Trees | Indices | Help |
---|
|
KD tree data structure for searching N-dimensional vectors.
The KD tree data structure can be used for all kinds of searches that involve N-dimensional vectors, e.g. neighbor searches (find all points within a radius of a given point) or finding all point pairs in a set that are within a certain radius of each other. See "Computational Geometry: Algorithms and Applications" (Mark de Berg, Marc van Kreveld, Mark Overmars, Otfried Schwarzkopf). Author: Thomas Hamelryck.
|
|||
KDTree KD tree implementation (C++, SWIG python wrapper) |
|
|||
|
|||
|
|||
|
|
Test all fixed radius neighbor search. Test all fixed radius neighbor search using the KD tree C module. o nr_points - number of points used in test o dim - dimension of coords o bucket_size - nr of points per tree node o radius - radius of search (typically 0.05 or so) |
Test neighbor search. Test neighbor search using the KD tree C module. o nr_points - number of points used in test o dim - dimension of coords o bucket_size - nr of points per tree node o radius - radius of search (typically 0.05 or so) |
Trees | Indices | Help |
---|
Generated by Epydoc 3.0.1 on Sat Aug 20 10:37:28 2011 | http://epydoc.sourceforge.net |