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, 25 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/KPMstats/gaussian_sample.m b/sourcecodes/bnt-master/KPMstats/gaussian_sample.m
new file mode 100644
index 00000000..a2f09df8
--- /dev/null
+++ b/sourcecodes/bnt-master/KPMstats/gaussian_sample.m
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+function x = gsamp(mu, covar, nsamp)
+%GSAMP	Sample from a Gaussian distribution.
+%
+%	Description
+%
+%	X = GSAMP(MU, COVAR, NSAMP) generates a sample of size NSAMP from a
+%	D-dimensional Gaussian distribution. The Gaussian density has mean
+%	vector MU and covariance matrix COVAR, and the matrix X has NSAMP
+%	rows in which each row represents a D-dimensional sample vector.
+%
+%	See also
+%	GAUSS, DEMGAUSS
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+d = size(covar, 1);
+
+mu = reshape(mu, 1, d);   % Ensure that mu is a row vector
+
+[evec, eval] = eig(covar);
+
+coeffs = randn(nsamp, d)*sqrt(eval);
+
+x = ones(nsamp, 1)*mu + coeffs*evec';