On Sun, Mar 16, 2014 at 4:33 PM, Eelco Hoogendoorn <hoogendoorn.ee...@gmail.com> wrote: >> >> 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.
Not sure what makes them "linalg-heavy" -- they're just trying to cover two application areas, machine learning and neuroscience. If that turns out to involve a lot of linear algebra, well, then... > 780 calls is not tons of use, and these projects are outliers id argue. But you haven't argued! You've just asserted. I admittedly didn't spend a lot of time figuring out what the "most representative" projects were, I just picked two high profile ones off the top of my head, but I ran the numbers and they came out the way they did. (I wasn't convinced @ was useful either when I started, I just figured it would be good to settle the infix operator question one way or the other. I was also surprised np.dot turned out to be used that heavily.) If you don't like my data, then show us yours :-). -- 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
