On Wed, Oct 26, 2016 at 3:23 PM, Charles R Harris <charlesr.har...@gmail.com > wrote:
> > > On Tue, Oct 25, 2016 at 10:14 AM, Stephan Hoyer <sho...@gmail.com> wrote: > >> I am also concerned about adding more special cases for NumPy scalars vs >> arrays. These cases are already confusing (e.g., making no distinction >> between 0d arrays and scalars) and poorly documented. >> >> On Mon, Oct 24, 2016 at 4:30 PM, Nathaniel Smith <n...@pobox.com> wrote: >> >>> On Mon, Oct 24, 2016 at 3:41 PM, Charles R Harris >>> <charlesr.har...@gmail.com> wrote: >>> > Hi All, >>> > >>> > I've been thinking about this some (a lot) more and have an alternate >>> > proposal for the behavior of the `**` operator >>> > >>> > if both base and power are numpy/python scalar integers, convert to >>> python >>> > integers and call the `**` operator. That would solve both the >>> precision and >>> > compatibility problems and I think is the option of least surprise. For >>> > those who need type preservation and modular arithmetic, the np.power >>> > function remains, although the type conversions can be surpirising as >>> it >>> > seems that the base and power should play different roles in >>> determining >>> > the type, at least to me. >>> > Array, 0-d or not, are treated differently from scalars and integers >>> raised >>> > to negative integer powers always raise an error. >>> > >>> > I think this solves most problems and would not be difficult to >>> implement. >>> > >>> > Thoughts? >>> >>> My main concern about this is that it adds more special cases to numpy >>> scalars, and a new behavioral deviation between 0d arrays and scalars, >>> when ideally we should be trying to reduce the >>> duplication/discrepancies between these. It's also inconsistent with >>> how other operations on integer scalars work, e.g. regular addition >>> overflows rather than promoting to Python int: >>> >>> In [8]: np.int64(2 ** 63 - 1) + 1 >>> /home/njs/.user-python3.5-64bit/bin/ipython:1: RuntimeWarning: >>> overflow encountered in long_scalars >>> #!/home/njs/.user-python3.5-64bit/bin/python3.5 >>> Out[8]: -9223372036854775808 >>> >>> So I'm inclined to try and keep it simple, like in your previous >>> proposal... theoretically of course it would be nice to have the >>> perfect solution here, but at this point it feels like we might be >>> overthinking this trying to get that last 1% of improvement. The thing >>> where 2 ** -1 returns 0 is just broken and bites people so we should >>> definitely fix it, but beyond that I'm not sure it really matters >>> *that* much what we do, and "special cases aren't special enough to >>> break the rules" and all that. >>> >>> > What I have been concerned about are the follow combinations that > currently return floats > > num: <type 'numpy.int8'>, exp: <type 'numpy.int8'>, res: <type > 'numpy.float32'> > num: <type 'numpy.int16'>, exp: <type 'numpy.int8'>, res: <type > 'numpy.float32'> > num: <type 'numpy.int16'>, exp: <type 'numpy.int16'>, res: <type > 'numpy.float32'> > num: <type 'numpy.int32'>, exp: <type 'numpy.int8'>, res: <type > 'numpy.float64'> > num: <type 'numpy.int32'>, exp: <type 'numpy.int16'>, res: <type > 'numpy.float64'> > num: <type 'numpy.int32'>, exp: <type 'numpy.int32'>, res: <type > 'numpy.float64'> > num: <type 'numpy.int64'>, exp: <type 'numpy.int8'>, res: <type > 'numpy.float64'> > num: <type 'numpy.int64'>, exp: <type 'numpy.int16'>, res: <type > 'numpy.float64'> > num: <type 'numpy.int64'>, exp: <type 'numpy.int32'>, res: <type > 'numpy.float64'> > num: <type 'numpy.int64'>, exp: <type 'numpy.int64'>, res: <type > 'numpy.float64'> > num: <type 'numpy.int64'>, exp: <type 'numpy.int64'>, res: <type > 'numpy.float64'> > num: <type 'numpy.uint64'>, exp: <type 'numpy.int8'>, res: <type > 'numpy.float64'> > num: <type 'numpy.uint64'>, exp: <type 'numpy.int16'>, res: <type > 'numpy.float64'> > num: <type 'numpy.uint64'>, exp: <type 'numpy.int32'>, res: <type > 'numpy.float64'> > num: <type 'numpy.uint64'>, exp: <type 'numpy.int64'>, res: <type > 'numpy.float64'> > num: <type 'numpy.uint64'>, exp: <type 'numpy.int64'>, res: <type > 'numpy.float64'> > > The other combinations of signed and unsigned integers to signed powers > currently raise ValueError due to the change to the power ufunc. The > exceptions that aren't covered by uint64 + signed (which won't change) seem > to occur when the exponent can be safely cast to the base type. I suspect > that people have already come to depend on that, especially as python > integers on 64 bit linux convert to int64. So in those cases we should > perhaps raise a FutureWarning instead of an error. > >>> np.int64(2)**np.array(-1, np.int64) 0.5 >>> np.__version__ '1.10.4' >>> np.int64(2)**np.array([-1, 2], np.int64) array([0, 4], dtype=int64) >>> np.array(2, np.uint64)**np.array([-1, 2], np.int64) array([0, 4], dtype=int64) >>> np.array([2], np.uint64)**np.array([-1, 2], np.int64) array([ 0.5, 4. ]) >>> np.array([2], np.uint64).squeeze()**np.array([-1, 2], np.int64) array([0, 4], dtype=int64) (IMO: If you have to break backwards compatibility, break forwards not backwards.) Josef http://www.stanlaurelandoliverhardy.com/nicemess.htm > > Chuck > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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