NAME

       mlpack_radical - radical

SYNOPSIS

        mlpack_radical [-h] [-v]

DESCRIPTION

       An  implementation of RADICAL, a method for independentcomponent analysis (ICA). Assuming that we have an
       input matrix X, thegoal is to find a square unmixing matrix W such that Y = W * X and the dimensions of Y
       are independent components. If the algorithm is runningparticularly slowly, try reducing  the  number  of
       replicates.

REQUIRED INPUT OPTIONS

       --input_file (-i) [string]
              Input dataset filename for ICA.

OPTIONAL INPUT OPTIONS

       --angles (-a) [int]
              Number of angles to consider in brute-force search during Radical2D. Default value 150.

       --help (-h)
              Default help info.

       --info [string]
              Get  help  on  a  specific  module  or  option.   Default value ''.  --noise_std_dev (-n) [double]
              Standard deviation of Gaussian noise. Default value 0.175.

       --objective (-O)
              If set, an estimate of the final objective function is printed.

       --replicates (-r) [int]
              Number of Gaussian-perturbed replicates to use (per point) in Radical2D. Default value 30.

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

       --sweeps (-S) [int]
              Number of sweeps; each sweep calls Radical2D once for each pair of dimensions. Default value

              0.

       --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_ic_file  (-o)  [string]  File  to  save   independent   components   to.   Default   value   ''.
       --output_unmixing_file (-u) [string] File to save unmixing matrix 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_radical(16 November 2017)