Struct rand::distributions::WeightedChoice

pub struct WeightedChoice<'a, T: 'a> { /* fields omitted */ }

A distribution that selects from a finite collection of weighted items.

Each item has an associated weight that influences how likely it is to be chosen: higher weight is more likely.

The Clone restriction is a limitation of the Distribution trait. Note that &T is (cheaply) Clone for all T, as is u32, so one can store references or indices into another vector.

Example

use rand::distributions::{Weighted, WeightedChoice, Distribution};

let mut items = vec!(Weighted { weight: 2, item: 'a' },
                     Weighted { weight: 4, item: 'b' },
                     Weighted { weight: 1, item: 'c' });
let wc = WeightedChoice::new(&mut items);
let mut rng = rand::thread_rng();
for _ in 0..16 {
     // on average prints 'a' 4 times, 'b' 8 and 'c' twice.
     println!("{}", wc.sample(&mut rng));
}

Methods

impl<'a, T: Clone> WeightedChoice<'a, T>

pub fn new(items: &'a mut [Weighted<T>]) -> WeightedChoice<'a, T>

Create a new WeightedChoice.

Panics if:

• items is empty
• the total weight is 0
• the total weight is larger than a u32 can contain.

Trait Implementations

impl<'a, T: Clone> Sample<T> for WeightedChoice<'a, T>

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

Deprecated since 0.5.0

: use Distribution instead

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

impl<'a, T: Clone> IndependentSample<T> for WeightedChoice<'a, T>

fn ind_sample<R: Rng>(&self, rng: &mut R) -> T

Deprecated since 0.5.0

: use Distribution instead

Generate a random value.

impl<'a, T: Debug + 'a> Debug for WeightedChoice<'a, T>

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

Formats the value using the given formatter.

impl<'a, T: Clone> Distribution<T> for WeightedChoice<'a, T>

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

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

Important traits for DistIter<'a, D, R, T>

impl<'a, D, R, T> Iterator for DistIter<'a, D, R, T> where
    D: Distribution<T>,
    R: Rng + 'a,  type Item = T;

fn sample_iter<'a, R>(&'a self, rng: &'a mut R) -> DistIter<'a, Self, R, T> where     Self: Sized,     R: Rng,

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