static void Main()
{
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
Application.Run(new LoadConfig());
// VariabilityModel model = new VariabilityModel("Model1");
// InfluenceModel infModel = new InfluenceModel(model, new NFProperty("Test"));
// BettyFileParser bfp = new BettyFileParser(model, infModel);
String[] file = System.IO.File.ReadAllLines(@"C:\Users\Tom\Desktop\Masterarbeit\SPLConqueror\SPLConqueror\BettyConqueror\FeatureModel0.afm");
// bfp.readConfiguration(file);
//ILArray<float> A = ILMath.tosingle()
//ILArray<float> A = ILMath.tosingle(ILMath.rand(3, 100));
// RDotNet.NativeLibrary.UnmanagedDll.SetDllDirectory(@"C:\Program Files\R\R-3.2.0\bin\i386\R.dll");
engine = REngine.GetInstance();
NumericVector group1 = engine.CreateNumericVector(new double[] { 30.02, 29.99, 30.11, 29.97, 30.01, 29.99 });
engine.SetSymbol("group1", group1);
// e.Evaluate("hist(group1)");
// e.Evaluate("hist(group1)");
Problem problem;
Operator crossover; // Crossover operator
Operator mutation; // Mutation operator
Operator selection; // Selection operator
var logger = Logger.Log;
// var appenders = logger.Logger.Repository.GetAppenders();
// var fileAppender = appenders[0] as log4net.Appender.FileAppender;
// fileAppender.File = "NSGAII.log";
//.ActivateOptions();
Dictionary <string, object> parameters; // Operator parameters
QualityIndicator indicators; // Object to get quality indicators
problem = new GeneratorProblem("Real", 30);
NSGAII algorithm = new NSGAII(problem);
indicators = null;
algorithm.SetInputParameter("populationSize", 200);
algorithm.SetInputParameter("maxEvaluations", 25000);
// Mutation and Crossover for Real codification
parameters = new Dictionary <string, object>();
parameters.Add("probability", 0.9);
parameters.Add("distributionIndex", 20.0);
crossover = CrossoverFactory.GetCrossoverOperator("SBXCrossover", parameters);
parameters = new Dictionary <string, object>();
parameters.Add("probability", 1.0 / problem.NumberOfVariables);
parameters.Add("distributionIndex", 20.0);
mutation = MutationFactory.GetMutationOperator("PolynomialMutation", parameters);
// Selection Operator
parameters = null;
selection = SelectionFactory.GetSelectionOperator("BinaryTournament2", parameters);
// Add the operators to the algorithm
algorithm.AddOperator("crossover", crossover);
algorithm.AddOperator("mutation", mutation);
algorithm.AddOperator("selection", selection);
// Add the indicator object to the algorithm
algorithm.SetInputParameter("indicators", indicators);
// Execute the Algorithm
long initTime = Environment.TickCount;
//SolutionSet population = algorithm.Execute();
long estimatedTime = Environment.TickCount - initTime;
logger.Info("Variables values have been writen to file VAR");
//population.PrintVariablesToFile("VAR");
logger.Info("Objectives values have been writen to file FUN");
// population.PrintObjectivesToFile("FUN");
Console.WriteLine("Time: " + estimatedTime);
Console.ReadLine();
//Application.Run(new Form1(e, group1, new double[] { 30.02, 29.99, 30.11, 29.97, 30.01, 29.99, 1.3, 1.7, 9.4, 11.1 }));
}
public void RunSimulation(object sender, EventArgs e)
{
var options = CreateAlgorithmOptions();
var problem = new ConfigurationProblem();
var algorithm = new NSGAII(problem);
algorithm.SetInputParameter("populationSize", options.NumberOfCromozoms);
algorithm.SetInputParameter("maxEvaluations", options.NumberOfCromozoms * options.NumberOfGenerations);
// Mutation and Crossover for Real codification
var parameters = new Dictionary <string, object>();
parameters.Add(ConfigurationCrossOver.CrossOverPercentageKey, options.CrossOverPercentage);
var crossover = new ConfigurationCrossOver(parameters); // Nobody cares about the factory
parameters = new Dictionary <string, object>();
parameters.Add(ConfigurationMutation.MutationPercentageKey, options.MutationPercentage);
var mutation = new ConfigurationMutation(parameters); // Nobody cares about the factory.
// Selection Operator
parameters = null;
var selection = new BinaryTournament2(parameters); // Nobody cares about the factory.
// Add the operators to the algorithm
algorithm.AddOperator("crossover", crossover);
algorithm.AddOperator("mutation", mutation);
algorithm.AddOperator("selection", selection);
// Add the indicator object to the algorithm
// nope
//algorithm.SetInputParameter("indicators", null);
var solution = algorithm.Execute();
var solutionsList = solution.SolutionsList;
var output = "";
using (var writer = new System.IO.StreamWriter("output.txt")) {
foreach (var sol in solutionsList)
{
var line = sol.Objective [0] + "\t" + sol.Objective [1];
output += line;
output += "\n";
writer.WriteLine(line);
}
output += "\n";
output += "\n";
writer.WriteLine();
writer.WriteLine();
var rank = new Ranking(solution);
var n = rank.GetNumberOfSubfronts();
for (var i = 0; i < n; i++)
{
var f = rank.GetSubfront(i);
foreach (var sol in f.SolutionsList)
{
var line = sol.Objective [0] + "\t" + sol.Objective [1];
output += line;
output += "\n";
writer.WriteLine(line);
}
output += "\n";
output += "\n";
writer.WriteLine();
writer.WriteLine();
}
}
resultsText.Buffer.Clear();
resultsText.Buffer.Text = output;
}