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

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

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

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

Performs the conversion.

Performs the conversion.

The resulting type after obtaining ownership.

Creates owned data from borrowed data, usually by cloning. Read more

🔬 This is a nightly-only experimental API. (toowned_clone_into)

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.