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.

mlpack-4.3.0                                     19 January 2024                          mlpack_range_search(1)