Cool.
Including the code in scikit-learn is often a bit of a process but it
might indeed be interesting.
You could just start with a pull request - or publish a gist if you
don't think you'll have time to work on the inclusion and leave that
part to someone else.
Cheers,
Andy
On 07/31/2015 05:38 AM, Timo Erkkilä wrote:
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 <mailto: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
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