Package uk.ac.cam.ch.wwmm.ptc.experimental.classifiers

Examples of uk.ac.cam.ch.wwmm.ptc.experimental.classifiers.MultinomialNaiveBayes

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      ce.pprintPrecisionRecallEval();
      //return;
    }
   
    ce = new ClassificationEvaluator();
    MultinomialNaiveBayes mnb = new MultinomialNaiveBayes(bagEvents);
    for(int i=0;i<testBagEvents.size();i++) {
      BagEvent be = testBagEvents.get(i);
      //Map<String,Double> results = mnb.testBag(be.getClassLabel(), be.getFeatures());
      Map<String,Double> results = mnb.testBag(be.getFeatures());
      System.out.println(be.getClassLabel() + "\t" + mnb.testBag(be.getFeatures()));
      ce.logEvent(be.getClassLabel(), mnb.bestResult(results));
    }
    System.out.println(ce.getAccuracy());
    System.out.println(ce.getKappa());     
    ce.pprintConfusionMatrix();
    ce.pprintPrecisionRecallEval();
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    }
       
    if(false) {
      ClassificationEvaluator ce = new ClassificationEvaluator();

      MultinomialNaiveBayes mnb = new MultinomialNaiveBayes(eventBags);
      for(int i=0;i<eventBags.size();i++) {
        BagEvent be = eventBags.get(i);
      //for(BagEvent be : eventBags) {
        Map<String,Double> results = mnb.testBag(be.getClassLabel(), be.getFeatures());
        System.out.println(be.getClassLabel() + "\t" + mnb.testBag(be.getFeatures()));
        ce.logEvent(be.getClassLabel(), mnb.bestResult(results));
        String rf = "MNB:" + mnb.bestResult(results);
        Event e = events.get(i);
        String [] sa = new String[e.getContext().length + 1];
        for(int j=0;j<e.getContext().length;j++) {
          sa[j] = e.getContext()[j];
        }
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      //return;
    }
   
    if(true) {
      ce = new ClassificationEvaluator();
      MultinomialNaiveBayes mnb = new MultinomialNaiveBayes(bagEvents);
     
      Element elem = mnb.toXML();
      Document doc = new Document(elem);
      Serializer ser = new Serializer(System.out);
      //ser.setIndent(2);
      ser.write(doc);
      mnb = new MultinomialNaiveBayes(elem);
      elem = mnb.toXML();
      doc = new Document(elem);
      ser = new Serializer(System.out);
      //ser.setIndent(2);
      ser.write(doc);
     
      for(int i=0;i<testBagEvents.size();i++) {
        BagEvent be = testBagEvents.get(i);
        //Map<String,Double> results = mnb.testBag(be.getClassLabel(), be.getFeatures());
        Map<String,Double> results = mnb.testBag(be.getFeatures());
        System.out.println(be.getClassLabel() + "\t" + mnb.testBag(be.getFeatures()));
        ce.logEvent(be.getClassLabel(), mnb.bestResult(results));
        if(!be.getClassLabel().equals(mnb.bestResult(results))) {
          System.out.println(be.getFeatures());
          System.out.println(bagsToSentences.get(be.getFeatures()));
        }
      }
      System.out.println(ce.getAccuracy());
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Related Classes of uk.ac.cam.ch.wwmm.ptc.experimental.classifiers.MultinomialNaiveBayes

  • nu.xom.Element
  • nu.xom.Elements
  • uk.ac.cam.ch.wwmm.ptc.experimental.DomainParaClassify
  • uk.ac.cam.ch.wwmm.ptc.experimental.ReactDocClassify
  • uk.ac.cam.ch.wwmm.ptc.experimental.ReactParaClassify
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