Transcript COMP171 Trees, Binary Trees, and Binary Search Trees Trees  Linear  access time of linked lists is prohibitive Does there exist any simple data structure.

COMP171
Trees, Binary Trees,
and Binary Search Trees
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Trees
 Linear

access time of linked lists is prohibitive
Does there exist any simple data structure for
which the running time of most operations (search,
insert, delete) is O(log N)?
 Trees




Basic concepts
Tree traversal
Binary tree
Binary search tree and its operations
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Trees
 A tree


T is a collection of nodes
T can be empty
(recursive definition) If not empty, a tree T consists
of
a (distinguished) node r (the root),
 and zero or more nonempty subtrees T1, T2, ...., Tk

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

Tree can be viewed as a ‘nested’ lists
Tree is also a graph …
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Some Terminologies

Child and Parent



Leaves


Every node except the root has one parent
A node can have an zero or more children
Leaves are nodes with no children
Sibling

nodes with same parent
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More Terminologies

Path


Length of a path



length of the unique path from the root to that node
Height of a node



number of edges on the path
Depth of a node


A sequence of edges
length of the longest path from that node to a leaf
all leaves are at height 0
The height of a tree = the height of the root
= the depth of the deepest leaf
Ancestor and descendant



If there is a path from n1 to n2
n1 is an ancestor of n2, n2 is a descendant of n1
Proper ancestor and proper descendant
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Example: UNIX Directory
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Example: Expression Trees
Leaves are operands (constants or variables)
 The internal nodes contain operators
 Will not be a binary tree if some operators are not
binary

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Tree Traversal
 Used
to print out the data in a tree in a certain
order
 Pre-order traversal




Print the data at the root
Recursively print out all data in the leftmost subtree
…
Recursively print out all data in the rightmost
subtree
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Preorder, Postorder and Inorder
 Preorder
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
traversal
node, left, right
prefix expression

++a*bc*+*defg
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Preorder, Postorder and Inorder
 Postorder


traversal
left, right, node
postfix expression
abc*+de*f+g*+
 Inorder


traversal
left, node, right
infix expression
a+b*c+d*e+f*g
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Example: Unix Directory Traversal
PreOrder
PostOrder
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Preorder, Postorder and Inorder
Pseudo Code
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Binary Trees

A tree in which no node can have more than two
children
Generic
binary tree

The depth of an “average” binary tree is considerably smaller
than N, even though in the worst case, the depth can be as large
as N – 1.
Worst-case
binary tree
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Convert a Generic Tree to a Binary Tree
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Binary Tree ADT

Possible operations on the Binary Tree ADT

Parent, left_child, right_child, sibling, root, etc

Implementation

Because a binary tree has at most two children, we can keep
direct pointers to them
 a linked list is physically a pointer, so is a tree.
Define a Binary Tree ADT later …

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A drawing of linked list with one pointer …
A drawing of binary tree with two pointers …
Struct BinaryNode {
double element; // the data
BinaryNode* left; // left child
BinaryNode* right; // right child
}
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Binary Search Trees (BST)
 A data
structure for efficient searching, insertion and deletion
 Binary search tree property

For every node X
 All the keys in its left
subtree are smaller than
the key value in X
 All the keys in its right
subtree are larger than the
key value in X
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Binary Search Trees
A binary search tree
Not a binary search tree
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Binary Search Trees
The same set of keys may have different BSTs
 Average
depth of a node is O(log N)
 Maximum depth of a node is O(N)
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Searching BST
 If
we are searching for 15, then we are done.
 If we are searching for a key < 15, then we
should search in the left subtree.
 If we are searching for a key > 15, then we
should search in the right subtree.
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Searching (Find)
 Find
X: return a pointer to the node that has
key X, or NULL if there is no such node
find(const double x, BinaryNode* t) const
 Time
complexity: O(height of the tree)
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Inorder Traversal of BST
 Inorder
traversal of BST prints out all the keys
in sorted order
Inorder: 2, 3, 4, 6, 7, 9, 13, 15, 17, 18, 20
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findMin/ findMax
Goal: return the node containing the smallest (largest)
key in the tree
 Algorithm: Start at the root and go left (right) as long as
there is a left (right) child. The stopping point is the
smallest (largest) element

BinaryNode* findMin(BinaryNode* t) const

Time complexity = O(height of the tree)
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Insertion
Proceed down the tree as you would with a find
 If X is found, do nothing (or update something)
 Otherwise, insert X at the last spot on the path traversed


Time complexity = O(height of the tree)
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void insert(double x, BinaryNode*& t)
{
if (t==NULL) t = new BinaryNode(x,NULL,NULL);
else if (x<t->element) insert(x,t->left);
else if (t->element<x) insert(x,t->right);
else ; // do nothing
}
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Deletion
 When
we delete a node, we need to consider
how we take care of the children of the
deleted node.

This has to be done such that the property of the
search tree is maintained.
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Deletion under Different Cases
 Case

Delete it immediately
 Case

1: the node is a leaf
2: the node has one child
Adjust a pointer from the parent to bypass that node
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Deletion Case 3
 Case


3: the node has 2 children
Replace the key of that node with the minimum
element at the right subtree
Delete that minimum element
Has
either no child or only right child because if it has a
left child, that left child would be smaller and would have
been chosen. So invoke case 1 or 2.

Time complexity = O(height of the tree)
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void remove(double x, BinaryNode*& t)
{
if (t==NULL) return;
if (x<t->element) remove(x,t->left);
else if (t->element < x) remove (x, t->right);
else if (t->left != NULL && t->right != NULL) // two children
{
t->element = finMin(t->right) ->element;
remove(t->element,t->right);
}
else
{
Binarynode* oldNode = t;
t = (t->left != NULL) ? t->left : t->right;
delete oldNode;
}
}
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Make a binary or BST ADT …
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For a generic (binary) tree:
Struct Node {
double element; // the data
Node* left; // left child
Node* right; // right child
}
class Tree {
public:
Tree();
// constructor
Tree(const Tree& t);
~Tree();
// destructor
bool empty() const;
double root(); // decomposition (access functions)
Tree& left();
Tree& right();
void insert(const double x); // compose x into a tree
access,
selection
update
void remove(const double x); // decompose x from a tree
(insert and remove are different from those of BST)
private:
Node* root;
}
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For BST tree:
Struct Node {
double element; // the data
Node* left; // left child
Node* right; // right child
}
class BST {
public:
BST();
// constructor
BST(const Tree& t);
~BST();
// destructor
bool empty() const;
double root(); // decomposition (access functions)
BST left();
BST right();
access,
selection
bool serch(const double x); // search an element
void insert(const double x); // compose x into a tree
void remove(const double x); // decompose x from a tree
update
private:
Node* root;
}
BST is for efficient search, insertion and removal,
so restricting these functions.
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class BST {
Weiss textbook:
public:
BST();
BST(const Tree& t);
~BST();
bool empty() const;
bool search(const double x); // contains
void insert(const double x); // compose x into a tree
void remove(const double x); // decompose x from a tree
private:
Struct Node {
double element;
Node* left;
Node* right;
Node(…) {…}; // constructuro for Node
}
Node* root;
void insert(const double x, Node*& t) const;
// recursive function
void remove(…)
Node* findMin(Node* t);
void makeEmpty(Node*& t); // recursive ‘destructor’
bool contains(const double x, Node* t) const;
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Comments:
root, left subtree, right subtree are missing:
1. we can’t write other tree algorithms, is implementation dependent,
BUT,
2. this is only for BST (we only need search, insert and remove, may not
need other tree algorithms)
so it’s two layers, the public for BST, and the private for Binary Tree.
3. it might be defined internally in ‘private’ part (actually it’s
implicitly done).
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A public non-recursive member function:
void insert(double x)
{
insert(x,root);
}
A private recursive member function:
void insert(double x, BinaryNode*& t)
{
if (t==NULL) t = new BinaryNode(x,NULL,NULL);
else if (x<t->element) insert(x,t->left);
else if (t->element<x) insert(x,t->right);
else ; // do nothing
}