mlpack.preprocess_binarize

preprocess_binarize(...)Binarize Data

>>> from mlpack import preprocess_binarize

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' parameter; if left unspecified, every dimension will be binarized. The threshold for binarization can also be specified with the 'threshold' parameter; the default threshold is 0.0.

The binarized matrix may be saved with the 'output' output parameter.

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

>>> preprocess_binarize(input=X, threshold=5)

>>> Y = output['output']

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

>>> preprocess_binarize(input=X, threshold=5, dimension=0)

>>> Y = output['output']

## input options

- input (numpy matrix or arraylike, float dtype): [required] Input data matrix.
- copy_all_inputs (bool): If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code.
- dimension (int): Dimension to apply the binarization. If not set, the program will binarize every dimension by default. Default value 0.
- threshold (float): Threshold to be applied for binarization. If not set, the threshold defaults to 0.0. Default value 0.
- verbose (bool): Display informational messages and the full list of parameters and timers at the end of execution.

## output options

The return value from the binding is a dict containing the following elements:

- output (numpy matrix, float dtype): Matrix in which to save the output.