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HEAPS

HEAPS. • Heaps • Properties of Heaps • HeapSort • Bottom-Up Heap Construction • Locators. Heaps. • A Heap is a Binary Tree H that stores a collection of keys at its internal nodes and that satisfies two additional properties: -1) Relational Property

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HEAPS

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  1. HEAPS • Heaps • Properties of Heaps • HeapSort • Bottom-Up Heap Construction • Locators

  2. Heaps •A Heap is a Binary Tree H that stores a collection of keys at its internal nodes and that satisfies two additional properties: -1)Relational Property -2)Structural Property

  3. Heap Properties • Heap-Order Property (Relational): In a heap H, for every node v (except the root), the key stored in v is greater than or equal to the key stored in v’s parent. • A structure fulfilling the Heap-Order Property • A structure which fails to fulfill the Heap-Order Property

  4. Heap Properties • Complete Binary Tree (Structural): A Binary Tree T is complete if each level but the last is full, and, in the last level, all of the internal nodes are to the left of the external nodes. • A structure fulfilling the Structural Property • A structure which fails to fulfill the Structural Property

  5. Heaps (contd.)

  6. Height of a Heap • Proposition: A heap H storing n keys has height h = [ log(n+1)ù ( ceiling of n+1 ) • Justification: Due to H being complete, we know: -# i of internal nodes is at least : 1 + 2 + 4 + ... 2 h-2 + 1 = 2 h-1 -1 + 1 = 2 h-1 -# i of internal nodes is at most: 1 + 2 + 4 + ... 2 h-1 = 2 h - 1 - Therefore: 2 h-1£ n and n £ 2 h - 1 - Which implies that: log(n + 1) £ h £ log n + 1 - Which in turn implies: h = | log(n+1)ù - Q.E.D.

  7. Height of a Heap (contd.) • Let’s look at that graphically: • Consider this heap which has height h = 4 and n =13 • Suppose two more nodes are added. To maintain completeness of the tree, the two external nodes in level 4 will become internal nodes: i.e. n = 15, h = 4 = log (15+1) • Add one more: n = 16, h = 5= ceiling {log(16+1)}

  8. Heap Insertion So here we go ... The key to insert is “6”

  9. Heap Insertion Add the key in the next available spot in the heap.

  10. Upheap • Upheap checks if the new node is smaller than its parent. If so, it switches the two • Upheap continues up the tree

  11. Removal From a Heap:RemoveMinElement() • The removal of the top key leaves a hole • We need to fix the heap • First, replace the hole with the last key in the heap • Then, begin Downheap

  12. Downheap Downheap compares the parent with the smallest child. If the child is smaller, it switches the two.

  13. Downheap Continues

  14. Downheap Continues

  15. End of Downheap • Downheap terminates when the key is greater than the keys of both its children or the bottom of the heap is reached. • (total #switches) < (height of tree - 1) =log N

  16. Implementation of a Heap • public class HeapSimplePriorityQueue implementsSimplePriorityQueue {BinaryTree T;Position last;Comparator comparator;... }

  17. Implementation of a Heap(cont.) • Two ways to find the insertion position z in a heap:

  18. Heap Sort • All heap methods run in logarithmic time or better • If we implement PriorityQueueSort using a heap for our priority queue, insertItem and removeMinElement each take O(logk), k being the number of elements in the heap at a given time. • We always have n or less elements in the heap, so the worst case time complexity of these methods is O(logn). • Thus each phase takes O(nlogn) time, so the algorithm runs in O(nlogn) time also. • This sort is known as heap-sort. • The O(nlogn) run time of heap-sort is much better than the O(n2 ) run time of selection and insertion sort.

  19. Bottom-Up Heap Construction • If all the keys to be stored are given in advance we can build a heap bottom-up in O(n) time. • Note: for simplicity, we describe bottom-up heap construction for the case for n keys where: n = 2 h –1 h being the height. • Steps: 1) Construct (n+1)/2 elementary heaps with one key each. 2) Construct (n+1)/4 heaps, each with 3 keys, by joining pairs of elementary heaps and adding a new key as the root. The new key may be swapped with a child in order to perserve heap- order property. 3) Construct (n+1)/8 heaps, each with 7 keys, by joining pairs of 3-key heaps and adding a new key. Again swaps may occur. ... 4) In the ith step, 2< i <h, we form (n+1)/2 iheaps, each storing 2 i -1 keys, by joining pairs of heaps storing (2 i-1 -1) keys. Swaps may occur.

  20. Bottom-Up Heap Construction(cont.)

  21. Bottom-Up Heap Construction(cont.)

  22. Bottom-Up Heap Construction(cont.)

  23. Bottom-Up Heap Construction(cont.)

  24. Analysis of Bottom-Up HeapConstruction • Proposition: Bottom-up heap construction with n keys takes O(n) time. - Insert (n + 1)/2 nodes - Insert (n + 1)/4 nodes - Upheap at most (n + 1)/4 nodes 1 level. - Insert (n + 1)/8 nodes - ... - Insert 1 node. - Upheap at most 1 node 1 level.

  25. Locators • Locators can be used to keep track of elements as they are moved around inside a positional container. • A Locator sticks with a specific element, even if that element changes positions in the container. • The Locator ADT supports the following fundamental methods: element(): Return the element of the item associated with the Locator. key(): Return the key of the item associated with the Locator. isContained(): Return true if and only if the Locator is associated with a container. container(): Return the container associated with the Locator.

  26. Locators and Positions • At this point, you may be wondering what the difference is between locators and positions, and why we need to distinguish between them. • It’s true that they have very similar methods • The difference is in their primary usage • Positions are used primarily to represent the internal form of a data structure • Locators are used primarily to keep track of data, regardless of what position it’s in (and even if it’s not in any position right now)

  27. Locators and Positions: Example • For example, consider the cs16 Valet Parking Service (started by the teaching assistants because they had too much free time on their hands). • When they began their business, Mike and Ryan decided to create a data structure to keep track of where exactly the cars were. • Ryan suggested having Positions represent what parking space (or part of the green) the car was in. • However, Mike realized that the car-owners coming into the CIT to reclaim their car wouldn’t remember what space the TAs had placed their car in. And the TA’s might want to move cars from space to space. • So he suggested using the UTAs as Locators. Each UTA would be able to get a car upon demand (when element() was called on them) and would know to come running when the car’s owner came by. (the owner would be their key) • And it was done, and the staff were able to dispense with the last remainders of their free time.

  28. Priority Queue with Locators • It is easy to extend the sequence-based and heap-based implementations of a Priority Queue to support Locators. • A Locator holds the key, element pair. • The Priority Queue ADT can be extended to implement the Locator ADT • A Locator in this implementation must have a instance variable pointing to its Position. • All of the methods of the Locator ADT can then be implemented in O(1) time.

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