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Changeset 8238


Ignore:
Timestamp:
07/05/12 16:55:26 (12 years ago)
Author:
gkronber
Message:

#1784 adapted Keijzer instances

Location:
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3
Files:
14 edited
3 copied

Legend:

Unmodified
Added
Removed
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/HeuristicLab.Problems.Instances.DataAnalysis-3.3.csproj

    r8226 r8238  
    134134    <Compile Include="Regression\Keijzer\KeijzerFunctionFifteen.cs" />
    135135    <Compile Include="Regression\Keijzer\KeijzerFunctionFour.cs" />
     136    <Compile Include="Regression\Keijzer\KeijzerFunctionNine.cs" />
    136137    <Compile Include="Regression\Keijzer\KeijzerFunctionOne.cs" />
    137138    <Compile Include="Regression\Keijzer\KeijzerFunctionSeven.cs" />
     
    139140    <Compile Include="Regression\Keijzer\KeijzerFunctionTen.cs" />
    140141    <Compile Include="Regression\Keijzer\KeijzerFunctionThirteen.cs" />
     142    <Compile Include="Regression\Keijzer\KeijzerFunctionThree.cs" />
    141143    <Compile Include="Regression\Keijzer\KeijzerFunctionTwelve.cs" />
     144    <Compile Include="Regression\Keijzer\KeijzerFunctionTwo.cs" />
    142145    <Compile Include="Regression\Keijzer\KeijzerInstanceProvider.cs" />
    143146    <Compile Include="Regression\Korns\KornsFunctionEight.cs" />
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionEight.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionNine : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionEight : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 9 f(x) = sqrt(x)"; } }
     29    public override string Name { get { return "Keijzer 8 f(x) = sqrt(x)"; } }
    3030    public override string Description {
    3131      get {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionEleven.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionTwelve : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionEleven : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 12 f(x, y) = xy + sin((x - 1)(y - 1))"; } }
     29    public override string Name { get { return "Keijzer 11 f(x, y) = xy + sin((x - 1)(y - 1))"; } }
    3030    public override string Description {
    3131      get {
    32         return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
    33         + "Authors: Maarten Keijzer" + Environment.NewLine
    34         + "Function: f(x, y) = xy + sin((x - 1)(y - 1))" + 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 intercal [-3, 3] (not ca. 360000 as described) "
    39         + ", but 5000 cases are created";
     32        return
     33          "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
     34          + "Authors: Maarten Keijzer" + Environment.NewLine
     35          + "Function: f(x, y) = xy + sin((x - 1)(y - 1))" + Environment.NewLine
     36          + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine
     37          + "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";
    4041      }
    4142    }
     
    4647    protected override int TrainingPartitionEnd { get { return 20; } }
    4748    protected override int TestPartitionStart { get { return 2500; } }
    48     protected override int TestPartitionEnd { get { return 5000; } }
     49    protected override int TestPartitionEnd { get { return 2600; } }
    4950
    5051    protected override List<List<double>> GenerateValues() {
    5152      List<List<double>> data = new List<List<double>>();
    5253      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
    53         data.Add(ValueGenerator.GenerateUniformDistributedValues(5000, -3, 3).ToList());
     54        data.Add(ValueGenerator.GenerateUniformDistributedValues(5020, -3, 3).ToList());
    5455      }
    5556
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionFifteen.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionSixteen : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionFifteen : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 16 f(x, y) = x^3 / 5 + y^3 / 2 - y - x"; } }
     29    public override string Name { get { return "Keijzer 15 f(x, y) = x³ / 5 + y³ / 2 - y - x"; } }
    3030    public override string Description {
    3131      get {
    3232        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
    3333        + "Authors: Maarten Keijzer" + Environment.NewLine
    34         + "Function: f(x, y) = x^3 / 5 + y^3 / 2 - y - x" + Environment.NewLine
     34        + "Function: f(x, y) = x³ / 5 + y³ / 2 - y - x" + Environment.NewLine
    3535        + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine
    3636        + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
    3737        + "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 intercal [-3, 3] (not ca. 360000 as described) "
     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) "
    3939        + ", but 5000 cases are created";
    4040      }
     
    4646    protected override int TrainingPartitionEnd { get { return 20; } }
    4747    protected override int TestPartitionStart { get { return 2500; } }
    48     protected override int TestPartitionEnd { get { return 5000; } }
     48    protected override int TestPartitionEnd { get { return 2600; } }
    4949
    5050    protected override List<List<double>> GenerateValues() {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionFive.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionSix : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionFive : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 6 f(x) = (30 * x * z) / ((x - 10)  * y^2)"; } }
     29    public override string Name { get { return "Keijzer 5 f(x) = (30 * x * z) / ((x - 10)  * y²)"; } }
    3030    public override string Description {
    3131      get {
    3232        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
    3333        + "Authors: Maarten Keijzer" + Environment.NewLine
    34         + "Function: f(x) = (30 * x * z) / ((x - 10)  * y^2)" + Environment.NewLine
     34        + "Function: f(x) = (30 * x * z) / ((x - 10)  * y²)" + Environment.NewLine
    3535        + "range(train): 1000 points x,z = rnd(-1, 1), y = rnd(1, 2)" + Environment.NewLine
    3636        + "range(test): 10000 points x,z = rnd(-1, 1), y = rnd(1, 2)" + Environment.NewLine
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionFour.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionFive : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionFour : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 5 f(x) = x ^ 3  * exp(-x) * cos(x) * sin(x) * (sin(x) ^ 2 * cos(x) - 1)"; } }
     29    public override string Name { get { return "Keijzer 4 f(x) = x³  * exp(-x) * cos(x) * sin(x) * (sin(x)² * cos(x) - 1)"; } }
    3030    public override string Description {
    3131      get {
    3232        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
    3333        + "Authors: Maarten Keijzer" + Environment.NewLine
    34         + "Function: f(x) = x ^ 3  * exp(-x) * cos(x) * sin(x) * (sin(x) ^ 2 * cos(x) - 1)" + Environment.NewLine
     34        + "Function: f(x) = x³  * exp(-x) * cos(x) * sin(x) * (sin(x)² * cos(x) - 1)" + Environment.NewLine
    3535        + "range(train): x = [0:0.05:10]" + Environment.NewLine
    3636        + "range(test): x = [0.05:0.05:10.05]" + Environment.NewLine
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionFourteen.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionFifteen : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionFourteen : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 15 f(x, y) = 8 / (2 + x^2 + y^2)"; } }
     29    public override string Name { get { return "Keijzer 14 f(x, y) = 8 / (2 + x² + y²)"; } }
    3030    public override string Description {
    3131      get {
    3232        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
    3333        + "Authors: Maarten Keijzer" + Environment.NewLine
    34         + "Function: f(x, y) = 8 / (2 + x^2 + y^2)" + Environment.NewLine
     34        + "Function: f(x, y) = 8 / (2 + x² + y²)" + Environment.NewLine
    3535        + "range(train): 20 Train cases x,y = rnd(-3, 3)" + Environment.NewLine
    3636        + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
    3737        + "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 intercal [-3, 3] (not ca. 360000 as described) "
     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) "
    3939        + ", but 5000 cases are created";
    4040      }
     
    4646    protected override int TrainingPartitionEnd { get { return 20; } }
    4747    protected override int TestPartitionStart { get { return 2500; } }
    48     protected override int TestPartitionEnd { get { return 5000; } }
     48    protected override int TestPartitionEnd { get { return 2600; } }
    4949
    5050    protected override List<List<double>> GenerateValues() {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionNine.cs

    r8226 r8238  
    2727  public class KeijzerFunctionNine : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 9 f(x) = sqrt(x)"; } }
     29    public override string Name { get { return "Keijzer 9 f(x) = arcsinh(x)  i.e. ln(x + sqrt(x² + 1))"; } }
    3030    public override string Description {
    3131      get {
    3232        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
    3333        + "Authors: Maarten Keijzer" + Environment.NewLine
    34         + "Function: f(x) = sqrt(x)" + Environment.NewLine
     34        + "Function: f(x) = arcsinh(x)  i.e. ln(x + sqrt(x² + 1))" + Environment.NewLine
    3535        + "range(train): x = [0:1:100]" + Environment.NewLine
    3636        + "range(test): x = [0:0.1:100]" + Environment.NewLine
     
    4242    protected override string[] AllowedInputVariables { get { return new string[] { "X" }; } }
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    44     protected override int TrainingPartitionEnd { get { return 101; } }
    45     protected override int TestPartitionStart { get { return 101; } }
    46     protected override int TestPartitionEnd { get { return 1102; } }
     44    protected override int TrainingPartitionEnd { get { return 100; } }
     45    protected override int TestPartitionStart { get { return 100; } }
     46    protected override int TestPartitionEnd { get { return 1100; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
     
    5555      for (int i = 0; i < data[0].Count; i++) {
    5656        x = data[0][i];
    57         results.Add(Math.Sqrt(x));
     57        results.Add(Math.Log(x + Math.Sqrt(x*x + 1)));
    5858      }
    5959      data.Add(results);
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionOne.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionFour : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionOne : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 4 f(x) = 0.3 * x *sin(2 * PI * x)"; } }
     29    public override string Name { get { return "Keijzer 1 f(x) = 0.3 * x *sin(2 * PI * x)"; } }
    3030    public override string Description {
    3131      get {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionSeven.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionEight : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionSeven : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 8 f(x) = log(x)"; } }
     29    public override string Name { get { return "Keijzer 7 f(x) = ln(x)"; } }
    3030    public override string Description {
    3131      get {
    3232        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
    3333          + "Authors: Maarten Keijzer" + Environment.NewLine
    34           + "Function: f(x) = log(x)" + Environment.NewLine
    35           + "range(train): x = [0:1:100]" + Environment.NewLine
    36           + "range(test): x = [0:0.1:100]" + Environment.NewLine
     34          + "Function: f(x) = ln(x)" + Environment.NewLine
     35          + "range(train): x = [1:1:100]" + Environment.NewLine
     36          + "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
    3838          + "Note: The problem starts with 1 to avoid log(0), which is minus infinity!";
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionSix.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionSeven : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionSix : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 7 f(x) = Sum(1 / i) From 1 to X"; } }
     29    public override string Name { get { return "Keijzer 6 f(x) = Sum(1 / i) From 1 to X"; } }
    3030    public override string Description {
    3131      get {
    3232        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
    3333        + "Authors: Maarten Keijzer" + Environment.NewLine
    34         + "Function: f(x) = (30 * x * y) / ((x - 10)  * y^2)" + Environment.NewLine
     34        + "Function: f(x) = Sum(1 / i) From 1 to X" + Environment.NewLine
    3535        + "range(train): x = [1:1:50]" + Environment.NewLine
    3636        + "range(test): x = [1:1:120]" + Environment.NewLine
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTen.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionEleven : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionTen : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 11 f(x, y) = x ^ y"; } }
     29    public override string Name { get { return "Keijzer 10 f(x, y) = x ^ y"; } }
    3030    public override string Description {
    3131      get {
     
    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 10100; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionThirteen.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionFourteen : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionThirteen : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 14 f(x, y) = 6 * sin(x) * cos(y)"; } }
     29    public override string Name { get { return "Keijzer 13 f(x, y) = 6 * sin(x) * cos(y)"; } }
    3030    public override string Description {
    3131      get {
     
    3636        + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
    3737        + "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 intercal [-3, 3] (not ca. 360000 as described) "
     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) "
    3939        + ", but 5000 cases are created";
    4040      }
     
    4646    protected override int TrainingPartitionEnd { get { return 20; } }
    4747    protected override int TestPartitionStart { get { return 2500; } }
    48     protected override int TestPartitionEnd { get { return 5000; } }
     48    protected override int TestPartitionEnd { get { return 2600; } }
    4949
    5050    protected override List<List<double>> GenerateValues() {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionThree.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionFour : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionThree : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 4 f(x) = 0.3 * x *sin(2 * PI * x)"; } }
     29    public override string Name { get { return "Keijzer 3 f(x) = 0.3 * x *sin(2 * PI * x)"; } }
    3030    public override string Description {
    3131      get {
     
    3333        + "Authors: Maarten Keijzer" + Environment.NewLine
    3434        + "Function: f(x) = 0.3 * x *sin(2 * PI * x)" + Environment.NewLine
    35         + "range(train): x = [-1:0.1:1]" + Environment.NewLine
    36         + "range(test): x = [-1:0.001:1]" + Environment.NewLine
     35        + "range(train): x = [-3:0.1:3]" + Environment.NewLine
     36        + "range(test): x = [-3:0.001:3]" + Environment.NewLine
    3737        + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
    3838      }
     
    4242    protected override string[] AllowedInputVariables { get { return new string[] { "X" }; } }
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    44     protected override int TrainingPartitionEnd { get { return 21; } }
    45     protected override int TestPartitionStart { get { return 21; } }
    46     protected override int TestPartitionEnd { get { return 2022; } }
     44    protected override int TrainingPartitionEnd { get { return 61; } }
     45    protected override int TestPartitionStart { get { return 61; } }
     46    protected override int TestPartitionEnd { get { return 6062; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    50       data.Add(ValueGenerator.GenerateSteps(-1, 1, 0.1).ToList());
    51       data[0].AddRange(ValueGenerator.GenerateSteps(-1, 1, 0.001));
     50      data.Add(ValueGenerator.GenerateSteps(-3, 3, 0.1).ToList());
     51      data[0].AddRange(ValueGenerator.GenerateSteps(-3, 3, 0.001));
    5252
    5353      double x;
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTwelve.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionThirteen : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionTwelve : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 13 f(x, y) = x^4 - x^3 + y^2 / 2 - y"; } }
     29    public override string Name { get { return "Keijzer 12 f(x, y) = x^4 - x³ + y² / 2 - y"; } }
    3030    public override string Description {
    3131      get {
    3232        return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
    3333        + "Authors: Maarten Keijzer" + Environment.NewLine
    34         + "Function: f(x, y) = x^4 - x^3 + y^2 / 2 - y" + Environment.NewLine
     34        + "Function: f(x, y) = x^4 - x³ + y² / 2 - y" + Environment.NewLine
    3535        + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine
    3636        + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
    3737        + "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 intercal [-3, 3] (not ca. 360000 as described) "
     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) "
    3939        + ", but 5000 cases are created";
    4040      }
     
    4646    protected override int TrainingPartitionEnd { get { return 20; } }
    4747    protected override int TestPartitionStart { get { return 2500; } }
    48     protected override int TestPartitionEnd { get { return 5000; } }
     48    protected override int TestPartitionEnd { get { return 2600; } }
    4949
    5050    protected override List<List<double>> GenerateValues() {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTwo.cs

    r8226 r8238  
    2525
    2626namespace HeuristicLab.Problems.Instances.DataAnalysis {
    27   public class KeijzerFunctionFour : ArtificialRegressionDataDescriptor {
     27  public class KeijzerFunctionTwo : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 4 f(x) = 0.3 * x *sin(2 * PI * x)"; } }
     29    public override string Name { get { return "Keijzer 2 f(x) = 0.3 * x *sin(2 * PI * x)"; } }
    3030    public override string Description {
    3131      get {
     
    3333        + "Authors: Maarten Keijzer" + Environment.NewLine
    3434        + "Function: f(x) = 0.3 * x *sin(2 * PI * x)" + Environment.NewLine
    35         + "range(train): x = [-1:0.1:1]" + Environment.NewLine
    36         + "range(test): x = [-1:0.001:1]" + Environment.NewLine
     35        + "range(train): x = [-2:0.1:2]" + Environment.NewLine
     36        + "range(test): x = [-2:0.001:2]" + Environment.NewLine
    3737        + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
    3838      }
     
    4242    protected override string[] AllowedInputVariables { get { return new string[] { "X" }; } }
    4343    protected override int TrainingPartitionStart { get { return 0; } }
    44     protected override int TrainingPartitionEnd { get { return 21; } }
    45     protected override int TestPartitionStart { get { return 21; } }
    46     protected override int TestPartitionEnd { get { return 2022; } }
     44    protected override int TrainingPartitionEnd { get { return 41; } }
     45    protected override int TestPartitionStart { get { return 41; } }
     46    protected override int TestPartitionEnd { get { return 4042; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
    4949      List<List<double>> data = new List<List<double>>();
    50       data.Add(ValueGenerator.GenerateSteps(-1, 1, 0.1).ToList());
    51       data[0].AddRange(ValueGenerator.GenerateSteps(-1, 1, 0.001));
     50      data.Add(ValueGenerator.GenerateSteps(-2, 2, 0.1).ToList());
     51      data[0].AddRange(ValueGenerator.GenerateSteps(-2, 2, 0.001));
    5252
    5353      double x;
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerInstanceProvider.cs

    r8224 r8238  
    4040    public override IEnumerable<IDataDescriptor> GetDataDescriptors() {
    4141      List<IDataDescriptor> descriptorList = new List<IDataDescriptor>();
     42      descriptorList.Add(new KeijzerFunctionOne());
     43      descriptorList.Add(new KeijzerFunctionTwo());
     44      descriptorList.Add(new KeijzerFunctionThree());
    4245      descriptorList.Add(new KeijzerFunctionFour());
    4346      descriptorList.Add(new KeijzerFunctionFive());
     
    4649      descriptorList.Add(new KeijzerFunctionEight());
    4750      descriptorList.Add(new KeijzerFunctionNine());
     51      descriptorList.Add(new KeijzerFunctionTen());
    4852      descriptorList.Add(new KeijzerFunctionEleven());
    4953      descriptorList.Add(new KeijzerFunctionTwelve());
     
    5155      descriptorList.Add(new KeijzerFunctionFourteen());
    5256      descriptorList.Add(new KeijzerFunctionFifteen());
    53       descriptorList.Add(new KeijzerFunctionSixteen());
    5457      return descriptorList;
    5558    }
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