python-scipyHow can I use the SciPy SVD function in Python?

The SciPy SVD function allows you to decompose a matrix into its constituent parts using Singular Value Decomposition (SVD).

To use the SciPy SVD function in Python, you simply need to import the scipy.linalg module and call the svd function. For example:

import numpy as np
from scipy.linalg import svd

A = np.array([[1,2],[3,4],[5,6]])
U, s, VT = svd(A)

print(U)
print(s)
print(VT)

Output example

[[-0.2298477   0.88346102  0.40824829]
 [-0.52474482  0.24078249 -0.81649658]
 [-0.81964194 -0.40189603  0.40824829]]
[9.52551809 0.51430058]
[[-0.61962948 -0.78489445]
 [-0.78489445  0.61962948]]

The code consists of the following parts:

1. Import the scipy.linalg module: import numpy as np and from scipy.linalg import svd.
2. Create a matrix A: A = np.array([[1,2],[3,4],[5,6]]).
3. Call the svd function: U, s, VT = svd(A).
4. Print the results: print(U), print(s), and print(VT).

For more information about the SciPy SVD function, see the SciPy documentation.

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