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function p=gaussian(x,m,C);
% p=gaussian(x,m,C);
%
% Evaluate the multi-variate density with mean vector m and covariance
% matrix C for the input vector x.
%
% p=gaussian(X,m,C);
%
% Vectorized version: Here X is a matrix of column vectors, and p is
% a vector of probabilities for each vector.
d=length(m);
if size(x,1)~=d
x=x';
end
N=size(x,2);
detC = det(C);
if rcond(C)<eps
% fprintf(1,'Covariance matrix close to singular. (gaussian.m)\n');
p = zeros(N,1);
else
m=m(:);
M=m*ones(1,N);
denom=(2*pi)^(d/2)*sqrt(abs(detC));
mahal=sum(((x-M)'*inv(C)).*(x-M)',2); % Chris Bregler's trick
numer=exp(-0.5*mahal);
p=numer/denom;
end
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