wolffd@0: function [covp, covf] = gpcovarp(net, x1, x2)
wolffd@0: %GPCOVARP Calculate the prior covariance for a Gaussian Process.
wolffd@0: %
wolffd@0: %	Description
wolffd@0: %
wolffd@0: %	COVP = GPCOVARP(NET, X1, X2) takes  a Gaussian Process data structure
wolffd@0: %	NET together with two matrices X1 and X2 of input vectors,  and
wolffd@0: %	computes the matrix of the prior covariance.  This is the function
wolffd@0: %	component of the covariance plus the exponential of the bias term.
wolffd@0: %
wolffd@0: %	[COVP, COVF] = GPCOVARP(NET, X1, X2) also returns the function
wolffd@0: %	component of the covariance.
wolffd@0: %
wolffd@0: %	See also
wolffd@0: %	GP, GPCOVAR, GPCOVARF, GPERR, GPGRAD
wolffd@0: %
wolffd@0: 
wolffd@0: %	Copyright (c) Ian T Nabney (1996-2001)
wolffd@0: 
wolffd@0: errstring = consist(net, 'gp', x1);
wolffd@0: if ~isempty(errstring);
wolffd@0:   error(errstring);
wolffd@0: end
wolffd@0: 
wolffd@0: if size(x1, 2) ~= size(x2, 2)
wolffd@0:   error('Number of variables in x1 and x2 must be the same');
wolffd@0: end
wolffd@0: 
wolffd@0: covf = gpcovarf(net, x1, x2);
wolffd@0: covp = covf + exp(net.bias);