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

[Neil Girdhar]

This relationship between outer an dot only holds for vectors.  For
tensors, and other kinds of vector spaces, I'm not sure if outer products
and dot products have anything to do with each other.

On Fri, Apr 17, 2015 at 11:11 AM, <josef.pktd at gmail.com> wrote:

> On Fri, Apr 17, 2015 at 10:59 AM, Sebastian Berg
> <sebastian at sipsolutions.net> wrote:
> > 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.
>
>
> Essentially yes, but I don't have an opinion about location or
> implementation in numpy, nor do I know enough.
>
> I always considered np.outer conceptually as belonging to linalg that
> provides a more convenient interface than np.dot if both arrays are
> 1-D.  (no need to add extra axis and transpose)
>
> Josef
>
> >
> >
> >> Josef
> >>
> >>
> >>
> >>
> >> >
> >> > - Sebastian
> >> >
> >> >
> >> >> Best,
> >> >>
> >> >> Matthew
> >> >> _______________________________________________
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> >> >>
> >> >
> >> >
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