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, 37 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/mlpbkp.m b/sourcecodes/bnt-master/netlab3.3/mlpbkp.m
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+function g = mlpbkp(net, x, z, deltas)
+%MLPBKP	Backpropagate gradient of error function for 2-layer network.
+%
+%	Description
+%	G = MLPBKP(NET, X, Z, DELTAS) takes a network data structure NET
+%	together with a matrix X of input vectors, a matrix  Z of hidden unit
+%	activations, and a matrix DELTAS of the  gradient of the error
+%	function with respect to the values of the output units (i.e. the
+%	summed inputs to the output units, before the activation function is
+%	applied). The return value is the gradient G of the error function
+%	with respect to the network weights. Each row of X corresponds to one
+%	input vector.
+%
+%	This function is provided so that the common backpropagation
+%	algorithm can be used by multi-layer perceptron network models to
+%	compute gradients for mixture density networks as well as standard
+%	error functions.
+%
+%	See also
+%	MLP, MLPGRAD, MLPDERIV, MDNGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Evaluate second-layer gradients.
+gw2 = z'*deltas;
+gb2 = sum(deltas, 1);
+
+% Now do the backpropagation.
+delhid = deltas*net.w2';
+delhid = delhid.*(1.0 - z.*z);
+
+% Finally, evaluate the first-layer gradients.
+gw1 = x'*delhid;
+gb1 = sum(delhid, 1);
+
+g = [gw1(:)', gb1, gw2(:)', gb2];