How to use

score

method

in

weka.classifiers.pmml.consumer.TreeModel$TreeNode

 preds = c.score(instance, classAtt);
 break;
} else if (c.getPredicate().evaluate(instance) == Predicate.Eval.UNKNOWN) {
  preds = m_defaultChild.score(instance, classAtt);
 } else {
  throw new Exception(

 preds = c.score(instance, classAtt);
 break;
} else if (c.getPredicate().evaluate(instance) == Predicate.Eval.UNKNOWN) {
  preds = m_defaultChild.score(instance, classAtt);
 } else {
  throw new Exception(

} else {
 if (trueNode != null) {
  preds = trueNode.score(instance, classAtt);
 } else {
  doNoTrueChild(classAtt, preds);

     + c.getID());
  dists[count++] = c.score(instance, classAtt);
 preds = trueNode.score(instance, classAtt);
} else {
 doNoTrueChild(classAtt, preds);

     + c.getID());
  dists[count++] = c.score(instance, classAtt);
 preds = trueNode.score(instance, classAtt);
} else {
 doNoTrueChild(classAtt, preds);

} else {
 if (trueNode != null) {
  preds = trueNode.score(instance, classAtt);
 } else {
  doNoTrueChild(classAtt, preds);

 preds = c.score(instance, classAtt);
 break;
} else if (c.getPredicate().evaluate(instance) == Predicate.Eval.UNKNOWN) {

 preds = c.score(instance, classAtt);
 break;
} else if (c.getPredicate().evaluate(instance) == Predicate.Eval.UNKNOWN) {

 preds = c.score(instance, classAtt);
 break;
} else if (c.getPredicate().evaluate(instance) == Predicate.Eval.UNKNOWN) {

 preds = c.score(instance, classAtt);
 break;
} else if (c.getPredicate().evaluate(instance) == Predicate.Eval.UNKNOWN) {

 /**
  * Compute predictions and optionally invoke the "none" missing value
  * handling strategy (invokes no true child).
  * 
  * @param instance the incoming vector of attribute and derived field
  *          values.
  * @param classAtt the class attribute.
  * @return the predicted probability distribution.
  * @throws Exception if something goes wrong.
  */
 protected double[] missingValueStrategyNone(double[] instance,
  Attribute classAtt) throws Exception {
  double[] preds = null;
  // look for a child whose predicate evaluates to TRUE
  for (TreeNode c : m_childNodes) {
   if (c.getPredicate().evaluate(instance) == Predicate.Eval.TRUE) {
    preds = c.score(instance, classAtt);
    break;
   }
  }
  if (preds == null) {
   preds = new double[classAtt.numValues()];
   // no true child strategy
   doNoTrueChild(classAtt, preds);
  }
  return preds;
 }
}

/**
 * Classifies the given test instance. The instance has to belong to a dataset
 * when it's being classified.
 * 
 * @param inst the instance to be classified
 * @return the predicted most likely class for the instance or
 *         Utils.missingValue() if no prediction is made
 * @exception Exception if an error occurred during the prediction
 */
@Override
public double[] distributionForInstance(Instance inst) throws Exception {
 if (!m_initialized) {
  mapToMiningSchema(inst.dataset());
 }
 double[] preds = null;
 if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
  preds = new double[1];
 } else {
  preds = new double[m_miningSchema.getFieldsAsInstances().classAttribute()
   .numValues()];
 }
 double[] incoming = m_fieldsMap.instanceToSchema(inst, m_miningSchema);
 preds = m_root.score(incoming, m_miningSchema.getFieldsAsInstances()
  .classAttribute());
 return preds;
}

 /**
  * Compute predictions and optionally invoke the "none" missing value
  * handling strategy (invokes no true child).
  * 
  * @param instance the incoming vector of attribute and derived field
  *          values.
  * @param classAtt the class attribute.
  * @return the predicted probability distribution.
  * @throws Exception if something goes wrong.
  */
 protected double[] missingValueStrategyNone(double[] instance,
  Attribute classAtt) throws Exception {
  double[] preds = null;
  // look for a child whose predicate evaluates to TRUE
  for (TreeNode c : m_childNodes) {
   if (c.getPredicate().evaluate(instance) == Predicate.Eval.TRUE) {
    preds = c.score(instance, classAtt);
    break;
   }
  }
  if (preds == null) {
   preds = new double[classAtt.numValues()];
   // no true child strategy
   doNoTrueChild(classAtt, preds);
  }
  return preds;
 }
}

/**
 * Classifies the given test instance. The instance has to belong to a dataset
 * when it's being classified.
 * 
 * @param inst the instance to be classified
 * @return the predicted most likely class for the instance or
 *         Utils.missingValue() if no prediction is made
 * @exception Exception if an error occurred during the prediction
 */
@Override
public double[] distributionForInstance(Instance inst) throws Exception {
 if (!m_initialized) {
  mapToMiningSchema(inst.dataset());
 }
 double[] preds = null;
 if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
  preds = new double[1];
 } else {
  preds = new double[m_miningSchema.getFieldsAsInstances().classAttribute()
   .numValues()];
 }
 double[] incoming = m_fieldsMap.instanceToSchema(inst, m_miningSchema);
 preds = m_root.score(incoming, m_miningSchema.getFieldsAsInstances()
  .classAttribute());
 return preds;
}