tslearn.preprocessing
.TimeSeriesScalerMeanVariance¶
-
class
tslearn.preprocessing.
TimeSeriesScalerMeanVariance
(mu=0.0, std=1.0)[source]¶ Scaler for time series. Scales time series so that their mean (resp. standard deviation) in each dimension is mu (resp. std).
Parameters: - mu : float (default: 0.)
Mean of the output time series.
- std : float (default: 1.)
Standard deviation of the output time series.
Notes
This method requires a dataset of equal-sized time series.
NaNs within a time series are ignored when calculating mu and std.
Examples
>>> TimeSeriesScalerMeanVariance(mu=0., ... std=1.).fit_transform([[0, 3, 6]]) array([[[-1.22474487], [ 0. ], [ 1.22474487]]]) >>> TimeSeriesScalerMeanVariance(mu=0., ... std=1.).fit_transform([[numpy.nan, 3, 6]]) array([[[nan], [-1.], [ 1.]]])
Methods
fit
(X[, y])A dummy method such that it complies to the sklearn requirements. fit_transform
(X[, y])Fit to data, then transform it. get_params
([deep])Get parameters for this estimator. set_params
(**params)Set the parameters of this estimator. transform
(X[, y])Fit to data, then transform it. -
fit
(X, y=None, **kwargs)[source]¶ A dummy method such that it complies to the sklearn requirements. Since this method is completely stateless, it just returns itself.
Parameters: - X
Ignored
Returns: - self
-
fit_transform
(X, y=None, **kwargs)[source]¶ Fit to data, then transform it.
Parameters: - X : array-like of shape (n_ts, sz, d)
Time series dataset to be rescaled.
Returns: - numpy.ndarray
Resampled time series dataset.
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get_params
(deep=True)[source]¶ 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.
-
set_params
(**params)[source]¶ 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.