function [s, V, D] = basicpca(x, dim)% BASICPCA  do simple linear PCA% % Usage:%    s = BASICPCA(x, dim)%    returns dim first PCA components of data, all scaled to%    unity variance%    [s, V, D] = BASICPCA(data, dim)%    returns dim first unscaled PCA components of data and the%    transformation matrices.%% Copyright (C) 1999-2000 Antti Honkela, Harri Valpola,% and Xavier Giannakopoulos.%% This package comes with ABSOLUTELY NO WARRANTY; for details% see License.txt in the program package.  This is free software,% and you are welcome to redistribute it under certain conditions;% see License.txt for details.% Remove the means from the data and calculate covariance matrixxn = x - mean(x, 2) * ones(1, size(x, 2));covm = xn * xn' ./ size(x, 2);% Calculate eigenvalues and find the greatest ones[V0, D0] = eig(covm);[S, I] = sort(-diag(D0));S = -S;% Return the sources normalized to unit variances = diag(sqrt(1./S(1:dim))) * V0(:,I(1:dim))' * xn;% Return unnormalized sources and transformation matricesif nargout == 3  s = V0(:,I(1:dim))' * xn;  V = V0(:,I);  D = S;end