Struct Weibull

pub struct Weibull<F>
where
    F: Float,
    OpenClosed01: Distribution<F>,
{ /* private fields */ }

The Weibull distribution Weibull(λ, k).

This is a family of continuous probability distributions with scale parameter λ (lambda) and shape parameter k. It is used to model reliability data, life data, and accelerated life testing data.

Density function

f(x; λ, k) = (k / λ) * (x / λ)^(k - 1) * exp(-(x / λ)^k) for x >= 0.

Plot

The following plot shows the Weibull distribution with various values of λ and k.

Example

use rand::prelude::*;
use rand_distr::Weibull;

let val: f64 = rand::rng().sample(Weibull::new(1., 10.).unwrap());
println!("{}", val);

Numerics

For small k like < 0.005, even with f64 a significant number of samples will be so small that they underflow to 0.0 or so big they overflow to inf. This is a limitation of the floating point representation and not specific to this implementation.

Implementations

impl<F> Weibull<F>

where F: Float, OpenClosed01: Distribution<F>,

pub fn new(scale: F, shape: F) -> Result<Weibull<F>, Error>

Construct a new Weibull distribution with given scale and shape.

Trait Implementations

impl<F> Clone for Weibull<F>

where F: Float + Clone, OpenClosed01: Distribution<F>,

fn clone(&self) -> Weibull<F>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<F> Debug for Weibull<F>

where F: Float + Debug, OpenClosed01: Distribution<F>,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de, F> Deserialize<'de> for Weibull<F>

where F: Float + Deserialize<'de>, OpenClosed01: Distribution<F>,

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<F> Distribution<F> for Weibull<F>

where F: Float, OpenClosed01: Distribution<F>,

fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> F

Generate a random value of T, using rng as the source of randomness.

fn sample_iter<R>(self, rng: R) -> Iter<Self, R, T>

where R: Rng, Self: Sized,

Create an iterator that generates random values of T, using rng as the source of randomness.

fn map<F, S>(self, func: F) -> Map<Self, F, T, S>

where F: Fn(T) -> S, Self: Sized,

Map sampled values to type S

impl<F> PartialEq for Weibull<F>

where F: Float + PartialEq, OpenClosed01: Distribution<F>,

fn eq(&self, other: &Weibull<F>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<F> Serialize for Weibull<F>

where F: Float + Serialize, OpenClosed01: Distribution<F>,

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl<F> Copy for Weibull<F>

where F: Float + Copy, OpenClosed01: Distribution<F>,

impl<F> StructuralPartialEq for Weibull<F>

where F: Float, OpenClosed01: Distribution<F>,