Struct rand_distr::Pert

pub struct Pert<N> { /* fields omitted */ }

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);

Methods

impl<N: Float> Pert<N> where
    StandardNormal: Distribution<N>,
    Exp1: Distribution<N>,
    Open01: Distribution<N>, 

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

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

Trait Implementations

impl<N: Clone> Clone for Pert<N>

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

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<N: Copy> Copy for Pert<N>

impl<N: Debug> Debug for Pert<N>

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

Formats the value using the given formatter.

impl<N: Float> Distribution<N> for Pert<N> where
    StandardNormal: Distribution<N>,
    Exp1: Distribution<N>,
    Open01: Distribution<N>, 

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

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

Important traits for DistIter<D, R, T>

impl<D, R, T> Iterator for DistIter<D, R, T> where
    D: Distribution<T>,
    R: Rng,  type Item = T;

fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T> where
    R: Rng, 

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