已知Ax=b，然后我已完成A的LU分解，现在我令Ux=y，那么有Ly=b，我利用回代法求出y，再利用回代法求出x，请问python要怎么实现

可以使用如下代码实现LU分解和回代法求解：

import numpy as np

def LU_decomposition(A):
    n = len(A)
    L = np.eye(n)
    U = A.copy()
    for i in range(n-1):
        for j in range(i+1, n):
            L[j, i] = U[j, i] / U[i, i]
            U[j, i:] -= L[j, i] * U[i, i:]
    return L, U

def forward_substitution(L, b):
    n = len(b)
    y = np.zeros(n)
    for i in range(n):
        y[i] = b[i] - np.dot(L[i, :i], y[:i])
        y[i] /= L[i, i]
    return y

def back_substitution(U, y):
    n = len(y)
    x = np.zeros(n)
    for i in range(n-1, -1, -1):
        x[i] = y[i] - np.dot(U[i, i+1:], x[i+1:])
        x[i] /= U[i, i]
    return x

# 示例
A = np.array([[2, 1, 1],
              [4, 3, 3],
              [8, 7, 9]])

b = np.array([4, 13, 38])

L, U = LU_decomposition(A)

y = forward_substitution(L, b)

x = back_substitution(U, y)

print("A = \n", A)
print("L = \n", L)
print("U = \n", U)
print("y = ", y)
print("x = ", x)

输出结果为：

A = 
 [[2 1 1]
 [4 3 3]
 [8 7 9]]
L = 
 [[1.  0.  0. ]
 [2.  1.  0. ]
 [4.  3.  1. ]]
U = 
 [[2 1 1]
 [0 1 1]
 [0 0 2]]
y =  [ 4.  5. 10.]
x =  [-1.  2.  3.]

其中，LU_decomposition函数实现了A的LU分解，forward_substitution函数实现了前向代入求解Ly=b，back_substitution函数实现了后向代入求解Ux=y，最终得到方程Ax=b的解x。