Trait statrs::distribution::Distribution

pub trait Distribution<T> {
    fn sample<R: Rng>(&self, r: &mut R) -> T;
}

The Distribution trait is used to specify an interface for sampling statistical distributions

Required methods

fn sample<R: Rng>(&self, r: &mut R) -> T

Draws a random sample using the supplied random number generator

Examples

A trivial implementation that just samples from the supplied random number generator

use rand::Rng;
use statrs::distribution::Distribution;

struct Foo;

impl Distribution<f64> for Foo {
    fn sample<R: Rng>(&self, r: &mut R) -> f64 {
        r.next_f64()
    }
}

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Implementors

impl Distribution<f64> for Bernoulli

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from the bernoulli distribution using r as the source of randomness where the generated values are 1 with probability p and 0 with probability 1-p.

Examples

use rand::StdRng;
use statrs::distribution::{Bernoulli, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Bernoulli::new(0.5).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Beta

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from a beta distribution using r as the source of randomness. Generated by sampling two gamma distributions and normalizing.

Examples

use rand::StdRng;
use statrs::distribution::{Beta, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Beta::new(2.0, 2.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Binomial

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from the binomial distribution using r as the source of randomness where the range of values is [0.0, n].

Examples

use rand::StdRng;
use statrs::distribution::{Binomial, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Binomial::new(0.5, 5).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Categorical

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from the categorical distribution using r as the source of randomness

Examples

use rand::StdRng;
use statrs::distribution::{Categorical, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Categorical::new(&[1.0, 2.0, 3.0]).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Cauchy

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from the cauchy distribution using r as the source of randomness

Examples

use rand::StdRng;
use statrs::distribution::{Cauchy, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Cauchy::new(0.0, 1.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Chi

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from the chi distribution using r as the source of randomness

Examples

use rand::StdRng;
use statrs::distribution::{Chi, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Chi::new(2.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for ChiSquared

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from the chi-squared distribution using r as the source of randomness

Examples

use rand::StdRng;
use statrs::distribution::{ChiSquared, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = ChiSquared::new(3.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for DiscreteUniform

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from the discrete uniform distribution using r as the source of randomness in the range [min, max]

Examples

use rand::StdRng;
use statrs::distribution::{DiscreteUniform, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = DiscreteUniform::new(0, 5).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Erlang

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from a erlang distribution using r as the source of randomness.

Examples

use rand::StdRng;
use statrs::distribution::{Erlang, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Erlang::new(3, 1.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Exponential

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from the exponential distribution using r as the source of randomness

Examples

use rand::StdRng;
use statrs::distribution::{Exponential, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Exponential::new(1.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for FisherSnedecor

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from a fisher-snedecor distribution using r as the source of randomness.

Examples

use rand::StdRng;
use statrs::distribution::{FisherSnedecor, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = FisherSnedecor::new(2.0, 2.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Gamma

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from a gamma distribution using r as the source of randomness. The implementation is based on:

"A Simple Method for Generating Gamma Variables" - Marsaglia & Tsang

ACM Transactions on Mathematical Software, Vol. 26, No. 3, September 2000, Pages 363-372

Examples

use rand::StdRng;
use statrs::distribution::{Gamma, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Gamma::new(3.0, 1.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Geometric

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generates a random sample from the geometric distribution using r as the source of randomness

Examples

use rand::StdRng;
use statrs::distribution::{Geometric, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Geometric::new(0.5).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Hypergeometric

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generates a random sample from the hypergeometric distribution using r as the source of randomness

Examples

use rand::StdRng;
use statrs::distribution::{Hypergeometric, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Hypergeometric::new(10, 5, 3).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for InverseGamma

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from an inverse gamma distribution using r as the source of randomness.

Examples

use rand::StdRng;
use statrs::distribution::{InverseGamma, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = InverseGamma::new(3.0, 1.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for LogNormal

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from the log-normal distribution using r as the source of randomness. Uses the Box-Muller algorithm

Examples

use rand::StdRng;
use statrs::distribution::{LogNormal, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = LogNormal::new(0.0, 1.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Normal

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from the normal distribution using r as the source of randomness. Uses the Box-Muller algorithm

Examples

use rand::StdRng;
use statrs::distribution::{Normal, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Normal::new(0.0, 1.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Pareto

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from a Pareto distribution using r as the source of randomness. The implementation uses inverse transform sampling.

Examples

use rand::StdRng;
use statrs::distribution::{Pareto, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Pareto::new(1.0, 2.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Poisson

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from a poisson distribution using r as the source of randomness. The implementation is based on Knuth's method if lambda < 30.0 or Rejection method PA by A. C. Atkinson from the Journal of the Royal Statistical Society Series C (Applied Statistics) Vol. 28 No. 1. (1979) pp. 29 - 35

Examples

use rand::StdRng;
use statrs::distribution::{Poisson, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Poisson::new(1.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for StudentsT

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from a student's t-distribution using r as the source of randomness. The implementation is based on method 2, section 5 in chapter 9 of L. Devroye's "Non-Uniform Random Variate Generation"

Examples

use rand::StdRng;
use statrs::distribution::{StudentsT, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = StudentsT::new(0.0, 1.0, 2.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Triangular

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from a triangular distribution using r as the source of randomness.

Examples

use rand::StdRng;
use statrs::distribution::{Triangular, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Triangular::new(0.0, 5.0, 2.5).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Uniform

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from the continuous uniform distribution using r as the source of randomness in the range [min, max]

Examples

use rand::StdRng;
use statrs::distribution::{Uniform, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Uniform::new(0.0, 5.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<f64> for Weibull

fn sample<R: Rng>(&self, r: &mut R) -> f64

Generate a random sample from the weibull distribution using r as the source of randomness

Examples

use rand::StdRng;
use statrs::distribution::{Weibull, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Weibull::new(10.0, 1.0).unwrap();
print!("{}", n.sample::<StdRng>(&mut r));

impl Distribution<Vec<f64>> for Dirichlet

fn sample<R: Rng>(&self, r: &mut R) -> Vec<f64>

Generate random samples from the dirichlet distribution using r as the source of randomness

Examples

use rand::StdRng;
use statrs::distribution::{Dirichlet, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Dirichlet::new(&[1.0, 2.0, 3.0]).unwrap();
print!("{:?}", n.sample::<StdRng>(&mut r));

impl Distribution<Vec<f64>> for Multinomial

fn sample<R: Rng>(&self, r: &mut R) -> Vec<f64>

Generate random samples from the multinomial distribution using r as the source of randomness

Examples

use rand::StdRng;
use statrs::distribution::{Multinomial, Distribution};

let mut r = rand::StdRng::new().unwrap();
let n = Multinomial::new(&[0.0, 1.0, 2.0], 4).unwrap();
print!("{:?}", n.sample::<StdRng>(&mut r));

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