Hi all,
I recently ported the Matlab version of SPGL1 to python. SPGL1 is an
extremely fast L1 solver (compressive sensing) that is fantastic with very
large datasets and also handles complex numbers naturally. It has
previously only been available in Matlab.
I'm a capable programmer and can refactor and comment it to follow all
scikit-learn guidelines, but I'd like to know if it would be a welcome
addition. I have permission from the creators to release it. They put it
under the LGPL license, so I'd have to figure out whether there are any
issues changing it over to BSD. Otherwise, it should be fine.
Thanks for any and all guidance.
(As an aside, I'm also going to port NESTA, which is an even faster L1
solver with somewhat better performance. Once it's ported and I have
permissions, I'd be happy to add it to scikit-learn as well.)
David Relyea
On Sat, Mar 21, 2015 at 9:32 AM, David Relyea <drrel...@gmail.com> wrote:
> Hi all,
>
> I recently ported the Matlab version of SPGL1 to python. SPGL1 is an
> extremely fast L1 solver (compressive sensing) that is fantastic with very
> large datasets and also handles complex numbers naturally. It has
> previously only been available in Matlab.
>
> I'm a capable programmer and can refactor it to follow all scikit-learn
> guidelines, but I'd like to know if it would be a welcome addition. I have
> permission from the creators to release it. They put it under the LGPL
> license, so I'd have to know whether there are any issues changing it over
> to BSD. Otherwise, it should be fine.
>
> Thanks for any and all guidance.
>
> (As an aside, I'm also going to port NESTA, which is an even faster L1
> solver with somewhat better performance. I'm happy to add either or both of
> them to scikit if desired.)
>
> David Relyea
>
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