def __init__(self, lower_bound, upper_bound, feature_range=(0, 1), copy=True, rescale_transformed_vars=True):
"""
Parameters
----------
lower_bound : np.ndarray
Array of the same length of the variable to which the transformation will be applied, containing lower
bounds of the variable. Each entry of the array can be either None or a number (see above).
upper_bound
Array of the same length of the variable to which the transformation will be applied, containing upper
bounds of the variable. Each entry of the array can be either None or a number (see above).
feature_range : tuple (min, max), optional
Desired range of transformed data (obtained with the MinMaxScaler after the
nonlinear transformation is computed). Default=(0, 1)
copy : bool, optional
Set to False to perform inplace row normalization and avoid a
copy in the MinMaxScaler (if the input is already a numpy array). Defaults to True.
rescale_transformed_vars : bool, optional
Whether to apply the MinMaxScaler after the nonlinear transformation. Defaults to True.
"""
BoundedVarTransformer.__init__(self, lower_bound, upper_bound)
MinMaxScaler.__init__(self, feature_range=feature_range, copy=copy)
self.rescale_transformed_vars = rescale_transformed_vars
def __init__(self, feature_range=(0, 1), copy=True):
MinMaxScaler.__init__(self, feature_range, copy)
def __init__(self):
MinMaxScaler.__init__(self, feature_range=(-1, 1))
