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
       --distances_file distances.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.