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1 function [e, edata, eprior] = rbferr(net, x, t)
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2 %RBFERR Evaluate error function for RBF network.
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3 %
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4 % Description
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5 % E = RBFERR(NET, X, T) takes a network data structure NET together
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6 % with a matrix X of input vectors and a matrix T of target vectors,
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7 % and evaluates the appropriate error function E depending on
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8 % NET.OUTFN. Each row of X corresponds to one input vector and each
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9 % row of T contains the corresponding target vector.
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10 %
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11 % [E, EDATA, EPRIOR] = RBFERR(NET, X, T) additionally returns the data
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12 % and prior components of the error, assuming a zero mean Gaussian
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13 % prior on the weights with inverse variance parameters ALPHA and BETA
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14 % taken from the network data structure NET.
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15 %
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16 % See also
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17 % RBF, RBFFWD, RBFGRAD, RBFPAK, RBFTRAIN, RBFUNPAK
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18 %
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19
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20 % Copyright (c) Ian T Nabney (1996-2001)
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21
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22 % Check arguments for consistency
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23 switch net.outfn
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24 case 'linear'
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25 errstring = consist(net, 'rbf', x, t);
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26 case 'neuroscale'
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27 errstring = consist(net, 'rbf', x);
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28 otherwise
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29 error(['Unknown output function ', net.outfn]);
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30 end
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31 if ~isempty(errstring);
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32 error(errstring);
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33 end
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34
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35 switch net.outfn
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36 case 'linear'
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37 y = rbffwd(net, x);
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38 edata = 0.5*sum(sum((y - t).^2));
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39 case 'neuroscale'
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40 y = rbffwd(net, x);
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41 y_dist = sqrt(dist2(y, y));
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42 % Take t as target distance matrix
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43 edata = 0.5.*(sum(sum((t-y_dist).^2)));
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44 otherwise
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45 error(['Unknown output function ', net.outfn]);
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46 end
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47
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48 % Compute Bayesian regularised error
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49 [e, edata, eprior] = errbayes(net, edata);
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50
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