population.py

# PY5 IMPORTED MODE CODE

from dna import DNA
from rocket import Rocket


class Population:
    # Needs "lifespan", "xy" parameter because Python modules have isolated namespaces.
    # (p5.js sketches share a single global scope)
    def __init__(
      self, mutation: float, length: int, lifespan: int, xy: tuple[float, float]
    ):
        """Population has variables to keep track of the mutation rate, current
        population list, and number of generations."""

        self.mutation_rate = mutation  # Mutation rate.
        self.generations = 0  # Number of generations
        self.x, self.y = xy
        # List to hold the current population.
        self.population = [
          Rocket(self.x, self.y, DNA(lifespan)) for _ in range(length)
        ]

    def live(self) -> None:
        """The run() method takes care of the simulation, updates the rocket's
        position, and draws it to the canvas."""

        for rocket in self.population:
            rocket.run()

    # Needs "target" parameter because Python modules have isolated namespaces.
    def fitness(self, target: Py5Vector2D) -> None:
        """Calculate the fitness for each rocket."""

        for rocket in self.population:
            rocket.calculate_fitness(target)

    def selection(self) -> None:
        """The selection method normalizes all the fitness values."""

        # Sum all the fitness values.
        total_fitness = sum(rocket.fitness for rocket in self.population)
        # Divide by the total to normalize the fitness values.
        for rocket in self.population:
            rocket.fitness /= total_fitness

    def reproduction(self) -> None:
        new_population = []  # Separate the list for the next generation.

        for _ in range(len(self.population)):
            # Now use the weighted selection algorithm.
            parent_a = self.weighted_selection()
            parent_b = self.weighted_selection()
            child = parent_a.crossover(parent_b)
            child.mutate(self.mutation_rate)
            # Rocket goes in the new population.
            new_population.append(Rocket(self.x, self.y, child))

        # Now the new population is the current one.
        self.population = new_population
        self.generations += 1

    def weighted_selection(self) -> DNA:
        # Start with the first element.
        index = 0
        # Pick a starting point.
        start = random()
        # At the finish line?
        while start > 0:
            # Move a distance according to fitness.
            start -= self.population[index].fitness
            # Pass the baton to the next element.
            index += 1
        # Undo moving to the next element since the finish has been reached.
        index -= 1
        return self.population[index].dna