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 weka.classifiers.Classifier

      }
      m_RunThread = new Thread() {
          public void run() {
            // Copy the current state of things
            m_Log.statusMessage("Setting up...");
            Classifier classifierToUse = classifier;


            StringBuffer outBuff = m_History.getNamedBuffer(name);
            DataSource source = null;
            Instances userTestStructure = null;
            ClassifierErrorsPlotInstances plotInstances = null;

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Examples of weka.classifiers.Classifier

    m_ClassifierEditor.setCapabilitiesFilter(filterClass);
    
    // Check capabilities
    m_StartBut.setEnabled(true);
    Capabilities currentFilter = m_ClassifierEditor.getCapabilitiesFilter();
    Classifier classifier = (Classifier) m_ClassifierEditor.getValue();
    Capabilities currentSchemeCapabilities =  null;
    if (classifier != null && currentFilter != null && 
        (classifier instanceof CapabilitiesHandler)) {
      currentSchemeCapabilities = ((CapabilitiesHandler)classifier).getCapabilities();

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Examples of weka.classifiers.Classifier

  }


  private List<Object> createLagFiller(Instances insts, String targetName)
      throws Exception {
    // Classifier lagFiller = new weka.classifiers.functions.LeastMedSq();
    Classifier lagFiller = new weka.classifiers.functions.LinearRegression();


    ArrayList<Attribute> atts = new ArrayList<Attribute>();
    atts.add(new Attribute("time"));
    atts.add(new Attribute("target"));
    Instances simple = new Instances("simple", atts, insts.numInstances());
    int targetIndex = insts.attribute(targetName).index();
    for (int i = 0; i < insts.numInstances(); i++) {
      double targetValue = insts.instance(i).value(targetIndex);
      double time = i;
      double[] vals = new double[2];
      vals[0] = time;
      vals[1] = targetValue;
      DenseInstance d = new DenseInstance(1.0, vals);
      simple.add(d);
    }


    simple.setClassIndex(1);
    lagFiller.buildClassifier(simple);
    System.err.println(lagFiller);
    simple = new Instances(simple, 0);


    List<Object> results = new ArrayList<Object>();
    results.add(lagFiller);

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Examples of weka.classifiers.Classifier

  public void acceptClassifier(BatchClassifierEvent ce) {
    if (ce.getTestSet() == null || ce.getTestSet().isStructureOnly()) {
      return; // can't evaluate empty/non-existent test instances
    }


    Classifier classifier = ce.getClassifier();


    try {
      if (ce.getGroupIdentifier() != m_currentBatchIdentifier) {
        if (m_setsComplete > 0) {
          if (m_logger != null) {

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Examples of weka.classifiers.Classifier

        System.out.println(ThresholdCurve.getNPointPrecision(inst, 11));
      } else {
        inst.setClassIndex(inst.numAttributes() - 1);
        ThresholdCurve tc = new ThresholdCurve();
        EvaluationUtils eu = new EvaluationUtils();
        Classifier classifier = new weka.classifiers.functions.Logistic();
        FastVector predictions = new FastVector();
        for (int i = 0; i < 2; i++) { // Do two runs.
          eu.setSeed(i);
          predictions.appendElements(eu.getCVPredictions(classifier, inst, 10));
          //System.out.println("\n\n\n");

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Examples of weka.classifiers.Classifier

   * Weka classifier.
   * 
   * @return the scheme name and options of the underlying Weka classifier
   */
  protected String getForecasterSpec() {
    Classifier c = getBaseForecaster();


    if (c instanceof OptionHandler) {
      return c.getClass().getName() + " "
          + Utils.joinOptions(((OptionHandler) c).getOptions());
    } else {
      return c.getClass().getName();
    }
  }

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Examples of weka.classifiers.Classifier

    // m_lastHistoricInstance = insts.instance(insts.numInstances() - 1);


    m_singleTargetForecasters = new ArrayList<SingleTargetForecaster>();
    for (int i = 0; i < m_fieldsToForecast.size(); i++) {
      SingleTargetForecaster f = new SingleTargetForecaster();
      Classifier c = AbstractClassifier.makeCopy(m_forecaster);
      f.setClassifier(c);
      f.buildForecaster(trainingData, m_fieldsToForecast.get(i));
      m_singleTargetForecasters.add(f);
    }

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Examples of weka.classifiers.Classifier


  private final static Logger logger = Logger
  .getLogger(WekaRegressionModelFactory.class);


  public static WekaRegressionModelFactory createForDir(String models, WekaOptions config, OpponentModelListener... listeners) throws IOException, ClassNotFoundException {
    Classifier preBetModel, preFoldModel, preCallModel, preRaiseModel, postBetModel, postFoldModel, postCallModel, postRaiseModel,
    showdown0Model, showdown1Model, showdown2Model, showdown3Model, showdown4Model, showdown5Model;
    ClassLoader classLoader = WekaRegressionModelFactory.class.getClassLoader();
    ObjectInputStream in = new ObjectInputStream(classLoader.getResourceAsStream(models+"preBet.model"));
    preBetModel = (Classifier)in.readObject();
    in.close();

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Examples of weka.classifiers.Classifier

        return trainingSet;
    }


    public double classify(LinkedHashMap<Integer, Double> sample) {
        try {
            Classifier cModel = (LibSVM) loadClassifier();
            ArffLoader loader = new ArffLoader();
            loader.setFile(new File(PropertiesGetter.getProperty("TrainingDataSetObject")));
            Instances structure = loader.getStructure();
            structure.setClassIndex(structure.numAttributes() - 1);
            int numAtts = structure.numAttributes();
            Instance instanceToClassify = getNewInstance(numAtts);
            Iterator<Entry<Integer, Double>> currentIt = sample.entrySet().iterator();
            Entry<Integer, Double> tempEntry;
            while (currentIt.hasNext()) {
                tempEntry = currentIt.next();
                instanceToClassify.setValue(tempEntry.getKey(), tempEntry.getValue());
            }
            instanceToClassify.setDataset(structure);
            instanceToClassify.setClassMissing();
            try {
                return cModel.classifyInstance(instanceToClassify);
            } catch (Exception ex) {
                Logger.getLogger(WekaWrapper.class.getName()).log(Level.SEVERE, null, ex);
                System.out.println(ex.toString());
                return -2;
            }

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Examples of weka.classifiers.Classifier

        }
        return newInst;
    }


    private Classifier loadClassifier() {
        Classifier cModel = null;
        InputStream is = null;
        try {
            ObjectInputStream objectInputStream = null;
            is = new FileInputStream(PropertiesGetter.getProperty("SVMModelFile"));
            objectInputStream = new ObjectInputStream(is);

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