How to Generate Normally Distributed Random Numbers in C/C ?

Generating Random Numbers Using Normal Distribution in C/C

The need to generate random numbers following a normal distribution often arises in various programming applications. In C/C , there are several techniques you can employ to achieve this.

One widely adopted approach is the Box-Muller transform. This method involves generating two independent uniform random numbers and transforming them using a mathematical formula to obtain normally distributed values. The Box-Muller transform is mathematically rigorous and produces accurate results.

Here's how to implement the Box-Muller transform in C/C :

#include <random>
#include <cmath>

// Generate a random number following a Gaussian distribution
double normal_rand() {
    static double z1;
    static bool ready = false;

    // If z1 is not ready, generate two uniform random numbers
    if (!ready) {
        double u1 = std::uniform_real_distribution<double>(0, 1)();
        double u2 = std::uniform_real_distribution<double>(0, 1)();
        z1 = std::sqrt(-2 * std::log(u1)) * std::cos(2 * M_PI * u2);
        ready = true;
    }

    // Return z1 and mark it as used
    ready = false;
    return z1;
}

In the above example, std::uniform_real_distribution generates uniform random numbers, while std::sqrt and std::cos perform the necessary mathematical transformations.

Utilizing the Box-Muller transform provides a straightforward and reliable way to generate random numbers following a normal distribution in C/C . By employing this technique, programmers can avoid the use of external libraries like Boost and leverage the standard C library's functionality.

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