Rust Random Choice

Chooses samples randomly by their weights/probabilities.

Advantages

• 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)
• Blazingly fast: O(n) (Roulette wheel selection algorithm: O(n * log n))
• Memory Usage: O(n); in place variant: O(1)
• The sum of the weights don't have to be 1.0, but must not overflow

This algorithm is based on the stochastic universal sampling algorithm.

Applications

• Evolutionary algorithms: Choose the n fittest populations by their fitness fi
• Monte Carlo Localization: Resampling of n particles by their weight w

Usage

Add this to your Cargo.toml:

toml [dependencies] random_choice = "*"

Examples

Default Way

``` extern crate randomchoice; use self::randomchoice::random_choice;

fn main() {

let capacity: usize = 500; let mut samples: Vec = Vec::withcapacity(capacity); let mut weights: Vec = Vec::withcapacity(capacity);

for i in 0..capacity { samples.push(i); weights.push(i as f64); }

let numberchoices = 10000; let choices = randomchoice().randomchoicef64(&samples, &weights, number_choices);

assert!(choices.len() == number_choices);

for choice in choices { print!("{}, ", choice); }

}

```

With Custom Seed

``` extern crate rand; extern crate randomchoice; use randomchoice::RandomChoice; use rand::SeedableRng;

fn main() { let mut samples = vec!["hi", "this", "is", "a", "test!"]; let weights: Vec = 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);
}

} ```