class Player():
def __init__(self, num, imr, amr, io, hidden_layers, activations):
layers = [io[0]] + hidden_layers + [io[1]]
self.brain = NeuralNetwork(layers,
activations,
'SSE',
imr=imr,
amr=amr)
self.num = num
self.imr = imr
self.amr = amr
self.io = io
self.hidden_layers = hidden_layers
self.activations = activations
self.fitness = 0
self.x = X
self.y = Y
self.r = R
# the player plays the game and returns its score (frames survived)
def calculate_fitness(self):
fitness = game.play(self)
self.fitness = fitness
def mutate(self):
self.brain.mutate()
def crossover(self, other):
brain = self.brain.crossover(other.brain)
child = Player(self.num, self.imr, self.amr, self.io,
self.hidden_layers, self.activations)
child.brain = brain
return child
def move(self, dx, dy):
#print(self.y)
self.x += dx
self.y += dy
def clone(self):
dolly = Player(self.num, self.imr, self.amr, self.io,
self.hidden_layers, self.activations)
dolly.brain = self.brain.clone()
return dolly
def decide(self, vision, n):
x = np.array(vision)
x[0:n] = x[0:n] / (game.WIDTH - self.x + self.r
) # bound the values [0,1]
p = self.brain.forward_propogation(x)[0][-1]
return p > THRESHOLD
def intersects(self, other):
# if the type is a rectangular barrier, check to see if any points on the barrier's perimeter is within the circle's radius
if type(other) == game.Barrier:
# check top and bottom
for x in range(other.x, other.x + other.width):
for y in [other.y, other.y + other.height]:
if dist([x, y], [self.x, self.y]) < self.r:
return True
# check left and right
for y in range(other.y, other.y + other.height):
for x in [other.x, other.x + other.width]:
if dist([x, y], [self.x, self.y]) < self.r:
return True
return False
# less than method used for sorting players by fitness
def __lt__(self, other):
return self.fitness < other.fitness
# equals method used for sorting players by fitness
def __eq__(self, other):
return self.fitness == other.fitness
def crossover(bird1, bird2):
return BirdAgent(NeuralNetwork.crossover(bird1.brain, bird2.brain))
