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Data Structures and Algorithms with the C++ STL

You're reading from   Data Structures and Algorithms with the C++ STL A guide for modern C++ practitioners

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781835468555
Length 458 pages
Edition 1st Edition
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John Farrier John Farrier
Author Profile Icon John Farrier
John Farrier
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Table of Contents (30) Chapters Close

Preface 1. Part 1: Mastering std::vector FREE CHAPTER
2. Chapter 1: The Basics of std::vector 3. Chapter 2: Mastering Iterators with std::vector 4. Chapter 3: Mastering Memory and Allocators with std::vector 5. Chapter 4: Mastering Algorithms with std::vector 6. Chapter 5: Making a Case for std::vector 7. Part 2: Understanding STL Data Structures
8. Chapter 6: Advanced Sequence Container Usage 9. Chapter 7: Advanced Ordered Associative Container Usage 10. Chapter 8: Advanced Unordered Associative Container Usage 11. Chapter 9: Advanced Container Adaptor Usage 12. Chapter 10: Advanced Container View Usage 13. Part 3: Mastering STL Algorithms
14. Chapter 11: Fundamental Algorithms and Searching 15. Chapter 12: Manipulation and Transformation 16. Chapter 13: Numeric and Range -Based Operations 17. Chapter 14: Permutations, Partitions, and Heaps 18. Chapter 15: STL with Ranges 19. Part 4: Creating STL-Compatible Types and Algorithms
20. Chapter 16: Creating STL-Types Containers 21. Chapter 17: Creating STL -Compatible Algorithms 22. Chapter 18: Type Traits and Policies 23. Part 5: STL Data Structures and Algorithms: Under the Hood
24. Chapter 19: Exception Safety 25. Chapter 20: Thread Safety and Concurrency with the STL 26. Chapter 21: STL Interaction with Concepts and Coroutines 27. Chapter 22: Parallel Algorithms with the STL 28. Index 29. Other Books You May Enjoy

Creating a custom iterator

One of the beauties of C++ is its flexibility, empowering developers to mold the language to suit their needs. This flexibility doesn’t stop with built-in functionality for container iteration. While std::vector comes with its set of built-in iterators, nothing is stopping us from creating our own. But why might we want to?

The appeal of custom iterators

Let’s examine the reasons you’d want to implement a custom iterator:

  • Enhanced abstraction: Consider a vector storing a matrix in a flat format. Wouldn’t it be more intuitive to iterate through rows or columns rather than individual elements? Custom iterators can facilitate this.
  • Data transformation: Perhaps you wish to iterate through the vector but retrieve transformed data, like the squared values of each element. Instead of changing the data before or during retrieval, a custom iterator can abstract this.
  • Filtered views: Imagine skipping over certain elements...
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