def mutate_add_neuron(self):
tmp = Neuron()
src = get_random_element(self.inputNodeList)
syn_in = Synapse(src, tmp, random.uniform(-1, 1))
src.add_output(syn_in)
tmp.add_input(syn_in)
dst = get_random_element(self.outputNodeList)
syn_out = Synapse(tmp, dst, random.uniform(-1, 1))
tmp.add_output(syn_out)
dst.add_input(syn_out)
self.inputNodeList.append(tmp)
self.outputNodeList.append(tmp)
self.inputOutputNodeList.append(tmp)
def copy(self):
q = []
m = {}
copy_network = Network()
original_actuator_list = self.actuatorList
copy_actuator_list = [Neuron(), Neuron(), Neuron(), Neuron()]
copy_network.actuatorList = copy_actuator_list
for i in range(len(original_actuator_list)):
q.append(original_actuator_list[i])
m[original_actuator_list[i]] = copy_actuator_list[i]
copy_actuator_list[i].k = original_actuator_list[i].k
copy_io_nodes = []
copy_i_nodes = []
copy_o_nodes = []
copy_sensors = []
for sensor in self.sensorList:
tmp = Neuron()
m[sensor] = tmp
tmp.k = sensor.k
q.append(sensor)
copy_sensors.append(tmp)
copy_i_nodes.append(tmp)
while len(q) > 0:
original_node = q.pop()
i_syns = original_node.inputList
o_syns = original_node.outputList
copy_node = m[original_node]
copy_node.k = original_node.k
for syn in i_syns:
input_original = syn.sourceNeuron
if input_original in list(m.keys()):
input_copy = m[input_original]
exists = False
for n in input_copy.outputList:
if n.sourceNeuron == input_copy and n.destinationNeuron == copy_node:
exists = True
break
if not exists:
copy_syn = Synapse(input_copy, copy_node, syn.weight)
copy_node.add_input(copy_syn)
input_copy.add_output(copy_syn)
else:
input_copy = Neuron()
input_copy.k = input_original.k
copy_io_nodes.append(input_copy)
copy_syn = Synapse(input_copy, copy_node, syn.weight)
input_copy.add_output(copy_syn)
copy_node.add_input(copy_syn)
m[input_original] = input_copy
q.append(input_original)
for syn in o_syns:
output_original = syn.destinationNeuron
if output_original in list(m.keys()):
output_copy = m[output_original]
exists = False
for n in output_copy.inputList:
if n.sourceNeuron == copy_node and n.destinationNeuron == output_copy:
exists = True
break
if not exists:
copy_syn = Synapse(copy_node, output_copy, syn.weight)
copy_node.add_output(copy_syn)
output_copy.add_input(copy_syn)
else:
output_copy = Neuron()
output_copy.k = output_original.k
copy_io_nodes.append(output_copy)
copy_syn = Synapse(copy_node, output_copy, syn.weight)
output_copy.add_input(copy_syn)
copy_node.add_output(copy_syn)
m[output_original] = output_copy
q.append(output_original)
for n in copy_io_nodes:
copy_i_nodes.append(n)
copy_o_nodes.append(n)
copy_o_nodes.extend(copy_actuator_list)
copy_network.sensorList = copy_sensors
copy_network.inputNodeList = copy_i_nodes
copy_network.outputNodeList = copy_o_nodes
copy_network.inputOutputNodeList = copy_io_nodes
return copy_network