Given a non-empty array of integers, return the k most frequent elements.
Example 1:
Input: nums = [1,1,1,2,2,3], k = 2
Output: [1,2]
Example 2:
Input: nums = [1], k = 1
Output: [1]
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.
class Solution { public: vector<int> topKFrequent(vector<int>& nums, int k) { unordered_map<int, int> mp; for (int i : nums) mp[i]++; vector<pair<int, int>> v(mp.begin(), mp.end()); sort(v.begin(), v.end(), cmp); vector<int> ans; for (int i = 0; i < k; ++i) ans.push_back(v[i].first); return ans; } private: static bool cmp(pair<int, int> a, pair<int, int> b) { return a.second > b.second; } };
In order to sort the map with it's value, we can't sort it directly because the iterator type on std::unordered_map
is a ForwardIterator, not a RandomAccessIterator, so the first requirement is unsatisfied. The type of the dereferenced iterator is pair<const Key, T>
, which is not MoveAssignable (can't assign to const
), so the second requirement is also unsatisfied.
we can use a vector to contain the unordered_map, then sort the vector.
Approach #2: Java.
class Solution { public List<Integer> topKFrequent(int[] nums, int k) { List<Integer>[] bucket = new List[nums.length+1]; Map<Integer, Integer> frequencyMap = new HashMap<Integer, Integer>(); for (int n : nums) { frequencyMap.put(n, frequencyMap.getOrDefault(n, 0) + 1); } for (int key : frequencyMap.keySet()) { int frequency = frequencyMap.get(key); if (bucket[frequency] == null) bucket[frequency] = new ArrayList<>(); bucket[frequency].add(key); } List<Integer> res = new ArrayList<>(); for (int pos = bucket.length - 1; pos >= 0 && res.size() < k; --pos) { if (bucket[pos] != null) res.addAll(bucket[pos]); } return res; } }
Approach #3: Python.
class Solution(object): def topKFrequent(self, nums, k): """ :type nums: List[int] :type k: int :rtype: List[int] """ return zip(*collections.Counter(nums).most_common(k))[0]
11.Use Counter to extract the top k frequent elements,
most_common(k) return a list of tuples, where the first item of the tuple is the element,
and the second item of the tuple is the count,
Thus,the built-in zip function could be used to extract the first item from the tuples
Time Submitted | Status | Runtime | Language |
---|---|---|---|
3 minutes ago | Accepted | 40 ms | python |
5 minutes ago | Accepted | 12 ms | java |
20 minutes ago | Accepted | 12 ms | cpp |