[source] # 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 ```php 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 [^1]: J. L. Bentley. (1975). Multidimensional Binary Search Trees Used for Associative Searching.