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Classe « Generator »

Méthode numpy.random.Generator.standard_normal

Signature de la méthode standard_normal

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

Description

help(Generator.standard_normal)

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

        Draw samples from a standard Normal distribution (mean=0, stdev=1).

        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
            A floating-point array of shape ``size`` of drawn samples, or a
            single sample if ``size`` was not specified.

        See Also
        --------
        normal :
            Equivalent function with additional ``loc`` and ``scale`` arguments
            for setting the mean and standard deviation.

        Notes
        -----
        For random samples from the normal distribution with mean ``mu`` and
        standard deviation ``sigma``, use one of::

            mu + sigma * rng.standard_normal(size=...)
            rng.normal(mu, sigma, size=...)

        Examples
        --------
        >>> rng = np.random.default_rng()
        >>> rng.standard_normal()
        2.1923875335537315 # random

        >>> s = rng.standard_normal(8000)
        >>> s
        array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311,  # random
               -0.38672696, -0.4685006 ])                                # random
        >>> s.shape
        (8000,)
        >>> s = rng.standard_normal(size=(3, 4, 2))
        >>> s.shape
        (3, 4, 2)

        Two-by-four array of samples from the normal distribution with
        mean 3 and standard deviation 2.5:

        >>> 3 + 2.5 * rng.standard_normal(size=(2, 4))
        array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random
               [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random

        

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