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)