lecture10 date structure types of graph and terminology

CS 103 1
Tree Traversal Techniques;
Heaps
• Tree Traversal Concept
• Tree Traversal Techniques: Preorder,
Inorder, Postorder
• Full Trees
• Almost Complete Trees
• Heaps

CS 103 2
Binary-Tree Related Definitions
• The children of any node in a binary tree are
ordered into a left child and a right child
• A node can have a left and
a right child, a left child
only, a right child only,
or no children
• The tree made up of a left
child (of a node x) and all its
descendents is called the left subtree of x
• Right subtrees are defined similarly
10
1
3
11
9
8
4 6
5
7
12

CS 103 3
A Binary-tree Node Class
class TreeNode {
public:
typedef int datatype;
TreeNode(datatype x=0, TreeNode *left=NULL,
TreeNode *right=NULL){
data=x; this->left=left; this->right=right; };
datatype getData( ) {return data;};
TreeNode *getLeft( ) {return left;};
TreeNode *getRight( ) {return right;};
void setData(datatype x) {data=x;};
void setLeft(TreeNode *ptr) {left=ptr;};
void setRight(TreeNode *ptr) {right=ptr;};
private:
datatype data; // different data type for other apps
TreeNode *left; // the pointer to left child
TreeNode *right; // the pointer to right child
};

CS 103 4
Binary Tree Class
class Tree {
public:
Tree(TreeNode *rootPtr=NULL){this->rootPtr=rootPtr;};
TreeNode *search(datatype x);
bool insert(datatype x); TreeNode * remove(datatype x);
TreeNode *getRoot(){return rootPtr;};
Tree *getLeftSubtree(); Tree *getRightSubtree();
bool isEmpty(){return rootPtr == NULL;};
private:
TreeNode *rootPtr;
};

CS 103 5
Binary Tree Traversal
• Traversal is the process of visiting every
node once
• Visiting a node entails doing some
processing at that node, but when describing
a traversal strategy, we need not concern
ourselves with what that processing is

CS 103 6
Binary Tree Traversal Techniques
• Three recursive techniques for binary tree
traversal
• In each technique, the left subtree is
traversed recursively, the right subtree is
traversed recursively, and the root is visited
• What distinguishes the techniques from one
another is the order of those 3 tasks

CS 103 7
Preoder, Inorder, Postorder
• In Preorder, the root
is visited before (pre)
the subtrees traversals
• In Inorder, the root is
visited in-between left
and right subtree traversal
• In Preorder, the root
is visited after (pre)
the subtrees traversals
Preorder Traversal:
1. Visit the root
2. Traverse left subtree
3. Traverse right subtree
Inorder Traversal:
2. Visit the root
Postorder Traversal:
3. Visit the root

CS 103 8
Illustrations for Traversals
• Assume: visiting a node
is printing its label
• Preorder:
1 3 5 4 6 7 8 9 10 11 12
• Inorder:
4 5 6 3 1 8 7 9 11 10 12
• Postorder:
4 6 5 3 8 11 12 10 9 7 1
1
3
11
9
8
4 6
5
7
12
10

CS 103 9
Illustrations for Traversals (Contd.)
• Assume: visiting a node
is printing its data
• Preorder: 15 8 2 6 3 7
11 10 12 14 20 27 22 30
• Inorder: 2 3 6 7 8 10 11
12 14 15 20 22 27 30
• Postorder: 3 7 6 2 10 14
12 11 8 22 30 27 20 15
6
15
8
2
3 7
11
10
14
12
20
27
22 30

CS 103 10
Code for the Traversal Techniques
• The code for visit
is up to you to
provide, depending
on the application
• A typical example
for visit(…) is to
print out the data
part of its input
node
void inOrder(Tree *tree){
if (tree->isEmpty( )) return;
inOrder(tree->getLeftSubtree( ));
visit(tree->getRoot( ));
inOrder(tree->getRightSubtree( ));
}
void preOrder(Tree *tree){
preOrder(tree->getLeftSubtree());
preOrder(tree->getRightSubtree());
}
void postOrder(Tree *tree){
postOrder(tree->getLeftSubtree( ));
postOrder(tree->getRightSubtree( ));
}

CS 103 11
Application of Traversal
Sorting a BST
• Observe the output of the inorder traversal
of the BST example two slides earlier
• It is sorted
• This is no coincidence
• As a general rule, if you output the keys
(data) of the nodes of a BST using inorder
traversal, the data comes out sorted in
increasing order

CS 103 12
Other Kinds of Binary Trees
(Full Binary Trees)
• Full Binary Tree: A full binary tree is a
binary tree where all the leaves are on the
same level and every non-leaf has two
children
• The first four full binary trees are:

CS 103 13
Examples of Non-Full Binary Trees
• These trees are NOT full binary trees: (do
you know why?)

CS 103 14
Canonical Labeling of
Full Binary Trees
• Label the nodes from 1 to n from the top to
the bottom, left to right
1 1
2 3
1
2 3
4 5 6 7
1
2 3
4
5 6 7
8 9 10 11
1213 14 15
Relationships between labels
of children and parent:
2i 2i+1
i

CS 103 15
Other Kinds of Binary Trees
(Almost Complete Binary trees)
• Almost Complete Binary Tree: An almost
complete binary tree of n nodes, for any
arbitrary nonnegative integer n, is the binary
tree made up of the first n nodes of a
canonically labeled full binary
1 1
2
1
2 3
4 5 6 7
1
2
1
2 3
4 5 6
1
2 3
4
1
2 3
4 5

CS 103 16
Depth/Height of Full Trees and
Almost Complete Trees
• The height (or depth ) h of such trees is O(log n)
• Proof: In the case of full trees,
– The number of nodes n is: n=1+2+22+23+…+2h=2h+1-1
– Therefore, 2h+1 = n+1, and thus, h=log(n+1)-1
– Hence, h=O(log n)
• For almost complete trees, the proof is left as an
exercise.

CS 103 17
Canonical Labeling of
Almost Complete Binary Trees
• Same labeling inherited from full binary
trees
• Same relationship holding between the
labels of children and parents:
Relationships between labels
of children and parent:
2i 2i+1
i

CS 103 18
Array Representation of Full Trees
and Almost Complete Trees
• A canonically label-able tree, like full binary trees
and almost complete binary trees, can be
represented by an array A of the same length as the
number of nodes
• A[k] is identified with node of label k
• That is, A[k] holds the data of node k
• Advantage:
– no need to store left and right pointers in the nodes 
save memory
– Direct access to nodes: to get to node k, access A[k]

CS 103 19
Illustration of Array Representation
• Notice: Left child of A[5] (of data 11) is A[2*5]=A[10] (of data 18),
and its right child is A[2*5+1]=A[11] (of data 12).
• Parent of A[4] is A[4/2]=A[2], and parent of A[5]=A[5/2]=A[2]
6
15
8
2 11
18 12
20
27
13
30
15 8 20 2 11 30 27 13 6 10 12
1 2 3 4 5 6 7 8 9 10 11

CS 103 20
Adjustment of Indexes
• Notice that in the previous slides, the node labels start
from 1, and so would the corresponding arrays
• But in C/C++, array indices start from 0
• The best way to handle the mismatch is to adjust the
canonical labeling of full and almost complete trees.
• Start the node labeling from 0 (rather than 1).
• The children of node k are now nodes (2k+1) and
(2k+2), and the parent of node k is (k-1)/2, integer
division.

CS 103 21
Application of Almost Complete
Binary Trees: Heaps
• A heap (or min-heap to be precise) is an
almost complete binary tree where
– Every node holds a data value (or key)
– The key of every node is ≤ the keys of the
children
Note:
A max-heap has the same definition except that the
Key of every node is >= the keys of the children

CS 103 22
Example of a Min-heap
16
5
8
15 11
18 12
20
27
33
30

CS 103 23
Operations on Heaps
• Delete the minimum value and return it.
This operation is called deleteMin.
• Insert a new data value
Applications of Heaps:
• A heap implements a priority queue, which is a queue
that orders entities not a on first-come first-serve basis,
but on a priority basis: the item of highest priority is at
the head, and the item of the lowest priority is at the tail
• Another application: sorting, which will be seen later

CS 103 24
DeleteMin in Min-heaps
• The minimum value in a min-heap is at the root!
• To delete the min, you can’t just remove the data
value of the root, because every node must hold a
key
• Instead, take the last node from the heap, move its
key to the root, and delete that last node
• But now, the tree is no longer a heap (still almost
complete, but the root key value may no longer be
≤ the keys of its children

CS 103 25
Illustration of First Stage of deletemin
16
5
8
15 11
18 12
20
27
33
30
16
8
15 11
18 12
20
27
33
30
16
8
15 11
18
12
20
27
33
30
16
8
15 11
18
12
20
27
33
30

CS 103 26
Restore Heap
• To bring the structure back to its
“heapness”, we restore the heap
• Swap the new root key with the smaller
child.
• Now the potential bug is at the one level
down. If it is not already ≤ the keys of its
children, swap it with its smaller child
• Keep repeating the last step until the “bug” key
becomes ≤ its children, or the it becomes a leaf

CS 103 27
Illustration of Restore-Heap
16
8
15 11
18
12
20
27
33
30
16
12
15 11
18
8
20
27
33
30
16
11
15 12
18
8
20
27
33
30
Now it is a correct heap

CS 103 28
Time complexity of insert and deletmin
• Both operations takes time proportional to the
height of the tree
– When restoring the heap, the bug moves from level to
level until, in the worst case, it becomes a leaf (in
deletemin) or the root (in insert)
– Each move to a new level takes constant time
– Therefore, the time is proportional to the number of
levels, which is the height of the tree.
• But the height is O(log n)
• Therefore, both insert and deletemin take O(log n)
time, which is very fast.

CS 103 29
Inserting into a minheap
• Suppose you want to insert a new value x
into the heap
• Create a new node at the “end” of the heap
(or put x at the end of the array)
• If x is >= its parent, done
• Otherwise, we have to restore the heap:
– Repeatedly swap x with its parent until either x
reaches the root of x becomes >= its parent

CS 103 30
Illustration of Insertion Into the Heap
• In class

CS 103 31
The Min-heap Class in C++
class Minheap{ //the heap is implemented with a dynamic array
public:
Minheap(int cap = 10){capacity=cap; length=0;
ptr = new datatype[cap];};
datatype deleteMin( );
void insert(datatype x);
bool isEmpty( ) {return length==0;};
int size( ) {return length;};
private:
datatype *ptr; // points to the array
int capacity;
int length;
void doubleCapacity(); //doubles the capacity when needed
};

CS 103 32
Code for deletemin
Minheap::datatype Minheap::deleteMin( ){
assert(length>0);
datatype returnValue = ptr[0];
length--; ptr[0]=ptr[length]; // move last value to root element
int i=0;
while ((2*i+1<length && ptr[i]>ptr[2*i+1]) ||
(2*i+2<length && (ptr[i]>ptr[2*i+1] ||
ptr[i]>ptr[2*i+2]))){ // “bug” still > at least one child
if (ptr[2*i+1] <= ptr[2*i+2]){ // left child is the smaller child
datatype tmp= ptr[i]; ptr[i]=ptr[2*i+1]; ptr[2*i+1]=tmp; //swap
i=2*i+1; }
else{ // right child if the smaller child. Swap bug with right child.
datatype tmp= ptr[i]; ptr[i]=ptr[2*i+2]; ptr[2*i+2]=tmp; // swap
i=2*i+2; }
}
return returnValue;
};

CS 103 33
Code for Insert
void Minheap::insert(datatype x){
if (length==capacity)
doubleCapacity();
ptr[length]=x;
int i=length;
length++;
while (i>0 && ptr[i] < ptr[i/2]){
datatype tmp= ptr[i];
ptr[i]=ptr[(i-1)/2];
ptr[(i-1)/2]=tmp;
i=(i-1)/2;
}
};

CS 103 34
Code for doubleCapacity
void Minheap::doubleCapacity(){
capacity = 2*capacity;
datatype *newptr = new datatype[capacity];
for (int i=0;i<length;i++)
newptr[i]=ptr[i];
delete [] ptr;
ptr = newptr;
};

lecture10 date structure types of graph and terminology

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