NAME

       mlpack_approx_kfn - approximate furthest neighbor search

SYNOPSIS

        mlpack_approx_kfn [-a string] [-e bool] [-x unknown] [-m unknown] [-k int] [-p int] [-t int] [-q unknown] [-r unknown] [-V bool] [-d unknown] [-n unknown] [-M unknown] [-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  (-r)',  specify  a  query  set  with
       '--query_file  (-q)',  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 (-a)'. Also specify the number of neighbors to search for
       with '--k (-k)'.

       Note that for 'qdafn' in lower dimensions, '--num_projections (-p)' may need to be set to a high value in
       order to return results for each query point.

       If no query set is specified, the reference set will be used as the query set.  The  '--output_model_file
       (-M)'  output parameter may be used to store the built model, and an input model may be loaded instead of
       specifying a reference set with the '--input_model_file (-m)' option.

       Results for each query point can be stored with the '--neighbors_file (-n)' and  '--distances_file  (-d)'
       output  parameters.  Each row of these output matrices holds the k distances or neighbor indices for each
       query point.

       For example, to find the 5 approximate furthest neighbors with ’reference_set.csv' as the  reference  set
       and  'query_set.csv'  as  the  query  set  using DrusillaSelect, storing the furthest neighbor indices to
       'neighbors.csv' and the furthest neighbor distances to 'distances.csv', one could call

       $ mlpack_approx_kfn --query_file query_set.csv --reference_file reference_set.csv --k  5  --algorithm  ds
       --neighbors_file neighbors.csv --distances_file distances.csv

       and  to perform approximate all-furthest-neighbors search with k=1 on the set ’data.csv' storing only the
       furthest neighbor distances to 'distances.csv', one could call

       $ mlpack_approx_kfn --reference_file reference_set.csv --k 1 --distances_file distances.csv

       A trained model can be re-used. If a model has been previously saved to ’model.bin', then we may  find  3
       approximate furthest neighbors on a query set ’new_query_set.csv' using that model and store the furthest
       neighbor indices into 'neighbors.csv' by calling

       $  mlpack_approx_kfn  --input_model_file  model.bin --query_file new_query_set.csv --k 3 --neighbors_file
       neighbors.csv

OPTIONAL INPUT OPTIONS

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

       --calculate_error (-e) [bool]
              If set, calculate the average distance error for the first furthest neighbor only.

       --exact_distances_file (-x) [unknown]
              Matrix containing exact distances to furthest neighbors;  this  can  be  used  to  avoid  explicit
              calculation when --calculate_error is set.

       --help (-h) [bool]
              Default help info.

       --info [string]
              Print help on a specific option. Default value ''.

       --input_model_file (-m) [unknown]
              File containing input model.

       --k (-k) [int]
              Number of furthest neighbors to search for.  Default value 0.  --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) [unknown]
              Matrix containing query points.

       --reference_file (-r) [unknown]
              Matrix containing the reference dataset.

       --verbose (-v) [bool]
              Display informational messages and the full list of parameters and timers at the end of execution.

       --version (-V) [bool]
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

       --distances_file (-d) [unknown]
              Matrix to save furthest neighbor distances to.

       --neighbors_file (-n) [unknown]
              Matrix to save neighbor indices to.

       --output_model_file (-M) [unknown]
              File to save output model to.

ADDITIONAL INFORMATION

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

mlpack-4.3.0                                     19 January 2024                            mlpack_approx_kfn(1)