CloneDetectionRoutines: Relative diff for matching

This increases precision for the code clone detection. (That's tested,
it has an effect.) Since we want to optimize the overall cost which is
also dependent on the max_sum, we need to take the maxabs into account
when building the cost matrix.

bears/codeclone_detection/CloneDetectionRoutines.py

@@ -83,6 +83,12 @@ def pad_count_vectors(cm1, cm2):
     return cm1, cm2
 
 
+def relative_difference(difference, maxabs):
+    if maxabs == 0:
+        return 1
+    return difference/maxabs
+
+
 def compare_functions(cm1, cm2):
     """
     Compares the functions represented by the given count matrices.
@@ -105,21 +111,24 @@ def compare_functions(cm1, cm2):
     #  side (columns). The fields in the matrix are the weighted nodes
     # connecting each element from one side to the other.
     diff_table = [(cv1,
-                   [(cv2, cv1.difference(cv2)) for cv2 in cm2.values()])
+                   [(cv2, cv1.difference(cv2), cv1.maxabs(cv2))
+                    for cv2 in cm2.values()])
                   for cv1 in cm1.values()]
-    cost_matrix = [[difference
-                    for cv2, difference in lst]
+
+    cost_matrix = [[relative_difference(difference, maxabs)
+                    for cv2, difference, maxabs in lst]
                    for cv1, lst in diff_table]
 
     # The munkres algorithm will calculate a matching such that the sum of
     # the taken fields is minimal. It thus will associate each variable
     # from one function to one on the other function.
     matching = munkres.compute(cost_matrix)
 
-    diff_sum = sum(cost_matrix[x][y] for x, y in matching)
+    diff_sum = sum(diff_table[x][1][y][1]
+                   for x, y in matching)
     # For each match we get the maximum of the absolute value of the count
     # vectors. Summed up with this we can normalize the whole thing.
-    max_sum = sum(diff_table[x][0].maxabs(diff_table[x][1][y][0])
+    max_sum = sum(diff_table[x][1][y][2]
                   for x, y in matching)
 
     if diff_sum == 0: