BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 103 insertions, 0 deletions

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+%DEMKNN1 Demonstrate nearest neighbour classifier.
+%
+%	Description
+%	The problem consists of data in a two-dimensional space.  The data is
+%	drawn from three spherical Gaussian distributions with priors 0.3,
+%	0.5 and 0.2; centres (2, 3.5), (0, 0) and (0,2); and standard
+%	deviations 0.2, 0.5 and 1.0. The first figure contains a scatter plot
+%	of the data.  The data is the same as in DEMGMM1.
+%
+%	The second figure shows the data labelled with the corresponding
+%	class given by the classifier.
+%
+%	See also
+%	DEM2DDAT, DEMGMM1, KNN
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+clc
+disp('This program demonstrates the use of the K nearest neighbour algorithm.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+% Generate the test data
+ndata = 250;
+randn('state', 42);
+rand('state', 42);
+
+[data, c] = dem2ddat(ndata);
+
+% Randomise data order
+data = data(randperm(ndata),:);
+
+clc
+disp('We generate the data in two-dimensional space from a mixture of')
+disp('three spherical Gaussians. The centres are shown as black crosses')
+disp('in the plot.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+fh1 = figure;
+plot(data(:, 1), data(:, 2), 'o')
+set(gca, 'Box', 'on')
+hold on
+title('Data')
+hp1 = plot(c(:, 1), c(:,2), 'k+')
+% Increase size of crosses
+set(hp1, 'MarkerSize', 8);
+set(hp1, 'LineWidth', 2);
+hold off
+
+clc
+disp('We next use the centres as training examplars for the K nearest')
+disp('neighbour algorithm.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+% Use centres as training data
+train_labels = [1, 0, 0; 0, 1, 0; 0, 0, 1];
+
+% Label the test data up to kmax neighbours
+kmax = 1;
+net = knn(2, 3, kmax, c, train_labels);
+[y, l] = knnfwd(net, data);
+
+clc
+disp('We now plot each data point coloured according to its classification.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+% Plot the result
+fh2 = figure;
+colors = ['b.'; 'r.'; 'g.'];
+for i = 1:3
+  thisX = data(l == i,1);
+  thisY = data(l == i,2);
+  hp(i) = plot(thisX, thisY, colors(i,:));
+  set(hp(i), 'MarkerSize', 12);
+  if i == 1
+    hold on
+  end
+end
+set(gca, 'Box', 'on');
+legend('Class 1', 'Class 2', 'Class 3', 2)
+hold on
+labels = ['1', '2', '3'];
+hp2 = plot(c(:, 1), c(:,2), 'k+');
+% Increase size of crosses
+set(hp2, 'MarkerSize', 8);
+set(hp2, 'LineWidth', 2);
+
+test_labels = labels(l(:,1));
+
+title('Training data and data labels')
+hold off
+
+disp('The demonstration is now complete: press any key to exit.')
+pause
+close(fh1);
+close(fh2);
+clear all; 
+