public static J48 ByARFF(string ARFF, string count)
{
java.io.StringReader ArffReader = new java.io.StringReader(ARFF);
ArffLoader.ArffReader ARFFData = new ArffLoader.ArffReader(ArffReader, Convert.ToInt32(count), true);
Instances structure = ARFFData.getStructure();
structure.setClassIndex(structure.numAttributes() - 1);
Instance inst;
while ((inst = ARFFData.readInstance(structure)) != null)
{
structure.add(inst);
}
//Instances data = ARFFData.getData();
//J48 cls = new J48();
//cls.buildClassifier(data);
//String[] options = new String[1];
//options[0] = "-U"; // unpruned tree
J48 tree = new J48(); // new instance of tree
//tree.setOptions(options); // set the options
tree.buildClassifier(structure); // build classifier
return(tree);
}
public static J48 ByHeaderAndData(string header, string dataARFF)
{
java.io.StringReader ArffReader = new java.io.StringReader(header);
ArffLoader.ArffReader ARFFData = new ArffLoader.ArffReader(ArffReader, 100, false);
Instances structure = ARFFData.getStructure();
structure.setClassIndex(structure.numAttributes() - 1);
ArffReader = new java.io.StringReader(dataARFF);
Instances data = ARFFData.getData();
Instance inst;
while ((inst = ARFFData.readInstance(data)) != null)
{
structure.add(inst);
}
J48 tree = new J48(); // new instance of tree
tree.buildClassifier(structure); // build classifier
return(tree);
}
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, 100);
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[0] = "-C"; // unpruned tree
options[1] = "0.1";
J48 tree = new J48(); // new instance of tree
tree.setOptions(options); // set the options
tree.buildClassifier(trainingSet); // build classifier
//save the resulting classifier
classifierTree = tree;
Reader treeDot = new StringReader(tree.graph());
TreeBuild treeBuild = new TreeBuild();
Node treeRoot = treeBuild.create(treeDot);
FeaturesUsed = getTreeFeatures(treeRoot);
}