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  • Book Overview & Buying C++ STL Cookbook
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C++ STL Cookbook

C++ STL Cookbook - Second Edition

By : Bill Weinman
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C++ STL Cookbook

C++ STL Cookbook

By: Bill Weinman

Overview of this book

C++ STL Cookbook is a comprehensive guide that provides practical solutions for mastering the latest features of the C++23 Standard Template Library (STL) through hands-on recipes. Beginning with new features in C++23, this book will help you understand the language's updated mechanics and library features, and offer insights into how they work. Unlike other books, this cookbook takes an implementation-specific, problem-solution approach that will help you overcome hurdles quickly. You'll learn core STL concepts, such as containers, algorithms, utility classes, lambda expressions, iterators, and more, through specific real-world recipes. Building on the success of the first edition, this updated guide includes a new chapter dedicated to the latest features introduced in C++23, such as improved modules, refined ranges, and coroutine-based generators. It also covers essential best practices for writing cleaner and more efficient code, including the use of coroutines, structured bindings, and std::span. Whether you're looking to deepen your understanding of the C++ STL or implement the latest features in your projects, this book provides valuable insights, clear and concise explanations and practical solutions to enhance your C++ programming skills.
Table of Contents (15 chapters)
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14
Index

Sample data sets with std::sample

The std::sample() algorithm takes a random sample of a sequence of values and populates a destination container with the sample. It is useful when analyzing a larger set of data, where the random sample is taken to be representative of the whole.

A sample set allows us to approximate the characteristics of a large set of data, without analyzing the full set. This provides efficiency in exchange for accuracy, a fair trade-off in many circumstances.

How to do it

In this recipe, we use an array of 200,000 random integers with a standard normal distribution. We'll sample a few hundred values to create a histogram of the frequency of each value.

  • We'll start with a simple function to return a rounded int from a double. The standard library lacks such a function and we'll need it later:
    int iround(const double& d) {
        return static_cast<int>(std::round(d));
    }

    The standard library provides...

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