# Struct rand_distr::WeightedIndex

source · `pub struct WeightedIndex<X>where`

X: SampleUniform + PartialOrd<X>,{ /* private fields */ }

**crate feature**only.

`alloc`

## Expand description

A distribution using weighted sampling of discrete items

Sampling a `WeightedIndex`

distribution returns the index of a randomly
selected element from the iterator used when the `WeightedIndex`

was
created. The chance of a given element being picked is proportional to the
weight of the element. The weights can use any type `X`

for which an
implementation of `Uniform<X>`

exists. The implementation guarantees that
elements with zero weight are never picked, even when the weights are
floating point numbers.

## Performance

Time complexity of sampling from `WeightedIndex`

is `O(log N)`

where
`N`

is the number of weights. As an alternative,
`rand_distr::weighted_alias`

supports `O(1)`

sampling, but with much higher initialisation cost.

A `WeightedIndex<X>`

contains a `Vec<X>`

and a `Uniform<X>`

and so its
size is the sum of the size of those objects, possibly plus some alignment.

Creating a `WeightedIndex<X>`

will allocate enough space to hold `N - 1`

weights of type `X`

, where `N`

is the number of weights. However, since
`Vec`

doesn’t guarantee a particular growth strategy, additional memory
might be allocated but not used. Since the `WeightedIndex`

object also
contains an instance of `X::Sampler`

, this might cause additional allocations,
though for primitive types, `Uniform<X>`

doesn’t allocate any memory.

Sampling from `WeightedIndex`

will result in a single call to
`Uniform<X>::sample`

(method of the `Distribution`

trait), which typically
will request a single value from the underlying `RngCore`

, though the
exact number depends on the implementation of `Uniform<X>::sample`

.

## Example

```
use rand::prelude::*;
use rand::distributions::WeightedIndex;
let choices = ['a', 'b', 'c'];
let weights = [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', 0.0), ('b', 3.0), ('c', 7.0)];
let dist2 = WeightedIndex::new(items.iter().map(|item| item.1)).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

source### impl<X> WeightedIndex<X>where

X: SampleUniform + PartialOrd<X>,

### impl<X> WeightedIndex<X>where

X: SampleUniform + PartialOrd<X>,

source#### pub fn new<I>(weights: I) -> Result<WeightedIndex<X>, WeightedError>where

I: IntoIterator,

<I as IntoIterator>::Item: SampleBorrow<X>,

X: for<'a> AddAssign<&'a X> + Clone + Default,

#### pub fn new<I>(weights: I) -> Result<WeightedIndex<X>, WeightedError>where

I: IntoIterator,

<I as IntoIterator>::Item: SampleBorrow<X>,

X: for<'a> AddAssign<&'a X> + Clone + Default,

Creates a new a `WeightedIndex`

`Distribution`

using the values
in `weights`

. The weights can use any type `X`

for which an
implementation of `Uniform<X>`

exists.

Returns an error if the iterator is empty, if any weight is `< 0`

, or
if its total value is 0.

source#### pub fn update_weights(

&mut self,

new_weights: &[(usize, &X)]

) -> Result<(), WeightedError>where

X: for<'a> AddAssign<&'a X> + for<'a> SubAssign<&'a X> + Clone + Default,

#### pub fn update_weights(

&mut self,

new_weights: &[(usize, &X)]

) -> Result<(), WeightedError>where

X: for<'a> AddAssign<&'a X> + for<'a> SubAssign<&'a X> + Clone + Default,

Update a subset of weights, without changing the number of weights.

`new_weights`

must be sorted by the index.

Using this method instead of `new`

might be more efficient if only a small number of
weights is modified. No allocations are performed, unless the weight type `X`

uses
allocation internally.

In case of error, `self`

is not modified.

## Trait Implementations

source### impl<X> Clone for WeightedIndex<X>where

X: Clone + SampleUniform + PartialOrd<X>,

<X as SampleUniform>::Sampler: Clone,

### impl<X> Clone for WeightedIndex<X>where

X: Clone + SampleUniform + PartialOrd<X>,

<X as SampleUniform>::Sampler: Clone,

source#### fn clone(&self) -> WeightedIndex<X>

#### fn clone(&self) -> WeightedIndex<X>

1.0.0 · source#### fn clone_from(&mut self, source: &Self)

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

`source`

. Read moresource### impl<X> Debug for WeightedIndex<X>where

X: Debug + SampleUniform + PartialOrd<X>,

<X as SampleUniform>::Sampler: Debug,

### impl<X> Debug for WeightedIndex<X>where

X: Debug + SampleUniform + PartialOrd<X>,

<X as SampleUniform>::Sampler: Debug,

source### impl<'de, X> Deserialize<'de> for WeightedIndex<X>where

X: SampleUniform + PartialOrd<X> + Deserialize<'de>,

<X as SampleUniform>::Sampler: Deserialize<'de>,

### impl<'de, X> Deserialize<'de> for WeightedIndex<X>where

X: SampleUniform + PartialOrd<X> + Deserialize<'de>,

<X as SampleUniform>::Sampler: Deserialize<'de>,

source#### fn deserialize<__D>(

__deserializer: __D

) -> Result<WeightedIndex<X>, <__D as Deserializer<'de>>::Error>where

__D: Deserializer<'de>,

#### fn deserialize<__D>(

__deserializer: __D

) -> Result<WeightedIndex<X>, <__D as Deserializer<'de>>::Error>where

__D: Deserializer<'de>,

source### impl<X> Distribution<usize> for WeightedIndex<X>where

X: SampleUniform + PartialOrd<X>,

### impl<X> Distribution<usize> for WeightedIndex<X>where

X: SampleUniform + PartialOrd<X>,

source#### fn sample<R>(&self, rng: &mut R) -> usizewhere

R: Rng + ?Sized,

#### fn sample<R>(&self, rng: &mut R) -> usizewhere

R: Rng + ?Sized,

`T`

, using `rng`

as the source of randomness.