function y = pdfgauss(X, arg1, arg2 )% PDFGAUSS Evaluates multivariate Gaussian distribution.%% Synopsis:%  y = pdfgauss(X, Mean, Cov)%  y = pdfgauss(X, model )%% Description:%  y = pdfgauss(X, Mean, Cov) evaluates a multi-variate Gaussian %  probability density function(s) for given input column vectors in X.%  Mean [dim x ncomp] and Cov [dim x dim x ncomp] describe a set of %  ncomp Gaussian distributions to be evaluted such that%%  y(i,j) = exp(-0.5(mahalan(X(:,j),Mean(:,i),Cov(:,:,i) )))/norm_const%%  where i=1:ncomp and j=1:size(X,2). If the Gaussians are%  uni-variate then the covariaves can be given as a vector%  Cov = [Cov_1, Cov_2, ..., Cov_comp].%%  y = pdfgauss( X, model ) takes Gaussian parameters from structure%  fields model.Mean and model.Cov.%% Input:%  X [dim x num_data] Input matrix of column vectors.%  Mean [dim x ncomp] Means of Gaussians.%  Cov [dim x dim x ncomp] Covarince matrices.%% Output:%  y [ncomp x num_data] Values of probability density function.%% Example:% % Univariate case%  x = linspace(-5,5,100);%  y = pdfgauss(x,0,1);%  figure; plot(x,y)%% Multivariate case%  [Ax,Ay] = meshgrid(linspace(-5,5,100), linspace(-5,5,100));%  y = pdfgauss([Ax(:)';Ay(:)'],[0;0],[1 0.5; 0.5 1]);%  figure; surf( Ax, Ay, reshape(y,100,100)); shading interp;%% See also %  GSAMP, PDFGMM.%% About: Statistical Pattern Recognition Toolbox% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac% <a href="http://www.cvut.cz">Czech Technical University Prague</a>% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>% Modifications:% 28-apr-2004, VF% process input argumentsif nargin < 3,  arg1 = c2s(arg1);  Mean = arg1.Mean;  Cov =  arg1.Cov;else  Mean = arg1;  Cov =  arg2;end% get dimensions[dim,num_data] = size(X);ncomp = size(Mean,2);% univariate variances can be given as a vectorif size(Cov,1) ~= size(Cov,2), Cov = reshape(Cov,1,1,ncomp); end% alloc memoryy = zeros(ncomp,num_data);% evaluate pdf for each componentfor i=1:ncomp,  dist = mahalan(X,Mean(:,i),Cov(:,:,i));  y(i,:) = exp(-0.5*dist)/sqrt((2*pi)^dim*det(Cov(:,:,i)));endreturn;