Classe « Generator »

Méthode numpy.random.Generator.uniform

Signature de la méthode uniform

Description

uniform.__doc__

        uniform(low=0.0, high=1.0, size=None)

        Draw samples from a uniform distribution.

        Samples are uniformly distributed over the half-open interval
        ``[low, high)`` (includes low, but excludes high).  In other words,
        any value within the given interval is equally likely to be drawn
        by `uniform`.

        Parameters
        ----------
        low : float or array_like of floats, optional
            Lower boundary of the output interval.  All values generated will be
            greater than or equal to low.  The default value is 0.
        high : float or array_like of floats
            Upper boundary of the output interval.  All values generated will be
            less than high.  The default value is 1.0.
        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 ``low`` and ``high`` are both scalars.
            Otherwise, ``np.broadcast(low, high).size`` samples are drawn.

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

        See Also
        --------
        integers : Discrete uniform distribution, yielding integers.
        random : Floats uniformly distributed over ``[0, 1)``.

        Notes
        -----
        The probability density function of the uniform distribution is

        .. math:: p(x) = \frac{1}{b - a}

        anywhere within the interval ``[a, b)``, and zero elsewhere.

        When ``high`` == ``low``, values of ``low`` will be returned.
        If ``high`` < ``low``, the results are officially undefined
        and may eventually raise an error, i.e. do not rely on this
        function to behave when passed arguments satisfying that
        inequality condition.

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

        >>> s = np.random.default_rng().uniform(-1,0,1000)

        All values are within the given interval:

        >>> np.all(s >= -1)
        True
        >>> np.all(s < 0)
        True

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

        >>> import matplotlib.pyplot as plt
        >>> count, bins, ignored = plt.hist(s, 15, density=True)
        >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
        >>> plt.show()