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1 function [e, edata, eprior] = gperr(net, x, t)
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2 %GPERR Evaluate error function for Gaussian Process.
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3 %
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4 % Description
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5 % E = GPERR(NET, X, T) takes a Gaussian Process data structure NET
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6 % together with a matrix X of input vectors and a matrix T of target
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7 % vectors, and evaluates the error function E. Each row of X
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8 % corresponds to one input vector and each row of T corresponds to one
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9 % target vector.
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10 %
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11 % [E, EDATA, EPRIOR] = GPERR(NET, X, T) additionally returns the data
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12 % and hyperprior components of the error, assuming a Gaussian prior on
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13 % the weights with mean and variance parameters PRMEAN and PRVARIANCE
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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 % GP, GPCOVAR, GPFWD, GPGRAD
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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 errstring = consist(net, 'gp', x, t);
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23 if ~isempty(errstring);
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24 error(errstring);
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25 end
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26
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27 cn = gpcovar(net, x);
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28
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29 edata = 0.5*(sum(log(eig(cn, 'nobalance'))) + t'*inv(cn)*t);
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30
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31 % Evaluate the hyperprior contribution to the error.
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32 % The hyperprior is Gaussian with mean pr_mean and variance
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33 % pr_variance
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34 if isfield(net, 'pr_mean')
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35 w = gppak(net);
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36 m = repmat(net.pr_mean, size(w));
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37 if size(net.pr_mean) == [1 1]
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38 eprior = 0.5*((w-m)*(w-m)');
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39 e2 = eprior/net.pr_var;
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40 else
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41 wpr = repmat(w, size(net.pr_mean, 1), 1)';
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42 eprior = 0.5*(((wpr - m').^2).*net.index);
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43 e2 = (sum(eprior, 1))*(1./net.pr_var);
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44 end
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45 else
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46 e2 = 0;
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47 eprior = 0;
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48 end
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49
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50 e = edata + e2;
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51
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