Bruce Southey wrote: > Hi, > I think what you are after is the multivariate normal distribution. > Indeed > Assuming I have it correctly, it is clearer to see (and probably more > accurate to compute) in the log form as: > > -(N/2)*log(2*PI) - 0.5*log(determinant of V) - 0.5*(transpose of > (x-mu))*inverse(V)*(x-mu) > > where N is the number of observations, PI is math constant, V is the > known variance co-variance matrix, x is vector of values, mu is the > known mean. > Sure, but I need the exponential form at the end (actually, in the real code, you can choose between log form and 'standard' form, but I removed this to make the example as simple as possible). > If so, then you can vectorize the density calculation. I am not sure to understand what you mean: the computation is already vectorized in _diag_gauss_den; there is no loop there, and the function expects x to be of shape (n, d), where d is the dimension and n the number of samples. The loop in multiple_gaussian in not on samples, but on densities, that is I need to compute the normal multivariate densities on the same data but with different (vector) means and (diagonal ) variances, for which I don't see any easy way to vectorize without huge memory usage (using rank 3 arrays).
Anyway, the problem I try to understand here is not related to gaussian kernel computation, but rather on cost difference for accessing row and columns depending on the underlying storage (C or Fortran). Don't try to find flaws in _diag_gauss_den, as it is just a toy example to make my point clearer. David ------------------------------------------------------------------------- Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT & business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.php&p=sourceforge&CID=DEVDEV _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
