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)
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]))
# This example follows from Beer's C++ source code available at:
# http://mypage.iu.edu/~rdbeer/
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from neatsociety.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")
