[Numpy-discussion] Consider improving numpy.outer's behavior with zero-dimensional vectors

[Neil Girdhar]

It also appears that cumsum has a lot of unnecessary overhead over
add.accumulate:

In [51]: %timeit np.add.accumulate(a)
The slowest run took 46.31 times longer than the fastest. This could mean
that an intermediate result is being cached
1000000 loops, best of 3: 372 ns per loop

In [52]: %timeit np.cum
np.cumprod     np.cumproduct  np.cumsum

In [52]: %timeit np.cumsum(a)
The slowest run took 18.44 times longer than the fastest. This could mean
that an intermediate result is being cached
1000000 loops, best of 3: 912 ns per loop

In [53]: %timeit np.add.accumulate(a.flatten())
The slowest run took 25.59 times longer than the fastest. This could mean
that an intermediate result is being cached
1000000 loops, best of 3: 834 ns per loop


On Tue, Apr 14, 2015 at 7:42 AM, Neil Girdhar <mistersheik at gmail.com> wrote:

> Okay, but by the same token, why do we have cumsum?  Isn't it identical to
>
> np.add.accumulate
>
> — or if you're passing in multidimensional data —
>
> np.add.accumulate(a.flatten())
>
> ?
>
> add.accumulate feels more generic, would make the other ufunc things more
> discoverable, and is self-documenting.
>
> Similarly, cumprod is just np.multiply.accumulate.
>
> Best,
>
> Neil
>
>
> On Sat, Apr 11, 2015 at 12:49 PM, Nathaniel Smith <njs at pobox.com> wrote:
>
>> Documentation and a call to warnings.warn(DeprecationWarning(...)), I
>> guess.
>>
>> On Sat, Apr 11, 2015 at 12:39 PM, Neil Girdhar <mistersheik at gmail.com>
>> wrote:
>> > I would be happy to, but I'm not sure what that involves?  It's just a
>> > documentation changelist?
>> >
>> > On Sat, Apr 11, 2015 at 12:29 PM, Nathaniel Smith <njs at pobox.com>
>> wrote:
>> >>
>> >> On Sat, Apr 11, 2015 at 12:06 PM, Neil Girdhar <mistersheik at gmail.com>
>> >> wrote:
>> >> >> On Wed, Apr 8, 2015 at 7:34 PM, Neil Girdhar <mistersheik at gmail.com
>> >
>> >> >> wrote:
>> >> >> > Numpy's outer product works fine with vectors. However, I seem to
>> >> >> > always
>> >> >> > want len(outer(a, b).shape) to be equal to len(a.shape) +
>> >> >> > len(b.shape).
>> >> >> > Wolfram-alpha seems to agree
>> >> >> > https://reference.wolfram.com/language/ref/Outer.html with
>> respect to
>> >> >> > matrix
>> >> >> > outer products.
>> >> >> You're probably right that this is the correct definition of the
>> outer
>> >> >> product in an n-dimensional world. But this seems to go beyond being
>> >> >> just a bug in handling 0-d arrays (which is the kind of corner case
>> >> >> we've fixed in the past); np.outer is documented to always ravel its
>> >> >> inputs to 1d.
>> >> >> In fact the implementation is literally just:
>> >> >> a = asarray(a)
>> >> >> b = asarray(b)
>> >> >> return multiply(a.ravel()[:, newaxis], b.ravel()[newaxis,:], out)
>> >> >> Sebastian's np.multiply.outer is much more generic and effective.
>> >> >> Maybe we should just deprecate np.outer? I don't see what use it
>> >> >> serves. (When and whether it actually got removed after being
>> >> >> deprecated would depend on how much use it actually gets in real
>> code,
>> >> >> which I certainly don't know while typing a quick email. But we
>> could
>> >> >> start telling people not to use it any time.)
>> >> >
>> >> >
>> >> > +1 with everything you said.
>> >>
>> >> Want to write a PR? :-)
>> >>
>> >> --
>> >> Nathaniel J. Smith -- http://vorpus.org
>> >> _______________________________________________
>> >> NumPy-Discussion mailing list
>> >> NumPy-Discussion at scipy.org
>> >> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>> >
>> >
>> >
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>>
>>
>>
>> --
>> Nathaniel J. Smith -- http://vorpus.org
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion at scipy.org
>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>
>
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