pyts.preprocessing
.MaxAbsScaler¶
-
class
pyts.preprocessing.
MaxAbsScaler
[source]¶ Scale each sample by its maximum absolute value.
Examples
>>> from pyts.preprocessing import MaxAbsScaler >>> X = [[1, 5, 3, 2, 10, 6, 4, 7], ... [1, -5, 3, 2, 2, 1, 0, 2]] >>> scaler = MaxAbsScaler() >>> scaler.transform(X) array([[ 0.1, 0.5, 0.3, 0.2, 1. , 0.6, 0.4, 0.7], [ 0.2, -1. , 0.6, 0.4, 0.4, 0.2, 0. , 0.4]])
Methods
__init__
(self)Initialize self. fit
(self[, X, y])Pass. fit_transform
(self, X[, y])Fit to data, then transform it. get_params
(self[, deep])Get parameters for this estimator. set_params
(self, \*\*params)Set the parameters of this estimator. transform
(self, X)Scale the data. -
fit_transform
(self, X, y=None, **fit_params)¶ Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters: - X : numpy array of shape [n_samples, n_features]
Training set.
- y : numpy array of shape [n_samples]
Target values.
- **fit_params : dict
Additional fit parameters.
Returns: - X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
-
get_params
(self, deep=True)¶ Get parameters for this estimator.
Parameters: - deep : bool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: - params : mapping of string to any
Parameter names mapped to their values.
-
set_params
(self, **params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Parameters: - **params : dict
Estimator parameters.
Returns: - self : object
Estimator instance.
-