8d1fb262da
* Added edit_distance test cases * Update edit_distance.py --------- Co-authored-by: Maxim Smolskiy <mithridatus@mail.ru>
47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
def edit_distance(source: str, target: str) -> int:
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"""
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Edit distance algorithm is a string metric, i.e., it is a way of quantifying how
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dissimilar two strings are to one another. It is measured by counting the minimum
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number of operations required to transform one string into another.
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This implementation assumes that the cost of operations (insertion, deletion and
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substitution) is always 1
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Args:
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source: the initial string with respect to which we are calculating the edit
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distance for the target
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target: the target string, formed after performing n operations on the source string
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>>> edit_distance("GATTIC", "GALTIC")
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1
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>>> edit_distance("NUM3", "HUM2")
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2
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>>> edit_distance("cap", "CAP")
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3
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>>> edit_distance("Cat", "")
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3
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>>> edit_distance("cat", "cat")
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0
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>>> edit_distance("", "123456789")
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9
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>>> edit_distance("Be@uty", "Beautyyyy!")
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5
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>>> edit_distance("lstring", "lsstring")
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1
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"""
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if len(source) == 0:
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return len(target)
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elif len(target) == 0:
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return len(source)
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delta = int(source[-1] != target[-1]) # Substitution
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return min(
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edit_distance(source[:-1], target[:-1]) + delta,
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edit_distance(source, target[:-1]) + 1,
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edit_distance(source[:-1], target) + 1,
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)
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if __name__ == "__main__":
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print(edit_distance("ATCGCTG", "TAGCTAA")) # Answer is 4
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