def test_best_chromosomes_sorts_them_in_descending_order():
chromosomes = [
Chromosome(np.array(list("ABCXXXXXXXXXXXXXXXXXXXXXXX"))),
Chromosome(np.array(list("ABXXXXXXXXXXXXXXXXXXXXXXXX"))),
Chromosome(np.array(list(string.ascii_uppercase))),
]
subject = Population(chromosomes)
expected = [chromosomes[2], chromosomes[0], chromosomes[1]]
assert subject.best_chromosomes() == expected
def main():
m = readMatrix('matrix_games/5x5.txt')
numR, numC = m.shape
Population.initPopulation = initPopulation
Population.evolve = evolve
p = Population(30, 2)
p.initPopulation(numC, numR)
p.evolve(m)
def test_parents_probabilities_returns_values_proportional_to_their_fitness():
chromosomes = [
Chromosome(np.array(list("ABCXXXXXXXXXXXXXXXXXXXXXXX"))),
Chromosome(np.array(list("ABXXXXXXXXXXXXXXXXXXXXXXXX"))),
Chromosome(np.array(list(string.ascii_uppercase))),
]
subject = Population(chromosomes)
result = subject.parents_probabilities()
expected = [0.12121212, 0.09090909, 0.78787879]
np.testing.assert_allclose(result, expected)
def test_random_parents_returns_two_random_chromosomes():
chromosomes = [
Chromosome(np.array(list("A" * 26))),
Chromosome(np.array(list("B" * 26))),
Chromosome(np.array(list("C" * 26))),
]
subject = Population(chromosomes)
random_parents = subject.random_parents()
assert len(random_parents) == 2
assert random_parents[0] == chromosomes[0]
assert random_parents[1] == chromosomes[0]
def test_init_assigns_chromosomes():
chromosomes = [
Chromosome(np.array(list("A" * 26))),
Chromosome(np.array(list("B" * 26))),
]
subject = Population(chromosomes)
assert subject.chromosomes == chromosomes
def evolve_previous_generation(self):
number_of_candidates_to_maintain = round(self.SIZE *
self.etilism_percentage)
next_generation = self.etilist_candidates()
for _ in range(self.n_crossovers):
parents = self.previous_generation.random_parents()
offspring = Crossover(parents)()
for chromosome in offspring:
next_generation.append(chromosome.mutated())
return Population(next_generation)
def main():
pygame.init()
window = pygame.display.set_mode((d_width, d_height))
circles = getCircles()
for circle in circles:
pygame.draw.circle(window, (255, 255, 255), circle[0], circle[1], 1)
Population.initPopulation = initPopulation
Population.evolve = evolve
p = Population(80, 3)
p.initPopulation()
p.evolve(window, circles)
#pygame.display.flip()
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
sys.exit(0)
def testNeuralNetWithGA():
net = NeuralNet(2, 2, 1)
t_model = utils.readTrainModel(os.path.join(utils.getResourcesPath(), 'logic_gates/NAND.txt'))
Population.initPopulation = initPopulation
Population.evolve = evolve
p = Population(70, 9)
p.initPopulation()
p.evolve(net, t_model)
print(net.getOutputs([0, 0]))
print(net.getOutputs([0, 1]))
print(net.getOutputs([1, 0]))
print(net.getOutputs([1, 1]))
print(net.getError(t_model))
#testPerceptron()
#testNeuralNet()
#testNeuralNetWithGA()
#numberRecognition()
def main():
global fitnessHistory
Population.initPopulation = initPopulation
Population.evolve = evolve
p = Population(30, 1)
#fig = plt.figure()
for i in range(1):
colors = ['b', 'r', 'g']
p.initPopulation()
p.evolve()
#ax = fig.add_subplot(111)
#ax.plot(np.arange(len(fitnessHistory)), fitnessHistory, c=colors[i])
fitnessHistory = []
h = np.array(history)
h = np.rot90(h, 1)
print(h)
plt.imshow(h, cmap='Greys', aspect='auto', interpolation='nearest')
plt.show()
def setup_population():
dm = DataManager.shared()
from genetic_algorithm.population import Population
from neural_network.neural_network import NeuralNetwork
population = Population.instantiate(NeuralNetwork.instantiate)
population.calculate_fitness(dm.get_X_train(), dm.get_y_train())
population.get_chromosomes().sort(key=lambda ann: ann.get_fitness(),
reverse=True)
print_population(population, 0)
return population
def __init__(self, screen):
self.screen = screen
# LOAD POPULATION
self.group = []
self.load()
self.population = Population(self.group)
self.game = Game(self.group, self.screen)
self.cfg = GA_SETTINGS
self._mutation_bins = np.cumsum([
self.cfg['probability_gaussian'],
self.cfg['probability_random_uniform']
])
self._crossover_bins = np.cumsum(
[self.cfg['probability_SBX'], self.cfg['probability_SPBX']])
self._SPBX_type = self.cfg['SPBX_type'].lower()
self._SBX_eta = self.cfg['SBX_eta']
self._mutation_rate = self.cfg['mutation_rate']
self._next_gen_size = self.cfg['num_offspring']
self.current_generation = 0
def main():
pygame.init()
maze = readMaze('mazes/9x15.txt')
numR, numC = maze.shape
window = pygame.display.set_mode((numC * square_l, numR * square_l))
renderMaze(window, maze)
Population.initPopulation = initPopulation
Population.evolve = evolve
p = Population(50, 30)
p.mutation_rate = 0.1
p.elites_num = 5
p.initPopulation()
p.evolve(window, maze)
print("done")
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
sys.exit(0)
def __init__(self, world, replay=False):
super().__init__()
self.world = world
self.title = 'Genetic Algorithm - Cars'
self.top = 150
self.left = 150
self.width = 1100
self.height = 700
self.max_fitness = 0.0
self.cars = []
self.population = Population([])
self.state = States.FIRST_GEN
self._next_pop = [
] # Used when you are in state 1, i.e. creating new cars from the old population
self.current_batch = 1
self.batch_size = get_boxcar_constant('run_at_a_time')
self.gen_without_improvement = 0
self.replay = replay
self.manual_control = False
self.current_generation = 0
self.leader = None # What car is leading
self.num_cars_alive = get_boxcar_constant('run_at_a_time')
self.batch_size = self.num_cars_alive
self._total_individuals_ran = 0
self._offset_into_population = 0 # Used if we display only a certain number at a
# Determine whether or not we are in the process of creating random cars.
# This is used for when we only run so many at a time. For instance if `run_at_a_time` is 20 and
# `num_parents` is 1500, then we can't just create 1500 cars. Instead we create batches of 20 to
# run at a time. This flag is for deciding when we are done with that so we can move on to crossover
# and mutation.
self._creating_random_cars = True
# Determine how large the next generation is
if get_ga_constant('selection_type').lower() == 'plus':
self._next_gen_size = get_ga_constant(
'num_parents') + get_ga_constant('num_offspring')
elif get_ga_constant('selection_type').lower() == 'comma':
self._next_gen_size = get_ga_constant('num_parents')
else:
raise Exception('Selection type "{}" is invalid'.format(
get_ga_constant('selection_type')))
if self.replay:
global args
self.floor = Floor(self.world)
self.state = States.REPLAY
self.num_replay_inds = len([
x for x in os.listdir(args.replay_from_folder)
if x.startswith('car_')
])
else:
self._set_first_gen()
# self.population = Population(self.cars)
# For now this is all I'm supporting, may change in the future. There really isn't a reason to use
# uniform or single point here because all the values have different ranges, and if you clip them, it
# can make those crossovers useless. Instead just use simulated binary crossover to ensure better crossover.
self._crossover_bins = np.cumsum([get_ga_constant('probability_SBX')])
self._mutation_bins = np.cumsum([
get_ga_constant('probability_gaussian'),
get_ga_constant('probability_random_uniform')
])
self.init_window()
self.stats_window.pop_size.setText(str(get_ga_constant('num_parents')))
self._set_number_of_cars_alive()
self.game_window.cars = self.cars
self._timer = QTimer(self)
self._timer.timeout.connect(self._update)
self._timer.start(1000 // get_boxcar_constant('fps'))
def __init__(self, settings, show=False, fps=2000):
super().__init__()
self.printIndividualSnakeFitness = True
self.setAutoFillBackground(True)
palette = self.palette()
palette.setColor(self.backgroundRole(), QtGui.QColor(240, 240, 240))
self.setPalette(palette)
self.settings = settings
self._SBX_eta = self.settings['SBX_eta']
self._mutation_bins = np.cumsum([self.settings['probability_gaussian'],
self.settings['probability_random_uniform']
])
self._crossover_bins = np.cumsum([self.settings['probability_SBX'],
self.settings['probability_SPBX']
])
self._SPBX_type = self.settings['SPBX_type'].lower()
self._mutation_rate = self.settings['mutation_rate']
# Determine size of next gen based off selection type
self._next_gen_size = None
if self.settings['selection_type'].lower() == 'plus':
self._next_gen_size = self.settings['num_parents'] + self.settings['num_offspring']
elif self.settings['selection_type'].lower() == 'comma':
self._next_gen_size = self.settings['num_offspring']
else:
raise Exception('Selection type "{}" is invalid'.format(self.settings['selection_type']))
self.board_size = settings['board_size']
#self.border = (0, 10, 0, 10) # Left, Top, Right, Bottom
self.border = (0, self.board_size[0], 0, self.board_size[1]) # Left, Top, Right, Bottom
self.snake_widget_width = SQUARE_SIZE[0] * self.board_size[0]
self.snake_widget_height = SQUARE_SIZE[1] * self.board_size[1]
# Allows padding of the other elements even if we need to restrict the size of the play area
self._snake_widget_width = max(self.snake_widget_width, 620)
self._snake_widget_height = max(self.snake_widget_height, 600)
self.top = 150
self.left = 150
self.width = self._snake_widget_width + 700 + self.border[0] + self.border[2]
self.height = self._snake_widget_height + self.border[1] + self.border[3] + 200
individuals: List[Individual] = []
#load the best snakes, if there are any
self.pathToBestSnakes = 'best_snakes/'
for f in os.scandir(self.pathToBestSnakes):
if f.is_dir():
individuals.append(load_snake(self.pathToBestSnakes,f.name))
#load up new snakes, minus the best snakes
for _ in range(self.settings['num_parents'] - len(individuals)):
individual = Snake(self.board_size, hidden_layer_architecture=self.settings['hidden_network_architecture'],
hidden_activation=self.settings['hidden_layer_activation'],
output_activation=self.settings['output_layer_activation'],
lifespan=self.settings['lifespan'],
apple_and_self_vision=self.settings['apple_and_self_vision'])
individuals.append(individual)
#clear out any saved snakes
try:
shutil.rmtree('./snakes/')
except OSError as e:
print("Error: %s : %s" % ('./snakes/', e.strerror))
self.best_fitness = 0
self.best_score = 0
self._current_individual = 0
self.population = Population(individuals)
self.snake = self.population.individuals[self._current_individual]
self.current_generation = 0
self.init_window()
self.timer = QtCore.QTimer(self)
self.timer.timeout.connect(self.update)
self.timer.start(1000//fps)
if show:
self.show()
def __init__(self, settings, show= (not NEW), fps=5000):
super().__init__()
self.setAutoFillBackground(True)
palette = self.palette()
palette.setColor(self.backgroundRole(), QtGui.QColor(0, 0, 0))
self.setPalette(palette)
self.settings = settings
self._SBX_eta = self.settings['SBX_eta']
self._mutation_bins = np.cumsum([self.settings['probability_gaussian'],
self.settings['probability_random_uniform']
])
self._crossover_bins = np.cumsum([self.settings['probability_SBX'],
self.settings['probability_SPBX']
])
self._SPBX_type = self.settings['SPBX_type'].lower()
self._mutation_rate = self.settings['mutation_rate']
# Determine size of next gen based off selection type
self._next_gen_size = None
if self.settings['selection_type'].lower() == 'plus':
self._next_gen_size = self.settings['num_parents'] + self.settings['num_offspring']
elif self.settings['selection_type'].lower() == 'comma':
self._next_gen_size = self.settings['num_offspring']
else:
raise Exception('Selection type "{}" is invalid'.format(self.settings['selection_type']))
self.board_size = settings['board_size']
self.border = (0, 10, 0, 10) # Left, Top, Right, Bottom
self.puzzle_widget_width = SQUARE_SIZE[0] * self.board_size[0]
self.puzzle_widget_height = SQUARE_SIZE[1] * self.board_size[1]
# Allows padding of the other elements even if we need to restrict the size of the play area
self._puzzle_widget_width = max(self.puzzle_widget_width, 620)
self._puzzle_widget_height = max(self.puzzle_widget_height, 600)
self.top = 150
self.left = 150
self.width = self._puzzle_widget_width + 700 + self.border[0] + self.border[2]
self.height = self._puzzle_widget_height + self.border[1] + self.border[3] + 200
self.nrGroupRange = settings['nrGroupRange']
self.nrBlocksRange = (settings['nrBlocksRange'][0] * self.nrGroupRange[0], settings['nrBlocksRange'][1] * self.nrGroupRange[1])
individuals: List[Individual] = []
if NEW:
for _ in range(self.settings['num_parents']):
nrG = random.randint(self.nrGroupRange[0], self.nrGroupRange[1])
nrC = random.randint(self.nrBlocksRange[0], self.nrBlocksRange[1])
individual = Puzzle(self.board_size, nrG, nrC,
hidden_layer_architecture=self.settings['hidden_network_architecture'],
hidden_activation=self.settings['hidden_layer_activation'],
output_activation=self.settings['output_layer_activation'])
individuals.append(individual)
else:
for i in range(NR_OLD_ONES):
individual = load_puzzle('population15', 'best_snake'+str(i+320), self.settings)
individuals.append(individual)
self.best_fitness = np.inf
self._current_individual = 0
self.population = Population(individuals)
self.puzzle = self.population.individuals[self._current_individual]
self.current_generation = 0
self.init_window()
self.timer = QtCore.QTimer(self)
self.timer.timeout.connect(self.update)
self.timer.start(1000./fps)
if show:
self.show()
def __init__(self, settings, show=True, fps=300):
super().__init__()
self.setAutoFillBackground(True)
palette = self.palette()
palette.setColor(self.backgroundRole(), QtGui.QColor(240, 240, 240))
self.setPalette(palette)
self.settings = settings
self._SBX_eta = self.settings['SBX_eta']
self._mutation_bins = np.cumsum([self.settings['probability_gaussian'],
self.settings['probability_random_uniform']
])
self._crossover_bins = np.cumsum([self.settings['probability_SBX'],
self.settings['probability_SPBX']
])
self._SPBX_type = self.settings['SPBX_type'].lower()
self._mutation_rate = self.settings['mutation_rate']
# Determine size of next gen based off selection type
self._next_gen_size = None
if self.settings['selection_type'].lower() == 'plus':
self._next_gen_size = self.settings['num_parents'] + self.settings['num_offspring']
elif self.settings['selection_type'].lower() == 'comma':
self._next_gen_size = self.settings['num_offspring']
else:
raise Exception('Selection type "{}" is invalid'.format(self.settings['selection_type']))
self.board_size = settings['board_size']
self.border = (0, 10, 0, 10) # Left, Top, Right, Bottom
self.snake_widget_width = SQUARE_SIZE[0] * self.board_size[0]
self.snake_widget_height = SQUARE_SIZE[1] * self.board_size[1]
# Allows padding of the other elements even if we need to restrict the size of the play area
self._snake_widget_width = max(self.snake_widget_width, 620)
self._snake_widget_height = max(self.snake_widget_height, 600)
self.top = 150
self.left = 150
self.width = self._snake_widget_width + 700 + self.border[0] + self.border[2]
self.height = self._snake_widget_height + self.border[1] + self.border[3] + 200
individuals: List[Individual] = []
#Load the snake/s from a pre-existing save
#You need to go to the load snake function and do it from there for now
#individual = load_snake(r'C:\Population',r'C:\Population\40')
#individuals.append(individual)
for _ in range(self.settings['num_parents']):
if (save_load = 1):
individual = load_snake(save_folder_location,save_folder_individual)
individuals.append(individual)
elif(save_load = 0):
individual = Snake(self.board_size, hidden_layer_architecture=self.settings['hidden_network_architecture'],
hidden_activation=self.settings['hidden_layer_activation'],
output_activation=self.settings['output_layer_activation'],
lifespan=self.settings['lifespan'],
apple_and_self_vision=self.settings['apple_and_self_vision'])
individuals.append(individual)
self.best_fitness = 0
self.best_score = 0
self._current_individual = 0
self.population = Population(individuals)
self.snake = self.population.individuals[self._current_individual]
self.current_generation = 406
print(self.current_generation)
self.init_window()
self.timer = QtCore.QTimer(self)
self.timer.timeout.connect(self.update)
self.timer.start(1000./fps)
if show:
self.show()
def __init__(self):
self._mutation_bins = np.cumsum([
settings['probability_gaussian'],
settings['probability_random_uniform']
])
self._crossover_bins = np.cumsum(
[settings['probability_SBX'], settings['probability_SPBX']])
self._SPBX_type = settings['SPBX_type'].lower()
self._SBX_eta = settings['SBX_eta']
self._mutation_rate = settings['mutation_rate']
# Determine size of next gen based off selection type
self._next_gen_size = None
if settings['selection_type'].lower() == 'plus':
self._next_gen_size = settings['num_parents'] + settings[
'num_offspring']
elif settings['selection_type'].lower() == 'comma':
self._next_gen_size = settings['num_offspring']
else:
raise Exception('Selection type "{}" is invalid'.format(
settings['selection_type']))
self.Window_Width = settings['Window_Width']
self.Window_Height = settings['Window_Height']
#### Pygame init ####
pygame.init()
self.screen = pygame.display.set_mode(
(self.Window_Width, self.Window_Height))
pygame.display.set_caption('Flappy Bird')
self.game_font = pygame.font.Font("04B_19.ttf", 40)
individuals: List[Individual] = []
# Create initial generation
for _ in range(settings['num_parents']):
individual = Bird(
hidden_layer_architecture=settings[
'hidden_network_architecture'],
hidden_activation=settings['hidden_layer_activation'],
output_activation=settings['output_layer_activation'])
individuals.append(individual)
self.population = Population(individuals)
self.current_generation = 0
# Best of single generation
self.winner = None
self.winner_index = -1
# Best paddle of all generations
self.champion = None
self.champion_index = -1
self.champion_fitness = -1 * np.inf
#### Pygame ####
# The loop will carry on until the user exit the game (e.g. clicks the close button).
game_active = True
# The clock will be used to control how fast the screen updates
clock = pygame.time.Clock()
# load assets
self.bg_surface = pygame.image.load(
"assets/background-day.png").convert()
self.bg_surface = pygame.transform.scale2x(self.bg_surface)
self.floor_surface = pygame.image.load("assets/base.png").convert()
self.floor_surface = pygame.transform.scale2x(self.floor_surface)
self.floor_x = 0
self.pipe_surface = pygame.image.load("assets/pipe-green.png")
self.pipe_surface = pygame.transform.scale2x(self.pipe_surface)
self.pipe_flip_surface = pygame.transform.flip(self.pipe_surface,
False, True)
self.pipe_list = []
self.pipe_list.extend(self.create_pipe())
# self.SPAWNPIPE = pygame.USEREVENT
# pygame.time.set_timer(self.SPAWNPIPE, 1200) # don't use this because sometimes the game lags and reuslts in pipes spawning closer than intended
self.spawn_pipe_counter = 0
# scores
self.score = 0
self.high_score = 0
while game_active and self.current_generation <= 100:
for event in pygame.event.get():
if event.type == pygame.QUIT:
game_active = False
pygame.quit()
# sys.exit() // this or exit()
exit()
# if event.type == self.SPAWNPIPE and game_active:
# self.pipe_list.extend(self.create_pipe())
# screen.fill(BLACK)
self.screen.blit(self.bg_surface, (0, 0))
# Pipes
self.spawn_pipe_counter += 1
# if self.spawn_pipe_counter % 72 == 0:
if self.spawn_pipe_counter % settings[
'pipe_interval_in_frames'] == 0:
self.pipe_list.extend(self.create_pipe())
self.pipe_list = self.move_pipes(self.pipe_list)
self.draw_pipes(self.pipe_list)
# Floor
self.floor_x = (self.floor_x - 6) % -self.Window_Width
self.draw_floor()
# TODO: should change this for better visibility
font = pygame.font.Font('freesansbold.ttf', 18)
generation_text = font.render(
"Generation: %d" % self.current_generation, True, WHITE)
score_text = font.render("Score: %d" % self.score, True, WHITE)
best_score_text = font.render("Best: %d" % self.high_score, True,
WHITE)
self.screen.blit(generation_text, (self.Window_Width - 150, 30))
self.screen.blit(score_text, (self.Window_Width - 150, 60))
self.screen.blit(best_score_text, (self.Window_Width - 150, 90))
self.still_alive = 0
# Loop through the paddles in the generation
for i, bird in enumerate(self.population.individuals):
# Update paddel if still alive
if bird.is_alive:
self.still_alive += 1
#----------------------------------------inputs for neural network--------------------------------------------
# next_pipe = next(pipe for pipe in self.pipe_list if pipe.right > settings['init_bird_x_pos'])
# next_next_pipe = next(pipe for pipe in self.pipe_list if pipe.right > next_pipe.right)
next_pipe, next_next_pipe = self.get_next_pipes()
bird.x_distance_to_next_pipe_center = next_pipe.right - settings[
'init_bird_x_pos']
bird.y_distance_to_next_pipe_center = (next_pipe.top -
150) - bird.y_pos
if next_next_pipe != None:
bird.x_distance_to_next_next_pipe_center = next_next_pipe.right - settings[
'init_bird_x_pos']
bird.y_distance_to_next_next_pipe_center = (
next_next_pipe.top - 150) - bird.y_pos
else:
bird.x_distance_to_next_next_pipe_center = None
bird.y_distance_to_next_next_pipe_center = None
inputs = np.array(
[[bird.y_speed], [bird.y_pos],
[bird.x_distance_to_next_pipe_center],
[bird.y_distance_to_next_pipe_center],
[bird.y_distance_to_next_next_pipe_center]])
# inputs = np.array([[paddle.x_pos], [ball_distance_left_wall], [ball_distance_right_wall], [balls[i].xspeed], [paddle.xspeed], [balls[i].y], [balls[i].yspeed]])
# inputs = np.array([[paddle.x_pos], [balls[i].xspeed], [paddle.xspeed], [balls[i].x]])
#----------------------------------------inputs for neural network--------------------------------------------
bird.update(inputs)
bird.move(self.pipe_list)
self.score = max(self.score, bird.score)
self.high_score = max(self.score, self.high_score)
# Draw every paddle except the best ones
if bird.is_alive and bird != self.winner and bird != self.champion:
bird.winner = False
bird.champion = False
bird.draw(self.screen)
# Draw the winning and champion paddle last
if self.winner is not None and self.winner.is_alive:
self.winner.draw(self.screen, winner=True)
if self.champion is not None and self.champion.is_alive:
self.champion.draw(self.screen, champion=True)
# Generate new generation when all have died out
if self.still_alive == 0:
self.next_generation()
# --- Go ahead and update the screen with what we've drawn.
pygame.display.update()
# --- Limit to 60 frames per second
clock.tick(60)
#Once we have exited the main program loop we can stop the game engine:
pygame.quit()
def __init__(self, NEW, old_ones, from_nr):
self.new = NEW
self.NR_OLD_ONES = old_ones
self.from_nr = from_nr
self.settings = settings
self._SBX_eta = self.settings['SBX_eta']
self._mutation_bins = np.cumsum([
self.settings['probability_gaussian'],
self.settings['probability_random_uniform']
])
self._crossover_bins = np.cumsum([
self.settings['probability_SBX'], self.settings['probability_SPBX']
])
self._SPBX_type = self.settings['SPBX_type'].lower()
self._mutation_rate = self.settings['mutation_rate']
# Determine size of next gen based off selection type
self._next_gen_size = None
if self.settings['selection_type'].lower() == 'plus':
self._next_gen_size = self.settings['num_parents'] + self.settings[
'num_offspring']
elif self.settings['selection_type'].lower() == 'comma':
self._next_gen_size = self.settings['num_offspring']
else:
raise Exception('Selection type "{}" is invalid'.format(
self.settings['selection_type']))
self.board_size = settings['board_size']
self.nrGroupRange = settings['nrGroupRange']
self.nrBlocksRange = (settings['nrBlocksRange'])
individuals: List[Individual] = []
if self.new:
for _ in range(self.settings['num_parents']):
nrG = random.randint(self.nrGroupRange[0],
self.nrGroupRange[1])
nrC = random.randint(self.nrBlocksRange[0],
self.nrBlocksRange[1])
individual = Puzzle(
self.board_size,
nrG,
nrC,
hidden_layer_architecture=self.
settings['hidden_network_architecture'],
hidden_activation=self.settings['hidden_layer_activation'],
output_activation=self.settings['output_layer_activation'])
individuals.append(individual)
else:
for i in range(self.NR_OLD_ONES):
individual = load_puzzle('population',
'best_snake' + str(i + self.from_nr),
self.settings)
individuals.append(individual)
self.best_fitness = np.inf
self._current_individual = 0
self.population = Population(individuals)
self.puzzle = self.population.individuals[self._current_individual]
self.current_generation = 0
def __init__(self, config: Optional[Config] = None):
super().__init__()
global args
self.config = config
self.top = 150
self.left = 150
self.width = 1100
self.height = 700
self.title = 'Super Mario Bros AI'
self.current_generation = 0
# This is the generation that is actual 0. If you load individuals then you might end up starting at gen 12, in which case
# gen 12 would be the true 0
self._true_zero_gen = 0
self._should_display = True
self._timer = QTimer(self)
self._timer.timeout.connect(self._update)
# Keys correspond with B, NULL, SELECT, START, U, D, L, R, A
# index 0 1 2 3 4 5 6 7 8
self.keys = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0], np.int8)
# I only allow U, D, L, R, A, B and those are the indices in which the output will be generated
# We need a mapping from the output to the keys above
self.ouput_to_keys_map = {
0: 4, # U
1: 5, # D
2: 6, # L
3: 7, # R
4: 8, # A
5: 0 # B
}
# Initialize the starting population
individuals: List[Individual] = []
# Load any individuals listed in the args.load_inds
num_loaded = 0
if args.load_inds:
# Overwrite the config file IF one is not specified
if not self.config:
try:
self.config = Config(
os.path.join(args.load_file, 'settings.config'))
except:
raise Exception(
f'settings.config not found under {args.load_file}')
set_of_inds = set(args.load_inds)
for ind_name in os.listdir(args.load_file):
if ind_name.startswith('best_ind_gen'):
ind_number = int(ind_name[len('best_ind_gen'):])
if ind_number in set_of_inds:
individual = load_mario(args.load_file, ind_name,
self.config)
# Set debug stuff if needed
if args.debug:
individual.name = f'm{num_loaded}_loaded'
individual.debug = True
individuals.append(individual)
num_loaded += 1
# Set the generation
self.current_generation = max(
set_of_inds) + 1 # +1 becauase it's the next generation
self._true_zero_gen = self.current_generation
# Load any individuals listed in args.replay_inds
if args.replay_inds:
# Overwrite the config file IF one is not specified
if not self.config:
try:
self.config = Config(
os.path.join(args.replay_file, 'settings.config'))
except:
raise Exception(
f'settings.config not found under {args.replay_file}')
for ind_gen in args.replay_inds:
ind_name = f'best_ind_gen{ind_gen}'
fname = os.path.join(args.replay_file, ind_name)
if os.path.exists(fname):
individual = load_mario(args.replay_file, ind_name,
self.config)
# Set debug stuff if needed
if args.debug:
individual.name = f'm_gen{ind_gen}_replay'
individual.debug = True
individuals.append(individual)
else:
raise Exception(
f'No individual named {ind_name} under {args.replay_file}'
)
# If it's not a replay then we need to continue creating individuals
else:
num_parents = max(self.config.Selection.num_parents - num_loaded,
0)
for _ in range(num_parents):
individual = Mario(self.config)
# Set debug stuff if needed
if args.debug:
individual.name = f'm{num_loaded}'
individual.debug = True
individuals.append(individual)
num_loaded += 1
self.best_fitness = 0.0
self._current_individual = 0
self.population = Population(individuals)
self.mario = self.population.individuals[self._current_individual]
self.max_distance = 0 # Track farthest traveled in level
self.max_fitness = 0.0
self.env = retro.make(game='SuperMarioBros-Nes',
state=f'Level{self.config.Misc.level}')
# Determine the size of the next generation based off selection type
self._next_gen_size = None
if self.config.Selection.selection_type == 'plus':
self._next_gen_size = self.config.Selection.num_parents + self.config.Selection.num_offspring
elif self.config.Selection.selection_type == 'comma':
self._next_gen_size = self.config.Selection.num_offspring
# If we aren't displaying we need to reset the environment to begin with
if args.no_display:
self.env.reset()
else:
self.init_window()
# Set the generation in the label if needed
if args.load_inds:
txt = "<font color='red'>" + str(
self.current_generation +
1) + '</font>' # +1 because we switch from 0 to 1 index
self.info_window.generation.setText(txt)
# if this is a replay then just set current_individual to be 'replay' and set generation
if args.replay_file:
self.info_window.current_individual.setText('Replay')
txt = f"<font color='red'>{args.replay_inds[self._current_individual] + 1}</font>"
self.info_window.generation.setText(txt)
self.show()
if args.no_display:
self._timer.start(1000 // 1000)
else:
self._timer.start(1000 // 60)
def __init__(self, settings, show=True, fps=1000): # tốc độ rắn và đồ hoạ
super().__init__()
self.setAutoFillBackground(True)
palette = self.palette()
palette.setColor(self.backgroundRole(), QtGui.QColor(240, 240, 240))
self.setPalette(palette)
self.settings = settings
self._SBX_eta = self.settings['SBX_eta']
self._mutation_bins = np.cumsum([
self.settings['probability_gaussian'], # tỉ lệ lai ghép
self.settings['probability_random_uniform'] # đột biến
])
self._crossover_bins = np.cumsum([
self.settings['probability_SBX'], self.settings['probability_SPBX']
])
self._SPBX_type = self.settings['SPBX_type'].lower()
self._mutation_rate = self.settings['mutation_rate']
# Determine size of next gen based off selection type
self._next_gen_size = None
if self.settings['selection_type'].lower() == 'plus': #chế độ plus
self._next_gen_size = self.settings['num_parents'] + self.settings[
'num_offspring']
elif self.settings['selection_type'].lower() == 'comma': #chế độ comma
self._next_gen_size = self.settings['num_offspring']
else:
raise Exception('Selection type "{}" is invalid'.format(
self.settings['selection_type']))
self.board_size = settings['board_size']
self.border = (0, 10, 0, 10) # Left, Top, Right, Bottom
self.snake_widget_width = SQUARE_SIZE[0] * self.board_size[0]
self.snake_widget_height = SQUARE_SIZE[1] * self.board_size[1]
# Cho phép đệm các yếu tố khác ngay cả khi chúng ta hạn chế kích thước của khu vực chơi
self._snake_widget_width = max(self.snake_widget_width, 620)
self._snake_widget_height = max(self.snake_widget_height, 600)
self.top = 150
self.left = 150
self.width = self._snake_widget_width + 700 + self.border[
0] + self.border[2]
self.height = self._snake_widget_height + self.border[1] + self.border[
3] + 200
individuals: List[Individual] = []
for _ in range(self.settings['num_parents']):
individual = Snake(
self.board_size,
hidden_layer_architecture=self.
settings['hidden_network_architecture'],
hidden_activation=self.settings['hidden_layer_activation'],
output_activation=self.settings['output_layer_activation'],
lifespan=self.settings['lifespan'],
apple_and_self_vision=self.settings['apple_and_self_vision'])
individuals.append(individual)
self.best_fitness = 0
self.best_score = 0
self._current_individual = 0
self.population = Population(individuals)
self.snake = self.population.individuals[self._current_individual]
self.current_generation = 0
self.init_window()
self.timer = QtCore.QTimer(self)
self.timer.timeout.connect(self.update)
self.timer.start(1000. / fps)
if show:
self.show()
def next_generation(self):
self._increment_generation()
# Calculate fitness of individuals
for individual in self.population.individuals:
individual.calculate_fitness()
self.population.individuals = elitism_selection(
self.population, self.cfg['num_parents'])
random.shuffle(self.population.individuals)
next_pop: List[Player] = []
while len(next_pop) < self._next_gen_size:
p1, p2 = roulette_wheel_selection(self.population, 2)
L = p1.brain.layer_nodes
c1_params = {}
c2_params = {}
# Each W_l and b_l are treated as their own chromosome.
# Because of this I need to perform crossover/mutation on each chromosome between parents
for l in range(0, L, 2):
p1_W_l = p1.brain.net[l].weight.data.numpy()
p2_W_l = p2.brain.net[l].weight.data.numpy()
p1_b_l = np.array([p1.brain.net[l].bias.data.numpy()])
p2_b_l = np.array([p2.brain.net[l].bias.data.numpy()])
# Crossover
# @NOTE: I am choosing to perform the same type of crossover on the weights and the bias.
c1_W_l, c2_W_l, c1_b_l, c2_b_l = self._crossover(
p1_W_l, p2_W_l, p1_b_l, p2_b_l)
# Mutation
# @NOTE: I am choosing to perform the same type of mutation on the weights and the bias.
self._mutation(c1_W_l, c2_W_l, c1_b_l, c2_b_l)
# Assign children from crossover/mutation
c1_params['W' + str(l)] = c1_W_l
c2_params['W' + str(l)] = c2_W_l
c1_params['b' + str(l)] = c1_b_l
c2_params['b' + str(l)] = c2_b_l
# Clip to [-1, 1]
np.clip(c1_params['W' + str(l)],
-1,
1,
out=c1_params['W' + str(l)])
np.clip(c2_params['W' + str(l)],
-1,
1,
out=c2_params['W' + str(l)])
np.clip(c1_params['b' + str(l)],
-1,
1,
out=c1_params['b' + str(l)])
np.clip(c2_params['b' + str(l)],
-1,
1,
out=c2_params['b' + str(l)])
# Create children from chromosomes generated above
brain = NN()
boat = Boat()
brain.transform_weights(c1_params)
p1 = Player(brain, boat)
brain = NN()
boat = Boat()
brain.transform_weights(c2_params)
p2 = Player(brain, boat)
# Add children to the next generation
next_pop.extend([p1, p2])
# Set the next generation
random.shuffle(next_pop)
self.group = next_pop
self.population = Population(self.group)
def first_generation(self):
chromosomes = []
for _ in range(self.SIZE):
chromosomes.append(Chromosome.random())
return Population(chromosomes)
def run_algorithm():
global FILE
if len(sys.argv) > 1:
FILE = sys.argv[1]
board_configuration = Config(FILE)
best_fitness = []
worst_fitness = []
avg_fitness = []
population = Population(board_configuration, POPULATION_SIZE)
start = time.time()
population.init_population()
population.calculate_fitness()
#population.print_population()
for i in range(GENERATIONS):
population.select_and_crossover()
population.calculate_fitness()
if i % 10 == 0:
population.save_best(i)
best, worst, avg = population.evaluate_population()
print("====== GENERATION {i} =======".format(i=i))
print("Best fitness: {}".format(best))
best_fitness.append(best)
worst_fitness.append(worst)
avg_fitness.append(avg)
#population.print_population()
population.save_best(GENERATIONS)
stop = time.time()
print("\nTime: {}".format(stop - start))
print("Worst: {}".format(worst_fitness[-1]))
print("Best: {}".format(min(best_fitness)))
print("Avg: {}".format(avg_fitness[-1]))
make_chart(best_fitness, worst_fitness, avg_fitness)