10129 - Play on Words

本文介绍了一种使用图论和深度优先搜索(DFS)解决单词排序问题的方法。通过对每个单词建立一个节点,并根据单词首尾字母形成有向边来构建图,进而分析节点出入度差值,判断是否能形成有效的单词序列。该方法适用于分析由一系列单词组成的特定问题。

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首先将单词看成是连接首字母和尾字母的有向边,然后在建立图的过程当中记录每个字母的入度和出度。当无向图建立好了之后,首先利用队列进行DFS遍历,判断图是否联通,如果图不联通则直接否定,如果图是联通的,那么逐个分析每个顶点的入度和出度只差,只有两种情况可以接收:第一种是所有顶点的出度和入度都相等,第二种情况是仅仅存在两个顶点的出度不等于入度,并且其中一个顶点的出度减去入度为1,另外一个顶点的入度减去出度为1,同时要保证出度与入度之间没有其他差值的情况存在即可,具体实现见如下源代码:

#include<iostream>
#include<vector>
#include<string>
#include<set>
#include<stack>
#include<queue>
#include<map>
#include<algorithm>
#include<cmath>
#include<iomanip>
#include<cstring>
#include<sstream>
using namespace std;

int T;

int main(){
	cin >> T;
	while (T--){
		int in[26], out[26];
		memset(in,0,sizeof(in));
		memset(out,0,sizeof(out));
		int N;
		cin >> N;
		int area[26][26];
		set<char> st;
		bool visit[26];
		memset(visit,false,sizeof(visit));
		memset(area,0,sizeof(area));
		for (int i = 0; i < N; i++){
			string s;
			cin >> s;
			st.insert(s[0]);
			st.insert(s[s.size()-1]);
			int index1 = s[0] - 'a';
			int index2 = s[s.size() - 1] - 'a';
			out[index1]++;
			in[index2]++;
			area[index1][index2] = 1;
			area[index2][index1] = 1;
		}
		int start = (*st.begin()) - 'a';
		queue<int> q;
		q.push(start);
		visit[start] = true;
		int amount = 0;
		while (!q.empty()){
			int index = q.front();
			q.pop();
			amount++;
			for (int i = 0; i < 26; i++){
				if (area[index][i] && !visit[i]){
					q.push(i);
					visit[i] = true;
				}
			}
		}
		if (amount != st.size()){
			cout << "The door cannot be opened." << endl;
		}
		else{
			int first = 0;
			int second = 0;
			int other = 0;
			for (int i = 0; i < 26; i++){
				if (out[i] - in[i] == 1) first++;
				else if (in[i] - out[i] == 1) second++;
				else if (abs(in[i] - out[i]) != 0) other++;
			}
			if ((first == 1 && second == 1 && other == 0) || (first == 0 && second == 0 && other == 0))
				cout << "Ordering is possible." << endl;
			else cout << "The door cannot be opened." << endl;
		}
	}
	//system("pause");
	return 0;
}

### c-TF-IDF Algorithm Implementation and Explanation #### Overview of c-TF-IDF In the context of information retrieval and text processing, c-TF-IDF extends traditional TF-IDF by incorporating cluster-specific term weights. This approach enhances the ability to identify important terms that are characteristic not only within individual documents but also across clusters of similar documents[^1]. #### Mathematical Foundation The core concept involves calculating two components: - **Cluster Term Frequency (CTF):** Measures how frequently a term appears in all documents belonging to a specific cluster. - **Inverse Cluster Frequency (ICF):** Reflects the rarity of a term across different clusters. Combining these metrics allows for identifying distinctive features unique to each cluster while down-weighting common words found throughout multiple clusters[^2]. #### Python Code Example Below is an example demonstrating how one might implement this method using `sklearn`: ```python from sklearn.feature_extraction.text import TfidfVectorizer import numpy as np def compute_ctfidf(documents_per_cluster): vectorizer = TfidfVectorizer().fit_transform([' '.join(docs) for docs in documents_per_cluster]) tfidf_matrix = vectorizer.toarray() # Compute average TF-IDF score per word over all clusters avg_tfidf_scores = np.mean(tfidf_matrix, axis=0) # Calculate CTF-IDF scores based on relative difference from mean ctfdif_scores = [] for i in range(len(documents_per_cluster)): doc_vector = tfidf_matrix[i] normalized_diff = abs(doc_vector - avg_tfidf_scores) ctfdif_scores.append(normalized_diff / max(normalized_diff)) return ctfdif_scores ``` This code snippet first computes standard TF-IDF values for every document grouped under their respective clusters. Then it calculates differences between individual document vectors and overall averages before normalizing them into final c-TF-IDF scores. #### Application Scenarios Such techniques find applications in areas like clustering evaluation, topic modeling, summarization tasks where distinguishing characteristics among groups play crucial roles[^3]. --related questions-- 1. What other methods exist besides c-TF-IDF for enhancing cluster-based text analysis? 2. Can you provide real-world examples illustrating successful uses of c-TF-IDF algorithms? 3. Are there any limitations associated with applying c-TF-IDF approaches compared to conventional ones? 4. How does adjusting parameters impact performance when implementing c-TF-IDF models?
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