David Cournapeau wrote: > 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. Why not?
> Isn't there any other way That depends on the details of gauss_den. A general note: for this kind of microoptimization puzzle, it's much easier to help if you can post a self contained example, preferably something fairly simple that still illustrates the speed issue, that we can experiment with. -tim ------------------------------------------------------------------------- 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
