public stressConstraint(double[,] stateVars, candidate c, GearEvaluator gt)
{
this.stateVars = stateVars;
this.gt = gt;
this.c = c;
this.findDerivBy = differentiate.Central2;
}
public outputSpeedConstraint(double[,] stateVars, GearEvaluator gt)
{
this.gt = gt;
this.outputSpeed = gt.udg.outputSpeed;
this.stateVars = stateVars;
this.findDerivBy = differentiate.Central2;
}
public outputLocationConstraint(double[,] stateVars, candidate c, GearEvaluator gt)
{
this.gt = gt;
this.c = c;
this.stateVars = stateVars;
this.findDerivBy = differentiate.Central2;
this.xtarget = gt.udg.outputLocation[0, 3];
this.ytarget = gt.udg.outputLocation[1, 3];
this.ztarget = gt.udg.outputLocation[2, 3];
this.theta1t = gt.udg.outputLocation[0, 2];
this.theta2t = gt.udg.outputLocation[1, 2];
this.theta3t = gt.udg.outputLocation[2, 2];
}
public static void runSearchProcess()
{
userDefinedGoals udg = new userDefinedGoals();
Form2 inputBox = new Form2(udg);
//inputBox.ShowDialog();
ruleSet.loadAndCompileSourceFiles(rulesets, Program.settings.recompileRules, Program.settings.compiledparamRules, Program.settings.execDir);
List <candidate> candidates = new List <candidate>();
candidate current = null;
candidate seedCandidate = new candidate(seed, Program.settings.numOfRuleSets);
Boolean found = false;
candidates.Add(seedCandidate);
GearEvaluator ge = new GearEvaluator(udg);
Guidance.PNormProportionalPopulationSelection GuidanceApproach
= new Guidance.PNormProportionalPopulationSelection(0, Guidance.optimize.minimize, true, true, 1);
int maxPop = 10000;
while (!found && (candidates.Count != 0))
{
current = candidates[0];
candidates.RemoveAt(0);
SearchIO.iteration = current.recipe.Count;
//RECOGNIZE
List <option> ruleChoices = rulesets[0].recognize(current.graph);
SearchIO.miscObject = candidates.Count;
//
if (current.recipe.Count >= 2)
{
found = isCurrentTheGoal(current, udg);
if (found == true)
{
ge.evalGT(current);
break;
}
}
//else current.f0 = double.PositiveInfinity;
for (int i = 0; i != ruleChoices.Count; i++)
{
candidate child = current.copy();
ruleChoices = rulesets[0].recognize(child.graph);
ruleChoices[i].apply(child.graph, null);
child.addToRecipe(ruleChoices[i]);
child.f0 = double.NaN;
child.f1 = double.NaN;
child.f2 = double.NaN;
ge.evalGT(child);
child.f3 = (child.f0) / 100 + child.f1;
//1 efficiency
//2 mass
//3 combination
addChildToSortedCandList(candidates, child, 3);
//candidates.Add(child);
}
}
Program.addAndShowGraphDisplay(current.graph, "Here is your gear train!!!");
candidate.saveToXml(current, "testTuned", settings.outputDirectory);
}
