NAME

       mlpack_knn - k-nearest-neighbors search

SYNOPSIS

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

DESCRIPTION

       This  program  will  calculate  the  k-nearest-neighbors of a set of points using kd-trees or cover trees
       (cover tree support is experimental and may be slow).  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 command will calculate the 5 nearest neighbors of each  point  in  'input.csv'
       and store the distances in 'distances.csv' and the neighbors in 'neighbors.csv':

       $ mlpack_knn --k 5 --reference_file input.csv --neighbors_file neighbors.csv

       The  output  is  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 nearest neighbor from the point  in  the  query
       set  with  index i. Row j and column i in the distances output matrix 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 nearest neighbor search  with  given  relative  error.   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 kNN model.

       --k (-k) [int]
              Number of nearest 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,  spill  trees,  and  octrees).
              Default value 20.

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

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

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

       --rho (-b) [double]
              Balance threshold (only valid for spill trees).  Default value 0.7.

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

       --tau (-u) [double]
              Overlapping size (only valid for spill trees).  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', 'spill', 'oct'. Default value 'kd'.

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

       --true_neighbors_file (-T) [string]
              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) [string]
              Matrix to output distances into.

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

       --output_model_file (-M) [unknown]
              If specified, the kNN 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-3.2.2                                    21 February 2020                                   mlpack_knn(1)