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, 49 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/glmerr.m b/sourcecodes/bnt-master/netlab3.3/glmerr.m
new file mode 100644
index 00000000..bab849c3
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/glmerr.m
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+function [e, edata, eprior, y, a] = glmerr(net, x, t)
+%GLMERR	Evaluate error function for generalized linear model.
+%
+%	Description
+%	 E = GLMERR(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 error function E. The choice
+%	of error function corresponds to the output unit activation function.
+%	Each row of X corresponds to one input vector and each row of T
+%	corresponds to one target vector.
+%
+%	[E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X, T) also returns the data
+%	and prior components of the total error.
+%
+%	[E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X) also returns a matrix Y
+%	giving the outputs of the models and a matrix A  giving the summed
+%	inputs to each output unit, where each row corresponds to one
+%	pattern.
+%
+%	See also
+%	GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMGRAD, 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, a] = glmfwd(net, x);
+
+switch net.outfn
+
+  case 'linear'  	% Linear outputs
+    edata = 0.5*sum(sum((y - t).^2));
+
+  case 'logistic'  	% Logistic outputs
+    edata = - sum(sum(t.*log(y) + (1 - t).*log(1 - y)));
+
+  case 'softmax'   	% Softmax outputs
+    edata = - sum(sum(t.*log(y)));
+
+  otherwise
+    error(['Unknown activation function ', net.outfn]);
+end
+
+[e, edata, eprior] = errbayes(net, edata);