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