# Struct rand::distributions::WeightedIndex [−][src]

`pub struct WeightedIndex<X: SampleUniform + PartialOrd> { /* fields omitted */ }`

## 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
value of the element. The weights can use any type `X`

for which an
implementation of `Uniform<X>`

exists.

# 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, 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), ('b', 3), ('c', 7)];
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

#### 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.

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

### 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,

### 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,

Generate a random value of `T`

, using `rng`

as the source of randomness.

Create an iterator that generates random values of `T`

, using `rng`

as
the source of randomness. Read more

## Auto Trait Implementations

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

X: RefUnwindSafe,

<X as SampleUniform>::Sampler: RefUnwindSafe,

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

X: Send,

<X as SampleUniform>::Sampler: Send,

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

X: Sync,

<X as SampleUniform>::Sampler: Sync,

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

X: Unpin,

<X as SampleUniform>::Sampler: Unpin,

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

X: UnwindSafe,

<X as SampleUniform>::Sampler: UnwindSafe,

## Blanket Implementations

Mutably borrows from an owned value. Read more