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.