bionic (1) mlpack_range_search.1.gz

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)