Provided by: mlpack-bin_2.2.5-1build1_amd64 bug

NAME

       mlpack_range_search - range search

SYNOPSIS

        mlpack_range_search [-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':

       $  range_search  --min=2 --max=5 --reference_file=input.csv --distances_file=distances.csv
       --neighbors_file=neighbors.csv

       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)
              Default help info.

       --info [string]
              Get  help  on  a  specific module or option.  Default value ''.  --input_model_file
              (-m) [string] File containing pre-trained range search model.  Default value ''.

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

       --query_file (-q) [string]
              File containing query points (optional).  Default value ''.

       --random_basis (-R)
              Before  tree-building,  project  the  data  onto   a   random   orthogonal   basis.
              --reference_file (-r) [string] File containing the reference dataset. Default value
              ''.

       --seed [int]
              Random seed (if 0, std::time(NULL) is used).  Default value 0.

       --single_mode (-s)
              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)
              Display  informational  messages  and the full list of parameters and timers at the
              end of execution.

       --version (-V)
              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) [string] If specified, the range search model will  be  saved  to
       the given file. Default value ''.

ADDITIONAL INFORMATION

ADDITIONAL INFORMATION

       For  further  information,  including  relevant papers, citations, and theory, For further
       information, including relevant papers, citations, and theory, consult  the  documentation
       found  at  http://www.mlpack.org  or included with your consult the documentation found at
       http://www.mlpack.org or included with  your  DISTRIBUTION  OF  MLPACK.   DISTRIBUTION  OF
       MLPACK.

                                                            mlpack_range_search(16 November 2017)