def __init__(self,
id,
layer,
boxid,
u,
sigma,
birth,
modelConfiguration={},
coord=None):
'''
特征神经元，每个神经元表达一个特征值
:param id: int id
:param layer: int 所属层
:param boxid: int 所属盒子id
:param u: list 中心特征值
:param sigma: list of list 方差
:param birth: int 出生年代
:param modelConfiguration: dict 模型配置
:param coord: list 坐标
'''
Neuron.__init__(self, id, layer, birth, modelConfiguration, coord)
self.states['activation'] = 0.
#self.state['clock'] = 0
self.features = {}
self.features['liveness'] = 0.
self.u = u
self.sigma = sigma
self.boxid = boxid
self.history_states = []
self.cur_capcity_count = 0
def createLayer(self, layerIndex, modelConfig):
if layerIndex < 0 or layerIndex >= len(self.definition.neuronCounts):
raise RuntimeError('createLayer failed: invaild layerIndex:' +
str(layerIndex))
count = self.definition.neuronCounts[layerIndex]
# 计算实际层编号
layerInterval = int((self.definition.config.range[0][1] -
self.definition.config.range[0][0]) /
(len(self.definition.neuronCounts) - 1))
layer = 0
if layerIndex == 0: layer = self.definition.config.range[0][0]
elif layerIndex == len(self.definition.neuronCounts) - 1:
layer = self.definition.config.range[0][1]
else:
layer = self.definition.config.range[0][
0] + layerIndex * layerInterval
while len(self.neurons) <= layerIndex:
self.neurons.insert(layerIndex, [])
# 生成神经元坐标
idGenerator = idGenerators.find(self.definition.idGenerator)
coords = self.__createCoord(layer, count)
# 创建神经元
for i in range(count):
coord = coords[i]
neuronid = idGenerator.getNeuronId(self, coord, None)
neuron = Neuron(neuronid, layer, 0, modelConfig, coord)
self.neurons[layerIndex].append(neuron)
return self.neurons[layerIndex]
def __createLayerNeuron(self, net, netdef, layerIndex, count, modelConfig):
if count <= 0: return None
# 计算实际层编号
layerInterval = int(
(netdef.config.range[0][1] - netdef.config.range[0][0]) /
(len(netdef.neuronCounts) - 1))
layer = 0
if layerIndex == 0: layer = 0
elif layerIndex == len(netdef.neuronCounts) - 1:
layer = netdef.config.range[0][1]
else:
layer = layerIndex * layerInterval
# 生成神经元坐标
idGenerator = networks.idGenerators.find(netdef.idGenerator)
while len(net.neurons) <= layerIndex:
net.neurons.insert(layerIndex, [])
coords = self.__createCoord(netdef, layer, count)
# 创建神经元
for i in range(count):
coord = coords[i]
neuronid = idGenerator.getNeuronId(net, coord, None)
neuron = Neuron(neuronid, layer, 0, modelConfig, coord)
net.neurons[layerIndex].append(neuron)
return net.neurons[layerIndex]
def __do_mutate_addnode(self, ind, session):
net = ind.genome
synapses = net.getSynapses()
synapse = None
if len(synapses) <= 0:
r, msg, synapse = self.____do_mutate_addconnection(ind)
else:
synapse = choice(synapses)
fromneuron = net.getNeuron(synapse.fromId)
toneuron = net.getNeuron(synapse.toId)
neuronModel = net.definition.models.hidden
synapseModel = net.definition.models.synapse
newNeuronLayer = int((toneuron.layer + fromneuron.layer) / 2)
idgenerator = networks.idGenerators.find(net.definition.idGenerator)
newNeuronid = idgenerator.getNeuronId(net, None, synapse)
newNeuron = Neuron(newNeuronid, newNeuronLayer, session.curTime,
neuronModel)
net.putneuron(newNeuron, fromneuron.id, toneuron.id, synapseModel)
net.remove(synapse)
return True, '', 'addnode', newNeuron
def put(self,
birth,
coord,
layer,
neuronModelConfig,
id=None,
inids=None,
outinds=None,
synapseModelConfig=None):
'''
添加神经元
:param birth: int or float 添加时间，必须
:param coord: Coordinate 坐标，可选
:param layer: int 层，必须
:param neuronModelConfig: dict 神经计算模型配置，必须
:param id int 神经元id,可选,如果无效,会尝试生成一个,但是要求coord有效
:param inids: int or list，输入神经元id
:param outinds: int or list，输入神经元id
:param synapseModelConfig: dict，突触计算模型配置
:return:
'''
if birth < 0: raise RuntimeError('添加神经元失败(NeuralNetwork.put):birth无效')
#if layer < 0:raise RuntimeError('添加神经元失败(NeuralNetwork.put):layer无效')
if neuronModelConfig is None:
raise RuntimeError(
'添加神经元失败(NeuralNetwork.put):neuronModelConfig无效')
idGenerator = idGenerators.find(
self.definition.get('idGenerator', 'default'))
if idGenerator is None:
raise RuntimeError(
"连接神经元失败(NeuralNetwork.connect(srcid,destid)):idGenerator无效")
if id is None:
id = idGenerator.getNeuronId(self, coord, None)
n = Neuron(id, layer, birth, neuronModelConfig, coord)
return self.putneuron(n, inids, outinds, synapseModelConfig)
def create(self,popParam):
# 取得个体类型，神经网络
indType = agent.individualTypes.find(popParam.indTypeName)
netdef = popParam.genomeDefinition
idGenerator = networks.idGenerators.find(netdef.idGenerator)
genomeFactory = indType.genomeFactory
if popParam.genomeFactory is not None: genomeFactory = popParam.genomeFactory
factoryParam = popParam.factoryParam
size = popParam.size
genomes = []
for i in range(size):
netid = idGenerator.getNetworkId()
net = NeuralNetwork(netid,netdef)
# 创建输入神经元
for i in range(netdef.neuronCounts[0]):
# 创建输入神经元
neuron = Neuron(idGenerator.getNeuronId(),0,0,netdef.models.input)
# 创建输入神经元的模块
module_sensor = Module(idGenerator.getModuleId(),0,[],[],[neuron])
module_sensor.attributes['attention'] = 'sensor'
# 创建输入神经元的值分布模块
module_sensor_distribution = Module(idGenerator.getModuleId(),0,[module_sensor],[],[])
module_sensor_distribution.attributes['attentation'] =
net.putneuron(neuron)
net.putmodule(module_sensor)
self.__createLayerNeuron(net,netdef,0,netdef.neuronCounts[0],netdef.models.input)
# 创建输出神经元
self.__createLayerNeuron(net,netdef,len(netdef.neuronCounts)-1,netdef.neuronCounts[-1], netdef.models.output)
# 创建输入神经元的值分布模块
# 创建中间神经元（如果有）
hiddenCount = netdef.neuronCounts[1:-1]
if not collections.isEmpty(hiddenCount):
for i in range(len(hiddenCount)):
self.__createLayerNeuron(net, netdef, i+1, netdef.neuronCounts[i+1], netdef.models.input)
genomes.append(net)
# 初始化连接
if popParam.factoryParam.connectionRate <= 0: #初始无连接
continue
elif popParam.factoryParam.connectionRate >= 1: #初始全连接
for i in range(len(net.neurons)-1):
for j,n1 in enumerate(net.neurons[i]): # 取第i层神经元
for k,n2 in enumerate(net.neurons[i+1]): # 取第i+1层神经元
synapseid = idGenerator.getSynapseId(net,n1.id,n2.id)
synapse = Synapse(synapseid,0,n1.id,n2.id,netdef.models.synapse)
net.synapses.append(synapse)
else: # 以一定概率进行连接
allsynapse = []
for i in range(len(net.neurons)-1):
for j,n1 in enumerate(net.neurons[i]): # 取第i层神经元
for k,n2 in enumerate(net.neurons[i+1]): # 取第i+1层神经元
allsynapse.append((n1,n2))
synapsecount = int(len(allsynapse)*popParam.factoryParam.connectionRate)
if synapsecount > 0 :
indexes = np.random.uniform(0,len(allsynapse)-1,synapsecount)
for i in indexes:
n1 = allsynapse[i][0]
n2 = allsynapse[i][1]
synapseid = idGenerator.getSynapseId(net, n1.id, n2.id)
synapse = Synapse(synapseid, 0, n1.id, n2.id, netdef.models.synapse)
net.synapses.append(synapse)
logging.debug('# neat工厂创建网络:'+str(net))
return genomes