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Méthode numpy.random.RandomState.random_integers

Signature de la méthode random_integers

Description

random_integers.__doc__

        random_integers(low, high=None, size=None)

        Random integers of type `np.int_` between `low` and `high`, inclusive.

        Return random integers of type `np.int_` from the "discrete uniform"
        distribution in the closed interval [`low`, `high`].  If `high` is
        None (the default), then results are from [1, `low`]. The `np.int_`
        type translates to the C long integer type and its precision
        is platform dependent.

        This function has been deprecated. Use randint instead.

        .. deprecated:: 1.11.0

        Parameters
        ----------
        low : int
            Lowest (signed) integer to be drawn from the distribution (unless
            ``high=None``, in which case this parameter is the *highest* such
            integer).
        high : int, optional
            If provided, the largest (signed) integer to be drawn from the
            distribution (see above for behavior if ``high=None``).
        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.  Default is None, in which case a
            single value is returned.

        Returns
        -------
        out : int or ndarray of ints
            `size`-shaped array of random integers from the appropriate
            distribution, or a single such random int if `size` not provided.

        See Also
        --------
        randint : Similar to `random_integers`, only for the half-open
            interval [`low`, `high`), and 0 is the lowest value if `high` is
            omitted.

        Notes
        -----
        To sample from N evenly spaced floating-point numbers between a and b,
        use::

          a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)

        Examples
        --------
        >>> np.random.random_integers(5)
        4 # random
        >>> type(np.random.random_integers(5))
        <class 'numpy.int64'>
        >>> np.random.random_integers(5, size=(3,2))
        array([[5, 4], # random
               [3, 3],
               [4, 5]])

        Choose five random numbers from the set of five evenly-spaced
        numbers between 0 and 2.5, inclusive (*i.e.*, from the set
        :math:`{0, 5/8, 10/8, 15/8, 20/8}`):

        >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.
        array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ]) # random

        Roll two six sided dice 1000 times and sum the results:

        >>> d1 = np.random.random_integers(1, 6, 1000)
        >>> d2 = np.random.random_integers(1, 6, 1000)
        >>> dsums = d1 + d2

        Display results as a histogram:

        >>> import matplotlib.pyplot as plt
        >>> count, bins, ignored = plt.hist(dsums, 11, density=True)
        >>> plt.show()