%% 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.xlsx');
%% 数据分析
num_size = 0.8; % 训练集占数据集比例
outdim = 3; % 最后3列为输出
num_samples = size(res, 1); % 样本个数
res = res(randperm(num_samples), :); % 打乱数据集（不希望打乱时，注释该行）
num_train_s = round(num_size * num_samples); % 训练集样本个数
f_ = size(res, 2) - outdim; % 输入特征维度
%% 划分训练集和测试集
P_train = res(1: num_train_s, 1: f_)';
T_train = res(1: num_train_s, f_ + 1: end)';
M = size(P_train, 2);
P_test = res(num_train_s + 1: end, 1: f_)';
T_test = res(num_train_s + 1: end, f_ + 1: end)';
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, ps_output] = mapminmax(T_train, 0, 1);
t_test = mapminmax('apply', T_test, ps_output);
%% 创建模型
num_hiddens = 50; % 隐藏层节点个数
activate_model = 'sig'; % 激活函数
[IW, B, LW, TF, TYPE] = elmtrain(p_train, t_train, num_hiddens, activate_model, 0);
%% 仿真测试
t_sim1 = elmpredict(p_train, IW, B, LW, TF, TYPE);
t_sim2 = elmpredict(p_test , IW, B, LW, TF, TYPE);
%% 数据反归一化
T_sim1 = mapminmax('reverse', t_sim1, ps_output);
T_sim2 = mapminmax('reverse', t_sim2, ps_output);
%% 绘图
for i = 1 : 3
figure(i)
%% 均方根误差
error1 = sqrt(sum((T_sim1(i, :) - T_train(i, :)).^2) ./ M);
error2 = sqrt(sum((T_sim2(i, :) - T_test (i, :)).^2) ./ N);
subplot(2, 1, 1)
plot(1: M, T_train(i, :), 'r-', 1: M, T_sim1(i, :), 'b-', 'LineWidth', 1)
legend('真实值', 'ELM预测值')
xlabel('预测样本')
ylabel('预测结果')
string = {'训练集预测结果对比'; ['RMSE=' num2str(error1)]};
title(string)
xlim([1, M])
grid
subplot(2, 1, 2)
plot(1: N, T_test(i, :), 'r-', 1: N, T_sim2(i, :), 'b-', 'LineWidth', 1)
legend('真实值', 'ELM预测值')
xlabel('预测样本')
ylabel('预测结果')
string = {'测试集预测结果对比'; ['RMSE=' num2str(error2)]};
title(string)
xlim([1, N])
grid
%% 相关指标计算
% R2
R1(i) = 1 - norm(T_train(i, :) - T_sim1(i, :))^2 / norm(T_train(i, :) - mean(T_train(i, :)))^2;
R2(i) = 1 - norm(T_test (i, :) - T_sim2(i, :))^2 / norm(T_test (i, :) - mean(T_test (i, :)))^2;
disp(['输出:', num2str(i), ' 训练集数据的R2为：', num2str(R1(i))])
disp(['输出:', num2str(i), ' 测试集数据的R2为：', num2str(R2(i))])
% MAE
mae1(i) = sum(abs(T_sim1(i, :) - T_train(i, :))) ./ M ;
mae2(i) = sum(abs(T_sim2(i, :) - T_test (i, :))) ./ N ;
disp(['输出:', num2str(i), ' 训练集数据的MAE为：', num2str(mae1(i))])
disp(['输出:', num2str(i), ' 测试集数据的MAE为：', num2str(mae2(i))])
% MBE
mbe1(i) = sum(T_sim1(i, :) - T_train(i, :)) ./ M ;
mbe2(i) = sum(T_sim2(i, :) - T_test (i, :)) ./ N ;
disp(['输出:', num2str(i), ' 训练集数据的MBE为：', num2str(mbe1(i))])
disp(['输出:', num2str(i), ' 测试集数据的MBE为：', num2str(mbe2(i))])
end
