# numpy matrix operations | randn() function

numpy.matlib.randn() is another function for doing matrix operations in numpy. It returns a matrix of random values from a univariate “normal” (Gaussian) distribution of mean 0 and variance 1.

Syntax : numpy.matlib.randn(*args)

Parameters :
*args : [Arguments] Shape of the output matrix. If given as N integers, each integer specifies the size of one dimension. If given as a tuple, this tuple gives the complete shape.If there are more than one argument and the first argument is a tuple then other arguments are ignored.

Return : The matrix of random values drawn from the standard normal distribution.

Code #1 :

 # Python program explaining  # numpy.matlib.randn() function     # importing matrix library from numpy  import numpy as geek  import numpy.matlib     # desired 3 x 4 random output matrix   out_mat = geek.matlib.randn((3, 4))   print ("Output matrix : ", out_mat)

Output :

Output matrix :  [[ 0.78620217  0.41624612 -0.28417131  0.1071018 ]
[ 0.77645105  0.30858858 -1.98901344  1.25977209]
[ 0.26279443 -0.41026178 -0.60834494  2.82552737]]


Code #2 :

 # Python program explaining  # numpy.matlib.randn() function     # importing numpy and matrix library  import numpy as geek  import numpy.matlib     # desired 1 x 5 random output matrix   out_mat = geek.matlib.randn(5)   print ("Output matrix : ", out_mat)

Output :

Output matrix :  [[ 0.34973625  0.28159132  0.72581405 -1.17511692  1.96773952]]


Code #3 :

 # Python program explaining  # numpy.matlib.randn() function     # importing numpy and matrix library  import numpy as geek  import numpy.matlib     # more than one argument given  out_mat = geek.matlib.randn((5, 3), 4)   print ("Output matrix : ", out_mat)

Output :

Output matrix :  [[ 0.56784957  0.82980325  1.16683558]
[-1.53444326 -0.27743273  0.65819067]
[ 0.99654573 -1.20399432 -0.25603147]
[ 1.74931585  0.58413453  1.67820029]
[-1.25643231  0.21610229  0.21694595]]


Note: For random samples from we can use  sigma * geek.matlib.randn(...) + mu .
For example, making a 3 x 3 matrix in which samples are taken from :

Code #4 :

 # Python program explaining  # numpy.matlib.randn() function     # importing numpy and matrix library  import numpy as geek  import numpy.matlib     # So, here mu = 3, sigma = 2  out_mat = 2 * geek.matlib.randn((3, 3)) + 3 print ("Output matrix : ", out_mat)

Output :

Output matrix :  [[ 4.04967121  0.26982021  2.3503067 ]
[ 5.57757131  2.40051874 -0.84588539]
[ 7.43715651  3.84004412  1.40514615]]


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