Examples of Classifier

OntoUML.Classifier
A representation of the model object 'Classifier'.
The following features are supported:
- {@link OntoUML.Classifier#getAttribute Attribute}
- {@link OntoUML.Classifier#isIsAbstract Is Abstract}
- {@link OntoUML.Classifier#getGeneral General}
- {@link OntoUML.Classifier#getGeneralization Generalization}
@see OntoUML.OntoUMLPackage#getClassifier() @model @generated
cc.mallet.classify.Classifier
Abstract parent of all Classifiers.
All classification techniques in MALLET are implemented as two classes: a trainer and a classifier. The trainer injests the training data and creates a classifier that holds the parameters set during training. The classifier applies those parameters to an Instance to produce a classification of the Instance.
A concrete classifier is required only to be able to classify an instance.
Methods for classifying an InstanceList are here. There are also methods for calculating precison, recall, and f1 from either InstanceLists (which are classified first) or an ArrayList of classifications. Similar functionality is also in {@link cc.mallet.classify.Trial}
A classifier holds a reference to the pipe that was used to create the Instances being classified. Most classifiers use this to make sure the Alphabets of the instances being classified are the same Alphabet objects used during training.
Alphabets are allowed to between training and classification. @see ClassifierTrainer @see Instance @see InstanceList @see Classification @see Trial
common.inspect.Classifier
@author forksapien
edu.msu.cme.rdp.classifier.Classifier
This is the class to do the classification. @author wangqion @version 1.0
edu.uga.cs.fluxbuster.classification.Classifier
This class runs the classifier on the clusters and stores the derived classes in the database. @author Chris Neasbitt
eu.admire.dispel.classifiers.Classifier
A representation of the model object 'Classifier'. @see eu.admire.dispel.classifiers.ClassifiersPackage#getClassifier() @model abstract="true" @generated
gannuCF.classifiers.Classifier
Generic template for creating your own classifiers for supervised WSD. @author Francisco Viveros-Jiménez
moa.classifiers.Classifier
org.apache.mahout.common.Classifier
Classifies documents based on a {@link org.apache.mahout.common.Model}.
org.dmd.util.parsing.Classifier
The Classifier class takes an input string, breaks it into tokens and classifies each token as either a basic token type eg. (Token.TEXT, Token.INTEGER, Token.FLOAT) or as a custom token type defined by the user. This approach is especially useful when an entire line of input has to parsed and checked against some allowable format. @see org.dmd.util.parsing.Token @see org.dmd.util.parsing.TokenArrayList
org.eclipse.uml2.uml.Classifier
org.geotools.filter.function.Classifier
The data structure returned by classification functions. We can take this object, tweak it, and then pass it to a ClassifyFunction. @author Cory Horner, Refractions Research @source $URL$
org.goai.classification.api.Classifier
sourceforge.net/doc.dev/weka/classifiers/Classifier.html Java ML classifier http://java-ml.sourceforge.net/api/0.1.7/net/sf/javaml/classification/Classifier.html @author Zoran Sevarac
org.omg.uml.foundation.core.Classifier
pattern.Classifier
quickml.supervised.classifier.Classifier
Created by alexanderhawk on 7/29/14.
weka.classifiers.Classifier
Abstract classifier. All schemes for numeric or nominal prediction in Weka extend this class. Note that a classifier MUST either implement distributionForInstance() or classifyInstance(). @author Eibe Frank (eibe@cs.waikato.ac.nz) @author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 5536 $

Examples of OntoUML.Classifier

     * <!-- end-user-doc -->
     * @generated NOT
     */
    private void updateContents() {
      CollectiveEditPart editpart = CollectiveEditPart.this;
      Classifier element = (Classifier) editpart.resolveSemanticElement();
      if (updateFaceIsExtensional((Collective) element)) {
        fFigureCollectiveMetaAttributeLabelFigure
            .setText("{extensional}");
        setIsEssentialTrue((Collective) element);
      } else {
        fFigureCollectiveMetaAttributeLabelFigure.setText("");
      }
      this.add(fFigureCollectiveMetaAttributeLabelFigure);


      EditPart compartmentEditPart = getChildBySemanticHint(OntoUML.diagram.part.OntoUMLVisualIDRegistry
          .getType(OntoUML.diagram.edit.parts.CollectiveAttributeCompartmentEditPart.VISUAL_ID));
      if (compartmentEditPart != null) {
        if (element.getAttribute().size() == 0)
          ((View) compartmentEditPart.getModel()).setVisible(false);
        else {
          if (((View) compartmentEditPart.getModel()).isVisible() == false)
            ((View) compartmentEditPart.getModel())
                .setVisible(true);

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Examples of OntoUML.Classifier

     * <!-- end-user-doc -->
     * @generated NOT
     */
    private void updateContents() {
      RoleMixinEditPart editpart = RoleMixinEditPart.this;
      Classifier element = (Classifier) editpart.resolveSemanticElement();
      EditPart compartmentEditPart = getChildBySemanticHint(OntoUML.diagram.part.OntoUMLVisualIDRegistry
          .getType(OntoUML.diagram.edit.parts.RoleMixinAttributeCompartmentEditPart.VISUAL_ID));
      if (compartmentEditPart != null) {
        if (element.getAttribute().size() == 0)
          ((View) compartmentEditPart.getModel()).setVisible(false);
        else {
          if (((View) compartmentEditPart.getModel()).isVisible() == false)
            ((View) compartmentEditPart.getModel())
                .setVisible(true);

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Examples of cc.mallet.classify.Classifier

        f++;
        InstanceList[] fold = cvIter.nextSplit();


        logger.info("Training token classifier on cv fold " + f + " / " + m_numCV + " (size=" + fold[0].size() + ")...");
        
        Classifier foldClassifier = m_trainer.train(fold[0]);
        Trial t1 = new Trial(foldClassifier, fold[0]);
        Trial t2 = new Trial(foldClassifier, fold[1]);


        logger.info("Within-fold accuracy = " + t1.getAccuracy());
        logger.info("Out-of-fold accuracy = " + t2.getAccuracy());

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Examples of cc.mallet.classify.Classifier

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


      return classifier.classify(instance);
    }

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Examples of cc.mallet.classify.Classifier

    Pipe clusterPipe = new OverlappingFeaturePipe();
    System.err.println("Training with " + training);
    InstanceList trainList = new InstanceList(clusterPipe);
    trainList.addThruPipe(new ClusterSampleIterator(training, random, 0.5, 100));
    System.err.println("Created " + trainList.size() + " instances.");
    Classifier me = new MaxEntTrainer().train(trainList);
    ClassifyingNeighborEvaluator eval =
      new ClassifyingNeighborEvaluator(me, "YES");
                                           
    Trial trial = new Trial(me, trainList);
    System.err.println(new ConfusionMatrix(trial));

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Examples of cc.mallet.classify.Classifier

          trainingInstances.add(pipe.pipe(inst));
        }
      }
      logger.info("generated " + trainingInstances.size()
          + " training instances");
      Classifier classifier = new MaxEntTrainer().train(trainingInstances);
      logger.info("InfoGain:\n");
      new InfoGain(trainingInstances).printByRank(System.out);
      logger.info("pairwise training accuracy="
          + new Trial(classifier, trainingInstances).getAccuracy());
      NeighborEvaluator neval = new PairwiseEvaluator(classifier, "YES",

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Examples of common.inspect.Classifier

    
@Override
public Classifier getClassifierForTF(String tf)throws Exception{
    tf= tf.toUpperCase();
    final int col= idCol.get(tf);
    Classifier ret= new HClassifier(tf, col);
    return ret;
}

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Examples of common.inspect.Classifier

}


public Classifier getClassifierForTF(String tf)throws Exception{
    tf= tf.toUpperCase();
    final int col= idCol.get(tf);
    Classifier ret= new HClassifier(tf, col);
    return ret;
}

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Examples of edu.msu.cme.rdp.classifier.Classifier

        FileInputStream inStream1 = new FileInputStream(s1);
        FileInputStream inStream2 = new FileInputStream(s2);
        BufferedWriter wt = new BufferedWriter(new FileWriter(detailFile));




        Classifier aClassifier = factory.createClassifier();
        TaxonTree root = null;
        SequenceReader parser = null;
        Sequence pSeq = null;
        int count = 0;


        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());
                } catch (Exception e) {
                    e.printStackTrace();
                }
            }


            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

        
        HierarchyTree rootTree = train.getRootTree();
        assertEquals(rootTree.getName(), "Bacteria");
        assertNotNull( rootTree.getSubclasses());
        
        Classifier aClassifier = train.createClassifier();
        
        //test addConfidence()
        
        HashMap<HierarchyTree, RankAssignment> determinedMap = new HashMap <HierarchyTree, RankAssignment>();
    HierarchyTree determinedGenusNode = train.getGenusNodebyIndex(4);
    HierarchyTree Enterobacteriales = determinedGenusNode.getParent().getParent();
    HierarchyTree Gammaproteobacteria = Enterobacteriales.getParent();
    
    HierarchyTree aNode = determinedGenusNode;
    while (aNode != null){      
      determinedMap.put(aNode, new RankAssignment(aNode, 0));
      aNode = aNode.getParent();
    }
    
    HierarchyTree bestNode = train.getGenusNodebyIndex(1);  // Vibrio
    aClassifier.addConfidence(bestNode, determinedMap);
    assertEquals( determinedMap.get(determinedGenusNode).getConfidence(), 0.0f, 0.0001);  //Enterobacter
    assertEquals( determinedMap.get(Enterobacteriales).getConfidence(), 0.0f, 0.0001);  //Enterobacteriales
    assertEquals( determinedMap.get(Gammaproteobacteria).getConfidence(), 1.0f, 0.0001);  //Gammaproteobacteria
        
    bestNode = train.getGenusNodebyIndex(4);   //Enterobacter
    aClassifier.addConfidence(bestNode, determinedMap);
    assertEquals( determinedMap.get(determinedGenusNode).getConfidence(), 1.0f, 0.0001);  //Enterobacter
    assertEquals( determinedMap.get(Enterobacteriales).getConfidence(), 1.0f, 0.0001);  //Enterobacteriales
    assertEquals( determinedMap.get(Gammaproteobacteria).getConfidence(), 2.0f, 0.0001);  //Gammaproteobacteria
    
    aClassifier.addConfidence(train.getGenusNodebyIndex(4), determinedMap);  //Enterobacter
    aClassifier.addConfidence(train.getGenusNodebyIndex(0), determinedMap);  //Clostridium
    assertEquals( determinedMap.get(determinedGenusNode).getConfidence(), 2.0f, 0.0001);  //Enterobacter
    assertEquals( determinedMap.get(Enterobacteriales).getConfidence(), 2.0f, 0.0001);  //Enterobacteriales
    assertEquals( determinedMap.get(Gammaproteobacteria).getConfidence(), 3.0f, 0.0001);  //Gammaproteobacteria
    assertEquals( determinedMap.get(rootTree).getConfidence(), 4.0f, 0.0001);  //Bacteria
        // end test addConfidence()
        
        
        // end of 
        dstream = System.class.getResourceAsStream("/test/classifier/testQuerySeq.fasta");
        in =  new InputStreamReader( dstream );
        BufferedReader infile = new BufferedReader(in);
        // test the first sequence
        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);
        classResult = (RankAssignment) it.next();
        assertEquals(classResult.getBestClass().getName() , "Proteobacteria");
        it.next();
        classResult = (RankAssignment) it.next();
        assertEquals(classResult.getBestClass().getName() , "Rhizobiales");
        it.next();
        classResult = (RankAssignment) it.next();
        assertEquals(classResult.getBestClass().getName() , "Rhizobium");
        
        //displayResult(result);
        // end of test first sequence
        
        // 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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