```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
%

% 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 data
if isstruct(D),
if strcmp(D.type,'som_map'), D=D.codebook; else D=D.data; end
end
[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 eigenvectors
P = D*V;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
```