Trees - What is a(General) Tree •  A(general) tree is a set of nodes with the following proper4es: –  The set can

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: What is a (General) Tree? •  A (general) tree is a set of nodes with the following proper4es: –  The set can be empty. –  Otherwise, the set is par44oned into k+1 disjoint subsets: •  a tree consists of a dis4nguished node r, called root, and zero or more nonempty sub ­trees T1, T2, … , Tk, each of whose roots are connected by an edge from r. •  T is a tree if either –  T has no nodes, or –  T is of the form: r T1 T2 Tk where r is a node and T1, T2, ..., Tk are trees. 3/7/11 CS202  ­ Fundamentals of Computer Science II 1 What is a (General) Tree? (cont.) •  The root of each sub ­tree is said to be child of r, and r is the parent of each sub ­tree root. •  If a tree is a collecFon of N nodes, then it has N ­1 edges. •  A path from node n1 to nk is defined as a sequence of nodes n1,n2, …,nk such that ni is parent of ni+1 (1 ≤ i < k) –  There is a path from every node to itself. –  There is exactly one path from the root to each node. 3/7/11 CS202  ­ Fundamentals of Computer Science II 2 Tree Terminology Parent – The parent of node n is the node directly above in the tree. Child – The child of node n is the node directly below in the tree. •  If node m is the parent of node n, node n is the child of node m. Root – The only node in the tree with no parent. Leaf – A node with no children. Siblings – Nodes with a common parent. Ancestor – An ancestor of node n is a node on the path from the root to n. Descendant – A descendant of node n is a node on the path from n to a leaf. Subtree – A subtree of node n is a tree that consists of a child (if any) of n and the child’s descendants (a tree which is rooted by a child of node n) 3/7/11 CS202  ­ Fundamentals of Computer Science II 3 A Tree – Example A B C D H E I F J P K L G M N Q – Node A has 6 children: B, C, D, E, F, G. – B, C, H, I, P, Q , K, L, M, N are leaves in the tree above. – K, L, M are siblings since F is parent of all of them. 3/7/11 CS202  ­ Fundamentals of Computer Science II 4 Level of a node Level – The level of node n is the number of nodes on the path from root to node n. DefiniFon: The level of node n in a tree T –  If n is the root of T, the level of n is 1. –  If n is not the root of T, its level is 1 greater than the level of its parent. 3/7/11 CS202  ­ Fundamentals of Computer Science II 5 Height of A Tree Height – The number of nodes on the longest path from the root to a leaf. •  The height of a tree T in terms of the levels of its nodes is defined as: –  If T is empty, its height is 0 –  If T is not empty, its height is equal to the maximum level of its nodes. •  Or, the height of a tree T can be defined as recursively as: –  If T is empty, its height is 0. –  If T is non ­empty tree, then since T is of the form: r T1 T2 Tk height(T) = 1 + max{height(T1),height(T2),...,height(Tk)} 3/7/11 CS202  ­ Fundamentals of Computer Science II 6 Binary Tree •  A binary tree T is a set of nodes with the following proper4es: –  The set can be empty. –  Otherwise, the set is par44oned into three disjoint subsets: •  a tree consists of a dis4nguished node r, called root, and •  two possibly empty sets are binary tree, called le< and right subtrees of r. •  T is a binary tree if either –  T has no nodes, or –  T is of the form: r TL TR where r is a node and TL and TR are binary trees. 3/7/11 CS202  ­ Fundamentals of Computer Science II 7 Binary Tree Terminology Le< Child – The lee child of node n is a node directly below and to the lee of node n in a binary tree. Right Child – The right child of node n is a node directly below and to the right of node n in a binary tree. Le< Subtree – In a binary tree, the lee subtree of node n is the lee child (if any) of node n plus its descendants. Right Subtree – In a binary tree, the right subtree of node n is the right child (if any) of node n plus its descendants. 3/7/11 CS202  ­ Fundamentals of Computer Science II 8 Binary Tree  ­ ­ Example •  A is the root. •  B is the left child of A, and C is the right child of A. •  D doesn t have a right child. •  H doesn t have a left child. •  B, F, G and I are leaves. •A •C •B •D •F •E •G •H •I 3/7/11 CS202  ­ Fundamentals of Computer Science II 9 Binary Tree – RepresenEng Algebraic Expressions 3/7/11 CS202  ­ Fundamentals of Computer Science II 10 Height of Binary Tree •  The height of a binary tree T can be defined as recursively as: –  If T is empty, its height is 0. –  If T is non ­empty tree, then since T is of the form r TL TR the height of T is 1 greater than the height of its root s taller subtree; ie. height(T) = 1 + max{height(TL),height(TR)} 3/7/11 CS202  ­ Fundamentals of Computer Science II 11 Height of Binary Tree (cont.) Binary trees with the same nodes but different heights 3/7/11 CS202  ­ Fundamentals of Computer Science II 12 Number of Binary trees with Same # of Nodes empty tree (1 tree) • n=0 n=1 • n=2 • • • • n=3 • • (2 trees) • • • • • • • • • (5 trees) • • • ( n!1)/ 2 n is even NumBT ( N ) = 2 " ( NumBT (i ) NumBT (n ! i ! 1)) i=0 (( n!1)/ 2 )!1 NumBT ( N ) = 2 n is odd " ( NumBT (i ) NumBT (n ! i ! 1)) i=0 + NumBT ((n ! 1) / 2 ) NumBT ((n ! 1) / 2 ) 3/7/11 CS202  ­ Fundamentals of Computer Science II 13 Full Binary Tree •  In a full binary tree of height h, all nodes that are at a level less than h have two children each. •  Each node in a full binary tree has lee and right subtrees of the same height. •  Among binary trees of height h, a full binary tree has as many leaves as possible, and they all are at level h. •  A full binary has no missing nodes. •  Recursive definiFon of full binary tree: –  If T is empty, T is a full binary tree of height 0. –  If T is not empty and has height h>0, T is a full binary tree if its root s subtrees are both full binary trees of height h ­1. 3/7/11 CS202  ­ Fundamentals of Computer Science II 14 Full Binary Tree – Example A full binary tree of height 3 3/7/11 CS202  ­ Fundamentals of Computer Science II 15 Complete Binary Tree •  A complete binary tree of height h is a binary tree that is full down to level h ­1, with level h filled in from lee to right. •  A binary tree T of height h is complete if 1.  All nodes at level h ­2 and above have two children each, and 2.  When a node at level h ­1 has children, all nodes to its lee at the same level have two children each, and 3.  When a node at level h ­1 has one child, it is a lee child. –  A full binary tree is a complete binary tree. 3/7/11 CS202  ­ Fundamentals of Computer Science II 16 Complete Binary Tree – Example 3/7/11 CS202  ­ Fundamentals of Computer Science II 17 Balanced Binary Tree •  A binary tree is height balanced (or balanced), if the height of any node s right subtree differs from the height of the node s lee subtree by no more than 1. •  A complete binary tree is a balanced tree. •  Later, we look at other height balanced trees. –  AVL trees –  Red ­Black trees, .... 3/7/11 CS202  ­ Fundamentals of Computer Science II 18 Maximum and Minimum Heights of a Binary Tree •  The efficiency of most of the binary tree operaFons depends on the height of the tree. •  The maximum number of key comparisons for retrieval, deleFon, and inserFon operaFons for BSTs is the height of the tree. •  The maximum of height of a binary tree with n nodes is n. •  Each level of a minimum height tree, except the last level, must contain as many nodes as possible. 3/7/11 CS202  ­ Fundamentals of Computer Science II 19 Maximum and Minimum Heights of a Binary Tree A maximum-height binary tree with seven nodes 3/7/11 Some binary trees of height 3 CS202  ­ Fundamentals of Computer Science II 20 CounEng the nodes in a full binary tree of height h 3/7/11 CS202  ­ Fundamentals of Computer Science II 21 Some Height Theorems Theorem: A full binary of height h≥0 has 2h ­1 nodes. –  The maximum number of nodes that a binary tree of height h can have is 2h ­1.  We cannot insert a new node into a full binary tree without increasing its height. 3/7/11 CS202  ­ Fundamentals of Computer Science II 22 Some Height Theorems Theorem 10 ­4: The minimum height of a binary tree with n nodes is Ⱥlog2(n+1)Ⱥ . Proof: Let h be the smallest integer such that n≤2h ­1. We can establish following facts: Fact 1 – A binary tree whose height is ≤ h ­1 has < n nodes. –  Otherwise h cannot be smallest integer in our assumpFon. Fact 2 – There exists a complete binary tree of height h that has exactly n nodes. –  A full binary tree of height h ­1 has 2h ­1 ­1 nodes. –  Since a binary tree of height h cannot have more than 2h ­1 nodes. –  At level h, we will reach n nodes. Fact 3 – The minimum height of a binary tree with n nodes is the smallest integer h such that n ≤2h ­1. So, 2h ­1 ­1 < n ≤ 2h ­1 2h ­1 < n+1 ≤ 2h h ­1 < log2(n+1) ≤ h Thus, h = Ⱥlog2(n+1)Ⱥ is the minimum height of a binary tree with n nodes. 3/7/11 CS202  ­ Fundamentals of Computer Science II 23 An Array ­Based ImplementaEon of Binary Trees const int MAX_NODES = 100; !// maximum number of nodes typedef string TreeItemType;! class TreeNode { ! ! !// node in the tree private: !TreeNode(); !TreeNode(const TreeItemType& nodeItem, int left, int right); ! !TreeItemType item; !int leftChild; ! !int rightChild; ! ! ! !// data portion !// index to left child !// index to right child !// friend class - can access private parts !friend class BinaryTree; }; // An array of tree nodes TreeNode[MAX_NODES] tree; int root; int free; 3/7/11 CS202  ­ Fundamentals of Computer Science II 24 An Array ­Based ImplementaEon (cont.) •  A free list keeps track of available nodes. •  To insert a new node into the tree, we first obtain an available node from the free list. •  When we delete a node from the tree, we have to place into the free list so that we can use it later. 3/7/11 CS202  ­ Fundamentals of Computer Science II 25 An Array ­Based RepresentaEon of a Complete Binary Tree •  If we know that our binary tree is a complete binary tree, we can use a simpler array ­based representaFon for complete binary trees •  without using leeChild and rightChild links •  We can number the nodes level by level, and lee to right (starFng from 0, the root will be 0). If a node is numbered as i, in the ith locaFon of the array, tree[i], contains this node without links. •  Using these numbers we can find leeChild, rightChild, and parent of a node i. The lee child (if it exists) of node i is tree[2*i+1]! The right child (if it exists) of node i is tree[2*i+2]! The parent (if it exists) of node i is 3/7/11 CS202  ­ Fundamentals of Computer Science II tree[(i-1)/2]! 26 An Array ­Based RepresentaEon of a Complete Binary Tree (cont.) 0 1 3 3/7/11 2 4 5 CS202  ­ Fundamentals of Computer Science II 27 Pointer ­Based ImplementaEon of Binary Trees 3/7/11 CS202  ­ Fundamentals of Computer Science II 28 A Pointer ­Based ImplementaEon of a Binary Tree Node typedef string TreeItemType;! class TreeNode { // node in the tree! private:! TreeNode() {}! TreeNode(const TreeItemType& nodeItem,! TreeNode *left = NULL,! TreeNode *right = NULL)! :item(nodeItem),leftChildPtr(left),rightChildPtr(right) {}! TreeItemType item; // data portion! TreeNode *leftChildPtr; // pointer to left child! TreeNode *rightChildPtr; // pointer to right child! friend class BinaryTree;! }; ! 3/7/11 CS202  ­ Fundamentals of Computer Science II 29 Binary Tree – TreeExcepEon.h class TreeException : public exception{ private: string msg; public: !virtual const char* what() const throw() !{ ! !return msg.c_str(); !}! TreeException(const string & message =""): !exception(), msg(message) {};! !~TreeException() throw() {}; }; // end TreeException 3/7/11 CS202  ­ Fundamentals of Computer Science II 30 The BinaryTree Class •  ProperFes –  TreeNode * root! •  Constructors –  BinaryTree();! –  BinaryTree(const TreeItemType& rootItem);! –  BinaryTree(const TreeItemType& rootItem, ! !! ! BinaryTree& leftTree, BinaryTree& rightTree);! –  BinaryTree(const BinaryTree& tree);! • void copyTree(TreeNode *treePtr, TreeNode* & newTreePtr) const;! •  Destructor –  ~BinaryTree();! • void destroyTree(TreeNode * &treePtr);! 3/7/11 CS202  ­ Fundamentals of Computer Science II 31 BinaryTree: Public Methods •  •  •  •  •  •  •  •  •  •  •  •  •  •  bool isEmpty()! TreeItemType rootData() const throw(TreeException)! void setRootData(const TreeItemType& newItem)! void attachLeft(const TreeItemType& newItem)! void attachRight(const TreeItemType& newItem)! void attachLeftSubtree(BinaryTree& leftTree)! void attachRightSubtree(BinaryTree& rightTree)! void detachLeftSubtree(BinaryTree& leftTree)! void detachRightSubtree(BinaryTree& rightTree)! BinaryTree leftSubtree()! BinaryTree rightSubtree()! void preorderTraverse(FunctionType visit_fn)! void inorderTraverse(FunctionType visit_fn)! void postorderTraverse(FunctionType visit_fn)! •  FunctionType is a pointer to a funcFon: •  typedef void (*FunctionType)(TreeItemType& anItem);! 3/7/11 CS202  ­ Fundamentals of Computer Science II 32 BinaryTree: ImplementaEon •  The complete implementaFon is in your text book •  In class, we will go through only some methods –  Skipping straighoorward methods •  Such as isEmpty, rootData, and setRootData funcFons –  Skipping some details •  Such as throwing excepFons 3/7/11 CS202  ­ Fundamentals of Computer Science II 33 // Default constructor! BinaryTree::BinaryTree() : root(NULL) {! }! // Protected constructor! BinaryTree::BinaryTree(TreeNode *nodePtr) : root(nodePtr) {! }! // Constructor! BinaryTree::BinaryTree(const TreeItemType& rootItem) {! ! !root = new TreeNode(rootItem, NULL, NULL);! }! 3/7/11 CS202  ­ Fundamentals of Computer Science II 34 // Constructor! BinaryTree::BinaryTree(const TreeItemType& rootItem, ! ! ! ! ! BinaryTree& leftTree, BinaryTree& rightTree) {! ! !root = new TreeNode(rootItem, NULL, NULL);! ! !attachLeftSubtree(leftTree);! ! !attachRightSubtree(rightTree);! }! void BinaryTree::attachLeftSubtree(BinaryTree& leftTree) {! ! !// Assertion: nonempty tree; no left child! ! !if (!isEmpty() && (root->leftChildPtr == NULL)) {! ! ! !root->leftChildPtr = leftTree.root;! ! ! !leftTree.root = NULL! ! !}! }! void BinaryTree::attachRightSubtree(BinaryTree& rightTree) {! ! !// Left as an exercise! }! 3/7/11 CS202  ­ Fundamentals of Computer Science II 35 // Copy constructor! BinaryTree::BinaryTree(const BinaryTree& tree) {! ! !copyTree(tree.root, root);! }! // Uses preorder traversal for the copy operation! // (Visits first the node and then the left and right children)! void BinaryTree::copyTree(TreeNode *treePtr, TreeNode *& newTreePtr) const { ! ! ! ! ! ! ! ! }! 3/7/11 !if (treePtr != NULL) { ! !// copy node! ! !newTreePtr = new TreeNode(treePtr->item, NULL, NULL);! ! !copyTree(treePtr->leftChildPtr, newTreePtr->leftChildPtr);! ! !copyTree(treePtr->rightChildPtr, newTreePtr->rightChildPtr);! !}! !else! ! !newTreePtr = NULL; !// copy empty tree! CS202  ­ Fundamentals of Computer Science II 36 // Destructor! BinaryTree::~BinaryTree() {! ! !destroyTree(root);! }! // Uses postorder traversal for the destroy operation! // (Visits first the left and right children and then the node)! void BinaryTree::destroyTree(TreeNode *& treePtr) {! ! ! ! ! ! ! }! 3/7/11 !if (treePtr != NULL){! ! !destroyTree(treePtr->leftChildPtr);! ! !destroyTree(treePtr->rightChildPtr);! ! !delete treePtr;! ! !treePtr = NULL;! !}! CS202  ­ Fundamentals of Computer Science II 37 Binary Tree Traversals •  Preorder Traversal –  The node is visited before its lee and right subtrees, •  Postorder Traversal –  The node is visited aeer both subtrees. •  Inorder Traversal –  The node is visited between the subtrees, –  Visit lee subtree, visit the node, and visit the right subtree. 3/7/11 CS202  ­ Fundamentals of Computer Science II 38 Binary Tree Traversals 3/7/11 CS202  ­ Fundamentals of Computer Science II 39 void BinaryTree::preorderTraverse(FunctionType visit) {! ! !preorder(root, visit);! }! void BinaryTree::inorderTraverse(FunctionType visit) {! ! !inorder(root, visit);! }! void BinaryTree::postorderTraverse(FunctionType visit) {! ! !postorder(root, visit);! }!  ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ ­ Remember that FunctionType is a pointer to a funcFon •  Variables that point to the address of a funcFon •  typedef void (*FunctionType)(TreeItemType& anItem);! Example of using inorderTraverse funcFon •  void display(TreeItemType& anItem) { cout << anItem << endl; } •  BinaryTree T1;! !T1.inorderTraverse(display);! 3/7/11 CS202  ­ Fundamentals of Computer Science II 40 void BinaryTree::preorder(TreeNode *treePtr, FunctionType visit) {! ! !if (treePtr != NULL) {! ! ! !visit(treePtr->item);! ! ! !preorder(treePtr->leftChildPtr, visit);! ! ! !preorder(treePtr->rightChildPtr, visit);! ! !}! }! void BinaryTree::inorder(TreeNode *treePtr, FunctionType visit) {! ! !if (treePtr != NULL) {! ! ! !inorder(treePtr->leftChildPtr, visit);! ! ! !visit(treePtr->item);! ! ! !inorder(treePtr->rightChildPtr, visit);! ! !}! }! void BinaryTree::postorder(TreeNode *treePtr, FunctionType visit) {! ! !if (treePtr != NULL) {! ! ! !postorder(treePtr->leftChildPtr, visit);! ! ! !postorder(treePtr->rightChildPtr, visit);! ! ! !visit(treePtr->item);! ! !}! 3/7/11 CS202  ­ Fundamentals of Computer Science II }! 41 Binary Search Tree •  An important applicaFon of binary trees is their use in searching. •  Binary search tree is a binary tree in which every node X contains a data value that saFsfies the following: a)  all data values in its lee subtree are smaller than the data value in X b)  the data value in X is smaller than all the values in its right subtree c)  the lee and right subtrees are also binary search trees 3/7/11 CS202  ­ Fundamentals of Computer Science II 42 Binary Search Tree 6 2 1 6 8 4 2 1 3 A binary search tree 3/7/11 8 4 3 7 Not a binary search tree, but a binary tree CS202  ­ Fundamentals of Computer Science II 43 Binary Search Trees – containing same data 3/7/11 CS202  ­ Fundamentals of Computer Science II 44 BinarySearchTree Class – UML Diagram 3/7/11 CS202  ­ Fundamentals of Computer Science II 45 The KeyedItem Class typedef desired-type-of-search-key KeyType;! class KeyedItem {! public:! ! !KeyedItem() { } ! ! !KeyedItem(const KeyType& keyValue) : searchKey(keyValue) { }! ! ! ! !KeyType getKey() const { ! ! !return searchKey;! !}! private:! ! !KeyType searchKey;! ! !// ... and other data items! };! 3/7/11 CS202  ­ Fundamentals of Computer Science II 46 The TreeNode Class typedef KeyedItem TreeItemType;! class TreeNode { !// a node in the tree! private:! ! !TreeNode() { }! ! !TreeNode(const TreeItemType& nodeItem,TreeNode *left = NULL, ! ! ! ! ! ! ! ! TreeNode *right = NULL) ! ! !: item(nodeItem), leftChildPtr(left), rightChildPtr(right){ } ! ! ! ! !TreeItemType item; ! !// a data item in the tree! !TreeNode *leftChildPtr; !// pointers to children ! !TreeNode *rightChildPtr; ! !// friend class - can access private parts! !friend class BinarySearchTree;! };! 3/7/11 CS202  ­ Fundamentals of Computer Science II 47 The BinarySearchTree Class •  ProperFes –  TreeNode * root! •  Constructors –  BinarySearchTree();! –  BinarySearchTree(const BinarySearchTree& tree);! •  Destructor –  ~BinarySearchTree();! 3/7/11 CS202  ­ Fundamentals of Computer Science II 48 The BinarySearchTree Class •  Public methods! –  bool isEmpty() const;! –  void searchTreeRetrieve(KeyType searchKey, TreeItemType& item);! –  void searchTreeInsert(const TreeItemType& newItem);! –  void searchTreeDelete(KeyType searchKey);! –  void preorderTraverse(FunctionType visit);! –  void inorderTraverse(FunctionType visit);! –  void postorderTraverse(FunctionType visit);! –  BinarySearchTree& operator=(const BinarySearchTree& rhs);! 3/7/11 CS202  ­ Fundamentals of Computer Science II 49 The BinarySearchTree Class •  Protected methods! –  void retrieveItem(TreeNode *treePtr, KeyType searchKey, ! !! ! ! ! !TreeItemType& item); ! –  void insertItem(TreeNode * &treePtr,const TreeItemType& item);! –  void deleteItem(TreeNode * &treePtr, KeyType searchKey);! –  void deleteNodeItem(TreeNode * &nodePtr);! –  void processLeftmost(TreeNode * &nodePtr, TreeItemType& item);! 3/7/11 CS202  ­ Fundamentals of Computer Science II 50 Searching (Retrieving) an Item in a BST void BinarySearchTree::searchTreeRetrieve(KeyType searchKey,! ! ! ! !TreeItemType& treeItem) const throw(TreeException) { ! ! !retrieveItem(root, searchKey, treeItem);! }! void BinarySearchTree::retrieveItem(TreeNode *treePtr, KeyType searchKey,! ! ! ! !TreeItemType& treeItem) const throw(TreeException) { ! ! ! ! ! ! ! ! ! }! 3/7/11 !if (treePtr == NULL)! ! !throw TreeException("TreeException: searchKey not found");! !else if (searchKey == treePtr->item.getKey())! ! !treeItem = treePtr->item;! !else if (searchKey < treePtr->item.getKey())! ! !retrieveItem(treePtr->leftChildPtr, searchKey, treeItem);! !else! ! !retrieveItem(treePtr->rightChildPtr, searchKey, treeItem);! CS202  ­ Fundamentals of Computer Science II 51 InserEng an Item into a BST Insert 5 Search determines the insertion point. 6 2 1 4 3 3/7/11 8 CS202  ­ Fundamentals of Computer Science II 5 52 InserEng an Item into a BST void BinarySearchTree::searchTreeInsert(const TreeItemType& newItem) {! ! !insertItem(root, newItem);! }! void BinarySearchTree::insertItem(TreeNode *& treePtr, ! ! ! ! !const TreeItemType& newItem) throw(TreeException) {! ! ! ! ! ! ! ! ! ! ! ! }! 3/7/11 !// Position of insertion found; insert after leaf! !if (treePtr == NULL) { ! ! !treePtr = new TreeNode(newItem, NULL, NULL);! ! !if (treePtr == NULL)! ! ! !throw TreeException("TreeException: insert failed"); ! !}! !// Else search for the insertion position! !else if (newItem.getKey() < treePtr->item.getKey()) ! ! !insertItem(treePtr->leftChildPtr, newItem);! !else! ! !insertItem(treePtr->rightChildPtr, newItem);! CS202  ­ Fundamentals of Computer Science II 53 InserEng an Item into a BST 3/7/11 CS202  ­ Fundamentals of Computer Science II 54 DeleEng An Item from a BST •  To delete an item from a BST, we have to locate that item in that BST. •  The deleted node can be: –  Case 1 – A leaf node. –  Case 2 – A node with only with child (with lee child or with right child). –  Case 3 – A node with two children. 3/7/11 CS202  ­ Fundamentals of Computer Science II 55 DeleEon – Case 1: A Leaf Node To remove the leaf containing the item, we have to set the pointer in its parent to NULL. 50 40 30 50 60 45 70 40 30 42 60 45 42 Delete 70 (A leaf node) 3/7/11 CS202  ­ Fundamentals of Computer Science II 56 DeleEon – Case 2: A Node with only a leV child 50 50 40 30 60 45 70 40 30 60 42 70 42 Delete 45 (A node with only a lee child) 3/7/11 CS202  ­ Fundamentals of Computer Science II 57 DeleEon – Case 2: A Node with only a right child 50 50 40 30 60 45 40 30 70 70 45 42 42 Delete 60 (A node with only a right child) 3/7/11 CS202  ­ Fundamentals of Computer Science II 58 DeleEon – Case 3: A Node with two children •  Locate the inorder successor of the node. •  Copy the item in this node into the node which contains the item which will be deleted. •  Delete the node of the inorder successor. 50 50 40 30 60 45 70 42 30 60 45 70 42 Delete 40 (A node with two children) 3/7/11 CS202  ­ Fundamentals of Computer Science II 59 DeleEon – Case 3: A Node with two children 3/7/11 CS202  ­ Fundamentals of Computer Science II 60 DeleEon – Case 3: A Node with two children Delete 2 3/7/11 CS202  ­ Fundamentals of Computer Science II 61 DeleEon from a BST void BinarySearchTree::searchTreeDelete(KeyType searchKey) ! ! ! ! ! ! ! ! !throw(TreeException) { ! ! !deleteItem(root, searchKey);! }! void BinarySearchTree::deleteItem(TreeNode * &treePtr, KeyType searchKey) ! ! ! ! ! ! ! ! !throw(TreeException) { ! ! !if (treePtr == NULL) // Empty tree! ! ! !throw TreeException("Delete failed"); ! ! !! ! !// Position of deletion found! ! !else if (searchKey == treePtr->item.getKey())! ! ! !deleteNodeItem(treePtr);! ! ! ! ! ! }! 3/7/11 !// Else search for the deletion position! !else if (searchKey < treePtr->item.getKey())! ! !deleteItem(treePtr->leftChildPtr, searchKey);! !else! ! !deleteItem(treePtr->rightChildPtr, searchKey);! CS202  ­ Fundamentals of Computer Science II 62 DeleEon from a BST void BinarySearchTree::deleteNodeItem(TreeNode * &nodePtr) {! ! !TreeNode *delPtr;! ! !TreeItemType replacementItem;! ! ! ! ! ! ! ! ! ! ! ! ! ! 3/7/11 !// (1) Test for a leaf! !if ( (nodePtr->leftChildPtr == NULL) && ! ! (nodePtr->rightChildPtr == NULL) ) {! ! !delete nodePtr;! ! !nodePtr = NULL;! !}! !// (2) Test for no left child! !else if (nodePtr->leftChildPtr == NULL){! ! !delPtr = nodePtr;! ! !nodePtr = nodePtr->rightChildPtr;! ! !delPtr->rightChildPtr = NULL; ! ! !delete delPtr;! !}! CS202  ­ Fundamentals of Computer Science II 63 DeleEon from a BST ! ! ! ! ! !// (3) Test for no right child! !else if (nodePtr->rightChildPtr == NULL) {! ! !// ...! ! !// Left as an exercise ! !}! ! ! ! ! ! ! !// (4) There are two children:! !// Retrieve and delete the inorder successor! !else {! ! !processLeftmost(nodePtr->rightChildPtr, replacementItem);! ! !nodePtr->item = replacementItem;! !}! }! 3/7/11 CS202  ­ Fundamentals of Computer Science II 64 DeleEon from a BST void BinarySearchTree::processLeftmost(TreeNode *&nodePtr, ! ! ! ! ! ! ! TreeItemType& treeItem){! ! ! ! ! ! ! ! ! ! }! 3/7/11 !if (nodePtr->leftChildPtr == NULL) {! ! !treeItem = nodePtr->item;! ! !TreeNode *delPtr = nodePtr;! ! !nodePtr = nodePtr->rightChildPtr;! ! !delPtr->rightChildPtr = NULL; // defense! ! !delete delPtr;! !}! !else! ! !processLeftmost(nodePtr->leftChildPtr, treeItem);! CS202  ­ Fundamentals of Computer Science II 65 Traversals •  The traversals for binary search trees are same as the traversals for the binary trees. Theorem: Inorder traversal of a binary search tree will visit its nodes in sorted search ­key order. Proof: Proof by inducFon on the height of the binary search tree T. Basis: h=0 no nodes are visited, empty list is in sorted order. Induc4ve Hypothesis: Assume that the theorem is true for all k, 0≤k<h Induc4ve Conclusion: We have to show that the theorem is true for k=h>0. T should be: Since the lengths of TL and TR are less than h, the theorem holds r for them. All the keys in TL are less than r, and all the keys in TR are greater than r. In inorder traversal, we visit TL first, then r, and then TR. T T Thus, the theorem holds for T with height k=h. L 3/7/11 R CS202  ­ Fundamentals of Computer Science II 66 Minimum Height •  Complete trees and full trees have minimum height. •  The height of an n ­node binary search tree ranges from Ⱥlog2(n+1)Ⱥ to n. •  InserFon in search ­key order produces a maximum ­height BST. •  InserFon in random order produces a near ­minimum ­height BST. •  That is, the height of an n ­node binary search tree –  Best Case – Ⱥlog2(n+1)Ⱥ O(log2n) –  Worst Case – n O(n) –  Average Case – close to Ⱥlog2(n+1)Ⱥ O(log2n) 3/7/11 CS202  ­ Fundamentals of Computer Science II 67 Average Height •  If we insert n items into an empty BST to create a BST with n nodes, –  How many different binary search trees with n nodes? –  What are their probabiliFes? •  There are n! different orderings of n keys. –  But how many different binary search trees with n nodes? n=0 1 BST (empty tree) n=1 1 BST (a binary tree with a single node) n=2 2 BSTs • • • • When n=3 n=3 5 BSTs • • Probabilities: 1/6 Insertion Order: 3,2,1 3/7/11 CS202  ­ Fundamentals of Computer Science II • •• • 1/6 3,1,2 2/6 2,1,3 2,3,1 • • • 1/6 1,3,2 • • 1/6 1,2,3 68 Order of OperaEons on BSTs 3/7/11 CS202  ­ Fundamentals of Computer Science II 69 Treesort •  We can use a binary search tree to sort an array. // Sorts n integers in an array anArray into ! // ascending order! treesort(inout anArray:ArrayType, in n:integer) ! ! ! !Insert anArray’s elements into a binary search ! !tree bTree! ! ! ! !Traverse bTree in inorder. As you visit bTree’s! !nodes, copy their data items into successive! !locations of anArray! 3/7/11 CS202  ­ Fundamentals of Computer Science II 70 Treesort Analysis •  InserFng an item into a binary search tree: –  Worst Case: O(n) –  Average Case: O(log2n) •  InserFng n items into a binary search tree: –  Worst Case: O(n2) –  Average Case: O(n*log2n) (1+2+...+n) = O(n2) •  Inorder traversal and copy items back into array O(n) •  Thus, treesort is O(n2) in worst case, and O(n*log2n) in average case. •  Treesort makes exactly same key comparisons of keys as does quicksort when the pivot for each sublist is chosen to be the first key 3/7/11 CS202  ­ Fundamentals of Computer Science II 71 Saving a BST into a file and restoring it to its original shape •  Save: –  Use a preorder traversal to save the nodes of the BST into a file •  Restore: –  Start with an empty BST –  Read the nodes from the file one by one and insert them into the BST 3/7/11 CS202  ­ Fundamentals of Computer Science II 72 Saving a BST into a file and restoring it to its original shape Preorder: 60 20 10 40 30 50 70 3/7/11 CS202  ­ Fundamentals of Computer Science II 73 Saving a BST into a file and restoring it to a minimum ­height BST •  Save: –  Use an inorder traversal to save the nodes of the BST into a file. The saved nodes will be in ascending order –  Save the number of nodes (n) in somewhere •  Restore: –  Read the number of nodes (n) –  Start with an empty BST –  Read the nodes from the file one by one to create a minimum ­ height binary search tree 3/7/11 CS202  ­ Fundamentals of Computer Science II 74 Building a minimum ­height BST // Builds a minimum-height binary search tree from n sorted! // values in a file. treePtr will point to the tree’s root.! readTree(out treePtr:TreeNodePtr, in n:integer)! ! ! !if (n>0) {! ! !treePtr = pointer to new node with NULL child pointers! ! ! ! ! !// construct the left subtree! !readTree(treePtr->leftChildPtr, n/2)! ! ! ! ! !// get the root! !Read item from file into treePtr->item! ! ! ! ! ! !}! !// construct the right subtree! !readTree(treePtr->rightChildPtr, (n-1)/2)! 3/7/11 CS202  ­ Fundamentals of Computer Science II 75 A full tree saved in a file by using inorder traversal 3/7/11 CS202  ­ Fundamentals of Computer Science II 76 A General Tree 3/7/11 CS202  ­ Fundamentals of Computer Science II 77 A Pointer ­Based ImplementaEon of General Trees 3/7/11 CS202  ­ Fundamentals of Computer Science II 78 A Pointer ­Based ImplementaEon of General Trees A pointer-based implementation of a general tree can also represent a binary tree. 3/7/11 CS202  ­ Fundamentals of Computer Science II 79 N ­ary Tree An n-ary tree is a generalization of a binary whose nodes each can have no more than n children. 3/7/11 CS202  ­ Fundamentals of Computer Science II 80 ...
View Full Document

This note was uploaded on 10/23/2011 for the course ENGINEERIN 102 taught by Professor Pablo during the Spring '11 term at Bilkent University.

Ask a homework question - tutors are online