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, 57 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlabKPM/glmhess_weighted.m b/sourcecodes/bnt-master/netlabKPM/glmhess_weighted.m
new file mode 100644
index 00000000..29689269
--- /dev/null
+++ b/sourcecodes/bnt-master/netlabKPM/glmhess_weighted.m
@@ -0,0 +1,57 @@
+function [h, hdata] = glmhess_weighted(net, x, t, eso_w, hdata)
+%GLMHESS Evaluate the Hessian matrix for a generalised linear model.
+%
+%	Description
+%	H = GLMHESS(NET, X, T) takes a GLM 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. Note that the target data
+%	is not required in the calculation, but is included to make the
+%	interface uniform with NETHESS.  For linear and logistic outputs, the
+%	computation is very simple and is  done (in effect) in one line in
+%	GLMTRAIN.
+%
+%	See also
+%	GLM, GLMTRAIN, HESSCHEK, NETHESS
+%
+%	Copyright (c) Ian T Nabney (1996-9)
+
+% Check arguments for consistency
+errstring = consist(net, 'glm', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+ndata = size(x, 1);
+nparams = net.nwts;
+nout = net.nout;
+p = glmfwd(net, x);
+inputs = [x ones(ndata, 1)];
+
+if nargin == 4
+   hdata = zeros(nparams);	% Full Hessian matrix
+   % Calculate data component of Hessian
+   switch net.outfn
+    
+   case 'softmax'
+    bb_start = nparams - nout + 1;	% Start of bias weights block
+    ex_hess = zeros(nparams);	% Contribution to Hessian from single example
+    for m = 1:ndata
+      X = x(m,:)'*x(m,:);
+      a = diag(p(m,:))-((p(m,:)')*p(m,:)); 
+      a=eso_w(m,1)*a;
+      ex_hess(1:nparams-nout,1:nparams-nout) = kron(a, X);
+      ex_hess(bb_start:nparams, bb_start:nparams) = a.*ones(net.nout, net.nout);
+      temp = kron(a, x(m,:));
+      ex_hess(bb_start:nparams, 1:nparams-nout) = temp;
+      ex_hess(1:nparams-nout, bb_start:nparams) = temp';
+      hdata = hdata + ex_hess;
+    end
+    
+    otherwise
+      error(['Unknown activation function ', net.actfn]);
+    end
+end
+
+[h, hdata] = hbayes(net, hdata);