sir, following is the error in my program, here AUC (area under curve) is 0 0 and OPTROCPT also zero why so this...

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Balaji M. Sontakke 2018-4-10

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https://ww2.mathworks.cn/matlabcentral/answers/393881-sir-following-is-the-error-in-my-program-here-auc-area-under-curve-is-0-0-and-optrocpt-also-ze

评论： Balaji M. Sontakke 2018-4-11

   clc
   clear all;
  c=[];
  addpath train;
  addpath test;
  mapping=getmapping(8,'u2');                                 %LBP
         W=[0,0,0,0,0,0,0; ...
            0,1,1,1,1,1,0; ...
            0,1,2,4,2,1,0; ...
            0,1,4,4,4,1,0; ...
            0,1,2,4,2,1,0; ...
            0,1,1,1,1,1,0; ...
            0,0,0,0,0,0,0];
   %%Training images
   for folder_idx = 1 : 10             % no of classes
   for i = 1 : 9                             % no of images per class
   thisfile = fullfile('train', num2str(folder_idx), [num2str(i) '.bmp ']); 
   B = imread(thisfile ); 
   X = double(B);
    X = imresize(X,[60 60],'bilinear');  
    H2=DSLBP(X,mapping,W);
    Gray=X;
    Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
    lpqhist=lpq(Gray,3,1,1,'nh');       
    % imshow(lpqhist);
    a=[H2,lpqhist];
    c=[c;a];
    disp(sprintf('Done',i));
   end 
   end
%%Testing images
d=[];
for folder_idx = 1 : 10     %no of classes
 for i = 1 : 3                     % no of images per class
   thisfile = fullfile('test', num2str(folder_idx), [num2str(i) '.bmp ']); 
   B = imread(thisfile ); 
    X = double(B);
    X = imresize(X,[60 60],'bilinear');  
    H2=DSLBP(X,mapping,W);
    Gray=X;
    Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
    lpqhist=lpq(Gray,3,1,1,'nh');       
    a=[H2,lpqhist];
    d=[d;a];
    disp(sprintf('Done',i));
   end 
   end
P_train=c;
P_test=d;
% %%PCA low dimension reduction
% 
P_train = P_train';
% if classes are 20 then eiganvectors not exceed then 179 
model = perform_pca(P_train,89);                       %rank(P_train)-1
test_features= linear_subspace_projection(P_test, model, 1);        
P_train=model.train';
P_test=test_features';
%%Normalisation
%    P_train=P_train/256;
%    P_test=P_test/256;

%%%%%%%%load label %%%%%%%%%%%%

 train_label=load('train_label.txt');  
 test_label=load('test_label.txt');
predictlabel = knnclassify(P_test, P_train, train_label,3,'euclidean','nearest');
cp = classperf(test_label,predictlabel);
cp.CorrectRate*100
    % Multiclass demo
  disp('_____________Multiclass demo_______________')
  disp('Runing Multiclass confusionmat')
[c_matrix,Result,RefereceResult]= confusion.getMatrix(test_label,predictlabel);
   [FPR, TPR,Thr, AUC, OPTROCPT]  = perfcurve(predictlabel, test_label,1)
% % 
 %plot(TPR,FPR,'--rs','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g','MarkerSize',10)
 figure,
 plot(TPR,FPR,'r-','LineWidth',4);
 xlabel('False positive rate')
 ylabel('True positive rate') 
 title('ROC Curve for Classification ')
 Tbl = table(FPR, TPR,Thr)
% 
% 
%%FAR = FPR = FP/(FP + TN)  and  FRR = FNR = FN/(FN + TP)
[FPR, FNR,Thr]  = perfcurve(predictlabel, test_label,1,'xCrit','FPR','yCrit','FNR','TVals','all')
Thr = Thr/10;
figure,
plot(Thr,FPR,'r--','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g','MarkerSize',10);
 xlabel('Threshold')
 ylabel('False positive rate / FAR') 
 title('ROC Curve for Classification ')
   figure,
   plot(Thr,FNR,'r--','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g','MarkerSize',10);
   xlabel('Threshold')
   ylabel('False Negative rate / FRR') 
   title('ROC Curve for Classification ')
   Tbl = table(FPR, FNR)