On Thu, Aug 7, 2014 at 12:24 AM, Charles R Harris <charlesr.har...@gmail.com> wrote: > > On Wed, Aug 6, 2014 at 4:57 PM, Nathaniel Smith <n...@pobox.com> wrote: >> >> On Wed, Aug 6, 2014 at 4:32 PM, Charles R Harris >> <charlesr.har...@gmail.com> wrote: >> > Should also mention that we don't have the ability to operate on stacked >> > vectors because they can't be identified by dimension info. One >> > workaround >> > is to add dummy dimensions where needed, another is to add two flags, >> > row >> > and col, and set them appropriately. Two flags are needed for backward >> > compatibility, i.e., both false is a traditional array. >> >> It's possible I could be convinced to like this, but it would take a >> substantial amount of convincing :-). It seems like a pretty big >> violation of orthogonality/"one obvious way"/etc. to have two totally >> different ways of representing row/column vectors. >> > > The '@' operator supports matrix stacks, so it would seem we also need to > support vector stacks. The new addition would only be effective with the '@' > operator. The main problem I see with flags is that adding them would > require an extensive audit of the C code to make sure they were preserved. > Another option, already supported to a large extent, is to have row and col > classes inheriting from ndarray that add nothing, except for a possible new > transpose type function/method. I did mock up such a class just for fun, and > also added a 'dyad' function. If we really don't care to support stacked > vectors we can get by without adding anything.
It's possible you could convince me that this is a good idea, but I'm starting at like -0.95 :-). Wouldn't it be vastly simpler to just have np.linalg.matvec, matmat, vecvec or something (each of which are single-liners in terms of @), rather than deal with two different ways of representing row/column vectors everywhere? -n -- Nathaniel J. Smith Postdoctoral researcher - Informatics - University of Edinburgh http://vorpus.org _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
