Lecture 13: Binary Search Trees

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1 cs2010: algorithms and data structures Lecture 13: Binary Search Trees Vasileios Koutavas School of Computer Science and Statistics Trinity College Dublin

2 Algorithms ROBERT SEDGEWICK KEVIN WAYNE 3.2 BINARY SEARCH TREES Algorithms F O U R T H E D I T I O N BSTs ordered operations deletion ROBERT SEDGEWICK KEVIN WAYNE

3 3.2 BINARY SEARCH TREES Algorithms BSTs ordered operations deletion ROBERT SEDGEWICK KEVIN WAYNE

4 Binary search trees Definition. A BST is a binary tree in symmetric order. A binary tree is either: Empty. Two disjoint binary trees (left and right). a subtree a left link null links root right child of root Symmetric order. Each node has a key, and every node s key is: Larger than all keys in its left subtree. Smaller than all keys in its right subtree. left link of E A keys smaller than E parent of A and R C E H S R 9 X key keys larger than E value associated with R 3

5 binary trees: more definitions leaves of tree: the nodes with no child nodes height of tree: the maximum number of links from the root to a leaf levels of tree: the maximum number of nodes from the root to a leaf (inl. root and leaf) size of tree: the number of nodes in the tree depth of a node: the number of links from the root to this node. 1

6 binary trees: more definitions leaves of tree: the nodes with no child nodes height of tree: the maximum number of links from the root to a leaf levels of tree: the maximum number of nodes from the root to a leaf (inl. root and leaf) size of tree: the number of nodes in the tree depth of a node: the number of links from the root to this node. Q: how many leafs in this tree? Q: what is the height of this tree? Q: how many levels in this tree? Q: what is the size of this tree? Q: what is the depth of H? 1

binary trees: more definitions leaves of tree: the nodes with no child nodes height of tree: the maximum number of links from the root to a leaf levels of tree: the maximum number of nodes from the

7 binary trees: more definitions leaves of tree: the nodes with no child nodes height of tree: the maximum number of links from the root to a leaf levels of tree: the maximum number of nodes from the root to a leaf (inl. root and leaf) size of tree: the number of nodes in the tree depth of a node: the number of links from the root to this node. Q: how many leafs in this tree? 4 Q: what is the height of this tree? 4 Q: how many levels in this tree? 5 Q: what is the size of this tree? 9 Q: what is the depth of H? 3 1

8 Binary search tree demo Search. If less, go left; if greater, go right; if equal, search hit. successful search for H S E X A R C H M 4

9 Binary search tree demo Insert. If less, go left; if greater, go right; if null, insert. insert G S E X A R C H G M 5

10 BST representation in Java Java definition. A BST is a reference to a root Node. A Node is composed of four fields: A Key and a Value. A reference to the left and right subtree. smaller keys larger keys private class Node { private Key key; private Value val; private Node left, right; public Node(Key key, Value val) { this.key = key; this.val = val; } } BST Node key left val right BST with smaller keys BST with larger keys Binary search tree Key and Value are generic types; Key is Comparable 6

BST implementation (skeleton) public class BST<Key extends Comparable<Key>, Value> { private Node root; root of BST private class Node { /* see previous slide */ } public void put(key key, Value

11 BST implementation (skeleton) public class BST<Key extends Comparable<Key>, Value> { private Node root; root of BST private class Node { /* see previous slide */ } public void put(key key, Value val) { /* see next slides */ } public Value get(key key) { /* see next slides */ } public void delete(key key) { /* see next slides */ } public Iterable<Key> iterator() { /* see next slides */ } } 7

12 BST search: Java implementation Get. Return value corresponding to given key, or null if no such key. public Value get(key key) { Node x = root; while (x!= null) { int cmp = key.compareto(x.key); if (cmp < 0) x = x.left; else if (cmp > 0) x = x.right; else if (cmp == 0) return x.val; } return null; } Cost. Number of compares is equal to 1 + depth of node. 8

13 BST insert Put. Associate value with key. Search for key, then two cases: Key in tree reset value. Key not in tree add new node. inserting L A C search for L ends at this null link A C create new node E E H M H L P M R P S R X S X A C E reset links on the way up H L M P R S X Insertion into a BST 9

BST insert: Java implementation Put. Associate value with key. public void put(key key, Value val) { root = put(root, key, val); } concise, but tricky, recursive code; read carefully!

14 BST insert: Java implementation Put. Associate value with key. public void put(key key, Value val) { root = put(root, key, val); } concise, but tricky, recursive code; read carefully! private Node put(node x, Key key, Value val) { if (x == null) return new Node(key, val); int cmp = key.compareto(x.key); if (cmp < 0) x.left = put(x.left, key, val); else if (cmp > 0) x.right = put(x.right, key, val); else if (cmp == 0) x.val = val; return x; } Cost. Number of compares is equal to 1 + depth of node. 10

15 Tree shape Many BSTs correspond to same set of keys. Number of compares for search/insert is equal to 1 + depth of node. best case A C E H R S X typical case E A C H R S X A C E worst case H R S X Bottom line. Tree shape depends on order of insertion. 11

16 BST insertion: random order visualization Ex. Insert keys in random order. 12

17 BSTs: mathematical analysis Proposition. If N distinct keys are inserted into a BST in random order, the expected number of compares for a search/insert is ~ 2 ln N. Pf. 1 1 correspondence with quicksort partitioning. Proposition. [Reed, 2003] If N distinct keys are inserted in random order, expected height of tree is ~ ln N. How Tall is a Tree? Bruce Reed CNRS, Paris, France reed@moka.ccr.jussieu.fr ABSTRACT Let H~ be the height of a random binary search tree on n nodes. We show that there exists constants a = and/3 = such that E(H~) = c~logn -/31oglogn + O(1), We also show that Var(H~) = O(1). But Worst-case height is N. [ exponentially small chance when keys are inserted in random order ] 15

18 ST implementations: summary implementation guarantee average case search insert search hit insert operations on keys sequential search (unordered list) N N ½ N N equals() binary search (ordered array) lg N N lg N ½ N compareto() BST N N 1.39 lg N 1.39 lg N compareto() Why not shuffle to ensure a (probabilistic) guarantee of ln N? 16

19 quiz Q: Which of the following are Binary Search Trees? Why? 2

CS2012 Programming Techniques II

10/02/2014 CS2012 Programming Techniques II Vasileios Koutavas Lecture 12 1 10/02/2014 Lecture 12 2 10/02/2014 Lecture 12 3 Answer until Thursday. Results Friday 10/02/2014 Lecture 12 4 Last Week Review: