List of classify() Examples

Examples of classify()

au.csiro.ontology.classification.IReasoner.classify()
Classifies the current axioms. @return
au.csiro.snorocket.core.NormalisedOntology.classify()
Starts the concurrent classification process.
au.csiro.snorocket.core.SnorocketReasoner.classify()
cc.mallet.classify.Classifier.classify()
com.clarkparsia.modularity.IncrementalClassifier.classify()
com.digitalpebble.classification.TextClassifier.classify()
Returns the probability for each possible label*
com.flaptor.hounder.classifier.bayes.BayesClassifier.classify()
Classifies a document, indicating the calculated confidence level the document belongs to the category with. @param doc the document token data, as generated by parseDocument. @return the confidence level of the document
com.github.pmerienne.trident.ml.nlp.KLDClassifier.classify()
edu.msu.cme.rdp.classifier.Classifier.classify()
Takes a query sequence, returns the classification result. For each query sequence, first assign it to a genus node using all the words for calculation. Then randomly chooses one-eighth of the all overlapping words in the query to calculate the joint probability. The number of times a genus was selected out of the number of bootstrap trials was used as an estimate of confidence in the assignment to that genus. @throws ShortSequenceException if the sequence length is less than the minimum sequence length.
edu.stanford.nlp.ie.regexp.NumberSequenceClassifier.classify()
Classify a {@link List} of {@link CoreLabel}s. @param document A {@link List} of {@link CoreLabel}s. @return the same {@link List}, but with the elements annotated with their answers.
gannuCF.classifiers.Classifier.classify()
Method for classifying a sample extracted as an array of weights. This method should be used together with its train counterpart when you want to control the Word Space Model by yourself. @param Sample An array of weights containing a sample. @return An array with the weights of each class. You should select the greater one as the answer.
jnisvmlight.SVMLightModel.classify()
org.apache.devicemap.DeviceMapClient.classify()
org.apache.mahout.classifier.AbstractVectorClassifier.classify()
Classify a vector returning a vector of numCategories-1 scores. It is assumed that the score for the missing category is one minus the sum of the scores that are returned. Note that the missing score is the 0-th score. @param instance A feature vector to be classified. @return A vector of probabilities in 1 of n-1 encoding.
org.apache.mahout.classifier.bayes.BayesClassifier.classify()
Classify the document and return the top numResults @param model The model @param document The document to classify @param defaultCategory The default category to assign @param numResults The maximum number of results to return, ranked by score.Ties are broken by comparing the category @return A Collection of {@link org.apache.mahout.classifier.ClassifierResult}s.
org.apache.mahout.clustering.classify.ClusterClassifier.classify()
org.apache.mahout.common.Classifier.classify()
Classify the document and return the top numResults @param model The model @param document The document to classify @param defaultCategory The default category to assign @param numResults The maximum number of results to return, ranked by score.Ties are broken by comparing the category @return A Collection of {@link org.apache.mahout.classifier.ClassifierResult}s.
org.apache.mahout.df.node.Node.classify()
predicts the label for the instance @param instance @return -1 if the label cannot be predicted
org.dbpedia.spotlight.spot.cooccurrence.classification.SpotClassifier.classify()
Classify a surface form candidate. @param surfaceFormOccurrence the surface form occurrence @return a Classification object containing the proposed classification and a confidence value
org.dmd.util.parsing.Classifier.classify()
The classify function breaks down the specified string into tokens and passes back an array of classified tokens. @param s The string to be parsed. @param returnSeps Set to true if the separators should be returned as tokens.
org.encog.neural.som.SOM.classify()
{@inheritDoc}
org.fnlp.app.tc.TextClassifier.classify()
org.fnlp.ml.classifier.bayes.BayesClassifier.classify()
org.fnlp.ml.classifier.knn.KNNClassifier.classify()
org.fnlp.ml.classifier.linear.Linear.classify()
org.mindswap.pellet.KnowledgeBase.classify()
org.mindswap.pellet.taxonomy.POTaxonomyBuilder.classify()
org.mindswap.pellet.taxonomy.TaxonomyBuilder.classify()
Classify the KB.
org.semanticweb.owl.inference.OWLReasoner.classify()
org.springframework.classify.BinaryExceptionClassifier.classify()

Examples of org.semanticweb.owl.inference.OWLReasoner.classify()

    */
    
    /* Classify the ontology if necessary */
    if (!reasoner.isClassified()){
      Timer timerClassify = timers.startTimer( "classification" );
      reasoner.classify();
      timerClassify.stop();
      classificationTime = timerClassify.getTotal();
    }
    
    /* Maps each class to the number of instances (including direct and indirect) */

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Examples of org.semanticweb.owl.inference.OWLReasoner.classify()

        double classificationTime = 0;
        
        if ( !(reasoner.isClassified()) ){
          logger.info("Classifying KB...");
          Timer timerClassify = timers.startTimer( "classification" );
          reasoner.classify();
          timerClassify.stop();
          classificationTime = timerClassify.getTotal();
        }
        
        /* Load Abox into reasoner */

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Examples of org.semanticweb.owl.inference.OWLReasoner.classify()

      /* Classify the ontology if necessary */
      if (!reasoner.isClassified())
      {
        /* Measure time using Benchmarking framework */
        Timer timerClassify = timers.startTimer("classification");
        reasoner.classify();
        timerClassify.stop();
        classificationTime = timerClassify.getTotal();
      }


      /* Load Abox into reasoner */

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Examples of org.semanticweb.owl.inference.OWLReasoner.classify()


      /* Load Abox into reasoner */
      reasoner.loadOntologies(Collections.singleton(abox));
      if (!reasoner.isClassified())
      {
        reasoner.classify();
      }


      /*
       * Maps each class to the number of instances (including direct and
       * indirect)

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Examples of org.semanticweb.owl.inference.OWLReasoner.classify()

            .singleton(approximatedTboxOntology));
        timers.createTimer("classification");
        if (!(reasoner.isClassified()))
        {
          Timer timerClassify = timers.startTimer("classification");
          reasoner.classify();
          timerClassify.stop();
          classificationTime += timerClassify.getTotal();
        }


        if (approximationResult.isOriginal())

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Examples of org.semanticweb.owl.inference.OWLReasoner.classify()

    timers.createTimer("classification");


    if (!reasoner.isClassified())
    {
      Timer timerClassify = timers.startTimer("classification");
      reasoner.classify();
      timerClassify.stop();
      classificationTime += timerClassify.getTotal();
    }
    // System.out.println("--->retrieving instances...");
    for (OWLClass cc : classes)

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Examples of org.springframework.classify.BinaryExceptionClassifier.classify()

          : noRollbackExceptionClasses;
      final BinaryExceptionClassifier classifier = new BinaryExceptionClassifier(exceptions, false);
      builder.transactionAttribute(new DefaultTransactionAttribute(attribute) {
        @Override
        public boolean rollbackOn(Throwable ex) {
          return classifier.classify(ex);
        }
      });
    }
    if (streams != null) {
      for (ItemStream stream : streams) {

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