Given a non-empty array of integers, return the k most frequent elements.
For example,
Given [1,1,1,2,2,3]
and k = 2, return [1,2]
.
Note:
- You may assume k is always valid, 1 ≤ k ≤ number of unique elements.
- Your algorithm's time complexity must be better than O(n log n), where n is the array's size.
传说中的哈希表。很多不知道的知识
class Solution {
public:
vector<int> topKFrequent(vector<int>& nums, int k) {
unordered_map<int, int> hash;
priority_queue<pair<int,int>> heap;
vector<int> ret;
for(int num:nums)
hash[num]++;
for(auto it:hash)
heap.push(make_pair(it.second, it.first));
for(int i=0; i<k; ++i) {
ret.push_back(heap.top().second);
heap.pop();
}
return ret;
}
};
知识点
1.priority_queue的用法,具体百度,竟然能自动排序。2.unordered_map,第一次见啊,好神奇啊
3.for循环中的int num:nums和aoto it:hash。什么鬼啊。感觉好简单
4.make_pair竟然能合二为一
5.it.first代表的key值,it.second代表的值!我去!
只能说66666
下面是小堆取代大堆
class Solution {
typedef pair<int, int> data;
public:
vector<int> topKFrequent(vector<int>& nums, int k) {
unordered_map<int, int> hash;
priority_queue<data, vector<data>, greater<data>> heap;
vector<int> ret;
for(int num:nums)
hash[num]++;
for(auto it:hash) {
heap.push(make_pair(it.second, it.first));
if(heap.size() > k) heap.pop();
}
while(!heap.empty()) {
ret.push_back(heap.top().second);
heap.pop();
}
return ret;
}
};
差不多啦。关键就是那些新的知识。真是瞎了我的狗眼。哈希啊。套路深啊