def evaluate(self, network, verbose=False):
if not isinstance(network, NeuralNetwork):
network = NeuralNetwork(network)
network.make_feedforward()
if not network.node_types[-1](-1000) < -0.95:
raise Exception(
"Network should be able to output value of -1, e.g. using a tanh node."
)
pairs = list(zip(self.INPUTS, self.OUTPUTS))
random.shuffle(pairs)
if not self.do_all:
pairs = [random.choice(pairs)]
rmse = 0.0
for (i, target) in pairs:
# Feed with bias
output = network.feed(i)
# Grab the output
output = output[-len(target):]
err = (target - output)
err[abs(err) < self.EPSILON] = 0
err = (err**2).mean()
# Add error
if verbose:
print("%r -> %r (%.2f)" % (i, output, err))
rmse += err
score = 1 / (1 + np.sqrt(rmse / len(pairs)))
return {'fitness': score}
def convert(self, individual):
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):
# Add a bias (translation)
coords = np.hstack((coords, [1]))
w = sum(weight * gabor_opt(*(np.dot(mat, coords)), sigma=sigma)
for (weight, sigma, mat) in individual.wavelets[conn_id])
cm[j, i] = w
# Rescale weights
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()
return net
def evaluate(self, network, verbose=False):
if not isinstance(network, NeuralNetwork):
network = NeuralNetwork(network)
network.make_feedforward()
if not network.node_types[-1](-1000) < -0.95:
raise Exception("Network should be able to output value of -1, e.g. using a tanh node.")
pairs = list(zip(self.INPUTS, self.OUTPUTS))
random.shuffle(pairs)
if not self.do_all:
pairs = [random.choice(pairs)]
rmse = 0.0
for (i, target) in pairs:
# Feed with bias
output = network.feed(i)
# Grab the output
output = output[-len(target):]
err = (target - output)
err[abs(err) < self.EPSILON] = 0;
err = (err ** 2).mean()
# Add error
if verbose:
print("%r -> %r (%.2f)" % (i, output, err))
rmse += err
score = 1/(1+np.sqrt(rmse / len(pairs)))
return {'fitness': score}
def convert(self, individual):
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):
# Add a bias (translation)
coords = np.hstack((coords, [1]))
w = sum(weight * gabor_opt(*(np.dot(mat, coords)), sigma=sigma)
for (weight, sigma, mat) in individual.wavelets[conn_id])
cm[j,i] = w
# Rescale weights
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()
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 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
