Changeset 8470 for branches/NCA/HeuristicLab.Algorithms.NCA/3.3/NcaAlgorithm.cs

Timestamp:

08/10/12 17:07:20 (12 years ago)

Author:

abeham

Message:

#1913: increased point size and converted K to a fixed value parameter

File:

• branches/NCA/HeuristicLab.Algorithms.NCA/3.3/NcaAlgorithm.cs (modified) (4 diffs)

branches/NCA/HeuristicLab.Algorithms.NCA/3.3/NcaAlgorithm.cs

@@ -45 +45 @@
   public sealed class NcaAlgorithm : FixedDataAnalysisAlgorithm<IClassificationProblem> {
     #region Parameter Properties
-    public IValueLookupParameter<IntValue> KParameter {
-      get { return (IValueLookupParameter<IntValue>)Parameters["K"]; }
+    public IFixedValueParameter<IntValue> KParameter {
+      get { return (IFixedValueParameter<IntValue>)Parameters["K"]; }
     }
     public IFixedValueParameter<IntValue> DimensionsParameter {
@@ -63 +63 @@
 
     #region Properties
-    private IntValue K {
-      get { return KParameter.Value; }
+    private int K {
+      get { return KParameter.Value.Value; }
+      set { KParameter.Value.Value = value; }
     }
     private int Dimensions {
@@ -85 +86 @@
     public NcaAlgorithm()
       : base() {
-      Parameters.Add(new ValueLookupParameter<IntValue>("K", "The K for the nearest neighbor.", new IntValue(1)));
+      Parameters.Add(new FixedValueParameter<IntValue>("K", "The K for the nearest neighbor.", new IntValue(1)));
       Parameters.Add(new FixedValueParameter<IntValue>("Dimensions", "The number of dimensions that NCA should reduce the data to.", new IntValue(2)));
       Parameters.Add(new ConstrainedValueParameter<INCAInitializer>("Initialization", "Which method should be used to initialize the matrix. Typically LDA (linear discriminant analysis) should provide a good estimate."));
@@ -110 +111 @@
 
     protected override void Run() {
-      var k = K.Value;
       var initializer = InitializationParameter.Value;
 
       var clonedProblem = (IClassificationProblemData)Problem.ProblemData.Clone();
-      var model = Train(clonedProblem, k, Dimensions, NeighborSamples, Iterations, initializer.Initialize(clonedProblem, Dimensions), ReportQuality, CancellationToken.None);
+      var model = Train(clonedProblem, K, Dimensions, NeighborSamples, Iterations, initializer.Initialize(clonedProblem, Dimensions), ReportQuality, CancellationToken.None);
       Results.Add(new Result("ClassificationSolution", "The classification solution.", model.CreateClassificationSolution(clonedProblem)));
     }