NumPy: Compute the factor of a given array by Singular Value Decomposition
NumPy: Linear Algebra Exercise-18 with Solution
Write a NumPy program to compute the factor of a given array by Singular Value Decomposition.
Sample Solution:
Python Code :
import numpy as np
a = np.array([[1, 0, 0, 0, 2], [0, 0, 3, 0, 0], [0, 0, 0, 0, 0], [0, 2, 0, 0, 0]], dtype=np.float32)
print("Original array:")
print(a)
U, s, V = np.linalg.svd(a, full_matrices=False)
q, r = np.linalg.qr(a)
print("Factor of a given array by Singular Value Decomposition:")
print("U=\n", U, "\ns=\n", s, "\nV=\n", V)
Sample Output:
Original array: [[ 1. 0. 0. 0. 2.] [ 0. 0. 3. 0. 0.] [ 0. 0. 0. 0. 0.] [ 0. 2. 0. 0. 0.]] Factor of a given array by Singular Value Decomposition: U= [[ 0. 1. 0. 0.] [ 1. 0. 0. 0.] [ 0. 0. 0. -1.] [ 0. 0. 1. 0.]] s= [ 3. 2.23606801 2. 0. ] V= [[-0. 0. 1. -0. 0. ] [ 0.44721359 -0. -0. -0. 0.89442718] [-0. 1. 0. -0. 0. ] [ 0. 0. 0. 1. 0. ]]
Python-Numpy Code Editor:
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