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.