2011/12/2 Ian Goodfellow <[email protected]>: > On Fri, Oct 7, 2011 at 5:14 AM, Olivier Grisel <[email protected]> > wrote: >> 2011/10/7 Ian Goodfellow <[email protected]>: >>> Thanks. Yes it does appear that liblinear uses only a 64 bit dense format, >>> so this memory usage is normal/caused by the implementation of liblinear. >>> >>> You may want to update the documentation hosted at this site: >>> http://scikit-learn.sourceforge.net/modules/svm.html# >>> >>> It has a section on "avoiding data copy" which only says that the data >>> should be C contiguous. >> >> Thanks for the report, this should be fixed: >> >> https://github.com/scikit-learn/scikit-learn/commit/bf68b538e8fe251303fc0f7469aad6e8bf56a1d0 >> >>> It looks like there's a different implementation of libsvm that uses a dense >>> format so I'll look into using that. >> >> Yes the libsvm in scikit-learn can use both dense and sparse inputs >> (actually we embed both the original sparse implementation and a dense >> fork of it). > > How can I access the dense version of libsvm via scikit-learn?
Those are the classes at the sklearn.svm level as opposed to the classes at the sklearn.svm.sparse level. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ All the data continuously generated in your IT infrastructure contains a definitive record of customers, application performance, security threats, fraudulent activity, and more. Splunk takes this data and makes sense of it. IT sense. And common sense. http://p.sf.net/sfu/splunk-novd2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
