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annotate toolboxes/FullBNT-1.0.7/netlab3.3/demknn1.m @ 0:e9a9cd732c1e tip

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 %DEMKNN1 Demonstrate nearest neighbour classifier.
wolffd@0 2 %
wolffd@0 3 % Description
wolffd@0 4 % The problem consists of data in a two-dimensional space. The data is
wolffd@0 5 % drawn from three spherical Gaussian distributions with priors 0.3,
wolffd@0 6 % 0.5 and 0.2; centres (2, 3.5), (0, 0) and (0,2); and standard
wolffd@0 7 % deviations 0.2, 0.5 and 1.0. The first figure contains a scatter plot
wolffd@0 8 % of the data. The data is the same as in DEMGMM1.
wolffd@0 9 %
wolffd@0 10 % The second figure shows the data labelled with the corresponding
wolffd@0 11 % class given by the classifier.
wolffd@0 12 %
wolffd@0 13 % See also
wolffd@0 14 % DEM2DDAT, DEMGMM1, KNN
wolffd@0 15 %
wolffd@0 16
wolffd@0 17 % Copyright (c) Ian T Nabney (1996-2001)
wolffd@0 18
wolffd@0 19 clc
wolffd@0 20 disp('This program demonstrates the use of the K nearest neighbour algorithm.')
wolffd@0 21 disp(' ')
wolffd@0 22 disp('Press any key to continue.')
wolffd@0 23 pause
wolffd@0 24 % Generate the test data
wolffd@0 25 ndata = 250;
wolffd@0 26 randn('state', 42);
wolffd@0 27 rand('state', 42);
wolffd@0 28
wolffd@0 29 [data, c] = dem2ddat(ndata);
wolffd@0 30
wolffd@0 31 % Randomise data order
wolffd@0 32 data = data(randperm(ndata),:);
wolffd@0 33
wolffd@0 34 clc
wolffd@0 35 disp('We generate the data in two-dimensional space from a mixture of')
wolffd@0 36 disp('three spherical Gaussians. The centres are shown as black crosses')
wolffd@0 37 disp('in the plot.')
wolffd@0 38 disp(' ')
wolffd@0 39 disp('Press any key to continue.')
wolffd@0 40 pause
wolffd@0 41 fh1 = figure;
wolffd@0 42 plot(data(:, 1), data(:, 2), 'o')
wolffd@0 43 set(gca, 'Box', 'on')
wolffd@0 44 hold on
wolffd@0 45 title('Data')
wolffd@0 46 hp1 = plot(c(:, 1), c(:,2), 'k+')
wolffd@0 47 % Increase size of crosses
wolffd@0 48 set(hp1, 'MarkerSize', 8);
wolffd@0 49 set(hp1, 'LineWidth', 2);
wolffd@0 50 hold off
wolffd@0 51
wolffd@0 52 clc
wolffd@0 53 disp('We next use the centres as training examplars for the K nearest')
wolffd@0 54 disp('neighbour algorithm.')
wolffd@0 55 disp(' ')
wolffd@0 56 disp('Press any key to continue.')
wolffd@0 57 pause
wolffd@0 58
wolffd@0 59 % Use centres as training data
wolffd@0 60 train_labels = [1, 0, 0; 0, 1, 0; 0, 0, 1];
wolffd@0 61
wolffd@0 62 % Label the test data up to kmax neighbours
wolffd@0 63 kmax = 1;
wolffd@0 64 net = knn(2, 3, kmax, c, train_labels);
wolffd@0 65 [y, l] = knnfwd(net, data);
wolffd@0 66
wolffd@0 67 clc
wolffd@0 68 disp('We now plot each data point coloured according to its classification.')
wolffd@0 69 disp(' ')
wolffd@0 70 disp('Press any key to continue.')
wolffd@0 71 pause
wolffd@0 72 % Plot the result
wolffd@0 73 fh2 = figure;
wolffd@0 74 colors = ['b.'; 'r.'; 'g.'];
wolffd@0 75 for i = 1:3
wolffd@0 76 thisX = data(l == i,1);
wolffd@0 77 thisY = data(l == i,2);
wolffd@0 78 hp(i) = plot(thisX, thisY, colors(i,:));
wolffd@0 79 set(hp(i), 'MarkerSize', 12);
wolffd@0 80 if i == 1
wolffd@0 81 hold on
wolffd@0 82 end
wolffd@0 83 end
wolffd@0 84 set(gca, 'Box', 'on');
wolffd@0 85 legend('Class 1', 'Class 2', 'Class 3', 2)
wolffd@0 86 hold on
wolffd@0 87 labels = ['1', '2', '3'];
wolffd@0 88 hp2 = plot(c(:, 1), c(:,2), 'k+');
wolffd@0 89 % Increase size of crosses
wolffd@0 90 set(hp2, 'MarkerSize', 8);
wolffd@0 91 set(hp2, 'LineWidth', 2);
wolffd@0 92
wolffd@0 93 test_labels = labels(l(:,1));
wolffd@0 94
wolffd@0 95 title('Training data and data labels')
wolffd@0 96 hold off
wolffd@0 97
wolffd@0 98 disp('The demonstration is now complete: press any key to exit.')
wolffd@0 99 pause
wolffd@0 100 close(fh1);
wolffd@0 101 close(fh2);
wolffd@0 102 clear all;
wolffd@0 103