Changeset 14308 for stable/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs

Timestamp:

09/26/16 18:25:24 (8 years ago)

Author:

gkronber

Message:

#2652: merged r14235 from trunk to stable

Location:

stable

Files:

• . (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis (modified) (1 prop)
• HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs (modified) (4 diffs)

stable/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs

@@ -39 +39 @@
     private const string KParameterName = "K";
     private const string NearestNeighbourRegressionModelResultName = "Nearest neighbour regression solution";
+    private const string WeightsParameterName = "Weights";
 
     #region parameter properties
     public IFixedValueParameter<IntValue> KParameter {
       get { return (IFixedValueParameter<IntValue>)Parameters[KParameterName]; }
+    }
+
+    public IValueParameter<DoubleArray> WeightsParameter {
+      get { return (IValueParameter<DoubleArray>)Parameters[WeightsParameterName]; }
     }
     #endregion
@@ -52 +57 @@
         else KParameter.Value.Value = value;
       }
+    }
+
+    public DoubleArray Weights {
+      get { return WeightsParameter.Value; }
+      set { WeightsParameter.Value = value; }
     }
     #endregion
@@ -63 +73 @@
       : base() {
       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)"));
       Problem = new RegressionProblem();
     }
+
     [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserialization() { }
+    private void AfterDeserialization() {
+      // BackwardsCompatibility3.3
+      #region Backwards compatible code, remove with 3.4
+      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)"));
+      }
+      #endregion
+    }
 
     public override IDeepCloneable Clone(Cloner cloner) {
@@ -74 +93 @@
     #region nearest neighbour
     protected override void Run() {
-      var solution = CreateNearestNeighbourRegressionSolution(Problem.ProblemData, K);
+      double[] weights = null;
+      if (Weights != null) weights = Weights.CloneAsArray();
+      var solution = CreateNearestNeighbourRegressionSolution(Problem.ProblemData, K, weights);
       Results.Add(new Result(NearestNeighbourRegressionModelResultName, "The nearest neighbour regression solution.", solution));
     }
 
-    public static IRegressionSolution CreateNearestNeighbourRegressionSolution(IRegressionProblemData problemData, int k) {
+    public static IRegressionSolution CreateNearestNeighbourRegressionSolution(IRegressionProblemData problemData, int k, double[] weights = null) {
       var clonedProblemData = (IRegressionProblemData)problemData.Clone();
-      return new NearestNeighbourRegressionSolution(Train(problemData, k), clonedProblemData);
+      return new NearestNeighbourRegressionSolution(Train(problemData, k, weights), clonedProblemData);
     }
 
-    public static INearestNeighbourModel Train(IRegressionProblemData problemData, int k) {
+    public static INearestNeighbourModel Train(IRegressionProblemData problemData, int k, double[] weights = null) {
       return new NearestNeighbourModel(problemData.Dataset,
         problemData.TrainingIndices,
         k,
         problemData.TargetVariable,
-        problemData.AllowedInputVariables);
+        problemData.AllowedInputVariables,
+        weights);
     }
     #endregion