Spring 2010CS 2251 Trees Chapter 6. Spring 2010CS 2252 Chapter Objectives Learn to use a tree to...
-
Upload
blaze-stokes -
Category
Documents
-
view
216 -
download
0
Transcript of Spring 2010CS 2251 Trees Chapter 6. Spring 2010CS 2252 Chapter Objectives Learn to use a tree to...
Spring 2010 CS 225 1
Trees
Chapter 6
Spring 2010 CS 225 2
Chapter Objectives• Learn to use a tree to represent a hierarchical
organization of information• Learn to use recursion to process trees• To understand the different ways of traversing a tree• To understand the difference between binary trees,
binary search trees, and heaps• To learn how to implement binary trees, binary
search trees, and heaps using linked data structures and arrays
• Learn to use a binary search tree to store information for efficient retrieval
• Learn how to use a Huffman tree to encode characters compactly
Spring 2010 CS 225 3
Trees
• Trees are nonlinear– useful for hierarchical organization of data
• Trees are recursive
Spring 2010 CS 225 4
Tree Terminology
• A tree consists of a collection of elements or nodes, with each node linked to its successors
• The node at the top of a tree is called its root• The links from a node to its successors are
called branches• The successors of a node are called its
children• The predecessor of a node is called its parent
Spring 2010 CS 225 5
Tree Terminology
• Each node in a tree has exactly one parent except for the root node, which has no parent
• Nodes that have the same parent are siblings• A node that has no children is called a leaf
node• A subtree of a node is a tree whose root is a
child of that node
Spring 2010 CS 225 6
Tree Terminology• The level of a node is a measure of its distance from
the root– If a node is the root, it has level 1– Otherwise, a nodes level is one more than the level of its
parent
• The height of a tree is the number of nodes in the longest path from the root to a leaf node– An empty tree has height 0– The height of a tree is the same as the largest level that any
node has
Spring 2010 CS 225 7
Binary Trees• In a binary tree, each node has at most two subtrees• A set of nodes T is a binary tree if either of the
following is true– T is empty– Its root node has two subtrees, TL and TR, such
that TL and TR are binary trees
Spring 2010 CS 225 8
Example 1:Expression tree
• Each node contains an operator or an operand– operator nodes have children– operand nodes are leaves
Spring 2010 CS 225 9
Example 2: Huffman tree• Represents Huffman codes for characters that might
appear in a text file• Huffman code uses different numbers of bits to
encode letters as opposed to ASCII or Unicode
Spring 2010 CS 225 10
Example 3: Binary Search Trees
• All elements in the left subtree precede those in the right subtree
Spring 2010 CS 225 11
Fullness and Completeness• A binary tree of height n is perfect if it has
(2n-1) nodes• A binary tree is full if every node has two
children except for the leaves• A tree is complete if only the rightmost
node(s) in the next to last row don't have two children
Spring 2010 CS 225 12
General Trees• Nodes of a general tree can have any number of
subtrees• A general tree can be represented using a binary tree
Spring 2010 CS 225 13
Tree Traversals
• Often we want to determine the nodes of a tree and their relationship– Do this by walking through the tree in a prescribed
order and visiting the nodes as they are encountered
• This process is called tree traversal
– Itarators need to visit all nodes of a tree exactly once
• Three kinds of tree traversal– Inorder– Preorder– Postorder
Spring 2010 CS 225 14
Preorder Tree Traversal
• Visit root node, traverse TL, traverse TR• Algorithmif the tree is empty returnelse visit the root do preorder traversal of left subtree do preorder traversal of right subtree
Spring 2010 CS 225 15
Inorder Tree Traversals
• Traverse TL, visit root node, traverse TR
• Algorithmif the tree is empty returnelse do preorder traversal of left subtree visit the root do preorder traversal of right subtree
Spring 2010 CS 225 16
Postorder Tree Traversals
• Traverse TL, Traverse TR, visit root node
• Algorithmif the tree is empty returnelse do preorder traversal of left subtree do preorder traversal of right subtree visit the root
Spring 2010 CS 225 17
Visualizing Tree Traversals
• You can visualize a tree traversal by imagining a mouse that walks along the edge of the tree– If the mouse always keeps the tree to the left, it
will trace a route known as the Euler tour• Preorder traversal if we record each node as the mouse
first encounters it• Inorder if each node is recorded as the mouse returns
from traversing its left subtree• Postorder if we record each node as the mouse last
encounters it
Spring 2010 CS 225 18
Visualizing Tree Traversals
Spring 2010 CS 225 19
Traversals of Binary Search Trees
• An inorder traversal of a binary search tree results in the nodes being visited in sequence by increasing data value
Spring 2010 CS 225 20
Traversals of Expression Trees
• An inorder traversal of an expression tree inserts parenthesis where they belong (infix form)
• A postorder traversal of an expression tree results in postfix form
• A preorder traversal of an expression results in prefix form
Spring 2010 CS 225 21
Practice
• Traverse the following trees in all three orders
Spring 2010 CS 225 22
Practice 2
• Draw an expression tree whose inorder traversal is
x / y + 3 * b / c• Draw an expression tree whose postorder
traversal is x y z + a b - c * / -• Draw an expression tree whose preorder
traversal is * + a - x y / c d
Spring 2010 CS 225 23
Implementing trees
• Trees are generally implemented with linked structure similar to what we used for linked lists
• Nodes need to have references to at least two child nodes as well as the data
• A node may also have a parent reference.
Spring 2010 CS 225 24
The Node<E> Class
• Just as for a linked list, a node consists of a data part and links to successor nodes
• The data part is a reference to type E• A binary tree node must have links to both its left and
right subtrees
Spring 2010 CS 225 25
The BinaryTree<E> Class
Spring 2010 CS 225 26
The BinaryTree<E> Class
Spring 2010 CS 225 27
Binary Search Trees
• Binary search tree definition– A set of nodes T is a binary search tree if
either of the following is true• T is empty• Its root has two subtrees such that each is a
binary search tree and the value in the root is greater than all values of the left subtree but less than all values in the right subtree
Spring 2010 CS 225 28
Binary Search Tree
http://www3.amherst.edu/~rjyanco94/literature/mothergoose/rhymes/thisisthehousethatjackbuilt.html
Spring 2010 CS 225 29
Searching a Binary Tree
• Searching for kept or jill
Spring 2010 CS 225 30
Class Search Tree
Spring 2010 CS 225 31
BinarySearchTree Class
Spring 2010 CS 225 32
BinarySearchTreeData
Spring 2010 CS 225 33
Binary Search Tree Insertion
if the root is null create new node containing item to be the rootelse if item is the same as root data item is already in tree, return falseelse if item is less than root data search left subtreeelse search right subtree
Spring 2010 CS 225 34
Binary Search Tree Delete
if root is null return nullelse if item is less than root data return result of deleting from left subtreeelse if item is greater than root data return result of deleting from right subtreeelse // need to replace the root save data in root to return replace the root (see next slide)
Spring 2010 CS 225 35
Replacing root of a subtree
if root has no children set parent reference to local root to nullelse if root has one child set parent reference to root to childelse // find the inorder predecessor if left child has no right child set parent reference to left child else find rightmost node in right child of left
subtree and move its data to root
Spring 2010 CS 225 36
Delete Example
Spring 2010 CS 225 37
Practice
• Show the tree that would be created from the following items in the given order
happy, depressed, manic, sad, ecstatic• What happens if you add them in a
different order?• What happens if we add mad to the tree?
Spring 2010 CS 225 38
Heaps and Priority Queues• In a heap, the value in a node is les
than all values in its two subtrees• A heap is a complete binary tree with
the following properties– The value in the root is the smallest item in
the tree– Every subtree is a heap
Spring 2010 CS 225 39
Inserting an Item into a Heap
Spring 2010 CS 225 40
Removing from a Heap
Spring 2010 CS 225 41
Implementing a Heap• Because a heap is a complete binary tree, it
can be implemented efficiently using an array instead of a linked data structure
• First element for storing a reference to the root data
• Use next two elements for storing the two children of the root
• Use elements with subscripts 3, 4, 5, and 6 for storing the four children of these two nodes and so on
Spring 2010 CS 225 42
Computing Positions
• Parent of a node at position c isparent = (c - 1) / 2
• Children of node at position p areleftchild = 2 p + 1rightchild = 2 p + 2
Spring 2010 CS 225 43
Inserting into a Heap Implemented as an ArrayList
Spring 2010 CS 225 44
Removing from a Heap Implemented as an ArrayList
Spring 2010 CS 225 45
Using Heaps
• The heap is not very useful as an ADT on its own– Will not create a Heap interface or code a
class that implements it– Will incorporate its algorithms when we
implement a priority queue class and Heapsort
• The heap is used to implement a special kind of queue called a priority queue
Spring 2010 CS 225 46
Priority Queues
• Sometimes a FIFO queue may not be the best way to implement a waiting line– What if some entries have higher priority than
others and need to be moved ahead in the line?• A priority queue is a data structure in which
only the highest-priority item is accessible
Spring 2010 CS 225 47
Insertion into a Priority Queue
• Imagine a print queue that prints the shortest documents first
Spring 2010 CS 225 48
The PriorityQueue Class• Java provides a PriorityQueue<E> class that implements
the Queue<E> interface given in Chapter 6. • Peek, poll, and remove methods return the smallest item
in the queue rather than the oldest item in the queue.
Spring 2010 CS 225 49
Design of a KWPriorityQueue Class
Spring 2010 CS 225 50
Huffman Trees
• A Huffman tree can be implemented using a binary tree and a PriorityQueue
• A straight binary encoding of an alphabet assigns a unique binary number to each symbol in the alphabet– Unicode for example
• The message “go eagles” requires 144 bits in Unicode but only 38 using Huffman coding
Spring 2010 CS 225 51
Huffman Tree Example
Spring 2010 CS 225 52
Huffman Trees
Spring 2010 CS 225 53
Chapter Review
• A tree is a recursive, nonlinear data structure that is used to represent data that is organized as a hierarchy
• A binary tree is a collection of nodes with three components: a reference to a data object, a reference to a left subtree, and a reference to a right subtree
• In a binary tree used for arithmetic expressions, the root node should store the operator that is evaluated last
Spring 2010 CS 225 54
Chapter Review• A binary search tree is a tree in which the data stored
in the left subtree of every node is less than the data stored in the root node, and the data stored in the right subtree is greater than the data stored in the root node
• A heap is a complete binary tree in which the data in each node is less than the data in both its subtrees
• Insertion and removal in a heap are both O(log n)
• A Huffman tree is a binary tree used to store a code that facilitates file compression