Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/netlab3.3/netevfwd.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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-1:000000000000 | 0:e9a9cd732c1e |
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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 |