Get Word List
by Isai Damier

/***************************************************************************
 * Author: Isai Damier
 * Title: Trie.java
 * Project: geekviewpoint
 * Package: datastructure
 *
 * Description:
 *  A trie is a tree data-structure that stores words by compressing common
 *  prefixes. To illustrate, following is a word list and its resulting trie.
 *
 *   WORDS: rat, rats, rattle, rate, rates, rating, can, cane,
 *          canny, cant, cans, cat, cats, cattle, cattles.
 *
 *   TRIE:
 *         ___________|____________
 *        |                        |
 *        r                        c
 *        a                ________a___________
 *        t~              |                    |
 *    ____|_____          n~                   t~
 *   |   |  |   |    _____|_____        _______|______
 *   s~  t  e~  i   |   |   |   |      |              t
 *       l  s~  n   e~  n   t~  s~     s~             l
 *       e~     g~      y~                            e~
 *                                                    s~
 *
 *   Each ~ in the figure indicates where a prefix is a word.
 *
 *   Generally, a trie has all the benefits of a hash table without
 *   any of the disadvantages.
 *
 *   --------------------------------------------------------------
 *          | HASH TABLE | TRIE     | explanations
 *   --------------------------------------------------------------
 *   Memory |    O(n)    |  < O(n)  | trie uses prefix compression.
 *          |            |          | Hence it does not store each
 *          |            |          | word explicitly
 *  ----------------------------------------------------------------
 *   Search |   O(1)     |  O(1)    | trie is technically faster.
 *          |            | pseudo-  | Given a word, computing a
 *          |            | constant | hash takes at least as long
 *          |            |          | as traversing a trie. Plus,
 *          |            |          | trie has no collision.
 *  ----------------------------------------------------------------
 *
 *  Tries are particularly superior to hash tables when it comes to solving
 *  problems such as word puzzles like boggle. In such puzzles the objective
 *  is to find how many words in a given list are valid. So if for example
 *  at a particular instance in boggle you have a list of one billion words
 *  all starting with zh-, whereas the dictionary has no words starting with
 *  zh-; then: if the dictionary is a hash table, you must compute the entire
 *  hashcode for each word and do one billion look-ups; if on the other hand
 *  the dictionary is a trie, you only do the equivalent of partially
 *  computing one hashcode! That's a saving of over one billion fold!
 *
 *  This implementations of trie uses an array to store the children
 *  nodes, where the numerical value of each char serves as index.
 **************************************************************************/ 
 package algorithms.trie;

import java.util.ArrayList;
import java.util.List;
import java.util.Stack;

public class Trie {
  //the root only serves to anchor the trie.

  private TrieNode root;

  public Trie() {
    root = new TrieNode('\0');
  }//constructor

  /*************************************************************************
   * Function: getWordList
   * @param word
   * @return List<String>
   *
   * Description: Return a lexicographically ordered list of all the words
   *   on the trie.
   *
   * Technical Details: This is a recursive pre-order depth-first traversal
   *   algorithm, pre-order is to Trie what in-order is to BST. In addition,
   *   because each prefix assembled is an optimal substructure, this is
   *   also a greedy algorithm: where the words are assembled during
   *   traversal and as soon as a word is encountered, it is added to the
   *   list of results.
   *
   *    0] initialize an empty list to hold the words to be added.
   *    1] for each child node of the root
   *    2]    call the recursive function getWordList to assemble
   *          all words stemming from the given prefix.
   *    3] return the list
   ************************************************************************/
  public List<String> getWordList() {
    List<String> result = new ArrayList<String>();
    for (TrieNode n : root.next) {
      if (null != n) {
        getWordList(result, n.value + "", n);
      }
    }
    return result;
  }//getWordList

  /**
   * Description: this function is the recursive portion of the
   *    overloaded function above.
   *
   *   0] if the given node n is a word, add the word to the result
   *   1] if n is a prefix, for each of its children
   *      call this recursive function getWordList to assemble
   *      all words stemming from the given child.
   */
  private void getWordList(List<String> result, String word, TrieNode n) {
    if (n.word) {
      result.add(word);
    }
    for (TrieNode t : n.next) {
      if (null != t) {
        getWordList(result, word + t.value, t);
      }
    }
  }
}
package algorithms.graph;

import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import org.junit.Test;
import static org.junit.Assert.*;

/***
 * The dictionary is assembled from the works of poet
 * Percy Bysshe Shelley (1792-1822)
 ***/
public class TrieTest {

  /**
   * Test of getWordList method, of class Trie.
   */
  @Test
  public void testGetWordList() {
    System.out.println("getWordList");
    Trie trie = new Trie();
    List<String> dictionary = Arrays.asList("His", "soul", "had", "wedded", "Wisdom",
            "and", "her", "dower", "is", "love", "justice", "clothed", "in",
            "which", "he", "sate", "apart", "from", "men", "as", "a",
            "lonely", "tower", "pitying", "the", "tumult", "of", "their",
            "dark", "estate");
    for (String w : dictionary) {
      trie.addWord(w);
    }
    List result = trie.getWordList();
    Collections.sort(dictionary);
    //Collections.sort(result);// result is already sorted by default
    assertEquals(dictionary, result);
  }
}