public static void classifierTwo(string classifierFileName, string predictionModel)
{
FileReader javaFileReader = new FileReader(classifierFileName);
weka.core.Instances wekaInsts = new weka.core.Instances(javaFileReader);
javaFileReader.close();
wekaInsts.setClassIndex(wekaInsts.numAttributes() - 1);
//Classifier nbTree = (Classifier)SerializationHelper.read(Model) as J48;
Instances testDataSet = new Instances(new BufferedReader(new FileReader(classifierFileName)));
testDataSet.setClassIndex(wekaInsts.numAttributes() - 1);
//testDataSet.setClassIndex(10);
Evaluation evaluation = new Evaluation(testDataSet);
J48 model = new J48();
//Classifier myClassifier = (Classifier)SerializationHelper.read(Model) as NaiveBayes;
//Classifier myClassifier = new NaiveBayes();
for (int i = 0; i < testDataSet.numInstances(); i++)
{
Instance instance = testDataSet.instance(i);
//evaluation.evaluateModelOnceAndRecordPrediction(myClassifier, instance);
//evaluation.evaluateModelOnce(myClassifier, instance);
}
foreach (object o in evaluation.predictions().toArray())
{
NominalPrediction prediction = o as NominalPrediction;
if (prediction != null)
{
double[] distribution = prediction.distribution();
double predicted = prediction.predicted();
for (int i = 0; i < distribution.Length; i++)
{
System.Console.WriteLine(distribution[i]);
}
System.Console.WriteLine(predicted);
}
}
System.Console.WriteLine(evaluation);
System.Console.ReadKey();
}
public static void classifierOne(string classifierFileName, string predictionModel)
{
FileReader javaFileReader = new FileReader(classifierFileName);
weka.core.Instances wekaInsts = new weka.core.Instances(javaFileReader);
javaFileReader.close();
wekaInsts.setClassIndex(wekaInsts.numAttributes() - 1);
Classifier cl = new SMO();
//Classifier cl = new NaiveBayes();
java.util.Random random = new java.util.Random(1);
Evaluation evaluation = new Evaluation(wekaInsts);
evaluation.crossValidateModel(cl, wekaInsts, 10, random);
foreach (object o in evaluation.getMetricsToDisplay().toArray())
{
}
int count = 0;
StringBuilder sb = new StringBuilder();
foreach (object o in evaluation.predictions().toArray())
{
NominalPrediction prediction = o as NominalPrediction;
if (prediction != null)
{
double[] distribution = prediction.distribution();
double predicted = prediction.predicted();
double actual = prediction.actual();
string revision = prediction.getRevision();
double weight = prediction.weight();
double margine = prediction.margin();
//bool equals = prediction.@equals();
string distributions = String.Empty;
for (int i = 0; i < distribution.Length; i++)
{
//System.Console.WriteLine(distribution[i]);
distributions += distribution[i];
}
var predictionLine = String.Format("{0} - {1} - {2} - {3} - {4} - {5}\n", actual, predicted, revision, weight, margine, distributions);
sb.Append(predictionLine);
//System.Console.WriteLine(predicted);
}
count++;
}
File_Helper.WriteToFile(sb, predictionModel + "NbCl.txt");
System.Console.WriteLine(count);
System.Console.ReadKey();
}
private void ResultButton_Click(object sender, RoutedEventArgs e)
{
if (LanguageComboBox.SelectedIndex < 0 ||
ContributersTextBox?.Text == "" ||
CommitsTextBox?.Text == "" ||
StarsTextBox?.Text == "" ||
ForksTextBox?.Text == "" ||
BranchesTextBox?.Text == "" ||
WatchersTextBox?.Text == "" ||
PullRequestsTextBox?.Text == "" ||
TotalIssuesTextBox?.Text == "" ||
OpenIssuesTextBox?.Text == "" ||
HasDownloadsComboBox.SelectedIndex < 0 ||
ReleaseCountsTextBox?.Text == ""
)
{
ResultLabel.Content = "Enter All Values";
return;
}
List <string> LinesToWrite = new List <string>();
LinesToWrite.Add("@relation \"GithubAll\"");
LinesToWrite.Add("");
LinesToWrite.Add("@attribute Language { PHP,JAVA,HTML,JavaScript,C}");
LinesToWrite.Add("@attribute Contributers real");
LinesToWrite.Add("@attribute Commits real");
LinesToWrite.Add("@attribute Stars real");
LinesToWrite.Add("@attribute Forks real");
LinesToWrite.Add("@attribute Branches real");
LinesToWrite.Add("@attribute Watchers real");
LinesToWrite.Add("@attribute PullRequests real");
LinesToWrite.Add("@attribute TotalIssues real");
LinesToWrite.Add("@attribute OpenIssues real");
LinesToWrite.Add("@attribute HasDownloads { TRUE,FALSE}");
LinesToWrite.Add("@attribute ReleaseCount real");
LinesToWrite.Add("@attribute isSuccessFull { TRUE,FALSE}");
LinesToWrite.Add("");
LinesToWrite.Add("@data");
string Value = ((ComboBoxItem)LanguageComboBox.SelectedItem).Content.ToString();
if (((ComboBoxItem)LanguageComboBox.SelectedItem).Content.ToString() == "C#/C++")
{
Value = "C";
}
LinesToWrite.Add(Value + "," +
ContributersTextBox.Text + "," +
CommitsTextBox.Text + "," +
StarsTextBox.Text + "," +
ForksTextBox.Text + "," +
BranchesTextBox.Text + "," +
WatchersTextBox.Text + "," +
PullRequestsTextBox.Text + "," +
TotalIssuesTextBox.Text + "," +
OpenIssuesTextBox.Text + "," +
((ComboBoxItem)HasDownloadsComboBox.SelectedItem).Content.ToString() + "," +
ReleaseCountsTextBox.Text + "," + "?"
);
System.IO.File.WriteAllLines("Dataset.arff", LinesToWrite);
Classifier cl = SerializationHelper.read(@"RF.model") as Classifier;
Instances testDataSet = new Instances(new java.io.FileReader("Dataset.arff"));
testDataSet.setClassIndex(testDataSet.numAttributes() - 1);
Evaluation evaluation = new Evaluation(testDataSet);
for (int i = 0; i < testDataSet.numInstances(); i++)
{
Instance instance = testDataSet.instance(i);
evaluation.evaluateModelOnceAndRecordPrediction(cl, instance);
}
double Prediction = -1;
foreach (object o in evaluation.predictions().toArray())
{
NominalPrediction prediction = o as NominalPrediction;
if (prediction != null)
{
double[] distribution = prediction.distribution();
Prediction = prediction.predicted();
}
}
if (Prediction == -1)
{
ResultLabel.Content = "Error Parsing";
}
else if (Prediction == 0)
{
ResultLabel.Content = "Successfull";
}
else
{
ResultLabel.Content = "UnSuccessfull";
}
}