Examples of opennlp.model.EventStream

opennlp.model.EventStream
A object which can deliver a stream of training events for the GIS procedure (or others such as IIS if and when they are implemented). EventStreams don't need to use opennlp.maxent.DataStreams, but doing so would provide greater flexibility for producing events from data stored in different formats.

    }
    String dataFileName = args[ai++];
    String modelFileName = args[ai];
    try {
      FileReader datafr = new FileReader(new File(dataFileName));
      EventStream es;
      if (!real) {
        es = new BasicEventStream(new PlainTextByLineDataStream(datafr), ",");
      } else {
        es = new RealBasicEventStream(new PlainTextByLineDataStream(datafr));
      }

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    AbstractModel posModel;
    
    if (!TrainUtil.isSequenceTraining(trainParams.getSettings())) {
      
      EventStream es = new POSSampleEventStream(samples, contextGenerator);
      
      posModel = TrainUtil.train(es, trainParams.getSettings(), manifestInfoEntries);
    }
    else {
      POSSampleSequenceStream ss = new POSSampleSequenceStream(samples, contextGenerator);

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  public static TokenizerModel train(ObjectStream<TokenSample> samples, TokenizerFactory factory,
      TrainingParameters mlParams) throws IOException {


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


    EventStream eventStream = new TokSpanEventStream(samples,
        factory.isUseAlphaNumericOptmization(),
        factory.getAlphaNumericPattern(), factory.getContextGenerator());


    AbstractModel maxentModel = TrainUtil.train(eventStream,
        mlParams.getSettings(), manifestInfoEntries);

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      throws IOException {
    Factory factory = new Factory();


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


    EventStream eventStream = new TokSpanEventStream(samples,
        useAlphaNumericOptimization, factory.getAlphanumeric(languageCode),
        factory.createTokenContextGenerator(languageCode,
            getAbbreviations(abbreviations)));


    AbstractModel maxentModel = TrainUtil.train(eventStream,

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       featureGenerator = createFeatureGenerator();


     AbstractModel nameFinderModel;


     if (!TrainUtil.isSequenceTraining(trainParams.getSettings())) {
       EventStream eventStream = new NameFinderEventStream(samples, type,
           new DefaultNameContextGenerator(featureGenerator));


       nameFinderModel = TrainUtil.train(eventStream, trainParams.getSettings(), manifestInfoEntries);
     }
     else {

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  public static final void main(String[] args) throws java.io.IOException {
    if (args.length != 0) {
      System.err.println("Usage: NameFinderEventStream < training files");
      System.exit(1);
    }
    EventStream es = new NameFinderEventStream(new NameSampleDataStream(
        new PlainTextByLineStream(new java.io.InputStreamReader(System.in))));
    while (es.hasNext()) {
      System.out.println(es.next());
    }
  }

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      TrainingParameters mlParams) throws IOException {


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


    // TODO: Fix the EventStream to throw exceptions when training goes wrong
    EventStream eventStream = new SDEventStream(samples,
        sdFactory.getSDContextGenerator(), sdFactory.getEndOfSentenceScanner());


    AbstractModel sentModel = TrainUtil.train(eventStream,
        mlParams.getSettings(), manifestInfoEntries);

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      ModelUtil.addCutoffAndIterations(manifestInfoEntries, cutoff, iterations);
      
      Factory factory = new Factory();


      // TODO: Fix the EventStream to throw exceptions when training goes wrong
      EventStream eventStream = new SDEventStream(samples,
          factory.createSentenceContextGenerator(languageCode),
          factory.createEndOfSentenceScanner(languageCode));
      
      HashSumEventStream hses = new HashSumEventStream(eventStream);
      GISModel sentModel = GIS.trainModel(hses, iterations, cutoff);

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      throws IOException {
    
    Map<String, String> manifestInfoEntries = new HashMap<String, String>();
    ModelUtil.addCutoffAndIterations(manifestInfoEntries, cutoff, iterations);
    
    EventStream es = new ChunkerEventStream(in, contextGenerator);
    HashSumEventStream hses = new HashSumEventStream(es);
    
    AbstractModel maxentModel = opennlp.maxent.GIS.trainModel(iterations, 
        new TwoPassDataIndexer(hses, cutoff));

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     if (generator != null)
       featureGenerator = generator;
     else 
       featureGenerator = createFeatureGenerator();
     
     EventStream eventStream = new NameFinderEventStream(samples, type,
         new DefaultNameContextGenerator(featureGenerator));
     HashSumEventStream hses = new HashSumEventStream(eventStream);
     AbstractModel nameFinderModel = GIS.trainModel(iterations, new TwoPassDataIndexer(hses, cutoff));
     
     manifestInfoEntries.put(BaseModel.TRAINING_EVENTHASH_PROPERTY,

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Related Classes of opennlp.model.EventStream

com.tamingtext.qa.AnswerTypeEventStream

CreateModel

opennlp.maxent.ModelApplier

opennlp.maxent.ModelTrainer

opennlp.maxent.RealBasicEventStream

opennlp.maxent.ScaleDoesntMatterTest

opennlp.PrepAttachDataUtil

opennlp.tools.chunker.ChunkerME

opennlp.tools.namefind.NameFinderEventStream

opennlp.tools.namefind.NameFinderEventStreamTest

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