www.pudn.com > HMM1.zip > gaussian_prob.m

function p = gaussian_prob(x, m, C, use_log) 
% GAUSSIAN_PROB Evaluate a multivariate Gaussian density. 
% p = gaussian_prob(X, m, C) 
% p(i) = N(X(:,i), m, C) where C = covariance matrix and each COLUMN of x is a datavector 
 
% p = gaussian_prob(X, m, C, 1) returns log N(X(:,i), m, C) (to prevents underflow). 
% 
% If X has size dxN, then p has size Nx1, where N = number of examples 
 
if nargin < 4, use_log = 0; end 
 
if length(m)==1 % scalar 
  x = x(:)'; 
end 
[d N] = size(x); 
%assert(length(m)==d); % slow 
m = m(:); 
M = m*ones(1,N); % replicate the mean across columns 
denom = (2*pi)^(d/2)*sqrt(abs(det(C))); 
mahal = sum(((x-M)'*inv(C)).*(x-M)',2);   % Chris Bregler's trick 
if any(mahal<0) 
  warning('mahal < 0 => C is not psd') 
end 
if use_log 
  p = -0.5*mahal - log(denom); 
else 
  p = exp(-0.5*mahal) / (denom+eps); 
end