R2_A_Taming the Herd

在本篇博客中,我们将探讨如何解决一个有趣的算法问题:修复受损的奶牛出逃记录,通过分析剩余的记录来确定奶牛可能的最少和最多出逃次数。这个问题涉及到逻辑推理和序列分析,我们将分享一种有效的解决方案。

题面

Taming the Herd

Early in the morning, Farmer John woke up to the sound of splintering wood. It was the cows, and they were breaking out of the barn again!

Farmer John was sick and tired of the cows’ morning breakouts, and he decided enough was enough: it was time to get tough. He nailed to the barn wall a counter tracking the number of days since the last breakout. So if a breakout occurred in the morning, the counter would be 00 that day; if the most recent breakout was 33 days ago, the counter would read 33. Farmer John meticulously logged the counter every day.

The end of the year has come, and Farmer John is ready to do some accounting. The cows will pay, he says! But lo and behold, some entries of his log are missing!

Farmer John is confident that the he started his log on the day of a breakout. Please help him determine, out of all sequences of events consistent with the log entries that remain, the minimum and maximum number of breakouts that may have take place over the course of the logged time.

Input

The first line contains a single integer NN (1≤N≤1001≤N≤100), denoting the number of days since Farmer John started logging the cow breakout counter.

The second line contains NN space-separated integers. The iith integer is either −1−1, indicating that the log entry for day ii is missing, or a non-negative integer aiai (at most 100100), indicating that on day ii the counter was at aiai.

Output

If there is no sequence of events consistent with Farmer John’s partial log and his knowledge that the cows definitely broke out of the barn on the morning of day 11, output a single integer −1−1. Otherwise, output two space-separated integers mm followed by MM, where mm is the minimum number of breakouts of any consistent sequence of events, and MM is the maximum.

Example

input

4
-1 -1 -1 1

output

2 3

Note

In this example, we can deduce that a breakout had to occur on day 3. Knowing that a breakout also occurred on day 1, the only remaining bit of uncertainty is whether a breakout occurred on day 2. Hence, there were between 2 and 3 breakouts in total.

题目大意

J用数字记录下了奶牛什么时候进行破坏。比如1是代表1天前进行了破坏,0代表今天进行破坏。但是这个记录损坏了,出现了一些-1,-1可以代表任意数字。任务是求奶牛进行破坏的次数的最大值与最小值。如果这份记录有矛盾,则输出-1。

题目分析

根据那些大于0的数字,我们可以去确定一些-1的具体数值。那么我们可以由那些数字倒着推回来。每当我们得到一个大于0的数n,那么前n天的记录也就清楚了,唯一确定了。当前n天的中如果出现有0的出现,那么我们比如可以确定,这份记录有矛盾。

代码

#include <cstdio>
using namespace std;
const int maxn = 1e5;
typedef long long ll;
int arr[107];
int main(int argc, char const *argv[]) {
  int n;
  scanf("%d", &n);
  for(int i = 0; i < n; i++)
    scanf("%d", &arr[i]);
  arr[0] = 0;
  bool flag = false;
  for(int i = 0; i < n; i++){
    // printf("%d\n", i);
    if(flag) break;

    if(arr[i] > 0){
      if(i - arr[i] < 0){flag = true; break;}
      if(arr[i - arr[i]] > 0) {
        flag = true;
        break;
      }else{
        for(int j = i - arr[i], cnt = 0; j < i; j++, cnt++){
          if(arr[j] == 0 && j != i - arr[i]){flag = true; break;}
          arr[j] = cnt;
        }
      }
    }
  }
  if(flag)
    printf("%d\n", -1);
  else{
    int fu = 0, zero = 0;
    for(int i = 0; i < n; i++){
      if(arr[i] == 0)
        zero++;
      if(arr[i] == -1)
        fu++;
    }
    printf("%d %d\n", zero, zero + fu);
  }
  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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