hdoj 4009 Transfer water 【求无根树最小树形图】

面对村庄搬迁至缺水山区的问题,本篇介绍了一种通过计算各户间的最优水源配置来实现最低成本供水的方法。利用图论中的朱刘算法,解决了如何在考虑家庭间矛盾关系的同时,确保每家都有水源供应的问题。

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Transfer water

Time Limit: 5000/3000 MS (Java/Others)    Memory Limit: 65768/65768 K (Java/Others)
Total Submission(s): 4402    Accepted Submission(s): 1564


Problem Description
XiaoA lives in a village. Last year flood rained the village. So they decide to move the whole village to the mountain nearby this year. There is no spring in the mountain, so each household could only dig a well or build a water line from other household. If the household decide to dig a well, the money for the well is the height of their house multiplies X dollar per meter. If the household decide to build a water line from other household, and if the height of which supply water is not lower than the one which get water, the money of one water line is the Manhattan distance of the two households multiplies Y dollar per meter. Or if the height of which supply water is lower than the one which get water, a water pump is needed except the water line. Z dollar should be paid for one water pump. In addition,therelation of the households must be considered. Some households may do not allow some other households build a water line from there house. Now given the 3‐dimensional position (a, b, c) of every household the c of which means height, can you calculate the minimal money the whole village need so that every household has water, or tell the leader if it can’t be done.
 

Input
Multiple cases. 
First line of each case contains 4 integers n (1<=n<=1000), the number of the households, X (1<=X<=1000), Y (1<=Y<=1000), Z (1<=Z<=1000). 
Each of the next n lines contains 3 integers a, b, c means the position of the i‐th households, none of them will exceeded 1000. 
Then next n lines describe the relation between the households. The n+i+1‐th line describes the relation of the i‐th household. The line will begin with an integer k, and the next k integers are the household numbers that can build a water line from the i‐th household. 
If n=X=Y=Z=0, the input ends, and no output for that. 
 

Output
One integer in one line for each case, the minimal money the whole village need so that every household has water. If the plan does not exist, print “poor XiaoA” in one line. 
 

Sample Input
2 10 20 30 1 3 2 2 4 1 1 2 2 1 2 0 0 0 0
 

Sample Output
30
Hint
In 3‐dimensional space Manhattan distance of point A (x1, y1, z1) and B(x2, y2, z2) is |x2‐x1|+|y2‐y1|+|z2‐z1|.
 



题意:一个村庄有N个家庭,他们都需要用水,已经给出每个家庭房子的坐标和高度。他们可以选择自己建水源(费用为房子高度乘以X),或者从有水源的村庄引一条渠道(费用为两者房子的曼哈顿距离乘以Y,若供应水源的家庭的房子高度低于得到水的家庭的房子高度,那么每修建一条水渠还需要额外的Z费用)。现在给你所有家庭自建水源后,可能引水的对象(有的家庭之间有矛盾,所以不会引水给对方)。问你需要的最少费用使得N个家庭都用上水,若不存在一种方案输出poor XiaoA。


思路:建立虚根N,连接所有家庭, 边权为他们自建水源的费用。所有家庭向他们可能引水的对象建边, 边权为引渠道的费用,然后跑一遍朱刘算法就ok了。


朱刘算法要注意自环的处理,虽然这道题不处理也能过。


AC代码:


#include <cstdio>
#include <cstring>
#include <cmath>
#include <cstdlib>
#include <queue>
#include <stack>
#include <vector>
#include <algorithm>
#define MAXN 1010
#define MAXM 1000000+1010
#define INF 0x3f3f3f3f
#define LL long long
using namespace std;
struct Node
{
    int x, y, h;
};
Node num[MAXN];
struct Edge
{
    int from, to, cost;
};
Edge edge[MAXM];
int edgenum;//边数
int pre[MAXN], in[MAXN];;
int vis[MAXN], id[MAXN];
int N, X, Y, Z;
int dis(Node a, Node b)//曼哈顿距离
{
    return abs(a.x - b.x) + abs(a.y - b.y) + abs(a.h - b.h);
}
void addEdge(int u, int v, int w)
{
    Edge E = {u, v, w};
    edge[edgenum++] = E;
}
void getMap()
{
    edgenum = 0;
    for(int i = 0; i < N; i++)
        scanf("%d%d%d", &num[i].x, &num[i].y, &num[i].h),
        addEdge(N, i, num[i].h * X);//建立虚根N
    int k, y;
    for(int i = 0; i < N; i++)
    {
        scanf("%d", &k);
        while(k--)
        {
            scanf("%d", &y);
            y--;
            if(i == y) continue;//加上保险  防止自环
            int c = dis(num[i], num[y]) * Y;;
            if(num[i].h < num[y].h)
                c += Z;
            addEdge(i, y, c);
        }
    }
}
LL zhuliu(int root, int n, int m, Edge *edge)
{
    LL res = 0;
    int u, v;
    while(1)
    {
        for(int i = 0; i < n; ++i)
            in[i] = INF;
        for(int i = 0; i < m; i++)
        {
            Edge E = edge[i];
            if(E.from != E.to && E.cost < in[E.to])
            {
                pre[E.to] = E.from;
                in[E.to] = E.cost;
            }
        }
        for(int i = 0; i < n; i++)
            if(in[i] == INF && i != root)
                return -1;
        int tn = 0;
        memset(id, -1, sizeof(id));
        memset(vis, -1, sizeof(vis));
        in[root] = 0;
        for(int i = 0; i < n; i++)
        {
            res += in[i];
            v = i;
            while(vis[v] != i && id[v] == -1 && v != root)
            {
                vis[v] = i;
                v = pre[v];
            }
            while(id[v] == -1 && v != root)
            {
                for(u = pre[v]; u != v; u = pre[u])
                    id[u] = tn;
                id[v] = tn++;
            }
        }
        if(tn == 0) break;
        for(int i = 0; i < n; i++)
            if(id[i] == -1)
                id[i] = tn++;
        for(int i = 0; i < m; i++)
        {
            v = edge[i].to;
            edge[i].from = id[edge[i].from];
            edge[i].to = id[edge[i].to];
            if(edge[i].from != edge[i].to)
                edge[i].cost -= in[v];
        }
        n = tn;
        root = id[root];
    }
    return res;
}
int main()
{
    while(scanf("%d%d%d%d", &N, &X, &Y, &Z), N||X||Y||Z)
    {
        getMap();
        LL ans = zhuliu(N, N+1, edgenum, edge);
        if(ans == -1)
            printf("poor XiaoA\n");
        else
            printf("%lld\n", ans);
    }
    return 0;
}



内容概要:本文介绍了基于Python实现的SSA-GRU(麻雀搜索算法优化门控循环单元)时间序列预测项目。项目旨在通过结合SSA的全局搜索能力和GRU的时序信息处理能力,提升时间序列预测的精度和效率。文中详细描述了项目的背景、目标、挑战及解决方案,涵盖了从数据预处理到模型训练、优化及评估的全流程。SSA用于优化GRU的超参数,如隐藏层单元数、学习率等,以解决传统方法难以捕捉复杂非线性关系的问题。项目还提供了具体的代码示例,包括GRU模型的定义、训练和验证过程,以及SSA的种群初始化、迭代更新策略和适应度评估函数。; 适合人群:具备一定编程基础,特别是对时间序列预测和深度学习有一定了解的研究人员和技术开发者。; 使用场景及目标:①提高时间序列预测的精度和效率,适用于金融市场分析、气象预报、工业设备故障诊断等领域;②解决传统方法难以捕捉复杂非线性关系的问题;③通过自动化参数优化,减少人工干预,提升模型开发效率;④增强模型在不同数据集和未知环境中的泛化能力。; 阅读建议:由于项目涉及深度学习和智能优化算法的结合,建议读者在阅读过程中结合代码示例进行实践,理解SSA和GRU的工作原理及其在时间序列预测中的具体应用。同时,关注数据预处理、模型训练和优化的每个步骤,以确保对整个流程有全面的理解。
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