fix typo

logmetric.py

@@ -98,25 +98,25 @@ class LogMetric(evaluate.Metric):
         pass
 
     # Jaccard Similarity to measure closeness of two log-messages
-    def get_jaccard_similarity(set1, set2):
+    def get_jaccard_similarity(self, set1, set2):
         intersection = set1.intersection(set2)
         union = set1.union(set2)
         return len(intersection) / len(union)
     
     # A score depending on the difference in length of two sentences
-    def get_length_score(sentence1, sentence2):
+    def get_length_score(self, sentence1, sentence2):
         s1len = len(sentence1)
         s2len = len(sentence2)
 
         return 1 - (abs(s1len - s2len) / max(s1len, s2len))
 
     # Combine a weighted average of different scores
-    def get_overall_similarity(sentence1, sentence2):
+    def get_overall_similarity(self, sentence1, sentence2):
         s1split = sentence1.split()
         s2split = sentence2.split()
 
-        jaccard_score = get_jaccard_similarity(set(s1split), set(s2split))
-        length_score = get_length_score(s1split, s2split)
+        jaccard_score = self.get_jaccard_similarity(set(s1split), set(s2split))
+        length_score = self.get_length_score(s1split, s2split)
 
         return (jaccard_score * 0.7 + length_score * 0.3) * 100.0
 
@@ -196,7 +196,7 @@ class LogMetric(evaluate.Metric):
                 monotonicallyIncreasingScore = 0.0
     
         # apply jaccard-similarity to every pred-ref pair and then take mean score * 100
-        local_score = np.mean([get_overall_similarity(p, r) for p,r in 
+        local_score = np.mean([self.get_overall_similarity(p, r) for p,r in 
                                        zip(
                                         list(map(lambda t: t[1], pred_logentries))[:min_logentries], 
                                         list(map(lambda t: t[1], ref_logentries))[:min_logentries]