function [y, a] = glmfwd(net, x)%GLMFWD	Forward propagation through generalized linear model.%%	Description%	Y = GLMFWD(NET, X) takes a generalized linear model data structure%	NET together with a matrix X of input vectors, and forward propagates%	the inputs through the network to generate a matrix Y of output%	vectors. Each row of X corresponds to one input vector and each row%	of Y corresponds to one output vector.%%	[Y, A] = GLMFWD(NET, X) also returns a matrix A  giving the summed%	inputs to each output unit, where each row corresponds to one%	pattern.%%	See also%	GLM, GLMPAK, GLMUNPAK, GLMERR, GLMGRAD%%	Copyright (c) Ian T Nabney (1996-2001)% Check arguments for consistencyerrstring = consist(net, 'glm', x);if ~isempty(errstring);  error(errstring);endndata = size(x, 1);a = x*net.w1 + ones(ndata, 1)*net.b1;switch net.outfn  case 'linear'     % Linear outputs    y = a;  case 'logistic'   % Logistic outputs    % Prevent overflow and underflow: use same bounds as glmerr    % Ensure that log(1-y) is computable: need exp(a) > eps    maxcut = -log(eps);    % Ensure that log(y) is computable    mincut = -log(1/realmin - 1);    a = min(a, maxcut);    a = max(a, mincut);    y = 1./(1 + exp(-a));  case 'softmax'   	% Softmax outputs    nout = size(a,2);    % Prevent overflow and underflow: use same bounds as glmerr    % 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;  otherwise    error(['Unknown activation function ', net.outfn]);end