414. Third Maximum Number

本文介绍了一种在O(n)时间复杂度内找到数组中第三大数的方法,通过排序和遍历,有效处理了重复元素,确保了结果的正确性。

Given a non-empty array of integers, return the third maximum number in this array. If it does not exist, return the maximum number. The time complexity must be in O(n).

Example 1:

Input: [3, 2, 1]

Output: 1

Explanation: The third maximum is 1.

 

Example 2:

Input: [1, 2]

Output: 2

Explanation: The third maximum does not exist, so the maximum (2) is returned instead.

 

Example 3:

Input: [2, 2, 3, 1]

Output: 1

Explanation: Note that the third maximum here means the third maximum distinct number.
Both numbers with value 2 are both considered as second maximum.

 

思路:

1,排序(小到大)

2,判断数组中有几个数,若小于3,则直接输出最大的

3,设置一个数组变量temp,从最大的那个数开始放入数组中,若是放到第三个 ,则满足最大的第三个数,则返回

      遍历结束,temp中还没有三个数的话,则返回temp[0],也就是数组中最大的数

#pragma once
#include<iostream>
#include<vector>
#include<algorithm>
using namespace std;

class Solution {
public:
	int thirdMax(vector<int>& nums) {
		vector<int> temp ;
		//排序
		sort(nums.begin(), nums.end());
		if (nums.size() < 3)
		{
			return nums[nums.size() - 1];
		}

		for (int i = nums.size() - 1; i >= 0; i--)
		{
			//最大的数加入temp中
			if (i == nums.size() - 1)
			{
				temp.push_back(nums[i]);
			}
			else
			{
				if (nums[i] < temp[temp.size() - 1])
				{
					temp.push_back(nums[i]);
					//如果有3个值进入,则第三个就是第三大的数
					if (temp.size() == 3)
					{
						return temp[temp.size()-1];
					}
				}
			}

		}

		//没有第三个最大的,则返回最大的数
		return temp[0];
	}
};

 

A piece of paper contains an array of n integers a1,&thinsp;a2,&thinsp;...,&thinsp;an. Your task is to find a number that occurs the maximum number of times in this array. However, before looking for such number, you are allowed to perform not more than k following operations — choose an arbitrary element from the array and add 1 to it. In other words, you are allowed to increase some array element by 1 no more than k times (you are allowed to increase the same element of the array multiple times). Your task is to find the maximum number of occurrences of some number in the array after performing no more than k allowed operations. If there are several such numbers, your task is to find the minimum one. Input The first line contains two integers n and k (1&thinsp;≤&thinsp;n&thinsp;≤&thinsp;105; 0&thinsp;≤&thinsp;k&thinsp;≤&thinsp;109) — the number of elements in the array and the number of operations you are allowed to perform, correspondingly. The third line contains a sequence of n integers a1,&thinsp;a2,&thinsp;...,&thinsp;an (|ai|&thinsp;≤&thinsp;109) — the initial array. The numbers in the lines are separated by single spaces. Output In a single line print two numbers — the maximum number of occurrences of some number in the array after at most k allowed operations are performed, and the minimum number that reaches the given maximum. Separate the printed numbers by whitespaces. Examples Inputcopy Outputcopy 5 3 6 3 4 0 2 3 4 Inputcopy Outputcopy 3 4 5 5 5 3 5 Inputcopy Outputcopy 5 3 3 1 2 2 1 4 2 Note In the first sample your task is to increase the second element of the array once and increase the fifth element of the array twice. Thus, we get sequence 6,&thinsp;4,&thinsp;4,&thinsp;0,&thinsp;4, where number 4 occurs 3 times. In the second sample you don't need to perform a single operation or increase each element by one. If we do nothing, we get array 5,&thinsp;5,&thinsp;5, if we increase each by one, we get 6,&thinsp;6,&thinsp;6. In both cases the maximum number of occurrences equals 3. So we should do nothing, as number 5 is less than number 6. In the third sample we should increase the second array element once and the fifth element once. Thus, we get sequence 3,&thinsp;2,&thinsp;2,&thinsp;2,&thinsp;2, where number 2 occurs 4 times.讲解这道题
最新发布
09-21
### 问题分析 此问题旨在不超过 `k` 次操作(每次操作可将数组中任意元素加 1)的条件下,找出数组中某个数字出现的最大次数以及达到该最大次数的最小数字。核心在于考虑如何利用有限的操作次数,让某个数字尽可能多地出现。 ### 算法思路 可以采用排序加滑动窗口的方法来解决该问题。 1. 对数组进行排序,这样方便后续操作和计算。 2. 利用滑动窗口来遍历数组,计算在不超过 `k` 次操作的情况下,窗口内元素能够变成同一个数字的最大窗口大小。 3. 在遍历过程中,记录最大窗口大小以及对应的最小数字。 ### 代码实现 ```python def max_frequency(nums, k): nums.sort() n = len(nums) left = 0 total = 0 max_count = 0 result_num = 0 for right in range(n): total += nums[right] # 计算当前窗口内元素全部变成 nums[right] 所需的操作次数 while (right - left + 1) * nums[right] - total > k: total -= nums[left] left += 1 # 更新最大出现次数和对应的数字 if right - left + 1 > max_count: max_count = right - left + 1 result_num = nums[right] return max_count, result_num nums = [1, 2, 4] k = 5 max_count, result_num = max_frequency(nums, k) print(f"最大出现次数: {max_count}, 达到最大次数的最小数字: {result_num}") ``` ### 复杂度分析 - **时间复杂度**:排序操作的时间复杂度为 $O(n log n)$,滑动窗口遍历数组的时间复杂度为 $O(n)$,因此总的时间复杂度为 $O(n log n)$。 - **空间复杂度**:排序所需的额外空间复杂度为 $O(log n)$,因此总的空间复杂度为 $O(log n)$。 ### 复杂度分析 - **时间复杂度**:排序操作的时间复杂度为 $O(n log n)$,滑动窗口遍历数组的时间复杂度为 $O(n)$,因此总的时间复杂度为 $O(n log n)$。 - **空间复杂度**:排序所需的额外空间复杂度为 $O(log n)$,因此总的空间复杂度为 $O(log n)$。 ### 代码解释 1. **排序**:对数组 `nums` 进行排序,方便后续计算和操作。 2. **滑动窗口**:使用 `left` 和 `right` 指针来表示窗口的左右边界,`total` 用于记录窗口内元素的总和。 3. **计算操作次数**:通过 `(right - left + 1) * nums[right] - total` 计算将窗口内元素全部变成 `nums[right]` 所需的操作次数。 4. **更新窗口**:如果操作次数超过 `k`,则将 `left` 指针右移,同时更新 `total`。 5. **记录结果**:在每次更新窗口后,更新最大出现次数和对应的数字。 ### 示例 假设 `nums = [1, 2, 4]`,`k = 5`。 - 排序后数组为 `[1, 2, 4]`。 - 初始时,`left = 0`,`right = 0`,`total = 1`。 - 当 `right = 1` 时,`total = 1 + 2 = 3`,操作次数为 `(2 - 0 + 1) * 2 - 3 = 3 <= 5`,窗口可以扩大。 - 当 `right = 2` 时,`total = 1 + 2 + 4 = 7`,操作次数为 `(3 - 0 + 1) * 4 - 7 = 9 > 5`,需要缩小窗口,将 `left` 右移一位,`left = 1`,`total = 2 + 4 = 6`,操作次数为 `(3 - 1 + 1) * 4 - 6 = 6 > 5`,继续缩小窗口,`left = 2`,`total = 4`,操作次数为 `(3 - 2 + 1) * 4 - 4 = 4 <= 5`。 - 最终最大窗口大小为 1,对应的数字为 4。
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