Examples of AdaptiveFeatureGenerator

opennlp.tools.util.featuregen.AdaptiveFeatureGenerator
An interface for generating features for name entity identification and for updating document level contexts.
Most implementors do not need the adaptive functionality of this interface, they should extend the {@link FeatureGeneratorAdapter} class instead.
Note:
Feature generation is not thread safe and a instance of a feature generator must only be called from one thread. The resources used by a feature generator are typically shared between man instances of features generators which are called from many threads and have to be thread safe. If that is not possible the {@link FeatureGeneratorFactory} must make a copyof the resource object for each feature generator instance. @see FeatureGeneratorAdapter @see FeatureGeneratorFactory

Examples of opennlp.tools.util.featuregen.AdaptiveFeatureGenerator

       throw new IllegalArgumentException("languageCode must not be null!");
     }
     
     Map<String, String> manifestInfoEntries = new HashMap<String, String>();


     AdaptiveFeatureGenerator featureGenerator;


     if (generator != null)
       featureGenerator = generator;
     else
       featureGenerator = createFeatureGenerator();

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Examples of opennlp.tools.util.featuregen.AdaptiveFeatureGenerator

       byte[] generatorDescriptor, final Map<String, Object> resources,
       int iterations, int cutoff) throws IOException {


     // TODO: Pass in resource manager ...


     AdaptiveFeatureGenerator featureGenerator = createFeatureGenerator(generatorDescriptor, resources);


     TokenNameFinderModel model = train(languageCode, type, samples, featureGenerator,
         resources, iterations, cutoff);


     if (generatorDescriptor != null) {

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Examples of opennlp.tools.util.featuregen.AdaptiveFeatureGenerator

    // If generator is provided always use that one
    if (generator != null) {
      contextGenerator = new DefaultNameContextGenerator(generator);
    } else {
      // If model has a generator use that one, otherwise create default
      AdaptiveFeatureGenerator featureGenerator = model.createFeatureGenerators();


      if (featureGenerator == null) {
        featureGenerator = createFeatureGenerator();
      }

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Examples of opennlp.tools.util.featuregen.AdaptiveFeatureGenerator

  }


  private static AdaptiveFeatureGenerator createFeatureGenerator(
          byte[] generatorDescriptor, final Map<String, Object> resources)
          throws IOException {
    AdaptiveFeatureGenerator featureGenerator;


    if (generatorDescriptor != null) {
      featureGenerator = GeneratorFactory.create(new ByteArrayInputStream(
              generatorDescriptor), new FeatureGeneratorResourceProvider() {

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Examples of opennlp.tools.util.featuregen.AdaptiveFeatureGenerator

      beamSize = Integer.parseInt(beamSizeString);
    }


    Map<String, String> manifestInfoEntries = new HashMap<String, String>();


    AdaptiveFeatureGenerator featureGenerator;


    if (generator != null) {
      featureGenerator = generator;
    } else {
      featureGenerator = createFeatureGenerator();

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Examples of opennlp.tools.util.featuregen.AdaptiveFeatureGenerator

          new PreviousMapFeatureGenerator()};
    }
  }


  public void addFeatureGenerator(AdaptiveFeatureGenerator generator) {
      AdaptiveFeatureGenerator generators[] = featureGenerators;


      featureGenerators = new AdaptiveFeatureGenerator[featureGenerators.length + 1];


      System.arraycopy(generators, 0, featureGenerators, 0, generators.length);

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Examples of opennlp.tools.util.featuregen.AdaptiveFeatureGenerator

    }
  }


  public NameContextGenerator createContextGenerator() {


    AdaptiveFeatureGenerator featureGenerator = createFeatureGenerators();


    if (featureGenerator == null) {
      featureGenerator = NameFinderME.createFeatureGenerator();
    }

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Examples of opennlp.tools.util.featuregen.AdaptiveFeatureGenerator

    }


    if (descriptorBytes != null) {
      InputStream descriptorIn = new ByteArrayInputStream(descriptorBytes);


      AdaptiveFeatureGenerator generator = null;
      try {
        generator = GeneratorFactory.create(descriptorIn, new FeatureGeneratorResourceProvider() {


          public Object getResource(String key) {
            if (artifactProvider != null) {

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Examples of opennlp.tools.util.featuregen.AdaptiveFeatureGenerator

       int iterations, int cutoff) throws IOException {
     
     Map<String, String> manifestInfoEntries = new HashMap<String, String>();
     ModelUtil.addCutoffAndIterations(manifestInfoEntries, cutoff, iterations);
     
     AdaptiveFeatureGenerator featureGenerator;
     
     if (generator != null)
       featureGenerator = generator;
     else 
       featureGenerator = createFeatureGenerator();

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Examples of opennlp.tools.util.featuregen.AdaptiveFeatureGenerator

          new PreviousMapFeatureGenerator()};
    }
  }


  public void addFeatureGenerator(AdaptiveFeatureGenerator generator) {
      AdaptiveFeatureGenerator generators[] = featureGenerators;


      featureGenerators = new AdaptiveFeatureGenerator[featureGenerators.length + 1];


      System.arraycopy(generators, 0, featureGenerators, 0, generators.length);

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