Python – scipy.linalg.norm

ScipyScientific ComputingProgramming

The norm() function of the scipy.linalg package is used to return one of eight different matrix norms or one of an infinite number of vector norms.

Syntax

scipy.linalg.norm(x)

Where x is an input array or a square matrix.

Example 1

Let us consider the following example −

# Importing the required libraries from scipy
from scipy import linalg
import numpy as np

# Define the input array
x = np.array([7 , 4])
print("Input array:\n", x)

# Calculate the L2 norm
r = linalg.norm(x)

# Calculate the L1 norm
s = linalg.norm(x, 3)

# Display the norm values
print("Norm Value of r :", r)
print("Norm Value of s :", s)

Output

The above program will generate the following output −

Input array:
[7 4]
Norm Value of r : 8.06225774829855
Norm Value of s : 7.410795055420619

Example 2

Let us take another example −

# Importing the required libraries from scipy
from scipy import linalg
import numpy as np

# Define the input array
x = np.array([[ 6, 7, 8], [9, -1, -2]])
print("Input Array :\n", x)

# Calculate the L2 norm
p = linalg.norm(x)

# Calculate the L1 norm
q = linalg.norm(x, axis=1)

# Display the norm values
print("Norm Values of P :", p)
print("Norm Values of Q :", q)

Output

It will produce the following output −

Input Array :
[[ 6 7 8]
[ 9 -1 -2]]
Norm Values of P : 15.329709716755891
Norm Values of Q : [12.20655562 9.2736185 ]
raja
Updated on 22-Dec-2021 10:05:34

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