diff --git a/callAll.m b/callAll.m
index b43c83c..adc2c97 100644
--- a/callAll.m
+++ b/callAll.m
@@ -15,13 +15,13 @@
 poolObj=parpool(32);
 
 [subjects,numbers]=structForAllDays(pathToFile);
-for i=1:size(subjects,2)
+parfor i=1:size(subjects,2)
     subject=subjects{i};
     for number=numbers(i,:)
         %fprintf('%s%i\n',subject,number);
         readEEGEciton(subject,number,windowEMG,windowEEG,shift,5,7500,pburgOrder,minEEGFreq,maxEEGFreq);
-        svmEciton(subject,number,false,k,maxExpC,maxPerClass,poolObj);
-        svmEciton(subject,number,EEG,k,maxExpC,maxPerClass,poolObj);
+        svmEciton(subject,number,false,k,maxExpC,maxPerClass);
+        svmEciton(subject,number,EEG,k,maxExpC,maxPerClass);
     end
 end
 delete(poolObj)
diff --git a/kfoldCV.m b/kfoldCV.m
index 7194ee3..94f27d8 100644
--- a/kfoldCV.m
+++ b/kfoldCV.m
@@ -1,5 +1,6 @@
 function [model,maxC] = kfoldCV(classification,trainingData,k,cExpMax,maxDataPerClass)
     noClasses=size(unique(classification),1);
+if cExpMax>0 %CV only if necessary
     cvAccurancy=zeros(2*cExpMax+1,1);
     parfor cExp=1:2*cExpMax+1
         c=2^(cExp-cExpMax-1);
@@ -21,8 +22,11 @@
         cvAccurancy(cExp)=mean(accurancy(:,1));
     end
     [~,maxC]=max(cvAccurancy);
+else
+    maxC=1; %no gridsearch since only one C possible
+end
     bestC=2^(maxC-cExpMax-1);
     [balancedClasses, balanceData]=balanceClasses(classification,trainingData,maxDataPerClass,noClasses);
     model=svmtrain(balancedClasses, balanceData(:,:),sprintf('-t 0 -c %f -q',bestC));
 
-end
\ No newline at end of file
+end
diff --git a/svmEciton.m b/svmEciton.m
index e9c04b1..6087e61 100644
--- a/svmEciton.m
+++ b/svmEciton.m
@@ -1,5 +1,6 @@
-function svmEciton(subject,number,EEG,k,maxExpC,maxPerClass,poolObj)
+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}
@@ -12,8 +13,8 @@
     end
     clear trainingDataEEG;
     clear trainingDataEMG;
-    addAttachedFiles(poolObj,'classification');
-    addAttachedFiles(poolObj,'trainingData');
+%    addAttachedFiles(poolObj,'classification');
+%    addAttachedFiles(poolObj,'trainingData');
 
     accurancy=zeros(k,3);
     maxC=zeros(k,1);
@@ -21,13 +22,15 @@
     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=classification(mod(randMap,k)==i-1);
+        leaveClasses=classes(mod(randMap,k)==i-1);
         remaining=trainingData(mod(randMap,k)~=i-1,:,:);
-        remainingClasses=classification(mod(randMap,k)~=i-1);
-        disp(datestr(datetime('now')))
-        fprintf('create %ith model\n',i)
+        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')))