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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

The accumulator idiom


These methods are easy to understand. However, for unbalanced trees, concatenating long lists could be slow, very slow.

We could eliminate list concatenation by sending the result list as an accumulator argument:

scala>   def preorderAcc[A](tree: BinTree[A], acc: List[A]): List[A] = tree match { 
     |     case Leaf => acc 
     |     case Branch(v, l, r) => v :: preorderAcc(l, preorderAcc(r, acc)) 
     |   } 
scala> println(preorderAcc(t, Nil)) 
List(1, 2, 5, 9) 

The method now takes an additional argument: acc. We always set it to Nil when we call the method.

As in preorder, we have two cases.

The first clause is as follows:

case Leaf => acc 

This just returns the already accumulated values, if any.

The second clause is as follows:

case Branch(v, l, r) => v :: preorderAcc(l, preorderAcc(r, acc)) 

This prepends the value v to the result of calling preorder on both the subtrees:

A nice way to trace out how the accumulator grows is to add a judicious print...

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