diff --git a/text/collection.txt b/text/collection.txt
index 77dfff3..d96f829 100644
--- a/text/collection.txt
+++ b/text/collection.txt
@@ -12,6 +12,6 @@
 Move/Rest EMG : 99.9535%
 Bewegungen EMG: 77.7352% bzw. 55.8440% wenn 0.5s vorher einbezogen
 EMG bei 1s Pause vor Bewegungsbeginn 70.4908% 
+allSubjectsAllDays:
+EMG: 56.11%, EEG: 42.2% (pburgOrder 50, EMGwindow 0.2, 1s Pause, c=1 fest)
 
-
-Vergleich mit/ohne grid Search
diff --git a/usedMcode/callAll.m b/usedMcode/callAll.m
index d0d6af9..5e100be 100644
--- a/usedMcode/callAll.m
+++ b/usedMcode/callAll.m
@@ -1,6 +1,6 @@
 addpath('/home/hohlochj/masterarbeit/usedMcode')
 pathToFile='/nfs/wsi/ti/messor/hohlochj/origData/';
-% pathToFile='/home/jph/Uni/masterarbeit/Block1_ReachingMovements/';
+%pathToFile='/home/jph/Uni/masterarbeit/Block1_ReachingMovements/';
 
 windowEMG=0.2; %try 1s
 windowEEG=1;
@@ -25,6 +25,10 @@
 
 meanAccurancysEMG=zeros([j,1]);
 meanAccurancysEEG=zeros([j,1]);
+maxCEMG=zeros([j,k,1]);
+maxCEEG=zeros([j,k,1]);
+cmScaledEMG=zeros([j,5,5]);
+cmScaledEEG=zeros([j,5,5]);
 
 j=1;
 for i=1:size(subjects,2)
@@ -32,15 +36,13 @@
     for number=numbers{i}
 %         fprintf('%s%i\n',subject,number);
         readEEG(pathToFile,subject,number,windowEMG,windowEEG,shift,5,7500,pburgOrder,minEEGFreq,maxEEGFreq);
-        meanAccurancysEMG(j)=svmEciton(subject,number,false,k,maxExpC,maxPerClass);
-        meanAccurancysEEG(j)=svmEciton(subject,number,EEG,k,maxExpC,maxPerClass);
+        [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);
         j=j+1;
     end
 end
 
-[ accEEG, accEMG, cmEEG, cmEMG ] = collectAccurancys( pathToFile, subjects, numbers );
-
-save(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s_callAll.mat',datestr(datetime('now'))));
+save(strcat(pathToFile,sprintf('../matlabData/%s_callAll.mat',datestr(datetime('now')))));
 delete(poolObj)
 
 
diff --git a/usedMcode/readEEG.m b/usedMcode/readEEG.m
index 2c4b875..148c4c3 100644
--- a/usedMcode/readEEG.m
+++ b/usedMcode/readEEG.m
@@ -15,30 +15,30 @@
         %fprintf('%ith file processed\n',i)
     end
 
-    %clear sig
+    clear sig
 
     trainingDataEEG=cell2mat(trainingDataEEGcell);
     trainingDataEMG=cell2mat(trainingDataEMGcell);
     classesMat=cell2mat(classesCell);
     kinMat=cell2mat(kin);
-    %clear trainingDataEEGcell trainingDataEMGcell classesCell kin
+    clear trainingDataEEGcell trainingDataEMGcell classesCell kin
 
     classificationWithPause=classifyAccordingToEMG(trainingDataEEG, trainingDataEMG,classesMat,shift,params.SamplingRate.NumericValue,threshold);
-    %clear classesMat
+    clear classesMat
 
     smoothClassification=zeros(size(classificationWithPause));
     for i=1:size(classificationWithPause,1)
         smoothClassification(i)=round(mode(classificationWithPause(max(i-2,1):min(i+2,end))));       
     end
 
-    %clear classificationWithPause
+    clear classificationWithPause
 
     trainingDataEEG=trainingDataEEG(smoothClassification~=-1,:,:);
     trainingDataEMG=trainingDataEMG(smoothClassification~=-1,:);
     classification=smoothClassification(smoothClassification~=-1);
     kinematics=kinMat(smoothClassification~=-1,:);
 
-    %clear smoothClassification i
+    clear smoothClassification i
     save(strcat(pathToFile,sprintf('../matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number)),'trainingDataEEG','trainingDataEMG','classification','kinematics','-v7.3');
     
 
diff --git a/usedMcode/svmEciton.m b/usedMcode/svmEciton.m
index c91d139..86c0611 100644
--- a/usedMcode/svmEciton.m
+++ b/usedMcode/svmEciton.m
@@ -1,5 +1,5 @@
-function [meanAccurancy]= svmEciton(subject,number,EEG,k,maxExpC,maxPerClass)
-    load(sprintf('/nfs/wsi/ti/messor/hohlochj/matlabData/%s%i200msWindowEMG1sWindowEEG200msShift1sPauseFreq0to200.mat',subject,number));
+function [meanAccurancy,maxC, cmScaled]= svmEciton(pathToFile,subject,number,EEG,k,maxExpC,maxPerClass)
+    load(strcat(pathToFile,sprintf('../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');
 
@@ -32,7 +32,7 @@
         [predictions,accurancy(i,:),~]=svmpredict(leaveClasses,leaveOut(:,:),model);
         cm=cm+confusionmat(leaveClasses,predictions); %confusion matrix
     end
-    meanAccurancy=mean(accurancy(:,1))
+    meanAccurancy=mean(accurancy(:,1));
 
     fig=figure;
     cmScaled=zeros(size(cm));
@@ -41,7 +41,7 @@
     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.mat',subject,number,EEG),'meanAccurancy','maxC','cmScaled','-v7.3');
+    saveas(fig,strcat(pathToFile,sprintf('../plots/%s%i%icm200ms1sPause.fig',subject,number,EEG)),'fig');
+    save(strcat(pathToFile,sprintf('../matlabData/%s%i%i200ms1sPause.mat',subject,number,EEG)),'meanAccurancy','maxC','cmScaled','-v7.3');
     fprintf('%s%i%i finished %s\n',subject,number,EEG,datestr(datetime('now')))
 end