Changeset 16495
 Timestamp:
 01/03/19 19:05:37 (4 years ago)
 Location:
 stable
 Files:

 8 edited
 1 copied
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 Removed

stable
 Property svn:mergeinfo changed
/trunk merged: 16264,16394,16431
 Property svn:mergeinfo changed

stable/HeuristicLab.Problems.Instances.DataAnalysis
 Property svn:mergeinfo changed
/trunk/HeuristicLab.Problems.Instances.DataAnalysis merged: 16264,16394,16431
 Property svn:mergeinfo changed

stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/HeuristicLab.Problems.Instances.DataAnalysis3.3.csproj
r16163 r16495 169 169 <Compile Include="Regression\PennML\PennMLRegressionDataDescriptor.cs" /> 170 170 <Compile Include="Regression\PennML\PennMLRegressionInstanceProvider.cs" /> 171 <Compile Include="Regression\Physics\AircraftLift.cs" /> 172 <Compile Include="Regression\Physics\AircraftMaximumLift.cs" /> 173 <Compile Include="Regression\Physics\FluidDynamics.cs" /> 174 <Compile Include="Regression\Physics\PhysicsInstanceProvider.cs" /> 175 <Compile Include="Regression\Physics\RocketFuelFlow.cs" /> 171 176 <Compile Include="Regression\VariableNetworks\LinearVariableNetwork.cs" /> 172 177 <Compile Include="Regression\VariableNetworks\GaussianProcessVariableNetwork.cs" /> 
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 2534 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 
stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/AircraftMaximumLift.cs
r16264 r16495 26 26 27 27 namespace HeuristicLab.Problems.Instances.DataAnalysis { 28 class AircraftMaximumLift : ArtificialRegressionDataDescriptor {29 public override string Name { get { return "Aircraft Maximum Lift Coefficient "; } }28 public class AircraftMaximumLift : ArtificialRegressionDataDescriptor { 29 public override string Name { get { return "Aircraft Maximum Lift Coefficient f(X) = x1  1/4 x4 x5 x6 (4 + 0.1 x2/x3  x2²/x3²) + x13 x14/x15 x18 x7  x13 x14/x15 x8 + x13 x14/x15 x9 + x16 x17/x15 x18 x10  x16 x17/x15 x11 + x16 x17/x15 x12"; } } 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  0.25 x4 x5 x6 (4 + 0.1 x2/x3  x2²/x3²) + x13 x14/x15 x18 x7  x13 x14/x15 x8 + x13 x14/x15 x9 + x16 x17/x15 x18 x10  x16 x17/x15 x11 + x16 x17/x15 x12" + Environment.NewLine + 36 "with x1 in [0.4, 0.8], " + 37 "x2 in [3, 4], " + 38 "x3 in [20, 30], " + 39 "x4, x13, x16 in [2, 5]," + 40 "x14, x17 in [1, 1.5], " + 41 "x15 in [5, 7]," + 42 "x18 in [10, 20]," + 43 "x8, x11 in [1, 1.5]," + 44 "x9, x12 in [1, 2]," + 45 "x7, x10 in [0.5, 1.5]"; 33 return "Slightly changed version of the problem instance given in: " + Environment.NewLine + 34 "Chen Chen, Changtong Luo, Zonglin Jiang, \"A multilevel block building algorithm for fast " + 35 "modeling generalized separable systems\", " + 36 "preprint on arXiv: https://arxiv.org/abs/1706.02281 ." + Environment.NewLine + 37 "Notably, this problem is missing from the peerreviewed version of the article in Expert Systems with Applications, Volume 109" + Environment.NewLine + 38 "Function: f(X) = x1  0.25 x4 x5 x6 (4 + 0.1 x2/x3  x2²/x3²) + x13 x14/x15 x18 x7  x13 x14/x15 x8 + x13 x14/x15 x9 + x16 x17/x15 x18 x10  x16 x17/x15 x11 + x16 x17/x15 x12" + Environment.NewLine + 39 "with x1 ∈ [0.4, 0.8]," + Environment.NewLine + 40 "x2 ∈ [3, 4]," + Environment.NewLine + 41 "x3 ∈ [20, 30]," + Environment.NewLine + 42 "x4, x13, x16 ∈ [2, 5]," + Environment.NewLine + 43 "x14, x17 ∈ [1, 1.5]," + Environment.NewLine + 44 "x15 ∈ [5, 7]," + Environment.NewLine + 45 "x18 ∈ [10, 20]," + Environment.NewLine + 46 "x8, x11 ∈ [1, 1.5]," + Environment.NewLine + 47 "x9, x12 ∈ [1, 2]," + Environment.NewLine + 48 "x7, x10 ∈ [0.5, 1.5]." + Environment.NewLine + 49 "Values for x5 and x6 have not been specified in the reference paper." + 50 " We therefore only use a single (x5) variable in place of ∆αW/c and set x6 to a constant value of 1.0." + Environment.NewLine + 51 "The range for x5 is [0..20]."; 46 52 } 47 53 } … … 78 84 79 85 80 var x5 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList(); // TODO: range for X5 is not specified in the paper 81 var x6 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 7.0).ToList(); // TODO: range for X6 is not specified in the paper 86 // in the reference paper \Delta alpha_w/c is replaced by two variables x5*x6. 87 var x5 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 20).ToList(); // range for X5 is not specified in the paper, we use [0°..20°] for ∆αW/c 88 var x6 = Enumerable.Repeat(1.0, x5.Count).ToList(); // range for X6 is not specified in the paper. In the maximum lift formular there is only a single variable ∆αW/c in place of x5*x6. 82 89 83 90 var x7 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 1.5).ToList(); … … 96 103 97 104 var x18 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 10.0, 20.0).ToList(); 105 98 106 99 107 List<double> fx = new List<double>(); 
stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/FluidDynamics.cs
r16264 r16495 26 26 27 27 namespace HeuristicLab.Problems.Instances.DataAnalysis { 28 class FluidDynamics : ArtificialRegressionDataDescriptor {29 public override string Name { get { return "Flow psi = x1*x2*x5*(1  x4²/x5²) + 1/(2*Pi) * x3*log(x5/x4)"; } }28 public class FluidDynamics : ArtificialRegressionDataDescriptor { 29 public override string Name { get { return "Flow Psi = V_inf r sin(th) (1  R²/r²) + G/(2 π) ln(r/R)"; } } 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*x5*(1  x4²/x5²) + 1/(2*Pi) * x3*log(x5/x4)" + Environment.NewLine + 36 "with x1 in [60,65], x2 in [30, 40], x3 in [5,10], x4 in [0.5,0.8], x5 in [0.2,0.5]"; 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 2534 https://doi.org/10.1016/j.eswa.2018.05.021. " + Environment.NewLine + 37 "Function: Psi = V_inf r sin(th) (1  R²/r²) + G/(2 π) ln(r/R)" + Environment.NewLine + 38 "with V_inf ∈ [60 m/s, 65 m/s]," + Environment.NewLine + 39 "th ∈ [30°, 40°]," + Environment.NewLine + 40 "r ∈ [0.2m, 0.5m]," + Environment.NewLine + 41 "R ∈ [0.5m, 0.8m]," + Environment.NewLine + 42 "G ∈ [5 m²/s, 10 m²/s]"; 37 43 } 38 44 } 39 45 40 protected override string TargetVariable { get { return " f(X)"; } }41 protected override string[] VariableNames { get { return new string[] { " x1", "x2", "x3", "x4", "x5", "f(X)" }; } }42 protected override string[] AllowedInputVariables { get { return new string[] { " x1", "x2", "x3", "x4", "x5" }; } }46 protected override string TargetVariable { get { return "Psi"; } } 47 protected override string[] VariableNames { get { return new string[] { "V_inf", "th", "r", "R", "G", "Psi" }; } } 48 protected override string[] AllowedInputVariables { get { return new string[] { "V_inf", "th", "r", "R", "G" }; } } 43 49 protected override int TrainingPartitionStart { get { return 0; } } 44 50 protected override int TrainingPartitionEnd { get { return 100; } } … … 58 64 59 65 List<List<double>> data = new List<List<double>>(); 60 var x1= ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 60.0, 65.0).ToList();61 var x2= ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 30.0, 40.0).ToList();62 var x3 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 10.0).ToList();63 var x4= ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 0.8).ToList();64 var x5 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.2, 0.5).ToList();66 var V_inf = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 60.0, 65.0).ToList(); 67 var th = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 30.0, 40.0).ToList(); 68 var r = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.2, 0.5).ToList(); 69 var R = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 0.8).ToList(); 70 var G = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5, 10).ToList(); 65 71 66 List<double> fx= new List<double>();67 data.Add( x1);68 data.Add( x2);69 data.Add( x3);70 data.Add( x4);71 data.Add( x5);72 data.Add( fx);72 List<double> Psi = new List<double>(); 73 data.Add(V_inf); 74 data.Add(th); 75 data.Add(r); 76 data.Add(R); 77 data.Add(G); 78 data.Add(Psi); 73 79 74 for (int i = 0; i < x1.Count; i++) { 75 double fxi = x1[i] * x2[i] * x5[i] * (1  (x4[i] * x4[i]) / (x5[i] * x5[i])) + 76 (1 / (2 * Math.PI)) * x3[i] * Math.Log(x5[i] / x4[i]); 77 fx.Add(fxi); 80 for (int i = 0; i < V_inf.Count; i++) { 81 var th_rad = Math.PI * th[i] / 180.0; 82 double Psi_i = V_inf[i] * r[i] * Math.Sin(th_rad) * (1  (R[i] * R[i]) / (r[i] * r[i])) + 83 (G[i] / (2 * Math.PI)) * Math.Log(r[i] / R[i]); 84 Psi.Add(Psi_i); 78 85 } 79 86 
stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/PhysicsInstanceProvider.cs
r16264 r16495 22 22 using System; 23 23 using System.Collections.Generic; 24 using HeuristicLab.Random; 24 25 25 26 namespace HeuristicLab.Problems.Instances.DataAnalysis { 26 class PhysicsInstanceProvider : ArtificialRegressionInstanceProvider { 27 public class PhysicsInstanceProvider : ArtificialRegressionInstanceProvider { 28 public override string Name { get { return "Physics Benchmark Problems"; } } 29 public override string Description { get { return ""; } } 30 public override Uri WebLink { get { return new Uri(@"https://doi.org/10.1016/j.eswa.2018.05.021"); } } 31 public override string ReferencePublication { 32 get { 33 return "Chen Chen, Changtong Luo, Zonglin Jiang, \"A multilevel block building algorithm for fast modeling generalized separable systems\", Expert Systems with Applications, Volume 109, 2018, Pages 2534 https://doi.org/10.1016/j.eswa.2018.05.021 as well as the (slightly different) preprint on arXiv: https://arxiv.org/abs/1706.02281"; 34 } 35 } 36 37 public int Seed { get; private set; } 38 39 public PhysicsInstanceProvider() : this((int)DateTime.Now.Ticks) { } 40 41 public PhysicsInstanceProvider(int seed) : base() { 42 Seed = seed; 43 } 44 27 45 public override IEnumerable<IDataDescriptor> GetDataDescriptors() { 46 var rand = new FastRandom(Seed); 47 28 48 return new List<IDataDescriptor>() 29 49 { 30 new RocketFuelFlow( 123),31 new AircraftLift( 456),32 new FluidDynamics( 789),33 new AircraftMaximumLift( 321)50 new RocketFuelFlow(rand.Next()), 51 new AircraftLift(rand.Next()), 52 new FluidDynamics(rand.Next()), 53 new AircraftMaximumLift(rand.Next()) 34 54 }; 35 }36 37 public override string Name { get { return "Physics Benchmark Problems"; } }38 public override string Description { get { return ""; } }39 public override Uri WebLink { get { return new Uri(@"https://arxiv.org/abs/1706.02281"); } }40 public override string ReferencePublication {41 get {42 return "Chen Chen, Changtong Luo, Zonglin Jiang, 2017 " +43 "\"A multilevel block search algorithm for fast modeling generalized separable systems.\" arXiv preprint arXiv:1706.02281, v3";44 }45 55 } 46 56 } 
stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/RocketFuelFlow.cs
r16264 r16495 26 26 27 27 namespace HeuristicLab.Problems.Instances.DataAnalysis { 28 class RocketFuelFlow : ArtificialRegressionDataDescriptor {29 public override string Name { get { return "Rocket Fuel Flow f(X) = 4000*x1*x2/sqrt(x3)"; } }28 public class RocketFuelFlow : ArtificialRegressionDataDescriptor { 29 public override string Name { get { return "Rocket Fuel Flow m_dot = p0 A / sqrt(T0) * sqrt(γ/R (2/(γ+1))^((γ+1) / (γ1)))"; } } 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) = 4000*x1*x2/sqrt(x3)" + Environment.NewLine + 36 "with x1 in [4,6], x2 in [0.5, 1.5], x3 in [250,260]"; 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 2534 https://doi.org/10.1016/j.eswa.2018.05.021. " + Environment.NewLine + 37 "Function: m_dot = p0 A / sqrt(T0) * sqrt(γ/R (2/(γ+1))^((γ+1) / (γ1)))" + Environment.NewLine + 38 "with p0 ∈ [4e5 Pa, 6e5 Pa]," + Environment.NewLine + 39 "A ∈ [0.5m², 1.5m²]," + Environment.NewLine + 40 "T0 ∈ [250°K, 260°K]," + Environment.NewLine + 41 "γ=1.4 and R=287 J/(kg*K)" + Environment.NewLine + 42 "The factor sqrt(γ/R (2/(γ+1))^((γ+1) / (γ1))) is constant as γ and R are constants."; 37 43 } 38 44 } 39 45 40 protected override string TargetVariable { get { return " f(X)"; } }41 protected override string[] VariableNames { get { return new string[] { " x1", "x2", "x3", "f(X)" }; } }42 protected override string[] AllowedInputVariables { get { return new string[] { " x1", "x2", "x3" }; } }46 protected override string TargetVariable { get { return "m_dot"; } } 47 protected override string[] VariableNames { get { return new string[] { "p0", "A", "T0", "m_dot" }; } } 48 protected override string[] AllowedInputVariables { get { return new string[] { "p0", "A", "T0" }; } } 43 49 protected override int TrainingPartitionStart { get { return 0; } } 44 50 protected override int TrainingPartitionEnd { get { return 100; } } … … 58 64 59 65 List<List<double>> data = new List<List<double>>(); 60 var x1 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 4.0, 6.0).ToList();61 var x2= ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 1.5).ToList();62 var x3= ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 250.0, 260.0).ToList();66 var p0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 4.0e5, 6.0e5).ToList(); 67 var A = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 1.5).ToList(); 68 var T0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 250.0, 260.0).ToList(); 63 69 64 List<double> fx = new List<double>(); 65 data.Add(x1); 66 data.Add(x2); 67 data.Add(x3); 68 data.Add(fx); 69 70 for (int i = 0; i < x1.Count; i++) { 71 double fxi = 4000 * x1[i] * x2[i] / Math.Sqrt(x3[i]); 72 fx.Add(fxi); 70 List<double> m_dot = new List<double>(); 71 data.Add(p0); 72 data.Add(A); 73 data.Add(T0); 74 data.Add(m_dot); 75 double R = 287.0; 76 double γ = 1.4; 77 var c = Math.Sqrt(γ / R * Math.Pow(2 / (γ + 1), (γ + 1) / (γ  1))); 78 for (int i = 0; i < p0.Count; i++) { 79 double m_dot_i = p0[i] * A[i] / Math.Sqrt(T0[i]) * c; 80 m_dot.Add(m_dot_i); 73 81 } 74 82
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