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, 111 insertions, 0 deletions

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+%DEMKMEAN Demonstrate simple clustering model trained with K-means.
+%
+%	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.
+%
+%	A cluster model with three components is trained using the batch K-
+%	means algorithm. The matrix of centres is printed after training. The
+%	second figure shows the data labelled with a colour derived from the
+%	corresponding  cluster
+%
+%	See also
+%	DEM2DDAT, DEMGMM1, KNN1, KMEANS
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Generate the data, fixing seeds for reproducible results
+ndata = 250;
+randn('state', 42);
+rand('state', 42);
+data = dem2ddat(ndata);
+
+% Randomise data order
+data = data(randperm(ndata),:);
+
+clc 
+disp('This demonstration illustrates the use of a cluster model to')
+disp('find centres that reflect the distribution of data points.')
+disp('We begin by generating the data from a mixture of three Gaussians')
+disp('in two-dimensional space and plotting it.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+fh1 = figure;
+plot(data(:, 1), data(:, 2), 'o')
+set(gca, 'Box', 'on')
+title('Data')
+
+% Set up cluster model
+ncentres = 3;
+centres = zeros(ncentres, 2);
+
+% Set up vector of options for kmeans trainer
+options = foptions;
+options(1)  = 1;		% Prints out error values.
+options(5) = 1;
+options(14) = 10;		% Number of iterations.
+
+clc
+disp('The model is chosen to have three centres, which are initialised')
+disp('at randomly selected data points.  We now train the model using')
+disp('the batch K-means algorithm with a maximum of 10 iterations and')
+disp('stopping tolerance of 1e-4.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+% Train the centres from the data
+[centres, options, post] = kmeans(centres, data, options);
+
+% Print out model
+disp(' ')
+disp('Note that training has terminated before 10 iterations as there')
+disp('has been no change in the centres or error function.')
+disp(' ')
+disp('The trained model has centres:')
+disp(centres);
+disp('Press any key to continue.')
+pause
+
+clc
+disp('We now plot each data point coloured according to its classification')
+disp('given by the nearest cluster centre.  The cluster centres are denoted')
+disp('by black crosses.')
+
+% 					Plot the result
+fh2 = figure;
+
+hold on
+colours = ['b.'; 'r.'; 'g.'];
+
+[tempi, tempj] = find(post);
+hold on
+for i = 1:3
+  % Select data points closest to ith centre
+  thisX = data(tempi(tempj == i), 1);
+  thisY = data(tempi(tempj == i), 2);
+  hp(i) = plot(thisX, thisY, colours(i,:));
+  set(hp(i), 'MarkerSize', 12);
+end
+set(gca, 'Box', 'on')
+legend('Class 1', 'Class 2', 'Class 3', 2)
+hold on
+plot(centres(:, 1), centres(:,2), 'k+', 'LineWidth', 2, ...
+  'MarkerSize', 8)
+title('Centres and data labels')
+hold off
+
+disp(' ')
+disp('Press any key to end.')
+pause
+
+close(fh1);
+close(fh2);
+clear all;
+