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