2012/8/24 Alex Wiltschko <[email protected]>:
> Hello all,
>
> Long time user of scikits-learn, first time posting here.
> I implemented a self-organizing (aka Kohonen) map with Numba. It's fast. I'd
> like to share it with the scientific python community. Can I contribute it
> to scikits-learn?

numba (because of llvm) is a too large dependency to be accepted in
scikit-learn for now. As anybody tried to make it work under windows
for instance?

Maybe in a year or two, once numba as reached more maturity we might
reimplement everything with it but it's way to early to think about
that now IMHO.

> Here's a notebook with a run-down of how it works:
> http://nbviewer.ipython.org/3407544 (the source:
> https://gist.github.com/3407544)
>
> I don't know much about how scikits-learn structures unsupervised learning
> methods, but if there's interest, and perhaps someone to point me to a brief
> run-down on how to format my work for inclusion in scikits-learn, I'd love
> the chance to contribute.

This is a very interesting work. You should blog it. It's great to
demonstrate the promise of the numba approach with real working
algorithm implementations.

In the mean time if your want to start a new github repo with
implementations of machine learning algorithm that rely on numba as
the execution accelerator and that follow the scikit-learn coding
conventions / style / community contribution rules I am sure others
might be interested in contributing / experimenting with numba.

-- 
Olivier
http://twitter.com/ogrisel - http://github.com/ogrisel

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