comparison toolboxes/FullBNT-1.0.7/netlab3.3/netevfwd.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 [y, extra, invhess] = netevfwd(w, net, x, t, x_test, invhess)
2 %NETEVFWD Generic forward propagation with evidence for network
3 %
4 % Description
5 % [Y, EXTRA] = NETEVFWD(W, NET, X, T, X_TEST) takes a network data
6 % structure NET together with the input X and target T training data
7 % and input test data X_TEST. It returns the normal forward propagation
8 % through the network Y together with a matrix EXTRA which consists of
9 % error bars (variance) for a regression problem or moderated outputs
10 % for a classification problem.
11 %
12 % The optional argument (and return value) INVHESS is the inverse of
13 % the network Hessian computed on the training data inputs and targets.
14 % Passing it in avoids recomputing it, which can be a significant
15 % saving for large training sets.
16 %
17 % See also
18 % MLPEVFWD, RBFEVFWD, GLMEVFWD, FEVBAYES
19 %
20
21 % Copyright (c) Ian T Nabney (1996-2001)
22
23 func = [net.type, 'evfwd'];
24 net = netunpak(net, w);
25 if nargin == 5
26 [y, extra, invhess] = feval(func, net, x, t, x_test);
27 else
28 [y, extra, invhess] = feval(func, net, x, t, x_test, invhess);
29 end