Daniel@0: function [e, edata, eprior, y, a] = glmerr(net, x, t)
Daniel@0: %GLMERR	Evaluate error function for generalized linear model.
Daniel@0: %
Daniel@0: %	Description
Daniel@0: %	 E = GLMERR(NET, X, T) takes a generalized linear model data
Daniel@0: %	structure NET together with a matrix X of input vectors and a matrix
Daniel@0: %	T of target vectors, and evaluates the error function E. The choice
Daniel@0: %	of error function corresponds to the output unit activation function.
Daniel@0: %	Each row of X corresponds to one input vector and each row of T
Daniel@0: %	corresponds to one target vector.
Daniel@0: %
Daniel@0: %	[E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X, T) also returns the data
Daniel@0: %	and prior components of the total error.
Daniel@0: %
Daniel@0: %	[E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X) also returns a matrix Y
Daniel@0: %	giving the outputs of the models and a matrix A  giving the summed
Daniel@0: %	inputs to each output unit, where each row corresponds to one
Daniel@0: %	pattern.
Daniel@0: %
Daniel@0: %	See also
Daniel@0: %	GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMGRAD, GLMTRAIN
Daniel@0: %
Daniel@0: 
Daniel@0: %	Copyright (c) Ian T Nabney (1996-2001)
Daniel@0: 
Daniel@0: % Check arguments for consistency
Daniel@0: errstring = consist(net, 'glm', x, t);
Daniel@0: if ~isempty(errstring);
Daniel@0:   error(errstring);
Daniel@0: end
Daniel@0: 
Daniel@0: [y, a] = glmfwd(net, x);
Daniel@0: 
Daniel@0: switch net.outfn
Daniel@0: 
Daniel@0:   case 'linear'  	% Linear outputs
Daniel@0:     edata = 0.5*sum(sum((y - t).^2));
Daniel@0: 
Daniel@0:   case 'logistic'  	% Logistic outputs
Daniel@0:     edata = - sum(sum(t.*log(y) + (1 - t).*log(1 - y)));
Daniel@0: 
Daniel@0:   case 'softmax'   	% Softmax outputs
Daniel@0:     edata = - sum(sum(t.*log(y)));
Daniel@0: 
Daniel@0:   otherwise
Daniel@0:     error(['Unknown activation function ', net.outfn]);
Daniel@0: end
Daniel@0: 
Daniel@0: [e, edata, eprior] = errbayes(net, edata);