%% CNN-GRU多变量回归预测
%% 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
P_train = xlsread('data','training set','B2：G191')';
T_train= xlsread('data','training set','H2：H191')';
% 测试集——44个样本
P_test=xlsread('data','test set','B2:G45')';
T_test=xlsread('data','test set','H2:H45')';
%% 划分训练集和测试集
M = size(P_train, 2);
N = size(P_test, 2);
%% 数据归一化
[inputn_train,inputps]=mapminmax(P_train);
inputn_test=mapminmax('apply',P_test,inputps);
[outputn_train,outputps]=mapminmax(T_train);
outputn_test=mapminmax('apply',T_test,outputps);
%% 创建元胞或向量，长度为训练集大小；
XrTrain = cell(size(inputn_train,2),1);
YrTrain = zeros(size(outputn_train,2),1);
for i=1:size(inputn_train,2)
XrTrain{i,1} = inputn_train(:,i);
YrTrain(i,1) = outputn_train(:,i);
end
% 创建元胞或向量，长度为测试集大小；
XrTest = cell(size(inputn_test,2),1);
YrTest = zeros(size(outputn_test,2),1);
for i=1:size(P_test,2)
XrTest{i,1} = inputn_test(:,i);
YrTest(i,1) = outputn_test(:,i);
end
%% 优化算法参数设置
SearchAgents_no = 8; % 数量
Max_iteration = 5; % 最大迭代次数
dim = 3; % 优化参数个数
lb = [1e-3,10 1e-4]; % 参数取值下界(学习率，隐藏层节点，正则化系数)
ub = [1e-2, 30,1e-1]; % 参数取值上界(学习率，隐藏层节点，正则化系数)
fitness = @(x)fical(x);
[Best_score,Best_pos,curve]=SSA(SearchAgents_no,Max_iteration,lb ,ub,dim,fitness)
Best_pos(1, 2) = round(Best_pos(1, 2));
best_hd = Best_pos(1, 2); % 最佳隐藏层节点数
best_lr= Best_pos(1, 1);% 最佳初始学习率
best_l2 = Best_pos(1, 3);% 最佳L2正则化系数
%% 创建混合CNN-GRU网络架构
% 输入特征维度
numFeatures = size(P_train,1);
% 输出特征维度
numResponses = 1;
FiltZise =4;
% 创建"CNN-GRU"模型
layers = [...
% 输入特征
sequenceInputLayer([numFeatures 1 1],'Name','input')
sequenceFoldingLayer('Name','fold')
% CNN特征提取
convolution2dLayer([FiltZise 1],32,'Padding','same','WeightsInitializer','he','Name','conv','DilationFactor',1);
batchNormalizationLayer('Name','bn')
reluLayer('Name','relu')
averagePooling2dLayer(1,'Stride',FiltZise,'Name','pool1')
% 展开层
sequenceUnfoldingLayer('Name','unfold')
% 平滑层
flattenLayer('Name','flatten')
% GRU特征学习
gruLayer(128,'Name','gru1','RecurrentWeightsInitializer','He','InputWeightsInitializer','He')
% GRU输出
gruLayer(best_hd,'OutputMode',"last",'Name','bil4','RecurrentWeightsInitializer','He','InputWeightsInitializer','He')
dropoutLayer(0.25,'Name','drop3')
% 全连接层
fullyConnectedLayer(numResponses,'Name','fc')
regressionLayer('Name','output') ];
layers = layerGraph(layers);
layers = connectLayers(layers,'fold/miniBatchSize','unfold/miniBatchSize');
%% CNN-GRU训练选项
% 批处理样本
MiniBatchSize =128;
% 最大迭代次数
MaxEpochs = 500;
% 一些参数调整
if gpuDeviceCount>0
mydevice = 'gpu';
else
mydevice = 'cpu';
end
options = trainingOptions( 'adam', ...
'MaxEpochs',500, ...
'MiniBatchSize',128,...
'GradientThreshold',1, ...
'InitialLearnRate',best_lr, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropPeriod',20, ...
'LearnRateDropFactor',0.8, ...
'L2Regularization',best_l2,...
'Verbose',false, ...
'ExecutionEnvironment',mydevice,...
'Plots','training-progress');
%% 训练混合网络
% rng(0);
%% 训练
net = trainNetwork(XrTrain,YrTrain,layers,options);
%% 预测
Y1Pred = predict(net,XrTrain,"ExecutionEnvironment",mydevice,"MiniBatchSize",numFeatures);
Y2Pred = predict(net,XrTest,"ExecutionEnvironment",mydevice,"MiniBatchSize",numFeatures);
%% 结果
Y1Pred =double(Y1Pred');
Y2Pred =double(Y2Pred');
%% 反归一化
output_train1=mapminmax('reverse',Y1Pred,outputps);
T_sim1=double(output_train1);
output_test1=mapminmax('reverse',Y2Pred,outputps);
T_sim2=double(output_test1);
%% 评价
figure
plot(1 : length(curve), curve,'linewidth',1.5);
title('SSA-CNN-GRU', 'FontSize', 10);
xlabel('迭代次数', 'FontSize', 10);
ylabel('适应度值mse', 'FontSize', 10);
grid off
%% 测试集结果
figure;
plotregression(T_test,T_sim2,['回归图']);
figure;
ploterrhist(T_test-T_sim2,['误差直方图']);
%% 均方根误差 RMSE
error1 = sqrt(sum((T_sim1 - T_train).^2)./M);
error2 = sqrt(sum((T_test - T_sim2).^2)./N);
%%
%决定系数
R1 = 1 - norm(T_train - T_sim1)^2 / norm(T_train - mean(T_train))^2;
R2 = 1 - norm(T_test - T_sim2)^2 / norm(T_test - mean(T_test ))^2;
%%
%均方误差 MSE
mse1 = sum((T_sim1 - T_train).^2)./M;
mse2 = sum((T_sim2 - T_test).^2)./N;
%%
%RPD 剩余预测残差
SE1=std(T_sim1-T_train);
RPD1=std(T_train)/SE1;
SE=std(T_sim2-T_test);
RPD2=std(T_test)/SE;
%% 平均绝对误差MAE
MAE1 = mean(abs(T_train - T_sim1));
MAE2 = mean(abs(T_test - T_sim2));
%% 平均绝对百分比误差MAPE
MAPE1 = mean(abs((T_train - T_sim1)./T_train));
MAPE2 = mean(abs((T_test - T_sim2)./T_test));
%% 训练集绘图
figure
%plot(1:M,T_train,'r-*',1:M,T_sim1,'b-o','LineWidth',1)
plot(1:M,T_train,'r-*',1:M,T_sim1,'b-o','LineWidth',1.5)
legend('真实值','SSA-CNN-GRU预测值')
xlabel('预测样本')
ylabel('预测结果')
string={'训练集预测结果对比';['(R^2 =' num2str(R1) ' RMSE= ' num2str(error1) ' MSE= ' num2str(mse1) ' RPD= ' num2str(RPD1) ')' ]};
title(string)
%% 预测集绘图
figure
plot(1:N,T_test,'r-*',1:N,T_sim2,'b-o','LineWidth',1.5)
legend('真实值','SSA-CNN-GRU预测值')
xlabel('预测样本')
ylabel('预测结果')
string={'测试集预测结果对比';['(R^2 =' num2str(R2) ' RMSE= ' num2str(error2) ' MSE= ' num2str(mse2) ' RPD= ' num2str(RPD2) ')']};
title(string)
%% 测试集误差图
figure
ERROR3=T_test-T_sim2;
plot(T_test-T_sim2,'b-*','LineWidth',1.5)
xlabel('测试集样本编号')
ylabel('预测误差')
title('测试集预测误差')
grid on;
legend('SSA-CNN-GRU预测输出误差')
%% 绘制线性拟合图
%% 训练集拟合效果图
figure
plot(T_train,T_sim1,'*r');
xlabel('真实值')
ylabel('预测值')
string = {'训练集效果图';['R^2_c=' num2str(R1) ' RMSEC=' num2str(error1) ]};
title(string)
hold on ;h=lsline;
set(h,'LineWidth',1,'LineStyle','-','Color',[1 0 1])
%% 预测集拟合效果图
figure
plot(T_test,T_sim2,'ob');
xlabel('真实值')
ylabel('预测值')
string1 = {'测试集效果图';['R^2_p=' num2str(R2) ' RMSEP=' num2str(error2) ]};
title(string1)
hold on ;h=lsline();
set(h,'LineWidth',1,'LineStyle','-','Color',[1 0 1])
%% 求平均
R3=(R1+R2)./2;
error3=(error1+error2)./2;
%% 总数据线性预测拟合图
tsim=[T_sim1,T_sim2]';
S=[T_train,T_test]';
figure
plot(S,tsim,'ob');
xlabel('真实值')
ylabel('预测值')
string1 = {'所有样本拟合预测图';['R^2_p=' num2str(R3) ' RMSEP=' num2str(error3) ]};
title(string1)
hold on ;h=lsline();
set(h,'LineWidth',1,'LineStyle','-','Color',[1 0 1])
%% 打印出评价指标
disp(['-----------------------误差计算--------------------------'])
disp(['评价结果如下所示：'])
disp(['平均绝对误差MAE为：',num2str(MAE2)])
disp(['均方误差MSE为： ',num2str(mse2)])
disp(['均方根误差RMSEP为： ',num2str(error2)])
disp(['决定系数R^2为： ',num2str(R2)])
disp(['剩余预测残差RPD为： ',num2str(RPD2)])
disp(['平均绝对百分比误差MAPE为： ',num2str(MAPE2)])
grid
