wolffd@0: function [h, hdata] = hbayes(net, hdata) 
wolffd@0: %HBAYES	Evaluate Hessian of Bayesian error function for network.
wolffd@0: %
wolffd@0: %	Description
wolffd@0: %	H = HBAYES(NET, HDATA) takes a network data structure NET together
wolffd@0: %	the data contribution to the Hessian for a set of inputs and targets.
wolffd@0: %	It returns the regularised Hessian using any zero mean Gaussian
wolffd@0: %	priors on the weights defined in NET.  In addition, if a MASK is
wolffd@0: %	defined in NET, then the entries in H that correspond to weights with
wolffd@0: %	a 0 in the mask are removed.
wolffd@0: %
wolffd@0: %	[H, HDATA] = HBAYES(NET, HDATA) additionally returns the data
wolffd@0: %	component of the Hessian.
wolffd@0: %
wolffd@0: %	See also
wolffd@0: %	GBAYES, GLMHESS, MLPHESS, RBFHESS
wolffd@0: %
wolffd@0: 
wolffd@0: %	Copyright (c) Ian T Nabney (1996-2001)
wolffd@0: 
wolffd@0: if (isfield(net, 'mask'))
wolffd@0:   % Extract relevant entries in Hessian
wolffd@0:   nmask_rows = size(find(net.mask), 1);
wolffd@0:   hdata = reshape(hdata(logical(net.mask*(net.mask'))), ...
wolffd@0:      nmask_rows, nmask_rows);
wolffd@0:   nwts = nmask_rows;
wolffd@0: else
wolffd@0:   nwts = net.nwts;
wolffd@0: end
wolffd@0: if isfield(net, 'beta')
wolffd@0:   h = net.beta*hdata;
wolffd@0: else
wolffd@0:   h = hdata;
wolffd@0: end
wolffd@0: 
wolffd@0: if isfield(net, 'alpha')
wolffd@0:   if size(net.alpha) == [1 1]
wolffd@0:     h = h + net.alpha*eye(nwts);
wolffd@0:   else
wolffd@0:     if isfield(net, 'mask')
wolffd@0:       nindx_cols = size(net.index, 2);
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:     h = h + diag(index*net.alpha);
wolffd@0:   end 
wolffd@0: end