感知机算法python实现

感知机算法

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    import random
    import numpy as np
    import matplotlib.pyplot as plt
    class percePtron:
    #初始化w向量和截距b，注意初始化为浮点类型
        def __init__(self):
            self.weight = [0.0, 0.0]
            self.bias = 0.0
        def draw(self, x1, x2, y1, y2):
            plt.scatter(x1, y1, label="y = 1")
            plt.scatter(x2, y2, label="y = -1")
            plt.legend()
            plt.xlabel('X1')
            plt.ylabel('X2')
            label_x = np.linspace(0, 4, 20)
            # print(-(self.bias + self.weight[0] * label_x) / self.weight[1])
            plt.plot(label_x, -(self.bias + self.weight[0] * label_x) / self.weight[1])
            plt.show()
        #  learning_rate=0.1表示为步长为0.1，step表示为训练次数，data为样本集
        def train(self, data, learning_rate=0.1, step=1000):
            self.weight = np.array(self.weight)
            for i in range(step):
                randomindex = random.randint(0, len(data) - 1)
                randomData = data[randomindex]
                #如果y(wx + b) <= 0，则表明这个点为误分类点
                if randomData[2] * (self.weight[0] * randomData[0] + self.weight[1] * randomData[1] + self.bias) <= 0:
                	#此处为梯度下降算法更新w和b
                    self.weight[0] = self.weight[0] + randomData[2] * randomData[0] * learning_rate
                    self.weight[1] = self.weight[1] + randomData[2] * randomData[1] * learning_rate
                    self.bias = self.bias + randomData[2] * learning_rate
                    print("weight : ", self.weight)
                    print("bias : ", self.bias)
    if __name__ == "__main__":
        data = [[1, 4, 1], [0.5, 2, 1], [2, 2.3, 1], [1, 0.5, -1], [2, 1, -1],
                [4, 1, -1], [3.5, 4, 1], [3, 2.2, -1]]
        x1 = []
        y1 = []
        x2 = []
        y2 = []
        for p in data:
            if p[2] > 0:
                x1.append(p[0])
                y1.append(p[1])
            else:
                x2.append(p[0])
                y2.append(p[1])
        x1 = np.array(x1)
        x2 = np.array(x2)
        y1 = np.array(y1)
        y2 = np.array(y2)
        model = percePtron()
        model.train(data)
        model.draw(x1, x2, y1, y2)

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运行前：

运行后：

哈哈，完美！

结束语：本人为机器学习初学者，若代码有误请大佬留言，本人会及时更正，谢谢！！！