[SciPy-Dev] np.any(), np.all()

[Benny Malengier]

There is an open issue for this since 2012:
https://github.com/numpy/numpy/issues/2269

Some python and c solutions are suggested in that thread.

Op wo 4 jul. 2018 om 06:44 schreef Phillip Feldman <
phillip.m.feldman at gmail.com>:

> Performing a test (to determine whether the loop should be aborted) after
> looking at each element would cause `np.any` to run slower if all elements
> are zero, or if the first non-zero element is near the end of the array.
> Possibly a separate method that operates in this fashion is needed?
>
> Phillip
>
>
> On Mon, Jul 2, 2018 at 11:03 AM, Dieter Werthmüller <
> dieter at werthmuller.org> wrote:
>
>> Dear devs,
>>
>> Sorry for posting a numpy-issue on the scipy-list. I am not (yet)
>> subscribed to the numpy list, but I believe that the two community have a
>> big enough overlap so it shouldn't matter too much where I post it.
>>
>> I recently encountered the issue that I found that np.any(x) is sort of
>> veeeery slow for big arrays, even if every element has a non-zero value and
>> I only need a True/False response. So my thinking was that if np.any(x)
>> encounters the first non-zero value it should simply return True, which
>> should take basically no time at all if every element in the array is
>> non-zero.
>>
>> Searching for it I found the following two old issues on numpy:
>>
>> 1. https://github.com/numpy/numpy/issues/2269
>>
>> It is an issue from 2010, but got some traffic again in 2016 and 2017.
>>
>>
>> 2. https://github.com/numpy/numpy/issues/3446
>>
>> This is a related issue from 2013, reporting a potential performance
>> regression between numpy 1.6.2 and 1.7.0, that got some traffic in 2016
>> again as well.
>>
>>
>> I just wanted to ask about the opinions of devs more familiar with these
>> two functions (np.all(), np.any()). Would there be better ways to check if
>> any element in a big (1D or higher dimensions)-array is non-zero (or the
>> reverse with np.all)?
>>
>>
>> Thanks,
>> Dieter
>>
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>
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