Examples of MaxentModel

opennlp.ccg.parse.tagger.ml.MaxentModel
@author Dennis N. Mehay @version $Revision: 1.1 $, $Date: 2010/09/21 04:12:41 $
opennlp.model.MaxentModel
Interface for maximum entropy models.
opennlp.tools.ml.model.MaxentModel
Interface for maximum entropy models.

Examples of opennlp.tools.ml.model.MaxentModel


    // build
    System.err.println("Training builder");
    ObjectStream<Event> bes = new ParserEventStream(parseSamples, rules, ParserEventTypeEnum.BUILD, mdict);
    Map<String, String> buildReportMap = new HashMap<String, String>();
    MaxentModel buildModel = TrainUtil.train(bes, mlParams.getSettings("build"), buildReportMap);
    mergeReportIntoManifest(manifestInfoEntries, buildReportMap, "build");


    parseSamples.reset();


    // tag
    POSModel posModel = POSTaggerME.train(languageCode, new PosSampleStream(parseSamples),
        mlParams.getParameters("tagger"), null, null);


    parseSamples.reset();


    // chunk
    ChunkerModel chunkModel = ChunkerME.train(languageCode,
        new ChunkSampleStream(parseSamples),
        new ChunkContextGenerator(), mlParams.getParameters("chunker"));


    parseSamples.reset();


    // check
    System.err.println("Training checker");
    ObjectStream<Event> kes = new ParserEventStream(parseSamples, rules, ParserEventTypeEnum.CHECK);
    Map<String, String> checkReportMap = new HashMap<String, String>();
    MaxentModel checkModel = TrainUtil.train(kes, mlParams.getSettings("check"), checkReportMap);
    mergeReportIntoManifest(manifestInfoEntries, checkReportMap, "check");


    // TODO: Remove cast for HeadRules
    return new ParserModel(languageCode, buildModel, checkModel,
        posModel, chunkModel, (opennlp.tools.parser.HeadRules) rules,

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Examples of opennlp.tools.ml.model.MaxentModel


    if (!isValid()) {
      throw new IllegalArgumentException("trainParams are not valid!");
    }


    MaxentModel model = doTrain(events);
    addToReport(AbstractTrainer.TRAINER_TYPE_PARAM,
        EventModelSequenceTrainer.SEQUENCE_VALUE);
    return model;
  }

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Examples of opennlp.tools.ml.model.MaxentModel

      beamSize = Integer.parseInt(beamSizeString);
    }


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


    MaxentModel nameFinderModel = null;


    SequenceClassificationModel<String> seqModel = null;


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

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Examples of opennlp.tools.ml.model.MaxentModel

      featureGenerator = generator;
    } else {
      featureGenerator = createFeatureGenerator();
    }


    MaxentModel nameFinderModel = null;


    SequenceClassificationModel<String> seqModel = null;


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

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Examples of opennlp.tools.ml.model.MaxentModel


  public String[] getOrderedTags(List<String> words, List<String> tags, int index,double[] tprobs) {


    if (modelPackage.getPosModel() != null) {


      MaxentModel posModel = modelPackage.getPosModel();


      double[] probs = posModel.eval(contextGen.getContext(index,
          words.toArray(new String[words.size()]),
          tags.toArray(new String[tags.size()]),null));


      String[] orderedTags = new String[probs.length];
      for (int i = 0; i < probs.length; i++) {
        int max = 0;
        for (int ti = 1; ti < probs.length; ti++) {
          if (probs[ti] > probs[max]) {
            max = ti;
          }
        }
        orderedTags[i] = posModel.getOutcome(max);
        if (tprobs != null){
          tprobs[i]=probs[max];
        }
        probs[max] = 0;
      }

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Examples of opennlp.tools.ml.model.MaxentModel


    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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Examples of opennlp.tools.ml.model.MaxentModel

    }


    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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Examples of opennlp.tools.ml.model.MaxentModel

    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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Examples of opennlp.tools.ml.model.MaxentModel


    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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Examples of opennlp.tools.ml.model.MaxentModel

      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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