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, 36 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/glmgrad.m b/sourcecodes/bnt-master/netlab3.3/glmgrad.m
new file mode 100644
index 00000000..d967804e
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/glmgrad.m
@@ -0,0 +1,36 @@
+function [g, gdata, gprior] = glmgrad(net, x, t)
+%GLMGRAD Evaluate gradient of error function for generalized linear model.
+%
+%	Description
+%	G = GLMGRAD(NET, X, T) takes a generalized linear model data
+%	structure NET  together with a matrix X of input vectors and a matrix
+%	T of target vectors, and evaluates the gradient G of the error
+%	function with respect to the network weights. The error function
+%	corresponds to the choice of output unit activation function. Each
+%	row of X corresponds to one input vector and each row of T
+%	corresponds to one target vector.
+%
+%	[G, GDATA, GPRIOR] = GLMGRAD(NET, X, T) also returns separately  the
+%	data and prior contributions to the gradient.
+%
+%	See also
+%	GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMERR, GLMTRAIN
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+errstring = consist(net, 'glm', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+y = glmfwd(net, x);
+delout = y - t;
+
+gw1 = x'*delout;
+gb1 = sum(delout, 1);
+
+gdata = [gw1(:)', gb1];
+
+[g, gdata, gprior] = gbayes(net, gdata);