KSGMIEstimator

class infomeasure.estimators.mutual_information.KSGMIEstimator(*data, cond=None, k: int = 4, noise_level=1e-10, minkowski_p=inf, offset: int = 0, normalize: bool = False, base: int | float | str = 'e')

Bases: BaseKSGMIEstimator, PValueMixin, MutualInformationEstimator

Estimator for mutual information using the Kraskov-Stoegbauer-Grassberger (KSG) method.

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.

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.

offsetint, 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.

Notes

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