Even if they would be useful, I'd rather avoid projects like "maintenance" or "speed things up". I think projects with a well-identified goal are more likely to be accepted by the PSF.
I like Olivier's proposals for SGD-based low-rank and non-negative matrix factorization but I'd rather merge them into a single project. For the non-negative constraint, I believe we just need to add a clipping-to-zero step at each iteration. Vlad's idea of a Cython-based non-negative least squares solver is very good and would be a nice bonus if time permits. For students, I'd strongly recommend to submit a first pull-request to scikit-learn *before* you apply for the GSOC. Last year, by the time he applied for GSOC, Vlad had already merged his NMF code. This made his application very credible and convincing. Mathieu ------------------------------------------------------------------------------ Virtualization & Cloud Management Using Capacity Planning Cloud computing makes use of virtualization - but cloud computing also focuses on allowing computing to be delivered as a service. http://www.accelacomm.com/jaw/sfnl/114/51521223/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
