public TrainingSet buildTSet(TrainingSet tSet, double[] w) {
WeightBasedRandom wRnd = new WeightBasedRandom(w);
int n = w.length;
Instance[] sample = new Instance[n];
Map<Integer, Instance> instances = tSet.getInstances();
for (int i = 0; i < n; i++) {
int instanceIndex = wRnd.nextInt();
sample[i] = instances.get(instanceIndex);
}
return new TrainingSet(sample);
}
@Override
public boolean train() {
baseClassifiers = new ArrayList<Classifier>();
int size = originalTSet.getSize();
/*
* Weights that define sample selection
*/
double[] w = new double[size];
/*
* Number of times instance was misclassified by classifiers that are
* currently in ensemble.
*/
int[] m = new int[size];
double w0 = 1.0 / size;
Arrays.fill(w, w0);
Arrays.fill(m, 0);
for (int i = 0; i < classifierPopulation; i++) {
if (verbose) {
System.out.println("Instance weights: " + Arrays.toString(w));
System.out.println("Instance misclassifications: " + Arrays.toString(m));
}
TrainingSet tSet = buildTSet(originalTSet, w);
Classifier baseClassifier = getClassifierForTraining(tSet);
baseClassifier.train();
updateWeights(originalTSet, w, m, baseClassifier);
baseClassifiers.add(baseClassifier);
}
return true;
}
public void updateWeights(TrainingSet tSet, double[] w, int[] m, Classifier baseClassifier) {
int n = w.length;
// update misclassification counts with results from latest classifier
for (int i = 0; i < n; i++) {
Instance instance = tSet.getInstance(i);
Concept actualConcept = baseClassifier.classify(instance);
Concept expectedConcept = instance.getConcept();
if (actualConcept == null || !(actualConcept.getName().equals(expectedConcept.getName()))) {
m[i]++;
}
}
// update weights
double sum = 0.0;
for (int i = 0; i < n; i++) {
sum += (1.0 + Math.pow(m[i], 4));
}
for (int i = 0; i < n; i++) {
w[i] = (1.0 + Math.pow(m[i], 4)) / sum;
}
}