def test_basic():
# create two output neurons (they won't receive any external inputs)
n1 = CTNeuron('OUTPUT', 1, -2.75, 1.0, 'sigmoid', 0.5)
n1.set_integration_step(0.04)
repr(n1)
str(n1)
n2 = CTNeuron('OUTPUT', 2, -1.75, 1.0, 'sigmoid', 0.5)
n2.set_integration_step(0.04)
repr(n2)
str(n2)
n1.set_init_state(-0.084000643)
n2.set_init_state(-0.408035109)
neurons_list = [n1, n2]
# create some synapses
conn_list = [(1, 1, 4.5), (1, 2, -1.0), (2, 1, 1.0), (2, 2, 4.5)]
# create the network
net = Network(neurons_list, conn_list, 0)
net.set_integration_step(0.03)
repr(net)
str(net)
# activates the network
print("{0:.7f} {1:.7f}".format(n1.output, n2.output))
outputs = []
for i in range(1000):
output = net.parallel_activate()
outputs.append(output)
print("{0:.7f} {1:.7f}".format(output[0], output[1]))
for s in net.synapses:
repr(s)
str(s)
def create_simple():
neurons = [Neuron('INPUT', 1, 0.0, 5.0, 'sigmoid'),
Neuron('HIDDEN', 2, 0.0, 5.0, 'sigmoid'),
Neuron('OUTPUT', 3, 0.0, 5.0, 'sigmoid')]
connections = [(1, 2, 0.5), (1, 3, 0.5), (2, 3, 0.5)]
map(repr, neurons)
return Network(neurons, connections, 1)
def test_basic():
# create two output neurons (they won't receive any external inputs)
n1 = CTNeuron('OUTPUT', 1, -2.75, 1.0, 'exp', 0.5)
n2 = CTNeuron('OUTPUT', 2, -1.75, 1.0, 'exp', 0.5)
n1.set_init_state(-0.084000643)
n2.set_init_state(-0.408035109)
neurons_list = [n1, n2]
# create some synapses
conn_list = [(1, 1, 4.5), (1, 2, -1.0), (2, 1, 1.0), (2, 2, 4.5)]
# create the network
net = Network(neurons_list, conn_list)
# activates the network
print("%.17f %.17f" % (n1.output, n2.output))
outputs = []
for i in range(1000):
output = net.parallel_activate()
outputs.append(output)
print("%.17f %.17f" % (output[0], output[1]))
import numpy as np
import matplotlib.pyplot as plt
from neat.ctrnn import CTNeuron, Network
# create two output neurons (they won't receive any external inputs)
N1 = CTNeuron('OUTPUT', 1, -2.75, 1.0, 'exp', 0.5)
N2 = CTNeuron('OUTPUT', 2, -1.75, 1.0, 'exp', 0.5)
N1.set_init_state(-0.084000643)
N2.set_init_state(-0.408035109)
neurons_list = [N1, N2]
# create some synapses
conn_list = [(1, 1, 4.5), (1, 2, -1.0), (2, 1, 1.0), (2, 2, 4.5)]
# create the network
net = Network(neurons_list, conn_list)
# activates the network
print("%.17f %.17f" % (N1.output, N2.output))
outputs = []
for i in range(1000):
output = net.parallel_activate()
outputs.append(output)
print("%.17f %.17f" % (output[0], output[1]))
outputs = np.array(outputs).T
plt.title("CTRNN model")
plt.ylabel("Outputs")
plt.xlabel("Time")
plt.grid()
plt.plot(outputs[0], "g-", label="output 0")
import numpy as np
import matplotlib.pyplot as plt
from neat.ctrnn import CTNeuron, Network
# create two output neurons (they won't receive any external inputs)
N1 = CTNeuron('OUTPUT', 1, -2.75, 1.0, 'sigmoid', 0.5)
N2 = CTNeuron('OUTPUT', 2, -1.75, 1.0, 'sigmoid', 0.5)
N1.set_init_state(-0.084000643)
N2.set_init_state(-0.408035109)
neurons_list = [N1, N2]
# create some synapses
conn_list = [(1, 1, 4.5), (1, 2, -1.0), (2, 1, 1.0), (2, 2, 4.5)]
# create the network
net = Network(neurons_list, conn_list, 0)
# activates the network
print("{0:.7f} {1:.7f}".format(N1.output, N2.output))
outputs = []
for i in range(1000):
output = net.parallel_activate()
outputs.append(output)
print("{0:.7f} {1:.7f}".format(output[0], output[1]))
outputs = np.array(outputs).T
plt.title("CTRNN model")
plt.ylabel("Outputs")
plt.xlabel("Time")
plt.grid()
plt.plot(outputs[0], "g-", label="output 0")