1 / 23

CS 261 – Winter 2009

CS 261 – Winter 2009. Trees. Trees. Ubiquitous – they are everywhere in CS Probably ranks third among the most used data structure: Vectors and Arrays Lists Trees. Tree Characteristics. A tree consists of a collection of nodes connected by directed arcs A tree has a single root node

leon
Download Presentation

CS 261 – Winter 2009

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CS 261 – Winter 2009 Trees

  2. Trees • Ubiquitous – they are everywhere in CS • Probably ranks third among the most used data structure: • Vectors and Arrays • Lists • Trees

  3. Tree Characteristics • A tree consists of a collection of nodes connected by directed arcs • A tree has a single root node • By convention, the root node is usually drawn at the top • A node that points to (one or more) other nodes is the parent of those nodes while the nodes pointed to are the children • Every node (except the root) has exactly one parent • Nodes with no children are leaf nodes • Nodes with children are interior nodes

  4. Tree Characteristics (cont.) • Nodes that have the same parent are siblings • The descendents of a node consist of its children, and their children, and so on • All nodes in a tree are descendents of the root node(except, of course, the root node itself) • Any node can be considered the root of a subtree • Like a subset, a subtree need not be “proper”(i.e., be strictly smaller than the original) • A subtree rooted at a node consists of that node and all of its descendents

  5. Tree Characteristics (cont.) • There is a single, unique path from the root to any node • Arcs don’t join together • A path’s length is equal to the number of arcs traversed • A node’s height is equal to the maximum path length from that node to a leaf node: • A leaf node has a height of 0 • The height of a tree is equal to the height of the root • A node’s depth is equal to the path length from the root to that node: • The root node has a depth of 0 • A tree’s depth is the maximum depth of all its leaf nodes (which, of course, is equal to the tree’s height)

  6. Tree Characteristics (cont.) A • Nodes D and E are children of node B • Node B is the parent of nodes D and E • Nodes B, D, and E are descendents of node A(as are all other nodes in the tree…except A) • E is an interior node • F is a leaf node Root(depth = 0, height = 4) B C Subtree rooted at node C D E F Leaf node(depth = 4, height = 0)

  7. Tree Characteristics (cont.) Are these trees? Yes No No

  8. Binary Tree • Nodes have no more than two children: • Children are generally ordered from left to right • Full Binary Tree: every leaf is at the same depth • Every internal node has 2 children • Height of n will have 2n+1 – 1 nodes • Height of n will have 2n leaves

  9. Binary Tree • Nodes have no more than two children: • Children are generally ordered from left to right • Full Binary Tree: every leaf is at the same depth • Every internal node has 2 children • Height of n will have 2n+1 – 1 nodes • Height of n will have 2n leaves • Complete Binary Tree: full except for the bottom level which is filled from left to right

  10. Relationship of Height to Number of Nodes • If a complete binary tree has N nodes, what it its height? • We will come back to this when We later have algorithms That run in time Proportional to the Path length

  11. Array Implementation • Complete binary tree have structure that is efficiently implemented with an array: • Children of node i are stored at 2i + 1 and 2i + 2 • Parent of node i is at floor((i - 1) / 2) a Root b c 0 a 1 b 2 c 3 d 4 e 5 f 6 7 d e f

  12. Vector Implementation (cont.) • If the tree is not complete (it is thin, unbalanced, etc.),the Array • implementation will be full of holes a b c d e f 0 a 1 b 2 c 3 4 d 5 6 e 7 8 9 10 11 12 13 f 14 15

  13. Dynamic Memory Implementation struct node { EleType value; struct node * left; // Left child. struct node * right; // Right child. }; Like the Link class in LinkedList: we will use this class in several data structures

  14. Binary Tree Application: Animal Game • Purpose: guess an animal using a sequence of questions • Internal nodes contain yes/no questions • Leaf nodes are animals • Initially, tree contains a single animal (e.g., a “cat”) stored in the root node • Start at root. • If internal node  ask yes/no question • Yes  go to left child and repeat step 2 • No  go to right child and repeat step 2 • If leaf node  ask “I know. Is it a …”: • If right  done • If wrong  “learn” new animal by asking for a yes/no question that distinguishes the new animal from the guess

  15. Binary Tree Traversals • Just as a list, it is often necessary to examine every node in a tree • A list is a simple linear structure: can be traversed either forward or backward – but usually forward • What order do we visit nodes in a tree? • Most common traversal orders: • Pre-order • In-order • Post-order

  16. Binary Tree Traversals (cont.) • All traversal algorithms have to: • Process node • Process left subtree • Process right subtree Traversal order determined by the order these operations are done. • Six possible traversal orders: • Node, left, right  Pre-order • Left, node, right  In-order • Left, right, node  Post-order • Node, right, left • Right, node, left • Right, left, node • Most common traversals.  • Subtrees are not • usually analyzed  from right to left.

  17. Pre-Order Traversal • Process order  Node, Left subtree, Right subtree // Not in the BinaryNode class. void preorder(BinaryNode node) { if (node != null){ process (node.obj); preorder(node.left); preorder(node.rght); } } Example result:p s a m a e l r t e e p s e a m r l a e t e

  18. Post-Order Traversal • Process order  Left subtree, Right subtree, Node void postorder(BinaryNode node) { if (node != null){ postorder(node.left); postorder(node.rght); process (node.obj); } } Example result:a a m s l t e e r e p p s e a m r l a e t e

  19. In-Order Traversal • Process order  Left subtree, Node, Right subtree void inorder(BinaryNode node) { if (node != null){ inorder(node.left); process (node.obj); inorder(node.rght); } } Example result:a sample tree p s e a m r l a e t e

  20. Binary Tree Traversals: Euler Tour • An Euler Tour “walks” around the tree’s perimeter • Each node is visited three times: • 1st visit: left side of node • 2nd visit: bottom side of node • 3rd visit: right side of node • Leaf nodes are visited three times in succession • Traversal order depends on when node processed: • Pre-order: 1st visit • In-order: 2nd visit • Post-order: 3rd visit p s e a m r l a e t e

  21. Traversal Example Pre-order:+ a * + b c d(Polish notation) In-order:a + (b + c) * d(parenthesis added) Post-order:a b c + d * +(reverse Polish notation) + a * d + c b

  22. Traversals • Computational complexity: • Each traversal requires constant work at each node (not including the recursive calls) • Order not important • Iterating over all n elements in a tree requires O(n) time • Problem with traversal code: • The process function must be rewritten (or replaced) for each new task • Programmer writing new process function sees the internal tree structure representation • Not good information hiding • Solution  Iterator (more on this later)

  23. Questions? • Do the worksheet on trees. • Next topic, how do we make a useful data structure out of a tree?

More Related