FastICA_25.rar_fast ica matlab_fastICA matlab_fastica_matlab fas

function [A, W] = fpica(X, whiteningMatrix, dewhiteningMatrix, approach, ... numOfIC, g, finetune, a1, a2, myy, stabilization, ... epsilon, maxNumIterations, maxFinetune, initState, ... guess, sampleSize, displayMode, displayInterval, ... s_verbose); %FPICA - Fixed point ICA. Main algorithm of FASTICA. % % [A, W] = fpica(whitesig, whiteningMatrix, dewhiteningMatrix, approach, % numOfIC, g, finetune, a1, a2, mu, stabilization, epsilon, % maxNumIterations, maxFinetune, initState, guess, sampleSize, % displayMode, displayInterval, verbose); % % Perform independent component analysis using Hyvarinen's fixed point % algorithm. Outputs an estimate of the mixing matrix A and its inverse W. % % whitesig :the whitened data as row vectors % whiteningMatrix :can be obtained with function whitenv % dewhiteningMatrix :can be obtained with function whitenv % approach [ 'symm' | 'defl' ] :the approach used (deflation or symmetric) % numOfIC [ 0 - Dim of whitesig ] :number of independent components estimated % g [ 'pow3' | 'tanh' | :the nonlinearity used % 'gaus' | 'skew' ] % finetune [same as g + 'off'] :the nonlinearity used in finetuning. % a1 :parameter for tuning 'tanh' % a2 :parameter for tuning 'gaus' % mu :step size in stabilized algorithm % stabilization [ 'on' | 'off' ] :if mu < 1 then automatically on % epsilon :stopping criterion % maxNumIterations :maximum number of iterations % maxFinetune :maximum number of iteretions for finetuning % initState [ 'rand' | 'guess' ] :initial guess or random initial state. See below % guess :initial guess for A. Ignored if initState = 'rand' % sampleSize [ 0 - 1 ] :percentage of the samples used in one iteration % displayMode [ 'signals' | 'basis' | :plot running estimate % 'filters' | 'off' ] % displayInterval :number of iterations we take between plots % verbose [ 'on' | 'off' ] :report progress in text format % % EXAMPLE % [E, D] = pcamat(vectors); % [nv, wm, dwm] = whitenv(vectors, E, D); % [A, W] = fpica(nv, wm, dwm); % % % This function is needed by FASTICA and FASTICAG % % See also FASTICA, FASTICAG, WHITENV, PCAMAT % @(#)$Id: fpica.m,v 1.7 2005/06/16 12:52:55 jarmo Exp $ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Global variable for stopping the ICA calculations from the GUI global g_FastICA_interrupt; if isempty(g_FastICA_interrupt) clear global g_FastICA_interrupt; interruptible = 0; else interruptible = 1; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Default values if nargin < 3, error('Not enough arguments!'); end [vectorSize, numSamples] = size(X); if nargin < 20, s_verbose = 'on'; end if nargin < 19, displayInterval = 1; end if nargin < 18, displayMode = 'on'; end if nargin < 17, sampleSize = 1; end if nargin < 16, guess = 1; end if nargin < 15, initState = 'rand'; end if nargin < 14, maxFinetune = 100; end if nargin < 13, maxNumIterations = 1000; end if nargin < 12, epsilon = 0.0001; end if nargin < 11, stabilization = 'on'; end if nargin < 10, myy = 1; end if nargin < 9, a2 = 1; end if nargin < 8, a1 = 1; end if nargin < 7, finetune = 'off'; end if nargin < 6, g = 'pow3'; end if nargin < 5, numOfIC = vectorSize; end % vectorSize = Dim if nargin < 4, approach = 'defl'; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Checking the data if ~isreal(X) error('Input has an imaginary part.'); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Checking the value for verbose switch lower(s_verbose) case 'on' b_verbose = 1; case 'off' b_verbose = 0; otherwise error(sprintf('Illegal value [ %s ] for parameter: ''verbose''\n', s_verbose)); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Checking the value for approach switch lower(approach) case 'symm' approachMode = 1; case 'defl' approachMode = 2; otherwise error(sprintf('Illegal value [ %s ] for parameter: ''approach''\n', approach)); end if b_verbose, fprintf('Used approach [ %s ].\n', approach); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Checking the value for numOfIC if vectorSize < numOfIC error('Must have numOfIC <= Dimension!'); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Checking the sampleSize if sampleSize > 1 sampleSize = 1; if b_verbose fprintf('Warning: Setting ''sampleSize'' to 1.\n'); end elseif sampleSize < 1 if (sampleSize * numSamples) < 1000 sampleSize = min(1000/numSamples, 1); if b_verbose fprintf('Warning: Setting ''sampleSize'' to %0.3f (%d samples).\n', ... sampleSize, floor(sampleSize * numSamples)); end end end if b_verbose if b_verbose & (sampleSize < 1) fprintf('Using about %0.0f%% of the samples in random order in every step.\n',sampleSize*100); end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Checking the value for nonlinearity. switch lower(g) case 'pow3' gOrig = 10; case 'tanh' gOrig = 20; case {'gaus', 'gauss'} gOrig = 30; case 'skew' gOrig = 40; otherwise error(sprintf('Illegal value [ %s ] for parameter: ''g''\n', g)); end if sampleSize ~= 1 gOrig = gOrig + 2; end if myy ~= 1 gOrig = gOrig + 1; end if b_verbose, fprintf('Used nonlinearity [ %s ].\n', g); end finetuningEnabled = 1; switch lower(finetune) case 'pow3' gFine = 10 + 1; case 'tanh' gFine = 20 + 1; case {'gaus', 'gauss'} gFine = 30 + 1; case 'skew' gFine = 40 + 1; case 'off' if myy ~= 1 gFine = gOrig; else gFine = gOrig + 1; end finetuningEnabled = 0; otherwise error(sprintf('Illegal value [ %s ] for parameter: ''finetune''\n', ... finetune)); end if b_verbose & finetuningEnabled fprintf('Finetuning enabled (nonlinearity: [ %s ]).\n', finetune); end switch lower(stabilization) case 'on' stabilizationEnabled = 1; case 'off' if myy ~= 1 stabilizationEnabled = 1; else stabilizationEnabled = 0; end otherwise error(sprintf('Illegal value [ %s ] for parameter: ''stabilization''\n', ... stabilization)); end if b_verbose & stabilizationEnabled fprintf('Using stabilized algorithm.\n'); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Some other parameters myyOrig = myy; % When we start fine-tuning we'll set myy = myyK * myy myyK = 0.01; % How many times do we try for convergence until we give up. failureLimit = 5; usedNlinearity = gOrig; stroke = 0; notFine = 1; long = 0; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Checking the value for initial state. switch lower(initState) case 'rand' initialStateMode = 0; case 'guess' if size(guess,1) ~= size(whiteningMatrix,2) initialStateMode = 0; if b_verbose fprintf('Warning: size of initial guess is incorrect. Using random initial guess.\n'); end else initialStateMode = 1; if size(guess,2) < numOfIC if b_verbose fprintf('Warning: initial guess only for first %d components. Using random initial guess for others.\n', size(guess,2)); end guess(:, size(guess, 2) + 1:numOfIC) = ... rand(vectorSize,numOfIC-size(guess,2))-.5; elseif size(guess,2)>numOfIC guess=guess(:,1:numOfIC); fprintf('Warning: Initial guess too large. The excess column are dropped.\n'); end if b_verbose, fprintf('Using initia