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# matrix operations | randn () function

`numpy.matlib.randn()` — another function to do matrix operations in numpy. It returns a matrix of random values ​​from a one-dimensional "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    # import 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 `   ` # import numpy library and matrix ` ` import ` ` numpy as geek ` ` import ` ` numpy.matlib `   ` # desired random output matrix 1 x 5 ` ` 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 `   ` # import numpy library and matrices ` ` 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: src = "http://espressocode.top/images/aresconcoidili748083.jpg" /> we can use ` sigma * geek.matlib.randn (...) + mu `
For example, creating a 3 x 3 matrix with samples from :

Code # 4:

 ` # Python program explaining ` ` # numpy.matlib .randn () function `   ` # import numpy library and matrix ` ` 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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