Source code for neurokit2.complexity.entropy_fuzzy

# -*- coding: utf-8 -*-
from .entropy_approximate import entropy_approximate
from .entropy_sample import entropy_sample

[docs] def entropy_fuzzy(signal, delay=1, dimension=2, tolerance="sd", approximate=False, **kwargs): """**Fuzzy Entropy (FuzzyEn)** Fuzzy entropy (FuzzyEn) of a signal stems from the combination between information theory and fuzzy set theory (Zadeh, 1965). A fuzzy set is a set containing elements with varying degrees of membership. This function can be called either via ``entropy_fuzzy()`` or ``complexity_fuzzyen()``, or ``complexity_fuzzyapen()`` for its approximate version. Note that the fuzzy corrected approximate entropy (cApEn) can also be computed via setting ``corrected=True`` (see examples). Parameters ---------- signal : Union[list, np.array, pd.Series] The signal (i.e., a time series) in the form of a vector of values. delay : int Time delay (often denoted *Tau* :math:`\\tau`, sometimes referred to as *lag*) in samples. See :func:`complexity_delay` to estimate the optimal value for this parameter. dimension : int Embedding Dimension (*m*, sometimes referred to as *d* or *order*). See :func:`complexity_dimension` to estimate the optimal value for this parameter. tolerance : float Tolerance (often denoted as *r*), distance to consider two data points as similar. If ``"sd"`` (default), will be set to :math:`0.2 * SD_{signal}`. See :func:`complexity_tolerance` to estimate the optimal value for this parameter. approximate : bool If ``True``, will compute the fuzzy approximate entropy (FuzzyApEn). **kwargs Other arguments. Returns ---------- fuzzyen : float The fuzzy entropy of the single time series. info : dict A dictionary containing additional information regarding the parameters used to compute fuzzy entropy. See Also -------- entropy_sample Examples ---------- ..ipython:: python import neurokit2 as nk signal = nk.signal_simulate(duration=2, frequency=5) fuzzyen, parameters = nk.entropy_fuzzy(signal) fuzzyen fuzzyapen, parameters = nk.entropy_fuzzy(signal, approximate=True) fuzzyapen fuzzycapen, parameters = nk.entropy_fuzzy(signal, approximate=True, corrected=True) fuzzycapen References ---------- * Ishikawa, A., & Mieno, H. (1979). The fuzzy entropy concept and its application. Fuzzy Sets and systems, 2(2), 113-123. * Zadeh, L. A. (1996). Fuzzy sets. In Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh (pp. 394-432). """ if approximate is False: out = entropy_sample( signal, delay=delay, dimension=dimension, tolerance=tolerance, fuzzy=True, **kwargs, ) else: out = entropy_approximate( signal, delay=delay, dimension=dimension, tolerance=tolerance, fuzzy=True, **kwargs, ) return out