protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); List <double> evenlySpacedSequence = SequenceGenerator.GenerateSteps(-5, 5, 0.4m).Select(v => (double)v).ToList(); List <List <double> > trainingData = new List <List <double> >() { evenlySpacedSequence, evenlySpacedSequence }; var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(trainingData).ToList(); var rand = new MersenneTwister((uint)Seed); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(combinations[i].ToList()); data[i].AddRange(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 1000, -5, 5).ToList()); } double x, y; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; y = data[1][i]; results.Add(1 / (1 + Math.Pow(x, -4)) + 1 / (1 + Math.Pow(y, -4))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); var rand = new MersenneTwister((uint)Seed); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 1024, 0.05, 6.05).ToList()); data[i].AddRange(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 5000, -0.25, 6.35)); } double x1, x2, x3, x4, x5; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x1 = data[0][i]; x2 = data[1][i]; x3 = data[2][i]; x4 = data[3][i]; x5 = data[4][i]; results.Add(10 / (5 + Math.Pow(x1 - 3, 2) + Math.Pow(x2 - 3, 2) + Math.Pow(x3 - 3, 2) + Math.Pow(x4 - 3, 2) + Math.Pow(x5 - 3, 2))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); var rand = new MersenneTwister((uint)Seed); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList()); } double x1, x2, x3, x4; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x1 = data[1][i]; x2 = data[2][i]; x3 = data[3][i]; x4 = data[4][i]; results.Add(0.81 + (24.3 * (((2.0 * x1) + (3.0 * Math.Pow(x2, 2))) / ((4.0 * Math.Pow(x3, 3)) + (5.0 * Math.Pow(x4, 4)))))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); var rand = new MersenneTwister((uint)Seed); data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper) data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper) data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper) double x0, x3, x4; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x0 = data[0][i]; x3 = data[3][i]; x4 = data[4][i]; results.Add(6.87 + (11 * Math.Sqrt(7.23 * x0 * x3 * x4))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); var rand = new MersenneTwister((uint)Seed); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 10000, 0, 1).ToList()); } double x1, x2, x3, x4, x5; double f; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x1 = data[0][i]; x2 = data[1][i]; x3 = data[2][i]; x4 = data[3][i]; x5 = data[4][i]; f = 10 * Math.Sin(Math.PI * x1 * x2) + 20 * Math.Pow(x3 - 0.5, 2) + 10 * x4 + 5 * x5; results.Add(f + NormalDistributedRandom.NextDouble(rand, 0, 1)); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { var rand = new MersenneTwister((uint)Seed); List <List <double> > data = new List <List <double> >(); var p0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 4.0e5, 6.0e5).ToList(); var A = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 1.5).ToList(); var T0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 250.0, 260.0).ToList(); var m_dot = new List <double>(); var m_dot_noise = new List <double>(); data.Add(p0); data.Add(A); data.Add(T0); data.Add(m_dot); data.Add(m_dot_noise); double R = 287.0; double γ = 1.4; var c = Math.Sqrt(γ / R * Math.Pow(2 / (γ + 1), (γ + 1) / (γ - 1))); for (int i = 0; i < p0.Count; i++) { double m_dot_i = p0[i] * A[i] / Math.Sqrt(T0[i]) * c; m_dot.Add(m_dot_i); } var sigma_noise = 0.05 * m_dot.StandardDeviationPop(); m_dot_noise.AddRange(m_dot.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise))); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -1, 1).ToList()); } double x1, x2, x3, x4, x5, x6, x7, x8, x9, x10; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x1 = data[0][i]; x2 = data[1][i]; x3 = data[2][i]; x4 = data[3][i]; x5 = data[4][i]; x6 = data[5][i]; x7 = data[6][i]; x8 = data[7][i]; x9 = data[8][i]; x10 = data[9][i]; results.Add(x1 * x2 + x3 * x4 + x5 * x6 + x1 * x7 * x9 + x3 * x6 * x10); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper) data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); double x0, x1, x2, x3; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x0 = data[0][i]; x1 = data[1][i]; x2 = data[2][i]; x3 = data[3][i]; results.Add(12.0 - (6.0 * ((Math.Tan(x0) / Math.Exp(x1)) * (Math.Log(x2) - Math.Tan(x3))))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); var rand = new MersenneTwister((uint)Seed); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList()); } double x0, x1, x3, x4; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x0 = data[0][i]; x1 = data[1][i]; x3 = data[3][i]; x4 = data[4][i]; results.Add(-5.41 + (4.9 * (((x3 - x0) + (x1 / x4)) / (3 * x4)))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(300, 0.05, 6.05).ToList()); } for (int i = 0; i < AllowedInputVariables.Count(); i++) { data[i].AddRange(ValueGenerator.GenerateUniformDistributedValues(1000, -0.25, 6.35)); } double x1, x2; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x1 = data[0][i]; x2 = data[1][i]; results.Add((x1 - 3) * (x2 - 3) + 2 * Math.Sin((x1 - 4) * (x2 - 4))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); var rand = new MersenneTwister((uint)Seed); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList()); } double x0, x1, x2, x3; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x0 = data[0][i]; x1 = data[1][i]; x2 = data[2][i]; x3 = data[3][i]; results.Add(22.0 + (4.2 * ((Math.Cos(x0) - Math.Tan(x1)) * (Math.Tanh(x2) / Math.Sin(x3))))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); List <double> oneVariableTestData = SequenceGenerator.GenerateSteps(-0.2m, 4.2m, 0.1m).Select(v => (double)v).ToList(); List <List <double> > testData = new List <List <double> >() { oneVariableTestData, oneVariableTestData }; var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList <IEnumerable <double> >(); var rand = new MersenneTwister((uint)Seed); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 100, 0.3, 4).ToList()); data[i].AddRange(combinations[i]); } double x1, x2; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x1 = data[0][i]; x2 = data[1][i]; results.Add(Math.Exp(-Math.Pow(x1 - 1, 2)) / (1.2 + Math.Pow(x2 - 2.5, 2))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); List <double> oneVariableTestData = SequenceGenerator.GenerateSteps(-0.05m, 6.05m, 0.02m).Select(v => (double)v).ToList(); List <List <double> > testData = new List <List <double> >() { oneVariableTestData, oneVariableTestData }; var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList <IEnumerable <double> >(); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(30, 0.1, 5.9).ToList()); data[i].AddRange(combinations[i]); } double x1, x2; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x1 = data[0][i]; x2 = data[1][i]; results.Add(6 * Math.Sin(x1) * Math.Cos(x2)); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); List <double> oneVariableTestData = SequenceGenerator.GenerateSteps(-3, 3, 0.1m).Select(v => (double)v).ToList(); List <List <double> > testData = new List <List <double> >() { oneVariableTestData, oneVariableTestData }; var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList(); var rand = new MersenneTwister((uint)Seed); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 20, -3, 3).ToList()); data[i].AddRange(combinations[i]); } double x, y; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; y = data[1][i]; results.Add(x * x * x / 5.0 + y * y * y / 2.0 - y - x); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(10000, 0, 1).ToList()); } double x1, x2, x3, x4, x5; double f; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x1 = data[0][i]; x2 = data[1][i]; x3 = data[2][i]; x4 = data[3][i]; x5 = data[4][i]; f = 0.1 * Math.Exp(4 * x1) + 4 / (1 + Math.Exp(-20 * (x2 - 0.5))) + 3 * x3 + 2 * x4 + x5; results.Add(f + NormalDistributedRandom.NextDouble(rand, 0, 1)); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); List <double> oneVariableTestData = SequenceGenerator.GenerateSteps(0, 1, 0.01m).Select(v => (double)v).ToList(); List <List <double> > testData = new List <List <double> >() { oneVariableTestData, oneVariableTestData }; var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList <IEnumerable <double> >(); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(100, 0, 1).ToList()); data[i].AddRange(combinations[i]); } double x, y; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; y = data[1][i]; results.Add(Math.Pow(x, y)); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); var rand = new MersenneTwister((uint)Seed); data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper) data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper) data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList()); double x0, x1, x2, x3; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x0 = data[0][i]; x1 = data[1][i]; x2 = data[2][i]; x3 = data[3][i]; results.Add(((Math.Sqrt(x0) / Math.Log(x1)) * (Math.Exp(x2) / Math.Pow(x3, 2)))); } data.Add(results); return(data); }
protected override List<List<double>> GenerateValues() { List<List<double>> data = new List<List<double>>(); data.Add(ValueGenerator.GenerateUniformDistributedValues(Seed, 520, -1, 1).ToList()); double x; List<double> results = new List<double>(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; results.Add(Math.Pow(x, 4) + Math.Pow(x, 3) + x*x + x); } data.Add(results); return data; }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); data.Add(ValueGenerator.GenerateUniformDistributedValues(Seed, 520, 0, 2).ToList()); double x; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; results.Add(Math.Log(x + 1) + Math.Log(x * x + 1)); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); data.Add(ValueGenerator.GenerateUniformDistributedValues(520, -1, 1).ToList()); double x; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; results.Add(Math.Sin(x * x) * Math.Cos(x) - 1); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); data.Add(ValueGenerator.GenerateUniformDistributedValues(1020, 0, 1).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(1020, 0, 1).ToList()); double x, y; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; y = data[1][i]; results.Add(Math.Pow(x, 4) - Math.Pow(x, 3) + y * y / 2 - y); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 1).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 1).ToList()); double x, y; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; y = data[1][i]; results.Add(Math.Sin(x) + Math.Sin(y * y)); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); } double x2; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x2 = data[2][i]; results.Add(-2.3 + (0.13 * Math.Sin(x2))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); } double x3; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x3 = data[3][i]; results.Add(1.57 + (24.3 * x3)); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); } double x0; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x0 = data[0][i]; results.Add(6.87 + (11 * Math.Cos(7.23 * x0 * x0 * x0))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); var rand = new MersenneTwister((uint)Seed); data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 1).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 1).ToList()); double x, y; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; y = data[1][i]; results.Add(Math.Pow(x, y)); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); var rand = new MersenneTwister((uint)Seed); for (int i = 0; i < 5; i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList()); } double x0; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x0 = data[0][i]; results.Add(213.80940889 - (213.80940889 * Math.Exp(-0.54723748542 * x0))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); } double x0, x4; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x0 = data[0][i]; x4 = data[4][i]; results.Add(2.0 - (2.1 * (Math.Cos(9.8 * x0) * Math.Sin(1.3 * x4)))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -1, 1).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 1, 2).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -1, 1).ToList()); double x, y, z; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x = data[0][i]; y = data[1][i]; z = data[2][i]; results.Add((30 * x * z) / ((x - 10) * Math.Pow(y, 2))); } data.Add(results); return(data); }
protected override List <List <double> > GenerateValues() { List <List <double> > data = new List <List <double> >(); data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper) data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList()); double x0; List <double> results = new List <double>(); for (int i = 0; i < data[0].Count; i++) { x0 = data[0][i]; results.Add(1.3 + (0.13 * Math.Sqrt(x0))); } data.Add(results); return(data); }