NLP/similarities
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update edit sim.

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This commit is contained in:
shibing624 2023-02-01 15:40:24 +08:00
parent 472164b766
commit d2405d940d
1 changed files with 10 additions and 3 deletions
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13
similarities/utils/distance.py
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@ -83,7 +83,7 @@ def edit_distance(str1, str2):
# very fast
# http://stackoverflow.com/questions/14260126/how-python-levenshtein-ratio-is-computed
import Levenshtein
d = 1.0 - Levenshtein.distance(str1, str2) / float(max(len(str1), len(str2)))
d = Levenshtein.distance(str1, str2) / float(max(len(str1), len(str2)))
except:
# https://docs.python.org/2/library/difflib.html
d = 1.0 - SequenceMatcher(lambda x: x == " ", str1, str2).ratio()
@ -206,8 +206,15 @@ if __name__ == '__main__':
str1_test = "你到底是谁?"
str2_test = "没想到我是谁，是真样子"
print('strs:', str1_test, ' vs ', str2_test)
print(edit_distance(str1_test, str2_test))
print('edit sim:', 1 - edit_distance(str1_test, str2_test))
print('edit_dist', edit_distance(str1_test, str2_test))
print('edit_sim:', 1 - edit_distance(str1_test, str2_test))
str1_test = "private Thread currentThread;"
str2_test = "private volatile Thread currentThread;"
print('strs:', str1_test, ' vs ', str2_test)
print('edit_dist', edit_distance(str1_test, str2_test))
print('edit_sim:', 1 - edit_distance(str1_test, str2_test))
print(num_of_common_sub_str(str1_test, str2_test))
print(max_min_normalize(vec1_test)) # 归一化（0-1）
print(z_score(vec1_test)) # 标准化（0附近，正负）