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function [e, edata, eprior, y, a] = glmerr_weighted(net, x, t, eso_w)
%GLMERR Evaluate error function for generalized linear model.
%
% Description
% E = GLMERR(NET, X, T) takes a generalized linear model data
% structure NET together with a matrix X of input vectors and a matrix
% T of target vectors, and evaluates the error function E. The choice
% of error function corresponds to the output unit activation function.
% Each row of X corresponds to one input vector and each row of T
% corresponds to one target vector.
%
% [E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X, T) also returns the data
% and prior components of the total error.
%
% [E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X) also returns a matrix Y
% giving the outputs of the models and a matrix A giving the summed
% inputs to each output unit, where each row corresponds to one
% pattern.
%
% See also
% GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMGRAD, GLMTRAIN
%
% Copyright (c) Ian T Nabney (1996-9)
% Check arguments for consistency
errstring = consist(net, 'glm', x, t);
if ~isempty(errstring);
error(errstring);
end
[y, a] = glmfwd(net, x);
%switch net.actfn
switch net.outfn
case 'softmax' % Softmax outputs
nout = size(a,2);
% Ensure that sum(exp(a), 2) does not overflow
maxcut = log(realmax) - log(nout);
% Ensure that exp(a) > 0
mincut = log(realmin);
a = min(a, maxcut);
a = max(a, mincut);
temp = exp(a);
y = temp./(sum(temp, 2)*ones(1,nout));
% Ensure that log(y) is computable
y(y<realmin) = realmin;
e_app=sum(t.*log(y),2);
edata = - eso_w'*e_app;
otherwise
error(['Unknown activation function ', net.actfn]);
end
[e, edata, eprior] = errbayes(net, edata);
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