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, 93 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/gp.m b/sourcecodes/bnt-master/netlab3.3/gp.m
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+function net = gp(nin, covar_fn, prior)
+%GP	Create a Gaussian Process.
+%
+%	Description
+%
+%	NET = GP(NIN, COVARFN) takes the number of inputs NIN  for a Gaussian
+%	Process model with a single output, together with a string COVARFN
+%	which specifies the type of the covariance function, and returns a
+%	data structure NET. The parameters are set to zero.
+%
+%	The fields in NET are
+%	  type = 'gp'
+%	  nin = number of inputs
+%	  nout = number of outputs: always 1
+%	  nwts = total number of weights and covariance function parameters
+%	  bias = logarithm of constant offset in covariance function
+%	  noise = logarithm of output noise variance
+%	  inweights = logarithm of inverse length scale for each input 
+%	  covarfn = string describing the covariance function:
+%	      'sqexp'
+%	      'ratquad'
+%	  fpar = covariance function specific parameters (1 for squared exponential,
+%	   2 for rational quadratic)
+%	  trin = training input data (initially empty)
+%	  trtargets = training target data (initially empty)
+%
+%	NET = GP(NIN, COVARFN, PRIOR) sets a Gaussian prior on the parameters
+%	of the model. PRIOR must contain the fields PR_MEAN and PR_VARIANCE.
+%	If PR_MEAN is a scalar, then the Gaussian is assumed to be isotropic
+%	and the additional fields NET.PR_MEAN and PR_VARIANCE are set.
+%	Otherwise,  the Gaussian prior has a mean defined by a column vector
+%	of parameters PRIOR.PR_MEAN and covariance defined by a column vector
+%	of parameters PRIOR.PR_VARIANCE. Each element of PRMEAN corresponds
+%	to a separate group of parameters, which need not be mutually
+%	exclusive. The membership of the groups is defined by the matrix
+%	PRIOR.INDEX in which the columns correspond to the elements of
+%	PRMEAN. Each column has one element for each weight in the matrix, in
+%	the order defined by the function GPPAK, and each element is 1 or 0
+%	according to whether the parameter is a member of the corresponding
+%	group or not.  The additional field NET.INDEX is set in this case.
+%
+%	See also
+%	GPPAK, GPUNPAK, GPFWD, GPERR, GPCOVAR, GPGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+net.type = 'gp';
+net.nin = nin;
+net.nout = 1;  % Only do single output GP
+
+% Store log parameters
+net.bias = 0;
+net.min_noise = sqrt(eps);  % Prevent output noise collapsing completely
+net.noise = 0;
+net.inweights = zeros(1,nin);  % Weights on inputs in covariance function
+
+covarfns = {'sqexp', 'ratquad'};
+
+if sum(strcmp(covar_fn, covarfns)) == 0
+  error('Undefined activation function. Exiting.');
+else
+  net.covar_fn = covar_fn;
+end
+
+switch covar_fn
+
+  case 'sqexp'		% Squared exponential
+    net.fpar = zeros(1,1);  % One function specific parameter
+    
+  case 'ratquad' 	% Rational quadratic
+    net.fpar = zeros(1, 2); % Two function specific parameters
+
+  otherwise
+    error(['Unknown covariance function ', covar_fn]);
+end
+
+net.nwts = 2 + nin + length(net.fpar);
+
+if nargin >= 3
+  if size(prior.pr_mean) == [1 1]
+    net.pr_mean = prior.pr_mean;
+    net.pr_var = prior.pr_var;
+  else
+    net.pr_mean = prior.pr_mean;
+    net.pr_var = prior.pr_var;
+    net.index = prior.index;
+  end  
+end
+
+% Store training data as needed for gpfwd
+net.tr_in = [];
+net.tr_targets = [];
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