Package opennlp.maxent

Examples of opennlp.maxent.TwoPassDataIndexer

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  public double[] probs() {
    return bestSequence.getProbs();
  }

  private static GISModel train(EventStream es, int iterations, int cut) throws IOException {
    return GIS.trainModel(iterations, new TwoPassDataIndexer(es, cut));
  }
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    }
    return (orderedTags);
  }

  public static GISModel train(EventStream es, int iterations, int cut) throws IOException {
    return opennlp.maxent.GIS.trainModel(iterations, new TwoPassDataIndexer(es, cut));
  }
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    return bestSequence.getProbs();
  }

 
  private static GISModel train(opennlp.maxent.EventStream es, int iterations, int cut) throws java.io.IOException {
    return opennlp.maxent.GIS.trainModel(iterations, new TwoPassDataIndexer(es, cut));
  }
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    //System.err.println("collapsedPunctuation: collapsedParses"+collapsedParses);
    return (Parse[]) collapsedParses.toArray(new Parse[collapsedParses.size()]);
  }

  public static GISModel train(opennlp.maxent.EventStream es, int iterations, int cut) throws java.io.IOException {
    return opennlp.maxent.GIS.trainModel(iterations, new TwoPassDataIndexer(es, cut));
  }
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    }
    return (Span[]) tokens.toArray(new Span[tokens.size()]);
  }

  public static void train(EventStream evc, File output) throws IOException {
    GISModel tokModel = opennlp.maxent.GIS.trainModel(100,new TwoPassDataIndexer(evc, 5),true);
    new SuffixSensitiveGISModelWriter(tokModel, output).persist();
  }
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    modelsByType = new HashMap<String,GISModel>();
    for(String type : eventsByType.keySet()) {
      DataIndexer di = null;
      List<Event> evs = eventsByType.get(type);
      if(evs.size() == 1) evs.add(evs.get(0));
      di = new TwoPassDataIndexer(new EventCollectorAsStream(new SimpleEventCollector(evs)), 1);
      modelsByType.put(type, GIS.trainModel(trainingCycles, di));
    }
  }
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    if(useUber) {
      List<Event> evs = new ArrayList<Event>();
      for(String prevTagg : evsByPrev.keySet()) {
        evs.addAll(evsByPrev.get(prevTagg));
      }
      DataIndexer di = new TwoPassDataIndexer(new EventCollectorAsStream(new SimpleEventCollector(evs)), featureCutOff);
      ubermodel = GIS.trainModel(trainingCycles, di);
    } else {
      for(String prevTagg : evsByPrev.keySet()) {
        if(Oscar3Props.getInstance().verbose) System.out.println(prevTagg);
        List<Event> evs = evsByPrev.get(prevTagg);
        if(featureSel) {
          evs = new FeatureSelector().selectFeatures(evs);           
        }   
        if(evs.size() == 1) {
          evs.add(evs.get(0));
        }
        DataIndexer di = null;
        try {
          di = new TwoPassDataIndexer(new EventCollectorAsStream(new SimpleEventCollector(evs)), featureCutOff);
          gmByPrev.put(prevTagg, GIS.trainModel(trainingCycles, di));
        } catch (Exception e) {
          di = new TwoPassDataIndexer(new EventCollectorAsStream(new SimpleEventCollector(evs)), 1);       
          gmByPrev.put(prevTagg, GIS.trainModel(trainingCycles, di));
        } 
      }
    }
  }
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      String outcome = "E";
      if(isChem.get(s)) outcome = "C";
      events.add(new Event(outcome, getFeatures(s)));
    }
    SimpleEventCollector sec = new SimpleEventCollector(events);
    DataIndexer di = new TwoPassDataIndexer(new EventCollectorAsStream(sec), 1);
    GISModel gis = GIS.trainModel(100, di);
   
    System.out.println(gis.getIndex("FOO"));
    if(true) return;
   
   
    long time = System.currentTimeMillis();
    StringGISModelWriter sgmw = new StringGISModelWriter(gis);
    sgmw.persist();
    String modelStr = sgmw.toString();

    //System.out.println(modelStr);
   
    StringGISModelReader sgmr = new StringGISModelReader(modelStr);
    gis = sgmr.getModel();
    System.out.println("Round trip in " + (System.currentTimeMillis() - time));
    System.out.println(modelStr.length());
   
    //if(true) return;
   
    GISModel gis2 = null;
   
    if(false) {
      List<Event> events2 = new ArrayList<Event>();
      for(String s : batch2) {
        double prob = gis.eval(getFeatures(s))[gis.getIndex("C")];
        String outcome = "E";
        if(isChem.get(s)) outcome = "C";
        events.add(new Event(outcome, probToFeatures(prob)));
      }
      sec = new SimpleEventCollector(events);
      di = new TwoPassDataIndexer(new EventCollectorAsStream(sec), 2);
      gis2 = GIS.trainModel(1000, di);     
    }
   
    double score = 0.0;
    List<Double> trialScores = new ArrayList<Double>();
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      Event event = new Event(type, f.toArray(new String[0]));
      events.add(event);
    }
    if(events.size() == 1) events.add(events.get(0));
    DataIndexer di = new TwoPassDataIndexer(new EventCollectorAsStream(new SimpleEventCollector(events)), 3);
    GISModel gm = GIS.trainModel(100, di);
   
    ClassificationEvaluator ce = new ClassificationEvaluator();

    List<Double> chemList = new ArrayList<Double>();
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      trainEvents.add(new Event(be.getClassLabel(), be.getFeatures().getSet().toArray(new String[0])));
    }
    for(BagEvent be : testBagEvents) {
      testEvents.add(new Event(be.getClassLabel(), be.getFeatures().getSet().toArray(new String[0])));
    }
    DataIndexer di = new TwoPassDataIndexer(new EventCollectorAsStream(new SimpleEventCollector(trainEvents)), 1);
    GISModel gm = GIS.trainModel(100, di);
   
    //ClassificationEvaluator ce = new ClassificationEvaluator();
   
    for(Event event : testEvents) {
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Related Classes of opennlp.maxent.TwoPassDataIndexer

  • opennlp.tools.chunker.ChunkerME
  • opennlp.tools.namefind.NameFinderME
  • opennlp.tools.parser.ParserME
  • opennlp.tools.postag.POSTaggerME
  • opennlp.tools.tokenize.TokenizerME
  • uk.ac.cam.ch.wwmm.oscar3.recogniser.memm.MEMM
  • uk.ac.cam.ch.wwmm.oscar3.recogniser.memm.rescorer.RescoreMEMMOut
  • uk.ac.cam.ch.wwmm.oscar3.subtypes.NESubtypes
  • uk.ac.cam.ch.wwmm.ptc.experimental.Attenuation
  • uk.ac.cam.ch.wwmm.ptc.experimental.DomainParaClassify
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