function main()
%基于OLS(正交最小二乘學習)的RBF網設計算法
tic,
SamNum=100;  %訓練樣本數
TestSamNum=101;  %測試樣本數
SP=0.6;  %隱節點擴展常數
ErrorLimit=0.9;

%根據樣本目標函數獲得樣本輸入/輸出
rand('state',sum(100*clock))  %產生均勻分布的隨機矩陣，元素0.0－1.0
NoiseVar=0.1;
Noise=NoiseVar*randn(1,SamNum);  %產生正態分布的隨機矩陣
SamIn=8*rand(1,SamNum)-4;
SamOutNoise=1.1*(1-SamIn+2*SamIn.^2).*exp(-SamIn.^2/2);
SamOut=SamOutNoise+Noise;

TestSamIn=-4:0.08:4;
TestSamOut=1.1*(1-TestSamIn+2*TestSamIn.^2).*exp(-TestSamIn.^2/2);

figure
hold on
grid
plot(SamIn,SamOut,'r+')
plot(TestSamIn,TestSamOut,'b-')
xlabel('Inputx');
ylabel('Outputy');

[InDim,MaxUnitNum]=size(SamIn); %樣本輸入維數和最大允許隱節點數

%計算隱節點輸出陣
Distance=dist(SamIn',SamIn);
HiddenUnitOut=radbas(Distance/SP);

PosSelected=[];
VectorsSelected=[];
HiddenUnitSelected=[];
ErrHistory=[];  %用于記錄每次增加隱節點后的訓練誤差
HiddenUnitOutSelected=[];
VectorsSelectFrom=HiddenUnitOut;
dd=sum((SamOut.*SamOut)')';
for k=1:MaxUnitNum
    %計算各隱節點輸出矢量與目標輸出矢量的夾角平方值
    PP=sum(VectorsSelectFrom.*VectorsSelectFrom)';
    Denominator=dd*PP';
    [xxx,SelectedNum]=size(PosSelected);
    if SelectedNum>0,
        [lin,xxx]=size(Denominator);
        Denominator(:,PosSelected)=ones(lin,1);
    end
    Angle=((SamOut*VectorsSelectFrom).^2)./Denominator;%1*100矩陣
    
    %選擇具有最大投影的矢量,得到相應的數據中心
    [value,pos]=max(Angle);
    PosSelected=[PosSelected pos];
    
    %計算RBF網訓練誤差
    HiddenUnitOutSelected=[HiddenUnitOutSelected; HiddenUnitOut(pos,:)];
    %HiddenUnitOutEx=[HiddenUnitOutSelected;ones(1,SamNum)];
   % W2Ex=SamOut*pinv(HiddenUnitOutEx);
   HiddenUnitOutEx=HiddenUnitOutSelected;
   W2Ex=SamOut*pinv(HiddenUnitOutEx);
    W2=W2Ex(:,1:k);
    %B2=W2Ex(:,k+1);
    NNOut=W2*HiddenUnitOutSelected;
    SSE=sumsqr(SamOut-NNOut)
    
    %記錄每次增加隱節點后的訓練誤差
    ErrHistory=[ErrHistory SSE];
    
    if SSE<ErrorLimit,break,end
    
    %作Gram-Schmidt正交化
    NewVector=VectorsSelectFrom(:,pos);
    ProjectionLen=NewVector'*VectorsSelectFrom/(NewVector'*NewVector);
    VectorsSelectFrom=VectorsSelectFrom-NewVector*ProjectionLen;
end
UnitCenters=SamIn(PosSelected);
[m,n]=size(UnitCenters);
m
n
%測試
TestDistance=dist(UnitCenters',TestSamIn);
TestHiddenUnitOut=radbas(TestDistance/SP);
TestNNOut=W2*TestHiddenUnitOut;

hold on
plot(TestSamIn,TestNNOut,'k-')
k
UnitCenters
W2

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