numpy.matlib.randn()

numpy.matlib.randn(*args) [source]

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:

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.

*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.

Notes

For random samples from $N(\mu, \sigma^2)$ , 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 $N(3, 6.25)$ :

>>> 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

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.matlib.randn.html

doc_NumPy

2025-01-10 15:47:30

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