Modern C++ idoms and recipes

meshell

59.2K views

GitHub

Open Source Your Knowledge, Become a Contributor

Technology knowledge has to be shared and made accessible for free. Join the movement.

Create Content

Previous: Filesystem library (Solution)

Pseudo-random number generation

The random number library provides classes that generate random and pseudo-random numbers. The library contains two types of components:

Engines, which are generators of random numbers (both pseudo-random number generators, which generate integer sequences with a uniform distribution, and true random number generators if available)
Distributions which convert the output of random number engines into various statistical distributions

Random number engines

All engines (except the random_device) produce integer numbers in a uniform distribution using seed data as entropy source.

Random number distributions

Random number distributions post-processe the output of an random number engine in such a way that the resulting output is distributed according to a defined statistical probability density function.

Example

The follwing example prints the uniform distribution of an random generator.

TODO

Play around with different engines and distributions.

play around with different engines and distributions.

#include <functional>
#include <map>
#include <algorithm>
#include <iostream>
#include <random>
#include <iomanip>
// Taken from "Modern C++ Programming Cookbook" by Marius Bancila
void generate_and_print(std::function<int()> rnd_generator,
                        const int iterations = 10000) {
    auto data = std::map<int, int>{};
    // generate random numbers
    for (auto n = 0; n < iterations; ++n) {
        ++data[rnd_generator()];
    }
    // find element with the most repetitions
    const auto& [_, max_rep] = *std::max_element(
            std::begin(data),
            std::end(data),
            [](const auto value_rep_pair_1, const auto& value_rep_pair_2) {
                return value_rep_pair_1.second < value_rep_pair_2.second;
            });
    // print bars
    constexpr char block_character= 219;
    const auto rep_unit_width = iterations / 50;
    for (auto i = max_rep / rep_unit_width; i > 0; --i) {
        for (const auto& [val, rep] : data) {
            std::cout << std::fixed
                      << std::setprecision(1)
                      << std::setw(3)
                      << ((rep / rep_unit_width) >= i ? block_character : ' ');
        }
        std::cout << "\n";
    }
    // print numbers
    for (const auto& [val, rep] : data) {
        std::cout << std::fixed
                  << std::setprecision(1)
                  << std::setw(3)
                  << val;
    }
    std::cout << "\n";
};
int main() {
    std::random_device rd{};
    // TODO play around with different engines.
    auto mtgen = std::mt19937{rd()};
    // TODO play around with different distributions.

Open Source Your Knowledge: become a Contributor and help others learn. Create New Content

Open Source Your Knowledge, Become a Contributor

19/19 Pseudo-random number generation

Pseudo-random number generation

Random number engines

Random number distributions

Example

TODO

c++17 std::optional

Apprendre le C++

Using Pragma For Compile Optimization

Computing with Data