Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/netlab3.3/mdnerr.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 e = mdnerr(net, x, t) | |
2 %MDNERR Evaluate error function for Mixture Density Network. | |
3 % | |
4 % Description | |
5 % E = MDNERR(NET, X, T) takes a mixture density network data structure | |
6 % NET, a matrix X of input vectors and a matrix T of target vectors, | |
7 % and evaluates the error function E. The error function is the | |
8 % negative log likelihood of the target data under the conditional | |
9 % density given by the mixture model parameterised by the MLP. Each | |
10 % row of X corresponds to one input vector and each row of T | |
11 % corresponds to one target vector. | |
12 % | |
13 % See also | |
14 % MDN, MDNFWD, MDNGRAD | |
15 % | |
16 | |
17 % Copyright (c) Ian T Nabney (1996-2001) | |
18 % David J Evans (1998) | |
19 | |
20 % Check arguments for consistency | |
21 errstring = consist(net, 'mdn', x, t); | |
22 if ~isempty(errstring) | |
23 error(errstring); | |
24 end | |
25 | |
26 % Get the output mixture models | |
27 mixparams = mdnfwd(net, x); | |
28 | |
29 % Compute the probabilities of mixtures | |
30 probs = mdnprob(mixparams, t); | |
31 % Compute the error | |
32 e = sum( -log(max(eps, sum(probs, 2)))); | |
33 |