ting-point samples drawn from the standard normal
    distribution.

See Also
--------
rand, numpy.random.RandomState.randn

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

    sigma * np.matlib.randn(...) + mu

Examples
--------
>>> np.random.seed(123)
>>> import numpy.matlib
>>> np.matlib.randn(1)
matrix([[-1.0856306]])
>>> np.matlib.randn(1, 2, 3)
matrix([[ 0.99734545,  0.2829785 , -1.50629471],
        [-0.57860025,  1.65143654, -2.42667924]])

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

>>> 2.5 * np.matlib.randn((2, 4)) + 3
matrix([[1.92771843, 6.16484065, 0.83314899, 1.30278462],
        [2.76322758, 6.72847407, 1.40274501, 1.8900451 ]])

r