Changeset 8742 for branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis

Timestamp:

10/05/12 11:58:17 (12 years ago)

Author:

mkommend

Message:

#1081: Merged trunk changes and fixed compilation errors due to the merge.

Location:

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3

Files:

• Classification/CSV/ClassifiactionCSVInstanceProvider.cs (modified) (3 diffs)
• Classification/ClassificationInstanceProvider.cs (modified) (1 diff)
• Classification/Data/UCI.zip (copied) (copied from trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/Data/UCI.zip)
• Clustering/CSV/ClusteringCSVInstanceProvider.cs (modified) (4 diffs)
• Clustering/ClusteringInstanceProvider.cs (modified) (1 diff)
• DataAnalysisImportType.cs (copied) (copied from trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/DataAnalysisImportType.cs)
• DataAnalysisInstanceProvider.cs (copied) (copied from trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/DataAnalysisInstanceProvider.cs)
• HeuristicLab.Problems.Instances.DataAnalysis-3.3.csproj (modified) (5 diffs)
• Plugin.cs.frame (modified) (1 diff)
• Regression/CSV/RegressionCSVInstanceProvider.cs (modified) (3 diffs)
• Regression/RegressionInstanceProvider.cs (modified) (1 diff)
• Regression/ValueGenerator.cs (modified) (2 diffs)
• Regression/Various/FriedmanTwo.cs (modified) (1 diff)
• TableFileParser.cs (modified) (1 diff)

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/CSV/ClassifiactionCSVInstanceProvider.cs

@@ -21 +21 @@
 
 using System;
+using System.Collections;
 using System.Collections.Generic;
+using System.Globalization;
 using System.IO;
 using System.Linq;
 using System.Text;
+using HeuristicLab.Common;
 using HeuristicLab.Problems.DataAnalysis;
 
@@ -61 +64 @@
 
       Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values);
-      string targetVar = csvFileParser.VariableNames.Where(x => dataset.DoubleVariables.Contains(x)).Last();
-      IEnumerable<string> allowedInputVars = dataset.DoubleVariables.Where(x => !x.Equals(targetVar));
+      string targetVar = dataset.DoubleVariables.Last();
 
-      ClassificationProblemData claData = new ClassificationProblemData(dataset, allowedInputVars, targetVar);
-
-      int trainingPartEnd = csvFileParser.Rows * 2 / 3;
-      claData.TrainingPartition.Start = 0;
-      claData.TrainingPartition.End = trainingPartEnd;
-      claData.TestPartition.Start = trainingPartEnd;
-      claData.TestPartition.End = csvFileParser.Rows;
-      int pos = path.LastIndexOf('\\');
-      if (pos < 0)
-        claData.Name = path;
-      else {
-        pos++;
-        claData.Name = path.Substring(pos, path.Length - pos);
+      // turn of input variables that are constant in the training partition
+      var allowedInputVars = new List<string>();
+      var trainingIndizes = Enumerable.Range(0, (csvFileParser.Rows * 2) / 3);
+      if (trainingIndizes.Count() >= 2) {
+        foreach (var variableName in dataset.DoubleVariables) {
+          if (dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 &&
+            variableName != targetVar)
+            allowedInputVars.Add(variableName);
+        }
+      } else {
+        allowedInputVars.AddRange(dataset.DoubleVariables.Where(x => x.Equals(targetVar)));
       }
 
-      return claData;
+      ClassificationProblemData classificationData = new ClassificationProblemData(dataset, allowedInputVars, targetVar);
+
+      int trainingPartEnd = trainingIndizes.Last();
+      classificationData.TrainingPartition.Start = trainingIndizes.First();
+      classificationData.TrainingPartition.End = trainingPartEnd;
+      classificationData.TestPartition.Start = trainingPartEnd;
+      classificationData.TestPartition.End = csvFileParser.Rows;
+
+      classificationData.Name = Path.GetFileName(path);
+
+      return classificationData;
+    }
+
+    public override IClassificationProblemData ImportData(string path, DataAnalysisImportType type) {
+      TableFileParser csvFileParser = new TableFileParser();
+      csvFileParser.Parse(path);
+
+      int trainingPartEnd = (csvFileParser.Rows * type.Training) / 100;
+      List<IList> values = csvFileParser.Values;
+      if (type.Shuffle) {
+        values = Shuffle(values);
+      }
+
+      Dataset dataset = new Dataset(csvFileParser.VariableNames, values);
+      string targetVar = dataset.DoubleVariables.Last();
+
+      // turn of input variables that are constant in the training partition
+      var allowedInputVars = new List<string>();
+      var trainingIndizes = Enumerable.Range(0, trainingPartEnd);
+      foreach (var variableName in dataset.DoubleVariables) {
+        if (trainingIndizes.Count() >= 2 && dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 &&
+          variableName != targetVar)
+          allowedInputVars.Add(variableName);
+      }
+
+      ClassificationProblemData classificationData = new ClassificationProblemData(dataset, allowedInputVars, targetVar);
+
+      classificationData.TrainingPartition.Start = 0;
+      classificationData.TrainingPartition.End = trainingPartEnd;
+      classificationData.TestPartition.Start = trainingPartEnd;
+      classificationData.TestPartition.End = csvFileParser.Rows;
+
+      classificationData.Name = Path.GetFileName(path);
+
+      return classificationData;
     }
 
@@ -86 +130 @@
     }
     public override void ExportData(IClassificationProblemData instance, string path) {
-      StringBuilder strBuilder = new StringBuilder();
-
-      foreach (var variable in instance.InputVariables) {
-        strBuilder.Append(variable + ";");
+      var strBuilder = new StringBuilder();
+      var colSep = CultureInfo.CurrentCulture.TextInfo.ListSeparator;
+      foreach (var variable in instance.Dataset.VariableNames) {
+        strBuilder.Append(variable.Replace(colSep, String.Empty) + colSep);
       }
-      strBuilder.Remove(strBuilder.Length - 1, 1);
+      strBuilder.Remove(strBuilder.Length - colSep.Length, colSep.Length);
       strBuilder.AppendLine();
 
-      Dataset dataset = instance.Dataset;
+      var dataset = instance.Dataset;
 
       for (int i = 0; i < dataset.Rows; i++) {
         for (int j = 0; j < dataset.Columns; j++) {
-          strBuilder.Append(dataset.GetValue(i, j) + ";");
+          if (j > 0) strBuilder.Append(colSep);
+          strBuilder.Append(dataset.GetValue(i, j));
         }
-        strBuilder.Remove(strBuilder.Length - 1, 1);
         strBuilder.AppendLine();
       }
 
-      using (StreamWriter writer = new StreamWriter(path)) {
+      using (var writer = new StreamWriter(path)) {
         writer.Write(strBuilder);
       }

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Classification/ClassificationInstanceProvider.cs

@@ -23 +23 @@
 
 namespace HeuristicLab.Problems.Instances.DataAnalysis {
-  public abstract class ClassificationInstanceProvider : ProblemInstanceProvider<IClassificationProblemData> {
+  public abstract class ClassificationInstanceProvider : DataAnalysisInstanceProvider<IClassificationProblemData> {
   }
 }

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Clustering/CSV/ClusteringCSVInstanceProvider.cs

@@ -21 +21 @@
 
 using System;
+using System.Collections;
 using System.Collections.Generic;
+using System.Globalization;
 using System.IO;
+using System.Linq;
 using System.Text;
+using HeuristicLab.Common;
 using HeuristicLab.Problems.DataAnalysis;
 
@@ -56 +60 @@
     public override IClusteringProblemData ImportData(string path) {
       var csvFileParser = new TableFileParser();
-
       csvFileParser.Parse(path);
 
-      var dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values);
-      var claData = new ClusteringProblemData(dataset, dataset.DoubleVariables);
+      Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values);
+      string targetVar = dataset.DoubleVariables.Last();
 
-      int trainingPartEnd = csvFileParser.Rows * 2 / 3;
-      claData.TrainingPartition.Start = 0;
-      claData.TrainingPartition.End = trainingPartEnd;
-      claData.TestPartition.Start = trainingPartEnd;
-      claData.TestPartition.End = csvFileParser.Rows;
-      int pos = path.LastIndexOf('\\');
-      if (pos < 0)
-        claData.Name = path;
-      else {
-        pos++;
-        claData.Name = path.Substring(pos, path.Length - pos);
+      // turn of input variables that are constant in the training partition
+      var allowedInputVars = new List<string>();
+      var trainingIndizes = Enumerable.Range(0, (csvFileParser.Rows * 2) / 3);
+      if (trainingIndizes.Count() >= 2) {
+        foreach (var variableName in dataset.DoubleVariables) {
+          if (dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 &&
+            variableName != targetVar)
+            allowedInputVars.Add(variableName);
+        }
+      } else {
+        allowedInputVars.AddRange(dataset.DoubleVariables.Where(x => x.Equals(targetVar)));
       }
 
-      return claData;
+      ClusteringProblemData clusteringData = new ClusteringProblemData(dataset, allowedInputVars);
+
+      int trainingPartEnd = trainingIndizes.Last();
+      clusteringData.TrainingPartition.Start = trainingIndizes.First();
+      clusteringData.TrainingPartition.End = trainingPartEnd;
+      clusteringData.TestPartition.Start = trainingPartEnd;
+      clusteringData.TestPartition.End = csvFileParser.Rows;
+
+      clusteringData.Name = Path.GetFileName(path);
+
+      return clusteringData;
+    }
+
+    public override IClusteringProblemData ImportData(string path, DataAnalysisImportType type) {
+      TableFileParser csvFileParser = new TableFileParser();
+      csvFileParser.Parse(path);
+
+      List<IList> values = csvFileParser.Values;
+      if (type.Shuffle) {
+        values = Shuffle(values);
+      }
+
+      Dataset dataset = new Dataset(csvFileParser.VariableNames, values);
+      string targetVar = dataset.DoubleVariables.Last();
+
+      // turn of input variables that are constant in the training partition
+      var allowedInputVars = new List<string>();
+      int trainingPartEnd = (csvFileParser.Rows * type.Training) / 100;
+      var trainingIndizes = Enumerable.Range(0, trainingPartEnd);
+      foreach (var variableName in dataset.DoubleVariables) {
+        if (trainingIndizes.Count() >= 2 && dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 &&
+          variableName != targetVar)
+          allowedInputVars.Add(variableName);
+      }
+
+      ClusteringProblemData clusteringData = new ClusteringProblemData(dataset, allowedInputVars);
+
+      clusteringData.TrainingPartition.Start = 0;
+      clusteringData.TrainingPartition.End = trainingPartEnd;
+      clusteringData.TestPartition.Start = trainingPartEnd;
+      clusteringData.TestPartition.End = csvFileParser.Rows;
+
+      clusteringData.Name = Path.GetFileName(path);
+
+      return clusteringData;
     }
 
@@ -83 +130 @@
     public override void ExportData(IClusteringProblemData instance, string path) {
       var strBuilder = new StringBuilder();
-
-      foreach (var variable in instance.InputVariables) {
-        strBuilder.Append(variable + ";");
+      var colSep = CultureInfo.CurrentCulture.TextInfo.ListSeparator;
+      foreach (var variable in instance.Dataset.VariableNames) {
+        strBuilder.Append(variable.Replace(colSep, String.Empty) + colSep);
       }
-      strBuilder.Remove(strBuilder.Length - 1, 1);
+      strBuilder.Remove(strBuilder.Length - colSep.Length, colSep.Length);
       strBuilder.AppendLine();
 
@@ -94 +141 @@
       for (int i = 0; i < dataset.Rows; i++) {
         for (int j = 0; j < dataset.Columns; j++) {
-          strBuilder.Append(dataset.GetValue(i, j) + ";");
+          if (j > 0) strBuilder.Append(colSep);
+          strBuilder.Append(dataset.GetValue(i, j));
         }
-        strBuilder.Remove(strBuilder.Length - 1, 1);
         strBuilder.AppendLine();
       }

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Clustering/ClusteringInstanceProvider.cs

@@ -23 +23 @@
 
 namespace HeuristicLab.Problems.Instances.DataAnalysis {
-  public abstract class ClusteringInstanceProvider : ProblemInstanceProvider<IClusteringProblemData> {
+  public abstract class ClusteringInstanceProvider : DataAnalysisInstanceProvider<IClusteringProblemData> {
   }
 }

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/HeuristicLab.Problems.Instances.DataAnalysis-3.3.csproj

@@ -98 +98 @@
   </PropertyGroup>
   <ItemGroup>
-    <Reference Include="HeuristicLab.Collections-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
-      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Collections-3.3.dll</HintPath>
-    </Reference>
-    <Reference Include="HeuristicLab.Common-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
-      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Common-3.3.dll</HintPath>
-    </Reference>
-    <Reference Include="HeuristicLab.Core-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
-      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Core-3.3.dll</HintPath>
-    </Reference>
-    <Reference Include="HeuristicLab.Data-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
-      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Data-3.3.dll</HintPath>
-    </Reference>
-    <Reference Include="HeuristicLab.PluginInfrastructure-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
-      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.PluginInfrastructure-3.3.dll</HintPath>
-    </Reference>
-    <Reference Include="HeuristicLab.Problems.Instances-3.3">
-      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Problems.Instances-3.3.dll</HintPath>
-    </Reference>
-    <Reference Include="HeuristicLab.Random-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
-      <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Random-3.3.dll</HintPath>
-    </Reference>
-    <Reference Include="ICSharpCode.SharpZipLib, Version=0.85.4.369, Culture=neutral, PublicKeyToken=1b03e6acf1164f73, processorArchitecture=MSIL">
+    <Reference Include="HeuristicLab.Collections-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL" />
+    <Reference Include="HeuristicLab.Common-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL" />
+    <Reference Include="HeuristicLab.Core-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL" />
+    <Reference Include="HeuristicLab.Data-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL" />
+    <Reference Include="HeuristicLab.PluginInfrastructure-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL" />
+    <Reference Include="HeuristicLab.Problems.Instances-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL" />
+    <Reference Include="HeuristicLab.Random-3.3, Version=3.3.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL" />
+    <Reference Include="ICSharpCode.SharpZipLib">
+      <HintPath>..\..\HeuristicLab.PluginInfrastructure\3.3\ICSharpCode.SharpZipLib.dll</HintPath>
       <Private>False</Private>
     </Reference>
@@ -137 +124 @@
     <Compile Include="Classification\ClassificationInstanceProvider.cs" />
     <Compile Include="Classification\CSV\ClassifiactionCSVInstanceProvider.cs" />
-    <Compile Include="Classification\RealWorld\Iris.cs" />
-    <Compile Include="Classification\RealWorld\Mammography.cs" />
-    <Compile Include="Classification\RealWorld\ClassificationRealWorldInstanceProvider.cs" />
     <Compile Include="Classification\ResourceClassificationDataDescriptor.cs" />
     <Compile Include="Classification\ResourceClassificationInstanceProvider.cs" />
+    <Compile Include="Classification\UCI\Mammography.cs" />
+    <Compile Include="Classification\UCI\UCIInstanceProvider.cs" />
+    <Compile Include="Classification\UCI\Wine.cs" />
     <Compile Include="Clustering\ClusteringInstanceProvider.cs" />
     <Compile Include="Clustering\CSV\ClusteringCSVInstanceProvider.cs" />
+    <Compile Include="DataAnalysisImportType.cs" />
+    <Compile Include="DataAnalysisInstanceProvider.cs" />
     <Compile Include="Plugin.cs" />
     <Compile Include="Properties\AssemblyInfo.cs" />
@@ -219 +208 @@
   </ItemGroup>
   <ItemGroup>
-    <EmbeddedResource Include="Classification\Data\ClassificationRealWorld.zip" />
+    <EmbeddedResource Include="Classification\Data\UCI.zip" />
     <None Include="HeuristicLab.snk" />
     <None Include="Plugin.cs.frame" />
@@ -234 +223 @@
   <Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
   <PropertyGroup>
-    <PreBuildEvent>set Path=%25Path%25;$(ProjectDir);$(SolutionDir)
+    <PreBuildEvent Condition=" '$(OS)' == 'Windows_NT' ">set Path=%25Path%25;$(ProjectDir);$(SolutionDir)
 set ProjectDir=$(ProjectDir)
 set SolutionDir=$(SolutionDir)
@@ -240 +229 @@
 
 call PreBuildEvent.cmd</PreBuildEvent>
+    <PreBuildEvent Condition=" '$(OS)' != 'Windows_NT' ">
+export ProjectDir=$(ProjectDir)
+export SolutionDir=$(SolutionDir)
+
+$SolutionDir/PreBuildEvent.sh
+</PreBuildEvent>
   </PropertyGroup>
   <!-- To modify your build process, add your task inside one of the targets below and uncomment it. 

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Plugin.cs.frame

@@ -25 +25 @@
   [Plugin("HeuristicLab.Problems.Instances.DataAnalysis", "3.3.7.$WCREV$")]
   [PluginFile("HeuristicLab.Problems.Instances.DataAnalysis-3.3.dll", PluginFileType.Assembly)]
+  [PluginDependency("HeuristicLab.Common", "3.3")]
   [PluginDependency("HeuristicLab.Core", "3.3")]
   [PluginDependency("HeuristicLab.Data", "3.3")]

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/CSV/RegressionCSVInstanceProvider.cs

@@ -21 +21 @@
 
 using System;
+using System.Collections;
 using System.Collections.Generic;
+using System.Globalization;
 using System.IO;
 using System.Linq;
 using System.Text;
+using HeuristicLab.Common;
 using HeuristicLab.Problems.DataAnalysis;
 
@@ -59 +62 @@
 
       Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values);
-      string targetVar = csvFileParser.VariableNames.Where(x => dataset.DoubleVariables.Contains(x)).Last();
+      string targetVar = dataset.DoubleVariables.Last();
 
-      IEnumerable<string> allowedInputVars = dataset.DoubleVariables.Where(x => !x.Equals(targetVar));
+      // turn of input variables that are constant in the training partition
+      var allowedInputVars = new List<string>();
+      var trainingIndizes = Enumerable.Range(0, (csvFileParser.Rows * 2) / 3);
+      foreach (var variableName in dataset.DoubleVariables) {
+        if (trainingIndizes.Count() >= 2 && dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 &&
+          variableName != targetVar)
+          allowedInputVars.Add(variableName);
+      }
 
-      IRegressionProblemData regData = new RegressionProblemData(dataset, allowedInputVars, targetVar);
+      IRegressionProblemData regressionData = new RegressionProblemData(dataset, allowedInputVars, targetVar);
 
-      int trainingPartEnd = csvFileParser.Rows * 2 / 3;
-      regData.TrainingPartition.Start = 0;
-      regData.TrainingPartition.End = trainingPartEnd;
-      regData.TestPartition.Start = trainingPartEnd;
-      regData.TestPartition.End = csvFileParser.Rows;
+      var trainingPartEnd = trainingIndizes.Last();
+      regressionData.TrainingPartition.Start = trainingIndizes.First();
+      regressionData.TrainingPartition.End = trainingPartEnd;
+      regressionData.TestPartition.Start = trainingPartEnd;
+      regressionData.TestPartition.End = csvFileParser.Rows;
 
-      int pos = path.LastIndexOf('\\');
-      if (pos < 0)
-        regData.Name = path;
-      else {
-        pos++;
-        regData.Name = path.Substring(pos, path.Length - pos);
+      regressionData.Name = Path.GetFileName(path);
+
+      return regressionData;
+    }
+
+    public override IRegressionProblemData ImportData(string path, DataAnalysisImportType type) {
+      TableFileParser csvFileParser = new TableFileParser();
+      csvFileParser.Parse(path);
+
+      List<IList> values = csvFileParser.Values;
+      if (type.Shuffle) {
+        values = Shuffle(values);
       }
-      return regData;
+      Dataset dataset = new Dataset(csvFileParser.VariableNames, values);
+      string targetVar = dataset.DoubleVariables.Last();
+
+      // turn of input variables that are constant in the training partition
+      var allowedInputVars = new List<string>();
+      int trainingPartEnd = (csvFileParser.Rows * type.Training) / 100;
+      trainingPartEnd = trainingPartEnd > 0 ? trainingPartEnd : 1;
+      var trainingIndizes = Enumerable.Range(0, trainingPartEnd);
+      if (trainingIndizes.Count() >= 2) {
+        foreach (var variableName in dataset.DoubleVariables) {
+          if (dataset.GetDoubleValues(variableName, trainingIndizes).Range() > 0 &&
+            variableName != targetVar)
+            allowedInputVars.Add(variableName);
+        }
+      } else {
+        allowedInputVars.AddRange(dataset.DoubleVariables.Where(x => x.Equals(targetVar)));
+      }
+
+      RegressionProblemData regressionData = new RegressionProblemData(dataset, allowedInputVars, targetVar);
+
+      regressionData.TrainingPartition.Start = 0;
+      regressionData.TrainingPartition.End = trainingPartEnd;
+      regressionData.TestPartition.Start = trainingPartEnd;
+      regressionData.TestPartition.End = csvFileParser.Rows;
+
+      regressionData.Name = Path.GetFileName(path);
+
+      return regressionData;
     }
 
@@ -85 +128 @@
     }
     public override void ExportData(IRegressionProblemData instance, string path) {
-      StringBuilder strBuilder = new StringBuilder();
-
-      foreach (var variable in instance.InputVariables) {
-        strBuilder.Append(variable + ";");
+      var strBuilder = new StringBuilder();
+      var colSep = CultureInfo.CurrentCulture.TextInfo.ListSeparator;
+      foreach (var variable in instance.Dataset.VariableNames) {
+        strBuilder.Append(variable.Replace(colSep, String.Empty) + colSep);
       }
-      strBuilder.Remove(strBuilder.Length - 1, 1);
+      strBuilder.Remove(strBuilder.Length - colSep.Length, colSep.Length);
       strBuilder.AppendLine();
 
-      Dataset dataset = instance.Dataset;
+      var dataset = instance.Dataset;
 
       for (int i = 0; i < dataset.Rows; i++) {
         for (int j = 0; j < dataset.Columns; j++) {
-          strBuilder.Append(dataset.GetValue(i, j) + ";");
+          if (j > 0) strBuilder.Append(colSep);
+          strBuilder.Append(dataset.GetValue(i, j));
         }
-        strBuilder.Remove(strBuilder.Length - 1, 1);
         strBuilder.AppendLine();
       }
 
-      using (StreamWriter writer = new StreamWriter(path)) {
+      using (var writer = new StreamWriter(path)) {
         writer.Write(strBuilder);
       }

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/RegressionInstanceProvider.cs

@@ -23 +23 @@
 
 namespace HeuristicLab.Problems.Instances.DataAnalysis {
-  public abstract class RegressionInstanceProvider : ProblemInstanceProvider<IRegressionProblemData> {
+  public abstract class RegressionInstanceProvider : DataAnalysisInstanceProvider<IRegressionProblemData> {
   }
 }

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/ValueGenerator.cs

@@ -23 +23 @@
 using System.Collections.Generic;
 using System.Linq;
+using HeuristicLab.Common;
 using HeuristicLab.Random;
 
@@ -40 +41 @@
       if (stepWidth <= 0) throw new ArgumentException("stepwith must be larger than zero.", "stepWidth");
       double x = start;
-      while (x <= end) {
+      // x<=end could skip the last value because of numerical problems
+      while (x < end || x.IsAlmost(end)) {
         yield return x;
         x += stepWidth;

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Various/FriedmanTwo.cs

@@ -36 +36 @@
     }
     protected override string TargetVariable { get { return "Y"; } }
-    protected override string[] InputVariables { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "Y" }; } }
-    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3", "X4", "X5" }; } }
+    protected override string[] InputVariables { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "Y" }; } }
+    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10" }; } }
     protected override int TrainingPartitionStart { get { return 0; } }
     protected override int TrainingPartitionEnd { get { return 5000; } }

branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.Instances.DataAnalysis/3.3/TableFileParser.cs

@@ -32 +32 @@
 namespace HeuristicLab.Problems.Instances.DataAnalysis {
   public class TableFileParser {
-    private const int BUFFER_SIZE = 1024;
+    private const int BUFFER_SIZE = 65536;
     private static readonly char[] POSSIBLE_SEPARATORS = new char[] { ',', ';', '\t' };
     private Tokenizer tokenizer;