# In C++, what is #include <random>?

**In C++, #include <random> is a header file that provides facilities for generating random numbers. It offers a powerful and flexible framework for generating various types of random sequences, addressing the limitations of the older rand() function.**

**Key components of <random>:**

**Engines:**- Generate sequences of pseudo-random numbers, acting as the base for random number production.
- Examples:
`std::mt19937`

: Mersenne Twister engine (widely used, good quality)`std::minstd_rand`

: Minimal Standard engine (smaller and faster, but lower quality)`std::random_device`

: Non-deterministic engine for seeding other engines (uses hardware sources for randomness)

**Distributions:**- Shape the raw output of engines into specific probability distributions.
- Examples:
`std::uniform_int_distribution`

: Generates uniformly distributed integers within a range`std::uniform_real_distribution`

: Generates uniformly distributed real numbers within a range`std::normal_distribution`

: Generates normally distributed real numbers`std::bernoulli_distribution`

: Generates Boolean values (true or false) with a specified probability- And more...

**Basic usage:**

**Include the header:**C++`#include <random>`

**Create an engine:**C++`std::mt19937 engine; // Mersenne Twister engine`

**Seed the engine (for non-deterministic results):**C++`std::random_device rd; engine.seed(rd());`

**Create a distribution:**C++`std::uniform_int_distribution<int> dist(1, 6); // Generate integers from 1 to 6`

**Generate random numbers:**C++`int random_number = dist(engine);`

**Advantages over rand():**

**More flexible:**Offers a variety of engines and distributions for different needs.**Better quality:**Produces higher-quality random sequences compared to`rand()`

.**Non-deterministic:**Can be seeded with non-deterministic sources for true randomness.**Extensible:**Allows for custom engines and distributions.

**Remember:**

- Use
`#include <random>`

instead of`rand()`

for modern C++ random number generation. - Choose the appropriate engine and distribution based on your specific requirements.
- Seed the engine properly for non-deterministic results.