Python/dynamic_programming/min_distance_up_bottom.py
Alexander Pantyukhin cf915e7042
add Levinstein distance with Dynamic Programming: up -> down approach (#7171)
* add Levinstein distance with Dynamic Programming: up -> down approach

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* Update dynamic_programming/min_distance_up_bottom.py

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Christian Clauss <cclauss@me.com>
2022-10-30 14:00:16 +01:00

56 lines
1.5 KiB
Python

"""
Author : Alexander Pantyukhin
Date : October 14, 2022
This is implementation Dynamic Programming up bottom approach
to find edit distance.
The aim is to demonstate up bottom approach for solving the task.
The implementation was tested on the
leetcode: https://leetcode.com/problems/edit-distance/
"""
"""
Levinstein distance
Dynamic Programming: up -> down.
"""
def min_distance_up_bottom(word1: str, word2: str) -> int:
"""
>>> min_distance_up_bottom("intention", "execution")
5
>>> min_distance_up_bottom("intention", "")
9
>>> min_distance_up_bottom("", "")
0
>>> min_distance_up_bottom("zooicoarchaeologist", "zoologist")
10
"""
from functools import lru_cache
len_word1 = len(word1)
len_word2 = len(word2)
@lru_cache(maxsize=None)
def min_distance(index1: int, index2: int) -> int:
# if first word index is overflow - delete all from the second word
if index1 >= len_word1:
return len_word2 - index2
# if second word index is overflow - delete all from the first word
if index2 >= len_word2:
return len_word1 - index1
diff = int(word1[index1] != word2[index2]) # current letters not identical
return min(
1 + min_distance(index1 + 1, index2),
1 + min_distance(index1, index2 + 1),
diff + min_distance(index1 + 1, index2 + 1),
)
return min_distance(0, 0)
if __name__ == "__main__":
import doctest
doctest.testmod()