Package opennlp.model

Examples of opennlp.model.OnePassDataIndexer

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   * @param iterations The number of iterations to use for GIS.
   * @param cutoff The number of times a feature must occur to be included.
   * @return A GIS model trained with specified
   */
  public GISModel trainModel(EventStream eventStream, int iterations, int cutoff) throws IOException {
    return trainModel(iterations, new OnePassDataIndexer(eventStream,cutoff),cutoff);
  }
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  writer = new SuffixSensitiveGISModelWriter(model, outputFile);

      } else if (type.equals("perceptron")) {
        //System.err.println("Perceptron training");
        model = new PerceptronTrainer().trainModel(maxit, new OnePassDataIndexer(es, cutoff), cutoff);

  writer = new SuffixSensitivePerceptronModelWriter(model, outputFile);

      } else {
        throw new RuntimeException("Unknown model type: " + type);
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  int numSequences;

  public AbstractModel trainModel(int iterations, SequenceStream sequenceStream, int cutoff, boolean useAverage) throws IOException {
    this.iterations = iterations;
    this.sequenceStream = sequenceStream;
    DataIndexer di = new OnePassDataIndexer(new SequenceStreamEventStream(sequenceStream),cutoff,false);
    numSequences = 0;
    for (Sequence s : sequenceStream) {
      numSequences++;
    }
    outcomeList  = di.getOutcomeList();
    predLabels = di.getPredLabels();
    pmap = new IndexHashTable<String>(predLabels, 0.7d);
     
    display("Incorporating indexed data for training...  \n");
    this.useAverage = useAverage;
    numEvents = di.getNumEvents();

    this.iterations = iterations;
    outcomeLabels = di.getOutcomeLabels();
    omap = new HashMap<String,Integer>();
    for (int oli=0;oli<outcomeLabels.length;oli++) {
      omap.put(outcomeLabels[oli], oli);
    }
    outcomeList = di.getOutcomeList();

    numPreds = predLabels.length;
    numOutcomes = outcomeLabels.length;
    if (useAverage) {
      updates = new int[numPreds][numOutcomes][3];
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            model = GIS.trainModel(100, new OnePassRealValueDataIndexer(es,0), USE_SMOOTHING);
          }
        }
        else if (type.equals("perceptron")){
          System.err.println("Perceptron training");
          model = new PerceptronTrainer().trainModel(10, new OnePassDataIndexer(es,0),0);
        }
        else {
          System.err.println("Unknown model type: "+type);
          model = null;
        }
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          }
          writer =  new SuffixSensitiveGISModelWriter(model, outputFile);
        }
        else if (type.equals("perceptron")){
          System.err.println("Perceptron training");
          model = new PerceptronTrainer().trainModel(10, new OnePassDataIndexer(es,0),0);
          writer = new SuffixSensitivePerceptronModelWriter(model, outputFile);
        }
        else {
          System.err.println("Unknown model type: "+type);
          model = null;
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   * @param iterations The number of iterations to use for GIS.
   * @param cutoff The number of times a feature must occur to be included.
   * @return A GIS model trained with specified
   */
  public GISModel trainModel(EventStream eventStream, int iterations, int cutoff) throws IOException {
    return trainModel(iterations, new OnePassDataIndexer(eventStream,cutoff),cutoff);
  }
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  writer = new SuffixSensitiveGISModelWriter(model, outputFile);

      } else if (type.equals("perceptron")) {
        //System.err.println("Perceptron training");
        model = new PerceptronTrainer().trainModel(maxit, new OnePassDataIndexer(es, cutoff), cutoff);

  writer = new SuffixSensitivePerceptronModelWriter(model, outputFile);

      } else {
        throw new RuntimeException("Unknown model type: " + type);
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  int numSequences;

  public AbstractModel trainModel(int iterations, SequenceStream sequenceStream, int cutoff, boolean useAverage) throws IOException {
    this.iterations = iterations;
    this.sequenceStream = sequenceStream;
    DataIndexer di = new OnePassDataIndexer(new SequenceStreamEventStream(sequenceStream),cutoff,false);
    numSequences = 0;
    for (Sequence s : sequenceStream) {
      numSequences++;
    }
    outcomeList  = di.getOutcomeList();
    predLabels = di.getPredLabels();
    pmap = new IndexHashTable<String>(predLabels, 0.7d);
     
    display("Incorporating indexed data for training...  \n");
    this.useAverage = useAverage;
    numEvents = di.getNumEvents();

    this.iterations = iterations;
    outcomeLabels = di.getOutcomeLabels();
    omap = new HashMap<String,Integer>();
    for (int oli=0;oli<outcomeLabels.length;oli++) {
      omap.put(outcomeLabels[oli], oli);
    }
    outcomeList = di.getOutcomeList();

    numPreds = predLabels.length;
    numOutcomes = outcomeLabels.length;
    if (useAverage) {
      updates = new int[numPreds][numOutcomes][3];
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  int numSequences;

  public AbstractModel trainModel(int iterations, SequenceStream sequenceStream, int cutoff, boolean useAverage) throws IOException {
    this.iterations = iterations;
    this.sequenceStream = sequenceStream;
    DataIndexer di = new OnePassDataIndexer(new SequenceStreamEventStream(sequenceStream),cutoff,false);
    numSequences = 0;
    for (Sequence s : sequenceStream) {
      numSequences++;
    }
    outcomeList  = di.getOutcomeList();
    predLabels = di.getPredLabels();
    pmap = new HashMap<String,Integer>();
    for (int pli=0;pli<predLabels.length;pli++) {
      pmap.put(predLabels[pli], pli);
    }
    display("Incorporating indexed data for training...  \n");
    this.useAverage = useAverage;
    numEvents = di.getNumEvents();

    this.iterations = iterations;
    outcomeLabels = di.getOutcomeLabels();
    omap = new HashMap<String,Integer>();
    for (int oli=0;oli<outcomeLabels.length;oli++) {
      omap.put(outcomeLabels[oli], oli);
    }
    outcomeList = di.getOutcomeList();

    numPreds = predLabels.length;
    numOutcomes = outcomeLabels.length;
    if (useAverage) {
      updates = new int[numPreds][numOutcomes][3];
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   * @param iterations The number of iterations to use for GIS.
   * @param cutoff The number of times a feature must occur to be included.
   * @return A GIS model trained with specified
   */
  public GISModel trainModel(EventStream eventStream, int iterations, int cutoff) throws IOException {
    return trainModel(iterations, new OnePassDataIndexer(eventStream,cutoff),cutoff);
  }
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Related Classes of opennlp.model.OnePassDataIndexer

  • CreateModel
  • opennlp.maxent.GISTrainer
  • opennlp.maxent.ModelTrainer
  • opennlp.perceptron.SimplePerceptronSequenceTrainer
  • java.util.ArrayList
  • java.util.Set
  • java.util.HashSet
  • java.util.List
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