Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/HeuristicLab.Algorithms.DataAnalysis-3.4.csproj
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/HeuristicLab.Algorithms.DataAnalysis-3.4.csproj	(revision 6575)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/HeuristicLab.Algorithms.DataAnalysis-3.4.csproj	(revision 6576)
@@ -129,7 +129,7 @@
       <SubType>Code</SubType>
     </Compile>
-    <Compile Include="Linear\LogitClassificationSolution.cs" />
-    <Compile Include="Linear\LogitModel.cs" />
     <Compile Include="Linear\MultinomialLogitClassification.cs" />
+    <Compile Include="Linear\MultinomialLogitClassificationSolution.cs" />
+    <Compile Include="Linear\MultinomialLogitModel.cs" />
     <Compile Include="Properties\AssemblyInfo.cs" />
     <Compile Include="RandomForest\RandomForestClassificationSolution.cs" />
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LogitClassificationSolution.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LogitClassificationSolution.cs	(revision 6575)
+++ 	(revision )
@@ -1,57 +1,0 @@
-#region License Information
-/* HeuristicLab
- * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
- *
- * This file is part of HeuristicLab.
- *
- * HeuristicLab is free software: you can redistribute it and/or modify
- * it under the terms of the GNU General Public License as published by
- * the Free Software Foundation, either version 3 of the License, or
- * (at your option) any later version.
- *
- * HeuristicLab is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- * GNU General Public License for more details.
- *
- * You should have received a copy of the GNU General Public License
- * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
- */
-#endregion
-
-using System;
-using System.Collections.Generic;
-using System.Drawing;
-using System.Linq;
-using HeuristicLab.Common;
-using HeuristicLab.Core;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-using HeuristicLab.Problems.DataAnalysis;
-
-namespace HeuristicLab.Algorithms.DataAnalysis {
-  /// <summary>
-  /// Represents a multinomial logit solution for a classification problem which can be visualized in the GUI.
-  /// </summary>
-  [Item("LogitClassificationSolution", "Represents a multinomial logit solution for a classification problem which can be visualized in the GUI.")]
-  [StorableClass]
-  public sealed class LogitClassificationSolution : ClassificationSolution {
-
-    public new LogitModel Model {
-      get { return (LogitModel)base.Model; }
-      set { base.Model = value; }
-    }
-
-    [StorableConstructor]
-    private LogitClassificationSolution(bool deserializing) : base(deserializing) { }
-    private LogitClassificationSolution(LogitClassificationSolution original, Cloner cloner)
-      : base(original, cloner) {
-    }
-    public LogitClassificationSolution(IClassificationProblemData problemData, LogitModel logitModel)
-      : base(logitModel, problemData) {
-    }
-
-    public override IDeepCloneable Clone(Cloner cloner) {
-      return new LogitClassificationSolution(this, cloner);
-    }
-  }
-}
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LogitModel.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LogitModel.cs	(revision 6575)
+++ 	(revision )
@@ -1,134 +1,0 @@
-#region License Information
-/* HeuristicLab
- * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
- *
- * This file is part of HeuristicLab.
- *
- * HeuristicLab is free software: you can redistribute it and/or modify
- * it under the terms of the GNU General Public License as published by
- * the Free Software Foundation, either version 3 of the License, or
- * (at your option) any later version.
- *
- * HeuristicLab is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- * GNU General Public License for more details.
- *
- * You should have received a copy of the GNU General Public License
- * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
- */
-#endregion
-
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Linq;
-using System.Text;
-using HeuristicLab.Common;
-using HeuristicLab.Core;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-using HeuristicLab.Problems.DataAnalysis;
-using SVM;
-
-namespace HeuristicLab.Algorithms.DataAnalysis {
-  /// <summary>
-  /// Represents a multinomial logit model for classification
-  /// </summary>
-  [StorableClass]
-  [Item("Multinomial Logit Model", "Represents a multinomial logit model for classification.")]
-  public sealed class LogitModel : NamedItem, IClassificationModel {
-
-    private alglib.logitmodel logitModel;
-    public alglib.logitmodel Model {
-      get { return logitModel; }
-      set {
-        if (value != logitModel) {
-          if (value == null) throw new ArgumentNullException();
-          logitModel = value;
-          OnChanged(EventArgs.Empty);
-        }
-      }
-    }
-
-    [Storable]
-    private string targetVariable;
-    [Storable]
-    private string[] allowedInputVariables;
-    [Storable]
-    private double[] classValues;
-    [StorableConstructor]
-    private LogitModel(bool deserializing)
-      : base(deserializing) {
-      if (deserializing)
-        logitModel = new alglib.logitmodel();
-    }
-    private LogitModel(LogitModel original, Cloner cloner)
-      : base(original, cloner) {
-      logitModel = new alglib.logitmodel();
-      logitModel.innerobj.w = (double[])original.logitModel.innerobj.w.Clone();
-      targetVariable = original.targetVariable;
-      allowedInputVariables = (string[])original.allowedInputVariables.Clone();
-      this.classValues = (double[])original.classValues.Clone();
-    }
-    public LogitModel(alglib.logitmodel logitModel, string targetVariable, IEnumerable<string> allowedInputVariables, double[] classValues)
-      : base() {
-      this.name = ItemName;
-      this.description = ItemDescription;
-      this.logitModel = logitModel;
-      this.targetVariable = targetVariable;
-      this.allowedInputVariables = allowedInputVariables.ToArray();
-      this.classValues = (double[])classValues.Clone();
-    }
-
-    public override IDeepCloneable Clone(Cloner cloner) {
-      return new LogitModel(this, cloner);
-    }
-
-    public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
-      double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables, rows);
-
-      int n = inputData.GetLength(0);
-      int columns = inputData.GetLength(1);
-      double[] x = new double[columns];
-      double[] y = new double[classValues.Length];
-
-      for (int row = 0; row < n; row++) {
-        for (int column = 0; column < columns; column++) {
-          x[column] = inputData[row, column];
-        }
-        alglib.mnlprocess(logitModel, x, ref y);
-        // find class for with the largest probability value
-        int maxProbClassIndex = 0;
-        double maxProb = y[0];
-        for (int i = 1; i < y.Length; i++) {
-          if (maxProb < y[i]) {
-            maxProb = y[i];
-            maxProbClassIndex = i;
-          }
-        }
-        yield return classValues[maxProbClassIndex];
-      }
-    }
-
-    #region events
-    public event EventHandler Changed;
-    private void OnChanged(EventArgs e) {
-      var handlers = Changed;
-      if (handlers != null)
-        handlers(this, e);
-    }
-    #endregion
-
-    #region persistence
-    [Storable]
-    private double[] LogitModelW {
-      get {
-        return logitModel.innerobj.w;
-      }
-      set {
-        logitModel.innerobj.w = value;
-      }
-    }
-    #endregion
-  }
-}
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassification.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassification.cs	(revision 6575)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassification.cs	(revision 6576)
@@ -98,5 +98,5 @@
       relClassError = alglib.mnlrelclserror(lm, inputMatrix, nRows);
 
-      LogitClassificationSolution solution = new LogitClassificationSolution(problemData, new LogitModel(lm, targetVariable, allowedInputVariables, classValues));
+      MultinomialLogitClassificationSolution solution = new MultinomialLogitClassificationSolution(problemData, new MultinomialLogitModel(lm, targetVariable, allowedInputVariables, classValues));
       return solution;
     }
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassificationSolution.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassificationSolution.cs	(revision 6576)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitClassificationSolution.cs	(revision 6576)
@@ -0,0 +1,57 @@
+#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Drawing;
+using System.Linq;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Problems.DataAnalysis;
+
+namespace HeuristicLab.Algorithms.DataAnalysis {
+  /// <summary>
+  /// Represents a multinomial logit solution for a classification problem which can be visualized in the GUI.
+  /// </summary>
+  [Item("Multinomial Logit Classification Solution", "Represents a multinomial logit solution for a classification problem which can be visualized in the GUI.")]
+  [StorableClass]
+  public sealed class MultinomialLogitClassificationSolution : ClassificationSolution {
+
+    public new MultinomialLogitModel Model {
+      get { return (MultinomialLogitModel)base.Model; }
+      set { base.Model = value; }
+    }
+
+    [StorableConstructor]
+    private MultinomialLogitClassificationSolution(bool deserializing) : base(deserializing) { }
+    private MultinomialLogitClassificationSolution(MultinomialLogitClassificationSolution original, Cloner cloner)
+      : base(original, cloner) {
+    }
+    public MultinomialLogitClassificationSolution(IClassificationProblemData problemData, MultinomialLogitModel logitModel)
+      : base(logitModel, problemData) {
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new MultinomialLogitClassificationSolution(this, cloner);
+    }
+  }
+}
Index: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitModel.cs
===================================================================
--- trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitModel.cs	(revision 6576)
+++ trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/MultinomialLogitModel.cs	(revision 6576)
@@ -0,0 +1,134 @@
+#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using System.Text;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Problems.DataAnalysis;
+using SVM;
+
+namespace HeuristicLab.Algorithms.DataAnalysis {
+  /// <summary>
+  /// Represents a multinomial logit model for classification
+  /// </summary>
+  [StorableClass]
+  [Item("Multinomial Logit Model", "Represents a multinomial logit model for classification.")]
+  public sealed class MultinomialLogitModel : NamedItem, IClassificationModel {
+
+    private alglib.logitmodel logitModel;
+    public alglib.logitmodel Model {
+      get { return logitModel; }
+      set {
+        if (value != logitModel) {
+          if (value == null) throw new ArgumentNullException();
+          logitModel = value;
+          OnChanged(EventArgs.Empty);
+        }
+      }
+    }
+
+    [Storable]
+    private string targetVariable;
+    [Storable]
+    private string[] allowedInputVariables;
+    [Storable]
+    private double[] classValues;
+    [StorableConstructor]
+    private MultinomialLogitModel(bool deserializing)
+      : base(deserializing) {
+      if (deserializing)
+        logitModel = new alglib.logitmodel();
+    }
+    private MultinomialLogitModel(MultinomialLogitModel original, Cloner cloner)
+      : base(original, cloner) {
+      logitModel = new alglib.logitmodel();
+      logitModel.innerobj.w = (double[])original.logitModel.innerobj.w.Clone();
+      targetVariable = original.targetVariable;
+      allowedInputVariables = (string[])original.allowedInputVariables.Clone();
+      this.classValues = (double[])original.classValues.Clone();
+    }
+    public MultinomialLogitModel(alglib.logitmodel logitModel, string targetVariable, IEnumerable<string> allowedInputVariables, double[] classValues)
+      : base() {
+      this.name = ItemName;
+      this.description = ItemDescription;
+      this.logitModel = logitModel;
+      this.targetVariable = targetVariable;
+      this.allowedInputVariables = allowedInputVariables.ToArray();
+      this.classValues = (double[])classValues.Clone();
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new MultinomialLogitModel(this, cloner);
+    }
+
+    public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
+      double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables, rows);
+
+      int n = inputData.GetLength(0);
+      int columns = inputData.GetLength(1);
+      double[] x = new double[columns];
+      double[] y = new double[classValues.Length];
+
+      for (int row = 0; row < n; row++) {
+        for (int column = 0; column < columns; column++) {
+          x[column] = inputData[row, column];
+        }
+        alglib.mnlprocess(logitModel, x, ref y);
+        // find class for with the largest probability value
+        int maxProbClassIndex = 0;
+        double maxProb = y[0];
+        for (int i = 1; i < y.Length; i++) {
+          if (maxProb < y[i]) {
+            maxProb = y[i];
+            maxProbClassIndex = i;
+          }
+        }
+        yield return classValues[maxProbClassIndex];
+      }
+    }
+
+    #region events
+    public event EventHandler Changed;
+    private void OnChanged(EventArgs e) {
+      var handlers = Changed;
+      if (handlers != null)
+        handlers(this, e);
+    }
+    #endregion
+
+    #region persistence
+    [Storable]
+    private double[] LogitModelW {
+      get {
+        return logitModel.innerobj.w;
+      }
+      set {
+        logitModel.innerobj.w = value;
+      }
+    }
+    #endregion
+  }
+}
