On Sun, Mar 16, 2014 at 2:39 PM, Eelco Hoogendoorn <hoogendoorn.ee...@gmail.com> wrote: > Note that I am not opposed to extra operators in python, and only mildly > opposed to a matrix multiplication operator in numpy; but let me lay out the > case against, for your consideration. > > First of all, the use of matrix semantics relative to arrays semantics is > extremely rare; even in linear algebra heavy code, arrays semantics often > dominate. As such, the default of array semantics for numpy has been a great > choice. Ive never looked back at MATLAB semantics.
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 > Secondly, I feel the urge to conform to a historical mathematical notation > is misguided, especially for the problem domain of linear algebra. Perhaps > in the world of mathematics your operation is associative or commutes, but > on your computer, the order of operations will influence both outcomes and > performance. Even for products, we usually care not only about the outcome, > but also how that outcome is arrived at. And along the same lines, I don't > suppose I need to explain how I feel about A@@-1 and the likes. Sure, it > isn't to hard to learn or infer this implies a matrix inverse, but why on > earth would I want to pretend the rich complexity of numerical matrix > inversion can be mangled into one symbol? Id much rather write inv or pinv, > or whatever particular algorithm happens to be called for given the > situation. Considering this isn't the num-lisp discussion group, I suppose I > am hardly the only one who feels so. > My impression from the other thread is that @@ probably won't end up existing, so you're safe here ;-). > On the whole, I feel the @ operator is mostly superfluous. I prefer to be > explicit about where I place my brackets. I prefer to be explicit about the > data layout and axes that go into a (multi)linear product, rather than rely > on obtuse row/column conventions which are not transparent across function > calls. When I do linear algebra, it is almost always vectorized over > additional axes; how does a special operator which is only well defined for > a few special cases of 2d and 1d tensors help me with that? 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. > Note that I don't think there is much harm in an @ operator; but I don't see > myself using it either. Aside from making textbook examples like a > gram-schmidt orthogonalization more compact to write, I don't see it having > much of an impact in the real world. 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 :-). -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
