Module « numpy.random »

Fonction logistic - module numpy.random

Signature de la fonction logistic

Description

logistic.__doc__

        logistic(loc=0.0, scale=1.0, size=None)

        Draw samples from a logistic distribution.

        Samples are drawn from a logistic distribution with specified
        parameters, loc (location or mean, also median), and scale (>0).

        .. note::
            New code should use the ``logistic`` method of a ``default_rng()``
            instance instead; please see the :ref:`random-quick-start`.

        Parameters
        ----------
        loc : float or array_like of floats, optional
            Parameter of the distribution. Default is 0.
        scale : float or array_like of floats, optional
            Parameter of the distribution. Must be non-negative.
            Default is 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 ``loc`` and ``scale`` are both scalars.
            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.

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

        See Also
        --------
        scipy.stats.logistic : probability density function, distribution or
            cumulative density function, etc.
        Generator.logistic: which should be used for new code.

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

        .. math:: P(x) = P(x) = \frac{e^{-(x-\mu)/s}}{s(1+e^{-(x-\mu)/s})^2},

        where :math:`\mu` = location and :math:`s` = scale.

        The Logistic distribution is used in Extreme Value problems where it
        can act as a mixture of Gumbel distributions, in Epidemiology, and by
        the World Chess Federation (FIDE) where it is used in the Elo ranking
        system, assuming the performance of each player is a logistically
        distributed random variable.

        References
        ----------
        .. [1] Reiss, R.-D. and Thomas M. (2001), "Statistical Analysis of
               Extreme Values, from Insurance, Finance, Hydrology and Other
               Fields," Birkhauser Verlag, Basel, pp 132-133.
        .. [2] Weisstein, Eric W. "Logistic Distribution." From
               MathWorld--A Wolfram Web Resource.
               http://mathworld.wolfram.com/LogisticDistribution.html
        .. [3] Wikipedia, "Logistic-distribution",
               https://en.wikipedia.org/wiki/Logistic_distribution

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

        >>> loc, scale = 10, 1
        >>> s = np.random.logistic(loc, scale, 10000)
        >>> import matplotlib.pyplot as plt
        >>> count, bins, ignored = plt.hist(s, bins=50)

        #   plot against distribution

        >>> def logist(x, loc, scale):
        ...     return np.exp((loc-x)/scale)/(scale*(1+np.exp((loc-x)/scale))**2)
        >>> lgst_val = logist(bins, loc, scale)
        >>> plt.plot(bins, lgst_val * count.max() / lgst_val.max())
        >>> plt.show()