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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? 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

Streams


Scala's streams are lazy lists, an infinite sequence of elements. For example, just like we zip two lists together, we could zip two streams too:

scala> def zip(p: Stream[Int], q: Stream[Int]): Stream[(Int, Int)] = 
     | (p.head, q.head) #:: zip(p.tail, q.tail) 
zip: (p: Stream[Int], q: Stream[Int])Stream[(Int, Int)] 
 
scala> val r = zip(Stream.from(9), Stream.from(10)) 
r: Stream[(Int, Int)] = Stream((9,10), ?) 

As you can see, the type of r variable is another stream. Also, the stream has a head element, a pair (9,10), and a function (also called a thunk). The function (in this case, zip) is called to produce successive elements.

As you can see, the tail is a question, meaning it is not yet computed; therefore, it is not yet known.

Why are streams called lazy? Note the definition of zip, which is defined recursively. There is no condition related to termination; the calls just recur. A recursive call is not evaluated right away but only when it is needed.

The idea is to force...

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