博客
关于我
LeetCode Most Common Word 最常见的词
阅读量:803 次
发布时间:2023-01-31

本文共 3035 字,大约阅读时间需要 10 分钟。

Here is an optimized version of the thought process and solution:

  • Data Preparation

    • Convert the entire paragraph to lowercase to handle case insensitivity.
    • Remove all punctuation marks (such as commas, periods, exclamation points, etc.) to isolate words.
    • Ensure words are properly separated by spaces to avoid partial words (e.g., "ball," becomes "ball").
  • Word Frequency Calculation

    • Traverse the prepared string, extracting each word by ignoring punctuation and case differences.
    • Use a hash map (dictionary) to count occurrences of each word.
    • For each character in the paragraph: If it's a letter, add it to the current word being built. If it's not a letter or reaches the end of the string, finalize the word and update its count in the hash map.
  • Filter Banned Words

    • Store banned words in a set for quick lookup.
    • Iterate through the hash map to exclude any words that exist in the banned set, keeping only valid words.
  • Determine Most Frequent Word

    • Sort the remaining words by their frequency in descending order.
    • Return the first word in this sorted list, as it by definition is unique and has the highest count according to the problem constraints.
  • Final Solution Code

    import java.util.HashMap;import java.util.HashSet;import java.util.Map;public class Solution {    public String mostCommonWord(String paragraph, String[] banned) {        // Convert paragraph to lowercase and remove punctuation        StringBuilder cleanParagraph = new StringBuilder();        for (char c : paragraph.toCharArray()) {            if (c >= 'a' && c <= 'z') {                cleanParagraph.append(c);            }        }        // Split into words        String[] words = cleanParagraph.toString().split(" +");        // Count frequency of each word        Map
    frequencyMap = new HashMap<>(); for (String word : words) { frequencyMap.put(word, frequencyMap.getOrDefault(word, 0) + 1); } // Create banned words set for quick lookup HashSet
    bannedWords = new HashSet<>(); for (String bw : banned) { bannedWords.add(bw.toLowerCase()); } // Exclude banned words and find the most frequent int maxCount = -1; String result = ""; for (Map.Entry
    entry : frequencyMap.entrySet()) { if (!bannedWords.contains(entry.getKey())) { if (entry.getValue() > maxCount) { maxCount = entry.getValue(); result = entry.getKey(); } } } return result; }}

    Explanation

    • The code first processes the input paragraph to remove punctuation and convert it to lowercase, ensuring uniformity in word processing.
    • It then splits the cleaned string into individual words and uses a hash map to count each word's occurrences.
    • Banned words are stored in a set for quick exclusion.
    • Finally, the code iterates through the frequency map, excluding banned words, and identifies the word with the highest count, which is then returned as the result.

    转载地址:http://oogyk.baihongyu.com/

    你可能感兴趣的文章
    Python合并两张图片 | 先叠透明度再合并 (附Demo)
    查看>>
    python各进制的表述与转换
    查看>>
    python各种库安装
    查看>>
    python可视化matplotlib_如何使用Python中最强大的可视化工具Matplotlib?
    查看>>
    python可维护性_使用这7大神器,让你的Python 代码更容易于维护
    查看>>
    python变量的详细教程_Python零基础入门教程之语法入门[变量](第二期)
    查看>>
    Python变量命名方法-ChatGPT4o作答
    查看>>
    Python变量与运算符
    查看>>
    python单元测试之unittest
    查看>>
    Python单元测试unittest实战
    查看>>
    python协程实时输出_Python 协程以及事件循环的问题,怎么知道 IO 读取结束了呢?...
    查看>>
    Python化身“安全老中医”:一键搞定工控系统漏洞,从零基础到精通,收藏这篇就够了!
    查看>>
    Python加速运行技巧
    查看>>
    python办公自动化基础遍历文件夹
    查看>>
    python办公自动化基础输出文件和文件夹
    查看>>
    python办公自动化基础搜索文件
    查看>>
    python前端之css
    查看>>
    Python制作进度条,原来有这么多方法
    查看>>
    Python制作简单的学生成绩管理系统
    查看>>
    python制作甘特图的基本知识(附Demo)
    查看>>