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, 41 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/mdn2gmm.m b/sourcecodes/bnt-master/netlab3.3/mdn2gmm.m
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+function gmmmixes = mdn2gmm(mdnmixes)
+%MDN2GMM Converts an MDN mixture data structure to array of GMMs.
+%
+%	Description
+%	GMMMIXES = MDN2GMM(MDNMIXES) takes an MDN mixture data structure
+%	MDNMIXES containing three matrices (for priors, centres and
+%	variances) where each row represents the corresponding parameter
+%	values for a different mixture model  and creates an array of GMMs.
+%	These can then be used with the standard Netlab Gaussian mixture
+%	model functions.
+%
+%	See also
+%	GMM, MDN, MDNFWD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+%	David J Evans (1998)
+
+% Check argument for consistency
+errstring = consist(mdnmixes, 'mdnmixes');
+if ~isempty(errstring)
+  error(errstring);
+end
+
+nmixes = size(mdnmixes.centres, 1);
+% Construct ndata structures containing the mixture model information.
+% First allocate the memory.
+tempmix = gmm(mdnmixes.dim_target, mdnmixes.ncentres, 'spherical');
+f = fieldnames(tempmix);
+gmmmixes = cell(size(f, 1), 1, nmixes);
+gmmmixes = cell2struct(gmmmixes, f,1);
+
+% Then fill each structure in turn using gmmunpak.  Assume that spherical
+% covariance structure is used.
+for i = 1:nmixes
+  centres = reshape(mdnmixes.centres(i, :), mdnmixes.dim_target, ...
+    mdnmixes.ncentres)';
+  gmmmixes(i) = gmmunpak(tempmix, [mdnmixes.mixcoeffs(i,:), ...
+      centres(:)', mdnmixes.covars(i,:)]);
+end
+