Daniel@0: function [y, a] = glmfwd(net, x)
Daniel@0: %GLMFWD	Forward propagation through generalized linear model.
Daniel@0: %
Daniel@0: %	Description
Daniel@0: %	Y = GLMFWD(NET, X) takes a generalized linear model data structure
Daniel@0: %	NET together with a matrix X of input vectors, and forward propagates
Daniel@0: %	the inputs through the network to generate a matrix Y of output
Daniel@0: %	vectors. Each row of X corresponds to one input vector and each row
Daniel@0: %	of Y corresponds to one output vector.
Daniel@0: %
Daniel@0: %	[Y, A] = GLMFWD(NET, X) also returns 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, GLMERR, GLMGRAD
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);
Daniel@0: if ~isempty(errstring);
Daniel@0:   error(errstring);
Daniel@0: end
Daniel@0: 
Daniel@0: ndata = size(x, 1);
Daniel@0: 
Daniel@0: a = x*net.w1 + ones(ndata, 1)*net.b1;
Daniel@0: 
Daniel@0: switch net.outfn
Daniel@0: 
Daniel@0:   case 'linear'     % Linear outputs
Daniel@0:     y = a;
Daniel@0: 
Daniel@0:   case 'logistic'   % Logistic outputs
Daniel@0:     % Prevent overflow and underflow: use same bounds as glmerr
Daniel@0:     % Ensure that log(1-y) is computable: need exp(a) > eps
Daniel@0:     maxcut = -log(eps);
Daniel@0:     % Ensure that log(y) is computable
Daniel@0:     mincut = -log(1/realmin - 1);
Daniel@0:     a = min(a, maxcut);
Daniel@0:     a = max(a, mincut);
Daniel@0:     y = 1./(1 + exp(-a));
Daniel@0: 
Daniel@0:   case 'softmax'   	% Softmax outputs
Daniel@0:     nout = size(a,2);
Daniel@0:     % Prevent overflow and underflow: use same bounds as glmerr
Daniel@0:     % Ensure that sum(exp(a), 2) does not overflow
Daniel@0:     maxcut = log(realmax) - log(nout);
Daniel@0:     % Ensure that exp(a) > 0
Daniel@0:     mincut = log(realmin);
Daniel@0:     a = min(a, maxcut);
Daniel@0:     a = max(a, mincut);
Daniel@0:     temp = exp(a);
Daniel@0:     y = temp./(sum(temp, 2)*ones(1,nout));
Daniel@0:     % Ensure that log(y) is computable
Daniel@0:     y(y<realmin) = realmin;
Daniel@0: 
Daniel@0:   otherwise
Daniel@0:     error(['Unknown activation function ', net.outfn]);
Daniel@0: end