A binary spatial tree that partitions the dataset into successively smaller and tighter ball nodes whose boundary are defined by a centroid and radius. Ball Trees work well in higher dimensions since the partitioning schema does not rely on a finite number of 1-dimensional axis aligned splits as with k-d trees.
Data Type Compatibility: Continuous
|1||max leaf size||30||int||The maximum number of samples that each leaf node can contain.|
|2||kernel||Euclidean||object||The distance kernel used to compute the distance between sample points.|
Return the path of a sample taken from the root node to a leaf node in an array.
public path(array $sample) : array
use Rubix\ML\Graph\Trees\BallTree; use Rubix\ML\Kernels\Distance\Euclidean; $tree = new BallTree(40, new Euclidean());
- S. M. Omohundro. (1989). Five Balltree Construction Algorithms.
- M. Dolatshah et al. (2015). Ball*-tree: Efficient spatial indexing for constrained nearest-neighbor search in metric spaces.