本文整理了Java中weka.core.Instances.randomize()方法的一些代码示例，展示了Instances.randomize()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Instances.randomize()方法的具体详情如下：
包路径：weka.core.Instances
类名称：Instances
方法名：randomize

Instances.randomize介绍

[英]Shuffles the instances in the set so that they are ordered randomly.
[中]洗牌集合中的实例，使其随机排序。

代码示例

代码示例来源：origin: com.googlecode.obvious/obviousx-weka

@Override
public void randomize(Random arg0) {
 super.randomize(arg0);
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Creates the training set for one fold of a cross-validation on the dataset.
 * The data is subsequently randomized based on the given random number
 * generator.
 * 
 * @param numFolds the number of folds in the cross-validation. Must be
 *          greater than 1.
 * @param numFold 0 for the first fold, 1 for the second, ...
 * @param random the random number generator
 * @return the training set
 * @throws IllegalArgumentException if the number of folds is less than 2 or
 *           greater than the number of instances.
 */
// @ requires 2 <= numFolds && numFolds < numInstances();
// @ requires 0 <= numFold && numFold < numFolds;
public Instances trainCV(int numFolds, int numFold, Random random) {
 Instances train = trainCV(numFolds, numFold);
 train.randomize(random);
 return train;
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Creates the training set for one fold of a cross-validation on the dataset.
 * The data is subsequently randomized based on the given random number
 * generator.
 * 
 * @param numFolds the number of folds in the cross-validation. Must be
 *          greater than 1.
 * @param numFold 0 for the first fold, 1 for the second, ...
 * @param random the random number generator
 * @return the training set
 * @throws IllegalArgumentException if the number of folds is less than 2 or
 *           greater than the number of instances.
 */
// @ requires 2 <= numFolds && numFolds < numInstances();
// @ requires 0 <= numFold && numFold < numFolds;
public Instances trainCV(int numFolds, int numFold, Random random) {
 Instances train = trainCV(numFolds, numFold);
 train.randomize(random);
 return train;
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Reset the visualize panel's buttons and the plot panels instances
 * 
 * @param inst the data
 * @param cIndex the color index
 */
private void plotReset(Instances inst, int cIndex) {
 if (m_splitListener == null) {
  m_submit.setText("Reset");
  m_submit.setActionCommand("Reset");
  // if (m_origInstances == null || m_origInstances == inst) {
  if (m_originalPlot == null || m_originalPlot.m_plotInstances == inst) {
   m_submit.setEnabled(false);
  } else {
   m_submit.setEnabled(true);
  }
 } else {
  m_submit.setEnabled(false);
 }
 m_plotInstances = inst;
 if (m_splitListener != null) {
  m_plotInstances.randomize(new Random());
 }
 m_xIndex = 0;
 m_yIndex = 0;
 m_cIndex = cIndex;
 cancelShapes();
}

代码示例来源：origin: hltfbk/Excitement-Open-Platform

/**
 * Train the required classifier with generated Weka ARFF file.
 * 
 */
private void trainClassifier() {
  for (int i = 0; i < numOfModelFiles; i++) {
    Instances data;
    try {
      data = loadInstancesFromARFF(wekaArffFile + String.valueOf(i)
          + ".arff", "class");
      java.util.Random rand = new java.util.Random();
      data.randomize(rand);
      // classifier = new LibLINEAR();
      // ((LibLINEAR) classifier).setSVMType(new SelectedTag(
      // LibLINEAR.SVMTYPE_L2_LR, LibLINEAR.TAGS_SVMTYPE));
      classifier.buildClassifier(data);
      // serialize model
      weka.core.SerializationHelper.write(
          modelFile + String.valueOf(i), classifier);
    } catch (IOException e) {
      // TODO Auto-generated catch block
      e.printStackTrace();
    } catch (Exception e) {
      // TODO Auto-generated catch block
      e.printStackTrace();
    }
  }
}

代码示例来源：origin: Waikato/meka

@Override
public void buildClassifier(Instances train) throws Exception {
   testCapabilities(train);
   
  if (getDebug()) System.out.print("-: Models: ");
  train = new Instances(train);
  m_Classifiers = ProblemTransformationMethod.makeCopies((ProblemTransformationMethod) m_Classifier, m_NumIterations);
  int sub_size = (train.numInstances()*m_BagSizePercent/100);
  for(int i = 0; i < m_NumIterations; i++) {
    if(getDebug()) System.out.print(""+i+" ");
    if (m_Classifiers[i] instanceof Randomizable) ((Randomizable)m_Classifiers[i]).setSeed(i);
    train.randomize(new Random(m_Seed+i));
    Instances sub_train = new Instances(train,0,sub_size);
    m_Classifiers[i].buildClassifier(sub_train);
  }
  if (getDebug()) System.out.println(":-");
}

代码示例来源：origin: nz.ac.waikato.cms.weka/meka

@Override
public void buildClassifier(Instances train) throws Exception {
   testCapabilities(train);
   
  if (getDebug()) System.out.print("-: Models: ");
  train = new Instances(train);
  m_Classifiers = MultilabelClassifier.makeCopies((MultilabelClassifier)m_Classifier, m_NumIterations);
  int sub_size = (train.numInstances()*m_BagSizePercent/100);
  for(int i = 0; i < m_NumIterations; i++) {
    if(getDebug()) System.out.print(""+i+" ");
    if (m_Classifiers[i] instanceof Randomizable) ((Randomizable)m_Classifiers[i]).setSeed(i);
    train.randomize(new Random(m_Seed+i));
    Instances sub_train = new Instances(train,0,sub_size);
    m_Classifiers[i].buildClassifier(sub_train);
  }
  if (getDebug()) System.out.println(":-");
}

代码示例来源：origin: net.sf.meka/meka

@Override
public void buildClassifier(Instances train) throws Exception {
   testCapabilities(train);
   
  if (getDebug()) System.out.print("-: Models: ");
  train = new Instances(train);
  m_Classifiers = ProblemTransformationMethod.makeCopies((ProblemTransformationMethod) m_Classifier, m_NumIterations);
  int sub_size = (train.numInstances()*m_BagSizePercent/100);
  for(int i = 0; i < m_NumIterations; i++) {
    if(getDebug()) System.out.print(""+i+" ");
    if (m_Classifiers[i] instanceof Randomizable) ((Randomizable)m_Classifiers[i]).setSeed(i);
    train.randomize(new Random(m_Seed+i));
    Instances sub_train = new Instances(train,0,sub_size);
    m_Classifiers[i].buildClassifier(sub_train);
  }
  if (getDebug()) System.out.println(":-");
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Generate a bunch of predictions ready for processing, by performing a
 * cross-validation on the supplied dataset.
 * 
 * @param classifier the Classifier to evaluate
 * @param data the dataset
 * @param numFolds the number of folds in the cross-validation.
 * @exception Exception if an error occurs
 */
public ArrayList<Prediction> getCVPredictions(Classifier classifier,
 Instances data, int numFolds) throws Exception {
 ArrayList<Prediction> predictions = new ArrayList<Prediction>();
 Instances runInstances = new Instances(data);
 Random random = new Random(m_Seed);
 runInstances.randomize(random);
 if (runInstances.classAttribute().isNominal() && (numFolds > 1)) {
  runInstances.stratify(numFolds);
 }
 for (int fold = 0; fold < numFolds; fold++) {
  Instances train = runInstances.trainCV(numFolds, fold, random);
  Instances test = runInstances.testCV(numFolds, fold);
  ArrayList<Prediction> foldPred = getTrainTestPredictions(classifier,
   train, test);
  predictions.addAll(foldPred);
 }
 return predictions;
}

代码示例来源：origin: net.sf.meka.thirdparty/mulan

private MultipleEvaluation innerCrossValidate(MultiLabelLearner learner, MultiLabelInstances data, boolean hasMeasures, List<Measure> measures, int someFolds) {
    Evaluation[] evaluation = new Evaluation[someFolds];

    Instances workingSet = new Instances(data.getDataSet());
    workingSet.randomize(new Random(seed));
    for (int i = 0; i < someFolds; i++) {
      System.out.println("Fold " + (i + 1) + "/" + someFolds);
      try {
        Instances train = workingSet.trainCV(someFolds, i);
        Instances test = workingSet.testCV(someFolds, i);
        MultiLabelInstances mlTrain = new MultiLabelInstances(train, data.getLabelsMetaData());
        MultiLabelInstances mlTest = new MultiLabelInstances(test, data.getLabelsMetaData());
        MultiLabelLearner clone = learner.makeCopy();
        clone.build(mlTrain);
        if (hasMeasures)
          evaluation[i] = evaluate(clone, mlTest, measures);
        else
          evaluation[i] = evaluate(clone, mlTest);
      } catch (Exception ex) {
        Logger.getLogger(Evaluator.class.getName()).log(Level.SEVERE, null, ex);
      }
    }
    MultipleEvaluation me = new MultipleEvaluation(evaluation, data);
    me.calculateStatistics();
    return me;
  }
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Perform a cross validation for attribute selection. With subset evaluators
 * the number of times each attribute is selected over the cross validation is
 * reported. For attribute evaluators, the average merit and average ranking +
 * std deviation is reported for each attribute.
 * 
 * @return the results of cross validation as a String
 * @exception Exception if an error occurs during cross validation
 */
public String CrossValidateAttributes() throws Exception {
 Instances cvData = new Instances(m_trainInstances);
 Instances train;
 Random random = new Random(m_seed);
 cvData.randomize(random);
 if (!(m_ASEvaluator instanceof UnsupervisedSubsetEvaluator)
  && !(m_ASEvaluator instanceof UnsupervisedAttributeEvaluator)) {
  if (cvData.classAttribute().isNominal()) {
   cvData.stratify(m_numFolds);
  }
 }
 for (int i = 0; i < m_numFolds; i++) {
  // Perform attribute selection
  train = cvData.trainCV(m_numFolds, i, random);
  selectAttributesCVSplit(train);
 }
 return CVResultsString();
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Builds the clusterer.
 * 
 * @param data the training instances.
 * @throws Exception if something goes wrong.
 */
@Override
public void buildClusterer(Instances data) throws Exception {
 m_numberOfClusters = -1;
 m_cobwebTree = null;
 m_numberSplits = 0;
 m_numberMerges = 0;
 // can clusterer handle the data?
 getCapabilities().testWithFail(data);
 // randomize the instances
 data = new Instances(data);
 if (getSeed() >= 0) {
  data.randomize(new Random(getSeed()));
 }
 for (int i = 0; i < data.numInstances(); i++) {
  updateClusterer(data.instance(i));
 }
 updateFinished();
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Builds the clusterer.
 * 
 * @param data the training instances.
 * @throws Exception if something goes wrong.
 */
@Override
public void buildClusterer(Instances data) throws Exception {
 m_numberOfClusters = -1;
 m_cobwebTree = null;
 m_numberSplits = 0;
 m_numberMerges = 0;
 // can clusterer handle the data?
 getCapabilities().testWithFail(data);
 // randomize the instances
 data = new Instances(data);
 if (getSeed() >= 0) {
  data.randomize(new Random(getSeed()));
 }
 for (int i = 0; i < data.numInstances(); i++) {
  updateClusterer(data.instance(i));
 }
 updateFinished();
}

代码示例来源：origin: nz.ac.waikato.cms.weka/weka-stable

/**
 * Signify that this batch of input to the filter is finished. If the filter
 * requires all instances prior to filtering, output() may now be called to
 * retrieve the filtered instances. Any subsequent instances filtered should
 * be filtered based on setting obtained from the first batch (unless the
 * setInputFormat has been re-assigned or new options have been set). This
 * implementation randomizes all the instances received in the batch.
 * 
 * @return true if there are instances pending output
 * @throws IllegalStateException if no input format has been set.
 */
@Override
public boolean batchFinished() {
 if (getInputFormat() == null) {
  throw new IllegalStateException("No input instance format defined");
 }
 if (!isFirstBatchDone()) {
  getInputFormat().randomize(m_Random);
 }
 for (int i = 0; i < getInputFormat().numInstances(); i++) {
  push(getInputFormat().instance(i), false); // No need to copy because of bufferInput()
 }
 flushInput();
 m_NewBatch = true;
 m_FirstBatchDone = true;
 return (numPendingOutput() != 0);
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Signify that this batch of input to the filter is finished. If the filter
 * requires all instances prior to filtering, output() may now be called to
 * retrieve the filtered instances. Any subsequent instances filtered should
 * be filtered based on setting obtained from the first batch (unless the
 * setInputFormat has been re-assigned or new options have been set). This
 * implementation randomizes all the instances received in the batch.
 * 
 * @return true if there are instances pending output
 * @throws IllegalStateException if no input format has been set.
 */
@Override
public boolean batchFinished() {
 if (getInputFormat() == null) {
  throw new IllegalStateException("No input instance format defined");
 }
 if (!isFirstBatchDone()) {
  getInputFormat().randomize(m_Random);
 }
 for (int i = 0; i < getInputFormat().numInstances(); i++) {
  push(getInputFormat().instance(i), false); // No need to copy because of bufferInput()
 }
 flushInput();
 m_NewBatch = true;
 m_FirstBatchDone = true;
 return (numPendingOutput() != 0);
}

代码示例来源：origin: net.sf.meka/meka

public static double[][] LEAD(Instances D, Classifier h, Random r, String MDType)  throws Exception {
  Instances D_r = new Instances(D);
  D_r.randomize(r);
  Instances D_train = new Instances(D_r,0,D_r.numInstances()*60/100);
  Instances D_test = new Instances(D_r,D_train.numInstances(),D_r.numInstances()-D_train.numInstances());
  BR br = new BR();
  br.setClassifier(h);
  Result result = Evaluation.evaluateModel((MultiLabelClassifier)br,D_train,D_test,"PCut1","1");
  return LEAD(D_test, result, MDType);
}

代码示例来源：origin: Waikato/meka

public static double[][] LEAD(Instances D, Classifier h, Random r, String MDType)  throws Exception {
  Instances D_r = new Instances(D);
  D_r.randomize(r);
  Instances D_train = new Instances(D_r,0,D_r.numInstances()*60/100);
  Instances D_test = new Instances(D_r,D_train.numInstances(),D_r.numInstances()-D_train.numInstances());
  BR br = new BR();
  br.setClassifier(h);
  Result result = Evaluation.evaluateModel((MultiLabelClassifier)br,D_train,D_test,"PCut1","1");
  return LEAD(D_test, result, MDType);
}

代码示例来源：origin: net.sf.meka.thirdparty/mulan

@Override
protected void buildInternal(MultiLabelInstances trainingSet)
    throws Exception {
  Instances dataSet = new Instances(trainingSet.getDataSet());
  for (int i = 0; i < numOfModels; i++) {
    dataSet.randomize(rand);
    RemovePercentage rmvp = new RemovePercentage();
    rmvp.setInputFormat(dataSet);
    rmvp.setPercentage(percentage);
    rmvp.setInvertSelection(true);
    Instances trainDataSet = Filter.useFilter(dataSet, rmvp);
    MultiLabelInstances train = new MultiLabelInstances(trainDataSet, trainingSet.getLabelsMetaData());
    ensemble[i].build(train);
  }
}

代码示例来源：origin: net.sf.meka/meka

/**
 * LEAD - Performs LEAD on dataset 'D', using BR with base classifier 'h', under random seed 'r'.
 * <br>
 * WARNING: changing this method will affect the perfomance of e.g., BCC -- on the other hand the original BCC paper did not use LEAD, so don't worry.
 */
public static double[][] LEAD(Instances D, Classifier h, Random r)  throws Exception {
  Instances D_r = new Instances(D);
  D_r.randomize(r);
  Instances D_train = new Instances(D_r,0,D_r.numInstances()*60/100);
  Instances D_test = new Instances(D_r,D_train.numInstances(),D_r.numInstances()-D_train.numInstances());
  BR br = new BR();
  br.setClassifier(h);
  Result result = Evaluation.evaluateModel((MultiLabelClassifier)br,D_train,D_test,"PCut1","1");
  return LEAD2(D_test,result);
}

代码示例来源：origin: Waikato/meka

/**
 * LEAD - Performs LEAD on dataset 'D', using BR with base classifier 'h', under random seed 'r'.
 * <br>
 * WARNING: changing this method will affect the perfomance of e.g., BCC -- on the other hand the original BCC paper did not use LEAD, so don't worry.
 */
public static double[][] LEAD(Instances D, Classifier h, Random r)  throws Exception {
  Instances D_r = new Instances(D);
  D_r.randomize(r);
  Instances D_train = new Instances(D_r,0,D_r.numInstances()*60/100);
  Instances D_test = new Instances(D_r,D_train.numInstances(),D_r.numInstances()-D_train.numInstances());
  BR br = new BR();
  br.setClassifier(h);
  Result result = Evaluation.evaluateModel((MultiLabelClassifier)br,D_train,D_test,"PCut1","1");
  return LEAD2(D_test,result);
}