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, 61 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/gtm.m b/sourcecodes/bnt-master/netlab3.3/gtm.m
new file mode 100644
index 00000000..9d1b3ab3
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/gtm.m
@@ -0,0 +1,61 @@
+function net = gtm(dim_latent, nlatent, dim_data, ncentres, rbfunc, ...
+   prior)
+%GTM	Create a Generative Topographic Map.
+%
+%	Description
+%
+%	NET = GTM(DIMLATENT, NLATENT, DIMDATA, NCENTRES, RBFUNC), takes the
+%	dimension of the latent space DIMLATENT, the number of data points
+%	sampled in the latent space NLATENT, the dimension of the data space
+%	DIMDATA, the number of centres in the RBF model NCENTRES, the
+%	activation function for the RBF RBFUNC and returns a data structure
+%	NET. The parameters in the RBF and GMM sub-models are set by calls to
+%	the corresponding creation routines RBF and GMM.
+%
+%	The fields in NET are
+%	  type = 'gtm'
+%	  nin = dimension of data space
+%	  dimlatent = dimension of latent space
+%	  rbfnet = RBF network data structure
+%	  gmmnet = GMM data structure
+%	  X = sample of latent points
+%
+%	NET = GTM(DIMLATENT, NLATENT, DIMDATA, NCENTRES, RBFUNC, PRIOR),
+%	sets a Gaussian zero mean prior on the parameters of the RBF model.
+%	PRIOR must be a scalar and represents the inverse variance of the
+%	prior distribution.  This gives rise to a weight decay term in the
+%	error function.
+%
+%	See also
+%	GTMFWD, GTMPOST, RBF, GMM
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+net.type = 'gtm';
+% Input to functions is data
+net.nin = dim_data;
+net.dim_latent = dim_latent;
+
+% Default is no regularisation
+if nargin == 5
+   prior = 0.0;
+end
+
+% Only allow scalar prior
+if isstruct(prior) | size(prior) ~= [1 1]
+   error('Prior must be a scalar');
+end
+
+% Create RBF network
+net.rbfnet = rbf(dim_latent, ncentres, dim_data, rbfunc, ...
+   'linear', prior);
+
+% Mask all but output weights
+net.rbfnet.mask = rbfprior(rbfunc, dim_latent, ncentres, dim_data);
+
+% Create field for GMM output model
+net.gmmnet = gmm(dim_data, nlatent, 'spherical');
+
+% Create empty latent data sample
+net.X = [];
\ No newline at end of file