// View-Based Classification
public static void ViewBasedClassification(Instances insts, int folds, int view, int k)
{
java.util.Random rand = new java.util.Random(1);
insts.setClassIndex(insts.numAttributes() - 1);
Vote vote = new Vote();
Classifier[] classifiers = new Classifier[k];
for (int i = 1; i <= k; i++)
{
IBk IBk = new IBk(); // Instance-based (IB) learner
IBk.setKNN(i);
classifiers[i - 1] = IBk;
}
vote.setClassifiers(classifiers);
vote.setCombinationRule(new SelectedTag(Vote.MAJORITY_VOTING_RULE, Vote.TAGS_RULES));
vote.buildClassifier(insts); // Generate classifiers
AbstractOutput output = new PlainText();
output.setBuffer(new java.lang.StringBuffer());
output.setHeader(insts);
Evaluation evaluation = new Evaluation(insts);
evaluation.crossValidateModel(vote, insts, folds, rand, output); // n-fold cross validation
// Parse the output string
string str = output.getBuffer().toString();
str = str.Replace(" ", " ").Replace(" ", " ").Replace(" ", " ").Replace(" ", " ");
string[] line = str.Split('\n');
predicted[view] = new List <int>();
predicted[view].Clear();
actual.Clear();
for (int i = 1; i < line.Length - 1; i++)
{
String[] linesplit = line[i].Trim().Split(' ');
int index = linesplit[1].IndexOf(':'); // Actual class
actual.Add(Convert.ToInt16(linesplit[1].Substring(0, index)) - 1);
index = linesplit[2].IndexOf(':'); // Predicted class
predicted[view].Add(Convert.ToInt16(linesplit[2].Substring(0, index)) - 1);
}
}
