private static void AddPopulationSizeExperiment(ExperimentSet newSet, int populationSize)
{
var newExperiment = new Experiment();
newSet.Experiments.Add(newExperiment);
newExperiment.PopulationSize = populationSize;
newExperiment.Label = "Population Size: " + populationSize;
}
private static void AddMutationPercentExperiment(ExperimentSet newSet, double mutationPercent)
{
var newExperiment = new Experiment();
newSet.Experiments.Add(newExperiment);
newExperiment.PercentToMutate = mutationPercent;
newExperiment.Label = "Mutation Percent: " + mutationPercent;
}
private static void AddCrossoverPercentExperiment(ExperimentSet newSet, double crossoverPercent)
{
var newExperiment = new Experiment();
newSet.Experiments.Add(newExperiment);
newExperiment.PercentToCrossover = crossoverPercent;
newExperiment.Label = "Crossover Percent: " + crossoverPercent;
}
private static void AddCrossoverMethodExperiment(ExperimentSet newSet, ICrossoverMethod crossoverMethod, string title)
{
var newExperiment = new Experiment();
newSet.Experiments.Add(newExperiment);
newExperiment.CrossoverMethod = crossoverMethod;
newExperiment.Label = "Crossover Method: " + title;
}
private static void AddCrossoverHandlerExperiment(ExperimentSet newSet, ICrossoverHandler crossoverHandler, string title)
{
var newExperiment = new Experiment();
newSet.Experiments.Add(newExperiment);
newExperiment.CrossoverHandler = crossoverHandler;
newExperiment.Label = "Selection Process: " + title;
}
public static float fitness(float[,] groundTruth, float[] userTrusts, UserUpdate[,,] updates, FitnessParameter fitnessParams)
{
return(Experiment.execute(groundTruth, userTrusts, updates, fitnessParams));
}
private static int ExecuteExperiment(Experiment experiment)
{
//returns milliseconds to get target string
const int timesToTest = 20;
var tests = 0;
//var generationTotal = 0;
var timeTotal = 0;
while (tests < timesToTest)
{
var time = DateTime.Now;
tests++;
var hypothesisList = new List<Hypothesis>();
for (var i = 0; i < experiment.PopulationSize; i++)
{
hypothesisList.Add(Hypothesis.CreateRandomHypothesis());
}
var generations = 0;
while (true)
{
SortAndCull(Program.Comparer, experiment.PopulationSize, experiment.Evaluator, hypothesisList);
var bestHypothesis = hypothesisList.FirstOrDefault();
if (bestHypothesis != null)
{
var bestString = bestHypothesis.String;
if (bestString.Equals(experiment.TargetString)) break;
}
var newHypothesisList = experiment.CrossoverHandler.CrossoverPopulation(hypothesisList, experiment.PercentToCrossover, experiment.ChildPerPair);
MutatePopulation(newHypothesisList, experiment.PercentToMutate, experiment.MutationIntensity);
hypothesisList.AddRange(newHypothesisList);
generations++;
}
var timeElapsed = (DateTime.Now - time).Milliseconds;
//generationTotal += generations;
timeTotal += timeElapsed;
//Console.WriteLine("Test "+tests+" Found in: " + generations + " generations, "+timeElapsed+" ms");
}
//Console.WriteLine("Average to find the goal over "+timesToTest+" trials: "+(generationTotal/timesToTest)+" generations, "+(timeTotal/timesToTest)+" ms");
//Console.ReadLine();
return timeTotal / timesToTest;
}