From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001
From: ziejd2
Date: Thu, 28 Sep 2017 15:04:40 -0500
Subject: 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
---
 .../bnt-master/netlabKPM/mlphdotv_weighted.m       | 73 ++++++++++++++++++++++
 1 file changed, 73 insertions(+)
 create mode 100644 sourcecodes/bnt-master/netlabKPM/mlphdotv_weighted.m

(limited to 'sourcecodes/bnt-master/netlabKPM/mlphdotv_weighted.m')

diff --git a/sourcecodes/bnt-master/netlabKPM/mlphdotv_weighted.m b/sourcecodes/bnt-master/netlabKPM/mlphdotv_weighted.m
new file mode 100644
index 00000000..f3cc52e1
--- /dev/null
+++ b/sourcecodes/bnt-master/netlabKPM/mlphdotv_weighted.m
@@ -0,0 +1,73 @@
+function hdv = mlphdotv_weighted(net, x, t, eso_w, v)
+%MLPHDOTV Evaluate the product of the data Hessian with a vector. 
+%
+%	Description
+%
+%	HDV = MLPHDOTV(NET, X, T, V) takes an MLP network data structure NET,
+%	together with the matrix X of input vectors, the matrix T of target
+%	vectors and an arbitrary row vector V whose length equals the number
+%	of parameters in the network, and returns the product of the data-
+%	dependent contribution to the Hessian matrix with V. The
+%	implementation is based on the R-propagation algorithm of
+%	Pearlmutter.
+%
+%	See also
+%	MLP, MLPHESS, HESSCHEK
+%
+
+%	Copyright (c) Ian T Nabney (1996-9)
+
+% Check arguments for consistency
+errstring = consist(net, 'mlp', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+ndata = size(x, 1);
+
+[y, z] = mlpfwd(net, x);		% Standard forward propagation.
+zprime = (1 - z.*z);			% Hidden unit first derivatives.
+zpprime = -2.0*z.*zprime;		% Hidden unit second derivatives.
+
+vnet = mlpunpak(net, v);	% 		Unpack the v vector.
+
+% Do the R-forward propagation.
+
+ra1 = x*vnet.w1 + ones(ndata, 1)*vnet.b1;
+rz = zprime.*ra1;
+ra2 = rz*net.w2 + z*vnet.w2 + ones(ndata, 1)*vnet.b2;
+
+switch net.actfn
+  case 'softmax'     % Softmax outputs
+  
+    nout = size(t, 2);
+    ry = y.*ra2 - y.*(sum(y.*ra2, 2)*ones(1, nout));
+
+  otherwise
+    error(['Unknown activation function ', net.actfn]);  
+end
+
+% Evaluate a weighted delta for the output units.
+temp = y - t;
+for m=1:ndata,
+      delout(m,:)=eso_w(m,1)*temp(m,:);
+end
+clear temp;
+
+% Do the standard backpropagation.
+
+delhid = zprime.*(delout*net.w2');
+
+% Now do the R-backpropagation.
+
+rdelhid = zpprime.*ra1.*(delout*net.w2') + zprime.*(delout*vnet.w2') + ...
+          zprime.*(ry*net.w2');
+
+% Finally, evaluate the components of hdv and then merge into long vector.
+
+hw1 = x'*rdelhid;
+hb1 = sum(rdelhid, 1);
+hw2 = z'*ry + rz'*delout;
+hb2 = sum(ry, 1);
+
+hdv = [hw1(:)', hb1, hw2(:)', hb2];
-- 
cgit 1.4.1