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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.
1#include <stdio.h>
2#include <stdlib.h>
3#include <string.h>
4
5#define MAX 10000
6
7typedef struct {
8 int value;
9 int count;
10} Freq;
11
12int compare(const void *a, const void *b) {
13 return ((Freq *)b)->count - ((Freq *)a)->count;
14}
15
16int* topKFrequent(int* nums, int numsSize, int k, int* returnSize) {
17 int freqMap[2 * MAX + 1] = {0};
18 Freq freqArray[numsSize];
19 int uniqueCount = 0;
20
21 for(int i = 0; i < numsSize; i++) {
22 freqMap[nums[i] + MAX]++;
23 }
24
25 for(int i = 0; i < 2 * MAX + 1; i++) {
26 if(freqMap[i]) {
27 freqArray[uniqueCount].value = i - MAX;
28 freqArray[uniqueCount].count = freqMap[i];
29 uniqueCount++;
30 }
31 }
32
33 qsort(freqArray, uniqueCount, sizeof(Freq), compare);
34
35 *returnSize = k;
36 int *result = (int*)malloc(sizeof(int) * k);
37 for(int i = 0; i < k; i++) {
38 result[i] = freqArray[i].value;
39 }
40 return result;
41}
42
43int main() {
44 int nums[] = {1, 1, 1, 2, 2, 3};
45 int k = 2;
46 int returnSize;
47 int* result = topKFrequent(nums, 6, k, &returnSize);
48 for(int i = 0; i < returnSize; i++) {
49 printf("%d ", result[i]);
50 }
51 free(result);
52 return 0;
53}
54We use a frequency map to track occurrences of each number. Then, we create a struct array for frequencies, sort it, and return the top k elements.
This approach involves using bucket sort where we create buckets for frequency counts and then extract the top k frequent elements.
Time Complexity: O(n + k).
Space Complexity: O(n).
1using System;
2using System.Collections.Generic;
using System.Linq;
public class Solution {
public int[] TopKFrequent(int[] nums, int k) {
var freqMap = new Dictionary<int, int>();
foreach (var num in nums) {
if (!freqMap.ContainsKey(num))
freqMap[num] = 0;
freqMap[num]++;
}
List<int>[] buckets = new List<int>[nums.Length + 1];
foreach (var pair in freqMap) {
int freq = pair.Value;
if (buckets[freq] == null)
buckets[freq] = new List<int>();
buckets[freq].Add(pair.Key);
}
List<int> res = new List<int>();
for (int i = buckets.Length - 1; i >= 0 && res.Count < k; --i) {
if (buckets[i] != null)
res.AddRange(buckets[i].ToArray());
}
return res.Take(k).ToArray();
}
public static void Main(string[] args) {
int[] nums = new int[] {1, 1, 1, 2, 2, 3};
int k = 2;
Solution sol = new Solution();
int[] result = sol.TopKFrequent(nums, k);
Console.WriteLine(string.Join(", ", result));
}
}
In this C# implementation, frequency of elements is handled with lists representing buckets, aiding the direct extraction of frequent elements.