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

Instances.deleteWithMissingClass介绍

[英]Removes all instances with a missing class value from the dataset.
[中]从数据集中删除缺少类值的所有实例。

代码示例

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

@Override
public void deleteWithMissingClass() {
 super.deleteWithMissingClass();
}

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

/**
 * Method for building a classifier tree.
 * 
 * @param data the data to build the tree from
 * @throws Exception if something goes wrong
 */
public void buildClassifier(Instances data) throws Exception {
 // remove instances with missing class
 data = new Instances(data);
 data.deleteWithMissingClass();
 buildTree(data, false);
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Method for building a classifier tree.
 * 
 * @param data the data to build the tree from
 * @throws Exception if something goes wrong
 */
public void buildClassifier(Instances data) throws Exception {
 // remove instances with missing class
 data = new Instances(data);
 data.deleteWithMissingClass();
 buildTree(data, false);
}

代码示例来源：origin: com.github.fracpete/multisearch-weka-package

/**
 * Loads test data, if required.
 *
 * @param data
 *            the current training data
 * @throws Exception
 *             if test sets are not compatible with training data
 */
protected void loadTestData(Instances data) throws Exception {
 String msg;
 m_SearchSpaceTestInst = null;
 if (m_SearchSpaceTestSet.exists()
  && !m_SearchSpaceTestSet.isDirectory()) {
  m_SearchSpaceTestInst = DataSource.read(m_SearchSpaceTestSet
 .getAbsolutePath());
  m_SearchSpaceTestInst.setClassIndex(data.classIndex());
  msg = data.equalHeadersMsg(m_SearchSpaceTestInst);
  if (msg != null) {
 throw new IllegalArgumentException(
  "Test set for search space not compatible with training dta:\n"
   + msg);
  }
  m_SearchSpaceTestInst.deleteWithMissingClass();
  log("Using test set for search space: " + m_SearchSpaceTestSet);
 }
}

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

/**
 * Builds Id3 decision tree classifier.
 *
 * @param data the training data
 * @exception Exception if classifier can't be built successfully
 */
public void buildClassifier(Instances data) throws Exception {
 // can classifier handle the data?
 getCapabilities().testWithFail(data);
 // remove instances with missing class
 data = new Instances(data);
 data.deleteWithMissingClass();
 
 makeTree(data);
}

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

/**
 * Generates the classifier.
 *
 * @param instances set of instances serving as training data 
 * @throws Exception if the classifier has not been generated successfully
 */
public void buildClassifier(Instances instances) throws Exception {
 
 // can classifier handle the data?
 getCapabilities().testWithFail(instances);
 // remove instances with missing class
 instances = new Instances(instances);
 instances.deleteWithMissingClass();
 
 m_Train = new Instances(instances, 0, instances.numInstances());
 // initializes class attributes ** java-speaking! :-) **
 init_m_Attributes();
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Generates the classifier.
 *
 * @param instances set of instances serving as training data 
 * @throws Exception if the classifier has not been generated successfully
 */
public void buildClassifier(Instances instances) throws Exception {
 
 // can classifier handle the data?
 getCapabilities().testWithFail(instances);
 // remove instances with missing class
 instances = new Instances(instances);
 instances.deleteWithMissingClass();
 
 m_Train = new Instances(instances, 0, instances.numInstances());
 // initializes class attributes ** java-speaking! :-) **
 init_m_Attributes();
}

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

/**
 * Generates the classifier.
 *
 * @param instances set of instances serving as training data 
 * @throws Exception if the classifier has not been generated successfully
 */
public void buildClassifier(Instances instances) throws Exception {
 
 // can classifier handle the data?
 getCapabilities().testWithFail(instances);
 // remove instances with missing class
 instances = new Instances(instances);
 instances.deleteWithMissingClass();
 
 m_Train = new Instances(instances, 0, instances.numInstances());
 m_MinArray = new double [m_Train.numAttributes()];
 m_MaxArray = new double [m_Train.numAttributes()];
 for (int i = 0; i < m_Train.numAttributes(); i++) {
  m_MinArray[i] = m_MaxArray[i] = Double.NaN;
 }
 Enumeration enu = m_Train.enumerateInstances();
 while (enu.hasMoreElements()) {
  updateMinMax((Instance) enu.nextElement());
 }
}

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

/**
 * 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

/**
 * Method for building a pruneable classifier tree.
 *
 * @param data the data for building the tree
 * @throws Exception if something goes wrong
 */
public void buildClassifier(Instances data) throws Exception {
 // remove instances with missing class
 data = new Instances(data);
 data.deleteWithMissingClass();
 
 buildTree(data, m_subtreeRaising || !m_cleanup);
 if (m_collapseTheTree) {
  collapse();
 }
 if (m_pruneTheTree) {
  prune();
 }
 if (m_cleanup) {
  cleanup(new Instances(data, 0));
 }
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Method for building a pruneable classifier tree.
 *
 * @param data the data for building the tree
 * @throws Exception if something goes wrong
 */
public void buildClassifier(Instances data) throws Exception {
 // remove instances with missing class
 data = new Instances(data);
 data.deleteWithMissingClass();
 
 buildTree(data, m_subtreeRaising || !m_cleanup);
 if (m_collapseTheTree) {
  collapse();
 }
 if (m_pruneTheTree) {
  prune();
 }
 if (m_cleanup) {
  cleanup(new Instances(data, 0));
 }
}

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

/**
 * Build the clusterer on the filtered data.
 * 
 * @param data the training data
 * @throws Exception if the clusterer could not be built successfully
 */
@Override
public void buildClusterer(Instances data) throws Exception {
 if (m_Clusterer == null) {
  throw new Exception("No base clusterer has been set!");
 }
 // remove instances with missing class
 if (data.classIndex() > -1) {
  data = new Instances(data);
  data.deleteWithMissingClass();
 }
 m_Filter.setInputFormat(data); // filter capabilities are checked here
 data = Filter.useFilter(data, m_Filter);
 // can clusterer handle the data?
 getClusterer().getCapabilities().testWithFail(data);
 m_FilteredInstances = data.stringFreeStructure();
 m_Clusterer.buildClusterer(data);
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Build the clusterer on the filtered data.
 * 
 * @param data the training data
 * @throws Exception if the clusterer could not be built successfully
 */
@Override
public void buildClusterer(Instances data) throws Exception {
 if (m_Clusterer == null) {
  throw new Exception("No base clusterer has been set!");
 }
 // remove instances with missing class
 if (data.classIndex() > -1) {
  data = new Instances(data);
  data.deleteWithMissingClass();
 }
 m_Filter.setInputFormat(data); // filter capabilities are checked here
 data = Filter.useFilter(data, m_Filter);
 // can clusterer handle the data?
 getClusterer().getCapabilities().testWithFail(data);
 m_FilteredInstances = data.stringFreeStructure();
 m_Clusterer.buildClusterer(data);
}

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

/**
 * Builds the classifier
 * 
 * @param data the training data to be used for generating the boosted
 *          classifier.
 * @throws Exception if the classifier could not be built successfully
 */
@Override
public void buildClassifier(Instances data) throws Exception {
 // can classifier handle the data?
 getCapabilities().testWithFail(data);
 // remove instances with missing class
 Instances train = new Instances(data);
 train.deleteWithMissingClass();
 if (m_Classifier == null) {
  throw new Exception("A base classifier has not been specified!");
 }
 if (getDebug()) {
  System.out.println("Start training ...");
 }
 m_NumClasses = train.numClasses();
 // convert the training dataset into single-instance dataset
 m_ConvertToProp.setWeightMethod(getWeightMethod());
 m_ConvertToProp.setInputFormat(train);
 train = Filter.useFilter(train, m_ConvertToProp);
 train.deleteAttributeAt(0); // remove the bag index attribute
 m_Classifier.buildClassifier(train);
}

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

/**
 * Generates a classifier.
 *
 * @param data set of instances serving as training data
 * @throws Exception if the classifier has not been
 * generated successfully
 */
public void buildClassifier(Instances data) throws Exception {
   this.instanceConverter = new WekaToSamoaInstanceConverter();
  getCapabilities().testWithFail(data);
  data = new Instances(data);
  data.deleteWithMissingClass();
  m_ActualClassifier.resetLearning();
  for (int i = 0; i < data.numInstances(); i++)
    updateClassifier(data.instance(i));
   
}

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

/**
 * Builds the classifier.
 * 
 * @param data the data to train with
 * @throws Exception if classifier can't be built successfully
 */
@Override
public void buildClassifier(Instances data) throws Exception {
 reset();
 m_header = new Instances(data, 0);
 if (m_selectedSplitMetric == GINI_SPLIT) {
  m_splitMetric = new GiniSplitMetric();
 } else {
  m_splitMetric = new InfoGainSplitMetric(m_minFracWeightForTwoBranchesGain);
 }
 data = new Instances(data);
 data.deleteWithMissingClass();
 for (int i = 0; i < data.numInstances(); i++) {
  updateClassifier(data.instance(i));
 }
 // can classifier handle the data?
 getCapabilities().testWithFail(data);
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Builds the classifier.
 * 
 * @param data the data to train with
 * @throws Exception if classifier can't be built successfully
 */
@Override
public void buildClassifier(Instances data) throws Exception {
 reset();
 m_header = new Instances(data, 0);
 if (m_selectedSplitMetric == GINI_SPLIT) {
  m_splitMetric = new GiniSplitMetric();
 } else {
  m_splitMetric = new InfoGainSplitMetric(m_minFracWeightForTwoBranchesGain);
 }
 data = new Instances(data);
 data.deleteWithMissingClass();
 for (int i = 0; i < data.numInstances(); i++) {
  updateClassifier(data.instance(i));
 }
 // can classifier handle the data?
 getCapabilities().testWithFail(data);
}

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

/**
 * Builds the classifiers.
 *
 * @param insts the training data.
 * @throws Exception if a classifier can't be built
 */
public void buildClassifier(Instances insts) throws Exception {
 Instances newInsts;
 // can classifier handle the data?
 getCapabilities().testWithFail(insts);
 // remove instances with missing class
 insts = new Instances(insts);
 insts.deleteWithMissingClass();
 
 m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, insts.numClasses());
 m_ClassFilters = new MakeIndicator[insts.numClasses()];
 for (int i = 0; i < insts.numClasses(); i++) {
  m_ClassFilters[i] = new MakeIndicator();
  m_ClassFilters[i].setAttributeIndex("" + (insts.classIndex() + 1));
  m_ClassFilters[i].setValueIndex(i);
  m_ClassFilters[i].setNumeric(true);
  m_ClassFilters[i].setInputFormat(insts);
  newInsts = Filter.useFilter(insts, m_ClassFilters[i]);
  m_Classifiers[i].buildClassifier(newInsts);
 }
}

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

/**
 * Builds the classifier from the given training data.
 */
@Override
public void buildClassifier(Instances data) throws Exception {
 data = new Instances(data);
 data.deleteWithMissingClass();
 getCapabilities().testWithFail(data);
 m_Filter = new MultiFilter();
 Filter[] twoFilters = new Filter[2];
 PartitionMembership pm = new PartitionMembership();
 pm.setPartitionGenerator(getPartitionGenerator());
 MultiInstanceWrapper miw = new MultiInstanceWrapper();
 miw.setFilter(pm);
 twoFilters[0] = miw;
 twoFilters[1] = new Remove();
 ((Remove) twoFilters[1]).setAttributeIndices("1");
 m_Filter.setFilters(twoFilters);
 m_Filter.setInputFormat(data);
 Instances propositionalData = Filter.useFilter(data, m_Filter);
 // can classifier handle the data?
 getClassifier().getCapabilities().testWithFail(propositionalData);
 m_Classifier.buildClassifier(propositionalData);
}