AVL Trees (Part 1)

0. Complete the Prerequisites

Before continuing on, make sure you’re caught up on the lectures through 7/8. Also make sure to pull the starter code.

1. First 4 AVL Tree methods

In this assignment your task is to complete a series of algorithms involving a Self-Balancing Binary Search Trees, AVL Trees. The method signatures and their explanations that you need to implement are provided in the SelfBalancingBST Interface. We have provided the implementation for an EmptyBST, and it is your job to provide the outstanding implementation for a non-empty BST in the NonEmptyBST class. The methods left for you to complete have a “TODO” comment above them. We have provided getHeight, getRight, getLeft, getElement, and isEmpty.

You can test your implemention in the Main class.

The entire AVL Tree programming task has been broken up into two smaller exercises, with the methods required by the SelfBalancingBST interface being divided up between ex11 and ex12.

For ex11, we are expecting you to complete findMin, rotateLeft, rotateRight, and insert. Note that rotateLeft and rotateRight are not part of the SelfBalancingBST interface, but are private methods that you will need to use in your implementation of insert and remove.

The remove, findMax, and contains methods will count as part of ex12, but feel free to work ahead.

We have provided the fields, constructor, size, height, getRight, getLeft, isEmpty and getValue. The constructor takes no arguments and creates an empty AVLTree. You can either use a null element value to indicate that a tree is empty or explicitly keep track of whether a tree is empty with a boolean field (you would have to add this field).

Inserting and removing elements to a self-balancing tree may result is a different root object after the insertion or removal because of self-balancing operations. This is why these methods as declared in the interface return the potentially different post-operation root of the tree. In your AVLTree implementation, this means you will need to recapture the result whenever you recursively do something to a child.

Self balancing trees are one of the more difficult Data Structures to implement so we highly recommend making use of the debugger to help with any issues you may run into. Here is a tutorial on how to get started with the dubugger in IntelliJ: https://www.jetbrains.com/help/idea/debugging-your-first-java-application.html. The TAs also went over this during the review on 7/7. The recording is posted to the course webpage. We are happy to help you with this in office hours, too. As always you should be writing your own JUnits to test your code instead of only relying on the autograder.

Hints and Notes

It may be helpful to write some additional helper methods beyond the basic rotation methods. Some ideas might be:

• updateHeightAndSize: a void method that updates the _size and _height fields of the AVL tree. This would be called after a rotation, or after an insertion or deletion.

• fixIfImbalanced: a void method that checks if the tree is imbalanced. If so, perform the necessary rotations. This would be called after an insertion or deletion.

These are just two ideas, feel free to structure your code however you think makes sense!

2. Make a Backup Checkpoint “Commit”

“Push” your work up to GitHub for backup. By creating “commits”, which you can think of as versioned checkpoints in your workspace, you are not at risk of losing your work. It’s easy to revert back to an old version or to restore your entire workspace on a different computer.

1. Select the Git menu along the top of your screen and then choose “Commit”.
2. Notice the files listed under Changes. These are files you’ve made modifications to since your last backup.
3. Ensure all the files that you’d like to backup are selected. Your cursor should be inside of a message box where you will write a nice description of the modifications you’ve made to your code, like “Finished EX04!”, and then hit the “Commit” button.
   • If you do not want to backup all changed files, you can select them individually. For this course you’re encouraged to back everything up.
   • IntelliJ might tell you there are some warnings or errors here, and you should just commit anyway!
4. If you open the Git at the bottom of your screen, you should see this commit added to your chain of git commits. However, it has just been added to your local main branch, and needs to be pushed to your remote backup.
5. Select the Git menu along the top of your screen again and then choose “Push”.
6. A pop-up should appear that displays: “main -> backup : main”, which means your latest local commit on the local main branch is going to be pushed to the main branch on the remote backup. If you see “main -> origin : main”, just click where it says origin and select backup. Hit the “Push” button.
7. If you want to see your backed up work on Github, navigate to the following URL but replace USERNAME with your GitHub username:
   • https://github.com/summer-210/summer210-workspace-USERNAME
   • You can also verify it was pushed successfully by opening the Git view along the bottom of your screen and verifying the latest commit appears on the local and remote branches. (The text next to the tag icons should say backup & main, next to the latest commit message)

4. Submit to Gradescope for Grading

All that’s left now is to hand-in your work on Gradescope for grading.

Before doing so, you need to know that before an assignment’s deadline you can resubmit work as many times as you need to without penalty. Portions of assignments are autograded and will provide near-immediate feedback. We want you to resubmit as many times as it takes you in order to earn full autograding credit!

Login to Gradescope and select the assignment named “EX11 - AVL Trees Part 1” You’ll see an area to upload a zip file. To produce a zip file for autograding, return back to IntelliJ.

Mac Users

Along the bottom of your window, you should see an option to open a terminal integrated into IntelliJ.

Type the following command (all on a single line):

./submit.sh ex11

In the file explorer pane, look to find the zip file named “ex11_submission.zip”. The script will call it an ex11 submission since that is the package we zipped. If you right click on this file “Open in -> Finder” on Mac, the zip file’s location on your computer will open. Upload this file to Gradescope to submit your work for this exercise.

Windows Users

We are working on rewriting the script to work for Windows! Until then, please navigate to your course workspace in a File Explorer window. Then right click on the src folder in your exercises directory and compress the directory into a zip folder. You can name it “ex11_submission.zip”

When you upload it to Gradescope, please delete any files that showed up in the src/ folder that were not actually part of ex11.