poj 1328Radar Installation

本文探讨了在无限海岸线上,通过雷达安装实现岛屿覆盖的最小数量问题。使用二维坐标系,通过输入岛屿的位置和雷达的覆盖距离,计算并输出所需雷达安装的数量。通过实例演示了解决此类问题的方法。

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Language:
Radar Installation
Time Limit: 1000MS Memory Limit: 10000K
Total Submissions: 53901 Accepted: 12143

Description

Assume the coasting is an infinite straight line. Land is in one side of coasting, sea in the other. Each small island is a point locating in the sea side. And any radar installation, locating on the coasting, can only cover d distance, so an island in the sea can be covered by a radius installation, if the distance between them is at most d. 

We use Cartesian coordinate system, defining the coasting is the x-axis. The sea side is above x-axis, and the land side below. Given the position of each island in the sea, and given the distance of the coverage of the radar installation, your task is to write a program to find the minimal number of radar installations to cover all the islands. Note that the position of an island is represented by its x-y coordinates. 
 
Figure A Sample Input of Radar Installations


Input

The input consists of several test cases. The first line of each case contains two integers n (1<=n<=1000) and d, where n is the number of islands in the sea and d is the distance of coverage of the radar installation. This is followed by n lines each containing two integers representing the coordinate of the position of each island. Then a blank line follows to separate the cases. 

The input is terminated by a line containing pair of zeros 

Output

For each test case output one line consisting of the test case number followed by the minimal number of radar installations needed. "-1" installation means no solution for that case.

Sample Input

3 2
1 2
-3 1
2 1

1 2
0 2

0 0

Sample Output

Case 1: 2
Case 2: 1

Source

#include <iostream>
#include <cstdio>
#include <cstring>
#include <cmath>
#include <algorithm>

using namespace std;
double x,y;

struct point
{
    double  x1;
    double  x2;
} p[1001];

int cmp(point a,point b)
{
    return a.x1<b.x1;
}

int main()
{
    int n,d;
    //int ans=1;
    int kase=0;
    while(~scanf("%d%d",&n,&d)&&n+d)
    {
        int ans=1;
        for(int i=0; i<n; i++)
        {
            cin>>x>>y;
            p[i].x1=x-sqrt(d*d-y*y);
            p[i].x2=x+sqrt(d*d-y*y);
            if(y>d||y<0||d<=0)
                ans=-1;
        }
        sort(p,p+n,cmp);
        double xx=p[0].x2;
        for(int i=1; i<n && ans!=-1; i++)
        {
            if(p[i].x1>xx)
            {
                ans++;
                xx=p[i].x2;
            }
            else if(p[i].x2<xx)
                {
                    xx=p[i].x2;
                }
        }
        printf("Case %d: %d\n",++kase,ans);
    }
}

内容概要:本文针对国内加密货币市场预测研究较少的现状,采用BP神经网络构建了CCi30指数预测模型。研究选取2018年3月1日至2019年3月26日共391天的数据作为样本,通过“试凑法”确定最优隐结点数目,建立三层BP神经网络模型对CCi30指数收盘价进行预测。论文详细介绍了数据预处理、模型构建、训练及评估过程,包括数据归一化、特征工程、模型架构设计(如输入层、隐藏层、输出层)、模型编译与训练、模型评估(如RMSE、MAE计算)以及结果可视化。研究表明,该模型在短期内能较准确地预测指数变化趋势。此外,文章还讨论了隐层节点数的优化方法及其对预测性能的影响,并提出了若干改进建议,如引入更多技术指标、优化模型架构、尝试其他时序模型等。 适合人群:对加密货币市场预测感兴趣的研究人员、投资者及具备一定编程基础的数据分析师。 使用场景及目标:①为加密货币市场投资者提供一种新的预测工具和方法;②帮助研究人员理解BP神经网络在时间序列预测中的应用;③为后续研究提供改进方向,如数据增强、模型优化、特征工程等。 其他说明:尽管该模型在短期内表现出良好的预测性能,但仍存在一定局限性,如样本量较小、未考虑外部因素影响等。因此,在实际应用中需谨慎对待模型预测结果,并结合其他分析工具共同决策。
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