Function randn

numpy/matlib.py:279–330

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 th

(*args)

    return asmatrix(np.random.rand(*args))

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.

    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 ]])

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

def repmat(a, m, n):
    """