diff toolboxes/FullBNT-1.0.7/netlab3.3/demkmn1.m @ 0:e9a9cd732c1e tip

first hg version after svn

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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;
+