python实现感知机

 感知机理论链接：https://www.zybuluo.com/hanbingtao/note/433855

import numpy as np
#定义激活函数
def acti_fun(x):
    return 1 if x > 0 else 0
#创建感知器类
class Perception(object):
     #初始化权重
    def __init__(self):
        self.weights = 0
        self.bias = 1
    #定义训练函数，包括训练次数iter,学习率rate
    def train(self,input_vecs,labels,iter,rate):
        for i in range(iter):
            for input_vec,label in zip(input_vecs,labels):
                output = acti_fun(sum(np.array(input_vec) * self.weights) + self.bias)
                bias = label - output
                #更新权重
                self.weights += rate*bias*np.array(input_vec)
                self.bias +=  rate*bias
        return self.weights,self.bias
    #定义预测函数
 
    def predict(self,input_data):
        input_data = np.array(input_data)
        pred = []
        for each in input_data:
            pred_each = acti_fun(sum(np.array(each)*np.array(self.weights)) + self.bias)
            pred.append(pred_each)
        return pred
#测试
if __name__=='__main__':
    input_vecs = [[1,1],[1,0],[0,1],[0,0]]
    labels = [1,0,0,0]
    inputdata = [[-1,0],[1,1]]  #可以单个[1,0]，也可以多个[[-1,0],[1,1]]
    inputdata = np.mat(inputdata)
    p = Perception()
    p.train(input_vecs,labels,50,0.1)
    predict = p.predict(inputdata)
    print(predict)

参考链接：https://blog.csdn.net/momaojia/article/details/75127541