Controls for KD algorithm used in the package (see knn for details).
Usage
control_kd(
algorithm = "dual_tree",
epsilon = 0,
leaf_size = 20,
random_basis = FALSE,
rho = 0.7,
tau = 0,
tree_type = "kd",
...
)Arguments
- algorithm
Type of neighbor search:
'naive','single_tree','dual_tree','greedy'.- epsilon
If specified, will do approximate nearest neighbor search with given relative error.
- leaf_size
Leaf size for tree building (used for kd-trees, vp trees, random projection trees, UB trees, R trees, R* trees, X trees, Hilbert R trees, R+ trees, R++ trees, spill trees, and octrees).
- random_basis
Before tree-building, project the data onto a random orthogonal basis.
- rho
Balance threshold (only valid for spill trees).
- tau
Overlapping size (only valid for spill trees).
- tree_type
Type of tree to use:
'kd','vp','rp','max-rp','ub','cover','r','r-star','x','ball','hilbert-r','r-plus','r-plus-plus','spill','oct'.- ...
Additional arguments.