function [g, gdata, gprior] = rbfgrad(net, x, t)%RBFGRAD Evaluate gradient of error function for RBF network.%%	Description%	G = RBFGRAD(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 gradient G of the error function with respect to%	the network weights (i.e. including the hidden unit parameters). The%	error function is sum of squares. Each row of X corresponds to one%	input vector and each row of T contains the corresponding target%	vector. If the output function is 'NEUROSCALE' then the gradient is%	only computed for the output layer weights and biases.%%	[G, GDATA, GPRIOR] = RBFGRAD(NET, X, T) also returns separately  the%	data and prior contributions to the gradient. In the case of multiple%	groups in the prior, GPRIOR is a matrix with a row for each group and%	a column for each weight parameter.%%	See also%	RBF, RBFFWD, RBFERR, RBFPAK, RBFUNPAK, RBFBKP%%	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);endndata = size(x, 1);[y, z, n2] = rbffwd(net, x);switch net.outfncase 'linear'   % Sum squared error at output units   delout = y - t;   gdata = rbfbkp(net, x, z, n2, delout);   [g, gdata, gprior] = gbayes(net, gdata);case 'neuroscale'   % Compute the error gradient with respect to outputs   y_dist = sqrt(dist2(y, y));   D = (t - y_dist)./(y_dist+diag(ones(ndata, 1)));   temp = y';   gradient = 2.*sum(kron(D, ones(1, net.nout)) .* ...      (repmat(y, 1, ndata) - repmat((temp(:))', ndata, 1)), 1);   gradient = (reshape(gradient, net.nout, ndata))';   % Compute the error gradient   gdata = rbfbkp(net, x, z, n2, gradient);   [g, gdata, gprior] = gbayes(net, gdata);otherwise   error(['Unknown output function ', net.outfn]);end