You could move some of the cost to index-creation time by converting the
per-row indices into flattened indices:
In [1]: a = np.random.random((5, 6))
In [2]: i = a.argmax(axis=1)
In [3]: a[np.arange(len(a)), i]
Out[3]: array([0.95774465, 0.90940106, 0.98025448, 0.97836906, 0.80483784])
In [4
On 01-11-2019 09:51, Allan Haldane wrote:
> 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 inde
On 31-10-2019 01:44, Elliot Hallmark wrote:
> Depends on how big your array is. Numpy C code is 150x+ faster than
> python overhead. Fancy indexing can be expensive in my experience.
> Without trying I'd guess arr[:, argmax(arr, axis=1)] does what you want,
It does not.
> but even if it is, try
> On my system plain fancy indexing is fastest
Hardly surprising, since take_along_axis is doing that under the hood,
after constructing the index for you :)
https://github.com/numpy/numpy/blob/v1.17.0/numpy/lib/shape_base.py#L58-L172
I deliberately didn't expose the internal function that const
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 µ
Depends on how big your array is. Numpy C code is 150x+ faster than python
overhead. Fancy indexing can be expensive in my experience. Without trying
I'd guess arr[:, argmax(arr, axis=1)] does what you want, but even if it
is, try profiling the two and see. I highly doubt such would be even 1% o
On 30/10/2019 22:42, Elliot Hallmark wrote:
> I wouldn't be surprised at all if calling max in addition to argmax
> wasn't as fast or faster than indexing the array using argmax.
> Regardless, just use that then profile when you're done with the
> whole thing and see if there's any gains to be made
I wouldn't be surprised at all if calling max in addition to argmax wasn't
as fast or faster than indexing the array using argmax. Regardless, just
use that then profile when you're done with the whole thing and see if
there's any gains to be made. Very likely not here.
-elliot
On Wed, Oct 30, 20
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 ar
max(axis=1)?
On Wed, Oct 30, 2019, 7:33 PM Daniele Nicolodi 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 bette
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:
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