Python NumPy matlib.randn() Function

NumPy matlib.randn() Function:

The matlib.randn() function of the Numpy module returns a matrix filled with random floats sampled from a univariate normal (Gaussian) distribution with mean 0 and variance 1.

Syntax:

numpy.matlib.randn(*args)

Parameters

*args: This is required. It is the shape of the output. Each integer specifies the size of one dimension If given as N integers. If given as a tuple, this tuple gives the complete/entire shape.

Return Value:

A matrix of random values taken from a standard normal distribution of shape specified by *args is returned.

NumPy matlib.randn() Function in Python

Example1

Approach:

Import numpy module using the import keyword.
Import matlib function of numpy module using the import keyword
Pass the shape of the matrix(as a tuple), as an argument to the matlib.randn() function of numpy module to create a matrix of the given shape with random values from the standard normal distribution, N(0, 1) i.e, with mean 0 and variance 1.
Store it in a variable.
Print the above-obtained matrix with random values of the given shape.
The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np
# Import matlib function of numpy module using the import keyword
import numpy.matlib
# Pass the shape of the matrix(as a tuple), as an argument to the matlib.randn() 
# function of numpy module to create a matrix of the given shape with random values 
# from the standard normal distribution, N(0, 1) i.e, with mean 0 and variance 1.
# Store it in a variable.
gvn_matrx = np.matlib.randn((2,2))
# Print the above obtained matrix with random values of the given shape
print("The above obtained matrix with random values of the given shape:")
print(gvn_matrx)

Output:

The above obtained matrix with random values of the given shape:
[[ 0.83740804 -1.54515964]
 [ 3.44807217 -0.49712937]]

Example2: Normal Distribution

The method below can be used to build a matrix containing random values from a normal distribution, N(μ, σ2).

σ * np.matlib.randn() + μ

where μ = mean

σ = variance

Approach:

Import numpy module using the import keyword.
Import matlib function of numpy module using the import keyword.
Apply the above formula by passing mean and variance as random values and shape as argument(as a tuple) to the randn() function of the numpy module(here mean is 3 and variance is 4).
Print the above matrix with random values of normal distribution for the given shape.
The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np
# Import matlib function of numpy module using the import keyword
import numpy.matlib
# Apply the above formula by passing mean and variance as random values and shape as argument(as a tuple) to the randn() function of the numpy module.
# here mean is 3 and variance is 4
# Store it in a variable.
gvn_matrx = 4*np.matlib.randn((4,4)) + 3
# Print the above matrix with random values of normal distribution for the given shape
print("The above matrix with random values of normal distribution for the given shape:")
print(gvn_matrx)

Output:

The above matrix with random values of normal distribution for the given shape:
[[ 0.90043018 4.70289111 -0.65385717 2.37142165]
 [-0.01856318 0.37620644 -2.68371203 7.39011586]
 [-0.64833481 1.48203811 8.30356936 5.16294525]
 [ 0.96125199 4.84063464 2.54766651 2.06913251]]

NumPy matlib.randn() Function in Python

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