private double[] incomingParamVector(double[] incomingInst) throws Exception {
  Instances miningSchemaI=m_miningSchema.getFieldsAsInstances();
  double[] incomingPV=new double[m_parameterList.size()];
  for (int i=0; i < m_parameterList.size(); i++) {
    incomingPV[i]=1.0;
    for (int j=0; j < miningSchemaI.numAttributes(); j++) {
      PPCell cellEntry=m_ppMatrix[i][j];
      Predictor p=null;
      if (cellEntry != null) {
        if ((p=getFactor(cellEntry.m_predictorName)) != null) {
          if ((int)incomingInst[p.m_miningSchemaIndex] == (int)cellEntry.m_value) {
            incomingPV[i]*=1.0;
          }
 else {
            incomingPV[i]*=0.0;
          }
        }
 else         if ((p=getCovariate(cellEntry.m_predictorName)) != null) {
          incomingPV[i]*=Math.pow(incomingInst[p.m_miningSchemaIndex],cellEntry.m_value);
        }
 else {
          throw new Exception("[GeneralRegression] can't find predictor " + cellEntry.m_predictorName + " in either the list of factors or covariates");
        }
      }
    }
  }
  return incomingPV;
}

private double [ ] incomingParamVector ( double [ ] incomingInst ) throws Exception { Instances miningSchemaI = m_miningSchema . getFieldsAsInstances ( ) ; double [ ] incomingPV = new double [ m_parameterList . size ( ) ] ; for ( int i = 0 ; i < m_parameterList . size ( ) ; i ++ ) { incomingPV [ i ] = 1.0 ; for ( int j = 0 ; j < miningSchemaI . numAttributes ( ) ; j ++ ) { PPCell cellEntry = m_ppMatrix [ i ] [ j ] ; Predictor p = null ; if ( cellEntry != null ) { if ( ( p = getFactor ( cellEntry . m_predictorName ) ) != null ) { if ( ( int ) incomingInst [ p . m_miningSchemaIndex ] == ( int ) cellEntry . m_value ) { incomingPV [ i ] *= 1.0 ; } else { incomingPV [ i ] *= 0.0 ; } } else if ( ( p = getCovariate ( cellEntry . m_predictorName ) ) != null ) { incomingPV [ i ] *= Math . pow ( incomingInst [ p . m_miningSchemaIndex ] , cellEntry . m_value ) ; } else { throw new Exception ( "[GeneralRegression] can't find predictor " + cellEntry . m_predictorName + " in either the list of factors or covariates" ) ; } } } } return incomingPV ; }
construct the incoming parameter vector based on the values in the incoming test instance .
