Trait rand::seq::SliceRandom

source · 
pub trait SliceRandom {
    type Item;

    fn choose<R>(&self, rng: &mut R) -> Option<&Self::Item>
    where
        R: Rng + ?Sized;
    fn choose_mut<R>(&mut self, rng: &mut R) -> Option<&mut Self::Item>
    where
        R: Rng + ?Sized;
    fn choose_multiple<R>(
        &self,
        rng: &mut R,
        amount: usize
    ) -> SliceChooseIter<'_, Self, Self::Item> ⓘ
    where
        R: Rng + ?Sized;
    fn choose_weighted<R, F, B, X>(
        &self,
        rng: &mut R,
        weight: F
    ) -> Result<&Self::Item, WeightedError>
    where
        R: Rng + ?Sized,
        F: Fn(&Self::Item) -> B,
        B: SampleBorrow<X>,
        X: SampleUniform + for<'a> AddAssign<&'a X> + PartialOrd<X> + Clone + Default;
    fn choose_weighted_mut<R, F, B, X>(
        &mut self,
        rng: &mut R,
        weight: F
    ) -> Result<&mut Self::Item, WeightedError>
    where
        R: Rng + ?Sized,
        F: Fn(&Self::Item) -> B,
        B: SampleBorrow<X>,
        X: SampleUniform + for<'a> AddAssign<&'a X> + PartialOrd<X> + Clone + Default;
    fn choose_multiple_weighted<R, F, X>(
        &self,
        rng: &mut R,
        amount: usize,
        weight: F
    ) -> Result<SliceChooseIter<'_, Self, Self::Item>, WeightedError>
    where
        R: Rng + ?Sized,
        F: Fn(&Self::Item) -> X,
        X: Into<f64>;
    fn shuffle<R>(&mut self, rng: &mut R)
    where
        R: Rng + ?Sized;
    fn partial_shuffle<R>(
        &mut self,
        rng: &mut R,
        amount: usize
    ) -> (&mut [Self::Item], &mut [Self::Item])
    where
        R: Rng + ?Sized;
}
Expand description

Extension trait on slices, providing random mutation and sampling methods.

This trait is implemented on all [T] slice types, providing several methods for choosing and shuffling elements. You must use this trait:

use rand::seq::SliceRandom;

let mut rng = rand::thread_rng();
let mut bytes = "Hello, random!".to_string().into_bytes();
bytes.shuffle(&mut rng);
let str = String::from_utf8(bytes).unwrap();
println!("{}", str);

Example output (non-deterministic):

l,nmroHado !le

Required Associated Types§

The element type.

Required Methods§

Returns a reference to one random element of the slice, or None if the slice is empty.

For slices, complexity is O(1).

Example
use rand::thread_rng;
use rand::seq::SliceRandom;

let choices = [1, 2, 4, 8, 16, 32];
let mut rng = thread_rng();
println!("{:?}", choices.choose(&mut rng));
assert_eq!(choices[..0].choose(&mut rng), None);

Returns a mutable reference to one random element of the slice, or None if the slice is empty.

For slices, complexity is O(1).

Chooses amount elements from the slice at random, without repetition, and in random order. The returned iterator is appropriate both for collection into a Vec and filling an existing buffer (see example).

In case this API is not sufficiently flexible, use index::sample.

For slices, complexity is the same as index::sample.

Example
use rand::seq::SliceRandom;

let mut rng = &mut rand::thread_rng();
let sample = "Hello, audience!".as_bytes();

// collect the results into a vector:
let v: Vec<u8> = sample.choose_multiple(&mut rng, 3).cloned().collect();

// store in a buffer:
let mut buf = [0u8; 5];
for (b, slot) in sample.choose_multiple(&mut rng, buf.len()).zip(buf.iter_mut()) {
    *slot = *b;
}

Similar to choose, but where the likelihood of each outcome may be specified.

The specified function weight maps each item x to a relative likelihood weight(x). The probability of each item being selected is therefore weight(x) / s, where s is the sum of all weight(x).

For slices of length n, complexity is O(n). See also choose_weighted_mut, distributions::weighted.

Example
use rand::prelude::*;

let choices = [('a', 2), ('b', 1), ('c', 1)];
let mut rng = thread_rng();
// 50% chance to print 'a', 25% chance to print 'b', 25% chance to print 'c'
println!("{:?}", choices.choose_weighted(&mut rng, |item| item.1).unwrap().0);

Similar to choose_mut, but where the likelihood of each outcome may be specified.

The specified function weight maps each item x to a relative likelihood weight(x). The probability of each item being selected is therefore weight(x) / s, where s is the sum of all weight(x).

For slices of length n, complexity is O(n). See also choose_weighted, distributions::weighted.

Similar to choose_multiple, but where the likelihood of each element’s inclusion in the output may be specified. The elements are returned in an arbitrary, unspecified order.

The specified function weight maps each item x to a relative likelihood weight(x). The probability of each item being selected is therefore weight(x) / s, where s is the sum of all weight(x).

If all of the weights are equal, even if they are all zero, each element has an equal likelihood of being selected.

The complexity of this method depends on the feature partition_at_index. If the feature is enabled, then for slices of length n, the complexity is O(n) space and O(n) time. Otherwise, the complexity is O(n) space and O(n * log amount) time.

Example
use rand::prelude::*;

let choices = [('a', 2), ('b', 1), ('c', 1)];
let mut rng = thread_rng();
// First Draw * Second Draw = total odds
// -----------------------
// (50% * 50%) + (25% * 67%) = 41.7% chance that the output is `['a', 'b']` in some order.
// (50% * 50%) + (25% * 67%) = 41.7% chance that the output is `['a', 'c']` in some order.
// (25% * 33%) + (25% * 33%) = 16.6% chance that the output is `['b', 'c']` in some order.
println!("{:?}", choices.choose_multiple_weighted(&mut rng, 2, |item| item.1).unwrap().collect::<Vec<_>>());

Shuffle a mutable slice in place.

For slices of length n, complexity is O(n).

Example
use rand::seq::SliceRandom;
use rand::thread_rng;

let mut rng = thread_rng();
let mut y = [1, 2, 3, 4, 5];
println!("Unshuffled: {:?}", y);
y.shuffle(&mut rng);
println!("Shuffled:   {:?}", y);

Shuffle a slice in place, but exit early.

Returns two mutable slices from the source slice. The first contains amount elements randomly permuted. The second has the remaining elements that are not fully shuffled.

This is an efficient method to select amount elements at random from the slice, provided the slice may be mutated.

If you only need to choose elements randomly and amount > self.len()/2 then you may improve performance by taking amount = values.len() - amount and using only the second slice.

If amount is greater than the number of elements in the slice, this will perform a full shuffle.

For slices, complexity is O(m) where m = amount.

Implementations on Foreign Types§

Implementors§