Examples of weka.core.Instance

weka.core.Instance
te empty instance with three attribute values
Instance inst = new Instance(3);

// Set instance's values for the attributes "length", "weight", and "position"
inst.setValue(length, 5.3);
inst.setValue(weight, 300);
inst.setValue(position, "first");

// Set instance's dataset to be the dataset "race"
inst.setDataset(race);

// Print the instance
System.out.println("The instance: " + inst);
...

All methods that change an instance are safe, ie. a change of an instance does not affect any other instances. All methods that change an instance's attribute values clone the attribute value vector before it is changed. If your application heavily modifies instance values, it may be faster to create a new instance from scratch. @author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5970 $

   */
  public static Instances scramble(Instances src, Instances dst, final int attIndex, int[] perm) {


    for (int i = 0; i < src.numInstances(); i++) {


      Instance scrambled = dst.instance(i);


      if (attIndex > 0)
        scrambled.setValue(attIndex - 1, src.instance(i).value(attIndex - 1));
      scrambled.setValue(attIndex, src.instance(perm[i]).value(attIndex));
    }


    return dst;
  }

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          double lambda_d = 1.0;
          for(ClassifierInstance c : ensemble){
            double k = this.pureBoostOption.isSet() ? lambda_d : MiscUtils.poisson(lambda_d, this.classifierRandom);
            if (k > 0.0) {
                  Instance weightedInst = (Instance) inst.copy();
                  weightedInst.setWeight(inst.weight() * k);
                  c.getClassifier().trainOnInstance(weightedInst);
              }
              if (c.getClassifier().correctlyClassifies(inst)) {
                  c.setScms(c.getScms() + lambda_d);
                  lambda_d *= this.trainingWeightSeenByModel / (2 * c.getScms());

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        filter.input(inst);
      } catch (Exception e) {
        e.printStackTrace();
      }
      
      Instance filteredValue;
      while((filteredValue = filter.output()) != null){
        collector.emit(new Values(filteredValue));
      }
    }

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  @Override
  public void execute(Tuple input) {
    //Get the post
    Post newPost = (Post)input.getValue(0);
    //Turn it into an instance
    Instance inst = new DenseInstance(2);
    inst.setDataset(INST_HEADERS);
    inst.setValue(0, newPost.getTitle());
    inst.setValue(1, newPost.getSubReddit());
    //emit these to a new bolt that collects instances
    _collector.emit(new Values(inst));
    _collector.ack(input);
  }

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        // Create an empty training set
        Instances trainingSet = initializeDataSet(maxNumFeats, trainingSamples.size());
        int numAtts = maxNumFeats + 1;
        Iterator<Entry<Integer, Double>> currentIt;
        Entry<Integer, Double> tempEntry;
        Instance iExample;
        int i = 0;
        Instance originalCopy = getNewInstance(numAtts);
        for (LinkedHashMap<Integer, Double> currentSample : trainingSamples) {
            iExample = (Instance) originalCopy.copy();
            iExample.setDataset(trainingSet);
            currentIt = currentSample.entrySet().iterator();
            while (currentIt.hasNext()) {
                tempEntry = currentIt.next();
                iExample.setValue(tempEntry.getKey(), tempEntry.getValue());

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            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());

View Full Code Here

            }
        }
    }


    private Instance getNewInstance(int numAttributes) {
        Instance newInst = new Instance(numAttributes);
        for (int j = 0; j < numAttributes; j++) {
            newInst.setValue(j, 0.0);
        }
        return newInst;
    }

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    classAttribut = dataSet.attribute(dataSet.classIndex());
  }
  
  public Instance getInstance(OWLOntology ontology, Query query){
    List<String> featureList = getFeatures(ontology, query);
    Instance instance = new Instance(featureList.size() + 1);
    instance.setDataset(dataSet);
    for(int i = 0; i < featureList.size(); i ++){
      Attribute attr = instance.attribute(i);
      if(attr.isNumeric()){
        instance.setValue(i, Double.valueOf(featureList.get(i)));
      }else{
        instance.setValue(i, featureList.get(i));
      }
    }
    return instance;
  }

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    }
    return translator.getResult(index);
  }
  
  public String predict(OWLOntology ontology, Query query){
    Instance instance = translator.getInstance(ontology, query);
    return predict(instance);
  }

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    System.out.println("Test started!");
    System.out.println("Onotolgy: " + ontology);
    
    for(Query query: getQuery(ontology)){
      Instance instance = translator.getInstance(ontology, query);
      
      System.out.println("********************************");
      System.out.println("Query: " + query.getTask());
      
      testWithReasoner(ontology, query);

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