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* 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
"""
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Author : Turfa Auliarachman
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Date : October 12, 2016
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This is a pure Python implementation of Dynamic Programming solution to the edit
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distance problem.
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The problem is :
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Given two strings A and B. Find the minimum number of operations to string B such that
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A = B. The permitted operations are removal, insertion, and substitution.
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"""
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class EditDistance:
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"""
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Use :
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solver = EditDistance()
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editDistanceResult = solver.solve(firstString, secondString)
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"""
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def __init__(self):
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self.word1 = ""
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self.word2 = ""
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self.dp = []
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def __min_dist_top_down_dp(self, m: int, n: int) -> int:
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if m == -1:
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return n + 1
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elif n == -1:
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return m + 1
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elif self.dp[m][n] > -1:
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return self.dp[m][n]
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else:
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if self.word1[m] == self.word2[n]:
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self.dp[m][n] = self.__min_dist_top_down_dp(m - 1, n - 1)
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else:
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insert = self.__min_dist_top_down_dp(m, n - 1)
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delete = self.__min_dist_top_down_dp(m - 1, n)
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replace = self.__min_dist_top_down_dp(m - 1, n - 1)
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self.dp[m][n] = 1 + min(insert, delete, replace)
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return self.dp[m][n]
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def min_dist_top_down(self, word1: str, word2: str) -> int:
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"""
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>>> EditDistance().min_dist_top_down("intention", "execution")
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5
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>>> EditDistance().min_dist_top_down("intention", "")
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9
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>>> EditDistance().min_dist_top_down("", "")
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0
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"""
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self.word1 = word1
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self.word2 = word2
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self.dp = [[-1 for _ in range(len(word2))] for _ in range(len(word1))]
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return self.__min_dist_top_down_dp(len(word1) - 1, len(word2) - 1)
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def min_dist_bottom_up(self, word1: str, word2: str) -> int:
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"""
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>>> EditDistance().min_dist_bottom_up("intention", "execution")
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5
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>>> EditDistance().min_dist_bottom_up("intention", "")
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9
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>>> EditDistance().min_dist_bottom_up("", "")
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0
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"""
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self.word1 = word1
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self.word2 = word2
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m = len(word1)
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n = len(word2)
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self.dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)]
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for i in range(m + 1):
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for j in range(n + 1):
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if i == 0: # first string is empty
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self.dp[i][j] = j
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elif j == 0: # second string is empty
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self.dp[i][j] = i
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elif word1[i - 1] == word2[j - 1]: # last characters are equal
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self.dp[i][j] = self.dp[i - 1][j - 1]
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else:
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insert = self.dp[i][j - 1]
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delete = self.dp[i - 1][j]
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replace = self.dp[i - 1][j - 1]
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self.dp[i][j] = 1 + min(insert, delete, replace)
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return self.dp[m][n]
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if __name__ == "__main__":
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solver = EditDistance()
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print("****************** Testing Edit Distance DP Algorithm ******************")
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print()
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S1 = input("Enter the first string: ").strip()
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S2 = input("Enter the second string: ").strip()
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print()
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print(f"The minimum edit distance is: {solver.min_dist_top_down(S1, S2)}")
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print(f"The minimum edit distance is: {solver.min_dist_bottom_up(S1, S2)}")
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print()
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print("*************** End of Testing Edit Distance DP Algorithm ***************")
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