Advanced NumPy Exercises - Normalize a 5x5 array row-wise with random values
Write a NumPy program to create a 5x5 array with random values and normalize it row-wise.
Sample Solution:
Python Code:
import numpy as np
# create 5x5 array with random values
nums = np.random.rand(5, 5)
print("Original array elements:")
print(nums)
# normalize row-wise
norm_arr = nums / np.linalg.norm(nums, axis=1, keepdims=True)
print("\nNormalize Array row-wise:")
print(norm_arr)
Output:
Original array elements: [[0.96984536 0.03044869 0.01721181 0.89185135 0.97561925] [0.63986486 0.99058455 0.67101686 0.93148062 0.4123554 ] [0.58921689 0.79738557 0.59987027 0.14101544 0.68292227] [0.74824255 0.66308133 0.34566079 0.18339684 0.92431556] [0.73276253 0.13083497 0.96101991 0.57190127 0.66658679]] Normalize Array row-wise: [[0.59142903 0.01856816 0.01049607 0.54386689 0.59495005] [0.37713294 0.58384526 0.39549376 0.54900971 0.24304008] [0.43566728 0.58958731 0.44354439 0.10426689 0.50495308] [0.52816143 0.46804874 0.24399134 0.12945419 0.65244595] [0.48861401 0.08724218 0.64081852 0.38134998 0.44448731]]
Explanation:
In the above code -
nums = np.random.rand(5, 5): This line creates a 5x5 NumPy array with random values between 0 and 1.
np.linalg.norm(nums, axis=1, keepdims=True): This calculates the Euclidean norm of each row in nums. The axis=1 argument specifies that the norm should be calculated along the rows, and keepdims=True ensures that the result is a column vector with the same number of dimensions as nums.
norm_arr = nums / np.linalg.norm(nums, axis=1, keepdims=True): This line divides each element in nums by the corresponding row norm, effectively normalizing the rows of nums. The resulting array norm_arr has the same shape as nums, but with each row normalized to unit length.
Python-Numpy Code Editor: