An Implementation of the Trie dictionary tree

Today, I saw Trie's principles. I thought of a question I had not answered in my previous interview. I 'd like to try it out by writing some code.

A typical Trie application is used for statistics and sorting, and to query a large number of strings, but not limited to strings). Therefore, it is often used by the search engine system for text word frequency statistics. If the maximum length of a keyword is 5, you can use the trie tree to retrieve the specified keyword from 26 ^ 5 = 11881376 possible keywords using 5 comparisons. Log2n comparison is required at least for the binary search tree.

If you want to calculate the term frequency, you need to modify the Node class. For a given word table, this data structure can be used to search for words in the text in a poor word table.

package algorithm;import java.util.ArrayList;import java.util.HashMap;import java.util.List;public class Trie {    private Node root=new Node();                                                       public static void main(String[] args) {        Trie trie = new Trie();                                                               List<String> keyWords=new ArrayList<String>();        keyWords.add("ca123");        keyWords.add("mu456");        keyWords.add("zh789");                                                               trie.buildTrie(keyWords);                                                               trie.search("ca123");        trie.search("ca124");        trie.search("mu445");        trie.search("zh789");    }                                                       public void buildTrie(List<String> keyWords){        for(String key:keyWords){            Node parent=root;            for (int i = 0; i < key.length(); i++) {                char nodeKey=key.charAt(i);                Node node = parent.getChildNodes().get(nodeKey);                if (node!=null) {                    parent=node;                } else {                    Node childNode = new Node();                    parent.getChildNodes().put(nodeKey, childNode);                    parent=childNode;                }            }            parent.setValue(key);        }    }                                                       public void search(String key){        Node parent=root;        for (int i = 0; i < key.length(); i++) {            Node node = parent.getChildNodes().get(key.charAt(i));            if (node == null) {                System.out.println("The word "+key+" is not in dictionary");                return;            }            parent=node;        }        System.out.println("The word "+parent.getValue()+" is in the dictionary");    }                                                   }class Node {    private String value;    private HashMap<Character, Node> childNodes;                                                       public String getValue() {        return value;    }    public void setValue(String value) {        this.value = value;    }    public HashMap<Character, Node> getChildNodes() {        if (childNodes==null) {            childNodes=new HashMap<Character, Node>();        }        return childNodes;    }    public void setChildNodes(HashMap<Character, Node> childNodes) {        this.childNodes = childNodes;    }                                                   }

This article from the "Ying: Good memory as bad pen" blog, please be sure to keep this source http://yingtju.blog.51cto.com/3760152/1299564