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