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, 52 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/mdnprob.m b/sourcecodes/bnt-master/netlab3.3/mdnprob.m
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+function [prob,a] = mdnprob(mixparams, t)
+%MDNPROB Computes the data probability likelihood for an MDN mixture structure.
+%
+%	Description
+%	PROB = MDNPROB(MIXPARAMS, T) computes the probability P(T) of each
+%	data vector in T under the Gaussian mixture model represented by the
+%	corresponding entries in MIXPARAMS. Each row of T represents a single
+%	vector.
+%
+%	[PROB, A] = MDNPROB(MIXPARAMS, T) also computes the activations A
+%	(i.e. the probability P(T|J) of the data conditioned on each
+%	component density) for a Gaussian mixture model.
+%
+%	See also
+%	MDNERR, MDNPOST
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+%	David J Evans (1998)
+
+% Check arguments for consistency
+errstring = consist(mixparams, 'mdnmixes');
+if ~isempty(errstring)
+  error(errstring);
+end
+
+ntarget    = size(t, 1);
+if ntarget ~= size(mixparams.centres, 1)
+  error('Number of targets does not match number of mixtures')
+end
+if size(t, 2) ~= mixparams.dim_target
+  error('Target dimension does not match mixture dimension')
+end
+
+dim_target = mixparams.dim_target;
+ntarget    = size(t, 1);
+
+% Calculate squared norm matrix, of dimension (ndata, ncentres)
+% vector (ntarget * ncentres)
+dist2 = mdndist2(mixparams, t);
+
+% Calculate variance factors
+variance = 2.*mixparams.covars;
+
+% Compute the normalisation term
+normal  = ((2.*pi).*mixparams.covars).^(dim_target./2);
+
+% Now compute the activations
+a = exp(-(dist2./variance))./normal;
+
+% Accumulate negative log likelihood of targets
+prob = mixparams.mixcoeffs.*a;