tsvd_sklearn.py

import numpy as np
from sklearn.decomposition import TruncatedSVD

A = np.array([[-1, 2, 0], [2, 0, -2], [0, -2, 1]])
print("Original Matrix:")
print(A)

svd =  TruncatedSVD(n_components = 2)
A_transf = svd.fit_transform(A)

print("Singular values:")
print(svd.singular_values_)

print("Transformed Matrix after reducing to 2 features:")
print(A_transf)