KSGCTEEstimator

class infomeasure.estimators.transfer_entropy.KSGCTEEstimator(source, dest, *, cond=None, k: int = 4, ksg_id: int = 1, noise_level=1e-08, minkowski_p=inf, 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)

Bases: BaseKSGTEEstimator, ConditionalTransferEntropyEstimator

Estimator for conditional transfer entropy using the Kraskov-Stoegbauer-Grassberger (KSG) method.

Attributes:

source, dest, condarray_like

The source (X), destination (Y), and conditional (Z) data used to estimate the conditional transfer entropy.

kint

Number of nearest neighbors to consider.

noise_levelfloat, None or False

Standard deviation of Gaussian noise to add to the data. Adds \(\mathcal{N}(0, ext{noise}^2)\) to each data point.

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.

step_sizeint

Step size between elements for the state space reconstruction.

src_hist_len, dest_hist_len, cond_hist_lenint, optional

Number of past observations to consider for the source, destination, and conditional data.

prop_timeint, optional

Not compatible with the cond parameter / conditional TE.

Notes

The estimator supports two variants:

• Type I (ksg_id=1): Uses strict inequality for counting neighbors in marginal spaces (dist < eps).

• Type II (ksg_id=2): Uses non-strict inequality (dist <= eps) and a modified formula.

Changing the number of nearest neighbors k can change the outcome, but the default value of \(k=4\) is recommended by [KSG11].