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Classe « Generator »

Méthode numpy.random.Generator.random

Signature de la méthode random

def random(self, size=None, dtype=<class 'numpy.float64'>, out=None) 

Description

help(Generator.random)

        random(size=None, dtype=np.float64, out=None)

        Return random floats in the half-open interval [0.0, 1.0).

        Results are from the "continuous uniform" distribution over the
        stated interval.  To sample :math:`Unif[a, b), b > a` use `uniform`
        or multiply the output of `random` by ``(b - a)`` and add ``a``::

            (b - a) * random() + a

        Parameters
        ----------
        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.
        dtype : dtype, optional
            Desired dtype of the result, only `float64` and `float32` are supported.
            Byteorder must be native. The default value is np.float64.
        out : ndarray, optional
            Alternative output array in which to place the result. If size is not None,
            it must have the same shape as the provided size and must match the type of
            the output values.

        Returns
        -------
        out : float or ndarray of floats
            Array of random floats of shape `size` (unless ``size=None``, in which
            case a single float is returned).

        See Also
        --------
        uniform : Draw samples from the parameterized uniform distribution.

        Examples
        --------
        >>> rng = np.random.default_rng()
        >>> rng.random()
        0.47108547995356098 # random
        >>> type(rng.random())
        <class 'float'>
        >>> rng.random((5,))
        array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428]) # random

        Three-by-two array of random numbers from [-5, 0):

        >>> 5 * rng.random((3, 2)) - 5
        array([[-3.99149989, -0.52338984], # random
               [-2.99091858, -0.79479508],
               [-1.23204345, -1.75224494]])

        

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