private void createUnderGroundParking_Click(object sender, RoutedEventArgs e)
{
Apartment newAGoutput = this.AG.generator(this.Plot, this.ExistingParameter, this.ExistingTarget);
List <Apartment> newAGOutputSet = FinalizeApartment.finalizeAGoutput(newAGoutput, TuringAndCorbusierPlugIn.InstanceClass.page1Settings.MaxFloorAreaRatio, TuringAndCorbusierPlugIn.InstanceClass.page1Settings.MaxBuildingCoverage, true);
if (newAGOutputSet.Count != 0)
{
returnValue = newAGOutputSet;
}
this.Close();
}
private void useProposedArea_Click(object sender, RoutedEventArgs e)
{
List <Target> sortedTarget = SuggestTarget.bestTarget(this.ExistingTarget, getProposedArea());
for (int i = 0; i < sortedTarget.Count(); i++)
{
Apartment newAGoutput = this.AG.generator(this.Plot, this.ExistingParameter, this.ExistingTarget);
List <Apartment> newAGOutputSet = FinalizeApartment.finalizeAGoutput(newAGoutput, TuringAndCorbusierPlugIn.InstanceClass.page1Settings.MaxFloorAreaRatio, TuringAndCorbusierPlugIn.InstanceClass.page1Settings.MaxBuildingCoverage, false);
if (newAGOutputSet.Count != 0)
{
returnValue = newAGOutputSet;
break;
}
}
this.Close();
}
public static List <Apartment> giantAnteater(Plot plot, ApartmentGeneratorBase ag, Target target, bool previewOn)
{
double mutationProbability = ag.GAParameterSet[0];
double elitismPercentage = ag.GAParameterSet[1];
double initialBoost = ag.GAParameterSet[2];
int population = (int)ag.GAParameterSet[3];
int maxGen = (int)ag.GAParameterSet[4];
double fitnessFactor = ag.GAParameterSet[5];
double mutationFactor = ag.GAParameterSet[6];
//Initialize Minimum and Maximum value
double[] tempMaxInput = ag.MaxInput.Clone() as double[];
double[] tempMinInput = ag.MinInput.Clone() as double[];
//create initial genes
Random myRandom = new Random((int)DateTime.Now.Ticks);
double goodAngle = maxRectDirection(plot);
//double goodAngle = Math.PI * 152 / 180;
List <ParameterSet> offspringGenes = new List <ParameterSet>();
//for (int i = 0; i < initialBoost; i++)
//{
// CoreType tempCoreType = ag.GetRandomCoreType();
// double[] oneGene = new double[ag.MinInput.Length];
// //if (ag.IsCoreProtrude)
// // tempMaxInput[2] = tempCoreType.GetDepth();
// for (int j = 0; j < ag.MinInput.Length; j++)
// {
// if (i % 2 == 0 && j == 3 && ag.GetType() == typeof(AG1))
// {
// oneGene[j] = goodAngle;
// }
// else
// {
// double parameterForGene = (tempMaxInput[j] - tempMinInput[j]) * myRandom.NextDouble() + tempMinInput[j];
// oneGene[j] = parameterForGene;
// }
// oneGene[0] = Math.Floor(oneGene[0]);
// oneGene[1] = Math.Floor(oneGene[1]);
// }
// ParameterSet a = new ParameterSet(oneGene, ag.GetType().ToString(), tempCoreType);
// offspringGenes.Add(a);
//}
double otherAngles = population * initialBoost;//(population - 1) * initialBoost;
for (int i = 0; i < (int)otherAngles; i++)
{
CoreType tempCoreType = ag.GetRandomCoreType();
double[] oneGene = new double[ag.MinInput.Length];
//if (ag.IsCoreProtrude)
// tempMaxInput[2] = tempCoreType.GetDepth();
for (int j = 0; j < ag.MinInput.Length; j++)
{
if (/*i % 2 == 0 &&*/ j == 3 && ag.GetType() == typeof(AG1))
{
double parameterForGene = ((goodAngle + Math.PI / 2 * (i % 4) / 2) % (Math.PI * 2) + Math.PI * ((int)(i / 4) % 2) % (Math.PI * 2));
//oneGene[j] = parameterForGene;
//double mainAngle = Math.PI * i / otherAngles;
//oneGene[j] = mainAngle;
oneGene[j] = parameterForGene;
}
else
{
double parameterForGene = (tempMaxInput[j] - tempMinInput[j]) * myRandom.NextDouble() + tempMinInput[j];
//width - 100씩
if (j == 2)
{
parameterForGene = Math.Round(parameterForGene / 100) * 100;
}
oneGene[j] = parameterForGene;
}
oneGene[0] = Math.Floor(oneGene[0]);
oneGene[1] = Math.Floor(oneGene[1]);
}
ParameterSet a = new ParameterSet(oneGene);
offspringGenes.Add(a);
}
//initializing end condition
bool endCondition = true;
//start genetic algorithm
int genCount = 0;
ParameterSet bestGene = offspringGenes[0];
while (endCondition)
{
//evaluate fitness`
List <List <double> > evaluation = new List <List <double> >(evaluateFitness(plot, ag, target, offspringGenes, fitnessFactor, previewOn));
List <double> fitnessValues = new List <double>(evaluation[0]);
//sort genes and fitness values
RhinoList <ParameterSet> myRhinoList = new RhinoList <ParameterSet>(offspringGenes);
myRhinoList.Sort(fitnessValues.ToArray());
myRhinoList.Reverse();
fitnessValues.Sort();
fitnessValues.Reverse();
offspringGenes = myRhinoList.ToList();
var radcheck = offspringGenes.Select(n => n.Parameters[3]);
/*
* //write
* Rhino.RhinoApp.WriteLine(genCount.ToString());
* Rhino.RhinoApp.WriteLine(evaluation[1][0].ToString());
* Rhino.RhinoApp.WriteLine(evaluation[1][offspringGenes.Count-1].ToString());
* ParameterSet geneToShow = offspringGenes[0];
*
* for (int i = 0; i < geneToShow.Parameters.Length; i++)
* {
* Rhino.RhinoApp.WriteLine(geneToShow.Parameters[i].ToString());
* }
*/
//create new generation
List <ParameterSet> tempGenes = new List <ParameterSet>();
//Add elites to new generation
int eliteNum = (int)(population * elitismPercentage);
for (int i = 0; i < eliteNum; i++)
{
tempGenes.Add(offspringGenes[i]);
}
//crossover & mutation
for (int i = 0; i < population - eliteNum; i++)
{
ParameterSet newOffspring = crossover(offspringGenes, fitnessValues, (int)myRandom.Next(0, int.MaxValue), ag.GetType().ToString());
if (myRandom.NextDouble() < mutationProbability)
{
newOffspring = mutation(newOffspring, ag, mutationFactor, (int)myRandom.Next(0, int.MaxValue));
}
tempGenes.Add(newOffspring);
}
offspringGenes = tempGenes;
genCount += 1;
if (genCount == maxGen)
{
endCondition = false;
}
GC.Collect();
//Rhino.RhinoApp.Wait();
//finalize before end
if (endCondition == false)
{
//evaluate fitness
evaluation = new List <List <double> >(evaluateFitness(plot, ag, target, offspringGenes, fitnessFactor, previewOn));
fitnessValues = new List <double>(evaluation[0]);
//sort genes and fitness values
myRhinoList = new RhinoList <ParameterSet>(offspringGenes);
myRhinoList.Sort(fitnessValues.ToArray());
myRhinoList.Reverse();
fitnessValues.Sort();
fitnessValues.Reverse();
offspringGenes = myRhinoList.ToList();
bestGene = offspringGenes[0];
}
Rhino.RhinoApp.Wait();
}
//best 1
Apartment bestOutput = ag.generator(plot, bestGene, target);
return(new Apartment[] { bestOutput }.ToList());
//best 5
//var uniqueGenes = offspringGenes.Distinct();
//Apartment[] bestOutputs = offspringGenes.Take(5).Select(n => ag.generator(plot, n, target)).ToArray();
//return bestOutputs.ToList();
//best 10
//var uniqueGenes = offspringGenes.Distinct();
//Apartment[] bestOutputs = offspringGenes.Take(10).Select(n=>ag.generator(plot, n, target)).ToArray();
//return new Apartment[] { bestOutput }.ToList();
//return bestOutputs.ToList();
//all
Apartment[] bestOutputs = offspringGenes.Select(n => ag.generator(plot, n, target)).ToArray();
return(bestOutputs.ToList());
if (bestOutput.ParameterSet == null)
{
return(FinalizeApartment.finalizeAGoutput(bestOutput, TuringAndCorbusierPlugIn.InstanceClass.page1Settings.MaxFloorAreaRatio, TuringAndCorbusierPlugIn.InstanceClass.page1Settings.MaxBuildingCoverage, false));
}
if (bestOutput.ParameterSet.Parameters != null)
{
List <Apartment> output = FinalizeApartment.finalizeAGoutput(bestOutput, TuringAndCorbusierPlugIn.InstanceClass.page1Settings.MaxFloorAreaRatio, TuringAndCorbusierPlugIn.InstanceClass.page1Settings.MaxBuildingCoverage, false);
bool IsSatisfyingLegalParking = false;
List <Apartment> satisFyingLegalParkingOutput = new List <Apartment>();
foreach (Apartment i in output)
{
if (i.GetLegalParkingLotOfCommercial() + i.GetLegalParkingLotofHousing() < i.ParkingLotOnEarth.GetCount() + i.ParkingLotUnderGround.Count)
{
satisFyingLegalParkingOutput.Add(i);
IsSatisfyingLegalParking = true;
}
}
if (IsSatisfyingLegalParking == false)
{
targetError tempErrorMessage = new targetError();
List <Apartment> tempNewOutput = tempErrorMessage.showDialogAndReturnValue(ag, plot, bestGene, target, output);
bool tempSatisfyingLegalParking = false;
List <Apartment> tempSatisFyingLegalParkingOutput = new List <Apartment>();
foreach (Apartment i in tempNewOutput)
{
if (i.GetLegalParkingLotOfCommercial() + i.GetLegalParkingLotofHousing() < i.ParkingLotOnEarth.GetCount() + i.ParkingLotUnderGround.Count)
{
tempSatisfyingLegalParking = true;
tempSatisFyingLegalParkingOutput.Add(i);
}
}
if (tempSatisfyingLegalParking == false)
{
System.Windows.MessageBox.Show("선택한 설계로 법정 주차대수를 만족하기 어려운 대지입니다.");
return(output);
}
else
{
return(tempSatisFyingLegalParkingOutput);
}
}
else
{
return(output);
}
}
else
{
System.Windows.MessageBox.Show(CommonFunc.GetApartmentType(ag.GetAGType) + " 타입 설계에 적합하지 않은 대지입니다.");
}
return(FinalizeApartment.finalizeAGoutput(bestOutput, TuringAndCorbusierPlugIn.InstanceClass.page1Settings.MaxFloorAreaRatio, TuringAndCorbusierPlugIn.InstanceClass.page1Settings.MaxBuildingCoverage, false));
}