NAME

       mlpack_kfn - k-furthest-neighbors search

SYNOPSIS

        mlpack_kfn [-a string] [-e double] [-m unknown] [-k int] [-l int] [-p double] [-q unknown] [-R bool] [-r unknown] [-s int] [-t string] [-D unknown] [-T unknown] [-V bool] [-d unknown] [-n unknown] [-M unknown] [-h -v]

DESCRIPTION

       This  program  will calculate the k-furthest-neighbors of a set of points. You may specify a separate set
       of reference points and query points, or just a reference set which will be used as  both  the  reference
       and query set.

       For  example, the following will calculate the 5 furthest neighbors of eachpoint in 'input.csv' and store
       the distances in 'distances.csv' and the neighbors in 'neighbors.csv':

       $  mlpack_kfn  --k  5  --reference_file   input.csv   --distances_file   distances.csv   --neighbors_file
       neighbors.csv

       The output files are organized such that row i and column j in the neighbors output matrix corresponds to
       the  index  of  the  point in the reference set which is the j'th furthest neighbor from the point in the
       query set with index i.  Row i and column j in the distances output  file  corresponds  to  the  distance
       between those two points.

OPTIONAL INPUT OPTIONS

       --algorithm (-a) [string]
              Type of neighbor search: 'naive', 'single_tree', 'dual_tree', 'greedy'. Default value 'dual_tree'.

       --epsilon (-e) [double]
              If  specified,  will do approximate furthest neighbor search with given relative error. Must be in
              the range [0,1). Default value 0.

       --help (-h) [bool]
              Default help info.

       --info [string]
              Print help on a specific option. Default value ''.

       --input_model_file (-m) [unknown]
              Pre-trained kFN model.

       --k (-k) [int]
              Number of furthest neighbors to find. Default value 0.

       --leaf_size (-l) [int]
              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.

       --percentage (-p) [double]
              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)  %  of  the  distance  as  the  true  furthest
              neighbor. Default value 1.

       --query_file (-q) [unknown]
              Matrix containing query points (optional).

       --random_basis (-R) [bool]
              Before tree-building, project the data onto a random orthogonal basis.

       --reference_file (-r) [unknown]
              Matrix containing the reference dataset.

       --seed (-s) [int]
              Random seed (if 0, std::time(NULL) is used).  Default value 0.

       --tree_type (-t) [string]
              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'.

       --true_distances_file (-D) [unknown]
              Matrix of true distances to compute the effective error (average relative error)  (it  is  printed
              when -v is specified).

       --true_neighbors_file (-T) [unknown]
              Matrix of true neighbors to compute the recall (it is printed when -v is specified).

       --verbose (-v) [bool]
              Display informational messages and the full list of parameters and timers at the end of execution.

       --version (-V) [bool]
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

       --distances_file (-d) [unknown]
              Matrix to output distances into.

       --neighbors_file (-n) [unknown]
              Matrix to output neighbors into.

       --output_model_file (-M) [unknown]
              If specified, the kFN model will be output here.

ADDITIONAL INFORMATION

       For  further  information,  including  relevant  papers, citations, and theory, consult the documentation
       found at http://www.mlpack.org or included with your distribution of mlpack.

mlpack-4.3.0                                     19 January 2024                                   mlpack_kfn(1)