On Wed, Oct 26, 2016 at 1:39 PM, <josef.p...@gmail.com> wrote: > > > 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.) >
Current master is different. I'm not too worried in the array cases as the results for negative exponents were zero except then raising -1 to a power. Since that result is incorrect raising an error falls on the fine line between bug fix and compatibility break. If the pre-releases cause too much trouble. Chuck
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion