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, 34 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/netunpak.m b/sourcecodes/bnt-master/netlab3.3/netunpak.m
new file mode 100644
index 00000000..7f253d2f
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+++ b/sourcecodes/bnt-master/netlab3.3/netunpak.m
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+function net = netunpak(net, w)
+%NETUNPAK Separates weights vector into weight and bias matrices. 
+%
+%	Description
+%	NET = NETUNPAK(NET, W) takes an net network data structure NET and  a
+%	weight vector W, and returns a network data structure identical to
+%	the input network, except that the componenet weight matrices have
+%	all been set to the corresponding elements of W.  If there is  a MASK
+%	field in the NET data structure, then the weights in W are placed in
+%	locations corresponding to non-zero entries in the mask (so W should
+%	have the same length as the number of non-zero entries in the MASK).
+%
+%	See also
+%	NETPAK, NETFWD, NETERR, NETGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+unpakstr = [net.type, 'unpak'];
+
+% Check if we are being passed a masked set of weights
+if (isfield(net, 'mask'))
+   if length(w) ~= size(find(net.mask), 1)
+      error('Weight vector length does not match mask length')
+   end
+   % Do a full pack of all current network weights
+   pakstr = [net.type, 'pak'];
+   fullw = feval(pakstr, net);
+   % Replace current weights with new ones
+   fullw(logical(net.mask)) = w;
+   w = fullw;
+end
+
+net = feval(unpakstr, net, w);
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