KozachenkoLeonenkoEntropyEstimator#

class infomeasure.estimators.entropy.KozachenkoLeonenkoEntropyEstimator(data, *, k: int = 4, noise_level=1e-10, minkowski_p=inf, base: int | float | str = 'e')[source]

Bases: RandomGeneratorMixin, EntropyEstimator

Kozachenko-Leonenko estimator for Shannon entropies.

Attributes:

dataarray_like: The data used to estimate the entropy.
kint: The number of nearest neighbors to consider.
noise_levelfloat: The standard deviation of the Gaussian noise to add to the data to avoid issues with zero distances.
minkowski_pfloat, \(1 \leq p \leq \infty\): The power parameter for the Minkowski metric. Default is np.inf for maximum norm. Use 2 for Euclidean distance.

Raises:

ValueError: If the number of nearest neighbors is not a positive integer
ValueError: If the noise level is negative
ValueError: If the Minkowski power parameter is invalid

Notes

Changing the number of nearest neighbors k can change the outcome, but the default value of \(k=4\) is recommended by [KStogbauerG11].