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);endif 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 matrixif 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  endend