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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

Node definitions


Similar to lists, our binary tree is a trait, BinTree[+A]:

sealed trait BinTree[+A] 
case object Leaf extends BinTree[Nothing] 
case class Branch[A](value: A, left: BinTree[A], right: BinTree[A]) extends BinTree[A] 

The sealed trait BinTree[+A] array defines a sealed trait. As it is sealed, we can extend it only in the same source file. We saw in Chapter 3, Lists how this helps the compiler to check for exhaustive pattern matching:

case object Leaf extends BinTree[Nothing] 

The Leaf node is a terminator node, just like we have the Nil node in lists. Just like Nil, Leaf is a case object, as we just need only one instance of it:

case class Branch[A](value: A, left: BinTree[A], right: BinTree[A]) extends BinTree[A] 

The Branch node holds a value, of type A, and a left and right subtree. These subtrees could be either branches or leaves.

Thus, we define the binary tree in terms of itself; in other words, it is a recursively defined structure, similar to List:

Note

Note that this...

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