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

            String item=cache.getItem(url);
            if (null==item){
                LOGGER.warn("Page " + url + "is in notIncluded for " + catName + " but not in cache");
                continue;
            }
            double classifierScore = classifier.classify(DocumentParser.parse(item, classifier.getMaxTuple()));
            boolean classifierIncluded = (classifierScore > 0.5);
            addToIncNotIncMap(mp, url, false, classifierIncluded, classifierScore);            
        }
        return mp;
    }

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Examples of com.github.pmerienne.trident.ml.nlp.KLDClassifier.classify()

    kldClassifier.update(0, tokenizer.tokenize(NOSQL_WIKI));
    kldClassifier.update(0, tokenizer.tokenize(MYSQL_WIKI));
    kldClassifier.update(1, tokenizer.tokenize(LILIUM_WIKI));
    kldClassifier.update(1, tokenizer.tokenize(ROSE_WIKI));


    assertEquals(0, (int) kldClassifier.classify(tokenizer.tokenize(DATABASE_WIKI)));
    assertEquals(1, (int) kldClassifier.classify(tokenizer.tokenize(FLOWER_WIKI)));
  }


  @Test
  public void testWithReuters() {

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Examples of edu.msu.cme.rdp.classifier.Classifier.classify()


        try {
            parser = new SequenceReader(inStream1);
            while((pSeq = parser.readNextSequence()) != null) {
                try {
                    ClassificationResult result = aClassifier.classify(pSeq);
                    root = reconstructTree(result, root, SAMPLE1);
                    wt.write(ClassificationResultFormatter.getOutput(result, format));


                } catch (ShortSequenceException e) {
                    System.out.println(e.getMessage());

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Examples of edu.msu.cme.rdp.classifier.Classifier.classify()

            }


            parser = new SequenceReader(inStream2);
            while((pSeq = parser.readNextSequence()) != null) {
                try {
                    ClassificationResult result = aClassifier.classify(pSeq);
                    root = reconstructTree(result, root, SAMPLE2);
                    wt.write(ClassificationResultFormatter.getOutput(result, format));


                } catch (ShortSequenceException e) {
                    System.out.println(e.getMessage());

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Examples of edu.msu.cme.rdp.classifier.Classifier.classify()

        String sequence = "";
        infile.readLine();
        sequence = infile.readLine();
        sequence = sequence.toUpperCase();
        ClassifierSequence pSeq = new ClassifierSequence("name", "title", sequence);
        ClassificationResult result = aClassifier.classify(pSeq);
        
        Iterator it = result.getAssignments().iterator();
        RankAssignment classResult = (RankAssignment) it.next();
        assertEquals(classResult.getBestClass().getName() , rootTree.getName());
        assertEquals(classResult.getConfidence(), 1.0, 0.1);

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Examples of edu.msu.cme.rdp.classifier.Classifier.classify()

        // test the second sequence
        infile.readLine();
        sequence = infile.readLine();
        sequence = sequence.toUpperCase();
        pSeq = new ClassifierSequence("name", "title", sequence);
        result = aClassifier.classify(pSeq);
        
        
        it = result.getAssignments().iterator();
        classResult = (RankAssignment) it.next();
        assertEquals(classResult.getBestClass().getName() , rootTree.getName());

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Examples of edu.stanford.nlp.ie.regexp.NumberSequenceClassifier.classify()

      }
      copyL.add(new CoreLabel(l.get(i)));
    }
    // run NumberSequenceClassifier
    AbstractSequenceClassifier<CoreLabel> nsc = new NumberSequenceClassifier();
    copyL = nsc.classify(copyL);
    // update entity only if it was not O
    for (int i = 0; i < sz; i++) {
      E before = l.get(i);
      CoreLabel nscAnswer = copyL.get(i);
      if (before.get(CoreAnnotations.NamedEntityTagAnnotation.class) == null && before.get(CoreAnnotations.NamedEntityTagAnnotation.class).equals(BACKGROUND_SYMBOL) &&

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Examples of gannuCF.classifiers.Classifier.classify()

      ArrayList<String> sample=new ArrayList<String>(window.size());      
      for(AmbiguousWord word:window)
      {
        sample.add(word.getLemma());        
      }
      float w[]=c.classify(sample);
      for(int j=0;j<target.getSenses().size();j++)
      {
        ArrayList<String> dwords=new ArrayList<String>(window.size());
        for(AmbiguousWord word:window)
        {

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Examples of jnisvmlight.SVMLightModel.classify()

    int precision = 0;
    for (int i = 0; i < N; i++) {


      // Classify a test vector using the Java object
      // (in a real application, this should not be one of the training vectors)
      double d = model.classify(traindata[i]);
      if ((traindata[i].getLabel() < 0 && d < 0)
          || (traindata[i].getLabel() > 0 && d > 0)) {
        precision++;
      }
      if (i % 10 == 0) {

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Examples of org.apache.devicemap.DeviceMapClient.classify()


        String test = "Mozilla/5.0 (Linux; U; Android 2.2; en; HTC Aria A6380 Build/ERE27) AppleWebKit/540.13+ (KHTML, like Gecko) Version/3.1 Mobile Safari/524.15.0";


        long startn = System.nanoTime();


        Map<String, String> m = client.classify(test);


        long diffn = (System.nanoTime() - startn) / 1000;


        System.out.println("Test lookup: '" + m.get("id") + "' time: " + diffn + "usec");

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