Java データ構造とアルゴリズム: ビッグ データ分析の実践ガイド

import java.util.HashMap;
import java.util.StringJoiner;

public class WordFrequencyCounter {

    public static void main(String[] args) {
        String text = "This is an example text to count word frequencies";

        // 使用哈希表存储单词及其频率
        HashMap<String, Integer> frequencyMap = new HashMap<>();

        // 将文本拆分为单词并将其添加到哈希表中
        String[] words = text.split(" ");
        for (String word : words) {
            frequencyMap.put(word, frequencyMap.getOrDefault(word, 0) + 1);
        }

        // 从哈希表中打印每个单词及其频率
        StringJoiner output = new StringJoiner("\n");
        for (String word : frequencyMap.keySet()) {
            output.add(word + ": " + frequencyMap.get(word));
        }
        System.out.println(output);
    }
}

import java.util.*;

public class SocialNetworkAnalyzer {

    public static void main(String[] args) {
        // 创建一个图来表示社交网络
        Map<String, Set<String>> graph = new HashMap<>();

        // 添加节点和边到图中
        graph.put("Alice", new HashSet<>(Arrays.asList("Bob", "Carol")));
        graph.put("Bob", new HashSet<>(Collections.singleton("Dave")));
        ...

        // 使用广度优先搜索找到与 Alice 相关的所有节点
        Queue<String> queue = new LinkedList<>();
        queue.add("Alice");
        Set<String> visited = new HashSet<>();
        while (!queue.isEmpty()) {
            String current = queue.remove();
            visited.add(current);
            for (String neighbor : graph.get(current)) {
                if (!visited.contains(neighbor)) {
                    queue.add(neighbor);
                }
            }
        }

        // 打印与 Alice 相关的所有节点
        System.out.println(visited);
    }
}