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Changeset 8900


Ignore:
Timestamp:
11/12/12 13:51:49 (12 years ago)
Author:
sforsten
Message:

#1979:

  • renamed Keijzer 1-3
  • corrected training and test partition of Keijzer 9 and 10
  • changed interval for some variables in Korn 7, 9, 15 to avoid infinity and NaN values
Location:
trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression
Files:
8 edited

Legend:

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

    r8825 r8900  
    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 100; } }
    45     protected override int TestPartitionStart { get { return 100; } }
    46     protected override int TestPartitionEnd { get { return 1100; } }
     44    protected override int TrainingPartitionEnd { get { return 101; } }
     45    protected override int TestPartitionStart { get { return 101; } }
     46    protected override int TestPartitionEnd { get { return 1102; } }
    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.Log(x + Math.Sqrt(x*x + 1)));
     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

    r8825 r8900  
    2727  public class KeijzerFunctionOne : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 1 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); Interval [-1, 1]"; } }
    3030    public override string Description {
    3131      get {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTen.cs

    r8825 r8900  
    4444    protected override int TrainingPartitionEnd { get { return 100; } }
    4545    protected override int TestPartitionStart { get { return 100; } }
    46     protected override int TestPartitionEnd { get { return 10100; } }
     46    protected override int TestPartitionEnd { get { return 10301; } }
    4747
    4848    protected override List<List<double>> GenerateValues() {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionThree.cs

    r8825 r8900  
    2727  public class KeijzerFunctionThree : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 3 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); Interval [-3, 3]"; } }
    3030    public override string Description {
    3131      get {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionTwo.cs

    r8825 r8900  
    2727  public class KeijzerFunctionTwo : ArtificialRegressionDataDescriptor {
    2828
    29     public override string Name { get { return "Keijzer 2 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); Interval [-2, 2]"; } }
    3030    public override string Description {
    3131      get {
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionFiveteen.cs

    r8825 r8900  
    4040        + "For each test a training matrix is filled with random numbers between -50 and +50. The test case "
    4141        + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"" + Environment.NewLine + Environment.NewLine
    42         + "Note: Because of the logarithm only non-negatic values are created for the input variables!";
     42        + "Note: Because of the logarithm and the exponential function as denominator only non-negatic values are created for their input variables!";
    4343      }
    4444    }
     
    5454      List<List<double>> data = new List<List<double>>();
    5555      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
    56       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)
    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

    r8825 r8900  
    4040        + "For each test a training matrix is filled with random numbers between -50 and +50. The test case "
    4141        + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"" + Environment.NewLine + Environment.NewLine
    42         + "Note: Because of the square root and the logarithm only non-negatic values are created for the input variables!";
     42        + "Note: Because of the square root and the logarithm only non-negatic values are created for their input variables!" + Environment.NewLine
     43        + "Because of the exponential function only only non-positive values are created for its input variable!";
    4344      }
    4445    }
     
    5556      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
    5657      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
    57       data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
    58       data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); 
    59       data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); 
     58      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 0).ToList()); // note: range is only [-50,0] to prevent NaN values (deviates from gp benchmark paper)
     59      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
     60      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
    6061
    6162      double x0, x1, x2, x3;
  • trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Korns/KornsFunctionSeven.cs

    r8825 r8900  
    3939        + "We test each of the test cases on matrices of 10000 rows by 1 to 5 columns with no noise. "
    4040        + "For each test a training matrix is filled with random numbers between -50 and +50. The test case "
    41         + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"";
     41        + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"" + Environment.NewLine
     42        + "Note: Because of the exponential function only non-negatic values are created for its input variable!";
    4243      }
    4344    }
     
    5253    protected override List<List<double>> GenerateValues() {
    5354      List<List<double>> data = new List<List<double>>();
    54       for (int i = 0; i < AllowedInputVariables.Count(); i++) {
    55         data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
    56       }
     55      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());
     57      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
     58      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
     59      data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
    5760
    5861      double x0;
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