Changeset 5770 for branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4

Timestamp:

03/21/11 00:19:08 (14 years ago)

Author:

mkommend

Message:

#1418: Changed data type of estimation limits and corrected some bugs.

Location:

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4

Files:

• MultiObjective/SymbolicRegressionMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs (modified) (2 diffs)
• MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs (modified) (2 diffs)
• MultiObjective/SymbolicRegressionMultiObjectiveProblem.cs (modified) (4 diffs)
• MultiObjective/SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer.cs (modified) (6 diffs)
• MultiObjective/SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer.cs (modified) (3 diffs)
• SingleObjective/SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.cs (modified) (2 diffs)
• SingleObjective/SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs (modified) (2 diffs)
• SingleObjective/SymbolicRegressionSingleObjectiveProblem.cs (modified) (5 diffs)
• SingleObjective/SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer.cs (modified) (6 diffs)
• SingleObjective/SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer.cs (modified) (3 diffs)

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs

@@ -46 +46 @@
     public override Core.IOperation Apply() {
       IEnumerable<int> rows = GenerateRowsToEvaluate();
-      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, SymbolicExpressionTreeParameter.ActualValue, LowerEstimationLimitParameter.ActualValue.Value, UpperEstimationLimitParameter.ActualValue.Value, ProblemDataParameter.ActualValue, rows);
+      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, SymbolicExpressionTreeParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
       QualitiesParameter.ActualValue = new DoubleArray(qualities);
       return base.Apply();
@@ -61 +61 @@
     public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
-      LowerEstimationLimitParameter.ExecutionContext = context;
-      UpperEstimationLimitParameter.ExecutionContext = context;
+      EstimationLimitsParameter.ExecutionContext = context;
 
-      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, LowerEstimationLimitParameter.ActualValue.Value, UpperEstimationLimitParameter.ActualValue.Value, problemData, rows);
+      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
 
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
-      LowerEstimationLimitParameter.ExecutionContext = null;
-      UpperEstimationLimitParameter.ExecutionContext = null;
+      EstimationLimitsParameter.ExecutionContext = null;
 
       return quality;

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator.cs

@@ -47 +47 @@
     public override IOperation Apply() {
       IEnumerable<int> rows = GenerateRowsToEvaluate();
-      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, SymbolicExpressionTreeParameter.ActualValue, LowerEstimationLimitParameter.ActualValue.Value, UpperEstimationLimitParameter.ActualValue.Value, ProblemDataParameter.ActualValue, rows);
+      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, SymbolicExpressionTreeParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
       QualitiesParameter.ActualValue = new DoubleArray(qualities);
       return base.Apply();
@@ -67 +67 @@
     public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
-      LowerEstimationLimitParameter.ExecutionContext = context;
-      UpperEstimationLimitParameter.ExecutionContext = context;
+      EstimationLimitsParameter.ExecutionContext = context;
 
-      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, LowerEstimationLimitParameter.ActualValue.Value, UpperEstimationLimitParameter.ActualValue.Value, problemData, rows);
+      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
 
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
-      LowerEstimationLimitParameter.ExecutionContext = null;
-      UpperEstimationLimitParameter.ExecutionContext = null;
+      EstimationLimitsParameter.ExecutionContext = null;
 
       return quality;

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveProblem.cs

@@ -35 +35 @@
     private const int InitialMaximumTreeDepth = 8;
     private const int InitialMaximumTreeLength = 25;
-    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
-    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
-    private const string LowerEstimationLimitParameterDescription = "The lower limit for the estimated value that can be returned by the symbolic regression model.";
-    private const string UpperEstimationLimitParameterDescription = "The upper limit for the estimated value that can be returned by the symbolic regression model.";
+    private const string EstimationLimitsParameterName = "EstimationLimits";
+    private const string EstimationLimitsParameterDescription = "The lower and upper limit for the estimated value that can be returned by the symbolic regression model.";
 
     #region parameter properties
-    public IFixedValueParameter<DoubleValue> LowerEstimationLimitParameter {
-      get { return (IFixedValueParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
-    }
-    public IFixedValueParameter<DoubleValue> UpperEstimationLimitParameter {
-      get { return (IFixedValueParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
+    public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter {
+      get { return (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
     }
     #endregion
+
     #region properties
-    public DoubleValue LowerEstimationLimit {
-      get { return LowerEstimationLimitParameter.Value; }
+    public DoubleLimit EstimationLimits {
+      get { return EstimationLimitsParameter.Value; }
     }
-    public DoubleValue UpperEstimationLimit {
-      get { return UpperEstimationLimitParameter.Value; }
-    }
+
     #endregion
+
     [StorableConstructor]
     protected SymbolicRegressionMultiObjectiveProblem(bool deserializing) : base(deserializing) { }
@@ -63 +58 @@
     public SymbolicRegressionMultiObjectiveProblem()
       : base(new RegressionProblemData(), new SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
-      Parameters.Add(new FixedValueParameter<DoubleValue>(LowerEstimationLimitParameterName, LowerEstimationLimitParameterDescription, new DoubleValue()));
-      Parameters.Add(new FixedValueParameter<DoubleValue>(UpperEstimationLimitParameterName, UpperEstimationLimitParameterDescription, new DoubleValue()));
+      Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription, new DoubleLimit()));
 
       Maximization = new BoolArray(new bool[] { true, false });
@@ -85 +79 @@
         var mean = targetValues.Average();
         var range = targetValues.Max() - targetValues.Min();
-        UpperEstimationLimit.Value = mean + PunishmentFactor * range;
-        LowerEstimationLimit.Value = mean - PunishmentFactor * range;
+        EstimationLimits.Upper = mean + PunishmentFactor * range;
+        EstimationLimits.Lower = mean - PunishmentFactor * range;
       }
     }
@@ -97 +91 @@
     protected override void ParameterizeOperators() {
       base.ParameterizeOperators();
-      var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
-      foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
-        op.LowerEstimationLimitParameter.ActualName = LowerEstimationLimitParameterName;
-        op.UpperEstimationLimitParameter.ActualName = UpperEstimationLimitParameterName;
+      if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
+        var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
+        foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
+          op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
+        }
       }
     }

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveTrainingBestSolutionAnalyzer.cs

@@ -20 +20 @@
 #endregion
 
-using System.Collections.Generic;
-using System.Linq;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.Operators;
-using HeuristicLab.Optimization;
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
@@ -41 +37 @@
     private const string ProblemDataParameterName = "ProblemData";
     private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
-    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
-    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
+    private const string EstimationLimitsParameterName = "EstimationLimits";
     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
     #region parameter properties
@@ -51 +46 @@
       get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
     }
-    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
-      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
-    }
-
-    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
-      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
+    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
+      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
     }
     public IValueParameter<BoolValue> ApplyLinearScalingParameter {
@@ -64 +55 @@
 
     #region properties
-    public IRegressionProblemData ProblemData {
-      get { return ProblemDataParameter.ActualValue; }
-    }
-    public ISymbolicDataAnalysisExpressionTreeInterpreter SymbolicDataAnalysisTreeInterpreter {
-      get { return SymbolicDataAnalysisTreeInterpreterParameter.ActualValue; }
-    }
-    public DoubleValue UpperEstimationLimit {
-      get { return UpperEstimationLimitParameter.ActualValue; }
-    }
-    public DoubleValue LowerEstimationLimit {
-      get { return LowerEstimationLimitParameter.ActualValue; }
-    }
     public BoolValue ApplyLinearScaling {
       get { return ApplyLinearScalingParameter.Value; }
@@ -88 +67 @@
       Parameters.Add(new LookupParameter<IRegressionProblemData>(ProblemDataParameterName, "The problem data for the symbolic regression solution."));
       Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
-      Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit for the estimated values produced by the symbolic regression model."));
-      Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit for the estimated values produced by the symbolic regression model."));
+      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
     }
@@ -98 +76 @@
 
     protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
-      var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreter, LowerEstimationLimit.Value, UpperEstimationLimit.Value);
-      var solution = new SymbolicRegressionSolution(model, ProblemData);
+      var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
+      var solution = new SymbolicRegressionSolution(model, ProblemDataParameter.ActualValue);
       if (ApplyLinearScaling.Value)
         solution.ScaleModel();

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer.cs

@@ -35 +35 @@
   public sealed class SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer : SymbolicDataAnalysisMultiObjectiveValidationBestSolutionAnalyzer<ISymbolicRegressionSolution, ISymbolicRegressionMultiObjectiveEvaluator, IRegressionProblemData>,
     ISymbolicDataAnalysisBoundedOperator {
-    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
-    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
+    private const string EstimationLimitsParameterName = "EstimationLimits";
     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
 
     #region parameter properties
-    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
-      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
-    }
-
-    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
-      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
+    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
+      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
     }
     public IValueParameter<BoolValue> ApplyLinearScalingParameter {
@@ -62 +57 @@
     public SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer()
       : base() {
-      Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit for the estimated values produced by the symbolic regression model."));
-      Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit for the estimated values produced by the symbolic regression model."));
+      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
     }
@@ -71 +65 @@
 
     protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double[] bestQuality) {
-      var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, LowerEstimationLimitParameter.ActualValue.Value, UpperEstimationLimitParameter.ActualValue.Value);
+      var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
       var solution = new SymbolicRegressionSolution(model, ProblemDataParameter.ActualValue);
       if (ApplyLinearScaling.Value)

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.cs

@@ -46 +46 @@
     public override IOperation Apply() {
       IEnumerable<int> rows = GenerateRowsToEvaluate();
-      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, SymbolicExpressionTreeParameter.ActualValue, LowerEstimationLimitParameter.ActualValue.Value, UpperEstimationLimitParameter.ActualValue.Value, ProblemDataParameter.ActualValue, rows);
+      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, SymbolicExpressionTreeParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
       Quality = new DoubleValue(quality);
       return base.Apply();
@@ -60 +60 @@
     public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
-      LowerEstimationLimitParameter.ExecutionContext = context;
-      UpperEstimationLimitParameter.ExecutionContext = context;
+      EstimationLimitsParameter.ExecutionContext = context;
 
-      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, LowerEstimationLimitParameter.ActualValue.Value, UpperEstimationLimitParameter.ActualValue.Value, problemData, rows);
+      double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
 
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
-      LowerEstimationLimitParameter.ExecutionContext = null;
-      UpperEstimationLimitParameter.ExecutionContext = null;
+      EstimationLimitsParameter.ExecutionContext = null;
 
       return mse;

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.cs

@@ -47 +47 @@
     public override IOperation Apply() {
       IEnumerable<int> rows = GenerateRowsToEvaluate();
-      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, SymbolicExpressionTreeParameter.ActualValue, LowerEstimationLimitParameter.ActualValue.Value, UpperEstimationLimitParameter.ActualValue.Value, ProblemDataParameter.ActualValue, rows);
+      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, SymbolicExpressionTreeParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
       Quality = new DoubleValue(quality);
       return base.Apply();
@@ -66 +66 @@
     public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
-      LowerEstimationLimitParameter.ExecutionContext = context;
-      UpperEstimationLimitParameter.ExecutionContext = context;
+      EstimationLimitsParameter.ExecutionContext = context;
 
-      double r2 = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, LowerEstimationLimitParameter.ActualValue.Value, UpperEstimationLimitParameter.ActualValue.Value, problemData, rows);
+      double r2 = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
 
       SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
-      LowerEstimationLimitParameter.ExecutionContext = null;
-      UpperEstimationLimitParameter.ExecutionContext = null;
+      EstimationLimitsParameter.ExecutionContext = null;
 
       return r2;

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveProblem.cs

@@ -23 +23 @@
 using HeuristicLab.Common;
 using HeuristicLab.Core;
-using HeuristicLab.Data;
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
@@ -35 +34 @@
     private const int InitialMaximumTreeDepth = 8;
     private const int InitialMaximumTreeLength = 25;
-    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
-    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
-    private const string LowerEstimationLimitParameterDescription = "The lower limit for the estimated value that can be returned by the symbolic regression model.";
-    private const string UpperEstimationLimitParameterDescription = "The upper limit for the estimated value that can be returned by the symbolic regression model.";
+    private const string EstimationLimitsParameterName = "EstimationLimits";
+    private const string EstimationLimitsParameterDescription = "The limits for the estimated value that can be returned by the symbolic regression model.";
 
     #region parameter properties
-    public IFixedValueParameter<DoubleValue> LowerEstimationLimitParameter {
-      get { return (IFixedValueParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
-    }
-    public IFixedValueParameter<DoubleValue> UpperEstimationLimitParameter {
-      get { return (IFixedValueParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
+    public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter {
+      get { return (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
     }
     #endregion
     #region properties
-    public DoubleValue LowerEstimationLimit {
-      get { return LowerEstimationLimitParameter.Value; }
-    }
-    public DoubleValue UpperEstimationLimit {
-      get { return UpperEstimationLimitParameter.Value; }
+    public DoubleLimit EstimationLimits {
+      get { return EstimationLimitsParameter.Value; }
     }
     #endregion
@@ -63 +54 @@
     public SymbolicRegressionSingleObjectiveProblem()
       : base(new RegressionProblemData(), new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
-      Parameters.Add(new FixedValueParameter<DoubleValue>(LowerEstimationLimitParameterName, LowerEstimationLimitParameterDescription, new DoubleValue()));
-      Parameters.Add(new FixedValueParameter<DoubleValue>(UpperEstimationLimitParameterName, UpperEstimationLimitParameterDescription, new DoubleValue()));
+      Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription, new DoubleLimit()));
 
       Maximization.Value = true;
@@ -86 +76 @@
         var mean = targetValues.Average();
         var range = targetValues.Max() - targetValues.Min();
-        UpperEstimationLimit.Value = mean + PunishmentFactor * range;
-        LowerEstimationLimit.Value = mean - PunishmentFactor * range;
+        EstimationLimits.Upper = mean + PunishmentFactor * range;
+        EstimationLimits.Lower = mean - PunishmentFactor * range;
       }
     }
@@ -98 +88 @@
     protected override void ParameterizeOperators() {
       base.ParameterizeOperators();
-      var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
-      foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
-        op.LowerEstimationLimitParameter.ActualName = LowerEstimationLimitParameterName;
-        op.UpperEstimationLimitParameter.ActualName = UpperEstimationLimitParameterName;
+      if (Parameters.ContainsKey(EstimationLimitsParameterName)) {
+        var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
+        foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>()) {
+          op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
+        }
       }
     }

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer.cs

@@ -20 +20 @@
 #endregion
 
-using System.Collections.Generic;
-using System.Linq;
 using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.Operators;
-using HeuristicLab.Optimization;
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
@@ -41 +37 @@
     private const string ProblemDataParameterName = "ProblemData";
     private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicDataAnalysisTreeInterpreter";
-    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
-    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
+    private const string EstimationLimitsParameterName = "EstimationLimits";
     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
     #region parameter properties
@@ -51 +46 @@
       get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
     }
-    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
-      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
-    }
-
-    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
-      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
+    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
+      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
     }
     public IValueParameter<BoolValue> ApplyLinearScalingParameter {
@@ -64 +55 @@
 
     #region properties
-    public IRegressionProblemData ProblemData {
-      get { return ProblemDataParameter.ActualValue; }
-    }
-    public ISymbolicDataAnalysisExpressionTreeInterpreter SymbolicDataAnalysisTreeInterpreter {
-      get { return SymbolicDataAnalysisTreeInterpreterParameter.ActualValue; }
-    }
-    public DoubleValue UpperEstimationLimit {
-      get { return UpperEstimationLimitParameter.ActualValue; }
-    }
-    public DoubleValue LowerEstimationLimit {
-      get { return LowerEstimationLimitParameter.ActualValue; }
-    }
     public BoolValue ApplyLinearScaling {
       get { return ApplyLinearScalingParameter.Value; }
     }
     #endregion
+
     [StorableConstructor]
     private SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer(bool deserializing) : base(deserializing) { }
@@ -87 +67 @@
       Parameters.Add(new LookupParameter<IRegressionProblemData>(ProblemDataParameterName, "The problem data for the symbolic regression solution."));
       Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The symbolic data analysis tree interpreter for the symbolic expression tree."));
-      Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit for the estimated values produced by the symbolic regression model."));
-      Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit for the estimated values produced by the symbolic regression model."));
+      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
     }
@@ -96 +75 @@
 
     protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
-      var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreter, LowerEstimationLimit.Value, UpperEstimationLimit.Value);
-      var solution = new SymbolicRegressionSolution(model, ProblemData);
+      var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
+      var solution = new SymbolicRegressionSolution(model, ProblemDataParameter.ActualValue);
       if (ApplyLinearScaling.Value)
         solution.ScaleModel();

branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer.cs

@@ -35 +35 @@
   public sealed class SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer : SymbolicDataAnalysisSingleObjectiveValidationBestSolutionAnalyzer<ISymbolicRegressionSolution, ISymbolicRegressionSingleObjectiveEvaluator, IRegressionProblemData>,
     ISymbolicDataAnalysisBoundedOperator {
-    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
-    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
+    private const string EstimationLimitsParameterName = "EstimationLimits";
     private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
+
     #region parameter properties
-    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
-      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
-    }
-
-    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
-      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
+    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
+      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
     }
     public IValueParameter<BoolValue> ApplyLinearScalingParameter {
@@ -62 +58 @@
     public SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer()
       : base() {
-      Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit for the estimated values produced by the symbolic regression model."));
-      Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit for the estimated values produced by the symbolic regression model."));
+      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The lower and upper limit for the estimated values produced by the symbolic regression model."));
       Parameters.Add(new ValueParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the produced symbolic regression solution should be linearly scaled.", new BoolValue(true)));
     }
@@ -72 +67 @@
 
     protected override ISymbolicRegressionSolution CreateSolution(ISymbolicExpressionTree bestTree, double bestQuality) {
-      var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, LowerEstimationLimitParameter.ActualValue.Value, UpperEstimationLimitParameter.ActualValue.Value);
+      var model = new SymbolicRegressionModel(bestTree, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper);
       var solution = new SymbolicRegressionSolution(model, ProblemDataParameter.ActualValue);
       if (ApplyLinearScaling.Value)