class KuKuModel(Model):
def __init__(self,proprioception_input_length,sensory_input_length,reservoir_size):
# Build the Reservoir
tau = .1 # execution timestep for the cortical rate model
sigma = .001 # intra-reservoir weights
eps = .1 # learning rate
som_size = 10*10
self.sensory_input_length = sensory_input_length
self.proprioception_input_length = proprioception_input_length
full_reservoir_input_length = proprioception_input_length+som_size
# Nodes: units, tau, method
self.reservoir_input = esn.Node((full_reservoir_input_length,), 0, esn._load )
self.reservoir = esn.Node((reservoir_size,), tau, esn._reservoir )
self.reservoir_output = esn.Node((som_size,), 0, esn._load )
# Arcs: target, source, weight, eps
# input from som
self.d_P = esn.Arc( self.reservoir, self.reservoir_input, sigma, 0 )
self.d_P.initConnections( numpy.random.randn, self.reservoir.shape+self.reservoir_input.shape ) # type of init numpy func
#print d_P.connections
# recurrent connections intra node
self.r_P = esn.Arc( self.reservoir, self.reservoir, sigma, 0 )
self.r_P.initConnections( numpy.random.randn, self.reservoir.shape+self.reservoir.shape ) # type of init numpy func
#print r_P.connections
# input from som
self.d_out = esn.Arc( self.reservoir_output, self.reservoir, 0, eps )
self.d_out.initConnections( numpy.random.randn, self.reservoir_output.shape+self.reservoir.shape ) # type of init numpy func
#print d_out.connections
from minisom import MiniSom
self.som = MiniSom(10, 10, sensory_input_length,sigma=0.3,learning_rate=0.1,normalize=True)
self.previous_som_activation = numpy.zeros((10,10))
def run(self,inp):
self.som.train_single_instance(inp[:self.sensory_input_length])
self.reservoir_input.update(numpy.append(self.previous_som_activation.flatten().copy(),inp[-self.proprioception_input_length:])) # 3
self.reservoir.update(self.d_P.read())
self.reservoir_output.update(self.d_out.read())
print "error:",self.som.activation_map.flatten() - self.reservoir_output.state
self.d_out.learn(self.som.activation_map.flatten() - self.reservoir_output.state )
self.previous_som_activation = self.som.activation_map.flatten().copy()
