Choosing the right random number distribution

Choosing the right random number distribution depends on the characteristics of the random numbers you need. Here are a few common distributions and their use cases:

std::uniform_int_distribution

• Generates random integers uniformly distributed within a specified range
• Use when you need random integers with equal probability across the range

1#include <iostream>
2#include <random>
3
4int main() {
5  std::random_device rd;
6  std::mt19937 gen(rd());
7  std::uniform_int_distribution<int> dist(1, 6);
8
9  for (int i = 0; i < 5; ++i) {
10    std::cout << dist(gen) << ' ';
11  }
12}

13 5 2 6 1

std::uniform_real_distribution

• Generates random floating-point numbers uniformly distributed within a specified range
• Use when you need random real numbers with equal probability across the range

1#include <iostream>
2#include <random>
3
4int main() {
5  std::random_device rd;
6  std::mt19937 gen(rd());
7  std::uniform_real_distribution<double> dist(
8    0.0, 1.0);  
9
10  for (int i = 0; i < 5; ++i) {
11    std::cout << dist(gen) << ' ';
12  }
13}

10.278498 0.534335 0.95655 0.157167 0.424719

std::normal_distribution

• Generates random numbers according to a normal (Gaussian) distribution
• Use when you need random numbers centered around a mean with a specified standard deviation

1#include <iostream>
2#include <random>
3
4int main() {
5  std::random_device rd;
6  std::mt19937 gen(rd());
7  std::normal_distribution<double> dist(
8    5.0, 2.0);  
9
10  for (int i = 0; i < 5; ++i) {
11    std::cout << dist(gen) << ' ';
12  }
13}

16.20473 3.94903 7.31954 1.69591 4.97183

Other distributions like std::exponential_distribution, std::poisson_distribution, and std::gamma_distribution are available for more specific use cases.

Consider the properties of your random number requirements, such as the range, probability distribution, and data type, to select the appropriate distribution.