/// <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>
/// 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;
}
/// <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());
}
