import numpy as np
 
# 入力
n = int(input())
s = int(input())
 
# 1. 乱数の種
np.random.seed(s)
 
# 2. n 次正方行列 A の生成（対角優位に修正）
A = np.random.randint(0, 2, (n, n)).astype(float)
for i in range(n):
    A[i, i] += np.sum(A[i])
 
# 3. n 次元ベクトル b の生成
b = np.random.randint(0, 2, n).astype(float)
 
# 4. Jacobi 法
x = np.zeros(n)
for _ in range(100):
    x_new = np.zeros(n)
    for i in range(n):
        s = b[i] - np.dot(A[i, :], x) + A[i, i] * x[i]
        x_new[i] = s / A[i, i]
 
    # 収束判定
    if np.linalg.norm(np.dot(A, x_new) - b) < 1e-6:
        x = x_new
        break
    x = x_new
 
# 結果をスペース区切りで出力
print(*x)
 

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4
2