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Learning Functional Data Structures and Algorithms

You're reading from   Learning Functional Data Structures and Algorithms Learn functional data structures and algorithms for your applications and bring their benefits to your work now

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781785888731
Length 318 pages
Edition 1st Edition
Languages
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Authors (2):
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Raju Kumar Mishra Raju Kumar Mishra
Author Profile Icon Raju Kumar Mishra
Raju Kumar Mishra
Atul S. Khot Atul S. Khot
Author Profile Icon Atul S. Khot
Atul S. Khot
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Toc

Table of Contents (14) Chapters Close

Preface 1. Why Functional Programming? 2. Building Blocks FREE CHAPTER 3. Lists 4. Binary Trees 5. More List Algorithms 6. Graph Algorithms 7. Random Access Lists 8. Queues 9. Streams, Laziness, and Algorithms 10. Being Lazy - Queues and Deques 11. Red-Black Trees 12. Binomial Heaps 13. Sorting

Binary number equivalence

There is a surprising equivalent in the processes of binomial heap insertion and incrementing a binary number. For example, the following figure shows the addition of 1 to a number, say 6, and the equivalent tree insertion into a binomial heap:

Binary number equivalence

The binary addition happens from right to left, whereas binomial insertion happens from left to right. Also, the link up triggers changes to the heap similar to the way the carry triggers further changes to the number:

Binary number equivalence

Merging

The insert method is a simplified case of the merge operation. We will discuss the case clauses one by one. This will help us see how the concepts detailed before fit together. Here are the first two cases:

  def merge(ts1: List[Node], ts2: List[Node]): List[Node] = (ts1, ts2) match { 
    case (ts1, Nil) => ts1 
    case (Nil, ts2) => ts2 
    case (t1 :: ts11, t2 :: ts22) if (t1.rank < t2.rank) => t1 :: merge(ts11, ts2) 
    case (t1 :: ts11, t2 :: ts22) if (t2.rank < t1.rank) =&gt...
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