estimator

infomeasure.estimator(*data, cond=None, measure: str = None, approach: str = None, step_size: int = 1, prop_time: int = 0, src_hist_len: int = 1, dest_hist_len: int = 1, cond_hist_len: int = 1, **kwargs: any) → EstimatorType

Get an estimator for a specific measure.

This function provides a simple interface to get an Estimator for a specific measure.

If you are only interested in the global result, use the functional interfaces:

• entropy

• cross_entropy

• mutual_information

• conditional_mutual_information

• transfer_entropy

• conditional_transfer_entropy

Estimators available:

1. Entropy:

   • ansb: Asymptotic NSB entropy estimator.

   • bayes: Bayesian entropy estimator.

   • bonachela: Bonachela entropy estimator.

   • [chao_shen, cs]: Chao-Shen entropy estimator.

   • [chao_wang_jost, cwj]: Chao Wang Jost entropy estimator.

   • discrete: Discrete entropy estimator.

   • grassberger: Grassberger entropy estimator.

   • kernel: Kernel entropy estimator.

   • [metric, kl]: Kozachenko-Leonenko entropy estimator.

   • [miller_madow, mm]: Miller-Madow entropy estimator.

   • nsb: NSB (Nemenman-Shafee-Bialek) entropy estimator.

   • [ordinal, symbolic, permutation]: Ordinal / Permutation entropy estimator.

   • renyi: Renyi entropy estimator.

   • [shrink, js]: Shrinkage (James-Stein) entropy estimator.

   • tsallis: Tsallis entropy estimator.

   • zhang: Zhang entropy estimator.

2. Mutual Information:

   • ansb: Asymptotic NSB mutual information estimator.

   • bayes: Bayesian mutual information estimator.

   • bonachela: Bonachela mutual information estimator.

   • chao_shen: Chao-Shen mutual information estimator.

   • chao_wang_jost: Chao Wang Jost mutual information estimator.

   • discrete: Discrete mutual information estimator.

   • grassberger: Grassberger mutual information estimator.

   • kernel: Kernel mutual information estimator.

   • [metric, ksg]: Kraskov-Stoegbauer-Grassberger mutual information estimator.

   • [miller_madow, mm]: Miller-Madow mutual information estimator.

   • nsb: NSB (Nemenman-Shafee-Bialek) mutual information estimator.

   • [ordinal, symbolic, permutation]: Ordinal mutual information estimator.

   • renyi: Renyi mutual information estimator.

   • shrink: Shrinkage (James-Stein) mutual information estimator.

   • tsallis: Tsallis mutual information estimator.

   • zhang: Zhang mutual information estimator.

3. Transfer Entropy:

   • ansb: Asymptotic NSB transfer entropy estimator.

   • bayes: Bayesian transfer entropy estimator.

   • bonachela: Bonachela transfer entropy estimator.

   • chao_shen: Chao-Shen transfer entropy estimator.

   • chao_wang_jost: Chao Wang Jost transfer entropy estimator.

   • discrete: Discrete transfer entropy estimator.

   • grassberger: Grassberger transfer entropy estimator.

   • kernel: Kernel transfer entropy estimator.

   • [metric, ksg]: Kraskov-Stoegbauer-Grassberger transfer entropy estimator.

   • [miller_madow, mm]: Miller-Madow transfer entropy estimator.

   • nsb: NSB (Nemenman-Shafee-Bialek) transfer entropy estimator.

   • [ordinal, symbolic, permutation]: Ordinal transfer entropy estimator.

   • renyi: Renyi transfer entropy estimator.

   • shrink: Shrinkage (James-Stein) transfer entropy estimator.

   • tsallis: Tsallis transfer entropy estimator.

   • zhang: Zhang transfer entropy estimator.

Parameters:

*data

The data used to estimate the measure. For entropy: a single array-like data. A tuple of data for joint entropy. For cross-entropy: two array-like data. Second input RV relative to the first. For mutual information: arbitrary number of array-like data. For transfer entropy: two array-like data. Source and destination.

condarray_like, optional

Only if the measure is conditional transfer entropy.

measurestr

The measure to estimate. Options: entropy, cross_entropy, mutual_information, transfer_entropy, conditional_mutual_information, conditional_transfer_entropy; aliases: h, hx, mi, te, cmi, cte.

approachstr

The name of the estimator to use. Find the available estimators in the docstring of this function.

*args: tuple

Additional arguments to pass to the estimator.

**kwargs: dict

Additional keyword arguments to pass to the estimator.

Returns:

Estimator

The estimator instance.

Raises:

ValueError

If the measure is not recognised.