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Classe « RandomState »

Méthode numpy.random.RandomState.standard_normal

Signature de la méthode standard_normal

def standard_normal(self, size=None) 

Description

help(RandomState.standard_normal)

        standard_normal(size=None)

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

        .. note::
            New code should use the
            `~numpy.random.Generator.standard_normal`
            method of a `~numpy.random.Generator` instance instead;
            please see the :ref:`random-quick-start`.

        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.

        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.
        random.Generator.standard_normal: which should be used for new code.

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

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

        Examples
        --------
        >>> np.random.standard_normal()
        2.1923875335537315 #random

        >>> s = np.random.standard_normal(8000)
        >>> s
        array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311,  # random
               -0.38672696, -0.4685006 ])                                # random
        >>> s.shape
        (8000,)
        >>> s = np.random.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 * np.random.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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