Algorithm Notes
Summary: Edit Distance — notes not yet curated.
Time: Estimate via loops/recurrences; common classes: O(1), O(log n), O(n), O(n log n), O(n^2)
Space: Count auxiliary structures and recursion depth.
Tip: See the Big-O Guide for how to derive bounds and compare trade-offs.
Big-O Guide
Source
"""
Edit Distance
TODO: Add problem description
"""
from src.interview_workbook.leetcode._registry import register_problem
from src.interview_workbook.leetcode._types import Category, Difficulty
class Solution:
def solve(self, *args) -> int:
"""Compute minimum edit distance (Levenshtein distance)."""
if len(args) != 2:
return ""
word1, word2 = args
m, n = len(word1), len(word2)
dp = [[0] * (n + 1) for _ in range(m + 1)]
for i in range(m + 1):
dp[i][0] = i
for j in range(n + 1):
dp[0][j] = j
for i in range(1, m + 1):
for j in range(1, n + 1):
if word1[i - 1] == word2[j - 1]:
dp[i][j] = dp[i - 1][j - 1]
else:
dp[i][j] = 1 + min(
dp[i - 1][j], # delete
dp[i][j - 1], # insert
dp[i - 1][j - 1], # replace
)
return dp[m][n]
def demo():
"""Run a demo for the Edit Distance problem."""
solver = Solution()
word1 = "horse"
word2 = "ros"
result = solver.solve(word1, word2)
return str(result)
register_problem(
id=72,
slug="edit_distance",
title="Edit Distance",
category=Category.DP_2D,
difficulty=Difficulty.HARD,
tags=["string", "dynamic_programming"],
url="https://leetcode.com/problems/edit-distance/",
notes="",
)