Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/netlab3.3/rbfjacob.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
---|---|
date | Tue, 10 Feb 2015 15:05:51 +0000 |
parents | |
children |
line wrap: on
line source
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 consistency errstring = consist(net, 'rbf', x); if ~isempty(errstring); error(errstring); end if ~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 n2 switch net.actfn case '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 Jacobian for 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