protected ScatterPlotContent(ScatterPlotContent original, Cloner cloner)
   : this() {
   this.qualities = original.qualities.Select(s => s.ToArray()).ToArray();
   this.solutions = original.solutions.Select(s => s.ToArray()).ToArray();
   this.paretoFront = original.paretoFront.Select(s => s.ToArray()).ToArray();
   this.objectives = original.objectives;
 }

 protected SymbolicDataAnalysisModel(SymbolicDataAnalysisModel original, Cloner cloner)
   : base(original, cloner) {
   this.symbolicExpressionTree = cloner.Clone(original.symbolicExpressionTree);
   this.interpreter = cloner.Clone(original.interpreter);
   this.lowerEstimationLimit = original.lowerEstimationLimit;
   this.upperEstimationLimit = original.upperEstimationLimit;
 }

 protected ConnectionInfo(ConnectionInfo original, Cloner cloner)
   : base(original, cloner) {
   from = cloner.Clone(original.from);
   connectorFrom = original.ConnectorFrom;
   to = cloner.Clone(original.To);
   connectorTo = original.ConnectorTo;
 }

 private Solution(Solution original, Cloner cloner)
   : base(original, cloner) {
   this.Length = original.Length;
   this.Width = original.Width;
   this.Tree = cloner.Clone(original.Tree);
   this.Quality = original.Quality;
 }

    public override void CalculateImpactAndReplacementValues(ISymbolicDataAnalysisModel model, ISymbolicExpressionTreeNode node,
      IDataAnalysisProblemData problemData, IEnumerable<int> rows, out double impactValue, out double replacementValue, out double newQualityForImpactsCalculation,
      double qualityForImpactsCalculation = Double.NaN) {
      var classificationModel = (ISymbolicClassificationModel)model;
      var classificationProblemData = (IClassificationProblemData)problemData;

      if (double.IsNaN(qualityForImpactsCalculation))
        qualityForImpactsCalculation = CalculateQualityForImpacts(classificationModel, classificationProblemData, rows);

      replacementValue = CalculateReplacementValue(classificationModel, node, classificationProblemData, rows);
      var constantNode = new ConstantTreeNode(new Constant()) { Value = replacementValue };

      var cloner = new Cloner();
      var tempModel = cloner.Clone(classificationModel);
      var tempModelNode = (ISymbolicExpressionTreeNode)cloner.GetClone(node);

      var tempModelParentNode = tempModelNode.Parent;
      int i = tempModelParentNode.IndexOfSubtree(tempModelNode);
      tempModelParentNode.RemoveSubtree(i);
      tempModelParentNode.InsertSubtree(i, constantNode);

      OnlineCalculatorError errorState;
      var dataset = classificationProblemData.Dataset;
      var targetClassValues = dataset.GetDoubleValues(classificationProblemData.TargetVariable, rows);
      var estimatedClassValues = tempModel.GetEstimatedClassValues(dataset, rows);
      newQualityForImpactsCalculation = OnlineAccuracyCalculator.Calculate(targetClassValues, estimatedClassValues, out errorState);
      if (errorState != OnlineCalculatorError.None) newQualityForImpactsCalculation = 0.0;

      impactValue = qualityForImpactsCalculation - newQualityForImpactsCalculation;
    }

    private CMAParameters(CMAParameters original, Cloner cloner)
      : base(original, cloner) {
      this.AxisRatio = original.AxisRatio;
      if (original.B != null) this.B = (double[,])original.B.Clone();
      if (original.BDz != null) this.BDz = (double[])original.BDz.Clone();
      if (original.C != null) this.C = (double[,])original.C.Clone();
      this.CCov = original.CCov;
      this.CCovSep = original.CCovSep;
      this.CC = original.CC;
      this.ChiN = original.ChiN;
      this.CS = original.CS;
      if (original.D != null) this.D = (double[])original.D.Clone();
      this.Damps = original.Damps;
      this.InitialIterations = original.InitialIterations;
      this.Mu = original.Mu;
      this.MuCov = original.MuCov;
      this.MuEff = original.MuEff;
      if (original.PC != null) this.PC = (double[])original.PC.Clone();
      if (original.PS != null) this.PS = (double[])original.PS.Clone();
      this.Sigma = original.Sigma;
      if (original.SigmaBounds != null) this.SigmaBounds = (double[,])original.SigmaBounds.Clone();
      if (original.Weights != null) this.Weights = (double[])original.Weights.Clone();

      if (original.QualityHistory != null) this.QualityHistory = new Queue<double>(original.QualityHistory);
      this.QualityHistorySize = original.QualityHistorySize;
    }

 protected InversionMoveRelativeAttribute(InversionMoveRelativeAttribute original, Cloner cloner)
   : base(original, cloner) {
   this.Edge1Source = original.Edge1Source;
   this.Edge1Target = original.Edge1Target;
   this.Edge2Source = original.Edge2Source;
   this.Edge2Target = original.Edge2Target;
 }

 protected OperatorGraph(OperatorGraph original, Cloner cloner)
   : base(original, cloner) {
   operators = cloner.Clone(original.operators);
   initialOperator = cloner.Clone(original.initialOperator);
   visualizationInfo = cloner.Clone(original.visualizationInfo);
   Initialize();
 }

 protected JobPermission(JobPermission original, Cloner cloner)
   : base(original, cloner) {
   this.GrantedByUserId = original.GrantedByUserId;
   this.GrantedUserId = original.GrantedUserId;
   this.JobId = original.JobId;
   this.Permission = original.Permission;
 }

 protected NcaModel(NcaModel original, Cloner cloner)
   : base(original, cloner) {
   this.transformationMatrix = (double[,])original.transformationMatrix.Clone();
   this.allowedInputVariables = (string[])original.allowedInputVariables.Clone();
   this.nnModel = cloner.Clone(original.nnModel);
   this.classValues = (double[])original.classValues.Clone();
 }

 private PathTSPTour(PathTSPTour original, Cloner cloner)
   : base(original, cloner) {
   this.coordinates = cloner.Clone(original.coordinates);
   this.permutation = cloner.Clone(original.permutation);
   this.quality = cloner.Clone(original.quality);
   Initialize();
 }

 private MultinomialLogitModel(MultinomialLogitModel original, Cloner cloner)
   : base(original, cloner) {
   logitModel = new alglib.logitmodel();
   logitModel.innerobj.w = (double[])original.logitModel.innerobj.w.Clone();
   allowedInputVariables = (string[])original.allowedInputVariables.Clone();
   classValues = (double[])original.classValues.Clone();
 }

 protected SolutionSimilarityCalculator(SolutionSimilarityCalculator original, Cloner cloner)
   : base(original, cloner) {
   SolutionVariableName = original.SolutionVariableName;
   QualityVariableName = original.QualityVariableName;
   ExecuteInParallel = original.ExecuteInParallel;
   MaxDegreeOfParallelism = original.MaxDegreeOfParallelism;
 }

 private Solution(Solution original, Cloner cloner)
   : base(original, cloner) {
   SymbolicExpressionTree = cloner.Clone(original.SymbolicExpressionTree);
   World = cloner.Clone(original.World);
   MaxTimeSteps = original.MaxTimeSteps;
   Quality = original.Quality;
 }

 protected Algorithm(Algorithm original, Cloner cloner)
   : base(original, cloner) {
   PlatformId = original.PlatformId;
   AlgorithmClassId = original.AlgorithmClassId;
   DataTypeName = original.DataTypeName;
   DataTypeTypeName = original.DataTypeTypeName;
 }

 private TwoPointFiveMove(TwoPointFiveMove original, Cloner cloner)
   : base(original, cloner) {
   this.Index1 = original.Index1;
   this.Index2 = original.Index2;
   this.IsInvert = original.IsInvert;
   this.Permutation = cloner.Clone(original.Permutation);
 }

 private Solution(Solution original, Cloner cloner)
   : base(original, cloner) {
   Tree = cloner.Clone(original.Tree);
   Path = (string)original.Path.Clone();
   NrOfRounds = original.NrOfRounds;
   Enemies = cloner.Clone(original.Enemies);
 }

    private NearestNeighbourModel(NearestNeighbourModel original, Cloner cloner)
      : base(original, cloner) {
      kdTree = new alglib.nearestneighbor.kdtree();
      kdTree.approxf = original.kdTree.approxf;
      kdTree.boxmax = (double[])original.kdTree.boxmax.Clone();
      kdTree.boxmin = (double[])original.kdTree.boxmin.Clone();
      kdTree.buf = (double[])original.kdTree.buf.Clone();
      kdTree.curboxmax = (double[])original.kdTree.curboxmax.Clone();
      kdTree.curboxmin = (double[])original.kdTree.curboxmin.Clone();
      kdTree.curdist = original.kdTree.curdist;
      kdTree.debugcounter = original.kdTree.debugcounter;
      kdTree.idx = (int[])original.kdTree.idx.Clone();
      kdTree.kcur = original.kdTree.kcur;
      kdTree.kneeded = original.kdTree.kneeded;
      kdTree.n = original.kdTree.n;
      kdTree.nodes = (int[])original.kdTree.nodes.Clone();
      kdTree.normtype = original.kdTree.normtype;
      kdTree.nx = original.kdTree.nx;
      kdTree.ny = original.kdTree.ny;
      kdTree.r = (double[])original.kdTree.r.Clone();
      kdTree.rneeded = original.kdTree.rneeded;
      kdTree.selfmatch = original.kdTree.selfmatch;
      kdTree.splits = (double[])original.kdTree.splits.Clone();
      kdTree.tags = (int[])original.kdTree.tags.Clone();
      kdTree.x = (double[])original.kdTree.x.Clone();
      kdTree.xy = (double[,])original.kdTree.xy.Clone();

      k = original.k;
      targetVariable = original.targetVariable;
      allowedInputVariables = (string[])original.allowedInputVariables.Clone();
      if (original.classValues != null)
        this.classValues = (double[])original.classValues.Clone();
    }

 protected Arc(Arc original, Cloner cloner)
   : base(original, cloner) {
   Source = cloner.Clone(original.Source);
   Target = cloner.Clone(original.Target);
   label = original.Label;
   weight = original.Weight;
 }

 protected Job(Job original, Cloner cloner)
   : base(original, cloner) {
   this.dueDate = original.DueDate;
   this.index = original.Index;
   this.tasks = cloner.Clone(original.Tasks);
   RegisterEventHandlers();
 }