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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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Table of Contents (14) Chapters Close

Preface 1. Why Functional Programming? FREE CHAPTER 2. Building Blocks 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

Implementing a priority queue


For our queue implementation, the following invariant holds. If the out list is empty, the in list has to be empty (that is, the entire queue is empty). During the pivot step, the invariant is temporarily violated and then restored:

scala> case class Fifo(out: List[Int], in: List[Int]) { 
     | 
     |   def check(): Boolean = (out, in) match { 
     |     case (Nil, x :: xs) => false 
     |     case _ => true 
     |   } 
     |   require(check, "Invariant Failed - out.em") 
     | } 
defined class Fifo 

Note the use of the require method to make sure the invariant always holds. We use a method called check; this method, in turn, uses pattern matching to check both the lists.

Here's the first clause:

case (Nil, x :: xs) => false 

This clause matches when the out list is empty and the in list is non-empty, that is, it has at least one element. We know this violates our invariant and the object construction...

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