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This approach uses a hash map to count the frequency of each element. We then use a min-heap to keep track of the top k elements.
Time Complexity: O(n log n) due to sorting.
Space Complexity: O(n) for storing frequencies.
1using System;
2using System.Collections.Generic;
3using System.Linq;
4
5public class Solution {
6 public int[] TopKFrequent(int[] nums, int k) {
7 var freqMap = new Dictionary<int, int>();
8 foreach (var num in nums) {
9 if (!freqMap.ContainsKey(num))
10 freqMap[num] = 0;
11 freqMap[num]++;
12 }
13
14 return freqMap.OrderByDescending(x => x.Value).Take(k).Select(x => x.Key).ToArray();
15 }
16
17 public static void Main(string[] args) {
18 int[] nums = new int[] {1, 1, 1, 2, 2, 3};
19 int k = 2;
20 Solution sol = new Solution();
21 int[] result = sol.TopKFrequent(nums, k);
22 Console.WriteLine(string.Join(", ", result));
23 }
24}
25We use a dictionary to count frequencies, then order by descending frequency and take the top k elements.
We manually record each element's frequency and sort the list based on counts into a frequency bucket. Then, we retrieve the top k elements.