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06 Nov 2002 (Updated )

Pattern analysis toolbox.

%DEMHMC1 Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians.
%	Description
%	The problem consists of generating data from a mixture of two
%	Gaussians in two dimensions using a hybrid Monte Carlo algorithm with
%	persistence. A mixture model is then fitted to the sample to compare
%	it with the  true underlying generator.
%	See also

%	Copyright (c) Ian T Nabney (1996-2001)

dim = 2;            	% Data dimension
ncentres = 2;		% Number of centres in mixture model

seed = 42;              % Seed for random weight initialization.
randn('state', seed);
rand('state', seed);

disp('This demonstration illustrates the use of the hybrid Monte Carlo')
disp('algorithm to sample from a mixture of two Gaussians.')
disp('The means of the two components are [0 0] and [2 2].')
disp(' ')
disp('First we set up the parameters of the mixture model we are sampling')
disp(' ')
disp('Press any key to continue.')

% Set up mixture model to sample from
mix = gmm(dim, ncentres, 'spherical');
mix.centres(1, :) = [0 0];
mix.centres(2, :) = [2 2];
x = [0 1];  % Start vector

% Set up vector of options for hybrid Monte Carlo.

nsamples = 160;		% Number of retained samples.

options = foptions;     % Default options vector.
options(1) = 1;		% Switch on diagnostics.
options(5) = 1;		% Use persistence
options(7) = 50;	% Number of steps in trajectory.
options(14) = nsamples;	% Number of Monte Carlo samples returned. 
options(15) = 30;	% Number of samples omitted at start of chain.
options(18) = 0.02;

disp(['Next we take ', num2str(nsamples),' samples from the distribution.'...
    , 'The first ', num2str(options(15))])
disp('samples at the start of the chain are omitted.  As persistence')
disp('is used, the momentum has a small random component added at each step.')
disp([num2str(options(7)), ...
    ' iterations are used at each step and the step size is ',...
disp('Sampling starts at the point [0 1].')
disp('The new state is accepted if the threshold value is greater than')
disp('a random number between 0 and 1.')
disp(' ')
disp('Negative step numbers indicate samples discarded from the start of the')
disp(' ')
disp('Press any key to continue.')

[samples, energies] = hmc('dempot', x, options, 'demgpot', mix);

disp(' ')
disp('Press any key to continue.')
disp('The plot shows the samples generated by the HMC function.')
disp('The different colours are used to show how the samples move from')
disp('one component to the other over time.')
disp(' ')
disp('Press any key to continue.')
probs = exp(-energies);
fh1 = figure;
% Plot data in 4 groups
ngroups = 4;
g1end = floor(nsamples/ngroups);
g2end = floor(2*nsamples/ngroups);
g3end = floor(3*nsamples/ngroups);
p1 = plot(samples(1:g1end,1), samples(1:g1end,2), 'k.', 'MarkerSize', 12);
hold on
lstrings = char(['Samples 1-' int2str(g1end)], ...
  ['Samples ' int2str(g1end+1) '-' int2str(g2end)], ...
  ['Samples ' int2str(g2end+1) '-' int2str(g3end)], ...
  ['Samples ' int2str(g3end+1) '-' int2str(nsamples)]);
p2 = plot(samples(g1end+1:g2end,1), samples(g1end+1:g2end,2), ...
  'r.', 'MarkerSize', 12);
p3 = plot(samples(g2end+1:g3end,1), samples(g2end+1:g3end,2), ...
  'g.', 'MarkerSize', 12);
p4 = plot(samples(g3end+1:nsamples,1), samples(g3end+1:nsamples,2), ...
  'b.', 'MarkerSize', 12);
legend([p1 p2 p3 p4], lstrings, 2);

disp('We now fit a Gaussian mixture model to the sampled data.')
disp('The model has spherical covariance structure and the correct')
disp('number of components.')
disp(' ')
disp('Press any key to continue.')
% Fit a mixture model to the sample
newmix = gmm(dim, ncentres, 'spherical');
options = foptions;
options(1) = -1;	% Switch off all diagnostics
options(14) = 5;	% Just use 5 iterations of k-means in initialisation
% Initialise the model parameters from the samples
newmix = gmminit(newmix, samples, options);

% Set up vector of options for EM trainer
options = zeros(1, 18);
options(1)  = 1;		% Prints out error values.
options(14) = 15;		% Max. Number of iterations.

disp('We now train the model using the EM algorithm for 15 iterations')
disp(' ')
disp('Press any key to continue')
[newmix, options, errlog] = gmmem(newmix, samples, options);

% Print out model
disp(' ')
disp('The trained model has parameters ')
disp('    Priors        Centres         Variances')
disp([newmix.priors' newmix.centres newmix.covars'])
disp('Note the close correspondence between these parameters and those')
disp('of the distribution used to generate the data')
disp(' ')
disp('    Priors        Centres         Variances')
disp([mix.priors' mix.centres mix.covars'])
disp(' ')
disp('Press any key to exit')

clear all;

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