A multi-dimensional binary spatial tree for fast nearest neighbor queries. The K-d tree construction algorithm separates data points into bounded hypercubes or boxes that are used to determine when to prune off sections of nodes during nearest neighbor and range searches. Pruning allows K-d Tree to perform searches in sub-linear time.
Interfaces: Binary Tree, Spatial
Data Type Compatibility: Continuous
|1||max leaf size||30||int||The maximum number of samples that each leaf node can contain.|
|2||kernel||Euclidean||Distance||The distance kernel used to compute the distance between sample points.|
use Rubix\ML\Graph\Trees\KDTree; use Rubix\ML\Kernels\Distance\Euclidean; $tree = new KDTree(30, new Euclidean());
This tree does not have any additional methods.
- J. L. Bentley. (1975). Multidimensional Binary Search Trees Used for Associative Searching.