def evaluate_population(genomes, create_func, force_func):
for g in genomes:
net = create_func(g)
fitness = 0
for runs in range(runs_per_net):
sim = CartPole()
fitness += run_simulation(sim, net, force_func)
# The genome's fitness is its average performance across all runs.
g.fitness = fitness / float(runs_per_net)
print(c)
print()
print('Python implementation of network:')
print(nn.create_feed_forward_function(c))
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'nn_config'))
net = nn.create_feed_forward_phenotype(c)
sim = CartPole()
print()
print("Initial conditions:")
print(" x = %.4f" % sim.x)
print(" x_dot = %.4f" % sim.dx)
print(" theta = %.4f" % sim.theta)
print("theta_dot = %.4f" % sim.dtheta)
print()
n = run_simulation(sim, net, discrete_actuator_force)
print('Pole balanced for %d of %d time steps' % (n, num_steps))
print()
print("Final conditions:")
print(" x = %.4f" % sim.x)
print(" x_dot = %.4f" % sim.dx)
print(" theta = %.4f" % sim.theta)
print("theta_dot = %.4f" % sim.dtheta)
print()
print("Making movie...")
make_movie(net, discrete_actuator_force, 15.0, "nn_movie.mp4")
