function [wf,MAE,RMSE,MRE]=SubclustKMeanRBFSamExtRegTimeCPerr( )
clc;
close all;
clear;
data=xlsread('trainingdata.xlsx','processed data');
A=xlsread('trainingdata.xlsx','sample selection');
B=xlsread('trainingdata.xlsx','test sample number');
SamTrainNum=A(:,1);
SamTestNum=B(:,1);

%-----------数据归一化------------%
data1=[data(:,1:4) data(:,6)]';
[data2,PS]=mapminmax(data1,0,1);
data=data2';
%-----------------------------------%
%选择训练样本和测试样本%
% Z=randperm(61);
% Trrand=Z(1:51);
% Terand=Z(52:61);
train=data(SamTrainNum,:);
test=data(SamTestNum,:);
%-----------------------------------%
[Clust]=subclust(train,0.45);%减聚类,确定节点数及中心
[A B]=size(Clust);
SamNum=51;  %训练样本个数
TestSamNum=10;   %测试样本个数
Overlap=1;    %

%输入输出矩阵
%训练数据
SamIn=train(:,1:4)';
SamOut=train(:,5)';
%预测数据
TestSamIn=test(:,1:4)';
TestSamOut=test(:,5)';

%节点数
InDim=4;  %输入节点
ClusterNum=A;  %隐含节点
%K-均值聚类
[IDX,C,sumd,D]=kmeans(SamIn',A,'start',Clust(:,1:4)) 
%sumd是在一个聚类内部，所有点到中心的聚类和
%D所有点到k个聚类中心的距离
%计算各隐层结点的扩展常数
Centers=C';
AllDistances=dist(Centers',Centers);
Maximun=max(max(sumd));
for i=1:ClusterNum
   AllDistances(i,i)=Maximun+1;
end
Spreads=Overlap*min(AllDistances)';

%用最小二乘法得到权值
Distance=dist(Centers',SamIn);
SpreadsMat=repmat(Spreads,1,SamNum);
HiddenUnitOut=radbas(Distance./SpreadsMat);
HiddenUnitOutEx=[HiddenUnitOut' ones(SamNum,1)]';
W2Ex=SamOut*pinv(HiddenUnitOutEx);
W2=W2Ex(:,1:ClusterNum);
B2=W2Ex(:,ClusterNum+1);

%预测数据输出
TestDistance=dist(Centers',TestSamIn);
TestSpreadsMat=repmat(Spreads,1,TestSamNum);
TestHiddenUnitOut=radbas(TestDistance./TestSpreadsMat); %高斯函数，距离除以宽度，使用的是指数转移函数
[n2,m2]=size(TestSamIn);
for i=1:m2
    TestNNOut(:,i)=W2*TestHiddenUnitOut(:,i)+B2;
end
%反归一化
ReMapTest=mapminmax('reverse',[   TestSamIn
    TestNNOut
  ],PS);
TestNNOut=ReMapTest( 5, : );
ReMapSam=mapminmax('reverse',[   TestSamIn
    TestSamOut
  ],PS);
TestSamOut=ReMapSam( 5, : );
%平均绝对误差
Y=[TestNNOut
    -TestSamOut];
    Ycha=sum(Y);

   MAE=mean (abs(Ycha))
%误差均方根
RMSE=sqrt(mean(Ycha.^2))
%平均相对误差
MRE=mean(abs(Ycha./TestSamOut))

R=corrcoef(TestNNOut,TestSamOut)

A=sum(abs(TestNNOut-mean(TestNNOut))+abs(TestSamOut-mean(TestNNOut))).^2
WIA=1-mean(sum(TestNNOut-TestSamOut).^2)/A

% PE=RMSE./mean(TestSamOut)

f=figure('Visible','on');
plot(TestNNOut,'-r*');
hold on
plot(TestSamOut,':go');
legend('模拟预测值','真实值')
hold off
wf=webfigure(f);
end
%---------------------------------------以上------------------------%