[Numpy-discussion] Integers to negative integer powers, time for a decision.
Charles R Harris
charlesr.harris at gmail.com
Sat Oct 8 09:59:06 EDT 2016
On Sat, Oct 8, 2016 at 4:40 AM, Nathaniel Smith <njs at pobox.com> wrote:
> On Fri, Oct 7, 2016 at 6:12 PM, Charles R Harris
> <charlesr.harris at gmail.com> wrote:
> > Hi All,
> > The time for NumPy 1.12.0 approaches and I like to have a final decision
> > the treatment of integers to negative integer powers with the `**`
> > The two alternatives looked to be
> > Raise an error for arrays and numpy scalars, including 1 and -1 to
> > powers.
> > Pluses
> > Backward compatible
> > Allows common powers to be integer, e.g., arange(3)**2
> > Consistent with inplace operators
> > Fixes current wrong behavior.
> > Preserves type
> > Minuses
> > Integer overflow
> > Computational inconvenience
> > Inconsistent with Python integers
> > Always return a float
> > Pluses
> > Computational convenience
> > Minuses
> > Loss of type
> > Possible backward incompatibilities
> > Not applicable to inplace operators
> I guess I could be wrong, but I think the backwards incompatibilities
> are going to be *way* too severe to make option 2 possible in
Backwards compatibility is also a major concern for me. Here are my
- Add an fpow ufunc that always converts to float, it would not accept
- Raise errors in current power ufunc (**), for ints to negative ints.
The power ufunc will change in the following ways
- +1, -1 to negative ints will error, currently they work
- n > 1 ints to negative ints will error, currently warn and return zero
- 0 to negative ints will error, they currently return the minimum
The `**` operator currently calls the power ufunc, leave that as is for
backward almost compatibility. The remaining question is numpy scalars,
which we can make either compatible with Python, or with NumPy arrays. I'm
leaning towards NumPy array compatibility mostly on account of type
preservation and the close relationship between zero dimensionaly arrays
The fpow function could be backported to NumPy 1.11 if that would be
helpful going forward.
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