public static double StaticEvaluation2D(
Func <double, double, XY> BaseFn,
ParetoResultAnalyzer pra1, ParetoResultAnalyzer pra2,
ParetoVariable pv1, ParetoVariable pv2
)
{
XY BaseResult = BaseFn(pv1.val, pv2.val);
return(pra1.fn(BaseResult) * pra1.weight + pra2.fn(BaseResult) * pra2.weight);
}
public static double Gradient2D(
Func <double, double, XY> BaseFn,
ParetoResultAnalyzer pra1, ParetoResultAnalyzer pra2,
ParetoVariable pv1, ParetoVariable pv2,
out double df_dpv1, out double df_dpv2
)
{
double f = StaticEvaluation2D(BaseFn, pra1, pra2, pv1, pv2);
df_dpv1 = (StaticEvaluation2D(BaseFn, pra1, pra2, pv1.get_increment(), pv2) - f) / pv1.dx;
df_dpv2 = (StaticEvaluation2D(BaseFn, pra1, pra2, pv1, pv2.get_increment()) - f) / pv2.dx;
return(f);
//Debug.WriteLine("Coming from Gradient2D() to say that: (df_dpv1, df_dpv2) == " + "(" + df_dpv1 + ", " + df_dpv2 + ")");
}
public static void FindSolution2D(
Func <double, double, XY> BaseFn,
ParetoResultAnalyzer pra1, ParetoResultAnalyzer pra2,
ParetoVariable pv1, ParetoVariable pv2,
int numTrials, string outPath = ""
)
{
//Find 2-dimensional pareto solution using gradient descent, given two input biasing functions.
File.Delete(outPath);
double f = 0;
double df_dpv1 = 0;
double df_dpv2 = 0;
for (int trialsElapsed = 0; trialsElapsed < numTrials; trialsElapsed++)
{
f = Gradient2D(BaseFn, pra1, pra2, pv1, pv2, out df_dpv1, out df_dpv2);
if (trialsElapsed % 100 == 0)
{
Debug.WriteLine("Iteration: " + trialsElapsed + ".");
Debug.WriteLine("Static evaluation at f == (pv1, pv2): " + f + " == (" + pv1.val + ", " + pv2.val + ")");
Debug.WriteLine("(dpv1, dpv2) == " + "(" + df_dpv1 + ", " + df_dpv2 + ")");
}
pv1.adjust(df_dpv1);
pv2.adjust(df_dpv2);
if (trialsElapsed % 100 == 0)
{
Console.WriteLine(trialsElapsed);
ExportData.AppendXYtoCSV(outPath, BaseFn(pv1.val, pv2.val));
}
}
}
