NAME

       mlpack_range_search - range search

SYNOPSIS

        mlpack_range_search [-m unknown] [-l int] [-U double] [-L double] [-N bool] [-q unknown] [-R bool] [-r unknown] [-s int] [-S bool] [-t string] [-V bool] [-d string] [-n string] [-M unknown] [-h -v]

DESCRIPTION

       This  program  implements range search with a Euclidean distance metric. For a given query
       point, a given range, and a given set of reference points, the program will return all  of
       the  reference  points  with  distance  to  the  query  point  in the given range. This is
       performed for an entire set of query points. You may specify a separate set  of  reference
       and  query points, or only a reference set -- which is then used as both the reference and
       query set.  The given range is taken to be inclusive (that  is,  points  with  a  distance
       exactly equal to the minimum and maximum of the range are included in the results).

       For example, the following will calculate the points within the range [2, 5] of each point
       in  'input.csv'  and  store  the  distances  in'distances.csv'  and   the   neighbors   in
       'neighbors.csv'

       $  mlpack_range_search  --min  2 --max 5 --distances_file input --distances_file distances
       --neighbors_file neighbors

       The output files are organized such that line i corresponds to the points found for  query
       point  i.  Because sometimes 0 points may be found in the given range, lines of the output
       files may be empty. The points are not ordered in any specific manner.

       Because the number of points returned for each query point may differ, the resultant  CSV-
       like  files  may  not  be loadable by many programs. However, at this time a better way to
       store this non-square result is not known. As a result, any output files will  be  written
       as CSVs in this manner, regardless of the given extension.

OPTIONAL INPUT OPTIONS

       --help (-h) [bool]
              Default help info.

       --info [string]
              Print help on a specific option. Default value ''.

       --input_model_file (-m) [unknown]
              File containing pre-trained range search model.

       --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.

       --max (-U) [double]
              Upper bound in range (if not specified, +inf will be used. Default value 0.

       --min (-L) [double]
              Lower bound in range. Default value 0.

       --naive (-N) [bool]
              If true, O(n^2) naive mode is used for computation.

       --query_file (-q) [unknown]
              File 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.

       --single_mode (-S) [bool]
              If true, single-tree search is used (as opposed to dual-tree search).

       --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'.

       --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]
              File to output distances into. Default value ''.

       --neighbors_file (-n) [string]
              File to output neighbors into. Default value ''.

       --output_model_file (-M) [unknown]
              If specified, the range search model will be saved to the given file.

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.