# scipy.stats.differential_entropy

scipy.stats.differential_entropy(values, *, window_length=None, base=None, axis=0, method='auto')

  Given a sample of a distribution, estimate the differential entropy.

  Several estimation methods are available using the method parameter. By default, a method is selected based the size of the sample.

  Parameters
    values sequence

      Sample from a continuous distribution.

    window_length int, optional

      Window length for computing Vasicek estimate. Must be an integer between 1 and half of the sample size. If `None` (the default), it uses the heuristic value

      \[\left \lfloor \sqrt{n} + 0.5 \right \rfloor\]

      where \(n\) is the sample size. This heuristic was originally proposed in [2] and has become common in the literature.

    base float, optional

      The logarithmic base to use, defaults to `e` (natural logarithm).

    axis int, optional

      The axis along which the differential entropy is calculated. Default is 0.

    method{‘vasicek’, ‘van es’, ‘ebrahimi’, ‘correa’, ‘auto’}, optional

      The method used to estimate the differential entropy from the sample. Default is `'auto'`. See Notes for more information.

  Returns
    entropy float

      The calculated differential entropy.

  Notes

  This function will converge to the true differential entropy in the limit

  \[n \to \infty, \quad m \to \infty, \quad \frac{m}{n} \to 0\]

  The optimal choice of `window_length` for a given sample size depends on the (unknown) distribution. Typically, the smoother the density of the distribution, the larger the optimal value of `window_length` [1].

  The following options are available for the method parameter.

  - `'vasicek'` uses the estimator presented in [1]. This is one of the first and most influential estimators of differential entropy.

  - `'van es'` uses the bias-corrected estimator presented in [3], which is not only consistent but, under some conditions, asymptotically normal.

  - `'ebrahimi'` uses an estimator presented in [4], which was shown in simulation to have smaller bias and mean squared error than the Vasicek estimator.

  - `'correa'` uses the estimator presented in [5] based on local linear regression. In a simulation study, it had consistently smaller mean square error than the Vasiceck estimator, but it is more expensive to compute.

  - `'auto'` selects the method automatically (default). Currently, this selects `'van es'` for very small samples (<10), `'ebrahimi'` for moderate sample sizes (11-1000), and `'vasicek'` for larger samples, but this behavior is subject to change in future versions.

  All estimators are implemented as described in [6].

  References

  1(1,2)

    Vasicek, O. (1976). A test for normality based on sample entropy. Journal of the Royal Statistical Society: Series B (Methodological), 38(1), 54-59.

  2

    Crzcgorzewski, P., & Wirczorkowski, R. (1999). Entropy-based goodness-of-fit test for exponentiality. Communications in Statistics-Theory and Methods, 28(5), 1183-1202.

  3

    Van Es, B. (1992). Estimating functionals related to a density by a class of statistics based on spacings. Scandinavian Journal of Statistics, 61-72.

  4

    Ebrahimi, N., Pflughoeft, K., & Soofi, E. S. (1994). Two measures of sample entropy. Statistics & Probability Letters, 20(3), 225-234.

  5

    Correa, J. C. (1995). A new estimator of entropy. Communications in Statistics-Theory and Methods, 24(10), 2439-2449.

  6

    Noughabi, H. A. (2015). Entropy Estimation Using Numerical Methods. Annals of Data Science, 2(2), 231-241. https://link.springer.com/article/10.1007/s40745-015-0045-9

  Examples

      >>> from scipy.stats import differential_entropy, norm

  Entropy of a standard normal distribution:

      >>> rng = np.random.default_rng()
      >>> values = rng.standard_normal(100)
      >>> differential_entropy(values)
      1.3407817436640392

  Compare with the true entropy:

      >>> float(norm.entropy())
      1.4189385332046727

  For several sample sizes between 5 and 1000, compare the accuracy of the `'vasicek'`, `'van es'`, and `'ebrahimi'` methods. Specifically, compare the root mean squared error (over 1000 trials) between the estimate and the true differential entropy of the distribution.

      >>> from scipy import stats
      >>> import matplotlib.pyplot as plt
      >>>
      >>>
      >>> def rmse(res, expected):
      ...     '''Root mean squared error'''
      ...     return np.sqrt(np.mean((res - expected)**2))
      >>>
      >>>
      >>> a, b = np.log10(5), np.log10(1000)
      >>> ns = np.round(np.logspace(a, b, 10)).astype(int)
      >>> reps = 1000  # number of repetitions for each sample size
      >>> expected = stats.expon.entropy()
      >>>
      >>> method_errors = {'vasicek': [], 'van es': [], 'ebrahimi': []}
      >>> for method in method_errors:
      ...     for n in ns:
      ...        rvs = stats.expon.rvs(size=(reps, n), random_state=rng)
      ...        res = stats.differential_entropy(rvs, method=method, axis=-1)
      ...        error = rmse(res, expected)
      ...        method_errors[method].append(error)
      >>>
      >>> for method, errors in method_errors.items():
      ...     plt.loglog(ns, errors, label=method)
      >>>
      >>> plt.legend()
      >>> plt.xlabel('sample size')
      >>> plt.ylabel('RMSE (1000 trials)')
      >>> plt.title('Entropy Estimator Error (Exponential Distribution)')

scipy.stats.entropy scipy.stats.median_absolute_deviation