FAQ #
Each assignment will have an FAQ linked at the top. You can also access it by adding “/faq” to the end of the URL. The FAQ for Lab 10 is located here.
Introduction #
As usual, pull the files from the skeleton and open the Lab 10 directory in IntelliJ.
In this lab, we will introduce another data structure called a tree (you might have already seen this in CS 61A or another equivalent course). The meaning of the tree data structure originates from the notion of a tree in real life.
Below is a conceptual visualization of a tree. It’s not really a box and pointer diagram and should not be interpreted as a literal implementation in Java.
Note that while a tree in real life is rooted at the bottom and branches out and upwards, trees in computer science are typically drawn such that they are rooted at the top and branch out and downwards.
For this lab, we have not provided you with any local tests. Instead, we expect you to create your own test file and write your own tests! You can also use adhoc testing in the main methods to test quickly.
Vocabulary #
In order to be able to talk about trees, we first need to define some terminology.
Term  Description  Example Above 

Node  Single element in a tree  1, 2, 3, 4, 5, 6, and 7 are all nodes. 
Edge  A connection between two nodes  There is a direct edge from 1 to 2, but there is no direct edge from 1 to 7. 
Path  A series of one or more edges that connects two nodes without using an edge more than once  There is a path from 1 to 7, consisting of edges 1 > 3 and 3 > 7. 
Leaf  A node with no children  4, 5, 6, and 7 are leaf nodes. 
Root  The topmost node, which does not have a parent  1 is the root node. 
Relationships #
These following terms are defined with respect to a particular node.
Term  Description 

Child  A node directly below the current node 
Parent  Single node connected directly above the current node 
Siblings  Nodes with the same parent 
Ancestors  All nodes on the path from the selected node up to and including the root 
Descendants  All the children, children’s children, and so forth of a node 
Subtree  The tree consisting of the node and all of its descendants 
Height  The length of the path from the node to its deepest descendant. By length, we mean the number of nodes traveled along a given path. Therefore, the height of a leaf node is 1. (Note: There is an alternate definition of height as the number of edges traversed. In that case, the height of a leaf would be 0.) 
Depth  The length of the path from the node to the root. The depth of the root node is 0. 
Sometimes we’ll refer to the height and depth of a tree as a whole. In these cases, it’s usually assumed that we’re referring to the height of the root node and the depth of the deepest leaf, respectively.
Definition #
For a linked structure of nodes to be considered a tree:
 nodes must have either no parents or 1 parent
 there can be no cycles (paths that start and end at the same node)
 there can only be one path between any two nodes in a tree
 all nodes must be a descendant of the root (except the root itself)
Discussion: Examples of Trees #
Discuss with your partner whether the following examples would be considered trees as we’ve introduced them above.
 Linked List
 Family Tree
Trees are naturally recursive structures, so you’ll find that we implement a lot the methods in this lab using recursion because it tends to be the simplest solution. Unlike linked lists, trees can have one or more children which can make iterative approaches more difficult (this ultimately depends on the structure / implementation of your tree).
Amoeba Family Tree #
An amoeba family tree is an example of our above definition of a tree that you will be working with for this lab. It is simpler than a normal family tree because amoebas do not need partners in order to reproduce. An amoeba has one parent, zero or more siblings, and zero or more children. An amoeba also has a name.
Below is the skeleton code for an Amoeba
. The full code is located in the
AmoebaFamily.java
file.
class Amoeba {
public String name;
public Amoeba parent;
public ArrayList<Amoeba> children;
}
And below is a box and pointer diagram of our Amoeba
.
Amoebas (or amoebae) live dull lives. All they do is reproduce, so all we need to keep track of them are the following methods:
/* Creates an AmoebaFamily, where the first Amoeba's name is NAME. */
public AmoebaFamily(String name) {
root = new Amoeba(name, null);
}
/* Adds a new Amoeba with childName to this AmoebaFamily as the youngest
child of the Amoeba named parentName. This AmoebaFamily must contain an
Amoeba named parentName. */
public void addChild(String parentName, String childName) {
if (root != null) {
root.addChildHelper(parentName, childName);
}
}
The Amoeba
objects are the nodes of our tree, represented by the
AmoebaFamily
object. You will find that most of the methods of this
AmoebaFamily
will be implemented through recursive helper methods of the
Amoeba
class.
This organization structure should feel similar. Before for our
DLList
we had theDLList
class and within it the inner classNode
. TheDLList
encapsulated the recursive structure of theNode
s. Now we will have ourAmoebaFamily
which will encapsulate the recursive structure of theAmoeba
s.
Void Recursive Methods #
In the AmoebaFamily
class, we provide the method addChild
to add a new
Amoeba
to the AmoebaFamily
tree. The method implements a general traversal
of the tree using a void recursive method in the Amoeba
class. Read through
this method and related functionality to get an understanding of how we are
traversing and manipulating the Object.
Nonvoid Recursive Methods #
So far, the methods we’ve seen for the AmoebaFamily
class have not returned
anything; they have only been modifying the state of the AmoebaFamily
. We will
be able to do cooler things if we can return values, and the next example will
take advantage of this.
Exercise: longestName
#
Implement the longestName
method in the AmoebaFamily
class. Make this as
similar to longestNameLength
as possible. You will find that these nonvoid
recursive methods follow a specific pattern, one that you will likely draw upon
again when solving problems that you face in the future.
You will not need to use longestNameLength
directly in your implementation of
longestName
.
Binary Trees #
We’ll now move on from trees and explore a common, special case of the tree data
structure: the binary tree. A binary tree is a tree in which each node has at
most two children. Rather than store the children in an ArrayList
as was done
in Amoeba
nodes, one normally just has two separate variables left
and
right
for the left and right children of the binary tree.
Exercise: BinaryTree
#
The file BinaryTree.java
defines a BinaryTree
class and a TreeNode
class.
First, read over the code and then implement the following three methods.
Note: you may use the sampleTreeX
methods to generate sample trees to test the
functions that you write. In addition, consider writing your own methods to
generate more trees to test your code.
Exercise 1: height
#
First, if you have a partner, switch which partner is coding if you haven’t recently.
Implement the height
method in the BinaryTree
class. The height of an empty tree is
0; the height of a onenode tree is 1; the height of any other tree is 1 + the
greater of the heights of the two children.
Exercise 2: isCompletelyBalanced
#
Add an isCompletelyBalanced
method for the BinaryTree
class. A tree with no
nodes and a tree with one node are both completely balanced; any other tree is
completely balanced if and only if the height of its left child is equal to the
height of its right child, and its left and right children are also completely
balanced. Make sure you test your code with trees of height 3 or more to ensure
that your code works!
Exercise 3: fibTree
#
This exercise deals with “Fibonacci trees”, trees that represents the recursive
call structure of the Fibonacci computation. (The Fibonacci sequence is defined
as follows: \(F_0 = 0, F_1 = 1\), and each subsequent number in the sequence is
the sum of the previous two.) The root of a Fibonacci tree should contain the
value of the N
th Fibonacci number, the left subtree should be the tree
representing the computation of the N1
th Fibonacci number, and the right
subtree should be the tree representing the computation of the N2
th
Fibonacci number. The two exceptions to this rule are when we pass in 0 or 1 to
the fibTree
method. The first few Fibonacci trees appear below.
Function  Tree 

fibtree(0) 

fibtree(1) 

fibtree(2) 

fibtree(3) 

fibtree(4) 

fibtree(5) 
Write the static fibTree
method in BinaryTree
that takes in a nonnegative
integer N
, and returns a BinaryTree
that stores the N
th Fibonacci value
using the representation above.
Remember how we’ve been delegating structuring all our methods so far for the best code!
Traversals #
The remainder of this lab will cover the tools and techniques related to traversing the notes of a tree structure.
Three processing sequences for nodes in a binary tree occur commonly enough to have names:

preorder: process the root, process the left subtree (in preorder), process the right subtree (in preorder)

postorder: process the left subtree (in postorder), process the right subtree (in postorder), process the root.

inorder: process the left subtree (in inorder), process the root, process the right subtree (in inorder).
Note that for preorder and postorder, it can often be equally valid to process the right and then the left, although it depends on the application. In this course we will typically assume that the processing will be done left and then right unless we specify otherwise. On most exams we will be precise in what order nodes will be processed.
Exercise: Processing Sequences #
With your partner, determine the preorder, inorder, and postorder processing sequence for the tree below. This exercise will not be graded, but understanding how traversals work is a crucial part of understanding how to operate on trees. We will rely on these for many topics in the rest of the course.
Answers below (highlight to reveal):
Stacks and Queues #
Before we go into more traversals, we will go over some a few more data structures that will help us when we implement the traversals later in the lab.
One of the first true data structures we encountered was the LinkedList
data
structure. That data structure allowed for adding and removing at any position.
However sometimes that is too powerful and we do not need all of that
functionality. Thus, we will introduce the stack and the queue data
structures, which are like lists, but have more limited, precise functionality.
As you go through this section, think about how you can implement stacks and
queues by using a LinkedList
.
A stack models a stack of papers, or plates in a restaurant, or boxes in a garage or closet. A new item is placed on the top of the stack, and only the top item on the stack can be accessed at any particular time. Stack operations include the following:
 Pushing an item onto the stack.
 Accessing the top item of the stack.
 Popping the top item off the stack.
 Checking if the stack is empty.
As a result, we often refer to stacks as processing in LIFO (lastin, firstout) ordering.
A queue is a waiting line. As with stacks, access to a queue’s elements is restricted. Queue operations include:
 Adding an item to the back of the queue.
 Accessing the item at the front of the queue.
 Removing the front item.
 Checking if the queue is empty.
As a result, we often refer to queues as processing in FIFO (firstin, firstout) ordering.
Exercise: Stacks and Queues #
Exercise 1 #
Suppose that the following sequence of operations is executed using an initially empty stack. What ends up in the stack?
push A
push B
pop
push C
push D
pop
push E
pop
Answer below (highlight to reveal) Note the ordering is such that the bottom of the stack is to the left and the top of the stack is to the right:
Exercise 2 #
Suppose that the following sequence of operations is executed using an initially empty queue. What ends up in the queue?
add A
add B
remove
add C
add D
remove
add E
remove
Answer below (highlight to reveal) Note the ordering is such that the front of the queue is to the left and the back of the queue is to the right:
Stacks and Queues: Building a Tree Iterator #
We now consider the problem of returning the elements of a tree one by one,
using an iterator. To do this, we will implement the interface
java.util.Iterator
. The first technique to traverse trees that we will examine
is depthfirst iteration or depthfirst search. The general idea is
that we walk along each branch, traveling as far down each branch as possible
before turning back and examining the next one. This depth first search traversal
should feel similar to the preorder and postorder we defined above. See if you
can see the connection!
We will also use a nested iterator class to hide the details of the iteration from the outer class. As with previous iterators, we need to maintain statesaving information that lets us find the next tree element to return, and we now must determine what that information might include. To help work this out, we’ll consider the sample tree below, with elements to be returned depth first as indicated by the numbers.
The first element to be returned is the one labeled “1”. Once that’s done, we have a choice whether to return “2” or “5”.
Suppose we break ties by lowest node label. As a result, based on our tiebreaking rule, we will pick “2” over “5”. Once we return element “2”, we have a choice between return “3” or “4” next. But don’t forget about “5” from earlier!
Next, we return “3”, again based on our tiebreaking rule. At this point, we still remember that we need to return to “4” and “5” as in the diagram below.
That means that our statesaving information must include not just a single pointer of what to return next, but a whole collection of “bookmarks” to nodes we’ve passed along the way.
More generally, we will maintain a collection that we’ll call the fringe,
containing all the nodes in the tree that are candidates for returning next. The
next
method will choose one of the elements of the fringe as the one to
return, then add the returned element’s children to the fringe as candidates for
the next element to return. hasNext
returns true when the fringe isn’t empty.
The iteration sequence will then depend on the order we take nodes out of the
fringe. Depthfirst iteration, for example, results from storing the fringe
elements in a stack, a lastin firstout structure. The java.util
class
library conveniently contains a Stack
class with push
and pop
methods. We
illustrate this process on a binary tree in which TreeNode
s have 0, 1, or 2
children named left
and right
. Here is the code:
public class DepthFirstIterator implements Iterator<TreeNode> {
/* Where we will store our bookmarks. */
private Stack<TreeNode> fringe = new Stack<TreeNode>();
public DepthFirstIterator(TreeNode root) {
if (root != null) {
fringe.push(root);
}
}
/* Returns whether or not we have any more "bookmarks" to visit. */
public boolean hasNext() {
return !fringe.isEmpty();
}
/* Throws an exception if we have no more nodes to visit in our traversal.
Otherwise, it picks the most recent entry to our stack and "explores" it.
Exploring it requires visiting the node and adding its children to the
fringe, since we must eventually visit them too. */
public TreeNode next() {
if (!hasNext()) {
throw new NoSuchElementException("tree ran out of elements");
}
TreeNode node = fringe.pop();
if (node.right != null) {
fringe.push(node.right);
}
if (node.left != null) {
fringe.push(node.left);
}
return node;
}
/* This is left unimplemented for this example. */
public void remove() {
throw new UnsupportedOperationException();
}
}
Exercise: Stack Contents #
For the following exercises, assume that we push the right child before the left child of a node onto the fringe.
Exercise 1 #
What numbers are on the stack when element 4 in the tree below has just been
returned by next
?
Answer below (highlight to reveal) Note the ordering is such that the front of the queue is to the left and the back of the queue is to the right:
Exercise 2 #
For the same image above, what numbers are on the stack when element 6 in the
tree below has just been returned by next
?
Answer below (highlight to reveal) Note the ordering is such that the front of the queue is to the left and the back of the queue is to the right:
Exercise 3 #
Suppose the next
code pushes the left child onto the stack before the right:
if (node.left != null) {
fringe.push(node.left);
}
if (node.right != null) {
fringe.push(node.right);
}
In what order are the elements of the tree above returned?
Answer below (highlight to reveal):
Optional Exercise: Tree Iterators #
Optional Exercise 1: AmoebaDFSIterator
#
Switch which partner is doing the coding if you have not done so recently.
Complete the definition of the AmoebaDFSIterator
class (within the
AmoebaFamily
class). It should successively return names of Amoeba
s from the
family in depthfirst preorder. You will need to add Amoeba
s to the stack as you do
above, with the right child being pushed before the left child. Thus, for the
family set up in the AmoebaFamily
main method, the name “Amos McCoy” should be
returned by the first call to next
; the second call to next
should return
the name “mom/dad”; and so on. Do not change any of the framework code.
Organizing your code as described in the previous step will result in a process
that takes time proportional to the number of Amoeba
s in the tree to return
them all. Moreover, the constructor and hasNext
both run in constant time,
while next
runs in time proportional to the number of children of the element
being returned.
Add some code at the end of the AmoebaFamily
main method to test your
solution. We suggest using the tree examples and answers above as a starting
point for your tests.
Optional Exercise 2: AmoebaBFSIterator
#
Now write the AmoebaBFSIterator
class (within the AmoebaFamily
class). This
will result in Amoeba
names being returned in breadthfirst order. That is,
the name of the root of the family will be returned first, then the names of its
children (oldest first), then the names of all their children, and so on. Another
way to think about breadthfirst search is a level order traversal. All of the
nodes at depth 0 will be processed, then at depth 1, then at depth 2, etc.
Your code for your AmoebaBFSIterator
should be very similar to that of
AmoebaDFSIterator
with one big exception. In AmoebaBFSIterator
instead of
using a stack you should use a queue. By making this one change you will greatly
alter the behavior of your iterator. We recommend using java.util.LinkedList
to implement your queue.
There is a
java.util.Queue
but it is just an interface. There are a number of Java classes which implement this interface, one of which beingjava.util.LinkedList
. Any nonabstract subclass of this interface should work for this exercise.
For the family constructed in the AmoebaFamily
main method, your modification will
result in the following iteration sequence:
Amos McCoy
mom/dad
auntie
me
Fred
Wilma
Mike
Homer
Marge
Bart
Lisa
Bill
Hilary
Note: You will need to change the return value of the iterator
method to
AmoebaBFSIterator
to see the above output.
Before implementing this, try working out this traversal on paper with the prior exercises to gain intuition for it. Keep track of the state of the queue during the traversal and try to see how the difference between the queue’s FIFO behavior and the stack’s LIFO behavior results in a different traversal.
Conclusion #
Extra Readings #
The next lab will cover binary search trees. You may find Shewchuk’s notes on Binary Search Trees helpful.
Deliverables #
To finish this lab, make sure to finish the following:
 Read through the lab and understand how the
AmoebaFamily
andBinaryTree
s work. Make sure to also read through the section on Stacks and Queues.  Read through and understand the conceptual exercises. Discuss your answers with your partner or neighbor.
 Complete the following methods in
AmoebaFamily
:public String longestName()
 Complete the following methods in
BinaryTree
:public int height()
public boolean isCompletelyBalanced()
public static BinaryTree fibTree()