1. © 2004 Goodrich, Tamassia Trees1 Lecture 04 Trees Topics Trees Binary Trees Binary Search trees

2. © 2004 Goodrich, Tamassia Trees2 In computer science, a tree is an abstract model of a hierarchical structure A tree consists of nodes with a parent-child relation Applications: Organization charts File systems Programming environments Computers”R”Us SalesR&DManufacturing LaptopsDesktops US International EuropeAsiaCanada

3. © 2004 Goodrich, Tamassia Trees3 subtree Trees Terminology Root: node without parent (A) Internal node: node with at least one child (A, B, C, F) External node (a.k.a. leaf ): node without children (E, I, J, K, G, H, D) Ancestors of a node: parent, grandparent, grand-grandparent, etc. Depth of a node: number of ancestors Height of a tree: maximum depth of any node (3) Descendant of a node: child, grandchild, grand-grandchild, etc. A B DC GH E F IJ K Subtree: tree consisting of a node and its descendants

4. © 2004 Goodrich, Tamassia Trees4  A node is represented by an object storing Element Parent node Sequence of children nodes Node objects implement the Position ADT B D A CE F B  ADF  C  E

5. © 2004 Goodrich, Tamassia Trees5 Operations: We use positions to abstract nodes Generic methods: integer size() boolean isEmpty() Iterator elements() Iterator positions() Accessor methods: position root() position parent(p) positionIterator children(p) Query methods: boolean isInternal(p) boolean isExternal(p) boolean isRoot(p) Update method: object replace (p, o) Additional update methods may be defined by data structures implementing the Tree ADT

6. © 2004 Goodrich, Tamassia Trees6 A traversal visits the nodes of a tree in a systematic manner In a preorder traversal, a node is visited before its descendants Application: print a structured document Make Money Fast! 1. MotivationsReferences2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 678 9 Algorithm preOrder(v) visit(v) for each child w of v preorder (w)

7. © 2004 Goodrich, Tamassia Trees7 In a postorder traversal, a node is visited after its descendants Application: compute space used by files in a directory and its subdirectories Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) cs16/ homeworks/ todo.txt 1K programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 456 8

8. © 2004 Goodrich, Tamassia Trees8 Binary Trees A binary tree is a tree with the following properties: Each internal node has at most two children (exactly two for proper binary trees) The children of a node are an ordered pair We call the children of an internal node left child and right child Alternative recursive definition: a binary tree is either a tree consisting of a single node, or a tree whose root has an ordered pair of children, each of which is a binary tree Applications: arithmetic expressions decision processes searching A B C FG D E H I

9. © 2004 Goodrich, Tamassia Trees9 Binary Trees A node is represented by an object storing Element Parent node Left child node Right child node Node objects implement the Position ADT B D A CE   BADCE 

10. © 2004 Goodrich, Tamassia Trees10 Binary Trees nodes are stored in an array … let rank(node) be defined as follows: rank(root) = 1 if node is the left child of parent(node), rank(node) = 2*rank(parent(node)) if node is the right child of parent(node), rank(node) = 2*rank(parent(node))+1 1 23 6 7 45 1011 A HG FE D C B J

11. © 2004 Goodrich, Tamassia Trees11 Binary Trees Arithmetic Expression Tree: Binary tree associated with an arithmetic expression internal nodes: operators external nodes: operands Example: arithmetic expression tree for the expression (2  ( a  1)  (3  b))    2 a1 3b

12. © 2004 Goodrich, Tamassia Trees12 Binary Trees Decision Tree: Binary tree associated with a decision process internal nodes: questions with yes/no answer external nodes: decisions Example: dining decision Want a fast meal? How about coffee?On expense account? StarbucksSpike’sAl FornoCafé Paragon Yes No YesNoYesNo

13. © 2004 Goodrich, Tamassia Trees13 Binary Trees Notation n number of nodes e number of external nodes i number of internal nodes h height Properties: e  i  1 n  2e  1 h  i h  (n  1)  2 e  2 h h  log 2 e h  log 2 (n  1)  1

14. © 2004 Goodrich, Tamassia Trees14 Binary Trees The BinaryTree ADT extends the Tree ADT, i.e., it inherits all the methods of the Tree ADT (e.g., postOrder/ preOrder traversals) Additional methods: position left(p) position right(p) boolean hasLeft(p) boolean hasRight(p) Update methods may be defined by data structures implementing the BinaryTree ADT

15. © 2004 Goodrich, Tamassia Trees15 Binary Trees In an inorder traversal a node is visited after its left subtree and before its right subtree Algorithm inOrder(v) if hasLeft (v) inOrder (left (v)) visit(v) if hasRight (v) inOrder (right (v)) 3 1 2 5 6 79 8 4

16. © 2004 Goodrich, Tamassia Trees16 Binary Trees Print Arithmetic Expressions: Application and specialization of an inorder traversal print operand or operator when visiting node print “(“ before traversing left subtree print “)“ after traversing right subtree Algorithm printExpression(v) if hasLeft (v) print( “(’’ ) inOrder (left(v)) print(v.element ()) if hasRight (v) inOrder (right(v)) print ( “)’’ )    2 a1 3b ((2  ( a  1))  (3  b))

17. © 2004 Goodrich, Tamassia Trees17 Binary Trees Evaluate Arithmetic Expressions: Specialization of a postorder traversal recursive method returning the value of a subtree when visiting an internal node, combine the values of the subtrees Algorithm evalExpr(v) if isExternal (v) return v.element () else x  evalExpr(leftChild (v)) y  evalExpr(rightChild (v))   v. operatorElement () return x  y    2 51 32

18. © 2004 Goodrich, Tamassia Trees18 Binary Trees Euler Tour Traversal: Generic traversal of a binary tree Includes a special cases the preorder, postorder and inorder traversals Walk around the tree and visit each node three times: on the left (preorder) from below (inorder) on the right (postorder)    2 51 32 L B R 

19. © 2004 Goodrich, Tamassia Trees19 Binary Trees Generic algorithm that can be specialized by redefining certain steps Implemented by means of an abstract Java class Visit methods that can be redefined by subclasses Template method eulerTour Recursively called on the left and right children A Result object with fields leftResult, rightResult and finalResult keeps track of the output of the recursive calls to eulerTour public abstract class EulerTour { protected BinaryTree tree; protected void visitExternal(Position p, Result r) { } protected void visitLeft(Position p, Result r) { } protected void visitBelow(Position p, Result r) { } protected void visitRight(Position p, Result r) { } protected Object eulerTour(Position p) { Result r = new Result(); if tree.isExternal(p) { visitExternal(p, r); } else { visitLeft(p, r); r.leftResult = eulerTour(tree.left(p)); visitBelow(p, r); r.rightResult = eulerTour(tree.right(p)); visitRight(p, r); return r.finalResult; } …

20. © 2004 Goodrich, Tamassia Trees20 Binary Trees We show how to specialize class EulerTour to evaluate an arithmetic expression Assumptions External nodes store Integer objects Internal nodes store Operator objects supporting method operation (Integer, Integer) public class EvaluateExpression extends EulerTour { protected void visitExternal(Position p, Result r) { r.finalResult = (Integer) p.element(); } protected void visitRight(Position p, Result r) { Operator op = (Operator) p.element(); r.finalResult = op.operation( (Integer) r.leftResult, (Integer) r.rightResult ); } … }

21. © 2004 Goodrich, Tamassia Trees21 Binary Search Trees A binary search tree is a binary tree storing keys (or key-value entries) at its internal nodes and satisfying the following property: Let u, v, and w be three nodes such that u is in the left subtree of v and w is in the right subtree of v. We have key(u)  key(v)  key(w) External nodes do not store items An inorder traversal of a binary search trees visits the keys in increasing order 6 92 418

22. © 2004 Goodrich, Tamassia Trees22 Binary Search Trees To search for a key k, we trace a downward path starting at the root The next node visited depends on the outcome of the comparison of k with the key of the current node If we reach a leaf, the key is not found and we return nukk Example: find(4): Call TreeSearch(4,root) Algorithm TreeSearch(k, v) if T.isExternal (v) return v if k  key(v) return TreeSearch(k, T.left(v)) else if k  key(v) return v else { k  key(v) } return TreeSearch(k, T.right(v)) 6 9 2 4 1 8   

23. © 2004 Goodrich, Tamassia Trees23 Binary Search Trees - Insertion To perform operation inser(k, o), we search for key k (using TreeSearch) Assume k is not already in the tree, and let let w be the leaf reached by the search We insert k at node w and expand w into an internal node Example: insert 5 6 92 418 6 9 2 4 18 5    w w

24. © 2004 Goodrich, Tamassia Trees24 Binary Search Trees - Deletion To perform operation remove( k ), we search for key k Assume key k is in the tree, and let let v be the node storing k If node v has a leaf child w, we remove v and w from the tree with operation removeExternal( w ), which removes w and its parent Example: remove 4 6 9 2 4 18 5 v w 6 9 2 5 18  

25. © 2004 Goodrich, Tamassia Trees25 Binary Search Trees - Deletion (cont.) We consider the case where the key k to be removed is stored at a node v whose children are both internal we find the internal node w that follows v in an inorder traversal we copy key(w) into node v we remove node w and its left child z (which must be a leaf) by means of operation removeExternal( z ) Example: remove 3 3 1 8 6 9 5 v w z 2 5 1 8 6 9 v 2

26. © 2004 Goodrich, Tamassia Trees26 Binary Search Trees - Performance Consider a dictionary with n items implemented by means of a binary search tree of height h the space used is O(n) methods find, insert and remove take O(h) time The height h is O(n) in the worst case and O(log n) in the best case