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

          // Instantiate the Remove filter
          Remove removeIDFilter = new Remove();
          removeIDFilter.setAttributeIndices("first");
      
      // Randomize the data
      data.randomize(random);
    
      // Perform cross-validation
        Instances predictedData = null;
        Evaluation eval = new Evaluation(data);

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    // Instantiate the Remove filter
        Remove removeIDFilter = new Remove();
      removeIDFilter.setAttributeIndices("first");
        
    // Randomize the data
    test.randomize(random);
    
    // Apply log filter
//      Filter logFilter = new LogFilter();
//      logFilter.setInputFormat(train);
//      train = Filter.useFilter(train, logFilter);

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        // Instantiate the Remove filter
        Remove removeIDFilter = new Remove();
      removeIDFilter.setAttributeIndices("first");
        
    // Randomize the data
    data.randomize(random);
  
    // Perform cross-validation
      Instances predictedData = null;
      Evaluation eval = new Evaluation(data);

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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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    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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    switch (mode) {
    case EVAL_TUNED_SPLIT:
      Instances trainData = null, evalData = null;
      Instances data = new Instances(instances);
      Random random = new Random(m_Seed);
      data.randomize(random);
      data.stratify(numFolds);
      
      // Make sure that both subsets contain at least one positive instance
      for (int subsetIndex = 0; subsetIndex < numFolds; subsetIndex++) {
        trainData = data.trainCV(numFolds, subsetIndex, random);

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      final Instances dataSet = new Instances(e.getDataSet());
      m_foldThread = new Thread() {
    public void run() {
      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("CrossValidationFoldMaker : "

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    }
    m_InitOptions = ((OptionHandler)m_Classifier).getOptions();
    m_BestPerformance = -99;
    m_NumAttributes = trainData.numAttributes();
    Random random = new Random(m_Seed);
    trainData.randomize(random);
    m_TrainFoldSize = trainData.trainCV(m_NumFolds, 0).numInstances();


    // Check whether there are any parameters to optimize
    if (m_CVParams.size() == 0) {
       m_Classifier.buildClassifier(trainData);

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        Vector segmentList = new Vector(q + 1);


        //Set random seed
        Random random = new Random(m_Seed);


        data.randomize(random);


        //create index arrays for different segments


        int currentDataIndex = 0;

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                                }
                            }
                        }
                    }


                    TP.randomize(random);


                    if( getTrainSize() > TP.numInstances() ){
                        throw new Exception("The training set size of " + getTrainSize() + ", is greater than the training pool "
                        + TP.numInstances() );
                    }

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