very interesting, however, it would be better if you provide exact code. I didn't use timeit and I have some troubles with the module.
Regards, D. David Cournapeau wrote: > Hi, > > While profiling some code, I noticed that sum in numpy is kind of > slow once you use axis argument: > > import numpy as N > a = N.random.randn(1e5, 30) > %timeit N.sum(a) #-> 26.8ms > %timeit N.sum(a, 1) #-> 65.5ms > %timeit N.sum(a, 0) #-> 141ms > > Now, if I use some tricks, I get: > > %timeit N.sum(a) #-> 26.8 ms > %timeit N.dot(a, N.ones(a.shape[1], a.dtype)) #-> 11.3ms > %timeit N.dot(N.ones((1, a.shape[0]), a.dtype), a) #-> 15.5ms > > I realize that dot uses optimized libraries (atlas in my case) and all, > but is there any way to improve this situation ? > > Cheers, > > David > _______________________________________________ > Numpy-discussion mailing list > [email protected] > http://projects.scipy.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ Numpy-discussion mailing list [email protected] http://projects.scipy.org/mailman/listinfo/numpy-discussion
