Changeset 5275 for branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic

Timestamp:

01/11/11 15:03:46 (14 years ago)

Author:

gkronber

Message:

Merged changes from trunk to data analysis exploration branch and added fractional distance metric evaluator. #1142

Location:

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic

Files:

• Analyzer/ValidationBestScaledSymbolicVectorRegressionSolutionAnalyzer.cs (modified) (3 diffs)
• Evaluators/MultiObjectiveSymbolicVectorRegressionEvaluator.cs (modified) (2 diffs)
• Evaluators/PartialDerivativeEvaluator.cs (modified) (1 diff)
• Evaluators/SingleObjectiveSymbolicVectorRegressionEvaluator.cs (modified) (2 diffs)
• Evaluators/SymbolicRegressionNormalizedMeanSquaredErrorEvaluator.cs (modified) (2 diffs)
• Evaluators/SymbolicVectorRegressionEvaluator.cs (modified) (3 diffs)
• Evaluators/SymbolicVectorRegressionNormalizedMseEvaluator.cs (modified) (2 diffs)
• Evaluators/SymbolicVectorRegressionScaledMseEvaluator.cs (modified) (3 diffs)
• Evaluators/SymbolicVectorRegressionScaledNormalizedMseEvaluator.cs (modified) (3 diffs)
• MultiObjectiveSymbolicVectorRegressionProblem.cs (modified) (2 diffs)
• SingleObjectiveSymbolicVectorRegressionProblem.cs (modified) (2 diffs)
• SymbolicVectorRegressionGrammar.cs (modified) (2 diffs)
• SymbolicVectorRegressionProblem.cs (modified) (3 diffs)
• SymbolicVectorRegressionSolutionLinearScaler.cs (modified) (3 diffs)

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Analyzer/ValidationBestScaledSymbolicVectorRegressionSolutionAnalyzer.cs

@@ -33 +33 @@
 using HeuristicLab.Problems.DataAnalysis.Symbolic;
 using HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Interfaces;
+using HeuristicLab.Common;
 
 namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Analyzers {
@@ -133 +134 @@
     }
     #endregion
-
+    [StorableConstructor]
+    protected ValidationBestScaledSymbolicVectorRegressionSolutionAnalyzer(bool deserializing) : base(deserializing) { }
+    protected ValidationBestScaledSymbolicVectorRegressionSolutionAnalyzer(ValidationBestScaledSymbolicVectorRegressionSolutionAnalyzer original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public ValidationBestScaledSymbolicVectorRegressionSolutionAnalyzer()
       : base() {
@@ -151 +156 @@
       Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The result collection where the best symbolic regression solution should be stored."));
       Parameters.Add(new LookupParameter<DoubleValue>(BestKnownQualityParameterName, "The best known (validation) quality achieved on the data set."));
-
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new ValidationBestScaledSymbolicVectorRegressionSolutionAnalyzer(this, cloner);
     }
 

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/MultiObjectiveSymbolicVectorRegressionEvaluator.cs

@@ -32 +32 @@
 using HeuristicLab.Problems.DataAnalysis.Symbolic;
 using HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Interfaces;
+using HeuristicLab.Common;
 
 namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
@@ -42 +43 @@
     }
     #endregion
-    public MultiObjectiveSymbolicVectorRegressionEvaluator(bool deserializing) : base(deserializing) { }
+    [StorableConstructor]
+    protected MultiObjectiveSymbolicVectorRegressionEvaluator(bool deserializing) : base(deserializing) { }
+    protected MultiObjectiveSymbolicVectorRegressionEvaluator(MultiObjectiveSymbolicVectorRegressionEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public MultiObjectiveSymbolicVectorRegressionEvaluator()
       : base() {

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/PartialDerivativeEvaluator.cs

@@ -39 +39 @@
   public class PartialDerivativeEvaluator : SingleObjectiveSymbolicVectorRegressionEvaluator {
 
-    public PartialDerivativeEvaluator(bool deserializing) : base(deserializing) { }
+    [StorableConstructor]
+    protected PartialDerivativeEvaluator(bool deserializing) : base(deserializing) { }
+    protected PartialDerivativeEvaluator(PartialDerivativeEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public PartialDerivativeEvaluator()
       : base() {
     }
-
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new PartialDerivativeEvaluator(this, cloner);
+    }
     public override double Evaluate(SymbolicExpressionTree tree, ISymbolicExpressionTreeInterpreter interpreter, MultiVariateDataAnalysisProblemData problemData, IEnumerable<string> targetVariables, IEnumerable<int> rows, DoubleArray lowerEstimationBound, DoubleArray upperEstimationBound) {
 

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SingleObjectiveSymbolicVectorRegressionEvaluator.cs

@@ -32 +32 @@
 using HeuristicLab.Problems.DataAnalysis.Symbolic;
 using HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Interfaces;
+using HeuristicLab.Common;
 
 namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
@@ -42 +43 @@
     }
     #endregion
-    public SingleObjectiveSymbolicVectorRegressionEvaluator(bool deserializing) : base(deserializing) { }
+    [StorableConstructor]
+    protected SingleObjectiveSymbolicVectorRegressionEvaluator(bool deserializing) : base(deserializing) { }
+    protected SingleObjectiveSymbolicVectorRegressionEvaluator(SingleObjectiveSymbolicVectorRegressionEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public SingleObjectiveSymbolicVectorRegressionEvaluator()
       : base() {
       Parameters.Add(new LookupParameter<DoubleValue>(QualityParameterName, "The quality of the symbolic vector regression solution."));
     }
-
 
     public override IOperation Apply() {

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicRegressionNormalizedMeanSquaredErrorEvaluator.cs

@@ -29 +29 @@
 using HeuristicLab.Problems.DataAnalysis.Evaluators;
 using HeuristicLab.Problems.DataAnalysis.Symbolic;
+using HeuristicLab.Common;
 
 namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
@@ -34 +35 @@
   [StorableClass]
   public class SymbolicRegressionNormalizedMeanSquaredErrorEvaluator : SingleObjectiveSymbolicRegressionEvaluator {
+    [StorableConstructor]
+    protected SymbolicRegressionNormalizedMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { }
+    protected SymbolicRegressionNormalizedMeanSquaredErrorEvaluator(SymbolicRegressionNormalizedMeanSquaredErrorEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public SymbolicRegressionNormalizedMeanSquaredErrorEvaluator()
       : base() {
     }
-
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SymbolicRegressionNormalizedMeanSquaredErrorEvaluator(this, cloner);
+    }
     public override double Evaluate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, Dataset dataset, string targetVariable, IEnumerable<int> rows) {
       double nmse = Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, dataset, targetVariable, rows);

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicVectorRegressionEvaluator.cs

@@ -31 +31 @@
 using HeuristicLab.Problems.DataAnalysis.Regression;
 using HeuristicLab.Problems.DataAnalysis.Symbolic;
+using HeuristicLab.Common;
 
 namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
@@ -105 +106 @@
     #endregion
 
-    public SymbolicVectorRegressionEvaluator(bool deserializing) : base(deserializing) { }
+    [StorableConstructor]
+    protected SymbolicVectorRegressionEvaluator(bool deserializing) : base(deserializing) { }
+    protected SymbolicVectorRegressionEvaluator(SymbolicVectorRegressionEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public SymbolicVectorRegressionEvaluator()
       : base() {
@@ -118 +123 @@
       Parameters.Add(new ValueParameter<PercentValue>(RelativeNumberOfEvaluatedSamplesParameterName, "The relative number of samples of the dataset partition, which should be randomly chosen for evaluation between the start and end index.", new PercentValue(1)));
     }
-
     public static IEnumerable<int> GenerateRowsToEvaluate(int seed, double relativeAmount, int start, int end) {
       if (end < start) throw new ArgumentException("Start value is larger than end value.");

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicVectorRegressionNormalizedMseEvaluator.cs

@@ -30 +30 @@
 using HeuristicLab.Problems.DataAnalysis.Regression.Symbolic;
 using HeuristicLab.Problems.DataAnalysis.Symbolic;
+using HeuristicLab.Common;
 
 namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
@@ -37 +38 @@
 
 
-    public SymbolicVectorRegressionNormalizedMseEvaluator(bool deserializing) : base(deserializing) { }
+    [StorableConstructor]
+    protected SymbolicVectorRegressionNormalizedMseEvaluator(bool deserializing) : base(deserializing) { }
+    protected SymbolicVectorRegressionNormalizedMseEvaluator(SymbolicVectorRegressionNormalizedMseEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public SymbolicVectorRegressionNormalizedMseEvaluator()
       : base() {
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SymbolicVectorRegressionNormalizedMseEvaluator(this, cloner);
     }
 

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicVectorRegressionScaledMseEvaluator.cs

@@ -30 +30 @@
 using HeuristicLab.Problems.DataAnalysis.Regression.Symbolic;
 using HeuristicLab.Problems.DataAnalysis.Symbolic;
+using HeuristicLab.Common;
 
 namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
@@ -48 +49 @@
     #endregion
 
+    [StorableConstructor]
+    protected SymbolicVectorRegressionScaledMseEvaluator(bool deserializing) : base(deserializing) { }
+    protected SymbolicVectorRegressionScaledMseEvaluator(SymbolicVectorRegressionScaledMseEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public SymbolicVectorRegressionScaledMseEvaluator()
       : base() {
@@ -53 +59 @@
       Parameters.Add(new LookupParameter<DoubleArray>(BetaParameterName, "The beta parameter for linear scaling."));
     }
-
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SymbolicVectorRegressionScaledMseEvaluator(this, cloner);
+    }
     public override double[] Evaluate(SymbolicExpressionTree tree, ISymbolicExpressionTreeInterpreter interpreter, MultiVariateDataAnalysisProblemData problemData, IEnumerable<string> targetVariables, IEnumerable<int> rows, DoubleArray lowerEstimationBound, DoubleArray upperEstimationBound) {
       List<string> targetVariablesList = targetVariables.ToList();

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicVectorRegressionScaledNormalizedMseEvaluator.cs

@@ -30 +30 @@
 using HeuristicLab.Problems.DataAnalysis.Regression.Symbolic;
 using HeuristicLab.Problems.DataAnalysis.Symbolic;
+using HeuristicLab.Common;
 
 namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
@@ -48 +49 @@
     #endregion
 
+    [StorableConstructor]
+    protected SymbolicVectorRegressionScaledNormalizedMseEvaluator(bool deserializing) : base(deserializing) { }
+    protected SymbolicVectorRegressionScaledNormalizedMseEvaluator(SymbolicVectorRegressionScaledNormalizedMseEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public SymbolicVectorRegressionScaledNormalizedMseEvaluator()
       : base() {
@@ -53 +59 @@
       Parameters.Add(new LookupParameter<DoubleArray>(BetaParameterName, "The beta parameter for linear scaling."));
     }
-
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SymbolicVectorRegressionScaledNormalizedMseEvaluator(this, cloner);
+    }
     public override double Evaluate(SymbolicExpressionTree tree, ISymbolicExpressionTreeInterpreter interpreter, MultiVariateDataAnalysisProblemData problemData, IEnumerable<string> targetVariables, IEnumerable<int> rows, DoubleArray lowerEstimationBound, DoubleArray upperEstimationBound) {
       List<string> targetVariablesList = targetVariables.ToList();

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/MultiObjectiveSymbolicVectorRegressionProblem.cs

@@ -68 +68 @@
     [StorableConstructor]
     protected MultiObjectiveSymbolicVectorRegressionProblem(bool deserializing) : base(deserializing) { }
+    protected MultiObjectiveSymbolicVectorRegressionProblem(MultiObjectiveSymbolicVectorRegressionProblem original, Cloner cloner)
+      : base(original, cloner) {
+        Initialize();
+    }
     public MultiObjectiveSymbolicVectorRegressionProblem()
       : base() {
@@ -84 +88 @@
 
     public override IDeepCloneable Clone(Cloner cloner) {
-      MultiObjectiveSymbolicVectorRegressionProblem clone = (MultiObjectiveSymbolicVectorRegressionProblem)base.Clone(cloner);
-      clone.Initialize();
-      return clone;
+      return new MultiObjectiveSymbolicVectorRegressionProblem(this, cloner);
     }
 

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/SingleObjectiveSymbolicVectorRegressionProblem.cs

@@ -75 +75 @@
     #endregion
 
+    [StorableConstructor]
+    private SingleObjectiveSymbolicVectorRegressionProblem(bool deserializing) : base() { }
+    private SingleObjectiveSymbolicVectorRegressionProblem(SingleObjectiveSymbolicVectorRegressionProblem original, Cloner cloner)
+      : base(original, cloner) {
+        Initialize();
+    }
     public SingleObjectiveSymbolicVectorRegressionProblem()
       : base() {
@@ -87 +93 @@
     }
 
-    [StorableConstructor]
-    private SingleObjectiveSymbolicVectorRegressionProblem(bool deserializing) : base() { }
 
     [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserializationHook() {
+    private void AfterDeserialization() {
       Initialize();
     }
 
     public override IDeepCloneable Clone(Cloner cloner) {
-      SingleObjectiveSymbolicVectorRegressionProblem clone = (SingleObjectiveSymbolicVectorRegressionProblem)base.Clone(cloner);
-      clone.Initialize();
-      return clone;
+      return new SingleObjectiveSymbolicVectorRegressionProblem(this, cloner);
     }
-
     private void RegisterParameterValueEvents() {
       EvaluatorParameter.ValueChanged += new EventHandler(EvaluatorParameter_ValueChanged);

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/SymbolicVectorRegressionGrammar.cs

@@ -33 +33 @@
   public class SymbolicVectorRegressionGrammar : MultiVariateExpressionGrammar {
     public SymbolicVectorRegressionGrammar() : this(1) { }
-
+    [StorableConstructor]
+    protected SymbolicVectorRegressionGrammar(bool deserializing) : base(deserializing) { }
+    protected SymbolicVectorRegressionGrammar(SymbolicVectorRegressionGrammar original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public SymbolicVectorRegressionGrammar(int dimension)
       : base(dimension) {
       Initialize();
     }
-
-    protected SymbolicVectorRegressionGrammar(SymbolicVectorRegressionGrammar original) : base(original.Dimension) {
-    // fill ???
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SymbolicVectorRegressionGrammar(this, cloner);
     }
 
@@ -115 +118 @@
       }
     }
-
-    public override IDeepCloneable Clone(Cloner cloner) {
-      var clone = new SymbolicVectorRegressionGrammar(this);
-      cloner.RegisterClonedObject(this, clone);
-      return clone;
-    }
   }
 }

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/SymbolicVectorRegressionProblem.cs

@@ -158 +158 @@
     [StorableConstructor]
     protected SymbolicVectorRegressionProblem(bool deserializing) : base(deserializing) { }
+    protected SymbolicVectorRegressionProblem(SymbolicVectorRegressionProblem original, Cloner cloner)
+      : base(original, cloner) {
+      operators = original.operators.Select(x => (IOperator)cloner.Clone(x)).ToList();
+      RegisterParameterEvents();
+      RegisterParameterValueEvents();
+    }
     public SymbolicVectorRegressionProblem()
       : base() {
@@ -184 +190 @@
 
     [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserializationHook() {
+    private void AfterDeserialization() {
       // BackwardsCompatibility3.3
       #region Backwards compatible code (remove with 3.4)
@@ -194 +200 @@
 
     public override IDeepCloneable Clone(Cloner cloner) {
-      SymbolicVectorRegressionProblem clone = (SymbolicVectorRegressionProblem)base.Clone(cloner);
-      clone.operators = operators.Select(x => (IOperator)cloner.Clone(x)).ToList();
-      clone.RegisterParameterEvents();
-      clone.RegisterParameterValueEvents();
-      return clone;
+      return new SymbolicVectorRegressionProblem(this, cloner);
     }
 

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/SymbolicVectorRegressionSolutionLinearScaler.cs

@@ -29 +29 @@
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
+using HeuristicLab.Common;
 
 namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic {
@@ -54 +55 @@
       get { return (ILookupParameter<DoubleArray>)Parameters[BetaParameterName]; }
     }
-
+    [StorableConstructor]
+    protected SymbolicVectorRegressionSolutionLinearScaler(bool deserializing) : base(deserializing) { }
+    protected SymbolicVectorRegressionSolutionLinearScaler(SymbolicVectorRegressionSolutionLinearScaler original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public SymbolicVectorRegressionSolutionLinearScaler()
       : base() {
@@ -62 +67 @@
       Parameters.Add(new LookupParameter<DoubleArray>(BetaParameterName, "Beta parameter for linear transformation."));
     }
-
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SymbolicVectorRegressionSolutionLinearScaler(this, cloner);
+    }
     public override IOperation Apply() {
       SymbolicExpressionTree tree = SymbolicExpressionTreeParameter.ActualValue;