comparison toolboxes/FullBNT-1.0.7/netlabKPM/mlphess_weighted.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [h, hdata] = mlphess_weighted(net, x, t, eso_w, hdata)
+%MLPHESS Evaluate the Hessian matrix for a multi-layer perceptron network.
+%
+%	Description
+%	H = MLPHESS(NET, X, T) takes an MLP network data structure NET, a
+%	matrix X of input values, and a matrix T of target values and returns
+%	the full Hessian matrix H corresponding to the second derivatives of
+%	the negative log posterior distribution, evaluated for the current
+%	weight and bias values as defined by NET.
+%
+%	[H, HDATA] = MLPHESS(NET, X, T) returns both the Hessian matrix H and
+%	the contribution HDATA arising from the data dependent term in the
+%	Hessian.
+%
+%	H = MLPHESS(NET, X, T, HDATA) takes a network data structure NET, a
+%	matrix X of input values, and a matrix T of  target values, together
+%	with the contribution HDATA arising from the data dependent term in
+%	the Hessian, and returns the full Hessian matrix H corresponding to
+%	the second derivatives of the negative log posterior distribution.
+%	This version saves computation time if HDATA has already been
+%	evaluated for the current weight and bias values.
+%
+%	See also
+%	MLP, HESSCHEK, MLPHDOTV, EVIDENCE
+%
+
+%	Copyright (c) Ian T Nabney (1996-9)
+
+% Check arguments for consistency
+errstring = consist(net, 'mlp', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+if nargin == 4
+  % Data term in Hessian needs to be computed
+  hdata = datahess(net, x, t, eso_w);
+end
+
+[h, hdata] = hbayes(net, hdata);
+
+% Sub-function to compute data part of Hessian
+function hdata = datahess(net, x, t, eso_w)
+
+hdata = zeros(net.nwts, net.nwts);
+
+for v = eye(net.nwts);
+  hdata(find(v),:) = mlphdotv_weighted(net, x, t, eso_w, v);
+end
+
+return