[Numpy-discussion] Use-case for np.choose
josef.pktd at gmail.com
josef.pktd at gmail.com
Sun Nov 8 00:06:35 EST 2009
On Sat, Nov 7, 2009 at 7:53 PM, David Goldsmith <d.l.goldsmith at gmail.com> wrote:
> Thanks, Anne.
>
> On Sat, Nov 7, 2009 at 1:32 PM, Anne Archibald <peridot.faceted at gmail.com>
> wrote:
>>
>> 2009/11/7 David Goldsmith <d.l.goldsmith at gmail.com>:
>
> <snip>
>
>>
>> > Also, my experimenting suggests that the index array ('a', the first
>> > argument in the func. sig.) *must* have shape (choices.shape[-1],) -
>> > someone
>> > please let me know ASAP if this is not the case, and please furnish me
>> > w/ a
>> > counterexample because I was unable to generate one myself.
>>
>> It seems like a and each of the choices must have the same shape
>
> So in essence, at least as it presently functions, the shape of 'a'
> *defines* what the individual choices are within 'choices`, and if 'choices'
> can't be parsed into an integer number of such individual choices, that's
> when an exception is raised?
>
>>
>> (with
>>
>> the exception that choices acn be scalars), but I would consider this
>> a bug.
>
> OK, then we definitely need more people to opine on this, because, if the
> the two don't match, our established policy is to document *desired*
> behavior, not extant behavior (and file a bug ticket).
>
>>
>> Really, a and all the choices should be broadcast to the same
>> shape. Or maybe it doesn't make sense to broadcast a - it could be
>
> Thus begging the question: does anyone actually have an extant, specific
> use-case?
>
>>
>> valuable to know that the result is always exactly the same shape as a
>> - but broadcasting all the choice arrays presents an important
>> improvement of choose over fancy indexing.
>
> Then perhaps we need either another function, or a flag specifying which
> behavior this one should exhibit.
>
>>
>> There's a reason choose
>> accepts a sequence of arrays as its second argument, rather than a
>> higher-dimensional array.
>
> And that reason is probably supposed to be transparent above, but I've
> confused it by this point, so can you please reiterate it here, in so many
> words. :-)
>From looking at a few special cases, I think that full broadcasting rules apply.
First a and all choice array are broadcast to the same shape,
then the selection is done according to the elements of (the broadcasted) a.
For broadcasting it doesn't matter whether they are scalars or 1d or 2d or
a 2d single column array. (I haven't tried more than 2 dimensions)
The examples look a bit messy, but broadcasting is relatively straightforward.
(I think, np.where is a bit easier to use because `a` is just a
condition and doesn't
require an index array)
Josef
>>> np.choose(1, (3,4))
4
>>> np.choose(0, (3,4))
3
>>> np.choose(0, (np.arange(3)[:,None],np.arange(4),0))
array([[0, 0, 0, 0],
[1, 1, 1, 1],
[2, 2, 2, 2]])
>>> np.choose(2, (np.arange(3)[:,None],np.arange(4),0))
array([[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]])
>>> np.choose(1, (np.arange(3)[:,None],np.arange(4),0))
array([[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3]])
>>> np.choose([1,2,0,0], (np.arange(3)[:,None],np.arange(4),0))
array([[0, 0, 0, 0],
[0, 0, 1, 1],
[0, 0, 2, 2]])
>>> np.choose(np.array([[1,2,0,0]]), (np.arange(3)[:,None],np.arange(4),0))
array([[0, 0, 0, 0],
[0, 0, 1, 1],
[0, 0, 2, 2]])
>>> np.choose(np.array([[1,2,0]]).T, (np.arange(3)[:,None],np.arange(4),0))
array([[0, 1, 2, 3],
[0, 0, 0, 0],
[2, 2, 2, 2]])
>
> Thanks again,
>
> DG
>>
>> Anne
>>
>> > Thanks,
>> >
>> > DG
>> >
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>> >
>> >
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