Classe « Generator »

Méthode numpy.random.Generator.zipf

Signature de la méthode zipf

Description

zipf.__doc__

        zipf(a, size=None)

        Draw samples from a Zipf distribution.

        Samples are drawn from a Zipf distribution with specified parameter
        `a` > 1.

        The Zipf distribution (also known as the zeta distribution) is a
        continuous probability distribution that satisfies Zipf's law: the
        frequency of an item is inversely proportional to its rank in a
        frequency table.

        Parameters
        ----------
        a : float or array_like of floats
            Distribution parameter. Must be greater than 1.
        size : int or tuple of ints, optional
            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
            ``m * n * k`` samples are drawn.  If size is ``None`` (default),
            a single value is returned if ``a`` is a scalar. Otherwise,
            ``np.array(a).size`` samples are drawn.

        Returns
        -------
        out : ndarray or scalar
            Drawn samples from the parameterized Zipf distribution.

        See Also
        --------
        scipy.stats.zipf : probability density function, distribution, or
            cumulative density function, etc.

        Notes
        -----
        The probability density for the Zipf distribution is

        .. math:: p(x) = \frac{x^{-a}}{\zeta(a)},

        where :math:`\zeta` is the Riemann Zeta function.

        It is named for the American linguist George Kingsley Zipf, who noted
        that the frequency of any word in a sample of a language is inversely
        proportional to its rank in the frequency table.

        References
        ----------
        .. [1] Zipf, G. K., "Selected Studies of the Principle of Relative
               Frequency in Language," Cambridge, MA: Harvard Univ. Press,
               1932.

        Examples
        --------
        Draw samples from the distribution:

        >>> a = 2. # parameter
        >>> s = np.random.default_rng().zipf(a, 1000)

        Display the histogram of the samples, along with
        the probability density function:

        >>> import matplotlib.pyplot as plt
        >>> from scipy import special  # doctest: +SKIP

        Truncate s values at 50 so plot is interesting:

        >>> count, bins, ignored = plt.hist(s[s<50],
        ...         50, density=True)
        >>> x = np.arange(1., 50.)
        >>> y = x**(-a) / special.zetac(a)  # doctest: +SKIP
        >>> plt.plot(x, y/max(y), linewidth=2, color='r')  # doctest: +SKIP
        >>> plt.show()