On Sat, Jun 4, 2016 at 8:07 PM, Charles R Harris <charlesr.har...@gmail.com> wrote:
> > > On Sat, Jun 4, 2016 at 5:27 PM, <josef.p...@gmail.com> wrote: > >> >> >> On Sat, Jun 4, 2016 at 6:10 PM, Nathaniel Smith <n...@pobox.com> wrote: >> >>> On Sat, Jun 4, 2016 at 2:07 PM, V. Armando Sole <s...@esrf.fr> wrote: >>> > Also in favor of 2. Always return a float for '**' >>> >>> Even if we did want to switch to this, it's such a major >>> backwards-incompatible change that I'm not sure how we could actually >>> make the transition without first making it an error for a while. >>> >> >> AFAIU, only the dtype for int**int would change. So, what would be the >> problem with FutureWarnings as with other dtype changes that were done in >> recent releases. >> >> > The main problem I see with that is that numpy integers would behave > differently than Python integers, and the difference would be silent. With > option 1 it is possible to write code that behaves the same up to overflow > and the error message would supply a warning when the exponent should be > float. One could argue that numpy scalar integer types could be made to > behave like python integers, but then their behavior would differ from > numpy arrays and numpy scalar arrays. > I'm not sure I understand. Do you mean np.arange(5)**2 would behave differently than np.arange(5)**np.int_(2) or 2**2 would behave differently than np.int_(2)**np.int(2) ? AFAICS, there are many cases where numpy scalars don't behave like python scalars. Also, does different behavior mean different type/dtype or different numbers. (The first I can live with, the second requires human memory usage, which is a scarce resource.) >>> 2**(-2) 0.25 Josef > > Chuck > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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