diff toolboxes/FullBNT-1.0.7/netlab3.3/demhmc1.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demhmc1.m	Tue Feb 10 15:05:51 2015 +0000
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+%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
+%	DEMHMC3, HMC, DEMPOT, DEMGPOT
+%
+
+%	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);
+
+clc
+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('from.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+% 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;
+
+clc
+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 ',...
+    num2str(options(18))])
+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('chain.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+[samples, energies] = hmc('dempot', x, options, 'demgpot', mix);
+
+disp(' ')
+disp('Press any key to continue.')
+pause
+clc
+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.')
+pause
+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);
+
+clc
+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.')
+pause
+% 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')
+pause
+[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')
+pause
+
+close(fh1);
+clear all;
+