static void mainToOutput(String[] args, PrintWriter output) throws Exception {
    if (!parseArgs(args)) {
      return;
    }
    AdaptiveLogisticModelParameters lmp = AdaptiveLogisticModelParameters
        .loadFromFile(new File(modelFile));

    CsvRecordFactory csv = lmp.getCsvRecordFactory();
    csv.setIdName(idColumn);

    AdaptiveLogisticRegression lr = lmp.createAdaptiveLogisticRegression();

    State<Wrapper, CrossFoldLearner> best = lr.getBest();
    if (best == null) {
      output.printf("%s\n",
          "AdaptiveLogisticRegression has not be trained probably.");
      return;
    }
    CrossFoldLearner learner = best.getPayload().getLearner();

    BufferedReader in = TrainAdaptiveLogistic.open(inputFile);
    BufferedWriter out = new BufferedWriter(new FileWriter(outputFile));

    out.write(idColumn + ",target,score");
    out.newLine();

    String line = in.readLine();
    csv.firstLine(line);
    line = in.readLine();
    Map<String, Double> results = new HashMap<String, Double>();
    int k = 0;
    while (line != null) {
      Vector v = new SequentialAccessSparseVector(lmp.getNumFeatures());
      csv.processLine(line, v, false);
      Vector scores = learner.classifyFull(v);
      results.clear();
      if (maxScoreOnly) {
        results.put(csv.getTargetLabel(scores.maxValueIndex()),
            scores.maxValue());
      } else {
        for (int i = 0; i < scores.size(); i++) {
          results.put(csv.getTargetLabel(i), scores.get(i));
        }
      }

      for (Map.Entry<String,Double> entry : results.entrySet()) {
        out.write(csv.getIdString(line) + ',' + entry.getKey() + ',' + entry.getValue());
        out.newLine();
      }
      k++;
      if (k % 100 == 0) {
        output.printf(Locale.ENGLISH, "%d records processed \n", k);
      }
      line = in.readLine();
    }
    out.flush();
    out.close();
    output.printf(Locale.ENGLISH, "%d records processed totally.\n", k);
  }