Changeset 16495 for stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/AircraftLift.cs
- Timestamp:
- 01/03/19 19:05:37 (6 years ago)
- Location:
- stable
- Files:
-
- 3 edited
- 1 copied
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stable
- Property svn:mergeinfo changed
/trunk merged: 16264,16394,16431
- Property svn:mergeinfo changed
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stable/HeuristicLab.Problems.Instances.DataAnalysis
- Property svn:mergeinfo changed
/trunk/HeuristicLab.Problems.Instances.DataAnalysis merged: 16264,16394,16431
- Property svn:mergeinfo changed
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stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/AircraftLift.cs
r16264 r16495 26 26 27 27 namespace HeuristicLab.Problems.Instances.DataAnalysis { 28 class AircraftLift : ArtificialRegressionDataDescriptor {29 public override string Name { get { return "Aircraft Lift Coefficient C_L = x1*(x2 - 2) + x3*x4*x5/x6"; } }28 public class AircraftLift : ArtificialRegressionDataDescriptor { 29 public override string Name { get { return "Aircraft Lift Coefficient C_L = C_La (a - a0) + C_Ld_e d_e S_HT / S_ref"; } } 30 30 31 31 public override string Description { 32 32 get { 33 return "A full description of this problem instance is given in the paper: A multilevel block building algorithm for fast modeling generalized separable systems. " + Environment.NewLine + 34 "Authors: Chen Chen, Changtong Luo, Zonglin Jiang" + Environment.NewLine + 35 "Function: f(X) = x1*(x2 - 2) + x3*x4*x5/x6" + Environment.NewLine + 36 "with x1 in [0.4, 0.8], x2 in [5, 10], x3 in [0.4, 0.8], x4 in [5, 10], x5 in [1, 1.5], x6 in [5, 7]"; 33 return "A full description of this problem instance is given in: " + Environment.NewLine + 34 "Chen Chen, Changtong Luo, Zonglin Jiang, \"A multilevel block building algorithm for fast " + 35 "modeling generalized separable systems\", Expert Systems with Applications, Volume 109, 2018, " + 36 "Pages 25-34 https://doi.org/10.1016/j.eswa.2018.05.021. " + Environment.NewLine + 37 "Function: C_L = C_La (a - a0) + C_Ld_e d_e S_HT / S_ref" + Environment.NewLine + 38 "with C_La ∈ [0.4, 0.8]," + Environment.NewLine + 39 "a ∈ [5°, 10°]," + Environment.NewLine + 40 "C_Ld_e ∈ [0.4, 0.8]," + Environment.NewLine + 41 "d_e ∈ [5°, 10°]," + Environment.NewLine + 42 "S_HT ∈ [1m², 1.5m²]," + Environment.NewLine + 43 "S_ref ∈ [5m², 7m²]," + Environment.NewLine + 44 "a0 is set to -2°"; 37 45 } 38 46 } 39 47 40 protected override string TargetVariable { get { return " f(X)"; } }41 protected override string[] VariableNames { get { return new string[] { " x1", "x2", "x3", "x4", "x5", "x6", "f(X)" }; } }42 protected override string[] AllowedInputVariables { get { return new string[] { " x1", "x2", "x3", "x4", "x5", "x6" }; } }48 protected override string TargetVariable { get { return "C_L"; } } 49 protected override string[] VariableNames { get { return new string[] { "C_La", "a", "a0", "C_Ld_e", "d_e", "S_HT", "C_L" }; } } 50 protected override string[] AllowedInputVariables { get { return new string[] { "C_La", "a", "a0", "C_Ld_e", "d_e", "S_HT" }; } } 43 51 protected override int TrainingPartitionStart { get { return 0; } } 44 52 protected override int TrainingPartitionEnd { get { return 100; } } … … 58 66 59 67 List<List<double>> data = new List<List<double>>(); 60 var x1= ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.4, 0.8).ToList();61 var x2= ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 10.0).ToList();62 var x3= ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.4, 0.8).ToList();63 var x4= ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 10.0).ToList();64 var x5= ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();65 var x6= ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 7.0).ToList();68 var C_La = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.4, 0.8).ToList(); 69 var a = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 10.0).ToList(); 70 var C_Ld_e = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.4, 0.8).ToList(); 71 var d_e = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 10.0).ToList(); 72 var S_HT = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList(); 73 var S_ref = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 7.0).ToList(); 66 74 67 List<double> fx= new List<double>();68 data.Add( x1);69 data.Add( x2);70 data.Add( x3);71 data.Add( x4);72 data.Add( x5);73 data.Add( x6);74 data.Add( fx);75 List<double> C_L = new List<double>(); 76 data.Add(C_La); 77 data.Add(a); 78 data.Add(C_Ld_e); 79 data.Add(d_e); 80 data.Add(S_HT); 81 data.Add(S_ref); 82 data.Add(C_L); 75 83 76 for (int i = 0; i < x1.Count; i++) { 77 double fxi = x1[i] * (x2[i] - 2.0) + x3[i] * x4[i] * x5[i] / x6[i]; 78 fx.Add(fxi); 84 double a0 = -2.0; 85 86 for (int i = 0; i < C_La.Count; i++) { 87 double C_Li = C_La[i] * (a[i] - a0) + C_Ld_e[i] * d_e[i] * S_HT[i] / S_ref[i]; 88 C_L.Add(C_Li); 79 89 } 80 90
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