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, 193 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/metrop.m b/sourcecodes/bnt-master/netlab3.3/metrop.m
new file mode 100644
index 00000000..730afc6b
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/metrop.m
@@ -0,0 +1,193 @@
+function [samples, energies, diagn] = metrop(f, x, options, gradf, varargin)
+%METROP	Markov Chain Monte Carlo sampling with Metropolis algorithm.
+%
+%	Description
+%	 SAMPLES = METROP(F, X, OPTIONS) uses the Metropolis algorithm to
+%	sample from the distribution P ~ EXP(-F), where F is the first
+%	argument to METROP.   The Markov chain starts at the point X and each
+%	candidate state is picked from a Gaussian proposal distribution and
+%	accepted or rejected according to the Metropolis criterion.
+%
+%	SAMPLES = METROP(F, X, OPTIONS, [], P1, P2, ...) allows additional
+%	arguments to be passed to F().  The fourth argument is ignored, but
+%	is included for compatibility with HMC and the optimisers.
+%
+%	[SAMPLES, ENERGIES, DIAGN] = METROP(F, X, OPTIONS) also returns a log
+%	of the energy values (i.e. negative log probabilities) for the
+%	samples in ENERGIES and DIAGN, a structure containing diagnostic
+%	information (position and acceptance threshold) for each step of the
+%	chain in DIAGN.POS and DIAGN.ACC respectively.  All candidate states
+%	(including rejected ones) are stored in DIAGN.POS.
+%
+%	S = METROP('STATE') returns a state structure that contains the state
+%	of the two random number generators RAND and RANDN. These are
+%	contained in fields randstate,  randnstate.
+%
+%	METROP('STATE', S) resets the state to S.  If S is an integer, then
+%	it is passed to RAND and RANDN. If S is a structure returned by
+%	METROP('STATE') then it resets the generator to exactly the same
+%	state.
+%
+%	The optional parameters in the OPTIONS vector have the following
+%	interpretations.
+%
+%	OPTIONS(1) is set to 1 to display the energy values and rejection
+%	threshold at each step of the Markov chain. If the value is 2, then
+%	the position vectors at each step are also displayed.
+%
+%	OPTIONS(14) is the number of samples retained from the Markov chain;
+%	default 100.
+%
+%	OPTIONS(15) is the number of samples omitted from the start of the
+%	chain; default 0.
+%
+%	OPTIONS(18) is the variance of the proposal distribution; default 1.
+%
+%	See also
+%	HMC
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+if nargin <= 2
+  if ~strcmp(f, 'state')
+    error('Unknown argument to metrop');
+  end
+  switch nargin
+    case 1
+      % Return state of sampler
+      samples = get_state(f);	% Function defined in this module
+      return;
+    case 2
+      % Set the state of the sampler
+      set_state(f, x);		% Function defined in this module
+      return;
+  end
+end
+
+if 0
+seed = 42;
+randn('state', seed);
+rand('state', seed)
+end
+
+display = options(1);
+if options(14) > 0
+  nsamples = options(14);
+else
+  nsamples = 100;
+end
+if options(15) >= 0
+  nomit = options(15);
+else
+  nomit = 0;
+end
+if options(18) > 0.0
+  std_dev = sqrt(options(18));
+else
+  std_dev = 1.0;   % default
+end			
+nparams = length(x);
+
+% Set up string for evaluating potential function.
+f = fcnchk(f, length(varargin));
+
+samples = zeros(nsamples, nparams);		% Matrix of returned samples.
+if nargout >= 2
+  en_save = 1;
+  energies = zeros(nsamples, 1);
+else
+  en_save = 0;
+end
+if nargout >= 3
+  diagnostics = 1;
+  diagn_pos = zeros(nsamples, nparams);
+  diagn_acc = zeros(nsamples, 1);
+else
+  diagnostics = 0;
+end
+
+% Main loop.
+n = - nomit + 1;
+Eold = feval(f, x, varargin{:});	% Evaluate starting energy.
+nreject = 0;				% Initialise count of rejected states.
+while n <= nsamples
+
+  xold = x;
+  % Sample a new point from the proposal distribution
+  x = xold + randn(1, nparams)*std_dev;
+  %fprintf('netlab propose: xold = %5.3f,%5.3f, xnew = %5.3f,%5.3f\n',...
+  %	xold(1), xold(2), x(1), x(2));
+
+  % Now apply Metropolis algorithm.
+  Enew = feval(f, x, varargin{:});	% Evaluate new energy.
+  a = exp(Eold - Enew);			% Acceptance threshold.
+  if (diagnostics & n > 0)
+    diagn_pos(n,:) = x;
+    diagn_acc(n,:) = a;
+  end
+  if (display > 1)
+    fprintf(1, 'New position is\n');
+    disp(x);
+  end
+
+  r = rand(1);
+  %fprintf('netlab: n=%d, a=%f/%f=%5.3f (%5.3f), r=%5.3f\n',...
+  %	  n, exp(-Enew), exp(-Eold), a, exp(-Enew)/exp(-Eold), r);
+  if a > r	% Accept the new state.
+    Eold = Enew;
+    if (display > 0)
+      fprintf(1, 'Finished step %4d  Threshold: %g\n', n, a);
+    end
+  else			% Reject the new state
+    if n > 0
+      nreject = nreject + 1;
+    end
+    x = xold;	% Reset position 
+    if (display > 0)
+      fprintf(1, '  Sample rejected %4d.  Threshold: %g\n', n, a);
+    end
+  end
+  if n > 0
+    samples(n,:) = x;			% Store sample.
+    if en_save 
+      energies(n) = Eold;		% Store energy.
+    end
+  end
+  n = n + 1;
+end
+
+if (display > 0)
+  fprintf(1, '\nFraction of samples rejected:  %g\n', ...
+          nreject/(nsamples));
+end
+
+if diagnostics
+  diagn.pos = diagn_pos;
+  diagn.acc = diagn_acc;
+end
+
+% Return complete state of the sampler.
+function state = get_state(f)
+
+state.randstate = rand('state');
+state.randnstate = randn('state');
+return
+
+% Set state of sampler, either from full state, or with an integer
+function set_state(f, x)
+
+if isnumeric(x)
+  rand('state', x);
+  randn('state', x);
+else
+  if ~isstruct(x)
+    error('Second argument to metrop must be number or state structure');
+  end
+  if (~isfield(x, 'randstate') | ~isfield(x, 'randnstate'))
+    error('Second argument to metrop must contain correct fields')
+  end
+  rand('state', x.randstate);
+  randn('state', x.randnstate);
+end
+return