Tuesday coding tips are super short posts about various tidbits mainly from C++, but also from other programming languages I use. You can also follow the #TuesdayCodingTips hashtag on Mastodon and Linkedin.
Classic C way of generating random numbers is to set the current time as a seed and then modulo results of the rand function to get somewhat random numbers (modulo can introduce bias based on the implementation of rand).
#include <iostream>
#include <random>
// Get random integer in range <min, max)
int getRandomNumber(int min, int max)
{
return rand() % (max - min) + min;
}
int main()
{
// seeding with current time to get different output each time
srand(time(NULL));
for (int i = 0; i < 10; i++)
std::cout << getRandomNumber(10, 20) << std::endl;
}
C++11 introduces the concept of engines, adaptors, and distributions. Engines are seedable and give you random numbers similar to rand() calls. However, each engine is implemented differently, impacting its speed and memory footprint. Adapters tamper with RNG output, such as shuffling the order of output values or discarding them.
Distributions transform the output from the generators to get values in certain ranges and probabilistic properties, such as the middle value being the most probable (normal distribution). Distributions are more nuanced than just using the modulo operator so their outputs are more suitable for cryptography use-cases.
#include <iostream>
#include <random>
// Get random integer in range <min, max)
int getRandomNumber(int min, int max)
{
return rand() % (max - min) + min;
}
int main()
{
std::random_device rd; // non-seedable source of non-deterministic numbers from hardware
std::mt19937 generator(rd()); // deterministic generator with uniform distribution
// seeded with totally random number
std::uniform_int_distribution<> distribution(10, 19);
for (int i = 0; i < 10; i++)
std::cout << distribution(generator) << std::endl;
}