mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-11-23 21:11:08 +00:00
39e5bc5980
* Refactor bottom-up function to be class method
* Add type hints
* Update convolve function namespace
* Remove depreciated np.float
* updating DIRECTORY.md
* updating DIRECTORY.md
* updating DIRECTORY.md
* updating DIRECTORY.md
* Renamed function for consistency
* updating DIRECTORY.md
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
Co-authored-by: Chris O <46587501+ChrisO345@users.noreply.github.com>
104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
"""
|
|
Author : Turfa Auliarachman
|
|
Date : October 12, 2016
|
|
|
|
This is a pure Python implementation of Dynamic Programming solution to the edit
|
|
distance problem.
|
|
|
|
The problem is :
|
|
Given two strings A and B. Find the minimum number of operations to string B such that
|
|
A = B. The permitted operations are removal, insertion, and substitution.
|
|
"""
|
|
|
|
|
|
class EditDistance:
|
|
"""
|
|
Use :
|
|
solver = EditDistance()
|
|
editDistanceResult = solver.solve(firstString, secondString)
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.word1 = ""
|
|
self.word2 = ""
|
|
self.dp = []
|
|
|
|
def __min_dist_top_down_dp(self, m: int, n: int) -> int:
|
|
if m == -1:
|
|
return n + 1
|
|
elif n == -1:
|
|
return m + 1
|
|
elif self.dp[m][n] > -1:
|
|
return self.dp[m][n]
|
|
else:
|
|
if self.word1[m] == self.word2[n]:
|
|
self.dp[m][n] = self.__min_dist_top_down_dp(m - 1, n - 1)
|
|
else:
|
|
insert = self.__min_dist_top_down_dp(m, n - 1)
|
|
delete = self.__min_dist_top_down_dp(m - 1, n)
|
|
replace = self.__min_dist_top_down_dp(m - 1, n - 1)
|
|
self.dp[m][n] = 1 + min(insert, delete, replace)
|
|
|
|
return self.dp[m][n]
|
|
|
|
def min_dist_top_down(self, word1: str, word2: str) -> int:
|
|
"""
|
|
>>> EditDistance().min_dist_top_down("intention", "execution")
|
|
5
|
|
>>> EditDistance().min_dist_top_down("intention", "")
|
|
9
|
|
>>> EditDistance().min_dist_top_down("", "")
|
|
0
|
|
"""
|
|
self.word1 = word1
|
|
self.word2 = word2
|
|
self.dp = [[-1 for _ in range(len(word2))] for _ in range(len(word1))]
|
|
|
|
return self.__min_dist_top_down_dp(len(word1) - 1, len(word2) - 1)
|
|
|
|
def min_dist_bottom_up(self, word1: str, word2: str) -> int:
|
|
"""
|
|
>>> EditDistance().min_dist_bottom_up("intention", "execution")
|
|
5
|
|
>>> EditDistance().min_dist_bottom_up("intention", "")
|
|
9
|
|
>>> EditDistance().min_dist_bottom_up("", "")
|
|
0
|
|
"""
|
|
self.word1 = word1
|
|
self.word2 = word2
|
|
m = len(word1)
|
|
n = len(word2)
|
|
self.dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)]
|
|
|
|
for i in range(m + 1):
|
|
for j in range(n + 1):
|
|
if i == 0: # first string is empty
|
|
self.dp[i][j] = j
|
|
elif j == 0: # second string is empty
|
|
self.dp[i][j] = i
|
|
elif word1[i - 1] == word2[j - 1]: # last characters are equal
|
|
self.dp[i][j] = self.dp[i - 1][j - 1]
|
|
else:
|
|
insert = self.dp[i][j - 1]
|
|
delete = self.dp[i - 1][j]
|
|
replace = self.dp[i - 1][j - 1]
|
|
self.dp[i][j] = 1 + min(insert, delete, replace)
|
|
return self.dp[m][n]
|
|
|
|
|
|
if __name__ == "__main__":
|
|
solver = EditDistance()
|
|
|
|
print("****************** Testing Edit Distance DP Algorithm ******************")
|
|
print()
|
|
|
|
S1 = input("Enter the first string: ").strip()
|
|
S2 = input("Enter the second string: ").strip()
|
|
|
|
print()
|
|
print(f"The minimum edit distance is: {solver.min_dist_top_down(S1, S2)}")
|
|
print(f"The minimum edit distance is: {solver.min_dist_bottom_up(S1, S2)}")
|
|
print()
|
|
print("*************** End of Testing Edit Distance DP Algorithm ***************")
|