public List <string> Classify(string model, string test)
{
List <string> ret = new List <string>();
try
{
java.io.ObjectInputStream ois = new java.io.ObjectInputStream(new java.io.FileInputStream(model));
weka.classifiers.Classifier cl = (weka.classifiers.Classifier)ois.readObject();
ois.close();
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(test));
insts.setClassIndex(insts.numAttributes() - 1);
for (int i = 0; i < 1; 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) + ", " + predicted);
for (int j = 0; j < distrs.Length; j++)
{
string predicted = insts.classAttribute().value(j);
string distr = distrs[j].ToString("#0.000");
ret.Add(predicted + "," + distr);
}
}
return(ret);
}
catch
{
return(ret);
}
}
public int ClassifyInstance(double[] attributes, out double[] percentages)
{
double classificationResult = 1.0;
testInstance.setDataset(dataSet);
testInstance.setClassMissing();
dataSet.add(testInstance);
for (int i = 0; i < attributes.Length; i++)
{
testInstance.setValue(i, attributes[i]);
}
classificationResult = m_cl.classifyInstance(testInstance);
dataSet.delete(0);
percentages = m_cl.distributionForInstance(testInstance);
return((int)classificationResult);
}
public void Test2()
{
java.io.ObjectInputStream ois = new java.io.ObjectInputStream(new java.io.FileInputStream("D:\\android_analysis\\som_model.model"));
weka.classifiers.Classifier cl = (weka.classifiers.Classifier)ois.readObject();
ois.close();
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("D:\\android_analysis\\test1.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
for (int i = 0; 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) + ", " + predicted);
}
}
public double[] ClassifyNewData(double[] newData)
{
weka.core.Instance newInstance = new weka.core.Instance(1, newData);
newInstance.setDataset(playerData);
return(classifier.distributionForInstance(newInstance));
}