BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 42 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/KPMstats/pca.m b/sourcecodes/bnt-master/KPMstats/pca.m
new file mode 100644
index 00000000..4b7063d6
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMstats/pca.m
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+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
+