sgu184:Patties

184. Patties
time limit per test: 0.25 sec.
memory limit per test: 4096 KB
input: standard input
output: standard output



Petya is well-known with his famous cabbage patties. Petya's birthday will come very soon, and he wants to invite as many guests as possible. But the boy wants everybody to try his specialty of the house. That's why he needs to know the number of the patties he can cook using the stocked ingredients. Petya has P grams of flour, M milliliters of milk and C grams of cabbage. He has plenty of other ingredients. Petya knows that he needs K grams of flour, R milliliters of milk and V grams of cabbage to cook one patty. Please, help Petya calculate the maximum number of patties he can cook.

Input
The input file contains integer numbers P, M, C, K, R and V, separated by spaces and/or line breaks (1 <= P, M, C, K, R, V <= 10000).

Output
Output the maximum number of patties Petya can cook.

Sample test(s)

Input
 
3000 1000 500 30 15 60
Output
 
8

Author: Andrew V. Lazarev
Resource: ACM International Collegiate Programming Contest 2003-2004 
North-Eastern European Region, Southern Subregion
Date: 2003 October, 9







偶尔刷刷水题有益身心健康~~
#include <cstdio>
#include <algorithm>
using namespace std;
int main()
{
  int a, b, c, d, e, f, ans;
  scanf("%d%d%d", &a, &b, &c);
  scanf("%d%d%d", &d, &e, &f);
  ans = a/d;
  ans = min(ans, b/e);
  ans = min(ans, c/f);
  printf("%d", ans);
  return 0;	
}





Server time: 2014-11-22 09:07:00 Online Contester Team © 2002 - 2014. All rights reserved.
内容概要:本文介绍了基于Python实现的SSA-GRU(麻雀搜索算法优化门控循环单元)时间序列预测项目。项目旨在通过结合SSA的全局搜索能力和GRU的时序信息处理能力,提升时间序列预测的精度和效率。文中详细描述了项目的背景、目标、挑战及解决方案,涵盖了从数据预处理到模型训练、优化及评估的全流程。SSA用于优化GRU的超参数,如隐藏层单元数、学习率等,以解决传统方法难以捕捉复杂非线性关系的问题。项目还提供了具体的代码示例,包括GRU模型的定义、训练和验证过程,以及SSA的种群初始化、迭代更新策略和适应度评估函数。; 适合人群:具备一定编程基础,特别是对时间序列预测和深度学习有一定了解的研究人员和技术开发者。; 使用场景及目标:①提高时间序列预测的精度和效率,适用于金融市场分析、气象预报、工业设备故障诊断等领域;②解决传统方法难以捕捉复杂非线性关系的问题;③通过自动化参数优化,减少人工干预,提升模型开发效率;④增强模型在不同数据集和未知环境中的泛化能力。; 阅读建议:由于项目涉及深度学习和智能优化算法的结合,建议读者在阅读过程中结合代码示例进行实践,理解SSA和GRU的工作原理及其在时间序列预测中的具体应用。同时,关注数据预处理、模型训练和优化的每个步骤,以确保对整个流程有全面的理解。
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