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.% Jianbo Shi, 1997d=length(m);if size(x,1)~=d   x=x';endN=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