[Numpy-discussion] It looks like Py 3.5 will include a dedicated infix matrix multiply operator

[Eelco Hoogendoorn]

  Different people work on different code and have different experiences
  here -- yours may or may be typical yours. Pauli did some quick checks
  on scikit-learn & nipy & scipy, and found that in their test suites,
  uses of np.dot and uses of elementwise-multiplication are ~equally
  common: https://github.com/numpy/numpy/pull/4351#issuecomment-37717330h

Yeah; these are examples of linalg-heavy packages. Even there, dot does not dominate.


  My impression from the other thread is that @@ probably won't end up
  existing, so you're safe here ;-).

I know; my point is that the same objections apply to @, albeit in weaker form.


  Einstein notation is coming up on its 100th birthday and is just as
  blackboard-friendly as matrix product notation. Yet there's still a
  huge number of domains where the matrix notation dominates. It's cool
  if you aren't one of the people who find it useful, but I don't think
  it's going anywhere soon.

Einstein notation is just as blackboard friendly; but also much more computer-future proof. I am not saying matrix multiplication is going anywhere soon; but as far as I can tell that is all inertia; historical circumstance has not accidentially prepared it well for numerical needs, as far as I can tell.


The analysis in the PEP found ~780 calls to np.dot, just in the two
projects I happened to look at. @ will get tons of use in the real
world. Maybe all those people who will be using it would be happier if
they were using einsum instead, I dunno, but it's an argument you'll
have to convince them of, not me :-).

780 calls is not tons of use, and these projects are outliers id argue.

  I just read for the first time two journal articles in econometrics that use einsum notation.
  I have no idea what their formulas are supposed to mean, no sum signs and no matrix algebra.
If they could have been expressed more clearly otherwise, of course this is what they should have done; but could they? b_i = A_ij x_j isnt exactly hard to read, but if it was some form of complicated product, its probably tensor notation was their best bet.
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