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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "c19a2efe",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import random \n",
"import time\n",
"import math\n",
"import random\n",
"import numpy as np\n",
"from scipy import stats\n",
"from random import randint\n",
"from util import *\n",
"from stats import Histogram\n",
"\n",
"def randomFloat(low, high):\n",
" \"\"\"\n",
" sample float within range\n",
" Parameters\n",
" low : low valuee\n",
" high : high valuee\n",
" \"\"\"\n",
" return random.random() * (high-low) + low\n",
"\n",
"def randomInt(minv, maxv):\n",
" \"\"\"\n",
" sample int within range\n",
" Parameters\n",
" minv : low valuee\n",
" maxv : high valuee\n",
" \"\"\"\n",
" return randint(minv, maxv)\n",
"\n",
"def randIndex(lData):\n",
" \"\"\"\n",
" random index of a list\n",
" Parameters\n",
" lData : list data\n",
" \"\"\"\n",
" return randint(0, len(lData)-1)\n",
"\n",
"def randomUniformSampled(low, high):\n",
" \"\"\"\n",
" sample float within range\n",
"\n",
" Parameters\n",
" low : low value\n",
" high : high value\n",
" \"\"\"\n",
" return np.random.uniform(low, high)\n",
"\n",
"def randomUniformSampledList(low, high, size):\n",
" \"\"\"\n",
" sample floats within range to create list\n",
" Parameters\n",
" low : low value\n",
" high : high value\n",
" size ; size of list to be returned\n",
" \"\"\"\n",
" return np.random.uniform(low, high, size)\n",
"\n",
"def randomNormSampled(mean, sd):\n",
" \"\"\"\n",
" sample float from normal\n",
" Parameters\n",
" mean : mean\n",
" sd : std deviation\n",
" \"\"\"\n",
" return np.random.normal(mean, sd)\n",
"\n",
"def randomNormSampledList(mean, sd, size):\n",
" \"\"\"\n",
" sample float list from normal \n",
" Parameters\n",
" mean : mean\n",
" sd : std deviation\n",
" size : size of list to be returned\n",
" \"\"\"\n",
" return np.random.normal(mean, sd, size)\n",
"\n",
"def randomSampledList(sampler, size):\n",
" \"\"\"\n",
" sample list from given sampler \n",
" Parameters\n",
" sampler : sampler object\n",
" size : size of list to be returned\n",
" \"\"\"\n",
" return list(map(lambda i : sampler.sample(), range(size)))\n",
"\n",
"\n",
"def minLimit(val, minv):\n",
" \"\"\"\n",
" min limit\n",
"\n",
" Parameters\n",
" val : value\n",
" minv : min limit\n",
" \"\"\"\n",
" if (val < minv):\n",
" val = minv\n",
" return val\n",
"\n",
"\n",
"def rangeLimit(val, minv, maxv):\n",
" \"\"\"\n",
" range limit\n",
" Parameters\n",
" val : value\n",
" minv : min limit\n",
" maxv : max limit\n",
" \"\"\"\n",
" if (val < minv):\n",
" val = minv\n",
" elif (val > maxv):\n",
" val = maxv\n",
" return val\n",
"\n",
"\n",
"def sampleUniform(minv, maxv):\n",
" \"\"\"\n",
" sample int within range\n",
" Parameters\n",
" minv ; int min limit\n",
" maxv : int max limit\n",
" \"\"\"\n",
" return randint(minv, maxv)\n",
"\n",
"\n",
"def sampleFromBase(value, dev):\n",
" \"\"\"\n",
" sample int wrt base\n",
" Parameters\n",
" value : base value\n",
" dev : deviation\n",
" \"\"\"\n",
" return randint(value - dev, value + dev)\n",
"\n",
"\n",
"def sampleFloatFromBase(value, dev):\n",
" \"\"\"\n",
" sample float wrt base\n",
" Parameters\n",
" value : base value\n",
" dev : deviation\n",
" \"\"\"\n",
" return randomFloat(value - dev, value + dev)\n",
"\n",
"\n",
"def distrUniformWithRanndom(total, numItems, noiseLevel):\n",
" \"\"\"\n",
" uniformly distribute with some randomness and preserves total\n",
" Parameters\n",
" total : total count\n",
" numItems : no of bins\n",
" noiseLevel : noise level fraction\n",
" \"\"\"\n",
" perItem = total / numItems\n",
" var = perItem * noiseLevel\n",
" items = []\n",
" for i in range(numItems):\n",
" item = perItem + randomFloat(-var, var)\n",
" items.append(item)\t\n",
"\n",
" #adjust last item\n",
" sm = sum(items[:-1])\n",
" items[-1] = total - sm\n",
" return items\n",
"\n",
"\n",
"def isEventSampled(threshold, maxv=100):\n",
" \"\"\"\n",
" sample event which occurs if sampled below threshold\n",
" Parameters\n",
" threshold : threshold for sampling\n",
" maxv : maximum values\n",
" \"\"\"\n",
" return randint(0, maxv) < threshold\n",
"\n",
"\n",
"def sampleBinaryEvents(events, probPercent):\n",
" \"\"\"\n",
" sample binary events\n",
" Parameters\n",
" events : two events\n",
" probPercent : probability as percentage\n",
" \"\"\"\n",
" if (randint(0, 100) < probPercent):\n",
" event = events[0]\n",
" else:\n",
" event = events[1]\n",
" return event\n",
"\n",
"\n",
"def addNoiseNum(value, sampler):\n",
" \"\"\"\n",
" add noise to numeric value\n",
" Parameters\n",
" value : base value\n",
" sampler : sampler for noise\n",
" \"\"\"\n",
" return value * (1 + sampler.sample())\n",
"\n",
"\n",
"def addNoiseCat(value, values, noise):\t\n",
" \"\"\"\n",
" add noise to categorical value i.e with some probability change value\n",
" Parameters\n",
" value : cat value\n",
" values : cat values\n",
" noise : noise level fraction\n",
" \"\"\"\n",
" newValue = value\n",
" threshold = int(noise * 100)\n",
" if (isEventSampled(threshold)):\t\t\n",
" newValue = selectRandomFromList(values)\n",
" while newValue == value:\n",
" newValue = selectRandomFromList(values)\n",
" return newValue\n",
"\n",
"\n",
"def sampleWithReplace(data, sampSize):\n",
" \"\"\"\n",
" sample with replacement\n",
" Parameters\n",
" data : array\n",
" sampSize : sample size\n",
" \"\"\"\n",
" sampled = list()\n",
" le = len(data)\n",
" if sampSize is None:\n",
" sampSize = le\n",
" for i in range(sampSize):\n",
" j = random.randint(0, le - 1)\n",
" sampled.append(data[j])\n",
" return sampled\n",
"\n",
"class CumDistr:\n",
" \"\"\"\n",
" cumulative distr\n",
" \"\"\"\n",
"\n",
" def __init__(self, data, numBins = None):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" data : array\n",
" numBins : no of bins\n",
" \"\"\"\n",
" if not numBins:\n",
" numBins = int(len(data) / 5)\n",
" res = stats.cumfreq(data, numbins=numBins)\n",
" self.cdistr = res.cumcount / len(data)\n",
" self.loLim = res.lowerlimit\n",
" self.upLim = res.lowerlimit + res.binsize * res.cumcount.size\n",
" self.binWidth = res.binsize\n",
"\n",
" def getDistr(self, value):\n",
" \"\"\"\n",
" get cumulative distribution\n",
"\n",
" Parameters\n",
" value : value\n",
" \"\"\"\n",
" if value <= self.loLim:\n",
" d = 0.0\n",
" elif value >= self.upLim:\n",
" d = 1.0\n",
" else:\n",
" bin = int((value - self.loLim) / self.binWidth)\n",
" d = self.cdistr[bin]\n",
" return d\n",
"\n",
"class BernoulliTrialSampler:\n",
" \"\"\"\n",
" bernoulli trial sampler return True or False\n",
" \"\"\"\n",
"\n",
" def __init__(self, pr):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" pr : probability\n",
" \"\"\"\n",
" self.pr = pr\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" return random.random() < self.pr\n",
"\n",
"class PoissonSampler:\n",
" \"\"\"\n",
" poisson sampler returns number of events\n",
" \"\"\"\n",
" def __init__(self, rateOccur, maxSamp):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" rateOccur : rate of occurence\n",
" maxSamp : max limit on no of samples\n",
" \"\"\"\n",
" self.rateOccur = rateOccur\n",
" self.maxSamp = int(maxSamp)\n",
" self.pmax = self.calculatePr(rateOccur)\n",
"\n",
" def calculatePr(self, numOccur):\n",
" \"\"\"\n",
" calulates probability\n",
"\n",
" Parameters\n",
" numOccur : no of occurence\n",
" \"\"\"\n",
" p = (self.rateOccur ** numOccur) * math.exp(-self.rateOccur) / math.factorial(numOccur)\n",
" return p\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" done = False\n",
" samp = 0\n",
" while not done:\n",
" no = randint(0, self.maxSamp)\n",
" sp = randomFloat(0.0, self.pmax)\n",
" ap = self.calculatePr(no)\n",
" if sp < ap:\n",
" done = True\n",
" samp = no\n",
" return samp\n",
"\n",
"class ExponentialSampler:\n",
" \"\"\"\n",
" returns interval between events\n",
" \"\"\"\n",
" def __init__(self, rateOccur, maxSamp = None):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" rateOccur : rate of occurence\n",
" maxSamp : max limit on interval\n",
" \"\"\"\n",
" self.interval = 1.0 / rateOccur\n",
" self.maxSamp = int(maxSamp) if maxSamp is not None else None\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" sampled = np.random.exponential(scale=self.interval)\n",
" if self.maxSamp is not None:\n",
" while sampled > self.maxSamp:\n",
" sampled = np.random.exponential(scale=self.interval)\n",
" return sampled\n",
"\n",
"class UniformNumericSampler:\n",
" \"\"\"\n",
" uniform sampler for numerical values\n",
" \"\"\"\n",
" def __init__(self, minv, maxv):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" minv : min value\n",
" maxv : max value\n",
" \"\"\"\n",
" self.minv = minv\n",
" self.maxv = maxv\n",
"\n",
" def isNumeric(self):\n",
" \"\"\"\n",
" returns true\n",
" \"\"\"\n",
" return True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" samp =\tsampleUniform(self.minv, self.maxv) if isinstance(self.minv, int) else randomFloat(self.minv, self.maxv)\n",
" return samp\t\n",
"\n",
"class UniformCategoricalSampler:\n",
" \"\"\"\n",
" uniform sampler for categorical values\n",
" \"\"\"\n",
" def __init__(self, cvalues):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" cvalues : categorical value list\n",
" \"\"\"\n",
" self.cvalues = cvalues\n",
"\n",
" def isNumeric(self):\n",
" return False\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" return selectRandomFromList(self.cvalues)\t\n",
"\n",
"class NormalSampler:\n",
" \"\"\"\n",
" normal sampler\n",
" \"\"\"\n",
" def __init__(self, mean, stdDev):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" mean : mean\n",
" stdDev : std deviation\n",
" \"\"\"\n",
" self.mean = mean\n",
" self.stdDev = stdDev\n",
" self.sampleAsInt = False\n",
"\n",
" def isNumeric(self):\n",
" return True\n",
"\n",
" def sampleAsIntValue(self):\n",
" \"\"\"\n",
" set True to sample as int\n",
" \"\"\"\n",
" self.sampleAsInt = True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" samp = np.random.normal(self.mean, self.stdDev)\n",
" if self.sampleAsInt:\n",
" samp = int(samp)\n",
" return samp\n",
"\n",
"class LogNormalSampler:\n",
" \"\"\"\n",
" log normal sampler\n",
" \"\"\"\n",
" def __init__(self, mean, stdDev):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" mean : mean\n",
" stdDev : std deviation\n",
" \"\"\"\n",
" self.mean = mean\n",
" self.stdDev = stdDev\n",
"\n",
" def isNumeric(self):\n",
" return True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" return np.random.lognormal(self.mean, self.stdDev)\n",
"\n",
"class NormalSamplerWithTrendCycle:\n",
" \"\"\"\n",
" normal sampler with cycle and trend\n",
" \"\"\"\n",
" def __init__(self, mean, stdDev, dmean, cycle, step=1):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" mean : mean\n",
" stdDev : std deviation\n",
" dmean : trend delta\n",
" cycle : cycle values wrt base mean\n",
" step : adjustment step for cycle and trend\n",
" \"\"\"\n",
" self.mean = mean\n",
" self.cmean = mean\n",
" self.stdDev = stdDev\n",
" self.dmean = dmean\n",
" self.cycle = cycle\n",
" self.clen = len(cycle) if cycle is not None else 0\n",
" self.step = step\n",
" self.count = 0\n",
"\n",
" def isNumeric(self):\n",
" return True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" s = np.random.normal(self.cmean, self.stdDev)\n",
" self.count += 1\n",
" if self.count % self.step == 0:\n",
" cy = 0\n",
" if self.clen > 1:\n",
" coff = self.count % self.clen\n",
" cy = self.cycle[coff]\n",
" tr = self.count * self.dmean\n",
" self.cmean = self.mean + tr + cy\n",
" return s\n",
"\n",
"\n",
"class ParetoSampler:\n",
" \"\"\"\n",
" pareto sampler\n",
" \"\"\"\n",
" def __init__(self, mode, shape):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" mode : mode\n",
" shape : shape\n",
" \"\"\"\n",
" self.mode = mode\n",
" self.shape = shape\n",
"\n",
" def isNumeric(self):\n",
" return True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" return (np.random.pareto(self.shape) + 1) * self.mode\n",
"\n",
"class GammaSampler:\n",
" \"\"\"\n",
" pareto sampler\n",
" \"\"\"\n",
" def __init__(self, shape, scale):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" shape : shape\n",
" scale : scale\n",
" \"\"\"\n",
" self.shape = shape\n",
" self.scale = scale\n",
"\n",
" def isNumeric(self):\n",
" return True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" return np.random.gamma(self.shape, self.scale)\n",
"\n",
"class GaussianRejectSampler:\n",
" \"\"\"\n",
" gaussian sampling based on rejection sampling\n",
" \"\"\"\n",
" def __init__(self, mean, stdDev):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" mean : mean\n",
" stdDev : std deviation\n",
" \"\"\"\n",
" self.mean = mean\n",
" self.stdDev = stdDev\n",
" self.xmin = mean - 3 * stdDev\n",
" self.xmax = mean + 3 * stdDev\n",
" self.ymin = 0.0\n",
" self.fmax = 1.0 / (math.sqrt(2.0 * 3.14) * stdDev)\n",
" self.ymax = 1.05 * self.fmax\n",
" self.sampleAsInt = False\n",
"\n",
" def isNumeric(self):\n",
" return True\n",
"\n",
" def sampleAsIntValue(self):\n",
" \"\"\"\n",
" sample as int value\n",
" \"\"\"\n",
" self.sampleAsInt = True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" done = False\n",
" samp = 0\n",
" while not done:\n",
" x = randomFloat(self.xmin, self.xmax)\n",
" y = randomFloat(self.ymin, self.ymax)\n",
" f = self.fmax * math.exp(-(x - self.mean) * (x - self.mean) / (2.0 * self.stdDev * self.stdDev))\n",
" if (y < f):\n",
" done = True\n",
" samp = x\n",
" if self.sampleAsInt:\n",
" samp = int(samp)\n",
" return samp\n",
"\n",
"class DiscreteRejectSampler:\n",
" \"\"\"\n",
" non parametric sampling for discrete values using given distribution based \n",
" on rejection sampling\t\n",
" \"\"\"\n",
" def __init__(self, xmin, xmax, step, *values):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" xmin : min value\n",
" xmax : max value\n",
" step : discrete step\n",
" values : distr values\n",
" \"\"\"\n",
" self.xmin = xmin\n",
" self.xmax = xmax\n",
" self.step = step\n",
" self.distr = values\n",
" if (len(self.distr) == 1):\n",
" self.distr = self.distr[0]\t\n",
" numSteps = int((self.xmax - self.xmin) / self.step)\n",
" #print(\"{:.3f} {:.3f} {:.3f} {}\".format(self.xmin, self.xmax, self.step, numSteps))\n",
" assert len(self.distr)\t== numSteps + 1, \"invalid number of distr values expected {}\".format(numSteps + 1)\n",
" self.ximin = 0\n",
" self.ximax = numSteps\n",
" self.pmax = float(max(self.distr))\n",
"\n",
" def isNumeric(self):\n",
" return True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" done = False\n",
" samp = None\n",
" while not done:\n",
" xi = randint(self.ximin, self.ximax)\n",
" #print(formatAny(xi, \"xi\"))\n",
" ps = randomFloat(0.0, self.pmax)\n",
" pa = self.distr[xi]\n",
" if ps < pa:\n",
" samp = self.xmin + xi * self.step\n",
" done = True\n",
" return samp\n",
"\n",
"\n",
"class TriangularRejectSampler:\n",
" \"\"\"\n",
" non parametric sampling using triangular distribution based on rejection sampling\t\n",
" \"\"\"\n",
" def __init__(self, xmin, xmax, vertexValue, vertexPos=None):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" xmin : min value\n",
" xmax : max value\n",
" vertexValue : distr value at vertex\n",
" vertexPos : vertex pposition\n",
" \"\"\"\n",
" self.xmin = xmin\n",
" self.xmax = xmax\n",
" self.vertexValue = vertexValue\n",
" if vertexPos: \n",
" assert vertexPos > xmin and vertexPos < xmax, \"vertex position outside bound\"\n",
" self.vertexPos = vertexPos\n",
" else:\n",
" self.vertexPos = 0.5 * (xmin + xmax)\n",
" self.s1 = vertexValue / (self.vertexPos - xmin)\n",
" self.s2 = vertexValue / (xmax - self.vertexPos)\n",
"\n",
" def isNumeric(self):\n",
" return True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" done = False\n",
" samp = None\n",
" while not done:\n",
" x = randomFloat(self.xmin, self.xmax)\n",
" y = randomFloat(0.0, self.vertexValue)\n",
" f = (x - self.xmin) * self.s1 if x < self.vertexPos else (self.xmax - x) * self.s2\n",
" if (y < f):\n",
" done = True\n",
" samp = x\n",
"\n",
" return samp;\t\n",
"\n",
"class NonParamRejectSampler:\n",
" \"\"\"\n",
" non parametric sampling using given distribution based on rejection sampling\t\n",
" \"\"\"\n",
" def __init__(self, xmin, binWidth, *values):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" xmin : min value\n",
" binWidth : bin width\n",
" values : distr values\n",
" \"\"\"\n",
" self.values = values\n",
" if (len(self.values) == 1):\n",
" self.values = self.values[0]\n",
" self.xmin = xmin\n",
" self.xmax = xmin + binWidth * (len(self.values) - 1)\n",
" #print(self.xmin, self.xmax, binWidth)\n",
" self.binWidth = binWidth\n",
" self.fmax = 0\n",
" for v in self.values:\n",
" if (v > self.fmax):\n",
" self.fmax = v\n",
" self.ymin = 0\n",
" self.ymax = self.fmax\n",
" self.sampleAsInt = True\n",
"\n",
" def isNumeric(self):\n",
" return True\n",
"\n",
" def sampleAsFloat(self):\n",
" self.sampleAsInt = False\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" done = False\n",
" samp = 0\n",
" while not done:\n",
" if self.sampleAsInt:\n",
" x = random.randint(self.xmin, self.xmax)\n",
" y = random.randint(self.ymin, self.ymax)\n",
" else:\n",
" x = randomFloat(self.xmin, self.xmax)\n",
" y = randomFloat(self.ymin, self.ymax)\n",
" bin = int((x - self.xmin) / self.binWidth)\n",
" f = self.values[bin]\n",
" if (y < f):\n",
" done = True\n",
" samp = x\n",
" return samp\n",
"\n",
"class JointNonParamRejectSampler:\n",
" \"\"\"\n",
" non parametric sampling using given distribution based on rejection sampling\t\n",
" \"\"\"\n",
" def __init__(self, xmin, xbinWidth, xnbin, ymin, ybinWidth, ynbin, *values):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" xmin : min value for x\n",
" xbinWidth : bin width for x\n",
" xnbin : no of bins for x\n",
" ymin : min value for y\n",
" ybinWidth : bin width for y\n",
" ynbin : no of bins for y\n",
" values : distr values\n",
" \"\"\"\n",
" self.values = values\n",
" if (len(self.values) == 1):\n",
" self.values = self.values[0]\n",
" assert len(self.values) == xnbin * ynbin, \"wrong number of values for joint distr\"\n",
" self.xmin = xmin\n",
" self.xmax = xmin + xbinWidth * xnbin\n",
" self.xbinWidth = xbinWidth\n",
" self.ymin = ymin\n",
" self.ymax = ymin + ybinWidth * ynbin\n",
" self.ybinWidth = ybinWidth\n",
" self.pmax = max(self.values)\n",
" self.values = np.array(self.values).reshape(xnbin, ynbin)\n",
"\n",
" def isNumeric(self):\n",
" return True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" done = False\n",
" samp = 0\n",
" while not done:\n",
" x = randomFloat(self.xmin, self.xmax)\n",
" y = randomFloat(self.ymin, self.ymax)\n",
" xbin = int((x - self.xmin) / self.xbinWidth)\n",
" ybin = int((y - self.ymin) / self.ybinWidth)\n",
" ap = self.values[xbin][ybin]\n",
" sp = randomFloat(0.0, self.pmax)\n",
" if (sp < ap):\n",
" done = True\n",
" samp = [x,y]\n",
" return samp\n",
"\n",
"\n",
"class JointNormalSampler:\n",
" \"\"\"\n",
" joint normal sampler\t\n",
" \"\"\"\n",
" def __init__(self, *values):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" values : 2 mean values followed by 4 values for covar matrix\n",
" \"\"\"\n",
" lvalues = list(values)\n",
" assert len(lvalues) == 6, \"incorrect number of arguments for joint normal sampler\"\n",
" mean = lvalues[:2]\n",
" self.mean = np.array(mean)\n",
" sd = lvalues[2:]\n",
" self.sd = np.array(sd).reshape(2,2)\n",
"\n",
" def isNumeric(self):\n",
" return True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" return list(np.random.multivariate_normal(self.mean, self.sd))\n",
"\n",
"\n",
"class MultiVarNormalSampler:\n",
" \"\"\"\n",
" muti variate normal sampler\t\n",
" \"\"\"\n",
" def __init__(self, numVar, *values):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" numVar : no of variables\n",
" values : numVar mean values followed by numVar x numVar values for covar matrix\n",
" \"\"\"\n",
" lvalues = list(values)\n",
" assert len(lvalues) == numVar + numVar * numVar, \"incorrect number of arguments for multi var normal sampler\"\n",
" mean = lvalues[:numVar]\n",
" self.mean = np.array(mean)\n",
" sd = lvalues[numVar:]\n",
" self.sd = np.array(sd).reshape(numVar,numVar)\n",
"\n",
" def isNumeric(self):\n",
" return True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" return list(np.random.multivariate_normal(self.mean, self.sd))\n",
"\n",
"class CategoricalRejectSampler:\n",
" \"\"\"\n",
" non parametric sampling for categorical attributes using given distribution based \n",
" on rejection sampling\t\n",
" \"\"\"\n",
" def __init__(self, *values):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" values : list of tuples which contains a categorical value and the corresponsding distr value\n",
" \"\"\"\n",
" self.distr = values\n",
" if (len(self.distr) == 1):\n",
" self.distr = self.distr[0]\n",
" maxv = 0\n",
" for t in self.distr:\n",
" if t[1] > maxv:\n",
" maxv = t[1]\n",
" self.maxv = maxv\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" done = False\n",
" samp = \"\"\n",
" while not done:\n",
" t = self.distr[randint(0, len(self.distr)-1)]\t\n",
" d = randomFloat(0, self.maxv)\t\n",
" if (d <= t[1]):\n",
" done = True\n",
" samp = t[0]\n",
" return samp\n",
"\n",
"\n",
"class DistrMixtureSampler:\n",
" \"\"\"\n",
" distr mixture sampler\n",
" \"\"\"\n",
" def __init__(self, mixtureWtDistr, *compDistr):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" mixtureWtDistr : sampler that returns index into sampler list\n",
" compDistr : sampler list\n",
" \"\"\"\n",
" self.mixtureWtDistr = mixtureWtDistr\n",
" self.compDistr = compDistr\n",
" if (len(self.compDistr) == 1):\n",
" self.compDistr = self.compDistr[0]\n",
"\n",
" def isNumeric(self):\n",
" return True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" comp = self.mixtureWtDistr.sample()\n",
"\n",
" #sample sampled comp distr\n",
" return self.compDistr[comp].sample()\n",
"\n",
"class AncestralSampler:\n",
" \"\"\"\n",
" ancestral sampler using conditional distribution\n",
" \"\"\"\n",
" def __init__(self, parentDistr, childDistr, numChildren):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" parentDistr : parent distr\n",
" childDistr : childdren distribution dictionary\n",
" numChildren : no of children\n",
" \"\"\"\n",
" self.parentDistr = parentDistr\n",
" self.childDistr = childDistr\n",
" self.numChildren = numChildren\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" parent = self.parentDistr.sample()\n",
"\n",
" #sample all children conditioned on parent\n",
" children = []\n",
" for i in range(self.numChildren):\n",
" key = (parent, i)\n",
" child = self.childDistr[key].sample()\n",
" children.append(child)\n",
" return (parent, children)\n",
"\n",
"class ClusterSampler:\n",
" \"\"\"\n",
" sample cluster and then sample member of sampled cluster\n",
" \"\"\"\n",
" def __init__(self, clusters, *clustDistr):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" clusters : dictionary clusters\n",
" clustDistr : distr for clusters\n",
" \"\"\"\n",
" self.sampler = CategoricalRejectSampler(*clustDistr)\n",
" self.clusters = clusters\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" cluster = self.sampler.sample()\n",
" member = random.choice(self.clusters[cluster])\n",
" return (cluster, member)\n",
"\n",
"\n",
"class MetropolitanSampler:\n",
" \"\"\"\n",
" metropolitan sampler\t\n",
" \"\"\"\n",
" def __init__(self, propStdDev, min, binWidth, values):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" propStdDev : proposal distr std dev\n",
" min : min domain value for target distr\n",
" binWidth : bin width\n",
" values : target distr values\n",
" \"\"\"\n",
" self.targetDistr = Histogram.createInitialized(min, binWidth, values)\n",
" self.propsalDistr = GaussianRejectSampler(0, propStdDev)\n",
" self.proposalMixture = False\n",
"\n",
" # bootstrap sample\n",
" (minv, maxv) = self.targetDistr.getMinMax()\n",
" self.curSample = random.randint(minv, maxv)\n",
" self.curDistr = self.targetDistr.value(self.curSample)\n",
" self.transCount = 0\n",
"\n",
" def initialize(self):\n",
" \"\"\"\n",
" initialize\n",
" \"\"\"\n",
" (minv, maxv) = self.targetDistr.getMinMax()\n",
" self.curSample = random.randint(minv, maxv)\n",
" self.curDistr = self.targetDistr.value(self.curSample)\n",
" self.transCount = 0\n",
"\n",
" def setProposalDistr(self, propsalDistr):\n",
" \"\"\"\n",
" set custom proposal distribution\n",
" Parameters\n",
" propsalDistr : proposal distribution\n",
" \"\"\"\n",
" self.propsalDistr = propsalDistr\n",
"\n",
"\n",
" def setGlobalProposalDistr(self, globPropStdDev, proposalChoiceThreshold):\n",
" \"\"\"\n",
" set custom proposal distribution\n",
" Parameters\n",
" globPropStdDev : global proposal distr std deviation\n",
" proposalChoiceThreshold : threshold for using global proposal distribution\n",
" \"\"\"\n",
" self.globalProposalDistr = GaussianRejectSampler(0, globPropStdDev)\n",
" self.proposalChoiceThreshold = proposalChoiceThreshold\n",
" self.proposalMixture = True\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" samples value\n",
" \"\"\"\n",
" nextSample = self.proposalSample(1)\n",
" self.targetSample(nextSample)\n",
" return self.curSample;\n",
"\n",
" def proposalSample(self, skip):\n",
" \"\"\"\n",
" sample from proposal distribution\n",
" Parameters\n",
" skip : no of samples to skip\n",
" \"\"\"\n",
" for i in range(skip):\n",
" if not self.proposalMixture:\n",
" #one proposal distr\n",
" nextSample = self.curSample + self.propsalDistr.sample()\n",
" nextSample = self.targetDistr.boundedValue(nextSample)\n",
" else:\n",
" #mixture of proposal distr\n",
" if random.random() < self.proposalChoiceThreshold:\n",
" nextSample = self.curSample + self.propsalDistr.sample()\n",
" else:\n",
" nextSample = self.curSample + self.globalProposalDistr.sample()\n",
" nextSample = self.targetDistr.boundedValue(nextSample)\n",
"\n",
" return nextSample\n",
"\n",
" def targetSample(self, nextSample):\n",
" \"\"\"\n",
" target sample\n",
" Parameters\n",
" nextSample : proposal distr sample\n",
" \"\"\"\n",
" nextDistr = self.targetDistr.value(nextSample)\n",
"\n",
" transition = False\n",
" if nextDistr > self.curDistr:\n",
" transition = True\n",
" else:\n",
" distrRatio = float(nextDistr) / self.curDistr\n",
" if random.random() < distrRatio:\n",
" transition = True\n",
"\n",
" if transition:\n",
" self.curSample = nextSample\n",
" self.curDistr = nextDistr\n",
" self.transCount += 1\n",
"\n",
"\n",
" def subSample(self, skip):\n",
" \"\"\"\n",
" sub sample\n",
" Parameters\n",
" skip : no of samples to skip\n",
" \"\"\"\n",
" nextSample = self.proposalSample(skip)\n",
" self.targetSample(nextSample)\n",
" return self.curSample;\n",
"\n",
" def setMixtureProposal(self, globPropStdDev, mixtureThreshold):\n",
" \"\"\"\n",
" mixture proposal\n",
" Parameters\n",
" globPropStdDev : global proposal distr std deviation\n",
" mixtureThreshold : threshold for using global proposal distribution\n",
" \"\"\"\n",
" self.globalProposalDistr = GaussianRejectSampler(0, globPropStdDev)\n",
" self.mixtureThreshold = mixtureThreshold\n",
"\n",
" def samplePropsal(self):\n",
" \"\"\"\n",
" sample from proposal distr\n",
" \"\"\"\n",
" if self.globalPropsalDistr is None:\n",
" proposal = self.propsalDistr.sample()\n",
" else:\n",
" if random.random() < self.mixtureThreshold:\n",
" proposal = self.propsalDistr.sample()\n",
" else:\n",
" proposal = self.globalProposalDistr.sample()\n",
"\n",
" return proposal\n",
"\n",
"class PermutationSampler:\n",
" \"\"\"\n",
" permutation sampler by shuffling a list\n",
" \"\"\"\n",
" def __init__(self):\n",
" \"\"\"\n",
" initialize\n",
" \"\"\"\n",
" self.values = None\n",
" self.numShuffles = None\n",
"\n",
" @staticmethod\n",
" def createSamplerWithValues(values, *numShuffles):\n",
" \"\"\"\n",
" creator with values\n",
" Parameters\n",
" values : list data\n",
" numShuffles : no of shuffles or range of no of shuffles\n",
" \"\"\"\n",
" sampler = PermutationSampler()\n",
" sampler.values = values\n",
" sampler.numShuffles = numShuffles\n",
" return sampler\n",
"\n",
" @staticmethod\n",
" def createSamplerWithRange(minv, maxv, *numShuffles):\n",
" \"\"\"\n",
" creator with ramge min and max\n",
"\n",
" Parameters\n",
" minv : min of range\n",
" maxv : max of range\n",
" numShuffles : no of shuffles or range of no of shuffles\n",
" \"\"\"\n",
" sampler = PermutationSampler()\n",
" sampler.values = list(range(minv, maxv + 1))\n",
" sampler.numShuffles = numShuffles\n",
" return sampler\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" sample new permutation\n",
" \"\"\"\n",
" cloned = self.values.copy()\n",
" shuffle(cloned, *self.numShuffles)\n",
" return cloned\n",
"\n",
"class SpikeyDataSampler:\n",
" \"\"\"\n",
" samples spikey data\n",
" \"\"\"\n",
" def __init__(self, intvMean, intvScale, distr, spikeValueMean, spikeValueStd, spikeMaxDuration, baseValue = 0):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" intvMean : interval mean\n",
" intvScale : interval std dev\n",
" distr : type of distr for interval\n",
" spikeValueMean : spike value mean\n",
" spikeValueStd : spike value std dev\n",
" spikeMaxDuration : max duration for spike\n",
" baseValue : base or offset value\n",
" \"\"\"\n",
" if distr == \"norm\":\n",
" self.intvSampler = NormalSampler(intvMean, intvScale)\n",
" elif distr == \"expo\":\n",
" rate = 1.0 / intvScale\n",
" self.intvSampler = ExponentialSampler(rate)\n",
" else:\n",
" raise ValueError(\"invalid distribution\")\n",
"\n",
" self.spikeSampler = NormalSampler(spikeValueMean, spikeValueStd)\n",
" self.spikeMaxDuration = spikeMaxDuration\n",
" self.baseValue = baseValue\n",
" self.inSpike = False\n",
" self.spikeCount = 0\n",
" self.baseCount = 0\n",
" self.baseLength = int(self.intvSampler.sample())\n",
" self.spikeValues = list()\n",
" self.spikeLength = None\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" sample new value\n",
" \"\"\"\n",
" if self.baseCount <= self.baseLength:\n",
" sampled = self.baseValue\n",
" self.baseCount += 1\n",
" else:\n",
" if not self.inSpike:\n",
" #starting spike\n",
" spikeVal = self.spikeSampler.sample()\n",
" self.spikeLength = sampleUniform(1, self.spikeMaxDuration)\n",
" spikeMaxPos = 0 if self.spikeLength == 1 else sampleUniform(0, self.spikeLength-1)\n",
" self.spikeValues.clear()\n",
" for i in range(self.spikeLength):\n",
" if i < spikeMaxPos:\n",
" frac = (i + 1) / (spikeMaxPos + 1)\n",
" frac = sampleFloatFromBase(frac, 0.1 * frac)\n",
" elif i > spikeMaxPos:\n",
" frac = (self.spikeLength - i) / (self.spikeLength - spikeMaxPos)\n",
" frac = sampleFloatFromBase(frac, 0.1 * frac)\n",
" else:\n",
" frac = 1.0\n",
" self.spikeValues.append(frac * spikeVal)\n",
" self.inSpike = True\n",
" self.spikeCount = 0\n",
"\n",
"\n",
" sampled = self.spikeValues[self.spikeCount]\n",
" self.spikeCount += 1\n",
"\n",
" if self.spikeCount == self.spikeLength:\n",
" #ending spike\n",
" self.baseCount = 0\n",
" self.baseLength = int(self.intvSampler.sample())\n",
" self.inSpike = False\n",
"\n",
" return sampled\n",
"\n",
"\n",
"class EventSampler:\n",
" \"\"\"\n",
" sample event\n",
" \"\"\"\n",
" def __init__(self, intvSampler, valSampler=None):\n",
" \"\"\"\n",
" initializer\n",
"\n",
" Parameters\n",
" intvSampler : interval sampler\n",
" valSampler : value sampler\n",
" \"\"\"\n",
" self.intvSampler = intvSampler\n",
" self.valSampler = valSampler\n",
" self.trigger = int(self.intvSampler.sample())\n",
" self.count = 0\n",
"\n",
" def reset(self):\n",
" \"\"\"\n",
" reset trigger\n",
" \"\"\"\n",
" self.trigger = int(self.intvSampler.sample())\n",
" self.count = 0\n",
"\n",
" def sample(self):\n",
" \"\"\"\n",
" sample event\n",
" \"\"\"\n",
" if self.count == self.trigger:\n",
" sampled = self.valSampler.sample() if self.valSampler is not None else 1.0\n",
" self.trigger = int(self.intvSampler.sample())\n",
" self.count = 0\n",
" else:\n",
" sample = 0.0\n",
" self.count += 1\n",
" return sampled\n",
"\n",
"\n",
"\n",
"\n",
"def createSampler(data):\n",
" \"\"\"\n",
" create sampler\n",
"\n",
" Parameters\n",
" data : sampler description\n",
" \"\"\"\n",
" #print(data)\n",
" items = data.split(\":\")\n",
" size = len(items)\n",
" dtype = items[-1]\n",
" stype = items[-2]\n",
" sampler = None\n",
" if stype == \"uniform\":\n",
" if dtype == \"int\":\n",
" min = int(items[0])\n",
" max = int(items[1])\n",
" sampler = UniformNumericSampler(min, max)\n",
" elif dtype == \"float\":\n",
" min = float(items[0])\n",
" max = float(items[1])\n",
" sampler = UniformNumericSampler(min, max)\n",
" elif dtype == \"categorical\":\n",
" values = items[:-2]\n",
" sampler = UniformCategoricalSampler(values)\n",
" elif stype == \"normal\":\n",
" mean = float(items[0])\n",
" sd = float(items[1])\n",
" sampler = NormalSampler(mean, sd)\n",
" if dtype == \"int\":\n",
" sampler.sampleAsIntValue()\n",
" elif stype == \"nonparam\":\n",
" if dtype == \"int\" or dtype == \"float\":\n",
" min = int(items[0])\n",
" binWidth = int(items[1])\n",
" values = items[2:-2]\n",
" values = list(map(lambda v: int(v), values))\n",
" sampler = NonParamRejectSampler(min, binWidth, values)\n",
" if dtype == \"float\":\n",
" sampler.sampleAsFloat()\n",
" elif dtype == \"categorical\":\n",
" values = list()\n",
" for i in range(0, size-2, 2):\n",
" cval = items[i]\n",
" dist = int(items[i+1])\n",
" pair = (cval, dist)\n",
" values.append(pair)\n",
" sampler = CategoricalRejectSampler(values)\n",
" elif stype == \"discrete\":\n",
" vmin = int(items[0])\n",
" vmax = int(items[1])\n",
" step = int(items[2])\n",
" values = list(map(lambda i : int(items[i]), range(3, len(items)-2)))\n",
" sampler = DiscreteRejectSampler(vmin, vmax, step, values)\n",
" else:\n",
" raise ValueError(\"invalid sampler type \" + dtype)\n",
" return sampler\n"
]
}
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|