POJ - 2318 TOYS————计算几何

[HCTF 2018]WarmUp 1学习记录 #[HCTF 2018]WarmUp 1学习笔记 最近做了做ctf,觉得真的很有趣,后悔当初没早点做,不过之前的学习也让我得到了不少经验基础,废话不多说,这次ctf虽然基本上靠了网上的大佬们(因为比较菜),但自己也做了些思考,应该对新手也比较友好吧。 ##1 首先怎么找源码,我承认我确实刚开始不知道在哪找,不过也很快找到了,相信也会有部分新手和我差不多吧 然后下面控制台会有个temp0,然后直接回车 后面我就受到启发,直接在链接后面加上source.php,进行页面跳转,然后就可以开始PHP代码审计了。 阅读详情

alculate the number of toys that land in each bin of a partitioned toy box.
Mom and dad have a problem - their child John never puts his toys away when he is finished playing with them. They gave John a rectangular box to put his toys in, but John is rebellious and obeys his parents by simply throwing his toys into the box. All the toys get mixed up, and it is impossible for John to find his favorite toys.

John’s parents came up with the following idea. They put cardboard partitions into the box. Even if John keeps throwing his toys into the box, at least toys that get thrown into different bins stay separated. The following diagram shows a top view of an example toy box.
这里写图片描述
For this problem, you are asked to determine how many toys fall into each partition as John throws them into the toy box.
Input
The input file contains one or more problems. The first line of a problem consists of six integers, n m x1 y1 x2 y2. The number of cardboard partitions is n (0 < n <= 5000) and the number of toys is m (0 < m <= 5000). The coordinates of the upper-left corner and the lower-right corner of the box are (x1,y1) and (x2,y2), respectively. The following n lines contain two integers per line, Ui Li, indicating that the ends of the i-th cardboard partition is at the coordinates (Ui,y1) and (Li,y2). You may assume that the cardboard partitions do not intersect each other and that they are specified in sorted order from left to right. The next m lines contain two integers per line, Xj Yj specifying where the j-th toy has landed in the box. The order of the toy locations is random. You may assume that no toy will land exactly on a cardboard partition or outside the boundary of the box. The input is terminated by a line consisting of a single 0.
Output
The output for each problem will be one line for each separate bin in the toy box. For each bin, print its bin number, followed by a colon and one space, followed by the number of toys thrown into that bin. Bins are numbered from 0 (the leftmost bin) to n (the rightmost bin). Separate the output of different problems by a single blank line.
Sample Input
5 6 0 10 60 0
3 1
4 3
6 8
10 10
15 30
1 5
2 1
2 8
5 5
40 10
7 9
4 10 0 10 100 0
20 20
40 40
60 60
80 80
5 10
15 10
25 10
35 10
45 10
55 10
65 10
75 10
85 10
95 10
0
Sample Output
0: 2
1: 1
2: 1
3: 1
4: 0
5: 1

0: 2
1: 2
2: 2
3: 2
4: 2
Hint
As the example illustrates, toys that fall on the boundary of the box are “in” the box.


题意:
有一个矩形盒子,放入n块按照x轴排序的不相交的隔板,这样就将矩形盒子分成了n+1块区域。现在将m个玩具放入这个矩形盒子,统计每个区域的玩具个数。(玩具不会与隔板有重叠部分)
数据解释:
第一行6个整数: n(0 < n <= 5000), m (0 < m <= 5000), x1, y1, x2, y2 n代表隔板数目,m是玩具数目,x1,y1是抽屉左上角坐标,x2,y2是抽屉右下角坐标。 接下来n行给出n块板子的顶部x坐标ti,底部x坐标bi。 接下来m行给出m个玩具的坐标xi, yi。
思路:
先将线段存起来,在对每个玩具的的坐标与两个相邻的线段短点进行叉乘,若两个叉乘的值一正一负,则说明这个点在这两个线段之间。否则不在,
这里写图片描述
就是这种情况


具体还是看代码吧:

#include<cstdio>
#include<cmath>
#include<algorithm>
#include<iostream>
#include<cstring>
#include<vector>
using namespace std;
typedef long long ll;
const int MAXN=5000+5;
struct node{
    int x,y;
    node(){}
    node(int _x,int _y)
    {
        x=_x;
        y=_y;
    }
}p[MAXN];
struct line{
    node s,e;
    line(){}
    line(node _s,node _e)
    {
        s=_s;
        e=_e;
    }
}l[MAXN];

bool cmp1(node a,node b)
{
    return a.x==b.x?a.y<b.y:a.x<b.x;
}

int dir(node p1,node p2,node p3)//
{
    return (p3.x-p1.x)*(p2.y-p1.y)-(p2.x-p1.x)*(p3.y-p1.y);
}

int main()
{
    int n,m,x1,x2,y1,y2;
    int k=1;
    while(~scanf("%d",&n)&&n)
    {
        if(k)   k=0;
        else    printf("\n");
        int a[MAXN];
        memset(a,0,sizeof(a));
        scanf("%d %d %d %d %d",&m,&x1,&y1,&x2,&y2);
        l[0]=line(node(x1,y1),node(x1,y2));
        for(int i=1;i<=n;i++)
        {
            int x_1,x_2;
            scanf("%d %d",&x_1,&x_2);
            l[i]=line(node(x_1,y1),node(x_2,y2));
        }
        l[n+1]=line(node(x2,y1),node(x2,y2));
        for(int i=0;i<m;i++)
        {
            int x,y;
            scanf("%d %d",&x,&y);
            p[i]=node(x,y);
        }
        sort(p,p+m,cmp1);
        for(int i=0;i<m;i++)
        {
            for(int j=0;j<=n;j++)
            {
                if((dir(p[i],l[j].s,l[j].e)>0&&dir(p[i],l[j+1].s,l[j+1].e)<0)||(dir(p[i],l[j].s,l[j].e)<0&&dir(p[i],l[j+1].s,l[j+1].e)>0))
//              if(dir(p[i],l[j].s,l[j].e) * dir(p[i],l[j+1].s,l[j+1].e)<0)
                {
                    a[j]++;
                    break;
                }
            }
        }
        for(int i=0;i<n+1;i++)
            printf("%d: %d\n",i,a[i]);
    }
    return 0;
}
vmware虚拟机网络连接问题 主机:ubuntu14.04 Vitual Network Editor 阅读详情

相关推荐

STM32F105双路CAN通讯无法进入接收中断问题及解决方法

STM32F105RBT6双路CAN通讯无法进入接收中断问题及解决方法

m0_62383223的博客 3442

A - TOYS(POJ - 2318) 计算几何的一道基础题

Calculate the number of toys that land in each bin of a partitioned toy box.  计算每一个玩具箱里面玩具的数量 Mom and dad have a problem - their child John never puts his toys away when he is finished playing with

叶梨子 865

Skiplist,B,B+tree,LSM tree

SkipList 理解(如何实现): 即使是有序的链表,查找的时间复杂度也是O(n),但链表不能随机访问所以不适用于二分查找,使得查找的时间复杂度为O(logn)。 于是将有序链表和二分查找结合得到了SkipList,类似于二叉搜索树,这是一种以空间换时间的算法,通过在每个节点中增加向前的指针,从而提升查找效率(如下图所示)。 一开始如a所示,是一个有序的链表,时间复杂度O(n)。随后建立了e所示的SkipList,从最顶层往下查找,可以跳过许多元素,减少查找次数。 ​

weixin_45875458的博客 947

POJ 2318 TOYS

POJ 2318

Vontroy 515

代码实现 —— 基于 STM32 的可见光通信系统课程设计

基于STM32F103ZET6,源码如需移植,需要修改定时器捕获通道引脚,及定时器,如果你也有这个课设任务,希望对你有所帮助。

不善挽留 1万+

别再死记硬背了!用LabVIEW串口通信的3个真实项目案例,手把手教你从调试到部署

本文通过3个真实项目案例,详细讲解LabVIEW串口通信从调试到部署的全过程。涵盖Arduino温湿度监控、西门子PLC Modbus通信以及高可靠性通信框架设计,提供实用技巧和常见问题解决方案,帮助工程师快速掌握LabVIEW串口通信的实战应用。

weixin_30485379的博客 524

POJ 2318

第一道计算几何。 二分一下用叉积来判。。看了DIS上说要INT64,就改INT64了。。。 1 #include <iostream> 2 #include <cstdio> 3 #include <cstring> 4 #include <algorithm> 5 using namespace std; 6 c...

weixin_30338497的博客 123

SAP CDS View association 的工作原理,与数据库传统 join 操作的区别讲解

笔者前一篇教程,介绍了 SAP CDS View 里如何创建 association:本文就来一一对这些问题进行解答。首先我们来研究如何消费创建好的 SAP CDS View 中的 association 数据。在上图 ABAP Development Tool 里展示的 CDS View 界面,点击 F8 快捷键,进入 Data Preview 即数据预览界面。我们随便选中一条行项目,右键,发现右键菜单里,赫然有一条的菜单项。看来这个功能肯定和 association 相关了。

2007 年 ~ 2025 年,深耕 SAP 技术 18 年 1009

poj2318 TOYS 线划分矩形

题目链接:http://poj.org/problem?id=2318 //题目意思: //在一个矩形中用n条线来划分(划分的线不交叉此而且只能分左右方向 ), //给出m个点,求一个区域内分别有多少点。 //解题思路: //判断点在直线的左边还是右边//代码如下: #include&lt;iostream&gt; #include&lt;cstdio&gt; #includ...

java开发指南博客 【转载】 136

【Kubernetes】Kubernetes 安全沙箱深度对比:gVisor vs Kata Containers

Kubernetes安全沙箱深度对比:gVisor和Kata Containers在架构、性能与安全性方面各有特点。gVisor通过用户态内核(Sentry)实现软件隔离,启动快(毫秒级)但CPU开销大(20-30%),适合Web服务等场景。Kata Containers采用轻量级虚拟机提供硬件级隔离,启动较慢(1-2秒)但兼容性更好,适合金融等高安全需求。测试显示gVisor在动态请求下性能下降33%,而Kata性能接近原生。建议根据场景选择:多租户/快速启动用gVisor,敏感数据/极致安全用Kata,

九师兄 178

【PointNet++】制作点云数据集使用CloudCompare进行标注

使用PointNet++复现成功后,需要训练自己的数据集。于是使用cloudcompare参照ShapeNet的数据集格式去制作,最终保存的txt文件格式为“xyz+rgb+label”,一共7列。 自己按此制作好的数据集已成功训练及推测,于是记录下来让好兄弟好姐妹们少踩坑多吃肉。

纵马踏花向自由 5715

POJ - 2318 TOYS 二分+叉积

给出n条线段把一个区域分割成不同的地区,之后给你m个点,求每个地区有几个点。 线段是从左到右给出的,所以不用做什么处理。 对于每一个点我们直接二分,即可。 #include #include #include using namespace std; struct point{ int x,y; }; struct Node{ point a,b; }A[5010];

Little_boy_z的博客 316

实现两端固支梁热力耦合的有限元分析

在本文中,我们将使用Matlab实现两端固支梁的热力耦合有限元分析。我们假设梁的材料是均匀的,梁的截面积为1,长度为1,两端固支,且受到均匀分布的热荷载。接下来,我们需要组装全局刚度矩阵和热传导矩阵。对于两个相邻的元素,它们共享一个节点,因此需要使用连接矩阵将局部矩阵组装成全局矩阵。接下来,我们需要定义元素刚度矩阵和热传导矩阵。然后,我们需要定义载荷向量。然后,我们需要定义边界条件。在这个例子中,梁的两端固支,因此我们需要将两端节点的位移限制为0。最后,我们需要解决线性方程组,得到节点的位移和温度分布。

CyberSparkZ的博客 396

POJ 2318 - TOYS (计算几何)

POJ 2318- TOYS 一个比较适合入门的基础题 题目: Calculate the number of toys that land in each bin of a partitioned toy box. Mom and dad have a problem - their child John never puts his toys away when he is finished ...

weixin_45691686的博客 341

Statistics and Machine Learning Toolbox

Statistics and Machine Learning Toolbox(带书签),MATLAB统计学习工具

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

POJ 2318 TOYS(计算几何)

居然脑残认为一定从(0,0)点作为左下角的点,然后怒WA,然后请爽爷出手查,果断一针见血啊!Orz 嗯,第一题计算几何 //POJ2318 TOYS //叉积+二分 #include #include #include using namespace std; #define N 50005 #define ll long long struct Point{

452

WINCC7.3破解文件

WINCC7.3硬件够破解文件,有需要的下载!WINCC7.3硬件够破解文件,有需要的下载!

ACM-计算几何之Toys——poj2318

ACM 计算几何 Toys poj2318 点与线段位置关系

继续激情,继续奋斗。 2081

计算机控制技术及应用

《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术,以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索,主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式,计算机控制系统的数学描述与基本分析方法,以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。

计算几何入门:向量叉积与二分查找在POJ 2318 TOYS问题中的应用

向量叉积是计算几何中的基础工具,其符号可用于判断点与有向线段的相对位置(左侧或右侧)。这一原理在工程实践中常用于解决几何定位问题,例如判断点是否在多边形内、线段相交检测等。通过将几何关系转化为可计算的叉积符号,能够高效处理空间中的位置判断。在POJ 2318 TOYS问题中,利用隔板坐标的单调性,可将暴力遍历的O(m*n)复杂度优化为O(m*log n)的二分查找,显著提升算法效率。该问题典型地展示了如何将数学工具与算法优化结合,解决实际中的区域划分与点定位需求。

weixin_32642109的博客 322

【POJ 2318】TOYS(计算几何)

给你一个长方形,然后有若干条不相交的线把它分成若干个区域。 然后有一些点,保证不会再线上,然后问你每个区域有多少个点。

欢迎您的光顾awa 251

【计算几何】POJ 2318 TOYS

POJ 2138 TOYS 【传送门】 Time Limit:2000MS Memory Limit:65536K Total Submissions:20891 Accepted:9758 Description Calculate the number of toys that land in each bin of a partitione...

lesileqin的博客 210

poj2318 TOYS(计算几何)

TOYS Time Limit: 2000MS   Memory Limit: 65536K Total Submissions: 14257   Accepted: 6907 Description Calculate the number of toys that land in each bin of a partitioned t

yukang_0613的博客 322

(计算几何)POJ2318TOYS、POJ2398Toy Storage

POJ2318TOYS 题意: 给一个矩形和nnn条线将矩形分成n+1n+1n+1块区域,给mmm个点保证点不在线上,问每一块区域里有几个点。 思路: 很容易看出,一个点如果在一个区域内,它会在一条边的顺时针方向和另一条边的逆时针方向。所以用叉积。 如果一个点在一个区域里,设区域为ABCDABCDABCD,点为PPP。可得(AB→×AP→)∗(CD→×CP→)<0(\overrightarrow{AB}\times\overrightarrow{AP})*(\overrightarrow{CD}\ti

Z7784562的博客 193

计算几何 POJ 2318 TOYS

Problem Calculate the number of toys that land in each bin of a partitioned toy box. Mom and dad have a problem - their child John never puts his toys away when he is finished playing with them. They ...

FrankDura的博客 473

计算几何 POJ2318 POJ2398 Toys

题目 题目链接:POJ2318 POJ2398 n个夹层,m个玩具。 关于夹层中玩具数量的问题。 题解 2318 在判断时使质点(玩具)坐标从字与夹层的方向向量进行叉乘运算,根据右手定则判断该点位于板子的哪一侧。 分情况进行讨论: 夹板编号为 111 且玩具位于夹板左侧:放入 000 号夹层; 位于夹板右侧则一直向右进行直到碰到夹板 jjj 且位于其左侧:放于 j−1j-1j−1号夹层; 位于夹板右侧则一直向右进行直到最后一个夹板 jjj :放入 jjj 号夹层。 2398 与上题相比多了对夹

Zoybzo想AK 215

POJ 2318 TOYS(计算几何)

Calculate the number of toys that land in each bin of a partitioned toy box. Mom and dad have a problem - their child John never puts his toys away when he is finished playing with them. They gave Joh...

weixin_44705665的博客 130

poj 2318 toys 计算几何+二分

poj 2318 toys 计算几何+二分

klc的博客 414

【计算几何】 poj2318 TOYS

TOYS http://poj.org/problem?id=2318 Description Calculate the number of toys that land in each bin of a partitioned toy box.  Mom and dad have a problem - their child John never puts his t

Ted's 游乐园 1354
上一篇: 洛谷Oj-台阶问题————DP
下一篇: POJ - 2398 Toy Storage————计算几何
陶鸿杰
博客等级 码龄9年 93粉丝 298原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值