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* pre-commit: Upgrade psf/black for stable style 2023 Updating https://github.com/psf/black ... updating 22.12.0 -> 23.1.0 for their `2023 stable style`. * https://github.com/psf/black/blob/main/CHANGES.md#2310 > This is the first [psf/black] release of 2023, and following our stability policy, it comes with a number of improvements to our stable style… Also, add https://github.com/tox-dev/pyproject-fmt and https://github.com/abravalheri/validate-pyproject to pre-commit. I only modified `.pre-commit-config.yaml` and all other files were modified by pre-commit.ci and psf/black. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
"""
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This is a Python implementation of the levenshtein distance.
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Levenshtein distance is a string metric for measuring the
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difference between two sequences.
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For doctests run following command:
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python -m doctest -v levenshtein-distance.py
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or
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python3 -m doctest -v levenshtein-distance.py
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For manual testing run:
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python levenshtein-distance.py
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"""
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def levenshtein_distance(first_word: str, second_word: str) -> int:
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"""Implementation of the levenshtein distance in Python.
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:param first_word: the first word to measure the difference.
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:param second_word: the second word to measure the difference.
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:return: the levenshtein distance between the two words.
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Examples:
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>>> levenshtein_distance("planet", "planetary")
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3
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>>> levenshtein_distance("", "test")
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4
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>>> levenshtein_distance("book", "back")
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2
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>>> levenshtein_distance("book", "book")
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0
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>>> levenshtein_distance("test", "")
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4
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>>> levenshtein_distance("", "")
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0
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>>> levenshtein_distance("orchestration", "container")
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10
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"""
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# The longer word should come first
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if len(first_word) < len(second_word):
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return levenshtein_distance(second_word, first_word)
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if len(second_word) == 0:
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return len(first_word)
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previous_row = list(range(len(second_word) + 1))
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for i, c1 in enumerate(first_word):
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current_row = [i + 1]
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for j, c2 in enumerate(second_word):
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# Calculate insertions, deletions and substitutions
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insertions = previous_row[j + 1] + 1
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deletions = current_row[j] + 1
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substitutions = previous_row[j] + (c1 != c2)
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# Get the minimum to append to the current row
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current_row.append(min(insertions, deletions, substitutions))
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# Store the previous row
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previous_row = current_row
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# Returns the last element (distance)
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return previous_row[-1]
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if __name__ == "__main__":
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first_word = input("Enter the first word:\n").strip()
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second_word = input("Enter the second word:\n").strip()
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result = levenshtein_distance(first_word, second_word)
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print(f"Levenshtein distance between {first_word} and {second_word} is {result}")
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