Changeset 5275 for branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators

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/3.3/Evaluators

Files:

• OnlineCovarianceEvaluator.cs (modified) (2 diffs)
• SimpleEvaluator.cs (modified) (2 diffs)
• SimpleMSEEvaluator.cs (modified) (2 diffs)
• SimpleMeanAbsolutePercentageErrorEvaluator.cs (modified) (3 diffs)
• SimpleMeanAbsolutePercentageOfRangeErrorEvaluator.cs (modified) (3 diffs)
• SimpleNMSEEvaluator.cs (modified) (2 diffs)
• SimpleRSquaredEvaluator.cs (modified) (2 diffs)
• SimpleVarianceAccountedForEvaluator.cs (modified) (2 diffs)

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/OnlineCovarianceEvaluator.cs

@@ -25 +25 @@
   public class OnlineCovarianceEvaluator : IOnlineEvaluator {
 
-    private double xMean, yMean, Cn;
+    private double originalMean, estimatedMean, Cn;
     private int n;
     public double Covariance {
@@ -47 +47 @@
       n = 0;
       Cn = 0.0;
-      xMean = 0.0;
-      yMean = 0.0;
+      originalMean = 0.0;
+      estimatedMean = 0.0;
     }
 
-    public void Add(double x, double y) {
-      if (double.IsNaN(x) || double.IsInfinity(x) ||
-          double.IsNaN(y) || double.IsInfinity(y)) {
+    public void Add(double original, double estimated) {
+      if (double.IsNaN(estimated) || double.IsInfinity(estimated) ||
+          double.IsNaN(original) || double.IsInfinity(original)) {
         throw new ArgumentException("Covariance is not defined for series containing NaN or infinity elements");
       } else {
         n++;
         // online calculation of tMean
-
-        xMean = xMean + (x - xMean) / n;
-        double deltaY = y - yMean; // delta = (y - yMean(n-1))
-        yMean = yMean + deltaY / n;
+        originalMean = originalMean + (original - originalMean) / n;
+        double delta = estimated - estimatedMean; // delta = (y - yMean(n-1))
+        estimatedMean = estimatedMean + delta / n;
 
         // online calculation of covariance
-        Cn = Cn + deltaY * (x - xMean); // C(n) = C(n-1) + (y - yMean(n-1)) (x - xMean(n))
+        Cn = Cn + delta * (original - originalMean); // C(n) = C(n-1) + (y - yMean(n-1)) (t - tMean(n))       
       }
     }

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/SimpleEvaluator.cs

@@ -20 +20 @@
 #endregion
 
+using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Data;
 using HeuristicLab.Operators;
 using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
 namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
@@ -32 +34 @@
     public ILookupParameter<DoubleMatrix> ValuesParameter {
       get { return (ILookupParameter<DoubleMatrix>)Parameters["Values"]; }
+    }
+    [StorableConstructor]
+    protected SimpleEvaluator(bool deserializing) : base(deserializing) { }
+    protected SimpleEvaluator(SimpleEvaluator original, Cloner cloner)
+      : base(original, cloner) {
     }
     public SimpleEvaluator()

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/SimpleMSEEvaluator.cs

@@ -23 +23 @@
 using System.Collections.Generic;
 using System.Linq;
+using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Data;
 using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
 namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
@@ -34 +36 @@
     }
 
+    [StorableConstructor]
+    protected SimpleMSEEvaluator(bool deserializing) : base(deserializing) { }
+    protected SimpleMSEEvaluator(SimpleMSEEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SimpleMSEEvaluator(this, cloner);
+    }
     public SimpleMSEEvaluator() {
       Parameters.Add(new LookupParameter<DoubleValue>("MeanSquaredError", "The mean squared error of estimated values."));

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/SimpleMeanAbsolutePercentageErrorEvaluator.cs

@@ -23 +23 @@
 using System.Collections.Generic;
 using System.Linq;
+using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Data;
 using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
 namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
@@ -33 +35 @@
     }
 
+    [StorableConstructor]
+    protected SimpleMeanAbsolutePercentageErrorEvaluator(bool deserializing) : base(deserializing) { }
+    protected SimpleMeanAbsolutePercentageErrorEvaluator(SimpleMeanAbsolutePercentageErrorEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public SimpleMeanAbsolutePercentageErrorEvaluator() {
       Parameters.Add(new LookupParameter<PercentValue>("AverageRelativeError", "The average relative error of estimated values."));
@@ -43 +50 @@
                       select values[i, ESTIMATION_INDEX];
       AverageRelativeErrorParameter.ActualValue = new PercentValue(Calculate(original, estimated));
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SimpleMeanAbsolutePercentageErrorEvaluator(this, cloner);
     }
 

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/SimpleMeanAbsolutePercentageOfRangeErrorEvaluator.cs

@@ -27 +27 @@
 using HeuristicLab.Data;
 using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
 namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
@@ -35 +36 @@
     }
 
+    [StorableConstructor]
+    protected SimpleMeanAbsolutePercentageOfRangeErrorEvaluator(bool deserializing) : base(deserializing) { }
+    protected SimpleMeanAbsolutePercentageOfRangeErrorEvaluator(SimpleMeanAbsolutePercentageOfRangeErrorEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
     public SimpleMeanAbsolutePercentageOfRangeErrorEvaluator() {
       Parameters.Add(new LookupParameter<PercentValue>("AveragePercentageOfRangeError", "The average relative (percentage of range) error of estimated values."));
@@ -45 +51 @@
                       select values[i, ESTIMATION_INDEX];
       AveragePercentageOfRangeErrorParameter.ActualValue = new PercentValue(Calculate(original, estimated));
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SimpleMeanAbsolutePercentageOfRangeErrorEvaluator(this, cloner);
     }
 

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/SimpleNMSEEvaluator.cs

@@ -23 +23 @@
 using System.Collections.Generic;
 using System.Linq;
+using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Data;
 using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
 namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
@@ -34 +36 @@
     }
 
+    [StorableConstructor]
+    protected SimpleNMSEEvaluator(bool deserializing) : base(deserializing) { }
+    protected SimpleNMSEEvaluator(SimpleNMSEEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SimpleNMSEEvaluator(this, cloner);
+    }
     public SimpleNMSEEvaluator() {
       Parameters.Add(new LookupParameter<DoubleValue>("NormalizedMeanSquaredError", "The normalized mean squared error (divided by variance) of estimated values."));

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/SimpleRSquaredEvaluator.cs

@@ -23 +23 @@
 using System.Collections.Generic;
 using System.Linq;
+using HeuristicLab.Common;
 using HeuristicLab.Core;
 using HeuristicLab.Data;
 using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
 namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
@@ -32 +34 @@
       get { return (ILookupParameter<DoubleValue>)Parameters["RSquared"]; }
     }
-
+    [StorableConstructor]
+    protected SimpleRSquaredEvaluator(bool deserializing) : base(deserializing) { }
+    protected SimpleRSquaredEvaluator(SimpleRSquaredEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SimpleRSquaredEvaluator(this, cloner);
+    }
     public SimpleRSquaredEvaluator() {
       Parameters.Add(new LookupParameter<DoubleValue>("RSquared", "The squared Pearson's Product Moment Correlation (R²) of estimated values and original values."));

branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/SimpleVarianceAccountedForEvaluator.cs

@@ -27 +27 @@
 using HeuristicLab.Data;
 using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
 namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
@@ -40 +41 @@
     }
 
+    [StorableConstructor]
+    protected SimpleVarianceAccountedForEvaluator(bool deserializing) : base(deserializing) { }
+    protected SimpleVarianceAccountedForEvaluator(SimpleVarianceAccountedForEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SimpleVarianceAccountedForEvaluator(this, cloner);
+    }
     public SimpleVarianceAccountedForEvaluator() {
       Parameters.Add(new LookupParameter<DoubleValue>("VarianceAccountedFor", "The variance of the original values accounted for by the estimated values (VAF(y,y') = 1 - var(y-y') / var(y) )."));