diff --git a/ss2013/1_Web Mining/Uebungen/4_Uebung/code/confusion_matrix.py b/ss2013/1_Web Mining/Uebungen/4_Uebung/code/confusion_matrix.py
index 32c18c50..52f7fb8c 100644
--- a/ss2013/1_Web Mining/Uebungen/4_Uebung/code/confusion_matrix.py	
+++ b/ss2013/1_Web Mining/Uebungen/4_Uebung/code/confusion_matrix.py	
@@ -14,8 +14,10 @@ conf_matr = [[0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,
 accuracy = 0
 document_count = 0
 precision = [0,0,0,0,0,0,0,0,0,0,0]
-precision_avg = 0
+precision_micro = 0
+precision_macro = 0
 recalls = [0,0,0,0,0,0,0,0,0,0,0]
+recall_micro = 0
 
 def load_predictionfile(file):
     global document_count
@@ -75,11 +77,11 @@ def prec():
         i += 1
 
 def macro_prec():
-    global precision_avg
+    global precision_macro
     global precision
     for p in precision:
-        precision_avg += p
-    precision_avg = float(precision_avg) / 11
+        precision_macro += p
+    precision_macro = float(precision_macro) / 11
 
        
 def pp_confusionmatrix():
@@ -113,7 +115,7 @@ def pp_prec():
     print line
     
 def pp_macroprec():
-    print "Precision Macroavg: "+str(round(precision_avg*100,4))+"%"
+    print "Precision Macroavg: "+str(round(precision_macro*100,4))+"%"
 
 def recall():
     i = 0
@@ -138,18 +140,83 @@ def pp_recall():
         i += 1
     print line
     
+def prec_micro():
+    i = 0
+    result = [0,0,0,0]
+    for c in classes:
+        res = conf_micro_class(i)
+        result[0] += res[0]
+        result[1] += res[1]
+        result[2] += res[2]
+        result[3] += res[3]
+        i += 1
+    
+    global precision_micro
+    precision_micro = float(result[0]) / float(result[0]+result[1])
+        
+def conf_micro_class(class_):
+    i = 0
+    ok_values = 0 #class ok, is ok
+    wrong_values = 0 # class ok, is not ok
+    wrong_values_others = 0 # class not ok, is not ok
+    ok_values_others = 0 # class not ok, is ok    
+    for conf in conf_matr:
+        j = 0
+        for c in conf:
+            if i == class_:
+                if i == j:
+                    ok_values += c
+                else:
+                    wrong_values += c
+            else:
+                if i == j:
+                    ok_values_others += c
+                else:
+                    wrong_values_others += c                                    
+            j += 1 
+        i += 1
+    return [ok_values,wrong_values,wrong_values_others,ok_values_others]
+    
+def pp_microprec():
+    print "Precision Microavg: "+str(round(precision_micro*100,4))+"%"
+    
+def recall_micro():
+    i = 0
+    result = [0,0,0,0]
+    for c in classes:
+        res = conf_micro_class(i)
+        result[0] += res[0]
+        result[1] += res[1]
+        result[2] += res[2]
+        result[3] += res[3]
+        i += 1
+    
+    global recall_micro
+    recall_micro = float(result[0]) / float(result[0]+result[2])
+
+def pp_microrecall():
+    print "Recall Microavg: "+str(round(recall_micro*100,4))+"%"
+
 if __name__ == '__main__':
     load_predictionfile(predfile)
     #print preds
     confusion_matrix()
     pp_confusionmatrix()
+    
     accuracy()
     pp_accuracy()
+    
     prec()
     pp_prec()    
     macro_prec()
     pp_macroprec()
+    prec_micro()
+    pp_microprec()
+    
+    
     recall()
     pp_recall()
+    recall_micro()
+    pp_microrecall()    
     
     
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