Given a non-empty array of integers, return thekmost frequent elements. 给定一个不为空的数字数组,返回出现频率最高的k个元素。 Example 1: Input: nums = [1,1,1,2,2,3], k = 2 Output: [1,2] Example 2: Input:nums = [1], k = 1Output:[1] Note: You may assumekis always valid...
Given a non-empty array of integers, return the k most frequent elements. 题目链接:Top K Frequent Elements 一句话理解题意:输出数组中出现次数对多的k个数。 在如果用C语言来写这个题目,思路就是先按数的大小排序,然后再用一个结构体数组保存每个数的出现次次数。 因为数组已经有序了,所...
https://leetcode.com/problems/top-k-frequent-elements/ Given a non-empty array of integers, return thekmost frequent elements. For example, Given[ 1,1,1,2,2,3]and k = 2, return[1,2]. Note: You may assumekis always valid, 1 ≤k≤ number of unique elements. Your algorithm's t...
Given a non-empty array of integers, return thekmost frequent elements. Example 1: Input: nums = [1,1,1,2,2,3], k =2Output: [1,2] Example 2: Input: nums = [1], k = 1Output: [1] Note: You may assumekis always valid, 1 ≤k≤ number of unique elements. Your algorithm's...
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...
heappop(heap)[1] for _ in range(k)] 代码(Go) func topKFrequent(nums []int, k int) []int { // 统计 nums 中每个数字出现的次数, // 时间复杂度为 O(n) ,空间复杂度为 O(n) numToCnt := make(map[int]int) for _, num := range nums { // num 如果不在 num_to_cnt 中,则...
347. Top K Frequent Elements 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....
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 ...
Practical/ data analysis/ top-k elementstime sliding windowdata streams analysissliding window modelapproximate answersfiltered space savingsliding window algorithm/ C6130 Data handling techniquesmost frequent elements is one of the many problems associated with data streams analysis. It is a well-known ...
This paper considers the privacy of the CMS and presents an algorithm to extract the most frequent elements (also known as top-K) and their estimate from a CMS. This is possible for universes of a limited size; when the attacker has access to the sketch, its hash functions and the ...