my thought was to try `take` or `take_along_axis`:
ind = np.argmin(a, axis=1) np.take_along_axis(a, ind[:,None], axis=1)
But those functions tend to simply fall back to fancy indexing, and are pretty slow. On my system plain fancy indexing is fastest:
%timeit a[np.arange(N),ind]
1.58 µs ± 18.1 ns per loop
%timeit np.take_along_axis(a, ind[:,None], axis=1)
6.49 µs ± 57.3 ns per loop
%timeit np.min(a, axis=1)
9.51 µs ± 64.1 ns per loop
Probably `take_along_axis` was designed with uses like yours in mind, but it is not very optimized.
(I think numpy is lacking a category of efficient indexing/search/reduction functions, like 'findfirst', 'groupby', short-circuiting any_*/all_*/nonzero, the proposed oindex/vindex, better gufunc broadcasting. There is slow but gradual infrastructure work towards these, potentially).
Cheers, Allan
On 10/30/19 11:31 PM, Daniele Nicolodi wrote:
On 30/10/2019 19:10, Neal Becker wrote:
max(axis=1)?
Hi Neal,
I should have been more precise in stating the problem. Getting the values in the array for which I'm looking at the maxima is only one step in a more complex piece of code for which I need the indexes along the second axis of the array. I would like to avoid to have to iterate the array more than once.
Thank you!
Cheers, Dan
On Wed, Oct 30, 2019, 7:33 PM Daniele Nicolodi <daniele@grinta.net mailto:daniele@grinta.net> wrote:
Hello, this is a very basic question, but I cannot find a satisfying answer. Assume a is a 2D array and that I get the index of the maximum value along the second dimension: i = a.argmax(axis=1) Is there a better way to get the value of the maximum array entries along the second axis other than: v = a[np.arange(len(a)), i] ?? Thank you. Cheers, Daniele _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org <mailto:NumPy-Discussion@python.org> https://mail.python.org/mailman/listinfo/numpy-discussion
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