Changeset 8331 for branches/ScatterSearch (trunk integration)/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionNine.cs
- Timestamp:
- 07/26/12 09:51:13 (12 years ago)
- Location:
- branches/ScatterSearch (trunk integration)
- Files:
-
- 2 edited
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branches/ScatterSearch (trunk integration)
- Property svn:ignore
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old new 21 21 protoc.exe 22 22 _ReSharper.HeuristicLab 3.3 Tests 23 Google.ProtocolBuffers-2.4.1.473.dll
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- Property svn:mergeinfo changed
- Property svn:ignore
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branches/ScatterSearch (trunk integration)/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Keijzer/KeijzerFunctionNine.cs
r8086 r8331 27 27 public class KeijzerFunctionNine : ArtificialRegressionDataDescriptor { 28 28 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))"; } } 30 30 public override string Description { 31 31 get { 32 32 return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine 33 33 + "Authors: Maarten Keijzer" + Environment.NewLine 34 + "Function: f(x) = sqrt(x)" + Environment.NewLine34 + "Function: f(x) = arcsinh(x) i.e. ln(x + sqrt(x² + 1))" + Environment.NewLine 35 35 + "range(train): x = [0:1:100]" + Environment.NewLine 36 36 + "range(test): x = [0:0.1:100]" + Environment.NewLine … … 42 42 protected override string[] AllowedInputVariables { get { return new string[] { "X" }; } } 43 43 protected override int TrainingPartitionStart { get { return 0; } } 44 protected override int TrainingPartitionEnd { get { return 10 1; } }45 protected override int TestPartitionStart { get { return 10 1; } }46 protected override int TestPartitionEnd { get { return 110 2; } }44 protected override int TrainingPartitionEnd { get { return 100; } } 45 protected override int TestPartitionStart { get { return 100; } } 46 protected override int TestPartitionEnd { get { return 1100; } } 47 47 48 48 protected override List<List<double>> GenerateValues() { … … 55 55 for (int i = 0; i < data[0].Count; i++) { 56 56 x = data[0][i]; 57 results.Add(Math. Sqrt(x));57 results.Add(Math.Log(x + Math.Sqrt(x*x + 1))); 58 58 } 59 59 data.Add(results);
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