pyts.preprocessing
.MaxAbsScaler¶
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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__
()Initialize self. fit
([X, y])Pass. fit_transform
(X[, y])Fit to data, then transform it. get_params
([deep])Get parameters for this estimator. set_output
(*[, transform])Set output container. set_params
(**params)Set the parameters of this estimator. transform
(X)Scale the data. -
fit_transform
(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 : array-like of shape (n_samples, n_features)
Input samples.
- y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_params : dict
Additional fit parameters.
Returns: - X_new : ndarray array of shape (n_samples, n_features_new)
Transformed array.
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get_params
(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 : dict
Parameter names mapped to their values.
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set_output
(*, transform=None)¶ Set output container.
See Introducing the set_output API for an example on how to use the API.
Parameters: - transform : {“default”, “pandas”}, default=None
Configure output of transform and fit_transform.
- “default”: Default output format of a transformer
- “pandas”: DataFrame output
- None: Transform configuration is unchanged
Returns: - self : estimator instance
Estimator instance.
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set_params
(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). 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 : estimator instance
Estimator instance.
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