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 au.csiro.ontology.classification.IReasoner.classify()

        baseAxioms.add(f.createRoleInclusion(rlhs, rrhs));
        
        // The first time the classify method is called it runs a full 
        // classification
        reasoner.loadAxioms(baseAxioms);
        reasoner.classify();
        
        // If classification worked properly then Endocarditis should be 
        // classified not only as an Inflammation but also as a HeartDisease 
        // and a CriticalDisease
        Ontology t = reasoner.getClassifiedOntology();

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Examples of au.csiro.snorocket.core.NormalisedOntology.classify()

            System.out.println("Could not find version " + version + " in input files");
        }
        System.out.println("Loading axioms");
        no.loadAxioms(new HashSet<Axiom>(ont.getStatedAxioms()));
        System.out.println("Running classification");
        no.classify();
        System.out.println("Computing taxonomy");
        no.buildTaxonomy();
        System.out.println("Done");


        // If a relationship that is part of a role group is added incrementally

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Examples of au.csiro.snorocket.core.SnorocketReasoner.classify()

        baseAxioms.add(f.createRoleInclusion(rlhs, rrhs));
        
        // The first time the classify method is called it runs a full 
        // classification
        reasoner.loadAxioms(baseAxioms);
        reasoner.classify();
        
        // If classification worked properly then Endocarditis should be 
        // classified not only as an Inflammation but also as a HeartDisease 
        // and a CriticalDisease
        Ontology t = reasoner.getClassifiedOntology();

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Examples of cc.mallet.classify.Classifier.classify()

      if (! useOutOfFold || ! m_table.containsKey(instName))
        return m_tokenClassifier.classify(instance);
      
      Classifier classifier = (Classifier) m_table.get(instName);


      return classifier.classify(instance);
    }


    // serialization
    private static final long serialVersionUID = 1;
    private static final int CURRENT_SERIAL_VERSION = 1;

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Examples of com.clarkparsia.modularity.IncrementalClassifier.classify()

    
    // Get an instance of the incremental classifier
    IncrementalClassifier classifier = new IncrementalClassifier( ontology );


    // trigger classification
    classifier.classify();
        
    // persist the current state of the classifier to a file
    try {
      System.out.print( "Saving the state of the classifier to the file ... " );
      System.out.flush();

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Examples of com.clarkparsia.modularity.IncrementalClassifier.classify()


    // We classify the ontology and use a specific timer to keep track of
    // the time required for the classification
    Timer t = timers.createTimer( "First classification" );
    t.start();
    classifier.classify();
    t.stop();
        
    System.out.println( "\nClassification time: " + t.getTotal() + "ms");
    System.out.println( "Subclasses of " + pain + ": " + classifier.getSubClasses( pain, true ).getFlattened() + "\n");

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Examples of com.clarkparsia.modularity.IncrementalClassifier.classify()


    // Now we create a second timer to keep track of the performance of the
    // second classification
    t = timers.createTimer("Second classification");
    t.start();
    classifier.classify();
    t.stop();
    
    System.out.println( "\nClassification time: " + t.getTotal() + "ms");
    System.out.println( "Subclasses of " + pain + ": " + classifier.getSubClasses( pain, true ).getFlattened() + "\n");

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Examples of com.clarkparsia.modularity.IncrementalClassifier.classify()

    OWL.manager.applyChange( new RemoveAxiom( ontologyTBox, axiom ) );


    // Now we create a third timer to keep track of the performance of the
    // third classification
    timers.startTimer( "Third classification" );
    classifier.classify();
    timers.stopTimer( "Third classification" );
    
    System.out.println( "\nClassification time: " + t.getTotal() + "ms");
    System.out.println( "Subclasses of " + pain + ": " + classifier.getSubClasses( pain, true ).getFlattened() + "\n");

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Examples of com.digitalpebble.classification.TextClassifier.classify()

            .getClassifier(new File(resourceDir));
        // create a document from a String
        String[] tokens = Tokenizer.tokenize(text.toString(), true);
        Document doc = classifier.createDocument(tokens);
        // classify
        double[] scores = classifier.classify(doc);
        // get best label
        String label = classifier.getBestLabel(scores);
        System.out.println("Classified as : "+label);
      } catch (Exception e) {
        // TODO Auto-generated catch block

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Examples of com.flaptor.hounder.classifier.bayes.BayesClassifier.classify()

            String item=cache.getItem(url);
            if (null==item){
                LOGGER.warn("Page " + url + "is in included for " + catName + " but not in cache");
                continue;
            }
            double classifierScore = classifier.classify(DocumentParser.parse(item, classifier.getMaxTuple()));
            boolean classifierIncluded = (classifierScore > 0.5);
            addToIncNotIncMap(mp, url, true, classifierIncluded, classifierScore);            
        }
        // traverse the list of not included urls and check what the classifier say
        for (String url: notIncludedUrlsList){

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