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1 function jac = rbfjacob(net, x)
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2 %RBFJACOB Evaluate derivatives of RBF network outputs with respect to inputs.
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3 %
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4 % Description
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5 % G = RBFJACOB(NET, X) takes a network data structure NET and a matrix
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6 % of input vectors X and returns a three-index matrix G whose I, J, K
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7 % element contains the derivative of network output K with respect to
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8 % input parameter J for input pattern I.
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9 %
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10 % See also
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11 % RBF, RBFGRAD, RBFBKP
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12 %
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13
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14 % Copyright (c) Ian T Nabney (1996-2001)
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15
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16 % Check arguments for consistency
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17 errstring = consist(net, 'rbf', x);
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18 if ~isempty(errstring);
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19 error(errstring);
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20 end
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21
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22 if ~strcmp(net.outfn, 'linear')
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23 error('Function only implemented for linear outputs')
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24 end
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25
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26 [y, z, n2] = rbffwd(net, x);
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27
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28 ndata = size(x, 1);
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29 jac = zeros(ndata, net.nin, net.nout);
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30 Psi = zeros(net.nin, net.nhidden);
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31 % Calculate derivative of activations wrt n2
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32 switch net.actfn
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33 case 'gaussian'
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34 dz = -z./(ones(ndata, 1)*net.wi);
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35 case 'tps'
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36 dz = 2*(1 + log(n2+(n2==0)));
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37 case 'r4logr'
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38 dz = 2*(n2.*(1+2.*log(n2+(n2==0))));
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39 otherwise
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40 error(['Unknown activation function ', net.actfn]);
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41 end
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42
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43 % Ignore biases as they cannot affect Jacobian
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44 for n = 1:ndata
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45 Psi = (ones(net.nin, 1)*dz(n, :)).* ...
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46 (x(n, :)'*ones(1, net.nhidden) - net.c');
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47 % Now compute the Jacobian
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48 jac(n, :, :) = Psi * net.w2;
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49 end |
