Package opennlp.tools.ml.model

Examples of opennlp.tools.ml.model.MaxentModel

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

    TrainerType trainerType = TrainerFactory.getTrainerType(trainParams.getSettings());

    MaxentModel posModel = null;
    SequenceClassificationModel<String> seqPosModel = null;
    if (TrainerType.EVENT_MODEL_TRAINER.equals(trainerType)) {
      ObjectStream<Event> es = new POSSampleEventStream(samples, contextGenerator);

      EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams.getSettings(),
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    }

    HashSumEventStream hses = new HashSumEventStream(events);
    DataIndexer indexer = getDataIndexer(events);

    MaxentModel model = doTrain(indexer);

    addToReport("Training-Eventhash", hses.calculateHashSum().toString(16));
    addToReport(AbstractTrainer.TRAINER_TYPE_PARAM, EventTrainer.EVENT_VALUE);
    return model;
  }
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    Map<String, String> manifestInfoEntries = new HashMap<String, String>();

    TrainerType trainerType = TrainerFactory.getTrainerType(mlParams.getSettings());


    MaxentModel chunkerModel = null;
    SequenceClassificationModel<String> seqChunkerModel = null;

    if (TrainerType.EVENT_MODEL_TRAINER.equals(trainerType)) {
      ObjectStream<Event> es = new ChunkerEventStream(in, factory.getContextGenerator());
      EventTrainer trainer = TrainerFactory.getEventTrainer(mlParams.getSettings(),
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    Map<String, String> manifestInfoEntries = new HashMap<String, String>();

    ObjectStream<Event> es = new ChunkerEventStream(in, contextGenerator);

    MaxentModel maxentModel = TrainUtil.train(es, mlParams.getSettings(), manifestInfoEntries);

    return new ChunkerModel(lang, maxentModel, manifestInfoEntries);
  }
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      featureGenerators = new FeatureGenerator[]{defaultFeatureGenerator};
    }

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

    MaxentModel model = TrainUtil.train(
            new DocumentCategorizerEventStream(samples, featureGenerators),
            mlParams.getSettings(), manifestInfoEntries);

    return new DoccatModel(languageCode, model, manifestInfoEntries);
  }
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          TrainingParameters mlParams, DoccatFactory factory)
          throws IOException {

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

    MaxentModel model = TrainUtil.train(
            new DocumentCategorizerEventStream(samples, factory.getFeatureGenerators()),
            mlParams.getSettings(), manifestInfoEntries);

    return new DoccatModel(languageCode, model, manifestInfoEntries, factory);
  }
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      modelFileName = args[ai++];
      dataFileName = args[ai++];

      ModelApplier predictor = null;
      try {
        MaxentModel m = new GenericModelReader(new File(modelFileName)).getModel();
        predictor = new ModelApplier(m);
      } catch (Exception e) {
        e.printStackTrace();
        System.exit(0);
      }
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    ObjectStream<Event> eventStream = new TokSpanEventStream(samples,
        factory.isUseAlphaNumericOptmization(),
        factory.getAlphaNumericPattern(), factory.getContextGenerator());

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

    return new TokenizerModel(maxentModel, manifestInfoEntries,
        factory);
  }
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    ObjectStream<Event> eventStream = new TokSpanEventStream(samples,
        useAlphaNumericOptimization, factory.getAlphanumeric(languageCode),
        factory.createTokenContextGenerator(languageCode,
            getAbbreviations(abbreviations)));

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

    return new TokenizerModel(languageCode, maxentModel, abbreviations,
        useAlphaNumericOptimization, manifestInfoEntries);
  }
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    // TODO: Fix the EventStream to throw exceptions when training goes wrong
    ObjectStream<Event> eventStream = new SDEventStream(samples,
        sdFactory.getSDContextGenerator(), sdFactory.getEndOfSentenceScanner());

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

    return new SentenceModel(languageCode, sentModel, manifestInfoEntries,
        sdFactory);
  }
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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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