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));
}
public void LearnModel()
{
Init();
foreach (Feature currFeature in DomPool.SelectorFeatures)
{
String featureString = currFeature.ToString();
HashSet <HtmlNode> resNodes = DomPool.RunXpathQuery(featureString);
foreach (HtmlNode nd in resNodes)
{
if (!allNodes.Contains(nd))
{
continue;
}
nodeFeatures[nd].Add(featureString);
}
}
FastVector fvWekaAttributes = GetDataSetAtts();
Instances trainingSet = new Instances("TS", fvWekaAttributes, 10);
trainingSet.setClassIndex(fvWekaAttributes.size() - 1);
foreach (HtmlNode currNode in allNodes)
{
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 (nodeFeatures[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);
item.setValue(classFeature, (DomPool.TargetNodes.Contains(currNode)?"yes":"no"));
item.setDataset(trainingSet);
if (DomPool.TargetNodes.Contains(currNode))
{
for (int t = 0; t < (DomPool.NonTargetNodes.Count() / DomPool.TargetNodes.Count()); t++)
{
trainingSet.add(new SparseInstance(item));
}
}
else
{
trainingSet.add(item);
}
}
//String[] options = new String[2];
//options = new string[] { "-C", "0.05" }; // unpruned tree
NaiveBayes cls = new NaiveBayes(); // new instance of tree
//cls.setOptions(weka.core.Utils.splitOptions("-C 1.0 -L 0.0010 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\""));
//cls.setOptions(options); // set the options
cls.buildClassifier(trainingSet); // build classifier
//save the resulting classifier
classifier = cls;
// Reader treeDot = new StringReader(tree.graph());
// TreeBuild treeBuild = new TreeBuild();
// Node treeRoot = treeBuild.create(treeDot);
FeaturesUsed = new HashSet <string>();
foreach (Feature f in DomPool.SelectorFeatures)
{
FeaturesUsed.Add(f.ToString());
}
}
