Package opennlp.tools.ml.model

Examples of opennlp.tools.ml.model.OnePassDataIndexer

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    int cutoff = getCutoff();
    boolean sortAndMerge = isSortAndMerge();
    DataIndexer indexer = null;

    if (DATA_INDEXER_ONE_PASS_VALUE.equals(dataIndexerName)) {
      indexer = new OnePassDataIndexer(events, cutoff, sortAndMerge);
    } else if (DATA_INDEXER_TWO_PASS_VALUE.equals(dataIndexerName)) {
      indexer = new TwoPassDataIndexer(events, cutoff, sortAndMerge);
    } else {
      throw new IllegalStateException("Unexpected data indexer name: "
          + dataIndexerName);
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   * @param iterations The number of iterations to use for GIS.
   * @param cutoff The number of times a feature must occur to be included.
   * @return A GIS model trained with specified
   */
  public GISModel trainModel(ObjectStream<Event> eventStream, int iterations, int cutoff) throws IOException {
    return trainModel(iterations, new OnePassDataIndexer(eventStream,cutoff),cutoff);
  }
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  // << members related to AbstractSequenceTrainer

  public AbstractModel trainModel(int iterations, SequenceStream sequenceStream, int cutoff, boolean useAverage) throws IOException {
    this.iterations = iterations;
    this.sequenceStream = sequenceStream;
    DataIndexer di = new OnePassDataIndexer(new SequenceStreamEventStream(sequenceStream),cutoff,false);
    numSequences = 0;

    sequenceStream.reset();

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

    outcomeList  = di.getOutcomeList();
    predLabels = di.getPredLabels();
    pmap = new IndexHashTable<String>(predLabels, 0.7d);

    display("Incorporating indexed data for training...  \n");
    this.useAverage = useAverage;
    numEvents = di.getNumEvents();

    this.iterations = iterations;
    outcomeLabels = di.getOutcomeLabels();
    omap = new HashMap<String,Integer>();
    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];
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Related Classes of opennlp.tools.ml.model.OnePassDataIndexer

  • opennlp.tools.ml.AbstractEventTrainer
  • opennlp.tools.ml.maxent.GISTrainer
  • opennlp.tools.ml.perceptron.SimplePerceptronSequenceTrainer
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