# 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 random_choice;
use self::random_choice::random_choice;
# fn main() {
let capacity: usize = 500;
let mut samples: Vec<usize> = Vec::with_capacity(capacity);
let mut weights: Vec<f64> = Vec::with_capacity(capacity);
for i in 0..capacity {
samples.push(i);
weights.push(i as f64);
}
let number_choices = 10000;
let choices = random_choice().random_choice_f64(&samples, &weights, number_choices);
assert!(choices.len() == 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);
}
}
```