Examples of opennlp.tools.util.featuregen.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

    byte descriptorBytes[] = (byte[]) artifactMap.get(GENERATOR_DESCRIPTOR_ENTRY_NAME);
    
    if (descriptorBytes != null) {
      InputStream descriptorIn = new ByteArrayInputStream(descriptorBytes);
  
      AdaptiveFeatureGenerator generator = null;
      try {
        generator = GeneratorFactory.create(descriptorIn, new FeatureGeneratorResourceProvider() {
  
          public Object getResource(String key) {
            return artifactMap.get(key);

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    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();
      
      contextGenerator = new DefaultNameContextGenerator(featureGenerator);

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  }
  
  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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   public static TokenNameFinderModel train(String languageCode, String type, ObjectStream<NameSample> samples, 
       TrainingParameters trainParams, AdaptiveFeatureGenerator generator, final Map<String, Object> resources) throws IOException {
     
     Map<String, String> manifestInfoEntries = new HashMap<String, String>();
     
     AdaptiveFeatureGenerator featureGenerator;
     
     if (generator != null)
       featureGenerator = generator;
     else 
       featureGenerator = createFeatureGenerator();

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       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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          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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          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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    byte descriptorBytes[] = (byte[]) artifactMap.get(GENERATOR_DESCRIPTOR_ENTRY_NAME);
    
    if (descriptorBytes != null) {
      InputStream descriptorIn = new ByteArrayInputStream(descriptorBytes);
  
      AdaptiveFeatureGenerator generator = null;
      try {
        generator = GeneratorFactory.create(descriptorIn, new FeatureGeneratorResourceProvider() {
  
          public Object getResource(String key) {
            return artifactMap.get(key);

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    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();


      contextGenerator = new DefaultNameContextGenerator(featureGenerator);

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  }


  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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Related Classes of opennlp.tools.util.featuregen.AdaptiveFeatureGenerator

opennlp.tools.namefind.DefaultNameContextGenerator

opennlp.tools.namefind.NameFinderME

opennlp.tools.namefind.TokenNameFinderFactory

opennlp.tools.namefind.TokenNameFinderModel

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