Hi,
I was wondering if there was any way to speed up the following code:
y = N.zeros((n, K))
for i in range(K):
y[:, i] = gauss_den(data, mu[i, :], va[i, :])
Where K is of order 1e1, n of order 1e5. Normally, gauss_den is a quite
expensive function, but the profiler tells me that the indexing y[:,i]
takes almost as much time as the gauss_den computation (which computes n
exp !). To see if the profiler is "right", i replaces with the (non
valid) following function:
y = N.zeros((n, K))
for i in range(K):
yt = gauss_den(data, mu[i, :], va[i, :])
return y
Where more than 99% of the code is spent inside gauss_den.
I guess the problem is coming from the fact that y being C order, y[:,
i] needs accessing data in a non 'linear' way. Is there a way to speed
this up ? I did something like this:
y = N.zeros((K, n))
for i in range(K):
y[i] = gauss_den(data, mu[i, :], va[i, :])
return y.T
which works, but I don't like it very much. Isn't there any other way ?
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
