NAME

       mlpack_lsh - all k-approximate-nearest-neighbor search with lsh

SYNOPSIS

        mlpack_lsh [-h] [-v]

DESCRIPTION

       This  program  will calculate the k approximate-nearest-neighbors of a set of points using
       locality-sensitive hashing. You may specify a separate set of reference points  and  query
       points, or just a reference set which will be used as both the reference and query set.

       For  example,  the  following  will  return  5  neighbors  from the data for each point in
       'input.csv' and store the distances in 'distances.csv'  and  the  neighbors  in  the  file
       'neighbors.csv':

       $ lsh -k 5 -r input.csv -d distances.csv -n neighbors.csv

       The  output  files are organized such that row i and column j in the neighbors output file
       corresponds to the index of the point in the reference  set  which  is  the  i'th  nearest
       neighbor  from  the  point  in  the  query  set  with  index j.  Row i and column j in the
       distances output file corresponds to the distance between those two points.

       Because this is approximate-nearest-neighbors search, results may be different from run to
       run. Thus, the --seed option can be specified to set the random seed.

OPTIONAL INPUT OPTIONS

       --bucket_size (-B) [int]
              The size of a bucket in the second level hash.  Default value 500.

       --hash_width (-H) [double]
              The  hash  width  for the first-level hashing in the LSH preprocessing. By default,
              the LSH class automatically estimates a hash width for its use. Default value 0.

       --help (-h)
              Default help info.

       --info [string]
              Get help on a specific module or option.   Default  value  ''.   --input_model_file
              (-m)   [string]   File   to   load  LSH  model  from.  (Cannot  be  specified  with
              --reference_file.) Default value ’'.

       --k (-k) [int]
              Number of nearest neighbors to find. Default value 0.

       --num_probes (-T) [int]
              Number of additional probes for multiprobe LSH; if  0,  traditional  LSH  is  used.
              Default value

              0.

       --projections (-K) [int]
              The number of hash functions for each table Default value 10.

       --query_file (-q) [string]
              File  containing query points (optional).  Default value ''.  --reference_file (-r)
              [string]   File   containing   the   reference   dataset.   Default    value    ''.
              --second_hash_size  (-S)  [int]  The  size of the second level hash table.  Default
              value 99901.

       --seed (-s) [int]
              Random seed. If 0, 'std::time(NULL)' is used.  Default value 0.

       --tables (-L) [int]
              The number of hash tables to be used. Default value 30.  --true_neighbors_file (-t)
              [string]  File of true neighbors to compute recall with (the recall is printed when
              -v is specified).  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

       --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] File to save LSH 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_lsh(16 November 2017)