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, 51 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/gperr.m b/sourcecodes/bnt-master/netlab3.3/gperr.m
new file mode 100644
index 00000000..402d63bd
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/gperr.m
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+function [e, edata, eprior] = gperr(net, x, t)
+%GPERR	Evaluate error function for Gaussian Process.
+%
+%	Description
+%	E = GPERR(NET, X, T) takes a Gaussian Process data structure NET
+%	together  with a matrix X of input vectors and a matrix T of target
+%	vectors, and evaluates the error function E. Each row of X
+%	corresponds to one input vector and each row of T corresponds to one
+%	target vector.
+%
+%	[E, EDATA, EPRIOR] = GPERR(NET, X, T) additionally returns the data
+%	and hyperprior components of the error, assuming a Gaussian prior on
+%	the weights with mean and variance parameters PRMEAN and PRVARIANCE
+%	taken from the network data structure NET.
+%
+%	See also
+%	GP, GPCOVAR, GPFWD, GPGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+errstring = consist(net, 'gp', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+cn = gpcovar(net, x);
+
+edata = 0.5*(sum(log(eig(cn, 'nobalance'))) + t'*inv(cn)*t);
+
+% Evaluate the hyperprior contribution to the error.
+% The hyperprior is Gaussian with mean pr_mean and variance
+% pr_variance
+if isfield(net, 'pr_mean')
+  w = gppak(net);
+  m = repmat(net.pr_mean, size(w));
+  if size(net.pr_mean) == [1 1]
+    eprior = 0.5*((w-m)*(w-m)');
+    e2 = eprior/net.pr_var;
+  else
+    wpr = repmat(w, size(net.pr_mean, 1), 1)';
+    eprior = 0.5*(((wpr - m').^2).*net.index);
+    e2 = (sum(eprior, 1))*(1./net.pr_var);
+  end
+else
+  e2 = 0;
+  eprior = 0;
+end
+
+e = edata + e2;
+