c = cvpartition(fullDataY, 'KFold', 10);
kSVMModel = fitcsvm(fullDataX, fullDataY, 'Standardize', true, 'CVPartition', c);
scorekSVMModel = fitSVMPosterior(kSVMModel);
[predictions, post_scores] = kfoldPredict(scorekSVMModel);
for jj = 1:kSVMModel.KFold
indTrainFold{jj} = find(training(c,jj)==1);
indTestFold{jj} = find(test(c,jj)==1);
[predFold{jj}] = predict(kSVMModel.Trained{jj}, fullDataX(indTestFold{jj},:));
cmFold = confusionchart(fullDataY(indTestFold{jj},:), predFold{jj});
TN(jj) = cmFold.NormalizedValues(1,1);
TP(jj) = cmFold.NormalizedValues(2,2);
FP(jj) = cmFold.NormalizedValues(1,2);
FN(jj) = cmFold.NormalizedValues(2,1);
close all;
end
cm = confusionchart(fullDataY, predictions);
sum(TN) == cm.NormalizedValues(1,1);
sum(TP) == cm.NormalizedValues(2,2);
sum(FP) == cm.NormalizedValues(1,2);
sum(FN) == cm.NormalizedValues(2,1);
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