matlab 独立分量分析 fastica,icaplot,remmean,whiten，盲源分离,去均值,白化处理.zip

function [Out1, Out2, Out3] = fastica(mixedsig, varargin) %FASTICA - Fast Independent Component Analysis % % FastICA for Matlab 7.x and 6.x % Version 2.5, October 19 2005 % Copyright (c) Hugo G�vert, Jarmo Hurri, Jaakko S�rel�, and Aapo Hyv�rinen. % % FASTICA(mixedsig) estimates the independent components from given % multidimensional signals. Each row of matrix mixedsig is one % observed signal. FASTICA uses Hyvarinen's fixed-point algorithm, % see http://www.cis.hut.fi/projects/ica/fastica/. Output from the % function depends on the number output arguments: % % [icasig] = FASTICA (mixedsig); the rows of icasig contain the % estimated independent components. % % [icasig, A, W] = FASTICA (mixedsig); outputs the estimated separating % matrix W and the corresponding mixing matrix A. % % [A, W] = FASTICA (mixedsig); gives only the estimated mixing matrix % A and the separating matrix W. % % Some optional arguments induce other output formats, see below. % % A graphical user interface for FASTICA can be launched by the % command FASTICAG % % FASTICA can be called with numerous optional arguments. Optional % arguments are given in parameter pairs, so that first argument is % the name of the parameter and the next argument is the value for % that parameter. Optional parameter pairs can be given in any order. % % OPTIONAL PARAMETERS: % % Parameter name Values and description % %====================================================================== % --Basic parameters in fixed-point algorithm: % % 'approach' (string) The decorrelation approach used. Can be % symmetric ('symm'), i.e. estimate all the % independent component in parallel, or % deflation ('defl'), i.e. estimate independent % component one-by-one like in projection pursuit. % Default is 'defl'. % % 'numOfIC' (integer) Number of independent components to % be estimated. Default equals the dimension of data. % %====================================================================== % --Choosing the nonlinearity: % % 'g' (string) Chooses the nonlinearity g used in % the fixed-point algorithm. Possible values: % % Value of 'g': Nonlinearity used: % 'pow3' (default) g(u)=u^3 % 'tanh' g(u)=tanh(a1*u) % 'gauss g(u)=u*exp(-a2*u^2/2) % 'skew' g(u)=u^2 % % 'finetune' (string) Chooses the nonlinearity g used when % fine-tuning. In addition to same values % as for 'g', the possible value 'finetune' is: % 'off' fine-tuning is disabled. % % 'a1' (number) Parameter a1 used when g='tanh'. % Default is 1. % 'a2' (number) Parameter a2 used when g='gaus'. % Default is 1. % % 'mu' (number) Step size. Default is 1. % If the value of mu is other than 1, then the % program will use the stabilized version of the % algorithm (see also parameter 'stabilization'). % % % 'stabilization' (string) Values 'on' or 'off'. Default 'off'. % This parameter controls wether the program uses % the stabilized version of the algorithm or % not. If the stabilization is on, then the value % of mu can momentarily be halved if the program % senses that the algorithm is stuck between two % points (this is called a stroke). Also if there % is no convergence before half of the maximum % number of iterations has been reached then mu % will be halved for the rest of the rounds. % %====================================================================== % --Controlling convergence: % % 'epsilon' (number) Stopping criterion. Default is 0.0001. % % 'maxNumIterations' (integer) Maximum number of iterations. % Default is 1000. % % 'maxFinetune' (integer) Maximum number of iterations in % fine-tuning. Default 100. % % 'sampleSize' (number) [0 - 1] Percentage of samples used in % one iteration. Samples are chosen in random. % Default is 1 (all samples). % % 'initGuess' (matrix) Initial guess for A. Default is random. % You can now do a "one more" like this: % [ica, A, W] = fastica(mix, 'numOfIC',3); % [ica2, A2, W2] = fastica(mix, 'initGuess', A, 'numOfIC', 4); % %====================================================================== % --Graphics and text output: % % 'verbose' (string) Either 'on' or 'off'. Default is % 'on': report progress of algorithm in text format. % % 'displayMode' (string) Plot running estimates of independent % components: 'signals', 'basis', 'filters' or % 'off'. Default is 'off'. % % 'displayInterval' Number of iterations between plots. % Default is 1 (plot after every iteration). % %====================================================================== % --Controlling reduction of dimension and whitening: % % Reduction of dimension is controlled by 'firstEig' and 'lastEig', or % alternatively by 'interactivePCA'. % % 'firstEig' (integer) This and 'lastEig' specify the range for % eigenvalues that are retained, 'firstEig' is % the index of largest eigenvalue to be % retained. Default is 1. % % 'lastEig' (integer) This is the index of the last (smallest) % eigenvalue to be retained. Default equals the % dimension of data. % % 'interactivePCA' (string) Either 'on' or 'off'. When set 'on', the % eigenvalues are shown to the user and the % range can be specified interactively. Default % is 'off'. Can also be set to 'gui'. Then the user % can use the same GUI that's in FASTICAG. % % If you already know the eigenvalue decomposition of the covariance % matrix, you can avoid computing it again by giving it with the % following options: % % 'pcaE' (matrix) Eigenvectors % 'pcaD' (matrix) Eigenvalues % % If you already know the whitened data, you can give it directly to % the algorithm using the following options: % % 'whiteSig' (matrix) Whitened signal % 'whiteMat' (matrix) Whitening matrix % 'dewhiteMat' (matrix) dewhitening matrix % % If values for all the 'whiteSig', 'whiteSig' and 'dewhiteMat' are % supplied, they will be used in computing the ICA. PCA and whitening % are not performed. Though 'mixedsig' is not used in the main % algorithm it still must be entered - some values are still % calculated from it. % % Performing preprocessing only is possible by the option: % % 'only' (string) Compute only PCA i.e. reduction of % dimension ('pca') or only PCA plus whitening % ('white'). Default is 'all': do ICA estimation % as well. This option changes the output % format accordingly. For example: % % [whitesig, WM, DWM] = FASTICA(mixedsig, % 'only', 'white') % returns the whitened signals, the whitening matrix % (WM) and the dewhitening matrix (DWM). (See also %