基于MATLAB的fastica算法的实现

function [Gn, pi1,pi2,SIR_G,SIR_Gc] = icacalcpi( W, Q, A ); % Performance indices for extraction/separation % (SCruces 01Jan03) % % These performance indices resembles Amari's index % and work properly when the sources are normalized % to unit variance. if isempty(W) | (size(W,2) ~= size(A,1)) disp('First press button "RUN ALGORITHM"'); Gn = []; pi1 = []; pi2 = []; return end % G = ( W * Q * A ); [G,SIR_G, SIR_Gc] = sirgmeasure(A,W*Q); [Nrows, Ncols] = size( G ); % pi1: Gives an idea of the capability of extraction that the algorithm % achieved. Computes some normalized mean interference due to the other sources % that remains in the extracted/separated signals. This index ideally % should be zero. However, it does not take into account if the % extracted sources are different or not (this can be controled with the % performance index pi2). D=pinv(diag(max(abs(G.')))); Gn =D*abs(G); pi1=(sum(sum(Gn))-Nrows)/(Nrows*Ncols-Nrows); % pi2: Indicates the divergence from Unitarity/Semi-Unitarity % after the normalization of the rows of G to unit norm to remove % any possible scaling indeterminacy. Idealy should be zero. GG=abs(G*G'); D=pinv(diag(sqrt(diag(GG)))); GGn=D*GG*D; if Nrows>1 pi2=(sum(sum(GGn))-Nrows)/(Nrows*(Nrows-1)); else pi2=0; end