mlpack_preprocess_binarize(1)

NAME
mlpack_preprocess_binarize - binarize data

SYNOPSIS
 mlpack_preprocess_binarize -i unknown [-d int] [-t double] [-V bool] [-o unknown] [-h -v]

DESCRIPTION

This utility takes a dataset and binarizes the variables into either 0 or 1 given threshold. User can apply binarization on a dimension or the whole dataset. The dimension to apply binarization to can be specified using the ’--dimension (-d)' parameter; if left unspecified, every dimension will be binarized. The threshold for binarization can also be specified with the ’--threshold (-t)' parameter; the default threshold is 0.0.

The binarized matrix may be saved with the '--output_file (-o)' output parameter.

For example, if we want to set all variables greater than 5 in the dataset ’X.csv' to 1 and variables less than or equal to 5.0 to 0, and save the result to 'Y.csv', we could run

$ mlpack_preprocess_binarize --input_file X.csv --threshold 5 --output_file Y.csv

But if we want to apply this to only the first (0th) dimension of 'X.csv', we could instead run

$ mlpack_preprocess_binarize --input_file X.csv --threshold 5 --dimension 0 --output_file Y.csv

REQUIRED INPUT OPTIONS

--input_file (-i) [unknown]
    Input data matrix.

OPTIONAL INPUT OPTIONS

--dimension (-d) [int]
    Dimension to apply the binarization. If not set, the program will binarize every dimension by default. Default value 0.

--help (-h) [bool]
    Default help info.

--info [string]
    Print help on a specific option. Default value ''.

--threshold (-t) [double]
    Threshold to be applied for binarization. If not set, the threshold defaults to 0.0. Default value 0.

--verbose (-v) [bool]
    Display informational messages and the full list of parameters and timers at the end of execution.

--version (-V) [bool]
    Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

--output_file (-o) [unknown] Matrix in which to save the output.

ADDITIONAL INFORMATION

For further information, including relevant papers, citations, and theory, consult the documentation found at http://www.mlpack.org or included with your distribution of mlpack.