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/rbfjacob.m b/sourcecodes/bnt-master/netlab3.3/rbfjacob.m
new file mode 100644
index 00000000..6a6ec73a
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+++ b/sourcecodes/bnt-master/netlab3.3/rbfjacob.m
@@ -0,0 +1,49 @@
+function jac = rbfjacob(net, x)
+%RBFJACOB Evaluate derivatives of RBF network outputs with respect to inputs.
+%
+%	Description
+%	G = RBFJACOB(NET, X) takes a network data structure NET and a matrix
+%	of input vectors X and returns a three-index matrix G whose I, J, K
+%	element contains the derivative of network output K with respect to
+%	input parameter J for input pattern I.
+%
+%	See also
+%	RBF, RBFGRAD, RBFBKP
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+errstring = consist(net, 'rbf', x);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+if ~strcmp(net.outfn, 'linear')
+  error('Function only implemented for linear outputs')
+end
+
+[y, z, n2] = rbffwd(net, x);
+
+ndata = size(x, 1);
+jac = zeros(ndata, net.nin, net.nout);
+Psi = zeros(net.nin, net.nhidden);
+% Calculate derivative of activations wrt n2
+switch net.actfn
+case 'gaussian'
+  dz = -z./(ones(ndata, 1)*net.wi);
+case 'tps'
+  dz = 2*(1 + log(n2+(n2==0)));
+case 'r4logr'
+  dz = 2*(n2.*(1+2.*log(n2+(n2==0))));
+otherwise
+   error(['Unknown activation function ', net.actfn]);
+end
+
+% Ignore biases as they cannot affect Jacobian
+for n = 1:ndata
+  Psi = (ones(net.nin, 1)*dz(n, :)).* ...
+    (x(n, :)'*ones(1, net.nhidden) - net.c');
+  % Now compute the Jacobian
+  jac(n, :, :) =  Psi * net.w2;
+end
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