3427: Dark roads

本文探讨了在不牺牲市民安全感的前提下,通过优化道路照明来减少能耗的方法,具体介绍了如何在夜晚关闭某些街道的照明,同时确保从每个交汇点到其他交汇点至少有一条被照亮的道路,以此达到节省开支的目的。

http://cs.scu.edu.cn/soj/problem.action?id=3427

Description

Economic times these days are tough, even in Byteland. To reduce the operating costs, the government of Byteland has decided to optimize the road lighting. Till now every road was illuminated all night long, which costs 1 Bytelandian Dollar per meter and day. To save money, they decided to no longer illuminate every road, but to switch off the road lighting of some streets. To make sure that the inhabitants of Byteland still feel safe, they want to optimize the lighting in such a way, that after darkening some streets at night, there will still be at least one illuminated path from every junction in Byteland to every other junction.

What is the maximum daily amount of money the government of Byteland can save, without making their inhabitants feel unsafe?

Input Specification

The input file contains several test cases. Each test case starts with two numbersm andn, the number of junctions in Byteland and the number of roads in Byteland, respectively. Input is terminated bym=n=0. Otherwise,1 ≤ m ≤ 200000 andm-1 ≤ n ≤ 200000. Then follown integer triplesx, y, z specifying that there will be a bidirectional road betweenx andy with lengthz meters (0 ≤ x, y < m andx ≠ y). The graph specified by each test case is connected. The total length of all roads in each test case is less than 231.

Output Specification

For each test case print one line containing the maximum daily amount the government can save.

Sample Input
7 11
0 1 7
0 3 5
1 2 8
1 3 9
1 4 7
2 4 5
3 4 15
3 5 6
4 5 8
4 6 9
5 6 11
0 0
Sample Output
51
¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥¥做了这道题以后感觉自己对kruskal算法又有了一个新的认识,以前的充其量只能
算是模板,真正自己敲得就是这道题了!!嘎嘎!!大笑奖励自己一下!
#include<stdio.h>
#include<iostream>
#include<algorithm>
#define NN 200000000
using namespace std;
struct node
{
int l,r,value;
}g[200005];
int cmp(const node&a,const node&b)
{
return a.value<b.value;
}
int set[200005];
int find(int x)
{
if(x!=set[x])
set[x]=find(set[x]);
return set[x];
}
void merge(int x,int y)
{
int fx,fy;
fx=find(x);
fy=find(y);
if(fx==fy)
return ;
if(fx>fy)
set[fx]=fy;
else
set[fy]=fx;
}
int main()
{
int i,j,k,m,n,x,y,z,ww;
int map[105][105];
while(scanf("%d%d",&m,&n),m,n)
{
for(i=0;i<m;i++)
     set[i]=i;
     ww=0;
for(i=0;i<n;i++)
   {
     scanf("%d%d%d",&g[i].l,&g[i].r,&g[i].value);
     ww+=g[i].value;
     }
sort(g,g+n,cmp);
/*printf("\n");
for(i=0;i<n;i++)
printf("%d %d %d\n",g[i].l,g[i].r,g[i].value);
        printf("\n");*/
int sum=0;
for(i=0;i<n;i++)
{
if(find(g[i].l)!=find(g[i].r))
{
merge(g[i].l,g[i].r);
sum+=g[i].value;
//printf("%d %d %d\n",g[i].l,g[i].r,g[i].value);
}
}
printf("%d\n",ww-sum);
}
return 0;
}
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边学边练,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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