/**
* Draw a instance out from a cluster.
*
* @param instIdx index of the instance to be drawn out
* @param t index of the cluster which the instance previously belong to
* @param T the current working partition
* @param input the input statistics
*/
private void reduce_x(int instIdx, int t, Partition T, Input input) {
// Update the prior probability of the cluster
ArrayList<Integer> indices = T.find(t);
double sum = 0.0;
for (int i = 0; i < indices.size(); i++) {
if (indices.get(i) == instIdx) {
continue;
}
sum += input.Px[indices.get(i)];
}
T.Pt[t] = sum;
if (T.Pt[t] < 0) {
System.out.format("Warning: probability < 0 (%s)\n", T.Pt[t]);
T.Pt[t] = 0;
}
// Update prob of each attribute in the cluster
double[][] mArray = input.Pyx.getArray();
for (int i = 0; i < m_numAttributes; i++) {
sum = 0.0;
for (int j = 0; j < indices.size(); j++) {
if (indices.get(j) == instIdx) {
continue;
}
sum += mArray[i][indices.get(j)];
}
T.Py_t.set(i, t, sum / T.Pt[t]);
}
}
/**
* Process the input and compute the statistics of the training data
*
* @return an Input object which holds the statistics about the training data
*/
private Input sIB_ProcessInput() {
double valSum = 0.0;
for (int i = 0; i < m_numInstances; i++) {
valSum = 0.0;
for (int v = 0; v < m_data.instance(i).numValues(); v++) {
valSum += m_data.instance(i).valueSparse(v);
}
if (valSum <= 0) {
if (m_verbose) {
System.out.format("Instance %s sum of value = %s <= 0, removed.\n", i, valSum);
}
m_data.delete(i);
m_numInstances--;
}
}
// get the term-document matrix
Input input = new Input();
input.Py_x = getTransposedNormedMatrix(m_data);
if (m_uniformPrior) {
input.Pyx = input.Py_x.copy();
normalizePrior(m_data);
} else {
input.Pyx = getTransposedMatrix(m_data);
}
input.sumVals = getTotalSum(m_data);
input.Pyx.timesEquals(1 / input.sumVals);
// prior probability of documents, ie. sum the columns from the Pyx matrix
input.Px = new double[m_numInstances];
for (int i = 0; i < m_numInstances; i++) {
for (int j = 0; j < m_numAttributes; j++) {
input.Px[i] += input.Pyx.get(j, i);
}
}
// prior probability of terms, ie. sum the rows from the Pyx matrix
input.Py = new double[m_numAttributes];
for (int i = 0; i < input.Pyx.getRowDimension(); i++) {
for (int j = 0; j < input.Pyx.getColumnDimension(); j++) {
input.Py[i] += input.Pyx.get(i, j);
}
}
MI(input.Pyx, input);
return input;
}
/**
* Initialize the partition
*
* @param input object holding the statistics of the training data
* @return the initialized partition
*/
private Partition sIB_InitT(Input input) {
Partition T = new Partition();
int avgSize = (int) Math.ceil((double) m_numInstances / m_numCluster);
ArrayList<Integer> permInstsIdx = new ArrayList<Integer>();
ArrayList<Integer> unassigned = new ArrayList<Integer>();
for (int i = 0; i < m_numInstances; i++) {
unassigned.add(i);
}
while (unassigned.size() != 0) {
int t = random.nextInt(unassigned.size());
permInstsIdx.add(unassigned.get(t));
unassigned.remove(t);
}
for (int i = 0; i < m_numCluster; i++) {
int r2 = avgSize > permInstsIdx.size() ? permInstsIdx.size() : avgSize;
for (int j = 0; j < r2; j++) {
T.Pt_x[permInstsIdx.get(j)] = i;
}
for (int j = 0; j < r2; j++) {
permInstsIdx.remove(0);
}
}
// initialize the prior prob of each cluster, and the probability
// for each attribute within the cluster
for (int i = 0; i < m_numCluster; i++) {
ArrayList<Integer> indices = T.find(i);
for (int j = 0; j < indices.size(); j++) {
T.Pt[i] += input.Px[indices.get(j)];
}
double[][] mArray = input.Pyx.getArray();
for (int j = 0; j < m_numAttributes; j++) {
double sum = 0.0;
for (int k = 0; k < indices.size(); k++) {
sum += mArray[j][indices.get(k)];
}
sum /= T.Pt[i];
T.Py_t.set(j, i, sum);
}
}
if (m_verbose) {
System.out.println("Initializing...");
}
return T;
}