wolffd@0: function y = gauss(mu, covar, x)
wolffd@0: %GAUSS	Evaluate a Gaussian distribution.
wolffd@0: %
wolffd@0: %	Description
wolffd@0: %
wolffd@0: %	Y = GAUSS(MU, COVAR, X) evaluates a multi-variate Gaussian  density
wolffd@0: %	in D-dimensions at a set of points given by the rows of the matrix X.
wolffd@0: %	The Gaussian density has mean vector MU and covariance matrix COVAR.
wolffd@0: %
wolffd@0: %	See also
wolffd@0: %	GSAMP, DEMGAUSS
wolffd@0: %
wolffd@0: 
wolffd@0: %	Copyright (c) Ian T Nabney (1996-2001)
wolffd@0: 
wolffd@0: [n, d] = size(x);
wolffd@0: 
wolffd@0: [j, k] = size(covar);
wolffd@0: 
wolffd@0: % Check that the covariance matrix is the correct dimension
wolffd@0: if ((j ~= d) | (k ~=d))
wolffd@0:   error('Dimension of the covariance matrix and data should match');
wolffd@0: end
wolffd@0:    
wolffd@0: invcov = inv(covar);
wolffd@0: mu = reshape(mu, 1, d);    % Ensure that mu is a row vector
wolffd@0: 
wolffd@0: x = x - ones(n, 1)*mu;
wolffd@0: fact = sum(((x*invcov).*x), 2);
wolffd@0: 
wolffd@0: y = exp(-0.5*fact);
wolffd@0: 
wolffd@0: y = y./sqrt((2*pi)^d*det(covar));