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:
- mufloat (default: 0.)
Mean of the output time series.
- stdfloat (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 metadata routing of this object.
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[, 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:
- Xarray-like of shape (n_ts, sz, d)
Time series dataset to be rescaled.
- Returns:
- numpy.ndarray
Resampled time series dataset.
- get_metadata_routing()[source]¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- set_output(*, transform=None)[source]¶
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:
- selfestimator instance
Estimator instance.
- 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:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
Examples using tslearn.preprocessing.TimeSeriesScalerMeanVariance
¶
Longest Commom Subsequence with a custom distance metric
DTW computation with a custom distance metric