trie | 易学教程

Can I use a trie that has a whole word on each node?

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问题 I want to implement a trie to check for the validity of paths, so I would have a tree built that contains all the possible path constructions by breaking it down by directory. So something like /guest/friendsList/search would go from the root node to it's child guest , then guest's child friendsList , and then friendsList's child search . If search is a leaf node then my string /guest/friendsList/search would be considered valid. Is this something a trie would be useful for. All the

algorithm to print trie alphabetically

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问题 I've been busy trying to code a trie ordered tree data structure in C. My program reads in words in a sentence one at a time from a .txt, then it stores each word in a trie without duplicates. It then grabs all other words in that sentence and stores them in a subtrie of the word that was stored. For example if we had the following sentence: "contribute to open source. " My code does the following... root ab'c'defghijklmn'o'pqr's''t'uvwxyz 'o' 'p' 'o''o'-subtrie-> "contribute", "open",

What is the runtime of my algorithm?

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问题 I'm writing an algorithm that will first take a config file of various endpoints and their associated method like the following: /guest guestEndpoint /guest/lists listEndpoint /guest/friends guestFriendsEndpoint /guest/X/friends guetFriendsEndpoint /guest/X/friends/X guestFriendsEndpoint /X/guest guestEndpoint /X/lists listEndpoint /options optionsEndpoint X here represents a wildcard, so any string would match with this. The algorithm would take this as input and build a tree with each node

Finding longest common substring using Trie

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问题 How can i find LCS (longest common substring) among two or more strings using trie ? I have an idea like this - suppose my first string is "abbcabdd". then i will first insert "abbcabdd" in trie ,then "bbcabdd", then "bcabdd" .... , then "d" and repeat this process for every string . Then calculate the longest substring by traversing the trie. but i think it is not efficient. Is there any other improved method ? 回答1: What you are describing is exactly a suffix tree - Use an algorithm

Traversing a trie to get all words

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问题 I have written Perl code to actually create a Trie datastructure given a set of words in an array. Now I have problems traversing and printing the words. Also pasted the Dumper output of the Datastructure created. The final set of words after traversal doesn't seem to be right since the traversal logic is certainly missing something. But the trie creation is fine and works fast. Can someone help me here? The top level of the trie is a hash Each hash item has a key which is a letter and each

How to find the longest word in a trie?

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问题 I'm having trouble understanding the concept of a trie. From the "trie" wikipedia entry I have this picture: If I see this correctly, all leaf nodes in a trie will have the entire word spelled out and all parent nodes hold the characters leading up the the final leaf node. So, if I have a class called DigitalTreeNode defined by public class DigitalTreeNode { public boolean isAWord; public String wordToHere; (compiles all the characters in a word together) public Map<String, DTN> children; }

Implementing trie for efficient search of products on my website

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问题 I have got a list of say million of products. Now when the user at my website types something, I need to show him some relevant products for help. The search should be fast. I think trie implementation will be fine for me. But i am confused with the very idea of implementation. I need to have the tree ready always , so that i can search n show the result instantaneously. If i start inserting the elements while calling the javascript function , it will take too long. Can anyone suggest me,

中文分词算法之词典机制性能优化与测试

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在之前的两篇博文中文分词算法之基于词典的正向最大匹配算法和中文分词算法之基于词典的逆向最大匹配算法中，我们对分词实现和词典实现都做了优化，本文对词典实现做进一步优化，并和之前的多个实现做一个对比，使用的词典下载地址，使用的测试文本下载地址。优化TrieV3的关键在于把虚拟根节点（/）的子节点（词表首字母）提升为多个相互独立的根节点，并对这些根节点建立索引。优化的依据是根节点（词表首字母）的数量庞大，索引查找的速度远远超过二分查找。下面看看进一步优化后的TrieV4和之前的TrieV3的对比： /** * 获取字符对应的根节点 * 如果节点不存在 * 则增加根节点后返回新增的节点 * @param character 字符 * @return 字符对应的根节点 */ private TrieNode getRootNodeIfNotExistThenCreate(char character){ TrieNode trieNode = getRootNode(character); if(trieNode == null){ trieNode = new TrieNode(character); addRootNode(trieNode); } return trieNode; } /** * 新增一个根节点 * @param

JavaScript: 实现简单的中文分词

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中文分词在大数据横行的今天是越来越有用武之地了。它不仅被广泛用于专业的中文搜索引擎中，而且在关键词屏蔽、黑白名单以及文本相似度等方面也能大显身手。中文分词最简单也最常用的方式是基于字典查找的方式，通过遍历待分词字符串并在字典中进行查找匹配以达到分词的目的。本文即是采用这种方式。字典在本文中，完全依赖于字典，因此需要准备好字典。一般面对不同的领域用不同的字典。比如面向医学的，则字典会添加许多医学术语方面的词。可以很容易的找到常用词的字典，比如搜狗输入法自带的字典等。停止词停止词不能用于成词。停止词主要包括无意义的字符(如的、地、得)或词。常规实现本文由于只是简单的介绍和实现，所以定义好了简单的字典和停止词，如下代码所示： <!DOCTYPE html> <html> <head> <meta charset="utf-8"> <title>简单的中文分词</title> <meta name="author" content="" /> <meta http-equiv="X-UA-Compatible" content="IE=7" /> <meta name="keywords" content="简单的中文分词" /> <meta name="description" content="简单的中文分词" /> </head> <body> <script type=

中文分词算法之基于词典的正向最大匹配算法

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基于词典的正向最大匹配算法（最长词优先匹配），算法会根据词典文件自动调整最大长度，分词的好坏完全取决于词典。算法流程图如下： Java实现代码如下： /** * 基于词典的正向最大匹配算法 * @author 杨尚川 */ public class WordSeg { private static final List<String> DIC = new ArrayList<>(); private static final int MAX_LENGTH; static{ try { System.out.println("开始初始化词典"); int max=1; int count=0; List<String> lines = Files.readAllLines(Paths.get("D:/dic.txt"), Charset.forName("utf-8")); for(String line : lines){ DIC.add(line); count++; if(line.length()>max){ max=line.length(); } } MAX_LENGTH = max; System.out.println("完成初始化词典，词数目："+count); System.out.println("最大分词长度："+MAX_LENGTH); }