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]]