body_spec = BodyImplementation({
("CoreComponent", "E"):
PartSpec(arity=2, outputs=1, inputs=2),
"2Params":
PartSpec(arity=2,
inputs=2,
outputs=2,
params=[
ParamSpec("param_a", default=-1),
ParamSpec("param_b", default=15)
])
})
brain_spec = NeuralNetImplementation({
"Simple":
NeuronSpec(params=["bias"]),
"Oscillator":
NeuronSpec(params=["period", "phaseOffset", "amplitude"])
})
def main():
args = parser.parse_args()
# brain_spec = get_brain_spec(args)
# body_spec = get_body_spec(args)
brain_parser = NeuralNetworkParser(brain_spec)
pb_robot = yaml_to_robot(body_spec, brain_spec, yaml_robot)
print "yaml converted to pb"
import random
import unittest
from revolve.generate import NeuralNetworkGenerator
from revolve.spec import NeuralNetImplementation, NeuronSpec
brain_spec = NeuralNetImplementation(
{
"Simple":
NeuronSpec(
params=["bias"],
layers=["hidden"],
),
"Oscillator":
NeuronSpec(
params=[
"period",
"phase_offset",
"amplitude",
],
layers=[
"hidden",
"output",
],
),
}, )
class TestNeuralNetGenerator(unittest.TestCase):
"""
Some simple tests for generating a neural network
outputs=2,
params=[
ParamSpec(
"param_a",
default=-1
),
ParamSpec(
"param_b",
default=15
),
],
),
}, )
brain_spec = NeuralNetImplementation({
"Simple" : NeuronSpec(params=["bias"]),
"Oscillator": NeuronSpec(
params=["period", "phaseOffset", "amplitude"]
)
})
# Enter the test cases below (make alterations to the basic yaml object)
# Body test cases
missing_body = yaml_to_robot(body_spec, brain_spec, basic_yaml_object)
missing_body.body.root.Clear()
missing_id = yaml_to_robot(body_spec, brain_spec, basic_yaml_object)
missing_id.body.root.id = ""
missing_part_type = yaml_to_robot(body_spec, brain_spec, basic_yaml_object)
missing_part_type.body.root.type = ""
import random
import unittest
from revolve.generate import NeuralNetworkGenerator
from revolve.spec import NeuralNetImplementation, NeuronSpec
brain_spec = NeuralNetImplementation(
{
"Simple": NeuronSpec(params=["bias"], layers=["hidden"]),
"Oscillator": NeuronSpec(
params=["period", "phase_offset", "amplitude"],
layers=["hidden", "output"]
)
}
)
class TestNeuralNetGenerator(unittest.TestCase):
"""
Some simple tests for generating a neural network
"""
def test_valid(self):
"""
Tests a valid neural network is generated
:return:
"""
seed = random.randint(0, 10000)
random.seed(seed)
gen = NeuralNetworkGenerator(
def get_extended_brain_spec(conf):
"""
Returns the brain specification corresponding to the
given config.
:param conf:
:return:
"""
epsilon = conf.brain_mutation_epsilon
return NeuralNetImplementation({
"Input": NeuronSpec(
layers=["input"]
),
"Sigmoid": NeuronSpec(
params=[
ParamSpec("bias", min_value=-1, max_value=1, default=0, epsilon=epsilon),
ParamSpec("gain", min_value=0, max_value=1, default=.5, epsilon=epsilon)
],
layers=["output", "hidden"]
),
"Simple": NeuronSpec(
params=[
ParamSpec("bias", min_value=-1, max_value=1, epsilon=epsilon),
ParamSpec("gain", min_value=0, max_value=1, default=.5, epsilon=epsilon)
],
layers=["output", "hidden"]
),
# "Gain": NeuronSpec(
# params=[
# ParamSpec("gain", min_value=0, max_value=1, default=.5, epsilon=epsilon)
# ],
# layers=["output", "hidden"]
# ),
"Bias": NeuronSpec(
params=[
ParamSpec("bias", min_value=-1.0, max_value=1.0, epsilon=epsilon),
],
layers=["output", "hidden"]
),
"Oscillator": NeuronSpec(
params=[
ParamSpec("period", min_value=0, max_value=10, epsilon=epsilon),
ParamSpec("phase_offset", min_value=0, max_value=3.14, epsilon=epsilon),
ParamSpec("amplitude", min_value=0, default=1, max_value=10000, epsilon=epsilon)
],
layers=["output", "hidden"]
),
# these neurons are for the nonlinear oscillator CPG model found in Ijspeert (2005):
"V-Neuron": NeuronSpec(
params=[
ParamSpec("alpha", min_value = 0.05, max_value = 10.0, epsilon = epsilon),
ParamSpec("tau", min_value = 1.0, max_value = 50.0, epsilon = epsilon),
ParamSpec("energy", min_value = 0.0, max_value = 25.0, epsilon = epsilon)
],
layers = ["output", "hidden"]
),
"X-Neuron": NeuronSpec(
params=[
ParamSpec("tau", min_value = 0.01, max_value = 5.0, epsilon = epsilon),
],
layers = ["output", "hidden"]
),
"DifferentialCPG": NeuronSpec(
params=[
ParamSpec("bias", min_value = -1.0, max_value = 1.0, epsilon = epsilon),
],
layers = ["output", "hidden"]
),
# # Leaky integrator
# "Leaky": NeuronSpec(
# params=[
# ParamSpec("bias", min_value = 0.01, max_value = 10.0, epsilon = epsilon),
# ParamSpec("tau", min_value = 0.01, max_value = 10.0, epsilon = epsilon),
# ],
# layers = ["output", "hidden"]
# ),
# "NL-Main": NeuronSpec(
# params=[
# ParamSpec("a", min_value = 0.01, max_value = 10.0, epsilon = epsilon),
# ParamSpec("amplitude", min_value = 0.01, max_value = 10.0, epsilon = epsilon)
# ],
# layers = ["output", "hidden"]
# )
# "NL-Theta": NeuronSpec(
# params=[
# ParamSpec("freq", min_value = 0.01, max_value = 10.0, epsilon = epsilon)
# ],
# layers = ["output", "hidden"]
# )
# "QuadNeuron": NeuronSpec(
# params=[],
# layers = ["output", "hidden"]
# )
})