class DinoNeurons:
def __init__(self, input_list):
"""
:param input_list:
"""
self.input_list = input_list
self.neuron_jump = Neuron(input_liste=input_list,
max_value=1.0,
activation_function=sigmoid)
self.neuron_no_jump = Neuron(input_liste=input_list,
max_value=1.0,
activation_function=sigmoid)
def need_to_jump(self):
return self.neuron_jump.compute() > self.neuron_no_jump.compute()
def reset(self):
self.neuron_jump = Neuron(input_liste=self.input_list,
max_value=1.0,
activation_function=sigmoid)
self.neuron_no_jump = Neuron(input_liste=self.input_list,
max_value=1.0,
activation_function=sigmoid)
def __init__(self, input_list, gen: genome):
"""
:param input_list:
"""
super().__init__(gen)
self.input_list = input_list
self.neuron_jump = Neuron(input_liste=input_list,
max_value=5.0,
activation_function=sigmoid)
self.neuron_no_jump = Neuron(input_liste=input_list,
max_value=5.0,
activation_function=sigmoid)
# Load the gnome
index_gen = 0
# jump neuron - 1 to remove the bias node
for index_weight in range(self.neuron_jump.get_weight_len() - 1):
self.neuron_jump.weight_liste[index_weight] = gen[index_gen]
index_gen += 1
# bias node for jump neuron
self.neuron_jump.set_threshold(gen[index_gen])
index_gen += 1
# no jump neuron - 1 to remove the bias node
for index_weight in range(self.neuron_no_jump.get_weight_len() - 1):
self.neuron_no_jump.weight_liste[index_weight] = gen[index_gen]
index_gen += 1
# bias node for jump neuron
self.neuron_no_jump.set_threshold(gen[index_gen])
self.dino_score = None
def reset(self):
self.neuron_jump = Neuron(input_liste=self.input_list,
max_value=1.0,
activation_function=sigmoid)
self.neuron_no_jump = Neuron(input_liste=self.input_list,
max_value=1.0,
activation_function=sigmoid)
def __init__(self, input_list):
"""
:param input_list:
"""
self.input_list = input_list
self.neuron_jump = Neuron(input_liste=input_list,
max_value=1.0,
activation_function=sigmoid)
self.neuron_no_jump = Neuron(input_liste=input_list,
max_value=1.0,
activation_function=sigmoid)
class DinoNeurons(Individual):
def __init__(self, input_list, gen: genome):
"""
:param input_list:
"""
super().__init__(gen)
self.input_list = input_list
self.neuron_jump = Neuron(input_liste=input_list,
max_value=5.0,
activation_function=sigmoid)
self.neuron_no_jump = Neuron(input_liste=input_list,
max_value=5.0,
activation_function=sigmoid)
# Load the gnome
index_gen = 0
# jump neuron - 1 to remove the bias node
for index_weight in range(self.neuron_jump.get_weight_len() - 1):
self.neuron_jump.weight_liste[index_weight] = gen[index_gen]
index_gen += 1
# bias node for jump neuron
self.neuron_jump.set_threshold(gen[index_gen])
index_gen += 1
# no jump neuron - 1 to remove the bias node
for index_weight in range(self.neuron_no_jump.get_weight_len() - 1):
self.neuron_no_jump.weight_liste[index_weight] = gen[index_gen]
index_gen += 1
# bias node for jump neuron
self.neuron_no_jump.set_threshold(gen[index_gen])
self.dino_score = None
def set_score(self, score):
self.dino_score = score
def need_to_jump(self):
return self.neuron_jump.compute() > self.neuron_no_jump.compute()
def get_score(self):
if self.dino_score is None:
raise ValueError("The score is not set")
return self.dino_score
def to_dict(self):
return {'score': self.get_score(), 'genome': self.genome}
PYTHON_LOGGER.setLevel(logging.DEBUG)
# Absolute path to the folder location of this python file
FOLDER_ABSOLUTE_PATH = os.path.normpath(
os.path.dirname(os.path.abspath(__file__)))
if __name__ == "__main__":
from neural import sigmoid
from dino_game import GameController, width
game_speed = Input(value=0)
distance_next_obstacle = Input(value=0)
gap_between_obstacles = Input(value=0)
input_list = [game_speed, distance_next_obstacle, gap_between_obstacles]
neuron_jump = Neuron(input_liste=input_list,
max_value=1.0,
activation_function=sigmoid)
neuron_no_jump = Neuron(input_liste=input_list,
max_value=1.0,
activation_function=sigmoid)
controller = GameController(numbers_of_dino=1)
while True:
if controller.game_is_over():
neuron_jump = Neuron(input_liste=input_list,
max_value=1.0,
activation_function=sigmoid)
neuron_no_jump = Neuron(input_liste=input_list,
max_value=1.0,
activation_function=sigmoid)
controller.restart_game()