Did things

Cargo.toml

@@ -5,16 +5,7 @@ authors = ["logaritmisk <anders.e.olsson@gmail.com>"]
 edition = "2018"
 
 [dependencies]
-rayon = "1.0"
-
-[[example]]
-name = "parabole"
-path = "examples/parabole.rs"
-
-[dev-dependencies]
-criterion = "0.2"
 rand = "0.6"
 
-[[bench]]
+[dev-dependencies]
-name = "parabole"
+approx = "0.3"
-harness = false

benches/parabole.rs

@@ -1,138 +0,0 @@
-#[macro_use]
-extern crate criterion;
-extern crate genetisk;
-extern crate rand;
-extern crate rayon;
-
-use std::cmp::Ordering;
-
-use criterion::Criterion;
-use rand::distributions::{IndependentSample, Range};
-use rayon::prelude::*;
-
-use genetisk::{Individual, Simulation, Wrapper};
-
-#[derive(Clone, Copy, Debug)]
-struct Fitness(f64);
-
-impl PartialEq for Fitness {
-    fn eq(&self, other: &Fitness) -> bool {
-        (self.0 - other.0).abs() < 0.0001
-    }
-}
-
-impl Eq for Fitness {}
-
-impl PartialOrd for Fitness {
-    fn partial_cmp(&self, other: &Fitness) -> Option<Ordering> {
-        self.0.partial_cmp(&other.0)
-    }
-}
-
-impl Ord for Fitness {
-    fn cmp(&self, other: &Fitness) -> Ordering {
-        self.0.partial_cmp(&other.0).unwrap_or(Ordering::Equal)
-    }
-}
-
-#[derive(Clone, Debug)]
-struct Parabole {
-    x: f64,
-}
-
-impl Individual for Parabole {
-    type Fitness = Fitness;
-
-    fn mate(&self, other: &Parabole) -> Parabole {
-        Parabole {
-            x: (self.x + other.x) / 2.0,
-        }
-    }
-
-    fn mutate(&mut self) {
-        let mut rng = rand::weak_rng();
-
-        let between = Range::new(-1.0, 1.0);
-        let offset = between.ind_sample(&mut rng);
-
-        self.x += offset;
-    }
-
-    fn fitness(&self) -> Self::Fitness {
-        Fitness(10.0 - ((self.x + 3.0) * (self.x + 3.0)))
-    }
-}
-
-fn criterion_benchmark(c: &mut Criterion) {
-    c.bench_function("calculate", move |b| {
-        let initial = (0..1000)
-            .map(|i| Parabole { x: f64::from(i) })
-            .collect::<Vec<_>>();
-
-        let mut simulation = Simulation::with_population(initial);
-
-        b.iter(|| {
-            simulation.calculate();
-        });
-    });
-
-    c.bench_function("evolve", move |b| {
-        let initial = (0..1000)
-            .map(|i| Parabole { x: f64::from(i) })
-            .collect::<Vec<_>>();
-
-        let population_size = initial.len();
-
-        let mut simulation = Simulation::with_population(initial);
-
-        b.iter(|| {
-            simulation.evolve(|parents, population| {
-                // Mate top 10 to get 5 children.
-                parents
-                    .iter()
-                    .enumerate()
-                    .filter(|&(n, _)| n % 2 == 0)
-                    .map(|(_, wrapper)| wrapper)
-                    .take(5)
-                    .zip(
-                        parents
-                            .iter()
-                            .enumerate()
-                            .filter(|&(n, _)| n % 2 == 1)
-                            .map(|(_, wrapper)| wrapper)
-                            .take(5),
-                    )
-                    .map(|(a, b)| a.individual.mate(&b.individual))
-                    .map(|individual| Wrapper {
-                        individual,
-                        fitness: None,
-                    })
-                    .for_each(|wrapper| {
-                        population.push(wrapper);
-                    });
-
-                // Mutate all to get new children.
-                parents
-                    .par_iter()
-                    .map(|wrapper| wrapper.individual.clone())
-                    .map(|mut individual| {
-                        individual.mutate();
-
-                        Wrapper {
-                            individual,
-                            fitness: None,
-                        }
-                    })
-                    .collect_into_vec(population);
-
-                // Add all parents again.
-                population.extend(parents.iter().cloned());
-            });
-
-            simulation.population.truncate(population_size);
-        });
-    });
-}
-
-criterion_group!(benches, criterion_benchmark);
-criterion_main!(benches);

examples/parabole.rs

@@ -1,126 +0,0 @@
-use std::cmp::Ordering;
-
-use genetisk::{Individual, Simulation, Wrapper};
-use rand::distributions::{IndependentSample, Range};
-use rayon::prelude::*;
-
-#[derive(Clone, Copy, Debug)]
-struct Fitness(f64);
-
-impl PartialEq for Fitness {
-    fn eq(&self, other: &Fitness) -> bool {
-        (self.0 - other.0).abs() < 0.0001
-    }
-}
-
-impl Eq for Fitness {}
-
-impl PartialOrd for Fitness {
-    fn partial_cmp(&self, other: &Fitness) -> Option<Ordering> {
-        self.0.partial_cmp(&other.0)
-    }
-}
-
-impl Ord for Fitness {
-    fn cmp(&self, other: &Fitness) -> Ordering {
-        self.0.partial_cmp(&other.0).unwrap_or(Ordering::Equal)
-    }
-}
-
-#[derive(Clone, Debug)]
-struct Parabole {
-    x: f64,
-}
-
-impl Individual for Parabole {
-    type Fitness = Fitness;
-
-    fn mate(&self, other: &Parabole) -> Parabole {
-        Parabole {
-            x: (self.x + other.x) / 2.0,
-        }
-    }
-
-    fn mutate(&mut self) {
-        let mut rng = rand::weak_rng();
-
-        let between = Range::new(-1.0, 1.0);
-        let offset = between.ind_sample(&mut rng);
-
-        self.x += offset;
-    }
-
-    fn fitness(&self) -> Self::Fitness {
-        Fitness(10.0 - ((self.x + 3.0) * (self.x + 3.0)))
-    }
-}
-
-fn main() {
-    let initial = (0..300)
-        .map(|i| Parabole { x: i as f64 })
-        .collect::<Vec<_>>();
-
-    let population_size = initial.len();
-
-    let mut simulation = Simulation::with_population(initial);
-
-    simulation.calculate();
-
-    for _ in 0..250_000 {
-        simulation.evolve(|parents, population| {
-            // Mate top 10 to get 5 children.
-            parents
-                .iter()
-                .enumerate()
-                .filter(|&(n, _)| n % 2 == 0)
-                .map(|(_, wrapper)| wrapper)
-                .take(5)
-                .zip(
-                    parents
-                        .iter()
-                        .enumerate()
-                        .filter(|&(n, _)| n % 2 == 1)
-                        .map(|(_, wrapper)| wrapper)
-                        .take(5),
-                )
-                .map(|(a, b)| a.individual.mate(&b.individual))
-                .map(|individual| Wrapper {
-                    individual,
-                    fitness: None,
-                })
-                .for_each(|wrapper| {
-                    population.push(wrapper);
-                });
-
-            // Mutate all to get new children.
-            parents
-                .par_iter()
-                .map(|wrapper| wrapper.individual.clone())
-                .map(|mut individual| {
-                    individual.mutate();
-
-                    Wrapper {
-                        individual,
-                        fitness: None,
-                    }
-                })
-                .collect_into_vec(population);
-
-            // Add all parents again.
-            population.extend(parents.iter().cloned());
-        });
-
-        simulation.population.truncate(population_size);
-    }
-
-    println!(
-        "{:#?}",
-        simulation
-            .population
-            .iter()
-            .take(10)
-            .map(|individual| &individual.individual)
-            .collect::<Vec<_>>()
-    );
-    println!();
-}

src/lib.rs

@@ -1,170 +1,135 @@
-use rayon::prelude::*;
+// https://en.wikipedia.org/wiki/Test_functions_for_optimization
 
-pub trait Individual: Send {
+/*
-    type Fitness: Send + Ord;
+pub trait Genotype {
-
+    //
-    fn fitness(&self) -> Self::Fitness;
-
-    fn mate(&self, other: &Self) -> Self;
-    fn mutate(&mut self);
 }
 
-pub trait Select<T>: Send
+pub trait Phenotype {
-where
+    //
-    T: Individual,
-{
-    fn select(&self, population: Vec<Wrapper<T>>) -> Vec<Wrapper<T>>;
 }
+*/
 
-pub struct MaximizeSelector {
+pub mod crossover {
-    count: usize,
+    use std::cmp;
-}
 
-impl MaximizeSelector {
+    use rand::distributions::Uniform;
-    pub fn new(count: usize) -> Self {
+    use rand::Rng;
-        MaximizeSelector { count }
+
+    pub fn single_point<T, R: Rng>(rng: &mut R, left: &[T], right: &[T]) -> (Vec<T>, Vec<T>)
+    where
+        T: Clone,
+    {
+        let min_len = cmp::min(left.len(), right.len());
+        let cross_point = rng.sample(Uniform::from(1..min_len));
+
+        let first = left[..cross_point]
+            .iter()
+            .chain(right[cross_point..].iter())
+            .cloned()
+            .collect::<Vec<_>>();
+
+        let second = right[..cross_point]
+            .iter()
+            .chain(left[cross_point..].iter())
+            .cloned()
+            .collect::<Vec<_>>();
+
+        (first, second)
     }
 }
 
-impl<T> Select<T> for MaximizeSelector
+#[cfg(test)]
-where
+mod tests {
-    T: Individual,
+    use std::cmp::Ordering;
-{
+    use std::f64;
-    fn select(&self, mut population: Vec<Wrapper<T>>) -> Vec<Wrapper<T>> {
-        population.sort_unstable_by(|a, b| b.fitness.cmp(&a.fitness));
 
-        population.into_iter().take(self.count).collect()
+    use super::*;
-    }
-}
 
-pub struct MinimizeSelector {
+    // use approx::assert_relative_eq;
-    count: usize,
+    use rand::distributions::{Standard, Uniform};
-}
+    use rand::{thread_rng, Rng};
 
-impl MinimizeSelector {
+    #[test]
-    pub fn new(count: usize) -> Self {
+    fn single_objective_sphere() {
-        MinimizeSelector { count }
+        let mut rng = thread_rng();
-    }
-}
 
-impl<T> Select<T> for MinimizeSelector
+        fn evaluate(x: &[f64]) -> f64 {
-where
+            x.iter().map(|x| x.powi(2)).sum()
-    T: Individual,
+        }
-{
-    fn select(&self, mut population: Vec<Wrapper<T>>) -> Vec<Wrapper<T>> {
-        population.sort_unstable_by(|a, b| a.fitness.cmp(&b.fitness));
 
-        population.into_iter().take(self.count).collect()
+        fn generate<R: Rng>(rng: &mut R) -> Vec<f64> {
-    }
+            let between = Uniform::new_inclusive(-10.0, 10.0);
-}
 
-#[derive(Clone)]
+            (0..10).map(|_| rng.sample(between)).collect::<Vec<_>>()
-pub struct Wrapper<T>
+        }
-where
-    T: Individual,
-{
-    pub individual: T,
-    pub fitness: Option<T::Fitness>,
-}
 
-pub struct Simulation<T>
+        fn mutate<R: Rng>(rng: &mut R, individual: &mut Vec<f64>) {
-where
+            let idx = rng.gen_range(0, individual.len());
-    T: Individual,
-{
-    pub population: Vec<Wrapper<T>>,
-}
 
-impl<T> Simulation<T>
+            individual[idx] += rng.sample(Uniform::new_inclusive(-1.0, 1.0));
-where
+        }
-    T: Individual,
-{
-    pub fn with_population(initial: Vec<T>) -> Self {
-        let mut population = Vec::with_capacity(initial.len() * 2);
 
-        initial
+        // step 1 (generate)
-            .into_iter()
+        let mut population = (0..100)
-            .map(|individual| Wrapper {
+            .map(|_| (generate(&mut rng), f64::MAX))
-                individual,
+            .collect::<Vec<_>>();
-                fitness: None,
+
-            })
+        for _ in 0..1000 {
-            .for_each(|wrapper| {
+            // step 2 (evaluate)
-                population.push(wrapper);
+            population
+                .iter_mut()
+                .for_each(|(individual, fitness)| *fitness = evaluate(&individual));
+
+            population
+                .sort_unstable_by(|(_, a), (_, b)| a.partial_cmp(&b).unwrap_or(Ordering::Equal));
+
+            // step 3 (select)
+            let parents = population
+                .iter()
+                .step_by(2)
+                .zip(population.iter().skip(1).step_by(2))
+                .take(5)
+                .map(|((a, _), (b, _))| (a, b))
+                .collect::<Vec<_>>();
+
+            // step 4 (crossover)
+            let mut children = Vec::with_capacity(parents.len() * 2);
+
+            parents
+                .into_iter()
+                .map(|(left, right)| crossover::single_point(&mut rng, left, right))
+                .for_each(|(first, second)| {
+                    children.push((first, f64::MAX));
+                    children.push((second, f64::MAX));
+                });
+
+            // step 5 (survivial pressure)
+            population.truncate(population.len() - children.len());
+
+            // step 6 (mutate)
+            population.iter_mut().for_each(|(individual, fitness)| {
+                let random: f64 = rng.sample(Standard);
+
+                if random < 0.3 {
+                    mutate(&mut rng, individual);
+
+                    *fitness = f64::MAX;
+                }
             });
 
-        Simulation { population }
+            // step 7 (add children)
-    }
+            population.extend_from_slice(&children);
+        }
 
-    #[inline]
+        population
-    pub fn calculate(&mut self) {
-        self.population
             .iter_mut()
-            .filter(|wrapper| wrapper.fitness.is_none())
+            .for_each(|(individual, fitness)| *fitness = evaluate(&individual));
-            .for_each(|wrapper| wrapper.fitness = Some(wrapper.individual.fitness()));
 
-        self.population
+        population.sort_unstable_by(|(_, a), (_, b)| a.partial_cmp(&b).unwrap_or(Ordering::Equal));
-            .sort_unstable_by(|a, b| b.fitness.cmp(&a.fitness));
-    }
 
-    pub fn evolve<F>(&mut self, func: F)
+        println!("{:#?}", population);
-    where
-        F: Fn(&[Wrapper<T>], &mut Vec<Wrapper<T>>),
-    {
-        let mut population = Vec::with_capacity(self.population.len());
 
-        func(&self.population, &mut population);
+        assert!(false);
-
-        self.population = population;
-
-        self.calculate();
-    }
-}
-
-pub struct ParSimulation<T>
-where
-    T: Individual,
-{
-    pub population: Vec<Wrapper<T>>,
-}
-
-impl<T> ParSimulation<T>
-where
-    T: Individual,
-{
-    pub fn with_population(initial: Vec<T>) -> Self {
-        let mut population = Vec::with_capacity(initial.len() * 2);
-
-        initial
-            .into_par_iter()
-            .map(|individual| Wrapper {
-                individual,
-                fitness: None,
-            })
-            .collect_into_vec(&mut population);
-
-        ParSimulation { population }
-    }
-
-    #[inline]
-    pub fn calculate(&mut self) {
-        self.population
-            .par_iter_mut()
-            .filter(|wrapper| wrapper.fitness.is_none())
-            .for_each(|wrapper| wrapper.fitness = Some(wrapper.individual.fitness()));
-
-        self.population
-            .par_sort_unstable_by(|a, b| b.fitness.cmp(&a.fitness));
-    }
-
-    pub fn evolve<F>(&mut self, func: F)
-    where
-        F: Fn(&[Wrapper<T>], &mut Vec<Wrapper<T>>),
-    {
-        let mut population = Vec::with_capacity(self.population.len());
-
-        func(&self.population, &mut population);
-
-        self.population = population;
-
-        self.calculate();
     }
 }