On Tue, Mar 3, 2015 at 7:12 PM, Ralf Gommers <ralf.gomm...@gmail.com> wrote:
> > > On Wed, Mar 4, 2015 at 1:34 AM, Charles R Harris < > charlesr.har...@gmail.com> wrote: > >> >> >> On Tue, Mar 3, 2015 at 5:31 PM, Charles R Harris < >> charlesr.har...@gmail.com> wrote: >> >>> >>> >>> On Tue, Mar 3, 2015 at 5:21 PM, Jaime Fernández del Río < >>> jaime.f...@gmail.com> wrote: >>> >>>> On Tue, Mar 3, 2015 at 4:11 PM, Charles R Harris < >>>> charlesr.har...@gmail.com> wrote: >>>> >>>>> Hi All, >>>>> >>>>> This is with reference to issue #5626 >>>>> <https://github.com/numpy/numpy/issues/5626>. Currently linalg.norm >>>>> converts the input like so `x = asarray(x)`. This can produce integer >>>>> arrays, which in turn may create problems of overflow, or the failure of >>>>> the abs functions for minimum values of signed integer types. I propose to >>>>> convert the input to a minimum precision of float32. However, this will be >>>>> a change in behavior. I'd guess that that might not be much of a problem, >>>>> as otherwise it is likely that this problem would have been reported >>>>> earlier. >>>>> >>>>> Thoughts? >>>>> >>>> >>>> Not sure if it makes sense here, but elsewhere (I think it was polyval) >>>> we let object arrays through unchanged. >>>> >>> >>> That would still work. I'm thinking something like >>> >>> x = asarray(x) >>> dt = result_type(x, np.float32) >>> if x.dtype.type is not dt.type: >>> x = x.astype(dt) >>> >>> >> I'd actually like to add a `min_dtype` keyword to asarray, We need it in >> several places. >> > > That sounds like a good idea. > Not sure what idea you are referring to, but I"ve added a `precision` keyword in gh- 5634. <https://github.com/numpy/numpy/pull/5634> Chuck
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