bionic (1) mlpack_approx_kfn.1.gz

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

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)