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SVMCrossVal.git
somtoolbox2
pcaproj.m
starting som prediction fine-tuned class-performance visualisation
Christoph Budziszewski
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4dbef18
at 2009-01-21 16:34:25
pcaproj.m
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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 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; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%