NAME

       mlpack_approx_kfn - approximate furthest neighbor search

SYNOPSIS

        mlpack_approx_kfn [-h] [-v]

DESCRIPTION

       This program implements two strategies for furthest neighbor search. These strategies are:

              •  The  'qdafn'  algorithm  from 'Approximate Furthest Neighbor in High Dimensions' by R. Pagh, F.
                 Silvestri, J. Sivertsen, and M. Skala, in Similarity Search and Applications 2015 (SISAP).

              •  The 'DrusillaSelect' algorithm from 'Fast approximate furthest  neighbors  with  data-dependent
                 candidate  selection,  by  R.R.  Curtin and A.B. Gardner, in Similarity Search and Applications
                 2016 (SISAP).

       These two strategies give approximate results for the furthest neighbor search problem and can be used as
       fast replacements for other furthest neighbor techniques such as those found in the  mlpack_kfn  program.
       Note  that  typically,  the  'ds'  algorithm  requires  far fewer tables and projections than the 'qdafn'
       algorithm.

       Specify a reference set (set to search in) with --reference_file, specify a query set with  --query_file,
       and specify algorithm parameters with --num_tables (-t) and --num_projections (-p) (or don't and defaults
       will  be  used).  The algorithm to be used (either 'ds'---the default---or 'qdafn') may be specified with
       --algorithm. Also specify the number of neighbors to search for with --k. Each of those options also  has
       short names; see the detailed parameter documentation below.

       If no query file is specified, the reference set will be used as the query set. A model may be saved with
       --output_model_file  (-M),  and  an  input model may be loaded instead of specifying a reference set with
       --input_model_file (-m).

       Results for each query point are stored in the files specified by --neighbors_file and  --distances_file.
       This  is  in the same format as the mlpack_kfn and mlpack_knn programs: each row holds the k distances or
       neighbor indices for each query point.

OPTIONAL INPUT OPTIONS

       --algorithm (-a) [string]
              Algorithm to use: 'ds' or 'qdafn'. Default value 'ds'.

       --calculate_error (-e)
              If  set,  calculate  the  average  distance  error  for  the   first   furthest   neighbor   only.
              --distances_file  (-d)  [string]  File  to save furthest neighbor distances to.  Default value ''.
              --exact_distances_file (-x) [string] File containing exact distances to furthest  neighbors;  this
              can be used to avoid explicit calculation when --calculate_error is set.  Default value ''.

       --help (-h)
              Default help info.

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

       --k (-k) [int]
              Number of furthest neighbors to search for.  Default value 0.  --neighbors_file (-n) [string] File
              to  save  furthest neighbor indices to.  Default value ''.  --num_projections (-p) [int] Number of
              projections to use in each hash table.  Default value 5.

       --num_tables (-t) [int]
              Number of hash tables to use. Default value 5.

       --query_file (-q) [string]
              File containing query points. Default value ’'.  --reference_file (-r)  [string]  File  containing
              reference points. Default value ’'.

       --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

       --output_model_file (-M) [string] File to save output model to. 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_approx_kfn(16 November 2017)