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

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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-1:000000000000 0:e9a9cd732c1e
1 function [h, hdata] = mlphess_weighted(net, x, t, eso_w, hdata)
2 %MLPHESS Evaluate the Hessian matrix for a multi-layer perceptron network.
3 %
4 % Description
5 % H = MLPHESS(NET, X, T) takes an MLP network data structure NET, a
6 % matrix X of input values, and a matrix T of target values and returns
7 % the full Hessian matrix H corresponding to the second derivatives of
8 % the negative log posterior distribution, evaluated for the current
9 % weight and bias values as defined by NET.
10 %
11 % [H, HDATA] = MLPHESS(NET, X, T) returns both the Hessian matrix H and
12 % the contribution HDATA arising from the data dependent term in the
13 % Hessian.
14 %
15 % H = MLPHESS(NET, X, T, HDATA) takes a network data structure NET, a
16 % matrix X of input values, and a matrix T of target values, together
17 % with the contribution HDATA arising from the data dependent term in
18 % the Hessian, and returns the full Hessian matrix H corresponding to
19 % the second derivatives of the negative log posterior distribution.
20 % This version saves computation time if HDATA has already been
21 % evaluated for the current weight and bias values.
22 %
23 % See also
24 % MLP, HESSCHEK, MLPHDOTV, EVIDENCE
25 %
26
27 % Copyright (c) Ian T Nabney (1996-9)
28
29 % Check arguments for consistency
30 errstring = consist(net, 'mlp', x, t);
31 if ~isempty(errstring);
32 error(errstring);
33 end
34
35 if nargin == 4
36 % Data term in Hessian needs to be computed
37 hdata = datahess(net, x, t, eso_w);
38 end
39
40 [h, hdata] = hbayes(net, hdata);
41
42 % Sub-function to compute data part of Hessian
43 function hdata = datahess(net, x, t, eso_w)
44
45 hdata = zeros(net.nwts, net.nwts);
46
47 for v = eye(net.nwts);
48 hdata(find(v),:) = mlphdotv_weighted(net, x, t, eso_w, v);
49 end
50
51 return