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Design a data structure that supports all following operations in average O(1) time.
Note: Duplicate elements are allowed.
insert(val): Inserts an item val to the collection.
remove(val): Removes an item val from the collection if present.
getRandom: Returns a random element from current collection of elements. The probability of each element being returned is linearly related to the number of same value the collection contains.
Example:
// Init an empty collection.
RandomizedCollection collection = new RandomizedCollection();
// Inserts 1 to the collection. Returns true as the collection did not contain 1.
collection.insert(1);
// Inserts another 1 to the collection. Returns false as the collection contained 1. Collection now contains [1,1].
collection.insert(1);
// Inserts 2 to the collection, returns true. Collection now contains [1,1,2].
collection.insert(2);
// getRandom should return 1 with the probability 2/3, and returns 2 with the probability 1/3.
collection.getRandom();
// Removes 1 from the collection, returns true. Collection now contains [1,2].
collection.remove(1);
// getRandom should return 1 and 2 both equally likely.
collection.getRandom();
class RandomizedCollection {
public:
/** Initialize your data structure here. */
RandomizedCollection() {
}
/** Inserts a value to the collection. Returns true if the collection did not already contain the specified element. */
bool insert(int val) {
hmap[val].push_back(values.size());
values.emplace_back(val, hmap[val].size()-1);
return hmap[val].size()==1;
}
/** Removes a value from the collection. Returns true if the collection contained the specified element. */
bool remove(int val) {
if(!hmap.count(val)) return false;
int index = hmap[val].back();
hmap[val].pop_back();
auto const&last_entry = values.back();
hmap[last_entry.first][last_entry.second] = index;
values[index]=last_entry;
if(hmap[val].empty())hmap.erase(val);
return true;
}
/** Get a random element from the collection. */
int getRandom() {
// write your code here
return values[rand()%values.size()].first;
}
vector<pair<int,int>> values;
unordered_map<int,vector<int>> hmap;
Design a data structure that supports all following operations in average O(1) time.
Note: Duplicate elements are allowed.
insert(val)
: Inserts an item val to the collection.remove(val)
: Removes an item val from the collection if present.getRandom
: Returns a random element from current collection of elements. The probability of each element being returned is linearly related to the number of same value the collection contains.Example:
这题是之前那道 Insert Delete GetRandom O(1) 的拓展,与其不同的是,之前那道题不能有重复数字,而这道题可以有,那么就不能像之前那道题那样建立每个数字和其坐标的映射了,但是我们可以建立数字和其所有出现位置的集合之间的映射,虽然写法略有不同,但是思路和之前那题完全一样,都是将数组最后一个位置的元素和要删除的元素交换位置,然后删掉最后一个位置上的元素。对于 insert 函数,我们将要插入的数字在 nums 中的位置加入 m[val] 数组的末尾,然后在数组 nums 末尾加入 val,我们判断是否有重复只要看 m[val] 数组只有刚加的 val 一个值还是有多个值。remove 函数是这题的难点,我们首先看 HashMap 中有没有 val,没有的话直接返回 false。然后我们取出 nums 的尾元素,把尾元素 HashMap 中的位置数组中的最后一个位置更新为 m[val] 的尾元素,这样我们就可以删掉 m[val] 的尾元素了,如果 m[val] 只有一个元素,那么我们把这个映射直接删除。然后将 nums 数组中的尾元素删除,并把尾元素赋给 val 所在的位置,注意我们在建立 HashMap 的映射的时候需要用堆而不是普通的 vector 数组,因为我们每次 remove 操作后都会移除 nums 数组的尾元素,如果我们用 vector 来保存数字的坐标,而且只移出末尾数字的话,有可能出现前面的坐标大小超过了此时 nums 的大小的情况,就会出错,所以我们用优先队列对所有的相同数字的坐标进行自动排序,每次把最大位置的坐标移出即可,参见代码如下:
解法一:
有网友指出上面的方法其实不是真正的 O(1) 时间复杂度,因为优先队列的 push 不是常数级的,博主一看果然是这样的,为了严格的遵守 O(1) 的时间复杂度,我们将优先队列换成 unordered_set,其插入删除的操作都是常数量级的,其他部分基本不用变,参见代码如下:
解法二:
Github 同步地址:
#381
类似题目:
Insert Delete GetRandom O(1)
参考资料:
https://leetcode.com/problems/insert-delete-getrandom-o1-duplicates-allowed/
https://leetcode.com/problems/insert-delete-getrandom-o1-duplicates-allowed/discuss/85635/c-two-solutions
https://leetcode.com/problems/insert-delete-getrandom-o1-duplicates-allowed/discuss/85541/C%2B%2B-128m-Solution-Real-O(1)-Solution
https://leetcode.com/problems/insert-delete-getrandom-o1-duplicates-allowed/discuss/197641/C%2B%2B-30-ms-hashmap-hashset-and-vector
LeetCode All in One 题目讲解汇总(持续更新中...)
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