Definition
A random-number generator (RNG) is a program or algorithm that generates a sequence of numbers that are supposed to be statistically independent and uniformly distributed; thus, they appear random. Such numbers are essential for various applications, including statistical sampling, computer simulations, cryptography, and gambling systems. The elementary statement N = RND(0) can generate a random number and assign its value to the variable N.
Examples
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Simple Uniform Random Number Generation in Python:
1import random 2number = random.uniform(0, 1) 3print("Random number between 0 and 1 is", number) -
As part of Statistical Sampling:
1import random 2 3population = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 4sample = random.sample(population, 3) 5print("Random sample from population:", sample) -
Random Numbers for Cryptographic Purposes with
secretsmodule:1import secrets 2crypto_random_number = secrets.randbelow(100) 3print("Crypto-safe random number below 100:", crypto_random_number)
Frequently Asked Questions
What are the types of random-number generators?
Random-number generators can be classified into two main types:
- Pseudo-Random Number Generators (PRNGs): These use deterministic algorithms to produce sequences of numbers that only simulate randomness.
- True Random Number Generators (TRNGs): These derive randomness from physical phenomena and are therefore truly unpredictable.
How is randomness checked in numbers?
Randomness is verified using statistical tests such as the Chi-square test, Kolmogorov-Smirnov test, and autocorrelation checks. Failures in these tests usually indicate patterns or predictable elements.
Where are random-number generators used?
Random-number generators are utilized in various domains: simulations in science and engineering, statistical sampling, cryptographic key generation, procedural generation in gaming, and many more areas where unbiased random selections are necessary.
What is secrets module in Python?
The secrets module in Python provides functions for generating cryptographically strong random numbers suitable for managing data such as passwords, account authentication, and security tokens.
Are random-number generators truly random?
PRNGs are deterministic and, hence, not truly random; they rely on an initial seed value and an algorithm to produce numbers that only appear random. TRNGs can achieve higher levels of unpredictability by utilizing physical processes.
Related Terms
- Algorithm: A sequence of computational steps that transforms input into output.
- Probability: A branch of mathematics dealing with the likelihood of different outcomes.
- Statistical Sampling: The process of selecting a subset of individuals from a population to estimate characteristics of the whole population.
- Cryptography: The practice and study of techniques for securing communication and data from third parties.
Online References
- Random Number Generators: Good Practices and Efficient Implementations
- Python’s secrets module documentation
- NIST Random Number Tests
Suggested Books for Further Studies
- “The Art of Computer Programming, Vol. 2: Seminumerical Algorithms” by Donald E. Knuth
- “Numerical Recipes: The Art of Scientific Computing” by William H. Press, Saul A. Teukolsky, William T. Vetterling, Brian P. Flannery
- “Simulation Modeling and Analysis” by Averill M. Law
Fundamentals of Random-Number Generator: Computer Science Basics Quiz
Thank you for exploring the concept of random-number generators and testing your understanding with our comprehensive quiz. Keep learning and enhancing your computer science knowledge!
