public void Test()
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("D:\\android_analysis\\attributes.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
cl.buildClassifier(insts);
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = (int)(insts.numInstances() * 0.66);
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
double[] distrs = cl.distributionForInstance(currentInst);
string actual = insts.classAttribute().value((int)currentInst.classValue());
string predicted = insts.classAttribute().value((int)predictedClass);
System.Console.WriteLine("ID: " + (i + 1) + ", " + actual + " --> " + predicted);
}
}
public static Classifier GetWekaClassifier(string algorithm, string trainingSetPath)
{
Classifier classifier = null;
switch (algorithm)
{
case "KNN":
{
classifier = new weka.classifiers.lazy.IB1();
WekaNETBridge.WekaClassification wekaClassification = new WekaNETBridge.WekaClassification(trainingSetPath, classifier);
classifier = wekaClassification.CreateClassifier();
}
break;
case "NBayes":
{
classifier = new weka.classifiers.bayes.NaiveBayes();
WekaNETBridge.WekaClassification wekaClassification = new WekaNETBridge.WekaClassification(trainingSetPath, classifier);
classifier = wekaClassification.CreateClassifier();
}
break;
case "JRip":
{
classifier = new weka.classifiers.rules.JRip();
WekaNETBridge.WekaClassification wekaClassification = new WekaNETBridge.WekaClassification(trainingSetPath, classifier);
classifier = wekaClassification.CreateClassifier();
}
break;
case "J48":
{
classifier = new weka.classifiers.trees.J48();
WekaNETBridge.WekaClassification wekaClassification = new WekaNETBridge.WekaClassification(trainingSetPath, classifier);
classifier = wekaClassification.CreateClassifier();
}
break;
case "NeuralNets":
{
classifier = new weka.classifiers.functions.MultilayerPerceptron();
WekaNETBridge.WekaClassification wekaClassification = new WekaNETBridge.WekaClassification(trainingSetPath, classifier);
classifier = wekaClassification.CreateClassifier();
}
break;
case "SVM":
{
classifier = new weka.classifiers.functions.SMO();
WekaNETBridge.WekaClassification wekaClassification = new WekaNETBridge.WekaClassification(trainingSetPath, classifier);
classifier = wekaClassification.CreateClassifier();
}
break;
}
return(classifier);
}
public static void CalculateSuccessForDt(weka.core.Instances originalInsts)
{
try
{
var form = Form.ActiveForm as Form1;
form.successPrcDt.Text = "Training...";
form.successRtDt.Text = "../" + testSize;
weka.core.Instances insts = originalInsts;
// Pre-process
insts = ConvertNumericToNominal(insts);
insts = Normalize(insts);
// Classify
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
int numCorrect = 0;
double percentage = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
percentage = (double)numCorrect / (double)testSize * 100.0;
form.successRtDt.Text = numCorrect + "/" + testSize;
form.successPrcDt.Text = String.Format("{0:0.00}", percentage) + "%";
}
succesRates.Add(Classifier.ID3, percentage);
classifiers.Add(Classifier.ID3, cl);
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
MessageBox.Show(ex.ToString(), "Error for Decision Tree", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
catch (Exception)
{
MessageBox.Show("Error for Decision Tree", "Error for Decision Tree", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
public static string classifyTest()
{
try
{
String result = "";
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("C:\\Program Files\\Weka-3-7\\data\\iris.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
// Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
result += "Performing " + percentSplit + "% split evaluation.\n";
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
//Console.WriteLine(numCorrect + " out of " + testSize + " correct (" + (double)((double)numCorrect / (double)testSize * 100.0) + "%)");
result += (numCorrect + " out of " + testSize + " correct (" + (double)((double)numCorrect / (double)testSize * 100.0) + "%)");
return(result);
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
return(ex.Message);
}
}
private void initialiseClassifier()
{
// Loads the file to grab the filename.
GameObject player = GameObject.Find("Player");
PlayerBehaviour playerBehaviourScript = player.GetComponent <PlayerBehaviour>();
//loads training and test data, builds new classifier and trains it
insts = new weka.core.Instances(new java.io.FileReader(playerBehaviourScript.stringToEdit));
insts.setClassIndex(4);
j48Classifier = new weka.classifiers.trees.J48();
//Optionals methods currently testing for improvements
//j48Classifier.setUnpruned (true);
//j48Classifier.setUseLaplace (true);
j48Classifier.buildClassifier(insts);
//NO CURRENT TEST SET READY TO USE
//weka.core.Instances testData = new weka.core.Instances(new java.io.FileReader("TestSet.arff"));
//testData.setClassIndex(5);
//weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(insts);
//eval.evaluateModel (j48Classifier,testData);
Debug.Log(j48Classifier.toSummaryString());
}
public static void classifyTest()
{
try
{
Console.WriteLine("Hello Java, from C#!");
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("D:/iris.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
Console.WriteLine("Performing " + 33 + "% split evaluation.");
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = insts.numInstances() * 33 / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
numCorrect++;
}
Console.WriteLine(numCorrect + " out of " + testSize + " correct (" +
(double)((double)numCorrect / (double)testSize * 100.0) + "%)");
}
catch (Exception ex)
{
}
}
public static void ClassifyTest()
{
try
{
weka.core.Instances insts = new ConverterUtils.DataSource("weka").getDataSet();
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = insts.numInstances()*percentSplit/100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
numCorrect++;
}
Console.WriteLine(numCorrect + " out of " + testSize + " correct (" +
(double) ((double) numCorrect/(double) testSize*100.0) + "%)");
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
}
public static string classifyTest(string file, string classifier)
{
string data = "No data";
try
{
//weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("C:\\Users\\kinli\\source\\repos\\WebApplication2\\WebApplication2\\iris.arff"));
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(file));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
if (classifier == "J48")
{
cl = new weka.classifiers.trees.J48();
}
else if (classifier == "MLP")
{
cl = new weka.classifiers.functions.MultilayerPerceptron();
}
else if (classifier == "NaiveBayes")
{
cl = new weka.classifiers.bayes.NaiveBayes();
}
//data = ("Performing " + percentSplit + "% split evaluation.\n");
data = ("Performing use training set evaluation.\n");
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myRandom);
/*
* int trainSize = insts.numInstances() * percentSplit / 100;
* int testSize = insts.numInstances() - trainSize;
* weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
*
* cl.buildClassifier(train);
* int numCorrect = 0;
* for (int i = trainSize; i < insts.numInstances(); i++)
* {
* weka.core.Instance currentInst = insts.instance(i);
* double predictedClass = cl.classifyInstance(currentInst);
* if (predictedClass == insts.instance(i).classValue())
* numCorrect++;
* }*/
cl.buildClassifier(insts);
int numCorrect = 0;
for (int i = 0; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
data = data + (numCorrect + " out of " + insts.numInstances() + " correct (" +
(double)((double)numCorrect / (double)insts.numInstances() * 100.0) + "%)");
}
catch (java.lang.Exception ex)
{
data = "Error";
ex.printStackTrace();
}
return(data);
}
private List<string> ComputePlateBasedClassification(int Classes, int MinObjectsNumber, cPlate CurrentPlateToProcess)
{
CurrentPlateToProcess.ComputePlateBasedDescriptors();
weka.core.Instances insts = CurrentPlateToProcess.CreateInstancesWithClassesWithPlateBasedDescriptor(Classes);
weka.classifiers.trees.J48 ClassificationModel = new weka.classifiers.trees.J48();
ClassificationModel.setMinNumObj(MinObjectsNumber);
weka.core.Instances train = new weka.core.Instances(insts, 0, insts.numInstances());
ClassificationModel.buildClassifier(train);
// display the tree
string DotString = ClassificationModel.graph().Remove(0, ClassificationModel.graph().IndexOf("{") + 2);
int DotLenght = DotString.Length;
string NewDotString = DotString.Remove(DotLenght - 3, 3);
ComputeAndDisplayGraph(NewDotString);
return ComputeGraph(NewDotString, Classes);
}
/// <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;
}
private cExtendedTable GenerateArtifactMessage(cExtendedTable TmpTable, cPlate PlateToProcess)
{
cLinearize Lin = new cLinearize();
Lin.SetInputData(TmpTable);
Lin.Run();
cExtendedTable LINTable = Lin.GetOutPut();
cClustering Cluster = new cClustering();
Cluster.SetInputData(LINTable);
Cluster.ParamAlgoForClustering = this.ParamAlgoForClustering;
if (Cluster.Run().IsSucceed == false) return null;
this.ClusteredTable = Cluster.GetOutPut();
// now clustering
//if (!KMeans((int)cGlobalInfo.OptionsWindow.numericUpDownSystErrorIdentKMeansClasses.Value, PlateToProcess, CurrentDescSel))
//{
// List<string> ListMessageError = new List<string>();
// ListMessageError.Add("K-Means Error");
// return ListMessageError;
//}
//// and finally classification
// return this.ComputePlateBasedClassification(MinObjectsNumber);
//}
cExtendedTable ET = PlateToProcess.ListWells.GetPositionRelatedSignatures();
ET.Add(this.ClusteredTable[this.ClusteredTable.Count - 1]);
weka.core.Instances insts = ET.CreateWekaInstancesWithClasses(); //CurrentPlateToProcess.CreateInstancesWithClassesWithPlateBasedDescriptor(Classes);
weka.classifiers.trees.J48 ClassificationModel = new weka.classifiers.trees.J48();
ClassificationModel.setMinNumObj(MinObjectsNumber);
weka.core.Instances train = new weka.core.Instances(insts, 0, insts.numInstances());
ClassificationModel.buildClassifier(train);
string DotString = ClassificationModel.graph().Remove(0, ClassificationModel.graph().IndexOf("{") + 2);
int DotLenght = DotString.Length;
string NewDotString = DotString.Remove(DotLenght - 3, 3);
// display the tree is requested
//cDisplayTree DT = new cDisplayTree();
//DT.SetInputData(NewDotString);
//DT.Run();
cExtendedTable ToReturn = new cExtendedTable(4, 1, 0);
ToReturn.ListRowNames = new List<string>();
ToReturn.ListTags = new List<object>();
int CurrentPos = 0;
int NextReturnPos = CurrentPos;
List<int> ListNodeId = new List<int>();
string TmpDotString = NewDotString;
int TmpClass = 0;
string ErrorString = "";
int ErrorMessage = 0;
ToReturn[0].Name = "Edge artifact"; // edge
ToReturn[1].Name = "Column artifact"; // col
ToReturn[2].Name = "Row artifact"; // row
ToReturn[3].Name = "Bowl artifact"; // bowl
#region build message
while (NextReturnPos != -1)
{
int NextBracket = NewDotString.IndexOf("[");
string StringToProcess = NewDotString.Remove(NextBracket - 1);
string StringToProcess1 = StringToProcess.Remove(0, 1);
if (StringToProcess1.Contains("N") == false)
{
int Id = Convert.ToInt32(StringToProcess1);
int LabelPos = NewDotString.IndexOf("label=\"");
string LabelString = NewDotString.Remove(0, LabelPos + 7);
LabelPos = LabelString.IndexOf("\"");
string FinalLabel = LabelString.Remove(LabelPos);
// if (TmpClass < Classes)
{
if ((FinalLabel == "Dist_To_Border") || (FinalLabel == "Col_Pos") || (FinalLabel == "Row_Pos") || (FinalLabel == "Dist_To_Center"))
{
if ((FinalLabel == "Dist_To_Border") && (!ErrorString.Contains(" an edge effect")) && (!ErrorString.Contains(" a bowl effect")) && (ErrorMessage < 2))
{
if (TmpClass > 0) ErrorString += " combined with";
ErrorString += " an " + cGlobalInfo.ListArtifacts[0];
ErrorMessage++;
ToReturn[0][0] = 1;
}
else if ((FinalLabel == "Col_Pos") && (!ErrorString.Contains(" a column artifact")) && (ErrorMessage < 2))
{
if (TmpClass > 0) ErrorString += " combined with";
ErrorString += " a " + cGlobalInfo.ListArtifacts[1];
ErrorMessage++;
ToReturn[1][0] = 1;
}
else if ((FinalLabel == "Row_Pos") && (!ErrorString.Contains(" a row artifact")) && (ErrorMessage < 2))
{
if (TmpClass > 0) ErrorString += " combined with";
ErrorString += " a " + cGlobalInfo.ListArtifacts[2];
ErrorMessage++;
ToReturn[2][0] = 1;
}
else if ((FinalLabel == "Dist_To_Center") && (!ErrorString.Contains(" a bowl effect")) && (!ErrorString.Contains(" an edge effect")) && (ErrorMessage < 2))
{
if (TmpClass > 0) ErrorString += " combined with";
ErrorString += " a " + cGlobalInfo.ListArtifacts[3];
ErrorMessage++;
ToReturn[3][0] = 1;
}
TmpClass++;
}
}
}
NextReturnPos = NewDotString.IndexOf("\n");
if (NextReturnPos != -1)
{
string TmpString = NewDotString.Remove(0, NextReturnPos + 1);
NewDotString = TmpString;
}
}
if (TmpClass == 0)
{
string NoError = "No systematic error detected !";
ToReturn.ListTags.Add(NoError);
//ToReturn.Add(NoError);
return ToReturn;
}
string FinalString = "You have a systematic error !\nThis is " + ErrorString;
NewDotString = TmpDotString;
NextReturnPos = 0;
while (NextReturnPos != -1)
{
int NextBracket = NewDotString.IndexOf("[");
string StringToProcess = NewDotString.Remove(NextBracket - 1);
string StringToProcess1 = StringToProcess.Remove(0, 1);
if (StringToProcess1.Contains("N"))
{
//// this is an edge
string stringNodeIdxStart = StringToProcess1.Remove(StringToProcess1.IndexOf("-"));
int NodeIdxStart = Convert.ToInt32(stringNodeIdxStart);
string stringNodeIdxEnd = StringToProcess1.Remove(0, StringToProcess1.IndexOf("N") + 1);
int NodeIdxSEnd = Convert.ToInt32(stringNodeIdxEnd);
int LabelPos = NewDotString.IndexOf("label=");
LabelPos += 7;
string CurrLabelString = NewDotString.Remove(0, LabelPos);
}
NextReturnPos = NewDotString.IndexOf("\n");
if (NextReturnPos != -1)
{
string TmpString = NewDotString.Remove(0, NextReturnPos + 1);
NewDotString = TmpString;
}
}
ToReturn.ListTags.Add(FinalString + ".");
#endregion
return ToReturn;
}
public void classifyTest()
{
try
{
CSV2Arff();
java.io.FileReader arrfFile = new java.io.FileReader("D:/final_version/Gesture-Gis-master/GestureGis2/ComparisonFeaturefile.arff");
weka.core.Instances insts = new weka.core.Instances(arrfFile);
//weka.core.Instances insts2 = new weka.core.Instances(new java.io.FileReader("D:/Gesture-Gis-master/GestureGis2/ComparisonFeaturefile.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
//int percentSplit = 66;
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
//Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
//randomize the order of the instances in the dataset.
//weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
//myRandom.setInputFormat(insts);
//insts = weka.filters.Filter.useFilter(insts, myRandom);
int count = insts.numInstances();
int trainSize = count - 1;
int testSize = count - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
//weka.core.Instance current = insts2.instance(0);
int numCorrect = 0;
/*for (int i = trainSize; i < insts.numInstances(); i++)
* {
* weka.core.Instance currentInst = insts.instance(i);
* double predictedClass = cl.classifyInstance(currentInst);
* if (predictedClass == insts.instance(i).classValue())
* numCorrect++;
* }*/
int index = count - 1;
weka.core.Instance currentInst = insts.instance(index);
double predictedClass = cl.classifyInstance(currentInst);
int pre = (int)predictedClass;
if (predictedClass == insts.instance(index).classValue())
{
numCorrect++;
}
//insts.instance(index).classAttribute();
//insts.attribute(11);
string s = insts.toString();
s = s.Substring(s.IndexOf("{") + 1);
s = s.Substring(0, s.IndexOf("}"));
s = s.Substring(0, s.Length);
string[] ae = s.Split(',');
/*ArrayList arr = new ArrayList();
* string path_class = @"D:\final_version\Gesture-Gis-master\GestureGis2\Classfile.txt";
* using (StreamReader reader = new StreamReader(path_class))
* {
* while (!reader.EndOfStream)
* {
* arr.Add(reader.ReadLine());
* }
* reader.Close();
* }*/
PredictedClassbyWeka = (string)(ae[pre]);
arrfFile.close();
//insts.instance(index).attribute(3);
/*System.Diagnostics.Debug.WriteLine(numCorrect + " out of " + testSize + " correct (" +
* (double)((double)numCorrect / (double)testSize * 100.0) + "%)");
* Console.WriteLine(numCorrect + " out of " + testSize + " correct (" +
* (double)((double)numCorrect / (double)testSize * 100.0) + "%)");*/
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
}
