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