Classe « RandomState »

Méthode numpy.random.RandomState.poisson

Signature de la méthode poisson

Description

poisson.__doc__

        poisson(lam=1.0, size=None)

        Draw samples from a Poisson distribution.

        The Poisson distribution is the limit of the binomial distribution
        for large N.

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

        Parameters
        ----------
        lam : float or array_like of floats
            Expectation of interval, must be >= 0. A sequence of expectation
            intervals must be broadcastable over the requested size.
        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 ``lam`` is a scalar. Otherwise,
            ``np.array(lam).size`` samples are drawn.

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

        See Also
        --------
        Generator.poisson: which should be used for new code.

        Notes
        -----
        The Poisson distribution

        .. math:: f(k; \lambda)=\frac{\lambda^k e^{-\lambda}}{k!}

        For events with an expected separation :math:`\lambda` the Poisson
        distribution :math:`f(k; \lambda)` describes the probability of
        :math:`k` events occurring within the observed
        interval :math:`\lambda`.

        Because the output is limited to the range of the C int64 type, a
        ValueError is raised when `lam` is within 10 sigma of the maximum
        representable value.

        References
        ----------
        .. [1] Weisstein, Eric W. "Poisson Distribution."
               From MathWorld--A Wolfram Web Resource.
               http://mathworld.wolfram.com/PoissonDistribution.html
        .. [2] Wikipedia, "Poisson distribution",
               https://en.wikipedia.org/wiki/Poisson_distribution

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

        >>> import numpy as np
        >>> s = np.random.poisson(5, 10000)

        Display histogram of the sample:

        >>> import matplotlib.pyplot as plt
        >>> count, bins, ignored = plt.hist(s, 14, density=True)
        >>> plt.show()

        Draw each 100 values for lambda 100 and 500:

        >>> s = np.random.poisson(lam=(100., 500.), size=(100, 2))