pyts.preprocessing
.StandardScaler¶
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class
pyts.preprocessing.
StandardScaler
(with_mean=True, with_std=True)[source]¶ Standardize time series by removing mean and scaling to unit variance.
Parameters: - with_mean : bool (default = True)
If True, center the data before scaling.
- with_std : bool (default = True)
If True, scale the data to unit variance.
Examples
>>> from pyts.preprocessing import StandardScaler >>> X = [[0, 2, 0, 4, 4, 6, 4, 4], ... [1, 0, 3, 2, 2, 2, 0, 2]] >>> scaler = StandardScaler() >>> scaler.transform(X) array([[-1.5, -0.5, -1.5, 0.5, 0.5, 1.5, 0.5, 0.5], [-0.5, -1.5, 1.5, 0.5, 0.5, 0.5, -1.5, 0.5]])
Methods
__init__
(self[, with_mean, with_std])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)Perform standardization by centering and scaling. -
__init__
(self, with_mean=True, with_std=True)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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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.
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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.
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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.