annotate toolboxes/FullBNT-1.0.7/netlab3.3/gauss.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 function y = gauss(mu, covar, x)
wolffd@0 2 %GAUSS Evaluate a Gaussian distribution.
wolffd@0 3 %
wolffd@0 4 % Description
wolffd@0 5 %
wolffd@0 6 % Y = GAUSS(MU, COVAR, X) evaluates a multi-variate Gaussian density
wolffd@0 7 % in D-dimensions at a set of points given by the rows of the matrix X.
wolffd@0 8 % The Gaussian density has mean vector MU and covariance matrix COVAR.
wolffd@0 9 %
wolffd@0 10 % See also
wolffd@0 11 % GSAMP, DEMGAUSS
wolffd@0 12 %
wolffd@0 13
wolffd@0 14 % Copyright (c) Ian T Nabney (1996-2001)
wolffd@0 15
wolffd@0 16 [n, d] = size(x);
wolffd@0 17
wolffd@0 18 [j, k] = size(covar);
wolffd@0 19
wolffd@0 20 % Check that the covariance matrix is the correct dimension
wolffd@0 21 if ((j ~= d) | (k ~=d))
wolffd@0 22 error('Dimension of the covariance matrix and data should match');
wolffd@0 23 end
wolffd@0 24
wolffd@0 25 invcov = inv(covar);
wolffd@0 26 mu = reshape(mu, 1, d); % Ensure that mu is a row vector
wolffd@0 27
wolffd@0 28 x = x - ones(n, 1)*mu;
wolffd@0 29 fact = sum(((x*invcov).*x), 2);
wolffd@0 30
wolffd@0 31 y = exp(-0.5*fact);
wolffd@0 32
wolffd@0 33 y = y./sqrt((2*pi)^d*det(covar));