def run(method, setup, generations=250, popsize=100):
# Create task and genotype->phenotype converter
size = 11
task_kwds = dict(size=size)
if setup == 'big-little':
task_kwds['targetshape'] = ShapeDiscriminationTask.makeshape('box', size//3)
task_kwds['distractorshapes'] = [ShapeDiscriminationTask.makeshape('box', 1)]
elif setup == 'triup-down':
task_kwds['targetshape'] = np.triu(np.ones((size//3, size//3)))
task_kwds['distractorshapes'] = [np.tril(np.ones((size//3, size//3)))]
task = ShapeDiscriminationTask(**task_kwds)
substrate = Substrate()
substrate.add_nodes((size, size), 'l')
substrate.add_connections('l', 'l')
if method == 'wavelet':
num_inputs = 6 if deltas else 4
geno = lambda: WaveletGenotype(inputs=num_inputs)
pop = SimplePopulation(geno, popsize=popsize)
developer = WaveletDeveloper(substrate=substrate, add_deltas=True, sandwich=True)
else:
geno_kwds = dict(feedforward=True,
inputs=6,
weight_range=(-3.0, 3.0),
prob_add_conn=0.1,
prob_add_node=0.03,
bias_as_node=False,
types=['sin', 'bound', 'linear', 'gauss', 'sigmoid', 'abs'])
if method == 'nhn':
pass
elif method == '0hnmax':
geno_kwds['max_nodes'] = 7
elif method == '1hnmax':
geno_kwds['max_nodes'] = 8
geno = lambda: NEATGenotype(**geno_kwds)
pop = NEATPopulation(geno, popsize=popsize, target_species=8)
developer = HyperNEATDeveloper(substrate=substrate,
sandwich=True,
add_deltas=True,
node_type='tanh')
# Run and save results
results = pop.epoch(generations=generations,
evaluator=partial(evaluate, task=task, developer=developer),
solution=partial(solve, task=task, developer=developer),
)
return results
def run(method, setup, generations=100, popsize=100):
""" Use hyperneat for a walking gait task
"""
# Create task and genotype->phenotype converter
if setup == 'easy':
task_kwds = dict(field='eight',
observation='eight',
max_steps=3000,
friction_scale=0.3,
damping=0.3,
motor_torque=10,
check_coverage=False,
flush_each_step=False,
initial_pos=(282, 300, np.pi*0.35))
elif setup == 'hard':
task_kwds = dict(field='eight',
observation='eight_striped',
max_steps=3000,
friction_scale=0.3,
damping=0.3,
motor_torque=10,
check_coverage=False,
flush_each_step=True,
force_global=True,
initial_pos=(282, 300, np.pi*0.35))
elif setup == 'force':
task_kwds = dict(field='eight',
observation='eight',
max_steps=3000,
friction_scale=0.1,
damping=0.9,
motor_torque=3,
check_coverage=True,
flush_each_step=True,
force_global=True,
initial_pos=(17, 256, np.pi*0.5))
elif setup == 'prop':
task_kwds = dict(field='eight',
observation='eight_striped',
max_steps=3000,
friction_scale=0.3,
damping=0.3,
motor_torque=10,
check_coverage=False,
flush_each_step=False,
initial_pos=(282, 300, np.pi*0.35))
elif setup == 'cover':
task_kwds = dict(field='eight',
observation='eight_striped',
max_steps=3000,
friction_scale=0.1,
damping=0.9,
motor_torque=3,
check_coverage=True,
flush_each_step=False,
initial_pos=(17, 256, np.pi*0.5))
task = LineFollowingTask(**task_kwds)
# The line following experiment has quite a specific topology for its network:
substrate = Substrate()
substrate.add_nodes([(0,0)], 'bias')
substrate.add_nodes([(r, theta) for r in np.linspace(0,1,3)
for theta in np.linspace(-1, 1, 5)], 'input')
substrate.add_nodes([(r, theta) for r in np.linspace(0,1,3)
for theta in np.linspace(-1, 1, 3)], 'layer')
substrate.add_connections('input', 'layer',-1)
substrate.add_connections('bias', 'layer', -2)
substrate.add_connections('layer', 'layer',-3)
if method == 'wvl':
geno = lambda: WaveletGenotype(inputs=4, layers=3)
pop = SimplePopulation(geno, popsize=popsize)
developer = WaveletDeveloper(substrate=substrate,
add_deltas=False,
sandwich=False,
node_type='tanh')
else:
geno_kwds = dict(feedforward=True,
inputs=4,
outputs=3,
weight_range=(-3.0, 3.0),
prob_add_conn=0.1,
prob_add_node=0.03,
bias_as_node=False,
types=['sin', 'bound', 'linear', 'gauss', 'sigmoid', 'abs'])
if method == 'nhn':
pass
elif method == '0hnmax':
geno_kwds['max_nodes'] = 7
elif method == '1hnmax':
geno_kwds['max_nodes'] = 8
geno = lambda: NEATGenotype(**geno_kwds)
pop = NEATPopulation(geno, popsize=popsize, target_species=8)
developer = HyperNEATDeveloper(substrate=substrate,
add_deltas=False,
sandwich=False,
node_type='tanh')
results = pop.epoch(generations=generations,
evaluator=partial(evaluate, task=task, developer=developer),
solution=partial(solve, task=task, developer=developer),
)
return results
