function [P,V,me,l] = pcaproj(D,arg1,arg2)%PCAPROJ Projects data vectors using Principal Component Analysis.%% [P,V,me,l] = pcaproj(D, odim)% P =          pcaproj(D, V, me)%%  Input and output arguments ([]'s are optional)%   D      (matrix) size dlen x dim, the data matrix%          (struct) data or map struct            %   odim   (scalar) how many principal vectors are used%  %   P      (matrix) size dlen x odim, the projections%   V      (matrix) size dim x odim, principal eigenvectors (unit length)%   me     (vector) size 1 x dim, center point of D%   l      (vector) size 1 x odim, the corresponding eigenvalues, %                   relative to total sum of eigenvalues%                   % See also SAMMON, CCA.% Contributed to SOM Toolbox 2.0, February 2nd, 2000 by Juha Vesanto% Copyright (c) by Juha Vesanto% http://www.cis.hut.fi/projects/somtoolbox/% juuso 191297 070200%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%error(nargchk(2, 3, nargin)); % check the number of input arguments% the dataif isstruct(D),   if strcmp(D.type,'som_map'), D=D.codebook; else D=D.data; endend[dlen dim] = size(D);if nargin==2,   odim = arg1;      % autocorrelation matrix  A = zeros(dim);  me = zeros(1,dim);  for i=1:dim,     me(i) = mean(D(isfinite(D(:,i)),i));     D(:,i) = D(:,i) - me(i);   end    for i=1:dim,     for j=i:dim,       c = D(:,i).*D(:,j); c = c(isfinite(c));      A(i,j) = sum(c)/length(c); A(j,i) = A(i,j);     end  end    % eigenvectors, sort them according to eigenvalues, and normalize  [V,S]   = eig(A);  eigval  = diag(S);  [y,ind] = sort(abs(eigval));   eigval  = eigval(flipud(ind));  V       = V(:,flipud(ind));   for i=1:odim, V(:,i) = (V(:,i) / norm(V(:,i))); end    % take only odim first eigenvectors  V = V(:,1:odim);  l = abs(eigval)/sum(abs(eigval));  l = l(1:odim); else % nargin==3,   V = arg1;  me = arg2;  odim = size(V,2);      D = D-me(ones(dlen,1),:);  end  % project the data using odim first eigenvectorsP = D*V;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%