def test_run():
xor_inputs = [[0, 0], [0, 1], [1, 0], [1, 1]]
xor_outputs = [0, 1, 1, 0]
def eval_fitness(genomes):
for g in genomes:
net = nn.create_feed_forward_phenotype(g)
error = 0.0
for inputs, expected in zip(xor_inputs, xor_outputs):
# Serial activation propagates the inputs through the entire network.
output = net.serial_activate(inputs)
error += (output[0] - expected) ** 2
# When the output matches expected for all inputs, fitness will reach
# its maximum value of 1.0.
g.fitness = 1 - error
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
pop = population.Population(config)
pop.run(eval_fitness, 10)
winner = pop.statistics.best_genome()
# Validate winner.
for g in pop.statistics.most_fit_genomes:
assert winner.fitness >= g.fitness
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
def test_run():
xor_inputs = [[0, 0], [0, 1], [1, 0], [1, 1]]
xor_outputs = [0, 1, 1, 0]
def eval_fitness(genomes):
for g in genomes:
net = nn.create_feed_forward_phenotype(g)
error = 0.0
for inputs, expected in zip(xor_inputs, xor_outputs):
# Serial activation propagates the inputs through the entire network.
output = net.serial_activate(inputs)
error += (output[0] - expected)**2
# When the output matches expected for all inputs, fitness will reach
# its maximum value of 1.0.
g.fitness = 1 - error
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
pop = population.Population(config)
pop.run(eval_fitness, 10)
winner = pop.statistics.best_genome()
# Validate winner.
for g in pop.statistics.most_fit_genomes:
assert winner.fitness >= g.fitness
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
def train_network(self, checkpoint):
self.checkpoint = checkpoint
# Simulation
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'config.txt')
pop = population.Population(config_path)
# Load checkpoint
if self.checkpoint != None:
pop.load_checkpoint(self.checkpoint)
#pe = parallel.ParallelEvaluator(args.numCores,worker_evaluate_genome)
pop.run(self.eval_fitness, self.generations)
pop.save_checkpoint("checkpoint")
# Log statistics.
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Show output of the most fit genome against training data.
winner = pop.statistics.best_genome()
# Save best network
import pickle
with open('winner.pkl', 'wb') as output:
pickle.dump(winner, output, 1)
#visualize.draw_net("config.txt", winner, view=False, node_names=None, filename=self.game_name + "net")
'''
def run():
local_dir = os.path.dirname(__file__)
pop = population.Population(os.path.join(local_dir, 'nn_config'))
pe = parallel.ParallelEvaluator(4, eval_fitness)
pop.run(pe.evaluate, 1000)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Display the most fit genome.
print('\nBest genome:')
winner = pop.statistics.best_genome()
print(winner)
# Verify network output against a few randomly-generated sequences.
winner_net = nn.create_recurrent_phenotype(winner)
for n in range(4):
print('\nRun {0} output:'.format(n))
seq = [random.choice((0, 1)) for _ in range(N)]
winner_net.reset()
for s in seq:
winner_net.activate([s, 0])
for s in seq:
output = winner_net.activate([0, 1])
print("expected {0:1.5f} got {1:1.5f}".format(s, output[0]))
# Visualize the winner network and plot/log statistics.
visualize.draw_net(winner, view=True, filename="nn_winner.gv")
visualize.draw_net(winner, view=True, filename="nn_winner-enabled.gv", show_disabled=False)
visualize.draw_net(winner, view=True, filename="nn_winner-enabled-pruned.gv", show_disabled=False, prune_unused=True)
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
def run():
local_dir = os.path.dirname(__file__)
pop = population.Population(os.path.join(local_dir, "nn_config"))
pe = parallel.ParallelEvaluator(4, eval_fitness)
pop.run(pe.evaluate, 1000)
# Log statistics.
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
print("Number of evaluations: {0}".format(pop.total_evaluations))
# Show output of the most fit genome against a random input.
winner = pop.statistics.best_genome()
print("\nBest genome:\n{!s}".format(winner))
print("\nOutput:")
winner_net = nn.create_recurrent_phenotype(winner)
for n in range(4):
print("\nRun {0} output:".format(n))
seq = [random.choice((0, 1)) for _ in range(N)]
winner_net.reset()
for s in seq:
winner_net.activate([s, 0])
for s in seq:
output = winner_net.activate([0, 1])
print("expected {0:1.5f} got {1:1.5f}".format(s, output[0]))
def run():
local_dir = os.path.dirname(__file__)
pop = population.Population(os.path.join(local_dir, 'nn_config'))
pe = parallel.ParallelEvaluator(4, eval_fitness)
pop.run(pe.evaluate, 1000)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Display the most fit genome.
print('\nBest genome:')
winner = pop.statistics.best_genome()
print(winner)
# Verify network output against a few randomly-generated sequences.
winner_net = nn.create_recurrent_phenotype(winner)
for n in range(4):
print('\nRun {0} output:'.format(n))
seq = [random.choice((0, 1)) for _ in range(N)]
winner_net.reset()
for s in seq:
winner_net.activate([s, 0])
for s in seq:
output = winner_net.activate([0, 1])
print("expected {0:1.5f} got {1:1.5f}".format(s, output[0]))
# Visualize the winner network and plot/log statistics.
visualize.draw_net(winner, view=True, filename="nn_winner.gv")
visualize.draw_net(winner, view=True, filename="nn_winner-enabled.gv", show_disabled=False)
visualize.draw_net(winner, view=True, filename="nn_winner-enabled-pruned.gv", show_disabled=False, prune_unused=True)
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
def run():
local_dir = os.path.dirname(__file__)
pop = population.Population(os.path.join(local_dir, 'nn_config'))
pe = parallel.ParallelEvaluator(4, eval_fitness)
pop.run(pe.evaluate, 1000)
# Log statistics.
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Show output of the most fit genome against a random input.
winner = pop.statistics.best_genome()
print('\nBest genome:\n{!s}'.format(winner))
print('\nOutput:')
winner_net = nn.create_recurrent_phenotype(winner)
for n in range(4):
print('\nRun {0} output:'.format(n))
seq = [random.choice((0, 1)) for _ in range(N)]
winner_net.reset()
for s in seq:
winner_net.activate([s, 0])
for s in seq:
output = winner_net.activate([0, 1])
print("expected {0:1.5f} got {1:1.5f}".format(s, output[0]))
def train_network(self, checkpoint):
self.checkpoint = checkpoint
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'config.txt')
pop = population.Population(config_path)
# Load checkpoint
if self.checkpoint != None:
pop.load_checkpoint(self.checkpoint)
pop.run(self.eval_fitness, self.generations)
pop.save_checkpoint("checkpoint")
# Log statistics.
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Show output of the most fit genome against training data.
winner = pop.statistics.best_genome()
# Save best network
import pickle
with open('winner.pkl', 'wb') as output:
pickle.dump(winner, output, 1)
def train_network(env):
def evaluate_genome(g):
net = nn.create_feed_forward_phenotype(g)
return simulate_species(net,
env,
args.episodes,
args.max_steps,
render=args.render)
def eval_fitness(genomes):
for g in genomes:
fitness = evaluate_genome(g)
g.fitness = fitness
# Simulation
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'gym_config')
pop = population.Population(config_path)
# Load checkpoint
if args.checkpoint:
pop.load_checkpoint(args.checkpoint)
# Start simulation
if args.render:
pop.run(eval_fitness, args.generations)
else:
pe = parallel.ParallelEvaluator(args.numCores, worker_evaluate_genome)
pop.run(pe.evaluate, args.generations)
pop.save_checkpoint("checkpoint")
# Log statistics.
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Show output of the most fit genome against training data.
winner = pop.statistics.best_genome()
# Save best network
import pickle
with open('winner.pkl', 'wb') as output:
pickle.dump(winner, output, 1)
print('\nBest genome:\n{!s}'.format(winner))
print('\nOutput:')
raw_input("Press Enter to run the best genome...")
winner_net = nn.create_feed_forward_phenotype(winner)
for i in range(100):
simulate_species(winner_net, env, 1, args.max_steps, render=True)
def __init__(self, env, config, threads: int = 4):
print("\n------------------------------------------")
print(" NEAT USING THE ")
print(" NEAT-PYTHON LIBRARY ")
print("------------------------------------------\n")
self.env = env
# Simulation
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, config)
pop = population.Population(config_path) # Get the config file
# Load checkpoint
if args.checkpoint:
pop.load_checkpoint(args.checkpoint)
# Start simulation
try:
if threads == 1:
pop.run(self.eval_fitness, args.generations)
else:
pe = parallel.ParallelEvaluator(threads, self.evaluate_genome)
pop.run(pe.evaluate, args.generations)
except KeyboardInterrupt:
print("\nExited.")
pop.save_checkpoint("checkpoint") # Save the current checkpoint
# Log statistics.
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Show output of the most fit genome against training data.
winner = pop.statistics.best_genome()
# Save best network
name = input("Net name: ")
import pickle
with open(name + '.pkl', 'wb') as output:
pickle.dump(winner, output, 1)
print("Net saved as " + name + '.pkl')
print('\nBest genome:\n{!s}'.format(winner))
input("Press enter to run the best genome...")
winner_net = nn.create_feed_forward_phenotype(winner)
self.simulate_species(winner_net, env, 1, args.max_steps, render=True)
def save_stats(self, file_prefix):
'''
Save info about the network to a number of files with the given file prefix
file_prefix: string
returns: None
'''
best_genome = self.pop.statistics.best_genome()
visualize.plot_stats(self.pop.statistics, filename="%s_stats.png" % file_prefix)
visualize.plot_species(self.pop.statistics, filename="%s_stats.png" % file_prefix)
visualize.draw_net(best_genome, view=False, filename="%s_stats.gv" % file_prefix)
statistics.save_stats(self.pop.statistics, filename="%s_stats.csv" % file_prefix)
statistics.save_species_count(self.pop.statistics, filename="%s_species_count.csv" % file_prefix)
statistics.save_species_fitness(self.pop.statistics, filename="%s_species_fitness.csv" % file_prefix)
def train(checkpoint, config):
print("Starting...")
pop = population.Population(config)
# HINT change checkpoints for new try or reloading
try:
pop.load_checkpoint(checkpoint)
except:
print("Checkpoint not found, starting from scratch: ", checkpoint)
trainfunc = partial(eval_fitness_internalfight, num_runs=4, steplength=100, x=16, y=16)
pop.run(trainfunc, 20)
pop.save_checkpoint(checkpoint)
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
winner = pop.statistics.best_genome()
print('\nBest genome:\n{!s}'.format(winner))
def main():
print("Starting...")
pop = population.Population(
os.path.join(os.path.dirname(__file__), 'nn_config'))
#HINT change checkpoints for new try or reloading
pop.load_checkpoint(
os.path.join(os.path.dirname(__file__), 'checkpoints/popv1.cpt'))
pop.run(eval_fitness_internalfight, 5)
pop.save_checkpoint(
os.path.join(os.path.dirname(__file__), 'checkpoints/popv1.cpt'))
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
winner = pop.statistics.best_genome()
print('\nBest genome:\n{!s}'.format(winner))
print('\nOutput:')
winner_net = nn.create_recurrent_phenotype(winner)
mooreNeighborhood = ca.Neighborhood(gocl.initNeighborhood)
gameOfLife = ca.CellularAutomaton(mooreNeighborhood)
gameOfLife.parameters = gameOfLife.world['parameters']
newSpecies(gameOfLife, {
'species': 'test',
'color': 'Blue',
'position': {
'x': 0,
'y': 0
}
})
currentDecision = partial(netDecision, net=winner_net)
evolve = gameOfLife.evolve
c = 0
while c < 20:
dec = {}
recursionDecision(gameOfLife.world['space'], dec, currentDecision)
gameOfLife.decisions = dec
evolve()
c += 1
print("Ended with: ", countSpecies(gameOfLife.world['space'], 'test'))
# Serial activation propagates the inputs through the entire network.
output = net.serial_activate(inputs)
sum_square_error += (output[0] - expected) ** 2
# When the output matches expected for all inputs, fitness will reach
# its maximum value of 1.0.
g.fitness = 1 - sum_square_error
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'xor2_config')
pop = population.Population(config_path)
pop.run(eval_fitness, 300)
# Log statistics.
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Show output of the most fit genome against training data.
winner = pop.statistics.best_genome()
print('\nBest genome:\n{!s}'.format(winner))
print('\nOutput:')
winner_net = nn.create_feed_forward_phenotype(winner)
for inputs, expected in zip(xor_inputs, xor_outputs):
output = winner_net.serial_activate(inputs)
print("expected {0:1.5f} got {1:1.5f}".format(expected, output[0]))
def test_concurrent_nn():
"""This is a stripped-down copy of the `memory` example."""
# num_tests is the number of random examples each network is tested against.
num_tests = 16
# N is the length of the test sequence.
N = 4
def eval_fitness(genomes):
for g in genomes:
net = nn.create_recurrent_phenotype(g)
error = 0.0
for _ in range(num_tests):
# Create a random sequence, and feed it to the network with the
# second input set to zero.
seq = [random.choice((0, 1)) for _ in range(N)]
net.reset()
for s in seq:
inputs = [s, 0]
net.activate(inputs)
# Set the second input to one, and get the network output.
for s in seq:
inputs = [0, 1]
output = net.activate(inputs)
error += (output[0] - s)**2
g.fitness = -(error / (N * num_tests))**0.5
# Demonstration of how to add your own custom activation function.
def sinc(x):
return 1.0 if x == 0 else math.sin(x) / x
# This sinc function will be available if my_sinc_function is included in the
# config file activation_functions option under the pheotype section.
# Note that sinc is not necessarily useful for this example, it was chosen
# arbitrarily just to demonstrate adding a custom activation function.
activation_functions.add('my_sinc_function', sinc)
local_dir = os.path.dirname(__file__)
pop = population.Population(os.path.join(local_dir, 'recurrent_config'))
pop.run(eval_fitness, 10)
# Visualize the winner network and plot/log statistics.
# visualize.draw_net(winner, view=True, filename="nn_winner.gv")
# visualize.draw_net(winner, view=True, filename="nn_winner-enabled.gv", show_disabled=False)
# visualize.draw_net(winner, view=True, filename="nn_winner-enabled-pruned.gv", show_disabled=False, prune_unused=True)
# visualize.plot_stats(pop.statistics)
# visualize.plot_species(pop.statistics)
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
winner = pop.statistics.best_genome()
repr(winner)
str(winner)
for g in winner.node_genes:
repr(g)
str(g)
for g in winner.conn_genes:
repr(g)
str(g)
pop.run(eval_fitness, 300)
print('Number of evaluations: {0}'.format(pop.total_evaluations))
# Display the most fit genome.
winner = pop.statistics.best_genome()
print('\nBest genome:\n{!s}'.format(winner))
# Verify network output against training data.
print('\nOutput:')
winner_net = nn.create_feed_forward_phenotype(winner)
for inputs, expected in zip(xor_inputs, xor_outputs):
output = winner_net.serial_activate(inputs)
print("expected {0:1.5f} got {1:1.5f}".format(expected, output[0]))
# Visualize the winner network and plot/log statistics.
visualize.plot_stats(pop.statistics)
visualize.plot_species(pop.statistics)
visualize.draw_net(winner, view=True, filename="xor2-all.gv")
visualize.draw_net(winner,
view=True,
filename="xor2-enabled.gv",
show_disabled=False)
visualize.draw_net(winner,
view=True,
filename="xor2-enabled-pruned.gv",
show_disabled=False,
prune_unused=True)
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
def test_concurrent_nn():
"""This is a stripped-down copy of the `memory` example."""
# num_tests is the number of random examples each network is tested against.
num_tests = 16
# N is the length of the test sequence.
N = 4
def eval_fitness(genomes):
for g in genomes:
net = nn.create_recurrent_phenotype(g)
error = 0.0
for _ in range(num_tests):
# Create a random sequence, and feed it to the network with the
# second input set to zero.
seq = [random.choice((0, 1)) for _ in range(N)]
net.reset()
for s in seq:
inputs = [s, 0]
net.activate(inputs)
# Set the second input to one, and get the network output.
for s in seq:
inputs = [0, 1]
output = net.activate(inputs)
error += (output[0] - s) ** 2
g.fitness = -(error / (N * num_tests)) ** 0.5
# Demonstration of how to add your own custom activation function.
def sinc(x):
return 1.0 if x == 0 else math.sin(x) / x
# This sinc function will be available if my_sinc_function is included in the
# config file activation_functions option under the pheotype section.
# Note that sinc is not necessarily useful for this example, it was chosen
# arbitrarily just to demonstrate adding a custom activation function.
activation_functions.add('my_sinc_function', sinc)
local_dir = os.path.dirname(__file__)
pop = population.Population(os.path.join(local_dir, 'recurrent_config'))
pop.run(eval_fitness, 10)
# Visualize the winner network and plot/log statistics.
# visualize.draw_net(winner, view=True, filename="nn_winner.gv")
# visualize.draw_net(winner, view=True, filename="nn_winner-enabled.gv", show_disabled=False)
# visualize.draw_net(winner, view=True, filename="nn_winner-enabled-pruned.gv", show_disabled=False, prune_unused=True)
# visualize.plot_stats(pop.statistics)
# visualize.plot_species(pop.statistics)
statistics.save_stats(pop.statistics)
statistics.save_species_count(pop.statistics)
statistics.save_species_fitness(pop.statistics)
winner = pop.statistics.best_genome()
repr(winner)
str(winner)
for g in winner.node_genes:
repr(g)
str(g)
for g in winner.conn_genes:
repr(g)
str(g)
