Expand description
§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_simpleleverages 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
Macros§
- generate_
seeds - Macro to generate an array of random seeds in a
stdenvironment.
Structs§
- Bernoulli
- Bernoulli Distribution
- Beta
- Beta Distribution
- Cauchy
- Cauchy Distribution
- Chi
- Chi Distribution
- ChiSquare
- Chi-Square Distribution
- Erlang
- Erlang Distribution
- Exponential
- Exponential Distribution
- FDistribution
- F-distribution
- Frechet
- Frechet Distribution
- Gamma
- Gamma Distribution
- Geometric
- Geometric Distribution
- Gunbel
- Gunbel Distribution
- Half
Cauchy - Half Cauchy Distribution
- Half
Normal - Half Normal Distribution
- Inverse
Gaussian - Represents an Inverse Gaussian (IG) distribution.
- Laplace
- Laplace Distribution
- Levy
- Lévy Distribution
- LogLaplace
- Log-Laplace Distribution
- LogNormal
- Log Normal Distribution
- Normal
- Normal Distribution
- Power
Function - Power function distribution
- Rayleigh
- Rayleigh distribution.
- Reflected
Weibull - Reflected Weibull Distribution
- TDistribution
- t-distribution (Student’s t-distribution)
- Triangular
- Represents a triangular distribution defined by a minimum value, maximum value, and mode.
- Uniform
- Uniform distribution
- Weibull
- Weibull Distribution