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, 124 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/kmeansNetlab.m b/sourcecodes/bnt-master/netlab3.3/kmeansNetlab.m
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index 00000000..09935f7b
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+++ b/sourcecodes/bnt-master/netlab3.3/kmeansNetlab.m
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+function [centres, options, post, errlog] = kmeans(centres, data, options)
+%KMEANS	Trains a k means cluster model.
+%
+%	Description
+%	 CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means
+%	algorithm to set the centres of a cluster model. The matrix DATA
+%	represents the data which is being clustered, with each row
+%	corresponding to a vector. The sum of squares error function is used.
+%	The point at which a local minimum is achieved is returned as
+%	CENTRES.  The error value at that point is returned in OPTIONS(8).
+%
+%	[CENTRES, OPTIONS, POST, ERRLOG] = KMEANS(CENTRES, DATA, OPTIONS)
+%	also returns the cluster number (in a one-of-N encoding) for each
+%	data point in POST and a log of the error values after each cycle in
+%	ERRLOG.    The optional parameters have the following
+%	interpretations.
+%
+%	OPTIONS(1) is set to 1 to display error values; also logs error
+%	values in the return argument ERRLOG. If OPTIONS(1) is set to 0, then
+%	only warning messages are displayed.  If OPTIONS(1) is -1, then
+%	nothing is displayed.
+%
+%	OPTIONS(2) is a measure of the absolute precision required for the
+%	value of CENTRES at the solution.  If the absolute difference between
+%	the values of CENTRES between two successive steps is less than
+%	OPTIONS(2), then this condition is satisfied.
+%
+%	OPTIONS(3) is a measure of the precision required of the error
+%	function at the solution.  If the absolute difference between the
+%	error functions between two successive steps is less than OPTIONS(3),
+%	then this condition is satisfied. Both this and the previous
+%	condition must be satisfied for termination.
+%
+%	OPTIONS(14) is the maximum number of iterations; default 100.
+%
+%	See also
+%	GMMINIT, GMMEM
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+[ndata, data_dim] = size(data);
+[ncentres, dim] = size(centres);
+
+if dim ~= data_dim
+  error('Data dimension does not match dimension of centres')
+end
+
+if (ncentres > ndata)
+  error('More centres than data')
+end
+
+% Sort out the options
+if (options(14))
+  niters = options(14);
+else
+  niters = 100;
+end
+
+store = 0;
+if (nargout > 3)
+  store = 1;
+  errlog = zeros(1, niters);
+end
+
+% Check if centres and posteriors need to be initialised from data
+if (options(5) == 1)
+  % Do the initialisation
+  perm = randperm(ndata);
+  perm = perm(1:ncentres);
+
+  % Assign first ncentres (permuted) data points as centres
+  centres = data(perm, :);
+end
+% Matrix to make unit vectors easy to construct
+id = eye(ncentres);
+
+% Main loop of algorithm
+for n = 1:niters
+
+  % Save old centres to check for termination
+  old_centres = centres;
+  
+  % Calculate posteriors based on existing centres
+  d2 = dist2(data, centres);
+  % Assign each point to nearest centre
+  [minvals, index] = min(d2', [], 1);
+  post = id(index,:);
+
+  num_points = sum(post, 1);
+  % Adjust the centres based on new posteriors
+  for j = 1:ncentres
+    if (num_points(j) > 0)
+      centres(j,:) = sum(data(find(post(:,j)),:), 1)/num_points(j);
+    end
+  end
+
+  % Error value is total squared distance from cluster centres
+  e = sum(minvals);
+  if store
+    errlog(n) = e;
+  end
+  if options(1) > 0
+    fprintf(1, 'Cycle %4d  Error %11.6f\n', n, e);
+  end
+
+  if n > 1
+    % Test for termination
+    if max(max(abs(centres - old_centres))) < options(2) & ...
+        abs(old_e - e) < options(3)
+      options(8) = e;
+      return;
+    end
+  end
+  old_e = e;
+end
+
+% If we get here, then we haven't terminated in the given number of 
+% iterations.
+options(8) = e;
+if (options(1) >= 0)
+  disp(maxitmess);
+end
+