function p=gaussian_prob(x, m, C, use_log)% p=gaussian(x,m,C);%% Evaluate the multi-variate density with mean vector m and covariance% matrix C for the input vector x.% Vectorized version: Here X is a matrix of column vectors, and p is % a vector of probabilities for each vector.if nargin<4, use_log = 0; endd=length(m);if size(x,1)~=d   x=x';endN=size(x,2);m=m(:);M=m*ones(1,N);denom=(2*pi)^(d/2)*sqrt(abs(det(C)));invC = inv(C);mahal=sum(((x-M)'*invC).*(x-M)',2);   % Chris Bregler's trickif use_log  p = -0.5*mahal - log(denom);else  numer=exp(-0.5*mahal);  p=numer/denom;end