Package opennlp.tools.ml.model

Examples of opennlp.tools.ml.model.TwoPassDataIndexer


    return train(languageCode, parseSamples, rules, params);
  }

  @Deprecated
  public static AbstractModel train(ObjectStream<Event>  es, int iterations, int cut) throws java.io.IOException {
    return opennlp.tools.ml.maxent.GIS.trainModel(iterations, new TwoPassDataIndexer(es, cut));
  }
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   * @deprecated Please do not use anymore, use the ObjectStream train methods instead! This method
   * will be removed soon.
   */
  @Deprecated
  public static AbstractModel train(ObjectStream<Event> es, int iterations, int cut) throws java.io.IOException {
    return opennlp.tools.ml.maxent.GIS.trainModel(iterations, new TwoPassDataIndexer(es, cut));
  }
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    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);
    }
    return indexer;
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  @Test
  public void testPerceptronOnPrepAttachData() throws IOException {
    MaxentModel model =
        new PerceptronTrainer().trainModel(400,
        new TwoPassDataIndexer(createTrainingStream(), 1, false), 1);

    testModel(model, 0.7650408516959644);
  }
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  @Test
  public void testQNOnPrepAttachData() throws IOException {
    AbstractModel model =
        new QNTrainer(true).trainModel(
            100, new TwoPassDataIndexer(createTrainingStream(), 1));

    testModel(model, 0.8155484030700668);
  }
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  @Test
  public void testMaxentOnPrepAttachData() throws IOException {
    AbstractModel model =
        new GISTrainer(true).trainModel(100,
        new TwoPassDataIndexer(createTrainingStream(), 1), 1);

    testModel(model, 0.7997028967566229);
  }
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  @Test
  public void testMaxentOnPrepAttachData2Threads() throws IOException {
    AbstractModel model =
        new GISTrainer(true).trainModel(100,
            new TwoPassDataIndexer(createTrainingStream(), 1),
            new UniformPrior(), 1, 2);

    testModel(model, 0.7997028967566229);
  }
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