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

    train.compactify();
    test.compactify();
    
    // randomize the final sets
    train.randomize(rand);
    test.randomize(rand);
  } else {
    
    // Numeric target 
    int trainSize = 
      Utils.probRound(runInstances.numInstances() * m_TrainPercent / 100, rand);

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    Instances[] testData = new Instances[m_numFolds];
    LFSMethods[] searchResults = new LFSMethods[m_numFolds];


    Random random = new Random(m_seed);
    Instances dataCopy = new Instances(data);
    dataCopy.randomize(random);


    if (dataCopy.classAttribute().isNominal()) {
      dataCopy.stratify(m_numFolds);
    }

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    double minC=0, maxC=0;


    /** Resampling  **/
    if(Double.parseDouble(m_resamplePercent.getText())<100) {
        inst = new Instances(m_data, 0, m_data.numInstances());
        inst.randomize( new Random(Integer.parseInt(m_rseed.getText())) );
        
        //System.err.println("gettingPercent: " +
        //                   Math.round(
        //                     Double.parseDouble(m_resamplePercent.getText())
        //                     / 100D * m_data.numInstances()

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      return;
    }


    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);


    double[][] alphas = new double[m_numFoldsPruning][];
    double[][] errors = new double[m_numFoldsPruning][];

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      m_foldThread = new Thread() {
    public void run() {
      boolean errorOccurred = false;
      try {
        Random random = new Random(getSeed());
        dataSet.randomize(random);
        if (dataSet.classIndex() >= 0 && 
      dataSet.attribute(dataSet.classIndex()).isNominal()) {
    dataSet.stratify(getFolds());
    if (m_logger != null) {
      m_logger.logMessage(Messages.getInstance().getString("CrossValidationFoldMaker_AcceptDataSet_LogMessage_Text_First") + getCustomName() + Messages.getInstance().getString("CrossValidationFoldMaker_AcceptDataSet_LogMessage_Text_Second"));

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    // remove instances with missing class
    Instances newData = new Instances(data);
    newData.deleteWithMissingClass();
    
    Random random = new Random(m_Seed);
    newData.randomize(random);
    if (newData.classAttribute().isNominal() && (m_NumXValFolds > 1)) {
      newData.stratify(m_NumXValFolds);
    }
    Instances train = newData;               // train on all data by default
    Instances test = newData;               // test on training data by default

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    train.compactify();
    test.compactify();
    
    // randomize the final sets
    train.randomize(rand);
    test.randomize(rand);
  } else {
    
    // Numeric target 
    int trainSize = 
      Utils.probRound(runInstances.numInstances() * m_TrainPercent / 100, rand);

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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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  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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      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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