public double[] GetNetOutput(NEATNetwork net, double[][] input, IEvolutionState state)
{
double[] output;
int netDepth = net.MaxDepth();
net.LoadSensors(input[0]);
for (int relax = 0; relax < netDepth; relax++)
{
net.Activate(state);
}
output = net.GetOutputResults();
net.Flush();
return(output);
}
public virtual void Evaluate(IEvolutionState state, Individual ind, int subpopulation, int threadnum)
{
if (ind.Evaluated)
{
return;
}
if (!(ind is NEATIndividual))
{
state.Output.Fatal("Whoa! It's not a NEATIndividual!!!", null);
}
NEATIndividual neatInd = (NEATIndividual)ind;
if (!(neatInd.Fitness is SimpleFitness))
{
state.Output.Fatal("Whoa! It's not a SimpleFitness!!!", null);
}
//The four possible input combinations to xor
//The first number is for biasing
double[][] input =
{
new [] { 1.0, 0.0, 0.0 }, // output 0
new [] { 1.0, 0.0, 1.0 }, // 1
new [] { 1.0, 1.0, 0.0 }, // 1
new [] { 1.0, 1.0, 1.0 }, // 0
};
double[] output = new double[4];
double[] expectedOut = { 0.0, 1.0, 1.0, 0.0 };
NEATNetwork net = neatInd.CreateNetwork();
int netDepth = net.MaxDepth();
// Load and activate the network on each input
for (int i = 0; i < input.Length; i++)
{
net.LoadSensors(input[i]);
for (int relax = 0; relax < netDepth; relax++)
{
net.Activate(state);
}
// only have one output, so let's get it
output[i] = net.GetOutputResults()[0];
net.Flush();
}
// calculate fitness
double errorSum = 0;
for (int i = 0; i < output.Length; i++)
{
errorSum += Math.Abs(output[i] - expectedOut[i]);
}
double fitness = (4.0 - errorSum) * (4.0 - errorSum);
// this is from the original code for counting as ideal
bool ideal = true;
for (int i = 0; i < output.Length; i++)
{
if (Math.Abs(output[i] - expectedOut[i]) > 0.5)
{
ideal = false;
break;
}
}
((SimpleFitness)neatInd.Fitness).SetFitness(state, fitness, ideal);
neatInd.Evaluated = true;
}
