I thought it would be a good idea to figure out where I could contribute it
early on in case it affects design decisions. The code is definitely not
done or publicly available yet. It sounds like sklearn is the right place
for this, then. Unit tests and examples are already part of my plan, and I
am certainly willing write documentation. I will take a look at the
guidelines
I'm implementing MARS as part of a project for work, but my company is very
much okay with my contributing the MARS portion of the project to an open
source library of some kind. The way they see it, if we meet a few good
Python developers in the long run as a result of getting involved with the
community then it will have been worth the extra effort.
So, I will continue my development with sklearn in mind and will be in
touch. I'll also comment on the issue tracker to that effect.
On Thu, Jan 10, 2013 at 11:48 AM, Andreas Mueller
<amuel...@ais.uni-bonn.de>wrote:
> Hi Jason.
> Thanks for wanting to contribute MARS to sklearn.
>
> There is even an issue requesting the feature ;)
>
> https://github.com/scikit-learn/scikit-learn/issues/845
> I think it would be great addition.
>
> You should be aware of the fact that contributing to sklearn is a bit
> more than just implementing the algorithm.
> For your code to be merged, you'd also have to provide documentation,
> examples and unit-tests.
>
> Also, it would be good to know if any of the core developers are
> familiar enough with the method to take
> care of it in the future.
>
>
> Cheers,
> Andy
>
>
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