№5 с нормальным методом Гаусса

import numpy as np

def gauss(A):
    A = np.array(A, dtype=float)
    n, m = A.shape

    def print_matrix(matrix):
        print(np.array_str(matrix, precision=5, suppress_small=True))

    print("Расширенная матрица СЛАУ:")
    print_matrix(A)
    print()

    for i in range(n):
        max_row = i + np.argmax(np.abs(A[i:, i]))
        A[[i, max_row]] = A[[max_row, i]]

        div = A[i, i]
        A[i] /= div

        for j in range(i + 1, n):
            factor = A[j, i]
            A[j] -= factor * A[i]

    print("Прямой ход метода Гаусса:")
    print_matrix(A)
    print()

    # Обратный ход
    for j in range(n - 1, -1, -1):
        for i in range(j - 1, -1, -1):
            d = A[i, j] / A[j, j]
            A[i, j:] -= A[j, j:] * d

    print("Обратный ход метода Гаусса:")
    print_matrix(A)
    print()

    s = [round(A[i, -1] / A[i, i], 4) for i in range(n)]
    print("Решение СЛАУ:")
    print(*s)


n = 5
v = 8
A = [
    [(v + i) / 100 if i != j else v + i for j in range(n)]
    for i in range(n)
]
V = [v + i for i in range(n)]
B = [sum(A[i][j] * V[j] for j in range(n)) for i in range(n)]
res_matrix = [A[i] + [B[i]] for i in range(n)]

gauss(res_matrix)