/// <summary>
/// Plate by plate classification
/// </summary>
/// <param name="NeutralClass">Neutral class</param>
/// <param name="IdxClassifier">Classifier Index (0:J48), (1:SVM), (2:NN), (3:KNN)</param>
private void ClassificationPlateByPlate(int NeutralClass, int IdxClassifier)
{
int NumberOfPlates = cGlobalInfo.CurrentScreening.ListPlatesActive.Count;
for (int PlateIdx = 0; PlateIdx < NumberOfPlates; PlateIdx++)
{
cPlate CurrentPlateToProcess = cGlobalInfo.CurrentScreening.ListPlatesActive.GetPlate(cGlobalInfo.CurrentScreening.ListPlatesActive[PlateIdx].GetName());
cInfoClass InfoClass = CurrentPlateToProcess.GetNumberOfClassesBut(NeutralClass);
// return;
if (InfoClass.NumberOfClass <= 1)
{
richTextBoxInfoClassif.AppendText(CurrentPlateToProcess.GetName() + " not processed.\n");
continue;
}
weka.core.Instances insts = CurrentPlateToProcess.CreateInstancesWithClasses(InfoClass, NeutralClass);
Classifier ClassificationModel = null;
string Text = "";
switch (IdxClassifier)
{
case 0: // J48
ClassificationModel = new weka.classifiers.trees.J48();
weka.classifiers.trees.J48 J48Model = (weka.classifiers.trees.J48)ClassificationModel;
J48Model.setMinNumObj((int)cGlobalInfo.OptionsWindow.numericUpDownJ48MinNumObjects.Value);
Text = "J48 - ";
break;
case 1: // SVM
ClassificationModel = new weka.classifiers.functions.SMO();
Text = "SVM - ";
break;
case 2: // NN
ClassificationModel = new weka.classifiers.functions.MultilayerPerceptron();
Text = "Neural Network - ";
break;
case 3: // KNN
ClassificationModel = new weka.classifiers.lazy.IBk((int)cGlobalInfo.OptionsWindow.numericUpDownKofKNN.Value);
Text = "K-Nearest Neighbor(s) - ";
break;
case 4: // Random Forest
ClassificationModel = new weka.classifiers.trees.RandomForest();
Text = "Random Forest - ";
break;
default:
break;
}
richTextBoxInfoClassif.AppendText(Text + InfoClass.NumberOfClass + " classes - Plate: ");
richTextBoxInfoClassif.AppendText(CurrentPlateToProcess.GetName() + " OK \n");
weka.core.Instances train = new weka.core.Instances(insts, 0, insts.numInstances());
ClassificationModel.buildClassifier(train);
cGlobalInfo.ConsoleWriteLine(ClassificationModel.ToString());
weka.classifiers.Evaluation evaluation = new weka.classifiers.Evaluation(insts);
evaluation.crossValidateModel(ClassificationModel, insts, 2, new java.util.Random(1));
cGlobalInfo.ConsoleWriteLine(evaluation.toSummaryString());
cGlobalInfo.ConsoleWriteLine(evaluation.toMatrixString());
// update classification information of the current plate
switch (IdxClassifier)
{
case 0: // J48
weka.classifiers.trees.J48 CurrentClassifier = (weka.classifiers.trees.J48)(ClassificationModel);
CurrentPlateToProcess.GetInfoClassif().StringForTree = CurrentClassifier.graph().Remove(0, CurrentClassifier.graph().IndexOf("{") + 2);
break;
/*case 1: // SVM
break;
case 2: // NN
break;
case 3: // KNN
break;*/
default:
break;
}
CurrentPlateToProcess.GetInfoClassif().StringForQuality = evaluation.toSummaryString();
CurrentPlateToProcess.GetInfoClassif().ConfusionMatrix = evaluation.toMatrixString();
foreach (cWell TmpWell in CurrentPlateToProcess.ListActiveWells)
{
weka.core.Instance currentInst = TmpWell.CreateInstanceForNClasses(InfoClass).instance(0);
double predictedClass = ClassificationModel.classifyInstance(currentInst);
TmpWell.SetClass(InfoClass.ListBackAssociation[(int)predictedClass]);
}
}
return;
}
/// <summary>
/// Global classification
/// </summary>
/// <param name="NeutralClass">Neutral class</param>
/// <param name="IdxClassifier">Classifier Index (0:J48), (1:SVM), (2:NN), (3:KNN)</param>
private void ClassificationGlobal(int NeutralClass, int IdxClassifier)
{
cInfoClass InfoClass = cGlobalInfo.CurrentScreening.GetNumberOfClassesBut(NeutralClass);
if (InfoClass.NumberOfClass <= 1)
{
richTextBoxInfoClassif.AppendText("Screening not processed.\n");
return;
}
cExtendedTable TrainingTable = cGlobalInfo.CurrentScreening.ListPlatesActive.GetListActiveWells().GetAverageDescriptorValues(cGlobalInfo.CurrentScreening.ListDescriptors.GetActiveDescriptors(), false, true);
weka.core.Instances insts = cGlobalInfo.CurrentScreening.CreateInstancesWithClasses(InfoClass, NeutralClass);
Classifier ClassificationModel = null;
switch (IdxClassifier)
{
case 0: // J48
ClassificationModel = new weka.classifiers.trees.J48();
weka.classifiers.trees.J48 J48Model = (weka.classifiers.trees.J48)ClassificationModel;
J48Model.setMinNumObj((int)cGlobalInfo.OptionsWindow.numericUpDownJ48MinNumObjects.Value);
richTextBoxInfoClassif.AppendText("\nC4.5 : " + InfoClass.NumberOfClass + " classes");
break;
case 1: // SVM
ClassificationModel = new weka.classifiers.functions.SMO();
break;
case 2: // NN
ClassificationModel = new weka.classifiers.functions.MultilayerPerceptron();
break;
case 3: // KNN
ClassificationModel = new weka.classifiers.lazy.IBk((int)cGlobalInfo.OptionsWindow.numericUpDownKofKNN.Value);
break;
case 4: // Random Forest
ClassificationModel = new weka.classifiers.trees.RandomForest();
break;
default:
break;
}
weka.core.Instances train = new weka.core.Instances(insts, 0, insts.numInstances());
ClassificationModel.buildClassifier(train);
cGlobalInfo.ConsoleWriteLine(ClassificationModel.ToString());
weka.classifiers.Evaluation evaluation = new weka.classifiers.Evaluation(insts);
evaluation.crossValidateModel(ClassificationModel, insts, 2, new java.util.Random(1));
cGlobalInfo.ConsoleWriteLine(evaluation.toSummaryString());
cGlobalInfo.ConsoleWriteLine(evaluation.toMatrixString());
// update classification information of the current plate
string Text = "";
switch (IdxClassifier)
{
case 0: // J48
Text = "J48 - ";
break;
case 1: // SVM
// ClassificationModel = new weka.classifiers.functions.SMO();
Text = "SVM - ";
break;
case 2: // NN
// ClassificationModel = new weka.classifiers.functions.MultilayerPerceptron();
Text = "Neural Network - ";
break;
case 3: // KNN
// ClassificationModel = new weka.classifiers.lazy.IBk((int)CompleteScreening.GlobalInfo.OptionsWindow.numericUpDownKofKNN.Value);
Text = "K-Nearest Neighbor(s) - ";
break;
default:
break;
}
richTextBoxInfoClassif.AppendText(Text + InfoClass.NumberOfClass + " classes.");
// CurrentPlateToProcess.GetInfoClassif().StringForQuality = evaluation.toSummaryString();
// CurrentPlateToProcess.GetInfoClassif().ConfusionMatrix = evaluation.toMatrixString();
foreach (cPlate CurrentPlateToProcess in cGlobalInfo.CurrentScreening.ListPlatesActive)
{
foreach (cWell TmpWell in CurrentPlateToProcess.ListActiveWells)
{
// return;
weka.core.Instance currentInst = TmpWell.CreateInstanceForNClasses(InfoClass).instance(0);
double predictedClass = ClassificationModel.classifyInstance(currentInst);
double[] ClassConfidence = ClassificationModel.distributionForInstance(currentInst);
double ConfidenceValue = ClassConfidence[(int)predictedClass];
TmpWell.SetClass(InfoClass.ListBackAssociation[(int)predictedClass], ConfidenceValue);
}
}
return;
}