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Ignore:
Timestamp:
12/19/12 11:16:51 (12 years ago)
Author:
abeham
Message:

#1985: Updated branch from trunk

Location:
branches/RuntimeOptimizer
Files:
36 edited

Legend:

Unmodified
Added
Removed

  • branches/RuntimeOptimizer

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis

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

    r8885 r9078  
    9191
    9292    protected override IClassificationProblemData ImportData(string path, ClassificationImportType type, TableFileParser csvFileParser) {
    93       int trainingPartEnd = (csvFileParser.Rows * type.Training) / 100;
     93      int trainingPartEnd = (csvFileParser.Rows * type.TrainingPercentage) / 100;
    9494      List<IList> values = csvFileParser.Values;
    9595      if (type.Shuffle) {
    … …  
    9797        if (type.UniformlyDistributeClasses) {
    9898          values = Shuffle(values, csvFileParser.VariableNames.ToList().FindIndex(x => x.Equals(type.TargetVariable)),
    99                            type.Training, out trainingPartEnd);
     99                           type.TrainingPercentage, out trainingPartEnd);
    100100        }
    101101      }

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

    r8877 r9078  
    100100      // turn of input variables that are constant in the training partition
    101101      var allowedInputVars = new List<string>();
    102       int trainingPartEnd = (csvFileParser.Rows * type.Training) / 100;
     102      int trainingPartEnd = (csvFileParser.Rows * type.TrainingPercentage) / 100;
    103103      var trainingIndizes = Enumerable.Range(0, trainingPartEnd);
    104104      if (trainingIndizes.Count() >= 2) {

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/DataAnalysisImportType.cs

    r8598 r9078  
    2323  public class DataAnalysisImportType {
    2424    public bool Shuffle { get; set; }
    25     public int Training { get; set; }
     25    public int TrainingPercentage { get; set; }
    2626  }
    2727}

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

    r8877 r9078  
    9797      // turn of input variables that are constant in the training partition
    9898      var allowedInputVars = new List<string>();
    99       int trainingPartEnd = (csvFileParser.Rows * type.Training) / 100;
     99      int trainingPartEnd = (csvFileParser.Rows * type.TrainingPercentage) / 100;
    100100      trainingPartEnd = trainingPartEnd > 0 ? trainingPartEnd : 1;
    101101      var trainingIndizes = Enumerable.Range(0, trainingPartEnd);

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionEleven.cs

    r8825 r9078  
    3636          + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine
    3737          + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
    38           + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + Environment.NewLine
    39           + "Note: Test partition has been adjusted to only 100 random uniformly distributed test cases in the interval [-3, 3] (not ca. 360000 as described) "
    40           + ", but 5000 cases are created";
     38          + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
    4139      }
    4240    }
    … …  
    4644    protected override int TrainingPartitionStart { get { return 0; } }
    4745    protected override int TrainingPartitionEnd { get { return 20; } }
    48     protected override int TestPartitionStart { get { return 2500; } }
    49     protected override int TestPartitionEnd { get { return 2600; } }
     46    protected override int TestPartitionStart { get { return 20; } }
     47    protected override int TestPartitionEnd { get { return 20 + (601 * 601); } }
    5048
    5149    protected override List<List<double>> GenerateValues() {
    5250      List<List<double>> data = new List<List<double>>();
     51      List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-3, 3, 0.01).ToList();
     52      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
     53
     54      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList();
     55
    5356      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
    54         data.Add(ValueGenerator.GenerateUniformDistributedValues(5020, -3, 3).ToList());
     57        data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList());
     58        data[i].AddRange(combinations[i]);
    5559      }
    5660

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionFifteen.cs

    r8825 r9078  
    3535        + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine
    3636        + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
    37         + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + Environment.NewLine
    38         + "Note: Test partition has been adjusted to only 100 random uniformly distributed test cases in the interval [-3, 3] (not ca. 360000 as described) "
    39         + ", but 5000 cases are created";
     37        + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
    4038      }
    4139    }
    … …  
    4543    protected override int TrainingPartitionStart { get { return 0; } }
    4644    protected override int TrainingPartitionEnd { get { return 20; } }
    47     protected override int TestPartitionStart { get { return 2500; } }
    48     protected override int TestPartitionEnd { get { return 2600; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 20 + (601 * 601); } }
    4947
    5048    protected override List<List<double>> GenerateValues() {
    5149      List<List<double>> data = new List<List<double>>();
     50      List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-3, 3, 0.01).ToList();
     51      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
     52
     53      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList();
     54
    5255      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
    53         data.Add(ValueGenerator.GenerateUniformDistributedValues(5000, -3, 3).ToList());
     56        data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList());
     57        data[i].AddRange(combinations[i]);
    5458      }
    5559
    … …  
    5963        x = data[0][i];
    6064        y = data[1][i];
    61         results.Add(Math.Pow(x, 3) / 5 + Math.Pow(y, 3) / 2 - y - x);
     65        results.Add(x * x * x / 5.0 + y * y * y / 2.0 - y - x);
    6266      }
    6367      data.Add(results);

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionFourteen.cs

    r8825 r9078  
    3535        + "range(train): 20 Train cases x,y = rnd(-3, 3)" + Environment.NewLine
    3636        + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
    37         + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + Environment.NewLine
    38         + "Note: Test partition has been adjusted to only 100 random uniformly distributed test cases in the interval [-3, 3] (not ca. 360000 as described) "
    39         + ", but 5000 cases are created";
     37        + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
    4038      }
    4139    }
    … …  
    4543    protected override int TrainingPartitionStart { get { return 0; } }
    4644    protected override int TrainingPartitionEnd { get { return 20; } }
    47     protected override int TestPartitionStart { get { return 2500; } }
    48     protected override int TestPartitionEnd { get { return 2600; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 20 + (601 * 601); } }
    4947
    5048    protected override List<List<double>> GenerateValues() {
    5149      List<List<double>> data = new List<List<double>>();
     50      List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-3, 3, 0.01).ToList();
     51      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
     52
     53      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList();
     54
    5255      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
    53         data.Add(ValueGenerator.GenerateUniformDistributedValues(5000, -3, 3).ToList());
     56        data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList());
     57        data[i].AddRange(combinations[i]);
    5458      }
    5559
    … …  
    5963        x = data[0][i];
    6064        y = data[1][i];
    61         results.Add(8 / (2 + Math.Pow(x, 2) + Math.Pow(y, 2)));
     65        results.Add(8.0 / (2.0 + x * x + y * y));
    6266      }
    6367      data.Add(results);

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionSeven.cs

    r8825 r9078  
    3636          + "range(test): x = [1:0.1:100]" + Environment.NewLine
    3737          + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + Environment.NewLine
    38           + "Note: The problem starts with 1 to avoid log(0), which is minus infinity!";
     38          + "Note: The problem starts with 1 to avoid log(0)!";
    3939      }
    4040    }

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTen.cs

    r8900 r9078  
    4444    protected override int TrainingPartitionEnd { get { return 100; } }
    4545    protected override int TestPartitionStart { get { return 100; } }
    46     protected override int TestPartitionEnd { get { return 10301; } }
     46    protected override int TestPartitionEnd { get { return 100 + (101 * 101); } }
    4747
    4848    protected override List<List<double>> GenerateValues() {

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionThirteen.cs

    r8825 r9078  
    3535        + "range(train): 20 Train cases x,y = rnd(-3, 3)" + Environment.NewLine
    3636        + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
    37         + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + Environment.NewLine
    38         + "Note: Test partition has been adjusted to only 100 random uniformly distributed test cases in the interval [-3, 3] (not ca. 360000 as described) "
    39         + ", but 5000 cases are created";
     37        + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
    4038      }
    4139    }
    … …  
    4543    protected override int TrainingPartitionStart { get { return 0; } }
    4644    protected override int TrainingPartitionEnd { get { return 20; } }
    47     protected override int TestPartitionStart { get { return 2500; } }
    48     protected override int TestPartitionEnd { get { return 2600; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 20 + (601 * 601); } }
    4947
    5048    protected override List<List<double>> GenerateValues() {
    5149      List<List<double>> data = new List<List<double>>();
     50      List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-3, 3, 0.01).ToList();
     51      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
     52
     53      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList();
     54
    5255      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
    53         data.Add(ValueGenerator.GenerateUniformDistributedValues(5000, -3, 3).ToList());
     56        data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList());
     57        data[i].AddRange(combinations[i]);
    5458      }
    5559

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTwelve.cs

    r8825 r9078  
    3131      get {
    3232        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
    33         + "Authors: Maarten Keijzer" + Environment.NewLine
    34         + "Function: f(x, y) = x^4 - x³ + y² / 2 - y" + Environment.NewLine
    35         + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine
    36         + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
    37         + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)" + Environment.NewLine + Environment.NewLine
    38         + "Note: Test partition has been adjusted to only 100 random uniformly distributed test cases in the interval [-3, 3] (not ca. 360000 as described) "
    39         + ", but 5000 cases are created";
     33               + "Authors: Maarten Keijzer" + Environment.NewLine
     34               + "Function: f(x, y) = x^4 - x³ + y² / 2 - y" + Environment.NewLine
     35               + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine
     36               + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
     37               + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
    4038      }
    4139    }
    … …  
    4543    protected override int TrainingPartitionStart { get { return 0; } }
    4644    protected override int TrainingPartitionEnd { get { return 20; } }
    47     protected override int TestPartitionStart { get { return 2500; } }
    48     protected override int TestPartitionEnd { get { return 2600; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 20 + (601 * 601); } }
    4947
    5048    protected override List<List<double>> GenerateValues() {
    5149      List<List<double>> data = new List<List<double>>();
     50      List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-3, 3, 0.01).ToList();
     51      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
     52
     53      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList();
     54
    5255      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
    53         data.Add(ValueGenerator.GenerateUniformDistributedValues(5000, -3, 3).ToList());
     56        data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList());
     57        data[i].AddRange(combinations[i]);
    5458      }
    5559

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionEleven.cs

    r8825 r9078  
    2727  public class KornsFunctionEleven : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Korns 11 y = 6.87 + (11 * cos(7.23 * X0 * X0 * X0))"; } }
     29    public override string Name { get { return "Korns 11 y = 6.87 + (11 * cos(7.23 * X0³))"; } }
    3030    public override string Description {
    3131      get {
    … …  
    4646    protected override string[] AllowedInputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4" }; } }
    4747    protected override int TrainingPartitionStart { get { return 0; } }
    48     protected override int TrainingPartitionEnd { get { return 5000; } }
    49     protected override int TestPartitionStart { get { return 5000; } }
    50     protected override int TestPartitionEnd { get { return 10000; } }
     48    protected override int TrainingPartitionEnd { get { return 10000; } }
     49    protected override int TestPartitionStart { get { return 10000; } }
     50    protected override int TestPartitionEnd { get { return 20000; } }
    5151
    5252    protected override List<List<double>> GenerateValues() {

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionFiveteen.cs

    r8900 r9078  
    5454      List<List<double>> data = new List<List<double>>();
    5555      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
    56       data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
     56      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
    5757      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
    5858      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionNine.cs

    r8900 r9078  
    5656      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
    5757      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
    58       data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 0).ToList()); // note: range is only [-50,0] to prevent NaN values (deviates from gp benchmark paper)
     58      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
    5959      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
    6060      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionSeven.cs

    r8900 r9078  
    5353    protected override List<List<double>> GenerateValues() {
    5454      List<List<double>> data = new List<List<double>>();
    55       data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
     55      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
    5656      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
    5757      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionEight.cs

    r8825 r9078  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 20; } }
    45     protected override int TestPartitionStart { get { return 250; } }
    46     protected override int TestPartitionEnd { get { return 350; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 520; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    50       data.Add(ValueGenerator.GenerateUniformDistributedValues(500, 0, 4).ToList());
     50      data.Add(ValueGenerator.GenerateUniformDistributedValues(520, 0, 4).ToList());
    5151
    5252      double x;

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionEleven.cs

    r8825 r9078  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 20; } }
    45     protected override int TestPartitionStart { get { return 500; } }
    46     protected override int TestPartitionEnd { get { return 1000; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 1020; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionFive.cs

    r8825 r9078  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 20; } }
    45     protected override int TestPartitionStart { get { return 250; } }
    46     protected override int TestPartitionEnd { get { return 350; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 520; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    50       data.Add(ValueGenerator.GenerateUniformDistributedValues(500, -1, 1).ToList());
     50      data.Add(ValueGenerator.GenerateUniformDistributedValues(520, -1, 1).ToList());
    5151
    5252      double x;
    … …  
    5454      for (int i = 0; i < data[0].Count; i++) {
    5555        x = data[0][i];
    56         results.Add(Math.Sin(Math.Pow(x, 2)) * Math.Cos(x) - 1);
     56        results.Add(Math.Sin(x * x) * Math.Cos(x) - 1);
    5757      }
    5858      data.Add(results);

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionFour.cs

    r8825 r9078  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 20; } }
    45     protected override int TestPartitionStart { get { return 250; } }
    46     protected override int TestPartitionEnd { get { return 350; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 520; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    50       data.Add(ValueGenerator.GenerateUniformDistributedValues(500, -1, 1).ToList());
     50      data.Add(ValueGenerator.GenerateUniformDistributedValues(520, -1, 1).ToList());
    5151
    5252      double x;
    … …  
    5454      for (int i = 0; i < data[0].Count; i++) {
    5555        x = data[0][i];
    56         results.Add(Math.Pow(x, 6) + Math.Pow(x, 5) + Math.Pow(x, 4) + Math.Pow(x, 3) + Math.Pow(x, 2) + x);
     56        results.Add(Math.Pow(x, 6) + Math.Pow(x, 5) + Math.Pow(x, 4) + Math.Pow(x, 3) + x * x + x);
    5757      }
    5858      data.Add(results);

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionNine.cs

    r8825 r9078  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 20; } }
    45     protected override int TestPartitionStart { get { return 500; } }
    46     protected override int TestPartitionEnd { get { return 1000; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 1020; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    … …  
    5656        x = data[0][i];
    5757        y = data[1][i];
    58         results.Add(Math.Sin(x) + Math.Sin(Math.Pow(y, 2)));
     58        results.Add(Math.Sin(x) + Math.Sin(y * y));
    5959      }
    6060      data.Add(results);

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionOne.cs

    r8825 r9078  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 20; } }
    45     protected override int TestPartitionStart { get { return 250; } }
    46     protected override int TestPartitionEnd { get { return 350; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 520; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    50       data.Add(ValueGenerator.GenerateUniformDistributedValues(500, -1, 1).ToList());
     50      data.Add(ValueGenerator.GenerateUniformDistributedValues(520, -1, 1).ToList());
    5151
    5252      double x;

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionSeven.cs

    r8825 r9078  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 20; } }
    45     protected override int TestPartitionStart { get { return 250; } }
    46     protected override int TestPartitionEnd { get { return 350; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 520; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    50       data.Add(ValueGenerator.GenerateUniformDistributedValues(500, 0, 2).ToList());
     50      data.Add(ValueGenerator.GenerateUniformDistributedValues(520, 0, 2).ToList());
    5151
    5252      double x;
    … …  
    5454      for (int i = 0; i < data[0].Count; i++) {
    5555        x = data[0][i];
    56         results.Add(Math.Log(x + 1) + Math.Log(Math.Pow(x, 2) + 1));
     56        results.Add(Math.Log(x + 1) + Math.Log(x * x + 1));
    5757      }
    5858      data.Add(results);

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionSix.cs

    r8825 r9078  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 20; } }
    45     protected override int TestPartitionStart { get { return 250; } }
    46     protected override int TestPartitionEnd { get { return 350; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 520; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    50       data.Add(ValueGenerator.GenerateUniformDistributedValues(500, -1, 1).ToList());
     50      data.Add(ValueGenerator.GenerateUniformDistributedValues(520, -1, 1).ToList());
    5151
    5252      double x;
    … …  
    5454      for (int i = 0; i < data[0].Count; i++) {
    5555        x = data[0][i];
    56         results.Add(Math.Sin(x) + Math.Sin(x + Math.Pow(x, 2)));
     56        results.Add(Math.Sin(x) + Math.Sin(x + x*x));
    5757      }
    5858      data.Add(results);

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionTen.cs

    r8825 r9078  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 20; } }
    45     protected override int TestPartitionStart { get { return 500; } }
    46     protected override int TestPartitionEnd { get { return 1000; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 1020; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    50       data.Add(ValueGenerator.GenerateUniformDistributedValues(1000, 0, 1).ToList());
    51       data.Add(ValueGenerator.GenerateUniformDistributedValues(1000, 0, 1).ToList());
     50      data.Add(ValueGenerator.GenerateUniformDistributedValues(1020, 0, 1).ToList());
     51      data.Add(ValueGenerator.GenerateUniformDistributedValues(1020, 0, 1).ToList());
    5252
    5353      double x, y;

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionThree.cs

    r8825 r9078  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 20; } }
    45     protected override int TestPartitionStart { get { return 250; } }
    46     protected override int TestPartitionEnd { get { return 350; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 520; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    50       data.Add(ValueGenerator.GenerateUniformDistributedValues(500, -1, 1).ToList());
     50      data.Add(ValueGenerator.GenerateUniformDistributedValues(520, -1, 1).ToList());
    5151
    5252      double x;

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionTwelve.cs

    r8825 r9078  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 20; } }
    45     protected override int TestPartitionStart { get { return 500; } }
    46     protected override int TestPartitionEnd { get { return 1000; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 1020; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    50       data.Add(ValueGenerator.GenerateUniformDistributedValues(1000, 0, 1).ToList());
    51       data.Add(ValueGenerator.GenerateUniformDistributedValues(1000, 0, 1).ToList());
     50      data.Add(ValueGenerator.GenerateUniformDistributedValues(1020, 0, 1).ToList());
     51      data.Add(ValueGenerator.GenerateUniformDistributedValues(1020, 0, 1).ToList());
    5252
    5353      double x, y;
    … …  
    5656        x = data[0][i];
    5757        y = data[1][i];
    58         results.Add(Math.Pow(x, 4) - Math.Pow(x, 3) + Math.Pow(y, 2) / 2 - y);
     58        results.Add(Math.Pow(x, 4) - Math.Pow(x, 3) + y * y / 2 - y);
    5959      }
    6060      data.Add(results);

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionTwo.cs

    r8825 r9078  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 20; } }
    45     protected override int TestPartitionStart { get { return 250; } }
    46     protected override int TestPartitionEnd { get { return 350; } }
     45    protected override int TestPartitionStart { get { return 20; } }
     46    protected override int TestPartitionEnd { get { return 520; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    50       data.Add(ValueGenerator.GenerateUniformDistributedValues(500, -1, 1).ToList());
     50      data.Add(ValueGenerator.GenerateUniformDistributedValues(520, -1, 1).ToList());
    5151
    5252      double x;
    … …  
    5454      for (int i = 0; i < data[0].Count; i++) {
    5555        x = data[0][i];
    56         results.Add(Math.Pow(x, 4) + Math.Pow(x, 3) + Math.Pow(x, 2) + x);
     56        results.Add(Math.Pow(x, 4) + Math.Pow(x, 3) + x*x + x);
    5757      }
    5858      data.Add(results);

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/KotanchekFunction.cs

    r8825 r9078  
    3434        + "Function: F1(X1, X2) = exp(-(X1 - 1))² / (1.2 + (X2 -2.5)²" + Environment.NewLine
    3535        + "Training Data: 100 points X1, X2 = Rand(0.3, 4)" + Environment.NewLine
    36         + "Test Data: 2026 points (X1, X2) = (-0.2:0.1:4.2)" + Environment.NewLine
     36        + "Test Data: 45*45 points (X1, X2) = (-0.2:0.1:4.2)" + Environment.NewLine
    3737        + "Function Set: +, -, *, /, square, e^x, e^-x, x^eps, x + eps, x * eps";
    3838      }
    … …  
    4444    protected override int TrainingPartitionEnd { get { return 100; } }
    4545    protected override int TestPartitionStart { get { return 100; } }
    46     protected override int TestPartitionEnd { get { return 2126; } }
     46    protected override int TestPartitionEnd { get { return 100 + (45 * 45); } }
    4747
    4848    protected override List<List<double>> GenerateValues() {

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/RationalPolynomialThreeDimensional.cs

    r8825 r9078  
    3434        + "Function: F5(X1, X2, X3) = 30 * ((X1 - 1) * (X3 -1)) / (X2² * (X1 - 10))" + Environment.NewLine
    3535        + "Training Data: 300 points X1, X3 = Rand(0.05, 2), X2 = Rand(1, 2)" + Environment.NewLine
    36         + "Test Data: 2701 points X1, X3 = (-0.05:0.15:2.1), X2 = (0.95:0.1:2.05)" + Environment.NewLine
     36        + "Test Data: (14*12*14) points X1, X3 = (-0.05:0.15:2.05), X2 = (0.95:0.1:2.05)" + Environment.NewLine
    3737        + "Function Set: +, -, *, /, square, x^eps, x + eps, x * eps";
    3838      }
    … …  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 300; } }
    45     protected override int TestPartitionStart { get { return 1000; } }
    46     protected override int TestPartitionEnd { get { return 3700; } }
     45    protected override int TestPartitionStart { get { return 300; } }
     46    protected override int TestPartitionEnd { get { return 300 + (15*12*15); } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    5050
    51       int n = 1000;
     51      int n = 300;
    5252      data.Add(ValueGenerator.GenerateUniformDistributedValues(n, 0.05, 2).ToList());
    5353      data.Add(ValueGenerator.GenerateUniformDistributedValues(n, 1, 2).ToList());
    … …  
    5555
    5656      List<List<double>> testData = new List<List<double>>() {
    57         ValueGenerator.GenerateSteps(-0.05, 2.1, 0.15).ToList(),
     57        ValueGenerator.GenerateSteps(-0.05, 2.05, 0.15).ToList(),
    5858        ValueGenerator.GenerateSteps( 0.95, 2.05, 0.1).ToList(),
    59         ValueGenerator.GenerateSteps(-0.05, 2.1, 0.15).ToList()
     59        ValueGenerator.GenerateSteps(-0.05, 2.05, 0.15).ToList()
    6060      };
    6161

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/RationalPolynomialTwoDimensional.cs

    r8825 r9078  
    3434        + "Function: F8(X1, X2) = ((X1 - 3)^4 + (X2 - 3)³ - (X2 -3)) / ((X2 - 2)^4 + 10)" + Environment.NewLine
    3535        + "Training Data: 50 points X1, X2 = Rand(0.05, 6.05)" + Environment.NewLine
    36         + "Test Data: 1157 points X1, X2 = (-0.25:0.2:6.35)" + Environment.NewLine
     36        + "Test Data: 34*34 points X1, X2 = (-0.25:0.2:6.35)" + Environment.NewLine
    3737        + "Function Set: +, -, *, /, square, x^eps, x + eps, x * eps";
    3838      }
    … …  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 50; } }
    45     protected override int TestPartitionStart { get { return 1000; } }
    46     protected override int TestPartitionEnd { get { return 2157; } }
     45    protected override int TestPartitionStart { get { return 50; } }
     46    protected override int TestPartitionEnd { get { return 50 + (34 * 34); } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    … …  
    5050
    5151      List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-0.25, 6.35, 0.2).ToList();
     52
    5253      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
     54      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
    5355
    54       var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
    5556      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
    56         data.Add(ValueGenerator.GenerateUniformDistributedValues(1000, 0.05, 6.05).ToList());
     57        data.Add(ValueGenerator.GenerateUniformDistributedValues(50, 0.05, 6.05).ToList());
    5758        data[i].AddRange(combinations[i]);
    5859      }

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/RippleFunction.cs

    r8825 r9078  
    2727  public class RippleFunction : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Vladislavleva-7  F7(X1, X2) = (X1 - 3)(X2 - 3) + 2 * sin((X1 - 4)(X2 - 4))"; } }
     29    public override string Name { get { return "Vladislavleva-7 F7(X1, X2) = (X1 - 3)(X2 - 3) + 2 * sin((X1 - 4)(X2 - 4))"; } }
    3030    public override string Description {
    3131      get {
    … …  
    4444    protected override int TrainingPartitionEnd { get { return 300; } }
    4545    protected override int TestPartitionStart { get { return 300; } }
    46     protected override int TestPartitionEnd { get { return 1300; } }
     46    protected override int TestPartitionEnd { get { return 300 + 1000; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    5050      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
    51         data.Add(ValueGenerator.GenerateUniformDistributedValues(TrainingPartitionEnd, 0.05, 6.05).ToList());
     51        data.Add(ValueGenerator.GenerateUniformDistributedValues(300, 0.05, 6.05).ToList());
    5252      }
    5353
    5454      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
    55         data[i].AddRange(ValueGenerator.GenerateUniformDistributedValues(TrainingPartitionEnd, -0.25, 6.35));
     55        data[i].AddRange(ValueGenerator.GenerateUniformDistributedValues(1000, -0.25, 6.35));
    5656      }
    5757

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/SalutowiczFunctionTwoDimensional.cs

    r8825 r9078  
    3333        + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
    3434        + "Function: F3(X1, X2) = exp(-X1) * X1³ * cos(X1) * sin(X1) * (cos(X1)sin(X1)² - 1)(X2 - 5)" + Environment.NewLine
    35         + "Training Data: 601 points X1 = (0.05:0.1:10), X2 = (0.05:2:10.05)" + Environment.NewLine
    36         + "Test Data: 4840 points X1 = (-0.5:0.05:10.5), X2 = (-0.5:0.5:10.5)" + Environment.NewLine
     35        + "Training Data: 600 points X1 = (0.05:0.1:10), X2 = (0.05:2:10.05)" + Environment.NewLine
     36        + "Test Data: 221 * 23 points X1 = (-0.5:0.05:10.5), X2 = (-0.5:0.5:10.5)" + Environment.NewLine
    3737        + "Function Set: +, -, *, /, square, e^x, e^-x, sin(x), cos(x), x^eps, x + eps, x + eps";
    3838      }
    … …  
    4242    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } }
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    44     protected override int TrainingPartitionEnd { get { return 601; } }
    45     protected override int TestPartitionStart { get { return 601; } }
    46     protected override int TestPartitionEnd { get { return 5441; } }
     44    protected override int TrainingPartitionEnd { get { return 600; } }
     45    protected override int TestPartitionStart { get { return 600; } }
     46    protected override int TestPartitionEnd { get { return 600 + (221 * 23); } }
    4747
    4848    protected override List<List<double>> GenerateValues() {

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Vladislavleva/SineCosineFunction.cs

    r8825 r9078  
    3434        + "Function: F6(X1, X2) = 6 * sin(X1) * cos(X2)" + Environment.NewLine
    3535        + "Training Data: 30 points X1, X2 = Rand(0.1, 5.9)" + Environment.NewLine
    36         + "Test Data: 961 points X1, X2 = (-0.05:0.02:6.05)" + Environment.NewLine
     36        + "Test Data: 306*306 points X1, X2 = (-0.05:0.02:6.05)" + Environment.NewLine
    3737        + "Function Set: +, -, *, /, square, e^x, e^-x, x^eps, x + eps, x * eps";
    3838      }
    … …  
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    4444    protected override int TrainingPartitionEnd { get { return 30; } }
    45     protected override int TestPartitionStart { get { return 500; } }
    46     protected override int TestPartitionEnd { get { return 1461; } }
     45    protected override int TestPartitionStart { get { return 30; } }
     46    protected override int TestPartitionEnd { get { return 30 + (306 * 306); } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    … …  
    5353
    5454      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
    55         data.Add(ValueGenerator.GenerateUniformDistributedValues(500, 0.1, 5.9).ToList());
     55        data.Add(ValueGenerator.GenerateUniformDistributedValues(30, 0.1, 5.9).ToList());
    5656        data[i].AddRange(combinations[i]);
    5757      }

  • branches/RuntimeOptimizer/HeuristicLab.Problems.Instances.DataAnalysis/3.3/TimeSeries/CSV/TimeSeriesPrognosisCSVInstanceProvider.cs

    r8885 r9078  
    8787      // turn of input variables that are constant in the training partition
    8888      var allowedInputVars = new List<string>();
    89       int trainingPartEnd = (csvFileParser.Rows * type.Training) / 100;
     89      int trainingPartEnd = (csvFileParser.Rows * type.TrainingPercentage) / 100;
    9090      trainingPartEnd = trainingPartEnd > 0 ? trainingPartEnd : 1;
    9191      var trainingIndizes = Enumerable.Range(0, trainingPartEnd);
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