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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

Inserting a node

When we insert a node, we color it red. This means that the newly inserted node will never change the black height of any ancestor!

However, it could violate another invariant, namely a red node can never have a red parent. Once we fix this invariant violation, we are done! Here's the code to do this:

sealed trait Color 
case object Red extends Color 
case object Black extends Color 

We know the advantages of sealing a trait. The Color trait can now be used only in this source file. This helps the compiler check for exhaustive matching. As seen earlier, the List and BinTree traits were also sealed.

The case object idiom creates singleton objects, just like List.Nil and Bintree.Leaf.

Next, we define our Tree class:

sealed abstract class Tree { 
  def color: Color 
} 

We create two concrete instances of the Tree class. The internal nodes are modeled by the case class, that is, Node:

case class Node(color: Color, left: Tree, value: Int, right: Tree) extends Tree { 
  override...
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