def distances(self, hyp, ref, bestonly):
# print(hyp)
# print(ref)
bo = 1 if bestonly else 0
dist = Levenshtein.compare_lists(hyp, ref, self.threshold, bo)
# replace special value -1 meaning "above threshold"0
# print(dist)
# print(dist==-1)
dist[dist == -1] = np.NaN
for i, hyp_ngram in enumerate(hyp):
for j, ref_ngram in enumerate(ref):
if dist[i][j] != 0:
ref_ngram_split = ref_ngram.split(' ')
all_matched = True
hyp_ngram_split = hyp_ngram.split(' ')
if len(ref_ngram_split) == len(hyp_ngram_split):
for k, hyp_w in enumerate(hyp_ngram_split):
ref_w = ref_ngram_split[k]
if self.wordnet_similarity_distance(
hyp_word=hyp_w, ref_word=ref_w) > 0.5:
all_matched = False
break
if all_matched:
dist[i][j] = 0
print(dist)
return dist
def distances(self, hyp, ref, bestonly):
bo = 1 if bestonly else 0
dist = Levenshtein.compare_lists(hyp, ref, self.threshold, bo)
# replace special value -1 meaning "above threshold"
dist[dist == -1] = np.NaN
return dist
def distances(self, hyp, ref, bestonly):
bo = 1 if bestonly else 0
dist = Levenshtein.compare_lists(hyp, ref, self.threshold, bo)
# replace special value -1 meaning "above threshold"
dist[dist == -1] = np.NaN
return dist
