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github
|
zhuhan1236/dhn-caffe-master
|
matcaffe_demo_vgg_mean_pix.m
|
.m
|
dhn-caffe-master/matlab/caffe/matcaffe_demo_vgg_mean_pix.m
| 3,069 |
utf_8
|
04b831d0f205ef0932c4f3cfa930d6f9
|
function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
%
% Demo of the matlab wrapper based on the networks used for the "VGG" entry
% in the ILSVRC-2014 competition and described in the tech. report
% "Very Deep Convolutional Networks for Large-Scale Image Recognition"
% http://arxiv.org/abs/1409.1556/
%
% INPUT
% im - color image as uint8 HxWx3
% use_gpu - 1 to use the GPU, 0 to use the CPU
% model_def_file - network configuration (.prototxt file)
% model_file - network weights (.caffemodel file)
%
% OUTPUT
% scores 1000-dimensional ILSVRC score vector
%
% EXAMPLE USAGE
% model_def_file = 'zoo/deploy.prototxt';
% model_file = 'zoo/model.caffemodel';
% use_gpu = true;
% im = imread('../../examples/images/cat.jpg');
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file);
%
% NOTES
% mean pixel subtraction is used instead of the mean image subtraction
%
% PREREQUISITES
% You may need to do the following before you start matlab:
% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
% Or the equivalent based on where things are installed on your system
% init caffe network (spews logging info)
matcaffe_init(use_gpu, model_def_file, model_file);
% mean BGR pixel
mean_pix = [103.939, 116.779, 123.68];
% prepare oversampled input
% input_data is Height x Width x Channel x Num
tic;
input_data = {prepare_image(im, mean_pix)};
toc;
% do forward pass to get scores
% scores are now Width x Height x Channels x Num
tic;
scores = caffe('forward', input_data);
toc;
scores = scores{1};
% size(scores)
scores = squeeze(scores);
% scores = mean(scores,2);
% [~,maxlabel] = max(scores);
% ------------------------------------------------------------------------
function images = prepare_image(im, mean_pix)
% ------------------------------------------------------------------------
IMAGE_DIM = 256;
CROPPED_DIM = 224;
% resize to fixed input size
im = single(im);
if size(im, 1) < size(im, 2)
im = imresize(im, [IMAGE_DIM NaN]);
else
im = imresize(im, [NaN IMAGE_DIM]);
end
% RGB -> BGR
im = im(:, :, [3 2 1]);
% oversample (4 corners, center, and their x-axis flips)
images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
indices_y = [0 size(im,1)-CROPPED_DIM] + 1;
indices_x = [0 size(im,2)-CROPPED_DIM] + 1;
center_y = floor(indices_y(2) / 2)+1;
center_x = floor(indices_x(2) / 2)+1;
curr = 1;
for i = indices_y
for j = indices_x
images(:, :, :, curr) = ...
permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
curr = curr + 1;
end
end
images(:,:,:,5) = ...
permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:), ...
[2 1 3]);
images(:,:,:,10) = images(end:-1:1, :, :, curr);
% mean BGR pixel subtraction
for c = 1:3
images(:, :, c, :) = images(:, :, c, :) - mean_pix(c);
end
|
github
|
willamowius/openmcu-master
|
echo_diagnostic.m
|
.m
|
openmcu-master/libs/speex/libspeex/echo_diagnostic.m
| 2,076 |
utf_8
|
8d5e7563976fbd9bd2eda26711f7d8dc
|
% Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'out_file'.
function out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
F=fopen(rec_file,'rb');
rec=fread(F,Inf,'short');
fclose (F);
F=fopen(play_file,'rb');
play=fread(F,Inf,'short');
fclose (F);
rec = [rec; zeros(1024,1)];
play = [play; zeros(1024,1)];
N = length(rec);
corr = real(ifft(fft(rec).*conj(fft(play))));
acorr = real(ifft(fft(play).*conj(fft(play))));
[a,b] = max(corr);
if b > N/2
b = b-N;
end
printf ("Far end to near end delay is %d samples\n", b);
if (b > .3*tail_length)
printf ('This is too much delay, try delaying the far-end signal a bit\n');
else if (b < 0)
printf ('You have a negative delay, the echo canceller has no chance to cancel anything!\n');
else
printf ('Delay looks OK.\n');
end
end
end
N2 = round(N/2);
corr1 = real(ifft(fft(rec(1:N2)).*conj(fft(play(1:N2)))));
corr2 = real(ifft(fft(rec(N2+1:end)).*conj(fft(play(N2+1:end)))));
[a,b1] = max(corr1);
if b1 > N2/2
b1 = b1-N2;
end
[a,b2] = max(corr2);
if b2 > N2/2
b2 = b2-N2;
end
drift = (b1-b2)/N2;
printf ('Drift estimate is %f%% (%d samples)\n', 100*drift, b1-b2);
if abs(b1-b2) < 10
printf ('A drift of a few (+-10) samples is normal.\n');
else
if abs(b1-b2) < 30
printf ('There may be (not sure) excessive clock drift. Is the capture and playback done on the same soundcard?\n');
else
printf ('Your clock is drifting! No way the AEC will be able to do anything with that. Most likely, you''re doing capture and playback from two different cards.\n');
end
end
end
acorr(1) = .001+1.00001*acorr(1);
AtA = toeplitz(acorr(1:tail_length));
bb = corr(1:tail_length);
h = AtA\bb;
out = (rec - filter(h, 1, play));
F=fopen(out_file,'w');
fwrite(F,out,'short');
fclose (F);
|
github
|
themattinthehatt/rlvm-master
|
myProcessOptions.m
|
.m
|
rlvm-master/lib/minConf/myProcessOptions.m
| 674 |
utf_8
|
b94d252a960faa95a3074129247619e6
|
function [varargout] = myProcessOptions(options,varargin)
% Similar to processOptions, but case insensitive and
% using a struct instead of a variable length list
options = toUpper(options);
for i = 1:2:length(varargin)
if isfield(options,upper(varargin{i}))
v = getfield(options,upper(varargin{i}));
if isempty(v)
varargout{(i+1)/2}=varargin{i+1};
else
varargout{(i+1)/2}=v;
end
else
varargout{(i+1)/2}=varargin{i+1};
end
end
end
function [o] = toUpper(o)
if ~isempty(o)
fn = fieldnames(o);
for i = 1:length(fn)
o = setfield(o,upper(fn{i}),getfield(o,fn{i}));
end
end
end
|
github
|
themattinthehatt/rlvm-master
|
minConf_PQN.m
|
.m
|
rlvm-master/lib/minConf/minConf_PQN.m
| 8,246 |
utf_8
|
982955326f59fecc4cf6993c3b7428aa
|
function [x,f,funEvals] = minConf_PQN(funObj,x,funProj,options)
% function [x,f] = minConf_PQN(funObj,funProj,x,options)
%
% Function for using a limited-memory projected quasi-Newton to solve problems of the form
% min funObj(x) s.t. x in C
%
% The projected quasi-Newton sub-problems are solved the spectral projected
% gradient algorithm
%
% @funObj(x): function to minimize (returns gradient as second argument)
% @funProj(x): function that returns projection of x onto C
%
% options:
% verbose: level of verbosity (0: no output, 1: final, 2: iter (default), 3:
% debug)
% optTol: tolerance used to check for optimality (default: 1e-5)
% progTol: tolerance used to check for progress (default: 1e-9)
% maxIter: maximum number of calls to funObj (default: 500)
% maxProject: maximum number of calls to funProj (default: 100000)
% numDiff: compute derivatives numerically (0: use user-supplied
% derivatives (default), 1: use finite differences, 2: use complex
% differentials)
% suffDec: sufficient decrease parameter in Armijo condition (default: 1e-4)
% corrections: number of lbfgs corrections to store (default: 10)
% adjustStep: use quadratic initialization of line search (default: 0)
% bbInit: initialize sub-problem with Barzilai-Borwein step (default: 1)
% SPGoptTol: optimality tolerance for SPG direction finding (default: 1e-6)
% SPGiters: maximum number of iterations for SPG direction finding (default:10)
nVars = length(x);
% Set Parameters
if nargin < 4
options = [];
end
[verbose,numDiff,optTol,progTol,maxIter,maxProject,suffDec,corrections,adjustStep,...
SPGoptTol,SPGprogTol,SPGiters,SPGtestOpt] = ...
myProcessOptions(...
options,'verbose',2,'numDiff',0,'optTol',1e-5,'progTol',1e-9,'maxIter',500,'maxProject',100000,'suffDec',1e-4,...
'corrections',10,'adjustStep',0,'SPGoptTol',1e-6,'SPGprogTol',1e-10,'SPGiters',10,'SPGtestOpt',0);
% Output Parameter Settings
if verbose >= 3
fprintf('Running PQN...\n');
fprintf('Number of L-BFGS Corrections to store: %d\n',corrections);
fprintf('Maximum number of SPG iterations: %d\n',SPGiters);
fprintf('SPG optimality tolerance: %.2e\n',SPGoptTol);
fprintf('SPG progress tolerance: %.2e\n',SPGprogTol);
fprintf('PQN optimality tolerance: %.2e\n',optTol);
fprintf('PQN progress tolerance: %.2e\n',progTol);
fprintf('Quadratic initialization of line search: %d\n',adjustStep);
fprintf('Maximum number of function evaluations: %d\n',maxIter);
fprintf('Maximum number of projections: %d\n',maxProject);
end
% Output Log
if verbose >= 2
fprintf('%10s %10s %10s %15s %15s %15s\n','Iteration','FunEvals','Projections','Step Length','Function Val','Opt Cond');
end
% Make objective function (if using numerical derivatives)
funEvalMultiplier = 1;
if numDiff
if numDiff == 2
useComplex = 1;
else
useComplex = 0;
end
funObj = @(x)autoGrad(x,useComplex,funObj);
funEvalMultiplier = nVars+1-useComplex;
end
% Project initial parameter vector
x = funProj(x);
projects = 1;
% Evaluate initial parameters
[f,g] = funObj(x);
funEvals = 1;
% Check Optimality of Initial Point
projects = projects+1;
if max(abs(funProj(x-g)-x)) < optTol
if verbose >= 1
fprintf('First-Order Optimality Conditions Below optTol at Initial Point\n');
end
return;
end
i = 1;
while funEvals <= maxIter
% Compute Step Direction
if i == 1
p = funProj(x-g);
projects = projects+1;
S = zeros(nVars,0);
Y = zeros(nVars,0);
Hdiag = 1;
else
y = g-g_old;
s = x-x_old;
[S,Y,Hdiag] = lbfgsUpdate(y,s,corrections,verbose==3,S,Y,Hdiag);
% Make Compact Representation
k = size(Y,2);
L = zeros(k);
for j = 1:k
L(j+1:k,j) = S(:,j+1:k)'*Y(:,j);
end
N = [S/Hdiag Y];
M = [S'*S/Hdiag L;L' -diag(diag(S'*Y))];
HvFunc = @(v)lbfgsHvFunc2(v,Hdiag,N,M);
% Solve Sub-problem
[p,subProjects] = solveSubProblem(x,g,HvFunc,funProj,SPGoptTol,SPGprogTol,SPGiters,SPGtestOpt);
projects = projects+subProjects;
end
d = p-x;
g_old = g;
x_old = x;
% Check that Progress can be made along the direction
gtd = g'*d;
if gtd > -progTol
if verbose >= 1
fprintf('Directional Derivative below progTol\n');
end
break;
end
% Select Initial Guess to step length
if i == 1 || adjustStep == 0
t = 1;
else
t = min(1,2*(f-f_old)/gtd);
end
% Bound Step length on first iteration
if i == 1
t = min(1,1/sum(abs(g)));
end
% Evaluate the Objective and Gradient at the Initial Step
if t == 1
x_new = p;
else
x_new = x + t*d;
end
[f_new,g_new] = funObj(x_new);
funEvals = funEvals+1;
% Backtracking Line Search
f_old = f;
while f_new > f + suffDec*g'*(x_new-x) || ~isLegal(f_new)
temp = t;
% Backtrack to next trial value
if ~isLegal(f_new) || ~isLegal(g_new)
if verbose == 3
fprintf('Halving Step Size\n');
end
t = t/2;
else
if verbose == 3
fprintf('Cubic Backtracking\n');
end
t = polyinterp([0 f gtd; t f_new g_new'*d]);
end
% Adjust if change is too small/large
if t < temp*1e-3
if verbose == 3
fprintf('Interpolated value too small, Adjusting\n');
end
t = temp*1e-3;
elseif t > temp*0.6
if verbose == 3
fprintf('Interpolated value too large, Adjusting\n');
end
t = temp*0.6;
end
% Check whether step has become too small
if max(abs(t*d)) < progTol || t == 0
if verbose == 3
fprintf('Line Search failed\n');
end
t = 0;
f_new = f;
g_new = g;
break;
end
% Evaluate New Point
f_prev = f_new;
t_prev = temp;
x_new = x + t*d;
[f_new,g_new] = funObj(x_new);
funEvals = funEvals+1;
end
% Take Step
x = x_new;
f = f_new;
g = g_new;
optCond = max(abs(funProj(x-g)-x));
projects = projects+1;
% Output Log
if verbose >= 2
fprintf('%10d %10d %10d %15.5e %15.5e %15.5e\n',i,funEvals*funEvalMultiplier,projects,t,f,optCond);
end
% Check optimality
if optCond < optTol
if verbose >= 1
fprintf('First-Order Optimality Conditions Below optTol\n');
end
break;
end
if max(abs(t*d)) < progTol
if verbose >= 1
fprintf('Step size below progTol\n');
end
break;
end
if abs(f-f_old) < progTol
if verbose >= 1
fprintf('Function value changing by less than progTol\n');
end
break;
end
if funEvals*funEvalMultiplier > maxIter
if verbose >= 1
fprintf('Function Evaluations exceeds maxIter\n');
end
break;
end
if projects > maxProject
if verbose >= 1
fprintf('Number of projections exceeds maxProject\n');
end
break;
end
i = i + 1;
% pause
end
end
function [p,subProjects] = solveSubProblem(x,g,H,funProj,optTol,progTol,maxIter,testOpt)
% Uses SPG to solve for projected quasi-Newton direction
options.verbose = 0;
options.optTol = optTol;
options.progTol = progTol;
options.maxIter = maxIter;
options.testOpt = testOpt;
options.feasibleInit = 1;
funObj = @(p)subHv(p,x,g,H);
[p,f,funEvals,subProjects] = minConf_SPG(funObj,x,funProj,options);
end
function [f,g] = subHv(p,x,g,HvFunc)
d = p-x;
Hd = HvFunc(d);
f = g'*d + (1/2)*d'*Hd;
g = g + Hd;
end
|
github
|
themattinthehatt/rlvm-master
|
minConf_QNST.m
|
.m
|
rlvm-master/lib/minConf/minConf_QNST.m
| 5,460 |
utf_8
|
d3af055fa412ac52b199c8238fc83783
|
function [x,f,funEvals] = minConf_QNST(funObj1,funObj2,x,funProj,options)
nVars = length(x);
if nargin < 5
options = [];
end
[verbose,numDiff,optTol,progTol,maxIter,maxProject,suffDec,corrections,adjustStep,bbInit,...
BBSToptTol,BBSTprogTol,BBSTiters,BBSTtestOpt] = ...
myProcessOptions(...
options,'verbose',2,'numDiff',0,'optTol',1e-5,'progTol',1e-9,'maxIter',500,'maxProject',100000,'suffDec',1e-4,...
'corrections',10,'adjustStep',0,'bbInit',0,'BBSToptTol',1e-6,'BBSTprogTol',1e-10,'BBSTiters',10,'BBSTtestOpt',0);
% Output Parameter Settings
if verbose >= 3
fprintf('Running QNST...\n');
fprintf('Number of L-BFGS Corrections to store: %d\n',corrections);
fprintf('Spectral initialization of BBST: %d\n',bbInit);
fprintf('Maximum number of BBST iterations: %d\n',BBSTiters);
fprintf('BBST optimality tolerance: %.2e\n',BBSToptTol);
fprintf('BBST progress tolerance: %.2e\n',BBSTprogTol);
fprintf('PQN optimality tolerance: %.2e\n',optTol);
fprintf('PQN progress tolerance: %.2e\n',progTol);
fprintf('Quadratic initialization of line search: %d\n',adjustStep);
fprintf('Maximum number of function evaluations: %d\n',maxIter);
fprintf('Maximum number of projections: %d\n',maxProject);
end
if verbose
fprintf('%10s %10s %10s %15s %15s\n','Iteration','FunEvals','Projections','Step Length','Function Val');
end
% Evaluate Initial Objective
[f1,g] = funObj1(x);
f = f1+funObj2(x);
funEvals = 1;
projects = 0;
% Check optimality
optCond = max(abs(x-funProj(x-g,1)));
projects = 1;
if optCond < optTol
if verbose >= 1
fprintf('First-Order Optimality Conditions Below optTol at Initial Point\n');
end
return;
end
i = 1;
while 1
if 0 % BBST
if i == 1
alpha = 1;
else
y = g-g_old;
s = x-x_old;
alpha = (s'*s)/(s'*y);
if alpha <= 1e-10 || alpha > 1e10
alpha = min(1,1/sum(abs(g)));
end
end
p = funProj(x-alpha*g,alpha);
projects = projects+1;
else % QNST
if i == 1
p = funProj(x-g,1);
projects = projects+1;
S = zeros(nVars,0);
Y = zeros(nVars,0);
Hdiag = 1;
else
y = g-g_old;
s = x-x_old;
[S,Y,Hdiag] = lbfgsUpdate(y,s,corrections,verbose==3,S,Y,Hdiag);
% Make Compact Representation
k = size(Y,2);
L = zeros(k);
for j = 1:k
L(j+1:k,j) = S(:,j+1:k)'*Y(:,j);
end
N = [S/Hdiag Y];
M = [S'*S/Hdiag L;L' -diag(diag(S'*Y))];
HvFunc = @(v)lbfgsHvFunc2(v,Hdiag,N,M);
if bbInit
% Use Barzilai-Borwein step to initialize sub-problem
alpha = (s'*s)/(s'*y);
if alpha <= 1e-10 || alpha > 1e10
alpha = 1/norm(g);
end
% Solve Sub-problem
xSubInit = funProj(x-alpha*g,alpha);
projects = projects+1;
else
xSubInit = x;
end
% Solve Sub-problem
[p,subProjects] = solveSubProblem(x,g,HvFunc,funObj2,funProj,BBSToptTol,BBSTprogTol,BBSTiters,BBSTtestOpt,xSubInit);
projects = projects+subProjects;
end
end
d = p-x;
g_old = g;
x_old = x;
% Bound Step length on first iteration
t = 1;
if i == 1
t = min(1,1/sum(abs(g)));
end
if t == 1
x_new = p;
else
x_new = x+t*d;
end
[f1_new,g_new] = funObj1(x_new);
f_new = f1_new + funObj2(x_new);
funEvals = funEvals+1;
f_old = f;
while f_new > f
if verbose
fprintf('Backtracking\n');
end
t = t/2;
x_new = x+t*d;
[f1_new,g_new] = funObj1(x_new);
f_new = f1_new + funObj2(x_new);
funEvals = funEvals+1;
end
x = x_new;
f = f_new;
g = g_new;
% Check Optimality
optCond = max(abs(x-funProj(x-g,1)));
projects = projects+1;
if verbose
fprintf('%10d %10d %10d %15.5e %15.5e %15.5e\n',i,funEvals,projects,t,f,optCond);
end
if optCond < optTol
if verbose
fprintf('First-order optimality below optTol\n');
end
break;
end
if max(abs(x-x_old)) < progTol
if verbose >= 1
fprintf('Step size below progTol\n');
end
break;
end
if abs(f-f_old) < progTol
if verbose >= 1
fprintf('Function value changing by less than progTol\n');
end
break;
end
if funEvals > maxIter
if verbose
fprintf('Exceeded maxIter funEvals\n');
end
break
end
i = i + 1;
end
end
function [p,subProjects] = solveSubProblem(x,g,H,funObj2,funProj,optTol,progTol,maxIter,testOpt,x_init)
% Uses BBST to solve for quasi-Newton soft-threshold direction
options.verbose = 0;
options.optTol = optTol;
options.progTol = progTol;
options.maxIter = maxIter;
options.testOpt = testOpt;
funObj = @(p)subHv(p,x,g,H);
[p,f,funEvals,subProjects] = minConf_BBST(funObj,funObj2,x_init,funProj,options);
end
function [f,g] = subHv(p,x,g,HvFunc)
d = p-x;
Hd = HvFunc(d);
f = g'*d + (1/2)*d'*Hd;
g = g + Hd;
end
|
github
|
themattinthehatt/rlvm-master
|
WolfeLineSearch.m
|
.m
|
rlvm-master/lib/minFunc_2012/minFunc/WolfeLineSearch.m
| 10,590 |
utf_8
|
f962bc5ae0a1e9f80202a9aaab106dab
|
function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS_interp,LS_multi,maxLS,progTol,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at starting location
% g: gradient at starting location
% gtd: directional derivative at starting location
% c1: sufficient decrease parameter
% c2: curvature parameter
% debug: display debugging information
% LS_interp: type of interpolation
% maxLS: maximum number of iterations
% progTol: minimum allowable step length
% doPlot: do a graphical display of interpolation
% funObj: objective function
% varargin: parameters of objective function
%
% Outputs:
% t: step length
% f_new: function value at x+t*d
% g_new: gradient value at x+t*d
% funEvals: number function evaluations performed by line search
% H: Hessian at initial guess (only computed if requested
% Evaluate the Objective and Gradient at the Initial Step
if nargout == 5
[f_new,g_new,H] = funObj(x + t*d,varargin{:});
else
[f_new,g_new] = funObj(x+t*d,varargin{:});
end
funEvals = 1;
gtd_new = g_new'*d;
% Bracket an Interval containing a point satisfying the
% Wolfe criteria
LSiter = 0;
t_prev = 0;
f_prev = f;
g_prev = g;
gtd_prev = gtd;
nrmD = max(abs(d));
done = 0;
while LSiter < maxLS
%% Bracketing Phase
if ~isLegal(f_new) || ~isLegal(g_new)
if debug
fprintf('Extrapolated into illegal region, switching to Armijo line-search\n');
end
t = (t + t_prev)/2;
% Do Armijo
if nargout == 5
[t,x_new,f_new,g_new,armijoFunEvals,H] = ArmijoBacktrack(...
x,t,d,f,f,g,gtd,c1,LS_interp,LS_multi,progTol,debug,doPlot,saveHessianComp,...
funObj,varargin{:});
else
[t,x_new,f_new,g_new,armijoFunEvals] = ArmijoBacktrack(...
x,t,d,f,f,g,gtd,c1,LS_interp,LS_multi,progTol,debug,doPlot,saveHessianComp,...
funObj,varargin{:});
end
funEvals = funEvals + armijoFunEvals;
return;
end
if f_new > f + c1*t*gtd || (LSiter > 1 && f_new >= f_prev)
bracket = [t_prev t];
bracketFval = [f_prev f_new];
bracketGval = [g_prev g_new];
break;
elseif abs(gtd_new) <= -c2*gtd
bracket = t;
bracketFval = f_new;
bracketGval = g_new;
done = 1;
break;
elseif gtd_new >= 0
bracket = [t_prev t];
bracketFval = [f_prev f_new];
bracketGval = [g_prev g_new];
break;
end
temp = t_prev;
t_prev = t;
minStep = t + 0.01*(t-temp);
maxStep = t*10;
if LS_interp <= 1
if debug
fprintf('Extending Braket\n');
end
t = maxStep;
elseif LS_interp == 2
if debug
fprintf('Cubic Extrapolation\n');
end
t = polyinterp([temp f_prev gtd_prev; t f_new gtd_new],doPlot,minStep,maxStep);
elseif LS_interp == 3
t = mixedExtrap(temp,f_prev,gtd_prev,t,f_new,gtd_new,minStep,maxStep,debug,doPlot);
end
f_prev = f_new;
g_prev = g_new;
gtd_prev = gtd_new;
if ~saveHessianComp && nargout == 5
[f_new,g_new,H] = funObj(x + t*d,varargin{:});
else
[f_new,g_new] = funObj(x + t*d,varargin{:});
end
funEvals = funEvals + 1;
gtd_new = g_new'*d;
LSiter = LSiter+1;
end
if LSiter == maxLS
bracket = [0 t];
bracketFval = [f f_new];
bracketGval = [g g_new];
end
%% Zoom Phase
% We now either have a point satisfying the criteria, or a bracket
% surrounding a point satisfying the criteria
% Refine the bracket until we find a point satisfying the criteria
insufProgress = 0;
Tpos = 2;
LOposRemoved = 0;
while ~done && LSiter < maxLS
% Find High and Low Points in bracket
[f_LO LOpos] = min(bracketFval);
HIpos = -LOpos + 3;
% Compute new trial value
if LS_interp <= 1 || ~isLegal(bracketFval) || ~isLegal(bracketGval)
if debug
fprintf('Bisecting\n');
end
t = mean(bracket);
elseif LS_interp == 2
if debug
fprintf('Grad-Cubic Interpolation\n');
end
t = polyinterp([bracket(1) bracketFval(1) bracketGval(:,1)'*d
bracket(2) bracketFval(2) bracketGval(:,2)'*d],doPlot);
else
% Mixed Case %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
nonTpos = -Tpos+3;
if LOposRemoved == 0
oldLOval = bracket(nonTpos);
oldLOFval = bracketFval(nonTpos);
oldLOGval = bracketGval(:,nonTpos);
end
t = mixedInterp(bracket,bracketFval,bracketGval,d,Tpos,oldLOval,oldLOFval,oldLOGval,debug,doPlot);
end
% Test that we are making sufficient progress
if min(max(bracket)-t,t-min(bracket))/(max(bracket)-min(bracket)) < 0.1
if debug
fprintf('Interpolation close to boundary');
end
if insufProgress || t>=max(bracket) || t <= min(bracket)
if debug
fprintf(', Evaluating at 0.1 away from boundary\n');
end
if abs(t-max(bracket)) < abs(t-min(bracket))
t = max(bracket)-0.1*(max(bracket)-min(bracket));
else
t = min(bracket)+0.1*(max(bracket)-min(bracket));
end
insufProgress = 0;
else
if debug
fprintf('\n');
end
insufProgress = 1;
end
else
insufProgress = 0;
end
% Evaluate new point
if ~saveHessianComp && nargout == 5
[f_new,g_new,H] = funObj(x + t*d,varargin{:});
else
[f_new,g_new] = funObj(x + t*d,varargin{:});
end
funEvals = funEvals + 1;
gtd_new = g_new'*d;
LSiter = LSiter+1;
armijo = f_new < f + c1*t*gtd;
if ~armijo || f_new >= f_LO
% Armijo condition not satisfied or not lower than lowest
% point
bracket(HIpos) = t;
bracketFval(HIpos) = f_new;
bracketGval(:,HIpos) = g_new;
Tpos = HIpos;
else
if abs(gtd_new) <= - c2*gtd
% Wolfe conditions satisfied
done = 1;
elseif gtd_new*(bracket(HIpos)-bracket(LOpos)) >= 0
% Old HI becomes new LO
bracket(HIpos) = bracket(LOpos);
bracketFval(HIpos) = bracketFval(LOpos);
bracketGval(:,HIpos) = bracketGval(:,LOpos);
if LS_interp == 3
if debug
fprintf('LO Pos is being removed!\n');
end
LOposRemoved = 1;
oldLOval = bracket(LOpos);
oldLOFval = bracketFval(LOpos);
oldLOGval = bracketGval(:,LOpos);
end
end
% New point becomes new LO
bracket(LOpos) = t;
bracketFval(LOpos) = f_new;
bracketGval(:,LOpos) = g_new;
Tpos = LOpos;
end
if ~done && abs(bracket(1)-bracket(2))*nrmD < progTol
if debug
fprintf('Line-search bracket has been reduced below progTol\n');
end
break;
end
end
%%
if LSiter == maxLS
if debug
fprintf('Line Search Exceeded Maximum Line Search Iterations\n');
end
end
[f_LO LOpos] = min(bracketFval);
t = bracket(LOpos);
f_new = bracketFval(LOpos);
g_new = bracketGval(:,LOpos);
% Evaluate Hessian at new point
if nargout == 5 && funEvals > 1 && saveHessianComp
[f_new,g_new,H] = funObj(x + t*d,varargin{:});
funEvals = funEvals + 1;
end
end
%%
function [t] = mixedExtrap(x0,f0,g0,x1,f1,g1,minStep,maxStep,debug,doPlot);
alpha_c = polyinterp([x0 f0 g0; x1 f1 g1],doPlot,minStep,maxStep);
alpha_s = polyinterp([x0 f0 g0; x1 sqrt(-1) g1],doPlot,minStep,maxStep);
if alpha_c > minStep && abs(alpha_c - x1) < abs(alpha_s - x1)
if debug
fprintf('Cubic Extrapolation\n');
end
t = alpha_c;
else
if debug
fprintf('Secant Extrapolation\n');
end
t = alpha_s;
end
end
%%
function [t] = mixedInterp(bracket,bracketFval,bracketGval,d,Tpos,oldLOval,oldLOFval,oldLOGval,debug,doPlot);
% Mixed Case %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
nonTpos = -Tpos+3;
gtdT = bracketGval(:,Tpos)'*d;
gtdNonT = bracketGval(:,nonTpos)'*d;
oldLOgtd = oldLOGval'*d;
if bracketFval(Tpos) > oldLOFval
alpha_c = polyinterp([oldLOval oldLOFval oldLOgtd
bracket(Tpos) bracketFval(Tpos) gtdT],doPlot);
alpha_q = polyinterp([oldLOval oldLOFval oldLOgtd
bracket(Tpos) bracketFval(Tpos) sqrt(-1)],doPlot);
if abs(alpha_c - oldLOval) < abs(alpha_q - oldLOval)
if debug
fprintf('Cubic Interpolation\n');
end
t = alpha_c;
else
if debug
fprintf('Mixed Quad/Cubic Interpolation\n');
end
t = (alpha_q + alpha_c)/2;
end
elseif gtdT'*oldLOgtd < 0
alpha_c = polyinterp([oldLOval oldLOFval oldLOgtd
bracket(Tpos) bracketFval(Tpos) gtdT],doPlot);
alpha_s = polyinterp([oldLOval oldLOFval oldLOgtd
bracket(Tpos) sqrt(-1) gtdT],doPlot);
if abs(alpha_c - bracket(Tpos)) >= abs(alpha_s - bracket(Tpos))
if debug
fprintf('Cubic Interpolation\n');
end
t = alpha_c;
else
if debug
fprintf('Quad Interpolation\n');
end
t = alpha_s;
end
elseif abs(gtdT) <= abs(oldLOgtd)
alpha_c = polyinterp([oldLOval oldLOFval oldLOgtd
bracket(Tpos) bracketFval(Tpos) gtdT],...
doPlot,min(bracket),max(bracket));
alpha_s = polyinterp([oldLOval sqrt(-1) oldLOgtd
bracket(Tpos) bracketFval(Tpos) gtdT],...
doPlot,min(bracket),max(bracket));
if alpha_c > min(bracket) && alpha_c < max(bracket)
if abs(alpha_c - bracket(Tpos)) < abs(alpha_s - bracket(Tpos))
if debug
fprintf('Bounded Cubic Extrapolation\n');
end
t = alpha_c;
else
if debug
fprintf('Bounded Secant Extrapolation\n');
end
t = alpha_s;
end
else
if debug
fprintf('Bounded Secant Extrapolation\n');
end
t = alpha_s;
end
if bracket(Tpos) > oldLOval
t = min(bracket(Tpos) + 0.66*(bracket(nonTpos) - bracket(Tpos)),t);
else
t = max(bracket(Tpos) + 0.66*(bracket(nonTpos) - bracket(Tpos)),t);
end
else
t = polyinterp([bracket(nonTpos) bracketFval(nonTpos) gtdNonT
bracket(Tpos) bracketFval(Tpos) gtdT],doPlot);
end
end
|
github
|
themattinthehatt/rlvm-master
|
minFunc_processInputOptions.m
|
.m
|
rlvm-master/lib/minFunc_2012/minFunc/minFunc_processInputOptions.m
| 4,103 |
utf_8
|
8822581c3541eabe5ce7c7927a57c9ab
|
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,optTol,progTol,method,...
corrections,c1,c2,LS_init,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,useNegCurv,precFunc,...
LS_type,LS_interp,LS_multi,DerivativeCheck] = ...
minFunc_processInputOptions(o)
% Constants
SD = 0;
CSD = 1;
BB = 2;
CG = 3;
PCG = 4;
LBFGS = 5;
QNEWTON = 6;
NEWTON0 = 7;
NEWTON = 8;
TENSOR = 9;
verbose = 1;
verboseI= 1;
debug = 0;
doPlot = 0;
method = LBFGS;
cgSolve = 0;
o = toUpper(o);
if isfield(o,'DISPLAY')
switch(upper(o.DISPLAY))
case 0
verbose = 0;
verboseI = 0;
case 'FINAL'
verboseI = 0;
case 'OFF'
verbose = 0;
verboseI = 0;
case 'NONE'
verbose = 0;
verboseI = 0;
case 'FULL'
debug = 1;
case 'EXCESSIVE'
debug = 1;
doPlot = 1;
end
end
DerivativeCheck = 0;
if isfield(o,'DERIVATIVECHECK')
switch(upper(o.DERIVATIVECHECK))
case 1
DerivativeCheck = 1;
case 'ON'
DerivativeCheck = 1;
end
end
LS_init = 0;
LS_type = 1;
LS_interp = 2;
LS_multi = 0;
Fref = 1;
Damped = 0;
HessianIter = 1;
c2 = 0.9;
if isfield(o,'METHOD')
m = upper(o.METHOD);
switch(m)
case 'TENSOR'
method = TENSOR;
case 'NEWTON'
method = NEWTON;
case 'MNEWTON'
method = NEWTON;
HessianIter = 5;
case 'PNEWTON0'
method = NEWTON0;
cgSolve = 1;
case 'NEWTON0'
method = NEWTON0;
case 'QNEWTON'
method = QNEWTON;
Damped = 1;
case 'LBFGS'
method = LBFGS;
case 'BB'
method = BB;
LS_type = 0;
Fref = 20;
case 'PCG'
method = PCG;
c2 = 0.2;
LS_init = 2;
case 'SCG'
method = CG;
c2 = 0.2;
LS_init = 4;
case 'CG'
method = CG;
c2 = 0.2;
LS_init = 2;
case 'CSD'
method = CSD;
c2 = 0.2;
Fref = 10;
LS_init = 2;
case 'SD'
method = SD;
LS_init = 2;
end
end
maxFunEvals = getOpt(o,'MAXFUNEVALS',1000);
maxIter = getOpt(o,'MAXITER',500);
optTol = getOpt(o,'OPTTOL',1e-5);
progTol = getOpt(o,'PROGTOL',1e-9);
corrections = getOpt(o,'CORRECTIONS',100);
corrections = getOpt(o,'CORR',corrections);
c1 = getOpt(o,'C1',1e-4);
c2 = getOpt(o,'C2',c2);
LS_init = getOpt(o,'LS_INIT',LS_init);
cgSolve = getOpt(o,'CGSOLVE',cgSolve);
qnUpdate = getOpt(o,'QNUPDATE',3);
cgUpdate = getOpt(o,'CGUPDATE',2);
initialHessType = getOpt(o,'INITIALHESSTYPE',1);
HessianModify = getOpt(o,'HESSIANMODIFY',0);
Fref = getOpt(o,'FREF',Fref);
useComplex = getOpt(o,'USECOMPLEX',0);
numDiff = getOpt(o,'NUMDIFF',0);
LS_saveHessianComp = getOpt(o,'LS_SAVEHESSIANCOMP',1);
Damped = getOpt(o,'DAMPED',Damped);
HvFunc = getOpt(o,'HVFUNC',[]);
bbType = getOpt(o,'BBTYPE',0);
cycle = getOpt(o,'CYCLE',3);
HessianIter = getOpt(o,'HESSIANITER',HessianIter);
outputFcn = getOpt(o,'OUTPUTFCN',[]);
useMex = getOpt(o,'USEMEX',1);
useNegCurv = getOpt(o,'USENEGCURV',1);
precFunc = getOpt(o,'PRECFUNC',[]);
LS_type = getOpt(o,'LS_type',LS_type);
LS_interp = getOpt(o,'LS_interp',LS_interp);
LS_multi = getOpt(o,'LS_multi',LS_multi);
end
function [v] = getOpt(options,opt,default)
if isfield(options,opt)
if ~isempty(getfield(options,opt))
v = getfield(options,opt);
else
v = default;
end
else
v = default;
end
end
function [o] = toUpper(o)
if ~isempty(o)
fn = fieldnames(o);
for i = 1:length(fn)
o = setfield(o,upper(fn{i}),getfield(o,fn{i}));
end
end
end
|
github
|
levyfan/bow-master
|
evalData.m
|
.m
|
bow-master/src/main/matlab/KISSME/toolbox/evalData.m
| 4,143 |
utf_8
|
fa4260fdbaa73509795057250201aece
|
function [ds,rocPlot] = evalData(pairs, ds, params)
% EVALDATA Evaluate results and plot figures
%
% Input:
% pairs - [1xN] struct. N is the number of pairs. Fields: pairs.fold
% pairs.match, pairs.img1, pairs.img2.
% ds - [1xF] data struct. F is the number of folds.
% ds.method.dist is required to compute tpr, fpr, etc.
% params - Parameter struct with the following fields:
% params.title - Title for ROC plot.
% params.saveDir - Directory to which all the plots are saved
%
% Output:
% ds - Augmented result data struct
% rocPlot - handle to the ROC figure
%
% copyright by Martin Koestinger (2011)
% Graz University of Technology
% contact [email protected]
%
% For more information, see <a href="matlab:
% web('http://lrs.icg.tugraz.at/members/koestinger')">the ICG Web site</a>.
%
if ~isfield(params,'title')
params.title = 'ROC';
end
matches = logical([pairs.match]);
%-- EVAL FOLDS --%
un = unique([pairs.fold]);
for c=1:length(un)
testMask = [pairs.fold] == un(c);
% eval fold
names = fieldnames(ds(c));
for nameCounter=1:length(names)
%tpr, fpr
[ds(c).(names{nameCounter}).tpr, ds(c).(names{nameCounter}).fpr] = ...
icg_roc(matches(testMask),-ds(c).(names{nameCounter}).dist);
[ignore, ds(c).(names{nameCounter}).eerIdx] = min(abs(ds(c).(names{nameCounter}).tpr ...
- (1-ds(c).(names{nameCounter}).fpr)));
%eer
ds(c).(names{nameCounter}).eer = ...
ds(c).(names{nameCounter}).tpr(ds(c).(names{nameCounter}).eerIdx);
end
h = myplotroc(ds(c),matches(testMask),names,params);
title(sprintf('%s Fold: %d',params.title, c));
%save figure if save dir is specified
if isfield(params,'saveDir')
exportAndCropFigure(h,sprintf('Fold%d',c),params.saveDir);
end
close;
end
%-- EVAL ALL --%
names = fieldnames(ds);
for nameCounter=1:length(names)
s = [ds.(names{nameCounter})];
ms.(names{nameCounter}).std = std([s.eer]);
ms.(names{nameCounter}).dist = [s.dist];
ms.(names{nameCounter}).se = ms.(names{nameCounter}).std/sqrt(length(un));
[ms.(names{nameCounter}).tpr, ms.(names{nameCounter}).fpr, ms.(names{nameCounter}).thresh] = icg_roc(matches,-[s.dist]);
[ignore, ms.(names{nameCounter}).eerIdx] = min(abs(ms.(names{nameCounter}).tpr ...
- (1-ms.(names{nameCounter}).fpr)));
ms.(names{nameCounter}).eer = ms.(names{nameCounter}).tpr(ms.(names{nameCounter}).eerIdx);
ms.(names{nameCounter}).type = names{nameCounter};
ms.(names{nameCounter}).roccolor = s(1).roccolor;
end
[rocPlot.h,rocPlot.hL] = myplotroc(ms,matches,names,params);
if isfield(params,'saveDir')
exportAndCropFigure(rocPlot.h,'overall.png',params.saveDir);
end
end
%--------------------------------------------------------------------------
function [h,l] = myplotroc(ds,matches,names,params)
legendEntries = cell(1,length(names));
rocColors = prism(length(names)); %hsv(length(names))
for nameCounter=1:length(names)
roccolor = rocColors(nameCounter,:);
if isfield(ds.(names{nameCounter}),'roccolor');
roccolor = ds.(names{nameCounter}).roccolor;
end
%plot roc
if nameCounter==1
h = icg_plotroc(matches,-ds.(names{nameCounter}).dist);
hold on; plot(ds.(names{nameCounter}).fpr,ds.(names{nameCounter}).tpr,'Color',roccolor,'LineWidth',2,'LineStyle','-'); hold off;
else
hold on; plot(ds.(names{nameCounter}).fpr,ds.(names{nameCounter}).tpr,'Color',roccolor,'LineWidth',2,'LineStyle','-'); hold off;
end
legendEntries{nameCounter} = sprintf('%s (%.3f)',upper(names{nameCounter}),ds.(names{nameCounter}).eer);
end
grid on;
ha = get(gca);
set(get(get(ha.Children(end),'Annotation'),'LegendInformation'),'IconDisplayStyle','off');
set(get(get(ha.Children(end-1),'Annotation'),'LegendInformation'),'IconDisplayStyle','off');
l = legend(legendEntries,'Location', 'SouthEast');
drawnow;
end
%--------------------------------------------------------------------------
|
github
|
levyfan/bow-master
|
LearnAlgoLMNN.m
|
.m
|
bow-master/src/main/matlab/KISSME/toolbox/learnAlgos/LearnAlgoLMNN.m
| 2,829 |
utf_8
|
f833d30dfe0476ecab72fc14f7cacc8a
|
%LEARNALGOLMNN Wrapper class to the actual LMNN code
classdef LearnAlgoLMNN < LearnAlgo
properties
p %parameters
s %struct
available
fhanlde
end
properties (Constant)
type = 'lmnn'
end
methods
function obj = LearnAlgoLMNN(p)
if nargin < 1
p = struct();
end
if ~isfield(p,'knn')
p.knn = 1;
end
if ~isfield(p,'maxiter')
p.maxiter = 1000; %std
end
if ~isfield(p,'validation')
p.validation = 0;
end
if ~isfield(p,'roccolor')
p.roccolor = 'k';
end
if ~isfield(p,'quiet')
p.quiet = 1;
end
obj.p = p;
check(obj);
end
function bool = check(obj)
bool = exist('lmnn.m') == 2;
if ~bool
fprintf('Sorry %s not available\n',obj.type);
end
obj.fhanlde = @lmnn;
if isunix && exist('lmnn2.m') == 2;
obj.fhanlde = @lmnn2;
end
obj.available = bool;
end
function s = learnPairwise(obj,X,idxa,idxb,matches)
if ~obj.available
s = struct();
return;
end
obj.p.knn = 1;
X = X(:,[idxa(matches) idxb(matches)]); %m x d
y = [1:sum(matches) 1:sum(matches)];
tic;
[s.L, s.Det] = obj.fhanlde(X,consecutiveLabels(y),obj.p.knn, ...
'maxiter',obj.p.maxiter,'validation',obj.p.validation, ...
'quiet',obj.p.quiet);
s.M = s.L'*s.L;
s.t = toc;
s.learnAlgo = obj;
s.roccolor = obj.p.roccolor;
end
function s = learn(obj,X,y)
if ~obj.available
s = struct();
return;
end
tic;
[s.L, s.Det] = obj.fhanlde(X,consecutiveLabels(y),obj.p.knn, ...
'maxiter', obj.p.maxiter,'validation',obj.p.validation, ...
'quiet',obj.p.quiet);
s.M = s.L'*s.L;
s.t = toc;
s.learnAlgo = obj;
s.roccolor = obj.p.roccolor;
end
function d = dist(obj, s, X, idxa,idxb)
d = cdistM(s.M,X,idxa,idxb);
end
end
end
% lmnn2 needs consecutive integers as labels
function ty = consecutiveLabels(y)
uniqueLabels = unique(y);
ty = zeros(size(y));
for cY=1:length(uniqueLabels)
mask = y == uniqueLabels(cY);
ty(mask ) = cY;
end
end
|
github
|
levyfan/bow-master
|
icg_roc.m
|
.m
|
bow-master/src/main/matlab/KISSME/toolbox/helper/icg_roc.m
| 1,425 |
utf_8
|
11d04e9c4c3db15aa1c3b9b771eff30e
|
function [tpr,fpr,thresh] = icg_roc(tp,confs)
% ICG_ROC computes ROC measures (tpr,fpr)
%
% Input:
% tp - [m x n] matrix of zero-one labels. one row per class.
% confs - [m x n] matrix of classifier scores. one row per class.
%
% Output:
% tpr - true positive rate in interval [0,1], [m x n+1] matrix
% fpr - false positive rate in interval [0,1], [m x n+1] matrix
% confs - thresholds over interval
%
% Example:
% icg_plotroc([ones(1,10) zeros(1,10)],20:-1:1);
% produces a perfect step curve
%
% copyright by Martin Koestinger (2011)
% Graz University of Technology
% contact [email protected]
%
% For more information, see <a href="matlab:
% web('http://lrs.icg.tugraz.at/members/koestinger')">the ICG Web site</a>.
%
m = size(tp,1);
n = size(tp,2);
tpr = zeros(m,n+1);
fpr = zeros(m,n+1);
thresh = zeros(m,n);
for c=1:m
[tpr(c,:),fpr(c,:),thresh(c,:)] = icg_roc_one_class(tp(c,:),confs(c,:));
end
end
function [tpr,fpr,confs] = icg_roc_one_class(tp,confs)
[confs,idx] = sort(confs,'descend');
tps = tp(idx);
% calc recall/precision
tpr = zeros(1,numel(tps));
fpr = zeros(1,numel(tps));
tp = 0;
fp = 0;
tn = sum(tps < 0.5);
fn = numel(tps) - tn;
for i=1:numel(tps)
if tps(i) > 0.5
tp = tp + 1;
fn = fn - 1;
else
fp = fp + 1;
tn = tn - 1;
end
tpr(i) = tp/(tp+fn);
fpr(i) = fp/(fp+tn);
end
fpr = [0 fpr];
tpr = [0 tpr];
end
|
github
|
levyfan/bow-master
|
lmnn.m
|
.m
|
bow-master/src/main/matlab/KISSME/toolbox/lib/LMNN/lmnn.m
| 15,400 |
utf_8
|
cb91112611f161bfe0a081b291878dea
|
function [L,Det]=lmnn(x,y,varargin);
%
% function [L,Det]=lmnn(maxiter,L,x,y,Kg,'Parameter1',Value1,'Parameter2',Value2,...);
%
% Input:
%
% x = input matrix (each column is an input vector)
% y = labels
% (*optional*) L = initial transformation matrix (e.g eye(size(x,1)))
% (*optional*) Kg = attract Kg nearest similar labeled vectos
%
% Parameters:
% stepsize = (default 1e-09)
% tempid = (def 0) saves state every 10 iterations in temp#.mat
% save = (def 0) save the initial computation
% skip = (def 0) loads the initial computation instead of
% recomputing (only works if previous run was on exactly the same data)
% correction = (def 15) how many steps between each update
% The number of impostors are fixed for until next "correction"
% factor = (def 1.1) multiplicative factor by which the
% "correction" gab increases
% obj = (def 1) if 1, solver solves in L, if 0, solver solves in L'*L
% thresho = (def 1e-9) cut off for change in objective function (if
% improvement is less, stop)
% thresha = (def 1e-22) cut off for stepsize, if stepsize is
% smaller stop
% validation = (def 0) fraction of training data to be used as
% validation set (best output is stored in Det.bestL)
% valcount = (def 50) every "valcount" steps do validation
% maxiter = maximum number of iterations (default: 10000)
% scale = (def. 0) if 1, all data gets re-scaled s.t. average
% distance to closest neighbor is 1
% quiet = {0,1} surpress output (default=0)
%
%
% Output:
%
% L = linear transformation xnew=L*x
%
% Det.obj = objective function over time
% Det.nimp = number of impostors over time
% Det.pars = all parameters used in run
% Det.time = time needed for computation
% Det.iter = number of iterations
% Det.verify = verify (results of validation - if used)
%
% Version 1.0
% copyright by Kilian Q. Weinbergerr (2005)
% University of Pennsylvania
% contact [email protected]
%
tempnum=num2str(round(rand(1).*1000));
fprintf('Tempfile: %s\n',tempnum);
fprintf('LMNN stable\n');
if(nargin==0)
help lmnn;
return;
end;
if(length(varargin)>0 & isnumeric(varargin{1}))
% check if neighborhood or L have been passed on
Kg=varargin{1};
fprintf('Setting neighborhood to k=%i\n',Kg);
if(length(varargin)>1 & ~isstr(varargin{2}))
L=varargin{2};
fprintf('Setting initial transformation!\n');
end;
% skip Kgand L parameters
newvarargin={};copy=0;j=1;
for i=1:length(varargin)
if(isstr(varargin{i})) copy=1;end;
if(copy)newvarargin{j}=varargin{i};j=j+1;end;
end;
varargin=newvarargin;
clear('newvarargin','copy');
else
fprintf('Neigborhood size not specified. Setting k=3\n');
Kg=3;
end;
if(exist('L')~=1)
fprintf(['Initial starting point not specified.\nStarting with identity matrix.\n']);
L=eye(size(x,1));
end;
tic
% checks
D=size(L,2);
x=x(1:D,:);
if(size(x,1)>length(L)) error('x and L must have matching dimensions!\n');end;
% set parameters
pars.stepsize=1e-07;
pars.minstepsize=0;
pars.tempid=-1;
pars.save=0;
pars.skip=0;
pars.maxiter=10000;
pars.factor=1.1;
pars.correction=15;
pars.thresho=1e-7;
pars.thresha=1e-22;
pars.ifraction=1;
pars.scale=0;
pars.obj=1;
pars.union=1;
pars.tabularasa=Inf;
pars.quiet=0;
pars.validation=0;
pars.validationstep=50;
pars.earlystopping=0;
pars.aggressive=0;
pars.valcount=50;
pars.stepgrowth=1.01;
pars.reg=0;
pars.weight1=0.5;
pars.maximp=100000;
pars.fifo=0;
pars.guard=0;
pars.graphics=0;
pars=extractpars(varargin,pars);
% verification dataset
%i=randperm(size(x,2));
i=1:size(x,2);
tr=ceil(size(x,2)*(1-pars.validation));
ve=size(x,2)-tr;
xo=x;
yo=y;
x=xo(:,i(1:tr));
y=yo(i(1:tr));
xv=xo(:,i(tr+1:end));
yv=yo(i(tr+1:end));
verify=[];besterr=inf;
clear('xo','yo');
lowesterr=inf;
verify=[];
bestL=L;
if(~pars.quiet)
pars
end;
tempname=sprintf('temp%i.mat',pars.tempid);
% Initializationip
[D,N]=size(x);
fprintf('%i input vectors with %i dimensions\n',N,D);
[gen,NN]=getGenLS(x,y,Kg,pars);
obj=zeros(1,pars.maxiter);
nimp=zeros(1,pars.maxiter);
if(~pars.quiet) fprintf('Total number of genuine pairs: %i\n',size(gen,2));end;
[imp]= checkup(L,x,y,NN(end,:),pars);
if(~pars.quiet)fprintf('Total number of imposture pairs: %i\n',size(imp,2));end;
if(pars.reg==1)
dfG=vec(eye(D));
else
dfG=vec(SOD(x,gen(1,:),gen(2,:)));
end;
% Fifo=zeros(1,size(imp,2));
if(pars.scale)
Lx=L*x;
sc=sqrt(mean(sum( ((Lx-Lx(:,NN(end,:)))).^2)));
L=L./sc;
end;
df=zeros(D^2,1);
correction=pars.correction;
tabularasa=pars.tabularasa;
ifraction=pars.ifraction;
stepsize=pars.stepsize;
lastcor=1;
if(pars.graphics)
hpl=scat(L*x,3,y+0,'size',120);
end;
for nnid=1:Kg; a1{nnid}=[];a2{nnid}=[];end;
df=zeros(size(dfG));
% Main Loop
for iter=1:pars.maxiter
% save old position
Lold=L;dfold=df;
for nnid=1:Kg; a1old{nnid}=a1{nnid};a2old{nnid}=a2{nnid};end;
if(iter>1)L=step(L,mat((dfG.*pars.weight1+df.*(1-pars.weight1))),stepsize,pars);end;
if(~pars.quiet)fprintf('%i.',iter);end;
Lx=L*x;
%Lx2=sum(Lx.^2);
totalactive=0;
if(pars.graphics & mod(iter,10)==0)
set(hpl,'XData',Lx(1,:),'YData',Lx(2,:));
set(hpl,'YData',Lx(3,:));
set(hpl,'CData',y);
axis tight;
drawnow;
end;
g0=cdist(Lx,imp(1,:),imp(2,:));
if(pars.guard) kk=Kg;
else kk=1;
end;
for nnid=kk:Kg
Ni(nnid,1:N)=(sum((Lx-Lx(:,NN(nnid,:))).^2)+1);
end;
g1=Ni(:,imp(1,:));
g2=Ni(:,imp(2,:));
act1=[];act2=[];
if(pars.validation>0 & (mod(iter,pars.validationstep)==0 | iter==1))
verify=[verify Ltest2in(Lx,y,L*xv,yv,Ni,Kg,pars)];
if(verify(end)<=besterr) fprintf('*');besterr=verify(end);bestL= ...
L;Det.bestiter=iter;
end;
if(pars.earlystopping>0 & length(verify)>pars.earlystopping & all(verify(end-pars.earlystopping:end)>besterr)) ...
fprintf('Validation error is no longer improving!\n');break;
end;
end;
clear('Lx','Lx2');
% objv=dfG'*vec((L'*L));
for nnid=Kg:-1:kk
act1=find(g0<g1(nnid,:));
act2=find(g0<g2(nnid,:));
active=[act1 act2];
if(~isempty(a1{nnid}) | ~isempty(a2{nnid}))
try
[plus1,minus1]=sd(act1(:)',a1{nnid}(:)');
[plus2,minus2]=sd(act2(:)',a2{nnid}(:)');
catch
lasterr
keyboard;
end;
else
plus1=act1;plus2=act2;
minus1=[];minus2=[];
end;
% [isminus2,i]=sort(imp(1,minus2));minus2=minus2(i);
MINUS1a=[imp(1,minus1) imp(2,minus2)]; MINUS1b=[imp(1,[plus1 plus2])];
MINUS2a=[NN(nnid,imp(1,minus1)) NN(nnid,imp(2,minus2))]; MINUS2b=[imp(2,[plus1 plus2])];
[isplus2,i]= sort(imp(2,plus2));plus2=plus2(i);
PLUS1a=[imp(1,plus1) isplus2]; PLUS1b=[imp(1,[minus1 minus2])];
PLUS2a=[NN(nnid,imp(1,plus1)) NN(nnid,isplus2)]; PLUS2b=[imp(2,[minus1 minus2])];
loss1=max(g1(nnid,:)-g0,0);
loss2=max(g2(nnid,:)-g0,0);
% ;
[PLUS ,pweight]=count([PLUS1a;PLUS2a]);
[MINUS,mweight]=count([MINUS1a;MINUS2a]);
df2=SODW(x,PLUS(1,:),PLUS(2,:),pweight)-SODW(x,MINUS(1,:),MINUS(2,:),mweight);
df4=SOD(x,PLUS1b,PLUS2b)-SOD(x,MINUS1b,MINUS2b);
df=df+vec(df2+df4);
a1{nnid}=act1;a2{nnid}=act2;
totalactive=totalactive+length(active);
end;
if(any(any(isnan(df))))
fprintf('Gradient has NaN value!\n');
keyboard;
end;
%obj(iter)=objv;
obj(iter)=(dfG.*pars.weight1+df.*(1-pars.weight1))'*vec(L'*L)+totalactive.*(1-pars.weight1);
if(isnan(obj(iter)))
fprintf('Obj is NAN!\n');
keyboard;
end;
nimp(iter)=totalactive;
delta=obj(iter)-obj(max(iter-1,1));
if(~pars.quiet)fprintf([' Obj:%2.2f Nimp:%i Delta:%2.4f max(G):' ...
' %2.4f' ...
' \n '],obj(iter),nimp(iter),delta,max(max(abs(df))));
end;
if(iter>1 & delta>0 & correction~=pars.correction)
stepsize=stepsize*0.5;
fprintf('***correcting stepsize***\n');
if(stepsize<pars.minstepsize) stepsize=pars.minstepsize;end;
if(~pars.aggressive)
L=Lold;
df=dfold;
for nnid=1:Kg; a1{nnid}=a1old{nnid};a2{nnid}=a2old{nnid};end;
obj(iter)=obj(iter-1);
end;
% correction=1;
hitwall=1;
else
if(correction~=pars.correction)stepsize=stepsize*pars.stepgrowth;end;
hitwall=0;
end;
if(iter>10 & (max(abs(diff(obj(iter-3:iter))))<pars.thresho*obj(iter) ...
| stepsize<pars.thresha))
if(pars.correction-correction>=5)
correction=1;
else
switch(pars.obj)
case 0
if(~pars.quiet)fprintf('Stepsize too small. No more progress!\n');end;
break;
case 1
pars.obj=0;
pars.correction=15;
pars.stepsize=1e-9;
correction=min(correction,pars.correction);
if(~pars.quiet | 1)
fprintf('\nVerifying solution! %i\n',obj(iter));
end;
case 3
if(~pars.quiet)fprintf('Stepsize too small. No more progress!\n');end;
break;
end;
end;
end;
if(pars.tempid>=0 & mod(iter,50)==0) time=toc;save(tempname,'L','iter','obj','pars','time','verify');end;
correction=correction-1;
if(correction==0)
if(pars.quiet)fprintf('\n');end;
[Vio]=checkup(L,x,y,NN(nnid,:),pars);
Vio=setdiff(Vio',imp','rows')';
if(pars.maximp<inf)
i=randperm(size(Vio,2));
Vio=Vio(:,i(1:min(pars.maximp,size(Vio,2))));
end;
ol=size(imp,2);
[imp i1 i2]=unique([imp Vio].','rows');
imp=imp.';
if(size(imp,2)~=ol)
for nnid=1:Kg;
a1{nnid}=i2(a1{nnid});
a2{nnid}=i2(a2{nnid});
end;
end;
fprintf('Added %i active constraints. New total: %i\n\n',size(imp,2)-ol,size(imp,2));
if(ifraction<1)
i=1:size(imp,2);
imp=imp(:,i(1:ceil(length(i)*ifraction)));
if(~pars.quiet)fprintf('Only use %2.2f of them.\n',ifraction);end;
ifraction=ifraction+pars.ifraction;
end;
% put next correction a little more into the future if no new impostors were added
if(size(imp,2)-ol<=0)
pars.correction=min(pars.correction*2+2,300);
correction=pars.correction-1;
else
pars.correction=round(pars.correction*pars.factor);
correction=pars.correction;
end;
lastcor=iter;
end;
end;
% Output
Det.obj=obj(1:iter);
Det.nimp=nimp;
Det.pars=pars;
Det.time=toc;
Det.iter=iter;
Det.verify=verify;
if(pars.validation>0)
Det.minL=L;
L=bestL;
Det.verify=verify;
end;
function [err,yy,Value]=Ltest2in(Lx,y,LxT,yTest,Ni,Kg,pars);
% function [err,yy,Value]=Ltest2(L,x,y,xTest,yTest,Kg,varargin);
%
% Initializationip
[D,N]=size(Lx);
Lx2=sum(Lx.^2);
MM=min(y);
y=y-MM+1;
un=unique(y);
Value=zeros(length(un),length(yTest));
B=500;
NTe=size(LxT,2);
for n=1:B:NTe
nn=n:n+min(B-1,NTe-n);
DD=distance(Lx,LxT(:,nn));
for i=1:length(un)
% Main Loopfor iter=1:pars.maxiter
testlabel=un(i);
enemy=find(y~=testlabel);
friend=find(y==testlabel);
Df=mink(DD(friend,:),Kg);
Value(i,nn)=sumiflessv2(DD,Ni(:,enemy),enemy)+sumiflessh2(DD,Df+1,enemy);
if(pars.reg==0)
Value(i,nn)=Value(i,nn)+sum(Df);
end;
end;
end;
fprintf('\n');
[temp,yy]=min(Value);
yy=un(yy)+MM-1;
err=sum(yy~=yTest)./length(yTest);
fprintf('Energy error:%2.2f%%\n',err*100);
function err=validation(Lx,y,LxT,yTest,Ni,Kg);
if(isempty(LxT)) err=0;return;end;
MM=min(y);
y=y-MM+1;
un=unique(y);
Value=zeros(length(un),length(yTest));
B=500;
if(size(Lx,2)>50000) B=250;end;
NTe=size(LxT,2);
for n=1:B:NTe
fprintf('%2.2f%%: ',n/NTe*100);
nn=n:n+min(B-1,NTe-n);
DD=distance(Lx,LxT(:,nn));
for i=1:length(un)
testlabel=un(i);
fprintf('%i.',testlabel+MM-1);
enemy=find(y~=testlabel);
friend=find(y==testlabel);
Df=sort(DD(friend,:));
Value(i,nn)=sum(Df(1:Kg,:))+sumiflessv(DD(enemy,:),Ni(enemy))+sumiflessh(DD(enemy,:),Df(Kg,:));
end;
fprintf('\n');
end;
fprintf('\n');
[temp,yy]=min(Value);
yy=un(yy)+MM-1;
err=sum(yy~=yTest)./length(yTest);
fprintf('Energy error:%2.2f%%\n',err*100);
function L=step(L,G,stepsize,pars);
% do step in gradient direction
if(size(L,1)~=size(L,2)) pars.obj=1;end;
switch(pars.obj)
case 0 % updating Q
Q=L'*L;
Q=Q-stepsize.*G;
case 1 % updating L
G=2.*(L*G);
L=L-stepsize.*G;
return;
case 2 % multiplicative update
Q=L'*L;
Q=Q-stepsize.*G+stepsize^2/4.*G*inv(Q)*G;
return;
case 3
Q=L'*L;
Q=Q-stepsize.*G;
Q=diag(Q);
L=diag(sqrt(max(Q,0)));
return;
otherwise
error('Objective function has to be 0,1,2\n');
end;
% decompose Q
[L,dd]=eig(Q);
dd=real(diag(dd));
L=real(L);
% reassemble Q (ignore negative eigenvalues)
j=find(dd<1e-10);
if(~isempty(j))
if(~pars.quiet)fprintf('[%i]',length(j));end;
end;
dd(j)=0;
[temp,ii]=sort(-dd);
L=L(:,ii);
dd=dd(ii);
% Q=L*diag(dd)*L';
L=(L*diag(sqrt(dd)))';
%for i=1:size(L,1)
% if(L(i,1)~=0) L(i,:)=L(i,:)./sign(L(i,1));end;
%end;
function imp=getImpLS(x,y,Kg,Ki,pars);
[D,N]=size(x);
filename=sprintf('.LSKInn%i.mat',pars.tempid);
if(pars.skip) load(filename);
else
un=unique(y);
Inn=zeros(Ki,N);
for c=un
fprintf('%i nearest imposture neighbors for class %i :',Ki,c);
i=find(y==c);
j=find(y~=c);
nn=LSKnn(x(:,j),x(:,i),1:Ki);
Inn(:,i)=j(nn);
fprintf('\r');
end;
fprintf('\n');
end;
imp=[vec(Inn(1:Ki,:)')'; vec(repmat(1:N,Ki,1)')'];
imp=unique(imp','rows')'; % Delete dublicates
if(pars.save)
save(filename,'Inn');
end;
function [gen,NN]=getGenLS(x,y,Kg,pars);
if(~pars.quiet)fprintf('Computing nearest neighbors ...\n');end;
filename=sprintf('.LSKGnn%i.mat',pars.tempid);
[D,N]=size(x);
if(pars.skip) load(filename);
else
un=unique(y);
Gnn=zeros(Kg,N);
for c=un
fprintf('%i nearest genuine neighbors for class %i:',Kg,c);
i=find(y==c);
nn=LSKnn(x(:,i),x(:,i),2:Kg+1);
Gnn(:,i)=i(nn);
fprintf('\r');
end;
fprintf('\n');
NN=Gnn;
gen1=vec(Gnn(1:Kg,:)')';
gen2=vec(repmat(1:N,Kg,1)')';
gen=[gen1;gen2];
if(pars.save)
save(filename,'gen','NN');
end;
end;
function imp=checkup(L,x,y,NN,pars);
if(~pars.quiet)fprintf('Computing nearest neighbors ...\n');end;
[D,N]=size(x);
Lx=L*x;
Ni=sum((Lx-Lx(:,NN)).^2)+1;
un=unique(y);
imp=[];
index=1:N;
for c=un(1:end-1)
if(~pars.quiet)fprintf('All nearest impostor neighbors for class %i :',c);end;
i=index(find(y(index)==c));
index=index(find(y(index)~=c));
limps=LSImps2(Lx(:,index),Lx(:,i),Ni(index),Ni(i),pars);
if(size(limps,2)>pars.maximp)
ip=randperm(size(limps,2));
ip=ip(1:pars.maximp);
limps=limps(:,ip);
end;
imp=[imp [i(limps(2,:));index(limps(1,:))]];
if(~pars.quiet)fprintf('\r');end;
end;
try
imp=unique(sort(imp)','rows')';
catch
keyboard;
end;
function limps=LSImps2(X1,X2,Thresh1,Thresh2,pars);
B=750;
[D,N2]=size(X2);
N1=size(X1,2);
limps=[];
for i=1:B:N2
BB=min(B,N2-i);
try
newlimps=findimps3Dac(X1,X2(:,i:i+BB), Thresh1,Thresh2(i:i+BB));
if(~isempty(newlimps) & newlimps(end)==0)
[minv,endpoint]=min(min(newlimps));
newlimps=newlimps(:,1:endpoint-1);
end;
newlimps=unique(newlimps','rows')';
% if(~all(all(newlimps==newlimps2))) keyboard;end;
% newlimps2=findimps3D2(X1,X2(:,i:i+BB), Thresh1,Thresh2(i:i+BB));
% newlimps=unique((sort(newlimps))','rows')';
% if(length(newlimps2)~=length(newlimps) | ~all(all(newlimps2==newlimps)))
% keyboard;
%end;
catch
keyboard;
end;
newlimps(2,:)=newlimps(2,:)+i-1;
limps=[limps newlimps];
if(~pars.quiet)fprintf('(%i%%) ',round((i+BB)/N2*100)); end;
end;
if(~pars.quiet)fprintf(' [%i] ',size(limps,2));end;
function NN=LSKnn(X1,X2,ks,pars);
B=750;
[D,N]=size(X2);
NN=zeros(length(ks),N);
DD=zeros(length(ks),N);
for i=1:B:N
BB=min(B,N-i);
fprintf('.');
Dist=distance(X1,X2(:,i:i+BB));
fprintf('.');
[dist,nn]=mink(Dist,max(ks));
clear('Dist');
fprintf('.');
NN(:,i:i+BB)=nn(ks,:);
clear('nn','dist');
fprintf('(%i%%) ',round((i+BB)/N*100));
end;
|
github
|
levyfan/bow-master
|
knnclassify.m
|
.m
|
bow-master/src/main/matlab/KISSME/toolbox/lib/LMNN/knnclassify.m
| 2,559 |
utf_8
|
02d7cf7e68cc0dc6f86bc8765e02e66b
|
function [Eval,Details]=LSevaluate(L,xTr,lTr,xTe,lTe,KK);
% function [Eval,Details]=LSevaluate(L,xTr,yTr,xTe,yTe,Kg);
%
% INPUT:
% L : transformation matrix (learned by LMNN)
% xTr : training vectors (each column is an instance)
% yTr : training labels (row vector!!)
% xTe : test vectors
% yTe : test labels
% Kg : number of nearest neighbors
%
% Good luck!
%
% copyright by Kilian Q. Weinberger, 2006
%
% version 1.1 (04/13/07)
% Little bugfix, couldn't handle single test vectors beforehand.
% Thanks to Karim T. Abou-Moustafa for pointing it out to me.
%
MM=min([lTr lTe]);
if(nargin<6)
KK=1:2:3;
end;
if(length(KK)==1) outputK=ceil(KK/2);KK=1:2:KK;else outputK=1:length(KK);end;
Kn=max(KK);
D=length(L);
xTr=L*xTr(1:D,:);
xTe=L*xTe(1:D,:);
B=700;
[NTr]=size(xTr,2);
[NTe]=size(xTe,2);
Eval=zeros(2,length(KK));
lTr2=zeros(length(KK),NTr);
lTe2=zeros(length(KK),NTe);
iTr=zeros(Kn,NTr);
iTe=zeros(Kn,NTe);
sx1=sum(xTr.^2,1);
sx2=sum(xTe.^2,1);
for i=1:B:max(NTr,NTe)
if(i<=NTr)
BTr=min(B-1,NTr-i);
%Dtr=distance(xTr,xTr(:,i:i+BTr));
Dtr=addh(addv(-2*xTr'*xTr(:,i:i+BTr),sx1),sx1(i:i+BTr));
% [dist,nn]=sort(Dtr);
[dist,nn]=mink(Dtr,Kn+1);
nn=nn(2:Kn+1,:);
lTr2(:,i:i+BTr)=LSKnn2(lTr(nn),KK,MM);
iTr(:,i:i+BTr)=nn;
Eval(1,:)=sum((lTr2(:,1:i+BTr)~=repmat(lTr(1:i+BTr),length(KK),1))',1)./(i+BTr);
end;
if(i<=NTe)
BTe=min(B-1,NTe-i);
Dtr=addh(addv(-2*xTr'*xTe(:,i:i+BTe),sx1),sx2(i:i+BTe));
[dist,nn]=mink(Dtr,Kn);
lTe2(:,i:i+BTe)=LSKnn2(reshape(lTr(nn),max(KK),BTe+1),KK,MM);
iTe(:,i:i+BTe)=nn;
Eval(2,:)=sum((lTe2(:,1:i+BTe)~=repmat(lTe(1:i+BTe),length(KK),1))',1)./(i+BTe);
end;
fprintf('%2.2f%%.:\n',(i+BTr)/max(NTr,NTe)*100);
disp(Eval.*100);
end;
Details.lTe2=lTe2;
Details.lTr2=lTr2;
Details.iTe=iTe;
Details.iTr=iTr;
Eval=Eval(:,outputK);
function yy=LSKnn2(Ni,KK,MM);
% function yy=LSKnn2(Ni,KK,MM);
%
if(nargin<2)
KK=1:2:3;
end;
N=size(Ni,2);
Ni=Ni-MM+1;
classes=unique(unique(Ni))';
%yy=zeros(1,size(Ni,2));
%for i=1:size(Ni,2)
% n=zeros(max(un),1);
% for j=1:size(Ni,1)
% n(Ni(j,i))=n(Ni(j,i))+1;
% end;
% [temp,yy(i)]=max(n);
%end;
T=zeros(length(classes),N,length(KK));
for i=1:length(classes)
c=classes(i);
for k=KK
% NNi=Ni(1:k,:)==c;
% NNi=NNi+(Ni(1,:)==c).*0.01;% give first neighbor tiny advantage
try
T(i,:,k)=sum(Ni(1:k,:)==c,1);
catch
keyboard;
end;
end;
end;
yy=zeros(max(KK),N);
for k=KK
[temp,yy(k,1:N)]=max(T(:,:,k)+T(:,:,1).*0.01);
yy(k,1:N)=classes(yy(k,:));
end;
yy=yy(KK,:);
yy=yy+MM-1;
|
github
|
levyfan/bow-master
|
energyclassify.m
|
.m
|
bow-master/src/main/matlab/KISSME/toolbox/lib/LMNN/energyclassify.m
| 2,926 |
utf_8
|
9155befdcfcd16052c23bbab1cf7b530
|
function [err,yy,Value]=energyclassify(L,x,y,xTest,yTest,Kg,varargin);
% function [err,yy,Value]=energyclassify(L,xTr,yTr,xTe,yTe,Kg,varargin);
%
% INPUT:
% L : transformation matrix (learned by LMNN)
% xTr : training vectors (each column is an instance)
% yTr : training labels (row vector!!)
% xTe : test vectors
% yTe : test labels
% Kg : number of nearest neighbors
%
% Good luck!
%
% copyright by Kilian Q. Weinberger, 2006
% checks
D=length(L);
x=x(1:D,:);
xTest=xTest(1:D,:);
if(size(x,1)>length(L)) error('x and L must have matching dimensions!\n');end;
% set parameters
pars.alpha=1e-09;
pars.tempid=0;
pars.save=0;
pars.speed=10;
pars.skip=0;
pars.factor=1;
pars.correction=15;
pars.prod=0;
pars.thresh=1e-16;
pars.ifraction=1;
pars.scale=0;
pars.obj=0;
pars.union=1;
pars.margin=0;
pars.tabularasa=Inf;
pars.blocksize=500;
pars=extractpars(varargin,pars);
pars
tempname=sprintf('temp%i.mat',pars.tempid);
% Initializationip
[D,N]=size(x);
[gen,NN]=getGenLS(x,y,Kg,pars);
if(pars.scale)
fprintf('Scaling input vectors!\n');
sc=sqrt(mean(sum( ((x-x(:,NN(end,:)))).^2)));
x=x./sc;
xTest=xTest./sc;
end;
Lx=L*x;
Lx2=sum(Lx.^2);
LxT=L*xTest;
for inn=1:Kg
Ni(inn,:)=sum((Lx-Lx(:,NN(inn,:))).^2)+1;
end;
MM=min(y);
y=y-MM+1;
un=unique(y);
Value=zeros(length(un),length(yTest));
B=pars.blocksize;
if(size(x,2)>50000) B=250;end;
NTe=size(xTest,2);
for n=1:B:NTe
fprintf('%2.2f%%: ',n/NTe*100);
nn=n:n+min(B-1,NTe-n);
DD=distance(Lx,LxT(:,nn));
for i=1:length(un)
% Main Loopfor iter=1:maxiter
testlabel=un(i);
fprintf('%i.',testlabel+MM-1);
enemy=find(y~=testlabel);
friend=find(y==testlabel);
Df=mink(DD(friend,:),Kg);
Value(i,nn)=sumiflessv2(DD,Ni(:,enemy),enemy)+sumiflessh2(DD,Df,enemy)+sum(Df);
% Value(i,nn)=sumiflessh2(DD,Df+pars.margin,enemy)+sum(Df);
end;
fprintf('\n');
end;
fprintf('\n');
[temp,yy]=min(Value);
yy=un(yy)+MM-1;
err=sum(yy~=yTest)./length(yTest);
fprintf('Energy error:%2.2f%%\n',err*100);
function [gen,NN]=getGenLS(x,y,Kg,pars);
fprintf('Computing nearest neighbors ...\n');
[D,N]=size(x);
if(pars.skip) load('.LSKGnn.mat');
else
un=unique(y);
Gnn=zeros(Kg,N);
for c=un
fprintf('%i nearest genuine neighbors for class %i:',Kg,c);
i=find(y==c);
nn=LSKnn(x(:,i),x(:,i),2:Kg+1);
Gnn(:,i)=i(nn);
fprintf('\n');
end;
end;
NN=Gnn;
gen1=vec(Gnn(1:Kg,:)')';
gen2=vec(repmat(1:N,Kg,1)')';
gen=[gen1;gen2];
if(pars.save)
save('.LSKGnn.mat','Gnn');
end;
function NN=LSKnn(X1,X2,ks,pars);
B=2000;
[D,N]=size(X2);
NN=zeros(length(ks),N);
DD=zeros(length(ks),N);
for i=1:B:N
BB=min(B,N-i);
fprintf('.');
Dist=distance(X1,X2(:,i:i+BB));
fprintf('.');
% [dist,nn]=sort(Dist);
[dist,nn]=mink(Dist,max(ks));
clear('Dist');
fprintf('.');
% keyboard;
NN(:,i:i+BB)=nn(ks,:);
clear('nn','dist');
fprintf('(%i%%) ',round((i+BB)/N*100));
end;
function v=vec(M);
% vectorizes a matrix
v=M(:);
|
github
|
fengweiigg/GRACE_Matlab_Toolbox-master
|
GRACE_Matlab_Toolbox.m
|
.m
|
GRACE_Matlab_Toolbox-master/GRACE_Matlab_Toolbox.m
| 5,933 |
utf_8
|
852fbc11927286fc4ac8b0d61dcc2624
|
function varargout = GRACE_Matlab_Toolbox(varargin)
% GRACE_MATLAB_TOOLBOX MATLAB code for GRACE_Matlab_Toolbox.fig
% GRACE_MATLAB_TOOLBOX, by itself, creates a new GRACE_MATLAB_TOOLBOX or raises the existing
% singleton*.
%
% H = GRACE_MATLAB_TOOLBOX returns the handle to a new GRACE_MATLAB_TOOLBOX or the handle to
% the existing singleton*.
%
% GRACE_MATLAB_TOOLBOX('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in GRACE_MATLAB_TOOLBOX.M with the given input arguments.
%
% GRACE_MATLAB_TOOLBOX('Property','Value',...) creates a new GRACE_MATLAB_TOOLBOX or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before GRACE_Matlab_Toolbox_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to GRACE_Matlab_Toolbox_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help GRACE_Matlab_Toolbox
% Last Modified by GUIDE v2.5 04-Sep-2015 23:20:36
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @GRACE_Matlab_Toolbox_OpeningFcn, ...
'gui_OutputFcn', @GRACE_Matlab_Toolbox_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before GRACE_Matlab_Toolbox is made visible.
function GRACE_Matlab_Toolbox_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to GRACE_Matlab_Toolbox (see VARARGIN)
% Choose default command line output for GRACE_Matlab_Toolbox
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes GRACE_Matlab_Toolbox wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = GRACE_Matlab_Toolbox_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --------------------------------------------------------------------
function GSMGADProcessing_Callback(hObject, eventdata, handles)
% open('GRACE_Matlab_Toolbox_preprocessing.fig'); %result??????????
run GRACE_Matlab_Toolbox_preprocessing
% hObject handle to GSMGADProcessing (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --------------------------------------------------------------------
function GSMGADProcessing_ClickedCallback(hObject, eventdata, handles)
% open('GRACE_Matlab_Toolbox_preprocessing.fig'); %result??????????
run GRACE_Matlab_Toolbox_preprocessing
% --------------------------------------------------------------------
function LeakageReductionSpatial_Callback(hObject, eventdata, handles)
% hObject handle to LeakageReductionSpatial (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
run GRACE_Matlab_Toolbox_LeakageReductionSpatial
% --------------------------------------------------------------------
function SHGrid_Callback(hObject, eventdata, handles)
% hObject handle to SHGrid (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% open('GRACE_Matlab_Toolbox_SHGrid.fig'); %result??????????
run GRACE_Matlab_Toolbox_SHGrid
% --------------------------------------------------------------------
function Grid2Series_Callback(hObject, eventdata, handles)
% hObject handle to Grid2Series (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
run GRACE_Matlab_Toolbox_Grid2Series
% --------------------------------------------------------------------
function HarmonicAnalysis_Callback(hObject, eventdata, handles)
% hObject handle to HarmonicAnalysis (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% open('GRACE_Matlab_Toolbox_HarmonicAnalysis.fig'); %result??????????
run GRACE_Matlab_Toolbox_HarmonicAnalysis
% --------------------------------------------------------------------
function GRACEProcessing_Callback(hObject, eventdata, handles)
% hObject handle to GRACEProcessing (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --------------------------------------------------------------------
function DataAnalysis_Callback(hObject, eventdata, handles)
% hObject handle to DataAnalysis (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
|
github
|
fengweiigg/GRACE_Matlab_Toolbox-master
|
GRACE_Matlab_Toolbox_preprocessing.m
|
.m
|
GRACE_Matlab_Toolbox-master/GRACE_Matlab_Toolbox_preprocessing.m
| 29,614 |
utf_8
|
7ba0deb4a2a5efbfd17e13c6705230a4
|
function varargout = GRACE_Matlab_Toolbox_preprocessing(varargin)
% GRACE_MATLAB_TOOLBOX_PREPROCESSING MATLAB code for GRACE_Matlab_Toolbox_preprocessing.fig
% GRACE_MATLAB_TOOLBOX_PREPROCESSING, by itself, creates a new GRACE_MATLAB_TOOLBOX_PREPROCESSING or raises the existing
% singleton*.
%
% H = GRACE_MATLAB_TOOLBOX_PREPROCESSING returns the handle to a new GRACE_MATLAB_TOOLBOX_PREPROCESSING or the handle to
% the existing singleton*.
%
% GRACE_MATLAB_TOOLBOX_PREPROCESSING('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in GRACE_MATLAB_TOOLBOX_PREPROCESSING.M with the given input arguments.
%
% GRACE_MATLAB_TOOLBOX_PREPROCESSING('Property','Value',...) creates a new GRACE_MATLAB_TOOLBOX_PREPROCESSING or raises
% the existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before GRACE_Matlab_Toolbox_preprocessing_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to GRACE_Matlab_Toolbox_preprocessing_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help GRACE_Matlab_Toolbox_preprocessing
% Last Modified by GUIDE v2.5 23-Mar-2015 18:15:17
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @GRACE_Matlab_Toolbox_preprocessing_OpeningFcn, ...
'gui_OutputFcn', @GRACE_Matlab_Toolbox_preprocessing_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before GRACE_Matlab_Toolbox_preprocessing is made visible.
function GRACE_Matlab_Toolbox_preprocessing_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to GRACE_Matlab_Toolbox_preprocessing (see VARARGIN)
% Choose default command line output for GRACE_Matlab_Toolbox_preprocessing
handles.output = hObject;
guidata(hObject, handles);
initialize_gui(hObject, handles, false);
% --- Outputs from this function are returned to the command line.
function varargout = GRACE_Matlab_Toolbox_preprocessing_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --------------------------------------------------------------------
function initialize_gui(fig_handle, handles, isreset)
% If the metricdata field is present and the reset flag is false, it means
% we are we are just re-initializing a GUI by calling it from the cmd line
% while it is up. So, bail out as we dont want to reset the data.
if isfield(handles, 'metricdata') && ~isreset
return;
end
% Update handles structure
guidata(handles.figure1, handles);
% --- Main Function in this Toolbox ---
function PushbuttonCalculate_Callback(hObject, eventdata, handles)
if ~isempty(get(handles.EditOpenControlFile,'String'))
GRACE_Matlab_Toolbox_preprocessing_core(get(handles.EditOpenControlFile,'String'));
return;
elseif ~isempty(get(handles.EditSaveControlFile,'String'))
GRACE_Matlab_Toolbox_preprocessing_core(get(handles.EditSaveControlFile,'String'));
return;
else
warndlg('Control file should be specified in STEP3/STEP1!','Warning');
end
% Specify the GRACE Level2 Files
function PushButtonOpenFiles_Callback(hObject, eventdata, handles)
% Open GRACE GSM Files
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*','GRACE-files (*)'; ...
'*.gfc','ICGEM-files (*.gfc)'; ...
'*.*', 'All Files (*.*)'}, ...
'Pick GRACE Level2 files', ...
'MultiSelect', 'on');
if filterindex==1
set(handles.InputFileListbox,'string',InputFilename);% Show the Files in the listbox
handles.InputFilename=InputFilename;
handles.InputPathname=InputPathname;
guidata(hObject,handles);
else
warndlg('Input files should be specified!!!','Warning');
return;
end
function InputFileListbox_Callback(hObject, eventdata, handles)
function InputFileListbox_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% Specify the Maximum Degree
function EditMaxDegreeOutput_Callback(hObject, eventdata, handles)
handles.MaxDegreeOutput=str2double(get(hObject,'String'));
guidata(hObject, handles);
function EditMaxDegreeOutput_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% Specify the Gaussin Filter Radius
function EditFilterRadius_Callback(hObject, eventdata, handles)
guidata(hObject, handles);
function EditFilterRadius_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% Specify the path of C20, C21, S21, C22, S22
function CheckboxC20_Callback(hObject, eventdata, handles)
if get(handles.CheckboxC20,'value')==1
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*.*', 'All Files (*.*)'}, ...
'Pick C20 file');
if filterindex==1 % file selected
% Show the Directory and File in the textbox
set(handles.EditC20, 'String', [InputPathname,InputFilename]);
handles.PathC20=[InputPathname,InputFilename];
guidata(hObject, handles);
else
set(handles.CheckboxC20,'value',0);
set(handles.EditC20, 'String', 'NAN');
end
else
set(handles.EditC20, 'String', 'NAN');
end
function CheckboxC21S21_Callback(hObject, eventdata, handles)
if get(handles.CheckboxC21S21,'value')==1
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*.*', 'All Files (*.*)'}, ...
'Pick C21 & S21 file');
if filterindex==1 % file selected
% Show the Directory and File in the textbox
set(handles.EditC21S21, 'String', [InputPathname,InputFilename]);
handles.PathC21S21=[InputPathname,InputFilename];
guidata(hObject, handles);
else
set(handles.CheckboxC21S21,'value',0);
set(handles.EditC21S21, 'String', 'NAN');
end
else
set(handles.EditC21S21, 'String', 'NAN');
end
function CheckboxC22S22_Callback(hObject, eventdata, handles)
if get(handles.CheckboxC22S22,'value')==1
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*.*', 'All Files (*.*)'}, ...
'Pick C22 & S22 file');
if filterindex==1 % file selected
% Show the Directory and File in the textbox
set(handles.EditC22S22, 'String', [InputPathname,InputFilename]);
handles.PathC22S22=[InputPathname,InputFilename];
guidata(hObject, handles);
else
set(handles.CheckboxC22S22,'value',0);
set(handles.EditC22S22, 'String', 'NAN');
end
else
set(handles.EditC22S22, 'String', 'NAN');
end
function CheckboxDegree1_Callback(hObject, eventdata, handles)
if get(handles.CheckboxDegree1,'value')==1
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*.*', 'All Files (*.*)'}, ...
'Pick Degree 1 file');
if filterindex==1 % file selected
% Show the Directory and File in the textbox
set(handles.EditDegree1, 'String', [InputPathname,InputFilename]);
handles.PathDegree1=[InputPathname,InputFilename];
guidata(hObject, handles);
else
set(handles.CheckboxDegree1,'value',0);
set(handles.EditDegree1, 'String', 'NAN');
end
else
set(handles.EditDegree1, 'String', 'NAN');
end
function EditC20_Callback(hObject, eventdata, handles)
function EditC20_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditC21S21_Callback(hObject, eventdata, handles)
function EditC21S21_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditC22S22_Callback(hObject, eventdata, handles)
function EditC22S22_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditDegree1_Callback(hObject, eventdata, handles)
function EditDegree1_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% Specify the Destriping method
function RadiobuttonNonedestriping_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',1);
set(handles.RadiobuttonSwenson,'value',0);
set(handles.RadiobuttonChambers2007,'value',0);
set(handles.RadiobuttonChambers2012,'value',0);
set(handles.RadiobuttonChenP3M6,'value',0);
set(handles.RadiobuttonChenP4M6,'value',0);
set(handles.RadiobuttonDuan,'value',0);
function RadiobuttonSwenson_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',0);
set(handles.RadiobuttonSwenson,'value',1);
set(handles.RadiobuttonChambers2007,'value',0);
set(handles.RadiobuttonChambers2012,'value',0);
set(handles.RadiobuttonChenP3M6,'value',0);
set(handles.RadiobuttonChenP4M6,'value',0);
set(handles.RadiobuttonDuan,'value',0);
function RadiobuttonChambers2007_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',0);
set(handles.RadiobuttonSwenson,'value',0);
set(handles.RadiobuttonChambers2007,'value',1);
set(handles.RadiobuttonChambers2012,'value',0);
set(handles.RadiobuttonChenP3M6,'value',0);
set(handles.RadiobuttonChenP4M6,'value',0);
set(handles.RadiobuttonDuan,'value',0);
function RadiobuttonChambers2012_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',0);
set(handles.RadiobuttonSwenson,'value',0);
set(handles.RadiobuttonChambers2007,'value',0);
set(handles.RadiobuttonChambers2012,'value',1);
set(handles.RadiobuttonChenP3M6,'value',0);
set(handles.RadiobuttonChenP4M6,'value',0);
set(handles.RadiobuttonDuan,'value',0);
function RadiobuttonChenP3M6_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',0);
set(handles.RadiobuttonSwenson,'value',0);
set(handles.RadiobuttonChambers2007,'value',0);
set(handles.RadiobuttonChambers2012,'value',0);
set(handles.RadiobuttonChenP3M6,'value',1);
set(handles.RadiobuttonChenP4M6,'value',0);
set(handles.RadiobuttonDuan,'value',0);
function RadiobuttonChenP4M6_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',0);
set(handles.RadiobuttonSwenson,'value',0);
set(handles.RadiobuttonChambers2007,'value',0);
set(handles.RadiobuttonChambers2012,'value',0);
set(handles.RadiobuttonChenP3M6,'value',0);
set(handles.RadiobuttonChenP4M6,'value',1);
set(handles.RadiobuttonDuan,'value',0);
function RadiobuttonDuan_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',0);
set(handles.RadiobuttonSwenson,'value',0);
set(handles.RadiobuttonChambers2007,'value',0);
set(handles.RadiobuttonChambers2012,'value',0);
set(handles.RadiobuttonChenP3M6,'value',0);
set(handles.RadiobuttonChenP4M6,'value',0);
set(handles.RadiobuttonDuan,'value',1);
% Specify the GIA removed or not
function RadiobuttonGIAnotRemoved_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonGIAnotRemoved, 'value', 1);
set(handles.RadiobuttonGIARemovedGeru, 'value', 0);
function RadiobuttonGIARemovedGeru_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonGIAnotRemoved, 'value', 0);
set(handles.RadiobuttonGIARemovedGeru, 'value', 1);
% Specify the Output Directory
function PushbuttonOutputDirectory_Callback(hObject, eventdata, handles)
OutputPathname= uigetdir('','Pick the output directory');
if OutputPathname==0
% set(handles.EditOutputDirectory, 'String', 'Output directry must be specified!!!');
warndlg('Output directry must be specified!!!','Warning');
else
set(handles.EditOutputDirectory, 'String', OutputPathname); % show directory
handles.OutputPathname=[OutputPathname,'/'];
guidata(hObject, handles);
end
function EditOutputDirectory_Callback(hObject, eventdata, handles)
function EditOutputDirectory_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% Specify the Output Format
function EditOutputSHFilename_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonOutputFormatSH,'value',1);
set(handles.RadiobuttonOutputFormatGrid1degree,'value',0);
set(handles.RadiobuttonOutputFormatGrid025degree,'value',0);
function EditOutputSHFilename_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditOutputGridFilename1degree_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonOutputFormatSH,'value',0);
set(handles.RadiobuttonOutputFormatGrid1degree,'value',1);
set(handles.RadiobuttonOutputFormatGrid025degree,'value',0);
function EditOutputGridFilename1degree_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditOutputGridFilename025degree_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonOutputFormatSH,'value',0);
set(handles.RadiobuttonOutputFormatGrid1degree,'value',0);
set(handles.RadiobuttonOutputFormatGrid025degree,'value',1);
function EditOutputGridFilename025degree_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function RadiobuttonOutputFormatSH_Callback(hObject, eventdata, handles)
set(handles.EditOutputGridFilename1degree,'String','');
set(handles.EditOutputGridFilename025degree,'String','');
set(handles.RadiobuttonOutputFormatSH,'value',1);
set(handles.RadiobuttonOutputFormatGrid1degree,'value',0);
set(handles.RadiobuttonOutputFormatGrid025degree,'value',0);
function RadiobuttonOutputFormatGrid1degree_Callback(hObject, eventdata, handles)
set(handles.EditOutputSHFilename,'String','');
set(handles.EditOutputGridFilename025degree,'String','');
set(handles.RadiobuttonOutputFormatSH,'value',0);
set(handles.RadiobuttonOutputFormatGrid1degree,'value',1);
set(handles.RadiobuttonOutputFormatGrid025degree,'value',0);
function RadiobuttonOutputFormatGrid025degree_Callback(hObject, eventdata, handles)
set(handles.EditOutputSHFilename,'String','');
set(handles.EditOutputGridFilename1degree,'String','');
set(handles.RadiobuttonOutputFormatSH,'value',0);
set(handles.RadiobuttonOutputFormatGrid1degree,'value',0);
set(handles.RadiobuttonOutputFormatGrid025degree,'value',1);
% Save the Control File
function PushbuttonSaveControlFile_Callback(hObject, eventdata, handles)
[InputFilename,InputPathname,FilterIndex] = uiputfile('GMT_Control_File.txt','Save control file');
if FilterIndex==1 % file saved
set(handles.EditSaveControlFile, 'String', [InputPathname,InputFilename]);
set(handles.EditOpenControlFile, 'String', '');
handles.PathControlFile=[InputPathname,InputFilename];
guidata(hObject, handles);
% Check the possible errors in Input Settings, Output Settings
if isempty(get(handles.InputFileListbox,'String'))
warndlg('Input files should be specified! Please select GRACE Level2 Files.','Warning');
return;
end
if isempty(get(handles.EditMaxDegreeOutput,'String'))
warndlg('Maximun Output Degree should be specified!','Warning');
return;
end
if isnan(str2double(get(handles.EditMaxDegreeOutput,'String')))
warndlg('Maximun Output Degree should be a number!','Warning');
return;
end
if isempty(get(handles.EditFilterRadius,'String'))
warndlg('Filter Radius should be specified!','Warning');
return;
end
if isnan(str2double(get(handles.EditFilterRadius,'String')))
warndlg('Gaussian Filter Radius should be a number!','Warning');
return;
end
if isempty(get(handles.EditOutputDirectory,'String'))
warndlg('Output Directory should be specified!','Warning');
return;
end
if get(handles.RadiobuttonOutputFormatSH,'value') && isempty(get(handles.EditOutputSHFilename,'String'))
warndlg('Name of SH output file should be specified!','Warning');
return;
end
if get(handles.RadiobuttonOutputFormatGrid1degree,'value') && isempty(get(handles.EditOutputGridFilename1degree,'String'))
warndlg('Name of Grid output file should be specified!','Warning');
return;
end
if get(handles.RadiobuttonOutputFormatGrid025degree,'value') && isempty(get(handles.EditOutputGridFilename025degree,'String'))
warndlg('Name of Grid output file should be specified!','Warning');
return;
end
% Get the destriping method option
if get(handles.RadiobuttonNonedestriping,'value')
option_destriping='NONE';
elseif get(handles.RadiobuttonSwenson,'value')
option_destriping='SWENSON';
elseif get(handles.RadiobuttonChambers2007,'value')
option_destriping='CHAMBERS2007';
elseif get(handles.RadiobuttonChambers2012,'value')
option_destriping='CHAMBERS2012';
elseif get(handles.RadiobuttonChenP3M6,'value')
option_destriping='CHENP3M6';
elseif get(handles.RadiobuttonChenP4M6,'value')
option_destriping='CHENP4M6';
elseif get(handles.RadiobuttonDuan,'value')
option_destriping='DUAN';
end
% Get the GIA option
if get(handles.RadiobuttonGIAnotRemoved,'value')
option_gia='GIA_notRemoved';
elseif get(handles.RadiobuttonGIARemovedGeru,'value')
option_gia='GIA_Removed_Geru';
end
% Get the output format
if get(handles.RadiobuttonOutputFormatSH,'value')
OutputFileFormat=['SH_MAT ', get(handles.EditMaxDegreeOutput,'String'),' ', get(handles.EditOutputSHFilename,'String')];
elseif get(handles.RadiobuttonOutputFormatGrid1degree,'value')
OutputFileFormat=['GRID_MAT ',get(handles.EditMaxDegreeOutput,'String'),' ',get(handles.EditOutputGridFilename1degree,'String'),' 1'];
elseif get(handles.RadiobuttonOutputFormatGrid025degree,'value')
OutputFileFormat=['GRID_MAT ',get(handles.EditMaxDegreeOutput,'String'),' ',get(handles.EditOutputGridFilename025degree,'String'),' 0.25'];
end
% Get the file extension and determine the format of input files
handles.InputFilename=get(handles.InputFileListbox,'String');
if iscell(handles.InputFilename) % more than one input file
[~, ~,FILE_TYPE]=fileparts([handles.InputPathname,handles.InputFilename{1}]);
else % only one input file
[~, ~,FILE_TYPE]=fileparts([handles.InputPathname,handles.InputFilename]);
end
if strcmp(FILE_TYPE,'.gfc') || strcmp(FILE_TYPE,'.GFC')
InputFileFormat='ICGEM';
else
InputFileFormat='GRACE';
end
% Create Control File
fid_write=fopen([InputPathname,InputFilename],'w');
if iscell(handles.InputFilename) % more than one input file
fprintf(fid_write,'%d\n',length(handles.InputFilename));
else % only one input file
fprintf(fid_write,'%d\n',1);
end
fprintf(fid_write,'%s\n',get(handles.EditFilterRadius,'String'));
fprintf(fid_write,'%s\n',option_destriping);
fprintf(fid_write,'%s\n',option_gia);
fprintf(fid_write,'%s\n',InputFileFormat);
fprintf(fid_write,'%s\n',OutputFileFormat);
fprintf(fid_write,'%s\n',get(handles.EditC20, 'String'));
fprintf(fid_write,'%s\n',get(handles.EditC21S21, 'String'));
fprintf(fid_write,'%s\n',get(handles.EditC22S22, 'String'));
fprintf(fid_write,'%s\n',get(handles.EditDegree1, 'String'));
fprintf(fid_write,'%s\n',handles.InputPathname);
fprintf(fid_write,'%s\n',strcat(get(handles.EditOutputDirectory,'String'),'/'));
if iscell(handles.InputFilename) % more than one input file
for jj=1:length(handles.InputFilename)-1
fprintf(fid_write,'%s',handles.InputFilename{jj});
fprintf(fid_write,'\n');
end
fprintf(fid_write,'%s',handles.InputFilename{length(handles.InputFilename)});
fclose(fid_write);
else % only one input file
fprintf(fid_write,'%s',handles.InputFilename);
fclose(fid_write);
end
end
function EditSaveControlFile_Callback(hObject, eventdata, handles)
function EditSaveControlFile_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% Read the Control File
function PushbuttonOpenControlFile_Callback(hObject, eventdata, handles)
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*.*', 'All Files (*.*)'}, ...
'Pick control file');
if filterindex==1 % file selected
% Show the Directory and File in the textbox
set(handles.EditOpenControlFile, 'String', [InputPathname,InputFilename]);
set(handles.EditSaveControlFile, 'String', '');
handles.PathControlFile=[InputPathname,InputFilename];
guidata(hObject, handles);
% Update the data
% Read the Control File
fid=fopen(handles.PathControlFile,'r');
num_file = fscanf(fid,'%d',1);
radius_filter = fscanf(fid,'%d',1);
destrip_method = fscanf(fid,'%s',1);
option_gia = fscanf(fid,'%s',1);
InputFileFormat = fscanf(fid,'%s',1);
OutputFileFormat_1= fscanf(fid,'%s',1);
if strcmp(OutputFileFormat_1,'SH_MAT')
OutputFileFormat_2= fscanf(fid,'%f',1); % max degree
OutputFileFormat_3= fscanf(fid,'%s',1); % output name
end
if strcmp(OutputFileFormat_1,'GRID_MAT')
OutputFileFormat_2= fscanf(fid,'%f',1); % max degree
OutputFileFormat_3= fscanf(fid,'%s',1); % output name
OutputFileFormat_4= fscanf(fid,'%f',1); % spatial resolution
end
dir_c20 = fscanf(fid,'%s',1);
dir_c21_s21 = fscanf(fid,'%s',1);
dir_c22_s22 = fscanf(fid,'%s',1);
dir_degree_1 = fscanf(fid,'%s',1);
dir_in = fscanf(fid,'%s',1);
dir_out = fscanf(fid,'%s',1);
file_name=cell(num_file,1);% Name list of input GRACE files
for i = 1:num_file % read the name list of files in control file
file_name{i} = fscanf(fid,'%s',1);
end
fclose(fid);
handles.InputPathname=dir_in;
% Show input files
set(handles.InputFileListbox,'String',file_name);
% Show C20 C21 S21 C22 S22 Degree 1 pathname
if exist(dir_c20,'file') && ~strcmp(dir_c20,'NAN')
set(handles.CheckboxC20,'Value',1);
set(handles.EditC20,'String',dir_c20);
end
if exist(dir_c21_s21,'file') && ~strcmp(dir_c21_s21,'NAN')
set(handles.CheckboxC21S21,'Value',1);
set(handles.EditC21S21,'String',dir_c21_s21);
end
if exist(dir_c22_s22,'file') && ~strcmp(dir_c22_s22,'NAN')
set(handles.CheckboxC22S22,'Value',1);
set(handles.EditC22S22,'String',dir_c22_s22);
end
if exist(dir_degree_1,'file') && ~strcmp(dir_degree_1,'NAN')
set(handles.CheckboxDegree1,'Value',1);
set(handles.EditDegree1,'String',dir_degree_1);
end
% Show destriping option
if strcmp(destrip_method,'NONE')
set(handles.RadiobuttonNonedestriping,'Value',1);
set(handles.RadiobuttonSwenson,'Value',0);
set(handles.RadiobuttonChambers2007,'Value',0);
set(handles.RadiobuttonChambers2012,'Value',0);
set(handles.RadiobuttonChenP3M6,'Value',0);
set(handles.RadiobuttonChenP4M6,'Value',0);
set(handles.RadiobuttonDuan,'Value',0);
elseif strcmp(destrip_method,'SWENSON')
set(handles.RadiobuttonNonedestriping,'Value',0);
set(handles.RadiobuttonSwenson,'Value',1);
set(handles.RadiobuttonChambers2007,'Value',0);
set(handles.RadiobuttonChambers2012,'Value',0);
set(handles.RadiobuttonChenP3M6,'Value',0);
set(handles.RadiobuttonChenP4M6,'Value',0);
set(handles.RadiobuttonDuan,'Value',0);
elseif strcmp(destrip_method,'CHAMBERS2007')
set(handles.RadiobuttonNonedestriping,'Value',0);
set(handles.RadiobuttonSwenson,'Value',0);
set(handles.RadiobuttonChambers2007,'Value',1);
set(handles.RadiobuttonChambers2012,'Value',0);
set(handles.RadiobuttonChenP3M6,'Value',0);
set(handles.RadiobuttonChenP4M6,'Value',0);
set(handles.RadiobuttonDuan,'Value',0);
elseif strcmp(destrip_method,'CHAMBERS2012')
set(handles.RadiobuttonNonedestriping,'Value',0);
set(handles.RadiobuttonSwenson,'Value',0);
set(handles.RadiobuttonChambers2007,'Value',0);
set(handles.RadiobuttonChambers2012,'Value',1);
set(handles.RadiobuttonChenP3M6,'Value',0);
set(handles.RadiobuttonChenP4M6,'Value',0);
set(handles.RadiobuttonDuan,'Value',0);
elseif strcmp(destrip_method,'CHENP3M6')
set(handles.RadiobuttonNonedestriping,'Value',0);
set(handles.RadiobuttonSwenson,'Value',0);
set(handles.RadiobuttonChambers2007,'Value',0);
set(handles.RadiobuttonChambers2012,'Value',0);
set(handles.RadiobuttonChenP3M6,'Value',1);
set(handles.RadiobuttonChenP4M6,'Value',0);
set(handles.RadiobuttonDuan,'Value',0);
elseif strcmp(destrip_method,'CHENP4M6')
set(handles.RadiobuttonNonedestriping,'Value',0);
set(handles.RadiobuttonSwenson,'Value',0);
set(handles.RadiobuttonChambers2007,'Value',0);
set(handles.RadiobuttonChambers2012,'Value',0);
set(handles.RadiobuttonChenP3M6,'Value',0);
set(handles.RadiobuttonChenP4M6,'Value',1);
set(handles.RadiobuttonDuan,'Value',0);
elseif strcmp(destrip_method,'DUAN')
set(handles.RadiobuttonNonedestriping,'Value',0);
set(handles.RadiobuttonSwenson,'Value',0);
set(handles.RadiobuttonChambers2007,'Value',0);
set(handles.RadiobuttonChambers2012,'Value',0);
set(handles.RadiobuttonChenP3M6,'Value',0);
set(handles.RadiobuttonChenP4M6,'Value',0);
set(handles.RadiobuttonDuan,'Value',1);
end
% Show GIA option
if strcmp(option_gia,'GIA_notRemoved')
set(handles.RadiobuttonGIAnotRemoved,'Value',1);
set(handles.RadiobuttonGIARemovedGeru,'Value',0);
elseif strcmp(option_gia,'GIA_Removed_Geru')
set(handles.RadiobuttonGIAnotRemoved,'Value',0);
set(handles.RadiobuttonGIARemovedGeru,'Value',1);
end
% Show filter radius
set(handles.EditFilterRadius,'string',radius_filter);
% Show output directory
if exist(dir_out,'dir')
set(handles.EditOutputDirectory,'String',dir_out);
end
% Show output format
if strcmp(OutputFileFormat_1,'SH_MAT')
set(handles.RadiobuttonOutputFormatSH,'value',1);
set(handles.RadiobuttonOutputFormatGrid1degree,'value',0);
set(handles.RadiobuttonOutputFormatGrid025degree,'value',0);
set(handles.EditMaxDegreeOutput,'String',OutputFileFormat_2);
set(handles.EditOutputSHFilename,'String',OutputFileFormat_3);
elseif strcmp(OutputFileFormat_1,'GRID_MAT') && OutputFileFormat_4==1
set(handles.RadiobuttonOutputFormatSH,'value',0);
set(handles.RadiobuttonOutputFormatGrid1degree,'value',1);
set(handles.RadiobuttonOutputFormatGrid025degree,'value',0);
set(handles.EditMaxDegreeOutput,'String',OutputFileFormat_2);
set(handles.EditOutputGridFilename1degree,'String',OutputFileFormat_3);
elseif strcmp(OutputFileFormat_1,'GRID_MAT') && OutputFileFormat_4==0.25
set(handles.RadiobuttonOutputFormatSH,'value',0);
set(handles.RadiobuttonOutputFormatGrid1degree,'value',0);
set(handles.RadiobuttonOutputFormatGrid025degree,'value',1);
set(handles.EditMaxDegreeOutput,'String',OutputFileFormat_2);
set(handles.EditOutputGridFilename025degree,'String',OutputFileFormat_3);
end
guidata(hObject, handles);
end
function EditOpenControlFile_Callback(hObject, eventdata, handles)
function EditOpenControlFile_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
|
github
|
fengweiigg/GRACE_Matlab_Toolbox-master
|
GRACE_Matlab_Toolbox_HarmonicAnalysis.m
|
.m
|
GRACE_Matlab_Toolbox-master/GRACE_Matlab_Toolbox_HarmonicAnalysis.m
| 22,263 |
utf_8
|
160d792627938e5f0f3ba354c01c48a6
|
function varargout = GRACE_Matlab_Toolbox_HarmonicAnalysis(varargin)
% GRACE_MATLAB_TOOLBOX_HARMONICANALYSIS MATLAB code for GRACE_Matlab_Toolbox_HarmonicAnalysis.fig
% GRACE_MATLAB_TOOLBOX_HARMONICANALYSIS, by itself, creates a new GRACE_MATLAB_TOOLBOX_HARMONICANALYSIS or raises the existing
% singleton*.
%
% H = GRACE_MATLAB_TOOLBOX_HARMONICANALYSIS returns the handle to a new GRACE_MATLAB_TOOLBOX_HARMONICANALYSIS or the handle to
% the existing singleton*.
%
% GRACE_MATLAB_TOOLBOX_HARMONICANALYSIS('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in GRACE_MATLAB_TOOLBOX_HARMONICANALYSIS.M with the given input arguments.
%
% GRACE_MATLAB_TOOLBOX_HARMONICANALYSIS('Property','Value',...) creates a new GRACE_MATLAB_TOOLBOX_HARMONICANALYSIS or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before GRACE_Matlab_Toolbox_HarmonicAnalysis_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to GRACE_Matlab_Toolbox_HarmonicAnalysis_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help GRACE_Matlab_Toolbox_HarmonicAnalysis
% Last Modified by GUIDE v2.5 25-Mar-2015 21:59:55
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @GRACE_Matlab_Toolbox_HarmonicAnalysis_OpeningFcn, ...
'gui_OutputFcn', @GRACE_Matlab_Toolbox_HarmonicAnalysis_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before GRACE_Matlab_Toolbox_HarmonicAnalysis is made visible.
function GRACE_Matlab_Toolbox_HarmonicAnalysis_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to GRACE_Matlab_Toolbox_HarmonicAnalysis (see VARARGIN)
% Choose default command line output for GRACE_Matlab_Toolbox_HarmonicAnalysis
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes GRACE_Matlab_Toolbox_HarmonicAnalysis wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = GRACE_Matlab_Toolbox_HarmonicAnalysis_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
function EditOpenSeries_Callback(hObject, eventdata, handles)
% hObject handle to EditOpenSeries (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditOpenSeries as text
% str2double(get(hObject,'String')) returns contents of EditOpenSeries as a double
% --- Executes during object creation, after setting all properties.
function EditOpenSeries_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditOpenSeries (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in PushbuttonOpenSeries.
function PushbuttonOpenSeries_Callback(hObject, eventdata, handles)
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*.mat','Matlab-files (*.mat)'}, ...
'Pick series file');
if filterindex==1 % file selected
set(handles.EditOpenSeries,'string',strcat(InputPathname,InputFilename));
[~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
if strcmp(FILE_TYPE,'.mat')
load(strcat(InputPathname,InputFilename));
if exist('time_series_grace','var')
time_series=time_series_grace;
end
if ~exist('time_series','var')
warndlg('There is no ''time_series'' or ''time_series_grace'' variable in .mat file!','Warning');
return;
end
if exist('time_grace','var')
time=time_grace;
end
if ~exist('time','var')
warndlg('There is no ''time'' or ''time_grace'' variable in .mat file!','Warning');
return;
end
grid_data(1,1,:)=time_series;
[ Amplitude1, Amplitude1_std, Phase1,Phase1_std, Amplitude2,...
Amplitude2_std, Phase2, Phase2_std, Trend, Trend_std,...
~, ~, ~] = gmt_harmonic(time,[],grid_data);
set(handles.EditAnnualAmplitude,'string',strcat(num2str(Amplitude1,'%3.2e'),'+/-',num2str(Amplitude1_std,'%3.2e')));
set(handles.EditAnnualPhase,'string',strcat(num2str(Phase1,'%4.1f'),'+/-',num2str(Phase1_std,'%4.1f')));
set(handles.EditSemiannualAmplitude,'string',strcat(num2str(Amplitude2,'%3.2e'),'+/-',num2str(Amplitude2_std,'%3.2e')));
set(handles.EditSemiannualPhase,'string',strcat(num2str(Phase2,'%4.1f'),'+/-',num2str(Phase2_std,'%4.1f')));
set(handles.EditTrend,'string',strcat(num2str(Trend,'%3.2e'),'+/-',num2str(Trend_std,'%3.2e')));
end
end
function EditAnnualAmplitude_Callback(hObject, eventdata, handles)
% hObject handle to EditAnnualAmplitude (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditAnnualAmplitude as text
% str2double(get(hObject,'String')) returns contents of EditAnnualAmplitude as a double
% --- Executes during object creation, after setting all properties.
function EditAnnualAmplitude_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditAnnualAmplitude (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditSemiannualAmplitude_Callback(hObject, eventdata, handles)
% hObject handle to EditSemiannualAmplitude (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditSemiannualAmplitude as text
% str2double(get(hObject,'String')) returns contents of EditSemiannualAmplitude as a double
% --- Executes during object creation, after setting all properties.
function EditSemiannualAmplitude_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditSemiannualAmplitude (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditTrend_Callback(hObject, eventdata, handles)
% hObject handle to EditTrend (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditTrend as text
% str2double(get(hObject,'String')) returns contents of EditTrend as a double
% --- Executes during object creation, after setting all properties.
function EditTrend_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditTrend (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditAnnualPhase_Callback(hObject, eventdata, handles)
% hObject handle to EditAnnualPhase (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditAnnualPhase as text
% str2double(get(hObject,'String')) returns contents of EditAnnualPhase as a double
% --- Executes during object creation, after setting all properties.
function EditAnnualPhase_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditAnnualPhase (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditSemiannualPhase_Callback(hObject, eventdata, handles)
% hObject handle to EditSemiannualPhase (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditSemiannualPhase as text
% str2double(get(hObject,'String')) returns contents of EditSemiannualPhase as a double
% --- Executes during object creation, after setting all properties.
function EditSemiannualPhase_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditSemiannualPhase (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditSaveSemiannualphase_Callback(hObject, eventdata, handles)
% hObject handle to EditSaveSemiannualphase (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditSaveSemiannualphase as text
% str2double(get(hObject,'String')) returns contents of EditSaveSemiannualphase as a double
% --- Executes during object creation, after setting all properties.
function EditSaveSemiannualphase_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditSaveSemiannualphase (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditSaveAnnualphase_Callback(hObject, eventdata, handles)
% hObject handle to EditSaveAnnualphase (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditSaveAnnualphase as text
% str2double(get(hObject,'String')) returns contents of EditSaveAnnualphase as a double
% --- Executes during object creation, after setting all properties.
function EditSaveAnnualphase_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditSaveAnnualphase (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditSaveTrend_Callback(hObject, eventdata, handles)
% hObject handle to EditSaveTrend (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditSaveTrend as text
% str2double(get(hObject,'String')) returns contents of EditSaveTrend as a double
% --- Executes during object creation, after setting all properties.
function EditSaveTrend_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditSaveTrend (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditSaveSemiannualamplitude_Callback(hObject, eventdata, handles)
% hObject handle to EditSaveSemiannualamplitude (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditSaveSemiannualamplitude as text
% str2double(get(hObject,'String')) returns contents of EditSaveSemiannualamplitude as a double
% --- Executes during object creation, after setting all properties.
function EditSaveSemiannualamplitude_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditSaveSemiannualamplitude (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditSaveAnnualamplitude_Callback(hObject, eventdata, handles)
% hObject handle to EditSaveAnnualamplitude (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditSaveAnnualamplitude as text
% str2double(get(hObject,'String')) returns contents of EditSaveAnnualamplitude as a double
% --- Executes during object creation, after setting all properties.
function EditSaveAnnualamplitude_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditSaveAnnualamplitude (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in PushbuttonOpenGrid.
function PushbuttonOpenGrid_Callback(hObject, eventdata, handles)
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*.mat','Matlab-files (*.mat)'}, ...
'Pick grid file');
if filterindex==1 % file selected
[~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
if strcmp(FILE_TYPE,'.mat')
load(strcat(InputPathname,InputFilename));
if exist('grid_data_grace','var')% there is 'cs_grace' variable in SH mat files
handles.grid_data=grid_data_grace;
elseif exist('grid_data','var') % no 'cs' variable in SH mat files
handles.grid_data=grid_data;
else
warndlg('There is no ''grid_data'' or ''grid_data_grace'' variable in .mat file!','Warning');
return;
end
if exist('time','var')
handles.time=time;
elseif exist('time_grace','var')
handles.time=time_grace;
else
warndlg('There is no ''time'' or ''time_grace'' variable in .mat file!','Warning');
return;
end
if exist('str_year','var')
handles.str_year=str_year;
end
if exist('str_month','var')
handles.str_month=str_month;
end
end
guidata(hObject,handles);
set(handles.EditOpenGrid,'String',strcat(InputPathname,InputFilename));
end
function EditOpenGrid_Callback(hObject, eventdata, handles)
% hObject handle to EditOpenGrid (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditOpenGrid as text
% str2double(get(hObject,'String')) returns contents of EditOpenGrid as a double
% --- Executes during object creation, after setting all properties.
function EditOpenGrid_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditOpenGrid (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Save Annual amplitude ---
function PushbuttonSaveAnnualamplitude_Callback(hObject, eventdata, handles)
[InputFilename,InputPathname,filterindex] = uiputfile('annual_amplitude.mat','Save annual amplitude file');
if filterindex==1 % file selected
[~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
if strcmp(FILE_TYPE,'.mat')
set(handles.EditSaveAnnualamplitude,'String',strcat(InputPathname,InputFilename));
end
end
% --- Save Annual phase ---
function PushbuttonSaveAnnualphase_Callback(hObject, eventdata, handles)
[InputFilename,InputPathname,filterindex] = uiputfile('annual_phase.mat','Save annual phase file');
if filterindex==1 % file selected
[~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
if strcmp(FILE_TYPE,'.mat')
set(handles.EditSaveAnnualphase,'String',strcat(InputPathname,InputFilename));
end
end
% --- Save Semiannual amplitude ---
function PushbuttonSaveSemiannualamplitude_Callback(hObject, eventdata, handles)
[InputFilename,InputPathname,filterindex] = uiputfile('semiannual_amplitude.mat','Save semi-annual amplitude file');
if filterindex==1 % file selected
[~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
if strcmp(FILE_TYPE,'.mat')
set(handles.EditSaveSemiannualamplitude,'String',strcat(InputPathname,InputFilename));
end
end
% --- Save Semiannual phase ---
function PushbuttonSaveSemiannualphase_Callback(hObject, eventdata, handles)
[InputFilename,InputPathname,filterindex] = uiputfile('semiannual_phase.mat','Save semi-annual phase file');
if filterindex==1 % file selected
[~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
if strcmp(FILE_TYPE,'.mat')
set(handles.EditSaveSemiannualphase,'String',strcat(InputPathname,InputFilename));
end
end
% --- Save Trend ---
function PushbuttonSaveTrend_Callback(hObject, eventdata, handles)
[InputFilename,InputPathname,filterindex] = uiputfile('trend.mat','Save trend file');
if filterindex==1 % file selected
[~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
if strcmp(FILE_TYPE,'.mat')
set(handles.EditSaveTrend,'String',strcat(InputPathname,InputFilename));
end
end
% --- Executes on button press in PushbuttonCalculate.
function PushbuttonCalculate_Callback(hObject, eventdata, handles)
hh=msgbox('Spatial Harmonic Analysis is in processing.');
pause(0.3);
close(hh);
[ Amplitude1, Amplitude1_std, Phase1,Phase1_std, Amplitude2,...
Amplitude2_std, Phase2, Phase2_std, Trend, Trend_std,...
~, ~, ~] = gmt_harmonic(handles.time,[],handles.grid_data);
if ~isempty(get(handles.EditSaveAnnualamplitude,'string'))
annual_amplitude=Amplitude1;
save(get(handles.EditSaveAnnualamplitude,'string'),'annual_amplitude');
end
if ~isempty(get(handles.EditSaveAnnualphase,'string'))
annual_phase=Phase1;
save(get(handles.EditSaveAnnualphase,'string'),'annual_phase');
end
if ~isempty(get(handles.EditSaveSemiannualamplitude,'string'))
semiannual_amplitude=Amplitude2;
save(get(handles.EditSaveSemiannualamplitude,'string'),'semiannual_amplitude');
end
if ~isempty(get(handles.EditSaveSemiannualphase,'string'))
semiannual_phase=Phase2;
save(get(handles.EditSaveSemiannualphase,'string'),'semiannual_phase');
end
if ~isempty(get(handles.EditSaveTrend,'string'))
trend=Trend;
save(get(handles.EditSaveTrend,'string'),'trend');
end
hh=msgbox('Spatial harmonic analysis is done.');
pause(1);
close(hh);
|
github
|
fengweiigg/GRACE_Matlab_Toolbox-master
|
GRACE_Matlab_Toolbox_SHGrid.m
|
.m
|
GRACE_Matlab_Toolbox-master/GRACE_Matlab_Toolbox_SHGrid.m
| 10,513 |
utf_8
|
034ca5e2ececb7735df50033a84e4717
|
function varargout = GRACE_Matlab_Toolbox_SHGrid(varargin)
% GRACE_MATLAB_TOOLBOX_SHGRID MATLAB code for GRACE_Matlab_Toolbox_SHGrid.fig
% GRACE_MATLAB_TOOLBOX_SHGRID, by itself, creates a new GRACE_MATLAB_TOOLBOX_SHGRID or raises the existing
% singleton*.
%
% H = GRACE_MATLAB_TOOLBOX_SHGRID returns the handle to a new GRACE_MATLAB_TOOLBOX_SHGRID or the handle to
% the existing singleton*.
%
% GRACE_MATLAB_TOOLBOX_SHGRID('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in GRACE_MATLAB_TOOLBOX_SHGRID.M with the given input arguments.
%
% GRACE_MATLAB_TOOLBOX_SHGRID('Property','Value',...) creates a new GRACE_MATLAB_TOOLBOX_SHGRID or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before GRACE_Matlab_Toolbox_SHGrid_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to GRACE_Matlab_Toolbox_SHGrid_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help GRACE_Matlab_Toolbox_SHGrid
% Last Modified by GUIDE v2.5 24-Mar-2015 16:22:39
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @GRACE_Matlab_Toolbox_SHGrid_OpeningFcn, ...
'gui_OutputFcn', @GRACE_Matlab_Toolbox_SHGrid_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
end
% --- Executes just before GRACE_Matlab_Toolbox_SHGrid is made visible.
function GRACE_Matlab_Toolbox_SHGrid_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to GRACE_Matlab_Toolbox_SHGrid (see VARARGIN)
% Choose default command line output for GRACE_Matlab_Toolbox_SHGrid
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes GRACE_Matlab_Toolbox_SHGrid wait for user response (see UIRESUME)
% uiwait(handles.figure1);
end
% --- Outputs from this function are returned to the command line.
function varargout = GRACE_Matlab_Toolbox_SHGrid_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
end
% --- Open SH coefficients file ---
function PushbuttonOpenSH_Callback(hObject, eventdata, handles)
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*.mat','Matlab-files (*.mat)'; ...
'*.gfc','ICGEM-files (*.gfc)'}, ...
'Pick SH coefficients file');
if filterindex==1 % file selected
[~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
if strcmp(FILE_TYPE,'.gfc') || strcmp(FILE_TYPE,'.GFC')
[cs,~]=gmt_readgfc(strcat(InputPathname,InputFilename));
elseif strcmp(FILE_TYPE,'.mat')
load(strcat(InputPathname,InputFilename));
if exist('cs_grace','var')% there is 'cs_grace' variable in SH mat files
handles.cs=cs_grace;
elseif exist('cs','var')
handles.cs=cs;
else
warndlg('There is no SH coefficients variable ''cs'' or ''cs_grace'' in .mat file!','Warning');
end
if exist('time','var')
handles.time=time;
end
if exist('time_grace','var')
handles.time=time_grace;
end
if exist('str_year','var')
handles.str_year=str_year;
end
if exist('str_month','var')
handles.str_month=str_month;
end
else
warndlg('Input file format is wrong!','Warning');
end
guidata(hObject,handles);
set(handles.EditOpenSH,'String',strcat(InputPathname,InputFilename));
end
end
% --- Save Grid file ---
function PushbuttonSaveGrid_Callback(hObject, eventdata, handles)
[InputFilename,InputPathname,FilterIndex] = uiputfile('grid_data.mat','Save grid file');
if get(handles.Radiobutton1Degree,'value')
type=1;
else
type=0.25;
end
if FilterIndex==1 && isfield(handles,'cs')% create output file if there's input file
set(handles.EditSaveGrid,'string',strcat(InputPathname,InputFilename));
hh=msgbox('Sphercial Harmonic Synthesis is in processing.');
pause(0.3);
close(hh);
grid_data=gmt_cs2grid(handles.cs,0,type);
save(strcat(InputPathname, InputFilename),'grid_data');
% add other variables if they are in input SH file *.mat
if isfield(handles,'str_year') % whether str_year exists in handles
str_year= handles.str_year;
save(strcat(InputPathname, InputFilename),'-append','str_year');
end
if isfield(handles,'str_month')
str_month= handles.str_month;
save(strcat(InputPathname, InputFilename),'-append','str_month');
end
if isfield(handles,'time')
time= handles.time;
save(strcat(InputPathname, InputFilename),'-append','time');
end
hh=msgbox('Spherical Harmonic Synthesis is done.');
pause(0.5);
close(hh);
end
end
% --- Open Grid file ---
function PushbuttonOpenGrid_Callback(hObject, eventdata, handles)
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*.mat','Matlab-files (*.mat)'}, ...
'Pick grid file');
if filterindex==1 % file selected
[~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
if strcmp(FILE_TYPE,'.mat')
load(strcat(InputPathname,InputFilename));
if exist('grid_data_grace','var')% there is 'cs_grace' variable in SH mat files
handles.grid_data=grid_data_grace;
elseif exist('grid_data','var') % no 'cs' variable in SH mat files
handles.grid_data=grid_data;
else
warndlg('There is no ''grid_data'' or ''grid_data_grace'' variable in .mat file!','Warning');
end
if exist('time','var')
handles.time=time;
end
if exist('time_grace','var')
handles.time=time_grace;
end
if exist('str_year','var')
handles.str_year=str_year;
end
if exist('str_month','var')
handles.str_month=str_month;
end
end
guidata(hObject,handles);
set(handles.EditOpenGrid,'String',strcat(InputPathname,InputFilename));
end
end
% --- Save SH coefficents file ---
function PushbuttonSaveSH_Callback(hObject, eventdata, handles)
degree_max=str2double(get(handles.EditMaxDegree,'string'));
if isnan(degree_max) || degree_max~=fix(degree_max) % not integer
warndlg('Input maximum degree is wrong!','Warning');
return;
end
[InputFilename,InputPathname,FilterIndex] = uiputfile('cs.mat','Save SH coefficients file');
if FilterIndex==1 && isfield(handles,'grid_data') % create output file
set(handles.EditSaveSH,'string',strcat(InputPathname,InputFilename));
hh=msgbox('Spherical Harmonic Analysis is in processing.');
pause(0.3);
close(hh);
cs=gmt_grid2cs(handles.grid_data,degree_max);
save(strcat(InputPathname, InputFilename),'cs');
% add other variables if they are in input SH file *.mat
if isfield(handles,'str_year') % whether str_year exists in handles
str_year= handles.str_year;
save(strcat(InputPathname, InputFilename),'-append','str_year');
end
if isfield(handles,'str_month')
str_month= handles.str_month;
save(strcat(InputPathname, InputFilename),'-append','str_month');
end
if isfield(handles,'time')
time= handles.time;
save(strcat(InputPathname, InputFilename),'-append','time');
end
hh=msgbox('Spherical Harmonic Analysis is done.');
pause(0.5);
close(hh);
end
end
function Radiobutton1Degree_Callback(hObject, eventdata, handles)
set(handles.Radiobutton1Degree,'value',1);
set(handles.Radiobutton025Degree,'value',0);
end
function Radiobutton025Degree_Callback(hObject, eventdata, handles)
set(handles.Radiobutton1Degree,'value',0);
set(handles.Radiobutton025Degree,'value',1);
end
function EditOpenSH_Callback(hObject, eventdata, handles)
end
function EditOpenSH_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
end
function EditSaveGrid_Callback(hObject, eventdata, handles)
end
function EditSaveGrid_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
end
function EditOpenGrid_Callback(hObject, eventdata, handles)
end
function EditOpenGrid_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
end
function EditSaveSH_Callback(hObject, eventdata, handles)
end
function EditSaveSH_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
end
function EditMaxDegree_Callback(hObject, eventdata, handles)
end
function EditMaxDegree_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditMaxDegree (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
end
|
github
|
fengweiigg/GRACE_Matlab_Toolbox-master
|
GRACE_Matlab_Toolbox_LeakageReductionSpatial.m
|
.m
|
GRACE_Matlab_Toolbox-master/GRACE_Matlab_Toolbox_LeakageReductionSpatial.m
| 12,935 |
utf_8
|
1347aa0cb7a59cc3b4ee3bce77b0377e
|
function varargout = GRACE_Matlab_Toolbox_LeakageReductionSpatial(varargin)
% GRACE_MATLAB_TOOLBOX_LEAKAGEREDUCTIONSPATIAL MATLAB code for GRACE_Matlab_Toolbox_LeakageReductionSpatial.fig
% GRACE_MATLAB_TOOLBOX_LEAKAGEREDUCTIONSPATIAL, by itself, creates a new GRACE_MATLAB_TOOLBOX_LEAKAGEREDUCTIONSPATIAL or raises the existing
% singleton*.
%
% H = GRACE_MATLAB_TOOLBOX_LEAKAGEREDUCTIONSPATIAL returns the handle to a new GRACE_MATLAB_TOOLBOX_LEAKAGEREDUCTIONSPATIAL or the handle to
% the existing singleton*.
%
% GRACE_MATLAB_TOOLBOX_LEAKAGEREDUCTIONSPATIAL('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in GRACE_MATLAB_TOOLBOX_LEAKAGEREDUCTIONSPATIAL.M with the given input arguments.
%
% GRACE_MATLAB_TOOLBOX_LEAKAGEREDUCTIONSPATIAL('Property','Value',...) creates a new GRACE_MATLAB_TOOLBOX_LEAKAGEREDUCTIONSPATIAL or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before GRACE_Matlab_Toolbox_LeakageReductionSpatial_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to GRACE_Matlab_Toolbox_LeakageReductionSpatial_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help GRACE_Matlab_Toolbox_LeakageReductionSpatial
% Last Modified by GUIDE v2.5 05-Sep-2015 17:37:21
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @GRACE_Matlab_Toolbox_LeakageReductionSpatial_OpeningFcn, ...
'gui_OutputFcn', @GRACE_Matlab_Toolbox_LeakageReductionSpatial_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before GRACE_Matlab_Toolbox_LeakageReductionSpatial is made visible.
function GRACE_Matlab_Toolbox_LeakageReductionSpatial_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to GRACE_Matlab_Toolbox_LeakageReductionSpatial (see VARARGIN)
% Choose default command line output for GRACE_Matlab_Toolbox_LeakageReductionSpatial
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes GRACE_Matlab_Toolbox_LeakageReductionSpatial wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = GRACE_Matlab_Toolbox_LeakageReductionSpatial_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
function EditOpenSH_Callback(hObject, eventdata, handles)
% hObject handle to EditOpenSH (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditOpenSH as text
% str2double(get(hObject,'String')) returns contents of EditOpenSH as a double
% --- Executes during object creation, after setting all properties.
function EditOpenSH_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditOpenSH (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in PushbuttonOpenSH.
function PushbuttonOpenSH_Callback(hObject, eventdata, handles)
% hObject handle to PushbuttonOpenSH (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*.mat','Matlab-files (*.mat)'; ...
'*.gfc','ICGEM-files (*.gfc)'}, ...
'Pick SH coefficients file');
if filterindex==1 % file selected
[~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
if strcmp(FILE_TYPE,'.gfc') || strcmp(FILE_TYPE,'.GFC')
[cs,~]=gmt_readgfc(strcat(InputPathname,InputFilename));
elseif strcmp(FILE_TYPE,'.mat')
load(strcat(InputPathname,InputFilename));
if exist('cs_grace','var')% there is 'cs_grace' variable in SH mat files
handles.cs=cs_grace;
elseif exist('cs','var')
handles.cs=cs;
else
warndlg('There is no SH coefficients variable ''cs'' or ''cs_grace'' in .mat file!','Warning');
end
if exist('time','var')
handles.time=time;
end
if exist('time_grace','var')
handles.time=time_grace;
end
if exist('str_year','var')
handles.str_year=str_year;
end
if exist('str_month','var')
handles.str_month=str_month;
end
else
warndlg('Input file format is wrong!','Warning');
end
guidata(hObject,handles);
set(handles.EditOpenSH,'String',strcat(InputPathname,InputFilename));
end
function EditSaveSH_Callback(hObject, eventdata, handles)
% hObject handle to EditSaveSH (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditSaveSH as text
% str2double(get(hObject,'String')) returns contents of EditSaveSH as a double
% --- Executes during object creation, after setting all properties.
function EditSaveSH_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditSaveSH (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in PushbuttonSaveSH.
function PushbuttonSaveSH_Callback(hObject, eventdata, handles)
[InputFilename,InputPathname,FilterIndex] = uiputfile('cs_leakage_removed.mat','Save SH coefficients file');
if FilterIndex==1 && isfield(handles,'cs') % create output file
set(handles.EditSaveSH,'string',strcat(InputPathname,InputFilename));
hh=msgbox('Leakage reduction in spatial domain is in processing.');
pause(0.3);
close(hh);
% Processing leakage reduction
% cs=gmt_grid2cs(handles.grid_data,degree_max);
% Get leakage reduction method
if get(handles.RadiobuttonOceanLeakageReduction,'value')
type='ocean';
else
type='land';
end
% Get the destriping method option
if get(handles.RadiobuttonNonedestriping,'value')
option_destriping='NONE';
elseif get(handles.RadiobuttonSwenson,'value')
option_destriping='SWENSON';
elseif get(handles.RadiobuttonChambers2007,'value')
option_destriping='CHAMBERS2007';
elseif get(handles.RadiobuttonChambers2012,'value')
option_destriping='CHAMBERS2012';
elseif get(handles.RadiobuttonChenP3M6,'value')
option_destriping='CHENP3M6';
elseif get(handles.RadiobuttonChenP4M6,'value')
option_destriping='CHENP4M6';
elseif get(handles.RadiobuttonDuan,'value')
option_destriping='DUAN';
end
radius_filter=str2double(get(handles.EditFilterRadius,'String'));
cs=gmt_cs2leakagefreecs(handles.cs,type,option_destriping,radius_filter);
save(strcat(InputPathname, InputFilename),'cs');
% add other variables if they are in input SH file *.mat
if isfield(handles,'str_year') % whether str_year exists in handles
str_year= handles.str_year;
save(strcat(InputPathname, InputFilename),'-append','str_year');
end
if isfield(handles,'str_month')
str_month= handles.str_month;
save(strcat(InputPathname, InputFilename),'-append','str_month');
end
if isfield(handles,'time')
time= handles.time;
save(strcat(InputPathname, InputFilename),'-append','time');
end
hh=msgbox('Leakage reduction in spatial domain is done.');
pause(0.5);
close(hh);
end
% --- Executes on button press in RadiobuttonOceanLeakageReduction.
function RadiobuttonOceanLeakageReduction_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonOceanLeakageReduction,'value',1);
set(handles.RadiobuttonLandLeakageReduction,'value',0);
% --- Executes on button press in RadiobuttonLandLeakageReduction.
function RadiobuttonLandLeakageReduction_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonOceanLeakageReduction,'value',0);
set(handles.RadiobuttonLandLeakageReduction,'value',1);
% Specify the Destriping method
function RadiobuttonNonedestriping_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',1);
set(handles.RadiobuttonSwenson,'value',0);
set(handles.RadiobuttonChambers2007,'value',0);
set(handles.RadiobuttonChambers2012,'value',0);
set(handles.RadiobuttonChenP3M6,'value',0);
set(handles.RadiobuttonChenP4M6,'value',0);
set(handles.RadiobuttonDuan,'value',0);
function RadiobuttonSwenson_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',0);
set(handles.RadiobuttonSwenson,'value',1);
set(handles.RadiobuttonChambers2007,'value',0);
set(handles.RadiobuttonChambers2012,'value',0);
set(handles.RadiobuttonChenP3M6,'value',0);
set(handles.RadiobuttonChenP4M6,'value',0);
set(handles.RadiobuttonDuan,'value',0);
function RadiobuttonChambers2007_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',0);
set(handles.RadiobuttonSwenson,'value',0);
set(handles.RadiobuttonChambers2007,'value',1);
set(handles.RadiobuttonChambers2012,'value',0);
set(handles.RadiobuttonChenP3M6,'value',0);
set(handles.RadiobuttonChenP4M6,'value',0);
set(handles.RadiobuttonDuan,'value',0);
function RadiobuttonChambers2012_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',0);
set(handles.RadiobuttonSwenson,'value',0);
set(handles.RadiobuttonChambers2007,'value',0);
set(handles.RadiobuttonChambers2012,'value',1);
set(handles.RadiobuttonChenP3M6,'value',0);
set(handles.RadiobuttonChenP4M6,'value',0);
set(handles.RadiobuttonDuan,'value',0);
function RadiobuttonChenP3M6_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',0);
set(handles.RadiobuttonSwenson,'value',0);
set(handles.RadiobuttonChambers2007,'value',0);
set(handles.RadiobuttonChambers2012,'value',0);
set(handles.RadiobuttonChenP3M6,'value',1);
set(handles.RadiobuttonChenP4M6,'value',0);
set(handles.RadiobuttonDuan,'value',0);
function RadiobuttonChenP4M6_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',0);
set(handles.RadiobuttonSwenson,'value',0);
set(handles.RadiobuttonChambers2007,'value',0);
set(handles.RadiobuttonChambers2012,'value',0);
set(handles.RadiobuttonChenP3M6,'value',0);
set(handles.RadiobuttonChenP4M6,'value',1);
set(handles.RadiobuttonDuan,'value',0);
function RadiobuttonDuan_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonNonedestriping,'value',0);
set(handles.RadiobuttonSwenson,'value',0);
set(handles.RadiobuttonChambers2007,'value',0);
set(handles.RadiobuttonChambers2012,'value',0);
set(handles.RadiobuttonChenP3M6,'value',0);
set(handles.RadiobuttonChenP4M6,'value',0);
set(handles.RadiobuttonDuan,'value',1);
function EditFilterRadius_Callback(hObject, eventdata, handles)
guidata(hObject, handles);
function EditFilterRadius_CreateFcn(hObject, eventdata, handles)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
|
github
|
fengweiigg/GRACE_Matlab_Toolbox-master
|
GRACE_Matlab_Toolbox_Grid2Series.m
|
.m
|
GRACE_Matlab_Toolbox-master/GRACE_Matlab_Toolbox_Grid2Series.m
| 10,462 |
utf_8
|
2af44185348673e29b28f1c5effed356
|
function varargout = GRACE_Matlab_Toolbox_Grid2Series(varargin)
% GRACE_MATLAB_TOOLBOX_GRID2SERIES MATLAB code for GRACE_Matlab_Toolbox_Grid2Series.fig
% GRACE_MATLAB_TOOLBOX_GRID2SERIES, by itself, creates a new GRACE_MATLAB_TOOLBOX_GRID2SERIES or raises the existing
% singleton*.
%
% H = GRACE_MATLAB_TOOLBOX_GRID2SERIES returns the handle to a new GRACE_MATLAB_TOOLBOX_GRID2SERIES or the handle to
% the existing singleton*.
%
% GRACE_MATLAB_TOOLBOX_GRID2SERIES('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in GRACE_MATLAB_TOOLBOX_GRID2SERIES.M with the given input arguments.
%
% GRACE_MATLAB_TOOLBOX_GRID2SERIES('Property','Value',...) creates a new GRACE_MATLAB_TOOLBOX_GRID2SERIES or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before GRACE_Matlab_Toolbox_Grid2Series_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to GRACE_Matlab_Toolbox_Grid2Series_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help GRACE_Matlab_Toolbox_Grid2Series
% Last Modified by GUIDE v2.5 24-Mar-2015 18:05:04
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @GRACE_Matlab_Toolbox_Grid2Series_OpeningFcn, ...
'gui_OutputFcn', @GRACE_Matlab_Toolbox_Grid2Series_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before GRACE_Matlab_Toolbox_Grid2Series is made visible.
function GRACE_Matlab_Toolbox_Grid2Series_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to GRACE_Matlab_Toolbox_Grid2Series (see VARARGIN)
% Choose default command line output for GRACE_Matlab_Toolbox_Grid2Series
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes GRACE_Matlab_Toolbox_Grid2Series wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = GRACE_Matlab_Toolbox_Grid2Series_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
function EditInputGrid_Callback(hObject, eventdata, handles)
% hObject handle to EditInputGrid (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of EditInputGrid as text
% str2double(get(hObject,'String')) returns contents of EditInputGrid as a double
% --- Executes during object creation, after setting all properties.
function EditInputGrid_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditInputGrid (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Open Grid file ---
function PushbuttonInputGrid_Callback(hObject, eventdata, handles)
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*.mat','Matlab-files (*.mat)'}, ...
'Pick grid file');
if filterindex==1 % file selected
[~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
if strcmp(FILE_TYPE,'.mat')
load(strcat(InputPathname,InputFilename));
if exist('grid_data_grace','var')% there is 'cs_grace' variable in SH mat files
handles.grid_data=grid_data_grace;
elseif exist('grid_data','var') % no 'cs' variable in SH mat files
handles.grid_data=grid_data;
else
warndlg('There is no ''grid_data'' or ''grid_data_grace'' variable in .mat file!','Warning');
end
if exist('time','var')
handles.time=time;
end
if exist('time_grace','var')
handles.time=time_grace;
end
if exist('str_year','var')
handles.str_year=str_year;
end
if exist('str_month','var')
handles.str_month=str_month;
end
end
guidata(hObject,handles);
set(handles.EditInputGrid,'String',strcat(InputPathname,InputFilename));
end
% --- Open boundary file ---
function PushbuttonBoundaryFile_Callback(hObject, eventdata, handles)
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*.bln','Boundary-files (*.bln)'}, ...
'Pick boundary file');
if filterindex==1 % file selected
[~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
if strcmp(FILE_TYPE,'.bln')
handles.boundaryfile=strcat(InputPathname,InputFilename);
end
guidata(hObject,handles);
set(handles.EditBoundaryFile,'String',strcat(InputPathname,InputFilename));
end
% --- Open mask file ---
function PushbuttonMaskFile_Callback(hObject, eventdata, handles)
[InputFilename, InputPathname, filterindex] = uigetfile( ...
{'*.*','All files (*.*)'}, ...
'Pick mask file');
if filterindex==1 % file selected
% [~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
% if strcmp(FILE_TYPE,'.bln')
handles.boundaryfile=strcat(InputPathname,InputFilename);
% end
guidata(hObject,handles);
set(handles.EditMaskFile,'String',strcat(InputPathname,InputFilename));
end
function RadiobuttonBoundaryfile_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonBoundaryfile,'value',1);
set(handles.RadiobuttonMaskfile,'value',0);
function RadiobuttonMaskfile_Callback(hObject, eventdata, handles)
set(handles.RadiobuttonBoundaryfile,'value',0);
set(handles.RadiobuttonMaskfile,'value',1);
function EditBoundaryFile_Callback(hObject, eventdata, handles)
function EditBoundaryFile_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditBoundaryFile (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function EditMaskFile_Callback(hObject, eventdata, handles)
function EditMaskFile_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditMaskFile (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Save series file ---
function PushbuttonOutputSeries_Callback(hObject, eventdata, handles)
[InputFilename,InputPathname,filterindex] = uiputfile('time_series.mat','Save time series file');
if filterindex==1 % file selected
[~,~,FILE_TYPE]=fileparts(strcat(InputPathname,InputFilename));
if strcmp(FILE_TYPE,'.mat')
set(handles.EditOutputSeries,'String',strcat(InputPathname,InputFilename));
hh=msgbox('Grid2Series is in processing.');
pause(0.3);
close(hh);
% end
if get(handles.RadiobuttonBoundaryfile,'value')
time_series=gmt_grid2series(handles.grid_data,handles.boundaryfile,'line');
elseif get(handles.RadiobuttonMaskfile,'value')
time_series=gmt_grid2series(handles.grid_data,handles.maskfile,'mask');
end
save(strcat(InputPathname, InputFilename),'time_series');
% add other variables if they are in input SH file *.mat
if isfield(handles,'str_year') % whether str_year exists in handles
str_year= handles.str_year;
save(strcat(InputPathname, InputFilename),'-append','str_year');
end
if isfield(handles,'str_month')
str_month= handles.str_month;
save(strcat(InputPathname, InputFilename),'-append','str_month');
end
if isfield(handles,'time')
time= handles.time;
save(strcat(InputPathname, InputFilename),'-append','time');
end
hh=msgbox('Grid2Series is done.');
pause(0.5);
close(hh);
end
end
function EditOutputSeries_Callback(hObject, eventdata, handles)
function EditOutputSeries_CreateFcn(hObject, eventdata, handles)
% hObject handle to EditOutputSeries (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in PushbuttonOutputSeries.
% hObject handle to PushbuttonOutputSeries (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
|
github
|
fengweiigg/GRACE_Matlab_Toolbox-master
|
gmt_destriping_ddk.m
|
.m
|
GRACE_Matlab_Toolbox-master/GRACE_functions/gmt_destriping_ddk.m
| 3,472 |
utf_8
|
631d74e74801849ae929bbec74c6b556
|
function dataDDK=gmt_destriping_ddk(number,data)
% DDK filtering
%
% INPUT:
% number the type of DDK filter
% data spherical harmonic coefficients before filtering
%
% OUTPUT:
% grid_filter equi-angular grid N*2N, N=180 or 720
%
% DDK1d11: filtered with inverse signal degree power law 1e11*deg^4 (DDK5) weakest smoothing
% DDK5d11: filtered with inverse signal degree power law 5e11*deg^4 (DDK4) |
% DDK1d12: filtered with inverse signal degree power law 1e12*deg^4 (DDK3) |
% DDK1d13: filtered with inverse signal degree power law 1e13*deg^4 (DDK2) |
% DDK1d14: filtered with inverse signal degree power law 1e14*deg^4 (DDK1) strongest smoothing
% FENG Wei 18/10/2016
%
% State Key Laboratory of Geodesy and Earth's Dynamics
% Institute of Geodesy and Geophysics, Chinese Academy of Sciences
% [email protected]
% This function is created based on the code from Roelof Rietbroek.
% Copyright Roelof Rietbroek 2016
% This software is licensed under the MIT license see https://github.com/strawpants/GRACE-filter/blob/master/LICENSE
% URL: https://github.com/strawpants/GRACE-filter
switch number
case 1 %strongest DDK1
file='Wbd_2-120.a_1d14p_4';
case 2 % DDK2
file='Wbd_2-120.a_1d13p_4';
case 3 % DDK3
file='Wbd_2-120.a_1d12p_4';
case 4 % DDK4
file='Wbd_2-120.a_5d11p_4';
case 5 % DDK5
file='Wbd_2-120.a_1d11p_4';
case 6
file='Wbd_2-120.a_5d10p_4';
case 7
file='Wbd_2-120.a_1d10p_4';
case 8
file='Wbd_2-120.a_5d9p_4';
end
% rep=pwd;
% cd('C:\_PROGZ\GRACE\DDK\filtercoef\')
dat=read_BIN([file]);
% cd(rep)
% Block interpretation
% size ith block
clear sz nend nstart
sz(1)=dat.blockind(1);
nstart(1)=1;
nend(1)=1+sz(1)^2-1;
for ij=2:dat.nblocks
sz(ij)=dat.blockind(ij)-dat.blockind(ij-1);
nstart(ij)=1+sum(sz(1:ij-1).^2);
nend(ij)=nstart(ij)+sz(ij).^2-1;
end
% GRACE dataset
nmax=size(data.C,1)-1;
ntime=size(data.C,3);
% Initialization
dataDDK=data;
% CAS de l'ordre 0
ordre=0;
% block d'ordre 0, degree 2-> 120
ij=ordre+1;
block=reshape(dat.pack1(nstart(ij):nend(ij)),sz(ij),sz(ij));
% on garde degree 2-> nmax
block=block(1:nmax-1,1:nmax-1);
% GRACE ordre 0 degree 2->nmax
for klm=1:ntime
coef=squeeze(data.C(3:end,ordre+1,klm));
%coefF=block*coef;
% remplacement
dataDDK.C(3:end,ij,klm)=block*coef;
end
ordre=1;
while ordre<nmax+1
% block d'ordre ordre, degree 2-> 120
ij=2*(ordre);
blockC=reshape(dat.pack1(nstart(ij):nend(ij)),sz(ij),sz(ij));
blockS=reshape(dat.pack1(nstart(ij+1):nend(ij+1)),sz(ij+1),sz(ij+1));
% on garde degree 2-> nmax
fin=min(nmax-1,nmax-ordre+1);
blockC=blockC(1:fin,1:fin);
blockS=blockS(1:fin,1:fin);
% GRACE ordre ordre degree 2->nmax
deb=max(3,ordre+1);
for klm=1:ntime
coefC=squeeze(data.C(deb:end,ordre+1,klm));
coefS=squeeze(data.S(deb:end,ordre+1,klm));
% coefCF=blockC*coefC;
% coefSF=blockS*coefS;
% remplacement
dataDDK.C(deb:end,ordre+1,klm)=blockC*coefC;
dataDDK.S(deb:end,ordre+1,klm)=blockS*coefS;
end
% increment
ordre=ordre+1;
end
% imagesc(log10(abs(data.C(:,:,1))))
% set(gca,'Clim',[-12 0])
% colorbar
% figure(2)
% imagesc(log10(abs(dataDDK.C(:,:,1))))
% set(gca,'Clim',[-12 0])
% colorbar
|
github
|
fengweiigg/GRACE_Matlab_Toolbox-master
|
xyz2plm.m
|
.m
|
GRACE_Matlab_Toolbox-master/GRACE_functions/simons/xyz2plm.m
| 9,366 |
utf_8
|
04d6208d0c886556d07c21d6c75a52f5
|
function [lmcosi,dw]=xyz2plm(fthph,L,method,lat,lon,cnd)
% [lmcosi,dw]=XYZ2PLM(fthph,L,method,lat,lon,cnd)
%
% Forward real spherical harmonic transform in the 4pi normalized basis.
%
% Converts a spatially gridded field into spherical harmonics.
% For complete and regular spatial samplings [0 360 -90 90].
% If regularly spaced and complete, do not specify lat,lon.
% If not regularly spaced, fthph, lat and lon are column vectors.
%
% INPUT:
%
% fthph Real-valued function whose transform we seek:
% [1] MxN matrix of values corresponding to a regular grid
% defined by lat,lon as described below, OR
% [2] an MNx1 vector of values corrsponding to a set of
% latitude and longitude values given by lat,lon as below
% L Maximum degree of the expansion (Nyquist checked)
% method 'im' By inversion (fast, accurate, preferred),
% uses FFT on equally spaced longitudes, ok to
% specify latitudes only as long as nat>=(L+1),
% note: works with the orthogonality of the
% cosine/sine of the longitude instead of with
% the orthogonality of the Legendre polynomials.
% 'gl' By Gauss-Legendre integration (fast, inaccurate)
% note: resampling to GL integration points,
% uses FFT on equally spaced longitudes
% 'simpson' By Simpson integation (fast, inaccurate),
% note: requires equidistant latitude spacing,
% uses FFT on equally spaced longitudes
% 'irr' By inversion (irregular samplings)
% 'fib' By Riemann sum on a Fibonacci grid (not done yet)
% lat Latitude range for the grid or set of function values:
% [1] if unspecified, we assume [90 -90] and a regular grid
% [2] 1x2 vector [maximumlatitude minimumlatitude] in degrees
% [3] an MNx1 vector of values with the explicit latitudes
% lon Longitude range for the grid or set of function values:
% [1] if unspecified, we assume [0 360] and a regular grid
% [2] 1x2 vector [maximumlatitude minimumlatitude] in degrees
% [3] an MNx1 vector of values with the explicit longitudes
% cnd Eigenvalue tolerance in the irregular case
%
% OUTPUT:
%
% lmcosi Matrix listing l,m,cosine and sine coefficients
% dw Eigenvalue spectrum in the irregular case
%
% Note that the MEAN of the input data deviates from C(1), as sampled
% fields lose the orthogonality. The inversion approaches should recover
% the exact value of C(1), the true mean of the data, not the sample
% mean.
%
% lmcosi=xyz2plm(ones(randi(100),randi(100))); lmcosi(1,3) is close to one
%
% See also PLM2XYZ, PLM2SPEC, PLOTPLM, etc.
%
% Last modified by fjsimons-at-alum.mit.edu, 09/04/2014
t0=clock;
defval('method','im')
defval('lon',[])
defval('lat',[])
defval('dw',[])
defval('cnd',[])
as=0;
% If no grid is specified, assumes equal spacing and complete grid
if isempty(lat) & isempty(lon)
% Test if data is 2D, and periodic over longitude
fthph=reduntest(fthph);
polestest(fthph)
% Make a complete grid
nlon=size(fthph,2);
nlat=size(fthph,1);
% Nyquist wavelength
Lnyq=min([ceil((nlon-1)/2) nlat-1]);
% Colatitude and its increment
theta=linspace(0,pi,nlat);
as=1; % Equally spaced
% Calculate latitude/longitude sampling interval; no wrap-around left
dtheta=pi/(nlat-1);
dphi=2*pi/nlon;
switch method
% Even without lat/lon can still choose the full inversion method
% without Fourier transformation
case 'irr'
[LON,LAT]=meshgrid(linspace(0,2*pi*(1-1/nlon),nlon),...
linspace(pi/2,-pi/2,nlat));
lat=LAT(:); lon=LON(:); fthph=fthph(:);
theta=pi/2-lat;
clear LON LAT
end
elseif isempty(lon)
% If only latitudes are specified; make equal spacing longitude grid
% Latitudes can be unequally spaced for 'im', 'irr' and 'gl'.
fthph=reduntest(fthph);
theta=(90-lat)*pi/180;
dtheta=(lat(1)-lat(2))*pi/180;
nlat=length(lat);
nlon=size(fthph,2);
dphi=2*pi/nlon;
Lnyq=min([ceil((nlon-1)/2) ceil(pi/dtheta)]);
else
% Irregularly sampled data
fthph=fthph(:);
theta=(90-lat)*pi/180;
lat=lat(:)*pi/180;
lon=lon(:)*pi/180;
nlon=length(lon);
nlat=length(lat);
% Nyquist wavelength
adi=[abs(diff(sort(lon))) ; abs(diff(sort(lat)))];
Lnyq=ceil(pi/min(adi(~~adi)));
method='irr';
end
% Decide on the Nyquist frequency
defval('L',Lnyq);
% Never use Libbrecht algorithm... found out it wasn't that good
defval('libb',0)
%disp(sprintf('Lnyq= %i ; expansion out to degree L= %i',Lnyq,L))
if L>Lnyq | nlat<(L+1)
warning('XYZ2PLM: Function undersampled. Aliasing will occur.')
end
% Make cosine and sine matrices
[m,l,mz]=addmon(L);
lmcosi=[l m zeros(length(l),2)];
% Define evaluation points
switch method
case 'gl'
% Highest degree of integrand will always be 2*L
[w,x]=gausslegendrecof(2*L,[],[-1 1]);
% Function interpolated at Gauss-Legendre latitudes; 2D no help
fthph=interp1(theta,fthph,acos(x),'spline');
case {'irr','simpson','im'}
% Where else to evaluate the Legendre polynomials
x=cos(theta);
otherwise
error('Specify valid method')
end
fnpl=sprintf('%s/LSSM-%i-%i.mat',...
fullfile(getenv('IFILES'),'LEGENDRE'),L,length(x));
if exist(fnpl,'file')==2 & as==1
load(fnpl)
else
% Evaluate Legendre polynomials at selected points
Plm=repmat(NaN,length(x),addmup(L));
if L>200
h=waitbar(0,'Evaluating all Legendre polynomials');
end
in1=0;
in2=1;
for l=0:L
if libb==0
Plm(:,in1+1:in2)=(legendre(l,x(:)','sch')*sqrt(2*l+1))';
else
Plm(:,in1+1:in2)=(libbrecht(l,x(:)','sch')*sqrt(2*l+1))';
end
in1=in2;
in2=in1+l+2;
if L>200
waitbar((l+1)/(L+1),h)
end
end
if L>200
delete(h)
end
if as==1
% save(fnpl,'Plm')% Plm is not saved anymore! % Feng Wei modified
end
end
switch method
case {'irr'}
Plm=[Plm.*cos(lon(:)*m(:)') Plm.*sin(lon(:)*m(:)')];
% Add these into the sensitivity matrix
[C,merr,mcov,chi2,L2err,rnk,dw]=datafit(Plm,fthph,[],[],cnd);
lmcosi(:,3)=C(1:end/2);
lmcosi(:,4)=C(end/2+1:end);
case {'im','gl','simpson'}
% Perhaps demean the data for Fourier transform
defval('dem',0)
if dem
meanm=mean(fthph,2);
fthph=fthph-repmat(meanm,1,nlon);
end
% Calculate integration over phi by the fast Fourier
% transform. Integration of real input field with respect to the second
% dimension of r, at wavenumber m, thus at constant latitude. You get
% as many wavenumbers m as there are longitudes; only use to L. With
% Matlab's FFT, need to multiply by sampling interval.
gfft=dphi*fft(fthph,nlon,2);
if dem
% Add the azimuthal mean back in there
gfft(:,1)=2*pi*meanm;
end
% Note these things are only half unique - the maximum m is nlon/2
% But no Nyquist theory exists for the Legendre transform...
a=real(gfft);
b=-imag(gfft);
in1=0;
in2=1;
otherwise
error('Specify valid method')
end
switch method
case 'im'
% Loop over the orders. This speeds it up versus 'irr'
for ord=0:L
a(:,1)=a(:,1)/2;
b(:,1)=b(:,1)/2;
Pm=Plm(:,mz(ord+1:end)+ord)*pi;
[lmcosi(mz(ord+1:end)+ord,3)]=datafit(Pm,a(:,ord+1),[],[],cnd);
[lmcosi(mz(ord+1:end)+ord,4)]=datafit(Pm,b(:,ord+1),[],[],cnd);
end
case 'simpson'
% Loop over the degrees. Could go up to l=nlon if you want
for l=0:L,
% Integrate over theta using Simpson's rule
clm=simpson(theta,...
repmat(sin(theta(:)),1,l+1).*a(:,1:l+1).*Plm(:,in1+1:in2));
slm=simpson(theta,...
repmat(sin(theta(:)),1,l+1).*b(:,1:l+1).*Plm(:,in1+1: ...
in2));
in1=in2;
in2=in1+l+2;
% And stick it in a matrix [l m Ccos Csin]
lmcosi(addmup(l-1)+1:addmup(l),3)=clm(:)/4/pi;
lmcosi(addmup(l-1)+1:addmup(l),4)=slm(:)/4/pi;
end
case 'gl'
% Loop over the degrees. Could go up to l=nlon if you want
for l=0:L,
% Integrate over theta using Gauss-Legendre integration
clm=sum(a(:,1:l+1).*(diag(w)*Plm(:,in1+1:in2)));
slm=sum(b(:,1:l+1).*(diag(w)*Plm(:,in1+1:in2)));
in1=in2;
in2=in1+l+2;
% And stick it in a matrix [l m Ccos Csin]
lmcosi(addmup(l-1)+1:addmup(l),3)=clm(:)/4/pi;
lmcosi(addmup(l-1)+1:addmup(l),4)=slm(:)/4/pi;
end
rnk=[];
end
% Get rid of machine precision error
lmcosi(abs(lmcosi(:,3))<eps,3)=0;
lmcosi(abs(lmcosi(:,4))<eps,4)=0;
%disp(sprintf('XYZ2PLM (Analysis) took %8.4f s',etime(clock,t0)))
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function grd=reduntest(grd)
% Tests if last longitude repeats last (0,360)
% and removes last data column
if sum(abs(grd(:,1)-grd(:,end))) >= size(grd,2)*eps*10
% disp(sprintf('Data violate wrap-around by %8.4e',...
% sum(abs(grd(:,1)-grd(:,end))))) %comment out, Feng Wei modified
end
grd=grd(:,1:end-1);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function polestest(grd)
% Tests if poles (-90,90) are identical over longitudes
var1=var(grd(1,:));
var2=var(grd(end,:));
if var1>eps*10 | var2>eps*10
% disp(sprintf('Poles violated by %8.4e and %8.4e',var1,var2)) %comment out, Feng Wei modified
end
|
github
|
fengweiigg/GRACE_Matlab_Toolbox-master
|
testddk.m
|
.m
|
GRACE_Matlab_Toolbox-master/GRACE_functions/ddk_filtercoef/testddk.m
| 2,100 |
utf_8
|
b83bf6d30660ea515968e1e59b24a607
|
function nouv=ddk(number,data);
% ddk filtering
switch number
case 1 %strongest
file='Wbd_2-120.a_1d14p_4';
case 2
file='Wbd_2-120.a_1d13p_4';
case 3
file='Wbd_2-120.a_1d12p_4';
case 4
file='Wbd_2-120.a_5d11p_4';
case 5
file='Wbd_2-120.a_1d11p_4';
end
dat=read_BIN(file);
% Block interpretation
% size ith block
clear sz nend nstart
sz(1)=dat.blockind(1);
nstart(1)=1;
nend(1)=1+sz(1)^2-1;
for ij=2:dat.nblocks
sz(ij)=dat.blockind(ij)-dat.blockind(ij-1);
nstart(ij)=1+sum(sz(1:ij-1).^2);
nend(ij)=nstart(ij)+sz(ij).^2-1;
end
% GRACE dataset
nmax=size(data.C,1)-1;
ntime=size(data.C,3);
% Initialization
dataDDK=data;
% CAS de l'ordre 0
ordre=0;
% block d'ordre 0, degree 2-> 120
ij=ordre+1;
block=reshape(dat.pack1(nstart(ij):nend(ij)),sz(ij),sz(ij));
% on garde degree 2-> nmax
block=block(1:nmax-1,1:nmax-1);
% GRACE ordre 0 degree 2->nmax
for klm=1:ntime
coef=squeeze(data.C(3:end,ordre+1,klm));
coefF=block*coef;
% remplacement
dataDDK.C(3:end,ij,klm)=coefF;
end
ordre=1;
while ordre<nmax+1
% block d'ordre ordre, degree 2-> 120
ij=2*(ordre);
blockC=reshape(dat.pack1(nstart(ij):nend(ij)),sz(ij),sz(ij));
blockS=reshape(dat.pack1(nstart(ij+1):nend(ij+1)),sz(ij+1),sz(ij+1));
% on garde degree 2-> nmax
fin=min(nmax-1,nmax-ordre+1);
blockC=blockC(1:fin,1:fin);
blockS=blockS(1:fin,1:fin);
% GRACE ordre ordre degree 2->nmax
deb=max(3,ordre+1);
for klm=1:ntime
coefC=squeeze(data.C(deb:end,ordre+1,klm));
coefS=squeeze(data.S(deb:end,ordre+1,klm));
coefCF=blockC*coefC;
coefSF=blockS*coefS;
% remplacement
dataDDK.C(deb:end,ordre+1,klm)=coefCF;
dataDDK.S(deb:end,ordre+1,klm)=coefSF;
end
% increment
ordre=ordre+1;
end
% imagesc(log10(abs(data.C(:,:,1))))
% set(gca,'Clim',[-12 0])
% colorbar
% figure(2)
% imagesc(log10(abs(dataDDK.C(:,:,1))))
% set(gca,'Clim',[-12 0])
% colorbar
|
github
|
fengweiigg/GRACE_Matlab_Toolbox-master
|
read_BIN.m
|
.m
|
GRACE_Matlab_Toolbox-master/GRACE_functions/ddk_filtercoef/read_BIN.m
| 4,694 |
utf_8
|
099496edd812b75154803c4cb6023ab2
|
%function which reads in a binary file containing symmetric/full or block
%diagonal matrices and associated vectors and parameters
%Roelof Rietbroek, 7-1-2008
%updated: 29-07-2008
%
%usage: dat=read_BIN(file)
%returns a structure array 'dat' with the file content
% the matrix remains in packed form (dat.pack1 field)
%or: dat=read_BIN(file,'F')
% also expands the matrix to its full form (dat.mat1 field)
% Warning: this option may cause excessive RAM memory use with large matrices
%function now also works with Octave
function dat=read_BIN(file,varargin)
unpack=false;
for i=1:size(varargin,2)
switch varargin{i}
case {'F'}
unpack=true; % unpack matrix in full size
end
end
%open file for read acces in little endian format
[fid,message]=fopen(file,'r','ieee-le');
%check for errors
if (fid == -1)
message
dat=[];
return;
end
%read BINARY version and type from file
dat.version(1,1:8)=fread(fid,8,'uint8=>char')';
dat.type(1,1:8)=fread(fid,8,'uint8=>char')';
dat.descr(1,1:80)=fread(fid,80,'uint8=>char')';
%read indices
%integers:inint,indbls,inval1,inval2,ipval1,ipval2
metaint=fread(fid,6,'integer*4');
%put index data in structure array
n_ints=metaint(1);
n_dbls=metaint(2);
dat.nval1=metaint(3);
dat.nval2=metaint(4);
dat.pval1=metaint(5);
dat.pval2=metaint(6);
%Type dependent index data
switch dat.type
case {'BDSYMV0_','BDFULLV0'}
%read additional nblocks parameter
dat.nblocks=fread(fid,1,'integer*4');
end
%get meta data
%integers
if(n_ints > 0)
list=fread(fid,n_ints*24,'uint8=>char');
dat.ints_d=reshape(list,24,n_ints)';
dat.ints=fread(fid,n_ints,'integer*4');
end
%doubles
if(n_dbls > 0)
list=fread(fid,n_dbls*24,'uint8=>char');
dat.dbls_d=reshape(list,24,n_dbls)';
dat.dbls=fread(fid,n_dbls,'real*8');
end
%side description meta data
list=fread(fid,dat.nval1*24,'uint8=>char');
%reshape characters and put in dat struct array
dat.side1_d=reshape(list,24,dat.nval1)';
%type specific meta data
switch dat.type
case {'BDSYMV0_','BDFULLV0'}
%read additional nblocks parameter
dat.blockind=fread(fid,dat.nblocks,'integer*4');
end
%data (type dependent)
switch dat.type
case 'SYMV0___'
dat.pack1=fread(fid,dat.pval1,'real*8');
if( unpack)
row=[1:dat.nval1];
col=row;
%the array ind is upper triangular
ind=triu(row'*ones(1,dat.nval1)) +triu(ones(dat.nval1,1)*(col.*(col-1))/2);
%copy data from packed vector to full array
dat.mat1=dat.pack1(ind+ind'-diag(diag(ind)));
clear ind
dat=rmfield(dat,'pack1');
end
case 'SYMV1___'
dat.vec1=fread(fid,dat.nval1,'real*8');
dat.pack1=fread(fid,dat.pval1,'real*8');
if( unpack)
row=[1:dat.nval1];
col=row;
%the array ind is upper triangular
ind=triu(row'*ones(1,dat.nval1)) +triu(ones(dat.nval1,1)*(col.*(col-1))/2);
%copy data from packed vector to full array
dat.mat1=dat.pack1(ind+ind'-diag(diag(ind)));
clear ind
dat=rmfield(dat,'pack1');
end
case 'SYMV2___'
dat.vec1=fread(fid,dat.nval1,'real*8');
dat.vec2=fread(fid,dat.nval1,'real*8');
dat.pack1=fread(fid,dat.pval1,'real*8');
if(unpack)
row=[1:dat.nval1];
col=row;
%the array ind is upper triangular
ind=triu(row'*ones(1,dat.nval1)) +triu(ones(dat.nval1,1)*(col.*(col-1))/2);
%copy data from packed vector to full array
dat.mat1=dat.pack1(ind+ind'-diag(diag(ind)));
clear ind
dat=rmfield(dat,'pack1');
end
case 'BDSYMV0_'
dat.pack1=fread(fid,dat.pval1,'real*8');
if(unpack)
dat.mat1=[];
skip=0;
skipentries=0;
for i=1:dat.nblocks
sz=dat.blockind(i)-skip;
row=[1:sz];
col=row;
ind=triu(row'*ones(1,sz)) +triu(ones(sz,1)*(col.*(col-1))/2);
ind=triu(ind+skipentries)
skip=dat.blockind(i);
dat.mat1=blkdiag(dat.mat1,dat.pack1(ind+ind'-diag(diag(ind))));
skipentries=skipentries+(sz*(sz+1))/2;
end
clear ind
dat=rmfield(dat,'pack1');
end
case 'BDFULLV0'
dat.pack1=fread(fid,dat.pval1,'real*8');
if(unpack)
dat.mat1=[];
skip=0;
skipentries=0;
for i=1:dat.nblocks
sz=dat.blockind(i)-skip;
dat.mat1=blkdiag(dat.mat1,reshape(dat.pack1(skipentries+1:skipentries+sz^2),sz,sz));
%reset indices
skip=dat.blockind(i);
skipentries=skipentries+sz^2;
end
dat=rmfield(dat,'pack1');
end
case 'FULLSQV0'
dat.pack1=fread(fid,dat.pval1^2,'real*8');
if(unpack)
dat.mat1=reshape(dat.pack1,dat.nval1,dat.nval1);
dat=rmfield(dat,'pack1');
end
end
fclose(fid);
|
github
|
zhimingluo/MovingObjectSegmentation-master
|
training.m
|
.m
|
MovingObjectSegmentation-master/SBMI/Cascade/training.m
| 3,369 |
utf_8
|
13d359faf4fd100e37021dd78cacb7d2
|
function training(video)
previousMethod = 'MSCNN'; % BasicCNN or MSCNN
opts.expDir = [previousMethod 'net/', video];
opts.train.batchSize = 5 ;
opts.train.numEpochs = 20;
opts.train.continue = true ;
opts.train.useGpu = true ;
opts.train.learningRate = 1e-3;
opts.train.expDir = opts.expDir;
% --------------------------------------------------------------------
% Prepare data
% --------------------------------------------------------------------
imgDir = ['../SBMIDataset/' video '/input'];
labelDir = ['../SBMIDataset/' video '/groundtruth'];
grayDir = ['../' previousMethod '/result/', video];
imdb = getImdb_new(video, imgDir, labelDir, grayDir);
imdb.half_size = 15;
%%%%%%Yi%%%%%% redefined the net
load('net');
net.layers{1} = struct('type', 'conv', ...
'filters', 0.01 * randn(7, 7, 4, 32, 'single'), ...
'biases', zeros(1, 32, 'single'), ...
'stride', 1, ...
'pad', 0) ;
net.layers{end-1} = struct('type', 'conv', ...
'filters', 0.1*randn(1,1,64,1, 'single'), ...
'biases', zeros(1, 1, 'single'), ...
'stride', 1, ...
'pad', 0) ;
net.layers{end} = struct('type', 'sigmoidcrossentropyloss');
load('meanPixel.mat');
imdb.meanPixel = meanPixel;
[net,info] = cnn_train_adagrad(net, imdb, @getBatch,...
opts.train, 'errorType', 'euclideanloss', ...
'conserveMemory', true);
end
function [im, labels, mask] = getBatch(imdb, batch)
half_size = imdb.half_size;
meanPixel = imdb.meanPixel;
meanPixel(:,:,4) =0;
for ii = 1 : numel(batch)
imagename = imdb.images.name{batch(ii)};
im_ii = single(imread(imagename));
labelname = imdb.images.labels{batch(ii)};
roi = imread(labelname);
labels_ii = zeros(size(roi, 1), size(roi, 2));
labels_ii( roi == 50 ) = 0.25; %shade
labels_ii( roi == 170 ) = 0.75; %object boundary
labels_ii( roi == 255 ) = 1; %foreground
% resize the image to half size
if size(im_ii, 1) > 400 || size(im_ii, 2) >400
im_ii = imresize(im_ii, 0.5, 'nearest');
labels_ii = imresize(labels_ii, 0.5, 'nearest');
end
grayname =imdb.images.gray_name{batch(ii)};
im_ii(:,:,4) = single(imread(grayname));
im_large = padarray(im_ii, [half_size, half_size], 'symmetric');
im_ii = bsxfun(@minus, im_large, meanPixel);
im(:, :, :, ii) = im_ii;
labels(:, :, 1, ii) = labels_ii;
labels(:, :, 2, ii) = double(imdb.mask);
end
end
function imdb = getImdb_new(video, imgDir, labelDir, grayDir)
files = [dir([imgDir '/*.jpg']); dir([imgDir '/*.png'])];
label_files = dir([labelDir '/*.png']);
gray_files = dir([grayDir '/*.png']);
names = {};
labels = {};
gray_names = {};
load(['../split/' video '.mat']);
for ii = 1:numel(train_index)
k = train_index(ii);
names{end+1} = [imgDir '/' files(k).name];
labels{end+1} = [labelDir '/' label_files(k).name];
gray_names{end+1} = [grayDir '/' gray_files(k).name];
end
im = imread(labels{1});
mask = ones(size(im,1),size(im,2));
if size(mask,1) > 400 || size(mask,2) >400
mask = imresize(mask, 0.5, 'nearest');
end
imdb.mask = single(mask);
imdb.images.set = ones(1,numel(names));
imdb.images.name = names ;
imdb.images.gray_name = gray_names;
imdb.images.labels = labels;
end
|
github
|
zhimingluo/MovingObjectSegmentation-master
|
cnn_train_adagrad.m
|
.m
|
MovingObjectSegmentation-master/SBMI/Cascade/cnn_train_adagrad.m
| 11,673 |
utf_8
|
313227873e4c91a0c6db387ba794ec99
|
function [net, info] = cnn_train_adagrad(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a suitable
% getBatch function.
opts.train = [] ;
opts.val = [] ;
opts.numEpochs = 300 ;
opts.batchSize = 256 ;
opts.useGpu = false ;
opts.learningRate = 0.001 ;
opts.continue = false ;
opts.expDir = fullfile('data','exp') ;
opts.conserveMemory = false ;
opts.sync = true ;
opts.prefetch = false ;
opts.weightDecay = 0.0005 ;
opts.errorType = 'multiclass' ;
opts.plotDiagnostics = false ;
opts.delta = 1e-8;
opts.display = 1;
opts.snapshot = 1;
opts.test_interval = 1;
opts = vl_argparse(opts, varargin) ;
if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir); end
if isempty(opts.train), opts.train = find(imdb.images.set==1); end
if isempty(opts.val), opts.val = find(imdb.images.set==2); end
if isnan(opts.train), opts.train = []; end
% -------------------------------------------------------------------------
% Network initialization
% -------------------------------------------------------------------------
for i=1:numel(net.layers)
if ~strcmp(net.layers{i}.type,'conv'), continue; end
net.layers{i}.filtersMomentum = zeros(size(net.layers{i}.filters), ...
class(net.layers{i}.filters)) ;
net.layers{i}.biasesMomentum = zeros(size(net.layers{i}.biases), ...
class(net.layers{i}.biases)) ; %#ok<*ZEROLIKE>
if ~isfield(net.layers{i}, 'filtersLearningRate')
net.layers{i}.filtersLearningRate = 1 ;
end
if ~isfield(net.layers{i}, 'biasesLearningRate')
net.layers{i}.biasesLearningRate = 1 ;
end
if ~isfield(net.layers{i}, 'filtersWeightDecay')
net.layers{i}.filtersWeightDecay = 1 ;
end
if ~isfield(net.layers{i}, 'biasesWeightDecay')
net.layers{i}.biasesWeightDecay = 1 ;
end
end
if opts.useGpu
net = vl_simplenn_move(net, 'gpu') ;
for i=1:numel(net.layers)
if ~strcmp(net.layers{i}.type,'conv'), continue; end
net.layers{i}.filtersMomentum = gpuArray(net.layers{i}.filtersMomentum) ;
net.layers{i}.biasesMomentum = gpuArray(net.layers{i}.biasesMomentum) ;
end
end
G_f = cell(numel(net.layers), 1);
G_b = cell(numel(net.layers), 1);
for l=1:numel(net.layers)
if ~strcmp(net.layers{l}.type, 'conv'), continue ; end
G_f{l} = zeros(size(net.layers{l}.filters), 'single');
G_b{l} = zeros(size(net.layers{l}.biases), 'single');
end
% -------------------------------------------------------------------------
% Train and validate
% -------------------------------------------------------------------------
rng(0) ;
if opts.useGpu
one = gpuArray(single(1)) ;
else
one = single(1) ;
end
info.train.objective = [] ;
info.train.error = [] ;
info.train.topFiveError = [] ;
info.train.speed = [] ;
info.val.objective = [] ;
info.val.error = [] ;
info.val.topFiveError = [] ;
info.val.speed = [] ;
lr = opts.learningRate ;
res = [] ;
for epoch=1:opts.numEpochs
% fast-forward to where we stopped
modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep));
modelFigPath = fullfile(opts.expDir, 'net-train.pdf') ;
if opts.continue
if exist(modelPath(epoch),'file')
if epoch == opts.numEpochs
load(modelPath(epoch), 'net', 'info') ;
end
continue ;
end
if epoch > 1
fprintf('resuming by loading epoch %d\n', epoch-1) ;
load(modelPath(epoch-1), 'net', 'info') ;
end
end
train = opts.train(randperm(numel(opts.train))) ;
val = opts.val ;
info.train.objective(end+1) = 0 ;
info.train.error(end+1) = 0 ;
info.train.topFiveError(end+1) = 0 ;
info.train.speed(end+1) = 0 ;
info.val.objective(end+1) = 0 ;
info.val.error(end+1) = 0 ;
info.val.topFiveError(end+1) = 0 ;
info.val.speed(end+1) = 0 ;
for t=1:opts.batchSize:numel(train)
% get next image batch and labels
batch = train(t:min(t+opts.batchSize-1, numel(train))) ;
batch_time = tic ;
fprintf('training: epoch %02d: processing batch %3d of %3d ...', epoch, ...
fix(t/opts.batchSize)+1, ceil(numel(train)/opts.batchSize)) ;
[im, labels] = getBatch(imdb, batch) ;
if opts.prefetch
nextBatch = train(t+opts.batchSize:min(t+2*opts.batchSize-1, numel(train))) ;
getBatch(imdb, nextBatch) ;
end
if opts.useGpu
im = gpuArray(im) ;
end
% backprop
net.layers{end}.class = labels ;
res = vl_simplenn(net, im, one, res, ...
'conserveMemory', opts.conserveMemory, ...
'sync', opts.sync) ;
% gradient step
for l=1:numel(net.layers)
if ~strcmp(net.layers{l}.type, 'conv'), continue ; end
g_f = (net.layers{l}.filtersLearningRate) * ...
(opts.weightDecay * net.layers{l}.filtersWeightDecay) * net.layers{l}.filters + ...
(net.layers{l}.filtersLearningRate) / numel(batch) * res(l).dzdw{1};
g_b = (net.layers{l}.biasesLearningRate) * ...
(opts.weightDecay * net.layers{l}.biasesWeightDecay) * net.layers{l}.biases + ...
(net.layers{l}.biasesLearningRate) / numel(batch) * res(l).dzdw{2};
G_f{l} = G_f{l} + g_f .^ 2;
G_b{l} = G_b{l} + g_b .^ 2;
net.layers{l}.filters = net.layers{l}.filters - lr ./ (opts.delta + sqrt(G_f{l})) .* g_f;
net.layers{l}.biases = net.layers{l}.biases - lr ./ (opts.delta + sqrt(G_b{l})) .* g_b;
end
% print information
batch_time = toc(batch_time) ;
speed = numel(batch)/batch_time ;
info.train = updateError(opts, info.train, net, res, batch_time) ;
fprintf(' %.2f s (%.1f images/s)', batch_time, speed) ;
n = t + numel(batch) - 1 ;
switch opts.errorType
case 'multiclass'
fprintf(' err %.1f err5 %.1f', ...
info.train.error(end)/n*100, info.train.topFiveError(end)/n*100) ;
fprintf('\n') ;
case 'binary'
fprintf(' err %.1f', ...
info.train.error(end)/n*100) ;
fprintf('\n') ;
case 'euclideanloss'
fprintf(' err %.1f', info.train.error(end) / n);
fprintf('\n') ;
end
% debug info
if opts.plotDiagnostics
figure(2) ; vl_simplenn_diagnose(net,res) ; drawnow ;
end
end % next batch
% evaluation on validation set
if epoch == 1 || rem(epoch, opts.test_interval) == 0 || epoch == opts.numEpochs
for t=1:opts.batchSize:numel(val)
batch_time = tic ;
batch = val(t:min(t+opts.batchSize-1, numel(val))) ;
fprintf('validation: epoch %02d: processing batch %3d of %3d ...', epoch, ...
fix(t/opts.batchSize)+1, ceil(numel(val)/opts.batchSize)) ;
[im, labels] = getBatch(imdb, batch) ;
if opts.prefetch
nextBatch = val(t+opts.batchSize:min(t+2*opts.batchSize-1, numel(val))) ;
getBatch(imdb, nextBatch) ;
end
if opts.useGpu
im = gpuArray(im) ;
end
net.layers{end}.class = labels ;
res = vl_simplenn(net, im, [], res, ...
'disableDropout', true, ...
'conserveMemory', opts.conserveMemory, ...
'sync', opts.sync) ;
% print information
batch_time = toc(batch_time) ;
speed = numel(batch)/batch_time ;
info.val = updateError(opts, info.val, net, res, batch_time) ;
fprintf(' %.2f s (%.1f images/s)', batch_time, speed) ;
n = t + numel(batch) - 1 ;
switch opts.errorType
case 'multiclass'
fprintf(' err %.1f err5 %.1f', ...
info.val.error(end)/n*100, info.val.topFiveError(end)/n*100) ;
fprintf('\n') ;
case 'binary'
fprintf(' err %.1f', ...
info.val.error(end)/n*100) ;
fprintf('\n') ;
case 'euclideanloss'
fprintf(' err %.1f', info.val.error(end) / n);
fprintf('\n') ;
end
end
end
% save
info.train.objective(end) = info.train.objective(end) / numel(train) ;
info.train.error(end) = info.train.error(end) / numel(train) ;
info.train.topFiveError(end) = info.train.topFiveError(end) / numel(train) ;
info.train.speed(end) = numel(train) / info.train.speed(end) ;
info.val.objective(end) = info.val.objective(end) / numel(val) ;
info.val.error(end) = info.val.error(end) / numel(val) ;
info.val.topFiveError(end) = info.val.topFiveError(end) / numel(val) ;
info.val.speed(end) = numel(val) / info.val.speed(end) ;
if epoch == 1 || rem(epoch, opts.snapshot) == 0 || epoch == opts.numEpochs
save(modelPath(epoch), 'net', 'info') ;
end
if epoch == 1 || rem(epoch, opts.display) == 0 || epoch == opts.numEpochs
figure(1) ; clf ;
subplot(1,2,1) ;
semilogy(1:epoch, info.train.objective, 'k') ; hold on ;
semilogy([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.objective([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
xlabel('training epoch') ; ylabel('energy') ;
grid on ;
h=legend('train', 'val') ;
set(h,'color','none');
title('objective') ;
subplot(1,2,2) ;
switch opts.errorType
case 'multiclass'
plot(1:epoch, info.train.error, 'k') ; hold on ;
plot(1:epoch, info.train.topFiveError, 'k--') ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.topFiveError([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b--') ;
h=legend('train','train-5','val','val-5') ;
case 'binary'
plot(1:epoch, info.train.error, 'k') ; hold on ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
h=legend('train','val') ;
case 'euclideanloss'
plot(1 : epoch, info.train.error, 'k'); hold on;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
h = legend('train', 'val') ;
end
grid on ;
xlabel('training epoch') ; ylabel('error') ;
set(h,'color','none') ;
title('error') ;
drawnow ;
print(1, modelFigPath, '-dpdf') ;
end
end
% -------------------------------------------------------------------------
function info = updateError(opts, info, net, res, speed)
% -------------------------------------------------------------------------
predictions = gather(res(end-1).x) ;
sz = size(predictions) ;
n = prod(sz(1:2)) ;
labels = net.layers{end}.class ;
info.objective(end) = info.objective(end) + sum(double(gather(res(end).x))) ;
info.speed(end) = info.speed(end) + speed ;
switch opts.errorType
case 'multiclass'
[~,predictions] = sort(predictions, 3, 'descend') ;
error = ~bsxfun(@eq, predictions, reshape(labels, 1, 1, 1, [])) ;
info.error(end) = info.error(end) +....
sum(sum(sum(error(:,:,1,:))))/n ;
info.topFiveError(end) = info.topFiveError(end) + ...
sum(sum(sum(min(error(:,:,1:5,:),[],3))))/n ;
case 'binary'
error = bsxfun(@times, predictions, labels) < 0 ;
info.error(end) = info.error(end) + sum(error(:))/n ;
case 'euclideanloss'
error = euclideanloss(sigmoid(predictions), labels);
info.error(end) = info.error(end) + error;
end
|
github
|
zhimingluo/MovingObjectSegmentation-master
|
cnn_train_adagrad_ms.m
|
.m
|
MovingObjectSegmentation-master/SBMI/MSCNN/cnn_train_adagrad_ms.m
| 13,645 |
utf_8
|
f04bf6fcc2468adcfb032edb55192a75
|
function [net, info] = cnn_train_adagrad_ms(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a suitable
% getBatch function.
opts.train = [] ;
opts.val = [] ;
opts.numEpochs = 300 ;
opts.batchSize = 256 ;
opts.useGpu = false ;
opts.learningRate = 0.001 ;
opts.continue = false ;
opts.expDir = fullfile('data','exp') ;
opts.conserveMemory = false ;
opts.sync = true ;
opts.prefetch = false ;
opts.weightDecay = 0.0005 ;
opts.errorType = 'multiclass' ;
opts.plotDiagnostics = false ;
opts.delta = 1e-8;
opts.display = 1;
opts.snapshot = 1;
opts.test_interval = 1;
opts = vl_argparse(opts, varargin) ;
if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir); end
if isempty(opts.train), opts.train = find(imdb.images.set==1); end
if isempty(opts.val), opts.val = find(imdb.images.set==2); end
if isnan(opts.train), opts.train = []; end
% -------------------------------------------------------------------------
% Network initialization
% -------------------------------------------------------------------------
for i=1:numel(net.layers)
if ~strcmp(net.layers{i}.type,'conv'), continue; end
net.layers{i}.filtersMomentum = zeros(size(net.layers{i}.filters), ...
class(net.layers{i}.filters)) ;
net.layers{i}.biasesMomentum = zeros(size(net.layers{i}.biases), ...
class(net.layers{i}.biases)) ; %#ok<*ZEROLIKE>
if ~isfield(net.layers{i}, 'filtersLearningRate')
net.layers{i}.filtersLearningRate = 1 ;
end
if ~isfield(net.layers{i}, 'biasesLearningRate')
net.layers{i}.biasesLearningRate = 1 ;
end
if ~isfield(net.layers{i}, 'filtersWeightDecay')
net.layers{i}.filtersWeightDecay = 1 ;
end
if ~isfield(net.layers{i}, 'biasesWeightDecay')
net.layers{i}.biasesWeightDecay = 1 ;
end
end
if opts.useGpu
net = vl_simplenn_move(net, 'gpu') ;
for i=1:numel(net.layers)
if ~strcmp(net.layers{i}.type,'conv'), continue; end
net.layers{i}.filtersMomentum = gpuArray(net.layers{i}.filtersMomentum) ;
net.layers{i}.biasesMomentum = gpuArray(net.layers{i}.biasesMomentum) ;
end
end
G_f = cell(numel(net.layers), 1);
G_b = cell(numel(net.layers), 1);
for l=1:numel(net.layers)
if ~strcmp(net.layers{l}.type, 'conv'), continue ; end
G_f{l} = zeros(size(net.layers{l}.filters), 'single');
G_b{l} = zeros(size(net.layers{l}.biases), 'single');
end
% -------------------------------------------------------------------------
% Train and validate
% -------------------------------------------------------------------------
rng(0) ;
if opts.useGpu
one = gpuArray(single(1)) ;
else
one = single(1) ;
end
info.train.objective = [] ;
info.train.error = [] ;
info.train.topFiveError = [] ;
info.train.speed = [] ;
info.val.objective = [] ;
info.val.error = [] ;
info.val.topFiveError = [] ;
info.val.speed = [] ;
lr = opts.learningRate ;
res = [] ;
for epoch=1:opts.numEpochs
% fast-forward to where we stopped
modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep));
modelFigPath = fullfile(opts.expDir, 'net-train.pdf') ;
if opts.continue
if exist(modelPath(epoch),'file')
if epoch == opts.numEpochs
load(modelPath(epoch), 'net', 'info') ;
end
continue ;
end
if epoch > 1
fprintf('resuming by loading epoch %d\n', epoch-1) ;
load(modelPath(epoch-1), 'net', 'info') ;
end
end
train = opts.train(randperm(numel(opts.train))) ;
val = opts.val ;
info.train.objective(end+1) = 0 ;
info.train.error(end+1) = 0 ;
info.train.topFiveError(end+1) = 0 ;
info.train.speed(end+1) = 0 ;
info.val.objective(end+1) = 0 ;
info.val.error(end+1) = 0 ;
info.val.topFiveError(end+1) = 0 ;
info.val.speed(end+1) = 0 ;
for t=1:opts.batchSize:numel(train)
% get next image batch and labels
batch = train(t:min(t+opts.batchSize-1, numel(train))) ;
batch_time = tic ;
fprintf('training: epoch %02d: processing batch %3d of %3d ...', epoch, ...
fix(t/opts.batchSize)+1, ceil(numel(train)/opts.batchSize)) ;
[im_ori, labels_ori] = getBatch(imdb, batch) ;
if opts.prefetch
nextBatch = train(t+opts.batchSize:min(t+2*opts.batchSize-1, numel(train))) ;
getBatch(imdb, nextBatch) ;
end
for ss = 1 : numel(imdb.scales)
[im, labels] = rescale_im(im_ori, labels_ori, imdb.scales(ss),...
imdb.mask, imdb.half_size, imdb.meanPixel);
if opts.useGpu
im = gpuArray(im) ;
end
% backprop
net.layers{end}.class = labels ;
res = vl_simplenn(net, im, one, res, ...
'conserveMemory', opts.conserveMemory, ...
'sync', opts.sync) ;
% gradient step
for l=1:numel(net.layers)
if ~strcmp(net.layers{l}.type, 'conv'), continue ; end
g_f = (net.layers{l}.filtersLearningRate) * ...
(opts.weightDecay * net.layers{l}.filtersWeightDecay) * net.layers{l}.filters + ...
(net.layers{l}.filtersLearningRate) / numel(batch) * res(l).dzdw{1};
g_b = (net.layers{l}.biasesLearningRate) * ...
(opts.weightDecay * net.layers{l}.biasesWeightDecay) * net.layers{l}.biases + ...
(net.layers{l}.biasesLearningRate) / numel(batch) * res(l).dzdw{2};
G_f{l} = G_f{l} + g_f .^ 2;
G_b{l} = G_b{l} + g_b .^ 2;
net.layers{l}.filters = net.layers{l}.filters - lr ./ (opts.delta + sqrt(G_f{l})) .* g_f;
net.layers{l}.biases = net.layers{l}.biases - lr ./ (opts.delta + sqrt(G_b{l})) .* g_b;
end
end
% print information
batch_time = toc(batch_time) ;
speed = numel(batch)/batch_time ;
info.train = updateError(opts, info.train, net, res, batch_time) ;
fprintf(' %.2f s (%.1f images/s)', batch_time, speed) ;
n = t + numel(batch) - 1 ;
switch opts.errorType
case 'multiclass'
fprintf(' err %.1f err5 %.1f', ...
info.train.error(end)/n*100, info.train.topFiveError(end)/n*100) ;
fprintf('\n') ;
case 'binary'
fprintf(' err %.1f', ...
info.train.error(end)/n*100) ;
fprintf('\n') ;
case 'euclideanloss'
fprintf(' err %.1f', info.train.error(end) / n);
fprintf('\n') ;
end
% debug info
if opts.plotDiagnostics
figure(2) ; vl_simplenn_diagnose(net,res) ; drawnow ;
end
end % next batch
% evaluation on validation set
if epoch == 1 || rem(epoch, opts.test_interval) == 0 || epoch == opts.numEpochs
for t=1:opts.batchSize:numel(val)
batch_time = tic ;
batch = val(t:min(t+opts.batchSize-1, numel(val))) ;
fprintf('validation: epoch %02d: processing batch %3d of %3d ...', epoch, ...
fix(t/opts.batchSize)+1, ceil(numel(val)/opts.batchSize)) ;
[im, labels] = getBatch(imdb, batch) ;
if opts.prefetch
nextBatch = val(t+opts.batchSize:min(t+2*opts.batchSize-1, numel(val))) ;
getBatch(imdb, nextBatch) ;
end
if opts.useGpu
im = gpuArray(im) ;
end
net.layers{end}.class = labels ;
res = vl_simplenn(net, im, [], res, ...
'disableDropout', true, ...
'conserveMemory', opts.conserveMemory, ...
'sync', opts.sync) ;
% print information
batch_time = toc(batch_time) ;
speed = numel(batch)/batch_time ;
info.val = updateError(opts, info.val, net, res, batch_time) ;
fprintf(' %.2f s (%.1f images/s)', batch_time, speed) ;
n = t + numel(batch) - 1 ;
switch opts.errorType
case 'multiclass'
fprintf(' err %.1f err5 %.1f', ...
info.val.error(end)/n*100, info.val.topFiveError(end)/n*100) ;
fprintf('\n') ;
case 'binary'
fprintf(' err %.1f', ...
info.val.error(end)/n*100) ;
fprintf('\n') ;
case 'euclideanloss'
fprintf(' err %.1f', info.val.error(end) / n);
fprintf('\n') ;
end
end
end
% save
info.train.objective(end) = info.train.objective(end) / numel(train) ;
info.train.error(end) = info.train.error(end) / numel(train) ;
info.train.topFiveError(end) = info.train.topFiveError(end) / numel(train) ;
info.train.speed(end) = numel(train) / info.train.speed(end) ;
info.val.objective(end) = info.val.objective(end) / numel(val) ;
info.val.error(end) = info.val.error(end) / numel(val) ;
info.val.topFiveError(end) = info.val.topFiveError(end) / numel(val) ;
info.val.speed(end) = numel(val) / info.val.speed(end) ;
if epoch == 1 || rem(epoch, opts.snapshot) == 0 || epoch == opts.numEpochs
save(modelPath(epoch), 'net', 'info') ;
end
if epoch == 1 || rem(epoch, opts.display) == 0 || epoch == opts.numEpochs
figure(1) ; clf ;
subplot(1,2,1) ;
semilogy(1:epoch, info.train.objective, 'k') ; hold on ;
semilogy([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.objective([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
xlabel('training epoch') ; ylabel('energy') ;
grid on ;
h=legend('train', 'val') ;
set(h,'color','none');
title('objective') ;
subplot(1,2,2) ;
switch opts.errorType
case 'multiclass'
plot(1:epoch, info.train.error, 'k') ; hold on ;
plot(1:epoch, info.train.topFiveError, 'k--') ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.topFiveError([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b--') ;
h=legend('train','train-5','val','val-5') ;
case 'binary'
plot(1:epoch, info.train.error, 'k') ; hold on ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
h=legend('train','val') ;
case 'euclideanloss'
plot(1 : epoch, info.train.error, 'k'); hold on;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
h = legend('train', 'val') ;
end
grid on ;
xlabel('training epoch') ; ylabel('error') ;
set(h,'color','none') ;
title('error') ;
drawnow ;
print(1, modelFigPath, '-dpdf') ;
end
end
end
% -------------------------------------------------------------------------
function info = updateError(opts, info, net, res, speed)
% -------------------------------------------------------------------------
predictions = gather(res(end-1).x) ;
sz = size(predictions) ;
n = prod(sz(1:2)) ;
labels = net.layers{end}.class ;
info.objective(end) = info.objective(end) + sum(double(gather(res(end).x))) ;
info.speed(end) = info.speed(end) + speed ;
switch opts.errorType
case 'multiclass'
[~,predictions] = sort(predictions, 3, 'descend') ;
error = ~bsxfun(@eq, predictions, reshape(labels, 1, 1, 1, [])) ;
info.error(end) = info.error(end) +....
sum(sum(sum(error(:,:,1,:))))/n ;
info.topFiveError(end) = info.topFiveError(end) + ...
sum(sum(sum(min(error(:,:,1:5,:),[],3))))/n ;
case 'binary'
labels = labels(:,:,1,:);
[~,predictions] = sort(predictions, 3, 'descend') ;
predictions = predictions(:,:,1,:);
error = ~bsxfun(@eq, predictions, labels) ;
info.error(end) = info.error(end) + sum(error(:))/n ;
case 'euclideanloss'
error = euclideanloss(sigmoid(predictions), labels);
info.error(end) = info.error(end) + error;
end
end
function [im, labels] = rescale_im(im_ori, label_ori, scale, mask, half_size, meanPixel)
mask = imresize(mask, scale, 'nearest');
for i = 1:size(im_ori,4)
im_ii = imresize(im_ori(:,:,:,i), scale, 'nearest');
im_large = padarray(im_ii, [half_size, half_size], 'symmetric');
im_ii = bsxfun(@minus, im_large, meanPixel);
label_ii = imresize(label_ori(:,:,:,i), scale, 'nearest');
im(:, :, :, i) = im_ii;
labels(:, :, 1, i) = label_ii;
labels(:, :, 2, i) = double(mask);
end
end
|
github
|
zhimingluo/MovingObjectSegmentation-master
|
regular_training_ms.m
|
.m
|
MovingObjectSegmentation-master/SBMI/MSCNN/regular_training_ms.m
| 2,734 |
utf_8
|
ac270ae6d004596e4f3555eef3262c42
|
function regular_training_ms(video)
opts.dataDir = 'net/traffic_Simple' ;
opts.expDir = ['net/' video] ;
opts.train.batchSize = 5 ;
opts.train.numEpochs = 20;
opts.train.continue = false ;
opts.train.useGpu = true ;
opts.train.learningRate = 1e-3;
opts.train.expDir = opts.expDir ;
scales = [1, 0.75, 0.5];
%opts = vl_argparse(opts, varargin) ;
% --------------------------------------------------------------------
% Prepare data
% --------------------------------------------------------------------
imgDir = ['../SBMIDataset/' video '/input'];
labelDir = ['../SBMIDataset/' video '/groundtruth'];
imdb = getImdb(video,imgDir,labelDir);
imdb.half_size = 15;
%%%%%%Yi%%%%%% redefined the net
load('net');
net.layers{end-1} = struct('type', 'conv', ...
'filters', 0.1*randn(1,1,64,1, 'single'), ...
'biases', zeros(1, 1, 'single'), ...
'stride', 1, ...
'pad', 0) ;
net.layers{end} = struct('type', 'sigmoidcrossentropyloss');
load('meanPixel.mat');
imdb.meanPixel = meanPixel;
imdb.scales = scales;
[net,info] = cnn_train_adagrad_ms(net, imdb, @getBatch,...
opts.train,'errorType','euclideanloss',...
'conserveMemory', true);
end
function [im, labels] = getBatch(imdb, batch)
% --------------------------------------------------------------------
half_size = imdb.half_size;
meanPixel = imdb.meanPixel;
for ii = 1:numel(batch)
imagename = imdb.images.name{batch(ii)};
im_ii = single(imread(imagename));
labelname = imdb.images.labels{batch(ii)};
roi = imread(labelname);
labels_ii = zeros(size(roi, 1), size(roi, 2));
labels_ii( roi == 50 ) = 0.25; %shade
labels_ii( roi == 170 ) = 0.75; %object boundary
labels_ii( roi == 255 ) = 1; %foreground
% resize the image to half size
if size(im_ii,1) > 400 || size(im_ii,2) >400
im_ii = imresize(im_ii, 0.5, 'nearest');
labels_ii = imresize(labels_ii, 0.5, 'nearest');
end
im(:,:,:,ii) = im_ii;
labels(:,:,1,ii) = labels_ii;
end
end
function imdb = getImdb(video, imgDir, labelDir)
files = [dir([imgDir '/*.png']); dir([imgDir '/*.jpg'])];
label_files = dir([labelDir '/*.png']);
names = {};labels = {};
load(['../split/' video '.mat']);
for ii = 1:numel(train_index)
names{end+1} = [imgDir '/' files(train_index(ii)).name];
labels{end+1} = [labelDir '/' label_files(train_index(ii)).name];
end
im = imread(labels{1});
mask = ones(size(im,1),size(im,2));
if size(mask,1) > 400 || size(mask,2) >400
mask = imresize(mask, 0.5, 'nearest');
end
imdb.mask = single(mask);
imdb.images.set = ones(1,numel(names));
imdb.images.name = names ;
imdb.images.labels = labels;
end
|
github
|
zhimingluo/MovingObjectSegmentation-master
|
regular_training.m
|
.m
|
MovingObjectSegmentation-master/SBMI/BasicCNN/regular_training.m
| 2,730 |
utf_8
|
a9196ae9e46f45d17b86ba05cff18112
|
function regular_training(video)
opts.expDir = ['net/' video] ;
opts.train.batchSize = 5 ;
opts.train.numEpochs = 20;
opts.train.continue = false ;
opts.train.useGpu = true ;
opts.train.learningRate = 1e-3;
opts.train.expDir = opts.expDir ;
%opts = vl_argparse(opts, varargin) ;
% --------------------------------------------------------------------
% Prepare data
% --------------------------------------------------------------------
imgDir = ['../SBMIDataset/' video '/input'];
labelDir = ['../SBMIDataset/' video '/groundtruth'];
imdb = getImdb(video,imgDir,labelDir);
imdb.half_size = 15;
%%%%%%Yi%%%%%% redefined the net
load('net');
net.layers{end-1} = struct('type', 'conv', ...
'filters', 0.1*randn(1,1,64,1, 'single'), ...
'biases', zeros(1, 1, 'single'), ...
'stride', 1, ...
'pad', 0) ;
net.layers{end} = struct('type', 'sigmoidcrossentropyloss');
%load(['../meanPixel/', category, '_', video, '_meanPixel']);
load('meanPixel.mat');
imdb.meanPixel = meanPixel;
[net,info] = cnn_train_adagrad(net, imdb, @getBatch,...
opts.train,'errorType','euclideanloss',...
'conserveMemory', true);
end
function [im, labels] = getBatch(imdb, batch)
% --------------------------------------------------------------------
half_size = imdb.half_size;
meanPixel = imdb.meanPixel;
for ii = 1:numel(batch)
imagename = imdb.images.name{batch(ii)};
im_ii = single(imread(imagename));
labelname = imdb.images.labels{batch(ii)};
roi = imread(labelname);
labels_ii = zeros(size(roi,1),size(roi,2));
labels_ii( roi == 50 ) = 0.25; %shade
labels_ii( roi == 170 ) = 0.75; %object boundary
labels_ii( roi == 255 ) = 1; %foreground
% resize the image to half size
if size(im_ii,1) > 400 || size(im_ii,2) >400
im_ii = imresize(im_ii, 0.5, 'nearest');
labels_ii = imresize(labels_ii, 0.5, 'nearest');
end
im_large = padarray(im_ii,[half_size,half_size],'symmetric');
im_ii = bsxfun(@minus, im_large, meanPixel);
im(:,:,:,ii) = im_ii;
labels(:,:,1,ii) = labels_ii;
labels(:,:,2,ii) = double(ones(size(labels_ii,1),size(labels_ii,2)));
end
end
function imdb = getImdb(video, imgDir, labelDir)
files = [dir([imgDir '/*.png']); dir([imgDir '/*.jpg'])];
label_files = dir([labelDir '/*.png']);
names = {};labels = {};
load(['../split/' video '.mat']);
for ii = 1:numel(train_index)
names{end+1} = [imgDir '/' files(train_index(ii)).name];
labels{end+1} = [labelDir '/' label_files(train_index(ii)).name];
end
imdb.images.set = ones(1,numel(names));
imdb.images.name = names ;
imdb.images.labels = labels;
end
|
github
|
zhimingluo/MovingObjectSegmentation-master
|
cnn_train_adagrad.m
|
.m
|
MovingObjectSegmentation-master/SBMI/BasicCNN/cnn_train_adagrad.m
| 11,359 |
utf_8
|
b97433eec24865c7f9bc705af9dcbb58
|
function [net, info] = cnn_train_adagrad(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a suitable
% getBatch function.
opts.train = [] ;
opts.val = [] ;
opts.numEpochs = 300 ;
opts.batchSize = 256 ;
opts.useGpu = false ;
opts.learningRate = 0.001 ;
opts.continue = false ;
opts.expDir = fullfile('data','exp') ;
opts.conserveMemory = false ;
opts.sync = true ;
opts.prefetch = false ;
opts.weightDecay = 0.0005 ;
opts.errorType = 'multiclass' ;
opts.plotDiagnostics = false ;
opts.delta = 1e-8;
opts.display = 1;
opts.snapshot = 1;
opts.test_interval = 1;
opts = vl_argparse(opts, varargin) ;
if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir) ; end
if isempty(opts.train), opts.train = find(imdb.images.set==1) ; end
if isempty(opts.val), opts.val = find(imdb.images.set==2) ; end
if isnan(opts.train), opts.train = [] ; end
% -------------------------------------------------------------------------
% Network initialization
% -------------------------------------------------------------------------
for i=1:numel(net.layers)
if ~strcmp(net.layers{i}.type,'conv'), continue; end
net.layers{i}.filtersMomentum = zeros(size(net.layers{i}.filters), ...
class(net.layers{i}.filters)) ;
net.layers{i}.biasesMomentum = zeros(size(net.layers{i}.biases), ...
class(net.layers{i}.biases)) ; %#ok<*ZEROLIKE>
if ~isfield(net.layers{i}, 'filtersLearningRate')
net.layers{i}.filtersLearningRate = 1 ;
end
if ~isfield(net.layers{i}, 'biasesLearningRate')
net.layers{i}.biasesLearningRate = 1 ;
end
if ~isfield(net.layers{i}, 'filtersWeightDecay')
net.layers{i}.filtersWeightDecay = 1 ;
end
if ~isfield(net.layers{i}, 'biasesWeightDecay')
net.layers{i}.biasesWeightDecay = 1 ;
end
end
if opts.useGpu
net = vl_simplenn_move(net, 'gpu') ;
for i=1:numel(net.layers)
if ~strcmp(net.layers{i}.type,'conv'), continue; end
net.layers{i}.filtersMomentum = gpuArray(net.layers{i}.filtersMomentum) ;
net.layers{i}.biasesMomentum = gpuArray(net.layers{i}.biasesMomentum) ;
end
end
G_f = cell(numel(net.layers), 1);
G_b = cell(numel(net.layers), 1);
for l=1:numel(net.layers)
if ~strcmp(net.layers{l}.type, 'conv'), continue ; end
G_f{l} = zeros(size(net.layers{l}.filters), 'single');
G_b{l} = zeros(size(net.layers{l}.biases), 'single');
end
% -------------------------------------------------------------------------
% Train and validate
% -------------------------------------------------------------------------
rng(0) ;
if opts.useGpu
one = gpuArray(single(1)) ;
else
one = single(1) ;
end
info.train.objective = [] ;
info.train.error = [] ;
info.train.topFiveError = [] ;
info.train.speed = [] ;
info.val.objective = [] ;
info.val.error = [] ;
info.val.topFiveError = [] ;
info.val.speed = [] ;
lr = opts.learningRate ;
res = [] ;
for epoch=1:opts.numEpochs
% fast-forward to where we stopped
modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep));
modelFigPath = fullfile(opts.expDir, 'net-train.pdf') ;
if opts.continue
if exist(modelPath(epoch),'file')
if epoch == opts.numEpochs
load(modelPath(epoch), 'net', 'info') ;
end
continue ;
end
if epoch > 1
fprintf('resuming by loading epoch %d\n', epoch-1) ;
load(modelPath(epoch-1), 'net', 'info') ;
end
end
train = opts.train(randperm(numel(opts.train))) ;
val = opts.val ;
info.train.objective(end+1) = 0 ;
info.train.error(end+1) = 0 ;
info.train.topFiveError(end+1) = 0 ;
info.train.speed(end+1) = 0 ;
info.val.objective(end+1) = 0 ;
info.val.error(end+1) = 0 ;
info.val.topFiveError(end+1) = 0 ;
info.val.speed(end+1) = 0 ;
for t=1:opts.batchSize:numel(train)
% get next image batch and labels
batch = train(t:min(t+opts.batchSize-1, numel(train))) ;
batch_time = tic ;
fprintf('training: epoch %02d: processing batch %3d of %3d ...', epoch, ...
fix(t/opts.batchSize)+1, ceil(numel(train)/opts.batchSize)) ;
[im, labels] = getBatch(imdb, batch) ;
if opts.prefetch
nextBatch = train(t+opts.batchSize:min(t+2*opts.batchSize-1, numel(train))) ;
getBatch(imdb, nextBatch) ;
end
if opts.useGpu
im = gpuArray(im) ;
end
% backprop
net.layers{end}.class = labels ;
res = vl_simplenn(net, im, one, res, ...
'conserveMemory', opts.conserveMemory, ...
'sync', opts.sync) ;
% gradient step
for l=1:numel(net.layers)
if ~strcmp(net.layers{l}.type, 'conv'), continue ; end
g_f = (net.layers{l}.filtersLearningRate) * ...
(opts.weightDecay * net.layers{l}.filtersWeightDecay) * net.layers{l}.filters + ...
(net.layers{l}.filtersLearningRate) / numel(batch) * res(l).dzdw{1};
g_b = (net.layers{l}.biasesLearningRate) * ...
(opts.weightDecay * net.layers{l}.biasesWeightDecay) * net.layers{l}.biases + ...
(net.layers{l}.biasesLearningRate) / numel(batch) * res(l).dzdw{2};
G_f{l} = G_f{l} + g_f .^ 2;
G_b{l} = G_b{l} + g_b .^ 2;
net.layers{l}.filters = net.layers{l}.filters - lr ./ (opts.delta + sqrt(G_f{l})) .* g_f;
net.layers{l}.biases = net.layers{l}.biases - lr ./ (opts.delta + sqrt(G_b{l})) .* g_b;
end
% print information
batch_time = toc(batch_time) ;
speed = numel(batch)/batch_time ;
info.train = updateError(opts, info.train, net, res, batch_time) ;
fprintf(' %.2f s (%.1f images/s)', batch_time, speed) ;
n = t + numel(batch) - 1 ;
switch opts.errorType
case 'multiclass'
fprintf(' err %.1f err5 %.1f', ...
info.train.error(end)/n*100, info.train.topFiveError(end)/n*100) ;
fprintf('\n') ;
case 'binary'
fprintf(' err %.1f', ...
info.train.error(end)/n*100) ;
fprintf('\n') ;
case 'euclideanloss'
fprintf(' err %.1f', info.train.error(end) / n);
fprintf('\n') ;
end
% debug info
if opts.plotDiagnostics
figure(2) ; vl_simplenn_diagnose(net,res) ; drawnow ;
end
end % next batch
% evaluation on validation set
if epoch == 1 || rem(epoch, opts.test_interval) == 0 || epoch == opts.numEpochs
for t=1:opts.batchSize:numel(val)
batch_time = tic ;
batch = val(t:min(t+opts.batchSize-1, numel(val))) ;
fprintf('validation: epoch %02d: processing batch %3d of %3d ...', epoch, ...
fix(t/opts.batchSize)+1, ceil(numel(val)/opts.batchSize)) ;
[im, labels] = getBatch(imdb, batch) ;
if opts.prefetch
nextBatch = val(t+opts.batchSize:min(t+2*opts.batchSize-1, numel(val))) ;
getBatch(imdb, nextBatch) ;
end
if opts.useGpu
im = gpuArray(im) ;
end
net.layers{end}.class = labels ;
res = vl_simplenn(net, im, [], res, ...
'disableDropout', true, ...
'conserveMemory', opts.conserveMemory, ...
'sync', opts.sync) ;
% print information
batch_time = toc(batch_time) ;
speed = numel(batch)/batch_time ;
info.val = updateError(opts, info.val, net, res, batch_time) ;
fprintf(' %.2f s (%.1f images/s)', batch_time, speed) ;
n = t + numel(batch) - 1 ;
switch opts.errorType
case 'multiclass'
fprintf(' err %.1f err5 %.1f', ...
info.val.error(end)/n*100, info.val.topFiveError(end)/n*100) ;
fprintf('\n') ;
case 'binary'
fprintf(' err %.1f', ...
info.val.error(end)/n*100) ;
fprintf('\n') ;
case 'euclideanloss'
fprintf(' err %.1f', info.val.error(end) / n);
fprintf('\n') ;
end
end
end
% save
info.train.objective(end) = info.train.objective(end) / numel(train) ;
info.train.error(end) = info.train.error(end) / numel(train) ;
info.train.topFiveError(end) = info.train.topFiveError(end) / numel(train) ;
info.train.speed(end) = numel(train) / info.train.speed(end) ;
info.val.objective(end) = info.val.objective(end) / numel(val) ;
info.val.error(end) = info.val.error(end) / numel(val) ;
info.val.topFiveError(end) = info.val.topFiveError(end) / numel(val) ;
info.val.speed(end) = numel(val) / info.val.speed(end) ;
if epoch == 1 || rem(epoch, opts.snapshot) == 0 || epoch == opts.numEpochs
save(modelPath(epoch), 'net', 'info') ;
end
if epoch == 1 || rem(epoch, opts.display) == 0 || epoch == opts.numEpochs
figure(1) ; clf ;
subplot(1,2,1) ;
semilogy(1:epoch, info.train.objective, 'k') ; hold on ;
semilogy([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.objective([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
xlabel('training epoch') ; ylabel('energy') ;
grid on ;
h=legend('train', 'val') ;
set(h,'color','none');
title('objective') ;
subplot(1,2,2) ;
switch opts.errorType
case 'multiclass'
plot(1:epoch, info.train.error, 'k') ; hold on ;
plot(1:epoch, info.train.topFiveError, 'k--') ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.topFiveError([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b--') ;
h=legend('train','train-5','val','val-5') ;
case 'binary'
plot(1:epoch, info.train.error, 'k') ; hold on ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
h=legend('train','val') ;
case 'euclideanloss'
plot(1 : epoch, info.train.error, 'k'); hold on;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
h = legend('train', 'val') ;
end
grid on ;
xlabel('training epoch') ; ylabel('error') ;
set(h,'color','none') ;
title('error') ;
drawnow ;
print(1, modelFigPath, '-dpdf') ;
end
end
% -------------------------------------------------------------------------
function info = updateError(opts, info, net, res, speed)
% -------------------------------------------------------------------------
predictions = gather(res(end-1).x) ;
sz = size(predictions) ;
n = prod(sz(1:2)) ;
labels = net.layers{end}.class ;
info.objective(end) = info.objective(end) + sum(double(gather(res(end).x))) ;
info.speed(end) = info.speed(end) + speed ;
switch opts.errorType
case 'multiclass'
[~,predictions] = sort(predictions, 3, 'descend') ;
error = ~bsxfun(@eq, predictions, reshape(labels, 1, 1, 1, [])) ;
info.error(end) = info.error(end) +....
sum(sum(sum(error(:,:,1,:))))/n ;
info.topFiveError(end) = info.topFiveError(end) + ...
sum(sum(sum(min(error(:,:,1:5,:),[],3))))/n ;
case 'binary'
error = bsxfun(@times, predictions, labels) < 0 ;
info.error(end) = info.error(end) + sum(error(:))/n ;
case 'euclideanloss'
error = euclideanloss(sigmoid(predictions), labels);
info.error(end) = info.error(end) + error;
end
|
github
|
zhimingluo/MovingObjectSegmentation-master
|
training.m
|
.m
|
MovingObjectSegmentation-master/CDNet/Cascade/training.m
| 3,369 |
utf_8
|
55bd6c6fc17bd619f84337f5ab575009
|
function training(video, method, frames)
opts.expDir = ['net/' method '/' num2str(frames) '/' video];
opts.train.batchSize = 5 ;
opts.train.numEpochs = 20;
opts.train.continue = true ;
opts.train.useGpu = true ;
opts.train.learningRate = 1e-3;
opts.train.expDir = opts.expDir;
% --------------------------------------------------------------------
% Prepare data
% --------------------------------------------------------------------
imgDir = ['../' video '/input'];
labelDir = ['../' video '/GT'];
grayDir = fullfile('../result/', method, '/', num2str(num_frames), video);
imdb = getImdb_new(imgDir, labelDir, grayDir);
mask = imread(['../' video '/ROI.bmp']);
mask = mask(:, :, 1);
A = max(max(mask));
mask(mask == A) = 1;
if size(mask, 1) > 400 || size(mask,2) > 400
mask = imresize(mask, 0.5, 'nearest');
end
imdb.mask = single(double(mask));
imdb.half_size = 15;
%%%%%%Yi%%%%%% redefined the net
load('net');
net.layers{1} = struct('type', 'conv', ...
'filters', 0.01 * randn(7, 7, 4, 32, 'single'), ...
'biases', zeros(1, 32, 'single'), ...
'stride', 1, ...
'pad', 0) ;
net.layers{end-1} = struct('type', 'conv', ...
'filters', 0.1*randn(1,1,64,1, 'single'), ...
'biases', zeros(1, 1, 'single'), ...
'stride', 1, ...
'pad', 0) ;
net.layers{end} = struct('type', 'sigmoidcrossentropyloss');
load('meanPixel.mat');
imdb.meanPixel = meanPixel;
[net,info] = cnn_train_adagrad(net, imdb, @getBatch,...
opts.train, 'errorType', 'euclideanloss', ...
'conserveMemory', true);
end
function [im, labels, mask] = getBatch(imdb, batch)
half_size = imdb.half_size;
meanPixel = imdb.meanPixel;
meanPixel(:,:,4) =0;
for ii = 1 : numel(batch)
imagename = imdb.images.name{batch(ii)};
im_ii = single(imread(imagename));
labelname = imdb.images.labels{batch(ii)};
roi = imread(labelname);
labels_ii = zeros(size(roi, 1), size(roi, 2));
labels_ii( roi == 50 ) = 0.25; %shade
labels_ii( roi == 170 ) = 0.75; %object boundary
labels_ii( roi == 255 ) = 1; %foreground
% resize the image to half size
if size(im_ii, 1) > 400 || size(im_ii, 2) >400
im_ii = imresize(im_ii, 0.5, 'nearest');
labels_ii = imresize(labels_ii, 0.5, 'nearest');
end
grayname =imdb.images.gray_name{batch(ii)};
im_ii(:,:,4) = single(imread(grayname));
im_large = padarray(im_ii, [half_size, half_size], 'symmetric');
im_ii = bsxfun(@minus, im_large, meanPixel);
im(:, :, :, ii) = im_ii;
labels(:, :, 1, ii) = labels_ii;
labels(:, :, 2, ii) = double(imdb.mask);
end
end
function imdb = getImdb_new(imgDir, labelDir, grayDir)
files = dir([imgDir '/*.jpg']);
label_files = dir([labelDir '/*.png']);
names = {};
labels = {};
gray_names = {};
for ii = 1:numel(files)
names{end+1} = [imgDir '/' files(ii).name];
labels{end+1} = [labelDir '/' label_files(ii).name];
%prob_name = strrep(label_files(ii).name, 'gt', 'in');
prob_name = strrep(label_files(ii).name, 'gt', 'in');
gray_names{end+1} = [grayDir '/' prob_name];
end
imdb.images.set = ones(1,numel(names));
imdb.images.name = names ;
imdb.images.gray_name = gray_names;
imdb.images.labels = labels;
end
|
github
|
zhimingluo/MovingObjectSegmentation-master
|
cnn_train_adagrad.m
|
.m
|
MovingObjectSegmentation-master/CDNet/Cascade/cnn_train_adagrad.m
| 11,673 |
utf_8
|
313227873e4c91a0c6db387ba794ec99
|
function [net, info] = cnn_train_adagrad(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a suitable
% getBatch function.
opts.train = [] ;
opts.val = [] ;
opts.numEpochs = 300 ;
opts.batchSize = 256 ;
opts.useGpu = false ;
opts.learningRate = 0.001 ;
opts.continue = false ;
opts.expDir = fullfile('data','exp') ;
opts.conserveMemory = false ;
opts.sync = true ;
opts.prefetch = false ;
opts.weightDecay = 0.0005 ;
opts.errorType = 'multiclass' ;
opts.plotDiagnostics = false ;
opts.delta = 1e-8;
opts.display = 1;
opts.snapshot = 1;
opts.test_interval = 1;
opts = vl_argparse(opts, varargin) ;
if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir); end
if isempty(opts.train), opts.train = find(imdb.images.set==1); end
if isempty(opts.val), opts.val = find(imdb.images.set==2); end
if isnan(opts.train), opts.train = []; end
% -------------------------------------------------------------------------
% Network initialization
% -------------------------------------------------------------------------
for i=1:numel(net.layers)
if ~strcmp(net.layers{i}.type,'conv'), continue; end
net.layers{i}.filtersMomentum = zeros(size(net.layers{i}.filters), ...
class(net.layers{i}.filters)) ;
net.layers{i}.biasesMomentum = zeros(size(net.layers{i}.biases), ...
class(net.layers{i}.biases)) ; %#ok<*ZEROLIKE>
if ~isfield(net.layers{i}, 'filtersLearningRate')
net.layers{i}.filtersLearningRate = 1 ;
end
if ~isfield(net.layers{i}, 'biasesLearningRate')
net.layers{i}.biasesLearningRate = 1 ;
end
if ~isfield(net.layers{i}, 'filtersWeightDecay')
net.layers{i}.filtersWeightDecay = 1 ;
end
if ~isfield(net.layers{i}, 'biasesWeightDecay')
net.layers{i}.biasesWeightDecay = 1 ;
end
end
if opts.useGpu
net = vl_simplenn_move(net, 'gpu') ;
for i=1:numel(net.layers)
if ~strcmp(net.layers{i}.type,'conv'), continue; end
net.layers{i}.filtersMomentum = gpuArray(net.layers{i}.filtersMomentum) ;
net.layers{i}.biasesMomentum = gpuArray(net.layers{i}.biasesMomentum) ;
end
end
G_f = cell(numel(net.layers), 1);
G_b = cell(numel(net.layers), 1);
for l=1:numel(net.layers)
if ~strcmp(net.layers{l}.type, 'conv'), continue ; end
G_f{l} = zeros(size(net.layers{l}.filters), 'single');
G_b{l} = zeros(size(net.layers{l}.biases), 'single');
end
% -------------------------------------------------------------------------
% Train and validate
% -------------------------------------------------------------------------
rng(0) ;
if opts.useGpu
one = gpuArray(single(1)) ;
else
one = single(1) ;
end
info.train.objective = [] ;
info.train.error = [] ;
info.train.topFiveError = [] ;
info.train.speed = [] ;
info.val.objective = [] ;
info.val.error = [] ;
info.val.topFiveError = [] ;
info.val.speed = [] ;
lr = opts.learningRate ;
res = [] ;
for epoch=1:opts.numEpochs
% fast-forward to where we stopped
modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep));
modelFigPath = fullfile(opts.expDir, 'net-train.pdf') ;
if opts.continue
if exist(modelPath(epoch),'file')
if epoch == opts.numEpochs
load(modelPath(epoch), 'net', 'info') ;
end
continue ;
end
if epoch > 1
fprintf('resuming by loading epoch %d\n', epoch-1) ;
load(modelPath(epoch-1), 'net', 'info') ;
end
end
train = opts.train(randperm(numel(opts.train))) ;
val = opts.val ;
info.train.objective(end+1) = 0 ;
info.train.error(end+1) = 0 ;
info.train.topFiveError(end+1) = 0 ;
info.train.speed(end+1) = 0 ;
info.val.objective(end+1) = 0 ;
info.val.error(end+1) = 0 ;
info.val.topFiveError(end+1) = 0 ;
info.val.speed(end+1) = 0 ;
for t=1:opts.batchSize:numel(train)
% get next image batch and labels
batch = train(t:min(t+opts.batchSize-1, numel(train))) ;
batch_time = tic ;
fprintf('training: epoch %02d: processing batch %3d of %3d ...', epoch, ...
fix(t/opts.batchSize)+1, ceil(numel(train)/opts.batchSize)) ;
[im, labels] = getBatch(imdb, batch) ;
if opts.prefetch
nextBatch = train(t+opts.batchSize:min(t+2*opts.batchSize-1, numel(train))) ;
getBatch(imdb, nextBatch) ;
end
if opts.useGpu
im = gpuArray(im) ;
end
% backprop
net.layers{end}.class = labels ;
res = vl_simplenn(net, im, one, res, ...
'conserveMemory', opts.conserveMemory, ...
'sync', opts.sync) ;
% gradient step
for l=1:numel(net.layers)
if ~strcmp(net.layers{l}.type, 'conv'), continue ; end
g_f = (net.layers{l}.filtersLearningRate) * ...
(opts.weightDecay * net.layers{l}.filtersWeightDecay) * net.layers{l}.filters + ...
(net.layers{l}.filtersLearningRate) / numel(batch) * res(l).dzdw{1};
g_b = (net.layers{l}.biasesLearningRate) * ...
(opts.weightDecay * net.layers{l}.biasesWeightDecay) * net.layers{l}.biases + ...
(net.layers{l}.biasesLearningRate) / numel(batch) * res(l).dzdw{2};
G_f{l} = G_f{l} + g_f .^ 2;
G_b{l} = G_b{l} + g_b .^ 2;
net.layers{l}.filters = net.layers{l}.filters - lr ./ (opts.delta + sqrt(G_f{l})) .* g_f;
net.layers{l}.biases = net.layers{l}.biases - lr ./ (opts.delta + sqrt(G_b{l})) .* g_b;
end
% print information
batch_time = toc(batch_time) ;
speed = numel(batch)/batch_time ;
info.train = updateError(opts, info.train, net, res, batch_time) ;
fprintf(' %.2f s (%.1f images/s)', batch_time, speed) ;
n = t + numel(batch) - 1 ;
switch opts.errorType
case 'multiclass'
fprintf(' err %.1f err5 %.1f', ...
info.train.error(end)/n*100, info.train.topFiveError(end)/n*100) ;
fprintf('\n') ;
case 'binary'
fprintf(' err %.1f', ...
info.train.error(end)/n*100) ;
fprintf('\n') ;
case 'euclideanloss'
fprintf(' err %.1f', info.train.error(end) / n);
fprintf('\n') ;
end
% debug info
if opts.plotDiagnostics
figure(2) ; vl_simplenn_diagnose(net,res) ; drawnow ;
end
end % next batch
% evaluation on validation set
if epoch == 1 || rem(epoch, opts.test_interval) == 0 || epoch == opts.numEpochs
for t=1:opts.batchSize:numel(val)
batch_time = tic ;
batch = val(t:min(t+opts.batchSize-1, numel(val))) ;
fprintf('validation: epoch %02d: processing batch %3d of %3d ...', epoch, ...
fix(t/opts.batchSize)+1, ceil(numel(val)/opts.batchSize)) ;
[im, labels] = getBatch(imdb, batch) ;
if opts.prefetch
nextBatch = val(t+opts.batchSize:min(t+2*opts.batchSize-1, numel(val))) ;
getBatch(imdb, nextBatch) ;
end
if opts.useGpu
im = gpuArray(im) ;
end
net.layers{end}.class = labels ;
res = vl_simplenn(net, im, [], res, ...
'disableDropout', true, ...
'conserveMemory', opts.conserveMemory, ...
'sync', opts.sync) ;
% print information
batch_time = toc(batch_time) ;
speed = numel(batch)/batch_time ;
info.val = updateError(opts, info.val, net, res, batch_time) ;
fprintf(' %.2f s (%.1f images/s)', batch_time, speed) ;
n = t + numel(batch) - 1 ;
switch opts.errorType
case 'multiclass'
fprintf(' err %.1f err5 %.1f', ...
info.val.error(end)/n*100, info.val.topFiveError(end)/n*100) ;
fprintf('\n') ;
case 'binary'
fprintf(' err %.1f', ...
info.val.error(end)/n*100) ;
fprintf('\n') ;
case 'euclideanloss'
fprintf(' err %.1f', info.val.error(end) / n);
fprintf('\n') ;
end
end
end
% save
info.train.objective(end) = info.train.objective(end) / numel(train) ;
info.train.error(end) = info.train.error(end) / numel(train) ;
info.train.topFiveError(end) = info.train.topFiveError(end) / numel(train) ;
info.train.speed(end) = numel(train) / info.train.speed(end) ;
info.val.objective(end) = info.val.objective(end) / numel(val) ;
info.val.error(end) = info.val.error(end) / numel(val) ;
info.val.topFiveError(end) = info.val.topFiveError(end) / numel(val) ;
info.val.speed(end) = numel(val) / info.val.speed(end) ;
if epoch == 1 || rem(epoch, opts.snapshot) == 0 || epoch == opts.numEpochs
save(modelPath(epoch), 'net', 'info') ;
end
if epoch == 1 || rem(epoch, opts.display) == 0 || epoch == opts.numEpochs
figure(1) ; clf ;
subplot(1,2,1) ;
semilogy(1:epoch, info.train.objective, 'k') ; hold on ;
semilogy([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.objective([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
xlabel('training epoch') ; ylabel('energy') ;
grid on ;
h=legend('train', 'val') ;
set(h,'color','none');
title('objective') ;
subplot(1,2,2) ;
switch opts.errorType
case 'multiclass'
plot(1:epoch, info.train.error, 'k') ; hold on ;
plot(1:epoch, info.train.topFiveError, 'k--') ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.topFiveError([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b--') ;
h=legend('train','train-5','val','val-5') ;
case 'binary'
plot(1:epoch, info.train.error, 'k') ; hold on ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
h=legend('train','val') ;
case 'euclideanloss'
plot(1 : epoch, info.train.error, 'k'); hold on;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
h = legend('train', 'val') ;
end
grid on ;
xlabel('training epoch') ; ylabel('error') ;
set(h,'color','none') ;
title('error') ;
drawnow ;
print(1, modelFigPath, '-dpdf') ;
end
end
% -------------------------------------------------------------------------
function info = updateError(opts, info, net, res, speed)
% -------------------------------------------------------------------------
predictions = gather(res(end-1).x) ;
sz = size(predictions) ;
n = prod(sz(1:2)) ;
labels = net.layers{end}.class ;
info.objective(end) = info.objective(end) + sum(double(gather(res(end).x))) ;
info.speed(end) = info.speed(end) + speed ;
switch opts.errorType
case 'multiclass'
[~,predictions] = sort(predictions, 3, 'descend') ;
error = ~bsxfun(@eq, predictions, reshape(labels, 1, 1, 1, [])) ;
info.error(end) = info.error(end) +....
sum(sum(sum(error(:,:,1,:))))/n ;
info.topFiveError(end) = info.topFiveError(end) + ...
sum(sum(sum(min(error(:,:,1:5,:),[],3))))/n ;
case 'binary'
error = bsxfun(@times, predictions, labels) < 0 ;
info.error(end) = info.error(end) + sum(error(:))/n ;
case 'euclideanloss'
error = euclideanloss(sigmoid(predictions), labels);
info.error(end) = info.error(end) + error;
end
|
github
|
zhimingluo/MovingObjectSegmentation-master
|
ms_regular_training.m
|
.m
|
MovingObjectSegmentation-master/CDNet/MSCNN/ms_regular_training.m
| 2,806 |
utf_8
|
84fba4417e546a61491110751487c70a
|
function ms_regular_training(video, method, frames)
opts.expDir = ['net/' method '/' num2str(frames) '/' video] ;
opts.train.batchSize = 5 ;
opts.train.numEpochs = 20;
opts.train.continue = false ;
opts.train.useGpu = true ;
opts.train.learningRate = 1e-3;
opts.train.expDir = opts.expDir ;
scales = [1, 0.75, 0.5];
%opts = vl_argparse(opts, varargin) ;
% --------------------------------------------------------------------
% Prepare data
% --------------------------------------------------------------------
imgDir = ['../CDNetDataset/' method '/' num2str(frames) 'frames/' video '/input'];
labelDir = ['../CDNetDataset/' method '/' num2str(frames) 'frames/' video '/GT'];
imdb = getImdb(imgDir,labelDir);
mask = imread(['../CDNetDataset/' method '/' num2str(frames) 'frames/' video '/ROI.bmp']);
mask = mask(:,:,1);
A = max(max(mask));
mask(mask == A) = 1;
if size(mask,1) > 400 || size(mask,2) >400
mask = imresize(mask, 0.5, 'nearest');
end
imdb.mask = single(double(mask));
imdb.half_size = 15;
%%%%%%Yi%%%%%% redefined the net
load('net');
net.layers{end-1} = struct('type', 'conv', ...
'filters', 0.1*randn(1,1,64,1, 'single'), ...
'biases', zeros(1, 1, 'single'), ...
'stride', 1, ...
'pad', 0) ;
net.layers{end} = struct('type', 'sigmoidcrossentropyloss');
load('meanPixel.mat');
imdb.meanPixel = meanPixel;
imdb.scales = scales;
[net,info] = cnn_train_adagrad_ms(net, imdb, @getBatch,...
opts.train,'errorType','euclideanloss',...
'conserveMemory', true);
end
function [im, labels] = getBatch(imdb, batch)
% --------------------------------------------------------------------
half_size = imdb.half_size;
meanPixel = imdb.meanPixel;
for ii = 1:numel(batch)
imagename = imdb.images.name{batch(ii)};
im_ii = single(imread(imagename));
labelname = imdb.images.labels{batch(ii)};
roi = imread(labelname);
labels_ii = zeros(size(roi, 1), size(roi, 2));
labels_ii( roi == 50 ) = 0.25; %shade
labels_ii( roi == 170 ) = 0.75; %object boundary
labels_ii( roi == 255 ) = 1; %foreground
% resize the image to half size
if size(im_ii,1) > 400 || size(im_ii,2) >400
im_ii = imresize(im_ii, 0.5, 'nearest');
labels_ii = imresize(labels_ii, 0.5, 'nearest');
end
im(:,:,:,ii) = im_ii;
labels(:,:,1,ii) = labels_ii;
end
end
function imdb = getImdb(imgDir, labelDir)
files = dir([imgDir '/*.jpg']);
label_files = dir([labelDir '/*.png']);
names = {};labels = {};
for ii = 1:numel(files)
names{end+1} = [imgDir '/' files(ii).name];
labels{end+1} = [labelDir '/' label_files(ii).name];
end
imdb.images.set = ones(1,numel(names));
imdb.images.name = names ;
imdb.images.labels = labels;
end
|
github
|
zhimingluo/MovingObjectSegmentation-master
|
cnn_train_adagrad_ms.m
|
.m
|
MovingObjectSegmentation-master/CDNet/MSCNN/cnn_train_adagrad_ms.m
| 13,645 |
utf_8
|
f04bf6fcc2468adcfb032edb55192a75
|
function [net, info] = cnn_train_adagrad_ms(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a suitable
% getBatch function.
opts.train = [] ;
opts.val = [] ;
opts.numEpochs = 300 ;
opts.batchSize = 256 ;
opts.useGpu = false ;
opts.learningRate = 0.001 ;
opts.continue = false ;
opts.expDir = fullfile('data','exp') ;
opts.conserveMemory = false ;
opts.sync = true ;
opts.prefetch = false ;
opts.weightDecay = 0.0005 ;
opts.errorType = 'multiclass' ;
opts.plotDiagnostics = false ;
opts.delta = 1e-8;
opts.display = 1;
opts.snapshot = 1;
opts.test_interval = 1;
opts = vl_argparse(opts, varargin) ;
if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir); end
if isempty(opts.train), opts.train = find(imdb.images.set==1); end
if isempty(opts.val), opts.val = find(imdb.images.set==2); end
if isnan(opts.train), opts.train = []; end
% -------------------------------------------------------------------------
% Network initialization
% -------------------------------------------------------------------------
for i=1:numel(net.layers)
if ~strcmp(net.layers{i}.type,'conv'), continue; end
net.layers{i}.filtersMomentum = zeros(size(net.layers{i}.filters), ...
class(net.layers{i}.filters)) ;
net.layers{i}.biasesMomentum = zeros(size(net.layers{i}.biases), ...
class(net.layers{i}.biases)) ; %#ok<*ZEROLIKE>
if ~isfield(net.layers{i}, 'filtersLearningRate')
net.layers{i}.filtersLearningRate = 1 ;
end
if ~isfield(net.layers{i}, 'biasesLearningRate')
net.layers{i}.biasesLearningRate = 1 ;
end
if ~isfield(net.layers{i}, 'filtersWeightDecay')
net.layers{i}.filtersWeightDecay = 1 ;
end
if ~isfield(net.layers{i}, 'biasesWeightDecay')
net.layers{i}.biasesWeightDecay = 1 ;
end
end
if opts.useGpu
net = vl_simplenn_move(net, 'gpu') ;
for i=1:numel(net.layers)
if ~strcmp(net.layers{i}.type,'conv'), continue; end
net.layers{i}.filtersMomentum = gpuArray(net.layers{i}.filtersMomentum) ;
net.layers{i}.biasesMomentum = gpuArray(net.layers{i}.biasesMomentum) ;
end
end
G_f = cell(numel(net.layers), 1);
G_b = cell(numel(net.layers), 1);
for l=1:numel(net.layers)
if ~strcmp(net.layers{l}.type, 'conv'), continue ; end
G_f{l} = zeros(size(net.layers{l}.filters), 'single');
G_b{l} = zeros(size(net.layers{l}.biases), 'single');
end
% -------------------------------------------------------------------------
% Train and validate
% -------------------------------------------------------------------------
rng(0) ;
if opts.useGpu
one = gpuArray(single(1)) ;
else
one = single(1) ;
end
info.train.objective = [] ;
info.train.error = [] ;
info.train.topFiveError = [] ;
info.train.speed = [] ;
info.val.objective = [] ;
info.val.error = [] ;
info.val.topFiveError = [] ;
info.val.speed = [] ;
lr = opts.learningRate ;
res = [] ;
for epoch=1:opts.numEpochs
% fast-forward to where we stopped
modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep));
modelFigPath = fullfile(opts.expDir, 'net-train.pdf') ;
if opts.continue
if exist(modelPath(epoch),'file')
if epoch == opts.numEpochs
load(modelPath(epoch), 'net', 'info') ;
end
continue ;
end
if epoch > 1
fprintf('resuming by loading epoch %d\n', epoch-1) ;
load(modelPath(epoch-1), 'net', 'info') ;
end
end
train = opts.train(randperm(numel(opts.train))) ;
val = opts.val ;
info.train.objective(end+1) = 0 ;
info.train.error(end+1) = 0 ;
info.train.topFiveError(end+1) = 0 ;
info.train.speed(end+1) = 0 ;
info.val.objective(end+1) = 0 ;
info.val.error(end+1) = 0 ;
info.val.topFiveError(end+1) = 0 ;
info.val.speed(end+1) = 0 ;
for t=1:opts.batchSize:numel(train)
% get next image batch and labels
batch = train(t:min(t+opts.batchSize-1, numel(train))) ;
batch_time = tic ;
fprintf('training: epoch %02d: processing batch %3d of %3d ...', epoch, ...
fix(t/opts.batchSize)+1, ceil(numel(train)/opts.batchSize)) ;
[im_ori, labels_ori] = getBatch(imdb, batch) ;
if opts.prefetch
nextBatch = train(t+opts.batchSize:min(t+2*opts.batchSize-1, numel(train))) ;
getBatch(imdb, nextBatch) ;
end
for ss = 1 : numel(imdb.scales)
[im, labels] = rescale_im(im_ori, labels_ori, imdb.scales(ss),...
imdb.mask, imdb.half_size, imdb.meanPixel);
if opts.useGpu
im = gpuArray(im) ;
end
% backprop
net.layers{end}.class = labels ;
res = vl_simplenn(net, im, one, res, ...
'conserveMemory', opts.conserveMemory, ...
'sync', opts.sync) ;
% gradient step
for l=1:numel(net.layers)
if ~strcmp(net.layers{l}.type, 'conv'), continue ; end
g_f = (net.layers{l}.filtersLearningRate) * ...
(opts.weightDecay * net.layers{l}.filtersWeightDecay) * net.layers{l}.filters + ...
(net.layers{l}.filtersLearningRate) / numel(batch) * res(l).dzdw{1};
g_b = (net.layers{l}.biasesLearningRate) * ...
(opts.weightDecay * net.layers{l}.biasesWeightDecay) * net.layers{l}.biases + ...
(net.layers{l}.biasesLearningRate) / numel(batch) * res(l).dzdw{2};
G_f{l} = G_f{l} + g_f .^ 2;
G_b{l} = G_b{l} + g_b .^ 2;
net.layers{l}.filters = net.layers{l}.filters - lr ./ (opts.delta + sqrt(G_f{l})) .* g_f;
net.layers{l}.biases = net.layers{l}.biases - lr ./ (opts.delta + sqrt(G_b{l})) .* g_b;
end
end
% print information
batch_time = toc(batch_time) ;
speed = numel(batch)/batch_time ;
info.train = updateError(opts, info.train, net, res, batch_time) ;
fprintf(' %.2f s (%.1f images/s)', batch_time, speed) ;
n = t + numel(batch) - 1 ;
switch opts.errorType
case 'multiclass'
fprintf(' err %.1f err5 %.1f', ...
info.train.error(end)/n*100, info.train.topFiveError(end)/n*100) ;
fprintf('\n') ;
case 'binary'
fprintf(' err %.1f', ...
info.train.error(end)/n*100) ;
fprintf('\n') ;
case 'euclideanloss'
fprintf(' err %.1f', info.train.error(end) / n);
fprintf('\n') ;
end
% debug info
if opts.plotDiagnostics
figure(2) ; vl_simplenn_diagnose(net,res) ; drawnow ;
end
end % next batch
% evaluation on validation set
if epoch == 1 || rem(epoch, opts.test_interval) == 0 || epoch == opts.numEpochs
for t=1:opts.batchSize:numel(val)
batch_time = tic ;
batch = val(t:min(t+opts.batchSize-1, numel(val))) ;
fprintf('validation: epoch %02d: processing batch %3d of %3d ...', epoch, ...
fix(t/opts.batchSize)+1, ceil(numel(val)/opts.batchSize)) ;
[im, labels] = getBatch(imdb, batch) ;
if opts.prefetch
nextBatch = val(t+opts.batchSize:min(t+2*opts.batchSize-1, numel(val))) ;
getBatch(imdb, nextBatch) ;
end
if opts.useGpu
im = gpuArray(im) ;
end
net.layers{end}.class = labels ;
res = vl_simplenn(net, im, [], res, ...
'disableDropout', true, ...
'conserveMemory', opts.conserveMemory, ...
'sync', opts.sync) ;
% print information
batch_time = toc(batch_time) ;
speed = numel(batch)/batch_time ;
info.val = updateError(opts, info.val, net, res, batch_time) ;
fprintf(' %.2f s (%.1f images/s)', batch_time, speed) ;
n = t + numel(batch) - 1 ;
switch opts.errorType
case 'multiclass'
fprintf(' err %.1f err5 %.1f', ...
info.val.error(end)/n*100, info.val.topFiveError(end)/n*100) ;
fprintf('\n') ;
case 'binary'
fprintf(' err %.1f', ...
info.val.error(end)/n*100) ;
fprintf('\n') ;
case 'euclideanloss'
fprintf(' err %.1f', info.val.error(end) / n);
fprintf('\n') ;
end
end
end
% save
info.train.objective(end) = info.train.objective(end) / numel(train) ;
info.train.error(end) = info.train.error(end) / numel(train) ;
info.train.topFiveError(end) = info.train.topFiveError(end) / numel(train) ;
info.train.speed(end) = numel(train) / info.train.speed(end) ;
info.val.objective(end) = info.val.objective(end) / numel(val) ;
info.val.error(end) = info.val.error(end) / numel(val) ;
info.val.topFiveError(end) = info.val.topFiveError(end) / numel(val) ;
info.val.speed(end) = numel(val) / info.val.speed(end) ;
if epoch == 1 || rem(epoch, opts.snapshot) == 0 || epoch == opts.numEpochs
save(modelPath(epoch), 'net', 'info') ;
end
if epoch == 1 || rem(epoch, opts.display) == 0 || epoch == opts.numEpochs
figure(1) ; clf ;
subplot(1,2,1) ;
semilogy(1:epoch, info.train.objective, 'k') ; hold on ;
semilogy([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.objective([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
xlabel('training epoch') ; ylabel('energy') ;
grid on ;
h=legend('train', 'val') ;
set(h,'color','none');
title('objective') ;
subplot(1,2,2) ;
switch opts.errorType
case 'multiclass'
plot(1:epoch, info.train.error, 'k') ; hold on ;
plot(1:epoch, info.train.topFiveError, 'k--') ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.topFiveError([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b--') ;
h=legend('train','train-5','val','val-5') ;
case 'binary'
plot(1:epoch, info.train.error, 'k') ; hold on ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
h=legend('train','val') ;
case 'euclideanloss'
plot(1 : epoch, info.train.error, 'k'); hold on;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
h = legend('train', 'val') ;
end
grid on ;
xlabel('training epoch') ; ylabel('error') ;
set(h,'color','none') ;
title('error') ;
drawnow ;
print(1, modelFigPath, '-dpdf') ;
end
end
end
% -------------------------------------------------------------------------
function info = updateError(opts, info, net, res, speed)
% -------------------------------------------------------------------------
predictions = gather(res(end-1).x) ;
sz = size(predictions) ;
n = prod(sz(1:2)) ;
labels = net.layers{end}.class ;
info.objective(end) = info.objective(end) + sum(double(gather(res(end).x))) ;
info.speed(end) = info.speed(end) + speed ;
switch opts.errorType
case 'multiclass'
[~,predictions] = sort(predictions, 3, 'descend') ;
error = ~bsxfun(@eq, predictions, reshape(labels, 1, 1, 1, [])) ;
info.error(end) = info.error(end) +....
sum(sum(sum(error(:,:,1,:))))/n ;
info.topFiveError(end) = info.topFiveError(end) + ...
sum(sum(sum(min(error(:,:,1:5,:),[],3))))/n ;
case 'binary'
labels = labels(:,:,1,:);
[~,predictions] = sort(predictions, 3, 'descend') ;
predictions = predictions(:,:,1,:);
error = ~bsxfun(@eq, predictions, labels) ;
info.error(end) = info.error(end) + sum(error(:))/n ;
case 'euclideanloss'
error = euclideanloss(sigmoid(predictions), labels);
info.error(end) = info.error(end) + error;
end
end
function [im, labels] = rescale_im(im_ori, label_ori, scale, mask, half_size, meanPixel)
mask = imresize(mask, scale, 'nearest');
for i = 1:size(im_ori,4)
im_ii = imresize(im_ori(:,:,:,i), scale, 'nearest');
im_large = padarray(im_ii, [half_size, half_size], 'symmetric');
im_ii = bsxfun(@minus, im_large, meanPixel);
label_ii = imresize(label_ori(:,:,:,i), scale, 'nearest');
im(:, :, :, i) = im_ii;
labels(:, :, 1, i) = label_ii;
labels(:, :, 2, i) = double(mask);
end
end
|
github
|
zhimingluo/MovingObjectSegmentation-master
|
regular_training.m
|
.m
|
MovingObjectSegmentation-master/CDNet/BasicCNN/regular_training.m
| 2,883 |
utf_8
|
1cd69f4eb86fc4053c598bf51bbc5147
|
function regular_training(video, method, frames)
opts.expDir = ['net/' method '/' num2str(frames) '/' video] ;
opts.train.batchSize = 5 ;
opts.train.numEpochs = 20;
opts.train.continue = false ;
opts.train.useGpu = true ;
opts.train.learningRate = 1e-3;
opts.train.expDir = opts.expDir ;
% --------------------------------------------------------------------
% Prepare data
% --------------------------------------------------------------------
imgDir = ['../CDNetDataset/' method '/' num2str(frames) 'frames/' video '/input'];
labelDir = ['../CDNetDataset/' method '/' num2str(frames) 'frames/' video '/GT'];
imdb = getImdb(imgDir,labelDir);
mask = imread(['../CDNetDataset/' method '/' num2str(frames) 'frames/' video '/ROI.bmp']);
mask = mask(:,:,1);
A = max(max(mask));
mask(mask == A) = 1;
if size(mask,1) > 400 || size(mask,2) >400
mask = imresize(mask, 0.5, 'nearest');
end
imdb.mask = single(double(mask));
imdb.half_size = 15;
%%%%%%Yi%%%%%% redefined the net
load('net');
net.layers{end-1} = struct('type', 'conv', ...
'filters', 0.1*randn(1,1,64,1, 'single'), ...
'biases', zeros(1, 1, 'single'), ...
'stride', 1, ...
'pad', 0) ;
net.layers{end} = struct('type', 'sigmoidcrossentropyloss');
load('meanPixel.mat');
imdb.meanPixel = meanPixel;
[net,info] = cnn_train_adagrad(net, imdb, @getBatch,...
opts.train,'errorType','euclideanloss',...
'conserveMemory', true);
end
function [im, labels] = getBatch(imdb, batch)
% --------------------------------------------------------------------
half_size = imdb.half_size;
meanPixel = imdb.meanPixel;
for ii = 1:numel(batch)
imagename = imdb.images.name{batch(ii)};
im_ii = single(imread(imagename));
labelname = imdb.images.labels{batch(ii)};
roi = imread(labelname);
labels_ii = zeros(size(roi,1),size(roi,2));
labels_ii( roi == 50 ) = 0.25; %shade
labels_ii( roi == 170 ) = 0.75; %object boundary
labels_ii( roi == 255 ) = 1; %foreground
% resize the image to half size
if size(im_ii,1) > 400 || size(im_ii,2) >400
im_ii = imresize(im_ii, 0.5, 'nearest');
labels_ii = imresize(labels_ii, 0.5, 'nearest');
end
im_large = padarray(im_ii,[half_size,half_size],'symmetric');
im_ii = bsxfun(@minus, im_large, meanPixel);
im(:,:,:,ii) = im_ii;
labels(:,:,1,ii) = labels_ii;
labels(:,:,2,ii) = double(imdb.mask);
end
end
function imdb = getImdb(imgDir, labelDir)
files = dir([imgDir '/*.jpg']);
label_files = dir([labelDir '/*.png']);
names = {};labels = {};
for ii = 1:numel(files)
names{end+1} = [imgDir '/' files(ii).name];
labels{end+1} = [labelDir '/' label_files(ii).name];
end
imdb.images.set = ones(1,numel(names));
imdb.images.name = names ;
imdb.images.labels = labels;
end
|
github
|
zhimingluo/MovingObjectSegmentation-master
|
cnn_train_adagrad.m
|
.m
|
MovingObjectSegmentation-master/CDNet/BasicCNN/cnn_train_adagrad.m
| 11,359 |
utf_8
|
b97433eec24865c7f9bc705af9dcbb58
|
function [net, info] = cnn_train_adagrad(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a suitable
% getBatch function.
opts.train = [] ;
opts.val = [] ;
opts.numEpochs = 300 ;
opts.batchSize = 256 ;
opts.useGpu = false ;
opts.learningRate = 0.001 ;
opts.continue = false ;
opts.expDir = fullfile('data','exp') ;
opts.conserveMemory = false ;
opts.sync = true ;
opts.prefetch = false ;
opts.weightDecay = 0.0005 ;
opts.errorType = 'multiclass' ;
opts.plotDiagnostics = false ;
opts.delta = 1e-8;
opts.display = 1;
opts.snapshot = 1;
opts.test_interval = 1;
opts = vl_argparse(opts, varargin) ;
if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir) ; end
if isempty(opts.train), opts.train = find(imdb.images.set==1) ; end
if isempty(opts.val), opts.val = find(imdb.images.set==2) ; end
if isnan(opts.train), opts.train = [] ; end
% -------------------------------------------------------------------------
% Network initialization
% -------------------------------------------------------------------------
for i=1:numel(net.layers)
if ~strcmp(net.layers{i}.type,'conv'), continue; end
net.layers{i}.filtersMomentum = zeros(size(net.layers{i}.filters), ...
class(net.layers{i}.filters)) ;
net.layers{i}.biasesMomentum = zeros(size(net.layers{i}.biases), ...
class(net.layers{i}.biases)) ; %#ok<*ZEROLIKE>
if ~isfield(net.layers{i}, 'filtersLearningRate')
net.layers{i}.filtersLearningRate = 1 ;
end
if ~isfield(net.layers{i}, 'biasesLearningRate')
net.layers{i}.biasesLearningRate = 1 ;
end
if ~isfield(net.layers{i}, 'filtersWeightDecay')
net.layers{i}.filtersWeightDecay = 1 ;
end
if ~isfield(net.layers{i}, 'biasesWeightDecay')
net.layers{i}.biasesWeightDecay = 1 ;
end
end
if opts.useGpu
net = vl_simplenn_move(net, 'gpu') ;
for i=1:numel(net.layers)
if ~strcmp(net.layers{i}.type,'conv'), continue; end
net.layers{i}.filtersMomentum = gpuArray(net.layers{i}.filtersMomentum) ;
net.layers{i}.biasesMomentum = gpuArray(net.layers{i}.biasesMomentum) ;
end
end
G_f = cell(numel(net.layers), 1);
G_b = cell(numel(net.layers), 1);
for l=1:numel(net.layers)
if ~strcmp(net.layers{l}.type, 'conv'), continue ; end
G_f{l} = zeros(size(net.layers{l}.filters), 'single');
G_b{l} = zeros(size(net.layers{l}.biases), 'single');
end
% -------------------------------------------------------------------------
% Train and validate
% -------------------------------------------------------------------------
rng(0) ;
if opts.useGpu
one = gpuArray(single(1)) ;
else
one = single(1) ;
end
info.train.objective = [] ;
info.train.error = [] ;
info.train.topFiveError = [] ;
info.train.speed = [] ;
info.val.objective = [] ;
info.val.error = [] ;
info.val.topFiveError = [] ;
info.val.speed = [] ;
lr = opts.learningRate ;
res = [] ;
for epoch=1:opts.numEpochs
% fast-forward to where we stopped
modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep));
modelFigPath = fullfile(opts.expDir, 'net-train.pdf') ;
if opts.continue
if exist(modelPath(epoch),'file')
if epoch == opts.numEpochs
load(modelPath(epoch), 'net', 'info') ;
end
continue ;
end
if epoch > 1
fprintf('resuming by loading epoch %d\n', epoch-1) ;
load(modelPath(epoch-1), 'net', 'info') ;
end
end
train = opts.train(randperm(numel(opts.train))) ;
val = opts.val ;
info.train.objective(end+1) = 0 ;
info.train.error(end+1) = 0 ;
info.train.topFiveError(end+1) = 0 ;
info.train.speed(end+1) = 0 ;
info.val.objective(end+1) = 0 ;
info.val.error(end+1) = 0 ;
info.val.topFiveError(end+1) = 0 ;
info.val.speed(end+1) = 0 ;
for t=1:opts.batchSize:numel(train)
% get next image batch and labels
batch = train(t:min(t+opts.batchSize-1, numel(train))) ;
batch_time = tic ;
fprintf('training: epoch %02d: processing batch %3d of %3d ...', epoch, ...
fix(t/opts.batchSize)+1, ceil(numel(train)/opts.batchSize)) ;
[im, labels] = getBatch(imdb, batch) ;
if opts.prefetch
nextBatch = train(t+opts.batchSize:min(t+2*opts.batchSize-1, numel(train))) ;
getBatch(imdb, nextBatch) ;
end
if opts.useGpu
im = gpuArray(im) ;
end
% backprop
net.layers{end}.class = labels ;
res = vl_simplenn(net, im, one, res, ...
'conserveMemory', opts.conserveMemory, ...
'sync', opts.sync) ;
% gradient step
for l=1:numel(net.layers)
if ~strcmp(net.layers{l}.type, 'conv'), continue ; end
g_f = (net.layers{l}.filtersLearningRate) * ...
(opts.weightDecay * net.layers{l}.filtersWeightDecay) * net.layers{l}.filters + ...
(net.layers{l}.filtersLearningRate) / numel(batch) * res(l).dzdw{1};
g_b = (net.layers{l}.biasesLearningRate) * ...
(opts.weightDecay * net.layers{l}.biasesWeightDecay) * net.layers{l}.biases + ...
(net.layers{l}.biasesLearningRate) / numel(batch) * res(l).dzdw{2};
G_f{l} = G_f{l} + g_f .^ 2;
G_b{l} = G_b{l} + g_b .^ 2;
net.layers{l}.filters = net.layers{l}.filters - lr ./ (opts.delta + sqrt(G_f{l})) .* g_f;
net.layers{l}.biases = net.layers{l}.biases - lr ./ (opts.delta + sqrt(G_b{l})) .* g_b;
end
% print information
batch_time = toc(batch_time) ;
speed = numel(batch)/batch_time ;
info.train = updateError(opts, info.train, net, res, batch_time) ;
fprintf(' %.2f s (%.1f images/s)', batch_time, speed) ;
n = t + numel(batch) - 1 ;
switch opts.errorType
case 'multiclass'
fprintf(' err %.1f err5 %.1f', ...
info.train.error(end)/n*100, info.train.topFiveError(end)/n*100) ;
fprintf('\n') ;
case 'binary'
fprintf(' err %.1f', ...
info.train.error(end)/n*100) ;
fprintf('\n') ;
case 'euclideanloss'
fprintf(' err %.1f', info.train.error(end) / n);
fprintf('\n') ;
end
% debug info
if opts.plotDiagnostics
figure(2) ; vl_simplenn_diagnose(net,res) ; drawnow ;
end
end % next batch
% evaluation on validation set
if epoch == 1 || rem(epoch, opts.test_interval) == 0 || epoch == opts.numEpochs
for t=1:opts.batchSize:numel(val)
batch_time = tic ;
batch = val(t:min(t+opts.batchSize-1, numel(val))) ;
fprintf('validation: epoch %02d: processing batch %3d of %3d ...', epoch, ...
fix(t/opts.batchSize)+1, ceil(numel(val)/opts.batchSize)) ;
[im, labels] = getBatch(imdb, batch) ;
if opts.prefetch
nextBatch = val(t+opts.batchSize:min(t+2*opts.batchSize-1, numel(val))) ;
getBatch(imdb, nextBatch) ;
end
if opts.useGpu
im = gpuArray(im) ;
end
net.layers{end}.class = labels ;
res = vl_simplenn(net, im, [], res, ...
'disableDropout', true, ...
'conserveMemory', opts.conserveMemory, ...
'sync', opts.sync) ;
% print information
batch_time = toc(batch_time) ;
speed = numel(batch)/batch_time ;
info.val = updateError(opts, info.val, net, res, batch_time) ;
fprintf(' %.2f s (%.1f images/s)', batch_time, speed) ;
n = t + numel(batch) - 1 ;
switch opts.errorType
case 'multiclass'
fprintf(' err %.1f err5 %.1f', ...
info.val.error(end)/n*100, info.val.topFiveError(end)/n*100) ;
fprintf('\n') ;
case 'binary'
fprintf(' err %.1f', ...
info.val.error(end)/n*100) ;
fprintf('\n') ;
case 'euclideanloss'
fprintf(' err %.1f', info.val.error(end) / n);
fprintf('\n') ;
end
end
end
% save
info.train.objective(end) = info.train.objective(end) / numel(train) ;
info.train.error(end) = info.train.error(end) / numel(train) ;
info.train.topFiveError(end) = info.train.topFiveError(end) / numel(train) ;
info.train.speed(end) = numel(train) / info.train.speed(end) ;
info.val.objective(end) = info.val.objective(end) / numel(val) ;
info.val.error(end) = info.val.error(end) / numel(val) ;
info.val.topFiveError(end) = info.val.topFiveError(end) / numel(val) ;
info.val.speed(end) = numel(val) / info.val.speed(end) ;
if epoch == 1 || rem(epoch, opts.snapshot) == 0 || epoch == opts.numEpochs
save(modelPath(epoch), 'net', 'info') ;
end
if epoch == 1 || rem(epoch, opts.display) == 0 || epoch == opts.numEpochs
figure(1) ; clf ;
subplot(1,2,1) ;
semilogy(1:epoch, info.train.objective, 'k') ; hold on ;
semilogy([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.objective([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
xlabel('training epoch') ; ylabel('energy') ;
grid on ;
h=legend('train', 'val') ;
set(h,'color','none');
title('objective') ;
subplot(1,2,2) ;
switch opts.errorType
case 'multiclass'
plot(1:epoch, info.train.error, 'k') ; hold on ;
plot(1:epoch, info.train.topFiveError, 'k--') ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.topFiveError([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b--') ;
h=legend('train','train-5','val','val-5') ;
case 'binary'
plot(1:epoch, info.train.error, 'k') ; hold on ;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
h=legend('train','val') ;
case 'euclideanloss'
plot(1 : epoch, info.train.error, 'k'); hold on;
plot([1 opts.test_interval : opts.test_interval : epoch epoch], info.val.error([1 opts.test_interval : opts.test_interval : epoch epoch]), 'b') ;
h = legend('train', 'val') ;
end
grid on ;
xlabel('training epoch') ; ylabel('error') ;
set(h,'color','none') ;
title('error') ;
drawnow ;
print(1, modelFigPath, '-dpdf') ;
end
end
% -------------------------------------------------------------------------
function info = updateError(opts, info, net, res, speed)
% -------------------------------------------------------------------------
predictions = gather(res(end-1).x) ;
sz = size(predictions) ;
n = prod(sz(1:2)) ;
labels = net.layers{end}.class ;
info.objective(end) = info.objective(end) + sum(double(gather(res(end).x))) ;
info.speed(end) = info.speed(end) + speed ;
switch opts.errorType
case 'multiclass'
[~,predictions] = sort(predictions, 3, 'descend') ;
error = ~bsxfun(@eq, predictions, reshape(labels, 1, 1, 1, [])) ;
info.error(end) = info.error(end) +....
sum(sum(sum(error(:,:,1,:))))/n ;
info.topFiveError(end) = info.topFiveError(end) + ...
sum(sum(sum(min(error(:,:,1:5,:),[],3))))/n ;
case 'binary'
error = bsxfun(@times, predictions, labels) < 0 ;
info.error(end) = info.error(end) + sum(error(:))/n ;
case 'euclideanloss'
error = euclideanloss(sigmoid(predictions), labels);
info.error(end) = info.error(end) + error;
end
|
github
|
gramuah/pose-errors-master
|
writeNumObjClass.m
|
.m
|
pose-errors-master/src/writeNumObjClass.m
| 303 |
utf_8
|
a8dcea39cdb9809fe77f44ecbf61b417
|
function writeNumObjClass(outdir, objects)
if ~exist(outdir, 'file'), mkdir(outdir); end;
global fid
fid = fopen(fullfile(outdir, ['classes.tex']), 'w');
for obj=1:length(objects)
pr('\\input{%s}\n', objects{obj});
end
fclose(fid);
function pr(varargin)
global fid;
fprintf(fid, varargin{:});
|
github
|
gramuah/pose-errors-master
|
writeTexObject.m
|
.m
|
pose-errors-master/src/writeTexObject.m
| 17,543 |
utf_8
|
03d088c85af1a2b7ee209ac56eaf02e6
|
function writeTexObject(name, outdir, gt, metric_type, dataset, detector)
% writeTexObject(name, outdir, gt)
%
% Adds latex code to an existing file for one object:
switch metric_type
case 1
metric = 'AOS';
case 2
metric = 'AVP';
case 3
metric = 'PEAP';
case 4
metric = 'MAE';
case 5
metric = 'MedError';
end
if ~exist(outdir, 'file'), mkdir(outdir); end;
global fid
fid = fopen(fullfile(outdir, [name '.tex']), 'w');
pr('\\subsection{%s}\n\n', name);
% create table
if ~isempty(gt)
pr('\\textbf{Statistics for %s class:}\n', name);
pr('\n\n')
pr('Number of objects = %d, No difficult objects = %d\n', length(gt.isdiff), sum(~gt.isdiff));
pr('\n\n')
pr('Truncated: None = %d, Truncated = %d\n', sum(~gt.isdiff)-sum(gt.istrunc), sum(gt.istrunc));
pr('\n\n')
if ~isempty(gt.details{1})
details = cat(1, gt.details{:});
pr('Occluded: None = %d, Occluded = %d\n', hist([details.occ_level], 1:2));
pr('\n\n')
a = find(gt.istrunc == 1);
b = find(gt.isocc == 1);
[c1,ia1] = setdiff(a,b);
occtrunc = max(length(a), length(b)) - length(ia1);
pr('Occluded and Truncated: %d\n', occtrunc);
sv = [details.side_visible];
names = fieldnames(details(1).side_visible);
pr('\n\n')
pr('side visible: \n');
pr('\\begin{verbatim}\n');
for k = 1:numel(names)
pr(' %s: Yes = %d No = %d \n', names{k}, sum([sv.(names{k})]==1), sum([sv.(names{k})]==0));
end
pr('\\end{verbatim}\n');
pr('\n\n')
pr('part visible: \n');
pr('\\begin{verbatim}\n');
sv = [details.part_visible];
names = fieldnames(details(1).part_visible);
for k = 1:numel(names)
pr(' part %d = %s: Yes = %d No = %d \n', k, names{k}, sum([sv.(names{k})]==1), sum([sv.(names{k})]==0));
end
pr('\\end{verbatim}\n');
end
pr('\n\n');
end
pr('\\clearpage')
pr('\n\n');
pr('Figure \\ref{fig1%s} summarizes the viewpoint distribution on the test set for %s class considering the 8 views described in Section \\ref{info}. Figure \\ref{fig2%s} shows the analysis and the impact on the pose performance of each type of pose error. We also report in Figure \\ref{fig3%s} the success rate for %s class considering each of the 8 viewpoints described in Section \\ref{info}. ', name, name, name, name, name);
if exist(fullfile(outdir(1:end-4), name, 'analysisI/plot_2.pdf'), 'file')
pr('\\begin{figure}[h]\n');
pr('\\centering\n');
pr('\\includegraphics[width=0.65\\textwidth,trim = 20mm 65mm 25mm 60mm, clip]{../%s/analysisI/plot_3.pdf}\n', name);
pr('\\caption{\\textbf{Analysis of Pose Distribution.} Histogram represents viewpoint distribution on test set for %s class from %s dataset.}\n', name, dataset);
pr('\\label{fig1%s}\n', name);
pr('\\end{figure}\n');
pr('\n\n');
end
if exist(fullfile(outdir(1:end-4), name, 'analysisI/plot_2.pdf'), 'file')
pr('\\begin{figure}[h]\n');
pr('\\centering\n');
pr('\\subfloat[]{\n');
pr('\\label{fig2%sa}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 20mm 65mm 25mm 60mm, clip]{../%s/analysisI/plot_2.pdf}\n', name);
pr('}\n');
pr('\\subfloat[]{\n');
pr('\\label{fig2%sb}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 15mm 65mm 25mm 60mm, clip]{../%s/analysisI/plot_1.pdf}\n', name);
pr('}\n');
pr('\\caption{\\textbf{Analysis of the Main Pose Estimation Errors.} (a) Given the correct detections, pie chart shows the fraction of pose error that are due to Opposite viewpoints (Opposite), confusion with Nearby viewpoints (Nearby), confusion with Other rotations (Other), and correct pose estimations (Correct) for %s class. (b) Impact of pose errors in terms of %s. \\textcolor{blue}{Blue Bars} show the %s performance obtained when all estimations are considered. \\textcolor{green}{Green Bars} display the %s improvement by removing all estimations of one type: \\textbf{OTH} removes confusion with other rotation viewpoints; \\textbf{NEAR} removes confusion with nearby viewpoints; \\textbf{OPP} removes confusion with opposite viewpoints. \\textcolor{BrickRed}{Brick Red Bars} display the %s improvement by correcting all estimations of one type: \\textbf{OTH} corrects confusion with other rotation viewpoints; \\textbf{NEAR} corrects confusion with nearby viewpoints; \\textbf{OPP} corrects confusion with opposite viewpoints.}\n', name, metric, metric, metric, metric);
pr('\\label{fig2%s}\n', name);
pr('\\end{figure}\n');
pr('\n\n');
end
if exist(fullfile(outdir(1:end-4), name, 'analysisI/plot_4.pdf'), 'file')
pr('\\begin{figure}[h]\n')
pr('\\centering\n');
pr('\\includegraphics[width=0.85\\textwidth,trim = 20mm 65mm 25mm 65mm, clip]{../%s/analysisI/plot_4.pdf} \n', name);
pr('\\caption{\\textbf{Success rate for %s class considering 8 views.} For each of the 8 viewpoints, we report: In \\textcolor{blue}{Blue}, the percentage of the correct pose estimations. In \\textcolor{cyan}{Cyan}, the percentage of confusion with the opposite viewpoints. In \\textcolor{yellow}{Yellow}, the percentage of confusion with nearby viewpoints. In \\textcolor{BrickRed}{Brick Red}, the percentage of the confusions with other rotations.}\n', name);
pr('\\label{fig3%s}\n', name);
pr('\\end{figure}\n');
pr('\n\n');
end
pr('\\clearpage')
pr('\n\n');
pr('Figure \\ref{fig4%s} summarizes the detection and pose estimation performances for %s class. Figure \\ref{fig4%sa} shows the model performance working on continuous pose estimation. For AVP and PEAP the results are obtained considering a threshold equal to $\\frac{\\pi}{12}$. Figures \\ref{fig4%sb} and \\ref{fig4%sc} report the AVP and PEAP performances achieve working on discrete pose estimation. The AVP and PEAP metrics are obtained considering different number of the views: 4, 8, 16 and 24.\n', name, name, name, name, name);
if exist(fullfile(outdir(1:end-4), name, 'analysisIII/curves/plot_1.pdf'), 'file')
pr('\\begin{figure}[h]\n')
pr('\\centering\n');
pr('\\subfloat[]{\n');
pr('\\label{fig4%sa}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 20mm 65mm 25mm 65mm, clip]{../%s/analysisIII/curves/plot_1.pdf} \n', name);
pr('}\\\\ \n');
pr('\\subfloat[]{\n');
pr('\\label{fig4%sb}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 20mm 65mm 25mm 65mm, clip]{../%s/analysisIII/curves/plot_2.pdf} \n', name);
pr('}\n');
pr('\\subfloat[]{\n');
pr('\\label{fig4%sc}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 20mm 65mm 25mm 65mm, clip]{../%s/analysisIII/curves/plot_3.pdf} \n', name);
pr('}\n');
pr('\\caption{\\textbf{Precision/Recall Curves.} (a) Performance working on continuous pose estimation. For AVP and PEAP the results are obtained considering a threshold equal to $\\frac{\\pi}{12}$. (b) AVP and (c) PEAP performances working on discrete pose estimation. Results obtained by considering 4, 8, 16, and 24 views.}\n');
pr('\\label{fig4%s}\n', name);
pr('\\end{figure}\n');
pr('\n\n');
end
pr('\\clearpage')
pr('\n\n');
pr('Figures \\ref{fig5%s}, \\ref{fig6%s}, \\ref{fig7%s}, \\ref{fig8%s} and \\ref{fig9%s} summarize the main object characteristic influences on detection and pose estimation performances for %s class. Figure \\ref{fig5%s} reports the effect of object size characteristic on object detection and pose estimation performances. Figure \\ref{fig6%s} shows the influence of aspect ratio characteristic on object detection and pose estimation performances. Figure \\ref{fig7%s} represents the influence of occluded and truncated objects on detection and pose estimation performances. Figure \\ref{fig8%s} shows the visible side influence. Figure \\ref{fig9%s} reports the part visibily effect. \n', name, name, name ,name, name, name, name, name, name, name, name);
if exist(fullfile(outdir(1:end-4), name, 'analysisII/obj_charact_detection/plot_2.pdf'), 'file')
pr('\\begin{figure}[h]\n')
pr('\\centering\n');
pr('\\subfloat[]{\n');
pr('\\label{fig5%sa}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 15mm 65mm 25mm 65mm, clip]{../%s/analysisII/obj_charact_detection/plot_2.pdf} \n', name);
pr('}\n')
pr('\\subfloat[]{\n');
pr('\\label{fig5%sb}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 20mm 65mm 25mm 65mm, clip]{../%s/analysisII/obj_charact_pose/plot_5.pdf} \n', name);
pr('}\n')
pr('\\caption{\\textbf{Effect of Object Size Characteristic on Object Detection and Pose Estimation Performances.} (a) Detection analysis: AP variance. XS and S are considered EXTRA SMALL and SMALL objects, M are MEDIUM objects, L and XL are LARGE and EXTRA LARGE objects. (b) Pose Estimation Results in terms of %s. \\textcolor{blue}{Blue Bars} show the results when all pose estimations are considered, \\textcolor{green}{Green Bars} show the results when only the pose estimations corresponding this object size are considered and \\textcolor{BrickRed}{Brick Red Bars} display the results when the pose estimations corresponding this object size are removed.}\n', metric);
pr('\\label{fig5%s}\n', name);
pr('\\end{figure}\n');
pr('\n\n');
end
if exist(fullfile(outdir(1:end-4), name, 'analysisII/obj_charact_detection/plot_3.pdf'), 'file')
pr('\\begin{figure}[h]\n')
pr('\\centering\n');
pr('\\subfloat[]{\n');
pr('\\label{fig6%sa}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 15mm 65mm 25mm 65mm, clip]{../%s/analysisII/obj_charact_detection/plot_3.pdf} \n', name);
pr('}\n')
pr('\\subfloat[]{\n');
pr('\\label{fig6%sb}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 20mm 65mm 25mm 65mm, clip]{../%s/analysisII/obj_charact_pose/plot_6.pdf} \n', name);
pr('}\n')
pr('\\caption{\\textbf{Effect of Object Size Characteristic on Object Detection and Pose Estimation.} (a) Detection analysis: AP variance. XS and S are considered EXTRA SMALL and SMALL objects, M are MEDIUM objects, L and XL are LARGE and EXTRA LARGE objects. (b) Pose Estimation Results in terms of %s. \\textcolor{blue}{Blue Bars} show the results when all pose estimations are considered, \\textcolor{green}{Green Bars} show the results when only the pose estimations corresponding this object size are considered and \\textcolor{BrickRed}{Brick Red Bars} display the results when the pose estimations corresponding this object size are removed.}\n', metric);
pr('\\label{fig6%s}\n', name);
pr('\\end{figure}\n');
pr('\n\n');
end
if exist(fullfile(outdir(1:end-4), name, 'analysisII/obj_charact_detection/plot_5.pdf'), 'file')
pr('\\begin{figure}[h]\n')
pr('\\centering\n');
pr('\\subfloat[]{\n');
pr('\\label{fig7%sa}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 15mm 65mm 25mm 65mm, clip]{../%s/analysisII/obj_charact_detection/plot_5.pdf} \n', name);
pr('}\n')
pr('\\subfloat[]{\n');
pr('\\label{fig7%sb}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 15mm 65mm 25mm 65mm, clip]{../%s/analysisII/obj_charact_pose/plot_3.pdf} \n', name);
pr('}\n')
pr('\\caption{\\textbf{Effect of Occluded and Truncated Objects on Detection and Pose Estimation Performances.} (a) Detection analysis: AP variance. N non occluded or truncated object, T/O truncated or occluded object. Results on Pose Estimation in terms of the %s metric. \\textcolor{blue}{Blue Bars} show the results when all pose estimations are considered. \\textcolor{green}{Green Bars} display the results when the occluded and truncated objects are removed. \\textcolor{BrickRed}{Brick Red Bars} show the performance when we are only considered the occluded and truncated objects.}\n', metric);
pr('\\label{fig7%s}\n', name);
pr('\\end{figure}\n');
pr('\n\n');
end
if exist(fullfile(outdir(1:end-4), name, 'analysisII/obj_charact_detection/plot_8.pdf'), 'file')
pr('\\begin{figure}[h]\n')
pr('\\centering\n');
pr('\\subfloat[]{\n');
pr('\\label{fig8%sa}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 15mm 65mm 25mm 65mm, clip]{../%s/analysisII/obj_charact_detection/plot_8.pdf} \n', name);
pr('}\n');
pr('\\subfloat[]{\n');
pr('\\label{fig8%sb}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 10mm 65mm 25mm 65mm, clip]{../%s/analysisII/obj_charact_pose/plot_2.pdf} \n', name);
pr('}\n');
pr('\\caption{\\textbf{Effect of Visible Sides on Object Detection and Pose Estimation Performances.} (a) Detection analysis: AP performance. Pose Estimation analysis: (b) Pose Estimation analysis: %s performance. Black dashed lines indicate overall. Visible side: \\textbf{1} = performance when the side is visible; \\textbf{0} = performance when the side is not visible.}\n', metric);
pr('\\label{fig8%s}\n', name);
pr('\\end{figure}\n');
pr('\n\n');
end
if exist(fullfile(outdir(1:end-4), name, 'analysisII/obj_charact_detection/plot_7.pdf'), 'file')
pr('\\begin{figure}[h]\n')
pr('\\centering\n');
pr('\\subfloat[]{\n');
pr('\\label{fig9%sa}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 15mm 65mm 25mm 65mm, clip]{../%s/analysisII/obj_charact_detection/plot_7.pdf} \n', name);
pr('}\n')
pr('\\subfloat[]{\n');
pr('\\label{fig9%sb}\n', name);
pr('\\includegraphics[width=0.45\\textwidth,trim = 10mm 65mm 25mm 65mm, clip]{../%s/analysisII/obj_charact_pose/plot_1.pdf} \n', name);
pr('}\n')
pr('\\caption{\\textbf{Part Visibility Influence on Object Detection and Pose Estimation Performances.} (a) Detection analysis: AP performance. Pose Estimation analysis: (b) Pose Estimation analysis: %s performance. Black dashed lines indicate overall performance. Visible part: \\textbf{1} = performance when the part is visible; \\textbf{0} = performance when the part is not visible. The correspondence between the part number and the part name is indicated at the beginning of this Section (see Statistics for %s class).}\n', metric, name);
pr('\\label{fig9%s}\n', name);
pr('\\end{figure}\n');
pr('\n\n');
end
pr('\\clearpage')
pr('\n\n');
pr('Figure \\ref{fig10%s} provides a summary of the sensitivity to each characteristic and the potential impact on improving pose estimation robustness. The worst-performing and best-performing combinations for each object characteristic are averaged over the selected object categories. The difference between the best and the worst performance indicates sensitivity; the difference between the best and the overall indicates the potential impact.\n', name);
if strcmp(detector(length(detector)-1:length(detector)), 'gt')
pr('\\begin{figure}[h]\n');
pr('\\centering\n');
pr('\\includegraphics[width=0.65\\textwidth,trim = 10mm 65mm 25mm 65mm, clip]{../%s/analysisII/obj_charact_pose/plot_7.pdf} \n', name);
pr('\\caption{\\textbf{Summary of Sensitivity and Impact of Object Characteristics.} We show the performance of the highest performing and lowest performing subsets within each characteristic (occlusion/truncation (occ-trn), bounding box area or object size (size), aspect ratio (asp), visible sides (side) and part visibility (part)). Overall accuracy is indicated by the black dashed line. The difference between max and min indicates sensitivity; the difference between max and overall indicates the impact.\n');
pr('}\n');
pr('\\label{fig10%s}\n', name);
pr('\\end{figure}\n');
else
pr('\\begin{figure}[h]\n');
pr('\\centering\n');
pr('\\includegraphics[width=0.85\\textwidth,trim = 10mm 65mm 25mm 65mm, clip]{../%s/analysisII/obj_charact_pose/plot_7.pdf} \n', name);
pr('\\caption{\\textbf{Summary of Sensitivity and Impact of Object Characteristics.} We show the %s performance of the highest performing and lowest performing subsets within each characteristic (occlusion/truncation (occ-trn), difficult objects (diff), bounding box area or object size (size), aspect ratio (asp), visible sides (side) and part visibility (part)). Overall accuracy is indicated by the black dashed line. The difference between max and min indicates sensitivity; the difference between max and overall indicates the impact.\n', metric);
pr('}\n');
pr('\\label{fig10%s}\n', name);
pr('\\end{figure}\n');
end
pr('\\clearpage\n');
if exist(fullfile(outdir(1:end-4), name, 'analysisIII/ov_analysis/plot_1.pdf'), 'file')
pr('Figure \\ref{fig11%s} shows an analysis of the influence of the overlap criterion considering the %s metric. For this overlap criterion analysis we follow the PASCAL VOC formulation: to be considered a true positive, the area of overlap between the predicted BB and GT BB must exceed a threshold.\n', name, metric);
pr('\\begin{figure}[h]\n');
pr('\\centering\n');
pr('\\includegraphics[width=0.85\\textwidth,trim = 15mm 65mm 25mm 65mm, clip]{../%s/analysisIII/ov_analysis/plot_1.pdf} \n', name);
pr('\\caption{\\textbf{Simultaneous Object Detection and Pose Estimation.} The detection performance (AP) is represented in red and the pose estimation performance (%s) in blue.\n', metric);
pr('}\n');
pr('\\label{fig11%s}\n', name);
pr('\\end{figure}\n');
end
pr('\\clearpage\n');
fclose(fid);
function pr(varargin)
global fid;
fprintf(fid, varargin{:});
|
github
|
gramuah/pose-errors-master
|
matchDetectionsWithGroundTruth.m
|
.m
|
pose-errors-master/src/matchDetectionsWithGroundTruth.m
| 5,491 |
utf_8
|
cd0e182e842c1e3357082c6701324245
|
function [det, gt] = matchDetectionsWithGroundTruth(dataset, dataset_params, objname, ann, det, localization)
% [det, gt] = matchDetectionsWithGroundTruth(dataset, dataset_params, objname, ann, det, localization)
%
% Determines which detections are correct based on dataset and localization
% criteria. See matchDetectionsWithGroundTruth_VOC documentation (below) for
% details.
%
switch dataset
case {'PASCAL3D+'}
o = strcmp(dataset_params.objnames_all, objname);
gt = ann.gt(o);
[det, gt] = matchDetectionsWithGroundTruth_PASCAL3D(dataset_params, gt, det, localization);
otherwise
error('unknown dataset: %s', dataset);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [det, gt] = matchDetectionsWithGroundTruth_PASCAL3D(dataset_params, gt, det, localization)
% [det, gt] = matchDetectionsWithGroundTruth_PASCAL3D(dataset_params, det, gt, localization)
%
% Returns the intersection over union, intersection/object, and
% intersection/gt areas and index of closest ground truth for each
% detection
%
% Input:
% Dataset parameters:
% dataset_params.(iuthresh_weak, idthresh_weak, iuthresh_strong, idthresh_strong)
% Ground truth anntoations:
% gt.(bbox, rnum, isdiff)
% Object detection results:
% det.(bbox, conf, rnum)
% Localization criteria: 'weak' or 'strong'
%
% Output
% Adds to the input det struct:
% the index of best-matching ground truth (gtnum), the corresponding
% overlap measures, the label (1=true, 0=difficult, -1=false), and whether
% the detection is a duplicate (label=-1 in this case)
% det.(bbox, conf, rnum, gtnum, isdiff, ov, ov_obj, ov_gt, label, isduplicate)
% Adds to the input gt struct:
% (1) index (detnum) of highest scoring detection with ov>ovthresh;
% (2) index (detnum_ov) of maximum overlap detection
% (3) the overlaps with the detection of maximum overlap (ov, ov_obj,
% ov_gt), which may not be the same detection
% gt.(bbox, rnum, isdiff, detnum):
switch localization
case 'weak' % also duplicate detections are ignored, per last line
iuthresh = dataset_params.iuthresh_weak; % intersection/union threshold
idthresh = dataset_params.idthresh_weak; % intersection/det_area threshold
case 'strong'
iuthresh = dataset_params.iuthresh_strong;
idthresh = dataset_params.idthresh_strong;
case 'weak_1'
iuthresh = 0.2;
idthresh = 0;
case 'weak_2'
iuthresh = 0.3;
idthresh = 0;
case 'weak_3'
iuthresh = 0.4;
idthresh = 0;
case 'strong_1'
iuthresh = 0.6;
idthresh = 0;
case 'strong_2'
iuthresh = 0.7;
idthresh = 0;
case 'strong_3'
iuthresh = 0.8;
idthresh = 0;
case 'strong_4'
iuthresh = 0.9;
idthresh = 0;
otherwise
error('invalid localization criterion: %s', localization)
end
Ngt = size(gt.bbox, 1);
gt.detnum = zeros(Ngt, 1);
gt.detnum_ov = zeros(Ngt, 1);
gt.ov = zeros(Ngt, 1);
gt.ov_obj = zeros(Ngt, 1);
gt.ov_gt = zeros(Ngt, 1);
Nd = size(det.bbox, 1);
det.gtnum = zeros(Nd, 1);
det.ov = zeros(Nd, 1);
det.ov_obj = zeros(Nd, 1);
det.ov_gt = zeros(Nd, 1);
det.isdiff = zeros(Nd, 1);
det.label = -ones(Nd, 1);
det.label_occ = zeros(Nd, 1);
det.label_trunc = zeros(Nd, 1);
det.isduplicate = false(Nd, 1);
isdetected = zeros(Ngt, 1);
[sv, si] = sort(det.conf, 'descend');
for dtmp = 1:Nd
d = si(dtmp);
indgt = find(gt.rnum == det.rnum(d));
if isempty(indgt), continue; end
bbgt = gt.bbox(indgt, [1 3 2 4]); % ground truth in same image
box = det.bbox(d, [1 3 2 4]); % detection window
bi=[max(box(1),bbgt(:, 1)) max(box(3),bbgt(:, 3)) ...
min(box(2),bbgt(:, 2)) min(box(4),bbgt(:, 4))];
iw=bi(:, 3)-bi(:, 1)+1;
ih=bi(:, 4)-bi(:, 2)+1;
ind = find(iw >0 & ih > 0); % others have no intersection
if ~isempty(ind)
gtarea = (bbgt(ind, 2)-bbgt(ind, 1)+1).*(bbgt(ind, 4)-bbgt(ind, 3)+1);
detarea = (box(2)-box(1)+1)*(box(4)-box(3)+1);
intersectArea = iw(ind).*ih(ind);
unionArea =gtarea + detarea - intersectArea;
i = find(((intersectArea ./ unionArea) >= iuthresh) & ...
((intersectArea ./ detarea) >= idthresh)...
& (~isdetected(indgt(ind))));
if ~isempty(i) % correct detection
[det.ov(d), i] = max((intersectArea ./ unionArea) .* (~isdetected(indgt(ind))));
gti = indgt(ind(i));
if gt.isdiff(gti)
det.label(d) = 0;
det.isdiff(d) = 1;
else
det.label(d) = 1;
end
if gt.istrunc(gti)
det.label_trunc(d) = 1;
end
if gt.isocc(gti)
det.label_occ(d) = 1;
end
gt.detnum(gti) = d;
isdetected(gti) = 1;
else % no correct detection, or extra detection
[det.ov(d), i] = max(intersectArea ./ unionArea);
gti = indgt(ind(i));
if gt.isdiff(gti)
det.label(d) = 0;
det.isdiff(d) = 1;
else
if det.ov(d)>=iuthresh && ((intersectArea(i) ./ detarea) >= idthresh)
det.isduplicate(d) = true;
end
end
end
det.ov_obj(d) = intersectArea(i) ./ detarea;
det.ov_gt(d) = intersectArea(i) ./ gtarea(i);
det.gtnum(d) = gti;
if det.ov(d) > gt.ov(gti)
gt.ov(gti) = det.ov(d);
gt.ov_obj(gti) = intersectArea(i) ./ detarea;
gt.ov_gt(gti) = intersectArea(i) ./ gtarea(i);
gt.detnum_ov(gti) = d;
end
end
end
|
github
|
gramuah/pose-errors-master
|
analyzeDetections.m
|
.m
|
pose-errors-master/src/analyzeDetections.m
| 19,712 |
utf_8
|
659b017995dcdb25acaf67df4197f3aa
|
function result = analyzeDetections(dataset, dataset_params, objname, det, ann, localization)
% result = analyzeDetections(dataset, dataset_params, objname, det, ann, localization)
%
% Input:
% dataset: name of the dataset (e.g., PASCAL3D+)
% dataset_params: parameters of the dataset
% objname: name of the object class
% det.(bbox, conf, rnum): object detection results
% ann: dataset annotations
% localization: 'weak' or 'strong' to specify localization criteria
%
% Output:
% result: set of precision-recall and aos/avp/peap/errors statistics
switch dataset
case {'PASCAL3D+'}
result = analyzeDetections_PASCAL3D(dataset, dataset_params, objname, ...
ann, det, localization);
otherwise
error('dataset %s is unknown\n', dataset);
end
result.name = objname;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function result = analyzeDetections_PASCAL3D(dataset, dataset_params, objname, ...
ann, det, localization)
rec = ann.rec;
[det.conf, si] = sort(det.conf, 'descend');
det.bbox = det.bbox(si, :);
det.rnum = det.rnum(si);
det.view = det.view(si, :);
[det, gt] = matchDetectionsWithGroundTruth(dataset, dataset_params, objname, ann, det, localization);
result.localization = localization;
result.gt = gt;
result.gt.bbox_conf = zeros(gt.N, 4);
result.gt.bbox_conf(gt.detnum>0, 1:4) = det.bbox(gt.detnum(gt.detnum>0), :);
result.gt.bbox_ov = zeros(gt.N, 4);
result.gt.bbox_ov(gt.detnum_ov>0, 1:4) = det.bbox(gt.detnum_ov(gt.detnum_ov>0), :);
result.det.bbox = det.bbox;
result.det.view = det.view;
result.det.conf = det.conf;
result.det.gtnum = det.gtnum;
result.det.rnum = det.rnum;
result.det.isduplicate = det.isduplicate;
%% Obtaining Precision-recall curves
% Overall
npos = sum(~[gt.isdiff]);
dummy = [];
[result.pose, dummy] = averagePoseDetectionPrecision(det, gt, npos);
% Detection and pose estimation considering the difficult objects as well
npos = length(gt.bbox);%
result.pose.diff = averagePoseDetectionPrecision(det, gt, npos, 1);
result.diff_nondiff(1) = result.pose.diff;
result.gt.diffnondiff = zeros(gt.N, 1);
deto = det;
npos = 0;
rec = ann.rec;
for k = 1:gt.N
r = gt.rnum(k);
o = gt.onum(k);
if rec(r).objects(o).difficult == 1
result.gt.trunc_occ(k) = 1;
i = (det.label==0 & det.gtnum==k);
deto.label(i) = 1;
npos = npos+1;
else
i = (det.label==1 & det.gtnum==k);
deto.label(i) = 0;
end
end
dummy = [];
[result.diff_nondiff(2), dummy] = averagePoseDetectionPrecision(deto, gt, npos);
%% Object Characteristic Analysis
% (occ/trunc. objects, object size, aspect ratio, part visibility, visible side)
% Occlusion
result.gt.occ_level = zeros(gt.N, 1);
for level = 1:2
deto = det;
npos = 0;
for k = 1:gt.N
if gt.isdiff(k), continue; end;
r = gt.rnum(k);
o = gt.onum(k);
if rec(r).objects(o).detailedannotation
result.gt.occ_level(k) = rec(r).objects(o).details.occ_level;
if rec(r).objects(o).details.occ_level~=level;
i = (det.label==1 & det.gtnum==k);
deto.label(i) = 0;
else
npos = npos+1;
end
end
end
dummy = [];
[result.occ(level), dummy] = averagePoseDetectionPrecision(deto, gt, npos);
end
% Truncation
result.gt.truncated = zeros(gt.N, 1);
for val = 0:1
deto = det;
npos = 0;
for k = 1:gt.N
if gt.isdiff(k), continue; end;
r = gt.rnum(k);
o = gt.onum(k);
if rec(r).objects(o).truncated~=val
result.gt.truncated(k) = rec(r).objects(o).truncated;
i = (det.label==1 & det.gtnum==k);
deto.label(i) = 0;
else
npos = npos+1;
end
end
dummy = [];
[result.truncated(val+1), dummy] = averagePoseDetectionPrecision(deto, gt, npos);
end
% Truncation and Occlusion
result.gt.trunc_occ = zeros(gt.N, 1);
for val = 1:2
deto = det;
npos = 0;
for k = 1:gt.N
if gt.isdiff(k), continue; end;
r = gt.rnum(k);
o = gt.onum(k);
if val == 2
if rec(r).objects(o).truncated == 1 || rec(r).objects(o).occluded == 1
result.gt.trunc_occ(k) = 1;
npos = npos+1;
else
i = (det.label==1 & det.gtnum==k);
deto.label(i) = 0;
end
else
if rec(r).objects(o).truncated == 0 && rec(r).objects(o).occluded == 0
result.gt.trunc_occ(k) = 0;
npos = npos+1;
else
i = (det.label==1 & det.gtnum==k);
deto.label(i) = 0;
end
end
end
dummy = [];
[result.trunc_occ(val), dummy] = averagePoseDetectionPrecision(deto, gt, npos);
end
% Ignore truncated and occluded objects: remove objects with labels: isocc == 1 and
% istrunc == 1
ob_gtindex = [];
ob_gtindex= unique(find((gt.istrunc == 0) & (gt.isocc==0)));
hh=1;
for jj= 1:length(ob_gtindex)
if gt.detnum(ob_gtindex(jj)) ~= 0
result.pose.isocc(hh) = gt.detnum(ob_gtindex(jj));
hh= hh+1;
end
end
det2=[];
if hh > 1
det2.bbox = det.bbox(result.pose.isocc,:);
det2.conf = det.conf(result.pose.isocc,:);
det2.rnum = det.rnum(result.pose.isocc,:);
det2.view = det.view(result.pose.isocc,:);
det2.nimages = det.nimages;
det2.N = det.N;
det2.gtnum = det.gtnum(result.pose.isocc,:);
det2.ov = det.ov(result.pose.isocc,:);
det2.ov_obj = det.ov_obj(result.pose.isocc,:);
det2.ov_gt = det.ov_gt(result.pose.isocc,:);
det2.isdiff = det.isdiff(result.pose.isocc,:);
det2.label = det.label(result.pose.isocc,:);
det2.label_occ = det.label_occ(result.pose.isocc,:);
det2.label_trunc = det.label_trunc(result.pose.isocc,:);
det2.isduplicate = det.isduplicate(result.pose.isocc,:);
gt2=[];
for j=1:length(result.pose.isocc)
gt2.isdiff(j) = gt.isdiff(det.gtnum(result.pose.isocc(j)), :);
end
if ~isempty(gt2)
ah=0;
ai=unique(find((gt.istrunc == 1) | (gt.isocc==1)));
for j = 1: length(ai);
if gt.isdiff(ai(j)) == 0
ah = ah+1;
end
end
npos = sum(~[gt.isdiff])-ah;
else
npos = 0;
end
dummy = [];
[result.pose.ignoreocc, dummy] = averagePoseDetectionPrecision(det2, gt, npos);
else
result.pose.ignoreocc = [];
end
% Considering only truncated and occluded objects: remove objects with labels: isocc == 0 and
% istrunc == 0
ob_gtindex = [];
ob_gtindex= unique(find((gt.istrunc == 1) & (gt.isocc==1)));
hh=1;
for jj= 1:length(ob_gtindex)
if gt.detnum(ob_gtindex(jj)) ~= 0
result.pose.occind(hh) = gt.detnum(ob_gtindex(jj));
hh= hh+1;
end
end
det2=[];
if hh > 1
det2.bbox = det.bbox(result.pose.occind,:);
det2.conf = det.conf(result.pose.occind,:);
det2.rnum = det.rnum(result.pose.occind,:);
det2.view = det.view(result.pose.occind,:);
det2.nimages = det.nimages;
det2.N = det.N;
det2.gtnum = det.gtnum(result.pose.occind,:);
det2.ov = det.ov(result.pose.occind,:);
det2.ov_obj = det.ov_obj(result.pose.occind,:);
det2.ov_gt = det.ov_gt(result.pose.occind,:);
det2.isdiff = det.isdiff(result.pose.occind,:);
det2.label = det.label(result.pose.occind,:);
det2.label_occ = det.label_occ(result.pose.occind,:);
det2.label_trunc = det.label_trunc(result.pose.occind,:);
det2.isduplicate = det.isduplicate(result.pose.occind,:);
gt2=[];
for j=1:length(result.pose.occind)
gt2.isdiff(j) = gt.isdiff(det.gtnum(result.pose.occind(j)), :);
end
if ~isempty(gt2)
ah=0;
ai=unique(find((gt.istrunc == 0) | (gt.isocc==0)));
for j = 1: length(ai);
if gt.isdiff(ai(j)) == 0
ah = ah+1;
end
end
npos = sum(~[gt.isdiff])-ah;
else
npos = 0;
end
dummy = [];
[result.pose.onlyocc, dummy] = averagePoseDetectionPrecision(det2, gt, npos);
else
result.pose.onlyocc = [];
end
% BBox Area
bb = gt.bbox(~[gt.isdiff], :);
gtarea = (bb(:, 3)-bb(:, 1)+1).*(bb(:, 4)-bb(:, 2)+1);
[sa, si] = sort(gtarea, 'ascend');
athresh = [0 sa(round([1/10 3/10 7/10 9/10]*size(bb,1)))'];
alabel(~[gt.isdiff]) = sum(repmat(gtarea, [1 5])>repmat(athresh, [size(bb, 1) 1]), 2);
alabel(logical([gt.isdiff])) = 0;
result.gt.area = alabel;
for a = 1:5
deto = det;
npos = sum(alabel==a &~ [gt.isdiff]');
ind = find(deto.label==1);
gti = deto.gtnum(ind);
ind = ind(alabel(gti)~=a);
deto.label(ind) = 0;
dummy = [];
[result.area(a), dummy] = averagePoseDetectionPrecision(deto, gt, npos);
end
areathresh = athresh;
% BBox Height
alabel = [];
bb = gt.bbox(~[gt.isdiff], :);
gtheight = (bb(:, 4)-bb(:, 2)+1);
[sa, si] = sort(gtheight, 'ascend');
athresh = [0 sa(round([1/10 3/10 7/10 9/10]*size(bb,1)))'];
alabel(~[gt.isdiff]) = sum(repmat(gtheight, [1 5])>repmat(athresh, [size(bb, 1) 1]), 2);
alabel(logical([gt.isdiff])) = 0;
for a = 1:5
deto = det;
npos = sum(alabel==a &~ [gt.isdiff]');
ind = find(deto.label==1);
gti = deto.gtnum(ind);
ind = ind(alabel(gti)~=a);
deto.label(ind) = 0;
dummy = [];
[result.height(a), dummy] = averagePoseDetectionPrecision(deto, gt, npos);
end
result.gt.height = alabel;
heightthresh = athresh;
% Aspect Ratio
bb = gt.bbox(~[gt.isdiff], :);
gtaspect = (bb(:, 3)-bb(:, 1)+1)./(bb(:, 4)-bb(:, 2)+1);
[sa, si] = sort(gtaspect, 'ascend');
athresh = [0 sa(round([1/10 3/10 7/10 9/10]*size(bb,1)))'];
alabel(~[gt.isdiff]) = sum(repmat(gtaspect, [1 5])>repmat(athresh, [size(bb, 1) 1]), 2);
alabel(logical([gt.isdiff])) = 0;
for a = 1:5
deto = det;
npos = sum(alabel==a &~ [gt.isdiff]');
ind = find(deto.label==1);
gti = deto.gtnum(ind);
ind = ind(alabel(gti)~=a);
deto.label(ind) = 0;
dummy = [];
[result.aspect(a), dummy] = averagePoseDetectionPrecision(deto, gt, npos);
end
result.gt.aspect = alabel;
aspectthresh = athresh;
% Pose estimation vs BBox Area
bb = gt.bbox;
gtarea = (bb(:, 3)-bb(:, 1)+1).*(bb(:, 4)-bb(:, 2)+1);
[sa, si] = sort(gtarea, 'ascend');
athresh = [0 sa(round([1/10 3/10 7/10 9/10]*size(bb,1)))'];
alabel = sum(repmat(gtarea, [1 5])>repmat(athresh, [size(bb, 1) 1]), 2);
result.gt.area = alabel;
for a = 1:5
det2=[];
ind=[];
dummy = [];
[ii dummy] = find(alabel==a);
npos_aux=length(find(gt.isdiff(ii)==0));
hh=1;
for jj= 1:length(ii)
if gt.detnum(ii(jj)) ~= 0 && gt.isdiff(ii(jj)) == 0
ind(hh) = gt.detnum(ii(jj));
hh= hh+1;
end
end
det2.bbox = det.bbox(ind,:);
det2.conf = det.conf(ind,:);
det2.rnum = det.rnum(ind,:);
det2.view = det.view(ind,:);
det2.nimages = det.nimages;
det2.N = det.N;
det2.gtnum = det.gtnum(ind,:);
det2.ov = det.ov(ind,:);
det2.ov_obj = det.ov_obj(ind,:);
det2.ov_gt = det.ov_gt(ind,:);
det2.isdiff = det.isdiff(ind,:);
det2.label = det.label(ind,:);
det2.label_occ = det.label_occ(ind,:);
det2.label_trunc = det.label_trunc(ind,:);
det2.isduplicate = det.isduplicate(ind,:);
gt2 = [];
for j=1:length(det2.gtnum)
if det2.gtnum(j) ~= 0
gt2.isdiff(j) = gt.isdiff(det2.gtnum(j), :);
end
end
if ~isempty(gt2)
npos = npos_aux;%sum(~[gt2.isdiff]);
else
npos = 0;
end
dummy = [];
[result.pose.onlythissize(a), dummy] = averagePoseDetectionPrecision(det2, gt, npos);
det2=[];
ind=[];
ii=[];
val=[];
dummy = [];
[ii dummy] = find(alabel~=a);
npos_aux=length(find(gt.isdiff(ii)==0));
hh=1;
for jj= 1:length(ii)
if gt.detnum(ii(jj)) ~= 0 && gt.isdiff(ii(jj)) == 0
ind(hh) = gt.detnum(ii(jj));
hh= hh+1;
end
end
det2.bbox = det.bbox(ind,:);
det2.conf = det.conf(ind,:);
det2.rnum = det.rnum(ind,:);
det2.view = det.view(ind,:);
det2.nimages = det.nimages;
det2.N = det.N;
det2.gtnum = det.gtnum(ind,:);
det2.ov = det.ov(ind,:);
det2.ov_obj = det.ov_obj(ind,:);
det2.ov_gt = det.ov_gt(ind,:);
det2.isdiff = det.isdiff(ind,:);
det2.label = det.label(ind,:);
det2.label_occ = det.label_occ(ind,:);
det2.label_trunc = det.label_trunc(ind,:);
det2.isduplicate = det.isduplicate(ind,:);
gt2 = [];
for j=1:length(det2.gtnum)
if det2.gtnum(j) ~= 0
gt2.isdiff(j) = gt.isdiff(det2.gtnum(j), :);
end
end
if ~isempty(gt2)
npos = npos_aux;%sum(~[gt2.isdiff]);
else
npos = 0;
end
dummy = [];
[result.pose.ignorethissize(a), dummy] = averagePoseDetectionPrecision(det2, gt, npos);
end
% Pose estimation vs Aspect Ratio
alabel = [];
bb = gt.bbox;
gtaspect = (bb(:, 3)-bb(:, 1)+1)./(bb(:, 4)-bb(:, 2)+1);
[sa, si] = sort(gtaspect, 'ascend');
athresh = [0 sa(round([1/10 3/10 7/10 9/10]*size(bb,1)))'];
alabel = sum(repmat(gtaspect, [1 5])>repmat(athresh, [size(bb, 1) 1]), 2);
for a = 1:5
det2=[];
ii=[];
ind=[];
[ii dummy] = find(alabel==a);
npos_aux=length(find(gt.isdiff(ii)==0));
hh=1;
for jj= 1:length(ii)
if gt.detnum(ii(jj)) ~= 0 && gt.isdiff(ii(jj)) == 0
ind(hh) = gt.detnum(ii(jj));
hh= hh+1;
end
end
det2.bbox = det.bbox(ind,:);
det2.conf = det.conf(ind,:);
det2.rnum = det.rnum(ind,:);
det2.view = det.view(ind,:);
det2.nimages = det.nimages;
det2.N = det.N;
det2.gtnum = det.gtnum(ind,:);
det2.ov = det.ov(ind,:);
det2.ov_obj = det.ov_obj(ind,:);
det2.ov_gt = det.ov_gt(ind,:);
det2.isdiff = det.isdiff(ind,:);
det2.label = det.label(ind,:);
det2.label_occ = det.label_occ(ind,:);
det2.label_trunc = det.label_trunc(ind,:);
det2.isduplicate = det.isduplicate(ind,:);
gt2 = [];
for j=1:length(det2.gtnum)
if det2.gtnum(j) ~= 0
gt2.isdiff(j) = gt.isdiff(det2.gtnum(j), :);
end
end
if ~isempty(gt2)
npos = npos_aux;%sum(~[gt2.isdiff]);
else
npos = 0;
end
dummy = [];
[result.pose.onlythisaspect(a), dummy] = averagePoseDetectionPrecision(det2, gt, npos);
det2=[];
ind=[];
ii=[];
val=[];
[ii dummy] =find(alabel~=a);
npos_aux=length(find(gt.isdiff(ii)==0));
hh=1;
for jj= 1:length(ii)
if gt.detnum(ii(jj)) ~= 0 && gt.isdiff(ii(jj)) == 0
ind(hh) = gt.detnum(ii(jj));
hh= hh+1;
end
end
det2.bbox = det.bbox(ind,:);
det2.conf = det.conf(ind,:);
det2.rnum = det.rnum(ind,:);
det2.view = det.view(ind,:);
det2.nimages = det.nimages;
det2.N = det.N;
det2.gtnum = det.gtnum(ind,:);
det2.ov = det.ov(ind,:);
det2.ov_obj = det.ov_obj(ind,:);
det2.ov_gt = det.ov_gt(ind,:);
det2.isdiff = det.isdiff(ind,:);
det2.label = det.label(ind,:);
det2.label_occ = det.label_occ(ind,:);
det2.label_trunc = det.label_trunc(ind,:);
det2.isduplicate = det.isduplicate(ind,:);
gt2 = [];
for j=1:length(det2.gtnum)
if det2.gtnum(j) ~= 0
gt2.isdiff(j) = gt.isdiff(det2.gtnum(j), :);
end
end
if ~isempty(gt2)
npos = npos_aux;%sum(~[gt2.isdiff]);
else
npos = 0;
end
dummy = [];
[result.pose.ignorethisaspect(a), dummy] = averagePoseDetectionPrecision(det2, gt, npos);
end
% Parts
i = find(~[gt.isdiff], 1, 'first');
if rec(gt.rnum(i)).objects(gt.onum(i)).detailedannotation
pnames = fieldnames(rec(gt.rnum(i)).objects(gt.onum(i)).details.part_visible);
for p = 1:numel(pnames)
name = pnames{p};
for val = 0:1
deto = det;
npos = 0;
for k = 1:gt.N
r = gt.rnum(k);
o = gt.onum(k);
if rec(r).objects(o).detailedannotation
result.gt.part.(name)(k) = rec(r).objects(o).details.part_visible.(name);
if rec(r).objects(o).details.part_visible.(name)~=val
deto.label(det.label==1 & det.gtnum==k) = 0;
else
if gt.isdiff(k) == 0
npos = npos+1;
end
end
end
end
dummy = [];
[result.pose.part.(name)(val+1), dummy] = averagePoseDetectionPrecision(deto, gt, npos);
end
end
end
% Side
i = find(~[gt.isdiff], 1, 'first');
if rec(gt.rnum(i)).objects(gt.onum(i)).detailedannotation
pnames = fieldnames(rec(gt.rnum(i)).objects(gt.onum(i)).details.side_visible);
for p = 1:numel(pnames)
name = pnames{p};
for val = 0:1 %0 = non-visible 1 = visible
deto = det;
npos = 0;
for k = 1:gt.N
r = gt.rnum(k);
o = gt.onum(k);
if rec(r).objects(o).detailedannotation
result.gt.side.(name)(k) = rec(r).objects(o).details.side_visible.(name);
if rec(r).objects(o).details.side_visible.(name)~=val
deto.label(det.label==1 & det.gtnum==k) = 0;
else
if gt.isdiff(k) == 0
npos = npos+1;
end
end
end
end
dummy = [];
[result.pose.side.(name)(val+1), dummy] =averagePoseDetectionPrecision(deto, gt, npos);
end
end
end
%% Statistics of missed vs. detected
% result.counts stores counts of properties of all and missed objects
% result.overlap stores maximum overlap of different kinds of objects
missedthresh = 0.05;
missed = true(gt.N, 1);
missed(det.gtnum(result.pose.p>=missedthresh & det.label==1)) = false;
missed(gt.isdiff) = false;
found = ~missed;
found(gt.isdiff) = false;
% occlusion/truncation
gtoccludedL = result.gt.occ_level(:)>=2 | result.gt.truncated(:);
gtoccludedM = result.gt.occ_level(:)>=3 | result.gt.truncated(:);
result.counts.missed.total = sum(missed);
result.counts.missed.occludedL = sum(missed.*gtoccludedL(:));
result.counts.missed.occludedM = sum(missed.*gtoccludedM(:));
result.counts.all.total = sum(missed)+sum(found);
result.counts.all.occludedL = sum(gtoccludedL);
result.counts.all.occludedM = sum(gtoccludedM);
result.overlap.all.all = mean(gt.ov);
gtnum = det.gtnum(det.gtnum==1);
result.overlap.detected.all = mean(gt.ov(gtnum));
ind = gtoccludedL(gtnum);
result.overlap.detected.occludedL = mean(gt.ov(gtnum(ind)));
result.overlap.all.occludedL = mean(gt.ov(gtoccludedL));
ind = gtoccludedM(gtnum);
result.overlap.detected.occludedM = mean(gt.ov(gtnum(ind)));
result.overlap.all.occludedM = mean(gt.ov(gtoccludedM));
% area
alabel = result.gt.area(:);
alabel(logical([gt.isdiff])) = 0;
result.counts.missed.area = hist(alabel(missed & alabel>0), 1:5);
result.counts.all.area = hist(alabel(alabel>0), 1:5);
for k = 1:5
ind = det.gtnum>0;
ind(ind) = alabel(det.gtnum(ind))==k;
result.overlap.detected.area(k) = mean(gt.ov(det.gtnum(ind)));
result.overlap.all.area(k) = mean(gt.ov(alabel==k));
end
% aspect
alabel = result.gt.aspect(:);
alabel(logical([gt.isdiff])) = 0;
result.counts.all.aspectratio = hist(alabel(alabel>0), 1:5);
result.counts.missed.aspectratio = hist(alabel(missed & alabel>0), 1:5);
for k = 1:5
ind = det.gtnum>0;
ind(ind) = alabel(det.gtnum(ind))==k;
result.overlap.detected.aspectratio(k) = mean(gt.ov(det.gtnum(ind)));
result.overlap.all.aspectratio(k) = mean(gt.ov(alabel==k));
end
|
github
|
gramuah/pose-errors-master
|
analyzePoseError.m
|
.m
|
pose-errors-master/src/analyzePoseError.m
| 7,939 |
utf_8
|
ff08935c01bb85fd65958948f3efd616
|
function [result, resulclass] = analyzePoseError(dataset, dataset_params, ann, objind, det, localization)
% result = analyzePoseError(dataset, dataset_params, ann, objind, similar_ind, det)
% Pose Error Analysis.
switch dataset
case {'PASCAL3D+'}
[result, resulclass] = analyzePoseError_PASCAL3D(dataset, ...
dataset_params, ann, objind, det, localization);
otherwise
error('dataset %s is unknown\n', dataset);
end
function [result, resulclass] = analyzePoseError_PASCAL3D(dataset, dataset_params, ann, objind, det, localization)
[sv, si] = sort(det.conf, 'descend');
det.bbox = det.bbox(si, :);
det.conf = det.conf(si);
det.rnum = det.rnum(si);
det.view = det.view(si, :);
objname = dataset_params.objnames_all{objind};
% Regular
[det, gt] = matchDetectionsWithGroundTruth(dataset, dataset_params, objname, ann, det, localization);
npos = sum(~[gt.isdiff]);
result.iscorrect = (det.label>=0);
[result.pose, resulclass] = averagePoseDetectionPrecision(det, gt, npos);
%% Pose Error Analysis
% Ignore opposite error: remove estimations that have label 2
det2 = [];
ii = [];
ii = find(result.pose.labels_pose == 1 | result.pose.labels_pose == 3 | ...
result.pose.labels_pose == 4 | result.pose.labels_pose == 0);
hh=1;
result.pose.isopp = [];
for jj = 1: length(ii)
result.pose.isopp(hh) = ii(jj);
hh = hh+1;
end
if ~isempty(result.pose.isopp)
det2.bbox = det.bbox(result.pose.isopp,:);
det2.conf = det.conf(result.pose.isopp,:);
det2.rnum = det.rnum(result.pose.isopp,:);
det2.view = det.view(result.pose.isopp,:);
det2.nimages = det.nimages;
det2.N = det.N;
det2.gtnum = det.gtnum(result.pose.isopp,:);
det2.ov = det.ov(result.pose.isopp,:);
det2.ov_obj = det.ov_obj(result.pose.isopp,:);
det2.ov_gt = det.ov_gt(result.pose.isopp,:);
det2.isdiff = det.isdiff(result.pose.isopp,:);
det2.label = det.label(result.pose.isopp,:);
det2.label_occ = det.label_occ(result.pose.isopp,:);
det2.label_trunc = det.label_trunc(result.pose.isopp,:);
det2.isduplicate = det.isduplicate(result.pose.isopp,:);
ah=0;
ai=find(result.pose.labels_pose == 2);
for j = 1: length(ai);
if det.isdiff(ai(j)) == 0
ah = ah+1;
end
end
npos = sum(~[gt.isdiff])-ah;
dummy = [];
[result.pose.ignoreopp, dummy] = averagePoseDetectionPrecision(det2, gt, npos);
else
result.pose.ignoreopp = result.pose;
end
% reassigns opposite error to correct estimations
ii = [];
ii = find(result.pose.labels_pose == 2);
hh=1;
for jj = 1: length(ii)
result.pose.isopp2(hh) = ii(jj);
hh = hh +1;
end
det3 = det;
if hh > 1
for j=1:length(result.pose.isopp2)
det3.view(result.pose.isopp2(j),1) = gt.viewpoint(det3.gtnum(result.pose.isopp2(j)), :).azimuth;
end
end
npos = sum(~[gt.isdiff]);
dummy = [];
[result.pose.correctopp, dummy] = averagePoseDetectionPrecision(det3, gt, npos);
% Ignore nearby error: remove estimations that have label 3
ii = [];
ii = find(result.pose.labels_pose == 1 | result.pose.labels_pose == 2 | ...
result.pose.labels_pose == 4 | result.pose.labels_pose == 0);
hh=1;
for jj= 1:length(ii)
result.pose.isnearby(hh) = ii(jj);
hh= hh+1;
end
det2 = [];
if hh > 1
det2.bbox = det.bbox(result.pose.isnearby,:);
det2.conf = det.conf(result.pose.isnearby,:);
det2.rnum = det.rnum(result.pose.isnearby,:);
det2.view = det.view(result.pose.isnearby,:);
det2.nimages = det.nimages;
det2.N = det.N;
det2.gtnum = det.gtnum(result.pose.isnearby,:);
det2.ov = det.ov(result.pose.isnearby,:);
det2.ov_obj = det.ov_obj(result.pose.isnearby,:);
det2.ov_gt = det.ov_gt(result.pose.isnearby,:);
det2.isdiff = det.isdiff(result.pose.isnearby,:);
det2.label = det.label(result.pose.isnearby,:);
det2.label_occ = det.label_occ(result.pose.isnearby,:);
det2.label_trunc = det.label_trunc(result.pose.isnearby,:);
det2.isduplicate = det.isduplicate(result.pose.isnearby,:);
ah=0;
ai=find(result.pose.labels_pose == 3);
for j = 1: length(ai);
if det.isdiff(ai(j)) == 0
ah = ah+1;
end
end
npos = sum(~[gt.isdiff])-ah;
dummy = [];
[result.pose.ignorenearby, dummy] = averagePoseDetectionPrecision(det2, gt, npos);
else
NAMES = fieldnames(det);
for na=1:length(NAMES)
det2= setfield(det2,NAMES{na},[]);
end
npos = 0;
result.pose.ignorenearby = [];
end
% reassigns nearby error to correct estimations
ii = [];
ii = find(result.pose.labels_pose == 3);
hh=1;
for jj= 1:length(ii)
result.pose.isnearby2(hh) = ii(jj);
hh= hh+1;
end
det3 = det;
if hh > 1
for j=1:length(result.pose.isnearby2)
det3.view(result.pose.isnearby2(j),1) = ...
gt.viewpoint(det3.gtnum(result.pose.isnearby2(j)), :).azimuth;
end
end
npos = sum(~[gt.isdiff]);
dummy =[];
[result.pose.correctnearby, dummy] = averagePoseDetectionPrecision(det3, gt, npos);
% Ignore other error: remove estimations that have label 4
ii = [];
ii = find(result.pose.labels_pose == 1 | result.pose.labels_pose == 2 | ...
result.pose.labels_pose == 3 | result.pose.labels_pose == 0);
hh=1;
for jj= 1:length(ii)
result.pose.isother(hh) = ii(jj);
hh= hh+1;
end
det2 = [];
if hh > 1
det2.bbox = det.bbox(result.pose.isother,:);
det2.conf = det.conf(result.pose.isother,:);
det2.rnum = det.rnum(result.pose.isother,:);
det2.view = det.view(result.pose.isother,:);
det2.nimages = det.nimages;
det2.N = det.N;
det2.gtnum = det.gtnum(result.pose.isother,:);
det2.ov = det.ov(result.pose.isother,:);
det2.ov_obj = det.ov_obj(result.pose.isother,:);
det2.ov_gt = det.ov_gt(result.pose.isother,:);
det2.isdiff = det.isdiff(result.pose.isother,:);
det2.label = det.label(result.pose.isother,:);
det2.label_occ = det.label_occ(result.pose.isother,:);
det2.label_trunc = det.label_trunc(result.pose.isother,:);
det2.isduplicate = det.isduplicate(result.pose.isother,:);
ah=0;
ai=find(result.pose.labels_pose == 4);
for j = 1: length(ai);
if det.isdiff(ai(j)) == 0
ah = ah+1;
end
end
npos = sum(~[gt.isdiff])-ah;
dummy=[];
[result.pose.ignoreother, dummy] = averagePoseDetectionPrecision(det2, gt, npos);
else
result.pose.ignoreother = [];
end
% reassigns other error to correct estimations
ii = [];
ii = find(result.pose.labels_pose == 4);
hh=1;
for jj= 1:length(ii)
result.pose.isother2(hh) = ii(jj);
hh= hh+1;
end
det3 = det;
if hh > 1
for j=1:length(result.pose.isother2)
det3.view(result.pose.isother2(j),1) = ...
gt.viewpoint(det3.gtnum(result.pose.isother2(j)), :).azimuth;
end
end
npos = sum(~[gt.isdiff]);
dummy = [];
[result.pose.correctother, dummy] = averagePoseDetectionPrecision(det3, gt, npos);
% Only correct estimation: remove estimations that have label 2,3 and 4
ii = [];
det2 = [];
ii = find(result.pose.labels_pose == 1);
hh=1;
for jj= 1:length(ii)
if det.isdiff(ii(jj)) == 0
result.pose.ignall(hh) = ii(jj);
hh= hh+1;
end
end
det2.bbox = det.bbox(result.pose.ignall,:);
det2.conf = det.conf(result.pose.ignall,:);
det2.rnum = det.rnum(result.pose.ignall,:);
det2.view = det.view(result.pose.ignall,:);
det2.nimages = det.nimages;
det2.N = det.N;
det2.gtnum = det.gtnum(result.pose.ignall,:);
det2.ov = det.ov(result.pose.ignall,:);
det2.ov_obj = det.ov_obj(result.pose.ignall,:);
det2.ov_gt = det.ov_gt(result.pose.ignall,:);
det2.isdiff = det.isdiff(result.pose.ignall,:);
det2.label = det.label(result.pose.ignall,:);
det2.label_occ = det.label_occ(result.pose.ignall,:);
det2.label_trunc = det.label_trunc(result.pose.ignall,:);
det2.isduplicate = det.isduplicate(result.pose.ignall,:);
npos = length(result.pose.ignall);
dummy = [];
[result.pose.ignoreall, dummy] = averagePoseDetectionPrecision(det2, gt, npos);
|
github
|
gramuah/pose-errors-master
|
writeTexHeader.m
|
.m
|
pose-errors-master/src/writeTexHeader.m
| 2,580 |
utf_8
|
dcb5ce28f51374b9f67f7518477d7740
|
function writeTexHeader(outdir, detname)
ch = sprintf('%c', '%');
if ~exist(outdir, 'file'), mkdir(outdir); end;
global fid
fid = fopen(fullfile(outdir, ['header.tex']), 'w');
pr('\\section{Information}\n');
pr('\\label{info}')
pr('The \\textbf{%s} detector is analyzed. This is an automatically generated report.\n\n', detname);
pr('Our diagnostic tool analyzes the frequency and impact of different types of false positives, and the infuence on the performance of the main object characteristics. Analyzing the different types of false pose estimations of the methods, we can gather very interesting information to improve them. Since it is difficult to characterize the error modes for generic rotations, we restrict our analysis to only the predicted azimuth. We discretize the azimuth angle into $K$ bins, such that the bin centers have an equidistant spacing of $\\frac{2\\pi}{K}$. For our evaluations we set $K=24$. Thus, we define the following types of error modes. \\textit{Opposite viewpoint error}, which measures the efect of flipped estimates (\\textit{e.g}. confusion between frontal and rear views of a car). \\textit{Nearby viewpoint errors}. Nearby pose bins are confused due to they are very correlated in terms of appearance. Finally, the \\textit{Other rotation errors}, which include the rest of false positives. \n\n We also provide the success rate for each pose estimator considering 8 viewpoints: F: frontal. F-L: frontal-left. L: Left. L-RE: left-rear. RE: rear. RE-R: rear-right. R: right. R-F: right-frontal.\n\n');
pr('With respect to the impact of the main object characteristic, the following characteristic are considered in our study: occlusion/truncation, which indicates whether the object is occluded/truncated or not; object size and aspect ratio, which organizes the objects in different sets, depending on their size or aspect ratio; visible sides, which indicates if the object is in frontal, rear or side view position; and part visibility, which marks whether a 3D part is visible or not. For the object size, we measure the pixel area of the bounding box. We assign each object to a size category, depending on the object percentile size within its object category: extra-small (XS: bottom 10$\\%c$); small (S: next 20$\\%c$); large (L: next 80$\\%c$); extra-large (XL: next 100$\\%c$). Likewise, for the aspect ratio, objects are categorized into extra-tall (XT), tall (T), wide (W), and extra-wide (XW), using the same percentiles.\n', ch, ch, ch, ch);
fclose(fid);
function pr(varargin)
global fid;
fprintf(fid, varargin{:});
|
github
|
gramuah/pose-errors-master
|
writeTableResults.m
|
.m
|
pose-errors-master/src/writeTableResults.m
| 15,577 |
utf_8
|
0e5f03d0af7f21ac22d7f5a429be3e10
|
function writeTableResults(outdir, detector, res, avp_matrix, peap_matrix, dataset, objects, metric_type)
switch metric_type
case 1
metric = 'AOS';
case 2
metric = 'AVP';
case 3
metric = 'PEAP';
case 4
metric = 'MAE';
case 5
metric = 'MedError';
end
if ~exist(outdir, 'file'), mkdir(outdir); end;
global fid
fid = fopen(fullfile(outdir, ['results.tex']), 'w');
pr('Note that the results shown in this section are obtained by computing the average over \\textbf{the selected object classes} (\\textit{i.e}:');
for obj=1:length(objects)
if obj == length(objects)
pr(' %s) included in the %s dataset.\n', objects{obj}, dataset)
else
pr(' %s, ', objects{obj})
end
end
pr('\n\n')
pr('Tables \\ref{cont_pose} and \\ref{disc_pose} summarize the detection and pose estimation results. Table \\ref{cont_pose} shows the model performance working on continuous pose estimation. For AVP and PEAP the results are obtained considering a threshold equal to $\\frac{\\pi}{12}$.\n Table \\ref{disc_pose} summarizes the results achieved working on discrete pose estimation. The AVP and PEAP metrics are obtained considering different viewpoint discretizations: 4, 8, 16 and 24 views.', length(objects), dataset)
pr('\n\n');
pr('\\begin{table}[h]\n')
pr('\\caption{\\textbf{%s: Detection and Continuous Pose Estimation Results on %s dataset. For AVP and PEAP the results are obtained considering a threshold equal to $\\frac{\\pi}{12}$.}}\n', detector, dataset);
pr('\\label{cont_pose}\n')
pr('\\begin{center}\n');
pr('\\resizebox{\\textwidth}{!}{\n')
pr('\\begin{tabular}{|c||')
for obj=1:length(objects)+1
if obj == length(objects)+1
pr('c|}\n');
else
pr('c|');
end
end
pr('\\hline\n')
pr('Metric & ')
for obj=1:length(objects)
pr('%s & ', objects{obj});
end
pr('AVG \\\\ \n');
pr('\\hline\n')
pr('AP & ');
for obj=1:length(objects)+1
if obj == length(objects) + 1
cad = mean([res.ap])*100;
pr('%.1f \\\\ \n', cad );
else
cad = res(obj).ap*100;
pr('%.1f & ', cad);
end
end
pr('AOS & ');
for obj=1:length(objects)+1
if obj == length(objects) + 1
cad = mean([res.aos])*100;
pr('%.1f \\\\ \n', cad );
else
cad = res(obj).aos*100;
pr('%.1f & ', cad);
end
end
pr('AVP ($\\frac{\\pi}{12}$) & ');
for obj=1:length(objects)+1
if obj == length(objects) + 1
cad = mean([res.avp])*100;
pr('%.1f \\\\ \n', cad );
else
cad = res(obj).avp*100;
pr('%.1f & ', cad);
end
end
pr('PEAP ($\\frac{\\pi}{12}$) & ');
for obj=1:length(objects)+1
if obj == length(objects) + 1
cad = mean([res.peap])*100;
pr('%.1f \\\\ \n', cad );
else
cad = res(obj).peap*100;
pr('%.1f & ', cad);
end
end
pr('\\hline\n');
pr('\\end{tabular}\n');
pr('}');
pr('\\end{center}\n');
pr('\\end{table}\n');
pr('\\begin{table}[h]\n')
pr('\\vspace{-0.4cm}')
pr('\\caption{\\textbf{%s: Detection and Discrete Pose Estimation Results on %s dataset. Results obtained considering: 4, 8, 16 and 24 Views.}}\n', detector, dataset);
pr('\\label{disc_pose}\n')
pr('\\begin{center}\n');
pr('\\resizebox{\\textwidth}{!}{\n')
pr('\\begin{tabular}{|c||')
for obj=1:length(objects)+1
if obj == length(objects)+1
pr('c|}\n');
else
pr('c|');
end
end
pr('\\hline\n')
pr('Metric & ')
for obj=1:length(objects)
pr('%s & ', objects{obj});
end
pr('AVG \\\\ \n');
pr('\\hline\n')
pr('AP & ');
for obj=1:length(objects)+1
if obj == length(objects) + 1
cad = mean([res.ap])*100;
pr('%.1f \\\\ \n', cad );
else
cad = res(obj).ap*100;
pr('%.1f & ', cad);
end
end
pr('\\hline');
pr('\\hline\n');
pr('AVP (4 views) & ');
for obj=1:length(objects)+1
if obj == length(objects) + 1
cad = mean(avp_matrix(1,:))*100;
pr('%.1f \\\\ \n', cad );
else
cad = res(obj).avp_views(1)*100;
pr('%.1f & ', cad);
end
end
pr('PEAP (4 views) & ');
for obj=1:length(objects)+1
if obj == length(objects) + 1
cad = mean(peap_matrix(1,:))*100;
pr('%.1f \\\\ \n', cad );
else
cad = res(obj).peap_views(1)*100;
pr('%.1f & ', cad);
end
end
pr('\\hline');
pr('\\hline\n');
pr('AVP (8 views) & ');
for obj=1:length(objects)+1
if obj == length(objects) + 1
cad = mean(avp_matrix(2,:))*100;
pr('%.1f \\\\ \n', cad );
else
cad = res(obj).avp_views(2)*100;
pr('%.1f & ', cad);
end
end
pr('PEAP (8 views) & ');
for obj=1:length(objects)+1
if obj == length(objects) + 1
cad = mean(peap_matrix(2,:))*100;
pr('%.1f \\\\ \n', cad );
else
cad = res(obj).peap_views(2)*100;
pr('%.1f & ', cad);
end
end
pr('\\hline');
pr('\\hline\n');
pr('AVP (16 views) & ');
for obj=1:length(objects)+1
if obj == length(objects) + 1
cad = mean(avp_matrix(3,:))*100;
pr('%.1f \\\\ \n', cad );
else
cad = res(obj).avp_views(3)*100;
pr('%.1f & ', cad);
end
end
pr('PEAP (16 views) & ');
for obj=1:length(objects)+1
if obj == length(objects) + 1
cad = mean(peap_matrix(3,:))*100;
pr('%.1f \\\\ \n', cad );
else
cad = res(obj).peap_views(3)*100;
pr('%.1f & ', cad);
end
end
pr('\\hline');
pr('\\hline\n');
pr('AVP (24 views) & ');
for obj=1:length(objects)+1
if obj == length(objects) + 1
cad = mean(avp_matrix(4,:))*100;
pr('%.1f \\\\ \n', cad );
else
cad = res(obj).avp_views(4)*100;
pr('%.1f & ', cad);
end
end
pr('PEAP (24 views) & ');
for obj=1:length(objects)+1
if obj == length(objects) + 1
cad = mean(peap_matrix(4,:))*100;
pr('%.1f \\\\ \n', cad );
else
cad = res(obj).peap_views(4)*100;
pr('%.1f & ', cad);
end
end
pr('\\hline\n');
pr('\\end{tabular}\n');
pr('}');
pr('\\end{center}\n');
pr('\\end{table}\n');
pr('Figure \\ref{fig1} summarizes the impact on the pose performance of each type of error. Figure \\ref{fig1a} reports, for the correct detections obtained by the %s model, the frequency and impact on the pose performance of each type of false positive. For this figure a pose estimation is considered as: a) correct if its error is $< 15^\\circ$; b) opposite if its pose error is $> 165^\\circ$; c) nearby if its pose error is $\\in [15^\\circ; 30^\\circ]$; d) other for the rest of situations. Figure \\ref{fig1b} shows the impact of different type of pose errors.\n', detector);
pr('Figure \\ref{fig2} shows the success rate considering the 8 viewpoints described in Section \\ref{info}. Hence, considering only the correct detections, Figure \\ref{fig1b} also summarizes the percentage of success and error on pose estimation. \n');
pr('\n');
pr('\\begin{figure}[h]\n');
pr('\\centering\n');
pr('\\subfloat[]{\n');
pr('\\label{fig1a}\n');
pr('\\includegraphics[width=0.45\\textwidth,trim = 25mm 65mm 20mm 65mm, clip]{../plot_3.pdf} \n');
pr('}\n');
pr('\\subfloat[]{\n');
pr('\\label{fig1b}\n');
pr('\\includegraphics[width=0.45\\textwidth,trim = 15mm 65mm 25mm 65mm, clip]{../plot_6.pdf}\n');
pr('}\n');
pr('\\caption{\\textbf{Analysis and Impact of Pose Estimation Errors.} (a) Pie chart shows the fraction of pose errors that are due to Opposite viewpoints (Opposite), confusion with Nearby viewpoints (Nearby), confusion with Other rotations (Other). It also reports the percentage of correct pose estimations (Correct). (b) Impact of Pose Errors. \\textcolor{blue}{Blue Bars} display the %s performance obtained when all estimations are considered. \\textcolor{green}{Green Bars} display the %s improvement by \\textbf{removing} all estimations of one type: \\textbf{OTH} removes confusion with other rotation viewpoints; \\textbf{NEAR} removes confusion with nearby viewpoints; \\textbf{OPP} removes confusion with opposite viewpoints. \\textcolor{BrickRed}{Brick Red Bars} show the %s improvement by \\textbf{correcting} all estimations of one type: \\textbf{OTH}, \\textbf{NEAR} and \\textbf{OPP}.} \n', metric, metric, metric);
pr('\\label{fig1}\n');
pr('\\end{figure}\n');
pr('\n');
pr('\\begin{figure}[h]\n');
pr('\\centering\n');
pr('\\includegraphics[width=0.85\\textwidth,trim = 15mm 65mm 25mm 65mm, clip]{../plot_7.pdf} \n');
pr('\\caption{\\textbf{Success Rate considering 8 viewpoints.} For each of the 8 viewpoints, we report: In \\textcolor{blue}{Blue}, the percentage of the correct pose estimations. In \\textcolor{cyan}{Cyan}, the percentage of confusion with the opposite viewpoints. In \\textcolor{yellow}{Yellow}, the percentage of confusion with nearby viewpoints. In \\textcolor{BrickRed}{Brick Red}, the percentage of the confusions with other rotations.}\n');
pr('\\label{fig2}\n');
pr('\\end{figure}\n');
pr('\n');
pr('\\clearpage\n');
pr('Figure \\ref{fig3} summarizes the main object characteristic influences. Figure \\ref{fig3a} shows the influence of the side visibility. Figures \\ref{fig3b} and \\ref{fig3c} report the aspect ratio and object size effects, respectively.\n');
pr('Figure \\ref{fig4} provides a summary of the sensitivity to each characteristic and the potential impact on improving pose estimation robustness. The worst-performing and best-performing combinations for each object characteristic are averaged over the selected object categories. The difference between the best and the worst performance indicates sensitivity; the difference between the best and the overall indicates the potential impact.\n');
pr('\\begin{figure}[h]\n');
pr('\\centering\n');
pr('\\subfloat[]{\n');
pr('\\label{fig3a}\n');
pr('\\includegraphics[width=0.3\\textwidth,trim = 15mm 65mm 25mm 65mm, clip]{../plot_2.pdf}\n');
pr('}\n');
pr('\\subfloat[]{\n');
pr('\\label{fig3b}\n');
pr('\\includegraphics[width=0.3\\textwidth,trim = 15mm 65mm 25mm 65mm, clip]{../plot_8.pdf}\n');
pr('}\n');
pr('\\subfloat[]{\n');
pr('\\label{fig3c}\n');
pr('\\includegraphics[width=0.3\\textwidth,trim = 15mm 65mm 25mm 65mm, clip]{../plot_9.pdf}\n');
pr('}\n');
pr('\\caption{\\textbf{Object Characteristic Influences in terms of %s}. (a) Side Visibility Influence. Visible side: \\textbf{1} = performance obtained when the corresponding side is visible; \\textbf{0} = accuracy achieved when the side is not visible. (b) Aspect Ratio Effect. We show the overall accuracy by the black dashed line, and the performance achieved when we only consider: XT , T, W and XW objects. (c) Object Size Effect. We show the overall accuracy by the black dashed line, and the performance achieved when we only consider: XS, S, L and XL objects.}\n', metric);
pr('\\label{fig3}\n');
pr('\\end{figure}\n');
if strcmp(detector(length(detector)-1:length(detector)), 'gt')
pr('\\begin{figure}[h]\n');
pr('\\centering\n');
pr('\\includegraphics[width=0.85\\textwidth,trim = 10mm 65mm 25mm 65mm, clip]{../plot_1.pdf}\n');
pr('\\caption{\\textbf{Summary of Sensitivity and Impact of Object Characteristics.} We show the %s performance of the highest performing and lowest performing subsets within each characteristic (occlusion/truncation (occ-trn), bounding box area or object size (size), aspect ratio (asp), visible sides (side) and part visibility (part)). Overall accuracy is indicated by the black dashed line. The difference between max and min indicates sensitivity; the difference between max and overall indicates the impact.\n', metric);
pr('}\n');
pr('\\label{fig4}\n');
pr('\\end{figure}\n');
else
pr('\\begin{figure}[h]\n');
pr('\\centering\n');
pr('\\includegraphics[width=0.85\\textwidth,trim = 10mm 65mm 25mm 65mm, clip]{../plot_1.pdf}\n');
pr('\\caption{\\textbf{Summary of Sensitivity and Impact of Object Characteristics.} We show the %s performance of the highest performing and lowest performing subsets within each characteristic (occlusion/truncation (occ-trn), difficult objects (diff), bounding box area or object size (size), aspect ratio (asp), visible sides (side) and part visibility (part)). Overall accuracy is indicated by the black dashed line. The difference between max and min indicates sensitivity; the difference between max and overall indicates the impact.\n', metric);
pr('}\n');
pr('\\label{fig4}\n');
pr('\\end{figure}\n');
end
pr('\\clearpage\n');
pr('Figure \\ref{fig5} shows an analysis of the influence of the overlap criterion considering all the metrics. Figure \\ref{fig5a} shows AOS, AVP and PEAP, and Figure \\ref{fig5b} reports MAE and MedError. For this overlap criterion analysis we follow the PASCAL VOC formulation: to be considered a true positive, the area of overlap between the predicted BB and GT BB must exceed a threshold.\n');
pr('\\begin{figure}[h]\n');
pr('\\centering\n');
pr('\\subfloat[]{\n');
pr('\\label{fig5a}\n');
pr('\\includegraphics[width=0.45\\textwidth,trim = 15mm 65mm 20mm 65mm, clip]{../plot_4.pdf}\n');
pr('}\n');
pr('\\subfloat[]{\n');
pr('\\label{fig5b}\n');
pr('\\includegraphics[width=0.45\\textwidth,trim = 15mm 65mm 20mm 65mm, clip]{../plot_5.pdf}\n');
pr('}\n');
pr('\\caption{\\textbf{Simultaneous Object Detection and Pose Estimation.} Analysis of pose estimation performance considering different metrics and different overlap criteria. (a) AP, AOS, AVP and PEAP. (b) MAE and MedError.}\n');
pr('\\label{fig5}\n');
pr('\\end{figure}\n');
pr('\n\n')
pr('Finally, we summarize the main pose estimation errors (Figure \\ref{fig6}) and the success rate considering 8 viewpoints (Figure \\ref{fig7}) for each of the selected object classes: ')
for obj=1:length(objects)
if obj == length(objects)
pr(' %s from %s dataset.\n', objects{obj}, dataset)
else
pr(' %s, ', objects{obj})
end
end
pr('\n\n')
pr('\\begin{figure}[h]\n')
pr('\\centering\n');
for obj=1:length(objects)
name = objects{obj};
if exist(fullfile(outdir(1:end-4), name, 'analysisI/plot_2.pdf'), 'file')
pr('\\subfloat[%s]{\n', name);
pr('\\label{fig6%c}\n', char(96 + obj));
if mod(obj,3) ~= 0
pr('\\includegraphics[width=0.3\\textwidth,trim = 25mm 65mm 20mm 65mm, clip]{../%s/analysisI/plot_2.pdf} \n', name);
pr('}\n');
else
pr('\\includegraphics[width=0.3\\textwidth,trim = 25mm 65mm 20mm 65mm, clip]{../%s/analysisI/plot_2.pdf} \n', name);
pr('}\\\\n');
end
end
end
pr('\\caption{\\textbf{Main Pose Estimation Errors for all selected Object Classes from %s dataset.}}\n', dataset);
pr('\\label{fig6}\n');
pr('\\end{figure}\n');
pr('\\begin{figure}[h]\n')
pr('\\centering\n');
for obj=1:length(objects)
name = objects{obj};
if exist(fullfile(outdir(1:end-4), name, 'analysisI/plot_4.pdf'), 'file')
pr('\\subfloat[%s]{\n', name);
pr('\\label{fig7%c}\n', char(96 + obj));
if mod(obj,3) ~= 0
pr('\\includegraphics[width=0.3\\textwidth,trim = 20mm 65mm 25mm 65mm, clip]{../%s/analysisI/plot_4.pdf} \n', name);
pr('}\n');
else
pr('\\includegraphics[width=0.3\\textwidth,trim = 20mm 65mm 25mm 65mm, clip]{../%s/analysisI/plot_4.pdf} \n', name);
pr('}\\\\n');
end
end
end
pr('\\caption{\\textbf{Success Rate considering 8 viewpoints for all selected Object Classes from %s dataset.}}\n', dataset);
pr('\\label{fig7}\n');
pr('\\end{figure}\n');
fclose(fid);
function pr(varargin)
global fid;
fprintf(fid, varargin{:});
|
github
|
gramuah/pose-errors-master
|
displayPerCharacteristicPosePlots.m
|
.m
|
pose-errors-master/src/displayPerCharacteristicPosePlots.m
| 35,305 |
utf_8
|
6f6e849114b00ea0f0b75027b316bc43
|
function [resultclass, f] = displayPerCharacteristicPosePlots(resultfp, result, detector, error_type)
%function [resutclass,f] = displayPerCharacteristicDetPlots(results_all, error_type)
%
% Object characteristic effect on pose estimation: save and display plots
%
% Inputs:
% result: detection results
% resultfp: pose error results (false positives)
% error_type: metric to analysis
close all
switch error_type
case 1
metric = 'AOS';
case 2
metric = 'AVP';
case 3
metric = 'PEAP';
case 4
metric = 'MAE';
case 5
metric = 'MedError';
end
%% Impact Bar Chart
% Obtained pose results:
tmp = [resultfp.pose];
AP = mean([tmp.ap]);
AOS = mean([tmp.aos]);
AVP = mean([tmp.avp15]);
PEAP = mean([tmp.peap15]);
MAE = mean([tmp.mean_error]);
MedErr = mean([tmp.median_error]);
resultclass.MAE = MAE;
resultclass.MedError = MedErr;
fs = 18;
f = 1;
%% occ vs non-occluded
tmp = [result.pose.ignoreocc];
occ_flag = 0;
if ~isempty(tmp)
occ_flag = 1;
ignore_OCC_aos = mean([tmp.aos]);
ignore_OCC_avp = mean([tmp.avp15]);
ignore_OCC_peap = mean([tmp.peap15]);
ignore_OCC_mae = mean([tmp.mean_error]);
ignore_OCC_mederr = mean([tmp.median_error]);
else
occ_flag = 0;
ignore_OCC_aos = 0;
ignore_OCC_avp = 0;
ignore_OCC_peap = 0;
ignore_OCC_mae = 0;
ignore_OCC_mederr = 0;
end
tmp = [result.pose.onlyocc];
occ_flag = 0;
if ~isempty(tmp)
occ_flag = 1;
only_OCC_aos = mean([tmp.aos]);
only_OCC_avp = mean([tmp.avp15]);
only_OCC_peap = mean([tmp.peap15]);
only_OCC_mae = mean([tmp.mean_error]);
only_OCC_mederr = mean([tmp.median_error]);
else
occ_flag = 0;
only_OCC_aos = 0;
only_OCC_avp = 0;
only_OCC_peap = 0;
only_OCC_mae = 0;
only_OCC_mederr = 0;
end
switch error_type
case 1
y = [0, 0, 0; ...
AOS, ignore_OCC_aos, only_OCC_aos];
x = [-2 2];
figure(f), hold off;
barh(x, y);
axis([0 1 0 4]);
xlim = [0 1];
set(gca, 'xlim', xlim);
set(gca, 'xminortick', 'on');
set(gca, 'ticklength', get(gca, 'ticklength'));
set(gca, 'ytick', 2)
set(gca, 'yticklabel', {' '});
ylabel('AOS','fontsize', fs,'Rotation',90);
set(gca, 'fontsize', fs);
title([resultfp.name ': Trunc/Occ. Objects'])
case 2
y = [0, 0, 0;
AVP, ignore_OCC_avp, only_OCC_avp];
x = [-2 2];
figure(f), hold off;
barh(x, y);
axis([0 1 0 4]);
xlim = [0 1];
set(gca, 'xlim', xlim);
set(gca, 'xminortick', 'on');
set(gca, 'ticklength', get(gca, 'ticklength'));
set(gca, 'ytick', 2)
set(gca, 'yticklabel', {' '});
ylabel('AVP','fontsize', fs,'Rotation',90);
set(gca, 'fontsize', fs);
title([resultfp.name ': Trunc/Occ. Objects'])
case 3
y = [0, 0, 0;
PEAP, ignore_OCC_peap, only_OCC_peap];
x = [-2 2];
figure(f), hold off;
barh(x, y);
axis([0 1 0 4]);
xlim = [0 1];
set(gca, 'xlim', xlim);
set(gca, 'xminortick', 'on');
set(gca, 'ticklength', get(gca, 'ticklength'));
set(gca, 'ytick', 2)
set(gca, 'yticklabel', {' '});
ylabel('PEAP','fontsize', fs,'Rotation',90);
set(gca, 'fontsize', fs);
title([resultfp.name ': Trunc/Occ. Objects'])
case 4
y = [0, 0, 0; ...
MAE, ignore_OCC_mae, only_OCC_mae];
x = [-2 2];
figure(f), hold off;
barh(x, y);
axis([0 ceil(max([MAE ignore_OCC_mae only_OCC_mae])+10) 0 4]);
xlim = [0 ceil((max([MAE ignore_OCC_mae only_OCC_mae])+10))];
set(gca, 'xlim', xlim);
set(gca, 'xminortick', 'on');
set(gca, 'ticklength', get(gca, 'ticklength'));
set(gca, 'ytick', 2)
set(gca, 'yticklabel', {' '});
ylabel('MAE','fontsize', fs,'Rotation',90);
set(gca, 'fontsize', fs);
title([resultfp.name ': Trunc/Occ. Objects'])
case 5
y = [0, 0, 0; ...
MedErr, ignore_OCC_mederr, only_OCC_mederr];
x = [-2 2];
figure(f), hold off;
barh(x, y);
axis([0 ceil(max([MedErr ignore_OCC_mederr only_OCC_mederr])+10) 0 4]);
xlim = [0 ceil((max([MedErr ignore_OCC_mederr only_OCC_mederr])+10))];
set(gca, 'xlim', xlim);
set(gca, 'xminortick', 'on');
set(gca, 'ticklength', get(gca, 'ticklength'));
set(gca, 'ytick', 2)
set(gca, 'yticklabel', {' '});
ylabel('MedError','fontsize', fs,'Rotation',90);
set(gca, 'fontsize', fs);
title([resultfp.name ': Trunc/Occ. Objects'])
end
%% diff vs non-diff
%%%%
f = f + 1;
tmp = [result.pose.diff];
diff_aos = mean([tmp.aos]);
diff_avp = mean([tmp.avp15]);
diff_peap = mean([tmp.peap15]);
diff_mae = mean([tmp.mean_error]);
diff_mederr = mean([tmp.median_error]);
tmp = [result.diff_nondiff(2)];
only_diff_aos = mean([tmp.aos]);
only_diff_avp = mean([tmp.avp15]);
only_diff_peap = mean([tmp.peap15]);
only_diff_mae = mean([tmp.mean_error]);
only_diff_mederr = mean([tmp.median_error]);
switch error_type
case 1
y = [0, 0, 0; ...
AOS, diff_aos, only_diff_aos];
x = [-2 2];
figure(f), hold off;
barh(x, y);
axis([0 1 0 4]);
xlim = [0 1];
set(gca, 'xlim', xlim);
set(gca, 'xminortick', 'on');
set(gca, 'ticklength', get(gca, 'ticklength'));
set(gca, 'ytick', 2)
set(gca, 'yticklabel', {' '});
ylabel('AOS','fontsize', fs,'Rotation',90);
set(gca, 'fontsize', fs);
title([resultfp.name ': Difficult Objects'])
case 2
y = [0, 0, 0;
AVP, diff_avp, only_diff_avp];
x = [-2 2];
figure(f), hold off;
barh(x, y);
axis([0 1 0 4]);
xlim = [0 1];
set(gca, 'xlim', xlim);
set(gca, 'xminortick', 'on');
set(gca, 'ticklength', get(gca, 'ticklength'));
set(gca, 'ytick', 2)
set(gca, 'yticklabel', {' '});
ylabel('AVP','fontsize', fs,'Rotation',90);
set(gca, 'fontsize', fs);
title([resultfp.name ': Difficult Objects'])
case 3
y = [0, 0, 0;
PEAP, diff_peap, only_diff_peap];
x = [-2 2];
figure(f), hold off;
barh(x, y);
axis([0 1 0 4]);
xlim = [0 1];
set(gca, 'xlim', xlim);
set(gca, 'xminortick', 'on');
set(gca, 'ticklength', get(gca, 'ticklength'));
set(gca, 'ytick', 2)
set(gca, 'yticklabel', {' '});
ylabel('PEAP','fontsize', fs,'Rotation',90);
set(gca, 'fontsize', fs);
title([resultfp.name ': Difficult Objects'])
case 4
y = [0, 0, 0; ...
MAE, diff_mae, only_diff_mae];
x = [-2 2];
figure(f), hold off;
barh(x, y);
axis([0 ceil(max([MAE ignore_OCC_mae only_OCC_mae])+10) 0 4]);
xlim = [0 ceil((max([MAE ignore_OCC_mae only_OCC_mae])+10))];
set(gca, 'xlim', xlim);
set(gca, 'xminortick', 'on');
set(gca, 'ticklength', get(gca, 'ticklength'));
set(gca, 'ytick', 2)
set(gca, 'yticklabel', {' '});
ylabel('MAE','fontsize', fs,'Rotation',90);
set(gca, 'fontsize', fs);
title([resultfp.name ': Difficult Objects'])
case 5
y = [0, 0, 0; ...
MedErr, diff_mederr, only_diff_mederr];
x = [-2 2];
figure(f), hold off;
barh(x, y);
axis([0 ceil(max([MedErr ignore_OCC_mederr only_OCC_mederr])+10) 0 4]);
xlim = [0 ceil((max([MedErr ignore_OCC_mederr only_OCC_mederr])+10))];
set(gca, 'xlim', xlim);
set(gca, 'xminortick', 'on');
set(gca, 'ticklength', get(gca, 'ticklength'));
set(gca, 'ytick', 2)
set(gca, 'yticklabel', {' '});
ylabel('MedError','fontsize', fs,'Rotation',90);
set(gca, 'fontsize', fs);
title([resultfp.name ': Difficult Objects'])
end
%% object size analysis
%%%%
f = f + 1;
onlythissize_aos = zeros(1, length(result.pose.onlythissize));
onlythissize_avp = zeros(1, length(result.pose.onlythissize));
onlythissize_peap = zeros(1, length(result.pose.onlythissize));
onlythissize_mae = zeros(1, length(result.pose.onlythissize));
onlythissize_mederr = zeros(1, length(result.pose.onlythissize));
ignorethissize_aos = zeros(1, length(result.pose.onlythissize));
ignorethissize_avp = zeros(1, length(result.pose.onlythissize));
ignorethissize_peap = zeros(1, length(result.pose.onlythissize));
ignorethissize_mae = zeros(1, length(result.pose.onlythissize));
ignorethissize_mederr = zeros(1, length(result.pose.onlythissize));
for i=1:length(result.pose.onlythissize)
tmp = [result.pose.onlythissize(i)];
onlythissize_aos(i) = mean([tmp.aos]);
onlythissize_avp(i) = mean([tmp.avp15]);
onlythissize_peap(i) = mean([tmp.peap15]);
onlythissize_mae(i) = mean([tmp.mean_error]);
onlythissize_mederr(i) = mean([tmp.median_error]);
tmp = [result.pose.ignorethissize(i)];
ignorethissize_aos(i) = mean([tmp.aos]);
ignorethissize_avp(i) = mean([tmp.avp15]);
ignorethissize_peap(i) = mean([tmp.peap15]);
ignorethissize_mae(i) = mean([tmp.mean_error]);
ignorethissize_mederr(i) = mean([tmp.median_error]);
end
switch error_type
case 1
resultclass.extrasmall = onlythissize_aos(1);
resultclass.small = onlythissize_aos(2);
resultclass.large = onlythissize_aos(4);
resultclass.extralarge = onlythissize_aos(5);
y1 = [AOS, onlythissize_aos(1), ignorethissize_aos(1);
AOS, onlythissize_aos(2), ignorethissize_aos(2);
AOS, onlythissize_aos(3), ignorethissize_aos(3);
AOS, onlythissize_aos(4), ignorethissize_aos(4);
AOS, onlythissize_aos(5), ignorethissize_aos(5)];
case 2
resultclass.extrasmall = onlythissize_avp(1);
resultclass.small = onlythissize_avp(2);
resultclass.large = onlythissize_avp(4);
resultclass.extralarge = onlythissize_avp(5);
y1 = [AVP, onlythissize_avp(1), ignorethissize_avp(1);
AVP, onlythissize_avp(2), ignorethissize_avp(2);
AVP, onlythissize_avp(3), ignorethissize_avp(3);
AVP, onlythissize_avp(4), ignorethissize_avp(4);
AVP, onlythissize_avp(5), ignorethissize_avp(5)];
case 3
resultclass.extrasmall = onlythissize_peap(1);
resultclass.small = onlythissize_peap(2);
resultclass.large = onlythissize_peap(4);
resultclass.extralarge = onlythissize_peap(5);
y1 = [PEAP, onlythissize_peap(1), ignorethissize_peap(1);
AVP, onlythissize_peap(2), ignorethissize_peap(2);
AVP, onlythissize_peap(3), ignorethissize_peap(3);
AVP, onlythissize_peap(4), ignorethissize_peap(4);
AVP, onlythissize_peap(5), ignorethissize_peap(5)];
case 4
resultclass.extrasmall = onlythissize_mae(1);
resultclass.small = onlythissize_mae(2);
resultclass.large = onlythissize_mae(4);
resultclass.extralarge = onlythissize_mae(5);
y1 = [MAE, onlythissize_mae(1), ignorethissize_mae(1);
MAE, onlythissize_mae(2), ignorethissize_mae(2);
MAE, onlythissize_mae(3), ignorethissize_mae(3);
MAE, onlythissize_mae(4), ignorethissize_mae(4);
MAE, onlythissize_mae(5), ignorethissize_mae(5)];
case 5
resultclass.extrasmall = onlythissize_mederr(1);
resultclass.small = onlythissize_mederr(2);
resultclass.large = onlythissize_mederr(4);
resultclass.extralarge = onlythissize_mederr(5);
y1 = [MedErr, onlythissize_mederr(1), ignorethissize_mederr(1);
MedErr, onlythissize_mederr(2), ignorethissize_mederr(2);
MedErr, onlythissize_mederr(3), ignorethissize_mederr(3);
MedErr, onlythissize_mederr(4), ignorethissize_mederr(4);
MedErr, onlythissize_mederr(5), ignorethissize_mederr(5)];
end
x = [1 2 3 4 5];
figure(f), hold off;
barh(x, y1);
if (error_type == 1) || (error_type == 2) || (error_type == 3)
xlim = [0 1];
else
xlim = [0 ceil(max(max(y1))+10)];
end
set(gca, 'xlim', xlim);
set(gca, 'xminortick', 'on');
set(gca, 'ticklength', get(gca, 'ticklength'));
xlabel(metric, 'fontsize', fs);
set(gca, 'ytick', 1:5)
set(gca, 'yticklabel', {'XS', 'S', 'M', 'L', 'XL'});
set(gca, 'fontsize', fs);
title([resultfp.name ': Object Size Influence'], 'fontsize', fs, 'fontweight', 'bold')
%% aspect ratio analysis
%%%%
f = f + 1;
onlythisaspect_aos = zeros(1, length(result.pose.onlythisaspect));
onlythisaspect_avp = zeros(1, length(result.pose.onlythisaspect));
onlythisaspect_peap = zeros(1, length(result.pose.onlythisaspect));
onlythisaspect_mae = zeros(1, length(result.pose.onlythisaspect));
onlythisaspect_mederr = zeros(1, length(result.pose.onlythisaspect));
ignorethisaspect_aos = zeros(1, length(result.pose.onlythisaspect));
ignorethisaspect_avp = zeros(1, length(result.pose.onlythisaspect));
ignorethisaspect_peap = zeros(1, length(result.pose.onlythisaspect));
ignorethisaspect_mae = zeros(1, length(result.pose.onlythisaspect));
ignorethisaspect_mederr = zeros(1, length(result.pose.onlythisaspect));
for i = 1:length(result.pose.onlythisaspect)
tmp = [result.pose.onlythisaspect(i)];
onlythisaspect_aos(i) = mean([tmp.aos]);
onlythisaspect_avp(i) = mean([tmp.avp15]);
onlythisaspect_peap(i) = mean([tmp.peap15]);
onlythisaspect_mae(i) = mean([tmp.mean_error]);
onlythisaspect_mederr(i) = mean([tmp.median_error]);
tmp = [result.pose.ignorethisaspect(i)];
ignorethisaspect_aos(i) = mean([tmp.aos]);
ignorethisaspect_avp(i) = mean([tmp.avp15]);
ignorethisaspect_peap(i) = mean([tmp.peap15]);
ignorethisaspect_mae(i) = mean([tmp.mean_error]);
ignorethisaspect_mederr(i) = mean([tmp.median_error]);
end
switch error_type
case 1
resultclass.extratall = onlythisaspect_aos(1);
resultclass.tall = onlythisaspect_aos(2);
resultclass.wide = onlythisaspect_aos(4);
resultclass.extrawide = onlythisaspect_aos(5);
y1 = [AOS, onlythisaspect_aos(1), ignorethisaspect_aos(1);
AOS, onlythisaspect_aos(2), ignorethisaspect_aos(2);
AOS, onlythisaspect_aos(3), ignorethisaspect_aos(3);
AOS, onlythisaspect_aos(4), ignorethisaspect_aos(4);
AOS, onlythisaspect_aos(5), ignorethisaspect_aos(5)];
case 2
resultclass.extratall = onlythisaspect_avp(1);
resultclass.tall = onlythisaspect_avp(2);
resultclass.wide = onlythisaspect_avp(4);
resultclass.extrawide = onlythisaspect_avp(5);
y1 = [AVP, onlythisaspect_avp(1), ignorethisaspect_avp(1);
AVP, onlythisaspect_avp(2), ignorethisaspect_avp(2);
AVP, onlythisaspect_avp(3), ignorethisaspect_avp(3);
AVP, onlythisaspect_avp(4), ignorethisaspect_avp(4);
AVP, onlythisaspect_avp(5), ignorethisaspect_avp(5)];
case 3
resultclass.extratall = onlythisaspect_peap(1);
resultclass.tall = onlythisaspect_peap(2);
resultclass.wide = onlythisaspect_peap(4);
resultclass.extrawide = onlythisaspect_peap(5);
y1 = [PEAP, onlythisaspect_peap(1), ignorethisaspect_peap(1);
PEAP, onlythisaspect_peap(2), ignorethisaspect_peap(2);
PEAP, onlythisaspect_peap(3), ignorethisaspect_peap(3);
PEAP, onlythisaspect_peap(4), ignorethisaspect_peap(4);
PEAP, onlythisaspect_peap(5), ignorethisaspect_peap(5)];
case 4
resultclass.extratall = onlythisaspect_mae(1);
resultclass.tall = onlythisaspect_mae(2);
resultclass.wide = onlythisaspect_mae(4);
resultclass.extrawide = onlythisaspect_mae(5);
y1 = [MAE, onlythisaspect_mae(1), ignorethisaspect_mae(1);
MAE, onlythisaspect_mae(2), ignorethisaspect_mae(2);
MAE, onlythisaspect_mae(3), ignorethisaspect_mae(3);
MAE, onlythisaspect_mae(4), ignorethisaspect_mae(4);
MAE, onlythisaspect_mae(5), ignorethisaspect_mae(5)];
case 5
resultclass.extratall = onlythisaspect_mederr(1);
resultclass.tall = onlythisaspect_mederr(2);
resultclass.wide = onlythisaspect_mederr(4);
resultclass.extrawide = onlythisaspect_mederr(5);
y1 = [MedErr, onlythisaspect_mederr(1), ignorethisaspect_mederr(1);
MedErr, onlythisaspect_mederr(2), ignorethisaspect_mederr(2);
MedErr, onlythisaspect_mederr(3), ignorethisaspect_mederr(3);
MedErr, onlythisaspect_mederr(4), ignorethisaspect_mederr(4);
MedErr, onlythisaspect_mederr(5), ignorethisaspect_mederr(5)];
end
x = [1 2 3 4 5];
figure(f), hold off;
barh(x, y1);
if (error_type == 1) || (error_type == 2) || (error_type == 3)
xlim = [0 1];
else
xlim = [0 round(max(max(y1))+10)];
end
set(gca, 'xlim', xlim);
set(gca, 'xminortick', 'on');
set(gca, 'ticklength', get(gca, 'ticklength'));
xlabel(metric, 'fontsize', fs);
set(gca, 'ytick', 1:5)
set(gca, 'yticklabel', {'XT', 'T', 'M', 'W', 'XW'});
set(gca, 'fontsize', fs);
title([resultfp.name ': Aspect Ratio Influence'], 'fontsize', fs, 'fontweight', 'bold')
%%%%
fs = 18;
f = f + 1;
if strcmp(detector(length(detector)-1:length(detector)), 'gt')
fnames = {'side', 'part'};
xticklab = {'occ-trn', 'size', 'asp', 'side', 'part'};
valid = true(size(xticklab));
for af = 1:numel(fnames)
if ~isfield(result(1).pose, fnames{af})
valid(f) = false;
continue;
end
if af == 1
if occ_flag
switch error_type
case 1
maxval(1)= max([AOS, ignore_OCC_aos, only_OCC_aos]);
minval(1)= min([AOS, ignore_OCC_aos, only_OCC_aos]);
case 2
maxval(1)= max([AVP, ignore_OCC_avp, only_OCC_avp]);
minval(1)= min([AVP, ignore_OCC_avp, only_OCC_avp]);
case 3
maxval(1)= max([PEAP, ignore_OCC_peap, only_OCC_peap]);
minval(1)= min([PEAP, ignore_OCC_peap, only_OCC_peap]);
case 4
maxval(1)= max([MAE, ignore_OCC_mae, only_OCC_mae]);
minval(1)= min([MAE, ignore_OCC_mae, only_OCC_mae]);
case 5
maxval(1)= max([MedErr, ignore_OCC_mederr, only_OCC_mederr]);
minval(1)= min([MedErr, ignore_OCC_mederr, only_OCC_mederr]);
end
else
switch error_type
case 1
maxval(1) = AOS;
minval(1) = 0;
case 2
maxval(1) = AVP;
minval(1) = 0;
case 3
maxval(1) = PEAP;
minval(1) = 0;
case 4
maxval(1) = MAE;
minval(1) = 0;
case 5
maxval(1) = MedErr;
minval(1) = 0;
end
end
resultclass.ap = AP;
resultclass.aos = AOS;
resultclass.avp = AVP;
resultclass.peap = PEAP;
resultclass.mae = MAE;
resultclass.mederr = MedErr;
switch error_type
case 1
tmp = [resultfp.pose];
avgval = mean([tmp.aos]);
maxval(2)= max([AOS, onlythissize_aos, ignorethissize_aos]);
minval(2)= min([AOS, onlythissize_aos, ignorethissize_aos]);
maxval(3)= max([AOS, onlythisaspect_aos, ignorethisaspect_aos]);
minval(3)= min([AOS, onlythisaspect_aos, ignorethisaspect_aos]);
case 2
tmp = [resultfp.pose];
avgval = mean([tmp.avp15]);
maxval(2)= max([AVP, onlythissize_avp, ignorethissize_avp]);
minval(2)= min([AVP, onlythissize_avp, ignorethissize_avp]);
maxval(3)= max([AVP, onlythisaspect_avp, ignorethisaspect_avp]);
minval(3)= min([AVP, onlythisaspect_avp, ignorethisaspect_avp]);
case 3
tmp = [resultfp.pose];
avgval = mean([tmp.peap15]);
maxval(2)= max([PEAP, onlythissize_peap, ignorethissize_peap]);
minval(2)= min([PEAP, onlythissize_peap, ignorethissize_peap]);
maxval(3)= max([PEAP, onlythisaspect_peap, ignorethisaspect_peap]);
minval(3)= min([PEAP, onlythisaspect_peap, ignorethisaspect_peap]);
case 4
tmp = [resultfp.pose];
avgval = mean([tmp.mean_error]);
maxval(2)= max([MAE, onlythissize_mae, ignorethissize_mae]);
minval(2)= min([MAE, onlythissize_mae, ignorethissize_mae]);
maxval(3)= max([MAE, onlythisaspect_mae, ignorethisaspect_mae]);
minval(3)= min([MAE, onlythisaspect_mae, ignorethisaspect_mae]);
case 5
tmp = [resultfp.pose];
avgval = mean([tmp.median_error]);
maxval(2)= max([MedErr, onlythissize_mederr, ignorethissize_mederr]);
minval(2)= min([MedErr, onlythissize_mederr, ignorethissize_mederr]);
maxval(3)= max([MedErr, onlythisaspect_mederr, ignorethisaspect_mederr]);
minval(3)= min([MedErr, onlythisaspect_mederr, ignorethisaspect_mederr]);
end
resultclass.maxval(1) = maxval(1);
resultclass.minval(1) = minval(1);
resultclass.maxval(2) = maxval(2);
resultclass.minval(2) = minval(2);
resultclass.maxval(3) = maxval(3);
resultclass.minval(3) = minval(3);
end
switch error_type
case 1
maxval(af+3) = getMaxVal([result.pose.(fnames{af})], 'aos');
minval(af+3) = getMinVal([result.pose.(fnames{af})], 'aos');
case 2
maxval(af+3) = getMaxVal([result.pose.(fnames{af})], 'avp15');
minval(af+3) = getMinVal([result.pose.(fnames{af})], 'avp15');
case 3
maxval(af+3) = getMaxVal([result.pose.(fnames{af})], 'peap15');
minval(af+3) = getMinVal([result.pose.(fnames{af})], 'peap15');
case 4
maxval(af+3) = getMaxVal([result.pose.(fnames{af})], 'mean_error');
minval(af+3) = getMinVal([result.pose.(fnames{af})], 'mean_error');
case 5
maxval(af+3) = getMaxVal([result.pose.(fnames{af})], 'median_error');
minval(af+3) = getMinVal([result.pose.(fnames{af})], 'median_error');
end
resultclass.maxval(af+3) = maxval(af+3);
resultclass.minval(af+3) = minval(af+3);
end
maxval = maxval(:, valid); minval = minval(:, valid);
fnames = xticklab(valid); xticklab = xticklab(valid);
maxval = mean(maxval, 1);
minval = mean(minval, 1);
figure(f), hold off;
plot([1 numel(fnames)], [avgval avgval], 'k--', 'linewidth', 2);
hold on;
errorbar(1:numel(fnames), avgval*ones(1, numel(fnames)), avgval-minval, ...
maxval-avgval, 'r+', 'linewidth', 2);
for x = 1:numel(fnames)
if (error_type == 1) || (error_type == 2) || (error_type == 3)
text(x+0.12, minval(x)+0.01, sprintf('%0.3f', minval(x)), ...
'fontsize', fs, 'fontweight', 'bold');
text(x+0.12, maxval(x)-0.02, sprintf('%0.3f', maxval(x)), ...
'fontsize', fs, 'fontweight', 'bold');
else
text(x+0.12, minval(x), sprintf('%0.1f', minval(x)), 'fontsize', fs, 'fontweight', 'bold');
text(x+0.12, maxval(x), sprintf('%0.1f', maxval(x)), 'fontsize', fs, 'fontweight', 'bold');
end
end
text(0.1, avgval, sprintf('%0.3f', avgval), 'fontsize', fs, 'fontweight', 'bold');
if (error_type == 1) || (error_type == 2) || (error_type == 3)
ymax = min(round((max(maxval)+0.15)*10)/10,1);
else
ymax = max(maxval) + 5;
end
axis([0 numel(fnames)+1 0 ymax]);
ylabel(metric, 'fontsize', fs, 'fontweight', 'bold');
set(gca, 'xtick', 1:numel(fnames));
set(gca, 'xticklabel', xticklab);
set(gca, 'ygrid', 'on')
set(gca, 'xgrid', 'on')
set(gca, 'fontsize', fs, 'fontweight', 'bold');
set(gca, 'ticklength', [0.001 0.001]);
title([resultfp.name ': Sensitivity and Impact'])
else
fnames = {'side', 'part'};
xticklab = {'occ-trn', 'diff', 'size', 'asp', 'side', 'part'};
af=1;
valid = true(size(xticklab));
for af = 1:numel(fnames)
if ~isfield(result(1).pose, fnames{af})
valid(f) = false;
continue;
end
if af == 1
if occ_flag
switch error_type
case 1
maxval(1)= max([AOS, ignore_OCC_aos, only_OCC_aos]);
minval(1)= min([AOS, ignore_OCC_aos, only_OCC_aos]);
case 2
maxval(1)= max([AVP, ignore_OCC_avp, only_OCC_avp]);
minval(1)= min([AVP, ignore_OCC_avp, only_OCC_avp]);
case 3
maxval(1)= max([PEAP, ignore_OCC_peap, only_OCC_peap]);
minval(1)= min([PEAP, ignore_OCC_peap, only_OCC_peap]);
case 4
maxval(1)= max([MAE, ignore_OCC_mae, only_OCC_mae]);
minval(1)= min([MAE, ignore_OCC_mae, only_OCC_mae]);
case 5
maxval(1)= max([MedErr, ignore_OCC_mederr, only_OCC_mederr]);
minval(1)= min([MedErr, ignore_OCC_mederr, only_OCC_mederr]);
end
else
switch error_type
case 1
maxval(1) = AOS;
minval(1) = 0;
case 2
maxval(1) = AVP;
minval(1) = 0;
case 3
maxval(1) = PEAP;
minval(1) = 0;
case 4
maxval(1) = MAE;
minval(1) = 0;
case 5
maxval(1) = MedErr;
minval(1) = 0;
end
end
resultclass.ap = AP;
resultclass.aos = AOS;
resultclass.avp = AVP;
resultclass.peap = PEAP;
resultclass.mae = MAE;
resultclass.mederr = MedErr;
switch error_type
case 1
tmp = [resultfp.pose];
avgval = mean([tmp.aos]);
maxval(2)= max(AOS, diff_aos);
minval(2)= min(AOS, diff_aos);
maxval(3)= max([AOS, onlythissize_aos, ignorethissize_aos]);
minval(3)= min([AOS, onlythissize_aos, ignorethissize_aos]);
maxval(4)= max([AOS, onlythisaspect_aos, ignorethisaspect_aos]);
minval(4)= min([AOS, onlythisaspect_aos, ignorethisaspect_aos]);
case 2
tmp = [resultfp.pose];
avgval = mean([tmp.avp15]);
maxval(2)= max(AVP, diff_avp);
minval(2)= min(AVP, diff_avp);
maxval(3)= max([AVP, onlythissize_avp, ignorethissize_avp]);
minval(3)= min([AVP, onlythissize_avp, ignorethissize_avp]);
maxval(4)= max([AVP, onlythisaspect_avp, ignorethisaspect_avp]);
minval(4)= min([AVP, onlythisaspect_avp, ignorethisaspect_avp]);
case 3
tmp = [resultfp.pose];
avgval = mean([tmp.peap15]);
maxval(2)= max(PEAP, diff_peap);
minval(2)= min(PEAP, diff_peap);
maxval(3)= max([PEAP, onlythissize_peap, ignorethissize_peap]);
minval(3)= min([PEAP, onlythissize_peap, ignorethissize_peap]);
maxval(4)= max([PEAP, onlythisaspect_peap, ignorethisaspect_peap]);
minval(4)= min([PEAP, onlythisaspect_peap, ignorethisaspect_peap]);
case 4
tmp = [resultfp.pose];
avgval = mean([tmp.mean_error]);
maxval(2)= max(MAE, diff_mae);
minval(2)= min(MAE, diff_mae);
maxval(3)= max([MAE, onlythissize_mae, ignorethissize_mae]);
minval(3)= min([MAE, onlythissize_mae, ignorethissize_mae]);
maxval(4)= max([MAE, onlythisaspect_mae, ignorethisaspect_mae]);
minval(4)= min([MAE, onlythisaspect_mae, ignorethisaspect_mae]);
case 5
tmp = [resultfp.pose];
avgval = mean([tmp.median_error]);
maxval(2)= max(MedErr, diff_mederr);
minval(2)= min(MedErr, diff_mederr);
maxval(3)= max([MedErr, onlythissize_mederr, ignorethissize_mederr]);
minval(3)= min([MedErr, onlythissize_mederr, ignorethissize_mederr]);
maxval(4)= max([MedErr, onlythisaspect_mederr, ignorethisaspect_mederr]);
minval(4)= min([MedErr, onlythisaspect_mederr, ignorethisaspect_mederr]);
end
resultclass.maxval(1) = maxval(1);
resultclass.minval(1) = minval(1);
resultclass.maxval(2) = maxval(2);
resultclass.minval(2) = minval(2);
resultclass.maxval(3) = maxval(3);
resultclass.minval(3) = minval(3);
resultclass.maxval(4) = maxval(4);
resultclass.minval(4) = minval(4);
end
switch error_type
case 1
maxval(af+4) = getMaxVal([result.pose.(fnames{af})], 'aos');
minval(af+4) = getMinVal([result.pose.(fnames{af})], 'aos');
case 2
maxval(af+4) = getMaxVal([result.pose.(fnames{af})], 'avp15');
minval(af+4) = getMinVal([result.pose.(fnames{af})], 'avp15');
case 3
maxval(af+4) = getMaxVal([result.pose.(fnames{af})], 'peap15');
minval(af+4) = getMinVal([result.pose.(fnames{af})], 'peap15');
case 4
maxval(af+4) = getMaxVal([result.pose.(fnames{af})], 'mean_error');
minval(af+4) = getMinVal([result.pose.(fnames{af})], 'mean_error');
case 5
maxval(af+4) = getMaxVal([result.pose.(fnames{af})], 'median_error');
minval(af+4) = getMinVal([result.pose.(fnames{af})], 'median_error');
end
resultclass.maxval(af+4) = maxval(af+4);
resultclass.minval(af+4) = minval(af+4);
end
maxval = maxval(:, valid); minval = minval(:, valid);
fnames = xticklab(valid); xticklab = xticklab(valid);
maxval = mean(maxval, 1);
minval = mean(minval, 1);
figure(f), hold off;
plot([1 numel(fnames)], [avgval avgval], 'k--', 'linewidth', 2);
hold on;
errorbar(1:numel(fnames), avgval*ones(1, numel(fnames)), avgval-minval, ...
maxval-avgval, 'r+', 'linewidth', 2);
for x = 1:numel(fnames)
if (error_type == 1) || (error_type == 2) || (error_type == 3)
text(x+0.12, minval(x)+0.01, sprintf('%0.3f', minval(x)), ...
'fontsize', fs, 'fontweight', 'bold');
text(x+0.12, maxval(x)-0.02, sprintf('%0.3f', maxval(x)), ...
'fontsize', fs, 'fontweight', 'bold');
else
text(x+0.12, minval(x), sprintf('%0.1f', minval(x)), 'fontsize', fs, 'fontweight', 'bold');
text(x+0.12, maxval(x), sprintf('%0.1f', maxval(x)), 'fontsize', fs, 'fontweight', 'bold');
end
end
text(0.1, avgval, sprintf('%0.3f', avgval), 'fontsize', fs, 'fontweight', 'bold');
if (error_type == 1) || (error_type == 2) || (error_type == 3)
ymax = min(round((max(maxval)+0.15)*10)/10,1);
else
ymax = max(maxval) + 5;
end
axis([0 numel(fnames)+1 0 ymax]);
ylabel(metric, 'fontsize', fs, 'fontweight', 'bold');
set(gca, 'xtick', 1:numel(fnames));
set(gca, 'xticklabel', xticklab);
set(gca, 'ygrid', 'on')
set(gca, 'xgrid', 'on')
set(gca, 'fontsize', fs, 'fontweight', 'bold');
set(gca, 'ticklength', [0.001 0.001]);
title([resultfp.name ': Sensitivity and Impact'])
end
%% Gets the maximum value of a particular variable name for any field in the structure
function maxy = getMaxVal(s, fname, maxy)
if ~exist('maxy', 'var') || isempty(maxy)
maxy = -Inf;
end
if numel(s)>1
for k = 1:numel(s)
maxy = max(maxy, getMaxVal(s(k), fname, maxy));
end
return;
end
names = fieldnames(s);
for k = 1:numel(names)
if ~isstruct(s.(names{k})) && ~strcmp(names{k}, fname)
continue;
end
for j = 1:numel(s.(names{k}))
if strcmp(names{k}, fname)
maxy = max(maxy, s.(fname)(j));
else
maxy = max(maxy, getMaxVal(s.(names{k})(j), fname, maxy));
end
end
end
function miny = getMinVal(s, fname, miny)
if ~exist('miny', 'var') || isempty(miny)
miny = Inf;
end
if numel(s)>1
for k = 1:numel(s)
miny = min(miny, getMinVal(s(k), fname, miny));
end
return;
end
names = fieldnames(s);
for k = 1:numel(names)
if ~isstruct(s.(names{k})) && ~strcmp(names{k}, fname)
continue;
end
for j = 1:numel(s.(names{k}))
if strcmp(names{k}, fname)
%if s.npos>=5 % special case
miny = min(miny, s.(fname)(j));
%end
else
miny = min(miny, getMinVal(s.(names{k})(j), fname, miny));
end
end
end
%% Removes vowels from a string
function str = removeVowels(str)
for v = 'aeiou'
str(str==v) = [];
end
|
github
|
gramuah/pose-errors-master
|
averagePoseDetectionPrecision.m
|
.m
|
pose-errors-master/src/averagePoseDetectionPrecision.m
| 11,758 |
utf_8
|
c860088de2a9ab843d34b5c7d4ea649d
|
function [result, resultclass] = averagePoseDetectionPrecision(det, gt, npos, diff_flag)
% result = averagePoseDetectionPrecision(det, gt, npos, diff_flag)
%
% Computes full interpolated average precision
% Normally, p = tp ./ (fp + tp)
%
% Input:
% det(ndet, 1): detections
% gt: ground truth annotations
% npos: the number of ground truth positive examples
% diff_flag: set true to consider the difficult objects (by default 0)
%
% Output:
% result.(labels, conf, npos, r, p, ap, ap_std, MAE, MedErr, std_err, ...
% aos, avp):
% the precision-recall and aos, avp and peap precision-recall curves.
% Pose estimation analysis: aos, avp, peap, MAE and MedErr
%
if nargin < 4
diff_flag = 0;
end
[sv, si] = sort(det.conf, 'descend');
label = det.label(si);
label_pose = zeros(length(det.label),1); % label (-1, 1; 0=don't care)
if diff_flag
ind_tp=find(label==1 | label==0);
else
ind_tp=find(label==1);
end
% compute average precision in pose estimation
tmp=zeros(1,length(label));
tmp_v=zeros(1,length(label));
tmp_v15=zeros(1,length(label));
tp_peap_15=zeros(1,length(label));
fp_peap_15=ones(1,length(label));
tp_peap_30=zeros(1,length(label));
fp_peap_30=ones(1,length(label));
viewcountvector = zeros(1,8);
viewcountvector_opp = zeros(1,8);
viewcountvector_near = zeros(1,8);
viewcountvector_oth = zeros(1,8);
gtviewcountvector = gt.gtviewvector;
error_azimuth = zeros(1, length(ind_tp));
vnum_test = [4 8 16 24];
tmp_bins=zeros(length(label), size(vnum_test,2));
tp_peap_bins=zeros(length(label), size(vnum_test,2));
fp_peap_bins=ones(length(label), size(vnum_test,2));
resultclass.obj = zeros(1,length(label));
resultclass.err = zeros(1,length(label));
for h=1:length(ind_tp)
if gt.viewpoint(det.gtnum(ind_tp(h))).azimuth ~= 0
gtview = gt.viewpoint(det.gtnum(ind_tp(h))).azimuth;
else
gtview = gt.viewpoint(det.gtnum(ind_tp(h))).azimuth_coarse;
end
error_azimuth(h) = min(mod(det.view(ind_tp(h),1)-gtview,360), ...
mod(gtview-det.view(ind_tp(h),1),360));
resultclass.obj(h) = det.gtnum(ind_tp(h));
resultclass.err(h) = error_azimuth(h);
tmp(ind_tp(h))=(1 + cos((error_azimuth(h)*pi)/180))/2; % AOS
if error_azimuth(h) < 15 % pi/12 --> AVP_15
tmp_v15(ind_tp(h))=1;
tp_peap_15(ind_tp(h))=1;
fp_peap_15(ind_tp(h))=0;
else
tmp_v15(ind_tp(h))=0;
fp_peap_15(ind_tp(h))=1;
end
if error_azimuth(h) < 30 % pi/6 --> AVP_30
tmp_v(ind_tp(h))=1;
tp_peap_30(ind_tp(h))=1;
fp_peap_30(ind_tp(h))=0;
else
tmp_v(ind_tp(h))=0;
fp_peap_30(ind_tp(h))=1;
end
for av = 1:size(vnum_test,2)
azimuth_interval = [0 (360/(vnum_test(av)*2)):(360/vnum_test(av)):360-(360/(vnum_test(av)*2))];
view_gt = find_interval(gtview, azimuth_interval);
view_pr = find_interval(det.view(ind_tp(h),1), azimuth_interval);
if view_pr == view_gt
tmp_bins(ind_tp(h),av)=1;
tp_peap_bins(ind_tp(h),av)=1;
fp_peap_bins(ind_tp(h),av)=0;
else
tmp_bins(ind_tp(h),av)=0;
fp_peap_bins(ind_tp(h),av)=1;
end
end
%% error classification:
% label 1 --> error < 15 (correct);
% label 2 --> error > 165 (opposite);
% label 3 --> 15 < error < 30 (nearby);
% label 4 --> others
flag = 0;
if error_azimuth(h) <= 15
label_pose(ind_tp(h)) = 1;
flag = 1;
if gtview <= 25 || gtview > 340
viewcountvector(1) = viewcountvector(1) + 1;
end
if gtview <= 70 && gtview > 25
viewcountvector(2) = viewcountvector(2) + 1;
end
if gtview <= 115 && gtview > 70
viewcountvector(3) = viewcountvector(3) + 1;
end
if gtview <= 160 && gtview > 115
viewcountvector(4) = viewcountvector(4) + 1;
end
if gtview <= 205 && gtview > 160
viewcountvector(5) = viewcountvector(5) + 1;
end
if gtview <= 250 && gtview > 205
viewcountvector(6) = viewcountvector(6) + 1;
end
if gtview <= 295 && gtview > 250
viewcountvector(7) = viewcountvector(7) + 1;
end
if gtview <= 340 && gtview > 295
viewcountvector(8) = viewcountvector(8) + 1;
end
end
if error_azimuth(h) > 165
label_pose(ind_tp(h)) = 2;
flag = 1;
if gtview <= 25 || gtview > 340
viewcountvector_opp(1) = viewcountvector_opp(1) + 1;
end
if gtview <= 70 && gtview > 25
viewcountvector_opp(2) = viewcountvector_opp(2) + 1;
end
if gtview <= 115 && gtview > 70
viewcountvector_opp(3) = viewcountvector_opp(3) + 1;
end
if gtview <= 160 && gtview > 115
viewcountvector_opp(4) = viewcountvector_opp(4) + 1;
end
if gtview <= 205 && gtview > 160
viewcountvector_opp(5) = viewcountvector_opp(5) + 1;
end
if gtview <= 250 && gtview > 205
viewcountvector_opp(6) = viewcountvector_opp(6) + 1;
end
if gtview <= 295 && gtview > 250
viewcountvector_opp(7) = viewcountvector_opp(7) + 1;
end
if gtview <= 340 && gtview > 295
viewcountvector_opp(8) = viewcountvector_opp(8) + 1;
end
end
if error_azimuth(h) > 15 && error_azimuth(h) <= 30
label_pose(ind_tp(h)) = 3;
flag = 1;
if gtview <= 25 || gtview > 340
viewcountvector_near(1) = viewcountvector_near(1) + 1;
end
if gtview <= 70 && gtview > 25
viewcountvector_near(2) = viewcountvector_near(2) + 1;
end
if gtview <= 115 && gtview > 70
viewcountvector_near(3) = viewcountvector_near(3) + 1;
end
if gtview <= 160 && gtview > 115
viewcountvector_near(4) = viewcountvector_near(4) + 1;
end
if gtview <= 205 && gtview > 160
viewcountvector_near(5) = viewcountvector_near(5) + 1;
end
if gtview <= 250 && gtview > 205
viewcountvector_near(6) = viewcountvector_near(6) + 1;
end
if gtview <= 295 && gtview > 250
viewcountvector_near(7) = viewcountvector_near(7) + 1;
end
if gtview <= 340 && gtview > 295
viewcountvector_near(8) = viewcountvector_near(8) + 1;
end
end
if flag == 0
label_pose(ind_tp(h)) = 4;
if gtview <= 25 || gtview > 340
viewcountvector_oth(1) = viewcountvector_oth(1) + 1;
end
if gtview <= 70 && gtview > 25
viewcountvector_oth(2) = viewcountvector_oth(2) + 1;
end
if gtview <= 115 && gtview > 70
viewcountvector_oth(3) = viewcountvector_oth(3) + 1;
end
if gtview <= 160 && gtview > 115
viewcountvector_oth(4) = viewcountvector_oth(4) + 1;
end
if gtview <= 205 && gtview > 160
viewcountvector_oth(5) = viewcountvector_oth(5) + 1;
end
if gtview <= 250 && gtview > 205
viewcountvector_oth(6) = viewcountvector_oth(6) + 1;
end
if gtview <= 295 && gtview > 250
viewcountvector_oth(7) = viewcountvector_oth(7) + 1;
end
if gtview <= 340 && gtview > 295
viewcountvector_oth(8) = viewcountvector_oth(8) + 1;
end
end
end
if ~isempty(error_azimuth)
mean_error = mean(error_azimuth);
median_error = median(error_azimuth);
std_error = std(error_azimuth);
else
mean_error = 0;
median_error = 0;
std_error = 0;
end
tp = cumsum(label==1);
fp = cumsum(label==-1);
conf = sv;
acc=cumsum(tmp);
accV=cumsum(tmp_v);
accV_15 = cumsum(tmp_v15);
tp_peap_15 = cumsum(tp_peap_15);
fp_peap_15 = cumsum(fp_peap_15);
tp_peap_30 = cumsum(tp_peap_30);
fp_peap_30 = cumsum(fp_peap_30);
for av=1:size(vnum_test,2)
accV_bins = cumsum(tmp_bins(:,av));
tp_peap = cumsum(tp_peap_bins(:,av));
fp_peap = cumsum(fp_peap_bins(:,av));
r_peap(:,av) = tp_peap / npos;
p_peap(:,av) = tp_peap./(fp_peap+tp_peap);
p_avp_bins(:,av) = accV_bins ./(fp+tp);
end
r = tp / npos;
p = tp ./ (tp + fp);
r_peap15 = tp_peap_15 / npos;
p_peap15 = tp_peap_15 ./ (tp_peap_15 + fp_peap_15);
r_peap30 = tp_peap_30 / npos;
p_peap30 = tp_peap_30 ./ (tp_peap_30 + fp_peap_30);
p_aos = acc./(fp+tp)';
p_avp30=accV./(fp+tp)';
p_avp15=accV_15 ./(fp+tp)';
result = struct('labels', label, 'labels_pose', label_pose, 'hist_views', ...
viewcountvector, 'hist_gt_views', gtviewcountvector, ...
'hist_opp_views', viewcountvector_opp, 'hist_near_views', viewcountvector_near, ...
'hist_oth_views', viewcountvector_oth,'conf', conf, 'r', r, 'p', p, ...
'p_aos', p_aos, 'p_avp30', p_avp30, ...
'p_avp15', p_avp15, 'p_peap15', p_peap15, 'r_peap15', r_peap15, ...
'p_peap30', p_peap30, 'r_peap30', r_peap30);
result.npos = npos;
result.mean_error = mean_error;
result.median_error = median_error;
result.std_error = std_error;
for av=1:size(vnum_test,2)
result.p_avp_bins(:,av) = p_avp_bins(:,av);
result.p_peap_bins(:,av) = p_peap(:,av);
result.r_peap_bins(:,av) = r_peap(:,av);
end
%% Error types
result.opp_error= (length(find(error_azimuth>165))/length(error_azimuth))*100;
result.nearby_error= (length(find(error_azimuth>15 & error_azimuth<30))/length(error_azimuth))*100;
result.no_error= (length(find(error_azimuth<=15))/length(error_azimuth))*100;
result.other_error= 100 - (result.opp_error+result.nearby_error+result.no_error);
result.opp_error_count= length(find(error_azimuth>165));
result.nearby_error_count= length(find(error_azimuth>15 & error_azimuth<30));
result.no_error_count= length(find(error_azimuth<=15));
result.other_error_count= length(error_azimuth) - (result.opp_error_count+result.nearby_error_count+result.no_error_count);
% compute interpolated precision and precision (AP)
istp = (label==1);
[result.ap, result.pi] = calcule_ap(r, p);
missed = zeros(npos-sum(label==1),1);
result.ap_stderr = std([p(istp(:)) ; missed])/sqrt(npos);
%% compute interpolated precision and normalized precision for pose (AOS, AVP and PEAP)
% AOS
[result.aos, result.aos_pi] = calcule_ap(r, p_aos);
result.aos_stderr = std([p_aos(istp(:))' ; missed])/sqrt(npos);
% AVP
[result.avp30, result.avp_pi30] = calcule_ap(r, p_avp30);
result.avp_stderr30 = std([p_avp30(istp(:))' ; missed])/sqrt(npos);
[result.avp15, result.avp_pi15] = calcule_ap(r, p_avp15);
result.avp_stderr15 = std([p_avp15(istp(:))' ; missed])/sqrt(npos);
% PEAP
[result.peap30, result.peap_pi30] = calcule_ap(r_peap30', p_peap30);
result.peap_stderr30 = std([p_peap30(istp(:))' ; missed])/sqrt(npos);
[result.peap15, result.peap_pi15] = calcule_ap(r_peap15', p_peap15);
result.peap_stderr15 = std([p_peap15(istp(:))' ; missed])/sqrt(npos);
for av=1:size(vnum_test,2)
[result.avp_bin(av), result.avp_pi_bin(:,av)] = calcule_ap(r, p_avp_bins(:,av));
[result.peap_bin(av), result.peap_pi_bin(:,av)] = calcule_ap(r_peap(:,av), p_peap(:,av));
result.avp_stderr_bin(av) = std([p_avp_bins(istp(:), av) ; missed])/sqrt(npos);
end
function ind = find_interval(azimuth, a)
for i = 1:numel(a)
if azimuth < a(i)
break;
end
end
ind = i - 1;
if azimuth > a(end)
ind = 1;
end
function [ap, mpre] = calcule_ap(rec, prec)
mrec = [0; rec; 1];
if size(prec,1) >= size(prec,2)
mpre = [0; prec; 0];
else
mpre = [0; prec'; 0];
end
for i = numel(mpre)-1:-1:1
mpre(i) = max(mpre(i), mpre(i+1));
end
i = find(mrec(2:end) ~= mrec(1:end-1)) + 1;
ap = sum((mrec(i) - mrec(i-1)) .* mpre(i));
|
github
|
gramuah/pose-errors-master
|
displayPerCharacteristicDetPlots.m
|
.m
|
pose-errors-master/src/displayPerCharacteristicDetPlots.m
| 15,123 |
utf_8
|
8b2b7edcad03a9fefdfea69282b0546b
|
function [resutclass,f] = displayPerCharacteristicDetPlots(results_all, error_type)
%function [resutclass,f] = displayPerCharacteristicDetPlots(results_all, error_type)
%
% Object characteristic effect on detection: save and display plots
%
% Inputs:
% results_all: detection results
% error_type: metric to analysis
close all
drawline = true;
makeMultiCategoryPlot(1, results_all, 'occ', ...
[results_all(1).name ': Occluded Objects'], 1, {'N', 'O'}, drawline);
makeMultiCategoryPlot(2, results_all, 'area', ...
[results_all(1).name ': Object Size Influence'], 1, {'XS', 'S', 'M', 'L', 'XL'}, drawline);
makeMultiCategoryPlot(3, results_all, 'aspect', ...
[results_all(1).name ': Aspect Ratio Influence'], 1, {'XT', 'T', 'M', 'W', 'XW'}, drawline);
makeMultiCategoryPlot(4, results_all, 'truncated', ...
[results_all(1).name ': Truncated Objects'], 1, {'N', 'T'}, drawline);
makeMultiCategoryPlot(5, results_all, 'trunc_occ', ...
[results_all(1).name ': Trunc/Occ. Objects'], 1, {'N', 'T/O'}, drawline);
makeMultiCategoryPlot(6, results_all, 'diff_nondiff', ...
[results_all(1).name ': Difficult Objects'], 1, {'All', 'Only Diff'}, drawline);
f=6;
fs = 18;
%% Visible parts
f=f+1;
tickstr = {};
np=0;
for o = 1:numel(results_all)
if isfield(results_all(o).pose, 'part')
pnames = fieldnames(results_all(o).pose.part);
for p = 1:numel(pnames)
np=np+1;
results_all(o).tmp((p-1)*2+(1:2)) = results_all(o).pose.part.(pnames{p});
end
end
end
drawline = false;
makeMultiCategoryPlot(f, results_all, 'tmp', ...
[results_all(o).name ': Part Visibility Effect'], 1, tickstr, drawline);
axisval = axis;
n=0;
for o = 1:numel(results_all)
if isfield(results_all(o).pose, 'part')
pnames = fieldnames(results_all(o).pose.part);
n=n+1;
for p = 1:numel(pnames)
text(n+1, -0.071*axisval(4), sprintf('%d\n0/1', p), 'fontsize', fs);
n = n+2;
end
end
end
if isfield(results_all, 'tmp')
results_all = rmfield(results_all, 'tmp');
end
%% Visible sides
f=f+1;
tickstr = {};
np=0;
for o = 1:numel(results_all)
if isfield(results_all(o).pose, 'side')
pnames = fieldnames(results_all(o).pose.side);
pnames = pnames(2:4);
for p = 1:numel(pnames)
np=np+1;
results_all(o).tmp((p-1)*2+(1:2)) = results_all(o).pose.side.(pnames{p});
end
end
end
drawline = false;
makeMultiCategoryPlot(f, results_all, 'tmp', ...
[results_all(o).name ': Visible Side Influence'], 1, tickstr, drawline);
axisval = axis;
n=0;
for o = 1:numel(results_all)
if isfield(results_all(o).pose, 'side')
pnames = fieldnames(results_all(o).pose.side);
pnames = pnames(2:4);
n=n+1;
for p = 1:numel(pnames)
name = pnames{p}; if numel(name)>5, name = removeVowels(name); end;
text(n+1, -0.071*axisval(4), sprintf('%s\n0/1', name), 'fontsize', fs);
n = n+2;
end
end
end
if isfield(results_all, 'tmp')
results_all = rmfield(results_all, 'tmp');
end
%% Visible parts vs pose estimation
f=f+1;
tickstr = {};
np=0;
for o = 1:numel(results_all)
if isfield(results_all(o).pose, 'part')
pnames = fieldnames(results_all(o).pose.part);
for p = 1:numel(pnames)
np=np+1;
results_all(o).pose.tmp((p-1)*2+(1:2)) = results_all(o).pose.part.(pnames{p});
end
end
end
drawline = false;
makeMultiCategoryPlotPose(f, results_all.pose, 'tmp', ...
[results_all(o).name ': Part Visibility Effect'], 1, tickstr, drawline, error_type);
axisval = axis;
n=0;
for o = 1:numel(results_all)
if isfield(results_all(o).pose, 'part')
pnames = fieldnames(results_all(o).pose.part);
n=n+1;
for p = 1:numel(pnames)
text(n+1, -0.071*axisval(4), sprintf('%d\n0/1', p), 'fontsize', fs);
n = n+2;
end
end
end
if isfield(results_all, 'tmp')
results_all = rmfield(results_all, 'tmp');
end
%% Visible sides vs pose estimation
f=f+1;
tickstr = {};
np=0;
for o = 1:numel(results_all)
if isfield(results_all(o).pose, 'side')
pnames = fieldnames(results_all(o).pose.side);
pnames = pnames(2:4);
for p = 1:numel(pnames)
switch error_type
case 1
resutclass.side_1(p) = results_all(o).pose.side.(pnames{p})(1).aos;
if isnan(resutclass.side_1(p))
resutclass.side_1(p) = results_all(o).pose.aos;
end
resutclass.side_2(p) = results_all(o).pose.side.(pnames{p})(2).aos;
if isnan(resutclass.side_2(p))
resutclass.side_2(p) = results_all(o).pose.aos;
end
case 2
resutclass.side_1(p) = results_all(o).pose.side.(pnames{p})(1).avp15;
if isnan(resutclass.side_1(p))
resutclass.side_1(p) = results_all(o).pose.avp15;
end
resutclass.side_2(p) = results_all(o).pose.side.(pnames{p})(2).avp15;
if isnan(resutclass.side_2(p))
resutclass.side_2(p) = results_all(o).pose.avp15;
end
case 3
resutclass.side_1(p) = results_all(o).pose.side.(pnames{p})(1).peap15;
if isnan(resutclass.side_1(p))
resutclass.side_1(p) = results_all(o).pose.peap15;
end
resutclass.side_2(p) = results_all(o).pose.side.(pnames{p})(2).peap15;
if isnan(resutclass.side_2(p))
resutclass.side_2(p) = results_all(o).pose.avp15;
end
case 4
resutclass.side_1(p) = results_all(o).pose.side.(pnames{p})(1).mean_error;
if isnan(resutclass.side_1(p))
resutclass.side_1(p) = results_all(o).pose.mean_error;
end
resutclass.side_2(p) = results_all(o).pose.side.(pnames{p})(2).mean_error;
if isnan(resutclass.side_2(p))
resutclass.side_2(p) = results_all(o).pose.mean_error;
end
case 5
resutclass.side_1(p) = results_all(o).pose.side.(pnames{p})(1).median_error;
if isnan(resutclass.side_1(p))
resutclass.side_1(p) = results_all(o).pose.median_error;
end
resutclass.side_2(p) = results_all(o).pose.side.(pnames{p})(2).median_error;
if isnan(resutclass.side_2(p))
resutclass.side_2(p) = results_all(o).pose.median_error;
end
end
np=np+1;
results_all(o).pose.tmp2((p-1)*2+(1:2)) = results_all(o).pose.side.(pnames{p});
end
end
end
drawline = false;
makeMultiCategoryPlotPose(f, results_all.pose, 'tmp2', ...
[results_all(o).name ': Visible Side Influence'], 1, tickstr, drawline, error_type);
axisval = axis;
n=0;
for o = 1:numel(results_all)
if isfield(results_all(o).pose, 'side')
pnames = fieldnames(results_all(o).pose.side);
pnames = pnames(2:4);
n=n+1;
for p = 1:numel(pnames)
name = pnames{p}; if numel(name)>5, name = removeVowels(name); end;
text(n+1, -0.071*axisval(4), sprintf('%s\n0/1', name), 'fontsize', fs);
n = n+2;
end
end
end
if isfield(results_all, 'tmp2')
results_all = rmfield(results_all, 'tmp2');
end
if isfield(results_all, 'tmp2')
results_all = rmfield(results_all, 'tmp2');
end
function makeMultiCategoryPlot(f, results, rname, title_str, xtickstep, xticklab, drawline)
fs = 18;
setupplot(f);
if ~isfield(results(1), rname)
return;
end
nobj = numel(results);
rangex = 0;
maxy = 0;
xticks = [];
firsttick = zeros(nobj,1);
for o = 1:nobj
result = results(o);
nres = numel(results(o).(rname));
rangex = rangex(end)+1+(1:nres);
plotapnbars(result.(rname), rangex, drawline);
maxy = max(maxy, round(max(([result.(rname).ap]+0.15))*10)/10);
h=plot(rangex([1 end]), [1 1]*result.pose.ap, 'k--', 'linewidth', 2);
firsttick(o) = rangex(1);
xticks = [xticks rangex(1:xtickstep:end)];
end
maxy = min(maxy, 1);
if numel(xticklab)==nres
xticklab = repmat(xticklab, [1 nobj]);
end
axis([0 rangex(end)+1 0 maxy]);
for o = 1:numel(results)
if strcmp(results(o).name, 'diningtable')
results(o).name = 'diningtable';
elseif strcmp(results(o).name, 'aeroplane')
results(o).name = 'aeroplane';
end
end
title(title_str, 'fontsize', fs);
set(gca, 'xtick', xticks);
ylabel('AP', 'fontsize', fs);
set(gca, 'xticklabel', xticklab);
set(gca, 'ygrid', 'on')
set(gca, 'xgrid', 'on')
set(gca, 'fontsize', fs);
set(gca, 'ticklength', [0.001 0.001]);
function makeMultiCategoryPlotPose(f, results, ...
rname, title_str, xtickstep, xticklab, drawline, error_type)
fs = 18;
setupplot(f);
if ~isfield(results(1), rname)
return;
end
nobj = numel(results);
rangex = 0;
maxy = 0;
xticks = [];
firsttick = zeros(nobj,1);
for o = 1:nobj
result = results(o);
nres = numel(results(o).(rname));
rangex = rangex(end)+1+(1:nres);
plotapnbarspose(result.(rname), rangex, drawline, error_type);
switch error_type
case 1
maxy = max(maxy, round(max(([result.(rname).aos]+0.15))*10)/10);
h=plot(rangex([1 end]), [1 1]*result.aos, 'k--', 'linewidth', 2);
ylabel('AOS', 'fontsize', fs, 'FontWeight', 'bold');
maxy = min(maxy, 1);
case 2
maxy = max(maxy, round(max(([result.(rname).avp15]+0.15))*10)/10);
h=plot(rangex([1 end]), [1 1]*result.avp15, 'k--', 'linewidth', 2);
ylabel('AVP', 'fontsize', fs, 'FontWeight', 'bold');
maxy = min(maxy, 1);
case 3
maxy = max(maxy, round(max(([result.(rname).peap15]+0.15))*10)/10);
h=plot(rangex([1 end]), [1 1]*result.peap15, 'k--', 'linewidth', 2);
ylabel('PEAP', 'fontsize', fs, 'FontWeight', 'bold');
maxy = min(maxy, 1);
case 4
maxy = max(maxy, round(max(([result.(rname).mean_error]))) + 80);
h=plot(rangex([1 end]), [1 1]*result.mean_error, 'k--', 'linewidth', 2);
ylabel('MAE', 'fontsize', fs, 'FontWeight', 'bold');
case 5
maxy = max(maxy, round(max(([result.(rname).median_error]))) + 80);
h=plot(rangex([1 end]), [1 1]*result.median_error, 'k--', 'linewidth', 2);
ylabel('MedError', 'fontsize', fs, 'FontWeight', 'bold');
end
firsttick(o) = rangex(1);
xticks = [xticks rangex(1:xtickstep:end)];
end
if numel(xticklab)==nres
xticklab = repmat(xticklab, [1 nobj]);
end
axis([0 rangex(end)+1 0 maxy]);
title(title_str, 'fontsize', fs);
set(gca, 'xtick', xticks);
set(gca, 'xticklabel', xticklab, 'fontsize', fs);
set(gca, 'ygrid', 'on')
set(gca, 'xgrid', 'on')
set(gca, 'fontsize', fs);
set(gca, 'ticklength', [0.001 0.001]);
function plotapnbars(resall, x, drawline)
fs = 18;
for k = 1:numel(resall)
res =resall(k);
if isnan(res.ap), res.ap = 0; end
if ~isnan(res.ap_stderr)
errorbar(x(k), res.ap, res.ap_stderr, 'r', 'linewidth', 1);
end
hold on;
plot(x(k), res.ap, '+', 'linewidth', 4, 'markersize', 2);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
text(x(k)+0.12, res.ap, sprintf('%0.2f', res.ap), 'fontsize', fs, 'FontWeight', 'bold');
end
if drawline
plot(x, [resall.ap], 'b-', 'linewidth', 4);
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).ap], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
function plotapnbarspose(resall, x, drawline, error_type)
fs = 18;
for k = 1:numel(resall)
res =resall(k);
switch error_type
case 1
err_name = res.aos;
err_name_std = res.aos_stderr;
case 2
err_name = res.avp15;
err_name_std = res.avp_stderr15;
case 3
err_name = res.peap15;
err_name_std = res.peap_stderr15;
case 4
err_name = res.mean_error;
err_name_std = res.std_error;
case 5
err_name = res.median_error;
err_name_std = res.std_error;
end
if isnan(err_name), err_name = 0; end
if ~isnan(res.ap_stderr)
errorbar(x(k), err_name, err_name_std, 'r', 'linewidth', 1);
end
hold on;
plot(x(k), err_name, '+', 'linewidth', 4, 'markersize', 2);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
if (error_type == 1) || (error_type == 2) || (error_type == 3)
text(x(k)+0.12, err_name, sprintf('%0.2f', err_name), 'FontSize', fs, 'FontWeight', 'bold');
else
text(x(k)+0.12, err_name, sprintf('%0.1f', err_name), 'FontSize', fs, 'FontWeight', 'bold');
end
end
switch error_type
case 1
if drawline
plot(x, [resall.aos], 'b-', 'linewidth', 4);
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).aos], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
case 2
if drawline
plot(x, [resall.avp15], 'b-', 'linewidth', 4);
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).avp15], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
case 3
if drawline
plot(x, [resall.peap15], 'b-', 'linewidth', 4);
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).peap15], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
case 4
if drawline
plot(x, [resall.mean_error], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).mean_error], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
case 5
if drawline
plot(x, [resall.median_error], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).median_error], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
end
function str = removeVowels(str)
for v = 'aeiou'
str(str==v) = [];
end
function setupplot(f)
figure(f), hold off
|
github
|
gramuah/pose-errors-master
|
VOCevalseg.m
|
.m
|
pose-errors-master/src/VOCcode/VOCevalseg.m
| 3,330 |
utf_8
|
df4ea45a026fbf0caff5d9fb135af1a2
|
%VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEVALSEG(VOCopts,ID) returns the per class
% percentage ACCURACIES, the average accuracy AVACC and the confusion
% matrix CONF.
%
% [ACCURACIES,AVACC,CONF,RAWCOUNTS] = VOCEVALSEG(VOCopts,ID) also returns
% the unnormalised confusion matrix, which contains raw pixel counts.
function [accuracies,avacc,conf,rawcounts] = VOCevalseg(VOCopts,id)
% image test set
[gtids,t]=textread(sprintf(VOCopts.seg.imgsetpath,VOCopts.testset),'%s %d');
% number of labels = number of classes plus one for the background
num = VOCopts.nclasses+1;
confcounts = zeros(num);
count=0;
tic;
for i=1:length(gtids)
% display progress
if toc>1
fprintf('test confusion: %d/%d\n',i,length(gtids));
drawnow;
tic;
end
imname = gtids{i};
% ground truth label file
gtfile = sprintf(VOCopts.seg.clsimgpath,imname);
[gtim,map] = imread(gtfile);
gtim = double(gtim);
% results file
resfile = sprintf(VOCopts.seg.clsrespath,id,VOCopts.testset,imname);
[resim,map] = imread(resfile);
resim = double(resim);
% Check validity of results image
maxlabel = max(resim(:));
if (maxlabel>VOCopts.nclasses),
error('Results image ''%s'' has out of range value %d (the value should be <= %d)',imname,maxlabel,VOCopts.nclasses);
end
szgtim = size(gtim); szresim = size(resim);
if any(szgtim~=szresim)
error('Results image ''%s'' is the wrong size, was %d x %d, should be %d x %d.',imname,szresim(1),szresim(2),szgtim(1),szgtim(2));
end
%pixel locations to include in computation
locs = gtim<255;
% joint histogram
sumim = 1+gtim+resim*num;
hs = histc(sumim(locs),1:num*num);
count = count + numel(find(locs));
confcounts(:) = confcounts(:) + hs(:);
end
% confusion matrix - first index is true label, second is inferred label
%conf = zeros(num);
conf = 100*confcounts./repmat(1E-20+sum(confcounts,2),[1 size(confcounts,2)]);
rawcounts = confcounts;
% Percentage correct labels measure is no longer being used. Uncomment if
% you wish to see it anyway
%overall_acc = 100*sum(diag(confcounts)) / sum(confcounts(:));
%fprintf('Percentage of pixels correctly labelled overall: %6.3f%%\n',overall_acc);
accuracies = zeros(VOCopts.nclasses,1);
fprintf('Accuracy for each class (intersection/union measure)\n');
for j=1:num
gtj=sum(confcounts(j,:));
resj=sum(confcounts(:,j));
gtjresj=confcounts(j,j);
% The accuracy is: true positive / (true positive + false positive + false negative)
% which is equivalent to the following percentage:
accuracies(j)=100*gtjresj/(gtj+resj-gtjresj);
clname = 'background';
if (j>1), clname = VOCopts.classes{j-1};end;
fprintf(' %14s: %6.3f%%\n',clname,accuracies(j));
end
accuracies = accuracies(1:end);
avacc = mean(accuracies);
fprintf('-------------------------\n');
fprintf('Average accuracy: %6.3f%%\n',avacc);
|
github
|
gramuah/pose-errors-master
|
VOClabelcolormap.m
|
.m
|
pose-errors-master/src/VOCcode/VOClabelcolormap.m
| 691 |
utf_8
|
0bfcd3122e62038f83e2d64f456d556b
|
% VOCLABELCOLORMAP Creates a label color map such that adjacent indices have different
% colors. Useful for reading and writing index images which contain large indices,
% by encoding them as RGB images.
%
% CMAP = VOCLABELCOLORMAP(N) creates a label color map with N entries.
function cmap = labelcolormap(N)
if nargin==0
N=256
end
cmap = zeros(N,3);
for i=1:N
id = i-1; r=0;g=0;b=0;
for j=0:7
r = bitor(r, bitshift(bitget(id,1),7 - j));
g = bitor(g, bitshift(bitget(id,2),7 - j));
b = bitor(b, bitshift(bitget(id,3),7 - j));
id = bitshift(id,-3);
end
cmap(i,1)=r; cmap(i,2)=g; cmap(i,3)=b;
end
cmap = cmap / 255;
|
github
|
gramuah/pose-errors-master
|
VOCwritexml.m
|
.m
|
pose-errors-master/src/VOCcode/VOCwritexml.m
| 1,166 |
utf_8
|
5eee01a8259554f83bf00cf9cf2992a2
|
function VOCwritexml(rec, path)
fid=fopen(path,'w');
writexml(fid,rec,0);
fclose(fid);
function xml = writexml(fid,rec,depth)
fn=fieldnames(rec);
for i=1:length(fn)
f=rec.(fn{i});
if ~isempty(f)
if isstruct(f)
for j=1:length(f)
fprintf(fid,'%s',repmat(char(9),1,depth));
fprintf(fid,'<%s>\n',fn{i});
writexml(fid,rec.(fn{i})(j),depth+1);
fprintf(fid,'%s',repmat(char(9),1,depth));
fprintf(fid,'</%s>\n',fn{i});
end
else
if ~iscell(f)
f={f};
end
for j=1:length(f)
fprintf(fid,'%s',repmat(char(9),1,depth));
fprintf(fid,'<%s>',fn{i});
if ischar(f{j})
fprintf(fid,'%s',f{j});
elseif isnumeric(f{j})&&numel(f{j})==1
fprintf(fid,'%s',num2str(f{j}));
else
error('unsupported type');
end
fprintf(fid,'</%s>\n',fn{i});
end
end
end
end
|
github
|
gramuah/pose-errors-master
|
VOCreadrecxml.m
|
.m
|
pose-errors-master/src/VOCcode/VOCreadrecxml.m
| 1,914 |
utf_8
|
174191a85122cb6b823846389450728e
|
function rec = VOCreadrecxml(path)
x=VOCreadxml(path);
x=x.annotation;
rec=rmfield(x,'object');
rec.size.width=str2double(rec.size.width);
rec.size.height=str2double(rec.size.height);
rec.size.depth=str2double(rec.size.depth);
rec.segmented=strcmp(rec.segmented,'1');
rec.imgname=[x.folder '/JPEGImages/' x.filename];
rec.imgsize=str2double({x.size.width x.size.height x.size.depth});
rec.database=rec.source.database;
for i=1:length(x.object)
rec.objects(i)=xmlobjtopas(x.object(i));
end
function p = xmlobjtopas(o)
p.class=o.name;
if isfield(o,'pose')
if strcmp(o.pose,'Unspecified')
p.view='';
else
p.view=o.pose;
end
else
p.view='';
end
if isfield(o,'truncated')
p.truncated=strcmp(o.truncated,'1');
else
p.truncated=false;
end
if isfield(o,'occluded')
p.occluded=strcmp(o.occluded,'1');
else
p.occluded=false;
end
if isfield(o,'difficult')
p.difficult=strcmp(o.difficult,'1');
else
p.difficult=false;
end
p.label=['PAS' p.class p.view];
if p.truncated
p.label=[p.label 'Trunc'];
end
if p.occluded
p.label=[p.label 'Occ'];
end
if p.difficult
p.label=[p.label 'Diff'];
end
p.orglabel=p.label;
p.bbox=str2double({o.bndbox.xmin o.bndbox.ymin o.bndbox.xmax o.bndbox.ymax});
p.bndbox.xmin=str2double(o.bndbox.xmin);
p.bndbox.ymin=str2double(o.bndbox.ymin);
p.bndbox.xmax=str2double(o.bndbox.xmax);
p.bndbox.ymax=str2double(o.bndbox.ymax);
if isfield(o,'polygon')
warning('polygon unimplemented');
p.polygon=[];
else
p.polygon=[];
end
if isfield(o,'mask')
warning('mask unimplemented');
p.mask=[];
else
p.mask=[];
end
if isfield(o,'part')&&~isempty(o.part)
p.hasparts=true;
for i=1:length(o.part)
p.part(i)=xmlobjtopas(o.part(i));
end
else
p.hasparts=false;
p.part=[];
end
|
github
|
gramuah/pose-errors-master
|
VOCxml2struct.m
|
.m
|
pose-errors-master/src/VOCcode/VOCxml2struct.m
| 1,920 |
utf_8
|
6a873dba4b24c57e9f86a15ee12ea366
|
function res = VOCxml2struct(xml)
xml(xml==9|xml==10|xml==13)=[];
[res,xml]=parse(xml,1,[]);
function [res,ind]=parse(xml,ind,parent)
res=[];
if ~isempty(parent)&&xml(ind)~='<'
i=findchar(xml,ind,'<');
res=trim(xml(ind:i-1));
ind=i;
[tag,ind]=gettag(xml,i);
if ~strcmp(tag,['/' parent])
error('<%s> closed with <%s>',parent,tag);
end
else
while ind<=length(xml)
[tag,ind]=gettag(xml,ind);
if strcmp(tag,['/' parent])
return
else
[sub,ind]=parse(xml,ind,tag);
if isstruct(sub)
if isfield(res,tag)
n=length(res.(tag));
fn=fieldnames(sub);
for f=1:length(fn)
res.(tag)(n+1).(fn{f})=sub.(fn{f});
end
else
res.(tag)=sub;
end
else
if isfield(res,tag)
if ~iscell(res.(tag))
res.(tag)={res.(tag)};
end
res.(tag){end+1}=sub;
else
res.(tag)=sub;
end
end
end
end
end
function i = findchar(str,ind,chr)
i=[];
while ind<=length(str)
if str(ind)==chr
i=ind;
break
else
ind=ind+1;
end
end
function [tag,ind]=gettag(xml,ind)
if ind>length(xml)
tag=[];
elseif xml(ind)=='<'
i=findchar(xml,ind,'>');
if isempty(i)
error('incomplete tag');
end
tag=xml(ind+1:i-1);
ind=i+1;
else
error('expected tag');
end
function s = trim(s)
for i=1:numel(s)
if ~isspace(s(i))
s=s(i:end);
break
end
end
for i=numel(s):-1:1
if ~isspace(s(i))
s=s(1:i);
break
end
end
|
github
|
gramuah/pose-errors-master
|
PASreadrectxt.m
|
.m
|
pose-errors-master/src/VOCcode/PASreadrectxt.m
| 3,179 |
utf_8
|
3b0bdbeb488c8292a1744dace066bb73
|
function record=PASreadrectxt(filename)
[fd,syserrmsg]=fopen(filename,'rt');
if (fd==-1),
PASmsg=sprintf('Could not open %s for reading',filename);
PASerrmsg(PASmsg,syserrmsg);
end;
matchstrs=initstrings;
record=PASemptyrecord;
notEOF=1;
while (notEOF),
line=fgetl(fd);
notEOF=ischar(line);
if (notEOF),
matchnum=match(line,matchstrs);
switch matchnum,
case 1, [imgname]=strread(line,matchstrs(matchnum).str);
record.imgname=char(imgname);
case 2, [x,y,c]=strread(line,matchstrs(matchnum).str);
record.imgsize=[x y c];
case 3, [database]=strread(line,matchstrs(matchnum).str);
record.database=char(database);
case 4, [obj,lbl,xmin,ymin,xmax,ymax]=strread(line,matchstrs(matchnum).str);
record.objects(obj).label=char(lbl);
record.objects(obj).bbox=[min(xmin,xmax),min(ymin,ymax),max(xmin,xmax),max(ymin,ymax)];
case 5, tmp=findstr(line,' : ');
[obj,lbl]=strread(line(1:tmp),matchstrs(matchnum).str);
record.objects(obj).label=char(lbl);
record.objects(obj).polygon=sscanf(line(tmp+3:end),'(%d, %d) ')';
case 6, [obj,lbl,mask]=strread(line,matchstrs(matchnum).str);
record.objects(obj).label=char(lbl);
record.objects(obj).mask=char(mask);
case 7, [obj,lbl,orglbl]=strread(line,matchstrs(matchnum).str);
lbl=char(lbl);
record.objects(obj).label=lbl;
record.objects(obj).orglabel=char(orglbl);
if strcmp(lbl(max(end-8,1):end),'Difficult')
record.objects(obj).difficult=true;
lbl(end-8:end)=[];
else
record.objects(obj).difficult=false;
end
if strcmp(lbl(max(end-4,1):end),'Trunc')
record.objects(obj).truncated=true;
lbl(end-4:end)=[];
else
record.objects(obj).truncated=false;
end
t=find(lbl>='A'&lbl<='Z');
t=t(t>=4);
if ~isempty(t)
record.objects(obj).view=lbl(t(1):end);
lbl(t(1):end)=[];
else
record.objects(obj).view='';
end
record.objects(obj).class=lbl(4:end);
otherwise, %fprintf('Skipping: %s\n',line);
end;
end;
end;
fclose(fd);
return
function matchnum=match(line,matchstrs)
for i=1:length(matchstrs),
matched(i)=strncmp(line,matchstrs(i).str,matchstrs(i).matchlen);
end;
matchnum=find(matched);
if isempty(matchnum), matchnum=0; end;
if (length(matchnum)~=1),
PASerrmsg('Multiple matches while parsing','');
end;
return
function s=initstrings
s(1).matchlen=14;
s(1).str='Image filename : %q';
s(2).matchlen=10;
s(2).str='Image size (X x Y x C) : %d x %d x %d';
s(3).matchlen=8;
s(3).str='Database : %q';
s(4).matchlen=8;
s(4).str='Bounding box for object %d %q (Xmin, Ymin) - (Xmax, Ymax) : (%d, %d) - (%d, %d)';
s(5).matchlen=7;
s(5).str='Polygon for object %d %q (X, Y)';
s(6).matchlen=5;
s(6).str='Pixel mask for object %d %q : %q';
s(7).matchlen=8;
s(7).str='Original label for object %d %q : %q';
return
|
github
|
gramuah/pose-errors-master
|
plotFigure10.m
|
.m
|
pose-errors-master/src/utils/plot_figures/plotFigure10.m
| 6,834 |
utf_8
|
029d97648c31c4be2ed6446dc55aebdc
|
function plotFigure10()
%% plot Figure 10 from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = { 'vdpm','vpskps', '3ddpm','bhf'};
objnames = {'aeroplane', 'bicycle', 'boat', 'bus', 'car', ...
'chair', 'diningtable', 'motorbike', 'sofa', 'train', 'tvmonitor'};
% Visible parts vs pose estimation
for obj = 1: length(objnames)
for d = 1:length(detectors)
tmp(d) = load ([resultDir, '/', detectors{d}, '/', objnames{obj}, ...
'/results_I_', objnames{obj} '_strong.mat']);
end
f=f+1;
tickstr = {};
np=0;
clear results_all;
for o = 1:numel(tmp)
if isfield(tmp(o).result.pose, 'part')
pnames = fieldnames(tmp(o).result.pose.part);
results_all(o).aos = tmp(o).result.pose.aos;
results_all(o).avp = tmp(o).result.pose.avp15;
results_all(o).mean = tmp(o).result.pose.mean_error;
results_all(o).medError = tmp(o).result.pose.median_error;
for p = 1:numel(pnames)
np=np+1;
results_all(o).tmp((p-1)*2+(1:2)) = tmp(o).result.pose.part.(pnames{p});
end
end
end
N = length(detectors);
drawline = false;
xticks = makeMultiCategoryPlotPose(f, results_all, 'tmp', ...
[objnames{obj} ': Visible Parts'], 1, tickstr, drawline, 1, N);
axisval = axis;
n=0;
for o = 1:numel(results_all)
if isfield(tmp(o).result.pose, 'part')
pnames = fieldnames(tmp(o).result.pose.part);
n=n+1;
for p = 1:numel(pnames)
name = pnames{p};
hold on;
text(n+1, -0.051*axisval(4), sprintf('%d', p), 'fontsize', fs-2);
n = n+2;
end
end
end
au = '0/1';
text(xticks(round(numel(pnames) / 2)+2), -0.12*axisval(4), sprintf('%s', au), 'fontsize', fs);
text(xticks(round(numel(pnames) / 2) + 2 + (2*numel(pnames))), -0.12*axisval(4), sprintf('%s', au), 'fontsize', fs);
text(xticks(round(numel(pnames) / 2) + 2 + (4*numel(pnames))), -0.12*axisval(4), sprintf('%s', au), 'fontsize', fs);
text(xticks(round(numel(pnames) / 2) + 2 + (6*numel(pnames))), -0.12*axisval(4), sprintf('%s', au), 'fontsize', fs);
text(xticks(round(numel(pnames) / 2)), 0.3, sprintf('%s', 'VDPM'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (2*numel(pnames))), 0.3, sprintf('%s', 'V&K'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (4*numel(pnames)))-1, 0.3, sprintf('%s', 'DPM+VOC-VP'), 'fontsize', fs, 'FontWeight', 'bold');
text(xticks(round(numel(pnames) / 2) + (6*numel(pnames))), 0.3, sprintf('%s', 'BHF'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
hold off;
end
Nfig = f;
for f = 1:Nfig
print('-dpdf', ['-f' num2str(f)], ...
fullfile(resultDir, sprintf('plot_%d.pdf', f)));
end
close all;
function xticks = makeMultiCategoryPlotPose(f, results, ...
rname, title_str, xtickstep, xticklab, drawline, error_type, N)
fs = 18;
setupplot(f);
if ~isfield(results(1), rname)% || any(isnan([results(1).(rname).apn]))
return;
end
nobj = numel(results);
rangex = 0;
maxy = 0;
xticks = [];
yticks = [];
firsttick = zeros(nobj,1);
for o = 1:nobj
result = results(o);
nres = numel(results(o).(rname));
rangex = rangex(end)+1+(1:nres);
shift_y(o) = result.aos;
plotapnbarspose(result.(rname), rangex, drawline, error_type, shift_y(o));
maxy = max(maxy, round(max(([result.aos]+0.15))*10)/10);
my(o) = max(maxy, round(max(([result.aos]+0.15))*10)/10);
h=plot(rangex([1 end]), [1 1]*result.aos, 'k--', 'linewidth', 2);
hold on;
text(rangex(1)-6, result.aos, sprintf('%0.2f', result.aos), ...
'FontSize', fs, 'FontWeight', 'bold');
maxy = min(maxy, 1);
firsttick(o) = rangex(1);
xticks = [xticks rangex(1:xtickstep:end)];
end
if numel(xticklab)==nres
xticklab = repmat(xticklab, [1 nobj]);
end
axis([0 rangex(end)+1 0 max(my) + 0.05]);
title(title_str, 'fontsize', fs);
set(gca, 'xtick', xticks);
set(gca, 'ytick', 0:10);
ylabel('AOS');
set(gca, 'xticklabel', xticklab, 'fontsize', fs);
set(gca, 'yticklabel', [], 'fontsize', fs)
set(gca, 'ygrid', 'on')
set(gca, 'xgrid', 'on')
set(gca, 'fontsize', fs);
set(gca, 'ticklength', [0.001 0.001]);
function plotapnbarspose(resall, x, drawline, error_type, shift_y)
fs = 18;
for k = 1:numel(resall)
res =resall(k);
switch error_type
case 1
err_name = res.aos;
err_name_std = res.aos_stderr;
case 2
err_name = res.avp;
err_name_std = res.avp_stderr;
case 3
err_name = res.mean_error;
err_name_std = res.std_error;
case 4
err_name = res.median_error;
err_name_std = res.std_error;
end
if isnan(err_name), err_name = 0; end
if ~isnan(res.apn_stderr)
errorbar(x(k), err_name , err_name_std, 'r', 'linewidth', 1);
end
hold on;
plot(x(k), err_name, '+', 'linewidth', 4, 'markersize', 2);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
switch error_type
case 1
if drawline
plot(x, [resall.aos], 'b-', 'linewidth', 4);
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).aos], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
case 2
if drawline
plot(x, [resall.avp], 'b-', 'linewidth', 4);
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).avp] + shift_y, 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
case 3
if drawline
plot(x, [resall.mean_error], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).mean_error] + shift_y, 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
case 4
if drawline
plot(x, [resall.median_error], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).median_error] + shift_y, 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
end
function setupplot(f)
figure(f), hold off
|
github
|
gramuah/pose-errors-master
|
plotFigure9c.m
|
.m
|
pose-errors-master/src/utils/plot_figures/plotFigure9c.m
| 4,647 |
utf_8
|
60ea095bf103f6922a56f6c8bb3956b2
|
function plotFigure9c()
%% plot Figure 9(c) from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = {'vdpm', 'vpskps','3ddpm', 'bhf'};
% Visible parts vs pose estimation
for obj = 1: length(detectors)
tmp(obj) = load ([resultDir, '/', detectors{obj}, '/results_total.mat']);
end
f=f+1;
tickstr = {};
np=0;
clear results_all;
for o = 1:numel(tmp)
pnames = {'ET', 'T', 'W', 'EW'};
results_all(o).aos = mean([tmp(o).resulttotal(:).aos]);
results_all(o).avp = mean([tmp(o).resulttotal(:).avp]);
maxval_a = zeros(1,length(tmp(o).resulttotal(1).extratall));
minval_a = zeros(1,length(tmp(o).resulttotal(1).tall));
maxval_a = zeros(1,length(tmp(o).resulttotal(1).wide));
minval_a = zeros(1,length(tmp(o).resulttotal(1).extrawide));
for mx = 1:length(tmp(o).resulttotal)
maxval_a = maxval_a + tmp(o).resulttotal(mx).extratall;
minval_a = minval_a + tmp(o).resulttotal(mx).tall;
maxval_b = maxval_a + tmp(o).resulttotal(mx).wide;
minval_b = minval_a + tmp(o).resulttotal(mx).extrawide;
end
maxval_a = maxval_a/length(tmp(o).resulttotal);
minval_a = minval_a/length(tmp(o).resulttotal);
maxval_b = maxval_b/length(tmp(o).resulttotal);
minval_b = minval_b/length(tmp(o).resulttotal);
maxval=[results_all(o).aos, results_all(o).aos, results_all(o).aos, results_all(o).aos];
minval=[maxval_a, minval_a,maxval_b, minval_b];
for p = 1:numel(pnames)
results_all(o).tmp((p-1)*2+(1:2)) = [maxval(p), minval(p)];
end
end
N = length(detectors);
drawline = false;
xticks = makeMultiCategoryPlotPose(f, results_all, 'tmp', ...
['Aspect Ratio Influence'], 1, tickstr, drawline, 1, N);
axisval = axis;
n=0;
for o = 1:numel(results_all)
n=n+1;
for p = 1:numel(pnames)
name = pnames{p};
hold on;
text(n+1, -0.071*axisval(4), sprintf('%s', name), 'fontsize', fs, 'fontweight', 'bold');
n = n+2;
end
end
text(xticks(round(numel(pnames) / 2)) , 0.7, sprintf('%s', 'VDPM'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (2*numel(pnames))), 0.7, sprintf('%s', 'V&K'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (4*numel(pnames)))-2, 0.7, sprintf('%s', 'DPM+VOC-VP'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (6*numel(pnames)))+1.5, 0.7, sprintf('%s', 'BHF'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
hold off;
Nfig = f;
for f = 1:Nfig
print('-dpdf', ['-f' num2str(f)], ...
fullfile(resultDir, sprintf('plot_%d.pdf', f)));
end
hold off;
close all;
function xticks = makeMultiCategoryPlotPose(f, results, ...
rname, title_str, xtickstep, xticklab, drawline, error_type, N)
fs = 18;
setupplot(f);
if ~isfield(results(1), rname)
return;
end
nobj = numel(results);
rangex = 0;
maxy = 0;
xticks = [];
yticks = [];
firsttick = zeros(nobj,1);
for o = 1:nobj
result = results(o);
nres = numel(results(o).(rname));
rangex = rangex(end)+1+(1:nres);
shift_y(o) = result.aos;
plotapnbarspose(result.(rname), rangex, drawline, error_type, shift_y(o));
maxy = max(maxy, round(max(([result.aos]+0.15))*10)/10);
my(o) = max(maxy, round(max(([result.aos]+0.15))*10)/10);
hold on;
h=plot(rangex([1 end]), [1 1]*result.aos, 'k--', 'linewidth', 2);
text(rangex(1)-2.5, result.aos, sprintf('%0.2f', result.aos), ...
'FontSize', fs, 'FontWeight', 'bold');
maxy = min(maxy, 1);
firsttick(o) = rangex(1);
xticks = [xticks rangex(1:xtickstep:end)];
end
if numel(xticklab)==nres
xticklab = repmat(xticklab, [1 nobj]);
end
axis([0 rangex(end)+1 0 max(my) + 0.05]);
title(title_str, 'fontsize', fs);
set(gca, 'xtick', xticks);
box on
ylabel('AOS');
set(gca, 'xticklabel', xticklab, 'fontsize', fs);
set(gca, 'yticklabel', [], 'fontsize', fs)
set(gca, 'ygrid', 'on')
set(gca, 'xgrid', 'on')
set(gca, 'fontsize', fs);
set(gca, 'ticklength', [0.001 0.001]);
function plotapnbarspose(resall, x)
fs = 18;
for k = 1:numel(resall)
hold on;
plot(x(k), resall(k), '+', 'linewidth', 4, 'markersize', 2);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
for k = 2:2:numel(resall)
text(x(k)-0.12, resall(k), sprintf('%0.2f', resall(k)), 'FontSize', fs, 'FontWeight', 'bold');
end
for i=1:2:numel(x)-1
plot(x([i i+1]), [resall([i i+1])], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
function setupplot(f)
figure(f), hold off
|
github
|
gramuah/pose-errors-master
|
plotFigure4a.m
|
.m
|
pose-errors-master/src/utils/plot_figures/plotFigure4a.m
| 4,099 |
utf_8
|
b50b1944f9dc8db0abb6e694983fce6f
|
function plotFigure4a()
%% plot Figure 4(a) from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = {'vdpm-gt','vpskps-gt', 'bhf-gt'};
% Visible parts vs pose estimation
for obj = 1: length(detectors)
tmp(obj) = load ([resultDir, '/', detectors{obj}, '/results_total.mat']);
end
f=f+1;
tickstr = {};
np=0;
clear results_all;
for o = 1:numel(tmp)
pnames = {'fr', 're', 'side'};
results_all(o).aos = mean([tmp(o).resulttotal(:).aos]);
results_all(o).avp = mean([tmp(o).resulttotal(:).avp]);
maxval_a = zeros(1,length(tmp(o).resulttotal(1).side_1));
minval_a = zeros(1,length(tmp(o).resulttotal(1).side_2));
for mx = 1:length(tmp(o).resulttotal)
maxval_a = maxval_a + tmp(o).resulttotal(mx).side_1;
minval_a = minval_a + tmp(o).resulttotal(mx).side_2;
end
maxval = maxval_a/length(tmp(o).resulttotal);
minval = minval_a/length(tmp(o).resulttotal);
for p = 1:numel(pnames)
results_all(o).tmp((p-1)*2+(1:2)) = [maxval(p), minval(p)];
end
end
N = length(detectors);
drawline = false;
xticks = makeMultiCategoryPlotPose(f, results_all, 'tmp', ...
['Visible Side Characteristic Overview'], 1, tickstr, drawline, 1, N);
axisval = axis;
n=0;
for o = 1:numel(results_all)
n=n+1;
for p = 1:numel(pnames)
name = pnames{p};
hold on;
text(n+0.5, -0.071*axisval(4), sprintf('%s\n 0/1', name), 'fontsize', fs, 'fontweight', 'bold');
n = n+2;
end
end
text(xticks(round(numel(pnames) / 2)) , 0.2, sprintf('%s', 'VDPM'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (2*numel(pnames)))+1, 0.2, sprintf('%s', 'V&K'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (4*numel(pnames)))+1, 0.2, sprintf('%s', 'BHF'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
hold off;
Nfig = f;
for f = 1:Nfig
print('-dpdf', ['-f' num2str(f)], ...
fullfile(resultDir, sprintf('plot_%d.pdf', f)));
end
hold off;
close all;
function xticks = makeMultiCategoryPlotPose(f, results, ...
rname, title_str, xtickstep, xticklab, drawline, error_type, N)
fs = 18;
setupplot(f);
if ~isfield(results(1), rname)% || any(isnan([results(1).(rname).apn]))
return;
end
nobj = numel(results);
rangex = 0;
maxy = 0;
xticks = [];
yticks = [];
firsttick = zeros(nobj,1);
for o = 1:nobj
result = results(o);
nres = numel(results(o).(rname));
rangex = rangex(end)+1+(1:nres);
shift_y(o) = result.aos;
plotapnbarspose(result.(rname), rangex, drawline, error_type, shift_y(o));
maxy = max(maxy, round(max(([result.aos]+0.15))*10)/10);
my(o) = max(maxy, round(max(([result.aos]+0.15))*10)/10);
hold on;
h=plot(rangex([1 end]), [1 1]*result.aos, 'k--', 'linewidth', 2);
text(rangex(1)-2.5, result.aos, sprintf('%0.2f', result.aos), ...
'FontSize', fs, 'FontWeight', 'bold');
%ylabel('AOS', 'fontsize', fs, 'FontWeight', 'bold');
maxy = min(maxy, 1);
firsttick(o) = rangex(1);
xticks = [xticks rangex(1:xtickstep:end)];
end
if numel(xticklab)==nres
xticklab = repmat(xticklab, [1 nobj]);
end
axis([0 rangex(end)+1 0 max(my) + 0.05]);
title(title_str, 'fontsize', fs);
set(gca, 'xtick', xticks);
%set(gca, 'ytick', 0:10);
box on
ylabel('AOS');
set(gca, 'xticklabel', xticklab, 'fontsize', fs);
set(gca, 'yticklabel', [], 'fontsize', fs)
set(gca, 'ygrid', 'on')
set(gca, 'xgrid', 'on')
set(gca, 'fontsize', fs);
set(gca, 'ticklength', [0.001 0.001]);
function plotapnbarspose(resall, x)
fs = 18;
for k = 1:numel(resall)
hold on;
plot(x(k), resall(k), '+', 'linewidth', 4, 'markersize', 2);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
text(x(k)+0.12, resall(k), sprintf('%0.2f', resall(k)), 'FontSize', fs, 'FontWeight', 'bold');
end
for i=1:2:numel(x)-1
plot(x([i i+1]), [resall([i i+1])], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
function setupplot(f)
figure(f), hold off
|
github
|
gramuah/pose-errors-master
|
plotFigure9b.m
|
.m
|
pose-errors-master/src/utils/plot_figures/plotFigure9b.m
| 4,716 |
utf_8
|
83ef7d0ecc26b31b2a73badb85ef0142
|
function plotFigure9b()
%% plot Figure 9(b) from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = {'vdpm','vpskps', '3ddpm', 'bhf'};
% Visible parts vs pose estimation
for obj = 1: length(detectors)
tmp(obj) = load ([resultDir, '/', detectors{obj}, '/results_total.mat']);
end
f=f+1;
tickstr = {};
np=0;
clear results_all;
for o = 1:numel(tmp)
pnames = {'ES', 'S', 'L', 'EL'};
results_all(o).aos = mean([tmp(o).resulttotal(:).aos]);
results_all(o).avp = mean([tmp(o).resulttotal(:).avp]);
maxval_a = zeros(1,length(tmp(o).resulttotal(1).extrasmall));
minval_a = zeros(1,length(tmp(o).resulttotal(1).small));
maxval_a = zeros(1,length(tmp(o).resulttotal(1).large));
minval_a = zeros(1,length(tmp(o).resulttotal(1).extralarge));
for mx = 1:length(tmp(o).resulttotal)
maxval_a = maxval_a + tmp(o).resulttotal(mx).extrasmall;
minval_a = minval_a + tmp(o).resulttotal(mx).small;
maxval_b = maxval_a + tmp(o).resulttotal(mx).large;
minval_b = minval_a + tmp(o).resulttotal(mx).extralarge;
end
maxval_a = maxval_a/length(tmp(o).resulttotal);
minval_a = minval_a/length(tmp(o).resulttotal);
maxval_b = maxval_b/length(tmp(o).resulttotal);
minval_b = minval_b/length(tmp(o).resulttotal);
maxval=[results_all(o).aos, results_all(o).aos, results_all(o).aos, results_all(o).aos];
minval=[maxval_a, minval_a,maxval_b, minval_b];
for p = 1:numel(pnames)
results_all(o).tmp((p-1)*2+(1:2)) = [maxval(p), minval(p)];
end
end
N = length(detectors);
drawline = false;
xticks = makeMultiCategoryPlotPose(f, results_all, 'tmp', ...
['Object Size Influence'], 1, tickstr, drawline, 1, N);
axisval = axis;
n=0;
for o = 1:numel(results_all)
n=n+1;
for p = 1:numel(pnames)
name = pnames{p};
hold on;
text(n+1, -0.071*axisval(4), sprintf('%s', name), 'fontsize', fs, 'fontweight', 'bold');
n = n+2;
end
end
text(xticks(round(numel(pnames) / 2)) , 0.7, sprintf('%s', 'VDPM'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (2*numel(pnames))), 0.7, sprintf('%s', 'V&K'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (4*numel(pnames)))-2, 0.7, sprintf('%s', 'DPM+VOC-VP'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (6*numel(pnames)))+1.5, 0.7, sprintf('%s', 'BHF'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
hold off;
Nfig = f;
for f = 1:Nfig
print('-dpdf', ['-f' num2str(f)], ...
fullfile(resultDir, sprintf('plot_%d.pdf', f)));
end
hold off;
close all;
function xticks = makeMultiCategoryPlotPose(f, results, ...
rname, title_str, xtickstep, xticklab, drawline, error_type, N)
fs = 18;
setupplot(f);
if ~isfield(results(1), rname)
return;
end
nobj = numel(results);
rangex = 0;
maxy = 0;
xticks = [];
yticks = [];
firsttick = zeros(nobj,1);
for o = 1:nobj
result = results(o);
nres = numel(results(o).(rname));
rangex = rangex(end)+1+(1:nres);
shift_y(o) = result.aos;
plotapnbarspose(result.(rname), rangex, drawline, error_type, shift_y(o));
maxy = max(maxy, round(max(([result.aos]+0.15))*10)/10);
my(o) = max(maxy, round(max(([result.aos]+0.15))*10)/10);
hold on;
h=plot(rangex([1 end]), [1 1]*result.aos, 'k--', 'linewidth', 2);
text(rangex(1)-2.5, result.aos, sprintf('%0.2f', result.aos), ...
'FontSize', fs, 'FontWeight', 'bold');
maxy = min(maxy, 1);
firsttick(o) = rangex(1);
xticks = [xticks rangex(1:xtickstep:end)];
end
if numel(xticklab)==nres
xticklab = repmat(xticklab, [1 nobj]);
end
axis([0 rangex(end)+1 0 max(my) + 0.05]);
title(title_str, 'fontsize', fs);
set(gca, 'xtick', xticks);
%set(gca, 'ytick', 0:10);
box on
ylabel('AOS');
set(gca, 'xticklabel', xticklab, 'fontsize', fs);
set(gca, 'yticklabel', [], 'fontsize', fs)
set(gca, 'ygrid', 'on')
set(gca, 'xgrid', 'on')
set(gca, 'fontsize', fs);
set(gca, 'ticklength', [0.001 0.001]);
function plotapnbarspose(resall, x, drawline, error_type, shift_y)
fs = 18;
for k = 1:numel(resall)
hold on;
plot(x(k), resall(k), '+', 'linewidth', 4, 'markersize', 2);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
for k = 2:2:numel(resall)
text(x(k)-0.12, resall(k), sprintf('%0.2f', resall(k)), 'FontSize', fs, 'FontWeight', 'bold');
end
for i=1:2:numel(x)-1
plot(x([i i+1]), [resall([i i+1])], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
function setupplot(f)
figure(f), hold off
|
github
|
gramuah/pose-errors-master
|
plotFigure5.m
|
.m
|
pose-errors-master/src/utils/plot_figures/plotFigure5.m
| 6,499 |
utf_8
|
ed30bc380ffcbf3d1de53920c67a86f5
|
function plotFigure5()
%% plot Figure 5 from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = { 'vdpm-gt','vpskps-gt','bhf-gt'};
objnames = {'aeroplane', 'bicycle', 'boat', 'bus', 'car', ...
'chair', 'diningtable', 'motorbike', 'sofa', 'train', 'tvmonitor'};
% Visible parts vs pose estimation
for obj = 1: length(objnames)
for d = 1:length(detectors)
tmp(d) = load ([resultDir, '/', detectors{d}, '/', objnames{obj}, ...
'/results_I_', objnames{obj} '_strong.mat']);
end
f=f+1;
tickstr = {};
np=0;
clear results_all;
for o = 1:numel(tmp)
if isfield(tmp(o).result.pose, 'part')
pnames = fieldnames(tmp(o).result.pose.part);
results_all(o).aos = tmp(o).result.pose.aos;
results_all(o).avp = tmp(o).result.pose.avp15;
results_all(o).mean = tmp(o).result.pose.mean_error;
results_all(o).medError = tmp(o).result.pose.median_error;
for p = 1:numel(pnames)
np=np+1;
results_all(o).tmp((p-1)*2+(1:2)) = tmp(o).result.pose.part.(pnames{p});
end
end
end
N = length(detectors);
drawline = false;
xticks = makeMultiCategoryPlotPose(f, results_all, 'tmp', ...
[objnames{obj} ': Visible Parts'], 1, tickstr, drawline, 1, N);
axisval = axis;
n=0;
for o = 1:numel(results_all)
if isfield(tmp(o).result.pose, 'part')
pnames = fieldnames(tmp(o).result.pose.part);
n=n+1;
for p = 1:numel(pnames)
name = pnames{p};
hold on;
text(n+1, -0.051*axisval(4), sprintf('%d', p), 'fontsize', fs-2);
n = n+2;
end
end
end
au = '0/1';
text(xticks(round(numel(pnames) / 2)+2), -0.12*axisval(4), sprintf('%s', au), 'fontsize', fs);
text(xticks(round(numel(pnames) / 2) + 2 + (2*numel(pnames))), -0.12*axisval(4), sprintf('%s', au), 'fontsize', fs);
text(xticks(round(numel(pnames) / 2) + 2 + (4*numel(pnames))), -0.12*axisval(4), sprintf('%s', au), 'fontsize', fs);
text(xticks(round(numel(pnames) / 2)), 0.3, sprintf('%s', 'VDPM'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (2*numel(pnames))), 0.3, sprintf('%s', 'V&K'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (4*numel(pnames))), 0.3, sprintf('%s', 'BHF'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
hold off;
end
Nfig = f;
for f = 1:Nfig
print('-dpdf', ['-f' num2str(f)], ...
fullfile(resultDir, sprintf('plot_%d.pdf', f)));
end
close all;
function xticks = makeMultiCategoryPlotPose(f, results, ...
rname, title_str, xtickstep, xticklab, drawline, error_type, N)
fs = 18;
setupplot(f);
if ~isfield(results(1), rname)% || any(isnan([results(1).(rname).apn]))
return;
end
nobj = numel(results);
rangex = 0;
maxy = 0;
xticks = [];
yticks = [];
firsttick = zeros(nobj,1);
for o = 1:nobj
result = results(o);
nres = numel(results(o).(rname));
rangex = rangex(end)+1+(1:nres);
shift_y(o) = result.aos;
plotapnbarspose(result.(rname), rangex, drawline, error_type, shift_y(o));
maxy = max(maxy, round(max(([result.aos]+0.15))*10)/10);
my(o) = max(maxy, round(max(([result.aos]+0.15))*10)/10);
h=plot(rangex([1 end]), [1 1]*result.aos, 'k--', 'linewidth', 2);
hold on;
text(rangex(1)-6, result.aos, sprintf('%0.2f', result.aos), ...
'FontSize', fs, 'FontWeight', 'bold');
maxy = min(maxy, 1);
firsttick(o) = rangex(1);
xticks = [xticks rangex(1:xtickstep:end)];
end
if numel(xticklab)==nres
xticklab = repmat(xticklab, [1 nobj]);
end
axis([0 rangex(end)+1 0 max(my) + 0.05]);
title(title_str, 'fontsize', fs);
set(gca, 'xtick', xticks);
set(gca, 'ytick', 0:10);
ylabel('AOS');
set(gca, 'xticklabel', xticklab, 'fontsize', fs);
set(gca, 'yticklabel', [], 'fontsize', fs)
set(gca, 'ygrid', 'on')
set(gca, 'xgrid', 'on')
set(gca, 'fontsize', fs);
set(gca, 'ticklength', [0.001 0.001]);
function plotapnbarspose(resall, x, drawline, error_type, shift_y)
fs = 18;
for k = 1:numel(resall)
res =resall(k);
switch error_type
case 1
err_name = res.aos;
err_name_std = res.aos_stderr;
case 2
err_name = res.avp;
err_name_std = res.avp_stderr;
case 3
err_name = res.mean_error;
err_name_std = res.std_error;
case 4
err_name = res.median_error;
err_name_std = res.std_error;
end
if isnan(err_name), err_name = 0; end
if ~isnan(res.apn_stderr)
errorbar(x(k), err_name , err_name_std, 'r', 'linewidth', 1);
end
hold on;
plot(x(k), err_name, '+', 'linewidth', 4, 'markersize', 2);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
switch error_type
case 1
if drawline
plot(x, [resall.aos], 'b-', 'linewidth', 4);
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).aos], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
case 2
if drawline
plot(x, [resall.avp], 'b-', 'linewidth', 4);
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).avp] + shift_y, 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
case 3
if drawline
plot(x, [resall.mean_error], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).mean_error] + shift_y, 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
case 4
if drawline
plot(x, [resall.median_error], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
else % draw every other
for i=1:2:numel(x)
plot(x([i i+1]), [resall([i i+1]).median_error] + shift_y, 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
end
end
function setupplot(f)
figure(f), hold off
|
github
|
gramuah/pose-errors-master
|
plotFigure4c.m
|
.m
|
pose-errors-master/src/utils/plot_figures/plotFigure4c.m
| 4,593 |
utf_8
|
078fde124eb8a63a7dcbe0a1577947bf
|
function plotFigure4c()
%% plot Figure 4(c) from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = {'vdpm-gt', 'vpskps-gt', 'bhf-gt'};
% Visible parts vs pose estimation
for obj = 1: length(detectors)
tmp(obj) = load ([resultDir, '/', detectors{obj}, '/results_total.mat']);
end
f=f+1;
tickstr = {};
np=0;
clear results_all;
for o = 1:numel(tmp)
pnames = {'ET', 'T', 'W', 'EW'};
results_all(o).aos = mean([tmp(o).resulttotal(:).aos]);
results_all(o).avp = mean([tmp(o).resulttotal(:).avp]);
maxval_a = zeros(1,length(tmp(o).resulttotal(1).extratall));
minval_a = zeros(1,length(tmp(o).resulttotal(1).tall));
maxval_a = zeros(1,length(tmp(o).resulttotal(1).wide));
minval_a = zeros(1,length(tmp(o).resulttotal(1).extrawide));
for mx = 1:length(tmp(o).resulttotal)
maxval_a = maxval_a + tmp(o).resulttotal(mx).extratall;
minval_a = minval_a + tmp(o).resulttotal(mx).tall;
maxval_b = maxval_a + tmp(o).resulttotal(mx).wide;
minval_b = minval_a + tmp(o).resulttotal(mx).extrawide;
end
maxval_a = maxval_a/length(tmp(o).resulttotal);
minval_a = minval_a/length(tmp(o).resulttotal);
maxval_b = maxval_b/length(tmp(o).resulttotal);
minval_b = minval_b/length(tmp(o).resulttotal);
maxval=[results_all(o).aos, results_all(o).aos, results_all(o).aos, results_all(o).aos];
minval=[maxval_a, minval_a,maxval_b, minval_b];
for p = 1:numel(pnames)
results_all(o).tmp((p-1)*2+(1:2)) = [maxval(p), minval(p)];
end
end
N = length(detectors);
drawline = false;
xticks = makeMultiCategoryPlotPose(f, results_all, 'tmp', ...
['Aspect Ratio Influence'], 1, tickstr, drawline, 1, N);
axisval = axis;
n=0;
for o = 1:numel(results_all)
n=n+1;
for p = 1:numel(pnames)
name = pnames{p};
hold on;
text(n+1, -0.071*axisval(4), sprintf('%s', name), 'fontsize', fs, 'fontweight', 'bold');
n = n+2;
end
end
text(xticks(round(numel(pnames) / 2)) , 0.2, sprintf('%s', 'VDPM'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (2*numel(pnames)))+1, 0.2, sprintf('%s', 'V&K'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (4*numel(pnames)))+2, 0.2, sprintf('%s', 'BHF'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
hold off;
Nfig = f;
for f = 1:Nfig
print('-dpdf', ['-f' num2str(f)], ...
fullfile(resultDir, sprintf('plot_%d.pdf', f)));
end
hold off;
close all;
function xticks = makeMultiCategoryPlotPose(f, results, ...
rname, title_str, xtickstep, xticklab, drawline, error_type, N)
fs = 18;
setupplot(f);
if ~isfield(results(1), rname)% || any(isnan([results(1).(rname).apn]))
return;
end
nobj = numel(results);
rangex = 0;
maxy = 0;
xticks = [];
yticks = [];
firsttick = zeros(nobj,1);
for o = 1:nobj
result = results(o);
nres = numel(results(o).(rname));
rangex = rangex(end)+1+(1:nres);
shift_y(o) = result.aos;
plotapnbarspose(result.(rname), rangex, drawline, error_type, shift_y(o));
maxy = max(maxy, round(max(([result.aos]+0.15))*10)/10);
my(o) = max(maxy, round(max(([result.aos]+0.15))*10)/10);
hold on;
h=plot(rangex([1 end]), [1 1]*result.aos, 'k--', 'linewidth', 2);
text(rangex(1)-2.5, result.aos, sprintf('%0.2f', result.aos), ...
'FontSize', fs, 'FontWeight', 'bold');
%ylabel('AOS', 'fontsize', fs, 'FontWeight', 'bold');
maxy = min(maxy, 1);
firsttick(o) = rangex(1);
xticks = [xticks rangex(1:xtickstep:end)];
end
if numel(xticklab)==nres
xticklab = repmat(xticklab, [1 nobj]);
end
axis([0 rangex(end)+1 0 max(my) + 0.05]);
title(title_str, 'fontsize', fs);
set(gca, 'xtick', xticks);
box on
ylabel('AOS');
set(gca, 'xticklabel', xticklab, 'fontsize', fs);
set(gca, 'yticklabel', [], 'fontsize', fs)
set(gca, 'ygrid', 'on')
set(gca, 'xgrid', 'on')
set(gca, 'fontsize', fs);
set(gca, 'ticklength', [0.001 0.001]);
function plotapnbarspose(resall, x)
fs = 18;
for k = 1:numel(resall)
hold on;
plot(x(k), resall(k), '+', 'linewidth', 4, 'markersize', 2);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
for k = 2:2:numel(resall)
text(x(k)-0.12, resall(k), sprintf('%0.2f', resall(k)), 'FontSize', fs, 'FontWeight', 'bold');
end
for i=1:2:numel(x)-1
plot(x([i i+1]), [resall([i i+1])], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
function setupplot(f)
figure(f), hold off
|
github
|
gramuah/pose-errors-master
|
plotFigure4b.m
|
.m
|
pose-errors-master/src/utils/plot_figures/plotFigure4b.m
| 4,566 |
utf_8
|
d189abcb9012e29ad8400c07fe4a1493
|
function plotFigure4b()
%% plot Figure 4(b) from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = {'vdpm-gt','vpskps-gt', 'bhf-gt'};
% Visible parts vs pose estimation
for obj = 1: length(detectors)
tmp(obj) = load ([resultDir, '/', detectors{obj}, '/results_total.mat']);
end
f=f+1;
tickstr = {};
np=0;
clear results_all;
for o = 1:numel(tmp)
pnames = {'ES', 'S', 'L', 'EL'};
results_all(o).aos = mean([tmp(o).resulttotal(:).aos]);
results_all(o).avp = mean([tmp(o).resulttotal(:).avp]);
maxval_a = zeros(1,length(tmp(o).resulttotal(1).extrasmall));
minval_a = zeros(1,length(tmp(o).resulttotal(1).small));
maxval_a = zeros(1,length(tmp(o).resulttotal(1).large));
minval_a = zeros(1,length(tmp(o).resulttotal(1).extralarge));
for mx = 1:length(tmp(o).resulttotal)
maxval_a = maxval_a + tmp(o).resulttotal(mx).extrasmall;
minval_a = minval_a + tmp(o).resulttotal(mx).small;
maxval_b = maxval_a + tmp(o).resulttotal(mx).large;
minval_b = minval_a + tmp(o).resulttotal(mx).extralarge;
end
maxval_a = maxval_a/length(tmp(o).resulttotal);
minval_a = minval_a/length(tmp(o).resulttotal);
maxval_b = maxval_b/length(tmp(o).resulttotal);
minval_b = minval_b/length(tmp(o).resulttotal);
maxval=[results_all(o).aos, results_all(o).aos, results_all(o).aos, results_all(o).aos];
minval=[maxval_a, minval_a,maxval_b, minval_b];
for p = 1:numel(pnames)
results_all(o).tmp((p-1)*2+(1:2)) = [maxval(p), minval(p)];
end
end
N = length(detectors);
drawline = false;
xticks = makeMultiCategoryPlotPose(f, results_all, 'tmp', ...
['Object Size Influence'], 1, tickstr, drawline, 1, N);
axisval = axis;
n=0;
for o = 1:numel(results_all)
n=n+1;
for p = 1:numel(pnames)
name = pnames{p};
hold on;
text(n+1, -0.071*axisval(4), sprintf('%s', name), 'fontsize', fs, 'fontweight', 'bold');
n = n+2;
end
end
text(xticks(round(numel(pnames) / 2)) , 0.2, sprintf('%s', 'VDPM'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (2*numel(pnames)))+1, 0.2, sprintf('%s', 'V&K'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (4*numel(pnames)))+1, 0.2, sprintf('%s', 'BHF'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
hold off;
Nfig = f;
for f = 1:Nfig
print('-dpdf', ['-f' num2str(f)], ...
fullfile(resultDir, sprintf('plot_%d.pdf', f)));
end
hold off;
close all;
function xticks = makeMultiCategoryPlotPose(f, results, ...
rname, title_str, xtickstep, xticklab, drawline, error_type, N)
fs = 18;
setupplot(f);
if ~isfield(results(1), rname)
return;
end
nobj = numel(results);
rangex = 0;
maxy = 0;
xticks = [];
yticks = [];
firsttick = zeros(nobj,1);
for o = 1:nobj
result = results(o);
nres = numel(results(o).(rname));
rangex = rangex(end)+1+(1:nres);
shift_y(o) = result.aos;
plotapnbarspose(result.(rname), rangex, drawline, error_type, shift_y(o));
maxy = max(maxy, round(max(([result.aos]+0.15))*10)/10);
my(o) = max(maxy, round(max(([result.aos]+0.15))*10)/10);
hold on;
h=plot(rangex([1 end]), [1 1]*result.aos, 'k--', 'linewidth', 2);
text(rangex(1)-2.5, result.aos, sprintf('%0.2f', result.aos), ...
'FontSize', fs, 'FontWeight', 'bold');
maxy = min(maxy, 1);
firsttick(o) = rangex(1);
xticks = [xticks rangex(1:xtickstep:end)];
end
if numel(xticklab)==nres
xticklab = repmat(xticklab, [1 nobj]);
end
axis([0 rangex(end)+1 0 max(my) + 0.05]);
title(title_str, 'fontsize', fs);
set(gca, 'xtick', xticks);
%set(gca, 'ytick', 0:10);
box on
ylabel('AOS');
set(gca, 'xticklabel', xticklab, 'fontsize', fs);
set(gca, 'yticklabel', [], 'fontsize', fs)
set(gca, 'ygrid', 'on')
set(gca, 'xgrid', 'on')
set(gca, 'fontsize', fs);
set(gca, 'ticklength', [0.001 0.001]);
function plotapnbarspose(resall, x, drawline, error_type, shift_y)
fs = 18;
for k = 1:numel(resall)
hold on;
plot(x(k), resall(k), '+', 'linewidth', 4, 'markersize', 2);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
for k = 2:2:numel(resall)
text(x(k)-0.12, resall(k), sprintf('%0.2f', resall(k)), 'FontSize', fs, 'FontWeight', 'bold');
end
for i=1:2:numel(x)-1
plot(x([i i+1]), [resall([i i+1])], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
function setupplot(f)
figure(f), hold off
|
github
|
gramuah/pose-errors-master
|
plotFigure9a.m
|
.m
|
pose-errors-master/src/utils/plot_figures/plotFigure9a.m
| 4,149 |
utf_8
|
c29f1498294dfcc7870ca22632824264
|
function plotFigure9a()
%% plot Figure 9(a) from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = {'vdpm','vpskps','3ddpm', 'bhf'};
% Visible parts vs pose estimation
for obj = 1: length(detectors)
tmp(obj) = load ([resultDir, '/', detectors{obj}, '/results_total.mat']);
end
f=f+1;
tickstr = {};
np=0;
clear results_all;
for o = 1:numel(tmp)
pnames = {'fr', 're', 'side'};
results_all(o).aos = mean([tmp(o).resulttotal(:).aos]);
results_all(o).avp = mean([tmp(o).resulttotal(:).avp]);
maxval_a = zeros(1,length(tmp(o).resulttotal(1).side_1));
minval_a = zeros(1,length(tmp(o).resulttotal(1).side_2));
for mx = 1:length(tmp(o).resulttotal)
maxval_a = maxval_a + tmp(o).resulttotal(mx).side_1;
minval_a = minval_a + tmp(o).resulttotal(mx).side_2;
end
maxval = maxval_a/length(tmp(o).resulttotal);
minval = minval_a/length(tmp(o).resulttotal);
for p = 1:numel(pnames)
results_all(o).tmp((p-1)*2+(1:2)) = [maxval(p), minval(p)];
end
end
N = length(detectors);
drawline = false;
xticks = makeMultiCategoryPlotPose(f, results_all, 'tmp', ...
['Visible Side Characteristic Overview'], 1, tickstr, drawline, 1, N);
axisval = axis;
n=0;
for o = 1:numel(results_all)
n=n+1;
for p = 1:numel(pnames)
name = pnames{p};
hold on;
text(n+0.5, -0.071*axisval(4), sprintf('%s\n 0/1', name), 'fontsize', fs, 'fontweight', 'bold');
n = n+2;
end
end
text(xticks(round(numel(pnames) / 2)) , 0.7, sprintf('%s', 'VDPM'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (2*numel(pnames))), 0.7, sprintf('%s', 'V&K'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (4*numel(pnames)))-2, 0.7, sprintf('%s', 'DPM+VOC-VP'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
text(xticks(round(numel(pnames) / 2) + (6*numel(pnames)))+1.5, 0.7, sprintf('%s', 'BHF'), 'fontsize', fs, 'FontWeight', 'bold', 'Color', 'red');
hold off;
Nfig = f;
for f = 1:Nfig
print('-dpdf', ['-f' num2str(f)], ...
fullfile(resultDir, sprintf('plot_%d.pdf', f)));
end
hold off;
close all;
function xticks = makeMultiCategoryPlotPose(f, results, ...
rname, title_str, xtickstep, xticklab, drawline, error_type, N)
fs = 18;
setupplot(f);
if ~isfield(results(1), rname)
return;
end
nobj = numel(results);
rangex = 0;
maxy = 0;
xticks = [];
yticks = [];
firsttick = zeros(nobj,1);
for o = 1:nobj
result = results(o);
nres = numel(results(o).(rname));
rangex = rangex(end)+1+(1:nres);
shift_y(o) = result.aos;
plotapnbarspose(result.(rname), rangex, drawline, error_type, shift_y(o));
maxy = max(maxy, round(max(([result.aos]+0.15))*10)/10);
my(o) = max(maxy, round(max(([result.aos]+0.15))*10)/10);
hold on;
h=plot(rangex([1 end]), [1 1]*result.aos, 'k--', 'linewidth', 2);
text(rangex(1)-2.5, result.aos, sprintf('%0.2f', result.aos), ...
'FontSize', fs, 'FontWeight', 'bold');
maxy = min(maxy, 1);
firsttick(o) = rangex(1);
xticks = [xticks rangex(1:xtickstep:end)];
end
if numel(xticklab)==nres
xticklab = repmat(xticklab, [1 nobj]);
end
axis([0 rangex(end)+1 0 max(my) + 0.05]);
title(title_str, 'fontsize', fs);
set(gca, 'xtick', xticks);
%set(gca, 'ytick', 0:10);
box on
ylabel('AOS');
set(gca, 'xticklabel', xticklab, 'fontsize', fs);
set(gca, 'yticklabel', [], 'fontsize', fs)
set(gca, 'ygrid', 'on')
set(gca, 'xgrid', 'on')
set(gca, 'fontsize', fs);
set(gca, 'ticklength', [0.001 0.001]);
function plotapnbarspose(resall, x)
fs = 18;
for k = 1:numel(resall)
hold on;
plot(x(k), resall(k), '+', 'linewidth', 4, 'markersize', 2);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
text(x(k)+0.12, resall(k), sprintf('%0.2f', resall(k)), 'FontSize', fs, 'FontWeight', 'bold');
end
for i=1:2:numel(x)-1
plot(x([i i+1]), [resall([i i+1])], 'b-', 'linewidth', 4);
set(gca, 'fontsize', fs, 'FontWeight', 'bold');
end
function setupplot(f)
figure(f), hold off
|
github
|
kstrotz/URToolbox-master
|
installURToolbox.m
|
.m
|
URToolbox-master/installURToolbox.m
| 10,069 |
utf_8
|
7fad6ecb89fa082153fe4d8db039c4b5
|
function installURToolbox(replaceExisting)
% INSTALLURTOOLBOX installs UR Toolbox for MATLAB.
% INSTALLURTOOLBOX installs UR Toolbox into the following
% locations:
% Source: Destination
% URToolboxFunctions: matlabroot\toolbox\optitrack
% URToolboxSupport: matlabroot\toolbox\optitrack\OptiTrackToolboxSupport
%
% INSTALLURTOOLBOX(true) installs UR Toolbox regardless of
% whether a copy of the UR toolbox exists in the MATLAB root.
%
% INSTALLURTOOLBOX(false) installs UR Toolbox only if no copy
% of the UR toolbox exists in the MATLAB root.
%
% M. Kutzer 17Feb2016, USNA
% Updates
% TODO - Allow users to create a local version if admin rights are not
% possible.
%% Install/Update required toolboxes
ToolboxUpdate('Transformation');
ToolboxUpdate('Plotting');
%% Assign tool/toolbox specific parameters
dirName = 'ur';
%% Check inputs
if nargin == 0
replaceExisting = [];
end
%% Installation error solution(s)
adminSolution = sprintf(...
['Possible solution:\n',...
'\t(1) Close current instance of MATLAB\n',...
'\t(2) Open a new instance of MATLAB "as administrator"\n',...
'\t\t(a) Locate MATLAB shortcut\n',...
'\t\t(b) Right click\n',...
'\t\t(c) Select "Run as administrator"\n']);
%% Check for toolbox directory
toolboxRoot = fullfile(matlabroot,'toolbox',dirName);
isToolbox = exist(toolboxRoot,'file');
if isToolbox == 7
% Apply replaceExisting argument
if isempty(replaceExisting)
choice = questdlg(sprintf(...
['MATLAB Root already contains the UR Toolbox.\n',...
'Would you like to replace the existing toolbox?']),...
'Yes','No');
elseif replaceExisting
choice = 'Yes';
else
choice = 'No';
end
% Replace existing or cancel installation
switch choice
case 'Yes'
% TODO - check if NatNet SDK components are running and close
% them prior to removing directory
rmpath(toolboxRoot);
[isRemoved, msg, msgID] = rmdir(toolboxRoot,'s');
if isRemoved
fprintf('Previous version of UR Toolbox removed successfully.\n');
else
fprintf('Failed to remove old UR Toolbox folder:\n\t"%s"\n',toolboxRoot);
fprintf(adminSolution);
error(msgID,msg);
end
case 'No'
fprintf('UR Toolbox currently exists, installation cancelled.\n');
return
case 'Cancel'
fprintf('Action cancelled.\n');
return
otherwise
error('Unexpected response.');
end
end
%% Create Scorbot Toolbox Path
[isDir,msg,msgID] = mkdir(toolboxRoot);
if isDir
fprintf('UR toolbox folder created successfully:\n\t"%s"\n',toolboxRoot);
else
fprintf('Failed to create UR Toolbox folder:\n\t"%s"\n',toolboxRoot);
fprintf(adminSolution);
error(msgID,msg);
end
%% Migrate toolbox folder contents
toolboxContent = 'URToolboxFunctions';
if ~isdir(toolboxContent)
error(sprintf(...
['Change your working directory to the location of "installURToolbox.m".\n',...
'\n',...
'If this problem persists:\n',...
'\t(1) Unzip your original download of "URToolbox" into a new directory\n',...
'\t(2) Open a new instance of MATLAB "as administrator"\n',...
'\t\t(a) Locate MATLAB shortcut\n',...
'\t\t(b) Right click\n',...
'\t\t(c) Select "Run as administrator"\n',...
'\t(3) Change your "working directory" to the location of "installURToolbox.m"\n',...
'\t(4) Enter "installURToolbox" (without quotes) into the command window\n',...
'\t(5) Press Enter.']));
end
files = dir(toolboxContent);
wb = waitbar(0,'Copying UR Toolbox toolbox contents...');
n = numel(files);
fprintf('Copying UR Toolbox contents:\n');
for i = 1:n
% source file location
source = fullfile(toolboxContent,files(i).name);
% destination location
destination = toolboxRoot;
if files(i).isdir
switch files(i).name
case '.'
%Ignore
case '..'
%Ignore
otherwise
fprintf('\t%s...',files(i).name);
nDestination = fullfile(destination,files(i).name);
[isDir,msg,msgID] = mkdir(nDestination);
if isDir
[isCopy,msg,msgID] = copyfile(source,nDestination,'f');
if isCopy
fprintf('[Complete]\n');
else
bin = msg == char(10);
msg(bin) = [];
bin = msg == char(13);
msg(bin) = [];
fprintf('[Failed: "%s"]\n',msg);
end
else
bin = msg == char(10);
msg(bin) = [];
bin = msg == char(13);
msg(bin) = [];
fprintf('[Failed: "%s"]\n',msg);
end
end
else
fprintf('\t%s...',files(i).name);
[isCopy,msg,msgID] = copyfile(source,destination,'f');
if isCopy == 1
fprintf('[Complete]\n');
else
bin = msg == char(10);
msg(bin) = [];
bin = msg == char(13);
msg(bin) = [];
fprintf('[Failed: "%s"]\n',msg);
end
end
waitbar(i/n,wb);
end
set(wb,'Visible','off');
%% Migrate toolbox support folder
pythonRoot = fullfile('C:\Python34\Lib');
toolboxContent = 'URToolboxSupport';
if ~isdir(toolboxContent)
error(sprintf(...
['Change your working directory to the location of "installURToolbox.m".\n',...
'\n',...
'If this problem persists:\n',...
'\t(1) Unzip your original download of "URToolbox" into a new directory\n',...
'\t(2) Open a new instance of MATLAB "as administrator"\n',...
'\t\t(a) Locate MATLAB shortcut\n',...
'\t\t(b) Right click\n',...
'\t\t(c) Select "Run as administrator"\n',...
'\t(3) Change your "working directory" to the location of "installURToolbox.m"\n',...
'\t(4) Enter "installURToolbox" (without quotes) into the command window\n',...
'\t(5) Press Enter.']));
end
files = dir(toolboxContent);
wb = waitbar(0,'Copying UR Toolbox toolbox contents...');
n = numel(files);
fprintf('Copying UR Toolbox contents:\n');
for i = 1:n
% source file location
source = fullfile(toolboxContent,files(i).name);
% destination location
destination = pythonRoot;
if files(i).isdir
switch files(i).name
case '.'
%Ignore
case '..'
%Ignore
otherwise
fprintf('\t%s...',files(i).name);
nDestination = fullfile(destination,files(i).name);
[isDir,msg,msgID] = mkdir(nDestination);
if isDir
[isCopy,msg,msgID] = copyfile(source,nDestination,'f');
if isCopy
fprintf('[Complete]\n');
else
bin = msg == char(10);
msg(bin) = [];
bin = msg == char(13);
msg(bin) = [];
fprintf('[Failed: "%s"]\n',msg);
end
else
bin = msg == char(10);
msg(bin) = [];
bin = msg == char(13);
msg(bin) = [];
fprintf('[Failed: "%s"]\n',msg);
end
end
else
fprintf('\t%s...',files(i).name);
[isCopy,msg,msgID] = copyfile(source,destination,'f');
if isCopy == 1
fprintf('[Complete]\n');
else
bin = msg == char(10);
msg(bin) = [];
bin = msg == char(13);
msg(bin) = [];
fprintf('[Failed: "%s"]\n',msg);
end
end
waitbar(i/n,wb);
end
set(wb,'Visible','off');
%% Save toolbox path
%addpath(genpath(toolboxRoot),'-end');
addpath(toolboxRoot,'-end');
savepath;
%% Rehash toolbox cache
fprintf('Rehashing Toolbox Cache...');
rehash TOOLBOXCACHE
fprintf('[Complete]\n');
end
function ToolboxUpdate(toolboxName)
%% Setup functions
ToolboxVer = str2func( sprintf('%sToolboxVer',toolboxName) );
installToolbox = str2func( sprintf('install%sToolbox',toolboxName) );
%% Check current version
try
A = ToolboxVer;
catch ME
A = [];
fprintf('No previous version of %s detected.\n',toolboxName);
end
%% Setup temporary file directory
fprintf('Downloading the %s Toolbox...',toolboxName);
tmpFolder = sprintf('%sToolbox',toolboxName);
pname = fullfile(tempdir,tmpFolder);
%% Download and unzip toolbox (GitHub)
if (strcmp(toolboxName,'Transformation')) || (strcmp(toolboxName,'Plotting'))
url = sprintf('https://github.com/kutzer/%sToolbox/archive/master.zip',toolboxName);
else
url = sprintf('https://github.com/kstrotz/%sToolbox/archive/master.zip',toolboxName);
end
try
fnames = unzip(url,pname);
fprintf('SUCCESS\n');
confirm = true;
catch
confirm = false;
end
%% Check for successful download
if ~confirm
error('InstallToolbox:FailedDownload','Failed to download updated version of %s Toolbox.',toolboxName);
end
%% Find base directory
install_pos = strfind(fnames, sprintf('install%sToolbox.m',toolboxName) );
sIdx = cell2mat( install_pos );
cIdx = ~cell2mat( cellfun(@isempty,install_pos,'UniformOutput',0) );
pname_star = fnames{cIdx}(1:sIdx-1);
%% Get current directory and temporarily change path
cpath = cd;
cd(pname_star);
%% Install ScorBot Toolbox
installToolbox(true);
%% Move back to current directory and remove temp file
cd(cpath);
[ok,msg] = rmdir(pname,'s');
if ~ok
warning('Unable to remove temporary download folder. %s',msg);
end
%% Complete installation
fprintf('Installation complete.\n');
end
|
github
|
kstrotz/URToolbox-master
|
URToolboxUpdate.m
|
.m
|
URToolbox-master/URToolboxFunctions/URToolboxUpdate.m
| 1,675 |
utf_8
|
f9676ebb610a475bb41b827d331033ad
|
function URToolboxUpdate
% URTOOLBOXUPDATE download and update the UR Toolbox.
%
% M. Kutzer 27Feb2016, USNA
% TODO - Find a location for "URToolbox Example SCRIPTS"
% TODO - update function for general operation
% Install UR Toolbox
ToolboxUpdate('UR');
end
function ToolboxUpdate(toolboxName)
%% Setup functions
ToolboxVer = str2func( sprintf('%sToolboxVer',toolboxName) );
installToolbox = str2func( sprintf('install%sToolbox',toolboxName) );
%% Check current version
A = ToolboxVer;
%% Setup temporary file directory
fprintf('Downloading the %s Toolbox...',toolboxName);
tmpFolder = sprintf('%sToolbox',toolboxName);
pname = fullfile(tempdir,tmpFolder);
%% Download and unzip toolbox (GitHub)
url = sprintf('https://github.com/kstrotz/%sToolbox/archive/master.zip',toolboxName);
try
fnames = unzip(url,pname);
fprintf('SUCCESS\n');
confirm = true;
catch
confirm = false;
end
%% Check for successful download
if ~confirm
error('InstallToolbox:FailedDownload','Failed to download updated version of %s Toolbox.',toolboxName);
end
%% Find base directory
install_pos = strfind(fnames, sprintf('install%sToolbox.m',toolboxName) );
sIdx = cell2mat( install_pos );
cIdx = ~cell2mat( cellfun(@isempty,install_pos,'UniformOutput',0) );
pname_star = fnames{cIdx}(1:sIdx-1);
%% Get current directory and temporarily change path
cpath = cd;
cd(pname_star);
%% Install ScorBot Toolbox
installToolbox(true);
%% Move back to current directory and remove temp file
cd(cpath);
[ok,msg] = rmdir(pname,'s');
if ~ok
warning('Unable to remove temporary download folder. %s',msg);
end
%% Complete installation
fprintf('Installation complete.\n');
end
|
github
|
Mark-Kramer/Spike-Ripple-Detector-Method-master
|
spike_ripple_detector.m
|
.m
|
Spike-Ripple-Detector-Method-master/spike_ripple_detector.m
| 12,942 |
utf_8
|
8552c4cead1053e7c0b1f5b34f70d65e
|
% Spike-ripple detector.
% Developed by Catherine Chu, Arthur Chan, and Mark Kramer.
%
% INPUTS:
% data = the time series data, in this case EEG from one electrode.
% time = the time axis for the data, in units of seconds.
% ADVANCED INPUTS:
% varargin = 'PercentileEnvelope', value
% set 'value' between 0 and 1 to choose the
% envelope threshold. If this parameter is not specified, the default
% envelope threshold of 0.85 is used.
% varargin = 'SharedMaxMin', [value1, value2]
% where value1 = max_min_threshold
% value2 = peak_threshold
%
% OUTPUT:
% res = structure that holds each candidate spike-ripple event.
% res.INPOS %Start time [s]
% res.FIPOS %End time [s]
% res.LEN %Duration [s]
% res.freq %Frequency of ripple [Hz].
% res.zc %Zero-crossing of ripple [Hz]
% res.fano %Fano factor of ripple.
% res.Lhite %Difference between spike peak and start of interval.
% res.Rhite %Difference between spike peak and end of interval.
% res.Ctime %Time difference between start of ripple and start of spike.
% res.Vpeak %Peak voltage of spike.
% diagnostics = structure that holds method diagnostics (see code).
%
% DEPENDENCIES:
% findseq.m developed by Oleg Komarov.
function [res,diagnostics] = spike_ripple_detector(data,time, varargin)
Fs = 1/(time(2)-time(1)); %Sampling frequency.
res = {}; %Structure to store results.
fprintf('Design the filter ... \n')
fNQ = Fs/2; %Define Nyquist frequeuncy.
d = fdesign.bandpass('Fst1,Fp1,Fp2,Fst2,Ast1,Ap,Ast2',...
(60)/fNQ,... %Freq @ edge first stop band.
(100)/fNQ,... %Freq @ edge of start of passband.
(300)/fNQ,... %Freq @ edge of end of passband
(350)/fNQ,... %Freq @ edge second stop band
80,... %Attenuation in the first stop band in decibels
0.1,... %Amount of ripple allowed in the pass band.
40); %Attenuation in the second stop band in decibels
Hd = design(d,'equiripple'); %Design the filter
[num, den] = tf(Hd); %Convert filter to numerator, denominator expression.
order = length(num); %Get filter order.
diagnostics.num = num;
diagnostics.den = den;
diagnostics.order = order;
dfilt = filter(num, den, data); %Filter the data.
dfilt = [dfilt(floor(order/2+1):end); zeros(floor(order/2),1)]; %Shift after filtering.
if any(isnan(dfilt)) % If there's a nan in the data,
nan_dfilt = find(isnan(dfilt)); % ... find nans,
temp_for_hilbert = dfilt; % ... make dfilt for hilbert,
temp_for_hilbert(nan_dfilt)=0; % ... and put 0s at nans
amp = abs(hilbert(temp_for_hilbert)); %Compute amplitude envelope.
else
amp = abs(hilbert(dfilt)); %Compute amplitude envelope.
end
if any(strcmp(varargin, 'PercentileEnvelope')) %Choose envelope threshold (ADVANCED)
i0 = 1+find(strcmp(varargin, 'PercentileEnvelope'));
percentile_envelope = varargin{i0}; %... as input,
else %... or,
percentile_envelope = 0.85; %... as default envelope threshold.
end
threshold = quantile(amp, percentile_envelope); %Set amplitude threshold.
fprintf(['Percentile envelope = ' num2str(percentile_envelope) ' ... \n'])
diagnostics.threshold = threshold; %... save as diagnoistic to return.
if any(strcmp(varargin, 'SharedMaxMin')) %Choose max&min threshold (ADVANCED)
i0 = 1+find(strcmp(varargin, 'SharedMaxMin')); %... as input
thresholds = varargin{i0};
max_min_threshold = thresholds(1);
peak_threshold = thresholds(2);
else % ... or get a sampling of max-start values.
n_max_min = 10000; %For 10,000 resamples,
N_time = length(data);
win_max_min = round(0.050*Fs); %... and window interval of 50 ms,
max_min_distribution = zeros(n_max_min,1); %... create a surrogate distribution,
parfor n=1:n_max_min %... for each surrogate,
istart=randi(N_time-win_max_min); %... choose a random time index.
%... compute max value - value @ start of interval.
max_min_distribution(n) = max(data(istart:istart+win_max_min-1))-data(istart);
end
percentile_max_and_peaks = 0.95; %Set max & peak threshold,
%Get threshold for max-start values.
max_min_threshold = quantile(max_min_distribution, percentile_max_and_peaks);
%Get threshold for max voltage values.
peak_threshold = quantile(data, percentile_max_and_peaks);
end
binary_above = amp > threshold;
above = find(amp > threshold); %Find amp's above threshold.
t_separation = 0.005; %Set small time seperation to 5 ms,
%... and merge small separations.
small_separation = find(diff(above) > 1 & diff(above) < round(t_separation*Fs));
for js=0:length(small_separation)-1
ileft = small_separation(end-js);
iright = ileft+1;
binary_above(above(ileft):above(iright))=1;
end
above=find(binary_above);
[VALUES, INPOS, FIPOS, LEN] = findseq(diff(above));
i_values=find(VALUES==1); %Locate sequences of value=1.
diagnostics.sequences_above = length(i_values);
if ~isempty(i_values) %If we find sequences of 1's,
INPOS = INPOS(i_values); %...save start index of each sequence,
FIPOS = FIPOS(i_values); %...save end index of each sequence,
LEN = LEN(i_values); %...and save length.
INPOS = time(above(INPOS)); %Convert detections to TIME.
FIPOS = time(above(FIPOS));
LEN = LEN/Fs;
long_enough = find(LEN > 0.02); %Find intervals > 20 ms.
diagnostics.number_long_enough = length(long_enough);
if ~isempty(long_enough) %If we find intervals > 20 ms,
fprintf(['Found sequences > 20 ms ... \n' ])
INPOS=INPOS(long_enough); %... get those intervals.
FIPOS=FIPOS(long_enough);
LEN =LEN(long_enough);
%Find intervals away from first/last time index.
away_from_edges = find(INPOS > 1 & FIPOS < time(end)-1);
INPOS=INPOS(away_from_edges);
FIPOS=FIPOS(away_from_edges);
LEN =LEN(away_from_edges);
% For each interval, compute zero-crossings, frequency, fano, left-right-max value, and time of max.
zc = zeros(length(INPOS),1);
freq = zeros(length(INPOS),1);
fano = zeros(length(INPOS),1);
Lhite = zeros(length(INPOS),1);
Rhite = zeros(length(INPOS),1);
Ctime = zeros(length(INPOS),1);
Vpeak = zeros(length(INPOS),1);
parfor k=1:length(INPOS) % Find candidate HFO interval.
good = find(time >= INPOS(k) & time < FIPOS(k));
d0 = dfilt(good); % Get filtered data.
d0 = d0 - mean(d0); % Subtract mean.
d0((d0>0))=1; % Set values > 0 equal to 1.
d0((d0<0))=0; % Set values < 0 equal to 0.
zc0 = find(diff(d0)==1); % ZC when transition from 0 to 1.
zc(k)=length(zc0); % Count ZC.
ISI0 = diff(zc0); % Distance between ZC.
freq(k)=mean(1/(mean(ISI0)/Fs)); % Approx freq.
fano(k)=var(ISI0)/mean(ISI0); % Fano factor of of zero crossing times.
center_time = mean(time(good)); % Center time of window.
good = find(time >= center_time-0.05 & time < center_time+0.05); %+/- 50 ms around center.
dorig = data(good); % Get unfiltered data.
dorig = smooth(dorig,11); % Smooth it.
[mx, imx] = max(dorig); % Find max.
Lhite(k) = mx-dorig(1); % Difference between max & left (or start) of interval.
Rhite(k) = mx-dorig(end); % Difference betweem max & right (or end) of interval.
Ctime(k) = INPOS(k) - time(good(imx)); % Time from ripple start to max
Vpeak(k) = mx; % Max values.
end
% Find intervals that pass tests.
threshold_fano = 1; %Fix Fano threshold.
%To classify as a spike-ripple detection, must have:
good = find(zc >= 3 ... % At least 3 ZC.
& fano < threshold_fano ... % Fano < 1.
& Lhite > max_min_threshold ... % Max - start value > threshold.
& Rhite > max_min_threshold ... % Max - end value > threshold.
& Ctime < 0 ... % Ripple begin before peak.
& Vpeak > peak_threshold); % Max > threshold.
end
% Save candidate spike-ripple events.
INPOS=INPOS(good);
FIPOS=FIPOS(good);
LEN =LEN(good);
zc = zc(good);
freq = freq(good);
fano = fano(good);
Lhite = Lhite(good);
Rhite = Rhite(good);
Ctime = Ctime(good);
Vpeak = Vpeak(good);
diagnostics.number_detections = length(good);
%Sort detections by starting time.
[~, isort] = sort(INPOS, 'ascend');
INPOS = INPOS(isort);
FIPOS = FIPOS(isort);
LEN = LEN(isort);
freq = freq(isort);
zc = zc(isort);
fano = fano(isort);
Lhite = Lhite(isort);
Rhite = Rhite(isort);
Ctime = Ctime(isort);
Vpeak = Vpeak(isort);
fprintf(['Candidate spike-ripple events = ' num2str(length(good)) ' ... \n' ])
%Save the results for each candidate spike-ripple event.
res.INPOS = INPOS; %Start time [s]
res.FIPOS = FIPOS; %End time [s]
res.LEN = LEN; %Duration [s]
res.freq = freq; %Frequency of ripple [Hz].
res.zc = zc; %Zero-crossing of ripple [Hz]
res.fano = fano; %Fano factor of ripple.
res.Lhite = Lhite; %Difference between spike peak and start of interval.
res.Rhite = Rhite; %Difference between spike peak and end of interval.
res.Ctime = Ctime; %Time difference between start of ripple and start of spike.
res.Vpeak = Vpeak; %Peak voltage of spike.
end
end
|
github
|
Mark-Kramer/Spike-Ripple-Detector-Method-master
|
spike_ripple_visualizer.m
|
.m
|
Spike-Ripple-Detector-Method-master/spike_ripple_visualizer.m
| 5,754 |
utf_8
|
9fddc4b8bfa03634727d213351a8345c
|
% Function to visualize and classify the candidate spike ripple events
% detected in spike_ripple_detector.m
%
% This function produces a figure showing the (1) the original data, (2)
% the filtered data, and (3) the spectrogram surrounding each candidate spike ripple
% event.
%
% For each candidate spike ripple event, the user enters "y" for YES or "n"
% for NO at the command line. The results of these classifications are then
% returned in the output.
% INPUTS.
% data = the LFP data (same as for spike_ripple_detector.m)
% time = the time axis (same as for spike_ripple_detector.m)
% res0 = the 1st output of spike_ripple_detector.m
% diagnostics = the 2nd output of spike_ripple_detector.m
% OUTPUTS.
% expert_classify = the classification of each candidate spike ripple
% event as 'y' or 'n'.
function [expert_classify] = spike_ripple_visualizer(data, time, res0, diagnostics)
Fs = 1/(time(10)-time(9));
num = diagnostics.num;
den = diagnostics.den;
order = diagnostics.order;
dfilt = filter(num, den, data); %Filter it.
dfilt = [dfilt(floor(order/2)+1:end); zeros(floor(order/2),1)]; %Shift after filtering.
n_detections = length(res0.INPOS);
counter = 1;
for k=1:n_detections
INPOS = res0.INPOS(k);
FIPOS = res0.FIPOS(k);
LEN = res0.LEN(k);
freq = res0.freq(k);
zc = res0.zc(k);
fano = res0.fano(k);
Lhite = res0.Lhite(k);
Rhite = res0.Rhite(k);
Ctime = res0.Ctime(k);
Vpeak = res0.Vpeak(k);
igood = find(time > INPOS-0.5 & time < FIPOS+0.5);
t = time(igood);
dat = data(igood);
datf = dfilt(igood);
dspec = data(igood)-mean(data(igood));
params.Fs = Fs; % Sampling frequency [Hz]
params.fpass = [30 250]; % Frequencies to visualize in spectra [Hz]
movingwin = [0.200,0.005]; % Window size, Step size [s]
params.tEDF = t;
[S,S_times,S_freq] = hannspecgramc(dspec,movingwin,params);
%Smooth the spectra.
t_smooth = 11;
dt_S = S_times(2)-S_times(1);
myfilter = fspecial('gaussian',[1 t_smooth], 1);
if k==1
fprintf(['Smooth spectra over +/- ' num2str((t_smooth-1)/2*(dt_S)*1000,3) ' ms \n'])
end
S_smooth = imfilter(S, myfilter, 'replicate'); % Smooth the spectrum.
res{counter}.INPOS = INPOS;
res{counter}.FIPOS = FIPOS;
res{counter}.LEN = LEN;
res{counter}.freq = freq;
res{counter}.zc = zc;
res{counter}.fano = fano;
res{counter}.Lhite = Lhite;
res{counter}.Rhite = Rhite;
res{counter}.Ctime = Ctime;
res{counter}.Vpeak = Vpeak;
res{counter}.threshold = diagnostics.threshold;
res{counter}.t = t;
res{counter}.data = dat;
res{counter}.dfilt = datf;
res{counter}.S_smooth = S_smooth;
res{counter}.S_times = S_times;
res{counter}.S_freq = S_freq;
res{counter}.k = k;
counter = counter + 1;
end
%% Visualize
expert_classify = cell(length(res),1);
ek = 1;
while ek <= length(res)
INPOS = res{ek}.INPOS;
FIPOS = res{ek}.FIPOS;
LEN = res{ek}.LEN;
freq = res{ek}.freq;
zc = res{ek}.zc;
fano = res{ek}.fano;
Lhite = res{ek}.Lhite;
Rhite = res{ek}.Rhite;
Ctime = res{ek}.Ctime;
Vpeak = res{ek}.Vpeak;
data = res{ek}.data;
dfilt = res{ek}.dfilt;
amp = abs(hilbert(dfilt));
t = res{ek}.t;
threshold = res{ek}.threshold;
S_times = res{ek}.S_times;
S_freq = res{ek}.S_freq;
S_smooth = res{ek}.S_smooth;
t0 = 0;
t = t-t0;
INPOS = INPOS - t0;
FIPOS = FIPOS - t0;
S_times = S_times - t0;
subplot(3,1,1)
plot(t, data)
ax = axis;
axis tight
hold on
plot([INPOS INPOS], [ax(3) ax(4)], 'k')
plot([FIPOS FIPOS], [ax(3) ax(4)], 'k')
hold off
title(['# ' num2str(length(INPOS)) ', @ ' num2str(k) ...
' DUR ' num2str(LEN*1000,3) ', FQ ' num2str(freq,3) ...
', LHT ' num2str(Lhite,3) ', RHT ' num2str(Rhite,3) ', CT ' num2str(Ctime,3)...
', VPK ' num2str(Vpeak,3) ...
', ZC ' num2str(zc) ', FF ' num2str(fano,2)])
subplot(3,1,2)
plot(t, dfilt)
hold on
plot(t, threshold*ones(size(t)), 'k')
axis tight
ax = axis;
plot([INPOS INPOS], [ax(3) ax(4)], 'k')
plot([FIPOS FIPOS], [ax(3) ax(4)], 'k')
hold off
%Compute the spectrogram.
subplot(3,1,3)
colormap(jet)
imagesc(S_times,S_freq,log10(S_smooth)') %, [-3 -0.5])
axis xy
ax = axis;
hold on
plot([INPOS INPOS], [ax(3) ax(4)], 'k')
plot([FIPOS FIPOS], [ax(3) ax(4)], 'k')
hold off
xlim([INPOS-0.5, FIPOS+0.5])
input0 = input(['Event ' num2str(ek) ' of ' num2str(length(res)) ', Is there an HFO? y/[n]/b: '], 's');
switch input0
case 'b'
ek = ek-1; fprintf(['going back one to ' num2str(ek) '\n'])
case '' %If you enter nothing,
expert_classify{ek,1} = 'n'; %... it's "no".
ek = ek+1;
otherwise
expert_classify{ek,1} = input0;
ek = ek+1;
end
end
end
|
github
|
Mark-Kramer/Spike-Ripple-Detector-Method-master
|
findseq.m
|
.m
|
Spike-Ripple-Detector-Method-master/findseq.m
| 5,921 |
utf_8
|
21500cb2129c3e3e6c06539f219a9c9a
|
function varargout = findseq(A,dim)
% FINDSEQ Find sequences of repeated (adjacent/consecutive) numeric values
%
% FINDSEQ(A) Find sequences of repeated numeric values in A along the
% first non-singleton dimension. A should be numeric.
%
% FINDSEQ(...,DIM) Look for sequences along the dimension specified by the
% positive integer scalar DIM.
%
% OUT = findseq(...)
% OUT is a "m by 4" numeric matrix where m is the number of sequences found.
%
% Each sequence has 4 columns where:
% - 1st col.: the value being repeated
% - 2nd col.: the position of the first value of the sequence
% - 3rd col.: the position of the last value of the sequence
% - 4th col.: the length of the sequence
%
% [VALUES, INPOS, FIPOS, LEN] = findseq(...)
% Get OUT as separate outputs.
%
% If no sequences are found no value is returned.
% To convert positions into subs/coordinates use IND2SUB
%
%
% Examples:
%
% % There are sequences of 20s, 1s and NaNs (column-wise)
% A = [ 20, 19, 3, 2, NaN, NaN
% 20, 23, 1, 1, 1, NaN
% 20, 7, 7, NaN, 1, NaN]
%
% OUT = findseq(A)
% OUT =
% 20 1 3 3
% 1 14 15 2
% NaN 16 18 3
%
% % 3D sequences: NaN, 6 and 0
% A = [ 1, 4
% NaN, 5
% 3, 6];
% A(:,:,2) = [ 0, 0
% NaN, 0
% 0, 6];
% A(:,:,3) = [ 1, 0
% 2, 5
% 3, 6];
%
% OUT = findseq(A,3)
% OUT =
% 6 6 18 3
% 0 10 16 2
% NaN 2 8 2
%
% Additional features:
% - <a href="matlab: web('http://www.mathworks.com/matlabcentral/fileexchange/28113','-browser')">FEX findseq page</a>
% - <a href="matlab: web('http://www.mathworks.com/matlabcentral/fileexchange/6436','-browser')">FEX rude by us page</a>
%
% See also: DIFF, FIND, SUB2IND, IND2SUB
% Author: Oleg Komarov ([email protected])
% Tested on R14SP3 (7.1) and on R2012a. In-between compatibility is assumed.
% 02 jul 2010 - Created
% 05 jul 2010 - Reorganized code and fixed bug when concatenating results
% 12 jul 2010 - Per Xiaohu's suggestion fixed bug in output dimensions when A is row vector
% 26 aug 2010 - Cast double on logical instead of single
% 28 aug 2010 - Per Zachary Danziger's suggestion reorganized check structure to avoid bug when concatenating results
% 22 mar 2012 - Per Herbert Gsenger's suggestion fixed bug in matching initial and final positions; minor change to distribution of OUT if multiple outputs; added 3D example
% 08 nov 2013 - Fixed major bug in the sorting of Final position that relied on regularity conditions not always verified
% NINPUTS
%error(narginchk(1,2,nargin));
% NOUTPUTS
%error(nargoutchk(0,4,nargout));
% IN
if ~isnumeric(A)
error('findseq:fmtA', 'A should be numeric')
elseif isempty(A) || isscalar(A)
varargout{1} = [];
return
elseif islogical(A)
A = double(A);
end
% DIM
szA = size(A);
if nargin == 1 || isempty(dim)
% First non singleton dimension
dim = find(szA ~= 1,1,'first');
elseif ~(isnumeric(dim) && dim > 0 && rem(dim,1) == 0) || dim > numel(szA)
error('findseq:fmtDim', 'DIM should be a scalar positive integer <= ndims(A)');
end
% Less than two elements along DIM
if szA(dim) == 1
varargout{1} = [];
return
end
% ISVECTOR
if nnz(szA ~= 1) == 1
A = A(:);
dim = 1;
szA = size(A);
end
% Detect 0, NaN, Inf and -Inf
OtherValues = cell(1,4);
OtherValues{1} = A == 0;
OtherValues{2} = isnan(A) ;
OtherValues{3} = A == Inf;
OtherValues{4} = A == -Inf;
Values = [0,NaN, Inf,-Inf];
% Remove zeros
A(OtherValues{1}) = NaN;
% Make the bread
bread = NaN([szA(1:dim-1),1,szA(dim+1:end)]);
% [1] Get chunks of "normal" values
Out = mainengine(A,bread,dim,szA);
% [2] Get chunks of 0, NaN, Inf and -Inf
for c = 1:4
if nnz(OtherValues{c}) > 1
% Logical to double and NaN padding
OtherValues{c} = double(OtherValues{c});
OtherValues{c}(~OtherValues{c}) = NaN;
% Call mainengine and concatenate results
tmp = mainengine(OtherValues{c}, bread,dim,szA);
if ~isempty(tmp)
Out = [Out; [repmat(Values(c),size(tmp,1),1) tmp(:,2:end)]]; %#ok
end
end
end
% Distribute output
if nargout < 2
varargout = {Out};
else
varargout = num2cell(Out(:,1:nargout),1);
end
end
% MAINENGINE This functions uses run length encoding and retrieve positions
function Out = mainengine(meat,bread,dim,szMeat)
% Make a sandwich
sandwich = cat(dim, bread, meat, bread);
% Find chunks (run length encoding engine)
IDX = diff(diff(sandwich,[],dim) == 0,[],dim);
% Initial and final row/col subscripts
[rIn, cIn] = find(IDX == 1);
[rFi, cFi] = find(IDX == -1);
% Make sure row/col subs correspond (relevant if dim > 1)
[In, idx] = sortrows([rIn, cIn],1);
Fi = [rFi, cFi];
Fi = Fi(idx,:);
% Calculate length of blocks
if dim < 3
Le = Fi(:,dim) - In(:,dim) + 1;
else
md = prod(szMeat(2:dim-1));
Le = (Fi(:,2) - In(:,2))/md + 1;
end
% Convert to linear index
InPos = sub2ind(szMeat,In(:,1),In(:,2));
FiPos = sub2ind(szMeat,Fi(:,1),Fi(:,2));
% Assign output
Out = [meat(InPos),... % Values
InPos ,... % Initial positions
FiPos ,... % Final positions
Le ]; % Length of the blocks
end
|
github
|
dschick/udkm1DsimML-master
|
bool2str.m
|
.m
|
udkm1DsimML-master/helpers/functions/bool2str.m
| 185 |
utf_8
|
7a576a35879a9481b26e8782d0918d2b
|
%% bool2str
% Returns the according string for a boolean input.
function str = bool2str(bool)
if bool
str = 'true';
else
str = 'false';
end%if
end%function
|
github
|
dschick/udkm1DsimML-master
|
dataHash.m
|
.m
|
udkm1DsimML-master/helpers/functions/dataHash.m
| 15,297 |
utf_8
|
8592e240500ffb55915cf0df7bffbd33
|
function Hash = dataHash(Data, Opt)
%% DATAHASH - Checksum for Matlab array of any type
% This function creates a hash value for an input of any type. The type and
% dimensions of the input are considered as default, such that UINT8([0,0]) and
% UINT16(0) have different hash values. Nested STRUCTs and CELLs are parsed
% recursively.
%
% Hash = DataHash(Data, Opt)
% INPUT:
% Data: Array of these built-in types:
% (U)INT8/16/32/64, SINGLE, DOUBLE, (real or complex)
% CHAR, LOGICAL, CELL (nested), STRUCT (scalar or array, nested),
% function_handle.
% Opt: Struct to specify the hashing algorithm and the output format.
% Opt and all its fields are optional.
% Opt.Method: String, known methods for Java 1.6 (Matlab 2009a):
% 'SHA-1', 'SHA-256', 'SHA-384', 'SHA-512', 'MD2', 'MD5'.
% Known methods for Java 1.3 (Matlab 6.5):
% 'MD5', 'SHA-1'.
% Default: 'MD5'.
% Opt.Format: String specifying the output format:
% 'hex', 'HEX': Lower/uppercase hexadecimal string.
% 'double', 'uint8': Numerical vector.
% 'base64': Base64 encoded string, only printable
% ASCII characters, 33% shorter than 'hex'.
% Default: 'hex'.
% Opt.Input: Type of the input as string, not case-sensitive:
% 'array': The contents, type and size of the input [Data] are
% considered for the creation of the hash. Nested CELLs
% and STRUCT arrays are parsed recursively. Empty arrays of
% different type reply different hashs.
% 'file': [Data] is treated as file name and the hash is calculated
% for the files contents.
% 'bin': [Data] is a numerical, LOGICAL or CHAR array. Only the
% binary contents of the array is considered, such that
% e.g. empty arrays of different type reply the same hash.
% Default: 'array'.
%
% OUTPUT:
% Hash: String, DOUBLE or UINT8 vector. The length depends on the hashing
% method.
%
% EXAMPLES:
% % Default: MD5, hex:
% DataHash([]) % 7de5637fd217d0e44e0082f4d79b3e73
% % MD5, Base64:
% Opt.Format = 'base64';
% Opt.Method = 'MD5';
% DataHash(int32(1:10), Opt) % bKdecqzUpOrL4oxzk+cfyg
% % SHA-1, Base64:
% S.a = uint8([]);
% S.b = {{1:10}, struct('q', uint64(415))};
% Opt.Method = 'SHA-1';
% DataHash(S, Opt) % ZMe4eUAp0G9TDrvSW0/Qc0gQ9/A
% % SHA-1 of binary values:
% Opt.Method = 'SHA-1';
% Opt.Input = 'bin';
% DataHash(1:8, Opt) % 826cf9d3a5d74bbe415e97d4cecf03f445f69225
%
% NOTE:
% Function handles and user-defined objects cannot be converted uniquely:
% - The subfunction ConvertFuncHandle uses the built-in function FUNCTIONS,
% but the replied struct can depend on the Matlab version.
% - It is tried to convert objects to UINT8 streams in the subfunction
% ConvertObject. A conversion by STRUCT() might be more appropriate.
% Adjust these subfunctions on demand.
%
% MATLAB CHARs have 16 bits! In consequence the string 'hello' is treated as
% UINT16('hello') for the binary input method.
%
% DataHash uses James Tursa's smart and fast TYPECASTX, if it is installed:
% http://www.mathworks.com/matlabcentral/fileexchange/17476
% As fallback the built-in TYPECAST is used automatically, but for large
% inputs this can be more than 10 times slower.
% For Matlab 6.5 installing typecastx is obligatory to run DataHash.
%
% Tested: Matlab 6.5, 7.7, 7.8, WinXP, Java: 1.3.1_01, 1.6.0_04.
% Author: Jan Simon, Heidelberg, (C) 2011 matlab.THISYEAR(a)nMINUSsimon.de
%
% See also: TYPECAST, CAST.
% FEX:
% Michael Kleder, "Compute Hash", no structs and cells:
% http://www.mathworks.com/matlabcentral/fileexchange/8944
% Tim, "Serialize/Deserialize", converts structs and cells to a byte stream:
% http://www.mathworks.com/matlabcentral/fileexchange/29457
% Jan Simon, "CalcMD5", MD5 only, faster C-mex, no structs and cells:
% http://www.mathworks.com/matlabcentral/fileexchange/25921
% $JRev: R-j V:010 Sum:q3tnsDSkI19o Date:09-Sep-2011 13:32:19 $
% $License: BSD (use/copy/change/redistribute on own risk, mention the author) $
% $File: Tools\GLFile\DataHash.m $
% History:
% 001: 01-May-2011 21:52, First version.
% 007: 10-Jun-2011 10:38, [Opt.Input], binary data, complex values considered.
% Main function: ===============================================================
% Java is needed:
if ~usejava('jvm')
error(['JSimon:', mfilename, ':NoJava'], ...
'*** %s: Java is required.', mfilename);
end
% typecastx creates a shared data copy instead of the deep copy as Matlab's
% TYPECAST - for a [1000x1000] DOUBLE array this is 100 times faster!
persistent usetypecastx
if isempty(usetypecastx)
usetypecastx = ~isempty(which('typecastx')); % Run the slow WHICH once only
end
% Default options: -------------------------------------------------------------
Method = 'MD5';
OutFormat = 'hex';
isFile = false;
isBin = false;
% Check number and type of inputs: ---------------------------------------------
nArg = nargin;
if nArg == 2
if isa(Opt, 'struct') == 0 % Bad type of 2nd input:
error(['JSimon:', mfilename, ':BadInput2'], ...
'*** %s: 2nd input [Opt] must be a struct.', mfilename);
end
% Specify hash algorithm:
if isfield(Opt, 'Method')
Method = upper(Opt.Method);
end
% Specify output format:
if isfield(Opt, 'Format')
OutFormat = Opt.Format;
end
% Check if the Input type is specified - default: 'array':
if isfield(Opt, 'Input')
if strcmpi(Opt.Input, 'File')
isFile = true;
if ischar(Data) == 0
error(['JSimon:', mfilename, ':CannotOpen'], ...
'*** %s: 1st input is not a file name', mfilename);
end
if exist(Data, 'file') ~= 2
error(['JSimon:', mfilename, ':FileNotFound'], ...
'*** %s: File not found: %s.', mfilename, Data);
end
elseif strncmpi(Opt.Input, 'bin', 3) % Accept 'binary'
isBin = true;
if (isnumeric(Data) || ischar(Data) || islogical(Data)) == 0
error(['JSimon:', mfilename, ':BadDataType'], ...
'*** %s: 1st input is not numeric, CHAR or LOGICAL.', mfilename);
end
end
end
elseif nArg ~= 1 % Bad number of arguments:
error(['JSimon:', mfilename, ':BadNInput'], ...
'*** %s: 1 or 2 inputs required.', mfilename);
end
% Create the engine: -----------------------------------------------------------
try
Engine = java.security.MessageDigest.getInstance(Method);
catch
error(['JSimon:', mfilename, ':BadInput2'], ...
'*** %s: Invalid algorithm: [%s].', mfilename, Method);
end
% Create the hash value: -------------------------------------------------------
if isFile
% Read the file and calculate the hash:
FID = fopen(Data, 'r');
if FID < 0
error(['JSimon:', mfilename, ':CannotOpen'], ...
'*** %s: Cannot open file: %s.', mfilename, Data);
end
Data = fread(FID, Inf, '*uint8');
fclose(FID);
Engine.update(Data);
if usetypecastx
Hash = typecastx(Engine.digest, 'uint8');
else
Hash = typecast(Engine.digest, 'uint8');
end
elseif isBin % Contents of an elementary array:
if usetypecastx % Faster typecastx:
if isreal(Data)
Engine.update(typecastx(Data(:), 'uint8'));
else
Engine.update(typecastx(real(Data(:)), 'uint8'));
Engine.update(typecastx(imag(Data(:)), 'uint8'));
end
Hash = typecastx(Engine.digest, 'uint8');
else % Matlab's TYPECAST is less elegant:
if isnumeric(Data)
if isreal(Data)
Engine.update(typecast(Data(:), 'uint8'));
else
Engine.update(typecast(real(Data(:)), 'uint8'));
Engine.update(typecast(imag(Data(:)), 'uint8'));
end
elseif islogical(Data) % TYPECAST cannot handle LOGICAL
Engine.update(typecast(uint8(Data(:)), 'uint8'));
elseif ischar(Data) % TYPECAST cannot handle CHAR
Engine.update(typecast(uint16(Data(:)), 'uint8'));
Engine.update(typecast(Data(:), 'uint8'));
end
Hash = typecast(Engine.digest, 'uint8');
end
elseif usetypecastx % Faster typecastx:
Engine = CoreHash_(Data, Engine);
Hash = typecastx(Engine.digest, 'uint8');
else % Slower built-in TYPECAST:
Engine = CoreHash(Data, Engine);
Hash = typecast(Engine.digest, 'uint8');
end
% Convert hash specific output format: -----------------------------------------
switch OutFormat
case 'hex'
Hash = sprintf('%.2x', double(Hash));
case 'HEX'
Hash = sprintf('%.2X', double(Hash));
case 'double'
Hash = double(reshape(Hash, 1, []));
case 'uint8'
Hash = reshape(Hash, 1, []);
case 'base64'
Hash = fBase64_enc(double(Hash));
otherwise
error(['JSimon:', mfilename, ':BadOutFormat'], ...
'*** %s: [Opt.Format] must be: HEX, hex, uint8, double, base64.', ...
mfilename);
end
% return;
% ******************************************************************************
function Engine = CoreHash_(Data, Engine)
% This mothod uses the faster typecastx version.
% Consider the type and dimensions of the array to distinguish arrays with the
% same data, but different shape: [0 x 0] and [0 x 1], [1,2] and [1;2],
% DOUBLE(0) and SINGLE([0,0]):
Engine.update([uint8(class(Data)), typecastx(size(Data), 'uint8')]);
if isstruct(Data) % Hash for all array elements and fields:
F = sort(fieldnames(Data)); % Ignore order of fields
Engine = CoreHash_(F, Engine); % Catch the fieldnames
for iS = 1:numel(Data) % Loop over elements of struct array
for iField = 1:length(F) % Loop over fields
Engine = CoreHash_(Data(iS).(F{iField}), Engine);
end
end
elseif iscell(Data) % Get hash for all cell elements:
for iS = 1:numel(Data)
Engine = CoreHash_(Data{iS}, Engine);
end
elseif isnumeric(Data) || islogical(Data) || ischar(Data)
if isempty(Data) == 0
if isreal(Data) % TRUE for LOGICAL and CHAR also:
Engine.update(typecastx(Data(:), 'uint8'));
else % typecastx accepts complex input:
Engine.update(typecastx(real(Data(:)), 'uint8'));
Engine.update(typecastx(imag(Data(:)), 'uint8'));
end
end
elseif isa(Data, 'function_handle')
Engine = CoreHash(ConvertFuncHandle(Data), Engine);
else % Most likely this is a user-defined object:
try
Engine = CoreHash(ConvertObject(Data), Engine);
catch
warning(['JSimon:', mfilename, ':BadDataType'], ...
['Type of variable not considered: ', class(Data)]);
end
end
% return;
% ******************************************************************************
function Engine = CoreHash(Data, Engine)
% This methods uses the slower TYPECAST of Matlab
% See CoreHash_ for comments.
Engine.update([uint8(class(Data)), typecast(size(Data), 'uint8')]);
if isstruct(Data) % Hash for all array elements and fields:
F = sort(fieldnames(Data)); % Ignore order of fields
Engine = CoreHash(F, Engine); % Catch the fieldnames
for iS = 1:numel(Data) % Loop over elements of struct array
for iField = 1:length(F) % Loop over fields
Engine = CoreHash(Data(iS).(F{iField}), Engine);
end
end
elseif iscell(Data) % Get hash for all cell elements:
for iS = 1:numel(Data)
Engine = CoreHash(Data{iS}, Engine);
end
elseif isempty(Data)
elseif isnumeric(Data)
if isreal(Data)
Engine.update(typecast(Data(:), 'uint8'));
else
Engine.update(typecast(real(Data(:)), 'uint8'));
Engine.update(typecast(imag(Data(:)), 'uint8'));
end
elseif islogical(Data) % TYPECAST cannot handle LOGICAL
Engine.update(typecast(uint8(Data(:)), 'uint8'));
elseif ischar(Data) % TYPECAST cannot handle CHAR
Engine.update(typecast(uint16(Data(:)), 'uint8'));
elseif isa(Data, 'function_handle')
Engine = CoreHash(ConvertFuncHandle(Data), Engine);
else % Most likely a user-defined object:
try
Engine = CoreHash(ConvertObject(Data), Engine);
catch
warning(['JSimon:', mfilename, ':BadDataType'], ...
['Type of variable not considered: ', class(Data)]);
end
end
% return;
% ******************************************************************************
function FuncKey = ConvertFuncHandle(FuncH)
% The subfunction ConvertFuncHandle converts function_handles to a struct
% using the Matlab function FUNCTIONS. The output of this function changes
% with the Matlab version, such that DataHash(@sin) replies different hashes
% under Matlab 6.5 and 2009a.
% An alternative is using the function name and name of the file for
% function_handles, but this is not unique for nested or anonymous functions.
% If the MATLABROOT is removed from the file's path, at least the hash of
% Matlab's toolbox functions is (usually!) not influenced by the version.
% Finally I'm in doubt if there is a unique method to hash function handles.
% Please adjust the subfunction ConvertFuncHandles to your needs.
% The Matlab version influences the conversion by FUNCTIONS:
% 1. The format of the struct replied FUNCTIONS is not fixed,
% 2. The full paths of toolbox function e.g. for @mean differ.
FuncKey = functions(FuncH);
% ALTERNATIVE: Use name and path. The <matlabroot> part of the toolbox functions
% is replaced such that the hash for @mean does not depend on the Matlab
% version.
% Drawbacks: Anonymous functions, nested functions...
% funcStruct = functions(FuncH);
% funcfile = strrep(funcStruct.file, matlabroot, '<MATLAB>');
% FuncKey = uint8([funcStruct.function, ' ', funcfile]);
% Finally I'm afraid there is no unique method to get a hash for a function
% handle. Please adjust this conversion to your needs.
% return;
% ******************************************************************************
function DataBin = ConvertObject(DataObj)
% Convert a user-defined object to a binary stream. There cannot be a unique
% solution, so this part is left for the user...
warning off
DataBin = struct(DataObj);
warning on
% disp(DataBin);
% Perhaps a direct conversion is implemented:
% DataBin = uint8(DataObj);
% Or perhaps this is better:
% DataBin = struct(DataObj);
% return;
% ******************************************************************************
function Out = fBase64_enc(In)
% Encode numeric vector of UINT8 values to base64 string.
Pool = [65:90, 97:122, 48:57, 43, 47]; % [0:9, a:z, A:Z, +, /]
v8 = [128; 64; 32; 16; 8; 4; 2; 1];
v6 = [32, 16, 8, 4, 2, 1];
In = reshape(In, 1, []);
X = rem(floor(In(ones(8, 1), :) ./ v8(:, ones(length(In), 1))), 2);
Y = reshape([X(:); zeros(6 - rem(numel(X), 6), 1)], 6, []);
Out = char(Pool(1 + v6 * Y));
% return;
|
github
|
dschick/udkm1DsimML-master
|
mtimesx_test_ssspeed.m
|
.m
|
udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_ssspeed.m
| 415,311 |
utf_8
|
c663b5bc66edbfec752f88862a1805d1
|
% Test routine for mtimesx, op(single) * op(single) speed vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_ssspeed
% Filename: mtimesx_test_ssspeed.m
% Programmer: James Tursa
% Version: 1.0
% Date: September 27, 2009
% Copyright: (c) 2009 by James Tursa, All Rights Reserved
%
% This code uses the BSD License:
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
%
% Syntax (arguments in brackets [ ] are optional):
%
% T = mtimesx_test_ddspeed( [N [,D]] )
%
% Inputs:
%
% N = Number of runs to make for each individual test. The test result will
% be the median of N runs. N must be even. If N is odd, it will be
% automatically increased to the next even number. The default is 10,
% which can take *hours* to run. Best to run this program overnight.
% D = The string 'details'. If present, this will cause all of the
% individual intermediate run results to print as they happen.
%
% Output:
%
% T = A character array containing a summary of the results.
%
%--------------------------------------------------------------------------
function ttable = mtimesx_test_ssspeed(nn,details)
global mtimesx_ttable
disp(' ');
disp('****************************************************************************');
disp('* *');
disp('* WARNING WARNING WARNING WARNING WARNING WARNING WARNING WARNING *');
disp('* *');
disp('* This test program can take several *hours* to complete, particularly *');
disp('* when using the default number of runs as 10. It is strongly suggested *');
disp('* to close all applications and run this program overnight to get the *');
disp('* best possible result with minimal impacts to your computer usage. *');
disp('* *');
disp('* The program will be done when you see the message: DONE ! *');
disp('* *');
disp('****************************************************************************');
disp(' ');
try
input('Press Enter to start test, or Ctrl-C to exit ','s');
catch
ttable = '';
return
end
start_time = datenum(clock);
if nargin >= 1
n = nn;
else
n = 10;
end
if nargin < 2
details = false;
else
if( isempty(details) ) % code to get rid of the lint message
details = true;
else
details = true;
end
end
RC = ' Real*Real Real*Cplx Cplx*Real Cplx*Cplx';
compver = [computer ', ' version ', mtimesx mode ' mtimesx ', median of ' num2str(n) ' runs'];
k = length(compver);
nl = 199;
mtimesx_ttable = char([]);
mtimesx_ttable(1:nl,1:74) = ' ';
mtimesx_ttable(1,1:k) = compver;
mtimesx_ttable(2,:) = RC;
for r=3:(nl-2)
mtimesx_ttable(r,:) = ' -- -- -- --';
end
mtimesx_ttable(nl,1:6) = 'DONE !';
disp(' ');
disp(compver);
disp('Test program for function mtimesx:')
disp('----------------------------------');
rsave = 2;
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = single(rand(1,1));
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400));
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1));
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1));
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500));
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000));
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1));
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = single(rand(1,1));
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400));
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = single(rand(1,1));
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1));
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500));
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000));
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1));
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * (real).''');
disp(' ');
rsave = r;
mtimesx_ttable(r,:) = RC;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1));
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1));
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000));
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1));
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000));
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000));
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1));
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1));
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000));
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1));
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000));
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000));
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1));
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1));
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000));
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1));
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000));
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000));
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1));
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1));
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000));
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1));
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000));
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000));
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = single(rand(1,1));
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400));
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1));
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1));
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500));
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000));
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1));
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = single(rand(1,1));
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400));
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = single(rand(1,1));
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1));
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500));
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000));
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1));
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1));
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400));
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1));
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500));
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000));
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1));
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1));
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400));
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1));
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500));
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000));
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1));
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1));
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000));
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1));
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000));
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000));
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1));
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000));
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1));
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000));
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000));
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1));
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000));
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1));
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000));
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000));
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1));
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000));
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1));
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000));
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000));
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1));
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400));
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1));
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500));
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000));
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1));
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1));
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400));
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1));
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500));
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000));
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1));
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1));
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400));
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1));
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500));
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000));
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1));
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1));
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400));
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1));
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500));
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000));
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1));
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1));
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000));
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1));
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000));
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000));
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1));
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000));
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1));
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000));
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000));
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1));
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000));
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1));
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000));
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000));
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1));
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000));
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1));
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000));
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000));
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1));
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400));
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1));
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500));
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000));
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1));
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1));
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400));
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1));
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500));
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000));
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1));
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = single(rand(1,1));
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400));
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1));
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1));
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500));
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000));
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1));
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = single(rand(1,1));
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400));
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = single(rand(1,1));
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1));
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500));
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000));
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1));
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1));
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1));
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000));
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1));
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000));
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000));
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1));
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1));
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000));
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1));
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000));
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000));
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1));
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1));
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000));
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1));
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000));
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000));
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1));
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1));
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000));
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1));
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000));
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000));
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000));
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = single(rand(1,1));
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400));
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1));
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1));
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500));
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000));
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1));
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000));
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000));
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = single(rand(1,1));
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400));
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = single(rand(1,1));
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1));
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500));
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000));
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1));
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000));
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs ... symmetric cases op(A) * op(A)']);
disp(' ');
disp('real');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(2000));
maxtimesymCN('Matrix'' * Same ',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymNC('Matrix * Same'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymTN('Matrix.'' * Same ',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymNT('Matrix * Same.'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymGC('conj(Matrix) * Same'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymCG('Matrix'' * conj(Same)',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymGT('conj(Matrix) * Same.'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymTG('Matrix.'' * conj(Same)',A,n,details,r);
r = rsave;
disp(' ');
disp('complex');
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymCN('Matrix'' * Same ',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymNC('Matrix * Same'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymTN('Matrix.'' * Same ',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymNT('Matrix * Same.'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymGC('conj(Matrix) * Same'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymCG('Matrix'' * conj(Same)',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymGT('conj(Matrix) * Same.'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymTG('Matrix.'' * conj(Same)',A,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs ... special scalar cases']);
disp(' ');
disp('(scalar) * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(1);
B = single(rand(2500));
maxtimeNN('( 1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(1 + 1i);
B = single(rand(2500));
maxtimeNN('( 1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(1 - 1i);
B = single(rand(2500));
maxtimeNN('( 1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(1 + 2i);
B = single(rand(2500));
maxtimeNN('( 1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1);
B = single(rand(2500));
maxtimeNN('(-1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 1i);
B = single(rand(2500));
maxtimeNN('(-1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1 - 1i);
B = single(rand(2500));
maxtimeNN('(-1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 2i);
B = single(rand(2500));
maxtimeNN('(-1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(2 + 1i);
B = single(rand(2500));
maxtimeNN('( 2+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(2 - 1i);
B = single(rand(2500));
maxtimeNN('( 2-1i) * Matrix ',A,B,n,details,r);
disp(' ');
disp('(scalar) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(1);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('( 1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(1 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('( 1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(1 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('( 1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(1 + 2i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('( 1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('(-1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('(-1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('(-1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 2i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('(-1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(2 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('( 2+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(2 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('( 2-1i) * Matrix ',A,B,n,details,r);
disp(' ');
disp('(scalar) * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(1);
B = single(rand(2500));
maxtimeNC('( 1+0i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(1 + 1i);
B = single(rand(2500));
maxtimeNC('( 1+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(1 - 1i);
B = single(rand(2500));
maxtimeNC('( 1-1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(1 + 2i);
B = single(rand(2500));
maxtimeNC('( 1+2i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(-1);
B = single(rand(2500));
maxtimeNC('(-1+0i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 1i);
B = single(rand(2500));
maxtimeNC('(-1+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 - 1i);
B = single(rand(2500));
maxtimeNC('(-1-1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 2i);
B = single(rand(2500));
maxtimeNC('(-1+2i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(2 + 1i);
B = single(rand(2500));
maxtimeNC('( 2+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(2 - 1i);
B = single(rand(2500));
maxtimeNC('( 2-1i) * Matrix'' ',A,B,n,details,r);
disp(' ');
disp('(scalar) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(1);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('( 1+0i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(1 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('( 1+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(1 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('( 1-1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(1 + 2i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('( 1+2i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(-1);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('(-1+0i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('(-1+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('(-1-1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 2i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('(-1+2i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(2 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('( 2+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(2 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('( 2-1i) * Matrix'' ',A,B,n,details,r);
disp(' ');
disp('(scalar) * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(1);
B = single(rand(2500));
maxtimeNT('( 1+0i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(1 + 1i);
B = single(rand(2500));
maxtimeNT('( 1+1i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(1 - 1i);
B = single(rand(2500));
maxtimeNT('( 1-1i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(1 + 2i);
B = single(rand(2500));
maxtimeNT('( 1+2i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(-1);
B = single(rand(2500));
maxtimeNT('(-1+0i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 1i);
B = single(rand(2500));
maxtimeNT('(-1+1i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 - 1i);
B = single(rand(2500));
maxtimeNT('(-1-1i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 2i);
B = single(rand(2500));
maxtimeNT('(-1+2i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(2 + 1i);
B = single(rand(2500));
maxtimeNT('( 2+1i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(2 - 1i);
B = single(rand(2500));
maxtimeNT('( 2-1i) * Matrix.'' ',A,B,n,details,r);
disp(' ');
disp('(scalar) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(1);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNT('( 1+0i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(1 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNT('( 1+1i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(1 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNT('( 1-1i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(1 + 2i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNT('( 1+2i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(-1);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNT('(-1+0i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNT('(-1+1i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNT('(-1-1i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 2i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNT('(-1+2i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(2 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNT('( 2+1i) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(2 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNT('( 2-1i) * Matrix.'' ',A,B,n,details,r);
disp(' ');
disp('(scalar) * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(1);
B = single(rand(2500));
maxtimeNG('( 1+0i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(1 + 1i);
B = single(rand(2500));
maxtimeNG('( 1+1i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(1 - 1i);
B = single(rand(2500));
maxtimeNG('( 1-1i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(1 + 2i);
B = single(rand(2500));
maxtimeNG('( 1+2i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(-1);
B = single(rand(2500));
maxtimeNG('(-1+0i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 1i);
B = single(rand(2500));
maxtimeNG('(-1+1i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(-1 - 1i);
B = single(rand(2500));
maxtimeNG('(-1-1i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 2i);
B = single(rand(2500));
maxtimeNG('(-1+2i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(2 + 1i);
B = single(rand(2500));
maxtimeNG('( 2+1i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(2 - 1i);
B = single(rand(2500));
maxtimeNG('( 2-1i) * conj(Matrix) ',A,B,n,details,r);
disp(' ');
disp('(scalar) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(1);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNG('( 1+0i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(1 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNG('( 1+1i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(1 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNG('( 1-1i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(1 + 2i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNG('( 1+2i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(-1);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNG('(-1+0i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNG('(-1+1i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(-1 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNG('(-1-1i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 2i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNG('(-1+2i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(2 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNG('( 2+1i) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(2 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxtimeNG('( 2-1i) * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(' --- DONE ! ---');
disp(' ');
disp(['Summary of Timing Tests, ' num2str(n) ' runs, + = percent faster, - = percent slower:']);
disp(' ');
mtimesx_ttable(1,1:k) = compver;
disp(mtimesx_ttable);
disp(' ');
ttable = mtimesx_ttable;
running_time(datenum(clock) - start_time);
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*B;
mtoc(k) = toc;
tic;
mtimesx(A,B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*B.';
mtoc(k) = toc;
tic;
mtimesx(A,B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*B';
mtoc(k) = toc;
tic;
mtimesx(A,B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*B;
mtoc(k) = toc;
tic;
mtimesx(A,'T',B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*B.';
mtoc(k) = toc;
tic;
mtimesx(A,'T',B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*B';
mtoc(k) = toc;
tic;
mtimesx(A,'T',B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,'T',B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*B;
mtoc(k) = toc;
tic;
mtimesx(A,'C',B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*B.';
mtoc(k) = toc;
tic;
mtimesx(A,'C',B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*B';
mtoc(k) = toc;
tic;
mtimesx(A,'C',B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,'C',B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*B;
mtoc(k) = toc;
tic;
mtimesx(A,'G',B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*B.';
mtoc(k) = toc;
tic;
mtimesx(A,'G',B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*B';
mtoc(k) = toc;
tic;
mtimesx(A,'G',B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,'G',B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymCN(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*A;
mtoc(k) = toc;
tic;
mtimesx(A,'C',A);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymNC(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*A';
mtoc(k) = toc;
tic;
mtimesx(A,A,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymTN(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*A;
mtoc(k) = toc;
tic;
mtimesx(A,'T',A);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymNT(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*A.';
mtoc(k) = toc;
tic;
mtimesx(A,A,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymCG(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*conj(A);
mtoc(k) = toc;
tic;
mtimesx(A,'C',A,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymGC(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*A';
mtoc(k) = toc;
tic;
mtimesx(A,'G',A,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymTG(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*conj(A);
mtoc(k) = toc;
tic;
mtimesx(A,'T',A,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymGT(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*A.';
mtoc(k) = toc;
tic;
mtimesx(A,'G',A,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeout(T,A,B,p,r)
global mtimesx_ttable
mtimesx_ttable(r,1:length(T)) = T;
if( isreal(A) && isreal(B) )
lt = length(T);
b = repmat(' ',1,30-lt);
x = [T b sprintf('%10.0f%%',-p)];
mtimesx_ttable(r,1:length(x)) = x;
elseif( isreal(A) && ~isreal(B) )
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,42:41+length(x)) = x;
elseif( ~isreal(A) && isreal(B) )
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,53:52+length(x)) = x;
else
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymout(T,A,p,r)
global mtimesx_ttable
if( isreal(A) )
lt = length(T);
b = repmat(' ',1,30-lt);
x = [T b sprintf('%10.0f%%',-p)];
mtimesx_ttable(r,1:length(x)) = x;
else
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,1:length(T)) = T;
mtimesx_ttable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function running_time(d)
h = 24*d;
hh = floor(h);
m = 60*(h - hh);
mm = floor(m);
s = 60*(m - mm);
ss = floor(s);
disp(' ');
rt = sprintf('Running time hh:mm:ss = %2.0f:%2.0f:%2.0f',hh,mm,ss);
if( rt(28) == ' ' )
rt(28) = '0';
end
if( rt(31) == ' ' )
rt(31) = '0';
end
disp(rt);
disp(' ');
return
end
|
github
|
dschick/udkm1DsimML-master
|
mtimesx_build.m
|
.m
|
udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_build.m
| 16,405 |
utf_8
|
838ce3d9c7bc33beb0d2f75546ead978
|
% mtimesx_build compiles mtimesx.c with BLAS libraries
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_build
% Filename: mtimesx_build.m
% Programmer: James Tursa
% Version: 1.40
% Date: October 4, 2010
% Copyright: (c) 2009, 2010 by James Tursa, All Rights Reserved
%
% This code uses the BSD License:
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
%
%--
%
% mtimesx_build compiles mtimesx.c and mtimesx_RealTimesReal.c with the BLAS
% libraries libmwblas.lib (if present) or libmwlapack.lib (if libmwblas.lib
% is not present). This function basically works as follows:
%
% - Opens the current mexopts.bat file in the directory [prefdir], and
% checks to make sure that the compiler selected is cl or lcc. If it
% is not, then a warning is issued and the compilation continues with
% the assumption that the microsoft BLAS libraries will work.
%
% - Looks for the file libmwblas.lib or libmwlapack.lib files in the
% appropriate directory: [matlabroot '\extern\lib\win32\microsoft']
% or [matlabroot '\extern\lib\win32\lcc']
% or [matlabroot '\extern\lib\win64\microsoft']
% or [matlabroot '\extern\lib\win64\lcc']
%
% - Changes directory to the directory of the file mtimesx.m.
%
% - Compiles mtimesx.c (which includes mtimesx_RealTimesReal.c) along with
% either libmwblas.lib or libmwlapack.lib depending on the version of
% MATLAB. The resulting exedcutable mex file is placed in the same
% directory as the source code. The files mtimesx.m, mtimesx.c, and
% mtimesx_RealTimesReal.c must all be in the same directory.
%
% - Changes the directory back to the original directory.
%
% Change Log:
% 2009/Sep/27 --> 1.00, Initial Release
% 2010/Feb/15 --> 1.10, Fixed largearrardims typo to largeArrayDims
% 2010/Oct/04 --> 1.40, Updated support for OpenMP compiling
%
%**************************************************************************
function mtimesx_build(x)
disp(' ');
disp('... Build routine for mtimesx');
TRUE = 1;
FALSE = 0;
%\
% Check for number of inputs & outputs
%/
noopenmp = FALSE;
if( nargin == 1 )
if( isequal(upper(x),'NOOPENMP') )
noopenmp = TRUE;
else
error('Invalid input.');
end
elseif( nargin ~= 0 )
error('Too many inputs. Expected none.');
end
if( nargout ~= 0 )
error('Too many outputs. Expected none.');
end
%\
% Check for non-PC
%/
disp('... Checking for PC');
try
% ispc does not appear in MATLAB 5.3
pc = ispc ;
catch
% if ispc fails, assume we are on a Windows PC if it's not unix
pc = ~isunix ;
end
if( ~pc )
disp('Non-PC auto build is not currently supported. You will have to');
disp('manually compile the mex routine. E.g., as follows:');
disp(' ');
disp('>> blas_lib = ''the_actual_path_and_name_of_your_systems_BLAS_library''');
disp('>> mex(''-DDEFINEUNIX'',''mtimesx.c'',blas_lib)');
disp(' ');
disp('or');
disp(' ');
disp('>> mex(''-DDEFINEUNIX'',''-largeArrayDims'',''mtimesx.c'',blas_lib)');
disp(' ');
error('Unable to compile mtimesx.c');
end
%\
% Check to see that mtimesx.c source code is present
%/
disp('... Finding path of mtimesx C source code files');
try
mname = mfilename('fullpath');
catch
mname = mfilename;
end
cname = [mname(1:end-6) '.c'];
if( isempty(dir(cname)) )
disp('Cannot find the file mtimesx.c in the same directory as the');
disp('file mtimesx_build.m. Please ensure that they are in the same');
disp('directory and try again. The following file was not found:');
disp(' ');
disp(cname);
disp(' ');
error('Unable to compile mtimesx.c');
end
disp(['... Found file mtimesx.c in ' cname]);
%\
% Check to see that mtimesx_RealTimesReal.c source code is present
%/
rname = [mname(1:end-13) 'mtimesx_RealTimesReal.c'];
if( isempty(dir(rname)) )
disp('Cannot find the file mtimesx_RealTimesReal.c in the same');
disp('directory as the file mtimesx_build.m. Please ensure that');
disp('they are in the same directory and try again. The');
disp('following file was not found:');
disp(' ');
disp(rname);
disp(' ');
error('Unable to compile mtimesx.c');
end
disp(['... Found file mtimesx_RealTimesReal.c in ' rname]);
%\
% Open the current mexopts.bat file
%/
mexopts = [prefdir '\mexopts.bat'];
fid = fopen(mexopts);
if( fid == -1 )
error('A C/C++ compiler has not been selected with mex -setup');
end
disp(['... Opened the mexopts.bat file in ' mexopts]);
disp('... Reading the mexopts.bat file to find the compiler and options used.');
%\
% Check for the correct compiler selected.
%/
ok_cl = FALSE;
ok_lcc = FALSE;
omp_option = '';
compiler = '(unknown)';
compilername = '';
while( TRUE )
tline = fgets(fid);
if( isequal(tline,-1) )
break;
else
if( isempty(compilername) )
y = findstr(tline,'OPTS.BAT');
if( ~isempty(y) )
x = findstr(tline,'rem ');
if( ~isempty(x) )
compilername = tline(x+4:y-1);
end
end
end
x = findstr(tline,'COMPILER=lcc');
if( ~isempty(x) )
ok_lcc = TRUE;
libdir = 'lcc';
compiler = 'LCC';
disp(['... ' compiler ' is the selected compiler']);
break;
end
x = findstr(tline,'COMPILER=cl');
if( ~isempty(x) )
ok_cl = TRUE;
libdir = 'microsoft';
compiler = ['Microsoft_' compilername '_cl'];
omp_option = ' /openmp';
disp(['... ' compiler ' is the selected compiler']);
break;
end
x = findstr(tline,'COMPILER=bcc32');
if( ~isempty(x) )
ok_cl = TRUE;
libdir = 'microsoft';
compiler = ['Borland_' compilername '_bcc32'];
disp(['... ' compiler ' is the selected compiler']);
disp('... Assuming that Borland will link with Microsoft libraries');
break;
end
x = findstr(tline,'COMPILER=icl');
if( ~isempty(x) )
ok_cl = TRUE;
if( pc )
omp_option = ' -Qopenmp';
else
omp_option = ' -openmp';
end
libdir = 'microsoft';
compiler = ['Intel_' compilername '_icl'];
disp(['... ' compiler ' is the selected compiler']);
disp('... Assuming that Intel will link with Microsoft libraries');
break;
end
x = findstr(tline,'COMPILER=wc1386');
if( ~isempty(x) )
ok_cl = TRUE;
libdir = 'microsoft';
compiler = ['Watcom_' compilername '_wc1386'];
disp(['... ' compiler ' is the selected compiler']);
disp('... Assuming that Watcom will link with Microsoft libraries');
break;
end
x = findstr(tline,'COMPILER=gcc');
if( ~isempty(x) )
ok_cl = TRUE;
libdir = 'microsoft';
omp_option = ' -fopenmp';
compiler = 'GCC';
disp(['... ' compiler ' is the selected compiler']);
disp('... Assuming that GCC will link with Microsoft libraries');
break;
end
end
end
fclose(fid);
%\
% MS Visual C/C++ or lcc compiler has not been selected
%/
if( ~(ok_cl | ok_lcc) )
warning('... Supported C/C++ compiler has not been selected with mex -setup');
warning('... Assuming that Selected Compiler will link with Microsoft libraries');
warning('... Continuing at risk ...');
libdir = 'microsoft';
end
%\
% If an OpenMP supported compiler is potentially present, make sure that the
% necessary compile option is present in the mexopts.bat file on the COMPFLAGS
% line. If necessary, build a new mexopts.bat file with the correct option
% added to the COMPFLAGS line.
%/
while( TRUE )
ok_openmp = FALSE;
ok_compflags = FALSE;
xname = '';
if( isempty(omp_option) )
disp('... OpenMP compiler not detected ... you may want to check this website:');
disp(' http://openmp.org/wp/openmp-compilers/');
elseif( noopenmp )
disp(['... OpenMP compiler potentially detected, but not checking for ''' omp_option ''' compile option']);
else
disp('... OpenMP compiler potentially detected');
disp(['... Checking to see that the ''' omp_option ''' compile option is present']);
fid = fopen(mexopts);
while( TRUE )
tline = fgets(fid);
if( isequal(tline,-1) )
break;
else
x = findstr(tline,'set COMPFLAGS');
if( ~isempty(x) )
ok_compflags = TRUE;
x = findstr(tline,omp_option);
if( ~isempty(x) )
ok_openmp = TRUE;
end
break;
end
end
end
fclose(fid);
if( ~ok_compflags )
warning(['... COMPFLAGS line not found ... ''' omp_option ''' will not be added.']);
elseif( ~ok_openmp )
disp(['... The ''' omp_option ''' compile option is not present ... adding it']);
xname = [mname(1:end-6) '_mexopts.bat'];
disp(['... Creating custom options file ' xname ' with the ''' omp_option ''' option added.']);
fid = fopen(mexopts);
fidx = fopen(xname,'w');
if( fidx == -1 )
xname = '';
warning(['... Unable to create custom mexopts.bat file ... ''' omp_option ''' will not be added']);
else
while( TRUE )
tline = fgets(fid);
if( isequal(tline,-1) )
break;
else
x = findstr(tline,'set COMPFLAGS');
if( ~isempty(x) )
n = numel(tline);
e = n;
while( tline(e) < 32 )
e = e - 1;
end
tline = [tline(1:e) omp_option tline(e+1:n)];
end
fwrite(fidx,tline);
end
end
fclose(fidx);
end
fclose(fid);
end
end
%\
% Construct full file name of libmwblas.lib and libmwlapack.lib. Note that
% not all versions have both files. Earlier versions only had the lapack
% file, which contained both blas and lapack routines.
%/
comp = computer;
mext = mexext;
lc = length(comp);
lm = length(mext);
cbits = comp(max(1:lc-1):lc);
mbits = mext(max(1:lm-1):lm);
if( isequal(cbits,'64') | isequal(mbits,'64') )
compdir = 'win64';
largearraydims = '-largeArrayDims';
else
compdir = 'win32';
largearraydims = '';
end
lib_blas = [matlabroot '\extern\lib\' compdir '\' libdir '\libmwblas.lib'];
d = dir(lib_blas);
if( isempty(d) )
disp('... BLAS library file not found, so linking with the LAPACK library');
lib_blas = [matlabroot '\extern\lib\' compdir '\' libdir '\libmwlapack.lib'];
end
disp(['... Using BLAS library lib_blas = ''' lib_blas '''']);
%\
% Save old directory and change to source code directory
%/
cdold = cd;
if( length(mname) > 13 )
cd(mname(1:end-13));
end
%\
% Do the compile
%/
disp('... Now attempting to compile ...');
disp(' ');
try
if( isunix )
if( isempty(largearraydims) )
if( isempty(xname) )
disp(['mex(''-DDEFINEUNIX'',''' cname ''',lib_blas,''-DCOMPILER=' compiler ''')']);
disp(' ');
mex('-DDEFINEUNIX',cname,lib_blas,['-DCOMPILER=' compiler]);
else
disp(['mex(''-f'',''' xname ''',''-DDEFINEUNIX'',''' cname ''',lib_blas,''-DCOMPILER=' compiler ''')']);
disp(' ');
mex('-f',xname,'-DDEFINEUNIX',cname,lib_blas,['-DCOMPILER=' compiler]);
end
else
if( isempty(xname) )
disp(['mex(''-DDEFINEUNIX'',''' cname ''',''' largearraydims ''',lib_blas,''-DCOMPILER=' compiler ''')']);
disp(' ');
mex('-DDEFINEUNIX',largearraydims,cname,lib_blas,['-DCOMPILER=' compiler]);
else
disp(['mex(''-f'',''' xname ''',''-DDEFINEUNIX'',''' cname ''',''' largearraydims ''',lib_blas,''-DCOMPILER=' compiler ''')']);
disp(' ');
mex('-f',xname,'-DDEFINEUNIX',largearraydims,cname,lib_blas,['-DCOMPILER=' compiler]);
end
end
else
if( isempty(largearraydims) )
if( isempty(xname) )
disp(['mex(''' cname ''',lib_blas,''-DCOMPILER=' compiler ''')']);
disp(' ');
mex(cname,lib_blas,['-DCOMPILER=' compiler]);
else
disp(['mex(''-f'',''' xname ''',''' cname ''',lib_blas,''-DCOMPILER=' compiler ''')']);
disp(' ');
mex('-f',xname,cname,lib_blas,['-DCOMPILER=' compiler]);
end
else
if( isempty(xname) )
disp(['mex(''' cname ''',''' largearraydims ''',lib_blas,''-DCOMPILER=' compiler ''')']);
disp(' ');
mex(cname,largearraydims,lib_blas,['-DCOMPILER=' compiler]);
else
disp(['mex(''-f'',''' xname ''',''' cname ''',''' largearraydims ''',lib_blas,''-DCOMPILER=' compiler ''')']);
disp(' ');
mex('-f',xname,cname,largearraydims,lib_blas,['-DCOMPILER=' compiler]);
end
end
end
disp('... mex mtimesx.c build completed ... you may now use mtimesx.');
disp(' ');
mtimesx;
break;
catch
if( noopenmp )
cd(cdold);
disp(' ');
disp('... Well, *that* didn''t work either!');
disp(' ');
disp('The mex command failed. This may be because you have already run');
disp('mex -setup and selected a non-C compiler, such as Fortran. If this');
disp('is the case, then rerun mex -setup and select a C/C++ compiler.');
disp(' ');
error('Unable to compile mtimesx.c');
else
disp(' ');
disp('... Well, *that* didn''t work ...');
disp(' ');
if( isequal(omp_option,' /openmp') )
disp('This may be because an OpenMP compile option was added that the');
disp('compiler did not like. For example, the Standard versions of the');
disp('Microsoft C/C++ compilers do not support OpenMP, only the');
disp('Professional versions do. Attempting to compile again but this');
disp(['time will not add the ''' omp_option ''' option.'])
else
disp('This may be because an OpenMP compile option was added that the');
disp('compiler did not like. Attempting to compile again, but this time');
disp(['will not add the ''' omp_option ''' option.'])
end
disp(' ');
noopenmp = TRUE;
end
end
end
%\
% Restore old directory
%/
cd(cdold);
return
end
|
github
|
dschick/udkm1DsimML-master
|
mtimesx_test_nd.m
|
.m
|
udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_nd.m
| 14,364 |
utf_8
|
0d3b436cea001bccb9c6cccdaa21b34d
|
% Test routine for mtimesx, multi-dimensional speed and equality to MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_nd
% Filename: mtimesx_test_nd.m
% Programmer: James Tursa
% Version: 1.40
% Date: October 4, 2010
% Copyright: (c) 2009,2010 by James Tursa, All Rights Reserved
%
% This code uses the BSD License:
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
%
% Syntax:
%
% A = mtimesx_test_nd % default n=4 is used
% A = mtimesx_test_nd(n)
%
% where n = number of repetitions (should be 4 <= n <= 100)
%
% Output:
%
% Prints out speed and equality test results.
% A = cell array with tabled results.
%
% 2010/Oct/04 --> 1.40, Added OpenMP support for custom code
% Expanded sparse * single and sparse * nD support
%
%--------------------------------------------------------------------------
function Cr = mtimesx_test_nd(n)
mtimesx; % load the mex routine into memory
if( nargin == 0 )
n = 4;
else
n = floor(n);
if( ~(n >= 4 && n <= 100) )
n = 4;
end
end
cn = sprintf('%g',n);
disp(' ');
disp('MTIMESX multi-dimensional equality and speed tests');
disp('--------------------------------------------------');
disp(' ');
disp('(M x K) * ( K x N) equality tests, SPEED mode, M,K,N <= 4');
trans = 'NGTC';
cmpx = {'real ','cmpx '};
mtimesx('speed');
smallok = true;
for m=1:4
for k=1:4
for n=1:4
for transa=1:4
if( transa <= 2 )
ma = m;
ka = k;
else
ma = k;
ka = m;
end
for transb=1:4
if( transb <= 2 )
kb = k;
nb = n;
else
kb = n;
nb = k;
end
for cmplxa=1:2
if( cmplxa == 1 )
A = floor(rand(ma,ka)*100+1);
else
A = floor(rand(ma,ka)*100+1) + floor(rand(ma,ka)*100+1)*1i;
end
for cmplxb=1:2
if( cmplxb == 1 )
B = floor(rand(kb,nb)*100+1);
else
B = floor(rand(kb,nb)*100+1) + floor(rand(kb,nb)*100+1)*1i;
end
Cm = mtimesx_sparse(A,trans(transa),B,trans(transb));
Cx = mtimesx(A,trans(transa),B,trans(transb));
if( isequal(Cm,Cx) )
disp(['(' cmpx{cmplxa} num2str(m) ' x ' num2str(k) ')' trans(transa) ...
' * (' cmpx{cmplxb} num2str(k) ' x ' num2str(n) ')' trans(transb) ' EQUAL']);
else
disp(['(' cmpx{cmplxa} num2str(m) ' x ' num2str(k) ')' trans(transa) ...
' * (' cmpx{cmplxb} num2str(k) ' x ' num2str(n) ')' trans(transb) ' NOT EQUAL']);
smallok = false;
end
end
end
end
end
end
end
end
if( mtimesx('openmp') )
disp(' ');
disp('(M x K) * ( K x N) equality tests, SPEEDOMP mode, M,K,N <= 4');
mtimesx('speedomp');
smallokomp = true;
for m=1:4
for k=1:4
for n=1:4
for transa=1:4
if( transa <= 2 )
ma = m;
ka = k;
else
ma = k;
ka = m;
end
for transb=1:4
if( transb <= 2 )
kb = k;
nb = n;
else
kb = n;
nb = k;
end
for cmplxa=1:2
if( cmplxa == 1 )
A = floor(rand(ma,ka)*100+1);
else
A = floor(rand(ma,ka)*100+1) + floor(rand(ma,ka)*100+1)*1i;
end
A = reshape(repmat(A,1000,1),ma,ka,1000);
for cmplxb=1:2
if( cmplxb == 1 )
B = floor(rand(kb,nb)*100+1);
else
B = floor(rand(kb,nb)*100+1) + floor(rand(kb,nb)*100+1)*1i;
end
B = reshape(repmat(B,1000,1),kb,nb,1000);
Cm = mtimesx_sparse(A(:,:,1),trans(transa),B(:,:,1),trans(transb));
Cx = mtimesx(A,trans(transa),B,trans(transb));
if( isequal(Cm,Cx(:,:,1)) )
disp(['(' cmpx{cmplxa} num2str(m) ' x ' num2str(k) ')' trans(transa) ...
' * (' cmpx{cmplxb} num2str(k) ' x ' num2str(n) ')' trans(transb) ' EQUAL']);
else
disp(['(' cmpx{cmplxa} num2str(m) ' x ' num2str(k) ')' trans(transa) ...
' * (' cmpx{cmplxb} num2str(k) ' x ' num2str(n) ')' trans(transb) ' NOT EQUAL']);
smallokomp = false;
end
end
end
end
end
end
end
end
end
disp(' ');
if( smallok )
disp('All small matrix multiplies are OK in SPEED mode');
else
disp('ERROR --> One or more of the small matrix multiplies was not equal in SPEED mode');
end
if( mtimesx('openmp') )
if( smallokomp )
disp('All small matrix multiplies are OK in SPEEDOMP mode');
else
disp('ERROR --> One or more of the small matrix multiplies was not equal in SPEEDOMP mode');
end
end
disp(' ');
disp(['mtimesx multi-dimensional test routine using ' cn ' repetitions']);
if( mtimesx('OPENMP') )
topm = 6;
else
topm = 4;
end
Cr = cell(6,topm+1);
Cr{1,1} = 'All operands real';
for m=2:topm+1
if( m == 2 )
mtimesx('BLAS');
elseif( m == 3 )
mtimesx('LOOPS');
elseif( m == 4 )
mtimesx('MATLAB');
elseif( m == 5 )
mtimesx('SPEED');
elseif( m == 6 )
mtimesx('LOOPSOMP');
else
mtimesx('SPEEDOMP');
end
Cr{1,m} = mtimesx;
disp(' ');
disp('--------------------------------------------------------------');
disp('--------------------------------------------------------------');
disp(' ');
disp(['MTIMESX mode: ' mtimesx]);
disp(' ');
disp('(real 3x5x1x4x3x2x1x8) * (real 5x7x3x1x3x2x5) example');
Cr{2,1} = '(3x5xND) *(5x7xND)';
A = rand(3,5,1,4,3,2,1,8);
B = rand(5,7,3,1,3,2,5);
% mtimes
tm = zeros(1,n);
for k=1:n
clear Cm
A(1) = 2*A(1);
B(1) = 2*B(1);
tic
Cm = zeros(3,7,3,4,3,2,5,8);
for k1=1:3
for k2=1:4
for k3=1:3
for k4=1:2
for k5=1:5
for k6=1:8
Cm(:,:,k1,k2,k3,k4,k5,k6) = A(:,:,1,k2,k3,k4,1,k6) * B(:,:,k1,1,k3,k4,k5);
end
end
end
end
end
end
tm(k) = toc;
end
% mtimesx
tx = zeros(1,n);
for k=1:n
clear Cx
tic
Cx = mtimesx(A,B);
tx(k) = toc;
end
% results
tm = median(tm);
tx = median(tx);
if( tx < tm )
faster = sprintf('%7.1f',100*(tm)/tx-100);
slower = '';
else
faster = sprintf('%7.1f',-(100*(tx)/tm-100));
slower = ' (i.e., slower)';
end
Cr{2,m} = faster;
disp(' ');
disp(['mtimes Elapsed time ' num2str(tm) ' seconds.']);
disp(['MTIMESX Elapsed time ' num2str(tx) ' seconds.']);
disp(['MTIMESX ' mtimesx ' mode is ' faster '% faster than MATLAB mtimes' slower])
if( isequal(Cx,Cm) )
disp(['MTIMESX ' mtimesx ' mode result matches mtimes: EQUAL'])
else
dx = max(abs(Cx(:)-Cm(:)));
disp(['MTIMESX ' mtimesx ' mode result does not match mtimes: NOT EQUAL , max diff = ' num2str(dx)])
end
disp(' ');
disp('--------------------------------------------------------------');
disp('(real 3x3x1000000) * (real 3x3x1000000) example');
Cr{3,1} = '(3x3xN) *(3x3xN)';
A = rand(3,3,1000000);
B = rand(3,3,1000000);
% mtimes
tm = zeros(1,n);
for k=1:n
clear Cm
A(1) = 2*A(1);
B(1) = 2*B(1);
tic
Cm = zeros(3,3,1000000);
for k1=1:1000000
Cm(:,:,k1) = A(:,:,k1) * B(:,:,k1);
end
tm(k) = toc;
end
% mtimesx
tx = zeros(1,n);
for k=1:n
clear Cx
tic
Cx = mtimesx(A,B);
tx(k) = toc;
end
% results
tm = median(tm);
tx = median(tx);
if( tx < tm )
faster = sprintf('%7.1f',100*(tm)/tx-100);
slower = '';
else
faster = sprintf('%7.1f',-(100*(tx)/tm-100));
slower = ' (i.e., slower)';
end
Cr{3,m} = faster;
disp(' ');
disp(['mtimes Elapsed time ' num2str(tm) ' seconds.']);
disp(['MTIMESX Elapsed time ' num2str(tx) ' seconds.']);
disp(['MTIMESX ' mtimesx ' mode is ' faster '% faster than MATLAB mtimes' slower])
if( isequal(Cx,Cm) )
disp(['MTIMESX ' mtimesx ' mode result matches mtimes: EQUAL'])
else
dx = max(abs(Cx(:)-Cm(:)));
disp(['MTIMESX ' mtimesx ' mode result does not match mtimes: NOT EQUAL , max diff = ' num2str(dx)])
end
disp(' ');
disp('--------------------------------------------------------------');
disp('(real 2x2x2000000) * (real 2x2x2000000) example');
Cr{4,1} = '(2x2xN) *(2x2xN)';
A = rand(2,2,2000000);
B = rand(2,2,2000000);
% mtimes
tm = zeros(1,n);
for k=1:n
clear Cm
A(1) = 2*A(1);
B(1) = 2*B(1);
tic
Cm = zeros(2,2,2000000);
for k1=1:2000000
Cm(:,:,k1) = A(:,:,k1) * B(:,:,k1);
end
tm(k) = toc;
end
% mtimesx
tx = zeros(1,n);
for k=1:n
clear Cx
tic
Cx = mtimesx(A,B);
tx(k) = toc;
end
% results
tm = median(tm);
tx = median(tx);
if( tx < tm )
faster = sprintf('%7.1f',100*(tm)/tx-100);
slower = '';
else
faster = sprintf('%7.1f',-(100*(tx)/tm-100));
slower = ' (i.e., slower)';
end
Cr{4,m} = faster;
disp(' ');
disp(['mtimes Elapsed time ' num2str(tm) ' seconds.']);
disp(['MTIMESX Elapsed time ' num2str(tx) ' seconds.']);
disp(['MTIMESX ' mtimesx ' mode is ' faster '% faster than MATLAB mtimes' slower])
if( isequal(Cx,Cm) )
disp(['MTIMESX ' mtimesx ' mode result matches mtimes: EQUAL'])
else
dx = max(abs(Cx(:)-Cm(:)));
disp(['MTIMESX ' mtimesx ' mode result does not match mtimes: NOT EQUAL , max diff = ' num2str(dx)])
end
disp(' ');
disp('--------------------------------------------------------------');
disp('(real 2x2x2000000) * (real 1x1x2000000) example');
Cr{5,1} = '(2x2xN) *(1x1xN)';
A = rand(2,2,2000000);
B = rand(1,1,2000000);
% mtimes
tm = zeros(1,n);
for k=1:n
clear Cm
A(1) = 2*A(1);
B(1) = 2*B(1);
tic
Cm = zeros(2,2,2000000);
for k1=1:2000000
Cm(:,:,k1) = A(:,:,k1) * B(:,:,k1);
end
tm(k) = toc;
end
% mtimesx
tx = zeros(1,n);
for k=1:n
clear Cx
tic
Cx = mtimesx(A,B);
tx(k) = toc;
end
% results
tm = median(tm);
tx = median(tx);
if( tx < tm )
faster = sprintf('%7.1f',100*(tm)/tx-100);
slower = '';
else
faster = sprintf('%7.1f',-(100*(tx)/tm-100));
slower = ' (i.e., slower)';
end
Cr{5,m} = faster;
disp(' ');
disp(['mtimes Elapsed time ' num2str(tm) ' seconds.']);
disp(['MTIMESX Elapsed time ' num2str(tx) ' seconds.']);
disp(['MTIMESX ' mtimesx ' mode is ' faster '% faster than MATLAB mtimes' slower])
if( isequal(Cx,Cm) )
disp(['MTIMESX ' mtimesx ' mode result matches mtimes: EQUAL'])
else
dx = max(abs(Cx(:)-Cm(:)));
disp(['MTIMESX ' mtimesx ' mode result does not match mtimes: NOT EQUAL , max diff = ' num2str(dx)])
end
try
bsxfun(@times,1,1);
Cr{6,1} = 'above vs bsxfun';
A = rand(2,2,2000000);
B = rand(1,1,2000000);
% bsxfun
tm = zeros(1,n);
for k=1:n
clear Cm
A(1) = 2*A(1);
B(1) = 2*B(1);
tic
Cm = bsxfun(@times,A,B);
tm(k) = toc;
end
% mtimesx
tx = zeros(1,n);
for k=1:n
clear Cx
tic
Cx = mtimesx(A,B);
tx(k) = toc;
end
% results
tm = median(tm);
tx = median(tx);
if( tx < tm )
faster = sprintf('%7.1f',100*(tm)/tx-100);
slower = '';
else
faster = sprintf('%7.1f',-(100*(tx)/tm-100));
slower = ' (i.e., slower)';
end
Cr{6,m} = faster;
disp(' ');
disp(['bsxfun Elapsed time ' num2str(tm) ' seconds.']);
disp(['MTIMESX Elapsed time ' num2str(tx) ' seconds.']);
disp(['MTIMESX ' mtimesx ' mode is ' faster '% faster than MATLAB bsxfun with @times' slower])
if( isequal(Cx,Cm) )
disp(['MTIMESX ' mtimesx ' mode result matches bsxfun with @times: EQUAL'])
else
dx = max(abs(Cx(:)-Cm(:)));
disp(['MTIMESX ' mtimesx ' mode result does not match bsxfun with @times: NOT EQUAL , max diff = ' num2str(dx)])
end
catch
disp('Could not perform comparison with bsxfun, possibly because your version of');
disp('MATLAB does not have it. You can download a substitute for bsxfun from the');
disp('FEX here: http://www.mathworks.com/matlabcentral/fileexchange/23005-bsxfun-substitute');
end
end
disp(' ');
disp('Percent Faster Results Table');
disp(' ');
disp(Cr);
disp(' ');
disp('Done');
disp(' ');
end
|
github
|
dschick/udkm1DsimML-master
|
mtimesx_test_sdequal.m
|
.m
|
udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_sdequal.m
| 350,821 |
utf_8
|
7e6a367b3ad6154ce1e4da70a91ba4cf
|
% Test routine for mtimesx, op(single) * op(double) equality vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_sdequal
% Filename: mtimesx_test_sdequal.m
% Programmer: James Tursa
% Version: 1.0
% Date: September 27, 2009
% Copyright: (c) 2009 by James Tursa, All Rights Reserved
%
% This code uses the BSD License:
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
%
% Syntax:
%
% T = mtimesx_test_ddequal
%
% Output:
%
% T = A character array containing a summary of the results.
%
%--------------------------------------------------------------------------
function dtable = mtimesx_test_sdequal
global mtimesx_dtable
disp(' ');
disp('****************************************************************************');
disp('* *');
disp('* WARNING WARNING WARNING WARNING WARNING WARNING WARNING WARNING *');
disp('* *');
disp('* This test program can take an hour or so to complete. It is suggested *');
disp('* that you close all applications and run this program during your lunch *');
disp('* break or overnight to minimize impacts to your computer usage. *');
disp('* *');
disp('* The program will be done when you see the message: DONE ! *');
disp('* *');
disp('****************************************************************************');
disp(' ');
try
input('Press Enter to start test, or Ctrl-C to exit ','s');
catch
dtable = '';
return
end
start_time = datenum(clock);
compver = [computer ', ' version ', mtimesx mode ' mtimesx];
k = length(compver);
RC = ' Real*Real Real*Cplx Cplx*Real Cplx*Cplx';
mtimesx_dtable = char([]);
mtimesx_dtable(157,74) = ' ';
mtimesx_dtable(1,1:k) = compver;
mtimesx_dtable(2,:) = RC;
for r=3:157
mtimesx_dtable(r,:) = ' -- -- -- --';
end
disp(' ');
disp(compver);
disp('Test program for function mtimesx:')
disp('----------------------------------');
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * (real)');
disp(' ');
rsave = 2;
r = rsave;
%if( false ) % debug jump
if( isequal([]*[],mtimesx([],[])) )
disp('Empty * Empty EQUAL');
else
disp('Empty * Empty NOT EQUAL <---');
end
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000);
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = rand(1,1);
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40);
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1);
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1);
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500);
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000);
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1);
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffNN('Matrix * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = rand(1,1) + rand(1,1)*1i;
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1) + rand(1,1)*1i;
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNN('Matrix * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = rand(1,1);
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40);
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1);
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1);
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500);
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000);
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1);
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffNN('Matrix * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNN('Matrix * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * (real).''');
disp(' ');
if( isequal([]*[].',mtimesx([],[],'T')) )
disp('Empty * Empty.'' EQUAL');
else
disp('Empty * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = rand(10000,1);
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1);
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1);
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000);
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1);
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000);
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000);
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1) + rand(1,1)*1i;
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1) + rand(1,1)*1i;
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1);
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1);
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000);
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1);
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000);
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000);
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * (real)''');
disp(' ');
if( isequal([]*[]',mtimesx([],[],'C')) )
disp('Empty * Empty'' EQUAL');
else
disp('Empty * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = rand(10000,1);
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1);
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1);
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000);
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1);
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000);
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000);
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffNC('Matrix * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1) + rand(1,1)*1i;
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1) + rand(1,1)*1i;
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNC('Matrix * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1);
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1);
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000);
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1);
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000);
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000);
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffNC('Matrix * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNC('Matrix * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * conj(real)');
disp(' ');
%if( false ) % debug jump
if( isequal([]*conj([]),mtimesx([],[],'G')) )
disp('Empty * conj(Empty) EQUAL');
else
disp('Empty * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000);
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = rand(1,1);
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40);
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1);
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1);
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500);
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000);
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1);
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = rand(1,1) + rand(1,1)*1i;
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1) + rand(1,1)*1i;
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj((real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = rand(1,1);
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40);
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1);
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1);
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500);
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000);
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1);
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * (real)');
disp(' ');
if( isequal([]'*[],mtimesx([],'C',[])) )
disp('Empty.'' * Empty EQUAL');
else
disp('Empty.'' * Empty NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000);
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1);
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40);
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1);
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500);
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000);
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1);
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1) + rand(1,1)*1i;
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1);
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40);
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1);
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500);
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000);
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1);
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * (real).''');
disp(' ');
if( isequal([].'*[]',mtimesx([],'T',[],'C')) )
disp('Empty.'' * Empty.'' EQUAL');
else
disp('Empty.'' * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = rand(10000,1);
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1);
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000);
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1);
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000);
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000);
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1) + rand(1,1)*1i;
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1);
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000);
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1);
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000);
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000);
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * (real)''');
disp(' ');
if( isequal([].'*[]',mtimesx([],'T',[],'C')) )
disp('Empty.'' * Empty'' EQUAL');
else
disp('Empty.'' * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = rand(10000,1);
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1);
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000);
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1);
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000);
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000);
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1) + rand(1,1)*1i;
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1);
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000);
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1);
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000);
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000);
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * conj(real)');
disp(' ');
if( isequal([]'*conj([]),mtimesx([],'C',[],'G')) )
disp('Empty.'' * conj(Empty) EQUAL');
else
disp('Empty.'' * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000);
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1);
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40);
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1);
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500);
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000);
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1);
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1) + rand(1,1)*1i;
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1);
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40);
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1);
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500);
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000);
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1);
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * (real)');
disp(' ');
if( isequal([]'*[],mtimesx([],'C',[])) )
disp('Empty'' * Empty EQUAL');
else
disp('Empty'' * Empty NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000);
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1);
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40);
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1);
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500);
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000);
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1);
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffCN('Matrix'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1) + rand(1,1)*1i;
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCN('Matrix'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1);
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40);
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1);
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500);
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000);
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1);
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffCN('Matrix'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCN('Matrix'' * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * (real).''');
disp(' ');
if( isequal([]'*[]',mtimesx([],'C',[],'C')) )
disp('Empty'' * Empty.'' EQUAL');
else
disp('Empty'' * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = rand(10000,1);
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1);
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000);
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1);
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000);
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000);
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1) + rand(1,1)*1i;
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1);
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000);
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1);
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000);
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000);
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * (real)''');
disp(' ');
if( isequal([]'*[]',mtimesx([],'C',[],'C')) )
disp('Empty'' * Empty'' EQUAL');
else
disp('Empty'' * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = rand(10000,1);
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1);
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000);
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1);
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000);
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000);
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1) + rand(1,1)*1i;
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1);
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000);
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1);
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000);
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000);
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * conj(real)');
disp(' ');
if( isequal([]'*conj([]),mtimesx([],'C',[],'G')) )
disp('Empty'' * conj(Empty) EQUAL');
else
disp('Empty'' * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000);
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1);
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40);
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1);
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500);
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000);
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1);
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1) + rand(1,1)*1i;
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1);
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40);
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1);
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500);
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000);
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1);
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * (real)');
disp(' ');
if( isequal(conj([])*[],mtimesx([],'G',[])) )
disp('conj(Empty) * Empty EQUAL');
else
disp('conj(Empty) * Empty NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000);
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = rand(1,1);
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40);
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1);
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1);
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500);
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000);
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1);
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = rand(1,1) + rand(1,1)*1i;
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1) + rand(1,1)*1i;
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = rand(1,1);
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40);
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1);
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1);
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500);
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000);
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1);
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * (real).''');
disp(' ');
if( isequal(conj([])*[].',mtimesx([],'G',[],'T')) )
disp('conj(Empty) * Empty.'' EQUAL');
else
disp('conj(Empty) * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = rand(10000,1);
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1);
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1);
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000);
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1);
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000);
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000);
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1) + rand(1,1)*1i;
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1) + rand(1,1)*1i;
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1);
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1);
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000);
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1);
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000);
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000);
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * (real)''');
disp(' ');
if( isequal(conj([])*[]',mtimesx([],'G',[],'C')) )
disp('conj(Empty) * Empty'' EQUAL');
else
disp('conj(Empty) * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = rand(10000,1);
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1);
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1);
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000);
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1);
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000);
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000);
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = rand(1,1) + rand(1,1)*1i;
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1) + rand(1,1)*1i;
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1);
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1);
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000);
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1);
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000);
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000);
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * conj(real)');
disp(' ');
if( isequal(conj([])*conj([]),mtimesx([],'G',[],'G')) )
disp('conj(Empty) * conj(Empty) EQUAL');
else
disp('conj(Empty) * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000);
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = rand(1,1);
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40);
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1);
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1);
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500);
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000);
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1);
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000);
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = rand(1,1) + rand(1,1)*1i;
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = rand(1,1) + rand(1,1)*1i;
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000);
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = rand(1,1);
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40);
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1);
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1);
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500);
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000);
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1);
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000);
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ... symmetric cases op(A) * op(A)');
disp(' ');
disp('real');
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(2000));
maxdiffsymCN('Matrix'' * Same ',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymNC('Matrix * Same''',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymTN('Matrix.'' * Same ',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymNT('Matrix * Same.''',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymGC('conj(Matrix) * Same''',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymCG('Matrix'' * conj(Same)',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymGT('conj(Matrix) * Same.'' ',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymTG('Matrix.'' * conj(Same)',A,r);
r = rsave;
disp(' ' );
disp('complex');
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymCN('Matrix'' * Same ',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymNC('Matrix * Same''',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymTN('Matrix.'' * Same ',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymNT('Matrix * Same.''',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymGC('conj(Matrix) * Same''',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymCG('Matrix'' * conj(Same)',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymGT('conj(Matrix) * Same.''',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymTG('Matrix.'' * conj(Same)',A,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp('Numerical Comparison Tests ... special scalar cases');
disp(' ');
disp('(scalar) * (real)');
disp(' ');
r = r + 1;
mtimesx_dtable(r,:) = ' Real*Real Real*Cplx Cplx*Real Cplx*Cplx';
rsave = r;
r = r + 1;
A = single(1);
B = rand(2500);
maxdiffNN('( 1+0i) * Matrix ',A,B,r);
r = r + 1;
A = single(1 + 1i);
B = rand(2500);
maxdiffNN('( 1+1i) * Matrix ',A,B,r);
r = r + 1;
A = single(1 - 1i);
B = rand(2500);
maxdiffNN('( 1-1i) * Matrix ',A,B,r);
r = r + 1;
A = single(1 + 2i);
B = rand(2500);
maxdiffNN('( 1+2i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1);
B = rand(2500);
maxdiffNN('(-1+0i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1 + 1i);
B = rand(2500);
maxdiffNN('(-1+1i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1 - 1i);
B = rand(2500);
maxdiffNN('(-1-1i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1 + 2i);
B = rand(2500);
maxdiffNN('(-1+2i) * Matrix ',A,B,r);
r = r + 1;
A = single(2 + 1i);
B = rand(2500);
maxdiffNN('( 2+1i) * Matrix ',A,B,r);
r = r + 1;
A = single(2 - 1i);
B = rand(2500);
maxdiffNN('( 2-1i) * Matrix ',A,B,r);
disp(' ');
disp('(scalar) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(1);
B = rand(2500) + rand(2500)*1i;
maxdiffNN('( 1+0i) * Matrix ',A,B,r);
r = r + 1;
A = single(1 + 1i);
B = rand(2500) + rand(2500)*1i;
maxdiffNN('( 1+1i) * Matrix ',A,B,r);
r = r + 1;
A = single(1 - 1i);
B = rand(2500) + rand(2500)*1i;
maxdiffNN('( 1-1i) * Matrix ',A,B,r);
r = r + 1;
A = single(1 + 2i);
B = rand(2500) + rand(2500)*1i;
maxdiffNN('( 1+2i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1);
B = rand(2500) + rand(2500)*1i;
maxdiffNN('(-1+0i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1 + 1i);
B = rand(2500) + rand(2500)*1i;
maxdiffNN('(-1+1i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1 - 1i);
B = rand(2500) + rand(2500)*1i;
maxdiffNN('(-1-1i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1 + 2i);
B = rand(2500) + rand(2500)*1i;
maxdiffNN('(-1+2i) * Matrix ',A,B,r);
r = r + 1;
A = single(2 + 1i);
B = rand(2500) + rand(2500)*1i;
maxdiffNN('( 2+1i) * Matrix ',A,B,r);
r = r + 1;
A = single(2 - 1i);
B = rand(2500) + rand(2500)*1i;
maxdiffNN('( 2-1i) * Matrix ',A,B,r);
disp(' ');
disp('(scalar) * (complex)''');
disp(' ');
%r = rsave;
r = r + 1;
A = single(1);
B = rand(2500) + rand(2500)*1i;
maxdiffNC('( 1+0i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(1 + 1i);
B = rand(2500) + rand(2500)*1i;
maxdiffNC('( 1+1i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(1 - 1i);
B = rand(2500) + rand(2500)*1i;
maxdiffNC('( 1-1i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(1 + 2i);
B = rand(2500) + rand(2500)*1i;
maxdiffNC('( 1+2i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(-1);
B = rand(2500) + rand(2500)*1i;
maxdiffNC('(-1+0i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(-1 + 1i);
B = rand(2500) + rand(2500)*1i;
maxdiffNC('(-1+1i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(-1 - 1i);
B = rand(2500) + rand(2500)*1i;
maxdiffNC('(-1-1i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(-1 + 2i);
B = rand(2500) + rand(2500)*1i;
maxdiffNC('(-1+2i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(2 + 1i);
B = rand(2500) + rand(2500)*1i;
maxdiffNC('( 2+1i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(2 - 1i);
B = rand(2500) + rand(2500)*1i;
maxdiffNC('( 2-1i) * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(' --- DONE ! ---');
disp(' ');
disp('Summary of Numerical Comparison Tests, max relative element difference:');
disp(' ');
mtimesx_dtable(1,1:k) = compver;
disp(mtimesx_dtable);
disp(' ');
dtable = mtimesx_dtable;
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNN(T,A,B,r)
Cm = A*B;
Cx = mtimesx(A,B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCN(T,A,B,r)
Cm = A'*B;
Cx = mtimesx(A,'C',B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTN(T,A,B,r)
Cm = A.'*B;
Cx = mtimesx(A,'T',B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGN(T,A,B,r)
Cm = conj(A)*B;
Cx = mtimesx(A,'G',B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNC(T,A,B,r)
Cm = A*B';
Cx = mtimesx(A,B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCC(T,A,B,r)
Cm = A'*B';
Cx = mtimesx(A,'C',B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTC(T,A,B,r)
Cm = A.'*B';
Cx = mtimesx(A,'T',B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGC(T,A,B,r)
Cm = conj(A)*B';
Cx = mtimesx(A,'G',B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNT(T,A,B,r)
Cm = A*B.';
Cx = mtimesx(A,B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCT(T,A,B,r)
Cm = A'*B.';
Cx = mtimesx(A,'C',B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTT(T,A,B,r)
Cm = A.'*B.';
Cx = mtimesx(A,'T',B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGT(T,A,B,r)
Cm = conj(A)*B.';
Cx = mtimesx(A,'G',B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNG(T,A,B,r)
Cm = A*conj(B);
Cx = mtimesx(A,B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCG(T,A,B,r)
Cm = A'*conj(B);
Cx = mtimesx(A,'C',B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTG(T,A,B,r)
Cm = A.'*conj(B);
Cx = mtimesx(A,'T',B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGG(T,A,B,r)
Cm = conj(A)*conj(B);
Cx = mtimesx(A,'G',B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymCN(T,A,r)
Cm = A'*A;
Cx = mtimesx(A,'C',A);
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymNC(T,A,r)
Cm = A*A';
Cx = mtimesx(A,A,'C');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymTN(T,A,r)
Cm = A.'*A;
Cx = mtimesx(A,'T',A);
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymNT(T,A,r)
Cm = A*A.';
Cx = mtimesx(A,A,'T');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymTG(T,A,r)
Cm = A.'*conj(A);
Cx = mtimesx(A,'T',A,'G');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymGT(T,A,r)
Cm = conj(A)*A.';
Cx = mtimesx(A,'G',A,'T');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymCG(T,A,r)
Cm = A'*conj(A);
Cx = mtimesx(A,'C',A,'G');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymGC(T,A,r)
Cm = conj(A)*A';
Cx = mtimesx(A,'G',A,'C');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffout(T,A,B,Cm,Cx,r)
global mtimesx_dtable
lt = length(T);
b = repmat(' ',1,30-lt);
if( isequal(Cm,Cx) )
disp([T b ' EQUAL']);
d = 0;
else
Cm = Cm(:);
Cx = Cx(:);
if( isreal(Cm) && isreal(Cx) )
rx = Cx ~= Cm;
d = max(abs((Cx(rx)-Cm(rx))./Cm(rx)));
else
Cmr = real(Cm);
Cmi = imag(Cm);
Cxr = real(Cx);
Cxi = imag(Cx);
rx = Cxr ~= Cmr;
ix = Cxi ~= Cmi;
dr = max(abs((Cxr(rx)-Cmr(rx))./max(abs(Cmr(rx)),abs(Cmr(rx)))));
di = max(abs((Cxi(ix)-Cmi(ix))./max(abs(Cmi(ix)),abs(Cxi(ix)))));
if( isempty(dr) )
d = di;
elseif( isempty(di) )
d = dr;
else
d = max(dr,di);
end
end
disp([T b ' NOT EQUAL <--- Max relative difference: ' num2str(d)]);
end
mtimesx_dtable(r,1:length(T)) = T;
if( isreal(A) && isreal(B) )
if( d == 0 )
x = [T b ' 0'];
else
x = [T b sprintf('%11.2e',d)];
end
mtimesx_dtable(r,1:length(x)) = x;
elseif( isreal(A) && ~isreal(B) )
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,42:41+length(x)) = x;
elseif( ~isreal(A) && isreal(B) )
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,53:52+length(x)) = x;
else
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymout(T,A,Cm,Cx,r)
global mtimesx_dtable
lt = length(T);
b = repmat(' ',1,30-lt);
if( isequal(Cm,Cx) )
disp([T b ' EQUAL']);
d = 0;
else
Cm = Cm(:);
Cx = Cx(:);
if( isreal(Cm) && isreal(Cx) )
rx = Cx ~= Cm;
d = max(abs((Cx(rx)-Cm(rx))./Cm(rx)));
else
Cmr = real(Cm);
Cmi = imag(Cm);
Cxr = real(Cx);
Cxi = imag(Cx);
rx = Cxr ~= Cmr;
ix = Cxi ~= Cmi;
dr = max(abs((Cxr(rx)-Cmr(rx))./max(abs(Cmr(rx)),abs(Cmr(rx)))));
di = max(abs((Cxi(ix)-Cmi(ix))./max(abs(Cmi(ix)),abs(Cxi(ix)))));
if( isempty(dr) )
d = di;
elseif( isempty(di) )
d = dr;
else
d = max(dr,di);
end
end
disp([T b ' NOT EQUAL <--- Max relative difference: ' num2str(d)]);
end
if( isreal(A) )
if( d == 0 )
x = [T b ' 0'];
else
x = [T b sprintf('%11.2e',d)];
end
mtimesx_dtable(r,1:length(x)) = x;
else
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,1:length(T)) = T;
mtimesx_dtable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function running_time(d)
h = 24*d;
hh = floor(h);
m = 60*(h - hh);
mm = floor(m);
s = 60*(m - mm);
ss = floor(s);
disp(' ');
rt = sprintf('Running time hh:mm:ss = %2.0f:%2.0f:%2.0f',hh,mm,ss);
if( rt(28) == ' ' )
rt(28) = '0';
end
if( rt(31) == ' ' )
rt(31) = '0';
end
disp(rt);
disp(' ');
return
end
|
github
|
dschick/udkm1DsimML-master
|
mtimesx_test_ddequal.m
|
.m
|
udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_ddequal.m
| 94,229 |
utf_8
|
219fa3623cf14a54da7d267a29e61151
|
% Test routine for mtimesx, op(double) * op(double) equality vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_ddequal
% Filename: mtimesx_test_ddequal.m
% Programmer: James Tursa
% Version: 1.0
% Date: September 27, 2009
% Copyright: (c) 2009 by James Tursa, All Rights Reserved
%
% This code uses the BSD License:
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
%
% Syntax:
%
% T = mtimesx_test_ddequal
%
% Output:
%
% T = A character array containing a summary of the results.
%
%--------------------------------------------------------------------------
function dtable = mtimesx_test_ddequal
global mtimesx_dtable
disp(' ');
disp('****************************************************************************');
disp('* *');
disp('* WARNING WARNING WARNING WARNING WARNING WARNING WARNING WARNING *');
disp('* *');
disp('* This test program can take an hour or so to complete. It is suggested *');
disp('* that you close all applications and run this program during your lunch *');
disp('* break or overnight to minimize impacts to your computer usage. *');
disp('* *');
disp('* The program will be done when you see the message: DONE ! *');
disp('* *');
disp('****************************************************************************');
disp(' ');
input('Press Enter to start test, or Ctrl-C to exit ','s');
start_time = datenum(clock);
compver = [computer ', ' version ', mtimesx mode ' mtimesx];
k = length(compver);
RC = ' Real*Real Real*Cplx Cplx*Real Cplx*Cplx';
mtimesx_dtable = char([]);
mtimesx_dtable(162,74) = ' ';
mtimesx_dtable(1,1:k) = compver;
mtimesx_dtable(2,:) = RC;
for r=3:162
mtimesx_dtable(r,:) = ' -- -- -- --';
end
disp(' ');
disp(compver);
disp('Test program for function mtimesx:')
disp('----------------------------------');
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * (real)');
disp(' ');
rsave = 2;
r = rsave;
%if( false ) % debug jump
if( isequal([]*[],mtimesx([],[])) )
disp('Empty * Empty EQUAL');
else
disp('Empty * Empty NOT EQUAL <---');
end
r = r + 1;
A = rand(1,1);
B = rand(1,10000);
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = rand(1,1);
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40);
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1);
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1);
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500);
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000);
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1);
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffNN('Matrix * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNN('Matrix * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = rand(1,1);
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40);
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1);
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1);
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500);
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000);
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1);
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffNN('Matrix * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNN('Matrix * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * (real).''');
disp(' ');
if( isequal([]*[].',mtimesx([],[],'T')) )
disp('Empty * Empty.'' EQUAL');
else
disp('Empty * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = rand(10000,1);
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1);
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1);
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000);
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1);
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000);
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000);
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1);
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1);
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000);
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1);
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000);
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000);
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * (real)''');
disp(' ');
if( isequal([]*[]',mtimesx([],[],'C')) )
disp('Empty * Empty'' EQUAL');
else
disp('Empty * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = rand(10000,1);
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1);
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1);
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000);
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1);
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000);
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000);
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffNC('Matrix * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNC('Matrix * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1);
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1);
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000);
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1);
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000);
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000);
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffNC('Matrix * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNC('Matrix * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * conj(real)');
disp(' ');
%if( false ) % debug jump
if( isequal([]*conj([]),mtimesx([],[],'G')) )
disp('Empty * conj(Empty) EQUAL');
else
disp('Empty * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000);
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = rand(1,1);
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40);
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1);
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1);
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500);
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000);
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1);
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1) + rand(1,1)*1i;
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj((real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = rand(1,1);
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40);
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1);
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1);
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500);
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000);
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1);
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * (real)');
disp(' ');
if( isequal([]'*[],mtimesx([],'C',[])) )
disp('Empty.'' * Empty EQUAL');
else
disp('Empty.'' * Empty NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = rand(1,10000);
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1);
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40);
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1);
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500);
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000);
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1);
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1) + rand(1,1)*1i;
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1);
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40);
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1);
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500);
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000);
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1);
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * (real).''');
disp(' ');
if( isequal([].'*[]',mtimesx([],'T',[],'C')) )
disp('Empty.'' * Empty.'' EQUAL');
else
disp('Empty.'' * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = rand(10000,1);
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1);
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000);
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1);
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000);
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000);
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1) + rand(1,1)*1i;
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1);
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000);
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1);
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000);
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000);
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * (real)''');
disp(' ');
if( isequal([].'*[]',mtimesx([],'T',[],'C')) )
disp('Empty.'' * Empty'' EQUAL');
else
disp('Empty.'' * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = rand(10000,1);
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1);
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000);
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1);
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000);
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000);
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1) + rand(1,1)*1i;
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1);
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000);
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1);
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000);
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000);
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * conj(real)');
disp(' ');
if( isequal([]'*conj([]),mtimesx([],'C',[],'G')) )
disp('Empty.'' * conj(Empty) EQUAL');
else
disp('Empty.'' * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = rand(1,10000);
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1);
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40);
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1);
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500);
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000);
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1);
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1) + rand(1,1)*1i;
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1);
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40);
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1);
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500);
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000);
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1);
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * (real)');
disp(' ');
if( isequal([]'*[],mtimesx([],'C',[])) )
disp('Empty'' * Empty EQUAL');
else
disp('Empty'' * Empty NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = rand(1,10000);
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1);
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40);
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1);
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500);
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000);
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1);
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffCN('Matrix'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1) + rand(1,1)*1i;
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCN('Matrix'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1);
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40);
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1);
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500);
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000);
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1);
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffCN('Matrix'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCN('Matrix'' * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * (real).''');
disp(' ');
if( isequal([]'*[]',mtimesx([],'C',[],'C')) )
disp('Empty'' * Empty.'' EQUAL');
else
disp('Empty'' * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = rand(10000,1);
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1);
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000);
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1);
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000);
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000);
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1) + rand(1,1)*1i;
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1);
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000);
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1);
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000);
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000);
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * (real)''');
disp(' ');
if( isequal([]'*[]',mtimesx([],'C',[],'C')) )
disp('Empty'' * Empty'' EQUAL');
else
disp('Empty'' * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = rand(10000,1);
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1);
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000);
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1);
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000);
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000);
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1) + rand(1,1)*1i;
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1);
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000);
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1);
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000);
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000);
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * conj(real)');
disp(' ');
if( isequal([]'*conj([]),mtimesx([],'C',[],'G')) )
disp('Empty'' * conj(Empty) EQUAL');
else
disp('Empty'' * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = rand(1,10000);
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1);
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40);
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1);
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500);
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000);
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1);
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1) + rand(1,1)*1i;
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1);
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40);
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1);
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500);
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000);
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1);
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * (real)');
disp(' ');
if( isequal(conj([])*[],mtimesx([],'G',[])) )
disp('conj(Empty) * Empty EQUAL');
else
disp('conj(Empty) * Empty NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000);
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = rand(1,1);
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40);
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1);
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1);
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500);
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000);
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1);
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = rand(1,1);
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40);
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1);
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1);
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500);
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000);
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1);
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * (real).''');
disp(' ');
if( isequal(conj([])*[].',mtimesx([],'G',[],'T')) )
disp('conj(Empty) * Empty.'' EQUAL');
else
disp('conj(Empty) * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = rand(10000,1);
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1);
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1);
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000);
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1);
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000);
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000);
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1);
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1);
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000);
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1);
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000);
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000);
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * (real)''');
disp(' ');
if( isequal(conj([])*[]',mtimesx([],'G',[],'C')) )
disp('conj(Empty) * Empty'' EQUAL');
else
disp('conj(Empty) * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = rand(10000,1);
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1);
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1);
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000);
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1);
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000);
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000);
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1);
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1);
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000);
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1);
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000);
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000);
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1,1000) + rand(1,1000)*1i;
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * conj(real)');
disp(' ');
if( isequal(conj([])*conj([]),mtimesx([],'G',[],'G')) )
disp('conj(Empty) * conj(Empty) EQUAL');
else
disp('conj(Empty) * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = rand(1,10000);
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = rand(1,1);
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40);
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1);
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1);
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500);
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000);
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1);
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000);
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = rand(1,1) + rand(1,1)*1i;
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000);
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = rand(1,1);
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40);
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1);
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1);
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500);
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000);
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1);
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000);
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,10000) + rand(1,10000)*1i;
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,40) + rand(10,20,30,40)*1i;
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1) + rand(1000,1)*1i;
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = rand(1000,1000) + rand(1000,1000)*1i;
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ... symmetric cases op(A) * op(A)');
disp(' ');
disp('real');
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(2000);
maxdiffsymCN('Matrix'' * Same ',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymNC('Matrix * Same''',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymTN('Matrix.'' * Same ',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymNT('Matrix * Same.''',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymGC('conj(Matrix) * Same''',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymCG('Matrix'' * conj(Same)',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymGT('conj(Matrix) * Same.'' ',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymTG('Matrix.'' * conj(Same)',A,r);
r = rsave;
disp(' ' );
disp('complex');
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymCN('Matrix'' * Same ',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymNC('Matrix * Same''',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymTN('Matrix.'' * Same ',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymNT('Matrix * Same.''',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymGC('conj(Matrix) * Same''',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymCG('Matrix'' * conj(Same)',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymGT('conj(Matrix) * Same.''',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymTG('Matrix.'' * conj(Same)',A,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp('Numerical Comparison Tests ... special scalar cases');
disp(' ');
disp('(scalar) * (real)');
disp(' ');
r = r + 1;
mtimesx_dtable(r,:) = ' Real*Real Real*Cplx Cplx*Real Cplx*Cplx';
rsave = r;
r = r + 1;
A = 1;
B = rand(2500);
maxdiffNN('( 1+0i) * Matrix ',A,B,r);
r = r + 1;
A = 1 + 1i;
B = rand(2500);
maxdiffNN('( 1+1i) * Matrix ',A,B,r);
r = r + 1;
A = 1 - 1i;
B = rand(2500);
maxdiffNN('( 1-1i) * Matrix ',A,B,r);
r = r + 1;
A = 1 + 2i;
B = rand(2500);
maxdiffNN('( 1+2i) * Matrix ',A,B,r);
r = r + 1;
A = -1;
B = rand(2500);
maxdiffNN('(-1+0i) * Matrix ',A,B,r);
r = r + 1;
A = -1 + 1i;
B = rand(2500);
maxdiffNN('(-1+1i) * Matrix ',A,B,r);
r = r + 1;
A = -1 - 1i;
B = rand(2500);
maxdiffNN('(-1-1i) * Matrix ',A,B,r);
r = r + 1;
A = -1 + 2i;
B = rand(2500);
maxdiffNN('(-1+2i) * Matrix ',A,B,r);
r = r + 1;
A = 2 + 1i;
B = rand(2500);
maxdiffNN('( 2+1i) * Matrix ',A,B,r);
r = r + 1;
A = 2 - 1i;
B = rand(2500);
maxdiffNN('( 2-1i) * Matrix ',A,B,r);
disp(' ');
disp('(scalar) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = 1;
B = rand(2500) + rand(2500)*1i;
maxdiffNN('( 1+0i) * Matrix ',A,B,r);
r = r + 1;
A = 1 + 1i;
B = rand(2500) + rand(2500)*1i;
maxdiffNN('( 1+1i) * Matrix ',A,B,r);
r = r + 1;
A = 1 - 1i;
B = rand(2500) + rand(2500)*1i;
maxdiffNN('( 1-1i) * Matrix ',A,B,r);
r = r + 1;
A = 1 + 2i;
B = rand(2500) + rand(2500)*1i;
maxdiffNN('( 1+2i) * Matrix ',A,B,r);
r = r + 1;
A = -1;
B = rand(2500) + rand(2500)*1i;
maxdiffNN('(-1+0i) * Matrix ',A,B,r);
r = r + 1;
A = -1 + 1i;
B = rand(2500) + rand(2500)*1i;
maxdiffNN('(-1+1i) * Matrix ',A,B,r);
r = r + 1;
A = -1 - 1i;
B = rand(2500) + rand(2500)*1i;
maxdiffNN('(-1-1i) * Matrix ',A,B,r);
r = r + 1;
A = -1 + 2i;
B = rand(2500) + rand(2500)*1i;
maxdiffNN('(-1+2i) * Matrix ',A,B,r);
r = r + 1;
A = 2 + 1i;
B = rand(2500) + rand(2500)*1i;
maxdiffNN('( 2+1i) * Matrix ',A,B,r);
r = r + 1;
A = 2 - 1i;
B = rand(2500) + rand(2500)*1i;
maxdiffNN('( 2-1i) * Matrix ',A,B,r);
disp(' ');
disp('(scalar) * (complex)''');
disp(' ');
%r = rsave;
r = r + 1;
A = 1;
B = rand(2500) + rand(2500)*1i;
maxdiffNC('( 1+0i) * Matrix'' ',A,B,r);
r = r + 1;
A = 1 + 1i;
B = rand(2500) + rand(2500)*1i;
maxdiffNC('( 1+1i) * Matrix'' ',A,B,r);
r = r + 1;
A = 1 - 1i;
B = rand(2500) + rand(2500)*1i;
maxdiffNC('( 1-1i) * Matrix'' ',A,B,r);
r = r + 1;
A = 1 + 2i;
B = rand(2500) + rand(2500)*1i;
maxdiffNC('( 1+2i) * Matrix'' ',A,B,r);
r = r + 1;
A = -1;
B = rand(2500) + rand(2500)*1i;
maxdiffNC('(-1+0i) * Matrix'' ',A,B,r);
r = r + 1;
A = -1 + 1i;
B = rand(2500) + rand(2500)*1i;
maxdiffNC('(-1+1i) * Matrix'' ',A,B,r);
r = r + 1;
A = -1 - 1i;
B = rand(2500) + rand(2500)*1i;
maxdiffNC('(-1-1i) * Matrix'' ',A,B,r);
r = r + 1;
A = -1 + 2i;
B = rand(2500) + rand(2500)*1i;
maxdiffNC('(-1+2i) * Matrix'' ',A,B,r);
r = r + 1;
A = 2 + 1i;
B = rand(2500) + rand(2500)*1i;
maxdiffNC('( 2+1i) * Matrix'' ',A,B,r);
r = r + 1;
A = 2 - 1i;
B = rand(2500) + rand(2500)*1i;
maxdiffNC('( 2-1i) * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp('Numerical Comparison Tests ... special (scalar) * (sparse) cases');
disp('Real * Real, Real * Cmpx, Cmpx * Real, Cmpx * Cmpx');
disp(' ');
r = r + 1;
mtimesx_dtable(r,:) = RC;
% rsave = r;
r = r + 1;
A = rand(1,1);
B = sprand(5000,5000,.1);
maxdiffNN('Scalar * Sparse',A,B,r);
A = rand(1,1);
B = sprand(5000,5000,.1); B = B + B*2i;
maxdiffNN('Scalar * Sparse',A,B,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1);
maxdiffNN('Scalar * Sparse',A,B,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1); B = B + B*2i;
maxdiffNN('Scalar * Sparse',A,B,r);
r = r + 1;
A = rand(1,1);
B = sprand(5000,5000,.1);
maxdiffNT('Scalar * Sparse.''',A,B,r);
A = rand(1,1);
B = sprand(5000,5000,.1); B = B + B*2i;
maxdiffNT('Scalar * Sparse.''',A,B,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1);
maxdiffNT('Scalar * Sparse.''',A,B,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1); B = B + B*2i;
maxdiffNT('Scalar * Sparse.''',A,B,r);
r = r + 1;
A = rand(1,1);
B = sprand(5000,5000,.1);
maxdiffNC('Scalar * Sparse''',A,B,r);
A = rand(1,1);
B = sprand(5000,5000,.1); B = B + B*2i;
maxdiffNC('Scalar * Sparse''',A,B,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1);
maxdiffNC('Scalar * Sparse''',A,B,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1); B = B + B*2i;
maxdiffNC('Scalar * Sparse''',A,B,r);
r = r + 1;
A = rand(1,1);
B = sprand(5000,5000,.1);
maxdiffNG('Scalar * conj(Sparse)',A,B,r);
A = rand(1,1);
B = sprand(5000,5000,.1); B = B + B*2i;
maxdiffNG('Scalar * conj(Sparse)',A,B,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1);
maxdiffNG('Scalar * conj(Sparse)',A,B,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1); B = B + B*2i;
maxdiffNG('Scalar * conj(Sparse)',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(' --- DONE ! ---');
disp(' ');
disp('Summary of Numerical Comparison Tests, max relative element difference:');
disp(' ');
mtimesx_dtable(1,1:k) = compver;
disp(mtimesx_dtable);
disp(' ');
dtable = mtimesx_dtable;
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNN(T,A,B,r)
Cm = A*B;
Cx = mtimesx(A,B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCN(T,A,B,r)
Cm = A'*B;
Cx = mtimesx(A,'C',B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTN(T,A,B,r)
Cm = A.'*B;
Cx = mtimesx(A,'T',B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGN(T,A,B,r)
Cm = conj(A)*B;
Cx = mtimesx(A,'G',B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNC(T,A,B,r)
Cm = A*B';
Cx = mtimesx(A,B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCC(T,A,B,r)
Cm = A'*B';
Cx = mtimesx(A,'C',B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTC(T,A,B,r)
Cm = A.'*B';
Cx = mtimesx(A,'T',B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGC(T,A,B,r)
Cm = conj(A)*B';
Cx = mtimesx(A,'G',B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNT(T,A,B,r)
Cm = A*B.';
Cx = mtimesx(A,B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCT(T,A,B,r)
Cm = A'*B.';
Cx = mtimesx(A,'C',B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTT(T,A,B,r)
Cm = A.'*B.';
Cx = mtimesx(A,'T',B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGT(T,A,B,r)
Cm = conj(A)*B.';
Cx = mtimesx(A,'G',B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNG(T,A,B,r)
Cm = A*conj(B);
Cx = mtimesx(A,B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCG(T,A,B,r)
Cm = A'*conj(B);
Cx = mtimesx(A,'C',B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTG(T,A,B,r)
Cm = A.'*conj(B);
Cx = mtimesx(A,'T',B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGG(T,A,B,r)
Cm = conj(A)*conj(B);
Cx = mtimesx(A,'G',B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymCN(T,A,r)
Cm = A'*A;
Cx = mtimesx(A,'C',A);
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymNC(T,A,r)
Cm = A*A';
Cx = mtimesx(A,A,'C');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymTN(T,A,r)
Cm = A.'*A;
Cx = mtimesx(A,'T',A);
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymNT(T,A,r)
Cm = A*A.';
Cx = mtimesx(A,A,'T');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymTG(T,A,r)
Cm = A.'*conj(A);
Cx = mtimesx(A,'T',A,'G');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymGT(T,A,r)
Cm = conj(A)*A.';
Cx = mtimesx(A,'G',A,'T');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymCG(T,A,r)
Cm = A'*conj(A);
Cx = mtimesx(A,'C',A,'G');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymGC(T,A,r)
Cm = conj(A)*A';
Cx = mtimesx(A,'G',A,'C');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffout(T,A,B,Cm,Cx,r)
global mtimesx_dtable
lt = length(T);
b = repmat(' ',1,30-lt);
if( isequal(Cm,Cx) )
disp([T b ' EQUAL']);
d = 0;
else
Cm = Cm(:);
Cx = Cx(:);
if( isreal(Cm) && isreal(Cx) )
rx = Cx ~= Cm;
d = max(abs((Cx(rx)-Cm(rx))./Cm(rx)));
else
Cmr = real(Cm);
Cmi = imag(Cm);
Cxr = real(Cx);
Cxi = imag(Cx);
rx = Cxr ~= Cmr;
ix = Cxi ~= Cmi;
dr = max(abs((Cxr(rx)-Cmr(rx))./max(abs(Cmr(rx)),abs(Cmr(rx)))));
di = max(abs((Cxi(ix)-Cmi(ix))./max(abs(Cmi(ix)),abs(Cxi(ix)))));
if( isempty(dr) )
d = di;
elseif( isempty(di) )
d = dr;
else
d = max(dr,di);
end
end
disp([T b ' NOT EQUAL <--- Max relative difference: ' num2str(d)]);
end
mtimesx_dtable(r,1:length(T)) = T;
if( isreal(A) && isreal(B) )
if( d == 0 )
x = [T b ' 0'];
else
x = [T b sprintf('%11.2e',d)];
end
mtimesx_dtable(r,1:length(x)) = x;
elseif( isreal(A) && ~isreal(B) )
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,42:41+length(x)) = x;
elseif( ~isreal(A) && isreal(B) )
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,53:52+length(x)) = x;
else
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymout(T,A,Cm,Cx,r)
global mtimesx_dtable
lt = length(T);
b = repmat(' ',1,30-lt);
if( isequal(Cm,Cx) )
disp([T b ' EQUAL']);
d = 0;
else
Cm = Cm(:);
Cx = Cx(:);
if( isreal(Cm) && isreal(Cx) )
rx = Cx ~= Cm;
d = max(abs((Cx(rx)-Cm(rx))./Cm(rx)));
else
Cmr = real(Cm);
Cmi = imag(Cm);
Cxr = real(Cx);
Cxi = imag(Cx);
rx = Cxr ~= Cmr;
ix = Cxi ~= Cmi;
dr = max(abs((Cxr(rx)-Cmr(rx))./max(abs(Cmr(rx)),abs(Cmr(rx)))));
di = max(abs((Cxi(ix)-Cmi(ix))./max(abs(Cmi(ix)),abs(Cxi(ix)))));
if( isempty(dr) )
d = di;
elseif( isempty(di) )
d = dr;
else
d = max(dr,di);
end
end
disp([T b ' NOT EQUAL <--- Max relative difference: ' num2str(d)]);
end
if( isreal(A) )
if( d == 0 )
x = [T b ' 0'];
else
x = [T b sprintf('%11.2e',d)];
end
mtimesx_dtable(r,1:length(x)) = x;
else
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,1:length(T)) = T;
mtimesx_dtable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function running_time(d)
h = 24*d;
hh = floor(h);
m = 60*(h - hh);
mm = floor(m);
s = 60*(m - mm);
ss = floor(s);
disp(' ');
rt = sprintf('Running time hh:mm:ss = %2.0f:%2.0f:%2.0f',hh,mm,ss);
if( rt(28) == ' ' )
rt(28) = '0';
end
if( rt(31) == ' ' )
rt(31) = '0';
end
disp(rt);
disp(' ');
return
end
|
github
|
dschick/udkm1DsimML-master
|
mtimesx_test_dsequal.m
|
.m
|
udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_dsequal.m
| 350,693 |
utf_8
|
325490ae690791eb9f0e7d03408cc540
|
% Test routine for mtimesx, op(double) * op(single) equality vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_dsequal
% Filename: mtimesx_test_dsequal.m
% Programmer: James Tursa
% Version: 1.0
% Date: September 27, 2009
% Copyright: (c) 2009 by James Tursa, All Rights Reserved
%
% This code uses the BSD License:
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
%
% Syntax:
%
% T = mtimesx_test_ddequal
%
% Output:
%
% T = A character array containing a summary of the results.
%
%--------------------------------------------------------------------------
function dtable = mtimesx_test_dsequal
global mtimesx_dtable
disp(' ');
disp('****************************************************************************');
disp('* *');
disp('* WARNING WARNING WARNING WARNING WARNING WARNING WARNING WARNING *');
disp('* *');
disp('* This test program can take an hour or so to complete. It is suggested *');
disp('* that you close all applications and run this program during your lunch *');
disp('* break or overnight to minimize impacts to your computer usage. *');
disp('* *');
disp('* The program will be done when you see the message: DONE ! *');
disp('* *');
disp('****************************************************************************');
disp(' ');
try
input('Press Enter to start test, or Ctrl-C to exit ','s');
catch
dtable = '';
return
end
start_time = datenum(clock);
compver = [computer ', ' version ', mtimesx mode ' mtimesx];
k = length(compver);
RC = ' Real*Real Real*Cplx Cplx*Real Cplx*Cplx';
mtimesx_dtable = char([]);
mtimesx_dtable(157,74) = ' ';
mtimesx_dtable(1,1:k) = compver;
mtimesx_dtable(2,:) = RC;
for r=3:157
mtimesx_dtable(r,:) = ' -- -- -- --';
end
disp(' ');
disp(compver);
disp('Test program for function mtimesx:')
disp('----------------------------------');
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * (real)');
disp(' ');
rsave = 2;
r = rsave;
%if( false ) % debug jump
if( isequal([]*[],mtimesx([],[])) )
disp('Empty * Empty EQUAL');
else
disp('Empty * Empty NOT EQUAL <---');
end
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000));
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = single(rand(1,1));
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40));
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1));
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1));
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500));
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000));
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1));
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffNN('Matrix * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNN('Matrix * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = single(rand(1,1));
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40));
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1));
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1));
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500));
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000));
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1));
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffNN('Matrix * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNN('Matrix * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * (real).''');
disp(' ');
if( isequal([]*[].',mtimesx([],[],'T')) )
disp('Empty * Empty.'' EQUAL');
else
disp('Empty * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = single(rand(10000,1));
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1));
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1));
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000));
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1));
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000));
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000));
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1));
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1));
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000));
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1));
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000));
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000));
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * (real)''');
disp(' ');
if( isequal([]*[]',mtimesx([],[],'C')) )
disp('Empty * Empty'' EQUAL');
else
disp('Empty * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = single(rand(10000,1));
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1));
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1));
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000));
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1));
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000));
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000));
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffNC('Matrix * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNC('Matrix * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1));
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1));
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000));
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1));
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000));
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000));
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffNC('Matrix * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNC('Matrix * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * conj(real)');
disp(' ');
%if( false ) % debug jump
if( isequal([]*conj([]),mtimesx([],[],'G')) )
disp('Empty * conj(Empty) EQUAL');
else
disp('Empty * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000));
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = single(rand(1,1));
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40));
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1));
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1));
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500));
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000));
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1));
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj((real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = single(rand(1,1));
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40));
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1));
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1));
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500));
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000));
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1));
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * (real)');
disp(' ');
if( isequal([]'*[],mtimesx([],'C',[])) )
disp('Empty.'' * Empty EQUAL');
else
disp('Empty.'' * Empty NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000));
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1));
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40));
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1));
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500));
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000));
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1));
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1));
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40));
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1));
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500));
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000));
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1));
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * (real).''');
disp(' ');
if( isequal([].'*[]',mtimesx([],'T',[],'C')) )
disp('Empty.'' * Empty.'' EQUAL');
else
disp('Empty.'' * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = single(rand(10000,1));
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1));
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000));
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1));
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000));
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000));
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1));
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000));
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1));
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000));
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000));
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * (real)''');
disp(' ');
if( isequal([].'*[]',mtimesx([],'T',[],'C')) )
disp('Empty.'' * Empty'' EQUAL');
else
disp('Empty.'' * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = single(rand(10000,1));
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1));
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000));
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1));
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000));
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000));
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1));
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000));
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1));
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000));
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000));
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * conj(real)');
disp(' ');
if( isequal([]'*conj([]),mtimesx([],'C',[],'G')) )
disp('Empty.'' * conj(Empty) EQUAL');
else
disp('Empty.'' * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000));
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1));
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40));
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1));
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500));
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000));
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1));
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1));
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40));
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1));
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500));
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000));
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1));
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * (real)');
disp(' ');
if( isequal([]'*[],mtimesx([],'C',[])) )
disp('Empty'' * Empty EQUAL');
else
disp('Empty'' * Empty NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000));
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1));
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40));
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1));
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500));
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000));
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1));
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffCN('Matrix'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCN('Matrix'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1));
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40));
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1));
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500));
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000));
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1));
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffCN('Matrix'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCN('Matrix'' * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * (real).''');
disp(' ');
if( isequal([]'*[]',mtimesx([],'C',[],'C')) )
disp('Empty'' * Empty.'' EQUAL');
else
disp('Empty'' * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = single(rand(10000,1));
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1));
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000));
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1));
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000));
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000));
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1));
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000));
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1));
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000));
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000));
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * (real)''');
disp(' ');
if( isequal([]'*[]',mtimesx([],'C',[],'C')) )
disp('Empty'' * Empty'' EQUAL');
else
disp('Empty'' * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = single(rand(10000,1));
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1));
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000));
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1));
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000));
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000));
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1));
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000));
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1));
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000));
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000));
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * conj(real)');
disp(' ');
if( isequal([]'*conj([]),mtimesx([],'C',[],'G')) )
disp('Empty'' * conj(Empty) EQUAL');
else
disp('Empty'' * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000));
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1));
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40));
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1));
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500));
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000));
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1));
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1));
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40));
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1));
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500));
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000));
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1));
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1) + rand(1000,1)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * (real)');
disp(' ');
if( isequal(conj([])*[],mtimesx([],'G',[])) )
disp('conj(Empty) * Empty EQUAL');
else
disp('conj(Empty) * Empty NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000));
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = single(rand(1,1));
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40));
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1));
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1));
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500));
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000));
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1));
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = single(rand(1,1));
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40));
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1));
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1));
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500));
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000));
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1));
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * (real).''');
disp(' ');
if( isequal(conj([])*[].',mtimesx([],'G',[],'T')) )
disp('conj(Empty) * Empty.'' EQUAL');
else
disp('conj(Empty) * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = single(rand(10000,1));
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1));
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1));
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000));
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1));
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000));
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000));
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1));
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1));
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000));
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1));
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000));
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000));
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * (real)''');
disp(' ');
if( isequal(conj([])*[]',mtimesx([],'G',[],'C')) )
disp('conj(Empty) * Empty'' EQUAL');
else
disp('conj(Empty) * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = single(rand(10000,1));
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1));
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1));
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000));
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1));
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000));
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000));
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1));
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1));
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000));
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1));
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000));
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000));
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = rand(10000,1)+ rand(10000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * conj(real)');
disp(' ');
if( isequal(conj([])*conj([]),mtimesx([],'G',[],'G')) )
disp('conj(Empty) * conj(Empty) EQUAL');
else
disp('conj(Empty) * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000));
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = single(rand(1,1));
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40));
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1));
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1));
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500));
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000));
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1));
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000));
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000));
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = single(rand(1,1));
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40));
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1));
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1));
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500));
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000));
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1));
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000));
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,10000)+ rand(1,10000)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1,1000) + rand(1,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = rand(1000,1000) + rand(1000,1000)*1i;
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ... symmetric cases op(A) * op(A)');
disp(' ');
disp('real');
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = rand(2000);
maxdiffsymCN('Matrix'' * Same ',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymNC('Matrix * Same''',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymTN('Matrix.'' * Same ',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymNT('Matrix * Same.''',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymGC('conj(Matrix) * Same''',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymCG('Matrix'' * conj(Same)',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymGT('conj(Matrix) * Same.'' ',A,r);
r = r + 1;
A = rand(2000);
maxdiffsymTG('Matrix.'' * conj(Same)',A,r);
r = rsave;
disp(' ' );
disp('complex');
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymCN('Matrix'' * Same ',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymNC('Matrix * Same''',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymTN('Matrix.'' * Same ',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymNT('Matrix * Same.''',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymGC('conj(Matrix) * Same''',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymCG('Matrix'' * conj(Same)',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymGT('conj(Matrix) * Same.''',A,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxdiffsymTG('Matrix.'' * conj(Same)',A,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp('Numerical Comparison Tests ... special scalar cases');
disp(' ');
disp('(scalar) * (real)');
disp(' ');
r = r + 1;
mtimesx_dtable(r,:) = ' Real*Real Real*Cplx Cplx*Real Cplx*Cplx';
rsave = r;
r = r + 1;
A = 1;
B = single(rand(2500));
maxdiffNN('( 1+0i) * Matrix ',A,B,r);
r = r + 1;
A = 1 + 1i;
B = single(rand(2500));
maxdiffNN('( 1+1i) * Matrix ',A,B,r);
r = r + 1;
A = 1 - 1i;
B = single(rand(2500));
maxdiffNN('( 1-1i) * Matrix ',A,B,r);
r = r + 1;
A = 1 + 2i;
B = single(rand(2500));
maxdiffNN('( 1+2i) * Matrix ',A,B,r);
r = r + 1;
A = -1;
B = single(rand(2500));
maxdiffNN('(-1+0i) * Matrix ',A,B,r);
r = r + 1;
A = -1 + 1i;
B = single(rand(2500));
maxdiffNN('(-1+1i) * Matrix ',A,B,r);
r = r + 1;
A = -1 - 1i;
B = single(rand(2500));
maxdiffNN('(-1-1i) * Matrix ',A,B,r);
r = r + 1;
A = -1 + 2i;
B = single(rand(2500));
maxdiffNN('(-1+2i) * Matrix ',A,B,r);
r = r + 1;
A = 2 + 1i;
B = single(rand(2500));
maxdiffNN('( 2+1i) * Matrix ',A,B,r);
r = r + 1;
A = 2 - 1i;
B = single(rand(2500));
maxdiffNN('( 2-1i) * Matrix ',A,B,r);
disp(' ');
disp('(scalar) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = 1;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('( 1+0i) * Matrix ',A,B,r);
r = r + 1;
A = 1 + 1i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('( 1+1i) * Matrix ',A,B,r);
r = r + 1;
A = 1 - 1i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('( 1-1i) * Matrix ',A,B,r);
r = r + 1;
A = 1 + 2i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('( 1+2i) * Matrix ',A,B,r);
r = r + 1;
A = -1;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('(-1+0i) * Matrix ',A,B,r);
r = r + 1;
A = -1 + 1i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('(-1+1i) * Matrix ',A,B,r);
r = r + 1;
A = -1 - 1i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('(-1-1i) * Matrix ',A,B,r);
r = r + 1;
A = -1 + 2i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('(-1+2i) * Matrix ',A,B,r);
r = r + 1;
A = 2 + 1i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('( 2+1i) * Matrix ',A,B,r);
r = r + 1;
A = 2 - 1i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('( 2-1i) * Matrix ',A,B,r);
disp(' ');
disp('(scalar) * (complex)''');
disp(' ');
%r = rsave;
r = r + 1;
A = 1;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('( 1+0i) * Matrix'' ',A,B,r);
r = r + 1;
A = 1 + 1i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('( 1+1i) * Matrix'' ',A,B,r);
r = r + 1;
A = 1 - 1i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('( 1-1i) * Matrix'' ',A,B,r);
r = r + 1;
A = 1 + 2i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('( 1+2i) * Matrix'' ',A,B,r);
r = r + 1;
A = -1;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('(-1+0i) * Matrix'' ',A,B,r);
r = r + 1;
A = -1 + 1i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('(-1+1i) * Matrix'' ',A,B,r);
r = r + 1;
A = -1 - 1i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('(-1-1i) * Matrix'' ',A,B,r);
r = r + 1;
A = -1 + 2i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('(-1+2i) * Matrix'' ',A,B,r);
r = r + 1;
A = 2 + 1i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('( 2+1i) * Matrix'' ',A,B,r);
r = r + 1;
A = 2 - 1i;
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('( 2-1i) * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(' --- DONE ! ---');
disp(' ');
disp('Summary of Numerical Comparison Tests, max relative element difference:');
disp(' ');
mtimesx_dtable(1,1:k) = compver;
disp(mtimesx_dtable);
disp(' ');
dtable = mtimesx_dtable;
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNN(T,A,B,r)
Cm = A*B;
Cx = mtimesx(A,B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCN(T,A,B,r)
Cm = A'*B;
Cx = mtimesx(A,'C',B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTN(T,A,B,r)
Cm = A.'*B;
Cx = mtimesx(A,'T',B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGN(T,A,B,r)
Cm = conj(A)*B;
Cx = mtimesx(A,'G',B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNC(T,A,B,r)
Cm = A*B';
Cx = mtimesx(A,B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCC(T,A,B,r)
Cm = A'*B';
Cx = mtimesx(A,'C',B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTC(T,A,B,r)
Cm = A.'*B';
Cx = mtimesx(A,'T',B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGC(T,A,B,r)
Cm = conj(A)*B';
Cx = mtimesx(A,'G',B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNT(T,A,B,r)
Cm = A*B.';
Cx = mtimesx(A,B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCT(T,A,B,r)
Cm = A'*B.';
Cx = mtimesx(A,'C',B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTT(T,A,B,r)
Cm = A.'*B.';
Cx = mtimesx(A,'T',B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGT(T,A,B,r)
Cm = conj(A)*B.';
Cx = mtimesx(A,'G',B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNG(T,A,B,r)
Cm = A*conj(B);
Cx = mtimesx(A,B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCG(T,A,B,r)
Cm = A'*conj(B);
Cx = mtimesx(A,'C',B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTG(T,A,B,r)
Cm = A.'*conj(B);
Cx = mtimesx(A,'T',B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGG(T,A,B,r)
Cm = conj(A)*conj(B);
Cx = mtimesx(A,'G',B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymCN(T,A,r)
Cm = A'*A;
Cx = mtimesx(A,'C',A);
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymNC(T,A,r)
Cm = A*A';
Cx = mtimesx(A,A,'C');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymTN(T,A,r)
Cm = A.'*A;
Cx = mtimesx(A,'T',A);
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymNT(T,A,r)
Cm = A*A.';
Cx = mtimesx(A,A,'T');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymTG(T,A,r)
Cm = A.'*conj(A);
Cx = mtimesx(A,'T',A,'G');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymGT(T,A,r)
Cm = conj(A)*A.';
Cx = mtimesx(A,'G',A,'T');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymCG(T,A,r)
Cm = A'*conj(A);
Cx = mtimesx(A,'C',A,'G');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymGC(T,A,r)
Cm = conj(A)*A';
Cx = mtimesx(A,'G',A,'C');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffout(T,A,B,Cm,Cx,r)
global mtimesx_dtable
lt = length(T);
b = repmat(' ',1,30-lt);
if( isequal(Cm,Cx) )
disp([T b ' EQUAL']);
d = 0;
else
Cm = Cm(:);
Cx = Cx(:);
if( isreal(Cm) && isreal(Cx) )
rx = Cx ~= Cm;
d = max(abs((Cx(rx)-Cm(rx))./Cm(rx)));
else
Cmr = real(Cm);
Cmi = imag(Cm);
Cxr = real(Cx);
Cxi = imag(Cx);
rx = Cxr ~= Cmr;
ix = Cxi ~= Cmi;
dr = max(abs((Cxr(rx)-Cmr(rx))./max(abs(Cmr(rx)),abs(Cmr(rx)))));
di = max(abs((Cxi(ix)-Cmi(ix))./max(abs(Cmi(ix)),abs(Cxi(ix)))));
if( isempty(dr) )
d = di;
elseif( isempty(di) )
d = dr;
else
d = max(dr,di);
end
end
disp([T b ' NOT EQUAL <--- Max relative difference: ' num2str(d)]);
end
mtimesx_dtable(r,1:length(T)) = T;
if( isreal(A) && isreal(B) )
if( d == 0 )
x = [T b ' 0'];
else
x = [T b sprintf('%11.2e',d)];
end
mtimesx_dtable(r,1:length(x)) = x;
elseif( isreal(A) && ~isreal(B) )
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,42:41+length(x)) = x;
elseif( ~isreal(A) && isreal(B) )
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,53:52+length(x)) = x;
else
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymout(T,A,Cm,Cx,r)
global mtimesx_dtable
lt = length(T);
b = repmat(' ',1,30-lt);
if( isequal(Cm,Cx) )
disp([T b ' EQUAL']);
d = 0;
else
Cm = Cm(:);
Cx = Cx(:);
if( isreal(Cm) && isreal(Cx) )
rx = Cx ~= Cm;
d = max(abs((Cx(rx)-Cm(rx))./Cm(rx)));
else
Cmr = real(Cm);
Cmi = imag(Cm);
Cxr = real(Cx);
Cxi = imag(Cx);
rx = Cxr ~= Cmr;
ix = Cxi ~= Cmi;
dr = max(abs((Cxr(rx)-Cmr(rx))./max(abs(Cmr(rx)),abs(Cmr(rx)))));
di = max(abs((Cxi(ix)-Cmi(ix))./max(abs(Cmi(ix)),abs(Cxi(ix)))));
if( isempty(dr) )
d = di;
elseif( isempty(di) )
d = dr;
else
d = max(dr,di);
end
end
disp([T b ' NOT EQUAL <--- Max relative difference: ' num2str(d)]);
end
if( isreal(A) )
if( d == 0 )
x = [T b ' 0'];
else
x = [T b sprintf('%11.2e',d)];
end
mtimesx_dtable(r,1:length(x)) = x;
else
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,1:length(T)) = T;
mtimesx_dtable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function running_time(d)
h = 24*d;
hh = floor(h);
m = 60*(h - hh);
mm = floor(m);
s = 60*(m - mm);
ss = floor(s);
disp(' ');
rt = sprintf('Running time hh:mm:ss = %2.0f:%2.0f:%2.0f',hh,mm,ss);
if( rt(28) == ' ' )
rt(28) = '0';
end
if( rt(31) == ' ' )
rt(31) = '0';
end
disp(rt);
disp(' ');
return
end
|
github
|
dschick/udkm1DsimML-master
|
mtimesx_test_sdspeed.m
|
.m
|
udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_sdspeed.m
| 388,309 |
utf_8
|
1ed55a613d5cbfe9a11579562f600c9a
|
% Test routine for mtimesx, op(single) * op(double) speed vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_sdspeed
% Filename: mtimesx_test_sdspeed.m
% Programmer: James Tursa
% Version: 1.0
% Date: September 27, 2009
% Copyright: (c) 2009 by James Tursa, All Rights Reserved
%
% This code uses the BSD License:
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
%
% Syntax (arguments in brackets [ ] are optional):
%
% T = mtimesx_test_ddspeed( [N [,D]] )
%
% Inputs:
%
% N = Number of runs to make for each individual test. The test result will
% be the median of N runs. N must be even. If N is odd, it will be
% automatically increased to the next even number. The default is 10,
% which can take *hours* to run. Best to run this program overnight.
% D = The string 'details'. If present, this will cause all of the
% individual intermediate run results to print as they happen.
%
% Output:
%
% T = A character array containing a summary of the results.
%
%--------------------------------------------------------------------------
function ttable = mtimesx_test_sdspeed(nn,details)
global mtimesx_ttable
disp(' ');
disp('****************************************************************************');
disp('* *');
disp('* WARNING WARNING WARNING WARNING WARNING WARNING WARNING WARNING *');
disp('* *');
disp('* This test program can take several *hours* to complete, particularly *');
disp('* when using the default number of runs as 10. It is strongly suggested *');
disp('* to close all applications and run this program overnight to get the *');
disp('* best possible result with minimal impacts to your computer usage. *');
disp('* *');
disp('* The program will be done when you see the message: DONE ! *');
disp('* *');
disp('****************************************************************************');
disp(' ');
try
input('Press Enter to start test, or Ctrl-C to exit ','s');
catch
ttable = '';
return
end
start_time = datenum(clock);
if nargin >= 1
n = nn;
else
n = 10;
end
if nargin < 2
details = false;
else
if( isempty(details) ) % code to get rid of the lint message
details = true;
else
details = true;
end
end
RC = ' Real*Real Real*Cplx Cplx*Real Cplx*Cplx';
compver = [computer ', ' version ', mtimesx mode ' mtimesx ', median of ' num2str(n) ' runs'];
k = length(compver);
mtimesx_ttable = char([]);
mtimesx_ttable(100,74) = ' ';
mtimesx_ttable(1,1:k) = compver;
mtimesx_ttable(2,:) = RC;
for r=3:170
mtimesx_ttable(r,:) = ' -- -- -- --';
end
disp(' ');
disp(compver);
disp('Test program for function mtimesx:')
disp('----------------------------------');
rsave = 2;
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = rand(1,1);
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400);
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1);
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1);
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500);
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000);
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1);
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = rand(1,1) + rand(1,1)*1i;
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1) + rand(1,1)*1i;
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = rand(1,1);
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400);
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = rand(1,1);
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1);
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500);
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000);
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1);
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * (real).''');
disp(' ');
rsave = r;
mtimesx_ttable(r,:) = RC;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1);
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1);
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000);
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1);
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000);
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000);
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1) + rand(1,1)*1i;
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1) + rand(1,1)*1i;
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1);
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1);
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000);
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1);
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000);
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000);
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1);
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1);
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000);
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1);
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000);
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000);
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1) + rand(1,1)*1i;
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1) + rand(1,1)*1i;
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1);
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1);
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000);
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1);
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000);
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000);
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = rand(1,1);
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400);
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1);
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1);
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500);
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000);
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1);
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = rand(1,1) + rand(1,1)*1i;
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1) + rand(1,1)*1i;
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = rand(1,1);
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400);
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = rand(1,1);
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1);
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500);
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000);
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1);
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1);
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400);
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1);
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500);
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000);
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1);
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1) + rand(1,1)*1i;
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1);
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400);
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1);
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500);
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000);
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1);
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1);
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000);
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1);
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000);
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000);
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1) + rand(1,1)*1i;
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1);
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000);
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1);
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000);
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000);
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1);
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000);
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1);
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000);
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000);
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1) + rand(1,1)*1i;
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1);
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000);
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1);
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000);
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000);
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1);
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400);
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1);
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500);
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000);
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1);
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1) + rand(1,1)*1i;
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1);
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400);
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1);
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500);
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000);
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1);
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1);
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400);
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1);
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500);
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000);
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1);
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1) + rand(1,1)*1i;
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1);
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400);
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1);
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500);
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000);
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1);
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1);
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000);
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1);
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000);
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000);
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1) + rand(1,1)*1i;
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1);
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000);
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1);
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000);
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000);
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1);
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000);
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1);
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000);
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000);
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1) + rand(1,1)*1i;
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1);
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000);
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1);
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000);
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000);
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1);
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400);
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1);
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500);
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000);
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1);
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1) + rand(1,1)*1i;
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500));
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1);
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400);
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1);
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500);
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000);
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1);
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,1) + rand(2000,1)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = rand(1,1);
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400);
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1);
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1);
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500);
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000);
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1);
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = rand(1,1) + rand(1,1)*1i;
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1) + rand(1,1)*1i;
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = rand(1,1);
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400);
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = rand(1,1);
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1);
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500);
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000);
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1);
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1);
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1);
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000);
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1);
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000);
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000);
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1) + rand(1,1)*1i;
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1) + rand(1,1)*1i;
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1);
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1);
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000);
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1);
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000);
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000);
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1);
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1);
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000);
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1);
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000);
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000);
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1));
B = rand(1,1) + rand(1,1)*1i;
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1) + rand(1,1)*1i;
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1);
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = rand(1,1);
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000);
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1);
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000);
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000);
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1000000,1) + rand(1000000,1)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000);
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = rand(1,1);
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400);
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1);
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1);
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500);
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000);
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1);
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000);
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000));
B = rand(1,1) + rand(1,1)*1i;
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1));
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400));
B = rand(1,1) + rand(1,1)*1i;
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000));
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1));
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000));
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000);
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = rand(1,1);
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400);
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = rand(1,1);
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1);
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500);
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000);
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1);
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000);
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1000000) + rand(1,1000000)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(1,2000) + rand(1,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = single(rand(2000,2000) + rand(2000,2000)*1i);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs ... symmetric cases op(A) * op(A)']);
disp(' ');
disp('real');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = single(rand(2000));
maxtimesymCN('Matrix'' * Same ',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymNC('Matrix * Same'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymTN('Matrix.'' * Same ',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymNT('Matrix * Same.'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymGC('conj(Matrix) * Same'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymCG('Matrix'' * conj(Same)',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymGT('conj(Matrix) * Same.'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000));
maxtimesymTG('Matrix.'' * conj(Same)',A,n,details,r);
r = rsave;
disp(' ');
disp('complex');
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymCN('Matrix'' * Same ',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymNC('Matrix * Same'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymTN('Matrix.'' * Same ',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymNT('Matrix * Same.'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymGC('conj(Matrix) * Same'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymCG('Matrix'' * conj(Same)',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymGT('conj(Matrix) * Same.'' ',A,n,details,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxtimesymTG('Matrix.'' * conj(Same)',A,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs ... special scalar cases']);
disp(' ');
disp('(scalar) * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(1);
B = rand(2500);
maxtimeNN('( 1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(1 + 1i);
B = rand(2500);
maxtimeNN('( 1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(1 - 1i);
B = rand(2500);
maxtimeNN('( 1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(1 + 2i);
B = rand(2500);
maxtimeNN('( 1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1);
B = rand(2500);
maxtimeNN('(-1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 1i);
B = rand(2500);
maxtimeNN('(-1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1 - 1i);
B = rand(2500);
maxtimeNN('(-1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 2i);
B = rand(2500);
maxtimeNN('(-1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(2 + 1i);
B = rand(2500);
maxtimeNN('( 2+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(2 - 1i);
B = rand(2500);
maxtimeNN('( 2-1i) * Matrix ',A,B,n,details,r);
disp(' ');
disp('(scalar) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(1);
B = rand(2500) + rand(2500)*1i;
maxtimeNN('( 1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(1 + 1i);
B = rand(2500) + rand(2500)*1i;
maxtimeNN('( 1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(1 - 1i);
B = rand(2500) + rand(2500)*1i;
maxtimeNN('( 1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(1 + 2i);
B = rand(2500) + rand(2500)*1i;
maxtimeNN('( 1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1);
B = rand(2500) + rand(2500)*1i;
maxtimeNN('(-1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 1i);
B = rand(2500) + rand(2500)*1i;
maxtimeNN('(-1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1 - 1i);
B = rand(2500) + rand(2500)*1i;
maxtimeNN('(-1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 2i);
B = rand(2500) + rand(2500)*1i;
maxtimeNN('(-1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(2 + 1i);
B = rand(2500) + rand(2500)*1i;
maxtimeNN('( 2+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = single(2 - 1i);
B = rand(2500) + rand(2500)*1i;
maxtimeNN('( 2-1i) * Matrix ',A,B,n,details,r);
disp(' ');
disp('(scalar) * (complex)''');
disp(' ');
%r = rsave;
r = r + 1;
A = single(1);
B = rand(2500) + rand(2500)*1i;
maxtimeNC('( 1+0i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(1 + 1i);
B = rand(2500) + rand(2500)*1i;
maxtimeNC('( 1+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(1 - 1i);
B = rand(2500) + rand(2500)*1i;
maxtimeNC('( 1-1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(1 + 2i);
B = rand(2500) + rand(2500)*1i;
maxtimeNC('( 1+2i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(-1);
B = rand(2500) + rand(2500)*1i;
maxtimeNC('(-1+0i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 1i);
B = rand(2500) + rand(2500)*1i;
maxtimeNC('(-1+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 - 1i);
B = rand(2500) + rand(2500)*1i;
maxtimeNC('(-1-1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(-1 + 2i);
B = rand(2500) + rand(2500)*1i;
maxtimeNC('(-1+2i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(2 + 1i);
B = rand(2500) + rand(2500)*1i;
maxtimeNC('( 2+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = single(2 - 1i);
B = rand(2500) + rand(2500)*1i;
maxtimeNC('( 2-1i) * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(' --- DONE ! ---');
disp(' ');
disp(['Summary of Timing Tests, ' num2str(n) ' runs, + = percent faster, - = percent slower:']);
disp(' ');
mtimesx_ttable(1,1:k) = compver;
disp(mtimesx_ttable);
disp(' ');
ttable = mtimesx_ttable;
running_time(datenum(clock) - start_time);
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*B;
mtoc(k) = toc;
tic;
mtimesx(A,B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*B.';
mtoc(k) = toc;
tic;
mtimesx(A,B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*B';
mtoc(k) = toc;
tic;
mtimesx(A,B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*B;
mtoc(k) = toc;
tic;
mtimesx(A,'T',B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*B.';
mtoc(k) = toc;
tic;
mtimesx(A,'T',B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*B';
mtoc(k) = toc;
tic;
mtimesx(A,'T',B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,'T',B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*B;
mtoc(k) = toc;
tic;
mtimesx(A,'C',B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*B.';
mtoc(k) = toc;
tic;
mtimesx(A,'C',B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*B';
mtoc(k) = toc;
tic;
mtimesx(A,'C',B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,'C',B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*B;
mtoc(k) = toc;
tic;
mtimesx(A,'G',B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*B.';
mtoc(k) = toc;
tic;
mtimesx(A,'G',B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*B';
mtoc(k) = toc;
tic;
mtimesx(A,'G',B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,'G',B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymCN(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*A;
mtoc(k) = toc;
tic;
mtimesx(A,'C',A);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymNC(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*A';
mtoc(k) = toc;
tic;
mtimesx(A,A,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymTN(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*A;
mtoc(k) = toc;
tic;
mtimesx(A,'T',A);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymNT(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*A.';
mtoc(k) = toc;
tic;
mtimesx(A,A,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymCG(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*conj(A);
mtoc(k) = toc;
tic;
mtimesx(A,'C',A,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymGC(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*A';
mtoc(k) = toc;
tic;
mtimesx(A,'G',A,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymTG(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*conj(A);
mtoc(k) = toc;
tic;
mtimesx(A,'T',A,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymGT(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*A.';
mtoc(k) = toc;
tic;
mtimesx(A,'G',A,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeout(T,A,B,p,r)
global mtimesx_ttable
mtimesx_ttable(r,1:length(T)) = T;
if( isreal(A) && isreal(B) )
lt = length(T);
b = repmat(' ',1,30-lt);
x = [T b sprintf('%10.0f%%',-p)];
mtimesx_ttable(r,1:length(x)) = x;
elseif( isreal(A) && ~isreal(B) )
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,42:41+length(x)) = x;
elseif( ~isreal(A) && isreal(B) )
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,53:52+length(x)) = x;
else
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymout(T,A,p,r)
global mtimesx_ttable
if( isreal(A) )
lt = length(T);
b = repmat(' ',1,30-lt);
x = [T b sprintf('%10.0f%%',-p)];
mtimesx_ttable(r,1:length(x)) = x;
else
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,1:length(T)) = T;
mtimesx_ttable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function running_time(d)
h = 24*d;
hh = floor(h);
m = 60*(h - hh);
mm = floor(m);
s = 60*(m - mm);
ss = floor(s);
disp(' ');
rt = sprintf('Running time hh:mm:ss = %2.0f:%2.0f:%2.0f',hh,mm,ss);
if( rt(28) == ' ' )
rt(28) = '0';
end
if( rt(31) == ' ' )
rt(31) = '0';
end
disp(rt);
disp(' ');
return
end
|
github
|
dschick/udkm1DsimML-master
|
mtimesx_test_ddspeed.m
|
.m
|
udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_ddspeed.m
| 121,611 |
utf_8
|
32613fb321b2de56bd52cb4b4567187d
|
% Test routine for mtimesx, op(double) * op(double) speed vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_ddspeed
% Filename: mtimesx_test_ddspeed.m
% Programmer: James Tursa
% Version: 1.0
% Date: September 27, 2009
% Copyright: (c) 2009 by James Tursa, All Rights Reserved
%
% This code uses the BSD License:
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
%
% Syntax (arguments in brackets [ ] are optional):
%
% T = mtimesx_test_ddspeed( [N [,D]] )
%
% Inputs:
%
% N = Number of runs to make for each individual test. The test result will
% be the median of N runs. N must be even. If N is odd, it will be
% automatically increased to the next even number. The default is 10,
% which can take *hours* to run. Best to run this program overnight.
% D = The string 'details'. If present, this will cause all of the
% individual intermediate run results to print as they happen.
%
% Output:
%
% T = A character array containing a summary of the results.
%
%--------------------------------------------------------------------------
function ttable = mtimesx_test_ddspeed(nn,details)
global mtimesx_ttable
disp(' ');
disp('****************************************************************************');
disp('* *');
disp('* WARNING WARNING WARNING WARNING WARNING WARNING WARNING WARNING *');
disp('* *');
disp('* This test program can take several *hours* to complete, particularly *');
disp('* when using the default number of runs as 10. It is strongly suggested *');
disp('* to close all applications and run this program overnight to get the *');
disp('* best possible result with minimal impacts to your computer usage. *');
disp('* *');
disp('* The program will be done when you see the message: DONE ! *');
disp('* *');
disp('****************************************************************************');
disp(' ');
try
input('Press Enter to start test, or Ctrl-C to exit ','s');
catch
ttable = '';
return
end
start_time = datenum(clock);
if nargin >= 1
n = nn;
else
n = 10;
end
if nargin < 2
details = false;
else
if( isempty(details) ) % code to get rid of the lint message
details = true;
else
details = true;
end
end
RC = ' Real*Real Real*Cplx Cplx*Real Cplx*Cplx';
compver = [computer ', ' version ', mtimesx mode ' mtimesx ', median of ' num2str(n) ' runs'];
k = length(compver);
nl = 199;
mtimesx_ttable = char([]);
mtimesx_ttable(1:nl,1:74) = ' ';
mtimesx_ttable(1,1:k) = compver;
mtimesx_ttable(2,:) = RC;
for r=3:(nl-2)
mtimesx_ttable(r,:) = ' -- -- -- --';
end
mtimesx_ttable(nl,1:6) = 'DONE !';
disp(' ');
disp(compver);
disp('Test program for function mtimesx:')
disp('----------------------------------');
rsave = 2;
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = rand(1,1);
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400);
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1);
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1);
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500);
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000);
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1);
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = rand(1,1);
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400);
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = rand(1,1);
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1);
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500);
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000);
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1);
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * (real).''');
disp(' ');
rsave = r;
mtimesx_ttable(r,:) = RC;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1);
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1);
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000);
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1);
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000);
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000);
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1);
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1);
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000);
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1);
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000);
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000);
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1);
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1);
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000);
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1);
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000);
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000);
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1);
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1);
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000);
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1);
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000);
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000);
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = rand(1,1);
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400);
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1);
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1);
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500);
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000);
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1);
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1) + rand(1,1)*1i;
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = rand(1,1);
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400);
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = rand(1,1);
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1);
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500);
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000);
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1);
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1);
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400);
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1);
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500);
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000);
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1);
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1) + rand(1,1)*1i;
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1);
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400);
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1);
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500);
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000);
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1);
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1);
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000);
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1);
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000);
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000);
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1) + rand(1,1)*1i;
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1);
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000);
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1);
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000);
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000);
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1);
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000);
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1);
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000);
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000);
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1) + rand(1,1)*1i;
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1);
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000);
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1);
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000);
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000);
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1);
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400);
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1);
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500);
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000);
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1);
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1) + rand(1,1)*1i;
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1);
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400);
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1);
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500);
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000);
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1);
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1);
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400);
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1);
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500);
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000);
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1);
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1) + rand(1,1)*1i;
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1);
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400);
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1);
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500);
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000);
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1);
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1);
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000);
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1);
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000);
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000);
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1) + rand(1,1)*1i;
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1);
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000);
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1);
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000);
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000);
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1);
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000);
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1);
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000);
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000);
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1) + rand(1,1)*1i;
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1);
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000);
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1);
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000);
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000);
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1);
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400);
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1);
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500);
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000);
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1);
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1) + rand(1,1)*1i;
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1);
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400);
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1);
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500);
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000);
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1);
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = rand(1,1);
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400);
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1);
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1);
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500);
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000);
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1);
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = rand(1,1);
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400);
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = rand(1,1);
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1);
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500);
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000);
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1);
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1);
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1);
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000);
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1);
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000);
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000);
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1);
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1);
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000);
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1);
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000);
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000);
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1);
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1);
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000);
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1);
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000);
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000);
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1);
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = rand(1,1);
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000);
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1);
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000);
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000);
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(1,10000000) + rand(1,10000000)*1i;
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(2500,1) + rand(2500,1)*1i;
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(1,2000) + rand(1,2000)*1i;
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000);
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = rand(1,1);
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400);
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1);
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1);
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500);
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000);
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1);
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000);
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = rand(1,1) + rand(1,1)*1i;
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000);
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = rand(1,1);
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400);
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = rand(1,1);
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1);
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500);
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000);
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1);
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000);
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(1,1000000) + rand(1,1000000)*1i;
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = rand(10,20,30,400) + rand(10,20,30,400)*1i;
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = rand(1,1) + rand(1,1)*1i;
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = rand(10000000,1) + rand(10000000,1)*1i;
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = rand(1,2500) + rand(1,2500)*1i;
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,1) + rand(2000,1)*1i;
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = rand(2000,2000) + rand(2000,2000)*1i;
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs ... symmetric cases op(A) * op(A)']);
disp(' ');
disp('real');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(2000);
maxtimesymCN('Matrix'' * Same ',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymNC('Matrix * Same'' ',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymTN('Matrix.'' * Same ',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymNT('Matrix * Same.'' ',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymGC('conj(Matrix) * Same'' ',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymCG('Matrix'' * conj(Same)',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymGT('conj(Matrix) * Same.'' ',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymTG('Matrix.'' * conj(Same)',A,n,details,r);
r = rsave;
disp(' ');
disp('complex');
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymCN('Matrix'' * Same ',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymNC('Matrix * Same'' ',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymTN('Matrix.'' * Same ',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymNT('Matrix * Same.'' ',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymGC('conj(Matrix) * Same'' ',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymCG('Matrix'' * conj(Same)',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymGT('conj(Matrix) * Same.'' ',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymTG('Matrix.'' * conj(Same)',A,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs ... special scalar cases']);
disp(' ');
disp('(scalar) * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = r + 1;
A = 1;
B = rand(2500);
maxtimeNN('( 1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 1 + 1i;
B = rand(2500);
maxtimeNN('( 1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 1 - 1i;
B = rand(2500);
maxtimeNN('( 1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 1 + 2i;
B = rand(2500);
maxtimeNN('( 1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1;
B = rand(2500);
maxtimeNN('(-1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1 + 1i;
B = rand(2500);
maxtimeNN('(-1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1 - 1i;
B = rand(2500);
maxtimeNN('(-1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1 + 2i;
B = rand(2500);
maxtimeNN('(-1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 2 + 1i;
B = rand(2500);
maxtimeNN('( 2+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 2 - 1i;
B = rand(2500);
maxtimeNN('( 2-1i) * Matrix ',A,B,n,details,r);
disp(' ');
disp('(scalar) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = 1;
B = rand(2500) + rand(2500)*1i;
maxtimeNN('( 1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 1 + 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNN('( 1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 1 - 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNN('( 1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 1 + 2i;
B = rand(2500) + rand(2500)*1i;
maxtimeNN('( 1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1;
B = rand(2500) + rand(2500)*1i;
maxtimeNN('(-1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1 + 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNN('(-1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1 - 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNN('(-1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1 + 2i;
B = rand(2500) + rand(2500)*1i;
maxtimeNN('(-1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 2 + 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNN('( 2+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 2 - 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNN('( 2-1i) * Matrix ',A,B,n,details,r);
disp(' ');
disp('(scalar) * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = r + 1;
A = 1;
B = rand(2500);
maxtimeNT('( 1+0i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = 1 + 1i;
B = rand(2500);
maxtimeNT('( 1+1i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = 1 - 1i;
B = rand(2500);
maxtimeNT('( 1-1i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = 1 + 2i;
B = rand(2500);
maxtimeNT('( 1+2i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = -1;
B = rand(2500);
maxtimeNT('(-1+0i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = -1 + 1i;
B = rand(2500);
maxtimeNT('(-1+1i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = -1 - 1i;
B = rand(2500);
maxtimeNT('(-1-1i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = -1 + 2i;
B = rand(2500);
maxtimeNT('(-1+2i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = 2 + 1i;
B = rand(2500);
maxtimeNT('( 2+1i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = 2 - 1i;
B = rand(2500);
maxtimeNT('( 2-1i) * Matrix.''',A,B,n,details,r);
disp(' ');
disp('(scalar) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = 1;
B = rand(2500) + rand(2500)*1i;
maxtimeNT('( 1+0i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = 1 + 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNT('( 1+1i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = 1 - 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNT('( 1-1i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = 1 + 2i;
B = rand(2500) + rand(2500)*1i;
maxtimeNT('( 1+2i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = -1;
B = rand(2500) + rand(2500)*1i;
maxtimeNT('(-1+0i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = -1 + 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNT('(-1+1i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = -1 - 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNT('(-1-1i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = -1 + 2i;
B = rand(2500) + rand(2500)*1i;
maxtimeNT('(-1+2i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = 2 + 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNT('( 2+1i) * Matrix.''',A,B,n,details,r);
r = r + 1;
A = 2 - 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNT('( 2-1i) * Matrix.''',A,B,n,details,r);
disp(' ');
disp('(scalar) * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = r + 1;
A = 1;
B = rand(2500);
maxtimeNC('( 1+0i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 1 + 1i;
B = rand(2500);
maxtimeNC('( 1+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 1 - 1i;
B = rand(2500);
maxtimeNC('( 1-1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 1 + 2i;
B = rand(2500);
maxtimeNC('( 1+2i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = -1;
B = rand(2500);
maxtimeNC('(-1+0i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = -1 + 1i;
B = rand(2500);
maxtimeNC('(-1+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = -1 - 1i;
B = rand(2500);
maxtimeNC('(-1-1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = -1 + 2i;
B = rand(2500);
maxtimeNC('(-1+2i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 2 + 1i;
B = rand(2500);
maxtimeNC('( 2+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 2 - 1i;
B = rand(2500);
maxtimeNC('( 2-1i) * Matrix'' ',A,B,n,details,r);
disp(' ');
disp('(scalar) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = 1;
B = rand(2500) + rand(2500)*1i;
maxtimeNC('( 1+0i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 1 + 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNC('( 1+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 1 - 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNC('( 1-1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 1 + 2i;
B = rand(2500) + rand(2500)*1i;
maxtimeNC('( 1+2i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = -1;
B = rand(2500) + rand(2500)*1i;
maxtimeNC('(-1+0i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = -1 + 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNC('(-1+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = -1 - 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNC('(-1-1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = -1 + 2i;
B = rand(2500) + rand(2500)*1i;
maxtimeNC('(-1+2i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 2 + 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNC('( 2+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 2 - 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNC('( 2-1i) * Matrix'' ',A,B,n,details,r);
disp(' ');
disp('(scalar) * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = r + 1;
A = 1;
B = rand(2500);
maxtimeNG('( 1+0i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = 1 + 1i;
B = rand(2500);
maxtimeNG('( 1+1i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = 1 - 1i;
B = rand(2500);
maxtimeNG('( 1-1i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = 1 + 2i;
B = rand(2500);
maxtimeNG('( 1+2i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = -1;
B = rand(2500);
maxtimeNG('(-1+0i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = -1 + 1i;
B = rand(2500);
maxtimeNG('(-1+1i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = -1 - 1i;
B = rand(2500);
maxtimeNG('(-1-1i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = -1 + 2i;
B = rand(2500);
maxtimeNG('(-1+2i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = 2 + 1i;
B = rand(2500);
maxtimeNG('( 2+1i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = 2 - 1i;
B = rand(2500);
maxtimeNG('( 2-1i) * conj(Matrix)',A,B,n,details,r);
disp(' ');
disp('(scalar) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = 1;
B = rand(2500) + rand(2500)*1i;
maxtimeNG('( 1+0i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = 1 + 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNG('( 1+1i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = 1 - 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNG('( 1-1i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = 1 + 2i;
B = rand(2500) + rand(2500)*1i;
maxtimeNG('( 1+2i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = -1;
B = rand(2500) + rand(2500)*1i;
maxtimeNG('(-1+0i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = -1 + 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNG('(-1+1i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = -1 - 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNG('(-1-1i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = -1 + 2i;
B = rand(2500) + rand(2500)*1i;
maxtimeNG('(-1+2i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = 2 + 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNG('( 2+1i) * conj(Matrix)',A,B,n,details,r);
r = r + 1;
A = 2 - 1i;
B = rand(2500) + rand(2500)*1i;
maxtimeNG('( 2-1i) * conj(Matrix)',A,B,n,details,r);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs ... special sparse cases']);
disp('Real * Real, Real * Cmpx, Cmpx * Real, Cmpx * Cmpx');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = sprand(5000,5000,.1);
maxtimeNN('Scalar * Sparse',A,B,n,details,r);
A = rand(1,1);
B = sprand(5000,5000,.1); B = B + B*2i;
maxtimeNN('Scalar * Sparse',A,B,n,details,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1);
maxtimeNN('Scalar * Sparse',A,B,n,details,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1); B = B + B*2i;
maxtimeNN('Scalar * Sparse',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = sprand(5000,5000,.1);
maxtimeNT('Scalar * Sparse.''',A,B,n,details,r);
A = rand(1,1);
B = sprand(5000,5000,.1); B = B + B*2i;
maxtimeNT('Scalar * Sparse.''',A,B,n,details,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1);
maxtimeNT('Scalar * Sparse.''',A,B,n,details,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1); B = B + B*2i;
maxtimeNT('Scalar * Sparse.''',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = sprand(5000,5000,.1);
maxtimeNC('Scalar * Sparse''',A,B,n,details,r);
A = rand(1,1);
B = sprand(5000,5000,.1); B = B + B*2i;
maxtimeNC('Scalar * Sparse''',A,B,n,details,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1);
maxtimeNC('Scalar * Sparse''',A,B,n,details,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1); B = B + B*2i;
maxtimeNC('Scalar * Sparse''',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = sprand(5000,5000,.1);
maxtimeNG('Scalar * conj(Sparse)',A,B,n,details,r);
A = rand(1,1);
B = sprand(5000,5000,.1); B = B + B*2i;
maxtimeNG('Scalar * conj(Sparse)',A,B,n,details,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1);
maxtimeNG('Scalar * conj(Sparse)',A,B,n,details,r);
A = rand(1,1) + rand(1,1)*1i;
B = sprand(5000,5000,.1); B = B + B*2i;
maxtimeNG('Scalar * conj(Sparse)',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(' --- DONE ! ---');
disp(' ');
disp(['Summary of Timing Tests, ' num2str(n) ' runs, + = percent faster, - = percent slower:']);
disp(' ');
mtimesx_ttable(1,1:k) = compver;
disp(mtimesx_ttable);
disp(' ');
ttable = mtimesx_ttable;
running_time(datenum(clock) - start_time);
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*B;
mtoc(k) = toc;
tic;
mtimesx(A,B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*B.';
mtoc(k) = toc;
tic;
mtimesx(A,B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*B';
mtoc(k) = toc;
tic;
mtimesx(A,B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*B;
mtoc(k) = toc;
tic;
mtimesx(A,'T',B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*B.';
mtoc(k) = toc;
tic;
mtimesx(A,'T',B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*B';
mtoc(k) = toc;
tic;
mtimesx(A,'T',B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,'T',B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*B;
mtoc(k) = toc;
tic;
mtimesx(A,'C',B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*B.';
mtoc(k) = toc;
tic;
mtimesx(A,'C',B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*B';
mtoc(k) = toc;
tic;
mtimesx(A,'C',B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,'C',B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*B;
mtoc(k) = toc;
tic;
mtimesx(A,'G',B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*B.';
mtoc(k) = toc;
tic;
mtimesx(A,'G',B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*B';
mtoc(k) = toc;
tic;
mtimesx(A,'G',B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,'G',B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymCN(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*A;
mtoc(k) = toc;
tic;
mtimesx(A,'C',A);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymNC(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*A';
mtoc(k) = toc;
tic;
mtimesx(A,A,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymTN(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*A;
mtoc(k) = toc;
tic;
mtimesx(A,'T',A);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymNT(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*A.';
mtoc(k) = toc;
tic;
mtimesx(A,A,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymCG(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*conj(A);
mtoc(k) = toc;
tic;
mtimesx(A,'C',A,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymGC(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*A';
mtoc(k) = toc;
tic;
mtimesx(A,'G',A,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymTG(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*conj(A);
mtoc(k) = toc;
tic;
mtimesx(A,'T',A,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymGT(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*A.';
mtoc(k) = toc;
tic;
mtimesx(A,'G',A,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeout(T,A,B,p,r)
global mtimesx_ttable
mtimesx_ttable(r,1:length(T)) = T;
if( isreal(A) && isreal(B) )
lt = length(T);
b = repmat(' ',1,30-lt);
x = [T b sprintf('%10.0f%%',-p)];
mtimesx_ttable(r,1:length(x)) = x;
elseif( isreal(A) && ~isreal(B) )
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,42:41+length(x)) = x;
elseif( ~isreal(A) && isreal(B) )
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,53:52+length(x)) = x;
else
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymout(T,A,p,r)
global mtimesx_ttable
if( isreal(A) )
lt = length(T);
b = repmat(' ',1,30-lt);
x = [T b sprintf('%10.0f%%',-p)];
mtimesx_ttable(r,1:length(x)) = x;
else
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,1:length(T)) = T;
mtimesx_ttable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function running_time(d)
h = 24*d;
hh = floor(h);
m = 60*(h - hh);
mm = floor(m);
s = 60*(m - mm);
ss = floor(s);
disp(' ');
rt = sprintf('Running time hh:mm:ss = %2.0f:%2.0f:%2.0f',hh,mm,ss);
if( rt(28) == ' ' )
rt(28) = '0';
end
if( rt(31) == ' ' )
rt(31) = '0';
end
disp(rt);
disp(' ');
return
end
|
github
|
dschick/udkm1DsimML-master
|
mtimesx_sparse.m
|
.m
|
udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_sparse.m
| 3,015 |
utf_8
|
eeb3eb2df4d70c69695b45188807e91c
|
% mtimesx_sparse does sparse matrix multiply of two inputs
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_sparse
% Filename: mtimesx_sparse.m
% Programmer: James Tursa
% Version: 1.00
% Date: September 27, 2009
% Copyright: (c) 2009 by James Tursa, All Rights Reserved
%
% This code uses the BSD License:
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
%
%--
%
% mtimesx_sparse is a helper function for mtimesx and is not intended to be called
% directly by the user.
%
% ---------------------------------------------------------------------------------------------------------------------------------
function result = mtimesx_sparse(a,transa,b,transb)
if( transa == 'N' )
if( transb == 'N' )
result = a * b;
elseif( transb == 'G' )
result = a * conj(b);
elseif( transb == 'T' )
result = a * b.';
else
result = a * b';
end
elseif( transa == 'G' )
if( transb == 'N' )
result = conj(a) * b;
elseif( transb == 'G' )
result = conj(a) * conj(b);
elseif( transb == 'T' )
result = conj(a) * b.';
else
result = conj(a) * b';
end
elseif( transa == 'T' )
if( transb == 'N' )
result = a.' * b;
elseif( transb == 'G' )
result = a.' * conj(b);
elseif( transb == 'T' )
result = a.' * b.';
else
result = a.' * b';
end
else
if( transb == 'N' )
result = a' * b;
elseif( transb == 'G' )
result = a' * conj(b);
elseif( transb == 'T' )
result = a' * b.';
else
result = a' * b';
end
end
end
|
github
|
dschick/udkm1DsimML-master
|
mtimesx_test_dsspeed.m
|
.m
|
udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_dsspeed.m
| 388,140 |
utf_8
|
53e3e8d0e86784747c58c68664ae0d85
|
% Test routine for mtimesx, op(double) * op(single) speed vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_dsspeed
% Filename: mtimesx_test_dsspeed.m
% Programmer: James Tursa
% Version: 1.0
% Date: September 27, 2009
% Copyright: (c) 2009 by James Tursa, All Rights Reserved
%
% This code uses the BSD License:
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
%
% Syntax (arguments in brackets [ ] are optional):
%
% T = mtimesx_test_ddspeed( [N [,D]] )
%
% Inputs:
%
% N = Number of runs to make for each individual test. The test result will
% be the median of N runs. N must be even. If N is odd, it will be
% automatically increased to the next even number. The default is 10,
% which can take *hours* to run. Best to run this program overnight.
% D = The string 'details'. If present, this will cause all of the
% individual intermediate run results to print as they happen.
%
% Output:
%
% T = A character array containing a summary of the results.
%
%--------------------------------------------------------------------------
function ttable = mtimesx_test_dsspeed(nn,details)
global mtimesx_ttable
disp(' ');
disp('****************************************************************************');
disp('* *');
disp('* WARNING WARNING WARNING WARNING WARNING WARNING WARNING WARNING *');
disp('* *');
disp('* This test program can take several *hours* to complete, particularly *');
disp('* when using the default number of runs as 10. It is strongly suggested *');
disp('* to close all applications and run this program overnight to get the *');
disp('* best possible result with minimal impacts to your computer usage. *');
disp('* *');
disp('* The program will be done when you see the message: DONE ! *');
disp('* *');
disp('****************************************************************************');
disp(' ');
try
input('Press Enter to start test, or Ctrl-C to exit ','s');
catch
ttable = '';
return
end
start_time = datenum(clock);
if nargin >= 1
n = nn;
else
n = 10;
end
if nargin < 2
details = false;
else
if( isempty(details) ) % code to get rid of the lint message
details = true;
else
details = true;
end
end
RC = ' Real*Real Real*Cplx Cplx*Real Cplx*Cplx';
compver = [computer ', ' version ', mtimesx mode ' mtimesx ', median of ' num2str(n) ' runs'];
k = length(compver);
mtimesx_ttable = char([]);
mtimesx_ttable(100,74) = ' ';
mtimesx_ttable(1,1:k) = compver;
mtimesx_ttable(2,:) = RC;
for r=3:170
mtimesx_ttable(r,:) = ' -- -- -- --';
end
disp(' ');
disp(compver);
disp('Test program for function mtimesx:')
disp('----------------------------------');
rsave = 2;
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = single(rand(1,1));
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400));
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1));
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1));
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500));
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000));
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1));
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = single(rand(1,1));
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400));
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = single(rand(1,1));
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1));
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500));
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000));
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1));
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNN('Scalar * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNN('Vector * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeNN('Scalar * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNN('Array * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeNN('Vector i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeNN('Vector o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNN('Vector * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeNN('Matrix * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNN('Matrix * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * (real).''');
disp(' ');
rsave = r;
mtimesx_ttable(r,:) = RC;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1));
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1));
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000));
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1));
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000));
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000));
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1));
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1));
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000));
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1));
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000));
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000));
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNT('Scalar * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNT('Vector * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNT('Array * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeNT('Vector i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeNT('Vector o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNT('Vector * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeNT('Matrix * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNT('Matrix * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1));
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1));
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000));
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1));
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000));
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000));
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1));
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1));
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000));
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1));
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000));
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000));
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNC('Scalar * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNC('Vector * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNC('Array * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeNC('Vector i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeNC('Vector o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNC('Vector * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeNC('Matrix * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNC('Matrix * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real) * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = single(rand(1,1));
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400));
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1));
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1));
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500));
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000));
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1));
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = single(rand(1,1));
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400));
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = single(rand(1,1));
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1));
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500));
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000));
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1));
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeNG('Scalar * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNG('Vector * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeNG('Scalar * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeNG('Array * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeNG('Vector i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeNG('Vector o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNG('Vector * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeNG('Matrix * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeNG('Matrix * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1));
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400));
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1));
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500));
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000));
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1));
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1));
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400));
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1));
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500));
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000));
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1));
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTN('Scalar.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTN('Vector.'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeTN('Scalar.'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeTN('Vector.'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeTN('Vector.'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTN('Vector.'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeTN('Matrix.'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTN('Matrix.'' * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1));
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000));
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1));
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000));
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000));
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1));
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000));
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1));
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000));
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000));
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTT('Scalar.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTT('Vector.'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeTT('Vector.'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeTT('Vector.'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTT('Vector.'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeTT('Matrix.'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTT('Matrix.'' * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1));
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000));
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1));
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000));
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000));
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1));
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000));
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1));
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000));
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000));
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTC('Scalar.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTC('Vector.'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeTC('Vector.'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeTC('Vector.'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTC('Vector.'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeTC('Matrix.'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTC('Matrix.'' * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real).'' * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1));
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400));
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1));
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500));
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000));
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1));
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1));
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400));
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1));
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500));
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000));
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1));
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeTG('Scalar.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeTG('Vector.'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeTG('Scalar.'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeTG('Vector.'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeTG('Vector.'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTG('Vector.'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeTG('Matrix.'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeTG('Matrix.'' * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1));
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400));
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1));
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500));
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000));
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1));
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1));
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400));
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1));
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500));
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000));
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1));
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCN('Scalar'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCN('Vector'' * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeCN('Scalar'' * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeCN('Vector'' i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeCN('Vector'' o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCN('Vector'' * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeCN('Matrix'' * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCN('Matrix'' * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1));
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000));
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1));
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000));
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000));
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1));
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000));
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1));
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000));
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000));
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCT('Scalar'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCT('Vector'' * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeCT('Vector'' i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeCT('Vector'' o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCT('Vector'' * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeCT('Matrix'' * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCT('Matrix'' * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1));
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000));
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1));
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000));
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000));
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1));
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000));
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1));
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000));
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000));
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCC('Scalar'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCC('Vector'' * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeCC('Vector'' i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeCC('Vector'' o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCC('Vector'' * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeCC('Matrix'' * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCC('Matrix'' * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('(real)'' * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1));
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400));
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1));
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500));
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000));
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1));
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1));
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400));
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1));
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500));
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000));
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1));
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeCG('Scalar'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeCG('Vector'' * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeCG('Scalar'' * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10000000,1) + rand(10000000,1)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeCG('Vector'' i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2500) + rand(1,2500)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeCG('Vector'' o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,1) + rand(2000,1)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCG('Vector'' * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeCG('Matrix'' * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeCG('Matrix'' * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = single(rand(1,1));
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400));
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1));
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1));
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500));
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000));
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1));
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = single(rand(1,1));
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400));
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = single(rand(1,1));
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1));
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500));
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000));
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1));
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGN('conj(Scalar) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGN('conj(Vector) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeGN('conj(Scalar) * Array ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGN('conj(Array) * Scalar ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeGN('conj(Vector) i Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeGN('conj(Vector) o Vector ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGN('conj(Vector) * Matrix ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeGN('conj(Matrix) * Vector ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGN('conj(Matrix) * Matrix ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * (real).''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1));
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1));
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000));
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1));
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000));
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000));
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1));
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1));
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000));
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1));
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000));
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000));
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGT('conj(Scalar) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGT('conj(Vector) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGT('conj(Array) * Scalar.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeGT('conj(Vector) i Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeGT('conj(Vector) o Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGT('conj(Vector) * Matrix.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeGT('conj(Matrix) * Vector.'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGT('conj(Matrix) * Matrix.'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * (real)''');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1));
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1));
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000));
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1));
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000));
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000));
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1));
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,40) + rand(10,20,30,40)*1i;
B = single(rand(1,1));
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000));
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1));
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000));
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000));
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGC('conj(Scalar) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1000000,1) + rand(1000000,1)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGC('conj(Vector) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGC('conj(Array) * Scalar'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxtimeGC('conj(Vector) i Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(2500,1) + rand(2500,1)*1i);
maxtimeGC('conj(Vector) o Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGC('conj(Vector) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(1,2000) + rand(1,2000)*1i);
maxtimeGC('conj(Matrix) * Vector'' ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGC('conj(Matrix) * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs']);
disp(' ');
disp('conj(real) * conj(real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000));
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = single(rand(1,1));
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400));
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1));
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1));
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500));
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000));
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1));
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000));
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1);
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1);
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400);
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000);
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000));
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = single(rand(1,1));
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400));
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = single(rand(1,1));
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1));
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500));
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000));
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1));
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000));
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(1,1000000) + rand(1,1000000)*1i);
maxtimeGG('conj(Scalar) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1000000) + rand(1,1000000)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGG('conj(Vector) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,1) + rand(1,1)*1i;
B = single(rand(10,20,30,400) + rand(10,20,30,400)*1i);
maxtimeGG('conj(Scalar) * conj(Array) ',A,B,n,details,r);
r = r + 1;
A = rand(10,20,30,400) + rand(10,20,30,400)*1i;
B = single(rand(1,1) + rand(1,1)*1i);
maxtimeGG('conj(Array) * conj(Scalar) ',A,B,n,details,r);
r = r + 1;
A = rand(1,10000000) + rand(1,10000000)*1i;
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxtimeGG('conj(Vector) i conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2500,1) + rand(2500,1)*1i;
B = single(rand(1,2500) + rand(1,2500)*1i);
maxtimeGG('conj(Vector) o conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(1,2000) + rand(1,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGG('conj(Vector) * conj(Matrix) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,1) + rand(2000,1)*1i);
maxtimeGG('conj(Matrix) * conj(Vector) ',A,B,n,details,r);
r = r + 1;
A = rand(2000,2000) + rand(2000,2000)*1i;
B = single(rand(2000,2000) + rand(2000,2000)*1i);
maxtimeGG('conj(Matrix) * conj(Matrix) ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs ... symmetric cases op(A) * op(A)']);
disp(' ');
disp('real');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = rsave;
r = r + 1;
A = rand(2000);
maxtimesymCN('Matrix'' * Same ',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymNC('Matrix * Same'' ',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymTN('Matrix.'' * Same ',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymNT('Matrix * Same.'' ',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymGC('conj(Matrix) * Same'' ',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymCG('Matrix'' * conj(Same)',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymGT('conj(Matrix) * Same.'' ',A,n,details,r);
r = r + 1;
A = rand(2000);
maxtimesymTG('Matrix.'' * conj(Same)',A,n,details,r);
r = rsave;
disp(' ');
disp('complex');
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymCN('Matrix'' * Same ',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymNC('Matrix * Same'' ',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymTN('Matrix.'' * Same ',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymNT('Matrix * Same.'' ',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymGC('conj(Matrix) * Same'' ',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymCG('Matrix'' * conj(Same)',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymGT('conj(Matrix) * Same.'' ',A,n,details,r);
r = r + 1;
A = rand(2000) + rand(2000)*1i;
maxtimesymTG('Matrix.'' * conj(Same)',A,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp(['Timing Tests ... median of ' num2str(n) ' runs ... special scalar cases']);
disp(' ');
disp('(scalar) * (real)');
disp(' ');
r = r + 1;
mtimesx_ttable(r,:) = RC;
rsave = r;
r = r + 1;
A = 1;
B = single(rand(2500));
maxtimeNN('( 1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 1 + 1i;
B = single(rand(2500));
maxtimeNN('( 1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 1 - 1i;
B = single(rand(2500));
maxtimeNN('( 1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 1 + 2i;
B = single(rand(2500));
maxtimeNN('( 1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1;
B = single(rand(2500));
maxtimeNN('(-1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1 + 1i;
B = single(rand(2500));
maxtimeNN('(-1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1 - 1i;
B = single(rand(2500));
maxtimeNN('(-1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1 + 2i;
B = single(rand(2500));
maxtimeNN('(-1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 2 + 1i;
B = single(rand(2500));
maxtimeNN('( 2+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 2 - 1i;
B = single(rand(2500));
maxtimeNN('( 2-1i) * Matrix ',A,B,n,details,r);
disp(' ');
disp('(scalar) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = 1;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('( 1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 1 + 1i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('( 1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 1 - 1i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('( 1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 1 + 2i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('( 1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('(-1+0i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1 + 1i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('(-1+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1 - 1i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('(-1-1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = -1 + 2i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('(-1+2i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 2 + 1i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('( 2+1i) * Matrix ',A,B,n,details,r);
r = r + 1;
A = 2 - 1i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNN('( 2-1i) * Matrix ',A,B,n,details,r);
disp(' ');
disp('(scalar) * (complex)''');
disp(' ');
%r = rsave;
r = r + 1;
A = 1;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('( 1+0i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 1 + 1i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('( 1+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 1 - 1i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('( 1-1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 1 + 2i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('( 1+2i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = -1;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('(-1+0i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = -1 + 1i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('(-1+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = -1 - 1i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('(-1-1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = -1 + 2i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('(-1+2i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 2 + 1i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('( 2+1i) * Matrix'' ',A,B,n,details,r);
r = r + 1;
A = 2 - 1i;
B = single(rand(2500) + rand(2500)*1i);
maxtimeNC('( 2-1i) * Matrix'' ',A,B,n,details,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(' --- DONE ! ---');
disp(' ');
disp(['Summary of Timing Tests, ' num2str(n) ' runs, + = percent faster, - = percent slower:']);
disp(' ');
mtimesx_ttable(1,1:k) = compver;
disp(mtimesx_ttable);
disp(' ');
ttable = mtimesx_ttable;
running_time(datenum(clock) - start_time);
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*B;
mtoc(k) = toc;
tic;
mtimesx(A,B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*B.';
mtoc(k) = toc;
tic;
mtimesx(A,B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*B';
mtoc(k) = toc;
tic;
mtimesx(A,B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeNG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*B;
mtoc(k) = toc;
tic;
mtimesx(A,'T',B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*B.';
mtoc(k) = toc;
tic;
mtimesx(A,'T',B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*B';
mtoc(k) = toc;
tic;
mtimesx(A,'T',B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeTG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,'T',B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*B;
mtoc(k) = toc;
tic;
mtimesx(A,'C',B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*B.';
mtoc(k) = toc;
tic;
mtimesx(A,'C',B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*B';
mtoc(k) = toc;
tic;
mtimesx(A,'C',B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeCG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,'C',B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGN(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*B;
mtoc(k) = toc;
tic;
mtimesx(A,'G',B);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGT(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*B.';
mtoc(k) = toc;
tic;
mtimesx(A,'G',B,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGC(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*B';
mtoc(k) = toc;
tic;
mtimesx(A,'G',B,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeGG(T,A,B,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*conj(B);
mtoc(k) = toc;
tic;
mtimesx(A,'G',B,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
B(1,1) = 2*B(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimeout(T,A,B,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymCN(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*A;
mtoc(k) = toc;
tic;
mtimesx(A,'C',A);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymNC(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*A';
mtoc(k) = toc;
tic;
mtimesx(A,A,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymTN(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*A;
mtoc(k) = toc;
tic;
mtimesx(A,'T',A);
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymNT(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A*A.';
mtoc(k) = toc;
tic;
mtimesx(A,A,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymCG(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A'*conj(A);
mtoc(k) = toc;
tic;
mtimesx(A,'C',A,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymGC(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*A';
mtoc(k) = toc;
tic;
mtimesx(A,'G',A,'C');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymTG(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
A.'*conj(A);
mtoc(k) = toc;
tic;
mtimesx(A,'T',A,'G');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymGT(T,A,n,details,r)
pp(n) = 0;
mtoc(n) = 0;
xtoc(n) = 0;
for k=1:n
tic;
conj(A)*A.';
mtoc(k) = toc;
tic;
mtimesx(A,'G',A,'T');
xtoc(k) = toc;
pp(k) = (100 * (xtoc(k) - mtoc(k)) / min(mtoc(k),xtoc(k)));
A(1,1) = 2*A(1,1); % prevent JIT accelerator from interfering with timing
end
if( details )
disp('MATLAB mtimes times:');
disp(mtoc);
disp('mtimesx times:')
disp(xtoc);
disp('mtimesx percent faster times (+ = faster, - = slower)');
disp(-pp);
end
p = median(pp);
ap = abs(p);
sp = sprintf('%6.1f',ap);
if( ap < 5 )
c = '(not significant)';
else
c = '';
end
if( p < 0 )
a = [' <' repmat('-',[1,floor((ap+5)/10)])];
disp([T ' mtimesx is ' sp '% faster than MATLAB mtimes' a c]);
else
disp([T ' mtimesx is ' sp '% slower than MATLAB mtimes ' c]);
end
maxtimesymout(T,A,p,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimeout(T,A,B,p,r)
global mtimesx_ttable
mtimesx_ttable(r,1:length(T)) = T;
if( isreal(A) && isreal(B) )
lt = length(T);
b = repmat(' ',1,30-lt);
x = [T b sprintf('%10.0f%%',-p)];
mtimesx_ttable(r,1:length(x)) = x;
elseif( isreal(A) && ~isreal(B) )
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,42:41+length(x)) = x;
elseif( ~isreal(A) && isreal(B) )
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,53:52+length(x)) = x;
else
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxtimesymout(T,A,p,r)
global mtimesx_ttable
if( isreal(A) )
lt = length(T);
b = repmat(' ',1,30-lt);
x = [T b sprintf('%10.0f%%',-p)];
mtimesx_ttable(r,1:length(x)) = x;
else
x = sprintf('%10.0f%%',-p);
mtimesx_ttable(r,1:length(T)) = T;
mtimesx_ttable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function running_time(d)
h = 24*d;
hh = floor(h);
m = 60*(h - hh);
mm = floor(m);
s = 60*(m - mm);
ss = floor(s);
disp(' ');
rt = sprintf('Running time hh:mm:ss = %2.0f:%2.0f:%2.0f',hh,mm,ss);
if( rt(28) == ' ' )
rt(28) = '0';
end
if( rt(31) == ' ' )
rt(31) = '0';
end
disp(rt);
disp(' ');
return
end
|
github
|
dschick/udkm1DsimML-master
|
mtimesx_test_ssequal.m
|
.m
|
udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_ssequal.m
| 355,156 |
utf_8
|
4c01cb508f7cf6adb1b848f98ee9ca41
|
% Test routine for mtimesx, op(single) * op(single) equality vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_ssequal
% Filename: mtimesx_test_ssequal.m
% Programmer: James Tursa
% Version: 1.0
% Date: September 27, 2009
% Copyright: (c) 2009 by James Tursa, All Rights Reserved
%
% This code uses the BSD License:
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this list of conditions and the following disclaimer.
% * Redistributions in binary form must reproduce the above copyright
% notice, this list of conditions and the following disclaimer in
% the documentation and/or other materials provided with the distribution
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
% POSSIBILITY OF SUCH DAMAGE.
%
% Syntax:
%
% T = mtimesx_test_ssequal
%
% Output:
%
% T = A character array containing a summary of the results.
%
%--------------------------------------------------------------------------
function dtable = mtimesx_test_ssequal
global mtimesx_dtable
disp(' ');
disp('****************************************************************************');
disp('* *');
disp('* WARNING WARNING WARNING WARNING WARNING WARNING WARNING WARNING *');
disp('* *');
disp('* This test program can take an hour or so to complete. It is suggested *');
disp('* that you close all applications and run this program during your lunch *');
disp('* break or overnight to minimize impacts to your computer usage. *');
disp('* *');
disp('* The program will be done when you see the message: DONE ! *');
disp('* *');
disp('****************************************************************************');
disp(' ');
try
input('Press Enter to start test, or Ctrl-C to exit ','s');
catch
dtable = '';
return
end
start_time = datenum(clock);
compver = [computer ', ' version ', mtimesx mode ' mtimesx];
k = length(compver);
RC = ' Real*Real Real*Cplx Cplx*Real Cplx*Cplx';
mtimesx_dtable = char([]);
mtimesx_dtable(157,74) = ' ';
mtimesx_dtable(1,1:k) = compver;
mtimesx_dtable(2,:) = RC;
for r=3:157
mtimesx_dtable(r,:) = ' -- -- -- --';
end
disp(' ');
disp(compver);
disp('Test program for function mtimesx:')
disp('----------------------------------');
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * (real)');
disp(' ');
rsave = 2;
r = rsave;
%if( false ) % debug jump
if( isequal([]*[],mtimesx([],[])) )
disp('Empty * Empty EQUAL');
else
disp('Empty * Empty NOT EQUAL <---');
end
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000));
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = single(rand(1,1));
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40));
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1));
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1));
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500));
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000));
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1));
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffNN('Matrix * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNN('Matrix * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = single(rand(1,1));
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40));
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1));
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1));
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500));
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000));
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1));
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffNN('Matrix * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNN('Scalar * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNN('Vector * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffNN('Scalar * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNN('Array * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffNN('Vector i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffNN('Vector o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNN('Vector * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffNN('Matrix * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNN('Matrix * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * (real).''');
disp(' ');
if( isequal([]*[].',mtimesx([],[],'T')) )
disp('Empty * Empty.'' EQUAL');
else
disp('Empty * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = single(rand(10000,1));
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1));
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1));
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000));
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1));
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000));
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000));
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1));
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1));
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000));
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1));
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000));
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000));
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNT('Scalar * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNT('Vector * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNT('Array * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffNT('Vector i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffNT('Vector o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNT('Vector * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffNT('Matrix * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNT('Matrix * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * (real)''');
disp(' ');
if( isequal([]*[]',mtimesx([],[],'C')) )
disp('Empty * Empty'' EQUAL');
else
disp('Empty * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = single(rand(10000,1));
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1));
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1));
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000));
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1));
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000));
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000));
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffNC('Matrix * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNC('Matrix * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1));
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1));
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000));
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1));
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000));
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000));
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffNC('Matrix * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNC('Scalar * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNC('Vector * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNC('Array * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffNC('Vector i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffNC('Vector o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNC('Vector * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffNC('Matrix * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNC('Matrix * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real) * conj(real)');
disp(' ');
%if( false ) % debug jump
if( isequal([]*conj([]),mtimesx([],[],'G')) )
disp('Empty * conj(Empty) EQUAL');
else
disp('Empty * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000));
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = single(rand(1,1));
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40));
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1));
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1));
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500));
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000));
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1));
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj((real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = single(rand(1,1));
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40));
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1));
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1));
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500));
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000));
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1));
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffNG('Scalar * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNG('Vector * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffNG('Scalar * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffNG('Array * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffNG('Vector i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffNG('Vector o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNG('Vector * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffNG('Matrix * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffNG('Matrix * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * (real)');
disp(' ');
if( isequal([]'*[],mtimesx([],'C',[])) )
disp('Empty.'' * Empty EQUAL');
else
disp('Empty.'' * Empty NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000));
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1));
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40));
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1));
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500));
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000));
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1));
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1));
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40));
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1));
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500));
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000));
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1));
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTN('Scalar.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTN('Vector.'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffTN('Scalar.'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffTN('Vector.'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffTN('Vector.'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTN('Vector.'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffTN('Matrix.'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTN('Matrix.'' * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * (real).''');
disp(' ');
if( isequal([].'*[]',mtimesx([],'T',[],'C')) )
disp('Empty.'' * Empty.'' EQUAL');
else
disp('Empty.'' * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = single(rand(10000,1));
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1));
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000));
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1));
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000));
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000));
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1));
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000));
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1));
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000));
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000));
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTT('Scalar.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTT('Vector.'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffTT('Vector.'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffTT('Vector.'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTT('Vector.'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffTT('Matrix.'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTT('Matrix.'' * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * (real)''');
disp(' ');
if( isequal([].'*[]',mtimesx([],'T',[],'C')) )
disp('Empty.'' * Empty'' EQUAL');
else
disp('Empty.'' * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = single(rand(10000,1));
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1));
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000));
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1));
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000));
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000));
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1));
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000));
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1));
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000));
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000));
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTC('Scalar.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTC('Vector.'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffTC('Vector.'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffTC('Vector.'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTC('Vector.'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffTC('Matrix.'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTC('Matrix.'' * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real).'' * conj(real)');
disp(' ');
if( isequal([]'*conj([]),mtimesx([],'C',[],'G')) )
disp('Empty.'' * conj(Empty) EQUAL');
else
disp('Empty.'' * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000));
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1));
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40));
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1));
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500));
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000));
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1));
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1));
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40));
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1));
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500));
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000));
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1));
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex).'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffTG('Scalar.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffTG('Vector.'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffTG('Scalar.'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffTG('Vector.'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffTG('Vector.'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTG('Vector.'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffTG('Matrix.'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffTG('Matrix.'' * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * (real)');
disp(' ');
if( isequal([]'*[],mtimesx([],'C',[])) )
disp('Empty'' * Empty EQUAL');
else
disp('Empty'' * Empty NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000));
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1));
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40));
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1));
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500));
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000));
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1));
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffCN('Matrix'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCN('Matrix'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1));
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40));
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1));
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500));
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000));
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1));
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffCN('Matrix'' * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCN('Scalar'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCN('Vector'' * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffCN('Scalar'' * Array ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffCN('Vector'' i Vector ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffCN('Vector'' o Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCN('Vector'' * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffCN('Matrix'' * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCN('Matrix'' * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * (real).''');
disp(' ');
if( isequal([]'*[]',mtimesx([],'C',[],'C')) )
disp('Empty'' * Empty.'' EQUAL');
else
disp('Empty'' * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = single(rand(10000,1));
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1));
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000));
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1));
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000));
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000));
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1));
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000));
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1));
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000));
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000));
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCT('Scalar'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCT('Vector'' * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffCT('Vector'' i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffCT('Vector'' o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCT('Vector'' * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffCT('Matrix'' * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCT('Matrix'' * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * (real)''');
disp(' ');
if( isequal([]'*[]',mtimesx([],'C',[],'C')) )
disp('Empty'' * Empty'' EQUAL');
else
disp('Empty'' * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = single(rand(10000,1));
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1));
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000));
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1));
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000));
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000));
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1));
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000));
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1));
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000));
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000));
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCC('Scalar'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCC('Vector'' * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffCC('Vector'' i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffCC('Vector'' o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCC('Vector'' * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffCC('Matrix'' * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCC('Matrix'' * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('(real)'' * conj(real)');
disp(' ');
if( isequal([]'*conj([]),mtimesx([],'C',[],'G')) )
disp('Empty'' * conj(Empty) EQUAL');
else
disp('Empty'' * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000));
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1));
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40));
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1));
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500));
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000));
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1));
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(real)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1));
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40));
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1));
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500));
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000));
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1));
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('(complex)'' * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffCG('Scalar'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffCG('Vector'' * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffCG('Scalar'' * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10000000,1) + rand(10000000,1)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffCG('Vector'' i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,2500) + rand(1,2500)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffCG('Vector'' o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1) + rand(1000,1)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCG('Vector'' * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffCG('Matrix'' * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffCG('Matrix'' * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * (real)');
disp(' ');
if( isequal(conj([])*[],mtimesx([],'G',[])) )
disp('conj(Empty) * Empty EQUAL');
else
disp('conj(Empty) * Empty NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000));
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = single(rand(1,1));
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40));
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1));
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1));
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500));
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000));
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1));
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = single(rand(1,1));
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40));
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1));
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1));
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500));
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000));
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1));
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGN('conj(Scalar) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGN('conj(Vector) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffGN('conj(Scalar) * Array ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGN('conj(Array) * Scalar ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffGN('conj(Vector) i Vector ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffGN('conj(Vector) o Vector ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGN('conj(Vector) * Matrix ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffGN('conj(Matrix) * Vector ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGN('conj(Matrix) * Matrix ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * (real).''');
disp(' ');
if( isequal(conj([])*[].',mtimesx([],'G',[],'T')) )
disp('conj(Empty) * Empty.'' EQUAL');
else
disp('conj(Empty) * Empty.'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = single(rand(10000,1));
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1));
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1));
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000));
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1));
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000));
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000));
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1));
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1));
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000));
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1));
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000));
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000));
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex).''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGT('conj(Scalar) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGT('conj(Vector) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGT('conj(Array) * Scalar.'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffGT('conj(Vector) i Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffGT('conj(Vector) o Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGT('conj(Vector) * Matrix.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffGT('conj(Matrix) * Vector.'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGT('conj(Matrix) * Matrix.'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * (real)''');
disp(' ');
if( isequal(conj([])*[]',mtimesx([],'G',[],'C')) )
disp('conj(Empty) * Empty'' EQUAL');
else
disp('conj(Empty) * Empty'' NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = single(rand(10000,1));
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1));
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1));
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000));
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1));
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000));
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000));
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (real)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1));
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1));
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000));
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1));
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000));
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000));
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex) * (complex)''');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGC('conj(Scalar) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(10000,1)+ rand(10000,1)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGC('conj(Vector) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGC('conj(Array) * Scalar'' ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(1,10000000) + rand(1,10000000)*1i);
maxdiffGC('conj(Vector) i Vector'' ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(2500,1) + rand(2500,1)*1i);
maxdiffGC('conj(Vector) o Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGC('conj(Vector) * Matrix'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1,1000) + rand(1,1000)*1i);
maxdiffGC('conj(Matrix) * Vector'' ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGC('conj(Matrix) * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp('Numerical Comparison Tests ...');
disp(' ');
disp('conj(real) * conj(real)');
disp(' ');
if( isequal(conj([])*conj([]),mtimesx([],'G',[],'G')) )
disp('conj(Empty) * conj(Empty) EQUAL');
else
disp('conj(Empty) * conj(Empty) NOT EQUAL <---');
end
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000));
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = single(rand(1,1));
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40));
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1));
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1));
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500));
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000));
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1));
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000));
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(real) * conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1));
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1));
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40));
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000));
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1));
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000));
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(real)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000));
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = single(rand(1,1));
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40));
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1));
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1));
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500));
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000));
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1));
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000));
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
%--------------------------------------------------------------------------
disp(' ');
disp('conj(complex)* conj(complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(1,10000) + rand(1,10000)*1i);
maxdiffGG('conj(Scalar) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,10000)+ rand(1,10000)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGG('conj(Vector) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,1) + rand(1,1)*1i);
B = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
maxdiffGG('conj(Scalar) * conj(Array) ',A,B,r);
r = r + 1;
A = single(rand(10,20,30,40) + rand(10,20,30,40)*1i);
B = single(rand(1,1) + rand(1,1)*1i);
maxdiffGG('conj(Array) * conj(Scalar) ',A,B,r);
r = r + 1;
A = single(rand(1,10000000) + rand(1,10000000)*1i);
B = single(rand(10000000,1) + rand(10000000,1)*1i);
maxdiffGG('conj(Vector) i conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(2500,1) + rand(2500,1)*1i);
B = single(rand(1,2500) + rand(1,2500)*1i);
maxdiffGG('conj(Vector) o conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1,1000) + rand(1,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGG('conj(Vector) * conj(Matrix) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1) + rand(1000,1)*1i);
maxdiffGG('conj(Matrix) * conj(Vector) ',A,B,r);
r = r + 1;
A = single(rand(1000,1000) + rand(1000,1000)*1i);
B = single(rand(1000,1000) + rand(1000,1000)*1i);
maxdiffGG('conj(Matrix) * conj(Matrix) ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp('----------------------------------');
disp(' ');
disp('Numerical Comparison Tests ... symmetric cases op(A) * op(A)');
disp(' ');
disp('real');
r = r + 1;
mtimesx_dtable(r,:) = RC;
rsave = r;
r = r + 1;
A = single(rand(2000));
maxdiffsymCN('Matrix'' * Same ',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymNC('Matrix * Same''',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymTN('Matrix.'' * Same ',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymNT('Matrix * Same.''',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymGC('conj(Matrix) * Same''',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymCG('Matrix'' * conj(Same)',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymGT('conj(Matrix) * Same.'' ',A,r);
r = r + 1;
A = single(rand(2000));
maxdiffsymTG('Matrix.'' * conj(Same)',A,r);
r = rsave;
disp(' ');
disp('complex');
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymCN('Matrix'' * Same ',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymNC('Matrix * Same''',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymTN('Matrix.'' * Same ',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymNT('Matrix * Same.''',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymGC('conj(Matrix) * Same''',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymCG('Matrix'' * conj(Same)',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymGT('conj(Matrix) * Same.''',A,r);
r = r + 1;
A = single(rand(2000) + rand(2000)*1i);
maxdiffsymTG('Matrix.'' * conj(Same)',A,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%end % debug jump
disp(' ');
disp('Numerical Comparison Tests ... special scalar cases');
disp(' ');
disp('(scalar) * (real)');
disp(' ');
r = r + 1;
mtimesx_dtable(r,:) = ' Real*Real Real*Cplx Cplx*Real Cplx*Cplx';
rsave = r;
r = r + 1;
A = single(1);
B = single(rand(2500));
maxdiffNN('( 1+0i) * Matrix ',A,B,r);
r = r + 1;
A = single(1 + 1i);
B = single(rand(2500));
maxdiffNN('( 1+1i) * Matrix ',A,B,r);
r = r + 1;
A = single(1 - 1i);
B = single(rand(2500));
maxdiffNN('( 1-1i) * Matrix ',A,B,r);
r = r + 1;
A = single(1 + 2i);
B = single(rand(2500));
maxdiffNN('( 1+2i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1);
B = single(rand(2500));
maxdiffNN('(-1+0i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1 + 1i);
B = single(rand(2500));
maxdiffNN('(-1+1i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1 - 1i);
B = single(rand(2500));
maxdiffNN('(-1-1i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1 + 2i);
B = single(rand(2500));
maxdiffNN('(-1+2i) * Matrix ',A,B,r);
r = r + 1;
A = single(2 + 1i);
B = single(rand(2500));
maxdiffNN('( 2+1i) * Matrix ',A,B,r);
r = r + 1;
A = single(2 - 1i);
B = single(rand(2500));
maxdiffNN('( 2-1i) * Matrix ',A,B,r);
disp(' ');
disp('(scalar) * (complex)');
disp(' ');
r = rsave;
r = r + 1;
A = single(1);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('( 1+0i) * Matrix ',A,B,r);
r = r + 1;
A = single(1 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('( 1+1i) * Matrix ',A,B,r);
r = r + 1;
A = single(1 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('( 1-1i) * Matrix ',A,B,r);
r = r + 1;
A = single(1 + 2i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('( 1+2i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('(-1+0i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('(-1+1i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('(-1-1i) * Matrix ',A,B,r);
r = r + 1;
A = single(-1 + 2i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('(-1+2i) * Matrix ',A,B,r);
r = r + 1;
A = single(2 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('( 2+1i) * Matrix ',A,B,r);
r = r + 1;
A = single(2 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNN('( 2-1i) * Matrix ',A,B,r);
disp(' ');
disp('(scalar) * (complex)''');
disp(' ');
%r = rsave;
r = r + 1;
A = single(1);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('( 1+0i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(1 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('( 1+1i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(1 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('( 1-1i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(1 + 2i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('( 1+2i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(-1);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('(-1+0i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(-1 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('(-1+1i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(-1 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('(-1-1i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(-1 + 2i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('(-1+2i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(2 + 1i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('( 2+1i) * Matrix'' ',A,B,r);
r = r + 1;
A = single(2 - 1i);
B = single(rand(2500) + rand(2500)*1i);
maxdiffNC('( 2-1i) * Matrix'' ',A,B,r);
running_time(datenum(clock) - start_time);
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
disp(' ');
disp(' --- DONE ! ---');
disp(' ');
disp('Summary of Numerical Comparison Tests, max relative element difference:');
disp(' ');
mtimesx_dtable(1,1:k) = compver;
disp(mtimesx_dtable);
disp(' ');
dtable = mtimesx_dtable;
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNN(T,A,B,r)
Cm = A*B;
Cx = mtimesx(A,B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCN(T,A,B,r)
Cm = A'*B;
Cx = mtimesx(A,'C',B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTN(T,A,B,r)
Cm = A.'*B;
Cx = mtimesx(A,'T',B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGN(T,A,B,r)
Cm = conj(A)*B;
Cx = mtimesx(A,'G',B);
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNC(T,A,B,r)
Cm = A*B';
Cx = mtimesx(A,B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCC(T,A,B,r)
Cm = A'*B';
Cx = mtimesx(A,'C',B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTC(T,A,B,r)
Cm = A.'*B';
Cx = mtimesx(A,'T',B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGC(T,A,B,r)
Cm = conj(A)*B';
Cx = mtimesx(A,'G',B,'C');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNT(T,A,B,r)
Cm = A*B.';
Cx = mtimesx(A,B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCT(T,A,B,r)
Cm = A'*B.';
Cx = mtimesx(A,'C',B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTT(T,A,B,r)
Cm = A.'*B.';
Cx = mtimesx(A,'T',B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGT(T,A,B,r)
Cm = conj(A)*B.';
Cx = mtimesx(A,'G',B,'T');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffNG(T,A,B,r)
Cm = A*conj(B);
Cx = mtimesx(A,B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffCG(T,A,B,r)
Cm = A'*conj(B);
Cx = mtimesx(A,'C',B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffTG(T,A,B,r)
Cm = A.'*conj(B);
Cx = mtimesx(A,'T',B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffGG(T,A,B,r)
Cm = conj(A)*conj(B);
Cx = mtimesx(A,'G',B,'G');
maxdiffout(T,A,B,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymCN(T,A,r)
Cm = A'*A;
Cx = mtimesx(A,'C',A);
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymNC(T,A,r)
Cm = A*A';
Cx = mtimesx(A,A,'C');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymTN(T,A,r)
Cm = A.'*A;
Cx = mtimesx(A,'T',A);
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymNT(T,A,r)
Cm = A*A.';
Cx = mtimesx(A,A,'T');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymTG(T,A,r)
Cm = A.'*conj(A);
Cx = mtimesx(A,'T',A,'G');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymGT(T,A,r)
Cm = conj(A)*A.';
Cx = mtimesx(A,'G',A,'T');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymCG(T,A,r)
Cm = A'*conj(A);
Cx = mtimesx(A,'C',A,'G');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymGC(T,A,r)
Cm = conj(A)*A';
Cx = mtimesx(A,'G',A,'C');
maxdiffsymout(T,A,Cm,Cx,r);
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffout(T,A,B,Cm,Cx,r)
global mtimesx_dtable
lt = length(T);
b = repmat(' ',1,30-lt);
if( isequal(Cm,Cx) )
disp([T b ' EQUAL']);
d = 0;
else
Cm = Cm(:);
Cx = Cx(:);
if( isreal(Cm) && isreal(Cx) )
rx = Cx ~= Cm;
d = max(abs((Cx(rx)-Cm(rx))./Cm(rx)));
else
Cmr = real(Cm);
Cmi = imag(Cm);
Cxr = real(Cx);
Cxi = imag(Cx);
rx = Cxr ~= Cmr;
ix = Cxi ~= Cmi;
dr = max(abs((Cxr(rx)-Cmr(rx))./max(abs(Cmr(rx)),abs(Cmr(rx)))));
di = max(abs((Cxi(ix)-Cmi(ix))./max(abs(Cmi(ix)),abs(Cxi(ix)))));
if( isempty(dr) )
d = di;
elseif( isempty(di) )
d = dr;
else
d = max(dr,di);
end
end
disp([T b ' NOT EQUAL <--- Max relative difference: ' num2str(d)]);
end
mtimesx_dtable(r,1:length(T)) = T;
if( isreal(A) && isreal(B) )
if( d == 0 )
x = [T b ' 0'];
else
x = [T b sprintf('%11.2e',d)];
end
mtimesx_dtable(r,1:length(x)) = x;
elseif( isreal(A) && ~isreal(B) )
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,42:41+length(x)) = x;
elseif( ~isreal(A) && isreal(B) )
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,53:52+length(x)) = x;
else
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function maxdiffsymout(T,A,Cm,Cx,r)
global mtimesx_dtable
lt = length(T);
b = repmat(' ',1,30-lt);
if( isequal(Cm,Cx) )
disp([T b ' EQUAL']);
d = 0;
else
Cm = Cm(:);
Cx = Cx(:);
if( isreal(Cm) && isreal(Cx) )
rx = Cx ~= Cm;
d = max(abs((Cx(rx)-Cm(rx))./Cm(rx)));
else
Cmr = real(Cm);
Cmi = imag(Cm);
Cxr = real(Cx);
Cxi = imag(Cx);
rx = Cxr ~= Cmr;
ix = Cxi ~= Cmi;
dr = max(abs((Cxr(rx)-Cmr(rx))./max(abs(Cmr(rx)),abs(Cmr(rx)))));
di = max(abs((Cxi(ix)-Cmi(ix))./max(abs(Cmi(ix)),abs(Cxi(ix)))));
if( isempty(dr) )
d = di;
elseif( isempty(di) )
d = dr;
else
d = max(dr,di);
end
end
disp([T b ' NOT EQUAL <--- Max relative difference: ' num2str(d)]);
end
if( isreal(A) )
if( d == 0 )
x = [T b ' 0'];
else
x = [T b sprintf('%11.2e',d)];
end
mtimesx_dtable(r,1:length(x)) = x;
else
if( d == 0 )
x = ' 0';
else
x = sprintf('%11.2e',d);
end
mtimesx_dtable(r,1:length(T)) = T;
mtimesx_dtable(r,64:63+length(x)) = x;
end
return
end
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
function running_time(d)
h = 24*d;
hh = floor(h);
m = 60*(h - hh);
mm = floor(m);
s = 60*(m - mm);
ss = floor(s);
disp(' ');
rt = sprintf('Running time hh:mm:ss = %2.0f:%2.0f:%2.0f',hh,mm,ss);
if( rt(28) == ' ' )
rt(28) = '0';
end
if( rt(31) == ' ' )
rt(31) = '0';
end
disp(rt);
disp(' ');
return
end
|
github
|
dschick/udkm1DsimML-master
|
xml_write.m
|
.m
|
udkm1DsimML-master/helpers/functions/xml_io_tools/xml_write.m
| 18,325 |
utf_8
|
24bd3dc683e5a0a0ad4080deaa6a93a5
|
function DOMnode = xml_write(filename, tree, RootName, Pref)
%XML_WRITE Writes Matlab data structures to XML file
%
% DESCRIPTION
% xml_write( filename, tree) Converts Matlab data structure 'tree' containing
% cells, structs, numbers and strings to Document Object Model (DOM) node
% tree, then saves it to XML file 'filename' using Matlab's xmlwrite
% function. Optionally one can also use alternative version of xmlwrite
% function which directly calls JAVA functions for XML writing without
% MATLAB middleware. This function is provided as a patch to existing
% bugs in xmlwrite (in R2006b).
%
% xml_write(filename, tree, RootName, Pref) allows you to specify
% additional preferences about file format
%
% DOMnode = xml_write([], tree) same as above except that DOM node is
% not saved to the file but returned.
%
% INPUT
% filename file name
% tree Matlab structure tree to store in xml file.
% RootName String with XML tag name used for root (top level) node
% Optionally it can be a string cell array storing: Name of
% root node, document "Processing Instructions" data and
% document "comment" string
% Pref Other preferences:
% Pref.ItemName - default 'item' - name of a special tag used to
% itemize cell or struct arrays
% Pref.XmlEngine - let you choose the XML engine. Currently default is
% 'Xerces', which is using directly the apache xerces java file.
% Other option is 'Matlab' which uses MATLAB's xmlwrite and its
% XMLUtils java file. Both options create identical results except in
% case of CDATA sections where xmlwrite fails.
% Pref.CellItem - default 'true' - allow cell arrays to use 'item'
% notation. See below.
% Pref.RootOnly - default true - output variable 'tree' corresponds to
% xml file root element, otherwise it correspond to the whole file.
% Pref.StructItem - default 'true' - allow arrays of structs to use
% 'item' notation. For example "Pref.StructItem = true" gives:
% <a>
% <b>
% <item> ... <\item>
% <item> ... <\item>
% <\b>
% <\a>
% while "Pref.StructItem = false" gives:
% <a>
% <b> ... <\b>
% <b> ... <\b>
% <\a>
%
%
% Several special xml node types can be created if special tags are used
% for field names of 'tree' nodes:
% - node.CONTENT - stores data section of the node if other fields
% (usually ATTRIBUTE are present. Usually data section is stored
% directly in 'node'.
% - node.ATTRIBUTE.name - stores node's attribute called 'name'.
% - node.COMMENT - create comment child node from the string. For global
% comments see "RootName" input variable.
% - node.PROCESSING_INSTRUCTIONS - create "processing instruction" child
% node from the string. For global "processing instructions" see
% "RootName" input variable.
% - node.CDATA_SECTION - stores node's CDATA section (string). Only works
% if Pref.XmlEngine='Xerces'. For more info, see comments of F_xmlwrite.
% - other special node types like: document fragment nodes, document type
% nodes, entity nodes and notation nodes are not being handled by
% 'xml_write' at the moment.
%
% OUTPUT
% DOMnode Document Object Model (DOM) node tree in the format
% required as input to xmlwrite. (optional)
%
% EXAMPLES:
% MyTree=[];
% MyTree.MyNumber = 13;
% MyTree.MyString = 'Hello World';
% xml_write('test.xml', MyTree);
% type('test.xml')
% %See also xml_tutorial.m
%
% See also
% xml_read, xmlread, xmlwrite
%
% Written by Jarek Tuszynski, SAIC, jaroslaw.w.tuszynski_at_saic.com
%% Check Matlab Version
v = ver('MATLAB');
v = str2double(regexp(v.Version, '\d.\d','match','once'));
if (v<7)
error('Your MATLAB version is too old. You need version 7.0 or newer.');
end
%% default preferences
DPref.TableName = {'tr','td'}; % name of a special tags used to itemize 2D cell arrays
DPref.ItemName = 'item'; % name of a special tag used to itemize 1D cell arrays
DPref.StructItem = true; % allow arrays of structs to use 'item' notation
DPref.CellItem = true; % allow cell arrays to use 'item' notation
DPref.StructTable= 'Html';
DPref.CellTable = 'Html';
DPref.XmlEngine = 'Matlab'; % use matlab provided XMLUtils
%DPref.XmlEngine = 'Xerces'; % use Xerces xml generator directly
DPref.PreserveSpace = false; % Preserve or delete spaces at the beggining and the end of stings?
RootOnly = true; % Input is root node only
GlobalProcInst = [];
GlobalComment = [];
GlobalDocType = [];
%% read user preferences
if (nargin>3)
if (isfield(Pref, 'TableName' )), DPref.TableName = Pref.TableName; end
if (isfield(Pref, 'ItemName' )), DPref.ItemName = Pref.ItemName; end
if (isfield(Pref, 'StructItem')), DPref.StructItem = Pref.StructItem; end
if (isfield(Pref, 'CellItem' )), DPref.CellItem = Pref.CellItem; end
if (isfield(Pref, 'CellTable')), DPref.CellTable = Pref.CellTable; end
if (isfield(Pref, 'StructTable')), DPref.StructTable= Pref.StructTable; end
if (isfield(Pref, 'XmlEngine' )), DPref.XmlEngine = Pref.XmlEngine; end
if (isfield(Pref, 'RootOnly' )), RootOnly = Pref.RootOnly; end
if (isfield(Pref, 'PreserveSpace')), DPref.PreserveSpace = Pref.PreserveSpace; end
end
if (nargin<3 || isempty(RootName)), RootName=inputname(2); end
if (isempty(RootName)), RootName='ROOT'; end
if (iscell(RootName)) % RootName also stores global text node data
rName = RootName;
RootName = char(rName{1});
if (length(rName)>1), GlobalProcInst = char(rName{2}); end
if (length(rName)>2), GlobalComment = char(rName{3}); end
if (length(rName)>3), GlobalDocType = char(rName{4}); end
end
if(~RootOnly && isstruct(tree)) % if struct than deal with each field separatly
fields = fieldnames(tree);
for i=1:length(fields)
field = fields{i};
x = tree(1).(field);
if (strcmp(field, 'COMMENT'))
GlobalComment = x;
elseif (strcmp(field, 'PROCESSING_INSTRUCTION'))
GlobalProcInst = x;
elseif (strcmp(field, 'DOCUMENT_TYPE'))
GlobalDocType = x;
else
RootName = field;
t = x;
end
end
tree = t;
end
%% Initialize jave object that will store xml data structure
RootName = varName2str(RootName);
if (~isempty(GlobalDocType))
% n = strfind(GlobalDocType, ' ');
% if (~isempty(n))
% dtype = com.mathworks.xml.XMLUtils.createDocumentType(GlobalDocType);
% end
% DOMnode = com.mathworks.xml.XMLUtils.createDocument(RootName, dtype);
warning('xml_io_tools:write:docType', ...
'DOCUMENT_TYPE node was encountered which is not supported yet. Ignoring.');
end
DOMnode = com.mathworks.xml.XMLUtils.createDocument(RootName);
%% Use recursive function to convert matlab data structure to XML
root = DOMnode.getDocumentElement;
struct2DOMnode(DOMnode, root, tree, DPref.ItemName, DPref);
%% Remove the only child of the root node
root = DOMnode.getDocumentElement;
Child = root.getChildNodes; % create array of children nodes
nChild = Child.getLength; % number of children
if (nChild==1)
node = root.removeChild(root.getFirstChild);
while(node.hasChildNodes)
root.appendChild(node.removeChild(node.getFirstChild));
end
while(node.hasAttributes) % copy all attributes
root.setAttributeNode(node.removeAttributeNode(node.getAttributes.item(0)));
end
end
%% Save exotic Global nodes
if (~isempty(GlobalComment))
DOMnode.insertBefore(DOMnode.createComment(GlobalComment), DOMnode.getFirstChild());
end
if (~isempty(GlobalProcInst))
n = strfind(GlobalProcInst, ' ');
if (~isempty(n))
proc = DOMnode.createProcessingInstruction(GlobalProcInst(1:(n(1)-1)),...
GlobalProcInst((n(1)+1):end));
DOMnode.insertBefore(proc, DOMnode.getFirstChild());
end
end
% Not supported yet as the code below does not work
% if (~isempty(GlobalDocType))
% n = strfind(GlobalDocType, ' ');
% if (~isempty(n))
% dtype = DOMnode.createDocumentType(GlobalDocType);
% DOMnode.insertBefore(dtype, DOMnode.getFirstChild());
% end
% end
%% save java DOM tree to XML file
if (~isempty(filename))
if (strcmpi(DPref.XmlEngine, 'Xerces'))
xmlwrite_xerces(filename, DOMnode);
else
xmlwrite(filename, DOMnode);
end
end
%% =======================================================================
% === struct2DOMnode Function ===========================================
% =======================================================================
function [] = struct2DOMnode(xml, parent, s, TagName, Pref)
% struct2DOMnode is a recursive function that converts matlab's structs to
% DOM nodes.
% INPUTS:
% xml - jave object that will store xml data structure
% parent - parent DOM Element
% s - Matlab data structure to save
% TagName - name to be used in xml tags describing 's'
% Pref - preferenced
% OUTPUT:
% parent - modified 'parent'
% perform some conversions
if (ischar(s) && min(size(s))>1) % if 2D array of characters
s=cellstr(s); % than convert to cell array
end
% if (strcmp(TagName, 'CONTENT'))
% while (iscell(s) && length(s)==1), s = s{1}; end % unwrap cell arrays of length 1
% end
TagName = varName2str(TagName);
%% == node is a 2D cell array ==
% convert to some other format prior to further processing
nDim = nnz(size(s)>1); % is it a scalar, vector, 2D array, 3D cube, etc?
if (iscell(s) && nDim==2 && strcmpi(Pref.CellTable, 'Matlab'))
s = var2str(s, Pref.PreserveSpace);
end
if (nDim==2 && (iscell (s) && strcmpi(Pref.CellTable, 'Vector')) || ...
(isstruct(s) && strcmpi(Pref.StructTable, 'Vector')))
s = s(:);
end
if (nDim>2), s = s(:); end % can not handle this case well
nItem = numel(s);
nDim = nnz(size(s)>1); % is it a scalar, vector, 2D array, 3D cube, etc?
%% == node is a cell ==
if (iscell(s)) % if this is a cell or cell array
if ((nDim==2 && strcmpi(Pref.CellTable,'Html')) || (nDim< 2 && Pref.CellItem))
% if 2D array of cells than can use HTML-like notation or if 1D array
% than can use item notation
if (strcmp(TagName, 'CONTENT')) % CONTENT nodes already have <TagName> ... </TagName>
array2DOMnode(xml, parent, s, Pref.ItemName, Pref ); % recursive call
else
node = xml.createElement(TagName); % <TagName> ... </TagName>
array2DOMnode(xml, node, s, Pref.ItemName, Pref ); % recursive call
parent.appendChild(node);
end
else % use <TagName>...<\TagName> <TagName>...<\TagName> notation
array2DOMnode(xml, parent, s, TagName, Pref ); % recursive call
end
%% == node is a struct ==
elseif (isstruct(s)) % if struct than deal with each field separatly
if ((nDim==2 && strcmpi(Pref.StructTable,'Html')) || (nItem>1 && Pref.StructItem))
% if 2D array of structs than can use HTML-like notation or
% if 1D array of structs than can use 'items' notation
node = xml.createElement(TagName);
array2DOMnode(xml, node, s, Pref.ItemName, Pref ); % recursive call
parent.appendChild(node);
elseif (nItem>1) % use <TagName>...<\TagName> <TagName>...<\TagName> notation
array2DOMnode(xml, parent, s, TagName, Pref ); % recursive call
else % otherwise save each struct separatelly
fields = fieldnames(s);
node = xml.createElement(TagName);
for i=1:length(fields) % add field by field to the node
field = fields{i};
x = s.(field);
switch field
case {'COMMENT', 'CDATA_SECTION', 'PROCESSING_INSTRUCTION'}
if iscellstr(x) % cell array of strings -> add them one by one
array2DOMnode(xml, node, x(:), field, Pref ); % recursive call will modify 'node'
elseif ischar(x) % single string -> add it
struct2DOMnode(xml, node, x, field, Pref ); % recursive call will modify 'node'
else % not a string - Ignore
warning('xml_io_tools:write:badSpecialNode', ...
['Struct field named ',field,' encountered which was not a string. Ignoring.']);
end
case 'ATTRIBUTE' % set attributes of the node
if (isempty(x)), continue; end
if (isstruct(x))
attName = fieldnames(x); % get names of all the attributes
for k=1:length(attName) % attach them to the node
att = xml.createAttribute(varName2str(attName(k)));
att.setValue(var2str(x.(attName{k}),Pref.PreserveSpace));
node.setAttributeNode(att);
end
else
warning('xml_io_tools:write:badAttribute', ...
'Struct field named ATTRIBUTE encountered which was not a struct. Ignoring.');
end
otherwise % set children of the node
struct2DOMnode(xml, node, x, field, Pref ); % recursive call will modify 'node'
end
end % end for i=1:nFields
parent.appendChild(node);
end
%% == node is a leaf node ==
else % if not a struct and not a cell than it is a leaf node
switch TagName % different processing depending on desired type of the node
case 'COMMENT' % create comment node
com = xml.createComment(s);
parent.appendChild(com);
case 'CDATA_SECTION' % create CDATA Section
cdt = xml.createCDATASection(s);
parent.appendChild(cdt);
case 'PROCESSING_INSTRUCTION' % set attributes of the node
OK = false;
if (ischar(s))
n = strfind(s, ' ');
if (~isempty(n))
proc = xml.createProcessingInstruction(s(1:(n(1)-1)),s((n(1)+1):end));
parent.insertBefore(proc, parent.getFirstChild());
OK = true;
end
end
if (~OK)
warning('xml_io_tools:write:badProcInst', ...
['Struct field named PROCESSING_INSTRUCTION need to be',...
' a string, for example: xml-stylesheet type="text/css" ', ...
'href="myStyleSheet.css". Ignoring.']);
end
case 'CONTENT' % this is text part of already existing node
txt = xml.createTextNode(var2str(s, Pref.PreserveSpace)); % convert to text
parent.appendChild(txt);
otherwise % I guess it is a regular text leaf node
txt = xml.createTextNode(var2str(s, Pref.PreserveSpace));
node = xml.createElement(TagName);
node.appendChild(txt);
parent.appendChild(node);
end
end % of struct2DOMnode function
%% =======================================================================
% === array2DOMnode Function ============================================
% =======================================================================
function [] = array2DOMnode(xml, parent, s, TagName, Pref)
% Deal with 1D and 2D arrays of cell or struct. Will modify 'parent'.
nDim = nnz(size(s)>1); % is it a scalar, vector, 2D array, 3D cube, etc?
switch nDim
case 2 % 2D array
for r=1:size(s,1)
subnode = xml.createElement(Pref.TableName{1});
for c=1:size(s,2)
v = s(r,c);
if iscell(v), v = v{1}; end
struct2DOMnode(xml, subnode, v, Pref.TableName{2}, Pref ); % recursive call
end
parent.appendChild(subnode);
end
case 1 %1D array
for iItem=1:numel(s)
v = s(iItem);
if iscell(v), v = v{1}; end
struct2DOMnode(xml, parent, v, TagName, Pref ); % recursive call
end
case 0 % scalar -> this case should never be called
if ~isempty(s)
if iscell(s), s = s{1}; end
struct2DOMnode(xml, parent, s, TagName, Pref );
end
end
%% =======================================================================
% === var2str Function ==================================================
% =======================================================================
function str = var2str(object, PreserveSpace)
% convert matlab variables to a string
switch (1)
case isempty(object)
str = '';
case (isnumeric(object) || islogical(object))
if ndims(object)>2, object=object(:); end % can't handle arrays with dimention > 2
str=mat2str(object); % convert matrix to a string
% mark logical scalars with [] (logical arrays already have them) so the xml_read
% recognizes them as MATLAB objects instead of strings. Same with sparse
% matrices
if ((islogical(object) && isscalar(object)) || issparse(object)),
str = ['[' str ']'];
end
if (isinteger(object)),
str = ['[', class(object), '(', str ')]'];
end
case iscell(object)
if ndims(object)>2, object=object(:); end % can't handle cell arrays with dimention > 2
[nr nc] = size(object);
obj2 = object;
for i=1:length(object(:))
str = var2str(object{i}, PreserveSpace);
if (ischar(object{i})), object{i} = ['''' object{i} '''']; else object{i}=str; end
obj2{i} = [object{i} ','];
end
for r = 1:nr, obj2{r,nc} = [object{r,nc} ';']; end
obj2 = obj2.';
str = ['{' obj2{:} '}'];
case isstruct(object)
str='';
warning('xml_io_tools:write:var2str', ...
'Struct was encountered where string was expected. Ignoring.');
case isa(object, 'function_handle')
str = ['[@' char(object) ']'];
case ischar(object)
str = object;
otherwise
str = char(object);
end
%% string clean-up
str=str(:); str=str.'; % make sure this is a row vector of char's
if (~isempty(str))
str(str<32|str==127)=' '; % convert no-printable characters to spaces
if (~PreserveSpace)
str = strtrim(str); % remove spaces from begining and the end
str = regexprep(str,'\s+',' '); % remove multiple spaces
end
end
%% =======================================================================
% === var2Namestr Function ==============================================
% =======================================================================
function str = varName2str(str)
% convert matlab variable names to a sting
str = char(str);
p = strfind(str,'0x');
if (~isempty(p))
for i=1:length(p)
before = str( p(i)+(0:3) ); % string to replace
after = char(hex2dec(before(3:4))); % string to replace with
str = regexprep(str,before,after, 'once', 'ignorecase');
p=p-3; % since 4 characters were replaced with one - compensate
end
end
str = regexprep(str,'_COLON_',':', 'once', 'ignorecase');
str = regexprep(str,'_DASH_' ,'-', 'once', 'ignorecase');
|
github
|
dschick/udkm1DsimML-master
|
xml_read.m
|
.m
|
udkm1DsimML-master/helpers/functions/xml_io_tools/xml_read.m
| 23,864 |
utf_8
|
5bba7e1f07a293d773aed5616ad3d5c9
|
function [tree, RootName, DOMnode] = xml_read(xmlfile, Pref)
%XML_READ reads xml files and converts them into Matlab's struct tree.
%
% DESCRIPTION
% tree = xml_read(xmlfile) reads 'xmlfile' into data structure 'tree'
%
% tree = xml_read(xmlfile, Pref) reads 'xmlfile' into data structure 'tree'
% according to your preferences
%
% [tree, RootName, DOMnode] = xml_read(xmlfile) get additional information
% about XML file
%
% INPUT:
% xmlfile URL or filename of xml file to read
% Pref Preferences:
% Pref.ItemName - default 'item' - name of a special tag used to itemize
% cell arrays
% Pref.ReadAttr - default true - allow reading attributes
% Pref.ReadSpec - default true - allow reading special nodes
% Pref.Str2Num - default 'smart' - convert strings that look like numbers
% to numbers. Options: "always", "never", and "smart"
% Pref.KeepNS - default true - keep or strip namespace info
% Pref.NoCells - default true - force output to have no cell arrays
% Pref.Debug - default false - show mode specific error messages
% Pref.NumLevels- default infinity - how many recursive levels are
% allowed. Can be used to speed up the function by prunning the tree.
% Pref.RootOnly - default true - output variable 'tree' corresponds to
% xml file root element, otherwise it correspond to the whole file.
% Pref.CellItem - default 'true' - leave 'item' nodes in cell notation.
% OUTPUT:
% tree tree of structs and/or cell arrays corresponding to xml file
% RootName XML tag name used for root (top level) node.
% Optionally it can be a string cell array storing: Name of
% root node, document "Processing Instructions" data and
% document "comment" string
% DOMnode output of xmlread
%
% DETAILS:
% Function xml_read first calls MATLAB's xmlread function and than
% converts its output ('Document Object Model' tree of Java objects)
% to tree of MATLAB struct's. The output is in format of nested structs
% and cells. In the output data structure field names are based on
% XML tags, except in cases when tags produce illegal variable names.
%
% Several special xml node types result in special tags for fields of
% 'tree' nodes:
% - node.CONTENT - stores data section of the node if other fields are
% present. Usually data section is stored directly in 'node'.
% - node.ATTRIBUTE.name - stores node's attribute called 'name'.
% - node.COMMENT - stores node's comment section (string). For global
% comments see "RootName" output variable.
% - node.CDATA_SECTION - stores node's CDATA section (string).
% - node.PROCESSING_INSTRUCTIONS - stores "processing instruction" child
% node. For global "processing instructions" see "RootName" output variable.
% - other special node types like: document fragment nodes, document type
% nodes, entity nodes, notation nodes and processing instruction nodes
% will be treated like regular nodes
%
% EXAMPLES:
% MyTree=[];
% MyTree.MyNumber = 13;
% MyTree.MyString = 'Hello World';
% xml_write('test.xml', MyTree);
% [tree treeName] = xml_read ('test.xml');
% disp(treeName)
% gen_object_display()
% % See also xml_examples.m
%
% See also:
% xml_write, xmlread, xmlwrite
%
% Written by Jarek Tuszynski, SAIC, jaroslaw.w.tuszynski_at_saic.com
% References:
% - Function inspired by Example 3 found in xmlread function.
% - Output data structures inspired by xml_toolbox structures.
%% default preferences
DPref.TableName = {'tr','td'}; % name of a special tags used to itemize 2D cell arrays
DPref.ItemName = 'item'; % name of a special tag used to itemize 1D cell arrays
DPref.CellItem = false; % leave 'item' nodes in cell notation
DPref.ReadAttr = true; % allow reading attributes
DPref.ReadSpec = true; % allow reading special nodes: comments, CData, etc.
DPref.KeepNS = true; % Keep or strip namespace info
DPref.Str2Num = 'smart';% convert strings that look like numbers to numbers
DPref.NoCells = true; % force output to have no cell arrays
DPref.NumLevels = 1e10; % number of recurence levels
DPref.PreserveSpace = false; % Preserve or delete spaces at the beggining and the end of stings?
RootOnly = true; % return root node with no top level special nodes
Debug = false; % show specific errors (true) or general (false)?
tree = [];
RootName = [];
%% Check Matlab Version
v = ver('MATLAB');
version = str2double(regexp(v.Version, '\d.\d','match','once'));
if (version<7.1)
error('Your MATLAB version is too old. You need version 7.1 or newer.');
end
%% read user preferences
if (nargin>1)
if (isfield(Pref, 'TableName')), DPref.TableName = Pref.TableName; end
if (isfield(Pref, 'ItemName' )), DPref.ItemName = Pref.ItemName; end
if (isfield(Pref, 'CellItem' )), DPref.CellItem = Pref.CellItem; end
if (isfield(Pref, 'Str2Num' )), DPref.Str2Num = Pref.Str2Num ; end
if (isfield(Pref, 'NoCells' )), DPref.NoCells = Pref.NoCells ; end
if (isfield(Pref, 'NumLevels')), DPref.NumLevels = Pref.NumLevels; end
if (isfield(Pref, 'ReadAttr' )), DPref.ReadAttr = Pref.ReadAttr; end
if (isfield(Pref, 'ReadSpec' )), DPref.ReadSpec = Pref.ReadSpec; end
if (isfield(Pref, 'KeepNS' )), DPref.KeepNS = Pref.KeepNS; end
if (isfield(Pref, 'RootOnly' )), RootOnly = Pref.RootOnly; end
if (isfield(Pref, 'Debug' )), Debug = Pref.Debug ; end
if (isfield(Pref, 'PreserveSpace')), DPref.PreserveSpace = Pref.PreserveSpace; end
end
if ischar(DPref.Str2Num), % convert from character description to numbers
DPref.Str2Num = find(strcmpi(DPref.Str2Num, {'never', 'smart', 'always'}))-1;
if isempty(DPref.Str2Num), DPref.Str2Num=1; end % 1-smart by default
end
%% read xml file using Matlab function
if isa(xmlfile, 'org.apache.xerces.dom.DeferredDocumentImpl');
% if xmlfile is a DOMnode than skip the call to xmlread
try
try
DOMnode = xmlfile;
catch ME
error('Invalid DOM node: \n%s.', getReport(ME));
end
catch %#ok<CTCH> catch for mablab versions prior to 7.5
error('Invalid DOM node. \n');
end
else % we assume xmlfile is a filename
if (Debug) % in debuging mode crashes are allowed
DOMnode = xmlread(xmlfile);
else % in normal mode crashes are not allowed
try
try
DOMnode = xmlread(xmlfile);
catch ME
error('Failed to read XML file %s: \n%s',xmlfile, getReport(ME));
end
catch %#ok<CTCH> catch for mablab versions prior to 7.5
error('Failed to read XML file %s\n',xmlfile);
end
end
end
Node = DOMnode.getFirstChild;
%% Find the Root node. Also store data from Global Comment and Processing
% Instruction nodes, if any.
GlobalTextNodes = cell(1,3);
GlobalProcInst = [];
GlobalComment = [];
GlobalDocType = [];
while (~isempty(Node))
if (Node.getNodeType==Node.ELEMENT_NODE)
RootNode=Node;
elseif (Node.getNodeType==Node.PROCESSING_INSTRUCTION_NODE)
data = strtrim(char(Node.getData));
target = strtrim(char(Node.getTarget));
GlobalProcInst = [target, ' ', data];
GlobalTextNodes{2} = GlobalProcInst;
elseif (Node.getNodeType==Node.COMMENT_NODE)
GlobalComment = strtrim(char(Node.getData));
GlobalTextNodes{3} = GlobalComment;
% elseif (Node.getNodeType==Node.DOCUMENT_TYPE_NODE)
% GlobalTextNodes{4} = GlobalDocType;
end
Node = Node.getNextSibling;
end
%% parse xml file through calls to recursive DOMnode2struct function
if (Debug) % in debuging mode crashes are allowed
[tree RootName] = DOMnode2struct(RootNode, DPref, 1);
else % in normal mode crashes are not allowed
try
try
[tree RootName] = DOMnode2struct(RootNode, DPref, 1);
catch ME
error('Unable to parse XML file %s: \n %s.',xmlfile, getReport(ME));
end
catch %#ok<CTCH> catch for mablab versions prior to 7.5
error('Unable to parse XML file %s.',xmlfile);
end
end
%% If there were any Global Text nodes than return them
if (~RootOnly)
if (~isempty(GlobalProcInst) && DPref.ReadSpec)
t.PROCESSING_INSTRUCTION = GlobalProcInst;
end
if (~isempty(GlobalComment) && DPref.ReadSpec)
t.COMMENT = GlobalComment;
end
if (~isempty(GlobalDocType) && DPref.ReadSpec)
t.DOCUMENT_TYPE = GlobalDocType;
end
t.(RootName) = tree;
tree=t;
end
if (~isempty(GlobalTextNodes))
GlobalTextNodes{1} = RootName;
RootName = GlobalTextNodes;
end
%% =======================================================================
% === DOMnode2struct Function ===========================================
% =======================================================================
function [s TagName LeafNode] = DOMnode2struct(node, Pref, level)
%% === Step 1: Get node name and check if it is a leaf node ==============
[TagName LeafNode] = NodeName(node, Pref.KeepNS);
s = []; % initialize output structure
%% === Step 2: Process Leaf Nodes (nodes with no children) ===============
if (LeafNode)
if (LeafNode>1 && ~Pref.ReadSpec), LeafNode=-1; end % tags only so ignore special nodes
if (LeafNode>0) % supported leaf node types
try
try % use try-catch: errors here are often due to VERY large fields (like images) that overflow java memory
s = char(node.getData);
if (isempty(s)), s = ' '; end % make it a string
% for some reason current xmlread 'creates' a lot of empty text
% fields with first chatacter=10 - those will be deleted.
if (~Pref.PreserveSpace || s(1)==10)
if (isspace(s(1)) || isspace(s(end))), s = strtrim(s); end % trim speces is any
end
if (LeafNode==1), s=str2var(s, Pref.Str2Num, 0); end % convert to number(s) if needed
catch ME % catch for mablab versions 7.5 and higher
warning('xml_io_tools:read:LeafRead', ...
'This leaf node could not be read and was ignored. ');
getReport(ME)
end
catch %#ok<CTCH> catch for mablab versions prior to 7.5
warning('xml_io_tools:read:LeafRead', ...
'This leaf node could not be read and was ignored. ');
end
end
if (LeafNode==3) % ProcessingInstructions need special treatment
target = strtrim(char(node.getTarget));
s = [target, ' ', s];
end
return % We are done the rest of the function deals with nodes with children
end
if (level>Pref.NumLevels+1), return; end % if Pref.NumLevels is reached than we are done
%% === Step 3: Process nodes with children ===============================
if (node.hasChildNodes) % children present
Child = node.getChildNodes; % create array of children nodes
nChild = Child.getLength; % number of children
% --- pass 1: how many children with each name -----------------------
f = [];
for iChild = 1:nChild % read in each child
[cname cLeaf] = NodeName(Child.item(iChild-1), Pref.KeepNS);
if (cLeaf<0), continue; end % unsupported leaf node types
if (~isfield(f,cname)),
f.(cname)=0; % initialize first time I see this name
end
f.(cname) = f.(cname)+1; % add to the counter
end % end for iChild
% text_nodes become CONTENT & for some reason current xmlread 'creates' a
% lot of empty text fields so f.CONTENT value should not be trusted
if (isfield(f,'CONTENT') && f.CONTENT>2), f.CONTENT=2; end
% --- pass 2: store all the children as struct of cell arrays ----------
for iChild = 1:nChild % read in each child
[c cname cLeaf] = DOMnode2struct(Child.item(iChild-1), Pref, level+1);
if (cLeaf && isempty(c)) % if empty leaf node than skip
continue; % usually empty text node or one of unhandled node types
elseif (nChild==1 && cLeaf==1)
s=c; % shortcut for a common case
else % if normal node
if (level>Pref.NumLevels), continue; end
n = f.(cname); % how many of them in the array so far?
if (~isfield(s,cname)) % encountered this name for the first time
if (n==1) % if there will be only one of them ...
s.(cname) = c; % than save it in format it came in
else % if there will be many of them ...
s.(cname) = cell(1,n);
s.(cname){1} = c; % than save as cell array
end
f.(cname) = 1; % initialize the counter
else % already have seen this name
s.(cname){n+1} = c; % add to the array
f.(cname) = n+1; % add to the array counter
end
end
end % for iChild
end % end if (node.hasChildNodes)
%% === Step 4: Post-process struct's created for nodes with children =====
if (isstruct(s))
fields = fieldnames(s);
nField = length(fields);
% Detect structure that looks like Html table and store it in cell Matrix
if (nField==1 && strcmpi(fields{1},Pref.TableName{1}))
tr = s.(Pref.TableName{1});
fields2 = fieldnames(tr{1});
if (length(fields2)==1 && strcmpi(fields2{1},Pref.TableName{2}))
% This seems to be a special structure such that for
% Pref.TableName = {'tr','td'} 's' corresponds to
% <tr> <td>M11</td> <td>M12</td> </tr>
% <tr> <td>M12</td> <td>M22</td> </tr>
% Recognize it as encoding for 2D struct
nr = length(tr);
for r = 1:nr
row = tr{r}.(Pref.TableName{2});
Table(r,1:length(row)) = row; %#ok<AGROW>
end
s = Table;
end
end
% --- Post-processing: convert 'struct of cell-arrays' to 'array of structs'
% Example: let say s has 3 fields s.a, s.b & s.c and each field is an
% cell-array with more than one cell-element and all 3 have the same length.
% Then change it to array of structs, each with single cell.
% This way element s.a{1} will be now accessed through s(1).a
vec = zeros(size(fields));
for i=1:nField, vec(i) = f.(fields{i}); end
if (numel(vec)>1 && vec(1)>1 && var(vec)==0) % convert from struct of
s = cell2struct(struct2cell(s), fields, 1); % arrays to array of struct
end % if anyone knows better way to do above conversion please let me know.
end
%% === Step 5: Process nodes with attributes =============================
if (node.hasAttributes && Pref.ReadAttr)
if (~isstruct(s)), % make into struct if is not already
ss.CONTENT=s;
s=ss;
end
Attr = node.getAttributes; % list of all attributes
for iAttr = 1:Attr.getLength % for each attribute
name = char(Attr.item(iAttr-1).getName); % attribute name
name = str2varName(name, Pref.KeepNS); % fix name if needed
value = char(Attr.item(iAttr-1).getValue); % attribute value
value = str2var(value, Pref.Str2Num, 1); % convert to number if possible
s.ATTRIBUTE.(name) = value; % save again
end % end iAttr loop
end % done with attributes
if (~isstruct(s)), return; end %The rest of the code deals with struct's
%% === Post-processing: fields of "s"
% convert 'cell-array of structs' to 'arrays of structs'
fields = fieldnames(s); % get field names
nField = length(fields);
for iItem=1:length(s) % for each struct in the array - usually one
for iField=1:length(fields)
field = fields{iField}; % get field name
% if this is an 'item' field and user want to leave those as cells
% than skip this one
if (strcmpi(field, Pref.ItemName) && Pref.CellItem), continue; end
x = s(iItem).(field);
if (iscell(x) && all(cellfun(@isstruct,x(:))) && numel(x)>1) % it's cell-array of structs
% numel(x)>1 check is to keep 1 cell-arrays created when Pref.CellItem=1
try % this operation fails sometimes
% example: change s(1).a{1}.b='jack'; s(1).a{2}.b='john'; to
% more convinient s(1).a(1).b='jack'; s(1).a(2).b='john';
s(iItem).(field) = [x{:}]'; %#ok<AGROW> % converted to arrays of structs
catch %#ok<CTCH>
% above operation will fail if s(1).a{1} and s(1).a{2} have
% different fields. If desired, function forceCell2Struct can force
% them to the same field structure by adding empty fields.
if (Pref.NoCells)
s(iItem).(field) = forceCell2Struct(x); %#ok<AGROW>
end
end % end catch
end
end
end
%% === Step 4: Post-process struct's created for nodes with children =====
% --- Post-processing: remove special 'item' tags ---------------------
% many xml writes (including xml_write) use a special keyword to mark
% arrays of nodes (see xml_write for examples). The code below converts
% s.item to s.CONTENT
ItemContent = false;
if (isfield(s,Pref.ItemName))
s.CONTENT = s.(Pref.ItemName);
s = rmfield(s,Pref.ItemName);
ItemContent = Pref.CellItem; % if CellItem than keep s.CONTENT as cells
end
% --- Post-processing: clean up CONTENT tags ---------------------
% if s.CONTENT is a cell-array with empty elements at the end than trim
% the length of this cell-array. Also if s.CONTENT is the only field than
% remove .CONTENT part and store it as s.
if (isfield(s,'CONTENT'))
if (iscell(s.CONTENT) && isvector(s.CONTENT))
x = s.CONTENT;
for i=numel(x):-1:1, if ~isempty(x{i}), break; end; end
if (i==1 && ~ItemContent)
s.CONTENT = x{1}; % delete cell structure
else
s.CONTENT = x(1:i); % delete empty cells
end
end
if (nField==1)
if (ItemContent)
ss = s.CONTENT; % only child: remove a level but ensure output is a cell-array
s=[]; s{1}=ss;
else
s = s.CONTENT; % only child: remove a level
end
end
end
%% =======================================================================
% === forceCell2Struct Function =========================================
% =======================================================================
function s = forceCell2Struct(x)
% Convert cell-array of structs, where not all of structs have the same
% fields, to a single array of structs
%% Convert 1D cell array of structs to 2D cell array, where each row
% represents item in original array and each column corresponds to a unique
% field name. Array "AllFields" store fieldnames for each column
AllFields = fieldnames(x{1}); % get field names of the first struct
CellMat = cell(length(x), length(AllFields));
for iItem=1:length(x)
fields = fieldnames(x{iItem}); % get field names of the next struct
for iField=1:length(fields) % inspect all fieldnames and find those
field = fields{iField}; % get field name
col = find(strcmp(field,AllFields),1);
if isempty(col) % no column for such fieldname yet
AllFields = [AllFields; field]; %#ok<AGROW>
col = length(AllFields); % create a new column for it
end
CellMat{iItem,col} = x{iItem}.(field); % store rearanged data
end
end
%% Convert 2D cell array to array of structs
s = cell2struct(CellMat, AllFields, 2);
%% =======================================================================
% === str2var Function ==================================================
% =======================================================================
function val=str2var(str, option, attribute)
% Can this string 'str' be converted to a number? if so than do it.
val = str;
len = numel(str);
if (len==0 || option==0), return; end % Str2Num="never" of empty string -> do not do enything
if (len>10000 && option==1), return; end % Str2Num="smart" and string is very long -> probably base64 encoded binary
digits = '(Inf)|(NaN)|(pi)|[\t\n\d\+\-\*\.ei EI\;\,]';
s = regexprep(str, digits, ''); % remove all the digits and other allowed characters
if (~all(~isempty(s))) % if nothing left than this is probably a number
if (~isempty(strfind(str, ' '))), option=2; end %if str has white-spaces assume by default that it is not a date string
if (~isempty(strfind(str, '['))), option=2; end % same with brackets
str(strfind(str, '\n')) = ';';% parse data tables into 2D arrays, if any
if (option==1) % the 'smart' option
try % try to convert to a date, like 2007-12-05
datenum(str); % if successful than leave it as string
catch %#ok<CTCH> % if this is not a date than ...
option=2; % ... try converting to a number
end
end
if (option==2)
if (attribute)
num = str2double(str); % try converting to a single number using sscanf function
if isnan(num), return; end % So, it wasn't really a number after all
else
num = str2num(str); %#ok<ST2NM> % try converting to a single number or array using eval function
end
if(isnumeric(num) && numel(num)>0), val=num; end % if convertion to a single was succesful than save
end
elseif ((str(1)=='[' && str(end)==']') || (str(1)=='{' && str(end)=='}')) % this looks like a (cell) array encoded as a string
try
val = eval(str);
catch %#ok<CTCH>
val = str;
end
elseif (~attribute) % see if it is a boolean array with no [] brackets
str1 = lower(str);
str1 = strrep(str1, 'false', '0');
str1 = strrep(str1, 'true' , '1');
s = regexprep(str1, '[01 \;\,]', ''); % remove all 0/1, spaces, commas and semicolons
if (~all(~isempty(s))) % if nothing left than this is probably a boolean array
num = str2num(str1); %#ok<ST2NM>
if(isnumeric(num) && numel(num)>0), val = (num>0); end % if convertion was succesful than save as logical
end
end
%% =======================================================================
% === str2varName Function ==============================================
% =======================================================================
function str = str2varName(str, KeepNS)
% convert a sting to a valid matlab variable name
if(KeepNS)
str = regexprep(str,':','_COLON_', 'once', 'ignorecase');
else
k = strfind(str,':');
if (~isempty(k))
str = str(k+1:end);
end
end
str = regexprep(str,'-','_DASH_' ,'once', 'ignorecase');
if (~isvarname(str)) && (~iskeyword(str))
str = genvarname(str);
end
%% =======================================================================
% === NodeName Function =================================================
% =======================================================================
function [Name LeafNode] = NodeName(node, KeepNS)
% get node name and make sure it is a valid variable name in Matlab.
% also get node type:
% LeafNode=0 - normal element node,
% LeafNode=1 - text node
% LeafNode=2 - supported non-text leaf node,
% LeafNode=3 - supported processing instructions leaf node,
% LeafNode=-1 - unsupported non-text leaf node
switch (node.getNodeType)
case node.ELEMENT_NODE
Name = char(node.getNodeName);% capture name of the node
Name = str2varName(Name, KeepNS); % if Name is not a good variable name - fix it
LeafNode = 0;
case node.TEXT_NODE
Name = 'CONTENT';
LeafNode = 1;
case node.COMMENT_NODE
Name = 'COMMENT';
LeafNode = 2;
case node.CDATA_SECTION_NODE
Name = 'CDATA_SECTION';
LeafNode = 2;
case node.DOCUMENT_TYPE_NODE
Name = 'DOCUMENT_TYPE';
LeafNode = 2;
case node.PROCESSING_INSTRUCTION_NODE
Name = 'PROCESSING_INSTRUCTION';
LeafNode = 3;
otherwise
NodeType = {'ELEMENT','ATTRIBUTE','TEXT','CDATA_SECTION', ...
'ENTITY_REFERENCE', 'ENTITY', 'PROCESSING_INSTRUCTION', 'COMMENT',...
'DOCUMENT', 'DOCUMENT_TYPE', 'DOCUMENT_FRAGMENT', 'NOTATION'};
Name = char(node.getNodeName);% capture name of the node
warning('xml_io_tools:read:unkNode', ...
'Unknown node type encountered: %s_NODE (%s)', NodeType{node.getNodeType}, Name);
LeafNode = -1;
end
|
github
|
happynear/MTCNN_face_detection_alignment-master
|
test.m
|
.m
|
MTCNN_face_detection_alignment-master/code/codes/camera_demo/test.m
| 8,824 |
utf_8
|
484a3e8719f2102fd5aa6c841209b5ed
|
function varargout = test(varargin)
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @test_OpeningFcn, ...
'gui_OutputFcn', @test_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
function test_OpeningFcn(hObject, eventdata, handles, varargin)
handles.output = hObject;
guidata(hObject, handles);
function varargout = test_OutputFcn(hObject, eventdata, handles)
varargout{1} = handles.output;
function pushbutton1_Callback(hObject, eventdata, handles)
threshold=[0.6 0.7 str2num(get(findobj('tag','edit4'),'string'))]
factor=0.709;
minsize=str2num(get(findobj('tag','edit6'),'string'));
stop=0.03;
mypath=get(findobj('tag','edit1'),'string')
addpath(mypath);
caffe.reset_all();
caffe.set_mode_cpu();
mypath=get(findobj('tag','edit3'),'string')
addpath(mypath);
cameraid=get(findobj('tag','edit2'),'string')
camera=imaqhwinfo;
camera=camera.InstalledAdaptors{str2num(cameraid)}
vid1= videoinput(camera,1,get(findobj('tag','edit5'),'string'));
warning off all
usbVidRes1=get(vid1,'videoResolution');
nBands1=get(vid1,'NumberOfBands');
hImage1=imshow(zeros(usbVidRes1(2),usbVidRes1(1),nBands1));
preview(vid1,hImage1);
prototxt_dir = './model/det1.prototxt';
model_dir = './model/det1.caffemodel';
PNet=caffe.Net(prototxt_dir,model_dir,'test');
prototxt_dir = './model/det2.prototxt';
model_dir = './model/det2.caffemodel';
RNet=caffe.Net(prototxt_dir,model_dir,'test');
prototxt_dir = './model/det3.prototxt';
model_dir = './model/det3.caffemodel';
ONet=caffe.Net(prototxt_dir,model_dir,'test');
prototxt_dir = './model/det4.prototxt';
model_dir = './model/det4.caffemodel';
LNet=caffe.Net(prototxt_dir,model_dir,'test');
rec=rectangle('Position',[1 1 1 1],'Edgecolor','r');
while (1)
img=getsnapshot(vid1);
[total_boxes point]=detect_face(img,minsize,PNet,RNet,ONet,threshold,false,factor);
try
delete(rec);
catch
end
numbox=size(total_boxes,1);
for j=1:numbox;
rec(j)=rectangle('Position',[total_boxes(j,1:2) total_boxes(j,3:4)-total_boxes(j,1:2)],'Edgecolor','g','LineWidth',3);
rec(6*numbox+j)=rectangle('Position',[point(1,j),point(6,j),5,5],'Curvature',[1,1],'FaceColor','g','LineWidth',3);
rec(12*numbox+j)=rectangle('Position',[point(2,j),point(7,j),5,5],'Curvature',[1,1],'FaceColor','g','LineWidth',3);
rec(18*numbox+j)=rectangle('Position',[point(3,j),point(8,j),5,5],'Curvature',[1,1],'FaceColor','g','LineWidth',3);
rec(24*numbox+j)=rectangle('Position',[point(4,j),point(9,j),5,5],'Curvature',[1,1],'FaceColor','g','LineWidth',3);
rec(30*numbox+j)=rectangle('Position',[point(5,j),point(10,j),5,5],'Curvature',[1,1],'FaceColor','g','LineWidth',3);
end
pause(stop)
end
function edit1_Callback(hObject, eventdata, handles)
% hObject handle to edit1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit1 as text
% str2double(get(hObject,'String')) returns contents of edit1 as a double
% --- Executes during object creation, after setting all properties.
function edit1_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit2_Callback(hObject, eventdata, handles)
% hObject handle to edit2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit2 as text
% str2double(get(hObject,'String')) returns contents of edit2 as a double
% --- Executes during object creation, after setting all properties.
function edit2_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit3_Callback(hObject, eventdata, handles)
% hObject handle to edit3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit3 as text
% str2double(get(hObject,'String')) returns contents of edit3 as a double
% --- Executes during object creation, after setting all properties.
function edit3_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit4_Callback(hObject, eventdata, handles)
% hObject handle to edit4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit4 as text
% str2double(get(hObject,'String')) returns contents of edit4 as a double
% --- Executes during object creation, after setting all properties.
function edit4_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit5_Callback(hObject, eventdata, handles)
% hObject handle to edit5 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit5 as text
% str2double(get(hObject,'String')) returns contents of edit5 as a double
% --- Executes during object creation, after setting all properties.
function edit5_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit5 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit6_Callback(hObject, eventdata, handles)
% hObject handle to edit6 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit6 as text
% str2double(get(hObject,'String')) returns contents of edit6 as a double
% --- Executes during object creation, after setting all properties.
function edit6_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit6 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
|
github
|
UCL-SML/pilco-matlab-master
|
print_pdf.m
|
.m
|
pilco-matlab-master/doc/plots/print_pdf.m
| 7,570 |
utf_8
|
97a41030f45e2c5d10cc569c07e12a19
|
%PRINT_PDF Prints cropped figures to pdf with fonts embedded
%
% Examples:
% print_pdf filename
% print_pdf(filename, fig_handle)
%
% This function saves a figure as a pdf nicely, without the need to specify
% multiple options. It improves on MATLAB's print command (using default
% options) in several ways:
% - The figure borders are cropped
% - Fonts are embedded (as subsets)
% - Lossless compression is used on vector graphics
% - High quality jpeg compression is used on bitmaps
% - Dotted/dashed line dash lengths vary with line width (as on screen)
% - Grid lines given their own dot style, instead of dashed
%
% This function requires that you have ghostscript installed on your system
% and that the executable binary is on your system's path. Ghostscript can
% be downloaded from: http://www.ghostscript.com
%
%IN:
% filename - string containing the name (optionally including full or
% relative path) of the file the figure is to be saved as. A
% ".pdf" extension is added if not there already. If a path is
% not specified, the figure is saved in the current directory.
% fig_handle - The handle of the figure to be saved. Default: current
% figure.
%
% Copyright (C) Oliver Woodford 2008
% This function is inspired by Peder Axensten's SAVEFIG (fex id: 10889)
% which is itself inspired by EPS2PDF (fex id: 5782)
% The idea of editing the EPS file to change line styles comes from Jiro
% Doke's FIXPSLINESTYLE (fex id: 17928)
% The idea of changing dash length with line width came from comments on
% fex id: 5743, but the implementation is mine :)
% $Id: print_pdf.m,v 1.25 2008/12/15 16:52:07 ojw Exp $
function print_pdf(name, fig)
if nargin < 2
fig = gcf;
end
% Set paper size
% set(fig, 'PaperPositionMode', 'auto');
% Print to eps file
tmp_nam = [tempname '.eps'];
print(fig, '-depsc2', '-painters', '-r864', tmp_nam);
% Fix the line styles
fix_lines(tmp_nam);
% Construct the filename
if numel(name) < 5 || ~strcmpi(name(end-3:end), '.pdf')
name = [name '.pdf']; % Add the missing extension
end
% Construct the command string for ghostscript. This assumes that the
% ghostscript binary is on your path - you can also give the complete path,
% e.g. cmd = '"C:\Program Files\gs\gs8.63\bin\gswin32c.exe"';
cmd = 'gs';
if ispc
cmd = [cmd 'win32c.exe'];
end
options = [' -q -dNOPAUSE -dBATCH -dEPSCrop -sDEVICE=pdfwrite -dPDFSETTINGS=/prepress -sOutputFile="' name '" -f "' tmp_nam '"'];
% Convert to pdf
[status result] = system([cmd options]);
% Check status
if status
% Something went wrong
if isempty(strfind(result, 'not recognized'))
fprintf('%s\n', result);
else
% Ghostscript isn't on the path - try to find it
cmd = find_ghostscript;
if isempty(cmd)
fprintf('Ghostscript not found.\n');
else
system([cmd options]);
end
end
end
% Delete the temporary file
delete(tmp_nam);
return
function cmd = find_ghostscript
% Find the full path to a ghostscript executable
cmd = '';
if ispc
% For Windows, look in the default location
default_location = 'C:\Program Files\gs\';
executable = '\bin\gswin32c.exe';
else
% This case isn't supported. Contact me if you have a fix.
return
end
dir_list = dir(default_location);
ver_num = 0;
for a = 1:numel(dir_list)
% If there are multiple versions, use the newest
ver_num2 = sscanf(dir_list(a).name, 'gs%g');
if ~isempty(ver_num2) && ver_num2 > ver_num
cmd2 = [default_location dir_list(a).name executable];
if exist(cmd2, 'file') == 2
cmd = ['"' cmd2 '"'];
ver_num = ver_num2;
end
end
end
return
function fix_lines(fname)
% Improve the style of lines used and set grid lines to an entirely new
% style using dots, not dashes
% Read in the file
fh = fopen(fname, 'rt');
fstrm = char(fread(fh, '*uint8')');
fclose(fh);
% Make sure all line width commands come before the line style definitions,
% so that dash lengths can be based on the correct widths
% Find all line style sections
ind = [regexp(fstrm, '[\n\r]SO[\n\r]'),... % This needs to be here even though it doesn't have dots/dashes!
regexp(fstrm, '[\n\r]DO[\n\r]'),...
regexp(fstrm, '[\n\r]DA[\n\r]'),...
regexp(fstrm, '[\n\r]DD[\n\r]')];
ind = sort(ind);
% Find line width commands
[ind2 ind3] = regexp(fstrm, '[\n\r]\d* w[\n\r]', 'start', 'end');
% Go through each line style section and swap with any line width commands
% near by
b = 1;
m = numel(ind);
n = numel(ind2);
for a = 1:m
% Go forwards width commands until we pass the current line style
while b <= n && ind2(b) < ind(a)
b = b + 1;
end
if b > n
% No more width commands
break;
end
% Check we haven't gone past another line style (including SO!)
if a < m && ind2(b) > ind(a+1)
continue;
end
% Are the commands close enough to be confident we can swap them?
if (ind2(b) - ind(a)) > 8
continue;
end
% Move the line style command below the line width command
fstrm(ind(a)+1:ind3(b)) = [fstrm(ind(a)+4:ind3(b)) fstrm(ind(a)+1:ind(a)+3)];
b = b + 1;
end
% Find any grid line definitions and change to GR format
% Find the DO sections again as they may have moved
ind = int32(regexp(fstrm, '[\n\r]DO[\n\r]'));
if ~isempty(ind)
% Find all occurrences of what are believed to be axes and grid lines
ind2 = int32(regexp(fstrm, '[\n\r] *\d* *\d* *mt *\d* *\d* *L[\n\r]'));
if ~isempty(ind2)
% Now see which DO sections come just before axes and grid lines
ind2 = repmat(ind2', [1 numel(ind)]) - repmat(ind, [numel(ind2) 1]);
ind2 = any(ind2 > 0 & ind2 < 12); % 12 chars seems about right
ind = ind(ind2);
% Change any regions we believe to be grid lines to GR
fstrm(ind+1) = 'G';
fstrm(ind+2) = 'R';
end
end
% Isolate line style definition section
first_sec = findstr(fstrm, '% line types:');
[second_sec remaining] = strtok(fstrm(first_sec+1:end), '/');
[dummy remaining] = strtok(remaining, '%');
% Define the new styles, including the new GR format
% Dot and dash lengths have two parts: a constant amount plus a line width
% variable amount. The constant amount comes after dpi2point, and the
% variable amount comes after currentlinewidth. If you want to change
% dot/dash lengths for a one particular line style only, edit the numbers
% in the /DO (dotted lines), /DA (dashed lines), /DD (dot dash lines) and
% /GR (grid lines) lines for the style you want to change.
new_style = {'/dom { dpi2point 1 currentlinewidth 0.08 mul add mul mul } bdef',... % Dot length macro based on line width
'/dam { dpi2point 2 currentlinewidth 0.04 mul add mul mul } bdef',... % Dash length macro based on line width
'/SO { [] 0 setdash 0 setlinecap } bdef',... % Solid lines
'/DO { [1 dom 1.2 dom] 0 setdash 0 setlinecap } bdef',... % Dotted lines
'/DA { [4 dam 1.5 dam] 0 setdash 0 setlinecap } bdef',... % Dashed lines
'/DD { [1 dom 1.2 dom 4 dam 1.2 dom] 0 setdash 0 setlinecap } bdef',... % Dot dash lines
'/GR { [0 dpi2point mul 4 dpi2point mul] 0 setdash 1 setlinecap } bdef'}; % Grid lines - dot spacing remains constant
new_style = sprintf('%s\r', new_style{:});
% Save the file with the section replaced
fh = fopen(fname, 'wt');
fprintf(fh, '%s%s%s%s', fstrm(1:first_sec), second_sec, new_style, remaining);
fclose(fh);
fprintf('pdf successfully printed\n');
return
|
github
|
UCL-SML/pilco-matlab-master
|
draw_pendubot.m
|
.m
|
pilco-matlab-master/scenarios/pendubot/draw_pendubot.m
| 2,880 |
utf_8
|
d1b44901843520801be32a25b7efd3b2
|
%% draw_pendubot.m
% *Summary:* Draw the Pendubot system with reward, applied torque,
% and predictive uncertainty of the tips of the pendulums
%
% function draw_pendubot(theta1, theta2, force, cost, text1, text2, M, S)
%
%
% *Input arguments:*
%
% theta1 angle of inner pendulum
% theta2 angle of outer pendulum
% f1 torque applied to inner pendulum
% f2 torque applied to outer pendulum
% cost cost structure
% .fcn function handle (it is assumed to use saturating cost)
% .<> other fields that are passed to cost
% text1 (optional) text field 1
% text2 (optional) text field 2
% M (optional) mean of state
% S (optional) covariance of state
%
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-03-08
function draw_pendubot(theta1, theta2, force, cost, text1, text2, M, S)
%% Code
l = 0.6;
xmin = -2*l;
xmax = 2*l;
umax = 2;
height = 0;
% Draw double pendulum
clf; hold on
sth1 = sin(theta1); sth2 = sin(theta2);
cth1 = cos(theta1); cth2 = cos(theta2);
pendulum1 = [0, 0; -l*sth1, l*cth1];
pendulum2 = [-l*sth1, l*cth1; -l*(sth1-sth2), l*(cth1+cth2)];
plot(pendulum1(:,1), pendulum1(:,2),'r','linewidth',4)
plot(pendulum2(:,1), pendulum2(:,2),'r','linewidth',4)
% plot target location
plot(0,2*l,'k+','MarkerSize',20);
plot([xmin, xmax], [-height, -height],'k','linewidth',2)
% plot inner joint
plot(0,0,'k.','markersize',24)
plot(0,0,'y.','markersize',14)
% plot outer joint
plot(-l*sth1, l*cth1,'k.','markersize',24)
plot(-l*sth1, l*cth1,'y.','markersize',14)
% plot tip of outer joint
plot(-l*(sth1-sth2), l*(cth1+cth2),'k.','markersize',24)
plot(-l*(sth1-sth2), l*(cth1+cth2),'y.','markersize',14)
plot(0,-2*l,'.w','markersize',0.005)
% % Draw sample positions of the joints
% if nargin > 6
% samples = gaussian(M,S+1e-8*eye(4),1000);
% t1 = samples(3,:); t2 = samples(4,:);
% plot(-l*sin(t1),l*cos(t1),'b.','markersize',2)
% plot(-l*(sin(t1)-sin(t2)),l*(cos(t1)+cos(t2)),'r.','markersize',2)
% end
% plot ellipses around tips of pendulums (if M, S exist)
try
if max(max(S))>0
[M1 S1 M2 S2] = getPlotDistr_pendubot(M, S, l, l);
error_ellipse(S1, M1, 'style','b'); % inner pendulum
error_ellipse(S2, M2, 'style','r'); % outer pendulum
end
catch
end
% Draw useful information
% plot applied torque
plot([0 force/umax*xmax],[-0.5, -0.5],'g','linewidth',10)
% plot immediate reward
reward = 1-cost.fcn(cost,[0, 0, theta1, theta2]',zeros(4));
plot([0 reward*xmax],[-0.7, -0.7],'y','linewidth',10)
text(0,-0.5,'applied torque (inner joint)')
text(0,-0.7,'immediate reward')
if exist('text1','var')
text(0,-0.9, text1)
end
if exist('text2','var')
text(0,-1.1, text2)
end
set(gca,'DataAspectRatio',[1 1 1],'XLim',[xmin xmax],'YLim',[-2*l 2*l]);
axis off
drawnow;
|
github
|
UCL-SML/pilco-matlab-master
|
loss_pendubot.m
|
.m
|
pilco-matlab-master/scenarios/pendubot/loss_pendubot.m
| 4,307 |
utf_8
|
acf6555f4c0bb7d9c47e31649d69239e
|
%% loss_pendubot.m
% *Summary:* Pendubot loss function; the loss is
% $1-\exp(-0.5*d^2*a)$, where $a>0$ and $d^2$ is the squared difference
% between the actual and desired position of the tip of the outer pendulum.
% The mean and the variance of the loss are computed by averaging over the
% Gaussian distribution of the state $p(x) = \mathcal N(m,s)$ with mean $m$
% and covariance matrix $s$.
% Derivatives of these quantities are computed when desired.
%
%
% function [L, dLdm, dLds, S2] = loss_pendubot(cost, m, s)
%
%
% *Input arguments:*
%
% cost cost structure
% .p lengths of the 2 pendulums [2 x 1 ]
% .width array of widths of the cost (summed together)
% .expl (optional) exploration parameter
% .angle (optional) array of angle indices
% .target target state [D x 1 ]
% m mean of state distribution [D x 1 ]
% s covariance matrix for the state distribution [D x D ]
%
% *Output arguments:*
%
% L expected cost [1 x 1 ]
% dLdm derivative of expected cost wrt. state mean vector [1 x D ]
% dLds derivative of expected cost wrt. state covariance matrix [1 x D^2]
% S2 variance of cost [1 x 1 ]
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-03-08
%
%% High-Level Steps
% # Precomputations
% # Define static penalty as distance from target setpoint
% # Trigonometric augmentation
% # Calculate loss
function [L, dLdm, dLds, S2] = loss_pendubot(cost, m, s)
%% Code
if isfield(cost,'width'); cw = cost.width; else cw = 1; end
if ~isfield(cost,'expl') || isempty(cost.expl); b = 0; else b = cost.expl; end
% 1. Some precomputations
D0 = size(s,2); D = D0; % state dimension
D1 = D0 + 2*length(cost.angle); % state dimension (with sin/cos)
M = zeros(D1,1); M(1:D0) = m; S = zeros(D1); S(1:D0,1:D0) = s;
Mdm = [eye(D0); zeros(D1-D0,D0)]; Sdm = zeros(D1*D1,D0);
Mds = zeros(D1,D0*D0); Sds = kron(Mdm,Mdm);
% 2. Define static penalty as distance from target setpoint
ell1 = cost.p(1); ell2 = cost.p(2); C = [ell1 0 ell2 0; 0 ell1 0 ell2];
Q = zeros(D1); Q(D+1:D+4,D+1:D+4) = C'*C;
% 3. Trigonometric augmentation
if D1-D0 > 0
target = [cost.target(:); gTrig(cost.target(:), 0*s, cost.angle)];
i = 1:D0; k = D0+1:D1;
[M(k) S(k,k) C mdm sdm Cdm mds sds Cds] = gTrig(M(i),S(i,i),cost.angle);
[S Mdm Mds Sdm Sds] = ...
fillIn(S,C,mdm,sdm,Cdm,mds,sds,Cds,Mdm,Sdm,Mds,Sds,i,k,D1);
end
% 4. Calculate loss
L = 0; dLdm = zeros(1,D0); dLds = zeros(1,D0*D0); S2 = 0;
for i = 1:length(cw) % scale mixture of immediate costs
cost.z = target; cost.W = Q/cw(i)^2;
[r rdM rdS s2 s2dM s2dS] = lossSat(cost, M, S);
L = L + r; S2 = S2 + s2;
dLdm = dLdm + rdM(:)'*Mdm + rdS(:)'*Sdm;
dLds = dLds + rdM(:)'*Mds + rdS(:)'*Sds;
if (b~=0 || ~isempty(b)) && abs(s2)>1e-12
L = L + b*sqrt(s2);
dLdm = dLdm + b/sqrt(s2) * ( s2dM(:)'*Mdm + s2dS(:)'*Sdm )/2;
dLds = dLds + b/sqrt(s2) * ( s2dM(:)'*Mds + s2dS(:)'*Sds )/2;
end
end
% normalize
n = length(cw); L = L/n; dLdm = dLdm/n; dLds = dLds/n; S2 = S2/n;
% Fill in covariance matrix...and derivatives ----------------------------
function [S Mdm Mds Sdm Sds] = ...
fillIn(S,C,mdm,sdm,Cdm,mds,sds,Cds,Mdm,Sdm,Mds,Sds,i,k,D)
X = reshape(1:D*D,[D D]); XT = X'; % vectorized indices
I=0*X; I(i,i)=1; ii=X(I==1)'; I=0*X; I(k,k)=1; kk=X(I==1)';
I=0*X; I(i,k)=1; ik=X(I==1)'; ki=XT(I==1)';
Mdm(k,:) = mdm*Mdm(i,:) + mds*Sdm(ii,:); % chainrule
Mds(k,:) = mdm*Mds(i,:) + mds*Sds(ii,:);
Sdm(kk,:) = sdm*Mdm(i,:) + sds*Sdm(ii,:);
Sds(kk,:) = sdm*Mds(i,:) + sds*Sds(ii,:);
dCdm = Cdm*Mdm(i,:) + Cds*Sdm(ii,:);
dCds = Cdm*Mds(i,:) + Cds*Sds(ii,:);
S(i,k) = S(i,i)*C; S(k,i) = S(i,k)'; % off-diagonal
SS = kron(eye(length(k)),S(i,i)); CC = kron(C',eye(length(i)));
Sdm(ik,:) = SS*dCdm + CC*Sdm(ii,:); Sdm(ki,:) = Sdm(ik,:);
Sds(ik,:) = SS*dCds + CC*Sds(ii,:); Sds(ki,:) = Sds(ik,:);
|
github
|
UCL-SML/pilco-matlab-master
|
dynamics_pendubot.m
|
.m
|
pilco-matlab-master/scenarios/pendubot/dynamics_pendubot.m
| 2,465 |
utf_8
|
d04c0a8c0506c14bafcb992ee945135a
|
%% dynamics_pendubot.m
% *Summary:* Implements ths ODE for simulating the Pendubot
% dynamics, where an input torque f can be applied to the inner link
%
% function dz = dynamics_pendubot(t,z,f)
%
%
% *Input arguments:*
%
% t current time step (called from ODE solver)
% z state [4 x 1]
% f (optional): torque f(t) applied to inner pendulum
%
% *Output arguments:*
%
% dz if 3 input arguments: state derivative wrt time
% if only 2 input arguments: total mechanical energy
%
% Note: It is assumed that the state variables are of the following order:
% dtheta1: [rad/s] angular velocity of inner pendulum
% dtheta2: [rad/s] angular velocity of outer pendulum
% theta1: [rad] angle of inner pendulum
% theta2: [rad] angle of outer pendulum
%
% A detailed derivation of the dynamics can be found in:
%
% M.P. Deisenroth:
% Efficient Reinforcement Learning Using Gaussian Processes, Appendix C,
% KIT Scientific Publishing, 2010.
%
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-03-08
function dz = dynamics_pendubot(t,z,f)
%% Code
m1 = 0.5; % [kg] mass of 1st link
m2 = 0.5; % [kg] mass of 2nd link
b1 = 0.0; % [Ns/m] coefficient of friction (1st joint)
b2 = 0.0; % [Ns/m] coefficient of friction (2nd joint)
l1 = 0.5; % [m] length of 1st pendulum
l2 = 0.5; % [m] length of 2nd pendulum
g = 9.82; % [m/s^2] acceleration of gravity
I1 = m1*l1^2/12; % moment of inertia around pendulum midpoint (inner link)
I2 = m2*l2^2/12; % moment of inertia around pendulum midpoint (outer link)
if nargin == 3 % compute time derivatives
A = [l1^2*(0.25*m1+m2) + I1, 0.5*m2*l1*l2*cos(z(3)-z(4));
0.5*m2*l1*l2*cos(z(3)-z(4)), l2^2*0.25*m2 + I2 ];
b = [g*l1*sin(z(3))*(0.5*m1+m2) - 0.5*m2*l1*l2*z(2)^2*sin(z(3)-z(4))...
+ f(t) - b1*z(1);
0.5*m2*l2*( l1*z(1)^2*sin(z(3)-z(4)) + g*sin(z(4)) ) - b2*z(2)];
x = A\b;
dz = zeros(4,1);
dz(1) = x(1);
dz(2) = x(2);
dz(3) = z(1);
dz(4) = z(2);
else % compute total mechanical energy
dz = m1*l1^2*z(1)^2/8 + I1*z(1)^2/2 + m2/2*(l1^2*z(1)^2 ...
+ l2^2*z(2)^2/4 + l1*l2*z(1)*z(2)*cos(z(3)-z(4))) + I2*z(2)^2/2 ...
+ m1*g*l1*cos(z(3))/2 + m2*g*(l1*cos(z(3))+l2*cos(z(4))/2);
end
|
github
|
UCL-SML/pilco-matlab-master
|
getPlotDistr_pendubot.m
|
.m
|
pilco-matlab-master/scenarios/pendubot/getPlotDistr_pendubot.m
| 2,792 |
utf_8
|
90cd4837258b5d69ecce949b34088e6f
|
%% getPlotDistr_pendubot.m
% *Summary:* Compute means and covariances of the Cartesian coordinates of
% the tips both the inner and outer pendulum assuming that the joint state
% $x$ of the cart-double-pendulum system is Gaussian, i.e., $x\sim N(m, s)$
%
%
% function [M1, S1, M2, S2] = getPlotDistr_pendubot(m, s, ell1, ell2)
%
%
%
% *Input arguments:*
%
% m mean of full state [6 x 1]
% s covariance of full state [6 x 6]
% ell1 length of inner pendulum
% ell2 length of outer pendulum
%
% Note: this code assumes that the following order of the state:
% 1: pend1 angular velocity,
% 2: pend2 angular velocity,
% 3: pend1 angle,
% 4: pend2 angle
%
% *Output arguments:*
%
% M1 mean of tip of inner pendulum [2 x 1]
% S1 covariance of tip of inner pendulum [2 x 2]
% M2 mean of tip of outer pendulum [2 x 1]
% S2 covariance of tip of outer pendulum [2 x 2]
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modification: 2013-03-27
%
%% High-Level Steps
% # Augment input distribution to complex angle representation
% # Compute means of tips of pendulums (in Cartesian coordinates)
% # Compute covariances of tips of pendulums (in Cartesian coordinates)
function [M1, S1, M2, S2] = getPlotDistr_pendubot(m, s, ell1, ell2)
%% Code
% 1. Augment input distribution
[m1 s1 c1] = gTrig(m, s, [3 4], [ell1, ell2]); % map input through sin/cos
m1 = [m; m1]; % mean of joint
c1 = s*c1; % cross-covariance between input and prediction
s1 = [s c1; c1' s1]; % covariance of joint
% 2. Compute means of tips of pendulums (in Cartesian coordinates)
M1 = [-m1(5); m1(6)]; % [-l*sin(t1), l*cos(t1)]
M2 = [-m1(5) + m1(7); m1(6) + m1(8)]; % [-l*(sin(t1)-sin(t2)),l*(cos(t1)+cos(t2))]
% 2. Put covariance matrices together (Cart. coord.)
% first set of coordinates (tip of 1st pendulum)
s11 = s1(5,5);
s12 = -s1(5,6);
s22 = s1(6,6);
S1 = [s11 s12; s12' s22];
% second set of coordinates (tip of 2nd pendulum)
s11 = s1(5,5) + s1(7,7) - s1(5,7) - s1(7,5); % ell1*sin(t1) + ell2*sin(t2)
s22 = s1(6,6) + s1(8,8) + s1(6,8) + s1(8,6); % ell1*cos(t1) + ell2*cos(t2)
s12 = -(s1(5,6) + s1(5,8) + s1(7,6) + s1(7,8));
S2 = [s11 s12; s12' s22];
% make sure we have proper covariances (sometimes numerical problems occur)
try
chol(S1);
catch
warning('matrix S1 not pos.def. (getPlotDistr)');
S1 = S1 + (1e-6 - min(eig(S1)))*eye(2);
end
try
chol(S2);
catch
warning('matrix S2 not pos.def. (getPlotDistr)');
S2 = S2 + (1e-6 - min(eig(S2)))*eye(2);
end
|
github
|
UCL-SML/pilco-matlab-master
|
loss_dp.m
|
.m
|
pilco-matlab-master/scenarios/doublePendulum/loss_dp.m
| 4,296 |
utf_8
|
5aa6f225ab08ea5144106b58baf9624c
|
%% loss_dp.m
% *Summary:* Double-Pendulum loss function; the loss is
% $1-\exp(-0.5*d^2*a)$, where $a>0$ and $d^2$ is the squared difference
% between the actual and desired position of the tip of the outer pendulum.
% The mean and the variance of the loss are computed by averaging over the
% Gaussian distribution of the state $p(x) = \mathcal N(m,s)$ with mean $m$
% and covariance matrix $s$.
% Derivatives of these quantities are computed when desired.
%
%
% function [L, dLdm, dLds, S2] = loss_dp(cost, m, s)
%
%
% *Input arguments:*
%
% cost cost structure
% .p lengths of the 2 pendulums [2 x 1 ]
% .width array of widths of the cost (summed together)
% .expl (optional) exploration parameter
% .angle (optional) array of angle indices
% .target target state [D x 1 ]
% m mean of state distribution [D x 1 ]
% s covariance matrix for the state distribution [D x D ]
%
% *Output arguments:*
%
% L expected cost [1 x 1 ]
% dLdm derivative of expected cost wrt. state mean vector [1 x D ]
% dLds derivative of expected cost wrt. state covariance matrix [1 x D^2]
% S2 variance of cost [1 x 1 ]
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-03-08
%
%% High-Level Steps
% # Precomputations
% # Define static penalty as distance from target setpoint
% # Trigonometric augmentation
% # Calculate loss
function [L, dLdm, dLds, S2] = loss_dp(cost, m, s)
%% Code
if isfield(cost,'width'); cw = cost.width; else cw = 1; end
if ~isfield(cost,'expl') || isempty(cost.expl); b = 0; else b = cost.expl; end
% 1. Some precomputations
D0 = size(s,2); D = D0; % state dimension
D1 = D0 + 2*length(cost.angle); % state dimension (with sin/cos)
M = zeros(D1,1); M(1:D0) = m; S = zeros(D1); S(1:D0,1:D0) = s;
Mdm = [eye(D0); zeros(D1-D0,D0)]; Sdm = zeros(D1*D1,D0);
Mds = zeros(D1,D0*D0); Sds = kron(Mdm,Mdm);
% 2. Define static penalty as distance from target setpoint
ell1 = cost.p(1); ell2 = cost.p(2); C = [ell1 0 ell2 0; 0 ell1 0 ell2];
Q = zeros(D1); Q(D+1:D+4,D+1:D+4) = C'*C;
% 3. Trigonometric augmentation
if D1-D0 > 0
target = [cost.target(:); gTrig(cost.target(:), 0*s, cost.angle)];
i = 1:D0; k = D0+1:D1;
[M(k) S(k,k) C mdm sdm Cdm mds sds Cds] = gTrig(M(i),S(i,i),cost.angle);
[S Mdm Mds Sdm Sds] = ...
fillIn(S,C,mdm,sdm,Cdm,mds,sds,Cds,Mdm,Sdm,Mds,Sds,i,k,D1);
end
% 4. Calculate loss
L = 0; dLdm = zeros(1,D0); dLds = zeros(1,D0*D0); S2 = 0;
for i = 1:length(cw) % scale mixture of immediate costs
cost.z = target; cost.W = Q/cw(i)^2;
[r rdM rdS s2 s2dM s2dS] = lossSat(cost, M, S);
L = L + r; S2 = S2 + s2;
dLdm = dLdm + rdM(:)'*Mdm + rdS(:)'*Sdm;
dLds = dLds + rdM(:)'*Mds + rdS(:)'*Sds;
if (b~=0 || ~isempty(b)) && abs(s2)>1e-12
L = L + b*sqrt(s2);
dLdm = dLdm + b/sqrt(s2) * ( s2dM(:)'*Mdm + s2dS(:)'*Sdm )/2;
dLds = dLds + b/sqrt(s2) * ( s2dM(:)'*Mds + s2dS(:)'*Sds )/2;
end
end
% normalize
n = length(cw); L = L/n; dLdm = dLdm/n; dLds = dLds/n; S2 = S2/n;
% Fill in covariance matrix...and derivatives ----------------------------
function [S Mdm Mds Sdm Sds] = ...
fillIn(S,C,mdm,sdm,Cdm,mds,sds,Cds,Mdm,Sdm,Mds,Sds,i,k,D)
X = reshape(1:D*D,[D D]); XT = X'; % vectorized indices
I=0*X; I(i,i)=1; ii=X(I==1)'; I=0*X; I(k,k)=1; kk=X(I==1)';
I=0*X; I(i,k)=1; ik=X(I==1)'; ki=XT(I==1)';
Mdm(k,:) = mdm*Mdm(i,:) + mds*Sdm(ii,:); % chainrule
Mds(k,:) = mdm*Mds(i,:) + mds*Sds(ii,:);
Sdm(kk,:) = sdm*Mdm(i,:) + sds*Sdm(ii,:);
Sds(kk,:) = sdm*Mds(i,:) + sds*Sds(ii,:);
dCdm = Cdm*Mdm(i,:) + Cds*Sdm(ii,:);
dCds = Cdm*Mds(i,:) + Cds*Sds(ii,:);
S(i,k) = S(i,i)*C; S(k,i) = S(i,k)'; % off-diagonal
SS = kron(eye(length(k)),S(i,i)); CC = kron(C',eye(length(i)));
Sdm(ik,:) = SS*dCdm + CC*Sdm(ii,:); Sdm(ki,:) = Sdm(ik,:);
Sds(ik,:) = SS*dCds + CC*Sds(ii,:); Sds(ki,:) = Sds(ik,:);
|
github
|
UCL-SML/pilco-matlab-master
|
dynamics_dp.m
|
.m
|
pilco-matlab-master/scenarios/doublePendulum/dynamics_dp.m
| 2,575 |
utf_8
|
67e3af4bfc5975be48d8c58ec68c0c33
|
%% dynamics_dp.m
% *Summary:* Implements ths ODE for simulating the double pendulum
% dynamics, where an input torque can be applied to both links,
% f1:torque at inner joint, f2:torque at outer joint
%
% function dz = dynamics_dp(t, z, f1, f2)
%
%
% *Input arguments:*
%
% t current time step (called from ODE solver)
% z state [4 x 1]
% f1 (optional): torque f1(t) applied to inner pendulum
% f2 (optional): torque f2(t) applied to outer pendulum
%
% *Output arguments:*
%
% dz if 4 input arguments: state derivative wrt time
% if only 2 input arguments: total mechanical energy
%
% Note: It is assumed that the state variables are of the following order:
% dtheta1: [rad/s] angular velocity of inner pendulum
% dtheta2: [rad/s] angular velocity of outer pendulum
% theta1: [rad] angle of inner pendulum
% theta2: [rad] angle of outer pendulum
%
% A detailed derivation of the dynamics can be found in:
%
% M.P. Deisenroth:
% Efficient Reinforcement Learning Using Gaussian Processes, Appendix C,
% KIT Scientific Publishing, 2010.
%
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-03-08
function dz = dynamics_dp(t, z, f1, f2)
%% Code
m1 = 0.5; % [kg] mass of 1st link
m2 = 0.5; % [kg] mass of 2nd link
b1 = 0.0; % [Ns/m] coefficient of friction (1st joint)
b2 = 0.0; % [Ns/m] coefficient of friction (2nd joint)
l1 = 0.5; % [m] length of 1st pendulum
l2 = 0.5; % [m] length of 2nd pendulum
g = 9.82; % [m/s^2] acceleration of gravity
I1 = m1*l1^2/12; % moment of inertia around pendulum midpoint (1st link)
I2 = m2*l2^2/12; % moment of inertia around pendulum midpoint (2nd link)
if nargin == 4 % compute time derivatives
A = [l1^2*(0.25*m1+m2) + I1, 0.5*m2*l1*l2*cos(z(3)-z(4));
0.5*m2*l1*l2*cos(z(3)-z(4)), l2^2*0.25*m2 + I2 ];
b = [g*l1*sin(z(3))*(0.5*m1+m2) - 0.5*m2*l1*l2*z(2)^2*sin(z(3)-z(4)) ...
+ f1(t)-b1*z(1);
0.5*m2*l2*(l1*z(1)^2*sin(z(3)-z(4))+g*sin(z(4))) + f2(t)-b2*z(2)];
x = A\b;
dz = zeros(4,1);
dz(1) = x(1);
dz(2) = x(2);
dz(3) = z(1);
dz(4) = z(2);
else % compute total mechanical energy
dz = m1*l1^2*z(1)^2/8 + I1*z(1)^2/2 + m2/2*(l1^2*z(1)^2 ...
+ l2^2*z(2)^2/4 + l1*l2*z(1)*z(2)*cos(z(3)-z(4))) + I2*z(2)^2/2 ...
+ m1*g*l1*cos(z(3))/2 + m2*g*(l1*cos(z(3))+l2*cos(z(4))/2);
end
|
github
|
UCL-SML/pilco-matlab-master
|
draw_dp.m
|
.m
|
pilco-matlab-master/scenarios/doublePendulum/draw_dp.m
| 2,955 |
utf_8
|
a367561c45b347a90f121a366b70c407
|
%% draw_dp.m
% *Summary:* Draw the double-pendulum system with reward, applied torques,
% and predictive uncertainty of the tips of the pendulums
%
% function draw_dp(theta1, theta2, f1, f2, cost, text1, text2, M, S)
%
% *Input arguments:*
%
% theta1 angle of inner pendulum
% theta2 angle of outer pendulum
% f1 torque applied to inner pendulum
% f2 torque applied to outer pendulum
% cost cost structure
% .fcn function handle (it is assumed to use saturating cost)
% .<> other fields that are passed to cost
% text1 (optional) text field 1
% text2 (optional) text field 2
% M (optional) mean of state
% S (optional) covariance of state
%
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-03-07
function draw_dp(theta1, theta2, f1, f2, cost, text1, text2, M, S)
%% Code
l = 0.6;
xmin = -2*l;
xmax = 2*l;
umax = 2;
height = 0;
% Draw double pendulum
clf; hold on
sth1 = sin(theta1); sth2 = sin(theta2);
cth1 = cos(theta1); cth2 = cos(theta2);
pendulum1 = [0, 0; -l*sth1, l*cth1];
pendulum2 = [-l*sth1, l*cth1; -l*(sth1-sth2), l*(cth1+cth2)];
plot(pendulum1(:,1), pendulum1(:,2),'r','linewidth',4)
plot(pendulum2(:,1), pendulum2(:,2),'r','linewidth',4)
% plot target location
plot(0,2*l,'k+','MarkerSize',20);
plot([xmin, xmax], [-height, -height],'k','linewidth',2)
% plot inner joint
plot(0,0,'k.','markersize',24)
plot(0,0,'y.','markersize',14)
% plot outer joint
plot(-l*sth1, l*cth1,'k.','markersize',24)
plot(-l*sth1, l*cth1,'y.','markersize',14)
% plot tip of outer joint
plot(-l*(sth1-sth2), l*(cth1+cth2),'k.','markersize',24)
plot(-l*(sth1-sth2), l*(cth1+cth2),'y.','markersize',14)
plot(0,-2*l,'.w','markersize',0.005)
% % Draw sample positions of the joints
% if nargin > 7
% samples = gaussian(M,S+1e-8*eye(4),1000);
% t1 = samples(3,:); t2 = samples(4,:);
% plot(-l*sin(t1),l*cos(t1),'b.','markersize',2)
% plot(-l*(sin(t1)-sin(t2)),l*(cos(t1)+cos(t2)),'r.','markersize',2)
% end
% plot ellipses around tips of pendulums (if M, S exist)
try
if max(max(S))>0
[M1 S1 M2 S2] = getPlotDistr_dp(M, S, l, l);
error_ellipse(S1,M1,'style','b'); % inner pendulum
error_ellipse(S2,M2,'style','r'); % outer pendulum
end
catch
end
% Show other useful information
% plot applied torques
plot([0 f1/umax*xmax],[-0.3, -0.3],'g','linewidth',10)
plot([0 f2/umax*xmax],[-0.5, -0.5],'g','linewidth',10)
% plot reward
reward = 1-cost.fcn(cost,[0, 0, theta1, theta2]',zeros(4));
plot([0 reward*xmax],[-0.7, -0.7],'y','linewidth',10)
text(0,-0.3,'applied torque (inner joint)')
text(0,-0.5,'applied torque (outer joint)')
text(0,-0.7,'immediate reward')
if exist('text1','var')
text(0,-0.9, text1)
end
if exist('text2','var')
text(0,-1.1, text2)
end
set(gca,'DataAspectRatio',[1 1 1],'XLim',[xmin xmax],'YLim',[-2*l 2*l]);
axis off
drawnow;
|
github
|
UCL-SML/pilco-matlab-master
|
getPlotDistr_dp.m
|
.m
|
pilco-matlab-master/scenarios/doublePendulum/getPlotDistr_dp.m
| 2,772 |
utf_8
|
9be4d2bdc8b4beb731e6776c5dd791a1
|
%% getPlotDistr_dp.m
% *Summary:* Compute means and covariances of the Cartesian coordinates of
% the tips both the inner and outer pendulum assuming that the joint state
% $x$ of the cart-double-pendulum system is Gaussian, i.e., $x\sim N(m, s)$
%
%
% function [M1, S1, M2, S2] = getPlotDistr_dp(m, s, ell1, ell2)
%
%
% *Input arguments:*
%
% m mean of full state [6 x 1]
% s covariance of full state [6 x 6]
% ell1 length of inner pendulum
% ell2 length of outer pendulum
%
% Note: this code assumes that the following order of the state:
% 1: pend1 angular velocity,
% 2: pend2 angular velocity,
% 3: pend1 angle,
% 4: pend2 angle
%
% *Output arguments:*
%
% M1 mean of tip of inner pendulum [2 x 1]
% S1 covariance of tip of inner pendulum [2 x 2]
% M2 mean of tip of outer pendulum [2 x 1]
% S2 covariance of tip of outer pendulum [2 x 2]
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modification: 2013-03-27
%
%% High-Level Steps
% # Augment input distribution to complex angle representation
% # Compute means of tips of pendulums (in Cartesian coordinates)
% # Compute covariances of tips of pendulums (in Cartesian coordinates)
function [M1, S1, M2, S2] = getPlotDistr_dp(m, s, ell1, ell2)
%% Code
% 1. Augment input distribution
[m1 s1 c1] = gTrig(m, s, [3 4], [ell1, ell2]); % map input through sin/cos
m1 = [m; m1]; % mean of joint
c1 = s*c1; % cross-covariance between input and prediction
s1 = [s c1; c1' s1]; % covariance of joint
% 2. Compute means of tips of pendulums (in Cartesian coordinates)
M1 = [-m1(5); m1(6)]; % [-l*sin(t1), l*cos(t1)]
M2 = [-m1(5) + m1(7); m1(6) + m1(8)]; % [-l*(sin(t1)-sin(t2)),l*(cos(t1)+cos(t2))]
% 2. Put covariance matrices together (Cart. coord.)
% first set of coordinates (tip of 1st pendulum)
s11 = s1(5,5);
s12 = -s1(5,6);
s22 = s1(6,6);
S1 = [s11 s12; s12' s22];
% second set of coordinates (tip of 2nd pendulum)
s11 = s1(5,5) + s1(7,7) - s1(5,7) - s1(7,5); % ell1*sin(t1) + ell2*sin(t2)
s22 = s1(6,6) + s1(8,8) + s1(6,8) + s1(8,6); % ell1*cos(t1) + ell2*cos(t2)
s12 = -(s1(5,6) + s1(5,8) + s1(7,6) + s1(7,8));
S2 = [s11 s12; s12' s22];
% make sure we have proper covariances (sometimes numerical problems occur)
try
chol(S1);
catch
warning('matrix S1 not pos.def. (getPlotDistr)');
S1 = S1 + (1e-6 - min(eig(S1)))*eye(2);
end
try
chol(S2);
catch
warning('matrix S2 not pos.def. (getPlotDistr)');
S2 = S2 + (1e-6 - min(eig(S2)))*eye(2);
end
|
github
|
UCL-SML/pilco-matlab-master
|
draw_cp.m
|
.m
|
pilco-matlab-master/scenarios/cartPole/draw_cp.m
| 2,120 |
utf_8
|
7d6605b0e25af85fb84cf253639ba531
|
%% draw_cp.m
% *Summary:* Draw the cart-pole system with reward, applied force, and
% predictive uncertainty of the tip of the pendulum
%
% function draw_cp(x, theta, force, cost, text1, text2, M, S)
%
%
% *Input arguments:*
%
% x position of the cart
% theta angle of pendulum
% force force applied to cart
% cost cost structure
% .fcn function handle (it is assumed to use saturating cost)
% .<> other fields that are passed to cost
% M (optional) mean of state
% S (optional) covariance of state
% text1 (optional) text field 1
% text2 (optional) text field 2
%
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-03-07
function draw_cp(x, theta, force, cost, text1, text2, M, S)
%% Code
l = 0.6;
xmin = -3;
xmax = 3;
height = 0.1;
width = 0.3;
maxU = 10;
% Compute positions
cart = [ x + width, height
x + width, -height
x - width, -height
x - width, height
x + width, height ];
pendulum = [x, 0; x+2*l*sin(theta), -cos(theta)*2*l];
clf; hold on
plot(0,2*l,'k+','MarkerSize',20,'linewidth',2)
plot([xmin, xmax], [-height-0.03, -height-0.03],'k','linewidth',2)
% Plot force
plot([0 force/maxU*xmax],[-0.3, -0.3],'g','linewidth',10)
% Plot reward
reward = 1-cost.fcn(cost,[x, 0, 0, theta]', zeros(4));
plot([0 reward*xmax],[-0.5, -0.5],'y','linewidth',10)
% Plot the cart-pole
fill(cart(:,1), cart(:,2),'k','edgecolor','k');
plot(pendulum(:,1), pendulum(:,2),'r','linewidth',4)
% Plot the joint and the tip
plot(x,0,'y.','markersize',24)
plot(pendulum(2,1),pendulum(2,2),'y.','markersize',24)
% plot ellipse around tip of pendulum (if M, S exist)
try
[M1 S1] = getPlotDistr_cp(M,S,2*l);
error_ellipse(S1,M1,'style','b');
catch
end
% Text
text(0,-0.3,'applied force')
text(0,-0.5,'immediate reward')
if exist('text1','var')
text(0,-0.9, text1)
end
if exist('text2','var')
text(0,-1.1, text2)
end
set(gca,'DataAspectRatio',[1 1 1],'XLim',[xmin xmax],'YLim',[-1.4 1.4]);
axis off;
drawnow;
|
github
|
UCL-SML/pilco-matlab-master
|
loss_cp.m
|
.m
|
pilco-matlab-master/scenarios/cartPole/loss_cp.m
| 4,079 |
utf_8
|
0dfef958e239873ab8f870cbcc59b496
|
%% loss_cp.m
% *Summary:* Cart-Pole loss function; the loss is
% $1-\exp(-0.5*d^2*a)$, where $a>0$ and $d^2$ is the squared difference
% between the actual and desired position of tip of the pendulum.
% The mean and the variance of the loss are computed by averaging over the
% Gaussian state distribution $p(x) = \mathcal N(m,s)$ with mean $m$
% and covariance matrix $s$.
% Derivatives of these quantities are computed when desired.
%
%
% function [L, dLdm, dLds, S2] = loss_cp(cost, m, s)
%
%
% *Input arguments:*
%
% cost cost structure
% .p length of pendulum [1 x 1 ]
% .width array of widths of the cost (summed together)
% .expl (optional) exploration parameter
% .angle (optional) array of angle indices
% .target target state [D x 1 ]
% m mean of state distribution [D x 1 ]
% s covariance matrix for the state distribution [D x D ]
%
% *Output arguments:*
%
% L expected cost [1 x 1 ]
% dLdm derivative of expected cost wrt. state mean vector [1 x D ]
% dLds derivative of expected cost wrt. state covariance matrix [1 x D^2]
% S2 variance of cost [1 x 1 ]
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-05-16
%
%% High-Level Steps
% # Precomputations
% # Define static penalty as distance from target setpoint
% # Trigonometric augmentation
% # Calculate loss
function [L, dLdm, dLds, S2] = loss_cp(cost, m, s)
%% Code
if isfield(cost,'width'); cw = cost.width; else cw = 1; end
if ~isfield(cost,'expl') || isempty(cost.expl); b = 0; else b = cost.expl; end
% 1. Some precomputations
D0 = size(s,2); % state dimension
D1 = D0 + 2*length(cost.angle); % state dimension (with sin/cos)
M = zeros(D1,1); M(1:D0) = m; S = zeros(D1); S(1:D0,1:D0) = s;
Mdm = [eye(D0); zeros(D1-D0,D0)]; Sdm = zeros(D1*D1,D0);
Mds = zeros(D1,D0*D0); Sds = kron(Mdm,Mdm);
% 2. Define static penalty as distance from target setpoint
ell = cost.p; % pendulum length
Q = zeros(D1); Q([1 D0+1],[1 D0+1]) = [1 ell]'*[1 ell]; Q(D0+2,D0+2) = ell^2;
% 3. Trigonometric augmentation
if D1-D0 > 0
% augment target
target = [cost.target(:); gTrig(cost.target(:), 0*s, cost.angle)];
% augment state
i = 1:D0; k = D0+1:D1;
[M(k) S(k,k) C mdm sdm Cdm mds sds Cds] = gTrig(M(i),S(i,i),cost.angle);
% compute derivatives (for augmentation)
X = reshape(1:D1*D1,[D1 D1]); XT = X'; % vectorized indices
I=0*X; I(i,i)=1; ii=X(I==1)'; I=0*X; I(k,k)=1; kk=X(I==1)';
I=0*X; I(i,k)=1; ik=X(I==1)'; ki=XT(I==1)';
Mdm(k,:) = mdm*Mdm(i,:) + mds*Sdm(ii,:); % chainrule
Mds(k,:) = mdm*Mds(i,:) + mds*Sds(ii,:);
Sdm(kk,:) = sdm*Mdm(i,:) + sds*Sdm(ii,:);
Sds(kk,:) = sdm*Mds(i,:) + sds*Sds(ii,:);
dCdm = Cdm*Mdm(i,:) + Cds*Sdm(ii,:);
dCds = Cdm*Mds(i,:) + Cds*Sds(ii,:);
S(i,k) = S(i,i)*C; S(k,i) = S(i,k)'; % off-diagonal
SS = kron(eye(length(k)),S(i,i)); CC = kron(C',eye(length(i)));
Sdm(ik,:) = SS*dCdm + CC*Sdm(ii,:); Sdm(ki,:) = Sdm(ik,:);
Sds(ik,:) = SS*dCds + CC*Sds(ii,:); Sds(ki,:) = Sds(ik,:);
end
% 4. Calculate loss!
L = 0; dLdm = zeros(1,D0); dLds = zeros(1,D0*D0); S2 = 0;
for i = 1:length(cw) % scale mixture of immediate costs
cost.z = target; cost.W = Q/cw(i)^2;
[r rdM rdS s2 s2dM s2dS] = lossSat(cost, M, S);
L = L + r; S2 = S2 + s2;
dLdm = dLdm + rdM(:)'*Mdm + rdS(:)'*Sdm;
dLds = dLds + rdM(:)'*Mds + rdS(:)'*Sds;
if (b~=0 || ~isempty(b)) && abs(s2)>1e-12
L = L + b*sqrt(s2);
dLdm = dLdm + b/sqrt(s2) * ( s2dM(:)'*Mdm + s2dS(:)'*Sdm )/2;
dLds = dLds + b/sqrt(s2) * ( s2dM(:)'*Mds + s2dS(:)'*Sds )/2;
end
end
% normalize
n = length(cw); L = L/n; dLdm = dLdm/n; dLds = dLds/n; S2 = S2/n;
|
github
|
UCL-SML/pilco-matlab-master
|
getPlotDistr_cp.m
|
.m
|
pilco-matlab-master/scenarios/cartPole/getPlotDistr_cp.m
| 2,037 |
utf_8
|
bd4a0c9d5da54b0aa0d1a011ee47db5b
|
%% getPlotDistr_cp.m
% *Summary:* Compute means and covariances of the Cartesian coordinates of
% the tips both the inner and outer pendulum assuming that the joint state
% $x$ of the cart-double-pendulum system is Gaussian, i.e., $x\sim N(m, s)$
%
%
% function [M, S] = getPlotDistr_cp(m, s, ell)
%
%
%
% *Input arguments:*
%
% m mean of full state [4 x 1]
% s covariance of full state [4 x 4]
% ell length of pendulum
%
% Note: this code assumes that the following order of the state:
% 1: cart pos.,
% 2: cart vel.,
% 3: pendulum angular velocity,
% 4: pendulum angle
%
% *Output arguments:*
%
% M mean of tip of pendulum [2 x 1]
% S covariance of tip of pendulum [2 x 2]
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modification: 2013-03-27
%
%% High-Level Steps
% # Augment input distribution to complex angle representation
% # Compute means of tips of pendulums (in Cartesian coordinates)
% # Compute covariances of tips of pendulums (in Cartesian coordinates)
function [M, S] = getPlotDistr_cp(m, s, ell)
%% Code
% 1. Augment input distribution to complex angle representation
[m1 s1 c1] = gTrig(m,s,4,ell); % map input distribution through sin/cos
m1 = [m; m1]; % mean of joint
c1 = s*c1; % cross-covariance between input and prediction
s1 = [s c1; c1' s1]; % covariance of joint
% 2. Compute means of tips of pendulums (in Cartesian coordinates)
M = [m1(1)+m1(5); -m1(6)];
% 3. Compute covariances of tips of pendulums (in Cartesian coordinates)
s11 = s1(1,1) + s1(5,5) + s1(1,5) + s1(5,1); % x+l sin(theta)
s22 = s1(6,6); % -l*cos(theta)
s12 = -(s1(1,6)+s1(5,6)); % cov(x+l*sin(th), -l*cos(th)
S = [s11 s12; s12' s22];
try
chol(S);
catch
warning('matrix S not pos.def. (getPlotDistr)');
S = S + (1e-6 - min(eig(S)))*eye(2);
end
|
github
|
UCL-SML/pilco-matlab-master
|
dynamics_cp.m
|
.m
|
pilco-matlab-master/scenarios/cartPole/dynamics_cp.m
| 1,720 |
utf_8
|
3782addcb8146afff9473fcc8b22a948
|
%% dynamics_cp.m
% *Summary:* Implements ths ODE for simulating the cart-pole dynamics.
%
% function dz = dynamics_cp(t, z, f)
%
%
% *Input arguments:*
%
% t current time step (called from ODE solver)
% z state [4 x 1]
% f (optional): force f(t)
%
% *Output arguments:*
%
% dz if 3 input arguments: state derivative wrt time
% if only 2 input arguments: total mechanical energy
%
%
% Note: It is assumed that the state variables are of the following order:
% x: [m] position of cart
% dx: [m/s] velocity of cart
% dtheta: [rad/s] angular velocity
% theta: [rad] angle
%
%
% A detailed derivation of the dynamics can be found in:
%
% M.P. Deisenroth:
% Efficient Reinforcement Learning Using Gaussian Processes, Appendix C,
% KIT Scientific Publishing, 2010.
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-03-08
function dz = dynamics_cp(t,z,f)
%% Code
l = 0.5; % [m] length of pendulum
m = 0.5; % [kg] mass of pendulum
M = 0.5; % [kg] mass of cart
b = 0.1; % [N/m/s] coefficient of friction between cart and ground
g = 9.82; % [m/s^2] acceleration of gravity
if nargin==3
dz = zeros(4,1);
dz(1) = z(2);
dz(2) = ( 2*m*l*z(3)^2*sin(z(4)) + 3*m*g*sin(z(4))*cos(z(4)) ...
+ 4*f(t) - 4*b*z(2) )/( 4*(M+m)-3*m*cos(z(4))^2 );
dz(3) = (-3*m*l*z(3)^2*sin(z(4))*cos(z(4)) - 6*(M+m)*g*sin(z(4)) ...
- 6*(f(t)-b*z(2))*cos(z(4)) )/( 4*l*(m+M)-3*m*l*cos(z(4))^2 );
dz(4) = z(3);
else
dz = (M+m)*z(2)^2/2 + 1/6*m*l^2*z(3)^2 + m*l*(z(2)*z(3)-g)*cos(z(4))/2;
end
|
github
|
UCL-SML/pilco-matlab-master
|
augment_unicycle.m
|
.m
|
pilco-matlab-master/scenarios/unicycle/augment_unicycle.m
| 2,372 |
utf_8
|
3d526959966588683c66fa49b3cf8b0b
|
%% augment_unicycle.m
% *Summary:* The function computes the $(x,y)$ velocities of the contact point
% in both absolute and unicycle coordinates as well as the the unicycle
% coordinates of the contact point themselves.
%
% function r = augment(s)
%
% *Input arguments:*
%
% s state of the unicycle (including the torques). [1 x 18]
% The state is assumed to be given as follows:
% dx empty (to be filled by this function)
% dy empty (to be filled by this function)
% dxc empty (to be filled by this function)
% dyc empty (to be filled by this function)
% dtheta roll angular velocity
% dphi yaw angular velocity
% dpsiw wheel angular velocity
% dpsif pitch angular velocity
% dpsit turn table angular velocity
% x x position
% y y position
% xc empty (to be filled by this function)
% yc empty (to be filled by this function)
% theta roll angle
% phi yaw angle
% psiw wheel angle
% psif pitch angle
% psit turn table angle
%
% *Output arguments:*
%
% r additional variables that are computed based on s: [1 x 6]
% dx x velocity of contact point (global coordinates)
% dy y velocity of contact point (global coordinates)
% dxc x velocity of contact point (unicycle coordinates)
% dyc y velocity of contact point (unicycle coordinates)
% xc x position of contact point (unicycle coordinates)
% yc y position of contact point (unicycle coordinates)
%
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-03-27
function r = augment_unicycle(s)
%% Code
rw = 0.225; % wheel radius in meters
% x velocity of contact point (global coordinates)
r(1) = rw*cos(s(15))*s(7);
% y velocity of contact point (global coordinates)
r(2) = rw*sin(s(15))*s(7);
% (x,y) velocities of contact point (unicycle coordinates)
A = -[cos(s(15)) sin(s(15)); -sin(s(15)) cos(s(15))];
dA = -s(6)*[-sin(s(15)) cos(s(15)); -cos(s(15)) -sin(s(15))];
r(3:4) = A*r(1:2)' + dA*s(10:11)';
% (x,y) coordinates of contact point (unicycle coordinates)
r(5:6) = A*s(10:11)';
|
github
|
UCL-SML/pilco-matlab-master
|
loss_unicycle.m
|
.m
|
pilco-matlab-master/scenarios/unicycle/loss_unicycle.m
| 5,277 |
utf_8
|
b20d304ef5667ff9e596087b42191a58
|
%% loss_unicycle.m
% Robotic unicycle loss function. The loss is $1-\exp(-0.5*a*d^2)$, where
% $a$ is a (positive) constant and $d^2$ is the squared difference between
% the current configuration of the unicycle and a target set point.
%
% The mean and the variance of the loss are computed by averaging over the
% Gaussian distribution of the state $p(x) = \mathcal N(m,s)$ with mean $m$
% and covariance matrix $s$, plus cost.expl times the standard deviation of
% the loss (averaged wrt the same Gaussian), where the exploration paramater
% cost.expl defaults to zero.
%
% Negative values of the exploration parameter are used to encourage
% exploration and positive values avoid regions of uncertainty in the
% policy. Derivatives are computed when desired.
%
% The mean and the variance of the loss are computed by averaging over the
% Gaussian distribution of the state $p(x) = \mathcal N(m,s)$ with mean $m$
% and covariance matrix $s$.
% Derivatives of these quantities are computed when desired.
%
%
% function [L, dLdm, dLds, S2] = loss_unicycle(cost, m, s)
%
%
% *Input arguments:*
%
%
% cost cost structure
% .p parameters: [radius of wheel, length of rod] [2 x 1 ]
% .width array of widths of the cost (summed together)
% .expl (optional) exploration parameter; default: 0
% m mean of state distribution [D x 1 ]
% s covariance matrix for the state distribution [D x D ]
%
% *Output arguments:*
%
% L expected cost [1 x 1 ]
% dLdm derivative of expected cost wrt. state mean vector [1 x D ]
% dLds derivative of expected cost wrt. state covariance matrix [1 x D^2]
% S2 variance of cost [1 x 1 ]
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-03-26
%
%% High-Level Steps
% # Precomputations
% # Define static penalty as distance from target setpoint
% # Trigonometric augmentation
% # Calculate loss
function [L, dLdm, dLds, S2] = loss_unicycle(cost, m, s)
%% Code
rw = cost.p(1); r = cost.p(2);
if isfield(cost,'width'); cw = cost.width; else cw = 1; end
if ~isfield(cost,'expl') || isempty(cost.expl); b = 0; else b = cost.expl; end
I6 = 8; I9 = 10; % coordinates of theta and psi
Ixc = 6; Iyc = 7; % coordinates of xc and yc
% 1. Some precomputations
D = size(s,2); % state dimension
D0 = D + 2; % state dimension (augmented with I6-I9 and I6+I9)
D1 = D0 + 8; % state dimension (with sin/cos)
L = 0; dLdm = zeros(1,D); dLds = zeros(1,D*D); S2 = 0;
P = [eye(D); zeros(2,D)]; P(D+1:end,I6) = [1;-1]; P(D+1:end,I9) = [1;1];
M = zeros(D1,1); M(1:D0) = P*m; S = zeros(D1); S(1:D0,1:D0) = P*s*P';
Mdm = [P; zeros(D1-D0,D)]; Sdm = zeros(D1*D1,D);
Mds = zeros(D1,D*D); Sds = kron(Mdm,Mdm);
% 2. Define static penalty as distance from target setpoint
Q = zeros(D+10);
C1 = [rw r/2 r/2];
Q([D+4 D+6 D+8],[D+4 D+6 D+8]) = 8*(C1'*C1); % dz
C2 = [1 -r];
Q([Ixc D+9],[Ixc D+9]) = 0.5*(C2'*C2); % dx
C3 = [1 -(r+rw)];
Q([Iyc D+3],[Iyc D+3]) = 0.5*(C3'*C3); % dy
Q(9,9) = (1/(4*pi))^2; % yaw angle loss
target = zeros(D1,1); target([D+4 D+6 D+8 D+10]) = 1; % target setpoint
% 3. Trigonometric augmentation
i = 1:D0; k = D0+1:D1;
[M(k) S(k,k) C mdm sdm Cdm mds sds Cds] = ...
gTrig(M(i),S(i,i),[I6 D+1 D+2 I9]);
[S Mdm Mds Sdm Sds] = ...
fillIn(S,C,mdm,sdm,Cdm,mds,sds,Cds,Mdm,Sdm,Mds,Sds,i,k,D1);
% 4. Calculate loss
for i = 1:length(cw) % scale mixture of immediate costs
cost.z = target; cost.W = Q/cw(i)^2;
[r rdM rdS s2 s2dM s2dS] = lossSat(cost, M, S);
L = L + r; S2 = S2 + s2;
dLdm = dLdm + rdM(:)'*Mdm + rdS(:)'*Sdm;
dLds = dLds + rdM(:)'*Mds + rdS(:)'*Sds;
if (b~=0 || ~isempty(b)) && abs(s2)>1e-12
L = L + b*sqrt(s2);
dLdm = dLdm + b/sqrt(s2) * ( s2dM(:)'*Mdm + s2dS(:)'*Sdm )/2;
dLds = dLds + b/sqrt(s2) * ( s2dM(:)'*Mds + s2dS(:)'*Sds )/2;
end
end
% normalize
n = length(cw); L = L/n; dLdm = dLdm/n; dLds = dLds/n; S2 = S2/n;
% Fill in covariance matrix...and derivatives ----------------------------
function [S Mdm Mds Sdm Sds] = ...
fillIn(S,C,mdm,sdm,Cdm,mds,sds,Cds,Mdm,Sdm,Mds,Sds,i,k,D)
X = reshape(1:D*D,[D D]); XT = X'; % vectorized indices
I=0*X; I(i,i)=1; ii=X(I==1)'; I=0*X; I(k,k)=1; kk=X(I==1)';
I=0*X; I(i,k)=1; ik=X(I==1)'; ki=XT(I==1)';
Mdm(k,:) = mdm*Mdm(i,:) + mds*Sdm(ii,:); % chainrule
Mds(k,:) = mdm*Mds(i,:) + mds*Sds(ii,:);
Sdm(kk,:) = sdm*Mdm(i,:) + sds*Sdm(ii,:);
Sds(kk,:) = sdm*Mds(i,:) + sds*Sds(ii,:);
dCdm = Cdm*Mdm(i,:) + Cds*Sdm(ii,:);
dCds = Cdm*Mds(i,:) + Cds*Sds(ii,:);
S(i,k) = S(i,i)*C; S(k,i) = S(i,k)'; % off-diagonal
SS = kron(eye(length(k)),S(i,i)); CC = kron(C',eye(length(i)));
Sdm(ik,:) = SS*dCdm + CC*Sdm(ii,:); Sdm(ki,:) = Sdm(ik,:);
Sds(ik,:) = SS*dCds + CC*Sds(ii,:); Sds(ki,:) = Sds(ik,:);
|
github
|
UCL-SML/pilco-matlab-master
|
draw_unicycle.m
|
.m
|
pilco-matlab-master/scenarios/unicycle/draw_unicycle.m
| 5,222 |
utf_8
|
4e5a8f9508c83cf1118de1536b5b45b0
|
%% draw_unicycle.m
% *Summary:* Draw the unicycle with cost and applied torques
%
% function draw_unicycle(latent, plant,t2,cost,text1, text2)
%
%
% *Input arguments:*
%
% latent state of the unicycle (including the torques)
% plant plant structure
% .dt sampling time
% .dyno state indices that are passed ont the cost function
% t2 supersampling frequency (in case you want smoother plots)
% cost cost structure
% .fcn function handle (it is assumed to use saturating cost)
% .<> other fields that are passed to cost
% text1 (optional) text field 1
% text2 (optional) text field 2
%
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-04-04
function draw_unicycle(latent,plant,t2,cost,text1, text2)
%% Code
clf; set(gca,'FontSize',16);
t1 = plant.dt;
rw = 0.225; % wheel radius
rf = 0.54; % frame center of mass to wheel
rt = 0.27; % frame centre of mass to turntable
rr = rf+rt; % distance wheel to turntable
M = 24; MM = 2*pi*(0:M)/M;
RR = ['r-';'r-';'r-';'k-';'b-';'b-';'b-'];
ii = 10000;
qq = latent;
clear q;
xi = t1*(0:size(qq,1)-1); xn = 0:t2:(size(qq,1)-1)*t1;
for i = 1:size(qq,2), q(:,i) = interp1(xi,qq(:,i),xn); end
for i=1:size(q,1)
x = q(i,10);
y = q(i,11);
theta = q(i,14);
phi = q(i,15);
psiw = -q(i,16);
psif = q(i,17);
psit = q(i,18);
A = [ cos(phi) sin(phi) 0
-sin(phi)*cos(theta) cos(phi)*cos(theta) -sin(theta)
-sin(phi)*sin(theta) cos(phi)*sin(theta) cos(theta) ]';
r = rw*[cos(psiw+MM); zeros(1,M+1); sin(psiw+MM)+1];
R{1} = bsxfun(@plus,A*r,[x; y; 0]);
r = rw*[cos(psiw) -cos(psiw); 0 0; sin(psiw)+1 -sin(psiw)+1];
R{2} = bsxfun(@plus,A*r,[x; y; 0]);
r = rw*[sin(psiw) -sin(psiw); 0 0; -cos(psiw)+1 cos(psiw)+1];
R{3} = bsxfun(@plus,A*r,[x; y; 0]);
r = [0 rr*sin(psif); 0 0; rw rw+rr*cos(psif)];
R{4} = bsxfun(@plus,A*r,[x; y; 0]);
r = [rr*sin(psif)+rw*cos(psif)*cos(psit+MM); rw*sin(psit+MM); rw+rr*cos(psif)-rw*sin(psif)*cos(psit+MM)];
R{5} = bsxfun(@plus,A*r,[x; y; 0]);
r = [rr*sin(psif)+rw*cos(psif)*cos(psit) rr*sin(psif)-rw* ...
cos(psif)*cos(psit); rw*sin(psit) -rw*sin(psit); rw+rr* ...
cos(psif)-rw*sin(psif)*cos(psit) rw+rr*cos(psif)+rw* ...
sin(psif)*cos(psit)];
R{6} = bsxfun(@plus,A*r,[x; y; 0]);
r = [rr*sin(psif)+rw*cos(psif)*sin(psit) rr*sin(psif)-rw* ...
cos(psif)*sin(psit); -rw*cos(psit) rw*cos(psit); rw+rr* ...
cos(psif)-rw*sin(psif)*sin(psit) rw+rr*cos(psif)+rw* ...
sin(psif)*sin(psit)];
R{7} = bsxfun(@plus,A*r,[x; y; 0]);
hold off
aa = linspace(0,2*pi,201); plot3(2*sin(aa),2*cos(aa),0*aa,'k:','LineWidth',2);
hold on
r = A*[0; 0; rw] + [x; y; 0];
P = [r R{1}(:,1:M/4+1) r]; fill3(P(1,:),P(2,:),P(3,:),'r','EdgeColor','none');
P = [r R{1}(:,M/2+1:3*M/4+1) r]; fill3(P(1,:),P(2,:),P(3,:),'r','EdgeColor','none');
r = A*[rr*sin(psif); 0; rw+rr*cos(psif) ] + [x; y; 0];
P = [r R{5}(:,1:M/4+1) r]; fill3(P(1,:),P(2,:),P(3,:),'b','EdgeColor','none');
P = [r R{5}(:,M/2+1:3*M/4+1) r]; fill3(P(1,:),P(2,:),P(3,:),'b','EdgeColor','none');
for j = [1 4 5];
plot3(R{j}(1,:),R{j}(2,:),R{j}(3,:),RR(j,:),'LineWidth',2)
end
axis equal; axis([-2 2 -2 2 0 1.5]);
xlabel 'x [m]';
ylabel 'y [m]';
grid on
% draw controls:
ut = q(i,end-1);
uw = q(i,end);
L = cost.fcn(cost,q(i,plant.dyno)',zeros(length(plant.dyno)));
utM = 10;
uwM = 50;
oo = [4 -3.07 0]/6.4; o1 = [-0.5 2 2.0]; o2 = [-0.5 2 1.6]; o3 = [-0.5 2 1.2];
o0 = 1.5*ut/utM;
plot3([o1(1) o1(1)+o0*oo(1)],[o1(2) o1(2)+o0*oo(2)],[o1(3) o1(3)+o0*oo(3)],'b','LineWidth',5)
plot3([o1(1)-1.5*oo(1) o1(1)+1.5*oo(1) o1(1)+1.5*oo(1) o1(1)-1.5*oo(1) o1(1)-1.5*oo(1)],...
[o1(2)-1.5*oo(2) o1(2)+1.5*oo(2) o1(2)+1.5*oo(2) o1(2)-1.5*oo(2) o1(2)-1.5*oo(2)],...
[o1(3)+0.04 o1(3)+0.04 o1(3)-0.04 o1(3)-0.04 o1(3)+0.04], 'b');
plot3([-0.5 -0.5],[2 2],o1(3)+[-0.06 0.06],'b');
o0 = 1.5*uw/uwM;
plot3([o2(1) o2(1)+o0*oo(1)],[o2(2) o2(2)+o0*oo(2)],[o2(3) o2(3)+o0*oo(3)],'r','LineWidth',5)
plot3([o2(1)-1.5*oo(1) o2(1)+1.5*oo(1) o2(1)+1.5*oo(1) o2(1)-1.5*oo(1) o2(1)-1.5*oo(1)],...
[o2(2)-1.5*oo(2) o2(2)+1.5*oo(2) o2(2)+1.5*oo(2) o2(2)-1.5*oo(2) o2(2)-1.5*oo(2)],...
[o2(3)+0.04 o2(3)+0.04 o2(3)-0.04 o2(3)-0.04 o2(3)+0.04], 'r');
plot3([-0.5 -0.5],[2 2],o2(3)+[-0.06 0.06],'r');
o0 = 3*L-1.5;
plot3([o3(1)-1.5*oo(1) o3(1)+o0*oo(1)],[o3(2)-1.5*oo(2) o3(2)+o0*oo(2)],[o3(3)-1.5*oo(3) o3(3)+o0*oo(3)],'k','LineWidth',5)
plot3([o3(1)-1.5*oo(1) o3(1)+1.5*oo(1) o3(1)+1.5*oo(1) o3(1)-1.5*oo(1) o3(1)-1.5*oo(1)],...
[o3(2)-1.5*oo(2) o3(2)+1.5*oo(2) o3(2)+1.5*oo(2) o3(2)-1.5*oo(2) o3(2)-1.5*oo(2)],...
[o3(3)+0.04 o3(3)+0.04 o3(3)-0.04 o3(3)-0.04 o3(3)+0.04], 'k');
text(-0.5-1.5*oo(1), 2-1.5*oo(2), 2.2,'Disc torque max \pm 10 Nm','Color','b');
text(-0.5-1.5*oo(1), 2-1.5*oo(2), 1.8,'Wheel torque max \pm 50 Nm','Color','r');
text(-0.5-1.5*oo(1), 2-1.5*oo(2), 1.4,'Instantaneous Cost','Color','k');
if nargin > 4
text(2,1,1.8,text1);
text(2,1,1.4,text2);
end
drawnow
% pause(plant.dt/2);
end
|
github
|
UCL-SML/pilco-matlab-master
|
dynamics_unicycle.m
|
.m
|
pilco-matlab-master/scenarios/unicycle/dynamics_unicycle.m
| 10,284 |
utf_8
|
60b3423e2e04ed55c08fbc8bb164bf6c
|
%% dynamics_unicycle.m
% *Summary:* Implements ths ODE for simulating the cart-pole dynamics.
%
% function dz = dz = dynamics_unicycle(t, z, V, U)
%
%
% *Input arguments:*
%
% t current time step (called from ODE solver)
% z state [12 x 1]
% V torque applied to the flywheel
% U torque applied to the wheel
%
% *Output arguments:*
%
% dz state derivative wrt time
%
%
% Note: It is assumed that the state variables are of the following order:
% state: z = [dtheta, dphi, dpsiw, dpsif, dspit,
% x, y, theta, phi, psiw, psif, psit]
%
% theta: tilt of the unicycle
% phi: orientation of the unicycle
% psiw: angle of wheel (rotation)
% psif: angle of fork
% psit: angle of turntable (rotation)
%
% dtheta angular velocity of tilt of the unicycle
% dphi angular velocity of orientation of the unicycle
% dpsiw angular velocity of wheel
% dpsif angular velocity of fork
% dpsit angular velocity of turntable
% x x-position of contact point in plane
% y y-position of contact point in plane
% theta tilt of the unicycle
% phi orientation of the unicycle
% psiw angle of wheel (rotation)
% psif angle of fork
% psit angle of turntable (rotation)
%
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen,
% based on derivations by David Forster
%
% Last modified: 2013-03-18
function dz = dynamics_unicycle(t, z, V, U)
%% Code
T = 0; % no friction
dtheta = z(1); dphi = z(2); dpsiw = z(3); dpsif = z(4); dpsit = z(5); x = z(6);
y = z(7); theta = z(8); phi = z(9); psiw = z(10); psif = z(11); psit = z(12);
clear psit psiw % dynamics can't possibly depend on these
% plant characteristics
mt = 10.0; % turntable mass
mw = 1.0; % wheel mass
mf = 23.5; % frame mass
rw = 0.225; % wheel radius
rf = 0.54; % frame center of mass to wheel
rt = 0.27; % frame centre of mass to turntable
r = rf+rt; % distance wheel to turntable
Cw = 0.0484; % moment of inertia of wheel around axle
Aw = 0.0242; % moment of inertia of wheel perpendicular to axle
Cf = 0.8292; % moment of inertia of frame
Bf = 0.4608; % moment of inertia of frame
Af = 0.4248; % moment of inertia of frame
Ct = 0.2; % moment of inertia of turntable around axle
At = 1.3; % moment of inertia of turntable perpendicular to axle
g = 9.82; % acceleration of gravity
st = sin(theta); ct = cos(theta); sf = sin(psif); cf = cos(psif);
A = [ -Ct*sf Ct*cf*ct 0 0 Ct;
0 Cw*st+At*st-rf*(-mf*(st*rf+cf*st*rw)-mt*(st*r+cf*st*rw))+rt*mt*(st*r+cf*st*rw) -cf*rw*(rf*(mf+mt)+rt*mt) -Cw-At-rf*(mf*rf+mt*r)-rt*mt*r 0;
cf*(-Af*sf-Ct*sf)-sf*(-Bf*cf-At*cf+rf*(-mf*(cf*rf+rw)-mt*(cf*r+rw))-rt*mt*(cf*r+rw)) Aw*ct+cf*(Af*cf*ct+Ct*cf*ct)-sf*(-Bf*sf*ct-At*sf*ct+rf*(-mf*sf*ct*rf-mt*sf*ct*r)-rt*mt*sf*ct*r) 0 0 Ct*cf;
-Aw-rw*(mf*(cf*rf+rw)+mw*rw+mt*(cf*r+rw))+sf*(-Af*sf-Ct*sf)+cf*(-Bf*cf-At*cf+rf*(-mf*(cf*rf+rw)-mt*(cf*r+rw))-rt*mt*(cf*r+rw)) -rw*(mt*sf*ct*r+mf*sf*ct*rf)+sf*(Af*cf*ct+Ct*cf*ct)+cf*(-Bf*sf*ct-At*sf*ct+rf*(-mf*sf*ct*rf-mt*sf*ct*r)-rt*mt*sf*ct*r) 0 0 Ct*sf;
0 2*Cw*st+At*st-rf*(-mt*(st*r+cf*st*rw)-mf*(st*rf+cf*st*rw))+rt*mt*(st*r+cf*st*rw)+rw*(mw*st*rw+sf*(mf*sf*st*rw+mt*sf*st*rw)+cf*(mt*(st*r+cf*st*rw)+mf*(st*rf+cf*st*rw))) -Cw-rt*mt*cf*rw+rw*(-mw*rw+sf*(-mf*sf*rw-mt*sf*rw)+cf*(-mf*cf*rw-mt*cf*rw))-rf*(mt*cf*rw+mf*cf*rw) -Cw-At-rf*(mf*rf+mt*r)-rt*mt*r-rw*cf*(mf*rf+mt*r) 0 ];
b = zeros(5,1);
b(1) = -V(t)+Ct*(-dphi*sf*dpsif*ct-dphi*cf*st*dtheta-cf*dpsif*dtheta);
b(2) = -U(t)+Cw*dphi*ct*dtheta-(-dphi*cf*ct+sf*dtheta)*Bf*(dphi*sf*ct+cf*dtheta)+(dphi*sf*ct+cf*dtheta)*Af*(-dphi*cf*ct+sf*dtheta)+At*dphi*ct*dtheta-(dphi*sf*ct+cf*dtheta)*Ct*(dphi*cf*ct-sf*dtheta+dpsit)+(dphi*cf*ct-sf*dtheta)*At*(dphi*sf*ct+cf*dtheta)-rf*(-mf*g*sf*ct-mf*(sf*dpsif*(-dphi*st+dpsiw)*rw+cf*dphi*ct*dtheta*rw-(-dphi*cf*ct+sf*dtheta)*(dtheta*rw+(dphi*sf*ct+cf*dtheta)*rf)+dphi*ct*dtheta*rf-(-dphi*st+dpsif)*sf*(-dphi*st+dpsiw)*rw)-mt*g*sf*ct-mt*(sf*dpsif*(-dphi*st+dpsiw)*rw+cf*dphi*ct*dtheta*rw-(-dphi*st+dpsif)*sf*(-dphi*st+dpsiw)*rw+dphi*ct*dtheta*(rf+rt)+(dphi*cf*ct-sf*dtheta)*(dtheta*rw+(dphi*sf*ct+cf*dtheta)*(rf+rt))))-rt*(-mt*g*sf*ct-mt*(sf*dpsif*(-dphi*st+dpsiw)*rw+cf*dphi*ct*dtheta*rw-(-dphi*st+dpsif)*sf*(-dphi*st+dpsiw)*rw+dphi*ct*dtheta*(rf+rt)+(dphi*cf*ct-sf*dtheta)*(dtheta*rw+(dphi*sf*ct+cf*dtheta)*(rf+rt))));
b(3) = -T*ct-2*dphi*st*Aw*dtheta-dtheta*Cw*(-dphi*st+dpsiw)+cf*(-Af*(dphi*sf*dpsif*ct+dphi*cf*st*dtheta+cf*dpsif*dtheta)-(dphi*sf*ct+cf*dtheta)*Cf*(-dphi*st+dpsif)+(-dphi*st+dpsif)*Bf*(dphi*sf*ct+cf*dtheta)+Ct*(-dphi*sf*dpsif*ct-dphi*cf*st*dtheta-cf*dpsif*dtheta))-sf*(-Bf*(dphi*cf*dpsif*ct-dphi*sf*st*dtheta-dpsif*sf*dtheta)-(-dphi*st+dpsif)*Af*(-dphi*cf*ct+sf*dtheta)+(-dphi*cf*ct+sf*dtheta)*Cf*(-dphi*st+dpsif)-At*(dphi*cf*dpsif*ct-dphi*sf*st*dtheta-dpsif*sf*dtheta)-(dphi*cf*ct-sf*dtheta)*At*(-dphi*st+dpsif)+(-dphi*st+dpsif)*Ct*(dphi*cf*ct-sf*dtheta+dpsit)+rf*(mf*g*st-mf*((dphi*sf*ct+cf*dtheta)*sf*(-dphi*st+dpsiw)*rw+(dphi*cf*dpsif*ct-dphi*sf*st*dtheta-dpsif*sf*dtheta)*rf+(-dphi*cf*ct+sf*dtheta)*(-cf*(-dphi*st+dpsiw)*rw-(-dphi*st+dpsif)*rf))+mt*g*st-mt*(-(dphi*cf*ct-sf*dtheta)*(-cf*(-dphi*st+dpsiw)*rw-(-dphi*st+dpsif)*(rf+rt))+(dphi*cf*dpsif*ct-dphi*sf*st*dtheta-dpsif*sf*dtheta)*(rf+rt)+(dphi*sf*ct+cf*dtheta)*sf*(-dphi*st+dpsiw)*rw))+rt*(mt*g*st-mt*(-(dphi*cf*ct-sf*dtheta)*(-cf*(-dphi*st+dpsiw)*rw-(-dphi*st+dpsif)*(rf+rt))+(dphi*cf*dpsif*ct-dphi*sf*st*dtheta-dpsif*sf*dtheta)*(rf+rt)+(dphi*sf*ct+cf*dtheta)*sf*(-dphi*st+dpsiw)*rw)));
b(4) = -dphi^2*st*Aw*ct-dphi*ct*Cw*(-dphi*st+dpsiw)-rw*(mw*dphi*ct*(-dphi*st+dpsiw)*rw-mt*g*st-mw*g*st+mf*((dphi*sf*ct+cf*dtheta)*sf*(-dphi*st+dpsiw)*rw+(dphi*cf*dpsif*ct-dphi*sf*st*dtheta-dpsif*sf*dtheta)*rf+(-dphi*cf*ct+sf*dtheta)*(-cf*(-dphi*st+dpsiw)*rw-(-dphi*st+dpsif)*rf))-mf*g*st+mt*(-(dphi*cf*ct-sf*dtheta)*(-cf*(-dphi*st+dpsiw)*rw-(-dphi*st+dpsif)*(rf+rt))+(dphi*cf*dpsif*ct-dphi*sf*st*dtheta-dpsif*sf*dtheta)*(rf+rt)+(dphi*sf*ct+cf*dtheta)*sf*(-dphi*st+dpsiw)*rw))+sf*(-Af*(dphi*sf*dpsif*ct+dphi*cf*st*dtheta+cf*dpsif*dtheta)-(dphi*sf*ct+cf*dtheta)*Cf*(-dphi*st+dpsif)+(-dphi*st+dpsif)*Bf*(dphi*sf*ct+cf*dtheta)+Ct*(-dphi*sf*dpsif*ct-dphi*cf*st*dtheta-cf*dpsif*dtheta))+cf*(-Bf*(dphi*cf*dpsif*ct-dphi*sf*st*dtheta-dpsif*sf*dtheta)-(-dphi*st+dpsif)*Af*(-dphi*cf*ct+sf*dtheta)+(-dphi*cf*ct+sf*dtheta)*Cf*(-dphi*st+dpsif)-At*(dphi*cf*dpsif*ct-dphi*sf*st*dtheta-dpsif*sf*dtheta)-(dphi*cf*ct-sf*dtheta)*At*(-dphi*st+dpsif)+(-dphi*st+dpsif)*Ct*(dphi*cf*ct-sf*dtheta+dpsit)+rf*(mf*g*st-mf*((dphi*sf*ct+cf*dtheta)*sf*(-dphi*st+dpsiw)*rw+(dphi*cf*dpsif*ct-dphi*sf*st*dtheta-dpsif*sf*dtheta)*rf+(-dphi*cf*ct+sf*dtheta)*(-cf*(-dphi*st+dpsiw)*rw-(-dphi*st+dpsif)*rf))+mt*g*st-mt*(-(dphi*cf*ct-sf*dtheta)*(-cf*(-dphi*st+dpsiw)*rw-(-dphi*st+dpsif)*(rf+rt))+(dphi*cf*dpsif*ct-dphi*sf*st*dtheta-dpsif*sf*dtheta)*(rf+rt)+(dphi*sf*ct+cf*dtheta)*sf*(-dphi*st+dpsiw)*rw))+rt*(mt*g*st-mt*(-(dphi*cf*ct-sf*dtheta)*(-cf*(-dphi*st+dpsiw)*rw-(-dphi*st+dpsif)*(rf+rt))+(dphi*cf*dpsif*ct-dphi*sf*st*dtheta-dpsif*sf*dtheta)*(rf+rt)+(dphi*sf*ct+cf*dtheta)*sf*(-dphi*st+dpsiw)*rw)));
b(5) = -T*st+2*Cw*dphi*ct*dtheta+(dphi*sf*ct+cf*dtheta)*Af*(-dphi*cf*ct+sf*dtheta)-rt*(-mt*g*sf*ct-mt*(sf*dpsif*(-dphi*st+dpsiw)*rw+cf*dphi*ct*dtheta*rw-(-dphi*st+dpsif)*sf*(-dphi*st+dpsiw)*rw+dphi*ct*dtheta*(rf+rt)+(dphi*cf*ct-sf*dtheta)*(dtheta*rw+(dphi*sf*ct+cf*dtheta)*(rf+rt))))-(dphi*sf*ct+cf*dtheta)*Ct*(dphi*cf*ct-sf*dtheta+dpsit)+At*dphi*ct*dtheta+rw*(2*mw*rw*dphi*ct*dtheta+sf*(mf*(-cf*dpsif*(-dphi*st+dpsiw)*rw+sf*dphi*ct*dtheta*rw+(dphi*sf*ct+cf*dtheta)*(dtheta*rw+(dphi*sf*ct+cf*dtheta)*rf)-(-dphi*st+dpsif)*(-cf*(-dphi*st+dpsiw)*rw-(-dphi*st+dpsif)*rf))-mf*g*cf*ct-mt*g*cf*ct-mt*(cf*dpsif*(-dphi*st+dpsiw)*rw-sf*dphi*ct*dtheta*rw+(-dphi*st+dpsif)*(-cf*(-dphi*st+dpsiw)*rw-(-dphi*st+dpsif)*(rf+rt))-(dphi*sf*ct+cf*dtheta)*(dtheta*rw+(dphi*sf*ct+cf*dtheta)*(rf+rt))))+cf*(mf*(sf*dpsif*(-dphi*st+dpsiw)*rw+cf*dphi*ct*dtheta*rw-(-dphi*cf*ct+sf*dtheta)*(dtheta*rw+(dphi*sf*ct+cf*dtheta)*rf)+dphi*ct*dtheta*rf-(-dphi*st+dpsif)*sf*(-dphi*st+dpsiw)*rw)+mt*g*sf*ct+mf*g*sf*ct+mt*(sf*dpsif*(-dphi*st+dpsiw)*rw+cf*dphi*ct*dtheta*rw-(-dphi*st+dpsif)*sf*(-dphi*st+dpsiw)*rw+dphi*ct*dtheta*(rf+rt)+(dphi*cf*ct-sf*dtheta)*(dtheta*rw+(dphi*sf*ct+cf*dtheta)*(rf+rt)))))+(dphi*cf*ct-sf*dtheta)*At*(dphi*sf*ct+cf*dtheta)-rf*(-mt*g*sf*ct-mt*(sf*dpsif*(-dphi*st+dpsiw)*rw+cf*dphi*ct*dtheta*rw-(-dphi*st+dpsif)*sf*(-dphi*st+dpsiw)*rw+dphi*ct*dtheta*(rf+rt)+(dphi*cf*ct-sf*dtheta)*(dtheta*rw+(dphi*sf*ct+cf*dtheta)*(rf+rt)))-mf*g*sf*ct-mf*(sf*dpsif*(-dphi*st+dpsiw)*rw+cf*dphi*ct*dtheta*rw-(-dphi*cf*ct+sf*dtheta)*(dtheta*rw+(dphi*sf*ct+cf*dtheta)*rf)+dphi*ct*dtheta*rf-(-dphi*st+dpsif)*sf*(-dphi*st+dpsiw)*rw))-(-dphi*cf*ct+sf*dtheta)*Bf*(dphi*sf*ct+cf*dtheta);
dz = zeros(12,1);
dz(1:5) = -A\b;
dz(6) = rw*cos(phi)*dpsiw;
dz(7) = rw*sin(phi)*dpsiw;
dz(8:12) = z(1:5);
|
github
|
UCL-SML/pilco-matlab-master
|
draw_cdp.m
|
.m
|
pilco-matlab-master/scenarios/cartDoublePendulum/draw_cdp.m
| 3,088 |
utf_8
|
04065885f673565eaffc1971cde2a74e
|
%% draw_cdp.m
% *Summary:* Draw the cart-double-pendulum system with reward, applied force,
% and predictive uncertainty of the tips of the pendulums
%
% function draw_cdp(x, theta2, theta3, force, cost, M, S, text1, text2)
%
%
% *Input arguments:*
%
% x position of the cart
% theta2 angle of inner pendulum
% theta3 angle of outer pendulum
% force force applied to cart
% cost cost structure
% .fcn function handle (it is assumed to use saturating cost)
% .<> other fields that are passed to cost
% M (optional) mean of state
% S (optional) covariance of state
% text1 (optional) text field 1
% text2 (optional) text field 2
%
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-03-27
function draw_cdp(x, theta2, theta3, force, cost, M, S, text1, text2)
%% Code
scale = 1;
l = 0.3*scale;
xmin = -3*scale;
xmax = 3*scale;
height = 0.07*scale;
width = 0.25*scale;
font_size = 12;
% Compute positions
cart = [ x + width, height
x + width, -height
x - width, -height
x - width, height
x + width, height ];
pend2 = [x, 0;
x-2*l*sin(theta2), cos(theta2)*2*l];
pend3 = [x-2*l*sin(theta2), 2*l*cos(theta2);
x-2*l*sin(theta2)-2*l*sin(theta3), 2*l*cos(theta2)+2*l*cos(theta3)];
% plot cart double pendulum
clf
hold on
plot(0,4*l,'k+','MarkerSize',2*font_size,'linewidth',2);
plot([xmin, xmax], [-height-0.03*scale, -height-0.03*scale], ...
'Color','b','LineWidth',3);
plot([0 force/20*xmax],[-0.3, -0.3].*scale, 'Color', 'g', 'LineWidth', font_size);
% Plot reward
reward = 1-cost.fcn(cost, [x, 0, 0, 0, theta2, theta3]',zeros(6));
plot([0 reward*xmax],[-0.5, -0.5].*scale, 'Color', 'y', 'LineWidth', font_size);
% Draw Cart
plot(cart(:,1), cart(:,2),'Color','k','LineWidth',3);
fill(cart(:,1), cart(:,2),'k');
% Draw Pendulum2
plot(pend2(:,1), pend2(:,2),'Color','r','LineWidth', round(font_size/2));
% Draw Pendulum3
plot(pend3(:,1), pend3(:,2),'Color','r','LineWidth', round(font_size/2));
% joint at cart
plot(x,0,'o','MarkerSize', round((font_size+4)/2),'Color','y','markerface','y');
% 2nd joint
plot(pend3(1,1),pend3(1,2),'o','MarkerSize', ...
round((font_size+4)/2),'Color','y','markerface','y');
% tip of 2nd joint
plot(pend3(2,1),pend3(2,2),'o','MarkerSize', ...
round((font_size+4)/2),'Color','y','markerface','y');
% plot ellipses around tip of pendulum (if M, S exist)
try
if max(max(S))>0
[M1 S1 M2 S2] = getPlotDistr_cdp(M, S, 2*l, 2*l);
error_ellipse(S1,M1,'style','b'); % inner pendulum
error_ellipse(S2,M2,'style','r'); % outer pendulum
end
catch
end
text(0,-0.3*scale,'applied force','fontsize', font_size)
text(0,-0.5*scale,'immediate reward','fontsize', font_size)
if exist('text1','var')
text(0,-0.7*scale, text1,'fontsize', font_size);
if exist('text2','var')
text(0,-0.9*scale, text2,'fontsize', font_size)
end
end
set(gca,'DataAspectRatio',[1 1 1],'XLim',[xmin xmax],'YLim',[-1.4 1.4].*scale);
axis off
drawnow;
|
github
|
UCL-SML/pilco-matlab-master
|
getPlotDistr_cdp.m
|
.m
|
pilco-matlab-master/scenarios/cartDoublePendulum/getPlotDistr_cdp.m
| 2,860 |
utf_8
|
bd0fa24486bfa30d58e3ee07349684d0
|
%% getPlotDistr_cdp.m
% *Summary:* Compute means and covariances of the Cartesian coordinates of
% the tips both the inner and outer pendulum assuming that the joint state
% $x$ of the cart-double-pendulum system is Gaussian, i.e., $x\sim N(m, s)$
%
%
% function [M1, S1, M2, S2] = getPlotDistr_cdp(m, s, ell1, ell2)
%
%
%
% *Input arguments:*
%
% m mean of full state [6 x 1]
% s covariance of full state [6 x 6]
% ell1 length of inner pendulum
% ell2 length of outer pendulum
%
% Note: this code assumes that the following order of the state:
% 1: cart pos.,
% 2: cart vel.,
% 3: pend1 angular velocity,
% 4: pend2 angular velocity,
% 5: pend1 angle,
% 6: pend2 angle
%
% *Output arguments:*
%
% M1 mean of tip of inner pendulum [2 x 1]
% S1 covariance of tip of inner pendulum [2 x 2]
% M2 mean of tip of outer pendulum [2 x 1]
% S2 covariance of tip of outer pendulum [2 x 2]
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modification: 2013-03-06
%
%% High-Level Steps
% # Augment input distribution to complex angle representation
% # Compute means of tips of pendulums (in Cartesian coordinates)
% # Compute covariances of tips of pendulums (in Cartesian coordinates)
function [M1, S1, M2, S2] = getPlotDistr_cdp(m, s, ell1, ell2)
%% Code
% 1. Augment input distribution (complex representation)
[m1 s1 c1] = gTrig(m, s, [5 6], [ell1, ell2]); % map input through sin/cos
m1 = [m; m1]; % mean of joint
c1 = s*c1; % cross-covariance between input and prediction
s1 = [s c1; c1' s1]; % covariance of joint
% 2. Mean of the tips of the pendulums (Cart. coord.)
M1 = [m1(1) - m1(7); m1(8)]; % p2: E[x -l1\sin\theta_2]; E[l2\cos\theta_2]
M2 = [M1(1) - m1(9); M1(2) + m1(10)]; % p3: mean of cart. coord.
% 2. Put covariance matrices together (Cart. coord.)
% first set of coordinates (tip of 1st pendulum)
S1(1,1) = s1(1,1) + s1(7,7) -2*s1(1,7);
S1(2,2) = s1(8,8);
S1(1,2) = s1(1,8) - s1(7,8);
S1(2,1) = S1(1,2)';
% second set of coordinates (tip of 2nd pendulum)
S2(1,1) = S1(1,1) + s1(9,9) + 2*(s1(1,9) - s1(7,9));
S2(2,2) = s1(8,8) + s1(10,10) + 2*s1(8,10);
S2(1,2) = s1(1,8) - s1(7,8) - s1(9,8) ...
+ s1(1,10) - s1(7,10) - s1(9,10);
S2(2,1) = S2(1,2)';
% make sure we have proper covariances (sometimes numerical problems occur)
try
chol(S1);
catch
warning('matrix S1 not pos.def. (getPlotDistr)');
S1 = S1 + (1e-6 - min(eig(S1)))*eye(2);
end
try
chol(S2);
catch
warning('matrix S2 not pos.def. (getPlotDistr)');
S2 = S2 + (1e-6 - min(eig(S2)))*eye(2);
end
|
github
|
UCL-SML/pilco-matlab-master
|
loss_cdp.m
|
.m
|
pilco-matlab-master/scenarios/cartDoublePendulum/loss_cdp.m
| 4,261 |
utf_8
|
50a62e605756c9036c520f7886803a73
|
%% loss_cdp.m
% *Summary:* Cart-Double-Pendulum loss function; the loss is
% $1-\exp(-0.5*d^2*a)$, where $a>0$ and $d^2$ is the squared difference
% between the actual and desired position of the end of the outer pendulum.
% The mean and the variance of the loss are computed by averaging over the
% Gaussian distribution of the state $p(x) = \mathcal N(m,s)$ with mean $m$
% and covariance matrix $s$.
% Derivatives of these quantities are computed when desired.
%
%
% function [L, dLdm, dLds, S2] = loss_cdp(cost, m, s)
%
%
% *Input arguments:*
% cost cost structure
% .p lengths of the 2 pendulums [2 x 1 ]
% .width array of widths of the cost (summed together)
% .expl (optional) exploration parameter
% .angle (optional) array of angle indices
% .target target state [D x 1 ]
% m mean of state distribution [D x 1 ]
% s covariance matrix for the state distribution [D x D ]
%
% *Output arguments:*
%
% L expected cost [1 x 1 ]
% dLdm derivative of expected cost wrt. state mean vector [1 x D ]
% dLds derivative of expected cost wrt. state covariance matrix [1 x D^2]
% S2 variance of cost [1 x 1 ]
%
% Copyright (C) 2008-2013 by
% Marc Deisenroth, Andrew McHutchon, Joe Hall, and Carl Edward Rasmussen.
%
% Last modified: 2013-03-07
%
%% High-Level Steps
% # Precomputations
% # Define static penalty as distance from target setpoint
% # Trigonometric augmentation
% # Calculate loss
function [L, dLdm, dLds, S2] = loss_cdp(cost, m, s)
%% Code
if isfield(cost,'width'); cw = cost.width; else cw = 1; end
if ~isfield(cost,'expl') || isempty(cost.expl); b = 0; else b = cost.expl; end
% 1. Some precomputations
D0 = size(s,2); D = D0; % state dimension
D1 = D0 + 2*length(cost.angle); % state dimension (with sin/cos)
M = zeros(D1,1); M(1:D0) = m; S = zeros(D1); S(1:D0,1:D0) = s;
Mdm = [eye(D0); zeros(D1-D0,D0)]; Sdm = zeros(D1*D1,D0);
Mds = zeros(D1,D0*D0); Sds = kron(Mdm,Mdm);
% 2. Define static penalty as distance from target setpoint
target = [cost.target(:); gTrig(cost.target(:), 0*s, cost.angle)];
ell1 = cost.p(1); ell2 = cost.p(2);
C = [1 -ell1 0 -ell2 0; 0 0 ell1 0 ell2];
Q = zeros(D1); Q([1 D+1:D+4],[1 D+1:D+4]) = C'*C;
% 3. Trigonometric augmentation
i = 1:D0; k = D0+1:D1;
[M(k) S(k,k) C mdm sdm Cdm mds sds Cds] = gTrig(M(i),S(i,i),cost.angle);
[S Mdm Mds Sdm Sds] = ...
fillIn(S,C,mdm,sdm,Cdm,mds,sds,Cds,Mdm,Sdm,Mds,Sds,i,k,D1);
% 4. Calculate loss
L = 0; dLdm = zeros(1,D0); dLds = zeros(1,D0*D0); S2 = 0;
for i = 1:length(cw) % scale mixture of immediate costs
cost.z = target;
cost.W = Q/cw(i)^2;
[r rdM rdS s2 s2dM s2dS] = lossSat(cost, M, S);
L = L + r; S2 = S2 + s2;
dLdm = dLdm + rdM(:)'*Mdm + rdS(:)'*Sdm;
dLds = dLds + rdM(:)'*Mds + rdS(:)'*Sds;
if (b~=0 || ~isempty(b)) && abs(s2)>1e-12
L = L + b*sqrt(s2);
dLdm = dLdm + b/sqrt(s2) * ( s2dM(:)'*Mdm + s2dS(:)'*Sdm )/2;
dLds = dLds + b/sqrt(s2) * ( s2dM(:)'*Mds + s2dS(:)'*Sds )/2;
end
end
% normalize
n = length(cw); L = L/n; dLdm = dLdm/n; dLds = dLds/n; S2 = S2/n;
% Fill in covariance matrix...and derivatives ----------------------------
function [S Mdm Mds Sdm Sds] = ...
fillIn(S,C,mdm,sdm,Cdm,mds,sds,Cds,Mdm,Sdm,Mds,Sds,i,k,D)
X = reshape(1:D*D,[D D]); XT = X'; % vectorised indices
I=0*X; I(i,i)=1; ii=X(I==1)'; I=0*X; I(k,k)=1; kk=X(I==1)';
I=0*X; I(i,k)=1; ik=X(I==1)'; ki=XT(I==1)';
Mdm(k,:) = mdm*Mdm(i,:) + mds*Sdm(ii,:); % chainrule
Mds(k,:) = mdm*Mds(i,:) + mds*Sds(ii,:);
Sdm(kk,:) = sdm*Mdm(i,:) + sds*Sdm(ii,:);
Sds(kk,:) = sdm*Mds(i,:) + sds*Sds(ii,:);
dCdm = Cdm*Mdm(i,:) + Cds*Sdm(ii,:);
dCds = Cdm*Mds(i,:) + Cds*Sds(ii,:);
S(i,k) = S(i,i)*C; S(k,i) = S(i,k)'; % off-diagonal
SS = kron(eye(length(k)),S(i,i)); CC = kron(C',eye(length(i)));
Sdm(ik,:) = SS*dCdm + CC*Sdm(ii,:); Sdm(ki,:) = Sdm(ik,:);
Sds(ik,:) = SS*dCds + CC*Sds(ii,:); Sds(ki,:) = Sds(ik,:);
|
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