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