Random number generation

True random value generator

To generate true random values that can be used for cryptography std::random_device has to be used as generator.

#include <iostream>
#include <random>

int main()
{
   std::random_device crypto_random_generator;
   std::uniform_int_distribution<int> int_distribution(0,9);
   
   int actual_distribution[10] = {0,0,0,0,0,0,0,0,0,0};
   
   for(int i = 0; i < 10000; i++) {
       int result = int_distribution(crypto_random_generator);
       actual_distribution[result]++;
   }

   for(int i = 0; i < 10; i++) {
       std::cout << actual_distribution[i] << " ";
   }
   
   return 0;
}

std::random_device is used in the same way as a pseudo random value generator is used.

However std::random_device may be implemented in terms of an implementation-defined pseudo-random number engine if a non-deterministic source (e.g. a hardware device) isn't available to the implementation.

Detecting such implementations should be possible via the entropy member function (opens new window) (which return zero when the generator is completely deterministic), but many popular libraries (both GCC's libstdc++ and LLVM's libc++) always return zero, even when they're using high-quality external randomness.

Generating a pseudo-random number

A pseudo-random number generator generates values that can be guessed based on previously generated values. In other words: it is deterministic. Do not use a pseudo-random number generator in situations where a true random number is required.

#include <iostream>
#include <random>

int main()
{
   std::default_random_engine pseudo_random_generator;
   std::uniform_int_distribution<int> int_distribution(0, 9);
   
   int actual_distribution[10] = {0,0,0,0,0,0,0,0,0,0};
   
   for(int i = 0; i < 10000; i++) {
       int result = int_distribution(pseudo_random_generator);
       actual_distribution[result]++;
   }

   for(int i = 0; i <= 9; i++) {
       std::cout << actual_distribution[i] << " ";
   }
   
   return 0;
}

This code creates a random number generator, and a distribution that generates integers in the range [0,9] with equal likelihood. It then counts how many times each result was generated.

The template parameter of std::uniform_int_distribution<T> (opens new window) specifies the type of integer that should be generated. Use std::uniform_real_distribution<T> (opens new window) to generate floats or doubles.

Using the generator for multiple distributions

The random number generator can (and should) be used for multiple distributions.

#include <iostream>
#include <random>

int main()
{
   std::default_random_engine pseudo_random_generator;
   std::uniform_int_distribution<int> int_distribution(0, 9);
   std::uniform_real_distribution<float> float_distribution(0.0, 1.0);
   std::discrete_distribution<int> rigged_dice({1,1,1,1,1,100});
   
   std::cout << int_distribution(pseudo_random_generator) << std::endl;
   std::cout << float_distribution(pseudo_random_generator) << std::endl;
   std::cout << (rigged_dice(pseudo_random_generator) + 1) << std::endl;
   
   return 0;
}

In this example, only one generator is defined. It is subsequently used to generate a random value in three different distributions. The rigged_dice distribution will generate a value between 0 and 5, but almost always generates a 5, because the chance to generate a 5 is 100 / 105.

Remarks

Random number generation in C++ is provided by the <random> header. This header defines random devices, pseudo-random generators and distributions.

Random devices return random numbers provided by operating system. They should either be used for initialization of pseudo-random generators or directly for cryptographic purposes.

Pseudo-random generators return integer pseudo-random numbers based on their initial seed. The pseudo-random number range typically spans all values of an unsigned type. All pseudo-random generators in the standard library will return the same numbers for the same initial seed for all platforms.

Distributions consume random numbers from pseudo-random generators or random devices and produce random numbers with necessary distribution. Distributions are not platform-independent and can produce different numbers for the same generators with the same initial seeds on different platforms.