normalized_levenshtein.py 1.8 KB

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  1. # Copyright (c) 2018 luozhouyang
  2. #
  3. # Permission is hereby granted, free of charge, to any person obtaining a copy
  4. # of this software and associated documentation files (the "Software"), to deal
  5. # in the Software without restriction, including without limitation the rights
  6. # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  7. # copies of the Software, and to permit persons to whom the Software is
  8. # furnished to do so, subject to the following conditions:
  9. #
  10. # The above copyright notice and this permission notice shall be included in all
  11. # copies or substantial portions of the Software.
  12. #
  13. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  14. # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  15. # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  16. # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  17. # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  18. # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  19. # SOFTWARE.
  20. from .string_distance import NormalizedStringDistance
  21. from .string_similarity import NormalizedStringSimilarity
  22. from .levenshtein import Levenshtein
  23. class NormalizedLevenshtein(NormalizedStringDistance, NormalizedStringSimilarity):
  24. def __init__(self):
  25. self.levenshtein = Levenshtein()
  26. def distance(self, s0, s1):
  27. if s0 is None:
  28. raise TypeError("Argument s0 is NoneType.")
  29. if s1 is None:
  30. raise TypeError("Argument s1 is NoneType.")
  31. if s0 == s1:
  32. return 0.0
  33. m_len = max(len(s0), len(s1))
  34. if m_len == 0:
  35. return 0.0
  36. return self.levenshtein.distance(s0, s1) / m_len
  37. def similarity(self, s0, s1):
  38. return 1.0 - self.distance(s0, s1)