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, 29 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/demgpot.m b/sourcecodes/bnt-master/netlab3.3/demgpot.m
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+++ b/sourcecodes/bnt-master/netlab3.3/demgpot.m
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+function g = demgpot(x, mix)
+%DEMGPOT Computes the gradient of the negative log likelihood for a mixture model.
+%
+%	Description
+%	This function computes the gradient of the negative log of the
+%	unconditional data density P(X) with respect to the coefficients of
+%	the data vector X for a Gaussian mixture model.  The data structure
+%	MIX defines the mixture model, while the matrix X contains the data
+%	vector as a row vector. Note the unusual order of the arguments: this
+%	is so that the function can be used in DEMHMC1 directly for sampling
+%	from the distribution P(X).
+%
+%	See also
+%	DEMHMC1, DEMMET1, DEMPOT
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Computes the potential gradient
+
+temp = (ones(mix.ncentres,1)*x)-mix.centres;
+temp = temp.*(gmmactiv(mix,x)'*ones(1, mix.nin));
+% Assume spherical covariance structure
+if ~strcmp(mix.covar_type, 'spherical')
+  error('Spherical covariance only.')
+end
+temp = temp./(mix.covars'*ones(1, mix.nin));
+temp = temp.*(mix.priors'*ones(1, mix.nin));
+g = sum(temp, 1)/gmmprob(mix, x);
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