Hey Liu.
You can probably fork it in github and submit your code as a module in
sklearn, then you would have a healthy test period to finally put it in the
latest release.
If you have it on open access, I would be happy to test it and help you
with the methods, specially because most of the methods like fit and train
have to be consistent.
Regards
Leon
On Wed, Nov 28, 2012 at 4:08 PM, <[email protected]> wrote:
> Dear scikit-learn community:
>
>
> Block Sparse Bayesian Learning is a powerful CS algorithm for recovering
> block sparse signals with structures, and shows the additional benefits of
> reconstruct non-sparse signals, see Dr. zhilin zhang's websites:
> http://dsp.ucsd.edu/~zhilin/BSBL.html
>
>
> I currently implement the BSBL-BO algorithm by Zhang and a fast version of
> BSBL algorithm recently proposed by us, called BSBL-FM, in python. Plus many
> demos using these two codes. Does scikit-learn community welcome such type of
> code ? what is the procedure to submit the code in the mainstream of scikit
> learn?
>
> Thanks for the great project!
>
> Liu benyuan
>
>
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--
Leon Palafox, M.Sc
PhD Candidate
Iba Laboratory
+81-3-5841-8436
University of Tokyo
Tokyo, Japan.
------------------------------------------------------------------------------
Keep yourself connected to Go Parallel:
INSIGHTS What's next for parallel hardware, programming and related areas?
Interviews and blogs by thought leaders keep you ahead of the curve.
http://goparallel.sourceforge.net
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