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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? 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

A stream is generally a lazy and linear sequence collection. Stream elements are evaluated only when needed. Streams are already defined in Scala and Clojure. Since elements of a stream are evaluated lazily, it can be of infinite length. In this section, we will explore streams in Scala and Clojure. Let me start with stream in Scala. List, which we are going to explore in the coming chapter, differs from stream only in lazy computation. Other properties of list are similar to stream.

Stream in Scala

Stream is a class defined in Scala. Stream constructor can be used to create Stream object. Stream elements are evaluated when they are required. Stream-calculated values are cached so that they can be used further. Let us explorer the characteristics of streams in Scala:

scala> import scala.Stream 
 
import scala.Stream   
 
scala> var ms = Stream("a","b","c","d","e") 
 
ms: scala.collection.immutable.Stream[String] = Stream(a, ?...
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