Examples of weka.core.Instance.dataset()

weka.core.Instance.dataset()
Returns the dataset this instance has access to. (ie. obtains information about attribute types from) Null if the instance doesn't have access to a dataset. @return the dataset the instance has accesss to

    if (setAnyPeriodic) {
      setPeriodicValues(result);
    }


    m_lastHistoricInstance = new DenseInstance(result);
    m_lastHistoricInstance.setDataset(result.dataset());


    if (m_extraneousAttributeRemover != null) {
      m_extraneousAttributeRemover.input(result);
      result = m_extraneousAttributeRemover.output();
    }

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      // set the correct value here - it can't be done after the fact because
      // of other filters that create the product of time and something else.
      if (incrementTime) {
        double newTime = m_lastTimeValue + 1;
        int timeIndex = result.dataset().attribute(m_timeStampName).index();
        result.setValue(timeIndex, newTime);
        m_lastTimeValue = newTime;
      }
    } else {
      // if we have a genuine time stamp field then make sure

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      }
    } else {
      // if we have a genuine time stamp field then make sure
      // that we keep track of the most recent time value
      if (m_adjustForTrends) {
        int timeIndex = result.dataset().attribute(m_timeStampName).index();
        if (incrementTime) {


          double newTime = weka.classifiers.timeseries.core.Utils
              .advanceSuppliedTimeValue(m_lastTimeValue, m_dateBasedPeriodicity);

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      if (!isDateBased()) {
        throw new Exception("This periodicity is not date timestamp-based");
      }


      int origIndex = result.dataset().attribute(timeStampName).index();
      Calendar c = new GregorianCalendar();


      boolean applyTrainingSkipAdjust = true;
      long localSkipAdjust = 0;

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  newInstance = new SparseInstance(oldInstance.weight(), newValues);
      else
  newInstance = new Instance(oldInstance.weight(), newValues);


      // copy string/relational values from input to output
      copyValues(newInstance, false, oldInstance.dataset(), getOutputFormat());


      result.add(newInstance);
    }


    return result;

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        if (m_Filter.input(e.getInstance())) {
          Instance filteredInstance = m_Filter.output();
          if (filteredInstance != null) {
            if (!m_structurePassedOn) {
              // pass on the new structure first
              m_ie.setStructure(new Instances(filteredInstance.dataset(), 0));
              notifyInstanceListeners(m_ie);
              m_structurePassedOn = true;
            }


            m_ie.setInstance(filteredInstance);

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          }
          Instance filteredInstance = m_Filter.output();
          if (filteredInstance != null) {
            if (!m_structurePassedOn) {
              // pass on the new structure first
              m_ie.setStructure(new Instances(filteredInstance.dataset(), 0));
              notifyInstanceListeners(m_ie);
              m_structurePassedOn = true;
            }


            m_ie.setInstance(filteredInstance);

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        }
        m_instanceCount++;
        
        if (!m_structurePassedOn) {
          // pass on the new structure first
          m_ie.setStructure(new Instances(filteredInstance.dataset(), 0));
          notifyInstanceListeners(m_ie);
          m_structurePassedOn = true;
        }


        m_ie.setInstance(filteredInstance);

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    int status = e.getStatus();
    int oldNumAtts = 0;
    if (status == IncrementalClassifierEvent.NEW_BATCH) {
      oldNumAtts = e.getStructure().numAttributes();
    } else {
      oldNumAtts = currentI.dataset().numAttributes();
    }
    if (status == IncrementalClassifierEvent.NEW_BATCH) {
      m_instanceEvent = new InstanceEvent(this, null, 0);
      // create new header structure
      Instances oldStructure = new Instances(e.getStructure(), 0);

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        String textTitle = ce.getClassifier().getClass().getName();
        textTitle = 
    textTitle.substring(textTitle.lastIndexOf('.')+1,
            textTitle.length());
        String results = Messages.getInstance().getString("IncrementalClassifierEvaluator_AcceptClassifier_Result_Text_First") + textTitle 
    +  Messages.getInstance().getString("IncrementalClassifierEvaluator_AcceptClassifier_Result_Text_Second") + inst.dataset().relationName() + "\n\n"
    + m_eval.toSummaryString();
              if (inst.classIndex() >= 0 && 
                  inst.classAttribute().isNominal() &&
                  (m_outputInfoRetrievalStats)) {
                results += "\n" + m_eval.toClassDetailsString();

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