Crate rand_simple

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§rand_simple

Crate

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:

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§

  • std環境で乱数の種の配列を生成するマクロ

Structs§