python

import numpy as np

def loaddataset(filename):
    fp = open(filename)
    # 存放数据
    dataset = []
    # 存放标签
    labelset = []
    for i in fp.readlines():
        a = i.strip().split()
        # 每个数据行的最后一个是标签
        dataset.append([float(j) for j in a[:len(a) - 1]])
        labelset.append(int(float(a[-1])))
    return dataset, labelset

# x为输入层神经元个数，y为隐层神经元个数，z输出层神经元个数
def parameter_initialization(x, y, z):
    # 隐层阈值
    value1 = np.random.randint(-5, 5, (1, y)).astype(np.float64)
    # 输出层阈值
    value2 = np.random.randint(-5, 5, (1, z)).astype(np.float64)
    # 输入层与隐层的连接权重
    weight1 = np.random.randint(-5, 5, (x, y)).astype(np.float64)
    # 隐层与输出层的连接权重
    weight2 = np.random.randint(-5, 5, (y, z)).astype(np.float64)
    return weight1, weight2, value1, value2

def sigmoid(z):
    return 1 / (1 + np.exp(-z))

'''
weight1:输入层与隐层的连接权重
weight2:隐层与输出层的连接权重
value1:隐层阈值
value2:输出层阈值
'''

def trainning(dataset, labelset, weight1, weight2, value1, value2):
    # x为步长
    x = 0.01
    for i in range(len(dataset)):
        # 输入数据
        inputset = np.mat(dataset[i]).astype(np.float64)
        # 数据标签
        outputset = np.mat(labelset[i]).astype(np.float64)
        # 隐层输入
        input1 = np.dot(inputset, weight1).astype(np.float64)
        # 隐层输出
        output2 = sigmoid(input1 - value1).astype(np.float64)
        # 输出层输入
        input2 = np.dot(output2, weight2).astype(np.float64)
        # 输出层输出
        output3 = sigmoid(input2 - value2).astype(np.float64)
        # 更新公式由矩阵运算表示
        a = np.multiply(output3, 1 - output3)
        g = np.multiply(a, outputset - output3)
        b = np.dot(g, np.transpose(weight2))
        c = np.multiply(output2, 1 - output2)
        e = np.multiply(b, c)

        value1_change = -x * e
        value2_change = -x * g
        weight1_change = x * np.dot(np.transpose(inputset), e)
        weight2_change = x * np.dot(np.transpose(output2), g)

        # 更新参数
        value1 += value1_change
        value2 += value2_change
        weight1 += weight1_change
        weight2 += weight2_change
    return weight1, weight2, value1, value2

def testing(dataset, labelset, weight1, weight2, value1, value2):
    # 记录预测正确的个数
    rightcount = 0
    for i in range(len(dataset)):
        # 计算每一个样例通过该神经网路后的预测值
        inputset = np.mat(dataset[i]).astype