Lab- RedBlackTrees.docx - Lab: Data Structures and...

Lab: Data Structures and Algorithms
Topic: Red Black Trees + BST
Objective
 Working with Red Black Trees
Solutions to tasks 2 and 3 will be considered as Assignment 3, Start from Task 2 or
3 and complete the task 1 in the end
Task 1:
■ Similar to the cases of Insertion, submit the rules for cases of Deletion.
■ Examples of each case are also required.
■ You may not create a power point for it, you may write down the cases on a Microsoft
Word, or text file and submit that file. Handwritten rules are also acceptable, the choice is
yours.
Task 2
:
For a BST, create the following functions:
1. Copy Constructor
2. A function other than copy constructor, which creates copy of a tree passed as parameter to the
function into the tree that called the function. The name of this function should be copyTree
3. Destructor (should destroy the complete tree without memory leakage or dangling pointers)
4. A function other than destructor, which when called, destroys the tree that called the function.
You can name this function as destroyTree
Task 3
:
For a BST, create a function that deletes all the duplicate nodes (if any) present in the tree.
.

The complete explanation of Red-Black tree is given below. I have copied
everything from Wikipedia. You can do the same to copy 6 rules of deletion as long
as you understand them (task 1).
Partial code of insert and delete are also given in the explanation below. This is
more than enough to implement the project. If you intend to copy the codes given
below without understanding it, trust me, it won’t run. You can try.
NOTE THAT R-B TREES ARE A PART OF YOUR COURSE IRRESPECTIVE
OF THE FACT WHETHER YOU IMPLEMENT IT AS A PROJECT OT NOT.
Red–black tree
From Wikipedia, the free encyclopedia
Red–black tree
Type Tree
Invented 1972
Invented by Rudolf Bayer
Time complexity in big O notation
Average Worst case
Search O(log n) O(log n)
Insert O(log n) O(log n)
Delete O(log n) O(log n)
A red–black tree is a binary search tree with an extra bit of data per node, its color, which can be
either red or black. The extra bit of storage ensures an approximately balanced tree by constraining
how nodes are colored from any path from the root to the leaf.
[1]
Thus, it is a data structure which is a
type of self-balancing binary search tree .
Balance is preserved by painting each node of the tree with one of two colors (typically called 'red'
and 'black') in a way that satisfies certain properties, which collectively constrain how unbalanced
the tree can become in the worst case. When the tree is modified, the new tree is subsequently