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

NAME

       mlpack_emst - fast euclidean minimum spanning tree

SYNOPSIS

        mlpack_emst [-h] [-v]

DESCRIPTION

       This program can compute the Euclidean minimum spanning tree of a set of input points using the dual-tree
       Boruvka algorithm.

       The  output  is  saved  in  a  three-column  matrix,  where  each row indicates an edge. The first column
       corresponds to the lesser index of the edge; the second column corresponds to the greater  index  of  the
       edge; and the third column corresponds to the distance between the two points.

REQUIRED INPUT OPTIONS

       --input_file (-i) [string]
              Data input file.

OPTIONAL INPUT OPTIONS

       --help (-h)
              Default help info.

       --info [string]
              Get help on a specific module or option.  Default value ''.

       --leaf_size (-l) [int]
              Leaf  size  in  the  kd-tree. One-element leaves give the empirically best performance, but at the
              cost of greater memory requirements.  Default value 1.

       --naive (-n)
              Compute the MST using O(n^2) naive algorithm.

       --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_file (-o) [string]
              Data output file. Stored as an edge list.  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_emst(16 November 2017)