Examples of edu.stanford.nlp.classify.LogisticClassifier

edu.stanford.nlp.classify.LogisticClassifier
A classifier for binary logistic regression problems. This uses the standard statistics textbook formulation of binary logistic regression, which is more efficient than using the LinearClassifier class. @author Galen Andrew @author Sarah Spikes (sdspikes@cs.stanford.edu) (Templatization) @author Ramesh Nallapati nmramesh@cs.stanford.edu {@link #justificationOf(Collection)} @param < L> The type of the labels in the Dataset @param < F> The type of the features in the Dataset

    if (scoreClassifierType.equals(ClassifierType.LR)) {
      LogisticClassifierFactory<String, ScorePhraseMeasures> logfactory = new LogisticClassifierFactory<String, ScorePhraseMeasures>();
      LogPrior lprior = new LogPrior();
      lprior.setSigma(constVars.LRSigma);
      classifier = logfactory.trainClassifier(dataset, lprior, false);
      LogisticClassifier logcl = ((LogisticClassifier) classifier);


      String l = (String) logcl.getLabelForInternalPositiveClass();
      Counter<String> weights = logcl.weightsAsGenericCounter();
      if (l.equals(Boolean.FALSE.toString())) {
        Counters.multiplyInPlace(weights, -1);
      }
      List<Pair<String, Double>> wtd = Counters.toDescendingMagnitudeSortedListWithCounts(weights);
      Redwood.log(ConstantsAndVariables.minimaldebug, "The weights are " + StringUtils.join(wtd.subList(0, Math.min(wtd.size(), 200)), "\n"));

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      Counter<String> sc = classifier.scoresOf(d);
      score = sc.getCount(Boolean.TRUE.toString());


    } else if (scoreClassifierType.equals(ClassifierType.LR)) {


      LogisticClassifier logcl = ((LogisticClassifier) classifier);


      String l = (String) logcl.getLabelForInternalPositiveClass();
      boolean flipsign = false;
      if (l.equals(Boolean.FALSE.toString())) {
        flipsign = true;
      }
      Counter<ScorePhraseMeasures> feat = null;
      if (forLearningPatterns)
        feat = getPhraseFeaturesForPattern(label, word);
      else
        feat = this.getFeatures(label, word, patternsThatExtractedPat, allSelectedPatterns);


      RVFDatum<String, ScorePhraseMeasures> d = new RVFDatum<String, ScorePhraseMeasures>(feat, Boolean.FALSE.toString());
      score = logcl.probabilityOf(d);
      if (flipsign)
        score = 1 - score;


    } else if (scoreClassifierType.equals(ClassifierType.RF)) {

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