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The C++ Programmer's Mindset

The C++ Programmer's Mindset

By : Sam Morley
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The C++ Programmer's Mindset

The C++ Programmer's Mindset

By: Sam Morley

Overview of this book

Solve complex problems in C++ by learning how to think like a computer scientist. This book introduces computational thinking—a framework for solving problems using decomposition, abstraction, and pattern recognition—and shows you how to apply it using modern C++ features. You'll learn how to break down challenges, choose the right abstractions, and build solutions that are both maintainable and efficient. Through small examples and a large case study, this book guides you from foundational concepts to high-performance applications. You’ll explore reusable templates, algorithms, modularity, and even parallel computing and GPU acceleration. With each chapter, you’ll not only expand your C++ skillset, but also refine the way you approach and solve real-world problems. Written by a seasoned research engineer and C++ developer, this book combines practical insight with academic rigor. Whether you're designing algorithms or profiling production code, this book helps you deliver elegant, effective solutions with confidence.
Table of Contents (19 chapters)
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18
Index

Clustering Data

The remaining challenge is to cluster the data that we can now read from files so we can analyze the locality of the data (to find out where the rubber ducks originate). For this task, we’re going to implement a -means clustering, which is a fairly basic data science tool that labels data according to its proximity to other data. The algorithm itself is relatively straightforward, but we will also need lots of supporting structure to embed our geographic data into an appropriate space and to decide on the number of clusters to fit.

The -means clustering itself is an exercise in implementing a standard algorithm efficiently. However, finding the number of clusters is a different problem entirely. For this, we will need to find an appropriate method of scoring different clusterings that will allow us to choose the best value of . This will be combined with some kind of search over a range of values. For this part, we will need us to construct our own algorithm...

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