编写python代码，实现感知器的 and和OR 操作算法

AND操作感知器算法：

import numpy as np

# 定义感知器类
class Perceptron:
    def __init__(self, input_num, activator):
        self.activator = activator  # 激活函数
        self.weights = np.zeros(input_num)  # 权重向量
        self.bias = 0  # 偏置项

    # 计算感知器输出
    def _output(self, input_vec):
        # 计算向量内积
        return self.activator(np.dot(input_vec, self.weights) + self.bias)

    # 迭代训练感知器
    def train(self, input_vecs, labels, iteration, rate):
        for i in range(iteration):
            self._one_iteration(input_vecs, labels, rate)

    # 单次迭代训练感知器
    def _one_iteration(self, input_vecs, labels, rate):
        for input_vec, label in zip(input_vecs, labels):
            output = self._output(input_vec)
            # 更新权重
            self.weights += rate * (label - output) * input_vec
            # 更新偏置项
            self.bias += rate * (label - output)

# 激活函数
def f(x):
    return 1 if x > 0 else 0

# 训练数据集
input_vecs = [[1, 1], [1, 0], [0, 1], [0, 0]]
labels = [1, 0, 0, 0]

# 创建感知器实例并训练
p = Perceptron(2, f)
p.train(input_vecs, labels, 10, 0.1)

# 测试感知器
print('1 and 1 =', p._output([1, 1]))  # 1
print('1 and 0 =', p._output([1, 0]))  # 0
print('0 and 1 =', p._output([0, 1]))  # 0
print('0 and 0 =', p._output([0, 0]))  # 0

OR操作感知器算法：

import numpy as np

# 定义感知器类
class Perceptron:
    def __init__(self, input_num, activator):
        self.activator = activator  # 激活函数
        self.weights = np.zeros(input_num)  # 权重向量
        self.bias = 0  # 偏置项

    # 计算感知器输出
    def _output(self, input_vec):
        # 计算向量内积
        return self.activator(np.dot(input_vec, self.weights) + self.bias)

    # 迭代训练感知器
    def train(self, input_vecs, labels, iteration, rate):
        for i in range(iteration):
            self._one_iteration(input_vecs, labels, rate)

    # 单次迭代训练感知器
    def _one_iteration(self, input_vecs, labels, rate):
        for input_vec, label in zip(input_vecs, labels):
            output = self._output(input_vec)
            # 更新权重
            self.weights += rate * (label - output) * input_vec
            # 更新偏置项
            self.bias += rate * (label - output)

# 激活函数
def f(x):
    return 1 if x > 0 else 0

# 训练数据集
input_vecs = [[1, 1], [1, 0], [0, 1], [0, 0]]
labels = [1, 1, 1, 0]

# 创建感知器实例并训练
p = Perceptron(2, f)
p.train(input_vecs, labels, 10, 0.1)

# 测试感知器
print('1 or 1 =', p._output([1, 1]))  # 1
print('1 or 0 =', p._output([1, 0]))  # 1
print('0 or 1 =', p._output([0, 1]))  # 1
print('0 or 0 =', p._output([0, 0]))  # 0