# Struct rand::distributions::weighted::WeightedIndex

source · `pub struct WeightedIndex<X: SampleUniform + PartialOrd> { /* 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: SampleUniform + PartialOrd> WeightedIndex<X>

### impl<X: SampleUniform + PartialOrd> WeightedIndex<X>

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

Note: Updating floating-point weights may cause slight inaccuracies in the total weight.
This method may not return `WeightedError::AllWeightsZero`

when all weights
are zero if using floating-point weights.

## Trait Implementations§

source§### impl<X: Clone + SampleUniform + PartialOrd> Clone for WeightedIndex<X>where
X::Sampler: Clone,

### impl<X: Clone + SampleUniform + PartialOrd> Clone for WeightedIndex<X>where X::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 + SampleUniform + PartialOrd> Debug for WeightedIndex<X>where
X::Sampler: Debug,

### impl<X: Debug + SampleUniform + PartialOrd> Debug for WeightedIndex<X>where X::Sampler: Debug,

source§### impl<'de, X> Deserialize<'de> for WeightedIndex<X>where
X: Deserialize<'de> + SampleUniform + PartialOrd,
X::Sampler: Deserialize<'de>,

### impl<'de, X> Deserialize<'de> for WeightedIndex<X>where X: Deserialize<'de> + SampleUniform + PartialOrd, X::Sampler: Deserialize<'de>,

source§#### fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
__D: Deserializer<'de>,

#### fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where __D: Deserializer<'de>,

source§### impl<X> Distribution<usize> for WeightedIndex<X>where
X: SampleUniform + PartialOrd,

### impl<X> Distribution<usize> for WeightedIndex<X>where X: SampleUniform + PartialOrd,

source§#### fn 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.source§### impl<X: PartialEq + SampleUniform + PartialOrd> PartialEq<WeightedIndex<X>> for WeightedIndex<X>where
X::Sampler: PartialEq,

### impl<X: PartialEq + SampleUniform + PartialOrd> PartialEq<WeightedIndex<X>> for WeightedIndex<X>where X::Sampler: PartialEq,

source§#### fn eq(&self, other: &WeightedIndex<X>) -> bool

#### fn eq(&self, other: &WeightedIndex<X>) -> bool

`self`

and `other`

values to be equal, and is used
by `==`

.