class SOMTestAgent(Agent):
"""
A simple agent that receives 2D points and trains a kohonen SOM
and a Growing Neural Gas with them. It produces no meanigful
actions (i.e. it always emits 0).
"""
def __init__(self, **args):
# Call the superclass constructor
super(SOMTestAgent, self).__init__(**args)
# instantiate a SOM
self.som = SOM()
# intialize SOM training
N = SOMTestEnvironment.num_samples_per_distr * 5
self.som.init_training(radius_0=max(self.som.xdim, self.som.ydim),
training_length=N)
# instantiate a Growing Neural Gas
self.gng = EquilibriumGNG()
def __call__(self, sensation, reward=None):
# On receiving input train the SOM and GNG.
self.som.train(sensation)
self.gng.train(sensation)
# Return 0 for the action.
return 0
class SOMTestAgent(Agent):
"""
A simple agent that receives 2D points and trains a kohonen SOM
and a Growing Neural Gas with them. It produces no meanigful
actions (i.e. it always emits 0).
"""
def __init__(self,**args):
# Call the superclass constructor
super(SOMTestAgent,self).__init__(**args)
# instantiate a SOM
self.som = SOM()
# intialize SOM training
N = SOMTestEnvironment.num_samples_per_distr * 5
self.som.init_training(radius_0 = max(self.som.xdim,self.som.ydim),
training_length = N)
# instantiate a Growing Neural Gas
self.gng = EquilibriumGNG()
def __call__(self,sensation,reward=None):
# On receiving input train the SOM and GNG.
self.som.train(sensation)
self.gng.train(sensation)
# Return 0 for the action.
return 0
def __init__(self, **args):
# Call the superclass constructor
super(SOMTestAgent, self).__init__(**args)
# instantiate a SOM
self.som = SOM()
# intialize SOM training
N = SOMTestEnvironment.num_samples_per_distr * 5
self.som.init_training(radius_0=max(self.som.xdim, self.som.ydim),
training_length=N)
# instantiate a Growing Neural Gas
self.gng = EquilibriumGNG()
def __init__(self,**args):
# Call the superclass constructor
super(SOMTestAgent,self).__init__(**args)
# instantiate a SOM
self.som = SOM()
# intialize SOM training
N = SOMTestEnvironment.num_samples_per_distr * 5
self.som.init_training(radius_0 = max(self.som.xdim,self.som.ydim),
training_length = N)
# instantiate a Growing Neural Gas
self.gng = EquilibriumGNG()
