?? som.m
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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
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