@Override
public String toString() {
StringBuffer text = new StringBuffer();
text.append("\nsIB\n===\n");
text.append("\nNumber of clusters: " + m_numCluster + "\n");
for (int j = 0; j < m_numCluster; j++) {
text.append(
"\nCluster: "
+ j
+ " Size : "
+ bestT.size(j)
+ " Prior probability: "
+ Utils.doubleToString(bestT.Pt[j], 4)
+ "\n\n");
for (int i = 0; i < m_numAttributes; i++) {
text.append("Attribute: " + m_data.attribute(i).name() + "\n");
text.append(
"Probability given the cluster = "
+ Utils.doubleToString(bestT.Py_t.get(i, j), 4)
+ "\n");
}
}
return text.toString();
}
/**
* Compute the JS divergence between an instance and a cluster, used for test data
*
* @param inst instance to be clustered
* @param t index of the cluster
* @param pi1
* @param pi2
* @return the JS divergence
*/
private double JS(Instance inst, 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;
}
double sum = Utils.sum(inst.toDoubleArray());
double kl1 = 0.0, kl2 = 0.0, tmp = 0.0;
for (int i = 0; i < inst.numValues(); i++) {
tmp = inst.valueSparse(i) / sum;
if (tmp != 0) {
kl1 += tmp * Math.log(tmp / (tmp * pi1 + pi2 * bestT.Py_t.get(inst.index(i), t)));
}
}
for (int i = 0; i < m_numAttributes; i++) {
if ((tmp = bestT.Py_t.get(i, t)) != 0) {
kl2 += tmp * Math.log(tmp / (inst.value(i) * pi1 / sum + pi2 * tmp));
}
}
return pi1 * kl1 + pi2 * kl2;
}
