def readGenes(self, numInputs):
# should create all of its neurons based on its genes
for i in range(TweakingVariables.outputCount): # create output neurons
self.neurons.append(Neuron.Neuron())
self.mapOutputToNeuron(i, self.neurons[-1])
self.neurons[-1].type = -1
for gene in self.genes: # first create all of them!
self.neurons.append(Neuron.Neuron())
self.neurons[-1].type = int(gene.getSequence()[:1])
for gene in self.genes: # now connect them
neuronWorkingOn = self.neurons[TweakingVariables.outputCount + self.genes.index(gene)]
for codon in gene.getSequence()[1:]:
if TweakingVariables.convertLetterToNum(
codon
) != 0 and TweakingVariables.outputCount + numInputs + TweakingVariables.convertLetterToNum(
codon
) - 1 < len(
self.neurons
):
neuronWorkingOn.addChild(
self.neurons[
TweakingVariables.outputCount + numInputs + TweakingVariables.convertLetterToNum(codon) - 1
]
)
elif (
TweakingVariables.convertLetterToNum(codon) != 0
and TweakingVariables.convertLetterToNum(codon) < TweakingVariables.pureRange2
):
# not enough genes were supplied in creation, need to fix this
self.genes.append(Gene.Gene("1000"))
# create defaut gene
self.neurons.append(Neuron.Neuron())
self.neurons[-1].type = int(self.genes[-1].getSequence()[:1])
elif TweakingVariables.convertLetterToNum(codon) != 0:
neuronWorkingOn.addChild(self.neurons[int(codon) - (TweakingVariables.pureRange2)])
def randomMutation(number):
if number == 0: # determines the type of neuron
return str(1 + random.randint(0, TweakingVariables.range1 - 1))
# determines where the neuron leads
temp = random.randint(0, TweakingVariables.range2 - 1)
return str(TweakingVariables.convertNumToLetter(temp))
