static void Main(string[] args)
{
//Thread.CurrentThread.CurrentCulture = new CultureInfo("en-us");
//CultureInfo.DefaultThreadCurrentUICulture = CultureInfo.GetCultureInfo("en-US");
//Thread.CurrentThread.CurrentUICulture = new CultureInfo("en-US");
Thread.CurrentThread.CurrentCulture = CultureInfo.InvariantCulture;
var experimentParameters = new ExperimentParameters(
numberOfDimensions: 2,
basePopulationSize: 10,
offspringPopulationSize: 100,
numberOfGenerations: 100,
//seed: new Random().Next(),
seed: 1,
//seed: 1971749862,
//oneFifthRuleCheckInterval: 10,
//individualLearningRate: 0.1, //0.47287080450158792
//globalLearningRate: 0.1, //0.31622776601683794
typeOfParentsSelection: ParentsSelectionType.Even,
typeOfStdDeviationsRecombination: RecombinationType.Intermediate,
typeOfRotationsRecombination: RecombinationType.Intermediate,
useRedundantConstraintsRemoving: true,
useDataNormalization: true,
allowQuadraticTerms: true,
useRecombination: false,
trackEvolutionSteps: true,
useSeeding: true,
numberOfParentsSolutionsToSelect: 5,
numberOfPositivePoints: 500,
numberOfNegativePoints: 1500,
//ballnBoundaryValue: 10,
//simplexnBoundaryValue: 10,
typeOfBenchmark: BenchmarkType.Simplexn);
var enginesFactory = new EnginesFactory();
var engine = enginesFactory.Create(experimentParameters);
var distanceCalculator = new CanberraDistanceCalculator();
var positivePointsGenerator = new PositivePointsGenerator();
var positiveTrainingPoints = positivePointsGenerator.GeneratePoints(experimentParameters.NumberOfPositivePoints, engine.Benchmark.Domains, engine.Benchmark.Constraints);
var negativePointsGenerator = new NegativePointsGenerator(positiveTrainingPoints, distanceCalculator, new NearestNeighbourDistanceCalculator(distanceCalculator));
//var negativeTrainingPoints = negativePointsGenerator.GeneratePoints(experimentParameters.NumberOfNegativePoints, engine.Benchmark.Domains);
var negativeTrainingPoints = negativePointsGenerator.GeneratePoints(experimentParameters.NumberOfNegativePoints, engine.Benchmark.Domains, engine.Benchmark.Constraints);
var trainingPoints = positiveTrainingPoints.Concat(negativeTrainingPoints).ToArray();
var testPointsGenerator = new TestPointsGenerator();
var testPoints = testPointsGenerator.GeneratePoints(experimentParameters.NumberOfTestPoints, engine.Benchmark.Domains, engine.Benchmark.Constraints);
Console.WriteLine("Evolution starts!");
var mathModel = engine.SynthesizeModel(trainingPoints);
var statistics = engine.EvaluateModel(testPoints);
var visualization = PrepareVisualisation(experimentParameters, engine, positiveTrainingPoints, negativeTrainingPoints,
testPoints);
visualization.Show();
Console.WriteLine("==========================================");
Console.WriteLine("=============== PARAMETERS ===============");
Console.WriteLine("==========================================");
Console.WriteLine(experimentParameters.ToPrintableString());
Console.WriteLine();
Console.WriteLine("==========================================");
Console.WriteLine("============= REFERENCE MODEL ============");
Console.WriteLine("==========================================");
Console.WriteLine(mathModel.ReferenceModelInLpFormat);
Console.WriteLine();
Console.WriteLine("==========================================");
Console.WriteLine("============ SYNTHESIZED MODEL ===========");
Console.WriteLine("==========================================");
Console.WriteLine(mathModel.SynthesizedModelInLpFormat);
Console.WriteLine();
Console.WriteLine("==========================================");
Console.WriteLine("=============== STATISTICS ===============");
Console.WriteLine("==========================================");
Console.WriteLine(statistics.ToPrintableString());
Console.WriteLine();
Console.ReadKey();
}
