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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

Drop elements


We are chugging along nicely. Let's look at element removal:

Here comes the method definition:

scala> :paste 
// Entering paste mode (ctrl-D to finish) 
 
  def drop[A](l: List[A], n: Int): List[A] = 
    if (n <= 0) l 
    else l match { 
      case Nil => Nil 
      case _ :: t => drop(t, n - 1) 
    } 

We drop n (or less than n) elements starting at the beginning of the list. If there are less than n elements, we return a Nil (empty) list.

What is the complexity of the drop method? It is O(n), as we might end up traversing the entire list.

The dropWhile method takes a list and predicate function. It invokes the function on successive elements and drops them if it evaluates to true. It stops when the function evaluates to false:

scala> def dropWhile[A](l: List[A], f: A => Boolean): List[A] = l match { 
     |     case x :: xs if f(x) => dropWhile(xs, f) 
     |     case _ => l 
     |   } 
...
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