From 6b55d3eceb7c3930552126d39e1da2d649493d9f Mon Sep 17 00:00:00 2001
From: Chenjie Xu <cxuscience@gmail.com>
Date: Wed, 11 Nov 2020 16:29:21 +0800
Subject: [PATCH] =?UTF-8?q?=E3=80=9072.=20=E7=BC=96=E8=BE=91=E8=B7=9D?=
 =?UTF-8?q?=E7=A6=BB=E3=80=91=E3=80=90Python=E3=80=91?=
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---
 动态规划系列/编辑距离.md | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/动态规划系列/编辑距离.md b/动态规划系列/编辑距离.md
index 27b8c22..ae6ff4e 100644
--- a/动态规划系列/编辑距离.md
+++ b/动态规划系列/编辑距离.md
@@ -290,3 +290,31 @@ class Node {
 <p align='center'>
 <img src="../pictures/table_qr2.jpg" width=500 >
 </p>
+
+[ChenjieXu](https://github.com/ChenjieXu) 提供Python版本代码：  
+
+```python3
+def minDistance(word1, word2):
+    m, n = len(word1), len(word2)
+    # 创建 DP 数组
+    dp = [[0] * (n + 1) for _ in range(m + 1)]
+
+    # base case初始化
+    for i in range(m + 1):
+        dp[i][0] = i
+    for j in range(n + 1):
+        dp[0][j] = j
+
+    # 自底向上求解
+    for i in range(1, m + 1):
+        for j in range(1, n + 1):
+            # 状态转移方程
+            if word1[i - 1] == word2[j - 1]:
+                dp[i][j] = dp[i - 1][j - 1]
+            else:
+                dp[i][j] = min(dp[i - 1][j] + 1, 
+                               dp[i][j - 1] + 1,
+                               dp[i - 1][j - 1] + 1)
+    # 储存着整个 word1 和 word2 的最小编辑距离
+    return dp[m][n]
+````