On Mon, Jan 23, 2012 at 6:06 PM, Andreas <[email protected]> wrote:
> It might be as easy as that. > I guess I should try to see if this speeds up things. If you use algorithm="brute", there should be no speed-up (it computes all the distances and find those within the given radius...). If you use ball-tree, it should improve on low-dimensional data, make things worse otherwise. Question: can the triangle inequality tricks frequently used in K-means be used for this kind of radius search too? One could sample a few training points from the datasets and use them as "pivots". Mathieu ------------------------------------------------------------------------------ Try before you buy = See our experts in action! The most comprehensive online learning library for Microsoft developers is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3, Metro Style Apps, more. Free future releases when you subscribe now! http://p.sf.net/sfu/learndevnow-dev2 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
