ZOJ--Doki Doki Literature Club

本文介绍了视觉小说DokiDokiLiteratureClub!中的诗歌写作机制,并探讨如何通过选择合适的词语来最大化主角Sayori的幸福感。玩家需根据每个俱乐部成员对单词的喜好来创作诗歌,目标是使Sayori的幸福感达到最高。文章通过实例展示了如何计算幸福感,并提出了优化诗歌的策略,以确保使用每个单词仅一次。

Doki Doki Literature Club! is a visual novel developed by Team Salvato. The protagonist is invited by his childhood friend, Sayori, to join their high school's literature club. The protagonist then meets the other members of the club: Natsuki, Yuri, and the club president Monika. The protagonist starts to participate in the club's activities such as writing and sharing poetry, and grows close to the four girls. What a lovely story!

A very important feature of the game is its poetry writing mechanism. The player is given a list of various words to select from that will make up his poem. Each girl in the Literature Club has different word preferences, and will be very happy if the player's poem is full of her favorite words.

The poem writing mini-game (from wikipedia)

BaoBao is a big fan of the game and likes Sayori the most, so he decides to write a poem to please Sayori. A poem of m words s_1, s_2, \dots, s_m is nothing more than a sequence of m strings, and the happiness of Sayori after reading the poem is calculated by the formula H=∑​i=1​m​​(mi+1)⋅f(si​​) where H is the happiness and f(si​​) is Sayori's preference to the word si​​.

Given a list of n words and Sayori's preference to each word, please help BaoBao select m words from the list and finish the poem with these m words to maximize the happiness of Sayori.

Please note that each word can be used at most once!

Input

There are multiple test cases. The first line of input contains an integer (about 100), indicating the number of test cases. For each test case:

The first line contains two integers n and m (1≤mn≤100), indicating the number of words and the length of the poem.

For the following n lines, the i-th line contains a string consisting of lowercased English letters wi​​ (1≤∣wi​​∣≤15) and an integer f(wi​​) (−10​9​​≤f(wi​​)≤10​9​​), indicating the i-th word and Sayori's preference to this word. It's guaranteed that wi​​≠wj​​ for all ij.

Output

For each test case output one line containing an integer and strings  separated by one space, indicating the maximum possible happiness and the corresponding poem. If there are multiple poems which can achieve the maximum happiness, print the ‎词汇‎ smallest one.

Please, DO NOT output extra spaces at the end of each line, or your answer may be considered incorrect!

sequence of m strings a_1, a_2, \dots, a_m is lexicographically smaller than another sequence of m strings b_1, b_2, \dots, b_m, if there exists a k (1≤km) such that ai​​=bi​​ for all 1≤i<k and ak​​ is lexicographically smaller than bk​​.

string s_1 = a_1a_2\dots a_x is lexicographically smaller than another string s_2 = b_1b_2\dots b_y, if there exists a k (1≤k≤min(x,y)) such that ai​​=bi​​ for all 1≤i<k and ak​​<bk​​, or ai​​=bi​​ for all 1≤i≤min(x,y) and x<y.

Sample Input

4
10 8
hello 0
world 0
behind 0
far 1
be 2
spring 10
can 15
comes 20
winter 25
if 200
5 5
collegiate 0
programming -5
zhejiang 10
provincial 5
contest -45
3 2
bcda 1
bcd 1
bbbbb 1
3 2
a 1
aa 1
aaa 1

Sample Output

2018 if winter comes can spring be far behind
15 zhejiang provincial collegiate programming contest
3 bbbbb bcd
3 a aa
Author: WENG, Caizhi

 typedef long long ll;

sum+=((m-i)*s[i].count);

#include<string.h>
#include<iostream>
#include<stdio.h>
#include<algorithm>
#include<string>
using namespace std;
typedef long long ll;

struct ch
{
	string s;
	ll count;
};
bool cmp(ch a,ch b){
	if(a.count==b.count)
	return a.s<b.s;
	else
	return a.count>b.count;
}
int main()
{
	ios::sync_with_stdio(false);
	int T,n,m;
	cin>>T;
	while(T--)
	{
		cin>>n>>m;
		ch s[200];
		ll sum=0;
		for(int i=0;i<n;i++)
		cin>>s[i].s>>s[i].count;
		
		sort(s,s+n,cmp);
		
		for(int i=0;i<m;i++)
		{
			sum+=((m-i)*s[i].count);
		}
		
		cout<<sum;
		for(int i=0;i<m;i++)
		cout<<" "<<s[i].s;
		cout<<endl;
		
	}
	 
	return 0;
}

Nano-ESG数据资源库的构建基于2023年初至2024年秋季期间采集的逾84万条新闻文本,从中系统提炼出企业环境、社会及治理维度的信息。其构建流程首先依据特定术语在德语与英语新闻平台上检索,初步锁定与德国DAX 40成分股企业相关联的报道。随后借助嵌入技术对文本段落执行去重操作,以降低内容冗余。继而采用GLiNER这一跨语言零样本实体识别系统,排除与目标企业无关的文档。在此基础上,通过GPT-3.5与GPT-4o等大规模语言模型对文本进行双重筛选:一方面判定其与ESG议题的相关性,另一方面生成简明的内容概要。最终环节由GPT-4o模型完成,它对每篇文献进行ESG情感倾向(正面、中性或负面)的判定,并标注所涉及的ESG具体维度,从而形成具备时序特征的ESG情感与维度标注数据集。 该数据集适用于多类企业可持续性研究,例如ESG情感趋势分析、ESG维度细分类别研究,以及企业可持续性事件的时序演变追踪。研究者可利用数据集内提供的新闻摘要、情感标签与维度分类,深入考察企业在不同时期的环境、社会及治理表现。此外,借助Bertopic等主题建模方法,能够从数据中识别出与企业相关的核心ESG议题,并观察这些议题随时间的演进轨迹。该资源以其开放获取特性与连续的时间覆盖,为探究企业可持续性表现的动态变化提供了系统化的数据基础。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
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