#random #distribution #numbers #probability-distribution #algorithm

rand_simple

The simple random number generator that is independent from the other libraries and based on XOR shift

78 releases

new 0.2.30 Jul 7, 2024
0.2.28 May 10, 2024
0.2.26 Mar 23, 2024
0.2.23 Dec 29, 2023
0.1.11 Jul 28, 2023

#222 in Algorithms

Download history 3/week @ 2024-03-14 147/week @ 2024-03-21 24/week @ 2024-03-28 136/week @ 2024-04-04 38/week @ 2024-04-11 131/week @ 2024-05-09 13/week @ 2024-05-16 7/week @ 2024-05-23 105/week @ 2024-06-06 24/week @ 2024-06-13

129 downloads per month

MIT/Apache

145KB
2K SLoC

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

No runtime deps