1132 Cut Integer (20 point(s))

本文介绍了一段使用C++进行字符串操作的代码,主要功能是对输入的长整型数字进行判断,通过将数字转换为字符串,再将字符串分为两部分并转回长整型,最后判断这两部分的乘积是否能整除原数字。涉及的知识点包括C++的字符串处理、类型转换和条件判断。

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#include<cstdio>
#include<iostream> 
#include<string>
#include<algorithm>
using namespace std; 
typedef long long ll;
ll n, a, b, c;
string s;
int main() {
	scanf("%lld", &n);
	for(int i = 0; i < n; ++i) {
		scanf("%lld", &c);
		s = to_string(c);
		a = stoll(s.substr(0, s.length() / 2));
		b = stoll(s.substr(s.length() / 2));
		if(a * b != 0 && c % (a * b) == 0) printf("Yes\n");
		else printf("No\n");
	}	
	return 0;
}

 

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。
Write a C program to partitions a hypergraph G = (V, E) into 2 partitions. The Assignment Write a computer program that takes a netlist represented by a weighted hypergraph and partitions it into two partitions. Each node is associated with an area value and each edge has an edge cost. Your program should minimize the total cost of the cut set, while satisfying the area constraint that the total area of partition 1 should satisfy the balance criteria as described in the class. That is, if the area sum of all the nodes is A, then the area of partition 1 should be greater than or equal to ra-tio_factor *A – amax and less than or equal to ratio_factor *A + amax, where amax is the maximum value among all cell areas. The program should prompt the user for the value of ratio_factor. Assumptions and Requirements of the Implementation 1. Your program should not have any limitation on the maximum number of nodes and the edges of the hypergraph. Each hyperedge could connect any subset of nodes in the hypergraph. 2. Each node area is a non-negative integer, and each edge cost is a non-negative floating- point value. 3. All the ids are 0-based. Namely, the id of the first element is 0, instead of 1. 4. The output of each partition should include the list of node ids, sorted in the ascending order. 5. The partition with the smaller minimum node id is listed first in the output. 6. Use balance criteria as the tiebreaker when there are multiple cell moves giving the max-imum gain, as described in the class. 7. Use the input and output formats given in the Sample Test Cases section. Sample Test Cases Test1: Please enter the number of nodes: 4 Please enter each of the 4 nodes with its id and the node area: 0 1 1 1 2 1 3 1 Please enter the number of edges: 3 Please enter each of the 3 edges with the number of connected nodes and their node ids, followed by the edge cost: 2 0 1 1 2 1 2 3 2 2 3 1 Please enter the percentage of the ratio factor: 50 The node ids of the partition 0 are 0 The node ids of the partition 1 are 1, 2, 3 The total cut cost is 1 Test2: Please enter the number of nodes: 4 Please enter each of the 4 nodes with its id and the node area: 0 1 1 4 2 2 3 1 Please enter the number of edges: 3 Please enter each of the 3 edges with the number of connected nodes and their node ids, followed by the edge cost: 3 0 1 2 5 3 0 2 3 3 3 0 1 3 4 Please enter the percentage of ratio factor: 50 The node ids of the partition 0 are 3 The node ids of the partition 1 are 0, 1, 2 The total cut cost is 7
07-08
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