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      [temp_evec, temp_evals] = eig(x);   else      options.disp = 0;      [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);if ~evals_only   if evals == temp_evals(1:N)      % Originals were in order      evec = temp_evec(:, 1:N);      return   else      % Need to reorder the eigenvectors      for i=1:N         evec(:,i) = temp_evec(:,perm(i));      end   endend