def check_add_connection(genome_type, feed_forward):
indexer = InnovationIndexer(0)
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
config.input_nodes = 3
config.output_nodes = 4
config.hidden_nodes = 5
config.feedforward = feed_forward
N = config.input_nodes + config.hidden_nodes + config.output_nodes
connections = {}
for a in range(100):
g = genome_type.create_unconnected(a, config)
g.add_hidden_nodes(config.hidden_nodes)
for b in range(1000):
g.mutate_add_connection(indexer)
for c in g.conn_genes.values():
connections[c.key] = connections.get(c.key, 0) + 1
# TODO: The connections should be returned to the caller and checked
# against the constraints/assumptions particular to the network type.
for i in range(N):
values = []
for j in range(N):
values.append(connections.get((i, j), 0))
print("{0:2d}: {1}".format(i, " ".join("{0:3d}".format(x) for x in values)))
def run():
# Load the config file, which is assumed to live in
# the same directory as this script.
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'xor2_config'))
# For this network, we use two output neurons and use the difference between
# the "time to first spike" to determine the network response. There are
# probably a great many different choices one could make for an output encoding,
# and this choice may not be the best for tackling a real problem.
config.output_nodes = 2
pop = population.Population(config)
pop.run(eval_fitness, 200)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Visualize the winner network and plot statistics.
winner = pop.statistics.best_genome()
node_names = {0: 'A', 1: 'B', 2: 'Out1', 3: 'Out2'}
visualize.draw_net(winner, view=True, node_names=node_names)
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
# Verify network output against training data.
print('\nBest network output:')
net = iznn.create_phenotype(winner, *iz_params)
sum_square_error, simulated = simulate(winner)
# Create a plot of the traces out to the max time for each set of inputs.
plt.figure(figsize=(12, 12))
for r, (inputData, outputData, t0, t1, v0, v1, neuron_data) in enumerate(simulated):
response = compute_output(t0, t1)
print("{0!r} expected {1:.3f} got {2:.3f}".format(inputData, outputData, response))
axes = plt.subplot(4, 1, r + 1)
plt.title("Traces for XOR input {{{0:.1f}, {1:.1f}}}".format(*inputData), fontsize=12)
for i, s in neuron_data.items():
if i in net.outputs:
t, v = zip(*s)
plt.plot(t, v, "-", label="neuron {0:d}".format(i))
# Circle the first peak of each output.
circle0 = patches.Ellipse((t0, v0), 1.0, 10.0, color='r', fill=False)
circle1 = patches.Ellipse((t1, v1), 1.0, 10.0, color='r', fill=False)
axes.add_artist(circle0)
axes.add_artist(circle1)
plt.ylabel("Potential (mv)", fontsize=10)
plt.ylim(-100, 50)
plt.tick_params(labelsize=8)
plt.grid()
plt.xlabel("Time (in ms)", fontsize=10)
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.savefig("traces.png", dpi=90)
plt.show()
def run():
# Load the config file, which is assumed to live in
# the same directory as this script.
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'xor2_config'))
# For this network, we use two output neurons and use the difference between
# the "time to first spike" to determine the network response. There are
# probably a great many different choices one could make for an output encoding,
# and this choice may not be the best for tackling a real problem.
config.output_nodes = 2
pop = population.Population(config)
pop.run(eval_fitness, 200)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Visualize the winner network and plot statistics.
winner = pop.statistics.best_genome()
node_names = {0: 'A', 1: 'B', 2: 'Out1', 3: 'Out2'}
visualize.draw_net(winner, view=True, node_names=node_names)
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
# Verify network output against training data.
print('\nBest network output:')
net = iznn.create_phenotype(winner, *iz_params)
sum_square_error, simulated = simulate(winner)
# Create a plot of the traces out to the max time for each set of inputs.
plt.figure(figsize=(12, 12))
for r, (inputData, outputData, t0, t1, v0, v1,
neuron_data) in enumerate(simulated):
response = compute_output(t0, t1)
print("{0!r} expected {1:.3f} got {2:.3f}".format(
inputData, outputData, response))
axes = plt.subplot(4, 1, r + 1)
plt.title(
"Traces for XOR input {{{0:.1f}, {1:.1f}}}".format(*inputData),
fontsize=12)
for i, s in neuron_data.items():
if i in net.outputs:
t, v = zip(*s)
plt.plot(t, v, "-", label="neuron {0:d}".format(i))
# Circle the first peak of each output.
circle0 = patches.Ellipse((t0, v0), 1.0, 10.0, color='r', fill=False)
circle1 = patches.Ellipse((t1, v1), 1.0, 10.0, color='r', fill=False)
axes.add_artist(circle0)
axes.add_artist(circle1)
plt.ylabel("Potential (mv)", fontsize=10)
plt.ylim(-100, 50)
plt.tick_params(labelsize=8)
plt.grid()
plt.xlabel("Time (in ms)", fontsize=10)
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.savefig("traces.png", dpi=90)
plt.show()
