function jac = rbfjacob(net, x)%RBFJACOB Evaluate derivatives of RBF network outputs with respect to inputs.%%	Description%	G = RBFJACOB(NET, X) takes a network data structure NET and a matrix%	of input vectors X and returns a three-index matrix G whose I, J, K%	element contains the derivative of network output K with respect to%	input parameter J for input pattern I.%%	See also%	RBF, RBFGRAD, RBFBKP%%	Copyright (c) Ian T Nabney (1996-2001)% Check arguments for consistencyerrstring = consist(net, 'rbf', x);if ~isempty(errstring);  error(errstring);endif ~strcmp(net.outfn, 'linear')  error('Function only implemented for linear outputs')end[y, z, n2] = rbffwd(net, x);ndata = size(x, 1);jac = zeros(ndata, net.nin, net.nout);Psi = zeros(net.nin, net.nhidden);% Calculate derivative of activations wrt n2switch net.actfncase 'gaussian'  dz = -z./(ones(ndata, 1)*net.wi);case 'tps'  dz = 2*(1 + log(n2+(n2==0)));case 'r4logr'  dz = 2*(n2.*(1+2.*log(n2+(n2==0))));otherwise   error(['Unknown activation function ', net.actfn]);end% Ignore biases as they cannot affect Jacobianfor n = 1:ndata  Psi = (ones(net.nin, 1)*dz(n, :)).* ...    (x(n, :)'*ones(1, net.nhidden) - net.c');  % Now compute the Jacobian  jac(n, :, :) =  Psi * net.w2;end