comparison toolboxes/FullBNT-1.0.7/netlab3.3/demsom1.m @ 0:e9a9cd732c1e tip

first hg version after svn

+%DEMSOM1 Demonstrate SOM for visualisation.
+%
+%	Description
+%	 This script demonstrates the use of a SOM with  a two-dimensional
+%	grid to map onto data in  two-dimensional space.  Both on-line and
+%	batch training algorithms are shown.
+%
+%	See also
+%	SOM, SOMPAK, SOMTRAIN
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+
+randn('state', 42);
+rand('state', 42);
+nin = 2; 
+ndata = 300;
+% Give data an offset so that network has something to learn.
+x = rand(ndata, nin) + ones(ndata, 1)*[1.5 1.5];
+
+clc;
+disp('This demonstration of the SOM, or Kohonen network, shows how the')
+disp('network units after training lie in regions of high data density.')
+disp('First we show the data, which is generated uniformly from a square.')
+disp('Red crosses denote the data and black dots are the initial locations')
+disp('of the SOM units.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+net = som(nin, [8, 7]);
+c1 = sompak(net);
+h1 = figure;
+plot(x(:, 1), x(:, 2), 'r+');
+hold on
+plot(c1(:,1), c1(:, 2), 'k.');
+drawnow;  % Force figure to be drawn before training starts
+options = foptions;
+
+% Ordering phase
+options(1) = 1;
+options(14) = 50;
+%options(14) = 5; % Just for testing
+options(18) = 0.9;  % Initial learning rate
+options(16) = 0.05; % Final learning rate
+options(17) = 8;    % Initial neighbourhood size
+options(15) = 1;    % Final neighbourhood size
+
+disp('The SOM network is trained in two phases using an on-line algorithm.')
+disp('Initially the neighbourhood is set to 8 and is then reduced')
+disp('linearly to 1 over the first 50 iterations.')
+disp('Each iteration consists of a pass through the complete')
+disp('dataset, while the weights are adjusted after each pattern.')
+disp('The learning rate is reduced linearly from 0.9 to 0.05.')
+disp('This ordering phase puts the units in a rough grid shape.')
+disp('Blue circles denote the units at the end of this phase.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+net2 = somtrain(net, options, x);
+c2 = sompak(net2);
+plot(c2(:, 1), c2(:, 2), 'bo');
+drawnow;
+
+% Convergence phase
+options(1) = 1;
+options(14) = 400;
+options(18) = 0.05;
+options(16) = 0.01;
+options(17) = 0;
+options(15) = 0;
+
+disp('The second, convergence, phase of learning just updates the winning node.')
+disp('The learning rate is reduced from 0.05 to 0.01 over 400 iterations.')
+disp('Note how the error value does not decrease monotonically; it is')
+disp('difficult to decide when training is complete in a principled way.')
+disp('The units are plotted as green stars.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+net3 = somtrain(net2, options, x);
+c3 = sompak(net3);
+plot(c3(:, 1), c3(:, 2), 'g*');
+drawnow;
+
+% Now try batch training
+options(1) = 1;
+options(6) = 1;
+options(14) = 50;
+options(17) = 3;
+options(15) = 0;
+disp('An alternative approach to the on-line algorithm is a batch update')
+disp('rule.  Each unit is updated to be the average weights')
+disp('in a neighbourhood (which reduces from 3 to 0) over 50 iterations.');
+disp('Note how the error is even more unstable at first, though eventually')
+disp('it does converge.')
+disp('The final units are shown as black triangles.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+net4 = somtrain(net, options, x);
+c4 = sompak(net4);
+plot(c4(:, 1), c4(:, 2), 'k^')
+legend('Data', 'Initial weights', 'Weights after ordering', ...
+    'Weights after convergence', 'Batch weights', 2);
+drawnow;
+
+disp(' ')
+disp('Press any key to end.')
+disp(' ')
+pause
+
+close(h1);