public double classifyInstance(Instance instance) throws Exception {
  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;
}

public double classifyInstance ( Instance instance ) throws Exception { 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 ; }
classifies the given instance using the linear regression function .
