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 distribution generators

The C++ Standard Library provides a selection of random number distribution generators, each with its own properties. In this recipe, we examine a function to compare the different options by creating a histogram of their output.

How to do it…

Like the random number engines, the distribution generators have some common interface elements. Unlike the random number engines, the distribution generators have a variety of properties to set. We can create a template function to print a histogram of the various distributions, but the initializations of the various distribution generators vary significantly:

  • We start with some constants:
    constexpr size_t n_samples{ 10 * 1000 };
    constexpr size_t n_max{ 50 };

The n_samples constant is the number of samples to generate for each histogram – in this case, 10,000.

The n_max constant is used as a divisor while generating our histograms.

  • Our histogram function...