Struct Exp 

pub struct Exp<F>
where
    F: Float,
    Exp1: Distribution<F>,
{ /* private fields */ }

The exponential distribution Exp(λ).

The exponential distribution is a continuous probability distribution with rate parameter λ (lambda). It describes the time between events in a Poisson process, i.e. a process in which events occur continuously and independently at a constant average rate.

See Exp1 for an optimised implementation for λ = 1.

Density function

f(x) = λ * exp(-λ * x) for x > 0, when λ > 0.

For λ = 0, all samples yield infinity (because a Poisson process with rate 0 has no events).

Plot

The following plot illustrates the exponential distribution with various values of λ. The λ parameter controls the rate of decay as x approaches infinity, and the mean of the distribution is 1/λ.

Example

use rand_distr::{Exp, Distribution};

let exp = Exp::new(2.0).unwrap();
let v = exp.sample(&mut rand::rng());
println!("{} is from a Exp(2) distribution", v);

Implementations

impl<F> Exp<F>

where F: Float, Exp1: Distribution<F>,

pub fn new(lambda: F) -> Result<Exp<F>, Error>

Construct a new Exp with the given shape parameter lambda.

Remarks

For custom types N implementing the Float trait, the case lambda = 0 is handled as follows: each sample corresponds to a sample from an Exp1 multiplied by 1 / 0. Primitive types yield infinity, since 1 / 0 = infinity.

Trait Implementations

impl<F> Clone for Exp<F>

where F: Float + Clone, Exp1: Distribution<F>,

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F> Debug for Exp<F>

where F: Float + Debug, Exp1: Distribution<F>,

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

Formats the value using the given formatter.

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

where F: Float + Deserialize<'de>, Exp1: 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 Exp<F>

where F: Float, Exp1: 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 Exp<F>

where F: Float + PartialEq, Exp1: Distribution<F>,

fn eq(&self, other: &Exp<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 Exp<F>

where F: Float + Serialize, Exp1: 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 Exp<F>

where F: Float + Copy, Exp1: Distribution<F>,

impl<F> StructuralPartialEq for Exp<F>

where F: Float, Exp1: Distribution<F>,