diff --git a/oldMcode/correlation2.m b/oldMcode/correlation2.m
new file mode 100644
index 0000000..ce92014
--- /dev/null
+++ b/oldMcode/correlation2.m
@@ -0,0 +1,9 @@
+function [R2]=correlation2(A,B)
+    if size(A)~=size(B)
+        error('dimension missmatch')
+    end
+    R2=zeros(size(A,2),1);
+    for i=1:size(A,2)
+        R2(i)=corr(A(:,i),B(:,i))^2;
+    end
+end
\ No newline at end of file
diff --git a/oldMcode/noOfSynergies.m b/oldMcode/noOfSynergies.m
index 6dea77d..e62db76 100644
--- a/oldMcode/noOfSynergies.m
+++ b/oldMcode/noOfSynergies.m
@@ -1,42 +1,42 @@
-maxSize=6;
+maxSize=min(10,size(trainingData,2));
 
-mseAutoenc=zeros([maxSize,1]);
+r2Autoenc=zeros([maxSize,size(trainingData,2)]);
 for i=1:maxSize
     ae=trainAutoencoder(trainingData,i,'ShowProgressWindow',false);
     predicted=predict(ae,trainingData);
-    mseAutoenc(i)=mse(trainingData-predicted);
+    r2Autoenc(i,:)=correlation2(trainingData,predicted);
 end
 
 fig=figure();
 subplot(1,3,1);
-plot(mseAutoenc)
+plot(r2Autoenc)
 title('Autoencoder')
 xlabel('number of syergies')
-ylabel('mse')
+ylabel('R^2')
 
 %PCA
 [COEFF,SCORE,latent] = pca(trainingData,'Centered',false);
-msePCA=zeros([maxSize,1]);
+r2PCA=zeros([maxSize,size(trainingData,2)]);
 for i=1:maxSize
     predicted=SCORE(:,1:i)*COEFF(:,1:i)';
-    msePCA(i)=mse(trainingData-predicted);
+    r2PCA(i,:)=correlation2(trainingData,predicted);   
 end
 subplot(1,3,2);
-plot(msePCA)
+plot(r2PCA)
 title('PCA')
 xlabel('number of syergies')
-ylabel('mse')
+ylabel('R^2')
 
 %NNMF
-mseNNMF=zeros([maxSize,1]);
+r2NNMF=zeros([maxSize,size(trainingData,2)]);
 for i=1:maxSize
     [W,H]=nnmf(trainingData,i);
     predicted=W*H;
-    mseNNMF(i)=mse(trainingData-predicted);
+    r2NNMF(i,:)=correlation2(trainingData,predicted);
 end
 
 subplot(1,3,3);
-plot(mseNNMF)
+plot(r2NNMF)
 title('NNMF')
 xlabel('number of syergies')
-ylabel('mse')
\ No newline at end of file
+ylabel('R^2')
\ No newline at end of file