On Tue, 11 Oct 2016, Peter Creasey wrote: > >> I agree with Sebastian and Nathaniel. I don't think we can deviating from > >> the existing behavior (int ** int -> int) without breaking lots of existing > >> code, and if we did, yes, we would need a new integer power function.
> >> I think it's better to preserve the existing behavior when it gives > >> sensible results, and error when it doesn't. Adding another function > >> float_power for the case that is currently broken seems like the right way > >> to go. > I actually suspect that the amount of code broken by int**int->float > may be relatively small (though extremely annoying for those that it > happens to, and it would definitely be good to have statistics). I > mean, Numpy silently transitioned to int32+uint64->float64 not so long > ago which broke my code, but the world didn’t end. > If the primary argument against int**int->float seems to be the > difficulty of managing the transition, with int**int->Error being the > seen as the required yet *very* painful intermediate step for the > large fraction of the int**int users who didn’t care if it was int or > float (e.g. the output is likely to be cast to float in the next step > anyway), and fail loudly for those users who need int**int->int, then > if you are prepared to risk a less conservative transition (i.e. we > think that latter group is small enough) you could skip the error on > users and just throw a warning for a couple of releases, along the > lines of: > WARNING int**int -> int is going to be deprecated in favour of > int**int->float in Numpy 1.16. To avoid seeing this message, either > use “from numpy import __future_float_power__” or explicitly set the > type of one of your inputs to float, or use the new ipower(x,y) > function for integer powers. Sorry for coming too late to the discussion and after PR "addressing" the issue by issuing an error was merged [1]. I got burnt by new behavior while trying to build fresh pandas release on Debian (we are freezing for release way too soon ;) ) -- some pandas tests failed since they rely on previous non-erroring behavior and we got numpy 1.12.0~b1 which included [1] in unstable/testing (candidate release) now. I quickly glanced over the discussion but I guess I have missed actual description of the problem being fixed here... what was it?? previous behavior, int**int->int made sense to me as it seemed to be consistent with casting Python's pow result to int, somewhat fulfilling desired promise for in-place operations and being inline with built-in pow results as far as I see it (up to casting). Current handling and error IMHO is going against rudimentary algebra, where numbers can be brought to negative power (integer or not). [1] https://github.com/numpy/numpy/pull/8231 -- Yaroslav O. Halchenko Center for Open Neuroscience http://centerforopenneuroscience.org Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755 Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419 WWW: http://www.linkedin.com/in/yarik _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
