Package opennlp.model

Examples of opennlp.model.RealValueFileEventStream

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  }

  @Test
  public void testGradientAtInitialPoint() throws IOException {
    // given
    RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt", "UTF-8");
    DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
    LogLikelihoodFunction objectFunction = new LogLikelihoodFunction(testDataIndexer);
    // when
    double[] gradientAtInitialPoint = objectFunction.gradientAt(objectFunction.getInitialPoint());
    double[] expectedGradient = new double[] { -9, -14, -17, 20, 8.5, 9, 14, 17, -20, -8.5 };
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  }

  @Test
  public void testGradientAtNonInitialPoint() throws IOException {
    // given
    RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt", "UTF-8");
    DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
    LogLikelihoodFunction objectFunction = new LogLikelihoodFunction(testDataIndexer);
    // when
    double[] nonInitialPoint = new double[] { 0.2, 0.5, 0.2, 0.5, 0.2,
        0.5, 0.2, 0.5, 0.2, 0.5 };
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public class QNTrainerTest {
  @Test
  public void testTrainModelReturnsAQNModel() throws Exception {
    // given
    RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt"); 
    DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
    // when
    QNModel trainedModel = new QNTrainer(false).trainModel(testDataIndexer);
    // then
    assertNotNull(trainedModel);
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  }

  @Test
  public void testInTinyDevSet() throws Exception {
    // given
    RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt"); 
    DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
    // when
    QNModel trainedModel = new QNTrainer(15, true).trainModel(testDataIndexer);
    String[] features2Classify = new String[] {"feature2","feature3", "feature3", "feature3","feature3", "feature3", "feature3","feature3", "feature3", "feature3","feature3", "feature3"};
    double[] eval = trainedModel.eval(features2Classify);
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  }
 
  @Test
  public void testModel() throws IOException {
      // given
      RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt"); 
      DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
      // when
      QNModel trainedModel = new QNTrainer(15, true).trainModel(testDataIndexer);
     
      assertTrue(trainedModel.equals(trainedModel)); 
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  }
 
  @Test
  public void testSerdeModel() throws IOException {
      // given
      RealValueFileEventStream rvfes1 = new RealValueFileEventStream("src/test/resources/data/opennlp/maxent/real-valued-weights-training-data.txt"); 
      DataIndexer testDataIndexer = new OnePassRealValueDataIndexer(rvfes1,1);
      // when
     // QNModel trainedModel = new QNTrainer(5, 500, true).trainModel(new TwoPassDataIndexer(createTrainingStream()));
      QNModel trainedModel = new QNTrainer(5, 700, true).trainModel(testDataIndexer);
     
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Related Classes of opennlp.model.RealValueFileEventStream

  • opennlp.maxent.io.RealValueFileEventStreamTest
  • opennlp.maxent.io.SuffixSensitiveGISModelWriter
  • opennlp.maxent.quasinewton.LogLikelihoodFunctionTest
  • opennlp.maxent.quasinewton.QNTrainerTest
  • opennlp.maxent.RealValueModelTest
  • java.io.File
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