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brain tumor code errors

3 vues (au cours des 30 derniers jours)
Faisal Al-Wazir
Faisal Al-Wazir le 31 Mar 2023
greetings, my doctor gave me this code and made me have two sets of data
1-roi_imdata_n= data for brains without tumors = 1x67
2-roi_imdata_a= data for brains with tumors =1x100
I keep getting errors and I don't know what are the issues
load ('roi_imdata_n');
load ('roi_imdata_a');
%Normal = 67;
%Normal_learn = 47;
%Abnormal = 100;
%Abnormal_learn = 70;
%Nim = 167;
%train = 117;
%test = 50;
imdsTrain = {roi_imdata_n{1,1:Normal_learn},roi_imdata_a{1,1:Abnormal_learn}}';
Ytrain = [zeros(Normal_learn,1);ones(Abnormal_learn,1)];
Ylabels = {};
for i = 1:train
if Ytrain(i,1) == 0;
Ylabels{i,1} = 'false';
else
Ylabels{i,1} = 'true';
end
end
Yclass = categorical(Ylabels);
dTrain = table(imdsTrain,Yclass);
imdsValidation= {roi_imdata_n{1,(Normal_learn+1):Normal},roi_imdata_a{1,(Abnormal_learn+1):Abnormal}}';
Ytest = [zeros(Normal-Normal_learn,1);ones(Abnormal-Abnormal_learn,1)];
Ylabels = {};
for i = 1:test
if Ytest(i,1) == 0;
Ylabels{i,1} = 'false';
else
Ylabels{i,1} = 'true';
end
end
Yclass = categorical(Ylabels);
dTest = table(imdsValidation,Yclass);
layers = [
imageInputLayer([32 32 1])
convolution2dLayer(32,32, 'Padding' , 'same' )
batchNormalizationLayer
reluLayer
maxPooling2dLayer(5, 'Stride' ,2)
convolution2dLayer(32,32, 'Padding' , 'same' )
batchNormalizationLayer
reluLayer
maxPooling2dLayer(5, 'Stride' ,2)
convolution2dLayer(32,32, 'Padding' , 'same' )
batchNormalizationLayer
reluLayer
maxPooling2dLayer(5, 'Stride' ,2)
convolution2dLayer(32,32, 'Padding' , 'same' )
batchNormalizationLayer
reluLayer
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];
% Dt = gpuDeviceTable;
% D = gpuDevice(Dt{1,1});
% reset(D);
options = trainingOptions( 'sgdm' , ...
'Momentum', 0.6, ...
'L2Regularization', 0.1, ...
'InitialLearnRate' ,0.01, ...
'GradientThresholdMethod', 'global-l2norm', ...
'MaxEpochs' ,6, ...
'Shuffle' , 'every-epoch' , ...
'LearnRateSchedule', 'piecewise', ...
'LearnRateDropFactor', 0.2, ...
'LearnRateDropPeriod', 5, ...
'ValidationData' , dTest, ...
'ValidationFrequency' , 24, ...
'ExecutionEnvironment','cpu',...
'Verbose',false, ...
'MiniBatchSize', 3, ...
'BatchNormalizationStatistics', 'moving', ...
'OutputNetwork', 'best-validation-loss', ...
'Plots' , 'Training-Progress' );
net = trainNetwork(dTrain,layers,options);
save('cnn_trained.mat','net','dTrain','dTest','Ytrain','Ytest')
% delete(findall(0));
then he gave us a fill called LAST.m
% delete(findall(0));
clear all; close all; clc;
clear memory;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%% Loading Database %%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
load ('roi_imdata_n');
load ('roi_imdata_a');
Normal = 67 ;
Normal_learn = 47 ;
Abnormal = 100;
Abnormal_learn = 70 ;
Nim = 167;
train = 117;
test = 50;
% Defining Normal learn & test images
for i = 1:Nim;
if i <= Normal;
all_images{1,i}=roi_imdata_n{1,i};
else
all_images{1,i} =roi_imdata_a{1,i-(Normal)};
end
end
%Deep Learning directly on roi
dl_classify;
load("cnn_trained.mat")
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%% Features Extraction %%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
all_feat = [];
for i = 1:(Nim)
ai = all_images{1,i}; % Normilaization steps
t = double(reshape((ai),1,numel(ai)));
c=1;
% FO Statistical Features
all_feat(i,c) = mean(t);
c=c+1;
% all_feat(i,2) = std(t);
% all_feat(i,3) = std(t).^2;
all_feat(i,c) = mode(t);
c=c+1;
all_feat(i,c) = median(t);
c=c+1;
all_feat(i,c) = quantile(t,0.1);
c=c+1;
all_feat(i,c) = quantile(t,0.2);
c=c+1;
all_feat(i,c) = quantile(t,0.3);
c=c+1;
all_feat(i,c) = quantile(t,0.4);
c=c+1;
all_feat(i,c) = quantile(t,0.5);
c=c+1;
all_feat(i,c) = quantile(t,0.6);
c=c+1;
all_feat(i,c) = quantile(t,0.7);
c=c+1;
all_feat(i,c) = quantile(t,0.8);
c=c+1;
all_feat(i,c) = quantile(t,0.9);
% HO Features (Hist & GLCM)
% Histogram Feature
hist_levels= 16;
imhist_1= zeros(Nim, hist_levels);
imhist_1 = imhist(ai,hist_levels);
% Normalization
% imhist_1= imhist_1/sum(sum(imhist_1));
%
% % First Order Uniformity
% all_feat(i,[15:30]) = sum((imhist_1).^2);
%
% % First Order Entropy
% all_feat(i,[31:46]) = sum(imhist_1.*log(imhist_1+0.001));
%
% % GLCM Feature
% GLCM_levels= 16;
% GLCM= zeros(Nim, GLCM_levels, GLCM_levels);
% GLCM = graycomatrix(ai,'NumLevels', GLCM_levels, 'GrayLimits', [], 'Offset', [-2 2]);
%
% % Normalization
% GLCM= GLCM/sum(sum(GLCM));
%
% % First Order Uniformity
% all_feat(i,[47:62]) = sum((GLCM).^2);
%
% % First Order Entropy
% all_feat(i,[63:78]) = sum(GLCM.*log(GLCM+0.001));
end
% Init feature vectors
feat_learn = [all_feat(1:Normal_learn,:);all_feat((Normal+1):(Normal+Abnormal_learn),:)];
feat_test = [all_feat((Normal_learn+1):Normal,:);all_feat((Normal+Abnormal_learn+1):(Nim),:)];
learn_class = [zeros(Normal_learn,1);ones(Abnormal_learn,1)];
test_class = [zeros((Normal - Normal_learn),1);ones((Abnormal - Abnormal_learn),1)];
size1= size(feat_learn);
Nfeat= size1(2);
Pval= zeros(Nfeat,1);
for k1=1:Nfeat
[H,P] = ttest2(feat_learn(1:Normal_learn,k1),feat_learn(((Normal_learn+1):end),k1));
Pval(k1)= P;
end
figure(1)
plot(Pval);
textout= sprintf('T-Test: Number of useful features (P-Value < 0.05) = %d out of %d',sum(Pval<0.05), Nfeat);
disp(textout);
[h,w] = size(all_feat);
Hypothesis = zeros(w,1); P_value = zeros(w,1); Selected_Feature_ttest = zeros(w,2);
for i = 1 : w
[Hypothesis(i), P_value(i)] = ttest2(all_feat(1:Normal,i) , all_feat((Normal)+1:h,i));
Selected_Feature_ttest(i,1) = Hypothesis(i);
Selected_Feature_ttest(i,2) = P_value(i);
end
[P_val_sortT_test, IndxTest]= sort(Selected_Feature_ttest(:,2)); % IndxTest: represents the number of accepted features.
a = sum(P_val_sortT_test < 0.05);
IndxTest = sort(IndxTest(1:a,1));
Finalfeatures_ttest = all_feat(:,IndxTest(1:a));
feat_learn = [Finalfeatures_ttest(1:Normal_learn,:);Finalfeatures_ttest((Normal+1):(Normal+Abnormal_learn),:)];
feat_test= [Finalfeatures_ttest((Normal_learn+1):Normal,:);Finalfeatures_ttest((Normal+Abnormal_learn+1):(Nim),:)];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%% Training Classifiers %%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% First Classifier:
svmstruct_1 = fitcsvm(feat_learn,learn_class,'KernelFunction','rbf');
answer1 = predict(svmstruct_1,feat_learn);
[X,Y,T,AUC1] = perfcurve(learn_class,answer1,1);
figure, plot(X,Y);
xlabel('False positive rate'); ylabel('True positive rate');
title('ROC for classification by SVM - rbf Kernel - Training Group');
% Confusion matrix for first classifier:
conMat1 = confusionmat(learn_class,answer1)/train;
TP1= conMat1(1,1);
TN1= conMat1(2,2);
FP1= conMat1(1,2);
FN1= conMat1(2,1);
Sens1= TP1/(TP1+FN1);
Spec1= TN1/(TN1+FP1);
PPV1= TP1/(TP1+FP1);
NPV1= TN1/(TN1+FN1);
Acc1= (TP1+TN1)/(TP1+TN1+FP1+FN1);
error1 = sum(learn_class~= answer1)/train;
AUC1;
check1 = Acc1+error1;
% Second Classifier:
svmstruct_2 = fitcsvm(feat_learn,learn_class,'KernelFunction','polynomial');
answer2 = predict(svmstruct_2,feat_learn);
[X,Y,T,AUC2] = perfcurve(learn_class,answer2,1);
figure, plot(X,Y);
xlabel('False positive rate'); ylabel('True positive rate');
title('ROC for classification by SVM - polynomial Kernel - Training Group');
% Confusion matrix for second classifier:
conMat2 = confusionmat(learn_class,answer2)/train;
TP2= conMat2(1,1);
TN2= conMat2(2,2);
FP2= conMat2(1,2);
FN2= conMat2(2,1);
Sens2= TP2/(TP2+FN2);
Spec2= TN2/(TN2+FP2);
PPV2= TP2/(TP2+FP2);
NPV2= TN2/(TN2+FN2);
Acc2= (TP2+TN2)/(TP2+TN2+FP2+FN2);
error2 = sum(learn_class~= answer2)/train;
AUC2;
check2 = Acc2+error2;
%Third Classifier Linear SVM
svmstruct_3 = fitcsvm(feat_learn,learn_class,'KernelFunction','linear');
answer3 = predict(svmstruct_3,feat_learn);
[X,Y,T,AUC3] = perfcurve(learn_class,answer3,1);
figure, plot(X,Y);
xlabel('False positive rate'); ylabel('True positive rate');
title('ROC for classification by SVM - linear Kernel - Training Group');
% Confusion matrix for fourth classifier:
conMat3 = confusionmat(learn_class,answer3)/train;
TP3= conMat3(1,1);
TN3= conMat3(2,2);
FP3= conMat3(1,2);
FN3= conMat3(2,1);
Sens3= TP3/(TP3+FN3);
Spec3= TN3/(TN3+FP3);
PPV3= TP3/(TP3+FP3);
NPV3= TN3/(TN3+FN3);
Acc3= (TP3+TN3)/(TP3+TN3+FP3+FN3);
error3 = sum(learn_class~= answer3)/train;
AUC3;
check3 = Acc3+error3;
% Fourth Classifier:
%%%%%%%CNN%%%%%%%%%%%%%%%%%%%%%%
YPred_tr = classify(net,dTrain);
YPred_n = [];
for i =1:train
if YPred_tr(i,1) == 'true';
YPred_n(i,1) = 1;
else
YPred_n(i,1) = 0;
end
end
YPred_tr = YPred_n;
[X,Y,T,AUC4] = perfcurve(Ytrain,YPred_tr,1);
figure, plot(X,Y)
xlabel('False positive rate'); ylabel('True positive rate')
title('ROC for classification by CNN - Train Group')
% Confusion matrix for trainng CNN classifier:
conMat4 = confusionmat(Ytrain,YPred_tr)/test;
TP4= conMat4(1,1);
TN4= conMat4(2,2);
FP4= conMat4(1,2);
FN4= conMat4(2,1);
Sens4= TP4/(TP4+FN4);
Spec4= TN4/(TN4+FP4);
PPV4= TP4/(TP4+FP4);
NPV4= TN4/(TN4+FN4);
Acc4= (TP4+TN4)/(TP4+TN4+FP4+FN4);
error4= sum(YPred_tr ~= Ytrain)/numel(Ytrain);
AUC4;
check4 = Acc4+error4;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Fifth Classifier:
knnstruct_1 = fitcknn(feat_learn,learn_class,'NumNeighbors',1);
answer5 = predict(knnstruct_1,feat_learn);
[X,Y,T,AUC5] = perfcurve(learn_class,answer5,1);
figure, plot(X,Y);
xlabel('False positive rate'); ylabel('True positive rate');
title('ROC for classification by KNN - 1 - Training Group');
% Confusion matrix for fifth classifier:
conMat5 = confusionmat(learn_class,answer5)/train;
TP5= conMat5(1,1);
TN5= conMat5(2,2);
FP5= conMat5(1,2);
FN5= conMat5(2,1);
Sens5= TP5/(TP5+FN5);
Spec5= TN5/(TN5+FP5);
PPV5= TP5/(TP5+FP5);
NPV5= TN5/(TN5+FN5);
Acc5= (TP5+TN5)/(TP5+TN5+FP5+FN5);
error5 = sum(learn_class~= answer5)/train;
AUC5;
check5 = Acc5+error5;
% Sixth Classifier:
knnstruct_2 = fitcknn(feat_learn,learn_class,'NumNeighbors',2);
answer6 = predict(knnstruct_2,feat_learn);
[X,Y,T,AUC6] = perfcurve(learn_class,answer6,1);
figure, plot(X,Y);
xlabel('False positive rate'); ylabel('True positive rate');
title('ROC for classification by KNN - 2 - Training Group');
% Confusion matrix for sixth classifier:
conMat6 = confusionmat(learn_class,answer6)/train;
TP6= conMat6(1,1);
TN6= conMat6(2,2);
FP6= conMat6(1,2);
FN6= conMat6(2,1);
Sens6= TP6/(TP6+FN6);
Spec6= TN6/(TN6+FP6);
PPV6= TP6/(TP6+FP6);
NPV6= TN6/(TN6+FN6);
Acc6= (TP6+TN6)/(TP6+TN6+FP6+FN6);
error6 = sum(learn_class~= answer6)/train;
AUC6;
check6 = Acc6+error6;
% Seventh Classifier:
knnstruct_3 = fitcknn(feat_learn,learn_class,'NumNeighbors',3);
answer7 = predict(knnstruct_3,feat_learn);
[X,Y,T,AUC7] = perfcurve(learn_class,answer7,1);
figure, plot(X,Y);
xlabel('False positive rate'); ylabel('True positive rate');
title('ROC for classification by KNN - 3 - Training Group');
% Confusion matrix for seventh classifier:
conMat7 = confusionmat(learn_class,answer7)/train;
TP7= conMat7(1,1);
TN7= conMat7(2,2);
FP7= conMat7(1,2);
FN7= conMat7(2,1);
Sens7= TP7/(TP7+FN7);
Spec7= TN7/(TN7+FP7);
PPV7= TP7/(TP7+FP7);
NPV7= TN7/(TN7+FN7);
Acc7= (TP7+TN7)/(TP7+TN7+FP7+FN7);
error7 = sum(learn_class~= answer7)/train;
AUC7;
check7 = Acc7+error7;
% Eighth Classifier:
knnstruct_4 = fitcknn(feat_learn,learn_class,'NumNeighbors',4);
answer8 = predict(knnstruct_4,feat_learn);
[X,Y,T,AUC8] = perfcurve(learn_class,answer8,1);
figure, plot(X,Y);
xlabel('False positive rate'); ylabel('True positive rate');
title('ROC for classification by KNN - 4 - Training Group');
% Confusion matrix for eighth classifier:
conMat8 = confusionmat(learn_class,answer8)/train;
TP8= conMat8(1,1);
TN8= conMat8(2,2);
FP8= conMat8(1,2);
FN8= conMat8(2,1);
Sens8= TP8/(TP8+FN8);
Spec8= TN8/(TN8+FP8);
PPV8= TP8/(TP8+FP8);
NPV8= TN8/(TN8+FN8);
Acc8= (TP8+TN8)/(TP8+TN8+FP8+FN8);
error8 = sum(learn_class~= answer8)/train;
AUC8;
check8 = Acc8+error8;
% Ninth Classifier:
knnstruct_5 = fitcknn(feat_learn,learn_class,'NumNeighbors',5);
answer9 = predict(knnstruct_5,feat_learn);
[X,Y,T,AUC9] = perfcurve(learn_class,answer9,1);
figure, plot(X,Y);
xlabel('False positive rate'); ylabel('True positive rate');
title('ROC for classification by KNN - 5 - Training Group');
% Confusion matrix for ninth classifier:
conMat9 = confusionmat(learn_class,answer9)/train;
TP9= conMat9(1,1);
TN9= conMat9(2,2);
FP9= conMat9(1,2);
FN9= conMat9(2,1);
Sens9= TP9/(TP9+FN9);
Spec9= TN9/(TN9+FP9);
PPV9= TP9/(TP9+FP9);
NPV9= TN9/(TN9+FN9);
Acc9= (TP9+TN9)/(TP9+TN9+FP9+FN9);
error9 = sum(learn_class~= answer9)/train;
AUC9;
check9 = Acc9+error9;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%% Testing Classifiers %%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Testing SVM - rbf Classifier:
answer_1 = predict(svmstruct_1,feat_test);
[X,Y,T,AUC_1] = perfcurve(test_class,answer_1,1);
figure, plot(X,Y)
xlabel('False positive rate'); ylabel('True positive rate')
title('ROC for classification by SVM - rbf Kernel - Testing Group')
% Confusion matrix for first testing:
conMat_1 = confusionmat(test_class,answer_1)/test;
TP_1= conMat_1(1,1);
TN_1= conMat_1(2,2);
FP_1= conMat_1(1,2);
FN_1= conMat_1(2,1);
Sens_1= TP_1/(TP_1+FN_1);
Spec_1= TN_1/(TN_1+FP_1);
PPV_1= TP_1/(TP_1+FP_1);
NPV_1= TN_1/(TN_1+FN_1);
Acc_1= (TP_1+TN_1)/(TP_1+TN_1+FP_1+FN_1);
error_1 = sum(test_class~= answer_1)/test;
AUC_1;
check_1 = Acc_1+error_1;
% Testing SVM - polynomial Classifier:
answer_2 = predict(svmstruct_2,feat_test);
[X,Y,T,AUC_2] = perfcurve(test_class,answer_2,1);
figure, plot(X,Y)
xlabel('False positive rate'); ylabel('True positive rate')
title('ROC for classification by SVM - polynomial Kernel - Testing Group')
% Confusion matrix for second testing:
conMat_2 = confusionmat(test_class,answer_2)/test;
TP_2= conMat_2(1,1);
TN_2= conMat_2(2,2);
FP_2= conMat_2(1,2);
FN_2= conMat_2(2,1);
Sens_2= TP_2/(TP_2+FN_2);
Spec_2= TN_2/(TN_2+FP_2);
PPV_2= TP_2/(TP_2+FP_2);
NPV_2= TN_2/(TN_2+FN_2);
Acc_2= (TP_2+TN_2)/(TP_2+TN_2+FP_2+FN_2);
error_2 = sum(test_class~= answer_2)/test;
AUC_2;
check_2 = Acc_2+error_2;
% Testing SVM - linear Classifier:
answer_3 = predict(svmstruct_3,feat_test);
[X,Y,T,AUC_3] = perfcurve(test_class,answer_3,1);
figure, plot(X,Y)
xlabel('False positive rate'); ylabel('True positive rate')
title('ROC for classification by SVM - linear Kernel - Testing Group')
% Confusion matrix for second testing:
conMat_3 = confusionmat(test_class,answer_3)/test;
TP_3= conMat_3(1,1);
TN_3= conMat_3(2,2);
FP_3= conMat_3(1,2);
FN_3= conMat_3(2,1);
Sens_3= TP_3/(TP_3+FN_3);
Spec_3= TN_3/(TN_3+FP_3);
PPV_3= TP_3/(TP_3+FP_3);
NPV_3= TN_3/(TN_3+FN_3);
Acc_3= (TP_3+TN_3)/(TP_3+TN_3+FP_3+FN_3);
error_3 = sum(test_class~= answer_3)/test;
AUC_3;
check_3 = Acc_3+error_3;
%Testing CNN Classifier:
YPred_ts = classify(net,dTest);
YPred_n = [];
for i =1:test
if YPred_ts(i,1) == 'true';
YPred_n(i,1) = 1;
else
YPred_n(i,1) = 0;
end
end
YPred_ts = YPred_n;
[X,Y,T,AUC_4] = perfcurve(Ytest,YPred_ts,1);
figure, plot(X,Y)
xlabel('False positive rate'); ylabel('True positive rate')
title('ROC for classification by CNN - Test Group')
% Confusion matrix for testing CNN classifier:
conMat_4 = confusionmat(Ytest,YPred_ts)/test;
TP_4= conMat_4(1,1);
TN_4= conMat_4(2,2);
FP_4= conMat_4(1,2);
FN_4= conMat_4(2,1);
Sens_4= TP_4/(TP_4+FN_4);
Spec_4= TN_4/(TN_4+FP_4);
PPV_4= TP_4/(TP_4+FP_4);
NPV_4= TN_4/(TN_4+FN_4);
Acc_4= (TP_4+TN_4)/(TP_4+TN_4+FP_4+FN_4);
error_4= sum(YPred_ts ~= Ytest)/numel(Ytest);
AUC_4;
check_4 = Acc_4+error_4;
% Testing KNN-1 Classifier:
answer_5 = predict(knnstruct_1,feat_test);
[X,Y,~,AUC_5] = perfcurve(test_class,answer_5,1);
figure, plot(X,Y)
xlabel('False positive rate'); ylabel('True positive rate')
title('ROC for classification by KNN - 1 - Test Group')
% Confusion matrix for testing KNN-1 classifier:
conMat_5 = confusionmat(test_class,answer_5)/test;
TP_5= conMat_5(1,1);
TN_5= conMat_5(2,2);
FP_5= conMat_5(1,2);
FN_5= conMat_5(2,1);
Sens_5= TP_5/(TP_5+FN_5);
Spec_5= TN_5/(TN_5+FP_5);
PPV_5= TP_5/(TP_5+FP_5);
NPV_5= TN_5/(TN_5+FN_5);
Acc_5= (TP_5+TN_5)/(TP_5+TN_5+FP_5+FN_5);
error_5 = sum(test_class~= answer_5)/test;
AUC_5;
check_5 = Acc_5+error_5;
% Testing KNN-2 Classifier:
answer_6 = predict(knnstruct_2,feat_test);
[X,Y,T,AUC_6] = perfcurve(test_class,answer_6,1);
figure, plot(X,Y)
xlabel('False positive rate'); ylabel('True positive rate')
title('ROC for classification by KNN - 2 - Test Group')
% Confusion matrix for testing KNN-2 classifier:
conMat_6 = confusionmat(test_class,answer_6)/test;
TP_6= conMat_6(1,1);
TN_6= conMat_6(2,2);
FP_6= conMat_6(1,2);
FN_6= conMat_6(2,1);
Sens_6= TP_6/(TP_6+FN_6);
Spec_6= TN_6/(TN_6+FP_6);
PPV_6= TP_6/(TP_6+FP_6);
NPV_6= TN_6/(TN_6+FN_6);
Acc_6= (TP_6+TN_6)/(TP_6+TN_6+FP_6+FN_6);
error_6 = sum(test_class~= answer_6)/test;
AUC_6;
check_6 = Acc_6+error_6;
% Testing the KNN-3 Classifier:
answer_7 = predict(knnstruct_3,feat_test);
[X,Y,T,AUC_7] = perfcurve(test_class,answer_7,1);
figure, plot(X,Y)
xlabel('False positive rate'); ylabel('True positive rate')
title('ROC for classification by KNN - 3 - Test Group')
% Confusion matrix for testing KNN-3 classifier:
conMat_7 = confusionmat(test_class,answer_7)/test;
TP_7= conMat_7(1,1);
TN_7= conMat_7(2,2);
FP_7= conMat_7(1,2);
FN_7= conMat_7(2,1);
Sens_7= TP_7/(TP_7+FN_7);
Spec_7= TN_7/(TN_7+FP_7);
PPV_7= TP_7/(TP_7+FP_7);
NPV_7= TN_7/(TN_7+FN_7);
Acc_7= (TP_7+TN_7)/(TP_7+TN_7+FP_7+FN_7);
error_7 = sum(test_class~= answer_7)/test;
AUC_7;
check_7 = Acc_7+error_7;
% Testing the KNN-4 Classifier:
answer_8 = predict(knnstruct_4,feat_test);
[X,Y,T,AUC_8] = perfcurve(test_class,answer_8,1);
figure, plot(X,Y)
xlabel('False positive rate'); ylabel('True positive rate')
title('ROC for classification by KNN - 4 - Test Group')
% Confusion matrix for testing KNN-4 classifier:
conMat_8 = confusionmat(test_class,answer_8)/test;
TP_8= conMat_8(1,1);
TN_8= conMat_8(2,2);
FP_8= conMat_8(1,2);
FN_8= conMat_8(2,1);
Sens_8= TP_8/(TP_8+FN_8);
Spec_8= TN_8/(TN_8+FP_8);
PPV_8= TP_8/(TP_8+FP_8);
NPV_8= TN_8/(TN_8+FN_8);
Acc_8= (TP_8+TN_8)/(TP_8+TN_8+FP_8+FN_8);
error_8= sum(test_class~= answer_8)/test;
AUC_8;
check_8 = Acc_8+error_8;
% Testing the KNN-5 Classifier:
answer_9 = predict(knnstruct_5,feat_test);
[X,Y,T,AUC_9] = perfcurve(test_class,answer_9,1);
figure, plot(X,Y)
xlabel('False positive rate'); ylabel('True positive rate')
title('ROC for classification by KNN - 5 - Test Group')
% Confusion matrix for testing KNN-5 classifier:
conMat_9 = confusionmat(test_class,answer_9)/test;
TP_9= conMat_9(1,1);
TN_9= conMat_9(2,2);
FP_9= conMat_9(1,2);
FN_9= conMat_9(2,1);
Sens_9= TP_9/(TP_9+FN_9);
Spec_9= TN_9/(TN_9+FP_9);
PPV_9= TP_9/(TP_9+FP_9);
NPV_9= TN_9/(TN_9+FN_9);
Acc_9= (TP_9+TN_9)/(TP_9+TN_9+FP_9+FN_9);
error_9 = sum(test_class~= answer_9)/test;
AUC_9;
check_9 = Acc_9+error_9;
% Showing all results for all training classifiers
sens = [Sens1, Sens2, Sens3, Sens4, Sens5, Sens6, Sens7, Sens8, Sens9];
spec = [Spec1, Spec2, Spec3, Spec4, Spec5, Spec6, Spec7, Spec8, Spec9];
PPV = [PPV1, PPV2, PPV3, PPV4, PPV5, PPV6, PPV7, PPV8, PPV9];
NPV = [NPV1, NPV2, NPV3, NPV4, NPV5, NPV6, NPV7, NPV8, NPV9];
Acc = [Acc1, Acc2, Acc3, Acc4, Acc5, Acc6, Acc7, Acc8, Acc9];
error = [error1, error2, error3, error4, error5, error6, error7, error8, error9];
AUC = [AUC1, AUC2, AUC3,AUC4, AUC5, AUC6, AUC7, AUC8, AUC9];
% % Showing all results for all testing classifiers
sens_t = [Sens_1, Sens_2, Sens_3, Sens_4, Sens_5, Sens_6, Sens_7, Sens_8, Sens_9];
spec_t = [Spec_1, Spec_2, Spec_3, Spec_4, Spec_5, Spec_6, Spec_7, Spec_8, Spec_9];
PPV_t = [PPV_1, PPV_2, PPV_3, PPV_4, PPV_5, PPV_6, PPV_7, PPV_8, PPV_9];
NPV_t = [NPV_1, NPV_2, NPV_3, NPV_4, NPV_5, NPV_6, NPV_7, NPV_8, NPV_9];
Acc_t = [Acc_1, Acc_2, Acc_3, Acc_4, Acc_5, Acc_6, Acc_7, Acc_8, Acc_9];
error_t = [error_1, error_2 error_3, error_4, error_5, error_6, error_7, error_8, error_9];
AUC_t = [AUC_1, AUC_2 AUC_3, AUC_4, AUC_5, AUC_6, AUC_7, AUC_8, AUC_9];
[sens;spec;PPV;NPV;Acc;error;AUC]
[sens_t;spec_t;PPV_t;NPV_t;Acc_t;error_t;AUC_t]
% close all;

Réponse acceptée

Joss Knight
Joss Knight le 4 Avr 2023
Looks like your version of MATLAB predates the OutputNetwork training option, which was introduced in R2021b.

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