80 releases
0.2.32 | Sep 29, 2024 |
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0.2.30 | Jul 7, 2024 |
0.2.26 | Mar 23, 2024 |
0.2.23 | Dec 29, 2023 |
0.1.11 | Jul 28, 2023 |
#116 in Algorithms
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rand_simple
rand_simple
is a Rust crate designed for efficient generation of pseudo-random numbers based on the Xorshift160 algorithm. It offers the following key features:
-
Xorshift160 Algorithm:
rand_simple
leverages the proven Xorshift160 algorithm to create high-quality pseudo-random numbers. -
Rich Variety of Probability Distributions: This crate aims to implement over 40 types of probability distribution random numbers, as featured in the book "Probability Distribution Random Number Generation Methods for Computer Simulation" by Tetsuaki Yotsuji(計算機シミュレーションのための確率分布乱数生成法/著者 四辻 哲章/プレアデス出版). These distributions cover a wide range of scenarios for computer simulations.
-
Convenient Use of Various Structs: With a simple declaration like
use rand_simple::StructName;
, you gain access to a variety of structs for different probability distributions, making it easy to incorporate randomness into your applications.
If you are seeking an effective solution for random number generation in Rust, rand_simple
is a reliable choice. Start using it quickly and efficiently, taking advantage of its user-friendly features.
Usage Examples
For graph-based examples, please refer to this repository.
Uniform Distribution
// Generate a single seed value for initializing the random number generator
let seed: u32 = rand_simple::generate_seeds!(1_usize)[0];
// Create a new instance of the Uniform distribution with the generated seed
let mut uniform = rand_simple::Uniform::new(seed);
// Check the default range of the uniform distribution and print it
assert_eq!(format!("{uniform}"), "Range (Closed Interval): [0, 1]");
println!("Returns a random number -> {}", uniform.sample());
// When changing the parameters of the random variable
// Define new minimum and maximum values for the uniform distribution
let min: f64 = -1_f64;
let max: f64 = 1_f64;
// Attempt to set the new parameters for the uniform distribution
let result: Result<(f64, f64), &str> = uniform.try_set_params(min, max);
// Check the updated range of the uniform distribution and print it
assert_eq!(format!("{uniform}"), "Range (Closed Interval): [-1, 1]");
println!("Returns a random number -> {}", uniform.sample());
Normal Distribution
// Generate two seed values for initializing the random number generator
let seeds: [u32; 2_usize] = rand_simple::generate_seeds!(2_usize);
// Create a new instance of the Normal distribution with the generated seeds
let mut normal = rand_simple::Normal::new(seeds);
// Check the default parameters of the normal distribution (mean = 0, std deviation = 1) and print it
assert_eq!(format!("{normal}"), "N(Mean, Std^2) = N(0, 1^2)");
println!("Returns a random number -> {}", normal.sample());
// When changing the parameters of the random variable
// Define new mean and standard deviation values for the normal distribution
let mean: f64 = -3_f64;
let std: f64 = 2_f64;
// Attempt to set the new parameters for the normal distribution
let result: Result<(f64, f64), &str> = normal.try_set_params(mean, std);
// Check the updated parameters of the normal distribution and print it
assert_eq!(format!("{normal}"), "N(Mean, Std^2) = N(-3, 2^2)");
println!("Returns a random number -> {}", normal.sample());
Implementation Status
Continuous distribution
- Uniform distribution
- 3.1 Normal distribution
- 3.2 Half Normal distribution
- 3.3 Log-Normal distribution
- 3.4 Cauchy distribution
- Half-Cauchy distribution
- 3.5 Lévy distribution
- 3.6 Exponential distribution
- 3.7 Laplace distribution
- Log-Laplace distribution
- 3.8 Rayleigh distribution
- 3.9 Weibull distribution
- Reflected Weibull distribution
- Fréchet distribution
- 3.10 Gumbel distribution
- 3.11 Gamma distribution
- 3.12 Beta distribution
- 3.13 Dirichlet distribution
- 3.14 Power Function distribution
- 3.15 Exponential Power distribution
- Half Exponential Power distribution
- 3.16 Erlang distribution
- 3.17 Chi-Square distribution
- 3.18 Chi distribution
- 3.19 F distribution
- 3.20 t distribution
- 3.21 Inverse Gaussian distribution
- 3.22 Triangular distribution
- 3.23 Pareto distribution
- 3.24 Logistic distribution
- 3.25 Hyperbolic Secant distribution
- 3.26 Raised Cosine distribution
- 3.27 Arcsine distribution
- 3.28 von Mises distribution
- 3.29 Non-Central Gammma distribution
- 3.30 Non-Central Beta distribution
- 3.31 Non-Central Chi-Square distribution
- 3.32 Non-Central Chi distribution
- 3.33 Non-Central F distribution
- 3.34 Non-Central t distribution
- 3.35 Planck distribution
Discrete distributions
- Bernoulli distribution
- 4.1 Binomial distribution
- 4.2 Geometric distribution
- 4.3 Poisson distribution
- 4.4 Hypergeometric distribution
- 4.5 Multinomial distribution
- 4.6 Negative Binomial distribution
- 4.7 Negative Hypergeometric distribution
- 4.8 Logarithmic Series distribution
- 4.9 Yule-Simon distribution
- 4.10 Zipf-Mandelbrot distribution
- 4.11 Zeta distribution