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

    this.classifier = loadClassifier();
    translator = Translator.getInstance();
  }
  
  private Classifier loadClassifier(){
    Classifier classifier = null;
    Properties configProperties = loadConfigProperties();
    try {
      classifier = (Classifier) SerializationHelper.read(configProperties.getProperty("modelPath"));
    } catch (Exception e) {
      // TODO Auto-generated catch block

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Examples of weka.classifiers.Classifier

          //repTree.setNoPruning(true);// since we only use the tree as a classifier (as a conservative extension of what is currently done) and do not actually look at it, elimination of pruning is not a problem. 
          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
          
          {// serialise the classifier, this is the only way to store it.

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Examples of weka.classifiers.Classifier

          //repTree.setNoPruning(true);// since we only use the tree as a classifier (as a conservative extension of what is currently done) and do not actually look at it, elimination of pruning is not a problem. 
          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
          
          {// serialise the classifier, this is the only way to store it.

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Examples of weka.classifiers.Classifier

      uas_F=new RBoxPlot<String>("Time","F-measure",new File("time_"+name+"_f.pdf")),
      uas_Diff=new RBoxPlot<String>("Time","Diff-measure",new File("time_"+name+"_Diff.pdf"));
    SquareBagPlot gr_diff_to_f = new SquareBagPlot("f-measure","diff-based measure",new File("diff-to-f.pdf"),0,1,true);


    Set<Integer> allFrames = collectionOfTraces.get(UAVAllSeeds).tracesForUAVandFrame.get(UAVAllSeeds).keySet();
    Classifier classifiers[] = loadClassifierFromArff(arffName);
    ProgressIndicator progress = new ProgressIndicator("UAS", allFrames.size()*classifiers.length);
    LearnerEvaluationConfiguration initConfiguration = new LearnerEvaluationConfiguration(learnerInitConfiguration.config);
    initConfiguration.setLabelConverter(learnerInitConfiguration.getLabelConverter());// we do not copy if-then automata here because we do not wish to augment from if-then on every iteration because our properties are pairwise and this permits augmentation to be carried out first thing and not any more.
    initConfiguration.config.setUseConstraints(false);// do not use if-then during learning (refer to the explanation above)

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Examples of weka.classifiers.Classifier

          //repTree.setNoPruning(true);// since we only use the tree as a classifier (as a conservative extension of what is currently done) and do not actually look at it, elimination of pruning is not a problem. 
          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
          
          {// serialise the classifier, this is the only way to store it.

View Full Code Here

Examples of weka.classifiers.Classifier

          //repTree.setNoPruning(true);// since we only use the tree as a classifier (as a conservative extension of what is currently done) and do not actually look at it, elimination of pruning is not a problem. 
          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
          
          {// serialise the classifier, this is the only way to store it.

View Full Code Here

Examples of weka.classifiers.Classifier

         
      if(target.getSenses().size()>1)
    {
        Instances ins=this.loadFilteredInstances(target); 
      System.out.println("Trying "+target.getIndex());
      Classifier cModel=null;
      String Evaluator=this.getValue("evaluator");
      String Search=this.getValue("search");
      if(Evaluator==null)
        Evaluator="";
      if(Search==null)
        Search="";
      File f=new File("data/weka/"+this.classifier+"@"+Dictionary.normalizeLemmaforFile(target.getLemma())+"@"+Evaluator+"@"+Search+".csf");
      if(f.exists())
      {
        cModel=(Classifier)Util.loadObject(f);
      }
      else
      {
        cModel = (Classifier)Class.forName(this.classifier).newInstance();        
             cModel.buildClassifier(ins);
             Util.writeObject(f, cModel);
      }
      //Instantiate the classifier
        Instance base=ins.firstInstance();
      Instance sample=new Instance(base.numAttributes()); 
      sample.setDataset(ins);
      int c[]=new int[base.numAttributes()];
      for(int i=0;i<base.numAttributes();i++)
      {
        c[i]=0;
      }
      for(int i=0;i<base.numAttributes();i++)
      {
        Attribute att=base.attribute(i);        
        for(AmbiguousWord word:window)
         {
           if(word.getLemma().equals(att.name()))
           {
             c[i]++;  
           }
         }
      }
      for(int i=0;i<base.numAttributes();i++)
      {
        sample.setValue(i, ((double)c[i]));
      }
      
      double []w=cModel.distributionForInstance(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 weka.classifiers.Classifier

          //repTree.setNoPruning(true);// since we only use the tree as a classifier (as a conservative extension of what is currently done) and do not actually look at it, elimination of pruning is not a problem. 
          // As part of learning, we also prune some of the nodes where the ratio of correctly-classified pairs to those incorrectly classified is comparable.
          // The significant advantage of not pruning is that the result is no longer sensitive to the order of elements in the tree and hence does not depend on the order in which elements have been obtained by concurrent threads.
          //final weka.classifiers.lazy.IB1 ib1 = new weka.classifiers.lazy.IB1();
          //final weka.classifiers.trees.J48 classifier = new weka.classifiers.trees.J48();
          final Classifier classifier = repTree;
          classifier.buildClassifier(dataCollector.trainingData);
          System.out.println("Entries in the classifier: "+dataCollector.trainingData.numInstances());
          System.out.println(classifier);
          dataCollector=null;// throw all the training data away.
          
          {// serialise the classifier, this is the only way to store it.

View Full Code Here

Examples of weka.classifiers.Classifier

      uas_F=new RBoxPlot<String>("Time","F-measure",new File("time_"+name+"_f.pdf")),
      uas_Diff=new RBoxPlot<String>("Time","Diff-measure",new File("time_"+name+"_Diff.pdf"));
    SquareBagPlot gr_diff_to_f = new SquareBagPlot("f-measure","diff-based measure",new File("diff-to-f.pdf"),0,1,true);


    Set<Integer> allFrames = collectionOfTraces.get(UAVAllSeeds).tracesForUAVandFrame.get(UAVAllSeeds).keySet();
    Classifier classifiers[] = loadClassifierFromArff(arffName);
    ProgressIndicator progress = new ProgressIndicator("UAS", allFrames.size()*classifiers.length);
    LearnerEvaluationConfiguration initConfiguration = new LearnerEvaluationConfiguration(learnerInitConfiguration.config);
    initConfiguration.setLabelConverter(learnerInitConfiguration.getLabelConverter());// we do not copy if-then automata here because we do not wish to augment from if-then on every iteration because our properties are pairwise and this permits augmentation to be carried out first thing and not any more.
    initConfiguration.config.setUseConstraints(false);// do not use if-then during learning (refer to the explanation above)

View Full Code Here

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