347. Top K Frequent Elements

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(nlogn), where n is the array's size.

Thoughts:

1. Bucket sort:

2. MaxHeap Implementation (BUT O(nlogn))

3. TreeMap: Use freq as the key so can get freq in order

Code

class Solution(object):
    def topKFrequent(self, nums, k):
        """
        :type nums: List[int]
        :type k: int
        :rtype: List[int]
        """
        m = [0] * (len(nums) + 1)
        cnt = collections.Counter(nums)
        for i in range(len(m)):
            m[i] = []

        for v in cnt:
            m[cnt[v]].append(v)
        res = []
        for l in (m[::-1]):
            res += l
            if len(res) >= k: break
        return res

Code:

public class Solution {
    public List<Integer> topKFrequent(int[] nums, int k) {
        Map<Integer, Integer> map = new HashMap<>();
        for(int n: nums){
            map.put(n, map.getOrDefault(n,0)+1);
        }

        // corner case: if there is only one number in nums, we need the bucket has index 1.
        List<Integer>[] bucket = new List[nums.length+1];
        for(int n:map.keySet()){
            int freq = map.get(n);
            if(bucket[freq]==null)
                bucket[freq] = new LinkedList<>();
            bucket[freq].add(n);
        }

        List<Integer> res = new LinkedList<>();
        for(int i=bucket.length-1; i>0 && k>0; --i){
            if(bucket[i]!=null){
                List<Integer> list = bucket[i]; 
                res.addAll(list);
                k-= list.size();
            }
        }

        return res;
    }
}

Code: max heap T:O(nlogn); S:O(n)

// use maxHeap. Put entry into maxHeap so we can always poll a number with largest frequency
public class Solution {
    public List<Integer> topKFrequent(int[] nums, int k) {
        Map<Integer, Integer> map = new HashMap<>();
        for(int n: nums){
            map.put(n, map.getOrDefault(n,0)+1);
        }

        PriorityQueue<Map.Entry<Integer, Integer>> maxHeap = 
                         new PriorityQueue<>((a,b)->(b.getValue()-a.getValue()));
        for(Map.Entry<Integer,Integer> entry: map.entrySet()){
            maxHeap.add(entry);
        }

        List<Integer> res = new ArrayList<>();
        while(res.size()<k){
            Map.Entry<Integer, Integer> entry = maxHeap.poll();
            res.add(entry.getKey());
        }
        return res;
    }
}

Code: Java TreeMap: T:O(nlogn); S:O(n)

public class Solution {
    public List<Integer> topKFrequent(int[] nums, int k) {
        Map<Integer, Integer> map = new HashMap<>();
        for(int n: nums){
            map.put(n, map.getOrDefault(n,0)+1);
        }

        TreeMap<Integer, List<Integer>> freqMap = new TreeMap<>();
        for(int num : map.keySet()){
           int freq = map.get(num);
           if(!freqMap.containsKey(freq)){
               freqMap.put(freq, new LinkedList<>());
           }
           freqMap.get(freq).add(num);
        }

        List<Integer> res = new ArrayList<>();
        while(res.size()<k){
            Map.Entry<Integer, List<Integer>> entry = freqMap.pollLastEntry();
            res.addAll(entry.getValue());
        }
        return res;
    }
}

Code: Python having fun...

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]