public List <Apartment> showDialogAndReturnValue(ApartmentGeneratorBase AG, Plot plot, ParameterSet existingParameter, Target existingTarget, List <Apartment> existingOutput)
{
returnValue = existingOutput;
this.AG = AG;
this.Plot = plot;
this.ExistingParameter = existingParameter;
this.ExistingTarget = existingTarget;
this.ShowDialog();
return(returnValue);
}
private static ParameterSet mutation(ParameterSet gene, ApartmentGeneratorBase ag, double mutationFactor, int randomSeed)
{
//random gaussian
Random rand = new Random(randomSeed); //reuse this if you are generating many
bool offBound = true;
double randStdNormal = double.MaxValue;
while (offBound)
{
double u1 = rand.NextDouble(); //these are uniform(0,1) random doubles
double u2 = rand.NextDouble();
randStdNormal = Math.Sqrt(-2.0 * Math.Log(u1)) * Math.Sin(2.0 * Math.PI * u2); //random normal(0,1)
if (randStdNormal > -mutationFactor && randStdNormal < mutationFactor)
{
offBound = false;
}
}
double fitThreeSigma = Math.Max(-mutationFactor, randStdNormal);
fitThreeSigma = Math.Min(mutationFactor, fitThreeSigma);
fitThreeSigma /= (mutationFactor * 2);
//mutation
List <double> newGene = new List <double>();
for (int i = 0; i < gene.Parameters.Length; i++)
{
double parameterDomainLength = ag.MaxInput[i] - ag.MinInput[i];
double mutatedParameter = gene.Parameters[i] + parameterDomainLength * fitThreeSigma;
mutatedParameter = Math.Max(ag.MinInput[i], mutatedParameter);
mutatedParameter = Math.Min(ag.MaxInput[i], mutatedParameter);
if (i <= 1)
{
mutatedParameter = Math.Floor(mutatedParameter);
}
//width - 100 씩
if (i == 2)
{
mutatedParameter = Math.Round(mutatedParameter / 100) * 100;
}
newGene.Add(mutatedParameter);
}
ParameterSet resultGene = new ParameterSet(newGene.ToArray());
return(resultGene);
}
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));
}
private static List <List <double> > evaluateFitness(Plot plot, ApartmentGeneratorBase ag, Target target, List <ParameterSet> gene, double fitnessFactor, bool previewOn)
{
List <List <double> > result = new List <List <double> >();
List <double> grossAreaRatio = new List <double>();
List <double> parkinglotRatio = new List <double>();
List <double> targetAccuracy = new List <double>();
List <double> axisAccuracy = new List <double>();
bool drawSomeThing = false;
int i = 0;
var timer = new System.Threading.Timer((e) =>
{
drawSomeThing = true;
}
, drawSomeThing, TimeSpan.Zero, TimeSpan.FromMilliseconds(1000));
List <Apartment> agOutToDrawList = new List <Apartment>();
List <double> agOutToDrawGrossAreaList = new List <double>();
for (i = 0; i < gene.Count(); i++)
{
Apartment tempOutput = ag.generator(plot, gene[i], target);
grossAreaRatio.Add(tempOutput.GetGrossAreaRatio());
//targetAccuracy.Add(tempOutput.GetTargetAccuracy());
parkinglotRatio.Add(tempOutput.GetParkingScore());
axisAccuracy.Add(tempOutput.GetAxisAccuracy());
TuringAndCorbusierPlugIn.InstanceClass.page3.currentProgressFactor += 1;
TuringAndCorbusierPlugIn.InstanceClass.page3.updateProGressBar(TuringAndCorbusierPlugIn.InstanceClass.page3.currentProgressFactor.ToString() + "/" + TuringAndCorbusierPlugIn.InstanceClass.page3.currentWorkQuantity.ToString() + " 진행중");
if (previewOn)
{
agOutToDrawList.Add(tempOutput);
//agOutToDrawGrossAreaList.Add(tempOutput.GetGrossAreaRatio());
if (drawSomeThing)
{
TuringAndCorbusierPlugIn.InstanceClass.page3.preview(agOutToDrawList.Last());
Rhino.RhinoDoc.ActiveDoc.Views.Redraw();
Rhino.RhinoApp.Wait();
agOutToDrawList.Clear();
agOutToDrawGrossAreaList.Clear();
drawSomeThing = false;
}
}
}
double MaxFAR = TuringAndCorbusierPlugIn.InstanceClass.page1Settings.MaxFloorAreaRatio;
//법정 용적률에 근접한 유전자가 가장 높은 값 받음,
// -0 ~ -5 최대값 받음
//넘어가면 * 0.01 포인트 마이너스
//낮으면 *0.1포인트 마이너스
List <double> points = new List <double>();
for (int r = 0; r < grossAreaRatio.Count; r++)
{
double point = 0;
if (grossAreaRatio[r] < MaxFAR && grossAreaRatio[r] > MaxFAR - 5)
{
point = 1 + (5 - (MaxFAR - grossAreaRatio[r])) / MaxFAR;
}
else if (grossAreaRatio[r] > MaxFAR)
{
point = 1 - (grossAreaRatio[r] - MaxFAR) / MaxFAR / 10;
}
else
{
point = 1 - (MaxFAR - grossAreaRatio[r]) / MaxFAR;
}
points.Add(point);
}
List <double> tempGAR = new List <double>(grossAreaRatio);
RhinoList <double> FARList = new RhinoList <double>(tempGAR);
//FARList.Sort(points.ToArray());
//points.Sort();
double Cworst = FARList.First;
double Cbest = FARList.Last;
double k = fitnessFactor;
List <double> fitness = new List <double>();
//double Cworst = tempGAR[0];
//double Cbest = tempGAR[gene.Count - 1];
//double k = fitnessFactor;
//List<double> fitness = new List<double>();
double parkingWeight = 0.3;
double CworstR = parkinglotRatio.Min();
double CbestR = parkinglotRatio.Max();
double targetWeight = 0.4;
//double CworstT = targetAccuracy.Min();
//double CbestT = targetAccuracy.Max();
//fitnessvalue rate
//연면적 1 : 주차율(?) 0.3 : 유닛정확도 : 0.4
//for test
//string url = @"C://Users//user//Desktop//test";
//System.IO.DirectoryInfo dinfo = new System.IO.DirectoryInfo(url);
//string filename = "//garojutacklogV2" + (dinfo.GetFiles().Length + 1).ToString() + ".csv";
//System.IO.FileStream fs = new System.IO.FileStream(url + filename, System.IO.FileMode.CreateNew);
//fs.Close();
//fs.Dispose();
//System.IO.StreamWriter w = new System.IO.StreamWriter(fs.Name);
//string column = "FAR" + "," + "Floors" + "," + "FARPoint" + "," + "ParkingPoint" + "," + "AxisPoint" + "," + "Sum" + "," + "Use1F" + ",UseSetback";
//w.WriteLine(column);
for (int j = 0; j < gene.Count; j++)
{
double farfitnessVal = points[j] * 10;
double parkkingfitnessVal = parkinglotRatio[j];
double axisfitnessVal = axisAccuracy[j];
//double farfitnessVal = ((grossAreaRatio[j] - Cbest) * (k - 1) / k + (Cbest - Cworst) + 0.01) / (Cbest - Cworst + 0.01);
//double parkingval = ((parkinglotRatio[j] - CbestR) * (k - 1) / k + (CbestR - CworstR) + 0.01) / (CbestR - CworstR + 0.01) * parkingWeight;
//double targetval = ((targetAccuracy[j] - CbestT) * (k - 1) / k + (CbestT - CworstT) + 0.01) / (CbestT - CworstT + 0.01) * targetWeight;
//firstfloor test
double firstfloorBonus = 0;
//if (gene[j].using1F)
// firstfloorBonus = 1000;
//setback test
double setbackBonus = 0;
//if (gene[j].setback)
// setbackBonus = 1000;
fitness.Add(farfitnessVal + parkkingfitnessVal + axisfitnessVal + setbackBonus + firstfloorBonus);
//for test
//string format = grossAreaRatio[j].ToString() + "," + gene[j].Stories + "," + farfitnessVal + "," + parkkingfitnessVal + "," + axisfitnessVal + "," + (farfitnessVal + parkkingfitnessVal + axisfitnessVal).ToString() + "," + gene[j].using1F.ToString() + "," + gene[j].setback.ToString();
//w.WriteLine(format);
}
//for test
//w.Close();
//w.Dispose();
//tempGAR.Reverse();
//fitness.Reverse();
//FARList.Reverse();
result.Add(fitness);
result.Add(FARList.Select(n => n).ToList());
double maxfar = FARList.Max();
double maxfitness = fitness.Max();
TuringAndCorbusierPlugIn.InstanceClass.page3.updateProGressBar(TuringAndCorbusierPlugIn.InstanceClass.page3.currentProgressFactor.ToString() + "/" + TuringAndCorbusierPlugIn.InstanceClass.page3.currentWorkQuantity.ToString() + " 완료");
return(result);
//or, return nested list, containing gross area ratio.
}
private string GetConvertedAGName(ApartmentGeneratorBase AG)
{
return("AG1");
}
