KSGCMIEstimator

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

Bases: BaseKSGMIEstimator, ConditionalMutualInformationEstimator

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

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.

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.

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 [KSG11].