Examples of weka.core.Instances.sort()

weka.core.Instances.sort()
Sorts the instances based on an attribute. For numeric attributes, instances are sorted in ascending order. For nominal attributes, instances are sorted based on the attribute label ordering specified in the header. Instances with missing values for the attribute are placed at the end of the dataset. @param attIndex the attribute's index (index starts with 0)

      maxInfoGain = 0;
      value = 0;  
      
      double fstCover=0, sndCover=0, fstAccu=0, sndAccu=0;
      
      data.sort(att);
      // Find the las instance without missing value 
      for(int x=0; x<data.numInstances(); x++){
  Instance inst = data.instance(x);
  if(inst.isMissing(att)){
    total = x;

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     * @param defInfo the default information for data
     * @return the array of data after split
     */
    public Instances[] splitData(Instances insts, double defInfo){  
      Instances data = new Instances(insts);
      data.sort(att);
      int total=data.numInstances();// Total number of instances without 
      // missing value for att
      maxInfoGain = 0;
      value = 0;

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    // Copy data so that it can be sorted
    Instances data = new Instances(getInputFormat());


    // Sort input data
    data.sort(index);


    // Compute weight of instances without missing values
    double sumOfWeights = 0;
    for (int i = 0; i < data.numInstances(); i++) {
      if (data.instance(i).isMissing(index)) {

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    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }


    Instances input = getInputFormat();
    input.sort(0);   // make sure that bagID is sorted
    Instances output = getOutputFormat();
    Instances bagInsts = output.attribute(1).relation();
    Instance inst = new Instance(bagInsts.numAttributes());
    inst.setDataset(bagInsts);

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     * @param cl the class label to be predicted
     * @return the array of data after split
     */
    public Instances[] splitData(Instances insts, double defAcRt, double cl){
      Instances data = new Instances(insts);
      data.sort(att);
      int total=data.numInstances();// Total number of instances without 
      // missing value for att
      
      int split=1;                  // Current split position
      int prev=0;                   // Previous split position

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     * @param cl the class label to be predicted
     * @return the array of data after split
     */
    public Instances[] splitData(Instances insts, double defAcRt, double cl){
      Instances data = new Instances(insts);
      data.sort(att);
      int total=data.numInstances();// Total number of instances without 
      // missing value for att
      
      int split=1;                  // Current split position
      int prev=0;                   // Previous split position

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      maxInfoGain = 0;
      value = 0;  
      
      double fstCover=0, sndCover=0, fstAccu=0, sndAccu=0;
      
      data.sort(att);
      // Find the las instance without missing value 
      for(int x=0; x<data.numInstances(); x++){
  Instance inst = data.instance(x);
  if(inst.isMissing(att)){
    total = x;

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     * @param defInfo the default information for data
     * @return the array of data after split
     */
    public Instances[] splitData(Instances insts, double defInfo){  
      Instances data = new Instances(insts);
      data.sort(att);
      int total=data.numInstances();// Total number of instances without 
      // missing value for att
      maxInfoGain = 0;
      value = 0;

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     * @param attIndex the index of the attribute
     * @param pq the priority queue of candidtate splits
     */
    private void evaluateNumeric(int attIndex, PriorityQueue<HotTestDetails> pq) {
      Instances tempInsts = m_insts;
      tempInsts.sort(attIndex);
      
      // target sums/counts
      double targetLeft = 0;
      double targetRight = 0;

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    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }


    Instances input = getInputFormat();
    input.sort(0);   // make sure that bagID is sorted
    Instances output = getOutputFormat();
    Instances bagInsts = output.attribute(1).relation();
    Instance inst = new Instance(bagInsts.numAttributes());
    inst.setDataset(bagInsts);

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