本文整理了Java中weka.core.Instances.stratify()方法的一些代码示例，展示了Instances.stratify()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.stratify()方法的具体详情如下：
包路径：weka.core.Instances
类名称：Instances
方法名：stratify

Instances.stratify介绍

[英]Stratifies a set of instances according to its class values if the class attribute is nominal (so that afterwards a stratified cross-validation can be performed).
[中]如果class属性为nominal，则根据其类值对一组实例进行分层（以便之后可以执行分层交叉验证）。

代码示例

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

@Override
public void stratify(int arg0) {
 super.stratify(arg0);
}

代码示例来源：origin: stackoverflow.com

Instances dataSet = ...;
dataSet.stratify(numOfFolds); // use this
     //before splitting the dataset into train and test set!

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: Waikato/weka-trunk

/**
 * 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: 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: nz.ac.waikato.cms.weka/weka-stable

/**
 * Method for building a pruneable classifier tree.
 *
 * @param data the data to build the tree from 
 * @throws Exception if tree can't be built successfully
 */
public void buildClassifier(Instances data) 
   throws Exception {
 // remove instances with missing class
 data = new Instances(data);
 data.deleteWithMissingClass();
 
 Random random = new Random(m_seed);
 data.stratify(numSets);
 buildTree(data.trainCV(numSets, numSets - 1, random),
    data.testCV(numSets, numSets - 1), !m_cleanup);
 if (pruneTheTree) {
  prune();
 }
 if (m_cleanup) {
  cleanup(new Instances(data, 0));
 }
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * 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

/**
 * Method for building a pruneable classifier tree.
 *
 * @param data the data to build the tree from 
 * @throws Exception if tree can't be built successfully
 */
public void buildClassifier(Instances data) 
   throws Exception {
 // remove instances with missing class
 data = new Instances(data);
 data.deleteWithMissingClass();
 
 Random random = new Random(m_seed);
 data.stratify(numSets);
 buildTree(data.trainCV(numSets, numSets - 1, random),
    data.testCV(numSets, numSets - 1), !m_cleanup);
 if (pruneTheTree) {
  prune();
 }
 if (m_cleanup) {
  cleanup(new Instances(data, 0));
 }
}

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

newData.randomize(random);
if (newData.classAttribute().isNominal()) {
 newData.stratify(m_NumFolds);

代码示例来源：origin: Waikato/weka-trunk

newData.randomize(random);
if (newData.classAttribute().isNominal()) {
 newData.stratify(m_NumFolds);

代码示例来源：origin: Waikato/weka-trunk

trainData.stratify(m_NumFolds);

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

trainData.stratify(m_NumFolds);

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

allData.stratify(m_numFoldsBoosting);

代码示例来源：origin: Waikato/weka-trunk

allData.stratify(m_numFoldsBoosting);

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

data.stratify(m_Folds);

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

getInputFormat().stratify(m_NumFolds);
if (!m_Inverse) {
 instances = getInputFormat().testCV(m_NumFolds, m_Fold - 1);

代码示例来源：origin: Waikato/weka-trunk

getInputFormat().stratify(m_NumFolds);
if (!m_Inverse) {
 instances = getInputFormat().testCV(m_NumFolds, m_Fold - 1);

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

transformed.stratify(folds);

代码示例来源：origin: olehmberg/winter

data.randomize(random);
if (data.classAttribute().isNominal()) {
 data.stratify(numFolds);

代码示例来源：origin: Waikato/weka-trunk

&& !getPreserveOrder()) {
getStepManager().logBasic("Stratifying data");
dataSet.stratify(m_numFolds);