def getTheInputToOneNeuron(self,neuron_key):
from functions.from_function_fire_output import fireForOneValue
if neuron_key.find('in')!=-1:
return self.get_output_from_input_layer[neuron_key]
else:
input_to_neuron=None
for keys in self.neuron_to_neuron_weight:
target_neuron=keys[keys.find('to_')+3:]
from_neuron=keys[:keys.find('_to')]
if target_neuron==neuron_key and self.neuron_to_neuron_weight[keys]!=None:
#recursion
if input_to_neuron==None:
input_to_neuron=0
input_to_neuron+=self.getInputToNeuronFromTwoConnectedNeurons(from_neuron, target_neuron,self.getOutputFromOneNeuron(from_neuron))
if self.has_bias:
if input_to_neuron!=None:
input_to_neuron+=self.bias_to_neuron[neuron_key]
else:
pass
#print output_from_neuron_key
#print input_to_neuron
output=fireForOneValue('purelin',input_to_neuron)
return output
def getOutputFromOneNeuron(self,output_from_neuron_key):
from functions.from_function_fire_output import fireForOneValue
from functions.functions import compet
if output_from_neuron_key.find('in')!=-1:
return self.get_output_from_input_layer[output_from_neuron_key]
else:
input_to_neuron=None
for keys in self.neuron_to_neuron_weight:
target_neuron=keys[keys.find('to_')+3:]
from_neuron=keys[:keys.find('_to')]
if target_neuron==output_from_neuron_key and self.neuron_to_neuron_weight[keys]!=None:
#recursion
if input_to_neuron==None:
input_to_neuron=0
input_to_neuron+=self.getInputToNeuronFromTwoConnectedNeurons(from_neuron, target_neuron,self.getOutputFromOneNeuron(from_neuron))
if self.has_bias:
if input_to_neuron!=None:
input_to_neuron+=self.bias_to_neuron[output_from_neuron_key]
else:
pass
#print output_from_neuron_key
#print input_to_neuron
if self.neuron_function[output_from_neuron_key]=='compet':
output=compet(output_from_neuron_key,self)
# if auto_train_competitive_neuron:
# from pyneuron.trainer.Kohonen import KohonenTrainer
# t=KohonenTrainer(self)
# if output_from_neuron_key.find('out')!=-1:
# competitive_layer_key='out'
# elif output_from_neuron_key.find('hid')!=-1:
# competitive_layer_key=output_from_neuron_key[:output_from_neuron_key.find('_')]
# else:
# print("competitive layer key error")
# t.KohonenTrainForWinnerNeuron(self.input_list_to_input_layer, competitive_layer_key)
else:
output=fireForOneValue(self.neuron_function[output_from_neuron_key],input_to_neuron)
return output
