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# numpy.matlib.randn

All Samples(3)  |  Call(3)  |  Derive(0)  |  Import(0)
Return a random matrix with data from the "standard normal" distribution.

randn generates a matrix filled with random floats sampled from a
univariate "normal" (Gaussian) distribution of mean 0 and variance 1.

Parameters
----------
\*args : Arguments
Shape of the output.
If given as N integers, each integer specifies the size of one(more...)


        def randn(*args):
"""
Return a random matrix with data from the "standard normal" distribution.

randn generates a matrix filled with random floats sampled from a
univariate "normal" (Gaussian) distribution of mean 0 and variance 1.

Parameters
----------
\\*args : Arguments
Shape of the output.
If given as N integers, each integer specifies the size of one
dimension. If given as a tuple, this tuple gives the complete shape.

Returns
-------
Z : matrix of floats
A matrix of floating-point samples drawn from the standard normal
distribution.

--------
rand, random.randn

Notes
-----
For random samples from :math:N(\\mu, \\sigma^2), use:

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

Examples
--------
>>> import numpy.matlib
>>> np.matlib.randn(1)
matrix([[-0.09542833]])                                 #random
>>> np.matlib.randn(1, 2, 3)
matrix([[ 0.16198284,  0.0194571 ,  0.18312985],
[-0.7509172 ,  1.61055   ,  0.45298599]])       #random

Two-by-four matrix of samples from :math:N(3, 6.25):

>>> 2.5 * np.matlib.randn((2, 4)) + 3
matrix([[ 4.74085004,  8.89381862,  4.09042411,  4.83721922],
[ 7.52373709,  5.07933944, -2.64043543,  0.45610557]])  #random

"""
if isinstance(args[0], tuple):
args = args[0]
return asmatrix(np.random.randn(*args))


def test_randn():
x = np.matlib.randn(3)
# check matrix type, array would have shape (3,)
assert_(x.ndim == 2)



def test_randn():
x = np.matlib.randn(3)
# check matrix type, array would have shape (3,)
assert_(x.ndim == 2)



def test_randn():