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

    if (data.numInstances() < 2 * m_TrainPoolSize) {
      throw new Exception("The dataset must contain at least "
        + (2 * m_TrainPoolSize) + " instances");
    }
    Random random = new Random(m_Seed);
    data.randomize(random);
    Instances trainPool = new Instances(data, 0, m_TrainPoolSize);
    Instances test = new Instances(data, m_TrainPoolSize, 
           data.numInstances() - m_TrainPoolSize);
    int numTest = test.numInstances();
    double [][] instanceProbs = new double [numTest][numClasses];

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      // theInstances.stratify(10);
        
      CVincreased = false;
      cvr = new Random(getSeed());
      trainCopy = new Instances(m_theInstances);
      trainCopy.randomize(cvr);
      templl = 0.0;
      for (i = 0; i < numFolds; i++) {
  Instances cvTrain = trainCopy.trainCV(numFolds, i, cvr);
  if (num_clusters > cvTrain.numInstances()) {
    break CLUSTER_SEARCH;

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  }
  for (int k = 0; k < subsets[j].numInstances(); k++) {
    data.add(subsets[j].instance(k));
  }
  data.compactify();
  data.randomize(rand);
  m_classifiers[i][j].buildClassifier(data, i, j, 
              m_fitLogisticModels,
              m_numFolds, m_randomSeed);
      }
    }

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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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      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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        break;


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

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        }
        for (int k = 0; k < subsets[j].numInstances(); k++) {
          data.add(subsets[j].instance(k));
        }  
        data.compactify(); 
        data.randomize(rand);
        m_classifiers[i][j].buildClassifier(data, i, j, 
            m_fitLogisticModels,
            m_numFolds, m_randomSeed);
      }
    }

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        break;


        case 2: // Percent split
        m_Log.statusMessage("Randomizing instances...");
        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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          // 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
    data.randomize(random);
  
    // Perform cross-validation
      Instances predictedData = null;
      Evaluation eval = new Evaluation(data);

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