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, 93 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/mdnfwd.m b/sourcecodes/bnt-master/netlab3.3/mdnfwd.m
new file mode 100644
index 00000000..962fbe08
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/mdnfwd.m
@@ -0,0 +1,93 @@
+function [mixparams, y, z, a] = mdnfwd(net, x)
+%MDNFWD	Forward propagation through Mixture Density Network.
+%
+%	Description
+%	 MIXPARAMS = MDNFWD(NET, X) takes a mixture density network data
+%	structure NET and a matrix X of input vectors, and forward propagates
+%	the inputs through the network to generate a structure MIXPARAMS
+%	which contains the parameters of several mixture models.   Each row
+%	of X represents one input vector and the corresponding row of the
+%	matrices in MIXPARAMS  represents the parameters of a mixture model
+%	for the conditional probability of target vectors given the input
+%	vector.  This is not represented as an array of GMM structures to
+%	improve the efficiency of MDN training.
+%
+%	The fields in MIXPARAMS are
+%	  type = 'mdnmixes'
+%	  ncentres = number of mixture components
+%	  dimtarget = dimension of target space
+%	  mixcoeffs = mixing coefficients
+%	  centres = means of Gaussians: stored as one row per pattern
+%	  covars = covariances of Gaussians
+%	  nparams = number of parameters
+%
+%	[MIXPARAMS, Y, Z] = MDNFWD(NET, X) also generates a matrix Y of the
+%	outputs of the MLP and a matrix Z of the hidden unit activations
+%	where each row corresponds to one pattern.
+%
+%	[MIXPARAMS, Y, Z, A] = MLPFWD(NET, X) also returns a matrix A  giving
+%	the summed inputs to each output unit, where each row  corresponds to
+%	one pattern.
+%
+%	See also
+%	MDN, MDN2GMM, MDNERR, MDNGRAD, MLPFWD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+%	David J Evans (1998)
+
+% Check arguments for consistency
+errstring = consist(net, 'mdn', x);
+if ~isempty(errstring)
+  error(errstring);
+end
+
+% Extract mlp and mixture model descriptors
+mlpnet = net.mlp;
+mixes = net.mdnmixes;
+
+ncentres = mixes.ncentres;	% Number of components in mixture model
+dim_target = mixes.dim_target;	% Dimension of targets
+nparams = mixes.nparams;	% Number of parameters in mixture model
+
+% Propagate forwards through MLP
+[y, z, a] = mlpfwd(mlpnet, x);
+
+% Compute the postion for each parameter in the whole
+% matrix.  Used to define the mixparams structure
+mixcoeff  = [1:1:ncentres];
+centres   = [ncentres+1:1:(ncentres*(1+dim_target))];
+variances = [(ncentres*(1+dim_target)+1):1:nparams];
+
+% Convert output values into mixture model parameters
+
+% Use softmax to calculate priors
+% Prevent overflow and underflow: use same bounds as glmfwd
+% Ensure that sum(exp(y), 2) does not overflow
+maxcut = log(realmax) - log(ncentres);
+% Ensure that exp(y) > 0
+mincut = log(realmin);
+temp = min(y(:,1:ncentres), maxcut);
+temp = max(temp, mincut);
+temp = exp(temp);
+mixpriors = temp./(sum(temp, 2)*ones(1,ncentres));
+
+% Centres are just copies of network outputs
+mixcentres =  y(:,(ncentres+1):ncentres*(1+dim_target));
+
+% Variances are exp of network outputs
+mixwidths = exp(y(:,(ncentres*(1+dim_target)+1):nparams));
+
+% Now build up all the mixture model weight vectors
+ndata = size(x, 1);
+
+% Return parameters
+mixparams.type = mixes.type;
+mixparams.ncentres = mixes.ncentres;
+mixparams.dim_target = mixes.dim_target;
+mixparams.nparams = mixes.nparams;
+
+mixparams.mixcoeffs = mixpriors;
+mixparams.centres   = mixcentres;
+mixparams.covars      = mixwidths;
+