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1 Data Structures: Trees and Grammars Readings: Sections 6.1, 7.1-7.4 (more from Ch. 6 later)

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Presentation on theme: "1 Data Structures: Trees and Grammars Readings: Sections 6.1, 7.1-7.4 (more from Ch. 6 later)"— Presentation transcript:

1 1 Data Structures: Trees and Grammars Readings: Sections 6.1, (more from Ch. 6 later)

2 2 Goals for this Unit Continue focus on data structures and algorithms Understand concepts of reference- based data structures (e.g. linked lists, binary trees) –Some implementation for binary trees Understand usefulness of trees and hierarchies as useful data models –Recursion used to define data organization

3 3 Taxonomy of Data Structures From the text: –Data type: collection of values and operations Compare to Abstract Data Type! –Simple data types vs. composite data types Book: Data structures are composite data types –Definition: a collection of elements that are some combination of primitive and other composite data types

4 4 Books Classification of Data Structures Four groupings: –Linear Data Structures –Hierarchical –Graph –Sets and Tables When defining these, note an element has: –one or more information fields –relationships with other elements

5 5 Note on Our Book and Our Course Our books strategy –In Ch. 6, discuss principles of Lists Give an interface, then implement from scratch –In Ch. 7, discuss principles of Trees –Later, in Ch. 9, see what Java gives us Our courses strategy –We did Ch. 9 first. Saw List interfaces and operations –Then, Ch. 8 on maps and sets –Now, trees with some implementation too

6 6 Trees Represent… Concept of a tree very common and important. Tree terminology: –Nodes have one parent –A nodes children Leaf nodes: no children Root node: top or start; no parent Data structures that store trees Execution or processing that can be expressed as a tree –E.g. method calls as a program runs –Searching a maze or puzzle

7 7 Trees are Important Trees are important for cognition and computation –computer science –language processing (human or computer) parse trees –knowledge representation (or modeling of the real world) E.g. family trees; the Linnaean taxonomy (kingdom, phylum, …, species); etc.

8 8 Another Tree Example: File System What about file links (Unix) or shortcuts (Windows)? C:\ lab2 CS216 lab1lab3 MyMail school CS120 pers list.hcalc.cpp list.cpp

9 9 Another Tree Example: XML and HTML documents … My Page Blah blah blah End How is this a tree? What are the leaves?

10 10 Tree Data Structures Why this now? –Very useful in coding TreeMap in Java Collections Framework –Example of recursive data structures –Methods are recursive algorithms

11 11 Tree Definitions and Terms First, general trees vs. binary trees –Each node in a binary tree has at most two children General tree definition: –Set of nodes T (possibly empty?) with a distinguished node, the root –All other nodes form a set of disjoint subtrees T i each a tree in its own right each connected to the root with an edge Note the recursive definition –Each node is the root of a subtree

12 12 Picture of Tree Definition And all subtrees are recursively defined as: –a node with… –subtrees attached to it r T1T1 T2T2 T3T3

13 13 Tree Terminology A nodes parent A nodes children –Binary tree: left child and right child –Sibling nodes –Descendants, ancestors A nodes degree (how many children) Leaf nodes or terminal nodes Internal or non-terminal nodes

14 14 Recursive Data Structure Recursive Data Structure: a data structure that contains a pointer or reference to an instance of itself: public class TreeNode { T nodeItem; TreeNode left, right; TreeNode parent; … } Recursion is a natural way to express many algorithms. For recursive data-structures, recursive algorithms are a natural choice

15 15 General Trees Representing general trees is a bit harder –Each node has a list of child nodes Turns out that: –Binary trees are simpler and still quite useful From now on, lets focus on binary- trees only

16 16 ADT Tree Remember definition on an ADT? –Model of information: we just covered that –Operations? See pages in textbook Many are similar to ADT List or any data structure –The CRUD operations: create, replace, update, delete Important about this list of operations –some are in terms of one specified node, e.g. hasParent() –others are tree-wide, e.g. size(), traversal

17 17 Classes for Binary Trees (pp ) class LinkedBinaryTree (p. 425, bottom) –reference to root BinaryTreeNode –methods: tree-level operations class BinaryTreeNode (p. 416) –data: an object (of some type) –left: references root of left-subtree (or null) –right: references root of right-subtree (or null) –parent: references this nodes parent node Could this be null? When should it be? –methods: node-level operations

18 18 Two-class Strategy for Recursive Data Structures Common design: use two classes for a Tree or List Top class –has reference to first node –other things that apply to the whole data-structure object (e.g. the tree-object) both methods and fields Node class –Recursive definitions are here as references to other node objects –Also data (of course) –Methods defined in this class are recursive

19 19 Binary Tree and Node Class LinkedBinaryTree class has: –reference to root node –reference to a current node, a cursor –non-recursive methods like: boolean find(tgt) // see if tgt is in the whole tree Node class has: –data, references to left and right subtrees –recursive versions of methods like find: boolean find(tgt) // is tgt here or in my subtrees? Note: BinaryTree.find() just calls Node.find() on the root node! –Other methods work this way too

20 20 Why Does This Matter Now? This illustrates (again) important design ideas The tree itself is what were interested in –There are tree-level operations on it (ADT level operations) The implementation is a recursive data structure –There are recursive methods inside the lower-level classes that are closely related (same name!) to the ADT-level operation Principles? abstraction (hiding details), delegation (helper classes, methods)

21 21 ADT Tree Operations: Navigation Positioning: –toRoot(), toParent(), toLeftChild(), toRightChild(), find(Object o) Checking: –hasParent(), hasLeftChild(), etc. –equals(Object tree2) Book calls this a deep compare Do two distinct objects have the same structure and contents?

22 22 ADT Tree Operations: Mutators Mutators: –insertRight(Object o), insertLeft(Object o) create a new node containing new data make this new node be the child of the current node Important: We use these to build trees! –prune() delete the subtree rooted by the current node

23 23 Next: Implementation Next (in the book) –How to implement Java classes for binary trees –Class for node, another class for BinTree –Interface for both, then two implementations (array and reference) But for us: –Well skip some of this, particularly the array version –Well only look at reference-base implementation –After that: concept of a binary search tree

24 Understanding Implementations Lets review some of the methods on pp –(Done in class, looking at code in book.) Some topics discussed: –Node class. Parent reference or not? –Are two trees equal? –Traversal strategies: Section in book –visit() method and callback (also 7.3.2) 24

25 25 Binary Search Trees We often need collections that store items –Maybe a long series of inserts or deletions We want fast lookup, and often we want to access in sorted order –Lists: O(n) lookup –Could sort them for O(lg n) lookup Cost to sort is O(n lg n) and we might need to re-sort often as we insert, remove items Solution: search tree

26 26 Binary Search Trees Associated with each node is a key value that can be compared. Binary search tree property: –every node in the left subtree has key whose value is less than the value of the roots key value, and –every node in the right subtree has key whose value is greater than the value of the roots key value.

27 27 Example BINARY SEARCH TREE

28 28 Counterexample NOT A BINARY SEARCH TREE 7 15

29 29 Find and Insert in BST Find: look for where it should be If not there, thats where you insert

30 30 Recursion and Tree Operations Recursive code for tree operations is simple, natural, elegant Example: pseudo-code for Node.find() boolean find(Comparable tgt) { Node next = null; if (this.data matches tgt) return true else if (tgts data this.data next = this.rightChild // next points to left or right subtree if (next == null ) return false // no subtree else return next.find(tgt) // search on }

31 Order in BSTs How could we traverse a BST so that the nodes are visited in sorted order? –Does one of our traversal strategies work? A very useful property about BSTs Consider Javas TreeSet and TreeMap –A search tree (not a BST, but be one of its better cousins) In CS2150: AVL trees, Red-Black trees –Guarantee: search times are O(lg n) 31

32 Deleting from a BST Removing a node requires –Moving its left and right subtrees –Not hard if one not there –But if both there? Answer: not too tough, but wait for CS2150 to see! In CS2110, well not worry about this 32

33 Next: Grammars, Trees, Recursion Languages are governed by a set of rules called a grammar –Is a statement legal ? –Generate or derive a new legal statement Natural language grammars –Language processing by computers But, grammars used a lot in computing –Grammar for a programming language –Grammar for valid inputs, messages, data, etc. 33

34 34 Backus-Naur Form BNF is a widely-used notation for describing the grammar or formal syntax of programming languages or data BNF specifics a grammar as a set of derivation rules of this form: ::= Look at website and example there (also on next slide) –How are trees involved here? Is it recursive?

35 35 BNF for Postal Address 1. ::= 2. ::= | "." 3. ::= [ ] | 4. ::= [ ] 5. ::= "," Example:Ann Marie G. Jones 123 Main St. Hooville, VA Wheres the recursion?

36 36 Grammars in Language Rule-based grammars describe –how legal statements can be produced –how to tell if a statement is legal Study textbook, pp , to see rule- based grammar for simple Java-like arithmetic expressions –four rules for expressions, terms, factors, and letter –Study how a (possibly) legal statement is parsed to generate a parse tree

37 37 Computing Parse-Tree Example Expression: a * b + c

38 38 Grammar Terms and Concepts First, this is whats called a context-free grammar –For CS2110, lets not worry about what this means! (But in CS2102, you learn this.) A CFG has –a set of variables (AKA non-terminals) –a set of terminal symbols –a set of productions –a starting symbol

39 39 Previous Parse Tree Terminal symbols: – could be:+ * – could be:a b c Production: |

40 40 Natural Language Parse Tree Statement: The man bit the dog

41 41 How Can We Use Grammars? Parsing –Is a given statement a valid statement in the language? (Is the statement recognized by the grammar?) –Note this is what the Java compiler does as a first step toward creating an executable form of your program. (Find errors, or build executable.) Production –Generate a legal statement for this grammar –Demo: generate random statements! See link on website next to slides

42 42 Demos Poem-grammar data file { The tonight } { waves big yellow flowers slugs } { sigh portend like die } { warily grumpily } Note: no recursive productions in this example!


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