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.