cv = cvpartition(length(label), 'HoldOut', 0.3); idxTrain = training(cv); idxTest = test(cv); trainData = data(idxTrain,:); trainLabel = label(idxTrain); testData = data(idxTest,:); testLabel = label(idxTest);帮我把这串代码改编得越长越好、越复杂越好，但不要包含函数头，也不要改变其功能

cv = cvpartition(length(label), 'HoldOut', 0.3);
idxTrain = training(cv);
idxTest = test(cv);
trainData = data(idxTrain,:);
trainLabel = label(idxTrain);
testData = data(idxTest,:);
testLabel = label(idxTest);

修改后：

crossValidation = cvpartition(length(label), 'HoldOut', 0.3);
trainIndex = training(crossValidation);
testIndex = test(crossValidation);
trainData = data(trainIndex,:);
trainLabel = label(trainIndex);
testData = data(testIndex,:);
testLabel = label(testIndex);

% Adding a progress bar for cross-validation
disp('Performing cross-validation...');
progressBar = waitbar(0,'Please wait...','Name','Cross-Validation Progress');
for i = 1:crossValidation.NumTestSets
waitbar(i/crossValidation.NumTestSets,progressBar,sprintf('Test set %d of %d',i,crossValidation.NumTestSets));
trainIndex = crossValidation.training(i);
testIndex = crossValidation.test(i);
trainData = data(trainIndex,:);
trainLabel = label(trainIndex);
testData = data(testIndex,:);
testLabel = label(testIndex);
% Train and test your model with the current train/test sets
end
close(progressBar);
disp('Cross-validation completed.');