Module balltree 

Ball tree for efficient nearest neighbor searches

Ball trees are spatial data structures that organize points in a metric space into a tree structure. Each node represents a hypersphere (ball) that contains a subset of the points. This implementation shares similarities with KD-tree, but can be more efficient for high-dimensional data or when using general distance metrics beyond Euclidean.

Features

• Fast construction of ball trees with customizable leaf size
• Nearest neighbor queries with configurable k
• Range queries to find all points within a distance
• Support for all distance metrics defined in the distance module
• Suitable for high-dimensional data where KD-trees become less efficient

References

• Omohundro, S.M. (1989) “Five Balltree Construction Algorithms”
• Liu, T. et al. (2006) “An Investigation of Practical Approximate Nearest Neighbor Algorithms”
• scikit-learn ball tree implementation

Structs

BallTree

Ball tree for efficient nearest neighbor searches