def _create_neuron(dna: DNASequence, network: NeuralNetwork, neuron: NeuralNetworkNeuron):
dna.extend(DNASequences.NEURON_CREATE)
neuron_layer_index = network.layers.index(neuron.layer)
# write bias
float_encode(dna, neuron.bias)
for connection in neuron.connections:
dna.extend(DNASequences.NEURON_CREATE_LINK)
node = connection.node # type: NeuralNetworkNeuron
layer_size = len(node.layer.neurons) - 1
if layer_size == 0:
layer_size = 1
node_index = node.layer.neurons.index(node) / layer_size * __NEURON_INDEX_REMAP
if node.layer == network.input:
link_layer_index = -1
else:
link_layer_index = network.layers.index(node.layer)
relative_index = neuron_layer_index - link_layer_index - 1
float_encode(dna, relative_index + node_index)
float_encode(dna, connection.weight)
def encode(dna: DNASequence, value: float, accuracy: float = EPS):
r = (-1.0, 0.0, 1.0)
while True:
# check for match and range control values
if abs(r[0] - value) <= accuracy:
dna.extend(_SELECT_RANGE_START)
break
if abs(r[1] - value) <= accuracy:
dna.extend(_SELECT_RANGE_CENTER)
break
if abs(r[2] - value) <= accuracy:
dna.extend(_SELECT_RANGE_END)
break
if value < r[0] or value > r[2]:
dna.extend(_INCREASE_RANGE)
r = (r[0] * 2, r[1], r[2] * 2)
continue
if r[0] < value < r[1]:
dna.extend(_SELECT_FIRST_HALF)
r = (r[0], r[0] + (r[1] - r[0]) * 0.5, r[1])
continue
if r[1] < value < r[2]:
dna.extend(_SELECT_SECOND_HALF)
r = (r[1], r[1] + (r[2] - r[1]) * 0.5, r[2])
continue
assert False and "Problem"
def _create_layer(dna: DNASequence, network: NeuralNetwork, layer: NeuralNetworkLayer):
dna.extend(DNASequences.NEURON_CREATE_LAYER)
for neuron in layer.neurons:
_create_neuron(dna, network, neuron)