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Module « numpy.random »

Fonction zipf - module numpy.random

Signature de la fonction zipf

def zipf(a, size=None) 

Description

help(numpy.random.zipf)

        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
        discrete probability distribution that satisfies Zipf's law: the
        frequency of an item is inversely proportional to its rank in a
        frequency table.

        .. note::
            New code should use the `~numpy.random.Generator.zipf`
            method of a `~numpy.random.Generator` instance instead;
            please see the :ref:`random-quick-start`.

        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.
        random.Generator.zipf: which should be used for new code.

        Notes
        -----
        The probability mass function (PMF) for the Zipf distribution is

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

        for integers :math:`k \geq 1`, 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 = 4.0
        >>> n = 20000
        >>> s = np.random.zipf(a, n)

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

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

        `bincount` provides a fast histogram for small integers.

        >>> count = np.bincount(s)
        >>> k = np.arange(1, s.max() + 1)

        >>> plt.bar(k, count[1:], alpha=0.5, label='sample count')
        >>> plt.plot(k, n*(k**-a)/zeta(a), 'k.-', alpha=0.5,
        ...          label='expected count')   # doctest: +SKIP
        >>> plt.semilogy()
        >>> plt.grid(alpha=0.4)
        >>> plt.legend()
        >>> plt.title(f'Zipf sample, a={a}, size={n}')
        >>> plt.show()

        

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