private List <TyWeightsLearner> MixPopulations(List <TyWeightsLearner> oldPopulation, List <TyWeightsLearner> offspring, int generation)
{
List <TyWeightsLearner> tmpPopulation = new List <TyWeightsLearner>();
tmpPopulation.AddRange(oldPopulation);
tmpPopulation.AddRange(offspring);
tmpPopulation.Sort(FittestSort);
List <TyWeightsLearner> newPopulaton = new List <TyWeightsLearner>();
for (int i = 0; i < PopulationSize; i++)
{
newPopulaton.Add(tmpPopulation[i]);
}
const int NUM_RAND_CHILDREN = 2;
//replace the worst NUM_RAND_CHILDREN individuals by random learners:
for (int i = 0; i < NUM_RAND_CHILDREN; i++)
{
TyWeightsLearner randOffspring = new TyWeightsLearner(new TyStateWeights(_random, MIN_WEIGHT, MAX_WEIGHT), generation, _individualId);
newPopulaton[newPopulaton.Count - 1 - i] = randOffspring;
}
return(newPopulaton);
}
private void ComputeFitness(TyWeightsLearner learner, List <TyDeckHeroPair> myDeck, List <TyDeckHeroPair> enemyDeck, AbstractAgent enemyAgent)
{
learner.BeforeLearn();
TyMatchSetup training = new TyMatchSetup(TycheAgent.GetLearningAgent(learner.Weights), enemyAgent);
training.RunRounds(myDeck, enemyDeck, Rounds, MatchesPerRound);
learner.AfterLearn(training.TotalPlays, training.Agent0Wins);
}
private void CsvLog(TyWeightsLearner curLearner)
{
_csvHelper.SetColumn(CsvHelper.Column.Id, curLearner.Id);
_csvHelper.SetColumn(CsvHelper.Column.Generation, curLearner.GenerationBorn);
_csvHelper.SetColumn(CsvHelper.Column.NumPlays, curLearner.NumPlays);
_csvHelper.SetColumn(CsvHelper.Column.Current, curLearner.CurWinPercent);
_csvHelper.SetColumn(CsvHelper.Column.Average, curLearner.AverageWinPercent);
for (int i = 0; i < (int)TyStateWeights.WeightType.Count; i++)
{
var curWeightType = (TyStateWeights.WeightType)i;
var obj = Enum.Parse(typeof(CsvHelper.Column), curWeightType.ToString());
var targetEnum = (CsvHelper.Column)obj;
_csvHelper.SetColumn(targetEnum, curLearner.Weights.GetWeight(curWeightType));
}
_csvLog.AddCsvEntry(_csvHelper.GetLine());
}
public void Clear()
{
_individualId = 0;
_globalFileLog = new List <string>();
_csvLog = new CsvLog();
_csvHelper = new CsvHelper();
_csvLog.AddCsvEntry(CsvHelper.GetCsvHeader());
_random = new Random();
_currentPopulation = new List <TyWeightsLearner>();
for (int i = 0; i < PopulationSize; i++)
{
var learner = new TyWeightsLearner(_random, MIN_WEIGHT, MAX_WEIGHT, 0, _individualId);
_individualId++;
_currentPopulation.Add(learner);
}
}
/// <summary>
/// Chooses weighted (by fitness) random weights from either A or B
/// </summary>
public static TyStateWeights GetCrossedWeights(TyWeightsLearner lhs, TyWeightsLearner rhs, System.Random rand)
{
TyStateWeights newWeights = new TyStateWeights();
for (int i = 0; i < (int)TyStateWeights.WeightType.Count; i++)
{
var factorType = (TyStateWeights.WeightType)i;
float weight = rhs._weights.GetWeight(factorType);
float chanceLhs = lhs.Fitness / (lhs.Fitness + rhs.Fitness);
if (rand.RandFloat() < chanceLhs)
{
weight = lhs._weights.GetWeight(factorType);
}
newWeights.SetWeight(factorType, weight);
}
return(newWeights);
}
private List <TyWeightsLearner> GiveBirth(List <TyWeightsLearner> choosen, System.Random random, int generation)
{
List <TyWeightsLearner> children = new List <TyWeightsLearner>();
for (int i = 0; i < OffspringSize; i++)
{
List <TyWeightsLearner> parentsToChoose = new List <TyWeightsLearner>(choosen);
int firstId = random.Next(parentsToChoose.Count);
var first = parentsToChoose[firstId];
parentsToChoose.RemoveAt(firstId);
int secondId = random.Next(parentsToChoose.Count);
var second = parentsToChoose[secondId];
var childWeights = TyWeightsLearner.GetCrossedWeights(first, second, random);
childWeights = TyWeightsLearner.GetMutatedWeights(childWeights, random, MutationDeviation);
children.Add(new TyWeightsLearner(childWeights, generation, _individualId));
_individualId++;
}
return(children);
}
/// <summary> Sort from biggest to smallest fitness </summary>
private int FittestSort(TyWeightsLearner x, TyWeightsLearner y)
{
return(y.Fitness.CompareTo(x.Fitness));
}
