RenyiEntropyEstimator

class infomeasure.estimators.entropy.RenyiEntropyEstimator(*data, k: int = 4, alpha: float | int = None, base: int | float | str = 'e')

Bases: EntropyEstimator

Estimator for the Rényi entropy.

Attributes:

*dataarray_like

The data used to estimate the entropy.

kint

The number of nearest neighbors used in the estimation.

alphafloat | int

The Rényi parameter, order or exponent. Sometimes denoted as \(\alpha\) or \(q\).

Raises:

ValueError

If the Renyi parameter is not a positive number.

ValueError

If the number of nearest neighbors is not a positive integer.

Notes

The Rényi entropy is a generalization of Shannon entropy, where the small values of probabilities are emphasized for \(\alpha < 1\), and higher probabilities are emphasized for \(\alpha > 1\). For \(\alpha = 1\), it reduces to Shannon entropy. The Rényi-Entropy class can be particularly interesting for systems where additivity (in Shannon sense) is not always preserved, especially in nonlinear complex systems, such as when dealing with long-range forces.