kdtree for quick nearestneighbor lookup
This class provides an index into a set of kdimensional points which can be used to rapidly look up the nearest neighbors of any point.
The algorithm used is described in Maneewongvatana and Mount 1999. The general idea is that the kdtree is a binary trie, each of whose nodes represents an axisaligned hyperrectangle. Each node specifies an axis and splits the set of points based on whether their coordinate along that axis is greater than or less than a particular value.
During construction, the axis and splitting point are chosen by the “sliding midpoint” rule, which ensures that the cells do not all become long and thin.
The tree can be queried for the r closest neighbors of any given point (optionally returning only those within some maximum distance of the point). It can also be queried, with a substantial gain in efficiency, for the r approximate closest neighbors.
For large dimensions (20 is already large) do not expect this to run significantly faster than brute force. Highdimensional nearestneighbor queries are a substantial open problem in computer science.
Parameters :  data : arraylike, shape (n,m)
leafsize : positive integer


Attributes
data  
leafsize  
m  
maxes  
mins  
n 
Methods
count_neighbors(self, other, r, p)  Count how many nearby pairs can be formed. 
query(self, x[, k, eps, p, distance_upper_bound])  Query the kdtree for nearest neighbors 
query_ball_point(self, x, r, p, eps)  Find all points within distance r of point(s) x. 
query_ball_tree(self, other, r, p, eps)  Find all pairs of points whose distance is at most r 
query_pairs(self, r, p, eps)  Find all pairs of points whose distance is at most r. 
sparse_distance_matrix(self, max_distance, p)  Compute a sparse distance matrix 