wolffd@0: function net = som(nin, map_size)
wolffd@0: %SOM	Creates a Self-Organising Map.
wolffd@0: %
wolffd@0: %	Description
wolffd@0: %	NET = SOM(NIN, MAP_SIZE) creates a SOM NET with input dimension (i.e.
wolffd@0: %	data dimension) NIN and map dimensions MAP_SIZE.  Only two-
wolffd@0: %	dimensional maps are currently implemented.
wolffd@0: %
wolffd@0: %	The fields in NET are
wolffd@0: %	  type = 'som'
wolffd@0: %	  nin = number of inputs
wolffd@0: %	  map_dim = dimension of map (constrained to be 2)
wolffd@0: %	  map_size = grid size: number of nodes in each dimension
wolffd@0: %	  num_nodes = number of nodes: the product of values in map_size
wolffd@0: %	  map = map_dim+1 dimensional array containing nodes
wolffd@0: %	  inode_dist = map of inter-node distances using Manhatten metric
wolffd@0: %
wolffd@0: %	The map contains the node vectors arranged column-wise in the first
wolffd@0: %	dimension of the array.
wolffd@0: %
wolffd@0: %	See also
wolffd@0: %	KMEANS, SOMFWD, SOMTRAIN
wolffd@0: %
wolffd@0: 
wolffd@0: %	Copyright (c) Ian T Nabney (1996-2001)
wolffd@0: 
wolffd@0: net.type = 'som';
wolffd@0: net.nin = nin;
wolffd@0: 
wolffd@0: % Create Map of nodes
wolffd@0: if round(map_size) ~= map_size | (map_size < 1)
wolffd@0:     error('SOM specification must contain positive integers');
wolffd@0: end
wolffd@0: 
wolffd@0: net.map_dim = length(map_size);
wolffd@0: if net.map_dim ~= 2
wolffd@0:     error('SOM is a 2 dimensional map');
wolffd@0: end
wolffd@0: net.num_nodes = prod(map_size);
wolffd@0: % Centres are stored by column as first index of multi-dimensional array.
wolffd@0: % This makes extracting them later more easy.
wolffd@0: % Initialise with rand to create square grid
wolffd@0: net.map = rand([nin, map_size]);
wolffd@0: net.map_size = map_size;
wolffd@0: 
wolffd@0: % Crude function to compute inter-node distances
wolffd@0: net.inode_dist = zeros([map_size, net.num_nodes]);
wolffd@0: for m = 1:net.num_nodes
wolffd@0:     node_loc = [1+fix((m-1)/map_size(2)), 1+rem((m-1),map_size(2))];
wolffd@0:     for k = 1:map_size(1)
wolffd@0: 	for l = 1:map_size(2)
wolffd@0: 	    net.inode_dist(k, l, m) = round(max(abs([k l] - node_loc)));
wolffd@0: 	end
wolffd@0:     end
wolffd@0: end