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 which branches to prune off during nearest neighbor and range searches enabling them to complete 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.