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LossFunctions

Timestamp:

08/17/15 18:33:31 (9 years ago)

Author:

gkronber

Message:

#2434 reverse merge of r12871 (changes should be applied directly to trunk)

Location:

Files:

-                      r12871
+                      r12872
 using System;
 using System.Collections.Generic;
+using System.Diagnostics;
+using System.Linq;
 using HeuristicLab.Common;
-using HeuristicLab.Core;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 namespace HeuristicLab.Algorithms.DataAnalysis {
   // loss function for the weighted absolute error
+  [StorableClass]
+  [Item("Absolute error loss", "")]
+  public class AbsoluteErrorLoss : Item, ILossFunction {
+    public AbsoluteErrorLoss() { }
+  public class AbsoluteErrorLoss : ILossFunction {
     public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred) {
       var targetEnum = target.GetEnumerator();
 …
+    }
+    #region item implementation
+    private AbsoluteErrorLoss(AbsoluteErrorLoss original, Cloner cloner) : base(original, cloner) { }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new AbsoluteErrorLoss(this, cloner);
+    public override string ToString() {
+      return "Absolute error loss";
+    }
-    #endregion
+  }
+}

-                      r12871
+                      r12872
 using System.Collections.Generic;
-using HeuristicLab.Core;
 namespace HeuristicLab.Algorithms.DataAnalysis {
 …
   // target represents the target vector  (original targets from the problem data, never changed)
   // pred   represents the current vector of predictions (a weighted combination of models learned so far, this vector is updated after each step)
   public interface ILossFunction : IItem {
+  public interface ILossFunction {
     // returns the loss of the current prediction vector
     double GetLoss(IEnumerable<double> target, IEnumerable<double> pred);

-                      r12871
+                      r12872
 using System.Linq;
 using HeuristicLab.Common;
-using HeuristicLab.Core;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 namespace HeuristicLab.Algorithms.DataAnalysis {
   // Greedy Function Approximation: A Gradient Boosting Machine (page 9)
+  [StorableClass]
+  [Item("Logistic regression loss", "")]
+  public class LogisticRegressionLoss : Item, ILossFunction {
+    public LogisticRegressionLoss() { }
+  public class LogisticRegressionLoss : ILossFunction {
     public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred) {
       var targetEnum = target.GetEnumerator();
 …
+    }
+    #region item implementation
+    private LogisticRegressionLoss(LogisticRegressionLoss original, Cloner cloner) : base(original, cloner) { }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new LogisticRegressionLoss(this, cloner);
+    public override string ToString() {
+      return "Logistic regression loss";
+    }
-    #endregion
+  }
+}

-                      r12871
+                      r12872
 using System.Linq;
 using HeuristicLab.Common;
-using HeuristicLab.Core;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 namespace HeuristicLab.Algorithms.DataAnalysis {
   // relative error loss is a special case of weighted absolute error loss with weights = (1/target)
+  [StorableClass]
+  [Item("Relative error loss", "")]
+  public class RelativeErrorLoss : Item, ILossFunction {
+    public RelativeErrorLoss() { }
+  public class RelativeErrorLoss : ILossFunction {
     public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred) {
       var targetEnum = target.GetEnumerator();
 …
+    }
+    #region item implementation
+    private RelativeErrorLoss(RelativeErrorLoss original, Cloner cloner) : base(original, cloner) { }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new RelativeErrorLoss(this, cloner);
+    public override string ToString() {
+      return "Relative error loss";
+    }
-    #endregion
+  }
+}

-                      r12871
+                      r12872
 using System.Collections.Generic;
 using System.Linq;
-using HeuristicLab.Common;
-using HeuristicLab.Core;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 namespace HeuristicLab.Algorithms.DataAnalysis {
+  [StorableClass]
+  [Item("Squared error loss", "")]
+  public class SquaredErrorLoss : Item, ILossFunction {
+    public SquaredErrorLoss() { }
+  public class SquaredErrorLoss : ILossFunction {
     public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred) {
       var targetEnum = target.GetEnumerator();
 …
+    }
+    #region item implementation
+    private SquaredErrorLoss(SquaredErrorLoss original, Cloner cloner) : base(original, cloner) { }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SquaredErrorLoss(this, cloner);
+    public override string ToString() {
+      return "Squared error loss";
+    }
-    #endregion
+  }
+}

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