最小窗口子串
“最小窗口子串”问题的详细指南和 Python 实现。
1. 学习
“最小窗口子串”问题是滑动窗口部分的一个关键挑战。
此实现侧重于 Python 中的简单级逻辑。
在我们提供的解决方案中,我们优先考虑技术准确性和代码可读性。
2. Real-World Applications
3. Visual Intuition
可视化最小窗口子串的逻辑流程。
4. Prerequisites
5. Step-by-Step Thinking
1. Understand the problem
仔细阅读最小窗口子串的问题陈述。
2. Formulate brute force
起草一个简单的迭代解决方案。
3. Identify inefficiency
寻找冗余计算。
4. Optimize search path
使用散列或排序来加速该过程。
5. Final Implementation
清理生产标准代码。
问题陈述
给定长度分别为 m 和 n 的两个字符串 s 和 t,返回 s 的最小窗口子字符串,使得 t 中的每个字符(包括重复字符)都包含在窗口中。如果不存在这样的子字符串,则返回空字符串 ""。
答案保证是唯一的。
编写一个函数 minWindow(s: str, t: str) -> str。
- •m == len(s), n == len(t)
- •1 <= m, n <= 10^5
- •s and t consist of uppercase and lowercase English letters
示例
s = "ADOBECODEBANC", t = "ABC"
"BANC"
The minimum window substring "BANC" (indices 9-12) contains 'A', 'B', and 'C' from t.
s = "a", t = "a"
"a"
The entire string s is the minimum window.
s = "a", t = "aa"
""
Both 'a's from t must be included. Since s only has one 'a', return empty string.
Need a Hint?
Edge Cases to Watch
- 空输入结构
- 单元素输入
- 大数值范围
准备好解决了吗?
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面试见解和变化
复杂性分析分解
为什么时间: Directly evaluates all possibilities.
为什么选择太空: Uses standard local memory.
为什么时间: Optimized paths reduce total operations.
为什么选择太空: May trade memory for speed.
优化解决方案Python代码
优化解决方案Python代码
def min_window_opt(s, t):
if t == "": return ""
countT, window = {}, {}
for c in t: countT[c] = 1 + countT.get(c, 0)
have, need = 0, len(countT)
res, resLen = [-1, -1], float("infinity")
l = 0
for r in range(len(s)):
c = s[r]
window[c] = 1 + window.get(c, 0)
if c in countT and window[c] == countT[c]:
have += 1
while have == need:
if (r - l + 1) < resLen:
resLen = r - l + 1
res = [l, r]
window[s[l]] -= 1
if s[l] in countT and window[s[l]] < countT[s[l]]:
have -= 1
l += 1
l, r = res
return s[l : r + 1] if resLen != float("infinity") else ""暴力破解代码(剧透保护)
暴力破解代码(剧透保护)
def min_window_brute(s, t):
if not t: return ""
def contains_all(counts_s, counts_t):
for char in counts_t:
if counts_s.get(char, 0) < counts_t[char]:
return False
return True
t_counts = {}
for c in t: t_counts[c] = 1 + t_counts.get(c, 0)
res, res_len = "", float("inf")
for i in range(len(s)):
for j in range(i, len(s)):
sub = s[i : j + 1]
sub_counts = {}
for c in sub: sub_counts[c] = 1 + sub_counts.get(c, 0)
if contains_all(sub_counts, t_counts):
if (j - i + 1) < res_len:
res_len = j - i + 1
res = sub
return resAlgorithm Pattern Checklist
When dealing with Sliding Window data patterns.
- Are constraints clear?
- Is there a linear or logarithmic optimization possible?
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