Free cookie consent management tool by TermsFeed Policy Generator

Changeset 9007


Ignore:
Timestamp:
12/06/12 14:13:39 (12 years ago)
Author:
gkronber
Message:

#1979: cross-checked all regression problem instances with the GP benchmarks paper and adapted where I thought necessary.

Location:
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression
Files:
23 edited

Legend:

Unmodified
Added
Removed
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionEleven.cs

    r8825 r9007  
    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
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionFifteen.cs

    r8825 r9007  
    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);
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionFourteen.cs

    r8825 r9007  
    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);
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionSeven.cs

    r8825 r9007  
    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    }
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTen.cs

    r8900 r9007  
    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() {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionThirteen.cs

    r8825 r9007  
    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
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTwelve.cs

    r8825 r9007  
    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
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionEleven.cs

    r8825 r9007  
    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() {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionFiveteen.cs

    r8900 r9007  
    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());
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionNine.cs

    r8900 r9007  
    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());
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionSeven.cs

    r8900 r9007  
    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());
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionEight.cs

    r8825 r9007  
    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;
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionEleven.cs

    r8825 r9007  
    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() {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionFive.cs

    r8825 r9007  
    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);
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionFour.cs

    r8825 r9007  
    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);
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionNine.cs

    r8825 r9007  
    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);
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionOne.cs

    r8825 r9007  
    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;
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionSeven.cs

    r8825 r9007  
    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);
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionSix.cs

    r8825 r9007  
    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);
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionTen.cs

    r8825 r9007  
    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;
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionThree.cs

    r8825 r9007  
    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;
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionTwelve.cs

    r8825 r9007  
    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);
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Nguyen/NguyenFunctionTwo.cs

    r8825 r9007  
    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);
Note: See TracChangeset for help on using the changeset viewer.