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CSC 212 – Data Structures.  Fri., Dec. 17 th from 8AM – 10AM in OM 200  Plan on exam taking full 2 hours  If major problem, come talk to me ASAP 

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Presentation on theme: "CSC 212 – Data Structures.  Fri., Dec. 17 th from 8AM – 10AM in OM 200  Plan on exam taking full 2 hours  If major problem, come talk to me ASAP "— Presentation transcript:

1 CSC 212 – Data Structures

2  Fri., Dec. 17 th from 8AM – 10AM in OM 200  Plan on exam taking full 2 hours  If major problem, come talk to me ASAP  Exam covers material from entire semester  Open-book & open-note so bring what you’ve got  My handouts, solutions, & computers are not allowed  Cannot collaborate with a neighbor on the exam  Problems will be in a similar style to 2 midterms Final Exam

3 Inheritance  implements extends  implements & extends used for relationships  Both imply there exists an IS - A relationship public class Student extends Person {…} public class Cat extends Mammal { … } public class AQ implements Queue {…}

4  All Java classes extend exactly 1 other class  All fields & methods inherited from the superclass  Within subclass, can access non-private members  Private methods inherited, but cannot be accessed  Classes can implement any number of interfaces  Must implement methods from the interface Inheritance

5  Subclass can override/overload inherited methods  Instance’s  Instance’s type determines which method is called  Parameter list stays the same to override the method  Overload method by modifying parameter list  Field in superclass hidden by redeclaring in subclass  2 fields with the same name now in subclass variable’s  Use the field for variable’s type Overriding & Hiding

6 Exceptions in Java  throw  throw an exception when an error detected  Exception s are objects - need an instance to throw  trycatch  try executing code & catch errors to handle  trycatch  try only when you will catch 1 or more exceptions catch  Do not need to catch every exception  If it is never caught, program will crash  Not a bad thing – had an unfixable error! throws  Exceptions listed in methods’ throws clause  Uncaught exception only need to be listed  Should list even if thrown by another method

7 Abstract Methods abstract  Methods declared abstract cannot have body  IOU for subclasses which will eventually define it  abstract abstract  abstract methods only in abstract classes abstract  Cannot instantiate an abstract class  But could still have fields & (non-abstract) methods  abstract  abstract methods declared by interfaces  Interfaces cannot declare fields  public abstract  public abstract methods only in interfaces

8  Concrete implementations used to hold data  Not ADTs  Arrays are easier to use & provide quicker access  Also are impossible to grow  Implementing ADTs harder due to lack of flexibility  Slower access & more complex to use linked lists  Implementing ADTs easier with increased flexibility  Can be singly, doubly, or circularly linked Arrays vs. Linked Lists

9 Stack vs. Queue Order read if Queue Order read if Stack

10 QueueStackDeque Simplest ADTs

11 D EQUE Q UEUE S TACK addFront() addLast() enqueue()push() getFront() getLast() front()top() removeFront() removeLast() dequeue()pop() ADT Operations

12 import java.util.Iterator; import java.lang.Iterable; public interface Iterator { E next() throws NoSuchElementException; boolean hasNext(); void remove() throws UnsupportedOperationException; } public interface Iterable { Iterator iterator(); } Iterators & Iterables

13  Abstract work in processing with Iterator Iterable myList; Iterator it;... for (it = myList.iterator(); it.hasNext(); ) { Integer i = it.next();... }  Process Iterable objects in an even easier way... for (Integer i : myList) {... } More Iterator & Iterable

14  Collection which we can access all elements  Add element before an existing one  Return the 3 rd element in List  Loop over all elements without removing them  L IST ADTs differ in how they provide access  I NDEX L IST uses indices for absolution positioning  Can only use relative positions in N ODE L IST  All L ISTS are I TERABLE IndexList & NodeList

15 Sequence ADT  Combines D EQUE, I NDEX L IST, & P OSITION L IST  Includes all methods defined by these interfaces  Adds 2 methods to convert between systems  Get Position at index using atIndex(i)  indexOf(pos) returns index of a Position

16 Sequence ADT  Combines D EQUE, I NDEX L IST, & P OSITION L IST  Includes all methods defined by these interfaces  Adds 2 methods to convert between systems  Get Position at index using atIndex(i)  indexOf(pos) returns index of a Position

17 Trees vs. Binary Trees  Both represent parent-child relationships  Both consist of single "root" node & its descendants  Nodes can have at most one parent  Root nodes are orphans -- do not have a parent must  All others, the non-root nodes must have parent  Children not required for any node in the tree  No limit to number of children for non-binary trees  2 children for node in binary tree is the maximum

18 Traversal Methods  Many traversals, differ in order nodes visited  Do parent then do each kid in pre-order traversal

19 Traversal Methods  Many traversals, differ in order nodes visited  Do parent then do each kid in pre-order traversal  Post-order traversal does kids before doing parents

20 Traversal Methods  Many traversals, differ in order nodes visited  Do parent then do each kid in pre-order traversal  Post-order traversal does kids before doing parents  Do left kid, parent, then right kid in in-order traversal

21 Tree D  Visualization of Tree B D A CE F B AF CE Tree root size  6

22 BinaryTree   Picturing Linked BinaryTree B C A D   BACD BinaryTree root size  4

23 Priority Queue ADT  Priority queue uses strict ordering of data  Values assigned priority when added to the queue completely biased order  Priorities used to process in completely biased order First you get the sugar, then you get the power, then you get the women

24 Priority Queue ADT  PriorityQueue yet another Collection  Prioritize each datum contained in the collection  PQ is organized from lowest to highest priority  Access smallest priority only sort of like Queue  min() & removeMin() return priority & value  Implementation not defined: this is still an ADT  Remember that organization & order is theoretical only

25  PriorityQueue yet another Collection  Prioritize each datum contained in the collection  PQ is organized from lowest to highest priority  Access smallest priority only sort of like Queue  min() & removeMin() return priority & value  Implementation not defined: this is still an ADT  Remember that organization & order is theoretical only Priority Queue ADT order is theoretical only

26 Entry s in a PriorityQueue  PriorityQueues use Entry to hold data  As with Position, implementations may differ  Entry has 2 items that define how it gets used  PQ will only use key – the priority given to the Entry  Value is important data to be processed by program

27 Sequence -based Priority Queue

28 Heaps  Binary-tree based PQ implementation  Still structured using parent-child relationship  At most 2 children & 1 parent for each node in tree  Heaps must also satisfy 2 additional properties  Parent at least as important as its children  Structure must form a complete binary tree

29 Hints for Studying  Will NOT require memorizing:  ADT’s methods  Node implementations  Big-Oh time proofs  (Memorizing anything)

30 (& be ready to look up):  You should know (& be ready to look up):  How ADT implementations work (tracing & more)  For each method what it does & what it returns  Where & why each ADT would be used  For each ADT implementations, its pros & cons  How to compute big-Oh time complexity Hints for Studying

31 1. What does the ADT do? Where in the real-world is this found? 2. How is the ADT used? What are the applications of this ADT? How is it used and why? 3. How do we implement the ADT? Given the implementation, why do we do it like that? What tradeoffs does this implementation make? Studying For the Exam

32 “Subtle” Hint

33 Final Exam Schedule  Lab Mastery Exam is: Tues., Dec. 14 th from 2:45PM – 3:45PM in OM 119  Final Exam is: Fri., Dec. 17 th from 8AM – 10AM in OM 200


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