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* Add Damerau-Levenshtein distance algorithm * fix: precommit check * fix: doc correction * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * refactor: use variable for length and doc correction * Update damerau_levenshtein_distance.py * Update damerau_levenshtein_distance.py --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Christian Clauss <cclauss@me.com>
72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
"""
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This script is a implementation of the Damerau-Levenshtein distance algorithm.
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It's an algorithm that measures the edit distance between two string sequences
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More information about this algorithm can be found in this wikipedia article:
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https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance
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"""
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def damerau_levenshtein_distance(first_string: str, second_string: str) -> int:
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"""
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Implements the Damerau-Levenshtein distance algorithm that measures
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the edit distance between two strings.
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Parameters:
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first_string: The first string to compare
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second_string: The second string to compare
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Returns:
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distance: The edit distance between the first and second strings
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>>> damerau_levenshtein_distance("cat", "cut")
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1
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>>> damerau_levenshtein_distance("kitten", "sitting")
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3
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>>> damerau_levenshtein_distance("hello", "world")
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4
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>>> damerau_levenshtein_distance("book", "back")
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2
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>>> damerau_levenshtein_distance("container", "containment")
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3
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>>> damerau_levenshtein_distance("container", "containment")
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3
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"""
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# Create a dynamic programming matrix to store the distances
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dp_matrix = [[0] * (len(second_string) + 1) for _ in range(len(first_string) + 1)]
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# Initialize the matrix
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for i in range(len(first_string) + 1):
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dp_matrix[i][0] = i
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for j in range(len(second_string) + 1):
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dp_matrix[0][j] = j
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# Fill the matrix
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for i, first_char in enumerate(first_string, start=1):
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for j, second_char in enumerate(second_string, start=1):
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cost = int(first_char != second_char)
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dp_matrix[i][j] = min(
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dp_matrix[i - 1][j] + 1, # Deletion
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dp_matrix[i][j - 1] + 1, # Insertion
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dp_matrix[i - 1][j - 1] + cost, # Substitution
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)
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if (
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i > 1
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and j > 1
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and first_string[i - 1] == second_string[j - 2]
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and first_string[i - 2] == second_string[j - 1]
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):
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# Transposition
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dp_matrix[i][j] = min(dp_matrix[i][j], dp_matrix[i - 2][j - 2] + cost)
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return dp_matrix[-1][-1]
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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