That makes sense. The basic implementation is definitely short, just ~20 lines of code if you don't count comments etc. I can put the source code available so that you can judge whether it's good to take further. I am familiar with the documentation libraries you are using (Sphinx with Numpy style docstrings) in Scikit-Learn, but that's further down the line.
Cheers, Timo On Fri, Jul 31, 2015 at 10:53 AM, Gael Varoquaux < gael.varoqu...@normalesup.org> wrote: > > Is it required that an algorithm, which is implemented in Scikit-Learn, > scales > > well wrt n_samples? > > The requirement is 'be actually useful', which is something that is a bit > hard to judge :). > > I think that K-medoids is bordeline on this requirement, probably on the > right side of the border. I would tend to say that if the code clean and > reasonnably short (that last requirement is important), it comes with > good tests, examples and documentation, it should be possible to merge it > in. > > Sorry, we are indeed being picky. It's a struggle to find the right > feature set to keep the package maintainable while providing great value > to our users. > > Gaël > > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >
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