Package opennlp.tools.ml.model

Examples of opennlp.tools.ml.model.MaxentModel

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    assertEquals("person", names1[2].getType());
    assertTrue(!hasOtherAsOutcome(nameFinderModel));
  }

  private boolean hasOtherAsOutcome(TokenNameFinderModel nameFinderModel) {
    MaxentModel model = nameFinderModel.getNameFinderModel();
    for (int i = 0; i < model.getNumOutcomes(); i++) {
        String outcome = model.getOutcome(i);
        if (outcome.equals(NameFinderME.OTHER)) {
          return true;
        }
      }
    return false;
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*/
public class PerceptronPrepAttachTest {

  @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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    Map<String, String> trainParams = new HashMap<String, String>();
    trainParams.put(AbstractTrainer.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE);
    trainParams.put(AbstractTrainer.CUTOFF_PARAM, Integer.toString(1));
    trainParams.put("UseSkippedAveraging", Boolean.toString(true));

    MaxentModel model = TrainUtil.train(createTrainingStream(), trainParams, null);

    testModel(model, 0.773706362961129);
  }
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    trainParams.put(AbstractTrainer.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE);
    trainParams.put(AbstractTrainer.CUTOFF_PARAM, Integer.toString(1));
    trainParams.put(AbstractTrainer.ITERATIONS_PARAM, Integer.toString(500));
    trainParams.put("Tolerance", Double.toString(0.0001d));

    MaxentModel model = TrainUtil.train(createTrainingStream(), trainParams, null);

    testModel(model, 0.7677642980935875);
  }
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    trainParams.put(AbstractTrainer.ALGORITHM_PARAM, PerceptronTrainer.PERCEPTRON_VALUE);
    trainParams.put(AbstractTrainer.CUTOFF_PARAM, Integer.toString(1));
    trainParams.put(AbstractTrainer.ITERATIONS_PARAM, Integer.toString(500));
    trainParams.put("StepSizeDecrease", Double.toString(0.06d));

    MaxentModel model = TrainUtil.train(createTrainingStream(), trainParams, null);

    testModel(model, 0.7791532557563754);
  }
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    StringReader smallReader = new StringReader(smallValues);
    ObjectStream<Event> smallEventStream = new RealBasicEventStream(
        new PlainTextByLineStream(smallReader));

    MaxentModel smallModel = GIS.trainModel(100,
        new OnePassRealValueDataIndexer(smallEventStream, 0), false);
    String[] contexts = smallTest.split(" ");
    float[] values = RealValueFileEventStream.parseContexts(contexts);
    double[] smallResults = smallModel.eval(contexts, values);

    String smallResultString = smallModel.getAllOutcomes(smallResults);
    System.out.println("smallResults: " + smallResultString);

    StringReader largeReader = new StringReader(largeValues);
    ObjectStream<Event> largeEventStream = new RealBasicEventStream(
        new PlainTextByLineStream(largeReader));

    MaxentModel largeModel = GIS.trainModel(100,
        new OnePassRealValueDataIndexer(largeEventStream, 0), false);
    contexts = largeTest.split(" ");
    values = RealValueFileEventStream.parseContexts(contexts);
    double[] largeResults = largeModel.eval(contexts, values);

    String largeResultString = smallModel.getAllOutcomes(largeResults);
    System.out.println("largeResults: " + largeResultString);

    assertEquals(smallResults.length, largeResults.length);
    for (int i = 0; i < smallResults.length; i++) {
      System.out.println(String.format(
          "classifiy with smallModel: %1$s = %2$f", smallModel.getOutcome(i),
          smallResults[i]));
      System.out.println(String.format(
          "classifiy with largeModel: %1$s = %2$f", largeModel.getOutcome(i),
          largeResults[i]));
      assertEquals(smallResults[i], largeResults[i], 0.01f);
    }
  }
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  public void testQNOnPrepAttachDataWithParamsDefault() throws IOException {
   
    Map<String, String> trainParams = new HashMap<String, String>();
    trainParams.put(AbstractTrainer.ALGORITHM_PARAM, QNTrainer.MAXENT_QN_VALUE);
   
    MaxentModel model = TrainerFactory.getEventTrainer(trainParams, null)
                                      .train(createTrainingStream());
   
    testModel(model, 0.8115870264917059);
  }
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        AbstractEventTrainer.DATA_INDEXER_TWO_PASS_VALUE);
    trainParams.put(AbstractTrainer.CUTOFF_PARAM, Integer.toString(1));
    trainParams.put(QNTrainer.L1COST_PARAM, Double.toString(0.25));
    trainParams.put(QNTrainer.L2COST_PARAM, Double.toString(1.0));
   
    MaxentModel model = TrainerFactory.getEventTrainer(trainParams, null)
                                      .train(createTrainingStream());
   
    testModel(model, 0.8229759841544937);
  }
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        AbstractEventTrainer.DATA_INDEXER_TWO_PASS_VALUE);
    trainParams.put(AbstractTrainer.CUTOFF_PARAM, Integer.toString(1));
    trainParams.put(QNTrainer.L1COST_PARAM, Double.toString(1.0));
    trainParams.put(QNTrainer.L2COST_PARAM, Double.toString(0));
   
    MaxentModel model = TrainerFactory.getEventTrainer(trainParams, null)
                                      .train(createTrainingStream());
   
    testModel(model, 0.8180242634315424);
  }
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        AbstractEventTrainer.DATA_INDEXER_TWO_PASS_VALUE);
    trainParams.put(AbstractTrainer.CUTOFF_PARAM, Integer.toString(1));
    trainParams.put(QNTrainer.L1COST_PARAM, Double.toString(0));
    trainParams.put(QNTrainer.L2COST_PARAM, Double.toString(1.0));
   
    MaxentModel model = TrainerFactory.getEventTrainer(trainParams, null)
                                      .train(createTrainingStream());
   
    testModel(model, 0.8227283981183461);
  }
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Related Classes of opennlp.tools.ml.model.MaxentModel

  • opennlp.tools.chunker.ChunkerME
  • opennlp.tools.doccat.DocumentCategorizerME
  • opennlp.tools.ml.AbstractEventModelSequenceTrainer
  • opennlp.tools.ml.AbstractEventTrainer
  • opennlp.tools.ml.maxent.MaxentPrepAttachTest
  • opennlp.tools.ml.maxent.ModelApplier
  • opennlp.tools.ml.maxent.quasinewton.QNPrepAttachTest
  • opennlp.tools.ml.maxent.ScaleDoesntMatterTest
  • opennlp.tools.ml.perceptron.PerceptronPrepAttachTest
  • opennlp.tools.namefind.NameFinderME
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