Daniel@0: function g = rbfderiv(net, x)
Daniel@0: %RBFDERIV Evaluate derivatives of RBF network outputs with respect to weights.
Daniel@0: %
Daniel@0: %	Description
Daniel@0: %	G = RBFDERIV(NET, X) takes a network data structure NET and a matrix
Daniel@0: %	of input vectors X and returns a three-index matrix G whose I, J, K
Daniel@0: %	element contains the derivative of network output K with respect to
Daniel@0: %	weight or bias parameter J for input pattern I. The ordering of the
Daniel@0: %	weight and bias parameters is defined by RBFUNPAK.  This function
Daniel@0: %	also takes into account any mask in the network data structure.
Daniel@0: %
Daniel@0: %	See also
Daniel@0: %	RBF, RBFPAK, RBFGRAD, RBFBKP
Daniel@0: %
Daniel@0: 
Daniel@0: %	Copyright (c) Ian T Nabney (1996-2001)
Daniel@0: 
Daniel@0: % Check arguments for consistency
Daniel@0: errstring = consist(net, 'rbf', x);
Daniel@0: if ~isempty(errstring);
Daniel@0:   error(errstring);
Daniel@0: end
Daniel@0: 
Daniel@0: if ~strcmp(net.outfn, 'linear')
Daniel@0:   error('Function only implemented for linear outputs')
Daniel@0: end
Daniel@0: 
Daniel@0: [y, z, n2] = rbffwd(net, x);
Daniel@0: ndata = size(x, 1);
Daniel@0: 
Daniel@0: if isfield(net, 'mask')
Daniel@0:     nwts = size(find(net.mask), 1);
Daniel@0:     temp = zeros(1, net.nwts);
Daniel@0: else
Daniel@0:     nwts = net.nwts;
Daniel@0: end
Daniel@0: 
Daniel@0: g = zeros(ndata, nwts, net.nout);
Daniel@0: for k = 1 : net.nout
Daniel@0:   delta = zeros(1, net.nout);
Daniel@0:   delta(1, k) = 1;
Daniel@0:   for n = 1 : ndata
Daniel@0:       if isfield(net, 'mask')
Daniel@0: 	  temp = rbfbkp(net, x(n, :), z(n, :), n2(n, :), delta);
Daniel@0: 	  g(n, :, k) = temp(logical(net.mask));
Daniel@0:       else
Daniel@0: 	  g(n, :, k) = rbfbkp(net, x(n, :), z(n, :), n2(n, :),...
Daniel@0: 	      delta);
Daniel@0:       end
Daniel@0:   end
Daniel@0: end
Daniel@0: 
Daniel@0: