www.pudn.com > HMM1.zip > eigdec.m

function [evals, evec] = eigdec(x, N) 
%EIGDEC	Sorted eigendecomposition 
% 
%	Description 
%	 EVALS = EIGDEC(X, N computes the largest N eigenvalues of the 
%	matrix X in descending order.  [EVALS, EVEC] = EIGDEC(X, N) also 
%	computes the corresponding eigenvectors. 
% 
%	See also 
%	PCA, PPCA 
% 
 
%	Copyright (c) Ian T Nabney (1996-2001) 
 
if nargout == 1 
   evals_only = logical(1); 
else 
   evals_only = logical(0); 
end 
 
if N ~= round(N) | N < 1 | N > size(x, 2) 
   error('Number of PCs must be integer, >0, < dim'); 
end 
 
% Find the eigenvalues of the data covariance matrix 
if evals_only 
   % Use eig function as always more efficient than eigs here 
   temp_evals = eig(x); 
else 
   % Use eig function unless fraction of eigenvalues required is tiny 
   if (N/size(x, 2)) > 0.04 
     fprintf('netlab pca: using eig\n'); 
      [temp_evec, temp_evals] = eig(x); 
   else 
      options.disp = 0; 
      fprintf('netlab pca: using eigs\n'); 
      [temp_evec, temp_evals] = eigs(x, N, 'LM', options); 
   end 
   temp_evals = diag(temp_evals); 
end 
 
% Eigenvalues nearly always returned in descending order, but just 
% to make sure..... 
[evals perm] = sort(-temp_evals); 
evals = -evals(1:N); 
%evec=temp_evec(:,1:N); 
if ~evals_only 
  if evals == temp_evals(1:N) 
    % Originals were in order 
    evec = temp_evec(:, 1:N); 
    return 
  else 
    fprintf('netlab pca: sorting evec\n'); 
    % Need to reorder the eigenvectors 
    for i=1:N 
      evec(:,i) = temp_evec(:,perm(i)); 
    end 
  end 
end