static void Main(string[] args)
{
EALoop ea = new EALoop();
Console.WriteLine("STARTING");
SocialNetwork socialNetwork = new SocialNetwork();
DataCollector data = new DataCollector();
List <Agent> population = new List <Agent>();
Dialogue dialogue = new Dialogue();
int generations = 1;
for (int i = 0; i < populationSize; i++)
{
population.Add(new Agent(ea.AgentIdCounter));
ea.AgentIdCounter++;
}
List <Agent> speakerPool = new List <Agent>();
foreach (Agent a in population)
{
if (socialNetwork.getAgentsConnections(a) != null)
{
speakerPool.Add(a);
}
}
Console.WriteLine("Speaker pool: " + speakerPool.Count);
ea.dialogueThreads(socialNetwork, speakerPool, population);
ea.fitnessOfPopulation(population, socialNetwork);
population = ea.survivalSelection(population, socialNetwork);
data.addDiscreteGraph(population, socialNetwork, generations);
data.addFitnessData(population);
data.setDialogues(population);
ea.addFitnessDegreeData(population, socialNetwork, data);
data.setUniqueWords(population);
ea.updateAges(population);
while (generations < Totalgenerations)
{
generations++;
Console.WriteLine("\nGeneration number: " + generations);
//Console.WriteLine("Nodes in the socialnetwork: " + socialNetwork.socialNetwork.Count);
//-- MAKE CHILDREN --//
//Console.WriteLine("Size of population before children is made: " + population.Count);
population.AddRange(ea.makeChildren(population, socialNetwork));
//Console.WriteLine("Size of population after children was made: " + population.Count);
//-- PERFORM DIALOGUES --//
//Console.WriteLine("socialnetwork count "+socialNetwork.socialNetwork.Count);
speakerPool = new List <Agent>();
for (int q = 0; q < population.Count; q++)
{
//Console.WriteLine("index: " + q);
if (population[q] == null)
{
population.RemoveAt(q);
}
else if (socialNetwork.getAgentsConnections(population[q]) != null && socialNetwork.getAgentsConnections(population[q]).Count > 0)
{
speakerPool.Add(population[q]);
}
}
Console.WriteLine("Speaker pool: " + speakerPool.Count);
ea.dialogueThreads(socialNetwork, speakerPool, population);
//ea.succcessfullDialogues = 0;
//Console.WriteLine("Dialogues performed");
//-- CALCULATE FITNESS --//
ea.fitnessOfPopulation(population, socialNetwork);
//-- SURVIVAL SELECTION --//
population = ea.survivalSelection(population, socialNetwork);
//-- DATA GATHERING --//
data.setDialogues(population);
data.setUniqueWords(population);
data.addFitnessData(population);
if (generations % 5 == 0)
{
data.addDiscreteGraph(population, socialNetwork, generations);
}
ea.addFitnessDegreeData(population, socialNetwork, data);
ea.updateAges(population);
//Console.WriteLine("Size of population after survival selection: " + population.Count);
}
Console.WriteLine("fitness data: " + data.getFitnessdata().Count + "\ndialogue data: " + data.getDialogues().Count + "\nUnique words data: " + data.getUniqueWords().Count);
data.writeToFiles();
data.addDiscreteGraph(population, socialNetwork, generations);
Console.Write("Press any button to end");
}
private void addFitnessDegreeData(List <Agent> population, SocialNetwork socialNetwork, DataCollector d)
{
double C = 0.5;
double allConnections = 0;
double AvgWeight = 0;
double learnRateSum = 0;
double totalVocLen = 0;
double speakToParentsGene = 0;
double extrovertProb = 0;
foreach (Agent a in population)
{
double agentsAvgWeight = 0;
double agentsConnections = 0;
speakToParentsGene += a.getGenome().getNormalisedGenome()[5] * speakToParentsConstant;
List <double> genome = a.getGenome().getValuesGenome();
extrovertProb += ((genome[3] + (100 - genome[6]) / 200) * C) / 100;
totalVocLen += a.getVocabulary().getVocabulary().Count;
List <double> normGenome = a.getGenome().getNormalisedGenome();
learnRateSum += (normGenome[0] + normGenome[8] + normGenome[9] - normGenome[4] - normGenome[1] - normGenome[7]);
if (socialNetwork.getAgentsConnections(a) != null && socialNetwork.getAgentsConnections(a).Count > 0)
{
foreach (var i in socialNetwork.getAgentsConnections(a))
{
if (i.Value > 0.0)
{
agentsConnections++;
agentsAvgWeight += i.Value;
}
}
if (agentsConnections > 0)
{
AvgWeight += (agentsAvgWeight / agentsConnections);
allConnections += agentsConnections;
}
}
}
Console.WriteLine("Speak to parents prob: " + (speakToParentsGene / population.Count));
Console.WriteLine("Extrovert probability: " + (extrovertProb / population.Count));
Console.WriteLine("average vocabulary length: " + (totalVocLen / population.Count));
Console.WriteLine("Fittest agent: " + population[0].getFitness());
Console.WriteLine("Average connections: " + (allConnections / population.Count));
Console.WriteLine("Agents average weight: " + (AvgWeight / population.Count));
Console.WriteLine("Average learn rate: " + (learnRateSum / population.Count) + "\n\n");
d.addExtrovertData((extrovertProb / population.Count));
d.addSpeakToParentsGenome((speakToParentsGene / population.Count));
d.addAvgVocLen((totalVocLen / population.Count));
d.addFittest(population[0].getFitness());
d.setDegree(allConnections / population.Count);
d.addLearnRate((learnRateSum / population.Count));
}
