python实现感知机（学习AND操作）

前段时间看完了《人工智能》这本书，其中对神经网络和遗传算法比较感兴趣，所以就试着回过头去仔细认真的看，并总结归纳

今天用python，按照书上介绍的感知机的步骤敲了代码，初步实现了，但是由于太久没敲代码加上刚接触python，代码难看的不行，大神勿喷哈哈哈，下面是代码

def stepFunc(x):  #定义阶跃函数
    if x > 0:
        return 1
    else:
        return 0

def trainning():
    #初始化权重、阈值和学习速度
    weight = [0.5,0.5]
    threshold = 0.2
    learning_rate = 0.1

    #设置训练数据
    trainning_X = [[0,0],[0,1],[1,0],[1,1]]
    trainning_Y = [0,0,0,1]

    #误差总和变量
    bias = 1
    y = 0 #记录实际输出
    p = 1 #记录迭代次数
    while bias != 0:
        print("第",p,"次迭代")
        bias = 0
        e = 0
        i = 0
        for x in trainning_X:
            X = x[0] * weight[0] + x[1] * weight[1] - threshold
            y = stepFunc(X)
            #误差e = 期望输出 - 实际输出
            e = trainning_Y[i] - y

            if e != 0:
                # 调整权重
                weight[0] += learning_rate * x[0] * e
                weight[1] += learning_rate * x[1] * e
                bias += abs(e)
            i += 1
            print("x:", x, " y:", y, " 误差：", e," 修改后权重：w1:",weight[0]," w2:",weight[1])
        print("此次迭代误差总和为",bias)
        p += 1

    print("训练结束")
    return weight,threshold

def predict(test_X):
    print("测试数据：",test_X)
    print("预测如下：")
    for x in test_X:
        X = x[0] * weight[0] + x[1] * weight[1] - threshold
        y = stepFunc(X)
        print("x:", x, " y:", y)

weight,threshold = trainning()
test_X = [[1,0],[0,1],[1,1],[0,0]]  #测试数据
predict(test_X)

实验结果：


记录总结一下这个过程，明天试着写写多层神经网络（后向传送训练算法）