/**
* Returns class probabilities for an instance.
*
* @param instance the instance to compute the distribution for
* @return the class probabilities
* @throws Exception if distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance instance) throws Exception {
// replace missing values
m_replaceMissing.input(instance);
instance = m_replaceMissing.output();
// possibly convert nominal attributes
if (m_convertNominal) {
m_nominalToBinary.input(instance);
instance = m_nominalToBinary.output();
}
return m_tree.distributionForInstance(instance);
}
/**
* Classifies the given instance using the linear regression function.
*
* @param instance the test instance
* @return the classification
* @throws Exception if classification can't be done successfully
*/
public double classifyInstance(Instance instance) throws Exception {
// Transform the input instance
Instance transformedInstance = instance;
if (!m_checksTurnedOff) {
m_TransformFilter.input(transformedInstance);
m_TransformFilter.batchFinished();
transformedInstance = m_TransformFilter.output();
m_MissingFilter.input(transformedInstance);
m_MissingFilter.batchFinished();
transformedInstance = m_MissingFilter.output();
}
// Calculate the dependent variable from the regression model
return regressionPrediction(transformedInstance, m_SelectedAttributes, m_Coefficients);
}