1132 Cut Integer (20分)

本文探讨了一种特殊的整数切割问题,即将一个整数切割成两个较小的整数,然后检查原始整数是否能被这两个较小整数的乘积整除。文章提供了输入输出规范,并附带了一个C++实现示例。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

Cutting an integer means to cut a K digits lone integer Z into two integers of (K/2) digits long integers A and B. For example, after cutting Z = 167334, we have A = 167 and B = 334. It is interesting to see that Z can be devided by the product of A and B, as 167334 / (167 × 334) = 3. Given an integer Z, you are supposed to test if it is such an integer.

Input Specification:

Each input file contains one test case. For each case, the first line gives a positive integer N (≤ 20). Then N lines follow, each gives an integer Z (10 ≤ Z <2​31​​). It is guaranteed that the number of digits of Z is an even number.

Output Specification:

For each case, print a single line Yes if it is such a number, or No if not.

Sample Input:

3
167334
2333
12345678

Sample Output:

Yes
No
No
#include<iostream>
#include<cstring>
#include<vector>

using namespace std;

int main(){
    int n;
    cin >> n;

    for(int i = 0; i < n; i++){
        string s;
        cin >> s;
        int k = s.length() / 2;
        string s1, s2;
        s1 = s.substr(0, k);
        s2 = s.substr(k, k);
        int a, b, c;
        a = stoi(s);
        b = stoi(s1);
        c = stoi(s2);
        if(b == 0 || c == 0){
            printf("No\n");
        }else
        {
            if(a % (b * c) == 0){
            printf("Yes\n");
            }else
            {
            printf("No\n");
            }
        }
        
            
        
        // cout << s1 << " " << s2 << endl;
    }



    return 0;
}

 

### GrabCut算法的核心概念和原理 #### 高斯混合模型 (GMM) GrabCut算法利用了高斯混合模型来描述前景和背景的颜色布。对于每一个像素,该模型能够估计其属于前景或背景的概率。通过不断更新这些概率,算法逐步改进对图像中物体轮廓的理解[^1]。 #### 图割理论的应用 此算法还将图像割视为图论中的最小割问题。具体来说,在构建好的图形上寻找最佳路径以离目标物与周围环境,即找到使切割成本最低的一组边集合。这种做法不仅考虑到了单个像素的信息,还兼顾了邻近区域间的关联性,从而提高了最终结果的质量[^4]。 #### 迭代优化过程 为了更精确地区前景和背景,GrabCut采用了迭代的方式来进行优化。每次循环都会重新评估并调整当前划方案直至收敛至满意的结果为止。这一过程中涉及到交互式的用户输入指导以及自动化的参数微调机制,确保即使是在复杂背景下也能获得较为理想的输出效果。 ```python import numpy as np from sklearn.mixture import GaussianMixture def grabcut(image, mask=None, iterations=5): """ A simplified version of the GrabCut algorithm. Parameters: image : array_like Input color or grayscale image to be segmented. mask : array_like, optional Predefined labels for each pixel in `image`. iterations : int, default=5 Number of iterations performed by the algorithm. Returns: label_map : ndarray An integer array with shape equal to that of input 'image', where elements are either 0 (background) or 1 (foreground). """ # Initialize GMMs and other parameters here... for _ in range(iterations): # Update foreground/background models using current segmentation... # Compute probabilities based on updated models... # Perform graph cut operation to refine boundaries between FG/BG regions... return final_segmentation_labels if __name__ == "__main__": pass # Example usage would go here but is omitted due to space constraints. ```
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
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
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