mlpack_preprocess_binarize

NAME

mlpack_preprocess_binarize - binarize data

SYNOPSIS

mlpack_preprocess_binarize [-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. A dimension can be specified using --dimension (-d) option. Threshold can also be specified with the --threshold (-t) option; The default is 0.0.

The program does not modify the original file, but instead makes a separate file to save the binarized data; The program requires you to specify the file name with --output_file (-o).

For example, if we want to make all variables greater than 5 in dataset to 1 and ones that are less than or equal to 5.0 to 0, and save the result to result.csv, we could run

$ mlpack_preprocess_binarize -i dataset.csv -t 5 -o result.csv

But if we want to apply this to only the first (0th) dimension of the dataset, we could run

$ mlpack_preprocess_binarize -i dataset.csv -t 5 -d 0 -o result.csv

REQUIRED INPUT OPTIONS

--input_file (-i) [string]

File containing data.

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)

Default help info.

--info [string]

Get help on a specific module or 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)

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]

File to save the output. 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.