神经网络，单层感知器的训练

# 神经网络，单层感知器
# 参考《人工神经网络教程》韩力群
# 2018-3-28

import numpy

# 定义一个符号函数


class Perceptron(object):

    def __init__(self, eta=0.01, iter_num=20):
        self.eta = eta
        self.iter_num = iter_num
        self.x = numpy.array([[0, 0, 1, 1], [0, 1, 0, 1]])
        self.x = numpy.array([[-1, -1, -1, -1], [0, 0, 1, 1], [0, 1, 0, 1]])
        self.y_output = numpy.array([0, 0, 0, 1])
        # self.b = numpy.zeros(1)
        self.w = numpy.array([0.5, 0, 0])  # 权重w和偏置b初始化为0，0.5

    def Train(self):
        for index in range(4):
            print('第%s列'%index)
            m = self.x[:, index]

            for i in range(self.iter_num):
                y = numpy.dot(self.w, m)

                y = numpy.sign(y)
                dy = self.y_output[index] - y
                self.w = self.w + self.eta*dy*m.T
                print(self.w)
        return self.w


p1 = Perceptron()
p1.Train()