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 islinearly 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) { m[val].insert(nums.size()); nums.push_back(val); return m[val].size() == 1; } /** Removes a value from the collection. Returns true if the collection contained the specified element. */ bool remove(int val) { if (m[val].empty()) return false; int idx = *m[val].begin(); m[val].erase(idx); if (nums.size() - 1 != idx) { int t = nums.back(); nums[idx] = t; m[t].erase(nums.size() - 1); m[t].insert(idx); } nums.pop_back(); return true; } /** Get a random element from the collection. */ int getRandom() { return nums[rand() % nums.size()]; } private: vector<int> nums; unordered_map<int, unordered_set<int>> m; };