Hi everybody, I just updated the gist quite a lot, please take a look: http://nbviewer.ipython.org/7224672
I'll go to sleep and interpret it with a fresh eye tomorrow, but what's interesting at the moment is: KKT's performance is quite constant, PG with sparsity penalties (the new, simpler ones, not the implementation in current master, also with fixed stopping condition) is quite fast! Residual calculation is fixed and suggests that the solvers work well. I'd love to add non-negative lasso to this mix. However, I noticed that cd_fast.pyx is missing the positive=True option in multitask lasso (as well as the sparse variant). Is there any other reason for this or just that nobody needed it? Cheers, Vlad On Wed, Oct 30, 2013 at 10:40 AM, Vlad Niculae <zephy...@gmail.com> wrote: > Thanks Mathieu, well part of it comes from your gist (I added an > attribution now) ;) > > Non-negative lasso is really interesting, I forgot about it but I > think it would be very interesting to compare qualitatively. > > Vlad > > > > On Wed, Oct 30, 2013 at 10:15 AM, Olivier Grisel > <olivier.gri...@ensta.org> wrote: >> 2013/10/30 Mathieu Blondel <math...@mblondel.org>: >>> I think MiniBatchDictLearning supports only dense arrays, though. >>> >>> Mathieu >>> >>> PS: Very nice notebooks, Vlad and Olivier. >> >> This is all Vlad's work here. >> >> -- >> Olivier >> http://twitter.com/ogrisel - http://github.com/ogrisel >> >> ------------------------------------------------------------------------------ >> Android is increasing in popularity, but the open development platform that >> developers love is also attractive to malware creators. Download this white >> paper to learn more about secure code signing practices that can help keep >> Android apps secure. >> http://pubads.g.doubleclick.net/gampad/clk?id=65839951&iu=/4140/ostg.clktrk >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ November Webinars for C, C++, Fortran Developers Accelerate application performance with scalable programming models. Explore techniques for threading, error checking, porting, and tuning. Get the most from the latest Intel processors and coprocessors. See abstracts and register http://pubads.g.doubleclick.net/gampad/clk?id=60136231&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
