Examples of weka.core.Instances.instance()

weka.core.Instances.instance()
Returns the instance at the given position. @param index the instance's index (index starts with 0) @return the instance at the given position

      }
      String[] options = weka.core.Utils.splitOptions("-p 0");
      J48 cls = (J48)weka.core.SerializationHelper.read(modelfile);
      cls.setOptions(options);
      for(int i = 0; i < data.numInstances(); i++){
        double pred = cls.classifyInstance(data.instance(i));
        ClusterClass clusClass = ClusterClass.valueOf(
            data.classAttribute().value((int)pred).toUpperCase());
        if(!retval.containsKey(clusClass)){
          retval.put(clusClass, new ArrayList<StoredDomainCluster>());
        }

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    Attribute groupID = new Attribute("groupID", (FastVector) null);
    
    DataSource source = new DataSource("testMe.arff");
    Instances instances = source.getDataSet();
    //System.out.println(instances);
    System.out.println(instances.instance(0));
    
        
    Enumeration enu = instances.enumerateInstances();
    while(enu.hasMoreElements())
    {

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      } catch (Exception e) {
        log.error("Can't find keyphrases for stem " + str + ".");
        newInst[1] = Instance.missingValue();
      }
      data.add(new Instance(1.0, newInst));
      m_KEAFilter.input(data.instance(0));
      data = data.stringFreeStructure();
    }
    m_KEAFilter.batchFinished();
    
    while ((m_KEAFilter.output()) != null) {};

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        newInst[1] = Instance.missingValue();
      }


      data.add(new Instance(1.0, newInst));


      m_KEAFilter.input(data.instance(0));


      data = data.stringFreeStructure();
      if (m_debug) {
        log.debug("-- Document: " + str);
      }

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  totalWeight += m_instances.instance(noa).weight();
      }
    }
    if (m_valSize != 0) {
      for (int noa = 0; noa < valSet.numInstances(); noa++) {
  if (!valSet.instance(noa).classIsMissing()) {
    totalValWeight += valSet.instance(noa).weight();
  }
      }
    }
    m_stopped = false;

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      }
    }
    if (m_valSize != 0) {
      for (int noa = 0; noa < valSet.numInstances(); noa++) {
  if (!valSet.instance(noa).classIsMissing()) {
    totalValWeight += valSet.instance(noa).weight();
  }
      }
    }
    m_stopped = false;

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      ////////////////////////do validation testing if applicable
      if (m_valSize != 0) {
  right = 0;
  for (int nob = 0; nob < valSet.numInstances(); nob++) {
    m_currentInstance = valSet.instance(nob);
    if (!m_currentInstance.classIsMissing()) {
      //this is where the network updating occurs, for the validation set
      resetNetwork();
      calculateOutputs();
      right += (calculateErrors() / valSet.numClasses())

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        newInst[2] = Instance.missingValue();
      }


      data.add(new Instance(1.0, newInst));


      mauiFilter.input(data.instance(0));
      data = data.stringFreeStructure();
    }
    mauiFilter.batchFinished();


    while ((mauiFilter.output()) != null) {

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          smo.setKernel(Kernel.makeCopy(SMO.this.m_kernel));
          smo.buildClassifier(train, cl1, cl2, false, -1, -1);
    Instances test = insts.testCV(numFolds, i);
    for (int j = 0; j < test.numInstances(); j++) {
      double[] vals = new double[2];
      vals[0] = smo.SVMOutput(-1, test.instance(j));
      if (test.instance(j).classValue() == cl2) {
        vals[1] = 1;
      }
      data.add(new Instance(test.instance(j).weight(), vals));
    }

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          smo.buildClassifier(train, cl1, cl2, false, -1, -1);
    Instances test = insts.testCV(numFolds, i);
    for (int j = 0; j < test.numInstances(); j++) {
      double[] vals = new double[2];
      vals[0] = smo.SVMOutput(-1, test.instance(j));
      if (test.instance(j).classValue() == cl2) {
        vals[1] = 1;
      }
      data.add(new Instance(test.instance(j).weight(), vals));
    }
  }

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