/**
* Compute the JS divergence between an instance and a cluster, used for training data
*
* @param instIdx index of the instance
* @param input statistics of the input data
* @param T the whole partition
* @param t index of the cluster
* @param pi1
* @param pi2
* @return the JS divergence
*/
private double JS(int instIdx, Input input, Partition T, int t, double pi1, double pi2) {
if (Math.min(pi1, pi2) <= 0) {
System.out.format(
"Warning: zero or negative weights in JS calculation! (pi1 %s, pi2 %s)\n", pi1, pi2);
return 0;
}
Instance inst = m_data.instance(instIdx);
double kl1 = 0.0, kl2 = 0.0, tmp = 0.0;
for (int i = 0; i < inst.numValues(); i++) {
tmp = input.Py_x.get(inst.index(i), instIdx);
if (tmp != 0) {
kl1 += tmp * Math.log(tmp / (tmp * pi1 + pi2 * T.Py_t.get(inst.index(i), t)));
}
}
for (int i = 0; i < m_numAttributes; i++) {
if ((tmp = T.Py_t.get(i, t)) != 0) {
kl2 += tmp * Math.log(tmp / (input.Py_x.get(i, instIdx) * pi1 + pi2 * tmp));
}
}
return pi1 * kl1 + pi2 * kl2;
}
/**
* Put an instance into a new cluster and update.
*
* @param instIdx instance to be updated
* @param newt index of the new cluster this instance has been assigned to
* @param T the current working partition
* @param Px an array of prior probabilities of the instances
*/
private void updateAssignment(int instIdx, int newt, Partition T, double Px, Matrix Py_x) {
T.Pt_x[instIdx] = newt;
// update probability of attributes in the cluster
double mass = Px + T.Pt[newt];
double pi1 = Px / mass;
double pi2 = T.Pt[newt] / mass;
for (int i = 0; i < m_numAttributes; i++) {
T.Py_t.set(i, newt, pi1 * Py_x.get(i, instIdx) + pi2 * T.Py_t.get(i, newt));
}
T.Pt[newt] = mass;
}
