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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

Amortized deques


Coming back to deques, we replace the list in the queue with a stream so both in and out become Streams objects. If we try to keep both the streams balanced, we would have an efficient deque implementation.

In this case, by balance, we mean both the streams will have almost the same number of elements. For example, both the streams would be non-empty when the deque contains two or more elements.

Let's say no stream is bigger than the other by a factor, say, c > 1. If one stream becomes too long, we move the elements to the other.

Let's look at stream operations a bit more so we could understand the code that follows:

scala> val s = 1 #:: 2 #:: 3 #:: 4 #:: Stream.empty 
s: scala.collection.immutable.Stream[Int] = Stream(1, ?) 

We define s as a stream:

scala> val p = s.drop(2) 
p: scala.collection.immutable.Stream[Int] = Stream(3, ?) 
scala> p foreach println 
3 
4 

Calling the drop(n) method gives us another stream with the n elements in front removed:

scala> val...
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