2012/9/27 James Bergstra <[email protected]>: > Right, but just so we're clear, there are different levels of > upstream? If sklearn maintains a modified version of libsvm, then > "contributing upstream" is simply a matter of committing to this > modified branch. There is a further-upstream branch (author's > official version) that none of us controls, which has it's own release > cycle, but which in principle may change significantly, and change for > the better in directions that we will want to include.
By upstream I mean Lin et al. > Why do you want to rewrite the predict code, which seems to be already > working? > (Doesn't this further divergence from the libsvm code base just > increase the sklearn maintenance burden?) Because maintaining the scikit-learn wrappers for the prediction code for Liblinear turned out to be more work than rewriting it, and I suspect the same will be true for LibSVM. Additional benefits would be faster compiles and smaller library images. > The key thing seems to be how heavily patched is the svm.cpp already? > If it's completely rewritten, then trying to work with the original > project is silly, but I don't think it is. It seems like there are a > few things: > > (1) the use of PREFIX and the _DENSE_REP ifdef, and the extra > double-include file that drives that mechanism > > (2) changing the upper_bound in solution_info to a buffer of len 2 > instead of 2 different variables > > (3) what looks like algorithmic changes around line 1600 that I don't > understand > > I could certainly be wrong, but these things still look maintainable > as a patch set. Why do you want to break further away from the libsvm > trunk, rather than refactor things to be, if anything, *more* > compatible with it? As I said, this is something I have not yet started serious work on, so I haven't made a decision yet. I'll try fixing things in Doug's version, and I'll see how far I get with that approach. -- Lars Buitinck Scientific programmer, ILPS University of Amsterdam ------------------------------------------------------------------------------ Everyone hates slow websites. So do we. Make your web apps faster with AppDynamics Download AppDynamics Lite for free today: http://ad.doubleclick.net/clk;258768047;13503038;j? http://info.appdynamics.com/FreeJavaPerformanceDownload.html _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
