clc
clear
close all
%global grain = 20
%---------------------------------------------------
load input;
T=input(101:150,5);
%數據歸一化處理
[g_data,mx,stdx]=autosc(input);  %？？

%標準化處理
Xmax=max(g_data);
Xmin=min(g_data);
el=ones(150,1);e2=ones(150,5);
%標準化后的數據集g_data[]
g_data=e2-(el*Xmax-g_data)./(el*(Xmax-Xmin));
xi=g_data(1:100,1:4);yi=g_data(1:100,5);

%測試樣本
Xt=g_data(101:150,1:4);
Yt=g_data(101:150,5);
%---------------------------------------------------
% 參數設置
type = 'f';
kernel = 'RBF_kernel';
gam =50;              %3.5655/37.413 Reg3ularization parameter
sig2 =10;            %31.3729/Kernel 20.089parameter (bandwidth in the case of the 'RBF_kernel'
% 模型初始化
model = initlssvm(xi,yi,type,gam,sig2,kernel);  
%costfun = 'rcrossvalidate';
%costfun_args = {xi,yi,2};
%optfun = 'gridsearch';
%model = tunelssvm(model,[],optfun,{},costfun,costfun_args);  
model = robustlssvm(model);  % 訓練　
Yd = simlssvm(model,Xt);    % 回歸
err=Yt-Yd;
MSE=mse(err)  %均值平方誤差mean squared error
SSE=sse(err)  %平方和誤差sum squared error
%Error=(Yd-Yt)./Yd;%相對誤差
%Mean_Error=mean(abs(Error))%相對誤差絕對值的平均值
RMSE1=sqrt(mean((Yt-Yd).^2))%均方根誤差
%---------------------------------------------------
% 結果作圖
Yd=Yd.*(ones(50,1)*(Xmax(1,5)-Xmin(1,5)))+ones(50,1)*Xmin(1,5);
Yd=rescal(Yd,mx(1,5),stdx(1,5));  %???
Et=Yd-T
MSE1=mse(Et)
SSE1=sse(Et)
%load result 
%T=result(:,1);
%Yd=result(:,2);
%Et=Yd-T;

Error=(Yd-T)./Yd
MAXE=max(Et)
MINE=min(Et)
RMSE2=sqrt(mean((Et.^2)))
figure(1)
plot(1:length(T),T,'rd-',1:length(Yd),Yd,'b.:')
%axis([0,50,0.3,1]);
%title('')
legend('實際測量值','LS-SVM模型估計值');
xlabel('樣本個數')
ylabel('組分含量（%）')
%size(0.5,0.5)
figure(2)
%plot(1:length(Yt),Error)
%title('相對誤差曲線')
%xlabel('樣本個數')
%ylabel('相對誤差')
%figure(3)
%hold on
%plot(1:length(Yt),0,'k-')
plot([0;50],[0;0],'k-')
hold on
plot(1:length(Yt),Et)


title('絕對誤差曲線')
xlabel('樣本個數')
ylabel('誤差')