public void DnCamThread(Object Obj)
{
DnCamThreadSwitch = true;
DnCamProcessEvent.Reset();
while (true)
{
if (DnCamProcessEvent.IsSet)
{
int DnCamStationNum = (int)LD.Logic.PlcHandle.Instance.ReadValue(RightDnCamVisionNumAddr);
if (DnCamStationNum == 0)
{
VisionStateRightDnCamLeft.GrabingImg();
LD.Logic.PlcHandle.Instance.WriteValue(RightDnCamGrabedAddr, 1);
VisionStateRightDnCamLeft.DoLocal();
VisionStateRightDnCamLeft.ShowRlt();
RightDnCamLeftLocalRlt = VisionStateRightDnCamLeft.GetLocalRlt();
RightDnLeftLocalIsOk = true;
}
else if (DnCamStationNum == 1)
{
VisionStateRightDnCamRight.GrabingImg();
LD.Logic.PlcHandle.Instance.WriteValue(RightDnCamGrabedAddr, 1);
VisionStateRightDnCamLeft.DoLocal();
VisionStateRightDnCamLeft.ShowRlt();
RightDnCamLeftLocalRlt = VisionStateRightDnCamLeft.GetLocalRlt();
RightDnRightLocalIsOk = true;
}
DnCamProcessEvent.Reset();
if (!DnCamThreadSwitch)
{
break;
}
}
}
}
public void DnCamThread(object Obj)
{
DncamThreadSwitch = true;
DnCamProcessEvent.Reset();
while (true)
{
if (DnCamProcessEvent.IsSet)
{
int DnCamStationNum = (int)LD.Logic.PlcHandle.Instance.ReadValue(LeftDnCamVisionNumAddr);
if (DnCamStationNum == 0) //左下相机左侧定位
{
VisionStateDnLeftCamLeft.GrabingImg(); //1.0开始采图
LD.Logic.PlcHandle.Instance.WriteValue(LeftDnCamGrabedAddr, 1); //2.0告诉PLC采图完成
VisionStateDnLeftCamLeft.DoLocal(); //3.0执行定位
VisionStateDnLeftCamLeft.ShowRlt(); //4.0显示定位结果
LeftDnCamLeftLocalResult = VisionStateDnLeftCamLeft.GetLocalRlt();
LeftDnCamLeftLocalIsOk = true;
}
else if (DnCamStationNum == 1) //左下相机右侧定位
{
VisionStateDnLeftCamRight.GrabingImg();
LD.Logic.PlcHandle.Instance.WriteValue(LeftDnCamGrabedAddr, 1);
VisionStateDnLeftCamRight.DoLocal();
VisionStateDnLeftCamRight.ShowRlt();
LeftDnCamRightLocalResult = VisionStateDnLeftCamRight.GetLocalRlt();
LeftDnCamRightLocalIsOk = true;
}
}
DnCamProcessEvent.Reset();
if (!DncamThreadSwitch)
{
break;
}
}
}
public bool DoLocal()
{
FileLib.Logger.Pop(" 采图完成开始执行定位：", false, StationDescribe + "运行日志");
myLocalManager.SetParam(GrabImg, MyVisionPara.localPara);
myLocalManager.doLocal();
myLocalrlt = myLocalManager.GetResult();
FileLib.Logger.Pop(" 定位完成，返回定位结果：", false, StationDescribe + "运行日志");
return true;
}
public void UpCamThread(Object Obj)
{
UpCamThreadSwitch = true;
UpCamProcessEvent.Reset();
while (true)
{
if (UpCamProcessEvent.IsSet)
{
int UpCamStationNum = (int)LD.Logic.PlcHandle.Instance.ReadValue(UpCamVisionNumAddr);
if (UpCamStationNum == 0)
{
VisionStateUpCamLeftDn.GrabingImg(); //1.0 开始采图
LD.Logic.PlcHandle.Instance.WriteValue(UpCamGrabedAddr, 1); //2.0 告诉Plc采图完成
VisionStateUpCamLeftDn.DoLocal(); //3.0 执行定位
VisionStateUpCamLeftDn.ShowRlt(); //4.0 显示定位结果
UpCamLeftDnLocalRlt = VisionStateUpCamLeftDn.GetLocalRlt(); //5.0 获取定位结果
UpCamLeftDnLocalIsOk = true;
}
else if (UpCamStationNum == 1)
{
VisionStateUpCamRightDn.GrabingImg();
LD.Logic.PlcHandle.Instance.WriteValue(UpCamGrabedAddr, 1);
VisionStateUpCamRightDn.DoLocal();
VisionStateUpCamRightDn.ShowRlt();
UpCamRightDnLocalRlt = VisionStateUpCamRightDn.GetLocalRlt();
UpCamRightDnLocalIsOk = true;
}
UpCamProcessEvent.Reset();
}
else
{
Thread.Sleep(1);
}
if (UpCamLeftDnLocalIsOk && UpCamRightDnLocalIsOk && RightDnLeftLocalIsOk && RightDnRightLocalIsOk)
{
//1.0计算胶带当前的定位坐标，相对于示教坐标的偏差；
double LeftOffSetX = 0, LeftOffSetY = 0, RightOffSetX = 0, RightOffsetY = 0;
LeftOffSetX = RightDnCamLeftLocalRlt.PosToRot.Col - RightDnCamLeftLocalRlt.TeachPosToRot.Col;
LeftOffSetY = RightDnCamLeftLocalRlt.PosToRot.Row - RightDnCamLeftLocalRlt.TeachPosToRot.Row;
RightOffSetX = RightDnCamRightLocalRlt.PosToRot.Col - RightDnCamRightLocalRlt.TeachPosToRot.Col;
RightOffsetY = RightDnCamRightLocalRlt.PosToRot.Row - RightDnCamRightLocalRlt.TeachPosToRot.Row;
//2.0 用当前的胶带坐标相对于示教的偏移量， 调整Ipad的示教坐标
Point2Db TeachIpadPosLeft = new Point2Db(UpCamLeftDnLocalRlt.TeachPosToRot.Col, UpCamLeftDnLocalRlt.TeachPosToRot.Row);
Point2Db TeachIpadPosRight = new Point2Db(UpCamRightDnLocalRlt.TeachPosToRot.Col, UpCamRightDnLocalRlt.TeachPosToRot.Row);
TeachIpadPosLeft.Col = TeachIpadPosLeft.Col + LeftOffSetX;
TeachIpadPosLeft.Row = TeachIpadPosLeft.Row + LeftOffSetY;
TeachIpadPosRight.Col = TeachIpadPosRight.Col + RightOffSetX;
TeachIpadPosRight.Row = TeachIpadPosRight.Row + RightOffsetY;
//3.0 利用调整后的Ipad示教坐标 和 当前的Ipad坐标 计算出 偏移补偿量
Point2Db NowIpadLeftPos = new Point2Db(UpCamLeftDnLocalRlt.PosToRot.Col, UpCamLeftDnLocalRlt.PosToRot.Row);
Point2Db NowIpadRightPos = new Point2Db(UpCamRightDnLocalRlt.PosToRot.Col, UpCamRightDnLocalRlt.PosToRot.Row);
//4.0计算出偏移量
double SystemOffsetX = RightDnLeftVisionPara.localPara.localSetting.Offset_x;
double SystemOffsetY = RightDnLeftVisionPara.localPara.localSetting.Offset_y;
double SystemOffsetTheta = RightDnLeftVisionPara.localPara.localSetting.Offset_theta;
double AddX = 0, AddY = 0, AddTheta = 0;
MyVisionBase.CalculateTwoPtPos(NowIpadLeftPos, NowIpadRightPos, TeachIpadPosLeft, TeachIpadPosRight,
SystemOffsetX, SystemOffsetY, SystemOffsetTheta, out AddX, out AddY, out AddTheta);
FileLib.Logger.Pop(" 计算出的偏移补偿量：" + AddX.ToString("f3") + " " + AddY.ToString("f3") + " " +
AddTheta.ToString("f3"), false, "运行日志");
int NewAddX = (int)(AddX * 1000);
int NewAddY = (int)(AddY * 1000);
int NewAddTheta = (int)(AddTheta * 1000);
LD.Logic.PlcHandle.Instance.WriteValue(RightDnCamVisionFinishAddr, 1);
LD.Logic.PlcHandle.Instance.WriteValue(LD.Common.PlcDevice.V_05_Offset_X, -NewAddX);
LD.Logic.PlcHandle.Instance.WriteValue(LD.Common.PlcDevice.V_05_Offset_Y, NewAddY);
LD.Logic.PlcHandle.Instance.WriteValue(LD.Common.PlcDevice.V_05_Offset_R, NewAddTheta);
}
}
}
private void UpCamThread(Object Obj)
{
UpCamThreadSwitch = true;
UpCamProcessEvent.Reset();
while (true)
{
if (UpCamProcessEvent.IsSet)
{
int UpCamStationNum = (int)LD.Logic.PlcHandle.Instance.ReadValue(UpCamVisionNumAddr);
if (UpCamStationNum == 0) //上相机定位左上角
{
//1.0开始采图
VisionStateBaseUpCamLeftUp.GrabingImg();
//2.0告诉PLC采图完成
LD.Logic.PlcHandle.Instance.WriteValue(UpCamGrabedAddr, 1);
//3.0执行定位
VisionStateBaseUpCamLeftUp.DoLocal();
//4.0显示定位结果
VisionStateBaseUpCamLeftUp.ShowRlt();
//5.0获取定位结果
UpCamLeftUpLocalRlt = VisionStateBaseUpCamLeftUp.GetLocalRlt();
UpCamLeftUpLocalIsok = true;
}
else if (UpCamStationNum == 1)//上相机定位右上角
{
VisionStateBaseUpCamRightUp.GrabingImg();
LD.Logic.PlcHandle.Instance.WriteValue(UpCamGrabedAddr, 1);
VisionStateBaseUpCamRightUp.DoLocal();
VisionStateBaseUpCamRightUp.ShowRlt();
UpCamRightUpLocalRlt = VisionStateBaseUpCamLeftUp.GetLocalRlt();
UpCamRightUpLocalIsOk = true;
}
UpCamProcessEvent.Reset();
}
else
{
Thread.Sleep(1);
}
if (UpCamLeftUpLocalIsok && UpCamRightUpLocalIsOk && LeftDnCamLeftLocalIsOk && LeftDnCamRightLocalIsOk)
{
//1.0计算胶带当前的定位坐标，相对于示教坐标的偏差；
double LeftOffSetX = 0, LeftOffSetY = 0, RightOffSetX = 0, RightOffsetY = 0;
LeftOffSetX = LeftDnCamLeftLocalResult.PosToRot.Col - LeftDnCamLeftLocalResult.TeachPosToRot.Col;
LeftOffSetY = LeftDnCamLeftLocalResult.PosToRot.Row - LeftDnCamLeftLocalResult.TeachPosToRot.Row;
RightOffSetX = LeftDnCamRightLocalResult.PosToRot.Col - LeftDnCamRightLocalResult.TeachPosToRot.Col;
RightOffsetY = LeftDnCamRightLocalResult.PosToRot.Row - LeftDnCamRightLocalResult.TeachPosToRot.Row;
//2.0 用当前的胶带坐标相对于示教的偏移量， 调整Ipad的示教坐标
Point2Db TeachIpadPosLeft = new Point2Db(UpCamLeftUpLocalRlt.TeachPosToRot.Col, UpCamLeftUpLocalRlt.TeachPosToRot.Row);
Point2Db TeachIpadPosRight = new Point2Db(UpCamRightUpLocalRlt.TeachPosToRot.Col, UpCamRightUpLocalRlt.TeachPosToRot.Row);
TeachIpadPosLeft.Col = TeachIpadPosLeft.Col + LeftOffSetX;
TeachIpadPosLeft.Row = TeachIpadPosLeft.Row + LeftOffSetY;
TeachIpadPosRight.Col = TeachIpadPosRight.Col + RightOffSetX;
TeachIpadPosRight.Row = TeachIpadPosRight.Row + RightOffsetY;
//3.0 利用调整后的Ipad示教坐标 和 当前的Ipad坐标 计算出 偏移补偿量
Point2Db NowIpadLeftPos = new Point2Db(UpCamLeftUpLocalRlt.PosToRot.Col, UpCamLeftUpLocalRlt.PosToRot.Row);
Point2Db NowIpadRightPos = new Point2Db(UpCamRightUpLocalRlt.PosToRot.Col, UpCamRightUpLocalRlt.PosToRot.Row);
//4.0计算出偏移量
double AddX = 0, AddY = 0, AddTheta = 0;
MyVisionBase.CalculateThreeTapePos(NowIpadLeftPos, NowIpadRightPos, TeachIpadPosLeft, TeachIpadPosRight, out AddX, out AddY, out AddTheta);
FileLib.Logger.Pop(" 计算出的偏移补偿量：" + AddX.ToString("f3") + " " + AddY.ToString("f3") + " " + AddTheta.ToString("f3"), false, "运行日志");
int SystemOffSetX = 0, SystemOffsetY = 0;
SystemOffSetX = (int)(LeftDnCamLeftVisionPara.localPara.localSetting.Offset_x * 1000); //系统误差补偿
SystemOffsetY = (int)(LeftDnCamLeftVisionPara.localPara.localSetting.Offset_y * 1000);
int NewAddX = (int)(AddX * 1000) + SystemOffSetX;
int NewAddY = (int)(AddY * 1000) + SystemOffsetY;
int NewAddTheta = (int)(AddTheta * 1000);
LD.Logic.PlcHandle.Instance.WriteValue(LeftDnCamVisionFinishAddr, 1);
LD.Logic.PlcHandle.Instance.WriteValue(LD.Common.PlcDevice.V_03_Offset_X, NewAddX);
LD.Logic.PlcHandle.Instance.WriteValue(LD.Common.PlcDevice.V_03_Offset_Y, -NewAddY); //移动屏幕
LD.Logic.PlcHandle.Instance.WriteValue(LD.Common.PlcDevice.V_03_Offset_R, NewAddTheta);
//5.0 清空此次定位的数据，进入下次定位
UpCamLeftUpLocalIsok = false;
UpCamRightUpLocalIsOk = false;
LeftDnCamLeftLocalIsOk = false;
LeftDnCamRightLocalIsOk = false;
}
if (!UpCamThreadSwitch)
{
break;
}
}
}
private void IpadLcoalThread(object Obj)
{
FeederThreadSwitch = true;
while (true)
{
if (FeerderCamTrigEvent.IsSet)
{
int StateNum = (int)LD.Logic.PlcHandle.Instance.ReadValue(FeederCamStateNumAddr);
if (StateNum == 0) //Ipad左上角
{
VisionStateLeftUp.GrabingImg();
LD.Logic.PlcHandle.Instance.WriteValue(FeederCamGrabedAddr, 1);
VisionStateLeftUp.DoLocal();
VisionStateLeftUp.ShowRlt();
LeftUpLocalRlt = VisionStateLeftUp.GetLocalRlt();
LeftUpLocalIsOK = true;
}
else if (StateNum == 1) //Ipad右下角
{
VisionStateRightDn.GrabingImg();
LD.Logic.PlcHandle.Instance.WriteValue(FeederCamGrabedAddr, 1);
VisionStateRightDn.DoLocal();
VisionStateRightDn.ShowRlt();
RightDnLocalRlt = VisionStateRightDn.GetLocalRlt();
RightDnLocalIsOk = true;
}
}
if (LeftUpLocalIsOK && RightDnLocalIsOk)
{
//1.0Ipad放置时的示教坐标
Point2Db IpadLeftUpTeachPos = new Point2Db(LeftUpLocalRlt.TeachPosToRot.Col, LeftUpLocalRlt.TeachPosToRot.Row);
Point2Db IpadRightDnTeachPos = new Point2Db(RightDnLocalRlt.TeachPosToRot.Col, RightDnLocalRlt.TeachPosToRot.Row);
//2.0Ipad的当前坐标
Point2Db IpadLeftUpNowPos = new Point2Db(LeftUpLocalRlt.PosToRot.Col, LeftUpLocalRlt.PosToRot.Row);
Point2Db IpadRightDnNowPos = new Point2Db(RightDnLocalRlt.PosToRot.Col, RightDnLocalRlt.PosToRot.Row);
//3.0计算出偏移量
double AddX = 0, AddY = 0, AddTheta = 0;
MyVisionBase.CalculateThreeTapePos(IpadLeftUpNowPos, IpadRightDnNowPos, IpadLeftUpTeachPos, IpadRightDnTeachPos, out AddX, out AddY, out AddTheta);
FileLib.Logger.Pop(" 计算出的偏移补偿量：" + AddX.ToString("f3") + " " + AddY.ToString("f3") + " " + AddTheta.ToString("f3"), false, "运行日志");
int SystemOffSetX = 0, SystemOffsetY = 0;
SystemOffSetX = (int)(LeftUpVisionPara.localPara.localSetting.Offset_x * 1000); //系统误差补偿
SystemOffsetY = (int)(LeftUpVisionPara.localPara.localSetting.Offset_y * 1000); //系统误差补偿
int NewAddX = (int)(AddX * 1000) + SystemOffSetX;
int NewAddY = (int)(AddY * 1000) + SystemOffsetY;
int NewAddTheta = (int)(AddTheta * 1000);
LD.Logic.PlcHandle.Instance.WriteValue(FeederCamVisionFinishAddr, 1);
LD.Logic.PlcHandle.Instance.WriteValue(LD.Common.PlcDevice.V_01_Offset_X, NewAddX);
LD.Logic.PlcHandle.Instance.WriteValue(LD.Common.PlcDevice.V_01_Offset_Y, NewAddY); //移动屏幕
LD.Logic.PlcHandle.Instance.WriteValue(LD.Common.PlcDevice.V_01_Offset_R, NewAddTheta);
//4.0 清空此次定位的数据，进入下次定位
LeftUpLocalIsOK = false;
RightDnLocalIsOk = false;
}
Thread.Sleep(1);
if (!FeederThreadSwitch)
{
break;
}
}
}
public static void Run(SoftwareGraphLearningParameters parameters)
{
Build.Do();
var graphs = new List <IGWGraph <SGLNodeData, SGLEdgeData, SGLGraphData> >();
#region Graphen erzeugen
var erdösGraphCreator = new ErdösGraphCreator();
var categoryGraphCreator = new CategoryGraphCreator();
for (int i = 0; i < parameters.NumberOfGraphs; i++)
{
var newGraph = erdösGraphCreator.CreateGraph(new ErdösGraphCreationParameter(parameters));
// Kategoriengraph erzeugen
categoryGraphCreator.CreateCategoryGraph(newGraph);
graphs.Add(newGraph);
//var graph3D = newGraph.Wrap3D();
//new ShowGraph3D(graph3D).Request();
}
#endregion
#region Beobachtungen erzeugen
// TODO: Nach welchem Vorgehen Beobachtungen erzeugen?
// erstmal: für jede Kategorie: zufällig x [0,1): Prob(0) = x, Prob(1) = 1-x
for (int i = 0; i < parameters.NumberOfGraphs; i++)
{
var graph = graphs[i];
// Beobachtung für graph erzeugen
foreach (var node in graph.Nodes)
{
var categoryGraph = graph.Data.CategoryGraph;
var category = categoryGraph.Nodes.ToList().Find(catNode => catNode.Data.Category == node.Data.Category);
double probability = CodeBase.BaseProgram.Random.NextDouble();
if (probability <= category.Data.ObservationProbabilty)
{
node.Data.Observation = 0;
}
else
{
node.Data.Observation = 1;
}
}
}
//information about graph
/*
* var graphView = new GraphView();
* foreach (var graph in graphs)
* {
* graphView.GetGraphInfo(graph);
*
* } */
#endregion
#region Training
// zwei Parameter: Korrelationsparameter || Konformitätsparameter
// zusätzliche parameter (a (negativ), b) (vorgegeben in SoftwareGraphLearningParameters)
// Zielfunktion setzt sich aus zwei Zielen zusammen:
// Ziel 1: Homogenität: erstmal: Für jede Kategorie: Score := a * (Math.Abs(0.95 - homogenityRatio))
// Ziel 2: Unabhängigkeit: erstmal: Für jede Kategorie: Score += b, falls
// Ziel 3: Korrelation mit lokalen Beobachtungen
// Math.Sign([Mittelwert Knotenscore] - 0.5) == Math.Sign([Mittelwert Knotenlabeling] - 0.5)
// Ziel 3: Korrelation
//in results sind die durchschnittl homogenitäten der graphen für die 10 * 10 datenpunkte gespeichert
var results = new List <LocalResult>();
for (int k = 1; k <= 10; k++)
{
for (int l = 1; l <= 10; l++)
{
var conformity = k * 0.1;
var correlation = l * 0.1;
var isingModel = new IsingModel(conformity, correlation);
//in resultstemp sind homogenitäten für die graphen bei gleicher correlation + conformity
var resultsTemp = new List <LocalResult>();
int counter = 0;
foreach (var graph in graphs)
{
var localResult = new LocalResult(parameters.NumberCategories, conformity, correlation);
#region homogenities normal
// anhand der Observation & Korrelation/Konformität CRF-Scores berechnen
// Berechne Scores für nodes und edges
isingModel.CreateCRFScore(graph);
// Viterbiheuristik starten
var request = new SolveInference(graph, null, parameters.NumberLabels);
request.RequestInDefaultContext();
// sammeln der ergebnisse
var resultingLabeling = request.Solution.Labeling;
//labeling auf nodes mappen
var nodes = graph.Nodes.ToList();
foreach (var node in nodes)
{
node.Data.AssignedLabel = resultingLabeling[node.GraphId];
}
//homogenität berechnen -> in localResult speichern
var categoryGraph = graph.Data.CategoryGraph;
//durch jede kategorie gehen
foreach (var catNode in categoryGraph.Nodes)
{
int amountZeroLabeled = 0;
//für jeden knoten in aktueller kategorie, anzahl 0 labels zählen
foreach (var node in catNode.Data.Nodes)
{
if (node.Data.AssignedLabel == 0)
{
amountZeroLabeled++;
}
}
//homogenität = max(a; 1-a) a = anteil mit 0 gelabelt
var homogenityRatio = Math.Max((amountZeroLabeled * 1.0) / catNode.Data.NumberNodes,
1 - (amountZeroLabeled * 1.0) / catNode.Data.NumberNodes);
//homgenität speichern in homogenity array
localResult.Homs[catNode.Data.Category] = homogenityRatio;
}
#endregion
#region distinction isolated
//distinction isoliert berechnen
foreach (var edge in graph.Edges)
{ //set score of all inter edges to 0
if (edge.Data.Type == EdgeType.Inter)
{
edge.Data.Scores = new double[2, 2] {
{ 0, 0 }, { 0, 0 }
};
}
}
// Viterbiheuristik starten
request = new SolveInference(graph, null, parameters.NumberLabels);
request.RequestInDefaultContext();
// sammeln der ergebnisse
resultingLabeling = request.Solution.Labeling;
//labeling auf nodes mappen
nodes = graph.Nodes.ToList();
foreach (var node in nodes)
{
node.Data.LabelTemp = resultingLabeling[node.GraphId];
}
categoryGraph = graph.Data.CategoryGraph;
//durch jede kategorie gehen
foreach (var catNode in categoryGraph.Nodes)
{
int amountDifferentLabeled = 0;
//für jede kategorie, anzahl unterschiedlich gelabelter knoten zählen
foreach (var node in catNode.Data.Nodes)
{
if (node.Data.AssignedLabel != node.Data.LabelTemp)
{
amountDifferentLabeled++;
}
}
//distinctRatio = anteil der ungleich gelabelten nodes
var distinctRatio = (amountDifferentLabeled * 1.0) / catNode.Data.NumberNodes;
//distinctRatio in distinction array speichern
localResult.Distincts[catNode.Data.Category] = distinctRatio;
}
#endregion
resultsTemp.Add(localResult);
if (counter == 0)
{
graph.SaveAsJSON("exampleGraph_" + k + "_" + l + ".txt");
//var graph3D = graph.Wrap3D();
//new ShowGraph3D(graph3D).Request();
}
counter++;
}//end of foreach graph in graphs
//jetzt durchschnittswerte für alle in resultsTemp berechnen
//und diese durchschnitte in results speichern
var averageResult = new LocalResult(parameters.NumberCategories, conformity, correlation);
//values aufaddieren
foreach (var localresult in resultsTemp)
{
averageResult.AddValues(localresult);
}
//durchschnitte für kategorien
for (int i = 0; i < averageResult.Homs.Length; i++)
{
averageResult.Homs[i] /= resultsTemp.Count;
averageResult.Distincts[i] /= resultsTemp.Count;
}
//gesamtdurchschnitte
double avgHomogenity = 0;
double avgDistinction = 0;
for (int i = 0; i < averageResult.Homs.Length; i++)
{
avgHomogenity += averageResult.Homs[i];
avgDistinction += averageResult.Distincts[i];
}
avgHomogenity /= averageResult.Homs.Length;
avgDistinction /= averageResult.Distincts.Length;
averageResult.AvgHomogenity = avgHomogenity;
averageResult.AvgDistinction = avgDistinction;
averageResult.ResultValue = avgHomogenity + (1 - avgDistinction);
//ausgabe
Log.Post("Konformität: " + conformity + " - Korrelation: " + correlation + " ", LogCategory.Result);
Log.Post("Avg Homogenity: " + averageResult.AvgHomogenity, LogCategory.Result);
Log.Post("Avg Distinction: " + averageResult.AvgDistinction, LogCategory.Result);
Log.Post(Environment.NewLine, LogCategory.Result);
results.Add(averageResult);
}
}
/* Erwartungen:
*
* 1) gleiche Werte in allen Communities
* 2) Homogenität ansteigend in Correlation
*
*
* */
// Auswertung der Homogenität und Unabhängigkeit
var bestResult = results.MaxEntry(r => r.ResultValue);
Log.Post("Exhaustive Search Result:");
Log.Post("conformity: " + bestResult.Conformity);
Log.Post("correlation: " + bestResult.Correlation);
bestResult.SaveAsJSON(@"..\..\bestResult.txt");
results.SaveAsJSON(@"..\..\results.txt");
#endregion
#region OLM
var con = bestResult.Conformity;
var cor = bestResult.Correlation;
//create referenceLabeling for best parameters
var isingModell = new IsingModel(con, cor);
foreach (var graph in graphs)
{
isingModell.CreateCRFScore(graph);
var request = new SolveInference(graph, null, parameters.NumberLabels);
request.RequestInDefaultContext();
graph.Data.ReferenceLabeling = request.Solution.Labeling;
}
var req = new OLMRequest(OLMVariant.Default, graphs);
req.BasisMerkmale.AddRange(new IsingMerkmalNode(), new IsingMerkmalEdge());
req.LossFunctionValidation = LossFunction;
req.MaxIterations = 100;
req.RequestInDefaultContext();
double[] olmWeights = req.Result.ResultingWeights;
Log.Post("OLM Result: ");
for (int i = 0; i < olmWeights.Length; i++)
{
Log.Post(olmWeights[i] + "");
}
#endregion
}//end of Do Method
