Struct rand_distr::Pert

pub struct Pert<F>
where
    F: Float,
    StandardNormal: Distribution<F>,
    Exp1: Distribution<F>,
    Open01: Distribution<F>,
{ /* private fields */ }

The PERT distribution.

Similar to the Triangular distribution, the PERT distribution is parameterised by a range and a mode within that range. Unlike the Triangular distribution, the probability density function of the PERT distribution is smooth, with a configurable weighting around the mode.

Example

use rand_distr::{Pert, Distribution};

let d = Pert::new(0., 5., 2.5).unwrap();
let v = d.sample(&mut rand::thread_rng());
println!("{} is from a PERT distribution", v);

Implementations

impl<F> Pert<F>

where F: Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

pub fn new(min: F, max: F, mode: F) -> Result<Pert<F>, PertError>

Set up the PERT distribution with defined min, max and mode.

This is equivalent to calling Pert::new_shape with shape == 4.0.

pub fn new_with_shape( min: F, max: F, mode: F, shape: F ) -> Result<Pert<F>, PertError>

Set up the PERT distribution with defined min, max, mode and shape.

Trait Implementations

impl<F> Clone for Pert<F>

where F: Float + Clone, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

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

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<F> Debug for Pert<F>

where F: Float + Debug, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,

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

Formats the value using the given formatter.

impl<F> Distribution<F> for Pert<F>

where F: Float, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: 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) -> DistIter<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) -> DistMap<Self, F, T, S>

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

Create a distribution of values of ‘S’ by mapping the output of Self through the closure F

impl<F> Copy for Pert<F>

where F: Float + Copy, StandardNormal: Distribution<F>, Exp1: Distribution<F>, Open01: Distribution<F>,