Mercurial > hg > camir-aes2014
annotate toolboxes/FullBNT-1.0.7/netlab3.3/gpcovarf.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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children |
rev | line source |
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wolffd@0 | 1 function covf = gpcovarf(net, x1, x2) |
wolffd@0 | 2 %GPCOVARF Calculate the covariance function for a Gaussian Process. |
wolffd@0 | 3 % |
wolffd@0 | 4 % Description |
wolffd@0 | 5 % |
wolffd@0 | 6 % COVF = GPCOVARF(NET, X1, X2) takes a Gaussian Process data structure |
wolffd@0 | 7 % NET together with two matrices X1 and X2 of input vectors, and |
wolffd@0 | 8 % computes the matrix of the covariance function values COVF. |
wolffd@0 | 9 % |
wolffd@0 | 10 % See also |
wolffd@0 | 11 % GP, GPCOVAR, GPCOVARP, GPERR, GPGRAD |
wolffd@0 | 12 % |
wolffd@0 | 13 |
wolffd@0 | 14 % Copyright (c) Ian T Nabney (1996-2001) |
wolffd@0 | 15 |
wolffd@0 | 16 errstring = consist(net, 'gp', x1); |
wolffd@0 | 17 if ~isempty(errstring); |
wolffd@0 | 18 error(errstring); |
wolffd@0 | 19 end |
wolffd@0 | 20 |
wolffd@0 | 21 if size(x1, 2) ~= size(x2, 2) |
wolffd@0 | 22 error('Number of variables in x1 and x2 must be the same'); |
wolffd@0 | 23 end |
wolffd@0 | 24 |
wolffd@0 | 25 n1 = size(x1, 1); |
wolffd@0 | 26 n2 = size(x2, 1); |
wolffd@0 | 27 beta = diag(exp(net.inweights)); |
wolffd@0 | 28 |
wolffd@0 | 29 % Compute the weighted squared distances between x1 and x2 |
wolffd@0 | 30 z = (x1.*x1)*beta*ones(net.nin, n2) - 2*x1*beta*x2' ... |
wolffd@0 | 31 + ones(n1, net.nin)*beta*(x2.*x2)'; |
wolffd@0 | 32 |
wolffd@0 | 33 switch net.covar_fn |
wolffd@0 | 34 |
wolffd@0 | 35 case 'sqexp' % Squared exponential |
wolffd@0 | 36 covf = exp(net.fpar(1) - 0.5*z); |
wolffd@0 | 37 |
wolffd@0 | 38 case 'ratquad' % Rational quadratic |
wolffd@0 | 39 nu = exp(net.fpar(2)); |
wolffd@0 | 40 covf = exp(net.fpar(1))*((ones(size(z)) + z).^(-nu)); |
wolffd@0 | 41 |
wolffd@0 | 42 otherwise |
wolffd@0 | 43 error(['Unknown covariance function ', net.covar_fn]); |
wolffd@0 | 44 end |