cratepy.clustering.clusteringdata.MinMaxScaler

class MinMaxScaler(feature_range=(0, 1))

Bases: Standardizer

Min-Max scaling algorithm (wrapper).

Transform features by scaling each feature to a given min-max range.

Documentation: see here.

_feature_range

Desired range of transformed data (tuple(min, max)).

Type:

tuple[float], default=(0, 1)

get_standardized_data_matrix(self, data_matrix)

Standardize provided data matrix.

Standardization algorithm constructor.

Parameters:

feature_range (tuple[float], default=(0, 1)) – Desired range of transformed data (tuple(min, max)).

List of Public Methods

get_standardized_data_matrix

Standardize provided data matrix.

Methods

__init__(feature_range=(0, 1))

Standardization algorithm constructor.

Parameters:

feature_range (tuple[float], default=(0, 1)) – Desired range of transformed data (tuple(min, max)).

get_standardized_data_matrix(data_matrix)

Standardize provided data matrix.

Parameters:

data_matrix (numpy.ndarray (2d)) – Data matrix to be standardized (numpy.ndarray of shape (n_items, n_features)).

Returns:

data_matrix – Transformed data matrix (numpy.ndarray of shape (n_items, n_features)).

Return type:

numpy.ndarray (2d)