Cats and Fish HihoCoder - 1631(思维模拟)

本文介绍了一道关于猫咪吃鱼的算法题目,通过优先级排序和状态标记的方法,解决特定时间内剩余完整鱼和未吃完鱼的数量问题。

Cats and Fish HihoCoder - 1631

There are many homeless cats in PKU campus. They are all happy because the students in the cat club of PKU take good care of them. Li lei is one of the members of the cat club. He loves those cats very much. Last week, he won a scholarship and he wanted to share his pleasure with cats. So he bought some really tasty fish to feed them, and watched them eating with great pleasure. At the same time, he found an interesting question:

There are m fish and n cats, and it takes ci minutes for the ith cat to eat out one fish. A cat starts to eat another fish (if it can get one) immediately after it has finished one fish. A cat never shares its fish with other cats. When there are not enough fish left, the cat which eats quicker has higher priority to get a fish than the cat which eats slower. All cats start eating at the same time. Li Lei wanted to know, after x minutes, how many fish would be left.

Input

There are no more than 20 test cases.

For each test case:

The first line contains 3 integers: above mentioned m, n and x (0 < m <= 5000, 1 <= n <= 100, 0 <= x <= 1000).

The second line contains n integers c1,c2 … cn,  ci means that it takes the ith cat ci minutes to eat out a fish ( 1<= ci <= 2000).

Output

For each test case, print 2 integers p and q, meaning that there are p complete fish(whole fish) and q incomplete fish left after x minutes.

Sample Input

2 1 1
1
8 3 5
1 3 4
4 5 1
5 4 3 2 1

Sample Output

1 0
0 1
0 3

题意:

给你m条鱼,有n只猫,每只猫吃鱼的速度不同,问x秒后有多少鱼是没被吃的,有多少是没吃完的,吃的快的优先吃鱼

思路:

因为是吃的快的优先吃鱼,那么先将吃鱼的时间从大到小排个序,优先判断吃的快的,猫的速度为ai,那么只有当时间是ai的倍数的时候他才能完整的吃掉一条鱼,这时候我们可以用一个数组b来记录他的状态,这样最后可以通过直接便利b数组来求出有多少鱼没有被完整吃完
当时间i%ai=0的时候代表已经吃完一条鱼,此时将状态标记一下bi=0,表示这只猫空闲,现在就要再给他一条鱼,所以鱼的剩余数量cnt–(完整的鱼的剩余数量实际就是第一个答案)
否则状态bi为1代表正在吃
当没有鱼的时候就可以退出了。
有多少鱼还没吃完即正在吃,遍历b数组可得到第二个答案

code:

#include <bits/stdc++.h>
using namespace std;
int main(){
    int n,m,x,a[1009],b[1009];
    while(scanf("%d%d%d",&m,&n,&x) != EOF){
        memset(b,0,sizeof(b));
        for(int i = 1; i <= n; i++){
            scanf("%d",&a[i]);
        }
        sort(a+1,a+1+n);
        int cnt = m;//剩余的完整的鱼
        for(int i = 1; i <= x; i++){
            for(int j = 1; j <= n; j++){
                if(b[j] == 0) cnt--;//如果此时猫的状态为空闲就分给他一条鱼,总条数减一
                if(i % a[j] == 0) b[j] = 0;//吃完鱼标记为空闲
                else b[j] = 1;//正在吃鱼
                if(cnt == 0) break;
            }
            if(cnt == 0) break;
        }
        int ans = 0;//正在被吃的鱼条数
        for(int i = 1; i <= n; i++){
            if(b[i] == 1) ans++;
        }
        printf("%d %d\n",cnt,ans);
    }
    return 0;
}
内容概要:本文介绍了一个基于冠豪猪优化算法(CPO)的无人机三维路径规划项目,利用Python实现了在复杂三维环境中为无人机规划安全、高效、低能耗飞行路径的完整解决方案。项目涵盖空间环境建模、无人机动力学约束、路径编码、多目标代价函数设计以及CPO算法的核心实现。通过体素网格建模、动态障碍物处理、路径平滑技术和多约束融合机制,系统能够在高维、密集障碍环境下快速搜索出满足飞行可行性、安全性与能效最优的路径,并支持在线重规划以适应动态环境变化。文中还提供了关键模块的代码示例,包括环境建模、路径评估和CPO优化流程。; 适合人群:具备一定Python编程基础和优化算法基础知识,从事无人机、智能机器人、路径规划或智能优化算法研究的相关科研人员与工程技术人员,尤其适合研究生及有一定工作经验的研发工程师。; 使用场景及目标:①应用于复杂三维环境下的无人机自主导航与避障;②研究智能优化算法(如CPO)在路径规划中的实际部署与性能优化;③实现多目标(路径最短、能耗最低、安全性最高)耦合条件下的工程化路径求解;④构建可扩展的智能无人系统决策框架。; 阅读建议:建议结合文中模型架构与代码示例进行实践运行,重点关注目标函数设计、CPO算法改进策略与约束处理机制,宜在仿真环境中测试不同场景以深入理解算法行为与系统鲁棒性。
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