hi, its not np.any that is slow in this case its np.array([A, B, C]) np.dstack([A, B, C]) is better but writing it like this has the same performance as your code: a = empty([3] list(A.shape) a[0] = A>5; a[1] = B<2; a[2] = A>10; np.any(a, 0)
I'll check if creating an array from a sequence can be improved for this case. On 05.09.2013 10:54, Graeme B. Bell wrote: > > > Hi Julian, > > Thanks for the post. It's great to hear that the main numpy function is > improving in 1.8, though I think there is still plenty of value here for > performance junkies :-) > > I don't have 1.8beta installed (and I can't conveniently install it on my > machines just now). If you have time, and have the beta installed, could you > try this and mail me the output from the benchmark? I'm curious to know. > > # git clone https://github.com/gbb/numpy-fast-any-all.git > # cd numpy-fast-any-all > # python test-fast-any-all.py > > Graeme > > > On Sep 4, 2013, at 7:38 PM, Julian Taylor <jtaylor.deb...@googlemail.com> > wrote: > >>> >>> The result is 14 to 17x faster than np.any() for this use case.* >> >> any/all and boolean operations have been significantly speed up by >> vectorization in numpy 1.8 [0]. >> They are now around 10 times faster than before, especially if the >> boolean array fits into one of the cpu caching layers. > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
