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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

List of tree roots


Time to change gears; we know that binary trees' lookup and update operations are fast. Fast lookup is the result of storing actual data elements in binary trees, that is, all the roots linked up in a list.

Here are our tree definitions:

scala> sealed abstract class Tree { 
     |   def size: Int 
     | } 

Note the use of the sealed keyword. It is sealed, which means the definition could be used in this file only. We have an abstract method to know how many data elements (leaves) exist in this tree:

scala> case class Leaf(n: Int) extends Tree { 
     |   override def size = 1 
     | } 

The Leaf class actually holds the data items. As the name indicates, it is a leaf and there are no children under a leaf:

scala> case object Zero extends Tree { 
     |   override def size = 0 
     | } 

The Zero is a singleton object; it corresponds to the binary 0. We will see it in action pretty soon:

scala> case class One(t: Tree...
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