NAME

       mlpack_allkrann - all k-rank-approximate-nearest-neighbors

SYNOPSIS

        mlpack_allkrann [-h] [-v] [-a double] [-d string] [-X] [-m string] [-k int] [-l int] [-N] [-n string] [-M string] [-q string] [-R] [-r string] [-L] [--seed int] [-s] [-S int] [-t double] [--tree_type string] -V

DESCRIPTION

       This  program  will calculate the k rank-approximate-nearest-neighbors of a set of points.
       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. You must specify the rank
       approximation (in %) (and optionally the success probability).

       For example, the following will return 5 neighbors from the top 0.1%  of  the  data  (with
       probability 0.95) for each point in 'input.csv' and store the distances in 'distances.csv'
       and the neighbors in the file 'neighbors.csv':

       $ allkrann -k 5 -r input.csv -d distances.csv -n neighbors.csv --tau 0.1

       Note that tau must be set such that the number of points in the  corresponding  percentile
       of  the data is greater than k. Thus, if we choose tau = 0.1 with a dataset of 1000 points
       and k = 5, then we are attempting to choose 5 nearest neighbors out of the closest 1 point
       -- this is invalid and the program will terminate with an error message.

       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.

OPTIONS

       --alpha (-a) [double]
              The  desired  success  probability.  Default  value  0.95.   --distances_file  (-d)
              [string] File to output distances into. Default value ’'.

       --first_leaf_exact (-X)
              The flag to trigger sampling only after exactly exploring the first leaf.

       --help (-h)
              Default help info.

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

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

       --leaf_size (-l) [int]
              Leaf size for tree building (used for kd-trees, R trees,  and  R*  trees).  Default
              value 20.

       --naive (-N)
              If  true,  sampling  will  be  done  without  using  a tree.  --neighbors_file (-n)
              [string] File to output neighbors into. Default value ’'.  --output_model_file (-M)
              [string] If specified, the kNN model will be saved to the given file. Default value
              ''.

       --query_file (-q) [string]
              File containing query points (optional).  Default value ''.

       --random_basis (-R)
              Before  tree-building,  project  the  data  onto   a   random   orthogonal   basis.
              --reference_file (-r) [string] File containing the reference dataset. Default value
              ''.

       --sample_at_leaves (-L)
              The flag to trigger sampling at leaves.

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

       --single_mode (-s)
              If  true,  single-tree  search  is  used   (as   opposed   to   dual-tree   search.
              --single_sample_limit  (-S)  [int]  The limit on the maximum number of samples (and
              hence the largest node you can approximate).  Default value 20.

       --tau (-t) [double]
              The allowed rank-error in terms of the percentile of the data. Default value 5.

       --tree_type [string]
              Type of tree to use: 'kd', 'cover', 'r', or ’r-star'. Default value 'kd'.

       --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.

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_allkrann(1)