[Numpy-discussion] Consider improving numpy.outer's behavior with zero-dimensional vectors
Sebastian Berg
sebastian at sipsolutions.net
Fri Apr 17 10:59:58 EDT 2015
On Fr, 2015-04-17 at 10:47 -0400, josef.pktd at gmail.com wrote:
> On Fri, Apr 17, 2015 at 10:07 AM, Sebastian Berg
> <sebastian at sipsolutions.net> wrote:
> > On Do, 2015-04-16 at 15:28 -0700, Matthew Brett wrote:
> >> Hi,
> >>
> > <snip>
> >>
> >> So, how about a slight modification of your proposal?
> >>
> >> 1) Raise deprecation warning for np.outer for non 1D arrays for a few
> >> versions, with depraction in favor of np.multiply.outer, then
> >> 2) Raise error for np.outer on non 1D arrays
> >>
> >
> > I think that was Neil's proposal a bit earlier, too. +1 for it in any
> > case, since at least for the moment I doubt outer is used a lot for non
> > 1-d arrays. Possible step 3) make it work on higher dims after a long
> > period.
>
> sounds ok to me
>
> Some random comments of what I remember or guess in terms of usage
>
> I think there are at most very few np.outer usages with 2d or higher dimension.
> (statsmodels has two models that switch between 2d and 1d
> parameterization where we don't use outer but it has similar
> characteristics. However, we need to control the ravel order, which
> IIRC is Fortran)
>
> The current behavior of 0-D scalars in the initial post might be
> useful if a numpy function returns a scalar instead of a 1-D array in
> size=1. np.diag which is a common case, doesn't return a scalar (in my
> version of numpy).
>
> I don't know any use case where I would ever want to have the 2d
> behavior of np.multiply.outer.
> I guess we will or would have applications for outer along an axis,
> for example if x.shape = (100, 10), then we have
> x[:,None, :] * x[:, :, None] (I guess)
> Something like this shows up reasonably often in econometrics as
> "Outer Product". However in most cases we can avoid constructing this
> matrix and get the final results in a more memory efficient or faster
> way.
> (example an array of covariance matrices)
>
So basically outer product of stacked vectors (fitting basically into
how np.linalg functions now work). I think that might be a good idea,
but even then we first need to do the deprecation and it would be a long
term project. Or you add np.linalg.outer or such sooner and in the
longer run it will be an alias to that instead of np.multiple.outer.
> Josef
>
>
>
>
> >
> > - Sebastian
> >
> >
> >> Best,
> >>
> >> Matthew
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> >>
> >
> >
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