RenyiCTEEstimator#
- class infomeasure.estimators.transfer_entropy.RenyiCTEEstimator(source, dest, *, cond=None, k: int = 4, alpha: float | int = None, noise_level=1e-08, prop_time: int = 0, step_size: int = 1, src_hist_len: int = 1, dest_hist_len: int = 1, cond_hist_len: int = 1, offset: int = None, base: int | float | str = 'e', **kwargs)[source]
Bases:
BaseRenyiTEEstimator,ConditionalTransferEntropyEstimatorEstimator for the Renyi conditional transfer entropy.
- Attributes:
- source, dest, condarray_like
The source (X), destination (Y), and conditional (Z) data used to estimate the conditional transfer entropy.
- 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.
- step_size
int Step size between elements for the state space reconstruction.
- src_hist_len, dest_hist_len, cond_hist_len
int Number of past observations to consider for the source, destination, and conditional data.
- Raises:
ValueErrorIf the Renyi parameter is not a positive number.
ValueErrorIf the number of nearest neighbors is not a positive integer.
ValueErrorIf the step_size is not a non-negative 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.