RenyiCMIEstimator#
- class infomeasure.estimators.mutual_information.RenyiCMIEstimator(*data, cond=None, k: int = 4, alpha: float | int = None, noise_level=1e-08, offset: int = 0, normalize: bool = False, base: int | float | str = 'e', **kwargs)[source]
Bases:
BaseRenyiMIEstimator,ConditionalMutualInformationEstimatorEstimator for the conditional Renyi mutual information.
- Attributes:
- *dataarray_like,
shape(n_samples,) The data used to estimate the conditional mutual information. You can pass an arbitrary number of data arrays as positional arguments.
- condarray_like
The conditional data used to estimate the conditional mutual information.
- k
int The number of nearest neighbors used in the estimation.
- alpha
float|int The Rényi parameter, order or exponent. Sometimes denoted as \(\alpha\) or \(q\).
- 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
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.