def propagate(self):
MotivationUnit.propagate(self)
# For the fast WTA:
# Only once (when inhibition_sum not set = -1) all the activations in the WTA net are summed.
# From this sum the inhibition can be calculated for each individual unit.
if (WTAUnitFast.inhibition_sum == -1):
WTAUnitFast.inhibition_sum = 0
WTAUnitFast.net_sum = 0
for i in range(0, len(WTAUnitFast.wtaUnitList)):
WTAUnitFast.inhibition_sum += WTAUnitFast.weight_other * WTAUnitFast.wtaUnitList[
i].output_value
self.input_sum += WTAUnitFast.inhibition_sum + (
1 - WTAUnitFast.weight_other) * self.output_value
if (self.input_sum > 0):
# net_sum is used to keep track of all activations in the network for the next
# timestep - it is used for normalization in the applyOutput time step.
WTAUnitFast.net_sum += self.input_sum
def propagate(self):
MotivationUnit.propagate(self)
if (self.output_value > self.activation_threshold):
for item in self.__modulatedConnectedTo:
#print("Modulate: ", item[0].name, " / ", item[0].output_value)
item[1].addIncomingValue(item[0].output_value * item[2])