function covf = gpcovarf(net, x1, x2)%GPCOVARF Calculate the covariance function for a Gaussian Process.%%	Description%%	COVF = GPCOVARF(NET, X1, X2) takes  a Gaussian Process data structure%	NET together with two matrices X1 and X2 of input vectors,  and%	computes the matrix of the covariance function values COVF.%%	See also%	GP, GPCOVAR, GPCOVARP, GPERR, GPGRAD%%	Copyright (c) Ian T Nabney (1996-2001)errstring = consist(net, 'gp', x1);if ~isempty(errstring);  error(errstring);endif size(x1, 2) ~= size(x2, 2)  error('Number of variables in x1 and x2 must be the same');endn1 = size(x1, 1);n2 = size(x2, 1);beta = diag(exp(net.inweights));% Compute the weighted squared distances between x1 and x2z = (x1.*x1)*beta*ones(net.nin, n2) - 2*x1*beta*x2' ...   + ones(n1, net.nin)*beta*(x2.*x2)';switch net.covar_fn  case 'sqexp'		% Squared exponential    covf = exp(net.fpar(1) - 0.5*z);  case 'ratquad'	% Rational quadratic    nu = exp(net.fpar(2));    covf = exp(net.fpar(1))*((ones(size(z)) + z).^(-nu));  otherwise    error(['Unknown covariance function ', net.covar_fn]);  end