/// <summary>
/// display a GUI and generate the WEKA based clusterer
/// </summary>
/// <param name="InstancesList">list of the weka instance</param>
/// <returns>weka clusterer</returns>
public Clusterer BuildClusterer(cParamAlgo ClusteringAlgo, cExtendedTable Input)
{
this.InputTable = Input;
foreach (var item in Input)
{
this.ListDescriptors.Add(item.Name);
}
cListValuesParam Parameters = ClusteringAlgo.GetListValuesParam();
Clusterer ClustererToReturn = null;
Instances ListInstancesWithoutClasses = CreateInstancesWithoutClass(Input);
#region EM
if (ClusteringAlgo.Name == "EM")
{
ClustererToReturn = new EM();
if (Parameters.ListCheckValues.Get("checkBoxAutomatedClassNum").Value)
((EM)ClustererToReturn).setNumClusters(-1);
else
((EM)ClustererToReturn).setNumClusters((int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value);
((EM)ClustererToReturn).setMaxIterations((int)Parameters.ListDoubleValues.Get("numericUpDownMaxIterations").Value);
((EM)ClustererToReturn).setMinStdDev((double)Parameters.ListDoubleValues.Get("numericUpDownMinStdev").Value);
((EM)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
#region K Means
else if (ClusteringAlgo.Name == "K-Means")
{
ClustererToReturn = new SimpleKMeans();
((SimpleKMeans)ClustererToReturn).setNumClusters((int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value);
((SimpleKMeans)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
string DistanceType = (string)Parameters.ListTextValues.Get("comboBoxDistance").Value;
if (DistanceType == "Euclidean")
{
EuclideanDistance ED = new EuclideanDistance();
ED.setDontNormalize(!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value);
((SimpleKMeans)ClustererToReturn).setDistanceFunction(ED);
}
else if (DistanceType == "Manhattan")
{
ManhattanDistance MD = new ManhattanDistance();
MD.setDontNormalize(!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value);
((SimpleKMeans)ClustererToReturn).setDistanceFunction(MD);
}
else return null;
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
//#region K Means++
//else if (ClusteringAlgo.Name == "K-Means++")
//{
// ClustererToReturn = new SimpleKMeans();
// ((SimpleKMeans)ClustererToReturn).setNumClusters((int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value);
// ((SimpleKMeans)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
// string DistanceType = (string)Parameters.ListTextValues.Get("comboBoxDistance").Value;
// if (DistanceType == "Euclidean")
// {
// EuclideanDistance ED = new EuclideanDistance();
// ED.setDontNormalize(!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value);
// ((SimpleKMeans)ClustererToReturn).setDistanceFunction(ED);
// }
// else if (DistanceType == "Manhattan")
// {
// ManhattanDistance MD = new ManhattanDistance();
// MD.setDontNormalize(!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value);
// ((SimpleKMeans)ClustererToReturn).setDistanceFunction(MD);
// }
// else return null;
// ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
// this.NumberOfClusters = ClustererToReturn.numberOfClusters();
//}
//#endregion
#region hierarchical
else if (ClusteringAlgo.Name == "Hierarchical")
{
ClustererToReturn = new weka.clusterers.HierarchicalClusterer();
string OptionDistance = " -N " + (int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value;
string DistanceType = (string)Parameters.ListTextValues.Get("comboBoxDistance").Value;
OptionDistance += " -A \"weka.core.";
switch (DistanceType)
{
case "Euclidean":
OptionDistance += "EuclideanDistance";
break;
case "Manhattan":
OptionDistance += "ManhattanDistance";
break;
case "Chebyshev":
OptionDistance += "ChebyshevDistance";
break;
default:
break;
}
if (!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value)
OptionDistance += " -D";
OptionDistance += " -R ";
OptionDistance += "first-last\"";
string WekaOption = "-L " + (string)Parameters.ListTextValues.Get("comboBoxLinkType").Value + OptionDistance;
((HierarchicalClusterer)ClustererToReturn).setOptions(weka.core.Utils.splitOptions(WekaOption));
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
#region Farthest First
else if (ClusteringAlgo.Name == "FarthestFirst")
{
ClustererToReturn = new weka.clusterers.FarthestFirst();
((FarthestFirst)ClustererToReturn).setNumClusters((int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value);
((FarthestFirst)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
#region CobWeb
else if (ClusteringAlgo.Name == "CobWeb")
{
ClustererToReturn = new weka.clusterers.Cobweb();
((Cobweb)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
((Cobweb)ClustererToReturn).setAcuity((double)Parameters.ListDoubleValues.Get("numericUpDownAcuity").Value);
((Cobweb)ClustererToReturn).setCutoff((double)Parameters.ListDoubleValues.Get("numericUpDownCutOff").Value);
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
#region Manual
else if (ClusteringAlgo.Name == "Manual")
{
string DescriptorName = (string)Parameters.ListTextValues.Get("comboBoxForDescriptorManualClustering").Value;
// this.Classes = new double[ListInstancesWithoutClasses.numInstances()];
for (int IdxPt = 0; IdxPt < this.Classes.Count / 2; IdxPt++)
{
this.Classes[IdxPt] = 2;
}
this.NumberOfClusters = 2;
// break;
//int IdxDesc = -1;
//foreach (string item in this.ListDescriptors)
//{
// IdxDesc++;
// if (item == DescriptorName) break;
//}
//int Idx=0;
//foreach (Instance item in ListInstancesWithoutClasses)
//{
// this.Classes.Add(((int)item.value(IdxDesc)) % cGlobalInfo.ListCellularPhenotypes.Count);
//}
//// re - ordonner les valeurs du discripteur afin que les classes se suivent sans laisser de classe vide !!
//this.NumberOfClusters = cGlobalInfo.ListCellularPhenotypes.Count;
}
#endregion
else
{
System.Windows.Forms.MessageBox.Show("Clustering method not implemented !", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return null;
}
return ClustererToReturn;
}
public cFeedBackMessage Run()
{
base.Start();
if (!FeedBackMessage.IsSucceed) return FeedBackMessage;
if (this.Input.Count > 1)
{
this.FeedBackMessage = new cFeedBackMessage(false, this);
return this.FeedBackMessage;
}
#region parameters initialization
//object _firstValue = base.ListProperties.FindByName("Number of Clusters");
//int NumOfClusters = 0;
//if (_firstValue == null)
//{
// base.GenerateError("-Number of Clusters- not found !");
// return base.FeedBackMessage;
//}
//try
//{
// cProperty TmpProp = (cProperty)_firstValue;
// NumOfClusters = (int)TmpProp.GetValue();
//}
//catch (Exception)
//{
// base.GenerateError("-Number of Clusters- cast didn't work");
// return base.FeedBackMessage;
//}
#endregion
cMachineLearning MachineLearning = new cMachineLearning(/*cWell.GlobalInfo*/null);
if (ParamAlgoForClustering == null)
ParamAlgoForClustering = MachineLearning.AskAndGetClusteringAlgo();
if (ParamAlgoForClustering == null)
{
FeedBackMessage.IsSucceed = false;
FeedBackMessage.Message = "Invalid Parameters";
return FeedBackMessage;
}
MachineLearning.SelectedClusterer = MachineLearning.BuildClusterer(ParamAlgoForClustering, this.Input);
if (MachineLearning.SelectedClusterer != null)
{
cLinearize L = new cLinearize();
L.SetInputData(this.Input);
L.Run();
cExtendedTable Test = L.GetOutPut();
ClusterEvaluation CE = MachineLearning.EvaluteAndDisplayClusterer(/*richTextBoxInfoClustering*/null,
null,
MachineLearning.CreateInstancesWithoutClass(this.Input));
double[] Assign = CE.getClusterAssignments();
base.Output = new cExtendedTable(this.Input);
cExtendedList CL = new cExtendedList("Class ID");
CL.SetInfo(CE.clusterResultsToString());
base.Output.Add(CL);
for (int i = 0; i < Assign.Length; i++)
base.Output[base.Output.Count-1].Add(Assign[i]);
}
base.End();
return FeedBackMessage;
}
public cFeedBackMessage Run(bool IsDisplayResults)
{
cMachineLearning MachineLearning = new cMachineLearning(/*cWell.GlobalInfo*/null);
ParamAlgoForClustering = MachineLearning.AskAndGetClusteringAlgo();
if (MinObjectsNumber == -1)
MinObjectsNumber = (cGlobalInfo.CurrentScreening.Columns * cGlobalInfo.CurrentScreening.Rows * (int)cGlobalInfo.OptionsWindow.numericUpDownSystemMinWellRatio.Value) / 100;
foreach (cPlate CurrentPlate in PlatesToProcess)
{
cExtendedTable CurrentPlateResult = null;
foreach (var item in this.DescriptorsToProcess)
{
cExtendedTable ET = CurrentPlate.GetAverageValueList(item, false);
ET.Name = item.GetName();
cExtendedTable ResultTable = this.GenerateArtifactMessage(ET, CurrentPlate);
ResultTable.ListRowNames = new List<string>();
ResultTable.ListRowNames.Add(item.GetName());
if (CurrentPlateResult == null) CurrentPlateResult = new cExtendedTable(ResultTable);
else
{
cMerge M = new cMerge();
M.IsHorizontal = false;
M.SetInputData(CurrentPlateResult, ResultTable);
M.Run();
CurrentPlateResult = M.GetOutPut();
}
}
CurrentPlateResult.Name = CurrentPlate.GetName();
CurrentPlateResult.Tag = CurrentPlate;
this.Output.Add(CurrentPlateResult);
}
this.Output.Name = "Systematic Errors (" + PlatesToProcess.Count + " plate(s))";
if (IsDisplayResults)
{
cDisplayToWindow DTW = new cDisplayToWindow();
cDesignerMultiChoices DMC = new cDesignerMultiChoices();
foreach (var item in this.Output)
{
cDesignerSplitter DS = new cDesignerSplitter();
DS.Orientation = System.Windows.Forms.Orientation.Horizontal;
DS.Title = item.Name;
cViewerTable VT = new cViewerTable();
VT.SetInputData(item);
VT.DigitNumber = 0;
VT.Run();
DS.SetInputData(VT.GetOutPut());
cViewerHeatMap VHM = new cViewerHeatMap();
VHM.SetInputData(item);
VHM.IsAutomatedMinMax = false;
VHM.Min = 0;
VHM.Max = 1;
VHM.Run();
DS.SetInputData(VHM.GetOutPut());
DS.Run();
cExtendedControl TmpXCtrl = DS.GetOutPut();
TmpXCtrl.Tag = item.Tag;
DMC.SetInputData(TmpXCtrl);
}
DMC.Run();
DTW.SetInputData(DMC.GetOutPut());
DTW.Title = "Systematic Errors [" + PlatesToProcess.Count + " plate(s)]";
DTW.Run();
DTW.Display();
//cDisplayListExtendedTable DLET = new cDisplayListExtendedTable();
//DLET.SetInputData(this.Output);
//return DLET.Run();
}
return FeedBackMessage;
}
