NumPy: Normalize a 3x3 random matrix
Write a NumPy program to normalize a 3x3 random matrix.
Sample Solution:
Python Code :
# Importing the NumPy library as np
import numpy as np
# Generating a 3x3 array of random numbers between 0 and 1 using np.random.random()
x = np.random.random((3, 3))
# Printing the original array 'x'
print("Original Array:")
print(x)
# Finding the maximum and minimum values in array 'x' using x.max() and x.min()
xmax, xmin = x.max(), x.min()
# Normalizing the array 'x' using min-max scaling: (x - xmin) / (xmax - xmin)
x = (x - xmin) / (xmax - xmin)
# Printing the array 'x' after normalization
print("After normalization:")
print(x)
Sample Output:
Original Array: [[ 0.89372503 0.99865458 0.77120044] [ 0.67632984 0.99990084 0.64110391] [ 0.34845794 0.66557903 0.29031742]] After normalization: [[ 0.85036881 0.99824367 0.67769765] [ 0.54399864 1. 0.49435553] [ 0.08193614 0.52884777 0. ]]
Explanation:
x = np.random.random((3,3)): This line creates a 3x3 array x with random numbers between 0 and 1 using the np.random.random() function. The input tuple (3,3) specifies the output array shape.
xmax, xmin = x.max(), x.min(): This line finds the maximum and minimum values in the array x using the x.max() and x.min() methods, respectively. These values are stored in the variables xmax and xmin.
x = (x - xmin)/(xmax - xmin): This line normalizes the array x by rescaling its elements to the range [0, 1]. This is done by subtracting the minimum value xmin from all elements in the array and then dividing the result by the range (xmax - xmin). The normalized values are stored back into array x.
print(x): Finally print() function prints the normalized 3x3 array x.
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