import numpy as np

# 样本和标签
X = np.array([[3, 3],
              [4, 3],
              [1, 1]])
y = np.array([1, 1, -1])

# 初始化
w = np.array([3.0, 3.0])
b = -1.0
eta = 1  # 学习率

def predict(x):
    return np.sign(np.dot(w, x) + b)

iteration = 0
while True:
    error_count = 0
    for i in range(len(X)):
        if y[i] * (np.dot(w, X[i]) + b) <= 0:  # 误分类
            w = w + eta * y[i] * X[i]
            b = b + eta * y[i]
            iteration += 1
            print(f"迭代{iteration}: w={w}, b={b}")
            error_count += 1
    if error_count == 0:
        print("训练收敛！")
        break

print(f"最终结果: w={w}, b={b}")