public static void subXor(NetworkGenome cppn)
{
DecodedNetworks decodedNetwork = (DecodedNetworks)null;
double[] inputs = new double[4];
bool flag1 = true;
inputs[0] = 1.0;
inputs[1] = 0.0;
inputs[2] = 0.0;
inputs[3] = 0.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(2);
double[] outputs1 = decodedNetwork.GetOutputs();
cppn.Fitness = 0.0;
cppn.Fitness += Math.Min(1.0, Math.Max(0.0, 1.0 - outputs1[0]));
bool flag2 = flag1 && outputs1[0] < 0.5;
decodedNetwork.Flush();
inputs[0] = 0.0;
inputs[1] = 0.0;
inputs[2] = 0.0;
inputs[3] = 1.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(2);
double[] outputs2 = decodedNetwork.GetOutputs();
cppn.Fitness += Math.Min(1.0, Math.Max(0.0, 1.0 - outputs2[0]));
bool flag3 = flag2 && outputs2[0] < 0.5;
decodedNetwork.Flush();
inputs[0] = 0.0;
inputs[1] = 1.0;
inputs[2] = 0.0;
inputs[3] = 0.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(2);
double[] outputs3 = decodedNetwork.GetOutputs();
cppn.Fitness += Math.Min(1.0, Math.Max(0.0, outputs3[0]));
bool flag4 = flag3 && outputs3[0] >= 0.5;
decodedNetwork.Flush();
inputs[0] = 0.0;
inputs[1] = 0.0;
inputs[2] = 1.0;
inputs[3] = 0.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(2);
double[] outputs4 = decodedNetwork.GetOutputs();
cppn.Fitness += Math.Min(1.0, Math.Max(0.0, outputs4[0]));
bool flag5 = flag4 && outputs4[0] >= 0.5;
decodedNetwork.Flush();
if (!flag5)
{
return;
}
cppn.Fitness = 4.0;
}
public static void Xor(object target)
{
NetworkGenome genome = target as NetworkGenome;
double[] inputs = new double[2];
bool flag1 = true;
DecodedNetworks decodedNetwork = DecodedNetworks.DecodeGenome(genome);
inputs[0] = 0.0;
inputs[1] = 0.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs1 = decodedNetwork.GetOutputs();
genome.Fitness = 0.0;
genome.Fitness += Math.Min(1.0, Math.Max(0.0, 1.0 - outputs1[0]));
bool flag2 = flag1 && outputs1[0] < 0.5;
decodedNetwork.Flush();
inputs[0] = 1.0;
inputs[1] = 1.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs2 = decodedNetwork.GetOutputs();
genome.Fitness += Math.Min(1.0, Math.Max(0.0, 1.0 - outputs2[0]));
bool flag3 = flag2 && outputs2[0] < 0.5;
decodedNetwork.Flush();
inputs[0] = 0.0;
inputs[1] = 1.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs3 = decodedNetwork.GetOutputs();
genome.Fitness += Math.Min(1.0, Math.Max(0.0, outputs3[0]));
bool flag4 = flag3 && outputs3[0] >= 0.5;
decodedNetwork.Flush();
inputs[0] = 1.0;
inputs[1] = 0.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs4 = decodedNetwork.GetOutputs();
genome.Fitness += Math.Min(1.0, Math.Max(0.0, outputs4[0]));
bool flag5 = flag4 && outputs4[0] >= 0.5;
decodedNetwork.Flush();
if (!flag5)
{
return;
}
genome.Fitness = 4.0;
}
public static bool NormalXor(List <NetworkGenome> genomes, bool test)
{
double[] inputs = new double[2];
bool flag1 = false;
foreach (NetworkGenome genome in genomes)
{
bool flag2 = true;
DecodedNetworks decodedNetwork = DecodedNetworks.DecodeGenome(genome);
inputs[0] = 0.0;
inputs[1] = 0.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs1 = decodedNetwork.GetOutputs();
genome.Fitness = 0.0;
genome.Fitness += Math.Min(1.0, Math.Max(0.0, 1.0 - outputs1[0]));
bool flag3 = flag2 && outputs1[0] < 0.5;
decodedNetwork.Flush();
inputs[0] = 1.0;
inputs[1] = 1.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs2 = decodedNetwork.GetOutputs();
genome.Fitness += Math.Min(1.0, Math.Max(0.0, 1.0 - outputs2[0]));
bool flag4 = flag3 && outputs2[0] < 0.5;
decodedNetwork.Flush();
inputs[0] = 0.0;
inputs[1] = 1.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs3 = decodedNetwork.GetOutputs();
genome.Fitness += Math.Min(1.0, Math.Max(0.0, outputs3[0]));
bool flag5 = flag4 && outputs3[0] >= 0.5;
decodedNetwork.Flush();
inputs[0] = 1.0;
inputs[1] = 0.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs4 = decodedNetwork.GetOutputs();
genome.Fitness += Math.Min(1.0, Math.Max(0.0, outputs4[0]));
bool flag6 = flag5 && outputs4[0] >= 0.5;
decodedNetwork.Flush();
if (flag6)
{
genome.Fitness = 4.0;
}
flag1 = flag1 || flag6;
}
return(flag1);
}
//by phillip
static void MapWeights(NetworkGenome genome, DecodedNetworks cppn)
{
double[] coords = new double[4];
double[] output;
LinkGene temp;
SubstrateNodes source;
SubstrateNodes target;
int linkId = 0;
for (int i = 1; i < genome.Links.Count; i++)
{
temp = genome.Links[i];
source = (SubstrateNodes)genome.Nodes[temp.Source];
target = (SubstrateNodes)genome.Nodes[temp.Target];
if (temp.Source != 0)
{
coords[0] = source.Coordinate[0];
coords[1] = source.Coordinate[1];
}
else
{
coords[0] = target.Coordinate[0];
coords[1] = target.Coordinate[1];
}
coords[2] = target.Coordinate[0];
coords[3] = target.Coordinate[1];
cppn.Flush();
cppn.SetInputs(coords);
cppn.ActivateNetwork(5);
output = cppn.GetOutputs();
if (source.Id != 0)
{
temp.Weight = output[0];
}
else
{
temp.Weight = output[1];
}
}
if (saveSub)
{
NetworkGenome.SaveToFile(genome, "Sub.xml");
saveSub = false;
}
}
public static void XorCreate(NetworkGenome sub, DecodedNetworks cppn)
{
SubstrateNodes substrateNode1 = sub.Nodes.Find((Predicate <NodeGene>)(n => n.Type == NodeType.Output)) as SubstrateNodes;
double[] inputs = new double[4];
int id = 0;
for (int index = 0; index < sub.Nodes.Count - 1; ++index)
{
SubstrateNodes substrateNode2 = sub.Nodes[index] as SubstrateNodes;
cppn.Flush();
inputs[0] = substrateNode2.Coordinate[0];
inputs[1] = substrateNode2.Coordinate[1];
inputs[2] = substrateNode1.Coordinate[0];
inputs[3] = substrateNode1.Coordinate[1];
cppn.SetInputs(inputs);
cppn.ActivateNetwork(5);
double[] outputs = cppn.GetOutputs();
sub.Links.Add(new LinkGene(id, substrateNode2.Id, substrateNode1.Id, outputs[0]));
}
}
