Le 3 mars 2012 13:07, Joe Kington <jking...@wisc.edu> a écrit : > > > On Sat, Mar 3, 2012 at 9:26 AM, Robert Kern <robert.k...@gmail.com> wrote: > >> On Sat, Mar 3, 2012 at 15:22, Benjamin Root <ben.r...@ou.edu> wrote: >> > >> > >> > On Saturday, March 3, 2012, Robert Kern <robert.k...@gmail.com> wrote: >> >> On Sat, Mar 3, 2012 at 14:31, Ralf Gommers < >> ralf.gomm...@googlemail.com> >> >> wrote: >> >>> >> >>> >> >>> On Sat, Mar 3, 2012 at 3:05 PM, Robert Kern <robert.k...@gmail.com> >> >>> wrote: >> >>>> >> >>>> On Sat, Mar 3, 2012 at 13:59, Ralf Gommers < >> ralf.gomm...@googlemail.com> >> >>>> wrote: >> >>>> > >> >>>> > >> >>>> > On Thu, Mar 1, 2012 at 11:44 PM, Joe Kington <jking...@wisc.edu> >> >>>> > wrote: >> >>>> >> >> >>>> >> Is there a numpy function for testing floating point equality that >> >>>> >> returns >> >>>> >> a boolean array? >> >>>> >> >> >>>> >> I'm aware of np.allclose, but I need a boolean array. Properly >> >>>> >> handling >> >>>> >> NaN's and Inf's (as allclose does) would be a nice bonus. >> >>>> >> >> >>>> >> I wrote the function below to do this, but I suspect there's a >> method >> >>>> >> in >> >>>> >> numpy that I missed. >> >>>> > >> >>>> > >> >>>> > I don't think such a function exists, would be nice to have. How >> about >> >>>> > just >> >>>> > adding a keyword "return_array" to allclose to do so? >> >>>> >> >>>> As a general design principle, adding a boolean flag that changes the >> >>>> return type is worse than making a new function. >> >>> >> >>> >> >>> That's certainly true as a general principle. Do you have a concrete >> >>> suggestion in this case though? >> >> >> >> np.close() >> >> >> > >> > When I read that, I mentally think of "close" as in closing a file. I >> think >> > we need a synonym. >> >> np.isclose() >> > > Would it be helpful if I went ahead and submitted a pull request with the > function in my original question called "isclose" (along with a complete > docstring and a few tests)? > > One note: > At the moment, it deliberately compares NaN's as equal. E.g. > > isclose([np.nan, np.nan], [np.nan, np.nan]) > > will return: > > [True, True] > > This obviously runs counter to the standard way NaN's are handled (and > indeed the definition of NaN). > > However, in the context of a floating point "close to" function, I think > it makes the most sense. > > I've had this sitting around in a small project for awhile now, and it's > been more useful to have it compare NaN's as "approximately equal" than not > for my purposes at least. > > Nonetheless, it's something that needs additional consideration. > > Thanks, > -Joe >
It would be confusing if numpy.isclose().all() was different from numpy.allclose(). That being said, I agree it's useful to have NaNs compare equal in some cases, maybe it could be a new argument to the function? -=- Olivier
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