diff --git a/kfoldCV.m b/kfoldCV.m
index 29deb48..f49d0e6 100644
--- a/kfoldCV.m
+++ b/kfoldCV.m
@@ -1,32 +1,27 @@
 function [model] = kfoldCV(classification,trainingData,k,cExpMax)
     noClasses=size(unique(classification),2);
-    randMap=randperm(size(trainingData,1));
-    leaveOut=trainingData;
-    leaveClasses=classification;
-    remaining=trainingData(mod(randMap,k+1)~=0,:,:);
-    remainingClasses=classification(mod(randMap,k+1)~=0);
     cvAccurancy=zeros(2*cExpMax+1,1);
-    for cExp=-cExpMax:1:cExpMax
-        c=2^cExp;
+    parfor cExp=1:2*cExpMax+1
+        c=2^(cExp-cExpMax-1);
 
-        randomMapping=randperm(size(remaining,1));
+        randomMapping=randperm(size(trainingData,1));
         accurancy=zeros(k,3);
 
-        parfor i=1:k
-            trainData=remaining(mod(randomMapping,k)+1~=i,:,:);
-            testData=remaining(mod(randomMapping,k)+1==i,:,:);
-            trainClasses=remainingClasses(mod(randomMapping,k)+1~=i);
-            testClasses=remainingClasses(mod(randomMapping,k)+1==i);
+        for i=1:k
+            trainData=trainingData(mod(randomMapping,k)+1~=i,:,:);
+            testData=trainingData(mod(randomMapping,k)+1==i,:,:);
+            trainClasses=classification(mod(randomMapping,k)+1~=i);
+            testClasses=classification(mod(randomMapping,k)+1==i);
             [trainClasses,trainData]=balanceClasses(trainClasses,trainData,noClasses,0.00001);
             model=svmtrain(trainClasses,trainData(:,:),sprintf('-t 0 -c %f -q',c));
             [~, accurancy(i,:), ~]=svmpredict(testClasses, testData(:,:), model,'-q');
         end
 
-        cvAccurancy(cExp+cExpMax+1)=mean(accurancy(:,1));
+        cvAccurancy(cExp)=mean(accurancy(:,1));
     end
-    [~,i]=max(cvAccurancy);
-    bestC=2^(i-cExpMax-1);
-    [balanceLeftClasses, balanceLeftOut]=balanceClasses(leaveClasses,leaveOut,noClasses,0.00001);
+    [~,maxC]=max(cvAccurancy);
+    bestC=2^(maxC-cExpMax-1);
+    [balanceLeftClasses, balanceLeftOut]=balanceClasses(classification,trainingData,noClasses,0.00001);
     model=svmtrain(balanceLeftClasses, balanceLeftOut(:,:),sprintf('-t 0 -c %f -q',bestC));
 
 end
\ No newline at end of file
diff --git a/svm.m b/svm.m
index 3780e04..62cc6cf 100644
--- a/svm.m
+++ b/svm.m
@@ -37,12 +37,12 @@
 
 accurancy=zeros(rounds,3);
 means=zeros(rounds,1);
-for i=1:rounds
+for i=1:k
     randMap=randperm(size(trainingData,1));
-    leaveOut=trainingData(mod(randMap,k+2)==0,:,:);
-    leaveClasses=classification(mod(randMap,k+2)==0);
-    remaining=trainingData(mod(randMap,k+2)~=0,:,:);
-    remainingClasses=classification(mod(randMap,k+2)~=0);
+    leaveOut=trainingData(mod(randMap,k+1)==i,:,:);
+    leaveClasses=classification(mod(randMap,k+1)==i);
+    remaining=trainingData(mod(randMap,k+1)~=i,:,:);
+    remainingClasses=classification(mod(randMap,k+1)~=i);
 
     model=kfoldCV(remainingClasses,remaining,k,maxExpC);
 
@@ -50,5 +50,4 @@
     means(i)=mean(leaveClasses-predictions); %confusion matrix
     %cf: for each cue what was the prediction (maybe colourCoded)
 end
-m=mean(accurancy(:,1))
-mm=mean(means)
\ No newline at end of file
+meanAccurancy=mean(accurancy(:,1))
\ No newline at end of file