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K-d Tree

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

Parameters

#	Name	Default	Type	Description
1	maxLeafSize	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.

Example

use Rubix\ML\Graph\Trees\KDTree;
use Rubix\ML\Kernels\Distance\Euclidean;

$tree = new KDTree(30, new Euclidean());

Additional Methods

This tree does not have any additional methods.

References

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1. J. L. Bentley. (1975). Multidimensional Binary Search Trees Used for Associative Searching. ↩