Struct std::rand::distributions::RangeUnstable

pub struct Range<X> {
    // some fields omitted
}

Sample values uniformly between two bounds.

This gives a uniform distribution (assuming the RNG used to sample it is itself uniform & the SampleRange implementation for the given type is correct), even for edge cases like low = 0u8, high = 170u8, for which a naive modulo operation would return numbers less than 85 with double the probability to those greater than 85.

Types should attempt to sample in [low, high), i.e., not including high, but this may be very difficult. All the primitive integer types satisfy this property, and the float types normally satisfy it, but rounding may mean high can occur.

Example

use std::rand::distributions::{IndependentSample, Range}; fn main() { let between = Range::new(10, 10000); let mut rng = std::rand::thread_rng(); let mut sum = 0; for _ in 0..1000 { sum += between.ind_sample(&mut rng); } println!("{}", sum); }

use std::rand::distributions::{IndependentSample, Range};

fn main() {
    let between = Range::new(10, 10000);
    let mut rng = std::rand::thread_rng();
    let mut sum = 0;
    for _ in 0..1000 {
        sum += between.ind_sample(&mut rng);
    }
    println!("{}", sum);
}

Methods

impl<X> Range<X> where X: SampleRange, X: PartialOrd<X>

fn new(low: X, high: X) -> Range<X>

Create a new Range instance that samples uniformly from [low, high). Panics if low >= high.

Trait Implementations

impl<Sup> Sample<Sup> for Range<Sup> where Sup: SampleRange

fn sample<R>(&mut self, rng: &mut R) -> Sup where R: Rng

impl<Sup> IndependentSample<Sup> for Range<Sup> where Sup: SampleRange

fn ind_sample<R>(&self, rng: &mut R) -> Sup where R: Rng