How to use

distributionForInstance

method

in

weka.clusterers.EM

 @Override
 public double[] call() {
  double[] llk = new double[2];
  double loglk = 0.0, sOW = 0.0;
  try {
   for (int i = m_lowNum; i < m_highNum; i++) {
    Instance in = m_eData.instance(i);
    loglk += in.weight() * EM.this.logDensityForInstance(in);
    sOW += in.weight();
    if (m_changeWeights) {
     m_weights[i] = distributionForInstance(in);
    }
   }
   // completedETask(loglk, sOW, true);
  } catch (Exception ex) {
   // completedETask(0, 0, false);
  }
  llk[0] = loglk;
  llk[1] = sOW;
  return llk;
 }
}

 @Override
 public double[] call() {
  double[] llk = new double[2];
  double loglk = 0.0, sOW = 0.0;
  try {
   for (int i = m_lowNum; i < m_highNum; i++) {
    Instance in = m_eData.instance(i);
    loglk += in.weight() * EM.this.logDensityForInstance(in);
    sOW += in.weight();
    if (m_changeWeights) {
     m_weights[i] = distributionForInstance(in);
    }
   }
   // completedETask(loglk, sOW, true);
  } catch (Exception ex) {
   // completedETask(0, 0, false);
  }
  llk[0] = loglk;
  llk[1] = sOW;
  return llk;
 }
}

/**
 * The E step of the EM algorithm. Estimate cluster membership probabilities.
 * 
 * @param inst the training instances
 * @param change_weights whether to change the weights
 * @return the average log likelihood
 * @throws Exception if computation fails
 */
private double E(Instances inst, boolean change_weights) throws Exception {
 double loglk = 0.0, sOW = 0.0;
 for (int l = 0; l < inst.numInstances(); l++) {
  Instance in = inst.instance(l);
  loglk += in.weight() * logDensityForInstance(in);
  sOW += in.weight();
  if (change_weights) {
   m_weights[l] = distributionForInstance(in);
  }
 }
 if (sOW <= 0) { // In case all weights are zero
  return 0;
 }
 
 // reestimate priors
 /*
  * if (change_weights) { estimate_priors(inst); }
  */
 return loglk / sOW;
}

/**
 * The E step of the EM algorithm. Estimate cluster membership probabilities.
 * 
 * @param inst the training instances
 * @param change_weights whether to change the weights
 * @return the average log likelihood
 * @throws Exception if computation fails
 */
private double E(Instances inst, boolean change_weights) throws Exception {
 double loglk = 0.0, sOW = 0.0;
 for (int l = 0; l < inst.numInstances(); l++) {
  Instance in = inst.instance(l);
  loglk += in.weight() * logDensityForInstance(in);
  sOW += in.weight();
  if (change_weights) {
   m_weights[l] = distributionForInstance(in);
  }
 }
 if (sOW <= 0) { // In case all weights are zero
  return 0;
 }
 
 // reestimate priors
 /*
  * if (change_weights) { estimate_priors(inst); }
  */
 return loglk / sOW;
}