pub struct WeightedIndex<X: SampleUniform + PartialOrd> { /* private fields */ }
Available on crate feature alloc only.
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

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

Returns a copy of the value. Read more
Performs copy-assignment from source. Read more
Formats the value using the given formatter. Read more
Deserialize this value from the given Serde deserializer. Read more
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
Create a distribution of values of ‘S’ by mapping the output of Self through the closure F Read more
This method tests for self and other values to be equal, and is used by ==. Read more
This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason. Read more
Serialize this value into the given Serde serializer. Read more

Auto Trait Implementations

Blanket Implementations

Gets the TypeId of self. Read more
Immutably borrows from an owned value. Read more
Mutably borrows from an owned value. Read more

Returns the argument unchanged.

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

The resulting type after obtaining ownership.
Creates owned data from borrowed data, usually by cloning. Read more
Uses borrowed data to replace owned data, usually by cloning. Read more
The type returned in the event of a conversion error.
Performs the conversion.
The type returned in the event of a conversion error.
Performs the conversion.