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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

Strict versus lazy


Consider the operation of zipping up two lists. The zip method pairs elements from the first list with the elements from the second list. Here is a sample run:

scala> List(1,2,3).zip(List(4,5,6,7)) 
res8: List[(Int, Int)] = List((1,4), (2,5), (3,6)) 
 
scala> List(1,2,3).zip(Nil) 
res9: List[(Int, Nothing)] = List() 

All the elements of both the lists are visited to create a zipped list. The following figure shows the zip operation in action:

As another example of strict evaluation, consider the reverse method of List:

scala> List(1,2,3).reverse 
res11: List[Int] = List(3, 2, 1) 

The reverse method also visits all the elements of the list. On the other hand, consider the following:

scala> val q = List.range(1, 1000000).view.reverse 
q: scala.collection.SeqView[Int,List[Int]] = SeqViewR(...) 
 
scala> (q take 10) foreach println 
999999 
999998 
999997 
... 

Only the first 10 elements of the lazy lists are computed on demand.

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