Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/netlab3.3/gauss.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 = gauss(mu, covar, x) | |
2 %GAUSS Evaluate a Gaussian distribution. | |
3 % | |
4 % Description | |
5 % | |
6 % Y = GAUSS(MU, COVAR, X) evaluates a multi-variate Gaussian density | |
7 % in D-dimensions at a set of points given by the rows of the matrix X. | |
8 % The Gaussian density has mean vector MU and covariance matrix COVAR. | |
9 % | |
10 % See also | |
11 % GSAMP, DEMGAUSS | |
12 % | |
13 | |
14 % Copyright (c) Ian T Nabney (1996-2001) | |
15 | |
16 [n, d] = size(x); | |
17 | |
18 [j, k] = size(covar); | |
19 | |
20 % Check that the covariance matrix is the correct dimension | |
21 if ((j ~= d) | (k ~=d)) | |
22 error('Dimension of the covariance matrix and data should match'); | |
23 end | |
24 | |
25 invcov = inv(covar); | |
26 mu = reshape(mu, 1, d); % Ensure that mu is a row vector | |
27 | |
28 x = x - ones(n, 1)*mu; | |
29 fact = sum(((x*invcov).*x), 2); | |
30 | |
31 y = exp(-0.5*fact); | |
32 | |
33 y = y./sqrt((2*pi)^d*det(covar)); |