diff toolboxes/FullBNT-1.0.7/netlab3.3/gp.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gp.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,93 @@
+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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