Struct rand::distributions::weighted::alias_method::WeightedIndex
source · [−]pub struct WeightedIndex<W: Weight> { /* private fields */ }
Expand description
A distribution using weighted sampling to pick a discretely selected item.
Sampling a WeightedIndex<W>
distribution returns the index of a randomly
selected element from the vector used to create the WeightedIndex<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
Weight
exists.
Performance
Given that n
is the number of items in the vector used to create an
WeightedIndex<W>
, WeightedIndex<W>
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 WeightedIndex<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::distributions::weighted::alias_method::WeightedIndex;
use rand::prelude::*;
let choices = vec!['a', 'b', 'c'];
let weights = vec![2, 1, 1];
let dist = WeightedIndex::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 = WeightedIndex::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: Weight> WeightedIndex<W>
impl<W: Weight> WeightedIndex<W>
sourcepub fn new(weights: Vec<W>) -> Result<Self, WeightedError>
pub fn new(weights: Vec<W>) -> Result<Self, WeightedError>
Creates a new WeightedIndex
.
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: Weight> Distribution<usize> for WeightedIndex<W>
impl<W: Weight> Distribution<usize> for WeightedIndex<W>
sourcefn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> usize
Generate a random value of 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,
Self: Sized,
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,
Self: Sized,
D: Distribution<T>,
R: Rng, type Item = T;
Create an iterator that generates random values of T
, using rng
as
the source of randomness. Read more
Auto Trait Implementations
impl<W> RefUnwindSafe for WeightedIndex<W> where
W: RefUnwindSafe,
<W as SampleUniform>::Sampler: RefUnwindSafe,
impl<W> Send for WeightedIndex<W> where
W: Send,
<W as SampleUniform>::Sampler: Send,
impl<W> Sync for WeightedIndex<W> where
W: Sync,
<W as SampleUniform>::Sampler: Sync,
impl<W> Unpin for WeightedIndex<W> where
W: Unpin,
<W as SampleUniform>::Sampler: Unpin,
impl<W> UnwindSafe for WeightedIndex<W> where
W: UnwindSafe,
<W as SampleUniform>::Sampler: UnwindSafe,
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcefn clone_into(&self, target: &mut T)
fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more