mogp.m

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function p = mogP(x,means,covs,priors)
% p = mogP(x,means,covs,priors)
%
% Calculate the probability density for each of the clusters of a
% mixture of Gaussians (note that P is not normalized!)

% Copyright: D. Tax, R.P.W. Duin, [e-mail]
% Faculty of Applied Physics, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands


[N,d] = size(x);
k = length(priors);
p = zeros(N,k);

covtype = ndims(covs);
if ((covtype==2)&(size(covs,2)==1))
  covtype = 1;
end
switch covtype
  case 1
    D = distm(x,means);
    sig = ones(N,1)*(2.*covs');
    Z = (pi*sig).^(d/2);
    p = exp(-(D./sig))./Z;
  case 2
    Z = (2*pi).^(d/2);
    sig = prod(sqrt(covs),2);
    for i=1:k
      dif = x - ones(N,1)*means(i,:);
      p(:,i) = exp(-sum((dif.*dif)./(ones(N,1)*covs(i,:)) ,2)/2) ./ ...
                   (Z*sig(i));
    end
  case 3
    Z = (2*pi).^(d/2);
    for i=1:k
      dif = x - ones(N,1)*means(i,:);
      c = chol(squeeze(covs(i,:,:)));
      Dmah = dif/c;
      p(:,i) = exp(-sum(Dmah.*Dmah,2)/2) ./ (Z*prod(diag(c)));
    end
  otherwise
   error('The covariance matrix parameter is not well-defined');
end

% include the priors:
p = p.*(ones(N,1)*priors);

return