comparison toolboxes/FullBNT-1.0.7/KPMstats/eigdec.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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