public static IList <Neuron> GetNeurons(
INeuralGenome genome,
ENeurType neurType)
{
return(genome.NeuronLst
.Where(x => x.neurType == neurType)
.ToArray());
}
protected float GetNetworkOutputAsNb(INeuralGenome genome, int inNb)
{
var outputs = genome.FeedNetwork(ToBits(inNb));
double result = 0;
for (int i = 0; i < outputs.Count(); i++)
{
result += outputs[i] * Mathf.Pow(2, i);
}
return((float)result);
}
public override void Init(INeuralGenome targetGenome)
{
base.Init(targetGenome);
lastActivation = 0;
columns = 0;
distToNextColOnDeath = Vector2.zero;
isDead = false;
foreach (var neuron in genome.Neurons)
{
neuron.Value.Val = 0;
}
}
private double ParityFitness(INeuralGenome genome)
{
float error = 0;
for (int i = 0; i < 8; i++)
{
var expected = (i % 2 == 0) ? 1 : 0;
var output = (float)genome.FeedNetwork(ToBits(i))[0];
error += Mathf.Abs(expected - output);
}
genome.Fitness = 8 - error;
return(error);
}
private double CountingFitness(INeuralGenome genome)
{
float error = 0;
for (int i = 0; i < 7; i++)
{
var next3Bit = i + 1;
if (next3Bit >= 8)
{
next3Bit = 0;
}
error += Mathf.Abs(next3Bit - GetNetworkOutputAsNb(genome, i));
}
//genome.Fitness = Mathf.Pow(8 - error + 1, fitenssPower);
genome.Fitness = 8 - error;
return(error);
}
protected override double ComputeFitness(INeuralGenome genome)
{
float error = 0;
for (int i = 0; i < 2; i++)
{
for (int j = 0; j < 2; j++)
{
var targetOutput = i ^ j;
var output = genome.FeedNetwork(new double[2] {
i, j
})[0];
error += Mathf.Abs((float)(targetOutput - output));
}
}
genome.Fitness = Mathf.Pow(4 - error + 1, fitenssPower);
return(error);
}
protected override double ComputeFitness(INeuralGenome genome)
{
double error = 0;
for (int i = 0; i < MaxCount; i++)
{
var bits = ToBits(i, MaxCount);
var expected = OppositeBits(bits);
var outputs = genome.FeedNetwork(bits);
for (int j = 0; j < bits.Length; j++)
{
error += Mathf.Abs((float)(expected[j] - outputs[j]));
}
}
genome.Fitness = -error;
return(error);
}
private void ConnectNode(
RectTransform target,
Neuron targetNeuron,
INeuralGenome genome)
{
var outNeurons = genome.Network[targetNeuron]
.Select(x => x.receiver);
foreach (var neuronInnov in outNeurons)
{
var neuron = genome.Neurons[neuronInnov];
if (neuron == targetNeuron)
{
continue;
}
var conn = ConnectionManager.CreateConnection(
target,
nodes[neuron]);
conn.points[0].weight = 0.6f;
conn.points[1].weight = 0.6f;
conn.line.widthMultiplier = connectionWidth;
conn.points[0].direction =
ConnectionPoint.ConnectionDirection.East;
conn.points[1].direction =
ConnectionPoint.ConnectionDirection.West;
var synapse = genome.Network[targetNeuron]
.First(x => x.receiver == neuronInnov);
var gene = NeuralUtils.FindGene(genome.Genes, synapse);
var color = GetColor(gene);
conn.points[0].color = color;
conn.points[1].color = color;
if (!synapse.isEnabled)
{
conn.line.widthMultiplier *= 0.3f;
}
}
}
public void DrawGenome(INeuralGenome genome)
{
RemoveAllNodes();
if (autoAdjustMaxWeight)
{
maxWeight = GetAverageWeight(genome.Genes);
}
int count = 0;
foreach (var neuron in genome.NeuronLst)
{
var newNode = Instantiate(nodePrefab, nodesParent)
.GetComponent <RectTransform>();
newNode.name = "Node " + count;
var nodeText = neuron.innovNb.ToString();
if (neuron.neurType == ENeurType.bias)
{
nodeText = "bias" + nodeText;
}
else if (neuron.neurType == ENeurType.input)
{
nodeText = "in" + nodeText;
}
else if (neuron.neurType == ENeurType.output)
{
nodeText = "out" + nodeText;
}
newNode.GetChild(0).GetComponent <Text>().text = nodeText;
nodes.Add(neuron, newNode);
count++;
}
var inputNeurons = NeuralUtils.GetNeurons(
genome, ENeurType.input);
var outputNeurons = NeuralUtils.GetNeurons(
genome, ENeurType.output);
var bias = NeuralUtils.GetBias(genome);
foreach (var kv in nodes)
{
ConnectNode(kv.Value, kv.Key, genome);
if (inputNeurons.Contains(kv.Key))
{
int i = inputNeurons.IndexOf(kv.Key) + 1;
kv.Value.localPosition =
inputNodesPos.localPosition +
Vector3.down * distBetweenNodes * i;
}
else if (outputNeurons.Contains(kv.Key))
{
int i = outputNeurons.IndexOf(kv.Key);
kv.Value.localPosition =
outputNodesPos.localPosition +
Vector3.down * distBetweenNodes * i;
}
else if (bias == kv.Key)
{
kv.Value.localPosition = inputNodesPos.localPosition;
}
else
{
kv.Value.localPosition = GetPosOfNewNode();
}
}
}
protected override double ComputeFitness(INeuralGenome genome)
{
return(ParityFitness(genome));
}
public virtual void Init(INeuralGenome targetGenome)
{
genome = targetGenome;
}
public void Init(INeuralGenome genome_, Transform target_)
{
genome = genome_;
target = target_;
}
public static Neuron GetBias(INeuralGenome genome)
{
return(genome.NeuronLst
.FirstOrDefault(x => x.neurType == ENeurType.bias));
}
protected abstract double ComputeFitness(INeuralGenome genome);
protected override double ComputeFitness(INeuralGenome genome)
{
return(CountingFitness(genome));
}
