Examples of weka.core.Instances.randomize()

weka.core.Instances.randomize()
Shuffles the instances in the set so that they are ordered randomly. @param random a random number generator

  // create new train/test sources
  if (splitPercentage > 0) {
    testSetPresent = true;
    Instances tmpInst = trainSource.getDataSet(actualClassIndex);
    if (!preserveOrder)
      tmpInst.randomize(new Random(seed));
    int trainSize = 
            (int) Math.round(tmpInst.numInstances() * splitPercentage / 100);
    int testSize  = tmpInst.numInstances() - trainSize;
    Instances trainInst = new Instances(tmpInst, 0, trainSize);
    Instances testInst  = new Instances(tmpInst, trainSize, testSize);

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  inBag[j] = new boolean[data.numInstances()];
  bagData = resampleWithWeights(data, random, inBag[j]);
      } else {
  bagData = data.resampleWithWeights(random);
  if (bagSize < data.numInstances()) {
    bagData.randomize(random);
    Instances newBagData = new Instances(bagData, 0, bagSize);
    bagData = newBagData;
  }
      }

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    if(m_NumAntds != -1){
      grow(data);
    }
    else{


      data.randomize(m_Random);


      // Split data into Grow and Prune     
      data.stratify(m_Folds);
  
      Instances growData=data.trainCV(m_Folds, m_Folds-1, m_Random);

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    Instances newData = new Instances(data);
    m_BaseFormat = new Instances(data, 0);
    newData.deleteWithMissingClass();


    Random random = new Random(m_Seed);
    newData.randomize(random);
    if (newData.classAttribute().isNominal()) {
      newData.stratify(m_NumFolds);
    }


    // Create meta level

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      throw new Exception("No Instances set");
    }


    // Randomize on a copy of the original dataset
    Instances runInstances = new Instances(m_Instances);
    runInstances.randomize(new Random(run));
    /*if (runInstances.classAttribute().isNominal()) {
      runInstances.stratify(m_StepSize);
    }*/


    // Tell the resultproducer to send results to us

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  RidorRule rule = new RidorRule();                                
  rule.setPredictedClass(0);       // Predict the classes other than default
    
  for(int j = 0; j < m_Shuffle; j++){
    if(m_Shuffle > 1)
      data.randomize(m_Random);
        
    rule.buildClassifier(data);
        
    double wr, w; // Worth rate and worth
    if(m_IsAllErr){

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    // weights for root nodes of each fold for prepruning and postpruning.
    int expansion = 0;


    Random random = new Random(m_Seed);
    Instances cvData = new Instances(data);
    cvData.randomize(random);
    cvData = new Instances(cvData,0,(int)(cvData.numInstances()*m_SizePer)-1);
    cvData.stratify(m_numFoldsPruning);


    Instances[] train = new Instances[m_numFoldsPruning];
    Instances[] test = new Instances[m_numFoldsPruning];

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        break;


        case 2: // Percent split
        m_Log.statusMessage(Messages.getInstance().getString("ClustererPanel_StartClusterer_Run_Log_StatusMessage_Fourth"));
        inst.randomize(new Random(1));
        trainInst.randomize(new Random(1));
        int trainSize = trainInst.numInstances() * percent / 100;
        int testSize = trainInst.numInstances() - trainSize;
        Instances train = new Instances(trainInst, 0, trainSize);
        Instances test = new Instances(trainInst, trainSize, testSize);
        Instances testVis = new Instances(inst, trainSize, testSize);

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        break;


        case 1: // CV mode
        m_Log.statusMessage(Messages.getInstance().getString("AttributeSelectionPanel_StartAttributeSelection_Run_Log_StatusMessage_Text_Second"));
        Random random = new Random(seed);
        inst.randomize(random);
        if (inst.attribute(classIndex).isNominal()) {
    m_Log.statusMessage(Messages.getInstance().getString("AttributeSelectionPanel_StartAttributeSelection_Run_Log_StatusMessage_Text_Third"));
    inst.stratify(numFolds);
        }
        for (int fold = 0; fold < numFolds;fold++) {

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      m_Antds = new FastVector();  
      
      /* Split data into Grow and Prune*/
      m_Random = new Random(m_Seed);
      data.randomize(m_Random);
      data.stratify(m_Folds);
      Instances growData=data.trainCV(m_Folds, m_Folds-1, m_Random);
      Instances pruneData=data.testCV(m_Folds, m_Folds-1);
      
      grow(growData);      // Build this rule

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