def __init__(self, dimensions, goomba_genomes, seed_meta, gen_len_range, gen_time=200):
self.dimensions = dimensions
width = dimensions[0]
height = dimensions[1]
self.seed_meta = seed_meta
self.gen_len_range = gen_len_range
self.gen_time = gen_time
self.steps = 0
self.generation = 0
distrib = [TileState.Boundary] * 2 + [TileState.Dirty] + [TileState.Clean] * 7
self.state = [[choice(distrib) for _ in range(width)] for _ in range(height)]
for i in range(width):
self.state[i][0] = TileState.Boundary
self.state[i][height - 1] = TileState.Boundary
for j in range(height):
self.state[0][j] = TileState.Boundary
self.state[width - 1][j] = TileState.Boundary
self.init_dirt_distrib = []
for y in range(height):
for x in range(width):
if self.state[x][y] == TileState.Dirty:
self.init_dirt_distrib.append((x, y))
starts = self.start_locations(len(goomba_genomes))
self.goombas = [Goomba.from_sequences(s, p) for s, p in zip(goomba_genomes, starts)]
self.top_five = self.goombas[:5]
self.running = True
def __init__(self, dimensions, goomba_genomes, seed_meta, gen_len_range, gen_time=200):
self.dimensions = dimensions
width = dimensions[0]
height = dimensions[1]
self.seed_meta = seed_meta
self.gen_len_range = gen_len_range
self.gen_time = gen_time
self.steps = 0
self.generation = 0
distrib = [TileState.Boundary]*2 + [TileState.Dirty] + [TileState.Clean]*7
self.state = [[choice(distrib) for _ in range(width)] for _ in range(height)]
for i in range(width):
self.state[i][0] = TileState.Boundary
self.state[i][height-1] = TileState.Boundary
for j in range(height):
self.state[0][j] = TileState.Boundary
self.state[width-1][j] = TileState.Boundary
self.init_dirt_distrib = []
for y in range(height):
for x in range(width):
if self.state[x][y] == TileState.Dirty:
self.init_dirt_distrib.append((x, y))
starts = self.start_locations(len(goomba_genomes))
self.goombas = [Goomba.from_sequences(s, p) for s, p in zip(goomba_genomes, starts)]
self.top_five = self.goombas[:5]
self.running = True
def breed_pop(self):
"""Breed the current population, a better score rank means probably more children."""
pop_size = len(self.goombas)
num_clones = ceil(pop_size * World.CLONE_BEST_FRACTION)
num_bred = ceil(pop_size * World.BREED_FRACTION)
num_rand = ceil(pop_size * World.NEW_RANDOM_FRACTION)
# Order the population by final score
ordered_pop = sorted([gmba for gmba in self.goombas], key=lambda g: g.score(), reverse=True)
# The top few will be cloned into the next generation unchanged
top_dogs = ordered_pop[:num_clones]
new_goombas = [Goomba.from_sequences(dog.genome.sequences()) for dog in top_dogs]
# The bottom fraction is thrown out entirely
breeders = ordered_pop[:num_bred]
# The remaining goombas breed, with a higher likelihood as they rank higher
breed_weighted = dict(zip(breeders, linspace(World.REPRO_SUCCESS_RAMP, 1, len(breeders))))
breeding_pairs = weighted_choice(breed_weighted, 2 * (pop_size - (num_clones + num_rand)))
for i in range(0, len(breeding_pairs), 2):
new_goombas.append(breed(breeding_pairs[i], breeding_pairs[i + 1]))
for i in range(num_rand):
new_goombas.append(Goomba(Genome.random_coding(self.seed_meta, randrange(*self.gen_len_range))))
starts = self.start_locations(len(new_goombas))
for gmba, pos in zip(new_goombas, starts):
gmba.pos = pos
self.goombas = new_goombas
def breed_pop(self):
"""Breed the current population, a better score rank means probably more children."""
pop_size = len(self.goombas)
num_clones = ceil(pop_size * World.CLONE_BEST_FRACTION)
num_bred = ceil(pop_size * World.BREED_FRACTION)
num_rand = ceil(pop_size * World.NEW_RANDOM_FRACTION)
# Order the population by final score
ordered_pop = sorted([gmba for gmba in self.goombas],
key=lambda g: g.score(),
reverse=True)
# The top few will be cloned into the next generation unchanged
top_dogs = ordered_pop[:num_clones]
new_goombas = [Goomba.from_sequences(dog.genome.sequences()) for dog in top_dogs]
# The bottom fraction is thrown out entirely
breeders = ordered_pop[:num_bred]
# The remaining goombas breed, with a higher likelihood as they rank higher
breed_weighted = dict(zip(breeders, linspace(World.REPRO_SUCCESS_RAMP, 1, len(breeders))))
breeding_pairs = weighted_choice(breed_weighted, 2 * (pop_size - (num_clones + num_rand)))
for i in range(0, len(breeding_pairs), 2):
new_goombas.append(breed(breeding_pairs[i], breeding_pairs[i + 1]))
for i in range(num_rand):
new_goombas.append(Goomba(Genome.random_coding(self.seed_meta,
randrange(*self.gen_len_range))))
starts = self.start_locations(len(new_goombas))
for gmba, pos in zip(new_goombas, starts):
gmba.pos = pos
self.goombas = new_goombas
