comparison toolboxes/FullBNT-1.0.7/netlab3.3/pca.m @ 0:cc4b1211e677 tip

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646 (e263d8a21543) added further path and more save "camirversion.m"

+function [PCcoeff, PCvec] = pca(data, N)
+%PCA	Principal Components Analysis
+%
+%	Description
+%	 PCCOEFF = PCA(DATA) computes the eigenvalues of the covariance
+%	matrix of the dataset DATA and returns them as PCCOEFF.  These
+%	coefficients give the variance of DATA along the corresponding
+%	principal components.
+%
+%	PCCOEFF = PCA(DATA, N) returns the largest N eigenvalues.
+%
+%	[PCCOEFF, PCVEC] = PCA(DATA) returns the principal components as well
+%	as the coefficients.  This is considerably more computationally
+%	demanding than just computing the eigenvalues.
+%
+%	See also
+%	EIGDEC, GTMINIT, PPCA
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+if nargin == 1
+   N = size(data, 2);
+end
+
+if nargout == 1
+   evals_only = logical(1);
+else
+   evals_only = logical(0);
+end
+
+if N ~= round(N) | N < 1 | N > size(data, 2)
+   error('Number of PCs must be integer, >0, < dim');
+end
+
+% Find the sorted eigenvalues of the data covariance matrix
+if evals_only
+   PCcoeff = eigdec(cov(data), N);
+else
+  [PCcoeff, PCvec] = eigdec(cov(data), N);
+end
+