class Cell():
def __init__(self, res_size = 50, connectivity = 0.05, state = None):
self.res_size = res_size
self.connectivity = connectivity
# we'll hold off on actually making the esn until we get our neighbors
if state == None:
self.state = choice([True, False])
else:
self.state = state
def set_neighbors(self, neighbors):
self.neighbors = neighbors
self.input_len = len(neighbors)
self.esn = EchoStateNetwork(self.res_size, self.connectivity, self.input_len)
self.output_rear_weights = [random() for _ in range(self.esn.rear_len)] # only one visible node (output state node)
# ----- for learning ------
def logsig(self, x):
if x <= -50:
return 0.0001
if x >= 50:
return 0.9999
return 1.0 / (1 + (2.718281828**(-1*x)))
def logsig_deriv(self, x):
v = self.logsig(x)
return v*(1-v)
def prep_change(self):
""" Predict the next state for this cell
"""
# activate the esn from the neighbors
self.esn.set_forward_states([ x.state for x in self.neighbors])
self.esn.activate()
# choose the next state from the rear of our esn
total = 0.0
rear_states = self.esn.get_rear_states()
for i in range(self.esn.rear_len):
total += self.output_rear_weights[i] * rear_states[i]
total = self.logsig(total)
self.next_state = (total > 0.5)
def commit_change(self):
self.state = self.next_state
def commit_to_correct(self):
self.state = self.should_state
def get_correct_next_state(self):
"""total_on = sum( [ x.state for x in self.neighbors ] )
if total_on < 2:
self.should_state = False
elif total_on > 3:
self.should_state = False
elif total_on == 2 and self.state == False:
self.should_state = False
else:
self.should_state = True"""
# pick minority class
total_on = sum( [ x.state for x in self.neighbors ] )
if 1.0 * total_on / len(self.neighbors) <= 0.5:
self.should_state = True
else:
self.should_state = False
def update_weights(self):
"""Not used in this version. We are updating parameters instead"""
self.get_correct_next_state()
# if we are right, we don't have to do anything
if self.should_state == self.next_state:
return
else:
output_delta = (self.should_state - self.next_state) * self.logsig_deriv(self.next_state)
rear_deltas = [0.0] * self.esn.rear_len
rear_states = self.esn.get_rear_states()
# update the weights between the visible node and the rear layer
for r in range(self.esn.rear_len):
rear_deltas[r] = output_delta * self.output_rear_weights[r]
self.output_rear_weights[r] += output_delta * rear_states[r] *LEARNING_RATE
self.esn.update_weights(rear_deltas)
def abs_error(self):
return abs( self.should_state - self.next_state )
data = load_data.get_data()
move_data = data['move_data'][:, 0:1]
neuron_data = data['neuron_data'][:, 0:1]
next_frame_neuron_data = neuron_data[1:, 0:1]
train_in = neuron_data[train_idx]
train_out_truth = move_data[train_idx]
test_in = neuron_data[test_idx]
test_out_truth = move_data[test_idx]
#make our network and run dat dynamical boi
esn = EchoStateNetwork.EchoStateNetwork(train_in[0].shape[0],
train_out_truth[0].shape[0],
res_size=100,
leak_rate=.9,
spectral_radius=.8)
print("training network...")
esn.train(train_in, train_out_truth)
print("done training network.")
machine_out, err = esn.test(test_in, test_out_truth)
print("error is: ", err)
plt.plot(machine_out[:, 0], label="machine outputs")
plt.plot(test_out_truth, label="truth")
plt.legend(loc='best')
plt.show()
def set_neighbors(self, neighbors):
self.neighbors = neighbors
self.input_len = len(neighbors)
self.esn = EchoStateNetwork(self.res_size, self.connectivity, self.input_len)
self.output_rear_weights = [random() for _ in range(self.esn.rear_len)] # only one visible node (output state node)