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1 function net = som(nin, map_size)
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2 %SOM Creates a Self-Organising Map.
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3 %
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4 % Description
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5 % NET = SOM(NIN, MAP_SIZE) creates a SOM NET with input dimension (i.e.
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6 % data dimension) NIN and map dimensions MAP_SIZE. Only two-
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7 % dimensional maps are currently implemented.
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8 %
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9 % The fields in NET are
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10 % type = 'som'
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11 % nin = number of inputs
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12 % map_dim = dimension of map (constrained to be 2)
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13 % map_size = grid size: number of nodes in each dimension
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14 % num_nodes = number of nodes: the product of values in map_size
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15 % map = map_dim+1 dimensional array containing nodes
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16 % inode_dist = map of inter-node distances using Manhatten metric
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17 %
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18 % The map contains the node vectors arranged column-wise in the first
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19 % dimension of the array.
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20 %
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21 % See also
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22 % KMEANS, SOMFWD, SOMTRAIN
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23 %
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24
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25 % Copyright (c) Ian T Nabney (1996-2001)
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26
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27 net.type = 'som';
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28 net.nin = nin;
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29
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30 % Create Map of nodes
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31 if round(map_size) ~= map_size | (map_size < 1)
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32 error('SOM specification must contain positive integers');
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33 end
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34
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35 net.map_dim = length(map_size);
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36 if net.map_dim ~= 2
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37 error('SOM is a 2 dimensional map');
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38 end
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39 net.num_nodes = prod(map_size);
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40 % Centres are stored by column as first index of multi-dimensional array.
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41 % This makes extracting them later more easy.
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42 % Initialise with rand to create square grid
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43 net.map = rand([nin, map_size]);
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44 net.map_size = map_size;
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45
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46 % Crude function to compute inter-node distances
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47 net.inode_dist = zeros([map_size, net.num_nodes]);
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48 for m = 1:net.num_nodes
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49 node_loc = [1+fix((m-1)/map_size(2)), 1+rem((m-1),map_size(2))];
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50 for k = 1:map_size(1)
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51 for l = 1:map_size(2)
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52 net.inode_dist(k, l, m) = round(max(abs([k l] - node_loc)));
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53 end
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54 end
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55 end
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