Changeset 16529 for branches/2520_PersistenceReintegration/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs

Timestamp:

01/10/19 15:40:44 (5 years ago)

Author:

gkronber

Message:

#2520: merged r16491:16528 from trunk to persistence branch

Location:

branches/2520_PersistenceReintegration

Files:

• . (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis/3.4 (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs (modified) (6 diffs)

branches/2520_PersistenceReintegration/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs

@@ -41 +41 @@
     private const string NearestNeighbourRegressionModelResultName = "Nearest neighbour regression solution";
     private const string WeightsParameterName = "Weights";
+    private const string SelfMatchParameterName = "SelfMatch";
 
     #region parameter properties
@@ -46 +47 @@
       get { return (IFixedValueParameter<IntValue>)Parameters[KParameterName]; }
     }
-
+    public IFixedValueParameter<BoolValue> SelfMatchParameter {
+      get { return (IFixedValueParameter<BoolValue>)Parameters[SelfMatchParameterName]; }
+    }
     public IValueParameter<DoubleArray> WeightsParameter {
       get { return (IValueParameter<DoubleArray>)Parameters[WeightsParameterName]; }
@@ -59 +62 @@
       }
     }
-
+    public bool SelfMatch {
+      get { return SelfMatchParameter.Value.Value; }
+      set { SelfMatchParameter.Value.Value = value; }
+    }
     public DoubleArray Weights {
       get { return WeightsParameter.Value; }
@@ -75 +81 @@
       Parameters.Add(new FixedValueParameter<IntValue>(KParameterName, "The number of nearest neighbours to consider for regression.", new IntValue(3)));
       Parameters.Add(new OptionalValueParameter<DoubleArray>(WeightsParameterName, "Optional: use weights to specify individual scaling values for all features. If not set the weights are calculated automatically (each feature is scaled to unit variance)"));
+      Parameters.Add(new FixedValueParameter<BoolValue>(SelfMatchParameterName, "Should we use equal points for classification?", new BoolValue(false)));
       Problem = new RegressionProblem();
     }
@@ -84 +91 @@
       if (!Parameters.ContainsKey(WeightsParameterName)) {
         Parameters.Add(new OptionalValueParameter<DoubleArray>(WeightsParameterName, "Optional: use weights to specify individual scaling values for all features. If not set the weights are calculated automatically (each feature is scaled to unit variance)"));
+      }
+      if (!Parameters.ContainsKey(SelfMatchParameterName)) {
+        Parameters.Add(new FixedValueParameter<BoolValue>(SelfMatchParameterName, "Should we use equal points for classification?", new BoolValue(false)));
       }
       #endregion
@@ -96 +106 @@
       double[] weights = null;
       if (Weights != null) weights = Weights.CloneAsArray();
-      var solution = CreateNearestNeighbourRegressionSolution(Problem.ProblemData, K, weights);
+      var solution = CreateNearestNeighbourRegressionSolution(Problem.ProblemData, K, SelfMatch, weights);
       Results.Add(new Result(NearestNeighbourRegressionModelResultName, "The nearest neighbour regression solution.", solution));
     }
 
-    public static IRegressionSolution CreateNearestNeighbourRegressionSolution(IRegressionProblemData problemData, int k, double[] weights = null) {
+    public static IRegressionSolution CreateNearestNeighbourRegressionSolution(IRegressionProblemData problemData, int k, bool selfMatch = false, double[] weights = null) {
       var clonedProblemData = (IRegressionProblemData)problemData.Clone();
-      return new NearestNeighbourRegressionSolution(Train(problemData, k, weights), clonedProblemData);
+      return new NearestNeighbourRegressionSolution(Train(problemData, k, selfMatch, weights), clonedProblemData);
     }
 
-    public static INearestNeighbourModel Train(IRegressionProblemData problemData, int k, double[] weights = null) {
+    public static INearestNeighbourModel Train(IRegressionProblemData problemData, int k, bool selfMatch = false, double[] weights = null) {
       return new NearestNeighbourModel(problemData.Dataset,
         problemData.TrainingIndices,
         k,
+        selfMatch,
         problemData.TargetVariable,
         problemData.AllowedInputVariables,