Nine Chapter
  • Introduction
    • Summary
  • 1.Binary Search
    • Introduction
    • 458.Last position of target
    • 600.Smallest Rectangle Enclosing Black Pixels
    • 585.Maximum Number in Mountain Sequence
    • 183.Wood Cut
    • 62.Search in Rotated Sorted Array
    • 63.Search in Rotated Sorted Array II
    • 159.Find Minimum in Rotated Sorted Array
    • 160.Find Minimum in Rotated Sorted Array II
    • 75.Find Peak Element
    • 60.Search Insert Position
    • 28.Search a 2D Matrix
    • 240. Search a 2D Matrix II
    • 14.First Position of Target
    • 74.First Bad Version
    • 875. Koko Eating Bananas
    • 1011. Capacity To Ship Packages Within D Days (M)
    • 410. Split Array Largest Sum (H)
    • 475. Heaters (M)
    • 1044. Longest Duplicate Substring (H)
  • 2.Binary Tree
    • Summary
      • 二叉树八股文:递归改迭代
      • BST
      • Frame
    • 66.Binary Tree Preorder Traversal
    • 67.🌟Binary Tree Inorder Traversal
    • 145. Binary Tree Postorder Traversal (E)
    • 98.Validate Binary Search Tree(M)
    • 85.Insert Node in a Binary Search Tree
    • 104. Maximum Depth of Binary Tree(E)
    • 235. Lowest Common Ancestor of a Binary Search Tree (E)
    • 236.Lowest Common Ancestor of Binary Tree(M)
    • 578.Lowest Common Ancestor III
    • 1120.Subtree with Maximum Average
    • 596.Minimum Subtree
    • 480.Binary Tree Paths
    • 453.Flatten Binary Tree to Linked List
    • 110.Balanced Binary Tree
    • 376.Binary Tree Path Sum
    • 246.Binary Tree Path Sum II
    • 475.Binary Tree Maximum Path Sum II
    • 124.Binary Tree Maximum Path Sum (H)
    • Path Sum (*)
      • 112. Path Sum
      • 113. Path Sum II
      • 437. Path Sum III
    • 177.Convert Sorted Array to Binary Search Tree With Minimal Height
    • 7.Binary Tree Serialization
    • 72,73.Construct Binary Tree
    • Binary Search Tree Path
    • 245.Subtree
    • 469.Identical Binary Tree
    • 87.Remove Node in Binary Search Tree
    • 116.Populating Next Right Pointers in Each Node (M)
    • 114. Flatten Binary Tree to Linked List(M)
    • 654.Maximum Binary Tree (M)
    • 105. 🌟Construct Binary Tree from Preorder and Inorder Traversal (M)
    • 106. Construct Binary Tree from Inorder and Postorder Traversal (M)
    • 652. Find Duplicate Subtrees(M)
    • 230. Kth Smallest Element in a BST (M)
    • 538&1038. Convert BST to Greater Tree
    • 450. Delete Node in a BST (M)
    • 701. Insert into a Binary Search Tree (M)
    • 96. Unique Binary Search Trees
    • 95. Unique Binary Search Trees II (M)
    • 1373. Maximum Sum BST in Binary Tree (H)
    • 297. Serialize and Deserialize Binary Tree (H)
    • 222. Count Complete Tree Nodes (M)
    • 1120. Maximum Average Subtree
    • 341. Flatten Nested List Iterator
    • 333. Largest BST Subtree (M)
    • 543. Diameter of Binary Tree
    • Binary Tree Longest Consecutive Sequence(*)
      • 298.Binary Tree Longest Consecutive Sequence
      • 549. Binary Tree Longest Consecutive Sequence II (M)
  • 3.Breadth First Search
    • Introduction
      • BFS 算法解题套路框架
      • 双向 BFS 优化
    • 102.Binary Tree Level Order Traversal (M)
    • 103. Binary Tree Zigzag Level Order Traversal (M)
    • 107.Binary Tree Level Order Traversal II(M)
    • 618.Search Graph Nodes
    • 207.Course Schedule (M)
    • 210.Course Schedule II (M)
    • 611.Knight Shortest Path
    • 598.Zombie in Matrix
    • 133.Clone Graph (M)
    • 178.Graph Valid Tree
    • 7.Binary Tree Serialization
    • 574.Build Post Office
    • 573.Build Post Office II
    • 127.Topological Sorting
    • 127.Word Ladder
    • 126. Word Ladder II
    • (LeetCode)515.Find Largest Value in Each Tree Row
    • 111. Minimum Depth of Binary Tree (E)
    • 752. Open the Lock
    • 542. 01 Matrix (M)
    • 1306. Jump Game III (M)
  • 4.Depth First Search+BackTracking
    • Summary
      • FloodFill 算法
    • 136.Palindrome Partitioning
    • 39.Combination Sum
    • 40.Combination Sum II
    • 377. Combination Sum IV
    • 77.Combinations (M)
    • 78.Subsets (M)
    • 90.Subsets II (M)
    • 46.🌟Permutations
    • 47.Permutations II
    • 582.Word Break II
    • 490.The Maze (M)
    • 51.N-Queens (H)
    • 52. N-Queens II (H)
    • 698. Partition to K Equal Sum Subsets (M)
    • 22. Generate Parentheses (M)
    • 岛屿问题
      • 200.Number of Islands (M)
      • 1254. Number of Closed Islands (M)
      • 1020. Number of Enclaves (M)
      • 695. Max Area of Island (M)
      • 1905. Count Sub Islands (M)
      • 694. Number of Distinct Islands
    • 131. Palindrome Partitioning (M)
    • 967. Numbers With Same Consecutive Differences (M)
    • 79. Word Search (M)
    • 212. Word Search II (M)
    • 472. Concatenated Words (H)
    • Page 2
    • 291. Word Pattern II
    • 17. Letter Combinations of a Phone Number (M)
  • 5.LinkedList
    • Summary
      • 单链表的倒数第 k 个节点
      • Merge two/k sorted LinkedList
      • Middle of the Linked List
      • 判断链表是否包含环
      • 两个链表是否相交 Intersection of Two Linked Lists
      • 递归反转链表
      • 如何判断回文链表
    • 599.Insert into a Cyclic Sorted List
    • 21.Merge Two Sorted Lists (E)
    • 23.Merge k Sorted Lists (H)
    • 105.Copy List with Random Pointer
    • 141.Linked List Cycle (E)
    • 142.Linked List Cycle II (M)
    • 148.Sort List (M)
    • 86.Partition List (M)
    • 83.Remove Duplicates from Sorted List(E)
    • 82.Remove Duplicates from Sorted List II (M)
    • 206.Reverse Linked List (E)
    • 92.Reverse Linked List II (M)
    • 143.Reorder List (M)
    • 19.Remove Nth Node From End of List (E)
    • 170.Rotate List
    • 🤔25.Reverse Nodes in k-Group (H)
    • 452.Remove Linked List Elements
    • 167.Add Two Numbers
    • 221.Add Two Numbers II
    • 876. Middle of the Linked List (E)
    • 160. Intersection of Two Linked Lists (E)
    • 234. Palindrome Linked List (E)
    • 2130. Maximum Twin Sum of a Linked List (M)
  • 6.Array
    • Summary
      • 前缀和思路PrefixSum
      • 差分数组 Difference Array
      • 双指针Two Pointers
      • 滑动窗口算法算法
      • Sliding windows II
      • 二分搜索Binary Search
      • 排序算法
      • 快速选择算法
    • 604.Window Sum
    • 138.Subarray Sum
    • 41.Maximum Subarray
    • 42.Maximum Subarray II
    • 43.Maximum Subarray III
    • 620.Maximum Subarray IV
    • 621.Maximum Subarray V
    • 6.Merge Two Sorted Arrays
    • 88.Merge Sorted Array
    • 547.Intersection of Two Arrays
    • 548.Intersection of Two Arrays II
    • 139.Subarray Sum Closest
    • 65.Median of two Sorted Arrays
    • 636.132 Pattern
    • 402.Continuous Subarray Sum
    • 303. Range Sum Query - Immutable (E)
    • 304.Range Sum Query 2D - Immutable (M)
    • 560. Subarray Sum Equals K (M)
    • 370. Range Addition(M)
    • 1109. Corporate Flight Bookings(M)
    • 1094. Car Pooling (M)
    • 76. Minimum Window Substring(H)
    • 567. Permutation in String (M)
    • 438. Find All Anagrams in a String(M)
    • 3. Longest Substring Without Repeating Characters (M)
    • 380. Insert Delete GetRandom O(1) (M)
    • 710. Random Pick with Blacklist (H)
    • 528. Random Pick with Weight (M)
    • 26. Remove Duplicates from Sorted Array (E)
    • 27. Remove Element (E)
    • 283. Move Zeroes (E)
    • 659. Split Array into Consecutive Subsequences (M)
    • 4. Median of Two Sorted Arrays (H)
    • 48. Rotate Image (M)
    • 54. Spiral Matrix (M)
    • 59. Spiral Matrix II (M)
    • 918. Maximum Sum Circular Subarray
    • 128. Longest Consecutive Sequence (M)
    • 238. Product of Array Except Self (M)
    • 1438. Longest Continuous Subarray With Absolute Diff Less Than or Equal to Limit (M)
    • 1151. Minimum Swaps to Group All 1's Together (M)
    • 2134. Minimum Swaps to Group All 1's Together II
    • 2133. Check if Every Row and Column Contains All Numbers
    • 632. Smallest Range Covering Elements from K Lists (H)
    • 36. Valid Sudoku (M)
    • 383. Ransom Note
    • 228. Summary Ranges
  • 7.Two pointers
    • Summary
      • Two Sum
      • 2Sum 3Sum 4Sum 问题
    • 1.Two Sum I
    • 170.Two Sum III - Data structure design
    • 167.Two Sum II- Input array is sorted
    • 609.Two Sum - Less than or equal to target
    • 610.Two Sum - Difference equals to targe
    • 587.Two Sum - Unique pairs
    • 533.Two Sum - Closest to target
    • 443.Two Sum - Greater than target
    • 653. Two Sum IV - Input is a BST (M)
    • 57.3Sum
    • 59.3Sum Closest
    • 58.4Sum
    • 148.Sort Colors
    • 143.Sort Colors II
    • 31.Partition Array
    • 625.Partition Array II
    • 382.Triangle Count
      • 611. Valid Triangle Number
    • 521.Remove Duplicate Numbers in Array
    • 167. Two Sum II - Input Array Is Sorted (E)
    • 870. Advantage Shuffle (M)
    • 9. Palindrome Number (E)
    • 125. Valid Palindrome(E)
    • 5. Longest Palindromic Substring (M)
    • 42. Trapping Rain Water
    • 11. Container With Most Water (M)
    • 658. Find K Closest Elements (M)
    • 392. Is Subsequence
  • 8.Data Structure
    • Summary
      • 数据结构的存储方式
      • 单调栈
      • 单调队列
      • 二叉堆 Binary Heap
      • TreeMap
      • TreeSet
      • 🌟Trie
      • Trie Application
    • 155. Min Stack (E)
    • 716. Max Stack (E)
    • 1648. Sell Diminishing-Valued Colored Balls
    • 232. Implement Queue using Stacks (E)
    • 225. Implement Stack using Queues(E)
    • 84.Largest Rectangle in Histogram
    • 128.Hash Function
    • Max Tree
    • 544.Top k Largest Numbers
    • 545.Top k Largest Numbers II
    • 613.High Five
    • 606.Kth Largest Element II
    • 5.Kth Largest Element
    • 129.Rehashing
    • 4.Ugly Number II
    • 517.Ugly Number
    • 28. Implement strStr()
    • 594.strStr II
    • 146.LRU Cache
    • 460.LFU Cache
    • 486.Merge k Sorted Arrays
    • 130.Heapify
    • 215. Kth Largest Element in an Array (M)
    • 612.K Closest Points
    • 692. Top K Frequent Words
    • 347.Top K Frequent Elements
    • 601.Flatten 2D Vector
    • 540.Zigzag Iterator
    • 541.Zigzag Iterator II
    • 423.Valid Parentheses
    • 488.Happy Number
    • 547.Intersection of Two Arrays
    • 548.Intersection of Two Arrays II
    • 627.Longest Palindrome
    • 638.Strings Homomorphism
    • 138.Subarray Sum
    • 647.Substring Anagrams
    • 171.Anagrams
    • 739. Daily Temperatures(M)
    • 496. Next Greater Element I (E)
    • 503. Next Greater Element II(M)
    • 316. Remove Duplicate Letters(M) & 1081. Smallest Subsequence of Distinct Characters
    • 239. Sliding Window Maximum (H)
    • 355. Design Twitter (M)
    • 895. Maximum Frequency Stack (H)
    • 20. Valid Parentheses (E)
    • 921. Minimum Add to Make Parentheses Valid (M)
    • 1541. Minimum Insertions to Balance a Parentheses String (M)
    • 32. Longest Valid Parentheses (H)
    • Basic Calculator (*)
      • 224. Basic Calculator
      • 227. Basic Calculator II (M)
    • 844. Backspace String Compare
    • 295. Find Median from Data Stream
    • 208. Implement Trie (Prefix Tree)
    • 461.Kth Smallest Numbers in Unsorted Array
    • 1152.Analyze user website visit pattern
    • 811. Subdomain Visit Count (M)
    • 71. Simplify Path (M)
    • 362. Design Hit Counter
  • 9.Dynamic Programming
    • Summary
      • 最优子结构 Optimal Sustructure
      • 子序列解题模板
      • 空间压缩
      • 背包问题
        • Untitled
      • 股票买卖问题
      • KMP
    • 109.Triangle
    • 110.Minimum Path Sum
    • 114.Unique Paths
    • 115.Unique Paths II
    • 70.Climbing Stairs
    • 272.Climbing StairsII
    • 116.Jump Game
    • 117.Jump Game II
    • 322.Coin Change
    • 518. Coin Change 2 ()
    • Backpack I~VI
      • LintCode 563.Backpack V (M)
    • Best Time to Buy and Sell Stock(*)
      • 121. Best Time to Buy and Sell Stock
      • 122. Best Time to Buy and Sell Stock II (M)
      • 123. Best Time to Buy and Sell Stock III (H)
      • 188. Best Time to Buy and Sell Stock IV (H)
      • 309. Best Time to Buy and Sell Stock with Cooldown (M)
      • 714. Best Time to Buy and Sell Stock with Transaction Fee (M)
    • 394.Coins in a line
    • 395.Coins in a Line II
    • 509. Fibonacci Number (E)
    • 931. Minimum Falling Path Sum (M)
    • 494. Target Sum (M)
    • 72. Edit Distance (H)
    • 300.Longest Increasing Subsequence
    • 1143. Longest Common Subsequence (M)
    • 718. Maximum Length of Repeated Subarray
    • 583. Delete Operation for Two Strings (M)
    • 712. Minimum ASCII Delete Sum for Two Strings(M)
    • 53. Maximum Subarray (E)
    • 516. Longest Palindromic Subsequence (M)
    • 1312. Minimum Insertion Steps to Make a String Palindrome (H)
    • 416. Partition Equal Subset Sum (M)
    • 64. Minimum Path Sum(M)
    • 651. 4 Keys Keyboards (M)
    • House Robber (*)
      • 198. House Robber (M)
      • 213. House Robbber II
      • 337. House Robber III (M)
    • Word Break (*)
      • 139.Word Break (M)
    • 140. Word Break II (H)
    • 828. Count Unique Characters of All Substrings of a Given String (H)
    • 174. Dungeon Game (H)
    • 1567. Maximum Length of Subarray With Positive Product (M)
  • 10. Graph
    • Introduction
      • 有向图的环检测
      • 拓扑排序
      • 二分图判定
      • Union-Find
      • 最小生成树(Minimum Spanning Tree)算法
        • KRUSKAL 最小生成树算法
        • Prim 最小生成树算法
      • Dijkstra 最短路径算法
      • BFS vs DFS
    • 797. All Paths From Source to Target (M)
    • 785. Is Graph Bipartite? (M)
    • 886. Possible Bipartition (M)
    • 130. Surrounded Regions (M)
    • 990. Satisfiability of Equality Equations (M)
    • 721. Accounts Merge (M)
    • 323. Number of Connected Components in an Undirected Graph (M)
    • 261. Graph Valid Tree
    • 1135. Connecting Cities With Minimum Cost
    • 1584. Min Cost to Connect All Points (M)
    • 277. Find the Celebrity (M)
    • 743. Network Delay Time (M)
    • 1631. Path With Minimum Effort (M)
    • 1514. Path with Maximum Probability (M)
    • 589.Connecting Graph
    • 🌟787. Cheapest Flights Within K Stops (M)
    • 2050. Parallel Courses III (H)
    • 1293. Shortest Path in a Grid with Obstacles Elimination (H)
    • 864. Shortest Path to Get All Keys (H)
    • 269. Alien Dictionary (H)
    • 1192. Critical Connections in a Network (H)
    • 529. Minesweeper (M)
  • 11.Math
    • Page 1
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  1. 6.Array

380. Insert Delete GetRandom O(1) (M)

https://leetcode.com/problems/insert-delete-getrandom-o1/

Implement the RandomizedSet class:

  • RandomizedSet() Initializes the RandomizedSet object.

  • bool insert(int val) Inserts an item val into the set if not present. Returns true if the item was not present, false otherwise.

  • bool remove(int val) Removes an item val from the set if present. Returns true if the item was present, false otherwise.

  • int getRandom() Returns a random element from the current set of elements (it's guaranteed that at least one element exists when this method is called). Each element must have the same probability of being returned.

You must implement the functions of the class such that each function works in average O(1) time complexity.

Example 1:

Input
["RandomizedSet", "insert", "remove", "insert", "getRandom", "remove", "insert", "getRandom"]
[[], [1], [2], [2], [], [1], [2], []]
Output
[null, true, false, true, 2, true, false, 2]

Explanation
RandomizedSet randomizedSet = new RandomizedSet();
randomizedSet.insert(1); // Inserts 1 to the set. Returns true as 1 was inserted successfully.
randomizedSet.remove(2); // Returns false as 2 does not exist in the set.
randomizedSet.insert(2); // Inserts 2 to the set, returns true. Set now contains [1,2].
randomizedSet.getRandom(); // getRandom() should return either 1 or 2 randomly.
randomizedSet.remove(1); // Removes 1 from the set, returns true. Set now contains [2].
randomizedSet.insert(2); // 2 was already in the set, so return false.
randomizedSet.getRandom(); // Since 2 is the only number in the set, getRandom() will always return 2.

Constraints:

  • -231 <= val <= 231 - 1

  • At most 2 * 105 calls will be made to insert, remove, and getRandom.

  • There will be at least one element in the data structure when getRandom is called.

Solution:

就是说就是让我们实现如下一个类:

class RandomizedSet {
public:
    /** 如果 val 不存在集合中,则插入并返回 true,否则直接返回 false */
     bool insert(int val) {}
    
    /** 如果 val 在集合中,则删除并返回 true,否则直接返回 false */
    bool remove(int val) {}
    
    /** 从集合中等概率地随机获得一个元素 */
    int getRandom() {}
}

本题的难点在于两点:

1、插入,删除,获取随机元素这三个操作的时间复杂度必须都是 O(1)。

2、getRandom 方法返回的元素必须等概率返回随机元素,也就是说,如果集合里面有 n 个元素,每个元素被返回的概率必须是 1/n。

我们先来分析一下:对于插入,删除,查找这几个操作,哪种数据结构的时间复杂度是 O(1)?

HashSet 肯定算一个对吧。哈希集合的底层原理就是一个大数组,我们把元素通过哈希函数映射到一个索引上;如果用拉链法解决哈希冲突,那么这个索引可能连着一个链表或者红黑树。

那么请问对于这样一个标准的 HashSet,你能否在 O(1) 的时间内实现 getRandom 函数?

其实是不能的,因为根据刚才说到的底层实现,元素是被哈希函数「分散」到整个数组里面的,更别说还有拉链法等等解决哈希冲突的机制,基本做不到 O(1) 时间等概率随机获取元素。

但是,LinkedHashSet 只是给 HashSet 增加了有序性,依然无法按要求实现我们的 getRandom 函数,因为底层用链表结构存储元素的话,是无法在 O(1) 的时间内访问某一个元素的。

根据上面的分析,对于 getRandom 方法,如果想「等概率」且「在 O(1) 的时间」取出元素,一定要满足:底层用数组实现,且数组必须是紧凑的。

这样我们就可以直接生成随机数作为索引,从数组中取出该随机索引对应的元素,作为随机元素。

但如果用数组存储元素的话,插入,删除的时间复杂度怎么可能是 O(1) 呢?

可以做到!对数组尾部进行插入和删除操作不会涉及数据搬移,时间复杂度是 O(1)。

所以,如果我们想在 O(1) 的时间删除数组中的某一个元素 val,可以先把这个元素交换到数组的尾部,然后再 pop 掉。

交换两个元素必须通过索引进行交换对吧,那么我们需要一个哈希表 valToIndex 来记录每个元素值对应的索引。

如果仅实现insert/remove的话,那么可以直接用哈希表解决。 使用(动态)数组(vector in C++, list in Python and ArrayList in Java)可以很方便地实现getRandom操作,只要先随机取一个下标,然后返回该下标对应的元素即可。 因此考虑将这两种数据结构结合起来,用一个哈希表和一个数组来维护集合中的元素。

  • insert:可以直接在数组后面添加一个新元素,并在哈希表中记录下该元素对应的数组下标。

  • remove:先将待删除的元素与数组中最后一个元素调换,由于两个元素的顺序调换了,它们在哈希表中记录的下标也应该调换。调换完成后,待删除元素变成了数组中最后一个元素,故直接将数组长度减一并将该元素从哈希表中移除即可。

  • getRandom: 直接随机取一个下标然后返回该下标对应的元素即可。

class RandomizedSet {
public:
    // 存储元素的值
    vector<int> nums;
    // 记录每个元素对应在 nums 中的索引
    unordered_map<int,int> valToIndex;
    
    bool insert(int val) {
        // 若 val 已存在,不用再插入
        if (valToIndex.count(val)) {
            return false;
        }
        // 若 val 不存在,插入到 nums 尾部,
        // 并记录 val 对应的索引值
        valToIndex[val] = nums.size();
        nums.push_back(val);
        return true;
    }
     
    bool remove(int val) {
        // 若 val 不存在,不用再删除
        if (!valToIndex.count(val)) {
            return false;
        }
        // 先拿到 val 的索引
        int index = valToIndex[val];
        // 将最后一个元素对应的索引修改为 index
        valToIndex[nums.back()] = index;
        // 交换 val 和最后一个元素
        swap(nums[index], nums.back());
        // 在数组中删除元素 val
        nums.pop_back();
        // 删除元素 val 对应的索引
        valToIndex.erase(val);
        return true;
    }
    
    int getRandom() {
        // 随机获取 nums 中的一个元素
        return nums[rand() % nums.size()];
    }
};


JAVA Version:
public class RandomizedSet {
    
    List<Integer> list;
    Map<Integer, Integer> map;
    Random random;
    public RandomizedSet() {
        // do intialization if necessary
        // 用一个list存数,用一个HashMap记录下<val, index> 数对应的坐标(numToIndex)
        list = new ArrayList<>();
        map = new HashMap<>();
        random = new Random();
    }

    /*
     * @param val: a value to the set
     * @return: true if the set did not already contain the specified element or false
     */
    public boolean insert(int val) {
        if(map.containsKey(val)){
            return false;
        }
        list.add(val);
        map.put(val, list.size()-1);
        return true;
    }

    /*
     * @param val: a value from the set
     * @return: true if the set contained the specified element or false
     */
    public boolean remove(int val) {
        if(!map.containsKey(val)){
            return false;
        }
        int index = map.get(val);
        // list.remove(index); 
        // swap非常神来之笔,可以保证list中其他元素的index order不变,
        // 用map去numToIndex获取的数也是正确的数,
        // 但是map里面已经remove里这个元素的numToIndex,所以不会找到这个元素的
        Collections.swap(list, index, list.size()-1);
        map.put(list.get(index), index);
        map.remove(val);
        list.remove(list.size()-1);
        return true;
    }

    /*
     * @return: Get a random element from the set
     */
    public int getRandom() {
        int r = random.nextInt(list.size());
        // System.out.println(list.get(r));
        return list.get(r);
    }
}

注意 remove(val) 函数,对 nums 进行插入、删除、交换时,都要记得修改哈希表 valToIndex,否则会出现错误。

至此,这道题就解决了,每个操作的复杂度都是 O(1),且随机抽取的元素概率是相等的。

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除了 HashSet,还有一些类似的数据结构,比如哈希链表 LinkedHashSet,我们前文 和 讲过这类数据结构的实现原理,本质上就是哈希表配合双链表,元素存储在双链表中。

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