//public IList<Constraint> GetSynthesizedModel()
//{
// return SynthesizedModel;
//}
//public IList<Constraint> GetReferenceModel()
//{
// return Benchmark.Constraints;
//}
public virtual MathModel SynthesizeModel(Point[] trainingPoints)
{
var offspringPopulationSize = ExperimentParameters.OffspringPopulationSize;
var numberOfGenerations = ExperimentParameters.NumberOfGenerations;
Evaluator.PositivePoints = trainingPoints.Where(tp => tp.ClassificationType == ClassificationType.Positive).ToArray();
Evaluator.NegativePoints = trainingPoints.Where(tp => tp.ClassificationType == ClassificationType.Negative).ToArray();
BasePopulation = PopulationGenerator.GeneratePopulation(ExperimentParameters);
for (var i = 0; i < offspringPopulationSize; i++)
{
OffspringPopulation[i] = new Solution(ExperimentParameters);
}
InitialPopulation = BasePopulation.DeepCopyByExpressionTree();
Stoper.Restart();
for (var i = 0; i < numberOfGenerations; i++)
{
Evolve(offspringPopulationSize);
}
Stoper.Stop();
Statistics.TotalEvolutionTime = Stoper.Elapsed;
Statistics.MeanSingleGenerationEvolutionTime = TimeSpan.FromTicks(Statistics.TotalEvolutionTime.Ticks / numberOfGenerations);
Stoper.Restart();
SynthesizedModel = RedundantConstriantsRemover.ApplyProcessing(BasePopulation.First().GetConstraints(ExperimentParameters));
Stoper.Stop();
Statistics.RedundantConstraintsRemovingTime = Stoper.Elapsed;
Statistics.TotalSynthesisTime = Statistics.TotalEvolutionTime + Stoper.Elapsed;
Stoper.Reset();
MathModel = new MathModel(SynthesizedModel, Benchmark);
return(MathModel);
}
public MathModel SynthesizeModel(Point[] trainingPoints)
{
var means = trainingPoints.Means();
var standardDeviations = trainingPoints.StandardDeviations(means);
if (Parameters.UseDataNormalization)
{
trainingPoints = _pointsNormalizer.ApplyProcessing(trainingPoints);
NormalizedTrainingPoints = trainingPoints.DeepCopyByExpressionTree();
}
var evolutionEnginesFactory = new EnginesFactory();
var evolutionEngine = evolutionEnginesFactory.Create(Parameters.EvolutionParameters);
var positiveTrainingPoints = trainingPoints.Where(tp => tp.ClassificationType == ClassificationType.Positive).ToArray();
var negativeTrainingPoints = trainingPoints.Where(tp => tp.ClassificationType == ClassificationType.Negative).ToArray();
var evaluator = new Evaluator(positiveTrainingPoints, negativeTrainingPoints, _constraintsBuilder);
//HACK TODO
//var numberOfConstraintsCoefficients = Parameters.MaximumNumberOfConstraints * (Parameters.NumberOfDimensions + 1);
//var constraintsCoefficients = new List<double>(numberOfConstraintsCoefficients);
//var benchmarkConstraintsIndexer = 0;
//for (var i = 0; i < Parameters.MaximumNumberOfConstraints; i++)
//{
// if (benchmarkConstraintsIndexer >= Benchmark.Constraints.Length)
// benchmarkConstraintsIndexer = 0;
// constraintsCoefficients.AddRange(Benchmark.Constraints[benchmarkConstraintsIndexer].Terms.Select(t => t.Coefficient));
// constraintsCoefficients.Add(Benchmark.Constraints[benchmarkConstraintsIndexer++].LimitingValue);
//}
//PopulationGeneratorBase.ObjectCoefficients = constraintsCoefficients.ToArray();
//
Solution bestSolution;
if (Parameters.UseSeeding)
{
var singleConstraintModel = Parameters.TypeOfBenchmark == BenchmarkType.Balln && Parameters.AllowQuadraticTerms
? Benchmark.Constraints.Take(1).DeepCopyByExpressionTree().ToArray()
: new[] { new LinearConstraint(Benchmark.Constraints.First().Terms.Where(t => t.Type == TermType.Linear).ToArray(), 0) };
var singleConstraintBuilder = new ConstraintsBuilder(singleConstraintModel);
var singleConstraintEvaluator = new Evaluator(positiveTrainingPoints, negativeTrainingPoints, singleConstraintBuilder);
var seedingProcessor = new SeedingProcessor(singleConstraintEvaluator, _constraintsBuilder, positiveTrainingPoints);
bestSolution = evolutionEngine.RunEvolution(evaluator, seedingProcessor);
}
else
{
bestSolution = evolutionEngine.RunEvolution(evaluator);
}
CoreEvolutionSteps = evolutionEngine.EvolutionSteps;
Statistics.EvolutionStatistics = evolutionEngine.Statistics;
var synthesizedConstraints = _constraintsBuilder.BuildConstraints(bestSolution);
if (Parameters.UseDataNormalization)
{
NormalizedSynthesizedConstraints = synthesizedConstraints.DeepCopyByExpressionTree();
_constaintsDenormalizer = new StandardScoreConstraintsDenormalizer(means, standardDeviations);
synthesizedConstraints = _constaintsDenormalizer.ApplyProcessing(synthesizedConstraints);
}
if (Parameters.TrackEvolutionSteps)
{
var evolutionStepsAsSolutions = evolutionEngine.EvolutionStepsSimple.ToList();
foreach (var evolutionStepsAsSolution in evolutionStepsAsSolutions)
{
var evolutionStep = _constraintsBuilder.BuildConstraints(evolutionStepsAsSolution);
if (Parameters.UseDataNormalization)
{
NormalizedEvolutionSteps.Add(evolutionStep.DeepCopyByExpressionTree());
evolutionStep = _constaintsDenormalizer.ApplyProcessing(evolutionStep);
}
EvolutionSteps.Add(evolutionStep);
}
}
if (Parameters.UseRedundantConstraintsRemoving)
{
_stoper.Restart();
synthesizedConstraints = _redundantConstraintsRemover.ApplyProcessing(synthesizedConstraints);
_stoper.Stop();
Statistics.RedundantConstraintsRemovingTime = _stoper.Elapsed;
}
MathModel = new MathModel(synthesizedConstraints, Benchmark);
Statistics.NumberOfConstraints = synthesizedConstraints.Length;
Statistics.MeanAngle = Parameters.TypeOfBenchmark != BenchmarkType.Balln ? _meanAngleCalculator.Calculate(synthesizedConstraints, Benchmark.Constraints) : double.NaN;
return(MathModel);
}
