a sample_weight param seems reasonable to me Alex
On Wed, Apr 11, 2012 at 5:10 PM, Olivier Grisel <[email protected]> wrote: > Le 11 avril 2012 16:59, Michael Selik <[email protected]> a écrit : >> Certainly. It looks like a good approach would be to break out line 121 in >> mean_shift_.py: >>> my_mean = np.mean(points_within, axis=0) >> >> And provide a function instead that allows several methods of mean >> calculation -- flat kernel (current method), gaussian kernel, and/or >> accuracy-weighted kernel. >> >> Any thoughts before I get started? > > Have a look at other estimators that use stuff like precomputed > kernels, class_weight and sample_weight and try to reuse the idioms of > the rest of the library where applicable for consistency. > > git grep precomputed > git grep kernel > git grep class_weight > git grep sample_weight > > -- > Olivier > http://twitter.com/ogrisel - http://github.com/ogrisel > > ------------------------------------------------------------------------------ > Better than sec? Nothing is better than sec when it comes to > monitoring Big Data applications. Try Boundary one-second > resolution app monitoring today. Free. > http://p.sf.net/sfu/Boundary-dev2dev > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Better than sec? Nothing is better than sec when it comes to monitoring Big Data applications. Try Boundary one-second resolution app monitoring today. Free. http://p.sf.net/sfu/Boundary-dev2dev _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
