diff --git a/usedMcode/calcRidgeCorrelation.m b/usedMcode/calcRidgeCorrelation.m
deleted file mode 100644
index 7937a3c..0000000
--- a/usedMcode/calcRidgeCorrelation.m
+++ /dev/null
@@ -1,6 +0,0 @@
-function [correlation]=calcRidgeCorrelation(kin,data,coeffs)
-    prediction=coeffs(1)+coeffs(2:end)*data; %TODO check
-    
-    correlation=corr(kin,prediction);
-
-end
\ No newline at end of file
diff --git a/usedMcode/callAll.m b/usedMcode/callAll.m
index 8c14943..424a4f7 100644
--- a/usedMcode/callAll.m
+++ b/usedMcode/callAll.m
@@ -41,8 +41,8 @@
         readEEG(pathToFile,subject,number,windowEMG,windowEEG,shift,maxFile,threshold,pburgOrder,minEEGFreq,maxEEGFreq);
         [meanAccurancysEMG(j),maxCEMG(j,:),cmScaledEMG(j,:,:)]=svmEciton(pathToFile,subject,number,false,k,maxExpC,maxPerClass);
         [meanAccurancysEEG(j),maxCEEG(j,:),cmScaledEEG(j,:,:)]=svmEciton(pathToFile,subject,number,EEG,k,maxExpC,maxPerClass);
-        [correlationEMG(j)]=ridgeCorrelation(pathToFile,subject,number,false,k);
-        [correlationEEG(j)]=ridgeCorrelation(pathToFile,subject,number,EEG,k);
+        [correlationEMG(j)]=ridgeCV(pathToFile,subject,number,false,k);
+        [correlationEEG(j)]=ridgeCV(pathToFile,subject,number,EEG,k);
         j=j+1;
     end
 end
diff --git a/usedMcode/kFoldRidge.m b/usedMcode/kFoldRidge.m
new file mode 100644
index 0000000..dadfb6d
--- /dev/null
+++ b/usedMcode/kFoldRidge.m
@@ -0,0 +1,30 @@
+function [coeffs]=kFoldRidge(kin,data,k,ridgeParams)
+    if any(size(ridgeParams)>1)
+        
+        correlationPerParam=zeros(size(ridgeParams));
+        
+        for param=ridgeParams
+            
+            randomMapping=transpose(randperm(size(data,1)));
+            correlations=zeros(k,1);
+
+            parfor i=1:k
+                trainData=data(mod(randomMapping,k)+1~=i,:,:);
+                testData=data(mod(randomMapping,k)+1==i,:,:);
+                trainKin=kin(mod(randomMapping,k)+1~=i);
+                testKin=kin(mod(randomMapping,k)+1==i);
+                
+                coeffs=ridge(trainData,trainKin,param,0);
+                correlations(i)=ridgeCorrelation(testKin,testData,coeffs);
+            end
+
+           correlationPerParam(ridgeParams==param)=mean(correlations);
+        end
+        [~,maxRidgeParamIndex]=max(correlationPerParam);
+        coeffs=ridge(data,kin,ridgeParams(maxRidgeParamIndex),0);
+        
+        
+    else
+        coeffs=ridge(kin,data,ridgeParams(1),0);
+    end
+end
\ No newline at end of file
diff --git a/usedMcode/kfoldCV.m b/usedMcode/kfoldCV.m
index 94f27d8..af80d84 100644
--- a/usedMcode/kfoldCV.m
+++ b/usedMcode/kfoldCV.m
@@ -2,13 +2,13 @@
     noClasses=size(unique(classification),1);
 if cExpMax>0 %CV only if necessary
     cvAccurancy=zeros(2*cExpMax+1,1);
-    parfor cExp=1:2*cExpMax+1
+    for cExp=1:2*cExpMax+1
         c=2^(cExp-cExpMax-1);
 
         randomMapping=transpose(randperm(size(trainingData,1)));
         accurancy=zeros(k,3);
 
-        for i=1:k
+        parfor i=1:k
             trainData=trainingData(mod(randomMapping,k)+1~=i,:,:);
             testData=trainingData(mod(randomMapping,k)+1==i,:,:);
             trainClasses=classification(mod(randomMapping,k)+1~=i);
diff --git a/usedMcode/ridgeCV.m b/usedMcode/ridgeCV.m
new file mode 100644
index 0000000..1f18694
--- /dev/null
+++ b/usedMcode/ridgeCV.m
@@ -0,0 +1,29 @@
+function [correlation]=ridgeCV(pathToFile,subject,number,EEG,k,ridgeParams)
+    load(strcat(pathToFile,sprintf('../matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number)));
+    clear classification;
+    if EEG
+        trainingData=trainingDataEEG;
+    else
+        trainingData=trainingDataEMG;
+    end
+    clear trainingDataEEG;
+    clear trainingDataEMG;
+    
+    randMap=randperm(size(trainingData,1));
+    kin=kinematics;
+    correlations=zeros([k,1]);
+    
+    parfor i=1:k
+        leaveOut=trainingData(mod(randMap,k)==i-1,:,:);
+        leaveKin=kin(mod(randMap,k)==i-1,:);
+        remaining=trainingData(mod(randMap,k)~=i-1,:,:);
+        remainingKin=kin(mod(randMap,k)~=i-1,:);
+        %fprintf('%s create %ith model\n',datestr(datetime('now')),i)
+        
+        [coeffs]=kFoldRidge(remainingKin,remaining,k,ridgeParams);
+        
+        correlations(i)=ridgeCorrelation(leaveKin,leaveOut,coeffs);
+    end
+    
+    correlation=mean(correlations);
+end
\ No newline at end of file
diff --git a/usedMcode/ridgeCorrelation.m b/usedMcode/ridgeCorrelation.m
index 4d92957..5834a7e 100644
--- a/usedMcode/ridgeCorrelation.m
+++ b/usedMcode/ridgeCorrelation.m
@@ -1,29 +1,6 @@
-function [correlation]=ridgeCorrelation(pathToFile,subject,number,EEG,k,ridgeParams)
-    load(strcat(pathToFile,sprintf('../matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number)));
-    clear classification;
-    if EEG
-        trainingData=trainingDataEEG;
-    else
-        trainingData=trainingDataEMG;
-    end
-    clear trainingDataEEG;
-    clear trainingDataEMG;
+function [correlation]=ridgeCorrelation(kin,data,coeffs)
+    prediction=coeffs(1)+data*coeffs(2:end);
     
-    randMap=randperm(size(trainingData,1));
-    kin=kinematics;
-    correlations=zeros([k,1]);
-    
-    parfor i=1:k
-        leaveOut=trainingData(mod(randMap,k)==i-1,:,:);
-        leaveKin=kin(mod(randMap,k)==i-1,:);
-        remaining=trainingData(mod(randMap,k)~=i-1,:,:);
-        remainingKin=kin(mod(randMap,k)~=i-1,:);
-        %fprintf('%s create %ith model\n',datestr(datetime('now')),i)
-        
-        [coeffs]=kFoldRidge(remainingKin,remaining,k,ridgeParams);
-        
-        correlations(i)=ridgeCorrelation(leaveKin,leaveOut,coeffs);
-    end
-    
-    correlation=mean(correlations);
+    correlation=corr(kin,prediction);
+
 end
\ No newline at end of file