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, 65 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/rbfgrad.m b/sourcecodes/bnt-master/netlab3.3/rbfgrad.m
new file mode 100644
index 00000000..af6767ac
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/rbfgrad.m
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+function [g, gdata, gprior] = rbfgrad(net, x, t)
+%RBFGRAD Evaluate gradient of error function for RBF network.
+%
+%	Description
+%	G = RBFGRAD(NET, X, T) takes a network 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 (i.e. including the hidden unit parameters). The
+%	error function is sum of squares. Each row of X corresponds to one
+%	input vector and each row of T contains the corresponding target
+%	vector. If the output function is 'NEUROSCALE' then the gradient is
+%	only computed for the output layer weights and biases.
+%
+%	[G, GDATA, GPRIOR] = RBFGRAD(NET, X, T) also returns separately  the
+%	data and prior contributions to the gradient. In the case of multiple
+%	groups in the prior, GPRIOR is a matrix with a row for each group and
+%	a column for each weight parameter.
+%
+%	See also
+%	RBF, RBFFWD, RBFERR, RBFPAK, RBFUNPAK, RBFBKP
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+switch net.outfn
+case 'linear'
+   errstring = consist(net, 'rbf', x, t);
+case 'neuroscale'
+   errstring = consist(net, 'rbf', x);
+otherwise
+   error(['Unknown output function ', net.outfn]);
+end
+if ~isempty(errstring);
+  error(errstring);
+end
+
+ndata = size(x, 1);
+
+[y, z, n2] = rbffwd(net, x);
+
+switch net.outfn
+case 'linear'
+
+   % Sum squared error at output units
+   delout = y - t;
+
+   gdata = rbfbkp(net, x, z, n2, delout);
+   [g, gdata, gprior] = gbayes(net, gdata);
+
+case 'neuroscale'
+   % Compute the error gradient with respect to outputs
+   y_dist = sqrt(dist2(y, y));
+   D = (t - y_dist)./(y_dist+diag(ones(ndata, 1)));
+   temp = y';
+   gradient = 2.*sum(kron(D, ones(1, net.nout)) .* ...
+      (repmat(y, 1, ndata) - repmat((temp(:))', ndata, 1)), 1);
+   gradient = (reshape(gradient, net.nout, ndata))';
+   % Compute the error gradient
+   gdata = rbfbkp(net, x, z, n2, gradient);
+   [g, gdata, gprior] = gbayes(net, gdata);
+otherwise
+   error(['Unknown output function ', net.outfn]);
+end
+