Source code for neurokit2.complexity.entropy_shannon_joint

import numpy as np
import scipy.stats

from .entropy_shannon import _entropy_freq

[docs] def entropy_shannon_joint(x, y, base=2): """**Shannon's Joint Entropy** The joint entropy measures how much entropy is contained in a joint system of two random variables. Parameters ---------- x : Union[list, np.array, pd.Series] A :func:`symbolic <complexity_symbolize>` sequence in the form of a vector of values. y : Union[list, np.array, pd.Series] Another symbolic sequence with the same values. base: float The logarithmic base to use, defaults to ``2``. Note that ``scipy.stats.entropy()`` uses ``np.e`` as default (the natural logarithm). Returns -------- float The Shannon joint entropy. dict A dictionary containing additional information regarding the parameters used to compute Shannon entropy. See Also -------- entropy_shannon Examples ---------- .. ipython:: python import neurokit2 as nk x = ["A", "A", "A", "B", "A", "B"] y = ["A", "B", "A", "A", "A", "A"] jen, _ = nk.entropy_shannon_joint(x, y) jen """ # Get frequencies labels_x, freq_x = _entropy_freq(x) labels_y, freq_y = _entropy_freq(y) assert np.all(labels_y == labels_y), "The labels of x and y are not the same." return scipy.stats.entropy(freq_x, freq_y, base=base), {"Base": base}