function [g, gdata, gprior] = glmgrad(net, x, t)%GLMGRAD Evaluate gradient of error function for generalized linear model.%%	Description%	G = GLMGRAD(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 gradient G of the error%	function with respect to the network weights. The error function%	corresponds to the choice of output unit activation function. Each%	row of X corresponds to one input vector and each row of T%	corresponds to one target vector.%%	[G, GDATA, GPRIOR] = GLMGRAD(NET, X, T) also returns separately  the%	data and prior contributions to the gradient.%%	See also%	GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMERR, GLMTRAIN%%	Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)% Check arguments for consistencyerrstring = consist(net, 'glm', x, t);if ~isempty(errstring);  error(errstring);endy = glmfwd(net, x);delout = y - t;gw1 = x'*delout;gb1 = sum(delout, 1);gdata = [gw1(:)', gb1];if isfield(net, 'beta')  g1 = gdata*net.beta;else  g1 = gdata;endif isfield(net, 'alpha')  w = mlppak(net);  if size(net.alpha) == [1 1]    gprior = w;    g2 = net.alpha*gprior;  else    ngroups = size(net.alpha, 1);    gprior = net.index'.*(ones(ngroups, 1)*w);    g2 = net.alpha'*gprior;  endelse  gprior = 0;  g2 = 0;endg = g1 + g2;