private static bool isCurrentTheGoal(candidate current, userDefinedGoals udg)
{
double xP, y, z, theta1, theta2, theta3;
int gearcount = 0;
foreach (node n in current.graph.nodes)
{
if ((n.localLabels.Contains("gear")) || (n.localLabels.Contains("gear1")))
{
gearcount = gearcount + 1;
}
}
int i = gearcount;
xP = current.graph.nodes[gearcount].localVariables[3] - udg.outputLocation[0, 3];
y = current.graph.nodes[gearcount].localVariables[4] - udg.outputLocation[1, 3];
z = current.graph.nodes[gearcount].localVariables[5] - udg.outputLocation[2, 3];
theta1 = Math.Abs(current.graph.nodes[gearcount].localVariables[6]) - Math.Abs(udg.outputLocation[0, 2]);
theta2 = Math.Abs(current.graph.nodes[gearcount].localVariables[7]) - Math.Abs(udg.outputLocation[1, 2]);
theta3 = Math.Abs(current.graph.nodes[gearcount].localVariables[8]) - Math.Abs(udg.outputLocation[2, 2]);
double hVal = Math.Sqrt(xP * xP + y * y + z * z + theta1 * theta1 + theta2 * theta2 + theta3 * theta3);
if (hVal < .05)
{
return(true);
}
else
{
return(false);
}
}
private static bool isCurrentTheGoal(candidate current, userDefinedGoals udg)
{
double xP, y, z, theta1, theta2, theta3;
int gearcount = 0;
foreach (node n in current.graph.nodes)
{
if ((n.localLabels.Contains("gear")) || (n.localLabels.Contains("gear1")))
{
gearcount = gearcount + 1;
}
}
int i = gearcount;
xP = current.graph.nodes[gearcount].localVariables[3] - udg.outputLocation[0, 3];
y = current.graph.nodes[gearcount].localVariables[4] - udg.outputLocation[1, 3];
z = current.graph.nodes[gearcount].localVariables[5] - udg.outputLocation[2, 3];
theta1 = Math.Abs(current.graph.nodes[gearcount].localVariables[6]) - Math.Abs(udg.outputLocation[0, 2]);
theta2 = Math.Abs(current.graph.nodes[gearcount].localVariables[7]) - Math.Abs(udg.outputLocation[1, 2]);
theta3 = Math.Abs(current.graph.nodes[gearcount].localVariables[8]) - Math.Abs(udg.outputLocation[2, 2]);
double hVal = Math.Sqrt(xP * xP + y * y + z * z + theta1 * theta1 + theta2 * theta2 + theta3 * theta3);
if (hVal < .05)
{
return true;
}
else
{
return false;
}
}
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);
}
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);
}