/**
* 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 {
// Filter instance
m_Missing.input(instance);
m_Missing.batchFinished();
instance = m_Missing.output();
if (!m_onlyNumeric && m_NominalToBinary != null) {
m_NominalToBinary.input(instance);
m_NominalToBinary.batchFinished();
instance = m_NominalToBinary.output();
}
if (m_Filter != null) {
m_Filter.input(instance);
m_Filter.batchFinished();
instance = m_Filter.output();
}
double result = m_optimizer.SVMOutput(instance);
return result * m_x1 + m_x0;
}
/**
* Computes the distribution for a given instance
*
* @param instance the instance for which distribution is computed
* @return the distribution
* @throws Exception if the distribution can't be computed successfully
*/
@Override
public double[] distributionForInstance(Instance instance) throws Exception {
m_ReplaceMissingValues.input(instance);
instance = m_ReplaceMissingValues.output();
m_AttFilter.input(instance);
instance = m_AttFilter.output();
m_NominalToBinary.input(instance);
instance = m_NominalToBinary.output();
// Extract the predictor columns into an array
double[] instDat = new double[m_NumPredictors + 1];
int j = 1;
instDat[0] = 1;
for (int k = 0; k <= m_NumPredictors; k++) {
if (k != m_ClassIndex) {
instDat[j++] = instance.value(k);
}
}
double[] distribution = evaluateProbability(instDat);
return distribution;
}
