Svd, Singular Values, Eigenvalue, Eigenvectors, Sklearn.
Σ_array and then assign it to Σ’s top square matrix. m = A.shape[0] n = A.shape[1] Σ = np.zeros((m,n)) Σ[0: len(Σ_array), 0: len(Σ_array)] = np.diag(Σ_array) print(Σ) Σ Now we can multiply U, Σ, and V_transpose and we will get back the original matrix A: print("Matrix constructed from original