public HashSet <HtmlNode> RunOnTestSet()
{
HashSet <HtmlNode> classifierSelectedNodes = new HashSet <HtmlNode>();
InitTest();
foreach (string featureString in FeaturesUsed)
{
HashSet <HtmlNode> resNodes = DomPool.TESTRunXpathQuery(useNormalPerformanceQUERY(featureString));
foreach (HtmlNode nd in resNodes)
{
if (!testAllNodes.Contains(nd))
{
continue;
}
testNodeFeatures[nd].Add(featureString);
}
}
FastVector fvWekaAttributes = GetDataSetAtts();
Instances testSet = new Instances("TestSet", fvWekaAttributes, 10);
testSet.setClassIndex(fvWekaAttributes.size() - 1);
foreach (HtmlNode currNode in testAllNodes)
{
Instance item = new SparseInstance(fvWekaAttributes.size());
for (int i = 0; i < fvWekaAttributes.size() - 1; i++)
{
weka.core.Attribute currFeature = (weka.core.Attribute)fvWekaAttributes.elementAt(i);
if (testNodeFeatures[currNode].Contains(currFeature.name()))
{
item.setValue(currFeature, 1);
}
else
{
item.setValue(currFeature, 0);
}
}
//set the class
weka.core.Attribute classFeature = (weka.core.Attribute)fvWekaAttributes.elementAt(fvWekaAttributes.size() - 1);
//string rightVal = DomPool.TargetNodes.Contains(currNode) ? "yes" : "no";
item.setDataset(testSet);
double classifierdv = classifier.classifyInstance(item);
string classifierVal = classFeature.value((int)classifierdv);
if (classifierVal.Equals("yes"))
{
classifierSelectedNodes.Add(currNode);
}
testSet.add(item);
}
return(classifierSelectedNodes);
}
public void Classify()
{
// Train Dataset yolu
ConverterUtils.DataSource source1 = new ConverterUtils.DataSource(TrainPath);
Instances train = source1.getDataSet();
if (train.classIndex() == -1)
{
train.setClassIndex(train.numAttributes() - 1);
}
//Tahmin edilecek data'nın yolu
ConverterUtils.DataSource source2 = new ConverterUtils.DataSource(Application.StartupPath + "\\UrunPredict.arff");
Instances test = source2.getDataSet();
if (test.classIndex() == -1)
{
test.setClassIndex(train.numAttributes() - 1);
}
//Sınıflandırma algoritmasının belirlenmesi
/* weka.classifiers.lazy.IBk ibk = new weka.classifiers.lazy.IBk();
* //En yakın komşu 'k' değeri 3 olarak belirleniyor
* ibk.setKNN(3);
* ibk.buildClassifier(train);*/
//NAIVE BAYES ALGORITMASI UYGULAMA
NaiveBayes naiveBayes = new NaiveBayes();
naiveBayes.buildClassifier(train);
//Sınıflandırma işlemi uygulanıyor
double label = naiveBayes.classifyInstance(test.instance(0));
test.instance(0).setClassValue(label);
//Yeni sınıflandısrılan verinin train veri setine eklenmesi
string AddClassifiedData = txtSicaklik.Text + "," + txtNem.Text + "," + txtYagis.Text + "," + cmbDeniz.Text + "," + test.instance(0).stringValue(4);
StreamWriter Kayit = File.AppendText(TrainPath);
Kayit.WriteLine("\n" + AddClassifiedData);
Kayit.Close();
ShowImageAndInfo(test.instance(0).stringValue(4));
}