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)