KernelCMIEstimator

class infomeasure.estimators.mutual_information.KernelCMIEstimator(*data, cond=None, bandwidth: float | int = None, kernel: str = None, offset: int = 0, workers: int = 1, normalize: bool = False, base: int | float | str = 'e', **kwargs)

Bases: BaseKernelMIEstimator, ConditionalMutualInformationEstimator

Estimator for conditional mutual information using Kernel Density Estimation (KDE).

\[I(X;Y|Z) = \sum_{i=1}^{n} p(x_i, y_i, z_i) \log \left( \frac{p(z_i)p(x_i, y_i, z_i)}{p(x_i, z_i)p(y_i, z_i)} \right)\]

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.

bandwidthfloat | int

The bandwidth for the kernel.

kernelstr

Type of kernel to use, compatible with the KDE implementation kde_probability_density_function().

normalizebool, optional

If True, normalize the data before analysis.

Notes

A small bandwidth can lead to under-sampling, while a large bandwidth may over-smooth the data, obscuring details.