modemCls = "nengoros.comm.nodeFactory.modem.impl.DefaultModem"
# custom modem here
##################################
################# turtle 1
g = NodeGroup("zelvicka", True)
g.addNC(turtlesim, "zelva", "native")
# start native node called zelva
g.addNC(modemCls, "turtlemodem", "modem")
g.startGroup()
modem = g.getModem()
bigneuron = NeuralModule("TurtleController", modem)
bigneuron.createDecoder("turtle1/pose", "pose") # origin
bigneuron.createDecoder("turtle1/color_sensor", "color") # origin
bigneuron.createEncoder("turtle1/command_velocity", "velocity") # termination
many = net.add(bigneuron)
# Create a white noise input function with parameters: baseFreq, maxFreq (rad/s), RMS, Seed
input = FunctionInput("Randomized input", [FourierFunction(0.5, 10, 6, 12), FourierFunction(2, 11, 5, 17)], Units.UNK)
# Add the input node to the network and connect it to the smart enuron
net.add(input)
net.connect(input, many.getTermination("turtle1/command_velocity"))
# make neural network and connect it to the smart neuron
A = net.make("PositionData", neurons=10, dimensions=5, radius=20)
net.connect(many.getOrigin("turtle1/pose"), A)
# make neural network and connect it to the smart neuron
B = net.make("ColorData", neurons=10, dimensions=3, radius=120) # RGB values observed in range of 100
net.connect(many.getOrigin("turtle1/color_sensor"), B)
##################################
################# define the group and start it
finder = "resender.mpt.F2IPubSub"; # Jva (ROS) node that does this job
modem = "nengoros.comm.nodeFactory.modem.impl.DefaultModem"; # custom modem here
# create group with a name
g = NodeGroup("MinMaxFinder", True); # create independent group called..
g.addNC(finder, "Finder", "java"); # start java node and name it finder
g.addNC(modem,"Modem","modem") # add modem to the group
g.startGroup()
##################################
################# setup the smart neuron and add it to the Nengo network
modem = g.getModem()
neuron = NeuralModule('MinMaxFinder', modem) # construct the smart neuron
neuron.createEncoder("ann2rosFloatArr", "float",4) # termination = input of neuron (4xfloat)
neuron.createDecoder("ros2annFloatArr", "int",2) # origin = output of neuron (min and max)
many=net.add(neuron) # add it into the network
#Create a white noise input function with params: baseFreq, maxFreq [rad/s], RMS, seed
input=FunctionInput('Randomized input', [FourierFunction(.1, 10,1, 12),
FourierFunction(.4, 20,1.5, 11),
FourierFunction(.1, 10,0.9, 10),
FourierFunction(.5, 11,1.6, 17)],Units.UNK)
net.add(input) # Add to the network and connect to neuron
net.connect(input,neuron.getTermination('ann2rosFloatArr'))
print 'Configuration complete.'
# create group with a name
g = NodeGroup("zelvicka", True);
g.addNC(turtlesim, "zelva", "native"); # start native node called zelva
g.addNC(modem,"turtlemodem","modem")
g.startGroup()
##################################
################# get modem, create smart neuron with inputs/outputs
modem = g.getModem()
bigneuron = NeuralModule('TurtleController',modem)
bigneuron.createDecoder("turtle1/pose", "pose") # origin
bigneuron.createDecoder("turtle1/color_sensor", "color") # origin
bigneuron.createEncoder("turtle1/command_velocity", "velocity") # termination
# create nef.SimpleNode
many=net.add(bigneuron)
#Create a white noise input function with parameters: baseFreq, maxFreq (rad/s), RMS, Seed
input=FunctionInput('Randomized input', [FourierFunction(.5, 10, 6, 12),
FourierFunction(2, 11, 5, 17)],
Units.UNK)
# Add the input node to the network and connect it to the smart enuron
net.add(input)
net.connect(input,many.getTermination('turtle1/command_velocity'))
# make neural network and connect it to the smart neuron
A=net.make('PositionData',neurons=10,dimensions=5,radius=20)
################# define the group and start it
finder = "resender.mpt.F2IPubSub"
# Jva (ROS) node that does this job
modem = "nengoros.comm.nodeFactory.modem.impl.DefaultModem"
g = NodeGroup("MinMaxFinder", True)
g.addNC(finder, "Finder", "java")
g.addNC(modem, "Modem", "modem")
g.startGroup()
##################################
################# setup the smart neuron and add it to the Nengo network
modem = g.getModem()
neuron = NeuralModule("MinMaxFinder", modem)
neuron.createEncoder("ann2rosFloatArr", "float", 4)
neuron.createDecoder("ros2annFloatArr", "int", 2, False) # HERE: decorer is asynchronous
many = net.add(neuron)
# Create a white noise input function with params: baseFreq, maxFreq [rad/s], RMS, seed
input = FunctionInput(
"Randomized input",
[
FourierFunction(0.1, 10, 1, 12),
FourierFunction(0.4, 20, 1.5, 11),
FourierFunction(0.1, 10, 0.9, 10),
FourierFunction(0.5, 11, 1.6, 17),
],
Units.UNK,
)
##################################
################# get modem, create neural subsystem with inputs/outputs
modem = g.getModem() # get neuron
module = NeuralModule('TurtleController',modem) # create a module in Nengo with turtle modem
# configure modem, that is:
# -createDecoder = create origin (output) of Nengo module
# -what comes from ROS modules is decoded and passed to the output of subsystem in Nengo
# -createEncoder = create termination (input) of Nengo module
# -what comes from ANN in Nengo is encoded into messages and sent to ROS modules
# modem should be able to encode/decode (all) messages used in own NodeGroup
module.createDecoder("turtle1/pose", "pose") # origin
module.createDecoder("turtle1/color_sensor", "color") # origin
module.createEncoder("turtle1/command_velocity", "velocity") # termination
# build the subsystem in Nengo, thats it
subsystem=net.add(module)
##################################
################# create other ANN components (in Nengo)
# Create a white noise generator with parameters: baseFreq, maxFreq (rad/s), RMS, Seed
input=FunctionInput('Randomized input', [FourierFunction(.5, 10, 6, 12),
FourierFunction(2, 11, 5, 17)],
Units.UNK)
net.add(input) # add to Nengi gui
# make two neural networks which approximate its inputs
