Changeset 16723 for branches/2521_ProblemRefactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs

Timestamp:

03/28/19 16:54:20 (6 years ago)

Author:

abeham

Message:

#2521: merged changes from r15684 to trunk HEAD (r16716) and resolved all merge conflicts

• it doesn't build

Location:

branches/2521_ProblemRefactoring

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) (9 diffs)

branches/2521_ProblemRefactoring/HeuristicLab.Algorithms.DataAnalysis/3.4/NearestNeighbour/NearestNeighbourRegression.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -27 +27 @@
 using HeuristicLab.Optimization;
 using HeuristicLab.Parameters;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HEAL.Attic;
 using HeuristicLab.Problems.DataAnalysis;
 
@@ -36 +36 @@
   [Item("Nearest Neighbour Regression (kNN)", "Nearest neighbour regression data analysis algorithm (wrapper for ALGLIB).")]
   [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 150)]
-  [StorableClass]
+  [StorableType("3F940BE0-4F44-4F7F-A3EE-E47423C7F22D")]
   public sealed class NearestNeighbourRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {
     private const string KParameterName = "K";
     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; }
@@ -67 +73 @@
 
     [StorableConstructor]
-    private NearestNeighbourRegression(bool deserializing) : base(deserializing) { }
+    private NearestNeighbourRegression(StorableConstructorFlag _) : base(_) { }
     private NearestNeighbourRegression(NearestNeighbourRegression original, Cloner cloner)
       : base(original, cloner) {
@@ -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,