Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/netlab3.3/somfwd.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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-1:000000000000 | 0:e9a9cd732c1e |
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1 function [d2, win_nodes] = somfwd(net, x) | |
2 %SOMFWD Forward propagation through a Self-Organising Map. | |
3 % | |
4 % Description | |
5 % D2 = SOMFWD(NET, X) propagates the data matrix X through a SOM NET, | |
6 % returning the squared distance matrix D2 with dimension NIN by | |
7 % NUM_NODES. The $i$th row represents the squared Euclidean distance | |
8 % to each of the nodes of the SOM. | |
9 % | |
10 % [D2, WIN_NODES] = SOMFWD(NET, X) also returns the indices of the | |
11 % winning nodes for each pattern. | |
12 % | |
13 % See also | |
14 % SOM, SOMTRAIN | |
15 % | |
16 | |
17 % Copyright (c) Ian T Nabney (1996-2001) | |
18 | |
19 % Check for consistency | |
20 errstring = consist(net, 'som', x); | |
21 if ~isempty(errstring) | |
22 error(errstring); | |
23 end | |
24 | |
25 % Turn nodes into matrix of centres | |
26 nodes = (reshape(net.map, net.nin, net.num_nodes))'; | |
27 % Compute squared distance matrix | |
28 d2 = dist2(x, nodes); | |
29 % Find winning node for each pattern: minimum value in each row | |
30 [w, win_nodes] = min(d2, [], 2); |