partitioned_data):\n return {k : np.cov(partitioned_data[k].T, bias=True) for k in classes}\n\ndef calc_priors(partitioned_data, total):\n return {k: partitioned_data[k].shape[0] / total for k in classes}\n\ndef partition_data(data, labels):\n partitioned = {k: [] for k in classes}\n for sample_num in range(data.shape[0]):\n k = labels[sample_num][0]\n sample_features = data[sample_num]\n partitioned[k].append(sample_features)\n for k in classes:\n partitioned[k] = np.asarray(partitioned[k])\n return partitioned\n\ndef error_rate(prediction, actual):\n assert len(prediction) == len(actual)\n return np.count_nonzero(prediction - actual) / prediction.shape[0]\n\ndef classify(distributions, samples, priors):\n all_predictions = {}\n for key in samples.keys():\n predictions = []\n for sample in samples[key]:\n ll = {k: 0 for k in classes}\n for k in classes:\n sample = np.array(sample)\n ll[k] = distributions[k].logpdf(sample) + np.log(priors[k])\n predictions.append(max(ll, key=lambda key: ll[key]))\n all_predictions[key] = predictions\n return all_predictions\n\ndef pool_cov(covariances, priors):\n cov = np.zeros(covariances[0].shape)\n for k in classes:\n cov += priors[k] * covariances[k]\n return cov\ndef LDA(means, covariances, priors, inputs, c=0.0):\n pooled_cov = pool_cov(covariances, priors)\n pooled_cov += np.eye(features) * c * np.trace(pooled_cov)\n distributions = {k: multivariate_normal(means[k].flatten(), pooled_cov, allow_singular=True) for k in classes}\n return classify(distributions, inputs, priors)\ndef QDA(means, covariances, priors, inputs, c=0.0):\n temp_covariances, distributions = {}, {}\n for k in classes:\n temp_covariances[k] = np.eye(features) * c * np.trace(covariances[k]) + covariances[k]\n distributions[k] = multivariate_normal(means[k].flatten(), temp_covariances[k], allow_singular=True)\n return classify(distributions, inputs, priors)\n\n\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\ndef test_QDA(training_data, training_labels, validation_data, validation_labels, c=0.0):\n partitioned_training_data = partition_data(training_data, training_labels)\n means = empirical_mean(partitioned_training_data)\n covariances = empirical_cov(partitioned_training_data)\n priors = calc_priors(partitioned_training_data, training_data.shape[0])\n samples = {'validation' : validation_data}\n predictions = QDA(means, covariances, priors, samples, c)\n return error_rate(np.array([predictions['validation']]).T, validation_labels)\n\ndef test_LDA(training_data, training_labels, validation_data, validation_labels, c=0.0):\n partitioned_training_data = partition_data(training_data, training_labels)\n means = empirical_mean(partitioned_training_data)\n covariances = empirical_cov(partitioned_training_data)\n priors = calc_priors(partitioned_training_data, training_data.shape[0])\n samples = {'validation' : validation_data}\n predictions = LDA(means, covariances, priors, samples, c)\n return error_rate(np.array([predictions['validation']]).T, validation_labels)\n\n#print(test_QDA(training_data, training_labels, validation_data, validation_labels, .00064))\n#print(test_LDA(training_data, training_labels, validation_data, validation_labels))\n\ndef kaggle(c):\n data = sparse_to_np(spam_data[\"training_data\"])\n labels = spam_data[\"training_labels\"]\n test_data = sparse_to_np(spam_data[\"test_data\"])\n partitioned_data = partition_data(data, labels)\n\n means = empirical_mean(partitioned_data)\n partitioned_covariances = empirical_cov(partitioned_data)\n priors = calc_priors(partitioned_data, len(data))\n samples = {'training' : data}\n\n predictions = QDA(means, partitioned_covariances, priors, samples, c)\n train_predictions = predictions['training']\n #test_predictions = predictions['test']\n print(error_rate(np.array([train_predictions]).T, labels))\n #results_to_csv(np.array(test_predictions))\n #return\n\n#print(kaggle(.00064))\n#0.0004833252779120348 train error with 5000 @ .00064 no prior weighting (~95% test accuracy)\n\ndef opt_c_value(training_data, training_labels, validation_data, validation_labels, c_values):\n results = {}\n for c in c_values:\n results[c] = k_fold(training_data, training_labels, 5, c)\n print(\"Error rate \", results[c], \" achieved with c value: \", c)\n best_c = min(results, key=lambda key: results[key])\n print(\"Optimal c_value was \", best_c, \" with error: \", results[best_c])\n return best_c\n\ndef k_fold(data, labels, k, c):\n data, labels = permute_dictionaries(data, labels, np.random.randint(0,10000))\n data_partitions = np.array_split(data, k)\n label_partitions = np.array_split(labels, k)\n errors = []\n for k in range(k):\n validation_data = data_partitions[0]\n validation_labels = label_partitions[0]\n training_data = np.concatenate(data_partitions[1:])\n training_labels = np.concatenate(label_partitions[1:])\n\n error = test_QDA(training_data, training_labels, validation_data, validation_labels, c)\n\n data_partitions = np.roll(data_partitions, 1)\n label_partitions = np.roll(label_partitions, 1)\n errors.append(error)\n return sum(errors) / k\n\n#opt_c_value(training_data, training_labels, validation_data, validation_labels, np.arange(.0006, .0007, .0001))\n\n\n" }, { "alpha_fraction": 0.586129367351532, "alphanum_fraction": 0.6214480400085449, "avg_line_length": 35.99390411376953, "blob_id": "6878e852ea63c2daea4d0ed78c620440e06edc40", "content_id": "f7322233f06ebd299ae9937dc5c8f508ef2f494c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6229, "license_type": "no_license", "max_line_length": 102, "num_lines": 164, "path": "/decision_trees/spam_runner.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import numpy as np\r\nfrom utils import extract_column, results_to_csv, error_rate, plot_data\r\nfrom spam_utils import load_spam\r\nfrom decision_tree_starter import DecisionTree, RandomForest\r\n\r\n#RandomForest(trees, sample_size, bag_size, type_map, categories_map, seed)\r\n# fit(data, max_depth, min_samples)\r\n#\r\n#DecisionTree(type_map, categories_map)\r\n# fit(data, max_depth, min_samples, bag_size = None)\r\n#\r\n#100, 500, 70 .. 20 -> depth 36: .031884 or 26: .03478\r\n# classifier = RandomForest(100, 500, 70, type_map, categories_map, 20)\r\n\r\n\r\ndef plot_q2_5_3():\r\n data, test_data, feature_names, class_names = load_spam()\r\n type_map = dict((i, 'quantitative') for i in range(data.shape[1]))\r\n categories_map = {}\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:4137], data[4137:]\r\n\r\n train_accuracies = []\r\n valid_accuracries =[]\r\n depths = list(range(1,41))\r\n\r\n for max_depth in range(1,41):\r\n print(\"Computing max depth: \", max_depth)\r\n idy = valid.shape[1] - 1\r\n classifier = DecisionTree(type_map, categories_map, feature_names, class_names)\r\n classifier.fit(data, max_depth, 10)\r\n train_pred = classifier.predict(data)\r\n valid_pred = classifier.predict(valid)\r\n train_actual = extract_column(data, idy)\r\n valid_actual = extract_column(valid, idy)\r\n train_acc = 1 - error_rate(train_pred, train_actual)\r\n valid_acc = 1 -error_rate(valid_pred, valid_actual)\r\n train_accuracies.append(train_acc)\r\n valid_accuracries.append(valid_acc)\r\n\r\n plot_data(depths, train_accuracies, valid_accuracries, 'r', 'b', 'Training/Validation Accuracies')\r\n return\r\n\r\ndef q_2_5_2():\r\n data, test_data, feature_names, class_names = load_spam()\r\n type_map = dict((i, 'quantitative') for i in range(data.shape[1]))\r\n categories_map = {}\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:4137], data[4137:]\r\n\r\n classifier = DecisionTree(type_map, categories_map, feature_names, class_names)\r\n classifier.fit(data, 8, 15)\r\n samp_point = np.array([valid[0]])\r\n classifier.predict(samp_point, True)\r\n\r\n samp_point = np.array([valid[1]])\r\n classifier.predict(samp_point, True)\r\n\r\ndef kaggle():\r\n \"\"\"\r\n #run featurize.py with 5000 samples\r\n data, test_data, feature_names, class_names = load_spam()\r\n type_map = dict((i, 'quantitative') for i in range(data.shape[1]))\r\n categories_map = {}\r\n\r\n classifier = RandomForest(100, 500, 70, type_map, categories_map, 20)\r\n classifier.fit(data, 36, 10)\r\n predictions = classifier.predict(test_data)\r\n pred_train = classifier.predict(data)\r\n actual = extract_column(data, 9)\r\n print(error_rate(pred_train, actual))\r\n results_to_csv(predictions.flatten())\r\n\r\n #TESTING DECISION TREE\r\n data, test_data, feature_names, class_names = load_spam()\r\n type_map = dict((i, 'quantitative') for i in range(data.shape[1]))\r\n categories_map = {}\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:4137], data[4137:]\r\n\r\n best_i = -1\r\n best_error = 1\r\n for i in range(2, 50):\r\n classifier = DecisionTree(type_map, categories_map, feature_names, class_names)\r\n classifier.fit(data, 36, 10)\r\n predictions = classifier.predict(valid)\r\n actual = extract_column(valid, valid.shape[1] - 1)\r\n error = error_rate(predictions, actual)\r\n print(i, error)\r\n if error < best_error:\r\n best_error = error\r\n best_i = i\r\n print(best_i, best_error)\r\n # Best at depth 14 with error 0.11594202898550725\r\n \"\"\"\r\n \"\"\"\r\n data, test_data, feature_names, class_names = load_spam()\r\n type_map = dict((i, 'quantitative') for i in range(data.shape[1]))\r\n categories_map = {}\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:4137], data[4137:]\r\n\r\n best_i = -1\r\n best_error = 1\r\n best_j = -1\r\n print(\"Bagging, depth, error\")\r\n for i in range(10, 50):\r\n for j in range(30, 31):\r\n classifier = RandomForest(300, 300, i, type_map, categories_map, 20)\r\n classifier.fit(data, j, 10)\r\n predictions = classifier.predict(valid)\r\n actual = extract_column(valid, 9)\r\n error = error_rate(predictions, actual)\r\n print(i, j, error)\r\n if error < best_error:\r\n best_error = error\r\n best_i = i\r\n best_j = j\r\n print(best_i, best_j, best_error)\r\n \"\"\"\r\n return\r\n\r\ndef q2_4():\r\n print(\"******RUNNING SPAM DATA SET*****\")\r\n data, test_data, feature_names, class_names = load_spam()\r\n type_map = dict((i, 'quantitative') for i in range(data.shape[1]))\r\n categories_map = {}\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:4137], data[4137:]\r\n idy = data.shape[1] - 1\r\n\r\n classifier = DecisionTree(type_map, categories_map)\r\n classifier.fit(data, 14 , 10)\r\n train_predictions = classifier.predict(data)\r\n train_actual = extract_column(data, idy)\r\n valid_predictions = classifier.predict(valid)\r\n valid_actual = extract_column(valid, idy)\r\n\r\n print(\"Decision Tree training Accuracies: \", error_rate(train_predictions, train_actual))\r\n print(\"Decision Tree Validation Accuracies: \", error_rate(valid_predictions, valid_actual))\r\n\r\n classifier = RandomForest(300, 300, 2, type_map, categories_map, 20)\r\n classifier.fit(data, 10, 10)\r\n\r\n train_predictions = classifier.predict(data)\r\n train_actual = extract_column(data, idy)\r\n valid_predictions = classifier.predict(valid)\r\n valid_actual = extract_column(valid, idy)\r\n\r\n print(\"Random Forest training Accuracies: \", error_rate(train_predictions, train_actual))\r\n print(\"Random Forest Validation Accuracies: \", error_rate(valid_predictions, valid_actual))\r\n\r\n return\r\n\r\nif __name__ == \"__main__\":\r\n #plot_q2_5_3()\r\n #q_2_5_2()\r\n #kaggle()\r\n q2_4()" }, { "alpha_fraction": 0.6084234714508057, "alphanum_fraction": 0.6226522326469421, "avg_line_length": 27.81355857849121, "blob_id": "67b8d9f3dd8ca057ef221b44dbeb94e6d20bc507", "content_id": "a6caef550c3f29a4b87598f24869d39d34f5348b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1757, "license_type": "no_license", "max_line_length": 125, "num_lines": 59, "path": "/decision_trees/utils.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import numpy as np\r\nimport pandas\r\nfrom collections import Counter\r\nimport matplotlib.pyplot as plt\r\n\r\ndef results_to_csv(predictions):\r\n predictions = predictions.astype(int)\r\n df = pandas.DataFrame({'Category': predictions})\r\n df.index += 1\r\n df.to_csv('submission.csv', index_label='Id')\r\n\r\ndef sparse_to_np(sparse):\r\n temp = []\r\n for samp in range(sparse.shape[0]):\r\n row = sparse[samp].toarray()[0]\r\n temp.append(row)\r\n return np.asarray(temp)\r\n\r\ndef majority(data):\r\n idy = data.shape[1] - 1\r\n y = extract_column(data, idy)\r\n cnt = Counter(y.flatten())\r\n return cnt.most_common(1)[0][0]\r\n\r\ndef error_rate(prediction, actual):\r\n prediction = np.array(prediction.flatten())\r\n actual = np.array(actual.flatten())\r\n return np.count_nonzero(prediction - actual) / prediction.shape[0]\r\n\r\n\r\ndef extract_column(data, col):\r\n \"\"\"\r\n Extracts col column from data matrix\r\n Outputs: a 2d array (column vector)\r\n \"\"\"\r\n return data[:, [col]]\r\n\r\n\r\ndef find_index(val, thresholds):\r\n i = 0\r\n while i < len(thresholds):\r\n threshold = thresholds[i]\r\n if val < threshold:\r\n return i\r\n i += 1\r\n return i\r\n\r\ndef plot_data(depths, training, validation, clr_tr, clr_vld, title):\r\n fig = plt.figure()\r\n fig.set_size_inches(10, 5)\r\n fig.set_dpi(100)\r\n plt.subplot(1, 1, 1)\r\n plt.plot(depths, training, label=\"training\", color=clr_tr, marker='.', linestyle='dashed',linewidth=1, markersize=1)\r\n plt.plot(depths, validation, label=\"validation\", color=clr_vld, marker='.', linestyle='dashed',linewidth=1, markersize=1)\r\n plt.legend()\r\n plt.title(title + \" vs max depth\")\r\n plt.xlabel(\"Max depth\")\r\n plt.ylabel(\"Accuracy\")\r\n plt.show()" }, { "alpha_fraction": 0.6397821307182312, "alphanum_fraction": 0.6571576595306396, "avg_line_length": 40.462364196777344, "blob_id": "35c4f2819b1ad51eda75ec3096215e15226460ab", "content_id": "44b0c28b9cce3d98d1277bb5c953ca0a25dcedc7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7712, "license_type": "no_license", "max_line_length": 125, "num_lines": 186, "path": "/discriminant_analysis/mnist.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import numpy as np\nimport gc\nfrom scipy import io\nfrom scipy.stats import multivariate_normal\nimport cv2\nfrom save_csv import results_to_csv\nSZ = 28\n\nwinSize = (28, 28)\nblockSize = (12, 12)\nblockStride = (4, 4)\ncellSize = (12, 12)\nnbins = 9\nderivAperture = 1\nwinSigma = -1.\nhistogramNormType = 0\nL2HysThreshold = 0.2\ngammaCorrection = 1\nnlevels = 64\nsignedGradients = True\n\nhog = cv2.HOGDescriptor(winSize, blockSize, blockStride, cellSize, nbins, derivAperture, winSigma, histogramNormType,\n L2HysThreshold, gammaCorrection, nlevels, signedGradients)\n\ndef permute_dictionaries(data, labels, rand=25):\n perm = np.random.RandomState(seed=rand).permutation(training_data.shape[0])\n return data[perm], labels[perm]\n\ndef deskew(img):\n m = cv2.moments(img)\n if abs(m['mu02']) < 1e-2:\n return img.copy()\n skew = m['mu11']/m['mu02']\n M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])\n img = cv2.warpAffine(img,M,(SZ, SZ),flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)\n return img\n\ndef deskew_all(data):\n new_data = []\n for i in range(data.shape[0]):\n row_img = data[i]\n img = row_img.reshape((28, 28))\n new_data.append(np.array((hog.compute(deskew(img)))).flatten())\n return np.array(new_data)\n\ngc.enable()\n\nmnist_data = io.loadmat(\"mnist-data/mnist_data.mat\")\nprint(\"Loaded mnist data.\")\ntraining_data = mnist_data[\"training_data\"]\ntraining_labels = mnist_data[\"training_labels\"]\n\ntraining_data, training_labels = permute_dictionaries(training_data, training_labels,1000)\ntraining_data = deskew_all(training_data)\n\ntraining_data, validation_data = training_data[:50000], training_data[50000:]\ntraining_labels, validation_labels = training_labels[:50000], training_labels[50000:]\n\nclasses = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\nn, features = training_data.shape\n\nprint(\"\\nTraining data: \", training_data.shape)\nprint(\"Training data labels: \", training_labels.shape)\nprint(\"Validation data: \", validation_data.shape)\nprint(\"Validation labels: \", validation_labels.shape)\n\ndef empirical_mean(partitioned_data):\n return {k : np.sum(partitioned_data[k], 0, keepdims=True).transpose() / len(partitioned_data[k]) for k in classes}\n\ndef empirical_cov(partitioned_data):\n return {k : np.cov(partitioned_data[k].T, bias=True) for k in classes}\n\ndef calc_priors(partitioned_data, total):\n return {k: partitioned_data[k].shape[0] / total for k in classes}\n\ndef partition_data(data, labels):\n partitioned = {k: [] for k in classes}\n for sample_num in range(data.shape[0]):\n k = labels[sample_num][0]\n sample_features = data[sample_num]\n partitioned[k].append(sample_features)\n for k in classes:\n partitioned[k] = np.asarray(partitioned[k])\n return partitioned\n\ndef error_rate(prediction, actual):\n assert len(prediction) == len(actual)\n return np.count_nonzero(prediction - actual) / prediction.shape[0]\n\ndef classify(distributions, samples, priors):\n all_predictions = {}\n for key in samples.keys():\n predictions = []\n for sample in samples[key]:\n ll = {k: 0 for k in classes}\n for k in classes:\n sample = np.array(sample)\n ll[k] = distributions[k].logpdf(sample) + np.log(priors[k])\n predictions.append(max(ll, key=lambda key: ll[key]))\n all_predictions[key] = predictions\n return all_predictions\n\ndef pool_cov(covariances, priors):\n cov = np.zeros(covariances[0].shape)\n for k in classes:\n cov += priors[k] * covariances[k]\n return cov\n\ndef LDA(means, covariances, priors, inputs, c=0.0):\n pooled_cov = pool_cov(covariances, priors)\n pooled_cov += np.eye(features) * c * np.trace(pooled_cov)\n distributions = {k: multivariate_normal(means[k].flatten(), pooled_cov, allow_singular=True) for k in classes}\n return classify(distributions, inputs, priors)\ndef QDA(means, covariances, priors, inputs, c=0.0):\n temp_covariances, distributions = {}, {}\n for k in classes:\n temp_covariances[k] = np.eye(features) * c * np.trace(covariances[k]) + covariances[k]\n distributions[k] = multivariate_normal(means[k].flatten(), temp_covariances[k], allow_singular=True)\n return classify(distributions, inputs, priors)\n\n\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\"\"\"------------------------------------------------------------------------------------------------------------------\"\"\"\n\ndef test_QDA(training_data, training_labels, validation_data, validation_labels, c=0.0):\n partitioned_training_data = partition_data(training_data, training_labels)\n means = empirical_mean(partitioned_training_data)\n covariances = empirical_cov(partitioned_training_data)\n priors = calc_priors(partitioned_training_data, training_data.shape[0])\n samples = {'validation' : validation_data}\n predictions = QDA(means, covariances, priors, samples, c)\n return error_rate(np.array([predictions['validation']]).T, validation_labels)\n\ndef test_LDA(training_data, training_labels, validation_data, validation_labels, c=0.0):\n partitioned_training_data = partition_data(training_data, training_labels)\n means = empirical_mean(partitioned_training_data)\n covariances = empirical_cov(partitioned_training_data)\n priors = calc_priors(partitioned_training_data, training_data.shape[0])\n samples = {'validation' : validation_data}\n predictions = LDA(means, covariances, priors, samples, c)\n return error_rate(np.array([predictions['validation']]).T, validation_labels)\n\n#print(test_QDA(training_data, training_labels, validation_data, validation_labels, .0001))\n\ndef kaggle(c):\n data = deskew_all(mnist_data[\"training_data\"])\n labels = mnist_data[\"training_labels\"]\n test_data = deskew_all(mnist_data[\"test_data\"])\n partitioned_data = partition_data(data, labels)\n\n means = empirical_mean(partitioned_data)\n partitioned_covariances = empirical_cov(partitioned_data)\n priors = calc_priors(partitioned_data, len(data))\n samples = {'training' : data, 'test' : test_data}\n\n predictions = QDA(means, partitioned_covariances, priors, samples, c)\n train_predictions = predictions['training']\n test_predictions = predictions['test']\n print(error_rate(np.array([train_predictions]).T, labels))\n results_to_csv(np.array(test_predictions))\n return\n#kaggle(.0001)\n\ndef opt_c_value_QDA(training_data, training_labels, validation_data, validation_labels, c_values):\n results = {}\n for c in c_values:\n results[c] = test_QDA(training_data,training_labels,validation_data,validation_labels,c)\n print(\"Error rate \", results[c], \" achieved with c value: \", c)\n best_c = min(results, key=lambda key: results[key])\n print(\"Optimal c_value was \", best_c, \" with error: \", results[best_c])\n return best_c, results[best_c]\n\ndef opt_c_value_LDA(training_data, training_labels, validation_data, validation_labels, c_values):\n results = {}\n for c in c_values:\n results[c] = test_LDA(training_data,training_labels,validation_data,validation_labels,c)\n print(\"Error rate \", results[c], \" achieved with c value: \", c)\n best_c = min(results, key=lambda key: results[key])\n print(\"Optimal c_value was \", best_c, \" with error: \", results[best_c])\n return best_c\n\ndef gen_c_values(low_exp, high_exp):\n return [10**i for i in range(low_exp, high_exp + 1)]\n\nprint(opt_c_value_QDA(training_data, training_labels, validation_data, validation_labels, np.arange(.00047, .008, .0000001)))\n" }, { "alpha_fraction": 0.7415074110031128, "alphanum_fraction": 0.7494692206382751, "avg_line_length": 37.408164978027344, "blob_id": "857c1dbdc905462beb851cfddbfa2ecd1a4a0919", "content_id": "9e74345fe37eb6dda47134de5368eaa544821f23", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1884, "license_type": "no_license", "max_line_length": 115, "num_lines": 49, "path": "/svms/spam.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import sys\nif sys.version_info[0] < 3:\n raise Exception(\"Python 3 not detected.\")\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm\nfrom scipy import io\nfrom sklearn.metrics import accuracy_score\nfrom save_csv import results_to_csv\n\nfor data_name in [\"spam\"]:\n data = io.loadmat(\"data/%s_data.mat\" % data_name)\n print(\"\\nloaded %s data!\" % data_name)\n fields = \"test_data\", \"training_data\", \"training_labels\"\n for field in fields:\n print(field, data[field].shape)\n\ndef permute_dictionaries(training_data, training_labels):\n\t#takes two dictionaries and permutes both while keeping consistency\n\tperm = np.random.RandomState(seed=100).permutation(training_data.shape[0])\n\treturn (training_data[perm], training_labels[perm])\n\nspam_total_data = io.loadmat(\"data/%s_data.mat\" % \"spam\")\n\nspam_training_data = spam_total_data[\"training_data\"]\nspam_training_data_labels = spam_total_data[\"training_labels\"]\nspam_training_data, spam_training_data_labels = permute_dictionaries(spam_training_data, spam_training_data_labels)\nspam_test_data = spam_total_data[\"test_data\"]\nprint(\"train\")\nprint(spam_training_data)\nprint(\"test\")\nprint(spam_test_data)\n\nprint(\"spam_training_data\", spam_training_data.shape)\nprint(\"spam_training_data_labels\", spam_training_data_labels.shape)\nprint(\"spam_test_data\", spam_test_data.shape)\n\ndef problem6(training_data, training_data_labels, test_data, C_Value = 0):\n\n\tclassifier = svm.LinearSVC(random_state = 40, C = 10 ** C_Value)\n\n\tclassifier.fit(training_data, np.ravel(training_data_labels))\n\n\tpredict_training_results = classifier.predict(training_data)\n\tprint(accuracy_score(np.ravel(training_data_labels), np.ravel(predict_training_results)))\n\tpredict_test_results = classifier.predict(test_data)\n\tresults_to_csv(predict_test_results)\n\nproblem6(spam_training_data, spam_training_data_labels, spam_test_data, 1)\n\n\n" }, { "alpha_fraction": 0.655183732509613, "alphanum_fraction": 0.663385808467865, "avg_line_length": 33.2471923828125, "blob_id": "946fceb4b466015681b7c95f2fca8a4cea51cbc9", "content_id": "e9d9701ca32eabcc8b383961b9206773ccff7f5c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3048, "license_type": "no_license", "max_line_length": 90, "num_lines": 89, "path": "/discriminant_analysis/featurize2.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "'''\n**************** PLEASE READ ***************\n\nScript that reads in spam and ham messages and converts each training example\ninto a feature vector\n\nCode intended for UC Berkeley course CS 189/289A: Machine Learning\n\nRequirements:\n-scipy ('pip install scipy')\n\nTo add your own features, create a function that takes in the raw text and\nword frequency dictionary and outputs a int or float. Then add your feature\nin the function 'def generate_feature_vector'\n\nThe output of your file will be a .mat file. The data will be accessible using\nthe following keys:\n -'training_data'\n -'training_labels'\n -'test_data'\n\nPlease direct any bugs to [email protected]\n'''\n\nimport glob\nimport scipy.io\nimport numpy as np\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nNUM_TRAINING_EXAMPLES = 5172\nNUM_TEST_EXAMPLES = 5857\n\nBASE_DIR = './'\nSPAM_DIR = 'spam/'\nHAM_DIR = 'ham/'\nTEST_DIR = 'test/'\n\n# This method generates a design matrix with a list of filenames\n# Each file is a single training example\ndef generate_design_fit_matrix(filenames, vectorizer):\n print(\"Fitting and transforming training data\")\n all_text = []\n for filename in filenames:\n with open(filename, 'r', encoding='utf-8', errors='ignore') as f:\n try:\n text = f.read() # Read in text from file\n except Exception as e:\n # skip files we have trouble reading.\n continue\n text = text.replace('\\r\\n', ' ') # Remove newline character\n all_text.append(text)\n\n design_matrix = vectorizer.fit_transform(all_text)\n return design_matrix\n\ndef generate_design_matrix(test_filenames, vectorizer):\n print(\"Tranforming test data\")\n all_text = []\n for filename in test_filenames:\n with open(filename, 'r', encoding='utf-8', errors='ignore') as f:\n try:\n text = f.read() # Read in text from file\n except Exception as e:\n # skip files we have trouble reading.\n continue\n text = text.replace('\\r\\n', ' ') # Remove newline character\n all_text.append(text)\n return vectorizer.transform(all_text)\n\n# ************** Script starts here **************\n# DO NOT MODIFY ANYTHING BELOW\nvectorizer = TfidfVectorizer(max_features=5000, norm='l2', sublinear_tf=True)\n\nspam_filenames = glob.glob(BASE_DIR + SPAM_DIR + '*.txt')\nham_filenames = glob.glob(BASE_DIR + HAM_DIR + '*.txt')\nX = generate_design_fit_matrix(spam_filenames + ham_filenames, vectorizer)\n# Important: the test_filenames must be in numerical order as that is the\n# order we will be evaluating your classifier\ntest_filenames = [BASE_DIR + TEST_DIR + str(x) + '.txt' for x in range(NUM_TEST_EXAMPLES)]\ntest_design_matrix = generate_design_matrix(test_filenames, vectorizer)\n\n\nY = np.array([1]*len(spam_filenames) + [0]*len(ham_filenames)).reshape((-1, 1))\n\nfile_dict = {}\nfile_dict['training_data'] = X\nfile_dict['training_labels'] = Y\nfile_dict['test_data'] = test_design_matrix\nscipy.io.savemat('spam_data.mat', file_dict)\n" }, { "alpha_fraction": 0.642092764377594, "alphanum_fraction": 0.642092764377594, "avg_line_length": 27.964284896850586, "blob_id": "fccd0b765111feed190f5830af468b640270c40c", "content_id": "13444758d5951782a5a06fcb8ac541298fd12e99", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 841, "license_type": "no_license", "max_line_length": 60, "num_lines": 28, "path": "/decision_trees/spam_utils.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "from utils import extract_column\r\nfrom sklearn.impute import SimpleImputer\r\nimport numpy as np\r\nimport re\r\nimport pandas\r\nimport scipy.io\r\nfrom utils import sparse_to_np\r\n\r\ndef load_spam():\r\n path_train = 'datasets/spam-dataset/spam_data.mat'\r\n data = scipy.io.loadmat(path_train)\r\n X = data['training_data']\r\n X = sparse_to_np(X)\r\n y = data['training_labels']\r\n Z = data['test_data']\r\n Z = sparse_to_np(Z)\r\n #feature_names = data['feature_names']\r\n feature_names = []\r\n class_names = [\"Ham\", \"Spam\"]\r\n #print(X.shape)\r\n #print(y.shape)\r\n #print(Z.shape)\r\n data = np.hstack((X, y))\r\n\r\n feature_path = 'datasets/spam-dataset/feature_names.mat'\r\n feature_dict = scipy.io.loadmat(feature_path)\r\n feature_names = feature_dict['feature_names']\r\n return data, Z, feature_names, class_names\r\n\r\n" }, { "alpha_fraction": 0.6015936136245728, "alphanum_fraction": 0.6343048810958862, "avg_line_length": 35.56692886352539, "blob_id": "777e1d9e21e152c95e7003f5c00eabe031c3b007", "content_id": "4760fb0687b7665d4302f4d82bc2f08595bb9325", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4769, "license_type": "no_license", "max_line_length": 99, "num_lines": 127, "path": "/decision_trees/titanic_runner.py", "repo_name": "alexletu/traditional_ml_models", "src_encoding": "UTF-8", "text": "import numpy as np\r\nfrom utils import extract_column, results_to_csv, error_rate\r\nfrom decision_tree_starter import DecisionTree, RandomForest\r\nfrom titanic_utils import preprocess_titanic, load_titanic_data, gen_maps\r\n\r\n#RandomForest(trees, sample_size, bag_size, type_map, categories_map, seed)\r\n# fit(data, max_depth, min_samples)\r\n#\r\n#DecisionTree(type_map, categories_map)\r\n# fit(data, max_depth, min_samples, bag_size = None)\r\n#\r\n#100, 500, 70 .. 20 -> depth 36: .031884 or 26: .03478\r\n# classifier = RandomForest(100, 500, 70, type_map, categories_map, 20)\r\n\r\ndef q_2_6():\r\n data, test_data, feature_names, class_names = load_titanic_data()\r\n data = preprocess_titanic(data, True)\r\n\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:800], data[800:]\r\n\r\n type_map, categories_map = gen_maps(data)\r\n classifier = DecisionTree(type_map, categories_map, feature_names, class_names)\r\n classifier.fit(data, 3, 10)\r\n print(classifier)\r\n\r\ndef kaggle():\r\n data, test_data, feature_names, class_names = load_titanic_data()\r\n data = preprocess_titanic(data, True)\r\n test = preprocess_titanic(test_data, False)\r\n\r\n type_map, categories_map = gen_maps(data)\r\n classifier = DecisionTree(type_map, categories_map)\r\n\r\n classifier.fit(data, 4, 10)\r\n predictions = classifier.predict(test)\r\n pred_train = classifier.predict(data)\r\n actual = extract_column(data, 9)\r\n print(error_rate(pred_train, actual))\r\n results_to_csv(predictions.flatten())\r\n \"\"\"\r\n\r\n data, test_data, feature_names, class_names = load_titanic_data()\r\n data = preprocess_titanic(data, True)\r\n\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:800], data[800:]\r\n\r\n type_map, categories_map = gen_maps(data)\r\n classifier = DecisionTree(type_map, categories_map)\r\n\r\n #TESTING FOR BEST RANDOM FOREST\r\n best_i = -1\r\n best_error = 1\r\n best_j = -1\r\n print(\"Bagging, depth, error\")\r\n for i in range(1, 9):\r\n for j in range(1,20,1):\r\n classifier = RandomForest(300, 300, i, type_map, categories_map, 20)\r\n classifier.fit(data, j, 10)\r\n predictions = classifier.predict(valid)\r\n actual = extract_column(valid, 9)\r\n error = error_rate(predictions, actual)\r\n print(i, j, error)\r\n if error < best_error:\r\n best_error = error\r\n best_i = i\r\n best_j = j\r\n print(best_i, best_j, best_error)\r\n # best recorded is 2 10 at error .165829 (300 trees 300 samples)\r\n # best recorded is 2 5 at error .175879 (300 trees 300 samples)\r\n\r\n #TESTING FOR BEST DECISION TREE\r\n best_i = -1\r\n print(\"depth, error\")\r\n for i in range(1, 40):\r\n classifier = DecisionTree(type_map, categories_map,feature_names, class_names)\r\n classifier.fit(data, i, 10)\r\n predictions = classifier.predict(valid)\r\n actual = extract_column(valid, 9)\r\n error = error_rate(predictions, actual)\r\n print(i, j, error)\r\n if error < best_error:\r\n best_error = error\r\n best_i = i\r\n print(best_i, best_error)\r\n #best recorded at 4 at point .1758\r\n \"\"\"\r\ndef q_2_4():\r\n print(\"******RUNNING TITANIC DATA SET*****\")\r\n\r\n data, test_data, feature_names, class_names = load_titanic_data()\r\n data = preprocess_titanic(data, True)\r\n\r\n perm = np.random.RandomState(seed=20).permutation((data.shape[0]))\r\n data = data[perm]\r\n data, valid = data[:800], data[800:]\r\n idy = data.shape[1] - 1\r\n\r\n type_map, categories_map = gen_maps(data)\r\n classifier = DecisionTree(type_map, categories_map)\r\n classifier.fit(data, 4, 10)\r\n train_predictions = classifier.predict(data)\r\n train_actual = extract_column(data, idy)\r\n valid_predictions = classifier.predict(valid)\r\n valid_actual = extract_column(valid, idy)\r\n\r\n print(\"Decision Tree training Accuracies: \", error_rate(train_predictions, train_actual))\r\n print(\"Decision Tree Validation Accuracies: \", error_rate(valid_predictions, valid_actual))\r\n\r\n classifier = RandomForest(300, 300, 2, type_map, categories_map, 20)\r\n classifier.fit(data, 10, 10)\r\n train_predictions = classifier.predict(data)\r\n train_actual = extract_column(data, idy)\r\n valid_predictions = classifier.predict(valid)\r\n valid_actual = extract_column(valid, idy)\r\n\r\n print(\"Random Forest training Accuracies: \", error_rate(train_predictions, train_actual))\r\n print(\"Random Forest Validation Accuracies: \", error_rate(valid_predictions, valid_actual))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n #q_2_6()\r\n #kaggle()\r\n q_2_4()" } ]

13

jperilla/Machine-Learning

https://github.com/jperilla/Machine-Learning

53c800eb18af4ddf30e04708c549c8d8c90b8147

407275c2fca57c1f78ae11ef4c73ff87d4eef73a

1640e4b68f86c1f809b2539f74c7850e1406dba8

refs/heads/master

2020-07-10T16:04:21.396542

2020-06-22T18:30:16

204,306,544

2

0

null

[ { "alpha_fraction": 0.6546216011047363, "alphanum_fraction": 0.660359799861908, "avg_line_length": 40.06369400024414, "blob_id": "f6b32428cd04db1288540869617019ae7eb5b42c", "content_id": "c55394a2a88eee52dd23f1e71cf2d1b686adf773", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6448, "license_type": "no_license", "max_line_length": 119, "num_lines": 157, "path": "/Clustering and Feature Selection/runAlgorithms.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import sys\n\nimport pandas as pd\n\nimport naiveBayes as nb\nfrom kMeansClustering import KMeansClustering\nfrom machineLearningUtilities import split_test_train, one_hot_encoder, get_num_similarities, \\\n calculate_silhouette_coefficient\nfrom stepwiseForwardSelection import StepwiseForwardSelection\n\n\ndef run_on_glass(file):\n \"\"\"\n This function runs the SFS Algorithm wrapping Naive Bayes to reduce the feature set\n and then runs a K-means clustering algorithm to cluster the data. To test k-means as\n a classifier Naive Bayes is run again with the cluster labels generated by K-means.\n :param file: The file name that includes the data\n :return: Nothing\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Glass data set...\")\n # Read in glass data\n df_glass = pd.read_csv(file, header=None)\n df_glass.columns = [\"Id\", \"RI\", \"Na\", \"Mg\", \"Al\", \"Si\", \"K\", \"Ca\", \"Ba\", \"Fe\", \"Class\"]\n\n # This data set has no missing values, so we will skip that step\n\n # Drop Id\n df_glass_all = df_glass.drop('Id', axis=1)\n\n # One hot code the classes in order to use my previous naive bayes algorithm\n df_glass_encoded = one_hot_encoder(df_glass_all, [\"Class\"])\n\n # Split into test and training sets\n x_test, x_train = split_test_train(df_glass_encoded)\n x_test = x_test.reset_index(drop=True)\n x_train = x_train.reset_index(drop=True)\n\n # Run stepwise forward selection to reduce the feature set\n print(\"Running SFS on Glass data set...\")\n features = df_glass_all.columns.values.tolist()[0:9]\n print(\"All features...\")\n print(features)\n sfs = StepwiseForwardSelection(features, x_train.iloc[:, 0:9], x_test.iloc[:, 0:9],\n x_train[\"Class_1\"], x_test[\"Class_1\"], nb.learn, nb.test)\n optimized_feature_set = sfs.run()\n\n cluster_and_classify(optimized_feature_set, x_test, x_train)\n\n\ndef run_on_iris(file):\n \"\"\"\n This function runs the SFS Algorithm wrapping Naive Bayes to reduce the feature set\n and then runs a K-means clustering algorithm to cluster the data. To test k-means as\n a classifier Naive Bayes is run again with the cluster labels generated by K-means.\n :param file: The file name that includes the data\n :return: Nothing\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Iris data set...\")\n # Read in iris data\n df_iris = pd.read_csv(file, header=None)\n df_iris.columns = [\"sepal length in cm\", \"sepal width in cm\", \"petal length in cm\", \"petal width in cm\", \"Class\"]\n\n # This data set has no missing values, so we will skip that step\n\n # One hot code the classes in order to use my previous naive bayes algorithm\n df_iris_encoded = one_hot_encoder(df_iris, [\"Class\"])\n\n # Split into test and training sets\n x_test, x_train = split_test_train(df_iris_encoded)\n x_test = x_test.reset_index(drop=True)\n x_train = x_train.reset_index(drop=True)\n\n # Run stepwise forward selection to reduce the feature set\n print(\"Run Stepwise forward selection to reduce the feature set on Iris...\")\n print(\"All features...\")\n features = df_iris_encoded.columns.values.tolist()[0:4]\n print(features)\n sfs = StepwiseForwardSelection(features, x_train.iloc[:, 0:4], x_test.iloc[:, 0:4],\n x_train[\"Class_Iris-virginica\"], x_test[\"Class_Iris-virginica\"], nb.learn, nb.test)\n optimized_feature_set = sfs.run()\n\n cluster_and_classify(optimized_feature_set, x_test, x_train)\n\n\ndef run_on_spambase(file):\n \"\"\"\n This function runs the SFS Algorithm wrapping Naive Bayes to reduce the feature set\n and then runs a K-means clustering algorithm to cluster the data. To test k-means as\n a classifier Naive Bayes is run again with the cluster labels generated by K-means.\n :param file: The file name that includes the data\n :return: Nothing\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Spambase data set...\")\n # Read in spambase data\n df_spambase = pd.read_csv(file, header=None)\n df_spambase.rename(columns={57: 'Class'}, inplace=True)\n\n # This data set has no missing values, so we will skip that step\n\n # Split into test and training sets\n x_test, x_train = split_test_train(df_spambase)\n x_test = x_test.reset_index(drop=True)\n x_train = x_train.reset_index(drop=True)\n\n features = df_spambase.columns.values.tolist()[0:56]\n print(\"Running SFS on Spambase...\")\n print(\"All features...\")\n print(features)\n sfs = StepwiseForwardSelection(features, x_train.iloc[:, 0:56], x_test.iloc[:, 0:56],\n x_train[\"Class\"], x_test[\"Class\"], nb.learn, nb.test)\n optimized_feature_set = sfs.run()\n\n cluster_and_classify(optimized_feature_set, x_test, x_train)\n\n\ndef cluster_and_classify(optimized_feature_set, x_test, x_train):\n \"\"\"This function run the clustering and classification algorithms and\n tests clusters with the silhouette coffecient\"\"\"\n # Use k-means to cluster data\n print(\"Running K Means on Glass data set with optimized feature set...\")\n km = KMeansClustering(x_train[optimized_feature_set], 2)\n labels = km.run()\n # Train the training data with the cluster labels using Naive Bayes\n print(\"Training with Naive Bayes with k-means labels...\")\n model = nb.learn(pd.Series(labels), x_train[optimized_feature_set])\n # Test the naive bayes classifier on test data\n print(\"Testing Naive Bayes Classifier with cluster labels\")\n predictions = nb.test(x_test[optimized_feature_set], *model)\n print(\"Naive Bayes Classifier Performance = \" + str(get_num_similarities(labels, predictions) / len(labels) * 100))\n # Find the silhouette coefficient of the clusters\n print(\"Calculating the silhouette coefficient...\")\n sc = calculate_silhouette_coefficient(x_train[optimized_feature_set], labels)\n print(\"Silhouette Coefficient = \" + str(sc))\n\n\nif __name__== \"__main__\":\n if len(sys.argv) < 3:\n print(\"Please add options for data set (iris, glass, spambase) then file path when running this script\")\n print(\"Example: python runAlgorithms iris ./data/iris.data\")\n exit()\n\n if sys.argv[1] == \"glass\":\n run_on_glass(sys.argv[2])\n exit()\n\n if sys.argv[1] == \"iris\":\n run_on_iris(sys.argv[2])\n exit()\n\n if sys.argv[1] == \"spambase\":\n run_on_spambase(sys.argv[2])\n exit()\n\n exit()\n\n" }, { "alpha_fraction": 0.8518518805503845, "alphanum_fraction": 0.8518518805503845, "avg_line_length": 53, "blob_id": "4e5acdd5816c891a45ae8c1b27b1efc8d2f57ef9", "content_id": "4f7d39d3e4399cd045c72ec73dd26c08cc890f34", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 108, "license_type": "no_license", "max_line_length": 88, "num_lines": 2, "path": "/README.md", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "# Machine-Learning\nRepository for Machine Learning Projects from Johns Hopkins and Memorial Sloan Kettering\n" }, { "alpha_fraction": 0.5440627336502075, "alphanum_fraction": 0.5530246496200562, "avg_line_length": 30.880952835083008, "blob_id": "8d4a54cbc23d62c987b90a5f92486e50e9dfcb08", "content_id": "a494b722c5df88820b8b5929b5f4d80b9829076c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2678, "license_type": "no_license", "max_line_length": 98, "num_lines": 84, "path": "/Logistic Regression/logisticRegression.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import math\nimport random\n\nfrom machineLearningUtilities import get_num_similarities\n\n\nclass LogisticRegression:\n def __init__(self, features, x_train, x_train_classes, x_test, x_test_classes):\n self.features = features\n self.training_classes = x_train_classes\n self.train = x_train\n self.test = x_test\n self.test_classes = x_test_classes\n self.max_iterations = 50\n self.eta = 0.01 # learning rate\n self.weights = [0] * len(self.features)\n\n def learn(self):\n \"\"\"\n This function runs the logistic regression algorithm on the training set provided\n \"\"\"\n print(\"Running Logistic Regression...\")\n for j, value in enumerate(self.features):\n self.weights[j] = random.uniform(-1.0, 1.0)\n\n print(f\"Starting weights = {self.weights}\")\n converged = False\n i = 0\n while not converged:\n weight_deltas = [0] * len(self.features)\n\n for x_index, x_values in self.train.iterrows():\n o = 0\n for j, value in enumerate(self.features):\n o += self.weights[j] * float(x_values[j])\n\n y = self.sigmoid(o)\n\n for j, value in enumerate(self.features):\n weight_deltas[j] += (self.training_classes[x_index] - y) * float(x_values[j])\n\n for j, value in enumerate(self.features):\n self.weights[j] += self.eta * weight_deltas[j]\n\n if i > self.max_iterations:\n converged = True\n\n i += 1\n\n print(f\"Ending eights = {self.weights}\")\n\n def validate(self):\n \"\"\"\n This function determines accuracy of model using the test data set\n and applying the linear function using the weights\n \"\"\"\n print(\"Testing...\")\n predictions = []\n for x_index, x_values in self.test.iterrows():\n # Calculate linear value by adding up x values and their weights\n o = 0\n for j, value in enumerate(self.features):\n o += float(x_values[j]) * self.weights[j]\n\n y = self.sigmoid(o)\n\n if y > 0.5:\n predictions.append(1)\n else:\n predictions.append(0)\n\n return get_num_similarities(predictions, self.test_classes) / len(self.test_classes) * 100\n\n @staticmethod\n def sigmoid(x):\n \"\"\"\n This function calculates the logistic sigmoid of x\n :param x: The value\n :return: The logistic sigmoid\n \"\"\"\n try:\n return 1 / (1 + math.exp(-x))\n except OverflowError:\n return float('inf')\n" }, { "alpha_fraction": 0.5622423887252808, "alphanum_fraction": 0.5766693949699402, "avg_line_length": 32.92307662963867, "blob_id": "b28eefc5ec0d0dcfac5ae0690e52c34d5604cc09", "content_id": "18bdb5961394f26e6acd81fc61993b1c451be104", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4852, "license_type": "no_license", "max_line_length": 113, "num_lines": 143, "path": "/K-Nearest Neighbors/runAlgorithms.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import sys\nimport statistics\n\nimport pandas as pd\n\nfrom kNearestNeighbors import KNearestNeighbors\nfrom machineLearningUtilities import split_into_random_stratified_groups, one_hot_encoder\n\n\ndef run_on_ecoli(file, k):\n \"\"\"\n This function runs k-nearest neighbors on the Ecoli data set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Ecoli data set...\")\n df_ecoli = pd.read_csv(file, header=None, sep='\\s+')\n\n df_ecoli.columns = [\"Id\", \"mcg\", \"gvh\", \"lip\", \"chg\", \"aac\", \"alm1\", \"alm2\", \"Class\"]\n # This data set has no missing values, so we will skip that step\n\n # Drop Id\n df_ecoli = df_ecoli.drop(\"Id\", axis=1)\n\n run_k_nearest_neighbor_experiments(df_ecoli, k, True, classification=True)\n\n\ndef run_on_image(file, k):\n \"\"\"\n This function runs k-nearest neighbors on the Image Segmentation data set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Image Segmentation data set...\")\n df_image = pd.read_csv(file, header=None)\n print(df_image.head())\n\n df_image.columns = [\"Class\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\",\n \"16\", \"17\", \"18\", \"19\"]\n\n run_k_nearest_neighbor_experiments(df_image, k, True, classification=True)\n\n\ndef run_on_computer(file, k):\n \"\"\"\n This function runs k-nearest neighbors on the Computer Hardware data set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Computer Hardware data set...\")\n df_computer = pd.read_csv(file, header=None)\n print(df_computer.head())\n\n df_computer.columns = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"Class\", \"9\"]\n\n # One hot encode categorical values\n df_computer = one_hot_encoder(df_computer, [\"0\", \"1\"])\n print(df_computer.head())\n\n run_k_nearest_neighbor_experiments(df_computer, k, False, classification=False)\n\n\ndef run_on_forest(file, k):\n \"\"\"\n This function runs k-nearest neighbors on the Forest Fires data set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Forest Fires data set...\")\n df_forest = pd.read_csv(file, header=None)\n print(df_forest.head())\n\n df_forest.columns = []\n\n run_k_nearest_neighbor_experiments(df_forest, k, False, classification=False)\n\n\ndef run_k_nearest_neighbor_experiments(df, k, run_condensed, classification=True):\n # Split dataset 5-fold stratified\n print(f\"Size of total dataset = {len(df)}\")\n train1, train2, train3, train4, train5 = split_into_random_stratified_groups(df)\n # Run five experiments, using one of the sets as a test set each time\n k_scores = []\n k_condensed_scores = []\n datasets = [train1, train2, train3, train4, train5]\n for i, d in enumerate(datasets):\n print(\"-------------\")\n print(f\"Experiment #{i + 1}\")\n print(\"-------------\")\n df_test = datasets[i]\n print(len(df_test))\n training_sets = datasets.copy()\n del training_sets[i]\n df_train = pd.concat(training_sets)\n print(len(df_train))\n\n # Run K-Nearest Neighbors\n print(f\"k = {k}\")\n print(\"Running k nearest neighbors...\")\n knn = KNearestNeighbors(df_test, k, df.columns, classification)\n accuracy = knn.run(df_train)\n print('Percent accurate: ' + repr(accuracy) + '%')\n k_scores.append(accuracy)\n\n if run_condensed:\n # Run Condensed K-Nearest Neighbors\n knn = KNearestNeighbors(df_test, k, df.columns, classification)\n accuracy = knn.run_condensed(df_train)\n print('Percent accurate: ' + repr(accuracy) + '%')\n k_condensed_scores.append(accuracy)\n\n print(\"----------------------------------------\")\n print(f\"Averages over 5 experiments where k={k}\")\n print(\"----------------------------------------\")\n print(f\"k-Nearest Neighbors = {statistics.mean(k_scores)}\")\n if run_condensed:\n print(f\"Condensed k-Nearest Neighbors = {statistics.mean(k_condensed_scores)}\")\n\n\nif __name__== \"__main__\":\n if len(sys.argv) < 4:\n print(\"Please add options for data set (--ecoli, --image, --computer or --forest) \"\n \"and value of k then file path when running this script\")\n print(\"Example: python runAlgorithms --ecoli 3 ./data/iris.data\")\n exit()\n\n if sys.argv[1] == \"--ecoli\":\n run_on_ecoli(sys.argv[3], int(sys.argv[2]))\n exit()\n\n if sys.argv[1] == \"--image\":\n run_on_image(sys.argv[3], int(sys.argv[2]))\n exit()\n\n if sys.argv[1] == \"--computer\":\n run_on_computer(sys.argv[3], int(sys.argv[2]))\n exit()\n\n if sys.argv[1] == \"--forest\":\n run_on_forest(sys.argv[3], int(sys.argv[2]))\n exit()\n\n exit()\n\n" }, { "alpha_fraction": 0.5569592118263245, "alphanum_fraction": 0.573803186416626, "avg_line_length": 32.917293548583984, "blob_id": "a39ac21c75a6f62098449e522d462c81e5aae76c", "content_id": "dbdc0cfe3169a2ac662257117f39c06ee86286fa", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4512, "license_type": "no_license", "max_line_length": 113, "num_lines": 133, "path": "/Decision Trees/runAlgorithms.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import sys\nimport statistics\n\nimport pandas as pd\n\nfrom DecisionTree import DecisionTree\nfrom machineLearningUtilities import split_into_random_stratified_groups\n\n\ndef run_id3_on_abalone(file, prune=False):\n \"\"\"\n This function runs ID3 algorithm on the abalone data set, which is a classification set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Abalone data set...\")\n df_abalone = pd.read_csv(file, header=None)\n df_abalone.columns = [\"Sex\", \"Length\", \"Diameter\", \"Height\", \"Whole\", \"Shucked\",\n \"Viscera\", \"Shell\", \"Class\"]\n\n # This data set has no missing values, so we will skip that step\n\n # Encode Sex column\n df_abalone.loc[df_abalone[\"Sex\"] == \"M\", \"Sex\"] = 0\n df_abalone.loc[df_abalone[\"Sex\"] == \"F\", \"Sex\"] = 1\n df_abalone.loc[df_abalone[\"Sex\"] == \"I\", \"Sex\"] = 2\n print(df_abalone.head())\n\n run_id3_decision_tree(df_abalone, prune)\n\n\ndef run_id3_on_car(file, prune=False):\n \"\"\"\n This function runs ID3 algorithm on the car evaluation data set, which is a classification set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Car Evaluation data set...\")\n df_car = pd.read_csv(file, header=None)\n df_car.columns = [\"buying\", \"maint\", \"doors\", \"persons\", \"lug_boot\", \"safety\", \"Class\"]\n\n # This data set has no missing values, so we will skip that step\n\n print(df_car.head())\n\n run_id3_decision_tree(df_car, prune)\n\n\ndef run_id3_on_image(file, prune=False):\n \"\"\"\n This function runs the ID3 algorithm on the Image Segmentation data set, which is a classification set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Image Segmentation data set...\")\n df_image = pd.read_csv(file, header=None)\n df_image.columns = [\"Class\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\",\n \"16\", \"17\", \"18\", \"19\"]\n print(df_image.head())\n\n run_id3_decision_tree(df_image, prune)\n\n\ndef run_id3_decision_tree(df, prune=False):\n # Split dataset 5-fold stratified\n print(f\"Size of total dataset = {len(df)}\")\n train1, train2, train3, train4, train5 = split_into_random_stratified_groups(df)\n datasets = [train1, train2, train3, train4, train5]\n scores = []\n pruned_scores = []\n for i, d in enumerate(datasets):\n print(\"-------------\")\n print(f\"Experiment #{i + 1}\")\n print(\"-------------\")\n\n # Use one subset as a test set\n df_test = datasets[i]\n print(f\"Test set size = {len(df_test)}\")\n training_sets = datasets.copy()\n\n # Create a training set from remaining subsets\n del training_sets[i]\n df_train = pd.concat(training_sets)\n print(f\"Training set size = {len(df_train)}\")\n\n # Build the decision tree from the training set\n id3 = DecisionTree(df_train)\n id3.build_id3_tree()\n #id3.print_tree()\n\n # Test the decision tree\n accuracy = id3.validate(id3.root, df_test)\n print('Percent accurate: ' + repr(accuracy) + '%')\n scores.append(accuracy)\n\n # If pruning is turned on, test pruned tree accuracy\n if prune:\n p_accuracy = id3.validate_pruned_tree(df_test)\n print('Pruned Tree Percent Accurate: ' + repr(p_accuracy) + '%')\n pruned_scores.append(p_accuracy)\n\n print(\"----------------------------\")\n print(f\"Averages over 5 experiments\")\n print(\"----------------------------\")\n print(f\"ID3 Decision Tree Averages = {statistics.mean(scores)}%\")\n if prune:\n print(f\"Pruned ID3 Decision Tree Averages = {statistics.mean(pruned_scores)}%\")\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 3:\n print(\"Please add options for data set (--abalone --car --image --computer --forest --wine) \"\n \"and the full file path when running this script\")\n print(\"Example: python runAlgorithms.py --classification ./data/abalone.data\")\n exit()\n\n prune = False\n if len(sys.argv) == 4:\n prune = True if sys.argv[3] == \"-p\" else False\n\n if sys.argv[1] == \"--abalone\":\n run_id3_on_abalone(sys.argv[2], prune=prune)\n exit()\n\n if sys.argv[1] == \"--car\":\n run_id3_on_car(sys.argv[2], prune=prune)\n exit()\n\n if sys.argv[1] == \"--image\":\n run_id3_on_image(sys.argv[2], prune=prune)\n exit()\n\n exit()\n\n" }, { "alpha_fraction": 0.601307213306427, "alphanum_fraction": 0.6055363416671753, "avg_line_length": 37.776119232177734, "blob_id": "2e060ee258338b4bcf2fc2d11c9a32373d6dabf0", "content_id": "8829035408fb600b7af2cd45c2e568aa693809d4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2601, "license_type": "no_license", "max_line_length": 96, "num_lines": 67, "path": "/Clustering and Feature Selection/stepwiseForwardSelection.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "from machineLearningUtilities import get_num_similarities\n\n\nclass StepwiseForwardSelection:\n \"\"\"\n This clas performs Stepwise Forward Selection for feature selection\n \"\"\"\n def __init__(self, all_features, d_train, d_valid, c_train, c_test, f_learn, f_perf):\n \"\"\"\n This function initialize the parameters for SFS\n :param all_features: full set of features\n :param d_train: training data\n :param d_valid: validation data\n :param c_train: training classes\n :param c_test: validation classes\n :param f_learn: The learning function\n :param f_perf: The performance monitoring function\n \"\"\"\n self.features = all_features\n self.train = d_train\n self.valid = d_valid\n self.train_classes = c_train\n self.valid_classes = c_test\n self.learn = f_learn\n self.validate = f_perf\n self.base_performance = -float(\"inf\")\n\n def run(self):\n \"\"\"\n This function runs the SFS algorithm for feature selection\n :return: The set reduced set of features\n \"\"\"\n print(\"Running StepWise Forward Selection...\")\n features_0 = []\n while self.features:\n best_performance = -float(\"inf\")\n for feature in self.features:\n features_0.append(feature)\n hypothesis = self.learn(self.train_classes, self.train[features_0])\n current_performance = self.test_performance(self.valid[features_0], *hypothesis)\n if current_performance > best_performance:\n best_performance = current_performance\n best_feature = feature\n features_0.remove(feature)\n\n if best_performance > self.base_performance:\n self.base_performance = best_performance\n self.features.remove(best_feature)\n features_0.append(best_feature)\n else:\n break\n\n print(\"Final best performance...\")\n print(f\"{self.base_performance * 100}%\")\n print(\"Final feature set\")\n print(features_0)\n return features_0\n\n def test_performance(self, data, *hypothesis):\n \"\"\"\n This function measures performance using the validation function\n :param data: The data to test on\n :param hypothesis: The model to use\n :return: The performance as a percentage correct\n \"\"\"\n predictions = self.validate(data, *hypothesis)\n return get_num_similarities(self.valid_classes, predictions) / len(self.valid_classes)\n\n\n\n" }, { "alpha_fraction": 0.5696234703063965, "alphanum_fraction": 0.5713924765586853, "avg_line_length": 33.911502838134766, "blob_id": "12d0870f1912c32f87a2094d6059165de3ea8ebd", "content_id": "66629c6e7a5d3f08a8f2379dd8fd1b1cfa04826c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3957, "license_type": "no_license", "max_line_length": 105, "num_lines": 113, "path": "/K-Nearest Neighbors/kNearestNeighbors.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import operator\nimport statistics\n\nimport pandas as pd\nfrom machineLearningUtilities import euclidean_distance, get_percent_similarities, get_mean_squared_error\n\n\nclass KNearestNeighbors:\n \"\"\"\n This class implements the k nearest neighbors algorihtm\n \"\"\"\n def __init__(self, test, k, columns, classification=True):\n \"\"\"\n This function initialize the k nearest neighbors class with data and value of k\n :param test: The training dataset\n :param test: The test dataset\n :param k: k = number of clusters to create\n \"\"\"\n self.test = test\n self.k = k\n self.columns = columns\n self.classification = classification\n self.predictions = []\n\n def run(self, train):\n \"\"\"\n This function runs the k nearest neighbors algorithm\n \"\"\"\n for i, x in self.test.iterrows():\n neighbors = self.get_k_nearest_neighbors(train, x)\n result = self.get_prediction(neighbors)\n self.predictions.append(result)\n # print('> predicted=' + repr(result) + ', actual=' + repr(x[\"Class\"]))\n\n if self.classification:\n accuracy = get_percent_similarities(self.test[\"Class\"], self.predictions)\n else:\n accuracy = get_mean_squared_error(self.test[\"Class\"], self.predictions)\n\n return accuracy\n\n def run_condensed(self, train):\n \"\"\"\n This function runs the condensed k nearest neighbors algorithm\n \"\"\"\n print(\"Running Condensed k-Nearest Neighbors...\")\n Z = pd.DataFrame(columns=self.columns)\n # Loop through data adding only misclassified data\n # Sample data to shuffle it, selecting x at random\n for i, x in train.sample(frac=1).iterrows():\n additions = False\n if len(Z) <= self.k:\n Z = Z.append(x)\n additions = True\n else:\n z_closest = self.get_closest_point(x, Z)\n if z_closest[\"Class\"] != x[\"Class\"]:\n Z = Z.append(z_closest)\n additions = True\n\n if not additions:\n break\n\n return self.run(Z)\n\n def get_k_nearest_neighbors(self, train, test_instance):\n \"\"\" This function finds the k-nearest neighbors of the test instance in the training set\n :param: test_instance\n :returns: The k-nearest neighbors\n \"\"\"\n # Find distances from each\n distances = []\n for i, x in train.iterrows():\n distance = euclidean_distance(test_instance.drop(labels=['Class']), x.drop(labels=['Class']))\n distances.append((x, distance))\n\n # Sort distances ascending\n distances.sort(key=operator.itemgetter(1))\n\n # Find neighbors by taking k-nearest from the top\n neighbors = []\n for x in range(self.k):\n neighbors.append(distances[x][0])\n\n return neighbors\n\n def get_prediction(self, neighbors):\n \"\"\" This function gets the class based on votes for class in neighbors \"\"\"\n if self.classification:\n class_votes = {}\n for x in range(len(neighbors)):\n vote = neighbors[x][\"Class\"]\n if vote in class_votes:\n class_votes[vote] += 1\n else:\n class_votes[vote] = 1\n\n sorted_votes = sorted(class_votes, key=operator.itemgetter(1), reverse=True)\n return sorted_votes[0]\n else:\n return statistics.mean(x[\"Class\"] for x in neighbors)\n\n @staticmethod\n def get_closest_point(x, Z):\n min_distance = float('inf')\n closest = None\n for i, z in Z.iterrows():\n distance = euclidean_distance(x.drop(labels=['Class']), z.drop(labels=['Class']))\n if distance < min_distance:\n min_distance = distance\n closest = z\n\n return closest\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5238657593727112, "alphanum_fraction": 0.5322145223617554, "avg_line_length": 42.112728118896484, "blob_id": "fc1a8957c3b3b38e896b30a2bc9707703254dc30", "content_id": "e0f692d123ffbc2438d236d16b46a1204340d655", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 11858, "license_type": "no_license", "max_line_length": 119, "num_lines": 275, "path": "/Reinforcement Learning/raceTrack.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import copy\nimport pickle\nimport random\n\nimport numpy as np\n\nfrom car import Car\n\n\nclass RaceTrack:\n \"\"\"\n This class implements the race track algorithm\n \"\"\"\n\n def __init__(self, track, track_name, start_position_x, start_position_y, max_x, max_y):\n \"\"\"\n This function initializes the Race Track environment by setting the track, start and finish positions\n and setting the car to it's initial state. This function also renders the initial environment to the terminal\n :param track: The grid of the track with # = wall, S = possible start positions, F = possible finish positions,\n and . = open track\n :param start_position_x: The x value of the chosen start position\n :param start_position_y: The y value fo the chosen start position\n \"\"\"\n # Initialize track\n self.track_max_x = int(max_x)\n self.track_max_y = int(max_y)\n self.track_name = str(track_name)\n print(track_name)\n self.track = list()\n for row in track:\n self.track.append(list(row))\n self.start_state = Car(start_position_x, start_position_y, 0, 0, self.track_max_x, self.track_max_y)\n self.car = Car(start_position_x, start_position_y, 0, 0, self.track_max_x, self.track_max_y)\n self.cost = 0\n self.wall = '#'\n self.finish = 'F'\n self.num_crashes = 0\n self.__set_car_to_start()\n self.render_state()\n\n # Initialize MDP\n self.value_matrix = dict()\n self.actions = [tuple((0, -1)), tuple((-1, -1)), tuple((-1, 0)),\n tuple((1, 0)), tuple((0, 1)), tuple((1, 1)), tuple((1, -1)), tuple((-1, 1))]\n self.transitions = [1, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8] # based on probability of acceleration working\n self.rewards = dict()\n self.gamma = 0.9\n self.policy = None\n\n def run_racetrack_on_value_iteration(self, restart_on_crash=True):\n \"\"\"\n This function was used to run the race track simulator using the policy created in with the value iteration\n algorithm. It reads the policy in from a file, so that you do not need to rerun the value iteration\n each time.\n \"\"\"\n # Read in the policy created by value iteration\n print(\"Reading in policy created by value iteration...\")\n with open(f\"policy_{self.track_name}\", \"rb\") as file:\n self.policy = pickle.load(file)\n print(self.policy)\n\n # Run the race track simulation\n print(\"Running racetrack simulation...\")\n finished = False\n self.__set_car_to_start()\n current_movement_x, current_movement_y = self.__choose_action(self.car.get_state())\n while not finished and self.cost < 1000:\n print(f'a = {current_movement_x}, {current_movement_y}')\n print(f\"Starting state = {self.car.get_state()}\")\n self.car.accelerate(current_movement_x, current_movement_y)\n print(f'Current position = {self.car.position.x}, {self.car.position.y}')\n print(f'Current velocity = {self.car.velocity.x}, {self.car.velocity.y}')\n print(f'COST = {self.cost}')\n if self.car_has_crashed(self.car):\n print('THE CAR HAS CRASHED $$@@##$@$%!!')\n if restart_on_crash:\n self.__set_car_to_start()\n else:\n self.car.set_to_last_position()\n self.car.reset_velocity()\n self.num_crashes += 1\n current_movement_x, current_movement_y = self.__choose_action(self.car.get_state())\n\n if self.car_has_crossed_the_finish(self.car):\n print('CROSSED THE FINISH LINE!!!!!')\n finished = True\n else:\n self.cost += 1\n\n self.render_state()\n\n def run_racetrack_on_sarsa(self, episodes, restart_on_crash, epsilon=0.1, learning_rate=0.1):\n print(\"Running racetrack simulation with SARSA...\")\n self.__initialize_states()\n q = self.__init_q()\n for episode in range(episodes):\n print(f\"Episode: {episode}\")\n self.__set_car_to_start()\n action = self.__epsilon_greedy(q, epsilon, len(self.actions))\n finished = False\n self.cost = 0\n while not finished and self.cost < 20:\n current_state = self.car.get_state()\n reward = self.__get_reward(current_state, action)\n print(f'a = {action}')\n print(f\"Current state = {current_state}\")\n successor_car = Car(current_state[0], current_state[1],\n current_state[2], current_state[3],\n self.track_max_x, self.track_max_y)\n successor_car.accelerate(action[0], action[1])\n successor_state = successor_car.get_state()\n print(f'Successor state = {successor_state}')\n print(f'COST = {self.cost}')\n if self.car_has_crashed(successor_car):\n print('THE CAR HAS CRASHED $$@@##$@$%!!')\n if restart_on_crash:\n successor_car.set_state(tuple((self.start_state.position.x,\n self.start_state.position.y,\n 0, 0)))\n else:\n successor_car.set_to_last_position()\n\n if self.car_has_crossed_the_finish(successor_car):\n print('CROSSED THE FINISH LINE!!!!!')\n finished = True\n else:\n self.cost += 1\n\n successor_action = self.__epsilon_greedy(q, epsilon, len(self.actions))\n q[tuple((current_state, action))] = q[tuple((current_state, action))] + \\\n learning_rate * \\\n (reward + self.gamma *\n q[tuple((successor_state, successor_action))] -\n q[tuple((current_state, action))])\n self.car = successor_car\n action = successor_action\n\n self.render_state()\n\n def car_has_crashed(self, car):\n try:\n crashed = True if self.track[car.position.x][car.position.y] == self.wall else False\n return crashed\n except IndexError:\n return True\n\n def car_has_crossed_the_finish(self, car):\n return True if self.track[car.position.x][car.position.y] == self.finish else False\n\n def learn_value_iteration(self):\n self.__value_iteration__()\n\n def render_state(self):\n \"\"\"\n This function renders the current state of the race track to the screen with @ representing the current\n position of the car\n \"\"\"\n print(f\"Position = ({self.car.position.x}, {self.car.position.y})\")\n print(f\"Velocity = ({self.car.velocity.x}, {self.car.velocity.y})\")\n print(f\"Current cost = {self.cost}\")\n self.track[self.car.position.x][self.car.position.y] = '@'\n for row in self.track:\n print(\"\".join(row))\n\n def __set_car_to_start(self):\n print(\"Moving to Start Position\")\n self.car.position = self.start_state.position\n\n def __value_iteration__(self, epsilon=0.001):\n policy = dict()\n self.__initialize_states()\n\n # Run value iteration\n converged = False\n max_iterations = 20\n t = 0\n q = dict()\n previous_value_matrix = None\n while not converged and t < max_iterations:\n t += 1\n print(f\"Iterating: t = {t}\")\n for state, value in self.value_matrix.items():\n for a_index, action in enumerate(self.actions):\n if previous_value_matrix:\n q[tuple((state, action))] += self.__get_reward(state, action) + \\\n self.gamma * \\\n sum(self.__transition(i)\n * previous_value_matrix[self.__apply_action(state, a)]\n for i, a in enumerate(self.actions))\n else:\n q[tuple((state, action))] = self.__get_reward(state, action)\n\n q_for_this_state = {k: v for (k, v) in q.items() if k[0] == state}\n policy[state] = max(q_for_this_state, key=q_for_this_state.get)[1]\n self.value_matrix[state] = q_for_this_state[(state, policy[state])]\n\n # Calculate convergence\n if previous_value_matrix:\n diff = self.__get_max_difference(self.value_matrix, previous_value_matrix)\n print(f\"Max Difference = {diff}\")\n if diff < epsilon:\n print(\"Converging...\")\n converged = True\n\n previous_value_matrix = copy.deepcopy(self.value_matrix)\n\n # Write policy to file\n with open(f\"policy_{self.track_name}\", \"wb\") as file:\n pickle.dump(policy, file, protocol=pickle.HIGHEST_PROTOCOL)\n\n return policy\n\n def __initialize_states(self):\n # Initialize the value matrix to all zeros\n for r_index, row in enumerate(self.track):\n for c_index, cell in enumerate(row):\n for i in range(-5, 6):\n for j in range(-5, 6):\n state = tuple((r_index, c_index, i, j))\n self.value_matrix[state] = 0.0\n self.rewards[state] = self.__calculate_reward(c_index, r_index)\n\n def __calculate_reward(self, c_index, r_index):\n return -1 if self.track[r_index][c_index] == self.wall else \\\n (2 if self.track[r_index][c_index] == self.finish else 1)\n\n @staticmethod\n def __get_max_difference(value_matrix, previous_value_matrix):\n max_difference = 0\n for state, value in value_matrix.items():\n diff = value - previous_value_matrix[state]\n if diff > max_difference:\n max_difference = diff\n return max_difference\n\n def __transition(self, action):\n return self.transitions[action]\n\n def __get_reward(self, state, action):\n car = Car(state[0], state[1], state[2], state[3], self.track_max_x, self.track_max_y)\n car.accelerate(action[0], action[1])\n try:\n reward = self.rewards[car.get_state()]\n return reward\n except KeyError:\n return -1 # Went off board\n\n def __apply_action(self, state, action):\n car = Car(state[0], state[1], state[2], state[3], self.track_max_x, self.track_max_y)\n car.accelerate(action[0], action[1])\n state = car.get_state()\n return state\n\n def __choose_action(self, state):\n print(f'Choosing action from current state {state}')\n return self.policy[state]\n\n def __epsilon_greedy(self, q, epsilon, num_actions):\n if np.random.rand() < epsilon:\n q_for_this_state = {k: v for (k, v) in q.items() if k[0] == self.car.get_state()}\n action = max(q_for_this_state, key=q_for_this_state.get)[1]\n else:\n action = self.actions[np.random.randint(0, num_actions)]\n print(f\"Chosen action = {action}\")\n return action\n\n def __init_q(self):\n q = dict()\n for state, value in self.value_matrix.items():\n for a_index, action in enumerate(self.actions):\n q[tuple((state, action))] = random.randint(0, 100)\n\n print(f\"Q = {q}\")\n\n return q\n\n\n" }, { "alpha_fraction": 0.5162436366081238, "alphanum_fraction": 0.5263959169387817, "avg_line_length": 25.226667404174805, "blob_id": "0c4d48c2f27ee22e7da1cb4cba70591503caf4de", "content_id": "f1026ab35c1e1e9deaf03f163a587d2747720a4e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1970, "license_type": "no_license", "max_line_length": 90, "num_lines": 75, "path": "/Reinforcement Learning/car.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "class Car:\n\n def __init__(self, x, y, vx, vy, max_x, max_y):\n self.position = CarPosition(x, y)\n self.last_position = CarPosition(x, y)\n self.velocity = CarVelocity(vx, vy)\n self.last_velocity = CarVelocity(vx, vy)\n self.max_x = max_x\n self.max_y = max_y\n\n def accelerate(self, ax, ay):\n self.last_position = CarPosition(self.position.x, self.position.y)\n self.last_velocity = CarVelocity(self.velocity.x, self.velocity.y)\n self.velocity.accelerate(ax, ay)\n self.__move()\n\n def set_to_last_position(self):\n self.position = self.last_position\n\n def reset_velocity(self):\n self.velocity.x = 0\n self.velocity.y = 0\n\n def get_state(self):\n return tuple((self.position.x, self.position.y, self.velocity.x, self.velocity.y))\n\n def set_state(self, state):\n self.position.x = state[0]\n self.position.y = state[1]\n self.velocity.x = state[2]\n self.velocity.y = state[3]\n\n def __move(self):\n self.position.x += self.velocity.x\n self.position.y += self.velocity.y\n\n # Limit to max values on track\n if self.position.x < 0:\n self.position.x = 0\n\n if self.position.x >= self.max_x:\n self.position.x = self.max_x - 1\n\n if self.position.y < 0:\n self.position.y = 0\n\n if self.position.y >= self.max_y:\n self.position.y = self.max_y - 1\n\n\nclass CarPosition:\n def __init__(self, x, y):\n self.x = x\n self.y = y\n\n\nclass CarVelocity:\n def __init__(self, vx, vy):\n self.x = vx\n self.y = vy\n\n def accelerate(self, ax, ay):\n if self.x + ax > 5:\n self.x = 5\n elif self.x + ax < -5:\n self.x = -5\n else:\n self.x += ax\n\n if self.y + ay > 5:\n self.y = 5\n elif self.y + ay < -5:\n self.y = -5\n else:\n self.y += ay\n\n\n\n" }, { "alpha_fraction": 0.5967450141906738, "alphanum_fraction": 0.6380349397659302, "avg_line_length": 45.71831130981445, "blob_id": "07b2ab2ce6bf5ef3397938f736cd489c4058de44", "content_id": "47e66bf0c8d346a29b48a633d850a4a7d9c0cd35", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3318, "license_type": "no_license", "max_line_length": 109, "num_lines": 71, "path": "/Clustering and Feature Selection/naiveBayes.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "\ndef learn(classes, data):\n \"\"\"\n This function creates the naive bayes model.\n :param classes: the set of actual class values\n :param data: the set of data\n :return: the model, which consists of P(C=0), P(C=1), P(F=1|C=0), P(F=0|C=0), P(F=1|C=1) and P(F=0|C=1)\n \"\"\"\n # Create the model\n value_counts = classes.value_counts().to_dict()\n num_samples = len(classes)\n prob_0 = value_counts[0] / num_samples if 0 in value_counts else 0\n prob_1 = value_counts[1] / num_samples if 1 in value_counts else 1\n feature_probs_f0_given0 = []\n feature_probs_f1_given0 = []\n feature_probs_f0_given1 = []\n feature_probs_f1_given1 = []\n zero_class_indexes = [i for i, x in enumerate(classes) if x == 0]\n one_class_indexes = [i for i, x in enumerate(classes) if x == 1]\n num_zero_classes = len(zero_class_indexes)\n num_one_classes = len(one_class_indexes)\n for index, colName in enumerate(data.columns):\n f_value_counts_given_0 = data.iloc[zero_class_indexes, index].value_counts().to_dict()\n f_value_counts_given_1 = data.iloc[one_class_indexes, index].value_counts().to_dict()\n prob_f0_given_c0 = f_value_counts_given_0[0] / num_zero_classes if 0 in f_value_counts_given_0 else 0\n prob_f1_given_c0 = f_value_counts_given_0[1] / num_zero_classes if 1 in f_value_counts_given_0 else 0\n prob_f0_given_c1 = f_value_counts_given_1[0] / num_one_classes if 0 in f_value_counts_given_1 else 0\n prob_f1_given_c1 = f_value_counts_given_1[1] / num_one_classes if 1 in f_value_counts_given_1 else 0\n feature_probs_f0_given0.append(prob_f0_given_c0)\n feature_probs_f1_given0.append(prob_f1_given_c0)\n feature_probs_f0_given1.append(prob_f0_given_c1)\n feature_probs_f1_given1.append(prob_f1_given_c1)\n\n return (prob_0, prob_1, feature_probs_f0_given0,\n feature_probs_f1_given0, feature_probs_f0_given1,\n feature_probs_f1_given1)\n\n\ndef test(data, prob_0, prob_1, feature_probs_f0_given0,\n feature_probs_f1_given0, feature_probs_f0_given1,\n feature_probs_f1_given1):\n \"\"\"\n This function makes predictions for the naive bayes algorithm\n :param data: The data to create the model from for naive bayes\n :param prob_0: P(C=0)\n :param prob_1: P(C=1)\n :param feature_probs_f0_given0: P(F=0|C=0)\n :param feature_probs_f1_given0: P(F=1|C=0)\n :param feature_probs_f0_given1: P(F=0|C=1)\n :param feature_probs_f1_given1: P(F=1|C=1)\n :return: the predictions made on the set of values, based on the model given\n \"\"\"\n # Calculate predictions by calculating the probability of each\n # C=1 and C=0 in each sample(row)\n predictions = []\n for index, row in data.iterrows():\n c0_product = prob_0\n c1_product = prob_1\n for feature_index, feature in enumerate(row):\n if feature == 0:\n c0_product *= feature_probs_f0_given0[feature_index]\n c1_product *= feature_probs_f0_given1[feature_index]\n else:\n c0_product *= feature_probs_f1_given0[feature_index]\n c1_product *= feature_probs_f1_given1[feature_index]\n\n if c0_product > c1_product:\n predictions.append(0)\n else:\n predictions.append(1)\n\n return predictions\n" }, { "alpha_fraction": 0.6023854613304138, "alphanum_fraction": 0.6235091090202332, "avg_line_length": 36.068180084228516, "blob_id": "f918ee1a03b0ebd1e1c80c79baf1adb8b0b8405a", "content_id": "554d25fc16c5feaf4462cc301aa6c487c833f3da", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 17942, "license_type": "no_license", "max_line_length": 115, "num_lines": 484, "path": "/Classification/algorithms.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n# Import packages\nimport pandas as pd\nimport numpy as np\n\n\n# # Utilities\n\ndef one_hot_encoder(df, columns):\n \"\"\"\n This function one hot encodes a set of data\n :param df: data to encode\n :param columns: columns to encode\n :return: one hot encoded data\n \"\"\"\n one_hot = pd.get_dummies(df, columns=columns, prefix=columns)\n df = df.drop(columns, axis=1)\n return one_hot\n\n\ndef split_test_train(df):\n \"\"\"\n This function splits test and train sets by 2/3 and 1/3 randomly\n :param df: data to split\n :return: train and test set\n \"\"\"\n train = df.sample(frac=2 / 3)\n test = df.loc[~df.index.isin(train.index), :]\n return train, test\n\n\ndef get_num_similarities(array1, array2):\n \"\"\"\n This function compares two arrays for similiaries and returns number of equal elements\n :param array1: first array to compare\n :param array2: second array to compare\n :return: the number of similar elements\n \"\"\"\n a = np.array(array1)\n b = np.array(array2)\n return np.sum(a == b)\n\n\n# # Winnow-2\n# TODO: move this to a class\ndef promote(X, w, alpha):\n \"\"\"\n This function performs promotion for the winnow-2 algorithm\n :param X: the set of features values for the sample\n :param w: the current weight array\n :param alpha: alpha hyperparamter\n :return: new weight array\n \"\"\"\n for index, x in enumerate(X):\n if x == 1:\n w[index] *= alpha\n\n return w\n\n\ndef demote(X, w, alpha):\n \"\"\"\n This function performs demotion for the winnow-2 algorithm\n :param X: the set of features values for the sample\n :param w: the current weight array\n :param alpha: alpha hyperparamter\n :return: new weight array\n \"\"\"\n for index, x in enumerate(X):\n if x == 1:\n w[index] /= alpha\n\n return w\n\n\ndef make_prediction(X, w, theta):\n \"\"\"\n This function makes predictions for the winnow-2 algorithms\n :param X: the set of feature values for the sample\n :param w: the current weight array\n :param theta: theta hyperparameter\n :return: prediction 1 or 0\n \"\"\"\n sum = 0\n for index, x in enumerate(X):\n sum += w[index] * int(x)\n\n if sum > theta:\n return 1\n\n return 0\n\n\ndef winnow_2(X, w, cls, theta, alpha):\n \"\"\"\n This function implements the winnow-2 algorithm for a single example\n :param X: the feature set\n :param w: the weights\n :param cls: the correct class\n :param theta: theta hyperparameter\n :param alpha: alpha hyperparameter\n :return: new weights and prediction\n \"\"\"\n # Make the prediction on X\n prediction = make_prediction(X, w, theta)\n\n # Test prediction against class\n if prediction == cls:\n return w, prediction\n\n if prediction == 0 and cls == 1:\n w = promote(X, w, alpha)\n else:\n w = demote(X, w, alpha)\n\n return w, prediction\n\n\ndef train_winnow2(X_train, classes, alpha, theta):\n \"\"\"\n Run winnow-2 on the training set\n :param X_train: the training set\n :param classes: the set of actual classes to be compared to predictions\n :param alpha: the alpha hyperparameter\n :param theta: the theta hyperparameter\n :return: new weight array\n \"\"\"\n\n print(\"\\n\")\n print(\"WINNOW 2\")\n print(\"\\n\")\n\n # Initialize w to all 1s\n w = [1] * (X_train.shape[1])\n print('w = ' + str(w))\n print(\"Number of attributes = \" + str(len(w)))\n\n # TRAIN\n predictions = []\n for index, row in X_train.iterrows():\n (w, p) = winnow_2(row, w, classes[index], theta, alpha)\n predictions.append(p)\n\n # Compare predictions to actual\n print(\"Training Predictions (% Correct)\")\n print(get_num_similarities(classes, predictions) / len(X_train) * 100)\n\n return w\n\n\ndef test_winnow2(X_test, classes, alpha, theta, w):\n \"\"\"\n Run winnow-2 on the tes set, with the weights\n created by the training function\n :param X_test: the test set\n :param classes: the set of actual classes to be compared to predictions\n :param alpha: the alpha hyperparameter\n :param theta: the theta hyperparameter\n :param w: new weight array\n \"\"\"\n predictions = []\n for index, row in X_test.iterrows():\n (w, p) = winnow_2(row, w, classes[index], theta, alpha)\n predictions.append(p)\n\n # Compare predictions to actual\n print(\"Testing Predictions (% Correct)\")\n print(get_num_similarities(classes, predictions) / len(X_test) * 100)\n\n\n# # Naive Bayes\n# TODO: move this to a class\ndef get_naive_bayes_model(classes, features):\n \"\"\"\n This function creates the naive bayes model.\n :param classes: the set of actual class values\n :param features: the set of feature values for each sample\n :return: the model, which consists of P(C=0), P(C=1), P(F=1|C=0), P(F=0|C=0), P(F=1|C=1) and P(F=0|C=1)\n \"\"\"\n # Create the model\n value_counts = classes.value_counts().to_dict()\n num_samples = len(classes)\n prob_0 = value_counts[0] / num_samples if 0 in value_counts else 0\n prob_1 = value_counts[1] / num_samples if 1 in value_counts else 1\n feature_probs_f0_given0 = []\n feature_probs_f1_given0 = []\n feature_probs_f0_given1 = []\n feature_probs_f1_given1 = []\n zero_class_indexes = [i for i, x in enumerate(classes) if x == 0]\n one_class_indexes = [i for i, x in enumerate(classes) if x == 1]\n num_zero_classes = len(zero_class_indexes)\n num_one_classes = len(one_class_indexes)\n for index, colName in enumerate(features.columns):\n f_value_counts_given_0 = features.iloc[zero_class_indexes, index].value_counts().to_dict()\n f_value_counts_given_1 = features.iloc[one_class_indexes, index].value_counts().to_dict()\n prob_f0_given_c0 = f_value_counts_given_0[0] / num_zero_classes if 0 in f_value_counts_given_0 else 0\n prob_f1_given_c0 = f_value_counts_given_0[1] / num_zero_classes if 1 in f_value_counts_given_0 else 0\n prob_f0_given_c1 = f_value_counts_given_1[0] / num_one_classes if 0 in f_value_counts_given_1 else 0\n prob_f1_given_c1 = f_value_counts_given_1[1] / num_one_classes if 1 in f_value_counts_given_1 else 0\n feature_probs_f0_given0.append(prob_f0_given_c0)\n feature_probs_f1_given0.append(prob_f1_given_c0)\n feature_probs_f0_given1.append(prob_f0_given_c1)\n feature_probs_f1_given1.append(prob_f1_given_c1)\n\n print(\"THE MODEL\")\n print(\"Probability of C = 0:\", prob_0)\n print(\"Probability of C = 1:\", prob_1)\n print(\"Probabilities of f = 0, given c = 0:\", feature_probs_f0_given0)\n print(\"Probabilities of f = 1, given c = 0:\", feature_probs_f1_given0)\n print(\"Probabilities of f = 0, given c = 1:\", feature_probs_f0_given1)\n print(\"Probabilities of f = 1, given c = 1:\", feature_probs_f1_given1)\n return (prob_0, prob_1, feature_probs_f0_given0,\n feature_probs_f1_given0, feature_probs_f0_given1,\n feature_probs_f1_given1)\n\n\ndef naive_bayes_make_predictions(data, prob_0, prob_1, feature_probs_f0_given0,\n feature_probs_f1_given0, feature_probs_f0_given1,\n feature_probs_f1_given1):\n \"\"\"\n This function makes predictions for the naive bayes algorithm\n :param data: The data to create the model from for naive bayes\n :param prob_0: P(C=0)\n :param prob_1: P(C=1)\n :param feature_probs_f0_given0: P(F=0|C=0)\n :param feature_probs_f1_given0: P(F=1|C=0)\n :param feature_probs_f0_given1: P(F=0|C=1)\n :param feature_probs_f1_given1: P(F=1|C=1)\n :return: the predictions made on the set of values, based on the model given\n \"\"\"\n\n # Calculate predictions by calculating the probability of each \n # C=1 and C=0 in each sample(row)\n predictions = []\n for index, row in data.iterrows():\n c0_product = prob_0\n c1_product = prob_1\n for feature_index, feature in enumerate(row):\n if feature == 0:\n c0_product *= feature_probs_f0_given0[feature_index]\n c1_product *= feature_probs_f0_given1[feature_index]\n else:\n c0_product *= feature_probs_f1_given0[feature_index]\n c1_product *= feature_probs_f1_given1[feature_index]\n\n if c0_product > c1_product:\n predictions.append(0)\n else:\n predictions.append(1)\n\n return predictions\n\n\ndef naive_bayes(train_classes, test_classes, X_train, X_test):\n \"\"\"\n This function runs the naive bayes algorithm on a training set, then a test set\n and compares predictions with actual classes and outputs the predictions\n :param train_classes: the training set of classes\n :param test_classes: the test set of classes\n :param X_train: the training datset\n :param X_test: the test dataset\n \"\"\"\n\n print(\"\\n\")\n print(\"NAIVE BAYES\")\n print(\"\\n\")\n\n (prob_0, prob_1,\n feature_probs_f0_given0,\n feature_probs_f1_given0,\n feature_probs_f0_given1,\n feature_probs_f1_given1) = get_naive_bayes_model(train_classes, X_train)\n\n # TRAIN\n predictions_train = naive_bayes_make_predictions(X_train, prob_0, prob_1,\n feature_probs_f0_given0,\n feature_probs_f1_given0,\n feature_probs_f0_given1,\n feature_probs_f1_given1)\n\n # Compare training predictions to actual\n print(\"\\n\")\n print(\"TRAINING PREDICTIONS\")\n print(predictions_train)\n print(\"Training Predictions (% Correct)\")\n print(get_num_similarities(train_classes, predictions_train) / len(X_train) * 100)\n\n # TEST\n predictions = naive_bayes_make_predictions(X_test, prob_0, prob_1,\n feature_probs_f0_given0,\n feature_probs_f1_given0,\n feature_probs_f0_given1,\n feature_probs_f1_given1)\n\n # Compare training predictions to actual\n print(\"\\n\")\n print(\"TEST PREDICTIONS\")\n print(predictions)\n print(\"Testing Predictions (% Correct)\")\n print(get_num_similarities(test_classes, predictions) / len(X_test) * 100)\n\n\ndef run_both_breast():\n \"\"\"\n This function runs the Winnow-2 algorithm and the naive bayes algorithm\n on the breast cancer dataset and allows you to tune theta and alpha\n :param theta: the theta hyperparameter\n :param alpha: the alpha hyperparameter\n \"\"\"\n\n print(\"\\n\")\n print(\"\\n\")\n print(\"BREAST DATASET\")\n print(\"\\n\")\n print(\"\\n\")\n\n # Read in breast cancer data\n df_breast = pd.read_csv('./data/breast-cancer-wisconsin.data', header=None)\n\n # This data has nine attributes including the class attribute (which is Benign = 2, Malignant = 4)\n df_breast.columns = [\"Sample Id\", \"Clump Thickness\", \"Uniformity of Cell Size\", \"Uniformity of Cell Shape\",\n \"Marginal Adhesion\", \"Single Epithelial Cell Size\", \"Bare Nuclei\", \"Bland Chromatin\",\n \"Normal Nucleoli\", \"Mitoses\", \"Class: Benign or Malignant\"]\n\n # Find missing values and remove them, since there are so few\n # The documentation notes that there are16 missing values in group 1 and 6 denoted by '?'\n # I found 16 values in Group 6\n # Since there are so few missing values I dropped those rows\n df_breast_all = df_breast[df_breast[\"Bare Nuclei\"] != '?']\n\n # Drop Sample Id\n df_breast_all = df_breast_all.drop('Sample Id', axis=1)\n\n # Generate boolean classifiers (0 = Benign, 1 = Malignant)\n df_breast_all.loc[df_breast_all[\"Class: Benign or Malignant\"] == 2, \"Class: Benign or Malignant\"] = 0\n df_breast_all.loc[df_breast_all[\"Class: Benign or Malignant\"] == 4, \"Class: Benign or Malignant\"] = 1\n\n # Split breast dataset\n X_breast_train, X_breast_test = split_test_train(df_breast_all)\n print(\"Sample size = \", len(df_breast_all))\n print(\"Training set size = \", len(X_breast_train))\n print(\"Test set size = \", len(X_breast_test))\n\n # Run Winnow-2 on breast\n theta = 38 # Chosen based on box plot of sums\n alpha = 1.6 # Started with 2 and tuned until I got the best result\n print(\"theta = \" + str(theta))\n print(\"alpha = \" + str(alpha))\n w = train_winnow2(X_breast_train.iloc[:, 0:9], X_breast_train.iloc[:, 9], alpha, theta)\n test_winnow2(X_breast_test.iloc[:, 0:9], X_breast_test.iloc[:, 9], alpha, theta, w)\n\n # One hot encode\n columns_to_encode = df_breast_all.columns.values.tolist()\n del columns_to_encode[9]\n df_breast_encoded = one_hot_encoder(df_breast_all, columns_to_encode)\n\n # Split breast dataset\n X_breast_train_encoded, X_breast_test_encoded = split_test_train(df_breast_encoded)\n\n naive_bayes(X_breast_train_encoded[\"Class: Benign or Malignant\"],\n X_breast_test_encoded[\"Class: Benign or Malignant\"],\n X_breast_train_encoded.iloc[:, 1:90],\n X_breast_test_encoded.iloc[:, 1:90])\n\n\ndef run_both_iris():\n \"\"\"\n This function runs the Winnow-2 algorithm and the naive bayes algorithm\n on the iris dataset\n :return:\n \"\"\"\n print(\"\\n\")\n print(\"\\n\")\n print(\"IRIS DATASET\")\n print(\"\\n\")\n print(\"\\n\")\n # Read in Iris dataset and set column names\n df_iris = pd.read_csv('./data/iris.data', header=None)\n df_iris.columns = [\"sepal length (cm)\", \"sepal width (cm)\", \"petal length (cm)\", \"petal width (cm)\", \"Class\"]\n df_iris[df_iris[\"Class\"] == \"Iris-virginica\"].head()\n\n # Generate boolean classifiers (0 = not-Iris-virginica, 1 = Iris-virginica)\n df_iris.loc[df_iris[\"Class\"] == \"Iris-virginica\", \"Class_Bool\"] = 1\n df_iris.loc[df_iris[\"Class\"] != \"Iris-virginica\", \"Class_Bool\"] = 0\n\n # Split iris dataset\n X_iris_train, X_iris_test = split_test_train(df_iris)\n print(\"Sample size = \", len(df_iris))\n print(\"Training set size = \", len(X_iris_train))\n print(\"Test set size = \", len(X_iris_test))\n X_iris_train.head()\n\n # Run Winnow-2 on Iris, I made this a two class problem by running\n # it for Iris-virginica (1) or not-Iris-virginica (0)\n # Given more time, I would write the algorithm to handle all three classes\n theta = 16 # Chosen based on box plot of sums\n alpha = 2 # Started with 2 and tuned until I got the best result\n print(\"theta = \" + str(theta))\n print(\"alpha = \" + str(alpha))\n w = train_winnow2(X_iris_train.iloc[:, 0:4], X_iris_train.iloc[:, 5], alpha, theta)\n test_winnow2(X_iris_test.iloc[:, 0:4], X_iris_test.iloc[:, 5], alpha, theta, w)\n\n # Run Naive Bayes on Iris\n # I encoded these by hand based on the box plots that I created\n sepal_length_cond_1 = df_iris[\"sepal length (cm)\"] > 6\n sepal_length_cond_0 = df_iris[\"sepal length (cm)\"] <= 6\n sepal_width_cond_1 = (df_iris[\"sepal width (cm)\"] > 2.7) & (df_iris[\"sepal width (cm)\"] < 3.25)\n sepal_width_cond_0 = (df_iris[\"sepal width (cm)\"] <= 2.7) | (df_iris[\"sepal width (cm)\"] >= 3.25)\n petal_length_cond_1 = df_iris[\"petal length (cm)\"] > 5\n petal_length_cond_0 = df_iris[\"petal length (cm)\"] <= 5\n petal_width_cond_1 = df_iris[\"petal width (cm)\"] > 1.5\n petal_width_cond_0 = df_iris[\"petal width (cm)\"] <= 1.5\n\n df_iris_encoded = df_iris.copy()\n df_iris_encoded.loc[sepal_length_cond_1, \"sepal_length_cond_bool\"] = 1\n df_iris_encoded.loc[sepal_length_cond_0, \"sepal_length_cond_bool\"] = 0\n df_iris_encoded.loc[sepal_width_cond_1, \"sepal_width_cond_bool\"] = 1\n df_iris_encoded.loc[sepal_width_cond_0, \"sepal_width_cond_bool\"] = 0\n df_iris_encoded.loc[petal_length_cond_1, \"petal_length_cond_bool\"] = 1\n df_iris_encoded.loc[petal_length_cond_0, \"petal_length_cond_bool\"] = 0\n df_iris_encoded.loc[petal_width_cond_1, \"petal_width_cond_bool\"] = 1\n df_iris_encoded.loc[petal_width_cond_0, \"petal_width_cond_bool\"] = 0\n df_iris_encoded.head()\n\n # Split iris dataset\n X_iris_train, X_iris_test = split_test_train(df_iris_encoded)\n print(\"Sample size = \", len(df_iris))\n print(\"Training set size = \", len(X_iris_train))\n print(\"Test set size = \", len(X_iris_test))\n X_iris_train.head()\n\n naive_bayes(X_iris_train.iloc[:, 5], X_iris_test.iloc[:, 5], X_iris_train.iloc[:, 6:], X_iris_test.iloc[:, 6:])\n\n\ndef run_both_votes():\n \"\"\"This function runs the Winnow-2 algorithm and the naive bayes algorithm\n on the votes dataset\n \"\"\"\n print(\"\\n\")\n print(\"\\n\")\n print(\"VOTES DATASET\")\n print(\"\\n\")\n df_vote = pd.read_csv('./data/house-votes-84.data', header=None)\n df_vote.head()\n\n # Generate boolean classifiers (0 = republican, 1 = democrat)\n df_vote.loc[df_vote[0] == \"republican\", 0] = 0\n df_vote.loc[df_vote[0] == \"democrat\", 0] = 1\n\n # Generate boolean classifiers (0 = n, 1 = y)\n # Set ? to n, since there is no good way to impute votes\n df_vote.replace('n', 0, inplace=True)\n df_vote.replace('y', 1, inplace=True)\n df_vote.replace('?', 0, inplace=True)\n df_vote.head()\n\n # Split vote dataset\n X_vote_train, X_vote_test = split_test_train(df_vote)\n print(\"Sample size = \", len(df_vote))\n print(\"Training set size = \", len(X_vote_train))\n print(\"Test set size = \", len(X_vote_test))\n X_vote_train.head()\n\n # Run Winnow-2\n theta = 8.25 # Chosen based on box plot of sums\n alpha = 2 # Started with 2 and tuned until I got the best result\n print(\"theta = \" + str(theta))\n print(\"alpha = \" + str(alpha))\n w = train_winnow2(X_vote_train.iloc[:, 1:], X_vote_train.iloc[:, 0], alpha, theta)\n test_winnow2(X_vote_test.iloc[:, 1:], X_vote_test.iloc[:, 0], alpha, theta, w)\n\n # Run Naive Bayes\n naive_bayes(X_vote_train[0], X_vote_test[0], X_vote_train.iloc[:, 1:16], X_vote_test.iloc[:, 1:16])\n\n\nif __name__== \"__main__\":\n run_both_breast()\n run_both_iris()\n run_both_votes()\n\n" }, { "alpha_fraction": 0.6343877911567688, "alphanum_fraction": 0.6401979923248291, "avg_line_length": 28.769229888916016, "blob_id": "d6f584698b468566ffeb2e803a61e1f279042c2f", "content_id": "4780d1eb4e95a60ed74cfdffa69c9c4c84dbc4f0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4647, "license_type": "no_license", "max_line_length": 101, "num_lines": 156, "path": "/Clustering and Feature Selection/machineLearningUtilities.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import random\nimport pandas as pd\nimport numpy as np\n\n\ndef one_hot_encoder(df, columns):\n \"\"\"\n This function one hot encodes a set of data\n :param df: data to encode\n :param columns: columns to encode\n :return: one hot encoded data\n \"\"\"\n one_hot = pd.get_dummies(df, columns=columns, prefix=columns)\n df = df.drop(columns, axis=1)\n return one_hot\n\n\ndef split_test_train(df):\n \"\"\"\n This function splits test and train sets by 2/3 and 1/3 randomly\n :param df: data to split\n :return: train and test set\n \"\"\"\n train = df.sample(frac=2/3)\n test = df.loc[~df.index.isin(train.index), :]\n return train, test\n\n\ndef split_into_random_groups(df, k):\n \"\"\"\n This function splits a data set into k random groups\n :param df: data to split\n :param k: the number of groups\n :return: array of groups\n \"\"\"\n groups = []\n remaining = df.copy()\n if k > 0:\n for i in range(1, k):\n group = remaining.sample(frac=1/(k-(i-1)))\n remaining = remaining.loc[~remaining.index.isin(group.index), :]\n groups.append(group)\n\n groups.append(remaining)\n\n return groups\n\n\ndef generate_random_numbers(r, n):\n \"\"\"\n This function generates random integers in range(1:r)\n :param r: the range to generate random integers in\n :param n: the number of integers to generate\n :return: array of random numbers\n \"\"\"\n randoms = []\n for x in range(1, n+1):\n randoms.append(random.randint(1, r))\n\n return randoms\n\n\ndef get_num_similarities(array1, array2):\n \"\"\"\n This function compares two arrays for similiaries and returns number of equal elements\n :param array1: first array to compare\n :param array2: second array to compare\n :return: the number of similar elements\n \"\"\"\n a = np.array(array1)\n b = np.array(array2)\n return np.sum(a == b)\n\n\ndef get_euclidean_distance(array1, array2):\n \"\"\"\n This function computes the euclidean distance between two arrays\n :param array1: first array\n :param array2: second array\n :return: distance\n \"\"\"\n return np.linalg.norm(array1-array2)\n\n\ndef calculate_silhouette_coefficient(X, labels):\n \"\"\"\n This function calculates the silhouette coefficient of a data set, and it's labels for clustering\n :param X: The dataset\n :param labels: cluster labels\n :return: silhouette coefficient\n \"\"\"\n return np.mean(get_silhouettes(X, labels))\n\n\ndef get_mean_intra_cluster_distances(x, index, X, labels):\n \"\"\" \n This function gets the mean of all of the distances to each sample in the \n cluster from x\n :param x: The selected sample\n :param index: the index of the selected sample\n :param X: the sample dataset\n :param labels: the set of labels for data points (which cluster it's in)\n \"\"\"\n distances = []\n x_cluster = labels[index]\n rows_in_cluster = [i for i, value in enumerate(labels) if value == x_cluster]\n for i, sample in X.ix[rows_in_cluster].iterrows():\n # Find the distance to x\n d = get_euclidean_distance(x.values, sample.values)\n distances.append(d)\n \n return np.mean(distances)\n\n\ndef get_nearest_cluster_distance(x, index, X, labels):\n \"\"\"\n This function gets the distance to the cluster nearest to\n x, that is not the cluster that x is in.\n :param x: The data point to test distances from\n :param index: The index of the datapoint\n :param X: The data set\n :param labels: The cluster labels of each data point\n :return:\n \"\"\"\n minimum_distance = float('inf')\n clusters = set(labels)\n x_cluster = labels[index]\n for c in clusters:\n if c != x_cluster:\n distances = []\n rows_in_cluster = [i for i, value in enumerate(labels) if value == c]\n for i, sample in X.ix[rows_in_cluster].iterrows():\n distance = get_euclidean_distance(sample.values, x.values)\n distances.append(distance)\n\n mean = np.mean(distances)\n if mean < minimum_distance:\n minimum_distance = mean\n\n return minimum_distance\n\n\ndef get_silhouettes(X, labels):\n \"\"\" This function calculates the silhouette coefficient for each sample\n :param X: The dataset\n :param labels: cluster labels\n :return: silhouette coefficient\n \"\"\"\n silhouettes = []\n for index, x in X.iterrows():\n A = get_mean_intra_cluster_distances(x, index, X, labels)\n B = get_nearest_cluster_distance(x, index, X, labels)\n silhouette = (B - A) / np.maximum(A, B)\n silhouettes.append(np.nan_to_num(silhouette))\n\n return silhouettes\n\n\n\n" }, { "alpha_fraction": 0.5750651955604553, "alphanum_fraction": 0.5911139249801636, "avg_line_length": 39.02531814575195, "blob_id": "ba50ec2592c4a88d86853f099634a88f0d667a46", "content_id": "6133fca0e735b71191179cb3f4b263c5ffa3a96c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 12649, "license_type": "no_license", "max_line_length": 153, "num_lines": 316, "path": "/Logistic Regression/runAlgorithms.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import sys\nimport statistics\n\nimport pandas as pd\n\nfrom logisticRegression import LogisticRegression\nfrom machineLearningUtilities import split_into_random_stratified_groups, one_hot_encoder\nfrom naiveBayes import NaiveBayes\n\n\ndef run_naive_bayes(df, num_features):\n \"\"\"\n This function runs naive on the data frame and outputs statistics from five experiments\n :param df: The data set to run the algorithm on=\n :param num_features: The number of features in this dataset\n \"\"\"\n # Split dataset 5-fold stratified\n print(f\"Size of total dataset = {len(df)}\")\n train1, train2, train3, train4, train5 = split_into_random_stratified_groups(df)\n datasets = [train1, train2, train3, train4, train5]\n nb_scores = []\n for i, d in enumerate(datasets):\n print(\"-------------\")\n print(f\"Experiment #{i + 1}\")\n print(\"-------------\")\n\n # Use one subset as a test set\n df_test = datasets[i]\n print(f\"Test set size = {len(df_test)}\")\n training_sets = datasets.copy()\n\n # Create a training set from remaining subsets\n del training_sets[i]\n df_train = pd.concat(training_sets)\n print(f\"Training set size = {len(df_train)}\")\n\n # Create Naive Bayes\n nb = NaiveBayes(df_train.iloc[:, 0:num_features],\n df_train.iloc[:, num_features],\n df_test.iloc[:, 0:num_features],\n df_test.iloc[:, num_features])\n\n # Train with naive bayes\n nb.learn()\n\n # Test the accuracy of naive bayes\n nb_accuracy = nb.validate()\n print('Naive Bayes Percent accurate: ' + repr(nb_accuracy) + '%')\n nb_scores.append(nb_accuracy)\n\n return statistics.mean(nb_scores)\n\n\ndef run_logistic_regression(df, num_features):\n \"\"\"\n This function runs logistic regression on the data frame and outputs statistics from five experiments\n :param df: The data set to run the algorithm on=\n :param num_features: The number of features in this dataset\n \"\"\"\n # Split dataset 5-fold stratified\n print(f\"Size of total dataset = {len(df)}\")\n train1, train2, train3, train4, train5 = split_into_random_stratified_groups(df)\n datasets = [train1, train2, train3, train4, train5]\n lg_scores = []\n for i, d in enumerate(datasets):\n print(\"-------------\")\n print(f\"Experiment #{i + 1}\")\n print(\"-------------\")\n\n # Use one subset as a test set\n df_test = datasets[i]\n print(f\"Test set size = {len(df_test)}\")\n training_sets = datasets.copy()\n\n # Create a training set from remaining subsets\n del training_sets[i]\n df_train = pd.concat(training_sets)\n print(f\"Training set size = {len(df_train)}\")\n\n # Create Logistic Regression\n lg = LogisticRegression(df_train.columns[0:num_features],\n df_train.iloc[:, 0:num_features],\n df_train.iloc[:, num_features],\n df_test.iloc[:, 0:num_features],\n df_test.iloc[:, num_features])\n\n # Train with logistic regression\n lg.learn()\n\n # Test the logistic regression accuracy\n lg_accuracy = lg.validate()\n print('Logistic Regression Percent accurate: ' + repr(lg_accuracy) + '%')\n lg_scores.append(lg_accuracy)\n\n return statistics.mean(lg_scores)\n\n\ndef run_on_breast(file):\n \"\"\"\n This function runs logistic regression and naive bayes classifier on the breast data set, it encodes\n the classes to Benign = 0, Malignant = 1, and removes missing values\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Breast data set...\")\n df_breast = pd.read_csv(file, header=None)\n df_breast.columns = [\"Sample Id\", \"Clump Thickness\", \"Uniformity of Cell Size\", \"Uniformity of Cell Shape\",\n \"Marginal Adhesion\", \"Single Epithelial Cell Size\", \"Bare Nuclei\", \"Bland Chromatin\",\n \"Normal Nucleoli\", \"Mitoses\", \"Class\"]\n\n # Find missing values and remove them, since there are so few\n # The documentation notes that there are16 missing values in group 1 and 6 denoted by '?'\n # I found 16 values in Group 6\n # Since there are so few missing values I dropped those rows\n df_breast = df_breast[df_breast[\"Bare Nuclei\"] != '?']\n\n # Drop Sample Id\n df_breast = df_breast.drop('Sample Id', axis=1)\n\n # Generate boolean classifiers (0 = Benign, 1 = Malignant)\n df_breast.loc[df_breast[\"Class\"] == 2, \"Class\"] = 0\n df_breast.loc[df_breast[\"Class\"] == 4, \"Class\"] = 1\n\n # One hot encode breast data set for naive bayes\n columns_to_encode = df_breast.columns.values.tolist()\n del columns_to_encode[9]\n df_breast_encoded = one_hot_encoder(df_breast, columns_to_encode)\n\n lg_averages = run_logistic_regression(df_breast, 9)\n nb_averages = run_naive_bayes(df_breast_encoded, 9)\n\n print(\"----------------------------\")\n print(f\"Averages over 5 experiments\")\n print(\"----------------------------\")\n print(f\"Logistic Regression Averages = {lg_averages}%\")\n print(f\"Naive Bayes Averages = {nb_averages}%\")\n\n\ndef run_on_glass(file):\n \"\"\"\n This function runs logistic regression and naive bayes classifier on the glass data set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Glass data set...\")\n # Read in glass data\n df_glass = pd.read_csv(file, header=None)\n df_glass.columns = [\"Id\", \"RI\", \"Na\", \"Mg\", \"Al\", \"Si\", \"K\", \"Ca\", \"Ba\", \"Fe\", \"Class\"]\n\n # This data set has no missing values, so we will skip that step\n\n # Drop Id\n df_glass = df_glass.drop('Id', axis=1)\n\n # Encode the class\n df_glass = one_hot_encoder(df_glass, ['Class'])\n df_glass = df_glass.rename(columns={\"Class_1\": \"Class\"})\n df_glass = df_glass.drop(columns=['Class_2', 'Class_3', 'Class_5', 'Class_6', 'Class_7'])\n print(df_glass.head())\n\n # One hot code the data set for naive bayes\n columns_to_encode = df_glass.columns.values.tolist()\n del columns_to_encode[9]\n df_glass_encoded = one_hot_encoder(df_glass, columns_to_encode)\n df_glass_encoded = df_glass_encoded[[c for c in df_glass_encoded if c not in ['Class']] + ['Class']]\n\n lg_averages = run_logistic_regression(df_glass, 9)\n nb_averages = run_naive_bayes(df_glass_encoded, len(df_glass_encoded.columns) - 1)\n\n print(\"----------------------------\")\n print(f\"Averages over 5 experiments\")\n print(\"----------------------------\")\n print(f\"Logistic Regression Averages = {lg_averages}%\")\n print(f\"Naive Bayes Averages = {nb_averages}%\")\n\n\ndef run_on_iris(file):\n \"\"\"\n This function runs logistic regression and naive bayes classifier on the iris data set\n :param file: input file\n \"\"\"\n print(\"_______________________________\")\n print(\"Reading in Iris data set...\")\n # Read in iris data\n df_iris = pd.read_csv(file, header=None)\n df_iris.columns = [\"sepal length (cm)\", \"sepal width (cm)\", \"petal length (cm)\", \"petal width (cm)\", \"Class\"]\n\n # Generate boolean classifiers (0 = not-Iris-virginica, 1 = Iris-virginica)\n df_iris.loc[df_iris[\"Class\"] == \"Iris-virginica\", \"Class_Bool\"] = 1\n df_iris.loc[df_iris[\"Class\"] != \"Iris-virginica\", \"Class_Bool\"] = 0\n df_iris = df_iris.drop(columns=['Class'])\n df_iris = df_iris.rename(columns={\"Class_Bool\": \"Class\"})\n\n # I encoded these by hand based on the box plots that I created\n sepal_length_cond_1 = df_iris[\"sepal length (cm)\"] > 6\n sepal_length_cond_0 = df_iris[\"sepal length (cm)\"] <= 6\n sepal_width_cond_1 = (df_iris[\"sepal width (cm)\"] > 2.7) & (df_iris[\"sepal width (cm)\"] < 3.25)\n sepal_width_cond_0 = (df_iris[\"sepal width (cm)\"] <= 2.7) | (df_iris[\"sepal width (cm)\"] >= 3.25)\n petal_length_cond_1 = df_iris[\"petal length (cm)\"] > 5\n petal_length_cond_0 = df_iris[\"petal length (cm)\"] <= 5\n petal_width_cond_1 = df_iris[\"petal width (cm)\"] > 1.5\n petal_width_cond_0 = df_iris[\"petal width (cm)\"] <= 1.5\n\n df_iris_encoded = df_iris.copy()\n df_iris_encoded.loc[sepal_length_cond_1, \"sepal_length_cond_bool\"] = 1\n df_iris_encoded.loc[sepal_length_cond_0, \"sepal_length_cond_bool\"] = 0\n df_iris_encoded.loc[sepal_width_cond_1, \"sepal_width_cond_bool\"] = 1\n df_iris_encoded.loc[sepal_width_cond_0, \"sepal_width_cond_bool\"] = 0\n df_iris_encoded.loc[petal_length_cond_1, \"petal_length_cond_bool\"] = 1\n df_iris_encoded.loc[petal_length_cond_0, \"petal_length_cond_bool\"] = 0\n df_iris_encoded.loc[petal_width_cond_1, \"petal_width_cond_bool\"] = 1\n df_iris_encoded.loc[petal_width_cond_0, \"petal_width_cond_bool\"] = 0\n\n df_iris_encoded = df_iris_encoded.drop(columns=['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'])\n df_iris_encoded = df_iris_encoded[['sepal_length_cond_bool', 'sepal_width_cond_bool', 'petal_length_cond_bool', 'petal_width_cond_bool', 'Class']]\n\n lg_averages = run_logistic_regression(df_iris, 4)\n nb_averages = run_naive_bayes(df_iris_encoded, 4)\n\n print(\"----------------------------\")\n print(f\"Averages over 5 experiments\")\n print(\"----------------------------\")\n print(f\"Logistic Regression Averages = {lg_averages}%\")\n print(f\"Naive Bayes Averages = {nb_averages}%\")\n\n\ndef run_on_soybean(file):\n print(\"_______________________________\")\n print(\"Reading in Soybean data set...\")\n # Read in soybean data\n df_soybean = pd.read_csv(file, header=None)\n\n # Generate boolean classifiers (0 = not-Iris-virginica, 1 = Iris-virginica)\n df_soybean = df_soybean.rename(columns={35: 'Class'})\n df_soybean.loc[df_soybean[\"Class\"] == \"D1\", \"Class\"] = 0\n df_soybean.loc[df_soybean[\"Class\"] == \"D2\", \"Class\"] = 0\n df_soybean.loc[df_soybean[\"Class\"] == \"D3\", \"Class\"] = 1\n df_soybean.loc[df_soybean[\"Class\"] == \"D4\", \"Class\"] = 1\n\n # One hot encode breast data set for naive bayes\n columns_to_encode = df_soybean.columns.values.tolist()\n del columns_to_encode[35]\n df_soybean_encoded = one_hot_encoder(df_soybean, columns_to_encode)\n\n lg_averages = run_logistic_regression(df_soybean, 35)\n nb_averages = run_naive_bayes(df_soybean_encoded, 35)\n\n print(\"----------------------------\")\n print(f\"Averages over 5 experiments\")\n print(\"----------------------------\")\n print(f\"Logistic Regression Averages = {lg_averages}%\")\n print(f\"Naive Bayes Averages = {nb_averages}%\")\n\n\ndef run_on_votes(file):\n print(\"_______________________________\")\n print(\"Reading in Votes data set...\")\n # Read in votes data\n df_vote = pd.read_csv(file, header=None)\n\n # Generate boolean classifiers (0 = republican, 1 = democrat)\n df_vote.loc[df_vote[0] == \"republican\", 0] = 0\n df_vote.loc[df_vote[0] == \"democrat\", 0] = 1\n\n # Generate boolean classifiers (0 = n, 1 = y)\n # Set ? to n, since there is no good way to impute votes\n df_vote.replace('n', 0, inplace=True)\n df_vote.replace('y', 1, inplace=True)\n df_vote.replace('?', 0, inplace=True)\n print(df_vote.head())\n\n # Rename and reorder columns to work with algorithms\n df_vote = df_vote.rename(columns={0: 'Class', 1: 0, 2: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6,\n 8: 7, 9: 8, 10: 9, 11: 10, 12: 11, 13: 12, 14: 13, 15: 14,\n 16: 15})\n df_vote = df_vote[[c for c in df_vote if c not in ['Class']] + ['Class']]\n print(df_vote.head())\n\n lg_averages = run_logistic_regression(df_vote, 16)\n nb_averages = run_naive_bayes(df_vote, 16)\n\n print(\"----------------------------\")\n print(f\"Averages over 5 experiments\")\n print(\"----------------------------\")\n print(f\"Logistic Regression Averages = {lg_averages}%\")\n print(f\"Naive Bayes Averages = {nb_averages}%\")\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 3:\n print(\"Please add options for data set (--breast --glass --iris --soybean --vote) \"\n \"and the full file path when running this script\")\n print(\"Example: python runAlgorithms.py --breast ./data/breast.data\")\n exit()\n\n if sys.argv[1] == \"--breast\":\n run_on_breast(sys.argv[2])\n exit()\n\n if sys.argv[1] == \"--glass\":\n run_on_glass(sys.argv[2])\n exit()\n\n if sys.argv[1] == \"--iris\":\n run_on_iris(sys.argv[2])\n exit()\n\n if sys.argv[1] == \"--soybean\":\n run_on_soybean(sys.argv[2])\n exit()\n\n if sys.argv[1] == \"--votes\":\n run_on_votes(sys.argv[2])\n exit()\n\n exit()\n\n" }, { "alpha_fraction": 0.5721003413200378, "alphanum_fraction": 0.6056874394416809, "avg_line_length": 47.021507263183594, "blob_id": "bc67951e12b6cbdeba065744c682d96c86ca5446", "content_id": "ca2cf6c680d0d421558b8a1b17ca4f67b5b429aa", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4466, "license_type": "no_license", "max_line_length": 113, "num_lines": 93, "path": "/Logistic Regression/naiveBayes.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "from machineLearningUtilities import get_num_similarities\n\n\nclass NaiveBayesModel:\n def __init__(self, p0, p1, f0p0, f1p0, f0p1, f1p1):\n \"\"\"\n This class holds the model for the Naive Bayes algorithm\n :param p0: The probability of class = 0 in the entire data set\n :param p1: The probability of class = 1 in the entire data set\n :param fp00: An array that holds probabilities of f = 0, given c = 0\n :param fp10: An array that holds probabilities of f = 1, given c = 0\n :param fp01: An array that holds probabilities of f = 0, given c = 1\n :param fp11: An array that holds probabilities of f = 1, given c = 1\n \"\"\"\n self.prob_0 = p0\n self.prob_1 = p1\n self.feature_probs_f0_given0 = f0p0\n self.feature_probs_f1_given0 = f1p0\n self.feature_probs_f0_given1 = f0p1\n self.feature_probs_f1_given1 = f1p1\n\n\nclass NaiveBayes:\n def __init__(self, train, training_classes, test, test_classes):\n self.training_classes = training_classes\n self.test_classes = test_classes\n self.train = train\n self.test = test\n self.model = None\n self.predictions = []\n\n def learn(self):\n \"\"\"\n This function creates the naive bayes model.\n \"\"\"\n print(\"Training Naive Bayes...\")\n # Create the model\n value_counts = self.training_classes.value_counts().to_dict()\n num_samples = len(self.training_classes)\n prob_0 = value_counts[0] / num_samples if 0 in value_counts else 0\n prob_1 = value_counts[1] / num_samples if 1 in value_counts else 1\n feature_probs_f0_given0 = []\n feature_probs_f1_given0 = []\n feature_probs_f0_given1 = []\n feature_probs_f1_given1 = []\n zero_class_indexes = [i for i, x in enumerate(self.training_classes) if x == 0]\n one_class_indexes = [i for i, x in enumerate(self.training_classes) if x == 1]\n num_zero_classes = len(zero_class_indexes)\n num_one_classes = len(one_class_indexes)\n for index, colName in enumerate(self.train.columns):\n f_value_counts_given_0 = self.train.iloc[zero_class_indexes, index].value_counts().to_dict()\n f_value_counts_given_1 = self.train.iloc[one_class_indexes, index].value_counts().to_dict()\n prob_f0_given_c0 = f_value_counts_given_0[0] / num_zero_classes if 0 in f_value_counts_given_0 else 0\n prob_f1_given_c0 = f_value_counts_given_0[1] / num_zero_classes if 1 in f_value_counts_given_0 else 0\n prob_f0_given_c1 = f_value_counts_given_1[0] / num_one_classes if 0 in f_value_counts_given_1 else 0\n prob_f1_given_c1 = f_value_counts_given_1[1] / num_one_classes if 1 in f_value_counts_given_1 else 0\n feature_probs_f0_given0.append(prob_f0_given_c0)\n feature_probs_f1_given0.append(prob_f1_given_c0)\n feature_probs_f0_given1.append(prob_f0_given_c1)\n feature_probs_f1_given1.append(prob_f1_given_c1)\n\n self.model = NaiveBayesModel(prob_0, prob_1, feature_probs_f0_given0,\n feature_probs_f1_given0, feature_probs_f0_given1,\n feature_probs_f1_given1)\n\n def validate(self):\n \"\"\"\n This function makes predictions for the naive bayes algorithm\n \"\"\"\n print(\"Testing Naive Bayes Accuracy...\")\n if not self.model:\n print(\"Please call the function train first!\")\n return\n\n # Calculate predictions by calculating the probability of each\n # C=1 and C=0 in each sample(row)\n for index, row in self.test.iterrows():\n c0_product = self.model.prob_0\n c1_product = self.model.prob_1\n for feature_index, feature in enumerate(row):\n if feature == 0:\n c0_product *= self.model.feature_probs_f0_given0[feature_index]\n c1_product *= self.model.feature_probs_f0_given1[feature_index]\n else:\n c0_product *= self.model.feature_probs_f1_given0[feature_index]\n c1_product *= self.model.feature_probs_f1_given1[feature_index]\n\n if c0_product > c1_product:\n self.predictions.append(0)\n else:\n self.predictions.append(1)\n\n return get_num_similarities(self.predictions, self.test_classes) / len(self.test_classes) * 100\n" }, { "alpha_fraction": 0.6352075934410095, "alphanum_fraction": 0.6476175785064697, "avg_line_length": 29.773584365844727, "blob_id": "1b58297aa59c9eb916c080d22b70cc4a525ac5ca", "content_id": "9a66962d82beb1f34d2928eae6fb72787fef9797", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6527, "license_type": "no_license", "max_line_length": 101, "num_lines": 212, "path": "/K-Nearest Neighbors/machineLearningUtilities.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import math\nimport random\nimport pandas as pd\nimport numpy as np\n\n\ndef one_hot_encoder(df, columns):\n \"\"\"\n This function one hot encodes a set of data\n :param df: data to encode\n :param columns: columns to encode\n :return: one hot encoded data\n \"\"\"\n one_hot = pd.get_dummies(df, columns=columns, prefix=columns)\n df = df.drop(columns, axis=1)\n return one_hot\n\n\ndef split_test_train(df):\n \"\"\"\n This function splits test and train sets by 2/3 and 1/3 randomly\n :param df: data to split\n :return: train and test set\n \"\"\"\n train = df.sample(frac=2/3)\n test = df.loc[~df.index.isin(train.index), :]\n return train, test\n\n\ndef split_into_random_groups(df, k):\n \"\"\"\n This function splits a data set into k random groups\n :param df: data to split\n :param k: the number of groups\n :return: array of groups\n \"\"\"\n groups = []\n remaining = df.copy()\n if k > 0:\n for i in range(1, k):\n group = remaining.sample(frac=1/(k-(i-1)))\n remaining = remaining.loc[~remaining.index.isin(group.index), :]\n groups.append(group)\n\n groups.append(remaining)\n\n return groups\n\n\ndef split_into_random_stratified_groups(df):\n \"\"\"\n This function splits the data frame into 5 stratified groups for 5-fold cross validation\n :param df: The dataframe\n :param test: The test set\n :param train1: Training set 1\n :param train2: Training set 2\n :param train3: Training set 3\n :param train4: Training set 4\n :return:\n \"\"\"\n train1 = pd.DataFrame(columns=df.columns.values.tolist())\n train2 = pd.DataFrame(columns=df.columns.values.tolist())\n train3 = pd.DataFrame(columns=df.columns.values.tolist())\n train4 = pd.DataFrame(columns=df.columns.values.tolist())\n test = pd.DataFrame(columns=df.columns.values.tolist())\n\n unique_classes = df.Class.unique()\n for className in unique_classes:\n groups = split_into_random_groups(df.loc[df.Class == className], 5)\n train1 = train1.append(groups[0])\n train2 = train2.append(groups[1])\n train3 = train3.append(groups[2])\n train4 = train4.append(groups[3])\n test = test.append(groups[4])\n\n return train1, train2, train3, train4, test\n\n\ndef generate_random_numbers(r, n):\n \"\"\"\n This function generates random integers in range(1:r)\n :param r: the range to generate random integers in\n :param n: the number of integers to generate\n :return: array of random numbers\n \"\"\"\n randoms = []\n for x in range(1, n+1):\n randoms.append(random.randint(1, r))\n\n return randoms\n\n\ndef get_num_similarities(array1, array2):\n \"\"\"\n This function compares two arrays for similiaries and returns number of equal elements\n :param array1: first array to compare\n :param array2: second array to compare\n :return: the number of similar elements\n \"\"\"\n a = np.array(array1)\n b = np.array(array2)\n return np.sum(a == b)\n\n\ndef get_mean_squared_error(array1, array2):\n \"\"\"\n This function calculates the mean squared error between the two arrays\n :param array1: first array to compare\n :param array2: second array to compare\n :return: the mean squared error\n \"\"\"\n return np.mean((array1 - array2)**2 / 100)\n\n\ndef get_percent_similarities(array1, array2):\n \"\"\"\n This function compares two arrays for similiaries and returns number of equal elements\n :param array1: first array to compare\n :param array2: second array to compare\n :return: the number of similar elements\n \"\"\"\n a = np.array(array1)\n b = np.array(array2)\n return np.sum(a == b) / len(a) * 100\n\n\ndef euclidean_distance(array1, array2):\n \"\"\"\n This function computes the euclidean distance between two arrays\n :param array1: first array\n :param array2: second array\n :return: distance\n \"\"\"\n length = len(array1)\n distance = 0\n for x in range(length):\n distance += pow((array1[x] - array2[x]), 2)\n return math.sqrt(distance)\n\n\ndef calculate_silhouette_coefficient(X, labels):\n \"\"\"\n This function calculates the silhouette coefficient of a data set, and it's labels for clustering\n :param X: The dataset\n :param labels: cluster labels\n :return: silhouette coefficient\n \"\"\"\n return np.mean(get_silhouettes(X, labels))\n\n\ndef get_mean_intra_cluster_distances(x, index, X, labels):\n \"\"\" \n This function gets the mean of all of the distances to each sample in the \n cluster from x\n :param x: The selected sample\n :param index: the index of the selected sample\n :param X: the sample dataset\n :param labels: the set of labels for data points (which cluster it's in)\n \"\"\"\n distances = []\n x_cluster = labels[index]\n rows_in_cluster = [i for i, value in enumerate(labels) if value == x_cluster]\n for i, sample in X.ix[rows_in_cluster].iterrows():\n # Find the distance to x\n d = get_euclidean_distance(x.values, sample.values)\n distances.append(d)\n \n return np.mean(distances)\n\n\ndef get_nearest_cluster_distance(x, index, X, labels):\n \"\"\"\n This function gets the distance to the cluster nearest to\n x, that is not the cluster that x is in.\n :param x: The data point to test distances from\n :param index: The index of the datapoint\n :param X: The data set\n :param labels: The cluster labels of each data point\n :return:\n \"\"\"\n minimum_distance = float('inf')\n clusters = set(labels)\n x_cluster = labels[index]\n for c in clusters:\n if c != x_cluster:\n distances = []\n rows_in_cluster = [i for i, value in enumerate(labels) if value == c]\n for i, sample in X.ix[rows_in_cluster].iterrows():\n distance = get_euclidean_distance(sample.values, x.values)\n distances.append(distance)\n\n mean = np.mean(distances)\n if mean < minimum_distance:\n minimum_distance = mean\n\n return minimum_distance\n\n\ndef get_silhouettes(X, labels):\n \"\"\" This function calculates the silhouette coefficient for each sample\n :param X: The dataset\n :param labels: cluster labels\n :return: silhouette coefficient\n \"\"\"\n silhouettes = []\n for index, x in X.iterrows():\n A = get_mean_intra_cluster_distances(x, index, X, labels)\n B = get_nearest_cluster_distance(x, index, X, labels)\n silhouette = (B - A) / np.maximum(A, B)\n silhouettes.append(np.nan_to_num(silhouette))\n\n return silhouettes\n\n\n\n" }, { "alpha_fraction": 0.7446808218955994, "alphanum_fraction": 0.7517730593681335, "avg_line_length": 22.66666603088379, "blob_id": "5d7884ef703a220bd4604884cd8b1082e40258de", "content_id": "29789025f077168b127893c9055505fb40f48140", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 141, "license_type": "no_license", "max_line_length": 57, "num_lines": 6, "path": "/Clustering and Feature Selection/README.md", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "# Programming Project 2\n\nTo run use the following command from the root directory:\n```.env\npython runAlgorithms.py iris ./data/iris/data \n```" }, { "alpha_fraction": 0.5718494057655334, "alphanum_fraction": 0.5787234306335449, "avg_line_length": 38.153846740722656, "blob_id": "f790e46f3000509fe348fc879bcc14a933f72d1e", "content_id": "9a14a6d2ed8e4b3b4002d69b4386800ae178af63", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6110, "license_type": "no_license", "max_line_length": 124, "num_lines": 156, "path": "/Neural Networks/backpropgationNeuralNetwork.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import math\nimport random\n\nfrom machineLearningUtilities import get_num_similarities\n\n\nclass BackpropagationNeuralNetwork:\n \"\"\"\n This class performs the backpropagation algorithm to train an artificial neural network\n \"\"\"\n def __init__(self, features, x_train, x_train_classes, x_test, x_test_classes, num_hidden=1):\n self.features = features\n self.training_classes = x_train_classes\n self.train = x_train\n self.test = x_test\n self.test_classes = x_test_classes\n self.num_iterations = 20\n self.eta = 0.01 # learning rate\n self.network = list()\n self.num_hidden_layers = num_hidden\n\n def learn(self):\n \"\"\"\n This function runs the backpropagation algorithm on the training set provided\n \"\"\"\n print(\"Running Backpropagation...\")\n\n print(\"Initializing Network...\")\n num_inputs = len(self.train.columns) - 1\n hidden = [{'w': [random.uniform(-1.0, 1.0) for i in range(num_inputs + 1)]} for i in range(self.num_hidden_layers)]\n self.network.append(hidden)\n num_outputs = len(set([c for c in self.training_classes]))\n output = [{'w': [random.uniform(-1.0, 1.0) for i in range(self.num_hidden_layers + 1)]} for i in range(num_outputs)]\n self.network.append(output)\n\n print(\"Training Network...\")\n for iteration in range(self.num_iterations):\n error = 0\n for x_index, row in self.train.iterrows():\n outputs = self.forward_propagate(row)\n expected = [0 for i in range(num_outputs)]\n expected[int(self.training_classes[x_index])] = 1\n error += sum([(expected[i] - outputs[i]) ** 2 for i in range(len(expected))])\n self.backward_propagate_error(expected)\n self.update_weights(row)\n print('>iteration=%d, learning rate=%.3f, error=%.3f' % (iteration, self.eta, error))\n\n def make_predictions(self):\n predictions = list()\n for i, row in self.test.iterrows():\n prediction = self.predict(row)\n predictions.append(prediction)\n\n print(\"Predicted Classes = \")\n print(predictions)\n\n print(\"Expected Classes = \")\n print(list(self.test_classes))\n\n return get_num_similarities(predictions, self.test_classes) / len(self.test_classes) * 100\n\n def predict(self, row):\n \"\"\"\n This function determines accuracy of model using the test data set\n and applying the linear function using the weights\n \"\"\"\n outputs = self.forward_propagate(row)\n return outputs.index(max(outputs))\n\n @staticmethod\n def sigmoid(x):\n \"\"\"\n This function calculates the logistic sigmoid of x and is used as the transfer function\n :param x: The value\n :return: The logistic sigmoid\n \"\"\"\n try:\n return 1.0 / (1.0 + math.exp(-x))\n except OverflowError:\n return float('inf')\n\n @staticmethod\n def activate(weights, inputs):\n \"\"\"\n This function performs activation by calculating the weighted sum of the\n inputs plus the bias\n :param weights: the current weight vector with bias as the last element\n :param inputs: a vector of inputs\n :return: The activation\n \"\"\"\n activation = weights[-1]\n for i in range(len(weights) - 1):\n activation += weights[i] * float(inputs[i])\n return activation\n\n @staticmethod\n def transfer_derivative(y):\n \"\"\"\n This function calculates the derivative to be used as the slope of an output value\n :param y: The output value\n :return: The derivative / slope\n \"\"\"\n return y * (1.0 - y)\n\n def forward_propagate(self, inputs):\n \"\"\"\n This function implements forward propagation, the inputs are taken and outpuss\n are calculated by first running the activation function and then the transfer (sigmoid)\n function using the activation, each new input is appended to the output layer and\n returned\n :param inputs: The input vector\n :return: The output layer\n \"\"\"\n for layer in self.network:\n new_inputs = []\n for neuron in layer:\n x = self.activate(neuron['w'], inputs)\n neuron['output'] = self.sigmoid(x)\n new_inputs.append(neuron['output'])\n inputs = new_inputs\n return inputs\n\n def backward_propagate_error(self, expected):\n \"\"\"\n This function performs backpropagation by calcuating error betweeen the expected outputs\n and the actual outputs of forward propagation. These error calcuations are propagated back\n throught the network from output to hidden layer, looking for where the error occurred.\n :param expected:\n :return:\n \"\"\"\n for i in reversed(range(len(self.network))):\n layer = self.network[i]\n errors = list()\n if i != len(self.network) - 1:\n for j in range(len(layer)):\n error = 0.0\n for neuron in self.network[i + 1]:\n error += (neuron['w'][j] * neuron['delta'])\n errors.append(error)\n else:\n for j in range(len(layer)):\n neuron = layer[j]\n errors.append(expected[j] - neuron['output'])\n for j in range(len(layer)):\n neuron = layer[j]\n neuron['delta'] = errors[j] * self.transfer_derivative(neuron['output'])\n\n def update_weights(self, row):\n for i in range(len(self.network)):\n inputs = row[:-1]\n if i != 0:\n inputs = [neuron['output'] for neuron in self.network[i - 1]]\n for neuron in self.network[i]:\n for j in range(len(inputs)):\n neuron['w'][j] += self.eta * float(neuron['delta']) * float(inputs[j])\n neuron['w'][-1] += self.eta * float(neuron['delta'])\n\n\n" }, { "alpha_fraction": 0.6258193850517273, "alphanum_fraction": 0.631281852722168, "avg_line_length": 43.282257080078125, "blob_id": "0751c20b32ef733f985ed0e02291a7741cf94690", "content_id": "598244cc94ffb7d7bbdc5f1a0bb7d881546475d6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5492, "license_type": "no_license", "max_line_length": 121, "num_lines": 124, "path": "/Reinforcement Learning/runAlgorithms.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import re\nimport sys\n\nfrom raceTrack import RaceTrack\n\n\ndef run_race_track_simulator_value_iteration(restart_on_crash, track_file_path, run_value_iteration=False):\n \"\"\"\n This function runs the race track simulator\n :param restart_on_crash: True if you should continue from nearest current location on crash, False restart at\n starting position on crash\n :param track_file_path: The file path to the race track text file\n :param run_value_iteration: True if you want to run the value iteration to create the policy, if False\n it will attempt to read the policy from a file\n \"\"\"\n print(f\"Running Race Track simulator with options: Restart on crash? {'yes' if restart_on_crash else 'no'}, \"\n f\"Track file: {track_file_path}\")\n\n print(\"Race Track\")\n with open(track_file_path, \"r\") as file:\n max_x, max_y = file.readline().split(',')\n race_track = file.read().splitlines()\n print(race_track)\n starting_positions = find_starting_positions(race_track)\n track_simulator = RaceTrack(race_track, track_file_path[track_file_path.rindex('/')+1:],\n starting_positions[1][0], starting_positions[1][1], max_x, max_y)\n if run_value_iteration:\n track_simulator.learn_value_iteration()\n else:\n track_simulator.run_racetrack_on_value_iteration(restart_on_crash)\n\n\ndef run_race_track_simulator_sarsa(restart_on_crash, track_file_path, run_value_iteration=False):\n \"\"\"\n This function runs the race track simulator\n :param restart_on_crash: True if you should continue from nearest current location on crash, False restart at\n starting position on crash\n :param track_file_path: The file path to the race track text file\n :param run_value_iteration: True if you want to run the value iteration to create the policy, if False\n it will attempt to read the policy from a file\n \"\"\"\n print(f\"Running Race Track simulator with options: Restart on crash? {'yes' if restart_on_crash else 'no'}, \"\n f\"Track file: {track_file_path}\")\n\n print(\"Race Track\")\n with open(track_file_path, \"r\") as file:\n max_x, max_y = file.readline().split(',')\n race_track = file.read().splitlines()\n print(race_track)\n starting_positions = find_starting_positions(race_track)\n track_simulator = RaceTrack(race_track, track_file_path[track_file_path.rindex('/') + 1:],\n starting_positions[1][0], starting_positions[1][1], max_x, max_y)\n\n track_simulator.run_racetrack_on_sarsa(3, restart_on_crash)\n\n\ndef find_starting_positions(track):\n \"\"\"\n This function takes a track and finds the instances of S and returns these start positions with a list of tuples\n of (x, y) pair values\n :param track: A list of strings that make up a grid that has a set of S values somewhere that indicate the\n starting line\n :return: A list of tuples of (x,y) pair values that represent possible starting positions\n \"\"\"\n return find_positions(track, 'S')\n\n\ndef find_finish_positions(track):\n \"\"\"\n This function takes a track and finds the instances of F and returns these finish positions with a list of tuples\n of (x, y) pair values\n :param track: A list of strings that make up a grid that has a set of S values somewhere that indicate the\n finish line\n :return: A list of tuples of (x,y) pair values that represent possible finish positions\n \"\"\"\n return find_positions(track, 'S')\n\n\ndef find_positions(list_of_sentences, word):\n \"\"\"\n This function finds all occurrences of a word in a list of sentences\n :param list_of_sentences: The list of sentences to find occurrences of a word in\n :param word: The word or character to find\n :return: A list of tuples with all positions\n \"\"\"\n start = list()\n for index, row in enumerate(list_of_sentences):\n for match in re.finditer('S', row):\n start.append(tuple((index, match.start())))\n\n return start\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 5:\n print(\"Please add options (--crash-continue --crash-restart) and (--value or --sarsa) \"\n \"and (--create-policy --run)\"\n \"and the full file path to the race track file\")\n print(\"Example: python runAlgorithms.py --crash-continue --value --run ./data/L-track.txt \")\n exit()\n\n if sys.argv[1] == \"--crash-continue\":\n if sys.argv[2] == \"--value\" and sys.argv[3] == \"--create-policy\":\n run_race_track_simulator_value_iteration(False, sys.argv[4], True)\n exit()\n elif sys.argv[2] == \"--value\" and sys.argv[3] == \"--run\":\n run_race_track_simulator_value_iteration(False, sys.argv[4], False)\n exit()\n elif sys.argv[2] == \"--sarsa\":\n run_race_track_simulator_sarsa(False, sys.argv[4])\n exit()\n\n if sys.argv[1] == \"--crash-restart\":\n if sys.argv[2] == \"--value\" and sys.argv[3] == \"--create-policy\":\n run_race_track_simulator_value_iteration(True, sys.argv[4], True)\n exit()\n elif sys.argv[2] == \"--value\" and sys.argv[3] == \"--run\":\n run_race_track_simulator_value_iteration(True, sys.argv[4], False)\n exit()\n elif sys.argv[2] == \"--sarsa\":\n run_race_track_simulator_sarsa(True, sys.argv[4])\n exit()\n\n exit()\n\n" }, { "alpha_fraction": 0.538469135761261, "alphanum_fraction": 0.5417410135269165, "avg_line_length": 35.94505310058594, "blob_id": "4b00a0020209f42496547710e1c9efcb95d4c5b1", "content_id": "5793d2ca342d1da08f82cdd792684626ec6afce5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 10086, "license_type": "no_license", "max_line_length": 108, "num_lines": 273, "path": "/Decision Trees/DecisionTree.py", "repo_name": "jperilla/Machine-Learning", "src_encoding": "UTF-8", "text": "import math\nfrom collections import deque\n\nfrom machineLearningUtilities import get_num_similarities\n\n\nclass Node:\n def __init__(self, feature=None, value=None):\n self.feature = feature\n self.value = value\n self.label = None\n self.children = []\n self.next = None\n\n\nclass DecisionTree:\n \"\"\"\n This class implements a decision tree algorithm\n \"\"\"\n def __init__(self, train):\n self.train = train\n self.unique_labels = set(train[\"Class\"])\n self.root = None\n\n def build_id3_tree(self):\n \"\"\"\n This function builds the id3 decision tree from the root\n \"\"\"\n features = [col for col in self.train.columns if col != \"Class\"]\n self.root = self.build_id3_branch(self.train, features)\n\n def build_id3_branch(self, subset, features, value=None):\n \"\"\"\n This function builds a subset of the id3 decision tree.\n :param subset: The current subset\n :param features: The features left\n :param value: The value that led to this subset\n :return: the root of the current subset\n \"\"\"\n\n # Return if only one class left\n class_labels_left = set(subset[\"Class\"])\n if len(class_labels_left) == 1:\n leaf = Node(value=value)\n leaf.label = list(class_labels_left)[0]\n return leaf\n\n # Return if last feature\n if len(features) == 1:\n # print(\"Creating Leaf\")\n leaf = Node(value=value)\n leaf.label = self.mode(subset, \"Class\")\n return leaf\n\n # Otherwise, create a new decision node\n decision = Node(value=value)\n\n # Calculate the entropy I(c1, c2, ... ck) of the subset\n entropy = self.get_entropy(subset, \"Class\")\n # print(f\"entropy = {entropy}\")\n if entropy == 0:\n return None\n\n # Find the feature with the most information gain\n largest_gain_ratio = 0\n feature_largest_gain_ratio = None\n # print(features)\n for feature in features:\n\n # Find the expected entropy for this feature\n expected_entropy = self.get_expected_entropy(subset, feature)\n # print(f\"Expected Entropy for feature [{feature}] = {expected_entropy}\")\n\n # Calculate the Gain Ratio for this feature\n information_value = self.get_information_value(subset, feature)\n gain_ratio = (entropy - expected_entropy) / information_value\n # print(f\"Gain for feature {feature} = {gain_ratio}\")\n if gain_ratio > largest_gain_ratio:\n largest_gain_ratio = gain_ratio\n feature_largest_gain_ratio = feature\n\n # print(f\"Feature with largest gain = {feature_largest_gain_ratio} at {largest_gain_ratio}\")\n\n # For this feature, create a child node for each feature value\n decision.feature = feature_largest_gain_ratio\n feature_value_counts = subset[feature_largest_gain_ratio].value_counts()\n features.remove(feature_largest_gain_ratio)\n\n # Sort feature values\n sorted_fvs = []\n for key in sorted(feature_value_counts.keys()):\n sorted_fvs.append(key)\n\n for fv in sorted_fvs:\n child_subset = subset.loc[subset[feature_largest_gain_ratio] == fv]\n child_tree = self.build_id3_branch(child_subset, features, value=fv)\n if child_tree:\n decision.children.append(child_tree)\n\n return decision\n\n def get_entropy(self, data, column):\n entropy = 0\n num_samples = len(data)\n label_counts = data[column].value_counts()\n for label in self.unique_labels:\n if label in label_counts:\n num_this_label = label_counts[label]\n class_prob = num_this_label / num_samples\n entropy += (-class_prob * math.log2(class_prob))\n\n return entropy\n\n def get_expected_entropy(self, data, column):\n expected_entropy = 0\n num_samples = len(data)\n feature_value_counts = data[column].value_counts()\n unique_feature_values = set(data[column])\n for fv in unique_feature_values:\n num_this_feature_value = feature_value_counts[fv]\n feature_in_sample_prob = num_this_feature_value / num_samples\n expected_entropy += (feature_in_sample_prob\n * self.get_entropy_partition(data, column, fv, num_this_feature_value))\n\n return expected_entropy\n\n def get_entropy_partition(self, data, column, fv, num_total):\n entropy_partition = 0\n class_values_in_fv = data.loc[data[column] == fv, [\"Class\"]][\"Class\"]\n label_counts_in_fv = class_values_in_fv.value_counts()\n for label in self.unique_labels:\n if label in label_counts_in_fv:\n num_this_label = label_counts_in_fv[label]\n class_in_fv_prob = num_this_label / num_total\n entropy_partition += (-class_in_fv_prob * math.log2(class_in_fv_prob))\n\n return entropy_partition\n\n @staticmethod\n def get_information_value(data, column):\n iv = 0\n num_samples = len(data)\n feature_value_counts = data[column].value_counts()\n unique_feature_values = set(data[column])\n for fv in unique_feature_values:\n num_this_feature_value = feature_value_counts[fv]\n feature_in_sample_prob = num_this_feature_value / num_samples\n iv += (-feature_in_sample_prob * math.log2(feature_in_sample_prob))\n\n return iv\n\n def print_tree(self):\n \"\"\"\n This function prints the decision tree\n \"\"\"\n print(\"Printing Decision Tree...\")\n if self.root:\n nodes = deque()\n nodes.append(self.root)\n while len(nodes) > 0:\n node = nodes.popleft()\n if node:\n print(f\"Decision = {node.feature}\")\n if node.children:\n print(node.children)\n for child in node.children:\n if child:\n if child.value:\n print(f\"({child.value})\")\n\n if child.label:\n print(\"---Leaf Node---\")\n print(f\"Class Label = {child.label}\")\n else:\n nodes.append(child)\n else:\n print(\"Decision Tree has not been built.\")\n\n def validate_pruned_tree(self, data):\n pruned_tree = self.prune(self.root, data)\n return self.validate(pruned_tree, data)\n\n def prune(self, node, data):\n \"\"\"\n This function prunes the tree and validates accuracy against the validation set\n :return: pruned tree accuracy\n \"\"\"\n if len(node.children) == 0:\n return\n\n if len(data) == 0:\n return\n\n # Loop through children and flag for pruning\n for child in node.children:\n subset = data[data[node.feature] == child.value]\n self.prune(child, subset)\n\n # Test if pruning improves accuracy\n if self.can_prune(node, data):\n node.children = None\n node.label = self.mode(data, 'Class')\n print(f\"Pruning node, replacing with leaf node, label = {node.label}\")\n\n return node\n\n def can_prune(self, node, data):\n\n if len(data) == 0:\n return True\n\n correct = 0\n majority_class = self.mode(data, \"Class\")\n for index, row in data.iterrows():\n if row['Class'] == majority_class:\n correct += 1\n\n pruned_accuracy = (float(correct) / len(data) * 100)\n node_accuracy = self.validate(node, data)\n if pruned_accuracy >= node_accuracy:\n return True\n\n return False\n\n @staticmethod\n def validate(root, df_test):\n \"\"\"\n This function makes predictions on a test set, based on the decision tree built\n :param root: The root of the current tree\n :param df_test: The test set\n :return: accuracy\n \"\"\"\n if root:\n predictions = []\n for index, row in df_test.iterrows():\n # print(row)\n leaf_found = False\n node = root\n class_label = None\n while not leaf_found:\n # Check if node is a leaf\n if node.label:\n leaf_found = True\n class_label = node.label\n elif node.children:\n # Loop through current node's children\n for child in node.children:\n # print(f\"Decision = {node.feature}\")\n # print(f\"Child value = {child.value}\")\n if isinstance(child.value, str) and row[node.feature] == child.value:\n node = child\n break\n elif row[node.feature] <= child.value:\n node = child\n break\n elif child == node.children[-1]: # This is the last child\n node = child\n else:\n print(\"Something went wrong\")\n leaf_found = True\n\n # print(f\"Prediction = {class_label}\")\n predictions.append(class_label)\n\n # Compare predictions and actual class labels\n # print(f\"Comparing {len(predictions)} predictions against {len(df_test['Class'])} class labels...\")\n accuracy = get_num_similarities(df_test[\"Class\"], predictions) / len(predictions) * 100\n return accuracy\n\n @staticmethod\n def mode(subset, column):\n label_value_counts = subset[column].value_counts()\n return label_value_counts.argmax()\n" } ]

19

themmyloluwaa/complex_fibonacci

https://github.com/themmyloluwaa/complex_fibonacci

625f181b52155664a56057590d43d7e04cebd6ee

eaa130ba8c935eac68398a3512e52f771cdd0f78

6f270269b01010e0446bfe7a864d46e77e61947d

refs/heads/main

2023-03-13T09:45:47.739366

2021-03-03T10:03:38

342,226,981

0

null

[ { "alpha_fraction": 0.6207584738731384, "alphanum_fraction": 0.6307384967803955, "avg_line_length": 22.85714340209961, "blob_id": "221cd01c0c0327f6cf8e05082db4cd59c504e303", "content_id": "e27ebd9fd1162c0c6541383fb65a4eb5b1f6c51f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 501, "license_type": "no_license", "max_line_length": 66, "num_lines": 21, "path": "/worker/index.py", "repo_name": "themmyloluwaa/complex_fibonacci", "src_encoding": "UTF-8", "text": "import redis\nimport os\n\nREDIS_HOST = os.environ['REDIS_HOST']\nREDIS_PORT = os.environ['REDIS_PORT']\n\nredisClient = redis.Redis(host=REDIS_HOST, port=REDIS_PORT, db=0,)\n\n\ndef fib(index):\n if(index < 2):\n return 1\n return fib(index - 1) + fib(index - 2)\n\n\ndef sub(name: 'str'):\n pubsub = redisClient.pubsub()\n pubsub.subscribe('insert')\n for message in pubsub.listen():\n if message.get('type') == 'message':\n redisClient.hset('values', fib(message.get('value')))\n" }, { "alpha_fraction": 0.7868852615356445, "alphanum_fraction": 0.7868852615356445, "avg_line_length": 19.66666603088379, "blob_id": "f9bd35f6f8b8aaa2826186a4dec38d9f1ce9b4b4", "content_id": "f51297a1040bb791efc440e0634c4cd48829fe9f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Dockerfile", "length_bytes": 61, "license_type": "no_license", "max_line_length": 49, "num_lines": 3, "path": "/nginx/Dockerfile.dev", "repo_name": "themmyloluwaa/complex_fibonacci", "src_encoding": "UTF-8", "text": "FROM nginx\n\nCOPY ./default.conf etc/nginx/conf.d/default.conf" }, { "alpha_fraction": 0.8235294222831726, "alphanum_fraction": 0.8235294222831726, "avg_line_length": 58, "blob_id": "e18dba31d3441166bb5afe5b91a7671a549a54c6", "content_id": "fdce18c28d869bf240e2e62367b074f6bd7c995c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 119, "license_type": "no_license", "max_line_length": 97, "num_lines": 2, "path": "/README.md", "repo_name": "themmyloluwaa/complex_fibonacci", "src_encoding": "UTF-8", "text": "# complex_fibonacci\nA dockerized multi container application built in react and nodejs with postgres, redis and nginx \n" } ]

3

bruabas/pymy

https://github.com/bruabas/pymy

0b1ce02b5433f68aa900611edee85d33a56f9a9e

5b9d1a3c95d5bab466911a904d83e03526ed0ef6

681a860a1aed380d63e760edeb091886b845cfe6

refs/heads/master

2021-09-03T14:31:58.243231

2018-01-09T19:46:04

116,853,549

0

null

[ { "alpha_fraction": 0.4379595220088959, "alphanum_fraction": 0.4453119933605194, "avg_line_length": 29.981855392456055, "blob_id": "de28e80ff559bcf4a895d776d7e0bba66bcef56b", "content_id": "292181716f36634940792f45494cde3e6fecd768", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C", "length_bytes": 15369, "license_type": "no_license", "max_line_length": 123, "num_lines": 496, "path": "/src/pymy.c", "repo_name": "bruabas/pymy", "src_encoding": "UTF-8", "text": "\n#include <mysql.h>\n#include <Python.h>\n#include <stdlib.h>\n#include \"structmember.h\"\n#include \"datetime.h\"\n\ntypedef struct {\n PyObject_HEAD\n MYSQL *con; // MySQL connection handler\n} Database_Object;\n\ntypedef struct {\n PyObject_HEAD\n PyObject *fields; // Tuple with field names (Python strings)\n int *types; // Types of MySQL individual fields\n MYSQL_RES *res; // MySQL stored result \n MYSQL_ROW last; // last read row from result\n unsigned row; // last row index\n unsigned num_rows; // total number of rows in result\n} Table_Object;\n\nvoid \nTable_dealloc(PyObject *self)\n{\n Table_Object *s = (Table_Object*)self;\n mysql_free_result(s->res);\n Py_XDECREF(s->fields);\n free(s->types);\n Py_TYPE(self)->tp_free(self);\n}\n\nPyObject *\nconvert_mysql_value(char *cvalue, unsigned type)\n{\n PyObject *res;\n if(cvalue == NULL) {\n /*\n * NULL values return None type\n */\n Py_INCREF(Py_None);\n return Py_None;\n }\n\n switch(type) {\n /*\n * Integer types.\n */\n case MYSQL_TYPE_TINY:\n case MYSQL_TYPE_SHORT:\n case MYSQL_TYPE_LONG:\n case MYSQL_TYPE_INT24:\n case MYSQL_TYPE_LONGLONG:\n res = PyInt_FromString(cvalue, NULL, 10);\n break;\n\n /*\n * Floating point types.\n */\n case MYSQL_TYPE_DECIMAL:\n case MYSQL_TYPE_NEWDECIMAL:\n case MYSQL_TYPE_FLOAT:\n case MYSQL_TYPE_DOUBLE:\n res = PyFloat_FromDouble(atof(cvalue));\n break;\n\n /*\n * Date types.\n */\n case MYSQL_TYPE_DATE:\n res = PyDate_FromDate(atoi(cvalue), atoi(cvalue+5), atoi(cvalue+8));\n break;\n\n case MYSQL_TYPE_TIME:\n res = PyTime_FromTime(atoi(cvalue), atoi(cvalue+3), atoi(cvalue+6), 0);\n break;\n\n case MYSQL_TYPE_DATETIME:\n res = PyDateTime_FromDateAndTime(atoi(cvalue), atoi(cvalue+5), atoi(cvalue+8),\n atoi(cvalue+11), atoi(cvalue+14), atoi(cvalue+17), 0);\n break;\n\n case MYSQL_TYPE_TIMESTAMP:\n res = PyDateTime_FromDateAndTime(atoi(cvalue), atoi(cvalue+5), atoi(cvalue+8),\n atoi(cvalue+11), atoi(cvalue+14), atoi(cvalue+17), atoi(cvalue+20));\n break;\n\n /*\n * All the rest are kept as strings\n */\n default:\n res = PyString_FromString(cvalue);\n break;\n }\n\n return res;\n}\n\nPyObject *\nTable_getitem(PyObject *self, Py_ssize_t index)\n{\n Table_Object *s = (Table_Object*)self;\n if(index != s->row) {\n /*\n * Read a different row from MySQL stored result.\n */\n if(index >= s->num_rows) {\n /*\n * Out of range\n */\n PyErr_SetString(PyExc_IndexError, \"Row out of range\");\n return NULL;\n }\n\n mysql_data_seek(s->res, index);\n s->row = index;\n s->last = mysql_fetch_row(s->res);\n }\n\n /*\n * Create a dictionary with the fields and values.\n */\n PyObject *item = PyDict_New();\n if(item == NULL) return NULL;\n\n unsigned i;\n unsigned n = PyTuple_Size(s->fields);\n for(i=0; i<n; i++) {\n PyObject *key = PyTuple_GetItem(s->fields, i);\n PyObject *value = convert_mysql_value(s->last[i], s->types[i]);\n if(value == NULL) {\n Py_DECREF(item);\n return NULL;\n }\n if(PyDict_SetItem(item, key, value) < 0) {\n Py_DECREF(item);\n return NULL;\n }\n }\n\n return item; \n}\n\nPyObject *\nTable_getcolumn(PyObject *self, PyObject *args)\n{\n Table_Object *s = (Table_Object*)self;\n char *field;\n\n if(!PyArg_ParseTuple(args, \"s\", &field)) return NULL;\n\n /*\n * Find the desired field into the fields list\n */\n unsigned i;\n unsigned n = PyTuple_Size(s->fields);\n for(i=0; i<n; i++) {\n char *f = PyString_AS_STRING(PyTuple_GET_ITEM(s->fields, i));\n if(strcmp(field, f) == 0) break;\n }\n if(i >= n) {\n PyErr_SetString(PyExc_IndexError, \"Unknown field\");\n return NULL;\n }\n\n PyObject *col = PyTuple_New(s->num_rows);\n if(col == NULL) return NULL;\n\n unsigned j;\n mysql_data_seek(s->res, 0);\n for(j=0; j<s->num_rows; j++) {\n s->row = j;\n s->last = mysql_fetch_row(s->res);\n PyTuple_SetItem(col, j, convert_mysql_value(s->last[i], s->types[i]));\n }\n\n return col;\n}\n\nPy_ssize_t \nTable_getsize(PyObject *self)\n{\n Table_Object *s = (Table_Object*)self;\n return s->num_rows;\n}\n\n/*\n * Table can work as a (imutable) sequence.\n */\nstatic PySequenceMethods Table_as_sequence = {\n Table_getsize, /* sq_length */\n 0, /* sq_concat */\n 0, /* sq_repeat */\n Table_getitem, /* sq_item */\n 0, /* sq_ass_item */\n 0, /* sq_contains */\n 0, /* sq_inplace_concat */\n 0 /* sq_inplace_repeat */\n};\n\n/*\n * Members for Table Object\n */\nstatic PyMemberDef Table_members[] = {\n { \"fields\", T_OBJECT, offsetof(Table_Object, fields), READONLY, \"Field names\" },\n { NULL }\n};\n\n/*\n * C Methods for Table Object\n */\nstatic PyMethodDef Table_methods[] = {\n { \"column\", (PyCFunction)Table_getcolumn, METH_VARARGS, \"Tuple with all values of the column\" },\n { NULL }\n};\n\n/*\n * Table Type definition for Python\n */\nstatic PyTypeObject Table_Type = {\n PyVarObject_HEAD_INIT(NULL, 0)\n \"pymy.Table\", /* tp_name */\n sizeof(Table_Object), /* tp_basicsize */\n 0, /* tp_itemsize */\n (destructor)Table_dealloc, /* tp_dealloc */\n 0, /* tp_print */\n 0, /* tp_getattr */\n 0, /* tp_setattr */\n 0, /* tp_compare */\n 0, /* tp_repr */\n 0, /* tp_as_number */\n &Table_as_sequence, /* tp_as_sequence */\n 0, /* tp_as_mapping */\n 0, /* tp_hash */\n 0, /* tp_call */\n 0, /* tp_str */\n 0, /* tp_getattro */\n 0, /* tp_setattro */\n 0, /* tp_as_buffer */\n Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /* tp_flags */\n \"A Table with MySQL Query Results\", /* tp_doc */\n 0, /* tp_traverse */\n 0, /* tp_clear */\n 0, /* tp_richcompare */\n 0, /* tp_weaklistoffset */\n 0, /* tp_iter */\n 0, /* tp_iternext */\n Table_methods, /* tp_methods */\n Table_members, /* tp_members */\n 0, /* tp_getset */\n 0, /* tp_base */\n 0, /* tp_dict */\n 0, /* tp_descr_get */\n 0, /* tp_descr_set */\n 0, /* tp_dictoffset */\n 0, /* tp_init */\n 0, /* tp_alloc */\n 0, /* tp_new */\n};\n\nstatic PyObject *\nDatabase_new(PyTypeObject *type, PyObject *args, PyObject *keywords)\n{\n Database_Object *self = (Database_Object*)type->tp_alloc(type, 0);\n if(self != NULL) {\n self->con = mysql_init(NULL);\n if(self->con == NULL) {\n PyErr_SetString(PyExc_RuntimeError, mysql_error(self->con));\n Py_XDECREF(self);\n return NULL;\n }\n }\n\n return (PyObject *)self;\n}\n\nvoid\nDatabase_dealloc(PyObject *self)\n{\n Database_Object *s = (Database_Object*)self;\n if(s->con != NULL)\n mysql_close(s->con);\n Py_TYPE(self)->tp_free(self);\n}\n\nstatic int\nDatabase_init(Database_Object *self, PyObject *args, PyObject *keywords)\n{\n MYSQL *res;\n static char *keys[] = { \"database\", \"host\", \"user\", \"password\", NULL };\n char *db;\n char *host = \"localhost\";\n char *user = \"root\";\n char *pwd = \"\";\n \n if(!PyArg_ParseTupleAndKeywords(args, keywords, \"s|sss\", keys, &db, &host, &user, &pwd))\n return -1;\n \n Py_BEGIN_ALLOW_THREADS\n res = mysql_real_connect(self->con, host, user, pwd, db, 0, NULL, 0);\n Py_END_ALLOW_THREADS\n\n if(res == NULL) {\n PyErr_SetString(PyExc_RuntimeError, mysql_error(self->con));\n return -1;\n }\n\n return 0;\n}\n\nstatic PyObject *\nDatabase_query(PyObject *self, PyObject *args)\n{\n char *query;\n int nok;\n MYSQL_RES *res;\n Database_Object *s = (Database_Object*)self;\n\n /*\n * Send query to MySQL server\n */\n if(!PyArg_ParseTuple(args, \"s\", &query)) return NULL;\n Py_BEGIN_ALLOW_THREADS\n nok = mysql_query(s->con, query);\n Py_END_ALLOW_THREADS\n\n if(nok) {\n PyErr_SetString(PyExc_RuntimeError, mysql_error(s->con));\n return NULL;\n }\n\n /*\n * Read back results (store to memory).\n */\n Py_BEGIN_ALLOW_THREADS\n res = mysql_store_result(s->con);\n Py_END_ALLOW_THREADS\n\n if(res == NULL) {\n PyErr_SetString(PyExc_RuntimeError, mysql_error(s->con));\n return NULL;\n }\n\n /*\n * Count results.\n */\n unsigned rows = mysql_num_rows(res);\n unsigned fields = mysql_field_count(s->con);\n if((rows == 0) || (fields == 0)) {\n /*\n * No results.\n */\n mysql_free_result(res);\n Py_RETURN_NONE;\n }\n\n /*\n * Create new Table object.\n */\n Table_Object *result = PyObject_New(Table_Object, &Table_Type);\n if(result == NULL) {\n mysql_free_result(res);\n return NULL;\n }\n\n result->num_rows = rows;\n result->res = res;\n result->row = -1;\n result->last = NULL;\n\n /*\n * Create new Tuple with fields' names\n */\n result->fields = PyTuple_New(fields);\n result->types = malloc(fields * sizeof(int));\n unsigned i;\n for(i=0; ;i++) {\n MYSQL_FIELD *f = mysql_fetch_field(res);\n if(f == NULL) break;\n PyTuple_SetItem(result->fields, i, PyString_FromString(f->name));\n result->types[i] = (int)f->type;\n }\n\n return (PyObject*)result;\n}\n\nstatic PyObject *\nDatabase_exec(PyObject *self, PyObject *args)\n{\n char *query;\n int nok;\n Database_Object *s = (Database_Object*)self;\n\n /*\n * Send query to MySQL server\n */\n if(!PyArg_ParseTuple(args, \"s\", &query)) return NULL;\n Py_BEGIN_ALLOW_THREADS\n nok = mysql_query(s->con, query);\n Py_END_ALLOW_THREADS\n if(nok) {\n PyErr_SetString(PyExc_RuntimeError, mysql_error(s->con));\n return NULL;\n }\n\n /*\n * Return number of affected rows.\n */\n return PyInt_FromLong(mysql_affected_rows(s->con));\n}\n\n/*\n * C Methods for Connection Object\n */\nstatic PyMethodDef Database_methods[] = {\n { \"query\", (PyCFunction)Database_query, METH_VARARGS, \"Execute a SQL query and return a result (None) if no result\" },\n { \"execute\", (PyCFunction)Database_exec, METH_VARARGS, \"Execute a SQL command and return the number of affected rows\" },\n { NULL }\n};\n\n/*\n * Connection Type definition for Python\n */\nstatic PyTypeObject Database_Type = {\n PyVarObject_HEAD_INIT(NULL, 0)\n \"pymy.Database\", /* tp_name */\n sizeof(Database_Object), /* tp_basicsize */\n 0, /* tp_itemsize */\n (destructor)Database_dealloc, /* tp_dealloc */\n 0, /* tp_print */\n 0, /* tp_getattr */\n 0, /* tp_setattr */\n 0, /* tp_compare */\n 0, /* tp_repr */\n 0, /* tp_as_number */\n 0, /* tp_as_sequence */\n 0, /* tp_as_mapping */\n 0, /* tp_hash */\n 0, /* tp_call */\n 0, /* tp_str */\n 0, /* tp_getattro */\n 0, /* tp_setattro */\n 0, /* tp_as_buffer */\n Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /* tp_flags */\n \"MySQL Database Connection abstraction\", /* tp_doc */\n 0, /* tp_traverse */\n 0, /* tp_clear */\n 0, /* tp_richcompare */\n 0, /* tp_weaklistoffset */\n 0, /* tp_iter */\n 0, /* tp_iternext */\n Database_methods, /* tp_methods */\n 0, /* tp_members */\n 0, /* tp_getset */\n 0, /* tp_base */\n 0, /* tp_dict */\n 0, /* tp_descr_get */\n 0, /* tp_descr_set */\n 0, /* tp_dictoffset */\n (initproc)Database_init, /* tp_init */\n 0, /* tp_alloc */\n Database_new, /* tp_new */\n};\n\nstatic PyMethodDef module_methods[] = {\n {NULL} /* Sentinel */\n};\n\n#ifndef PyMODINIT_FUNC/* declarations for DLL import/export */\n#define PyMODINIT_FUNC void\n#endif\nPyMODINIT_FUNC\ninitpymy(void)\n{\n PyObject* module;\n\n /*\n * Initialize new object types \n */\n if (PyType_Ready(&Database_Type) < 0) return;\n if (PyType_Ready(&Table_Type) < 0) return;\n\n /*\n * Create a Python module\n */\n module = Py_InitModule3(\"pymy\", module_methods, \"MySQL wrapper module\");\n if (module == NULL) return;\n\n /*\n * Add Connection class into module's namespace.\n */\n Py_INCREF(&Database_Type);\n Py_INCREF(&Table_Type);\n PyModule_AddObject(module, \"Database\", (PyObject *)&Database_Type);\n\n PyDateTime_IMPORT ;\n}\n\n" }, { "alpha_fraction": 0.6940279006958008, "alphanum_fraction": 0.7048144936561584, "avg_line_length": 35.20000076293945, "blob_id": "3bd87cd595b2efc2e549ef5f4c5c442d21f01f7a", "content_id": "619100ea5a7393a468f81237c25c0515fce60822", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 3801, "license_type": "no_license", "max_line_length": 339, "num_lines": 105, "path": "/README.md", "repo_name": "bruabas/pymy", "src_encoding": "UTF-8", "text": "# pymy -- a Python MySQL connector\n\n\n## What is it\n**pymy** is a Pythonic wrapper for connecting and sending queries to a MariaDB or MySQL database. Once it is **not** an ORM nor a SQL parser, you must enjoy SQL as much as you enjoy Python itself! And neither it adheres to any PEP database connection specification, but -- it is simple, neat and useful, doing a good job most of the times.\n\nYou can query data as simply as \n```python\nimport pymy\ndb = pymy.Database('test_db', user='myuser', password='pwd1234')\nres = db.query('select * from Table1')\nfor row in res:\n print 'id = {}, name = {}'.format(row['id'], row['name'])\n```\n\nor execute a SQL command...\n```python\nimport pymy\ndb = pymy.Database('test_db', user='myuser', password='pwd1234')\nres = db.execute('insert into Table1 (phone) values (\"551112345678\")')\nprint '{} rows affected'.format(res)\n```\n\n## Installation Instructions\nI've only installed in Linux, but with some effort it should be possible to install it on MacOS and Windows (using Cygwin). -- Help wanted here.\n\n**Dependencies**\n+ libmysqlclient-dev\n+ python2.7\n\nClone the repository and execute **setup.py**\n```bash\ngit clone https://github.com/bruabas/pymy.git\ncd pymy\npython setup.py build\nsudo python setup.py install\n```\n\n## Roadmap\n- [x] convert MySQL numeric types into Python Numeric Types\n- [x] convert MySQL date/time types into Python datetime objects\n- [ ] convert BLOB data into bytearray() or something\n- [ ] provide a Generator for quering huge sets of data (no memory copy)\n- [ ] make a Python3 version\n- [ ] make installers for systems other than Linux\n\n\n## The API in a minute\n### pymy.Database class\n* constructor take one compulsory parameter, the **database name**\n* host address, user name and password are optional. If not supplied, address is supposed to be 'localhost', user is 'root' and password is blank.\n\n```python\ndb = pymy.Database(name='MyDatabase',\n host = '192.168.1.23',\n user = 'nonono',\n password = '1234')\n```\non success it returns a **pymy.Database** object that you will use to interact with the database. An exception (**RuntimeException**) is raised if any error occurs. \n\nTwo methods are available to acess the database: **query()** and **execute()**\n\n### pymy.Database.query( str )\n* **query()** takes a string as a SQL query that returns a result set (usually a **SELECT** clause)\n* Executes the SQL query on the server, returning a result set (**pymy.Table**) or None if the query returned no values.\n* Errors in SQL syntax or communication errors raise an exception\n\n### pymy.Database.execute( str )\n* **execute()** takes a string as a SQL query not returning a result set (clauses like **INCLUDE**, **DELETE**, etc)\n* Executes the SQL query on the server, returning the number of affected rows.\n* Errors in SQL syntax or communication errors raise an exception\n\n### pymy.Table class\n* is an iterable, list-like object, containing the results of a **Database.query( )**\n```python\n# result is a pymy.Table\n# you can use it as an iterable\nresult = db.query('select * from Employees')\nfor item in result:\n\tdo_something_with(item)\n\t\n# or as a list-like\nitem = result[2]\n```\n* its items are dictionaries with the **keys** corresponding to the **field names**\n```python\n# result is a pymy.Table\n# item is a dictionary\nresult = db.query('select FirstName, LastName from Clients')\nitem = result[2]\nname = item['FirstName']\n\n# or\nname = result[2]['FirstName']\n```\n\n### pymy.Table.column ( str )\n* This method returns a tuple with the values of all rows corresponding with the desired column\n```python\n# result is a pymy.Table\n# ages is a tuple\nresult = db.query('select * from Clients')\nages = result.column('age')\n# age is something like ( 34, 45, 33, 20, 60, 55 )\n```\n" }, { "alpha_fraction": 0.5947136282920837, "alphanum_fraction": 0.5991189479827881, "avg_line_length": 27.1875, "blob_id": "0f2aa70e8eef56c7907ed80cd9af44201f966b7b", "content_id": "5563458b7cb943d2416b7a26d5f25d4e913b41b3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 454, "license_type": "no_license", "max_line_length": 59, "num_lines": 16, "path": "/setup.py", "repo_name": "bruabas/pymy", "src_encoding": "UTF-8", "text": "\nfrom distutils.core import setup, Extension\nfrom subprocess import check_output\n\ncompile_args = check_output(['mysql_config', '--cflags'])\nlink_args = check_output(['mysql_config', '--libs'])\n\nsources = [ 'src/pymy.c' ]\n\next = Extension( 'pymy',\n sources,\n extra_compile_args = compile_args.split(),\n extra_link_args = link_args.split())\n\nsetup(\n name=\"pymy\", version=\"0.2\",\n ext_modules=[ ext ])\n\n\n" } ]

3

oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool

https://github.com/oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool

770770bcadbe40d6f0b117194b097ff8c57ba473

672f54bb527373d700b38d0389202526d748d12e

4baff9ce95c57b9196410f02d24e84a3df4e94d9

refs/heads/master

2022-02-10T05:04:27.726998

2018-11-05T13:40:10

null

0

null

[ { "alpha_fraction": 0.4493597149848938, "alphanum_fraction": 0.5098952054977417, "avg_line_length": 23.428571701049805, "blob_id": "7fabcfea5256ade29c7337cbca7b46d654817035", "content_id": "370953849c51be8589285bad3220554694035980", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 879, "license_type": "permissive", "max_line_length": 68, "num_lines": 35, "path": "/Quart2Euler.py", "repo_name": "oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool", "src_encoding": "UTF-8", "text": " ##四元数转Euler角##\nimport sys\nimport math\ndef quart2Euler (*nums):\n Euler = []\n w = float(nums[0]) # img items: x,y,z real items :w #\n x = float(nums[1])\n y = float(nums[2])\n z = float(nums[3])\n theta1 = math.atan2(2 * (w * x + y * z), 1 - 2 * (x * x + y * y))\n if theta1 < 0:\n theta1 += 2 * math.pi\n temp = w * y - z * x\n if temp >= 0.5:\n temp = 0.5\n elif temp <= -0.5:\n temp = -0.5\n else:\n pass\n theta2 = math.asin(2 * temp)\n theta3 = math.atan2(2 * (w * z + x * y), 1 - 2 * (z * z + y * y))\n if theta3 < 0:\n theta3 += 2 * math.pi\n ##弧度转角度##\n roll = theta1 * 180 / math.pi\n Euler.append(roll)\n pitch = theta2 * 180 / math.pi\n Euler.append(pitch)\n yaw = theta3 * 180 / math.pi\n Euler.append(yaw)\n return Euler\n\nif __name__ == '__main__':\n quart1 = (1,0,0,0)\n print(quart2Euler(*quart1))\n\n\n\n" }, { "alpha_fraction": 0.579155683517456, "alphanum_fraction": 0.5936675667762756, "avg_line_length": 33.45454406738281, "blob_id": "f10a086fc7b540552dbb07d8c87bf9183b5a3306", "content_id": "e60408c469042e14dda4a8d75c40a5d930b90bc3", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 786, "license_type": "permissive", "max_line_length": 107, "num_lines": 22, "path": "/Euler2Quart.py", "repo_name": "oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool", "src_encoding": "UTF-8", "text": "import math\nimport numpy as np\ndef Euler2quatern(*nums): ##input : 欧拉角列表 [roll,pitch,yaw],返回一个四元数列表\n\n roll = nums[0] / 2\n pitch = nums[1] / 2\n yaw = nums[2] / 2\n\n w = math.cos(roll) * math.cos(pitch) * math.cos(yaw) + math.sin(roll) * math.sin(pitch) * math.sin(yaw)\n\n x = math.sin(roll) * math.cos(pitch) * math.cos(yaw) - math.cos(roll) * math.sin(pitch) * math.sin(yaw)\n\n y = math.cos(roll) * math.sin(pitch) * math.cos(yaw) + math.sin(roll) * math.cos(pitch) * math.sin(yaw)\n\n z = math.cos(roll) * math.cos(pitch) * math.sin(yaw) + math.sin(roll) * math.sin(pitch) * math.cos(yaw)\n qua = [w, x, y, z]\n return qua\ndef main():\n testeuler = [0, 0, 0]\n print(Euler2quatern(*testeuler))\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.42750608921051025, "alphanum_fraction": 0.4946221113204956, "avg_line_length": 34.345176696777344, "blob_id": "edbd19515f8692c5a6de1a67dfae8454bc3548cf", "content_id": "e4d805f03e33395e0a2b6445e86a2c15a2cdedbf", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7306, "license_type": "permissive", "max_line_length": 111, "num_lines": 197, "path": "/QuartClass.py", "repo_name": "oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool", "src_encoding": "UTF-8", "text": "import sys\nimport math\nimport cv2\nimport numpy as np\n\"\"\"\nThis is a quaternion class that calculates the quaternion to the rotation matrix, the rotation vector, \nand the rotation of the two quaternions based on the correlation method provided. \nThis is a key step in the virtual human project.\n© Weinan Gan. All Rights Reserved.\n\"\"\"\nclass Quart():\n def __init__(self,*nums):\n self.w = float(nums[0])\n self.x = float(nums[1])\n self.y = float(nums[2])\n self.z = float(nums[3])\n def Quart2Euler(self): #四元数计算欧拉角#\n Euler = []\n w = self.w\n x = self.x\n y = self.y\n z = self.z\n theta1 = math.atan2(2 * (w * x + y * z), 1 - 2 * (x * x + y * y))\n if theta1 < 0:\n theta1 += 2*math.pi\n temp = w*y - z*x\n if temp >= 0.5:\n temp = 0.5\n elif temp <= -0.5:\n temp = -0.5\n else:\n pass\n theta2 = math.asin(2 * temp)\n theta3 = math.atan2(2 * (w * z + x * y), 1 - 2 * (z * z + y * y))\n if theta3 < 0:\n theta3 += 2*math.pi\n ##弧度转角度##\n roll = theta1 * 180 / math.pi\n Euler.append(roll)\n pitch = theta2 * 180 / math.pi\n Euler.append(pitch)\n yaw = theta3 * 180 / math.pi\n Euler.append(yaw)\n return Euler\n def Euler2quatern(*nums): ##input : 欧拉角列表 [roll,pitch,yaw]\n roll = nums[0] / 2\n pitch = nums[1] / 2\n yaw = nums[2] / 2\n w = math.cos(roll) * math.cos(pitch) * math.cos(yaw) + math.sin(roll) * math.sin(pitch) * math.sin(yaw)\n x = math.sin(roll) * math.cos(pitch) * math.cos(yaw) - math.cos(roll) * math.sin(pitch) * math.sin(yaw)\n y = math.cos(roll) * math.sin(pitch) * math.cos(yaw) + math.sin(roll) * math.cos(pitch) * math.sin(yaw)\n z = math.cos(roll) * math.cos(pitch) * math.sin(yaw) + math.sin(roll) * math.sin(pitch) * math.cos(yaw)\n qua = [w, x, y, z]\n return qua\n \"\"\"\n The transform from quart to rotvector\n \"\"\"\n\n def Quart2RotationMatrix(self): ##四元数计算旋转矩阵##\n R = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 0]], dtype=float)\n w = self.w\n x = self.x\n y = self.y\n z = self.z\n # calculate element of matrix\n R[0][0] = np.square(w) + np.square(x) - np.square(y) - np.square(z)\n R[0][1] = 2 * (x * y + w * z)\n R[0][2] = 2 * (x * z - w * y)\n R[1][0] = 2 * (x * y - w * z)\n R[1][1] = np.square(w) - np.square(x) + np.square(y) - np.square(z)\n R[1][2] = 2 * (w * x + y * z)\n R[2][0] = 2 * (x * z + w * y)\n R[2][1] = 2 * (y * z - w * x)\n R[2][2] = np.square(w) - np.square(x) - np.square(y) + np.square(z)\n return R\n def rotMat2rotvector(R): ##旋转矩阵转旋转向量##\n vector = cv2.Rodrigues(R)[0]\n v =[]\n for i in range(3):\n v.append(vector[i][0])\n return v\n def Quart2RotationVector(self): ##四元数计算旋转向量##\n R = self.Quart2RotationMatrix()\n v = Quart.rotMat2rotvector(R)\n return v\n\n \"\"\"\n the transform from rotvector to quartern\n \"\"\"\n\n def rotvector2rotMat(v): ##旋转向量转旋转矩阵##\n v1=np.array([[0],[0],[0]],dtype=float)\n v1[0][0] = v[0]\n v1[1][0] = v[1]\n v1[2][0] = v[2]\n Matrix = cv2.Rodrigues(v1)\n return Matrix[0]\n def rotMat2quatern(R): ##旋转矩阵转四元数###\n # this function can transform the rotation matrix into quatern\n q = np.zeros(4)\n K = np.zeros([4, 4])\n K[0, 0] = 1 / 3 * (R[0, 0] - R[1, 1] - R[2, 2])\n K[0, 1] = 1 / 3 * (R[1, 0] + R[0, 1])\n K[0, 2] = 1 / 3 * (R[2, 0] + R[0, 2])\n K[0, 3] = 1 / 3 * (R[1, 2] - R[2, 1])\n K[1, 0] = 1 / 3 * (R[1, 0] + R[0, 1])\n K[1, 1] = 1 / 3 * (R[1, 1] - R[0, 0] - R[2, 2])\n K[1, 2] = 1 / 3 * (R[2, 1] + R[1, 2])\n K[1, 3] = 1 / 3 * (R[2, 0] - R[0, 2])\n K[2, 0] = 1 / 3 * (R[2, 0] + R[0, 2])\n K[2, 1] = 1 / 3 * (R[2, 1] + R[1, 2])\n K[2, 2] = 1 / 3 * (R[2, 2] - R[0, 0] - R[1, 1])\n K[2, 3] = 1 / 3 * (R[0, 1] - R[1, 0])\n K[3, 0] = 1 / 3 * (R[1, 2] - R[2, 1])\n K[3, 1] = 1 / 3 * (R[2, 0] - R[0, 2])\n K[3, 2] = 1 / 3 * (R[0, 1] - R[1, 0])\n K[3, 3] = 1 / 3 * (R[0, 0] + R[1, 1] + R[2, 2])\n # print(R)\n # print(\"***********\")\n # print(K)\n D, V = np.linalg.eig(K)\n # print(K)\n pp = 0\n for i in range(1, 4):\n if(D[i] > D[pp]):\n pp = i\n # print(D[pp])\n # print(D)\n q = V[:, pp]\n q = np.array([q[3], q[0], q[1], q[2]])\n return q\n def rotvector2quart(v): ##旋转向量转四元数##\n rotmatrix = Quart.rotvector2rotMat(v)\n q = Quart.rotMat2quatern(rotmatrix)\n return q\n\n\n \"\"\"\n loss about VirtualHuman's action \n \"\"\"\n def calculateloss1(self,*nums): ##第一种方法，input：一个四元数元组\n R1 = np.matrix(self.Quart2RotationMatrix())\n quart2 = Quart(*nums)\n R2 = np.matrix(quart2.Quart2RotationMatrix())\n reverse_R1 = R1.I\n R3 = reverse_R1 * R2 ##这里本应该用转置的，但是由于浮点数计算误差带来问题，故选择求逆函数##\n loss_vector = Quart.rotMat2rotvector(np.array(R3))\n loss1 = (loss_vector[0][0]**2 + loss_vector[1][0]**2 + loss_vector[2][0]**2)**0.5\n return loss1\n def calculateloss2(self ,predic_v ): ##第二种方法,input predic_v = [a,b,c]##\n truth_v = self.Quart2RotationVector()\n loss_v_list = []\n for i in range(3):\n loss_v_list.append(truth_v[i][0]-predic_v[i])\n loss2 = (loss_v_list[0]**2 + loss_v_list[1]**2 + loss_v_list[2]**2)**0.5\n return loss2\n\n def Dicar_trans(self,*nums1): ###笛卡尔坐标转换这里用不到 input:调用对象旧坐标 output：新的坐标##\n Matrix = self.Quart2RotationMatrix()\n newcordinate = []\n oldcordinate = np.array(nums1)\n for i in range(len(Matrix)):\n newcordinate.append(np.dot(Matrix[i],oldcordinate))\n return newcordinate\ndef main():\n testquart = (-0.707100,0.707100,0.000000,0.000000)\n old = (0,0,1)\n quart = Quart(*testquart)\n print(quart.Quart2RotationVector())\n print(quart.Quart2RotationMatrix())\n print(quart.Dicar_trans(*old))\n \n\n \"\"\"\n testquart = (-0.707100, 0.707100, 0.000000, 0.000000)\n testeuler = ((270.00109895412885/180)*math.pi, -0.0, 0.0)\n quart = Quart(*testquart)\n print(\"欧拉角为：\",quart.Quart2Euler())\n print(\"四元数为：\",Quart.Euler2quatern(*testeuler))\n \"\"\"\n \"\"\"\n predict_quart = (-0.707100,0.707100,0.000000,0.000000)\n truth_quart = (-0.707100,0.707100,0.000000,0.000000)\n quart = Quart(*predict_quart)\n print(\"lossFuncition1的损失为：\",quart.calculateloss1(*truth_quart))\n \"\"\"\n \"\"\"\n testquart = (1, 0, 0, 0)\n quart = Quart(*testquart)\n print(quart.Quart2RotationVector())\n print(Quart.rotvector2quart([0,0,0]))\n v_test = [1,0,0]\n print(\"lossFunction2的损失为：\",quart.calculateloss2(v_test))\n \"\"\"\n\nif __name__ == \"__main__\":\n main()\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.8223684430122375, "alphanum_fraction": 0.8223684430122375, "avg_line_length": 379, "blob_id": "2b4766b8efefd08633fcc6f4b4d863725eb5a2ee", "content_id": "5a022fba7beb1b545a7b02ef8e5ef3d0e48021df", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 796, "license_type": "permissive", "max_line_length": 734, "num_lines": 2, "path": "/README.md", "repo_name": "oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool", "src_encoding": "UTF-8", "text": "# 欧拉角四元数旋转向量旋转矩阵旋转损失\nThis is my internship project in NetEase game AI Lab: multi-modal virtual human interaction, through the text to predict the virtual human action and then generate animation through Unity rendering, enhance the interactive experience of virtual characters. This is the calculation tool I used to refer to the Python version written independently by the relevant C code, including quaternions, rotation vectors, Euler angles, and the loss function between the predicted rotation and the true rotation. This part I listed separately as a calculation tool for everyone to use in the game AI experiment development process. QuartClass is a class that integrates all functions. I have tested all the features and it is stable and feasible.\n" }, { "alpha_fraction": 0.46081870794296265, "alphanum_fraction": 0.5426900386810303, "avg_line_length": 29.464284896850586, "blob_id": "a2faf44db32ee18e8fd2102377e0d421a0809ef6", "content_id": "704282a7e63d67b7b723aa17d60998538f703cfe", "detected_licenses": [ "MIT" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 855, "license_type": "permissive", "max_line_length": 69, "num_lines": 28, "path": "/Quart2RotationVector.py", "repo_name": "oliviaengg/Virtual-person-posture-rotation-representation-and-loss-calculation-tool", "src_encoding": "UTF-8", "text": "import numpy as np\nimport cv2\ndef quart2Rotationvector(*nums):\n R = np.array([[0,0,0],[0,0,0],[0,0,0]],dtype=float)\n#print(RotationMatrix)\n w = float(nums[0]) # img items: x,y,z\n # real items :w #\n x = float(nums[1])\n y = float(nums[2])\n z = float(nums[3])\n#calculate element of matrix\n R[0][0] = np.square(w) + np.square(x) - np.square(y) - np.square(z)\n R[0][1] = 2*(x*y + w*z)\n R[0][2] = 2*(x*z - w*y)\n R[1][0] = 2*(x*y - w*z)\n R[1][1] = np.square(w) - np.square(x) + np.square(y) - np.square(z)\n R[1][2] = 2*(w*x + y*z)\n R[2][0] = 2*(x*z + w*y)\n R[2][1] = 2*(y*z - w*x)\n R[2][2] = np.square(w) - np.square(x) - np.square(y) + np.square(z)\n vector = cv2.Rodrigues(R)\n return vector[0]\n\n\n\nif __name__ == '__main__':\n testquart = (-0.707100,0.707100,0.000000,0.000000)\n print(quart2Rotationvector(*testquart))\n\n\n" } ]

5

mudsill/nys-re

https://github.com/mudsill/nys-re

6ada28ee27244f055433d8e06df0a731a8a8436c

aedaf103f143b11feba15a6a11eb9ddaea66d434

def3460f3e53dc79c03a1f46c34f5fb4aaadb672

refs/heads/master

2020-03-28T17:09:01.737172

2018-09-21T13:34:27

null

0

null

[ { "alpha_fraction": 0.6932153105735779, "alphanum_fraction": 0.6932153105735779, "avg_line_length": 27.25, "blob_id": "f5b4fe5381dbe523ebe6a594165c57e6eddb6594", "content_id": "5c1ee6babfd4c80811972c5d1fcf69f21d37ce9c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 339, "license_type": "no_license", "max_line_length": 113, "num_lines": 12, "path": "/nys.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "from app import create_app, db\n\n\napp = create_app()\n\n\[email protected]_context_processor\ndef make_shell_context():\n from app import db\n from app.models import Filer, Disclosure, Entity, Link\n from app.engine import Engine\n return {'db': db, 'Filer': Filer, 'Disclosure': Disclosure, 'Entity': Entity, 'Link': Link, 'Engine': Engine}\n" }, { "alpha_fraction": 0.6706587076187134, "alphanum_fraction": 0.6706587076187134, "avg_line_length": 10.928571701049805, "blob_id": "253e2e836c2a8a0a00f7b25085c555d200ee8396", "content_id": "867470d621ba8d3a31d344907989771f3a7aa5a5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 167, "license_type": "no_license", "max_line_length": 31, "num_lines": 14, "path": "/app/cli.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "import click\n\nfrom engine import Engine\n\n\[email protected]()\ndef cli():\n pass\n\n\[email protected]()\ndef normalize():\n engine = Engine()\n engine.normalize_entities()\n" }, { "alpha_fraction": 0.5307454466819763, "alphanum_fraction": 0.5389900207519531, "avg_line_length": 32.45977020263672, "blob_id": "1e507bec2441d2a744f3730844aa405af07bfda3", "content_id": "944486925a98ce653c8b955333401939ea6e55e9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2911, "license_type": "no_license", "max_line_length": 103, "num_lines": 87, "path": "/app/engine.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "import logging\nimport requests\n\nfrom datetime import datetime\n\nfrom lxml import html\nfrom sqlalchemy import update\nfrom Levenshtein import distance\n\nfrom app import db\nfrom app.config import Config\nfrom app.models import Entity, Link, Filer, Disclosure\n\nlogging.basicConfig(level=logging.DEBUG)\n\n\nclass Engine:\n CORPORATE_SUFFIXES = ['corporation', 'incorporated', 'corp', 'co', 'inc',\n 'llc']\n ELECTED_OFFICIALS_URL = 'http://www.elections.ny.gov:8080/reports/rwservlet?cmdkey=nysboe_incumbnt'\n\n\n @property\n def app(self):\n if not hasattr(self, '_app'):\n self._app = create_app()\n return self._app\n\n\n @property\n def logger(self):\n if not hasattr(self, '_logger'):\n self._logger = logging.getLogger(self.__class__.__name__)\n return self._logger\n\n\n def normalize_entities(self):\n entities = Entity.query.yield_per(5000).enable_eagerloads(False)\n for entity in entities:\n name = entity.name.strip().lower()\n name_check = name.split(', ')\n last_name_check = name_check[0].split(' ')\n aliases = []\n if len(name_check) == 2 and len(last_name_check) == 1:\n # Check if this is a person\n first_names = ' '.join(name_check[1:])\n last_name = name_check[0]\n normalized_name = '%s %s' % (first_names, last_name)\n else:\n # Now check if this is a corporation\n name_partition = name.split(' ')\n name_suffix = name_partition[-1]\n not_found = True\n for suffix in self.CORPORATE_SUFFIXES:\n if distance(str(name_suffix), str(suffix)) <= 2:\n not_found = False\n if not_found:\n continue\n normalized_name = name_partition[:-1]\n aliases.append(name)\n details = {'aliases': aliases}\n updated = datetime.utcnow()\n self.logger.debug('Updating [%s]...', entity.uuid)\n db.session.query(Entity).filter(Entity.uuid == entity.uuid) \\\n .update({'name': normalized_name, 'details': details,\n 'updated': updated})\n db.session.commit()\n\n\n\n def stage_disclosures(self):\n disclosures = db.session(Disclosure).yield_per(5000) \\\n .enable_eagerloads(False)\n entities = db.session(Entity).yield_per(5000) \\\n .enable_eagerloads(False) \\\n .filter(Entity.type.ilike('%real estate%'))\n for disclosure in disclosures:\n updated = datetime.utcnow()\n contributor = disclosure.contributor\n\n\n def stage_filers(self):\n pass\n\n\n def stage_link(self):\n pass\n" }, { "alpha_fraction": 0.7142857313156128, "alphanum_fraction": 0.7142857313156128, "avg_line_length": 16.5, "blob_id": "5cec00a850c15e5ba36580ef131f176bbf2f4c43", "content_id": "7050780d75cdd1343d3e4901a6646ad7a1958f63", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 35, "license_type": "no_license", "max_line_length": 25, "num_lines": 2, "path": "/README.md", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "# nys_re\nNYS Real Estate Who's Who\n" }, { "alpha_fraction": 0.7198879718780518, "alphanum_fraction": 0.7198879718780518, "avg_line_length": 20.636363983154297, "blob_id": "b575c597dcc47d84105015c1db1d9d74cd308024", "content_id": "067ba8ec59459ad148c7328a4b4d3c4705432428", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 714, "license_type": "no_license", "max_line_length": 45, "num_lines": 33, "path": "/app/__init__.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "import logging\n\nfrom flask import Flask, request, current_app\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\nfrom flask_bootstrap import Bootstrap\n\nfrom config import Config\n\ndb = SQLAlchemy()\nmigrate = Migrate()\nbootstrap = Bootstrap()\n\n\ndef create_app(config_class=Config):\n \"\"\" \"\"\"\n app = Flask(__name__)\n app.config.from_object(config_class)\n\n db.init_app(app)\n migrate.init_app(app, db)\n bootstrap.init_app(app)\n\n from app import models\n\n from app.routes import bp as main_bp\n app.register_blueprint(main_bp)\n\n stream_handler = logging.StreamHandler()\n stream_handler.setLevel(logging.INFO)\n app.logger.addHandler(stream_handler)\n\n return app\n" }, { "alpha_fraction": 0.6690475940704346, "alphanum_fraction": 0.6738095283508301, "avg_line_length": 27, "blob_id": "985051b12ab922b0ba5146258daff92339130841", "content_id": "7f632ad2afc731f351709848a32ce6f3cc978d55", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 420, "license_type": "no_license", "max_line_length": 65, "num_lines": 15, "path": "/app/config.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "import os\n\nfrom dotenv import load_dotenv\n\nbasedir = os.path.abspath(os.path.dirname(__file__))\nload_dotenv(os.path.join('/home/anabase/nyd/nys-re/.env'))\n\n\nclass Config:\n SECRET_KEY = os.environ.get('SECRET_KEY') or 'a;sdlfkja3lkja;sdlfkjas;dlkf'\n SQLALCHEMY_DATABASE_URI = os.environ.get('DATABASE_URL') or \\\n 'sqlite:///' + os.path.join(basedir, 'app.db')\n SQLALCHEMY_TRACK_MODIFICATIONS = False\n\n ENTITIES_PER_PAGE = 25\n" }, { "alpha_fraction": 0.6524448990821838, "alphanum_fraction": 0.6529242396354675, "avg_line_length": 33.196720123291016, "blob_id": "059c31b6bea340ff4a1820c8741a084d332e8bd6", "content_id": "18f3f3e0e12a3fdc72ae92896c9fc7198c7eb775", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2086, "license_type": "no_license", "max_line_length": 80, "num_lines": 61, "path": "/app/models.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "from datetime import datetime\n\nfrom app import db\n\n\nclass ToDictMixin:\n @property\n def to_dict(self):\n return {c.name.split('.')[-1]: getattr(self, c.name) \\\n for c in self.__table__.columns}\n\n\nclass Entity(ToDictMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.Text)\n uuid = db.Column(db.Text)\n type = db.Column(db.Text)\n sector = db.Column(db.Text)\n timestamp = db.Column(db.DateTime, default=datetime.utcnow)\n updated = db.Column(db.DateTime, default=datetime.utcnow)\n details = db.Column(db.JSON)\n\n\nclass Link(ToDictMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n node_a = db.Column(db.Text)\n node_b = db.Column(db.Text)\n type = db.Column(db.Text)\n details = db.Column(db.JSON)\n updated = db.Column(db.DateTime, default=datetime.utcnow)\n\n\nclass Filer(ToDictMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n created = db.Column(db.DateTime, index=True, default=datetime.utcnow)\n run_id = db.Column(db.Text)\n uuid = db.Column(db.Text, unique=True)\n filer_id = db.Column(db.Text, index=True, unique=True)\n name = db.Column(db.Text, index=True)\n address = db.Column(db.Text, index=True)\n status = db.Column(db.Text)\n disclosures = db.relationship('Disclosure', backref='payer', lazy='dynamic')\n\n def __repr__(self):\n return '<Filer {}>'.format(self.filer_id)\n\n\nclass Disclosure(ToDictMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n created = db.Column(db.DateTime, index=True, default=datetime.utcnow)\n run_id = db.Column(db.Text)\n uuid = db.Column(db.Text, unique=True)\n filer_id = db.Column(db.Text)\n filer_uuid = db.Column(db.Text, db.ForeignKey('filer.uuid'), index=True)\n filing_year = db.Column(db.Integer)\n contributor = db.Column(db.Text, index=True)\n address = db.Column(db.Text, index=True)\n amount = db.Column(db.Float)\n date = db.Column(db.Date)\n report_code = db.Column(db.Text)\n schedule = db.Column(db.Text)\n" }, { "alpha_fraction": 0.599078357219696, "alphanum_fraction": 0.6006144285202026, "avg_line_length": 38.85714340209961, "blob_id": "9ceffced806f1801fc4f689f98cbd40032f4b779", "content_id": "05f06f71c5c9cebf4acc8f9be7a3b059570b001e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1953, "license_type": "no_license", "max_line_length": 87, "num_lines": 49, "path": "/app/routes.py", "repo_name": "mudsill/nys-re", "src_encoding": "UTF-8", "text": "from flask import Blueprint, render_template, current_app, request, url_for\nfrom flask_paginate import Pagination, get_page_parameter\nfrom sqlalchemy import func, or_\n\nfrom app import db\nfrom app.models import Entity, Link\n\nbp = Blueprint('main', __name__)\n\n\[email protected]('/')\[email protected]('/index')\ndef index():\n q = request.args.get('q')\n search = q and q is not None\n\n page = request.args.get(get_page_parameter(), default=1, type=int)\n entities = db.session.query(Entity.name, Entity.uuid, Entity.type)\\\n .paginate(page, current_app.config['ENTITIES_PER_PAGE'], False).items\n total = db.session.query(func.count(Entity.uuid)).scalar()\n pagination = Pagination(page=page, total=total, search=search,\n record_name='entities',\n per_page=current_app.config['ENTITIES_PER_PAGE'],\n bs_version=4,\n inner_window=5)\n return render_template('index.html', title='Home', entities=entities,\n pagination=pagination)\n\n\[email protected]('/entity/<string:e_uuid>')\ndef profile(e_uuid):\n entity = Entity.query.filter_by(uuid=e_uuid).first()\n links = Link.query.filter(or_(Link.node_a == e_uuid, Link.node_b == e_uuid)).all()\n links_with_names = []\n for link in links:\n link = link.to_dict\n link['node_a'] = db.session.query(Entity.name, Entity.uuid) \\\n .filter(Entity.uuid == link.get('node_a')).first().name\n link['node_b'] = db.session.query(Entity.name, Entity.uuid) \\\n .filter(Entity.uuid == link.get('node_b')).first().name\n links_with_names.append(link)\n title = 'Profile: %s' % entity.name\n return render_template('profile.html', title=title, entity=entity,\n links=links_with_names)\n\n\[email protected]('/about')\ndef about():\n return render_template('about.html', title='About')\n" } ]

8

PaoloTCS/DjangoWebProjectGitHubTCS

https://github.com/PaoloTCS/DjangoWebProjectGitHubTCS

931dc640bcd0ecd4fdd0b5182a14127a73e026f8

0ee4f85fd577d88523e35547f9ddad59ef141d24

308c751c864c966a530ded15b4ae1626d338e944

refs/heads/master

2019-07-10T06:18:37.542548

2017-05-12T11:29:53

90,991,311

0

null

[ { "alpha_fraction": 0.7446808218955994, "alphanum_fraction": 0.7446808218955994, "avg_line_length": 14.666666984558105, "blob_id": "60c337cdc2c194a3532ecc9254bd11a609594479", "content_id": "af4e4d0f6e70203f93cd9bd951a3cbadc14db72d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 47, "license_type": "no_license", "max_line_length": 38, "num_lines": 3, "path": "/DjangoWebProjectGitHubTCS/DjangoWebProjectGitHubTCS/__init__.py", "repo_name": "PaoloTCS/DjangoWebProjectGitHubTCS", "src_encoding": "UTF-8", "text": "\"\"\"\nPackage for DjangoWebProjectGitHubTCS.\n\"\"\"\n" } ]

1

nunixnunix04/league-table-maker

https://github.com/nunixnunix04/league-table-maker

4b529637c06839a679ddffd4386b575e93284ef9

ad63a4fa72e30e589b19d39b632926413c4fe268

b5eb75b47c4110f1832ecb630294f42be98f40f2

refs/heads/master

2020-12-05T02:55:58.212749

2020-01-05T23:25:24

231,989,190

0

null

[ { "alpha_fraction": 0.7537155151367188, "alphanum_fraction": 0.7664543390274048, "avg_line_length": 46.099998474121094, "blob_id": "77d651cedb962e23cfdb2947a0cd19f3facb6467", "content_id": "f8cc29ea4db0c438503a6832b26e2c463d844190", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 471, "license_type": "no_license", "max_line_length": 104, "num_lines": 10, "path": "/README.md", "repo_name": "nunixnunix04/league-table-maker", "src_encoding": "UTF-8", "text": "# League Table Maker\nMakes a detailed league table csv file from soccer season data.\n\n# How to Use\n1) Get the csv files from the league you want from the following website: https://datahub.io/sports-data\n2) Place the csv files in a folder named after the country of that league.\n3) Open up the file standings.py\n4) Change the variables 'country' and 'season' to your choosing.\n5) Run the program.\n6) The csv table has been created under the same folder as your raw data.\n" }, { "alpha_fraction": 0.605980396270752, "alphanum_fraction": 0.6189467906951904, "avg_line_length": 24.815603256225586, "blob_id": "372e88c44c59ca8e9f067da94e80213da607ca36", "content_id": "a618eeb01d613062879bbe4b3fa69f4ced3f7a9a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3779, "license_type": "no_license", "max_line_length": 114, "num_lines": 141, "path": "/standings.py", "repo_name": "nunixnunix04/league-table-maker", "src_encoding": "UTF-8", "text": "import csv\r\n\r\n# SETTINGS\r\ncountry = \"spain\"\r\nseason = \"1819\" # Use the following format (eg: 2018-19 --> \"1819\")\r\n\r\n\r\nfilename = country + \"/\" + \"season-\" + season + \"_csv.csv\"\r\n\r\n# Creates team\r\ndef create_team_list(reader):\r\n\tteam_list = []\r\n\tfor row in reader:\r\n\t\tteam_name = row[2]\r\n\t\tif team_name not in team_list:\r\n\t\t\tteam_list.append(team_name)\r\n\tdel team_list[0]\r\n\treturn team_list\r\n\r\nclass Team():\r\n\t\r\n\tdef __init__(self, team, reader):\r\n\t\tself.team = team\r\n\t\tself.reader = reader\r\n\t\tself.matches = self.get_list_of_matches()\r\n\t\tself.wins = self.get_wins()\r\n\t\tself.draws = self.get_draws()\r\n\t\tself.losses = self.get_losses()\r\n\t\tself.points = self.get_points()\r\n\t\tself.goals_for = self.get_goals_for()\r\n\t\tself.goals_against = self.get_goals_against()\r\n\t\tself.goal_difference = self.get_goal_difference()\r\n\r\n\tdef get_list_of_matches(self):\r\n\t\tlist_of_matches = []\r\n\t\tfor row in self.reader:\r\n\t\t\tif (self.team == row[2]):\r\n\t\t\t\trow.append(\"H\")\r\n\t\t\t\tlist_of_matches.append(row)\r\n\t\t\telif (self.team == row[3]):\r\n\t\t\t\trow.append(\"A\")\r\n\t\t\t\tlist_of_matches.append(row)\r\n\t\treturn list_of_matches\r\n\r\n\tdef get_wins(self):\r\n\t\twins = 0\r\n\t\tfor match in self.matches:\r\n\t\t\tif (match[6] == match[-1]):\r\n\t\t\t\twins += 1\r\n\t\treturn wins\r\n\r\n\tdef get_draws(self):\r\n\t\tdraws = 0\r\n\t\tfor match in self.matches:\r\n\t\t\tif match[6] == \"D\":\r\n\t\t\t\tdraws += 1\r\n\t\treturn draws\r\n\r\n\tdef get_losses(self):\r\n\t\tlosses = 0\r\n\t\tfor match in self.matches:\r\n\t\t\tif (match[6] != match[-1]) and (match[6] != \"D\"):\r\n\t\t\t\tlosses += 1\r\n\t\treturn losses\r\n\r\n\tdef get_points(self):\r\n\t\tpoints = (3 * self.wins) + (self.draws)\r\n\t\treturn points\r\n\r\n\r\n\tdef get_goals_for(self):\r\n\t\tgoals_for = 0\r\n\t\tfor match in self.matches:\r\n\t\t\tif (match[-1] == \"H\"):\r\n\t\t\t\tgoals_for += int(match[4])\r\n\t\t\telif (match[-1] == \"A\"):\r\n\t\t\t\tgoals_for += int(match[5])\r\n\t\treturn goals_for\r\n\r\n\tdef get_goals_against(self):\r\n\t\tgoals_against = 0\r\n\t\tfor match in self.matches:\r\n\t\t\tif (match[-1] == \"H\"):\r\n\t\t\t\tgoals_against += int(match[5])\r\n\t\t\telif (match[-1] == \"A\"):\r\n\t\t\t\tgoals_against += int(match[4])\r\n\t\treturn goals_against\r\n\r\n\tdef get_goal_difference(self):\r\n\t\tgoal_difference = self.goals_for - self.goals_against\r\n\t\treturn goal_difference\r\n\t\r\n\tdef print_list_of_matches(self):\r\n\t\tfor match in self.matches:\r\n\t\t\tprint(match[2] + \" \" + match[4] + \" - \" + match[5] + \" \" + match[3])\r\n\r\n\tdef print_season_summary(self):\r\n\t\tprint(\"Team: \" + self.team)\r\n\t\tprint(\"\\tGames Played: \" + str(len(self.matches)))\r\n\t\tprint(\"\\tWins: \" + str(self.wins))\r\n\t\tprint(\"\\tDraws: \" + str(self.draws))\r\n\t\tprint(\"\\tLosses: \" + str(self.losses))\r\n\t\tprint(\"\\tGoals For: \" + str(self.goals_for))\r\n\t\tprint(\"\\tGoals Against: \" + str(self.goals_against))\r\n\t\tprint(\"\\tGoal Difference: \" + str(self.goal_difference))\r\n\r\nleague = {}\r\nteam_objects = []\r\nwith open(filename) as f:\r\n\treader = csv.reader(f)\r\n\tteam_list = create_team_list(reader)\r\n\r\nfor team in team_list:\r\n\twith open(filename) as f:\r\n\t\treader = csv.reader(f)\r\n\t\tleague[team] = Team(team, reader)\r\n\t\tteam_objects.append(league[team])\r\n\r\nteam_objects_sorted = sorted(team_objects, key=lambda x: (x.points, x.goal_difference, x.goals_for), reverse=True)\r\n\r\nleague_table = [[\"#\",\"Team\",\"GP\",\"W\",\"D\",\"L\",\"GF\",\"GA\",\"GD\",\"Points\"]]\r\n\r\nfor i in range(len(team_objects_sorted)):\r\n\tteam_row = [\r\n\ti+1,\r\n\tteam_objects_sorted[i].team,\r\n\tlen(team_objects_sorted[i].matches),\r\n\tteam_objects_sorted[i].wins,\r\n\tteam_objects_sorted[i].draws,\r\n\tteam_objects_sorted[i].losses,\r\n\tteam_objects_sorted[i].goals_for,\r\n\tteam_objects_sorted[i].goals_against,\r\n\tteam_objects_sorted[i].goal_difference,\r\n\tteam_objects_sorted[i].points\r\n\t]\r\n\tleague_table.append(team_row)\r\n\r\noutfile_name = country + \"/\" + \"league_table_\" + filename[-12:-8] + \".csv\"\r\nwith open(outfile_name,\"w\",newline=\"\") as f:\r\n\twriter = csv.writer(f)\r\n\twriter.writerows(league_table)" } ]

2

beatcode/neuro_network

https://github.com/beatcode/neuro_network

bd0d6a41e53c95e26e2b024293c521a76edfa39a

7017b486223250f63c4dbc762bcdf0b0b06b63f4

1d00c5af1f3572c7606c32167260e8002943af27

refs/heads/master

2021-01-12T11:32:54.807627

2016-11-26T22:57:01

72,949,980

0

null

[ { "alpha_fraction": 0.6774193644523621, "alphanum_fraction": 0.6774193644523621, "avg_line_length": 21.14285659790039, "blob_id": "b43cf755b91dd12593489c086ac67d9e8b5befb6", "content_id": "7fc73f5fb63f1df0c1d4926ae4d611c3d2d67c22", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "PHP", "length_bytes": 155, "license_type": "no_license", "max_line_length": 89, "num_lines": 7, "path": "/ajax/PythonCall_play.php", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "<?php\n\n$input = $_POST['input'];\n$command = escapeshellcmd(\"python /var/www/html/neuronal_network/python/play.py $input\");\n$temp = passthru($command);\n\n?>\n" }, { "alpha_fraction": 0.5134692192077637, "alphanum_fraction": 0.5400437116622925, "avg_line_length": 24.677570343017578, "blob_id": "5aceca73b8351c1df467feb4352a2f1e2305c358", "content_id": "68398ea0b08eef46b58f2c494c7de18465a70aa2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 5497, "license_type": "no_license", "max_line_length": 183, "num_lines": 214, "path": "/js/script.js", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "$(document).ready(function() {\n $(\".chk\").click(function() {\n if ($(\".chk\").is(\":checked\")) {\n alert('In Entwicklung');\n \n clear_content();\n } else {\n weights()\n clear_content();\n }\n });\n});\n\nfunction delete_file(){\n $.ajax({\n type: 'POST',\n url: \"/neuronal_network/ajax/PythonCall_clear.php\",\n success: function(data) {\n alert(\"Gewichtungen gelösccht\");\n clear_content();\n }\n });\n}\n\nfunction weights(){\n $.ajax({\n type: 'POST',\n url: \"/neuronal_network/ajax/PythonCall_weights.php\",\n success: function(data) {\n alert(\"Gewichtungen errechnet\");\n // set_output(data);\n }\n });\n}\n\nfunction python() {\n\n set_player();\n\n if ($(\".chk\").is(\":checked\")) {\n\n $.ajax({\n type: 'POST',\n url: \"/neuronal_network/ajax/PythonCall_Train.php\",\n data: \"input=\" + $(\"#input\").val() + \"&output=\" + $(\"#output\").val(),\n success: function(data) {\n\n // set_output(data);\n }\n });\n\n } else {\n\n $.ajax({\n type: 'POST',\n url: \"/neuronal_network/ajax/PythonCall_play.php\",\n data: \"input=\" + ReadInput(),\n success: function(data) {\n $(\"#calc\").val(data);\n $(\"#input\").val(data);\n set_output(data);\n }\n });\n }\n}\n\n\nfunction set_player() {\n\n player = $(\"#player\").val();\n\n if (player == \"1\") {\n $(\"#player\").val(\"2\");\n } else if (player == \"2\") {\n $(\"#player\").val(\"1\");\n }\n}\n\nfunction ReadInput() {\n\n feld1 = document.getElementById(\"1\").value;\n feld2 = document.getElementById(\"2\").value;\n feld3 = document.getElementById(\"3\").value;\n feld4 = document.getElementById(\"4\").value;\n feld5 = document.getElementById(\"5\").value;\n feld6 = document.getElementById(\"6\").value;\n feld7 = document.getElementById(\"7\").value;\n feld8 = document.getElementById(\"8\").value;\n feld9 = document.getElementById(\"9\").value;\n\n var result = [feld1, feld2, feld3, feld4, feld5, feld6, feld7, feld8, feld9];\n return result;\n}\n\n\nfunction clear_content() {\n\n $(\"#input\").val(\"0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5\");\n $(\"#output\").val(\"\");\n $(\"#calc\").val(\"\");\n $(\"#zug\").val(\"0\");\n $(\"#player\").val(\"1\");\n\n var input = \"0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5\";\n set_output(input);\n\n}\n\n\nfunction set_output(val) {\n\n var output = val;\n output = output.replace(/\\s/g, \"\");\n var splits = output.split(\",\");\n\n setval_compute('0', '0', '1', splits[0]);\n setval_compute('0', '1', '2', splits[1]);\n setval_compute('0', '2', '3', splits[2]);\n setval_compute('1', '0', '4', splits[3]);\n setval_compute('1', '1', '5', splits[4]);\n setval_compute('1', '2', '6', splits[5]);\n setval_compute('2', '0', '7', splits[6]);\n setval_compute('2', '1', '8', splits[7]);\n setval_compute('2', '2', '9', splits[8]);\n}\n\n\nfunction get_zug() {\n return $(\"#zug\").val();\n}\n\nfunction count_Zug() {\n\n counter = $(\"#zug\").val();\n counter++;\n $(\"#zug\").val(counter);\n}\n\nfunction get_value() {\n var player = $(\"#player\").val();\n if (player == \"1\") {\n return \"1.0\";\n } else if (player == \"2\") {\n return \"0.0\";\n }\n\n}\n\n\n\nfunction setval_human(row, cell, feld, wert) {\n\n var wert = get_value();\n\n act_field = document.getElementById(feld).value;\n\n // training\n if ($(\".chk\").is(\":checked\")) {\n\n\n // Play\n if (act_field != '0.5') {\n\n } else if (wert == '0.0') {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<h2><i class=\"fa fa-circle-thin\"></i></h2> <input type = \"hidden\" id=\"' + feld + '\" value=\"' + wert + '\" >';\n } else if (wert == '1.0') {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<h2><i class=\"fa fa-times\"></i></h2> <input type = \"hidden\" id=\"' + feld + '\" value=\"' + wert + '\" >';\n } else {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<input type = \"hidden\" id=\"' + feld + '\" value=\"0.5\" >';\n }\n\n // aktueller output wird für den nächsten zug zum input\n if (get_zug() != 0) {\n $(\"#input\").val($(\"#output\").val());\n }\n\n // Output wird neu vom aktuellers Situation gelesen.\n $(\"#output\").val(ReadInput());\n count_Zug();\n\n // An Python senden\n python()\n\n } else {\n\n // Play\n if (act_field != '0.5') {\n\n } else if (wert == '0.0') {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<h2><i class=\"fa fa-circle-thin\"></i></h2> <input type = \"hidden\" id=\"' + feld + '\" value=\"' + wert + '\" >';\n } else if (wert == '1.0') {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<h2><i class=\"fa fa-times\"></i></h2> <input type = \"hidden\" id=\"' + feld + '\" value=\"' + wert + '\" >';\n } else {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<input type = \"hidden\" id=\"' + feld + '\" value=\"0.5\" >';\n }\n\n $(\"#output\").val(ReadInput());\n count_Zug();\n set_player();\n python();\n }\n\n}\n\nfunction setval_compute(row, cell, feld, wert) {\n if (wert == 0.0) {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<h2><i class=\"fa fa-circle-thin\"></i></h2> <input type = \"hidden\" id=\"' + feld + '\" value=\"' + wert + '\" >';\n } else if (wert == 1.0) {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<h2><i class=\"fa fa-times\"></i></h2> <input type = \"hidden\" id=\"' + feld + '\" value=\"' + wert + '\" >';\n } else {\n document.getElementById(\"board\").rows[row].cells[cell].innerHTML = '<input type = \"hidden\" id=\"' + feld + '\" value=\"0.5\" >';\n }\n\n}" }, { "alpha_fraction": 0.6624277234077454, "alphanum_fraction": 0.6965317726135254, "avg_line_length": 38.1136360168457, "blob_id": "07a284ba6b6e8dbe52b1ea0343d3280705c273a2", "content_id": "2ff1480c22b763595a448a2754656bb3277a70a5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1730, "license_type": "no_license", "max_line_length": 144, "num_lines": 44, "path": "/python/SaveWeights.py", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "#!/usr/bin/python\n# -*- coding: ascii -*-\n\n#Quelle: https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.pea0nt22f\nfrom numpy import exp, array, random, dot\nimport numpy as np\nimport sys, os\nfrom NeuralNetwork import NeuralNetwork\nfrom NeuralNetwork import NeuronLayer\n\nif __name__ == \"__main__\":\n\n #Seed the random number generator\n random.seed(1)\n\n # Create layer 1 (4 neurons, each with 3 inputs)\n layer1 = NeuronLayer(10, 9)\n\n # Create layer 2 (a single neuron with 4 inputs)\n layer2 = NeuronLayer(9, 10)\n\n # Parameter Uebergabe von PHP Script\n input_parameter = \"1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0\"\n \n # Parameter konvertieren I\n input_int = input_parameter.split(',')\n\n # Parameter konvertieren II\n input = np.array(input_int, dtype=np.float32)\n\n # Combine the layers to create a neural network\n neural_network = NeuralNetwork(layer1, layer2, input)\n \n # Lade die Input und Output Arrays\n training_set_inputs = np.loadtxt('/var/www/html/neuronal_network/data/input_1.txt', delimiter=\",\")\n training_set_outputs = np.loadtxt('/var/www/html/neuronal_network/data/output_1.txt', delimiter=\",\")\n \n # Train the neural network using the training set.\n # Do it x times and make small adjustments each time.\n neural_network.train(training_set_inputs, training_set_outputs, 350000)\n\n # Erstelle die neuen Array mit den Gewichtungen\n np.savetxt('/var/www/html/neuronal_network/data/weights_layer1.txt', neural_network.get_weights('1'), delimiter=\",\")\n np.savetxt('/var/www/html/neuronal_network/data/weights_layer2.txt', neural_network.get_weights('2'), delimiter=\",\")\n \n " }, { "alpha_fraction": 0.6557527780532837, "alphanum_fraction": 0.6759486198425293, "avg_line_length": 41.98684310913086, "blob_id": "41cf432f1cc8cc437e669a381854cd76adfe10f4", "content_id": "71e91dd4b29836332f00c10cb5e1bf772c494783", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3268, "license_type": "no_license", "max_line_length": 144, "num_lines": 76, "path": "/python/NeuralNetwork.py", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "#!/usr/bin/python\n# -*- coding: ascii -*-\n\n#Quelle: https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.pea0nt22f\nfrom numpy import exp, array, random, dot\nimport numpy as np\nimport sys, os\n\n\nclass NeuronLayer():\t\n\t\tdef __init__(self, number_of_neurons, number_of_inputs_per_neuron):\n \t\t\tself.synaptic_weights = 2 * random.random((number_of_inputs_per_neuron, number_of_neurons)) - 1\n\nclass NeuralNetwork():\n def __init__(self, layer1, layer2, input):\n\n self.input = input\n self.layer1 = layer1\n self.layer2 = layer2\n\n # The Sigmoid function, which describes an S shaped curve.\n # We pass the weighted sum of the inputs through this function to\n # normalise them between 0 and 1.\n def __sigmoid(self, x):\n return 1 / (1 + exp(-x))\n\n # The derivative of the Sigmoid function.\n # This is the gradient of the Sigmoid curve.\n # It indicates how confident we are about the existing weight.\n def __sigmoid_derivative(self, x):\n return x * (1 - x)\n\n\t\t\n # We train the neural network through a process of trial and error.\n # Adjusting the synaptic weights each time.\n def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):\n\t\t\t\n for iteration in xrange(number_of_training_iterations):\n # Pass the training set through our neural network\n output_from_layer_1, output_from_layer_2 = self.think(training_set_inputs)\n\n # Calculate the error for layer 2 (The difference between the desired output\n # and the predicted output).\n layer2_error = training_set_outputs - output_from_layer_2\n layer2_delta = layer2_error * self.__sigmoid_derivative(output_from_layer_2)\n\n # Calculate the error for layer 1 (By looking at the weights in layer 1,\n # we can determine by how much layer 1 contributed to the error in layer 2).\n layer1_error = layer2_delta.dot(self.layer2.synaptic_weights.T)\n layer1_delta = layer1_error * self.__sigmoid_derivative(output_from_layer_1)\n\n # Calculate how much to adjust the weights by\n layer1_adjustment = training_set_inputs.T.dot(layer1_delta)\n layer2_adjustment = output_from_layer_1.T.dot(layer2_delta)\n\n # Adjust the weights.\n self.layer1.synaptic_weights += layer1_adjustment\n self.layer2.synaptic_weights += layer2_adjustment\n\n\n # The neural network thinks.\n def think(self, inputs):\n\toutput_from_layer1 = self.__sigmoid(dot(inputs, self.layer1.synaptic_weights))\n\toutput_from_layer2 = self.__sigmoid(dot(output_from_layer1, self.layer2.synaptic_weights))\n\treturn output_from_layer1, output_from_layer2\n\n def set_weights(self):\n\tself.layer1.synaptic_weights = np.loadtxt('/var/www/html/neuronal_network/data/weights_layer1.txt', delimiter=\",\")\n\tself.layer2.synaptic_weights = np.loadtxt('/var/www/html/neuronal_network/data/weights_layer2.txt', delimiter=\",\")\n\t\t\n\t\t# The neural network prints its weights\n def get_weights(self, layer):\n \tif layer == '1':\n return self.layer1.synaptic_weights\n \telif layer == '2':\n\t return self.layer2.synaptic_weights\n\n" }, { "alpha_fraction": 0.6958299279212952, "alphanum_fraction": 0.7424366474151611, "avg_line_length": 39.766666412353516, "blob_id": "8b4008f4d1bba599f71c5f92e6a14cee695a5e6b", "content_id": "740c6b8df4ad2bb815dee68564be9f6f12d807c3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 1231, "license_type": "no_license", "max_line_length": 169, "num_lines": 30, "path": "/README.md", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "# neuroal_network\n\nZiel: Erste Gehversuche mit dem Einsatz eines einfachen Neuronalen Netzwerks am Beispile Tic Tac Toe. \n\nLive Demo\n=========\nhttp://107.170.159.73/neuronal_network/web.php\n\nEinstellungen\n=============\n9 Input | 9 Output | 1 Hiddenschicht mit 10 Neuronen | 3 ausgeprägte Games in Form von Trainingssätzen\n\nTodo (In Entwicklung)\n====\nEin Trainingsmodus mit welchem dem Netz eigene Trainingssätze mitgegeben werden kann. \n - Input.txt | Output.txt und Gewichtungen.txt löschen.\n - Spieler spielt im Trainingsmodus für beide Spieler einen Zug und sendet diesen an das Netzwerk.\n - Jeder gespielte Zug soll durch das senden an das Netzwerk als Trainingssatz in 2 txt Dateien Input.txt und Output.txt angefügt werden.\n - Das Netzwerk soll im Anschluss alle Traininssätze laden und sich trainieren. Die errechneten Gewichtungen sollen in der Datei Gewichtungen.txt abgespecheicht werden. \n \n\nPython Testen\n=============\n$ python tic.py \"0.5,0,0,0,0,0,0,0,0\"\n\nSollte Fehlerfrei 1.0, 1.0, 0.0, 0.1, 0.0, 0.3, 1.0, 1.0, 1.0 zurück geben.\n\nQuelle Python Code \n==================\nhttps://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.pea0nt22f\n" }, { "alpha_fraction": 0.7080292105674744, "alphanum_fraction": 0.7080292105674744, "avg_line_length": 18.571428298950195, "blob_id": "14f2d3cd0e61d6bf8e405cd9e2b7988750411451", "content_id": "aa6c34ef440e0e6ac86a01c8256b989d1f07b738", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "PHP", "length_bytes": 137, "license_type": "no_license", "max_line_length": 98, "num_lines": 7, "path": "/ajax/PythonCall_weights.php", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "<?php\n\n\n$command = escapeshellcmd(\"/usr/bin/python /var/www/html/neuronal_network/python/SaveWeights.py\");\n$output = exec($command);\n\n?>\n" }, { "alpha_fraction": 0.644859790802002, "alphanum_fraction": 0.644859790802002, "avg_line_length": 24.625, "blob_id": "49333021f56206ea9d554f24b87eef65f68f1e78", "content_id": "c54360a1ae3697ec3436d8512fc9d212e2142461", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "PHP", "length_bytes": 214, "license_type": "no_license", "max_line_length": 113, "num_lines": 8, "path": "/ajax/PythonCall_Train.php", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "<?php\n\n$input = $_POST['input'];\n$output = $_POST['output'];\n$command = escapeshellcmd(\"/usr/bin/python /var/www/html/neuronal_network/python/SetTraining.py $input $output\");\n$output = exec($command);\n\n?>\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5831601023674011, "alphanum_fraction": 0.5914760828018188, "avg_line_length": 26.514286041259766, "blob_id": "9ac9a4eee5352d4d05ace747310d2f2407594367", "content_id": "0731741b778ed0ce0dc59e6fff2b22fd2d531f54", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 962, "license_type": "no_license", "max_line_length": 86, "num_lines": 35, "path": "/python/SetTraining.py", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "#!/usr/bin/python\n# -*- coding: ascii -*-\n\nfrom numpy import exp, array, random, dot\nimport numpy as np\nimport sys, os\n\nif __name__ == \"__main__\":\n\n\n \n # # Parameter Uebergabe von PHP Script\n input_parameter = sys.argv[1]\n output_parameter = sys.argv[2]\n\n \n # # Parameter konvertieren I\n input_int = input_parameter.split(',')\n output_int = output_parameter.split(',')\n\n \n # # Parameter konvertieren II\n input = np.array(input_int, dtype=np.float32)\n output = np.array(output_int, dtype=np.float32)\n\n \n \n f_handleI = file('/var/www/html/neuronal_network/data/input_1.txt', 'a')\n np.savetxt(f_handleI, np.matrix(input), delimiter=\",\", fmt=\"%s\", newline='\\n')\n f_handleI.close()\n\n\n f_handleO = file('/var/www/html/neuronal_network/data/output_1.txt', 'a')\n np.savetxt(f_handleO, np.matrix(output), delimiter=\",\", fmt=\"%s\", newline='\\n')\n f_handleO.close()" }, { "alpha_fraction": 0.7190082669258118, "alphanum_fraction": 0.7190082669258118, "avg_line_length": 29.25, "blob_id": "22847516f90ad2fa7a6c9ad12ecdcd837e24b19b", "content_id": "ea4f74d5001f468292b19743e5732ec45e7c9df0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "PHP", "length_bytes": 121, "license_type": "no_license", "max_line_length": 83, "num_lines": 4, "path": "/ajax/PythonCall_clear.php", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "<?php\n$command = escapeshellcmd(\"python /var/www/html/neuronal_network/python/clear.py\");\n$temp = passthru($command);\n?>\n" }, { "alpha_fraction": 0.6517571806907654, "alphanum_fraction": 0.6757188439369202, "avg_line_length": 33.66666793823242, "blob_id": "54fd674db1d6ffa09227ba6fabc7b958c983715f", "content_id": "74fd54365df26c74652eecd70e0b8ac9df39bd61", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 626, "license_type": "no_license", "max_line_length": 144, "num_lines": 18, "path": "/python/clear.py", "repo_name": "beatcode/neuro_network", "src_encoding": "UTF-8", "text": "#!/usr/bin/python\n# -*- coding: ascii -*-\n\n#Quelle: https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.pea0nt22f\nfrom numpy import exp, array, random, dot\nimport numpy as np\nimport sys, os\n\nif __name__ == \"__main__\":\n\n f = open('/var/www/html/neuronal_network/data/input_1.txt', 'w')\n f.close()\n f = open('/var/www/html/neuronal_network/data/output_1.txt', 'w')\n f.close()\n f = open('/var/www/html/neuronal_network/data/weights_layer1.txt', 'w')\n f.close()\n f = open('/var/www/html/neuronal_network/data/weights_layer2.txt', 'w')\n f.close()\n " } ]

10

fstrub95/writers

https://github.com/fstrub95/writers

474b9fa31fb4bce8c0352c9e4928a491a5dfc73b

19834487453f596c23d22f9cf5f5e7f60309fb2a

89dc5bd11ab772bd97eee0a41d67ebe0cd0b55fa

refs/heads/master

2020-03-23T18:39:50.114265

2018-07-07T00:08:32

null

0

null

[ { "alpha_fraction": 0.4369220733642578, "alphanum_fraction": 0.45475560426712036, "avg_line_length": 41.91304397583008, "blob_id": "328f7e5588d9d8c15d3e9eb92186d3b1f9530001", "content_id": "d911d8a775b65b7010887a1926fd20e00f5aecdd", "detected_licenses": [ "Apache-2.0" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3028, "license_type": "permissive", "max_line_length": 111, "num_lines": 69, "path": "/ang_writer/Methods/write_core_ang.py", "repo_name": "fstrub95/writers", "src_encoding": "UTF-8", "text": "import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport re\r\n\r\n\r\nclass AngWriter:\r\n def write_header(self, ang_in, ang_out):\r\n with open(ang_in, 'r') as input_ang, open(ang_out, 'w') as output_ang:\r\n for line in input_ang:\r\n if line.startswith(\"#\"):\r\n output_ang.write(line)\r\n\r\n def replace_phase(self, segment, ang_in, ang_out):\r\n seg = plt.imread(segment)\r\n seg_c = np.concatenate(seg, axis=0) # convert into a column array\r\n phase = np.copy(seg_c).astype(int)\r\n phase[np.where(seg_c == 255)] = 1\r\n phase[np.where(seg_c < 255)] = 2\r\n ang = pd.read_csv(ang_in, delim_whitespace=True, comment='#', names={'euler1', 'euler2', 'euler3', 'x',\r\n 'y', 'iq', 'ci', 'fit', 'phase',\r\n 'col9', 'col10', 'col11', 'col12',\r\n 'col13'})\r\n ang.iloc[ang_in[:, 6] < 0] = 0 # remove any negative confidence index\r\n ang.iloc[:, 7] = phase\r\n ang.to_csv(ang_out, index=False, header=False, sep='\\t', mode='a', float_format='%.5f')\r\n\r\n\r\nclass Phase:\r\n def __init__(self, number):\r\n self.number = number\r\n self.materialName = ''\r\n self.formula = ''\r\n self.info = ''\r\n self.symmetry = 0\r\n self.latticeConstants = [0] * 6 # 3 constants & 3 angles\r\n self.numberFamilies = []\r\n self.hklFamilies = []\r\n self.elasticConstants = []\r\n self.categories = []\r\n\r\n\r\nclass PhaseHeader:\r\n def _init_(self, filename_in, filename_out): # read and store info about input .ang file\r\n with open(filename_in) as file_in:\r\n for line in file_in:\r\n if line.startswith(\"#\"):\r\n tokens = re.split('\\s+', line.strip())\r\n if tokens[1] == 'Phase':\r\n self.phaseId = float(tokens[2])\r\n elif tokens[1] == 'MaterialName':\r\n self.materialName = str(tokens[2])\r\n elif tokens[1] == 'Formula':\r\n self.formula = str(tokens[2])\r\n # on s'en tape de 'Info', jamais rempli\r\n elif tokens[1] == 'Symmetry':\r\n self.symmetry = int(tokens[2])\r\n elif tokens[1] == 'LatticeConstants':\r\n values = [0] * 6\r\n values[0] = float(tokens[2])\r\n values[1] = float(tokens[3])\r\n values[2] = float(tokens[4])\r\n values[3] = float(tokens[5])\r\n values[4] = float(tokens[6])\r\n values[5] = float(tokens[7])\r\n # Code shit with phase properties using class phase\r\n\r\n with open(filename_out) as file_out:\r\n file_out.write()" }, { "alpha_fraction": 0.6764705777168274, "alphanum_fraction": 0.6764705777168274, "avg_line_length": 31.75, "blob_id": "6470e5f8d6f065662afa586f2334f2150b4fd1e6", "content_id": "f9591265137cc00716445b66356facc7cfe2a094", "detected_licenses": [ "Apache-2.0" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 272, "license_type": "permissive", "max_line_length": 89, "num_lines": 8, "path": "/ang_writer/Modify_ang.py", "repo_name": "fstrub95/writers", "src_encoding": "UTF-8", "text": "import os\r\nfrom Methods.write_core_ang import AngWriter\r\n\r\nang_dir = \"C:\\\\Users\\\\SteveJobs\\\\PycharmProjects\\\\ang_writer\\\\Data\\\\\"\r\nang_in = \"test.ang\"\r\nang_out = \"test_out.ang\"\r\n\r\nang_object = AngWriter((os.path.join(ang_dir, ang_in)), (os.path.join(ang_dir, ang_out)))\r\n\r\n" } ]

2

Ethan-mxc/xzday

https://github.com/Ethan-mxc/xzday

b188924752e86dfdda3927d7a4b070479596d382

6cd2fe61aa0d50745471ab25549c35c2fee3c9c7

216ea71ca5511c2e4eb7eb04cf6b707c104e5085

refs/heads/master

2020-04-02T10:36:37.502260

2019-07-28T02:08:49

154,346,913

2

0

null

[ { "alpha_fraction": 0.6927223801612854, "alphanum_fraction": 0.7196765542030334, "avg_line_length": 19.66666603088379, "blob_id": "3922a11602f311922ebb403fd6542ca123b1227c", "content_id": "6e175d6c71f26fe40c9d0087c09d0a1d814f42e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 371, "license_type": "no_license", "max_line_length": 54, "num_lines": 18, "path": "/src/jiuye/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "'''\nCreated on 2018-1-4\n\n@author: corey\n'''\n#-*-coding:utf-8-*-\nimport xlrd\nfrom django.shortcuts import render\nfrom django.template.context_processors import request\nimport sys\ndef indexView(req):\n return render(req,'baseindex.html',locals())\n\ndef more(req):\n return render(req,'moreindex.html',locals())\n\ndef page_not_found(req):\n return render(req,'404.html')" }, { "alpha_fraction": 0.5879895687103271, "alphanum_fraction": 0.6052219271659851, "avg_line_length": 36.54901885986328, "blob_id": "88d6e565ebbb69edc8ed526ab9becd0efc9284b5", "content_id": "045833f75f1ed6036473b4099f4f3aaa8cebda58", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1915, "license_type": "no_license", "max_line_length": 132, "num_lines": 51, "path": "/src/forum/migrations/0003_auto_20180623_0910.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n# Generated by Django 1.11.2 on 2018-06-23 01:10\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('forum', '0002_auto_20180125_1210'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='application',\n name='receiver',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='appli_receiver', to='online.User'),\n ),\n migrations.AlterField(\n model_name='application',\n name='sender',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='appli_sender', to='online.User'),\n ),\n migrations.AlterField(\n model_name='comment',\n name='author',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='online.User'),\n ),\n migrations.AlterField(\n model_name='message',\n name='receiver',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='message_receiver', to='online.User'),\n ),\n migrations.AlterField(\n model_name='message',\n name='sender',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='message_sender', to='online.User'),\n ),\n migrations.AlterField(\n model_name='post',\n name='author',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='post_author', to='online.User'),\n ),\n migrations.AlterField(\n model_name='post',\n name='last_response',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='online.User'),\n ),\n ]\n" }, { "alpha_fraction": 0.511303186416626, "alphanum_fraction": 0.5166223645210266, "avg_line_length": 35.17073059082031, "blob_id": "f8d50ebbd232f7268a45e6645601935422548154", "content_id": "6e1f99bb9321e6849f1e75f4263d6bed065b2217", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1512, "license_type": "no_license", "max_line_length": 96, "num_lines": 41, "path": "/src/tools/iponline.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-*-coding:utf-8-*-\nfrom tools.link import *\nfrom online.models import *\nfrom home.models import *\nimport time\ndef pingall():\n computes = Compute.objects.all()\n for compute in computes:\n if(ping_ip(compute.ip)):\n Compute.objects.filter(ip = compute.ip).update(state = \"开机\")\n try:\n activecompute = Compute.objects.get(ip = compute.ip)\n except:\n print 'err'\n else:\n \n if (activecompute.activetime!=None):\n activetime = int(activecompute.activetime)+1\n Compute.objects.filter(ip = compute.ip).update(activetime = str(activetime))\n else:\n Compute.objects.filter(ip = compute.ip).update(activetime = '0')\n else:\n Compute.objects.filter(ip = compute.ip).update(state = \"关机\")\n try:\n idletcompute = Compute.objects.get(ip = compute.ip)\n except:\n print \"err\"\n else:\n if (idletcompute.idletime!=None):\n idletime = int(idletcompute.idletime)+1\n \n Compute.objects.filter(ip = compute.ip).update(idletime = str(idletime))\n else:\n Compute.objects.filter(ip = compute.ip).update(idletime = '0')\n return 1\ndef runpingall():\n while True:\n pingall()\n time.sleep(10) \nif __name__ == \"__main__\": \n runpingall()\n \n " }, { "alpha_fraction": 0.47608405351638794, "alphanum_fraction": 0.5109521746635437, "avg_line_length": 25.011627197265625, "blob_id": "50d2e2a658c8ff43b3187f47fa9f6f3ae798fb9c", "content_id": "112c7ddcc4810eb94d03252b91cd5640143beed2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2349, "license_type": "no_license", "max_line_length": 84, "num_lines": 86, "path": "/src/resume_tool/date_extractor.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# encoding:utf-8\nimport re\n\n# date_str1 2010.09\n#date_str1 = u\"[0-9 年/.]+\"\ndate_str1 = u\"[0-9]{4} *[年/.-]+[0-9]{1,2}[月]*\"\ndate_pattern1 = re.compile(date_str1)\n\n# 解决9/1990和09/1992这些case\ndate_str2 = u\"[0-9]{1,2}[月/.-]+19{1}[0-9]{2}[年]*|[0-9]{1,2}[月/.-]+20{1}[0-9]{2}[年]*\"\ndate_pattern2 = re.compile(date_str2)\n\n# 将items（list）所有空元素，过滤掉\ndef drop_null(items):\n result = []\n for item in items:\n if len(item.strip()) == 0:\n continue\n result.append(item.strip())\n return result\n\n# 抽取日期信息,并将日期按照顺序排列存入list\n# str必须保证unicode编码\ndef date_extract(str):\n result_list = []\n date_list = date_pattern1.findall(str)\n for d in date_list:\n source = d\n d = d.replace(\"19\", \" \")\n d = d.replace(\"20\", \" \")\n items_tmp = d.split(\" \")\n items = drop_null(items_tmp)\n\n j = 0\n for i in range(0, len(items)):\n k = 0\n pre = ''\n m = j + 2\n if source[j] != items[i][k]:\n while (j < m and j < len(source)):\n pre += source[j]\n j = j + 1\n j = j + len(items[i])\n items[i] = pre + items[i]\n # print items[i]\n #str = str.replace(items[i], '') #注释\n result_list.append(items[i])\n\n result_list.extend(date_pattern2.findall(str))\n return result_list\n\ndef get_education_number_from_date(input_str):\n date_size = 0;\n size = len(date_extract(input_str))\n if size % 2 == 0:\n date_size = size / 2\n else:\n date_size = (size + 1) / 2\n return date_size\n\n\ndef process(input_file_path):\n items = []\n items.__sizeof__()\n for line in open(input_file_path, 'r'):\n try:\n line = line.strip().decode('utf-8') # 设置编码格式\n except:\n line = line.strip().decode('gb2312') \n print line\n # normalize_date(line)\n date_list = date_extract(line)\n for d in date_list:\n print d\n print 'education number: '\n print get_education_number_from_date(line)\n print '-------'\n\n\ndef main():\n print 'this is main'\n input_file_path = 'data/samples'\n result_dic = process(input_file_path)\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.685512363910675, "alphanum_fraction": 0.685512363910675, "avg_line_length": 34.5, "blob_id": "958d792ed7a85ee76493d7d215eed6bce3100afa", "content_id": "b51552f50cebe39caabc40b85b004c244c252212", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 283, "license_type": "no_license", "max_line_length": 73, "num_lines": 8, "path": "/src/resume/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom resume import views\napp_name = 'resume'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^uploadFile/$',views.upload_file,name = 'uploadFile'),\n url(r'^uploadPFile/$', views.upload_file_person, name='uploadPFile'),\n]" }, { "alpha_fraction": 0.6416666507720947, "alphanum_fraction": 0.6416666507720947, "avg_line_length": 29.125, "blob_id": "420a195c0a714fdab971144fb206c33363ea6e32", "content_id": "d7f15a1d43a8bc54dee772747d6fb4481b9ca050", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 240, "license_type": "no_license", "max_line_length": 48, "num_lines": 8, "path": "/src/question/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom question import views\napp_name = 'question'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^info/$',views.info,name = 'info'),\n url(r'^list/$',views.list,name = 'list'),\n]" }, { "alpha_fraction": 0.5555555820465088, "alphanum_fraction": 0.5681818127632141, "avg_line_length": 21, "blob_id": "62bb137aab12f28f2a54b4e923690f8c9d10d2d1", "content_id": "bdcd87a5b7fc367362acf7be55d2bbd582df550b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 432, "license_type": "no_license", "max_line_length": 41, "num_lines": 18, "path": "/src/bgtool/docprint.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "GB18030", "text": "# -*- coding: cp936 -*-\n__author__ = 'corey'\n# -*- coding:utf-8 -*-\nimport docx\n\ndef printdoc():\n \n document = docx.Document(\"./1.docx\")\n #打印每个段落的内容\n for paragraph in document.paragraphs:\n print paragraph.text\n #打印每个表格的内容\n for table in document.tables:\n for row in table.rows:\n for cell in row.cells:\n print cell.text\n \nprintdoc()\n" }, { "alpha_fraction": 0.6147859692573547, "alphanum_fraction": 0.6342412233352661, "avg_line_length": 35.85714340209961, "blob_id": "269ededa130c173d34c3b7fa18e8a2e4948033e9", "content_id": "a1d109f2ec46450c97d07c4e02e90b4bbd8ea548", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 265, "license_type": "no_license", "max_line_length": 74, "num_lines": 7, "path": "/src/tools/mail.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-*-coding:utf-8-*-\nfrom django.core.mail import send_mail\ndef send_mymail(subject,body,send_to):\n print send_mail(subject,body, '[email protected]', send_to, fail_silently=False)\n return 1\nif __name__ == \"__main__\": \n send_mymail('我是谁', '他', ['[email protected]'])" }, { "alpha_fraction": 0.536644697189331, "alphanum_fraction": 0.5428968667984009, "avg_line_length": 35.40506362915039, "blob_id": "d5594e0c0874d41f73bf2d39c9840737ad56bfd1", "content_id": "b1c34e6ac9ea5f326e4828cb2cc578b115eb9b98", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2891, "license_type": "no_license", "max_line_length": 143, "num_lines": 79, "path": "/src/intr/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-*-coding:utf-8-*-\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import *\nfrom online.models import User\nimport sys \nreload(sys) \nsys.setdefaultencoding('utf8') \n\n\ndef isflag(req):\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(**condition) \n user_info['job'] = r.job\n user_info['edu'] = r.edu\n user_info['comp'] = r.comp\n user_info['myjob'] = r.myjob\n user_info['mylocal'] = r.mylocal\n user_info['mycomp'] = r.mycomp\n user_info['myintr'] = r.myintr\n user_info['mymoney'] = r.mymoney\n if cmp(r.comp,\"\")!=0 or cmp(r.edu,\"\")!=0 or cmp(r.job,\"\")!=0:\n flag = 0\n \n else:\n flag = 1\n \n if cmp(r.mycomp,\"\")!=0 or cmp(r.mymoney,\"\")!=0 or cmp(r.mylocal,\"\")!=0 or cmp(r.myintr,\"\")!=0 or cmp(r.myjob,\"\")!=0:\n xzflag = 0\n else:\n xzflag = 1 \n return flag,xzflag,user_info\n \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.session['islogin'] == True:\n flag,xzflag,user_info=isflag(req)\n return render(req,\"intr_index.html\", locals())\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n \ndef money(req):\n if req.method == 'POST':\n mycomp = req.POST['mycomp']\n myjob = req.POST['myjob']\n mylocal = req.POST['mylocal']\n mymoney = req.POST['mymoney']\n myintr = req.POST['myintr']\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(**condition)\n beans = r.beans\n beans +=10\n User.objects.filter(name=user_info['name']).update(beans=beans,myjob=myjob,mycomp=mycomp,mylocal=mylocal,mymoney=mymoney,myintr=myintr)\n flag,xzflag,user_info=isflag(req)\n return render(req,\"intr_index.html\", locals()) \n \ndef info(req):\n if req.method == 'POST':\n comp = req.POST['comp']\n job = req.POST['job']\n edu = req.POST['edu']\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(**condition)\n beans = r.beans\n beans +=10\n User.objects.filter(name=user_info['name']).update(beans=beans,job=job,comp=comp,edu=edu)\n flag,xzflag,user_info=isflag(req)\n return render(req,\"intr_index.html\", locals()) \n\n" }, { "alpha_fraction": 0.4760802388191223, "alphanum_fraction": 0.4934413433074951, "avg_line_length": 37.6716423034668, "blob_id": "978d16967dbcd8dd2157de8588dafd8ba60afacc", "content_id": "5998b023ca51b95374d4c8b75384141ba4173eb9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2704, "license_type": "no_license", "max_line_length": 114, "num_lines": 67, "path": "/src/company/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-*-coding:utf-8-*-\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import *\nfrom online.models import User\nimport json\nimport sys \nreload(sys) \nsys.setdefaultencoding('utf8') \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.session['islogin'] == True:\n return render(req,\"com_index.html\", locals())\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.method == 'POST':\n msg = \"模式错误\"\n return render(req,\"msg.html\", locals()) \n \n \ndef info(req):\n if req.method == 'POST' or req.method =='GET':\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(**condition)\n if r.beans<1:\n msg = '用户豆不够，请充值'\n return render(req,'msg.html', locals())\n else:\n try:\n if req.method == 'POST':\n text=req.POST.get(\"comtext\",'')\n else:\n text = req.GET.get('comtext')\n my_db = MynewcoderDB()\n sqlstr = \"SELECT * from company where 标题 like '%\"+text+\"%'\"\n infos = my_db.getInfo(sqlstr)\n compintr = infos[0]\n infos = infos[0]\n info = infos[1]\n jstemp = ['百度', '腾讯', '阿里巴巴']\n jsdtemp = [[84, 66, 1000, '百度', '百度'], [88, 60, 1000, '腾讯', '腾讯'], [86, 64, 1000, '阿里巴巴', '阿里巴巴']]\n try:\n jsname = json.dumps(jstemp)\n data = [float(compintr[8]) * 20, float(compintr[9]) * 20, 1000, compintr[1], compintr[1]]\n if not compintr[1] in jstemp:\n jsdtemp.append(data)\n jstemp.append(compintr[1])\n jsdata = json.dumps(jsdtemp)\n except:\n pass\n beans = r.beans - 0\n User.objects.filter(name=user_info['name']).update(beans=beans)\n req.session['beans'] = beans\n my_db.close()\n return render(req,\"com_index.html\", locals()) \n except Exception,e:\n msg = '没有该公司，请重新查询'\n return render(req,\"msg.html\", locals())\n\n" }, { "alpha_fraction": 0.48055499792099, "alphanum_fraction": 0.4842681288719177, "avg_line_length": 31.044025421142578, "blob_id": "14b353fd385ce1d87aa50f9c2f799aa0ca05fdad", "content_id": "b00f16eff25ce674356ebb165385ba8736769598", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "HTML", "length_bytes": 5291, "license_type": "no_license", "max_line_length": 243, "num_lines": 159, "path": "/src/forum/templates/post_detail.html", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "{% extends \"./base.html\" %}\n\n<link rel=\"stylesheet\" type=\"text/css\" href=\"/static/css/post_detail.css\"> \t\t\t\n\n\n{% block body %}\n\n\n\n\n\n<div id=\"all_post\">\n\t<div class=\"post\"> \n\t\t<div class=\"avatar\">\n\t\t<img src=\"{{ post.author.avatar}}\" border=\"0\"><br>\n 用户名：{{ post.author.username }}<br>\n\t\t积分：{{ post.author.levels }}<br>\n\t\t{% if user.username %}\n {% ifnotequal post.author.username user.username %}\n {%if not post.author|checkfriend:user %}\n\t\t\t\t <a href=\"{% url 'make_friend' user.username post.author.username %}\">加好友</a>\n\t\t {% else %}\n\t\t\t\t <a href=\"{% url 'send_message' post.author.id %}\">发消息</a>\n\t\t {% endif %}\n\t\t {% endifnotequal %}\n\t\t{% endif %}\n\t\t</div>\n\t\t<div class=\"content\">\n\t\t\t<font size=\"8\" face=\"arial\" color=\"red\">标题：{{ post.title }}</font>\n\t\t\t<br>{% autoescape off %}\n\t\t\t\t\t\t{{post.content}}\n\t\t\t\t\t{% endautoescape %}\n\t\t</div>\n\t</div>\n\t\n\t<div class=\"well\">\n <div class=\"vmaig-comment\">\n <div class=\"vmaig-comment-tx\">\n {% if user.img%}\n <img src=\"{{user.img}}\" width=\"40\"></img>\n {%else%}\n <img src=\"http://vmaig.qiniudn.com/image/tx/tx-default.jpg\" width=\"40\"></img>\n {%endif%}\n </div>\n <div class=\"vmaig-comment-edit clearfix\">\n <form id=\"vmaig-comment-form\" method=\"post\" role=\"form\">\n {% csrf_token %}\n <textarea id=\"comment2\" name=\"comment\" class=\"form-control\" rows=\"4\" placeholder=\"请输入评论限200字!\"></textarea>\n <button type=\"submit\" class=\"btn btn-vmaig-comments pull-right\">提交</button>\n </form>\n </div>\n <ul>\n {% for comment in comment_list%}\n <li>\n\t <div class=\"comment\">\n <div class=\"avatar_comment\">\n {% if comment.author.avatar %}\n \t<img src={{comment.author.avatar}} width=\"40\"></img><br>\n {%else%}\n \t<img src=\"http://vmaig.qiniudn.com/image/tx/tx-default.jpg\" width=\"40\"></img><br>\n {%endif%}\n \t用户名：{{ comment.author.username }}<br>\n\t\t\t积分：{{ comment.author.levels }}<br>\n\t\t\t{% if user.username %}\n\t\t\t {% ifnotequal comment.author.username user.username %}\n\t\t \t{%if not comment.author|checkfriend:user %}\n\t\t\t\t <a href=\"{% url 'make_friend' user.username comment.author.username %}\">加好友</a>\n {% else %}\n\t\t\t\t <a href=\"{% url 'send_message' comment.author.id %}\">发消息</a>\n\t\t {% endif %}\n\t\t\t {% endifnotequal %}\n\t\t\t{% endif %}\n </div>\n <div class=\"content_comment\">\n <p>{% if comment.comment_parent %}回复{{comment.comment_parent.author}} 发表于{{comment.comment_parent.created_at|date:\"Y-m-d H:i:s\"}}的内容《{{comment.comment_parent.content}}》; {% endif %}{{comment.created_at|date:\"Y-m-d H:i:s\" }}</p>\n <p>\n 评论：\n {% autoescape on%}\n {{ comment.content }}\n {% endautoescape %}\n </p>\n \n </div>\n\t <div class=\"floor\">\n\t\t{% ifequal forloop.counter 1%} \n\t\t沙发\n\t\t{% else %}\n\t\t\t{% ifequal forloop.counter 2%} \n\t\t\t\t板凳\n\t\t\t{% else %}\n\t\t\t\t{{ forloop.counter}}楼\n\t\t\t{% endifequal %}\n\t\t{% endifequal %}\n\t\t<a href=\"javascript:showDivFun({{comment.pk}})\">评论</a>\n\t </div>\n </div>\n </li>\n {% endfor%}\n </ul>\n </div>\n</div>\n\n<script src=\"/static/js/jquery.min.js\" language=\"javascript\" type=\"text/javascript\"></script>\n<script language=\"javascript\" type=\"text/javascript\">\n//弹出调用的方法\ncommentid=0;\nfunction showDivFun(comment_id){\n {% if not user.is_authenticated %}\n alert(\"请登录后评论！\")\n location.reload();\n {% endif %}\n commentid=comment_id;\n document.getElementById('popDiv').style.display='block';\n \n}\n//关闭事件\nfunction closeDivFun(){\n \n document.getElementById('popDiv').style.display='none';\n $.ajax({\n type:\"POST\",\n url:\"/makecomment/\",\n data:{\"comment\":$(\"#comment\").val(),\"comment_id\":commentid,\"post_id\":{{post.pk}},},\n //beforeSend:function(xhr){\n //xhr.setRequestHeader(\"X-CSRFToken\", $.cookie('csrftoken')); \n //},\n success:function(data,textStatus){\n\t\t\n location.reload();\n }\n\n });\n \n \n}\n \n $('#vmaig-comment-form').submit(function(){\n\t{% if not user.is_authenticated %}\n alert(\"请登录后评论！\")\n return false;\n {% endif %}\n $.ajax({\n type:\"POST\",\n url:\"{% url 'make_comment'%}\",\n data:{\"comment\":$(\"#comment2\").val(),\"post_id\":{{post.pk}}},\n success:function(data,textStatus){\n location.reload();\n }\n \n });\n return false;\n }); \n</script>\n \n\t\n</div>\n\n\n{% endblock %} \n \n \n" }, { "alpha_fraction": 0.6269841194152832, "alphanum_fraction": 0.6269841194152832, "avg_line_length": 49.46666717529297, "blob_id": "f8527cf49bcd8f15f00fbefa18216f765f52c902", "content_id": "98a9d40843594c60e3df2a56279eb05c3cb7e94f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 756, "license_type": "no_license", "max_line_length": 88, "num_lines": 15, "path": "/src/forum/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom forum.views import *\nfrom forum import *\napp_name = 'forum'\nurlpatterns = [\n url(r'index',index.as_view(),name='index'),\n url(r'^columns/$',views.columnall,name='column_all'),\n url(r'^column/(?P<column_pk>\\d+)/$',views.columndetail,name='column_detail'),\n url(r'^postdetail/(?P<post_pk>\\d+)/$', views.postdetail, name='post_detail'), \n url(r'^postlist/$', UserPostView.as_view(), name='user_post'),\n url(r'^post_create/$', PostCreate.as_view(), name='post_create'), \n url(r'^post_update/(?P<pk>\\d+)/$', PostUpdate.as_view(), name='post_update'), \n url(r'^post_delete/(?P<pk>\\d+)/$', PostDelete.as_view(), name='post_delete'), \n url(r'^search/$', SearchView.as_view(), name='search'),\n]" }, { "alpha_fraction": 0.5634675025939941, "alphanum_fraction": 0.5789473652839661, "avg_line_length": 25.387754440307617, "blob_id": "9d13287f8f84472922f2e318c21a96e67b37faaa", "content_id": "189281a3bacc0c2fe4c9a670f6f48a5b2b0e2fa2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1426, "license_type": "no_license", "max_line_length": 70, "num_lines": 49, "path": "/src/resume_tool/gwread.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# encoding:utf-8\nimport sys\nfrom codeinit import *\nimport jieba\nimport xlrd\nreload(sys)\nsys.setdefaultencoding('utf8')\ninput_encode = 'utf-8'\n# 构建专业知识库\n\n# 抽取技能信息,并将日期按照顺序排列存入list\ndef word_extract(str):\n wordcut = WordCut(str)\n word_dict = {}\n for word, flag in wordcut.cutWords():\n if flag == \"n\" or flag == \"vn\":\n if word not in word_dict:\n word_dict[word] = 1\n else:\n word_dict[word] += 1\n if flag == \"eng\":\n if word not in word_dict:\n word_dict[word] = 10\n else:\n word_dict[word] += 10\n\n import operator\n word_dict = sorted(word_dict.items(), key=operator.itemgetter(1))\n for ktemp,vtemp in word_dict:\n print ktemp,vtemp\n\ndef readexcel(keyword,filename = \"lagou.xls\"):\n book = xlrd.open_workbook(filename)#得到Excel文件的book对象，实例化对象\n sheet0 = book.sheet_by_index(0) # 通过sheet索引获得sheet对象\n nrows = sheet0.nrows # 获取行总数\n ncols = sheet0.ncols #获取列总数\n words = \"\"\n for i in range(0,nrows):\n print i\n row_data = sheet0.row_values(i) # 获得第1行的数据列表\n if row_data[0] == keyword:\n words = words+row_data[11]\n word_extract(words)\n\n\ndef run():\n readexcel(\"测试\")\nrun()" }, { "alpha_fraction": 0.672535240650177, "alphanum_fraction": 0.672535240650177, "avg_line_length": 42.769229888916016, "blob_id": "55df3f384795f58995b5b2a052714dde07be1ba3", "content_id": "850ef22d43839229187eac5c7707f449917941fc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 568, "license_type": "no_license", "max_line_length": 72, "num_lines": 13, "path": "/src/online/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom online import views\napp_name = 'online'\nurlpatterns = [\n url(r'^login/$',views.login,name = 'login'),\n url(r'^forget/$',views.forget,name = 'forget'),\n url(r'^register/$',views.register,name = 'register'),\n url(r'^changepasswd/$',views.changepasswd,name = 'changepasswd'),\n url(r'^gotologin/$', views.gotologin, name='gotologin'),\n url(r'^logout', views.logout,name = 'logout'),\n url(r'^getpasswd',views.getpasswd,name = 'getpasswd'),\n url(r'^changehead_image',views.changehead_image,'changehead_image'),\n]" }, { "alpha_fraction": 0.5486012101173401, "alphanum_fraction": 0.5576102137565613, "avg_line_length": 23.811763763427734, "blob_id": "34c3b2cdba2abd8c5afeb00ef6befe1ba7576af4", "content_id": "50047da3c8ecb4324c74a2d95dc86cc429d7ca90", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2247, "license_type": "no_license", "max_line_length": 83, "num_lines": 85, "path": "/src/resume_tool/project_extractor.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# encoding:utf-8\nimport re\nimport sys\nfrom tools import *\nreload(sys)\nsys.setdefaultencoding('utf8')\n\ninput_encode = 'utf-8'\n# 构建专业知识库\nmajor_set = []\nscore = {}\n\ndic = \"\"\nif cmp(get_os(),\"n\")==0:\n dic = sys.path[0]+\"\\\\resume_tool\\\\project_dic\"\nelse:\n dic = sys.path[0]+\"/resume_tool/project_dic\"\n \nfor v in open(dic, 'r'):\n vs = v.split()\n major_set.append(vs[0])\n score[vs[0]] = vs[1]\n \n\n# 将items（list）所有“!*!!”元素，过滤掉\ndef drop_null(items):\n result = []\n for item in items:\n if item.strip() == \"!*!!\":\n continue\n result.append(item.strip())\n return result\n\n\n# 抽取专业信息,并将日期按照顺序排列存入list\ndef project_extract(str):\n result_list = []\n score_list = []\n for major in major_set:\n if str.__contains__(major):\n result_list.append(major)\n # 歧义消解水利水利水电工程归并为水利水电工程\n result_list = list(set(result_list))\n for i in range(0, len(result_list)):\n for j in range(0, len(result_list)):\n if i == j:\n continue\n if result_list[i].__contains__(result_list[j]):\n result_list[j] = '!*!!'\n result_list = drop_null(result_list)\n\n # 歧序消解\n if len(result_list) > 1:\n result_dic = {}\n for item in result_list:\n index = str.find(item)\n result_dic[item] = index\n result_list = sorted(result_dic.items(), key=lambda d: d[1], reverse=False)\n result_list = [v[0] for v in result_list]\n for resultemp in result_list:\n score_list.append(score[resultemp])\n # print type(result_list)\n return result_list,score_list\n\n\ndef process(input_file_path):\n for line in open(input_file_path, 'r'):\n try:\n line = line.strip().decode('utf-8') # 设置编码格式\n except:\n line = line.strip().decode('gb2312') \n school_list,score_list = project_extract(line)\n for d in school_list:\n print d,score[d]\n #print '-------'\n\n\ndef main():\n print 'this is main'\n input_file_path = 'lcy.txt'\n result_dic = process(input_file_path)\n\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.5954500436782837, "alphanum_fraction": 0.5998021960258484, "avg_line_length": 37.42748260498047, "blob_id": "6ad9702dba24f71dfdbd417b8a8b2a1ddfcc12e2", "content_id": "422a7993201f23f9ea8a6ee6874bc9c5551b8405", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 5315, "license_type": "no_license", "max_line_length": 121, "num_lines": 131, "path": "/src/forum/models.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#coding:utf-8\nfrom django.db import models\nfrom django.conf import settings\nfrom django.contrib.auth.models import AbstractUser\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.db.models import signals\nfrom online.models import *\nimport datetime\nfrom django.core.urlresolvers import reverse\n# Create your models here.\n\nclass Nav(models.Model):\n name = models.CharField(max_length=40,verbose_name=u'导航条')\n url = models.CharField(max_length=200,blank=True,null=True,verbose_name=u'指向地址')\n create_time = models.DateTimeField(u'创建时间',default=datetime.datetime.now)\n class Meta:\n db_table = 'nav'\n verbose_name_plural = verbose_name = u\"导航条\"\n ordering = ['-create_time']\n\n def __unicode__(self):\n return self.name \n\nclass Column(models.Model): #板块\n name = models.CharField(max_length=30)\n description = models.TextField()\n img = models.CharField(max_length=200,default='/static/tx/default.jpg',verbose_name=u'图标')\n post_number = models.IntegerField(default=0) #主题数\n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n\n class Meta:\n db_table = 'column'\n ordering = ['-post_number']\n verbose_name_plural = u'板块'\n \n def __unicode__(self):\n return self.name\n\n def get_absolute_url(self):\n return \"../../forum\"+reverse('column_detail',urlconf = \"forum.urls\",kwargs={'column_pk': self.pk })\n \nclass PostType(models.Model): #文章类型\n type_name = models.CharField(max_length=30)\n description = models.TextField()\n created_at = models.DateTimeField(default=datetime.datetime.now)\n class Meta:\n db_table = 'posttype'\n verbose_name_plural = u'主题类型'\n def __unicode__(self):\n return self.type_name\n\nclass Post(models.Model): #文章\n title = models.CharField(max_length=30)\n author = models.ForeignKey(User,related_name='post_author') #作者\n column = models.ForeignKey(Column) #所属板块\n type_name = models.ForeignKey(PostType) #文章类型\n content = models.TextField()\n \n view_times = models.IntegerField(default=0) #浏览次数\n responce_times = models.IntegerField(default=0) #回复次数\n last_response = models.ForeignKey(User) #最后回复者\n \n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n \n \n class Meta:\n db_table = 'post'\n ordering = ['-created_at']\n verbose_name_plural = u'主题'\n \n def __unicode__(self):\n return self.title\n \n def description(self):\n return u'%s 发表了主题《%s》' % (self.author, self.title)\n \n def get_absolute_url(self):\n return \"../../forum\"+reverse('post_detail',urlconf = \"forum.urls\",kwargs={'post_pk': self.pk })\n \n \nclass Comment(models.Model): #评论 \n post = models.ForeignKey(Post) \n author = models.ForeignKey(User) \n comment_parent = models.ForeignKey('self', blank=True, null=True,related_name='childcomment') \n content = models.TextField()\n \n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n \n class Meta:\n db_table = 'comment'\n ordering = ['created_at']\n verbose_name_plural = u'评论'\n \n def __unicode__(self):\n return self.content\n\n def description(self):\n return u'%s 回复了您的帖子(%s) R:《%s》' % (self.author,self.post, self.content)\n \n def get_absolute_url(self):\n return \"../../forum\"+reverse('post_detail',urlconf = \"forum.urls\",kwargs= { 'post_pk': self.post.pk })\n \nclass Message(models.Model): #好友消息\n sender = models.ForeignKey(User,related_name='message_sender') #发送者\n receiver = models.ForeignKey(User,related_name='message_receiver') #接收者\n content = models.TextField()\n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n \n def description(self):\n return u'%s 给你发送了消息《%s》' % (self.sender, self.content)\n\n class Meta:\n db_table = 'message'\n verbose_name_plural = u'消息'\n \nclass Application(models.Model): #好友申请\n sender = models.ForeignKey(User,related_name='appli_sender') #发送者\n receiver = models.ForeignKey(User,related_name='appli_receiver') #接收者\n status = models.IntegerField(default=0) #申请状态 0:未查看 1:同意 2:不同意\n created_at = models.DateTimeField(auto_now_add = True)\n updated_at = models.DateTimeField(auto_now = True)\n def description(self):\n return u'%s 申请加好友' % self.sender \n\n class Meta:\n db_table = 'application'\n verbose_name_plural = u'好友申请'\n \n\n\n \n \n\n\n\n\n" }, { "alpha_fraction": 0.5009669661521912, "alphanum_fraction": 0.5196619033813477, "avg_line_length": 41.694190979003906, "blob_id": "0eedbbb347c02eaa71cb4ff8209f4e0f3520e16a", "content_id": "ec36f2ba1358a831292b9c4b630933df2360e3bb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 14363, "license_type": "no_license", "max_line_length": 269, "num_lines": 327, "path": "/src/vip/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-*-coding:utf-8-*-\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import *\nfrom online.models import User\nimport json\nimport sys\nimport time\nimport re\nfrom django.views.decorators.cache import cache_page\nreload(sys)\nsys.setdefaultencoding('utf8')\n\n\n#确定身份函数\ndef comfirm(req):\n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n user_role = user_info['role']\n if user_role != '管理员':\n msg = '您不是管理员'\n return 0\n else:\n return 1\n\ndef index(req):\n if comfirm(req):\n my_db = MynewcoderDB()\n sql = \"select * from vip\"\n infos = my_db.getInfo(sql)\n results = []\n for infos1_row in infos:\n results.append({'id':infos1_row[0],'vip':infos1_row[2]})\n sql_share = \"select * from share\"\n infos_share = my_db.getInfo(sql_share)\n resultsshare = []\n for infos_row in infos_share:\n resultsshare.append({'id':infos_row[0],'old':infos_row[1],'new':infos_row[2],'start_time':infos_row[3],'end_time':infos_row[4],'dingdanhao':infos_row[5],'invitation':infos_row[6],'change_time':infos_row[7]})\n my_db.close()\n return render(req,'index.html',locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\n\ndef vip_edit_action(req):\n if comfirm(req):#确认身份\n res_id = req.POST.get('res_id','0')\n res_vipid = req.POST.get('res_vipid','999999')\n res_mail = req.POST.get('res_mail','0')\n res_create_time = req.POST.get('res_create_time','2010-1-10')\n res_change_time = time.strftime('%Y-%m-%d %X', time.localtime() )\n res_deadline = req.POST.get('res_deadline','2020-1-1')\n my_db = MynewcoderDB()\n if res_id == '0':\n sql2 = \"insert into vip (vipid,vip,create_time,change_time,deadline)values('\" + res_vipid + \"','\" + res_mail + \"','\" + res_create_time + \"','\" + res_change_time + \"','\" + res_deadline + \"')\"\n infos2 = my_db.execute(sql2)\n my_db.commit()\n #已经存在的会员查询\n sql = \"select * from vip\"\n infos = my_db.getInfo(sql)\n results = []\n for infos1_row in infos:\n results.append({'id': infos1_row[0], 'vip': infos1_row[2]})\n #接下去是订单表查询\n sql_share = \"select * from share\"\n infos_share = my_db.getInfo(sql_share)\n resultsshare = []\n for infos_row in infos_share:\n resultsshare.append(\n {'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n my_db.close()\n return render(req, 'index.html', locals())\n\n sql2 = \"update vip set vipid='\"+res_vipid+\"' ,vip='\"+res_mail+\"', create_time ='\"+res_create_time+\"',change_time= '\"+res_change_time+\"',deadline='\"+res_deadline+\"' where id= '\"+res_id+\"' \"\n infos2 = my_db.execute(sql2)\n my_db.commit()\n sql4 = \"select * from vip where id =\"+res_id+\" \"\n infos4 = my_db.getInfo(sql4)\n results = []\n for infos1_row in infos4:\n results.append(\n {'id': infos1_row[0], 'vipid': infos1_row[1], 'vip': infos1_row[2], 'create_time': infos1_row[3],\n 'change_time': infos1_row[4], 'deadline': infos1_row[5]})\n return render(req, 'member_edit.html', locals())\n my_db.close()\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef order_edit_action(req):\n if comfirm(req):#确认身份\n res_id = req.POST.get('id','0')\n res_old = req.POST.get('old','999999')\n res_new = req.POST.get('new','0')\n res_start_time = req.POST.get('start_time','2010-1-10')\n res_end_time = req.POST.get('end_time','2010-1-10')\n res_dingdanhao = req.POST.get('dingdanhao','2020-1-1')\n res_invitation = req.POST.get('invitation', '2020-1-1')\n res_change_time = time.strftime('%Y-%m-%d %X', time.localtime() )\n my_db = MynewcoderDB()\n if res_id == '0':\n sql2 = \"insert into share (old,new,start_time,end_time,dingdanhao,invitation,change_time)values('\" + res_old + \"','\" + res_new + \"','\" + res_start_time + \"','\" + res_end_time + \"','\" + res_dingdanhao + \"','\" + res_invitation + \"','\" + res_change_time + \"')\"\n infos2 = my_db.execute(sql2)\n my_db.commit()\n #已经存在的会员查询\n sql = \"select * from share\"\n infos = my_db.getInfo(sql)\n resultsshare = []\n for infos1_row in infos:\n resultsshare.append({'id': infos1_row[0], 'dingdanhao': infos1_row[5]})\n #接下去是订单表查询\n sql_share = \"select * from vip\"\n infos_share = my_db.getInfo(sql_share)\n results = []\n for infos_row in infos_share:\n results.append({'id': infos_row[0], 'vipid': infos_row[1], 'vip': infos_row[2], 'create_time': infos_row[3],'change_time': infos_row[4], 'deadline': infos_row[5]})\n my_db.close()\n return render(req, 'index.html', locals())\n\n sql3 = \"update share set old='\"+res_old+\"', new ='\"+res_new+\"',start_time= '\"+res_start_time+\"',end_time='\"+res_end_time+\"',dingdanhao='\"+res_dingdanhao+\"',invitation='\"+res_invitation+\"',change_time='\"+res_change_time+\"' where id= '\"+res_id+\"' \"\n infos3 = my_db.execute(sql3)\n my_db.commit()\n sql4 = \"select * from share where id =\"+res_id+\" \"\n infos4 = my_db.getInfo(sql4)\n results = []\n for infos_row in infos4:\n results.append(\n {'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n return render(req, 'share_edit.html', locals())\n my_db.close()\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef show(req,res_id):\n if comfirm(req):\n try:\n my_db = MynewcoderDB()\n sql = \"select * from vip where id =\"+res_id+\" \"\n infos = my_db.getInfo(sql)\n results = []\n for infos_row in infos:\n results.append({'id': infos_row[0], 'vipid': infos_row[1], 'vip': infos_row[2], 'create_time': infos_row[3],\n 'change_time': infos_row[4], 'deadline': infos_row[5]})\n my_db.close()\n return render(req,'member_edit.html',locals())\n except Exception as e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef order_show(req,res_id):\n if comfirm(req):\n try:\n my_db = MynewcoderDB()\n sql = \"select * from share where id =\"+res_id+\" \"\n infos = my_db.getInfo(sql)\n results = []\n for infos_row in infos:\n results.append({'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n my_db.close()\n return render(req,'share_edit.html',locals())\n except Exception as e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef search(req):\n if comfirm(req):\n try:\n text = req.POST['vip']\n my_db = MynewcoderDB()\n sql1 = \"select * from vip where vip LIKE '%\"+text+\"%'\"\n infos1 = my_db.getInfo(sql1)\n results = []\n for infos1_row in infos1:\n results.append({'id':infos1_row[0],'vipid':infos1_row[1],'vip':infos1_row[2],'create_time':infos1_row[3],'change_time':infos1_row[4],'deadline':infos1_row[5]})\n my_db.close()\n return render(req,'member_edit.html',locals())\n except Exception as e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef share_search(req):\n if comfirm(req):\n try:\n text = req.POST['share_dingdanhao']\n my_db = MynewcoderDB()\n sql1 = \"select * from share where dingdanhao ='\"+text+\"'\"\n infos1 = my_db.getInfo(sql1)\n results = []\n for infos_row in infos1:\n results.append({'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n my_db.close()\n return render(req,'share_edit.html',locals())\n except Exception as e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\",locals())\n else:\n msg = \"请求错误\"\n return render(req, \"msg.html\", locals())\n\ndef vip_edit(req,res_id):\n if comfirm(req):#确认身份\n if str(res_id) == '0':\n return render(req,'new_vip_edit.html')\n try:\n my_db = MynewcoderDB()\n sql1 = \"select * from vip where id = \"+res_id+\" \"\n infos1 = my_db.getInfo(sql1)\n results = []\n for infos1_row in infos1:\n results.append({'id':infos1_row[0],'vipid':infos1_row[1],'vip':infos1_row[2],'create_time':infos1_row[3],'change_time':infos1_row[4],'deadline':infos1_row[5]})\n my_db.close()\n res = results[0]\n return render(req,'vip_edit.html',locals())\n except Exception as e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef order_edit(req,res_id):\n if comfirm(req):#确认身份\n if str(res_id) == '0':\n return render(req,'new_order_edit.html')\n try:\n my_db = MynewcoderDB()\n sql1 = \"select * from share where id = \"+res_id+\" \"\n infos1 = my_db.getInfo(sql1)\n results = []\n for infos_row in infos1:\n results.append({'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n my_db.close()\n res = results[0]\n return render(req,'order_edit.html',locals())\n except Exception as e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef delete(req,res_id):\n if comfirm(req):\n try:\n my_db = MynewcoderDB()\n #vip表的删除\n sql1 = \"delete from vip where id =\"+res_id+\" \"\n my_db.execute(sql1)\n my_db.commit()\n #############\n sql = \"select * from vip\"\n infos = my_db.getInfo(sql)\n results = []\n for infos1_row in infos:\n results.append({'id':infos1_row[0],'vipid':infos1_row[1],'vip':infos1_row[2],'create_time':infos1_row[3],'change_time':infos1_row[4],'deadline':infos1_row[5]})\n ####################\n sql_share = \"select * from share\"\n infos_share = my_db.getInfo(sql_share)\n resultsshare = []\n for infos_row in infos_share:\n resultsshare.append(\n {'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n my_db.close()\n return render(req,\"index.html\",locals())\n except Exception as e:\n msg = '没有找到相关信息'\n return render(req,\"msg\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n\ndef order_delete(req,res_id):\n if comfirm(req):\n try:\n my_db = MynewcoderDB()\n #vip表的删除\n sql1 = \"delete from share where id =\"+res_id+\" \"\n my_db.execute(sql1)\n my_db.commit()\n #####################\n sql = \"select * from share\"\n infos = my_db.getInfo(sql)\n resultsshare = []\n for infos_row in infos:\n resultsshare.append({'id': infos_row[0], 'old': infos_row[1], 'new': infos_row[2], 'start_time': infos_row[3],\n 'end_time': infos_row[4], 'dingdanhao': infos_row[5], 'invitation': infos_row[6],\n 'change_time': infos_row[7]})\n #####################\n sql_share = \"select * from vip\"\n infos_share = my_db.getInfo(sql_share)\n results = []\n for infos1_row in infos_share:\n results.append(\n {'id':infos1_row[0],'vipid':infos1_row[1],'vip':infos1_row[2],'create_time':infos1_row[3],'change_time':infos1_row[4],'deadline':infos1_row[5]})\n my_db.close()\n return render(req,\"index.html\",locals())\n except Exception as e:\n msg = '没有找到相关信息'\n return render(req,\"msg\",locals())\n else:\n msg = \"请求错误\"\n return render(req,\"msg.html\", locals())\n" }, { "alpha_fraction": 0.5809018611907959, "alphanum_fraction": 0.5895225405693054, "avg_line_length": 29.15999984741211, "blob_id": "d8863d7c6f6533cf381f5bcdfa796b3180431882", "content_id": "c8bd5597c2474cadcd777c9a959957cd2c9486f8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1584, "license_type": "no_license", "max_line_length": 97, "num_lines": 50, "path": "/src/resume_tool/eml2txt.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -*- coding:utf8 -*-\n\nimport email.utils\nimport sys\nimport logging\n\nimport chardet\nimport html2text\nimport re\nfrom tools import *\naddsys()\n\n\ndef get_charset(message):\n return message.get_content_charset()\n\n\ndef convert_eml_to_txt(msg):\n try:\n logging.debug('Converting email to txt: ' + str(file))\n for par in msg.walk():\n if not par.is_multipart(): # 这里要判断是否是multipart，是的话，里面的数据是无用的\n charset = get_charset(par)\n if charset == None:\n mailContent = par.get_payload(decode=True)\n else:\n mailContent = par.get_payload(decode=True).decode(charset, 'replace')\n return mailContent\n\n except Exception, e:\n logging.error('Error in file: ' + file + str(e))\n return \"\"\n\n\n# 将所有email文档转换为txt格式\ndef handle_emlfiles(emlfile):\n fp = open(emlfile, \"r\")\n msg = email.message_from_file(fp) # 创建消息对象\n emltext = 'content:{}'.format(convert_eml_to_txt(msg))\n # print chardet.detect(emltext)\n if (chardet.detect(emltext)['encoding'] == 'GB2312'):\n str_file = html2text.html2text(emltext.decode(\"gbk\", 'ignore').encode(\"utf-8\", 'ignore'))\n if ((chardet.detect(emltext)['encoding'] == 'utf-8') or (\n chardet.detect(emltext)['encoding'] == 'UTF-8-SIG')):\n str_file = html2text.html2text(emltext)\n #print str_file\n return str_file\n # for t in str_file:\n # txt = re.sub(r'[# * | ]?', '', t) # drop #*\n # return txt\n" }, { "alpha_fraction": 0.5557677745819092, "alphanum_fraction": 0.5630252361297607, "avg_line_length": 32.32051467895508, "blob_id": "d4766ff2d12ef1396093045e66e0c73aaa3dfdd6", "content_id": "59b6c73e0592895722b96b753fa34db397f93473", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2648, "license_type": "no_license", "max_line_length": 105, "num_lines": 78, "path": "/src/question/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-*-coding:utf-8-*-\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import *\nfrom online.models import User\nfrom collections import Counter\nimport json\nimport sys\nimport time \nreload(sys) \nsys.setdefaultencoding('utf8') \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n \n if req.session['islogin'] == True:\n return render(req, \"question_index.html\")\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n\n#读大段然后内存处理\ndef info(req):\n if req.method == 'GET':\n data = ['review-frontend','acm-solutions','review-java','front-end-interview','nine-chapter']\n type = req.GET.get('type')\n if type == None:\n type = '1'\n page = req.GET.get('page')\n if page == None:\n page = '1'\n sqlstr = \"select * from question where type = '\"+data[int(type)]+\"' limit \"+str(int(page)-1)+\",1\"\n countsqlstr = \"select * from question where type = '\"+data[int(type)] +\"'\"\n queinfo = que_sql(sqlstr)\n maxnumber = len(que_sql(countsqlstr))\n maxlist = []\n pagenumber = int(page)\n maxlist.append(1)\n if maxnumber>3:\n if pagenumber < 3:\n maxlist.append(2)\n maxlist.append(3)\n elif pagenumber > maxnumber-2:\n maxlist.append(maxnumber-2)\n maxlist.append(maxnumber-1)\n else:\n for i in range(pagenumber-1,pagenumber+2):\n maxlist.append(i)\n maxlist.append(maxnumber)\n else:\n number = maxnumber\n for i in range(1,number+1):\n maxlist.append(i)\n print maxlist\n return render(req, \"question_info.html\", locals()) \n\ndef list(req):\n if req.method == 'GET':\n data = ['review-frontend','acm-solutions','review-java','front-end-interview','nine-chapter']\n type = req.GET.get('type')\n if type == None:\n type = '1'\n sqlstr = \"select * from question where type = '\"+data[int(type)]+\"'\"\n data = que_sql(sqlstr)\n return render(req, \"question_list.html\", locals()) \n\ndef que_sql(sqlstr,):\n my_db = MynewcoderDB()\n infos = my_db.getInfo(sqlstr)\n print sqlstr\n my_db.close()\n return infos \n \n\n\n\n" }, { "alpha_fraction": 0.5699234008789062, "alphanum_fraction": 0.5929118990898132, "avg_line_length": 23.85714340209961, "blob_id": "e19509f4a8e95ec2f842e58d9be8c4a0883d12cf", "content_id": "45ee65a740eacbd096b3c1d7c0f4fcd90a7dc2ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1044, "license_type": "no_license", "max_line_length": 51, "num_lines": 42, "path": "/src/tools/dbcon.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "'''\nCreated on 2018-1-11\n\n@author: corey\n'''\nimport platform\nimport MySQLdb\nimport jieba.posseg as pseg\nimport jieba.analyse as ana\nimport jieba\nclass MynewcoderDB:\n def __init__(self):\n self.db_conn= MySQLdb.connect(\n host='118.24.92.135',\n port = 3306,\n user='meng',\n passwd='123456',\n db ='newcoder',\n charset='utf8',\n )\n self.db_cur = self.db_conn.cursor()\n def execute(self,sqlstr):\n self.db_cur.execute(sqlstr)\n def getInfo(self,sqlstr):\n sqlstr = sqlstr.decode(\"utf-8\")\n self.execute(sqlstr)\n return self.db_cur.fetchall()\n def commit(self):\n self.db_conn.commit()\n def close(self):\n self.db_cur.close()\n self.db_conn.close()\n\nclass WordCut:\n def __init__(self,sentence):\n self.sentence = sentence\n def cutWords(self):\n self.words = pseg.cut(self.sentence)\n return self.words\n def top(self,num=20):\n words = ana.extract_tags(self.sentence,num)\n return self.words\n" }, { "alpha_fraction": 0.5396512746810913, "alphanum_fraction": 0.5584926605224609, "avg_line_length": 48.7342643737793, "blob_id": "f7e42b36706577485f899ebee39537369e1c1855", "content_id": "7e8e44b8281429398fcce7b53bada7c0b4c29774", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 7112, "license_type": "no_license", "max_line_length": 171, "num_lines": 143, "path": "/src/forum/migrations/0001_initial.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n# Generated by Django 1.10 on 2018-01-25 02:38\nfrom __future__ import unicode_literals\n\nimport datetime\nfrom django.conf import settings\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Application',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('status', models.IntegerField(default=0)),\n ('created_at', models.DateTimeField(auto_now_add=True)),\n ('updated_at', models.DateTimeField(auto_now=True)),\n ('receiver', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='appli_receiver', to=settings.AUTH_USER_MODEL)),\n ('sender', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='appli_sender', to=settings.AUTH_USER_MODEL)),\n ],\n options={\n 'db_table': 'application',\n 'verbose_name_plural': '\\u597d\\u53cb\\u7533\\u8bf7',\n },\n ),\n migrations.CreateModel(\n name='Column',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=30)),\n ('description', models.TextField()),\n ('img', models.CharField(default=b'/static/tx/default.jpg', max_length=200, verbose_name='\\u56fe\\u6807')),\n ('post_number', models.IntegerField(default=0)),\n ('created_at', models.DateTimeField(auto_now_add=True)),\n ('updated_at', models.DateTimeField(auto_now=True)),\n ('manager', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='column_manager', to=settings.AUTH_USER_MODEL)),\n ('parent', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='childcolumn', to='forum.Column')),\n ],\n options={\n 'ordering': ['-post_number'],\n 'db_table': 'column',\n 'verbose_name_plural': '\\u677f\\u5757',\n },\n ),\n migrations.CreateModel(\n name='Comment',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('content', models.TextField()),\n ('created_at', models.DateTimeField(auto_now_add=True)),\n ('updated_at', models.DateTimeField(auto_now=True)),\n ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),\n ('comment_parent', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='childcomment', to='forum.Comment')),\n ],\n options={\n 'ordering': ['created_at'],\n 'db_table': 'comment',\n 'verbose_name_plural': '\\u8bc4\\u8bba',\n },\n ),\n migrations.CreateModel(\n name='Message',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('content', models.TextField()),\n ('created_at', models.DateTimeField(auto_now_add=True)),\n ('updated_at', models.DateTimeField(auto_now=True)),\n ('receiver', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='message_receiver', to=settings.AUTH_USER_MODEL)),\n ('sender', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='message_sender', to=settings.AUTH_USER_MODEL)),\n ],\n options={\n 'db_table': 'message',\n 'verbose_name_plural': '\\u6d88\\u606f',\n },\n ),\n migrations.CreateModel(\n name='Nav',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=40, verbose_name='\\u5bfc\\u822a\\u6761')),\n ('url', models.CharField(blank=True, max_length=200, null=True, verbose_name='\\u6307\\u5411\\u5730\\u5740')),\n ('create_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='\\u521b\\u5efa\\u65f6\\u95f4')),\n ],\n options={\n 'ordering': ['-create_time'],\n 'db_table': 'nav',\n 'verbose_name': '\\u5bfc\\u822a\\u6761',\n 'verbose_name_plural': '\\u5bfc\\u822a\\u6761',\n },\n ),\n migrations.CreateModel(\n name='Post',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('title', models.CharField(max_length=30)),\n ('content', models.TextField()),\n ('view_times', models.IntegerField(default=0)),\n ('responce_times', models.IntegerField(default=0)),\n ('created_at', models.DateTimeField(auto_now_add=True)),\n ('updated_at', models.DateTimeField(auto_now=True)),\n ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='post_author', to=settings.AUTH_USER_MODEL)),\n ('column', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='forum.Column')),\n ('last_response', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),\n ],\n options={\n 'ordering': ['-created_at'],\n 'db_table': 'post',\n 'verbose_name_plural': '\\u4e3b\\u9898',\n },\n ),\n migrations.CreateModel(\n name='PostType',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('type_name', models.CharField(max_length=30)),\n ('description', models.TextField()),\n ('created_at', models.DateTimeField(default=datetime.datetime.now)),\n ],\n options={\n 'db_table': 'posttype',\n 'verbose_name_plural': '\\u4e3b\\u9898\\u7c7b\\u578b',\n },\n ),\n migrations.AddField(\n model_name='post',\n name='type_name',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='forum.PostType'),\n ),\n migrations.AddField(\n model_name='comment',\n name='post',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='forum.Post'),\n ),\n ]\n" }, { "alpha_fraction": 0.6170940399169922, "alphanum_fraction": 0.6393162608146667, "avg_line_length": 28, "blob_id": "085f16d6af02f2e8fe7a6156373878a777b39109", "content_id": "7e3f68ed7a92d3c2d20a9dcb6187f309571bbc2e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 635, "license_type": "no_license", "max_line_length": 51, "num_lines": 20, "path": "/src/resume_tool/run.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\nfrom all_extractor2 import *\nfrom main import *\nimport sys\ndef runresume(input):\n #处理多线程问题\n import pythoncom\n pythoncom.CoInitialize()\n handle_document(input.decode('utf-8','ignore'))\n filenames = input.split(\".\")\n filename = filenames[0]\n for i in range(1,len(filenames)-1):\n filename = filename+filenames[i]\n result_list = process(filename+'.txt')\n return result_list,filename+'.txt'\n \nif __name__ == '__main__':\n filename = r\"\\1111114北京工业大学-李小龙-硕士-数字媒体专业历.doc\"\n path = os.getcwd()\n runresume(path+filename)\n \n" }, { "alpha_fraction": 0.6198630332946777, "alphanum_fraction": 0.6198630332946777, "avg_line_length": 31.44444465637207, "blob_id": "129f9d2d3bf58ce126577ca6b20eaaa81ee8e678", "content_id": "fd70728454274b690607a7511190ce83747554ef", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 292, "license_type": "no_license", "max_line_length": 57, "num_lines": 9, "path": "/src/school/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom school import views\napp_name = 'school'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^info/$',views.info,name = 'info'),\n url(r'^all/$',views.all,name = 'all'),\n url(r'^test_one/$', views.test_one, name='test_one'),\n]\n" }, { "alpha_fraction": 0.5962733030319214, "alphanum_fraction": 0.6035196781158447, "avg_line_length": 20.46666717529297, "blob_id": "916af44ef1ecee189411041644c05e9302a06667", "content_id": "a4172f7c3bb4c18de23296d2224e42114a954ac3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1016, "license_type": "no_license", "max_line_length": 52, "num_lines": 45, "path": "/src/resume_tool/skill_extractor.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# encoding:utf-8\nimport re\nimport sys\nfrom bgtool.codeinit import *\nfrom tools import *\nreload(sys)\nsys.setdefaultencoding('utf8')\n\ninput_encode = 'utf-8'\n# 构建专业知识库\n\n# 抽取技能信息,并将日期按照顺序排列存入list\ndef skill_extract(str):\n result_list = []\n dic = \"\"\n if cmp(get_os(),\"n\")==0:\n dic = sys.path[0]+\"\\\\resume_tool\\\\skill_dic\"\n else:\n dic = sys.path[0]+\"/resume_tool/skill_dic\"\n jieba.load_userdict(dic)\n wordcut = WordCut(str)\n for word, flag in wordcut.cutWords():\n if cmp(flag,\"skill\")==0:\n if word not in result_list:\n result_list.append(word)\n return result_list\n\n\ndef process(input_file_path):\n with open(input_file_path, \"r\") as f:\n text = f.read()\n skill_list = skill_extract(text)\n for skill in skill_list:\n print skill\n\n\n\ndef main():\n print 'this is main'\n input_file_path = 'lcy.txt'\n result_dic = process(input_file_path)\n\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.5870736241340637, "alphanum_fraction": 0.5969479084014893, "avg_line_length": 24.31818199157715, "blob_id": "17df535e864ba27e5c0339cb97a9f86c5ce328bf", "content_id": "b5fb9801c4339d9a4a53868e77b68da43cd5800e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1324, "license_type": "no_license", "max_line_length": 84, "num_lines": 44, "path": "/src/resume_tool/school_ship_extractor.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# encoding:utf-8\nimport re\n\ndegree_str1 = u\"(一等奖学金|二等奖学金|三等奖学金|优秀毕业生|优良毕业生|国家励志奖学金|学生标兵|优秀学生|优秀本科生|三好学生|优秀学生干部)\"\ndegree_pattern1 = re.compile(degree_str1)\n\n# 抽取学位信息,并将日期按照顺序排列存入list\ndef ship_extract(str):\n result_list = []\n school_list = degree_pattern1.findall(str)\n for d in school_list:\n # print d\n result_list.append(d)\n return result_list\n\n# 会破坏信息排布的顺序\n# 该函数并没有被用到\ndef remove_duplicate_data(degree_list):\n degree_dic = {}\n for k in degree_list:\n if k not in degree_dic:\n degree_dic.setdefault(k, 0)\n else:\n degree_list.remove(k) #去除第一个k对象\n return degree_list\n\ndef process(input_file_path):\n for line in open(input_file_path, 'r'):\n try:\n line = line.strip().decode('utf-8') # 设置编码格式\n except:\n line = line.strip().decode('gb2312') \n degree_list = ship_extract(line)\n for d in degree_list:\n print d\n print '-------'\n\ndef main():\n print 'this is main'\n input_file_path = 'lcy.txt'\n result_dic = process(input_file_path)\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.6614583134651184, "alphanum_fraction": 0.6614583134651184, "avg_line_length": 26.571428298950195, "blob_id": "f48e0821c5b797932b93e44d83002e7a61aaffab", "content_id": "c0fc01580658691076f0b184fd88abae820a11fb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 192, "license_type": "no_license", "max_line_length": 48, "num_lines": 7, "path": "/src/company/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom company import views\napp_name = 'company'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^info/$',views.info,name = 'info'),\n]" }, { "alpha_fraction": 0.739393949508667, "alphanum_fraction": 0.7515151500701904, "avg_line_length": 32.20000076293945, "blob_id": "67e2c5e74ba289f1836ba2da4374b82ee9fa922a", "content_id": "fea09f53bbeb3d1cf6d8933c0af738a7d3c56350", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 165, "license_type": "no_license", "max_line_length": 55, "num_lines": 5, "path": "/src/manager/models.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.db import models#do not understand only key\n\nclass TestData(models.Model):\n info_id = models.IntegerField()\n info = models.CharField(max_length=50)" }, { "alpha_fraction": 0.5875942707061768, "alphanum_fraction": 0.6010058522224426, "avg_line_length": 27.404762268066406, "blob_id": "46873db7f9b5da2ba246a6766f023fd44f10a742", "content_id": "9804df2dbe594b738c0407956a94db335aa6874c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1219, "license_type": "no_license", "max_line_length": 90, "num_lines": 42, "path": "/src/resume_tool/doc2txt.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -*- coding:utf8 -*-\n\nimport sys\nimport logging\nimport chardet\nfrom tools import *\n\naddsys()\n\n\n# Load MS Word document,change to txt and return the document object #import win32com\ndef convert_doc_to_txt(file): # takes in a filename and returns a word document object\n try:\n logging.debug('Converting word to txt: ' + str(file))\n wordapp = win32com.client.Dispatch('Word.Application')\n wordapp.Visible = False\n # 后缀名doc切词\n if file[-4:] == '.doc':\n doc2t = file[:-4]\n elif file[-5:] == '.docx':\n doc2t = file[:-5]\n print file\n wordapp.Documents.Open(file, False, False)# FileName, ConfirmConversions, ReadOnly\n wordapp.ActiveDocument.SaveAs(doc2t, FileFormat=2)\n wordapp.ActiveDocument.Close()\n fin = open(doc2t + '.txt', 'r')\n strfile = fin.read()\n #print strfile\n return strfile\n\n except Exception, e:\n logging.error('Error in file: ' + str(e))\n return \"\"\n\n\n# 将word文档转换为txt格式\ndef handle_docfiles(docfile):\n if docfile[-4:] == '.DOC':\n doc_file = docfile[:-4] + '.doc'\n else:\n doc_file = docfile\n convert_doc_to_txt(doc_file)\n" }, { "alpha_fraction": 0.6206896305084229, "alphanum_fraction": 0.6416791677474976, "avg_line_length": 25.68000030517578, "blob_id": "adf94bc5314bacb207b619df50fb7d19bbeae8ba", "content_id": "506c8a427b4454278e1a47a0e6ff1019d09104b0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 707, "license_type": "no_license", "max_line_length": 97, "num_lines": 25, "path": "/src/resume_tool/html2txt.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -*- coding:utf8 -*-\n\nimport sys\nimport logging\n\nimport re\nimport chardet\nimport html2text\nfrom tools import *\n\naddsys()\n\n# 将word文档转换为txt格式\ndef handle_htmlfiles(htmlfile):\n fin = open(htmlfile, 'r')\n strfile = fin.read()\n #print chardet.detect(strfile)\n # 文本格式的编码方式统一为utf-8\n if (chardet.detect(strfile)['encoding'] == 'GB2312'):\n str_file = html2text.html2text(strfile.decode(\"gbk\", 'ignore').encode(\"utf-8\", 'ignore'))\n if ((chardet.detect(strfile)['encoding'] == 'utf-8') or (\n chardet.detect(strfile)['encoding'] == 'UTF-8-SIG')):\n str_file = html2text.html2text(strfile)\n #print str_file\n return str_file\n" }, { "alpha_fraction": 0.5322144031524658, "alphanum_fraction": 0.5463707447052002, "avg_line_length": 50.30583572387695, "blob_id": "1289f0dd55511074306d40a7e7155d29107af99f", "content_id": "49218f97e13fe04e000518f81becdfe10073aba5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 26007, "license_type": "no_license", "max_line_length": 346, "num_lines": 497, "path": "/src/person/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-*-coding:utf-8-*-\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import *\nfrom online.models import User\nfrom collections import Counter\nimport json\nimport sys\nimport time\nimport xlrd\nreload(sys) \nsys.setdefaultencoding('utf-8')\ndef index(req):\n if req.method == 'GET':\n qz = 1\n req.session['qz'] = 1\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n \n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(**condition)\n if r.myresume != None and len(r.myresume)!=0:\n return render(req, \"person_index.html\",locals())\n else:\n jl = 1\n return render(req, \"person_index.html\", locals())\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n\ndef index2(req):\n if req.method == 'GET':\n qz = 0\n req.session['qz'] = 0\n try:\n islogin = req.session['islogin']\n except Exception, e:\n msg = '请登录'\n return render(req, 'msg.html', locals())\n\n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(**condition)\n if r.myresume != None and len(r.myresume) != 0:\n return render(req, \"person_index.html\", locals())\n else:\n jl = 1\n return render(req, \"person_index.html\", locals())\n else:\n msg = '请登录'\n return render(req, 'msg.html', locals())\n#读大段然后内存处理\ndef info(req):\n if req.method == 'POST':\n qz = req.session['qz']\n try:\n comp = req.POST['comp']\n except:\n comp = \"\"\n try:\n job = req.POST['job']\n except:\n job = \"\"\n try:\n becomp = req.POST['becomp']\n except:\n becomp = \"\"\n try:\n bejob = req.POST['bejob']\n except:\n bejob = \"\"\n edu = req.POST['edu']\n major = req.POST['major']\n degree = req.POST['degree']\n\n email=req.session['user_info']['email']\n sqlstr = \"INSERT INTO querylog (email,comp,job,becomp,bejob,edu,major,degree) VALUES ('\"+email+\"','\"+ comp+\"','\"+job+\"','\"+becomp+\"','\"+bejob+\"','\"+edu+\"','\"+major+\"','\"+degree+\"')\"\n my_db = MynewcoderDB()\n infos = my_db.execute(sqlstr)\n my_db.commit()\n my_db.close()\n\n \n \n comp_list = []\n pos_list = []\n degree_list = []\n edu_list = []\n major_list = []\n detail_list = []\n myedunum = 0\n #该大学去的最多岗位和公司,公司UI和岗位UI\n if len(edu)!=0:\n import random\n key = random.randint(1,30000)\n keynum = random.randint(200,1000)\n \n #为空测试结果一致\n sqlstr = \"select bz_job.company,bz_job.position from bz_job where bz_job.name in (select name from bz_edu where bz_edu.edu like '%\"+edu.encode(\"utf-8\")+\"%' and bz_edu.major like '%\"+major.encode(\"utf-8\")+\"%' and bz_edu.degree like '%\"+degree+\"%')\"\n eduinfo = person_sql(sqlstr)\n if len(eduinfo)<200:\n sqlstr = \"select bz_job.company,bz_job.position from bz_job where bz_job.name in (select name from bz_edu where bz_edu.edu like '%\"+edu.encode(\"utf-8\")+\"%' and bz_edu.degree like '%\"+degree.encode(\"utf-8\")+\"%')\"\n eduinfo = person_sql(sqlstr)\n if len(eduinfo)<200:\n sqlstr = \"select bz_job.company,bz_job.position from bz_job where bz_job.name in (select name from bz_edu where bz_edu.degree like '%\"+degree.encode(\"utf-8\")+\"%') limit \"+str(key)+\",\"+str(keynum)\n eduinfo = person_sql(sqlstr)\n for tcomp,tpos in eduinfo:\n if len(tcomp) != 0:\n comp_list.append(tcomp)\n if len(tpos) != 0:\n pos_list.append(tpos) \n compnumber = int(len(eduinfo)*3*3.14)\n pos_data,pos_num,pos_t_data,pos_t_num = data_single_handle(pos_list)\n comp_data,comp_num,comp_t_data,comp_t_num = data_single_handle(comp_list)\n \n if len(comp)!=0:\n #该公司的统计信息\n sqlstr = \"select bz_edu.degree, bz_edu.edu ,bz_edu.major from bz_edu where bz_edu.name in (select name from bz_job where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' and bz_job.position like '%\" + job.encode(\"utf-8\") + \"%')\"\n compinfo = person_sql(sqlstr)\n if len(compinfo)<200:\n sqlstr = \"select bz_edu.degree, bz_edu.edu ,bz_edu.major from bz_edu where bz_edu.name in (select name from bz_job where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%')\"\n compinfo = person_sql(sqlstr)\n edunumber = int(len(compinfo)*3*3.14)\n \n for td,te,tm in compinfo:\n if len(td) != 0:\n degree_list.append(td)\n if len(te) != 0:\n edu_list.append(te)\n if len(tm) != 0:\n major_list.append(tm)\n degree_list = change_dic(degree_list)\n a = {}\n if len(edu)!=0:\n templist = Counter(edu_list) \n myedunum = templist[edu]\n a['name'] = edu\n a['value'] = int(myedunum*3.14*3) \n edu_list = change_dic(edu_list)\n if len(a)!=0 and myedunum!=0:\n edu_list.pop(-1)\n edu_list.append(a)\n degree_t_data = degree_list\n degree_data = json.dumps(degree_list) \n \n major_data,major_num,major_t_data,major_t_num = data_single_handle(major_list)\n major_list = change_dic(major_list)\n \n #该公司以前的人都去哪了，即关联公司\n sqlstr = \"select company,count(company) from bz_job where name in (select bz_job.name from bz_job where bz_job.company like '%\"+ comp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ comp.encode(\"utf-8\")+\"%' group by company ORDER BY COUNT(company) DESC LIMIT 5\"\n lcomp_data,lcomp_num,lcomp_t_data,lcomp_t_num,lcomp_number = data_handle(sqlstr)\n #这是个人项目经历\n if len(edu)!=0:\n num = 4\n tcount = 0\n while(num):\n tcount = tcount+1\n sqlstr = \"select * from bz_job where bz_job.name in (SELECT * FROM (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\"+comp.encode(\"utf-8\")+\"%' and bz_edu.edu like '%\"+edu+\"%' and bz_edu.name=bz_job.name ORDER BY bz_job.time desc limit \"+str(tcount)+\",1) AS s)\"\n detailinfo = person_sql(sqlstr)\n if len(detailinfo)>1 and len(detailinfo)<10:\n detail_list.append(detailinfo)\n num = num -1\n if tcount > 20:\n break\n \n if len(job)!=0: \n sqlstr = \"select position,count(position) from bz_job where bz_job.position!='' and name in (select bz_job.name from bz_job where bz_job.position like '%\"+ job.encode(\"utf-8\")+\"%') and bz_job.position not like '%\"+ job.encode(\"utf-8\")+\"%' group by position ORDER BY COUNT(position) DESC LIMIT 5\"\n ljob_data,ljob_num,ljob_t_data,ljob_t_num,ljob_number= data_handle(sqlstr)\n sqlstr = \"select position from bz_job where bz_job.position!='' and name in (select bz_job.name from bz_job where bz_job.position like '%\"+ job.encode(\"utf-8\")+\"%') and bz_job.position not like '%\"+ job.encode(\"utf-8\")+\"%' group by position ORDER BY COUNT(position)\"\n ljob_number = int(len(person_sql(sqlstr))*3.14*3)\n if len(becomp)!=0:\n sqlstr = \"select company,count(company) from bz_job where bz_job.company!='' and name in (select bz_job.name from bz_job where bz_job.company like '%\"+ becomp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ becomp.encode(\"utf-8\")+\"%' group by company ORDER BY COUNT(company) DESC LIMIT 5\"\n bcomp_data,bcomp_num,bcomp_t_data,bcomp_t_num,bcomp_number = data_handle(sqlstr)\n if len(comp)!=0:\n sqlstr = \"select company from bz_job where bz_job.company!='' and name in (select bz_job.name from bz_job where bz_job.company like '%\" + becomp.encode(\n \"utf-8\") + \"%') and bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' \"\n print sqlstr\n tempnumber = int(len(person_sql(sqlstr)) * 3.14 * 3)\n if comp not in bcomp_t_data:\n bcomp_t_data.append(comp)\n bcomp_t_num.append(tempnumber)\n bcomp_data = json.dumps(bcomp_t_data)\n bcomp_num = json.dumps(bcomp_t_num)\n\n\n\n sqlstr = \"select company from bz_job where bz_job.company!='' and name in (select bz_job.name from bz_job where bz_job.company like '%\"+ becomp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ becomp.encode(\"utf-8\")+\"%'\"\n bcomp_number = int(len(person_sql(sqlstr)) * 3.14 * 3)\n if len(bejob)!=0:\n sqlstr = \"select position,count(position) from bz_job where bz_job.position!='' and name in (select bz_job.name from bz_job where bz_job.position like '%\"+ bejob.encode(\"utf-8\")+\"%') and bz_job.position not like '%\"+ bejob.encode(\"utf-8\")+\"%' group by position ORDER BY COUNT(position) DESC LIMIT 5\"\n bjob_data,bjob_num,bjob_t_data,bjob_t_num,bjob_number = data_handle(sqlstr)\n sqlstr = \"select position from bz_job where bz_job.position!='' and name in (select bz_job.name from bz_job where bz_job.position like '%\"+ bejob.encode(\"utf-8\")+\"%') and bz_job.position not like '%\"+ bejob.encode(\"utf-8\")+\"%'\"\n bjob_number = int(len(person_sql(sqlstr)) * 3.14 * 3)\n #缺少验证信息\n return render(req, \"person_info.html\", locals()) \n\n\n#这个方法直接sql全部处理完 \ndef test(req):\n if req.method == 'GET': \n comp = req.GET.get('comp')\n if comp == None:\n comp = \"\"\n job = req.GET.get('job')\n if job == None:\n job = \"\"\n edu = req.GET.get('edu')\n if edu == None:\n edu = \"\"\n major = req.GET.get('major')\n if major == None:\n major = \"\"\n degree = req.GET.get('degree')\n if degree == None:\n degree = \"\"\n becomp =req.GET.get('becomp')\n if becomp == None:\n becomp = \"\"\n bejob = req.GET.get('bejob')\n if bejob == None:\n bejob = \"\"\n comp_list = []\n pos_list = []\n degree_list = []\n edu_list = []\n major_list = []\n detail_list = []\n myedunum = 0\n #该大学去的最多岗位和公司,公司UI和岗位UI\n if len(edu)!=0:\n import random\n key = random.randint(1,30000)\n keynum = random.randint(200,1000)\n \n #为空测试结果一致\n sqlstr = \"select bz_job.company,bz_job.position from bz_job where bz_job.name in (select name from bz_edu where bz_edu.edu like '%\"+edu.encode(\"utf-8\")+\"%' and bz_edu.major like '%\"+major.encode(\"utf-8\")+\"%' and bz_edu.degree like '%\"+degree+\"%')\"\n eduinfo = person_sql(sqlstr)\n if len(eduinfo)<200:\n sqlstr = \"select bz_job.company,bz_job.position from bz_job where bz_job.name in (select name from bz_edu where bz_edu.edu like '%\"+edu.encode(\"utf-8\")+\"%' and bz_edu.degree like '%\"+degree.encode(\"utf-8\")+\"%')\"\n eduinfo = person_sql(sqlstr)\n if len(eduinfo)<200:\n sqlstr = \"select bz_job.company,bz_job.position from bz_job where bz_job.name in (select name from bz_edu where bz_edu.degree like '%\"+degree.encode(\"utf-8\")+\"%') \"\n eduinfo = person_sql(sqlstr)\n for tcomp,tpos in eduinfo:\n if len(tcomp) != 0:\n comp_list.append(tcomp)\n if len(tpos) != 0:\n pos_list.append(tpos) \n compnumber = int(len(eduinfo)*3*3.14)\n pos_data,pos_num,pos_t_data,pos_t_num = data_single_handle(pos_list)\n comp_data,comp_num,comp_t_data,comp_t_num = data_single_handle(comp_list)\n \n if len(comp)!=0:\n sqlstr = \"select * from company where 标题 like '%\" + comp.encode(\"utf-8\") + \"%'\"\n compintr = person_sql(sqlstr)\n compintr = compintr[0]\n jstemp = ['百度','腾讯','阿里巴巴']\n jsdtemp =[[84,66,1000,'百度','百度'],[88,60,1000,'腾讯','腾讯'],[86,64,1000,'阿里巴巴','阿里巴巴']]\n\n try:\n jsname = json.dumps(jstemp)\n data = [float(compintr[8]) * 20, float(compintr[9]) * 20, 1000, compintr[1], compintr[1]]\n if not compintr[1] in jstemp:\n jsdtemp.append(data)\n jstemp.append(compintr[1])\n jsdata = json.dumps(jsdtemp)\n except:\n compintr = []\n #该公司的统计信息\n sqlstr = \"select bz_edu.degree, bz_edu.edu ,bz_edu.major from bz_edu where bz_edu.name in (select name from bz_job where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' and bz_job.position like '%\" + job.encode(\"utf-8\") + \"%')\"\n compinfo = person_sql(sqlstr)\n if len(compinfo)<200:\n sqlstr = \"select bz_edu.degree, bz_edu.edu ,bz_edu.major from bz_edu where bz_edu.name in (select name from bz_job where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%')\"\n compinfo = person_sql(sqlstr)\n edunumber = int(len(compinfo)*3*3.14)\n \n for td,te,tm in compinfo:\n if len(td) != 0:\n degree_list.append(td)\n if len(te) != 0:\n edu_list.append(te)\n if len(tm) != 0:\n major_list.append(tm)\n degree_list = change_dic(degree_list)\n a = {}\n if len(edu)!=0:\n templist = Counter(edu_list) \n myedunum = templist[edu]\n a['name'] = edu\n a['value'] = int(myedunum*3.14*3) \n edu_list = change_dic(edu_list)\n if len(a)!=0 and myedunum!=0:\n edu_list.pop(-1)\n edu_list.append(a)\n degree_t_data = degree_list\n degree_data = json.dumps(degree_list)\n \n major_data,major_num,major_t_data,major_t_num = data_single_handle(major_list)\n major_list = change_dic(major_list)\n \n #该公司以前的人都去哪了，即关联公司\n sqlstr = \"select company,count(company) from bz_job where name in (select bz_job.name from bz_job where bz_job.company like '%\"+ comp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ comp.encode(\"utf-8\")+\"%' group by company ORDER BY COUNT(company) DESC LIMIT 5\"\n lcomp_data,lcomp_num,lcomp_t_data,lcomp_t_num,lcomp_number = data_handle(sqlstr)\n sqlstr = \"select company from bz_job where name in (select bz_job.name from bz_job where bz_job.company like '%\"+ comp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ comp.encode(\"utf-8\")+\"%'\"\n lcomp_number = int(len(person_sql(sqlstr))*3.14*3) \n #这是个人项目经历\n if len(edu)!=0:\n num = 4\n tcount = 0\n while(num):\n tcount = tcount+1\n sqlstr = \"select * from bz_job where bz_job.name in (SELECT * FROM (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\"+comp.encode(\"utf-8\")+\"%' and bz_edu.edu like '%\"+edu+\"%' and bz_edu.name=bz_job.name ORDER BY bz_edu.time desc limit \"+str(tcount)+\",1) AS s)\"\n detailinfo = person_sql(sqlstr)\n if len(detailinfo)>1 and len(detailinfo)<10:\n detail_list.append(detailinfo)\n num = num -1\n if tcount > 20:\n while(num):\n sqlstr = \"select * from bz_job where bz_job.name in (SELECT * FROM (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\"+comp.encode(\"utf-8\")+\"%' and bz_edu.degree like '%\"+degree+\"%' and bz_edu.name=bz_job.name ORDER BY bz_edu.time desc limit \"+str(tcount)+\",1) AS s)\"\n detailinfo = person_sql(sqlstr)\n tcount = tcount+1\n if len(detailinfo)>1 and len(detailinfo)<10:\n detail_list.append(detailinfo)\n num = num -1\n if tcount >100:\n num = 0\n break\n sqlstr = \"select position from bz_job where position!='' and bz_job.name in (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' and bz_edu.edu like '%\" + edu.encode(\"utf-8\") + \"%' and bz_job.name = bz_edu.name)\"\n test_number = int(len(person_sql(sqlstr))*3.14*3)\n \n if test_number<200*3.14*3:\n sqlstr = \"select position from bz_job where position!='' and bz_job.name in (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' and bz_edu.degree like '%\" + degree.encode(\"utf-8\") + \"%' and bz_job.name = bz_edu.name )\"\n test_number = int(len(person_sql(sqlstr))*3.14*3) \n sqlstr = \"select position,count(position) from bz_job where position!='' and bz_job.name in (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' and bz_edu.degree like '%\" + degree.encode(\"utf-8\") + \"%') group by position ORDER BY COUNT(position) DESC LIMIT 5\"\n test_data,test_num,test_t_data,test_t_num,testnumber = data_handle(sqlstr)\n else:\n sqlstr = \"select position,count(position) from bz_job where position!='' and bz_job.name in (select bz_job.name from bz_job,bz_edu where bz_job.company like '%\" + comp.encode(\"utf-8\") + \"%' and bz_edu.edu like '%\" + edu.encode(\"utf-8\") + \"%' and bz_job.name = bz_edu.name) group by position ORDER BY COUNT(position) DESC LIMIT 5\"\n test_data,test_num,test_t_data,test_t_num,testnumber = data_handle(sqlstr)\n\n \n if len(job)!=0: \n sqlstr = \"select position,count(position) from bz_job where bz_job.position!='' and name in (select bz_job.name from bz_job where bz_job.position like '%\"+ job.encode(\"utf-8\")+\"%') and bz_job.position not like '%\"+ job.encode(\"utf-8\")+\"%' group by position ORDER BY COUNT(position) DESC LIMIT 5\"\n ljob_data,ljob_num,ljob_t_data,ljob_t_num,ljob_number= data_handle(sqlstr)\n\n \n if len(becomp)!=0:\n sqlstr = \"select company,count(company) from bz_job where bz_job.company!='' and name in (select bz_job.name from bz_job where bz_job.company like '%\"+ becomp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ becomp.encode(\"utf-8\")+\"%' group by company ORDER BY COUNT(company) DESC LIMIT 5\"\n bcomp_data,bcomp_num,bcomp_t_data,bcomp_t_num,bcomp_number = data_handle(sqlstr)\n sqlstr = \"select company from bz_job where bz_job.company!='' and name in (select bz_job.name from bz_job where bz_job.company like '%\"+ becomp.encode(\"utf-8\")+\"%') and bz_job.company not like '%\"+ becomp.encode(\"utf-8\")+\"%'\"\n bcomp_number = int(len(person_sql(sqlstr))*3*3.14) \n \n if len(bejob)!=0:\n sqlstr = \"select position,count(position) from bz_job where bz_job.position!='' and name in (select bz_job.name from bz_job where bz_job.position like '%\"+ bejob.encode(\"utf-8\")+\"%') and bz_job.position not like '%\"+ bejob.encode(\"utf-8\")+\"%' group by position ORDER BY COUNT(position) DESC LIMIT 5\"\n bjob_data,bjob_num,bjob_t_data,bjob_t_num,bjob_number = data_handle(sqlstr)\n\n #添加了关键字浮动\n word_list =readexcel(comp)\n word_list = json.dumps(word_list)\n\n #添加了公司信息详情\n if len(comp)!=0:\n sqlstr =\"select count(college_college.college_name) from bz_edu,college_college where bz_edu.name in (select name from bz_job where bz_job.company like '%\"+comp+\"%' and bz_job.position like '%\"+job+\"%') and bz_edu.edu = college_college.college_name GROUP BY college_college.isTwo;\"\n twoinfo = person_sql(sqlstr)\n if len(twoinfo) == 1:\n sqlstr = \"select count(college_college.college_name) from bz_edu,college_college where bz_edu.name in (select name from bz_job where bz_job.company like '%\" + comp + \"%') and bz_edu.edu = college_college.college_name GROUP BY college_college.isTwo;\"\n twoinfo = person_sql(sqlstr)\n twodata = []\n twotemp = {}\n twotemp['name'] = \"非211学校\"\n twotemp['value'] = int(int(twoinfo[0][0])*3.14)\n twodata.append(twotemp)\n twotemp = {}\n twotemp['name'] = \"211学校\"\n twotemp['value'] = int(int(twoinfo[1][0])*3.14)\n twodata.append(twotemp)\n twodata = json.dumps(twodata)\n\n sqlstr =\"select count(college_college.college_name) from bz_edu,college_college where bz_edu.name in (select name from bz_job where bz_job.company like '%\"+comp+\"%' and bz_job.position like '%\"+job+\"%') and bz_edu.edu = college_college.college_name GROUP BY college_college.isNine;\"\n nineinfo = person_sql(sqlstr)\n if len(nineinfo) == 1:\n sqlstr = \"select count(college_college.college_name) from bz_edu,college_college where bz_edu.name in (select name from bz_job where bz_job.company like '%\" + comp + \"%') and bz_edu.edu = college_college.college_name GROUP BY college_college.isNine;\"\n nineinfo = person_sql(sqlstr)\n ninedata = []\n ninetemp = {}\n ninetemp['name'] = \"非985学校\"\n ninetemp['value'] = int(int(nineinfo[0][0])*3.14)\n ninedata.append(ninetemp)\n ninetemp = {}\n ninetemp['name'] = \"985学校\"\n ninetemp['value'] = int(int(nineinfo[1][0])*3.14)\n ninedata.append(ninetemp)\n ninedata = json.dumps(ninedata)\n\n excelFile = 'maindata.xls'\n data = xlrd.open_workbook(sys.path[0] + \"//\" + excelFile)\n table = data.sheets()[0]\n nrows = table.nrows\n ncols = table.ncols\n mainlist = []\n for i in xrange(nrows-100, nrows):\n rowValues = table.row_values(i)\n if str(rowValues[1]).find(str(comp))>=0:\n mainlist.append(rowValues)\n #缺少验证信息\n return render(req, \"person_info.html\", locals()) \n \ndef person_sql(sqlstr,):\n my_db = MynewcoderDB()\n infos = my_db.getInfo(sqlstr)\n my_db.close()\n return infos\n\ndef data_single_handle(comp_list):\n comp_data = []\n comp_num = []\n comp_list = change_dic(comp_list)\n for tdata in comp_list:\n comp_data.append(tdata['name'])\n comp_num.append(int(tdata['value']))\n temp_data = comp_data\n temp_num = comp_num\n comp_data = json.dumps(comp_data)\n comp_num = json.dumps(comp_num)\n return comp_data,comp_num,temp_data,temp_num \n\ndef data_handle(sqlstr):\n data = []\n num = []\n beforeinfo = person_sql(sqlstr)\n number = int(len(beforeinfo)*3.14*3)\n for tdata,tnum in beforeinfo:\n data.append(tdata)\n num.append(int(tnum*3.14*3)) \n tdata = data\n tnum = num\n data = json.dumps(data)\n num = json.dumps(num)\n return data,num,tdata,tnum,number\n\ndef change_dic(List):\n alist = []\n data = Counter(List).most_common(5)\n for tdata,tnum in data:\n a = {}\n a['name'] = tdata\n a['value'] = int(tnum*3.14*3)\n alist.append(a)\n return alist\n\ndef word_extract(str):\n wordcut = WordCut(str)\n word_dict = {}\n for word, flag in wordcut.cutWords():\n\n if flag == \"n\" or flag == \"vn\":\n if word not in word_dict:\n word_dict[word] = 1\n else:\n word_dict[word] += 1\n if flag == \"eng\":\n word = word.upper()\n if word not in word_dict:\n word_dict[word] = 10\n else:\n word_dict[word] += 10\n\n import operator\n word_dict = sorted(word_dict.items(), key=operator.itemgetter(1))\n word_list = []\n for ktemp,vtemp in word_dict:\n word = {}\n word['text'] = ktemp\n import random\n word['weight'] = int(vtemp)\n word_list.append(word)\n word_list=word_list[len(word_list)-20:-1]\n return word_list\n\ndef readexcel(keyword,filename = \"lagou.xls\"):\n book = xlrd.open_workbook(filename)#得到Excel文件的book对象，实例化对象\n sheet0 = book.sheet_by_index(0) # 通过sheet索引获得sheet对象\n nrows = sheet0.nrows # 获取行总数\n ncols = sheet0.ncols #获取列总数\n words = \"\"\n for i in range(0,nrows):\n row_data = sheet0.row_values(i) # 获得第1行的数据列表\n if row_data[0] == keyword:\n words = words+row_data[11]\n return word_extract(words)\n\n\n" }, { "alpha_fraction": 0.5702868103981018, "alphanum_fraction": 0.5961447954177856, "avg_line_length": 24.321428298950195, "blob_id": "acfa33c211a738fcda5d050578618e9278a34fad", "content_id": "8756fa3fd31805a80ac522c146c01bde1d37dd2f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2351, "license_type": "no_license", "max_line_length": 113, "num_lines": 84, "path": "/src/resume_tool/school_name_extractor.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# encoding:utf-8\nfrom major_extractor import *\n\nimport re\n\ninput_encode = 'utf-8'\n\n#|[\\u4e00-\\u9fa5]中专+|[\\u4e00-\\u9fa5]+大专\nschool_str1 = u\"([\\u4e00-\\u9fa5]+大学|[\\u4e00-\\u9fa5]+学院|[\\u4e00-\\u9fa5]+分校|[\\u4e00-\\u9fa5]+学校|[\\u4e00-\\u9fa5]+电大)\"\n#school_str1 = u\"[\\u4e00-\\u9fa5]+(大学|学院|分校|学校|电大)\"\nschool_pattern1 = re.compile(school_str1)\n\nstop_words = {}\n\n#stop_words[u\"至今\"] = 0\n\n#stop_words = {u\"至今\": \"\", u\"月\": \"\", u\"本科\": \"\", u\"研究生\": \"\", u\"硕士\": \"\", u\"硕士研究生\": \"\"}\nstop_words[u\"至今\"] = 0\nstop_words[u\"月\"] = 0\nstop_words[u'年'] = 0\nstop_words[u'日'] = 0\n\n#消除学校前面的专业\n# # 构建专业知识库\n# major_set = set([v.strip() for v in open('major_dic', 'r')])\n\n\n# 将items（list）所有空元素，过滤掉\ndef drop_null(items):\n result = []\n for item in items:\n if len(item.strip()) == 0:\n continue\n result.append(item.strip())\n return result\n\n# items 是学校,当学校名字中含有以stop_words开头时,将stop_wrods替换成空.\ndef drop_stop_words(items):\n result = []\n for item in items:\n if len(item.strip()) == 0:\n continue\n for sw in stop_words:\n if item.startswith(sw):\n item = item.replace(sw, \"\")\n result.append(item.strip())\n return result\n\n# 抽取日期信息,并将日期按照顺序排列存入list\ndef school_name_extract(str):\n result_list = []\n school_list = school_pattern1.findall(str)\n for d in school_list:\n # print d\n result_list.append(d)\n result_list = drop_null(result_list)\n result_list = drop_stop_words(result_list)\n return result_list\n\ndef get_education_number_from_school_name(input_str):\n date_size = 0\n size = len(school_name_extract(input_str))\n return size\n\ndef process(input_file_path):\n for line in open(input_file_path, 'r'):\n line = line.strip().decode(input_encode)\n print line\n school_list = school_name_extract(line)\n\n for d in school_list:\n print d\n\n print 'education number: '\n print get_education_number_from_school_name(line)\n print '-------'\n\ndef main():\n print 'this is main'\n input_file_path = 'data/samples'\n result_dic = process(input_file_path)\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.6340425610542297, "alphanum_fraction": 0.6340425610542297, "avg_line_length": 28.5, "blob_id": "6c795c3783ac633feb2120de1fb6d409000d87d7", "content_id": "54ebbcdf369d870986072931ab33f7ccfdaa9fab", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 235, "license_type": "no_license", "max_line_length": 48, "num_lines": 8, "path": "/src/intr/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom intr import views\napp_name = 'intr'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^info/$',views.info,name = 'info'),\n url(r'^money/$',views.money,name = 'money'),\n]" }, { "alpha_fraction": 0.45805981755256653, "alphanum_fraction": 0.46243616938591003, "avg_line_length": 38.53845977783203, "blob_id": "5a5b9ab55ce4339542aaf8a726a9627b67cedf28", "content_id": "e290818fe4079b0b423d4a90f08e284afe284f25", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4335, "license_type": "no_license", "max_line_length": 155, "num_lines": 104, "path": "/src/pay/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-*-coding:utf-8-*-\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import *\nfrom online.models import User\nimport sys \nreload(sys) \nsys.setdefaultencoding('utf8') \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n \n if req.session['islogin'] == True:\n return render(req,\"payindex.html\")\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\ndef setjian(req):\n if req.method == 'POST':\n try:\n text=req.POST.get(\"text\",'')\n intr=req.POST.get(\"intr\",'')\n myinfo = text.split()\n comp = myinfo[1]\n local = myinfo[3]\n gw = myinfo[5]\n my_db = MynewcoderDB()\n sqlstr = \"SELECT * from pay where 公司 = '\"+comp+\"' and 地点 = '\"+local+\"' and 岗位 = '\"+gw+\"' and 说明 ='\"+intr+\"'\"\n myinfos = my_db.getInfo(sqlstr)\n mynum = int(myinfos[0][4])\n mynum = mynum-1\n sqlstr = \"Update pay SET 可信度 = \"+str(mynum)+\" where 公司 = '\"+comp+\"' and 地点 = '\"+local+\"' and 岗位 = '\"+gw+\"' and 说明 ='\"+intr+\"'\"\n my_db.execute(sqlstr)\n my_db.commit()\n my_db.close()\n msg = \"点评成功\"\n return HttpResponse(msg)\n except Exception,e:\n print e\n msg = \"输入错误，请联系管理员\"\n return HttpResponse(msg)\n else:\n msg = \"点评成功\"\n return HttpResponse(msg) \ndef setjia(req):\n if req.method == 'POST':\n try:\n text=req.POST.get(\"text\",'')\n intr=req.POST.get(\"intr\",'')\n myinfo = text.split()\n comp = myinfo[1]\n local = myinfo[3]\n gw = myinfo[5]\n my_db = MynewcoderDB()\n sqlstr = \"SELECT * from pay where 公司 = '\"+comp+\"' and 地点 = '\"+local+\"' and 岗位 = '\"+gw+\"' and 说明 ='\"+intr+\"'\"\n myinfos = my_db.getInfo(sqlstr)\n mynum = int(myinfos[0][4])\n mynum = mynum+1\n sqlstr = \"Update pay SET 可信度 = \"+str(mynum)+\" where 公司 = '\"+comp+\"' and 地点 = '\"+local+\"' and 岗位 = '\"+gw+\"' and 说明 ='\"+intr+\"'\"\n my_db.execute(sqlstr)\n my_db.commit()\n my_db.close()\n msg = \"点评成功\"\n return HttpResponse(msg)\n except Exception,e:\n print e\n msg = \"输入错误，请联系管理员\"\n return HttpResponse(msg)\n else:\n msg = \"点评成功\"\n return HttpResponse(msg) \n \ndef getpay(req):\n if req.method == 'POST':\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(**condition)\n if r.beans<5:\n msg = '用户豆不够，请充值'\n return render(req,'msg.html', locals())\n else:\n try:\n text=req.POST.get(\"paytext\",'') \n my_db = MynewcoderDB()\n sqlstr = \"SELECT * from pay where 公司 like '%\"+text+\"%' OR 地点 like '%\"+text+\"%' OR 岗位 like '%\"+text+\"%' ORDER BY 可信度 DESC limit 10 \"\n print sqlstr\n infos = my_db.getInfo(sqlstr)\n beans = r.beans - 5\n User.objects.filter(name=user_info['name']).update(beans=beans)\n req.session['beans'] = beans\n my_db.close()\n return render(req,\"payindex.html\",locals())\n except Exception,e:\n print e\n msg = \"输入错误，请联系管理员\"\n return render(req,'msg.html', locals())\n else:\n return render(req,\"payindex.html\",{'infos':infos})\n\n" }, { "alpha_fraction": 0.6800356507301331, "alphanum_fraction": 0.7139037251472473, "avg_line_length": 42.153846740722656, "blob_id": "080d62dda117f6cd6b0bf922bbe796066099a088", "content_id": "2953fd6699f7a1333c2cd508fc13adc84fa5364b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1122, "license_type": "no_license", "max_line_length": 91, "num_lines": 26, "path": "/src/online/models.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.db import models#do not understand only key\nclass User(models.Model):\n name = models.CharField(max_length=50)\n passwd = models.CharField(max_length=50)\n email = models.CharField(max_length=50)\n graduate = models.CharField(max_length=250,blank=True, null=True)\n beans = models.IntegerField()\n vip = models.BinaryField()\n create_date = models.DateTimeField(blank=True, null=True)\n update_date = models.DateTimeField(blank=True, null=True)\n role = models.CharField(max_length=255, blank=True)\n head_image = models.ImageField(upload_to='images',max_length=255,blank=True, null=True)\n comp = models.CharField(max_length=50)\n job = models.CharField(max_length=50)\n edu = models.CharField(max_length=50)\n \n \n mycomp = models.CharField(max_length=50)\n myjob = models.CharField(max_length=50)\n mymoney = models.CharField(max_length=50)\n myintr = models.CharField(max_length=254)\n mylocal = models.CharField(max_length=50)\n mymoney = models.CharField(max_length=255)\n \n myresume = models.CharField(max_length=255,null=True)\n# Create your models here.\n" }, { "alpha_fraction": 0.5500991940498352, "alphanum_fraction": 0.5590277910232544, "avg_line_length": 36.69158935546875, "blob_id": "6f9b44b99da2f56b4db2b863edd0484bf2e29068", "content_id": "9a4dc443f070fca7d3e7359905108f09649b8107", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4252, "license_type": "no_license", "max_line_length": 76, "num_lines": 107, "path": "/src/resume/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-*-coding:utf-8-*-\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import *\nfrom online.models import User\nfrom resume_tool.run import *\nfrom resume_tool.all_extractor2 import *\nfrom resume_tool.main import *\nfrom django.shortcuts import redirect\n\nimport json\nimport sys \nimport random \nreload(sys) \nsys.setdefaultencoding('utf8') \n\nimport os\n \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n \n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(**condition)\n if r.myresume != None and len(r.myresume)!=0:\n flag = 0\n result_list = process(r.myresume)\n info_list = result_list[0]\n ship_list = result_list[1]\n skill_list = result_list[2]\n project_list = result_list[3]\n socre_list = result_list[4]\n word_list = []\n for skill in skill_list:\n word = {}\n word['text'] = skill\n word['weight'] = random.uniform(100, 1000)\n word_list.append(word)\n word_list = json.dumps(word_list)\n return render(req,\"resumeindex.html\", locals())\n else:\n flag = 1\n return render(req,\"resumeindex.html\", locals())\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n \ndef upload_file(req): \n if req.method == \"POST\": # 请求方法为POST时，进行处理 \n user_info = req.session['user_info']\n user_name = user_info['name']\n myFile =req.FILES.get(\"myfile\", None) # 获取上传的文件，如果没有文件，则默认为None \n if not myFile: \n return HttpResponse(\"no files for upload!\") \n filename = \"\"\n if cmp(get_os(),\"n\")==0:\n filename = sys.path[0]+\"\\\\\"+user_name+myFile.name\n else:\n filename = sys.path[0]+\"/\"+user_name+myFile.name\n destination = open(filename,'wb+') # 打开特定的文件进行二进制的写操作 \n for chunk in myFile.chunks(): # 分块写入文件 \n destination.write(chunk) \n destination.close()\n print filename\n result_list,myresume = runresume(filename)\n User.objects.filter(name=user_name).update(myresume=myresume)\n info_list = result_list[0]\n ship_list = result_list[1]\n skill_list = result_list[2]\n project_list = result_list[3]\n socre_list = result_list[4]\n word_list = []\n for skill in skill_list:\n word = {}\n word['text'] = skill\n word['weight'] = random.uniform(100, 1000)\n word_list.append(word)\n word_list = json.dumps(word_list)\n return render(req,\"resumeindex.html\", locals())\n\ndef upload_file_person(req):\n if req.method == \"POST\": # 请求方法为POST时，进行处理\n user_info = req.session['user_info']\n user_name = user_info['name']\n myFile =req.FILES.get(\"myfile\", None) # 获取上传的文件，如果没有文件，则默认为None\n if not myFile:\n return HttpResponse(\"no files for upload!\")\n filename = \"\"\n if cmp(get_os(),\"n\")==0:\n filename = sys.path[0]+\"\\\\\"+user_name+myFile.name\n else:\n filename = sys.path[0]+\"/\"+user_name+myFile.name\n destination = open(filename,'wb+') # 打开特定的文件进行二进制的写操作\n for chunk in myFile.chunks(): # 分块写入文件\n destination.write(chunk)\n destination.close()\n result_list,myresume = runresume(filename)\n User.objects.filter(name=user_name).update(myresume=myresume)\n return HttpResponseRedirect(\"../../person/index\")" }, { "alpha_fraction": 0.5429542064666748, "alphanum_fraction": 0.5468370914459229, "avg_line_length": 33.7303352355957, "blob_id": "9fdbbcb5af4ce067e13a8feaa33a2a7a2cbe4594", "content_id": "9ba51976592398265de28e8676f88d4d362d3a1b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6609, "license_type": "no_license", "max_line_length": 167, "num_lines": 178, "path": "/src/online/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#encoding:utf-8\nfrom django.shortcuts import render_to_response,render\nfrom django.http import HttpResponseRedirect\nfrom online.models import User\nfrom django.template import RequestContext\nimport time\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom django import forms\n \nmail_host=\"smtp.163.com\" #设置服务器\nmail_user=\"18309238981\" #用户名\nmail_pass=\"19920203\" #口令 \nmail_postfix=\"163.com\" #发件箱的后缀\n\nclass ImageUploadForm(forms.Form):\n image = forms.ImageField()\n\ndef forget(req):\n pass \n \ndef send_mail(to_list,sub,content): \n me=mail_user+\"<\"+mail_user+\"@\"+mail_postfix+\">\" \n msg = MIMEText(content,_subtype='plain',_charset='utf-8') \n msg['Subject'] = sub \n msg['From'] = me \n msg['To'] = \";\".join(to_list) \n try: \n server = smtplib.SMTP() \n server.connect(mail_host) \n server.login(mail_user,mail_pass) \n server.sendmail(me, to_list, msg.as_string()) \n server.close() \n return True \n except Exception, e: \n print str(e) \n return False\n\ndef gotologin(req):\n if req.method == 'GET':\n return render(req,'login.html', locals())\n\ndef login(req):\n if req.method == 'GET':\n msg = '请求方式错误'\n return render(req, 'msg.html', locals())\n\n name = req.POST['name']\n passwd = req.POST['passwd']\n # 获取的表单数据与数据库进行比较\n condition = {'email': name}\n try:\n r = User.objects.get(**condition)\n except User.DoesNotExist:\n msg = \"邮箱不存在，请先注册\"\n return render(req,'user_register.html', locals())\n\n else:\n if r.passwd == passwd:\n req.session['islogin'] = True\n user_info = {}\n user_info['id'] = r.id\n user_info['name'] = r.name\n user_info['email'] = r.email\n user_info['role'] = r.role\n req.session['beans'] = r.beans\n req.session['head_image'] = r.head_image.name\n\n user_role = user_info['role']\n print user_role\n if user_role == '管理员':\n req.session['user_info'] = user_info\n msg = \"登录成功！\"\n return render(req, \"manager_index.html\", locals())\n elif user_role == '普通用户':\n req.session['user_info'] = user_info\n msg = \"登录成功！\"\n return HttpResponseRedirect('/school/index/')\n else: # there are only 2 roles in system for now, will add 3rd role which is \"Super user\" in the future\n # req.session['user_info'] = user_info\n # msg = '您不是管理员'\n # return render(req,'msg.html', locals())\n return HttpResponseRedirect('/') # for super user, will jump to super_user_index.html\n\n\n # if(r.role == u\"管理员\"):\n # req.session['role']=True\n # else:\n # req.session['role']=False\n # req.session['user_info'] = user_info\n # msg=\"登录成功！\"\n # return HttpResponseRedirect('/')\n else:\n msg = '密码错误！'\n return render(req, 'login.html', locals())\n \ndef logout(req):\n req.session['islogin'] = False\n req.session['user_info'] = {}\n return HttpResponseRedirect('/')\n\n\ndef register(req):\n if req.method == 'GET':\n status = False\n return render(req,'user_register.html', locals())\n else:\n status = True\n name = req.POST['aname']\n email = req.POST['email']\n passwd = req.POST['apasswd']\n graduate = req.POST['graduate']\n filterResult=User.objects.filter(name=name)\n emailfilterResult = User.objects.filter(email=email)\n if len(filterResult)>0:\n msg = '用户名被人注册过了！'\n return render(req, 'user_register.html', locals())\n if len(emailfilterResult)>0:\n msg = '邮箱被人注册过了！'\n return render(req, 'user_register.html', locals())\n User.objects.create(name= name,passwd=passwd,email=email,graduate=graduate,beans=1,role=\"普通用户\",create_date=time.strftime('%Y-%m-%d %X', time.localtime() ))\n msg = '注册完成，请直接登录！'\n return render(req, 'login.html', locals())\n\n\ndef changepasswd(req):\n if not req.session['islogin']:\n msg = '你当前还没有登录，请先登录！'\n return render(req, 'login.html', locals())\n msg='changepasswd page'\n if req.method == 'GET':\n status = False\n return render(req,'changepasswd.html', locals())\n else:\n status=True\n passwd = req.POST['passwd']\n name = req.session['user_info']['name']\n User.objects.filter(name=name).update(passwd=passwd)\n req.session['islogin']=False\n msg=\"密码修改成功，请重新登录！\"\n return render(req, 'login.html', locals())\n \ndef getpasswd(req):\n msg='getpasswd page'\n if req.method == 'GET':\n status = False\n return render(req,'getpasswd.html', locals())\n else:\n status=True\n name = req.POST['name']\n try:\n r = User.objects.get(name = name)\n except User.DoesNotExist:\n msg=\"用户名不存在，请先注册！\"\n return render(req,'msg.html', locals())\n passwd = r.passwd\n email = r.email\n sub = \"能耗管理系统密码找回\"\n content = \"您的密码是：\"+str(passwd)\n send_mail(email,sub,content)\n msg=\"密码已经发送到您的邮箱，请查收！\"\n return render(req,'msg.html', locals())\n \ndef changehead_image(req):\n if req.method == 'GET':\n status = False\n return render_to_response('changehead_image.html', locals())\n if req.method == 'POST':\n form = ImageUploadForm( req.POST, req.FILES ) # 有文件上传要传如两个字段\n if form.is_valid():\n m = User.objects.get(name = req.session['user_info']['name'])\n m.head_image = form.cleaned_data['image'] # 直接在这里使用字段名获取即可\n m.save()\n req.session['head_image'] = m.head_image.name\n msg = \"image upload success\"\n return HttpResponseRedirect('/')\n msg = \"allowed only via POST\"\n return render_to_response('msg.html', locals())" }, { "alpha_fraction": 0.47985348105430603, "alphanum_fraction": 0.47985348105430603, "avg_line_length": 27.763158798217773, "blob_id": "21c86df6ada67a6cc8f7b5efccf7c3b626444c6e", "content_id": "8032d7320b6d6d72a0a2f7b224e0e3d1632925e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "JavaScript", "length_bytes": 1092, "license_type": "no_license", "max_line_length": 68, "num_lines": 38, "path": "/src/jiuye/static/js/pay.js", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "$(\"#mycom\").click(function(){\n\t var comtext = $(\"#comtext\").val();\n \t $.post(\"../info/\",{'comtext':comtext}, function(ret){ \n \t\t \t$(\"#containermain\").html(ret);\n })\n\n})\n\n$(\"#mypay\").click(function(){\n\t var paytext = $(\"#paytext\").val();\n \t $.post(\"../getpay/\",{'paytext':paytext}, function(ret){ \n \t\t \t$(\"#containermain\").html(ret); \t \t\t \t\n })\n \t\n})\n\n$(\"#jiabtn\").click(function(){\n\t var text = $(\"#myinfo\").text();\n\t var intr = $(\"#myintr\").text();\n \t $.post(\"../setjia/\",{'text':text,'intr':intr}, function(ret){ \n \t\t \t$(\"#retsult\").html(ret);\n \t\t \t$(\"#jiabtn\").attr(\"disabled\", true);\n \t\t \t$(\"#jianbtn\").attr(\"disabled\", true);\n \t\t \t//$(\"#containermain\").html(ret); \t \t\t \t\n })\n \t\n})\n$(\"#jianbtn\").click(function(){\n\t var text = $(\"#myinfo\").text();\n\t var intr = $(\"#myintr\").text();\n \t $.post(\"../setjian/\",{'text':text,'intr':intr}, function(ret){ \n \t\t \t$(\"#retsult\").html(ret);\n \t\t \t$(\"#jiabtn\").attr(\"disabled\", true);\n \t\t \t$(\"#jianbtn\").attr(\"disabled\", true);\n \t\t \t//$(\"#containermain\").html(ret); \t \t\t \t\n })\n \t\n})" }, { "alpha_fraction": 0.5319634675979614, "alphanum_fraction": 0.586758017539978, "avg_line_length": 20.899999618530273, "blob_id": "0e9d7cb118b1116bc63e786574fcc86ce0de85b5", "content_id": "7859a2462cb6365380f6f8593693c6316e6a729e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 438, "license_type": "no_license", "max_line_length": 51, "num_lines": 20, "path": "/src/online/migrations/0002_auto_20180117_2209.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n# Generated by Django 1.11.2 on 2018-01-17 14:09\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('online', '0001_initial'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='user',\n name='myintr',\n field=models.CharField(max_length=254),\n ),\n ]\n" }, { "alpha_fraction": 0.5, "alphanum_fraction": 0.5466472506523132, "avg_line_length": 18.08333396911621, "blob_id": "7039d3d4b9b8f9d1d8ea3b6fbe9679edad4d644f", "content_id": "eea1f922eb8b376b12bb9098d0886de318ca8293", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 796, "license_type": "no_license", "max_line_length": 49, "num_lines": 36, "path": "/src/tools/link.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-*- coding: utf-8 -*- \n\n''''' \n不同平台，实现对所在内网端的ip扫描 \n \n有时候需要知道所在局域网的有效ip，但是又不想找特定的工具来扫描。 \n使用方法 python ip_scaner.py 192.168.1.1 \n(会扫描192.168.1.1-255的ip) \n'''\n \nimport platform \nimport sys \nimport os \nimport time \nimport thread\n\n\n \ndef ping_ip(ip_str): \n cmd = [\"ping\", \"-{op}\".format(op=get_os()), \n \"1\", ip_str] #only one bag\n output = os.popen(\" \".join(cmd)).readlines() \n \n flag = False\n for line in list(output): \n if not line: \n continue\n if str(line).upper().find(\"TTL\") >=0: \n flag = True\n break\n if flag: \n return True\n return False\n \nif __name__ == \"__main__\": \n print ping_ip(\"172.19.10.120\")" }, { "alpha_fraction": 0.546832799911499, "alphanum_fraction": 0.5525367856025696, "avg_line_length": 38.892215728759766, "blob_id": "d0e7581d724b67bfc63397497e78d02f85c63269", "content_id": "7ac3b78a4d51ee9fd87c2cbb80c4d2fa8b9ced09", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6982, "license_type": "no_license", "max_line_length": 286, "num_lines": 167, "path": "/src/manager/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-*-coding:utf-8-*-\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import *\nfrom online.models import User\nfrom difflib import Match\n\nimport json\nimport sys \nimport os\nimport resume_tool\nimport re\nimport time\nimport datetime\nimport xlrd\n\nreload(sys) \nsys.setdefaultencoding('utf8') \n \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n user_role = user_info['role']\n if user_role == '管理员':\n return render(req, \"manager_index.html\", locals())\n else:\n msg = '您不是管理员'\n return render(req,'msg.html', locals())\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.method == 'POST':\n msg = \"模式错误\"\n return render(req,\"msg.html\", locals())\n\ndef file_upload(req):\n t = time.time()\n time_now = time.strftime('%Y%m%d_%H%M%S', time.localtime(t))\n\n try:\n islogin = req.session['islogin']\n except Exception, e:\n msg = '请登录'\n return render(req, 'msg.html', locals())\n\n\n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n user_role = user_info['role']\n if user_role != '管理员':\n msg = '您不是管理员'\n return render(req, 'msg.html', locals())\n\n if req.method == 'POST':\n excelin = req.FILES.get(\"excelin\", None)\n print sys.path[0] #D:\\jiuye0320\\jiuye\\src\n if not excelin: \n return HttpResponse(\"no files for upload!\") \n print excelin.name\n fileName = \"\"\n \n # 判断扩展名\n if re.search(r'.xlsx$', excelin.name) is not None:\n nameUnextended = excelin.name.split(\".xlsx\")[0]\n fileName = sys.path[0] + \"\\\\\" + \"excelfiles\\\\\" + nameUnextended + \"_uploadDate_\" + time_now + \".xlsx\"\n destination = open(fileName,'wb+') # 打开特定的文件进行二进制的写操作 \n for chunk in excelin.chunks(): # 分块写入文件 \n destination.write(chunk) \n destination.close()\n print \"xlsx\"\n elif re.search(r'.xls$', excelin.name) is not None:\n nameUnextended = excelin.name.split(\".xls\")[0]\n fileName = sys.path[0] + \"\\\\\" + \"excelfiles\\\\\" + nameUnextended + \"_uploadDate_\" + time_now + \".xls\"\n destination = open(fileName,'wb+') # 打开特定的文件进行二进制的写操作 \n for chunk in excelin.chunks(): # 分块写入文件 \n destination.write(chunk) \n destination.close()\n print \"xls\"\n else:# for later *.csv or other type file\n return HttpResponse(\"Wrong file extension! Please re-select file.\")\n print fileName\n \n #read and write line by line from uploaded Excel files to DB\n #file I/O test\n xlsx_data = xlrd.open_workbook(fileName)\n table = xlsx_data.sheet_by_index(0)\n nrows = table.nrows\n ncols = table.ncols\n colnames = table.row_values(0)\n sql_args = \"\"\n print nrows,ncols\n for i in range(1,nrows):\n WorkList = []\n row = table.row_values(i)\n for j in range(0, ncols):\n if type(row[j]) == float:\n row[j] = str(row[j])\n WorkList.append(row[j])\n# print WorkList[0] + \", \" + WorkList[1]\n my_db = MynewcoderDB()\n for temp in range(0,len(WorkList)):\n WorkList[temp]=WorkList[temp].replace(\"\\'\",\"-\");\n sql_query = \"select * from school where \"+\" 采集日期='\" + WorkList[0] + \"' and 公司='\" + WorkList[1] + \"'and 职位='\" + WorkList[2]+ \"'and 职位描述='\" + WorkList[3] + \"'and 投递方式= '\" + WorkList[4]+ \"'and 分类='\" + WorkList[5] + \"'and 地点= '\" + WorkList[6] + \"'and URL= '\" + WorkList[7] + \"'\"\n tempinfo = my_db.getInfo(sql_query)\n if tempinfo ==None or len(tempinfo)==0:\n sql_query = \"REPLACE INTO school VALUES ('\" + WorkList[0] + \"','\" + WorkList[1] + \"','\" + WorkList[2]+ \"','\" + WorkList[3] + \"','\" + WorkList[4] + \"','\" + WorkList[5] + \"','\" + WorkList[6]+ \"','\" + WorkList[7] + \"'\" +\",0)\"\n #the sql_query above may cause issue because the data from Excel file may contains '\"' symbol\n #the '\"' symbol may cause sql_query cannot be executed\n #print sql_query\n my_db.execute(sql_query)\n my_db.commit()\n WorkList = []#reset WorkList\n uploads = 1\n return render(req,\"manager_index.html\", locals()) \n\ndef bean_recharge(req):\n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n user_role = user_info['role']\n if user_role != '管理员':\n msg = '您不是管理员'\n return render(req, 'msg.html', locals())\n\n if req.method == 'POST':\n recharge_user_name = req.POST['recharge_user_name']\n recharge_beans = req.POST['recharge_beans']\n \n user_beans = User.objects.get(email=recharge_user_name).beans\n pre_beans = user_beans#充值前校招豆数量\n \n recharge_beans_int = int(recharge_beans)\n user_beans += recharge_beans_int\n \n User.objects.filter(email=recharge_user_name).update(beans=user_beans)\n \n aft_beans = User.objects.get(email=recharge_user_name).beans#充值后校招豆数量\n u_beans = aft_beans - pre_beans#充值数量\n \n return render(req,\"manager_index.html\", {'u_beans': u_beans})\n \ndef authorize(req):\n if req.session['islogin'] == True:\n user_info = req.session['user_info']\n user_role = user_info['role']\n if user_role != '管理员':\n msg = '您不是管理员'\n return render(req, 'msg.html', locals())\n\n if req.method == 'POST':\n authorize_user_name = req.POST['authorize_user_name']\n pre_user_role = User.objects.get(name=authorize_user_name).role\n User.objects.filter(name=authorize_user_name).update(role='管理员')#authorize to manager\n user_role = User.objects.get(name=authorize_user_name).role\n if pre_user_role == user_role:\n auth_msg = '用户 ' + authorize_user_name + ' 已经是管理员'\n else:\n auth_msg = '用户 ' + authorize_user_name + ' 管理员权限授权成功'\n role_data = [0, auth_msg]\n return render(req,\"manager_index.html\", {'role_data': role_data})\n" }, { "alpha_fraction": 0.6397306323051453, "alphanum_fraction": 0.6397306323051453, "avg_line_length": 32.11111068725586, "blob_id": "03c7296a97b66991cf8d40e3f8026a63f0710606", "content_id": "10cd822bb173d1e142d3060cff9ce29724cd03e0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 297, "license_type": "no_license", "max_line_length": 54, "num_lines": 9, "path": "/src/pay/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom pay import views\napp_name = 'pay'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^getpay/$',views.getpay,name = 'getpay'),\n url(r'^setjia/$',views.setjia,name = 'setjia'),\n url(r'^setjian/$',views.setjian,name = 'setjian'),\n]" }, { "alpha_fraction": 0.605681836605072, "alphanum_fraction": 0.6193181872367859, "avg_line_length": 54, "blob_id": "289791ecd9217a990466f10f6a7deddbddc5c798", "content_id": "27ea1038735481153ce7ca391be2d54faa544f50", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 880, "license_type": "no_license", "max_line_length": 87, "num_lines": 16, "path": "/src/vip/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom vip import views\napp_name = 'vip'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^search/$',views.search,name = 'search'),\n url(r'^share_search/$',views.share_search,name = 'share_search'),\n url(r'^delete/(?P<res_id>[0-9]+)$', views.delete, name='delete'),\n url(r'^order_delete/(?P<res_id>[0-9]+)$', views.order_delete, name='order_delete'),\n url(r'^vip_edit/(?P<res_id>[0-9]+)$',views.vip_edit,name='vip_edit'),\n url(r'^order_edit/(?P<res_id>[0-9]+)$', views.order_edit, name='order_edit'),\n url(r'^show/(?P<res_id>[0-9]+)$', views.show, name='show'),\n url(r'^order_show/(?P<res_id>[0-9]+)$', views.order_show, name='order_show'),\n url(r'^vip_edit/action$',views.vip_edit_action,name='vip_edit_action'),\n url(r'^order_edit/action$', views.order_edit_action, name='order_edit_action')\n]\n" }, { "alpha_fraction": 0.5795167088508606, "alphanum_fraction": 0.5855005979537964, "avg_line_length": 23.828571319580078, "blob_id": "e17fd388c4b35ec1b1896c624cec3f48b18a52e7", "content_id": "950ac890ad72375c7a0c33d2a8a2c5e048a0a280", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4501, "license_type": "no_license", "max_line_length": 102, "num_lines": 175, "path": "/src/resume_tool/all_extractor2.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#!/usr/bin/env python\n# encoding:utf-8\n\nfrom date_extractor import *\nfrom school_name_extractor import *\nfrom major_extractor import *\nfrom degree_extractor import *\nfrom school_ship_extractor import *\nfrom skill_extractor import *\nfrom project_extractor import *\n\nimport chardet\n\nimport re\n\nspace_pattern = re.compile('\\s')\n\ninput_encode = 'utf-8'\n\ndef init_info_dic():\n result = {}\n result['start_date'] = ''\n result['end_date'] = ''\n result['school'] = ''\n result['major'] = ''\n result['degree'] = '' \n return result\n\n# 获取教育经历的数目\ndef get_education_number(from_date, from_school):\n if from_school == 0 or from_school == 0:\n return max(from_date, from_school)\n else:\n return min(from_date, from_school)\n\n# 替换str中所有在list中出现的字符串\ndef replace_list_from_str(str, str_list):\n for s in str_list:\n str = str.replace(s, ' ')\n return str\n\n\ndef skill_info_extract(input_str):\n skill_list = skill_extract(input_str);\n return skill_list \n\n#项目相关\ndef project_info_extract(input_str):\n project_list,project_score = project_extract(input_str);\n return project_list,project_score \n\n\n#奖学金相关\ndef ship_info_extract(input_str):\n ship_list = ship_extract(input_str);\n return ship_list\n \n \n \n# PS: 解析的格式是utf-8\ndef school_info_extract(input_str):\n result = []\n if len(input_str.strip()) == 0:\n return result\n #input_str_src = space_pattern.sub('', input_str)\n \n \n #时间\n date_list = date_extract(input_str)\n tmp_str = replace_list_from_str(input_str, date_list)\n # for d in date_list:\n # print d\n # school_list = school_name_extract(input_str)\n # for d in school_list:\n # print d\n \n #专业\n major_list = major_extract(input_str)\n tmp_str = replace_list_from_str(tmp_str, major_list)\n # for d in major_list:\n # print d\n \n #学历\n degree_list = degree_extract(input_str)\n tmp_str = replace_list_from_str(tmp_str, degree_list)\n # for d in degree_list:\n # print d\n \n #学校\n school_list = school_name_extract(tmp_str)\n\n date_number = get_education_number_from_date(input_str)\n school_number = get_education_number_from_school_name(input_str)\n education_number = get_education_number(school_number, date_number)\n\n for i in range(0, education_number):\n tmp = init_info_dic()\n j = i * 2\n if j < len(date_list):\n tmp['start_date'] = date_list[j]\n j += 1\n if j < len(date_list):\n tmp['end_date'] = date_list[j]\n\n if i < len(school_list):\n tmp['school'] = school_list[i]\n\n if i < len(major_list):\n tmp['major'] = major_list[i]\n\n if i < len(degree_list):\n tmp['degree'] = degree_list[i]\n\n if tmp['end_date'] == '' and (input_str.__contains__(u\"至今\") or input_str.__contains__(u\"致今\")):\n tmp['end_date'] = '1970/01/01'\n\n result.append(tmp)\n return result\n\ndef process(input_file_path):\n result_list = []\n items = []\n items.__sizeof__()\n all_line = \"\"\n school_info_list = []\n for line in open(input_file_path, 'r'):\n try:\n line = line.strip().decode('utf-8') # 设置编码格式\n except:\n try:\n line = line.strip().decode('gb2312') \n except:\n continue \n all_line = all_line +line \n info_list = school_info_extract(line) \n for info in info_list:\n if len(info_list) != 0:\n school_info_list.append(info)\n \n result_list.append(school_info_list)\n #整体分析\n \n #奖学金分析\n ship_list = ship_info_extract(all_line)\n for ship in ship_list:\n print \"奖学金相关:\"+ship\n \n result_list.append(ship_list) \n #关键词分析\n skill_list = skill_info_extract(all_line)\n for temp in skill_list:\n print \"能力:\"+temp\n \n result_list.append(skill_list) \n \n project_list,socre_list = project_info_extract(all_line)\n \n for i in range (0,len(project_list)):\n print \"项目:\"+project_list[i],socre_list[i]\n \n result_list.append(project_list) \n result_list.append(socre_list) \n return result_list\n \n\n\ndef main():\n print 'this is main'\n input_file_path = r'12.txt'\n #input_file_path = 'data/test'\n result_dic = process(input_file_path)\n\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.5752941370010376, "alphanum_fraction": 0.5870588421821594, "avg_line_length": 21.972972869873047, "blob_id": "01178359375ded3f99d98f7180f51fd62e6fa62f", "content_id": "b258eef104cc6a2f7ea72403f37f5b7a7ddd7815", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 998, "license_type": "no_license", "max_line_length": 64, "num_lines": 37, "path": "/src/resume_tool/union_extractor.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#学生会相关\n# encoding:utf-8\nimport re\n\nunion_str1 = u\"(主席|部长|会长|班长|副主席|副部长|副会长|党支书|团支书|学习委员|宣传委员|生活委员)\"\nunion_pattern1 = re.compile(union_str1)\n\n# 抽取学位信息,并将日期按照顺序排列存入list\ndef union_extract(str):\n result_list = []\n school_list = union_pattern1.findall(str)\n for d in school_list:\n # print d\n result_list.append(d)\n return result_list\n\n# 会破坏信息排布的顺序\n\n\ndef process(input_file_path):\n for line in open(input_file_path, 'r'):\n try:\n line = line.strip().decode('utf-8') # 设置编码格式\n except:\n line = line.strip().decode('gb2312') \n degree_list = union_extract(line)\n for d in degree_list:\n print d\n print '-------'\n\ndef main():\n print 'this is main'\n input_file_path = 'lcy.txt'\n result_dic = process(input_file_path)\n\nif __name__ == '__main__':\n main()\n" }, { "alpha_fraction": 0.59375, "alphanum_fraction": 0.78125, "avg_line_length": 15, "blob_id": "6792a60874d7959c7b678214d7ff86197852c75f", "content_id": "90bc7d2ddd5208b0a6912c0e620e98d0e9a2847b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 50, "license_type": "no_license", "max_line_length": 16, "num_lines": 2, "path": "/README.md", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# 校招项目test1.0版\n基于Django1.11.2开发\n" }, { "alpha_fraction": 0.6572890281677246, "alphanum_fraction": 0.6572890281677246, "avg_line_length": 38.20000076293945, "blob_id": "4a26f6b10dda8bc0b2bc1bd3ae6d9938f6830cf4", "content_id": "186dca0464e6f3f01559dbf8957d1bcd7c2c780b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 391, "license_type": "no_license", "max_line_length": 63, "num_lines": 10, "path": "/src/manager/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom manager import views\napp_name = 'manager'\nurlpatterns = [\n# url(r'^index/$',views.index,name = 'index'),\n url(r'^$',views.index,name = 'manager_index'),\n url(r'^upload/$',views.file_upload,name = 'manager_index'),\n url(r'^bean/$',views.bean_recharge,name = 'manager_index'),\n url(r'^authorize/$',views.authorize,name = 'manager_index')\n]" }, { "alpha_fraction": 0.5124481320381165, "alphanum_fraction": 0.5539419054985046, "avg_line_length": 19.95652198791504, "blob_id": "67920bce6a5c380b2f5e844b5f8ddb391ff3aa5b", "content_id": "470d9378bf639f668bd961714a7d57c5938ae0d6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 482, "license_type": "no_license", "max_line_length": 46, "num_lines": 23, "path": "/src/forum/migrations/0002_auto_20180125_1210.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n# Generated by Django 1.10 on 2018-01-25 04:10\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('forum', '0001_initial'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='column',\n name='manager',\n ),\n migrations.RemoveField(\n model_name='column',\n name='parent',\n ),\n ]\n" }, { "alpha_fraction": 0.4646974205970764, "alphanum_fraction": 0.5497118234634399, "avg_line_length": 28.489360809326172, "blob_id": "ed85ebc2ca95af93c9a88fa83aee2869c985edc0", "content_id": "77371f687365e23a6d3ae036216bba88fa53acb9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1388, "license_type": "no_license", "max_line_length": 81, "num_lines": 47, "path": "/src/tools/wol.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "import struct\nimport sys\nimport socket\n\ndef to_hex_int(s):\n return int(s.upper(), 16)\n\ndef wolrun(mac,ip):\n ip_prefix = '.'.join(ip.split('.')[:-1]) \n destip =ip = '%s.%s'%(ip_prefix,'255')\n print destip \n dest = (destip, 9)\n spliter = \"\"\n if mac.count(\":\") == 5: spliter = \":\"\n if mac.count(\"-\") == 5: spliter = \"-\"\n if spliter == \"\":\n print(\"MAC address should be like XX:XX:XX:XX:XX:XX / XX-XX-XX-XX-XX-XX\")\n return False\n\n parts = mac.split(spliter)\n a1 = to_hex_int(parts[0])\n a2 = to_hex_int(parts[1]) \n a3 = to_hex_int(parts[2])\n a4 = to_hex_int(parts[3])\n a5 = to_hex_int(parts[4])\n a6 = to_hex_int(parts[5])\n addr = [a1, a2, a3, a4, a5, a6]\n\n packet = chr(255) + chr(255) + chr(255) + chr(255) + chr(255) + chr(255)\n\n for n in range(0,16):\n for a in addr:\n packet = packet + chr(a)\n\n packet = packet + chr(0) + chr(0) + chr(0) + chr(0) + chr(0) + chr(0)\n\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n s.setsockopt(socket.SOL_SOCKET,socket.SO_BROADCAST,1)\n s.sendto(packet,dest)\n\n print(\"WOL packet %d bytes sent !\" % len(packet))\n return True\nif __name__ == \"__main__\": \n #wolrun('00-23-7d-c7-a5-44','10.13.28.202')\n wolrun('00-1f-c6-ac-ec-ec','10.13.28.69')\n #wolrun('00-23-7d-c7-9a-a4','10.13.28.140')\n #wolrun('2c:60:0c:52:81:ce','172.0.19.125')\n\n\n" }, { "alpha_fraction": 0.6125158071517944, "alphanum_fraction": 0.6169405579566956, "avg_line_length": 26.75438690185547, "blob_id": "79930f1f0bd868667b9867d8b62d5fdf71912f36", "content_id": "a341045a5a4a7156e6a0bc88f0d2b283a92768fe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1618, "license_type": "no_license", "max_line_length": 107, "num_lines": 57, "path": "/src/resume_tool/pdf2txt.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -*- coding:utf8 -*-\nfrom cStringIO import StringIO\n\nfrom pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\nfrom pdfminer.converter import TextConverter\nfrom pdfminer.layout import LAParams\nfrom pdfminer.pdfpage import PDFPage\n\nimport sys\nimport logging\nimport chardet\nfrom tools import *\naddsys()\n\n\ndef convert_pdf_to_txt(path):\n try:\n logging.debug('Converting pdf to txt: ' + str(path))\n rsrcmgr = PDFResourceManager()\n retstr = StringIO()\n codec = 'utf-8'\n laparams = LAParams()\n device = TextConverter(rsrcmgr, retstr, codec=codec, laparams=laparams)\n fp = file(path, 'rb')\n interpreter = PDFPageInterpreter(rsrcmgr, device)\n password = \"\"\n maxpages = 0\n caching = True\n pagenos = set()\n\n for page in PDFPage.get_pages(fp, pagenos, maxpages=maxpages, password=password, caching=caching,\n check_extractable=True):\n interpreter.process_page(page)\n\n text = retstr.getvalue()\n\n fp.close()\n device.close()\n retstr.close()\n # print chardet.detect(text)\n print text\n return text\n except Exception, e:\n logging.error('Error in file: ' + path + str(e))\n return \"\"\n\n\n# 将pdf文档转换为txt格式\ndef handle_pdffiles(pdffile):\n # print pdffile\n # pdf2t = pdffile[:-4] # 以后缀.pdf切词\n convert_pdf_to_txt(pdffile)\n\n # print os.path.exists(pdf2t + \".txt\")\n # f = open(pdf2t + '.txt', 'w+') # 以txt格式保存\n # f.write(convert_pdf_to_txt(pdffile))\n # f.close()\n" }, { "alpha_fraction": 0.6180555820465088, "alphanum_fraction": 0.6284722089767456, "avg_line_length": 31.11111068725586, "blob_id": "f50335b960e5be3f2a7303e725d0b2d9d13f8058", "content_id": "ae1452c6897ae12017f050078c6a16882c231ca6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 288, "license_type": "no_license", "max_line_length": 51, "num_lines": 9, "path": "/src/person/urls.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "from django.conf.urls import url\nfrom person import views\napp_name = 'person'\nurlpatterns = [\n url(r'^index/$',views.index,name = 'index'),\n url(r'^index2/$', views.index2, name='index2'),\n url(r'^info/$',views.info,name = 'info'),\n url(r'^test/$',views.test,name = 'test'),\n]" }, { "alpha_fraction": 0.6824644804000854, "alphanum_fraction": 0.6872037649154663, "avg_line_length": 27.200000762939453, "blob_id": "dd346beab95bc82f78ff29ddbc9394d23c48ae17", "content_id": "fee6b04654890761827a4d9b69ec2cecc5f3d2fb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 518, "license_type": "no_license", "max_line_length": 75, "num_lines": 15, "path": "/src/resume_tool/tools.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -*- coding:utf8 -*-\nimport platform\ndef addsys():\n import sys #这里只是一个对sys的引用，只能reload才能进行重新加载\n stdi,stdo,stde=sys.stdin,sys.stdout,sys.stderr \n reload(sys) #通过import引用进来时,setdefaultencoding函数在被系统调用后被删除了，所以必须reload一次\n sys.stdin,sys.stdout,sys.stderr=stdi,stdo,stde \n sys.setdefaultencoding('utf-8')\n \ndef get_os(): \n os = platform.system() \n if os == \"Windows\": \n return \"n\"\n else: \n return \"c\"" }, { "alpha_fraction": 0.4630792438983917, "alphanum_fraction": 0.4810523986816406, "avg_line_length": 32.463768005371094, "blob_id": "0a94d4a2444d92c542a3a16cf25ae3b2d7bdca6a", "content_id": "3772eb847ff89d0103ef2ee4ae634e2cf4f038b8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9438, "license_type": "no_license", "max_line_length": 370, "num_lines": 276, "path": "/src/bgtool/com.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "GB18030", "text": "__author__ = 'corey'\n# -*- coding:utf-8 -*-\nimport urllib\nimport urllib2\nimport re,json\nfrom bs4 import BeautifulSoup\nimport requests\nimport MySQLdb\n\ndef changekey(key):\n return key.strip().replace(\"'\",'\"')\n\ndef get(url):\n try:\n request = urllib2.Request(url)\n #request.add_header('Pragma', 'no-cache')\n response = urllib2.urlopen(request,timeout=20)\n page = response.read()\n return page\n except:\n return \"\"\nclass Spider:\n \n def __init__(self):\n self.siteURL = 'https://www.nowcoder.com/discuss/'\n\n def getPage(self,pageIndex):\n self.Pindex = pageIndex\n url = self.siteURL + str(pageIndex)+\"?type=0&order=0&page=1\"\n return get(url)\n\n \n def getContents(self,pageIndex):\n page = self.getPage(pageIndex)\n soup = BeautifulSoup(page,\"lxml\")\n try:\n #标题\n self.title = soup.find(\"h1\",{\"class\":\"discuss-title\"})\n self.title = soup.h1.get_text().strip()\n #作者\n louzhu =soup.find(\"span\",{\"class\":\"post-name\"})\n louzhu = louzhu.find(\"a\")\n self.louzhu = louzhu.attrs['title']\n self.louzhu = changekey(self.louzhu)\n\n #时间\n lztime =soup.find(\"span\",{\"class\":\"post-time\"})\n lztime = lztime.get_text()\n lztime = lztime.split()\n self.lztime = lztime[1]+\" \"+lztime[2]\n self.lztime = changekey(self.lztime)\n\n #发表内容\n context =soup.find(\"div\",{\"class\":\"post-topic-des\"})\n context = context.get_text()\n context = context.split()\n self.context = context[0]\n for i in range(1,len(context)-2):\n self.context = self.context+context[i]\n self.context = changekey(self.context)\n #pattern = re.compile('h1 class=\"discuss-title\".*')\n #items = re.findall(pattern,page)\n #print items\n \n \n #评论数\n num = soup.findAll(\"h1\")\n num = num[2].get_text()\n num = num.encode(\"utf-8\")\n num = re.findall(r'(\\w*[0-9]+)\\w*',num)\n self.num = int(num[0])\n return True\n except:\n return False\n\n \n def getComPage(self,page):\n #最多pagesize是100\n #https://www.nowcoder.com/comment/list-by-page-v2?pageSize=200000&page=1&order=1&entityId=62719&entityType=8\n url = \"https://www.nowcoder.com/comment/list-by-page-v2?pageSize=100&page=\"+str(page)+\"&order=1&entityId=\"+str(self.Pindex)+\"&entityType=\"+str(8);\n return get(url)\n \n def getComments(self,pageid):\n ids = []\n subcom= []\n compage = self.getComPage(pageid)\n #每次主回复的消息\n self.compage =json.loads(compage)\n #回复的子回复，每次的深度最多为1\n if self.num>100:\n num = 100\n else:\n num = self.num\n for i in range(0,num):\n ids.append(self.compage['data']['comments'][i]['id'])\n self.ids = ids\n for idtemp in ids:\n subcom.append(json.loads(self.getID(idtemp)))\n self.subcom = subcom\n \n\n def getID(self,idindex):\n #https://www.nowcoder.com/comment/list-by-page-v2?token=&pageSize=10&page=1&order=1&entityId=1091685&entityType=2&_=1511231131699\n url = \"https://www.nowcoder.com/comment/list-by-page-v2?token=&pageSize=100&page=1&order=1&entityId=\"+str(idindex)+\"&entityType=2\"\n return get(url)\n def save(self,sqlstr):\n self.db_conn= MySQLdb.connect(\n host='localhost',\n port = 3306,\n user='liu',\n passwd='123456',\n db ='newcoder',\n charset='utf8',\n )\n \n self.db_cur = self.db_conn.cursor()\n try:\n \n self.db_cur.execute(sqlstr)\n except:\n print \"error\" \n self.db_cur.close()\n self.db_conn.commit()\n self.db_conn.close()\n \n def saveall(self):\n #保存标题等\n sqlstr = \"insert into discuss values('\"+str(self.Pindex)+\"','\"+self.title+\"','\"+self.louzhu+\"','\"+self.lztime+\"','\"+self.context+\"','\"+str(self.num)+\"')\"\n self.save(sqlstr)\n \n #保存一级评论\n if self.num >100:\n num = 100\n else:\n num = self.num\n for i in range (0,num):\n sqlstr = \"insert into comment values('\"+str(self.Pindex)+\"','0','\"+str(self.compage['data']['comments'][i]['id'])+\"','\"+self.compage['data']['comments'][i]['authorName']+\"','\"+str(self.compage['data']['comments'][i]['authorId'])+\"','\"+self.compage['data']['comments'][i]['content']+\"')\"\n self.save(sqlstr)\n \n #保存二级评论\n for i in range (0,len(self.subcom)):\n for j in range (0,int(self.subcom[i][\"data\"][\"totalCnt\"])):\n print i,j\n sqlstr = \"insert into comment values('\"+str(self.Pindex)+\"','\"+str(self.subcom[i]['data']['comments'][j]['toCommentId'])+\"','\"+str(self.subcom[i]['data']['comments'][j]['id'])+\"','\"+self.subcom[i]['data']['comments'][j]['authorName']+\"','\"+str(self.subcom[i]['data']['comments'][j]['authorId'])+\"','\"+self.subcom[i]['data']['comments'][j]['content']+\"')\"\n self.save(sqlstr)\n \n \n #该网页需要登录，对比cookie之后发现存在变量t记录登录信息，postman实验成功\n def getUser(self,userindex):\n url = \"https://www.nowcoder.com/profile/\"+str(userindex)+\"/basicinfo\"\n try:\n opener = urllib2.build_opener()\n opener.addheaders.append(('Cookie', 't=C139A5266EFAE233DD9C9FC10C0A1B5C'))\n page = opener.open(url,timeout=20)\n soup = BeautifulSoup(page,\"lxml\")\n istrue = soup.find(\"div\",{\"class\":\"null-tip\"})\n except:\n istrue = \"\"\n if istrue == None:\n try:\n name = soup.find(\"dd\",{\"id\":\"nicknamePart\"})\n name = name.get_text().strip()\n except:\n name = \"\"\n try:\n city = soup.find(\"li\",{\"class\":\"profile-city\"})\n city = city.get_text().strip()\n except:\n city = \"\"\n try:\n edu = soup.find(\"dd\",{\"id\":\"schoolInfoPart\"})\n edu = edu.get_text().strip()\n except:\n edu = \"\"\n try:\n intr = soup.find(\"dd\",{\"id\":\"introductionPart\"})\n intr = intr.get_text().strip()\n except:\n intr = \"\"\n try:\n liv = soup.find(\"dd\",{\"id\":\"livePlacePart\"})\n liv = liv.get_text().strip()\n except:\n liv = \"\"\n try:\n cur = soup.find(\"dd\",{\"id\":\"curIdentityPart\"})\n cur = cur.get_text().strip()\n except:\n cur = \"\"\n try:\n wor = soup.find(\"dd\",{\"id\":\"workTimePart\"})\n wor = wor.get_text().strip()\n except:\n wor = \"\"\n try:\n el = soup.find(\"dd\",{\"id\":\"eduLevelPart\"})\n el = el.get_text().strip()\n except:\n el = \"\"\n try:\n com = soup.find(\"dd\",{\"id\":\"companyInfoPart\"})\n com = com.get_text().strip()\n except:\n com = \"\"\n try:\n job = soup.find(\"dd\",{\"id\":\"jobInfoPart\"})\n job = job.get_text().strip()\n except:\n job = \"\"\n #写入数据库 \n sqlstr = \"insert into NCuser values('\"+str(userindex)+\"','\"+name+\"','\"+city+\"','\"+edu+\"','\"+intr+\"','\"+liv+\"','\"+cur+\"','\"+wor+\"','\"+el+\"','\"+com+\"','\"+job+\"')\"\n sqlstr = sqlstr.encode(\"utf-8\")\n try:\n self.db_cur.execute(sqlstr)\n print name,city,edu,intr,liv,cur,wor,el,com,job\n\n except:\n print \"error\"\n else:\n print \"跳过\",userindex\n \n def getAllUser(self):\n\n for i in range(176046,900000):\n self.db_conn= MySQLdb.connect(\n host='localhost',\n port = 3306,\n user='liu',\n passwd='123456',\n db ='newcoder',\n charset='utf8',\n )\n \n self.db_cur = self.db_conn.cursor()\n self.getUser(i)\n self.db_cur.close()\n self.db_conn.commit()\n self.db_conn.close()\n def getAllContents(self):\n for i in range(100,10000):\n try:\n istrue = self.getContents(i)\n #大于100的必须分开\n count = 1;\n if istrue:\n print i\n while(self.num>100):\n self.getComments(count)\n self.saveall()\n count = count+1\n self.num -=100\n self.getComments(count)\n self.saveall()\n \n else:\n print \"跳过\",i\n except:\n pass\n \n \n \nimport sys\nstdout = sys.stdout\nreload(sys)\nsys.stdout = stdout\n \nspider = Spider()\nspider.getAllContents()\n\n'''\nspider.getContents(62186)\n\nspider.getComments()\n\n#spider.printall()\n'''\n" }, { "alpha_fraction": 0.5195214152336121, "alphanum_fraction": 0.5491183996200562, "avg_line_length": 39.71794891357422, "blob_id": "1003f7d8094c538f6e8fb0228e04c6294f623d7f", "content_id": "2ba2b348f616ccd416636673bc134cf0d73b42a0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1588, "license_type": "no_license", "max_line_length": 114, "num_lines": 39, "path": "/src/online/migrations/0001_initial.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n# Generated by Django 1.11.2 on 2018-01-17 13:57\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='User',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=50)),\n ('passwd', models.CharField(max_length=50)),\n ('email', models.CharField(max_length=50)),\n ('beans', models.IntegerField()),\n ('vip', models.BinaryField()),\n ('create_date', models.DateTimeField(blank=True, null=True)),\n ('update_date', models.DateTimeField(blank=True, null=True)),\n ('role', models.CharField(blank=True, max_length=255)),\n ('head_image', models.ImageField(blank=True, max_length=255, null=True, upload_to=b'images')),\n ('comp', models.CharField(max_length=50)),\n ('job', models.CharField(max_length=50)),\n ('edu', models.CharField(max_length=50)),\n ('mycomp', models.CharField(max_length=50)),\n ('myjob', models.CharField(max_length=50)),\n ('myintr', models.CharField(max_length=255)),\n ('mylocal', models.CharField(max_length=50)),\n ('mymoney', models.CharField(max_length=255)),\n ],\n ),\n ]\n" }, { "alpha_fraction": 0.5183805227279663, "alphanum_fraction": 0.5315449833869934, "avg_line_length": 33.7068977355957, "blob_id": "0460f1d9633c73fecdca3a5b011640fac0d3f49f", "content_id": "4b53cf5c214be14182f373c26c71d9d7d287a7c8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4186, "license_type": "no_license", "max_line_length": 144, "num_lines": 116, "path": "/src/school/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-*-coding:utf-8-*-\nfrom django.shortcuts import render,render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import JsonResponse\nfrom tools.dbcon import *\nfrom online.models import User\nimport json\nimport sys\nimport re\nfrom django.views.decorators.cache import cache_page\nreload(sys) \nsys.setdefaultencoding('utf8') \ndef index(req):\n if req.method == 'GET':\n try:\n islogin = req.session['islogin']\n except Exception,e:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.session['islogin'] == True:\n return render(req,\"school_index.html\", locals())\n else:\n msg = '请登录'\n return render(req,'msg.html', locals())\n if req.method == 'POST':\n msg = \"模式错误\"\n return render(req,\"msg.html\", locals())\n\n\ndef all(req):\n if req.method == 'GET':\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(**condition)\n if r.beans < 1:\n msg = '用户豆不够，请充值'\n return render(req, 'msg.html', locals())\n else:\n try:\n text = req.GET.get(\"id\", '')\n my_db = MynewcoderDB()\n sqlstr = \"SELECT * from school where id = \"+text\n infos = my_db.getInfo(sqlstr)[0]\n beans = r.beans - 0\n User.objects.filter(name=user_info['name']).update(beans=beans)\n req.session['beans'] = beans\n my_db.close()\n return render(req, \"school_all.html\", locals())\n except Exception, e:\n msg = '没有该信息，请重新查询'\n return render(req, \"msg.html\", locals())\n\n#@cache_page(60 * 5)\ndef info(req):\n user_info = req.session['user_info']\n condition = {'name': user_info['name']}\n r = User.objects.get(**condition)\n if r.beans<1:\n msg = '用户豆不够，请充值'\n return render(req,'msg.html', locals())\n else:\n try:\n if req.method == 'POST':\n text=req.POST.get(\"comtext\",'')\n zw=req.POST.get(\"zw\",'')\n fl=req.POST.get(\"fl\",'')\n else:\n text = req.GET.get(\"comtext\")\n if text == None:\n text = \"\"\n zw = req.GET.get(\"zw\")\n if zw == None:\n zw = \"\"\n fl = \"\"\n my_db = MynewcoderDB()\n sqlstr = \"SELECT * from school where 公司 like '%\"+text+\"%'and 职位 like '%\"+zw+\"%' and 分类 like '%\"+fl+\"%' order by id DESC limit 10\"\n infos = my_db.getInfo(sqlstr)\n beans = r.beans - 0\n User.objects.filter(name=user_info['name']).update(beans=beans)\n req.session['beans'] = beans\n my_db.close()\n return render(req,\"school_index.html\", locals())\n except Exception,e:\n msg = '没有该信息，请重新查询'\n return render(req,\"msg.html\", locals())\n\n\ndef test_one(req):\n my_db = MynewcoderDB()\n sqlstr0 = \"SELECT * from school where 职位 like '%测试%'\"\n infos0 = my_db.getInfo(sqlstr0)\n num0 = 0\n for maindata in range(len(infos0)):\n num0 += 1\n sqlstr1 = \"SELECT * from school where 职位 like '%数据%'\"\n infos1 = my_db.getInfo(sqlstr1)\n num1 = 0\n for maindata in range(len(infos1)):\n num1 += 1\n sqlstr2 = \"SELECT * from school where 职位 like '%java%'\"\n infos2 = my_db.getInfo(sqlstr2)\n num2 = 0\n for maindata in range(len(infos2)):\n num2 += 1\n sqlstr3 = \"SELECT * from school where 职位 like '%游戏%'\"\n infos3 = my_db.getInfo(sqlstr3)\n num3 = 0\n for maindata in range(len(infos3)):\n num3 += 1\n sqlstr4 = \"SELECT * from school where 职位 LIKE '%测试%' \"\n infos4 = my_db.getInfo(sqlstr4)\n my_db.close()\n jsdata1 = ['数据','java','游戏','测试']\n jsdata2 = [num0, num1, num2, num3]\n return render(req,'test_one.html',locals())\n" }, { "alpha_fraction": 0.6525725722312927, "alphanum_fraction": 0.6581762433052063, "avg_line_length": 33.641178131103516, "blob_id": "97d94a46eb9638cea09e3e9e0f6742dbf1cf07ce", "content_id": "5b7b0010620ace060fdf16f1f377eb28f0d33748", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 6241, "license_type": "no_license", "max_line_length": 149, "num_lines": 170, "path": "/src/forum/views.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "#-*-coding:utf-8-*-\nfrom forum.models import Nav, Post, Comment, Application, Column, Message\nfrom django.views.generic import View,TemplateView,ListView,DetailView\nfrom django.utils.timezone import now, timedelta\nfrom datetime import datetime\nfrom django.core.cache import cache\nfrom django.template import RequestContext\nfrom django.shortcuts import render_to_response,get_object_or_404\nfrom django.views.generic.edit import CreateView,UpdateView,DeleteView,FormView\nfrom django.http import HttpResponse,HttpResponseRedirect\nfrom django.core.urlresolvers import reverse_lazy\nfrom online.models import *\nfrom form import MessageForm, PostForm\nfrom django.db.models import Q\n\nPAGE_NUM = 50\nclass BaseMixin(object):\n def get_context_data(self,*args,**kwargs):\n context = super(BaseMixin,self).get_context_data(**kwargs)\n try:\n context['nav_list'] = Nav.objects.all()\n context['column_list'] = Column.objects.all()[0:5]\n context['last_comments'] = Comment.objects.all().order_by(\"-created_at\")[0:10]\n except Exception as e:\n print u'[BaseMixin]加载基本信息出错'\n\n return context\n\n#首页\nclass index(BaseMixin,ListView):\n model = Post\n queryset = Post.objects.all()\n template_name = 'forumindex.html'\n context_object_name = 'post_list'\n paginate_by = PAGE_NUM #分页--每页的数目\n def get_context_data(self,**kwargs):\n kwargs['foruminfo'] =get_forum_info()\n kwargs['online_ips_count'] =get_online_ips_count()\n kwargs['hot_posts'] = self.queryset.order_by(\"-responce_times\")[0:10] \n return super(index,self).get_context_data(**kwargs)\n \n \n#得到论坛信息，贴子数，用户数，昨日发帖数，今日发帖数\ndef get_forum_info():\n #请使用缓存\n oneday = timedelta(days=1)\n today = now().date()\n lastday = today - oneday\n todayend = today + oneday\n post_number = Post.objects.count()\n\n lastday_post_number = cache.get('lastday_post_number', None)\n today_post_number = cache.get('today_post_number', None)\n\n if lastday_post_number is None:\n lastday_post_number = Post.objects.filter(created_at__range=[lastday,today]).count()\n cache.set('lastday_post_number',lastday_post_number,60*60)\n \n if today_post_number is None:\n today_post_number = Post.objects.filter(created_at__range=[today,todayend]).count()\n cache.set('today_post_number',today_post_number,60*60)\n\n info = {\"post_number\":post_number,\n \"lastday_post_number\":lastday_post_number,\n \"today_post_number\":today_post_number}\n return info\n\ndef get_online_ips_count():\n online_ips = cache.get(\"online_ips\", [])\n if online_ips:\n online_ips = cache.get_many(online_ips).keys()\n return len(online_ips)\n return 0\n\n#帖子\ndef postdetail(request,post_pk):\n post_pk = int(post_pk)\n post = Post.objects.get(pk=post_pk)\n comment_list = post.comment_set.all() \n #统计帖子的访问访问次数\n if 'HTTP_X_FORWARDED_FOR' in request.META:\n ip = request.META['HTTP_X_FORWARDED_FOR']\n else:\n ip = request.META['REMOTE_ADDR']\n title = post.title\n visited_ips = cache.get(title, [])\n\n if ip not in visited_ips:\n post.view_times += 1\n post.save()\n visited_ips.append(ip)\n cache.set(title,visited_ips,15*60)\n return render_to_response('post_detail.html',{'post':post,'comment_list':comment_list})\n#用户已发贴\nclass UserPostView(ListView):\n template_name = 'user_posts.html'\n context_object_name = 'user_posts'\n paginate_by = PAGE_NUM\n\n def get_queryset(self):\n user_posts = Post.objects.filter(author=self.request.user)\n return user_posts\n\n\n#发帖\nclass PostCreate(CreateView): \n model = Post\n template_name = 'form.html'\n form_class = PostForm\n #fields = ('title', 'column', 'type_name','content')\n #SAE django1.5中fields失效，不知原因,故使用form_class\n success_url = reverse_lazy('user_post')\n #这里我们必须使用reverse_lazy() 而不是reverse，因为在该文件导入时URL 还没有加载。\n\n def form_valid(self, form):\n #此处有待加强安全验证\n validate = self.request.POST.get('validate',None)\n formdata = form.cleaned_data\n if self.request.session.get('validate',None) != validate:\n return HttpResponse(\"验证码错误！<a href='/'>返回</a>\")\n user = User.objects.get(username=self.request.user.username)\n #form.instance.author = user\n #form.instance.last_response = user\n formdata['author'] = user\n formdata['last_response'] = user\n p = Post(**formdata)\n p.save()\n user.levels += 5 #发帖一次积分加 5\n user.save()\n return HttpResponse(\"发贴成功！<a href='/'>返回</a>\")\n \n#编辑贴\nclass PostUpdate(UpdateView): \n model = Post\n template_name = 'form.html'\n success_url = reverse_lazy('user_post') \n\n\n#删贴\nclass PostDelete(DeleteView):\n model = Post\n template_name = 'delete_confirm.html'\n success_url = reverse_lazy('user_post') \n#所有板块\ndef columnall(request):\n column_list = Column.objects.all()\n return render_to_response('column_list.html',{'column_list':column_list })\n\n#每个板块\ndef columndetail(request,column_pk):\n column_obj = Column.objects.get(pk=column_pk)\n column_posts = column_obj.post_set.all()\n \n return render_to_response('column_detail.html',{'column_obj':column_obj,'column_posts':column_posts },context_instance=RequestContext(request)) \n#搜索 \nclass SearchView(ListView):\n template_name = 'search_result.html'\n context_object_name = 'post_list'\n paginate_by = PAGE_NUM\n\n def get_context_data(self,**kwargs):\n kwargs['q'] = self.request.GET.get('srchtxt','')\n return super(SearchView,self).get_context_data(**kwargs)\n\n def get_queryset(self):\n #获取搜索的关键字\n q = self.request.GET.get('srchtxt','')\n #在帖子的标题和内容中搜索关键字\n post_list = Post.objects.only('title','content').filter(Q(title__icontains=q)|Q(content__icontains=q));\n return post_list\n" }, { "alpha_fraction": 0.5509138107299805, "alphanum_fraction": 0.5509138107299805, "avg_line_length": 33.818180084228516, "blob_id": "477de4a7a68bf5300cd7fdd165891043e2ba1d58", "content_id": "4ce4e26bee039baca35a81226c80fc65fbec3c3b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "HTML", "length_bytes": 427, "license_type": "no_license", "max_line_length": 88, "num_lines": 11, "path": "/src/forum/templates/column_detail.html", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "板块:{{ column_obj.name}}; 管理员:{{ column_obj.manager }}; 帖子:{{column_obj.post_number}}<br>\n\n{% for post in column_posts %}\n<a href=\"{{ post.get_absolute_url }}\" >{{ post.title }}</a>\n \n 发表于:{{ post.created_at|date:\"Y-m-d H:i:s\" }};\n 作者:{{ post.author }}；\n 最后回复:{{ post.last_response }}\n 回复点击数:{{ post.responce_times }}/{{ post.view_times }}\n\t<br>\n{% endfor %}\n" }, { "alpha_fraction": 0.4942159354686737, "alphanum_fraction": 0.5080333948135376, "avg_line_length": 25.598291397094727, "blob_id": "51b1e42788054e161c076b90ffde269936ba666a", "content_id": "ac3f289373bcbe7b23aebea830a0528df351ca21", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3200, "license_type": "no_license", "max_line_length": 113, "num_lines": 117, "path": "/src/resume_tool/codeinit.py", "repo_name": "Ethan-mxc/xzday", "src_encoding": "UTF-8", "text": "# -*- coding:utf-8 -*-\nimport MySQLdb\nimport jieba.posseg as pseg\nimport jieba.analyse as ana\nimport jieba\nimport re\n#数据库相关\nclass MynewcoderDB:\n def __init__(self):\n self.db_conn= MySQLdb.connect(\n host='118.24.92.135',\n port = 3306,\n user='meng',\n passwd='123456',\n db ='newcoder',\n charset='utf8',\n )\n self.db_cur = self.db_conn.cursor()\n def execute(self,sqlstr):\n self.db_cur.execute(sqlstr)\n def getInfo(self,sqlstr):\n sqlstr = sqlstr.decode(\"utf-8\")\n self.execute(sqlstr)\n return self.db_cur.fetchall()\n def commit(self):\n self.db_conn.commit()\n def close(self):\n self.db_cur.close()\n self.db_conn.close()\n\nclass WordCut:\n def __init__(self,sentence):\n self.sentence = sentence\n def cutWords(self):\n self.words = pseg.cut(self.sentence)\n return self.words\n def top(self,num=100):\n words = ana.extract_tags(self.sentence,num)\n return words\nclass Info:\n def __init__(self):\n jieba.load_userdict(\"words.txt\")\n def getAlllist(self,sqlstr):\n self.my_db = MynewcoderDB()\n infos = self.my_db.getInfo(sqlstr)\n \n for key in infos:\n self.id = key[0]\n self.findCode(key[1]+key[4])\n def findCode(self,infostr):\n pass\n \n#邀请码相关\nclass InviteCode(Info):\n def __init__(self,usestr=\"内推码\"):\n Info.__init__(self)\n self.usestr = usestr\n sqlstr = \"SELECT * from discuss WHERE context like '%\"+self.usestr+\"%' OR title like '%\"+self.usestr+\"%'\"\n self.getAlllist(sqlstr)\n \n #找到分词中邀请码最近的eng属性的词汇就是邀请码or内推码的内容\n def findCode(self,infostr):\n wordcut = WordCut(infostr)\n minilen = 1000\n i = 0\n lcyflagi = 0\n code = \"\"\n mycompany = \"\"\n for word, flag in wordcut.cutWords():\n i = i + 1\n if cmp(flag,\"company\")==0:\n mycompany = word\n if cmp(flag,\"lcy\")==0:\n lcyflagi = i\n if cmp(flag,\"eng\")==0:\n if (i-lcyflagi)<minilen:\n minilen = i-lcyflagi\n code = word\n if lcyflagi == 0:\n continue\n if code!=\"\" and lcyflagi!=0 and len(code)>6:\n print self.id,mycompany,code\n \n#类型相关\nclass TypeOffer(Info):\n def __init__(self):\n Info.__init__(self)\n sqlstr = \"SELECT * from discuss\"\n self.getAlllist(sqlstr)\n \n def findCode(self,infostr):\n wordcut = WordCut(infostr)\n mycompanylist = []\n for word, flag in wordcut.cutWords():\n if cmp(flag,\"company\")==0:\n mycompanylist.append(word)\n if(len(mycompanylist)>0 and len(infostr)>2000):\n print self.id\n for mycom in mycompanylist:\n print mycom\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\nif __name__==\"__main__\":\n icode = TypeOffer()\n" } ]

57

theuscarvalho/remessa-santander-grafica

https://github.com/theuscarvalho/remessa-santander-grafica

1a9aa5f370d5c8546085b55e977db381c6d13286

1e29f6ea4ebf50973a05561796f4627e79a34586

d5151661c573f2d052d271d9da1c453d210a01e6

refs/heads/master

2020-05-05T09:43:35.611230

2017-08-19T15:06:53

null

0

null

[ { "alpha_fraction": 0.61623615026474, "alphanum_fraction": 0.6715866923332214, "avg_line_length": 26.100000381469727, "blob_id": "55dccc782a8672837c8a690b8ac3dbd08a113340", "content_id": "c7f83da916280efdbbff5b5cf6e7235e8ef51096", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 271, "license_type": "no_license", "max_line_length": 57, "num_lines": 10, "path": "/test.py", "repo_name": "theuscarvalho/remessa-santander-grafica", "src_encoding": "UTF-8", "text": "from database import connection as conn\nfrom vigo import Boletos\nfrom datetime import datetime as dt\n\nboletos = Boletos(conn, dt(2014, 1, 1), dt(2014, 12, 1), \n \"EMITIR TODO MES\", \"'L'\", \"15\")\nprint boletos.count\n\nfor boleto in boletos:\n print boleto\n" }, { "alpha_fraction": 0.7421383857727051, "alphanum_fraction": 0.7421383857727051, "avg_line_length": 14.899999618530273, "blob_id": "0dd5f05908b83a7d5b4002e4242187119f094310", "content_id": "c917b78aacf932ef602c86e8abb35f10fb8b2aec", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 159, "license_type": "no_license", "max_line_length": 38, "num_lines": 10, "path": "/adapter.wsgi", "repo_name": "theuscarvalho/remessa-santander-grafica", "src_encoding": "UTF-8", "text": "import os\nimport sys\nsys.path.append('/var/www/appremessa')\n\nos.chdir(os.path.dirname(__file__))\n\nimport bottle\nimport app\n\napplication = bottle.default_app()\n" } ]

2

abeishekeeva/python-resume

https://github.com/abeishekeeva/python-resume

4971ac23abbe58c3e2d064007fea645f7ffd0d6f

108a6bfda564c9a2333dc2b740b99238eea32416

69502348eea9faa83f0178d48fbd845152efe5d9

refs/heads/master

2020-03-20T20:15:05.922793

2019-02-06T12:01:49

137,677,452

0

null

[ { "alpha_fraction": 0.7169230580329895, "alphanum_fraction": 0.7230769395828247, "avg_line_length": 45.47618865966797, "blob_id": "c44123843eaaedbe4bd4796888ae9b9f767d4a29", "content_id": "e31d9bd28a9db28aadf028ea293f5fdeb4acd9d2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 975, "license_type": "no_license", "max_line_length": 115, "num_lines": 21, "path": "/blog/views.py", "repo_name": "abeishekeeva/python-resume", "src_encoding": "UTF-8", "text": "from django.shortcuts import render\nfrom django.utils import timezone\nfrom .models import Post\nfrom django.shortcuts import render, get_object_or_404\n\n# Create your views here.\ndef post_list(request):\n posts = Post.objects.filter(published_date__lte=timezone.now(), tag=\"programming\").order_by('published_date')\n return render(request, 'blog/post_list.html', {'posts': posts})\n\ndef post_list_other(request):\n posts_other = Post.objects.filter(published_date__lte=timezone.now(), tag=\"general\").order_by('published_date')\n return render(request, 'blog/post_list.html', {'posts_other': posts_other})\n\ndef post_list_books(request):\n posts_books = Post.objects.filter(published_date__lte=timezone.now(), tag=\"books\").order_by('published_date')\n return render(request, 'blog/post_list.html', {'posts_other': posts_books})\n\ndef post_detail(request, pk):\n post = get_object_or_404(Post, pk=pk)\n return render(request, 'blog/post_detail.html', {'post': post})" }, { "alpha_fraction": 0.7523809671401978, "alphanum_fraction": 0.7523809671401978, "avg_line_length": 25.25, "blob_id": "4972d4c258ccaaa311342c7bd54668ed1896a021", "content_id": "5faef8bb0bdf2471969b8eb3396087906b97867b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 105, "license_type": "no_license", "max_line_length": 64, "num_lines": 4, "path": "/README.md", "repo_name": "abeishekeeva/python-resume", "src_encoding": "UTF-8", "text": "# python-resume\nOnline resume using Python and Bootstrap. Here is how it looks: \n\n![Screenshot](img.png)\n" } ]

2

astroboi-SH-KWON/remove_motif_error

https://github.com/astroboi-SH-KWON/remove_motif_error

f15616ace9469a2ea3a29bb4ea1ed2b64faca700

dea93b19183f0ed7e841346c0cfd23236a848da1

2f84576c24154d7af012400b12130c802213b3e4

refs/heads/main

2023-04-23T17:51:09.460710

2021-05-12T08:01:51

341,741,502

0

null

[ { "alpha_fraction": 0.6104294657707214, "alphanum_fraction": 0.6104294657707214, "avg_line_length": 35.11111068725586, "blob_id": "33eb883fef0540fc139babd8fa75b00415a5c472", "content_id": "6b0ba6af2821bec6e8c4b788dcb1f58f21c592f9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 326, "license_type": "no_license", "max_line_length": 101, "num_lines": 9, "path": "/astroboi_bio_tools/ToolLogicPrep.py", "repo_name": "astroboi-SH-KWON/remove_motif_error", "src_encoding": "UTF-8", "text": "\n\nclass ToolLogicPreps:\n def __init__(self):\n pass\n\n def sort_list_by_ele(self, data_list, ele_idx, up_down_flag=True):\n result_list = []\n for tmp_arr in sorted(data_list, key=lambda tmp_arr: tmp_arr[ele_idx], reverse=up_down_flag):\n result_list.append(tmp_arr)\n return result_list" }, { "alpha_fraction": 0.638126015663147, "alphanum_fraction": 0.638126015663147, "avg_line_length": 46.38461685180664, "blob_id": "3f62ae05902918b7a9ea24a6a3095a25298a5b53", "content_id": "cea718c8e64377effdf1154111b085ac14df6c0a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 619, "license_type": "no_license", "max_line_length": 92, "num_lines": 13, "path": "/LogicPrep.py", "repo_name": "astroboi-SH-KWON/remove_motif_error", "src_encoding": "UTF-8", "text": "\nfrom astroboi_bio_tools.ToolLogicPrep import ToolLogicPreps\nclass LogicPreps(ToolLogicPreps):\n def make_list_to_dict_by_elekey(self, input_list, key_idx):\n result_dict = {}\n for input_arr in input_list:\n if input_arr[key_idx] in result_dict:\n result_dict[input_arr[key_idx]].append(input_arr)\n else:\n result_dict.update({input_arr[key_idx]: [input_arr]})\n return result_dict\n\n def filterout_ele_w_trgt_str(self, input_list, arr_idx, trgt_str):\n return [input_arr for input_arr in input_list if trgt_str not in input_arr[arr_idx]]\n\n\n" }, { "alpha_fraction": 0.6008174419403076, "alphanum_fraction": 0.6226158142089844, "avg_line_length": 37.6315803527832, "blob_id": "da0d10019d6da5d9658181ca2007798c9ce29e1f", "content_id": "f6024081e643166011cc1f6a99dc0780d6b9f14a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 4404, "license_type": "no_license", "max_line_length": 154, "num_lines": 114, "path": "/Main.py", "repo_name": "astroboi-SH-KWON/remove_motif_error", "src_encoding": "UTF-8", "text": "import time\nimport os\nimport platform\n\nimport Util\nimport Logic\nimport LogicPrep\n#################### st env ####################\nWORK_DIR = os.getcwd() + \"/\"\nPROJECT_NAME = WORK_DIR.split(\"/\")[-2]\nSYSTEM_NM = platform.system()\n\nif SYSTEM_NM == 'Linux':\n # REAL\n pass\nelse:\n # DEV\n WORK_DIR = \"D:/000_WORK/KimNahye/20210217_ML/WORK_DIR_4_remove_motif_error/\"\n\nIN = 'input/'\nOU = 'output/'\n\n# input file ONLY for recount_motif_error() must be tsv format with \".txt\" extension\nMOTIF_ERROR_FL = [\n 'merged_NG_SsAPOBEC3B_LibB_Rep1_Seq_freq_2_13.txt'\n , 'Seq_freq_all_merged_RY_ABE8e_V106W.txt'\n , 'Seq_freq_all_merged_RY_YE1_BE4max.txt'\n , 'Seq_freq_all_merged_NG_ABE8e_V106W.txt'\n ]\n\nos.makedirs(WORK_DIR + IN, exist_ok=True)\nos.makedirs(WORK_DIR + OU, exist_ok=True)\n\n#################### en env ####################\n\n\"\"\"\n for single files\n input : MOTIF_ERROR_FL \n\"\"\"\ndef recount_motif_error():\n util = Util.Utils()\n logic = Logic.Logics()\n logic_prep = LogicPrep.LogicPreps()\n\n for err_fl_path in MOTIF_ERROR_FL:\n motif_err_fl = util.read_tsv_ignore_N_line(WORK_DIR + IN + err_fl_path)\n\n # filter out missing values\n flted_1_motif_err_fl = logic_prep.filterout_ele_w_trgt_str(motif_err_fl, 2, '-')\n motif_err_fl.clear()\n # #NAME? is removed\n flted_2_motif_err_fl = logic_prep.filterout_ele_w_trgt_str(flted_1_motif_err_fl, 2, 'N')\n flted_1_motif_err_fl.clear()\n flted_3_motif_err_fl = logic_prep.filterout_ele_w_trgt_str(flted_2_motif_err_fl, 2, 'n')\n flted_2_motif_err_fl.clear()\n\n motif_err_dict = logic_prep.make_list_to_dict_by_elekey(flted_3_motif_err_fl, 0)\n\n result_list = logic.recount_total_proportion_by_dictkey(motif_err_dict, 3)\n\n # head = ['Filename', 'INDEX', 'seq', 'Motif', 'Count', 'Total_cnt', 'Proportion', 'Substitution']\n head = ['Filename', 'seq', 'Motif', 'Count', 'Total_cnt', 'Proportion', 'Substitution']\n util.make_excel(WORK_DIR + OU + 'new_' + err_fl_path.replace('.txt', ''), head, result_list)\n\n\n\"\"\"\n for paired files\n input : null\n paired files must have file name pattern like \"_Rep1_\" and \"_Rep2_\"\n this method analyzes all patterned files in \"/input/\"\n\"\"\"\ndef recount_paired_motif_error():\n deli_ch = \"^\"\n\n util = Util.Utils()\n logic = Logic.Logics()\n logic_prep = LogicPrep.LogicPreps()\n\n sources = util.get_files_from_dir(WORK_DIR + IN + \"/*Rep1*.txt\")\n\n for paired_rep1 in sources:\n motif_err_fl = util.read_tsv_ignore_N_line(paired_rep1)\n motif_err_fl += util.read_tsv_ignore_N_line(paired_rep1.replace(\"_Rep1_\", \"_Rep2_\"))\n\n # filter out missing values\n flted_1_motif_err_fl = logic_prep.filterout_ele_w_trgt_str(motif_err_fl, 2, '-')\n motif_err_fl.clear()\n # #NAME? is removed\n flted_2_motif_err_fl = logic_prep.filterout_ele_w_trgt_str(flted_1_motif_err_fl, 2, 'N')\n flted_1_motif_err_fl.clear()\n flted_3_motif_err_fl = logic_prep.filterout_ele_w_trgt_str(flted_2_motif_err_fl, 2, 'n')\n flted_2_motif_err_fl.clear()\n\n \"\"\"\n Filename\tSeq\tMotif\tCount\tTotal_cnt\tProportion\tSubstitution\n TTTGACACACACACAGCACTCATAGCAC\tTTTGACACACACACAGCACTCATAGCACTCTAGACCCAAGCCACGAACGAGCGCGGTAAGCTTGGCGTAACTAGATCT\tCCCAAGCC\t540\t580\t0.931\tref_from_result\n TTTGACACACACACAGCACTCATAGCAC\tTTTGACACACACACAGCACTCATAGCACTCTAGACCCGAGCCACGAACGAGCGCGGTAAGCTTGGCGTAACTAGATCT\tCCCGAGCC\t22\t580\t0.0379\talt\n TTTGACACACACACAGCACTCATAGCAC\tTTTGACACACACACAGCACTCATAGCACTCTAGACCCAAACCACGAACGAGCGCGGTAAGCTTGGCGTAACTAGATCT\tCCCAAACC\t4\t580\t0.0069\talt\n \"\"\"\n\n motif_err_dict = logic.make_merged_list_to_dict_for_motiff_err(flted_3_motif_err_fl, 0, [1, 2, -1], deli_ch, 3)\n\n result_list = logic.recount_total_proportion_merged_dict_by_dictkey(motif_err_dict, deli_ch)\n\n head = ['Filename', 'seq', 'Motif', 'Count', 'Total_cnt', 'Proportion', 'Substitution']\n util.make_excel(paired_rep1.replace(\"input\", \"output\").replace('.txt', ''), head, result_list)\n\n\nif __name__ == '__main__':\n start_time = time.perf_counter()\n print(\"start [ \" + PROJECT_NAME + \" ]>>>>>>>>>>>>>>>>>>\")\n # recount_motif_error()\n recount_paired_motif_error()\n print(\"::::::::::: %.2f seconds ::::::::::::::\" % (time.perf_counter() - start_time))\n" }, { "alpha_fraction": 0.7951807379722595, "alphanum_fraction": 0.7951807379722595, "avg_line_length": 26.66666603088379, "blob_id": "8bfd8712d317d17a0705df025f83a4f7b7a515b6", "content_id": "646600826fece2ca6f52685b506d5c9235099ba7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 83, "license_type": "no_license", "max_line_length": 49, "num_lines": 3, "path": "/Util.py", "repo_name": "astroboi-SH-KWON/remove_motif_error", "src_encoding": "UTF-8", "text": "\nfrom astroboi_bio_tools.ToolUtil import ToolUtils\nclass Utils(ToolUtils):\n pass" }, { "alpha_fraction": 0.46979591250419617, "alphanum_fraction": 0.4746938645839691, "avg_line_length": 36.10606002807617, "blob_id": "cea04826aab367c7a3ebc7e24b5a4ce78d50ca42", "content_id": "ce0f042815ac3995cbe4b0ac6bd61fcf62944e66", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2450, "license_type": "no_license", "max_line_length": 108, "num_lines": 66, "path": "/Logic.py", "repo_name": "astroboi-SH-KWON/remove_motif_error", "src_encoding": "UTF-8", "text": "\nfrom astroboi_bio_tools.ToolLogic import ToolLogics\nclass Logics(ToolLogics):\n def recount_total_proportion_by_dictkey(self, motif_err_dict, idx):\n result_list = []\n for key, val_list in motif_err_dict.items():\n tot = 0\n for val_arr in val_list:\n tot += int(val_arr[idx])\n\n for val_arr in val_list:\n tmp_arr = val_arr[:5]\n # new total\n tmp_arr.append(tot)\n # new proportion\n if tot == 0:\n tmp_arr.append(0.0)\n else:\n tmp_arr.append(int(val_arr[idx]) / tot)\n tmp_arr.append(val_arr[-1])\n result_list.append(tmp_arr)\n return result_list\n\n def make_merged_list_to_dict_for_motiff_err(self, merged_list, key_idx, scnd_key_arr, deli_ch, val_idx):\n result_dict = {}\n for val_arr in merged_list:\n key = val_arr[key_idx]\n scnd_key = \"\"\n for i in scnd_key_arr:\n tmp_val = val_arr[i] + deli_ch\n scnd_key += tmp_val\n\n if key in result_dict:\n if scnd_key in result_dict[key]:\n result_dict[key][scnd_key] += int(val_arr[val_idx])\n else:\n result_dict[key].update({scnd_key: int(val_arr[val_idx])})\n else:\n result_dict.update({key: {scnd_key: int(val_arr[val_idx])}})\n\n return result_dict\n\n def recount_total_proportion_merged_dict_by_dictkey(self, motif_err_dict, deli_ch):\n result_list = []\n for key, val_dict in motif_err_dict.items():\n tot = 0\n for cnt in val_dict.values():\n tot += int(cnt)\n\n for scnd_key, cnt_val in val_dict.items():\n tmp_arr = [key]\n seq_motif_sub_arr = scnd_key.split(deli_ch)\n #add Seq, Motif\n tmp_arr += seq_motif_sub_arr[:2]\n # cnt\n tmp_arr.append(cnt_val)\n # new total\n tmp_arr.append(tot)\n # new proportion\n if tot == 0:\n tmp_arr.append(0.0)\n else:\n tmp_arr.append(int(cnt_val) / tot)\n #add Substitution\n tmp_arr.append(seq_motif_sub_arr[2])\n result_list.append(tmp_arr)\n return result_list\n" } ]

5

ku3i/flatcat

https://github.com/ku3i/flatcat

a7ceece24c25d42285d57532cef6b83fa7a8b6ea

015784b645b63ad7d2c982467a53679b43aac615

a7c6d4c9d7ae0c84b4a5c8cdd34fed77ed9ca11c

refs/heads/master

2023-09-04T08:01:59.618666

2021-09-29T15:42:07

337,092,707

1

0

null

[ { "alpha_fraction": 0.47789472341537476, "alphanum_fraction": 0.5052631497383118, "avg_line_length": 18, "blob_id": "f46a22746a46476ce783b5fce5758e867e3d0ea5", "content_id": "0f57bc28204665845acdc7ee55937291e8991767", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 950, "license_type": "no_license", "max_line_length": 58, "num_lines": 50, "path": "/src/common/median3.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/*---------------------------------+\n | Supreme Machines |\n | Sensorimotor Firmware |\n | Matthias Kubisch |\n | [email protected] |\n | November 2018 |\n +---------------------------------*/\n\n#ifndef SUPREME_MEDIAN3_HPP\n#define SUPREME_MEDIAN3_HPP\n\nnamespace supreme {\n\ntemplate <typename T> inline T median_of_3(T a, T b, T c);\n\n/* computes median-of-three of a given variable*/\ntemplate <typename T>\nclass Median3 {\n\tT val_1, val_2;\npublic:\n\tMedian3() : val_1(), val_2() {}\n\n\tT step(T val_0) {\n\t\tconst T result = median_of_3(val_0, val_1, val_2);\n\t\tval_2 = val_1;\n\t\tval_1 = val_0;\n\t\treturn result;\n\t}\n\n\tvoid reset(void) { val_1 = {}; val_2 = {}; }\n};\n\ntemplate <typename T>\ninline T median_of_3(T a, T b, T c)\n{\n\tif (a > b)\n\t\tif (b > c)\n\t\t\treturn b;\n\t\telse\n\t\t\treturn (a > c) ? c : a;\n\telse\n\t\tif (b < c)\n\t\t\treturn b;\n\t\telse\n\t\t\treturn (a > c) ? a : c;\n}\n\n} /* namespace supreme */\n\n#endif /* SUPREME_MEDIAN3_HPP */\n" }, { "alpha_fraction": 0.48901546001434326, "alphanum_fraction": 0.499864399433136, "avg_line_length": 28.015748977661133, "blob_id": "18f4d1447ceef5d10e5b93c8d67272c006afadf6", "content_id": "16bf2b72b3587a4271c10295467505594f67a550", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3687, "license_type": "no_license", "max_line_length": 121, "num_lines": 127, "path": "/src/midi/midi_in.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/*---------------------------------+\n | Matthias Kubisch |\n | [email protected] |\n | July 2017 |\n +---------------------------------*/\n\n#include <vector>\n#include <cassert>\n#include <string>\n#include <memory>\n#include <common/log_messages.h>\n#include \"RtMidi.h\"\n\n/*\n g++ -Wall -D__LINUX_ALSA__ -o test test.cpp RtMidi.cpp -lasound -lpthread\n*/\n\n/* [0,1] -> [-1,1] */\ninline float normed(float value) { return 2 * value - 1; }\n\nclass MidiIn\n{\n struct Data_t {\n Data_t() : raw(.0), unlocked(false), changed(false) {}\n float raw;\n mutable bool unlocked;\n mutable bool changed;\n };\n\n static constexpr std::size_t max_channel = 256;\n const Data_t default_val = Data_t{};\n\n std::unique_ptr<RtMidiIn> midi_ptr;\n std::vector<unsigned char> message;\n std::vector<Data_t> data;\n\n std::string interface_name;\n bool init_success;\n bool verbose;\n\npublic:\n MidiIn(unsigned port, bool verbose = true)\n : midi_ptr(nullptr)\n , message()\n , data(max_channel, default_val)\n , interface_name()\n , init_success(false)\n , verbose(verbose)\n {\n assert(data.size() == max_channel);\n try {\n midi_ptr = std::unique_ptr<RtMidiIn>(new RtMidiIn());\n }\n catch (RtMidiError &error) {\n error.printMessage();\n err_msg(__FILE__,__LINE__,\"Error while initializing MIDI system.\");\n }\n\n if (port < midi_ptr->getPortCount())\n {\n try {\n midi_ptr->openPort(port);\n }\n catch (RtMidiError &error) {\n error.printMessage();\n err_msg(__FILE__,__LINE__,\"Error while opening MIDI port: %u\", port);\n }\n } else {\n wrn_msg(\"No MIDI interface initialized. No device on MIDI port: %u\", port);\n return;\n }\n\n midi_ptr->ignoreTypes( false, false, false );\n\n init_success = true;\n interface_name = midi_ptr->getPortName(port);\n sts_msg(\"MIDI Interface '%s' successfully initialized.\", interface_name.c_str());\n }\n\n bool fetch() {\n if (not init_success) return false;\n\n try {\n std::size_t num_bytes = 0;\n do {\n const float dt = midi_ptr->getMessage( &message );\n num_bytes = message.size();\n\n if (num_bytes >= 2) {\n const unsigned char channel = message[1];\n data[channel].raw = static_cast<float>(message[2] / 127.0);\n data[channel].changed = true;\n if (verbose)\n dbg_msg(\"MIDI(%03u): ch=%03u val=%4.2f dt=%4.2f\", message[0], message[1], data[channel].raw, dt);\n }\n } while (num_bytes >= 2); /* until message queue cleared */\n\n }\n catch (RtMidiError &error) {\n error.printMessage();\n return false;\n }\n\n return true;\n }\n\n bool has_changed(std::size_t index) const {\n const Data_t& d = data.at(index);\n bool c = d.changed;\n d.changed = false;\n return c;\n }\n\n float operator[] (std::size_t index) const { return data.at(index).raw; }\n\n /* normed and unlocked */\n float get(std::size_t index, float unlock_initial = 0.0) const {\n const float val = normed(data.at(index).raw);\n\n if (data.at(index).unlocked) return val;\n else if (std::abs(val - unlock_initial) <= 1/127.) { /* check if we can unlock */\n data.at(index).unlocked = true;\n return val;\n }\n else return .0;\n }\n};\n\n\n" }, { "alpha_fraction": 0.5546143651008606, "alphanum_fraction": 0.5557289123535156, "avg_line_length": 27.213836669921875, "blob_id": "7290fdec0b3c33a249ee6d89707b85dc46824c52", "content_id": "c2002871ff2e6409bc8192cce445d8c3d155c03d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4486, "license_type": "no_license", "max_line_length": 133, "num_lines": 159, "path": "/src/control/behavior_state_machine.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BEHAVIOR_STATE_MACHINE_H_INCLUDED\n#define BEHAVIOR_STATE_MACHINE_H_INCLUDED\n\n#include <common/log_messages.h>\n#include <robots/simloid.h>\n#include <control/controlparameter.h>\n#include <control/jointcontrol.h>\n\n/*\n \\ | /\n -- o --\n / | \\\n*/\n\n//class State_Base {\n//public:\n// State_Base(const std::string& name)\n// : name(name)\n// { dbg_msg(\"Creating state: %\", name.c_str()); }\n//\n// virtual void entry(void) = 0;\n// virtual void step(void) = 0;\n//\n// const std::string& get_name(void) const { return name; }\n//\n//protected:\n// const std::string name;\n//};\n\nenum Behaviorial_State {\n// standing,\n// walking_forwards,\n// stop_walking_forwards,\n// walking_backwards,\n// walking_left,\n// walking_right,\n// running_forwards,\n// running_backwards,\n// running_left,\n// running_right,\n// stop_from_running,\n// roll_over_left,\n// roll_over_right,\n//\n turning_left,\n stop_turning_left,\n//\n// turning_right,\n// stop_turning_right,\n};\n\n/*\nTODO: table with allowed transitions:\n*/\n\n\nenum Direction { forwards, backwards, left, right };\nenum Gait { stand, walk, turn, run, stop };\n\nclass Behavioral_Statemachine {\npublic:\n Behavioral_Statemachine( const control::Control_Vector& parameter_set\n , robots::Simloid& robot\n , control::Jointcontrol& control\n , Behaviorial_State initial_state = Behaviorial_State::stop_turning_left/*walking_forwards*/)\n : parameter_set(parameter_set)\n , robot(robot)\n , control(control)\n , state(initial_state)\n , last_state(state)\n , command()\n {\n dbg_msg(\"Creating behavioral state machine: \\n Initial state is %u\", state);\n //TODO: how make the mapping from enum (state) to parameter_setID\n\n }\n\n void loop(void)\n {\n switch(state)\n {\n case turning_left:\n if (command.gait == Gait::stop)\n state = stop_turning_left;\n break;\n\n case stop_turning_left:\n if (command.gait == Gait::turn)\n state = turning_left;\n break;\n\n// case standing:\n// if (command.gait == Gait::walk)\n// state = walking_forwards;\n// break;\n//\n// case walking_forwards:\n// if (command.gait == Gait::stop)\n// state = stop_walking_forwards;\n//\n// //TODO control.set_control_parameter(parameter_set.get(current_behavior).get_parameter(), true);\n// break;\n//\n// case stop_walking_forwards:\n// /* TODO: wait for stop : robot.motion_stopped(threshold) */\n// if (command.gait == Gait::walk) {\n// state = walking_forwards;\n// }\n// else if (robot.motion_stopped(0.01))\n// {\n// state = standing;\n// dbg_msg(\"goto state: standing\");\n// }\n// break;\n\n default:\n assert(false);\n }\n\n if (last_state != state) {\n dbg_msg(\"State has changed from %u to %u\", last_state, state);\n last_state = state;\n }\n }\n\n\n /* command functions */\n void walk(void) { command.gait = Gait::walk; dbg_msg(\"walk\"); }\n void turn(void) { command.gait = Gait::turn; dbg_msg(\"turn\"); }\n void run (void) { command.gait = Gait::run; dbg_msg(\"run\" ); }\n void stop(void) { command.gait = Gait::stop; dbg_msg(\"stop\"); }\n /* stand is implicit and will be activated when stopping has succeeded */\n\n void forwards (void) { command.dir = Direction::forwards; dbg_msg(\"forwards\" ); }\n void backwards(void) { command.dir = Direction::backwards; dbg_msg(\"backwards\"); }\n void left (void) { command.dir = Direction::left; dbg_msg(\"left\" ); }\n void right (void) { command.dir = Direction::right; dbg_msg(\"right\" ); }\n\n Behaviorial_State get_state(void) const { return state; }\n\nprivate:\n const control::Control_Vector& parameter_set;\n robots::Simloid& robot;\n control::Jointcontrol& control;\n Behaviorial_State state, last_state;\n\n struct control_commands {\n control_commands()\n : gait(Gait::stand)\n , dir(Direction::forwards)\n { dbg_msg(\"Creating control commands.\"); }\n\n Gait gait;\n Direction dir;\n } command;\n\n\n};\n#endif // BEHAVIOR_STATE_MACHINE_H_INCLUDED\n" }, { "alpha_fraction": 0.7117347121238708, "alphanum_fraction": 0.7193877696990967, "avg_line_length": 27, "blob_id": "d4a8eaaba04f5ab29dc44ad71ff0c5d62b0f2f8b", "content_id": "76c9fcbf372f6f5cbb95458a139d885e4950793b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 392, "license_type": "no_license", "max_line_length": 72, "num_lines": 14, "path": "/src/learning/action_module.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef ACTION_MODULE_H_INCLUDED\n#define ACTION_MODULE_H_INCLUDED\n\nclass Action_Module_Interface\n{\npublic:\n virtual std::size_t get_number_of_actions(void) const = 0;\n virtual std::size_t get_number_of_actions_available(void) const = 0;\n virtual bool exists(const std::size_t action_index) const = 0;\n virtual ~Action_Module_Interface() {}\n};\n\n\n#endif // ACTION_MODULE_H_INCLUDED\n" }, { "alpha_fraction": 0.6328938007354736, "alphanum_fraction": 0.6453851461410522, "avg_line_length": 24.280702590942383, "blob_id": "5ad7c409f35bb60e57ec746cc3856eee9e58c086", "content_id": "baf23428a5c504a5e5270307955b04ab18ad7d8c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1441, "license_type": "no_license", "max_line_length": 110, "num_lines": 57, "path": "/src/draw/network3D.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* network3D.h */\n\n#ifndef NETWORK3D_H\n#define NETWORK3D_H\n\n#include <cassert>\n#include <vector>\n\n#include \"draw.h\"\n#include \"axes3D.h\"\n#include \"../common/log_messages.h\"\n\nclass network3D\n{\npublic:\n network3D(unsigned int number_of_nodes, const axes3D& axis)\n : number_of_nodes(number_of_nodes)\n , axis(axis)\n , pointer(0)\n , special_node(0)\n , activated_node(0)\n , n_pos(number_of_nodes)\n , n_size(number_of_nodes)\n , n_edges(number_of_nodes)\n {\n for (unsigned int i = 0; i < number_of_nodes; ++i)\n n_edges.at(i).assign(number_of_nodes, 0);\n }\n\n void draw(float x_angle, float y_angle) const;\n\n void update_node(unsigned int n, float x0, float x1, float x2, float s);\n void update_node(unsigned int n, float s);\n void update_edge(unsigned int i, unsigned int j, unsigned char op);\n void update_all_edges_of(unsigned int i, unsigned char op);\n void special (unsigned int n);\n void activated (unsigned int n);\n\n const Point& get_position(unsigned int index) const { assert(index < n_pos.size()); return n_pos[index]; }\n\nprivate:\n const unsigned int number_of_nodes;\n const axes3D& axis;\n\n unsigned int pointer;\n unsigned int special_node;\n unsigned int activated_node;\n\n std::vector<Point> n_pos;\n std::vector<float> n_size;\n\n /* size == 0, kein Knoten */\n std::vector<std::vector<unsigned char> > n_edges;\n\n};\n\n#endif /*NETWORK3D_H*/\n" }, { "alpha_fraction": 0.6324435472488403, "alphanum_fraction": 0.6324435472488403, "avg_line_length": 20.954545974731445, "blob_id": "acbfda02ac506c04f2e60b1bf2f9f7cb1ff79008", "content_id": "21430e2e474f031357cc3189998fe6541bc3f6fc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 487, "license_type": "no_license", "max_line_length": 86, "num_lines": 22, "path": "/src/control/pidcontrol.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef PIDCONTROL_H\r\n#define PIDCONTROL_H\r\n\n#include \"../main.h\"\n\nclass pidcontrol\n{\n private:\n double kp, ki, kd; // PID parameter\n double u; // output\n\n public:\n pid(void);\n void loop(const double set_point, const double process_variable);\n void reset(void);\n\n void set_control_parameter(const double kp, const double ki, const double kd);\n void set_limits(const double llower, const double lupper);\n};\n\n\n#endif // PIDCONTROL\n\n\n" }, { "alpha_fraction": 0.5765306353569031, "alphanum_fraction": 0.6030611991882324, "avg_line_length": 26.94285774230957, "blob_id": "32cad6e39b3f3eeee0d2c53c67cc3d8cf930e48f", "content_id": "991d62bf9af8f9573acdaa1ea60cee267176c37d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 980, "license_type": "no_license", "max_line_length": 100, "num_lines": 35, "path": "/src/draw/display.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <draw/display.h>\n\nnamespace draw {\n\nvoid hbar(float px, float py, float dx, float dy, float value, float max_value, Color4 const& color)\n{\n const float frac = clip(value/max_value, 0.f, 1.f);\n set_color(color, 0.3);\n fill_rect(px, py, dx, dy);\n set_color(color);\n fill_rect(px, py, dx*frac, dy);\n}\n\nvoid vbar(float px, float py, float dx, float dy, float value, float max_value, Color4 const& color)\n{\n const float frac = clip(value/max_value, 0.f, 1.f);\n set_color(color, 0.3);\n fill_rect(px, py, dx, dy);\n set_color(color);\n fill_rect(px, py, dx, dy*frac);\n}\n\nvoid block(float px, float py, float sx, float sy, float value, float max_value)\n{\n float frac = clip(fabs(value/max_value), 0.f, 1.f);\n\n if (value >= 0.f) glColor4f(.0f, .5f, 1.f, frac);\n else glColor4f(1.f, .5f, .0f, frac);\n\n draw_fill_rect(px, py, sx, sy);\n glColor4f(.9f, .9f, .9f, .5f);\n draw_rect(px, py, sx, sy);\n}\n\n} /* namespace draw */\n\n\n" }, { "alpha_fraction": 0.5724981427192688, "alphanum_fraction": 0.5824806690216064, "avg_line_length": 34.14912414550781, "blob_id": "18dc26e90039618558aeb92caf0122847bf10002", "content_id": "c62538484384dc1c1705a14a32adf1ae3a5a7bbc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4007, "license_type": "no_license", "max_line_length": 135, "num_lines": 114, "path": "/src/evolution/micro_evolution.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef MICRO_EVOLUTION_H_INCLUDED\n#define MICRO_EVOLUTION_H_INCLUDED\n\n//#include <evolution/fitness.h> //TODO check for circular dependency\n#include <evolution/individual.h>\n#include <evolution/population.h>\n#include <evolution/pool_strategy.h>\n\n/** 29.09.15: Elmar hat heute: \"Papa, mehr Gurke bitte, ohne Schale\" gesagt. */\n\nclass MicroEvolution {\n\n Population population;\n Individual child; // temp individual for crossover operation\n\n std::size_t candidate_idx;\n\n double moving_rate;\n double selection_bias;\n\n enum Trial_t {\n trial_initial,\n trial_crossover,\n trial_refreshing\n };\n\n Trial_t trial;\n uint32_t trial_count;\n std::string name;\n\npublic:\n MicroEvolution( const std::string& name\n , std::size_t population_size\n , std::size_t individual_size\n , double init_mutation_rate\n , double meta_mutation_rate\n , double moving_rate\n , double selection_bias )\n : population(population_size, individual_size, init_mutation_rate, meta_mutation_rate)\n , child(population[0])\n , candidate_idx(0)\n , moving_rate(moving_rate)\n , selection_bias(selection_bias)\n , trial(trial_initial)\n , trial_count(0)\n , name(name)\n { /* dbg_msg(\"Creating micro evolution.\"); */ }\n\n ~MicroEvolution() { /* dbg_msg(\"Destroying micro evolution.\"); */ }\n\n void generate_start_population(const VectorN& seed) {\n //dbg_msg(\"Generate start population.\");\n for (std::size_t i = 0; i < seed.size(); ++i)\n dbg_msg(\"[%u]: %+1.3f\", i, seed[i]); // TODO remove\n\n population.initialize_from_seed(seed);\n }\n\n const VectorN& get_next_candidate_genome(void) {\n\n //sts_msg(\"%s Trial: %2u (%d)\", name.c_str(), trial_count, trial);\n\n if (trial_count < population.get_size())\n {\n trial = trial_initial;\n candidate_idx = trial_count;\n return population[trial_count].genome;\n }\n else if (random_value(0.0, 1.0) > moving_rate) /**TODO: this is actually the inverse moving rate, rename!*/\n {\n /* select parents from pool and crossover */\n std::size_t parent_1 = biased_random_index_inv(population.get_size(), selection_bias);\n std::size_t parent_2 = biased_random_index_inv(population.get_size(), selection_bias);\n //sts_msg(\" crossing %2u and %2u\", parent_1, parent_2);\n\n trial = trial_crossover;\n candidate_idx = parent_1;\n crossover( population[parent_1]\n , population[parent_2]\n , child );\n\n child.mutate();\n return child.genome;\n }\n else\n {\n trial = trial_refreshing;\n candidate_idx = random_index(population.get_size());\n return population[candidate_idx].genome;\n }\n }\n\n /** no state machine */\n void set_candidate_fitness(const double fitness) {\n\n if (trial == trial_crossover) {\n child.fitness = fitness; // assign fitness to child in crossover trial\n std::size_t replace_idx = get_replacement_candidate_for(child, population);\n if (child.fitness > population[replace_idx].fitness) {\n //dbg_msg(\"Got replacement candidate %u (%1.3f > %1.3f)\", replace_idx, child.fitness, population[replace_idx].fitness);\n population[replace_idx] = child;\n } //else dbg_msg(\"not replaced (%1.3f < %1.3f)\", child.fitness, population[replace_idx].fitness);\n }\n else {\n population[candidate_idx].fitness.set_value(fitness); //averaging\n //dbg_msg(\"refreshing id:%u (%1.2f)\", candidate_idx, population[candidate_idx].fitness.get_value());\n }\n ++trial_count;\n dbg_msg(\"fit(%1.2f)\", population[candidate_idx].fitness.get_value());\n }\n\n};\n\n#endif // MICRO_EVOLUTION_H_INCLUDED\n" }, { "alpha_fraction": 0.6268116235733032, "alphanum_fraction": 0.6280193328857422, "avg_line_length": 33.5, "blob_id": "a164a319ddb194b4b54d80db3c6170672a970611", "content_id": "6872bee112a8052f1764404dd3f91331f340cd0b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1656, "license_type": "no_license", "max_line_length": 133, "num_lines": 48, "path": "/src/learning/reward.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef REWARD_H_INCLUDED\n#define REWARD_H_INCLUDED\n\n#include <common/integrator.h>\n#include <control/sensorspace.h>\n\nclass reward_base\n{\nprotected:\n std::vector<sensor_signal> rewards;\n std::vector<Integrator> aggregated_rewards;\n\npublic:\n reward_base(std::size_t number_of_policies_to_reserve)\n : rewards()\n , aggregated_rewards(number_of_policies_to_reserve)\n {\n dbg_msg(\"Creating reward base, reserving %u items.\", number_of_policies_to_reserve);\n rewards.reserve(number_of_policies_to_reserve);\n }\n\n virtual ~reward_base() {}\n\n double get_current_reward(std::size_t index) const { assert(index < rewards.size()); return rewards[index].current; }\n double get_last_reward (std::size_t index) const { assert(index < rewards.size()); return rewards[index].last; }\n const std::string& get_reward_name (std::size_t index) const { assert(index < rewards.size()); return rewards[index].name; }\n std::size_t get_number_of_policies(void) const { return rewards.size(); }\n\n double get_aggregated_last_reward(std::size_t index) const {\n return aggregated_rewards.at(index).get_avg_value();\n }\n\n void clear_aggregations(void) {\n for (std::size_t i = 0; i < aggregated_rewards.size(); ++i)\n aggregated_rewards[i].reset();\n }\n\n void execute_cycle(void) {\n assert(aggregated_rewards.size() >= rewards.size());\n for (std::size_t i = 0; i < rewards.size(); ++i) {\n rewards[i].execute_cycle();\n aggregated_rewards[i].add(rewards[i].current);\n }\n }\n};\n\n\n#endif // REWARD_H_INCLUDED\n" }, { "alpha_fraction": 0.5574687719345093, "alphanum_fraction": 0.5619888305664062, "avg_line_length": 35.00775146484375, "blob_id": "8fad1c0cccbf3b2ed844b19776860a836ed7136f", "content_id": "0189e45d5c5722838ff006d3343c1e344aa13b78", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4646, "license_type": "no_license", "max_line_length": 121, "num_lines": 129, "path": "/src/learning/predictor.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <learning/predictor.h>\n\n double Predictor_Base::calculate_prediction_error() {\n auto const &predictions = get_prediction();\n assertion( input.size() == predictions.size()\n , \"Input and prediction vector must have same size (%u =/= %u)\"\n , input.size(), predictions.size()\n );\n\n //test_range(input, -1.0, 1.0, \"input\");\n //test_range(predictions, -1.0, 1.0, \"predictions\");\n\n /* sum of squared distances to input */\n const double error = squared_distance(input, predictions);\n\n /** The prediction error is being normalized by the number\n * of weights/inputs and the max. input range [-1,+1] so\n * that it is independent of the size of input space.\n * Also it should be limited [0..1].\n */\n prediction_error = normalize_factor * sqrt(error);\n assert_in_range(prediction_error, predictor_constants::error_min, predictor_constants::error_max);\n return prediction_error;\n }\n\n\n Predictor::Predictor( const sensor_vector& input\n , const double learning_rate\n , const double random_weight_range\n , const std::size_t experience_size )\n : Predictor_Base(input, learning_rate, random_weight_range, experience_size)\n , weights(input.size())\n {\n //dbg_msg(\"Initialize simple predictor.\");\n initialize_from_input();\n predict(); // initialize prediction error\n }\n\n\n /* initialize weights and experience with random values\n */\n void Predictor::initialize_randomized(void)\n {\n assert(weights.size() == input.size());\n for (std::size_t m = 0; m < input.size(); ++m)\n weights[m] = input[m] + random_value(-random_weight_range,\n +random_weight_range);\n experience.assign(experience.size(), weights);\n prediction_error = predictor_constants::error_min;\n }\n\n\n /* initialize weights and experience from first input\n */\n void Predictor::initialize_from_input(void)\n {\n assert(weights.size() == input.size());\n weights = input.get();\n experience.assign(experience.size(), weights);\n prediction_error = predictor_constants::error_min;\n }\n\n\n /* copy assignment to base type\n */\n void Predictor::copy(Predictor_Base const& other)\n {\n Predictor_Base::operator=(other); // copy base members\n Predictor const& rhs = dynamic_cast<Predictor const&>(other); /**TODO definitely write a test for that crap :) */\n assert(weights.size() == rhs.weights.size());\n weights = rhs.weights;\n }\n\n\n /* make the prediction based on actual weights\n */\n double Predictor::predict(void) {\n //dbg_msg(\"predict\");\n /* prediction is trivial for simple predictor*/\n return calculate_prediction_error();\n }\n\n /* adapt the weights to the current\n * input sample and learn from experience\n */\n void Predictor_Base::adapt(void)\n {\n //dbg_msg(\"adapt\");\n if (experience.size() == 1)\n learn_from_input_sample();\n else\n {\n /** create random index to skip an arbitrary\n * sample and replaced it by current input */\n std::size_t rand_idx = random_index(experience.size());\n\n learn_from_experience(rand_idx); // adapt without new sample\n predict(); // refresh prediction error\n learn_from_input_sample(); // adapt to new sample\n\n /** Insert current input into random position of experience list.\n * This must be done after adaptation to guarantee a positive learning progress */\n experience[rand_idx] = input.get();\n }\n }\n\n\n\n void Predictor::learn_from_input_sample(void) {\n for (std::size_t m = 0; m < input.size(); ++m)\n weights[m] += learning_rate * (input[m] - weights[m]) / experience.size();\n }\n\n\n\n void Predictor::learn_from_experience(std::size_t skip_idx) {\n assert(experience.size() > 1);\n assert(experience[0].size() == weights.size());\n /* learn the list */\n for (std::size_t m = 0; m < weights.size(); ++m) {\n double delta = .0;\n for (std::size_t i = 0; i < experience.size(); ++i) {\n if (i != skip_idx)\n delta += (experience[i][m] - weights[m]);\n }\n delta *= learning_rate / (experience.size() - 1);\n weights[m] += delta;\n }\n }\n\n" }, { "alpha_fraction": 0.6500375270843506, "alphanum_fraction": 0.6511628031730652, "avg_line_length": 29.643678665161133, "blob_id": "922e16701143f39d5b4a3b295afb708dc4144112", "content_id": "b2de66c2d4038ede4c5060402fdc98f5d5ddb644", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2666, "license_type": "no_license", "max_line_length": 107, "num_lines": 87, "path": "/src/learning/payload.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef PAYLOAD_H_INCLUDED\n#define PAYLOAD_H_INCLUDED\n\n#include <vector>\n#include <common/static_vector.h>\n#include <common/log_messages.h>\n#include <learning/q_function.h>\n#include <learning/eligibility.h>\n\nclass Empty_Payload\n{\npublic:\n Empty_Payload() {}\n Empty_Payload& operator=(const Empty_Payload& /*other*/) { return *this; };\n};\n\nclass State_Payload\n{\npublic:\n State_Payload(const Action_Module_Interface& actions, std::size_t number_of_policies, double q_initial)\n : policies(number_of_policies, actions, q_initial)\n , eligibility_trace(actions.get_number_of_actions())\n {}\n\n State_Payload& operator=(const State_Payload& other) {\n assert(this != &other); // no self-assignment\n copy_with_flaws(other); // redirect copy-assignment\n return *this;\n }\n\n /* This must be used by the motor layer to copy states associated to the copied action.*/\n void copy_payload(std::size_t from_idx, std::size_t to_idx) {\n for (std::size_t policy_idx = 0; policy_idx < policies.size(); ++policy_idx)\n policies[policy_idx].copy_q_value(from_idx, to_idx);\n eligibility_trace[to_idx] = eligibility_trace[from_idx];\n }\n\nprivate:\n void copy_with_flaws(const State_Payload& other)\n {\n /* inherit flawed Q-values, i.e. mutate, hidden in the copy assignment [!] */\n policies = other.policies;\n\n /* inherit flawless eligibility traces */\n eligibility_trace = other.eligibility_trace;\n }\npublic:\n copyable_static_vector<Policy> policies;\n copyable_static_vector<Eligibility> eligibility_trace;\n\n friend class SARSA;\n friend class Epsilon_Greedy;\n friend class Boltzmann_Softmax;\n friend class State_Payload_Graphics;\n friend class Payload_Graphics;\n};\n\n\nclass Motor_Payload\n{\n typedef static_vector<State_Payload> state_vector_t;\n\n std::size_t index = 0;\n state_vector_t* states = nullptr;\n\npublic:\n Motor_Payload(){}\n Motor_Payload& operator=(const Motor_Payload& other) {\n assert(this != &other); // no self-assignment\n //copy_with_flaws(other); // redirect copy-assignment\n sts_msg(\"Copy action value from %lu to %lu.\", other.index, index);\n assert(other.index != index); /* If this was thrown, check payloads connected. */\n assert(states != nullptr);\n for (std::size_t i = 0; i < (*states).size(); ++i)\n (*states)[i].copy_payload(other.index, index);\n return *this;\n }\n\n /* Two-phase initialization for connecting payloads.*/\n void connect(std::size_t idx, state_vector_t* s) {\n index = idx;\n states = s;\n }\n};\n\n\n#endif // PAYLOAD_H_INCLUDED\n" }, { "alpha_fraction": 0.6885474920272827, "alphanum_fraction": 0.6885474920272827, "avg_line_length": 20.57575798034668, "blob_id": "0d79d2f95eeff9a7516975235febb8408e637390", "content_id": "b32ef3e097595649cf0e5bfa972872a1461c1e8e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 716, "license_type": "no_license", "max_line_length": 87, "num_lines": 33, "path": "/src/common/basic.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BASIC_H\r\n#define BASIC_H\r\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <stdarg.h>\n#include <errno.h>\n#include <string.h>\n#include <vector>\n#include <algorithm>\n#include <string>\n#include <sys/stat.h>\n#include <dirent.h>\n#include \"log_messages.h\"\n\n\n/* file opening, formatted yeah! */\nFILE * open_file(const char *mode, const char *format, ...);\n\nnamespace basic { /**IDEA: Consider moving this to file_io*/\n\nstd::string make_directory(const char *format, ...);\n\ntypedef std::vector<std::string> Filelist;\nFilelist list_directory(const char* target_dir = \"\", const char* filename_ending = \"\");\n\nstd::size_t get_file_size(FILE* fd);\n\nstd::string get_timestamp();\n\n} // namespace basic\n\r\n#endif // BASIC_H\r\n" }, { "alpha_fraction": 0.6209813952445984, "alphanum_fraction": 0.624365508556366, "avg_line_length": 18.700000762939453, "blob_id": "eda8368e26dea3948871f4404055799cccfca114", "content_id": "6f25a6fb89926fa52275e89c3018f1cc15afa4d1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 591, "license_type": "no_license", "max_line_length": 58, "num_lines": 30, "path": "/src/common/save_load.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SAVE_LOAD_H_INCLUDED\n#define SAVE_LOAD_H_INCLUDED\n\n#include <string>\n#include <common/file_io.h>\n\n\nnamespace common {\n\nclass Save_Load {\npublic:\n\n typedef file_io::CSV_File<double> csv_file_t;\n typedef std::string folder_t;\n\n\n// template<typename... Args>\n // void save(const char* folder_t, const Args&... args)\n\n // template<typename... Args>\n // void load(const char* folder_t, const Args&... args)\n\n// virtual void save(folder_t& f, ... ) = 0;\n// virtual void load(folder_t& f, ... ) = 0;\n virtual ~Save_Load() {}\n};\n\n}\n\n#endif /* SAVE_LOAD_H_INCLUDED */\n" }, { "alpha_fraction": 0.5953088998794556, "alphanum_fraction": 0.599933922290802, "avg_line_length": 32.172603607177734, "blob_id": "1955a3f7fa8fb026db5225c92e70aeccb9b63d52", "content_id": "211c20b04b427189d52b2141794d086b856fe4ce", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 12117, "license_type": "no_license", "max_line_length": 139, "num_lines": 365, "path": "/src/learning/sarsa.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/**\nTo later make an easier transition from discrete tabular based value functions\nto linear function approximators or neural networks you should encapsulate the\nfunction lookup as Q(s_t,a_t) where s,a can be scalars (indices) or vectors\nand where the adaption is just taking the reward and adapts accordingly, no matter what mechanism is behind.\n\n*/\n\n#ifndef SARSA_H_INCLUDED\n#define SARSA_H_INCLUDED\n\n#include <algorithm>\n\n#include <common/modules.h>\n#include <common/static_vector.h>\n\n#include <learning/reinforcement_learning.h>\n#include <learning/sarsa_constants.h>\n#include <learning/gmes.h>\n#include <learning/reward.h>\n#include <learning/payload.h>\n#include <learning/action_selection.h>\n#include <learning/eligibility.h>\n\n#include <robots/joint.h>\n\n/**\n \"There is a chance that the mouse is going to say ‘yes I see the best move, but...the hell with it’ and jump over the edge!\n All in the name of exploration.\"\n\n --Travis DeWolf--\n*/\n\nnamespace RL {\n typedef std::size_t State;\n typedef std::size_t Action;\n}\n\n/** TODO: überprüfe, ob für die off-policies Q-learning bessere Ergebnisse liefert als SARSA,\n * Antwort: Möglicherweise gibt es Probleme den Max-Operator zusammen mit nichtstationären Aktionen zu verwenden. */\nclass SARSA : public learning::RL_Interface {\npublic:\n\n SARSA( static_vector<State_Payload>& states\n , const reward_base& rewards\n , /*const*/ Action_Selection_Base& action_selection\n , std::size_t number_of_actions\n , const std::vector<double>& learning_rates\n , float discounting = sarsa_constants::GAMMA\n , float trace_decay = sarsa_constants::LAMBDA\n , RL::State initial_state = 0\n , RL::Action initial_action = 0 )\n : states(states)\n , rewards(rewards)\n , action_selection(action_selection)\n , current_state(initial_state)\n , last_state()\n , current_action(initial_action)\n , last_action()\n , number_of_policies(states[0].policies.size())\n , number_of_actions(number_of_actions)\n , current_policy(0)\n , deltaQ(number_of_policies)\n , learning_rates(learning_rates)\n , discounting(discounting)\n , trace_decay(trace_decay)\n , learning_enabled(true)\n {\n sts_msg(\"Creating discrete Reinforcement Learner: SARSA.\\\n \\n states = %u\\n actions = %u\\n policies = %u\"\n , states.size(), number_of_actions, number_of_policies);\n assert(number_of_policies > 0);\n assert(number_of_actions > 0);\n assert_in_range(learning_rates, 0.0001, 0.5);\n promise(learning_rates.size() >= number_of_policies, __FILE__, __LINE__\n , \"Number of learning rates %u must be equal to the number of policies %u\"\n , learning_rates.size(), number_of_policies);\n assert(states[0].policies.size() == rewards.get_number_of_policies() );\n\n sts_msg(\"GAMMA=%1.3f LAMBDA = %1.3f\", discounting, trace_decay);\n }\n\n double get_current_reward (std::size_t index) const { return rewards.get_current_reward(index); }\n bool positive_current_delta(std::size_t index) const { assert(index < number_of_policies); return deltaQ[index] > 0.0; }\n std::size_t get_current_policy (void) const { return current_policy; }\n std::size_t get_current_action (void) const { return current_action; }\n std::size_t get_current_state (void) const { return current_state; }\n std::size_t get_number_of_policies(void) const { return number_of_policies; }\n std::size_t get_number_of_actions (void) const { return number_of_actions; }\n\n void select_policy(std::size_t index, bool print_status = true)\n {\n if (index < number_of_policies) {\n current_policy = index;\n if (print_status)\n sts_msg(\"Selecting policy: %s\", rewards.get_reward_name(current_policy).c_str());\n }\n else wrn_msg(\"Invalid policy\");\n }\n\n void decay_eligibility_traces(void) {\n /**TODO non existing states should be reset to zero */\n for (std::size_t s = 0; s < states.size(); ++s)\n for (std::size_t a = 0; a < number_of_actions; ++a)\n states[s].eligibility_trace[a].decay(discounting*trace_decay);\n }\n\n\n\n\n void execute_cycle(RL::State new_state, RL::Action new_action)\n {\n save_prev_state_and_action();\n current_state = new_state;\n current_action = new_action;\n if (learning_enabled)\n learning_step();\n }\n\n void execute_cycle(RL::State new_state)\n {\n save_prev_state_and_action();\n\n /* set new state */\n current_state = new_state;\n\n /** action selection should not be part of Sarsa module */\n /* action selection (e.g.Epsilon Greedy or Boltzmann) */\n if (not random_actions)\n current_action = action_selection.select_action(current_state, current_policy);\n else\n current_action = action_selection.select_randomized();\n\n if (learning_enabled)\n learning_step();\n }\n\n void toggle_random_actions(void) {\n random_actions = not random_actions;\n sts_msg(\"Random actions: %s\", random_actions? \"ON\":\"OFF\");\n }\n\n void enable_learning(bool enable) {\n if (learning_enabled != enable) {\n sts_msg(\"SARSA learning=%s\", enable?\"ENABLED\":\"DISABLED\");\n learning_enabled = enable;\n }\n }\n\n bool is_exploring(void) const {\n return action_selection.is_exploring();\n }\n\nprivate:\n\n void learning_step(void)\n {\n /* Q-learning (SARSA) */\n assert(deltaQ.size() == number_of_policies);\n assert(states[last_state].eligibility_trace.size() == number_of_actions);\n\n /** TODO rethink: use q-learning for off-policies.. and sarsa for the on-policy learning step */\n\n for (std::size_t pi = 0; pi < number_of_policies; ++pi)\n //TODO testing\n //if (pi == current_policy)\n deltaQ[pi] = rewards.get_aggregated_last_reward(pi) + discounting * states[current_state].policies[pi].qvalues[current_action]\n - states[last_state ].policies[pi].qvalues[last_action ];\n\n //else deltaQ[pi] = .0;\n //TODO use get_max_q\n\n /* decay eligibility traces */\n if (current_state != last_state)\n decay_eligibility_traces();\n\n /* reset trace */\n states[last_state].eligibility_trace[last_action].reset();\n\n /* update all Q-Values according to their trace */\n /**TODO this method is inherently slow. try to improve */\n for (std::size_t s = 0; s < states.size(); ++s)\n for (std::size_t a = 0; a < number_of_actions; ++a)\n for (std::size_t pi = 0; pi < number_of_policies; ++pi)\n states[s].policies[pi].qvalues[a] += learning_rates[pi] * deltaQ[pi] * states[s].eligibility_trace[a].get();\n }\n\n\n void save_prev_state_and_action(void) { last_state = current_state; last_action = current_action; }\n\n static_vector<State_Payload>& states;\n const reward_base& rewards;\n Action_Selection_Base& action_selection;\n\n RL::State current_state;\n RL::State last_state;\n\n RL::Action current_action;\n RL::Action last_action;\n\n const std::size_t number_of_policies;\n const std::size_t number_of_actions;\n std::size_t current_policy;\n\n std::vector<double> deltaQ;\n\n const std::vector<double> learning_rates;\n float discounting; // aka Gamma\n float trace_decay; // aka Lambda\n\n bool random_actions = false;\n bool learning_enabled;\n\n friend class SARSA_Graphics;\n friend class Policy_Selector_Graphics;\n};\n\nclass pseudo_random_order {\n\n unsigned ptr = 0;\n std::vector<unsigned> cards;\n\n void shuffle(void) { std::random_shuffle(cards.begin(), cards.end()); }\n\npublic:\n pseudo_random_order(unsigned number_of_values)\n : cards()\n {\n cards.reserve(number_of_values);\n for (unsigned i=0; i < number_of_values; ++i)\n cards.emplace_back(i);\n shuffle();\n }\n\n unsigned next(void) {\n unsigned result = cards[ptr++];\n if (ptr >= cards.size()) { shuffle(); ptr = 0; }\n return result;\n }\n};\n\nclass Policy_Selector /**TODO: move to separate file */\n{\n SARSA& sarsa;\n const std::size_t number_of_policies;\n std::size_t current_policy;\n\n std::vector<uint64_t> policy_trial_duration;\n uint64_t cycles;\n bool random_policy_mode;\n mutable bool trial_has_ended;\n\n pseudo_random_order pseudo_rnd_order;\n\n struct policy_profile {\n std::vector<std::size_t> items;\n std::size_t current;\n bool play;\n\n policy_profile() : items(), current(), play() {}\n } profile;\n\n\n\npublic:\n Policy_Selector(SARSA& sarsa, const std::size_t number_of_policies, bool random_policy_mode = true, uint64_t default_duration_s = 10)\n : sarsa(sarsa)\n , number_of_policies(number_of_policies)\n , current_policy(0)\n , policy_trial_duration(number_of_policies, default_duration_s * 100) /* make constant in setup */\n , cycles(0)\n , random_policy_mode(random_policy_mode)\n , trial_has_ended(false)\n , pseudo_rnd_order(number_of_policies)\n , profile()\n {\n dbg_msg(\"Creating Policy Selector with %u policies.\", number_of_policies);\n select_random_policy();\n }\n\n void toggle_random_policy_mode(void) {\n random_policy_mode = !random_policy_mode;\n sts_msg(\"Random Policy Mode: %s\", random_policy_mode ? \"ON\" : \"OFF\");\n }\n\n void select_random_policy(void) {\n /* select a random policy which is different from the previous one */\n if (number_of_policies > 1) {\n /*\n current_policy += random_int(1, number_of_policies - 1);\n current_policy %= number_of_policies;\n */\n current_policy = pseudo_rnd_order.next();\n assert(current_policy < number_of_policies);\n }\n sarsa.select_policy(current_policy, false);\n cycles = 0;\n trial_has_ended = true;\n }\n\n void select_policy(std::size_t new_policy) {\n if (new_policy >= number_of_policies) {\n dbg_msg(\"No such policy: %u\", new_policy);\n return;\n }\n current_policy = new_policy;\n sarsa.select_policy(current_policy);\n cycles = 0;\n trial_has_ended = true;\n }\n\n void execute_cycle(void) {\n\n ++cycles;\n if (cycles < policy_trial_duration[current_policy]) return;\n\n if (profile.play)\n {\n if (profile.items.size() > 0) {\n select_policy(profile.items.at(profile.current++));\n if (profile.current >= profile.items.size())\n profile.current = 0;\n }\n else profile.play = false;\n }\n else if (random_policy_mode)\n {\n select_random_policy();\n }\n else {\n cycles = 0;\n trial_has_ended = true;\n }\n }\n\n void set_profile(std::vector<std::size_t> p) { profile.items = p; profile.current = 0; }\n void toggle_playing_profile(void) {\n profile.play = not profile.play;\n sts_msg(\"Policy Profile Mode: %s\", profile.play ? \"ON\" : \"OFF\");\n }\n\n\n void set_policy_trial_duration(std::size_t index, uint64_t duration) {\n assert(index < policy_trial_duration.size());\n policy_trial_duration[index] = duration;\n }\n\n uint64_t get_trial_time_left(void) const {\n return policy_trial_duration[current_policy] - cycles;\n }\n\n std::size_t size() const { return number_of_policies; }\n\n bool has_trial_ended(void) const {\n const bool result = trial_has_ended;\n trial_has_ended = false;\n return result;\n }\n\n std::size_t get_current_policy(void) const { return current_policy; }\n\n friend class Policy_Selector_Graphics;\n};\n\n#endif // SARSA_H_INCLUDED\n" }, { "alpha_fraction": 0.5357781648635864, "alphanum_fraction": 0.5822898149490356, "avg_line_length": 24.976743698120117, "blob_id": "f365546254cd05d55b60268e85c17703651894b2", "content_id": "ff79e5fe59c65eb7704b6e3153f780b6298070e9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1118, "license_type": "no_license", "max_line_length": 83, "num_lines": 43, "path": "/src/draw/plot3D.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* plot3D.cpp */\n\n#include \"plot3D.h\"\n\nvoid plot3D::draw(float x_angle, float y_angle) const\n{\n glPushMatrix();\n glTranslatef(axis.px, axis.py, axis.pz);\n glRotatef(y_angle, 1.0, 0.0, 0.0);\n glRotatef(x_angle, 0.0, 1.0, 0.0);\n\n glBegin(GL_LINE_STRIP);\n //glColor4ubv(color);\n for (unsigned int i = number_of_samples - 1; i != 0; --i) {\n// color[3] = 255*i/N; //TODO\n glColor4ubv(color);\n glVertex3f(0.5 * axis.width * signal0[(i + pointer) % number_of_samples],\n 0.5 * axis.height * signal1[(i + pointer) % number_of_samples],\n 0.5 * axis.depth * signal2[(i + pointer) % number_of_samples]);\n }\n glEnd();\n glPopMatrix();\n}\n\nvoid plot3D::add_sample(float s0, float s1, float s2)\n{\n increment_pointer();\n signal0[pointer] = s0;\n signal1[pointer] = s1;\n signal2[pointer] = s2;\n}\n\nvoid plot3D::add_sample(const std::vector<double>& sample)\n{\n assert(sample.size() >= 3);\n increment_pointer();\n signal0[pointer] = sample[0];\n signal1[pointer] = sample[1];\n signal2[pointer] = sample[2];\n}\n\n\n/* plot3D.cpp */\n\n" }, { "alpha_fraction": 0.5452603101730347, "alphanum_fraction": 0.5918803215026855, "avg_line_length": 31.582279205322266, "blob_id": "9ee7b634e2b11adf1d34c260398c846e4ac8c6eb", "content_id": "76350d8e2af5aea1ed7105786e4a9dcbdf4dafc0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5148, "license_type": "no_license", "max_line_length": 123, "num_lines": 158, "path": "/src/tests/predictor_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n\n#include <control/sensorspace.h>\n#include <learning/predictor.h>\n#include <learning/state_predictor.h>\n#include <common/log_messages.h>\n\nclass test_space : public sensor_vector {\npublic:\n test_space(const double sigma) : sensor_vector(3) {\n sensors.emplace_back(\"Foo\", [sigma](){ return 0.42 + rand_norm_zero_mean(sigma); });\n sensors.emplace_back(\"Bar\", [sigma](){ return 0.37 + rand_norm_zero_mean(sigma); });\n sensors.emplace_back(\"Baz\", [sigma](){ return -0.23 + rand_norm_zero_mean(sigma); });\n }\n};\n\nTEST_CASE( \"predictor adapts\" , \"[predictor]\")\n{\n test_space sensors{0.0};\n sensors.execute_cycle();\n Predictor pred{ sensors, 0.1, 0.01, 1 };\n pred.initialize_randomized();\n const std::vector<double>& w = pred.get_prediction();\n REQUIRE( w.size() == 3 );\n\n for (unsigned i = 0; i < 100; ++i) {\n sensors.execute_cycle();\n pred.adapt();\n }\n\n REQUIRE( close(w[0], 0.42, 0.001) );\n REQUIRE( close(w[1], 0.37, 0.001) );\n REQUIRE( close(w[2],-0.23, 0.001) );\n\n dbg_msg(\"%6.4f %6.4f %6.4f\", w[0], w[1], w[2]);\n}\n\nTEST_CASE( \"adapt with experience replay\" , \"[predictor]\")\n{\n srand((unsigned) time(0));\n test_space sensors(0.01); /** with random */\n sensors.execute_cycle();\n Predictor pred{ sensors, 0.1, 0.01, 100 };\n pred.initialize_randomized();\n\n const std::vector<double>& w = pred.get_prediction();\n REQUIRE( w.size() == 3 );\n\n for (unsigned i = 0; i < 1000; ++i) {\n sensors.execute_cycle();\n pred.adapt();\n }\n REQUIRE( close(w[0], 0.42, 0.1) );\n REQUIRE( close(w[1], 0.37, 0.1) );\n REQUIRE( close(w[2],-0.23, 0.1) );\n\n dbg_msg(\"%6.4f %6.4f %6.4f\", w[0], w[1], w[2]);\n}\n\nTEST_CASE( \"prediction error must be constant without learning step\" )\n{\n srand((unsigned) time(0));\n test_space sensors(0.0); /** without random */\n sensors.execute_cycle();\n Predictor pred{ sensors, 0.1, 0.01, 1 };\n pred.initialize_randomized();\n\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.predict();\n double pred_err_start = pred.get_prediction_error();\n dbg_msg(\"Prediction error start: %1.5f\", pred_err_start);\n REQUIRE( pred_err_start > 0.0 );\n\n for (unsigned i = 0; i < 13; ++i) {\n sensors.execute_cycle();\n double pred_err_new = pred.predict();\n REQUIRE( pred_err_new == pred.get_prediction_error() );\n dbg_msg(\"Prediction error %u: %1.5f %1.5f\", i, pred_err_start, pred_err_new);\n REQUIRE( close(pred_err_start, pred_err_new, 0.001) );\n }\n}\n\nTEST_CASE( \"prediction error must decrease after learning step\" )\n{\n srand((unsigned) time(0));\n test_space sensors(0.0); /** without random */\n sensors.execute_cycle();\n Predictor pred{ sensors, 0.1, 0.01, 1 };\n pred.initialize_randomized();\n\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.predict();\n\n double pred_err_start = pred.get_prediction_error();\n dbg_msg(\"Prediction error start: %1.3f\", pred_err_start);\n REQUIRE( pred_err_start > 0.0 );\n\n for (unsigned i = 0; i < 42; ++i) {\n sensors.execute_cycle();\n double pred_err_before = pred.predict();\n pred.adapt();\n double pred_err_after = pred.predict();\n REQUIRE( pred_err_after == pred.get_prediction_error() );\n dbg_msg(\"Prediction error %u: %1.5f %1.5f\", i, pred_err_before, pred_err_after);\n REQUIRE( pred_err_before > pred_err_after );\n }\n}\n\nTEST_CASE( \"prediction error is reset on (re-)initialization\" )\n{\n srand((unsigned) time(0));\n test_space sensors(0.1); /** with random */\n sensors.execute_cycle();\n Predictor pred{ sensors, 0.1, 0.01, 1 };\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.initialize_randomized();\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.predict();\n REQUIRE( pred.get_prediction_error() > 0.0 );\n pred.initialize_from_input();\n REQUIRE( pred.get_prediction_error() == 0.0 );\n sensors.execute_cycle();\n pred.predict();\n REQUIRE( pred.get_prediction_error() > 0.0 );\n}\n\n\nTEST_CASE( \"state predictor construction\", \"[predictor]\" )\n{\n test_space sensors(0.01);\n learning::State_Predictor pred(sensors, 0.01, 0.1, /*experience buffer = */100, /*hidden size = */2, /*time_delay=*/1);\n}\n\nTEST_CASE( \"state predictor adapts\" , \"[predictor]\")\n{\n test_space sensors{0.0};\n sensors.execute_cycle();\n learning::State_Predictor pred{ sensors, 0.05, 0.01, 1, 2, 1};\n pred.initialize_randomized();\n const std::vector<double>& w = pred.get_prediction();\n REQUIRE( w.size() == 3 );\n pred.predict();\n pred.adapt();\n double err = pred.get_prediction_error();\n\n for (unsigned i = 0; i < 1000; ++i) {\n sensors.execute_cycle();\n pred.predict();\n pred.adapt();\n REQUIRE( err > pred.get_prediction_error() );\n err = pred.get_prediction_error();\n if (i%100 == 0)\n dbg_msg(\"w0:%6.4f w1:%6.4f w2:%6.4f, error: %6.4f\", w[0], w[1], w[2], err);\n }\n REQUIRE( close(w[0], 0.42, 0.001) );\n REQUIRE( close(w[1], 0.37, 0.001) );\n REQUIRE( close(w[2],-0.23, 0.001) );\n}\n" }, { "alpha_fraction": 0.691236674785614, "alphanum_fraction": 0.6986076831817627, "avg_line_length": 30.30769157409668, "blob_id": "84b54f6ecca3e4dfd59e0c68dd53064b7854d759", "content_id": "0eb9027614077780a0a47bf4c9a0adedf5d28b13", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1221, "license_type": "no_license", "max_line_length": 90, "num_lines": 39, "path": "/src/robots/robot.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef ROBOT_H_INCLUDED\n#define ROBOT_H_INCLUDED\n\n#include <robots/joint.h>\n#include <robots/accel.h>\n\nnamespace robots {\n\n/**\n * This robot interface should unify all types of robots and make them available\n * to other components as a pure structure of joints, i.e. angles, velocities and torques.\n * This shall ensure the independence of the individual segment lengths, masses or other\n * construction details. All components should prefer to use this interface instead of\n * using a certain robot class directly.\n */\nclass Robot_Interface {\npublic:\n virtual std::size_t get_number_of_joints (void) const = 0;\n virtual std::size_t get_number_of_symmetric_joints (void) const = 0;\n virtual std::size_t get_number_of_accel_sensors (void) const = 0;\n\n virtual const Jointvector_t& get_joints(void) const = 0;\n virtual Jointvector_t& set_joints(void) = 0;\n\n virtual const Accelvector_t& get_accels(void) const = 0;\n virtual Accelvector_t& set_accels(void) = 0;\n\n virtual ~Robot_Interface() {}\n\n virtual bool execute_cycle(void) = 0;\n\n virtual double get_normalized_mechanical_power(void) const = 0;\n\n};\n\n} // namespace robots\n\n\n#endif // ROBOT_H_INCLUDED\n" }, { "alpha_fraction": 0.5097216367721558, "alphanum_fraction": 0.5408749580383301, "avg_line_length": 36.40495681762695, "blob_id": "cd1deb8eef93ddc45cfbc8e50a73cb560d1061d7", "content_id": "66769db110c63113e407066904373c53b3cfddf8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4526, "license_type": "no_license", "max_line_length": 156, "num_lines": 121, "path": "/src/learning/gmes_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GMES_GRAPHICS_H_INCLUDED\n#define GMES_GRAPHICS_H_INCLUDED\n\n#include <draw/draw.h>\n#include <draw/axes3D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n#include <draw/display.h>\n#include <learning/gmes.h>\n#include <learning/predictor_graphics.h>\n\nclass GMES_Graphics : public Graphics_Interface {\npublic:\n GMES_Graphics(const GMES& gmes, const sensor_vector& input, unsigned num_samples = 100)\n : gmes(gmes)\n , expert(gmes.expert)\n , input(input)\n , axis(.0, .0, .0, 1., 1., 1., 0)\n , plot(num_samples, axis, LineColorMix0[0])\n , graph(expert.size(), axis)\n , predictor_graphics()\n {\n predictor_graphics.reserve(expert.size());\n\n int dsize = (int) ceil(sqrt(expert.size()));\n for (unsigned int n = 0; n < expert.size(); ++n)\n {\n graph.update_node(n,\n -1.0 + 2.0/dsize*(n%dsize) + 1.0/dsize,\n -1.0 + 2.0/dsize*(n/dsize) + 1.0/dsize,\n -1.0,\n gmes_constants::initial_learning_capacity);\n\n predictor_graphics.emplace_back(expert[n].get_predictor());\n }\n sts_msg(\"Created GMES Graphics Extension\");\n }\n\n void update_on_load(void)\n {\n for (unsigned int i = 0; i < expert.size(); ++i) {\n if (!expert[i].does_exists()) continue;\n graph.update_node(i,\n expert[i].get_predictor().get_prediction()[0],\n expert[i].get_predictor().get_prediction()[1],\n expert[i].get_predictor().get_prediction()[2],\n fmin(2.0, expert[i].learning_capacity));\n for (unsigned int j = 0; j < expert.size(); ++j) {\n if (!expert[j].does_exists()) continue;\n graph.update_edge(i, j, (unsigned char) 255 * expert[i].transition[j]);\n graph.update_edge(j, i, (unsigned char) 255 * expert[j].transition[i]);\n }\n }\n }\n\n void execute_cycle(uint64_t /*cycle*/)\n {\n assert(input.size() >= 3);\n plot.add_sample((float) input[0], (float) input[1], (float) input[2]);\n\n for (unsigned int n = 0; n < expert.size(); ++n) {\n graph.update_edge(n, gmes.get_winner(), (unsigned char) 255 * expert[n].transition[gmes.get_winner()]);\n graph.update_edge(gmes.get_winner(), n, (unsigned char) 255 * expert[gmes.get_winner()].transition[n]);\n }\n\n graph.update_node(gmes.get_recipient(),\n fmin(2.0, expert[gmes.get_recipient()].learning_capacity));\n\n graph.update_node(gmes.get_winner(),\n expert[gmes.get_winner()].get_predictor().get_prediction()[0], /**TODO: this kind of drawing is only valid for simple predictor!*/\n expert[gmes.get_winner()].get_predictor().get_prediction()[1], /**TODO: maybe draw the average of all experience vectors */\n expert[gmes.get_winner()].get_predictor().get_prediction()[2],\n fmin(2.0, expert[gmes.get_winner()].learning_capacity));\n\n graph.activated(gmes.get_winner());\n graph.special(gmes.get_to_insert());\n }\n\n void draw_experience(const pref& p) const {\n glPushMatrix();\n glRotatef(p.y_angle, 1.0, 0.0, 0.0);\n glRotatef(p.x_angle, 0.0, 1.0, 0.0);\n glScalef(0.5, 0.5, 0.5);\n for (std::size_t i = 0; i < gmes.get_max_number_of_experts(); ++i)\n if (expert[i].exists)\n predictor_graphics[i].draw(p);\n glPopMatrix();\n }\n\n void draw(const pref& p) const\n {\n draw_experience(p);\n\n glColor4f(1.0, 1.0, 1.0, 1.0);\n glprintf(0.8, -0.7, 0.0, 0.03, \"%u/%u\", gmes.get_number_of_experts(), gmes.get_max_number_of_experts());\n //glprintf(0.8, -0.8, 0.0, 0.03, \"%u\" , gmes.get_to_insert());\n\n glLineWidth(2.0f);\n glColor4f(1.0, 1.0, 1.0, 0.2);\n axis.draw(p.x_angle, p.y_angle);\n\n glColor4f(1.0, 1.0, 1.0, 1.0);\n plot.draw(p.x_angle, p.y_angle);\n graph.draw(p.x_angle, p.y_angle);\n\n draw_vector2(-1.0, 1.2, 0.1, 2.0, gmes.get_activations());\n }\n\n const GMES& gmes;\n const Expert_Vector& expert;\n const sensor_vector& input;\n\n const axes3D axis;\n plot3D plot;\n network3D graph;\n\n std::vector<Predictor_Graphics> predictor_graphics;\n};\n\n#endif // GMES_GRAPHICS_H_INCLUDED\n" }, { "alpha_fraction": 0.5598926544189453, "alphanum_fraction": 0.5653818249702454, "avg_line_length": 31.661354064941406, "blob_id": "9429b1bd1f80a79c783256f13fa9e87943a5caac", "content_id": "457d5d99012eb5c0e066cb5ac6143a05a1c66387", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8198, "license_type": "no_license", "max_line_length": 158, "num_lines": 251, "path": "/src/learning/gmes.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <learning/gmes.h>\n\n GMES::GMES( Expert_Vector& expert\n , double learning_rate\n , bool one_shot_learning\n , std::size_t number_of_initial_experts\n , std::string const& name )\n : expert(expert)\n , Nmax(expert.size()) /** TODO: check usage of Nmax */\n , min_prediction_error(.0)\n , learning_progress(.0)\n , learning_rate(learning_rate)\n , one_shot_learning(one_shot_learning)\n , learning_enabled(true)\n , number_of_experts(0)\n , winner(0)\n , last_winner(0)\n , recipient(0)\n , to_insert(0)\n , activations(Nmax)\n , new_node(false)\n , name(name)\n {\n assert(in_range(number_of_initial_experts, std::size_t{1}, Nmax));\n for (std::size_t n = 0; n < number_of_initial_experts; ++n)\n expert[n].create_randomized();\n sts_msg(\"Created GMES (%s) with %u experts and learning rate %.4f\", name.c_str(), Nmax, learning_rate);\n number_of_experts = count_existing_experts();\n }\n\n GMES::~GMES() { dbg_msg(\"Destroying GMES (%s).\", name.c_str()); }\n\n /* gmes main loop\n */\n /** TODO: consider changing the call pattern of private member\n * methods to functions like x = f(y) and make them\n */\n void GMES::execute_cycle(void)\n {\n /* backup last winner */\n last_winner = winner;\n\n /* determine new winner */\n winner = determine_winner();\n\n if (learning_enabled) {\n /* if needed, insert expert before adaptation takes place */\n insert_expert_on_demand();\n\n /* adapt weights of winner */\n expert[winner].adapt_weights();\n\n /* estimate learning progress: L = -dE/dt */\n estimate_learning_progress();\n\n /* shift learning capacity proportional to progress in learning */\n adjust_learning_capacity();\n\n /* refreshes transitions according to adaptation */\n refresh_transitions();\n\n /* count experts */\n number_of_experts = count_existing_experts(); /** could be a member method of experts class */\n\n /* assert learning_capacity does not leak */\n check_learning_capacity();\n\n /* choose next expert to insert\n * if all available experts are in use,\n * find and take the one with max. learning capacity */\n to_insert = (number_of_experts < Nmax) ? number_of_experts : arg_max_capacity();\n\n } else /* learning_disabled */\n learning_progress = .0;\n\n update_activations();\n }\n\n\n /* if needed, insert expert\n * before adaptation takes place\n */\n void GMES::insert_expert_on_demand(void)\n {\n new_node = false;\n\n if (expert[winner].learning_capacity_is_exhausted() && to_insert != winner)\n {\n /* copy weights and payload */\n expert.copy(to_insert, winner, one_shot_learning);\n\n /* clear transitions emanating from 'to_insert' */\n expert[to_insert].clear_transitions();\n clear_transitions_to(to_insert);\n\n /* exchange learning capacity */\n const double share = (expert[winner].learning_capacity + expert[to_insert].learning_capacity)/2;\n expert[winner ].learning_capacity += share;\n expert[to_insert].learning_capacity -= share;\n\n /* set new transition */\n expert[to_insert].reset_transition(winner);\n winner = to_insert;\n new_node = true;\n }\n }\n\n\n /* estimate learning progress: L = -dE/dt\n */\n void GMES::estimate_learning_progress(void)\n {\n const double prediction_error_before_adaption = expert[winner].get_prediction_error();\n learning_progress = prediction_error_before_adaption - expert[winner].redo_prediction();\n //TODO assert_in_range(learning_progress, 0.0, 1.0);\n assert_in_range(learning_progress, -0.2, 1.0); //TODO FIXME:\n clip(learning_progress, 0., 1.);\n }\n\n\n /* adjust learning capacity by shifting a fraction of learning capacity\n * away from the winner towards a randomly picked expert\n */\n void GMES::adjust_learning_capacity(void)\n {\n const double delta_capacity = expert[winner].learning_capacity\n - expert[winner].learning_capacity * exp(-learning_rate * learning_progress); /** TODO: reorder eq. to: x * (1-exp) */\n\n recipient = random_index(Nmax);\n\n expert[winner ].learning_capacity -= delta_capacity;\n expert[recipient].learning_capacity += delta_capacity;\n }\n\n\n /* refreshes transitions according to adaptations\n */\n void GMES::refresh_transitions(void)\n {\n /* invalidate connections emanating from winner */\n for (std::size_t n = 0; n < Nmax; ++n) {\n expert[n].transition[winner] *= exp(-learning_rate * learning_progress); /** TODO: this factor can be computed beforehand and used several times*/\n expert[winner].transition[n] *= exp(-learning_rate * learning_progress);\n }\n\n /* validate the connection from last_winner to winner */\n expert[winner].reset_transition(last_winner);\n }\n\n\n /* refreshes the activation vector with the current\n * experts' activations, i.e. inverse prediction error\n */\n void GMES::update_activations(void)\n {\n /* compute activations */\n for (std::size_t n = 0; n < Nmax; ++n)\n activations[n] = expert[n].update_and_get_activation();\n }\n\n\n /* compute all predictions and determine\n * the expert with minimal prediction error\n */\n std::size_t GMES::determine_winner(void)\n {\n std::size_t winner = 0;\n double min_error = expert[0].make_prediction();\n\n for (std::size_t n = 1; n < Nmax; ++n)\n {\n if (expert[n].exists)\n {\n /* compute prediction error and\n * find the best predicting expert */\n if (expert[n].make_prediction() < min_error)\n {\n winner = n;\n min_error = expert[n].get_prediction_error();\n }\n }\n }\n assert(winner < Nmax);\n min_prediction_error = min_error;\n return winner;\n }\n\n\n /* find the expert for which the\n * learning capacity is maximal.\n */\n std::size_t GMES::arg_max_capacity(void) const\n {\n std::size_t with_max_capacity = 0;\n double max_capacity = 0.0;\n for (std::size_t n = 0; n < Nmax; ++n)\n {\n if (expert[n].exists && expert[n].learning_capacity > max_capacity) {\n max_capacity = expert[n].learning_capacity;\n with_max_capacity = n;\n }\n }\n return with_max_capacity;\n }\n\n\n /* count number of experts where\n * the 'exists' flag is set to true\n */\n std::size_t GMES::count_existing_experts(void) const\n {\n std::size_t num_experts = 0;\n for (std::size_t n = 0; n < Nmax; ++n)\n if (expert[n].exists)\n ++num_experts;\n return num_experts;\n }\n\n\n /* double check that the total learning\n * capacity stays constants all the time\n */\n void GMES::check_learning_capacity(void) const\n {\n double sum_capacity = 0.0;\n for (std::size_t n = 0; n < Nmax; ++n)\n sum_capacity += expert[n].learning_capacity;\n\n double leakage = std::abs(sum_capacity - Nmax * gmes_constants::initial_learning_capacity);\n\n if (leakage > 1e-10) //1e-12\n wrn_msg(\"Learning capacity (%e) is leaking %e\", sum_capacity, leakage);\n }\n\n\n /* remove all transitions emanating\n * from a a certain node 'to_clear'\n */\n void GMES::clear_transitions_to(std::size_t to_clear)\n {\n for (std::size_t n = 0; n < Nmax; ++n)\n expert[n].transition[to_clear] = .0;\n }\n\n\n void GMES::enable_learning(bool enable) {\n if (learning_enabled != enable) {\n sts_msg(\"GMES (%s) learning is now %s\", name.c_str(), enable? \"ENABLED\":\"DISABLED\");\n learning_enabled = enable;\n }\n }\n" }, { "alpha_fraction": 0.4451327323913574, "alphanum_fraction": 0.49823009967803955, "avg_line_length": 18.13559341430664, "blob_id": "255e38cde0f0b998c9311dc7fb567b6d5757a166", "content_id": "d40804d3a231b2960e260e77ee1b827fdd1119eb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1130, "license_type": "no_license", "max_line_length": 102, "num_lines": 59, "path": "/src/draw/axes.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* axes.cpp */\n\n#include \"axes.h\"\n\naxes::axes(float x, float y, float z, float w, float h, int flags, std::string namestr, float def_amp)\n: px(x), py(y), pz(z)\n, width(w), height(h)\n, flag(flags)\n, countNum(0)\n, max_amplitude(+def_amp)\n, min_amplitude(-def_amp)\n, font_height(clip(0.75 * height, .01, .04))\n, name(namestr)\n{\n /* init axes coordinates */\n a[0][0] = -0.5*width;\n a[0][1] = 0;\n\n a[1][0] = 0.5*width;\n a[1][1] = 0;\n\n a[2][0] = 0;\n a[2][1] = 0.5*height;\n\n a[3][0] = 0;\n a[3][1] = -0.5*height;\n}\n\nvoid axes::draw(void) const\n{\n glPushMatrix();\n glTranslatef(px, py, pz);\n\n glColor4ubv(white_trans);\n glLineWidth(1.0f);\n\n draw_rect(width, height);\n\n glColor4ubv(white_trans2);\n glprints(-0.5 * width, -0.5 * height + 0.5 * font_height, 0.0,\n font_height,\n name);\n\n switch(flag)\n {\n case 0:\n draw_line2D(a[0], a[1]);\n draw_line2D(a[2], a[3]);\n break;\n case 1:\n draw_line2D(a[0], a[1]);\n break;\n default:\n break;\n }\n glPopMatrix();\n}\n\n/* axes.cpp */\n\n" }, { "alpha_fraction": 0.5352681875228882, "alphanum_fraction": 0.5470242500305176, "avg_line_length": 34.350650787353516, "blob_id": "22cbb5606c316c3ccd944dd177d33dcfdce1fb74", "content_id": "f0c16cb24c922297630792b66ae333a7a1835cf0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2722, "license_type": "no_license", "max_line_length": 132, "num_lines": 77, "path": "/src/tests/test_robot.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef TEST_ROBOT_H_INCLUDED\n#define TEST_ROBOT_H_INCLUDED\n\n#include <robots/robot.h>\n#include <common/modules.h>\n\nclass Test_Robot : public robots::Robot_Interface {\npublic:\n\n Test_Robot(std::size_t num_joints, std::size_t num_sym_joints)\n : num_joints(num_joints)\n , num_sym_joints(num_sym_joints)\n , joints()\n , accels(0)\n {\n for (unsigned i = 0; i < num_joints; ++i)\n joints.emplace_back(i, robots::Joint_Type_Normal, i, \"joint\"+std::to_string(i), -1.0, +1.0, 0.0);\n\n /* assign symmetric joints */\n REQUIRE( num_joints >= num_sym_joints*2 );\n for (unsigned i = 0; i < num_sym_joints*2; i+=2) {\n joints[i ].type = robots::Joint_Type_Normal;\n joints[i+1].type = robots::Joint_Type_Symmetric;\n joints[i ].symmetric_joint = i + 1;\n joints[i+1].symmetric_joint = i;\n REQUIRE( i < joints.size() );\n }\n for (unsigned i = num_sym_joints*2; i < num_joints; ++i) {\n joints[i].type = robots::Joint_Type_Normal;\n joints[i].symmetric_joint = i;\n REQUIRE( i < joints.size() );\n }\n\n for (unsigned i = 0; i < num_joints; ++i) {\n dbg_msg(\"creating joint jID = %u symID= %u type = %s\", joints[i].joint_id\n , joints[i].symmetric_joint\n , joints[i].type==robots::Joint_Type_Normal? \"normal\":\"symmetric\");\n }\n }\n\n std::size_t get_number_of_joints (void) const { return num_joints; }\n std::size_t get_number_of_symmetric_joints (void) const { return num_sym_joints; }\n std::size_t get_number_of_accel_sensors (void) const { return 0; }\n\n const robots::Jointvector_t& get_joints(void) const { return joints; }\n robots::Jointvector_t& set_joints(void) { return joints; }\n\n const robots::Accelvector_t& get_accels(void) const { return accels; }\n robots::Accelvector_t& set_accels(void) { return accels; }\n\n bool execute_cycle(void) {\n for (auto& j: joints) { j.motor.transfer(); j.motor = .0; }\n return true;\n }\n\n double get_normalized_mechanical_power(void) const { return .0; }\n\n void set_random_inputs(void) {\n for (auto& jx : set_joints()) {\n jx.s_ang = random_value(-1.0,1.0);\n jx.s_vel = random_value(-1.0,1.0);\n jx.motor = random_value(-1.0,1.0);\n jx.motor.transfer(); // for filling backed\n }\n }\n\n std::size_t num_joints;\n std::size_t num_sym_joints;\n robots::Jointvector_t joints;\n robots::Accelvector_t accels;\n\n};\n\n\n\n\n#endif // TEST_ROBOT_H_INCLUDED\n" }, { "alpha_fraction": 0.636970579624176, "alphanum_fraction": 0.6455247402191162, "avg_line_length": 29.291139602661133, "blob_id": "b0c11583460cce11b9bc8853bc76d35ba13cbd80", "content_id": "8702af1b2ed6f83e2daba946b085ecb487f7b912", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4793, "license_type": "no_license", "max_line_length": 110, "num_lines": 158, "path": "/src/learning/predictor.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef PREDICTOR_H\r\n#define PREDICTOR_H\r\n\n#include <stdio.h>\n#include <stdarg.h>\n#include <memory>\n#include <common/modules.h>\n#include <common/vector_n.h>\n#include <common/log_messages.h>\n#include <common/static_vector.h>\n#include <common/save_load.h>\n#include <control/sensorspace.h>\n\n/** Notes regarding normalizing the prediction error\n * N: input size\n * a: max input range [-a,+a], a =!= 1\r\n * The max prediction error e_max = sqrt(N * (2a)^2)\n * Normalize factor is therefore 1/e_max\n */\n\n/** Experience Replay.\n * Do not learn directly every sample. But put new sample\n * on random position into experience replay list and learn the list on every step.\n */\nnamespace predictor_constants {\n const double error_max = 1.0;\n const double error_min = 0.0;\n}\n\nclass Predictor_Base;\ntypedef std::unique_ptr<Predictor_Base> Predictor_ptr;\n\n\nclass Predictor_Base : public common::Save_Load {\n\n Predictor_Base(const Predictor_Base& other) = delete;\n\nprotected:\n\n double calculate_prediction_error();\n\n /* constants */\n const sensor_input_interface& input;\n const double learning_rate;\n const double random_weight_range;\n const double normalize_factor;\n\n /* non-const */\n double prediction_error;\n std::vector<VectorN> experience; // replay buffer\n\n Predictor_Base& operator=(const Predictor_Base& other)\n {\n prediction_error = other.prediction_error;\n assert(experience.size() == other.experience.size());\n experience = other.experience;\n return *this;\n }\n\npublic:\n typedef VectorN vector_t;\n\n Predictor_Base( const sensor_input_interface& input\n , const double learning_rate\n , const double random_weight_range\n , const std::size_t experience_size )\n : input(input)\n , learning_rate(learning_rate)\n , random_weight_range(random_weight_range)\n , normalize_factor( 1.0 / (sqrt(input.size() * 4)))\n , prediction_error(predictor_constants::error_min)\n , experience(experience_size)\n {\n //dbg_msg(\"Experience Replay: %s (%ul)\", (experience_size > 1 ? \"on\" : \"off\"), experience_size);\n //dbg_msg(\"Input dimension: %u\", input.size());\n assert_in_range(input.size(), 1ul, 500ul);\n assert_in_range(experience_size, 1ul, 1000ul);\n assert_in_range(learning_rate, 0.0, +1.0);\n assert_in_range(random_weight_range, -1.0, +1.0);\n\n experience.assign(experience.size(), VectorN(input.size(), .0) ); // zero initialize experience anyhow\n }\n\n /* non-virtual */\n double get_prediction_error(void) const { return prediction_error; }\n std::vector<VectorN> const& get_experience(void) const { return experience; }\n void adapt(void);\n\n /* virtual */\n virtual ~Predictor_Base() = default;\n\n virtual void copy(Predictor_Base const& other) = 0;\n\n virtual double predict(void) = 0;\n virtual double verify (void) = 0; // verification can e.g. be prediction by default\n\n virtual void initialize_randomized(void) = 0;\n virtual void initialize_from_input(void) = 0;\n\n virtual vector_t const& get_prediction(void) const = 0;\n\n //virtual void draw(void) const = 0;\n\n virtual vector_t const& get_weights(void) const = 0;\n virtual vector_t & set_weights(void) = 0;\n\nprivate:\n virtual void learn_from_input_sample(void) = 0;\n virtual void learn_from_experience(std::size_t /*skip_idx*/) {\n assert(false && \"Learning from experience is not implemented yet.\");\n };\n};\n\n\n/** simple predictor */\r\nclass Predictor : public Predictor_Base {\n\n Predictor(const Predictor& other) = delete;\n Predictor& operator=(const Predictor& other) = delete;\n\npublic:\n\n Predictor( const sensor_vector& input\n , const double learning_rate\n , const double random_weight_range\n , const std::size_t experience_size = 1 );\n\n\n virtual ~Predictor() = default;\n\n void copy(Predictor_Base const& other) override;\n\n Predictor_Base::vector_t const& get_prediction(void) const override { return weights; }\n\n double predict(void) override;\n double verify(void) override { return predict(); }\n\n void initialize_randomized(void) override;\n void initialize_from_input(void) override;\n\n //void draw(void) const { assert(false);/* not implemented*/ }\n\n vector_t const& get_weights(void) const override { return weights; }\n vector_t & set_weights(void) override { return weights; }\n\nprivate:\n\n void learn_from_input_sample(void) override;\n void learn_from_experience(std::size_t skip_idx) override;\n\n VectorN weights;\n\n friend class Predictor_Graphics;\n};\r\n\n\n\r\n#endif // PREDICTOR_H\r\n" }, { "alpha_fraction": 0.5681225061416626, "alphanum_fraction": 0.5887691378593445, "avg_line_length": 34.11952209472656, "blob_id": "00297c0c43b333ad292bd2b1d09c912983497a02", "content_id": "5fe6b08decf31f397f2eeef9994629dde431115e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8815, "license_type": "no_license", "max_line_length": 126, "num_lines": 251, "path": "/src/learning/state_layer.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef STATE_LAYER_H_INCLUDED\n#define STATE_LAYER_H_INCLUDED\n\n#include <common/integrator.h>\n#include <robots/robot.h>\n#include <robots/joint.h>\n#include <control/jointcontrol.h>\n#include <control/control_vector.h>\n#include <control/sensorspace.h>\n#include <learning/expert.h>\n#include <learning/gmes.h>\n#include <learning/payload.h>\n#include <learning/state_predictor.h>\n#include <learning/learning_machine_interface.h>\n\n/* graphics */\n#include <draw/draw.h>\n#include <draw/axes.h>\n#include <draw/axes3D.h>\n#include <draw/plot1D.h>\n#include <draw/plot2D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n#include <draw/color_table.h>\n\n\nnamespace learning {\n\n\nnamespace state_layer_constants {\n const unsigned subspace_num_datapoints = 200; // 2s of data at 100Hz\n}\n\nclass State_Space : public sensor_vector {\npublic:\n State_Space(const robots::Jointvector_t& joints)\n : sensor_vector(joints.size())\n {\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_ang\", [&j](){ return j.s_ang; });\n\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_vel\", [&j](){ return j.s_vel; });\n\n\n /* TODO: why is there no accel data included*/\n\n sensors.emplace_back(\"bias\", [](){ return 0.1; });\n }\n};\n\nclass State_Layer : public control::Statemachine_Interface, public learning::Learning_Machine_Interface {\npublic:\n State_Layer( robots::Robot_Interface const& robot\n , static_vector_interface& payloads\n , std::size_t max_num_state_experts\n , double learning_rate\n , double growth_rate\n , std::size_t experience_size\n , std::size_t hidden_layer_size\n , std::size_t time_delay_size )\n : max_num_state_experts(max_num_state_experts)\n , payloads(payloads)\n , statespace(robot.get_joints())\n , experts(max_num_state_experts, payloads, statespace, learning_rate, experience_size, hidden_layer_size, time_delay_size)\n , gmes(experts, growth_rate, /* one shot learning = */false)\n {\n dbg_msg(\"Creating new competitive state layer.\");\n assert(payloads.size() == max_num_state_experts); /**TODO: re-factor that, move payloads<template> to state_layer*/\n assert(learning_rate > 0.);\n assert(growth_rate > 0.);\n }\n\n void execute_cycle(void) {\n statespace.execute_cycle();\n gmes.execute_cycle();\n }\n\n bool has_state_changed(void) const override { return gmes.has_state_changed(); };\n std::size_t get_state(void) const override { return gmes.get_state(); }\n\n double get_learning_progress(void) const override { return gmes.get_learning_progress(); }\n\n void enable_learning(bool b) override { gmes.enable_learning(b); }\n void toggle_learning(void) { gmes.enable_learning(not gmes.is_learning_enabled()); }\n\n void save(const std::string& foldername) const {\n const std::size_t num_experts = gmes.get_number_of_experts();\n sts_msg(\"Saving %lu state expert%s to folder %s\", num_experts, (num_experts>1?\"s\":\"\"), foldername.c_str());\n const std::string folder = basic::make_directory((foldername + \"/state\").c_str());\n for (std::size_t i = 0; i < num_experts; ++i)\n {\n //auto const& ctrl = get_controller_weights(i);\n //ctrl.save_to_file(folder + \"/motor_expert_\" + std::to_string(i) + \".dat\", i);\n }\n }\n\n std::size_t max_num_state_experts;\n static_vector_interface& payloads;\n State_Space statespace;\n Expert_Vector experts;\n GMES gmes;\n\n friend class State_Layer_Graphics;\n};\n\n\n\n\nstruct sensor_subspace_graphics : public Graphics_Interface\n{\n sensor_subspace_graphics( robots::Joint_Model const& j0\n , robots::Joint_Model const& j1\n , Vector3 pos, float size\n , ColorTable const& colortable)\n : j0(j0)\n , j1(j1)\n , axis_xy(pos.x + size/2 , pos.y + size/2, pos.z, size, size, 0, std::string(\"xy\"))\n , axis_dt(pos.x + (2.0 + size)/2, pos.y + size/2, pos.z, 2.0-size, size, 1, std::string(j0.name + \"/\" + j1.name))\n , plot_xy(state_layer_constants::subspace_num_datapoints, axis_xy, colors::magenta )\n , plot_j0(state_layer_constants::subspace_num_datapoints, axis_dt, colors::light0, \"j0\")\n , plot_j1(state_layer_constants::subspace_num_datapoints, axis_dt, colors::light1, \"j1\")\n , plot_p0(state_layer_constants::subspace_num_datapoints, axis_dt, colortable , \"p0\")\n , plot_p1(state_layer_constants::subspace_num_datapoints, axis_dt, colortable , \"p1\")\n {\n dbg_msg(\"Initialize subspace for joints:\\n\\t%2u: %s\\n\\t%2u: %s\", j0.joint_id, j0.name.c_str()\n , j1.joint_id, j1.name.c_str() );\n }\n\n void draw(const pref&) const {\n axis_xy.draw();\n plot_xy.draw();\n\n axis_dt.draw();\n plot_j0.draw(); // signals\n plot_j1.draw();\n plot_p0.draw_colored(); // predictions\n plot_p1.draw_colored();\n }\n\n void execute_cycle(float s0, float s1, unsigned expert_id) {\n plot_xy.add_sample(j0.s_ang, j1.s_ang); // TODO: add velocities\n plot_j0.add_sample(j0.s_ang);\n plot_j1.add_sample(j1.s_ang);\n plot_p0.add_colored_sample(s0, expert_id);\n plot_p1.add_colored_sample(s1, expert_id);\n }\n\n const robots::Joint_Model& j0;\n const robots::Joint_Model& j1;\n\n axes axis_xy;\n axes axis_dt;\n\n plot2D plot_xy;\n plot1D plot_j0, plot_j1; // joint sensor values\n colored_plot1D plot_p0, plot_p1; // predictions\n\n};\n\nclass State_Layer_Graphics : public Graphics_Interface {\npublic:\n\n State_Layer_Graphics( State_Layer const& state_layer\n , robots::Robot_Interface const& robot )\n : state_layer(state_layer)\n , num_experts()\n , max_experts(state_layer.gmes.get_max_number_of_experts())\n , winner()\n , subspace()\n , colortable(5, /*randomized*/true)\n , integrator()\n , axis_dt(-0.5,-1.50, 0.0, 1.0, 1.0, 1, \"PredErr\")\n , axis_ne(-0.5,-1.75, 0.0, 1.0, 0.5, 1, \"#Experts\")\n , pred_err(2000, axis_dt, colors::gray0)\n , pred_err_avg(2000, axis_dt, colors::white)\n , num_exp(2000, axis_ne, colors::cyan)\n {\n /** TODO\n + also for the rest of the joints\n + consider grouping the graphs in legs (4x 3D) instead of 6x 2D\n */\n const unsigned N = robot.get_number_of_symmetric_joints();\n const float size = 2.0/N;\n subspace.reserve(N);\n Vector3 pos(-1.0, 1.0, 0.);\n for (auto const& j0: robot.get_joints()) {\n if (j0.is_symmetric()) {\n robots::Joint_Model const& j1 = robot.get_joints()[j0.symmetric_joint];\n pos.y -= size;\n subspace.emplace_back(j1, j0, pos, size, colortable);\n }\n }\n }\n\n void draw(const pref& p) const {\n glColor3f(1.0, 1.0, 1.0);\n glprintf(-0.95, 0.95, 0., 0.025, \"%03u (%03u/%03u)\", winner, num_experts, max_experts);\n\n for (auto& s: subspace)\n s.draw(p);\n\n axis_dt.draw();\n axis_ne.draw();\n pred_err.draw();\n pred_err_avg.draw();\n num_exp.draw();\n };\n\n void execute_cycle() {\n\n num_experts = state_layer.gmes.get_number_of_experts(); // update number of experts\n winner = state_layer.gmes.get_winner();\n auto const& predictions = state_layer.experts[winner].get_predictor().get_prediction();\n\n for (auto& s: subspace) {\n auto s0 = predictions.at(s.j0.joint_id); /**TODO: how to access the velocities*/\n auto s1 = predictions.at(s.j1.joint_id);\n s.execute_cycle(s0, s1, winner);\n }\n\n integrator.add(state_layer.gmes.get_min_prediction_error());\n if (integrator.get_number_of_samples()==1000) {\n const float val = 100.0*integrator.get_avg_value_and_reset();\n avg_err = 0.99*avg_err + 0.01*val;\n pred_err.add_sample(val);\n pred_err_avg.add_sample(avg_err);\n num_exp.add_sample(state_layer.gmes.get_number_of_experts());\n }\n };\n\n State_Layer const& state_layer;\n std::size_t num_experts;\n std::size_t max_experts;\n std::size_t winner;\n\n std::vector<sensor_subspace_graphics> subspace;\n ColorTable colortable;\n\n Integrator integrator;\n float avg_err = 0.0;\n axes axis_dt;\n axes axis_ne;\n plot1D pred_err,pred_err_avg;\n plot1D num_exp;\n};\n\n} // namespace learning\n\n#endif // STATE_LAYER_H_INCLUDED\n" }, { "alpha_fraction": 0.6587436199188232, "alphanum_fraction": 0.6926994919776917, "avg_line_length": 33.64706039428711, "blob_id": "0c954b5e38fba275f8cddec1e5808b47004ea6ea", "content_id": "c67bb978059aa921197c79eff5f060eba438e79f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 589, "license_type": "no_license", "max_line_length": 82, "num_lines": 17, "path": "/src/learning/sarsa_constants.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SARSA_CONSTANTS_H_INCLUDED\n#define SARSA_CONSTANTS_H_INCLUDED\n\nnamespace sarsa_constants {\n\n /* sarsa */\n const double EPSILON = 0.1; // probability for non-greedy action\n const double GAMMA = 0.99; // discount factor\n const double LAMBDA = 0.9; // eligibility trace factor (better do not touch)\n const double ALPHA = 0.1; // Reinforcement Learning Rate\n\n const unsigned int policy_change_cycle = 5000; // 10 sec. @ 100Hz\n const unsigned int number_of_policies = 3;\n const unsigned int number_of_actions = 3;\n}\n\n#endif // SARSA_CONSTANTS_H_INCLUDED\n" }, { "alpha_fraction": 0.6523109078407288, "alphanum_fraction": 0.6523109078407288, "avg_line_length": 34.70000076293945, "blob_id": "0e033d7dfa353b7c28049f9bae876c9d37b5e902", "content_id": "a3ec9da8db85a869bc1946d49794e2f743f0ed25", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2856, "license_type": "no_license", "max_line_length": 139, "num_lines": 80, "path": "/src/learning/state_predictor.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef STATE_PREDICTOR_H\n#define STATE_PREDICTOR_H\n\n#include <learning/predictor.h>\n#include <learning/autoencoder.h>\n#include <learning/time_delay_network.h>\n\nnamespace learning {\n\nclass State_Predictor : public Predictor_Base {\n\n State_Predictor(const State_Predictor& other) = delete;\n State_Predictor& operator=(const State_Predictor& other) = delete;\n\npublic:\n\n State_Predictor( const sensor_vector& inputs\n , const double learning_rate\n , const double random_weight_range\n , const std::size_t experience_size\n , const std::size_t hidden_layer_size\n , const std::size_t time_delay_size)\n : Predictor_Base(inputs, learning_rate, random_weight_range, experience_size)\n , enc(inputs.size(), inputs.size(), hidden_layer_size, time_delay_size, random_weight_range )\n {\n dbg_msg(\"Initialize State Predictor using TDNN.\");\n }\n\n virtual ~State_Predictor() = default;\n\n void copy(Predictor_Base const& other) override {\n Predictor_Base::operator=(other); // copy base members\n State_Predictor const& rhs = dynamic_cast<State_Predictor const&>(other);\n enc = rhs.enc;\n dbg_msg(\"Copying state predictor weights.\");\n };\n\n Predictor_Base::vector_t const& get_prediction(void) const override { return enc.get_outputs(); }\n\n double predict(void) override {\n enc.propagate_and_shift(input);\n return calculate_prediction_error();\n };\n\n double verify(void) override {\n enc.propagate();\n return calculate_prediction_error();\n }\n\n void initialize_randomized(void) override {\n //enc.randomize_weight_matrix(random_weight_range);\n auto initial_experience = input.get();\n for (auto& w: initial_experience)\n w += random_value(-random_weight_range, random_weight_range);\n experience.assign(experience.size(), initial_experience);\n prediction_error = predictor_constants::error_min;\n };\n\n void initialize_from_input(void) override { assert(false && \"one shot learning not supported.\"); }\n\n void draw(void) const { assert(false); /*not implemented*/ }\n\n vector_t const& get_weights(void) const override { assert(false); return dummy; /*not implemented*/ }\n vector_t & set_weights(void) override { assert(false); return dummy; /*not implemented*/ }\n\nprivate:\n\n void learn_from_input_sample(void) override { enc.adapt(input, learning_rate); };\n void learn_from_experience(std::size_t /*skip_idx*/) override { assert(false && \"Learning from experience is not implemented yet.\"); };\n\n Timedelay_Network enc;\n\n VectorN dummy = {}; // remove when implementing get_weights\n\n friend class Predictor_Graphics;\n};\n\n} /* namespace learning */\n\n#endif /* STATE_PREDICTOR_H */\n" }, { "alpha_fraction": 0.5758234262466431, "alphanum_fraction": 0.6127737760543823, "avg_line_length": 34.390533447265625, "blob_id": "7ed04cad456e173bd6626651bfde2d6b5b418be2", "content_id": "22491b456f34fb857b8965700446e796e0078c03", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5981, "license_type": "no_license", "max_line_length": 112, "num_lines": 169, "path": "/src/tests/motor_predictor_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n\n#include <learning/predictor.h>\n#include <learning/motor_predictor.h>\n#include <common/log_messages.h>\n#include <tests/test_robot.h>\n\nnamespace local_tests {\n\nclass Test_Motor_Space : public sensor_vector {\npublic:\n Test_Motor_Space(const robots::Jointvector_t& joints, const double sigma)\n : sensor_vector(joints.size())\n {\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name, [&j, sigma](){ return j.motor.get() + rand_norm_zero_mean(sigma); });\n }\n};\n\n\n\nTEST_CASE( \"motor predictor adapts\" , \"[motor_predictor]\")\n{\n srand(2310); // set random seed\n\n Test_Robot robot(5,2);\n Test_Motor_Space motors{robot.get_joints(), 0.01};\n control::Control_Parameter params = control::get_initial_parameter(robot,{0.,0.,0.}, false);\n\n /* set constant outputs to learn from */\n auto& joints = robot.set_joints();\n REQUIRE( joints.size() == 5 );\n joints[0].motor = +.4223;\n joints[1].motor = +.3771;\n joints[2].motor = -.2342;\n joints[3].motor = -.1337;\n joints[4].motor = +.6789;\n\n /* assert that motor space is correct */\n motors.execute_cycle();\n REQUIRE( motors.size() == 5 );\n for (unsigned i = 0; i < 5; ++i)\n REQUIRE( close(motors[i], joints[i].motor.get(), 0.1) );\n\n /* initialize predictors */\n motors.execute_cycle();\n learning::Motor_Predictor pred{ robot, motors, 0.25, 0.01, 1, params, /*noise=*/.0 };\n pred.initialize_randomized();\n\n /* adapt a 'few' cycles */\n for (unsigned i = 0; i < 2500; ++i) {\n motors.execute_cycle();\n pred.predict();\n pred.adapt();\n }\n\n /* compare predictions with constant motor output */\n auto const& predictions = pred.get_prediction();\n print_vector(predictions);\n\n REQUIRE( predictions.size() == 5 );\n REQUIRE( close(predictions[0], 0.4223, 0.02) );\n REQUIRE( close(predictions[1], 0.3771, 0.02) );\n REQUIRE( close(predictions[2],-0.2342, 0.02) );\n REQUIRE( close(predictions[3],-0.1337, 0.02) );\n REQUIRE( close(predictions[4], 0.6789, 0.02) );\n\n}\n\n//TEST_CASE( \"motor predictor adapts with experience replay\" , \"[motor_predictor]\") /**TODO EXPERIENCE REPLAY */\n//{\n// srand((unsigned) time(0));\n// Test_Robot robot(5,2);\n// Test_Motor_Space motors(robot.get_joints(), 0.01); /** with random */\n// control::Control_Parameter params = control::get_initial_parameter(robot,{0.,0.,0.}, false);\n// motors.execute_cycle();\n// learning::Motor_Predictor pred{ robot, motors, 0.1, 0.01, 1 /**TODO*/, params};\n// pred.initialize_randomized();\n//\n// const std::vector<double>& w = pred.get_prediction();\n// REQUIRE( w.size() == 3 );\n//\n// for (unsigned i = 0; i < 1000; ++i) {\n// motors.execute_cycle();\n// pred.adapt();\n// }\n// REQUIRE( close(w[0], 0.42, 0.1) );\n// REQUIRE( close(w[1], 0.37, 0.1) );\n// REQUIRE( close(w[2],-0.23, 0.1) );\n//\n// dbg_msg(\"%6.4f %6.4f %6.4f\", w[0], w[1], w[2]);\n//}\n\nTEST_CASE( \"motor prediction error must be constant without learning step\", \"[motor_predictor]\" )\n{\n Test_Robot robot(5,2);\n srand((unsigned) time(0));\n Test_Motor_Space motors(robot.get_joints(), 0.0); /** without random */\n control::Control_Parameter params = control::get_initial_parameter(robot,{0.,0.,0.}, false);\n motors.execute_cycle();\n learning::Motor_Predictor pred{ robot, motors, 0.1, 0.01, 1, params, /*noise=*/.0 };\n pred.initialize_randomized();\n\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.predict();\n double pred_err_start = pred.get_prediction_error();\n dbg_msg(\"Prediction error start: %e\", pred_err_start);\n REQUIRE( pred_err_start > 0.0 );\n\n for (unsigned i = 0; i < 13; ++i) {\n motors.execute_cycle();\n double pred_err_new = pred.predict();\n REQUIRE( pred_err_new == pred.get_prediction_error() );\n dbg_msg(\"Prediction error %u: %e %e\", i, pred_err_start, pred_err_new);\n REQUIRE( close(pred_err_start, pred_err_new, 0.001) );\n }\n}\n\nTEST_CASE( \"motor prediction error must decrease after learning step\", \"[motor_predictor]\" )\n{\n srand((unsigned) time(0));\n Test_Robot robot(5,2);\n Test_Motor_Space motors(robot.get_joints(), 0.0); /** without random */\n control::Control_Parameter params = control::get_initial_parameter(robot,{0.,0.,0.}, false);\n motors.execute_cycle();\n learning::Motor_Predictor pred{ robot, motors, 0.1, 0.01, 1, params, /*noise=*/.0 };\n pred.initialize_randomized();\n\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.predict();\n\n double pred_err_start = pred.get_prediction_error();\n dbg_msg(\"+++Prediction error start: %e\", pred_err_start);\n REQUIRE( pred_err_start > 0.0 );\n\n for (unsigned i = 0; i < 42; ++i) {\n motors.execute_cycle();\n double pred_err_before = pred.predict();\n pred.adapt();\n double pred_err_after = pred.verify();//predict();\n REQUIRE( pred_err_after == pred.get_prediction_error() );\n dbg_msg(\"Prediction error %u: %e %e\", i, pred_err_before, pred_err_after);\n REQUIRE( pred_err_before > pred_err_after );\n }\n}\n\nTEST_CASE( \"motor prediction error is reset on (re-)initialization\", \"[motor_predictor]\")\n{\n srand((unsigned) time(0));\n Test_Robot robot(5,2);\n Test_Motor_Space motors(robot.get_joints(), 0.0); /** without random */\n control::Control_Parameter params = control::get_initial_parameter(robot,{0.,0.,0.}, false);\n motors.execute_cycle();\n learning::Motor_Predictor pred{ robot, motors, 0.1, 0.01, 1, params, /*noise=*/.0 };\n pred.initialize_randomized();\n\n REQUIRE( pred.get_prediction_error() == 0.0 );\n pred.predict();\n\n REQUIRE( pred.get_prediction_error() > 0.0 );\n /*not supported pred.initialize_from_input();\n REQUIRE( pred.get_prediction_error() == 0.0 );\n motors.execute_cycle();\n pred.predict();\n REQUIRE( pred.get_prediction_error() > 0.0 );\n */\n}\n\n} // namespace local_tests\n" }, { "alpha_fraction": 0.4707980453968048, "alphanum_fraction": 0.5042353868484497, "avg_line_length": 30.15277862548828, "blob_id": "590b9de375ab67d7376616395f7ec60448b3fbef", "content_id": "074a77415f9b8732dea313600e8a608c895729ec", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2243, "license_type": "no_license", "max_line_length": 137, "num_lines": 72, "path": "/src/control/jointcontrol_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/*---------------------------------+\n | Matthias Kubisch |\n | [email protected] |\n | July 2017 |\n +---------------------------------*/\n\n#ifndef JOINTCONTROL_GRAPHICS_H_INCLUDED\n#define JOINTCONTROL_GRAPHICS_H_INCLUDED\n\n#include <draw/draw.h>\n\n#include <robots/robot.h>\n#include <control/jointcontrol.h>\n\nnamespace control {\n\nclass Jointcontrol_Graphics : public Graphics_Interface\n{\n const robots::Robot_Interface& robot;\n const control::Jointcontrol& control;\n\npublic:\n Jointcontrol_Graphics(const robots::Robot_Interface& robot, const control::Jointcontrol& control) : robot(robot), control(control) {}\n\n void draw(const pref& /*p*/) const\n {\n const double line_height = 0.02;\n const double row_width = 0.15;\n const double xstart = -1.0;\n const double ystart = +1.0;\n\n double xpos = xstart;\n double ypos = ystart;\n\n glColor3f(1.0,1.0,1.0);\n\n /*print header*/\n glprintc(xpos, ypos, 0.0, 0.5*line_height, \"#\");\n for (std::size_t i = 0; i < robot.get_number_of_joints(); ++i) {\n if (robot.get_joints()[i].type == robots::Joint_Type_Normal) {\n xpos += row_width;\n glprintf(xpos, ypos, 0.0, 0.5*line_height, \"%2lu_%s\", i, robot.get_joints()[i].name.substr(0,16).c_str());\n }\n }\n for (std::size_t k = 0; k < control.core.input.size(); ++k)\n {\n xpos = xstart;\n ypos -= line_height;\n\n glColor3f(1.0,1.0,1.0);\n glprintf(xpos, ypos, 0.0, line_height, \"%2lu: \", k);\n for (std::size_t i = 0; i < robot.get_number_of_joints(); ++i)\n {\n if (robot.get_joints()[i].type == robots::Joint_Type_Normal) {\n const double w = control.core.weights[i][k];\n\n if (w == 0.0) glColor3f(0.5, 0.5, 0.5);\n else if (w > 0.0) glColor3f(1.0, 0.5, 0.5);\n else glColor3f(0.5, 0.5, 1.0);\n\n xpos += row_width;\n glprintf(xpos, ypos, 0.0, line_height, \"%+1.3f\", control.core.weights[i][k]);\n }\n }\n\n }\n }\n};\n\n} // namespace control\n\n#endif // JOINTCONTROL_GRAPHICS_H_INCLUDED\n" }, { "alpha_fraction": 0.6202531456947327, "alphanum_fraction": 0.6445147395133972, "avg_line_length": 22.121952056884766, "blob_id": "3b3e2d008778767d1df563cab9459e3a718e3d92", "content_id": "13569398079fd62e45b8e9714d4748d03b21b88a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 948, "license_type": "no_license", "max_line_length": 82, "num_lines": 41, "path": "/src/draw/plot3D.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* plot3D.h */\n\n#ifndef plot3D_H\n#define plot3D_H\n\n#include <vector>\n#include <cassert>\n#include \"axes3D.h\"\n\nclass plot3D\n{\npublic:\n plot3D(unsigned int number_of_samples, const axes3D& axis, const GLubyte c[4])\n : number_of_samples(number_of_samples)\n , pointer(0)\n , axis(axis)\n , signal0(number_of_samples)\n , signal1(number_of_samples)\n , signal2(number_of_samples)\n {\n for (unsigned int i = 0; i < 4; ++i) color[i] = c[i];\n }\n\n void draw(float x_angle, float y_angle) const;\n\n void add_sample(float s0, float s1, float s2);\n void add_sample(const std::vector<double>& sample);\n\nprivate:\n void increment_pointer(void) { ++pointer; pointer %= number_of_samples; }\n\n const unsigned int number_of_samples;\n unsigned int pointer;\n const axes3D& axis;\n std::vector<float> signal0;\n std::vector<float> signal1;\n std::vector<float> signal2;\n GLubyte color[4];\n};\n\n#endif /*plot3D_H*/\n" }, { "alpha_fraction": 0.748251736164093, "alphanum_fraction": 0.748251736164093, "avg_line_length": 19.428571701049805, "blob_id": "235e5f32c2dc6dfde0185edf9a52ea6e31eb59b7", "content_id": "4075ef63ea934d6554a826c4f5dd281cb47ce335", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 143, "license_type": "no_license", "max_line_length": 36, "num_lines": 7, "path": "/src/common/vector_n.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef VECTOR_N_H_INCLUDED\n#define VECTOR_N_H_INCLUDED\n\n#include <vector>\ntypedef std::vector<double> VectorN;\n\n#endif // VECTOR_N_H_INCLUDED\n" }, { "alpha_fraction": 0.5224782824516296, "alphanum_fraction": 0.5413675904273987, "avg_line_length": 23.28440284729004, "blob_id": "61cf9a46db5f5a27885e351bcb55832083b3dd8f", "content_id": "bd2fcd51f4b2d748fdf2e5727a423e4a377e2bfe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2647, "license_type": "no_license", "max_line_length": 89, "num_lines": 109, "path": "/src/common/vector2.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef Vector2_H_INCLUDED\n#define Vector2_H_INCLUDED\n\n#include <common/modules.h>\n\nclass Vector2\n{\npublic:\n\n Vector2(const Vector2& rhs) : x(rhs.x), y(rhs.y) {}\n Vector2(void) : x(.0), y(.0) {}\n Vector2(double x, double y) : x(x), y(y) {}\n Vector2(double val) : x(val), y(val) {}\n Vector2(const double val[2]) : x(val[0]), y(val[1]) {}\n\n Vector2& operator=(const Vector2& rhs) {\n this->x = rhs.x;\n this->y = rhs.y;\n return *this;\n }\n\n Vector2& operator=(const double& rhs) {\n this->x = rhs;\n this->y = rhs;\n return *this;\n }\n\n Vector2& operator+=(const Vector2& rhs) {\n this->x += rhs.x;\n this->y += rhs.y;\n return *this;\n }\n Vector2& operator-=(const Vector2& rhs) {\n this->x -= rhs.x;\n this->y -= rhs.y;\n return *this;\n }\n Vector2& operator*=(const double& rhs) {\n this->x *= rhs;\n this->y *= rhs;\n return *this;\n }\n Vector2& operator/=(const double& rhs) {\n this->x /= rhs;\n this->y /= rhs;\n return *this;\n }\n// void clip(double max_val) {\n// x = clip(x, max_val);\n// y = clip(y, max_val);\n// }\n double length() { return sqrt(x*x + y*y); }\n\n void normalize(void) {\n double l = 1.0 / length();\n x *= l;\n y *= l;\n }\n double angle_phi (void) const { return atan2(y,x); }\n\n void random(double lower, double upper)\n {\n x = random_value(lower, upper);\n y = random_value(lower, upper);\n }\n\n void zero(void) { x = .0; y = .0; }\n\n bool is_zero(void) const { return (x == .0 && y == .0); }\n\n double x, y;\n};\n\ninline Vector2 operator+(Vector2 lhs, const Vector2& rhs) {\n lhs += rhs;\n return lhs;\n}\ninline Vector2 operator-(Vector2 lhs, const Vector2& rhs) {\n lhs -= rhs;\n return lhs;\n}\ninline Vector2 operator*(Vector2 lhs, const double& rhs) {\n lhs *= rhs;\n return lhs;\n}\ninline Vector2 operator*(const double& lhs, Vector2 rhs) {\n rhs *= lhs;\n return rhs;\n}\ninline double operator*(const Vector2& lhs, const Vector2& rhs) { //scalar multiplication\n return lhs.x * rhs.x\n + lhs.y * rhs.y;\n}\ninline Vector2 operator/(Vector2 lhs, const double& rhs) {\n lhs /= rhs;\n return lhs;\n}\ninline double distance(const Vector2& lhs, const Vector2& rhs) {\n return sqrt((lhs.x - rhs.x) * (lhs.x - rhs.x)\n + (lhs.y - rhs.y) * (lhs.y - rhs.y));\n}\ninline Vector2 clip(const Vector2& v, double max_val) {\n Vector2 result;\n result.x = clip(v.x, max_val);\n result.y = clip(v.y, max_val);\n return result;\n}\n\n#endif // Vector2_H_INCLUDED\n" }, { "alpha_fraction": 0.6666666865348816, "alphanum_fraction": 0.675000011920929, "avg_line_length": 33.28571319580078, "blob_id": "eef576f32e7e74f58d2f94673e9c6de5b4752807", "content_id": "c71283dd61c41b715ce7bf87235b5608d2065894", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 960, "license_type": "no_license", "max_line_length": 79, "num_lines": 28, "path": "/src/learning/reinforcement_learning.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef REINFORCEMENT_LEARNING_H_INCLUDED\n#define REINFORCEMENT_LEARNING_H_INCLUDED\n\nnamespace learning {\n\nclass RL_Interface {\npublic:\n virtual ~RL_Interface() {}\n virtual std::size_t get_current_state (void) const = 0;\n virtual std::size_t get_current_action(void) const = 0;\n virtual std::size_t get_current_policy(void) const = 0;\n virtual bool is_exploring (void) const = 0;\n virtual bool positive_current_delta(std::size_t policy) const = 0;\n};\n\nclass Non_Learning : public RL_Interface {\npublic:\n Non_Learning(void) {}\n std::size_t get_current_state (void) const { return 0; }\n std::size_t get_current_action(void) const { return 0; }\n std::size_t get_current_policy(void) const { return 0; }\n bool is_exploring (void) const { return false; }\n bool positive_current_delta(std::size_t /*policy*/) const { return false; }\n};\n\n} // namespace learning\n\n#endif // REINFORCEMENT_LEARNING_H_INCLUDED\n" }, { "alpha_fraction": 0.5551941990852356, "alphanum_fraction": 0.5633242726325989, "avg_line_length": 30.810344696044922, "blob_id": "a4f7245d335c089d0453c5f1a96b5ccaae9c74d4", "content_id": "e93c8eeccb8acc7d1b0ab1b90a1d400db4b0fab3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5535, "license_type": "no_license", "max_line_length": 151, "num_lines": 174, "path": "/src/learning/autoencoder.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef AUTOENCODER_H_INCLUDED\n#define AUTOENCODER_H_INCLUDED\n\n#include <common/modules.h>\n#include <common/static_vector.h>\n#include <control/sensorspace.h>\n\n\nnamespace learning {\n\nclass Autoencoder {\n typedef VectorN vector_t;\n typedef copyable_static_vector<copyable_static_vector<double>> matrix_t;\n\npublic:\n Autoencoder( const std::size_t input_size\n , const std::size_t hidden_size\n , const double random_weight_range )\n : hidden(hidden_size)\n , outputs(input_size)\n , delta(outputs.size())\n , weights(hidden.size(), input_size)\n {\n dbg_msg(\"Creating Autoencoder with %u inputs and %u hidden units.\", input_size, hidden_size);\n assertion(hidden_size > 0, \"Hidden layer must have min. size of 1 (currently=%u)\", hidden_size);\n assertion(input_size > hidden_size, \"Input size (%u) must be greater than hidden layer size (%u).\", input_size, hidden_size);\n\n assert(weights.size() == hidden_size);\n assert(weights[0].size() == input_size);\n assert(outputs.size() == input_size);\n\n if (random_weight_range == 0.0)\n wrn_msg(\"Weights will be all zeros\");\n else\n randomize_weight_matrix(random_weight_range);\n }\n\n\n template <typename InputVector_t>\n void propagate(const InputVector_t& inputs) {\n /* encoder */\n propagate_forward(inputs);\n\n /* decoder*/\n propagate_backward(hidden);\n }\n\n\n template <typename InputVector_t>\n void propagate_forward(const InputVector_t& inputs)\n {\n assert(inputs.size() == outputs.size());\n\n /* encoder */\n for (std::size_t i = 0; i < hidden.size(); ++i) {\n double act = 0.;\n for (std::size_t j = 0; j < inputs.size(); ++j)\n act += weights[i][j] * inputs[j];\n hidden[i] = tanh(act);\n }\n }\n\n template <typename InputVector_t>\n void propagate_backward(const InputVector_t& hidden_input)\n {\n assert(hidden_input.size() == hidden.size());\n\n /* decoder*/\n for (std::size_t j = 0; j < outputs.size(); ++j) {\n double act = 0.;\n for (std::size_t i = 0; i < hidden_input.size(); ++i)\n act += weights[i][j] * hidden_input[i];\n outputs[j] = tanh(act);\n /** TODO: The decoder should not use the tanh activation function.\n This must also be considered in the weight change. */\n }\n }\n\n\n template <typename InputVector_t>\n void adapt(const InputVector_t& inputs, const double learning_rate) {\n assert(inputs.size() == outputs.size());\n assert_in_range(learning_rate, 0.0, 0.5);\n\n /** x_j : inputs[j]\n y_j : outputs[j]\n h_i : hidden[i]\n\n eta : learning_rate\n\n decoder weight change:\n error : e_j = (x_j - y_j)\n delta : d2_j = e_j * (1.0 + y_j) * (1.0 - y_j)\n weight: dw2_ji = eta * d2_j * h_i\n\n encoder weight change:\n error : eh_i = sum_j d2_j * w2_ji\n delta : d1_i = eh_i * (1.0 + h_i) * (1.0 - h_i)\n weight: dw1_ji = eta * d1_i * x_j\n\n */\n\n for (std::size_t j = 0; j < outputs.size(); ++j)\n delta[j] = (inputs[j] - outputs[j]) * tanh_(outputs[j]); /** this tanh_ is not needed, when transfer function of output layer is omitted */\n\n for (std::size_t i = 0; i < hidden.size(); ++i)\n {\n double error_i = .0;\n for (std::size_t j = 0; j < outputs.size(); ++j)\n error_i += delta[j] * weights[i][j];\n\n const double delta_i = error_i * tanh_(hidden[i]);\n for (std::size_t j = 0; j < outputs.size(); ++j)\n weights[i][j] += learning_rate * (delta_i * inputs[j] + delta[j] * hidden[i]);\n }\n }\n\n vector_t const& get_outputs() const { return outputs; }\n matrix_t const& get_weights() const { return weights; }\n\n\n void randomize_weight_matrix(const double random_weight_range) {\n assert_in_range(random_weight_range, 0.0, 0.5);\n const double normed_std_dev = random_weight_range / sqrt(weights[0].size());\n assert(normed_std_dev != 0.0);\n\n for (std::size_t i = 0; i < weights.size(); ++i) {\n for (std::size_t j = 0; j < weights[i].size(); ++j)\n weights[i][j] = rand_norm_zero_mean(normed_std_dev); // normalized by sqrt(N), N:#inputs\n }\n }\n\n template <typename InputVector_t, typename TargetVector_t>\n void train(const InputVector_t& inputs, const TargetVector_t& targets, const double learning_rate)\n {\n set_hidden(targets);\n adapt(inputs, learning_rate);\n }\n\n vector_t const& get_forward() const { return hidden; }\n\n template <typename InputVector_t>\n vector_t const& propagate_forward_and_get(const InputVector_t& inputs) {\n propagate_forward(inputs);\n return hidden;\n }\n\n template <typename InputVector_t>\n vector_t const& propagate_backward_and_get(const InputVector_t& hidden_input) {\n propagate_backward(hidden_input);\n return outputs;\n }\n\nprivate:\n\n template <typename InputVector_t>\n void set_hidden(const InputVector_t& x) {\n assert(x.size() == hidden.size());\n if (is_vector_zero(x))\n wrn_msg(\"Assigned hidden vector is all zeros.\");\n hidden = x;\n }\n\n vector_t hidden;\n vector_t outputs;\n vector_t delta;\n matrix_t weights;\n};\n\n\n} // learning\n\n\n#endif // AUTOENCODER_H_INCLUDED\n" }, { "alpha_fraction": 0.657505989074707, "alphanum_fraction": 0.6588801145553589, "avg_line_length": 33.2470588684082, "blob_id": "6ba53c8f0e525acf7b5a8c3a368df926881545aa", "content_id": "bf898143ca65a7e64d53acd67472766db25e1087", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2911, "license_type": "no_license", "max_line_length": 132, "num_lines": 85, "path": "/src/common/static_vector.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef STATIC_VECTOR_H_INCLUDED\n#define STATIC_VECTOR_H_INCLUDED\n\n#include <algorithm>\n#include <vector>\n#include <cassert>\n#include <common/log_messages.h>\n\nclass static_vector_interface {\npublic:\n virtual ~static_vector_interface() = default;\n virtual std::size_t size(void) const = 0;\n virtual void copy(std::size_t, std::size_t) = 0;\n};\n\n\n/** this vector wrapper class prevents direct access to the underlying vector.\n * no copying of elements allowed\n * constructed via emplace back\n * no resizing\n * save indexing\n */\n\ntemplate <typename element_t>\nclass static_vector : public static_vector_interface {\n//TODO static_vector(const static_vector& other) = delete; // non construction-copyable\n static_vector& operator=(const static_vector&) = delete; // non copyable\npublic:\n\n template<typename... Args>\n explicit static_vector(std::size_t number_of_elements, const Args&... args) : content() {\n content.reserve(number_of_elements);\n for (std::size_t index = 0; index < number_of_elements; ++index)\n content.emplace_back(/*index, */args...);\n }\n\n explicit static_vector(std::vector<element_t> const& vec) : content(vec) {}\n\n static_vector& operator=(std::vector<element_t> const& vec) {\n assert(content.size() == vec.size());\n this->content = vec;\n return *this;\n }\n\n virtual ~static_vector() = default;\n\n std::size_t size() const override final { return content.size(); }\n\n element_t& operator[] (std::size_t index) { return content.at(index); }\n const element_t& operator[] (std::size_t index) const { return content.at(index); }\n\n element_t get_max (void) const { return *std::max_element(content.begin(), content.end()); }\n element_t get_min (void) const { return *std::min_element(content.begin(), content.end()); }\n std::size_t get_argmax(void) const { return std::distance(content.begin(), std::max_element(content.begin(), content.end())); }\n std::size_t get_argmin(void) const { return std::distance(content.begin(), std::min_element(content.begin(), content.end())); }\n\n void copy(std::size_t dst, std::size_t src) override final { content.at(dst) = content.at(src); }\n\n void zero(void) { std::fill(content.begin(), content.end(), .0); }\n\n std::vector<element_t> const& get_content(void) const { return content; }\n\nprotected:\n std::vector<element_t> content;\n};\n\ntemplate <typename element_t>\nclass copyable_static_vector : public static_vector<element_t>\n{\npublic:\n template<typename... Args>\n explicit copyable_static_vector(const Args&... args)\n : static_vector<element_t>(args...)\n {}\n\n copyable_static_vector& operator=(const copyable_static_vector& other) {\n assert(this->content.size() == other.content.size());\n this->content = other.content;\n return *this;\n }\n};\n\n\n\n#endif // STATIC_VECTOR_H_INCLUDED\n" }, { "alpha_fraction": 0.5780494213104248, "alphanum_fraction": 0.5902724862098694, "avg_line_length": 34.36936950683594, "blob_id": "5526d568e27f29e1eb4d5e9bee0dd6e435b03278", "content_id": "f4c5dab8be261f36b0d989d59f49cd3595fa3987", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3927, "license_type": "no_license", "max_line_length": 157, "num_lines": 111, "path": "/src/common/robot_conf.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"./robot_conf.h\"\n\nnamespace { //TODO maybe set up a file constants.h\n const unsigned int max_number_of_joints = 64;\n const unsigned int max_number_of_accels = 50;\n const unsigned int max_number_of_bodies = max_number_of_joints + 1;\n}\n\nvoid\nRobot_Configuration::read_robot_info(std::string const& server_message)\n{\n sts_msg(\"Reading for configuration.\");\n if (0 == server_message.length())\n err_msg(__FILE__, __LINE__, \"Received no more bytes. Close socket and exit. (robot conf)\");\n\n\n /* response OK, parsing robot information */\n sts_msg(\"Received response, now checking configuration...\");\n int offset = 0;\n const char* msg = server_message.c_str();\n\n if (sscanf(msg, \"%u %u %u\\n%n\", &number_of_bodies, &number_of_joints, &number_of_accels, &offset) == 3) {\n sts_msg(\"Robot's configuration: \\n %3u Bodies\\n %3u Joints\\n %3u Acceleration Sensors\\n\\n\", number_of_bodies, number_of_joints, number_of_accels);\n } else\n err_msg(__FILE__, __LINE__, \"Unable to read the robot's configuration. Exit.\\n Message was: \\n%s\\n\", msg);\n\n\n /* checking for consistency */\n if ((number_of_joints < 1) || (number_of_joints > max_number_of_joints))\n err_msg(__FILE__, __LINE__, \"Number of joints (%u) out of range (1..%u). Exit.\", number_of_joints, max_number_of_joints);\n\n if (number_of_accels > max_number_of_accels)\n err_msg(__FILE__, __LINE__, \"Number of acceleration sensors (%u) out of range (0..%u). Exit.\", number_of_accels, max_number_of_accels);\n\n if ((number_of_bodies < 2) || (number_of_bodies > max_number_of_bodies))\n err_msg(__FILE__, __LINE__, \"Number of bodies (%u) out of range (2..%u). Exit.\", number_of_bodies, max_number_of_bodies);\n\n\n\n msg = read_joints(msg, &offset);\n msg = read_bodies(msg, &offset);\n accels.assign(number_of_accels, robots::Accel_Sensor());\n}\n\nconst char*\nRobot_Configuration::read_joints(const char* msg, int* offset)\n{\n char tmp_name[256];\n unsigned int tmp_id, tmp_type, tmp_sym;\n float tmp_jslo, tmp_jshi, tmp_jdef;\n\n joints.reserve(number_of_joints);\n joints.clear();\n\n\n for (unsigned int i = 0; i < number_of_joints; ++i)\n {\n msg += (*offset);\n if (sscanf(msg, \"%u %u %u %e %e %e %256s\\n%n\", &tmp_id, &tmp_type, &tmp_sym, &tmp_jslo, &tmp_jshi, &tmp_jdef, tmp_name, offset) == 7) {\n sts_msg(\"Joint %02u (%s), Type %u, a.w. %u, limits(%+1.2f, %+1.2f, %+1.2f)\", tmp_id, tmp_name, tmp_type, tmp_sym, tmp_jslo, tmp_jdef, tmp_jshi);\n } else\n err_msg(__FILE__, __LINE__, \"could not parse joint %u\", i);\n\n if (tmp_type > 1)\n err_msg(__FILE__, __LINE__, \"FIXME: Invalid Joint Type.\");\n\n joints.emplace_back( i\n , tmp_type?robots::Joint_Type_Symmetric:robots::Joint_Type_Normal\n , tmp_sym, tmp_name, tmp_jslo, tmp_jshi, tmp_jdef);\n\n assert(i == joints.size()-1);\n }\n\n return msg;\n}\n\nconst char*\nRobot_Configuration::read_bodies(const char* msg, int* offset)\n{\n char tmp_name[256];\n unsigned int tmp_id = 0;\n\n bodies.reserve(number_of_bodies);\n bodies.clear();\n sts_add(\"Reading bodies:\");\n for (unsigned int i = 0; i < number_of_bodies; ++i)\n {\n msg += (*offset);\n if (sscanf(msg, \"%u %256s\\n%n\", &tmp_id, tmp_name, offset) == 2) {\n sts_add(\"%02u (%s)\", tmp_id, tmp_name);\n } else\n err_msg(__FILE__, __LINE__, \"could not parse body %u\", i);\n\n bodies.emplace_back(tmp_name);\n assert(i == bodies.size()-1);\n }\n sts_msg(\"Done.\");\n return msg;\n}\n\nunsigned int\nRobot_Configuration::get_number_of_symmetric_joints(void) const\n{\n unsigned int num_sym_joints = 0;\n for (auto const& j : joints)\n if (robots::Joint_Type_Symmetric == j.type)\n ++num_sym_joints;\n\n assert(num_sym_joints * 2 <= number_of_joints);\n return num_sym_joints;\n}\n" }, { "alpha_fraction": 0.5319851636886597, "alphanum_fraction": 0.5369376540184021, "avg_line_length": 28.017963409423828, "blob_id": "95972f461f11c22166236a8ee3289282b60e653f", "content_id": "4bc23694fe92bc5f603f9479c933e9f8d09f4758", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4846, "license_type": "no_license", "max_line_length": 97, "num_lines": 167, "path": "/src/common/file_io.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef FILE_IO_H_INCLUDED\n#define FILE_IO_H_INCLUDED\n\n#include <cstdio>\n#include <vector>\n#include <string>\n#include <assert.h>\n#include <common/basic.h>\n#include <common/noncopyable.h>\n#include <common/log_messages.h>\n\nnamespace file_io {\n\nnamespace constants {\n const size_t format_length = 32;\n const char separator[] = \" \";\n}\n\ntemplate <typename T>\nclass CSV_File : public noncopyable\n{\n CSV_File(const CSV_File& other) = delete; // non construction-copyable\n CSV_File& operator=(const CSV_File&) = delete; // non copyable\npublic:\n CSV_File(const std::string& filename, const std::size_t rows, const std::size_t cols)\n : nbytes(cols * constants::format_length)\n , txtbuf((char *) malloc(nbytes + 1))\n , data(rows, std::vector<T>(cols))\n , max_rows(rows)\n , max_cols(cols)\n , filename(filename)\n {}\n\n ~CSV_File() { free(txtbuf); }\n\n bool read(void)\n {\n FILE* csv_file = open_file(\"r\", filename.c_str());\n bool result = true;\n unsigned int row = 0;\n while (row < max_rows)\n { // read next line\n if (getline(&txtbuf, &nbytes, csv_file) < 0) {\n wrn_msg(\"Error reading csv file %s in line %u\\n\", filename.c_str(), row + 1);\n result = false;\n break;\n }\n char* token = strtok(txtbuf, constants::separator); // get first token\n unsigned int col = 0;\n while (col < max_cols) {\n if (token != NULL)\n data[row][col++] = atof(token);\n else {\n wrn_msg(\"Unexpected columns size: %u (%u expected)\", col, max_cols);\n result = false;\n break;\n }\n token = strtok(NULL, constants::separator); // get next token\n }\n ++row;\n }\n fclose(csv_file);\n return result;\n }\n\n void write(void)\n {\n FILE* csv_file = open_file(\"w\", filename.c_str());\n for (std::size_t row = 0; row < max_rows; ++row)\n {\n for (std::size_t col = 0; col < max_cols; ++col)\n fprintf(csv_file, \"%+1.8e \", data[row][col]);\n fprintf(csv_file, \"\\n\");\n }\n fclose(csv_file);\n }\n\n template <typename Vector_t>\n void get_line(std::size_t row_index, Vector_t& line) const {\n assert(row_index < data.size());\n //dbg_msg(\"reading line %2u, size: %u, line size: %u\", row_index, max_cols, line.size());\n assert(line.size() == max_cols);\n line = data[row_index];\n }\n void get_line(std::size_t row_index, T& value) const {\n assert(row_index < data.size());\n //dbg_msg(\"reading line %2u, size: 1\", row_index);\n value = data[row_index][0];\n }\n\n template <typename Vector_t>\n void set_line(std::size_t row_index, Vector_t const& line) {\n assert(row_index < data.size() && line.size() == max_cols);\n //dbg_msg(\"writing line %2u, size: %u\", row_index, max_cols);\n data[row_index] = line;\n }\n void set_line(std::size_t row_index, const T& value) {\n assert(row_index < data.size());\n //dbg_msg(\"writing line %2u, size: 1\", row_index);\n data[row_index][0] = value;\n }\n\n std::string const& get_filename(void) const { return filename; }\n\nprivate:\n std::size_t nbytes;\n char* txtbuf;\n std::vector< std::vector<T> > data;\n const std::size_t max_rows;\n const std::size_t max_cols;\n const std::string filename;\n};\n\n\nclass Logfile\n{\n Logfile(const Logfile& other) = delete; // non construction-copyable\n Logfile& operator=(const Logfile&) = delete; // non copyable\npublic:\n Logfile(const std::string& filename, bool append = false)\n : filename(filename)\n , fd(open_file(append ? \"a\":\"w\", filename.c_str()))\n {}\n\n ~Logfile() {\n std::size_t fs = basic::get_file_size(fd);\n fclose(fd);\n if (fs == 0) { // no data written\n sts_msg(\"No data written, removing file: %s\", filename.c_str());\n remove(filename.c_str());\n }\n }\n\n void append(const char* format, ...)\n {\n va_list args;\n va_start(args, format);\n vfprintf(fd, format, args);\n fprintf(fd, \"\\n\");\n va_end(args);\n }\n void append(const std::vector<double> Vector)\n {\n for (std::size_t idx = 0; idx < Vector.size(); ++idx)\n fprintf(fd, \"%+1.8e \", Vector[idx]);\n fprintf(fd, \"\\n\");\n }\n\n void flush(void) { fflush(fd); }\n\n void next(void) {\n fflush(fd);\n fclose(fd);\n auto name = filename + \"_\" + std::to_string(++index);\n fd = open_file(\"w\", name.c_str());\n }\n\n\n const std::string filename;\n FILE* fd;\n std::size_t index = 0;\n};\n\n\n} // namespace file_io\n\n#endif // FILE_IO_H_INCLUDED\n" }, { "alpha_fraction": 0.5402750372886658, "alphanum_fraction": 0.5486247539520264, "avg_line_length": 28.941177368164062, "blob_id": "db7e490de0c60fd50e3975d2a1b9a64763f8217d", "content_id": "2bd50de68ec28b0cda7ea9129bc4f039b89699b4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2036, "license_type": "no_license", "max_line_length": 125, "num_lines": 68, "path": "/src/learning/q_function.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef Q_FUNCTION_H_INCLUDED\n#define Q_FUNCTION_H_INCLUDED\n\n#include <cassert>\n#include <common/modules.h>\n#include <common/static_vector.h>\n#include <common/log_messages.h>\n#include <learning/action_module.h>\n\nclass Policy {\n\n const Action_Module_Interface& actions;\n\npublic:\n\n static_vector<double> qvalues;\n\n Policy(const Action_Module_Interface& actions, double initial)\n : actions(actions)\n , qvalues(actions.get_number_of_actions())\n {\n// dbg_msg(\"Creating Policies.\");\n assert(actions.get_number_of_actions() >= 1);\n for (std::size_t i = 0; i < qvalues.size(); ++i)\n qvalues[i] = initial + rand_norm_zero_mean(0.01);\n }\n\n double get_max_q(void) const\n {\n double max_q = qvalues[0];\n for (std::size_t i = 1; i < qvalues.size(); ++i)\n if (actions.exists(i) and qvalues[i] > max_q)\n max_q = qvalues[i];\n return max_q;\n }\n std::size_t get_argmax_q(void) const\n {\n double max_q = qvalues[0];\n std::size_t argmax = 0;\n\n for (std::size_t i = 1; i < qvalues.size(); ++i)\n if (actions.exists(i) and qvalues[i] > max_q) {\n max_q = qvalues[i];\n argmax = i;\n }\n return argmax;\n }\n\n void copy_with_flaws(const Policy& other) {\n assert(qvalues.size() == other.qvalues.size());\n for (std::size_t a = 0; a < qvalues.size(); ++a)\n if (actions.exists(a))\n qvalues[a] = other.qvalues[a]\n + random_value(-0.05 * other.qvalues[a], /**TODO this is imprecise and may cause the trouble !!!*/\n +0.05 * other.qvalues[a]); /** TODO: put into method and use gaussian noise */\n }\n\n void copy_q_value(std::size_t from_idx, std::size_t to_idx) {\n qvalues[to_idx] = qvalues[from_idx];\n }\n\n Policy& operator=(const Policy& other) {\n this->copy_with_flaws(other);\n return *this;\n }\n};\n\n#endif // Q_FUNCTION_H_INCLUDED\n" }, { "alpha_fraction": 0.5952380895614624, "alphanum_fraction": 0.6020408272743225, "avg_line_length": 28.399999618530273, "blob_id": "bb483b51fe644fb0e26a0a262247e7e17c1266c2", "content_id": "a7b13fe736977444dcb9f96e66edaceb001c4667", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1470, "license_type": "no_license", "max_line_length": 90, "num_lines": 50, "path": "/src/control/controlmixer.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef CONTROLMIXER_H\n#define CONTROLMIXER_H\n\n#include <control/jointcontrol.h>\n#include <robots/robot.h>\n\nnamespace control {\n\nclass Controlmixer {\n\nstd::vector<Jointcontrol> control;\n\npublic:\n Controlmixer(robots::Robot_Interface& robot, std::size_t num_controller)\n : control()\n {\n for (std::size_t i = 0; i < num_controller; ++i)\n control.emplace_back(robot);\n }\n\n std::size_t size(void) const { return control.size(); }\n\n /* 'one-hot' switching */\n void set_active(std::size_t index) {\n for (std::size_t i = 0; i < control.size(); ++i)\n control[i].set_input_gain((index == i) ? 1. : 0.);\n }\n\n void fade(std::size_t i, std::size_t j, float val, float gain = 1.f) {\n const float g = clip(val, 0.f, 1.f);\n control.at(i).set_input_gain((1.f - g)* gain); // active if g -> 0\n control.at(j).set_input_gain( g * gain); // active if g -> 1\n }\n\n void set_control_parameter(std::size_t index, const Control_Parameter& controller) {\n control.at(index).set_control_parameter(controller);\n }\n\n Jointcontrol& operator[] (std::size_t index) { return control.at(index); }\n const Jointcontrol& operator[] (std::size_t index) const { return control.at(index); }\n\n void execute_cycle(void) { for (auto& c: control) c.execute_cycle(); }\n\n void reset(void) { for (auto& c: control) c.reset(); }\n\n};\n\n} /* namespace control */\n\n#endif /* CONTROLMIXER_H */\n" }, { "alpha_fraction": 0.5307372212409973, "alphanum_fraction": 0.5618417859077454, "avg_line_length": 33.89743423461914, "blob_id": "cd123f176969266757b58a1519ce4a21146b8dfe", "content_id": "0d6e28f17e2b30b73a36fa406fd9ddc5e90e5cce", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4083, "license_type": "no_license", "max_line_length": 119, "num_lines": 117, "path": "/src/draw/display.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef DISPLAY_H_INCLUDED\n#define DISPLAY_H_INCLUDED\n\n#include <vector>\n#include <limits>\n#include <draw/draw.h>\n#include <common/modules.h>\n#include <basic/color.h>\n\ntemplate <typename Element_t = double, typename Vector_t = std::vector<Element_t> > inline void\ndraw_vector( const double posx\n , const double posy\n , const double height\n , const double width\n , const Vector_t& vec\n , const Element_t& max_val = 1.0 )\n{\n glColor4f(1.0, 1.0, 1.0, 1.0);\n const double wbar = width/vec.size();\n const double hbar = height/std::abs(max_val);\n for (std::size_t i = 0; i < vec.size(); ++i)\n draw_fill_rect(posx + i*wbar, posy, wbar, hbar*clip(vec[i], 0.0, max_val));\n}\n\ntemplate <typename Element_t = double, typename Vector_t = std::vector<Element_t> > inline void\ndraw_vector2( const double posx\n , const double posy\n , const double height\n , const double width\n , const Vector_t& vec\n , const Element_t& max_val = 1.0 )\n{\n //const double wbar = std::min(width/vec.size(), 0.05);\n const double wbar = width/vec.size();\n\n for (std::size_t i = 0; i < vec.size(); ++i) {\n\n if (vec[i] > max_val)\n glColor3f(1.0, 0.0, 0.0);\n else if (vec[i] < -max_val)\n glColor3f(0.0, 0.0, 1.0);\n else\n glColor4f((vec[i] > 0) ? 1.0 : 0.5\n , 0.5\n , (vec[i] < 0) ? 1.0 : 0.5\n , 0.1 + 0.9 * fabs(clip(vec[i]/max_val , 1.0)));\n\n draw::fill_rect(posx + i*wbar, posy, 0.9*wbar, height);\n }\n}\n\nnamespace draw {\n\n\n\nvoid hbar(float px, float py, float dx, float dy, float value, float max_value, Color4 const& color = colors::white);\nvoid vbar(float px, float py, float dx, float dy, float value, float max_value, Color4 const& color = colors::white);\nvoid block(float px, float py, float sx, float sy, float value, float max_value);\n\ntemplate <typename Element_t = double, typename Vector_t = std::vector<Element_t> > inline void\nvector_dual( const double posx\n , const double posy\n , const double height\n , const double width\n , const Vector_t& vec1\n , const Vector_t& vec2\n , const Element_t& max_val = 1.0\n , bool with_numbers = true )\n{\n assert(vec1.size() == vec2.size());\n const double wbar = width/vec1.size();\n\n for (std::size_t i = 0; i < vec1.size(); ++i)\n {\n if (vec1[i] > 0.f) draw::vbar(posx+wbar*i, posy, wbar*.4f, height, +vec1[i], max_val, colors::cyan);\n else draw::vbar(posx+wbar*i, posy, wbar*.4f, height, -vec1[i], max_val, colors::magenta);\n if (with_numbers)\n glprintf(posx+wbar*i, posy-height*0.5, .0f, 0.1*wbar, \"%+3.1f\", vec1[i]);\n\n if (vec2[i] > 0.f) draw::vbar(posx+wbar*i+wbar*.4f, posy, wbar*.4f, height, +vec2[i], max_val, colors::cyan_l);\n else draw::vbar(posx+wbar*i+wbar*.4f, posy, wbar*.4f, height, -vec2[i], max_val, colors::magenta_l);\n if (with_numbers)\n glprintf(posx+wbar*i+wbar*.4f, posy-height*0.5, .0f, 0.1*wbar, \"%+3.1f\", vec2[i]);\n\n }\n}\n\ntemplate <typename Vector_t = std::vector<double> > inline void\nvec3( const float posx\n , const float posy\n , const float height\n , const float width\n , const Vector_t& vec\n , std::size_t max_elements = std::numeric_limits<std::size_t>::max())\n{\n const std::size_t len = std::min(vec.size(), max_elements);\n double max_value = 0;\n for (std::size_t i = 0; i < len; ++i)\n max_value = std::max(max_value, fabs(vec[i]));\n\n const float wbar = width/vec.size();\n const float px = posx - 0.5*width + 0.5*wbar;\n const float py = posy;\n\n for (std::size_t i = 0; i < len; ++i) {\n block(px + i*wbar, py, 0.9*wbar, 0.9*height, vec[i], max_value);\n }\n glColor3f(0.8,0.8,0.8);\n for (std::size_t i = 0; i < len; ++i) {\n glprintf(px + (i-0.40)*wbar, py, 0, 0.008, \"%.2f\", vec[i]);\n }\n\n}\n\n} /* namespace draw */\n\n#endif // DISPLAY_H_INCLUDED\n" }, { "alpha_fraction": 0.5428509712219238, "alphanum_fraction": 0.5482751131057739, "avg_line_length": 32.39855194091797, "blob_id": "01a873124223deec5a903741bb0ef469a1fa8614", "content_id": "79b3884e842b5c35a08f8142fda12f1b7e6c0044", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4609, "license_type": "no_license", "max_line_length": 124, "num_lines": 138, "path": "/src/control/control_core.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef CONTROL_CORE_H_INCLUDED\n#define CONTROL_CORE_H_INCLUDED\n\n#include <vector>\n#include <common/log_messages.h>\n#include <robots/robot.h>\n\n\nnamespace control {\n\nnamespace constants {\n const double initial_bias = 0.1;\n}\n\ninline std::size_t get_number_of_inputs(robots::Robot_Interface const& robot) {\n /* angle, velocity, motor output + xyz-acceleration + bias */\n return 3 * robot.get_number_of_joints() + 3 * robot.get_number_of_accel_sensors() + 1;\n}\n\nstruct sym_input {\n double x,y;\n\n sym_input& operator*=(const double& gain) {\n this->x *= gain;\n this->y *= gain;\n return *this;\n }\n};\n\nclass Fully_Connected_Symmetric_Core\n{\npublic:\n std::vector<std::vector<double> > weights;\n std::vector<sym_input> input;\n std::vector<double> activation;\n\n double gain = 1.0;\n\n Fully_Connected_Symmetric_Core(robots::Robot_Interface const& robot)\n : weights(robot.get_number_of_joints(), std::vector<double>(get_number_of_inputs(robot), 0.0))\n , input(get_number_of_inputs(robot))\n , activation(robot.get_number_of_joints())\n {\n /*dbg_msg(\"Fully connected symmetric core.\\n\\t weights: %u x %u \", weights.size(), weights.at(0).size());*/\n assert(get_number_of_inputs(robot) > 0);\n assert(robot.get_number_of_joints() > 0);\n }\n\n\n void prepare_inputs(const robots::Robot_Interface& robot)\n {\n std::size_t index = 0;\n for (auto const& jx : robot.get_joints())\n {\n auto const& jy = robot.get_joints()[jx.symmetric_joint];\n\n /**IDEA: consider using a virtual (integrated) angle */\n input[index++] = {jx.s_ang , jy.s_ang };\n input[index++] = {jx.s_vel , jy.s_vel };\n input[index++] = {jx.motor.get_backed(), jy.motor.get_backed()};\n }\n\n for (auto const& a : robot.get_accels())\n {\n input[index++] = {a.v.x, -a.v.x}; // mirror the x-axes\n input[index++] = {a.v.y, a.v.y};\n input[index++] = {a.v.z, a.v.z};\n }\n\n input[index++] = {constants::initial_bias, constants::initial_bias};\n assert(index == input.size());\n\n /* apply input gain */\n for (auto& i : input)\n i *= gain;\n }\n\n void update_outputs(const robots::Robot_Interface& robot, bool is_symmetric, bool is_switched)\n {\n assert(input.size() == weights[0].size());\n assert(activation.size() == robot.get_number_of_joints());\n assert(!(is_switched and is_symmetric));\n for (std::size_t i = 0; i < activation.size(); ++i)\n {\n activation[i] = .0;\n bool swap_inputs = is_switched != (is_symmetric and robot.get_joints()[i].type == robots::Joint_Type_Symmetric);\n for (std::size_t k = 0; k < input.size(); ++k)\n activation[i] += weights[i][k] * (swap_inputs ? input[k].y : input[k].x);\n }\n }\n\n void write_motors(robots::Robot_Interface& robot, bool is_switched)\n {\n robots::Jointvector_t& joints = robot.set_joints();\n assert(activation.size() == joints.size());\n\n for (std::size_t i = 0; i < activation.size(); ++i)\n joints[(is_switched ? joints[i].symmetric_joint : i)].motor += clip(activation[i], 1.0);\n }\n\n void apply_weights(robots::Robot_Interface const& /*robot*/, std::vector<double> const& params)\n {\n assert(params.size() == weights.size() * weights.at(0).size());\n std::size_t param_index = 0;\n for (auto& w_i : weights)\n for (auto& w_ik : w_i)\n w_ik = params[param_index++];\n\n assert(param_index == params.size());\n }\n\n void apply_symmetric_weights(robots::Robot_Interface const& robot, std::vector<double> const& params)\n {\n robots::Jointvector_t const& joints = robot.get_joints();\n\n std::size_t param_index = 0;\n for (std::size_t ix = 0; ix < robot.get_number_of_joints(); ++ix)\n {\n if (joints[ix].type == robots::Joint_Type_Normal)\n {\n std::size_t iy = joints[ix].symmetric_joint; // get symmetric counter part of ix\n assert(iy < robot.get_number_of_joints());\n for (std::size_t k = 0; k < weights[ix].size(); ++k)\n {\n weights[ix][k] = params[param_index++];\n weights[iy][k] = weights[ix][k];\n }\n }\n }\n assert(param_index == params.size());\n }\n\n};\n\n\n} // namespace control\n\n#endif // CONTROL_CORE_H_INCLUDED\n" }, { "alpha_fraction": 0.6021189093589783, "alphanum_fraction": 0.614479124546051, "avg_line_length": 22.59722137451172, "blob_id": "03fa8b0d6f9841d495cfb1c21b1ce79f2eb21fbe", "content_id": "8121d06056ee52b55a8adbd52da6f2f761d7eb0a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1699, "license_type": "no_license", "max_line_length": 91, "num_lines": 72, "path": "/src/common/socket_client.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/*---------------------------------+\n | Matthias Kubisch |\n | [email protected] |\n | July 2017 |\n +---------------------------------*/\n\n#ifndef SOCKET_CLIENT_H\n#define SOCKET_CLIENT_H\n\n#include <stdio.h>\n#include <string.h>\n#include <stdlib.h>\n#include <unistd.h>\n#include <string>\n#include <fcntl.h>\n#include <netinet/in.h>\n#include <arpa/inet.h>\n#include <netdb.h>\n#include <common/globalflag.h>\n#include <common/log_messages.h>\n#include <common/lock.h>\n\nnamespace network {\n\nnamespace constants {\n const unsigned seconds_us = 1000*1000;\n const unsigned default_port = 7777;\n const unsigned msglen = 8192;\n}\n\nstd::string hostname_to_ip(const char* hostname);\n\nclass Socket_Client\n{\n Socket_Client( const Socket_Client& other ) = delete; // non construction-copyable\n Socket_Client& operator=( const Socket_Client& ) = delete; // non copyable\n\npublic:\n Socket_Client()\n : srv_addr()\n , server()\n , sockfd(-1)\n , connection_established(false)\n , msgbuf()\n , mtx()\n { sts_msg(\"Creating client socket.\"); }\n\n ~Socket_Client(void) { sts_msg(\"Destroying client socket.\"); }\n\n bool open_connection(const char* server_addr, const unsigned short port);\n void close_connection(void);\n\n std::string recv(unsigned int time_out_us);\n void send(const char* format, ...); /* sends independent messages immediately */\n\n void append(const char* format, ...);\n void flush();\n void eat(void);\n\nprivate:\n struct sockaddr_in srv_addr;\n struct hostent *server;\n int sockfd;\n bool connection_established;\n\n std::string msgbuf;\n common::mutex_t mtx;\n};\n\n} /* namespace network */\n\n#endif /* SOCKET_CLIENT_H */\n" }, { "alpha_fraction": 0.5363878607749939, "alphanum_fraction": 0.5384615659713745, "avg_line_length": 36.36531448364258, "blob_id": "b272c67f36b1a5059b8a1904c021f0156b9f28d2", "content_id": "74d30990cf2953722539450d5d707d912077368d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 10127, "license_type": "no_license", "max_line_length": 119, "num_lines": 271, "path": "/src/evolution/evolution.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include \"evolution.h\"\n\n/**TODO: --watch does not work, when current trial == 0*/\n\n/* constructor for a new evolution */\nEvolution::Evolution(Evaluation_Interface &evaluation, const Setting& settings, const std::vector<double>& seed_genome)\n: evaluation(evaluation)\n, settings(settings)\n, projectname(create_project_name_and_folder(settings.project_name))\n, conffilename(FOLDER_PREFIX + projectname + \"/evolution.conf\")\n, configuration(settings.save_to_projectfile(conffilename))\n, state(Evolution_State::stopped)\n, seed(seed_genome)\n, population(settings.population_size, seed.size(), settings.init_mutation_rate, settings.meta_mutation_rate)\n, strategy()\n, evolution_log(FOLDER_PREFIX + projectname + \"/evolution.log\")\n, bestindiv_log(FOLDER_PREFIX + projectname + \"/bestindiv.log\")\n, verbose(settings.visuals)\n, playback_only(false)\n{\n common_setup();\n\n sts_msg(\"Setting up a new evolution project: %s\", projectname.c_str());\n\n if (seed.empty()) err_msg(__FILE__, __LINE__, \"Seed genome is missing.\");\n\n /*TODO move to initializer list? */\n if (settings.strategy == \"GENERATION\")\n strategy = Strategy_Pointer(new Generation_Based_Evolution( population,\n evaluation,\n configuration,\n settings.max_generations,\n 0,\n settings.selection_size,\n FOLDER_PREFIX + projectname,\n settings.visuals ));\n else if (settings.strategy == \"POOL\")\n strategy = Strategy_Pointer(new Pool_Evolution( population,\n evaluation,\n configuration,\n settings.max_trials,\n 0,\n settings.moving_rate,\n settings.selection_bias,\n FOLDER_PREFIX + projectname));\n else\n err_msg(__FILE__, __LINE__, \"Unknown type of evolution strategy.\");\n\n write_config();\n\n\n assert(strategy != nullptr);\n assert(not settings.fitness_function.empty());\n\n dbg_msg(\"Init population filename: %s\", settings.initial_population.c_str());\n if (not settings.initial_population.empty())\n strategy->load_start_population(settings.initial_population);\n else\n strategy->generate_start_population(seed);\n\n state = Evolution_State::running;\n sts_msg(\"Ready to create.\");\n\n}\n\n/* constructor for resuming previous evolution */\nEvolution::Evolution(Evaluation_Interface &evaluation, const Setting& settings, bool playback_only)\n: evaluation(evaluation)\n, settings(settings)\n, projectname(settings.project_name)\n, conffilename(FOLDER_PREFIX + projectname + \"/evolution.conf\")\n, configuration(conffilename)\n, state(Evolution_State::stopped)\n, seed()\n, population(settings.population_size,\n configuration.readUINT(\"INDIVIDUAL_SIZE\"),\n settings.init_mutation_rate,\n settings.meta_mutation_rate)\n, strategy()\n, evolution_log(FOLDER_PREFIX + projectname + \"/evolution.log\", true)\n, bestindiv_log(FOLDER_PREFIX + projectname + \"/bestindiv.log\", true)\n, verbose(settings.visuals)\n, playback_only(playback_only)\n{\n common_setup();\n\n if (settings.strategy == \"GENERATION\")\n strategy = Strategy_Pointer(new Generation_Based_Evolution( population,\n evaluation,\n configuration,\n settings.max_generations,\n settings.cur_generations,\n settings.selection_size,\n FOLDER_PREFIX + projectname,\n settings.visuals ));\n else if (settings.strategy == \"POOL\")\n strategy = Strategy_Pointer(new Pool_Evolution( population,\n evaluation,\n configuration,\n settings.max_trials,\n settings.cur_trials,\n settings.moving_rate,\n settings.selection_bias,\n FOLDER_PREFIX + projectname));\n else\n err_msg(__FILE__, __LINE__, \"Unknown type of evolution strategy.\");\n\n assert(strategy != nullptr);\n assert(not settings.fitness_function.empty());\n\n switch (configuration.readINT(\"STATUS\")) {\n case 1: sts_msg(\"Former evolution has not finished.\"); break;\n case 2: sts_msg(\"Former evolution was finished. \"); break;\n default:\n wrn_msg(\"Project has invalid status. Abort.\");\n state = Evolution_State::stopped;\n return;\n }\n\n sts_msg(\"Resume existing evolution project: %s\", projectname.c_str());\n strategy->resume();\n\n if (not playback_only) {\n state = Evolution_State::running;\n sts_msg(\"Ready for resuming.\");\n } else {\n state = Evolution_State::playback;\n sts_msg(\"Ready for playback.\");\n }\n}\n\n\nvoid\nEvolution::common_setup(void)\n{\n /* since evolution uses mutation,\n * we have to be for sure that random values\n * are initialized properly */\n sts_msg(\"Initializing random number generator.\");\n srand((unsigned) time(NULL));\n}\n\nvoid\nEvolution::write_config()\n{\n /* test if the project already exists */\n if (configuration.readUINT(\"STATUS\") > 0)\n err_msg(__FILE__, __LINE__, \"Project already exists. Please use '--resume' or '-r' instead. Exit.\");\n else\n sts_msg(\"Safely override existing configuration file.\");\n\n configuration.writeUINT(\"STATUS\" , 0); // evolution has not started yet\n configuration.writeUINT(\"INDIVIDUAL_SIZE\", population.get_individual_size());\n\n assert(strategy != nullptr);\n strategy->save_config(configuration);\n\n configuration.finish();\n dbg_msg(\"Done writing configuration file.\");\n}\n\nbool Evolution::loop(void)\n{\n switch(state)\n {\n case running:\n state = strategy->execute_trial();\n save_best_individual();\n save_statistics();\n /** TODO update GUI status */\n break;\n\n case finished:\n sts_msg(\"Finished.\");\n configuration.load();\n configuration.writeINT(\"STATUS\", 2);\n configuration.writeUINT(\"RANDOM_INIT\", settings.rnd.init);\n strategy->save_config(configuration);\n configuration.finish();\n state = Evolution_State::stopped;\n break;\n\n case playback:\n state = strategy->playback();\n break;\n\n case aborted: /* by user */\n wrn_msg(\"Aborted.\");\n prepare_quit();\n break;\n\n case stopped:\n sts_msg(\"Stopped.\");\n return false;\n\n default:\n err_msg(__FILE__, __LINE__, \"Invalid Evolution status.\");\n break;\n }\n\n return true;\n}\n\nvoid\nEvolution::prepare_quit(void)\n{\n if (not playback_only) {\n sts_msg(\"Saving data.\");\n configuration.load();\n configuration.writeINT(\"STATUS\", 1);\n configuration.writeUINT(\"RANDOM_INIT\", settings.rnd.init);\n strategy->save_config(configuration);\n configuration.finish();\n }\n sts_msg(\"Sending evolution stop signal.\");\n state = Evolution_State::stopped;\n}\n\n/* abort evolution externally before finished */\nvoid\nEvolution::finish(void)\n{\n sts_msg(\"Closing.\");\n dbg_msg(\"Current state is: %d\", state);\n\n if ((Evolution_State::running == state) || (Evolution_State::aborted == state))\n prepare_quit();\n else if (Evolution_State::playback == state)\n state = Evolution_State::stopped;\n\n}\n\n/* save best individual, write/append to file */\nvoid Evolution::save_best_individual(void)\n{\n if (not (Evolution_State::running == state or Evolution_State::finished == state)) {\n wrn_msg(\"Evolution status is NOT running or finished. Saving best individual aborted.\");\n return;\n }\n\n if (strategy->is_there_a_new_best_individual()) {\n const Individual& best = strategy->get_best_individual();\n if (verbose) sts_msg(\"Saving best individual.\");\n bestindiv_log.append(best.genome);\n bestindiv_log.flush(); // flush, to save data in case of error\n } //else\n //dbg_msg(\"Saving best individual skipped.\");\n}\n\nvoid Evolution::save_statistics(void)\n{\n assert(strategy != nullptr);\n statistics_t const& fstats = strategy->get_fitness_statistics();\n statistics_t const& mstats = strategy->get_mutation_statistics();\n evolution_log.append( \"%+1.8e %+1.8e %+1.8e %+1.8e %+1.8e %+1.8e\"\n , fstats.max, fstats.avg, fstats.min\n , mstats.max, mstats.avg, mstats.min );\n evolution_log.flush();\n}\n\nstd::string\ncreate_project_name_and_folder(std::string name)\n{\n if (name.empty()) { // no name given yet? create project name as time stamp\n name = basic::get_timestamp();\n wrn_msg(\"Create project's name from current time stamp.\");\n }\n sts_msg(\"Project name is '%s'\", name.c_str());\n basic::make_directory(\"%s%s\", FOLDER_PREFIX, name.c_str());\n return name;\n}\n\n" }, { "alpha_fraction": 0.5724815726280212, "alphanum_fraction": 0.5995085835456848, "avg_line_length": 17.5, "blob_id": "d1f60613832a39385f587f445d2233fd1ee2a2b9", "content_id": "3d6fe09f1241b05ca97df4d0db7040c12b38634a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 407, "license_type": "no_license", "max_line_length": 51, "num_lines": 22, "path": "/src/robots/accel.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef ACCEL_H_INCLUDED\n#define ACCEL_H_INCLUDED\n\n#include \"../../simloidTCP/src/basic/vector3.h\"\n\nnamespace robots {\n\nstruct Accel_Sensor\n{\n Accel_Sensor() : a(), v() {}\n void integrate(void) { v = 0.1 * a + 0.9 * v; }\n void reset (void) { v = 0.0; a = 0.0; }\n\n Vector3 a;\n Vector3 v;\n};\n\ntypedef std::vector<Accel_Sensor> Accelvector_t;\n\n} // namespace\n\n#endif // ACCEL_H_INCLUDED\n" }, { "alpha_fraction": 0.5091561079025269, "alphanum_fraction": 0.522937536239624, "avg_line_length": 31.29878044128418, "blob_id": "07aad3498c1a62fb01527c07b7fc21f1c50e8bb8", "content_id": "1f166930dfda1b1268faeb8c9925eaec5a0384d9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5297, "license_type": "no_license", "max_line_length": 154, "num_lines": 164, "path": "/src/learning/payload_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef PAYLOAD_GRAPHICS_H_INCLUDED\n#define PAYLOAD_GRAPHICS_H_INCLUDED\n\n#include <common/static_vector.h>\n#include <draw/color_table.h>\n#include <draw/graphics.h>\n\n#include <learning/gmes.h>\n#include <learning/gmes_graphics.h>\n#include <learning/expert.h>\n#include <learning/payload.h>\n#include <learning/sarsa.h>\n\nclass Payload_Graphics : public Graphics_Interface {\n\n const GMES& gmes;\n const GMES_Graphics& gmes_graphics;\n const static_vector<State_Payload>& payload;\n const SARSA& sarsa;\n const ColorTable table;\n\npublic:\n Payload_Graphics( const GMES& gmes\n , const GMES_Graphics& gmes_graphics\n , const static_vector<State_Payload>& payload\n , const SARSA& sarsa )\n : gmes(gmes)\n , gmes_graphics(gmes_graphics)\n , payload(payload)\n , sarsa(sarsa)\n , table(3)\n {\n dbg_msg(\"Creating payload graphics\");\n }\n\n void draw(const pref& p) const\n {\n glPushMatrix();\n glRotatef(p.y_angle, 1.f, 0.f, 0.f);\n glRotatef(p.x_angle, 0.f, 1.f, 0.f);\n\n for (std::size_t n = 0; n < gmes.get_max_number_of_experts(); ++n) {\n if (gmes.expert[n].does_exists()) {\n const Point& point = gmes_graphics.graph.get_position(n);\n\n unsigned int argmax_q = payload[n].policies[sarsa.get_current_policy()].get_argmax_q();\n const Color4& c = table.get_color(argmax_q);\n\n glColor3f(c.r, c.g, c.b);\n glprintf(point.x, point.y, point.z, 0.03, \"%u\", argmax_q);\n\n /** make that switchable */\n }\n }\n glEnd();\n glPopMatrix();\n }\n\n};\n\n#include <draw/graphics.h>\n#include <draw/display.h>\n#include <common/vector2.h>\n\nclass State_Payload_Graphics : public Graphics_Interface {\n\n typedef static_vector<State_Payload> Payload_Vector_t;\n\n const Payload_Vector_t& payloads;\n const Action_Module_Interface& actions;\n const std::size_t num_policies, num_states;\n\npublic:\n State_Payload_Graphics(const Payload_Vector_t& payloads, const Action_Module_Interface& actions)\n : payloads(payloads)\n , actions(actions)\n , num_policies(payloads[0].policies.size())\n , num_states(payloads.size())\n {\n dbg_msg(\"payload graphics for %u states and %u policies.\", num_states, num_policies);\n\n }\n void draw(const pref& /*p*/) const {\n\n /**TODO draw user selected policy a little bigger */\n const float space = 1.8/num_policies;\n const float height = 0.6*space/num_states;\n const float offy = space * num_policies/2;\n for (std::size_t i = 0; i < num_policies; ++i)\n for (std::size_t s = 0; s < num_states; ++s) {\n draw::vec3( 0.0\n , -(i*space) - s*height + offy\n , height\n , 2.0\n , payloads[s].policies[i].qvalues\n , actions.get_number_of_actions_available()\n );\n }\n }\n\n void draw2(const pref& /*p*/, unsigned real_num_states, unsigned cur_policy, unsigned cur_state, unsigned cur_action, unsigned RL_sel_action ) const {\n\n const unsigned N = real_num_states;\n assert(real_num_states<=num_states);\n const unsigned K = actions.get_number_of_actions_available();\n\n // draw rectangles\n const float ds = 2./(float) N;\n const float da = 2./(float) K;\n const float size = 0.05;\n\n const float Ds = 1 - ds/2;\n const float Da = 1 - da/2;\n\n const unsigned n = cur_state; // current state\n const unsigned k = cur_action; // current action\n\n for (unsigned s = 0; s < N; ++s)\n draw_square(-1, Ds - s*ds, size);\n draw_fill_square(-1, Ds - n*ds, size);\n\n for (unsigned a = 0; a < K; ++a)\n draw_square(1, Da - a*da, size);\n draw_fill_square(1, Da - k*da, size);\n\n draw_square(1, Da - RL_sel_action*da, 1.5*size);\n\n unsigned p = cur_policy;\n\n for (unsigned s = 0; s < N; ++s)\n {\n auto const& pay = payloads[s].policies[p];\n const unsigned a_maxq = pay.get_argmax_q();\n const unsigned a_minq = pay.get_argmin_q();\n\n const float qmax = pay.qvalues[a_maxq];\n const float qmin = pay.qvalues[a_minq];\n\n\n\n const float d = clip(1.f/(qmax-qmin), 0.001, 1000);\n\n for (unsigned a = 0; a < K; ++a)\n {\n glLineWidth(a==a_maxq? 1.5f: 0.5f);\n float val = clip( (pay.qvalues[a] - qmin) * d,0,1);\n //val =clip(val,-1.f,1.f);\n //sts_msg(\"val=%1.5f\", val);\n //assert(in_range(val, -0.1f,1.f));\n\n if (a == RL_sel_action and s == n)\n set_color(colors::yellow);\n else //if (val>0)\n set_color(colors::cyan, val);\n //else\n // set_color(colors::magenta, -val);\n draw_line(-1, Ds - s*ds, 1, Da - a*da);\n }\n }\n\n }\n};\n\n#endif // PAYLOAD_GRAPHICS_H_INCLUDED\n" }, { "alpha_fraction": 0.6008968353271484, "alphanum_fraction": 0.6008968353271484, "avg_line_length": 30.85714340209961, "blob_id": "b049fcd0ff72a2b1e8c02442132a69daf09ae9e0", "content_id": "055763c89c4caf70acdbc7b476d2c466283cff13", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2007, "license_type": "no_license", "max_line_length": 96, "num_lines": 63, "path": "/src/common/config.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* config.h */\n\n#ifndef CONFIG_H\n#define CONFIG_H\n\n#include <cstdio>\n#include <cstdlib>\n#include <cstring>\n#include <climits>\n#include <fstream>\n#include <map>\n\n#include <common/log_messages.h>\n#include <common/modules.h>\n\n\nclass config\n{\n config( const config& other ) = delete; // non construction-copyable\n config& operator=( const config& ) = delete; // non copyable\n\n typedef std::pair<std::string,std::string> element_t;\n\npublic:\n config(const std::string& filename, bool quit_on_fail = false)\n : fd()\n , filename(filename)\n , configuration()\n {\n if (not filename.empty())\n sts_msg(\"Configuration filename is: %s\", filename.c_str());\n else\n err_msg(__FILE__, __LINE__, \"No file name provided for configuration.\");\n\n if (not load() and quit_on_fail)\n err_msg(__FILE__, __LINE__, \"Configuration file does not exits, but was expected.\");\n }\n\n bool load(void);\n void clear(void) { configuration.clear(); }\n void finish(void);\n unsigned get_num_entries(void) const { return configuration.size(); }\n\n int readINT (std::string const& name, int def = {});\n unsigned readUINT(std::string const& name, unsigned def = {});\n bool readBOOL(std::string const& name, bool def = {});\n double readDBL (std::string const& name, double def = {});\n std::string readSTR (std::string const& name, std::string const& def = {});\n\n void writeINT (std::string const& name, int value);\n void writeUINT(std::string const& name, unsigned int value);\n void writeBOOL(std::string const& name, bool value);\n void writeDBL (std::string const& name, double value);\n void writeSTR (std::string const& name, std::string const& value);\n\nprivate:\n FILE* fd;\n const std::string filename;\n std::map<std::string,std::string> configuration;\n void parse(void);\n};\n\n#endif //CONFIG_H\n" }, { "alpha_fraction": 0.6447876691818237, "alphanum_fraction": 0.6602316498756409, "avg_line_length": 14.235294342041016, "blob_id": "9bc5448db3e8e30e776a413e03834cfd7dfc497a", "content_id": "cb8b2e94bf55b6e8db9bd3b9205f25d2a448f6b7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 259, "license_type": "no_license", "max_line_length": 37, "num_lines": 17, "path": "/src/draw/hsv.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef HSV_H\n#define HSV_H\n\n#include <basic/color.h>\n\nnamespace draw {\n\nstruct RGBColor { uint8_t r, g, b; };\nstruct HSVColor { uint8_t h, s, v; };\n\n\nRGBColor hsv2rgb(HSVColor hsv);\nHSVColor rgb2hsv(RGBColor rgb);\n\n} /* namespace draw */\n\n#endif /* HSV_H */\n" }, { "alpha_fraction": 0.5412371158599854, "alphanum_fraction": 0.5541236996650696, "avg_line_length": 18.399999618530273, "blob_id": "60dcaac89cfb5c10b208dfeb91d150782e786122", "content_id": "e562b78212dfe8f44b32e257b896b8cc51772be2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 776, "license_type": "no_license", "max_line_length": 64, "num_lines": 40, "path": "/src/common/integrator.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef INTEGRATOR_H_INCLUDED\n#define INTEGRATOR_H_INCLUDED\n\nclass Integrator {\n\n double sum;\n std::size_t number;\n double last;\n\npublic:\n Integrator() : sum(0.0), number(0), last(0.0) {}\n\n void add(double sample) {\n sum += sample;\n ++number;\n }\n\n double get_avg_value(void) const {\n if (number > 0) return sum/number;\n else return .0;\n }\n\n double get_avg_value_and_reset(void) {\n double result = get_avg_value();\n reset();\n last = result;\n return result;\n }\n\n std::size_t get_number_of_samples() const { return number; }\n\n double get_last(void) const { return last; }\n\n void reset(void) {\n sum = 0.0;\n number = 0;\n }\n};\n\n#endif // INTEGRATOR_H_INCLUDED\n" }, { "alpha_fraction": 0.586705207824707, "alphanum_fraction": 0.589595377445221, "avg_line_length": 23.714284896850586, "blob_id": "1a5019c4a55ab536d408dbd61eb41b8a8167b7db", "content_id": "e72910ba9a9d9a0f2cbdb850df982733f40a84f9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 692, "license_type": "no_license", "max_line_length": 109, "num_lines": 28, "path": "/src/common/view_manager.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef VIEW_MANAGER_H_INCLUDED\n#define VIEW_MANAGER_H_INCLUDED\n\n#include <common/log_messages.h>\n\nclass View_Manager {\n\n const unsigned num_views;\n unsigned view_id;\n\npublic:\n View_Manager(const std::size_t num_views) : num_views(num_views), view_id(0) {}\n\n unsigned get() const { return view_id; }\n void set(unsigned v) { if (v < num_views) view_id = v; else wrn_msg(\"Could not set view: %u\", v); }\n\n void key_pressed(SDL_Keysym const& keysym)\n {\n switch (keysym.sym)\n {\n case SDLK_v : ++view_id; if (view_id >= num_views) view_id = 0; break;\n default : return;\n }\n }\n\n};\n\n#endif // VIEW_MANAGER_H_INCLUDED\n" }, { "alpha_fraction": 0.629687488079071, "alphanum_fraction": 0.637499988079071, "avg_line_length": 31.40506362915039, "blob_id": "d4097b2d5ea4338d4e2d181dcea027af5c15e0dc", "content_id": "5bbf2647eb7e259e965c25290a94b82355d40936", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2560, "license_type": "no_license", "max_line_length": 100, "num_lines": 79, "path": "/src/learning/action_selection.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef ACTION_SELECTION_H_INCLUDED\n#define ACTION_SELECTION_H_INCLUDED\n\n#include <common/static_vector.h>\n#include <common/log_messages.h>\n#include <learning/action_module.h>\n#include <learning/payload.h>\n\ntemplate <typename Vector_t> std::size_t select_from_distribution(Vector_t const& distribution);\n\nclass Action_Selection_Base\n{\npublic:\n typedef static_vector<double> Vector_t;\n\nprotected:\n Vector_t selection_probabilities;\n const static_vector<State_Payload>& states;\n const Action_Module_Interface& actions;\n const double exploration_rate;\n\n bool explorative_selection = false;\n\npublic:\n Action_Selection_Base( const static_vector<State_Payload>& states\n , const Action_Module_Interface& actions\n , const double exploration_rate )\n : selection_probabilities(actions.get_number_of_actions())\n , states(states)\n , actions(actions)\n , exploration_rate(exploration_rate)\n {}\n const Vector_t& get_distribution(void) const { return selection_probabilities; }\n\n virtual ~Action_Selection_Base() = default;\n virtual std::size_t select_action( std::size_t current_state, std::size_t current_policy) = 0;\n\n bool is_exploring(void) { return explorative_selection; }\n\n std::size_t select_randomized(void) {\n selection_probabilities.zero(); // set all zeros\n const double portion = 1.0 / actions.get_number_of_actions_available();\n\n for (std::size_t i = 0; i < selection_probabilities.size(); ++i)\n if (actions.exists(i))\n selection_probabilities[i] = portion;\n\n explorative_selection = true;\n\n return select_from_distribution(selection_probabilities); // uniform, only available actions\n }\n\n};\n\nvoid print_distribution(const Action_Selection_Base::Vector_t& distribution);\n\n\n/** Selects an index from given discrete probability distribution.\n * The given distribution must sum up to 1.\n */\ntemplate <typename Vector_t>\nstd::size_t\nselect_from_distribution(Vector_t const& distribution)\n{\n const double x = random_value(0.0, 1.0);\n double sum = 0.0;\n for (std::size_t i = 0; i < distribution.size(); ++i)\n {\n assert((0.0 <= distribution[i]) and (distribution[i] <= 1.0));\n sum += distribution[i];\n if (x < sum)\n return i;\n assert(sum < 1.0);\n }\n dbg_msg(\"sum: %1.4f\", sum);\n assert(false);\n}\n\n#endif // ACTION_SELECTION_H_INCLUDED\n" }, { "alpha_fraction": 0.5764706134796143, "alphanum_fraction": 0.6025210022926331, "avg_line_length": 18.19354820251465, "blob_id": "75dc1f71b2580a005762f7665b860e111d8d478a", "content_id": "d1041ef77efb497f623b3485a9a50d07ca822327", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1190, "license_type": "no_license", "max_line_length": 73, "num_lines": 62, "path": "/src/common/stopwatch.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef STOPWATCH_H\n#define STOPWATCH_H\n\n#include <sys/time.h>\n#include <unistd.h>\n\n\nclass Stopwatch\n{\npublic:\n Stopwatch()\n : last_time_us(0)\n , current_time_us(0)\n , start_time_us(0)\n , timestamp()\n {\n reset();\n }\n\n void reset(void)\n {\n get_time();\n start_time_us = current_time_us;\n }\n\n unsigned long long get_time_passed_us(void)\n {\n last_time_us = current_time_us; // save last time stamp\n get_time();\n return current_time_us - last_time_us;\n }\n\n unsigned long long get_current_time_ms(void) {\n get_time();\n return current_time_us/1000;\n }\n\nprivate:\n void get_time()\n {\n gettimeofday(&timestamp, NULL);\n current_time_us = timestamp.tv_sec*1000*1000 + timestamp.tv_usec;\n }\n unsigned long long last_time_us; // last time\n unsigned long long current_time_us; // current time\n unsigned long long start_time_us;\n struct timeval timestamp;\n};\n\n/*\nunsigned int get_hours(unsigned long long time_ms)\n{\n return time_ms / (1000*3600);\n}\n\nunsigned int get_minutes(unsigned long long time_ms)\n{\n return time_ms % (1000*3600);\n}\n*/\n\n#endif // STOPWATCH_H\n" }, { "alpha_fraction": 0.6359530091285706, "alphanum_fraction": 0.6362541317939758, "avg_line_length": 32.867347717285156, "blob_id": "276919b24f375f8a8869124883c9ceebabe807c2", "content_id": "2f07cba578934d783c561373b5a233ab1485bc85", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3321, "license_type": "no_license", "max_line_length": 105, "num_lines": 98, "path": "/src/learning/homeokinetic_predictor.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef HOMEOKINETIC_PREDICTOR_H\n#define HOMEOKINETIC_PREDICTOR_H\n\n#include <control/sensorspace.h>\n#include <learning/predictor.h>\n#include <learning/homeokinesis.h>\n\nnamespace learning {\n\n/*--------------------------------------------+\n | Homeokinetic Prediction and Control Core |\n +--------------------------------------------*/\n\nclass Homeokinetic_Core : public Predictor_Base\n{\n Homeokinetic_Control core;\n\n Homeokinetic_Core(const Homeokinetic_Core& other) = delete;\n Homeokinetic_Core& operator=(const Homeokinetic_Core& other) = delete;\n\npublic:\n\n Homeokinetic_Core( sensor_input_interface const& input\n , std::size_t number_of_joints\n , double learning_rate\n , double random_weight_range\n , std::size_t context\n )\n : Predictor_Base(input, learning_rate, random_weight_range, 1)\n , core( input, number_of_joints, random_weight_range, context)\n {\n dbg_msg(\"Initialize Predictor using homeokinetic controller.\");\n }\n\n virtual ~Homeokinetic_Core() = default;\n\n void copy(Predictor_Base const& other) override {\n Predictor_Base::operator=(other); // copy base members\n Homeokinetic_Core const& rhs = dynamic_cast<Homeokinetic_Core const&>(other);\n core = rhs.core;\n dbg_msg(\"Copying homeokinetic pred/ctrl weights.\");\n };\n\n Predictor_Base::vector_t const& get_prediction(void) const override { return core.get_prediction(); }\n\n double predict(void) override {\n core.read_next_state(input); //TODO alt: consider to inject, actual motor commands\n core.predict();\n core.backup_state();\n core.control();\n return calculate_prediction_error();\n };\n\n double verify(void) override {\n core.predict();\n return calculate_prediction_error();\n }\n\n void initialize_randomized(void) override {\n core.randomize_weights(random_weight_range);\n prediction_error = predictor_constants::error_min;\n /*Note: experience buffer not randomized here. not used */\n };\n\n void initialize_from_input(void) override { assert(false && \"one shot learning not supported.\"); }\n\n void draw(void) const { assert(false && \"not implemented yet.\"); }\n\n Homeokinetic_Control::Vector_t& set_motor_data(void) { return core.set_motor_data(); }\n\n void learn_motor(void) { core.reconstruct(); core.adapt_controller(); }\n\n double get_timeloop_error(void) const { return core.get_timeloop_error(); }\n double get_prediction_error(void) const { return core.get_prediction_error(); }\n\n vector_t const& get_weights(void) const override { assert(false); return dummy; /*not implemented*/ }\n vector_t & set_weights(void) override { assert(false); return dummy; /*not implemented*/ }\n\nprivate:\n\n void learn_from_input_sample(void) override {\n core.reconstruct();\n core.adapt_prediction();\n core.adapt_controller();\n }\n\n void learn_from_experience(std::size_t /*skip_idx*/) override {\n assert(false && \"Learning from experience is not implemented yet.\");\n };\n\n\n VectorN dummy = {}; // remove when implementing get_weights\n //friend class Predictor_Graphics;\n};\n\n} /* namespace learning */\n\n#endif /* HOMEOKINETIC_PREDICTOR_H */\n\n\n" }, { "alpha_fraction": 0.5500461459159851, "alphanum_fraction": 0.5548893213272095, "avg_line_length": 35.42856979370117, "blob_id": "dd484ebed47f0a249f70ad2d8558f885d076cbcd", "content_id": "ab1f1ffbd9ea84730303e3b3c3fc1cec6bab2c49", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4336, "license_type": "no_license", "max_line_length": 147, "num_lines": 119, "path": "/src/control/controlparameter.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include <control/controlparameter.h>\n\nnamespace control {\n\n\n Control_Parameter::Control_Parameter( const std::string& filename\n , std::size_t number_of_params\n , bool symmetric\n , bool mirrored\n , unsigned robot_id\n , uint64_t rnd_init )\n : parameter()\n , symmetric(symmetric)\n , mirrored (mirrored )\n , robot_id (robot_id )\n , rnd_init (rnd_init )\n {\n sts_msg(\"Loading controller weights from CSV file:\\n '%s'\", filename.c_str());\n file_io::CSV_File<double> csv_file(filename, 1, number_of_params);\n if (csv_file.read()) {\n parameter.assign(number_of_params, 0.0);\n csv_file.get_line(0, parameter);\n } else\n wrn_msg(\"Could not read from file: %s\", filename.c_str());\n }\n\n\n Control_Parameter::Control_Parameter(const std::string& filename)\n : parameter()\n , symmetric()\n , mirrored ()\n , robot_id ()\n , rnd_init ()\n {\n sts_msg(\"Loading controller file: '%s'\", filename.c_str());\n file_io::Data_Reader dat_file(filename, /*verbose=*/false);\n assert(dat_file.read(\"parameter\", parameter)); /** TODO: remove assert dependence */\n\n symmetric = (\"symmetric\" == dat_file.read_string(\"symmetry\" )) ? true : false;\n mirrored = (\"original\" == dat_file.read_string(\"propagation\")) ? false : true;\n robot_id = dat_file.read_unsigned(\"robot_id\", 0);\n rnd_init = dat_file.read_unsigned(\"random_init\", 0);\n }\n\n\n Control_Parameter::Control_Parameter(const std::vector<double>& parameter, bool symmetric, bool mirrored, unsigned robot_id, uint64_t rnd_init)\n : parameter(parameter)\n , symmetric(symmetric)\n , mirrored (mirrored )\n , robot_id (robot_id )\n , rnd_init (rnd_init )\n {}\n\n void Control_Parameter::set_from_matrix(matrix_t const& weights)\n {\n assert(symmetric == false and mirrored == false); /**TODO accept mirrored weights */\n assert(weights.size() > 0 and weights[0].size() > 0);\n assert(parameter.size() == weights.size() * weights[0].size());\n\n std::size_t p = 0;\n for (auto const& wi : weights)\n for (auto const& wij : wi)\n parameter[p++] = wij;\n assert( p == parameter.size());\n }\n\n Control_Parameter::Control_Parameter(const Control_Parameter& other)\n : noncopyable()\n , parameter(other.parameter)\n , symmetric(other.symmetric)\n , mirrored (other.mirrored )\n , robot_id (other.robot_id )\n , rnd_init (other.rnd_init )\n { /*dbg_msg(\"Copying control parameter.\");*/ }\n\n\n Control_Parameter& Control_Parameter::operator=(const Control_Parameter& other)\n {\n if (this != &other) // avoid invalid self-assignment\n {\n parameter = other.parameter;\n symmetric = other.symmetric;\n mirrored = other.mirrored;\n robot_id = other.robot_id;\n rnd_init = other.rnd_init;\n }\n return *this;\n }\n\n void Control_Parameter::add_gaussian_noise(double sigma) {\n if (sigma == 0.0) return;\n const double s = sigma/sqrt(parameter.size());\n for (auto &u : parameter) {\n u += rand_norm_zero_mean(s);\n }\n }\n\n void Control_Parameter::print() const {\n for ( auto const& p : parameter ) printf(\"% 5.2f \", p);\n printf(\"\\n\");\n }\n\n void Control_Parameter::save_to_file(const std::string& filename, std::size_t id) const {\n sts_msg(\"Saving control parameter to file: %s\", filename.c_str());\n\n FILE* ctrl = open_file(\"w\", filename.c_str());\n\n fprintf(ctrl, \"name = \\\"motor-expert-%zu\\\"\\n\", id);\n if (robot_id > 0) fprintf(ctrl, \"robot_id = %u\\n\", robot_id);\n if (rnd_init > 0) fprintf(ctrl, \"random_init = %zu\\n\", rnd_init);\n fprintf(ctrl, \"symmetry = \\\"%s\\\"\\n\", symmetric ? \"symmetric\" : \"asymmetric\");\n fprintf(ctrl, \"propagation = \\\"%s\\\"\\n\", mirrored ? \"mirrored\" : \"original\");\n fprintf(ctrl, \"parameter = { \");\n for ( auto const& p : parameter ) fprintf(ctrl, \"%e \", p);\n fprintf(ctrl, \"}\\n\\n\");\n fclose(ctrl);\n }\n\n} /* namespace control */\n" }, { "alpha_fraction": 0.6003438830375671, "alphanum_fraction": 0.6037828326225281, "avg_line_length": 24.929935455322266, "blob_id": "aded085bc6c87633fb08a061ed7777887dfaaeb3", "content_id": "c3b4744170920b83ad0e7aa16eba85876502e7c0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4071, "license_type": "no_license", "max_line_length": 129, "num_lines": 157, "path": "/src/control/sensorspace.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SENSORSPACE_H_INCLUDED\n#define SENSORSPACE_H_INCLUDED\n\n#include <vector>\n#include <string>\n#include <queue>\n#include <cassert>\n#include <functional>\n#include <common/vector_n.h>\n\n/**TODO namespace */\n\nclass sensor_signal\n{\npublic:\n sensor_signal(const std::string& name, std::function<double()> lambda)\n : name(name)\n , lambda(lambda)\n , current(.0)\n , last(current)\n {}\n\n void execute_cycle(void) {\n last = current;\n current = lambda();\n }\n\n std::string name;\n std::function<double()> lambda;\n\n double operator() (void) const { return current; }\n\n double current;\n double last;\n};\n\n\nclass sensor_input_interface {\npublic:\n virtual std::size_t size(void) const = 0;\n virtual VectorN get(void) const = 0;\n virtual ~sensor_input_interface() {}\n virtual double operator[] (std::size_t index) const = 0;\n};\n\n\nclass sensor_vector : public sensor_input_interface\n{\nprotected:\n std::vector<sensor_signal> sensors;\n\npublic:\n sensor_vector(std::size_t number_of_elements = 0)\n : sensors()\n {\n //dbg_msg(\"Creating sensor vector, reserving space for %u elements.\", number_of_elements);\n sensors.reserve(number_of_elements);\n }\n\n /* embed 'normal' vector */\n explicit sensor_vector(const VectorN& plain)\n : sensor_vector(plain.size())\n {\n for (std::size_t i = 0; i < plain.size(); ++i)\n sensors.emplace_back(std::to_string(i) , [&plain, i](){ return plain[i]; });\n }\n\n virtual ~sensor_vector() { /*dbg_msg(\"Destroying sensor vector base.\");*/ };\n\n void execute_cycle(void) {\n for (auto& s : sensors) s.execute_cycle();\n }\n\n std::size_t size(void) const { return sensors.size(); }\n\n double operator[] (std::size_t index) { return sensors.at(index)(); }\n double operator[] (std::size_t index) const { return sensors.at(index)(); }\n\n VectorN get(void) const {\n VectorN result(sensors.size());\n for (std::size_t i = 0; i < sensors.size(); ++i)\n result[i] = sensors[i]();\n return result;\n }\n};\n\ntemplate <std::size_t NumTaps>\nclass time_embedded_signal\n{\npublic:\n time_embedded_signal(const std::string& name, std::function<double()> lambda)\n : name(name)\n , lambda(lambda)\n , buffer()\n {\n static_assert(NumTaps > 0, \"Min. length of tapped delay line is 1.\");\n buffer.assign(NumTaps, .0);\n assert(buffer.size() == NumTaps);\n }\n\n void execute_cycle(void) {\n buffer.push_front(lambda());\n buffer.pop_back();\n }\n\n std::string name;\n std::function<double()> lambda;\n\n double operator() (void) const { return buffer.front(); }\n\n double operator[] (std::size_t index) { return buffer.at(index); }\n double operator[] (std::size_t index) const { return buffer.at(index); }\n\n std::deque<double> buffer;\n};\n\ntemplate <std::size_t NumTaps>\nclass time_embedded_sensors : public sensor_input_interface\n{\nprotected:\n std::vector<time_embedded_signal<NumTaps>> sensors;\n\npublic:\n time_embedded_sensors(std::size_t number_of_elements)\n : sensors()\n {\n //dbg_msg(\"Creating time-embedded sensors, reserving space for %u delay lines of size %u.\", number_of_elements, NumTaps);\n sensors.reserve(number_of_elements);\n }\n\n virtual ~time_embedded_sensors() {};\n\n void execute_cycle(void) {\n for (auto& s : sensors) s.execute_cycle(); // propagate delay lines\n }\n\n std::size_t size(void) const { return sensors.size()*NumTaps + 1 /*bias*/; }\n\n\n double operator[] (std::size_t index) const {\n assert(index < size());\n const std::size_t i = index/NumTaps; // get buffer index\n const std::size_t j = index%NumTaps; // get element index\n if (i >= sensors.size()) return 0.1;\n return sensors[i][j];\n }\n\n VectorN get(void) const {\n VectorN result(size());\n for (std::size_t i = 0; i < size(); ++i)\n result[i] = (*this)[i];\n return result;\n }\n\n};\n\n#endif // SENSORSPACE_H_INCLUDED\n" }, { "alpha_fraction": 0.5258910655975342, "alphanum_fraction": 0.5319435000419617, "avg_line_length": 29.95833396911621, "blob_id": "e4d83b60b7e25e8ef5e9da5e69ea24bae44464f2", "content_id": "a31ab0106a0fe99796a96ab8f76ccf18a5f7051a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1487, "license_type": "no_license", "max_line_length": 123, "num_lines": 48, "path": "/src/common/settings.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include <common/settings.h>\n\n/**TODO create argument parser, similar to python, showing all arguments via --help */\n\nbool\nread_option_flag(int argc, char **argv, const char* short_name, const char* ext_name)\n{\n for (int i = 1; i < argc; ++i) {\n if ((strncmp(argv[i], ext_name, strlen(ext_name)) == 0) || (strncmp(argv[i], short_name, strlen(short_name)) == 0))\n return true;\n }\n return false;\n}\n\nstd::string\nread_string_option(int argc, char **argv, const char* short_name, const char* ext_name, const std::string default_value)\n{\n std::string result = default_value;\n\n for (int i = 1; i < argc; ++i)\n {\n if ((strncmp(argv[i], ext_name, strlen(ext_name)) == 0) || (strncmp(argv[i], short_name, strlen(short_name)) == 0))\n {\n if (argc < i+2) {\n printf(\"usage: %s %s <value>\\n\", argv[0], short_name);\n exit(EXIT_FAILURE);\n } else {\n result = argv[i+1];\n ++i;\n }\n }\n }\n return result;\n}\n\nstd::string\nread_options(int argc, char **argv, const char* default_path)\n{\n if (read_option_flag(argc, argv, \"-h\", \"--help\"))\n {\n printf(\"Help, Options: \\n\");\n printf(\" -s --settings : filename of settings file\\n\");\n printf(\" -h --help : show help \\n\");\n printf(\"\\n\");\n exit(EXIT_SUCCESS);\n }\n return read_string_option(argc, argv, \"-s\", \"--settings\", default_path);\n}\n" }, { "alpha_fraction": 0.6481481194496155, "alphanum_fraction": 0.6764705777168274, "avg_line_length": 34.30769348144531, "blob_id": "b962925b14d77c5adbc397fc265dc4229404d44e", "content_id": "b42842451112010b0f442d5b98a2016101ffda04", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 918, "license_type": "no_license", "max_line_length": 85, "num_lines": 26, "path": "/src/learning/gmes_constants.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GMES_CONSTANTS_H_INCLUDED\n#define GMES_CONSTANTS_H_INCLUDED\n\nnamespace gmes_constants\n{\n const double global_learning_rate = 35.0; // used for edges\n const double local_learning_rate = 0.005; // used for predictors\n\n const double initial_learning_capacity = 1.0;\n const double learning_capacity_exhausted = 0.01;\n const double initial_transition_validation = 1.0;\n const double transition_exist_treshold = 0.01;\n const double perceptive_width = 0.1; /** TODO: derive this factor */\n\n const std::size_t experience_size = 100;\n\n /* This setting could be used in combination with initial weights,\n * if we want to insert prior domain knowledge to the system.\n * Otherwise this must be 1. */\n const std::size_t number_of_initial_experts = 1;\n\n /* general */\n const double random_weight_range = 0.1;\n}\n\n#endif // GMES_CONSTANTS_H_INCLUDED\n" }, { "alpha_fraction": 0.615081787109375, "alphanum_fraction": 0.623323917388916, "avg_line_length": 50.44936752319336, "blob_id": "5a8a0b52461fa0d5cbfc30a3b793b70ce26c2854", "content_id": "14d3a8c21c2d058d7673650873eafd8a322a2c5c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8129, "license_type": "no_license", "max_line_length": 259, "num_lines": 158, "path": "/src/control/spaces.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SPACES_H_INCLUDED\n#define SPACES_H_INCLUDED\n\n#include <robots/joint.h>\n#include <control/jointcontrol.h>\n#include <control/sensorspace.h>\n\n#include <robots/simloid.h>\n\n#include <learning/action_module.h>\n#include <learning/payload.h>\n#include <learning/sarsa.h>\n#include <learning/gmes.h>\n#include <learning/learning_machine_interface.h>\n\n/** TODO:\n * Think of having intermediate 'terminal' states, i.e. that switch to a specific policy and\n * giving a binary like reward of 1 when reaching.\n */\n\nnamespace control {\n\nclass pendulum_sensor_space : public sensor_vector {\npublic:\n pendulum_sensor_space(const robots::Jointvector_t& joints)\n : sensor_vector(3)\n {\n assert(joints.size() == 1);\n sensors.emplace_back(\"[1] sin phi\" , [&joints](){ return +sin(M_PI * joints[0].s_ang); });\n sensors.emplace_back(\"[2] cos phi\" , [&joints](){ return -cos(M_PI * joints[0].s_ang); });\n sensors.emplace_back(\"[3] velocity\", [&joints](){ return +joints[0].s_vel; });\n }\n};\n\nclass pendulum_reward_space : public reward_base\n{\npublic:\n pendulum_reward_space( const GMES& gmes\n , const robots::Jointvector_t& joints )\n : reward_base(3)\n {\n assert(joints.size() == 1);\n\n /** Cite: >The reward for swinging up is simply given by a measure of the height of the pole. In order to incorporate exploration due\n * to optimistic value initialization, we chose to give the negative distance of the pole to the horizontal plane\n * located on the top position.<\n * Reward should be normed and have a max. value of zero, so optimistic initialization of Q-values is easily zero.\n */\n rewards.emplace_back(\"Intrinsic learning\", [&gmes ](){ return gmes.get_learning_progress(); });\n rewards.emplace_back(\"Pendulum swing-up\" , [&joints](){ return exp(-100*(std::abs(joints[0].s_ang)-1.0)*(std::abs(joints[0].s_ang)-1.0)); });\n rewards.emplace_back(\"Resting position\" , [&joints](){ return exp(-100*(std::abs(joints[0].s_ang)-0.0)*(std::abs(joints[0].s_ang)-0.0)); });\n\n /** in principle the swing-up are two tasks, swing-up and balancing. Think of splitting this up and introduce a task planner.\n * switch to balancing when swing up is finished, on the other hand this introduces the need for terminal conditions.\n */\n\n sts_msg(\"Creating %u reward signals for Pendulum.\", rewards.size());\n }\n};\n\n/* these rewards are probably all wrong!!!\n try using a distance based reward (distance to a virtual goal, e.g. which can hardly be reached)\n or which can be reached and terminates the trial.\n however distance to goal is not available on real machines... so can we accumulate positions...?\n\n Relation to HER:\n HER (hindsight experience replay)\n move the goal towards the actual reached position.\n moving the robot in different directions within the plane can be considered as moving to a specific location.\n if the action was taken, select the respective reward function of subpolicy that would maximize learning.\n but we already learn everything simultaneously. so this is a generalization of HER?\n\n try move over from discrete separate goals to a goal vector.. e.g. for the walking robot...simply the direction in 2D or 3D space to move!\n\n\n */\nclass walking_reward_space : public reward_base\n{\npublic:\n walking_reward_space(robots::Simloid const& robot)\n : reward_base(16)\n {\n /* add dummies for intrinsic learning, until state and motor layers are constructed */\n rewards.emplace_back(\"intrinsic state motivation\" , [](){ return .0; } );\n rewards.emplace_back(\"intrinsic motor motivation\" , [](){ return .0; } );\n\n switch(robot.robot_ID){\n case 10: /* Tadpole */\n rewards.emplace_back(\"walking forwards\" , [&robot](){ return robot.get_avg_velocity_forward();/* - std::abs(robot.get_avg_velocity_left()) - std::abs(robot.get_avg_rotational_speed()) )/(1. + robot.get_normalized_mechanical_power()); */ });\n rewards.emplace_back(\"walking backwards\" , [&robot](){ return -robot.get_avg_velocity_forward()/* - std::abs(robot.get_avg_velocity_left()) - std::abs(robot.get_avg_rotational_speed()) )/(1. + robot.get_normalized_mechanical_power())*/; });\n rewards.emplace_back(\"turning left\" , [&robot](){ return +0.1*robot.get_avg_rotational_speed()/*/(1. + robot.get_normalized_mechanical_power())*/; });\n rewards.emplace_back(\"turning right\" , [&robot](){ return -0.1*robot.get_avg_rotational_speed()/*/(1. + robot.get_normalized_mechanical_power())*/; });\n //rewards.emplace_back(\"stopping/resting\" , [&robot](){ return .1f/(1.f + /*robot.get_motion_level() +*/ robot.get_normalized_mechanical_power()); });\n break;\n case 31: /* Fourlegged */\n rewards.emplace_back(\"walking forwards\" , [&robot](){ return +robot.get_avg_velocity_forward(); });\n //rewards.emplace_back(\"walking backwards\" , [&robot](){ return -robot.get_avg_velocity_forward() });\n //rewards.emplace_back(\"turning left\" , [&robot](){ return +0.1*robot.get_avg_rotational_speed() });\n //rewards.emplace_back(\"turning right\" , [&robot](){ return -0.1*robot.get_avg_rotational_speed() });\n //rewards.emplace_back(\"stopping\" , [&robot](){ return -1.0/(1. + robot.get_normalized_mechanical_power())*/; });\n //rewards.emplace_back(\"get_up\" , [&robot](){ return +robot.get_avg_position().z - robot.get_accels()[0].v.y; });\n //rewards.emplace_back(\"walking left\" , [&robot](){ return +robot.get_avg_velocity_left(); });\n //rewards.emplace_back(\"walking right\" , [&robot](){ return -robot.get_avg_velocity_left(); });\n break;\n\n case 38: /* Hannah */\n rewards.emplace_back(\"walking forwards\" , [&robot](){ return +robot.get_avg_velocity_forward(); });\n sts_msg(\"Reward for Hannah.\");\n break;\n\n case 61: /* Flatcat */\n rewards.emplace_back(\"crawling forwards\" , [&robot](){ return +robot.get_avg_velocity_forward(); });\n sts_msg(\"Reward for Flatcat.\");\n default:\n break;\n }\n /** TODO: consider a penalty for dropping in the reward function.\n * However this should only apply for the walking robots, not the crawlers.\n *\n * TODO: make a policy for stopping, use simloid.is_motion_stopped? and minimize ctrl output.\n */\n sts_msg(\"Creating %u reward signals for walking.\" , rewards.size());\n }\n\n\n\n void add_intrinsic_rewards( learning::Learning_Machine_Interface const& state_learner\n , learning::Learning_Machine_Interface const& motor_learner )\n {\n rewards.at(0) = { \"intrinsic state motivation\", [&state_learner]() { return state_learner.get_learning_progress(); } };\n rewards.at(1) = { \"intrinsic motor motivation\", [&motor_learner]() { return motor_learner.get_learning_progress(); } };\n }\n\n};\n\n\n\n\n/**GMES spaces */\nclass GMES_joint_space : public sensor_vector\n{\npublic:\n GMES_joint_space(const robots::Joint_Model& joint)\n : sensor_vector(3)\n {\n sensors.emplace_back(\"[1] angle\" , [&joint](){ return joint.s_ang; });\n sensors.emplace_back(\"[2] velocity\" , [&joint](){ return joint.s_vel; });\n sensors.emplace_back(\"[3] torque\" , [&joint](){ return joint.motor.get(); }); //don't even think of removing that\n /**TODO: Think about: the introduction of the motor signal in the sensor space makes that inherently instable.\n * The adaption can skip the world in the loop and can directly influence the sensor space without actually moving the robot.\n * Also, reducing sensor space dimension reduces the overall cost. */\n }\n};\n\n} // namespace control\n\n\n#endif // SPACES_H_INCLUDED\n" }, { "alpha_fraction": 0.5631470084190369, "alphanum_fraction": 0.5828157067298889, "avg_line_length": 28.272727966308594, "blob_id": "e2e3a8fc3343a598ce6d429a5b34ce03503f6330", "content_id": "637a0fa23c02c196b9e7d02b12f8795921fd2411", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 966, "license_type": "no_license", "max_line_length": 72, "num_lines": 33, "path": "/src/learning/action_selection.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <learning/action_selection.h>\n\n/** Prints the values of the distribution to terminal.\n */\nvoid\nprint_distribution(const Action_Selection_Base::Vector_t& distribution)\n{\n sts_msg(\"Length: %u\", distribution.size());\n for (std::size_t i = 0; i < distribution.size(); ++i)\n printf(\"%+1.3f \", distribution[i]);\n printf(\"\\n\");\n}\n\n///** Selects an index from given discrete probability distribution.\n// * The given distribution must sum up to 1.\n// */\n//template <typename Vector_t>\n//std::size_t\n//select_from_distribution(Vector_t const& distribution)\n//{\n// const double x = random_value(0.0, 1.0);\n// double sum = 0.0;\n// for (std::size_t i = 0; i < distribution.size(); ++i)\n// {\n// assert((0.0 <= distribution[i]) and (distribution[i] <= 1.0));\n// sum += distribution[i];\n// if (x < sum)\n// return i;\n// assert(sum < 1.0);\n// }\n// dbg_msg(\"sum: %1.4f\", sum);\n// assert(false);\n//}\n" }, { "alpha_fraction": 0.6137579679489136, "alphanum_fraction": 0.6147770881652832, "avg_line_length": 34.36035919189453, "blob_id": "fb80f72a2e17e926c242618cfb1e780300cc3fb4", "content_id": "b67233e6a10cbde9de7b6aa40feb0ea21e9287cf", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3925, "license_type": "no_license", "max_line_length": 117, "num_lines": 111, "path": "/src/evolution/generation_based_strategy.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GENERATION_BASED_STRATEGY_H_INCLUDED\n#define GENERATION_BASED_STRATEGY_H_INCLUDED\n\n#include <evolution/evolution_strategy.h>\n#include <common/stopwatch.h>\n#include <common/modules.h>\n#include <common/log_messages.h>\n\n/** TODO:\n * create object time statistics */\n\nclass Generation_Based_Evolution: public Evolution_Strategy\n{\npublic:\n Generation_Based_Evolution(Population& population,\n Evaluation_Interface& evaluation,\n config& configuration,\n std::size_t max_generation,\n std::size_t cur_generation,\n const std::size_t selection_size,\n const std::string& project_folder_path,\n const bool verbose = true)\n : Evolution_Strategy(population, evaluation, configuration, project_folder_path, verbose)\n , max_generation(max_generation)\n , cur_generation(cur_generation)\n , selection_size(selection_size)\n , verbose(verbose)\n {\n assert(max_generation > 0);\n assert(max_generation >= cur_generation);\n assert(selection_size < population.get_size());\n assert(selection_size > 1);\n\n sts_msg(\"created generation-based policy.\");\n sts_msg(\"selection size is %u.\", selection_size);\n }\n\n ~Generation_Based_Evolution() { sts_msg(\"destroyed generation-based policy.\"); }\n\n bool evaluate_generation(void);\n\n void selection(void) {\n if (verbose) sts_msg(\"Selecting.\");\n population.sort_by_fitness();\n }\n\n void recombination_crossover(void);\n void mutation(void);\n bool show_selection(void);\n\n Evolution_State playback(void) { return show_selection()? Evolution_State::playback : Evolution_State::stopped; }\n\n void resume(void)\n {\n if (cur_generation > 0) {\n std::size_t max_gen_old = max_generation;\n max_generation += (cur_generation - (cur_generation % max_generation));\n if (max_generation > max_gen_old)\n wrn_msg(\"max. generation increased from %u to %u.\", max_gen_old, max_generation);\n\n load_state();\n recombination_crossover();\n mutation();\n sts_msg(\"Generation-based strategy is ready to resume.\");\n } else\n wrn_msg(\"Nothing to resume. Skip.\");\n }\n\n void save_config(config& configuration)\n {\n sts_msg(\"Saving generation-based strategy settings.\");\n configuration.writeUINT(\"MAX_GENERATIONS\" , max_generation);\n configuration.writeUINT(\"CURRENT_GENERATION\", cur_generation);\n configuration.writeUINT(\"SELECTION_SIZE\" , selection_size);\n }\n\n std::size_t get_max_generation(void) const { return max_generation; }\n std::size_t get_cur_generation(void) const { return cur_generation; }\n std::size_t get_selection_size(void) const { return selection_size; }\n\n std::size_t get_max_trials (void) const { return max_generation * population.get_size(); }\n std::size_t get_current_trial (void) const { return cur_generation * population.get_size(); }\n\n Evolution_State execute_trial(void)\n {\n if (!evaluate_generation()) {\n return Evolution_State::aborted;\n }\n selection();\n save_state();\n\n ++cur_generation;\n if (cur_generation >= max_generation) {\n return Evolution_State::finished;\n } // else\n recombination_crossover();\n mutation();\n return Evolution_State::running;\n }\n\n const Individual& get_best_individual(void) const { return population[0]; }\n bool is_there_a_new_best_individual(void) { return true; /* TODO implement */}\n\nprivate:\n std::size_t max_generation;\n std::size_t cur_generation;\n const std::size_t selection_size;\n const bool verbose;\n};\n\n#endif // GENERATION_BASED_STRATEGY_H_INCLUDED\n" }, { "alpha_fraction": 0.56627357006073, "alphanum_fraction": 0.5761792659759521, "avg_line_length": 38.62616729736328, "blob_id": "2d5555b1ba6bb3974ae453f1d655f7d2efb2e949", "content_id": "37f0033f71cdff60c640a655454099c0aad588b6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4240, "license_type": "no_license", "max_line_length": 138, "num_lines": 107, "path": "/src/learning/self_adj_motor_space.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SELF_ADJ_MOTOR_SPACE_H_INCLUDED\n#define SELF_ADJ_MOTOR_SPACE_H_INCLUDED\n\n#include <control/control_vector.h>\n\nnamespace control {\n\nclass self_adjusting_motor_space : public Action_Module_Interface\n{\n control::Jointcontrol control;\n\n std::size_t applied_policy;\n std::size_t applied_action;\n std::size_t applied_state;\n\n const learning::RL_Interface& learner;\n const bool self_adjusting;\n CompetitiveMotorLayer motor_layer;\n\npublic:\n\n self_adjusting_motor_space( robots::Robot_Interface& robot\n , const Control_Vector& parameter_set\n , const std::size_t max_actions\n , const std::size_t num_actions_begin\n , static_vector<State_Payload>& state_payload\n , const learning::RL_Interface& learner\n , bool self_adjusting\n , const double mutation_rate\n , const double learning_rate\n , const control::Minimal_Seed_t& seed )\n : control(robot)\n , applied_policy(0)\n , applied_action(0)\n , applied_state(0)\n , learner(learner)\n , self_adjusting(self_adjusting)\n , motor_layer(robot, state_payload, parameter_set, max_actions, num_actions_begin, mutation_rate, learning_rate, self_adjusting, seed)\n {\n dbg_msg(\"Creating self adjusting motor space.\");\n control.set_control_parameter(motor_layer.get_unit(0).weights); // initialize non-mutated start controller\n control.print_parameter();\n control.reset();\n }\n\n void execute_cycle(bool state_has_changed)\n {\n if (state_has_changed) { /* apply action learning on state change only */\n /* adjust previously selected weights*/\n motor_layer.adapt(learner.positive_current_delta(applied_policy));\n\n /* update and check state + action from learner */\n applied_policy = learner.get_current_policy();\n applied_action = learner.get_current_action();\n applied_state = learner.get_current_state();\n\n if (self_adjusting)\n motor_layer.enable_adaption(applied_policy == 0); /**TODO use enums for policies*/\n motor_layer.create_mutated_weights(applied_action);\n\n /* apply new weights */\n control.set_control_parameter(motor_layer.get_mutated_weights());\n }\n control.execute_cycle();\n }\n\n std::size_t get_number_of_actions (void) const { return motor_layer.get_number_of_motor_units(); }\n std::size_t get_number_of_actions_available(void) const { return motor_layer.get_number_of_available_motor_units(); }\n\n bool exists(const std::size_t action_index) const { return motor_layer.exists(action_index); }\n\n friend class self_adjusting_motor_space_graphics;\n};\n\n#include <draw/draw.h>\n\n/**TODO refactor and move to gmes_action_module*/\nclass self_adjusting_motor_space_graphics : public Graphics_Interface {\n const self_adjusting_motor_space& space;\n CompetitiveMotorLayer_Graphics motor_layer_graphics;\npublic:\n self_adjusting_motor_space_graphics(const self_adjusting_motor_space& space)\n : space(space)\n , motor_layer_graphics(space.motor_layer, -1.0, 0.0, 0.5) {}\n\n void execute_cycle(bool state_has_changed) {\n if (state_has_changed)\n motor_layer_graphics.execute_cycle();\n }\n void draw(const pref& p) const\n {\n motor_layer_graphics.draw(p);\n\n const MotorUnit& motor = space.motor_layer.get_unit(space.applied_action);\n glColor3f(1.0, 1.0, 1.0);\n glprintf(-0.9, 0.65, 0.0, 0.03, \"%3u:%2u\"\n , space.applied_state\n , space.applied_action );\n\n draw_vector2(-0.9, 0.6, 0.05, 1.0, motor.weights.get_parameter(), 3.0);\n }\n};\n\n\n} /* namespace control */\n\n#endif /* SELF_ADJ_MOTOR_SPACE_H_INCLUDED */\n" }, { "alpha_fraction": 0.568509042263031, "alphanum_fraction": 0.5880167484283447, "avg_line_length": 21.893617630004883, "blob_id": "957bf0c26c8f0d00d011b1b7aadd0086cef65692", "content_id": "4008242d0ddcc05649cb0f2562def0eca456fea9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2154, "license_type": "no_license", "max_line_length": 83, "num_lines": 94, "path": "/src/draw/plot1D.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* plot1D.cpp */\n\n#include \"plot1D.h\"\n\nvoid\nplot1D::draw(void) const\n{\n glPushMatrix();\n auto_scale();\n\n set_color(color);\n draw_line_strip();\n\n glPopMatrix();\n draw_statistics();\n}\n\nvoid\ncolored_plot1D::draw_colored(void) const\n{\n glPushMatrix();\n auto_scale();\n\n draw_colored_line_strip();\n\n glPopMatrix();\n set_color(color);\n draw_statistics();\n}\n\nvoid\nplot1D::auto_scale(void) const\n{\n const float scale = 2.0 / (axis.max_amplitude - axis.min_amplitude);\n const float offset = 0.0;// (axis.max_amplitude + axis.min_amplitude) / 2;\n\n glTranslatef(axis.px - 0.5 * axis.width, axis.py - offset, axis.pz);\n glScalef( axis.width / number_of_samples\n , 0.5 * axis.height * scale /2 //TODO scale is wrong, remove 0.5?\n , 1.0);\n\n}\n\nvoid\nplot1D::draw_line_strip(void) const\n{\n glLineWidth(1.0f);\n glBegin(GL_LINE_STRIP);\n for (unsigned i = number_of_samples+1; i-- > 1; ) { // zero omitted\n const unsigned pos = (i + pointer) % number_of_samples;\n glVertex2f( i, signal[pos]);\n }\n glEnd();\n}\n\nvoid\ncolored_plot1D::draw_colored_line_strip(void) const\n{\n glLineWidth(1.0f);\n glBegin(GL_LINE_STRIP);\n for (unsigned i = number_of_samples+1; i-- > 1; ) { // zero omitted\n const unsigned pos = (i + pointer) % number_of_samples;\n set_color(colortable.get_color(colors[pos]));\n glVertex2f( i, signal[pos]);\n }\n glEnd();\n}\n\nvoid\nplot1D::draw_statistics(void) const\n{\n glprintf( axis.px - 0.5 * axis.width\n , axis.py + 0.5 * axis.height - 1.1 * axis.font_height * (axis_id + 1)\n , axis.pz\n , axis.font_height\n , \"%+.4f %s\"\n , signal[pointer], name.c_str() );\n}\n\nvoid\nplot1D::add_sample(const float s)\n{\n increment_pointer();\n //assert(pointer < number_of_samples);\n signal[pointer] = s;\n\n /* autoscale //TODO überarbeiten */\n if (pointer == 0) {\n axis.max_amplitude *= decrement;\n axis.min_amplitude *= decrement;\n }\n axis.max_amplitude = std::max(axis.max_amplitude, s);\n axis.min_amplitude = std::min(axis.min_amplitude, s);\n}\n\n" }, { "alpha_fraction": 0.662446141242981, "alphanum_fraction": 0.6640625, "avg_line_length": 35.75247573852539, "blob_id": "939861ed977782a33208e4237490ed9f59ac8a9a", "content_id": "2dffe8698583f5573d89f0c0f1fd294dbe63b56f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3712, "license_type": "no_license", "max_line_length": 125, "num_lines": 101, "path": "/src/evolution/individual.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef INDIVIDUAL_H_INCLUDED\n#define INDIVIDUAL_H_INCLUDED\n\n#include <vector>\n#include <cassert>\n#include <float.h>\n#include <common/modules.h>\n#include <common/log_messages.h>\n#include <common/incremental_average.h>\n\nclass Individual;\n\nclass Fitness_Value\n{\npublic:\n\n explicit Fitness_Value(void) : fitness() {}\n\n double get_value(void) const { return fitness.get(); }\n void set_value(double value) { fitness.sample(value); } // average fitness incrementally\n\n void reset(void) { fitness.reset(); }\n std::size_t get_number_of_evaluations(void) const { return fitness.get_num_samples(); }\n\n double get_value_or_default(double default_value = 0.0) const {\n return (fitness.get_num_samples()>0) ? fitness.get() : default_value;\n }\n\nprivate:\n incremental_average fitness;\n\n friend bool operator==(const Fitness_Value& lhs, const Fitness_Value& rhs);\n friend bool operator!=(const Fitness_Value& lhs, const Fitness_Value& rhs);\n friend bool operator< (const Fitness_Value& lhs, const Fitness_Value& rhs);\n friend bool operator> (const Fitness_Value& lhs, const Fitness_Value& rhs);\n friend bool operator<=(const Fitness_Value& lhs, const Fitness_Value& rhs);\n friend bool operator>=(const Fitness_Value& lhs, const Fitness_Value& rhs);\n friend void crossover (const Individual& mother, const Individual& father, Individual& child);\n};\n\ninline bool operator==(const Fitness_Value& lhs, const Fitness_Value& rhs) { return lhs.fitness.get() == rhs.fitness.get(); }\ninline bool operator!=(const Fitness_Value& lhs, const Fitness_Value& rhs) { return !operator==(lhs,rhs); }\ninline bool operator< (const Fitness_Value& lhs, const Fitness_Value& rhs) { return lhs.fitness.get() < rhs.fitness.get(); }\ninline bool operator> (const Fitness_Value& lhs, const Fitness_Value& rhs) { return operator< (rhs,lhs); }\ninline bool operator<=(const Fitness_Value& lhs, const Fitness_Value& rhs) { return !operator> (lhs,rhs); }\ninline bool operator>=(const Fitness_Value& lhs, const Fitness_Value& rhs) { return !operator< (lhs,rhs); }\n\nvoid crossover(const Individual& mother, const Individual& father, Individual& child);\n\nclass Individual\n{\npublic:\n Individual(unsigned int individual_size, double init_mutation_rate, double meta_mutation_rate)\n : genome(individual_size)\n , fitness()\n , mutation_rate(init_mutation_rate)\n , meta_mutation_rate(meta_mutation_rate)\n {\n assert(individual_size > 0);\n assert(init_mutation_rate > .0);\n assert(meta_mutation_rate > .0);\n }\n\n ~Individual() {}\n\n\n Individual(const Individual& mother, const Individual& father)\n : genome(mother.genome.size())\n , fitness()\n , mutation_rate()\n , meta_mutation_rate()\n {\n crossover(mother, father, *this);\n }\n\n Individual& operator=(const Individual& other) {\n if (this != &other) // avoid invalid self-assignment\n {\n assert(other.genome.size() == genome.size());\n genome = other.genome;\n fitness = other.fitness;\n mutation_rate = other.mutation_rate;\n meta_mutation_rate = other.meta_mutation_rate;\n }\n return *this;\n }\n\n void mutate(void);\n void initialize_from_seed(const std::vector<double>& seed); /** TODO make a constructor of that */\n std::size_t get_size(void) const { return genome.size(); }\n\n std::vector<double> genome;\n Fitness_Value fitness;\n\n double mutation_rate;\n double meta_mutation_rate;\n};\n\nvoid crossover(const Individual& mother, const Individual& father, Individual& child);\n\n#endif // INDIVIDUAL_H_INCLUDED\n" }, { "alpha_fraction": 0.5903158783912659, "alphanum_fraction": 0.5933421850204468, "avg_line_length": 27.88524627685547, "blob_id": "192a1437f6e44caefc8ac015701d6fd3de16539b", "content_id": "c2e1cd0617adbbb17f1ff61835e0786c856f8fe1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5287, "license_type": "no_license", "max_line_length": 108, "num_lines": 183, "path": "/src/common/config.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"./config.h\"\n\nbool\nconfig::load(void)\n{\n /* try to open file for reading */\n fd = fopen(filename.c_str(), \"r\");\n if (fd == NULL) {\n wrn_msg(\"Cannot load configuration file %s\", filename.c_str());\n return false;\n }\n\n sts_msg(\"Configuration file %s is opened, parsing...\", filename.c_str());\n parse();\n fclose(fd); // and close\n sts_msg(\"Done.\");\n return true;\n}\n\nvoid\nconfig::parse(void)\n{\n unsigned number_of_entries = 0;\n\n char name[64];\n char value[1024];\n\n if (NULL == fd)\n err_msg(__FILE__, __LINE__, \"Cannot parse config file. File is not open.\");\n fseek(fd, 0, SEEK_SET);\n\n while(true) {\n if (2 == fscanf(fd, \"%64s = %1024s\\n\", name, value)) {\n sts_msg(\"| %2u %s = %s\", ++number_of_entries, name, value);\n configuration.insert(element_t(std::string{name}, std::string{value}));\n }\n else break;\n }\n}\n\nint\nconfig::readINT(std::string const& name, int def)\n{\n int result = def;\n if (configuration.find(name) != configuration.end())\n result = std::stoi(configuration[name]);\n\n sts_msg(\"Reading %s <int> = %d %s\", name.c_str(), result, (result == def)? \"(DEFAULT)\":\"\");\n return result;\n}\n\nunsigned\nconfig::readUINT(std::string const& name, unsigned def)\n{\n unsigned result = def;\n if (configuration.find(name) != configuration.end())\n result = std::stoul(configuration[name]);\n\n sts_msg(\"Reading %s <unsigned> = %u %s\", name.c_str(), result, (result == def)? \"(DEF)\":\"\");\n return result;\n}\n\nbool\nconfig::readBOOL(std::string const& name, bool def)\n{\n bool result = def;\n\n if (configuration.find(name) != configuration.end())\n {\n if (\"YES\" == configuration[name] or \"TRUE\" == configuration[name]) result = true;\n else if (\"NO\" == configuration[name] or \"FALSE\" == configuration[name]) result = false;\n else\n wrn_msg(\"unrecognized boolean value.\");\n }\n\n sts_msg(\"Reading %s <bool> = %s %s\", name.c_str(), result? \"TRUE\":\"FALSE\", (result == def)? \"(DEF)\":\"\");\n return result;\n}\n\ndouble\nconfig::readDBL(std::string const& name, double def)\n{\n double result = def;\n if (configuration.find(name) != configuration.end())\n result = std::stod(configuration[name]);\n\n sts_msg(\"Reading %s <double> = %f %s\", name.c_str(), result, (result == def)? \"(DEF)\":\"\");\n return result;\n}\n\nstd::string\nconfig::readSTR(std::string const& name, std::string const& def)\n{\n std::string result = def;\n if (configuration.find(name) != configuration.end())\n result = configuration[name];\n\n sts_msg(\"Reading %s <string> = \\\"%s\\\" %s\", name.c_str(), result.c_str(), (result == def)? \"(DEF)\":\"\");\n return result;\n}\n\nvoid\nconfig::writeINT(std::string const& name, int value)\n{\n if (configuration.find(name) != configuration.end()) {\n sts_msg(\"Replacing entry %s with value %d\", name.c_str(), value);\n configuration[name] = std::to_string(value);\n return;\n }\n\n sts_msg(\"Adding entry %s with value %d\", name.c_str(), value);\n configuration.insert(element_t(name, std::to_string(value)));\n}\n\nvoid\nconfig::writeUINT(std::string const& name, unsigned value)\n{\n if (configuration.find(name) != configuration.end()) {\n sts_msg(\"Replacing entry %s with value %u\", name.c_str(), value);\n configuration[name] = std::to_string(value);\n return;\n }\n\n sts_msg(\"Adding entry %s with value %u\", name.c_str(), value);\n configuration.insert(element_t(name, std::to_string(value)));\n}\n\nvoid\nconfig::writeBOOL(std::string const& name, bool value)\n{\n if (configuration.find(name) != configuration.end()) {\n sts_msg(\"Replacing entry %s with value %s\", name.c_str(), value ? \"YES\" : \"NO\");\n configuration[name] = value ? \"YES\" : \"NO\";\n return;\n }\n\n sts_msg(\"Adding entry %s with value %s\", name.c_str(), value ? \"YES\" : \"NO\");\n configuration.insert(element_t(name, value ? std::string{\"YES\"} : std::string{\"NO\"}));\n}\n\nvoid\nconfig::writeDBL(std::string const& name, double value)\n{\n if (configuration.find(name) != configuration.end()) {\n sts_msg(\"Replacing entry %s with value %f\", name.c_str(), value);\n configuration[name] = std::to_string(value);\n return;\n }\n\n sts_msg(\"Adding entry %s with value %f\", name.c_str(), value);\n configuration.insert(element_t(name, std::to_string(value)));\n}\n\nvoid\nconfig::writeSTR(std::string const& name, std::string const& value)\n{\n if (configuration.find(name) != configuration.end()) {\n sts_msg(\"Replacing entry %s with value %s\", name.c_str(), value.c_str());\n configuration[name] = value;\n return;\n }\n\n sts_msg(\"Adding entry %s with value %s\", name.c_str(), value.c_str());\n configuration.insert(element_t(name, value));\n}\n\nvoid\nconfig::finish(void)\n{\n /* open configuration file for writing */\n std::fstream fs;\n fs.open(filename.c_str(), std::fstream::out);\n\n if (fs.is_open())\n {\n sts_add(\"Writing configuration file...\");\n for (auto const& c: configuration)\n fs << c.first << \" = \" << c.second << std::endl;\n sts_msg(\"Done.\");\n fs.close();\n }\n else wrn_msg(\"Cannot initialize configuration file %s\", filename.c_str());\n}\n" }, { "alpha_fraction": 0.6007751822471619, "alphanum_fraction": 0.6062471270561218, "avg_line_length": 34.0880012512207, "blob_id": "6bf941c4e8bcba7de9ce991d7818b9cea65b354e", "content_id": "42e3de760130560520760f295b389e57d070ccc1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8772, "license_type": "no_license", "max_line_length": 115, "num_lines": 250, "path": "/src/evolution/pool_strategy.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef POOL_STRATEGY_H_INCLUDED\n#define POOL_STRATEGY_H_INCLUDED\n\n#include <vector>\n#include <string>\n#include <algorithm>\n\n#include <evolution/evolution_strategy.h>\n#include <evolution/evaluation_interface.h>\n#include <common/log_messages.h>\n#include <common/modules.h>\n\ninline double biased_random_value(double xrand, double bias)\n{\n return tanh(bias*xrand)/tanh(bias);\n}\n\ninline std::size_t biased_random_index(std::size_t N, double bias)\n{\n return floor(biased_random_value(random_value(), bias) * N);\n}\n\ninline std::size_t biased_random_index_inv(std::size_t N, double bias)\n{\n return N - biased_random_index(N, bias) -1; //N - (0..N-1) - 1\n}\n\n/* search the population (sorted-by-fitness) bottom-up and replace\n * the best individual you can get with lower fitness than yours.\n */\nstd::size_t get_replacement_candidate_for(Individual& opponent, Population& population);\n\n/* generation free */\nclass Pool_Evolution: public Evolution_Strategy\n{\npublic:\n Pool_Evolution(Population& population,\n Evaluation_Interface& evaluation,\n config& configuration,\n std::size_t max_trials,\n std::size_t current_trial,\n double moving_rate,\n double selection_bias,\n const std::string& project_folder_path,\n const bool verbose = true )\n : Evolution_Strategy(population, evaluation, configuration, project_folder_path, verbose)\n , population(population)\n , max_trials(max_trials)\n , current_trial(current_trial)\n , moving_rate(moving_rate)\n , selection_bias(selection_bias)\n , best_individual_has_changed(false)\n , current_playback_idx()\n {\n sts_msg(\"Created pool evolution strategy.\");\n assert(current_trial <= max_trials);\n assert(max_trials > 1);\n }\n\n ~Pool_Evolution() { sts_msg(\"Destroyed pool evolution strategy.\"); }\n\n bool evaluate(Individual& individual)\n {\n evaluation.constrain(individual.genome);\n if (evaluation.evaluate(individual.fitness, individual.genome, random_value(0.0, 1.0))) {\n sts_add(\"F=%+1.3f\", individual.fitness.get_value_or_default());\n return true;\n } else {\n sts_msg(\"Evaluation aborted without result.\");\n return false;\n }\n }\n\n bool crossover_trial(void)\n {\n /* select parents from pool and crossover */\n std::size_t parent_1 = biased_random_index_inv(population.get_size(), selection_bias);\n std::size_t parent_2 = biased_random_index_inv(population.get_size(), selection_bias);\n\n Individual child(population[parent_1], population[parent_2]);\n sts_add(\"[cross %2u + %2u]\", parent_1, parent_2);\n\n child.mutate();\n\n if (not evaluate(child)) return false;\n\n std::size_t replace_idx = get_replacement_candidate_for(child, population);\n\n if (child.fitness > population[replace_idx].fitness)\n {// if better than the replacement candidate, replace it.\n sts_add(\"[> %+1.3f] replace %2u\", population[replace_idx].fitness.get_value(), replace_idx);\n population[replace_idx] = child;\n\n best_individual_has_changed |= (replace_idx == 0);\n\n if (population[replace_idx].fitness.get_number_of_evaluations() == 0)\n err_msg(__FILE__, __LINE__, \"overriding a not yet tested one: %u\", replace_idx);\n }\n else sts_add(\"[< %+1.3f] discard\", population[replace_idx].fitness.get_value_or_default());\n\n return true;\n }\n\n bool refreshing_trial(void)\n {\n std::size_t candidate_idx = random_index(population.get_size());\n Individual candidate(population[candidate_idx]); // select candidate from pool\n sts_add(\"[refr. %2u (%2u)]\", candidate_idx, candidate.fitness.get_number_of_evaluations());\n bool result = evaluate(candidate);\n\n /* push_back p to population, replace the old one */\n population[candidate_idx] = candidate;\n\n return result;\n }\n\n bool initial_trial(void)\n {\n sts_add(\"[initial trial]\");\n assert(current_trial < population.get_size());\n std::size_t candidate_idx = current_trial;\n Individual& candidate(population[candidate_idx]); // select candidates successively from pool\n return evaluate(candidate);\n }\n\n Evolution_State execute_trial(void)\n {\n bool result = false;\n bool is_initial = current_trial < population.get_size();\n\n /* prepare */\n if (current_trial % population.get_size() == 0)\n evaluation.prepare_evaluation(current_trial, max_trials);\n\n sts_add(\"T: %u\", current_trial);\n if (is_initial) {\n result = initial_trial();\n } else if (random_value(0.0, 1.0) > moving_rate) {\n result = crossover_trial();\n } else {\n result = refreshing_trial();\n }\n printf(\"\\n\");\n\n if (not result) return Evolution_State::aborted;\n\n if (!is_initial)\n update_population_statistics();\n\n if (++current_trial < max_trials) {\n if (current_trial > 0 and (current_trial % population.get_size() == 0))\n save_state();\n return Evolution_State::running;\n }\n else {\n save_state();\n return Evolution_State::finished;\n }\n\n }\n\n Evolution_State playback(void)\n {\n sts_msg(\"playback individual: %u\", current_playback_idx);\n if (not evaluate(population[current_playback_idx]))\n return Evolution_State::aborted;\n\n update_population_statistics();\n\n if (++current_playback_idx < population.get_size()) return Evolution_State::playback;\n else return Evolution_State::stopped;\n }\n\n void resume(void)\n {\n if (current_trial > 0) {\n std::size_t max_trials_old = max_trials;\n max_trials += (current_trial - (current_trial % max_trials));\n if (max_trials > max_trials_old)\n wrn_msg(\"Max. trials increased from %u to %u.\", max_trials_old, max_trials);\n load_state(); /**TODO consider to move that out of the if clause */\n population.sort_by_fitness(); /**TODO consider to move that out of the if clause */\n sts_msg(\"Strategy ready to resume.\");\n } else\n wrn_msg(\"Nothing to resume. Skip.\");\n }\n\n void save_config(config& configuration)\n {\n sts_msg(\"Saving pool strategy settings.\");\n configuration.writeUINT(\"MAX_TRIALS\" , max_trials);\n configuration.writeUINT(\"CURRENT_TRIAL\" , current_trial);\n configuration.writeDBL (\"MOVING_RATE\" , moving_rate);\n configuration.writeDBL (\"SELECTION_BIAS\", selection_bias);\n }\n\n std::size_t get_max_trials (void) const { return max_trials; }\n std::size_t get_current_trial(void) const { return current_trial; }\n\n const Individual& get_best_individual(void) const\n {\n sts_msg(\"Get best individual (%1.4f)\", population.get_best_individual().fitness.get_value_or_default());\n return population.get_best_individual();\n }\n\n bool is_there_a_new_best_individual(void) {\n bool result = best_individual_has_changed;\n best_individual_has_changed = false;\n return result;\n }\n\n /* think about to use 'insertion sort' and keeping this list up-to-date all the time */\n void update_population_statistics(void)\n {\n double last_best_fitness = population.get_best_individual().fitness.get_value();\n population.sort_by_fitness();\n best_individual_has_changed |= (last_best_fitness != population.get_best_individual().fitness.get_value());\n\n fitness_stats .reset();\n mutation_stats.reset();\n\n for (std::size_t i = 0; i < population.get_size(); ++i) {\n\n if (population[i].fitness.get_number_of_evaluations() > 0) {\n fitness_stats .add_sample(population[i].fitness.get_value());\n mutation_stats.add_sample(population[i].mutation_rate);\n } else dbg_msg(\"not evaluated, skipping %u\", i);\n }\n\n fitness_stats .update_average();\n mutation_stats.update_average();\n\n if ((current_trial+1) % population.get_size() == 0)\n sts_msg(\"max:%+1.4f, avg:%+1.4f, min:%+1.4f\",\n fitness_stats.max,\n fitness_stats.avg,\n fitness_stats.min);\n }\n\n Population& population;\n std::size_t max_trials;\n std::size_t current_trial;\n const double moving_rate;\n const double selection_bias;\n\n bool best_individual_has_changed;\n std::size_t current_playback_idx;\n};\n\n#endif // POOL_STRATEGY_H_INCLUDED\n" }, { "alpha_fraction": 0.6700814366340637, "alphanum_fraction": 0.671920120716095, "avg_line_length": 29.95121955871582, "blob_id": "33c1674695bcca3c036295dcbb2994d6c84012fe", "content_id": "c86e3ec61f744266461b864edc396a67813212c4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3807, "license_type": "no_license", "max_line_length": 122, "num_lines": 123, "path": "/src/common/event_manager.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EVENT_MANAGER_H\n#define EVENT_MANAGER_H\n\n#include <SDL2/SDL_events.h>\n#include <SDL2/SDL_keyboard.h>\n#include <SDL2/SDL_keycode.h>\n#include <algorithm>\n#include <functional>\n#include <common/log_messages.h>\n#include <common/globalflag.h>\n#include <common/visuals.h>\n\nstruct Mouse_Click_Event\n{\n bool clicked;\n int position_x;\n int position_y;\n};\n\nstruct Joystick\n{\n float x0, y0;\n float x1, y1;\n};\n\nclass Event_Manager\n{\n\npublic:\n Event_Manager()\n : event()\n , joystick()\n , user_callback()\n , mouse_button_left()\n , mouse_button_right()\n , mouse_button_middle()\n , mouse_position_x()\n , mouse_position_y()\n , mouse_wheel_position()\n , mouse_wheel_position_last()\n {}\n\n void process_events(void);\n\n typedef std::function<void(SDL_Keysym const& key )> Keysym_t;\n typedef std::function<void(SDL_JoyButtonEvent const& joystick)> Joybutton_t;\n typedef std::function<void(SDL_JoyAxisEvent const& joystick)> Joyaxis_t;\n typedef std::function<void(SDL_JoyHatEvent const& joystick)> Joyhat_t;\n\n\n void reg_usr_cb_key_pressed (Keysym_t cb);\n void reg_usr_cb_key_released (Keysym_t cb);\n void reg_usr_cb_joystick_button_pressed (Joybutton_t cb);\n void reg_usr_cb_joystick_button_released(Joybutton_t cb);\n void reg_usr_cb_joystick_motion_axis (Joyaxis_t cb);\n void reg_usr_cb_joystick_motion_hat (Joyhat_t cb);\n //void reg_usr_cb_mouse (callback_type callback_function);\n\n Joystick const& get_joystick(void) const { return joystick; }\n\n int get_mouse_wheel_position (void) const { return mouse_wheel_position; }\n\nprivate:\n void handle_key_pressed (SDL_Keysym const& key);\n void handle_key_released(SDL_Keysym const& key);\n\n void handle_mouse_motion (SDL_MouseMotionEvent const& motion);\n void handle_mouse_wheel (SDL_MouseWheelEvent const& wheel );\n void handle_mouse_button_pressed (SDL_MouseButtonEvent const& button);\n void handle_mouse_button_released(SDL_MouseButtonEvent const& button);\n\n void handle_joystick_motion_axis (SDL_JoyAxisEvent const& joystick);\n void handle_joystick_motion_hat (SDL_JoyHatEvent const& joystick);\n void handle_joystick_button_pressed (SDL_JoyButtonEvent const& joystick);\n void handle_joystick_button_released(SDL_JoyButtonEvent const& joystick);\n\n /* private member callbacks */\n void on_left_mouse_button_pressed (void);\n void on_right_mouse_button_pressed (void);\n void on_middle_mouse_button_pressed(void);\n\n void on_left_mouse_button_released (void);\n void on_right_mouse_button_released (void);\n void on_middle_mouse_button_released(void);\n\n void on_mouse_wheel_up (void);\n void on_mouse_wheel_down(void);\n\n\n SDL_Event event;\n Joystick joystick;\n\n /* callbacks */\n struct UserCallbacks {\n Keysym_t key_pressed = nullptr; // think about a general callback register function for all events\n Keysym_t key_released = nullptr;\n Joybutton_t joystick_button_pressed = nullptr;\n Joybutton_t joystick_button_released = nullptr;\n Joyaxis_t joystick_motion_axis = nullptr;\n Joyhat_t joystick_motion_hat = nullptr;\n } user_callback;\n\n //callback_type user_callback_mouse;\n\n Mouse_Click_Event mouse_button_left;\n Mouse_Click_Event mouse_button_right;\n Mouse_Click_Event mouse_button_middle;\n\n int mouse_position_x;\n int mouse_position_y;\n int mouse_wheel_position;\n int mouse_wheel_position_last;\n\n};\n\nvoid on_button_pressed_ESCAPE (void);\nvoid on_button_pressed_SPACE (void);\nvoid on_button_pressed_BACKSPACE(void);\nvoid on_button_pressed_RETURN (void);\nvoid quit(void);\n\n\n#endif /* EVENT_MANAGER_H */\n" }, { "alpha_fraction": 0.5305282473564148, "alphanum_fraction": 0.567230761051178, "avg_line_length": 27.21290397644043, "blob_id": "873270635eaca42de3d871c02d66e8ccb0fc9421", "content_id": "9d393fd222a988f9458f5f0f5160445c7b3a6208", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8746, "license_type": "no_license", "max_line_length": 106, "num_lines": 310, "path": "/src/tests/main.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#define CATCH_CONFIG_MAIN\n#include <tests/catch.hpp>\n#include <common/modules.h>\n/* Framework tests\n * Di, 8.November 2016\n * Hillary oder Donald? */\n\nconst double tolerance = 0.0001; // DO NOT CHANGE!\n\n\n#include <common/incremental_average.h>\nTEST_CASE( \"Incremental Average\", \"[math]\" ) {\n incremental_average inc;\n\n REQUIRE( inc.get_num_samples() == 0 );\n\n inc.sample(-0.01337);\n\n REQUIRE( inc.get() == -0.01337 );\n REQUIRE( inc.get_num_samples() == 1 );\n\n inc.sample(-0.01337);\n\n REQUIRE( inc.get() == -0.01337 );\n REQUIRE( inc.get_num_samples() == 2 );\n\n inc.reset();\n for (unsigned i = 0; i < 10; ++i)\n inc.sample(1.0*i);\n\n REQUIRE( inc.get_num_samples() == 10 );\n REQUIRE( inc.get() == 4.5 );\n\n inc.reset();\n REQUIRE( inc.get_num_samples() == 0 );\n\n double sum = .0;\n for (unsigned int i = 0; i < 10000; ++i) {\n double r = random_value(0.0, 23.42);\n sum += r;\n inc.sample(r);\n }\n sum /= 10000;\n REQUIRE( close(inc.get(), sum, tolerance) );\n}\n\n#include <common/modules.h>\nTEST_CASE( \"unwrap\", \"[math]\" ) {\n\n /* Test of wrap2 and unwrap */\n double step = M_PI_4;\n double angle = -4*M_PI;\n double unwrapped = angle;\n\n for (unsigned int i = 0; i < 32; ++i)\n {\n double mod_angle = wrap2(angle);\n REQUIRE( close(mod_angle, wrap(angle), tolerance) ); // check 2nd version\n unwrapped = unwrap(mod_angle, unwrapped);\n REQUIRE( close(angle, unwrapped, tolerance) );\n angle += step;\n }\n for (unsigned int i = 0; i < 32; ++i)\n {\n double mod_angle = wrap2(angle);\n REQUIRE( close(mod_angle, wrap(angle), tolerance) ); // check 2nd version\n unwrapped = unwrap(mod_angle, unwrapped);\n REQUIRE( close(angle, unwrapped, tolerance) );\n angle -= step;\n }\n}\n\n#include <evolution/pool_strategy.h>\nTEST_CASE( \"biased_random_index\", \"[math]\") {\n srand(2342);\n /* test case for biased random index */\n double selection_bias = 1.0;\n unsigned N = 7;\n std::vector<unsigned> bins(N,0);\n std::size_t total = 50000;\n\n for (unsigned i = 0; i < total; ++i){\n unsigned k = biased_random_index_inv(N, selection_bias);\n REQUIRE( k < bins.size() );\n ++bins[k];\n }\n\n auto draw_bar = [](unsigned len) {\n std::string foo{};\n for (unsigned i = 0; i < len;++i) foo.append(\"=\");\n return foo;\n };\n\n unsigned counter = total;\n for (unsigned p = 0; p < bins.size(); ++p) {\n dbg_msg(\"%2u: %5u %5.2f %s\", p, bins[p], 100.0*bins[p]/total, draw_bar(50*bins[p]/total).c_str());\n REQUIRE( bins[p] < counter );\n counter = bins[p];\n }\n srand((unsigned) time(0));\n}\n\n\nTEST_CASE( \"random_index\", \"[math]\") {\n srand((unsigned) time(0));\n\n /* check that random index does not overshoot range */\n REQUIRE( random_index(0) == 0 );\n unsigned counter = 0;\n for (unsigned i = 1; i < 1337; ++i) {\n unsigned rnd_idx = random_index(i);\n if (rnd_idx >= i) ++counter;\n }\n REQUIRE( counter == 0);\n\n /* weakly check uniform distribution */\n const unsigned num_bins = 13;\n std::vector<unsigned> bins(num_bins);\n for (unsigned i = 0; i < 13000; ++i) {\n ++bins.at(random_index(num_bins));\n }\n\n for (auto& b : bins) {\n REQUIRE( in_range(b, 900u, 1100u));\n dbg_msg(\"%u\", b);\n }\n}\n\n\n#include <common/backed.h>\nTEST_CASE( \"backed value\", \"[common]\") {\n\n common::backed_t<int> value_unsigned;\n REQUIRE( value_unsigned.get() == 0 );\n REQUIRE( value_unsigned.get_backed() == 0 );\n value_unsigned = 44;\n value_unsigned = 50;\n value_unsigned += 5;\n REQUIRE( value_unsigned.get() == 55 );\n REQUIRE( value_unsigned.get_backed() == 0 );\n value_unsigned.transfer();\n REQUIRE( value_unsigned.get_backed() == 55 );\n value_unsigned.reset();\n REQUIRE( value_unsigned.get() == 0 );\n REQUIRE( value_unsigned.get_backed() == 0 );\n\n\n common::backed_t<bool> value_bool(true);\n REQUIRE( value_bool.get() == true );\n REQUIRE( value_bool.get_backed() == false );\n value_bool = true;\n REQUIRE( value_bool.get() == true );\n REQUIRE( value_bool.get_backed() == false );\n value_bool.transfer();\n REQUIRE( value_bool.get_backed() == true );\n value_bool.reset();\n REQUIRE( value_bool.get() == false );\n REQUIRE( value_bool.get_backed() == false );\n\n common::backed_t<double> value_double(.6, .9);\n REQUIRE( value_double.get() == .6 );\n REQUIRE( value_double.get_backed() == .9 );\n value_double = .3;\n value_double = .38;\n value_double += .02;\n REQUIRE( value_double.get() == .4 );\n REQUIRE( value_double.get_backed() == .9 );\n value_double.transfer();\n REQUIRE( value_double.get_backed() == .4 );\n value_double.reset();\n REQUIRE( value_double.get() == .0 );\n REQUIRE( value_double.get_backed() == .0 );\n\n}\n\nTEST_CASE( \"delayed values\", \"[common]\") {\n\n common::delayed_t<int> value_unsigned(0,1);\n REQUIRE( value_unsigned.get() == 0 );\n REQUIRE( value_unsigned.get_delayed() == 0 );\n value_unsigned = 44;\n value_unsigned = 50;\n value_unsigned += 5;\n REQUIRE( value_unsigned.get() == 55 );\n REQUIRE( value_unsigned.get_delayed() == 0 );\n value_unsigned.transfer();\n REQUIRE( value_unsigned.get_delayed() == 55 );\n value_unsigned.reset();\n REQUIRE( value_unsigned.get() == 0 );\n REQUIRE( value_unsigned.get_delayed() == 0 );\n\n\n common::delayed_t<bool> value_bool(true, 1);\n REQUIRE( value_bool.get() == true );\n REQUIRE( value_bool.get_delayed() == true );\n value_bool = false;\n REQUIRE( value_bool.get() == false );\n REQUIRE( value_bool.get_delayed() == true );\n value_bool.transfer();\n REQUIRE( value_bool.get_delayed() == false );\n value_bool = true;\n value_bool.transfer();\n REQUIRE( value_bool.get_delayed() == true );\n value_bool.reset();\n REQUIRE( value_bool.get() == false );\n REQUIRE( value_bool.get_delayed() == false );\n\n\n common::delayed_t<double> value_double(.001, 3);\n REQUIRE( value_double.get() == .001 );\n value_double = .654;\n REQUIRE( value_double.get() == .654 );\n REQUIRE( value_double.get_delayed() == .001 );\n value_double.transfer();\n\n value_double = .321;\n value_double += 1.0;\n REQUIRE( value_double.get() == 1.321 );\n REQUIRE( value_double.get_delayed() == .001 );\n value_double.transfer();\n\n //NO automatic reset.\n REQUIRE( value_double.get() == 1.321 );\n\n\n REQUIRE( value_double.get_delayed() == .001 );\n\n value_double = .1337;\n value_double.transfer();\n\n REQUIRE( value_double.get_delayed() == .654 );\n\n value_double = .2342;\n value_double.transfer();\n REQUIRE( value_double.get_delayed() == 1.321 );\n\n value_double.transfer();\n REQUIRE( value_double.get_delayed() == .1337 );\n\n value_double.transfer();\n REQUIRE( value_double.get_delayed() == .2342 );\n\n value_double.transfer();\n\n value_double = 3.1415;\n REQUIRE( value_double.get_delayed() != .0 );\n value_double.reset();\n REQUIRE( value_double.get() == .0 );\n REQUIRE( value_double.get_delayed() == .0 );\n}\n\nTEST_CASE( \"zeroing vectors\", \"[modules]\") {\n std::vector<double> vect_d = {1.1,2.2,3.3,4.4};\n std::vector<double> zero_d = { .0, .0, .0, .0};\n REQUIRE( vect_d.size() == 4);\n zero(vect_d);\n REQUIRE( vect_d.size() == 4);\n REQUIRE( (vect_d == zero_d) );\n\n std::vector<int> vect_i = {-1,+2,-3};\n std::vector<int> zero_i = { 0, 0, 0};\n REQUIRE( vect_i.size() == 3);\n zero(vect_i);\n REQUIRE( vect_i.size() == 3);\n REQUIRE( (vect_i == zero_i) );\n\n std::vector<bool> vect_b = { true, false};\n std::vector<bool> zero_b = {false, false};\n REQUIRE( vect_b.size() == 2);\n zero(vect_b);\n REQUIRE( vect_b.size() == 2);\n REQUIRE( (vect_b == zero_b) );\n}\n\n#include <common/integrator.h>\nTEST_CASE( \"integrator tests\", \"[integrator]\") {\n Integrator inc;\n\n REQUIRE( inc.get_number_of_samples() == 0 );\n\n inc.add(-0.01337);\n\n REQUIRE( inc.get_avg_value() == -0.01337 );\n REQUIRE( inc.get_number_of_samples() == 1 );\n\n inc.add(-0.01337);\n\n REQUIRE( inc.get_avg_value() == -0.01337 );\n REQUIRE( inc.get_number_of_samples() == 2 );\n\n inc.reset();\n for (unsigned i = 0; i < 10; ++i)\n inc.add(1.0*i);\n\n REQUIRE( inc.get_number_of_samples() == 10 );\n REQUIRE( inc.get_avg_value() == 4.5 );\n\n inc.reset();\n REQUIRE( inc.get_number_of_samples() == 0 );\n REQUIRE( inc.get_avg_value() == .0 );\n\n double sum = .0;\n for (unsigned int i = 0; i < 10000; ++i) {\n double r = random_value(0.0, 23.42);\n sum += r;\n inc.add(r);\n }\n sum /= 10000;\n REQUIRE( close(inc.get_avg_value(), sum, tolerance) );\n}\n" }, { "alpha_fraction": 0.6304271221160889, "alphanum_fraction": 0.6540769338607788, "avg_line_length": 26.77450942993164, "blob_id": "217efd39be354a6c81986ff476c29e78c77a8c7d", "content_id": "3d8592923e25b0d3af255f809f3dacdbe7cd77e8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2833, "license_type": "no_license", "max_line_length": 107, "num_lines": 102, "path": "/src/common/gui.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* gui.cpp */\n#include \"./gui.h\"\n\nfloat progress_val1;\nfloat progress_val2;\n\nextern GlobalFlag do_pause;\n\n\ngboolean\nupdate_progressbar1(gpointer data)\n{\n gtk_progress_bar_set_fraction(GTK_PROGRESS_BAR(data), progress_val1);\n return 1;\n}\n\ngboolean\nupdate_progressbar2(gpointer data)\n{\n gtk_progress_bar_set_fraction(GTK_PROGRESS_BAR(data), progress_val2);\n return 1;\n}\n\nint\nthread_gtk_main(void)\n{\n gtk_main();\n return 0;\n}\n\nvoid\non_button_start_clicked(GtkObject *object, gpointer /*user_data*/)\n{\n do_pause.toggle();\n\n if (do_pause.status()) {\n gtk_button_set_label(GTK_BUTTON(object), \"Start\");\n sts_msg(\"Paused.\");\n }\n else {\n gtk_button_set_label(GTK_BUTTON(object), \"Pause\");\n sts_msg(\"Continuing.\");\n }\n}\n\nGTK_gui::GTK_gui(void)\n: num_vscale(10)\n, init_result(gtk_init_check(0, NULL))\n, window(gtk_window_new(GTK_WINDOW_TOPLEVEL))\n, table(gtk_table_new(6, num_vscale, TRUE))\n, label(gtk_label_new(\"Progress Bar Example\"))\n, progressbar1(gtk_progress_bar_new())\n, progressbar2(gtk_progress_bar_new())\n, button_start(gtk_button_new_with_label(\"Start\"))\n, multiscale()\n, main_gtk()\n{\n\n multiscale.reserve(num_vscale);\n for (unsigned int i = 0; i < num_vscale; ++i)\n multiscale.emplace_back(0.0, 100.0, 50.0, 0.1);\n\n sts_msg(\"Starting GTK window.\");\n if (!init_result) {\n wrn_msg(\"Starting without GTK-Window.\");\n return;\n }\n\n setlocale(LC_NUMERIC, \"C\");\n\n gtk_container_add(GTK_CONTAINER(window), table);\n gtk_table_set_row_spacing(GTK_TABLE(table), 3, 100);\n //gtk_table_set_row_spacings(GTK_TABLE(table), 100);\n\n /* set positions */\n gtk_table_attach_defaults(GTK_TABLE(table), label , 0, num_vscale, 0, 1);\n gtk_table_attach_defaults(GTK_TABLE(table), progressbar1, 0, num_vscale, 1, 2);\n gtk_table_attach_defaults(GTK_TABLE(table), progressbar2, 0, num_vscale, 2, 3);\n\n for (unsigned int i = 0; i < num_vscale; ++i)\n gtk_table_attach_defaults(GTK_TABLE(table), multiscale[i].get(), 0+i, 1+i, 3, 5);\n\n gtk_table_attach_defaults(GTK_TABLE(table), button_start, 0, 1, 5, 6);\n\n /* Set the timeout to handle automatic updating of the progress bar */\n g_timeout_add(1000, update_progressbar1, progressbar1);\n g_timeout_add(500, update_progressbar2, progressbar2);\n\n /* signals */\n g_signal_connect(GTK_OBJECT (button_start), \"clicked\", GTK_SIGNAL_FUNC(on_button_start_clicked), NULL);\n g_signal_connect(window, \"delete_event\", G_CALLBACK(gtk_window_iconify), NULL); // minimize window\n\n /* show */\n gtk_widget_show(button_start);\n gtk_widget_show(progressbar1);\n gtk_widget_show(progressbar2);\n gtk_widget_show(label);\n gtk_widget_show(table);\n gtk_widget_show(window);\n\n main_gtk = std::unique_ptr<std::thread>(new std::thread(thread_gtk_main));\n}\n" }, { "alpha_fraction": 0.5736607313156128, "alphanum_fraction": 0.5885416865348816, "avg_line_length": 29.534090042114258, "blob_id": "1835243a08e664501e4e49f4f6ec114a3f362f86", "content_id": "8d3f97df90f56c492b9984fc3ed13ea63029045f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2688, "license_type": "no_license", "max_line_length": 109, "num_lines": 88, "path": "/src/tests/homeokinesis_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <common/modules.h>\n#include <learning/homeokinesis.h>\n#include <controller/pid_control.hpp>\n\nnamespace local_tests {\n\nnamespace homeokinesis_tests {\n\nstruct Test_Sensor_Space : public sensor_vector {\n Test_Sensor_Space(const robots::Jointvector_t& joints)\n : sensor_vector(2*joints.size() + 1)\n {\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_ang\", [&j](){ return j.s_ang; });\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_vel\", [&j](){ return j.s_vel; });\n\n sensors.emplace_back(\"bias\", [&](){ return 0.01; });\n assert(sensors.size() == 2*joints.size() + 1);\n }\n\n};\n\n\n\nTEST_CASE( \"homeokinetic controller construction + basic stuff\" , \"[homeokinesis]\")\n{\n srand(time(0)); // set random seed\n\n Test_Robot robot(5,2);\n robot.set_random_inputs(); // random initialize sensors\n\n Test_Sensor_Space sensors{robot.get_joints()};\n sensors.execute_cycle();\n\n std::vector<supreme::pid_control> pid(robot.get_joints().size());\n\n double random_range = 0.1;\n VectorN ext = {0,0,0};\n learning::Homeokinetic_Control homeoctrl(sensors, robot.get_joints().size(), random_range, random_range);\n\n REQUIRE( homeoctrl.get_curr_state().size() == sensors.size() );\n REQUIRE( homeoctrl.get_next_state().size() == sensors.size() );\n\n// REQUIRE( homeoctrl.control_enabled == false );\n\n for (std::size_t i = 0; i < sensors.size(); ++i)\n REQUIRE ( sensors[i] != 0.0 );\n\n auto const& x0 = homeoctrl.get_curr_state();\n for (std::size_t i = 0; i < x0.size(); ++i)\n REQUIRE ( x0[i] != 0.0 );\n\n // check predictor and controller weights are random initialized\n auto const& weights = homeoctrl.pred.get_weights();\n double sum = .0;\n int diff = 0;\n for (std::size_t i = 0; i < weights.size(); ++i)\n for (std::size_t j = 0; j < weights[i].size(); ++j) {\n diff += ( weights[i][j] != .0 )? 0 : 1;\n sum += weights[i][j];\n }\n REQUIRE( diff == 0 );\n const double max_range = 0.5 * random_range * weights.size()*weights[0].size();\n dbg_msg(\"Max rand: %e < %e\", std::abs(sum), max_range);\n REQUIRE( std::abs(sum) <= max_range ); // check small\n REQUIRE( std::abs(sum) != 0. ); // but not zero\n\n\n // check motor outputs are NON-ZERO\n sensors.execute_cycle();\n homeoctrl.execute_cycle(sensors);\n sts_add(\"[\");\n for (auto const& e: homeoctrl.get_motor_data()) {\n sts_add(\"%+1.2f\", e);\n REQUIRE ( e != 0.0 );\n }\n sts_msg(\"]\");\n\n}\n\n\n} /* homeokinesis_tests */\n\n} /* local_tests */\n\n" }, { "alpha_fraction": 0.6531281471252441, "alphanum_fraction": 0.6536327004432678, "avg_line_length": 35.009090423583984, "blob_id": "53ac8724d75de54175c08ee3d28630df4c1ad8a4", "content_id": "e5e438075df5890680f0a88a000028ff0d69e5bc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3964, "license_type": "no_license", "max_line_length": 112, "num_lines": 110, "path": "/src/learning/bimodel_predictor.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BIMODEL_PREDICTOR_H\n#define BIMODEL_PREDICTOR_H\n\n#include <control/sensorspace.h>\n#include <learning/predictor.h>\n#include <learning/forward_inverse_model.hpp>\n\nnamespace learning {\n\n/** TODO implement Adam! */\n\nclass BiModel_Predictor : public Predictor_Base\n{\n typedef NeuralModel<learning::TanhTransfer<>> NeuralModel_t;\n typedef learning::BidirectionalModel<NeuralModel_t,NeuralModel_t> BidirectionalModel_t;\n\n BidirectionalModel_t mod;\n\n /* inputs from external models */\n sensor_input_interface const& ctrl_context; // base to make prediction from\n model::vector_t& gradient; // vector to connect ext. back-propagation error information\n\n BiModel_Predictor(const BiModel_Predictor& other) = delete;\n BiModel_Predictor& operator=(const BiModel_Predictor& other) = delete;\n\n double regularization_rate;\n\npublic:\n\n BiModel_Predictor( sensor_input_interface const& input\n , sensor_input_interface const& ctrl_context\n , model::vector_t& gradient\n , double learning_rate\n , double random_weight_range\n , double regularization_rate\n )\n : Predictor_Base(input, learning_rate, random_weight_range, /*experience replay OFF */ 1)\n , mod(ctrl_context.size(), input.size(), random_weight_range)\n , ctrl_context(ctrl_context)\n , gradient(gradient)\n , regularization_rate(regularization_rate)\n {\n assert(gradient.size() == ctrl_context.size());\n }\n\n virtual ~BiModel_Predictor() = default;\n\n void copy(Predictor_Base const& other) override {\n Predictor_Base::operator=(other); // copy base members\n BiModel_Predictor const& rhs = dynamic_cast<BiModel_Predictor const&>(other);\n mod = rhs.mod;\n };\n\n Predictor_Base::vector_t const& get_prediction (void) const override { return mod.get_forward_result(); }\n Predictor_Base::vector_t const& get_reconstruction(void) const { return mod.get_inverse_result(); }\n\n double predict(void) override {\n mod.propagate_forward(ctrl_context);\n mod.propagate_inverse(input);\n\n return calculate_prediction_error();\n };\n\n double verify(void) override {\n mod.propagate_forward(ctrl_context);\n return calculate_prediction_error();\n }\n\n void initialize_randomized(void) override {\n mod.randomize_weights(random_weight_range);\n prediction_error = predictor_constants::error_min;\n /*Note: experience buffer not randomized here. because it is not used */\n };\n\n void initialize_from_input(void) override { assert(false && \"one shot learning not supported.\"); }\n\n void draw(void) const { assert(false && \"not implemented yet.\"); }\n\n double get_prediction_error(void) const { return mod.get_forward_error(); } //OK\n double get_reconstruction_error(void) const { return mod.get_inverse_error(); } //OK\n\n BidirectionalModel_t& get_model(void) { return mod; }\n\n model::vector_t get_gradient(void) const { return mod.get_backprop_gradient(); }\n\n vector_t const& get_weights(void) const override { assert(false); return dummy; /*not implemented*/ }\n vector_t & set_weights(void) override { assert(false); return dummy; /*not implemented*/ }\n\nprivate:\n\n void learn_from_input_sample(void) override\n {\n assert( gradient.size() == ctrl_context.size() );\n for (unsigned i = 0; i < gradient.size(); ++i)\n gradient[i] += ctrl_context[i]; /* add the predictor's back-propagated\n error information to the training target */\n mod.adapt(gradient, input, learning_rate, regularization_rate);\n zero(gradient); // clear, mark as used\n\n //TODO check if needed mod.constrain_weights();\n }\n\n\n VectorN dummy = {}; // remove when implementing get_weights\n\n};\n\n} /* namespace learning */\n\n#endif /* BIMODEL_PREDICTOR_H */\n\n\n\n" }, { "alpha_fraction": 0.5313807725906372, "alphanum_fraction": 0.5499103665351868, "avg_line_length": 23.970149993896484, "blob_id": "15f70d3b1cf241fff4bbc6e5c7f8f5d67969dee5", "content_id": "8e6fb97bda2dbf1f2d86e4fd0e9396eae13b3a5a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1673, "license_type": "no_license", "max_line_length": 116, "num_lines": 67, "path": "/src/robots/joint.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef JOINT_H\n#define JOINT_H\n\n#include <string>\n#include <common/log_messages.h>\n#include <common/backed.h>\n\nnamespace robots {\n\nenum Joint_Type {Joint_Type_Normal, Joint_Type_Symmetric};\n\n//TODO: template <typename Float_t = double>\nclass Joint_Model\n{\npublic:\n Joint_Model( unsigned int joint_id\n , Joint_Type type\n , unsigned int symmetric_joint\n , const std::string& name\n , double limit_lo\n , double limit_hi\n , double default_pos\n )\n : joint_id(joint_id)\n , s_ang(.0)\n , s_vel(.0)\n , s_cur(.0)\n , s_vol(.0)\n , s_tmp(.0)\n , motor(.0)\n , type(type)\n , symmetric_joint(symmetric_joint)\n , name(name)\n , limit_lo(limit_lo)\n , limit_hi(limit_hi)\n , default_pos(default_pos)\n {\n sts_add(\"J=%2u '%16s'\", joint_id, name.c_str());\n sts_add(\"L=(%+1.2f, %+1.2f, %+1.2f) (%+3.1f, %+3.1f, %+3.1f)\", limit_lo , limit_hi , default_pos\n , limit_lo*180, limit_hi*180, default_pos*180);\n sts_msg(\"T=%u, S=%2u\\n\", type, symmetric_joint);\n }\n\n const unsigned int joint_id;\n double s_ang;\n double s_vel;\n double s_cur;\n double s_vol;\n double s_tmp;\n common::backed_t<double> motor;\n\n Joint_Type type;\n unsigned int symmetric_joint;\n const std::string name;\n\n const double limit_lo;\n const double limit_hi;\n const double default_pos;\n\n bool is_symmetric() const { return type == Joint_Type_Symmetric; }\n};\n\ntypedef std::vector<Joint_Model> Jointvector_t;\n\n} /* namespace robots */\n\n#endif /* JOINT_H */\n" }, { "alpha_fraction": 0.6115702390670776, "alphanum_fraction": 0.6264463067054749, "avg_line_length": 15.80555534362793, "blob_id": "7251b0643e64cac6dd38f0bd4db5ed11048769ce", "content_id": "787f5a19c6e2d3a4fca8fe59039f30f7b880b92c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 605, "license_type": "no_license", "max_line_length": 46, "num_lines": 36, "path": "/src/draw/network2D.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* network2D.h */\n\n#ifndef NETWORK2D_H\n#define NETWORK2D_H\n\n#include \"axes.h\"\n\n\nclass network2D\n{\n private:\n float **X; // position vector\n float *size;\n /* size == 0, kein Knoten */\n unsigned char **edges;\n\n int pointer;\n int special_node;\n int activated_node;\n int N;\n float px, py;\n float width, height;\n GLubyte color[4];\n axes *A;\n\n public:\n network2D(int, axes*, const GLubyte[4]);\n void draw(void);\n\n void update_node(int, float, float, float);\n void update_edge(int, int, unsigned char);\n void special(int);\n void activated(int);\n};\n\n#endif /*NETWORK2D_H*/\n" }, { "alpha_fraction": 0.6466346383094788, "alphanum_fraction": 0.6520432829856873, "avg_line_length": 23.441177368164062, "blob_id": "c3f672f3fb57125a63ecb7cfd12f47ce22c15d50", "content_id": "2a449fab0509325b3ee7ecf381c8665b00d4518b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1664, "license_type": "no_license", "max_line_length": 83, "num_lines": 68, "path": "/src/common/robot_conf.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef ROBOT_CONF_H\r\n#define ROBOT_CONF_H\r\n\n#include <cstdio>\n#include <cstdlib>\n#include <vector>\n#include <assert.h>\n\n#include <robots/joint.h>\n#include <robots/accel.h>\n#include <common/socket_client.h>\n#include <basic/vector3.h>\n\n\nstruct Body_Segment /**TODO move to separate header */\n{\n Body_Segment(const char* name) : position(), velocity(), force(), name(name) {}\n Vector3 position;\n Vector3 velocity;\n Vector3 force;\n std::string name;\n};\ntypedef std::vector<Body_Segment> Bodyvector_t;\n\nclass Robot_Configuration\n{\n\npublic:\n unsigned number_of_joints = 0;\n unsigned number_of_accels = 0;\n unsigned number_of_bodies = 0;\n\n robots::Jointvector_t joints;\n robots::Accelvector_t accels;\n Bodyvector_t bodies;\n\n bool const interlaced;\n\n Robot_Configuration(std::string const& server_message, bool interlaced)\n : joints()\n , accels()\n , bodies()\n , interlaced(interlaced)\n {\n read_robot_info(server_message);\n }\n\n void delete_symmetry_information(void) { //TODO get rid of that method\n sts_msg(\"Deleting symmetry information.\");\n for (unsigned int i = 0; i < joints.size(); ++i) {\n joints[i].type = robots::Joint_Type_Normal;\n joints[i].symmetric_joint = i; // delete reference to other joints\n }\n assert(get_number_of_symmetric_joints() == 0);\n }\n\n unsigned int get_number_of_symmetric_joints(void) const;\n\n\n void read_robot_info(std::string const& server_message);\n\nprivate:\n const char* read_joints(const char* msg, int* offset);\n const char* read_bodies(const char* msg, int* offset);\n};\n\n\n#endif // ROBOT_CONF_H\n" }, { "alpha_fraction": 0.4806746244430542, "alphanum_fraction": 0.48629656434059143, "avg_line_length": 26.901960372924805, "blob_id": "c2889845469bfe8fe35c71947dcc0225c7e9a516", "content_id": "4793e2ceb12ce01d7a371b20796444289e267efe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1423, "license_type": "no_license", "max_line_length": 68, "num_lines": 51, "path": "/src/robots/simloid_log.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/*---------------------------------+\n | Matthias Kubisch |\n | [email protected] |\n | July 2017 |\n +---------------------------------*/\n\n#ifndef SIMLOID_LOG_H_INCLUDED\n#define SIMLOID_LOG_H_INCLUDED\n\n#include <sstream>\n#include <robots/simloid.h>\n\nnamespace robots {\n\nclass Simloid_Log : public Loggable<2048> {\n\n const Simloid& simloid;\n\npublic:\n Simloid_Log(const Simloid& simloid) : simloid(simloid) {}\n\n const char* log()\n {\n for (auto const& j : simloid.get_joints())\n append(\"%+e %+e %+e \", j.s_ang, j.s_vel, j.motor.get());\n\n for (auto const& a : simloid.get_accels())\n append(\"%+e %+e %+e \", a.v.x, a.v.y, a.v.z);\n\n append( \"%+e %+e %+e %+e %+e %+e %+e %+e %+e %+e %+e\"\n , simloid.get_avg_rotation_inf_ang()\n , simloid.get_avg_rotational_speed()\n , simloid.get_avg_velocity_forward()\n , simloid.get_avg_velocity_left()\n , simloid.get_normalized_mechanical_power()\n /** global coordinates */\n , simloid.get_avg_position().x\n , simloid.get_avg_position().y\n , simloid.get_avg_position().z\n , simloid.get_avg_velocity().x\n , simloid.get_avg_velocity().y\n , simloid.get_avg_velocity().z\n );\n\n return done();\n }\n};\n\n} // namespace robots\n\n#endif // SIMLOID_LOG_H_INCLUDED\n" }, { "alpha_fraction": 0.49117422103881836, "alphanum_fraction": 0.5495011806488037, "avg_line_length": 30.780487060546875, "blob_id": "249afbf67f048254f3da6045d78cdcdb2081b1d0", "content_id": "1d7d8996a7c303bebbd672056997100f17cdaac5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1303, "license_type": "no_license", "max_line_length": 98, "num_lines": 41, "path": "/src/common/misc.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include \"misc.h\"\n\n\nstd::string //TODO make stepsize as parameter\nget_time_from_cycle_counter(unsigned long long cycles)\n{\n char time_str[1024];\n unsigned int days = cycles / (100 * 3600 * 24);\n unsigned int hours = cycles / (100 * 3600) % 24;\n unsigned int minutes = (cycles / (100 * 60)) % 60;\n unsigned int seconds = (cycles / 100) % 60;\n unsigned int hsecs = (cycles % 100);\n\n if (days > 0)\n snprintf(time_str, 1024, \"%3u:%02u:%02u:%02u:%02u\", days, hours, minutes, seconds, hsecs);\n else\n snprintf(time_str, 1024, \"%02u:%02u:%02u:%02u\", hours, minutes, seconds, hsecs);\n return std::string(time_str);\n}\n\nstd::string to_str(const std::vector<double>& vect)\n{\n char buf[256] = \"\";\n int len = 0;\n if (vect.size() == 0) return buf;\n int n = snprintf(buf, sizeof(buf), \"%+1.2f\", vect[0]);\n for (std::size_t i = 1; i < vect.size(); ++i) {\n if (n >= 0 && n < ((int)sizeof(buf) - len)) {\n len += n;\n n = snprintf(buf + len, sizeof(buf) - len, \" %+1.2f\", vect[i]);\n } else break;\n }\n return buf;\n}\n\ntemplate <typename vector_t> void\nprint(const vector_t& content) {\n for (std::size_t i = 0; i < content.size(); ++i)\n std::cout << content[i] << \" \";\n std::cout << std::endl;\n}\n" }, { "alpha_fraction": 0.5112841725349426, "alphanum_fraction": 0.5225023031234741, "avg_line_length": 33.598175048828125, "blob_id": "75c820afcf4dc988b2584e9eb7f9fadacbb756d6", "content_id": "d21bf2ad10d58fa3990b6d228d762014ade7f3d9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 7577, "license_type": "no_license", "max_line_length": 121, "num_lines": 219, "path": "/src/learning/homeokinesis.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef HOMEOKINESIS_H\n#define HOMEOKINESIS_H\n\n#include <robots/robot.h>\n#include <control/sensorspace.h>\n#include <learning/forward_inverse_model.hpp>\n\nnamespace learning {\n\nnamespace homeokinetic_constants {\n const float learning_rate_pred = 0.005; //both values 0.001..0.01 is very nice;\n const float learning_rate_ctrl = 0.005;\n\n const float regularization_rate = 0.0;\n}\n\n/**TODO description\n\n + using L1 regularization_rate to prevent overfitting and encourage sparsity\n*/\n\nclass Homeokinetic_Control {\npublic:\n typedef NeuralModel<TanhTransfer<>> Forward_t;\n typedef BidirectionalModel<Forward_t,Forward_t> PredictionModel_t;\n typedef BidirectionalModel<Forward_t,Forward_t> ControllerModel_t;\n typedef model::vector_t Vector_t;\n\n Vector_t x0, x1, y0, y1;\n Vector_t X0, X1, Y0;\n\n ControllerModel_t ctrl;\n PredictionModel_t pred;\n\n float learning_rate_pred;\n float learning_rate_ctrl;\n float regularization_rate;\n\n Vector_t gradient; // error gradient signal, used to transport error information between Models\n\n struct Option_t {\n bool prediction_learning = true;\n bool controller_learning = true;\n bool verbose = true;\n } option = {};\n\n\n Homeokinetic_Control( sensor_input_interface const& input\n , std::size_t number_of_joints\n , float init_weight_range\n , unsigned context = 0\n , float learning_rate_pred = homeokinetic_constants::learning_rate_pred\n , float learning_rate_ctrl = homeokinetic_constants::learning_rate_ctrl\n , float regularization_rate = homeokinetic_constants::regularization_rate\n )\n : x0(input.size())\n , x1(input.size())\n , y0(number_of_joints + context)\n , y1(number_of_joints + context)\n , X0(input.size())\n , X1(input.size())\n , Y0(number_of_joints + context)\n , ctrl(/*in=*/x0.size(), /*out=*/y0.size(), init_weight_range)\n , pred(/*in=*/y0.size(), /*out=*/x1.size(), init_weight_range)\n , learning_rate_pred(learning_rate_pred)\n , learning_rate_ctrl(learning_rate_ctrl)\n , regularization_rate(regularization_rate)\n , gradient(number_of_joints + context)\n {\n sts_add(\"creating homeokinetic controller with\\n%u joints and \\n%u context neurons.\", number_of_joints, context);\n sts_msg(\"+ PRED learning rate = %e\", learning_rate_pred);\n sts_msg(\"+ CTRL Learning rate = %e\", learning_rate_ctrl);\n sts_msg(\"+ Normalization rate = %e\", regularization_rate);\n read_sensor_data(x0, input);\n control();\n }\n\n\n void execute_cycle(sensor_input_interface const& input)\n {\n /*--------------------------------------------+\n | Notation: |\n | lower case real sensor/motor data x, y |\n | upper case prediction/reconstruction X, Y |\n | time indices: x0 = x(t+0) = x(t) |\n | x1 = x(t+1) |\n +--------------------------------------------*/\n\n /* original order changed, because robot.update must be executed between steps 1. + 2. */\n\n /** 2.) Prediction */\n read_next_state(input);\n predict();\n\n /** 3.) Reconstruction */\n reconstruct();\n\n /** 4.) Adaption/Learning */\n adapt_prediction();\n adapt_controller();\n\n /** 0.) time-step border */\n backup_state();\n\n /** 1.) Control */\n control();\n\n if (option.verbose) {\n sts_add(\"pe=%+.3f, tle=%.3f\", pred.get_forward_error(), ctrl.get_inverse_error());\n print_vector(y0,\"y\");\n }\n }\n\n\n /*----------------------------------+\n | time-step border: |\n | copy sensory state x(t) = x(t+1) |\n +----------------------------------*/\n void backup_state(void) {\n x0 = x1;\n }\n\n /*------------------------------------+\n | create control command y(t+1) from |\n | current sensory state x(t) |\n +------------------------------------*/\n void control(void) {\n y1 = ctrl.propagate_forward(x0);\n }\n\n /*------------------------------------+\n | read next state x(t+1) from inputs |\n +------------------------------------*/\n template <typename Input_t>\n void read_next_state(Input_t const& input) {\n y0 = y1;\n read_sensor_data(x1, input);\n }\n\n /*--------------------------------------------+\n | make prediction x^(t+1) from x(t) and y(t) |\n +--------------------------------------------*/\n void predict(void) {\n X1 = pred.propagate_forward(y0); // make prediction\n }\n\n /*-------------------------------------------------------+\n | make reconstruction from real next state x(t+1) |\n | to assumed motor commands y^(t), |\n | make reconstruction from assumed motor commands y^(t) |\n | to reconstructed sensor state x^(t) |\n +-------------------------------------------------------*/\n void reconstruct(void) {\n Y0 = pred.propagate_inverse(x1);\n /* X0 = */ ctrl.propagate_inverse(Y0);\n }\n\n /* 4.a) ------------------------------+\n | learn predictive model to map from |\n | current motor commands y(t) |\n | to next sensor state x(t+1) |\n +------------------------------------*/\n void adapt_prediction(void) {\n if (option.prediction_learning) {\n pred.adapt(y0, x1, learning_rate_pred, regularization_rate);\n gradient = pred.get_backprop_gradient(); // get the back-propagated error information\n }\n }\n\n /* 4.b) ---------------------------------------------+\n | learn controller to map from x(t) to y^(t), |\n | the INVERSE learns to reconstruct x(t) from y^(t) |\n +---------------------------------------------------*/\n void adapt_controller(void) {\n if (option.controller_learning) {\n assert( gradient.size() == Y0.size() );\n for (unsigned i = 0; i < gradient.size(); ++i)\n gradient[i] += Y0[i]; // add the predictor's back-propagated error information to the target\n ctrl.adapt(x0, gradient, learning_rate_ctrl, regularization_rate);\n\n zero(gradient);\n }\n }\n\n\n Vector_t const& get_curr_state(void) const { return x0; }\n Vector_t const& get_next_state(void) const { return x1; }\n Vector_t const& get_prediction(void) const { return X1; }\n\n Vector_t const& get_motor_data(void) const { return y1; }\n Vector_t & set_motor_data(void) { return y1; }\n\n void randomize_weights(double range) {\n pred.randomize_weights(range);\n ctrl.randomize_weights(range);\n }\n\n double get_timeloop_error (void) const { return ctrl.get_inverse_error(); }\n double get_prediction_error (void) const { return pred.get_forward_error(); }\n\n double get_control_error (void) const { return ctrl.get_forward_error(); }\n double get_reconstruction_error(void) const { return pred.get_inverse_error(); }\n\nprivate:\n\n // makes a copy of the sensor data\n template <typename Vector_t, typename Input_t>\n void read_sensor_data(Vector_t& vec, Input_t const& input) {\n assert(input.size() == vec.size());\n for (std::size_t i = 0; i < input.size(); ++i)\n vec[i] = input[i];\n }\n\n friend class Homeokinetic_Graphics;\n};\n\n} /* namespace learning */\n\n#endif /* HOMEOKINESIS_H */\n" }, { "alpha_fraction": 0.5760056972503662, "alphanum_fraction": 0.5792096853256226, "avg_line_length": 27.09000015258789, "blob_id": "6779f72438670e4b21617920b51acc0ce4bc8ea6", "content_id": "5509911512b306de0093cd8ff183df058740b145", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2809, "license_type": "no_license", "max_line_length": 111, "num_lines": 100, "path": "/src/common/datalog.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef DATALOG_H_INCLUDED\n#define DATALOG_H_INCLUDED\n\n#include <string>\n#include <common/basic.h>\n#include <common/file_io.h>\n#include <common/log_messages.h>\n#include <common/settings.h>\n\nnamespace constants {\n const std::string logfolder = \"./data/\";\n const std::string logfileext = \".log\";\n}\n\nclass Datalog {\npublic:\n\n Datalog(int argc, char** argv, std::string const& folder = constants::logfolder )\n : filename ( read_string_option(argc, argv, \"--outfile\" , \"-o\", get_timestamped_file_name(folder)) )\n , logfile ( filename )\n , enabled ( read_option_flag (argc, argv, \"--enable_logging\", \"-l\") )\n , incl_video( read_option_flag (argc, argv, \"--include_video\" , \"-i\") )\n {\n sts_msg(\"Created data log file: %s (%s)\", filename.c_str(), enabled ? \"Enabled\":\"Disabled\");\n sts_msg(\"Video will%s be recorded.\", incl_video ? \"\":\" NOT\");\n }\n\n template<typename... Args>\n void log(const char* format, const Args&... args)\n {\n if (!enabled) return;\n\n logfile.append(format, args...);\n }\n\n void toggle_logging(void) {\n enabled = !enabled;\n sts_msg(\"Logging: %s\", enabled ? \"ON\":\"OFF\");\n if (not enabled)\n logfile.flush();\n }\n\n void set_enable(bool en) { enabled = en; }\n\n bool is_enabled(void) const { return enabled; }\n bool is_video_included(void) const { return enabled and incl_video; }\n\n void flush(void) { logfile.flush(); }\n void next (void) { logfile.next(); }\n\nprivate:\n const std::string filename;\n file_io::Logfile logfile;\n bool enabled;\n bool incl_video;\n\n std::string get_timestamped_file_name(const std::string& folder) {\n return basic::make_directory(folder.c_str()) // folder\n + basic::get_timestamp() // name as time stamp\n + constants::logfileext; // extension\n }\n};\n\n\ntemplate <unsigned BufferSize = 1024>\nclass Loggable {\n\n char buffer[BufferSize];\n std::size_t total_bytes = 0;\n\nprotected:\n\n template<typename... Args>\n void append(const char* format, const Args&... args)\n {\n if (total_bytes + 1 < BufferSize) {\n auto n = snprintf(buffer+total_bytes, BufferSize-total_bytes, format, args...);\n if (n < 0) err_msg(__FILE__,__LINE__,\" Encoding error.\");\n if (static_cast<unsigned>(n) < BufferSize-total_bytes) {\n total_bytes += n;\n return;\n }\n }\n err_msg(__FILE__,__LINE__,\" Buffer size of %u too small.\", BufferSize);\n }\n\n const char* done(void) {\n //dbg_msg(\"Bytes written %u\", total_bytes);\n total_bytes = 0;\n return buffer;\n }\n\npublic:\n virtual ~Loggable() {}\n virtual const char* log(void) = 0;\n\n};\n\n\n#endif // DATALOG_H_INCLUDED\n" }, { "alpha_fraction": 0.5406643748283386, "alphanum_fraction": 0.5715922117233276, "avg_line_length": 25.044776916503906, "blob_id": "94f8e4cb0fd44aaf380563c84fd79af6c2f8dae8", "content_id": "e6f9820d3e1eb1255770634d9ef869ae954ecdc9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1746, "license_type": "no_license", "max_line_length": 62, "num_lines": 67, "path": "/src/learning/homeokinesis_gfx.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/*---------------------------------+\n | Matthias Kubisch |\n | [email protected] |\n | August 2020 |\n +---------------------------------*/\n\n#ifndef HOMEOKINESIS_GRAPHICS_H\n#define HOMEOKINESIS_GRAPHICS_H\n\n#include <draw/draw.h>\n#include <draw/axes.h>\n#include <draw/axes3D.h>\n#include <draw/plot1D.h>\n#include <draw/plot2D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n\n#include <learning/homeokinesis.h>\n\nnamespace learning {\n\nclass Homeokinesis_Graphics : public Graphics_Interface\n{\n Homeokinetic_Control const& ctrl;\n\npublic:\n Homeokinesis_Graphics(Homeokinetic_Control const& ctrl)\n : ctrl(ctrl)\n , axes_err(0., -0.5, 0., 2.0, 1.0, 1, \"error\" , 0.001)\n , plot_pre(1000, axes_err, colors::cyan , \"pre\")\n , plot_tle(1000, axes_err, colors::magenta , \"tle\")\n , plot_rec(1000, axes_err, colors::yellow , \"rec\")\n , plot_ctr(1000, axes_err, colors::green , \"ctr\")\n {}\n\n void draw(const pref& /*p*/) const {\n axes_err.draw();\n plot_pre.draw();\n plot_tle.draw();\n plot_rec.draw();\n plot_ctr.draw();\n draw_motor_context();\n }\n\n void draw_motor_context(void) const\n {\n draw::vector_dual(-1,-0.8,0.06,1.0, ctrl.y0, ctrl.Y0);\n }\n\n void execute_cycle(uint64_t /*cycle*/) {\n plot_pre.add_sample(ctrl.get_prediction_error ());\n plot_tle.add_sample(ctrl.get_timeloop_error ());\n plot_rec.add_sample(ctrl.get_reconstruction_error());\n plot_ctr.add_sample(ctrl.get_control_error ());\n }\n\n axes axes_err;\n plot1D plot_pre;\n plot1D plot_tle;\n plot1D plot_rec;\n plot1D plot_ctr;\n};\n\n} /* namespace learning */\n\n#endif /* HOMEOKINESIS_GRAPHICS_H */\n\n" }, { "alpha_fraction": 0.6547368168830872, "alphanum_fraction": 0.6610526442527771, "avg_line_length": 19.65217399597168, "blob_id": "96e8faf0e3de5ff76af87ce9f6d3f74d14b775e9", "content_id": "572f69afa413856ca992e4c3d3e3afe03b747e31", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 475, "license_type": "no_license", "max_line_length": 77, "num_lines": 23, "path": "/src/common/change_limiter.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef CHANGE_LIMITER_H\n#define CHANGE_LIMITER_H\n\nnamespace common {\n\ntemplate <typename T>\nclass ChangeLimiter {\n\tT last_pos, last_vel;\n\tconst T max_value;\npublic:\n\tChangeLimiter(T max_value) : last_pos(), last_vel(), max_value(max_value) {}\n\n\tT step(T current) {\n\t const double vel = current - last_pos;\n\t\tlast_pos = last_pos + 0.5*clip((vel + last_vel)/2, max_value);\n\t last_vel = vel;\n\t\treturn last_pos;\n\t}\n};\n\n} /* namespace common */\n\n#endif // CHANGE_LIMITER_H\n" }, { "alpha_fraction": 0.2917245626449585, "alphanum_fraction": 0.2929152548313141, "avg_line_length": 65.2368392944336, "blob_id": "2d406c1f487c03b61866e9b2a6d6b62bd32cc295", "content_id": "9a64af574cdbeb1bddb58d5ace3663d76171ef96", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5039, "license_type": "no_license", "max_line_length": 142, "num_lines": 76, "path": "/src/common/setup.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SETUPSDL_H\r\n#define SETUPSDL_H\r\n\r\n#include <sys/time.h>\n#include <unistd.h>\n#include <cmath>\n#include <chrono>\n#include <SDL2/SDL.h>\n#include <thread>\n\n#include <common/globalflag.h>\n#include <common/basic.h>\n#include <common/event_manager.h>\n#include <common/stopwatch.h>\n#include <common/application_base.h>\r\n#include <common/visuals.h>\n#include <common/misc.h>\n#include <common/gui.h>\n#include <common/modules.h>\n#include <common/timer.h>\n#include <draw/draw.h>\n#include <external/gl2ps/gl2ps.h>\n\nvoid init_SDL(const bool visuals, const std::size_t window_width, const std::size_t window_height, const std::string& name = \"working title\");\nvoid deinit_SDL(void);\nvoid init_OpenGL(const std::size_t window_width, const std::size_t window_height);\nvoid init_controls(void);\nvoid signal_terminate_handler(int signum);\nint process_application(void *data);\n\nvoid ui_main_loop(GlobalFlag& do_quit, const GlobalFlag& do_drawing, Event_Manager& em, const Application_Base& app);\nvoid draw_screen(const double& fps, const Application_Base& app);\nvoid fps_controller(double& fps, const double& sp_fps);\n\n\n/* get rid of that macros, prevents gtk from linking!*/\n\n#define DEFINE_GLOBALS() \\\n \\\nextern float progress_val1; \\\nextern float progress_val2; \\\n \\\nextern GlobalFlag do_pause; \\\nextern GlobalFlag do_quit; \\\nextern GlobalFlag fast_forward; \\\nextern GlobalFlag draw_grid; \\\nextern GlobalFlag do_drawing; \\\nextern GlobalFlag screenshot; \\\n\n\n#define APPLICATION_MAIN() \\\n \\\nint main(int argc, char* argv[]) \\\n{ \\\n srand((unsigned) time(NULL)); \\\n signal(SIGINT, signal_terminate_handler); \\\n \\\n Event_Manager em; \\\n Application app(argc, argv, em); \\\n \\\n init_SDL(app.visuals_enabled(), app.window_width, app.window_height, app.name); \\\n \\\n /*GUI_Starter gui(app.visuals_enabled());*/ \\\n \\\n atexit(quit); \\\n std::thread app_thread(process_application, &app); \\\n \\\n if (app.visuals_enabled()) ui_main_loop(do_quit, do_drawing, em, app); \\\n sts_msg(\"waiting for main thread to join.\"); \\\n app_thread.join(); \\\n \\\n sts_msg(\"Bye.\"); \\\n return 0; \\\n} \\\n\r\n#endif // SETUPSDL_H\n" }, { "alpha_fraction": 0.637982189655304, "alphanum_fraction": 0.642433226108551, "avg_line_length": 31.095237731933594, "blob_id": "c528847e5dd28c910d0c649f90a0c83a0441fe4c", "content_id": "8ee4bfa369ddb4a1622a84a3301f36c2cba31e4d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 674, "license_type": "no_license", "max_line_length": 87, "num_lines": 21, "path": "/src/evolution/pool_strategy.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <evolution/pool_strategy.h>\n\n/** TODO think about to include a check of number of evaluations? */\nstd::size_t get_replacement_candidate_for(Individual& opponent, Population& population)\n{\n if (opponent.fitness < population.get_last_individual().fitness)\n return population.get_size() - 1;\n\n std::size_t i = population.get_size();\n std::size_t candidate_idx = i-1;\n\n while (i > 0) {\n --i;\n if (opponent.fitness > population[i].fitness)\n candidate_idx = i;\n else break;\n }\n //dbg_msg(\"Replacement candidate is %u\", candidate_idx);\n assert(candidate_idx < population.get_size());\n return candidate_idx;\n}\n" }, { "alpha_fraction": 0.589841365814209, "alphanum_fraction": 0.5973538160324097, "avg_line_length": 28.09787940979004, "blob_id": "be6a5d73822e2dd3ddbefd478c3d19920ea408b1", "content_id": "b050b19b2b3ea4c2d0fce9ecbd7899d547a77087", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 17838, "license_type": "no_license", "max_line_length": 171, "num_lines": 613, "path": "/src/robots/simloid.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* simloid.cpp */\n#include \"./simloid.h\"\n\nnamespace robots {\n\nSimloid::Simloid( bool interlaced_mode,\n unsigned short port,\n unsigned int robot_ID,\n unsigned int scene_ID,\n bool visuals,\n bool realtime,\n std::vector<double> modelparams,\n bool initially_fixed\n )\n : port(port)\n , robot_ID(robot_ID)\n , scene_ID(scene_ID)\n , visuals(visuals)\n , realtime(realtime)\n , child_pid()\n , mtx()\n , client()\n , connection_established(open_connection())\n , record_frame(false)\n , configuration(client.recv(5*network::constants::seconds_us), interlaced_mode)\n , timestamp()\n , body_position0(configuration.number_of_bodies)\n , average_position()\n , average_position0()\n , average_velocity()\n , average_rotation()\n , avg_rot_inf_ang()\n , avg_rot_inf_ang_last()\n , avg_velocity_forward()\n , avg_velocity_left()\n , left_id(get_body_id_by_name(configuration.bodies, \"left\"))\n , rift_id(get_body_id_by_name(configuration.bodies, \"rift\"))\n , initially_fixed(initially_fixed)\n{\n if (interlaced_mode) client.send(\"INTERLACED MODE\\n\");\n else client.send(\"SEQUENTIAL MODE\\n\");\n\n sts_msg(\"Done reading robot configuration. Sending acknowledge.\");\n client.send(\"ACK\\n\");\n\n assert(configuration.number_of_bodies > 0);\n\n if (connection_established)\n {\n sts_msg(\"Connection established.\");\n\n if (0 == modelparams.size()) init_robot();\n else {\n read_sensor_data();\n reinit_robot_model(modelparams);\n }\n }\n else\n wrn_msg(\"Cannot connect to robot.\");\n}\n\nSimloid::~Simloid()\n{\n if (connection_established)\n close_connection();\n}\n\nbool\nSimloid::open_connection(void)\n{\n sts_msg(\"Forking process.\");\n\n child_pid = fork();\n if (child_pid >= 0) // fork was successful\n {\n if (child_pid == 0) // child process\n {\n sts_msg(\"Child process says \\'hello\\'.\");\n sts_msg(\"Checking if processor is available...\");\n if (system(NULL)) sts_msg(\"OK.\");\n else err_msg(__FILE__, __LINE__, \"FAILED.\");\n\n char portarg[8], robotarg[8], scenearg[8], nographics[8] = \"\", norealtime[8] = \"\"; //TODO: aufnäumen, extra funktion oder so.\n snprintf(portarg, 8, \"%d\", port);\n snprintf(robotarg, 8, \"%d\", robot_ID);\n snprintf(scenearg, 8, \"%d\", scene_ID);\n\n if (!visuals) snprintf(nographics, 8, \"-ng\");\n if (!realtime) snprintf(norealtime, 8, \"-nr\");\n\n sts_msg(\"Starting simloid on port %s with robot '%s' and scene '%s'.\", portarg, robotarg, scenearg);\n if (!visuals) sts_msg(\"Starting without graphics.\");\n if (!realtime) sts_msg(\"Starting with maximal speed (no realtime).\");\n\n int i = execl(\"../simloidTCP/bin/Release/simloid\", \"simloid\", \"--port\", portarg, \"--robot\", robotarg, \"--scene\", scenearg, nographics, norealtime, (char *) 0);\n err_msg(__FILE__, __LINE__, \"\\n +++ Could not start simloid. Status: %d +++ \\n%s\\n\", i, strerror(errno));\n }\n else //Parent process\n {\n sts_msg(\"Parent process says \\'hello\\'.\");\n }\n }\n else\n {\n wrn_msg(\"Forking process failed.\");\n return false;\n }\n\n sts_msg(\"Waiting a second for simloid to start.\");\n sleep(1);\n sts_msg(\"Continuing.\");\n\n return client.open_connection(\"127.0.0.1\", port);\n}\n\nvoid\nSimloid::simulation_idle(double sec)\n{\n sts_msg(\"Waiting for %1.1f seconds.\", sec);\n /* pass 100 time steps per second */\n for (unsigned i = 0; i < round(100.0 * sec); ++i)\n {\n read_sensor_data();\n client.send(\"DONE\\n\");\n }\n}\n\nvoid\nSimloid::set_robot_to_default_position(void)\n{\n if (initially_fixed) dbg_msg(\"Robot is fixed during initialization.\");\n client.send(\"GRAVITY OFF\\n%s\", initially_fixed ? \"FIXED 0\\n\":\"\");\n\n double sec = 2; // should be enough\n sts_msg(\"Setting robot to default joint position.\");\n\n char msg[network::constants::msglen];\n /* pass 100 time steps per second */\n for (unsigned i = 0; i < round(100.0 * sec); ++i)\n {\n read_sensor_data();\n\n short n = snprintf(msg, network::constants::msglen, \"PX\");\n for (auto& j: configuration.joints)\n n += snprintf(msg + n, network::constants::msglen - n, \" %lf\", j.default_pos);\n\n snprintf(msg + n, network::constants::msglen - n, \"\\nDONE\\n\");\n client.send(msg);\n }\n read_sensor_data();\n\n client.send(\"%sGRAVITY ON\\nDONE\\n\", initially_fixed ? \"FIXED 0\\n\":\"\");\n read_sensor_data();\n}\n\nvoid\nSimloid::reset(void) //non-public\n{\n /* resetting simloid, resetting motor output */\n for (auto& j: configuration.joints)\n j.motor.reset();\n client.send(\"UA 0\\nRESET\\nDONE\\n\");\n read_sensor_data(); // note: a reset must be followed by an update\n}\n\nvoid\nSimloid::save_state(void)\n{\n if (!connection_established)\n {\n wrn_msg(\"Cannot save state. Not connected.\");\n return;\n }\n /* saving state of simloid */\n client.send(\"UA 0\\nNEWTIME\\nSAVE\\nDONE\\n\");\n read_sensor_data();\n\n // save initial position\n for (std::size_t i = 0; i < configuration.bodies.size(); ++i)\n body_position0[i] = configuration.bodies[i].position;\n\n update_avg_position();\n update_avg_velocity();\n average_position0 = average_position;\n}\n\n\nvoid\nSimloid::restore_state(void)\n{\n common::lock_t lock(mtx);\n\n if (!connection_established) {\n wrn_msg(\"Cannot restore state. Not connected.\");\n return;\n }\n\n /* restoring last snapshot of simloid, resetting motor output */\n for (auto& j: configuration.joints) j.motor.reset();\n reset_all_forces();\n client.send(\"UA 0\\nNEWTIME\\nRESTORE\\nDONE\\n\");\n read_sensor_data(); // note: a reset must be followed by an update\n\n /* TODO: irgendwie ist nachm restore und sensorupdate noch die alten werte da */\n}\n\nvoid\nSimloid::finish(void)\n{\n if (connection_established) {\n sts_msg(\"Closing connection to simloid.\");\n close_connection();\n }\n else wrn_msg(\"Cannot finish simloid. Already disconnected.\");\n return;\n}\n\nbool\nSimloid::idle(void)\n{\n common::lock_t lock(mtx);\n\n if (!connection_established) {\n wrn_msg(\"Not connected.\");\n return false;\n }\n\n send_pause_command();\n return true;\n}\n\nbool\nSimloid::update(void)\n{\n common::lock_t lock(mtx);\n\n if (!connection_established) {\n wrn_msg(\"Cannot update sensor values. Not connected.\");\n return false;\n }\n\n write_motor_data();\n read_sensor_data();\n\n update_avg_position();\n update_avg_velocity();\n update_rotation_z();\n update_robot_velocity();\n return true;\n}\n\n\nvoid Simloid::init_robot(void) // non-public\n{\n sts_msg(\"Initializing robot.\");\n\n /* initialize motor voltages */\n for (auto& j: configuration.joints) j.motor.reset();\n set_robot_to_default_position();\n save_state();\n}\n\nvoid\nSimloid::close_connection(void)\n{\n /* close connection and socket */\n sts_msg(\"Closing connection to server.\");\n client.send(\"EXIT\\n\");\n client.close_connection();\n sts_msg(\"TCP connection terminated. Waiting for simloid to exit.\");\n\n int status;\n while (waitpid(child_pid, &status, 0) != child_pid)\n {\n sts_msg(\"Tick...\");\n sleep(1);\n }\n sts_msg(\"Simloid finished.\");\n connection_established = false;\n}\n\ndouble\nread_double(const char *msg_buffer, unsigned int *offset)\n{\n double value = .0;\n const char *msg = msg_buffer + (*offset);\n int chars_read = 0;\n\n if (1 == sscanf(msg, \" %lf%n\", &value, &chars_read))\n {\n msg += chars_read;\n *offset += chars_read;\n }\n else wrn_msg(\"Cannot read expected (double) value from TCP message at offset %d.\", *offset);\n\n return value;\n}\n\nVector3\nread_vector3(const char *msg_buffer, unsigned int *offset)\n{\n const double x = read_double(msg_buffer, offset);\n const double y = read_double(msg_buffer, offset);\n const double z = read_double(msg_buffer, offset);\n return Vector3(x, y, z);\n}\n\nvoid\nSimloid::eat_server_msg(void) {\n dbg_msg(\"eating msg\");\n client.eat();\n dbg_msg(\"eating msg done\");\n}\n\nvoid\nSimloid::read_sensor_data(void)\n{\n static std::string srv_msg;\n unsigned charcount = 0;\n\n srv_msg = client.recv(60*network::constants::seconds_us);\n\n unsigned len = srv_msg.length();\n\n if (0 == len)\n {\n wrn_msg(\"Received no more bytes. Cancel reading sensory data.\");\n close_connection();\n return;\n }\n\n /* read time stamp */\n const char *server_message = srv_msg.c_str();\n\n unsigned frames_read = 0;\n\n do {\n\n timestamp = read_double(server_message, &charcount);\n\n /* read angles */\n for (auto& j: configuration.joints)\n j.s_ang = clip(read_double(server_message, &charcount));\n\n /* read angle rate */\n for (auto& j: configuration.joints)\n j.s_vel = clip(read_double(server_message, &charcount));\n\n /* read motor current */\n for (auto& j: configuration.joints)\n j.s_cur = read_double(server_message, &charcount);\n\n /* read acceleration sensors */\n for(auto& s: configuration.accels)\n s.a = read_vector3(server_message, &charcount);\n\n /* read body positions + velocities */\n for (auto& b: configuration.bodies)\n {\n b.position = read_vector3(server_message, &charcount);\n b.velocity = read_vector3(server_message, &charcount);\n }\n\n ++frames_read;\n\n } while (charcount < len-1);\n\n if (len-1 != charcount)\n wrn_msg(\"Server message has different number of bytes than expected, %u != %u\", len-1, charcount);\n if (frames_read > 1)\n wrn_msg(\"%u frame%s skipped.\", frames_read-1, frames_read > 2 ? \"s\":\"\");\n}\n\n\nvoid\nSimloid::write_motor_data(void)\n{\n static char msg[network::constants::msglen];\n unsigned n = snprintf(msg, network::constants::msglen, \"UX\");\n\n for (auto& j: configuration.joints)\n n += snprintf(msg + n, network::constants::msglen - n, \" %lf\", clip(j.motor.get()));\n\n auto const& bodies = configuration.bodies;\n for (unsigned i = 0; i < bodies.size(); ++i)\n if (bodies[i].force.length() > .0)\n n += snprintf(msg + n, network::constants::msglen - n, \"\\nFI %u %lf %lf %lf\", i, bodies[i].force.x,\n bodies[i].force.y,\n bodies[i].force.z);\n snprintf(msg + n, network::constants::msglen - n, \"\\n%sDONE\\n\", record_frame ? \"RECORD\\n\" : \"\");\n client.send(msg);\n\n /* transfer motor data u(t) to u(t-1) and reset value */\n for (auto& j: configuration.joints) {\n j.motor.transfer();\n j.motor = .0;\n }\n\n record_frame = false;\n return;\n}\n\nvoid\nSimloid::send_pause_command(void) { client.send(\"PAUSE\\nDONE\\n\"); }\n\nvoid\nSimloid::set_low_sensor_quality(bool low_quality)\n{\n common::lock_t lock(mtx);\n if (low_quality)\n client.send(\"SENSORS POOR\\n\");\n else\n client.send(\"SENSORS GOOD\\n\");\n}\n\nvoid\nSimloid::update_avg_position(void)\n{\n Vector3 position(.0);\n for (auto& b: configuration.bodies)\n position += b.position;\n\n average_position = position / configuration.bodies.size();\n}\n\nvoid\nSimloid::update_avg_velocity(void)\n{\n Vector3 velocity(.0);\n for (auto& b: configuration.bodies)\n velocity += b.velocity;\n\n average_velocity = velocity / configuration.bodies.size();\n}\n\nVector3\nSimloid::get_min_position(void) const\n{\n Vector3 min_position(DBL_MAX);\n for (auto& b: configuration.bodies)\n {\n min_position.x = std::min(min_position.x, b.position.x);\n min_position.y = std::min(min_position.y, b.position.y);\n min_position.z = std::min(min_position.z, b.position.z);\n }\n return min_position;\n}\n\nVector3\nSimloid::get_max_position(void) const\n{\n Vector3 max_position(-DBL_MAX);\n for (auto& b: configuration.bodies)\n {\n max_position.x = std::max(max_position.x, b.position.x);\n max_position.y = std::max(max_position.y, b.position.y);\n max_position.z = std::max(max_position.z, b.position.z);\n }\n return max_position;\n}\n\ndouble\nSimloid::get_motion_level(void) const\n{\n double sum_v = .0;\n for (auto& j: configuration.joints)\n sum_v += fabs(j.s_vel);\n return sum_v/configuration.number_of_joints;\n}\n\nbool\nSimloid::motion_stopped(double thrsh) const\n{\n return (get_motion_level() < thrsh);\n}\n\nbool\nSimloid::dropped(double level) const\n{\n assert(level >= 0 && level <= 1.0);\n return (configuration.bodies[0].position.z < level * body_position0[0].z);\n}\n\ndouble\nSimloid::dx_from_origin(void) const\n{\n return configuration.bodies[0].position.x - body_position0[0].x;\n}\n\ndouble\nSimloid::dy_from_origin(void) const\n{\n return configuration.bodies[0].position.y - body_position0[0].y;\n}\n\n/* for mean calculation of circular quantities see:\n https://en.wikipedia.org/wiki/Mean_of_circular_quantities\n */\nvoid\nSimloid::update_rotation_z(void)\n{\n assert(configuration.number_of_bodies == configuration.bodies.size());\n double sum_y = 0.0;\n double sum_x = 0.0;\n\n for (std::size_t i = 0; i < configuration.number_of_bodies; ++i) {\n Vector3 relative_position = configuration.bodies[i].position - average_position; // avg free position\n relative_position.normalize();\n const double len = relative_position.length();\n\n if (len != 0) assert_close(len, 1.0, 0.001, \"relative position\");\n\n const Vector3 relpos0 = body_position0[i] - average_position0;\n const double relative_rotation = relative_position.angle_phi() - relpos0.angle_phi();\n\n sum_y += sin(relative_rotation);\n sum_x += cos(relative_rotation);\n }\n\n average_rotation = atan2( sum_y/configuration.number_of_bodies\n , sum_x/configuration.number_of_bodies );\n\n avg_rot_inf_ang_last = avg_rot_inf_ang;\n avg_rot_inf_ang = unwrap(average_rotation, avg_rot_inf_ang);\n}\n\nvoid\nSimloid::update_robot_velocity(void)\n{\n const double& a = average_rotation;\n const double& x = average_velocity.x;\n const double& y = -average_velocity.y;\n\n avg_velocity_left = cos(a)*x - sin(a)*y;\n avg_velocity_forward = sin(a)*x + cos(a)*y;\n}\n\ndouble\nSimloid::get_normalized_mechanical_power(void) const\n{\n double power = .0;\n for (auto& j: configuration.joints)\n power += square(j.motor.get());\n return power/configuration.number_of_joints;\n}\n\nunsigned\nSimloid::get_body_id_by_name(const Bodyvector_t& bodies, const std::string& name) const {\n for (std::size_t i = 0; i < bodies.size(); ++i)\n if (bodies[i].name == name) return i;\n return bodies.size();\n}\n\nVector3\nSimloid::get_min_feet_pos(void) const {\n assert(left_id < configuration.bodies.size());\n assert(rift_id < configuration.bodies.size());\n return Vector3{ std::min(configuration.bodies[left_id].position.x, configuration.bodies[rift_id].position.x)\n , std::min(configuration.bodies[left_id].position.y, configuration.bodies[rift_id].position.y)\n , std::min(configuration.bodies[left_id].position.z, configuration.bodies[rift_id].position.z) };\n}\n\nVector3\nSimloid::get_max_feet_pos(void) const {\n assert(left_id < configuration.bodies.size());\n assert(rift_id < configuration.bodies.size());\n return Vector3{ std::max(configuration.bodies[left_id].position.x, configuration.bodies[rift_id].position.x)\n , std::max(configuration.bodies[left_id].position.y, configuration.bodies[rift_id].position.y)\n , std::max(configuration.bodies[left_id].position.z, configuration.bodies[rift_id].position.z) };\n}\n\nuint64_t\nSimloid::randomize_model(double rnd_amp, double growth, double friction, uint64_t inst)\n{\n common::lock_t lock(mtx);\n if (0 == inst) {/* not initialized yet? */\n inst = time(NULL);\n sts_msg(\"Initializing random seed, instance is: %lu\", inst);\n }\n\n sts_msg(\"Req. new model for robot %u and inst. %lu, amp. %lf, grow %lf, fric %lf\", robot_ID, inst, rnd_amp, growth, friction);\n eat_server_msg();\n client.send(\"MODEL %u 4 %lu %lf %lf %lf\\nRESET\\nDONE\\n\", robot_ID, inst, rnd_amp, growth, friction);\n configuration.read_robot_info( client.recv(5*network::constants::seconds_us) );\n client.send(\"ACK\\n\");\n //read_sensor_data();\n assert(configuration.number_of_bodies > 0);\n init_robot();\n return inst;\n}\n\nvoid\nSimloid::reinit_robot_model(std::vector<double> const& params)\n{\n common::lock_t lock(mtx);\n sts_msg(\"Requesting new model for robot_id %u with %u params\", robot_ID, params.size());\n client.send(\"MODEL %u %u %s\\nDONE\\n\", robot_ID, params.size(), common::to_string(params).c_str());\n configuration.read_robot_info( client.recv(5*network::constants::seconds_us) );\n client.send(\"ACK\\n\");\n assert(configuration.number_of_bodies > 0);\n //read_sensor_data();\n init_robot();\n}\n\nvoid\nSimloid::reinit_motor_model(std::vector<double> const& params)\n{\n common::lock_t lock(mtx);\n sts_msg(\"Requesting new motor model with %u params\", params.size());\n client.send(\"MOTOR %u %s\\nDONE\\n\", params.size(), common::to_string(params).c_str());\n}\n\n} // namespace robots\n" }, { "alpha_fraction": 0.6436519026756287, "alphanum_fraction": 0.6487874388694763, "avg_line_length": 28.20833396911621, "blob_id": "1d002bb6e7e9d6c5b09874a60c0314f5cc1c4b44", "content_id": "e1ad76a4864f30c269567937bba6227aac1a1ddc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3505, "license_type": "no_license", "max_line_length": 163, "num_lines": 120, "path": "/src/evolution/evolution_strategy.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EVOLUTION_POLICY_H_INCLUDED\n#define EVOLUTION_POLICY_H_INCLUDED\n\n#include <common/config.h>\n#include <evolution/population.h>\n#include <evolution/evaluation_interface.h>\n\n\nenum Evolution_State\n{\n stopped = 0,\n running,\n aborted,\n finished,\n playback\n};\n\nstruct statistics_t {\n statistics_t()\n : max(-DBL_MAX)\n , min(+DBL_MAX)\n , sum(0.0)\n , avg(0.0)\n , num_samples(0)\n {}\n\n void add_sample(double val) {\n max = std::max(val, max);\n min = std::min(val, min);\n sum += val;\n ++num_samples;\n }\n\n void update_average(void) {\n avg = sum/num_samples;\n }\n\n void reset() { *this = statistics_t{}; }\n\n double max;\n double min;\n double sum;\n double avg;\n uint64_t num_samples;\n};\n\n/* base class */\nclass Evolution_Strategy\n{\npublic:\n Evolution_Strategy(Population& population, Evaluation_Interface& evaluation, config& configuration, const std::string& project_folder_path, const bool verbose)\n : population(population)\n , evaluation(evaluation)\n , configuration(configuration)\n , csv_population(project_folder_path + \"/population.log\", population.get_size(), population.get_individual_size())\n , csv_mutation (project_folder_path + \"/mutation.log\" , population.get_size(), 1)\n , csv_fitness (project_folder_path + \"/fitness.log\" , population.get_size(), 1)\n , fitness_stats()\n , mutation_stats()\n , verbose(verbose)\n {}\n\n virtual ~Evolution_Strategy() { };\n\n virtual Evolution_State execute_trial(void) = 0;\n virtual Evolution_State playback(void) = 0;\n virtual void resume(void) = 0;\n\n virtual void save_config(config& configuration) = 0;\n\n statistics_t const& get_fitness_statistics (void) const { return fitness_stats; }\n statistics_t const& get_mutation_statistics(void) const { return mutation_stats; }\n\n virtual std::size_t get_max_trials (void) const = 0;\n virtual std::size_t get_current_trial(void) const = 0;\n\n virtual const Individual& get_best_individual(void) const = 0;\n virtual bool is_there_a_new_best_individual(void) = 0;\n\n void save_state(void) {\n if (verbose) sts_msg(\"Saving population state:\");\n save_population (population, csv_population);\n save_mutation_rates(population, csv_mutation);\n save_fitness_values(population, csv_fitness);\n }\n void load_state(void) {\n if (verbose) sts_msg(\"Loading population state:\");\n load_population (population, csv_population);\n load_mutation_rates(population, csv_mutation);\n load_fitness_values(population, csv_fitness);\n }\n\n void generate_start_population(const std::vector<double>& seed) {\n sts_msg(\"Generate start population.\");\n population.initialize_from_seed(seed);\n }\n\n void load_start_population(const std::string& filename) {\n dbg_msg(\"Will try to load the population from file %s\", filename.c_str());\n file_io::CSV_File<double> pop_csv(filename, population.get_size(), population.get_individual_size());\n load_population(population, pop_csv);\n }\n\n Population& population;\n Evaluation_Interface& evaluation;\n config& configuration;\n\n file_io::CSV_File<double> csv_population;\n file_io::CSV_File<double> csv_mutation;\n file_io::CSV_File<double> csv_fitness;\n\n statistics_t fitness_stats;\n statistics_t mutation_stats;\n\n\n const bool verbose;\n};\n\n\n#endif // EVOLUTION_POLICY_H_INCLUDED\n" }, { "alpha_fraction": 0.6105263233184814, "alphanum_fraction": 0.6509180068969727, "avg_line_length": 40.68367385864258, "blob_id": "e80d2f9807c5ecafb6f7184c428aa067fcbe7881", "content_id": "c3298bffff39e1a7f4b55523f724cb8e5326c8f2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4085, "license_type": "no_license", "max_line_length": 139, "num_lines": 98, "path": "/src/tests/learning.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <vector>\n\n#include <common/modules.h>\n#include <common/static_vector.h>\n#include <learning/payload.h>\n#include <learning/epsilon_greedy.h>\n\n\nclass no_actions : public Action_Module_Interface {\npublic:\n std::size_t get_number_of_actions(void) const { return 7; }\n std::size_t get_number_of_actions_available(void) const { return 6; }\n bool exists(const std::size_t action_index) const {\n if (action_index == 3) return false; // id 3 is not available\n else return true;\n }\n};\n\nTEST_CASE( \"Epsilon Greedy\" , \"[learning]\") {\n REQUIRE( true );\n\n const unsigned num_states = 10;\n const unsigned num_policies = 3;\n const no_actions actions;\n static_vector<State_Payload> states(num_states, actions, num_policies, 0.0);\n\n learning::Epsilon_Greedy greedy(states, actions, 0.01);\n\n const unsigned avail_actions = actions.get_number_of_actions_available();\n const unsigned rand_idx = random_index(avail_actions);\n\n Action_Selection_Base::Vector_t const& selection_probabilities = greedy.get_distribution();\n print_distribution(selection_probabilities);\n\n /*check that probabilities are restricted to [0,1] and sum up to 1 */\n auto check_probabilities = [&](Action_Selection_Base::Vector_t const& prob) {\n double sum = .0;\n for (unsigned i = 0; i < prob.size(); ++i) {\n sum += prob[i];\n REQUIRE( in_range(prob[i], 0.0, 1.0) );\n }\n REQUIRE( in_range(sum, 0.0, 1.0) );\n };\n\n check_probabilities(selection_probabilities);\n\n /* check distribution of selected actions */\n auto test_selection = [&](learning::Epsilon_Greedy g, unsigned state_id, unsigned policy_id, unsigned action_id) {\n states[state_id].policies[policy_id].qvalues[action_id] = 1.0; // set Q-value\n unsigned result = g.select_action(state_id, policy_id);\n states[state_id].policies[policy_id].qvalues[action_id] = 0.0; // clear it\n return result;\n };\n\n auto check_distribution = [&](learning::Epsilon_Greedy g, unsigned state_id, unsigned policy_id, unsigned action_id, double expected) {\n REQUIRE( state_id < states.size() );\n REQUIRE( policy_id < states[0].policies.size() );\n REQUIRE( action_id < states[0].policies[0].qvalues.size() );\n const unsigned total = 2000;\n unsigned counter = 0;\n for (unsigned i = 0; i < total; ++i)\n if (action_id == test_selection(g, state_id, policy_id, action_id)) ++counter;\n dbg_msg(\"Selection rate: %4u/%4u %4.1f ~ %4.1f\", counter, total, 100.0*counter/total, expected);\n REQUIRE( close(100.0*counter/total, expected, 3.0) ); // values in 3.0% tolerance\n };\n\n learning::Epsilon_Greedy greedy_10(states, actions, 0.10);\n learning::Epsilon_Greedy greedy_40(states, actions, 0.40);\n learning::Epsilon_Greedy greedy_70(states, actions, 0.70);\n\n check_distribution(greedy_10, 0,0,0, 90.0); check_probabilities(greedy_10.get_distribution());\n check_distribution(greedy_10, 1,2,3, 0.0); check_probabilities(greedy_10.get_distribution()); // action not available\n check_distribution(greedy_40, 9,1,6, 60.0); check_probabilities(greedy_40.get_distribution());\n check_distribution(greedy_70, 3,0,1, 30.0); check_probabilities(greedy_70.get_distribution());\n}\n\n\n\nTEST_CASE( \"select_from_distribution\" ,\"[eps_greedy]\")\n{\n Action_Selection_Base::Vector_t selection_probabilities{5};\n selection_probabilities[0] = 0.50; //50\n selection_probabilities[1] = 0.25; //75\n selection_probabilities[2] = 0.10; //85\n selection_probabilities[3] = 0.10; //95\n selection_probabilities[4] = 0.05; //100\n\n /* Testing binning.*/\n std::vector<std::size_t> bins(selection_probabilities.size());\n for (unsigned i = 0; i < 1000; ++i)\n ++bins[select_from_distribution(selection_probabilities)];\n\n for (unsigned i = 0; i < bins.size(); ++i) {\n dbg_msg(\"%+1.3f ~ %+1.3f\", bins[i]/1000.0, selection_probabilities[i]);\n REQUIRE( close(bins[i]/1000.0, selection_probabilities[i], 0.05) );\n }\n}\n" }, { "alpha_fraction": 0.6266195774078369, "alphanum_fraction": 0.6277974247932434, "avg_line_length": 24.34328269958496, "blob_id": "925aa0d0e5bb943c211d9f637639a488dbcd134e", "content_id": "8e0378b3de3217238ee722416f92b4608268b5c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1698, "license_type": "no_license", "max_line_length": 109, "num_lines": 67, "path": "/src/control/behavior_switcher.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BEHAVIOR_SWITCHER_H_INCLUDED\n#define BEHAVIOR_SWITCHER_H_INCLUDED\n\n#include <control/controlparameter.h>\n#include <control/control_vector.h>\n#include <control/jointcontrol.h>\n\nnamespace control {\n\n\nclass Behavior_Switcher\n{\npublic:\n Behavior_Switcher(const Control_Vector& parameter_set, Jointcontrol& control)\n : parameter_set(parameter_set)\n , control(control)\n , current_behavior(0)\n {\n dbg_msg(\"Creating Behavior Switcher.\");\n }\n\n void random(void) {\n current_behavior = random_index(parameter_set.size());\n sts_msg(\"Switching to random behavior: %u\", current_behavior);\n switch_behavior();\n }\n\n void next(void) {\n ++current_behavior;\n if (current_behavior >= parameter_set.size())\n current_behavior = 0;\n //sts_msg(\"Current behavior: %u\", current_behavior);\n switch_behavior();\n }\n\n bool step(void) {\n if (triggered) {\n next();\n triggered = false;\n return true;\n }\n else return false;\n }\n\n void trigger(void) { triggered = true; }\n\n Control_Parameter const& get_current_behavior(void) const { return parameter_set.get(current_behavior); }\n\nprivate:\n\n void switch_behavior(void) {\n control.set_control_parameter(parameter_set.get(current_behavior));\n\n //TODO is that still needed?\n control.switch_symmetric(parameter_set.get(current_behavior).is_mirrored());\n }\n\n const Control_Vector& parameter_set;\n Jointcontrol& control;\n std::size_t current_behavior;\n bool triggered = false;\n};\n\n\n} // namespace control\n\n#endif // BEHAVIOR_SWITCHER_H_INCLUDED\n" }, { "alpha_fraction": 0.4704939126968384, "alphanum_fraction": 0.5163566470146179, "avg_line_length": 18.60377311706543, "blob_id": "c9761e39b3540c0bc75489a48adae68a44c76246", "content_id": "890e6213451cefd547649e9053652860fbb6a60d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3122, "license_type": "no_license", "max_line_length": 70, "num_lines": 159, "path": "/src/draw/network2D.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* network2D.cpp */\n\n#include \"network2D.h\"\n#include \"draw.h\"\n\n///////////////////////////////////\n////// TODO REFACTOR THAT /////////\n///////////////////////////////////\n\nnetwork2D::network2D(int Na, axes *a, const GLubyte c[4])\n{\n int i;\n N = Na;\n\n special_node = 0;\n\tfor (i = 0; i < 4; i++) color[i] = c[i];\n\n /* allocate memory */\n /* Speicher reservieren für Zeilen-Zeiger */\n\tX = (float **) malloc(N * sizeof(float *));\n if (NULL == X) {\n\t\tfprintf(stderr, \"Fehler: Zu wenig Speicher.\\n\");\n\t\texit(-1);\n\t}\n\n\t/* Speicher reservieren für Spalten */\n\tfor (i = 0; i < N; i++) {\n\t\tX[i] = (float *) calloc(2, sizeof(float));\n\t\tif (NULL == X[i]) {\n\t\t\tfprintf(stderr, \"Fehler: Zu wenig Speicher.\\n\");\n\t\t\texit(-1);\n\t\t}\n\t}\n\n\n\tsize = (float*) calloc(N, sizeof(float));\n\n for (i = 0; i < N; i++) {\n X[i][0] = 0.0f;\n X[i][1] = 0.0f;\n\t\tsize[i] = 0.0f;\n }\n pointer = 0;\n\n /* link to axis */\n px = a->px;\n py = a->py;\n A = a;\n width = 0.5*a->width;\n height = 0.5*a->height;\n\n\n\t/* Speicher reservieren für Zeilen-Zeiger */\n\tedges = (unsigned char **) malloc(N * sizeof(unsigned char *));\n if (NULL == edges) {\n\t\tfprintf(stderr, \"Fehler: Zu wenig Speicher.\\n\");\n\t\texit(-1);\n\t}\n\n\t/* Speicher reservieren für Spalten */\n\tfor (i = 0; i < N; i++) {\n\t\tedges[i] = (unsigned char *) calloc(N, sizeof(unsigned char));\n\t\tif (NULL == edges[i]) {\n\t\t\tfprintf(stderr, \"Fehler: Zu wenig Speicher.\\n\");\n\t\t\texit(-1);\n\t\t}\n\t}\n\n}\n\nvoid network2D::draw()\n{\n /* draw nodes */\n\n\tint i,j;\n\tglColor4ub(255,255,255,255);\n\n\tfor (i = 0; i < N; i++) {\n\t\tif (size[i] > 0) { //node exists\n\t\t\tdraw_fill_square(X[i][0], X[i][1], 0.005);\n\t\t}\n\t}\n\n\tglColor4ub(255,255,255,96);\n\n\tfor (i = 0; i < N; i++) {\n\t\tif (size[i] > 0) { //node exists\n\t\t\tdraw_fill_square(X[i][0], X[i][1], 0.005+0.01*size[i]);\n\t\t}\n\t}\n\n\n\t/*glColor4ub(255,192,0,128);\n\tif (size[special_node] > 0)\n\t\tdraw_fill_rect(X[special_node][0], X[special_node][1], 0.02);\n\t*/\n\tglColor4ub(0,128,255,128);\n\tif (size[activated_node] > 0)\n\t\tdraw_fill_square(X[activated_node][0], X[activated_node][1], 0.015);\n\n\t/* draw edges */\n\tglLineWidth(1.0f);\n\n\tfor (i = 0; i < N; i++)\n\t\tfor (j = 0; j < N; j++)\n\t\t\tif (i != j)\n\t\t\t\tif (edges[j][i])\n\t\t\t\t{\n\t\t\t\t\tglColor4ub(255,255,255,edges[j][i]);\n\t\t\t\t\tdraw_line(X[i], X[j]);\n\t\t\t\t}\n\n}\n\nvoid network2D::special(int n)\n{\n\n\tif (n >= 0 && n < N)\n\t\tspecial_node = n;\n\telse {\n \tfprintf(stderr, \"Fehler: Falscher Knotenindex\");\n \texit(-1);\n }\n}\n\nvoid network2D::activated(int n)\n{\n\n\tif (n >= 0 && n < N)\n\t\tactivated_node = n;\n\telse {\n \tfprintf(stderr, \"Fehler: Falscher Knotenindex\");\n \texit(-1);\n }\n}\n\nvoid network2D::update_node(int n, float x0, float x1, float s)\n{\n if (n >= 0 && n < N) {\n \t\tX[n][0] = px + x0*width;\n \t\tX[n][1] = py + x1*height;\n \t\tsize[n] = s;\n } else {\n \tfprintf(stderr, \"Fehler: Falscher Knotenindex\");\n \texit(-1);\n }\n}\n\nvoid network2D::update_edge(int i, int j, unsigned char op)\n{\n if (j>=0 && j<N && i>=0 && i<N) {\n \tedges[i][j] = op;\n \t} else {\n \tfprintf(stderr, \"Fehler: Falscher Kantenindex\");\n \texit(-1);\n }\n}\n\n/* network2D.cpp */\n\n" }, { "alpha_fraction": 0.5695159435272217, "alphanum_fraction": 0.5935461521148682, "avg_line_length": 25.97222137451172, "blob_id": "c41104c586492e2b0b564913ea651da0ad504cfa", "content_id": "467ba93338a32bf04c43716520f190ef85580726", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2913, "license_type": "no_license", "max_line_length": 93, "num_lines": 108, "path": "/src/draw/plot2D.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* plot2D.cpp */\n\n#include \"plot2D.h\"\n\nvoid plot2D::autoscale(void) const\n{\n float scale = 2.0 / (axis.max_amplitude - axis.min_amplitude);\n float offset0 = 0;//(axis.max_amplitude + axis.min_amplitude) / 2;\n\n glTranslatef(axis.px + offset0, axis.py + offset0, axis.pz);\n glScalef( 0.5 * axis.width * scale\n , 0.5 * axis.height * scale\n , 1.0);\n}\n\nvoid plot2D::draw(void) const\n{\n glPushMatrix();\n autoscale();\n\n glLineWidth(1.0f);\n\n glBegin(GL_LINE_STRIP);\n for (unsigned i = number_of_samples; i-- > 1; ) { // zero omitted\n set_color(color, (float) i/number_of_samples);\n const unsigned pos = (i + pointer) % number_of_samples;\n glVertex2f( signal[pos].x - offset.x, signal[pos].y - offset.y );\n }\n glEnd();\n glPopMatrix();\n}\n\n//void plot2D::draw_in_time(void) const\n//{\n// glPushMatrix();\n// glTranslatef(px,py,pz);\n//\n// glBegin(GL_LINE_STRIP);\n// for (unsigned int i = number_of_samples - 1; i != 0; --i) {\n// glVertex3f(0.5 * width + i * width / number_of_samples,\n// 0.5 * height * signal[0][(i + pointer) % number_of_samples],\n// 0.5 * depth * signal[1][(i + pointer) % number_of_samples]);\n// }\n// glEnd();\n// glPopMatrix();\n//}\n\nvoid plot2D::adjust_amplitude(float s0, float s1) const\n{\n if (pointer == 0) {\n axis.max_amplitude *= decrement;\n axis.min_amplitude *= decrement;\n }\n\n axis.max_amplitude = std::max(axis.max_amplitude, std::max(s0, s1));\n axis.min_amplitude = std::min(axis.min_amplitude, std::min(s0, s1));\n}\n\nvoid plot2D::add_sample(float s0, float s1)\n{\n increment_pointer();\n signal[pointer].x = s0;\n signal[pointer].y = s1;\n\n adjust_amplitude(s0,s1);\n}\n\nvoid plot2D::add_sample(const std::vector<double>& sample)\n{\n assert(sample.size() >= 2);\n increment_pointer();\n signal[pointer].x = sample[0];\n signal[pointer].y = sample[1];\n\n adjust_amplitude(sample[0],sample[1]);\n}\n\nvoid colored_plot2D::draw_colored(void) const\n{\n glPushMatrix();\n autoscale();\n\n glLineWidth(1.0f);\n\n glBegin(GL_LINE_STRIP);\n for (unsigned i = number_of_samples; i-- > 1; ) { // zero omitted\n const unsigned pos = (i + pointer) % number_of_samples;\n set_color(colortable.get_color(colors[pos]), (float) i/number_of_samples);\n glVertex2f( signal[pos].x-offset.x, signal[pos].y-offset.y );\n }\n glEnd();\n glPopMatrix();\n}\n\nvoid colored_plot2D::draw_colored_scatter(void) const\n{\n glPushMatrix();\n autoscale();\n for (unsigned i = number_of_samples; i-- > 1; ) { // zero omitted\n const unsigned pos = (i + pointer) % number_of_samples;\n set_color(colortable.get_color(colors[pos]), 0.2f*(float) i/number_of_samples+0.10f);\n draw_fill_square(signal[pos].x-offset.x, signal[pos].y-offset.y, 0.02);\n }\n glPopMatrix();\n}\n\n\n/* plot2D.cpp */\n" }, { "alpha_fraction": 0.49416643381118774, "alphanum_fraction": 0.4962877333164215, "avg_line_length": 45.29910659790039, "blob_id": "8cfdc59cd21715f7df6d7aed621bbad42d18ec49", "content_id": "6588f7d6ebd1f8f417373e77da58436260ca81c3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 10371, "license_type": "no_license", "max_line_length": 209, "num_lines": 224, "path": "/src/learning/expert_vector.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EXPERT_VECTOR_H_INCLUDED\n#define EXPERT_VECTOR_H_INCLUDED\n\n#include <memory>\n#include <common/static_vector.h>\n#include <common/save_load.h>\n#include <control/sensorspace.h>\n#include <control/control_vector.h>\n#include <robots/robot.h>\n#include <learning/expert.h>\n#include <learning/predictor.h>\n#include <learning/state_predictor.h>\n#include <learning/motor_predictor.h>\n#include <learning/state_action_predictor.h>\n#include <learning/homeokinetic_predictor.h>\n#include <learning/bimodel_predictor.h>\n\n/* The Expert Vector merely work as a container\n * and should neither carry any information nor functionality\n * regarding the expert modules in it. However this is theory. :)\n */\n\nclass Expert_Vector : public common::Save_Load {\n\n std::vector<Expert> experts;\n static_vector_interface& payloads;\n\n Expert_Vector( const std::size_t max_number_of_experts\n , static_vector_interface& payloads )\n : experts()\n , payloads(payloads)\n {\n assert(payloads.size() == max_number_of_experts);\n assert(max_number_of_experts > 0);\n experts.reserve(max_number_of_experts);\n }\n\npublic:\n Expert_Vector(Expert_Vector&& other) = default;\n Expert_Vector& operator=(Expert_Vector&& other) = default;\n\n Expert& operator[] (const std::size_t index) { return experts.at(index); }\n const Expert& operator[] (const std::size_t index) const { return experts.at(index); }\n\n std::size_t size(void) const { return experts.size(); }\n\n void save(std::string f)\n {\n auto const cols = experts.at(0).get_predictor().get_weights().size();\n csv_file_t csv(f+\"experts.dat\", experts.size(), cols);\n for (std::size_t i = 0, line = 0; i < experts.size(); ++i) {\n if (experts[i].does_exists())\n csv.set_line(line++, experts[i].get_predictor().get_weights());\n }\n csv.write();\n }\n\n void load(std::string f)\n {\n auto const cols = experts.at(0).get_predictor().get_weights().size();\n csv_file_t csv(f+\"experts.dat\", experts.size(), cols);\n csv.read();\n for (std::size_t i = 0; i < experts.size(); ++i) {\n csv.get_line(i, experts[i].set_predictor().set_weights());\n experts[i].exists = !(i > 0 && is_vector_zero(experts[i].get_predictor().get_weights()));\n }\n }\n\n\n void copy(std::size_t to, std::size_t from, bool one_shot_learning) {\n\n experts.at(to).exists = true; // create\n\n if (one_shot_learning) experts.at(to).reinit_predictor_weights();\n else\n experts.at(to).predictor->copy( *(experts.at(from).predictor) );\n\n payloads.copy(to, from); /* take a flawed copy of the payload */\n }\n\n /* simple sensor state space constructor */\n Expert_Vector( const std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , const sensor_vector& input\n , const double local_learning_rate\n , const double random_weight_range\n , const std::size_t experience_size )\n : Expert_Vector(max_number_of_experts, payloads)\n {\n assert(local_learning_rate > 0.);\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back( Predictor_ptr( new Predictor(input, local_learning_rate, random_weight_range, experience_size) )\n , max_number_of_experts );\n }\n\n /* time-delay network sensor state space constructor */\n Expert_Vector( const std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , const sensor_vector& input\n , const double local_learning_rate\n , const std::size_t experience_size\n , const std::size_t hidden_layer_size\n , const std::size_t time_delay_size )\n : Expert_Vector(max_number_of_experts, payloads)\n {\n assert(local_learning_rate > 0.);\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back( Predictor_ptr( new learning::State_Predictor(input, local_learning_rate, gmes_constants::random_weight_range, experience_size, hidden_layer_size, time_delay_size) )\n , max_number_of_experts );\n }\n\n /* motor action space constructor */\n Expert_Vector( std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , sensor_vector const& motor_targets\n , double local_learning_rate\n , std::size_t experience_size\n , double noise_level\n , control::Control_Vector const& ctrl_params\n , robots::Robot_Interface const& robot )\n : Expert_Vector(max_number_of_experts, payloads)\n {\n sts_msg(\"Creating motor expert vector with %u elements in control parameter vector.\", ctrl_params.size());\n assert(local_learning_rate > 0.);\n assert(ctrl_params.size() == max_number_of_experts);\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back( Predictor_ptr( new learning::Motor_Predictor(robot, motor_targets, local_learning_rate, gmes_constants::random_weight_range, experience_size, ctrl_params.get(i), noise_level))\n , max_number_of_experts );\n }\n\n /* state action space constructor */\n Expert_Vector( const std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , const time_embedded_sensors<16>& input\n , const double local_learning_rate\n , const std::size_t experience_size\n , const std::size_t hidden_layer_size // ergibt sich aus num joints\n , const double random_weight_range\n )\n : Expert_Vector(max_number_of_experts, payloads)\n {\n assert(local_learning_rate > 0.);\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back( Predictor_ptr( new learning::State_Action_Predictor( input\n , local_learning_rate\n , random_weight_range\n , experience_size\n , hidden_layer_size\n ) )\n , max_number_of_experts );\n }\n\n\n\n /* homeokinetic expert constructor */\n Expert_Vector( std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , sensor_input_interface const& input\n , std::size_t number_of_motor_outputs\n , std::size_t number_of_context_units\n , double local_learning_rate\n , double random_weight_range\n )\n : Expert_Vector(max_number_of_experts, payloads)\n {\n assert(local_learning_rate > 0.);\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back( Predictor_ptr( new learning::Homeokinetic_Core( input\n , number_of_motor_outputs\n , local_learning_rate\n , random_weight_range\n , number_of_context_units\n ) )\n , max_number_of_experts );\n }\n\n\n /* bimodel expert constructor */\n Expert_Vector( std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , sensor_input_interface const& input\n , sensor_input_interface const& gateway\n , learning::model::vector_t& gradient\n , double local_learning_rate\n , double random_weight_range\n , double regularization_rate\n )\n : Expert_Vector(max_number_of_experts, payloads)\n {\n assert(local_learning_rate > 0.);\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back(\n Predictor_ptr( new learning::BiModel_Predictor( input\n , gateway\n , gradient\n , local_learning_rate\n , random_weight_range\n , regularization_rate\n ) )\n , max_number_of_experts );\n }\n\n};\n\n/* possible template constructor\n\ntemplate <typename PredictorType>\nclass Expert_Vector : public Expert_Vector_Base {\npublic:\n\n template<typename... Args>\n Expert_Vector( const std::size_t max_number_of_experts\n , static_vector_interface& payloads\n , const Args&... predictor_args)\n : Expert_Vector_Base(max_number_of_experts, payloads)\n {\n for (std::size_t i = 0; i < max_number_of_experts; ++i)\n experts.emplace_back( Predictor_ptr(new PredictorType(predictor_args...)), max_number_of_experts );\n }\n};\n\n*/\n\n#endif // EXPERT_VECTOR_H_INCLUDED\n" }, { "alpha_fraction": 0.6953405141830444, "alphanum_fraction": 0.7096773982048035, "avg_line_length": 35.39130401611328, "blob_id": "3e05216a5129c27b6ddb43826e9221e862234514", "content_id": "7f52c80267773ee0a0534489b9f9bc816a524c07", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 837, "license_type": "no_license", "max_line_length": 140, "num_lines": 23, "path": "/src/tests/homeokinetic_core_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <common/log_messages.h>\n#include <control/sensorspace.h>\n#include <learning/predictor.h>\n#include <learning/time_state_space.h>\n#include <learning/homeokinetic_predictor.h>\n#include <controller/pid_control.hpp>\n\nTEST_CASE( \"homeokinetic predictor construction\", \"[homeokinetic_core]\" )\n{\n typedef learning::Homeokinetic_Control::Vector_t ExtInputVector_t;\n typedef std::vector<supreme::pid_control> PID_joint_control_vector_t;\n\n Test_Robot robot(5,2);\n learning::Time_State_Space<8> inputs{robot};\n PID_joint_control_vector_t pid;\n ExtInputVector_t ext_input;\n\n //test_space sensors(0.01);\n learning::Homeokinetic_Core core(inputs, robot.get_joints().size(), /*learning_rate=*/0.01, /*random_weight_range=*/0.1, /*context=*/4);\n}\n" }, { "alpha_fraction": 0.6580827832221985, "alphanum_fraction": 0.6638880372047424, "avg_line_length": 32.25742721557617, "blob_id": "ae4bbaaff41f723d5873ffa5f4477b82ae04bbae", "content_id": "4afb89c0238a64886d2739d287402af9382ef9ec", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6718, "license_type": "no_license", "max_line_length": 137, "num_lines": 202, "path": "/src/common/modules.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* modules.h\n * contains common mathematical functions and helpers */\n\n#ifndef MODULES_H\n#define MODULES_H\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <string>\n#include <math.h>\n#include <float.h>\n#include <assert.h>\n#include <vector>\n#include <algorithm>\n#include \"vector_n.h\"\n#include <common/log_messages.h>\n\n/* tanh() on a vector_t */\ntemplate <typename Vector_t>\nvoid vector_tanh(Vector_t& vec) { for (auto& v : vec) v = tanh(v); }\n\n/* TODO: find a better name, this one is actually wrong */\ninline double tanh_(double x) { return (1.0 + x) * (1.0 - x); }\n\n/* sigmoid function */\ndouble sigmoid(double x);\n\n/* squares the var */\ninline double square(double x) { return x * x; }\n\n/* clips values to Interval [-1,+1] */\ndouble clip(double x);\n\n/* clips values to Interval [-ul_limit,+ul_limit] */\ndouble clip(double x, double ul_limit);\n\n/* clips values to Interval [l_limit, u_limit] */\ndouble clip(double x, double l_limit, double u_limit);\n\n/* clip() on a vector_t */\ntemplate <typename Vector_t>\nvoid vector_clip(Vector_t& vec) { for (auto& v : vec) v = clip(v); }\n\n/* checks if vector is zero */\ntemplate <typename Vector_t>\nbool is_vector_zero(Vector_t const& vec) {\n for (auto& v : vec)\n if (v != 0.0) return false;\n return true;\n}\n\n/* make vector zero */\ntemplate <typename Vector_t>\nvoid zero(Vector_t& v) {\n typename Vector_t::value_type s{};\n std::fill(v.begin(), v.end(), s);\n}\n\ntemplate <typename Vector_t>\nvoid print_vector(Vector_t const& vec, const char* name = \"\", const char* format = \"%+4.2f \") {\n printf(\"%s = [\", name);\n for (std::size_t i = 0; i < vec.size(); ++i)\n printf(format, vec[i]);\n printf(\"]\\n\");\n}\n\n/* min of 3 arguments */\ndouble fmin3(double x, double y, double z);\n\n/* max of 3 arguments */\ndouble fmax3(double x, double y, double z);\n\n/* computes the minimum of N arguments */\ndouble fminN(double *x, unsigned int N);\n\n/* computes the maximum of N arguments */\ndouble fmaxN(double *x, unsigned int N);\n\n/* computes median-of-three */\ndouble median3(double a, double b, double c);\n\n/* generates a double random value within interval [a,b] */\ndouble random_value(double a, double b);\n\n/* generates a integer random index within interval [0,N[ */\nunsigned int random_index(unsigned int N);\n\n/* generates a integer random value within interval [a,b] */\nint random_int(int a, int b);\n\n/* generates a pseudo-random double between 0.0 and 0.999... */\ndouble random_value(void);\n\n/* normally distributed random value */\ndouble random_value_norm(const double m, const double s, const double min, const double max);\n\n/* zero mean normal distributed random value with max. 3 sigma */\ninline double rand_norm_zero_mean(double sigma) { return random_value_norm(0.0, sigma, -3*sigma, 3*sigma); }\n\n/* random sign */\ninline int rand_sign(void) { return (random_index(2) == 0) ? -1 : 1; }\n\n/* returns a random vector of size N with values in [a,b]*/\nstd::vector<double> random_vector(std::size_t N, double a, double b);\n\n/* multiplies matrix by vector */\nvoid mult_mat_by_vect(double *result_vect, const double *mat, const double *vect, const unsigned int Zeilen, const unsigned int Spalten);\nvoid mult_mat_by_vect(VectorN& result_vect, const VectorN& mat, const VectorN& vect);\n\n/* computes the argument which minimizes the function */\nint argmin(double *f, unsigned int N);\n\n/* computes the argument which maximizes the function */\nint argmax(double *f, unsigned int N);\n\n/* computes the squared distance of two vectors */\ndouble squared_distance(double *x , double *y, unsigned int length);\n\n/* computes the squared distance of two vectors */\ntemplate <typename Vector_Type_A, typename Vector_Type_B>\ndouble squared_distance(Vector_Type_A const& x , Vector_Type_B const& y) {\n assert(x.size() == y.size());\n double d = .0;\n for (std::size_t i = 0; i < x.size(); ++i)\n d += square(x[i] - y[i]);\n return d;\n}\n\n/* true is integer is even */\ninline bool is_even(unsigned int number) { return (number%2 == 0); }\n\n/* signum */\ndouble sign(double x);\n\n/* floating point modulo, better than fmod */\ndouble modulo(double a, double b);\n\n/* maps arbitrary angles from interval -inf..+inf to -pi..+pi */\ndouble wrap(double angle);\n\n/* maps arbitrary angles from interval -inf..+inf to -pi..+pi by Martin Marmulla */\ndouble wrap2(double angle);\n\n/* unwraps angles of -pi..+pi to -inf..+inf */\ndouble unwrap(double new_angle, double last_angle);\n\n/* checks that value is close to refval by max distance of tolerance */\ninline bool close(double value, double refval, double tolerance) { return (fabs(value - refval) < tolerance); }\n\ninline bool toggle(bool& b) { b = !b; return b; }\n\n/* checks for vector that value is close to refval by max distance of tolerance */\ntemplate <typename Vector_t>\ninline bool close(Vector_t vec, Vector_t refvec, double tolerance) {\n assert(vec.size() == refvec.size());\n for (std::size_t i = 0; i < vec.size();++i)\n if (fabs(vec[i] - refvec[i]) > tolerance)\n return false;\n return true;\n}\n\n/* asserts that value is close to refval by max distance of tolerance */\ninline void assert_close(double value, double refval, double tolerance, const char* msg) {\n if (not close(value, refval, tolerance))\n err_msg(__FILE__, __LINE__, \"%s: value %f not close to %f by tolerance of %f.\\n\", msg, value, refval, tolerance);\n}\n\n/* checks if variable is in the given range [lower, upper]*/\ntemplate <typename T>\ninline bool in_range(T value, T lower, T upper) { return (lower <= value) and (value <= upper); }\n\ninline void test_range(double value, double lower, double upper, const char* msg)\n{\n if (not in_range(value, lower, upper))\n err_msg(__FILE__, __LINE__, \"%s: value %f out of range [%f %f].\\n\", msg, value, lower, upper);\n}\ninline void test_range(std::size_t value, std::size_t lower, std::size_t upper, const char* msg)\n{\n if (not in_range(value, lower, upper))\n err_msg(__FILE__, __LINE__, \"%s: value %lu out of range [%lu %lu].\\n\", msg, value, lower, upper);\n}\n\ntemplate <typename Vector_t>\ninline void test_range(Vector_t const& values, double lower, double upper, const char* msg)\n{\n for (std::size_t i = 0; i < values.size(); ++i)\n if (not in_range(values[i], lower, upper))\n err_msg(__FILE__, __LINE__, \"%s: value %f from vector index %u out of range [%f %f].\\n\", msg, values[i], i, lower, upper);\n}\n\n#define assert_in_range(VALUE, LOWER, UPPER) \\\ntest_range(VALUE, LOWER, UPPER, #VALUE); \\\n\nstd::string random_string(size_t length);\n\n\ntemplate <typename A_t, typename B_t>\nvoid check_vectors(A_t const& a, B_t const& b) {\n assertion(a.size() == b.size(), \"Incompatible vector lengths %u =/= %u\", a.size(), b.size());\n}\n\n#endif /*MODULES_H*/\n" }, { "alpha_fraction": 0.600806474685669, "alphanum_fraction": 0.6270161271095276, "avg_line_length": 16.714284896850586, "blob_id": "f4470256c03e045b8190e63e1dfa9cbbf30cc301", "content_id": "5e91a176439707f5ca3f9fab10d800d1f1e1fed7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 496, "license_type": "no_license", "max_line_length": 58, "num_lines": 28, "path": "/src/draw/axes3D.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* axes3D.h */\n\n#ifndef AXES3D_H\n#define AXES3D_H\n\n#include <GL/gl.h>\n#include <GL/glu.h>\n#include <GL/glut.h>\n\nclass axes3D\n{\n friend class plot2D;\n friend class plot3D;\n friend class network3D;\n\nprivate:\n float px, py, pz;\n float width, height, depth;\n GLfloat r[8][3], a[6][3];\n int flag; /* axes flag */\n\npublic:\n axes3D(float, float, float, float, float, float, int);\n void draw(float x_angle, float y_angle) const;\n void axesflag(int);\n};\n\n#endif /*AXES3D_H*/\n" }, { "alpha_fraction": 0.5576279759407043, "alphanum_fraction": 0.5754688382148743, "avg_line_length": 30.02201271057129, "blob_id": "d7b0854048d01b4469acb6f93f891d1102f99622", "content_id": "a3f65590b2b55ab8de2be8e3c901a61a3f6612f6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 9865, "license_type": "no_license", "max_line_length": 151, "num_lines": 318, "path": "/src/common/setup.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <common/setup.h>\n\nGlobalFlag do_pause (true );\nGlobalFlag do_quit (false);\nGlobalFlag fast_forward(false);\nGlobalFlag draw_grid (false);\nGlobalFlag do_drawing (false);\nGlobalFlag screenshot (false);\n\nstatic SDL_Window *window;\nstatic SDL_GLContext glcontext;\n\n/* screen properties, consider finding a better place */\nVisuals screen;\n\nfloat t_delay_ms = 10.0f; // convert to unsigned int\ndouble speed_factor = 1.0;\n\nint\nprocess_application(void *data)\n{\n auto cur_time = std::chrono::high_resolution_clock::now();\n auto lst_time = cur_time;\n\n SimpleTimer tim = SimpleTimer(10*1000/*us*/, true); // 10 ms timer\n\n Application_Base *a = static_cast<Application_Base *>(data);\n sts_msg(\"Setting up application.\");\n\n /**TODO better place?*/\n do_drawing.enable(); // enable drawing\n\n sts_msg(\"Entering application main loop.\");\n while (!do_quit.status())\n {\n if (do_pause.status()) {\n usleep(10000); // 10ms\n a->paused();\n }\n else\n {\n if (a->get_cycle_count() % 100 == 0) {\n lst_time = cur_time;\n cur_time = std::chrono::high_resolution_clock::now();\n long long elapsed_ms = std::chrono::duration_cast<std::chrono::milliseconds>(cur_time - lst_time).count();\n speed_factor = 1000 / static_cast<double>(elapsed_ms);\n }\n\n if (!a->loop())\n {\n sts_msg(\"Application main loop has terminated.\");\n break;\n }\n\n if (!fast_forward.status()) {\n while(!tim.check_if_timed_out_and_restart((unsigned)t_delay_ms*1000))\n usleep(100);\n }\n }\n }\n sts_msg(\"Finishing application.\");\n a->finish();\n return 0;\n}\n\nvoid\nfps_controller(double &fps, const double &sp_fps)\n{\n static double delay_ms = 1.0;\n static double time_passed_s;\n static Stopwatch stopwatch;\n\n /* controller */\n delay_ms += 0.01 * (fps - sp_fps);\n if (delay_ms < 0.0) delay_ms = 0.0;\n\n usleep((int) round(1000 * delay_ms));\n time_passed_s = stopwatch.get_time_passed_us() / 1000000.0;\n\n if (time_passed_s > 0)\n fps = 0.9 * fps + 0.1 / time_passed_s; // low pass filter\n}\n\nvoid\nui_main_loop(GlobalFlag& do_quit, const GlobalFlag& do_drawing, Event_Manager& em, const Application_Base& app)\n{\n sts_msg(\"Entering UI main loop.\");\n double fps = 0.0;\n\n while (!do_quit.status())\n {\n em.process_events();\n\n if (do_drawing.status())\n draw_screen(fps, app);\n\n fps_controller(fps, visuals_defaults::frames_per_second); // delay to reach stable 25 f/s\n\n }\n sts_msg(\"Leaving UI main loop.\");\n return;\n}\n\nvoid\ninit_SDL(const bool visuals, const std::size_t window_width, const std::size_t window_height, const std::string& name)\n{\n if (visuals)\n {\n sts_msg(\"Initializing SDL.\");\n /* Dimensions of our window. */\n assert_in_range(window_width , 100ul, 2048ul);\n assert_in_range(window_height, 100ul, 1024ul);\n\n screen.window_size_x = window_width;\n screen.window_size_y = window_height;\n\n /* First, initialize SDL's video subsystem. */\n sts_msg(\"Initializing SDL VIDEO.\");\n if (SDL_Init(SDL_INIT_VIDEO))\n err_msg(__FILE__, __LINE__, \"SDL Video initialization failed: %s\", SDL_GetError());\n\n sts_msg(\"Initializing SDL Window.\");\n window = SDL_CreateWindow( name.c_str() // title\n , SDL_WINDOWPOS_UNDEFINED // initial x position\n , SDL_WINDOWPOS_UNDEFINED // initial y position\n , window_width // width, in pixels\n , window_height // height, in pixels\n , SDL_WINDOW_OPENGL\n // | SDL_WINDOW_RESIZABLE // flags\n );\n\n if (window == NULL)\n err_msg(__FILE__, __LINE__, \"SDL Window initialization failed: %s\", SDL_GetError());\n\n sts_msg(\"Creating GL context.\");\n glcontext = SDL_GL_CreateContext(window);\n\n /* Color depth in bits of our window. */\n //TODO int bpp = info->vfmt->BitsPerPixel;\n\n// SDL_GL_SetAttribute(SDL_GL_RED_SIZE, 5);\n// SDL_GL_SetAttribute(SDL_GL_GREEN_SIZE, 5);\n// SDL_GL_SetAttribute(SDL_GL_BLUE_SIZE, 5);\n// SDL_GL_SetAttribute(SDL_GL_DEPTH_SIZE, 16);\n// SDL_GL_SetAttribute(SDL_GL_DOUBLEBUFFER, 1);\n\n /* Flags we will pass into SDL_SetVideoMode. */\n //int flags = SDL_OPENGL;// | SDL_FULLSCREEN;\n\n /* set the video mode */\n //if (SDL_SetVideoMode(screen.window_size_x, screen.window_size_y, bpp, flags) == 0)\n // err_msg(__FILE__, __LINE__, \"Initialization of video mode failed: %s\", SDL_GetError());\n\n init_OpenGL(screen.window_size_x, screen.window_size_y);\n\n /* Initialize GLUT */\n sts_msg(\"Initialize GLUT.\");\n int argc = 1; // fake arguments for glutInit()\n char * argv[] = {strdup(\"sim\"), NULL};\n glutInit(&argc, argv);\n\n } // done initializing visuals\n\n /* initializing control pad */\n init_controls();\n\n atexit(deinit_SDL);\n}\n\nvoid\ndeinit_SDL(void) {\n sts_msg(\"De-initialize SDL on exit.\");\n SDL_GL_DeleteContext(glcontext);\n SDL_DestroyWindow(window);\n SDL_Quit();\n}\n\nvoid\ninit_OpenGL(const std::size_t window_width, const std::size_t window_height)\n{\n sts_msg(\"Initialize OpenGL.\");\n double ratio = (double) window_width / (double) window_height;\n\n glEnable(GL_BLEND);\n glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);\n\n //glEnable(GL_LINE_SMOOTH);\n //glHint(GL_LINE_SMOOTH_HINT, GL_DONT_CARE);\n\n glShadeModel(GL_SMOOTH); // shading model Gouraud (smooth)\n\n /* Culling. */\n glCullFace(GL_BACK);\n glFrontFace(GL_CCW);\n glEnable(GL_CULL_FACE);\n\n /* Set the clear color. */\n glClearColor(0.0, 0.0, 0.0, 0.0);//glClearColor(0.05, 0.0, 0.1, 0.0);\n\n /* Setup our view port. */\n glViewport(0, 0, window_width, window_height);\n\n /* Change to the projection matrix and set our viewing volume. */\n glMatrixMode(GL_PROJECTION);\n glLoadIdentity();\n gluPerspective(visuals_defaults::gl_fovy,\n ratio,\n visuals_defaults::gl_zNear,\n visuals_defaults::gl_zFar);\n}\n\nvoid\ninit_controls()\n{\n sts_msg(\"Initializing controls.\");\n /* Initialize the SDL joystick subsystem */\n if (SDL_InitSubSystem(SDL_INIT_JOYSTICK))\n err_msg(__FILE__, __LINE__, \"SDL joystick initialization failed: %s\", SDL_GetError());\n\n int num_buttons = 0; // number of control pad buttons\n int num_axes = 0; // number of control pad axes pairs\n SDL_Joystick *ctrl;\n\n /* initialize control pad */\n int JNum = SDL_NumJoysticks();\n int joystick_ID = 0;\n\n if (JNum > 0)\n {\n if (JNum > 1) sts_msg(\"Available control pads: %d\", JNum);\n\n /* open control pad with jID 0 */\n ctrl = SDL_JoystickOpen(joystick_ID);\n if (NULL != ctrl)\n {\n num_buttons = SDL_JoystickNumButtons(ctrl);\n num_axes = SDL_JoystickNumAxes(ctrl);\n\n if (num_buttons < 1) wrn_msg(\"Control pad \\\"%s\\\" has no buttons available.\", SDL_JoystickName(ctrl));\n if (num_axes < 2) wrn_msg(\"Control pad \\\"%s\\\" has insufficient axes.\" , SDL_JoystickName(ctrl));\n\n sts_msg(\"Control pad \\\"%s\\\" found with %d axes and %d buttons.\", SDL_JoystickName(ctrl), num_axes, num_buttons);\n }\n else\n err_msg(__FILE__, __LINE__, \"control pad error: %s\", SDL_GetError());\n\n }\n else wrn_msg(\"There is no control pad available.\");\n\n return;\n}\n\nvoid\ndraw_screen(const double& fps, const Application_Base& app)\n{\n static FILE *fp;\n const unsigned long long cycles = app.get_cycle_count();\n\n /* prepare screen shot */\n if (screenshot.status())\n {\n\n int buffsize = 1024*1024;\n\n fp = open_file(\"wb\", \"screenshot_%llu.svg\", cycles);\n sts_msg(\"Prepare screen shot at cycle: %llu\",cycles);\n\n gl2psBeginPage(\"screenshot\", \"gl2ps\", NULL, GL2PS_SVG, GL2PS_SIMPLE_SORT,\n GL2PS_DRAW_BACKGROUND | GL2PS_USE_CURRENT_VIEWPORT,\n GL_RGBA, 0, NULL, 0, 0, 0, buffsize, fp, \"foo.svg\");\n }\n\n /* Clear the color and depth buffers. */\n glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);\n\n /* We don't want to modify the projection matrix. */\n glMatrixMode(GL_MODELVIEW);\n glLoadIdentity();\n\n glTranslatef( screen.x_position + screen.x_position_disp\n , screen.y_position + screen.y_position_disp\n ,-screen.zdist );\n\n if (screen.rotate_view && (screen.x_angle += 0.1f * screen.rot_factor) > 360.0f)\n screen.x_angle = 0.0f;\n\n const pref p = {screen.x_angle + screen.x_angle_disp, screen.y_angle + screen.y_angle_disp};\n app.draw(p); // draw application dependent content\n\n /* overlay grid */\n if (draw_grid.status()) {\n glColor4f(1.0f, 1.0f, 1.0f, 0.1f);\n glLineWidth(1.0f);\n draw_grid2D(1.0, 5);\n }\n\n if (screen.show_fps) {\n glColor3f(1.0, 1.0, 1.0);\n glprintf(-1.0,-1.0, 0.0, .025, \"%s (%lu), %d fps %1.2fx\", get_time_from_cycle_counter(cycles).c_str(), cycles, (int) round(fps), speed_factor);\n }\n\n /* finish screen shot */\n if (screenshot.status()) {\n if (GL2PS_OVERFLOW == gl2psEndPage())\n wrn_msg(\"Overflow while creating screen shot. Increase buffer size.\");\n fclose(fp);\n screenshot.disable();\n }\n\n SDL_GL_SwapWindow(window); // swap the buffers\n}\n\nvoid\nsignal_terminate_handler(int signum)\n{\n sts_msg(\"Got a SIGINT(%d) from user\\n\", signum);\n quit();\n}\n" }, { "alpha_fraction": 0.6905989646911621, "alphanum_fraction": 0.6905989646911621, "avg_line_length": 30.487499237060547, "blob_id": "39e92cbe80f2d489fe8e2afc106c83d1f934476c", "content_id": "f7d0730498a0d511e5e6b5e28ebbb948fa49f93c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2521, "license_type": "no_license", "max_line_length": 120, "num_lines": 80, "path": "/src/evolution/evolution.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EVOLUTION_H\r\n#define EVOLUTION_H\r\n\n#include <vector>\n#include <algorithm>\n#include <float.h>\n#include <math.h>\n#include <memory>\n\n#include <common/config.h>\n#include <common/stopwatch.h>\n#include <common/modules.h>\n#include <common/log_messages.h>\n#include <common/file_io.h>\n\n#include <robots/simloid.h>\n\n#include <evolution/individual.h>\n#include <evolution/population.h>\n#include <evolution/evaluation_interface.h>\n#include <evolution/evolution_strategy.h>\n#include <evolution/generation_based_strategy.h>\n#include <evolution/pool_strategy.h>\n#include <evolution/setting.h>\n\ntypedef std::shared_ptr<Evolution_Strategy> Strategy_Pointer;\n\nclass Evolution\n{\n Evolution( const Evolution& other ) = delete; // non construction-copyable\n Evolution& operator=( const Evolution& ) = delete; // non copyable\n\npublic:\n Evolution(Evaluation_Interface &evaluation, const Setting& settings, const std::vector<double>& seed_genome); // new\n Evolution(Evaluation_Interface &evaluation, const Setting& settings, bool playback_only); // resume or watch\n\n ~Evolution() { sts_msg(\"Evolution shut down.\"); }\n\n bool loop(void);\n void finish(void);\n\n statistics_t const& get_fitness_statistics (void) const { return strategy->get_fitness_statistics(); }\n statistics_t const& get_mutation_statistics(void) const { return strategy->get_mutation_statistics(); }\n\n std::size_t get_number_of_trials(void) const { return strategy->get_max_trials(); }\n std::size_t get_current_trial (void) const { return strategy->get_current_trial(); }\n std::size_t get_population_size (void) const { return population.get_size(); }\n\n std::vector<double> get_best_individuals_genome(void) const { return population.get_best_individual().genome; }\n\nprivate:\n Evaluation_Interface& evaluation;\n const Setting& settings;\n const std::string projectname;\n const std::string conffilename;\n config configuration;\n\n Evolution_State state;\n\n std::vector<double> seed;\n Population population;\n Strategy_Pointer strategy;\n\n file_io::Logfile evolution_log;\n file_io::Logfile bestindiv_log;\n\n const bool verbose;\n const bool playback_only;\n\n void common_setup(void);\n void save_best_individual(void);\n void save_statistics(void);\n void write_config(void);\n\n void prepare_quit();\n};\n\nstd::string create_project_name_and_folder(std::string name);\n\n#endif // EVOLUTION_H\n" }, { "alpha_fraction": 0.5971605777740479, "alphanum_fraction": 0.5989352464675903, "avg_line_length": 44.060001373291016, "blob_id": "d9171c8023ec598d422ad394fcbea1ffb832d23a", "content_id": "7b3ac275f4a74f005411a54e604ac4a0c8658d68", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2254, "license_type": "no_license", "max_line_length": 136, "num_lines": 50, "path": "/src/learning/time_state_space.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef TIME_STATE_SPACE_H\n#define TIME_STATE_SPACE_H\n\n#include <robots/robot.h>\n#include <control/sensorspace.h>\n\nnamespace learning {\n\ntemplate <std::size_t NumTaps>\nclass Time_State_Space : public time_embedded_sensors<NumTaps> {\npublic:\n Time_State_Space(const robots::Robot_Interface& robot)\n : time_embedded_sensors<NumTaps>(robot.get_joints().size())\n {\n auto const& joints = robot.get_joints();\n auto const& accels = robot.get_accels();\n\n for (robots::Joint_Model const& j : joints) {\n time_embedded_sensors<NumTaps>::sensors.emplace_back(j.name + \"_ang\", [&j](){ return j.s_ang; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(j.name + \"_vel\", [&j](){ return j.s_vel; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(j.name + \"_vol\", [&j](){ return j.motor.get_backed(); });\n }\n //for (robots::Joint_Model const& j : joints)\n // time_embedded_sensors<NumTaps>::sensors.emplace_back(j.name + \"_cur\", [&j](){ return j.motor.get_backed();/*j.s_cur;*/ });\n\n for (robots::Accel_Sensor const& a : accels) {\n time_embedded_sensors<NumTaps>::sensors.emplace_back(\"acc_x\", [&a](){ return a.a.x; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(\"acc_y\", [&a](){ return a.a.y; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(\"acc_z\", [&a](){ return a.a.z; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(\"vel_x\", [&a](){ return a.v.x; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(\"vel_y\", [&a](){ return a.v.y; });\n time_embedded_sensors<NumTaps>::sensors.emplace_back(\"vel_z\", [&a](){ return a.v.z; });\n }\n\n // don't know if that helps much.\n //time_embedded_sensors<NumTaps>::sensors.emplace_back(\"noise\", [](){ return 0.1 + rand_norm_zero_mean(0.1); });\n\n //IDEA: consider avg rotational speed... as the gyroscope\n\n /*\n for (robots::Joint_Model const& j : joints)\n time_embedded_sensors<NumTaps>::sensors.emplace_back(j.name + \"_mot\", [&j](){ return j.motor.get(); });*/\n\n /**Added bias internally */\n }\n};\n\n} /* namespace learning */\n\n#endif /* TIME_STATE_SPACE_H */\n\n" }, { "alpha_fraction": 0.5565939545631409, "alphanum_fraction": 0.5913810729980469, "avg_line_length": 24.342105865478516, "blob_id": "55d2c48dff3fc1311be9d6a172cf227c9ad0e5e0", "content_id": "7f69cd485352baeae8d24353cf872044d6bb5ea8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1926, "license_type": "no_license", "max_line_length": 122, "num_lines": 76, "path": "/src/common/visuals.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef VISUALS_H_INCLUDED\n#define VISUALS_H_INCLUDED\n\n#include <basic/color.h>\n#include <GL/gl.h>\n\nnamespace visuals_defaults {\n const unsigned int window_width = 512;\n const unsigned int window_height = 512;\n const unsigned int frames_per_second = 25;\n const float zdist = 1.7320508f + 0.01f; // z = sin(pi/3) / sin(pi/6) for 60 deg view angle to be 2 units width;\n const unsigned int rot_factor = 1;\n const float zoom_factor = 0.95;\n\n const float gl_fovy = 60.0;\n const float gl_zNear = 0.05;\n const float gl_zFar = 1024.0;\n}\n\n//TODO improve this\nstruct Visuals\n{\n Visuals()\n : window_size_x(visuals_defaults::window_width)\n , window_size_y(visuals_defaults::window_height)\n , mdy(0.0f)\n , mdx(0.0f)\n , x_angle(0.0f)\n , y_angle(20.0f)\n , x_angle_disp(0.0f)\n , y_angle_disp(0.0f)\n , x_position(.0f)\n , y_position(.0f)\n , x_position_disp(.0f)\n , y_position_disp(.0f)\n , zdist(visuals_defaults::zdist)\n , rotate_view(true)\n , rot_factor(visuals_defaults::rot_factor)\n , show_fps(true)\n , snap(1.0)\n {}\n\n void reset(void) {\n x_angle = .0f;\n y_angle = 20.0f;\n x_position = .0f;\n y_position = .0f;\n zdist = visuals_defaults::zdist;\n rotate_view = true;\n rot_factor = visuals_defaults::rot_factor;\n mdx = .0f;\n mdy = .0f;\n }\n\n void set_background_color(Color4 color) { glClearColor(color.r, color.g, color.b, color.a); }\n\n unsigned int window_size_x;\n unsigned int window_size_y;\n float mdy;\n float mdx;\n float x_angle;\n float y_angle;\n float x_angle_disp;\n float y_angle_disp;\n float x_position;\n float y_position;\n float x_position_disp;\n float y_position_disp;\n float zdist;\n bool rotate_view;\n int rot_factor;\n bool show_fps;\n float snap;\n};\n\n#endif // VISUALS_H_INCLUDED\n" }, { "alpha_fraction": 0.5237889289855957, "alphanum_fraction": 0.5730968713760376, "avg_line_length": 25.88372039794922, "blob_id": "018bd1f69f91a5a6aafd36cea0be056fffc0681f", "content_id": "b8167dd8d1d12b52efd100a545cb826f36d9df0c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2312, "license_type": "no_license", "max_line_length": 106, "num_lines": 86, "path": "/src/draw/network3D.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* network3D.cpp */\n\n#include \"network3D.h\"\n\nvoid network3D::draw(float x_angle, float y_angle) const\n{\n glPushMatrix();\n glTranslatef(axis.px, axis.py, axis.pz);\n glRotatef(y_angle, 1.0, 0.0, 0.0);\n glRotatef(x_angle, 0.0, 1.0, 0.0);\n\n /* draw nodes */\n glColor4ub(255,255,255,160);\n\n for (unsigned int i = 0; i < number_of_nodes; ++i)\n draw_solid_cube(n_pos[i].x, n_pos[i].y, n_pos[i].z, 0.005);\n\n glColor4ub(255,255,255,96);\n\n for (unsigned int i = 0; i < number_of_nodes; ++i)\n draw_solid_cube(n_pos[i].x, n_pos[i].y, n_pos[i].z, 0.005 + 0.01 * n_size[i]);\n\n\n glColor4ub(255,192,0,128);\n if (n_size[special_node] > 0)\n draw_solid_cube(n_pos[special_node].x, n_pos[special_node].y, n_pos[special_node].z, 0.02);\n\n glColor4ub(0,128,255,128);\n if (n_size[activated_node] > 0)\n draw_solid_cube(n_pos[activated_node].x, n_pos[activated_node].y, n_pos[activated_node].z, 0.015);\n\n /* draw edges */\n glLineWidth(1.0f);\n for (unsigned int i = 0; i < number_of_nodes; ++i)\n for (unsigned int j = i+1; j < number_of_nodes; ++j)\n if (n_edges[j][i] || n_edges[i][j])\n {\n glColor4ub(255, 128, 32, std::max(n_edges[j][i],n_edges[i][j]));\n draw_line(n_pos[i], n_pos[j]);\n }\n glPopMatrix();\n}\n\nvoid network3D::special(unsigned int n)\n{\n assert(n < number_of_nodes);\n special_node = n;\n}\n\nvoid network3D::activated(unsigned int n)\n{\n assert(n < number_of_nodes);\n activated_node = n;\n}\n\nvoid network3D::update_node(unsigned int n, float x0, float x1, float x2, float s)\n{\n assert(n < number_of_nodes);\n n_pos[n].x = x0 * axis.width / 2;\n n_pos[n].y = x1 * axis.height / 2;\n n_pos[n].z = x2 * axis.depth / 2;\n n_size[n] = s;\n}\n\nvoid network3D::update_node(unsigned int n, float s)\n{\n assert(n < number_of_nodes);\n n_size[n] = s;\n}\n\nvoid network3D::update_edge(unsigned int i, unsigned int j, unsigned char op)\n{\n assert(i < number_of_nodes && j < number_of_nodes);\n n_edges[i][j] = op;\n}\n\nvoid network3D::update_all_edges_of(unsigned int i, unsigned char op)\n{\n assert(i < number_of_nodes);\n for (unsigned int k = 0; k < number_of_nodes; ++k) {\n n_edges[i][k] = op;\n n_edges[k][i] = op;\n }\n}\n\n/* network3D.cpp */\n" }, { "alpha_fraction": 0.6401015520095825, "alphanum_fraction": 0.6472080945968628, "avg_line_length": 32.38983154296875, "blob_id": "d6ac16dabcb42fb2d1f05064b6074af84b132510", "content_id": "083b2fbb69a2397dd2df0eb55d5cb8d9cf6cad22", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1970, "license_type": "no_license", "max_line_length": 113, "num_lines": 59, "path": "/src/learning/epsilon_greedy.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EPSILON_GREEDY_H_INCLUDED\n#define EPSILON_GREEDY_H_INCLUDED\n\n#include <vector>\n#include <common/static_vector.h>\n#include <common/log_messages.h>\n#include <learning/action_selection.h>\n#include <learning/action_module.h>\n#include <learning/payload.h>\n\nnamespace learning {\n\nclass Epsilon_Greedy : public Action_Selection_Base\n{\n\npublic:\n Epsilon_Greedy( const static_vector<State_Payload>& states\n , const Action_Module_Interface& actions\n , const double exploration_rate )\n : Action_Selection_Base(states, actions, exploration_rate)\n {\n dbg_msg(\"Creating 'Epsilon Greedy' action selection.\");\n assert_in_range(exploration_rate, 0.01, 0.99);\n }\n\nprivate:\n void update_distribution(const std::size_t greedy_action)\n {\n assert(actions.exists(greedy_action));\n selection_probabilities.zero(); // set all zeros\n\n if (actions.get_number_of_actions_available() > 1) {\n const double non_greedy_portion = exploration_rate / (actions.get_number_of_actions_available() - 1);\n\n for (std::size_t i = 0; i < selection_probabilities.size(); ++i)\n if (actions.exists(i) and (i != greedy_action))\n selection_probabilities[i] = non_greedy_portion;\n\n selection_probabilities[greedy_action] = 1.0 - exploration_rate;\n }\n else if (actions.get_number_of_actions_available() == 1) {\n selection_probabilities[greedy_action] = 1.0;\n }\n else assert(false and \"no actions available\");\n }\n\npublic:\n std::size_t select_action(std::size_t current_state, std::size_t current_policy) override\n {\n update_distribution(states[current_state].policies[current_policy].get_argmax_q());\n\n /* create random variable and select */\n return select_from_distribution(selection_probabilities);\n }\n};\n\n} // namespace learning\n\n#endif // EPSILON_GREEDY_H_INCLUDED\n" }, { "alpha_fraction": 0.647251307964325, "alphanum_fraction": 0.6753926873207092, "avg_line_length": 30.83333396911621, "blob_id": "32efdf36054da21d4b44d75a33167a35b70c44f1", "content_id": "14d1ba65f5ca5563a02e6660ce2671e19ccbf63c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1528, "license_type": "no_license", "max_line_length": 103, "num_lines": 48, "path": "/src/learning/eigenzeit.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef LEARNING_EIGENZEIT_H\n#define LEARNING_EIGENZEIT_H\n\n#include <common/log_messages.h>\n#include <control/statemachine.h>\n\nnamespace learning {\n\n/** Eigenzeit.h\n ** The concept of eigenzeit constitutes a mechanism for translating the inherent time step (cycle)\n ** of usually 10ms (100Hz) into a 'time' or cycle which only depends on gmes' state changes. Eigenzeit\n ** passes only when a new state is recognized by gmes. If a state does not change within a given time\n ** it is recognized as a state transition into itself and another eigenzeit cycle is executed. */\n\nclass Eigenzeit\n{\n const control::Statemachine_Interface& statemachine;\n const uint64_t timeout_10ms;\n uint64_t system_cycle;\n uint64_t eigenzeit_cycle;\n\npublic:\n Eigenzeit(const control::Statemachine_Interface& statemachine, const uint64_t timeout_10ms = 100)\n : statemachine(statemachine)\n , timeout_10ms(timeout_10ms)\n , system_cycle(0)\n , eigenzeit_cycle(0)\n {\n dbg_msg(\"Creating Eigenzeit with max. timeout of %1.2f s\", timeout_10ms/(100.0));\n assert(timeout_10ms > 0);\n }\n\n void execute_cycle(void)\n {\n ++system_cycle;\n if (statemachine.has_state_changed() or (system_cycle >= timeout_10ms)) {\n system_cycle = 0;\n ++eigenzeit_cycle;\n }\n }\n\n bool has_progressed(void) const { return system_cycle == 0; }\n uint64_t get_cycle (void) const { return eigenzeit_cycle; }\n};\n\n} /* namespace learning */\n\n#endif /* LEARNING_EIGENZEIT_H */\n" }, { "alpha_fraction": 0.5770773887634277, "alphanum_fraction": 0.5878223776817322, "avg_line_length": 28.70212745666504, "blob_id": "fe3f4d14d3cf4b7cb9b17976f173fccdf9c7c624", "content_id": "ff4e2d6f9a9eee6b75c2c7036f79c2897bb62e43", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6980, "license_type": "no_license", "max_line_length": 138, "num_lines": 235, "path": "/src/common/udp.hpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef UDP_HPP\n#define UDP_HPP\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <unistd.h>\n#include <string.h>\n#include <sys/types.h>\n#include <sys/socket.h>\n#include <arpa/inet.h>\n#include <netinet/in.h>\n#include <time.h>\n\n#include <common/log_messages.h>\n#include <common/lock.h>\n\n/*\n This UDP class is optionally able to do a multicast connection.\n Attention: this might spam the network if datagram size and frequency is large.\n Example usage for UDP Multicast Sender and Receiver\n group: 239.255.255.252\n port: 1900\n\n Otherwise, just specify address (group) and port.\n\n*/\n\nnamespace network {\n\n const uint16_t default_port = 7331;\n\n template <typename T, typename B>\n std::size_t getfrom(T& var, const B* buf, std::size_t offset)\n {\n var = T{};\n memcpy(&var, buf+offset, sizeof(T));\n return offset+sizeof(T);\n }\n\n template <typename B>\n bool validate(const B* buf, std::size_t maxl)\n {\n B sum = B{0};\n for (std::size_t i = 0; i < maxl; ++i)\n sum += buf[i];\n return sum == 0;\n }\n\n\ntemplate <unsigned NBytes>\nclass UDPSender {\n\n int sockfd;\n struct sockaddr_in addr{};\n\n uint8_t msg[NBytes];\n uint8_t buf[NBytes];\n\n common::mutex_t l_buf{};\n common::mutex_t l_adr{};\n bool tx_ready = false;\n\npublic:\n UDPSender(std::string const& group, uint16_t port = default_port)\n : sockfd(socket(AF_INET, SOCK_DGRAM, 0)) /* create UDP socket */\n , msg(), buf()\n {\n /* check for problems*/\n if (sockfd < 0)\n err_msg(__FILE__,__LINE__,\"Cannot create UDP socket with group %s and port %u.\\n%s\", group.c_str(), port, strerror(errno));\n\n /* set destination */\n common::lock_t lock(l_adr);\n memset(&addr, 0, sizeof(addr)); //TODO constructor\n addr.sin_family = AF_INET;\n addr.sin_addr.s_addr = inet_addr(group.c_str());\n addr.sin_port = htons(port);\n }\n\n void transmit(void)\n {\n common::lock_t lock(l_adr);\n int nbytes = sendto(sockfd, msg, NBytes, 0, (struct sockaddr*) &addr, sizeof(addr));\n\n { common::lock_t lock(l_buf);\n std::swap(msg, buf); /* swap buffers */\n tx_ready = false; // reset flag, waiting for data\n } /*end lock*/\n\n if (nbytes < 0)\n wrn_msg(\"Sending failed: %s\", strerror(errno));\n\n promise(nbytes == NBytes,__FILE__,__LINE__,\"Sending failed, message too short.\");\n }\n\n void set_buffer(const uint8_t* src, std::size_t size) {\n assertion(size == NBytes, \"Buffer length does not match. %u =!= %u\", size, NBytes);\n common::lock_t lock(l_buf);\n memcpy(buf, src, NBytes);\n tx_ready = true; // set flag, ready for transmission\n }\n\n bool data_ready(void) const { return tx_ready; }\n\n void change_destination(std::string const& dest, uint16_t port = default_port)\n {\n common::lock_t lock(l_adr);\n addr.sin_addr.s_addr = inet_addr(dest.c_str());\n addr.sin_port = htons(port);\n }\n};\n\n\ntemplate <unsigned NBytes>\nclass UDPReceiver {\n\n int sockfd;\n struct sockaddr_in addr{};\n\n uint8_t msg[NBytes];\n bool rx_ready = false;\n\npublic:\n UDPReceiver(const char* group, uint16_t port = default_port, bool multicast = false)\n : sockfd(socket(AF_INET, SOCK_DGRAM, 0)) /* create UDP socket */\n , msg()\n {\n /* check for problems*/\n if (sockfd < 0)\n err_msg(__FILE__,__LINE__,\"Cannot create UDP socket with group %s and port %d.\\n%s\", group, port, strerror(errno));\n\n /* allow multiple sockets to use the same port */\n unsigned optval = 1;\n if (setsockopt(sockfd, SOL_SOCKET, SO_REUSEADDR, (char*) &optval, sizeof(optval)) < 0)\n err_msg(__FILE__,__LINE__,\"Reusing address failed. Cannot allow multiple sockets to use the same port.\\n%s\", strerror(errno));\n\n /* set destination */\n memset(&addr, 0, sizeof(addr));\n addr.sin_family = AF_INET;\n addr.sin_addr.s_addr = htonl(INADDR_ANY); /* <- Note: allow any, differs from sender */\n addr.sin_port = htons(port);\n\n /* bind receive address */\n if (bind(sockfd, (struct sockaddr*) &addr, sizeof(addr)) < 0)\n err_msg(__FILE__,__LINE__,\"Cannot bind receive address.\\n%s\", strerror(errno));\n\n if (multicast) {\n /* request the kernel to join a multicast group */\n struct ip_mreq mreq;\n mreq.imr_multiaddr.s_addr = inet_addr(group);\n mreq.imr_interface.s_addr = htonl(INADDR_ANY);\n if (setsockopt(sockfd, IPPROTO_IP, IP_ADD_MEMBERSHIP, (char*) &mreq, sizeof(mreq)) < 0)\n err_msg(__FILE__,__LINE__,\"Cannot set socket options for multicast group.\\n%s\", strerror(errno));\n }\n\n struct timeval read_timeout;\n read_timeout.tv_sec = 1;\n read_timeout.tv_usec = 0;\n if (setsockopt(sockfd, SOL_SOCKET, SO_RCVTIMEO, &read_timeout, sizeof(read_timeout)) < 0)\n err_msg(__FILE__,__LINE__,\"Cannot set socket options for read timeout.\\n%s\", strerror(errno));\n }\n\n void receive_message(void)\n {\n assert(rx_ready == false && \"Received data not acknowledged yet.\");\n\n socklen_t addrlen = sizeof(addr);\n int nbytes = recvfrom(sockfd, msg, NBytes, 0, (struct sockaddr *) &addr, &addrlen);\n if (nbytes < 0) {\n wrn_msg(\"Receiving failed: %s\", strerror(errno));\n return;\n }\n\n if (nbytes == 0) return;\n\n if (nbytes != NBytes)\n wrn_msg(\"Invalid message length: %u != %u.\", nbytes, NBytes);\n\n rx_ready = true;\n }\n\n const uint8_t* get_message(void) const { return msg; }\n\n bool data_received(void) const { return rx_ready; }\n\n void acknowledge(void) { rx_ready = false; /* clear flag, ready to receive */ }\n\n}; /* class UDP_Receiver */\n\n\ntemplate <unsigned N>\nclass Sendbuffer {\n\tstatic const unsigned NumSyncBytes = 2;\n\tstatic const uint8_t chk_init = 0xFE; /* (0xff + 0xff) % 256 */\n\tuint16_t ptr = NumSyncBytes;\n\tuint8_t buffer[N];\n\tuint8_t checksum = chk_init;\npublic:\n\tSendbuffer()\n\t{\n\t\tstatic_assert(N > NumSyncBytes, \"Invalid buffer size.\");\n\t\tfor (uint8_t i = 0; i < NumSyncBytes; ++i)\n\t\t\tbuffer[i] = 0xFF; // init sync bytes once\n\t}\n\tvoid add_byte(uint8_t byte) {\n\t\tassertion(ptr < (N-1), \"ptr=%u N=%u\", ptr, N);\n\t\tbuffer[ptr++] = byte;\n\t\tchecksum += byte;\n\t}\n\n template <typename T>\n\tSendbuffer& add(T var) {\n\t const uint8_t * const bytes = (const uint8_t *) &var;\n for (unsigned i = 0; i < sizeof(T); ++i)\n add_byte(bytes[i]);\n return *this;\n\t}\n\n\tvoid reset(void) { ptr = NumSyncBytes; }\n\n\tvoid add_checksum() {\n\t\tassertion(ptr < N, \"ptr=%u N=%u\", ptr, N);\n\t\tbuffer[ptr++] = ~checksum + 1; /* two's complement checksum */\n\t\tchecksum = chk_init;\n\t}\n\n\tconst uint8_t* get(void) const { return buffer; }\n\n\tuint16_t size(void) const { return ptr; }\n\n}; /* Sendbuffer */\n\n} /* namespace network */\n\n#endif /* UDP_HPP */\n" }, { "alpha_fraction": 0.6654788255691528, "alphanum_fraction": 0.6703786253929138, "avg_line_length": 22.144329071044922, "blob_id": "23c6f5599287e2808ab60bca3dd327ca55df2013", "content_id": "59319ff8415dab71c80ee484e930d8a182c883cc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2245, "license_type": "no_license", "max_line_length": 135, "num_lines": 97, "path": "/src/common/gui.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GUI_H\n#define GUI_H\n\n#include <vector>\n#include <memory>\n#include <thread>\n#include <locale.h>\n#include <gtk/gtk.h>\n#include <common/globalflag.h>\n#include <common/log_messages.h>\n\n\nextern float progress_val1;\nextern float progress_val2;\n\nextern GlobalFlag do_pause;\n\ngboolean update_progressbar1(gpointer data);\ngboolean update_progressbar2(gpointer data);\n\nint thread_gtk_main(void);\n\nvoid on_button_start_clicked(GtkObject *object, gpointer user_data);\n\nclass vertical_scaler\n{\npublic:\n vertical_scaler(double value_min, double value_max, double value_default, double increment)\n : adjustment((GtkAdjustment*) gtk_adjustment_new(value_default, value_min, value_max, increment, 1.0, 0.0))\n , vscale(gtk_vscale_new(adjustment))\n {\n gtk_scale_set_digits(GTK_SCALE(vscale), 1); //TODO num digits\n gtk_scale_set_value_pos(GTK_SCALE(vscale), GTK_POS_BOTTOM);\n gtk_widget_show(vscale);\n }\n\n GtkWidget* get(void) { return vscale; }\n\nprivate:\n GtkAdjustment *adjustment;\n GtkWidget *vscale;\n};\n\nclass gui_interface\n{\npublic:\n virtual ~gui_interface() {};\n};\n\nclass no_gui : public gui_interface\n{\npublic:\n no_gui() { sts_msg(\"No GUI loaded.\"); }\n};\n\nclass GTK_gui : public gui_interface\n{\n GTK_gui(const GTK_gui& other) = delete; // non construction-copyable\n GTK_gui& operator=( const GTK_gui&) = delete; // non copyable\n\npublic:\n GTK_gui();\n\n ~GTK_gui()\n {\n dbg_msg(\"closing GTK main thread\");\n gtk_main_quit();\n main_gtk->join();\n }\n\nprivate:\n const unsigned int num_vscale;\n const bool init_result;\n\n GtkWidget *window;\n GtkWidget *table;\n GtkWidget *label;\n GtkWidget *progressbar1;\n GtkWidget *progressbar2;\n GtkWidget *button_start;\n\n std::vector<vertical_scaler> multiscale;\n std::unique_ptr<std::thread> main_gtk;\n};\n\nclass GUI_Starter\n{\n std::unique_ptr<gui_interface> gui;\n\n GUI_Starter(const GUI_Starter& other) = delete; // non construction-copyable\n GUI_Starter& operator=( const GUI_Starter&) = delete; // non copyable\n\npublic:\n GUI_Starter(bool visuals_enabled = true) : gui(visuals_enabled ? (gui_interface*) new GTK_gui() : (gui_interface*) new no_gui()) {}\n};\n\n#endif /*GUI_H*/\n" }, { "alpha_fraction": 0.6325392723083496, "alphanum_fraction": 0.63608717918396, "avg_line_length": 33.88495635986328, "blob_id": "c2f64f4b92e8c8bc3a48a356053f9f38b310d9b9", "content_id": "e92bdb0c62ec0fdc03db70e3a77b70fd6940d4ed", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3949, "license_type": "no_license", "max_line_length": 100, "num_lines": 113, "path": "/src/learning/gmes.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GMES_H\r\n#define GMES_H\n\r\n#include <cmath>\n#include <vector>\n#include <cassert>\n#include <common/log_messages.h>\n#include <common/modules.h>\n#include <common/vector_n.h>\n#include <control/statemachine.h>\n#include <learning/expert_vector.h>\n#include <learning/gmes_constants.h>\n#include <learning/q_function.h>\n#include <learning/payload.h>\n#include <learning/learning_machine_interface.h>\r\n/* first object oriented implementation of GMES\n * 23.02.2015 (Elmar ist heute 16 Monate alt geworden) */\n\n\n/**\n * Überlege Dir eine geschachtelte Gmes-Schichten-Anordnung, welche zunächst aus Sensordaten (1)\n * Fixpunkte und verschiedene Attraktoren (2) erkennt und dann in \"kombinierte Gelenke\"-Features (3)\n * bishin zu Körperbewegungen oder -posen (4) erkennt.\n */\n\n/** TODO: namespace learning */\n/** TODO: compute the activations of GMES as softmax activation function (YL)*/\n\n\nclass GMES_Graphics;\nclass Payload_Graphics;\nclass Force_Field;\n\n/* Growing_Multi_Expert_Structure\n */\nclass GMES : public control::Statemachine_Interface\n , public learning::Learning_Machine_Interface\n{\n GMES(const GMES& other) = delete; // non construction-copyable\n\npublic:\n GMES(GMES&& other) = default;\n\n explicit GMES( Expert_Vector& expert\n , double learning_rate = gmes_constants::global_learning_rate\n , bool one_shot_learning = true\n , std::size_t number_of_initial_experts = gmes_constants::number_of_initial_experts\n , std::string const& name = \"...\");\n\n ~GMES();\n\n bool is_learning_enabled (void) const { return learning_enabled; }\n bool has_state_changed (void) const { return winner != last_winner; }\n bool has_new_node (void) const { return new_node; }\n\n std::size_t get_number_of_experts (void) const { return number_of_experts; }\n std::size_t get_max_number_of_experts (void) const { return expert.size(); }\n std::size_t get_winner (void) const { return winner; }\n std::size_t get_state (void) const { return winner; }\n std::size_t get_recipient (void) const { return recipient; }\n std::size_t get_to_insert (void) const { return to_insert; }\n\n double get_learning_progress (void) const { return learning_progress; }\n double get_min_prediction_error (void) const { return min_prediction_error; }\n\n VectorN const& get_activations (void) const { return activations; }\n\n\n void enable_learning(bool enable);\n void execute_cycle(void);\n void update_activations(void);\n\nprivate:\n\n std::size_t determine_winner (void);\n std::size_t arg_max_capacity (void) const;\n std::size_t count_existing_experts (void) const;\n void check_learning_capacity (void) const;\n\n void estimate_learning_progress(void);\n void adjust_learning_capacity (void);\n void refresh_transitions (void);\n void insert_expert_on_demand (void);\n\n void clear_transitions_to(std::size_t to_clear);\n\n Expert_Vector& expert;\n const std::size_t Nmax;\n\n double min_prediction_error;\n double learning_progress;\n const double learning_rate;\n\n const bool one_shot_learning;\n bool learning_enabled;\n\n std::size_t number_of_experts; // number of existing experts\n std::size_t winner; // winning expert\n std::size_t last_winner; // winning expert of last time step\n std::size_t recipient; // donee of the learning capacity consumed by the winner\n std::size_t to_insert; // expert to be inserted on demand\n\n VectorN activations;\n bool new_node;\n\n std::string name;\n\n friend class GMES_Graphics;\n friend class Payload_Graphics;\n friend class Force_Field;\n};\n\n#endif // GMES_H\r\n" }, { "alpha_fraction": 0.6044343113899231, "alphanum_fraction": 0.6073352694511414, "avg_line_length": 33.97101593017578, "blob_id": "05ec9ef9c086908d71187f2fc313c652105b7d12", "content_id": "3824b4f9799c4e5242500af17312653251e9c397", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4826, "license_type": "no_license", "max_line_length": 140, "num_lines": 138, "path": "/src/control/controlparameter.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef CONTROLPARAMETER_H_INCLUDED\n#define CONTROLPARAMETER_H_INCLUDED\n\n#include <vector>\n#include <memory>\n#include <common/basic.h>\n#include <common/modules.h>\n#include <common/log_messages.h>\n#include <common/noncopyable.h>\n#include <common/file_io.h>\n#include <common/datareader.h>\n\nnamespace control {\n\ntemplate <typename T> inline std::vector<T>& operator+=(std::vector<T>& lhs, std::vector<T>const & rhs);\ntemplate <typename T> inline std::vector<T>& operator-=(std::vector<T>& lhs, std::vector<T>const & rhs);\ntemplate <typename T> inline std::vector<T>& operator*=(std::vector<T>& lhs, T const& rhs);\ntemplate <typename T> inline std::vector<T>& operator/=(std::vector<T>& lhs, T const& rhs);\n\nclass Control_Parameter : public noncopyable\n{\npublic:\n typedef std::vector<std::vector<double>> matrix_t;\n\n explicit Control_Parameter( const std::string& filename\n , std::size_t number_of_params\n , bool symmetric\n , bool mirrored = false\n , unsigned robot_id = 0\n , uint64_t rnd_init = 0 );\n\n explicit Control_Parameter(const std::string& filename);\n\n explicit Control_Parameter( const std::vector<double>& parameter\n , bool symmetric = false\n , bool mirrored = false\n , unsigned robot_id = 0\n , uint64_t rnd_init = 0 );\n\n void set_from_matrix( matrix_t const& weights );\n\n explicit Control_Parameter() : parameter(), symmetric(), mirrored(), robot_id(), rnd_init() { assert(false and \"Should not be used.\"); }\n\n Control_Parameter(const Control_Parameter& other);\n\n Control_Parameter& operator=(const Control_Parameter& other);\n\n ~Control_Parameter() { /*dbg_msg(\"Destroying control parameters.\");*/ }\n\n const std::vector<double>& get_parameter(void) const { return parameter; }\n std::vector<double>& set_parameter(void) { return parameter; }\n std::size_t size (void) const { return parameter.size(); }\n\n const double& operator[](std::size_t idx) const { return parameter.at(idx); }\n double& operator[](std::size_t idx) { return parameter.at(idx); }\n\n bool is_symmetric(void) const { return symmetric; }\n bool is_mirrored (void) const { return mirrored; }\n\n unsigned get_robot_id(void) const { return robot_id; }\n uint64_t get_rnd_init(void) const { return rnd_init; }\n\n void add_gaussian_noise(double sigma);\n\n void print() const;\n\n void save_to_file(const std::string& filename, std::size_t id) const;\n\n /* arithmetic overloads */\n\n Control_Parameter& operator+=(const Control_Parameter& rhs) {\n this->parameter += rhs.parameter;\n return *this;\n }\n Control_Parameter& operator-=(const Control_Parameter& rhs) {\n this->parameter -= rhs.parameter;\n return *this;\n }\n Control_Parameter& operator*=(const double& rhs) {\n this->parameter *= rhs;\n return *this;\n }\n Control_Parameter& operator/=(const double& rhs) {\n this->parameter /= rhs;\n return *this;\n }\n\n\nprivate:\n\n std::vector<double> parameter;\n bool symmetric;\n bool mirrored;\n unsigned robot_id;\n uint64_t rnd_init;\n\n};\n\n\ninline Control_Parameter operator+(Control_Parameter lhs, const Control_Parameter& rhs) { lhs += rhs; return lhs; }\ninline Control_Parameter operator-(Control_Parameter lhs, const Control_Parameter& rhs) { lhs -= rhs; return lhs; }\ninline Control_Parameter operator*(Control_Parameter lhs, const double& rhs) { lhs *= rhs; return lhs; }\ninline Control_Parameter operator*(const double& lhs, Control_Parameter rhs) { rhs *= lhs; return rhs; }\ninline Control_Parameter operator/(Control_Parameter lhs, const double& rhs) { lhs /= rhs; return lhs; }\n\ntemplate <typename T>\ninline std::vector<T>& operator+=(std::vector<T>& lhs, std::vector<T>const& rhs) {\n assert(lhs.size() == rhs.size());\n for (std::size_t i = 0; i < lhs.size(); ++i)\n lhs[i] += rhs[i];\n return lhs;\n}\n\ntemplate <typename T>\ninline std::vector<T>& operator-=(std::vector<T>& lhs, std::vector<T>const& rhs) {\n assert(lhs.size() == rhs.size());\n for (std::size_t i = 0; i < lhs.size(); ++i)\n lhs[i] -= rhs[i];\n return lhs;\n}\n\ntemplate <typename T>\ninline std::vector<T>& operator*=(std::vector<T>& lhs, T const& rhs) {\n for (T& e: lhs) e *= rhs;\n return lhs;\n}\n\ntemplate <typename T>\ninline std::vector<T>& operator/=(std::vector<T>& lhs, T const& rhs) {\n for (T& e: lhs) e /= rhs;\n return lhs;\n}\n\n\n\n} // namespace control\n\n#endif // CONTROLPARAMETER_H_INCLUDED\n" }, { "alpha_fraction": 0.534007728099823, "alphanum_fraction": 0.5768402814865112, "avg_line_length": 26.6488094329834, "blob_id": "60cdf92f8af48c6eb7de7b83e87cdaeb59c93e91", "content_id": "6836fa55b695dc7df077fca73960058967751327", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4646, "license_type": "no_license", "max_line_length": 82, "num_lines": 168, "path": "/src/tests/neural_model_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <limits>\n#include <common/modules.h>\n#include <learning/forward_inverse_model.hpp>\n\n\nnamespace local_tests {\n\nnamespace neural_model_tests {\n\ntypedef learning::NeuralModel<learning::TanhTransfer<>> NeuralModelType;\ntypedef learning::InverseNeuralModel InverseModelType;\n\n\nTEST_CASE( \"neural_model construction\" , \"[neural_model]\")\n{\n srand(time(0)); // set random seed\n\n double random_range = 0.01;\n\n NeuralModelType model(13, 7, random_range);\n\n learning::model::vector_t inputs(13);\n\n /* check weights are not zero, but randomized */\n auto const& weights = model.get_weights();\n double sum = .0;\n int diff = 0;\n for (std::size_t i = 0; i < weights.size(); ++i)\n for (std::size_t j = 0; j < weights[i].size(); ++j) {\n diff += ( weights[i][j] != .0 )? 0 : 1;\n sum += weights[i][j];\n }\n\n /* check randomize_weight_matrix() is executed */\n REQUIRE( diff == 0 );\n const double max_range = 0.5* random_range * weights.size()*weights[0].size();\n dbg_msg(\"Max rand: %e < %e\", std::abs(sum), max_range);\n REQUIRE( std::abs(sum) <= max_range ); // check small\n REQUIRE( std::abs(sum) != 0. ); // but not zero\n\n // check matrix size\n REQUIRE( weights .size() == 7 );\n REQUIRE( weights[0].size() == 13 );\n\n //auto const& inputs = model.get_inputs ();\n auto const& outputs = model.get_outputs();\n\n // check vector size\n //REQUIRE( inputs.size() == 13 );\n REQUIRE( outputs.size() == 7 );\n\n // check in and outputs are zero on initialization\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( outputs[i] == .0 );\n\n// for (std::size_t i = 0; i < inputs.size(); ++i)\n// REQUIRE( inputs[i] == .0 );\n\n model.propagate(inputs);\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( outputs[i] == .0 );\n\n NeuralModelType model2 = model;\n}\n\n\nTEST_CASE( \"neural_model learning (non-linear)\", \"[neural_model]\")\n{\n srand(time(0)); // set random seed\n\n const double learning_rate = 0.005;\n\n std::vector<double> X = {1,0.5,1,-1,0,1,1,0,-1,-1,1,0.75,1,0,1,0.5,-1,1};\n std::vector<double> Y = {0.7,0,-0.8,0,-0.5,0.4,0,-0.5,-0.7,0.5};\n\n NeuralModelType model(X.size(), Y.size(), 0.01);\n\n /* check error is decreasing (forward) */\n auto const& Y_ = model.propagate(X);\n\n double err0 = squared_distance(Y, model.get_outputs());\n double err1;\n\n for (std::size_t trials = 0; trials < 500; ++trials) {\n // adapt\n model.adapt(X, Y, learning_rate);\n\n // verify\n model.propagate(X);\n err1 = squared_distance(Y, model.get_outputs());\n REQUIRE( err0 > err1 );\n err0 = err1;\n\n }\n\n model.propagate(X);\n print_vector(Y);\n print_vector(Y_);\n\n REQUIRE( close(Y_, Y, 0.01) );\n\n}\n\nTEST_CASE( \"inverse-neural model learning (non-linear)\", \"[inverse_neural_model]\")\n{\n srand(time(0)); // set random seed\n\n const double learning_rate = 0.0025;\n\n // still a reasonable number\n double f = InverseModelType::G(0.999999999999999943);\n sts_msg(\"%f\", f);\n REQUIRE( close(f, 18.714974, 0.00001) );\n\n // infinity\n double g = InverseModelType::G(1.0);\n REQUIRE( g == std::numeric_limits<double>::infinity() );\n\n // not a number anymore\n double h = InverseModelType::G(2.0);\n REQUIRE( std::isnan(h) );\n\n std::vector<double> X = {0.9,0.5,0.9,-0.9,0,0.9,0.9,0,-0.9,-0.9};\n std::vector<double> Y = {0.7,0,-0.8,0,-0.5,0.4,0,-0.5,-0.7,0.5};\n\n InverseModelType model(X.size(), Y.size(), 0.01);\n NeuralModelType proof(Y.size(), X.size(), 0.01); // for comparison\n\n\n /* check error is decreasing (forward) */\n auto const& Y_ = model.propagate(X);\n\n double err0 = squared_distance(Y, model.get_outputs());\n double err1;\n\n for (std::size_t trials = 0; trials < 800; ++trials) {\n // adapt\n model.adapt(X, Y, learning_rate);\n proof.adapt(Y, X, learning_rate*20); // for comparison\n\n // verify\n model.propagate(X);\n proof.propagate(Y);\n auto const& O = model.get_outputs();\n //print_vector(O,\"o\");\n err1 = squared_distance(Y, O);\n REQUIRE( err0 > err1 );\n err0 = err1;\n }\n\n /* Y_ = */ model.propagate(X);\n auto const& X_ = proof.propagate(Y_);\n\n print_vector(Y ,\"target output\");\n print_vector(Y_,\"___prediction\");\n REQUIRE( close(Y_, Y, 0.01) );\n\n print_vector(X ,\"_____original\");\n print_vector(X_,\"reconstructed\");\n REQUIRE( close(X_, X, 0.01) );\n}\n\n\n} /* namespace local_tests */\n} /* namespace neural_model_tests */\n\n" }, { "alpha_fraction": 0.5209237337112427, "alphanum_fraction": 0.5517668724060059, "avg_line_length": 29.861244201660156, "blob_id": "31bd5ae4b3b9eaed264f43c2d9d3eaad3aebdda9", "content_id": "03758aff8e539f01ebaffd230cc1625dc9892323", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6452, "license_type": "no_license", "max_line_length": 110, "num_lines": 209, "path": "/src/tests/time_delay_network_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <common/modules.h>\n#include <learning/time_delay_network.h>\n\n\nnamespace local_tests {\nnamespace time_delay_network_tests {\n\n\nstruct TD_Sensor_Space : public sensor_vector {\n TD_Sensor_Space(const robots::Jointvector_t& joints)\n : sensor_vector(3*joints.size() + 1)\n {\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_ang\", [&j](){ return j.s_ang; });\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_vel\", [&j](){ return j.s_vel; });\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_mot\", [&j](){ return j.motor.get_backed(); });\n\n sensors.emplace_back(\"bias\", [&](){ return 0.13; });\n assert(sensors.size() == 3*joints.size() + 1);\n }\n};\n\n\nTEST_CASE( \"FIR Synapse Test\" , \"[Time Delay Network]\")\n{\n\n Test_Robot robot(1,0);\n TD_Sensor_Space inputs{robot.get_joints()};\n\n const unsigned num_taps = 3;\n\n learning::FIR_type_synapse td_input(inputs.size(), num_taps);\n\n for (auto& j: robot.set_joints()) {\n j.s_ang = 1.1;\n j.s_vel = 2.2;\n j.motor = 3.3;\n j.motor.transfer();\n }\n\n inputs.execute_cycle();\n td_input.propagate(inputs);\n\n REQUIRE( td_input.get().size() == num_taps*inputs.size() );\n\n auto const& vec = td_input.get();\n\n// for (auto const& v : vec)\n// printf(\"%5.2f \", v);\n\n auto const& target = std::vector<double>{1.1, 2.2, 3.3, 0.13, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};\n REQUIRE( vec == target );\n\n for (auto& j: robot.set_joints()) {\n j.s_ang = 1.0;\n j.s_vel = 2.0;\n j.motor = 3.0;\n j.motor.transfer();\n }\n\n inputs.execute_cycle();\n td_input.propagate(inputs);\n auto const& target2 = std::vector<double>{1.0, 2.0, 3.0, 0.13, 1.1, 2.2, 3.3, 0.13, 0.0, 0.0, 0.0, 0.0};\n REQUIRE( vec == target2 );\n\n inputs.execute_cycle();\n td_input.propagate(inputs);\n auto const& target3 = std::vector<double>{1.0, 2.0, 3.0, 0.13, 1.0, 2.0, 3.0, 0.13, 1.1, 2.2, 3.3, 0.13};\n REQUIRE( vec == target3 );\n}\n\n\nTEST_CASE( \"time delay network construction\" , \"[Time Delay Network]\")\n{\n srand(time(0)); // set random seed\n\n Test_Robot robot(5,2);\n robot.set_random_inputs();\n TD_Sensor_Space inputs{robot.get_joints()};\n const double random_range = 0.1;\n learning::Timedelay_Network tdn(inputs.size(), inputs.size(), 3, 10, random_range);\n\n auto const& outputs = tdn.get_outputs();\n REQUIRE( outputs.size() == inputs.size() );\n\n for (std::size_t i = 0; i < outputs.size(); ++i) {\n REQUIRE( outputs[i] == .0 );\n REQUIRE( inputs[i] == .0 );\n }\n\n tdn.propagate_and_shift(inputs);\n\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( outputs[i] == .0 );\n\n /* check weights are not zero */\n auto const& w1 = tdn.get_weights().hi;\n double sum = .0;\n int diff = 0;\n for (std::size_t i = 0; i < w1.size(); ++i)\n for (std::size_t j = 0; j < w1[i].size(); ++j) {\n diff += ( w1[i][j] != .0 )? 0 : 1;\n sum += w1[i][j];\n }\n auto const& w2 = tdn.get_weights().oh;\n sum = .0;\n diff = 0;\n for (std::size_t i = 0; i < w2.size(); ++i)\n for (std::size_t j = 0; j < w2[i].size(); ++j) {\n diff += ( w2[i][j] != .0 )? 0 : 1;\n sum += w2[i][j];\n }\n\n /* check randomize_weight_matrix() is executed */\n REQUIRE( diff == 0 );\n const double max_range = 0.5* random_range * w1.size()*w1[0].size();\n dbg_msg(\"Max rand: %e < %e\", std::abs(sum), max_range);\n REQUIRE( std::abs(sum) <= max_range ); // check small\n REQUIRE( std::abs(sum) != 0. ); // but not zero\n\n\n /** check that autoencoder is copyable **/\n learning::Timedelay_Network tdn2 = tdn;\n}\n\n\nTEST_CASE( \"time delay network learning\", \"[Time Delay Network]\")\n{\n srand(time(0)); // set fixed seed\n\n Test_Robot robot(5,2);\n TD_Sensor_Space inputs{robot.get_joints()};\n\n robot.set_random_inputs();\n const unsigned number_of_taps = 10;\n\n const double learning_rate = 0.02;\n learning::Timedelay_Network tdn(inputs.size(), inputs.size(), 3, number_of_taps, 0.1);\n\n SECTION( \"vector_tanh computes tanh element-wise\") {\n std::vector<double> vec = {1.1, -5.0, 10.0};\n vector_tanh(vec);\n REQUIRE( not is_vector_zero(vec) );\n REQUIRE( vec[0] == tanh( 1.1) );\n REQUIRE( vec[1] == tanh(-5.0) );\n REQUIRE( vec[2] == tanh(10.0) );\n }\n\n SECTION(\" outputs are zero at initialization time.\") {\n\n REQUIRE( is_vector_zero(tdn.get_hidden()) );\n REQUIRE( is_vector_zero(tdn.get_outputs()) );\n\n inputs.execute_cycle();\n tdn.propagate_and_shift(inputs);\n\n print_vector(tdn.get_hidden() ,\"hidden\");\n print_vector(tdn.get_outputs(),\"output\");\n\n REQUIRE( not is_vector_zero(tdn.get_hidden()) );\n REQUIRE( not is_vector_zero(tdn.get_outputs()) );\n\n inputs.execute_cycle();\n tdn.propagate_and_shift(inputs);\n\n REQUIRE( not is_vector_zero(tdn.get_hidden()) );\n REQUIRE( not is_vector_zero(tdn.get_outputs()) );\n }\n\n SECTION(\" reducing squared distance over training time.\") {\n\n /* shift all inputs */\n for (std::size_t tap = 0; tap < number_of_taps; ++tap) {\n inputs.execute_cycle();\n tdn.propagate_and_shift(inputs);\n }\n\n /* adapt without shifting and see if error drops */\n sts_msg(\"Error before training: %e \", squared_distance(inputs, tdn.get_outputs()));\n\n std::size_t max_trials = 10000;\n std::size_t trials = 0;\n double err1,err0;\n for (; trials < max_trials; ++trials) {\n tdn.propagate(); /** without shifting */\n err0 = squared_distance(inputs, tdn.get_outputs());\n tdn.adapt(inputs, learning_rate);\n tdn.propagate();\n err1 = squared_distance(inputs, tdn.get_outputs());\n\n if (err0 <= err1) {\n dbg_msg(\"Prediction Error before %e and %e after adaption.\", err0, err1);\n break;\n }\n }\n sts_msg(\"Error after training of %lu trials: %e \",trials,squared_distance(inputs, tdn.get_outputs()));\n if (err1 > 10e-25)\n REQUIRE( (trials == max_trials) );\n else REQUIRE( true );\n\n }\n}\n\n}} // namespace local_tests\n\n\n" }, { "alpha_fraction": 0.32941803336143494, "alphanum_fraction": 0.41644421219825745, "avg_line_length": 17.534652709960938, "blob_id": "cfb0e81cb23ba4a8e40d02ed36479afa85e2e695", "content_id": "8de4b4c1d30c59733863a8faa40a4288de1d861b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1873, "license_type": "no_license", "max_line_length": 75, "num_lines": 101, "path": "/src/draw/axes3D.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* axes3D.cpp */\n\n#include \"axes3D.h\"\n#include \"draw.h\"\n\naxes3D::axes3D(float x, float y, float z, float w, float h, float d, int f)\n: px(x), py(y), pz(z), width(w), height(h), depth(d), flag(f)\n{\n /* init frame coordinates */\n r[0][0] = -.5*width;\n r[0][1] = +.5*height;\n r[0][2] = -.5*depth;\n\n r[1][0] = +.5*width;\n r[1][1] = +.5*height;\n r[1][2] = -.5*depth;\n\n r[2][0] = +.5*width;\n r[2][1] = -.5*height;\n r[2][2] = -.5*depth;\n\n r[3][0] = -.5*width;\n r[3][1] = -.5*height;\n r[3][2] = -.5*depth;\n\n r[4][0] = -.5*width;\n r[4][1] = +.5*height;\n r[4][2] = +.5*depth;\n\n r[5][0] = +.5*width;\n r[5][1] = +.5*height;\n r[5][2] = +.5*depth;\n\n r[6][0] = +.5*width;\n r[6][1] = -.5*height;\n r[6][2] = +.5*depth;\n\n r[7][0] = -.5*width;\n r[7][1] = -.5*height;\n r[7][2] = +.5*depth;\n\n\n /* init axes coordinates */\n a[0][0] = -.5*width;\n a[0][1] = 0;\n a[0][2] = 0;\n\n a[1][0] = +.5*width;\n a[1][1] = 0;\n a[1][2] = 0;\n\n a[2][0] = 0;\n a[2][1] = -.5*height;\n a[2][2] = 0;\n\n a[3][0] = 0;\n a[3][1] = +.5*height;\n a[3][2] = 0;\n\n a[4][0] = 0;\n a[4][1] = 0;\n a[4][2] = -.5*depth;\n\n a[5][0] = 0;\n a[5][1] = 0;\n a[5][2] = +.5*depth;\n}\n\nvoid axes3D::draw(float x_angle, float y_angle) const\n{\n glColor4ubv(white_trans);\n\n glPushMatrix();\n glTranslatef(px, py, pz);\n glRotatef(y_angle, 1.0, 0.0, 0.0);\n glRotatef(x_angle, 0.0, 1.0, 0.0);\n\n draw_cube(r[0],r[1],r[2],r[3],r[4],r[5],r[6],r[7]);\n\n switch(flag)\n {\n case 0:\n draw_line(a[0],a[1]);\n draw_line(a[2],a[3]);\n draw_line(a[4],a[5]);\n break;\n case 1:\n draw_line(a[0],a[1]);\n break;\n default:\n break;\n }\n glPopMatrix();\n}\n\nvoid axes3D::axesflag(int f)\n{\n flag = f;\n}\n\n/* axes.cpp */\n\n" }, { "alpha_fraction": 0.47461751103401184, "alphanum_fraction": 0.5048678517341614, "avg_line_length": 30.25, "blob_id": "de1f7899c99cac7396ce225b6a78fd4d4e1f6432", "content_id": "134cf3221c388d28bd1fc02cbb8d5c71caa65404", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2876, "license_type": "no_license", "max_line_length": 129, "num_lines": 92, "path": "/src/robots/pole.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"pole.h\"\n\nnamespace robots {\n\nbool\npole::execute_cycle(void)\n{\n /* apply action and update pole cart*/\n assert(joints.size() == 1);\n\n update_dynamics(joints[0].motor.get());\n\n joints[0].s_ang = wrap2(theta) / M_PI;\n joints[0].s_vel = tanh(theta_dot / (2*M_PI));\n joints[0].motor = force;\n assert(std::abs(joints[0].s_ang) <= 1.0);\n\n joints[0].motor.transfer();\n joints[0].motor = .0;\n return true;\n}\n\nvoid\npole::reset_state(bool tilted)\n{\n theta_dot = 0.0;\n if (tilted)\n theta = M_PI + rand_sign() * random_value( deg_to_rad(2.)\n , deg_to_rad(5.) );\n else\n theta = M_PI;\n}\n\n\n/*-----------------------------------------------------------------------*\n * Pole: Takes an action [-1,+1] and the current values of the two state *\n * variables theta and theta_dot and updates their values by estimating *\n * the state dt seconds later, using Explicit Euler's Method. *\n *-----------------------------------------------------------------------*/\nvoid\npole::update_dynamics(const double action)\n{\n assert(std::abs(action) <= 1.0);\n force = pole_constants::force_mag * clip(action, 1.0);\n\n for (std::size_t t = 0; t < 10; ++t)\n {\n double theta_dotdot = -0.2 * sign(theta_dot) /* dry friction */\n - pole_constants::friction * theta_dot /* fluid friction */\n - (pole_constants::gravity / pole_constants::length) * sin(theta) /* torque induced by gravity */\n + force * (pole_constants::length / pole_constants::mass); /* torque induced by motor force */\n\n /* Update using Euler's method */\n theta += theta_dot * pole_constants::dt;\n theta_dot += theta_dotdot * pole_constants::dt;\n }\n}\n\nbool\npole::top(double top_range) const\n{\n /* [-1,+1] is top, 0 is bottom */\n assert_in_range(top_range, 0.0, 0.5);\n const double angle = wrap2(theta) / M_PI;\n\n return std::abs(angle) > (1.0 - top_range);\n}\n\nvoid\npole::draw(const float pos_x, const float pos_y, const float size) const\n{\n const double s = size/pole_constants::max_x;\n\n double length = clip(force)*0.1;\n\n if (force < 0) glColor4f(1.0, .5, 0.0, 0.7);\n else glColor4f(0.5, 0.0, 1.0, 0.7);\n draw_fill_rect(pos_x+length/2, pos_y, std::abs(length), 0.01);\n\n glColor3f(1.0, 1.0, 1.0);\n glprintf(pos_x, pos_y + 0.1 * s, 0.0, 0.025, \"F=%+1.2f\", force / pole_constants::force_mag);\n\n glLineWidth(2.0f);\n draw_line( pos_x\n , pos_y\n , 0.0\n , pos_x + 2 * s * pole_constants::length * sin(theta)\n , pos_y - 2 * s * pole_constants::length * cos(theta)\n , 0.0);\n}\n\n} // namespace robots\n" }, { "alpha_fraction": 0.560606062412262, "alphanum_fraction": 0.5617715716362, "avg_line_length": 19.188236236572266, "blob_id": "9653824d0697040a1d52b5bba5c2aede60170762", "content_id": "9876a590e156ae04ff9a69fde68d615f3bd843f0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1716, "license_type": "no_license", "max_line_length": 103, "num_lines": 85, "path": "/src/common/backed.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BACKED_H_INCLUDED\n#define BACKED_H_INCLUDED\n\n#include <cassert>\n#include <deque>\n\nnamespace common {\n\ntemplate <typename T>\nclass backed_t\n{\n T actual, backup;\n\npublic:\n explicit backed_t(T const& actual = T(), T const& backup = T()) : actual(actual), backup(backup) {}\n\n void reset(void) { actual = T{}; backup = T{}; }\n\n void set_backed(T const& value) { backup = value; }\n\n void transfer() { backup = actual; }\n\n T const& get() const { return actual; }\n T const& get_backed() const { return backup; }\n\n backed_t& operator=(const T& value) {\n this->actual = value;\n return *this;\n }\n\n backed_t& operator+=(const T& value) {\n this->actual += value;\n return *this;\n }\n};\n\ntemplate <typename T>\nclass delayed_t\n{\n T actual;\n const unsigned steps;\n\n typedef std::deque<T> buffer_t;\n buffer_t backup;\n\npublic:\n explicit delayed_t(T const& init = T(), const unsigned steps = 1)\n : actual(init)\n , steps(steps)\n , backup()\n {\n assert(steps > 0);\n backup.assign(steps, init);\n }\n\n void reset(void) {\n backup.assign(steps, T{});\n actual = T{};\n assert(backup.size() == steps);\n }\n\n void transfer() {\n backup.push_back(actual);\n backup.pop_front();\n assert(backup.size() == steps);\n }\n\n T const& get() const { return actual; }\n T const& get_delayed() const { return backup.front(); }\n\n delayed_t& operator=(const T& value) {\n this->actual = value;\n return *this;\n }\n\n delayed_t& operator+=(const T& value) {\n this->actual += value;\n return *this;\n }\n};\n\n\n} // common\n\n#endif // BACKED_H_INCLUDED\n" }, { "alpha_fraction": 0.6282961368560791, "alphanum_fraction": 0.6440162062644958, "avg_line_length": 22.759037017822266, "blob_id": "4e0dbacb8f2262557a5bdde227c50a214bd6dca8", "content_id": "d10960f8ea0088cd1954db2c0dd10e67266568df", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1972, "license_type": "no_license", "max_line_length": 77, "num_lines": 83, "path": "/src/draw/plot2D.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* plot2D.h */\n\n#ifndef plot2D_H\n#define plot2D_H\n\n#include <cassert>\n#include <vector>\n#include <algorithm>\n#include <basic/color.h>\n#include <common/vector2.h>\n#include <draw/axes.h>\n#include <draw/axes3D.h>\n#include <draw/color_table.h>\n\n//TODO autoscale.\n// * use gltranslate and glscale for autoscale\n// * also autoadjust the offset\n// Draw in time?\n\nclass plot2D\n{\npublic:\n plot2D(unsigned int number_of_samples, axes& axis, const Color4& color)\n : number_of_samples(number_of_samples)\n , pointer(0)\n , axis(axis)\n , signal(number_of_samples)\n , color(color)\n , decrement(0.999)\n { }\n\n void draw(void) const;\n //void draw_in_time(void) const;\n void add_sample(float s0, float s1);\n void add_sample(const std::vector<double>& sample);\n void reset(void) { std::fill(signal.begin(), signal.end(), Vector2()); }\n\nprotected:\n void adjust_amplitude(float s0, float s1) const;\n void autoscale(void) const;\n void increment_pointer(void) { ++pointer; pointer %= number_of_samples; }\n\n const unsigned int number_of_samples;\n unsigned int pointer;\n axes& axis;\n\n std::vector<Vector2> signal;\n const Color4 color;\n\n const float decrement;\n\npublic:\n Vector2 offset = {};\n};\n\n\nclass colored_plot2D : public plot2D {\npublic:\n colored_plot2D( unsigned int number_of_samples\n , axes& a\n , ColorTable const& colortable\n /*, const char* name = \"\"*/)\n : plot2D(number_of_samples, a, colors::white0) //TODO, name)\n , colors(number_of_samples)\n , colortable(colortable)\n {}\n\n void add_colored_sample(float s0, float s1, unsigned color_index) {\n add_sample(s0, s1);\n colors.at(pointer) = color_index;\n }\n\n void draw_colored(void) const;\n void draw_colored_scatter(void) const;\nprivate:\n //void draw_colored_line_strip(void) const;\n\n std::vector<unsigned> colors;\n ColorTable const& colortable;\n\n};\n\n#endif /*plot2D_H*/\n" }, { "alpha_fraction": 0.5533024072647095, "alphanum_fraction": 0.5866164565086365, "avg_line_length": 36.93406677246094, "blob_id": "6890be0c799908612e01c1584a988beb94345e3e", "content_id": "71c1f71d68bf3269b3a8239f14fb5b930174060b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3452, "license_type": "no_license", "max_line_length": 159, "num_lines": 91, "path": "/src/learning/competitive_motor_layer_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef COMPETITIVE_MOTOR_LAYER_GRAPHICS_H_INCLUDED\n#define COMPETITIVE_MOTOR_LAYER_GRAPHICS_H_INCLUDED\n\n#include <draw/draw.h>\n#include <draw/axes3D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n#include <draw/display.h>\n\n#include <learning/competitive_motor_layer.h>\n\nclass CompetitiveMotorLayer_Graphics : Graphics_Interface {\npublic:\n CompetitiveMotorLayer_Graphics( const CompetitiveMotorLayer& motor_layer\n , const float posx\n , const float posy\n , const float width )\n : motor_layer(motor_layer)\n , axis(posx, posy, 0.0, width, width, width, 0)\n , graph(motor_layer.get_number_of_motor_units(), axis, white)\n {\n dbg_msg(\"Creating Graphics Module for Competitive Motor Layer.\");\n }\n\n void execute_cycle(void)\n {\n const std::vector<double>& weights = motor_layer.get_weights(motor_layer.last_selected_idx);\n assert(weights.size() >= 4); /**TODO make general */\n\n graph.update_node( motor_layer.last_selected_idx\n , weights[1] // zero omitted\n , weights[2]\n , weights[3]\n , fmin(2.0, motor_layer.get_unit(motor_layer.last_selected_idx).learning_capacity / MotorLayerConstants::initial_learning_capacity) );\n\n graph.update_node( motor_layer.recipient_idx\n , fmin(2.0, motor_layer.get_unit(motor_layer.recipient_idx).learning_capacity / MotorLayerConstants::initial_learning_capacity) );\n\n graph.activated(motor_layer.last_selected_idx);\n //graph.special(gmes.get_to_insert());\n\n //TODO use for current variate weight vector\n //const std::vector<double>& variate = motor_layer.get_variate_weights();\n //plot.add_sample((float) variate[1], (float) variate[2], (float) variate[3]);\n\n /**TODO display motor weights in the 'cube' but display weights belonging to different joints with different colors. */\n }\n\n void draw(const pref& p) const\n {\n /* plots and drawings */\n glLineWidth(2.0f);\n glColor4f(1.0, 1.0, 1.0, 0.2);\n axis.draw(p.x_angle, p.y_angle);\n\n glColor4f(1.0, 1.0, 1.0, 1.0);\n graph.draw(p.x_angle, p.y_angle);\n\n /* text */\n for (std::size_t i = 0; i < motor_layer.motor_units.size(); ++i) {\n\n if (motor_layer.last_selected_idx == i) glColor3f(1.0, 0.5, 1.0);\n else if (motor_layer.motor_units[i].exists)\n glColor3f(.9f, .9f, .9f);\n else\n glColor3f(.3f,.3f,.3f);\n\n glprintf(-1.1, -1.2 - 0.05*i, 0.0, 0.04, \"%2u\" , i);\n draw::hbar(-1.4, -1.2 - 0.05*i, 0.3, 0.02, 0.5* motor_layer.motor_units[i].learning_capacity, MotorLayerConstants::initial_learning_capacity);\n draw_vector2(-1.0, -1.2 - 0.05*i, 0.045, 2.0, motor_layer.motor_units[i].weights.get_parameter(), 3.0);\n }\n\n\n if (motor_layer.is_adaption_enabled()) glColor3f(.9f,.9f,.9f);\n else glColor3f(.3f,.3f,.3f);\n\n glprintf(-1.0, -1.1, 0.0, 0.05, \"learning: %s\", motor_layer.is_adaption_enabled() ? \"enabled\" : \"disabled\");\n }\n\nprivate:\n\n const CompetitiveMotorLayer& motor_layer;\n\n /*draw*/\n const axes3D axis;\n network3D graph;\n};\n\n\n#endif // COMPETITIVE_MOTOR_LAYER_GRAPHICS_H_INCLUDED\n" }, { "alpha_fraction": 0.5627448558807373, "alphanum_fraction": 0.5704537034034729, "avg_line_length": 32.24369812011719, "blob_id": "2d9b278c1f41d01e18b2ad0dd7465329e81db169", "content_id": "c16c160f8b0c432cc77a38e84894891a75a13346", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 7914, "license_type": "no_license", "max_line_length": 166, "num_lines": 238, "path": "/src/control/jointcontroller.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"jointcontroller.h\"\n\nJointcontroller::Jointcontroller( Robot_Configuration& configuration\n , bool symmetric_controller\n , double param_p\n , double param_d\n , double param_m\n , const std::string& seed_filename\n )\n: robot(configuration)\n, num_inputs(3 * robot.number_of_joints + 3 * robot.number_of_accels + 1) /* angle, velocity, motor output, + xyz-Accel + bias, Idea: add 1 for antisymmetric bias? */\n, total_num_params(0) // will be assigned later\n, activation(robot.number_of_joints)\n, weights(robot.number_of_joints, std::vector<double>(num_inputs, 0.0))\n, X(num_inputs)\n, Y(num_inputs)\n, seed_from_file()\n{\n sts_msg(\"Creating joint controller.\");\n if (robot.number_of_joints < 1) err_msg(__FILE__, __LINE__, \"No motor outputs.\");\n if (0 == robot.number_of_accels) wrn_msg(\"No use of acceleration sensors in controller.\");\n\n sts_msg(\"Controller type is %s\", symmetric_controller? \"symmetric\":\"asymmetric\");\n if (not symmetric_controller)\n robot.delete_symmetry_information();\n\n unsigned int num_sym_joints = robot.get_number_of_symmetric_joints();\n sts_msg(\"Number of symmetric joints is %u.\", num_sym_joints);\n\n if (num_sym_joints * 2 > robot.number_of_joints) err_msg(__FILE__, __LINE__, \"Invalid number of symmetric joints.\\n\");\n\n total_num_params = num_inputs * (robot.number_of_joints - num_sym_joints);\n seed_from_file.reserve(total_num_params);\n\n assert(weights.size() == robot.number_of_joints);\n assert(weights[0].size() == num_inputs);\n\n set_initial_parameter(param_p, param_d, param_m); // load defaults\n if (seed_filename != \"\")\n load_seed(seed_filename); // load seed\n\n print_parameter();\n sts_msg(\"Created controller with: \\n %u inputs\\n %u outputs\\n %u params.\", num_inputs, robot.number_of_joints, total_num_params);\n}\n\nconst std::vector<double>\nJointcontroller::get_control_parameter(void) const\n{\n std::vector<double> transmission_params(total_num_params);\n assert(weights.size() == robot.number_of_joints);\n assert(weights[0].size() == num_inputs);\n\n unsigned int p = 0;\n for (unsigned int i = 0; i < robot.number_of_joints; ++i)\n if (robot.joints[i].type == robots::Joint_Type_Normal)\n for (unsigned int k = 0; k < num_inputs; ++k)\n {\n if ((p < total_num_params) && (weights[i][k] == weights[robot.joints[i].symmetric_joint][k]))\n transmission_params[p++] = weights[i][k];\n else\n err_msg(__FILE__, __LINE__, \"Inconsistent parameter transmission.\");\n }\n\n assert(p == total_num_params);\n return transmission_params;\n}\n\nvoid /* load parameters from file */\nJointcontroller::load_seed(const std::string& filename)\n{\n sts_msg(\"Load controller weights from csv file '%s'\", filename.c_str());\n assert(seed_from_file.size() == 0);\n assert(not filename.empty());\n\n file_io::CSV_File<double> seed_csv(filename, 1, total_num_params);\n seed_csv.read();\n seed_from_file.assign(total_num_params, 0.0);\n seed_csv.get_line(0, seed_from_file);\n\n assert(seed_from_file.size() == total_num_params);\n set_control_parameter(seed_from_file); // apply weights\n}\n\nvoid\nJointcontroller::set_seed_parameter(void)\n{\n assert(seed_from_file.size() == total_num_params);\n set_control_parameter(seed_from_file);\n}\n\nvoid\nJointcontroller::set_control_parameter(const std::vector<double>& params)\n{\n assert(params.size() == total_num_params);\n assert(weights.size() == robot.number_of_joints);\n assert(weights[0].size() == num_inputs);\n\n unsigned int p = 0;\n for (unsigned int i = 0; i < robot.number_of_joints; ++i)\n {\n if (robot.joints[i].type == robots::Joint_Type_Normal)\n {\n assert(robot.joints[i].symmetric_joint < robot.number_of_joints);\n for (unsigned int k = 0; k < num_inputs; ++k)\n {\n weights[i][k] = params[p++]; // apply weights\n weights[robot.joints[i].symmetric_joint][k] = weights[i][k];\n }\n }\n }\n assert(p == params.size());\n}\n\nvoid\nJointcontroller::set_initial_parameter(double p, double d, double m)\n{\n /* set default weights for asymmetric joints */\n for (unsigned int i = 0; i < robot.number_of_joints; ++i)\n if (robot.joints[i].type == robots::Joint_Type_Normal)\n {\n weights[i][i*3 + 0] = -p; // spring\n weights[i][i*3 + 1] = d; // positive friction\n weights[i][i*3 + 2] = m; // motor neuron's self coupling\n\n /* divide by initial bias, because INITIAL BIAS < 1 */\n weights[i][num_inputs-1] = -weights[i][i*3 + 0] * robot.joints[i].default_pos * 1.0/INITIAL_BIAS;\n /* if p is zero this bias is also zero */\n }\n\n /* copy weights for symmetric joints */\n for (unsigned int i = 0; i < robot.number_of_joints; ++i)\n if (robot.joints[i].type == robots::Joint_Type_Symmetric)\n for (unsigned int k = 0; k < num_inputs; ++k)\n weights[i][k] = weights[robot.joints[i].symmetric_joint][k];\n}\n\nvoid\nJointcontroller::print_parameter(void) const\n{\n sts_msg(\"Printing controller parameter:\");\n\n /*print header*/\n printf(\" # |\");\n for (std::size_t i = 0; i < robot.number_of_joints; ++i)\n if (robot.joints[i].type == robots::Joint_Type_Normal)\n printf(\"%4lu |\", i);\n printf(\"\\n\");\n\n for (std::size_t k = 0; k < num_inputs; ++k)\n {\n printf(\"%2lu: |\", k);\n for (std::size_t i = 0; i < robot.number_of_joints; ++i)\n {\n if (robot.joints[i].type == robots::Joint_Type_Normal)\n printf(\"% 1.3f|\", weights[i][k]);\n }\n printf(\"\\n\");\n }\n printf(\"\\n\");\n}\n\ndouble\nJointcontroller::get_normalized_mechanical_power(void) const\n{\n double power = .0;\n for (auto& j : robot.joints)\n power += square(j.motor.get());\n return power/robot.number_of_joints;\n}\n\nvoid\nJointcontroller::reset(void)\n{\n for (auto& j : robot.joints)\n j.motor = random_value(-0.01, 0.01);\n\n /* reset integrated velocities from acceleration sensors */\n for (auto& a : robot.accels) a.reset();\n}\n\nvoid\nJointcontroller::loop(void)\n{\n unsigned int index = 0;\n for (auto& jx : robot.joints)\n {\n //virt_ang[i] = clip(virt_ang[i] + x[i][0]);\n auto& jy = robot.joints[jx.symmetric_joint];\n\n X[index] = jx.s_ang;\n Y[index++] = jy.s_ang;\n\n X[index] = jx.s_vel;\n Y[index++] = jy.s_vel;\n\n X[index] = jx.motor.get_backed();\n Y[index++] = jy.motor.get_backed(); //ganz wichtig!, bringt irre viel dynamic für den anfang\n }\n\n for (auto& a : robot.accels)\n {\n /* integrate velocities from acceleration sensors */\n a.integrate();\n\n X[index] = a.v.x;\n Y[index++] = -a.v.x; // mirror the x-axes\n\n X[index] = a.v.y;\n Y[index++] = a.v.y;\n\n X[index] = a.v.z;\n Y[index++] = a.v.z;\n\n }\n\n X[index] = INITIAL_BIAS; // bias\n Y[index++] = INITIAL_BIAS; // bias\n\n assert(index == num_inputs);\n assert(activation.size() == robot.number_of_joints);\n\n for (unsigned int i = 0; i < robot.number_of_joints; ++i)\n {\n activation[i] = 0;\n if (robot.joints[i].type == robots::Joint_Type_Symmetric)\n {\n for (unsigned int k = 0; k < num_inputs; ++k)\n activation[i] += weights[i][k] * Y[k];\n }\n else\n {\n for (unsigned int k = 0; k < num_inputs; ++k)\n activation[i] += weights[i][k] * X[k];\n }\n //TODO robot.joints[i].motor = tanh(activation[i]);\n robot.joints[i].motor = clip(activation[i], 1.0);\n }\n}\n" }, { "alpha_fraction": 0.7096478939056396, "alphanum_fraction": 0.7105624079704285, "avg_line_length": 46.543479919433594, "blob_id": "7d01c6f150b4a35e8bb3b10b757f796986607607", "content_id": "9db5a44405cbda3ea5cecf4a40367fec7de49b32", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2187, "license_type": "no_license", "max_line_length": 126, "num_lines": 46, "path": "/src/evolution/population.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef POPULATION_H_INCLUDED\n#define POPULATION_H_INCLUDED\n\n#include <vector>\n#include <algorithm>\n#include <common/file_io.h>\n#include <common/log_messages.h>\n#include <evolution/individual.h>\n\nclass Population\n{\n std::vector<Individual> individuals;\n\npublic:\n Population(std::size_t population_size, std::size_t individual_size, double init_mutation_rate, double meta_mutation_rate)\n : individuals(population_size, Individual(individual_size, init_mutation_rate, meta_mutation_rate))\n {\n assert(population_size > 0);\n sts_msg(\"[Population:] Initializing population with %u individuals of size %u\", population_size, individual_size);\n }\n\n ~Population() { dbg_msg(\"[Population:] Destroying population with %u individuals.\", get_size()); }\n\n void initialize_from_seed(const std::vector<double>& seed);\n void sort_by_fitness(void);\n\n const Individual& get_best_individual(void) const { return individuals.front(); }\n const Individual& get_last_individual(void) const { return individuals.back (); }\n\n std::size_t get_size (void) const { return individuals.size(); }\n std::size_t get_individual_size(void) const { return individuals[0].genome.size(); }\n\n Individual& operator[] (std::size_t index) { assert(index < individuals.size()); return individuals[index]; }\n const Individual& operator[] (std::size_t index) const { assert(index < individuals.size()); return individuals[index]; }\n\n friend void save_population (Population& population, file_io::CSV_File<double>& csv_population);\n friend void load_population (Population& population, file_io::CSV_File<double>& csv_population);\n friend void save_mutation_rates(Population& population, file_io::CSV_File<double>& csv_mutation);\n friend void load_mutation_rates(Population& population, file_io::CSV_File<double>& csv_mutation);\n friend void save_fitness_values(Population& population, file_io::CSV_File<double>& csv_fitness);\n friend void load_fitness_values(Population& population, file_io::CSV_File<double>& csv_fitness);\n friend void print_population (Population& population);\n\n};\n\n#endif // POPULATION_H_INCLUDED\n" }, { "alpha_fraction": 0.7098360657691956, "alphanum_fraction": 0.7098360657691956, "avg_line_length": 33.85714340209961, "blob_id": "a2140b161e72819b8a31b849174c7bece9371f0c", "content_id": "53d9b800c8de5461ffd833a616022b493732fde2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1220, "license_type": "no_license", "max_line_length": 133, "num_lines": 35, "path": "/src/learning/gmes_action_module.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GMES_ACTION_MODULE_H_INCLUDED\n#define GMES_ACTION_MODULE_H_INCLUDED\n\n#include <common/log_messages.h>\n#include <learning/action_module.h>\n#include <learning/motor_layer.h>\n#include <learning/reinforcement_learning.h>\n\nnamespace learning {\n\n/** Maybe we don't need this class at all. Consider to move that into motor layer*/\n\nclass gmes_action_module : public Action_Module_Interface {\n\n Motor_Layer const& motor_layer;\n\npublic:\n gmes_action_module( Motor_Layer const& motor_layer )\n : motor_layer(motor_layer)\n {\n dbg_msg(\"Creating gmes_action_module with max. number of actions %u:\", get_number_of_actions());\n }\n\n ~gmes_action_module() = default;\n\n control::Control_Parameter const& get_controller_weights(std::size_t id) const { return motor_layer.get_controller_weights(id); }\n\n std::size_t get_number_of_actions(void) const { return motor_layer.get_max_number_of_experts(); }\n std::size_t get_number_of_actions_available(void) const { return motor_layer.get_cur_number_of_experts(); }\n bool exists(const std::size_t action_index) const { return motor_layer.has_expert(action_index); }\n};\n\n} // namespace learning\n\n#endif // GMES_ACTION_MODULE_H_INCLUDED\n" }, { "alpha_fraction": 0.5785627365112305, "alphanum_fraction": 0.5931790471076965, "avg_line_length": 19.78481101989746, "blob_id": "d6847b59b1821df5d094041905759ed708420553", "content_id": "b87e31b1095cc7b67e87eab35373506e0b22a151", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1646, "license_type": "no_license", "max_line_length": 85, "num_lines": 79, "path": "/src/common/log_messages.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* log_messages.cpp */\n#include \"log_messages.h\"\n\n#define KNRM \"\\x1B[0m\"\n#define KRED \"\\x1B[31m\"\n#define KGRN \"\\x1B[32m\"\n#define KYEL \"\\x1B[33m\"\n#define KBLU \"\\x1B[34m\"\n#define KMAG \"\\x1B[35m\"\n#define KCYN \"\\x1B[36m\"\n#define KWHT \"\\x1B[37m\"\n\nvoid\nsts_msg(const char* format, ...)\n{\n va_list args;\n va_start(args, format);\n vprintf(format, args);\n printf(\"\\n\");\n va_end(args);\n}\n\nvoid\nsts_add(const char* format, ...)\n{\n va_list args;\n va_start(args, format);\n vprintf(format, args);\n printf(\" \");\n va_end(args);\n}\n\nvoid\ndbg_msg(const char* format, ...)\n{\n va_list args;\n va_start(args, format);\n printf(\"%s‹dbg›%s \", KGRN, KNRM);\n vprintf(format, args);\n printf(\"\\n\");\n va_end(args);\n}\n\nvoid\nwrn_msg(const char* format, ...)\n{\n static unsigned long wrn_msg_cnt = 0;\n va_list args;\n va_start(args, format);\n printf(\"%sWARNING %ld: %s\", KYEL, ++wrn_msg_cnt, KNRM);\n vprintf(format, args);\n printf(\"\\n\");\n va_end(args);\n}\n\nvoid\nerr_msg(const char* file, unsigned int line, const char* format, ...)\n{\n va_list args;\n va_start(args, format);\n printf(\"%sERROR:%s \", KRED, KNRM);\n vprintf(format, args);\n printf(\"%s File %s in line %d%s\\n\", KWHT, file, line, KNRM);\n va_end(args);\n exit(EXIT_FAILURE);\n}\n\nvoid\npromise(bool condition, const char* file, unsigned int line, const char* format, ...)\n{\n if (condition) return;\n va_list args;\n va_start(args, format);\n printf(\"%sASSERTION:%s \", KCYN, KNRM);\n vprintf(format, args);\n printf(\"%s\\nFile %s in line %d%s\\n\", KWHT, file, line, KNRM);\n va_end(args);\n exit(EXIT_FAILURE);\n}\n" }, { "alpha_fraction": 0.5892351269721985, "alphanum_fraction": 0.6024551391601562, "avg_line_length": 23.627906799316406, "blob_id": "4214ebfe74dc66802b3674562d49b2f80aa1045a", "content_id": "09d094bf83ea5efb8b012a8a80e33d2e24573cc9", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1059, "license_type": "no_license", "max_line_length": 120, "num_lines": 43, "path": "/src/draw/graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GRAPHICS_H_INCLUDED\n#define GRAPHICS_H_INCLUDED\n\n#include <draw/draw.h>\n#include <basic/vector3.h>\n\n/**TODO namespace draw and rename to graphics_base */\n\nstruct pref {\n float x_angle;\n float y_angle;\n};\n\nclass Graphics_Interface\n{\n Vector3 pos;\n double scale;\n\npublic:\n\n Graphics_Interface()\n : pos(0.0), scale(1.0) {}\n\n Graphics_Interface(double px, double py, double pz = 0.0, double s = 1.0)\n : pos(px, py, pz), scale(s) {}\n\n virtual ~Graphics_Interface() {}\n virtual void draw(const pref&) const = 0; // consider to make this protected/private\n\n void drawing(const pref& p) const {\n glPushMatrix();\n glTranslatef(pos.x, pos.y, pos.z);\n glScalef(scale, scale, scale);\n draw(p);\n glPopMatrix();\n }\n\n Graphics_Interface& set_position(double px, double py, double pz = 0.0) { pos = Vector3(px, py, pz); return *this; }\n Graphics_Interface& set_scale (double s ) { scale = s; return *this; }\n\n};\n\n#endif // GRAPHICS_H_INCLUDED\n" }, { "alpha_fraction": 0.6128472089767456, "alphanum_fraction": 0.623071014881134, "avg_line_length": 34.5, "blob_id": "ad0620905dbb681580adf3ddf3d28103ba2a6a37", "content_id": "2ecc482bed0594625b670847d28ee5aec96924df", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5184, "license_type": "no_license", "max_line_length": 138, "num_lines": 146, "path": "/src/tests/jointcontroller_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <vector>\n\n#include <common/modules.h>\n#include <common/log_messages.h>\n#include <robots/robot.h>\n#include <robots/joint.h>\n#include <control/controlparameter.h>\n#include <control/jointcontrol.h>\n#include <tests/test_robot.h>\n\n\nstatic void printf_param(control::Control_Parameter const& ctrl, robots::Robot_Interface const& robot) {\n auto num_inputs = control::get_number_of_inputs(robot);\n auto p = ctrl.get_parameter();\n unsigned j = 0;\n printf(\"%s \", (robot.get_joints().at(j).type == robots::Joint_Type_Symmetric)?\"~\":\"=\");\n for (std::size_t i = 0; i < p.size(); ++i) {\n if ((i > 0) and (i % num_inputs == 0)) printf(\"\\n%s \", (robot.get_joints().at(++j).type == robots::Joint_Type_Symmetric)?\"/\":\"=\");\n printf(\"% 5.2f \", p[i]);\n }\n printf(\"\\n\");\n}\n\n\n\nTEST_CASE( \"Make Asymmetric\" , \"[jointcontrol]\") {\n\n std::vector<std::pair<unsigned, unsigned>> vec = {{3,1}, {5,2}, {4,2}, {3,0}};\n for (auto& s : vec)\n { /* LOOP OVER TESTCASE */\n\n std::size_t num_joints = s.first;\n std::size_t num_sym_joints = s.second;\n\n Test_Robot robot(num_joints, num_sym_joints);\n\n control::Control_Parameter asym_ctrl = control::get_initial_parameter(robot, {1.1, 2.2, 3.3}, false);\n\n REQUIRE( not asym_ctrl.is_symmetric() );\n REQUIRE( asym_ctrl.get_parameter().size() == control::get_number_of_inputs(robot) * num_joints );\n printf_param(asym_ctrl, robot);\n\n control::Control_Parameter sym_ctrl = control::make_symmetric(robot, asym_ctrl);\n REQUIRE( sym_ctrl.is_symmetric() );\n REQUIRE( sym_ctrl.get_parameter().size() == control::get_number_of_inputs(robot) * (num_joints-num_sym_joints) );\n if (num_sym_joints > 0)\n REQUIRE( sym_ctrl.get_parameter().size() < asym_ctrl.get_parameter().size() );\n printf_param(sym_ctrl, robot);\n\n control::Control_Parameter new_asym_ctrl = control::make_asymmetric(robot, sym_ctrl);\n REQUIRE( not new_asym_ctrl.is_symmetric() );\n REQUIRE( new_asym_ctrl.get_parameter().size() == asym_ctrl.get_parameter().size() );\n REQUIRE( new_asym_ctrl.get_parameter() == asym_ctrl.get_parameter() );\n if (num_sym_joints > 0)\n REQUIRE( new_asym_ctrl.get_parameter().size() > sym_ctrl.get_parameter().size() );\n printf_param(new_asym_ctrl, robot);\n\n control::Control_Parameter new_sym_ctrl = control::make_symmetric(robot, asym_ctrl);\n REQUIRE( new_sym_ctrl.is_symmetric() );\n REQUIRE( new_sym_ctrl.get_parameter().size() == sym_ctrl.get_parameter().size() );\n REQUIRE( new_sym_ctrl.get_parameter() == sym_ctrl.get_parameter() );\n printf_param(new_sym_ctrl, robot);\n\n } /* END TEST CASE LOOP */\n}\n\nTEST_CASE( \"Making Asymmetric keeps structure\" , \"[jointcontrol]\") {\n\n printf(\"\\n____\\nASYMMETRIC\\n\");\n\n std::vector<std::pair<unsigned, unsigned>> vec = {{4,2}, {8,4}, {4,0}, {5,2}, {3,1}};\n\n for (auto& s : vec)\n { /* LOOP OVER TESTCASE */\n printf(\"\\n------------------------\\n\");\n\n const std::size_t num_joints = s.first;\n const std::size_t num_sym_joints = s.second;\n\n Test_Robot robot(num_joints, num_sym_joints);\n const std::size_t num_inputs = control::get_number_of_inputs(robot);\n\n const std::size_t num_sym_params = num_inputs*(num_joints-num_sym_joints);\n\n control::Fully_Connected_Symmetric_Core core(robot);\n robot.set_random_inputs();\n core.prepare_inputs(robot);\n\n printf(\"Inputs: \\n\");\n for (auto const& jx : robot.get_joints())\n printf(\"%5.2f %5.2f %5.2f\\n\", jx.s_ang, jx.s_vel, jx.motor.get_backed());\n printf(\"\\n\");\n\n\n //TODO control::Control_Parameter sym_ctrl = control::get_initial_parameter(robot, {1.1, 2.2, 3.3}, true);\n\n std::vector<double> rnd_parameter;\n\n\n for (std::size_t i = 0; i < num_sym_params; ++i)\n rnd_parameter.push_back(random_value());\n\n control::Control_Parameter sym_ctrl = control::Control_Parameter( rnd_parameter, /*symmetric*/true);\n\n\n\n /* randomize parameters */\n /*for (auto& p : sym_ctrl.set_parameter())\n p += random_value();*/\n\n REQUIRE( sym_ctrl.is_symmetric() );\n REQUIRE( sym_ctrl.get_parameter().size() == num_sym_params );\n core.apply_symmetric_weights(robot, sym_ctrl.get_parameter());\n core.update_outputs(robot, true, false);\n std::vector<double> sym_activation = core.activation;\n\n control::Control_Parameter asym_ctrl = control::make_asymmetric(robot, sym_ctrl);\n\n printf(\"\\n sym: \"); sym_ctrl.print();\n printf(\"\\n asm: \"); asym_ctrl.print();\n\n REQUIRE( not asym_ctrl.is_symmetric() );\n\n if (num_sym_joints > 0)\n REQUIRE( asym_ctrl.get_parameter().size() > sym_ctrl.get_parameter().size() );\n\n core.prepare_inputs(robot);\n core.apply_weights(robot, asym_ctrl.get_parameter());\n core.update_outputs(robot, false, false);\n\n printf(\"\\nActivations:\");\n for (auto s: sym_activation) printf(\"%e \", s);\n printf(\"\\n\");\n\n REQUIRE( close(sym_activation, core.activation, 0.000001) );\n //printf(\"%e =?= %e\", sym_activation, core.activation);\n\n printf_param( sym_ctrl, robot);\n printf(\"\\nasymmetric\\n\");\n printf_param(asym_ctrl, robot);\n\n }\n\n printf(\"\\n____\\nDONE\\n\");\n}\n\n" }, { "alpha_fraction": 0.5399810671806335, "alphanum_fraction": 0.5478284358978271, "avg_line_length": 28.098424911499023, "blob_id": "55b0ce947ca9f6aea10a947fcac3ecafbb7ef02b", "content_id": "2e65af604aef4bae786eb7e198feace60b08f08b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 7391, "license_type": "no_license", "max_line_length": 126, "num_lines": 254, "path": "/src/learning/time_delay_network.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef TIME_DELAY_NETWORK_H_INCLUDED\n#define TIME_DELAY_NETWORK_H_INCLUDED\n\n#include <deque>\n\n#include <common/modules.h>\n#include <common/static_vector.h>\n#include <control/sensorspace.h>\n\n\n/** TODO consider a time-delay autoencoder */\n\nnamespace learning {\n\n/*\n + time expansion through FIR-type synapses DONE\n + non-linear expansion through tanh or ReLU neurons\n + configurable number of hidden layers\n*/\n\n\n\n/* FIR-type Synapse\n Expands the input vector v(t) by its recent values v(t-1), v(t-2),... , with each\n tapped delay line having the same size of \"number of taps\" = N.\n\n v1(t) ---> [v1(t-1)] --->[v1(t-2)]---> ... --->[v1(t-N)]\n v2(t) ---> [v2(t-1)] --->[v2(t-2)]---> ... --->[v2(t-N)]\n ... ...\n vK(t) ---> [vK(t-1)] --->[vK(t-2)]---> ... --->[vK(t-N)]\n\n plus additional bias\n\n*/\nclass FIR_type_synapse {\n\n typedef std::vector<double> vector_t;\n typedef std::deque<vector_t> buffer_t; // replace by something which is more efficient\n\n std::size_t number_of_taps;\n std::size_t input_size; /* size of raw inputs */\n vector_t td_inputs; /* time delayed inputs */\n buffer_t buffer; /* structure holding all FIFOs */\n\n\n void expand(void) {\n std::size_t p = 0;\n for (auto const& vec : buffer)\n for (auto const& el : vec)\n td_inputs[p++] = el;\n assert(p == td_inputs.size());\n }\n\npublic:\n\n FIR_type_synapse(std::size_t input_size, std::size_t number_of_taps)\n : number_of_taps(number_of_taps)\n , input_size(input_size)\n , td_inputs(input_size*number_of_taps)\n , buffer(input_size)\n {\n /* initialize buffer with zero vectors */\n buffer.assign(number_of_taps, vector_t(input_size));\n\n assert(buffer.size() == number_of_taps);\n assert(buffer.size()*buffer[0].size() == td_inputs.size());\n dbg_msg(\"Created FIR-type Synapse of size %u x %u.\", input_size, number_of_taps);\n }\n\n vector_t const& get (void) const { return td_inputs; }\n buffer_t const& get_buffer(void) const { return buffer; }\n\n\n std::size_t size(void) const { return td_inputs.size(); }\n\n template <typename InputVector_t>\n void propagate(const InputVector_t& inputs)\n {\n assert(input_size == inputs.size());\n\n /* shift */\n buffer.pop_back(); // remove oldest input vector\n buffer.push_front(inputs.get()); // fill in new values\n assert(buffer.size() == number_of_taps);\n\n /*\n for (auto const& vec : buffer) {\n printf(\"[ \");\n for (auto const& el : vec)\n printf(\"%5.2f \", el);\n printf(\"]\\n\");\n }\n printf(\"\\n\");\n */\n\n expand(); // prepare vector for returning in 'get()'\n }\n\n\n\n};\n\n\n/**TODO move the tapped delay line to input space, in order to have all experts share the same tapping line. */\n\n/* Time-Delay feed-forward network\n with a single tanh-type hidden layer.\n*/\ntypedef std::vector<double> vector_t;\ntypedef copyable_static_vector<copyable_static_vector<double>> matrix_t;\n\nstruct TDNWeights {\n TDNWeights(std::size_t inp, std::size_t hid, std::size_t out): hi(hid, inp), oh(out, hid) /*, biases_oh(output.size())*/{}\n\n matrix_t hi;\n matrix_t oh;\n //TODO vector_t biases_oh;\n};\n\n\nclass Timedelay_Network\n{\n\n FIR_type_synapse td_input; /* time delayed inputs */\n vector_t hidden;\n vector_t output;\n vector_t delta;\n\n TDNWeights weights;\n\n void randomize(matrix_t& mat, double std_dev) {\n assert_in_range(std_dev, 0.0, 0.1);\n const double normed_stddev = std_dev / sqrt(mat[0].size()); // normalize by sqrt(N), N:#inputs\n for (std::size_t i = 0; i < mat.size(); ++i)\n for (std::size_t j = 0; j < mat[i].size(); ++j)\n mat[i][j] = rand_norm_zero_mean(normed_stddev);\n }\n\n void propagate_forward(vector_t& out, matrix_t const& mat, vector_t const& in) {\n assert(out.size() == mat.size());\n assert( in.size() == mat[0].size());\n\n for (std::size_t i = 0; i < out.size(); ++i) {\n double act = 0.;\n for (std::size_t j = 0; j < in.size(); ++j)\n act += mat[i][j] * in[j];\n out[i] = act;\n }\n }\n\npublic:\n\n Timedelay_Network( std::size_t input_size\n , std::size_t target_size\n , std::size_t hidden_size\n , std::size_t number_of_taps\n , double rnd_init_range)\n : td_input(input_size, number_of_taps)\n , hidden(hidden_size)\n , output(target_size)\n , delta (output.size())\n , weights(td_input.size(), hidden.size(), output.size())\n {\n randomize_weight_matrix(rnd_init_range);\n }\n\n\n void propagate(void) {\n /* time delayed input to hidden layer */\n propagate_forward(hidden, weights.hi, td_input.get());\n vector_tanh(hidden);\n\n /* hidden to output layer */\n propagate_forward(output, weights.oh, hidden);\n vector_tanh(output);\n }\n\n template <typename InputVector_t>\n void propagate_and_shift(const InputVector_t& inputs)\n {\n /* shift and time expand next inputs */\n td_input.propagate(inputs);\n propagate();\n }\n\n template <typename TargetVector_t>\n void adapt(const TargetVector_t& targets, double learning_rate)\n {\n assert_in_range(learning_rate, 0.0, 0.5);\n assert(targets.size() == output.size());\n\n /* delta error */\n for (std::size_t i = 0; i < output.size(); ++i)\n delta[i] = (targets[i] - output[i]) * tanh_(output[i]);\n\n\n /* estimate error for hidden units and adapt input to hidden weights */\n for (std::size_t i = 0; i < hidden.size(); ++i)\n {\n /* estimate 'hidden' errors */\n double error_i = .0;\n for (std::size_t j = 0; j < output.size(); ++j)\n error_i += weights.oh[j][i] * delta[j];\n\n /* adapt weights.hi */\n const double delta_i = error_i * tanh_(hidden[i]);\n for (std::size_t j = 0; j < output.size(); ++j)\n weights.hi[i][j] += learning_rate * delta_i * td_input.get()[j];\n }\n\n\n /* adapt weight_oh */\n for (std::size_t i = 0; i < output.size(); ++i)\n for (std::size_t j = 0; j < hidden.size(); ++j)\n weights.oh[i][j] += learning_rate * delta[i] * hidden[j];\n }\n\n /*\n x_j : inputs[j]\n t_j : targets[j]\n y_j : outputs[j]\n h_i : hidden[i]\n\n eta : learning_rate\n\n second layer weight change:\n error : e_j = (t_j - y_j)\n delta : d2_j = e_j * (1.0 + y_j) * (1.0 - y_j)\n weight: dw2_ji = eta * d2_j * h_i\n\n first layer weight change:\n error : eh_i = sum_j w2_ji * d2_j\n delta : d1_i = eh_i * (1.0 + h_i) * (1.0 - h_i)\n weight: dw1_ji = eta * d1_i * x_j\n\n */\n\n vector_t const& get_outputs() const { return output; }\n vector_t const& get_hidden() const { return hidden; }\n TDNWeights const& get_weights() const { return weights; }\n\n void randomize_weight_matrix(double random_weight_range)\n {\n randomize(weights.hi, random_weight_range);\n randomize(weights.oh, random_weight_range);\n }\n};\n\n\n\n\n} // namespace learning\n\n#endif // TIME_DELAY_NETWORK_H_INCLUDED\n" }, { "alpha_fraction": 0.5881384015083313, "alphanum_fraction": 0.5963756442070007, "avg_line_length": 17.90625, "blob_id": "34dd4fb384fa9620c0039bdab3eb94138b95cfbe", "content_id": "918ce1cc7e46fa0d9274afecde5c1f6fe82981ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 607, "license_type": "no_license", "max_line_length": 68, "num_lines": 32, "path": "/src/common/incremental_average.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef INCREMENTAL_AVERAGE_H\n#define INCREMENTAL_AVERAGE_H\n\nclass incremental_average {\npublic:\n\n explicit incremental_average(void) : mean{.0}, num_samples{0} {}\n\n void sample(double value) {\n ++num_samples;\n mean = mean + (value - mean) / num_samples;\n }\n\n double get(void) const {\n assert(num_samples>0);\n return mean;\n }\n\n std::size_t get_num_samples(void) const { return num_samples; }\n\n void reset(void) {\n mean = .0;\n num_samples = 0;\n }\n\nprivate:\n double mean;\n std::size_t num_samples;\n};\n\n\n#endif // INCREMENTAL_AVERAGE_H\n\n\n" }, { "alpha_fraction": 0.6181672215461731, "alphanum_fraction": 0.6302250623703003, "avg_line_length": 36.69696807861328, "blob_id": "913757b6b5c51dc9022875ed010d5ad7f5df0569", "content_id": "2baac6e1eb6b26cab82b3c8b9ee5ee555d062dff", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3732, "license_type": "no_license", "max_line_length": 140, "num_lines": 99, "path": "/src/evolution/generation_based_strategy.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include \"generation_based_strategy.h\"\n\nbool\nGeneration_Based_Evolution::show_selection(void)\n{\n fitness_stats.reset();\n double generation_rnd = random_value(0.0, 1.0); // provide a random value used by evaluation, same for every individual of a generation,\n\n sts_msg(\"Showing selection\");\n evaluation.prepare_generation(cur_generation, max_generation); // prepare generation\n for (std::size_t i = 0; i < selection_size; ++i)\n {\n sts_msg(\" showing individual no. %3u/%u \", i + 1, selection_size);\n evaluation.constrain(population[i].genome);\n if (evaluation.evaluate(population[i].fitness, population[i].genome, generation_rnd))\n {\n auto const& fitval = population[i].fitness.get_value();\n sts_msg(\" = %1.3f (% e)\", fitval, fitval);\n fitness_stats.add_sample(fitval);\n }\n else return false;\n }\n fitness_stats.update_average();\n sts_msg(\"Result: max: %1.2f avg: %1.2f min: %1.2f\\n\", fitness_stats.max, fitness_stats.avg, fitness_stats.min);\n return true;\n}\n\n\nbool\nGeneration_Based_Evolution::evaluate_generation(void)\n{\n fitness_stats .reset();\n mutation_stats.reset();\n\n double generation_rnd = random_value(0.0, 1.0); // provide a random value used by evaluation, same for every individual of a generation,\n\n if (verbose) sts_msg(\"Evaluate generation %u/%u:\", cur_generation, max_generation);\n evaluation.prepare_generation(cur_generation, max_generation); // prepare generation\n\n for (std::size_t i = 0; i < population.get_size(); ++i)\n {\n if (verbose) sts_msg(\" testing individual no.%3lu/%lu [%lu/%lu]\", i + 1, population.get_size(), cur_generation, max_generation);\n else printf(\"\\rtesting individual no.%3lu/%lu [%lu/%lu]\", i + 1, population.get_size(), cur_generation, max_generation);\n\n population[i].fitness.reset();\n evaluation.constrain(population[i].genome);\n if (evaluation.evaluate(population[i].fitness, population[i].genome, generation_rnd))\n {\n auto const& fitval = population[i].fitness.get_value();\n auto const& mutval = population[i].mutation_rate;\n\n if (verbose) sts_msg(\" fit=%+07.3f mu=%1.5f\\n\", fitval, mutval);\n\n fitness_stats .add_sample(fitval);\n mutation_stats.add_sample(mutval);\n }\n else\n {\n sts_msg(\"Stopped in generation %u, max=%+07.3ff\", cur_generation, fitness_stats.max);\n return false;\n }\n }\n fitness_stats .update_average();\n mutation_stats.update_average();\n\n sts_msg(\"\\rGeneration result: max=%+07.3f avg=%+07.3f min=%+07.3f\", fitness_stats.max, fitness_stats.avg, fitness_stats.min);\n\n return true;\n}\n\nvoid\nGeneration_Based_Evolution::recombination_crossover(void)\n{\n if (selection_size < 2) {\n wrn_msg(\"Recombination skipped, selection size is only 1.\");\n return;\n }\n if (verbose) sts_msg(\"Doing recombination with crossover.\");\n assert(selection_size > 1);\n for (std::size_t child_idx = selection_size; child_idx < population.get_size(); ++child_idx)\n {\n std::size_t mother_idx = random_index(selection_size);\n std::size_t father_idx = random_index(selection_size);\n while (mother_idx == father_idx)\n mother_idx = random_index(selection_size); // (!) this gets an infinite loop with selection_size = 1\n\n crossover(population[mother_idx],\n population[father_idx],\n population[ child_idx]);\n }\n}\n\nvoid\nGeneration_Based_Evolution::mutation(void)\n{\n if (verbose) sts_msg(\"Mutating\");\n for (std::size_t i = selection_size; i < population.get_size(); ++i)\n population[i].mutate();\n}\n" }, { "alpha_fraction": 0.5732483863830566, "alphanum_fraction": 0.5940678715705872, "avg_line_length": 36.39146041870117, "blob_id": "394299b0a378237a154b03dc38f4b5989cc5c1f0", "content_id": "a927ec42d6dea9fa5f5138a8d5d17e6ff034c8b5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 10519, "license_type": "no_license", "max_line_length": 156, "num_lines": 281, "path": "/src/learning/motor_layer.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef MOTOR_LAYER_H_INCLUDED\n#define MOTOR_LAYER_H_INCLUDED\n\n#include <robots/robot.h>\n#include <robots/joint.h>\n#include <control/jointcontrol.h>\n#include <control/control_vector.h>\n#include <control/controlparameter.h>\n#include <control/sensorspace.h>\n#include <learning/expert.h>\n#include <learning/gmes.h>\n#include <learning/payload.h>\n#include <learning/motor_predictor.h>\n\n/* graphics */\n#include <draw/draw.h>\n#include <draw/axes.h>\n#include <draw/axes3D.h>\n#include <draw/plot1D.h>\n#include <draw/plot2D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n#include <draw/display.h>\n\n\nnamespace learning {\n\nnamespace motor_layer_constants {\n const double number_of_experts = 19;\n const double learning_rate = 0.001;\n const double growth_rate = 1.0;\n const std::size_t experience_size = 100;\n const double noise_level = 0.005; // 0.01 is maybe too high\n}\n\n\nclass Motor_Space : public sensor_vector {\npublic:\n Motor_Space(const robots::Jointvector_t& joints)\n : sensor_vector(joints.size())\n {\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name, [&j](){ return clip(j.motor.get()); });\n //sensors.emplace_back(j.name, [&j](){ return clip(j.motor.get() + rand_norm_zero_mean(0.01), 1.0); });\n }\n};\n\n\nstd::size_t getnum(void){\n return random_index(5);\n}\n\nclass Motor_Layer : public learning::Learning_Machine_Interface {\npublic:\n Motor_Layer( robots::Robot_Interface const& robot\n , std::size_t max_num_motor_experts = motor_layer_constants::number_of_experts\n , const double learning_rate = motor_layer_constants::learning_rate\n , const double growth_rate = motor_layer_constants::growth_rate\n , std::size_t experience_size = motor_layer_constants::experience_size\n , double noise_level = motor_layer_constants::noise_level\n , std::string const& initial_parameter_folder = \"\"\n , control::Minimal_Seed_t seed = {0.,0.,0.}\n , std::size_t num_initial_experts = 1)\n : params(control::param_factory(robot, max_num_motor_experts, initial_parameter_folder, seed))\n , payloads(max_num_motor_experts)\n , motorspace(robot.get_joints())\n , experts(max_num_motor_experts, payloads, motorspace, learning_rate, experience_size, noise_level, params, robot )\n , gmes(experts, growth_rate, /* one shot learning = */false, num_initial_experts, \"motor-layer\")\n {\n sts_msg(\"Creating motor layer with max. %u experts and growth rate: %1.4f\", max_num_motor_experts, growth_rate);\n if (initial_parameter_folder != \"\")\n sts_msg(\"Reading initial parameters from '%s'\", initial_parameter_folder.c_str());\n\n sts_msg(\"Number of initial experts: %u (loaded and created: %u)\", num_initial_experts, params.size());\n assert(params.size() == max_num_motor_experts);\n assert(get_max_number_of_experts() == max_num_motor_experts);\n assert(get_cur_number_of_experts() == num_initial_experts);\n\n assert(learning_rate > 0.);\n assert(growth_rate > 0.);\n }\n\n void execute_cycle(void) {\n motorspace.execute_cycle();\n gmes.execute_cycle();\n }\n\n void enable_learning(bool b) override { gmes.enable_learning(b); }\n void toggle_learning(void) { gmes.enable_learning(not gmes.is_learning_enabled()); }\n double get_learning_progress(void) const override { return gmes.get_learning_progress(); }\n\n std::size_t get_state(void) const { return gmes.get_state(); }\n\n bool is_learning_enabled(void) const { return gmes.is_learning_enabled(); }\n\n /** This is somewhat ugly. But the only way I have figured out to do that. */\n const control::Control_Parameter& get_controller_weights(std::size_t id) const {\n// dbg_msg(\"Fetching controller weights.\");\n Predictor_Base const& other = experts[id].get_predictor();\n Motor_Predictor const& motor_pred = dynamic_cast<Motor_Predictor const&>(other);\n return motor_pred.get_controller_weights();\n }\n\n\n std::size_t get_max_number_of_experts() const { return experts.size(); }\n std::size_t get_cur_number_of_experts() const { return gmes.get_number_of_experts(); }\n\n bool has_expert(std::size_t id) const { return experts[id].does_exists(); }\n\n void save(const std::string& foldername) const {\n const std::size_t num_experts = get_cur_number_of_experts();\n sts_msg(\"Saving %lu motor expert%s to folder %s\", num_experts, (num_experts>1?\"s\":\"\"), foldername.c_str());\n const std::string folder = basic::make_directory((foldername + \"/motor\").c_str());\n for (std::size_t i = 0; i < num_experts; ++i)\n {\n auto const& ctrl = get_controller_weights(i);\n ctrl.save_to_file(folder + \"/motor_expert_\" + std::to_string(i) + \".dat\", i);\n }\n }\n\n void connect_payloads(static_vector<State_Payload>* s) {\n dbg_msg(\"Connecting payloads\");\n for (std::size_t i = 0; i < payloads.size(); ++i)\n payloads[i].connect(i, s);\n }\n\n control::Control_Vector params;\n static_vector<Motor_Payload> payloads;\n Motor_Space motorspace;\n Expert_Vector experts;\n GMES gmes;\n\n friend class Motor_Layer_Graphics;\n};\n\n\n\nnamespace constants {\n const unsigned subspace_num_datapoints = 200; // 2s of data at 100Hz\n}\n\nstruct motor_subspace_graphics : public Graphics_Interface\n{\n motor_subspace_graphics( robots::Joint_Model const& j0\n , robots::Joint_Model const& j1\n , Vector3 pos, float size\n , ColorTable const& colortable )\n : j0(j0)\n , j1(j1)\n , axis_xy(pos.x + size/2 , pos.y + size/2, pos.z, size, size, 0, std::string(\"xy\"))\n , axis_dt(pos.x + (2.0 + size)/2, pos.y + size/2, pos.z, 2.0-size, size, 1, std::string(j0.name + \"/\" + j1.name))\n , plot_xy(constants::subspace_num_datapoints, axis_xy, colors::magenta )\n , plot_j0(constants::subspace_num_datapoints, axis_dt, colors::light0 )\n , plot_j1(constants::subspace_num_datapoints, axis_dt, colors::light1 )\n , plot_p0(constants::subspace_num_datapoints, axis_dt, colortable )\n , plot_p1(constants::subspace_num_datapoints, axis_dt, colortable )\n {\n dbg_msg(\"Initialize motor subspace for joints:\\n\\t%2u: %s\\n\\t%2u: %s\", j0.joint_id, j0.name.c_str()\n , j1.joint_id, j1.name.c_str() );\n }\n\n void draw(const pref&) const {\n axis_xy.draw();\n plot_xy.draw();\n\n axis_dt.draw();\n plot_j0.draw(); // signals\n plot_j1.draw();\n plot_p0.draw_colored(); // predictions\n plot_p1.draw_colored();\n }\n\n void execute_cycle(float s0, float s1, unsigned expert_id) {\n plot_xy.add_sample(j0.motor.get(), j1.motor.get());\n plot_j0.add_sample(j0.motor.get());\n plot_j1.add_sample(j1.motor.get());\n plot_p0.add_colored_sample(s0, expert_id);\n plot_p1.add_colored_sample(s1, expert_id);\n }\n\n const robots::Joint_Model& j0;\n const robots::Joint_Model& j1;\n\n axes axis_xy;\n axes axis_dt;\n\n plot2D plot_xy;\n plot1D plot_j0, plot_j1; // joint motor commands\n colored_plot1D plot_p0, plot_p1; // predictions\n\n};\n\nclass Motor_Layer_Graphics : public Graphics_Interface {\npublic:\n\n Motor_Layer_Graphics( Motor_Layer const& motor_layer\n , robots::Robot_Interface const& robot )\n : motor_layer(motor_layer)\n , num_experts()\n , max_experts(motor_layer.gmes.get_max_number_of_experts())\n , winner()\n , subspace()\n , colortable(5, /*randomized*/true)\n {\n /** TODO\n + also for the rest of the joints\n + consider grouping the graphs in legs (4x 3D) instead of 6x 2D\n */\n const unsigned N = robot.get_number_of_symmetric_joints();\n const double size = 2.0/N;\n subspace.reserve(N);\n Vector3 pos(-1.0, 1.0, 0.);\n for (auto const& j0: robot.get_joints()) {\n if (j0.is_symmetric()) {\n robots::Joint_Model const& j1 = robot.get_joints()[j0.symmetric_joint];\n pos.y -= size;\n subspace.emplace_back(j1, j0, pos, size, colortable);\n }\n }\n }\n\n void draw(const pref& p) const {\n glColor3f(1.0, 1.0, 1.0);\n glprintf(1.05, 0.95, 0.0, 0.05, \"no. exp. = %03u (%03u/%03u)\", winner, num_experts, max_experts);\n\n auto const learning = motor_layer.is_learning_enabled();\n if (!learning) glColor3f(.3f,.3f,.3f);\n glprintf(1.05, 0.90, 0.0, 0.05, \"learning: %s\", learning ? \"enabled\" : \"disabled\");\n\n\n for (auto& s: subspace)\n s.draw(p);\n\n for (std::size_t i = 0; i < motor_layer.experts.size(); ++i)\n {\n if (winner == i) glColor3f(1.0, 0.5, 1.0);\n else if (motor_layer.experts[i].does_exists())\n glColor3f(.9f, .9f, .9f);\n else\n glColor3f(.3f,.3f,.3f);\n glprintf(-1.1, -1.5 - 0.05*i, 0.0, 0.04, \"%2u\" , i);\n\n auto const& col = motor_layer.experts[i].does_exists() ? colortable[i] : colors::gray;\n draw::hbar(-1.4, -1.5 - 0.05*i, 0.3, 0.02, 0.5* motor_layer.experts[i].get_learning_capacity(), gmes_constants::initial_learning_capacity, col);\n\n auto const& pred = motor_layer.experts[i].get_predictor();\n glPushMatrix();\n glTranslatef(0.0, -1.5 - 0.05*i , 0.0);\n pred.draw();\n glPopMatrix();\n }\n\n };\n\n void execute_cycle() {\n\n num_experts = motor_layer.gmes.get_number_of_experts(); // update number of experts\n winner = motor_layer.gmes.get_winner();\n auto const& predictions = motor_layer.experts[winner].get_predictor().get_prediction();\n\n for (auto& s: subspace) {\n auto s0 = predictions.at(s.j0.joint_id);\n auto s1 = predictions.at(s.j1.joint_id);\n s.execute_cycle(s0, s1, winner);\n }\n };\n\n Motor_Layer const& motor_layer;\n std::size_t num_experts;\n std::size_t max_experts;\n std::size_t winner;\n\n std::vector<motor_subspace_graphics> subspace;\n ColorTable colortable;\n};\n\n} // namespace learning\n\n\n#endif // MOTOR_LAYER_H_INCLUDED\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "alpha_fraction": 0.5425454378128052, "alphanum_fraction": 0.5592727065086365, "avg_line_length": 25.44230842590332, "blob_id": "37098e775257a65b91525bfbbb184b723110e38a", "content_id": "23a1a41a736b0da5ed6ef9cb50c3d796811edbfc", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1375, "license_type": "no_license", "max_line_length": 123, "num_lines": 52, "path": "/src/common/timer.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/*\n\n +---------------------------------+\n | Matthias Kubisch |\n | [email protected] |\n | November 2018 |\n +---------------------------------+\n\n*/\n\n#ifndef TIMER_HPP\n#define TIMER_HPP\n\n#include <chrono>\n\nclass SimpleTimer {\n uint64_t timeout_us;\n std::chrono::high_resolution_clock::time_point time_0; //what type?\n bool enabled;\n uint64_t elapsed_us;\n\npublic:\n SimpleTimer(uint64_t timeout_us, bool enabled = false)\n : timeout_us(timeout_us)\n , time_0(std::chrono::high_resolution_clock::now())\n , enabled(enabled)\n , elapsed_us()\n {}\n\n void start(void) { enabled = true; }\n void stop(void) { enabled = false; }\n\n bool check_if_timed_out_and_restart(uint64_t new_timeout_us) {\n timeout_us = new_timeout_us;\n return check_if_timed_out_and_restart();\n }\n\n bool check_if_timed_out_and_restart(void) {\n if (not enabled) return false;\n const auto time_1 = std::chrono::high_resolution_clock::now();\n elapsed_us = static_cast<uint64_t>(std::chrono::duration_cast<std::chrono::microseconds>(time_1 - time_0).count());\n bool is_timed_out = elapsed_us >= timeout_us;\n if (is_timed_out) {\n time_0 = time_1; // reset\n return true;\n } else return false;\n }\n\n uint64_t get_elapsed_us(void) const { return elapsed_us; }\n};\n\n#endif /* TIMER_HPP */\n" }, { "alpha_fraction": 0.5325947403907776, "alphanum_fraction": 0.5419074892997742, "avg_line_length": 30.938461303710938, "blob_id": "9d89f5a5bf4748562ee315107d3952c2e9789515", "content_id": "40060b02ae12477f677ccabf6ce67e5ae5af3e10", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6228, "license_type": "no_license", "max_line_length": 119, "num_lines": 195, "path": "/src/common/socket_server.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SOCKET_SERVER_H\n#define SOCKET_SERVER_H\n\n#include <fcntl.h>\n#include <stdio.h>\n#include <stdlib.h>\n#include <unistd.h>\n#include <string.h>\n#include <sys/types.h>\n#include <sys/socket.h>\n#include <netinet/in.h>\n#include <netinet/tcp.h>\n#include <arpa/inet.h>\n\nnamespace network {\n\n namespace constants {\n const unsigned max_connections = 1;\n const unsigned max_tcp_msg_len = 8192;\n const unsigned timeout_ms = 500;\n }\n\nclass Socket_Server\n{\n int sockfd;\n int connectfd = -1;\n uint16_t port;\n struct sockaddr_in serv_addr = {};\n std::string recv_stream = \"\";\n std::string current_client_addr = \"\";\n\npublic:\n Socket_Server(const uint16_t port)\n : sockfd(socket(AF_INET, SOCK_STREAM, 0))\n , port(port)\n {\n if (sockfd < 0)\n err_msg(__FILE__,__LINE__,\"Cannot create TCP socket listening to port %u.\\n%s\", port, strerror(errno));\n\n memset(&serv_addr, 0, sizeof(serv_addr)); /* clear struct */\n serv_addr.sin_family = AF_INET; /* socket address type */\n serv_addr.sin_port = htons(port); /* set port, convert unsigned to network byte order */\n serv_addr.sin_addr.s_addr = htonl(INADDR_ANY); /* set IP of own host (INADDR_ANY) */\n int flag = 1; /* set TCP_NODELAY flag */\n setsockopt(sockfd, IPPROTO_TCP, TCP_NODELAY, &flag, sizeof(int));\n\n /* make the socket non-blockable */\n int x = fcntl(sockfd ,F_GETFL, 0);\n if (fcntl(sockfd, F_SETFL, x | O_NONBLOCK))\n err_msg(__FILE__,__LINE__,\"could not set socket blocking\");\n\n /* set timeout */\n struct timeval read_timeout = timeval{};\n read_timeout.tv_usec = constants::timeout_ms * 1000;\n if (setsockopt(sockfd, SOL_SOCKET, SO_RCVTIMEO, &read_timeout, sizeof(read_timeout)) < 0)\n err_msg(__FILE__,__LINE__,\"Cannot set socket options for read timeout.\\n%s\", strerror(errno));\n\n /* bind socket to address and port */\n if (-1 == bind(sockfd, (struct sockaddr *) &serv_addr, sizeof(serv_addr)))\n {\n close(sockfd);\n err_msg(__FILE__,__LINE__,\"Failed binding the socket.\\nPlease wait until released or try another port.\\n\");\n }\n\n if (-1 == listen(sockfd, (int) constants::max_connections))\n {\n close(sockfd);\n err_msg(__FILE__,__LINE__,\"Failed listening to socket.\");\n }\n }\n\n ~Socket_Server() {\n close_connection();\n close(sockfd);\n }\n\n bool open_connection(void)\n {\n /* wait for client connection and accept, if any. */\n struct sockaddr_storage addr;\n socklen_t len = sizeof addr;\n\n\n connectfd = accept(sockfd, (struct sockaddr*) &addr, &len);\n if (connectfd < 0) {\n return false;\n }\n\n set_socket_blocking(connectfd, false);\n\n char client_addr[INET6_ADDRSTRLEN];\n unsigned port;\n if (addr.ss_family == AF_INET) { /* IPv4 */\n struct sockaddr_in *s = (struct sockaddr_in*) &addr;\n port = ntohs(s->sin_port);\n inet_ntop(AF_INET, &s->sin_addr, client_addr, sizeof client_addr);\n } else { // AF_INET6\n struct sockaddr_in6 *s = (struct sockaddr_in6*) &addr;\n port = ntohs(s->sin6_port);\n inet_ntop(AF_INET6, &s->sin6_addr, client_addr, sizeof client_addr);\n }\n\n sts_msg(\"Connection opened to client: %s:%u\", client_addr, port);\n current_client_addr = client_addr;\n return true;\n }\n\n void close_connection(void)\n {\n if (connectfd < 0) return; /* already closed, nothing to do */\n\n if (shutdown(connectfd, SHUT_RDWR) < 0)\n wrn_msg(\"Cannot shutdown TCP connection.\\n%s\", strerror(errno));\n else\n sts_msg(\"Connection successfully shut down.\");\n\n close(connectfd);\n sts_msg(\"Connection closed.\");\n }\n\n bool send_message(std::string const& msg) const\n {\n if (write(connectfd, msg.c_str(), msg.length()) < 0) {\n wrn_msg(\"Failed writing to TCP socket.\\n%s\", strerror(errno));\n return false;\n }\n return true;\n }\n\n std::string get_next_msg(void) const\n {\n /* create and clear buffer */\n char buffer[constants::max_tcp_msg_len];\n memset(buffer, 0, constants::max_tcp_msg_len);\n\n /* read from socket (blocking) */\n if (recv(connectfd, buffer, constants::max_tcp_msg_len, MSG_DONTWAIT) < 0)\n {\n if (errno != EAGAIN)\n wrn_msg(\"Failed reading from socket.\\n%s\", strerror(errno));\n return \"\";\n }\n\n /*if (0 == n)\n {\n printf(\"Reading no more bytes from socket. Exiting.\\n\");\n return std::string(\"EXIT\\n\");\n }*/\n\n return std::string(buffer);\n }\n\n /* return part of the stream until next newline character */\n std::string get_next_line(void)\n {\n std::string::size_type pos;\n\n recv_stream = get_next_msg();\n if (recv_stream.size() == 0) return \"\";\n\n // continue reading if there was data but no complete command, timeout 10ms\n unsigned t = 0;\n while((pos = recv_stream.find(\"\\n\", 0)) == std::string::npos) {\n usleep(1000);\n recv_stream += get_next_msg();\n if (++t >= 10) {\n wrn_msg(\"Timeout reading data line.\");\n break;\n }\n }\n\n std::string retstr = recv_stream.substr(0, pos);\n recv_stream.erase(0, pos+1);\n\n return retstr;\n }\n\n\n std::string const& get_current_client_address(void) const { return current_client_addr; }\n\n\n bool set_socket_blocking(int fd, bool blocking)\n {\n if (fd < 0) return false;\n int flags = fcntl(fd, F_GETFL, 0);\n if (flags == -1) return false;\n flags = blocking ? (flags & ~O_NONBLOCK) : (flags | O_NONBLOCK);\n return (fcntl(fd, F_SETFL, flags) == 0) ? true : false;\n }\n\n};\n\n} /* namespace network */\n\n#endif /* SOCKET_SERVER_H */\n" }, { "alpha_fraction": 0.6127395629882812, "alphanum_fraction": 0.6369785666465759, "avg_line_length": 36.74468231201172, "blob_id": "6e8cf63151f91ba671afbf2abd0b6f37b73240a7", "content_id": "50387d6aa4f6f29376e55d07e74dff0c669354ea", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1774, "license_type": "no_license", "max_line_length": 96, "num_lines": 47, "path": "/src/evolution/individual.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include \"individual.h\"\n\nvoid\ncrossover(const Individual& mother, const Individual& father, Individual& child)\n{\n assert(child.genome.size() == father.genome.size());\n assert(child.genome.size() == mother.genome.size());\n\n /* 50% chance to inherit each feature from either mother or father */\n for (unsigned int j = 0; j < child.genome.size(); j++)\n child.genome[j] = (random_value(0, 1.0) < 0.5) ? mother.genome[j] : father.genome[j];\n\n /* average the mutation rate */\n child.fitness.reset();\n child.mutation_rate = .5 * mother.mutation_rate + .5 * father.mutation_rate;\n child.meta_mutation_rate = .5 * mother.meta_mutation_rate + .5 * father.meta_mutation_rate;\n}\n\nvoid\nIndividual::initialize_from_seed(const std::vector<double>& seed)\n{\n assert(genome.size() == seed.size());\n double sigma = mutation_rate / sqrt(genome.size()); // normalize\n for (unsigned int j = 0; j < genome.size(); ++j)\n genome[j] = seed[j] + rand_norm_zero_mean(sigma);\n fitness.reset();\n}\n\nvoid\nIndividual::mutate(void)\n{\n assert_in_range(mutation_rate , 0.0001, 1.0 );\n assert_in_range(meta_mutation_rate, 0.0 , M_LN2);\n assert(genome.size() > 0);\n /** TODO search for literature reference of that. */\n const double rnd_val = random_value_norm(0.0, meta_mutation_rate, -M_LN2, M_LN2);\n const double factor = exp(rnd_val); // mutate the mutation rate\n assert_in_range(factor, 0.5, 2.0);\n\n mutation_rate *= factor;\n mutation_rate = clip(mutation_rate, 0.0001, 1.0);\n// sts_msg(\" mutating: mu = %1.5f, d mu = %1.3f\", mutation_rate, factor);\n const double sigma = mutation_rate / sqrt(genome.size()); // normalize\n\n for (auto& element : genome)\n element += rand_norm_zero_mean(sigma);\n}\n" }, { "alpha_fraction": 0.5536980628967285, "alphanum_fraction": 0.6137284636497498, "avg_line_length": 37.70588302612305, "blob_id": "12ca67c5f9ff78463ac1213e305c7832a13f5bce", "content_id": "b43f0384095143dbc529e62595a9d6182437707d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3948, "license_type": "no_license", "max_line_length": 128, "num_lines": 102, "path": "/src/draw/draw.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* draw.h */\n#ifndef DRAW_H\n#define DRAW_H\n\n#include <GL/gl.h>\n#include <GL/glu.h>\n#include <GL/glut.h>\n#include <stdio.h>\n#include <stdlib.h>\n#include <stdarg.h>\n\n#include <string.h>\n#include <string>\n\n#include <basic/color.h>\n\nconst GLubyte red [] = {255, 0, 0, 255};\nconst GLubyte green [] = { 0, 255, 0, 255};\nconst GLubyte blue [] = { 0, 0, 255, 255};\nconst GLubyte white [] = {255, 255, 255, 255};\nconst GLubyte white_trans [] = {255, 255, 255, 64};\nconst GLubyte white_trans2 [] = {255, 255, 255, 128};\nconst GLubyte cyan [] = { 0, 255, 255, 255};\nconst GLubyte yellow [] = {255, 255, 0, 255};\nconst GLubyte black [] = { 0, 0, 0, 255};\nconst GLubyte orange [] = {255, 128, 0, 255};\nconst GLubyte magenta [] = {255, 0, 255, 255};\n\nconst GLubyte LineColorMix0[4][4] = {{ 0, 128, 255, 255},\n { 0, 128, 255, 196},\n { 0, 128, 255, 128},\n { 0, 128, 255, 64}};\n\n\nconst GLubyte LineColorMix1[4][4] = {{255, 0, 0, 255},\n {255, 0, 0, 196},\n {255, 0, 0, 128},\n {255, 0, 0, 64}};\n\ninline void set_color(Color4 c) { glColor4d(c.r, c.g, c.b, c.a); }\ninline void set_color(Color4 c, float a) { glColor4d(c.r, c.g, c.b, a); }\n\n\nstruct Point\n{\n float x;\n float y;\n float z;\n};\n\n\nvoid draw_line(const GLfloat x[3], const GLfloat y[3]);\nvoid draw_line(const Point& p1, const Point& p2);\nvoid draw_line(const float x1, const float y1, const float z1,\n const float x2, const float y2, const float z2);\nvoid draw_line(const float x1, const float y1,\n const float x2, const float y2);\n\nvoid draw_line2D(const GLfloat x[2], const GLfloat y[2]);\n\nvoid draw_rect(const GLfloat x1[3], const GLfloat x2[3], const GLfloat x3[3], const GLfloat x4[3]) __attribute__ ((deprecated));\n\nvoid draw_rect(const float size_x, const float size_y);\nvoid draw_rect(const float x, const float y, const float size_x, const float size_y);\n\nvoid draw_square(const float size);\nvoid draw_square(const float x, const float y, const float size);\n\nvoid draw_cube(const GLfloat x1[3], const GLfloat x2[3], const GLfloat x3[3], const GLfloat x4[3],\n const GLfloat x5[3], const GLfloat x6[3], const GLfloat x7[3], const GLfloat x8[3]);\n\nvoid draw_solid_cube(const float x, const float y, const float z, const float size);\nvoid draw_wire_cube(const float x, const float y, const float z, const float size);\n\nvoid draw_fill_rect(const float size_x, const float size_y);\nvoid draw_fill_rect(const float x, const float y, const float size_x, const float size_y);\n\nvoid draw_fill_square(const float size);\nvoid draw_fill_square(const float x, const float y, const float size);\n\n\n\nvoid fill_rect(const GLfloat x1[3], const GLfloat x2[3], const GLfloat x3[3], const GLfloat x4[3]);\n\nvoid draw_grid2D(const float range, const int lines);\nvoid draw_grid3D(const float range, const int lines);\n\nvoid draw_text_small(const float x, const float y, const float z, const char *str) __attribute__ ((deprecated));\nvoid draw_text_medium(const float x, const float y, const float z, const char *str) __attribute__ ((deprecated));\n\nvoid output(const GLfloat x, const GLfloat y, const GLfloat z, const char *text) __attribute__ ((deprecated));\n\nvoid gl_msg(const float px, const float py, const float pz, const char* format, ...) __attribute__ ((deprecated));\n\nvoid glprintf(float x, float py, float pz, float scale_factor, const char* format, ...);\nvoid glprintc(float x, float py, float pz, float scale_factor, const char* str);\nvoid glprints(float x, float py, float pz, float scale_factor, const std::string str);\n\nnamespace draw {\n void fill_rect(float px, float py, float dx, float dy);\n}\n#endif /*DRAW_H*/\n" }, { "alpha_fraction": 0.5519034266471863, "alphanum_fraction": 0.5644166469573975, "avg_line_length": 31.4228572845459, "blob_id": "972e385ce11ba351cf474c26cb4a2d0edde5ceb3", "content_id": "607fa1adc12eb936e0e4355fbbbc83b6e6c8d71e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 11348, "license_type": "no_license", "max_line_length": 137, "num_lines": 350, "path": "/src/learning/forward_inverse_model.hpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BIDIRECTIONAL_MODELS_H_INCLUDED\n#define BIDIRECTIONAL_MODELS_H_INCLUDED\n\n#include <vector>\n#include <common/static_vector.h>\n#include <common/modules.h>\n\n\nnamespace learning {\n\n\n\nnamespace model {\n typedef double scalar_t;\n typedef std::vector<scalar_t> vector_t;\n typedef copyable_static_vector<copyable_static_vector<scalar_t>> matrix_t;\n\n template <typename Float_t>\n struct AdamData {\n static constexpr Float_t b1 = 0.9;\n static constexpr Float_t b2 = 0.999;\n static constexpr Float_t e0 = 10e-8;\n\n Float_t m, v, bt1, bt2;\n\n AdamData(void) : m(.0), v(.0), bt1(1.), bt2(1.) { }\n\n Float_t get(Float_t grad) {\n bt1 *= b1;\n bt2 *= b2;\n\n m = b1 * m + (1. - b1) * grad; // 1st momentum\n v = b2 * v + (1. - b2) * grad * grad; // 2nd momentum\n\n const Float_t M = m / (1. - bt1);\n const Float_t V = v / (1. - bt2);\n\n return (M / (sqrt(V) + e0));\n\n }\n };\n\n typedef copyable_static_vector<copyable_static_vector<AdamData<scalar_t>>> gradient_t;\n\n\n\n template <typename MatrixType>\n void randomize_weights(MatrixType& mat, double random_weight_range) {\n assert_in_range(random_weight_range, 0.0, 5.0);\n const double normed_std_dev = random_weight_range / sqrt(mat[0].size());\n assert(normed_std_dev != 0.0);\n\n for (std::size_t i = 0; i < mat.size(); ++i)\n for (std::size_t j = 0; j < mat[i].size(); ++j)\n mat[i][j] = rand_norm_zero_mean(normed_std_dev); // normalized by sqrt(N), N:#inputs\n\n }\n}\n\ntemplate <typename Vector_t>\nclass twopart_vector {\npublic:\n\n Vector_t& part0;\n Vector_t& part1;\n\n twopart_vector(Vector_t& part0, Vector_t& part1) : part0(part0), part1(part1) {\n assert(part0.size() > 0);\n assert(part1.size() > 0);\n }\n\n\n twopart_vector& operator=(Vector_t const& vec) {\n assert(vec.size() == size());\n for (std::size_t i = 0; i < size(); ++i)\n this->operator[](i) = vec[i];\n return *this;\n }\n\n twopart_vector& operator=(twopart_vector const& other) {\n assert(part0.size() == other.part0.size());\n assert(part1.size() == other.part1.size());\n part0 = other.part0;\n part1 = other.part1;\n return *this;\n }\n\n std::size_t size(void) const { return part0.size() + part1.size(); }\n\n model::scalar_t& operator[] (std::size_t index) {\n const auto s0 = part0.size();\n if (index < s0) return part0[index];\n else if (index < s0 + part1.size()) return part1[index-s0];\n else {\n assert(false);\n return part0[0];\n }\n }\n\n const model::scalar_t& operator[] (std::size_t index) const {\n const auto s0 = part0.size();\n if (index < s0) return part0[index];\n else if (index < s0 + part1.size()) return part1[index-s0];\n else {\n assert(false);\n return part0[0];\n }\n }\n};\n\ntemplate <typename T = model::scalar_t>\nclass LinearTransfer {\npublic:\n static T transfer(T const& x) { return x; }\n static T derive(T const& /*y*/) { return T{1}; }\n};\n\ntemplate <typename T = model::scalar_t>\nclass TanhTransfer {\npublic:\n static T transfer(T const& x) { return tanh(x); } // normal tangens hyperbolicus\n static T derive (T const& y) { return (1.0 + y) * (1.0 - y); } // this is y' with y=tanh(x)\n static T inverse (T const& x) { return atanh(x); /*log((1+x)/(1-x))/2*/ } // area tangens hyperbolicus = tanh^-1\n};\n\nstruct Weight_Statistics_t {\n\n static constexpr float zero_thrsh = 0.001;\n\n unsigned num = 0, total = 0;\n float avg = 0.f, ext = 0.f, vol = 0.f;\n\n};\n\ntemplate <typename Transfer_t>\nclass NeuralModel {\n\n model::vector_t y; // output, predictions\n model::matrix_t W; // Weights\n model::scalar_t E; // prediction error\n model::vector_t d; // delta error (used for back-propagation)\n\n model::gradient_t G; // Adam's Gradient Statistics\n\npublic:\n NeuralModel(std::size_t size_in, std::size_t size_out, double random_weight_range)\n : y(size_out)\n , W(size_out, size_in)\n , E()\n , d(size_out)\n , G(size_out, size_in)\n {\n model::randomize_weights(W, random_weight_range);\n }\n\n /* calculate and get back-propagated delta error */\n model::vector_t get_backprop_err(void) const\n {\n model::vector_t r(W[0].size());\n for (std::size_t j = 0; j < r.size(); ++j)\n for (std::size_t i = 0; i < d.size(); ++i)\n r[j] += d[i] * W[i][j];\n return r;\n }\n\n\n template <typename InputVector_t>\n model::vector_t const& propagate(InputVector_t const& in)\n {\n check_vectors(in, W[0]);\n\n for (std::size_t i = 0; i < y.size(); ++i) {\n model::scalar_t a = .0;\n for (std::size_t j = 0; j < in.size(); ++j)\n a += W[i][j] * in[j];\n y[i] = Transfer_t::transfer(a);\n }\n return y;\n }\n\n /* adapt should follow a propagation to fill y */\n template <typename InputVector_From_t, typename InputVector_To_t>\n void adapt(InputVector_From_t const& in, InputVector_To_t const& tar, double learning_rate, double regularization_rate)\n {\n check_vectors(tar, y);\n check_vectors(in, W[0]);\n assert_in_range(learning_rate, 0.0, 1.0);\n E = .0; // reset total sum of prediction errors\n for (std::size_t i = 0; i < y.size(); ++i) {\n const model::scalar_t e_i = tar[i] - y[i];\n E += square(e_i);\n d[i] = Transfer_t::derive(y[i]) * e_i; // remember delta error for back-propagation signal\n for (std::size_t j = 0; j < in.size(); ++j)\n //W[i][j] += learning_rate * d[i] * in[j] - regularization_rate * W[i][j];//* sign(W[i][j]);\n /* target learning L1 regularization */\n W[i][j] += learning_rate * G[i][j].get(d[i] * in[j]) - regularization_rate * W[i][j];\n }\n }\n\n model::matrix_t const& get_weights() const { return W; }\n model::vector_t const& get_outputs() const { return y; }\n model::scalar_t get_error () const { return E/y.size(); }\n\n void randomize_weights(double random_weight_range) { model::randomize_weights(W, random_weight_range); }\n\n\n void constrain_weights(void) {\n for (std::size_t i = 0; i < W.size(); ++i)\n for (std::size_t j = 0; j < W[i].size(); ++j)\n W[i][j] = clip(W[i][j], 5);\n }\n\n //move to w_statistics class\n Weight_Statistics_t get_weight_statistics(void) const {\n Weight_Statistics_t stat = {};\n stat.num = 0;\n stat.total = W.size() * W[0].size();\n for (std::size_t i = 0; i < W.size(); ++i)\n for (std::size_t j = 0; j < W[i].size(); ++j) {\n const float w = W[i][j];\n stat.avg += w;\n stat.vol += std::abs(w);\n if (fabs(w) > Weight_Statistics_t::zero_thrsh) ++stat.num;\n if (fabs(w) > fabs(stat.ext)) stat.ext = w;\n }\n\n stat.avg /= stat.total; // normalize by number of weights\n return stat;\n }\n\n}; /* LinearModel */\n\n\nclass InverseNeuralModel {\n\n model::vector_t y, a; // output, temp\n model::matrix_t M; // Weights\n model::scalar_t e; // prediction error\n\npublic:\n static constexpr auto& G = TanhTransfer<>::inverse;\n\n InverseNeuralModel(std::size_t size_in, std::size_t size_out, double random_weight_range)\n : y(size_out)\n , a(size_in)\n , M(size_out, size_in)\n , e()\n {\n model::randomize_weights(M, random_weight_range);\n }\n\n template <typename InputVector_t>\n model::vector_t const& propagate(InputVector_t const& in)\n {\n check_vectors(in, a);\n for (std::size_t j = 0; j < in.size(); ++j)\n a[j] = G(in[j]);\n\n for (std::size_t i = 0; i < y.size(); ++i) {\n y[i] = .0;\n for (std::size_t j = 0; j < a.size(); ++j)\n y[i] += M[i][j] * a[j];\n }\n\n return y;\n }\n\n /* adapt should follow a propagation to fill y */\n template <typename InputVector_From_t, typename InputVector_To_t>\n void adapt(InputVector_From_t const& in, InputVector_To_t const& tar, double learning_rate, double normalize_rate) {\n check_vectors(tar, y);\n check_vectors(in, a);\n assert_in_range(learning_rate, 0.0, 5.0);\n\n e = .0;\n for (std::size_t j = 0; j < in.size(); ++j)\n a[j] = G(in[j]);\n\n for (std::size_t i = 0; i < y.size(); ++i) {\n model::scalar_t err_i = learning_rate * (tar[i] - y[i]);\n e += square(tar[i] - y[i]);\n for (std::size_t j = 0; j < in.size(); ++j)\n M[i][j] += err_i * a[j] - normalize_rate * sign(M[i][j]);\n }\n }\n\n model::matrix_t const& get_weights() const { return M; }\n model::vector_t const& get_outputs() const { return y; }\n model::scalar_t get_error () const { return e/y.size(); }\n\n void randomize_weights(double random_weight_range) { model::randomize_weights(M, random_weight_range); }\n\n\n}; /* LinearModel */\n\n\ntemplate <typename ForwardType, typename InverseType>\nclass BidirectionalModel {\n\n ForwardType m_forward;\n InverseType m_inverse;\n\npublic:\n BidirectionalModel(std::size_t size_x, std::size_t size_y, double random_weight_range)\n : m_forward(size_x, size_y, random_weight_range)\n , m_inverse(size_y, size_x, random_weight_range)\n {}\n\n template <typename InputVector_t> model::vector_t const& propagate_forward(InputVector_t const& X) { return m_forward.propagate(X); }\n template <typename InputVector_t> model::vector_t const& propagate_inverse(InputVector_t const& Y) { return m_inverse.propagate(Y); }\n\n template <typename InputVector_X_t, typename InputVector_Y_t>\n void adapt(InputVector_X_t const& X, InputVector_Y_t const& Y, double learning_rate, double regularization_rate)\n {\n m_forward.adapt(/*from*/X, /*to*/Y, learning_rate, regularization_rate);\n m_inverse.adapt(/*from*/Y, /*to*/X, learning_rate, regularization_rate);\n }\n\n model::matrix_t const& get_weights () const { return m_forward.get_weights(); }\n model::matrix_t const& get_weights_inverse() const { return m_inverse.get_weights(); }\n\n model::vector_t const& get_forward_result() const { return m_forward.get_outputs(); }\n model::vector_t const& get_inverse_result() const { return m_inverse.get_outputs(); }\n\n model::scalar_t get_forward_error() const { return m_forward.get_error(); }\n model::scalar_t get_inverse_error() const { return m_inverse.get_error(); }\n\n\n void randomize_weights(double range) {\n m_forward.randomize_weights(range);\n m_forward.randomize_weights(range);\n }\n\n model::vector_t get_backprop_gradient(void) const { return m_forward.get_backprop_err(); }\n\n ForwardType const& get_forward_model(void) const { return m_forward; }\n InverseType const& get_inverse_model(void) const { return m_inverse; }\n\n\n void constrain_weights(void) {\n m_forward.constrain_weights();\n m_inverse.constrain_weights();\n }\n\n}; /* BidirectionalModel */\n\n\n} /* namespace learning */\n\n#endif /* BIDIRECTIONAL_MODELS_H_INCLUDED */\n" }, { "alpha_fraction": 0.6607192754745483, "alphanum_fraction": 0.6626707315444946, "avg_line_length": 32.52336502075195, "blob_id": "134abcd5b292da8b5dc9aad6db97ec887f708b69", "content_id": "bf1ed5aafe20f51420001208ad19596b605f625a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3587, "license_type": "no_license", "max_line_length": 127, "num_lines": 107, "path": "/src/control/jointcontrol.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef JOINTCONTROL_H_INCLUDED\n#define JOINTCONTROL_H_INCLUDED\n\n#include <vector>\n#include <cassert>\n\n#include <common/modules.h>\n#include <common/config.h>\n#include <common/log_messages.h>\n\n#include <control/controlparameter.h>\n#include <control/control_vector.h>\n#include <control/control_core.h>\n\n#include <robots/robot.h>\n#include <robots/joint.h>\n\nnamespace control {\n\nclass Jointcontrol;\n\nstruct Minimal_Seed_t {\n Minimal_Seed_t(const std::vector<double>& vec)\n : pgain(), damping(), motor_self()\n {\n assert(vec.size() >= 3);\n pgain = vec[0];\n damping = vec[1];\n motor_self = vec[2];\n }\n\n Minimal_Seed_t(double pgain, double damping, double motor_self) : pgain(pgain), damping(damping), motor_self(motor_self) {}\n\n std::vector<double> get_vector() const { return {pgain, damping, motor_self}; }\n double pgain;\n double damping;\n double motor_self;\n};\n\nstd::size_t get_number_of_inputs(robots::Robot_Interface const& robot);\nControl_Parameter get_initial_parameter(robots::Robot_Interface const& robot, const Minimal_Seed_t& seed, bool symmetric);\nControl_Parameter make_symmetric (robots::Robot_Interface const& robot, const Control_Parameter& other);\nControl_Parameter make_asymmetric (robots::Robot_Interface const& robot, const Control_Parameter& other);\nControl_Parameter turn_symmetry (robots::Robot_Interface const& robot, const Control_Parameter& other);\n\nControl_Parameter initialize_anyhow ( robots::Robot_Interface const& robot, Jointcontrol const& control\n , bool is_symmetric, const Minimal_Seed_t params_pdm, const std::string& filename);\n\nControl_Vector param_factory( const robots::Robot_Interface& robot\n , std::size_t number_of_motor_units\n , const std::string& folder\n , const control::Minimal_Seed_t& seed );\n\n/** TODO: check for initialization problem in controller when using csl hold */\n\nclass Jointcontrol\n{\npublic:\n Jointcontrol(robots::Robot_Interface& robot);\n\n void execute_cycle(void);\n void reset(void);\n\n void insert_motor_command(unsigned index, double value);\n\n void switch_symmetric(bool switched);\n void switch_symmetric() { switch_symmetric(not is_switched); }\n\n void set_control_parameter(const Control_Parameter& controller);\n void set_control_parameter(const std::vector<double>& params);\n\n double get_normalized_mechanical_power(void) const;\n double get_normalized_control_change(void) const;\n\n void print_parameter(void) const;\n\n std::size_t get_number_of_parameter (void) const { return number_of_params_asym; }\n std::size_t get_number_of_symmetric_parameter(void) const { return number_of_params_sym; }\n\n bool is_symmetric(void) const { return symmetric_controller; }\n\n double get_L1_norm(void);\n\n void set_input_gain(double g) { core.gain = clip(g, 0., 1.); }\n\nprivate:\n\n void apply_symmetric_weights(const std::vector<double>& params);\n void apply_weights (const std::vector<double>& params);\n void integrate_accels (void);\n\n robots::Robot_Interface& robot;\n Fully_Connected_Symmetric_Core core;\n\n const std::size_t number_of_params_sym;\n const std::size_t number_of_params_asym;\n\n bool symmetric_controller;\n bool is_switched;\n\n friend class Jointcontrol_Graphics;\n};\n\n\n} // namespace control\n\n#endif // JOINTCONTROL_H_INCLUDED\n" }, { "alpha_fraction": 0.5627689957618713, "alphanum_fraction": 0.5649210810661316, "avg_line_length": 38.26760482788086, "blob_id": "0f1efac37a082b5159c8120d6cf00819b61aff7d", "content_id": "5a34081cb49a98f31231ce7f866fb17e4276bbd2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2788, "license_type": "no_license", "max_line_length": 168, "num_lines": 71, "path": "/src/control/control_vector.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <control/control_vector.h>\n\n\nnamespace control {\n\n void randomize_control_parameter(Control_Parameter& params, double std_dev, double max_dev)\n {\n assert_in_range(std_dev, 0.0, max_dev);\n for (std::size_t i = 0; i < params.size(); ++i)\n params[i] += random_value_norm(.0, std_dev, -max_dev, +max_dev);\n }\n\n\n Control_Vector::Control_Vector( std::size_t max_number_of_parameter_sets\n , const std::string& foldername\n , bool include_mirrored )\n : max_number_of_parameter_sets(max_number_of_parameter_sets)\n , controls()\n {\n sts_msg(\"Initialize control vector from folder: %s\", foldername.c_str());\n controls.reserve(max_number_of_parameter_sets);\n if (not foldername.empty())\n {\n basic::Filelist files = basic::list_directory(foldername.c_str(), \".dat\");\n /* add files successively */\n if (files.size() > 0) {\n for (auto& f : files)\n controls.emplace_back(foldername + f);\n } else\n sts_msg(\"No files found in '%s'\", foldername.c_str());\n } else\n sts_msg(\"Initialize empty control vector.\");\n\n if (include_mirrored) {\n std::size_t len = controls.size();\n for (std::size_t i = 0; i < len; ++i)\n if (not controls[i].is_symmetric() and not controls[i].is_mirrored()) {\n controls.emplace_back(controls[i].get_parameter(), false, true);\n sts_msg(\"Adding mirrored variant of controller %lu\", i);\n }\n }\n if (controls.size() >= max_number_of_parameter_sets)\n wrn_msg(\"Maximum number of parameter sets (%lu) exceeded (%lu).\\nConsider checking the configuration file.\", max_number_of_parameter_sets, controls.size());\n }\n\n\n void Control_Vector::add( const std::string& filename\n , const std::size_t number_of_params\n , bool symmetric\n , bool mirrored )\n {\n assert(controls.size() <= max_number_of_parameter_sets);\n controls.emplace_back(filename, number_of_params, symmetric, mirrored);\n }\n\n\n void Control_Vector::add(const std::string& filename) {\n assert(controls.size() <= max_number_of_parameter_sets);\n controls.emplace_back(filename);\n }\n\n void Control_Vector::add(const Control_Parameter& params) {\n assert(controls.size() <= max_number_of_parameter_sets);\n controls.emplace_back(params);\n }\n\n void Control_Vector::reload(std::size_t index, const std::string& filename) {\n controls.at(index) = Control_Parameter(filename);\n }\n\n} /* namespace control */\n" }, { "alpha_fraction": 0.5117924809455872, "alphanum_fraction": 0.5227987170219421, "avg_line_length": 23.461538314819336, "blob_id": "1515ac75c1f9a98afe4b4899d0405e06f5fa0993", "content_id": "f3b9ed964d105798e747c4633d73dc8cc86317d3", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1272, "license_type": "no_license", "max_line_length": 77, "num_lines": 52, "path": "/src/control/pusher.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef PUSHER_H_INCLUDED\n#define PUSHER_H_INCLUDED\n\n#include <basic/vector3.h>\n\nclass robot_pusher {\n\n robots::Simloid& robot;\n double probability;\n double strength;\n unsigned body_index;\n Vector3 force;\n unsigned duration;\n bool active;\n\npublic:\n robot_pusher(robots::Simloid& robot, double probability, double strength)\n : robot(robot)\n , probability(probability)\n , strength(strength)\n , body_index(0)\n , force(0.)\n , duration(0)\n , active(false)\n {\n assert(probability < 1.0 and probability >= 0.);\n }\n\n void execute_cycle(void) {\n if (!active) {\n if (random_value() > 1.0 - probability)\n {\n force.random(-strength,+strength);\n body_index = random_index(robot.get_number_of_bodies());\n robot.set_force(body_index, force);\n duration = random_int(0, 25);\n active = true;\n }\n }\n else { /* still active */\n if (duration == 0)\n {\n force.zero();\n robot.set_force(body_index, force); // set force to zero\n active = false;\n } else\n --duration;\n }\n }\n};\n\n#endif // PUSHER_H_INCLUDED\n" }, { "alpha_fraction": 0.6998654007911682, "alphanum_fraction": 0.7012113332748413, "avg_line_length": 24.586206436157227, "blob_id": "fef6ea478015b3d0ad8d286a05e08517d3788f59", "content_id": "750d0b49e4bb336387352a79ef783637f8800c52", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1486, "license_type": "no_license", "max_line_length": 128, "num_lines": 58, "path": "/src/control/jointcontroller.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef JOINTCONTROLLER_H\r\n#define JOINTCONTROLLER_H\r\n\n#include <vector>\n#include <cmath>\n#include <cassert>\n\n#include <common/modules.h>\n#include <common/config.h>\n#include <common/robot_conf.h>\n#include <common/file_io.h>\n\n#define INITIAL_BIAS 0.1\n\n/** WARNING: This class is outdated, do not use it anymore.\n * Use Jointcontrol instead.\n */\n\nclass Jointcontroller\n{\npublic:\n Jointcontroller( Robot_Configuration& configuration, bool symmetric_controller\n , double param_p, double param_d, double param_m, const std::string& seed_filename) __attribute_deprecated__;\n\n ~Jointcontroller() { sts_msg(\"Destroying joint controller.\"); }\n\n void loop(void);\n void reset(void);\n\n const std::vector<double> get_control_parameter(void) const;\n unsigned int get_number_of_parameter(void) const { return total_num_params; }\n\n void set_control_parameter(const std::vector<double>& params);\n void set_seed_parameter(void);\n double get_normalized_mechanical_power(void) const;\n void print_parameter(void) const;\n\nprivate:\n Robot_Configuration& robot;\n\n const unsigned int num_inputs;\n unsigned int total_num_params;\n\n std::vector<double> activation;\n std::vector<std::vector<double> > weights;\n\n std::vector<double> X;\n std::vector<double> Y;\n\n std::vector<double> seed_from_file;\n\n void set_initial_parameter(double p, double d, double m);\n void load_seed(const std::string& filename);\n\n};\n\n\n#endif // JOINTCONTROLLER\n" }, { "alpha_fraction": 0.5432921051979065, "alphanum_fraction": 0.5670789480209351, "avg_line_length": 28.19444465637207, "blob_id": "978dee03b90a64bcaa94ff39b651731c1e938c86", "content_id": "0ed9d0b438fbe37492eeb3028cf0602c548237a1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1051, "license_type": "no_license", "max_line_length": 88, "num_lines": 36, "path": "/src/learning/predictor_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef PREDICTOR_GRAPHICS_H_INCLUDED\n#define PREDICTOR_GRAPHICS_H_INCLUDED\n\n#include <common/modules.h>\n#include <draw/graphics.h>\n#include <basic/color.h>\n#include <learning/predictor.h>\n\nclass Predictor_Graphics : public Graphics_Interface\n{\n const Predictor_Base& predictor;\n const Color4 color;\n\npublic:\n Predictor_Graphics(const Predictor_Base& predictor)\n : predictor(predictor)\n , color(random_value(0.2, 1.0), random_value(0.2, 1.0), random_value(0.2, 1.0), 0.2)\n {\n assert(predictor.get_experience()[0].size() >= 3);\n }\n\n void draw(const pref& /*p*/) const\n {\n auto const& experience = predictor.get_experience();\n if (experience.size() > 1) {\n set_color(color);\n for (std::size_t i = 0; i < experience.size(); ++i)\n draw_solid_cube( experience[i][0]\n , experience[i][1]\n , experience[i][2]\n , 0.02 );\n }\n }\n};\n\n#endif // PREDICTOR_GRAPHICS_H_INCLUDED\n" }, { "alpha_fraction": 0.45288196206092834, "alphanum_fraction": 0.4620310962200165, "avg_line_length": 24.418603897094727, "blob_id": "7d41fe26373680567ba0ba70726420d61e77d492", "content_id": "1828ad70ed2acf246cc56d38ee971ee0891d565b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1093, "license_type": "no_license", "max_line_length": 102, "num_lines": 43, "path": "/src/SConscript", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\"\"\"\n +----------------------------------+\n | Framework |\n | Build Script |\n | Matthias Kubisch |\n | [email protected] |\n | May 2019 |\n +----------------------------------+\n\n\"\"\"\n\nimport os\n\nfor root, dirs, files in os.walk(\".\"):\n for file in files:\n if file.endswith(\".cpp\"):\n print(os.path.join(root, file))\n\ncpppaths = ['.','../../simloidTCP/src']\n\n\nsrc_files = [ 'common/log_messages.cpp'\n , 'common/modules.cpp'\n , 'common/settings.cpp'\n , 'common/basic.cpp'\n , 'control/jointcontrol.cpp'\n , 'control/controlparameter.cpp'\n , 'control/control_vector.cpp'\n , 'learning/gmes.cpp'\n , 'learning/predictor.cpp'\n , 'learning/action_selection.cpp'\n , 'serial/rs232.c'\n ]\n\n# common flags\ncppflags = ['-O2', '-Wall', '-Wextra', '-Wno-psabi']\n\n# c++ only flags\ncxxflags = ['-std=c++11']\n\n\n\nLibrary('../libframework', source = src_files, CPPPATH=cpppaths, CPPFLAGS=cppflags, CXXFLAGS=cxxflags)\n" }, { "alpha_fraction": 0.5910329222679138, "alphanum_fraction": 0.5962347984313965, "avg_line_length": 22.470930099487305, "blob_id": "630bdbad76af62f3d8b284e3a8ea8bde25ef71a1", "content_id": "faced50cb8c8fee65d66f7116b5258b1e5b93640", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4037, "license_type": "no_license", "max_line_length": 104, "num_lines": 172, "path": "/src/common/socket_client.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* implements a simple socket client for simloid communication */\n\n#include \"./socket_client.h\"\n\nextern GlobalFlag do_quit;\n\nnamespace network {\n\n\nstd::string hostname_to_ip(const char* hostname)\n{\n struct hostent * h = gethostbyname(hostname);\n struct in_addr **addr_list;\n\n std::string ipstr = \"\";\n\n if (h != nullptr)\n {\n addr_list = (struct in_addr **) h->h_addr_list;\n if (addr_list[0] != nullptr)\n ipstr = inet_ntoa(*addr_list[0]);\n }\n\n sts_msg(\"resolving hostname: %s >> %s\", hostname, ipstr.c_str());\n return ipstr;\n}\n\n\nbool\nSocket_Client::open_connection(const char* server_addr, const unsigned short port)\n{\n if (connection_established)\n {\n wrn_msg(\"Already connected to server.\");\n return false;\n }\n\n /* create socket */\n sockfd = socket(AF_INET, SOCK_STREAM, 0);\n if (sockfd < 0)\n {\n wrn_msg(\"Cannot create socket\");\n return false;\n }\n\n srv_addr.sin_family = AF_INET;\n srv_addr.sin_port = htons(port);\n int result = inet_pton(AF_INET, server_addr, &srv_addr.sin_addr);\n\n if (0 > result)\n {\n wrn_msg(\"First parameter is not a valid address family.\");\n close(sockfd);\n return false;\n }\n else if (0 == result)\n {\n wrn_msg(\"Char string (second parameter does not contain valid IP address)\");\n close(sockfd);\n return false;\n }\n\n if (-1 == connect(sockfd, (struct sockaddr *)&srv_addr, sizeof(srv_addr)))\n {\n wrn_msg(\"Connect failed\");\n close(sockfd);\n return false;\n }\n\n /* make the socket non-blockable */\n int x = fcntl(sockfd ,F_GETFL, 0);\n fcntl(sockfd, F_SETFL, x | O_NONBLOCK);\n\n connection_established = true;\n sts_msg(\"Socket opened.\");\n return true;\n}\n\nvoid\nSocket_Client::close_connection(void)\n{\n if (connection_established)\n {\n shutdown(sockfd, SHUT_RDWR);\n close(sockfd);\n connection_established = false;\n sts_msg(\"Socket closed.\");\n }\n else\n wrn_msg(\"No connection left to be closed.\");\n}\n\nvoid\nSocket_Client::send(const char* format, ...)\n{\n char buffer[constants::msglen];\n memset(buffer, 0, constants::msglen);\n\n va_list args;\n va_start(args, format);\n vsnprintf(buffer, constants::msglen, format, args);\n va_end(args);\n\n common::lock_t lock(mtx);\n if ((unsigned) write(sockfd, buffer, strlen(buffer)) != strlen(buffer))\n err_msg(__FILE__, __LINE__, \"Send incomplete.\");\n}\n\nvoid\nSocket_Client::append(const char* format, ...)\n{\n char buffer[constants::msglen];\n memset(buffer, 0, constants::msglen);\n\n va_list args;\n va_start(args, format);\n vsnprintf(buffer, constants::msglen, format, args);\n va_end(args);\n\n msgbuf.append(buffer);\n}\n\nvoid\nSocket_Client::flush(void)\n{\n if (msgbuf.empty()) return;\n\n { common::lock_t lock(mtx);\n if ((unsigned) write(sockfd, msgbuf.c_str(), msgbuf.length()) != msgbuf.length())\n err_msg(__FILE__, __LINE__, \"Flush incomplete.\");\n } // end lock\n msgbuf.clear();\n}\n\nvoid\nSocket_Client::eat(void)\n{\n char buffer[constants::msglen];\n read(sockfd, buffer, constants::msglen);\n}\n\nstd::string\nSocket_Client::recv(unsigned int timeout_us = 0)\n{\n char buffer[constants::msglen];\n unsigned int time_spent_us = 0;\n unsigned int interval_us = 1;\n\n int len = read(sockfd, buffer, constants::msglen);\n\n while ((-1 == len) && (time_spent_us < timeout_us) && !do_quit.status())\n {\n usleep(interval_us);\n time_spent_us += interval_us; // sum up time spent\n\n if (interval_us < 4096) // double the interval for next sleep\n interval_us *= 2;\n\n len = read(sockfd, buffer, constants::msglen);\n }\n\n if (0 == len) return \"\";\n else if (0 > len) {\n if (do_quit.status()) wrn_msg(\"Received signal to exit during read. Cancel reception of data.\");\n else wrn_msg(\"Connection timed out.\");\n return \"\";\n }\n\n return std::string(buffer);\n}\n\n} /* namespace network */\n" }, { "alpha_fraction": 0.6228076219558716, "alphanum_fraction": 0.6276530623435974, "avg_line_length": 34.47941970825195, "blob_id": "f6213852e33a897f69c6116803443b4b4fcc1979", "content_id": "20972c3b96ce864ba6ef034cab43fa43bb5b4493", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 14653, "license_type": "no_license", "max_line_length": 171, "num_lines": 413, "path": "/src/control/jointcontrol.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include \"jointcontrol.h\"\n\nnamespace control {\n\n\nJointcontrol::Jointcontrol(robots::Robot_Interface& robot)\n: robot(robot)\n, core(robot)\n, number_of_params_sym(get_number_of_inputs(robot) * (robot.get_number_of_joints() - robot.get_number_of_symmetric_joints()))\n, number_of_params_asym(get_number_of_inputs(robot) * robot.get_number_of_joints())\n, symmetric_controller(false)\n, is_switched(false)\n{\n sts_msg(\"Creating joint controller.\");\n if (robot.get_number_of_joints() < 1) err_msg(__FILE__, __LINE__, \"No motor outputs.\");\n if (0 == robot.get_number_of_accel_sensors()) wrn_msg(\"No use of acceleration sensors in controller.\");\n\n sts_msg(\"Number of symmetric joints is %u.\", robot.get_number_of_symmetric_joints());\n\n sts_msg( \"Created controller with: \\n %u inputs\\n %u outputs\\n %u symmetric params\\n %u asymmetric params.\"\n , get_number_of_inputs(robot)\n , robot.get_number_of_joints()\n , number_of_params_sym\n , number_of_params_asym);\n reset();\n}\n\n\nvoid\nJointcontrol::switch_symmetric(bool switched)\n{\n if (not symmetric_controller)\n is_switched = switched;\n else\n is_switched = false;\n /* switching symmetry does not have any effect on symmetrical controller weights */\n}\n\n\nvoid\nJointcontrol::set_control_parameter(const Control_Parameter& controller)\n{\n if (controller.is_symmetric()) apply_symmetric_weights(controller.get_parameter());\n else apply_weights(controller.get_parameter());\n symmetric_controller = controller.is_symmetric();\n\n switch_symmetric(controller.is_mirrored());\n}\n\n\nvoid\nJointcontrol::set_control_parameter(const std::vector<double>& params)\n{\n if (symmetric_controller) apply_symmetric_weights(params);\n else apply_weights(params);\n}\n\n\nvoid\nJointcontrol::reset(void)\n{\n for (auto& j : robot.set_joints()) {\n j.motor.reset();\n j.motor = random_value(-0.01, 0.01);\n }\n for (auto& a : robot.set_accels()) a.reset(); // reset integrated velocities from acceleration sensors\n}\n\n\nvoid\nJointcontrol::insert_motor_command(unsigned index, double value)\n{\n assert(index < robot.set_joints().size());\n auto& j = robot.set_joints()[index];\n j.motor += value;\n}\n\n\nvoid\nJointcontrol::integrate_accels(void)\n{\n for (auto& a : robot.set_accels()) a.integrate(); // integrate velocities from acceleration sensors\n}\n\n\nvoid\nJointcontrol::execute_cycle(void)\n{\n integrate_accels();\n core.prepare_inputs(robot);\n core.update_outputs(robot, symmetric_controller, is_switched);\n core.write_motors (robot, is_switched);\n}\n\n\nvoid\nJointcontrol::apply_symmetric_weights(const std::vector<double>& params)\n{\n assert(params.size() == number_of_params_sym);\n core.apply_symmetric_weights(robot, params);\n}\n\nvoid\nJointcontrol::apply_weights(const std::vector<double>& params)\n{\n assert(params.size() == number_of_params_asym);\n core.apply_weights(robot, params);\n}\n\nvoid\nJointcontrol::print_parameter(void) const\n{\n robots::Jointvector_t const& joints = robot.get_joints();\n\n sts_msg(\"Printing controller parameter:\");\n printf(\"joints:%zu inputs:%zu\\n\", robot.get_number_of_joints(), get_number_of_inputs(robot));\n /*print header*/\n printf(\" # |\");\n for (std::size_t i = 0; i < robot.get_number_of_joints(); ++i)\n if (!symmetric_controller or joints[i].type == robots::Joint_Type_Normal)\n printf(\"%4zu |\", i);\n printf(\"\\n\");\n\n for (std::size_t k = 0; k < get_number_of_inputs(robot); ++k)\n {\n printf(\"%2zu: |\", k);\n for (std::size_t i = 0; i < robot.get_number_of_joints(); ++i)\n {\n if (!symmetric_controller or joints[i].type == robots::Joint_Type_Normal)\n printf(\"% 1.3f|\", core.weights[i][k]);\n }\n printf(\"\\n\");\n }\n printf(\"\\n\");\n}\n\ndouble\nJointcontrol::get_normalized_mechanical_power(void) const\n{\n double power = .0;\n for (auto const& j : robot.get_joints())\n power += square(j.motor.get());\n return power/robot.get_number_of_joints();\n}\n\ndouble\nJointcontrol::get_normalized_control_change(void) const\n{\n double change = .0;\n for (auto const& j : robot.get_joints())\n change += square(j.motor.get() - j.motor.get_backed());\n return change/robot.get_number_of_joints();\n}\n\nControl_Parameter\nget_initial_parameter(robots::Robot_Interface const& robot, const Minimal_Seed_t& seed, bool symmetric = false)\n{\n const std::size_t number_of_joints = robot.get_number_of_joints();\n const std::size_t number_of_inputs = get_number_of_inputs(robot);\n std::vector<double> params(number_of_joints*number_of_inputs);\n\n /* set default weights for asymmetric joints */\n for (std::size_t i = 0; i < number_of_joints; ++i)\n {\n const std::size_t pos = i*number_of_inputs + i * 3;\n\n params[pos + 0] = -seed.pgain; // spring\n params[pos + 1] = seed.damping; // positive friction\n params[pos + 2] = seed.motor_self; // motor neuron's self coupling\n\n /** Divide by initial bias, because bias in the control input will be < 1.\n * If pgain or default_pos is zero this bias is also zero.\n */\n params[(i+1)*number_of_inputs - 1] = -params[pos + 0] * robot.get_joints()[i].default_pos * 1.0 / constants::initial_bias;\n }\n\n if (symmetric)\n return make_symmetric(robot, Control_Parameter(params));\n\n return Control_Parameter(params); /* asymmetric */\n}\n\n/**IDEA: instead of throwing away the not used weights, we could average the corresponding weight pairs*/\nControl_Parameter\nmake_symmetric(robots::Robot_Interface const& robot, const Control_Parameter& other) {\n if (other.is_symmetric())\n return other;\n\n dbg_msg(\"Making controller symmetric.\");\n assert(robot.get_number_of_joints() > robot.get_number_of_symmetric_joints());\n const std::size_t num_joints = robot.get_number_of_joints() - robot.get_number_of_symmetric_joints();\n const std::size_t number_of_inputs = get_number_of_inputs(robot);\n const std::vector<double>& other_params = other.get_parameter();\n std::size_t p = 0;\n\n std::vector<double> params(number_of_inputs * num_joints);\n for (std::size_t i = 0; i < robot.get_number_of_joints(); ++i)\n {\n if (robot.get_joints()[i].type == robots::Joint_Type_Normal)\n for (std::size_t k = 0; k < number_of_inputs; ++k)\n params[p++] = other_params[i*number_of_inputs + k];\n }\n assert(p == params.size());\n return Control_Parameter(params, true);\n\n}\n\nbool is_sagittal_accel_sensor(robots::Robot_Interface const& robot, std::size_t input_pos) {\n return (input_pos == 3 * robot.get_number_of_joints());\n}\n\nstd::size_t swap_sym_joint_pos(robots::Robot_Interface const& robot, std::size_t joint_id, std::size_t input_pos) {\n\n std::size_t sym_id = robot.get_joints()[joint_id].symmetric_joint;\n assert(sym_id < robot.get_number_of_joints());\n assert(input_pos < get_number_of_inputs(robot));\n\n if (joint_id == sym_id){ /* not symmetric */\n //dbg_msg(\"1) not changed %u\", input_pos);\n return input_pos;\n }\n\n if (input_pos >= 3 * robot.get_number_of_joints()){ /* no joint pos, angle or torque */\n //dbg_msg(\"2) not changed %u\", input_pos);\n return input_pos;\n }\n\n std::size_t jid = input_pos/3;\n std::size_t rem = input_pos%3;\n assert(jid < robot.get_number_of_joints());\n\n std::size_t result = 0;\n sym_id = robot.get_joints()[jid].symmetric_joint;\n result = sym_id*3 + rem;\n\n //dbg_msg(\"Transforming %u to %u\", input_pos, result);\n assert(result < get_number_of_inputs(robot));\n\n return result;\n}\n\n\nControl_Parameter\nmake_asymmetric(robots::Robot_Interface const& robot, const Control_Parameter& other) {\n if (not other.is_symmetric()) {\n //dbg_msg(\"Skipping, already asymmetric.\");\n return other;\n }\n\n dbg_msg(\"Making controller asymmetric.\");\n const std::size_t number_of_inputs = get_number_of_inputs(robot);\n\n /**TODO it must be asserted that joint IDs are unique and contiguous over the full range, different places, each constructor*/\n\n std::vector<std::vector<double> > temp_weights(robot.get_number_of_joints(), std::vector<double>(number_of_inputs, 0.0));\n\n const std::vector<double>& other_params = other.get_parameter();\n const std::size_t expected_other_size = number_of_inputs * (robot.get_number_of_joints() - robot.get_number_of_symmetric_joints());\n\n /* check the correct number of input params */\n //dbg_msg(\"num other params %u (%u)\", other_params.size(), expected_other_size);\n assert(other_params.size() == expected_other_size);\n\n std::size_t param_index = 0;\n for (std::size_t ix = 0; ix < robot.get_number_of_joints(); ++ix)\n if (robot.get_joints()[ix].type == robots::Joint_Type_Normal)\n {\n std::size_t iy = robot.get_joints()[ix].symmetric_joint; // get symmetric counterpart of ix\n assert(robot.get_joints()[iy].symmetric_joint == ix);\n if (ix!=iy)\n assert(robot.get_joints()[iy].type == robots::Joint_Type_Symmetric);\n assert(iy < robot.get_number_of_joints());\n for (std::size_t k = 0; k < number_of_inputs; ++k)\n {\n temp_weights[ix][k] = other_params[param_index++];\n if (iy != ix) {\n temp_weights[iy][swap_sym_joint_pos(robot, ix, k)] = temp_weights[ix][k];\n }\n }\n }\n assert(param_index == other_params.size());\n\n // get them out\n std::vector<double> params;\n params.reserve(number_of_inputs * robot.get_number_of_joints());\n for (std::size_t ix = 0; ix < robot.get_number_of_joints(); ++ix) {\n printf(\"\\n\");\n for (std::size_t k = 0; k < number_of_inputs; ++k) {\n params.push_back(temp_weights[ix][k]);\n printf(\" %+5.2f\", temp_weights[ix][k]);\n }\n }\n printf(\"\\n\");\n\n other.print();\n\n assert(params.size() == number_of_inputs * robot.get_number_of_joints());\n\n return Control_Parameter(params, false);\n}\n\n\nControl_Parameter\nturn_symmetry(robots::Robot_Interface const& robot, const Control_Parameter& other) {\n if (other.is_symmetric() or !other.is_mirrored()) {\n dbg_msg(\"Nothing to turn to original.\");\n return other; /* nothing to do*/\n }\n\n dbg_msg(\"Turning controller to original.\");\n Control_Parameter orig = other; // make a safe copy\n auto& params = orig.set_parameter();\n orig.print();\n printf(\"\\n\");\n std::size_t number_of_inputs = get_number_of_inputs(robot);\n\n /* copy params to temp weight matrix */\n std::vector<std::vector<double> > weights{ robot.get_number_of_joints()\n , std::vector<double>(number_of_inputs, 0.0)};\n std::size_t p = 0;\n for (auto& w_i : weights)\n for (auto& w_ik : w_i)\n w_ik = params[p++];\n assert(p == params.size());\n\n p = 0;\n for (std::size_t ix = 0; ix < robot.get_number_of_joints(); ++ix) {\n for (std::size_t k = 0; k < number_of_inputs; ++k) {\n unsigned j = swap_sym_joint_pos(robot, ix, k);\n int sign = is_sagittal_accel_sensor(robot,k)? -1:1;\n params[p++] = sign*weights[robot.get_joints()[ix].symmetric_joint][j];\n }\n }\n\n assert(p == params.size());\n\n orig.print();\n\n return Control_Parameter(params, false, false);\n}\n\n/** this initializer is capable of reading a symmetric file and transform it to asymmetric */\nControl_Parameter\ninitialize_anyhow(robots::Robot_Interface const& robot, Jointcontrol const& control, bool force_symmetric, const Minimal_Seed_t params_pdm, const std::string& filename ) {\n\n if (filename.empty())\n return get_initial_parameter(robot, params_pdm, force_symmetric);\n\n sts_msg(\"Reading seed from file: %s\", filename.c_str());\n sts_msg(\"Trying to fetch %s controller.\", force_symmetric? \"a symmetric\" : \"an asymmetric\");\n\n std::size_t num_params = force_symmetric ? control.get_number_of_symmetric_parameter() : control.get_number_of_parameter();\n\n Control_Parameter param0( filename, num_params, force_symmetric );\n if (param0.get_parameter().size() == num_params)\n return param0; /* success */\n\n /* try again with different symmetry */\n bool new_symmetry = not force_symmetric;\n\n num_params = new_symmetry ? control.get_number_of_symmetric_parameter() : control.get_number_of_parameter();\n\n Control_Parameter param1( filename, num_params, new_symmetry );\n if (param1.get_parameter().size() == num_params)\n {/* success, now force correct symmetry */\n\n if (force_symmetric) return make_symmetric(robot, param1);\n else return make_asymmetric(robot, param1);\n }\n else\n err_msg(__FILE__,__LINE__,\"Could not read controller weights from file: %s.\", filename.c_str());\n\n return Control_Parameter();\n}\n\nControl_Vector param_factory( const robots::Robot_Interface& robot\n , std::size_t number_of_motor_units\n , const std::string& folder\n , const control::Minimal_Seed_t& seed )\n{\n sts_msg(\"Launching motor control parameter vector factory.\");\n control::Control_Vector params(number_of_motor_units, folder);\n\n sts_msg(\"Number of elements loaded from files: %u\", params.size());\n assert(number_of_motor_units>=params.size());\n\n if (number_of_motor_units > params.size()) {\n sts_msg(\"Added the rest (%u) as randomized values.\", number_of_motor_units - params.size());\n for (std::size_t i = params.size(); i < number_of_motor_units; ++i) {\n sts_msg(\"Create randomized motor control parameter no.: %u\", i);\n control::Control_Parameter p = control::get_initial_parameter(robot, seed, false/*symmetric?*/);//(i % 2 == 0));\n control::randomize_control_parameter(p, 0.1, 1.0);\n /**TODO make random parameters to settings, and constrain motor self not not go beyond zero */\n /**TODO also: make settings grouped and only give the local settings as ref */\n params.add(p);\n }\n }\n assert(params.size() == number_of_motor_units);\n sts_msg(\"Done creating %u motor control vectors.\", params.size());\n return params;\n}\n\ndouble Jointcontrol::get_L1_norm(void)\n{\n /** think about: shall we exclude bias weights*/\n double sum_abs_weights = .0;\n for (auto const& wi: core.weights)\n for (auto const& wik: wi)\n sum_abs_weights += std::abs(wik);\n return sum_abs_weights/number_of_params_asym;\n}\n\n} // namespace control\n" }, { "alpha_fraction": 0.5971502661705017, "alphanum_fraction": 0.606217622756958, "avg_line_length": 22.393939971923828, "blob_id": "93f4d254af88da60a32f81711b5c2208d26130fc", "content_id": "43c5b2726eee4d40f50170a471fee4b5ea7bc85c", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 772, "license_type": "no_license", "max_line_length": 149, "num_lines": 33, "path": "/src/draw/barplot.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BARPLOT_H_INCLUDED\n#define BARPLOT_H_INCLUDED\n\n#include <draw/draw.h>\n#include <draw/display.h>\n\nnamespace draw {\n\nclass BarPlot\n{\n const float x, y, w, h, maxval;\n float curval;\n Color4 const& color, max_color;\n\npublic:\n\n BarPlot(float x, float y, float w, float h, float maxval = 1.0, Color4 const& color = colors::white, Color4 const& max_color = colors::redorange)\n : x(x), y(y), w(w), h(h), maxval(maxval), curval(0.0), color(color), max_color(max_color)\n {}\n\n void add_sample(float x) { curval=x; }\n\n void draw() const {\n if (curval<maxval)\n vbar(x, y, w, h, curval, maxval, color);\n else\n vbar(x, y, w, h, curval, maxval, max_color);\n }\n};\n\n} /* namespace draw */\n\n#endif // BARPLOT_H_INCLUDED\n" }, { "alpha_fraction": 0.695557177066803, "alphanum_fraction": 0.695557177066803, "avg_line_length": 40.60606002807617, "blob_id": "a200f7ceb0f12a5bd74332848b38c6623e007e4f", "content_id": "a2e16ea8d69f90dc971b034d7bf90c3c90cf852b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1373, "license_type": "no_license", "max_line_length": 153, "num_lines": 33, "path": "/src/common/settings.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SETTINGS_H_INCLUDED\n#define SETTINGS_H_INCLUDED\n\n#include <string.h>\n#include <common/datareader.h>\n\nbool read_option_flag (int argc, char **argv, const char* short_name, const char* ext_name);\nstd::string read_string_option(int argc, char **argv, const char* short_name, const char* ext_name, const std::string default_value);\n\nstd::string read_options(int argc, char **argv, const char* default_path);\n\nclass Settings_Base {\n\n const std::string filename;\n file_io::Data_Reader settings_file;\n\npublic:\n\n Settings_Base(int argc, char **argv, const char* default_path)\n : filename(read_options(argc, argv, default_path))\n , settings_file(filename)\n {}\n\n file_io::uint_t read_uint (const file_io::key_t name, file_io::uint_t default_value) { return settings_file.read_unsigned(name, default_value); }\n file_io::float_t read_float(const file_io::key_t name, file_io::float_t default_value) { return settings_file.read_float (name, default_value); }\n file_io::string_t read_str (const file_io::key_t name, file_io::string_t default_value) { return settings_file.read_string (name, default_value); }\n file_io::vector_t read_vec (const file_io::key_t name, file_io::vector_t default_value) { return settings_file.read_vector (name, default_value); }\n\n virtual ~Settings_Base() {}\n};\n\n\n#endif // SETTINGS_H_INCLUDED\n" }, { "alpha_fraction": 0.5948219895362854, "alphanum_fraction": 0.6132686138153076, "avg_line_length": 38.11392593383789, "blob_id": "312f673e5eb4e2dc0918b3ed03a62cd415149420", "content_id": "e73539edbc8e498d90821a54188d448d639cd8f5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3090, "license_type": "no_license", "max_line_length": 115, "num_lines": 79, "path": "/src/learning/boltzmann_softmax.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef BOLTZMANN_SOFTMAX_H_INCLUDED\n#define BOLTZMANN_SOFTMAX_H_INCLUDED\n\n#include <vector>\n#include <common/static_vector.h>\n#include <common/log_messages.h>\n#include <learning/action_selection.h>\n#include <learning/action_module.h>\n#include <learning/payload.h>\n\nclass Boltzmann_Softmax : public Action_Selection_Base\n{\n /** TODO this does not work for non-existing actions,\n ** rethink variably sized action space, this makes EVERYTHING way too complicated */\n\n template <typename VectorType>\n static void boltzmann_layer(VectorType& output, const VectorType& input, const double inv_temp) {\n assert(output.size() == input.size());\n assert(output.size() > 0);\n\n const double maxq = input.get_max();\n double sum = .0;\n for (std::size_t i = 0; i < input.size(); ++i)\n sum += exp(inv_temp * (input[i] - maxq));\n\n assert(sum > .0);\n for (std::size_t i = 0; i < input.size(); ++i)\n output[i] = exp(inv_temp * (input[i] - maxq)) / sum;\n }\n\npublic:\n Boltzmann_Softmax( const static_vector<State_Payload>& states\n , const Action_Module_Interface& actions\n , const double exploration_rate )\n : Action_Selection_Base(states, actions, exploration_rate)\n {\n dbg_msg(\"Creating 'Boltzmann/Softmax' action selection.\");\n assert_in_range(exploration_rate, 0.01, 0.99);\n\n// dbg_msg(\"Begin Testing of Boltzmann/Softmax Module\");\n// const unsigned avail_actions = actions.get_number_of_actions_available();\n//\n// std::vector<double> qval = random_vector(avail_actions, -10.0, +10.0);\n//\n// boltzmann_layer(selection_probabilities, qval, 1.0);\n//\n// print_distribution(selection_probabilities);\n// double sump = .0;\n// for (unsigned i = 0; i < selection_probabilities.size(); ++i) {\n// sump = selection_probabilities[i];\n// assert(selection_probabilities[i] > 0 and selection_probabilities[i] < 1.0);\n// }\n// assert_close(sump, 1.0, 0.0001);\n//\n// dbg_msg(\"Testing binning.\");\n// std::vector<std::size_t> bins(selection_probabilities.size());\n// for (unsigned i = 0; i < 1000; ++i)\n// ++bins[select_from_distribution(selection_probabilities)];\n//\n// for (unsigned i = 0; i < bins.size(); ++i) {\n// printf(\"%+1.3f ~ %+1.3f\\n\", bins[i]/1000.0, selection_probabilities[i]);\n// assert_close(bins[i]/1000.0, selection_probabilities[i], 0.05);\n// }\n//\n// dbg_msg(\"End Testing of Boltzmann/Softmax Module\");\n }\n\n std::size_t select_action(std::size_t current_state, std::size_t current_policy)\n {\n assert(actions.get_number_of_actions_available() > 1);\n double inv_temp = 1.0;//TODO\n boltzmann_layer(selection_probabilities, states[current_state].policies[current_policy].qvalues, inv_temp);\n\n /* create random variable and select */\n return select_from_distribution(selection_probabilities);\n }\n};\n\n#endif // BOLTZMANN_SOFTMAX_H_INCLUDED\n" }, { "alpha_fraction": 0.7198795080184937, "alphanum_fraction": 0.7259036302566528, "avg_line_length": 19.75, "blob_id": "ca778c86270c66b1ec776e16fc470034a4600a32", "content_id": "58fb0639a53c2b7e7e91f998b2d1a6cfb33bc6a2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 332, "license_type": "no_license", "max_line_length": 51, "num_lines": 16, "path": "/src/control/statemachine.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef STATEMACHINE_H_INCLUDED\n#define STATEMACHINE_H_INCLUDED\n\nnamespace control {\n\nclass Statemachine_Interface {\npublic:\n virtual bool has_state_changed(void) const = 0;\n virtual std::size_t get_state(void) const = 0;\n virtual ~Statemachine_Interface() {}\n};\n\n\n} // namespace control\n\n#endif // STATEMACHINE_H_INCLUDED\n" }, { "alpha_fraction": 0.529197096824646, "alphanum_fraction": 0.5863747000694275, "avg_line_length": 19.797468185424805, "blob_id": "75e73718658c6b28e3d637e3e019cad18ec35d3c", "content_id": "1757e255d6c7737b72d6a266ece9806c406c97ff", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1644, "license_type": "no_license", "max_line_length": 70, "num_lines": 79, "path": "/src/tests/time_state_space_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <common/modules.h>\n#include <control/sensorspace.h>\n#include <learning/time_state_space.h>\n\n\nnamespace local_tests {\nnamespace time_state_space {\n\n\nTEST_CASE( \"TES Construction with length 1\", \"[time_embedded_signal]\")\n{\n double j = 13.37;\n time_embedded_signal<1> signal(\"foo\", [&j](){ return j; });\n\n REQUIRE( signal.buffer.size() == 1 );\n\n REQUIRE( signal() == 0.0 );\n\n signal.execute_cycle();\n REQUIRE( signal() == 13.37 );\n\n j = 23.42;\n\n REQUIRE( signal() == 13.37 );\n signal.execute_cycle();\n\n REQUIRE( signal() == 23.42 );\n}\n\nTEST_CASE( \"TES Construction with length N\", \"[time_embedded_signal]\")\n{\n double j = 13.37;\n const unsigned BUFLEN = 3;\n time_embedded_signal<BUFLEN> signal(\"foo\", [&j](){ return j; });\n signal.execute_cycle();\n\n REQUIRE( signal.buffer.size() == BUFLEN );\n\n REQUIRE( signal() == 13.37 );\n\n j = 23.42;\n\n REQUIRE( signal[0] == 13.37 );\n\n signal.execute_cycle();\n\n REQUIRE( signal[0] == 23.42 );\n REQUIRE( signal[1] == 13.37 );\n REQUIRE( signal[2] == 0.00 );\n\n j = 77.99;\n signal.execute_cycle();\n\n REQUIRE( signal[0] == 77.99 );\n REQUIRE( signal[1] == 23.42 );\n REQUIRE( signal[2] == 13.37 );\n\n signal.execute_cycle();\n signal.execute_cycle();\n\n REQUIRE( signal[0] == 77.99 );\n REQUIRE( signal[1] == 77.99 );\n REQUIRE( signal[2] == 77.99 );\n}\n\nTEST_CASE( \"TSS Construction\" , \"[Time_State_Space]\")\n{\n Test_Robot robot(3,0);\n learning::Time_State_Space<7> inputs{robot};\n\n //robot.set_joint\n\n}\n\n} /* time_state_space */\n} /* local_tests */\n\n" }, { "alpha_fraction": 0.5616732239723206, "alphanum_fraction": 0.5806220769882202, "avg_line_length": 28.44210433959961, "blob_id": "05c6288e8c5a35e22760f24df64b948d2a1191b2", "content_id": "11f5cbad17f4e23a3b958770827a2e99823c06cd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2797, "license_type": "no_license", "max_line_length": 125, "num_lines": 95, "path": "/src/common/basic.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* basic.cpp */\n\n#include \"basic.h\"\n#include <algorithm>\n\nFILE*\nopen_file(const char* mode, const char* format, ...)\n{\n char filename[1024];\n va_list args;\n va_start(args, format);\n vsnprintf(filename, 1024, format, args);\n va_end(args);\n\n FILE * fd = fopen(filename, mode);\n if (NULL == fd)\n {\n perror(\"Error\");\n err_msg(__FILE__, __LINE__, \"could not open file %s in mode %s.\", filename, mode);\n }\n\n return fd;\n}\n\nnamespace basic {\n\nstd::string\nmake_directory(const char *format, ...)\n{\n char foldername[256];\n va_list args;\n va_start(args, format);\n vsnprintf(foldername, 256, format, args);\n va_end(args);\n\n int md = mkdir(foldername, S_IRWXU | S_IRWXG | S_IROTH | S_IXOTH);\n if (!md) sts_msg(\"created folder %s\", foldername);\n else if (errno != EEXIST) err_msg(__FILE__, __LINE__, \"could not create folder %s.\\n %s\\n\", foldername, strerror(errno));\n\n return foldername;\n}\n\nFilelist list_directory(const char* target_dir, const char* filter)\n{\n const unsigned max_files = 1024;\n std::vector<std::string> files_in_directory;\n struct dirent *epdf;\n DIR *dpdf;\n\n if (0 == strcmp(target_dir,\"\"))\n dpdf = opendir(\"./\");\n else\n dpdf = opendir(target_dir);\n\n if (dpdf != NULL) {\n while ((epdf = readdir(dpdf)) and (files_in_directory.size() < max_files)) {\n if (strcmp(epdf->d_name, \".\") and strcmp(epdf->d_name, \"..\") and (nullptr != strstr(epdf->d_name, filter)))\n files_in_directory.emplace_back(epdf->d_name);\n }\n if (files_in_directory.size() > 1)\n std::sort(files_in_directory.begin(), files_in_directory.end());\n sts_msg(\"Read %u files in directory %s\", files_in_directory.size(), target_dir);\n for (std::size_t i = 0; i < std::min(std::size_t{10}, files_in_directory.size()); ++i)\n sts_msg(\"\\t%s\", files_in_directory[i].c_str());\n if (files_in_directory.size()>10)\n sts_msg(\"\\t...truncated.\");\n } else err_msg(__FILE__, __LINE__, \"Could not open directory %s\", target_dir);\n\n return files_in_directory;\n}\n\nstd::size_t get_file_size(FILE* fd)\n{\n // obtain file size\n if (fd == nullptr) return 0;\n fseek(fd, 0, SEEK_END);\n long int file_size = ftell(fd);\n rewind(fd);\n return (file_size > 0) ? file_size : 0;\n}\n\nstd::string get_timestamp(void) {\n time_t t0 = time(NULL); // initialize time\n struct tm * timeinfo = localtime(&t0); // get time info\n char timestamp[256];\n snprintf(\n timestamp, 256, \"%02d%02d%02d%02d%02d%02d\",\n timeinfo->tm_year-100, timeinfo->tm_mon + 1,\n timeinfo->tm_mday, timeinfo->tm_hour,\n timeinfo->tm_min, timeinfo->tm_sec\n );\n return timestamp;\n}\n\n} /* namespace basic */\n" }, { "alpha_fraction": 0.6840336322784424, "alphanum_fraction": 0.6857143044471741, "avg_line_length": 19.517240524291992, "blob_id": "99d6583f5540cc4997f6550bcded53561ba3d9ee", "content_id": "120f94d234aab299b7a352ce4bbd4d635473aafb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 595, "license_type": "no_license", "max_line_length": 67, "num_lines": 29, "path": "/src/common/misc.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef MISC_H_INCLUDED\n#define MISC_H_INCLUDED\n\n#include <iostream>\n#include <vector>\n#include <string>\n\nstd::string get_time_from_cycle_counter(unsigned long long cycles);\n\nstd::string to_str(const std::vector<double>& vect);\n\ntemplate <typename vector_t> void print(const vector_t& content);\n\nnamespace common {\n\ntemplate <typename T>\nstd::string to_string(std::vector<T> const& vect)\n{\n if (vect.size() == 0) return \"\";\n\n std::string result;\n for (auto const& v: vect) result.append(std::to_string(v)+\" \");\n return result;\n}\n\n} /* namespace common */\n\n\n#endif // MISC_H_INCLUDED\n" }, { "alpha_fraction": 0.53532874584198, "alphanum_fraction": 0.5411362051963806, "avg_line_length": 42.60389709472656, "blob_id": "d1e066948b5ba6deda3777b2fe979203229d5df0", "content_id": "6c7f159c372f60379af10799c32dbce19c270c94", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 13431, "license_type": "no_license", "max_line_length": 132, "num_lines": 308, "path": "/src/evolution/setting.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"./setting.h\"\n\n\nbool read_option_bool (int argc, char **argv, const std::string long_name, const std::string short_name, bool def = false);\nunsigned int read_option_uint (int argc, char **argv, const std::string long_name, const std::string short_name, unsigned int def);\nstd::string read_option_string(int argc, char **argv, const std::string long_name, const std::string short_name);\n\nSetting::Setting( int argc, char **argv )\n : project_name()\n , project_status(NEW)\n , visuals(not read_option_bool(argc, argv, \"--blind\", \"-b\"))\n , interlaced_mode(false)\n , tcp_port(read_option_uint(argc, argv, \"--port\", \"-p\", network::constants::default_port))\n , robot_ID(31)\n , scene_ID(0)\n , max_steps(1000)\n , max_power(100)\n , max_dctrl(100)\n , initial_steps(0)\n , efficient(true)\n , drop_penalty(true)\n , out_of_track_penalty(true)\n , stop_penalty(false)\n , symmetric_controller(true)\n , strategy(\"GENERATION\")\n , population_size(500)\n , selection_size(100)\n , max_generations(500)\n , cur_generations(0)\n , max_trials(max_generations * population_size)\n , cur_trials(0)\n , init_mutation_rate(0.01)\n , meta_mutation_rate(0.5)\n , moving_rate(0.5)\n , selection_bias(1.0) // (0,...,5]\n , seed()\n , initial_population()\n , param{3.0, -1.0, 1.0}\n , push()\n , fitness_function(\"FORWARDS\")\n , rnd({\"NONE\", 0.0, 0})\n , growth({1.0, 0.0})\n , friction(1.0)\n , low_sensor_quality(false)\n , L1_normalization(false)\n , target(.0)\n , drop_level(.5)\n , stop_level(0.0005)\n , corridor(.5)\n , initially_fixed(false)\n{\n if (read_option_bool(argc, argv, \"--help\", \"-h\"))\n {\n print_options();\n exit(EXIT_SUCCESS);\n }\n else if (read_option_bool(argc, argv, \"--new\" , \"-n\"))\n {\n project_name = read_option_string(argc, argv, \"--new\" , \"-n\");\n read_setting_file(read_option_string(argc, argv, \"--settings\", \"-s\"));\n project_status = NEW;\n }\n else if (read_option_bool(argc, argv, \"--resume\" , \"-r\"))\n {\n project_name = read_option_string(argc, argv, \"--resume\", \"-r\");\n read_project_file(project_name);\n project_status = RESUME;\n }\n else if (read_option_bool(argc, argv, \"--watch\", \"-w\"))\n {\n project_name = read_option_string(argc, argv, \"--watch\" , \"-w\");\n read_project_file(project_name);\n project_status = WATCH;\n }\n\n assert(project_name != \"\");\n}\n\nvoid Setting::read_setting_file(const std::string& setting_name)\n{\n if (setting_name == \"\") {\n wrn_msg(\"No settings file provided. Using default settings.\");\n return;\n }\n read_configuration(setting_name);\n}\n\nvoid Setting::read_project_file(const std::string& project_name) {\n read_configuration(FOLDER_PREFIX + project_name + \"/evolution.conf\");\n}\n\nvoid\nSetting::read_configuration(std::string const& filename)\n{\n /* read all static settings */\n sts_msg(\"Reading configuration: %s\", filename.c_str());\n config settings_file(filename, true /*quit on fail*/);\n\n robot_ID = settings_file.readINT (\"ROBOT\" , robot_ID );\n scene_ID = settings_file.readINT (\"SCENE\" , scene_ID );\n\n initial_steps = settings_file.readUINT(\"INITIAL_STEPS\" , initial_steps );\n max_steps = settings_file.readUINT(\"MAX_STEPS\" , max_steps );\n\n push.mode = settings_file.readUINT(\"PUSH_MODE\" , push.mode );\n push.body = settings_file.readUINT(\"PUSH_BODY\" , push.body );\n push.cycle = settings_file.readUINT(\"PUSH_CYCLE\" , push.cycle );\n push.steps = settings_file.readUINT(\"PUSH_STEPS\" , push.steps );\n push.strength = settings_file.readDBL (\"PUSH_STRENGTH\" , push.strength );\n\n assert ( push.steps <= push.cycle );\n\n efficient = settings_file.readBOOL(\"EFFICIENT\" , efficient );\n max_power = settings_file.readUINT(\"MAX_POWER\" , max_power );\n max_dctrl = settings_file.readUINT(\"MAX_DCTRL\" , max_dctrl );\n\n drop_penalty = settings_file.readBOOL(\"DROP_PENALTY\" , drop_penalty );\n drop_level = settings_file.readDBL (\"DROP_LEVEL\" , drop_level );\n\n out_of_track_penalty = settings_file.readBOOL(\"OUT_OF_TRACK_PENALTY\", out_of_track_penalty);\n corridor = settings_file.readDBL (\"CORRIDOR\" , corridor );\n\n stop_penalty = settings_file.readBOOL(\"STOP_PENALTY\" , stop_penalty );\n stop_level = settings_file.readDBL (\"STOP_LEVEL\" , stop_level );\n\n symmetric_controller = settings_file.readBOOL(\"SYMMETRIC_CONTROLLER\", symmetric_controller);\n strategy = settings_file.readSTR (\"STRATEGY\" , strategy );\n\n max_generations = settings_file.readUINT(\"MAX_GENERATIONS\" , max_generations );\n cur_generations = settings_file.readUINT(\"CURRENT_GENERATION\" , cur_generations );\n population_size = settings_file.readUINT(\"POPULATION_SIZE\" , population_size );\n selection_size = settings_file.readUINT(\"SELECTION_SIZE\" , selection_size );\n\n max_trials = settings_file.readUINT(\"MAX_TRIALS\" , max_trials );\n cur_trials = settings_file.readUINT(\"CURRENT_TRIAL\" , cur_trials );\n moving_rate = settings_file.readDBL (\"MOVING_RATE\" , moving_rate );\n selection_bias = settings_file.readDBL (\"SELECTION_BIAS\" , selection_bias );\n\n init_mutation_rate = settings_file.readDBL (\"INIT_MUTATION_RATE\" , init_mutation_rate );\n meta_mutation_rate = settings_file.readDBL (\"META_MUTATION_RATE\" , meta_mutation_rate );\n\n param.pgain = settings_file.readDBL (\"PARAM_P\" , param.pgain );\n param.damping = settings_file.readDBL (\"PARAM_D\" , param.damping );\n param.motor_self = settings_file.readDBL (\"PARAM_M\" , param.motor_self );\n\n seed = settings_file.readSTR (\"SEED\" , seed );\n initial_population = settings_file.readSTR (\"INIT_POPULATION\" , initial_population );\n fitness_function = settings_file.readSTR (\"FITNESS_FUNCTION\" , fitness_function );\n\n rnd.mode = settings_file.readSTR (\"RANDOM_MODE\" , rnd.mode );\n rnd.value = settings_file.readDBL (\"RANDOM_VALUE\" , rnd.value );\n rnd.init = settings_file.readUINT(\"RANDOM_INIT\" , rnd.init );\n\n growth.init = settings_file.readDBL (\"GROWTH_INIT\" , growth.init );\n growth.rate = settings_file.readDBL (\"GROWTH_RATE\" , growth.rate );\n\n friction = settings_file.readDBL (\"FRICTION\" , friction );\n target = settings_file.readDBL (\"TARGET\" , target );\n\n interlaced_mode = settings_file.readBOOL(\"INTERLACED\" , interlaced_mode );\n low_sensor_quality = settings_file.readBOOL(\"LOW_SENSOR_QUALITY\" , low_sensor_quality );\n\n initially_fixed = settings_file.readBOOL(\"INITIALLY_FIXED\" , initially_fixed );\n\n L1_normalization = settings_file.readBOOL(\"L1_NORMALIZATION\" , L1_normalization );\n assert(false == L1_normalization);\n}\n\nconst std::string&\nSetting::save_to_projectfile(const std::string& filename) const\n{\n sts_msg(\"Saving to project file.\");\n config project_file(filename);\n\n if (interlaced_mode) /* default is false for evolution, so only write if true */\n project_file.writeBOOL(\"INTERLACED\", true);\n\n project_file.writeUINT(\"ROBOT\" , robot_ID );\n project_file.writeUINT(\"SCENE\" , scene_ID );\n project_file.writeUINT(\"MAX_STEPS\" , max_steps );\n project_file.writeUINT(\"MAX_POWER\" , max_power );\n project_file.writeUINT(\"MAX_DCTRL\" , max_dctrl );\n project_file.writeUINT(\"INITIAL_STEPS\" , initial_steps );\n project_file.writeUINT(\"PUSH_MODE\" , push.mode );\n project_file.writeUINT(\"PUSH_BODY\" , push.body );\n project_file.writeUINT(\"PUSH_CYCLE\" , push.cycle );\n project_file.writeUINT(\"PUSH_STEPS\" , push.steps );\n project_file.writeUINT(\"PUSH_STRENGTH\" , push.strength );\n project_file.writeSTR (\"STRATEGY\" , strategy );\n\n assert(not fitness_function.empty());\n project_file.writeSTR (\"FITNESS_FUNCTION\" , fitness_function);\n\n if (\"\" != seed) project_file.writeSTR(\"SEED\" , seed);\n\n project_file.writeBOOL(\"EFFICIENT\" , efficient);\n project_file.writeBOOL(\"DROP_PENALTY\" , drop_penalty);\n project_file.writeDBL (\"DROP_LEVEL\" , drop_level);\n project_file.writeBOOL(\"OUT_OF_TRACK_PENALTY\", out_of_track_penalty);\n project_file.writeDBL (\"CORRIDOR\" , corridor);\n project_file.writeBOOL(\"STOP_PENALTY\" , stop_penalty);\n project_file.writeDBL (\"STOP_LEVEL\" , stop_level);\n project_file.writeBOOL(\"SYMMETRIC_CONTROLLER\", symmetric_controller);\n\n project_file.writeDBL (\"INIT_MUTATION_RATE\" , init_mutation_rate);\n project_file.writeDBL (\"META_MUTATION_RATE\" , meta_mutation_rate);\n\n project_file.writeUINT(\"POPULATION_SIZE\" , population_size);\n\n project_file.writeSTR (\"RANDOM_MODE\" , rnd.mode);\n project_file.writeDBL (\"RANDOM_VALUE\" , rnd.value);\n project_file.writeUINT(\"RANDOM_INIT\" , rnd.init);\n\n project_file.writeDBL (\"GROWTH_INIT\" , growth.init);\n project_file.writeDBL (\"GROWTH_RATE\" , growth.rate);\n\n project_file.writeDBL (\"FRICTION\" , friction);\n\n project_file.writeBOOL(\"LOW_SENSOR_QUALITY\" , low_sensor_quality);\n project_file.writeBOOL(\"L1_NORMALIZATION\" , L1_normalization);\n project_file.writeDBL (\"TARGET\" , target);\n\n project_file.writeBOOL(\"INITIALLY_FIXED\" , initially_fixed);\n\n /** NOTE: Strategy-specific settings are saved separately!\n e.g. selection bias, moving rate, max generations, current_trial */\n\n project_file.finish();\n return filename;\n}\n\n\nvoid\nSetting::print_options(void)\n{\n printf(\"Help, Options: \\n\");\n printf(\" -p --port change TCP port\\n\");\n printf(\" -n --new create new evolution\\n\");\n printf(\" -r --resume resume with existing evolution\\n\");\n printf(\" -w --watch watch existing evolution\\n\");\n printf(\" -s --settings settings file\\n\");\n printf(\" -b --blind no visuals\\n\");\n printf(\" -h --help display this help\\n\");\n printf(\"\\n\");\n}\n\n\nbool read_option_bool(int argc, char **argv, const std::string long_name, const std::string short_name, bool def)\n{\n bool value = def;\n\n for (int i = 1; i < argc; ++i)\n {\n const std::string option_name = argv[i];\n value = (long_name == option_name || short_name == option_name);\n if (value) break;\n }\n dbg_msg(\"Reading option '%s' = %s\", long_name.c_str(), value? \"true\":\"false\");\n return value;\n}\n\nunsigned int\nread_option_uint(int argc, char **argv, const std::string long_name, const std::string short_name, unsigned int def = 0)\n{\n unsigned int value = def;\n\n for (int i = 1; i < argc; ++i)\n {\n const std::string option_name = argv[i];\n if (long_name == option_name || short_name == option_name)\n {\n if (argc < i+2) {\n printf(\"usage: %s %s <uint>\\n\", argv[0], long_name.c_str());\n exit(0);\n } else {\n value = abs(atoi(argv[i+1]));\n ++i;\n }\n break;\n }\n }\n dbg_msg(\"Reading option '%s' = %u\", long_name.c_str(), value);\n return value;\n}\n\nstd::string\nread_option_string(int argc, char **argv, const std::string long_name, const std::string short_name)\n{\n std::string value;\n\n for (int i = 1; i < argc; ++i)\n {\n const std::string option_name = argv[i];\n if (long_name == option_name || short_name == option_name)\n {\n if (argc < i+2) {\n printf(\"usage: %s %s <string>\\n\", argv[0], long_name.c_str());\n exit(0);\n } else {\n value = argv[i+1];\n ++i;\n }\n break;\n }\n }\n dbg_msg(\"Reading option '%s' = '%s'\", long_name.c_str(), value.c_str());\n return value;\n}\n" }, { "alpha_fraction": 0.6996879577636719, "alphanum_fraction": 0.6996879577636719, "avg_line_length": 27.488889694213867, "blob_id": "ee6933cb462909a599bc7ba6342e2760016fcf20", "content_id": "2317dc21b4cd9cc80a63717bcc3254ac6e83e533", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1282, "license_type": "no_license", "max_line_length": 128, "num_lines": 45, "path": "/src/control/control_vector.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef CONTROL_VECTOR_H_INCLUDED\n#define CONTROL_VECTOR_H_INCLUDED\n\n#include <string>\n#include <memory>\n#include <common/basic.h>\n#include <common/modules.h>\n#include <common/static_vector.h>\n#include <control/controlparameter.h>\n\nnamespace control {\n\n\nvoid randomize_control_parameter(Control_Parameter& params, double std_dev, double max_dev);\n\n\nclass Control_Vector : public static_vector_interface\n{\npublic:\n Control_Vector(std::size_t max_number_of_parameter_sets, const std::string& foldername = \"\", bool include_mirrored = false);\n\n const Control_Parameter& get(std::size_t index) const { return controls.at(index); }\n\n void add( const std::string& filename\n , const std::size_t number_of_params\n , bool symmetric\n , bool mirrored );\n\n void add(const std::string& filename);\n void add(const Control_Parameter& params);\n\n void reload(std::size_t index, const std::string& filename);\n\n std::size_t size(void) const override { return controls.size(); }\n void copy(std::size_t dst, std::size_t src) override { controls.at(dst) = controls.at(src); }\n\nprivate:\n const std::size_t max_number_of_parameter_sets;\n std::vector<Control_Parameter> controls;\n};\n\n\n} // namespace control\n\n#endif // CONTROL_VECTOR_H_INCLUDED\n" }, { "alpha_fraction": 0.6237413883209229, "alphanum_fraction": 0.634870171546936, "avg_line_length": 23.506492614746094, "blob_id": "27c8728545d2f74d0ec3580c2ece1692980f040d", "content_id": "11240fa71e89816fba6a6d8650098d1b1785711e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1887, "license_type": "no_license", "max_line_length": 108, "num_lines": 77, "path": "/src/draw/plot1D.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* plot1D.h */\n\n#ifndef plot1D_H\n#define plot1D_H\n\n#include <vector>\n#include <algorithm>\n#include <draw/axes.h>\n#include <draw/draw.h>\n#include <draw/color_table.h>\n#include <common/modules.h>\n#include <common/log_messages.h>\n#include <basic/color.h>\n\nclass plot1D {\npublic:\n plot1D(unsigned int number_of_samples, axes& a, const Color4& c = colors::white0, const char* name = \"\")\n : number_of_samples(number_of_samples)\n , pointer(number_of_samples-1)\n , axis(a)\n , axis_id(axis.countNum++)\n , signal(number_of_samples)\n , color(c)\n , decrement(0.99)\n , name(name)\n { }\n\n virtual ~plot1D() = default;\n\n void draw(void) const;\n void add_sample(const float s);\n void reset(void) { std::fill(signal.begin(), signal.end(), .0); pointer = number_of_samples-1; }\n\nprotected:\n void increment_pointer(void) { ++pointer; pointer %= number_of_samples; }\n\n void auto_scale (void) const;\n void draw_statistics(void) const;\n void draw_line_strip(void) const;\n\n const unsigned int number_of_samples;\n unsigned int pointer;\n axes& axis;\n unsigned int axis_id;\n std::vector<float> signal;\n Color4 color;\n const float decrement;\n const std::string name;\n};\n\n\nclass colored_plot1D : public plot1D {\npublic:\n colored_plot1D( unsigned int number_of_samples\n , axes& a\n , ColorTable const& colortable\n , const char* name = \"\")\n : plot1D(number_of_samples, a, colors::white0, name)\n , colors(number_of_samples)\n , colortable(colortable)\n {}\n\n void add_colored_sample(float s, unsigned color_index) {\n add_sample(s),\n colors.at(pointer) = color_index;\n }\n\n void draw_colored(void) const;\n\nprivate:\n void draw_colored_line_strip(void) const;\n\n std::vector<unsigned> colors;\n ColorTable const& colortable;\n\n};\n#endif /*plot1D_H*/\n" }, { "alpha_fraction": 0.6771546006202698, "alphanum_fraction": 0.6771546006202698, "avg_line_length": 68.57142639160156, "blob_id": "1c0f6797af9f911cd752e84b13f96b6d8ca590c9", "content_id": "e6d6f7072781e607ca86e13fbd77ae89e78cd224", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1462, "license_type": "no_license", "max_line_length": 130, "num_lines": 21, "path": "/src/evolution/fitness.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"./fitness.h\"\n\nFitness_ptr assign_fitness( const robots::Simloid& robot\n , const Setting& settings)\n{\n Fitness_ptr fitness_function;\n const std::string& fitness(settings.fitness_function);\n\n if (\"FORWARDS\" == fitness) fitness_function = Fitness_ptr(new Fitness_Forwards (robot, settings, /*use_avg=*/true ));\n else if (\"FORWARDS_MIN\" == fitness) fitness_function = Fitness_ptr(new Fitness_Forwards (robot, settings, /*use_avg=*/false));\n else if (\"FORWARDS_FEET\"== fitness) fitness_function = Fitness_ptr(new Fitness_Forwards_Feet(robot, settings));\n else if (\"BACKWARDS\" == fitness) fitness_function = Fitness_ptr(new Fitness_Backwards(robot, settings, /*use_avg=*/true ));\n else if (\"BACKWARDS_MIN\"== fitness) fitness_function = Fitness_ptr(new Fitness_Backwards(robot, settings, /*use_avg=*/false));\n else if (\"SIDEWARDS\" == fitness) fitness_function = Fitness_ptr(new Fitness_Sidewards(robot, settings, /*use_avg=*/true ));\n else if (\"SIDEWARDS_MIN\"== fitness) fitness_function = Fitness_ptr(new Fitness_Sidewards(robot, settings, /*use_avg=*/false));\n else if (\"TURNING\" == fitness) fitness_function = Fitness_ptr(new Fitness_Turning (robot, settings));\n else if (\"STOPPING\" == fitness) fitness_function = Fitness_ptr(new Fitness_Stopping (robot, settings));\n else err_msg(__FILE__, __LINE__, \"Wrong name of fitness function.\");\n\n return fitness_function;\n}\n" }, { "alpha_fraction": 0.5379236936569214, "alphanum_fraction": 0.5740994215011597, "avg_line_length": 26.86864471435547, "blob_id": "4463f4788bccbc6abc34c1ed609efd9749fecd77", "content_id": "8dfddd7431f07018c05e3b25ac605ddeb5ae97ac", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6579, "license_type": "no_license", "max_line_length": 102, "num_lines": 236, "path": "/src/tests/forward_inverse_model_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <common/modules.h>\n#include <learning/forward_inverse_model.hpp>\n\n\nnamespace local_tests {\n\nnamespace forward_inverse_model_tests {\n\ntypedef learning::NeuralModel<learning::LinearTransfer<>> LinearNeural_t;\ntypedef learning::NeuralModel<learning::TanhTransfer<>> NeuralForward_t;\ntypedef learning::InverseNeuralModel NeuralInverse_t;\n\ntypedef learning::BidirectionalModel<LinearNeural_t,LinearNeural_t> BidirectionalLinearModelType;\ntypedef learning::BidirectionalModel<NeuralForward_t,NeuralInverse_t> BidirectionalNonlinearModelType;\n\n\nTEST_CASE( \"twopart_vector access and manipulation\" , \"[twopart_vector]\")\n{\n typedef std::vector<double> Vector_t;\n\n Vector_t v1(3);\n Vector_t v2(5);\n\n learning::twopart_vector<Vector_t> vec(v1,v2);\n\n // check sizes\n REQUIRE( vec .size() == 8 );\n REQUIRE( vec.part0.size() == 3 );\n REQUIRE( vec.part1.size() == 5 );\n\n for (std::size_t i = 0; i < vec.size(); ++i)\n REQUIRE( vec[i] == 0 );\n\n auto& A = vec.part0;\n auto& B = vec.part1;\n\n for (std::size_t i = 0; i < A.size(); ++i)\n REQUIRE( A[i] == 0 );\n\n for (std::size_t i = 0; i < B.size(); ++i)\n REQUIRE( B[i] == 0 );\n\n A[0] = 0;\n A[1] = 1;\n A[2] = 2;\n\n B[0] = 3;\n B[1] = 4;\n B[2] = 5;\n B[3] = 6;\n B[4] = 7;\n\n\n REQUIRE( v1 == A );\n REQUIRE( v2 == B );\n\n // elements can be read\n for (std::size_t i = 0; i < vec.size(); ++i) {\n dbg_msg(\"%u = %f\", i, vec[i]);\n REQUIRE( close(vec[i], i, 0.00001) );\n }\n\n // elements can be written\n vec[4] = 23;\n REQUIRE( B[1] == 23 );\n\n vec[1] = 17;\n REQUIRE( A[1] == 17 );\n}\n\nTEST_CASE( \"forward_inverse_model construction\" , \"[forward_inverse_model]\")\n{\n srand(time(0)); // set random seed\n\n double random_range = 0.01;\n\n BidirectionalLinearModelType model(13, 7, random_range);\n\n /* check weights are not zero, but randomized */\n auto const& weights = model.get_weights();\n double sum = .0;\n int diff = 0;\n for (std::size_t i = 0; i < weights.size(); ++i)\n for (std::size_t j = 0; j < weights[i].size(); ++j) {\n diff += ( weights[i][j] != .0 )? 0 : 1;\n sum += weights[i][j];\n }\n\n /* check randomize_weight_matrix() is executed */\n REQUIRE( diff == 0 );\n const double max_range = 0.5* random_range * weights.size()*weights[0].size();\n dbg_msg(\"Max rand: %e < %e\", std::abs(sum), max_range);\n REQUIRE( std::abs(sum) <= max_range ); // check small\n REQUIRE( std::abs(sum) != 0. ); // but not zero\n\n // check matrix size\n REQUIRE( weights .size() == 7 );\n REQUIRE( weights[0].size() == 13 );\n\n auto const& inputs = model.get_inverse_result();\n auto const& outputs = model.get_forward_result();\n\n // check vector size\n REQUIRE( inputs.size() == 13 );\n REQUIRE( outputs.size() == 7 );\n\n // check in and outputs are zero on initialization\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( outputs[i] == .0 );\n\n for (std::size_t i = 0; i < inputs.size(); ++i)\n REQUIRE( inputs[i] == .0 );\n\n model.propagate_forward(inputs);\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( outputs[i] == .0 );\n\n model.propagate_inverse(outputs);\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( inputs[i] == .0 );\n\n BidirectionalLinearModelType model2 = model;\n}\n\n\nTEST_CASE( \"forward_inverse_model learning (linear)\", \"[forward_inverse_model]\")\n{\n dbg_msg(\"linear\");\n srand(time(0)); // set random seed\n\n const double learning_rate = 0.005;\n\n std::vector<double> X = {1,0.5,1,-1,0,1,1,0,-1,-1,1,0.75,1,0,1,0.5,-1,1};\n std::vector<double> Y = {0.9,0,-0.9,0,-0.9,0.9,0,-0.9,-0.9,0.5};\n\n BidirectionalLinearModelType model(X.size(), Y.size(), 0.01);\n\n /* check error is decreasing (forward) */\n auto const& Y_ = model.propagate_forward(X);\n auto const& X_ = model.propagate_inverse(Y); // inverse direction\n\n double ery0 = squared_distance(Y, model.get_forward_result());\n double erx0 = squared_distance(X, model.get_inverse_result()); // inverse error\n\n double ery1, erx1;\n\n for (std::size_t trials = 0; trials < 500; ++trials) {\n // adapt\n model.adapt(X, Y, learning_rate);\n\n // verify\n model.propagate_forward(X);\n model.propagate_inverse(Y);\n\n ery1 = squared_distance(Y, model.get_forward_result());\n erx1 = squared_distance(X, model.get_inverse_result());\n //dbg_msg(\"E_fw: %+e | E_bw: %+e\",ery1, erx1);\n REQUIRE( ery0 > ery1 );\n REQUIRE( erx0 > erx1 );\n ery0 = ery1;\n erx0 = erx1;\n }\n\n model.propagate_forward(X);\n print_vector(Y);\n print_vector(Y_);\n\n REQUIRE( close(Y_, Y, 0.01) );\n\n model.propagate_inverse(Y);\n print_vector(X);\n print_vector(X_);\n\n REQUIRE( close(X_, X, 0.01) );\n\n}\n\nTEST_CASE( \"forward_inverse_model learning (non-linear)\", \"[forward_inverse_model]\")\n{\n dbg_msg(\"non-linear\");\n srand(time(0)); // set random seed\n\n const double learning_rate = 0.005;\n\n std::vector<double> X = {1,0.5,1,-1,0,1,1,0,-1,-1,1,0.75,1,0,1,0.5,-1,1};\n std::vector<double> Y = {0.9,0,-0.9,0,-0.9,0.9,0,-0.9,-0.9,0.5};\n\n BidirectionalNonlinearModelType model(X.size(), Y.size(), 0.01);\n\n /* check error is decreasing (forward) */\n auto const& Y_ = model.propagate_forward(X);\n auto const& X_ = model.propagate_inverse(Y); // inverse direction\n\n double ery0 = squared_distance(Y, model.get_forward_result());\n double erx0 = squared_distance(X, model.get_inverse_result()); // inverse error\n\n double ery1, erx1;\n\n for (std::size_t trials = 0; trials < 500; ++trials) {\n // adapt\n model.adapt(X, Y, learning_rate);\n\n // verify\n model.propagate_forward(X);\n model.propagate_inverse(Y);\n\n ery1 = squared_distance(Y, model.get_forward_result());\n erx1 = squared_distance(X, model.get_inverse_result());\n //dbg_msg(\"E_fw: %+e | E_bw: %+e\",ery1, erx1);\n REQUIRE( ery0 > ery1 );\n REQUIRE( erx0 >= erx1 );\n ery0 = ery1;\n erx0 = erx1;\n }\n\n model.propagate_forward(X);\n print_vector(Y);\n print_vector(Y_);\n for (unsigned i = 0; i < Y_.size(); ++i)\n REQUIRE( close(Y_[i], Y[i], 0.03) );\n\n dbg_msg(\"---\");\n model.propagate_inverse(Y);\n print_vector(X);\n print_vector(X_);\n\n REQUIRE( close(X_, X, 0.03) );\n\n}\n\n\n\n}} // namespace local_tests::forward_inverse_model_tests\n\n\n" }, { "alpha_fraction": 0.608428418636322, "alphanum_fraction": 0.6255487203598022, "avg_line_length": 26.768293380737305, "blob_id": "f6730e7dbce15d065baf93149c3e6754188d9b95", "content_id": "1c5e7e20e0da2493bc82d7916579976bd23f659e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2278, "license_type": "no_license", "max_line_length": 116, "num_lines": 82, "path": "/src/robots/pole.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef POLE_H\n#define POLE_H\n\n#include <cmath>\n#include <vector>\n#include <cassert>\n#include <common/modules.h>\n#include <common/static_vector.h>\n#include <common/vector_n.h>\n\n#include <draw/draw.h>\n\n#include <robots/joint.h>\n#include <robots/accel.h>\n#include <robots/robot.h>\n\nnamespace robots {\n\nnamespace pole_constants\n{\n const double gravity = 9.81;\n const double mass = 0.1;\n const double length = 0.5; /* actually half the pole's length */\n\n const double force_mag = 2.0;\n const double dt = 0.001; /* step size */\n const double max_x = 10.0; /* meters */\n const double friction = 0.5;\n}\n\ninline double deg_to_rad(double deg) { return deg*M_PI/180.0; }\n\nclass pole : public Robot_Interface\n{\nprivate:\n double theta; /* pole angle [rad] */\n double theta_dot; /* pole angular velocity [rad/s] */\n double force; /* force exerted to hinge joint */\n\n Jointvector_t joints;\n\n void reset_state(bool tilted = false);\n void update_dynamics(const double action);\n\n Accelvector_t accels;\n\npublic:\n pole(bool tilted = false)\n : theta()\n , theta_dot()\n , force()\n , joints()\n , accels() //empty\n {\n joints.reserve(1);\n joints.emplace_back(0, Joint_Type_Normal, 0, \"joint0\", -1.0, +1.0, 0.0);\n assert(joints.size() == 1);\n reset_state(tilted);\n }\n\n bool execute_cycle(void);\n\n void draw(const float pos_x, const float pos_y, const float size) const; //TODO make independent of pos and size\n bool top(double range = 0.01) const;\n double height(void) const { return -cos(theta); }\n\n /* implement the robot interface */\n std::size_t get_number_of_joints (void) const { return 1; }\n std::size_t get_number_of_symmetric_joints(void) const { return 0; }\n std::size_t get_number_of_accel_sensors (void) const { return 0; }\n\n const Jointvector_t& get_joints(void) const { return joints; }\n Jointvector_t& set_joints(void) { return joints; }\n\n const Accelvector_t& get_accels(void) const { return accels; }\n Accelvector_t& set_accels(void) { return accels; }\n\n double get_normalized_mechanical_power(void) const { return force * force; };\n};\n\n} // namespace robots\n#endif /* POLE_H */\n\n" }, { "alpha_fraction": 0.6151208877563477, "alphanum_fraction": 0.6199211478233337, "avg_line_length": 41.732601165771484, "blob_id": "a9f8a7f028403d2cd89b3b3afc05876df880532b", "content_id": "a8e8a5afc59431b7186186649b56ad5f2c09ba44", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 11666, "license_type": "no_license", "max_line_length": 165, "num_lines": 273, "path": "/src/learning/competitive_motor_layer.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef COMPETITIVE_MOTOR_LAYER_H_INCLUDED\n#define COMPETITIVE_MOTOR_LAYER_H_INCLUDED\n\n#include <vector>\n#include <sstream>\n#include <iostream>\n#include <iomanip>\n\n#include <common/log_messages.h>\n#include <common/misc.h>\n\n#include <control/controlparameter.h>\n\nnamespace MotorLayerConstants {\n const std::size_t initial_learning_capacity = 1000;\n const control::Minimal_Seed_t seed = {2.0, 0.0, 1.0};\n\n const double symmetry_ratio = 0.5;\n}\n\nclass MotorUnit {\npublic:\n MotorUnit(const control::Control_Parameter& seed, const bool exists = false)\n : weights(seed)\n , selection_count(0) // selections should also be counted by q for better analysis of competition for motor neurons\n , learning_capacity(MotorLayerConstants::initial_learning_capacity)\n , exists(exists)\n {\n dbg_msg(\"Creating Motor Unit with %u weights: (%s)\", seed.size(), to_str(seed.get_parameter()).c_str());\n }\n\n MotorUnit& operator=(const MotorUnit& other) {\n dbg_msg(\"Copy motor neuron.\");\n weights = other.weights; // copy with flaws?\n exists = true;\n selection_count = 0;\n learning_capacity = other.learning_capacity;\n return *this;\n }\n\n control::Control_Parameter weights;\n std::size_t selection_count;\n std::uint64_t learning_capacity;\n bool exists;\n};\n\n\nclass CompetitiveMotorLayer\n{\npublic:\n CompetitiveMotorLayer( const robots::Robot_Interface& robot\n , static_vector<State_Payload>& states\n , const control::Control_Vector& parameter_sets\n , const std::size_t number_of_motor_units\n , const std::size_t number_of_motor_units_begin\n , const double mutation_rate\n , const double learning_rate\n , bool do_adaption\n , const control::Minimal_Seed_t& seed = MotorLayerConstants::seed)\n : robot(robot)\n , states(states)\n , parameter_sets(parameter_sets)\n , motor_units()\n , mutated_weights(parameter_sets.get(0))\n , last_selected_idx()\n , recipient_idx()\n , do_adaption(do_adaption)\n , mutation_rate(mutation_rate)\n , learning_rate(learning_rate)\n {\n dbg_msg(\"Creating competitive motor layer with %u motor units.\", number_of_motor_units);\n assert_in_range(number_of_motor_units, 1ul, 100ul);\n assert_in_range(learning_rate, 0.0010, 0.5);\n assert_in_range(mutation_rate, 0.0001, 0.5);\n motor_units.reserve(number_of_motor_units);\n\n if (parameter_sets.size() <= number_of_motor_units)\n sts_msg(\"Initialize %u of %u parameter sets.\", parameter_sets.size(), number_of_motor_units);\n else\n wrn_msg(\"Can not initialize all %u parameter sets. Limit is %u.\", parameter_sets.size(), number_of_motor_units);\n\n for (std::size_t i = 0; i < std::min(parameter_sets.size(), number_of_motor_units); ++i)\n {\n motor_units.emplace_back(parameter_sets.get(i), true);\n }\n assert(motor_units.size() <= number_of_motor_units);\n\n //if ?\n for (std::size_t i = motor_units.size(); i < number_of_motor_units; ++i) {\n control::Control_Parameter params = control::get_initial_parameter(robot, seed, /*symmetric?*/(i % 2 == 0));\n\n control::randomize_control_parameter(params, 0.1, 1.0); /**TODO make to settings, and constrain motor self not not go beyond zero */\n /**TODO also: make settings grouped and only give the local settings as ref */\n motor_units.emplace_back(params, (i < number_of_motor_units_begin));\n }\n// else\n// for (std::size_t i = motor_units.size(); i < number_of_motor_units; ++i)\n// motor_units.emplace_back(/*placeholder motor units*/);\n\n assert(motor_units.size() == number_of_motor_units);\n }\n\n bool exists(const std::size_t idx) const { assert(idx < motor_units.size()); return motor_units[idx].exists; }\n\n /** Only the current active motor neuron is going to be adapted. This adaption happens\n * iff we receive a positive delta from the superordinate learning layer, e.g. sarsa/RL.\n * That means that only if we notice an increase in our reward expectation we take over\n * the just evaluated changes to our controller weights.\n * By using the delta signal we make sure that different policies with different relative\n * reward levels do not affect the weight adjustment and are treated more or less equally.\n */\n void adapt(bool positive_delta)\n {\n if (not do_adaption) return;\n assert(motor_units[last_selected_idx].exists == true); // must not be executed on non-existing units\n if (positive_delta) {\n do_adapt(last_selected_idx);\n }\n }\n\n /** Create a randomized variant of the current motor neuron's controller weights.\n * This mutated weights will be evaluated by the next eigenzeit cycle and in case\n * of success, i.e. a positive learning delta, the new mutated weights will be adopted.\n */\n void create_mutated_weights(std::size_t selected_idx)\n {\n assert(exists(selected_idx)); // must not be executed on non-existing units\n\n mutated_weights = motor_units[selected_idx].weights;\n\n if (do_adaption)\n for (std::size_t i = 0; i < mutated_weights.size(); ++i)\n mutated_weights[i] += random_value(-mutation_rate, +mutation_rate);\n /**TODO\n * I want to use here the same mechanics as in Individual::mutate(void) (evolution)\n */\n ++(motor_units[selected_idx].selection_count);\n last_selected_idx = selected_idx;\n }\n\n const std::vector<double>& get_weights(std::size_t index) const {\n assert(index < motor_units.size());\n return motor_units[index].weights.get_parameter();\n }\n\n const control::Control_Parameter& get_mutated_weights(void) const { return mutated_weights; }\n\n const MotorUnit& get_unit(std::size_t index) const {\n assert(index < motor_units.size());\n return motor_units[index];\n }\n std::size_t get_number_of_motor_units(void) const { return motor_units.size(); }\n\n std::size_t get_number_of_available_motor_units(void) const\n {\n std::size_t available_units = 0;\n for (std::size_t i = 0; i < motor_units.size(); ++i)\n if (motor_units[i].exists)\n ++available_units;\n return available_units;\n }\n\n void enable_adaption(bool enable) { do_adaption = enable; }\n bool is_adaption_enabled(void) const { return do_adaption; }\n\nprivate:\n\n /** We're using the learning capacity here to decide which module should be replaced\n * by the cloning. We choose the candidate with highest learning capacity to be\n * replaced by the one having the lowest learning capacity.\n */\n std::size_t find_replacement_candidate_for(std::size_t min_index)\n {\n /* always returns a valid index */\n std::size_t max_learning_capacity = motor_units[min_index].learning_capacity;\n std::size_t argmax = min_index;\n for (std::size_t idx = 0; idx < motor_units.size(); ++idx) {\n /* if there's a free slot, take it */\n if (not motor_units[idx].exists) return idx;\n if (motor_units[idx].learning_capacity > max_learning_capacity)\n {\n max_learning_capacity = motor_units[idx].learning_capacity;\n argmax = idx;\n }\n }\n return argmax;\n }\n\n /** Replace the least adapted motor neuron by a copy of the most adapted one.\n * When cloning the motor neuron, the corresponding q-values and eligibility\n * trace which the motor neuron is connected with will also be cloned.\n * TODO: Instead of just cloning, think of using a kind of crossover operation\n * for the creation of the new unit.\n */\n void clone(std::size_t current_idx)\n {\n std::size_t replace_idx = find_replacement_candidate_for(current_idx);\n dbg_msg(\"Cloning motor unit %u to %u\", current_idx, replace_idx);\n\n unsigned int learning_capacity_remainder = (motor_units[current_idx].learning_capacity + motor_units[replace_idx].learning_capacity) % 2;\n motor_units[current_idx].learning_capacity = (motor_units[current_idx].learning_capacity + motor_units[replace_idx].learning_capacity) / 2;\n\n motor_units[replace_idx] = motor_units[current_idx]; // replace\n motor_units[replace_idx].learning_capacity += learning_capacity_remainder;\n\n /** Change symmetry to balance the ratio of symmetric and asymmetric joint count */\n if (get_number_of_symmetric_units() < (motor_units.size()/2))\n motor_units[replace_idx].weights = make_symmetric(robot, motor_units[replace_idx].weights);\n else\n motor_units[replace_idx].weights = make_asymmetric(robot, motor_units[replace_idx].weights);\n\n /* copy associate Q-values and eligibility traces */\n for (std::size_t i = 0; i < states.size(); ++i)\n states[i].copy_payload(current_idx, replace_idx);\n }\n\n /** Shift the original weight vector of the selected motor neuron a little bit\n * towards the mutated weight vector. Decrease learning capacity.\n */\n void do_adapt(std::size_t current_idx) {\n --(motor_units[current_idx].learning_capacity);\n assert(mutated_weights.size() == motor_units[current_idx].weights.size()); /** TODO: this was thrown, CHECK!!! somehow large reward values will induce that*/\n for (std::size_t i = 0; i < motor_units[current_idx].weights.size(); ++i) {\n double delta = learning_rate * (mutated_weights[i] - motor_units[current_idx].weights[i]);\n motor_units[current_idx].weights[i] += delta;\n }\n\n /** When learning capacity is exhausted we clone the motor unit */\n if (motor_units[current_idx].learning_capacity == 0) {\n clone(current_idx);\n }\n\n /** Redistribute the learning capacity to the group of all (possible) motor units.\n * This enforces the growing up to the maximal size of the group.\n */\n recipient_idx = random_index(motor_units.size());\n ++(motor_units[recipient_idx].learning_capacity);\n\n /* assert that learning capacity does not drain */\n unsigned int sum = 0;\n for (std::size_t i = 0; i < motor_units.size(); ++i)\n sum += motor_units[i].learning_capacity;\n assert(sum == motor_units.size() * MotorLayerConstants::initial_learning_capacity);\n\n }\n\n std::size_t get_number_of_symmetric_units() {\n std::size_t num_sym = 0;\n for (std::size_t i = 0; i < motor_units.size(); ++i)\n if (motor_units[i].weights.is_symmetric())\n ++num_sym;\n return num_sym;\n }\n\n const robots::Robot_Interface& robot; // only needed for changing symmetry of controller weights\n static_vector<State_Payload>& states;\n const control::Control_Vector& parameter_sets;\n\n std::vector<MotorUnit> motor_units;\n control::Control_Parameter mutated_weights;\n std::size_t last_selected_idx;\n std::size_t recipient_idx;\n bool do_adaption;\n\n const double mutation_rate;\n const double learning_rate;\n\n friend class CompetitiveMotorLayer_Graphics;\n};\n\n\n\n#endif // COMPETITIVE_MOTOR_LAYER_H_INCLUDED\n" }, { "alpha_fraction": 0.5604562759399414, "alphanum_fraction": 0.5615969300270081, "avg_line_length": 24.288461685180664, "blob_id": "06a7b61ad22be89a00f75f8091f122b410bd2526", "content_id": "de175a3b0fa57fa2e4f06228e4880345f8a216e0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2630, "license_type": "no_license", "max_line_length": 82, "num_lines": 104, "path": "/src/evolution/setting.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* setting.h */\n\n#ifndef SETTING_H\n#define SETTING_H\n\n#include <stdio.h>\n\n#include <common/log_messages.h>\n#include <common/socket_client.h>\n#include <control/jointcontrol.h>\n#include <evolution/evolution_strategy.h>\n\n#define FOLDER_PREFIX \"../data/exp/\"\n\nenum PStatus {NEW, RESUME, WATCH}; /** TODO rename */\n\nclass Setting\n{\n private:\n void print_options(void);\n\n public:\n /* general */\n std::string project_name;\n PStatus project_status;\n bool visuals;\n bool interlaced_mode;\n\n /* simloid */\n unsigned short tcp_port;\n unsigned int robot_ID;\n unsigned int scene_ID;\n\n /* evaluation */\n unsigned int max_steps;\n unsigned int max_power;\n unsigned int max_dctrl;\n unsigned int initial_steps;\n\n bool efficient;\n bool drop_penalty;\n bool out_of_track_penalty;\n bool stop_penalty;\n bool symmetric_controller;\n\n /* evolution */\n std::string strategy;\n unsigned int population_size;\n unsigned int selection_size;\n unsigned int max_generations;\n unsigned int cur_generations;\n unsigned int max_trials;\n unsigned int cur_trials;\n double init_mutation_rate;\n double meta_mutation_rate;\n double moving_rate;\n double selection_bias;\n std::string seed;\n std::string initial_population;\n\n control::Minimal_Seed_t param;\n\n struct Push_Settings {\n unsigned int mode;\n unsigned int body;\n unsigned int cycle;\n unsigned int steps;\n double strength;\n } push;\n\n std::string fitness_function;\n\n struct Random_Mode_Settings {\n std::string mode;\n double value;\n mutable uint64_t init;\n } rnd;\n\n struct Growth_Settings {\n double init;\n double rate;\n } growth;\n\n double friction;\n\n bool low_sensor_quality;\n bool L1_normalization;\n\n double target;\n double drop_level;\n double stop_level;\n double corridor;\n\n bool initially_fixed;\n\n Setting(int argc, char **argv);\n void read_setting_file(const std::string& setting_name);\n void read_project_file(const std::string& project_name);\n void read_configuration(const std::string& filename);\n const std::string& save_to_projectfile(const std::string& filename) const;\n\n};\n\n#endif //SETTING_H\n" }, { "alpha_fraction": 0.4878166913986206, "alphanum_fraction": 0.49569645524024963, "avg_line_length": 28.045774459838867, "blob_id": "06cc6221e6cc9271242f1375b75f3661f5f9eb08", "content_id": "e30d87d7baf9e2c1b5f9e1577e53793f748fa489", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8249, "license_type": "no_license", "max_line_length": 107, "num_lines": 284, "path": "/src/common/datareader.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef DATAREADER_H_INCLUDED\n#define DATAREADER_H_INCLUDED\n\n#include <string>\n#include <vector>\n#include <map>\n\n#include <common/noncopyable.h>\n#include <common/basic.h>\n\nnamespace file_io {\n\n/*\n i = 5 int\n s = \"hallo\" string\n f = {0.1} float\n v = {0 1 1 2} vector\n # comment\n*/\n\n typedef std::string key_t;\n\n typedef uint64_t uint_t;\n typedef int64_t sint_t;\n typedef double float_t;\n typedef std::vector<double> vector_t;\n typedef std::string string_t;\n\nnamespace {\n\n const std::size_t key_size = 256;\n const std::size_t val_size = 1048576; // 1 kbyte\n const char separator[] = \" \";\n\n const char pattern_vec[] = \" %256[a-z_] = { %1048576[\\n\\t eE0-9.+-] } %n\";\n const char pattern_str[] = \" %256[a-z_] = \\\"%1048576[^\\\"]\\\" %n\";\n const char pattern_int[] = \" %256[a-z_] = %ld %n\";\n}\n\ninline bool is_empty(const char* msg) {\n while(*msg != '\\0') {\n if (not isspace(*msg))\n return false;\n msg++;\n }\n return true;\n}\n\nclass Data_Reader : public noncopyable{\n\n FILE* fd;\n const std::size_t file_size;\n char* txtbuf;\n char* refto_txtbuf;\n\n char key[key_size];\n\n char str_val[val_size];\n long int int_val;\n\n Data_Reader ( const Data_Reader& ) = delete; // non construction-copyable\n Data_Reader& operator=( const Data_Reader& ) = delete; // non copyable\n\n /* maps */\n std::map<key_t, vector_t> map_vec;\n std::map<key_t, string_t> map_str;\n std::map<key_t, sint_t> map_int;\n\n bool verbose;\n\npublic:\n Data_Reader(const std::string& filename, bool verbose = true)\n : fd(open_file(\"r\", filename.c_str()))\n , file_size(basic::get_file_size(fd))\n , txtbuf((char*) malloc (sizeof(char) * file_size))\n , refto_txtbuf(txtbuf)\n , int_val(0)\n , map_vec()\n , map_str()\n , map_int()\n , verbose(verbose)\n {\n if (verbose) sts_msg(\"Reading file %s \\n with size: %zu bytes.\", filename.c_str(), file_size);\n\n if (nullptr == txtbuf)\n err_msg(__FILE__, __LINE__, \"Cannot allocate memory.\");\n\n // copy complete file into buffer\n std::size_t result = fread(txtbuf, 1, file_size, fd);\n if (result != file_size)\n err_msg(__FILE__, __LINE__, \"Cannot read full file.\");\n\n fclose(fd);\n parse();\n }\n\n ~Data_Reader() {\n if (refto_txtbuf != nullptr)\n free(refto_txtbuf);\n }\n\nprivate:\n\n vector_t decode(char *value)\n {\n vector_t data;\n char* token = strtok(value, separator); // get first token\n while (nullptr != token) {\n if (not is_empty(token))\n data.emplace_back(atof(token));\n token = strtok(nullptr, separator); // get next token\n }\n return data;\n }\n\n void remove_comments(void)\n {\n bool comment_found = false;\n for (std::size_t i = 0; i < file_size; ++i)\n {\n if ('#' == txtbuf[i])\n comment_found = true;\n else if (comment_found && ('\\n' == txtbuf[i]))\n comment_found = false;\n\n if (comment_found)\n txtbuf[i] = ' '; // clear comments\n }\n }\n\n void parse(void) {\n\n remove_comments();\n int offset = 0;\n while (true)\n {\n if (2 == sscanf(txtbuf, pattern_vec, key, str_val, &offset))\n {\n vector_t data = decode(str_val);\n if (verbose) {\n printf(\"\\t%s = ( \", key);\n for (std::size_t i = 0; i < std::min(std::size_t{8}, data.size()); ++i)\n printf(\"%1.2f \", data[i]);\n if (data.size() > 8)\n printf(\"... ) N=%zu\\n\", data.size());\n else\n printf(\") \\n\");\n }\n if (0 == map_vec.count(key))\n map_vec.emplace(key, data);\n else wrn_msg(\"\\tSkipping vector %s, already in list.\", key);\n }\n else if (2 == sscanf(txtbuf, pattern_str, key, str_val, &offset)) {\n if (verbose) sts_msg(\"\\t%s = \\'%s\\'\", key, str_val);\n if (0 == map_str.count(key))\n map_str.emplace(key, str_val);\n else wrn_msg(\"\\tSkipping string %s, already in list.\", key);\n }\n else if (2 == sscanf(txtbuf, pattern_int, key, &int_val, &offset)) {\n if (verbose) sts_msg(\"\\t%s = <%ld>\", key, int_val);\n if (0 == map_int.count(key))\n map_int.emplace(key, int_val);\n else wrn_msg(\"\\tSkipping integer %s, already in list.\", key);\n }\n else {\n if (verbose) sts_msg(\"Done reading\");\n break;\n }\n txtbuf += offset;\n }\n }\n\npublic:\n bool read (const key_t& key, uint_t& value)\n {\n if (map_int.count(key)) {\n value = static_cast<uint_t>(map_int[key]);\n return true;\n }\n return false;\n }\n bool read (const key_t& key, sint_t& value)\n {\n if (map_int.count(key)) {\n value = map_int[key];\n return true;\n }\n return false;\n }\n bool read (const key_t& key, float_t& value)\n {\n if (map_vec.count(key)) {\n const vector_t& vec = map_vec[key];\n if (vec.size() == 1) {\n value = vec[0];\n return true;\n }\n }\n return false;\n }\n bool read (const key_t& key, string_t& value)\n {\n if (map_str.count(key)) {\n value = map_str[key];\n return true;\n }\n return false;\n }\n bool read (const key_t& key, vector_t& value)\n {\n if (map_vec.count(key)) {\n value = map_vec[key];\n return true;\n }\n return false;\n }\n\n uint_t read_unsigned(const key_t& key, const uint_t default_value = uint_t()) {\n uint_t result;\n if (not read(key, result)) {\n wrn_msg(\"Cannot read unsigned '%s'. Taking default: %u\", key.c_str(), default_value);\n return default_value;\n }\n return result;\n }\n\n sint_t read_signed(const key_t& key, const sint_t default_value = sint_t()) {\n sint_t result;\n if (not read(key, result)) {\n wrn_msg(\"Cannot read signed '%s'. Taking default: %d\", key.c_str(), default_value);\n return default_value;\n }\n return result;\n }\n\n float_t read_float(const key_t& key, const float_t default_value = float_t()) {\n float_t result;\n if (not read(key, result)) {\n wrn_msg(\"Cannot read float '%s'. Taking default: %f\", key.c_str(), default_value);\n return default_value;\n }\n return result;\n }\n\n string_t read_string(const key_t& key, const string_t default_value = string_t()) {\n string_t result;\n if (not read(key, result)) {\n wrn_msg(\"Cannot read string '%s'. Taking default: %s\", key.c_str(), default_value.c_str());\n return default_value;\n }\n return result;\n }\n\n vector_t read_vector(const key_t& key, const vector_t default_value = vector_t()) {\n vector_t result;\n if (not read(key, result)) {\n wrn_msg(\"Cannot read vector '%s'.\", key.c_str());\n return default_value;\n }\n return result;\n }\n\n template <typename result_t>\n result_t read_type(const key_t& key, const result_t default_value = result_t()) {\n result_t result;\n if (not read(key, result)) {\n wrn_msg(\"Cannot read '%s'\", key.c_str());\n return default_value;\n }\n return result;\n }\n\n// void write(const std::string& key, const uint_t& value) { }\n// void write(const std::string& key, const sint_t& value) { }\n// void write(const std::string& key, const float_t& value) { }\n// void write(const std::string& key, const string_t& value) { }\n// void write(const std::string& key, const vector_t& value) { }\n\n};\n\n\n} // namespace file_io\n\n#endif // DATAREADER_H_INCLUDED\n" }, { "alpha_fraction": 0.6285289525985718, "alphanum_fraction": 0.6404160261154175, "avg_line_length": 20.70967674255371, "blob_id": "d0bc7decf65e6e4a590aaf3a744ccabbbb83448d", "content_id": "6594fbbae7db371dbaad64f70cf7870e4239169a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 673, "license_type": "no_license", "max_line_length": 79, "num_lines": 31, "path": "/src/learning/eligibility.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef ELIGIBILITY_H_INCLUDED\n#define ELIGIBILITY_H_INCLUDED\n\n#include <cassert>\n#include <iostream>\n\n/** TODO: visualize traces by displaying the max trace per state.\n */\nclass Eligibility\n{\n double value;\n\npublic:\n Eligibility()\n : value(0.0)\n {}\n\n void decay(float factor) { value *= factor; }\n void reset(void) { value = 1.0; }\n double get(void) const { assert(in_range(value, 0.0, 1.0)); return value; }\n\n friend std::ostream& operator<< (std::ostream& os, const Eligibility& e);\n};\n\ninline std::ostream& operator<< (std::ostream& os, const Eligibility& e) {\n os << e.value;\n return os;\n}\n\n\n#endif // ELIGIBILITY_H_INCLUDED\n" }, { "alpha_fraction": 0.6454652547836304, "alphanum_fraction": 0.6643109321594238, "avg_line_length": 23.257143020629883, "blob_id": "4f9f9bed2850395069719683374e69eb3987a195", "content_id": "67b804f94f9c519ce7d23bee3c1544979fc0179a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 849, "license_type": "no_license", "max_line_length": 99, "num_lines": 35, "path": "/src/learning/motor_predictor_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef MOTOR_PREDICTOR_GRAPHICS_H_INCLUDED\n#define MOTOR_PREDICTOR_GRAPHICS_H_INCLUDED\n\n#include <common/modules.h>\n#include <draw/graphics.h>\n#include <draw/display.h>\n#include <basic/color.h>\n#include <learning/motor_predictor.h>\n\nnamespace learning {\n\nclass Motor_Predictor_Graphics : public Graphics_Interface\n{\n const Motor_Predictor& predictor;\n const Color4 color;\n\npublic:\n Motor_Predictor_Graphics(const Motor_Predictor& predictor, Color4 const& color = colors::white)\n : predictor(predictor)\n , color(color)\n {\n }\n\n void draw(const pref&) const {\n float s = 2.0/predictor.core.weights.size();\n unsigned i = 0;\n for (auto const& wi : predictor.core.weights)\n draw_vector2(0.0 + s*i++, 0.0, 0.045, s, wi, 3.0);\n }\n\n};\n\n}\n\n#endif /* MOTOR_PREDICTOR_GRAPHICS_H_INCLUDED */\n" }, { "alpha_fraction": 0.6269165277481079, "alphanum_fraction": 0.6269165277481079, "avg_line_length": 31.61111068725586, "blob_id": "406719aa8c69da92333a0be5d2c215c619b9febe", "content_id": "0ef291677a64e3180576cbc663b8736e7b67bab4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C", "length_bytes": 587, "license_type": "no_license", "max_line_length": 91, "num_lines": 18, "path": "/src/common/log_messages.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef LOG_MESSAGES_H\n#define LOG_MESSAGES_H\n\n#include <cstdlib>\n#include <cstdio>\n#include <cstdarg>\n\nvoid sts_msg(const char* format, ...);\nvoid sts_add(const char* format, ...);\nvoid dbg_msg(const char* format, ...);\nvoid wrn_msg(const char* format, ...);\nvoid err_msg(const char* file, unsigned int line, const char* format, ...);\nvoid promise(bool condition, const char* file, unsigned int line, const char* format, ...);\n\n#define assertion(COND, FMT, ...) \\\npromise(COND, __FILE__, __LINE__, #COND \" \" FMT, ##__VA_ARGS__) \\\n\n#endif // LOG_MESSAGES_H\n" }, { "alpha_fraction": 0.6624472737312317, "alphanum_fraction": 0.6624472737312317, "avg_line_length": 15.928571701049805, "blob_id": "59e0a9395b9edd70f8de14a2f0aaa180c6fa4369", "content_id": "1d5ef2b9432a1401cd55375e74301f9a7d19dcf7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 237, "license_type": "no_license", "max_line_length": 41, "num_lines": 14, "path": "/src/common/lock.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef LOCK_H_INCLUDED\n#define LOCK_H_INCLUDED\n\n#include <mutex>\n\n/** Self-unlocking LOCK */\nnamespace common {\n\ntypedef std::mutex mutex_t;\ntypedef std::lock_guard<mutex_t> lock_t;\n\n} // common\n\n#endif // LOCK_H_INCLUDED\n" }, { "alpha_fraction": 0.6533289551734924, "alphanum_fraction": 0.6539849042892456, "avg_line_length": 36.17073059082031, "blob_id": "e6446986388fa84eecdc1c2cb4256d951cde6dfd", "content_id": "4ac8967363672ad0a28a9a9194522a7385c129f6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3049, "license_type": "no_license", "max_line_length": 139, "num_lines": 82, "path": "/src/learning/state_action_predictor.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef STATE_ACTION_PREDICTOR_H\n#define STATE_ACTION_PREDICTOR_H\n\n#include <control/sensorspace.h>\n#include <learning/predictor.h>\n#include <learning/autoencoder.h>\n\nnamespace learning {\n\nclass State_Action_Predictor : public Predictor_Base {\n\n State_Action_Predictor(const State_Action_Predictor& other) = delete;\n State_Action_Predictor& operator=(const State_Action_Predictor& other) = delete;\n\npublic:\n\n State_Action_Predictor( const time_embedded_sensors<16>& inputs\n , const double learning_rate\n , const double random_weight_range\n , const std::size_t experience_size\n , const std::size_t hidden_layer_size // not needed, replace with num joints, remove\n )\n : Predictor_Base(inputs, learning_rate, random_weight_range, experience_size)\n , enc(inputs.size(), hidden_layer_size, random_weight_range )\n {\n //dbg_msg(\"Initialize State Action Predictor using Autoencoder and time-embedded inputs.\");\n }\n\n virtual ~State_Action_Predictor() = default;\n\n void copy(Predictor_Base const& other) override {\n Predictor_Base::operator=(other); // copy base members\n State_Action_Predictor const& rhs = dynamic_cast<State_Action_Predictor const&>(other);\n enc = rhs.enc;\n dbg_msg(\"Copying state predictor weights.\");\n };\n\n Predictor_Base::vector_t const& get_prediction(void) const override { return enc.get_outputs(); }\n\n double predict(void) override {\n enc.propagate(input);\n return calculate_prediction_error();\n };\n\n double verify(void) override {\n enc.propagate(input);\n return calculate_prediction_error();\n }\n\n void initialize_randomized(void) override {\n enc.randomize_weight_matrix(random_weight_range);\n auto initial_experience = input.get();\n for (auto& w: initial_experience)\n w += random_value(-random_weight_range, random_weight_range);\n experience.assign(experience.size(), initial_experience);\n prediction_error = predictor_constants::error_min;\n };\n\n void initialize_from_input(void) override { assert(false && \"one shot learning not supported.\"); }\n\n void draw(void) const { assert(false); /*not implemented*/ }\n\n Autoencoder const& get_encoder(void) const { return enc; }\n\n vector_t const& get_weights(void) const override { assert(false); return dummy; /*not implemented*/ }\n vector_t & set_weights(void) override { assert(false); return dummy; /*not implemented*/ }\n\nprivate:\n\n void learn_from_input_sample(void) override { enc.adapt(input, learning_rate); };\n void learn_from_experience(std::size_t /*skip_idx*/) override { assert(false && \"Learning from experience is not implemented yet.\"); };\n\n Autoencoder enc;\n\n VectorN dummy = {}; // remove when implementing get_weights\n\n friend class Predictor_Graphics;\n};\n\n} /* namespace learning */\n\n#endif /* State_Action_Predictor_H */\n\n" }, { "alpha_fraction": 0.5831242799758911, "alphanum_fraction": 0.5905189514160156, "avg_line_length": 27.152416229248047, "blob_id": "95cd662359b5c3d9976a4c7c891753003a52baa0", "content_id": "d879fcb6118e34eb607a6822ec45b070eb1275b0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 7573, "license_type": "no_license", "max_line_length": 129, "num_lines": 269, "path": "/src/evolution/fitness.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef FITNESS_H\n#define FITNESS_H\n\n#include <math.h>\n#include <memory>\n\n#include <common/modules.h>\n#include <common/config.h>\n\n#include <robots/simloid.h>\n\n#include <evolution/setting.h>\n\n/**TODO: how to incorporate L1 normalization into fitness?\n How to access the weight matrix?\n we only need to access the norm value L1 = sum |w_i|\n start with computing this value and print it to terminal after each evaluation.\n */\n\nstruct fitness_data\n{\n fitness_data()\n : fit (.0)\n , power(.0)\n , dctrl(.0)\n , temp (.0)\n , steps( 0)\n , max_steps(0)\n , dropped(false)\n , out_of_track(false)\n , stopped(false)\n {}\n double fit;\n double power;\n double dctrl;\n double temp;\n std::size_t steps;\n std::size_t max_steps;\n bool dropped;\n bool out_of_track;\n bool stopped;\n};\n\n\ninline double step_ratio(fitness_data const& data) { return clip(static_cast<double>(data.steps)/data.max_steps, 0.0, 1.0); }\n\nclass Fitness_Base {\npublic:\n Fitness_Base(const std::string& name, const robots::Simloid& robot, const Setting& settings)\n : robot(robot)\n , s(settings)\n {\n sts_msg(\"Fitness Name = %s\", name.c_str());\n sts_msg(\"DROPPED = %s\", s.drop_penalty ? \"YES\":\"NO\");\n sts_msg(\"OUT-OF-TRACK = %s\", s.out_of_track_penalty ? \"YES\":\"NO\");\n sts_msg(\"STOPPED = %s\", s.stop_penalty ? \"YES\":\"NO\");\n sts_msg(\"TARGET = %1.2f\", s.target);\n sts_msg(\"DROP_LEVEL = %1.2f\", s.drop_level);\n sts_msg(\"STOP_LEVEL = %1.2f\", s.stop_level);\n sts_msg(\"CORRIDOR = %1.2f\", s.corridor);\n\n assert(s.target >= .0);\n assert(s.corridor >= .0);\n assert_in_range(s.drop_level, 0., 1.);\n assert_in_range(s.stop_level, 0., 1.);\n }\n\n virtual void start (fitness_data& /*data*/) {};\n virtual void step (fitness_data& data) = 0;\n virtual void finish(fitness_data& data) = 0;\n virtual ~Fitness_Base() {};\n\nprivate:\n const unsigned min_steps = 100;\n\nprotected:\n const robots::Simloid& robot;\n const Setting& s; //settings\n\n bool dropped(void) { return s.drop_penalty && robot.dropped(s.drop_level); }\n bool stopped(fitness_data& data) { return s.stop_penalty && (data.steps > min_steps) && robot.motion_stopped(s.stop_level); }\n\n bool out_of_track_x(void) { return s.out_of_track_penalty && (fabs(robot.dx_from_origin()) > s.corridor); }\n bool out_of_track_y(void) { return s.out_of_track_penalty && (fabs(robot.dy_from_origin()) > s.corridor); }\n\n\n};\n\ntypedef std::shared_ptr<Fitness_Base> Fitness_ptr;\n\nFitness_ptr assign_fitness(const robots::Simloid& robot, const Setting& settings);\n\nclass Fitness_Forwards : public Fitness_Base\n{\npublic:\n Fitness_Forwards(const robots::Simloid& robot, const Setting& s, bool use_avg = true)\n : Fitness_Base(\"FORWARDS\", robot, s)\n , use_avg(use_avg)\n { sts_msg(\"Evolve getting forwards.\"); }\n\n void step(fitness_data& data) override\n {\n if (dropped()) data.dropped = true;\n if (stopped(data)) data.stopped = true;\n if (out_of_track_x()) data.out_of_track = true;\n }\n\n void finish(fitness_data& data) override\n {\n /* forwards has negative sign on y axis */\n data.fit = use_avg ? -robot.get_avg_position().y : -robot.get_max_position().y;\n\n if (s.target != .0 and data.fit > s.target) {\n data.fit = clip(data.fit, std::abs(s.target));\n //dbg_msg(\"Target overshot. \\n\");\n data.fit *= step_ratio(data);\n }\n\n if (data.dropped || data.out_of_track || data.stopped)\n data.fit -= 1.0;// - step_ratio(data);\n }\n\nprivate:\n bool use_avg;\n};\n\n\nclass Fitness_Forwards_Feet : public Fitness_Base\n{\npublic:\n Fitness_Forwards_Feet(const robots::Simloid& robot, const Setting& s)\n : Fitness_Base(\"FORWARDS_FEET\", robot, s)\n { sts_msg(\"Evolve walking forwards, taking only feet position into account.\"); }\n\n void step(fitness_data& data) override\n {\n if (dropped()) data.dropped = true;\n if (stopped(data)) data.stopped = true;\n if (out_of_track_x()) data.out_of_track = true;\n }\n\n void finish(fitness_data& data) override\n {\n /* forwards has negative sign on y axis */\n data.fit = -robot.get_max_feet_pos().y;\n\n if (data.dropped || data.out_of_track || data.stopped)\n data.fit -= 1.0 - step_ratio(data);\n }\n};\n\n\nclass Fitness_Backwards : public Fitness_Base\n{\npublic:\n Fitness_Backwards(const robots::Simloid& robot, const Setting& s, bool use_avg = true)\n : Fitness_Base(\"BACKWARDS\", robot, s)\n , use_avg(use_avg)\n { sts_msg(\"Evolve getting backwards.\"); }\n\n void step(fitness_data& data) override\n {\n if (dropped()) data.dropped = true;\n if (stopped(data)) data.stopped = true;\n if (out_of_track_x()) data.out_of_track = true;\n }\n\n void finish(fitness_data& data) override\n {\n /* backwards has positive sign on y axis */\n data.fit = use_avg ? robot.get_avg_position().y : robot.get_min_position().y;\n\n if (data.dropped || data.out_of_track || data.stopped)\n data.fit -= 1.0;// - step_ratio(data);\n }\n\nprivate:\n bool use_avg;\n};\n\n\nclass Fitness_Stopping : public Fitness_Base\n{\npublic:\n Fitness_Stopping(const robots::Simloid& robot, const Setting& s)\n : Fitness_Base(\"STOPPING\", robot, s)\n { sts_msg(\"Evolve stopping.\"); }\n\n void step(fitness_data& data) override\n {\n if (dropped()) data.dropped = true;\n }\n\n void finish(fitness_data& data) override\n {\n data.fit = 10.0/(1 + data.power);\n data.fit+= 10.0/(1 + data.dctrl);\n\n if (data.dropped)\n data.fit -= 10.0 * (1.0 - step_ratio(data));\n }\n};\n\n\nclass Fitness_Sidewards : public Fitness_Base\n{\npublic:\n Fitness_Sidewards(const robots::Simloid& robot, const Setting& s, bool use_avg = true)\n : Fitness_Base(\"SIDEWARDS\", robot, s)\n , use_avg(use_avg)\n { sts_msg(\"Evolve walking sidewards.\"); }\n\n void step(fitness_data& data) override\n {\n if (dropped()) data.dropped = true;\n if (stopped(data)) data.stopped = true;\n if (out_of_track_y()) data.out_of_track = true;\n\n data.temp = fmax((data.temp), fabs(robot.get_avg_position().y));\n }\n\n void finish(fitness_data& data) override\n {\n /* left has positive sign on x axis */\n data.fit = use_avg ? robot.get_avg_position().x : robot.get_min_position().x;\n data.fit -= data.temp;\n\n if (data.dropped)\n data.fit -= 1.0;// - step_ratio(data);\n\n /* no penalty for out of track here */\n }\nprivate:\n bool use_avg;\n};\n\nclass Fitness_Turning : public Fitness_Base\n{\npublic:\n Fitness_Turning(const robots::Simloid& robot, const Setting& s)\n : Fitness_Base(\"TURNING\", robot, s)\n { sts_msg(\"Evolve turning around.\"); }\n\n void start(fitness_data& data) override {\n data.temp = robot.get_avg_rotation();\n }\n\n void step(fitness_data& data) override\n {\n if (dropped()) data.dropped = true;\n if (stopped(data)) data.stopped = true;\n\n if (out_of_track_y() || out_of_track_x())\n data.out_of_track = true;\n\n /* sum of rotation */\n data.temp = unwrap(robot.get_avg_rotation(), data.temp);\n }\n\n void finish(fitness_data& data) override\n {\n data.fit = data.temp;\n\n if (data.dropped)\n data.fit -= 2*M_PI;// * (1.0 - step_ratio(data));\n }\n};\n\n#endif /* FITNESS_H */\n" }, { "alpha_fraction": 0.5431813597679138, "alphanum_fraction": 0.5604118704795837, "avg_line_length": 32.279720306396484, "blob_id": "d620f2a88e024abee9b2181b658827c1cd211b5c", "content_id": "f4f2fa4212537c1af20e9c5860e3a2e042c0b9f0", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 4759, "license_type": "no_license", "max_line_length": 148, "num_lines": 143, "path": "/src/robots/spinalcord_watch.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/*---------------------------------+\n | Matthias Kubisch |\n | [email protected] |\n | July 2017 |\n +---------------------------------*/\n\n#ifndef SPINALCORD_WATCH_H_INCLUDED\n#define SPINALCORD_WATCH_H_INCLUDED\n\n#include <draw/draw.h>\n#include <draw/axes.h>\n#include <draw/axes3D.h>\n#include <draw/plot1D.h>\n#include <draw/plot2D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n\n/** TODO:\n * make this class more compatible to different (simloid) robots, e.g. with different draw-setups (use new data reader for reading this setup)\n * draw acceleration sensors\n */\n\nnamespace robots {\n\nnamespace constants {\n const double vel_amp = 1.0;///3.0;\n}\n\n// consider using Motorplot from Hannah\n\nclass Spinalcord_Watch : public Graphics_Interface\n{\npublic:\n Spinalcord_Watch(const robots::Robot_Interface& robot, const std::size_t num_datapoints)\n : joints(robot.get_joints())\n , num_joints(robot.get_number_of_joints())\n , accels(robot.get_accels())\n , plot_axs()\n , plot_pos()\n , plot_vel()\n , plot_vol()\n , plot_cur()\n , plot_tmp()\n , subspace_axes()\n , subspace_portrait()\n , axes_accel(0.,-1.25, 0., 2.0, 0.25, 1, \"Accel\")\n , plot_accel_x(1000, axes_accel, colors::cyan , \"ax\")\n , plot_accel_y(1000, axes_accel, colors::magenta, \"ay\")\n , plot_accel_z(1000, axes_accel, colors::yellow , \"az\")\n {\n plot_axs.reserve(num_joints);\n plot_pos.reserve(num_joints);\n plot_vel.reserve(num_joints);\n plot_vol.reserve(num_joints);\n plot_cur.reserve(num_joints);\n plot_tmp.reserve(num_joints);\n\n subspace_axes.reserve(num_joints);\n subspace_portrait.reserve(num_joints);\n\n for (std::size_t i = 0; i < num_joints; ++i)\n {\n const double width = 1.0;\n const double height = 4.0 / num_joints;\n const double posx = (i%2==0)? -1.5 : 1.5;\n const double posy = 1.0 - height * (i/2 + 0.5);\n\n plot_axs.emplace_back(posx, posy, 0., width, height, 1, std::to_string(i) + ' ' + joints[i].name);\n plot_cur.emplace_back(num_datapoints, plot_axs[i], colors::yellow, \"cur\");\n plot_tmp.emplace_back(num_datapoints, plot_axs[i], colors::orange, \"tmp\");\n plot_vol.emplace_back(num_datapoints, plot_axs[i], colors::cyan , \"vol\");\n plot_vel.emplace_back(num_datapoints, plot_axs[i], colors::pidgin, \"vel\");\n plot_pos.emplace_back(num_datapoints, plot_axs[i], colors::white , \"pos\");\n\n subspace_axes .emplace_back(posx + ((i%2==0) ? -1:1) * (width*0.5+0.5*height), posy, 0., height, height, 0, 'j' + std::to_string(i));\n subspace_portrait.emplace_back(num_datapoints, subspace_axes[i], colors::white);\n\n }\n //TODO: assert(robot.get_number_of_accel_sensors() >= 1); //TODO make applicable for more than one sensor\n }\n\n void draw(const pref& /*p*/) const {\n for (std::size_t i = 0; i < num_joints; ++i) {\n plot_axs[i].draw();\n plot_cur[i].draw();\n plot_tmp[i].draw();\n plot_pos[i].draw();\n plot_vel[i].draw();\n plot_vol[i].draw();\n\n subspace_axes[i] .draw();\n subspace_portrait[i].draw();\n }\n axes_accel .draw();\n plot_accel_x.draw();\n plot_accel_y.draw();\n plot_accel_z.draw();\n }\n\n void execute_cycle(uint64_t /*cycle*/) {\n for (std::size_t i = 0; i < num_joints; ++i) {\n plot_cur[i].add_sample(joints[i].s_cur);\n plot_tmp[i].add_sample(joints[i].s_tmp);\n plot_pos[i].add_sample(joints[i].s_ang);\n plot_vel[i].add_sample(joints[i].s_vel * constants::vel_amp);\n plot_vol[i].add_sample(joints[i].motor.get_backed());\n\n subspace_portrait[i].add_sample(joints[i].s_ang,\n joints[i].s_vel * constants::vel_amp);\n }\n if (accels.size() > 0) {\n plot_accel_x.add_sample(accels[0].a.x);\n plot_accel_y.add_sample(accels[0].a.y);\n plot_accel_z.add_sample(accels[0].a.z);\n }\n }\n\n const robots::Jointvector_t& joints;\n const std::size_t num_joints;\n\n const robots::Accelvector_t& accels;\n\n std::vector<axes> plot_axs;\n\n std::vector<plot1D> plot_pos;\n std::vector<plot1D> plot_vel;\n std::vector<plot1D> plot_vol;\n std::vector<plot1D> plot_cur;\n std::vector<plot1D> plot_tmp;\n\n std::vector<axes> subspace_axes;\n std::vector<plot2D> subspace_portrait;\n\n axes axes_accel;\n plot1D plot_accel_x;\n plot1D plot_accel_y;\n plot1D plot_accel_z;\n};\n\n} // namespace robots\n\n#endif // SPINALCORD_WATCH_H_INCLUDED\n" }, { "alpha_fraction": 0.5707285404205322, "alphanum_fraction": 0.5877164602279663, "avg_line_length": 29.600000381469727, "blob_id": "113bf370ca0c243cae445bf3fd81ba2b39442bd1", "content_id": "57c770fb6e1bd87b1d976cdd017480790739d1da", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3061, "license_type": "no_license", "max_line_length": 89, "num_lines": 100, "path": "/src/tests/autoencoder_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n#include <tests/test_robot.h>\n\n#include <common/modules.h>\n#include <learning/autoencoder.h>\n\n\nnamespace local_tests {\n\nnamespace autoencoder_tests {\n\nstruct Test_Sensor_Space : public sensor_vector {\n Test_Sensor_Space(const robots::Jointvector_t& joints)\n : sensor_vector(2*joints.size() + 1)\n {\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_ang\", [&j](){ return j.s_ang; });\n for (robots::Joint_Model const& j : joints)\n sensors.emplace_back(j.name + \"_vel\", [&j](){ return j.s_vel; });\n\n sensors.emplace_back(\"bias\", [&](){ return 0.01; });\n assert(sensors.size() == 2*joints.size() + 1);\n }\n};\n\n\n\nTEST_CASE( \"autoencoder construction\" , \"[autoencoder]\")\n{\n srand(time(0)); // set random seed\n\n Test_Robot robot(5,2);\n Test_Sensor_Space inputs{robot.get_joints()};\n const double random_range = 0.1;\n learning::Autoencoder autoenc(inputs.size(), 3, random_range);\n\n auto const& outputs = autoenc.get_outputs();\n REQUIRE( outputs.size() == inputs.size() );\n\n for (std::size_t i = 0; i < outputs.size(); ++i) {\n REQUIRE( outputs[i] == .0 );\n REQUIRE( inputs[i] == .0 );\n }\n\n autoenc.propagate(inputs);\n\n for (std::size_t i = 0; i < outputs.size(); ++i)\n REQUIRE( outputs[i] == .0 );\n\n /* check weights are not zero */\n auto const& weights = autoenc.get_weights();\n double sum = .0;\n int diff = 0;\n for (std::size_t i = 0; i < weights.size(); ++i)\n for (std::size_t j = 0; j < weights[i].size(); ++j) {\n diff += ( weights[i][j] != .0 )? 0 : 1;\n sum += weights[i][j];\n }\n\n /* check randomize_weight_matrix() is executed */\n REQUIRE( diff == 0 );\n const double max_range = 0.5* random_range * weights.size()*weights[0].size();\n dbg_msg(\"Max rand: %e < %e\", std::abs(sum), max_range);\n REQUIRE( std::abs(sum) <= max_range ); // check small\n REQUIRE( std::abs(sum) != 0. ); // but not zero\n\n\n /** check that autoencoder is copyable **/\n learning::Autoencoder autoenc2 = autoenc;\n}\n\n\nTEST_CASE( \"auto encoder learning\", \"[autoencoder]\")\n{\n srand(time(0)); // set random seed\n\n Test_Robot robot(5,2);\n Test_Sensor_Space inputs{robot.get_joints()};\n\n for (auto& j: robot.set_joints())\n j.s_ang = 1.0;\n\n const double learning_rate = 0.02;\n learning::Autoencoder autoenc(inputs.size(), 3, 0.1);\n REQUIRE ( squared_distance(inputs, autoenc.get_outputs()) == .0 );\n\n for (std::size_t trials = 0; trials < 50; ++trials) {\n inputs.execute_cycle();\n autoenc.propagate(inputs);\n double err0 = squared_distance(inputs, autoenc.get_outputs());\n autoenc.adapt(inputs, learning_rate);\n autoenc.propagate(inputs);\n double err1 = squared_distance(inputs, autoenc.get_outputs());\n\n dbg_msg(\"Pred. Error before %e and %e after adaption (%u).\", err0, err1, trials);\n REQUIRE( err0 > err1 );\n }\n}\n\n}} // namespace local_tests::autoencoder_tests\n\n" }, { "alpha_fraction": 0.5960773825645447, "alphanum_fraction": 0.6024383902549744, "avg_line_length": 39.13829803466797, "blob_id": "e535557b1a969db286f743ebbdec0c04f58a7ff8", "content_id": "20744a1ed54906a55d07baaec0d3c3aabfe455b8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3773, "license_type": "no_license", "max_line_length": 146, "num_lines": 94, "path": "/src/common/application_base.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/*---------------------------------+\n | Matthias Kubisch |\n | [email protected] |\n | July 2017 |\n +---------------------------------*/\n\n#ifndef APPLICATION_BASE_H\n#define APPLICATION_BASE_H\n\n#include <string>\n#include <common/event_manager.h>\n#include <common/datalog.h>\n#include <common/globalflag.h>\n#include <draw/graphics.h>\n\nextern GlobalFlag do_pause;\n\nnamespace constants {\n const unsigned default_window_width = 400;\n const unsigned default_window_height = 400;\n}\n\nclass Application_Base\n{\npublic:\n virtual bool loop() = 0; /* for everything which has to be done repeatedly */\n virtual void finish() = 0; /* for things to be done before exiting */\n virtual void draw(const pref&) const = 0; /* things to draw */\n virtual bool visuals_enabled() { return true; }; /* returns true if the application was started with visuals */\n uint64_t get_cycle_count(void) const { return cycles; } /* returns the current application cycle */\n virtual void paused() {}\n\n\n virtual void user_callback_key_pressed (SDL_Keysym const& /*key*/) {}\n virtual void user_callback_key_released(SDL_Keysym const& /*key*/) {}\n virtual void user_callback_joystick_button_pressed (SDL_JoyButtonEvent const& /*joystick*/) {}\n virtual void user_callback_joystick_button_released(SDL_JoyButtonEvent const& /*joystick*/) {}\n virtual void user_callback_joystick_motion_axis (SDL_JoyAxisEvent const& /*joystick*/) {}\n virtual void user_callback_joystick_motion_hat (SDL_JoyHatEvent const& /*joystick*/) {}\n\n Application_Base( int argc\n , char** argv\n , Event_Manager& em\n , const std::string& name\n , unsigned width = constants::default_window_width\n , unsigned height = constants::default_window_height )\n : em(em)\n , name(name)\n , window_width(width)\n , window_height(height)\n , cycles(0)\n , logger(argc, argv)\n {\n sts_msg(\"Loading application...\");\n /* register key event */\n em.reg_usr_cb_key_pressed (std::bind(&Application_Base::base_callback_key_pressed , this, std::placeholders::_1));\n em.reg_usr_cb_key_released (std::bind(&Application_Base::base_callback_key_released , this, std::placeholders::_1));\n em.reg_usr_cb_joystick_button_pressed (std::bind(&Application_Base::user_callback_joystick_button_pressed , this, std::placeholders::_1));\n em.reg_usr_cb_joystick_button_released(std::bind(&Application_Base::user_callback_joystick_button_released, this, std::placeholders::_1));\n em.reg_usr_cb_joystick_motion_axis (std::bind(&Application_Base::user_callback_joystick_motion_axis , this, std::placeholders::_1));\n em.reg_usr_cb_joystick_motion_hat (std::bind(&Application_Base::user_callback_joystick_motion_hat , this, std::placeholders::_1));\n\n if (read_option_flag(argc, argv, \"-c\", \"--no_pause\"))\n do_pause.disable();\n }\n\n Event_Manager& em;\n const std::string name;\n const unsigned window_width;\n const unsigned window_height;\n\n uint64_t cycles;\n\n Datalog logger;\n\nprotected:\n virtual ~Application_Base() = default;\n\nprivate:\n void base_callback_key_pressed (const SDL_Keysym& keysym) {\n switch (keysym.sym) {\n case SDLK_RSHIFT: logger.toggle_logging(); break;\n default: break;\n }\n user_callback_key_pressed (keysym);\n };\n\n void base_callback_key_released(const SDL_Keysym& keysym) {\n user_callback_key_released(keysym);\n };\n};\n\n\n#endif // APPLICATION_BASE_H\n" }, { "alpha_fraction": 0.5518296360969543, "alphanum_fraction": 0.5818137526512146, "avg_line_length": 40.47208023071289, "blob_id": "5472ae96259f1d679a664a89bdc4cfda1daff5c9", "content_id": "588929c8485c1ca978ae7fe6e72e75f05f8d0414", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 8171, "license_type": "no_license", "max_line_length": 146, "num_lines": 197, "path": "/src/learning/sarsa_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SARSA_GRAPHICS_H_INCLUDED\n#define SARSA_GRAPHICS_H_INCLUDED\n\n#include <common/integrator.h>\n\n#include <draw/draw.h>\n#include <draw/axes.h>\n#include <draw/axes3D.h>\n#include <draw/plot1D.h>\n#include <draw/plot3D.h>\n#include <draw/network3D.h>\n#include <draw/color_table.h>\n#include <draw/graphics.h>\n\n#include \"sarsa.h\"\n\n/** REWARD DISPLAY\n\n Shows the rewards on different time scales\n\n 1) Immediate reward r(t), according to system time t.\n 2) Immediate reward r(T), according to eigentime T.\n 3) Trial reward, accumulated eigenstep rewards for a full trial (Episode), before switching to another policy\n 4) Bunch reward, trial rewards over time.\n\n */\n\nclass SARSA_Graphics : public Graphics_Interface {\npublic:\n SARSA_Graphics(const SARSA& sarsa) //TODO make position on the screen configurable\n : sarsa(sarsa)\n , num_policies(sarsa.get_number_of_policies())\n , last_policy(sarsa.get_current_policy())\n , table(4)\n , axis_reward_total(-0.0, -0.5, 0.0, 1.98, 0.98, 0, \"total\", 0.1)\n , axis_reward_systemstep()\n , axis_reward_eigenstep ()\n , axis_reward_trial ()\n , axis_reward_bunch()\n , plot_reward_total(2000, axis_reward_total, Color4::set_transparency(colors::cyan_l, 0.30), \"total\")\n , plot_reward_t_avg(2000, axis_reward_total, Color4::set_transparency(colors::cyan , 0.60), \"t_avg\")\n , plot_reward_t_max(2000, axis_reward_total, Color4::set_transparency(colors::orange, 0.60), \"t_max\")\n , plot_reward_systemstep()\n , plot_reward_eigenstep ()\n , plot_reward_trial ()\n , plot_reward_bunch ()\n //, plot_reward_trial_long()\n , reward_trial(num_policies)\n , reward_bunch(num_policies)\n {\n axis_reward_eigenstep .reserve(num_policies);\n axis_reward_systemstep.reserve(num_policies);\n axis_reward_trial .reserve(num_policies);\n axis_reward_bunch .reserve(num_policies);\n plot_reward_systemstep.reserve(num_policies);\n plot_reward_eigenstep .reserve(num_policies);\n plot_reward_trial .reserve(num_policies);\n plot_reward_bunch .reserve(num_policies);\n\n unsigned N = sarsa.get_number_of_policies();\n for (std::size_t i = 0; i < N; ++i) {\n float ypos = N/2 *0.2 - 0.2*i + 0.5;\n axis_reward_systemstep.emplace_back(+0.75, ypos, 0.0, 0.48, 0.18, 0, \"r(t)\" , 0.001);\n axis_reward_eigenstep .emplace_back(+0.25, ypos, 0.0, 0.48, 0.18, 0, \"r(T)\" , 0.001);\n axis_reward_trial .emplace_back(-0.25, ypos, 0.0, 0.48, 0.18, 0, \"R/trial\", 0.001);\n axis_reward_bunch .emplace_back(-0.75, ypos, 0.0, 0.48, 0.18, 0, sarsa.rewards.get_reward_name(i).substr(0,14), 0.001);\n\n plot_reward_systemstep.emplace_back(400 , axis_reward_systemstep[i], Color4::set_transparency(table.get_color(i), 1.00));\n plot_reward_eigenstep .emplace_back(400 , axis_reward_eigenstep [i], Color4::set_transparency(table.get_color(i), 0.80));\n plot_reward_trial .emplace_back(100 , axis_reward_trial [i], Color4::set_transparency(table.get_color(i), 0.60));\n plot_reward_bunch .emplace_back(100 , axis_reward_bunch [i], Color4::set_transparency(table.get_color(i), 0.40));\n //plot_reward_trial_long.emplace_back(2000, axis_reward_total , Color4::set_transparency(table.get_color(i), 0.75), \"L-Trial\");\n }\n\n dbg_msg(\"Creating Sarsa Graphics for %lu policies.\", num_policies);\n }\n\n void execute_cycle(uint64_t /*cycle*/, bool state_changed, bool trial_ended)\n {\n\n for (std::size_t i = 0; i < num_policies; ++i)\n plot_reward_systemstep[i].add_sample(sarsa.rewards.get_current_reward(i));\n\n\n if (state_changed)\n {\n assert(sarsa.rewards.get_number_of_policies() == num_policies);\n\n for (std::size_t i = 0; i < num_policies; ++i)\n {\n if (sarsa.get_current_policy() == i) {\n const double value = sarsa.rewards.get_aggregated_last_reward(i);\n plot_reward_eigenstep[i].add_sample(value);\n reward_trial[i].add(value);\n }\n }\n }\n\n if (/*last_policy != sarsa.get_current_policy() or*/ trial_ended) { // policy changed, trial ended\n std::size_t i = last_policy;\n last_policy = sarsa.get_current_policy();\n\n const float trial_reward_i = reward_trial[i].get_avg_value_and_reset();\n reward_bunch[i].add(trial_reward_i);\n plot_reward_trial[i].add_sample(trial_reward_i);\n\n if (reward_bunch[i].get_number_of_samples() >= 10)\n plot_reward_bunch[i].add_sample(reward_bunch[i].get_avg_value_and_reset());\n\n //plot_reward_trial_long[i].add_sample(trial_reward_i);\n\n ++trial_count;\n if (trial_count % num_policies == 0) {\n double total = .0;\n for (auto const& r: reward_trial)\n total += r.get_last();\n reward_t_avg = 0.95*reward_t_avg + 0.05*total;\n reward_t_max = std::max(reward_t_max, reward_t_avg);\n plot_reward_total.add_sample(total);\n plot_reward_t_avg.add_sample(reward_t_avg);\n plot_reward_t_max.add_sample(reward_t_max);\n }\n }\n }\n\n void draw(const pref& /*p*/) const\n {\n axis_reward_total.draw();\n plot_reward_total.draw();\n plot_reward_t_max.draw();\n plot_reward_t_avg.draw();\n for (std::size_t i = 0; i < num_policies; ++i) {\n axis_reward_systemstep[i].draw();\n axis_reward_eigenstep [i].draw();\n axis_reward_trial [i].draw();\n axis_reward_bunch [i].draw();\n plot_reward_systemstep[i].draw();\n plot_reward_eigenstep [i].draw();\n plot_reward_trial [i].draw();\n plot_reward_bunch [i].draw();\n //plot_reward_trial_long[i].draw();\n }\n\n glColor3f(1.0, 1.0, 1.0);\n glprintf(-0.9, 1.0, 0.0, 0.03, \"cur: %+.4f\", sarsa.get_current_reward(sarsa.get_current_policy()));\n }\n\nprivate:\n\n const SARSA& sarsa;\n const std::size_t num_policies;\n std::size_t last_policy;\n\n std::size_t trial_count = 0;\n double reward_t_avg = .0;\n double reward_t_max = .0;\n\n const ColorTable table;\n axes axis_reward_total;\n std::vector<axes> axis_reward_systemstep;\n std::vector<axes> axis_reward_eigenstep;\n std::vector<axes> axis_reward_trial;\n std::vector<axes> axis_reward_bunch;\n\n plot1D plot_reward_total;\n plot1D plot_reward_t_avg;\n plot1D plot_reward_t_max;\n std::vector<plot1D> plot_reward_systemstep;\n std::vector<plot1D> plot_reward_eigenstep;\n std::vector<plot1D> plot_reward_trial;\n std::vector<plot1D> plot_reward_bunch;\n //std::vector<plot1D> plot_reward_trial_long;\n\n std::vector<Integrator> reward_trial;\n std::vector<Integrator> reward_bunch;\n\n};\n\nclass Policy_Selector_Graphics : public Graphics_Interface {\n const Policy_Selector& policy_selector;\npublic:\n Policy_Selector_Graphics(const Policy_Selector& policy_selector) : policy_selector(policy_selector) {}\n\n void draw(const pref& /*p*/) const {\n unsigned int time_left = policy_selector.get_trial_time_left();\n unsigned int minutes = (time_left / 6000);\n unsigned int seconds = (time_left / 100) % 60;\n unsigned int hsecs = (time_left % 100);\n glColor3f(1.0,1.0,1.0);\n glprintf(0.0,-0.05, 0.0, 0.03, \"[%u] %s\", policy_selector.current_policy\n , policy_selector.sarsa.rewards.get_reward_name(policy_selector.current_policy).c_str());\n glprintf(0.0,-0.10, 0.0, 0.03, \"left: %u:%02u:%02u [%c]\", minutes, seconds, hsecs, policy_selector.profile.play ? '+' :\n policy_selector.random_policy_mode ? '~' : '=');\n }\n};\n\n#endif // SARSA_GRAPHICS_H_INCLUDED\n\n" }, { "alpha_fraction": 0.6621403098106384, "alphanum_fraction": 0.6637568473815918, "avg_line_length": 36.719512939453125, "blob_id": "738f5f94ff01223ef32f4fc23bccf28f291cd510", "content_id": "fc1dcb3746cf604843bd2dd036ae82dca637dd19", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3093, "license_type": "no_license", "max_line_length": 129, "num_lines": 82, "path": "/src/learning/expert.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EXPERT_H_INCLUDED\n#define EXPERT_H_INCLUDED\n\n#include <memory>\n#include <common/save_load.h>\n#include <learning/gmes_constants.h>\n#include <learning/predictor.h>\n#include <control/sensorspace.h>\n\n/** TODO\n * + get 3d graphical representation, note: must provided by the underlying type\n * + in general, an expert should not care if it exists or not, this should be supervised by the expert_vector.\n * + optional: restrict transitions to have max. k connections\n */\n\nclass Expert : public common::Save_Load {\n Expert(const Expert& other) = delete; // non construction-copyable\n\npublic:\n\n Expert( Predictor_ptr predictor\n , const std::size_t max_number_of_nodes )\n : exists(false)\n , predictor(std::move(predictor))\n , learning_capacity(gmes_constants::initial_learning_capacity)\n , perceptive_width(gmes_constants::perceptive_width)\n , transition(max_number_of_nodes)\n { }\n\n Expert(Expert&& other) = default;\n Expert& operator=(Expert&& other) = default;\n\n bool learning_capacity_is_exhausted(void) const { return learning_capacity < gmes_constants::learning_capacity_exhausted; }\n double get_learning_capacity (void) const { return learning_capacity; }\n double get_prediction_error (void) const { return predictor->get_prediction_error(); }\n void adapt_weights (void) { predictor->adapt(); }\n void reinit_predictor_weights (void) { predictor->initialize_from_input(); }\n\n double update_and_get_activation (void) const {\n if (not exists) return 0.0;\n double e = predictor->get_prediction_error();\n return exp(-e*e/perceptive_width);\n }\n\n /* make prediction and update prediction error */\n double make_prediction(void) { return predictor->predict(); }\n double redo_prediction(void) { return predictor->verify(); }\n\n void clear_transitions(void) { for (auto& t : transition) t = 0.0; }\n\n void create_randomized(void) {\n exists = true;\n predictor->initialize_randomized();\n }\n\n bool exists_transition(std::size_t index) const { return transition.at(index) > gmes_constants::transition_exist_treshold; }\n void reset_transition(std::size_t index) { transition.at(index) = gmes_constants::initial_transition_validation; }\n\n Predictor_Base const& get_predictor(void) const { return *predictor; }\n Predictor_Base & set_predictor(void) { return *predictor; }\n\n bool does_exists(void) const { return exists; }\n\n //Predictor_Base::vector_t const& get_weights(void) const { return predictor->get_weights(); }\n //Predictor_Base::vector_t & set_weights(void) { return predictor->set_weights(); }\n\n\nprivate:\n bool exists;\n Predictor_ptr predictor;\n double learning_capacity;\n const double perceptive_width;\n VectorN transition; // validity of connections\n\n friend class Expert_Vector;\n friend class GMES;\n friend class GMES_Graphics;\n friend class Force_Field;\n};\n\n\n#endif // EXPERT_H_INCLUDED\n" }, { "alpha_fraction": 0.5300957560539246, "alphanum_fraction": 0.5396716594696045, "avg_line_length": 28.806121826171875, "blob_id": "00017f2f78ee6c84b5c8cc920499a66583aaf4d8", "content_id": "50828274bd92c3ea43013bc710173cc5017fa34d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 2924, "license_type": "no_license", "max_line_length": 87, "num_lines": 98, "path": "/src/control/positioncontrol.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef POSITION_CONTROL_H\n#define POSITION_CONTROL_H\n\n#include <common/log_messages.h>\n#include <robots/robot.h>\n#include <robots/joint.h>\n#include <controller/pid_control.hpp>\n\n\nnamespace control {\n\n/** A simple multi-joint PID position controller,\n based on the supreme lib PID controller */\n\nclass position_control // <- rename this... to homeostatic controller ?\n{\n robots::Jointvector_t& joints;\n\n struct JointCtrl_t {\n\n JointCtrl_t(uint8_t id) : pid(id, 0.01), enabled(true) {}\n supreme::pid_control pid;\n bool enabled;\n //TODO other?\n };\n\n std::vector<JointCtrl_t> ctrl;\n\n\n float joint_range; //TODO make a proper mapping\n // find general solution, inc. the limits of the joints\n\npublic:\n\n bool enable_unruh = false;\n\n\n position_control(robots::Robot_Interface& robot, float joint_range = 1.f)\n : joints(robot.set_joints())\n , ctrl()\n , joint_range(joint_range)\n {\n for (unsigned i = 0; i < joints.size(); ++i)\n {\n //PID setup\n ctrl.emplace_back(i);\n auto& c = ctrl.back();\n c.pid.set_pid(6.0,0,0); //tadpole\n }\n }\n\n template <typename ControlVector_t, typename ExternalControl_t>\n void execute_cycle( ControlVector_t& vec, ExternalControl_t const& ext = {}\n , const bool enable_external_control = false\n , const bool disable_internal_control = false )\n {\n assert(joints.size() <= vec.size());\n\n if (disable_internal_control)\n for (unsigned i = 0; i < vec.size(); ++i) vec[i] = 0;\n\n if (enable_external_control) { // inject external control inputs\n const std::size_t N = std::min(ext.size(), vec.size()); // match lengths\n for (unsigned i = 0; i < N; ++i)\n vec[i] += ext[i];\n }\n\n for (std::size_t i = 0; i < joints.size(); ++i) {\n ctrl[i].pid.set_target_value(joint_range * vec[i]);\n joints[i].motor = ctrl[i].enabled ? ctrl[i].pid.step(joints[i].s_ang) : 0.;\n\n // simulate simple temperature behavior\n const float a = 0.9995;\n joints[i].s_tmp = joints[i].s_tmp*a + (1-a)*joints[i].s_cur;\n\n if (joints[i].s_tmp > 1.5 && ctrl[i].enabled) {\n ctrl[i].enabled = false;\n sts_msg(\"motor %u OFF\",i);\n }\n if (joints[i].s_tmp < 0.75 && !ctrl[i].enabled) {\n ctrl[i].enabled = true;\n sts_msg(\"motor %u REACTIVATED\",i);\n }\n\n if (enable_unruh) {\n // This is an unruh controller, use if nothing else works\n float err = vec[i]-joints[i].s_ang;\n joints[i].motor += clip(1*err + joints[i].motor.get_backed(), 1);\n vec[i] = joints[i].s_ang;\n }\n }\n }\n\n};\n\n} /* namespace control */\n\n#endif /* POSITION_CONTROL_H */\n\n\n\n" }, { "alpha_fraction": 0.5292682647705078, "alphanum_fraction": 0.5292682647705078, "avg_line_length": 19.9743595123291, "blob_id": "c385aac63af22ef5f00e14f59fa4c20cbdd964a2", "content_id": "4aa195c4cfe9f9c882cb3aca3a62ab31f02332f8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 820, "license_type": "no_license", "max_line_length": 85, "num_lines": 39, "path": "/src/common/globalflag.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef GLOBALFLAG_H\r\n#define GLOBALFLAG_H\n#include <common/lock.h>\n\nclass GlobalFlag\n{\n GlobalFlag( const GlobalFlag& other ) = delete; // non construction-copyable\n GlobalFlag& operator=( const GlobalFlag& ) = delete; // non copyable\n\n common::mutex_t mtx;\n bool flag;\n\npublic:\n GlobalFlag() : mtx(), flag(false) {}\n GlobalFlag(bool flag) : mtx(), flag(flag) {}\n\n void toggle(void) {\n common::lock_t lock(mtx);\n flag = !flag;\n }\n void enable(void) {\n if (!flag) {\n common::lock_t lock(mtx);\n flag = true;\n }\n }\n void disable(void) {\n if (flag) {\n common::lock_t lock(mtx);\n flag = false;\n }\n }\n bool status(void) const\n {\n return flag;\n }\n};\n\n#endif // GLOBALFLAG\n\n" }, { "alpha_fraction": 0.7343999743461609, "alphanum_fraction": 0.7391999959945679, "avg_line_length": 31.894737243652344, "blob_id": "dd40d4c6ec5d3aac8dc1885fe5750987ec30292c", "content_id": "85ff802dc104c4f52556da0ac22085fb9d4c1f14", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 625, "license_type": "no_license", "max_line_length": 97, "num_lines": 19, "path": "/src/evolution/evaluation_interface.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef EVALUATION_INTERFACE_H_INCLUDED\n#define EVALUATION_INTERFACE_H_INCLUDED\n\n#include <vector>\n\nclass Evaluation_Interface\n{\npublic:\n typedef std::vector<double> genome_t;\n\n virtual ~Evaluation_Interface() = default;\n virtual bool evaluate(Fitness_Value &fitness, const genome_t& genome, double rand_value) = 0;\n virtual void prepare_generation(unsigned cur_generation, unsigned max_generation) = 0;\n virtual void prepare_evaluation(unsigned cur_trial, unsigned max_trial) = 0;\n\n virtual void constrain(genome_t& /*genome*/) { /* implement optionally */ };\n};\n\n#endif // EVALUATION_INTERFACE_H_INCLUDED\n" }, { "alpha_fraction": 0.6103286147117615, "alphanum_fraction": 0.6103286147117615, "avg_line_length": 23.461538314819336, "blob_id": "a588f846c590d6acef636c727362bd3fac7c3bb5", "content_id": "8663d088b9c0d8ed4fcdc262334f8f98b01667ea", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 639, "license_type": "no_license", "max_line_length": 89, "num_lines": 26, "path": "/src/control/csl.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef CSL_H\r\n#define CSL_H\r\n\n//#include \"../main.h\"\n\nclass csl\n{\n private:\n double z; // csl hidden state\n double gi; // loop gain\n double gf; // feedback gain\n double u; // output\n\n public:\n csl(void);\n csl(double *motor_output, double *angle_sensor);\n csl(double *motor_output, double *angle_sensor, double *angular_velocity_sensor);\n void loop(const double mode);\n void reset(const double initial);\n\n void set_control_parameter(const double gi, const double gf);\n void set_joint_limits(const double llower, const double lupper);\n};\n\n\n#endif // CSL\n\n" }, { "alpha_fraction": 0.5076452493667603, "alphanum_fraction": 0.5188583135604858, "avg_line_length": 22.926828384399414, "blob_id": "b6a184c5f5163c8a781da4f5f1fb6773f09f43dc", "content_id": "9d1ec51e34e418f1e75e0c6d6f75120e64c500dd", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 981, "license_type": "no_license", "max_line_length": 81, "num_lines": 41, "path": "/src/common/autopause.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/*---------------------------------+\n | Matthias Kubisch |\n | [email protected] |\n | July 2017 |\n +---------------------------------*/\n\n#ifndef AUTOPAUSE_H_INCLUDED\n#define AUTOPAUSE_H_INCLUDED\n\n/**\n * AUTO PAUSE\n * Used to pause the application automatically after certain time steps (cycles).\n * Steps have to be provided as a list.\n */\n\nclass Auto_Pause {\n\n GlobalFlag& do_pause;\n std::vector<uint64_t> times;\n std::size_t ptr;\n\npublic:\n\n Auto_Pause(GlobalFlag& do_pause, std::vector<uint64_t> const& times)\n : do_pause(do_pause)\n , times(times)\n , ptr(0)\n {\n std::sort(this->times.begin(), this->times.end());\n }\n\n void execute_cycle(uint64_t current_cycles) {\n if (ptr < times.size() and current_cycles >= times[ptr]) {\n ++ptr;\n do_pause.enable();\n sts_msg(\"Automatically paused at step %llu\", current_cycles);\n }\n }\n};\n\n#endif // AUTOPAUSE_H_INCLUDED\n" }, { "alpha_fraction": 0.5275567173957825, "alphanum_fraction": 0.5541372299194336, "avg_line_length": 31.965347290039062, "blob_id": "de5578ca3700d3ee7bbbbbd3aa1774a85b58b7da", "content_id": "f8a8635612921d93294f3065309520d311ba2a18", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 6659, "license_type": "no_license", "max_line_length": 190, "num_lines": 202, "path": "/src/learning/forcefield.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef FORCEFIELD_H_INCLUDED\n#define FORCEFIELD_H_INCLUDED\n\n#include <basic/vector3.h>\n#include <draw/axes3D.h>\n#include <draw/network3D.h>\n#include <draw/graphics.h>\n\n#include <learning/gmes.h>\n#include <learning/expert.h>\n\nnamespace ff_constants\n{\n const double mass = 0.1; // mass of one mass point 10g\n const double k_spring = 0.05;\n const double damper = 2*2*sqrt(mass * k_spring); // constant for the dampers\n const double distance_0 = 0.2;\n const double fluid_friction = 1.0 * mass;\n const double center_gravity = 0.0001;\n\n\n /* wall collision and damping */\n const double wall_damping = 0.5;\n const double wall_decay = 0.8;\n const double sticktion = 0.1;\n}\n\nclass Particle\n{\npublic:\n Particle()\n : position(random_value(-0.50,0.50),random_value(-0.50,0.50),random_value(-0.50,0.50))\n , velocity(random_value(-0.10,0.10),random_value(-0.10,0.10),random_value(-0.10,0.10))\n , force(.0)\n , mass(ff_constants::mass)\n {}\n\n void reset(void)\n {\n position = 0.0;\n velocity = 0.0;\n force = 0.0;\n }\n\n Vector3 position;\n Vector3 velocity;\n Vector3 force;\n const double mass;\n};\n\n\ninline void wall_contact(Particle& p)\n{\n\n if (p.position.x < -1.0 || p.position.x > +1.0) {\n p.velocity.x *= -ff_constants::wall_decay;\n p.velocity.y *= (fabs(p.velocity.y) > ff_constants::sticktion) * ff_constants::wall_damping;\n p.velocity.z *= (fabs(p.velocity.z) > ff_constants::sticktion) * ff_constants::wall_damping;\n }\n\n if (p.position.y < -1.0 || p.position.y > +1.0) {\n p.velocity.x *= (fabs(p.velocity.x) > ff_constants::sticktion) * ff_constants::wall_damping;\n p.velocity.y *= -ff_constants::wall_decay;\n p.velocity.z *= (fabs(p.velocity.z) > ff_constants::sticktion) * ff_constants::wall_damping;\n }\n\n if (p.position.x < -1.0 || p.position.x > +1.0) {\n p.velocity.x *= (fabs(p.velocity.x) > ff_constants::sticktion) * ff_constants::wall_damping;\n p.velocity.y *= (fabs(p.velocity.y) > ff_constants::sticktion) * ff_constants::wall_damping;\n p.velocity.z *= -ff_constants::wall_decay;\n }\n p.position.clip(1.0);\n}\n\nVector3 gravity(const Vector3& vec1, const Vector3& vec2, double g_const, double min_distance)\n{\n Vector3 force(0.0);\n double r = distance(vec1, vec2);\n\n if (r < min_distance)\n force = -g_const * (vec1 - vec2);\n else\n force = -g_const * (vec1 - vec2) / (r * r);\n\n return force;\n}\n\nVector3 repell(const Vector3& vec1, const Vector3& vec2, double g_const, double min_distance)\n{\n Vector3 force(0.0);\n double r = distance(vec1, vec2);\n double k = g_const * clip(2*min_distance - fabs(r), 0.0, 2*min_distance);// + 0.0001;\n force = k * ((vec1 - vec2)/r);\n return force;\n}\n\nclass Force_Field : public Graphics_Interface {\npublic:\n Force_Field(const GMES& gmes)\n : gmes(gmes)\n , expert(gmes.expert)\n , activations(gmes.get_activations())\n , particle(expert.size())\n , dt(0.001)\n , ff_axis(.0, .0, .0, 1., 1., 1., 0)\n , ff_graph(expert.size(), ff_axis, white)\n {\n dbg_msg(\"Creating Forcefield\");\n }\n\n void calculate_new_node_position(void) {\n std::size_t winner = gmes.get_winner();\n particle[winner].reset();\n\n std::size_t count_transitions = 0;\n Vector3 new_position = 0.0;\n new_position.random(-0.5*ff_constants::distance_0, +0.5*ff_constants::distance_0);\n\n for (std::size_t k = 0; k < expert[winner].transition.size(); ++k) {\n if (expert[winner].exists_transition(k)) {\n new_position += particle[k].position;\n ++count_transitions;\n }\n }\n new_position /= count_transitions;\n particle[winner].position = new_position;\n }\n\n void execute_cycle(uint64_t cycle)\n {\n if (gmes.has_new_node())\n calculate_new_node_position();\n\n for (unsigned int i = 0; i < expert.size(); ++i)\n {\n particle[i].force = .0; //clear\n particle[i].force += -ff_constants::fluid_friction * particle[i].velocity;\n\n if (expert[i].exists) //TODO check also if node is isolated\n {\n particle[i].force += gravity(particle[i].position, Vector3(.0), ff_constants::center_gravity, ff_constants::distance_0);\n\n for (unsigned int k = 0; k < expert.size(); ++k)\n {\n if (i != k && expert[k].exists)\n {\n double dx = distance(particle[i].position, particle[k].position);\n\n if (expert[i].exists_transition(k) || expert[k].exists_transition(i)) {\n particle[i].force += -ff_constants::k_spring * clip(dx - ff_constants::distance_0, ff_constants::distance_0) * ((particle[i].position - particle[k].position)/dx);\n //TODO add damper forces\n }\n else\n particle[i].force += repell(particle[i].position, particle[k].position, 1.0, ff_constants::distance_0);\n\n }\n }\n }\n\n particle[i].velocity += particle[i].force / particle[i].mass * dt;\n particle[i].position += particle[i].velocity * dt;\n\n wall_contact(particle[i]);\n }\n\n /* update drawings */\n for (unsigned int i = 0; i < expert.size(); ++i)\n {\n ff_graph.update_node(i,\n particle[i].position.x,\n particle[i].position.y,\n particle[i].position.z,\n fmin(2.0, expert[i].learning_capacity));\n\n for (unsigned int k = 0; k < expert.size(); ++k)\n ff_graph.update_edge(i, k, (expert[i].exists_transition(k) ? expert[i].transition[k]*192 : 0)); //TODO use color of edge to display eligibility traces\n }\n ff_graph.activated(gmes.get_winner());\n ff_graph.special(gmes.get_to_insert());\n }\n\n void draw(const pref& p) const\n {\n ff_axis .draw(p.x_angle, p.y_angle);\n ff_graph.draw(p.x_angle, p.y_angle);\n glprintf(0.8, 0.7, 0.0, 0.03, \"%u/%u\", gmes.get_number_of_experts(), gmes.get_max_number_of_experts());\n }\n\n const GMES& gmes;\n const Expert_Vector& expert;\n const VectorN& activations;\n\n std::vector<Particle> particle;\n const double dt;\n\n /* drawing */\n axes3D ff_axis;\n network3D ff_graph;\n\n};\n\n#endif // FORCEFIELD_H_INCLUDED\n" }, { "alpha_fraction": 0.6788091063499451, "alphanum_fraction": 0.6837127804756165, "avg_line_length": 34.24691390991211, "blob_id": "16e55e98f85e007cbc768b23cf2832234f2603d6", "content_id": "80525a4e4d48f1ef910d79ee4be7fee99a36ad35", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5710, "license_type": "no_license", "max_line_length": 205, "num_lines": 162, "path": "/src/robots/simloid.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef SIMLOID_H\n#define SIMLOID_H\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <string.h>\n#include <math.h>\n#include <sys/wait.h>\n#include <sys/types.h>\n#include <signal.h>\n#include <unistd.h>\n#include <errno.h>\n#include <float.h>\n#include <assert.h>\n#include <algorithm>\n#include <vector>\n\n#include <common/lock.h>\n#include <common/modules.h>\n#include <common/socket_client.h>\n#include <common/basic.h>\n#include <common/misc.h>\n#include <common/log_messages.h>\n#include <common/robot_conf.h>\n#include <robots/robot.h>\n#include <robots/joint.h>\n#include <basic/vector3.h>\n\nnamespace robots {\n\n/**TODO think about how to get simloid thread save:\n * analyze which resources are accessed simultaneously and lock only them instead of locking every method.*/\n\nclass Simloid : public Robot_Interface\n{\npublic:\n const unsigned short port;\n const unsigned int robot_ID;\n const unsigned int scene_ID;\n const bool visuals;\n const bool realtime;\n\nprivate:\n pid_t child_pid;\n common::mutex_t mtx;\n\n network::Socket_Client client;\n bool connection_established;\n bool record_frame;\n Robot_Configuration configuration;\n\n double timestamp;\n std::vector<Vector3> body_position0;\n\n Vector3 average_position;\n Vector3 average_position0;\n Vector3 average_velocity;\n double average_rotation;\n double avg_rot_inf_ang;\n double avg_rot_inf_ang_last;\n\n double avg_velocity_forward;\n double avg_velocity_left;\n\n const unsigned left_id;\n const unsigned rift_id;\n\n bool initially_fixed;\n\n bool open_connection(void);\n void close_connection(void);\n void simulation_idle(double sec);\n void set_robot_to_default_position(void);\n void init_robot(void);\n void read_robot_configuration(void);\n void read_sensor_data(void);\n void write_motor_data(void);\n void send_pause_command(void);\n void reset(void);\n void eat_server_msg(void);\n void update_avg_position(void);\n void update_avg_velocity(void);\n void update_rotation_z(void);\n void update_robot_velocity(void);\n\npublic:\n Simloid(bool interlaced_mode, unsigned short port, unsigned int robot_ID, unsigned int scene_ID, bool visuals, bool realtime = true, std::vector<double> modelparams = {}, bool initially_fixed = false);\n ~Simloid(void);\n\n bool update(void); //locking\n bool idle(void); //locking\n bool is_connected(void) const { return connection_established; }\n void restore_state(void); //locking\n void save_state(void);\n void finish(void);\n\n void set_force(std::size_t body_index, const Vector3& force) { configuration.bodies.at(body_index).force = force; }\n\n void reset_all_forces(void) {\n for (std::size_t i = 0; i < configuration.bodies.size(); ++i)\n configuration.bodies[i].force.zero();\n }\n\n /* implements the robot interface */\n bool execute_cycle(void) { return update(); }\n\n std::size_t get_number_of_joints (void) const { return configuration.number_of_joints; }\n std::size_t get_number_of_symmetric_joints(void) const { return configuration.get_number_of_symmetric_joints(); }\n std::size_t get_number_of_accel_sensors (void) const { return configuration.number_of_accels; }\n std::size_t get_number_of_bodies (void) const { return configuration.number_of_bodies; }\n\n const Jointvector_t& get_joints(void) const { return configuration.joints; }\n Jointvector_t& set_joints(void) { return configuration.joints; }\n\n const Accelvector_t& get_accels(void) const { return configuration.accels; }\n Accelvector_t& set_accels(void) { return configuration.accels; }\n\n const Bodyvector_t& get_bodies(void) const { return configuration.bodies; }\n Bodyvector_t& set_bodies(void) { return configuration.bodies; }\n\n Vector3 get_min_position(void) const;\n Vector3 get_max_position(void) const;\n Vector3 get_min_feet_pos(void) const; /** Currently, this is handcrafted for bipeds, only! */\n Vector3 get_max_feet_pos(void) const; /** Currently, this is handcrafted for bipeds, only! */\n\n const Vector3& get_avg_position(void) const { return average_position; }\n const Vector3& get_avg_velocity(void) const { return average_velocity; }\n double get_avg_rotation(void) const { return average_rotation; }\n\n double get_avg_rotation_inf_ang(void) const { return avg_rot_inf_ang; }\n double get_avg_rotational_speed(void) const { return (avg_rot_inf_ang - avg_rot_inf_ang_last)*100; }\n\n double get_avg_velocity_forward(void) const { return avg_velocity_forward; }\n double get_avg_velocity_left (void) const { return avg_velocity_left; }\n\n double get_bodyheight0 (void) const { return body_position0[0].z; }\n\n double get_normalized_mechanical_power(void) const;\n\n double get_motion_level(void) const;\n bool motion_stopped(double thrsh) const;\n bool dropped(double level = 0.5) const;\n double dx_from_origin(void) const;\n double dy_from_origin(void) const;\n\n unsigned get_body_id_by_name(const Bodyvector_t& bodies, const std::string& name) const;\n\n void record_next_frame() { record_frame = true; }\n\n uint64_t randomize_model(double rnd_amp, double growth, double friction, uint64_t inst); //locking\n\n void reinit_robot_model(std::vector<double> const& params); //locking\n void reinit_motor_model(std::vector<double> const& params); //locking\n\n void set_low_sensor_quality(bool low_quality); //locking\n\n void toggle_body_fixed(unsigned index = 0) { common::lock_t lock(mtx); client.send(\"FIXED %u\\n\", index); } //locking\n};\n\n} // namespace robots\n\n#endif /* SIMLOID_H */\n" }, { "alpha_fraction": 0.75, "alphanum_fraction": 0.7554348111152649, "avg_line_length": 23.53333282470703, "blob_id": "65543a6709b226582e48763593b951779045b707", "content_id": "d46724ca1d1bc928c196a76bc8b5b0c3f58b16c7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 368, "license_type": "no_license", "max_line_length": 57, "num_lines": 15, "path": "/src/learning/learning_machine_interface.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef LEARNING_MACHINE_INTERFACE_HPP_INCLUDED\n#define LEARNING_MACHINE_INTERFACE_HPP_INCLUDED\n\nnamespace learning {\n\nclass Learning_Machine_Interface {\npublic:\n virtual double get_learning_progress(void) const = 0;\n virtual void enable_learning(bool b) = 0;\n virtual ~Learning_Machine_Interface() {}\n};\n\n}\n\n#endif // LEARNING_MACHINE_INTERFACE_HPP_INCLUDED\n" }, { "alpha_fraction": 0.6016523241996765, "alphanum_fraction": 0.6103448271751404, "avg_line_length": 28.741453170776367, "blob_id": "557d649d156402a35be2ac14497a4cc5be43a806", "content_id": "2645164a01a53666e1f0b5ac340a85ea344f9268", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 13920, "license_type": "no_license", "max_line_length": 152, "num_lines": 468, "path": "/src/common/event_manager.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "\n#include \"./event_manager.h\"\n\nextern GlobalFlag do_quit;\nextern GlobalFlag do_pause;\nextern GlobalFlag fast_forward;\nextern GlobalFlag draw_grid;\nextern GlobalFlag screenshot;\n\nextern Visuals screen;\nextern float t_delay_ms;\n\nvoid\nquit()\n{\n static bool q = false;\n if (!q) // prevent second call\n {\n q = true;\n sts_msg(\"Sending signal to exit.\");\n do_quit.enable();\n }\n}\n\nvoid\non_button_pressed_ESCAPE(void)\n{\n sts_msg(\"Pressed key ESC. Quit.\");\n quit();\n}\n\nvoid\non_button_pressed_SPACE(void)\n{\n do_pause.toggle();\n if (do_pause.status()) sts_msg(\"Paused.\");\n else sts_msg(\"Continuing.\");\n}\n\nvoid\non_button_pressed_BACKSPACE(void)\n{\n fast_forward.toggle();\n if (fast_forward.status()) sts_msg(\"Fast mode enabled.\");\n else sts_msg(\"Fast mode disabled.\");\n}\n\nvoid\non_button_pressed_RETURN(void)\n{\n dbg_msg(\"Pressed key RETURN.\");\n}\n\nvoid\non_button_pressed_G(void)\n{\n sts_msg(\"Toggled drawing grid.\");\n draw_grid.toggle();\n}\n\nvoid\non_button_pressed_S(void)\n{\n screenshot.enable();\n}\n\nvoid\non_button_pressed_F(void)\n{\n screen.show_fps = not screen.show_fps;\n sts_msg(\"Statistics: %s\", screen.show_fps ? \"ON\":\"OFF\");\n}\n\nvoid\non_button_pressed_PAGEUP(void)\n{\n if (!screen.rotate_view)\n screen.rotate_view = true;\n else if (screen.rot_factor < 20)\n ++screen.rot_factor;\n sts_msg(\"Rotation factor: %d\", screen.rot_factor);\n}\n\nvoid\non_button_pressed_PAGEDOWN(void)\n{\n if (!screen.rotate_view)\n screen.rotate_view = true;\n else if (screen.rot_factor > -20)\n --screen.rot_factor;\n sts_msg(\"Rotation factor: %d\", screen.rot_factor);\n}\n\nvoid\non_button_pressed_PLUS(void)\n{\n if (t_delay_ms > 2) t_delay_ms *= 0.5;\n else fast_forward.enable();\n sts_msg(\"Delay: %.2f ms (ff %d)\", t_delay_ms, fast_forward.status());\n}\n\nvoid\non_button_pressed_MINUS(void)\n{\n if (fast_forward.status())\n fast_forward.disable();\n else if (t_delay_ms < 1000) t_delay_ms *= 2.0;\n sts_msg(\"Delay: %.2f ms (ff %d)\", t_delay_ms, fast_forward.status());\n}\nvoid\nEvent_Manager::on_left_mouse_button_pressed(void)\n{\n mouse_button_left.clicked = true;\n mouse_button_left.position_x = event.button.x;\n mouse_button_left.position_y = event.button.y;\n screen.rotate_view = false;\n}\n\nvoid\nEvent_Manager::on_left_mouse_button_released(void)\n{\n mouse_button_left.clicked = false;\n\n screen.x_angle += screen.x_angle_disp;\n screen.y_angle += screen.y_angle_disp;\n screen.x_angle_disp = 0.0f;\n screen.y_angle_disp = 0.0f;\n\n /* snap to grid */\n screen.x_angle = screen.snap*round(screen.x_angle/screen.snap);\n screen.y_angle = screen.snap*round(screen.y_angle/screen.snap);\n\n sts_msg(\"new pos: % 7.2f, % 7.2f\", screen.x_angle, screen.y_angle);\n}\n\nvoid\nEvent_Manager::on_right_mouse_button_pressed(void)\n{\n mouse_button_right.clicked = true;\n mouse_button_right.position_x = event.button.x;\n mouse_button_right.position_y = event.button.y;\n}\n\nvoid\nEvent_Manager::on_right_mouse_button_released(void)\n{\n mouse_button_right.clicked = false;\n screen.x_position += screen.x_position_disp;\n screen.y_position += screen.y_position_disp;\n screen.x_position_disp = 0.0f;\n screen.y_position_disp = 0.0f;\n}\n\nvoid\nEvent_Manager::on_middle_mouse_button_pressed(void)\n{\n mouse_button_middle.clicked = true;\n mouse_button_middle.position_x = event.button.x;\n mouse_button_middle.position_y = event.button.y;\n screen.reset();\n}\n\nvoid\nEvent_Manager::on_middle_mouse_button_released(void)\n{\n mouse_button_middle.clicked = false;\n}\n\nvoid\nEvent_Manager::on_mouse_wheel_up(void)\n{\n ++mouse_wheel_position;\n if (screen.zdist * visuals_defaults::zoom_factor > visuals_defaults::gl_zNear)\n screen.zdist *= visuals_defaults::zoom_factor;\n}\n\nvoid\nEvent_Manager::on_mouse_wheel_down(void)\n{\n --mouse_wheel_position;\n if (screen.zdist < 8)\n screen.zdist /= visuals_defaults::zoom_factor;\n}\n\nvoid\nEvent_Manager::handle_mouse_wheel(SDL_MouseWheelEvent const& wheel)\n{\n if (wheel.y > 0)\n on_mouse_wheel_up();\n else if (wheel.y < 0)\n on_mouse_wheel_down();\n}\n\nvoid\nEvent_Manager::handle_key_pressed(SDL_Keysym const& key)\n{\n switch (key.sym)\n {\n case SDLK_ESCAPE: on_button_pressed_ESCAPE(); break;\n case SDLK_SPACE: on_button_pressed_SPACE(); break;\n case SDLK_BACKSPACE: on_button_pressed_BACKSPACE(); break;\n case SDLK_RETURN: on_button_pressed_RETURN(); break;\n /*\n case SDLK_TAB: break;\n case SDLK_UP: break;\n case SDLK_F1: break; */\n case SDLK_g: on_button_pressed_G(); break;\n case SDLK_f: on_button_pressed_F(); break;\n case SDLK_s: on_button_pressed_S(); break;\n /*\n case SDLK_0: break;\n case SDLK_2: break;\n */\n case SDLK_PLUS: on_button_pressed_PLUS(); break;\n case SDLK_MINUS: on_button_pressed_MINUS(); break;\n\n case SDLK_PAGEUP: on_button_pressed_PAGEUP(); break;\n case SDLK_PAGEDOWN: on_button_pressed_PAGEDOWN(); break;\n\n default: break;\n }\n\n if (nullptr != user_callback.key_pressed) user_callback.key_pressed(key);\n}\n\nvoid\nEvent_Manager::handle_key_released(SDL_Keysym const& key)\n{\n switch (key.sym)\n {\n /*case SDLK_ESCAPE: break;*/\n /*case SDLK_SPACE: break;*/\n /*case SDLK_BACKSPACE: break;*/\n /*\n case SDLK_TAB: break;\n case SDLK_UP: break;\n case SDLK_F1: break;\n case SDLK_1: break;\n case SDLK_0: break;\n case SDLK_2: break;\n case SDLK_RETURN: break;\n case SDLK_PLUS: break;\n case SDLK_MINUS: break;\n\n case SDLK_PAGEUP: break;\n case SDLK_PAGEDOWN: break;\n */\n default: break;\n }\n if (nullptr != user_callback.key_released) user_callback.key_released(key);\n}\n\nvoid\nEvent_Manager::handle_mouse_button_pressed(SDL_MouseButtonEvent const& m)\n{\n switch (m.button)\n {\n case SDL_BUTTON_LEFT : on_left_mouse_button_pressed (); break;\n case SDL_BUTTON_RIGHT : on_right_mouse_button_pressed (); break;\n case SDL_BUTTON_MIDDLE : on_middle_mouse_button_pressed(); break;\n default: break;\n }\n}\n\nvoid\nEvent_Manager::handle_mouse_button_released(SDL_MouseButtonEvent const& m)\n{\n switch (m.button)\n {\n case SDL_BUTTON_LEFT : on_left_mouse_button_released (); break;\n case SDL_BUTTON_RIGHT : on_right_mouse_button_released (); break;\n case SDL_BUTTON_MIDDLE : on_middle_mouse_button_released(); break;\n default: break;\n }\n}\n\nvoid\nEvent_Manager::handle_mouse_motion(SDL_MouseMotionEvent const& m)\n{\n mouse_position_x = m.x;\n mouse_position_y = m.y;\n\n if (mouse_button_left.clicked) {\n screen.mdx = (float) (mouse_button_left.position_x - mouse_position_x) / screen.window_size_x;\n screen.mdy = (float) (mouse_button_left.position_y - mouse_position_y) / screen.window_size_y;\n screen.x_angle_disp = -90 * screen.mdx;\n screen.y_angle_disp = -90 * screen.mdy;\n //dbg_msg(\"(%1.2f,%1.2f)\", screen.mdx, screen.mdy);\n }\n\n if (mouse_button_right.clicked) {\n screen.x_position_disp = -(float) (mouse_button_right.position_x - mouse_position_x) * 2 / std::min(screen.window_size_y, screen.window_size_x);\n screen.y_position_disp = +(float) (mouse_button_right.position_y - mouse_position_y) * 2 / std::min(screen.window_size_y, screen.window_size_x);\n }\n}\n\nvoid\nEvent_Manager::handle_joystick_motion_axis(SDL_JoyAxisEvent const& j)\n{\n switch(j.axis)\n {\n case 0: joystick.x0 = (float) (+j.value / 32767.0); /*dbg_msg(\"jx0\");*/ break;\n case 1: joystick.y0 = (float) (-j.value / 32767.0); /*dbg_msg(\"jy0\");*/ break;\n case 2: joystick.x1 = (float) (+j.value / 32767.0); /*dbg_msg(\"jx1\");*/ break;\n case 3: joystick.y1 = (float) (-j.value / 32767.0); /*dbg_msg(\"jy1\");*/ break;\n default: dbg_msg(\"unknown axis %u\", j.axis); break;\n }\n if (nullptr != user_callback.joystick_motion_axis) user_callback.joystick_motion_axis(j);\n}\n\nvoid\nEvent_Manager::handle_joystick_motion_hat(SDL_JoyHatEvent const& j)\n{\n switch(j.value)\n {\n case SDL_HAT_LEFTUP : /*dbg_msg(\"L-U\");*/ break;\n case SDL_HAT_UP : /*dbg_msg(\" U\");*/ break;\n case SDL_HAT_RIGHTUP : /*dbg_msg(\"R-U\");*/ break;\n case SDL_HAT_LEFT : /*dbg_msg(\"L \");*/ break;\n case SDL_HAT_CENTERED : /*dbg_msg(\" C \");*/ break;\n case SDL_HAT_RIGHT : /*dbg_msg(\"R \");*/ break;\n case SDL_HAT_LEFTDOWN : /*dbg_msg(\"L-D\");*/ break;\n case SDL_HAT_DOWN : /*dbg_msg(\" D\");*/ break;\n case SDL_HAT_RIGHTDOWN : /*dbg_msg(\"R-U\");*/ break;\n default: dbg_msg(\"unknown hat %u\", j.hat); break;\n }\n if (nullptr != user_callback.joystick_motion_hat) user_callback.joystick_motion_hat(j);\n}\n\nvoid\nEvent_Manager::handle_joystick_button_pressed(SDL_JoyButtonEvent const& joystick)\n{\n switch (joystick.button) //IDEA: consider making names for rumblepad\n {\n case 0: dbg_msg(\"Button 0 pressed\"); break;\n case 1: dbg_msg(\"Button 1 pressed\"); break;\n case 2: dbg_msg(\"Button 2 pressed\"); break;\n case 3: dbg_msg(\"Button 3 pressed\"); break;\n case 4: dbg_msg(\"Button 4 pressed\"); break;\n case 5: dbg_msg(\"Button 5 pressed\"); break;\n case 6: dbg_msg(\"Button 6 pressed\"); break;\n case 7: dbg_msg(\"Button 7 pressed\"); break;\n case 8: dbg_msg(\"Button 8 pressed. Escape.\"); quit(); break;\n case 9: dbg_msg(\"Button 9 pressed. Toggle Pause.\"); do_pause.toggle(); break;\n case 10: dbg_msg(\"Button A pressed\"); break;\n case 11: dbg_msg(\"Button B pressed\"); break;\n default: dbg_msg(\"unknown button %u\", joystick.button); break;\n }\n\n if (nullptr != user_callback.joystick_button_pressed) user_callback.joystick_button_pressed(joystick);\n}\n\nvoid\nEvent_Manager::handle_joystick_button_released(SDL_JoyButtonEvent const& joystick)\n{\n switch (joystick.button)\n {\n case 0: break;\n case 1: break;\n case 2: break;\n case 3: break;\n case 4: break;\n case 5: break;\n case 6: break;\n case 7: break;\n case 8: break;\n case 9: break;\n case 10: break;\n case 11: break;\n default: break;\n }\n if (nullptr != user_callback.joystick_button_released) user_callback.joystick_button_released(joystick);\n}\n\nvoid\nhandle_application_quit(void)\n{\n sts_msg(\"handle application quit\");\n quit();\n}\n\nvoid\nEvent_Manager::process_events(void)\n{\n /* Grab all the events off the queue. */\n while (SDL_PollEvent(&event))\n {\n switch (event.type)\n {\n /* all possible events */\n case SDL_KEYDOWN : handle_key_pressed (event.key.keysym); break;\n case SDL_KEYUP : handle_key_released (event.key.keysym); break;\n case SDL_MOUSEBUTTONDOWN : handle_mouse_button_pressed (event.button ); break;\n case SDL_MOUSEBUTTONUP : handle_mouse_button_released (event.button ); break;\n case SDL_MOUSEMOTION : handle_mouse_motion (event.motion ); break;\n case SDL_MOUSEWHEEL : handle_mouse_wheel (event.wheel ); break;\n case SDL_JOYAXISMOTION : handle_joystick_motion_axis (event.jaxis ); break;\n case SDL_JOYHATMOTION : handle_joystick_motion_hat (event.jhat ); break;\n case SDL_JOYBUTTONDOWN : handle_joystick_button_pressed (event.jbutton ); break;\n case SDL_JOYBUTTONUP : handle_joystick_button_released(event.jbutton ); break;\n case SDL_QUIT : handle_application_quit(); break;\n default:\n /*assertion(false, \"unhandled SDL event: %u\", event.type); */ break;\n }\n } /* while */\n}\n\nvoid\nEvent_Manager::reg_usr_cb_key_pressed(Keysym_t callback_function)\n{\n if (callback_function == nullptr)\n wrn_msg(\"Could not register user callback function for 'keyboard pressed'.\");\n else\n user_callback.key_pressed = callback_function;\n}\n\nvoid\nEvent_Manager::reg_usr_cb_key_released(Keysym_t func)\n{\n if (func == nullptr)\n wrn_msg(\"Could not register user callback function for 'keyboard released'.\");\n else\n user_callback.key_released = func;\n}\n\nvoid\nEvent_Manager::reg_usr_cb_joystick_button_pressed(Joybutton_t func)\n{\n if (func == nullptr)\n wrn_msg(\"Could not register user callback function for joystick button pressed.\");\n else\n user_callback.joystick_button_pressed = func;\n}\n\nvoid\nEvent_Manager::reg_usr_cb_joystick_button_released(Joybutton_t func)\n{\n if (func == nullptr)\n wrn_msg(\"Could not register user callback function for joystick button released.\");\n else\n user_callback.joystick_button_released = func;\n}\n\nvoid\nEvent_Manager::reg_usr_cb_joystick_motion_axis(Joyaxis_t func)\n{\n if (func == nullptr)\n wrn_msg(\"Could not register user callback function for joystick motion axis.\");\n else\n user_callback.joystick_motion_axis = func;\n}\n\nvoid\nEvent_Manager::reg_usr_cb_joystick_motion_hat(Joyhat_t func)\n{\n if (func == nullptr)\n wrn_msg(\"Could not register user callback function for joystick motion hat.\");\n else\n user_callback.joystick_motion_hat = func;\n}\n\n/*void\nEvent_Manager::register_user_callback_mouse(callback_type func)\n{\n if (func == nullptr)\n wrn_msg(\"Could not register user callback function for mouse.\");\n else\n user_callback_mouse = func;\n}*/\n" }, { "alpha_fraction": 0.49237126111984253, "alphanum_fraction": 0.5343292951583862, "avg_line_length": 27.089284896850586, "blob_id": "565e0bd7f058c316796705060a75e146d96b7e23", "content_id": "2c396b4bfab5c5c9a41763d424e515aa52efcd1f", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3146, "license_type": "no_license", "max_line_length": 93, "num_lines": 112, "path": "/src/robots/simloid_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/*---------------------------------+\n | Matthias Kubisch |\n | [email protected] |\n | July 2017 |\n +---------------------------------*/\n\n#ifndef SIMLOID_GRAPHICS_H\n#define SIMLOID_GRAPHICS_H\n\n#include <robots/simloid.h>\n#include <draw/draw.h>\n#include <draw/axes.h>\n#include <draw/plot2D.h>\n\nnamespace robots {\nnamespace constants {\n const double bsize = 0.01;\n}\n\nclass Simloid_Graphics : public Graphics_Interface {\n const Simloid& simloid;\n axes axis_position;\n plot2D plot_position;\n\npublic:\n Simloid_Graphics(const Simloid& simloid)\n : simloid(simloid)\n , axis_position(.5, .5, 0.0, 1.0, 1.0, 0, \"pos xy\")\n , plot_position(1000, axis_position, colors::magenta)\n {}\n\n void draw_body_position(void) const\n {\n const Bodyvector_t& bodies = simloid.get_bodies();\n const Vector3& bodypos = simloid.get_avg_position();\n Vector3 bodyvel_absolute = simloid.get_avg_velocity();\n\n double bodyvel_forward = simloid.get_avg_velocity_forward();\n double bodyvel_left = simloid.get_avg_velocity_left();\n\n glColor3f(1.0, 1.0, 1.0);\n for (auto const& b : bodies)\n {\n draw_rect( (b.position.x - bodypos.x)\n , (b.position.y - bodypos.y)\n , constants::bsize, constants::bsize);\n }\n\n /* velocity vector */\n glColor3f(1.0, 0.75, 0.0);\n bodyvel_absolute.clip(0.5);\n draw_line(0.0, 0.0, bodyvel_absolute.x, bodyvel_absolute.y);\n\n glColor3f(0.0, 1.0, 1.0);\n draw_line(0.0, 0.0, bodyvel_left, -bodyvel_forward);\n\n }\n\n void draw_body_rotation(void) const\n {\n const double rot_norm = -simloid.get_avg_rotation();\n const double rot_inf = simloid.get_avg_rotation_inf_ang();\n const double rot_speed = -clip(simloid.get_avg_rotational_speed(), 1.0)/2;\n\n const float sin_rot = sin(rot_norm)/2;\n const float cos_rot = cos(rot_norm)/2;\n\n /* compass */\n glColor3f(1.0, 0.0, 0.0);\n draw_line(0.0, 0.0, sin_rot, cos_rot); // north\n glColor3f(0.0, 1.0, 0.0);\n draw_line(0.0, 0.0, -sin_rot, -cos_rot); // south\n\n /* speed */\n glColor3f(1.0, 0.0, 1.0);\n draw_line( sin_rot\n , cos_rot\n , sin_rot + cos_rot * rot_speed\n , cos_rot - sin_rot * rot_speed);// rot_speed\n\n glColor3f(1.0, 1.0, 1.0);\n glprintf(-.5, .5, 0.0, 0.05, \"rot: %+1.2f (%+1.2f pi)\", rot_norm/M_PI, rot_inf/M_PI);\n\n }\n\n void draw(const pref& /*p*/) const\n {\n glPushMatrix();\n glTranslatef(-0.5, 0.5, 0.);\n\n glColor4f(1.0, 1.0, 1.0, 0.25);\n draw_rect(0.0, 0.0, 1.0, 1.0);\n draw_body_position();\n draw_body_rotation();\n\n glPopMatrix();\n\n axis_position.draw();\n plot_position.draw();\n }\n\n void execute_cycle(void) {\n const Vector3& bodypos = simloid.get_avg_position();\n plot_position.add_sample(bodypos.x, bodypos.y);\n }\n\n void reset(void) { plot_position.reset(); }\n};\n\n} /* namespace robots */\n\n#endif /* SIMLOID_GRAPHICS_H */\n" }, { "alpha_fraction": 0.7262181043624878, "alphanum_fraction": 0.7262181043624878, "avg_line_length": 24.352941513061523, "blob_id": "26a344ab901d5f878dfc9d66211508be14f9cf73", "content_id": "ecceca2758ad717e45622506b538673529cdd867", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 431, "license_type": "no_license", "max_line_length": 76, "num_lines": 17, "path": "/src/learning/eigenzeit_graphics.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef LEARNING_EIGENZEIT_GRAPHICS_H\n#define LEARNING_EIGENZEIT_GRAPHICS_H\n\n#include <learning/eigenzeit.h>\n\nnamespace learning {\n\nclass Eigenzeit_Graphics : public Graphics_Interface {\n const Eigenzeit& eigenzeit;\npublic:\n Eigenzeit_Graphics(const Eigenzeit& eigenzeit) : eigenzeit(eigenzeit) {}\n void draw(const pref& /*p*/) const { /*TODO*/ }\n};\n\n} /* namespace learning */\n\n#endif /* LEARNING_EIGENZEIT_GRAPHICS_H */\n" }, { "alpha_fraction": 0.6204379796981812, "alphanum_fraction": 0.6291970610618591, "avg_line_length": 18.02777862548828, "blob_id": "fa7de8608a023a2b0c87e8d68978b11a0e00447a", "content_id": "a4729a78040ebc5f19c3f88a056837a64324b069", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 685, "license_type": "no_license", "max_line_length": 105, "num_lines": 36, "path": "/src/draw/axes.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* axes.h */\n\n#ifndef AXES_H\n#define AXES_H\n\n#include <GL/gl.h>\n#include <GL/glu.h>\n#include <GL/glut.h>\n#include <string>\n\n#include \"draw.h\"\n#include \"../common/modules.h\"\n\nclass axes\n{\n friend class plot1D;\n friend class plot2D;\n\nprivate:\n float px, py, pz;\n float width, height;\n GLfloat a[4][2];\n int flag; /* axes flag */\n unsigned int countNum;\n float max_amplitude;\n float min_amplitude;\n float font_height;\n std::string name;\n\npublic:\n axes(float x, float y, float z, float w, float h, int flags, std::string name, float def_amp = 1.0f);\n void set_fontheight(float f) { font_height = f; }\n void draw(void) const;\n};\n\n#endif /*AXES_H*/\n" }, { "alpha_fraction": 0.5374696850776672, "alphanum_fraction": 0.5733435750007629, "avg_line_length": 20.877094268798828, "blob_id": "01b048ecf6da2287ba8a781ae4ba549d1df8cb33", "content_id": "a009c8987fbb0238695b861749c35ae8cee403fe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 7833, "license_type": "no_license", "max_line_length": 98, "num_lines": 358, "path": "/src/draw/draw.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "/* draw.c */\n\n#include \"draw.h\"\n\nvoid draw_line(const GLfloat x[3], const GLfloat y[3])\n{\n glBegin(GL_LINE_STRIP);\n glVertex3fv(x);\n glVertex3fv(y);\n glEnd();\n}\n\nvoid draw_line(const float x1, const float y1, const float z1,\n const float x2, const float y2, const float z2)\n{\n glBegin(GL_LINE_STRIP);\n glVertex3f(x1, y1, z1);\n glVertex3f(x2, y2, z2);\n glEnd();\n}\n\nvoid draw_line(const float x1, const float y1,\n const float x2, const float y2)\n{\n glBegin(GL_LINE_STRIP);\n glVertex2f(x1, y1);\n glVertex2f(x2, y2);\n glEnd();\n}\n\nvoid draw_line(const Point& p1, const Point& p2)\n{\n glBegin(GL_LINE_STRIP);\n glVertex3f(p1.x, p1.y, p1.z);\n glVertex3f(p2.x, p2.y, p2.z);\n glEnd();\n}\n\nvoid draw_line2D(const GLfloat x[2], const GLfloat y[2])\n{\n glBegin(GL_LINE_STRIP);\n glVertex2fv(x);\n glVertex2fv(y);\n glEnd();\n}\n\nvoid draw_rect(const GLfloat x1[3], const GLfloat x2[3], const GLfloat x3[3], const GLfloat x4[3])\n{\n glBegin(GL_LINE_STRIP);\n glVertex3fv(x1);\n glVertex3fv(x2);\n glVertex3fv(x3);\n glVertex3fv(x4);\n glVertex3fv(x1);\n glEnd();\n}\n\nvoid draw_cube(const GLfloat x1[3], const GLfloat x2[3], const GLfloat x3[3], const GLfloat x4[3],\n const GLfloat x5[3], const GLfloat x6[3], const GLfloat x7[3], const GLfloat x8[3])\n{\n glBegin(GL_LINE_STRIP);\n glVertex3fv(x1);\n glVertex3fv(x2);\n glVertex3fv(x3);\n glVertex3fv(x4);\n glVertex3fv(x1);\n glVertex3fv(x5);\n glVertex3fv(x6);\n glVertex3fv(x7);\n glVertex3fv(x8);\n glVertex3fv(x5);\n glEnd();\n glBegin(GL_LINE_STRIP);\n glVertex3fv(x2);\n glVertex3fv(x6);\n glEnd();\n glBegin(GL_LINE_STRIP);\n glVertex3fv(x3);\n glVertex3fv(x7);\n glEnd();\n glBegin(GL_LINE_STRIP);\n glVertex3fv(x4);\n glVertex3fv(x8);\n glEnd();\n\n\n}\n\nvoid draw_wire_cube(const float x, const float y, const float z, const float size)\n{\n glPushMatrix();\n glTranslatef(x, y, z);\n glutWireCube(size);\n glPopMatrix();\n}\n\nvoid draw_solid_cube(const float x, const float y, const float z, const float size)\n{\n glPushMatrix();\n glTranslatef(x, y, z);\n glutSolidCube(size);\n glPopMatrix();\n}\n\n\nvoid draw_grid2D(const float range, const int lines)\n{\n float v0[2], v1[2];\n float F = range/lines;\n\n for (int i = -lines; i <= lines; ++i)\n {\n v0[0] = i*F;\n v0[1] = -range;\n v1[0] = i*F;\n v1[1] = +range;\n draw_line2D(v0, v1);\n\n v0[0] = -range;\n v0[1] = i*F;\n v1[0] = +range;\n v1[1] = i*F;\n draw_line2D(v0, v1);\n }\n}\n\nvoid draw_grid3D(const float range, const int LINES)\n{\n float v0[3], v1[3];\n float F = range/LINES;\n\n for (int i = -LINES; i <= LINES; i++)\n for (int j = -LINES; j <= LINES; j++)\n {\n v0[0] = i*F;\n v0[1] = j*F;\n v0[2] = -range;\n v1[0] = i*F;\n v1[1] = j*F;\n v1[2] = +range;\n draw_line(v0, v1);\n\n v0[0] = j*F;\n v0[1] = -range;\n v0[2] = i*F;\n v1[0] = j*F;\n v1[1] = +range;\n v1[2] = i*F;\n draw_line(v0, v1);\n\n v0[0] = -range;\n v0[1] = i*F;\n v0[2] = j*F;\n v1[0] = +range;\n v1[1] = i*F;\n v1[2] = j*F;\n draw_line(v0, v1);\n }\n}\n\nvoid\ndraw_fill_rect(const float size_x, const float size_y)\n{\n const float sx = size_x/2;\n const float sy = size_y/2;\n\n glBegin(GL_QUADS);\n glVertex2f( sx, sy);\n glVertex2f(-sx, sy);\n glVertex2f(-sx,-sy);\n glVertex2f( sx,-sy);\n glEnd();\n}\n\nvoid\ndraw_fill_square(const float size)\n{\n draw_fill_rect(size, size);\n}\n\nvoid\ndraw_fill_square(const float x, const float y, const float size)\n{\n glPushMatrix();\n glTranslatef(x, y, .0f);\n draw_fill_rect(size, size);\n glPopMatrix();\n}\n\nvoid draw_fill_rect(const float x, const float y, const float size_x, const float size_y)\n{\n glPushMatrix();\n glTranslatef(x, y, .0f);\n draw_fill_rect(size_x, size_y);\n glPopMatrix();\n}\n\nvoid draw_rect(const float size_x, const float size_y)\n{\n const float sx = size_x/2;\n const float sy = size_y/2;\n\n glBegin(GL_LINE_STRIP);\n glVertex2f( sx, sy);\n glVertex2f(-sx, sy);\n glVertex2f(-sx,-sy);\n glVertex2f( sx,-sy);\n glVertex2f( sx, sy);\n glEnd();\n}\n\nvoid draw_square(const float size)\n{\n draw_rect(size, size);\n}\n\nvoid draw_square(const float x, const float y, const float size)\n{\n glPushMatrix();\n glTranslatef(x, y, .0f);\n draw_rect(size, size);\n glPopMatrix();\n}\n\nvoid draw_rect(const float x, const float y, const float size_x, const float size_y)\n{\n glPushMatrix();\n glTranslatef(x, y, .0f);\n draw_rect(size_x, size_y);\n glPopMatrix();\n}\n\n\nvoid fill_rect(const GLfloat x1[3], const GLfloat x2[3], const GLfloat x3[3], const GLfloat x4[3])\n{\n glBegin(GL_TRIANGLE_STRIP);\n glVertex3fv(x2);\n glVertex3fv(x1);\n glVertex3fv(x3);\n glVertex3fv(x4);\n glEnd();\n}\n\n//GLUT_BITMAP_9_BY_15\n//GLUT_BITMAP_8_BY_13\n//GLUT_BITMAP_HELVETICA_10\n//GLUT_BITMAP_HELVETICA_18\nvoid draw_text_small(const float x, const float y, const float z, const char *str)\n{\n glRasterPos3f(x,y,z);\n for (unsigned int j = 0; j < strlen(str); j++)\n glutBitmapCharacter(GLUT_BITMAP_8_BY_13,str[j]);\n\n}\n\n\nvoid output(const GLfloat x, const GLfloat y, const GLfloat z, char *text)\n{\n char *p;\n\n glPushMatrix();\n glTranslatef(x, y, z);\n for (p = text; *p; p++)\n glutStrokeCharacter(GLUT_STROKE_ROMAN, *p);\n glPopMatrix();\n}\n\nvoid draw_text_medium(const float x, const float y, const float z, const char *str)\n{\n glRasterPos3f(x,y,z);\n for (unsigned int j = 0; j < strlen(str); j++)\n glutBitmapCharacter(GLUT_BITMAP_9_BY_15,str[j]);\n}\n\nvoid gl_msg(const float px, const float py, const float pz, const char* format, ...)\n{\n char text[256];\n va_list args;\n va_start(args, format);\n int length = vsnprintf(text, 256, format, args);\n va_end(args);\n\n if (length > 0) {\n glRasterPos3f(px, py, pz);\n for (int i = 0; i < length; ++i)\n glutBitmapCharacter(GLUT_BITMAP_HELVETICA_10, text[i]);\n }\n\n}\n\nvoid\nglprintf(float x, float y, float z, float line_height, const char* format, ...)\n{\n char text[256];\n va_list args;\n va_start(args, format);\n int length = vsnprintf(text, 256, format, args);\n va_end(args);\n\n if (length > 0)\n {\n glPushMatrix();\n glTranslatef(x, y, z);\n const float size = line_height / 128.0f;\n glScalef(size, size, size);\n glLineWidth(1.0f);\n for (int i = 0; i < length; ++i)\n glutStrokeCharacter(GLUT_STROKE_MONO_ROMAN, text[i]);\n glPopMatrix();\n }\n}\n\nvoid\nglprintc(float x, float y, float z, float line_height, const char* str)\n{\n if (strlen(str) > 0)\n {\n glPushMatrix();\n glTranslatef(x, y, z);\n const float size = line_height / 128.0;\n glScalef(size, size, size);\n glLineWidth(1.0f);\n for (unsigned int i = 0; i < strlen(str); ++i)\n glutStrokeCharacter(GLUT_STROKE_MONO_ROMAN, str[i]);\n glPopMatrix();\n }\n}\n\nvoid\nglprints(float x, float y, float z, float line_height, const std::string str)\n{\n if (str.size() > 0)\n {\n glPushMatrix();\n glTranslatef(x, y, z);\n const float size = line_height / 128.0;\n glScalef(size, size, size);\n glLineWidth(1.0f);\n for (std::size_t i = 0; i < str.size(); ++i)\n glutStrokeCharacter(GLUT_STROKE_MONO_ROMAN, str[i]);\n glPopMatrix();\n }\n}\n\nnamespace draw {\n\nvoid fill_rect(float px, float py, float dx, float dy) {\n glBegin(GL_QUADS);\n glVertex2f(px , py);\n glVertex2f(px+dx, py);\n glVertex2f(px+dx, py+dy);\n glVertex2f(px , py+dy);\n glEnd();\n}\n\n}\n\n/* draw.c */\n\n" }, { "alpha_fraction": 0.6126821041107178, "alphanum_fraction": 0.6155955195426941, "avg_line_length": 38.161075592041016, "blob_id": "40d144a378c39b53d7d25687b654564934503def", "content_id": "9b6656612cd7e337581e2febf99cf919a0a9c516", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 5835, "license_type": "no_license", "max_line_length": 138, "num_lines": 149, "path": "/src/learning/motor_predictor.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef MOTOR_PREDICTOR_H_INCLUDED\n#define MOTOR_PREDICTOR_H_INCLUDED\n\n#include <common/log_messages.h>\n#include <robots/robot.h>\n#include <control/controlparameter.h>\n#include <control/jointcontrol.h>\n#include <control/control_core.h>\n#include <learning/predictor.h>\n\n//#include <draw/display.h>\n\nnamespace learning {\n\n\nclass Motor_Predictor : public Predictor_Base {\npublic:\n Motor_Predictor( robots::Robot_Interface const& robot\n , sensor_vector const& motor_targets\n , double learning_rate\n , double random_weight_range\n , std::size_t experience_size\n , control::Control_Parameter const& parameter\n , double noise_level\n )\n : Predictor_Base(motor_targets, learning_rate, random_weight_range, experience_size)\n , robot(robot)\n , core(robot)\n , motor_targets(motor_targets)\n , params(control::turn_symmetry(robot, control::make_asymmetric(robot, parameter)))\n , params_changed(false)\n , noise_level(noise_level)\n {\n core.apply_weights(robot, params.get_parameter());\n }\n\n void copy(Predictor_Base const& other) override {\n Predictor_Base::operator=(other); // copy base members\n Motor_Predictor const& rhs = dynamic_cast<Motor_Predictor const&>(other); /**TODO definitely write a test for that crap :) */\n core = rhs.core;\n }\n\n double predict(void) override {\n core.prepare_inputs(robot);\n add_noise_to_inputs(core.input, noise_level);\n assert(!(params.is_mirrored() and params.is_symmetric()));\n core.update_outputs(robot, params.is_symmetric(), params.is_mirrored());\n //vector_tanh(core.activation);\n vector_clip(core.activation);\n return calculate_prediction_error();\n }\n\n double verify(void) override {\n assert(!(params.is_mirrored() and params.is_symmetric()));\n core.update_outputs(robot, params.is_symmetric(), params.is_mirrored());\n vector_clip(core.activation);\n return calculate_prediction_error();\n }\n\n void initialize_randomized(void) override {\n\n for (auto& w_k : core.weights)\n for (auto& w_ik : w_k)\n w_ik += random_value(-random_weight_range,+random_weight_range); // don't override weights initialized by params\n\n auto initial_experience = input.get(); /**TODO this code is the same in state predictor, move to base?*/\n for (auto& w: initial_experience)\n w += random_value(-random_weight_range, random_weight_range);\n experience.assign(experience.size(), initial_experience);\n\n prediction_error = predictor_constants::error_min;\n\n }\n\n void initialize_from_input(void) override { assert(false && \"one shot learning not supported.\"); }\n\n Predictor_Base::vector_t const& get_prediction(void) const override { return core.activation; }\n\n control::Control_Parameter const& get_controller_weights() const {\n if (params_changed) {\n params.set_from_matrix(core.weights);\n //params.print();\n params_changed = false;\n }\n return params;\n }\n\n /*\n void draw(void) const {\n float s = 2.0/core.weights.size();\n unsigned i = 0;\n for (auto const& wi : core.weights)\n draw_vector2(0.0 + s*i++, 0.0, 0.045, s, wi, 3.0);\n }\n */\n\n vector_t const& get_weights(void) const override { assert(false); return dummy; /*not implemented*/ }\n vector_t & set_weights(void) override { assert(false); return dummy; /*not implemented*/ }\n\nprivate:\n robots::Robot_Interface const& robot;\n control::Fully_Connected_Symmetric_Core core;\n sensor_vector const& motor_targets;\n\n mutable control::Control_Parameter params; // for loading, saving, buffering\n mutable bool params_changed;\n const double noise_level;\n\n VectorN dummy = {}; // remove when implementing get_weights\n\n void learn_from_input_sample(void) override {\n /** Regarding the gradient descent on the motor controller weights:\n * TODO: We handle the 'recurrent' motor connections as just ordinary inputs, this can get stuck,\n * since it learns to copy only the old motor value's input weights.\n */\n auto const& inputs = core.input;\n VectorN const& predictions = core.activation;\n std::vector<VectorN>& weights = core.weights; // non const ref\n assert(motor_targets.size() == predictions.size());\n assert(motor_targets.size() == weights.size());\n assert(inputs.size() == weights.at(0).size());\n\n for (std::size_t k = 0; k < motor_targets.size(); ++k) { // for num of motor outputs\n double err = motor_targets[k] - predictions[k];\n for (std::size_t i = 0; i < weights[k].size(); ++i) { // for num of inputs\n weights[k][i] += learning_rate * err * tanh_(predictions[k]) * inputs[i].x;\n /** This gradient is intentionally wrong, derivative of clip transfer function would be not continuous. */\n }\n }\n params_changed = true; /**TODO: move to learn_from_experience, if supported */\n }\n\n void learn_from_experience(std::size_t /*skip_idx*/) override { assert(false && \"Learning from experience is not implemented yet.\"); }\n\n void add_noise_to_inputs(std::vector<control::sym_input>& inputs, double sigma) {\n const double s = sigma/sqrt(inputs.size());\n for (auto &in : inputs) {\n const double rndval = rand_norm_zero_mean(s);\n in.x += rndval;\n in.y += rndval;\n }\n }\n\n friend class Motor_Predictor_Graphics;\n};\n\n} // namespace learning\n\n#endif // MOTOR_PREDICTOR_H_INCLUDED\n" }, { "alpha_fraction": 0.6461870670318604, "alphanum_fraction": 0.65297931432724, "avg_line_length": 32.391754150390625, "blob_id": "4094a5675dae87cffab2ccf4ffd7d8015b1c3d57", "content_id": "5fb8e8f8aff58f03ea474eb0f7ffb4a5df2777ad", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 3239, "license_type": "no_license", "max_line_length": 149, "num_lines": 97, "path": "/src/evolution/population.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include \"population.h\"\n\n\nvoid\nPopulation::initialize_from_seed(const std::vector<double>& seed) //TODO take a vector of vectors and init the population or just use load_population\n{\n for (std::size_t i = 0; i < individuals.size(); ++i)\n individuals[i].initialize_from_seed(seed);\n}\n\nbool greater_than(const Individual& ind1, const Individual& ind2) {\n if (ind1.fitness.get_number_of_evaluations() > 0 and ind2.fitness.get_number_of_evaluations() > 0)\n return (ind1.fitness > ind2.fitness);\n else return false;\n}\n\nvoid\nPopulation::sort_by_fitness(void)\n{\n std::sort(individuals.begin(), individuals.end(), greater_than);\n}\n\nvoid\nsave_population(Population& population, file_io::CSV_File<double>& csv_population)\n{\n //sts_msg(\"Saving population.\");\n for (std::size_t idx = 0; idx < population.individuals.size(); ++idx)\n csv_population.set_line(idx, population.individuals[idx].genome);\n csv_population.write();\n}\n\nvoid\nload_population(Population& population, file_io::CSV_File<double>& csv_population)\n{\n if (csv_population.read())\n {\n sts_msg(\"Loading population.\");\n for (std::size_t idx = 0; idx < population.individuals.size(); ++idx)\n csv_population.get_line(idx, population.individuals[idx].genome);\n }\n else wrn_msg(\"Could not load population.\");\n}\n\nvoid\nsave_mutation_rates(Population& population, file_io::CSV_File<double>& csv_mutation)\n{\n //sts_msg(\"Saving mutation rates.\");\n for (std::size_t idx = 0; idx < population.individuals.size(); ++idx)\n csv_mutation.set_line(idx, population.individuals[idx].mutation_rate);\n csv_mutation.write();\n}\n\nvoid\nload_mutation_rates(Population& population, file_io::CSV_File<double>& csv_mutation)\n{\n if (csv_mutation.read()) {\n sts_msg(\"Loading mutation rates.\");\n for (std::size_t idx = 0; idx < population.individuals.size(); ++idx)\n csv_mutation.get_line(idx, population.individuals[idx].mutation_rate);\n }\n else wrn_msg(\"Could not load mutation rates.\");\n}\n\nvoid\nsave_fitness_values(Population& population, file_io::CSV_File<double>& csv_fitness)\n{\n //sts_msg(\"Saving fitness values.\");\n for (std::size_t idx = 0; idx < population.individuals.size(); ++idx)\n csv_fitness.set_line(idx, population.individuals[idx].fitness.get_value_or_default());\n csv_fitness.write();\n}\n\nvoid\nload_fitness_values(Population& population, file_io::CSV_File<double>& csv_fitness)\n{\n if (csv_fitness.read()) {\n sts_msg(\"Loading fitness values.\");\n for (std::size_t idx = 0; idx < population.individuals.size(); ++idx)\n {\n double fitness = -DBL_MIN;\n csv_fitness.get_line(idx, fitness);\n population.individuals[idx].fitness.set_value(fitness);\n }\n }\n else wrn_msg(\"Could not load fitness values.\");\n}\n\nvoid\nprint_population(Population& population)\n{\n sts_msg(\"Print population:\");\n for (std::size_t i = 0; i < population.individuals.size(); ++i) {\n sts_msg(\"%2u. %1.2f\", i, population.individuals[i].fitness);\n for (std::size_t k = 0; k < population.individuals[i].genome.size(); ++k)\n sts_msg(\"genome %u %1.3f\", k, population.individuals[i].genome[k]);\n }\n}\n" }, { "alpha_fraction": 0.5801281929016113, "alphanum_fraction": 0.5942307710647583, "avg_line_length": 31.5, "blob_id": "20b896459bc3509cf75f5d40ba1e2414468323bd", "content_id": "5165a747c9813deb03e697e9562a48cf3d6b9fee", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 1560, "license_type": "no_license", "max_line_length": 95, "num_lines": 48, "path": "/src/draw/color_table.h", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#ifndef COLOR_TABLE_H_INCLUDED\n#define COLOR_TABLE_H_INCLUDED\n\n#include <vector>\n#include <random>\n#include <algorithm>\n#include <basic/color.h>\n\n\nclass ColorTable\n{\n std::vector<Color4> colors;\n unsigned int num_variations;\n unsigned int max_colors;\n float get(unsigned int x) { return (x * 1.0)/(num_variations-1); }\n\npublic:\n ColorTable(unsigned int num_variations, bool initialize_randomized = false)\n : colors()\n , num_variations(num_variations)\n , max_colors(num_variations * num_variations * num_variations)\n {\n assert(num_variations > 1);\n //unsigned int i=0;\n colors.reserve(max_colors);\n for (unsigned int r = num_variations; r-- > 0; )\n for (unsigned int g = num_variations; g-- > 0; )\n for (unsigned int b = num_variations; b-- > 0; ) {\n colors.emplace_back(get(r), get(g), get(b), 1.0);\n //dbg_msg(\"%2u: %1.2f %1.2f %1.2f\",i++, get(r), get(g), get(b));\n }\n if (initialize_randomized) randomize();\n }\n\n const Color4& get_color(unsigned int index) const { return colors[index % max_colors]; }\n\n // Color4& operator[] (std::size_t index) { return colors[index % max_colors]; }\n const Color4& operator[] (std::size_t index) const { return colors[index % max_colors]; }\n\n\n void randomize() {\n auto engine = std::default_random_engine{};\n engine.seed(time(0));\n std::shuffle(std::begin(colors), std::end(colors), engine);\n }\n};\n\n#endif // COLOR_TABLE_H_INCLUDED\n" }, { "alpha_fraction": 0.5542522072792053, "alphanum_fraction": 0.5923753380775452, "avg_line_length": 21.733333587646484, "blob_id": "429932ccce71079274ee46b457af50ea0c85dd45", "content_id": "58079d7435bb632d22cc9d3d55f0a94ada2f2674", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "C++", "length_bytes": 341, "license_type": "no_license", "max_line_length": 57, "num_lines": 15, "path": "/src/tests/motor_layer_tests.cpp", "repo_name": "ku3i/flatcat", "src_encoding": "UTF-8", "text": "#include <tests/catch.hpp>\n\n#include <learning/motor_layer.h>\n#include <tests/test_robot.h>\n\nTEST_CASE(\" motor layer \", \"[Motor_Layer]\") {\n\n Test_Robot robot(5,2);\n learning::Motor_Layer motor(robot, 10, 0.01, 1.0, 1);\n\n for (unsigned int t = 0; t < 10; ++t) {\n robot.execute_cycle();\n motor.execute_cycle();\n }\n}\n" } ]

173

Nikovit/backup

https://github.com/Nikovit/backup

7b510e1e4c2499e309d423ed4468a2402a3ef47c

fbb8402f518434672996be5f1e741750490e1862

f786c6d31d272c1155da3604f15eaa096f8d3ebc

refs/heads/master

2021-01-20T12:55:21.800413

2016-07-21T07:03:20

63,659,713

0

null

[ { "alpha_fraction": 0.5458874702453613, "alphanum_fraction": 0.550000011920929, "avg_line_length": 40.80995559692383, "blob_id": "2477fe616d8beae17c9d33b8f7cd8b05bb557293", "content_id": "390658678e6fb9246a41cc47641807c0295287d7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 9240, "license_type": "no_license", "max_line_length": 122, "num_lines": 221, "path": "/ftpsync/pyftpsync.py", "repo_name": "Nikovit/backup", "src_encoding": "UTF-8", "text": "# -*- coding: iso-8859-1 -*-\n\"\"\"\nSimple folder synchronization using FTP.\n\n(c) 2012-2015 Martin Wendt; see https://github.com/mar10/pyftpsync\nLicensed under the MIT license: http://www.opensource.org/licenses/mit-license.php\n\nUsage examples:\n > pyftpsync.py --help\n > pyftpsync.py upload . ftps://example.com/myfolder\n\"\"\"\nfrom __future__ import print_function\n\nfrom pprint import pprint\n\nfrom ftpsync import __version__\nfrom ftpsync.targets import make_target, FsTarget\n\nfrom ftpsync.synchronizers import UploadSynchronizer, \\\n DownloadSynchronizer, BiDirSynchronizer, DEFAULT_OMIT\n\n\n#def disable_stdout_buffering():\n# \"\"\"http://stackoverflow.com/questions/107705/python-output-buffering\"\"\"\n# # Appending to gc.garbage is a way to stop an object from being\n# # destroyed. If the old sys.stdout is ever collected, it will\n# # close() stdout, which is not good.\n# gc.garbage.append(sys.stdout)\n# sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 0)\n#disable_stdout_buffering()\ntry:\n import argparse\nexcept ImportError:\n print(\"argparse missing (requires 2.7+, 3.2+ or pip/easy_install)\")\n raise\n\n\n\n\ndef namespace_to_dict(o):\n \"\"\"Convert an argparse namespace object to a dictionary.\"\"\"\n d = {}\n for k, v in o.__dict__.items():\n if not callable(v):\n d[k] = v\n return d\n\n\n#===============================================================================\n# run\n#===============================================================================\ndef run():\n parser = argparse.ArgumentParser(\n description=\"Synchronize folders over FTP.\",\n epilog=\"See also https://github.com/mar10/pyftpsync\"\n )\n\n qv_group = parser.add_mutually_exclusive_group()\n qv_group.add_argument(\"--verbose\", \"-v\", action=\"count\", default=3,\n help=\"increment verbosity by one (default: %(default)s, range: 0..5)\")\n qv_group.add_argument(\"--quiet\", \"-q\", action=\"count\", default=0,\n help=\"decrement verbosity by one\")\n\n parser.add_argument(\"--version\", action=\"version\", version=\"%s\" % __version__)\n parser.add_argument(\"--progress\", \"-p\",\n action=\"store_true\",\n default=False,\n help=\"show progress info, even if redirected or verbose < 3\")\n\n subparsers = parser.add_subparsers(help=\"sub-command help\")\n\n def __add_common_sub_args(parser):\n parser.add_argument(\"local\",\n metavar=\"LOCAL\",\n# required=True,\n default=\".\",\n help=\"path to local folder (default: %(default)s)\")\n parser.add_argument(\"remote\",\n metavar=\"REMOTE\",\n help=\"path to remote folder\")\n# upload_parser.add_argument(\"--dry-run\",\n# action=\"store_true\",\n# help=\"just simulate and log results; don't change anything\")\n parser.add_argument(\"-x\", \"--execute\",\n action=\"store_false\", dest=\"dry_run\", default=True,\n help=\"turn off the dry-run mode (which is ON by default), \"\n \"that would just print status messages but does \"\n \"not change anything\")\n parser.add_argument(\"-f\", \"--include-files\",\n help=\"wildcard for file names (default: all, \"\n \"separate multiple values with ',')\")\n parser.add_argument(\"-o\", \"--omit\",\n help=\"wildcard of files and directories to exclude (applied after --include)\")\n parser.add_argument(\"--store-password\",\n action=\"store_true\",\n help=\"save password to keyring if login succeeds\")\n parser.add_argument(\"--no-prompt\",\n action=\"store_true\",\n help=\"prevent prompting for missing credentials\")\n parser.add_argument(\"--no-color\",\n action=\"store_true\",\n help=\"prevent use of ansi terminal color codes\")\n\n # Create the parser for the \"upload\" command\n upload_parser = subparsers.add_parser(\"upload\",\n help=\"copy new and modified files to remote folder\")\n __add_common_sub_args(upload_parser)\n\n upload_parser.add_argument(\"--force\",\n action=\"store_true\",\n help=\"overwrite remote files, even if the target is newer (but no conflict was detected)\")\n upload_parser.add_argument(\"--resolve\",\n default=\"skip\",\n choices=[\"local\", \"skip\", \"ask\"],\n help=\"conflict resolving strategy (default: '%(default)s')\")\n upload_parser.add_argument(\"--delete\",\n action=\"store_true\",\n help=\"remove remote files if they don't exist locally\")\n upload_parser.add_argument(\"--delete-unmatched\",\n action=\"store_true\",\n help=\"remove remote files if they don't exist locally \"\n \"or don't match the current filter (implies '--delete' option)\")\n\n upload_parser.set_defaults(command=\"upload\")\n\n\n # Create the parser for the \"download\" command\n download_parser = subparsers.add_parser(\"download\",\n help=\"copy new and modified files from remote folder to local target\")\n __add_common_sub_args(download_parser)\n\n download_parser.add_argument(\"--force\",\n action=\"store_true\",\n help=\"overwrite local files, even if the target is newer (but no conflict was detected)\")\n download_parser.add_argument(\"--resolve\",\n default=\"skip\",\n choices=[\"remote\", \"skip\", \"ask\"],\n help=\"conflict resolving strategy (default: '%(default)s')\")\n download_parser.add_argument(\"--delete\",\n action=\"store_true\",\n help=\"remove local files if they don't exist on remote target\")\n download_parser.add_argument(\"--delete-unmatched\",\n action=\"store_true\",\n help=\"remove local files if they don't exist on remote target \"\n \"or don't match the current filter (implies '--delete' option)\")\n\n download_parser.set_defaults(command=\"download\")\n\n # Create the parser for the \"sync\" command\n sync_parser = subparsers.add_parser(\"sync\",\n help=\"synchronize new and modified files between remote folder and local target\")\n __add_common_sub_args(sync_parser)\n\n sync_parser.add_argument(\"--resolve\",\n default=\"ask\",\n choices=[\"old\", \"new\", \"local\", \"remote\", \"skip\", \"ask\"],\n help=\"conflict resolving strategy (default: '%(default)s')\")\n\n sync_parser.set_defaults(command=\"synchronize\")\n\n # Parse command line\n args = parser.parse_args()\n\n if not hasattr(args, \"command\"):\n parser.error(\"missing command (choose from 'upload', 'download', 'sync')\")\n\n # Post-process and check arguments\n args.verbose -= args.quiet\n del args.quiet\n\n if hasattr(args, \"delete_unmatched\") and args.delete_unmatched:\n args.delete = True\n\n ftp_debug = 0\n if args.verbose >= 5:\n ftp_debug = 1\n\n args.local_target = make_target(args.local, {\"ftp_debug\": ftp_debug})\n\n if args.remote == \".\":\n parser.error(\"'.' is expected to be the local target (not remote)\")\n args.remote_target = make_target(args.remote, {\"ftp_debug\": ftp_debug})\n if not isinstance(args.local_target, FsTarget) and isinstance(args.remote_target, FsTarget):\n parser.error(\"a file system target is expected to be local\")\n\n # Let the command handler do its thing\n opts = namespace_to_dict(args)\n if args.command == \"upload\":\n s = UploadSynchronizer(args.local_target, args.remote_target, opts)\n elif args.command == \"download\":\n s = DownloadSynchronizer(args.local_target, args.remote_target, opts)\n elif args.command == \"synchronize\":\n s = BiDirSynchronizer(args.local_target, args.remote_target, opts)\n else:\n parser.error(\"unknown command %s\" % args.command)\n\n try:\n s.run()\n except KeyboardInterrupt:\n print(\"\\nAborted by user.\")\n return\n finally:\n # prevent sporadic exceptions in ftplib, when closing in __del__\n s.local.close()\n s.remote.close()\n\n stats = s.get_stats()\n if args.verbose >= 4:\n pprint(stats)\n elif args.verbose >= 1:\n if args.dry_run:\n print(\"(DRY-RUN) \", end=\"\")\n print(\"Wrote %s/%s files in %s dirs. Elap: %s\"\n % (stats[\"files_written\"], stats[\"local_files\"], stats[\"local_dirs\"], stats[\"elap_str\"]))\n\n return\n\n\n# Script entry point\nif __name__ == \"__main__\":\n run()\n" }, { "alpha_fraction": 0.707952618598938, "alphanum_fraction": 0.7184433341026306, "avg_line_length": 27.57281494140625, "blob_id": "430ac348893cd4dce7270cd507f568bfd63c024b", "content_id": "9c426c1daa98763c7dec31c97fd1a6b2a8d9ce3a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3430, "license_type": "no_license", "max_line_length": 139, "num_lines": 103, "path": "/PostgreSQL_bacup2.py", "repo_name": "Nikovit/backup", "src_encoding": "UTF-8", "text": "#!/usr/local/bin/python3.5\n# -*- coding: utf-8 -*-\n\nimport os\nimport subprocess\nimport time\nimport logging\nimport sys\nimport datetime\nimport stat\nfrom os import listdir\nfrom os.path import isfile, join\nfrom datetime import timedelta, datetime\nimport os.path, time\nimport ftplib\n\n#Логирование\n# импортируем модуль\nimport logging\n# создаём объект с именем модуля\nlogger = logging.getLogger(__name__)\n# задаём уровень логгирования\nlogger.setLevel(logging.INFO)\n# создаём обрабочтик файла лога\nhandler = logging.FileHandler('backup.log')\n# задаём уровень логгирования\nhandler.setLevel(logging.INFO)\n# форматируем записи\nformatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\n# устанавливаем формат для обработчика\nhandler.setFormatter(formatter)\n# добавляем обработчик к логгеру\nlogger.addHandler(handler)\n\n\n#Создание дампов PostgreSQL\n#database = ['trade', 'buh2bd', 'salary']\ndatabase = ['template0']\nbackupdir='/home/garbage/backup/'\ndate = time.strftime('%Y-%m-%d')\n\nfor backup in database:\n subprocess.call('cd %s | pg_dump -U postgres %s | gzip -9 -c > %s/%s-%s.gz' % (backupdir, backup, backupdir, date, backup), shell=True)\n #filename = '%s%s-%s.gz' % (backupdir, date, backup) - не помню зачем мне это нужно\n logger.info(backup + ' дамп создан и заархивирован')\nelse:\n logger.error(backup + ' дамп не создан')\n\n\n\n# Удаление старых копий\nremoval_period=timedelta(days=30)\n\nonlyfiles = [f for f in listdir(backupdir) if isfile(join(backupdir, f))]\n\nfor filename in onlyfiles:\n how_long_ago_creation_date = datetime.now()-datetime.fromtimestamp(os.path.getctime(backupdir+filename))\n print(filename+\" created \"+str(how_long_ago_creation_date)+\" ago\")\n if (how_long_ago_creation_date>removal_period):\n print(\"Delete file: \"+filename)\n os.remove(backupdir+filename)\n\n\n#####Синхронизация с FTP######\n\n# # #Настройки FTP\n# host = '192.168.0.26'\n# ftp_user = 'buuser'\n# ftp_password = 'buuserpwd'\n# REMOTE_FOLDER = '1C8'\n# LOCAL_FOLDER = '/home/garbage/backup/'\n#\n#\n# #соединяемся с сервером\n# server = ftplib.FTP(host)\n# server.login(ftp_user, ftp_password)\n#\n# #делаем текущими папки для синхронизации\n# server.cwd(REMOTE_FOLDER)\n# os.chdir(LOCAL_FOLDER)\n#\n# #получаем список файлов с синхронизируемых папках\n# remote_files = set(server.nlst())\n# local_files = set(os.listdir(os.curdir))\n#\n# #загружаем недостающие файлы на ftp\n# for local_file in local_files - remote_files:\n# server.storbinary('STOR ' + local_file, open(local_file, 'rb'))\n#\n# #закрываем соединение с сервером\n# server.close()\n\nfrom ftpsync.targets import FsTarget\nfrom ftpsync.ftp_target import FtpTarget\nfrom ftpsync.synchronizers import BiDirSynchronizer\n\nlocal = FsTarget(\"/home/garbage/backup/\")\nuser =\"buuser\"\npasswd = \"buuserpwd\"\nremote = FtpTarget(\"1C8\", \"192.168.0.26\", user, passwd, tls=True)\nopts = {\"resolve\": \"skip\", \"verbose\": 1, \"dry_run\" : False}\ns = BiDirSynchronizer(local, remote, opts)\ns.run()\n\n\n\n\n\n\n\n\n\n\n\n\n" } ]

2

fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV

https://github.com/fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV

f3a31591963fe8cc9b9f09c88f096411af07420a

80d43859b5b0efc487629a1f683acc97835fbf3f

cb57a29dbb6a4e923eaa1fb1b7b17a77b7c9700a

refs/heads/main

2023-05-07T19:21:30.291433

2021-06-05T17:09:26

374,027,439

0

null

[ { "alpha_fraction": 0.698630154132843, "alphanum_fraction": 0.7397260069847107, "avg_line_length": 26.25, "blob_id": "52108a61c802cd3c8884dee29d13806b200aabda", "content_id": "8cd16189154e35d13e717d1e3a1d0dd3f1c39fbe", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 219, "license_type": "no_license", "max_line_length": 54, "num_lines": 8, "path": "/Deteccao/teste_opencv.py", "repo_name": "fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV", "src_encoding": "UTF-8", "text": "import cv2\nprint(cv2.__version__)\n\nimagem = cv2.imread('opencv-python.jpg')\nimagem_gray = cv2.cvtColor(imagem, cv2.COLOR_BGR2GRAY)\ncv2.imshow('Original', imagem)\ncv2.imshow('Original Cinza', imagem_gray)\ncv2.waitKey()\n\n" }, { "alpha_fraction": 0.6510066986083984, "alphanum_fraction": 0.6923937201499939, "avg_line_length": 37.826087951660156, "blob_id": "05629b15b622c34312b7d43212a2e0f0f147df5b", "content_id": "0496dca786992ba48b489839147557d8cee5ffee", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 894, "license_type": "no_license", "max_line_length": 110, "num_lines": 23, "path": "/Deteccao/exemplo2.py", "repo_name": "fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV", "src_encoding": "UTF-8", "text": "import cv2\n\nclassify_face = cv2.CascadeClassifier('cascades\\\\haarcascade_frontalface_default.xml')\nclassify_eye = cv2.CascadeClassifier('cascades\\\\haarcascade_eye.xml')\n\nimage = cv2.imread('pessoas\\\\beatles.jpg')\nimage_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nfaces_detected = classify_face.detectMultiScale(image_gray, scaleFactor=1.1, minNeighbors=9, minSize=(30, 30))\n\nfor (x, y, w, h) in faces_detected:\n image = cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 2)\n\n face = image[y:y+h, x:x+w]\n face_gray = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n eye_detected = classify_eye.detectMultiScale(face_gray, scaleFactor=1.1, minNeighbors=2)\n\n for(ox, oy, ow, oh) in eye_detected:\n cv2.rectangle(face, (ox, oy), (ox + ow, oy + oh), (0, 190, 0), 2)\n\n\n# cv2.imshow('Faces e olhos detectados', face_gray)\ncv2.imshow('Faces e olhos detectados', image)\ncv2.waitKey()\n\n" }, { "alpha_fraction": 0.6567901372909546, "alphanum_fraction": 0.7061728239059448, "avg_line_length": 26, "blob_id": "bbc73764f952d97d1ac69da33115d000b5684994", "content_id": "0b9b9040597f335abd51f5d66ccfff3e0485f6a8", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 405, "license_type": "no_license", "max_line_length": 88, "num_lines": 15, "path": "/Deteccao/exemplo5.py", "repo_name": "fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV", "src_encoding": "UTF-8", "text": "import cv2\nclassify_clock = cv2.CascadeClassifier('cascades\\\\relogios.xml')\n\nimage = cv2.imread('outros\\\\relogio2.jpg')\nimage_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\ndetected = classify_clock.detectMultiScale(image_gray, scaleFactor=1.01, minNeighbors=5)\n\nfor (x, y, w, h) in detected:\n cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)\n\n\n\ncv2.imshow('Clock image', image)\ncv2.waitKey()\n" }, { "alpha_fraction": 0.6611111164093018, "alphanum_fraction": 0.7037037014961243, "avg_line_length": 34.86666488647461, "blob_id": "c041e9032bcc1e1e847b98ab326eb90b9dd273df", "content_id": "e659305bde963ee20593815f4d792ccbdbbc50b7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 540, "license_type": "no_license", "max_line_length": 110, "num_lines": 15, "path": "/Deteccao/exemplo1.py", "repo_name": "fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV", "src_encoding": "UTF-8", "text": "import cv2\n\nclassify_face = cv2.CascadeClassifier('cascades\\\\haarcascade_frontalface_default.xml')\nimage = cv2.imread('pessoas\\\\pessoas2.jpg')\nimage_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nfaces_detected = classify_face.detectMultiScale(image_gray, scaleFactor=1.1, minNeighbors=9, minSize=(30, 30))\n# print(len(faces_detected))\n# print(faces_detected)\n\nfor (x, y, w, h) in faces_detected:\n # print(x, y, w, h)\n cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 2)\n\ncv2.imshow('Faces encontradas', image)\ncv2.waitKey()\n\n\n" }, { "alpha_fraction": 0.6419098377227783, "alphanum_fraction": 0.692307710647583, "avg_line_length": 24.133333206176758, "blob_id": "fc5e4a057cbad230ade8f88911b6f1b72b5975cb", "content_id": "328cf579c7f77e142b7c53e913b4178dc0470fd2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 377, "license_type": "no_license", "max_line_length": 70, "num_lines": 15, "path": "/Deteccao/exemplo6.py", "repo_name": "fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV", "src_encoding": "UTF-8", "text": "import cv2\nclassify_car = cv2.CascadeClassifier('cascades\\\\cars.xml')\n\nimage = cv2.imread('outros\\\\carro3.jpg')\nimage_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\ndetected = classify_car.detectMultiScale(image_gray, scaleFactor=1.01)\n\nfor (x, y, w, h) in detected:\n cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)\n\n\n\ncv2.imshow('Car image', image)\ncv2.waitKey()\n" }, { "alpha_fraction": 0.6659038662910461, "alphanum_fraction": 0.709382176399231, "avg_line_length": 28.133333206176758, "blob_id": "1b70a0157cc83256b22d599484094cd873b64205", "content_id": "6698e42ac13440daa8865f473bc42af1a69e9ab5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 437, "license_type": "no_license", "max_line_length": 80, "num_lines": 15, "path": "/Deteccao/detect_cats.py", "repo_name": "fabricioramosdev/Deteccao-de-Faces-com-Python-e-OpenCV", "src_encoding": "UTF-8", "text": "import cv2\nclassify_cat = cv2.CascadeClassifier('cascades\\\\haarcascade_frontalcatface.xml')\n\ncat_image = cv2.imread('animais\\\\gato2.jpg')\ncat_image_gray = cv2.cvtColor(cat_image, cv2.COLOR_BGR2GRAY)\n\ncat_detected = classify_cat.detectMultiScale(cat_image_gray, scaleFactor=1.08)\n\nfor (x, y, w, h) in cat_detected:\n cv2.rectangle(cat_image, (x, y), (x+w, y+h), (0, 255, 0), 2)\n\n\n\ncv2.imshow('Cat image gray', cat_image)\ncv2.waitKey()\n" } ]

6

thedongregga/wayback-machine-scraper

https://github.com/thedongregga/wayback-machine-scraper

31625666ee324750f79f7541869c3f34dfa5c064

32ba9503fa8438ee75d16909911821d6ca336e8f

2dc355ba0ef9b3bf89d6c4cc044f91b280924545

refs/heads/master

2023-03-06T23:36:53.193039

2021-02-15T19:00:15

null

0

null

[ { "alpha_fraction": 0.6047928333282471, "alphanum_fraction": 0.6121039986610413, "avg_line_length": 34.17142868041992, "blob_id": "2f12aa8f4d8c4c33892c4d01d587dbae1e6dfba7", "content_id": "89624fabc8f293226285184709a91c86d1e6c1a0", "detected_licenses": [ "ISC" ], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 2462, "license_type": "permissive", "max_line_length": 102, "num_lines": 70, "path": "/wayback_machine_scraper/mirror_spider.py", "repo_name": "thedongregga/wayback-machine-scraper", "src_encoding": "UTF-8", "text": "import os\nfrom datetime import datetime\n\ntry:\n from urllib.parse import quote_plus\nexcept ImportError:\n from urllib import quote_plus\n\nfrom scrapy.spiders import CrawlSpider, Rule\nfrom scrapy.linkextractors import LinkExtractor\n\nfrom scrapy_wayback_machine import WaybackMachineMiddleware\n\n\nclass MirrorSpider(CrawlSpider):\n name = 'mirror_spider'\n handle_httpstatus_list = [404]\n\n def __init__(self, domains, directory, allow=(), deny=(), unix=False):\n self.directory = directory\n self.unix = unix\n self.rules = (\n Rule(LinkExtractor(allow=allow, deny=deny), callback='save_page'),\n )\n\n # parse the allowed domains and start urls\n self.allowed_domains = []\n self.start_urls = []\n for domain in domains:\n url_parts = domain.split('://')\n unqualified_url = url_parts[-1]\n url_scheme = url_parts[0] if len(url_parts) > 1 else 'http'\n full_url = '{0}://{1}'.format(url_scheme, unqualified_url)\n bare_domain = unqualified_url.split('/')[0]\n self.allowed_domains.append(bare_domain)\n self.start_urls.append(full_url)\n\n super().__init__()\n\n def parse_start_url(self, response):\n # scrapy doesn't call the callbacks for the start urls by default,\n # this overrides that behavior so that any matching callbacks are called\n for rule in self._rules:\n if rule.link_extractor._link_allowed(response):\n if rule.callback:\n rule.callback(response)\n\n def save_page(self, response):\n # ignore 404s\n if response.status == 404:\n return\n\n # make the parent directory\n url_parts = response.url.split('://')[1].split('/')\n if os.name == 'nt':\n url_parts = [quote_plus(url_part) for url_part in url_parts]\n parent_directory = os.path.join(self.directory, *url_parts)\n os.makedirs(parent_directory, exist_ok=True)\n\n # construct the output filename\n time = response.meta['wayback_machine_time']\n if self.unix:\n filename = '{0}.snapshot'.format(time.timestamp())\n else:\n filename = '{0}.snapshot'.format(time.strftime(WaybackMachineMiddleware.timestamp_format))\n full_path = os.path.join(parent_directory, filename)\n\n # write out the file\n with open(full_path, 'wb') as f:\n f.write(response.body)\n" } ]

1

infknight/SILT

https://github.com/infknight/SILT

8852f9a630eb358e4a3fb33848b5f9de1797c38e

c581d111c10ba8a0c34aa77f7413974102344c69

1ec45da2851de4c285da9006d0c03127452ddabc

refs/heads/main

2023-03-03T03:33:39.566822

2021-02-12T18:07:48

338,394,418

0

null

[ { "alpha_fraction": 0.6249029636383057, "alphanum_fraction": 0.634699285030365, "avg_line_length": 37.20143127441406, "blob_id": "ff5733a33ea37982f1132ca424aba8d627bb6d99", "content_id": "122f20053b9c567a031dfc70c3518278b2eb54ee", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 37361, "license_type": "no_license", "max_line_length": 161, "num_lines": 978, "path": "/app.py", "repo_name": "infknight/SILT", "src_encoding": "UTF-8", "text": "from flask import Flask, render_template, url_for, flash, redirect, request, session, make_response\nfrom flask_wtf.file import FileField, FileAllowed\nfrom flask_sqlalchemy import SQLAlchemy\nfrom datetime import datetime\nfrom flask_bcrypt import Bcrypt\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, PasswordField, SubmitField, BooleanField, TextAreaField\nfrom wtforms.validators import DataRequired, Length, ValidationError, EqualTo, Email\nfrom flask_login import LoginManager, UserMixin, login_user, current_user, logout_user, login_required\nfrom PIL import Image\nimport re\nimport secrets\nimport os\nfrom flask import Flask, redirect, url_for\nimport time\nimport requests\nimport json\nimport pandas as pd\nimport folium\nimport urllib.parse\nfrom requests_oauthlib import OAuth1\nimport tweepy\n\napp = Flask(__name__)\n\n# this is the serects numbers\napp.config['SECRET_KEY'] = 'ea7b11f0714027a81e7f81404612d80d'\n\n# how to add the\n# DB_URL = 'postgresql+psycopg2://jasonjia:[email protected]/SILT_DB'.format(user=POSTGRES_USER,pw=POSTGRES_PW,url=POSTGRES_URL,db=POSTGRES_DB)\n# DB_URL1 = 'postgresql://jasonjia:[email protected]:5432/SILT_DB_test'\nDB_URL1 = 'postgresql://doadmin:jglyvd028l8ced6h@db-silt-db-do-user-8284135-0.b.db.ondigitalocean.com:25060/defaultdb'\napp.config['SQLALCHEMY_DATABASE_URI']=DB_URL1\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False # silence the deprecation warning\n\ndb = SQLAlchemy(app)\nbcrypt = Bcrypt(app)\nlogin_manager = LoginManager(app)\nlogin_manager.login_view = 'login'\n# bootstrap color\nlogin_manager.login_message_category = 'info'\n\n@login_manager.user_loader\ndef load_user(user_id):\n return User.query.get(int(user_id))\n\nclass User(db.Model, UserMixin):\n id = db.Column(db.Integer, primary_key = True)\n email = db.Column(db.String(180), unique = True, nullable = False)\n twitter_username = db.Column(db.String(50), unique=True, default = None)\n username = db.Column(db.String(30), unique = True, nullable = False)\n password = db.Column(db.String(), nullable = False)\n user_pic = db.Column(db.String(20), nullable = False, default='default.jpg')\n posts = db.relationship('Post', backref='author', lazy = True)\n posts_ac = db.relationship('Post_ac', backref='author', lazy = True)\n post_h = db.relationship('Post_h', backref='author', lazy = True)\n post_sp = db.relationship('Post_sp', backref='author', lazy = True)\n post_cr = db.relationship('Post_cr', backref='author', lazy = True)\n post_ev = db.relationship('Post_ev', backref='author', lazy = True)\n spotifyartist = db.relationship('SpotifyArtist', backref='author', lazy = True)\n\n def __init__(self, email, username, password):\n self.email = email\n self.username = username\n self.password = password\n\n def __repr__ (self):\n return f\"User('{self.username}', '{self.email}', '{self.user_pic}', '{self.id}')\"\n\n\nclass Post(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n title = db.Column(db.String(50), nullable = False)\n post_time = db.Column(db.DateTime, nullable = False, default=datetime.utcnow)\n content = db.Column(db.String, nullable = False)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n\n def __repr__(self):\n return f\"Post('{self.tile}', '{self.post_time}', '{self.content}')\"\n\n\nclass Post_ac(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n title = db.Column(db.String(50), nullable = False)\n post_time = db.Column(db.DateTime, nullable = False, default=datetime.utcnow)\n content = db.Column(db.String, nullable = False)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n\n def __repr__(self):\n return f\"Post('{self.tile}', '{self.post_time}', '{self.content}')\"\n\n\nclass Post_h(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n title = db.Column(db.String(50), nullable = False)\n post_time = db.Column(db.DateTime, nullable = False, default=datetime.utcnow)\n content = db.Column(db.String, nullable = False)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n\n def __repr__(self):\n return f\"Post('{self.tile}', '{self.post_time}', '{self.content}')\"\n\nclass Post_sp(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n title = db.Column(db.String(50), nullable = False)\n post_time = db.Column(db.DateTime, nullable = False, default=datetime.utcnow)\n content = db.Column(db.String, nullable = False)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n\n def __repr__(self):\n return f\"Post('{self.tile}', '{self.post_time}', '{self.content}')\"\n\n\nclass Post_cr(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n title = db.Column(db.String(50), nullable = False)\n post_time = db.Column(db.DateTime, nullable = False, default=datetime.utcnow)\n content = db.Column(db.String, nullable = False)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n\n def __repr__(self):\n return f\"Post('{self.tile}', '{self.post_time}', '{self.content}')\"\n\n\n\nclass Post_ev(db.Model):\n id = db.Column(db.Integer, primary_key = True)\n title = db.Column(db.String(50), nullable = False)\n post_time = db.Column(db.DateTime, nullable = False, default=datetime.utcnow)\n content = db.Column(db.String, nullable = False)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n\n def __repr__(self):\n return f\"Post('{self.tile}', '{self.post_time}', '{self.content}')\"\n\n\n\n\n\nclass SpotifyArtist(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n artist_name = db.Column(db.String(2000), nullable=False)\n artist_id = db.Column(db.String(2000), nullable=False)\n # time_range = db.Column(db.String(15), nullable=False)\n\n def __repr__(self):\n return f\"SpotifyArtist('{self.artist_name}', '{self.artist_id}')\"\n\n\n# do not change this\n# from form import account, LoginForm, update_account, PostForm, spotify_profile\n\n####################\n## FORMS ##\n####################\n\nclass account(FlaskForm):\n # user name not null and not too long. Add validation\n username = StringField('Username', validators=[DataRequired(), Length(min = 2, max = 30)])\n email = StringField('Email', validators=[DataRequired(), Length(min = 6), Email()])\n\n password = PasswordField('Password', validators=[DataRequired()])\n confirmed_password = PasswordField('Confirm Password', validators=[DataRequired(), EqualTo('password')])\n submit = SubmitField('Sign Up')\n # def tamu_email_validate(self, form, field):\n # # [A-Za-z0-9] firt character to match it.\n # if not re.search(r\"^[A-Za-z0-9](\\.?[a-z0-9]){5,}@tamu\\.edu$\", field.data):\n # raise ValidationError(\"Invalid Email Address\")\n # return True\n # def validate_email(self, email):\n # # [A-Za-z0-9] firt character to match it.\n # if not re.search(r\"^[A-Za-z0-9](\\.?[a-z0-9]){5,}@tamu\\.edu$\", field.data):\n # raise ValidationError(\"Invalid Email Address\")\n # # return True\n def validate_username(self, username):\n user = User.query.filter_by(username=username.data).first()\n if user:\n raise ValidationError('Username is taken, Please choose a new one')\n\n def validate_email(self, email):\n email = User.query.filter_by(email=email.data).first()\n if email:\n raise ValidationError('Email is taken, Please choose a new one')\n # if not re.search(r\"^[A-Za-z0-9](\\.?[a-z0-9]){5,}@tamu\\.edu$\", email):\n # raise ValidationError(\"Invalid Email Address\")\n\n\n\n\nclass LoginForm(FlaskForm):\n email = StringField('Email', validators=[DataRequired(), Length(min = 6)])\n password = PasswordField('Password', validators=[DataRequired()])\n remeber = BooleanField('Remember Me')\n submit = SubmitField('Login')\n\n\n\nclass update_account(FlaskForm):\n # user name not null and not too long. Add validation\n username = StringField('Username', validators=[DataRequired(), Length(min = 2, max = 30)])\n email = StringField('Email', validators=[DataRequired(), Length(min = 6), Email()])\n picture = FileField('Update Your Picture', validators=[FileAllowed(['jpg', 'png'])])\n submit = SubmitField('Update')\n def validate_username(self, username):\n if username.data != current_user.username:\n user = User.query.filter_by(username=username.data).first()\n if user:\n raise ValidationError('Username is taken, Please choose a new one')\n\n def validate_email(self, email):\n if email.data != current_user.email:\n email = User.query.filter_by(email=email.data).first()\n if email:\n raise ValidationError('Email is taken, Please choose a new one')\n\nclass PostForm(FlaskForm):\n title = StringField('Title', validators=[DataRequired()])\n content = TextAreaField('Content', validators=[DataRequired()])\n submit = SubmitField('Post')\n tweet = BooleanField('Post On Twitter')\n\nclass spotify_profile(FlaskForm):\n artist_name = StringField('Artist', validators=[DataRequired()])\n artist_id = StringField('Artist_ID', validators=[DataRequired()])\n # time_range = StringField('time_range')\n\n########################\n## END FORMS ##\n########################\n\n\[email protected](\"/\", methods=['GET', 'POST'])\[email protected](\"/home\", methods=['GET', 'POST'])\n# in terminal:\n# debug mode in flask: export FLASK_DEBUG=1\n# run flask: flask run\ndef home():\n posts = Post.query.all()\n return render_template(\"home.html\", posts=posts)\n\n\n# @app.route(\"/funny\")\n# def funny():\n# return render_template(\"funny.html\")\n#\n#\[email protected](\"/Events\", methods=['GET', 'POST'])\ndef eve():\n posts_ev = Post_ev.query.all()\n return render_template(\"Events.html\", posts=posts_ev)\n\[email protected](\"/funny\", methods=['GET', 'POST'])\ndef fun():\n posts_h = Post_h.query.all()\n return render_template(\"funny.html\", posts= posts_h)\n\[email protected](\"/studyLounge\", methods=['GET', 'POST'])\ndef study_lounge():\n posts_ac = Post_ac.query.all()\n return render_template(\"studylounge.html\", posts = posts_ac)\n\[email protected](\"/sports\", methods=['GET', 'POST'])\ndef sports():\n posts_sp = Post_sp.query.all()\n return render_template(\"sports.html\", posts = posts_sp)\n\[email protected](\"/course\", methods=['GET', 'POST'])\ndef course():\n posts_cr = Post_cr.query.all()\n return render_template(\"course.html\", posts = posts_cr)\n\n\[email protected]('/profile/<username>')\ndef user_profile(username):\n\n # data we query\n # dbArtists = SpotifyArtist.query.filter_by(user_id = current_user.id).first()\n\n\n data = User.query.filter_by(username = username).first()\n spotify_data = SpotifyArtist.query.filter_by(user_id = data.id).first()\n print (spotify_data)\n # print ((data))\n\n artistArr = []\n if (spotify_data != None):\n if (len(spotify_data.artist_name.split(',! ')) == 31):\n artistArr = spotify_data.artist_name.split(',! ')[20:-1]\n print(artistArr)\n # return render_template(\"user_profile.html\", posts=data, art = spotify_data)\n return render_template(\"user_profile.html\", posts=data, art=artistArr, len=len(artistArr))\n\n\n return str(username)\n\n\n\[email protected](\"/resources\")\ndef resources():\n return render_template(\"resources.html\")\n\ndef save_image(form_picture):\n random_h = secrets.token_hex(8)\n _, fext = os.path.splitext(form_picture.filename)\n picture_fn = random_h + fext\n # root path attrinbute\n picture_path = os.path.join(app.root_path, 'static/image', picture_fn)\n output_size = (125,125)\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\[email protected](\"/profile\", methods = ['GET', 'POST'])\n@login_required\ndef profile(artists=[], artist_ids=[]):\n\n time_range = ['short_term', 'medium_term', 'long_term']\n leng = 0\n\n print(artists)\n\n if (len(artists) != 0):\n # going to be 3\n artists_string = \"\"\n artists_id_string = \"\"\n time_range_string = \"\"\n for i in range(len(artists)):\n for j in range(len(artists[0])):\n # artists[i][j], artist_ids[i][j]\n artists_string+=artists[i][j]\n artists_string+=\",! \"\n artists_id_string+=artist_ids[i][j]\n artists_id_string+=\", \"\n\n print(artists_string)\n print(artists_id_string)\n spo = SpotifyArtist(artist_name = artists_string, artist_id = artists_id_string, author=current_user)\n db.session.add(spo)\n db.session.commit()\n\n # how can we save it to a online drive???\n #image_file = 'https://i.pinimg.com/originals/0c/3b/3a/0c3b3adb1a7530892e55ef36d3be6cb8.png'\n form = update_account()\n if form.validate_on_submit():\n if form.picture.data:\n pic_file = save_image(form.picture.data)\n current_user.user_pic = pic_file\n current_user.username = form.username.data\n current_user.email = form.email.data\n db.session.commit()\n flash('You account is updated! ', 'success')\n return redirect(url_for('profile'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n form.email.data = current_user.email\n image_file = url_for('static', filename = 'image/' + current_user.user_pic, width=100)\n\n dbArtists = SpotifyArtist.query.filter_by(user_id = current_user.id).first()\n print(\"dbArtists:\", dbArtists)\n # return render_template(\"home.html\", posts=posts)\n\n artistArr = []\n if (dbArtists != None):\n if (len(dbArtists.artist_name.split(',! ')) == 31):\n artistArr = dbArtists.artist_name.split(',! ')[20:-1]\n print(artistArr)\n\n return render_template(\"profile.html\", title='Profile', image_file = image_file, form = form, leng=len(artistArr), posts=artistArr)\n\n\[email protected](\"/register\", methods=['GET','POST'])\ndef register():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n form = account()\n if form.validate_on_submit():\n # hash the paswword to save to our database\n hashed_password = bcrypt.generate_password_hash(form.password.data).decode('utf-8')\n\n # create a new user\n user= User(username = form.username.data, email = form.email.data, password = hashed_password)\n db.session.add(user)\n db.session.commit()\n\n flash(f'Account created! You can now log in! ','success')\n # we also need to redirect user to home page\n return redirect(url_for('login'))\n return render_template('register.html', title = 'Register', form = form)\n\n\[email protected](\"/login\", methods = ['GET', 'POST'])\ndef login():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n form = LoginForm()\n if form.validate_on_submit():\n user = User.query.filter_by(email = form.email.data).first()\n if user and bcrypt.check_password_hash(user.password, form.password.data):\n login_user(user, remember = form.remeber.data)\n next_page = request.args.get('next')\n # special python return\n return redirect(next_page) if next_page else redirect(url_for('home'))\n else:\n flash('Login not successful. Please check your password and email.', 'danger')\n return render_template('login.html', title = 'Login', form = form)\n\[email protected](\"/logout\")\ndef logout():\n logout_user()\n return redirect(url_for('home'))\n\ngloabal_true = False\[email protected](\"/post/new\", methods=['GET', 'POST'])\n@login_required\ndef new_post():\n form = PostForm()\n # and form.tweet.data == True\n if gloabal_true == True:\n\n twitter_consumer_key = \"bw5c7K2tzsceOlgenVFDRnogU\"\n twitter_consumer_secret = \"CTXbMs9vFwFCdYrM2CGkVsSsLl53LpO43FNeAwTcX5zukDg36m\"\n token_url = 'https://api.twitter.com/1.1/statuses/update.json'\n token_secret = (session[\"twitter_secret\"])\n access_token = (session[\"twitter_token\"])\n print (\"Auth: \")\n print(access_token, token_secret)\n if form.tweet.data == True:\n print (\"it is true\")\n\n auth = tweepy.OAuthHandler(twitter_consumer_key, twitter_consumer_secret)\n auth.set_access_token(access_token, token_secret)\n # Create API object\n api = tweepy.API(auth)\n # Create a tweet\n api.update_status(form.content.data)\n # post_response = requests.post(resource_url, auth=tw, data=body)\n # post_response = requests.post(request_url, auth = tw)\n # body = {'code': code, 'redirect_uri': redirect_uri, 'grant_type': 'authorization_code', 'client_id': CLI_ID, 'client_secret': CLI_SEC}\n\n\n if form.validate_on_submit():\n post = Post(title=form.title.data, content=form.content.data, author=current_user)\n db.session.add(post)\n db.session.commit()\n flash('Your post has been created', 'success')\n return redirect(url_for('home'))\n return render_template('create_post.html', title = 'Forum', form = form)\n\n\[email protected](\"/post/new/ac\", methods=['GET', 'POST'])\n@login_required\ndef new_post_ac():\n form = PostForm()\n # and form.tweet.data == True\n if form.tweet == True:\n flash(\"make a tweet\",'success')\n if form.validate_on_submit():\n post = Post_ac(title=form.title.data, content=form.content.data, author=current_user)\n print (request.form.get('mycheckbox'))\n db.session.add(post)\n db.session.commit()\n flash('Your post has been created', 'success')\n return redirect(url_for('home'))\n return render_template('create_post.html', title = 'Forum', form = form)\n\[email protected](\"/post/new/h\", methods=['GET', 'POST'])\n@login_required\ndef new_post_h():\n form = PostForm()\n # and form.tweet.data == True\n if form.tweet == True:\n flash(\"make a tweet\",'success')\n if form.validate_on_submit():\n post = Post_h(title=form.title.data, content=form.content.data, author=current_user)\n print (request.form.get('mycheckbox'))\n db.session.add(post)\n db.session.commit()\n flash('Your post has been created', 'success')\n return redirect(url_for('home'))\n return render_template('create_post.html', title = 'Forum', form = form)\n\n\n\[email protected](\"/post/new/sp\", methods=['GET', 'POST'])\n@login_required\ndef new_post_sp():\n form = PostForm()\n # and form.tweet.data == True\n if form.tweet == True:\n flash(\"make a tweet\",'success')\n if form.validate_on_submit():\n post = Post_sp(title=form.title.data, content=form.content.data, author=current_user)\n print (request.form.get('mycheckbox'))\n db.session.add(post)\n db.session.commit()\n flash('Your post has been created', 'success')\n return redirect(url_for('home'))\n return render_template('create_post.html', title = 'Forum', form = form)\n\[email protected](\"/post/new/ev\", methods=['GET', 'POST'])\n@login_required\ndef new_post_ev():\n form = PostForm()\n # and form.tweet.data == True\n if form.tweet == True:\n flash(\"make a tweet\",'success')\n if form.validate_on_submit():\n post = Post_ev(title=form.title.data, content=form.content.data, author=current_user)\n print (request.form.get('mycheckbox'))\n db.session.add(post)\n db.session.commit()\n flash('Your post has been created', 'success')\n return redirect(url_for('home'))\n return render_template('create_post.html', title = 'Forum', form = form)\n\n\n\[email protected](\"/post/new/cr\", methods=['GET', 'POST'])\n@login_required\ndef new_post_cr():\n form = PostForm()\n # and form.tweet.data == True\n if form.tweet == True:\n flash(\"make a tweet\",'success')\n if form.validate_on_submit():\n post = Post_cr(title=form.title.data, content=form.content.data, author=current_user)\n print (request.form.get('mycheckbox'))\n db.session.add(post)\n db.session.commit()\n flash('Your post has been created', 'success')\n return redirect(url_for('home'))\n return render_template('create_post.html', title = 'Forum', form = form)\n\n# oauth = OAuth(app)\n#\n# twitter = oauth.remote_app('twitter',\n# consumer_key = 'bw5c7K2tzsceOlgenVFDRnogU',\n# consumer_secret='CTXbMs9vFwFCdYrM2CGkVsSsLl53LpO43FNeAwTcX5zukDg36m',\n# base_url='https://api.twitter.com/1.1/',\n# request_token_url='https://api.twitter.com/oauth/request_token',\n# access_token_url='https://api.twitter.com/oauth/access_toke',\n# authorize_url='https://api.twitter.com/oauth/authorize'\n# )\n\n\n\n# DELETE this\n\n\n\n\n\n\[email protected]('/twitter_login')\ndef twitterPostForRequestToken():\n request_url = 'https://api.twitter.com/oauth/request_token'\n\t# authorization = app.config['AUTHORIZATION']\n twitter_redirect_url = \"http%3A%2F%2Fsilt-tamu.herokuapp.com%2Ftwitter_callback\"\n # oauth_callback=\"http%3A%2F%2Fmyapp.com%3A3005%2Ftwitter%2Fprocess_callback\"\n\n\t# headers = {'Authorization': authorization, 'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'}\n #headers = {'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'}\n twitter_consumer_key = \"bw5c7K2tzsceOlgenVFDRnogU\"\n twitter_consumer_secret = \"CTXbMs9vFwFCdYrM2CGkVsSsLl53LpO43FNeAwTcX5zukDg36m\"\n tw = OAuth1(twitter_consumer_key, twitter_consumer_secret)\n headers = {'oauth_callback': twitter_redirect_url, 'oauth_consumer_key': twitter_consumer_key}\n #body = {'code': code, 'redirect_uri': redirect_uri, 'grant_type': 'authorization_code', 'client_id': CLI_ID, 'client_secret': CLI_SEC}\n\n post_response = requests.post(request_url, auth = tw)\n # print(\"Twitter Post Response:\")\n\n attrs = vars(post_response)\n twitter_oauth = attrs.get('_content')\n\n oauth_arr = str(twitter_oauth)[2:].split('&')\n # oauth_token = oauth_arr[0].split('=')[1]\n # oauth_token_secret = oauth_arr[1].split('=')[1]\n oauth_token = oauth_arr[0]\n oauth_token_secret = oauth_arr[1]\n # print (oauth_token)\n # print (oauth_token_secret)\n authorize_url = \"https://api.twitter.com/oauth/authorize?\" + oauth_token\n return redirect(authorize_url)\n\n\n # 200 code indicates access token was properly granted\n # if post_response.status_code == 200:\n # json = post_response.json()\n # return json['access_token'], json['refresh_token'], json['expires_in']\n # else:\n # print(\"LOGGING: \" + 'getToken:' + str(post_response.status_code))\n # # logging.error('getToken:' + str(post_response.status_code))\n # return None\n\n# https://yourCallbackUrl.com?oauth_token=NPcudxy0yU5T3tBzho7iCotZ3cnetKwcTIRlX0iwRl0&oauth_verifier=uw7NjWHT6OJ1MpJOXsHfNxoAhPKpgI8BlYDhxEjIBY\[email protected]('/twitter_callback')\ndef twitter_callback():\n url_parse = request.url\n parse_arr = url_parse.split('=')[1:]\n token = parse_arr[0].split('&')[0]\n verifier = parse_arr[1]\n # print (token, verifier)\n\n request_url = 'https://api.twitter.com/oauth/access_token'\n\t# authorization = app.config['AUTHORIZATION']\n # twitter_redirect_url = \"http%3A%2F%2F127.0.0.1%3A5000%2Ftwitter_callback\"\n # oauth_callback=\"http%3A%2F%2Fmyapp.com%3A3005%2Ftwitter%2Fprocess_callback\"\n\n\t# headers = {'Authorization': authorization, 'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'}\n #headers = {'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'}\n twitter_consumer_key = \"bw5c7K2tzsceOlgenVFDRnogU\"\n twitter_consumer_secret = \"CTXbMs9vFwFCdYrM2CGkVsSsLl53LpO43FNeAwTcX5zukDg36m\"\n\n # oauth = OAuth1(client_key,\n # client_secret=client_secret,\n # resource_owner_key=resource_owner_key,\n # resource_owner_secret=resource_owner_secret,\n # verifier=verifier)\n\n tw = OAuth1(twitter_consumer_key, client_secret=twitter_consumer_secret, resource_owner_key=token, verifier=verifier)\n post_response = requests.post(request_url, auth = tw)\n attrs = vars(post_response)\n # print (attrs)\n twitter_oauth = attrs.get('_content')\n # print (\"Content: \")\n # print (twitter_oauth)\n oauth_arr = str(twitter_oauth)[2:].split('&')\n # oauth_token = oauth_arr[0].split('=')[1]\n # oauth_token_secret = oauth_arr[1].split('=')[1]\n oauth_token = oauth_arr[0].split('=')[1]\n oauth_token_secret = oauth_arr[1].split('=')[1]\n\n print (oauth_token, oauth_token_secret)\n\n print(\"tokens:\")\n print(oauth_token, oauth_token_secret)\n\n session.pop('twitter_token', None) # delete visits\n session.pop('twitter_secret', None) # delete visits\n session['twitter_token'] = oauth_token\n session['twitter_secret'] = oauth_token_secret\n session.modified = True\n # posts = {\"status\": \"test tweet\"}\n # token_url = 'https://api.twitter.com/1.1/statuses/update.json'\n # tw = OAuth1(twitter_consumer_key,\n # resource_owner_key=oauth_token,\n # resource_owner_secret=oauth_token_secret,\n # client_secret=twitter_consumer_secret)\n # a = requests.post(token_url, data=posts, auth = tw)\n # print (vars(a))\n #\n # auth = tweepy.OAuthHandler(twitter_consumer_key, twitter_consumer_secret)\n # auth.set_access_token(oauth_token, oauth_token_secret)\n # # Create API object\n # api = tweepy.API(auth)\n # # Create a tweet\n # api.update_status(\"Hello Tweepy2\")\n return redirect('/')\n\n\n##############################\n# Spotify section\n##############################\n\n# Spotify Prerequirements\nCLI_ID = \"035c861c44084c46bf08f93efed2bb4c\"\nCLI_SEC = \"18cba64539fc4c39894f8b17b4e78b6e\"\nAPI_BASE = 'https://accounts.spotify.com'\nREDIRECT_URI = \"http://silt-tamu.herokuapp.com/api_callback\"\nSCOPE = 'playlist-modify-private,playlist-modify-public,user-top-read, user-library-read'\n\n# Set this to True for testing but you probaly want it set to False in production.\nSHOW_DIALOG = True\n# Spotify pre-requirements end\n\[email protected](\"/spotify_authorize\")\ndef authorize():\n client_id = CLI_ID\n redirect_uri = REDIRECT_URI\n # TODO: change scope value\n scope = SCOPE\n\n # state_key = createStateKey(15)\n # session['state_key'] = state_key\n\n authorize_url = 'https://accounts.spotify.com/en/authorize?'\n # parameters = 'response_type=code&client_id=' + client_id + '&redirect_uri=' + redirect_uri + '&scope=' + scope + '&state=' + state_key\n parameters = 'response_type=code&client_id=' + client_id + '&redirect_uri=' + redirect_uri + '&scope=' + scope\n response = make_response(redirect(authorize_url + parameters))\n print(\"response\")\n return response\n\n\n\"\"\"\nCalled after a new user has authorized the application through the Spotift API page.\nStores user information in a session and redirects user back to the page they initally\nattempted to visit.\n\"\"\"\[email protected]('/api_callback')\ndef callback():\n # make sure the response came from Spotify\n # if request.args.get('state') != session['state_key']:\n # \t# return render_template('index.html', error='State failed.')\n # print(\"Error: State Failed\")\n # return\n if request.args.get('error'):\n \t# return render_template('index.html', error='Spotify error.')\n print(\"Error: Spotify error\")\n\n else:\n code = request.args.get('code')\n # session.pop('state_key', None)\n \t# get access token to make requests on behalf of the user\n payload = getToken(code)\n if payload != None:\n session['token'] = payload[0]\n session['refresh_token'] = payload[1]\n session['token_expiration'] = time.time() + payload[2]\n else:\n # return render_template('index.html', error='Failed to access token.')\n return \"Failed to access token\"\n\n current_user = getUserInformation(session)\n print(\"CURRENT USER:\", current_user)\n\n session['user_id'] = current_user['id']\n # logging.info('new user:' + session['user_id'])\n print(\"LOGGING: \" + 'new user:' + session['user_id'])\n\n # track_ids = getAllTopTracks(session)\n artist_names, artist_ids = getAllTopArtists(session)\n\n # if form.validate_on_submit() and form.tweet.data == True:\n # post = Post(title=form.title.data, content=form.content.data, author=current_user)\n # db.session.add(post)\n # db.session.commit()\n # flash('Your post has been created', 'success')\n # return redirect(url_for('home'))\n\n\n\n # print(\"------------------Artists---------------------\")\n time_range = ['short_term', 'medium_term', 'long_term']\n\n # for i in range(len(artist_names)):\n # term = time_range[i]\n #\n # for j in range(len(artist_names[0])):\n # print(artist_names[i][j], artist_ids[i][j])\n # SpotifyArtist = SpotifyArtist(user_id= , artist_name=artist_names[i][j], artist_id=artist_ids[i][j], time_range=term)\n\n print(\"\\nright before printing track_ids\")\n return profile(artists=artist_names, artist_ids=artist_ids)\n\n\ndef getToken(code):\n token_url = 'https://accounts.spotify.com/api/token'\n\t# authorization = app.config['AUTHORIZATION']\n redirect_uri = REDIRECT_URI\n\n\t# headers = {'Authorization': authorization, 'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'}\n headers = {'Accept': 'application/json', 'Content-Type': 'application/x-www-form-urlencoded'}\n body = {'code': code, 'redirect_uri': redirect_uri, 'grant_type': 'authorization_code', 'client_id': CLI_ID, 'client_secret': CLI_SEC}\n post_response = requests.post(token_url, headers=headers, data=body)\n\n # 200 code indicates access token was properly granted\n if post_response.status_code == 200:\n json = post_response.json()\n return json['access_token'], json['refresh_token'], json['expires_in']\n else:\n print(\"LOGGING: \" + 'getToken:' + str(post_response.status_code))\n # logging.error('getToken:' + str(post_response.status_code))\n return None\n\n\n\"\"\"\nMakes a GET request with the proper headers. If the request succeeds, the json parsed\nresponse is returned. If the request fails because the access token has expired, the\ncheck token function is called to update the access token.\nReturns: Parsed json response if request succeeds or None if request fails\n\"\"\"\ndef makeGetRequest(session, url, params={}):\n\theaders = {\"Authorization\": \"Bearer {}\".format(session['token'])}\n\tresponse = requests.get(url, headers=headers, params=params)\n\n\t# 200 code indicates request was successful\n\tif response.status_code == 200:\n\t\treturn response.json()\n\n\t# if a 401 error occurs, update the access token\n\telif response.status_code == 401 and checkTokenStatus(session) != None:\n\t\treturn makeGetRequest(session, url, params)\n\telse:\n # print(\"LOGGING: makeGetRequest\")\n # print(\"LOGGING: makeGetRequest: \" + str(response.status_code))\n\t\t# logging.error('makeGetRequest:' + str(response.status_code))\n\t\treturn None\n\n\n\ndef getUserInformation(session):\n\turl = 'https://api.spotify.com/v1/me'\n\tpayload = makeGetRequest(session, url)\n\n\tif payload == None:\n\t\treturn None\n\n\treturn payload\n\n\"\"\"\nGets the top tracks of a user for all three time intervals. Used to display the top\ntracks on the TopTracks feature page.\nReturns: A list of tracks IDs for each of the three time intervals\n\"\"\"\ndef getAllTopTracks(session, limit=10):\n url = 'https://api.spotify.com/v1/me/top/tracks'\n track_ids = []\n time_range = ['short_term', 'medium_term', 'long_term']\n\n for time in time_range:\n track_range_ids = []\n\n params = {'limit': limit, 'time_range': time}\n payload = makeGetRequest(session, url, params)\n\n # print(\"------------------PAYLOAD---------------------\")\n # print(payload)\n # print(\"------------------PAYLOAD END-------------\")\n\n if payload == None:\n return None\n\n for track in payload['items']:\n track_range_ids.append(track['id'])\n\n track_ids.append(track_range_ids)\n\n return track_ids\n\n# TODO: situation where user has no tracks\ndef getAllTopArtists(session, limit=10):\n url = 'https://api.spotify.com/v1/me/top/artists'\n artist_names = []\n artist_ids = []\n time_range = ['short_term', 'medium_term', 'long_term']\n\n for time in time_range:\n track_range_ids = []\n\n params = {'limit': limit, 'time_range': time}\n payload = makeGetRequest(session, url, params)\n\n if payload == None:\n return None\n\n artist_range_names = []\n artist_range_ids = []\n\n for artist in payload['items']:\n artist_range_names.append(artist['name'])\n artist_range_ids.append(artist['id'])\n\n artist_names.append(artist_range_names)\n artist_ids.append(artist_range_ids)\n\n return artist_names, artist_ids\n\n##############################\n# Yelp API Section #\n##############################\n\"\"\" END POINTS \"\"\"\n# Business Search URL -- 'https://api.yelp.com/v3/businesses/search'\n# Phone Search URL -- 'https://api.yelp.com/v3/businesses/search/phone'\n# Transaction Search URL -- 'https://api.yelp.com/v3/transactions/{transaction_type}/search'\n# Business Details URL -- 'https://api.yelp.com/v3/businesses/{id}'\n# Business Match URL -- 'https://api.yelp.com/v3/businesses/matches'\n# Reviews URL -- 'https://api.yelp.com/v3/businesses/{id}/reviews'\n# Autocomplete URL -- 'https://api.yelp.com/v3/autocomplete'\n\n# Define my API key, Endpoint, and Header\nAPI_KEY = 'nTM36O5k4QpcgkccZVAMhP8U4BxpO68EYzIA7KPXpRmnT31qUK49B7sfYQ2uA2_uzGRr94oA9aIxdD4PyIa0hyaXIccmnOGCVQ2tMJg4s3-a24CLE3syjaMHsqWRX3Yx'\nENDPOINT_PREFIX = 'https://api.yelp.com/v3/'\nHEADERS = {'Authorization': 'bearer %s' % API_KEY}\nEMPTY_RESPONSE = json.dumps('')\n\n# render popular locations webpage / make yelp API calls with user input for 'term' key\[email protected](\"/popular_locations\", methods=['GET'])\ndef popular_locations():\n # get user input from html form\n term = request.args.get('searchInput', None)\n\n # Check if user inputted a term\n if term == None:\n print(\"No term provided for business search, return nothing.\")\n\n # Define Business Search paramters\n parameters = {\n 'location': 'College Station, TX',\n 'radius': 15000,\n 'term': term,\n 'sort_by': 'best_match',\n 'limit': 50\n }\n\n # Make request to Yelp API\n url = ENDPOINT_PREFIX + 'businesses/search'\n response = requests.get(url, params = parameters, headers = HEADERS)\n\n # Check for good status code - if so, get JSON response and populate map\n if response.status_code == 200:\n print('Got 200 for business search')\n\n # Try/catch for invalid user input for 'term': key-value\n try:\n # Convert JSON string to dictionary\n businessSearchData = response.json()\n\n # Create dataframe from API response (businesses, list of dictionaries)\n dFrame = pd.DataFrame.from_dict(businessSearchData['businesses'])\n\n # YELP MAP - RESTAURANTS MARKED\n # Get latitude and longitude from Yelp API response\n cStatLat = 30.627977\n cStatLong = -96.334404\n\n # Generate base map of college station\n yelpMap = folium.Map(location = [cStatLat, cStatLong], zoom_start = 13)\n\n # Generate map of restaurants - Iterate through dataframe and add business markers\n for row in dFrame.index:\n latLong = dFrame['coordinates'][row]\n latitude = latLong['latitude']\n longitude = latLong['longitude']\n name = dFrame['name'][row]\n rating = dFrame['rating'][row]\n price = dFrame['price'][row]\n location = dFrame['location'][row]\n\n # Get address-1 from Location dictionary\n for loc in location.keys():\n if loc == 'address1':\n address = location[loc]\n\n # Create popup message for pin\n details = ('{}' + '<br><br>' + 'Address: {}' + '<br>' + 'Price: {}' + '<br>' + 'Rating: {}/5').format(name, address, price, rating)\n\n # Resize popup pin\n test = folium.Html(details, script = True)\n popup = folium.Popup(test, max_width = 300, min_width = 300)\n\n # Create and business marker to map\n marker = folium.Marker(location = [latitude, longitude], popup = popup, icon = folium.Icon(color = \"darkred\"))\n marker.add_to(yelpMap)\n\n # Display map on webpage\n yelpMap.save('./templates/yelpMap.html')\n except KeyError:\n print('ERROR: User input provided an invalid key-value.')\n flash(f'There was an error with your input.', 'danger')\n return redirect(url_for('popular_locations'))\n else:\n print('Received non-200 response({}) for business search, returning empty response'.format(response.status_code))\n return EMPTY_RESPONSE\n return render_template('popularLocations.html', businessData = dFrame, isBusinessDataEmpty = dFrame.empty)\n\[email protected](\"/yelp_map\")\ndef yelp_map():\n return render_template('yelpMap.html')\n\[email protected](\"/empty_yelp_map\")\ndef empty_yelp_map():\n return render_template('./templates/blank_yelpMap.html')\n\nif __name__ == '__main__':\n app.run(debug=True)\n" }, { "alpha_fraction": 0.44692736864089966, "alphanum_fraction": 0.4581005573272705, "avg_line_length": 16.899999618530273, "blob_id": "431a97f5a77c098fe9ec11076dce84cd6025616d", "content_id": "bf0b623e6a86a8ecf8f9aae03b539e6d9d27686a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "HTML", "length_bytes": 179, "license_type": "no_license", "max_line_length": 28, "num_lines": 10, "path": "/templates/spotify.html", "repo_name": "infknight/SILT", "src_encoding": "UTF-8", "text": "{% extends \"base.html\" %}\n{% block content %}\n\n {% for key in genres %}\n <h1>{{key}}</h1>\n <p>{{genres[key]}}</p>\n <hr>\n {% endfor %}\n\n{% endblock content %}\n" }, { "alpha_fraction": 0.5520150065422058, "alphanum_fraction": 0.5730240345001221, "avg_line_length": 48.627906799316406, "blob_id": "9cfc910a4f0e504d0de3e5215deb7ca9a5d1cf8b", "content_id": "c92e92107e9bf7d8b7a9753bc9d54f28568c7714", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "HTML", "length_bytes": 12820, "license_type": "no_license", "max_line_length": 398, "num_lines": 258, "path": "/templates/resources.html", "repo_name": "infknight/SILT", "src_encoding": "UTF-8", "text": "{% extends \"base.html\" %}\n\n{% block content %}\n\n <!DOCTYPE html>\n<html>\n<head>\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n<style>\n body {\n font-family: Arial, Helvetica, sans-serif;\n }\n\n .flip-card {\n background-color: transparent;\n width: 300px;\n height: 300px;\n perspective: 1000px;\n }\n\n .richard-flip-card-inside {\n position: relative;\n width: 100%;\n height: 100%;\n text-align: center;\n transition: transform 0.6s;\n transform-style: preserve-3d;\n box-shadow: 0 4px 8px 0 rgba(0,0,0,0.2);\n }\n\n .richard-flip-card-front, .flip-card-back {\n position: absolute;\n width: 100%;\n height: 100%;\n -webkit-backface-visibility: hidden;\n backface-visibility: hidden;\n }\n\n .richard-flip-card-front {\n background-color: #bbb;\n color: black;\n }\n\n .flip-card-back {\n background-color: grey;\n color: white;\n transform: rotateY(180deg);\n }\n\n .flip-card:hover .richard-flip-card-inside {\n transform: rotateY(180deg);\n }\n\n *{\n box-sizing: border-box;\n }\n\n /* Create two equal columns that floats next to each other */\n .column {\n float: left;\n width: 33%;\n padding: 10px;\n }\n\n /* Clear floats after the columns */\n .row:after {\n content: \"\";\n display: table;\n clear: both;\n }\n .TopLeft{\n position: relative;\n display: block;\n margin-left: auto;\n margin-right: auto;\n }\n .MidLeft{\n position: relative;\n display: block;\n margin-left: auto;\n margin-right: auto;\n }\n .BotLeft{\n position: relative;\n display: block;\n margin-left: auto;\n margin-right: auto;\n }\n p{\n font-size: 14px;\n }\n\n</style>\n</head>\n<body>\n\n\n<h1 id=\"id1\",\n style=\"text-align:center;\n color: rgb(143, 142, 142);\n font-style: italic;\">Loading Inspirational Quotes Page\n </h1>\n<script src=\"https://ajax.googleapis.com/ajax/libs/jquery/3.4.1/jquery.min.js\"></script>\n<script>\nurl = \"https://api.forismatic.com/api/1.0/?method=getQuote&lang=en&format=jsonp&jsonp=?\";\n\n$.getJSON(url, function(data){\n document.getElementById(\"id1\").innerHTML = data.quoteText;\n});\n</script>\n\n <div class=\"row\">\n <div class=\"column\" style=\"background-color:#FFFFFF;\">\n <h2 style = \"color: #330000; text-align:center;\">Mental Health</h2>\n <p style = \"color: #330000; text-align:center;\">CAPS remains committed to supporting students' mental health and wellbeing. We care about you and know this can be a difficult time!</p>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n \n <img src=\"../static/image/mental_health.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Counseling and Pyschological Services</h5>\n <a style = \"color: #FFFFFF\", href=\"https://caps.tamu.edu/\", target=\"_blank\">Official TAMU Services</a>\n <p>CAPS is currently not taking walk-in appointments. To schedule an appointment to meet with a counselor, please visit our Services page. If you are in need of urgent, crisis-related services, you may call us at (979) 845-4427 during TAMU hours of operation to discuss options to assist you or visit our Emergency Resources page for additional resources.</p>\n\n </div>\n </div>\n </div>\n <br>\n <div class=\"flip-card MidLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n \n <img src=\"../static/image/mental_health_2.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>EMERGENCY RESOURCES</h5>\n <a style = \"color: #FFFFFF\", href=\"https://caps.tamu.edu/emergency-resources/\", target=\"_blank\">ONLY FOR EMERGENCIES</a>\n <p>If you are currently in a life threatening situation or your safety is at risk, call 911 or visit your nearest emergency room.</p>\n </div>\n </div>\n </div>\n <br>\n <div class=\"flip-card BotLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n \n <img src=\"../static/image/suicide_awareness_prevention.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Suicide Awareness and Prevention</h5>\n <a style = \"color: #FFFFFF\", href=\"https://caps.tamu.edu/suicide-awareness-prevention/\", target=\"_blank\">Not Another Aggie</a>\n <p>The Suicide Awareness & Prevention Office offers a variety of services for individuals, programs, departments, and student groups. Knowing the signs of suicide is important in helping someone who may be at risk. By offering your understanding, reassurance, and support, you can help your loved one or friend seek the help they need.</p>\n </div>\n </div>\n </div>\n </div>\n\n <div class=\"column\" style=\"background-color:#330000;\">\n <h2 style = \"color: #FFFFFF; text-align:center;\">Tutoring</h2>\n <p style = \"color: #FFFFFF; text-align:center;\">Our undergraduate peer tutors have performed well in the subject-areas they cover. They are trained in creating a student-centered, active learning environment.”</p>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n \n <img src=\"../static/image/tutoring.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Study Hub</h5>\n <a style = \"color: #FFFFFF\", href=\"http://studyhub.tamu.edu/\", target=\"_blank\">Welcome to StudyHUB!</a>\n <p>As a core value of Texas A&M University, Excellence can be enhanced by how we serve our students. We are committed to providing access to academic support so that Aggies can achieve academic excellence.</p>\n </div>\n </div>\n </div>\n <br>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n <img src=\"../static/image/Si_sessions.jpg\" width=\"780&height=589\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Supplemental Instruction</h5>\n \n <a style = \"color: #FFFFFF\", href=\"http://studyhub.tamu.edu/\", target=\"_blank\">SI Sessions</a>\n <p>SI is a peer-led, academic assistance program that can significantly improve performance for students who attend regularly. Students who attend 10 or more sessions throughout the semester have statistically been shown to earn half to a full letter grade higher than those students who do not attend.</p>\n </div>\n </div>\n </div>\n <br>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n \n <img src=\"../static/image/success_initiative.jpg\" width=\"780&height=520\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Texas Success Iniative</h5>\n <a style = \"color: #FFFFFF\", href=\"http://asc.tamu.edu/TX-Success-Initiative\", target=\"_blank\">College-level reading, writing and math</a>\n <p>The Texas Success Initiative (TSI) is a statewide program created by the Texas State Legislature that helps ensure that all incoming college students are prepared for college-level reading, writing and math. </p>\n </div>\n </div>\n </div>\n </div>\n\n <div class=\"column\" style=\"background-color:#FFFFFF;\">\n <h2 style = \"color: #330000; text-align:center;\">Traditions</h2>\n <p style = \"color: #330000; text-align:center;\">Learn about the storied traditions of Texas A&M. Learn about orientation, the Aggie Culture, the Corps, Gameday, Remembrance, and many more!</p>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n \n <img src=\"../static/image/traditions.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Fish Camp</h5>\n <a style = \"color: #FFFFFF\", href=\"https://www.tamu.edu/traditions/orientation/fish-camp/\", target=\"_blank\">Incoming Freshmen</a>\n <p>Each year, Texas A&M incoming freshmen are welcomed to the university at Fish Camp, a four day orientation program that takes place at the Lakeview Methodist Conference Center in Palestine, Texas. At Fish Camp, freshmen are given opportunities to learn Aggie traditions, make friends and learn more about life at Texas A&M. The camp is led entirely by Texas A&M students.</p>\n </div>\n </div>\n </div>\n <br>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n \n <img src=\"../static/image/msc.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Memorial Student Center</h5>\n <a style = \"color: #FFFFFF\", href=\"https://www.tamu.edu/traditions/remembrance/msc/\", target=\"_blank\">Home Away From Home</a>\n <p>The Memorial Student Center — or the MSC, as it is known on campus — was built and dedicated on Muster Day (April 21) 1951 to all of the Aggies that have lost their lives in wars past, present, or future.</p>\n </div>\n </div>\n </div>\n <br>\n <div class=\"flip-card TopLeft\">\n <div class=\"richard-flip-card-inside\">\n <div class=\"richard-flip-card-front\">\n \n <img src=\"../static/image/muster.jpg\" alt=\"Avatar\" style=\"width:300px;height:300px;\">\n </div>\n <div class=\"flip-card-back\">\n <h5>Muster</h5>\n <a style = \"color: #FFFFFF\", href=\"https://www.tamu.edu/traditions/remembrance/muster/\", target=\"_blank\">A&M’s Most Solemn and Visible Tradition</a>\n <p>At each Muster ceremony around the world, a speaker addresses the crowd before the “Roll Call for the Absent.” Names of those from that area who have died in the past year will be read, and as each name is called, a family member or friend will answer “Here” to show that Aggie is present in spirit. Then, a candle will be lit.</p>\n </div>\n </div>\n </div>\n </div>\n </div>\n\n</body>\n\n\n\n\n{% endblock content %}\n" }, { "alpha_fraction": 0.48159509897232056, "alphanum_fraction": 0.691717803478241, "avg_line_length": 15.717948913574219, "blob_id": "326717ed70e86bb7e34098f554ab52a25a778632", "content_id": "676fe72d45b04ef428e729086a7b4ffdb8c23dd5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Text", "length_bytes": 652, "license_type": "no_license", "max_line_length": 24, "num_lines": 39, "path": "/requirements.txt", "repo_name": "infknight/SILT", "src_encoding": "UTF-8", "text": "bcrypt==3.2.0\nbranca==0.4.1\ncertifi==2020.11.8\ncffi==1.14.3\nchardet==3.0.4\nclick==7.1.2\ndnspython==2.0.0\nemail-validator==1.1.2\nFlask==1.1.2\nFlask-Bcrypt==0.7.1\nFlask-Dance==3.1.0\nFlask-Login==0.5.0\nFlask-SQLAlchemy==2.4.4\nFlask-WTF==0.14.3\nfolium==0.11.0\ngunicorn==20.0.4\nidna==2.10\nitsdangerous==1.1.0\nJinja2==2.11.2\nMarkupSafe==1.1.1\nnumpy==1.19.4\noauthlib==3.1.0\npandas==1.1.4\nPillow==8.0.1\npsycopg2-binary==2.8.6\npycparser==2.20\nPySocks==1.7.1\npython-dateutil==2.8.1\npytz==2020.4\nrequests==2.25.0\nrequests-oauthlib==1.3.0\nsix==1.15.0\nspotipy==2.16.1\nSQLAlchemy==1.3.20\ntweepy==3.9.0\nurllib3==1.26.2\nURLObject==2.4.3\nWerkzeug==1.0.1\nWTForms==2.3.3\n" }, { "alpha_fraction": 0.727544903755188, "alphanum_fraction": 0.7559880018234253, "avg_line_length": 38.29411697387695, "blob_id": "fd073924c3542c6421f620b14f7a972c8f84906e", "content_id": "d6b7927b21cf16798c8fcaf9c3f6d062b6b40f40", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 668, "license_type": "no_license", "max_line_length": 255, "num_lines": 17, "path": "/README.md", "repo_name": "infknight/SILT", "src_encoding": "UTF-8", "text": "# SILT\n\n## General info\n###### SILT is an application to help guide freshman transition to college by providing them an outlet to meet similar students utilizing Spotify and Twitter API around the campus as well as use Yelp's API to suggest popular locations around the BCS area.\n###### You could view all the commit through here: https://github.tamu.edu/CSCE315-902-B9/sprint2 and here: https://github.tamu.edu/CSCE315-902-B9/sprint1\n\n\n## Installation and Setup\n### Please read the appropriate guide for your environment of choice:\n```\n$ pip install flask\n$ pip3 install psycogn2\n$ brew install postgres\n```\n## To run this project:\n$ export DEBUG_FLASK=1\n$ flask run\n" } ]

5

onikbakht/Login-process-with-python

https://github.com/onikbakht/Login-process-with-python

b2ec7432f2a98f1b8f819c3dbdad305e17eb0ef2

c11bd8db68ca39ca7282df2714edfb538287a44c

26bd772d93f35da9fabcc6a627bb17e550c59531

refs/heads/main

2023-02-20T04:05:29.916205

2021-01-19T16:41:30

331,041,425

1

0

null

[ { "alpha_fraction": 0.6141732335090637, "alphanum_fraction": 0.6209223866462708, "avg_line_length": 29.92173957824707, "blob_id": "97bddd94914329b7348d2ac6c1b7ce26840ce87e", "content_id": "17dcb8db47a193013eaa95ef87b22a2201469cf1", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 3556, "license_type": "no_license", "max_line_length": 102, "num_lines": 115, "path": "/Login-process-with-Python.py", "repo_name": "onikbakht/Login-process-with-python", "src_encoding": "UTF-8", "text": "#before you try the program you need to create an excel file\n# and enter the path of the created excel file into the ''path'' variable\nimport xlrd\nfrom xlutils.copy import copy\n\n#enter the path of your created excel file in '''here''' (its going to be used as your local database)\n#instead of '\\' use '\\\\' in the path you want to enter\npath='here'\n\n#the main menu function\ndef mainMenu():\n command=input(\"\"\"\n 1> log in\n 2> create account\n \"\"\")\n if command==\"1\":\n logIn()\n elif command ==\"2\":\n createAccount()\n else:\n print(\"Wrong entry\")\n mainMenu()\n\n# if the user chooses to create an account\ndef createAccount():\n username=input(\"enter the ''New'' user name\")\n #geting an username and checking if it already exists\n # if it already exists user can try another username or go back to main menu\n if userExCheck(username)!=0 :\n print(\"SORRY!! \\n Username already exists\")\n command=input(\"\"\"\n 1> try a new user name\n 2> go to main menu\"\"\")\n if command==\"1\":\n createAccount()\n elif command==\"2\":\n mainMenu()\n else:\n print(\"wrong entry \\n heading back to main menu\")\n mainMenu()\n #if the entered username doesnt exist we ask for password\n else:\n newpassword=input(\"enter the password please.... : \")\n #opening our excl db and dedicating the entered pasword to the entered username in excl db\n file = xlrd.open_workbook(path)\n filesheet = file.sheet_by_index(0)\n row = filesheet.nrows\n\n wb = copy(file)\n w_sheet = wb.get_sheet(0)\n\n\n w_sheet.write(row, 1, username)\n\n w_sheet.write(row, 2, newpassword)\n wb.save(path)\n print(\"account created!! successfully heading to main menu...\")\n mainMenu()\n\n\n#the function which checks if the username already exists\ndef userExCheck(username):\n file = xlrd.open_workbook(path)\n filesheet = file.sheet_by_index(0)\n row = 0\n for l in range(1, filesheet.nrows):\n\n if username == str(filesheet.cell_value(l, 1)):\n row = l\n return row\n break\n\n return row\n\n#checking if the entered password matches the username which we are checking\ndef passCheck(password,row):\n file = xlrd.open_workbook(path)\n filesheet = file.sheet_by_index(0)\n if password == str(filesheet.cell_value(row, 2)):\n passIsRight=True\n return passIsRight\n\n\n#when the user chooses to log in\ndef logIn():\n\n username=input(\"enter your Username\")\n #checking the existance of untered username\n if(userExCheck(username)==0):\n print(\"username does not exist!!\\n\\n\")\n mainMenu()\n #if the username exists we ask for the password\n else:\n row=userExCheck(username)\n loginpassword=input(\"enter yout Password\")\n if (passCheck(loginpassword,row)==True):\n print(\"LOGG IN SUCCESSFULLY ACOMPLISHED\")\n print(\"hi dude welcome to our site\\n\\n\")\n #if the password is wrong we give the user 2 more chances to enter the correct password\n else:\n for i in range(2,4):\n\n print(f\"attempt {i}th from 3\")\n loginpassword =input(\"enter the password\")\n passCheck(loginpassword,row)\n if (passCheck(loginpassword, row) == True):\n print(\"LOG IN SUCCESSFULLY ACOMPLISHED\")\n print(\"hi dude welcome to our site\\n\\n\")\n exit()\n\n print(\"Account susspended!!! \\n(too many tries)\")\n mainMenu()\n\n\nmainMenu()\n" } ]

1

SrikanthreddyR/BasicPythonPrograms

https://github.com/SrikanthreddyR/BasicPythonPrograms

6cc4334328b4e298cd1136aa74d755d3c33062c8

26fe0df46889d7596212fbd77801475b14d433d3

5082cafa4e26726f91d1fe0082d8b1fb0b09697c

refs/heads/master

2023-05-08T11:25:43.402602

2021-06-01T04:19:30

207,070,761

0

null

[ { "alpha_fraction": 0.389413982629776, "alphanum_fraction": 0.4385633170604706, "avg_line_length": 10.276596069335938, "blob_id": "e333538d59010800f1f8f8c46cfe6308ad115dc9", "content_id": "db4c04f0390e6f01708b3580f5a654ef08c4b65a", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 529, "license_type": "no_license", "max_line_length": 35, "num_lines": 47, "path": "/jump_numbers.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 11 19:34:13 2019\n\nJumps\n\n@author: Admin\n\"\"\"\n\nnum=int(input());\n\nval=0;\n\nans=0;\n\nmod=num%6;\n\nif (mod==1 or mod==3 or mod==0):\n print(\"YES\")\nelse:\n print(\"NO\")\n\n'''\nwhile(val<num):\n val=val+1;\n if (val==num):\n ans=1;\n break;\n \n val=val+2;\n if (val==num):\n ans=1;\n break;\n \n val=val+3;\n if (val==num):\n ans=1;\n break;\n \n print(val)\n\nif (ans==1):\n print(\"YES\");\nelse:\n print(\"NO\");\n \n'''" }, { "alpha_fraction": 0.4721906781196594, "alphanum_fraction": 0.4926220178604126, "avg_line_length": 20, "blob_id": "49f89b5dce2d82991cbf8352c32206ebd4295ac5", "content_id": "815dc29e38825af890dc12775601e12e46bbebde", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 881, "license_type": "no_license", "max_line_length": 67, "num_lines": 42, "path": "/GuessMovieName.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 1 19:27:20 2019\n\nGuess the movie name\n\n@author: Admin\n\"\"\"\nimport random\nmovies=[\"tagaru\",\"bellbottom\",\"manasare\",\"gajakesari\",\"vishvasam\"];\n\ndef play():\n name=list(random.choice(movies));\n print(name);\n user_guess=[\"_\"]*len(name);\n c=0;\n while(1):\n c=c+1;\n print(user_guess,\"\\n\");\n gl=input(\"Guess and enter the charecter : \");\n g=0;\n for i in range(len(user_guess)):\n if (name[i]==gl):\n user_guess[i]=gl;\n g=1;\n \n if(g==1):\n print(\"Guessed letter is correct\\n\",)\n else:\n print(\"try again\");\n \n if(name==user_guess):\n print(\"The movie name is : \", name);\n break;\n \n return c;\n\nprint(\"The Game is ON\");\n\ncount=play();\n\nprint(\"No of guesses: \",count);" }, { "alpha_fraction": 0.5064267516136169, "alphanum_fraction": 0.5475578308105469, "avg_line_length": 17.571428298950195, "blob_id": "a1f23f92b9364ea3762789b827bad169991df79d", "content_id": "7aa8c2819ff2f4a7295d8940341bf9351a1771ca", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 389, "license_type": "no_license", "max_line_length": 40, "num_lines": 21, "path": "/String Sort.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 18 18:37:34 2019\n\nString Sort\n\n@author: Admin\n\"\"\"\ninp_list=list(map(int,input().split()));\n\nfor i in range(len(inp_list)):\n if(inp_list[i]==0):\n del inp_list[i];\n inp_list.append(0);\n\n\nfor i in range(len(inp_list)):\n if (i!=len(inp_list)-1):\n print(inp_list[i],end=\" \");\n else:\n print(inp_list[i],end=\"\");" }, { "alpha_fraction": 0.4841269850730896, "alphanum_fraction": 0.5370370149612427, "avg_line_length": 16.5238094329834, "blob_id": "408c8a1578733fb2e8161aeba10113599b8baad0", "content_id": "b2f93a46e46b243641dda54f5b7d679b96468130", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 378, "license_type": "no_license", "max_line_length": 48, "num_lines": 21, "path": "/cal_sqrt.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 11 20:13:04 2019\n\nCalculate Q = Square root of [(2 * C * D)/H]\n\n@author: Admin\n\"\"\"\n\nimport math\n\nx=input();\n\ninp_list=list(map(int,x.split(\",\")));\n\nfor i in range(len(inp_list)):\n res=round(math.sqrt((2*50*inp_list[i])/30));\n if(i!=len(inp_list)-1):\n print(res,end=\",\");\n else:\n print(res,end=\"\");\n \n \n" }, { "alpha_fraction": 0.3457249104976654, "alphanum_fraction": 0.3779430091381073, "avg_line_length": 13.600000381469727, "blob_id": "5f06690e6e06b3b43edad972539c5caf2891ce02", "content_id": "5620214b87fa926f475089da43c2db4e4b79ac29", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 807, "license_type": "no_license", "max_line_length": 35, "num_lines": 55, "path": "/Magic_square.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "\"\"\"\n\nMagic Square \n\n\nCreated on Tue Mar 19 17:50:14 2019\n\n@author: Admin\n\"\"\"\n\nn=int(input());\n\n#list_length=n*n;\n\nmagic_square=[];\n\nfor i in range (n*n):\n magic_square.append(\"-\");\n \n\ndef index(p,q,i):\n pos=p*n+q;\n \n if(magic_square[pos]==\"-\"):\n magic_square[pos]=i;\n else:\n p=p+1;\n q=q-2;\n index(p,q,i);\n\nfor i in range (1,n*n):\n if(i==1):\n p=int(n/2);\n q=n-1;\n index(p,q,i);\n else:\n p=p-1;\n q=q+1;\n \n if(p==-1 and q==n):\n p=0;\n q=n-1;\n index(p,q,i);\n \n elif(p==-1):\n p=n-1;\n index(p,q,i);\n \n elif(q==n):\n q=0;\n index(p,q,i);\n else:\n index(p,q,i);\n\nprint(magic_square);\n " }, { "alpha_fraction": 0.4939759075641632, "alphanum_fraction": 0.5843373537063599, "avg_line_length": 11.769230842590332, "blob_id": "a616a4c66e1dae22dccb16b0f929871991b9ca89", "content_id": "bd6b29f77b1021b4812d0452e21342e4cb60f3da", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 166, "license_type": "no_license", "max_line_length": 35, "num_lines": 13, "path": "/factorial_of_n.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Feb 28 09:37:02 2019\n\n@author: Admin\n\"\"\"\n\n\nn=int(input());\nfactn=1;\nfor i in range(n):\n factn=factn*(i+1);\nprint(factn)\n" }, { "alpha_fraction": 0.5096524953842163, "alphanum_fraction": 0.5444015264511108, "avg_line_length": 15.193548202514648, "blob_id": "ab327347634dfcee0610ef00805e072953d3444c", "content_id": "276d1974a2d4dff4de20c911dce8f7df40c74aa5", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 518, "license_type": "no_license", "max_line_length": 35, "num_lines": 31, "path": "/Sorting_using_random.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Feb 28 11:59:58 2019\n\n@author: Admin\n\"\"\"\n\n\nimport random\n\nn=int(input());\ngiven_list=[]\n\nfor i in range (n):\n given_list.append(int(input()))\n \nsorted_list=given_list.copy()\nsorted_list.sort()\n\nwhile(1):\n i=random.randint(0,n-1);\n j=random.randint(0,n-1);\n \n x=given_list[i];\n given_list[i]=given_list[j];\n given_list[j]=x;\n \n if(given_list==sorted_list):\n for i in given_list:\n print(i,end=\" \")\n break;\n \n \n\n\n" }, { "alpha_fraction": 0.4714764952659607, "alphanum_fraction": 0.49832215905189514, "avg_line_length": 15.583333015441895, "blob_id": "66429fe91a9fa50d884ce41f57352791351d9b55", "content_id": "dbaff9b1b43aa95ef27191f423ef9055918b33df", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 596, "license_type": "no_license", "max_line_length": 38, "num_lines": 36, "path": "/upper_tranglr_matrix.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n\nUpper Traingular square matrix\n\nCreated on Thu Mar 21 18:00:55 2019\n\n@author: Admin\n\"\"\"\n\n\ndef UpTrgMatrix(matrix,num):\n for i in range(num):\n for j in range (num):\n if (i>j):\n matrix[i][j]=0;\n return matrix;\n\nnum=int(input())\nmat=[]\n\nfor i in range (num):\n x=input().split();\n mat.append(x);\n\nmat=UpTrgMatrix(mat,num);\n\nfor i in range(num):\n for j in range(num):\n if (j==num-1):\n print(mat[i][j], end=\"\")\n else:\n print(mat[i][j], end=\" \");\n \n if(i<num-1):\n print();" }, { "alpha_fraction": 0.4886499345302582, "alphanum_fraction": 0.5292711853981018, "avg_line_length": 18.85365867614746, "blob_id": "7de2f202a48b9713e90f9e70d35074b70e5b3ebf", "content_id": "cbf746d772bd11d1d063932d58d2914f12a2ab97", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 837, "license_type": "no_license", "max_line_length": 48, "num_lines": 41, "path": "/Flames.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 8 20:16:43 2019.\n\nFLAMES\n\n@author: Admin\n\"\"\"\n\nName1=list(input(\"Enter 1st Name: \"));\n\nName2=list(input(\"Enter 2nd Name: \"));\n\ncount=0;\ndel_count=0;\nfor i in range(len(Name1)):\n for j in range(len(Name2)):\n if (Name1[i]==Name2[j]):\n Name1[i]=\"+\"\n Name2[j]=\"*\";\n del_count=del_count+1;\n break;\n\ncount=len(Name1)+len(Name2)-del_count*2;\n\nprint(Name1,\" \",Name2,\" \",\"count=\",count);\n\n\nflames_list=['f','l','a','m','e','s'];\n\nfor n in range (5):\n z=[];\n flames_list[(count-1)%len(flames_list)]='-';\n i=(count-1)%len(flames_list);\n for j in range(i+1,len(flames_list)):\n z.append(flames_list[j]);\n for k in range(i):\n z.append(flames_list[k]);\n flames_list=list(z);\n\nprint(flames_list);\n \n \n\n" }, { "alpha_fraction": 0.4359672963619232, "alphanum_fraction": 0.4877384305000305, "avg_line_length": 13.720000267028809, "blob_id": "0a2879fd4836985f70fd1e34272dc957ef4b0928", "content_id": "3d5a0179a55b7287edcaca489c2c29718f1962be", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 367, "license_type": "no_license", "max_line_length": 36, "num_lines": 25, "path": "/generating_matrix.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Feb 28 09:56:09 2019\n\n@author: Admin\n\"\"\"\n\nx,y=input().split();\n\nx=int(x);\ny=int(y);\nn=x*y;\n\nmatrix=[]\nfor i in range(1,n+1):\n matrix.append(i)\n\nn=len(matrix)\nfor i in range(n):\n if((i+1)%y!=0):\n print(matrix[i], end=\" \")\n else :\n if((i+1)%y==0):\n print(matrix[i], end=\" \")\n print();" }, { "alpha_fraction": 0.4474708139896393, "alphanum_fraction": 0.5408560037612915, "avg_line_length": 11.2380952835083, "blob_id": "d78995e7df820e7075f9d8a752b4af116bf8a4b5", "content_id": "63032c9fc2b009889ceaa0dc8064d694846775e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 257, "license_type": "no_license", "max_line_length": 51, "num_lines": 21, "path": "/Numpy_Array.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n\n\n\nCreated on Tue Mar 19 17:32:50 2019\n\n@author: Admin\n\"\"\"\n\n\n\nimport numpy\n\n\nmagic_square=numpy.array([[1,2,3],[4,5,6],[7,8,9]])\n\nfor i in range(3):\n for j in range(3):\n print(magic_square[i][j], end=\" \");\n print();\n" }, { "alpha_fraction": 0.5959596037864685, "alphanum_fraction": 0.6060606241226196, "avg_line_length": 14.230769157409668, "blob_id": "02c90bbbd8497f01aa443757995a35cfd7acde91", "content_id": "6f8143c7a7a022d5a87058dfdaf6d2329f5f4f89", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 198, "license_type": "no_license", "max_line_length": 49, "num_lines": 13, "path": "/SpaceCountInFile.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo=open('C:/Users/rangamma/Music/sample.txt','r')\n\ntext=fo.read()\nprint(text);\n\ncount=0;\n\n## Counting number of bank spaces\nfor i in text: \n if(i==' '): \n count=count+1\n\nprint(count)\n" }, { "alpha_fraction": 0.4318181872367859, "alphanum_fraction": 0.49793389439582825, "avg_line_length": 17.076923370361328, "blob_id": "b4eadee81ffe3845b242f88f37131f260097fe64", "content_id": "96bb9b34ad3aef3fba90b72a706b06fbfab7bcd4", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 484, "license_type": "no_license", "max_line_length": 184, "num_lines": 26, "path": "/Special Characterin a string .py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 11 18:44:45 2019\n\nSpecial Character in a string\n\n@author: Admin\n\"\"\"\n\ns=input();\n\ns_list=list(s);\n\nc=0;\n\nfor i in range(len(s_list)):\n if (((ord(s_list[i])>=65) and (ord(s_list[i])<=90)) or ((ord(s_list[i])>=97) and (ord(s_list[i])<=122)) or ((ord(s_list[i])>=48) and (ord(s_list[i])<=57)) or (ord(s_list[i])==32)):\n c=0;\n else:\n c=1;\n break;\n\nif c==0:\n print(\"NO\");\nelse: \n print(\"YES\");\n \n \n" }, { "alpha_fraction": 0.6708860993385315, "alphanum_fraction": 0.6708860993385315, "avg_line_length": 16.55555534362793, "blob_id": "468831994d08751e632c1bfaa2bd7f69a37f2640", "content_id": "7b8a36bf0826bd8fdef119d0388ad1882e0f8c0b", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 158, "license_type": "no_license", "max_line_length": 49, "num_lines": 9, "path": "/WordCountInFile.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo=open('C:/Users/rangamma/Music/sample.txt','r')\n\ntext=fo.read()\n\nl=list(text.split()) ## Splitting text into words\n\nprint('Word Count:',len(l))\n\nfo.close()\n" }, { "alpha_fraction": 0.39929741621017456, "alphanum_fraction": 0.43676814436912537, "avg_line_length": 24.909090042114258, "blob_id": "27db08cb574138f903b56815a03b0be8ab0b3433", "content_id": "231d04ad8585008af71cf0f70320f469526d93e6", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 854, "license_type": "no_license", "max_line_length": 197, "num_lines": 33, "path": "/Counter_Spiral.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 4 19:26:00 2019\n\nMaximum Numeric in a string;\n\n@author: Admin\n\"\"\"\n\n\n\"\"\" for j in range(i,len(in_str)):\n if(in_str[i]=='0' or in_str[i]=='1' or in_str[i]=='2' or in_str[i]=='3' or in_str[i]=='4' or in_str[i]=='5' or in_str[i]=='6' or in_str[i]=='7' or in_str[i]=='8' or in_str[i]=='9'):\n x.append(in_str[i]);\n else:\n break;\n st=''.join(x);\n num.append(st);\n\"\"\"\n\n\nin_str=input();\n\nnum=[];\nx=[];\n\nfor i in range(len(in_str)):\n if(in_str[i]=='0' or in_str[i]=='1' or in_str[i]=='2' or in_str[i]=='3' or in_str[i]=='4' or in_str[i]=='5' or in_str[i]=='6' or in_str[i]=='7' or in_str[i]=='8' or in_str[i]=='9'):\n x.append(in_str[i]);\n\n else:\n x.append('-'); \n \nprint(x);" }, { "alpha_fraction": 0.5995671153068542, "alphanum_fraction": 0.6320346593856812, "avg_line_length": 13.46875, "blob_id": "010805bbbd7131ffa996d4111f0e0cdc0781a281", "content_id": "71f9df199504e84a9ed8af5270a995d9c4c96ade", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 462, "license_type": "no_license", "max_line_length": 44, "num_lines": 32, "path": "/computing paradox.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 12 12:52:23 2019\n\n@author: Admin\n\"\"\"\n\nn=int(input())\n\nsongs_list=input();\n\nsongs_list=list(map(int,songs_list.split()))\n\nfav_song_pos=int(input())\n\nfav_song=songs_list[fav_song_pos-1]\n\n#print(fav_song,\"fav song\")\n\nsorted_list=songs_list.copy()\n\n#print(sorted_list,\"cpopied\");\n\n\nsorted_list.sort()\n\n#print(sorted_list, \"sorted\");\n\nfor i in range(n):\n if fav_song==sorted_list[i]:\n print(i+1)\n break;" }, { "alpha_fraction": 0.5337662100791931, "alphanum_fraction": 0.5532467365264893, "avg_line_length": 19.276315689086914, "blob_id": "a9ff4e7aa8d6016f6af6efe947449b00e0a7c81e", "content_id": "9f111bea08fa6192a6d25f8306ff2d571159641d", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 1540, "license_type": "no_license", "max_line_length": 47, "num_lines": 76, "path": "/permutations_comb.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 18 19:32:51 2019\n\n@author: Admin\n\"\"\"\n\n#from itertools import permutations \n\nn,m=map(int,input().split());\n\ninp_list=list(map(int,input().split()));\n\n#perm = permutations(inp_list, m) \n# \n#perm=list(perm);\n#\n#val_list=[];\n#\n#for i in range(len(perm)):\n# s=sorted(perm[i]);\n# val=s[m-1]-s[0];\n# val_list.append(val);\n# \n#val_list=sorted(val_list);\n#\n#print(val_list[0]);\n\n\nimport sys; \n \n# arr[0..n-1] represents sizes of packets \n# m is number of students. \n# Returns minimum difference between maximum \n# and minimum values of distribution. \ndef findMinDiff(arr, n, m): \n \n # if there are no chocolates or number \n # of students is 0 \n if (m==0 or n==0): \n return 0\n \n # Sort the given packets \n arr.sort() \n \n # Number of students cannot be more than \n # number of packets \n if (n < m): \n return -1\n \n # Largest number of chocolates \n min_diff = sys.maxsize \n \n # Find the subarray of size m such that \n # difference between last (maximum in case \n # of sorted) and first (minimum in case of \n # sorted) elements of subarray is minimum. \n first = 0\n last = 0\n i=0\n while(i+m-1<n ): \n \n diff = arr[i+m-1] - arr[i] \n if (diff < min_diff): \n \n min_diff = diff \n first = i \n last = i + m - 1\n \n i+=1\n \n return (arr[last] - arr[first]) \n \n# Driver Code \nif __name__ == \"__main__\": \n print(findMinDiff(inp_list, n, m))" }, { "alpha_fraction": 0.5046948194503784, "alphanum_fraction": 0.5187793374061584, "avg_line_length": 21.473684310913086, "blob_id": "ae23602d591b5145ed778e0c0b21bb85e45a72e8", "content_id": "8ef6cffba2edc7a9b5c48ebcf455e929b505a949", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 426, "license_type": "no_license", "max_line_length": 48, "num_lines": 19, "path": "/getAlphabetInFile.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo= open(\"alphabetInFile.txt\",\"w+\")\n\ndef alphabetInFile(count,n):\n for i in range(count,n+count):\n while(i>90):\n i=i-26;\n fo.write(chr(i)+\" \")\n fo.write(\"\\n\")\n\ncount=65;\nwhile(True):\n m=input('Wanna enter a value ? Enter T\\F: ')\n if(m==\"T\" or m==\"t\"):\n n=int(input('Enter a number'));\n alphabetInFile(count,n)\n count=count+n;\n else:\n fo.close()\n break" }, { "alpha_fraction": 0.5285714268684387, "alphanum_fraction": 0.5555555820465088, "avg_line_length": 18.090909957885742, "blob_id": "1a17aa572ad5b516c6068027e8dd0939550a21fe", "content_id": "b0a57c404d4789ca4024db39b7c626c3b4da67fb", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 630, "license_type": "no_license", "max_line_length": 96, "num_lines": 33, "path": "/Binary_Matrix.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n\nGiven a matrix with N rows and M columns, the task is to check if the matrix is a Binary Matrix.\nA binary matrix is a matrix in which all the elements are either 0 or 1\n\nCreated on Thu Mar 21 19:21:14 2019\n\n@author: Admin\n\"\"\"\n\nN, M=input().split();\nmat=[];\n\nN=int(N);\nM=int(M);\n\nfor i in range (N):\n x=list(map(int,input().split()));\n mat.append(x);\n \nresult=\"True\";\n\nfor i in range (N):\n for j in range (M):\n if (mat[i][j]!=0) and (mat[i][j]!=1):\n result=\"False\"\n break;\n \nif result==\"True\":\n print(\"YES\",end=\"\");\nelse :\n print(\"NO\",end=\"\"); " }, { "alpha_fraction": 0.37841352820396423, "alphanum_fraction": 0.4161248505115509, "avg_line_length": 17.774999618530273, "blob_id": "a86145cc091ee1ae5b4d72236e060db210d8db2b", "content_id": "707e3f118f758f21d803a016433cad14fb7b17b2", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 769, "license_type": "no_license", "max_line_length": 189, "num_lines": 40, "path": "/Maximum Numeric in a string.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 4 19:26:00 2019\n\nMaximum Numeric in a string;\n\n@author: Admin\n\"\"\"\n\nin_str=input();\nx=[];\nnum=[];\nz=[];\n\nfor i in range(len(in_str)):\n if(in_str[i]=='0' or in_str[i]=='1' or in_str[i]=='2' or in_str[i]=='3' or in_str[i]=='4' or in_str[i]=='5' or in_str[i]=='6' or in_str[i]=='7' or in_str[i]=='8' or in_str[i]=='9'):\n x.append(in_str[i]);\n else:\n x.append('-');\n\nprint(x);\n\ni=0;\nwhile(i<len(x)-1):\n c=0\n while(x[i]!='-'):\n z.append(x[i]);\n if(i<len(x)):\n i=i+1;\n print(i)\n c=1\n \n if(c==1):\n st=''.join(z);\n num.append(st);\n if(x[i]=='-'):\n i=i+1;\n print(\"i=\",i);\n\nprint(\"num=\",num)\n \n \n " }, { "alpha_fraction": 0.48363634943962097, "alphanum_fraction": 0.5490909218788147, "avg_line_length": 11, "blob_id": "d7699c4218340c0cdcd15f4b1fed29629c4a1dd6", "content_id": "277fac4241c66609acd71a73ab1dfbc3cead4868", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 275, "license_type": "no_license", "max_line_length": 35, "num_lines": 23, "path": "/disct().py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 12 10:35:05 2019\n\n@author: Admin\n\"\"\"\n#import string\n\n#s=string.ascii_letters\n\n##print(s)\n\n#print(s[-4]);\n\n#print(int(li))...\n\n\ndef test(i,j):\n if(i==0):\n return j\n else:\n return test(i-1,i+j)\nprint(test(4,7))" }, { "alpha_fraction": 0.5442478060722351, "alphanum_fraction": 0.5486725568771362, "avg_line_length": 18.65217399597168, "blob_id": "045c675f2c81991ed022d00e2334516c2cfc3f97", "content_id": "f77d49f5c4fd5ab88c4e74de6e7af8ec7c21ee49", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 452, "license_type": "no_license", "max_line_length": 92, "num_lines": 23, "path": "/RemoveNewLineCharsInFile.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo=open('C:/Users/rangamma/Music/sample.txt','r')\n\nl=fo.readlines()\n\nf=[]\nfor i in l:\n if (i!='\\n'):\n if('\\n' in i):\n f.append(i[:len(i)-2])\n else:\n ## If \\n is not present at the end of the file, i.e. after last line in the file\n f.append(i)\nfo.close()\n\nentstr=\". \".join(f) ## To convert the list of lines to single string\n\nfo=open('sample.txt','w+')\n\nfo.write(entstr)\n\nfo.seek(0)\n\nprint(fo.read())\n" }, { "alpha_fraction": 0.8429751992225647, "alphanum_fraction": 0.8429751992225647, "avg_line_length": 59.5, "blob_id": "67db56689049dd4b43dc963e35376ac7b7a72f94", "content_id": "f5600ac95ccbeca2f7adb1368842702366724f0e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Markdown", "length_bytes": 121, "license_type": "no_license", "max_line_length": 93, "num_lines": 2, "path": "/README.md", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# Beginner Python Programs\nBasic Python Programs which one encounters in the intial stage of learning Python Programming\n" }, { "alpha_fraction": 0.6533665657043457, "alphanum_fraction": 0.6882793307304382, "avg_line_length": 19, "blob_id": "e2de0c34353c674233ec861663754163043569f1", "content_id": "5193a39445ebb7e36f46be29ebb59c18cd92f469", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 401, "license_type": "no_license", "max_line_length": 136, "num_lines": 20, "path": "/Duplicate Elements.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 1 18:35:34 2019\n\nDuplicate Elements\n\nWith a given list L of integers, write a program to print this list L after removing all duplicate values with original order preserved.\n\n@author: Admin\n\"\"\"\n\n\norg_list=list(map(int,input().split()));\n\nfinal_list=[];\n\nfinal_list.append(org_list[0]);\n\nfor i in range(1,len(org_list)):\n for j in range(final_list);\n\n" }, { "alpha_fraction": 0.5857142806053162, "alphanum_fraction": 0.5928571224212646, "avg_line_length": 8.399999618530273, "blob_id": "98933884348fda89d6df34f1737dc04df86c459a", "content_id": "3e0bfe521f50cc4c9fefd7855320de4c7a8fcd12", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 140, "license_type": "no_license", "max_line_length": 30, "num_lines": 15, "path": "/CopyFileContent.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo=open('sample.txt','r')\n\ns=fo.read()\n\nprint(s,'\\n')\n\nfo.close()\n\nfc=open('samplecopy.txt','w+')\n\nfc.write(s)\n\nfc.seek(0)\n\nprint(fc.read())" }, { "alpha_fraction": 0.4661654233932495, "alphanum_fraction": 0.4987468719482422, "avg_line_length": 13.107142448425293, "blob_id": "891c67f1c3192356a13aade88677be0afd5935e7", "content_id": "107c8a1f1c39a62610307cd89ad4dceca3079597", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 399, "license_type": "no_license", "max_line_length": 45, "num_lines": 28, "path": "/Equal_Transpose_Matrix.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar 21 18:58:38 2019\n\n\n\n@author: Admin\n\"\"\"\n\n\nnum=int(input())\nmat=[]\n\nfor i in range (num):\n x=input().split();\n mat.append(x);\n\nresult=\"True\";\n\nfor i in range (num):\n for j in range (num):\n if (i!=j) and (mat[i][j]!=mat[j][i]):\n result=\"False\"\n break;\nif result==\"True\":\n print(\"YES\");\nelse :\n print(\"NO\");\n " }, { "alpha_fraction": 0.4073319733142853, "alphanum_fraction": 0.4480651617050171, "avg_line_length": 16.571428298950195, "blob_id": "7badee1547fc9813ae328c0cdc71e74ddfad8fac", "content_id": "50e4113d1975d166b3d43bc83ebbcd3a9c143b81", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 491, "license_type": "no_license", "max_line_length": 68, "num_lines": 28, "path": "/check_rect.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 15 19:04:42 2019\n\n@author: Admin\n\"\"\"\n\ndef rectangle(z):\n count=0;\n a=z[0];\n b=z[1];\n c=z[2];\n d=z[3];\n if((a==b or a==c or a==d)):\n if ((a==b and c==d) or (a==c and b==d) or (a==d and b==c )):\n count=1;\n if (count==1):\n return \"YES\"\n print(\"YES\");\n else:\n return \"NO\"\n print(\"NO\");\n\nt=int(input());\n\nfor i in range(t):\n l=list(map(int,input().split()));\n rectangle(l);" }, { "alpha_fraction": 0.6296296119689941, "alphanum_fraction": 0.6296296119689941, "avg_line_length": 10, "blob_id": "65185c16bd66073700343cf556d1d434d23591bd", "content_id": "6d284aae11beb6f0b33fabb1e8969920f4118e0e", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 54, "license_type": "no_license", "max_line_length": 26, "num_lines": 5, "path": "/readlinesFromFile.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo=open('sample.txt','r+')\n\nl=fo.readlines()\n\nprint(l)" }, { "alpha_fraction": 0.6000000238418579, "alphanum_fraction": 0.6095238327980042, "avg_line_length": 11.411765098571777, "blob_id": "9de613b0b74d1b6ae3e0fdd3de58f05acf3cf550", "content_id": "5e17ad3d3585f6c67e6c897059e371497a9f3cd7", "detected_licenses": [], "is_generated": false, "is_vendor": false, "language": "Python", "length_bytes": 210, "license_type": "no_license", "max_line_length": 40, "num_lines": 17, "path": "/capitalizeWordsInFile.py", "repo_name": "SrikanthreddyR/BasicPythonPrograms", "src_encoding": "UTF-8", "text": "fo=open('sample.txt','r+')\n\nl=fo.read().split()\n\n## Capitalize first letter of every word\nfor i in range(len(l)):\n l[i]=l[i].capitalize()\n\ns=\" \".join(l)\n\nfo.seek(0)\n\nfo.write(s)\n\nfo.seek(0)\n\nprint(fo.read())" } ]

29