function svmEciton(subject,number,EEG,k,maxExpC,maxPerClass)
load(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number));
% fprintf('%i,%i,%i',size(trainingDataEMG,1),size(trainingDataEEG,1),size(classification,1))
addpath('/nfs/wsi/ti/messor/hohlochj/libsvm/matlab');
%k=2;
%maxExpC=0; % c\in {2^i|i=-maxExpC:1:maxExpC}
%choose to estimate based on EEG or EMG
if EEG
trainingData=trainingDataEEG;
else
trainingData=trainingDataEMG;
end
clear trainingDataEEG;
clear trainingDataEMG;
% addAttachedFiles(poolObj,'classification');
% addAttachedFiles(poolObj,'trainingData');
accurancy=zeros(k,3);
maxC=zeros(k,1);
noClasses=size(unique(classification),1);
cm=zeros(noClasses);
randMap=randperm(size(trainingData,1));
disp('startCV')
classes=classification; %necessary since otherwise classes are not passed to workers
clear classification;
parfor i=1:k
leaveOut=trainingData(mod(randMap,k)==i-1,:,:);
leaveClasses=classes(mod(randMap,k)==i-1);
remaining=trainingData(mod(randMap,k)~=i-1,:,:);
remainingClasses=classes(mod(randMap,k)~=i-1);
fprintf('%s create %ith model\n',datestr(datetime('now')),i)
[model,maxC(i)]=kfoldCV(remainingClasses,remaining,k,maxExpC,maxPerClass);
disp(datestr(datetime('now')))
[predictions,accurancy(i,:),~]=svmpredict(leaveClasses,leaveOut(:,:),model);
cm=cm+confusionmat(leaveClasses,predictions); %confusion matrix
%cf: for each cue what was the prediction (maybe colourCoded)
end
meanAccurancy=mean(accurancy(:,1))
fig=figure;
cmScaled=zeros(size(cm));
for i=1:size(cm,1)
cmScaled(i,:)=cm(i,:)/sum(cm(i,:));
end
imagesc(cmScaled)
colorbar();
saveas(fig,sprintf('/nfs/wsi/ti/messor/hohlochj/plots/%s%i%icm200ms1sPause.fig',subject,number,EEG),'fig');
save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i%i200ms1sPause.fig',subject,number,EEG),'meanAccurancy','maxC','cmScaled');
end