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Update levenshtein_distance.py (TheAlgorithms#11171)
* Update levenshtein_distance.py * Update levenshtein_distance.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update levenshtein_distance.py * Update levenshtein_distance.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update levenshtein_distance.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update levenshtein_distance.py * Update levenshtein_distance.py * [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> Co-authored-by: Christian Clauss <cclauss@me.com>
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‎strings/levenshtein_distance.py

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"""
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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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from collections.abc import Callable
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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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"""
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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.
@@ -47,7 +36,7 @@ def levenshtein_distance(first_word: str, second_word: str) -> int:
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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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# 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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return previous_row[-1]
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def levenshtein_distance_optimized(first_word: str, second_word: str) -> int:
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"""
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Compute the Levenshtein distance between two words (strings).
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The function is optimized for efficiency by modifying rows in place.
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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_optimized("planet", "planetary")
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3
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>>> levenshtein_distance_optimized("", "test")
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4
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>>> levenshtein_distance_optimized("book", "back")
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2
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>>> levenshtein_distance_optimized("book", "book")
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0
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>>> levenshtein_distance_optimized("test", "")
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4
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>>> levenshtein_distance_optimized("", "")
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0
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>>> levenshtein_distance_optimized("orchestration", "container")
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10
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"""
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if len(first_word) < len(second_word):
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return levenshtein_distance_optimized(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] + [0] * len(second_word)
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for j, c2 in enumerate(second_word):
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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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current_row[j + 1] = min(insertions, deletions, substitutions)
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previous_row = current_row
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return previous_row[-1]
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def benchmark_levenshtein_distance(func: Callable) -> None:
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"""
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Benchmark the Levenshtein distance function.
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:param str: The name of the function being benchmarked.
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:param func: The function to be benchmarked.
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"""
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from timeit import timeit
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stmt = f"{func.__name__}('sitting', 'kitten')"
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setup = f"from __main__ import {func.__name__}"
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number = 25_000
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result = timeit(stmt=stmt, setup=setup, number=number)
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print(f"{func.__name__:<30} finished {number:,} runs in {result:.5f} seconds")
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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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# Get user input for words
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first_word = input("Enter the first word for Levenshtein distance:\n").strip()
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second_word = input("Enter the second word for Levenshtein distance:\n").strip()
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# Calculate and print Levenshtein distances
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print(f"{levenshtein_distance(first_word, second_word) = }")
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print(f"{levenshtein_distance_optimized(first_word, second_word) = }")
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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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# Benchmark the Levenshtein distance functions
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benchmark_levenshtein_distance(levenshtein_distance)
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benchmark_levenshtein_distance(levenshtein_distance_optimized)

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