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, 91 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/rbfhess.m b/sourcecodes/bnt-master/netlab3.3/rbfhess.m
new file mode 100644
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+++ b/sourcecodes/bnt-master/netlab3.3/rbfhess.m
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+function [h, hdata] = rbfhess(net, x, t, hdata)
+%RBFHESS Evaluate the Hessian matrix for RBF network.
+%
+%	Description
+%	H = RBFHESS(NET, X, T) takes an RBF network data structure NET, a
+%	matrix X of input values, and a matrix T of target values and returns
+%	the full Hessian matrix H corresponding to the second derivatives of
+%	the negative log posterior distribution, evaluated for the current
+%	weight and bias values as defined by NET.  Currently, the
+%	implementation only computes the Hessian for the output layer
+%	weights.
+%
+%	[H, HDATA] = RBFHESS(NET, X, T) returns both the Hessian matrix H and
+%	the contribution HDATA arising from the data dependent term in the
+%	Hessian.
+%
+%	H = RBFHESS(NET, X, T, HDATA) takes a network data structure NET, a
+%	matrix X of input values, and a matrix T of  target values, together
+%	with the contribution HDATA arising from the data dependent term in
+%	the Hessian, and returns the full Hessian matrix H corresponding to
+%	the second derivatives of the negative log posterior distribution.
+%	This version saves computation time if HDATA has already been
+%	evaluated for the current weight and bias values.
+%
+%	See also
+%	MLPHESS, HESSCHEK, EVIDENCE
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+errstring = consist(net, 'rbf', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+if nargin == 3
+  % Data term in Hessian needs to be computed
+  [a, z] = rbffwd(net, x); 
+  hdata = datahess(net, z, t);
+end
+
+% Add in effect of regularisation
+[h, hdata] = hbayes(net, hdata);
+
+% Sub-function to compute data part of Hessian
+function hdata = datahess(net, z, t)
+
+% Only works for output layer Hessian currently
+if (isfield(net, 'mask') & ~any(net.mask(...
+      1:(net.nwts - net.nout*(net.nhidden+1)))))
+  hdata = zeros(net.nwts);
+  ndata = size(z, 1);
+  out_hess = [z ones(ndata, 1)]'*[z ones(ndata, 1)];
+  for j = 1:net.nout
+    hdata = rearrange_hess(net, j, out_hess, hdata);
+  end
+else
+  error('Output layer Hessian only.');
+end
+return
+
+% Sub-function to rearrange Hessian matrix
+function hdata = rearrange_hess(net, j, out_hess, hdata)
+
+% Because all the biases come after all the input weights,
+% we have to rearrange the blocks that make up the network Hessian.
+% This function assumes that we are on the jth output and that all outputs
+% are independent.
+
+% Start of bias weights block
+bb_start = net.nwts - net.nout + 1;
+% Start of weight block for jth output
+ob_start = net.nwts - net.nout*(net.nhidden+1) + (j-1)*net.nhidden...
+   + 1; 
+% End of weight block for jth output
+ob_end = ob_start + net.nhidden - 1; 
+% Index of bias weight
+b_index = bb_start+(j-1);   
+% Put input weight block in right place
+hdata(ob_start:ob_end, ob_start:ob_end) = out_hess(1:net.nhidden, ...
+   1:net.nhidden);
+% Put second derivative of bias weight in right place
+hdata(b_index, b_index) = out_hess(net.nhidden+1, net.nhidden+1);
+% Put cross terms (input weight v bias weight) in right place
+hdata(b_index, ob_start:ob_end) = out_hess(net.nhidden+1, ...
+   1:net.nhidden);
+hdata(ob_start:ob_end, b_index) = out_hess(1:net.nhidden, ...
+   net.nhidden+1);
+
+return 
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