A multi-dimensional binary search tree for fast nearest neighbor queries. The K-d tree construction algorithm separates data points into bounded hypercubes or bounding boxes that are used to prune off nodes during nearest neighbor and range searches.
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\KDTree; use Rubix\ML\Kernels\Distance\Euclidean; $tree = new KDTree(30, new Euclidean());
- J. L. Bentley. (1975). Multidimensional Binary Seach Trees Used for Associative Searching.