%% 清空环境变量
warning off % 关闭报警信息
close all % 关闭开启的图窗
clear % 清空变量
clc % 清空命令行
%% 导入数据
res = xlsread('数据集.xlsx');
%% 划分训练集和测试集
temp = randperm(357);
P_train = res(temp(1: 240), 1: 12)';
T_train = res(temp(1: 240), 13)';
M = size(P_train, 2);
P_test = res(temp(241: end), 1: 12)';
T_test = res(temp(241: end), 13)';
N = size(P_test, 2);
%% 数据归一化
[p_train, ps_input] = mapminmax(P_train, 0, 1);
p_test = mapminmax('apply', P_test, ps_input );
t_train = T_train;
t_test = T_test ;
%% 转置以适应模型
p_train = p_train'; p_test = p_test';
t_train = t_train'; t_test = t_test';
%% 训练模型
trees = 50; % 决策树数目
leaf = 1; % 最小叶子数
OOBPrediction = 'on'; % 打开误差图
OOBPredictorImportance = 'on'; % 计算特征重要性
Method = 'classification'; % 分类还是回归
net = TreeBagger(trees, p_train, t_train, 'OOBPredictorImportance', OOBPredictorImportance, ...
'Method', Method, 'OOBPrediction', OOBPrediction, 'minleaf', leaf);
importance = net.OOBPermutedPredictorDeltaError; % 重要性
%% 仿真测试
t_sim1 = predict(net, p_train);
t_sim2 = predict(net, p_test );
%% 格式转换
T_sim1 = str2num(cell2mat(t_sim1));
T_sim2 = str2num(cell2mat(t_sim2));
%% 性能评价
error1 = sum((T_sim1' == T_train)) / M * 100 ;
error2 = sum((T_sim2' == T_test )) / N * 100 ;
%% 绘制误差曲线
figure
plot(1 : trees, oobError(net), 'b-', 'LineWidth', 1)
legend('误差曲线')
xlabel('决策树数目')
ylabel('误差')
xlim([1, trees])
grid
%% 绘制特征重要性
figure
bar(importance)
legend('重要性')
xlabel('特征')
ylabel('重要性')
%% 数据排序
[T_train, index_1] = sort(T_train);
[T_test , index_2] = sort(T_test );
T_sim1 = T_sim1(index_1);
T_sim2 = T_sim2(index_2);
%% 绘图
figure
plot(1: M, T_train, 'r-*', 1: M, T_sim1, 'b-o', 'LineWidth', 1)
legend('真实值', '预测值')
xlabel('预测样本')
ylabel('预测结果')
string = {'训练集预测结果对比'; ['准确率=' num2str(error1) '%']};
title(string)
grid
figure
plot(1: N, T_test, 'r-*', 1: N, T_sim2, 'b-o', 'LineWidth', 1)
legend('真实值', '预测值')
xlabel('预测样本')
ylabel('预测结果')
string = {'测试集预测结果对比'; ['准确率=' num2str(error2) '%']};
title(string)
grid
%% 混淆矩阵
figure
cm = confusionchart(T_train, T_sim1);
cm.Title = 'Confusion Matrix for Train Data';
cm.ColumnSummary = 'column-normalized';
cm.RowSummary = 'row-normalized';
figure
cm = confusionchart(T_test, T_sim2);
cm.Title = 'Confusion Matrix for Test Data';
cm.ColumnSummary = 'column-normalized';
cm.RowSummary = 'row-normalized';
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