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, 56 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/gbayes.m b/sourcecodes/bnt-master/netlab3.3/gbayes.m
new file mode 100644
index 00000000..7b814e97
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/gbayes.m
@@ -0,0 +1,56 @@
+function [g, gdata, gprior] = gbayes(net, gdata)
+%GBAYES	Evaluate gradient of Bayesian error function for network.
+%
+%	Description
+%	G = GBAYES(NET, GDATA) takes a network data structure NET together
+%	the data contribution to the error gradient for a set of inputs and
+%	targets. It returns the regularised error gradient using any zero
+%	mean Gaussian priors on the weights defined in NET.  In addition, if
+%	a MASK is defined in NET, then the entries in G that correspond to
+%	weights with a 0 in the mask are removed.
+%
+%	[G, GDATA, GPRIOR] = GBAYES(NET, GDATA) additionally returns the data
+%	and prior components of the error.
+%
+%	See also
+%	ERRBAYES, GLMGRAD, MLPGRAD, RBFGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Evaluate the data contribution to the gradient.
+if (isfield(net, 'mask'))
+   gdata = gdata(logical(net.mask));
+end
+if isfield(net, 'beta')
+  g1 = gdata*net.beta;
+else
+  g1 = gdata;
+end
+
+% Evaluate the prior contribution to the gradient.
+if isfield(net, 'alpha')
+   w = netpak(net);
+   if size(net.alpha) == [1 1]
+      gprior = w;
+      g2 = net.alpha*gprior;
+   else
+      if (isfield(net, 'mask'))
+         nindx_cols = size(net.index, 2);
+         nmask_rows = size(find(net.mask), 1);
+         index = reshape(net.index(logical(repmat(net.mask, ...
+            1, nindx_cols))), nmask_rows, nindx_cols);
+      else
+         index = net.index;
+      end
+      
+      ngroups = size(net.alpha, 1);
+      gprior = index'.*(ones(ngroups, 1)*w);
+      g2 = net.alpha'*gprior;
+   end
+else
+  gprior = 0;
+  g2 = 0;
+end
+
+g = g1 + g2;