Struct rand_distr::weighted_alias::WeightedAliasIndex
source · [−]pub struct WeightedAliasIndex<W: AliasableWeight> { /* private fields */ }
Expand description
A distribution using weighted sampling to pick a discretely selected item.
Sampling a WeightedAliasIndex<W>
distribution returns the index of a randomly
selected element from the vector used to create the WeightedAliasIndex<W>
.
The chance of a given element being picked is proportional to the value of
the element. The weights can have any type W
for which a implementation of
AliasableWeight
exists.
Performance
Given that n
is the number of items in the vector used to create an
WeightedAliasIndex<W>
, it will require O(n)
amount of memory.
More specifically it takes up some constant amount of memory plus
the vector used to create it and a Vec<u32>
with capacity n
.
Time complexity for the creation of a WeightedAliasIndex<W>
is O(n)
.
Sampling is O(1)
, it makes a call to Uniform<u32>::sample
and a call
to Uniform<W>::sample
.
Example
use rand_distr::WeightedAliasIndex;
use rand::prelude::*;
let choices = vec!['a', 'b', 'c'];
let weights = vec![2, 1, 1];
let dist = WeightedAliasIndex::new(weights).unwrap();
let mut rng = thread_rng();
for _ in 0..100 {
// 50% chance to print 'a', 25% chance to print 'b', 25% chance to print 'c'
println!("{}", choices[dist.sample(&mut rng)]);
}
let items = [('a', 0), ('b', 3), ('c', 7)];
let dist2 = WeightedAliasIndex::new(items.iter().map(|item| item.1).collect()).unwrap();
for _ in 0..100 {
// 0% chance to print 'a', 30% chance to print 'b', 70% chance to print 'c'
println!("{}", items[dist2.sample(&mut rng)].0);
}
Implementations
sourceimpl<W: AliasableWeight> WeightedAliasIndex<W>
impl<W: AliasableWeight> WeightedAliasIndex<W>
sourcepub fn new(weights: Vec<W>) -> Result<Self, WeightedError>
pub fn new(weights: Vec<W>) -> Result<Self, WeightedError>
Creates a new WeightedAliasIndex
.
Returns an error if:
- The vector is empty.
- The vector is longer than
u32::MAX
. - For any weight
w
:w < 0
orw > max
wheremax = W::MAX / weights.len()
. - The sum of weights is zero.
Trait Implementations
sourceimpl<W: AliasableWeight> Clone for WeightedAliasIndex<W>where
Uniform<W>: Clone,
impl<W: AliasableWeight> Clone for WeightedAliasIndex<W>where
Uniform<W>: Clone,
1.0.0const fn clone_from(&mut self, source: &Self)
const fn clone_from(&mut self, source: &Self)
source
. Read moresourceimpl<W: AliasableWeight> Debug for WeightedAliasIndex<W>where
W: Debug,
Uniform<W>: Debug,
impl<W: AliasableWeight> Debug for WeightedAliasIndex<W>where
W: Debug,
Uniform<W>: Debug,
sourceimpl<W: AliasableWeight> Distribution<usize> for WeightedAliasIndex<W>
impl<W: AliasableWeight> Distribution<usize> for WeightedAliasIndex<W>
sourcefn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize
T
, using rng
as the source of randomness.sourcefn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>ⓘNotable 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;
where
R: Rng,
fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>ⓘNotable 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;
where
R: Rng,
D: Distribution<T>,
R: Rng, type Item = T;
T
, using rng
as
the source of randomness. Read more