How to use

new_estimators

method

in

weka.clusterers.EM

new_estimators();
estimate_priors(inst);

new_estimators();
estimate_priors(inst);

/**
 * The M step of the EM algorithm.
 * 
 * @param inst the training instances
 * @throws Exception if something goes wrong
 */
private void M(Instances inst) throws Exception {
 int i, j, l;
 new_estimators();
 estimate_priors(inst);
 // sum
 for (l = 0; l < inst.numInstances(); l++) {
  Instance in = inst.instance(l);
  for (i = 0; i < m_num_clusters; i++) {
   for (j = 0; j < m_num_attribs; j++) {
    if (inst.attribute(j).isNominal()) {
     m_model[i][j]
      .addValue(in.value(j), in.weight() * m_weights[l][i]);
    } else {
     m_modelNormal[i][j][0] += (in.value(j) * in.weight() * m_weights[l][i]);
     m_modelNormal[i][j][2] += in.weight() * m_weights[l][i];
     m_modelNormal[i][j][1] += (in.value(j) * in.value(j)
      * in.weight() * m_weights[l][i]);
    }
   }
  }
 }
 // re-estimate Gaussian parameters
 M_reEstimate(inst);
}

/**
 * The M step of the EM algorithm.
 * 
 * @param inst the training instances
 * @throws Exception if something goes wrong
 */
private void M(Instances inst) throws Exception {
 int i, j, l;
 new_estimators();
 estimate_priors(inst);
 // sum
 for (l = 0; l < inst.numInstances(); l++) {
  Instance in = inst.instance(l);
  for (i = 0; i < m_num_clusters; i++) {
   for (j = 0; j < m_num_attribs; j++) {
    if (inst.attribute(j).isNominal()) {
     m_model[i][j]
      .addValue(in.value(j), in.weight() * m_weights[l][i]);
    } else {
     m_modelNormal[i][j][0] += (in.value(j) * in.weight() * m_weights[l][i]);
     m_modelNormal[i][j][2] += in.weight() * m_weights[l][i];
     m_modelNormal[i][j][1] += (in.value(j) * in.value(j)
      * in.weight() * m_weights[l][i]);
    }
   }
  }
 }
 // re-estimate Gaussian parameters
 M_reEstimate(inst);
}