def __init__(self, _bias_):
self.debug = 1
if self.debug == 1:
print ""
print("Ejecutando " + self.__class__.__name__) + "...\n"
self.__neuron_0 = nas.neuron(_bias_)
self.__neuron_1 = nas.neuron(_bias_)
self.__neuron_2 = nas.neuron(_bias_)
self.__bias = int(_bias_)
self.__base_pattern = []
for base_pattern in range(16):
self.__base_pattern.append(0)
def __init__(self, _bias_):
self.debug = 1
if self.debug == 1:
print "\n"
print ""
print("Ejecutando " + self.__class__.__name__) + "..."
self.__neuron = nas.neuron(int(_bias_))
self.__bias = int(_bias_)
def _layer_1_1_(_neuron, _idnas, _learn):
def neuron_0():
_neuron.rst_sign_()
_neuron.ibn_(0, 0, int(_learn[0]))
_neuron.ibn_(0, 1, int(_learn[1]))
_neuron.ibn_(1, 0, int(_learn[2]))
_neuron.ibn_(1, 1, int(_learn[3]))
__rst_sign = {
0: neuron_0,
}
__rst_sign[int(_idnas)]()
#Capa neuronal -2 neuronas-
n1 = nas.neuron(1)
n2 = nas.neuron(2)
def _layer_2_1(id_nas, _learn_):
def neuron_1():
n1.rst_sign_()
n1.ibn_(0, 0, int(_learn_[0]))
n1.ibn_(0, 1, int(_learn_[1]))
n1.ibn_(1, 0, int(_learn_[2]))
n1.ibn_(1, 1, int(_learn_[3]))
def neuron_2():
n2.rst_sign_()
n2.ibn_(0, 0, int(_learn_[0]))
__learn = {
0: s0,
1: s1,
2: s2,
3: s3,
4: s4,
5: s5,
}
__learn[int(id_mode)]()
return _learn_
#Layer neuronal -0- In
#----------------------------------------------------------------------------------------------------------------------------------------------------------
n0 = nas.neuron(0)
n1 = nas.neuron(1)
n2 = nas.neuron(2)
n3 = nas.neuron(3)
def layer_0(id_nas, _learn_):
def neuron_0():
n0.rst_sign_()
n0.ibn_(0, 0, int(_learn_[0]))
n0.ibn_(0, 1, int(_learn_[1]))
n0.ibn_(1, 0, int(_learn_[2]))
n0.ibn_(1, 1, int(_learn_[3]))
def neuron_1():
n1.rst_sign_()