private double[] calculateRegionProbs(int j, int i) throws Exception {
double[] sumOfProbsForRegion = new double[m_trainingData.classAttribute().numValues()];
for (int u = 0; u < m_numOfSamplesPerRegion; u++) {
double[] sumOfProbsForLocation = new double[m_trainingData.classAttribute().numValues()];
m_weightingAttsValues[m_xAttribute] = getRandomX(j);
m_weightingAttsValues[m_yAttribute] = getRandomY(m_panelHeight - i - 1);
m_dataGenerator.setWeightingValues(m_weightingAttsValues);
double[] weights = m_dataGenerator.getWeights();
double sumOfWeights = Utils.sum(weights);
int[] indices = Utils.sort(weights);
// Prune 1% of weight mass
int[] newIndices = new int[indices.length];
double sumSoFar = 0;
double criticalMass = 0.99 * sumOfWeights;
int index = weights.length - 1;
int counter = 0;
for (int z = weights.length - 1; z >= 0; z--) {
newIndices[index--] = indices[z];
sumSoFar += weights[indices[z]];
counter++;
if (sumSoFar > criticalMass) {
break;
}
}
indices = new int[counter];
System.arraycopy(newIndices, index + 1, indices, 0, counter);
for (int z = 0; z < m_numOfSamplesPerGenerator; z++) {
m_dataGenerator.setWeightingValues(m_weightingAttsValues);
double[][] values = m_dataGenerator.generateInstances(indices);
for (int q = 0; q < values.length; q++) {
if (values[q] != null) {
System.arraycopy(values[q], 0, m_vals, 0, m_vals.length);
m_vals[m_xAttribute] = m_weightingAttsValues[m_xAttribute];
m_vals[m_yAttribute] = m_weightingAttsValues[m_yAttribute];
// classify the instance
m_dist = m_classifier.distributionForInstance(m_predInst);
for (int k = 0; k < sumOfProbsForLocation.length; k++) {
sumOfProbsForLocation[k] += (m_dist[k] * weights[q]);
}
}
}
}
for (int k = 0; k < sumOfProbsForRegion.length; k++) {
sumOfProbsForRegion[k] += (sumOfProbsForLocation[k] * sumOfWeights);
}
}
// average
Utils.normalize(sumOfProbsForRegion);
// cache
double[] tempDist = new double[sumOfProbsForRegion.length];
System.arraycopy(sumOfProbsForRegion, 0, tempDist, 0, sumOfProbsForRegion.length);
return tempDist;
}
