机器学习及其matlab实现—从基础到实践——HW3

Contents

• I. 清空环境变量
• II. 训练集/测试集产生
• III. 数据归一化
• IV. BP神经网络创建、训练及仿真测试
• V. 性能评价
• VI. 画图

I. 清空环境变量

clear allclc

II. 训练集/测试集产生

1. 导入数据

load concrete_data.mat

2. 随机产生训练集和测试集

temp = randperm(size(attributes,2));% 训练集——80个样本P_train = attributes(:,temp(1:80));T_train = strength(:,temp(1:80));% 测试集——23个样本P_test = attributes(:,temp(81:end));T_test = strength(:,temp(81:end));N = size(P_test,2);

III. 数据归一化

[p_train, ps_input] = mapminmax(P_train,0,1);p_test = mapminmax('apply',P_test,ps_input);[t_train,ps_output] = mapminmax(T_train,0,1);

IV. BP神经网络创建、训练及仿真测试

1. 创建网络

net = newff(p_train,t_train,11);

2. 设置训练参数

net.trainParam.epochs = 1000;net.trainParam.goal = 1e-3;net.trainParam.lr = 0.01;

3. 训练网络

net = train(net,p_train,t_train);

4. 仿真测试

t_sim = sim(net,p_test);

5. 数据反归一化

T_sim = mapminmax('reverse',t_sim,ps_output);

V. 性能评价

1. 绝对误差error

error = abs(T_sim - T_test)./T_test;

2. 决定系数R^2

R2 = (N * sum(T_sim .* T_test) - sum(T_sim) * sum(T_test))^2 / ((N * sum((T_sim).^2) - (sum(T_sim))^2) * (N * sum((T_test).^2) - (sum(T_test))^2));

3. 结果对比

result = [T_test' T_sim' error']

result =   28.1600   31.1291    0.1054   52.6500   53.0643    0.0079   30.9700   26.2469    0.1525   38.4600   38.0986    0.0094   41.1400   39.8364    0.0317   30.8300   30.8207    0.0003   36.1900   35.7525    0.0121   32.7100   33.8095    0.0336   41.0100   40.0879    0.0225   32.8400   33.3301    0.0149   33.9100   31.6726    0.0660   38.1900   38.3960    0.0054   26.4200   27.5678    0.0434   17.1900   18.4602    0.0739   35.5200   35.2201    0.0084   49.9700   51.0821    0.0223   48.7700   48.8715    0.0021   46.3600   46.2920    0.0015   31.5000   29.0295    0.0784   42.0800   42.0742    0.0001   36.4600   39.2970    0.0778   44.4800   44.6282    0.0033   58.5300   53.3348    0.0888

VI. 画图

figureplot(1:N,T_test,'b:*',1:N,T_sim,'r-o')legend('真实值','预测值')xlabel('预测样本')ylabel('混凝土抗压强度')string = {'测试集混凝土抗压强度预测结果对比';['R^2=' num2str(R2)]};title(string)

Published with MATLAB® R2015a