Book Image

C++20 STL Cookbook

By : Bill Weinman
Book Image

C++20 STL Cookbook

By: Bill Weinman

Overview of this book

Fast, efficient, and flexible, the C++ programming language has come a long way and is used in every area of the industry to solve many problems. The latest version C++20 will see programmers change the way they code as it brings a whole array of features enabling the quick deployment of applications. This book will get you up and running with using the STL in the best way possible. Beginning with new language features in C++20, this book will help you understand the language's mechanics and library features and offer insights into how they work. Unlike other books, the C++20 STL 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, while working on real-world recipes. This book is a reference guide for using the C++ STL with its latest capabilities and exploring the cutting-edge features in functional programming and lambda expressions. By the end of the book C++20 book, you'll be able to leverage the latest C++ features and save time and effort while solving tasks elegantly using the STL.
Table of Contents (13 chapters)

Compare random number engines

The random library provides a selection of random number generators, each with different strategies and properties. In this recipe, we examine a function to compare the different options by creating a histogram of their output.

How to do it…

In this recipe, we compare the different random number generators provided by the C++ random library:

  • We start with some constants to provide uniform parameters for the random number generators:
    constexpr size_t n_samples{ 1000 };
    constexpr size_t n_partitions{ 10 };
    constexpr size_t n_max{ 50 };

n_samples is the number of samples to examine, n_partitions is the number of partitions in which to display the samples, and n_max is the maximum size of a bar in the histogram (this will vary some due to rounding).

These numbers provide a reasonable display of the differences between the engines. Increasing the ratio of samples versus partitions tends to smooth out the curves and obscure the...