- algorithm
Type of neighbor search: 'naive', 'single_tree',
'dual_tree', 'greedy'. Default value "dual_tree" (character).
- epsilon
If specified, will do approximate furthest neighbor
search with given relative error. Must be in the range [0,1). Default
value "0" (numeric).
- input_model
Pre-trained kFN model (KFNModel).
- k
Number of furthest neighbors to find. Default value "0"
(integer).
- 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, and octrees). Default value "20"
(integer).
- percentage
If specified, will do approximate furthest neighbor
search. Must be in the range (0,1] (decimal form). Resultant neighbors will
be at least (p*100)
Default value "1" (numeric).
- query
Matrix containing query points (optional) (numeric
matrix).
- random_basis
Before tree-building, project the data onto a random
orthogonal basis. Default value "FALSE" (logical).
- reference
Matrix containing the reference dataset (numeric
matrix).
- seed
Random seed (if 0, std::time(NULL) is used). Default value
"0" (integer).
- 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',
'oct'. Default value "kd" (character).
- true_distances
Matrix of true distances to compute the effective
error (average relative error) (it is printed when -v is specified)
(numeric matrix).
- true_neighbors
Matrix of true neighbors to compute the recall (it
is printed when -v is specified) (integer matrix).
- verbose
Display informational messages and the full list of
parameters and timers at the end of execution. Default value
"getOption("mlpack.verbose", FALSE)" (logical).