public FitnessInfo Evaluate(FastCyclicNetwork phenome)
{
EvaluationCount++;
var fcn = FastCyclicNetworkAdapter.Convert(phenome);
ProtocolManager.Open();
var cFitness = ProtocolManager.Client.calculateXorPhenotypeFitness(fcn);
//ProtocolManager.Close(); adding this line implies a huge performance hit
var auxFitness = new AuxFitnessInfo[cFitness.AuxFitness.Count];
for (int i = 0; i < auxFitness.Length; i++)
{
auxFitness[i] = new AuxFitnessInfo(cFitness.AuxFitness[i].Name, cFitness.AuxFitness[i].Value);
}
var fitness = new FitnessInfo(cFitness.Fitness, auxFitness);
StopConditionSatisfied = cFitness.StopConditionSatisfied;
if (StopConditionSatisfied)
{
FitnessInfo fi = _xorBlackBoxEvaluator.Evaluate(phenome);
bool reallySatisfied = _xorBlackBoxEvaluator.StopConditionSatisfied;
Debug.Assert((fi._fitness >= 10) == _xorBlackBoxEvaluator.StopConditionSatisfied);
if (!reallySatisfied)
{
Console.Out.WriteLine("ERROR: really fitness is not calculated (" + fi._fitness + " versus " +
cFitness.Fitness + ")");
Console.Out.WriteLine(phenome.ToString());
//throw new Exception("Wheih :(");
}
else
{
Console.Out.WriteLine("Networks is really good");
}
}
// We do not use the NEAT genome decoder. The NEAT genome decoder would
// do the same as the following code, but provide us with an IBlackBox object.
// Because we pass on the object to our Java code, we need to be aware of its
// underlying structure. The additional layer of abstraction gets in the way.
return(fitness);
}
public List <FitnessInfo> Evaluate(List <FastCyclicNetwork> phenomes)
{
var populationInfo = new CPopulationInfo
{
Phenomes = FastCyclicNetworkAdapter.Convert(phenomes),
Generation = (int)_ea.CurrentGeneration
};
ProtocolManager.Open();
var fitnessInfo = ProtocolManager.Client.calculateSixMultiplexerPopulationFitness(populationInfo);
EvaluationCount += (uint)fitnessInfo.EvaluationCount;
var result = new List <FitnessInfo>(fitnessInfo.FitnessInfos.Count);
for (var i = 0; i < fitnessInfo.FitnessInfos.Count; i++)
{
var fi = fitnessInfo.FitnessInfos[i];
StopConditionSatisfied |= fi.StopConditionSatisfied;
// Verify that the stop condition is actually really satisfied; if not, there is an error in the Java evaluator and we throw an exception
if (fi.StopConditionSatisfied)
{
var sharpneatFitness = _binarySixMultiplexerEvaluator.Evaluate(phenomes[i]);
var reallySatisfied = sharpneatFitness._fitness >= 1000;
if (!reallySatisfied)
{
Console.Out.WriteLine("ERROR: " + sharpneatFitness._fitness + " versus " + fi.Fitness);
throw new Exception("Noes there is an error in my Java code :(");
}
else
{
Console.Out.WriteLine("Yeey, the result is really correct: " + sharpneatFitness._fitness + " versus " + fi.Fitness);
}
}
result.Add(new FitnessInfo(
fi.Fitness,
fi.AuxFitness.Select(aux => new AuxFitnessInfo(aux.Name, aux.Value)).ToArray())
);
}
return(result);
}
public List <FitnessInfo> Evaluate(List <FastCyclicNetwork> phenomes)
{
var populationInfo = new CPopulationInfo
{
Phenomes = FastCyclicNetworkAdapter.Convert(phenomes),
Generation = (int)_ea.CurrentGeneration
};
var fitnessInfo = calculateSimPopulationFitness(populationInfo);
EvaluationCount += (uint)fitnessInfo.EvaluationCount;
var result = new List <FitnessInfo>(fitnessInfo.FitnessInfos.Count);
foreach (var fi in fitnessInfo.FitnessInfos)
{
StopConditionSatisfied |= fi.StopConditionSatisfied;
result.Add(new FitnessInfo(
fi.Fitness,
fi.AuxFitness.Select(aux => new AuxFitnessInfo(aux.Name, aux.Value)).ToArray())
);
}
return(result);
}