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, 30 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/somfwd.m b/sourcecodes/bnt-master/netlab3.3/somfwd.m
new file mode 100644
index 00000000..0e980284
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+++ b/sourcecodes/bnt-master/netlab3.3/somfwd.m
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+function [d2, win_nodes] = somfwd(net, x)
+%SOMFWD	Forward propagation through a Self-Organising Map.
+%
+%	Description
+%	D2 = SOMFWD(NET, X) propagates the data matrix X through  a SOM NET,
+%	returning the squared distance matrix D2 with dimension NIN by
+%	NUM_NODES.  The $i$th row represents the squared Euclidean distance
+%	to each of the nodes of the SOM.
+%
+%	[D2, WIN_NODES] = SOMFWD(NET, X) also returns the indices of the
+%	winning nodes for each pattern.
+%
+%	See also
+%	SOM, SOMTRAIN
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check for consistency
+errstring = consist(net, 'som', x);
+if ~isempty(errstring)
+    error(errstring);
+end
+
+% Turn nodes into matrix of centres
+nodes = (reshape(net.map, net.nin, net.num_nodes))';
+% Compute squared distance matrix
+d2 = dist2(x, nodes);
+% Find winning node for each pattern: minimum value in each row
+[w, win_nodes] = min(d2, [], 2);