Assignment Description
In this lab we’ll practice AVL tree rotations and insertions, and see some silly test cases.
Lab Insight
AVL trees can be used to build data structures like sorted maps and sets. To be quite honest, Red-Black trees, another kind of self-balancing search tree, are used more often in these applications because inserting things into them is faster. However, the general search performance of AVL trees is better, and even though AVL trees sound complicated to implement, all they need is a careful implementation of rotations to work well. Either way, the general principle of using self-balancing trees for data is useful because it helps search for things in faster than the O(n) time we would need if all we had were linked lists. Without rolling the dice and hoping for the best (an actually workable strategy, as we will see in later data structures), balanced trees are the go-to for good worst case search performance. They are the basis for many other data structures (like the maps and sets we mentioned before) and algorithms (they’re better than naive lists if we’re going to be searching for things), and a useful data structure to have in our toolkits for a lot of problems.
Checking Out The Code
You can find the code for all our released assignments at the following public git repo.
You can either clone this or pull a zip file of the repo to your local computer. All the files for this MP are in the lab_avl
directory. You should copy that directory into the directory you setup for your docker container.
As usual, don’t forget to take a look at Doxygen for lab_avl.
Preparing Your Code
This semester for MPs we are using CMake rather than just make. This allows for us to use libraries such as Catch2 that can be installed in your system rather than providing them with each assignment. This change does mean that for each assignment you need to use CMake to build your own custom makefiles. To do this you need to run the following in the base directory of the assignment. Which in this assignment is the lab_avl
directory.
mkdir build
cd build
This first makes a new directory in your assignment directory called build
. This is where you will actually build the assignment and then moves to that directory. This is not included in the provided code since we are following industry standard practices and you would normally exclude the build directory from any source control system.
Now you need to actually run CMake as follows.
cmake ..
This runs CMake to initialize the current directory which is the build
directory you just made as the location to build the assignment. The one argument to CMake here is ..
which referes to the parent of the current directory which in this case is top of the assignment. This directory has the files CMake needs to setup your assignment to be build.
At this point you can in the build directory run make as described to build the various programs for the MP.
Implement Rotation Functions
You must implement rotateLeft()
, rotateRight()
, and rotateRightLeft()
. We
have implemented rotateLeftRight()
for you as an example for implementing
rotateRightLeft()
.
Implement the rebalance()
Function
You must implement rebalance()
function. rebalance()
should, given a
subtree, rotate the subtree so that it is balanced. You should assume that the
subtree’s left
and right
children are both already balanced trees. The
node’s height
should always be updated, even if no rotations are required.
Implement the insert()
Function
You must implement the insert()
function. insert()
should add a node with
a key and value at the correct location in the tree, then rebalance
appropriately (while returning from each recursive function) to fix the tree’s
balance.
Implement the remove()
Function
You must implement the remove()
function. remove()
should remove the node
with the specified key from the tree, then rebalance appropriately (while
returning from each recursive function) to fix the tree’s balance. You can
assume that the key exists in the tree. You may want to use the swap()
method.
To match the provided output (and grading scripts), you should use IOP (in order predecessor) for removing a node with 2 children.
Testing Your Code
To test your code, compile using make
:
make
Then run it with:
./testavl color
You will see that the output is colored — green means correct output, red means incorrect output, and underlined red means expected output that was not present. This mode is a bit experimental, and it might cause problems with your own debugging output (or other problems in general). To turn it off, simply leave off the “color” argument:
./testavl
You may also diff
your solution with our expected output:
./testavl | diff -u - soln_testavl.out
Type [Escape] [:] [q] [a] [ENTER] to exit vimdiff
.
To make the catch
test suite, run:
make test
After compiling the test suite, run the tests using:
./test
Submitting Your Work
The following files are used in grading:
avltree.hpp
avltree.h
All other files including any testing files you have added will not be used for grading.