KernelMIEstimator

class infomeasure.estimators.mutual_information.KernelMIEstimator(*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, MutualInformationEstimator

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

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

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.

bandwidthfloat | int

The bandwidth for the kernel.

kernelstr

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

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

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