本文整理了Java中weka.classifiers.rules.ZeroR.<init>()方法的一些代码示例，展示了ZeroR.<init>()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。ZeroR.<init>()方法的具体详情如下：
包路径：weka.classifiers.rules.ZeroR
类名称：ZeroR
方法名：<init>

ZeroR.<init>介绍

暂无

代码示例

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

/**
 * Default constructor.
 */
public CostSensitiveClassifier() {
 m_Classifier = new weka.classifiers.rules.ZeroR();
}

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

protected void resetOptions() {
 m_trainInstances = null;
 m_Evaluation = null;
 m_BaseClassifier = new ZeroR();
 m_folds = 5;
 m_seed = 1;
 m_threshold = 0.01;
}

代码示例来源：origin: Waikato/weka-trunk

protected void resetOptions() {
 m_trainInstances = null;
 m_Evaluation = null;
 m_BaseClassifier = new ZeroR();
 m_folds = 5;
 m_seed = 1;
 m_threshold = 0.01;
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Default constructor.
 */
public CostSensitiveClassifier() {
 m_Classifier = new weka.classifiers.rules.ZeroR();
}

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

/**
 * reset to defaults
 */
protected void resetOptions() {
 m_trainingInstances = null;
 m_ClassifierTemplate = new ZeroR();
 m_holdOutFile = new File("Click to set hold out or test instances");
 m_holdOutInstances = null;
 m_useTraining = false;
 m_splitPercent = "90";
 m_usePercentageSplit = false;
 m_evaluationMeasure = TAGS_EVALUATION[0];
 m_IRClassVal = -1;
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * reset to defaults
 */
protected void resetOptions() {
 m_trainingInstances = null;
 m_ClassifierTemplate = new ZeroR();
 m_holdOutFile = new File("Click to set hold out or test instances");
 m_holdOutInstances = null;
 m_useTraining = false;
 m_splitPercent = "90";
 m_usePercentageSplit = false;
 m_evaluationMeasure = TAGS_EVALUATION[0];
 m_IRClassVal = -1;
}

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

/**
  * Main method for testing this class.
  * 
  * @param argv the options
  */
 public static void main(String[] argv) {
  runClassifier(new ZeroR(), argv);
 }
}

代码示例来源：origin: Waikato/weka-trunk

/**
  * Main method for testing this class.
  * 
  * @param argv the options
  */
 public static void main(String[] argv) {
  runClassifier(new ZeroR(), argv);
 }
}

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

/** Creates a default ZeroR */
public Classifier getClassifier() {
 return new ZeroR();
}

代码示例来源：origin: Waikato/weka-trunk

/** Creates a default ZeroR */
public Classifier getClassifier() {
 return new ZeroR();
}

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

/**
 * returns the default classifier (fully configured) for the classify panel.
 * 
 * @return the default classifier, ZeroR by default
 */
public static Object getClassifier() {
 Object result;
 result = getObject("Classifier",
  weka.classifiers.rules.ZeroR.class.getName(),
  weka.classifiers.Classifier.class);
 if (result == null) {
  result = new weka.classifiers.rules.ZeroR();
 }
 return result;
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * returns the default classifier (fully configured) for the classify panel.
 * 
 * @return the default classifier, ZeroR by default
 */
public static Object getClassifier() {
 Object result;
 result = getObject("Classifier",
  weka.classifiers.rules.ZeroR.class.getName(),
  weka.classifiers.Classifier.class);
 if (result == null) {
  result = new weka.classifiers.rules.ZeroR();
 }
 return result;
}

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

/** Creates a default TLC */
public Classifier getClassifier() {
 TLC tlc = new TLC();
 // Use ZeroR so that we never perform worse than ZeroR...
 tlc.setClassifier(new weka.classifiers.rules.ZeroR());
 return tlc;
}

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

/**
 * Determine whether the scheme performs worse than ZeroR during testing
 * 
 * @param classifier the pre-trained classifier
 * @param evaluation the classifier evaluation object
 * @param train the training data
 * @param test the test data
 * @return index 0 is true if the scheme performs better than ZeroR
 * @throws Exception if there was a problem during the scheme's testing
 */
protected boolean[] testWRTZeroR(Classifier classifier,
 Evaluation evaluation, Instances train, Instances test) throws Exception {
 boolean[] result = new boolean[2];
 evaluation.evaluateModel(classifier, test);
 try {
  // Tested OK, compare with ZeroR
  Classifier zeroR = new weka.classifiers.rules.ZeroR();
  zeroR.buildClassifier(train);
  Evaluation zeroREval = new Evaluation(train);
  zeroREval.evaluateModel(zeroR, test);
  result[0] = Utils.grOrEq(zeroREval.errorRate(), evaluation.errorRate());
 } catch (Exception ex) {
  throw new Error("Problem determining ZeroR performance: "
   + ex.getMessage());
 }
 return result;
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Determine whether the scheme performs worse than ZeroR during testing
 * 
 * @param classifier the pre-trained classifier
 * @param evaluation the classifier evaluation object
 * @param train the training data
 * @param test the test data
 * @return index 0 is true if the scheme performs better than ZeroR
 * @throws Exception if there was a problem during the scheme's testing
 */
protected boolean[] testWRTZeroR(Classifier classifier,
 Evaluation evaluation, Instances train, Instances test) throws Exception {
 boolean[] result = new boolean[2];
 evaluation.evaluateModel(classifier, test);
 try {
  // Tested OK, compare with ZeroR
  Classifier zeroR = new weka.classifiers.rules.ZeroR();
  zeroR.buildClassifier(train);
  Evaluation zeroREval = new Evaluation(train);
  zeroREval.evaluateModel(zeroR, test);
  result[0] = Utils.grOrEq(zeroREval.errorRate(), evaluation.errorRate());
 } catch (Exception ex) {
  throw new Error("Problem determining ZeroR performance: "
   + ex.getMessage());
 }
 return result;
}

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

/**
 * returns the configured FilteredClassifier. Since the base classifier is
 * determined heuristically, derived tests might need to adjust it.
 * 
 * @return the configured FilteredClassifier
 */
protected FilteredClassifier getFilteredClassifier() {
 FilteredClassifier    result;
 
 result = new FilteredClassifier();
 
 result.setFilter(getFilter());
 result.setClassifier(new weka.classifiers.rules.ZeroR());
 
 return result;
}

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

/**
 * returns the configured FilteredClassifier. Since the base classifier is
 * determined heuristically, derived tests might need to adjust it.
 * 
 * @return the configured FilteredClassifier
 */
protected FilteredClassifier getFilteredClassifier() {
 FilteredClassifier    result;
 
 result = new FilteredClassifier();
 
 result.setFilter(getFilter());
 result.setClassifier(new weka.classifiers.rules.ZeroR());
 
 return result;
}

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

/**
 * returns the configured FilteredClassifier. Since the base classifier is
 * determined heuristically, derived tests might need to adjust it.
 * 
 * @return the configured FilteredClassifier
 */
protected FilteredClassifier getFilteredClassifier() {
 FilteredClassifier     result;
 
 result = super.getFilteredClassifier();
 ((NominalToString) result.getFilter()).setAttributeIndexes("1");
 result.setClassifier(new ZeroR());
 
 return result;
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * returns the configured FilteredClassifier. Since the base classifier is
 * determined heuristically, derived tests might need to adjust it.
 * 
 * @return the configured FilteredClassifier
 */
protected FilteredClassifier getFilteredClassifier() {
 FilteredClassifier     result;
 
 result = super.getFilteredClassifier();
 ((NominalToString) result.getFilter()).setAttributeIndexes("1");
 result.setClassifier(new ZeroR());
 
 return result;
}

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

/**
 * Initialize 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
 */
public void initializeClassifier(Instances data) throws Exception {
 super.buildClassifier(data);
 // can classifier handle the data?
 getCapabilities().testWithFail(data);
 // remove instances with missing class
 data = new Instances(data);
 data.deleteWithMissingClass();
 m_ZeroR = new weka.classifiers.rules.ZeroR();
 m_ZeroR.buildClassifier(data);
 m_NumClasses = data.numClasses();
 m_Betas = new double[m_Classifiers.length];
 m_NumIterationsPerformed = 0;
 m_TrainingData = new Instances(data);
 m_RandomInstance = new Random(m_Seed);
 if ((m_UseResampling)
   || (!(m_Classifier instanceof WeightedInstancesHandler))) {
  // Normalize weights so that they sum to one and can be used as sampling probabilities
  double sumProbs = m_TrainingData.sumOfWeights();
  for (int i = 0; i < m_TrainingData.numInstances(); i++) {
   m_TrainingData.instance(i).setWeight(m_TrainingData.instance(i).weight() / sumProbs);
  }
 }
}