>> 23.02.2014 00:03, Nathaniel Smith kirjoitti: >>> Currently numpy's 'dot' acts a bit weird for ndim>2 or ndim<1. In >>> practice this doesn't usually matter much, because these are very >>> rarely used. But, I would like to nail down the behaviour so we can >>> say something precise in the matrix multiplication PEP.
> On Sat, Feb 22, 2014 at 7:09 PM, Pauli Virtanen wrote: >> I'm not sure it's necessary to say much about this in the PEP. It should >> in my view concentrate on arguing why the new binop is needed in the >> Python language, and for that, restricting to 2D is good enough IMHO. >> How exactly Numpy makes use of the capability for > 2-dim arrays is >> something that should definitely be discussed. >> But I think this is a problem mainly interesting for Numpy devs, and not >> for CPython devs. On 2/24/2014 12:21 AM, Nathaniel Smith wrote: > I actually disagree strongly. I think it's very important to make > clear that we have a clear, well thought through, and > cross-project approach to what @ is supposed to mean I think Paul is right. We know `@` is supposed to mean "matrix multiply" when dealing with conformable 2d arrays. That is the real motivation of the PEP. I cannot see why the PEP itself would need to go beyond that. The behavior of `@` in other cases seems a discussion that should go *much* slower than that of the core of the PEP, which is to get an operator for matrix multiplication. Furthermore, I am not able to understand the principles behind the discussion of how `@` should behave in other cases. I do not think they are being clearly stated. (I have added a comment to the PEP asking for clarification.) To be concrete, if `@` is proposed to behave unlike Mathematica's `Dot` command, I would hope to hear a very clear mathematical motivation for this. (Specifically, I do not understand why `@` would do scalar product.) Otoh, if the proposal is just that `@` should behave just like NumPy's `dot` does, that should be simply stated. Cheers, Alan Isaac _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
