diophantus_equation.rs

use core::{
    iter::Sum,
    ops::{Mul, Sub},
};

use chromosome::Fitness;
use chromosome::{Chromosome, DefaultSimulator, FitnessSelector, Simulator};
use rand_pcg::Pcg64;
use rand_seeder::Seeder;

struct DiophantusEquation<'a, 'b, T> {
    coefficients: &'a Vec<T>,
    result: &'b T,
}

impl<'a, 'b, T> DiophantusEquation<'a, 'b, T> {
    fn new(coefficients: &'a Vec<T>, result: &'b T) -> Self {
        DiophantusEquation {
            coefficients: coefficients,
            result: result,
        }
    }
}

impl<'a, 'b, T: Mul<Output = T> + Sum + Sub<Output = T> + Into<f64> + Clone> Fitness
    for DiophantusEquation<'a, 'b, T>
{
    type Value = T;
    fn fitness(self: &Self, chromosome: &Chromosome<T>) -> T {
        (0..usize::min(self.coefficients.len(), chromosome.genes.len()))
            .map(|i| self.coefficients[i].clone() * chromosome.genes[i].clone())
            .sum::<T>()
            - self.result.clone()
    }

    fn is_ideal_fitness(&self, fitness: Self::Value) -> bool {
        fitness.into() == 0_f64
    }
}

#[test]
fn diophantus_equation() {
    let mut rng: Pcg64 = Seeder::from([23, 87, 85]).make_rng();
    let coefs = vec![2_i32, 23, 54, 1];
    let equation = DiophantusEquation::new(&coefs, &2);

    let sim_result = DefaultSimulator::new(vec![1; 4], 0.09, 10000).simulate(
        vec![
            Chromosome::new_random(equation.coefficients.len(), 0_i32..10, &mut rng),
            Chromosome::new_random(equation.coefficients.len(), 0_i32..10, &mut rng),
        ],
        FitnessSelector::from(equation),
        &mut rng,
    );

    assert!(sim_result.is_some())
}