CodeForces - 965E Short Code

本文介绍了一种使用Trie树和贪心算法来最小化代码中变量名总长度的方法,通过替换变量名为其前缀以保持唯一性,同时实现代码的压缩。

Discription

Arkady's code contains nn variables. Each variable has a unique name consisting of lowercase English letters only. One day Arkady decided to shorten his code.

He wants to replace each variable name with its non-empty prefix so that these new names are still unique (however, a new name of some variable can coincide with some old name of another or same variable). Among such possibilities he wants to find the way with the smallest possible total length of the new names.

A string aa is a prefix of a string bb if you can delete some (possibly none) characters from the end of bb and obtain aa.

Please find this minimum possible total length of new names.

Input

The first line contains a single integer nn (1≤n≤1051≤n≤105) — the number of variables.

The next nn lines contain variable names, one per line. Each name is non-empty and contains only lowercase English letters. The total length of these strings is not greater than 105105. The variable names are distinct.

Output

Print a single integer — the minimum possible total length of new variable names.

Examples

Input

3
codeforces
codehorses
code

Output

6

Input

5
abba
abb
ab
aa
aacada

Output

11

Input

3
telegram
digital
resistance

Output

3

Note

In the first example one of the best options is to shorten the names in the given order as "cod", "co", "c".

In the second example we can shorten the last name to "aac" and the first name to "a" without changing the other names.

在trie树上贪心

每个点x开一个map,存单词结尾在x子树内的点的深度集合。

如果点x没有被单词结尾覆盖,那么就把最深的放到这里。

向上合并的时候启发式合并

#include<bits/stdc++.h>
#include <typeinfo>
#define ll long long
using namespace std;
const int maxn=100005;
 
map<int,int> mmp[maxn];
map<int,int> :: iterator it;
int ch[maxn][26],cnt,n,N,ans,dep[maxn],word[maxn];
char S[maxn];
 
inline void ins(){
    int now=0,c,depth=0;
    for(int i=0;i<N;i++){
        c=S[i]-'a';
        if(!ch[now][c]) ch[now][c]=++cnt;
        now=ch[now][c],dep[now]=++depth;
    }
     
    word[now]++;
}
 
void dfs(int x){
    for(int i=0,to;i<26;i++) if(ch[x][i]){
        to=ch[x][i],dfs(to);
         
        if(mmp[to].size()>mmp[x].size()) swap(mmp[to],mmp[x]);
         
        for(it=mmp[to].begin();it!=mmp[to].end();++it) mmp[x][it->first]+=it->second;
             
        mmp[to].clear();
    }
     
    if(word[x]) mmp[x][dep[x]]+=word[x];
    else if(x){
        it=--mmp[x].lower_bound(1e9);
 
        ans-=(it->first)-dep[x];
         
        if(!(--(it->second))) mmp[x].erase(it);
         
        mmp[x][dep[x]]++;
    }
}
 
 
int main(){
    scanf("%d",&n);
    for(int i=1;i<=n;i++){
        fill(S,S+N,0);
        scanf("%s",S),N=strlen(S),ans+=N;
        ins();
    }
     
    dfs(0);
     
    printf("%d\n",ans);
    return 0;
}

 

基于径向基函数神经网络RBFNN的自适应滑模控制学习(Matlab代码实现)内容概要:本文介绍了基于径向基函数神经网络(RBFNN)的自适应滑模控制方法,并提供了相应的Matlab代码实现。该方法结合了RBF神经网络的非线性逼近能力和滑模控制的强鲁棒性,用于解决复杂系统的控制问题,尤其适用于存在不确定性和外部干扰的动态系统。文中详细阐述了控制算法的设计思路、RBFNN的结构与权重更新机制、滑模面的构建以及自适应律的推导过程,并通过Matlab仿真验证了所提方法的有效性和稳定性。此外,文档还列举了大量相关的科研方向和技术应用,涵盖智能优化算法、机器学习、电力系统、路径规划等多个领域,展示了该技术的广泛应用前景。; 适合人群:具备一定自动控制理论基础和Matlab编程能力的研究生、科研人员及工程技术人员,特别是从事智能控制、非线性系统控制及相关领域的研究人员; 使用场景及目标:①学习和掌握RBF神经网络与滑模控制相结合的自适应控制策略设计方法;②应用于电机控制、机器人轨迹跟踪、电力电子系统等存在模型不确定性或外界扰动的实际控制系统中,提升控制精度与鲁棒性; 阅读建议:建议读者结合提供的Matlab代码进行仿真实践,深入理解算法实现细节,同时可参考文中提及的相关技术方向拓展研究思路,注重理论分析与仿真验证相结合。
### Codeforces Problem 976C Solution in Python For solving problem 976C on Codeforces using Python, efficiency becomes a critical factor due to strict time limits aimed at distinguishing between efficient and less efficient solutions[^1]. Given these constraints, it is advisable to focus on optimizing algorithms and choosing appropriate data structures. The provided code snippet offers insight into handling string manipulation problems efficiently by customizing comparison logic for sorting elements based on specific criteria[^2]. However, for addressing problem 976C specifically, which involves determining the winner ('A' or 'B') based on frequency counts within given inputs, one can adapt similar principles of optimization but tailored towards counting occurrences directly as shown below: ```python from collections import Counter def determine_winner(): for _ in range(int(input())): count_map = Counter(input().strip()) result = "A" if count_map['A'] > count_map['B'] else "B" print(result) determine_winner() ``` This approach leverages `Counter` from Python’s built-in `collections` module to quickly tally up instances of 'A' versus 'B'. By iterating over multiple test cases through a loop defined by user input, this method ensures that comparisons are made accurately while maintaining performance standards required under tight computational resources[^3]. To further enhance execution speed when working with Python, consider submitting codes via platforms like PyPy instead of traditional interpreters whenever possible since they offer better runtime efficiencies especially important during competitive programming contests where milliseconds matter significantly.
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