Java代写 | Data Structure 数据结构 | Algorithm 算法

本次Java代写数据结构算法的主要内容是使用Java在Eclipse上完成数据结构的编程代码,涉及二叉树的插入、排序、旋转等。

You get ONE free extension pass for assignments during the semester, no questions asked. There will be a total of 5 assignments this semester, and you may use this one free extension pass for any of the 5 assignments.

A separate Sakai assignment will be opened for extensions AFTER the deadline for the regular submission has passed. The regular submission deadline for all assignments will be on a Friday, 11 PM, and the deadline for the corresponding extensions will be on the following Monday, 11 PM.


  • You will work on this assignment individually. Read DCS Academic Integrity Policy for Programming Assignments – you are responsible for abiding by the policy. In particular, note that “All Violations of the Academic Integrity Policy will be reported by the instructor to the appropriate Dean“.
  • IMPORTANT – READ THE FOLLOWING CAREFULLY!!!

    Assignments emailed to the instructor or TAs will be ignored–they will NOT be accepted for grading.
    We will only grade submissions in Sakai.

    If your program does not compile, you will not get any credit.

    Most compilation errors occur for two reasons:

    1. You are programming outside Eclipse, and you delete the “package” statement at the top of the file. If you do this, you are changing the program structure, and it will not compile when we test it.
    2. You make some last minute changes, and submit without compiling.

    To avoid these issues, (a) START EARLY, and give yourself plenty of time to work through the assignment, and (b) Submit a version well before the deadline so there is at least something in Sakai for us to grade. And you can keep submitting later versions (up to 10) – we will accept the LATEST version.



Summary

You will write an application to build a tree structure called Trie for a dictionary of English words, and use the Trie to generate completion lists for string searches.


Trie Structure

A Trie is a general tree, in that each node can have any number of children. It is used to store a dictionary (list) of words that can be searched on, in a manner that allows for efficient generation of completion lists.

The word list is originally stored in an array, and the trie is built off of this array. Here are some word lists, the corresponding tries, followed by an explanation of the structure and its correspondence to the word list.

Special Notes

  • Every leaf node represents a complete word, and every complete word is represented by some leaf node. (In other words, internal nodes do not represent complete words, only proper prefixes.)
  • No node, except for the root, can have a single child. In other words, every internal node has at least 2 children. Why? Because an internal node is a common prefix of several words. Consider these trees, in each of which an internal node has a single child (incorrect), and the equivalent correct tree:
  • A trie does NOT accept two words where one entire word is a prefix of the other, such as “free” and “freedom”. 
    (You will not come across this situation in any of the test cases for your implementation.)The process to build the tree (described in the Building a Trie section below), will create a single child of the root for the longest common prefix “free”, and this node will have a single child, a leaf node for the word “freedom”. But this is an incorrect tree because it will (a) violate the constraint that no node aside from the root can have a single child, and (b) violate the requirement that every complete word be a leaf node (the complete word “free” is not a leaf node).

Data Structure

Since the nodes in a trie have varying numbers of children, the structure is built using linked lists in which each node has three fields:

  • substring (which is a triplet of indexes)
  • first child, and
  • sibling, which is a pointer to the next sibling.

Building a Trie

A trie is built for a given list of words that is stored in array. The word list is input to the trie building algorithm. The trie starts out empty, inserting one word at a time.

Example 1

The following sequence shows the building of the above trie, one word at a time, with the complete data structure shown after each word is inserted.

Example 2

This shows the sequence of inserts in building Trie 7 shown earlier.

Prefix Search

Once the trie is set up for a list of words, you can compute word completions efficiently.

For instance, in the trie of Example 2 above (cat, muscle, …), suppose you wanted to find all words that started with “po” (prefix). The search would start at the root, and touch the nodes [0,0,2],(1,0,2),(2,0,1),(2,2,2),(3,2,3),[2,3,6],[6,3,5],[3,4,7],[4,4,5] . The nodes marked in red are the ones that hold words that begin with the given prefix.

Note that NOT ALL nodes in the tree are examined. In particular, after examining (1,0,2), the entire subtree rooted at that node is skipped. This makes the search efficient. (Searching all nodes in the tree would obviously be very inefficient, you might as well have searched the word array in that case, why bother building a trie!)


Implementation

Download the attached trie_project.zip file to your computer. DO NOT unzip it. Instead, follow the instructions on the Eclipse page under the section “Importing a Zipped Project into Eclipse” to get the entire project into your Eclipse workspace.

You will see a project called Trie with the following classes in the trie package: TrieNodeTrie, and TrieApp.

There are also a number of sample test files of words directly under the project folder (see the Testing section that follows.)

You will implement the following methods in the Trie class:

  • (50 pts) buildTrie: Starting with an empty trie, builds it up by inserting words from an input array, one word at a time. The words in the input array are all lower case, and comprise of letters ONLY.
  • (30 pts) completionList: For a given search prefix, scans the trie efficiently, gathers and returns an ArrayList of references to all leaf TrieNodes that hold words that begin with the search prefix (you should NOT create new nodes.) For instance, in the trie of Example 2 above, for search prefix “po” your implementation should return a list of references to these trie leaf nodes: [2,3,6],[6,3,5],[3,4,7],[4,4,5].
    NOTE:

    • The order in which the leaf nodes appear in the returned list does not matter.
    • You may NOT search the words array directly, since that would defeat the purpose of building the trie, which allows for more efficient prefix search. See the Prefix Search section above. If you search the array, you will NOT GET ANY credit, even if your result is correct.
    • If your prefix search examines unnecessary nodes (see Prefix Search section above), you will NOT GET ANY credit, even if your result is correct.

Make sure to read the comments in the code that precede classes, fields, and methods for code-specific details that do not appear here. Also, note that the methods are all static, and the Trie has a single private constructor, which means NO Trie instances are to be created – all manipulations are directly done via TrieNode instances.

You may NOT MAKE ANY CHANGES to the Trie.java file EXCEPT to (a) fill in the body of the required methods, or (b) add private helper methods. Otherwise, your submission will be penalized.

You may NOT MAKE ANY CHANGES to the TrieNode class (you will only be submitting Trie.java). When we test your submission, we will use the exact same version of TrieNode that we shipped to you.


Testing

You can test your program using the supplied TrieApp driver. It first asks for the name of an input file of words, with which it builds a trie by calling the Trie.buuldTree method. After the trie is built, it asks for search prefixes for which it computes completion lists, calling the Trie.completionList method.

Several sample word files are given with the project, directly under the project folder. (words0.txtwords1.txtwords2.txtwords3.txtwords4.txt). The first line of a word file is the number of the words, and the subsequent lines are the words, one per line.

There’s a convenient print method implemented in the Trie class that is used by TrieApp to output a tree for verification and debugging ONLY. Our testing script will NOT look at this output – see the Grading section below.

When we test your program:

  • Words will ONLY have letters in the alphabet.
  • All words–for building the trie as well as for prefix searches–will be input in lower case.
  • We will NOT input duplicate words.
  • We will NOT input two words such that one is a prefix of the other, as in “free” and “freedom”, i.e. a complete word will not be a prefix of another word.

 


程序代写代做C/C++/JAVA/安卓/PYTHON/留学生/PHP/APP开发/MATLAB


本网站支持淘宝 支付宝 微信支付  paypal等等交易。如果不放心可以用淘宝交易!

E-mail: [email protected]  微信:dmxyzl003


如果您使用手机请先保存二维码,微信识别。如果用电脑,直接掏出手机果断扫描。

发表评论