def convert(self, network):
""" Performs conversion.
:param network: Any object that is convertible to a :class:`~peas.networks.NeuralNetwork`.
"""
# Cast input to a neuralnetwork if it isn't
if not isinstance(network, NeuralNetwork):
network = NeuralNetwork(network)
# Since Stanley mentions to "fully activate" the CPPN,
# I assume this means it's a feedforward net, since otherwise
# there is no clear definition of "full activation".
# In an FF network, activating each node once leads to a stable condition.
# Check if the network has enough inputs.
required_inputs = 2 * self.substrate.dimensions + 1
if self.add_deltas:
required_inputs += self.substrate.dimensions
if network.cm.shape[0] <= required_inputs:
raise Exception(
"Network does not have enough inputs. Has %d, needs %d" %
(network.cm.shape[0], cm_dims + 1))
# Initialize connectivity matrix
cm = np.zeros((self.substrate.num_nodes, self.substrate.num_nodes))
for (i, j
), coords, conn_id, expr_id in self.substrate.get_connection_list(
self.add_deltas):
expression = True
if expr_id is not None:
network.flush()
expression = network.feed(coords,
self.activation_steps)[expr_id] > 0
if expression:
network.flush()
weight = network.feed(coords, self.activation_steps)[conn_id]
cm[j, i] = weight
# Rescale the CM
cm[np.abs(cm) < self.min_weight] = 0
cm -= (np.sign(cm) * self.min_weight)
cm *= self.weight_range / (self.weight_range - self.min_weight)
# Clip highest weights
cm = np.clip(cm, -self.weight_range, self.weight_range)
net = NeuralNetwork().from_matrix(cm, node_types=[self.node_type])
if self.sandwich:
net.make_sandwich()
if self.feedforward:
net.make_feedforward()
if not np.all(np.isfinite(net.cm)):
raise Exception("Network contains NaN/inf weights.")
return net
def convert(self, network):
""" Performs conversion.
:param network: Any object that is convertible to a :class:`~peas.networks.NeuralNetwork`.
"""
# Cast input to a neuralnetwork if it isn't
if not isinstance(network, NeuralNetwork):
network = NeuralNetwork(network)
# Since Stanley mentions to "fully activate" the CPPN,
# I assume this means it's a feedforward net, since otherwise
# there is no clear definition of "full activation".
# In an FF network, activating each node once leads to a stable condition.
# Check if the network has enough inputs.
required_inputs = 2 * self.substrate.dimensions + 1
if self.add_deltas:
required_inputs += self.substrate.dimensions
if network.cm.shape[0] <= required_inputs:
raise Exception("Network does not have enough inputs. Has %d, needs %d" %
(network.cm.shape[0], cm_dims+1))
# Initialize connectivity matrix
cm = np.zeros((self.substrate.num_nodes, self.substrate.num_nodes))
for (i,j), coords, conn_id, expr_id in self.substrate.get_connection_list(self.add_deltas):
expression = True
if expr_id is not None:
network.flush()
expression = network.feed(coords, self.activation_steps)[expr_id] > 0
if expression:
network.flush()
weight = network.feed(coords, self.activation_steps)[conn_id]
cm[j, i] = weight
# Rescale the CM
cm[np.abs(cm) < self.min_weight] = 0
cm -= (np.sign(cm) * self.min_weight)
cm *= self.weight_range / (self.weight_range - self.min_weight)
# Clip highest weights
cm = np.clip(cm, -self.weight_range, self.weight_range)
net = NeuralNetwork().from_matrix(cm, node_types=[self.node_type])
if self.sandwich:
net.make_sandwich()
if self.feedforward:
net.make_feedforward()
if not np.all(np.isfinite(net.cm)):
raise Exception("Network contains NaN/inf weights.")
return net
def evaluate(self, network, draw=False, drawname='Simulation'):
""" Evaluate the efficiency of the given network. Returns the
distance that the bot ran
"""
if not isinstance(network, NeuralNetwork):
network = NeuralNetwork(network)
h,w = self.field_friction.shape
if draw:
import pygame
pygame.init()
screen = pygame.display.set_mode((w, h))
pygame.display.set_caption(drawname)
clock = pygame.time.Clock()
running = True
font = pygame.font.Font(pygame.font.get_default_font(), 12)
field_image = pygame.image.load(self.observationpath)
# Initialize pymunk
self.space = space = pymunk.Space()
space.gravity = (0,0)
space.damping = self.damping
# Create objects
robot = Robot(space, self.field_friction, self.field_observation, self.initial_pos,
friction_scale=self.friction_scale, motor_torque=self.motor_torque,
force_global=self.force_global)
path = [(robot.body.position.int_tuple)]
cells = []
if network.cm.shape[0] != (3*5 + 3*3 + 1):
raise Exception("Network shape must be a 2 layer controller: 3x5 input + 3x3 hidden + 1 bias. Has %d." % network.cm.shape[0])
for step in range(self.max_steps):
net_input = np.array(list(robot.sensor_response()))
# The nodes used for output are somewhere in the middle of the network
# so we extract them using -4 and -6
action = network.feed(net_input)[[-4,-6]]
if self.flush_each_step:
network.flush()
robot.drive(*action)
robot.apply_friction()
space.step(1/50.0)
new_cell_covered = False
current_cell = int(robot.body.position.x // 32), int(robot.body.position.y // 32)
if current_cell not in cells:
cells.append(current_cell)
new_cell_covered = True
if len(cells) > self.coverage_memory:
cells.pop(0)
elif cells[-1] == current_cell:
new_cell_covered = True
if step % self.path_resolution == 0 and (not self.check_coverage or new_cell_covered):
path.append((robot.body.position.int_tuple))
if draw:
screen.fill((255, 255, 255))
screen.blit(field_image, (0,0))
txt = font.render('%d - %.0f' % (step, path_length(path)), False, (0,0,0) )
screen.blit(txt, (0,0))
# Draw path
if len(path) > 1:
pygame.draw.lines(screen, (0,0,255), False, path, 3)
for cell in cells:
pygame.draw.rect(screen, (200,200,0), (cell[0]*32, cell[1]*32, 32, 32), 2)
robot.draw(screen)
if pygame.event.get(pygame.QUIT):
break
for k in pygame.event.get(pygame.KEYDOWN):
if k.key == pygame.K_SPACE:
break
pygame.display.flip()
# clock.tick(50)
if draw:
pygame.quit()
self.last_path = path
dist = path_length(path)
speed = dist / self.max_steps
return {'fitness':1 + dist**2, 'dist':dist, 'speed':speed}
def evaluate(self, network, draw=False, drawname='Simulation'):
""" Evaluate the efficiency of the given network. Returns the
distance that the bot ran
"""
if not isinstance(network, NeuralNetwork):
network = NeuralNetwork(network)
h, w = self.field_friction.shape
if draw:
import pygame
pygame.init()
screen = pygame.display.set_mode((w, h))
pygame.display.set_caption(drawname)
clock = pygame.time.Clock()
running = True
font = pygame.font.Font(pygame.font.get_default_font(), 12)
field_image = pygame.image.load(self.observationpath)
# Initialize pymunk
self.space = space = pymunk.Space()
space.gravity = (0, 0)
space.damping = self.damping
# Create objects
robot = Robot(space,
self.field_friction,
self.field_observation,
self.initial_pos,
friction_scale=self.friction_scale,
motor_torque=self.motor_torque,
force_global=self.force_global)
path = [(robot.body.position.int_tuple)]
cells = []
if network.cm.shape[0] != (3 * 5 + 3 * 3 + 1):
raise Exception(
"Network shape must be a 2 layer controller: 3x5 input + 3x3 hidden + 1 bias. Has %d."
% network.cm.shape[0])
for step in range(self.max_steps):
net_input = np.array(list(robot.sensor_response()))
# The nodes used for output are somewhere in the middle of the network
# so we extract them using -4 and -6
action = network.feed(net_input)[[-4, -6]]
if self.flush_each_step:
network.flush()
robot.drive(*action)
robot.apply_friction()
space.step(1 / 50.0)
new_cell_covered = False
current_cell = int(robot.body.position.x // 32), int(
robot.body.position.y // 32)
if current_cell not in cells:
cells.append(current_cell)
new_cell_covered = True
if len(cells) > self.coverage_memory:
cells.pop(0)
elif cells[-1] == current_cell:
new_cell_covered = True
if step % self.path_resolution == 0 and (not self.check_coverage
or new_cell_covered):
path.append((robot.body.position.int_tuple))
if draw:
screen.fill((255, 255, 255))
screen.blit(field_image, (0, 0))
txt = font.render('%d - %.0f' % (step, path_length(path)),
False, (0, 0, 0))
screen.blit(txt, (0, 0))
# Draw path
if len(path) > 1:
pygame.draw.lines(screen, (0, 0, 255), False, path, 3)
for cell in cells:
pygame.draw.rect(screen, (200, 200, 0),
(cell[0] * 32, cell[1] * 32, 32, 32), 2)
robot.draw(screen)
if pygame.event.get(pygame.QUIT):
break
for k in pygame.event.get(pygame.KEYDOWN):
if k.key == pygame.K_SPACE:
break
pygame.display.flip()
# clock.tick(50)
if draw:
pygame.quit()
self.last_path = path
dist = path_length(path)
speed = dist / self.max_steps
return {'fitness': 1 + dist**2, 'dist': dist, 'speed': speed}
