PermutationEntropy

class gtda.time_series.PermutationEntropy(n_jobs=None)[source]

Entropies from sets of permutations arg-sorting rows in arrays.

Given a two-dimensional array A, another array A’ of the same size is computed by arg-sorting each row in A. The permutation entropy 1 of A is the (base 2) Shannon entropy of the probability distribution given by the relative frequencies of each arg-sorting permutation in A’.

Parameters

n_jobs (int or None, optional, default: None) – The number of jobs to use for the computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

See also

SlidingWindow, TakensEmbedding, SingleTakensEmbedding, gtda.diagrams.PersistenceEntropy

References

1

C. Bandt and B. Pompe, “Permutation Entropy: A Natural Complexity Measure for Time Series”; Phys. Rev. Lett., 88.17, 2002; DOI: 10.1103/physrevlett.88.174102.

__init__(n_jobs=None)[source]

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

fit(X, y=None)[source]

Do nothing and return the estimator unchanged.

This method is here to implement the usual scikit-learn API and hence work in pipelines.

Parameters

  • X (ndarray of shape (n_samples, n_points, n_dimensions)) – Input data.

  • y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.

Returns

self

Return type

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 (ndarray of shape (n_samples, n_points, n_dimensions)) – Input data.

  • y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.

Returns

Xt – One permutation entropy per entry in X along axis 0.

Return type

ndarray of int, shape (n_samples, 1)

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 – Parameter names mapped to their values.

Return type

mapping of string to any

set_params(**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 – Estimator instance.

Return type

object

transform(X, y=None)[source]

Calculate the permutation entropy of each two-dimensional array in X.

Parameters

  • X (ndarray of shape (n_samples, n_points, n_dimensions)) – Input data.

  • y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.

Returns

Xt – One permutation entropy per entry in X along axis 0.

Return type

ndarray of int, shape (n_samples, 1)