""" File: edit_distancde.py Created Time: 2023-07-04 Author: krahets (krahets@163.com) """ def edit_distance_dfs(s: str, t: str, i: int, j: int) -> int: """Edit distance: Brute force search""" # If both s and t are empty, return 0 if i == 0 and j == 0: return 0 # If s is empty, return the length of t if i == 0: return j # If t is empty, return the length of s if j == 0: return i # If the two characters are equal, skip these two characters if s[i - 1] == t[j - 1]: return edit_distance_dfs(s, t, i - 1, j - 1) # The minimum number of edits = the minimum number of edits from three operations (insert, remove, replace) + 1 insert = edit_distance_dfs(s, t, i, j - 1) delete = edit_distance_dfs(s, t, i - 1, j) replace = edit_distance_dfs(s, t, i - 1, j - 1) # Return the minimum number of edits return min(insert, delete, replace) + 1 def edit_distance_dfs_mem(s: str, t: str, mem: list[list[int]], i: int, j: int) -> int: """Edit distance: Memoized search""" # If both s and t are empty, return 0 if i == 0 and j == 0: return 0 # If s is empty, return the length of t if i == 0: return j # If t is empty, return the length of s if j == 0: return i # If there is a record, return it if mem[i][j] != -1: return mem[i][j] # If the two characters are equal, skip these two characters if s[i - 1] == t[j - 1]: return edit_distance_dfs_mem(s, t, mem, i - 1, j - 1) # The minimum number of edits = the minimum number of edits from three operations (insert, remove, replace) + 1 insert = edit_distance_dfs_mem(s, t, mem, i, j - 1) delete = edit_distance_dfs_mem(s, t, mem, i - 1, j) replace = edit_distance_dfs_mem(s, t, mem, i - 1, j - 1) # Record and return the minimum number of edits mem[i][j] = min(insert, delete, replace) + 1 return mem[i][j] def edit_distance_dp(s: str, t: str) -> int: """Edit distance: Dynamic programming""" n, m = len(s), len(t) dp = [[0] * (m + 1) for _ in range(n + 1)] # State transition: first row and first column for i in range(1, n + 1): dp[i][0] = i for j in range(1, m + 1): dp[0][j] = j # State transition: the rest of the rows and columns for i in range(1, n + 1): for j in range(1, m + 1): if s[i - 1] == t[j - 1]: # If the two characters are equal, skip these two characters dp[i][j] = dp[i - 1][j - 1] else: # The minimum number of edits = the minimum number of edits from three operations (insert, remove, replace) + 1 dp[i][j] = min(dp[i][j - 1], dp[i - 1][j], dp[i - 1][j - 1]) + 1 return dp[n][m] def edit_distance_dp_comp(s: str, t: str) -> int: """Edit distance: Space-optimized dynamic programming""" n, m = len(s), len(t) dp = [0] * (m + 1) # State transition: first row for j in range(1, m + 1): dp[j] = j # State transition: the rest of the rows for i in range(1, n + 1): # State transition: first column leftup = dp[0] # Temporarily store dp[i-1, j-1] dp[0] += 1 # State transition: the rest of the columns for j in range(1, m + 1): temp = dp[j] if s[i - 1] == t[j - 1]: # If the two characters are equal, skip these two characters dp[j] = leftup else: # The minimum number of edits = the minimum number of edits from three operations (insert, remove, replace) + 1 dp[j] = min(dp[j - 1], dp[j], leftup) + 1 leftup = temp # Update for the next round of dp[i-1, j-1] return dp[m] """Driver Code""" if __name__ == "__main__": s = "bag" t = "pack" n, m = len(s), len(t) # Brute force search res = edit_distance_dfs(s, t, n, m) print(f"To change {s} to {t}, the minimum number of edits required is {res}") # Memoized search mem = [[-1] * (m + 1) for _ in range(n + 1)] res = edit_distance_dfs_mem(s, t, mem, n, m) print(f"To change {s} to {t}, the minimum number of edits required is {res}") # Dynamic programming res = edit_distance_dp(s, t) print(f"To change {s} to {t}, the minimum number of edits required is {res}") # Space-optimized dynamic programming res = edit_distance_dp_comp(s, t) print(f"To change {s} to {t}, the minimum number of edits required is {res}")