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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? 2. Building Blocks FREE CHAPTER 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 prepend


Note that lists are great when we insert a node at the beginning, in other words, prepend a value to the head of a list. Let's see how that works.

We have the list with values 17, 199, and 337. We prepend the value 37 first. Next, we prepend 99 to the resulting list. Finally, we prepend 12 to the new resulting list again.

When we prepend the value 37, we just allocate the node; while constructing this node, we append the original list to 37. In other words, 37 becomes the head and the original list becomes the tail.

Note that there is no copying needed at all. We just allocate the new node and append the existing list to it. As this does not affect the persistence of the already existing data structure, we have a very efficient prepend operation.

The version V0 is, as before, list b. V1 gets created when 37 is prepended to the list, V2 when 99 is prepended, and V3 when 12 is prepended. Thus, the complexity of prepend is O(1).

Here is the prepend method:

scala>   def prepend[A...
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