Changeset 10556 for branches/ClassificationModelComparison/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleClassification.cs

Timestamp:

03/05/14 17:36:03 (11 years ago)

Author:

mkommend

Message:

#1998: Updated classification model comparison branch with trunk changes (remaining changes).

Location:

branches/ClassificationModelComparison/HeuristicLab.Algorithms.DataAnalysis

Files:

• . (modified) (1 prop)
• 3.4/NeuralNetwork/NeuralNetworkEnsembleClassification.cs (modified) (4 diffs)

branches/ClassificationModelComparison/HeuristicLab.Algorithms.DataAnalysis/3.4/NeuralNetwork/NeuralNetworkEnsembleClassification.cs

@@ -1 +1 @@
 #region License Information
 /* HeuristicLab
- * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
  *
  * This file is part of HeuristicLab.
@@ -26 +26 @@
 using HeuristicLab.Core;
 using HeuristicLab.Data;
-using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Optimization;
+using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 using HeuristicLab.Problems.DataAnalysis;
-using HeuristicLab.Problems.DataAnalysis.Symbolic;
-using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
-using HeuristicLab.Parameters;
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
@@ -161 +158 @@
       var solution = CreateNeuralNetworkEnsembleClassificationSolution(Problem.ProblemData, EnsembleSize, HiddenLayers, NodesInFirstHiddenLayer, NodesInSecondHiddenLayer, Decay, Restarts, out rmsError, out avgRelError, out relClassError);
       Results.Add(new Result(NeuralNetworkEnsembleClassificationModelResultName, "The neural network ensemble classification solution.", solution));
-      Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the neural network ensemble regression solution on the training set.", new DoubleValue(rmsError)));
-      Results.Add(new Result("Average relative error", "The average of relative errors of the neural network ensemble regression solution on the training set.", new PercentValue(avgRelError)));
+      Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the neural network ensemble classification solution on the training set.", new DoubleValue(rmsError)));
+      Results.Add(new Result("Average relative error", "The average of relative errors of the neural network ensemble classification solution on the training set.", new PercentValue(avgRelError)));
       Results.Add(new Result("Relative classification error", "The percentage of misclassified samples.", new PercentValue(relClassError)));
     }
@@ -201 +198 @@
       int info;
       alglib.mlpetraines(mlpEnsemble, inputMatrix, nRows, decay, restarts, out info, out rep);
-      if (info != 6) throw new ArgumentException("Error in calculation of neural network ensemble regression solution");
+      if (info != 6) throw new ArgumentException("Error in calculation of neural network ensemble classification solution");
 
       rmsError = alglib.mlpermserror(mlpEnsemble, inputMatrix, nRows);