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]);
}
}
private static long curveDistance(int x[], int y[], long abortIfGreaterThan) {
long answer = 0;
// for(int i = 0; i < x.length; i++) {
for (int i = x.length; --i >= 0; ) {
answer += sqrLookup[Math.abs(x[i] - y[i])];
if (answer > abortIfGreaterThan) {
return Long.MAX_VALUE;
}
}
return answer;
}
private boolean computeNewMeans() {
boolean changed = false;
double avg_delta = 0;
for (int i = 0; i < m_numClusters; i++) {
long delta = 0;
int sampleToCopyFrom = (int) Math.floor(Math.random() * m_initSamples.length);
if (m_tmpClusterCount[i] == 0) {
System.out.println(" Warning: zero sized cluster");
}
for (int j = 0; j < m_dimensions; j++) {
int newValue = 0;
if (m_tmpClusterCount[i] != 0) {
newValue = (int) Math.round((float) m_tmpClusterAccum[i][j] / m_tmpClusterCount[i]);
} else {
try {
newValue = m_initSamples[sampleToCopyFrom][j];
} catch (NullPointerException npe) {
int count = 0;
do {
sampleToCopyFrom = (int) Math.floor(Math.random() * m_initSamples.length);
count++;
if (count == 50) {
throw new RuntimeException();
}
} while (m_initSamples[sampleToCopyFrom] == null);
}
}
if (!changed) {
if (newValue != m_clusters[i][j]) {
changed = true;
}
}
delta += sqrLookup[Math.abs(newValue - m_clusters[i][j])];
m_clusters[i][j] = newValue;
}
avg_delta += (delta / m_dimensions);
}
avg_delta /= m_numClusters;
// System.out.println("avg_delta per dimension: " + avg_delta);
return changed;
}
