pyts.preprocessing.StandardScaler

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__([with_mean, with_std]) 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_params(**params) Set the parameters of this estimator.
transform(X) Perform standardization by centering and scaling.
__init__(with_mean=True, with_std=True)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(X=None, y=None)[source]

Pass.

Parameters:
X

Ignored

y

Ignored
Returns:
self : object
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, shape = (n_samples, n_timestamps)

Univariate time series.

y : None or array-like, shape = (n_samples,) (default = None)

Target values (None for unsupervised transformations).

**fit_params : dict

Additional fit parameters.
Returns:
X_new : array

Transformed array.
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.
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.
transform(X)[source]

Perform standardization by centering and scaling.

Parameters:
X : array-like, shape = (n_samples, n_timestamps)

Data to scale.
Returns:
X_new : array-like, shape = (n_samples, n_timestamps)

Scaled data.

Examples using pyts.preprocessing.StandardScaler

Scalers

Scalers

Scalers