testphase.asv

for MLP neural network.

function    [Per(RC) ConfMat{RC} LRExperts{RC} DExperts{RC}] testPhase(WI,WO,PT,TT,NTmp,NumNet)
% PT=NormInData(PT);
for i=1:size(PT,1)
    if NTmp(i,1)==NTmp(i,2)
        PT(i,:)=(-NTmp(i,1)/(NTmp(i,1)))+PT(i,:)./(NTmp(i,1));            
    else
        PT(i,:)=(-NTmp(i,1)/(NTmp(i,2)-NTmp(i,1))+PT(i,:)./(NTmp(i,2)-NTmp(i,1)));
    end
end

PT(size(PT,1)+1,:)=1;%+1 is for bias
for NC=1:NumNet
    for i=1:size(PT,2)
        Y1=(USF(PT(:,i)'*WI{NC,1}));        
        Y1=[Y1 1];
        try
            Yout(:,i)=(USF(Y1*WO{NC,1}))';
        catch
            Yout(:,i)=(USF(Y1*WO{NC,1}))';
        end
    end
    [m,nn]=max(Yout);
    [m1,nn1]=max(TT);
    counter1=0;
    for i=1:size(TT,2)
        if nn(1,i)==nn1(1,i) 
            counter1=counter1+1;
        end
%%   confusion matrix
        try
            confmat{nt,nn}(size(confmat{nt,nn},2)+1)=K-fix(K/(size(PT,2)/10))*(size(PT,2)/10); %K;
        catch
            confmat{nt,nn}(1)=K-fix(K/(size(PT,2)/10))*(size(PT,2)/10); %K;        
        end
%%   Learning Rate oF Each Expert
        for NC=1:NumNet
            if nn(1,i)==nn1(1,i) 
                try
                    DExperts{NC,nn1}(size(DExperts{NC,nn1},2)+1)=K-fix(K/(size(PT,2)/10))*(size(PT,2)/10); %K;
                catch
                    DExperts{NC,nn1}(1)=K-fix(K/(size(PT,2)/10))*(size(PT,2)/10); %K;        
                end
            end
        end
    end
    NC
    percentage1=100*counter1/size(TT,2)
end