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, 54 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/gmmunpak.m b/sourcecodes/bnt-master/netlab3.3/gmmunpak.m
new file mode 100644
index 00000000..9a503816
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/gmmunpak.m
@@ -0,0 +1,54 @@
+function mix = gmmunpak(mix, p)
+%GMMUNPAK Separates a vector of Gaussian mixture model parameters into its components.
+%
+%	Description
+%	MIX = GMMUNPAK(MIX, P) takes a GMM data structure MIX and  a single
+%	row vector of parameters P and returns a mixture data structure
+%	identical to the input MIX, except that the mixing coefficients
+%	PRIORS, centres CENTRES and covariances COVARS  (and, for PPCA, the
+%	lambdas and U (PCA sub-spaces)) are all set to the corresponding
+%	elements of P.
+%
+%	See also
+%	GMM, GMMPAK
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+errstring = consist(mix, 'gmm');
+if ~errstring
+  error(errstring);
+end
+if mix.nwts ~= length(p)
+  error('Invalid weight vector length')
+end
+
+mark1 = mix.ncentres;
+mark2 = mark1 + mix.ncentres*mix.nin;
+
+mix.priors = reshape(p(1:mark1), 1, mix.ncentres);
+mix.centres = reshape(p(mark1 + 1:mark2), mix.ncentres, mix.nin);
+switch mix.covar_type
+  case 'spherical'
+    mark3 = mix.ncentres*(2 + mix.nin);
+    mix.covars = reshape(p(mark2 + 1:mark3), 1, mix.ncentres);
+  case 'diag'
+    mark3 = mix.ncentres*(1 + mix.nin + mix.nin);
+    mix.covars = reshape(p(mark2 + 1:mark3), mix.ncentres, mix.nin);
+  case 'full'
+    mark3 = mix.ncentres*(1 + mix.nin + mix.nin*mix.nin);
+    mix.covars = reshape(p(mark2 + 1:mark3), mix.nin, mix.nin, ...
+      mix.ncentres);
+  case 'ppca'
+    mark3 = mix.ncentres*(2 + mix.nin);
+    mix.covars = reshape(p(mark2 + 1:mark3), 1, mix.ncentres);
+    % Now also extract k and eigenspaces
+    mark4 = mark3 + mix.ncentres*mix.ppca_dim;
+    mix.lambda = reshape(p(mark3 + 1:mark4), mix.ncentres, ...
+      mix.ppca_dim);
+    mix.U = reshape(p(mark4 + 1:end), mix.nin, mix.ppca_dim, ...
+      mix.ncentres);
+  otherwise
+    error(['Unknown covariance type ', mix.covar_type]);
+end
+