public static Instances getKnowledgeBase() {
if (knowledgeBase == null) {
try {
// load knowledgebase from file
CreateAppInsertIntoVm.knowledgeBase =
Action.loadKnowledge(Configuration.getInstance().getKBCreateAppInsertIntoVm());
// prediction is also performed therefore the classifier and the evaluator must be
// instantiated
if (!isOnlyLearning()) {
System.out.println("Classify data CreateAppInsertInto");
if (knowledgeBase.numInstances() > 0) {
classifier = new MultilayerPerceptron();
classifier.buildClassifier(knowledgeBase);
evaluation = new Evaluation(knowledgeBase);
evaluation.crossValidateModel(
classifier,
knowledgeBase,
10,
knowledgeBase.getRandomNumberGenerator(randomData.nextLong(1, 1000)));
System.out.println("Classified data CreateAppInsertInto");
} else {
System.out.println("No Instancedata for classifier CreateAppInsertIntoVm");
}
}
} catch (Exception e) {
e.printStackTrace();
}
}
return knowledgeBase;
}
// Create 70% training data set
public void generateTrainingDataSet() {
trainingDataSet = new Instances(instances);
int size = trainingDataSet.numInstances();
// Remove closing prize "close" attribute
trainingDataSet.deleteAttributeAt(0);
// Randomize data set
trainingDataSet.randomize(trainingDataSet.getRandomNumberGenerator(1));
}
/** runs 10fold CV over the training file */
public void execute() throws Exception {
// run filter
m_Filter.setInputFormat(m_Training);
Instances filtered = Filter.useFilter(m_Training, m_Filter);
// train classifier on complete file for tree
m_Classifier.buildClassifier(filtered);
// 10fold CV with seed=1
m_Evaluation = new Evaluation(filtered);
m_Evaluation.crossValidateModel(
m_Classifier, filtered, 10, m_Training.getRandomNumberGenerator(1));
}