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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

Streams meet queues

Going back to our functional FIFO queue implementation, do you remember we had to pay the price of occasional reversal? The invariant asserted that the out list would never be empty when the in list is non-empty.

In case the invariant is violated, we can restore it by reversing the in list and turning it into a new out list. We could exploit this laziness to address the once in a while (and possibly costly) reversal. The idea is to make the out list a Stream. The in list remains a strict list, as before. This helps us do the reversal on demand:

    case class LazyQueue(out: Stream[Int], outLen: Int, in: List[Int], inLen: Int) { 
 
    def push(elem: Int) = { 
      val p = makeLazyQueue(out, outLen, elem :: in, inLen + 1) 
      println(s"pushed: ${elem} - ${p}") 
      p 
    } 
 
    def pop: (Int, LazyQueue) = { 
      val q = (out.head, makeLazyQueue(out.tail, outLen - 1, in, inLen)) 
      println(s"popped: ${q._1} and ${q._2}") 
      q ...
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