private static void Main()
{
double baseNonlinearModel(double[] k, double[] x) => k[0] * Math.Pow(k[1], Math.Log(x[0])) * Math.Pow(k[2], x[1]) * Math.Pow(k[3], x[2]);
// 1) Analyze test data using tools for nonlinear model
RAProcessorNonLinear nlra = new RAProcessorNonLinear(TestSet.Data);
nlra.SetModel(
f: baseNonlinearModel,
g: (k, x, r) =>
{ //gradient function
r[0] = k[0] * Math.Pow(k[1], Math.Log(x[0])) * Math.Pow(k[2], x[1]) * Math.Pow(k[3], x[2]);
r[1] = k[0] * Math.Pow(k[1], Math.Log(x[0])) * Math.Pow(k[2], x[1]) * Math.Pow(k[3], x[2]) * Math.Log(x[0]) / k[1];
r[2] = k[0] * Math.Pow(k[1], Math.Log(x[0])) * Math.Pow(k[2], x[1]) * Math.Pow(k[3], x[2]) * x[1] / k[2];
r[3] = k[0] * Math.Pow(k[1], Math.Log(x[0])) * Math.Pow(k[2], x[1]) * Math.Pow(k[3], x[2]) * x[2] / k[3];
},
n: 4, //number of observed coefficients
v: new[] { 150.0, 1, 1, 1 });
nlra.ComputeRAResult();
RAResultNonLinear nlres = nlra.GetRAresult();
nlres.ComputeRAResultMetrics();
PrintRAResult(nlres, "Non-linear analysis processing result:");
// 2) Transform test dataset to format for linear regression and analyze with tools linear model
RAData linedata = TestSet.Data.CloneData(); // Clone base test data
linedata.OutputsTransform(Math.Log); //perform the logarithm of y
linedata.InputsTransform(0, Math.Log); //perform the logarithm of first parametr
RAProcessorLinear lra = new RAProcessorLinear(linedata);
lra.ComputeRAResult();
RAResultBase lres = lra.GetRAresult();
lres.ComputeRAResultMetrics();
PrintRAResult(lres, "Linear analysis processing result:");
// 3) Create RAResultBase with nonlinear model but with model coefficients obtained using linear regression analysis
RAResultBase checklra = new RAResultBase(TestSet.Data)
{
Coefficients = new double[]
{
Math.Exp(lres.Coefficients[0]),
Math.Exp(lres.Coefficients[1]),
Math.Exp(lres.Coefficients[2]),
Math.Exp(lres.Coefficients[3])
},
Model = baseNonlinearModel
};
checklra.ComputeValuations();
checklra.ComputeRAResultMetrics();
PrintRAResult(checklra, "Non-linear analysis metrics with coefficients obtained with linear analysis:");
Console.WriteLine("Test is passed.");
Console.ReadKey();
}
public static void PrintRAResult(RAResultBase result, string title)
{
List <string> coefs = new List <string>();
for (int i = 0; i < result.Coefficients.Length; i++)
{
coefs.Add("k" + i.ToString() + "=" + result.Coefficients[i].ToString());
}
Console.WriteLine(title);
Console.WriteLine("Coefficients: " + String.Join(" ", coefs));
Console.WriteLine();
Console.WriteLine("SSE = " + result.SSE.ToString());
Console.WriteLine("MSE = " + result.MSE.ToString());
Console.WriteLine("R2 = " + result.R2.ToString());
Console.WriteLine("St.Error = " + result.StandardError.ToString());
Console.WriteLine();
Console.WriteLine("valuations - outputs:");
for (int i = 0; i < result.Data.Valuations.Length; i++)
{
Console.WriteLine(result.Data.Valuations[i].ToString() + " - " + result.Data.Outputs[i].ToString());
}
Console.WriteLine();
Console.WriteLine();
}
