protected override List <List <double> > GenerateValues()
{
var rand = new MersenneTwister((uint)Seed);
List <List <double> > data = new List <List <double> >();
var x1 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.4, 0.8).ToList();
var x2 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 3.0, 4.0).ToList();
var x3 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 20.0, 30.0).ToList();
var x4 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 2.0, 5.0).ToList();
var x13 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 2.0, 5.0).ToList();
var x16 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 2.0, 5.0).ToList();
// in the reference paper \Delta alpha_w/c is replaced by two variables x5*x6.
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
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.
var x7 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 1.5).ToList();
var x10 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 1.5).ToList();
var x8 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();
var x11 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();
var x9 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 2.0).ToList();
var x12 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 2.0).ToList();
var x14 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();
var x17 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();
var x15 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 7.0).ToList();
var x18 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 10.0, 20.0).ToList();
List <double> fx = new List <double>();
List <double> fx_noise = new List <double>();
data.Add(x1);
data.Add(x2);
data.Add(x3);
data.Add(x4);
data.Add(x5);
data.Add(x6);
data.Add(x7);
data.Add(x8);
data.Add(x9);
data.Add(x10);
data.Add(x11);
data.Add(x12);
data.Add(x13);
data.Add(x14);
data.Add(x15);
data.Add(x16);
data.Add(x17);
data.Add(x18);
data.Add(fx);
data.Add(fx_noise);
for (int i = 0; i < x1.Count; i++)
{
double fxi = x1[i];
fxi = fxi - 0.25 * x4[i] * x5[i] * x6[i] * (4 + 0.1 * (x2[i] / x3[i]) - (x2[i] / x3[i]) * (x2[i] / x3[i]));
fxi = fxi + x13[i] * (x14[i] / x15[i]) * x18[i] * x7[i];
fxi = fxi - x13[i] * (x14[i] / x15[i]) * x8[i];
fxi = fxi + x13[i] * (x14[i] / x15[i]) * x9[i];
fxi = fxi + x16[i] * (x17[i] / x15[i]) * x18[i] * x10[i];
fxi = fxi - x16[i] * (x17[i] / x15[i]) * x11[i];
fxi = fxi + x16[i] * (x17[i] / x15[i]) * x12[i];
fx.Add(fxi);
}
var sigma_noise = 0.05 * fx.StandardDeviationPop();
fx_noise.AddRange(fx.Select(fxi => fxi + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
return(data);
}