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, 113 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/gmm.m b/sourcecodes/bnt-master/netlab3.3/gmm.m
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+function mix = gmm(dim, ncentres, covar_type, ppca_dim)
+%GMM	Creates a Gaussian mixture model with specified architecture.
+%
+%	Description
+%	 MIX = GMM(DIM, NCENTRES, COVARTYPE) takes the dimension of the space
+%	DIM, the number of centres in the mixture model and the type of the
+%	mixture model, and returns a data structure MIX. The mixture model
+%	type defines the covariance structure of each component  Gaussian:
+%	  'spherical' = single variance parameter for each component: stored as a vector
+%	  'diag' = diagonal matrix for each component: stored as rows of a matrix
+%	  'full' = full matrix for each component: stored as 3d array
+%	  'ppca' = probabilistic PCA: stored as principal components (in a 3d array
+%	    and associated variances and off-subspace noise
+%	 MIX = GMM(DIM, NCENTRES, COVARTYPE, PPCA_DIM) also sets the
+%	dimension of the PPCA sub-spaces: the default value is one.
+%
+%	The priors are initialised to equal values summing to one, and the
+%	covariances are all the identity matrix (or equivalent).  The centres
+%	are initialised randomly from a zero mean unit variance Gaussian.
+%	This makes use of the MATLAB function RANDN and so the seed for the
+%	random weight initialisation can be set using RANDN('STATE', S) where
+%	S is the state value.
+%
+%	The fields in MIX are
+%	  
+%	  type = 'gmm'
+%	  nin = the dimension of the space
+%	  ncentres = number of mixture components
+%	  covartype = string for type of variance model
+%	  priors = mixing coefficients
+%	  centres = means of Gaussians: stored as rows of a matrix
+%	  covars = covariances of Gaussians
+%	 The additional fields for mixtures of PPCA are
+%	  U = principal component subspaces
+%	  lambda = in-space covariances: stored as rows of a matrix
+%	 The off-subspace noise is stored in COVARS.
+%
+%	See also
+%	GMMPAK, GMMUNPAK, GMMSAMP, GMMINIT, GMMEM, GMMACTIV, GMMPOST, 
+%	GMMPROB
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+if ncentres < 1
+  error('Number of centres must be greater than zero')
+end
+
+mix.type = 'gmm';
+mix.nin = dim;
+mix.ncentres = ncentres;
+
+vartypes = {'spherical', 'diag', 'full', 'ppca'};
+
+if sum(strcmp(covar_type, vartypes)) == 0
+  error('Undefined covariance type')
+else
+  mix.covar_type = covar_type;
+end
+
+% Make default dimension of PPCA subspaces one.
+if strcmp(covar_type, 'ppca')
+  if nargin < 4
+    ppca_dim = 1;
+  end
+  if ppca_dim > dim
+    error('Dimension of PPCA subspaces must be less than data.')
+  end
+  mix.ppca_dim = ppca_dim;
+end
+
+% Initialise priors to be equal and summing to one
+mix.priors = ones(1,mix.ncentres) ./ mix.ncentres;
+
+% Initialise centres
+mix.centres = randn(mix.ncentres, mix.nin);
+
+% Initialise all the variances to unity
+switch mix.covar_type
+
+case 'spherical'
+  mix.covars = ones(1, mix.ncentres);
+  mix.nwts = mix.ncentres + mix.ncentres*mix.nin + mix.ncentres;
+case 'diag'
+  % Store diagonals of covariance matrices as rows in a matrix
+  mix.covars =  ones(mix.ncentres, mix.nin);
+  mix.nwts = mix.ncentres + mix.ncentres*mix.nin + ...
+    mix.ncentres*mix.nin;
+case 'full'
+  % Store covariance matrices in a row vector of matrices
+  mix.covars = repmat(eye(mix.nin), [1 1 mix.ncentres]);
+  mix.nwts = mix.ncentres + mix.ncentres*mix.nin + ...
+    mix.ncentres*mix.nin*mix.nin;
+case 'ppca'
+  % This is the off-subspace noise: make it smaller than
+  % lambdas
+  mix.covars = 0.1*ones(1, mix.ncentres);
+  % Also set aside storage for principal components and
+  % associated variances
+  init_space = eye(mix.nin);
+  init_space = init_space(:, 1:mix.ppca_dim);
+  init_space(mix.ppca_dim+1:mix.nin, :) = ...
+    ones(mix.nin - mix.ppca_dim, mix.ppca_dim);
+  mix.U = repmat(init_space , [1 1 mix.ncentres]);
+  mix.lambda = ones(mix.ncentres, mix.ppca_dim);
+  % Take account of additional parameters
+  mix.nwts = mix.ncentres + mix.ncentres*mix.nin + ...
+    mix.ncentres + mix.ncentres*mix.ppca_dim + ...
+    mix.ncentres*mix.nin*mix.ppca_dim;
+otherwise
+  error(['Unknown covariance type ', mix.covar_type]);               
+end
+