Changeset 8660 for branches/GP-MoveOperators/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Various
- Timestamp:
- 09/14/12 18:58:15 (12 years ago)
- Location:
- branches/GP-MoveOperators
- Files:
-
- 2 edited
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branches/GP-MoveOperators
- 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/GP-MoveOperators/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Various/SpatialCoevolution.cs
r7988 r8660 27 27 public class SpatialCoevolution : ArtificialRegressionDataDescriptor { 28 28 29 public override string Name { get { return "Spatial co-evolution F(x,y) = 1/(1 +power(x,-4)) + 1/(1+pow(y,-4))"; } }29 public override string Name { get { return "Spatial co-evolution F(x,y) = 1/(1 + x^(-4)) + 1/(1 + y^(-4))"; } } 30 30 public override string Description { 31 31 get { 32 32 return "Paper: Evolutionary consequences of coevolving targets" + Environment.NewLine 33 33 + "Authors: Ludo Pagie and Paulien Hogeweg" + Environment.NewLine 34 + "Function: F(x,y) = 1/(1+power(x,-4)) + 1/(1+pow(y,-4))" + Environment.NewLine 35 + "Terminal set: x, y" + Environment.NewLine 34 + "Function: F(x,y) = 1/(1 + x^(-4)) + 1/(1 + y^(-4))" + Environment.NewLine 35 + "Non-terminals: +, -, *, % (protected division), sin, cos, exp, ln(|x|) (protected log)" + Environment.NewLine 36 + "Terminals: only variables (no random constants)" + Environment.NewLine 36 37 + "The fitness of a solution is defined as the mean of the absolute differences between " 37 38 + "the target function and the solution over all problems on the basis of which it is evaluated. " 38 39 + "A solution is considered completely ’correct’ if, for all 676 problems in the ’complete’ " 39 40 + "problem set used in the static evaluation scheme, the absolute difference between " 40 + "solution and target function is less than 0 :01 (this is a so-called hit).";41 + "solution and target function is less than 0.01 (this is a so-called hit)."; 41 42 } 42 43 } … … 45 46 protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y" }; } } 46 47 protected override int TrainingPartitionStart { get { return 0; } } 47 protected override int TrainingPartitionEnd { get { return 1000; } }48 protected override int TestPartitionStart { get { return 1000; } }48 protected override int TrainingPartitionEnd { get { return 676; } } 49 protected override int TestPartitionStart { get { return 676; } } 49 50 protected override int TestPartitionEnd { get { return 1676; } } 50 51 … … 52 53 List<List<double>> data = new List<List<double>>(); 53 54 54 List<double> oneVariableTestData= ValueGenerator.GenerateSteps(-5, 5, 0.4).ToList();55 List<List<double>> t estData = new List<List<double>>() { oneVariableTestData, oneVariableTestData};56 var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(t estData).ToList<IEnumerable<double>>();55 List<double> evenlySpacedSequence = ValueGenerator.GenerateSteps(-5, 5, 0.4).ToList(); 56 List<List<double>> trainingData = new List<List<double>>() { evenlySpacedSequence, evenlySpacedSequence }; 57 var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(trainingData).ToList(); 57 58 58 59 for (int i = 0; i < AllowedInputVariables.Count(); i++) { 59 data.Add( ValueGenerator.GenerateUniformDistributedValues(1000, -5, 5).ToList());60 data[i].AddRange( combinations[i]);60 data.Add(combinations[i].ToList()); 61 data[i].AddRange(ValueGenerator.GenerateUniformDistributedValues(1000, -5, 5).ToList()); 61 62 } 62 63
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