How to do SVD in python?
This recipe helps you do SVD in python
Recipe Objective.
How to do SVD in python?
The singular value decomposition (SVD) provides a alternate way to resolve a matrix, into singular vectors and singular values. The SVD permits us to get a number of constant kind of info because the eigendecomposition.
The SVD is calculated by iteration in numerical numbers.
Table of Contents
Step 1- Importing Libraries.
# Singular-value decomposition
import numpy as np
from scipy.linalg import svd
Step 2- Creating Arrays.
We will create a 2-dimensional array of increasing digits to understand that there are no limits in SVD regarding digit size.
# define a matrix
X = np.array([[1, 10], [100, 1000], [10000, 100000],[1000000,10000000]])
print(X)
Step 3- Applying SVD
Now we will factorize the 2d matrix into singular values and singular vectors.
# SVD
y, z, W = svd(X)
print(y)
print(z)
print(W)
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