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Timestamp:
11/07/12 10:19:31 (10 years ago)
Author:
gkronber
Message:

#1967 worked on GPR evolution

Location:
branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression
Files:
6 added
5 edited

Legend:

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Removed
  • branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression

    • Property svn:ignore set to
      obj
      Plugin.cs
      *.user
  • branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessPolyTen.cs

    r8826 r8873  
    4848    protected override List<List<double>> GenerateValues() {
    4949      var mt = new MersenneTwister(31415);
    50       var normalRand = new NormalDistributedRandom(mt, 1, 1);
    5150
    5251      List<List<double>> data = new List<List<double>>();
    5352      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
    54         data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -10, 10).ToList());
     53        data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -1, 1).ToList());
    5554      }
    5655
    5756
    58       var hyp = (from i in Enumerable.Range(0, 12) // for each CovSEiso
    59                  from j in Enumerable.Range(0, 2)  // for each parameter (length, scale)
    60                  select normalRand.NextDouble())
    61                  .Concat(new double[] { -5.0 }); // noise
     57      var hyp = new double[]
     58        {
     59          0.0, 0.0,
     60          0.0, 0.0,
     61          0.0, 0.0,
     62          0.0, 0.0,
     63          0.0, 0.0,
     64          0.0, 0.0,
     65          0.0, 0.0,
     66          0.0, 0.0,
     67          0.0, 0.0,
     68          0.0, 0.0,
     69          0.0, 0.0,
     70          0.0, 0.0,
     71          0.0, 0.0,
     72          0.0, 0.0,
     73          0.0, 0.0,
     74          -5.0 // noise
     75        };
     76       
    6277
    6378      var covarianceFunction = new CovarianceSum();
  • branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/GaussianProcessSEIsoDependentNoise.cs

    r8826 r8873  
    6060      covarianceFunction.Terms.Add(prod);
    6161      covarianceFunction.Terms.Add(new CovarianceNoise());
    62       covarianceFunction.SetParameter(new double[] { Math.Log(0.1), Math.Log(Math.Sqrt(1)), Math.Log(0.5), Math.Log(Math.Sqrt(1)), Math.Log(Math.Sqrt(0.1)), Math.Log(Math.Sqrt(0.01)) });
     62      var hyp = new double[]
     63        {
     64          Math.Log(0.1), Math.Log(Math.Sqrt(1)), // SE iso
     65          Math.Log(0.5), Math.Log(Math.Sqrt(1)), // SE iso for noise
     66          Math.Log(Math.Sqrt(0.1)), // dependent noise
     67          Math.Log(Math.Sqrt(0.01)) // noise
     68        };
     69      covarianceFunction.SetParameter(hyp);
    6370
    6471      var mt = new MersenneTwister(31415);
  • branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/Util.cs

    r8826 r8873  
    3434    public static List<double> SampleGaussianProcess(IRandom random, ICovarianceFunction covFunction, List<List<double>> data) {
    3535
    36       double[,] x = new double[data[0].Count, 1];
     36      double[,] x = new double[data[0].Count, data.Count];
    3737      for (int i = 0; i < x.GetLength(0); i++)
    3838        for (int j = 0; j < x.GetLength(1); j++)
  • branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/VariousInstanceProvider.cs

    r8826 r8873  
    2424
    2525namespace HeuristicLab.Problems.Instances.DataAnalysis {
    26   public class VariousInstanceProvider : ArtificialRegressionInstanceProvider {
     26  public class InstanceProvider : ArtificialRegressionInstanceProvider {
    2727    public override string Name {
    28       get { return "Various Benchmark Problems"; }
     28      get { return "GPR Benchmark Problems"; }
    2929    }
    3030    public override string Description {
     
    4040    public override IEnumerable<IDataDescriptor> GetDataDescriptors() {
    4141      List<IDataDescriptor> descriptorList = new List<IDataDescriptor>();
    42       descriptorList.Add(new BreimanOne());
    43       descriptorList.Add(new FriedmanOne());
    44       descriptorList.Add(new FriedmanTwo());
    45       descriptorList.Add(new PolyTen());
    4642      descriptorList.Add(new GaussianProcessPolyTen());
    4743      descriptorList.Add(new GaussianProcessSEIso());
     44      descriptorList.Add(new GaussianProcessSEIso2dim());
    4845      descriptorList.Add(new GaussianProcessSEIsoDependentNoise());
    49       descriptorList.Add(new SpatialCoevolution());
    5046      return descriptorList;
    5147    }
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