function [g, gdata, gprior] = gbayes(net, gdata)%GBAYES	Evaluate gradient of Bayesian error function for network.%%	Description%	G = GBAYES(NET, GDATA) takes a network data structure NET together%	the data contribution to the error gradient for a set of inputs and%	targets. It returns the regularised error gradient using any zero%	mean Gaussian priors on the weights defined in NET.  In addition, if%	a MASK is defined in NET, then the entries in G that correspond to%	weights with a 0 in the mask are removed.%%	[G, GDATA, GPRIOR] = GBAYES(NET, GDATA) additionally returns the data%	and prior components of the error.%%	See also%	ERRBAYES, GLMGRAD, MLPGRAD, RBFGRAD%%	Copyright (c) Ian T Nabney (1996-2001)% Evaluate the data contribution to the gradient.if (isfield(net, 'mask'))   gdata = gdata(logical(net.mask));endif isfield(net, 'beta')  g1 = gdata*net.beta;else  g1 = gdata;end% Evaluate the prior contribution to the gradient.if isfield(net, 'alpha')   w = netpak(net);   if size(net.alpha) == [1 1]      gprior = w;      g2 = net.alpha*gprior;   else      if (isfield(net, 'mask'))         nindx_cols = size(net.index, 2);         nmask_rows = size(find(net.mask), 1);         index = reshape(net.index(logical(repmat(net.mask, ...            1, nindx_cols))), nmask_rows, nindx_cols);      else         index = net.index;      end            ngroups = size(net.alpha, 1);      gprior = index'.*(ones(ngroups, 1)*w);      g2 = net.alpha'*gprior;   endelse  gprior = 0;  g2 = 0;endg = g1 + g2;