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, 40 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/glmderiv.m b/sourcecodes/bnt-master/netlab3.3/glmderiv.m
new file mode 100644
index 00000000..bc8de671
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/glmderiv.m
@@ -0,0 +1,40 @@
+function g = glmderiv(net, x)
+%GLMDERIV Evaluate derivatives of GLM outputs with respect to weights.
+%
+%	Description
+%	G = GLMDERIV(NET, X) takes a network data structure NET and a matrix
+%	of input vectors X and returns a three-index matrix mat{g} whose  I,
+%	J, K element contains the derivative of network output K with respect
+%	to weight or bias parameter J for input pattern I. The ordering of
+%	the weight and bias parameters is defined by GLMUNPAK.
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+errstring = consist(net, 'glm', x);
+if ~isempty(errstring)
+    error(errstring);
+end
+
+ndata = size(x, 1);
+if isfield(net, 'mask')
+  nwts = size(find(net.mask), 1);
+  mask_array = logical(net.mask)*ones(1, net.nout);
+else
+  nwts = net.nwts;
+end
+g = zeros(ndata, nwts, net.nout);
+
+temp = zeros(net.nwts, net.nout);
+for n = 1:ndata
+    % Weight matrix w1
+    temp(1:(net.nin*net.nout), :) = kron(eye(net.nout), (x(n, :))');
+    % Bias term b1
+    temp(net.nin*net.nout+1:end, :) = eye(net.nout);
+    if isfield(net, 'mask')
+	g(n, :, :) = reshape(temp(find(mask_array)), nwts, net.nout);
+    else
+	g(n, :, :) = temp;
+    end
+end