Examples of weka.core.Instance.classIsMissing()

weka.core.Instance.classIsMissing()
Tests if an instance's class is missing. @return true if the instance's class is missing @throws UnassignedClassException if the class is not set or the instance doesn'thave access to a dataset

    // Get counts
    for (int k = 0; k < numInstances; k++) {
      Instance inst = data.instance(k);
      for (int i = 0; i < inst.numValues(); i++) {
        if (inst.index(i) != classIndex) {
          if (inst.isMissingSparse(i) || inst.classIsMissing()) {
            if (!inst.isMissingSparse(i)) {
              counts[inst.index(i)][(int)inst.valueSparse(i)][numClasses] += 
                inst.weight();
              counts[inst.index(i)][0][numClasses] -= inst.weight();
            } else if (!inst.classIsMissing()) {

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          if (inst.isMissingSparse(i) || inst.classIsMissing()) {
            if (!inst.isMissingSparse(i)) {
              counts[inst.index(i)][(int)inst.valueSparse(i)][numClasses] += 
                inst.weight();
              counts[inst.index(i)][0][numClasses] -= inst.weight();
            } else if (!inst.classIsMissing()) {
              counts[inst.index(i)][data.attribute(inst.index(i)).numValues()]
                [(int)inst.classValue()] += inst.weight();
              counts[inst.index(i)][0][(int)inst.classValue()] -= 
                inst.weight();
            } else {

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      m_PriorErrorEstimator = null;
      m_ErrorEstimator = null;


      for (int i = 0; i < train.numInstances(); i++) {
        Instance currentInst = train.instance(i);
        if (!currentInst.classIsMissing()) {
          addNumericTrainClass(currentInst.classValue(),
              currentInst.weight());
        }
      }

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    // Initialize counters
    double[] temp = new double[numClasses + 1];
    for (int k = 0; k < numInstances; k++) {
      Instance inst = data.instance(k);
      if (inst.classIsMissing()) {
        temp[numClasses] += inst.weight();
      } else {
        temp[(int)inst.classValue()] += inst.weight();
      }
    }

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  double sumWeightedXDiffSquared = 0;
  double sumWeightedYDiffSquared = 0;
  m_slope = 0;
  for (int j = 0; j < insts.numInstances(); j++) {
    Instance inst = insts.instance(j);
    if (!inst.isMissing(i) && !inst.classIsMissing()) {
      double xDiff = inst.value(i) - xMean;
      double yDiff = inst.classValue() - yMean;
      double weightedXDiff = inst.weight() * xDiff;
      double weightedYDiff = inst.weight() * yDiff;
      m_slope += weightedXDiff * yDiff;

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    m_K = 0;
  out:
    for (int it = 0; it < m_NumIterations; it++) {
      for (int i = 0; i < m_Train.numInstances(); i++) {
  Instance inst = m_Train.instance(i);
  if (!inst.classIsMissing()) {
    int prediction = makePrediction(m_K, inst);
    int classValue = (int) inst.classValue();
    if (prediction == classValue) {
      m_Weights[m_K]++;
    } else {

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    // Compute counts and sums
    Enumeration enumInsts = instances.enumerateInstances();
    while (enumInsts.hasMoreElements()) {
      Instance instance = (Instance) enumInsts.nextElement();
      if (!instance.classIsMissing()) {
  Enumeration enumAtts = instances.enumerateAttributes();
  attIndex = 0;
  while (enumAtts.hasMoreElements()) {
    Attribute attribute = (Attribute) enumAtts.nextElement();
    if (!instance.isMissing(attribute)) {

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    // Compute standard deviations
    enumInsts = instances.enumerateInstances();
    while (enumInsts.hasMoreElements()) {
      Instance instance = 
  (Instance) enumInsts.nextElement();
      if (!instance.classIsMissing()) {
  enumAtts = instances.enumerateAttributes();
  attIndex = 0;
  while (enumAtts.hasMoreElements()) {
    Attribute attribute = (Attribute) enumAtts.nextElement();
    if (!instance.isMissing(attribute)) {

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      if (att.isNominal()) {
  avgClassValues[j] = new double [att.numValues()];
  counts = new double [att.numValues()];
  for (int i = 0; i < getInputFormat().numInstances(); i++) {
    instance = getInputFormat().instance(i);
    if (!instance.classIsMissing() && 
        (!instance.isMissing(j))) {
      counts[(int)instance.value(j)] += instance.weight();
      avgClassValues[j][(int)instance.value(j)] += 
        instance.weight() * instance.classValue();
    }

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    int [] counts = new int [getInputFormat().numClasses()];
    double [] weights = new double [getInputFormat().numClasses()];
    int min = -1;
    for (int i = 0; i < getInputFormat().numInstances(); i++) {
      Instance current = getInputFormat().instance(i);
      if (current.classIsMissing() == false) {
        counts[(int)current.classValue()]++;
        weights[(int)current.classValue()]+= current.weight();
      }
    }

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