numpy matrix operations | randn() function
Last Updated :
21 Feb, 2019
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 :
import numpy as geek
import numpy.matlib
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 :
import numpy as geek
import numpy.matlib
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 :
import numpy as geek
import numpy.matlib
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 :
import numpy as geek
import numpy.matlib
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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