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1 function [e, edata, eprior, y, a] = glmerr(net, x, t)
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2 %GLMERR Evaluate error function for generalized linear model.
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3 %
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4 % Description
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5 % E = GLMERR(NET, X, T) takes a generalized linear model data
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6 % structure NET together with a matrix X of input vectors and a matrix
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7 % T of target vectors, and evaluates the error function E. The choice
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8 % of error function corresponds to the output unit activation function.
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9 % Each row of X corresponds to one input vector and each row of T
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10 % corresponds to one target vector.
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11 %
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12 % [E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X, T) also returns the data
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13 % and prior components of the total error.
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14 %
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15 % [E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X) also returns a matrix Y
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16 % giving the outputs of the models and a matrix A giving the summed
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17 % inputs to each output unit, where each row corresponds to one
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18 % pattern.
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19 %
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20 % See also
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21 % GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMGRAD, GLMTRAIN
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22 %
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23
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24 % Copyright (c) Ian T Nabney (1996-2001)
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25
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26 % Check arguments for consistency
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27 errstring = consist(net, 'glm', x, t);
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28 if ~isempty(errstring);
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29 error(errstring);
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30 end
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31
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32 [y, a] = glmfwd(net, x);
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33
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34 switch net.outfn
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35
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36 case 'linear' % Linear outputs
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37 edata = 0.5*sum(sum((y - t).^2));
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38
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39 case 'logistic' % Logistic outputs
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40 edata = - sum(sum(t.*log(y) + (1 - t).*log(1 - y)));
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41
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42 case 'softmax' % Softmax outputs
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43 edata = - sum(sum(t.*log(y)));
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44
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45 otherwise
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46 error(['Unknown activation function ', net.outfn]);
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47 end
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48
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49 [e, edata, eprior] = errbayes(net, edata);
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