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

diff --git a/sourcecodes/bnt-master/netlab3.3/demmet1.m b/sourcecodes/bnt-master/netlab3.3/demmet1.m
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+++ b/sourcecodes/bnt-master/netlab3.3/demmet1.m
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+function samples=demmet1(plot_wait)
+%DEMMET1 Demonstrate Markov Chain Monte Carlo sampling on a Gaussian.
+%
+%	Description
+%	The problem consists of generating data from a Gaussian in two
+%	dimensions using a Markov Chain Monte Carlo algorithm. The points are
+%	plotted one after another to show the path taken by the chain.
+%
+%	DEMMET1(PLOTWAIT) allows the user to set the time (in a whole number
+%	of seconds) between the plotting of points.  This is passed to PAUSE
+%
+%	See also
+%	DEMHMC1, METROP, GMM, DEMPOT
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+if nargin == 0 | plot_wait < 0
+  plot_wait = 0; % No wait if not specified or incorrect
+end
+dim = 2;            	% Data dimension
+ncentres = 1;		% 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 Markov chain Monte Carlo')
+disp('algorithm to sample from a Gaussian distribution.')
+disp('The mean is at [0 0].')
+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];
+x = [0 4];  % Start vector
+
+% Set up vector of options for hybrid Monte Carlo.
+
+nsamples = 150;		% Number of retained samples.
+
+options = foptions;     % Default options vector.
+options(1) = 0;		% Switch off diagnostics.
+options(14) = nsamples;	% Number of Monte Carlo samples returned. 
+options(18) = 0.1;
+
+clc
+disp('Next we take 150 samples from the distribution.')
+disp('Sampling starts at the point [0 4].')
+disp('The new state is accepted if the threshold value is greater than')
+disp('a random number between 0 and 1.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+
+[samples, energies] = metrop('dempot', x, options, '', mix);
+
+clc
+disp('The plot shows the samples generated by the MCMC function in order')
+disp('as an animation to show the path taken by the Markov chain.')
+disp('The different colours are used to show that the first few samples')
+disp('should be discarded as they lie too far from the mean.')
+disp(' ')
+disp('Press any key to continue.')
+pause
+probs = exp(-energies);
+fh1 = figure;
+g1end = floor(nsamples/4);
+
+for n = 1:nsamples
+  
+  if n < g1end
+    Marker = 'k.';
+    p1 = plot(samples(n,1), samples(n,2), Marker, ...
+      'EraseMode', 'none', 'MarkerSize', 12);
+    if n == 1
+      axis([-3 5 -2 5])
+    end
+  else
+    Marker = 'r.';
+    p2 = plot(samples(n,1), samples(n,2), Marker, ...
+      'EraseMode', 'none', 'MarkerSize', 12);
+  end
+  hold on
+  drawnow;  % Force drawing immediately
+  pause(plot_wait);
+end
+lstrings = char(['Samples 1-' int2str(g1end)], ...
+  ['Samples ' int2str(g1end+1) '-' int2str(nsamples)]);
+legend([p1 p2], lstrings, 1);
+
+disp(' ')
+disp('Press any key to exit.')
+pause
+close(fh1);
+clear all;
+