def simulate(population_size: int, num_generations: int, folder_name="test"):
if os.path.exists(f"sim/x_rmg/{folder_name}"):
raise Exception("Folder exists")
else:
os.makedirs(f"sim/x_rmg/{folder_name}")
population = []
for _ in range(population_size):
population.append(Organism())
for generation_number in range(1, num_generations + 1):
found_no_loss = False
to_mate = select(make_wheel(population), population_size // 2)
after_mating = population_after_mating(to_mate)
after_mutation = population_after_mutation(after_mating)
population = next_generation(population, after_mating, after_mutation,
population_size)
with open(f"sim/x_rmg/{folder_name}/{generation_number}.csv",
"w") as file:
file.write("Genome, Fitness, Wins (X), Losses (X), Draws (X)\n")
for organism in population:
if organism.get_fitness() == 0:
found_no_loss = True
file.write(
f"{organism.genome}, {organism.get_fitness()}, {organism.win_x}, {organism.loss_x}, {organism.draw_x}\n"
)
print(population[0].get_fitness())
if found_no_loss:
return population
return population
def next_generation(population_before_selection,
population_after_mating,
population_after_mutation,
population_size=100):
UMG = UnbeatableMoveGenerator(2)
combined_list = []
for organism in population_before_selection:
insort(combined_list, (mg_result(organism, UMG), organism))
for organism in population_after_mating:
insort(combined_list, (mg_result(organism, UMG), organism))
for organism in population_after_mutation:
insort(combined_list, (mg_result(organism, UMG), organism))
ret_list = []
for i in range(1, population_size + 1):
j = int(i + (2 * population_size) * (((i - 1) * (i - 2)) /
((population_size - 1) *
(population_size - 2))))
ret_list.append(combined_list[j - 1][1])
return sorted(ret_list)
if __name__ == "__main__":
print(
next_generation(
[Organism(), Organism(), Organism()],
[Organism(), Organism(), Organism()],
[Organism(), Organism(), Organism()], 3))
def fitness(organism: Organism, m: int):
if m == 0:
return 1 - organism.get_fitness()
else:
return (1 / m) * (1 - organism.get_fitness())
def bin_search(wheel, num):
mid = len(wheel) // 2
low, high, answer = wheel[mid]
if low <= num <= high:
return answer
elif high < num:
return bin_search(wheel[mid + 1:], num)
else:
return bin_search(wheel[:mid], num)
def select(wheel, N):
stepSize = 1.0 / N
answer = []
r = random.random()
answer.append(bin_search(wheel, r))
while len(answer) < N:
r += stepSize
if r > 1:
r %= 1
answer.append(bin_search(wheel, r))
return answer
if __name__ == "__main__":
pop = []
for i in range(10):
pop.append(Organism())
print(select(make_wheel(pop), 2))