1 Heaps and Priority Queues v2 Starring: Min Heap Co-Starring: Max Heap

2 Purpose: In this lecture we will discuss the Priority Queue ADT, the Java PriorityQueue Class and a Heap implementation of a priority Queue

3 Resources: Java Methods AB Data Structures Chapter 7 p.175 Java Essentials Study Guide Chapter 17.7/8 p.330 & Chapter 20.1 p.398 & Chapter 20A.4/5 p.418 Barrons Chapter 9 p.305 & Chapter 12 p.414 AP Java Text Chapter 19.7 p.843 & Chapter 21.4 p.950

4 Handouts: 1.Priority Queue Class CODE: PQ.java PriorityString.java PriorityQueueExample.java Jobs.java

5 Intro: So far we have seen data structures that maintain and process data in the following ways: Store data in order based on a Key Store data in order based on the “value” of the object Store data in order of arrival Store data in reverse order of its arrival Store data in random order

6 There are times when we need to maintain and process data based on their relative importance or Priority

7 For example, people waiting to but concert tickets. The priority of the order they are allowed to purchase tickets is based on the number on a wrist band that are distributed. The number on the wristband determines when they get served

8 If you go to an amusement park, you can select a predetermined time to get on an attraction. When the time comes, you get into a Priority Queue which gets processed first

9 In a hospital emergency room, a triage system identifies the urgent patients all of whom are seem before the normal patients regardless of their order of arrival

10 In operating systems, jobs are processed based on a specific priority so that a recently submitted job with a high priority will process before lower priority jobs even though these jobs were in the queue longer

11 What we Will Cover in This Lecture: Priority Queue ADT Priority Queue Java Class Priority Queue Implementations Heap Implementation The AP AB Requirements

12 Priority Queue ADT PQ’s Support functionality that maintains a large set of elements that are ranked in some way and to have quick access to the HIGHEST ranked element

13 Priority Queue ADT PQ is a collection of the same type object values These object values contain data and a priority These objects are ordered in a way that allows the HIGHEST priority item to be removed first

14 Example of a priority queue where items entered have a priority of 1,2 or 3: EMPTY PQ (ADD) A (2) B (2) A (2) C (2) B (2) A (2) (REMOVE MIN) C (2) B(2) (ADD) D (3) C (2) B(2) (REMOVE MIN) D (3) C (2) (ADD) E (1) D (3) C (2) F (1) E (1) D (3) C (2) (REMOVE MIN) F (1) D (3) C (2) D (3) C (2) D (3) EMPTY PQ

15 If several items maintain the same priority then the first one in the queue is processed first

16 Priority Queues Must Provide for the adding and removing of elements in a way that ensures the HIGHEST Priority Item is removed First Here is the Priority Queue Java Class:

17 public Class PriorityQueue { boolean isEmpty() boolean add(E x) E remove () E peek () Iterator E iterator() }

18 import java.util.PriorityQueue; SPVM PriorityQueue P = new PriorityQueue (10); P.add("Sally"); P.add("Betty"); P.add("Xavier"); P.add("Teresa"); P.add("David");

19 if(P.isEmpty()) { System.out.println("QUEUE EMPTY"); } else { System.out.println(P.size()); } P.remove(); System.out.println(P.peek());

20 Iterator i = P.iterator(); i = P.iterator(); while(i.hasNext()) { System.out.println(i.next()); } System.out.println(); for(String x:P) { System.out.println(x.toString()); }

21 The priority of each item in the queue is established using the Comparable interface Every time the remove or peek method is executed the SMALLEST element with respect to the compareTo method is returned

22 Other Priority Queue Implementations You are required to know Java’s PQ implementation as well as to understand their general principles that require any Data Structure to allow for : Quick insertion of PQ elements Quick retrieval of an element with the top priority Potential PQ implementations include:

23 Linked List where new nodes are inserted at the front of the list O(1) and the removal searches the list for the highest priority node O(n)

24 Linked List where nodes are inserted in priority order with the smallest elements in the front nodes O(n) and removal simply unlinks the front node O(1)

25 Array with nodes inserted at the end of the List O(1) and removals search for the highest priority element O(n)

26 A sorted Array where the highest priority elements (smallest values) are added at the end of the List O(n) and removal takes off he last element in the array O(1)

27 TreeSet where adding an element is O(Log N) and Removal is O(Log N)

28 Heap Implementation The most common implementation of a PQ is a Binary Heap With a PQ we use a Minimum Heap With these type of heaps the VALUE of every node is LESS THAN or EQUAL to that of its children

29 A HEAP is viewed as a Complete Binary Tree where there are no GAPS at any level. The Last level may have some leaves missing on the right, but every node except the last must have a node after it. The nodes are in the leftmost positions

30 Each addition to the heap MUST maintain the property where the parent node is at least as small as its children

31 Example:

32 The lower the number, the higher the priority The element with the HIGHEST priority is kept at the root so removing an element requires removal of the root and then restructuring of the heap

33 ReHeaping is a O(LogN) operation Inserting into the heap also requires Reheaping and is O(LogN) ALL elements added to a PQ must implement the Comparable interface and be of the same class

34 Inserting and Removing elements to the Heap

35 create a heap:

36 create a heap:

37 create a heap:

38 Create a heap:

39 Create a heap:

40 create heap:

41 create heap:

42 create heap:

43 Given the following Tree:

44 Match this heap with an indices of an Array: Heap PriorityArray Element (IGNORE ZERO)

45 Array Index:

46 Lets Insert an element with a priority of 5 We insert the new element in the next open slot ( index 10 or left child of element with Priority of 7)

47 We then Compare This element against its Parent

48 If the Parent is Greater, Then SWAP the elements

49 We AGAIN Compare This element against its Parent

50 If the Parent is Greater, Then SWAP the elements

51 Again We Compare This Element against its New Parent But since the Parent is SMALLER than the this element, we are done RE-HEAPING The order of the Heap is maintained as all children are LARGER than their parent

52 The Heap has been updated and its order of priority maintained

53 We can implement a Heap as a Binary Tree but it is more efficient to implement a Binary Heap as an Array Using your own array or the ArrayList

54 Non-Linked Representation of Binary Trees is a more efficient way of implementing heaps. Here all nodes are stored in an array in a certain order so that for each node it is easy to find its children and its parent

55 EXAMPLE: Given the previous PQ, the following is the construction of the array at each stage of insertion:

57 If you match the element number, starting at element 1, with the Binary Heap, you will see an exact correlation

58 Treeelement #s 2 1 / \ / \ / \ / \ / \ / \

59 Each level contains twice as many nodes as the preceding level.

60 The left and right children of the i-th node, if they are present, have the numbers 2i and 2i+1 and its parent has the number i/2 (truncated to an integer)

61 Using this algorithm, we can manipulate an array as a Binary HEAP

62 REMEMBER THAT A COMPLETE TREE is a tree where there are no GAPS at any level. The Last level may have some leaves missing on the right, but every node except the last must have a node after it. The nodes are in the leftmost positions

63 A Complete Tree

64 Binary Trees: a non-linked representation:

65 This property allows us to store a complete tree in an array where the element x[n] corresponds to node number n Count the elements of the array starting from element 1 (leave element 0 unused)

66 Heap remove deletes an element from the root of the heap Then the heap needs to be adjusted to preserve its ordering property and to keep it a complete tree. Remove the root (first element in the heap)

67 Place the last element of the heap into its root (first element). This is the RIGHTMOST node in the lowest tree level Then move down from level to level, swapping it with its smaller child until it falls into place

68 This process is called the REHEAP DOWN procedure

69 Using our previous example, execute a remove

70 We take the last element in the heap (10th index / value #7) and move it to the top of the heap Then swap this element with its smaller child Swap #7 with #4 Continue this until this node is smaller than its children

71 The heap now looks like this:

72 We can also have a Maximum Heap where all elements are Stored with the HIGHEST value at the top of the heap Here, all parents have a HIGHER value than their children

73 Here element x[1] of the array corresponds to the root of the heap. Example: queued items

74 TREEVIEW:

75 ARRAYVIEW:Element #Value 0unused

76 Priority Queues maintain data as well as a priority You can create a class that serves as the “element” that is stored in the Heap

77 In the PriorityString class there is a data part and an Integer that represents the objects priority The SMALLER the number the HIGHER the priority

78 Lets look at the PriorityString.java class in the handout

79 The ArrayPriorityQueue.java class implements the Priority Queue as an ArrayList

80 Lets look at the ArrayPriorityQueue.java class in the handout

81 Finally, to put it all together we use our Array Priority Queue in a Driver Program Lets look at PriorityQueueExample.java in our handout

82 RUN the Priority QueuesAndExamples.java RUN and Review the program JOBS.JAVA

83 AP AB Subset Requirements Students are expected to: Understand the PQ ADT & Use Java’s PQ How the PriorityQueue can be implemented (insert, remove, peek, reheap) in other ways than the Java Class Create a Class that is used as the element of a PQ Utilize a PQ implementation in a Driver program

84 LABS:Write a Heap Class Social Security Administration Multiple Choice from Barrons: Chapter 9 p.317 #s 14 to 18