Meeting 加虚拟边

在Farmer John将农场分为多个区块后,Bessie和Elsie试图找到最快的方式在某一点会面。通过构建一个包含各区块间旅行时间的图,并使用SPFA算法寻找最短路径,此问题得以解决。
Bessie and her friend Elsie decide to have a meeting. However, after Farmer John decorated his 
fences they were separated into different blocks. John's farm are divided into nn blocks labelled from 11 to nn. 
Bessie lives in the first block while Elsie lives in the nn-th one. They have a map of the farm 
which shows that it takes they titi minutes to travel from a block in EiEito another block 
in EiEi where Ei (1im)Ei (1≤i≤m) is a set of blocks. They want to know how soon they can meet each other 
and which block should be chosen to have the meeting.

InputThe first line contains an integer T (1T6)T (1≤T≤6), the number of test cases. Then TT test cases 
follow. 

The first line of input contains nn and mm. 2n1052≤n≤105. The following mm lines describe the sets Ei (1im)Ei (1≤i≤m). Each line will contain two integers ti(1ti109)ti(1≤ti≤109) and Si (Si>0)Si (Si>0) firstly. Then SiSi integer follows which are the labels of blocks in EiEi. It is guaranteed that mi=1Si106∑i=1mSi≤106.
OutputFor each test case, if they cannot have the meeting, then output "Evil John" (without quotes) in one line. 

Otherwise, output two lines. The first line contains an integer, the time it takes for they to meet. 
The second line contains the numbers of blocks where they meet. If there are multiple 
optional blocks, output all of them in ascending order.Sample Input

2
5 4
1 3 1 2 3
2 2 3 4
10 2 1 5
3 3 3 4 5
3 1
1 2 1 2

Sample Output

Case #1: 3
3 4
Case #2: Evil John

 

可以将给顶集合的元素连到一个虚拟结点上,求出最短路来再/2,这样避免了大量的重复加边,还避免了小数

 

#include<iostream>
#include<cstdio>
#include<cmath>
#include<cstring>
#include<sstream>
#include<algorithm>
#include<queue>
#include<vector>
#include<cmath>
#include<map>
#include<stack>
#include<set>
#include<memory>
#include<bitset>
#include<string>
#include<functional>
using namespace std;
typedef long long LL;
typedef unsigned long long ULL;
const int MAXN = 5e5 ;

#define INF 0x3f3f3f3f

/*
连接虚拟结点
到该点的距离为L
求出最短路/2 避免小数!
*/
LL T, d, n, m, cnt;
struct edge
{
    edge(LL _a,LL _b):to(_a),cost(_b){}
    LL to, cost;
};
vector<edge>E[MAXN];
LL    dist1[MAXN], dist2[MAXN];
bool vis[MAXN];
void addedge(LL f,LL to,LL dis)
{
    E[f].push_back(edge(to, dis));
    E[to].push_back(edge(f, dis));
}
void init()
{
    for (LL i = 0; i < MAXN; i++)
        E[i].clear();
}
void spfa(LL beg, LL lowcost[])
{
    queue<LL> q;
    memset(vis, false, sizeof(vis));
    for (int i = 0; i <= n + m; i++)
        lowcost[i] = INF;
    lowcost[beg] = 0;
    vis[beg] = true;
    q.push(beg);
    while (!q.empty())
    {
        LL f = q.front();
        q.pop();
        vis[f] = false;
        for (int i = 0; i < E[f].size(); i++)
        {
            if (lowcost[E[f][i].to] > lowcost[f] + E[f][i].cost)
            {
                lowcost[E[f][i].to] = lowcost[f] + E[f][i].cost;
                if (!vis[E[f][i].to])
                {
                    vis[E[f][i].to] = true;
                    q.push(E[f][i].to);
                }
            }
        }
    }
}
int main()
{
    scanf("%lld", &T);
    for(LL cas = 1;cas <= T; cas++)
    {
        init();
        scanf("%lld%lld", &n, &m);
        LL tmp, tt;
        for (LL i = 1; i <= m; i++)
        {
            scanf("%lld%lld", &d, &tmp);
            while (tmp--)
            {
                scanf("%lld", &tt);
                addedge(tt, n + i, d);
            }
        }
        spfa(1, dist1);
        spfa(n , dist2);
        LL ans = INF;
        for (int i = 0; i <= n; i++)
            ans = min(ans, max(dist1[i], dist2[i]));
        if (ans == INF)
            printf("Case #%lld: Evil John\n", cas);
        else
        {
            printf("Case #%lld: %lld\n", cas, ans / 2);
            bool f = false;
            for (int i = 1; i <= n; i++)
            {
                if (max(dist1[i], dist2[i]) == ans)
                {
                    if (!f)
                        printf("%d", i), f = true;
                    else
                        printf(" %d", i);
                }
            }
            printf("\n");
        }
    }
}

 

转载于:https://www.cnblogs.com/joeylee97/p/7395535.html

内容概要:本文介绍了ENVI Deep Learning V1.0的操作教程,重点讲解了如何利用ENVI软件进行深度学习模型的训练与应用,以实现遥感图像中特定目标(如集装箱)的自动提取。教程涵盖了从数据准备、标签图像创建、模型初始化与训练,到执行分类及结果优化的完整流程,并介绍了精度评价与通过ENVI Modeler实现一键化建模的方法。系统基于TensorFlow框架,采用ENVINet5(U-Net变体)架构,支持通过点、线、面ROI或分类图生成标签数据,适用于多/高光谱影像的单一类别特征提取。; 适合人群:具备遥感图像处理基础,熟悉ENVI软件操作,从事地理信息、测绘、环境监测等相关领域的技术人员或研究人员,尤其是希望将深度学习技术应用于遥感目标识别的初学者与实践者。; 使用场景及目标:①在遥感影像中自动识别和提取特定地物目标(如车辆、建筑、道路、集装箱等);②掌握ENVI环境下深度学习模型的训练流程与关键参数设置(如Patch Size、Epochs、Class Weight等);③通过模型调优与结果反馈提升分类精度,实现高效自动化信息提取。; 阅读建议:建议结合实际遥感项目练,重点关注标签数据制作、模型参数配置与结果后处理环节,充分利用ENVI Modeler进行自动化建模与参数优化,同时注意软硬件环境(特别是NVIDIA GPU)的配置要求以保障训练效率。
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