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, 36 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/gmmpost.m b/sourcecodes/bnt-master/netlab3.3/gmmpost.m
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index 00000000..f2b7e224
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+++ b/sourcecodes/bnt-master/netlab3.3/gmmpost.m
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+function [post, a] = gmmpost(mix, x)
+%GMMPOST Computes the class posterior probabilities of a Gaussian mixture model.
+%
+%	Description
+%	This function computes the posteriors POST (i.e. the probability of
+%	each component conditioned on the data P(J|X)) for a Gaussian mixture
+%	model.   The data structure MIX defines the mixture model, while the
+%	matrix X contains the data vectors.  Each row of X represents a
+%	single vector.
+%
+%	See also
+%	GMM, GMMACTIV, GMMPROB
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check that inputs are consistent
+errstring = consist(mix, 'gmm', x);
+if ~isempty(errstring)
+  error(errstring);
+end
+
+ndata = size(x, 1);
+
+a = gmmactiv(mix, x);
+
+post = (ones(ndata, 1)*mix.priors).*a;
+s = sum(post, 2);
+if any(s==0)
+   warning('Some zero posterior probabilities')
+   % Set any zeros to one before dividing
+   zero_rows = find(s==0);
+   s = s + (s==0);
+   post(zero_rows, :) = 1/mix.ncentres;
+end
+post = post./(s*ones(1, mix.ncentres));