function [e, edata, eprior] = rbferr(net, x, t)%RBFERR	Evaluate error function for RBF network.%%	Description%	E = RBFERR(NET, X, T) takes a network data structure NET together%	with a matrix X of input vectors and a matrix T of target vectors,%	and evaluates the appropriate error function E depending on%	NET.OUTFN.  Each row of X corresponds to one input vector and each%	row of T contains the corresponding target vector.%%	[E, EDATA, EPRIOR] = RBFERR(NET, X, T) additionally returns the data%	and prior components of the error, assuming a zero mean Gaussian%	prior on the weights with inverse variance parameters ALPHA and BETA%	taken from the network data structure NET.%%	See also%	RBF, RBFFWD, RBFGRAD, RBFPAK, RBFTRAIN, RBFUNPAK%%	Copyright (c) Ian T Nabney (1996-2001)% Check arguments for consistencyswitch net.outfncase 'linear'   errstring = consist(net, 'rbf', x, t);case 'neuroscale'   errstring = consist(net, 'rbf', x);otherwise   error(['Unknown output function ', net.outfn]);endif ~isempty(errstring);  error(errstring);endswitch net.outfncase 'linear'   y = rbffwd(net, x);   edata = 0.5*sum(sum((y - t).^2));case 'neuroscale'   y = rbffwd(net, x);   y_dist = sqrt(dist2(y, y));   % Take t as target distance matrix   edata = 0.5.*(sum(sum((t-y_dist).^2)));otherwise   error(['Unknown output function ', net.outfn]);end% Compute Bayesian regularised error[e, edata, eprior] = errbayes(net, edata);