Module balltree 

Ball Tree implementation for efficient nearest neighbor search

A Ball Tree is a space-partitioning data structure for organizing points in a k-dimensional space. It divides points into nested hyperspheres, which makes it particularly effective for high-dimensional data.

Advantages of Ball Trees over KD-Trees:

• Better performance in high dimensions
• More efficient for datasets with varying density
• Handles elongated clusters well

This implementation provides:

• Building balanced Ball Trees from point data
• Efficient exact nearest neighbor queries
• k-nearest neighbor searches
• Range queries for all points within a specified radius

Structs

BallTree

Ball Tree for efficient nearest neighbor searches in high dimensions