class Bird:
#constructor
def __init__(self, canvas, colour, y):
self.brain = NeuralNetwork(4, 4, 1)
self.move = 0
self.x = 70
self.y = y
self.isDead = False
self.canvas = canvas
self.colour = colour
self.prevLoc = None
self.radius = 10
self.surface = pygame.Surface((self.radius * 2, self.radius * 2))
self.surface.fill(BLUE)
self.prevLoc = (self.x - self.radius, self.y - self.radius)
self.fitness = 0
# self.score = 0
self.isChamp = False
self.distance = 0
#draws the "bird" to the canvas
def draw(self):
pygame.draw.circle(self.canvas, self.colour, [self.x, int(self.y)], self.radius)
#how the screen updates every frame
def update(self, pipe_x):
if not self.isDead:
# wont fly of the screen
if self.y - 10 <= 0:
self.y = 10
self.isDead = True
#kills it if it hits the bottom
elif self.y+10 >= self.canvas.get_height():
self.y = self.canvas.get_height() - 10
self.isDead = True
#update screen and send information to canvas
first_rect = self.create_rect(self.x, self.y, self.radius)
self.canvas.blit(self.surface, self.prevLoc)
#movement if its flapping up
if self.move != 0:
self.y -= 8
self.move -= 1
else:
self.y += 7
#increase the total distance that the birds have travelled
self.distance += 2
self.prevLoc = (self.x - self.radius, self.y - self.radius)
self.draw()
second_rect = self.create_rect(self.x, self.y, self.radius)
return first_rect, second_rect
elif self.fitness == 0:
pipe_distance = pipe_x - self.x
self.fitness = self.distance - pipe_distance
return [self.create_rect(self.x, self.y, self.radius)]
@staticmethod
#convience function for creating rectangles
def create_rect(x, y, radius):
return pygame.Rect(x - radius, y - radius, x + radius, y + radius)
#use the neural network to decide if it should fly
def flap(self, inputs):
inputs.append(self.y)
inputs = self.normalize(inputs)
output = self.brain.guess_ff(inputs)
#jumps if more than 50% confident
return True if output[0] > .50 else False
@staticmethod
#normalizes the data to a range between 0 and 1
def normalize(data):
normalized = []
mini = min(data)
maxi = max(data)
for i in data:
normalized.append((i - mini)/(maxi - mini))
return normalized
