public AbstractModel trainModel()

in opennlp-tools/src/main/java/opennlp/tools/ml/perceptron/SimplePerceptronSequenceTrainer.java [153:238]
69 lines of code
12 McCabe index (conditional complexity)

  public AbstractModel trainModel(int iterations, SequenceStream<Event> sequenceStream,
                                  int cutoff, boolean useAverage) throws IOException {
    this.iterations = iterations;
    this.sequenceStream = sequenceStream;

    trainingParameters.put(TrainingParameters.CUTOFF_PARAM, cutoff);
    trainingParameters.put(AbstractDataIndexer.SORT_PARAM, false);
    DataIndexer di = new OnePassDataIndexer();
    di.init(trainingParameters, reportMap);
    di.index(new SequenceStreamEventStream(sequenceStream));
    numSequences = 0;

    sequenceStream.reset();

    while (sequenceStream.read() != null) {
      numSequences++;
    }

    outcomeList = di.getOutcomeList();
    predLabels = di.getPredLabels();
    pmap = new HashMap<>();

    for (int i = 0; i < predLabels.length; i++) {
      pmap.put(predLabels[i], i);
    }

    logger.info("Incorporating indexed data for training... ");
    this.useAverage = useAverage;
    numEvents = di.getNumEvents();

    this.iterations = iterations;
    outcomeLabels = di.getOutcomeLabels();
    omap = new HashMap<>();
    for (int oli = 0; oli < outcomeLabels.length; oli++) {
      omap.put(outcomeLabels[oli], oli);
    }
    outcomeList = di.getOutcomeList();

    numPreds = predLabels.length;
    numOutcomes = outcomeLabels.length;
    if (useAverage) {
      updates = new int[numPreds][numOutcomes][3];
    }

    logger.info("done.");

    logger.info("\tNumber of Event Tokens: {} " +
        "\n\t Number of Outcomes: {} " +
        "\n\t Number of Predicates: {}", numEvents, numOutcomes, numPreds);

    params = new MutableContext[numPreds];
    if (useAverage) {
      averageParams = new MutableContext[numPreds];
    }

    int[] allOutcomesPattern = new int[numOutcomes];
    for (int oi = 0; oi < numOutcomes; oi++) {
      allOutcomesPattern[oi] = oi;
    }

    for (int pi = 0; pi < numPreds; pi++) {
      params[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
      if (useAverage) {
        averageParams[pi] = new MutableContext(allOutcomesPattern, new double[numOutcomes]);
      }
      for (int aoi = 0; aoi < numOutcomes; aoi++) {
        params[pi].setParameter(aoi, 0.0);
        if (useAverage) {
          averageParams[pi].setParameter(aoi, 0.0);
        }
      }
    }

    logger.info("Computing model parameters...");
    findParameters(iterations);
    logger.info("...done.");

    /* Create and return the model ****/
    String[] updatedPredLabels = predLabels;

    if (useAverage) {
      return new PerceptronModel(averageParams, updatedPredLabels, outcomeLabels);
    } else {
      return new PerceptronModel(params, updatedPredLabels, outcomeLabels);
    }
  }