public void addSample(int[] sample) {
if (m_state == state_initializingClusters) {
m_initSamples[m_initCounter++] = sample;
IntTouple it = new IntTouple(sample);
if (m_uniqueInitSamplesSet.add(it)) {
// new element
m_uniqueInitSamplesList.add(it);
}
if (m_initCounter == numInitSamples()) {
initializeClusters();
}
} else if (m_state == state_updating) {
Object[] pairing = determineClusterMembership(sample);
int memberOf = ((Integer) pairing[0]).intValue();
for (int i = 0; i < m_dimensions; i++) {
m_tmpClusterAccum[memberOf][i] += sample[i];
}
m_tmpClusterCount[memberOf]++;
m_error += ((Long) pairing[1]).longValue();
} else if (m_state == state_doneAllComputingError) {
Object[] pairing = determineClusterMembership(sample);
m_error += ((Long) pairing[1]).longValue();
} else {
throw new RuntimeException();
}
}
private void initializeClusters() {
if (m_uniqueInitSamplesList.size() == 0) {
throw new RuntimeException();
}
for (int i = 0; i < m_numClusters; i++) {
int randomIndex = (int) Math.floor(m_uniqueInitSamplesList.size() * Math.random());
m_clusters[i] =
initSmoothClusterRandomly((IntTouple) m_uniqueInitSamplesList.get(randomIndex));
m_uniqueInitSamplesList.remove(randomIndex);
}
m_state = state_updating;
for (int i = 0; i < m_initCounter; i++) {
addSample(m_initSamples[i]);
}
}