comparison toolboxes/FullBNT-1.0.7/netlab3.3/demns1.m @ 0:e9a9cd732c1e tip

first hg version after svn

+%DEMNS1	Demonstrate Neuroscale for visualisation.
+%
+%	Description
+%	This script demonstrates the use of the Neuroscale algorithm for
+%	topographic projection and visualisation.  A data sample is generated
+%	from a mixture of two Gaussians in 4d space, and an RBF is trained
+%	with the stress error function to project the data into 2d.  The
+%	training data and a test sample are both plotted in this projection.
+%
+%	See also
+%	RBF, RBFTRAIN, RBFPRIOR
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Generate the data
+% Fix seeds for reproducible results
+rand('state', 420);
+randn('state', 420);
+
+input_dim = 4;
+output_dim = 2;
+mix = gmm(input_dim, 2, 'spherical');
+mix.centres = [1 1 1 1; 0 0 0 0];
+mix.priors = [0.5 0.5];
+mix.covars = [0.1 0.1];
+
+ndata = 60;
+[data, labels] = gmmsamp(mix, ndata);
+
+clc
+disp('This demonstration illustrates the use of the Neuroscale model')
+disp('to perform a topographic projection of data.  We begin by generating')
+disp('60 data points from a mixture of two Gaussians in 4 dimensional space.')
+disp(' ')
+disp('Press any key to continue')
+pause
+
+ncentres = 10;
+net = rbf(input_dim, ncentres, output_dim, 'tps', 'neuroscale');
+dstring = ['the Sammon mapping.  The model has ', num2str(ncentres), ...
+    ' centres, two outputs, and uses'];
+clc
+disp('The Neuroscale model is an RBF with a Stress error measure as used in')
+disp(dstring)
+disp('thin plate spline basis functions.')
+disp(' ')
+disp('It is trained using the shadow targets algorithm for at most 60 iterations.')
+disp(' ')
+disp('Press any key to continue')
+pause
+
+% First row controls shadow targets, second row controls rbfsetbf
+options(1, :) = foptions;
+options(2, :) = foptions;
+options(1, 1) = 1;
+options(1, 2) = 1e-2;
+options(1, 3) = 1e-2;
+options(1, 6) = 1;    % Switch on PCA initialisation
+options(1, 14) = 60;
+options(2, 1) = -1;   % Switch off all warnings
+options(2, 5) = 1;
+options(2, 14) = 10;
+net2 = rbftrain(net, options, data);
+
+disp(' ')
+disp('After training the model, we project the training data by a normal')
+disp('forward propagation through the RBF network.  Because there are two')
+disp('outputs, the results can be plotted and visualised.')
+disp(' ')
+disp('Press any key to continue')
+pause
+
+% Plot the result
+y = rbffwd(net2, data);
+ClassSymbol1 = 'r.';
+ClassSymbol2 = 'b.';
+PointSize = 12;
+fh1 = figure;
+hold on;
+plot(y((labels==1),1),y(labels==1,2),ClassSymbol1, 'MarkerSize', PointSize)
+plot(y((labels>1),1),y(labels>1,2),ClassSymbol2, 'MarkerSize', PointSize)
+
+disp(' ')
+disp('In this plot, the red dots denote the first class and the blue')
+disp('dots the second class.')
+disp(' ')
+disp('Press any key to continue.')
+disp(' ')
+pause
+
+disp('We now generate a further 100 data points from the original distribution')
+disp('and plot their projection using star symbols.  Note that a Sammon')
+disp('mapping cannot be used to generalise to new data in this fashion.')
+
+[test_data, test_labels] = gmmsamp(mix, 100);
+ytest = rbffwd(net2, test_data);
+ClassSymbol1 = 'ro';
+ClassSymbol2 = 'bo';
+% Circles are rather large symbols
+PointSize = 6;
+hold on
+plot(ytest((test_labels==1),1),ytest(test_labels==1,2), ...
+  ClassSymbol1, 'MarkerSize', PointSize)
+plot(ytest((test_labels>1),1),ytest(test_labels>1,2),...
+  ClassSymbol2, 'MarkerSize', PointSize)
+hold on
+legend('Class 1', 'Class 2', 'Test Class 1', 'Test Class 2')
+disp('Press any key to exit.')
+pause
+
+close(fh1);
+clear all;
+