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Classe « Generator »

Méthode numpy.random.Generator.uniform

Signature de la méthode uniform

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

Description

help(Generator.uniform)

        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 high limit may be included in the returned array of 
            floats due to floating-point rounding in the equation 
            ``low + (high-low) * random_sample()``.  high - low must be 
            non-negative.  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.

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

        >>> rng = np.random.default_rng()
        >>> s = 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, _ = plt.hist(s, 15, density=True)
        >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
        >>> plt.show()

        

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