/**
* Copy the current partition into T
*
* @param T the target partition object
*/
private void copy(Partition T) {
if (T == null) {
T = new Partition();
}
System.arraycopy(Pt_x, 0, T.Pt_x, 0, Pt_x.length);
System.arraycopy(Pt, 0, T.Pt, 0, Pt.length);
T.L = L;
T.counter = counter;
double[][] mArray = Py_t.getArray();
double[][] tgtArray = T.Py_t.getArray();
for (int i = 0; i < mArray.length; i++) {
System.arraycopy(mArray[i], 0, tgtArray[i], 0, mArray[0].length);
}
}
/**
* Optimize the partition
*
* @param tmpT partition to be optimized
* @param input object describing the statistics of the training dataset
* @return the optimized partition
*/
private Partition sIB_OptimizeT(Partition tmpT, Input input) {
boolean done = false;
int change = 0, loopCounter = 0;
if (m_verbose) {
System.out.println("Optimizing...");
System.out.println("-------------");
}
while (!done) {
change = 0;
for (int i = 0; i < m_numInstances; i++) {
int old_t = tmpT.Pt_x[i];
// If the current cluster only has one instance left, leave it.
if (tmpT.size(old_t) == 1) {
if (m_verbose) {
System.out.format("cluster %s has only 1 doc remain\n", old_t);
}
continue;
}
// draw the instance out from its previous cluster
reduce_x(i, old_t, tmpT, input);
// re-cluster the instance
int new_t = clusterInstance(i, input, tmpT);
if (new_t != old_t) {
change++;
updateAssignment(i, new_t, tmpT, input.Px[i], input.Py_x);
}
}
tmpT.counter += change;
if (m_verbose) {
System.out.format("iteration %s , changes : %s\n", loopCounter, change);
}
done = checkConvergence(change, loopCounter);
loopCounter++;
}
// compute the sIB score
tmpT.L = sIB_local_MI(tmpT.Py_t, tmpT.Pt);
if (m_verbose) {
System.out.format("score (L) : %s \n", Utils.doubleToString(tmpT.L, 4));
}
return tmpT;
}