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 PRMEAN and PRVARIANCE.%	If PRMEAN is a scalar, then the Gaussian is assumed to be isotropic%	and the additional fields NET.PRMEAN and PRVARIANCE are set.%	Otherwise,  the Gaussian prior has a mean defined by a column vector%	of parameters PRIOR.PRMEAN and covariance defined by a column vector%	of parameters PRIOR.PRVARIANCE. 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 parametersnet.bias = 0;net.min_noise = sqrt(eps);  % Prevent output noise collapsing completelynet.noise = 0;net.inweights = zeros(1,nin);  % Weights on inputs in covariance functioncovarfns = {'sqexp', 'ratquad'};if sum(strcmp(covar_fn, covarfns)) == 0  error('Undefined activation function. Exiting.');else  net.covar_fn = covar_fn;endswitch 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]);endnet.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 gpfwdnet.tr_in = [];net.tr_targets = [];