Lab 7: Binary Search Trees
Deadline Note
This lab is a bit more complex than some of the previous labs, thus you will have until Wednesday (2013/11/13) just before midnight (23:59:59) to complete this lab.
Goals for this lab
By the time you have completed this lab, you should
 learn about Tree ADT
 implement parts of the Binary Search Tree ADT
 get familiar with using Stack and Queue ADT
 understand depth first and breadth first tree traversals
The purpose of this lab is for you to get familiar with the tree ADT. A tree is another basic abstract data type, which is widely used in computer science. Because of the treelike structure, there are multiple ways to traverse data in a tree. In this lab you will get familiar with and implement some frequently used BST operations.
Lab pairing
For this lab you may work with a partner. To facilitate your submissions as a pair you MUST pair up using the submission system. When you visit the project page from where you can view all your submission you should see a link to a page where you can join a group. Of the two people in the group, one of you must invite the other, and the other must confirm the invitation. Once you group up, any submissions that either you, or your partner have already made will be visible to each other in addition to all future submissions.
Lab preparation
After logging in, create the directory for this lab:
mkdir p cs24/lab7
Change into the lab7 directory and copy the files you will need for this lab:
cd cs24/lab7
cp ~bboe/public_html/cs24_f13/code/lab7/* .
Trees and Binary Search Trees
Trees
A tree, in general is a data structure that, similar to a List, consists of nodes with data and, this time, one or more pointers to other nodes. However, a tree structure has an important requirement: no two pointers point to the same node. This property is important, because tree must not have any loops. From the requirement it follows that each node may have only one parent (the node, that is pointing to it), except for the very top one, which is called root. The root has no parents, (similar to the head node in the List). Nodes that don’t have any children (no nodes pointed to) are called leaf nodes. (Similar to the tail node in the List). More about the trees are on the wiki page.
Binary Trees, Binary Search Trees
There is a special type of tree called the Binary Tree. Each of its nodes must have no more than two children, usually distinguished as left and right. Among the Binary Trees there are Binary Search Trees, for which there are additional requirements, enforcing data in the tree to be structured in some way. First of all, there must be no duplicate nodes in the tree (nodes with the same data value). Secondly, for every node its left subtree (a tree that has its left child as a root) should consist only of nodes whose values (the data contained within it) are all less than its value. Additionally, for every node its right subtree (a tree that has its right child as a root) should consist only of nodes with values greater than its value. Finally, left and right subtrees should also be a BST themselves. More on BSTs. Below is an example BST created when inserting the following integers: 5, 3, 7, 4, 2, 6, 8
Insert the 5:
5
Insert the 3:
5
/
3
Insert the 7:
5
/ \
3 7
Insert the 4:
5
/ \
3 7
\
4
Insert the 2:
5
/ \
3 7
/ \
2 4
Insert the 6:
5
/ \
3 7
/ \ /
2 4 6
And finally, insert the 8:
5
/ \
3 7
/ \ / \
2 4 6 8
Inserting and searching in BST
Since the data in a BST follows the aforementioned structure, BSTs have an advantage in search operation over the List. The search algorithm itself is very easy. You start at the root node and compare its data with one you are looking for. If it matches, you return and appriopriate value. When it does not match and the value you are searching for is greater than the node’s value traverse down the right child (why?). If the value you are searching for is less than the node’s value traverse down the left child (why?). This process is repeated until a match is found, or there are no children in the appropriate search direction.
When inserting a node in a BST it is important that the new tree still has original properties. Therefore, it is important to select proper position for a new node. The insertion algorithm is similar to search, for except when we will reach a NULL pointer, we create a node and insert it in that position.
Templates in C++
Thus far we have discussed abstract data values in the BST node without mentioning the type we want to store specifically. In theory, BST nodes can contain data of any type: integers, chars, strings, etc. But in C++ , when you declare a node class, you must specify the data type in the very beginning. If later we want to have the same structure with a different data type, we will have to duplicate the code and only change the type. We will have to keep adding code for every new type we want to use. For someone writing code, this quite inefficient and error prone. To avoid this routine, C++ provides templates. Templates allow you to create functions and classes with a generic type. The type is then passed as template parameter to the newly created function or class. Here is an example.
Creating a template class:
template <class T>
Node{
private:
T data;
public:
Node(T data): data(data) {}
};
Creating an integer and char nodes:
Node<int> node_int(20);
Node<char> node_char('z');
For more on templates see the week 6 example and/or this tutorial.
Creating a BST
For this lab you are given a few source files and your goal is to complete the
implementation of the BST class in bst.cpp
. The specification for each of
those functions (and all the other BST functions) are provided in bst.h
.
Initially, when you create a BST, the constructor should create only a root
pointer equal to NULL (an empty tree). If you want to grow your tree, you
should implement and use the insert
method for creating and adding nodes to
the tree.
Breadth First Search vs Depth First Search
After you’ve created your BST, it is time to traverse it! As you might notice, there are multiple possibilities to traverse the BST because at every step you have an option to move to the left child or to the right child or to the node on the same level (same number of nodes away from the root as the current one).
Commonly, depth first search (traversal) is the one where you consistently move towards left or right children and when you’ve reached the leaf node go one level up and move towards the child you haven’t checked out (the right or left one, respectively). If you’ve checked out all of the descendants of this node, go one level up.
For example, a depth first traversal of the above tree will output nodes in the following order (this assumes you first output the node you are “visiting” and then “visit” the left hand side before the right hand side):
5 3 2 4 7 6 8
The provided stack_output
function (in bst.h
) output should match the above
exactly (note that there is trailing space before the newline (for
simplicity). More on dfs.
Depth first traversal of a tree can be conveniently implemented with the help
of stack. We initialize the stack with the root node, and then loop until the
stack is empty. The first step of the loop is to do “something” with the value
of the node we’re currently looking at (may be to output it in the case of
stack_output
) and then we push both of its children (if they exist) on to the
stack. Note that because of the way items are added/removed from the stack we
push the righthand side on before the lefthand side to acheive the expected
lefttoright output.
Breadth first search (traversal) is the one where you consistently move to the nodes on the same level from left to right or in opposite directions. For example, a breadth first traversal of the above tree will output nodes in the following order:
5 3 7 2 4 6 8
Your queue_output
function output should match the above output exactly (note
that there is trailing space before you output the newline. More on
bfs.
Conveniently, breadth first search can be implemented by using queues rather
than stacks in a manor similar to how depth first search was implemented
(replace push
with enqueue
and pop
with dequeue
).
Provided Files

Makefile A simple makefile for building the lab. Just run
make
to compile everything. 
bst.h  This file contains the definition of the BST class. You do not have to modify this file but you are responsible for understanding all of the code.

_data_structures.h This file contains the definitions for the BinaryNode class as well as the Queue and Stack data structures. You do not have to modify this file but you are responsible for understanding all of the code.

bst.cpp This file contains stubs for the functions that you will implement. To complete the lab you must write the following methods for BST:
 destructor
 insert
 queue_output (BFS traversal)

driver.cpp Provides an interface for inserting nodes into the BST (based off the command line arguments), and testing whether or not the integers 0 through 10 are contained in the BST.
Hints
Traversing the BST using a Stack or Queue
The linear structure of a List allows us to traverse the elements without having to keep track of the ones we’ve already visited. However BSTs do not afford us this convenience. Consider the BST in the example above:
5
/ \
3 7
/ \ / \
2 4 6 8
Let’s say we are in the midst of a DFS and we are currently at node 4. How are we to find the next node (7)? Trying to individually store each node we traverse would lead to messy and inefficient code. A much better solution is to store the nodes in another data structure to keep track of them. Consider a DFS using a stack. We initialize by pushing 5 onto the stack:
5 Stack: 5
/ \
3 7
/ \ / \
2 4 6 8
We then iterate over the following steps until the stack is empty:
WHILE stack is not empty:
POP the last element
PUSH its left child
PUSH its RIGHT child
PRINT the element
END WHILE
After 1 iteration (the star indicates that we’ve printed this node):
*5 Stack: 3 7
/ \
3 7
/ \ / \
2 4 6 8
After 2 iterations:
*5 Stack: 2 4 7
/ \
*3 7
/ \ / \
2 4 6 8
After 2 iterations:
*5 Stack: 4 7
/ \
*3 7
/ \ / \
*2 4 6 8
After 3 iterations:
*5 Stack: 7
/ \
*3 7
/ \ / \
*2 *4 6 8
After 4 iterations:
*5 Stack: 6 8
/ \
*3 *7
/ \ / \
*2 *4 6 8
Until we’ve traversed all of the elements. In this lab you will have to perform a BFS in a similar manner using a Queue.
Declaring the Stack and Queue
To implement queue_output
and stack_output
you need to respectively declare
both a templated version of the Stack class, and one of the Queue class. If the
Stack, or Queue needed to store integers, you could declare them like:
Stack<int> stack_variable_name;
Queue<int> queue_variable_name;
However, you want to store a pointer to the proper tree node type,
BinaryNode
. To make things slightly more confusing, the BinaryNode type is
also templated so you need to specify what type the BinaryNode should be. If
you knew for certain the BinaryNode is to store a character, then you might
declare the Stack and Queue like:
Stack<BinaryNode<char>*> char_stack;
Queue<BinaryNode<char>*> char_queue;
However, your BST implementation is templated so it should work with whatever
type the user of your program uses. That’s where the magic T
variable as
declared in the line template <class T>
comes in handy. T
specifies what
type the class holds so you can simply replace char
in our previous example
with T
like the following in order to declare the Stack and Queue for the
respective functions:
Stack<BinaryNode<T>*> stack;
Queue<BinaryNode<T>*> queue;
Submitting the project
Only one person in a group need submit the assignment but make sure that both you and your partner can view the submission on the submission site. If you cannot, you need to complete the making a group process. Please review the submission instructions as needed. Note that you may resubmit this assignment as many times as necessary up until the deadline.