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? -- Michael Selik On Tuesday, April 10, 2012 at 7:05 PM, Olivier Grisel wrote: > Le 11 avril 2012 00:28, Michael Selik <[email protected] > (mailto:[email protected])> a écrit : > > Hello, > > > > As per the docs' suggestion to ask around before starting my own work: is > > anyone working on a weighted mean shift implementation? > > > > The purpose of this is to account for some observations being more reliable > > than others. Or perhaps I've misunderstood the current implementation and > > it already allows for this? > > I don't think so but you should really start from the existing > implementation to extend it when needed rather roll a whole new > implementation from scratch. > > -- > 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] > (mailto:[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
