C++ Weekly - Ep 483 - Stop Using rand, Start Using Random

Summary

In this episode of C++ Weekly, Jason Turner discusses the C++ random number generation system, focusing on the header and why developers should avoid using rand() or custom solutions. He explains how to use random devices and various distributions, such as uniform_int_distribution, with examples. Jason also highlights the importance of reproducibility using the Mersenne Twister engine and seed values. For those creating procedurally generated content, he demonstrates how to generate random objects using random byte sets and std::bit_cast. The episode provides a clear and practical guide to mastering random number generation in C++.

Takeaways

• 😀 C++'s <random> header offers a powerful framework for random number generation, moving beyond the use of the old rand() function.
• 😀 The 'default' random_device is used to create high entropy random numbers, typically relying on system sources like dev_random on Unix.
• 😀 Using a uniform_int_distribution guarantees a fair dice roll, generating integers between a specified range (e.g., 1 to 6).
• 😀 It's important to pair the uniform_int_distribution with a random engine to generate the random sequence needed for distributions.
• 😀 The sequence of numbers generated can vary every time the code is executed, providing different outputs for each run.
• 😀 For reproducibility, pseudorandom number generators like the Mersenne Twister can be seeded with specific values to generate consistent results.
• 😀 A seed value is used to ensure that the output remains the same every time the same seed is used, creating reproducible results.
• 😀 The default seed value for the Mersenne Twister engine is 5489, but you can explicitly set the seed for different sequences.
• 😀 C++ provides various distributions such as real number, integer, natural, gamma, and exponential, making random number generation more flexible.
• 😀 Generating random sets of bytes with std::bit_cast allows for procedural generation, like creating random objects in a game world.
• 😀 Understanding random number generation in C++ doesn't have to be overwhelming; once you know the basics, it can be simple and intuitive.

Q & A

What is the main topic discussed in this episode of C++ Weekly?

The main topic is the use of the <random> header and random number generation in C++. The episode also advises against using 'rand()' or custom, hand-rolled random number generation solutions.

Why is it important to stop using 'rand()' for random number generation?

'rand()' is often not suitable for serious random number generation because it lacks proper randomness, is not thread-safe, and doesn't provide high-quality randomness compared to the modern options provided in the <random> header.

What is the purpose of the 'random_device' in C++?

The 'random_device' is used to provide a source of high-quality randomness, typically relying on operating system-level entropy sources like /dev/random on Unix-like systems. It ensures a better randomness source than 'rand()'.

What kind of distribution does 'uniform_int_distribution' provide?

'uniform_int_distribution' provides a uniform distribution of integers within a specified range, ensuring that every integer in the range has an equal probability of being chosen.

How does 'uniform_int_distribution' work in C++?

'uniform_int_distribution' uses a random number generator (engine) to generate random integers. When invoked, it returns a random integer within the specified range by utilizing the underlying engine for the bit sequence.

What is a pseudorandom number generator (PRNG), and why is it used in C++?

A pseudorandom number generator (PRNG) produces numbers that appear random but are actually generated using a deterministic process. It is used in C++ when reproducibility is needed, such as in simulations or procedural game generation, where the sequence of random numbers needs to be consistent given the same seed.

What is the Mersenne Twister, and how is it used in C++ random number generation?

The Mersenne Twister is a popular pseudorandom number generator known for its high-quality randomness and large period. In C++, it can be used by providing a seed to generate reproducible random sequences.

How does changing the seed of a random number generator affect the results?

Changing the seed of a random number generator changes the sequence of random numbers it produces. The same seed will always produce the same sequence, allowing for reproducibility in random number generation.

What does the 'generate_canonical' function do in C++?

'generate_canonical' generates random floating-point values that are uniformly distributed between 0 and 1, with a specified precision. It provides a way to generate high-quality real number distributions.

What exercise is suggested for using random data with C++?

The exercise suggests generating a random set of bytes equal to the size of the object you want to create and using 'std::bit_cast' to convert those bytes into a random object, such as one used in a procedurally generated game world.