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, 52 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/netopt.m b/sourcecodes/bnt-master/netlab3.3/netopt.m
new file mode 100644
index 00000000..76807903
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/netopt.m
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+function [net, options, varargout] = netopt(net, options, x, t, alg);
+%NETOPT	Optimize the weights in a network model. 
+%
+%	Description
+%
+%	NETOPT is a helper function which facilitates the training of
+%	networks using the general purpose optimizers as well as sampling
+%	from the posterior distribution of parameters using general purpose
+%	Markov chain Monte Carlo sampling algorithms. It can be used with any
+%	function that searches in parameter space using error and gradient
+%	functions.
+%
+%	[NET, OPTIONS] = NETOPT(NET, OPTIONS, X, T, ALG) takes a network
+%	data structure NET, together with a vector OPTIONS of parameters
+%	governing the behaviour of the optimization algorithm, a matrix X of
+%	input vectors and a matrix T of target vectors, and returns the
+%	trained network as well as an updated OPTIONS vector. The string ALG
+%	determines which optimization algorithm (CONJGRAD, QUASINEW, SCG,
+%	etc.) or Monte Carlo algorithm (such as HMC) will be used.
+%
+%	[NET, OPTIONS, VARARGOUT] = NETOPT(NET, OPTIONS, X, T, ALG) also
+%	returns any additional return values from the optimisation algorithm.
+%
+%	See also
+%	NETGRAD, BFGS, CONJGRAD, GRADDESC, HMC, SCG
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+optstring = [alg, '(''neterr'', w, options, ''netgrad'', net, x, t)'];
+
+% Extract weights from network as single vector
+w = netpak(net);
+
+% Carry out optimisation
+[s{1:nargout}] = eval(optstring);
+w = s{1};
+
+if nargout > 1
+  options = s{2};
+
+  % If there are additional arguments, extract them
+  nextra = nargout - 2;
+  if nextra > 0
+    for i = 1:nextra
+      varargout{i} = s{i+2};
+    end
+  end
+end
+
+% Pack the weights back into the network
+net = netunpak(net, w);