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.dbpedia.spotlight.spot.cooccurrence.classification.SpotClassifier.classify()

        SurfaceFormOccurrence unigramOccurrence = new SurfaceFormOccurrence(new SurfaceForm("Berlin"),
                        taggedText, 41, Provenance.Undefined(), -1);


        try {
            unigramClassifier.classify(unigramOccurrence);
            ngramClassifier.classify(ngramOccurrence);
        } catch (Exception e) {
            throw new InitializationException("An error occurred while classifying a test spot using the co-occurrence " +
                    "based spot selector. This is most probably caused by an outdated spot selector model. Please " +
                    "check the spot selector models defined 'org.dbpedia.spotlight.spot.cooccurrence.classifier.*'.", e);
        }

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Examples of org.dmd.util.parsing.Classifier.classify()

                currLine = s.trim();


                if (currLine.length() == 0)
                    continue;
                
                tokens = classifier.classify(currLine, false);
                
                if (tokens.size() == 0)
                  continue;
                
                ConfigVersion currConfig = configFinder.getConfig(tokens.nth(0).getValue());

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Examples of org.encog.neural.som.SOM.classify()

    final MLData data1 = new BasicMLData(
        TestCompetitive.SOM_INPUT[0]);
    final MLData data2 = new BasicMLData(
        TestCompetitive.SOM_INPUT[1]);
    
    int result1 = network.classify(data1);
    int result2 = network.classify(data2);
    
    Assert.assertTrue(result1!=result2);


  }

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Examples of org.fnlp.app.tc.TextClassifier.classify()

    tc.train(reader, modelFile);
    /**
     * 分类器使用
     */
    String str = "韦德：不拿冠军就是失败 詹皇：没拿也不意味失败";
    String label = (String) tc.classify(str).getLabel(0);
    System.out.println("类别："+ label);
    
    System.out.println("读入模型");
    tc = null;
    tc = new TextClassifier(modelFile);

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Examples of org.fnlp.ml.classifier.bayes.BayesClassifier.classify()

        bayes.fS_CS(percents_cs[test]);
      int count=0;
      for(int i=0;i<testset.size();i++){
        Instance data = testset.getInstance(i);
        Integer gold = (Integer) data.getTarget();
        Predict<String> pres=bayes.classify(data, Type.STRING, 3);
        String pred_label=pres.getLabel();
        String gold_label = bayes.getLabel(gold);
        
        if(pred_label.equals(gold_label)){
          count++;

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Examples of org.fnlp.ml.classifier.knn.KNNClassifier.classify()

    System.out.println("Testing Knn...\n");
    count=0;
    for(int i=0;i<testset.size();i++){
      Instance data = testset.getInstance(i);
      Integer gold = (Integer) data.getTarget();
      Predict<String> pres=(Predict<String>) knn.classify(data, Type.STRING, 3);
      String pred_label=pres.getLabel();
      String gold_label = knn.getLabel(gold);
      
      if(pred_label.equals(gold_label)){
        //System.out.println(pred_label+" : "+testsetknn.getInstance(i).getTempData());

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Examples of org.fnlp.ml.classifier.linear.Linear.classify()

    testSet.loadThruPipes(new SequenceReader(testfile, false, "utf8"));
    System.out.println("测试样本个数: " + testSet.size()); // 
    String[][] labelsSet = new String[testSet.size()][];
    for (int i = 0; i < testSet.size(); i++) {
      Instance carrier = testSet.get(i);
      int[] pred = (int[]) cl.classify(carrier).getLabel(0);
      labelsSet[i] = labels.lookupString(pred);
    }
    
    String s = SimpleFormatter.format(testSet, labelsSet);
    System.out.println(s);

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Examples of org.mindswap.pellet.KnowledgeBase.classify()


    assertSubClass( kb, A, B );


    assertSubClass( kb, B, C );


    kb.classify();
  }


  @Test
  public void testNominalCache() {
    // this case tests isMergable check and specifically the correctness of

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Examples of org.mindswap.pellet.taxonomy.POTaxonomyBuilder.classify()

      else 
        individuals = kb.getIndividuals();  // Note: this is not an optimal solution  
      
      for( ATermAppl individual :  individuals)
        if (!ATermUtils.isBnode( individual ))
          builder.classify( individual );
    }
    else {
      builder = new POTaxonomyBuilder( kb, new PartClassesComparator( kb, p ) );
      
      if(filter)

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Examples of org.mindswap.pellet.taxonomy.TaxonomyBuilder.classify()

      builder.setKB( kb );
    } catch( Exception e ) {
      throw new RuntimeException( e );
    }
builder.setProgressMonitor( new SilentProgressMonitor() );
    builder.classify();
    Taxonomy<ATermAppl> taxonomy = builder.getTaxonomy();
    
//     taxonomy.getTop().print();


    return taxonomy;

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