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();
}