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use genotype::Genotype;
use niches_beta_rate::*;
use rayon::prelude::*;
use std::cmp::PartialEq;
use IndWithFitness;
use PopulationRefitnessFunctions::*;
pub trait PopulationRefitness<T: PartialEq + Send + Sync, G: Genotype<T>>: Send + Sync {
fn population_refitness(
&self,
individual_index: usize,
population: &[IndWithFitness<T, G>],
generation: u64,
progress: f64,
n_solutions: usize,
) -> f64;
}
#[derive(Debug)]
pub struct NichesAlpha(pub f64);
#[derive(Debug)]
pub struct NichesSigma(pub f64);
pub enum PopulationRefitnessFunctions {
None,
Niches(NichesAlpha, Box<NichesBetaRates>, NichesSigma),
}
impl<T: PartialEq + Send + Sync, G: Genotype<T>> PopulationRefitness<T, G>
for PopulationRefitnessFunctions
{
fn population_refitness(
&self,
individual_index: usize,
population: &[IndWithFitness<T, G>],
generation: u64,
progress: f64,
n_solutions: usize,
) -> f64 {
match self {
None => {
population[individual_index]
.fitness
.unwrap()
.original_fitness
}
Niches(alfa, beta, sigma) => {
let current_ind = &population[individual_index].ind;
let current_fitness = &population[individual_index].fitness;
let mut current_fitness = current_fitness.unwrap().original_fitness;
if current_fitness > 0.0 {
let mut m = population
.par_iter()
.enumerate()
.filter(|(i, _ind)| *i != individual_index)
.map(|(_i, ind)| current_ind.distance(&ind.ind))
.map(|d| {
if d >= sigma.0 {
0.0
} else {
1.0 - (d / sigma.0).powf(alfa.0)
}
})
.sum::<f64>();
current_fitness =
current_fitness.powf(beta.rate(generation, progress, n_solutions));
if m == 0.0 {
m = f64::EPSILON;
}
current_fitness / m
} else {
current_fitness
}
}
}
}
}