Changeset 13941 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkModel.cs

Timestamp:

06/28/16 13:33:17 (8 years ago)

Author:

mkommend

Message:

#2604:

• Base classes for data analysis, classification, and regression models
• Added target variable to classification and regression models
• Switched parameter order in data analysis solutions (model, problemdata)

File:

• trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkModel.cs (modified) (6 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkModel.cs

@@ -34 +34 @@
   [StorableClass]
   [Item("NeuralNetworkModel", "Represents a neural network for regression and classification.")]
-  public sealed class NeuralNetworkModel : NamedItem, INeuralNetworkModel {
+  public sealed class NeuralNetworkModel : ClassificationModel, INeuralNetworkModel {
 
     private alglib.multilayerperceptron multiLayerPerceptron;
@@ -48 +48 @@
     }
 
-    public IEnumerable<string> VariablesUsedForPrediction {
+    public override IEnumerable<string> VariablesUsedForPrediction {
       get { return allowedInputVariables; }
     }
 
-    public string TargetVariable {
-      get { return targetVariable; }
-    }
-
-    [Storable]
-    private string targetVariable;
     [Storable]
     private string[] allowedInputVariables;
@@ -82 +76 @@
       multiLayerPerceptron.innerobj.x = (double[])original.multiLayerPerceptron.innerobj.x.Clone();
       multiLayerPerceptron.innerobj.y = (double[])original.multiLayerPerceptron.innerobj.y.Clone();
-      targetVariable = original.targetVariable;
       allowedInputVariables = (string[])original.allowedInputVariables.Clone();
       if (original.classValues != null)
@@ -88 +81 @@
     }
     public NeuralNetworkModel(alglib.multilayerperceptron multiLayerPerceptron, string targetVariable, IEnumerable<string> allowedInputVariables, double[] classValues = null)
-      : base() {
+      : base(targetVariable) {
       this.name = ItemName;
       this.description = ItemDescription;
       this.multiLayerPerceptron = multiLayerPerceptron;
-      this.targetVariable = targetVariable;
       this.allowedInputVariables = allowedInputVariables.ToArray();
       if (classValues != null)
@@ -119 +111 @@
     }
 
-    public IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
+    public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
       double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables, rows);
 
@@ -145 +137 @@
     }
 
-    public INeuralNetworkRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
-      return new NeuralNetworkRegressionSolution(new RegressionProblemData(problemData), this);
-    }
-    IRegressionSolution IRegressionModel.CreateRegressionSolution(IRegressionProblemData problemData) {
-      return CreateRegressionSolution(problemData);
-    }
-    public INeuralNetworkClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
-      return new NeuralNetworkClassificationSolution(new ClassificationProblemData(problemData), this);
-    }
-    IClassificationSolution IClassificationModel.CreateClassificationSolution(IClassificationProblemData problemData) {
-      return CreateClassificationSolution(problemData);
+    public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
+      return new NeuralNetworkRegressionSolution(this, new RegressionProblemData(problemData));
+    }
+    public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
+      return new NeuralNetworkClassificationSolution(this, new ClassificationProblemData(problemData));
     }