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.
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.|
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\KDTree; use Rubix\ML\Kernels\Distance\Euclidean; $tree = new KDTree(30, new Euclidean());
- J. L. Bentley. (1975). Multidimensional Binary Search Trees Used for Associative Searching.