pycrostates.segmentation.excess_entropy_rate#

pycrostates.segmentation.excess_entropy_rate(segmentation, history_length, ignore_repetitions=False, log_base=2, n_jobs=1)[source]#

Estimate the entropy rate and the excess_entropy of the segmentation.

The entropy rate and the excess_entropy are estimated from a linear fit:

\[H(X_{n}^{(k)}) = a \cdot k + b\]

where a is the entropy rate and b the excess entropy as described in Von Wegner[1].

Parameters:

segmentationRawSegmentation | EpochsSegmentation: Segmentation object containing the microstate symbolic sequence.
history_lengthint: Maximum history length in sample to estimate the excess entropy rate.
ignore_repetitionsbool: If True, ignores state repetitions. For example, the input sequence AAABBCCD will be transformed into ABCD before any calculation. This is equivalent to setting the duration of all states to 1 sample.
log_basefloat | str: The log base to use. If string: * bits: log_base = 2 * natural: log_base = np.e * dits: log_base = 10 Default to bits.
n_jobsint | None: The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_config context manager that sets another value for n_jobs.

Returns:

afloat: Entropy rate (slope).
bfloat: Excess entropy (intercept).
residualfloat: Sum of squared residuals of the least squares fit.
lagsarray of shape (history_length,): Lag values in sample used for the fit.
joint_entropiesarray of shape (history_length,): Joint entropy value for each lag.

References

Examples using `pycrostates.segmentation.excess_entropy_rate`#