The algorithm in scikit-learn-extra are usually algorithms which did not meet the inclusion criteria (too early publication, not enough citations, etc.) However, the code quality is as good and tested than scikit-learn (usually they were PR in the main repository). Doing in this manner allows us to find the impact of the algorithms in practice and maybe considering waiving-up the inclusion criterion.
On Sat, 2 May 2020 at 06:59, sai_ng <jonpsy...@gmail.com> wrote: > Hey folks ! > Hope you're all doing well. > > I'm developing Random Fourier Feature implementation in c++ for a > repository. Scikits implementation on RBFSampler has been really helpful, > and I must say that I'm charmed but how compact, yet powerful each line of > code is. > > I'm writing this mail because I couldn't find your implementation of > Random Binning Features, is it under development?. I tried searching in the > issues but, to no avail. I noticed you've put few of your algorithms on a > different repository for ex: > https://scikit-learn-extra.readthedocs.io/en/latest/generated/sklearn_extra.kernel_approximation.Fastfood.html. > > > Overall, I'd like to know if it's under development or has there been any > draft/proposal or is it already implemented. I'd greatly appreciate if you > could point me to other sources (if not here) which have successfully > implemented it in code (preferably python/c++) > > "Hit me back, > Just to chat, > Your biggest fan, > This is stan" > ~ Eminem: Stan > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/
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