function [e, edata, eprior] = gperr(net, x, t)%GPERR	Evaluate error function for Gaussian Process.%%	Description%	E = GPERR(NET, X, T) takes a Gaussian Process data structure NET%	together  with a matrix X of input vectors and a matrix T of target%	vectors, and evaluates the error function E. Each row of X%	corresponds to one input vector and each row of T corresponds to one%	target vector.%%	[E, EDATA, EPRIOR] = GPERR(NET, X, T) additionally returns the data%	and hyperprior components of the error, assuming a Gaussian prior on%	the weights with mean and variance parameters PRMEAN and PRVARIANCE%	taken from the network data structure NET.%%	See also%	GP, GPCOVAR, GPFWD, GPGRAD%%	Copyright (c) Ian T Nabney (1996-2001)errstring = consist(net, 'gp', x, t);if ~isempty(errstring);  error(errstring);endcn = gpcovar(net, x);edata = 0.5*(sum(log(eig(cn, 'nobalance'))) + t'*inv(cn)*t);% Evaluate the hyperprior contribution to the error.% The hyperprior is Gaussian with mean pr_mean and variance% pr_varianceif isfield(net, 'pr_mean')  w = gppak(net);  m = repmat(net.pr_mean, size(w));  if size(net.pr_mean) == [1 1]    eprior = 0.5*((w-m)*(w-m)');    e2 = eprior/net.pr_var;  else    wpr = repmat(w, size(net.pr_mean, 1), 1)';    eprior = 0.5*(((wpr - m').^2).*net.index);    e2 = (sum(eprior, 1))*(1./net.pr_var);  endelse  e2 = 0;  eprior = 0;ende = edata + e2;