# rand_simple [![Crate](https://img.shields.io/crates/v/rand_simple.svg)](https://crates.io/crates/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)](http://www.pleiades-publishing.co.jp/pdf/pdf03.html). 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](https://github.com/Tremendous1192/demo_rand_simple). ### Uniform Distribution ```rust // 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 ```rust // 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 * [x] Uniform distribution * [x] 3.1 Normal distribution * [x] 3.2 Half Normal distribution * [x] 3.3 Log-Normal distribution * [x] 3.4 Cauchy distribution * [x] Half-Cauchy distribution * [x] 3.5 Lévy distribution * [x] 3.6 Exponential distribution * [x] 3.7 Laplace distribution * [x] Log-Laplace distribution * [x] 3.8 Rayleigh distribution * [x] 3.9 Weibull distribution * [x] Reflected Weibull distribution * [x] Fréchet distribution * [x] 3.10 Gumbel distribution * [x] 3.11 Gamma distribution * [x] 3.12 Beta distribution * [ ] 3.13 Dirichlet distribution * [x] 3.14 Power Function distribution * [ ] 3.15 Exponential Power distribution * [ ] Half Exponential Power distribution * [x] 3.16 Erlang distribution * [x] 3.17 Chi-Square distribution * [x] 3.18 Chi distribution * [x] 3.19 F distribution * [x] 3.20 t distribution * [x] 3.21 Inverse Gaussian distribution * [x] 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 * [x] Bernoulli distribution * [ ] 4.1 Binomial distribution * [x] 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