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, 94 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/gmminit.m b/sourcecodes/bnt-master/netlab3.3/gmminit.m
new file mode 100644
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+function mix = gmminit(mix, x, options)
+%GMMINIT Initialises Gaussian mixture model from data
+%
+%	Description
+%	MIX = GMMINIT(MIX, X, OPTIONS) uses a dataset X to initialise the
+%	parameters of a Gaussian mixture model defined by the data structure
+%	MIX.  The k-means algorithm is used to determine the centres. The
+%	priors are computed from the proportion of examples belonging to each
+%	cluster. The covariance matrices are calculated as the sample
+%	covariance of the points associated with (i.e. closest to) the
+%	corresponding centres. For a mixture of PPCA model, the PPCA
+%	decomposition is calculated for the points closest to a given centre.
+%	This initialisation can be used as the starting point for training
+%	the model using the EM algorithm.
+%
+%	See also
+%	GMM
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+[ndata, xdim] = size(x);
+
+% Check that inputs are consistent
+errstring = consist(mix, 'gmm', x);
+if ~isempty(errstring)
+  error(errstring);
+end
+
+% Arbitrary width used if variance collapses to zero: make it 'large' so
+% that centre is responsible for a reasonable number of points.
+GMM_WIDTH = 1.0;
+
+% Use kmeans algorithm to set centres
+options(5) = 1;	
+[mix.centres, options, post] = kmeansNetlab(mix.centres, x, options);
+
+% Set priors depending on number of points in each cluster
+cluster_sizes = max(sum(post, 1), 1);  % Make sure that no prior is zero
+mix.priors = cluster_sizes/sum(cluster_sizes); % Normalise priors
+
+switch mix.covar_type
+case 'spherical'
+   if mix.ncentres > 1
+      % Determine widths as distance to nearest centre 
+      % (or a constant if this is zero)
+      cdist = dist2(mix.centres, mix.centres);
+      cdist = cdist + diag(ones(mix.ncentres, 1)*realmax);
+      mix.covars = min(cdist);
+      mix.covars = mix.covars + GMM_WIDTH*(mix.covars < eps);
+   else
+      % Just use variance of all data points averaged over all
+      % dimensions
+      mix.covars = mean(diag(cov(x)));
+   end
+  case 'diag'
+    for j = 1:mix.ncentres
+      % Pick out data points belonging to this centre
+      c = x(find(post(:, j)),:);
+      diffs = c - (ones(size(c, 1), 1) * mix.centres(j, :));
+      mix.covars(j, :) = sum((diffs.*diffs), 1)/size(c, 1);
+      % Replace small entries by GMM_WIDTH value
+      mix.covars(j, :) = mix.covars(j, :) + GMM_WIDTH.*(mix.covars(j, :)<eps);
+    end
+  case 'full'
+    for j = 1:mix.ncentres
+      % Pick out data points belonging to this centre
+      c = x(find(post(:, j)),:);
+      diffs = c - (ones(size(c, 1), 1) * mix.centres(j, :));
+      mix.covars(:,:,j) = (diffs'*diffs)/(size(c, 1));
+      % Add GMM_WIDTH*Identity to rank-deficient covariance matrices
+      if rank(mix.covars(:,:,j)) < mix.nin
+	mix.covars(:,:,j) = mix.covars(:,:,j) + GMM_WIDTH.*eye(mix.nin);
+      end
+    end
+  case 'ppca'
+    for j = 1:mix.ncentres
+      % Pick out data points belonging to this centre
+      c = x(find(post(:,j)),:);
+      diffs = c - (ones(size(c, 1), 1) * mix.centres(j, :));
+      [tempcovars, tempU, templambda] = ...
+	ppca((diffs'*diffs)/size(c, 1), mix.ppca_dim);
+      if length(templambda) ~= mix.ppca_dim
+	error('Unable to extract enough components');
+      else 
+        mix.covars(j) = tempcovars;
+        mix.U(:, :, j) = tempU;
+        mix.lambda(j, :) = templambda;
+      end
+    end
+  otherwise
+    error(['Unknown covariance type ', mix.covar_type]);
+end
+