Given your related post on the issue tracker, I think you're trying to perform clustering. Use DBSCAN, which is a standard approach to clustering based on neighborhoods within radius.
On 10 September 2017 at 14:44, Martin Lee <tesl...@hotmail.com> wrote: > nbrs = NearestNeighbors(n_neighbors=10,radius=100.0,metric='euclide > an',algorithm='ball_tree').fit(testing1) > distances, indices = nbrs.kneighbors(testing1) > > just expect when each point distance less than 100 then group into one > group > > > Martin > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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