[Numpy-discussion] matrix default to column vector?

Tom K. tpk at kraussfamily.org
Sat Jun 6 21:57:06 EDT 2009

Fernando Perez wrote:
> On Sat, Jun 6, 2009 at 11:03 AM, Charles R
> Harris<charlesr.harris at gmail.com> wrote:
>> I don't think we can change the current matrix class, to do so would
>> break
>> too much code. It would be nice to extend it with an explicit inner
>> product,
>> but I can't think of any simple notation for it that python would parse.
> Maybe it's time to make another push on python-dev for the pep-225
> stuff for other operators?
> https://cirl.berkeley.edu/fperez/static/numpy-pep225/
> Last year I got pretty much zero interest from python-dev on this, but
> they were very very busy with 3.0 on the horizon.  Perhaps once they
> put 3.1 out would be a good time to champion this again.
> It's slightly independent of the matrix class debate, but perhaps
> having special operators for real matrix multiplication could ease
> some of the bottlenecks of this discussion.
> It would be great if someone could champion that discussion on
> python-dev though, I don't see myself finding the time for it another
> time around...

How about pep 211?

PEP 211 proposes a single new operator (@) that could be used for matrix
MATLAB has elementwise versions of multiply, exponentiation, and left and
right division using a preceding "." for the usual matrix versions (* ^ \
PEP 225 proposes "tilde" versions of + - * / % **.

While PEP 225 would allow a matrix exponentiation and right divide, I think
these things are much less  common than matrix multiply.  Plus, I think
following through with the PEP 225 implementation would create a
frankenstein of a language that would be hard to read.

So, I would argue for pushing for a single new operator that can then be
used to implement "dot" with a binary infix operator.  We can resurrect PEP
211 or start a new PEP or whatever, the main thing is to have a proposal
that makes sense.  Actually, what do you all think of this:
  @ --> matrix multiply
  @@ --> matrix exponentiation
and we leave it at that - let's not get too greedy and try for matrix
inverse via @/ or something.  

For the nd array operator, I would propose taking the last dimension of the
left array and "collapsing" it with the first dimension of the right array,
  shape (a0, ..., aL-1,k) @ (k, b0, ..., bM-1) --> (a0, ..., aL-1, b0, ...,
Does that make sense?

With this proposal, matrices go away and all our lives are sane again. :-) 
Long live the numpy ndarray!  Thanks to the creators for all your hard work
BTW - I love this stuff!

  - Tom K.
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