/// <summary>
/// perform an EM clustering over the entire screening data
/// </summary>
/// <param name="ClassNumber"></param>
private string ClusteringEMGlobalScreen(int ClassNumber, FormForEMInfo WindowEMinfo)
{
weka.core.Instances Ninsts = cGlobalInfo.CurrentScreening.CreateInstancesWithoutClass();// CreateInstanceWithoutClass(CurrentTable);
weka.clusterers.EM EMCluster = new EM();
EMCluster.setNumClusters(ClassNumber);
EMCluster.setMaxIterations((int)WindowEMinfo.numericUpDownMaxIterations.Value);
EMCluster.setMinStdDev((double)WindowEMinfo.numericUpDownMinStdev.Value);
EMCluster.setSeed((int)WindowEMinfo.numericUpDownSeedNumber.Value);
EMCluster.buildClusterer(Ninsts);
EMCluster.getClusterModelsNumericAtts();
if (EMCluster.numberOfClusters() > cGlobalInfo.ListWellClasses.Count)
{
richTextBoxInfoClustering.AppendText("\nCluster Number: more than " + cGlobalInfo.ListWellClasses.Count + ", clustering not operated.\n");
return null;
}
richTextBoxInfoClustering.AppendText("\n" + EMCluster.numberOfClusters() + " cluster(s) identified");
ClusterEvaluation eval = new ClusterEvaluation();
eval.setClusterer(EMCluster);
eval.evaluateClusterer(Ninsts);
cGlobalInfo.CurrentScreening.AssignClass(eval.getClusterAssignments());
return eval.clusterResultsToString();
}
/// <summary>
/// Perform an EM clustering on each plate independantely
/// </summary>
/// <param name="CurrentPlateToProcess">the plate to process</param>
/// <param name="ClassNumber">Number of class</param>
private void ClusteringEMSinglePlate(cPlate CurrentPlateToProcess, int ClassNumber, FormForEMInfo WindowEMinfo)
{
weka.core.Instances Ninsts = CurrentPlateToProcess.CreateInstancesWithoutClass();// CreateInstanceWithoutClass(CurrentTable);
weka.clusterers.EM EMCluster = new EM();
EMCluster.setNumClusters(ClassNumber);
EMCluster.setMaxIterations((int)WindowEMinfo.numericUpDownMaxIterations.Value);
EMCluster.setMinStdDev((double)WindowEMinfo.numericUpDownMinStdev.Value);
EMCluster.setSeed((int)WindowEMinfo.numericUpDownSeedNumber.Value);
EMCluster.buildClusterer(Ninsts);
EMCluster.getClusterModelsNumericAtts();
if (EMCluster.numberOfClusters() > cGlobalInfo.ListWellClasses.Count)
{
richTextBoxInfoClustering.AppendText("\n Plate " + CurrentPlateToProcess.GetName() + ", cluster Number: more than " + cGlobalInfo.ListWellClasses.Count + ", clustering not operated.\n");
return;
}
else
richTextBoxInfoClustering.AppendText("\n" + CurrentPlateToProcess.GetName() + ": " + EMCluster.numberOfClusters() + " cluster(s)");
ClusterEvaluation eval = new ClusterEvaluation();
eval.setClusterer(EMCluster);
eval.evaluateClusterer(Ninsts);
CurrentPlateToProcess.AssignClass(eval.getClusterAssignments());
}
/// <summary>
/// Perform an EM clustering on each plate independantely
/// </summary>
/// <param name="CurrentPlateToProcess">the plate to process</param>
/// <param name="ClassNumber">Number of class</param>
private void ClusteringEMSinglePlate(cPlate CurrentPlateToProcess, int ClassNumber, FormForEMInfo WindowEMinfo)
{
weka.core.Instances Ninsts = CurrentPlateToProcess.CreateInstancesWithoutClass();// CreateInstanceWithoutClass(CurrentTable);
if (Ninsts.numInstances() == 0)
{
MessageBox.Show("No active wells !", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
weka.clusterers.EM EMCluster = new EM();
EMCluster.setNumClusters(ClassNumber);
EMCluster.setMaxIterations((int)WindowEMinfo.numericUpDownMaxIterations.Value);
EMCluster.setMinStdDev((double)WindowEMinfo.numericUpDownMinStdev.Value);
EMCluster.setSeed((int)WindowEMinfo.numericUpDownSeedNumber.Value);
EMCluster.buildClusterer(Ninsts);
EMCluster.getClusterModelsNumericAtts();
if (EMCluster.numberOfClusters() > GlobalInfo.GetNumberofDefinedClass())
{
richTextBoxInfoClustering.AppendText("\n Plate " + CurrentPlateToProcess.Name + ", cluster Number: more than " + GlobalInfo.GetNumberofDefinedClass() + ", clustering not operated.\n");
return;
}
else
richTextBoxInfoClustering.AppendText("\n" + CurrentPlateToProcess.Name + ": " + EMCluster.numberOfClusters() + " cluster(s)");
ClusterEvaluation eval = new ClusterEvaluation();
eval.setClusterer(EMCluster);
eval.evaluateClusterer(Ninsts);
CurrentPlateToProcess.AssignClass(eval.getClusterAssignments());
}
/// <summary>
/// Perform an EM clustering on each plate independantely
/// </summary>
/// <param name="CurrentPlateToProcess">the plate to process</param>
/// <param name="ClassNumber">Number of class</param>
private void ClusteringEMSinglePlate(cPlate CurrentPlateToProcess, int ClassNumber)
{
weka.core.Instances Ninsts = CurrentPlateToProcess.CreateInstancesWithoutClass();// CreateInstanceWithoutClass(CurrentTable);
weka.clusterers.EM EMCluster = new EM();
EMCluster.setNumClusters(ClassNumber);
EMCluster.buildClusterer(Ninsts);
EMCluster.getClusterModelsNumericAtts();
if (EMCluster.numberOfClusters() > GlobalInfo.GetNumberofDefinedClass())
{
richTextBoxInfoClustering.AppendText("\n Plate " + CurrentPlateToProcess.Name + ", cluster Number: more than " + GlobalInfo.GetNumberofDefinedClass() + ", clustering not operated.\n");
return;
}
else
richTextBoxInfoClustering.AppendText("\n" + CurrentPlateToProcess.Name + ": " + EMCluster.numberOfClusters() + " cluster(s)");
ClusterEvaluation eval = new ClusterEvaluation();
eval.setClusterer(EMCluster);
eval.evaluateClusterer(Ninsts);
CurrentPlateToProcess.AssignClass(eval.getClusterAssignments());
}
/// <summary>
/// perform an EM clustering over the entire screening data
/// </summary>
/// <param name="ClassNumber"></param>
private void ClusteringEMGlobalScreen(int ClassNumber)
{
weka.core.Instances Ninsts = CompleteScreening.CreateInstancesWithoutClass();// CreateInstanceWithoutClass(CurrentTable);
weka.clusterers.EM EMCluster = new EM();
EMCluster.setNumClusters(ClassNumber);
EMCluster.buildClusterer(Ninsts);
EMCluster.getClusterModelsNumericAtts();
if (EMCluster.numberOfClusters() > GlobalInfo.GetNumberofDefinedClass())
{
richTextBoxInfoClustering.AppendText("\nCluster Number: more than " + GlobalInfo.GetNumberofDefinedClass() + ", clustering not operated.\n");
return;
}
else
richTextBoxInfoClustering.AppendText("\n" + EMCluster.numberOfClusters() + " cluster(s) identified");
ClusterEvaluation eval = new ClusterEvaluation();
eval.setClusterer(EMCluster);
eval.evaluateClusterer(Ninsts);
CompleteScreening.AssignClass(eval.getClusterAssignments());
}
private void buttonStartCluster_Click(object sender, EventArgs e)
{
FormSingleCellClusteringInfo WindowClusteringInfo = new FormSingleCellClusteringInfo(GlobalInfo);
if (WindowClusteringInfo.ShowDialog() != System.Windows.Forms.DialogResult.OK) return;
Instances ListInstances = GlobalInfo.CurrentScreen.CellBasedClassification.CreateInstancesWithoutClass(dt);
if ((WindowClusteringInfo.radioButtonAutomated.Checked) && (WindowClusteringInfo.radioButtonEM.Checked))
{
ClusterEvaluation eval;
Classes = new cExtendedList();
weka.clusterers.EM EMCluster = new EM();
if(WindowClusteringInfo.checkBoxEMAutomated.Checked)
EMCluster.setNumClusters(-1);
else
EMCluster.setNumClusters((int)WindowClusteringInfo.numericUpDownClassNumber.Value);
EMCluster.buildClusterer(ListInstances);
EMCluster.getClusterModelsNumericAtts();
eval = new ClusterEvaluation();
eval.setClusterer(EMCluster);
eval.evaluateClusterer(ListInstances);
Classes.AddRange(eval.getClusterAssignments());
NumClusters= eval.getNumClusters();
ReDraw();
FormForCellByCellClusteringResults WindowFormForCellByCellClusteringResults = new FormForCellByCellClusteringResults();
WindowFormForCellByCellClusteringResults.richTextBoxResults.Clear();
WindowFormForCellByCellClusteringResults.richTextBoxResults.AppendText(eval.clusterResultsToString());
if (WindowFormForCellByCellClusteringResults.ShowDialog() != System.Windows.Forms.DialogResult.OK) return;
}
else if (WindowClusteringInfo.radioButtonDescriptorBased.Checked)
{
Classes = new cExtendedList();
DataTable FinalDataTable = new DataTable();
int IdxDescForClassSelect = WindowClusteringInfo.comboBoxDescriptorForClass.SelectedIndex;
for (int IdxWell = 0; IdxWell < GlobalInfo.ListSelectedWell.Count; IdxWell++)
{
cWell TmpWell = GlobalInfo.ListSelectedWell[IdxWell];
// if (IdxWell == 0)
if (TmpWell.ListDescriptors[IdxDescForClassSelect].GetAssociatedType().DataType == eDataType.HISTOGRAM)
{
Classes.AddRange(TmpWell.ListDescriptors[IdxDescForClassSelect].GetOriginalValues());
}
else
{
double ClasseValue = TmpWell.ListDescriptors[IdxDescForClassSelect].GetValue();
for (int IdxCell = 0; IdxCell < TmpWell.CellNumber; IdxCell++)
Classes.Add(ClasseValue);
//TmpWell.AddDescriptors
}
}
List<double> ListClassValues = new List<double>();
foreach (var item in Classes.Distinct())
{
ListClassValues.Add(item);
}
//(List<double>)Classes.Distinct();
NumClusters = ListClassValues.Count();
//Classes = new cExtendedList();
for (int IdxClust = 0; IdxClust < Classes.Count; IdxClust++)
{
for (int IdxCl = 0; IdxCl < ListClassValues.Count; IdxCl++)
{
if (ListClassValues[IdxCl] == Classes[IdxClust])
{
Classes[IdxClust] = IdxCl;
break;
}
}
//Classes[IdxClust] = ListClassValues.Find(Classes[IdxClust]);
}
//int NumClusters =
ReDraw();
}
// ReDraw();
//if (MessageBox.Show("Do you want perform a j48 training process ?", "Question", MessageBoxButtons.YesNo, MessageBoxIcon.Question) != System.Windows.Forms.DialogResult.Yes) return;
weka.core.FastVector attVals = new FastVector();
for (int i = 0; i < NumClusters; i++)
attVals.addElement("Class__" + (i).ToString());
ListInstances.insertAttributeAt(new weka.core.Attribute("Class__", attVals), ListInstances.numAttributes());
for (int i = 0; i < Classes.Count; i++)
{
ListInstances.get(i).setValue(ListInstances.numAttributes() - 1, Classes[i]);
}
ListInstances.setClassIndex(ListInstances.numAttributes() - 1);
GlobalInfo.CurrentScreen.CellBasedClassification.ClassificationModel_CellBased = new weka.classifiers.trees.J48();
GlobalInfo.CurrentScreen.CellBasedClassification.SetJ48Tree((weka.classifiers.trees.J48)GlobalInfo.CurrentScreen.CellBasedClassification.ClassificationModel_CellBased,Classes.Count);
GlobalInfo.CurrentScreen.CellBasedClassification.J48Model.setMinNumObj((int)GlobalInfo.OptionsWindow.numericUpDownJ48MinNumObjects.Value);
weka.core.Instances train = new weka.core.Instances(ListInstances, 0, ListInstances.numInstances());
GlobalInfo.CurrentScreen.CellBasedClassification.ClassificationModel_CellBased.buildClassifier(train);
GlobalInfo.ConsoleWriteLine(GlobalInfo.CurrentScreen.CellBasedClassification.ClassificationModel_CellBased.ToString());
GlobalInfo.CurrentScreen.CellBasedClassification.evaluation = new weka.classifiers.Evaluation(ListInstances);
GlobalInfo.CurrentScreen.CellBasedClassification.evaluation.crossValidateModel(GlobalInfo.CurrentScreen.CellBasedClassification.ClassificationModel_CellBased, ListInstances, 2, new java.util.Random(1));
GlobalInfo.CurrentScreen.CellBasedClassification.DisplayTree(GlobalInfo).Show();
FormForCellbyCellClassif WindowFormForCellbyCellClassif = new FormForCellbyCellClassif();
if (WindowFormForCellbyCellClassif.ShowDialog() != System.Windows.Forms.DialogResult.OK) return;
int DescrCount = GlobalInfo.CurrentScreen.ListDescriptors.Count;
// first we update the descriptor
for (int i = 0; i < ListInstances.numClasses(); i++)
GlobalInfo.CurrentScreen.ListDescriptors.AddNew(new cDescriptorsType("Ratio_Class " + i, true, 1, GlobalInfo));
FormForProgress ProgressWindow = new FormForProgress();
ProgressWindow.Show();
int IdxProgress = 0;
int MaxProgress = 0;
foreach (cPlate CurrentPlateToProcess in GlobalInfo.CurrentScreen.ListPlatesAvailable)
MaxProgress += CurrentPlateToProcess.ParentScreening.Columns * CurrentPlateToProcess.ParentScreening.Rows;
ProgressWindow.progressBar.Maximum = MaxProgress;
attVals = new FastVector();
for (int i = 0; i < NumClusters; i++)
attVals.addElement(i.ToString());
//ParallelOptions options = new ParallelOptions();
//options.MaxDegreeOfParallelism = -1; // -1 is for unlimited. 1 is for sequential.
//Stopwatch stopwatch = new Stopwatch();
//stopwatch.Start();
//////for (int PlateIdx = 0; PlateIdx < NumberOfPlates; PlateIdx++)
//int NumberOfPlates = CompleteScreening.ListPlatesAvailable.Count;
//Parallel.For(0, NumberOfPlates, options, (PlateIdx) =>
//{
// cPlate CurrentPlateToProcess = CompleteScreening.ListPlatesActive.GetPlate((string)Parent.GlobalInfo.PlateListWindow.listBoxPlateNameToProcess.Items[PlateIdx]);
// for (int row = 0; row < Parent.Rows; row++)
// for (int col = 0; col < Parent.Columns; col++)
// {
// TempWell = CurrentPlateToProcess.GetWell(col, row, false);
// if (TempWell == null) continue;
// else
// {
// if (TempWell.GetClass() == this.ClassForClassif)
// Pos.Add(TempWell.ListDescriptors[Parent.ListDescriptors.CurrentSelectedDescriptor].GetValue());
// }
// }
//}
//);
foreach (cPlate CurrentPlateToProcess in GlobalInfo.CurrentScreen.ListPlatesAvailable)
//Parallel.ForEach(GlobalInfo.CurrentScreen.ListPlatesActive, options, CurrentPlateToProcess =>
{
//Parallel.ForEach(CurrentPlateToProcess.ListActiveWells, options, TmpWell =>
for(int j=0;j<CurrentPlateToProcess.ParentScreening.Rows;j++)
for (int k = 0; k < CurrentPlateToProcess.ParentScreening.Columns; k++)
{
cWell TmpWell = CurrentPlateToProcess.GetWell(k, j, false);
if (TmpWell == null) continue;
ProgressWindow.progressBar.Value = IdxProgress++;
//DataTable FinalDataTable = new DataTable();
//TmpWell.AssociatedPlate.DBConnection = new cDBConnection(TmpWell.AssociatedPlate, TmpWell.SQLTableName);
//TmpWell.AssociatedPlate.DBConnection.AddWellToDataTable(TmpWell, FinalDataTable, false);
DataTable FinalDataTable = TmpWell.GetDescDataTable(true);
Instances ListInstancesTOClassify = GlobalInfo.CurrentScreen.CellBasedClassification.CreateInstancesWithoutClass(FinalDataTable);
ListInstancesTOClassify.insertAttributeAt(new weka.core.Attribute("Class", attVals), ListInstancesTOClassify.numAttributes());
ListInstancesTOClassify.setClassIndex(ListInstancesTOClassify.numAttributes() - 1);
cExtendedList ListClasses = new cExtendedList();
for (int i = 0; i < ListInstancesTOClassify.numInstances(); i++)
{
double classId = GlobalInfo.CurrentScreen.CellBasedClassification.ClassificationModel_CellBased.classifyInstance(ListInstancesTOClassify.instance(i));
ListClasses.Add(classId);
}
List<double[]> Histo = ListClasses.CreateHistogram(0, ListInstances.numClasses() - 1, ListInstances.numClasses() - 1);
List<cDescriptor> LDesc = new List<cDescriptor>();
for (int IdxHisto = 0; IdxHisto < Histo[1].Length; IdxHisto++)
{
double Value = (100.0 * Histo[1][IdxHisto]) / (double)ListInstancesTOClassify.numInstances();
cDescriptor NewDesc = new cDescriptor(Value, GlobalInfo.CurrentScreen.ListDescriptors[IdxHisto + DescrCount], GlobalInfo.CurrentScreen);
LDesc.Add(NewDesc);
}
TmpWell.AddDescriptors(LDesc);
//TmpWell.AssociatedPlate.DBConnection.DB_CloseConnection();
}//);
}
ProgressWindow.Close();
if (WindowFormForCellbyCellClassif.checkBoxKeepOriginalDesc.Checked == false)
{
// int DescNumToRemove = GlobalInfo.CurrentScreen.ListDescriptors.Count -
for (int IdxDesc = 0; IdxDesc < DescrCount; IdxDesc++)
GlobalInfo.CurrentScreen.ListDescriptors.RemoveDesc(GlobalInfo.CurrentScreen.ListDescriptors[0], GlobalInfo.CurrentScreen);
}
GlobalInfo.CurrentScreen.ListDescriptors.UpDateDisplay();
GlobalInfo.CurrentScreen.UpDatePlateListWithFullAvailablePlate();
for (int idxP = 0; idxP < GlobalInfo.CurrentScreen.ListPlatesActive.Count; idxP++)
GlobalInfo.CurrentScreen.ListPlatesActive[idxP].UpDataMinMax();
if (WindowFormForCellbyCellClassif.checkBoxKeepOriginalDesc.Checked == false)
GlobalInfo.CurrentScreen.GetCurrentDisplayPlate().DisplayDistribution(0, false);
//WindowFormForCellbyCellClassif.Close();
//WindowClusteringInfo.Close();
}