Crate random_choice [−] [src]
Rust Random Choice
This is an implementation of the stochastic universal sampling algorithm: https://en.wikipedia.org/wiki/Stochastic_universal_sampling
Advantages
- Blazingly fast: O(n) (Roulette wheel selection algorithm: O(n * log n))
- Low Memory Usage: O(n); in place variant: O(1)
- There is a good diversity for the case, that all weights are equally distributed (in contrast to the roulette wheel selection algorithm which tends to select the same sample n times)
- The sum of the weights don't have to be 1.0, but must not overflow
Applications
- Evolutionary algorithms: Choose the n fittest populations by their fitness fi
- Monte Carlo Localization: Resampling of n particles by their weight w
Examples
Default Way
extern crate random_choice; use self::random_choice::random_choice; let mut samples = vec!["hi", "this", "is", "a", "test!"]; let weights: Vec<f64> = vec![5.6, 7.8, 9.7, 1.1, 2.0]; let number_choices = 100; let choices = random_choice().random_choice_f64(&samples, &weights, number_choices); for choice in choices { print!("{}, ", choice); }
With Custom Seed
extern crate rand; extern crate random_choice; use random_choice::RandomChoice; use rand::SeedableRng; fn main() { let mut samples = vec!["hi", "this", "is", "a", "test!"]; let weights: Vec<f64> = vec![5.6, 7.8, 9.7, 1.1, 2.0]; let rng = rand::StdRng::from_seed(&[5000, 44, 55, 199]); let mut random_choice = RandomChoice::new(rng); let number_choices = 100; let choices = random_choice.random_choice_f64(&mut samples, &weights, number_choices); for choice in choices { print!("{}, ", choice); } }
Structs
RandomChoice |
Functions
random_choice |
Creates a new RandomChoice struct using the ThreadRng |