TsallisMIEstimator#
- class infomeasure.estimators.mutual_information.TsallisMIEstimator(*data, cond=None, k: int = 4, q: float | int = None, noise_level: float = 1e-08, offset: int = 0, normalize: bool = False, base: int | float | str = 'e', **kwargs)[source]
Bases:
BaseTsallisMIEstimator,MutualInformationEstimatorEstimator for the Tsallis mutual information.
- Attributes:
- *dataarray_like,
shape(n_samples,) The data used to estimate the mutual information. You can pass an arbitrary number of data arrays as positional arguments.
- k
int The number of nearest neighbors used in the estimation.
- q
float The Tsallis parameter, order or exponent. Sometimes denoted as \(q\), analogous to the Rényi parameter \(\alpha\).
- noise_level
float The standard deviation of the Gaussian noise to add to the data to avoid issues with zero distances.
- offset
int,optional Number of positions to shift the data arrays relative to each other. Delay/lag/shift between the variables. Default is no shift.
- normalizebool,
optional If True, normalize the data before analysis.
- *dataarray_like,
Notes
In the \(q \to 1\) limit, the Jackson sum (q-additivity) reduces to ordinary summation, and the Tallis entropy reduces to Shannon Entropy. This class of entropy measure is in particularly useful in the study in connection with long-range correlated systems and with non-equilibrium phenomena.