Java 类weka.core.WeightedInstancesHandler 实例源码

项目：repo.kmeanspp.silhouette_score 文件：CheckClusterer.java

/**
 * Checks whether the scheme says it can handle instance weights.
 * 
 * @return true if the clusterer handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances clusterer...");
  if (m_Clusterer instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  } else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：repo.kmeanspp.silhouette_score 文件：CheckAssociator.java

/**
 * Checks whether the scheme says it can handle instance weights.
 * 
 * @return true if the associator handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances associator...");
  if (m_Associator instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  } else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：repo.kmeanspp.silhouette_score 文件：CheckAttributeSelection.java

/**
 * Checks whether the scheme says it can handle instance weights.
 * 
 * @return true if the scheme handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances scheme...");
  if (getTestObject() instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  } else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：repo.kmeanspp.silhouette_score 文件：CheckClassifier.java

/**
 * Checks whether the scheme says it can handle instance weights.
 * 
 * @return true if the classifier handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances classifier...");
  if (m_Classifier instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  } else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：repo.kmeanspp.silhouette_score 文件：CheckKernel.java

/**
 * Checks whether the scheme says it can handle instance weights.
 * 
 * @return true if the kernel handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances kernel...");
  if (m_Kernel instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  } else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：repo.kmeanspp.silhouette_score 文件：CheckEstimator.java

/**
 * Checks whether the scheme says it can handle instance weights.
 * 
 * @return true if the estimator handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances estimator...");
  if (m_Estimator instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  } else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：collective-classification-weka-package 文件：Weighting.java

/**
 * the standard collective classifier accepts only nominal, binary classes
 * otherwise an exception is thrown. Additionally, all classifiers must be
 * able to handle weighted instances.
 * @throws Exception if the data doesn't have a nominal, binary class
 */
@Override
protected void checkRestrictions() throws Exception {
  int         i;
  String      nonWeighted;

  super.checkRestrictions();

  // do all implement the WeightedInstancesHandler?
  nonWeighted = "";
  for (i = 0; i < getClassifiers().length; i++) {
    if (!(getClassifiers()[i] instanceof WeightedInstancesHandler)) {
      if (nonWeighted.length() > 0)
        nonWeighted += ", ";
      nonWeighted += getClassifiers()[i].getClass().getName();
    }
  }
  if (nonWeighted.length() > 0)
    throw new Exception(
        "The following classifier(s) cannot handle weighted instances:\n" 
        + nonWeighted);
}

项目：autoweka 文件：CheckClusterer.java

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the clusterer handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances clusterer...");
  if (m_Clusterer instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：autoweka 文件：CheckAssociator.java

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the associator handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances associator...");
  if (m_Associator instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：autoweka 文件：CheckAttributeSelection.java

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the scheme handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances scheme...");
  if (getTestObject() instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：autoweka 文件：CheckClassifier.java

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the classifier handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances classifier...");
  if (m_Classifier instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：autoweka 文件：CheckKernel.java

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the kernel handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances kernel...");
  if (m_Kernel instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：autoweka 文件：CheckEstimator.java

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the estimator handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances estimator...");
  if (m_Estimator instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：umple 文件：CheckClusterer.java

/**
 * Checks whether the scheme says it can handle instance weights.
 * 
 * @return true if the clusterer handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances clusterer...");
  if (m_Clusterer instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  } else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：umple 文件：CheckAssociator.java

/**
 * Checks whether the scheme says it can handle instance weights.
 * 
 * @return true if the associator handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances associator...");
  if (m_Associator instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  } else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：umple 文件：CheckAttributeSelection.java

/**
 * Checks whether the scheme says it can handle instance weights.
 * 
 * @return true if the scheme handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances scheme...");
  if (getTestObject() instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  } else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：umple 文件：CheckClassifier.java

/**
 * Checks whether the scheme says it can handle instance weights.
 * 
 * @return true if the classifier handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances classifier...");
  if (m_Classifier instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  } else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：umple 文件：CheckKernel.java

/**
 * Checks whether the scheme says it can handle instance weights.
 * 
 * @return true if the kernel handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances kernel...");
  if (m_Kernel instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  } else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：umple 文件：CheckEstimator.java

/**
 * Checks whether the scheme says it can handle instance weights.
 * 
 * @return true if the estimator handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances estimator...");
  if (m_Estimator instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  } else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：jbossBA 文件：CheckClusterer.java

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the clusterer handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances clusterer...");
  if (m_Clusterer instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：jbossBA 文件：CheckAssociator.java

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the associator handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances associator...");
  if (m_Associator instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：jbossBA 文件：CheckAttributeSelection.java

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the scheme handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances scheme...");
  if (getTestObject() instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：jbossBA 文件：CheckClassifier.java

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the classifier handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances classifier...");
  if (m_Classifier instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：jbossBA 文件：CheckKernel.java

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the kernel handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances kernel...");
  if (m_Kernel instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：jbossBA 文件：CheckEstimator.java

/**
 * Checks whether the scheme says it can handle instance weights.
 *
 * @return true if the estimator handles instance weights
 */
protected boolean[] weightedInstancesHandler() {

  boolean[] result = new boolean[2];

  print("weighted instances estimator...");
  if (m_Estimator instanceof WeightedInstancesHandler) {
    println("yes");
    result[0] = true;
  }
  else {
    println("no");
    result[0] = false;
  }

  return result;
}

项目：reactive-data 文件：AdaBoostM1WithBuiltClassifiers.java

@Override
public void buildClassifier(Instances data) throws Exception 
{
  /** Changed here: Using the provided classifiers */
  /** End */

  // can classifier handle the data?
  getCapabilities().testWithFail(data);

  // remove instances with missing class
  data = new Instances(data);
  data.deleteWithMissingClass();

  // only class? -> build ZeroR model
  if (data.numAttributes() == 1) {
    System.err.println(
  "Cannot build model (only class attribute present in data!), "
  + "using ZeroR model instead!");
    m_ZeroR = new weka.classifiers.rules.ZeroR();
    m_ZeroR.buildClassifier(data);
    return;
  }
  else {
    m_ZeroR = null;
  }

  m_NumClasses = data.numClasses();
  if ((!m_UseResampling) && 
(m_Classifier instanceof WeightedInstancesHandler)) {
    buildClassifierWithWeights(data);
  } else {
    buildClassifierUsingResampling(data);
  }
}

项目：repo.kmeanspp.silhouette_score 文件：RandomCommittee.java

/**
   * Builds the committee of randomizable classifiers.
   *
   * @param data the training data to be used for generating the
   * bagged classifier.
   * @exception Exception if the classifier could not be built successfully
   */
  public void buildClassifier(Instances data) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(data);

    // get fresh instances
    m_data = new Instances(data);
    super.buildClassifier(m_data);

    if (!(m_Classifier instanceof Randomizable)) {
      throw new IllegalArgumentException("Base learner must implement Randomizable!");
    }

    m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, m_NumIterations);

    Random random = m_data.getRandomNumberGenerator(m_Seed);

    // Resample data based on weights if base learner can't handle weights
    if (!(m_Classifier instanceof WeightedInstancesHandler)) {
      m_data = m_data.resampleWithWeights(random);
    }

    for (int j = 0; j < m_Classifiers.length; j++) {

      // Set the random number seed for the current classifier.
      ((Randomizable) m_Classifiers[j]).setSeed(random.nextInt());

      // Build the classifier.
//      m_Classifiers[j].buildClassifier(m_data);
    }

    buildClassifiers();

    // save memory
    m_data = null;
  }

项目：repo.kmeanspp.silhouette_score 文件：AdaBoostM1.java

/**
 * 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);
    }
  }
}

项目：repo.kmeanspp.silhouette_score 文件：LWL.java

/**
 * 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 {

  if (!(m_Classifier instanceof WeightedInstancesHandler)) {
    throw new IllegalArgumentException("Classifier must be a "
             + "WeightedInstancesHandler!");
  }

  // can classifier handle the data?
  getCapabilities().testWithFail(instances);

  // remove instances with missing class
  instances = new Instances(instances);
  instances.deleteWithMissingClass();

  // only class? -> build ZeroR model
  if (instances.numAttributes() == 1) {
    System.err.println(
 "Cannot build model (only class attribute present in data!), "
 + "using ZeroR model instead!");
    m_ZeroR = new weka.classifiers.rules.ZeroR();
    m_ZeroR.buildClassifier(instances);
    return;
  }
  else {
    m_ZeroR = null;
  }

  m_Train = new Instances(instances, 0, instances.numInstances());

  m_NNSearch.setInstances(m_Train);
}

项目：autoweka 文件：AdaBoostM1.java

/**
  * Boosting method.
  *
  * @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 buildClassifier(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();

   // only class? -> build ZeroR model
   if (data.numAttributes() == 1) {
     System.err.println(
  "Cannot build model (only class attribute present in data!), "
  + "using ZeroR model instead!");
     m_ZeroR = new weka.classifiers.rules.ZeroR();
     m_ZeroR.buildClassifier(data);
     return;
   }
   else {
     m_ZeroR = null;
   }

   m_NumClasses = data.numClasses();
   if ((!m_UseResampling) && 
(m_Classifier instanceof WeightedInstancesHandler)) {
     buildClassifierWithWeights(data);
   } else {
     buildClassifierUsingResampling(data);
   }
 }

项目：autoweka 文件：LWL.java

/**
 * 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 {

  if (!(m_Classifier instanceof WeightedInstancesHandler)) {
    throw new IllegalArgumentException("Classifier must be a "
             + "WeightedInstancesHandler!");
  }

  // can classifier handle the data?
  getCapabilities().testWithFail(instances);

  // remove instances with missing class
  instances = new Instances(instances);
  instances.deleteWithMissingClass();

  // only class? -> build ZeroR model
  if (instances.numAttributes() == 1) {
    System.err.println(
 "Cannot build model (only class attribute present in data!), "
 + "using ZeroR model instead!");
    m_ZeroR = new weka.classifiers.rules.ZeroR();
    m_ZeroR.buildClassifier(instances);
    return;
  }
  else {
    m_ZeroR = null;
  }

  m_Train = new Instances(instances, 0, instances.numInstances());

  m_NNSearch.setInstances(m_Train);
}

项目：umple 文件：RandomCommittee.java

/**
   * Builds the committee of randomizable classifiers.
   *
   * @param data the training data to be used for generating the
   * bagged classifier.
   * @exception Exception if the classifier could not be built successfully
   */
  public void buildClassifier(Instances data) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(data);

    // remove instances with missing class
    m_data = new Instances(data);
    m_data.deleteWithMissingClass();
    super.buildClassifier(m_data);

    if (!(m_Classifier instanceof Randomizable)) {
      throw new IllegalArgumentException("Base learner must implement Randomizable!");
    }

    m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, m_NumIterations);

    Random random = m_data.getRandomNumberGenerator(m_Seed);

    // Resample data based on weights if base learner can't handle weights
    if (!(m_Classifier instanceof WeightedInstancesHandler)) {
      m_data = m_data.resampleWithWeights(random);
    }

    for (int j = 0; j < m_Classifiers.length; j++) {

      // Set the random number seed for the current classifier.
      ((Randomizable) m_Classifiers[j]).setSeed(random.nextInt());

      // Build the classifier.
//      m_Classifiers[j].buildClassifier(m_data);
    }

    buildClassifiers();

    // save memory
    m_data = null;
  }

项目：umple 文件：AdaBoostM1.java

/**
 * 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);
    }
  }
}

项目：umple 文件：LWL.java

/**
 * 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 {

  if (!(m_Classifier instanceof WeightedInstancesHandler)) {
    throw new IllegalArgumentException("Classifier must be a "
             + "WeightedInstancesHandler!");
  }

  // can classifier handle the data?
  getCapabilities().testWithFail(instances);

  // remove instances with missing class
  instances = new Instances(instances);
  instances.deleteWithMissingClass();

  // only class? -> build ZeroR model
  if (instances.numAttributes() == 1) {
    System.err.println(
 "Cannot build model (only class attribute present in data!), "
 + "using ZeroR model instead!");
    m_ZeroR = new weka.classifiers.rules.ZeroR();
    m_ZeroR.buildClassifier(instances);
    return;
  }
  else {
    m_ZeroR = null;
  }

  m_Train = new Instances(instances, 0, instances.numInstances());

  m_NNSearch.setInstances(m_Train);
}

项目：jbossBA 文件：AdaBoostM1.java

/**
  * Boosting method.
  *
  * @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 buildClassifier(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();

   // only class? -> build ZeroR model
   if (data.numAttributes() == 1) {
     System.err.println(
  "Cannot build model (only class attribute present in data!), "
  + "using ZeroR model instead!");
     m_ZeroR = new weka.classifiers.rules.ZeroR();
     m_ZeroR.buildClassifier(data);
     return;
   }
   else {
     m_ZeroR = null;
   }

   m_NumClasses = data.numClasses();
   if ((!m_UseResampling) && 
(m_Classifier instanceof WeightedInstancesHandler)) {
     buildClassifierWithWeights(data);
   } else {
     buildClassifierUsingResampling(data);
   }
 }

项目：jbossBA 文件：LWL.java

/**
 * 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 {

  if (!(m_Classifier instanceof WeightedInstancesHandler)) {
    throw new IllegalArgumentException("Classifier must be a "
             + "WeightedInstancesHandler!");
  }

  // can classifier handle the data?
  getCapabilities().testWithFail(instances);

  // remove instances with missing class
  instances = new Instances(instances);
  instances.deleteWithMissingClass();

  // only class? -> build ZeroR model
  if (instances.numAttributes() == 1) {
    System.err.println(
 "Cannot build model (only class attribute present in data!), "
 + "using ZeroR model instead!");
    m_ZeroR = new weka.classifiers.rules.ZeroR();
    m_ZeroR.buildClassifier(instances);
    return;
  }
  else {
    m_ZeroR = null;
  }

  m_Train = new Instances(instances, 0, instances.numInstances());

  m_NNSearch.setInstances(m_Train);
}

项目：repo.kmeanspp.silhouette_score 文件：LogitBoost.java

/**
 * Builds the boosted classifier
 * 
 * @param data the data to train the classifier with
 * @throws Exception if building fails, e.g., can't handle data
 */
public void initializeClassifier(Instances data) throws Exception {

  m_RandomInstance = new Random(m_Seed);
  int classIndex = data.classIndex();

  if (m_Classifier == null) {
    throw new Exception("A base classifier has not been specified!");
  }

  if (!(m_Classifier instanceof WeightedInstancesHandler) && !m_UseResampling) {
    m_UseResampling = true;
  }

  // can classifier handle the data?
  getCapabilities().testWithFail(data);

  if (m_Debug) {
    System.err.println("Creating copy of the training data");
  }

  // remove instances with missing class
  m_data = new Instances(data);
  m_data.deleteWithMissingClass();

  // only class? -> build ZeroR model
  if (m_data.numAttributes() == 1) {
    System.err
      .println("Cannot build model (only class attribute present in data!), "
        + "using ZeroR model instead!");
    m_ZeroR = new weka.classifiers.rules.ZeroR();
    m_ZeroR.buildClassifier(m_data);
    return;
  } else {
    m_ZeroR = null;
  }

  m_NumClasses = m_data.numClasses();
  m_ClassAttribute = m_data.classAttribute();

  // Create the base classifiers
  if (m_Debug) {
    System.err.println("Creating base classifiers");
  }
  m_Classifiers = new ArrayList<Classifier[]>();

  // Build classifier on all the data
  int numInstances = m_data.numInstances();
  m_trainFs = new double[numInstances][m_NumClasses];
  m_trainYs = new double[numInstances][m_NumClasses];
  for (int j = 0; j < m_NumClasses; j++) {
    for (int i = 0, k = 0; i < numInstances; i++, k++) {
      m_trainYs[i][j] =
        (m_data.instance(k).classValue() == j) ? 1.0 - m_Offset
          : 0.0 + (m_Offset / (double) m_NumClasses);
    }
  }

  // Make class numeric
  m_data.setClassIndex(-1);
  m_data.deleteAttributeAt(classIndex);
  m_data.insertAttributeAt(new Attribute("'pseudo class'"), classIndex);
  m_data.setClassIndex(classIndex);
  m_NumericClassData = new Instances(m_data, 0);

  // Perform iterations
  m_probs = initialProbs(numInstances);
  m_logLikelihood = logLikelihood(m_trainYs, m_probs);
  m_NumGenerated = 0;
  if (m_Debug) {
    System.err.println("Avg. log-likelihood: " + m_logLikelihood);
  }
  m_sumOfWeights = m_data.sumOfWeights();
}

项目：umple 文件：LogitBoost.java

/**
  * Builds the boosted classifier
  * 
  * @param data the data to train the classifier with
  * @throws Exception if building fails, e.g., can't handle data
  */
 public void initializeClassifier(Instances data) throws Exception {

   m_RandomInstance = new Random(m_Seed);
   int classIndex = data.classIndex();

   if (m_Classifier == null) {
     throw new Exception("A base classifier has not been specified!");
   }

   if (!(m_Classifier instanceof WeightedInstancesHandler) &&
!m_UseResampling) {
     m_UseResampling = true;
   }

   // can classifier handle the data?
   getCapabilities().testWithFail(data);

   if (m_Debug) {
     System.err.println("Creating copy of the training data");
   }

   // remove instances with missing class
   m_data = new Instances(data);
   m_data.deleteWithMissingClass();

   // only class? -> build ZeroR model
   if (m_data.numAttributes() == 1) {
     System.err.println(
  "Cannot build model (only class attribute present in data!), "
  + "using ZeroR model instead!");
     m_ZeroR = new weka.classifiers.rules.ZeroR();
     m_ZeroR.buildClassifier(m_data);
     return;
   }
   else {
     m_ZeroR = null;
   }

   m_NumClasses = m_data.numClasses();
   m_ClassAttribute = m_data.classAttribute();

   // Create the base classifiers
   if (m_Debug) {
     System.err.println("Creating base classifiers");
   }
   m_Classifiers = new ArrayList<Classifier[]>();

   // Build classifier on all the data
   int numInstances = m_data.numInstances();
   m_trainFs = new double [numInstances][m_NumClasses];
   m_trainYs = new double [numInstances][m_NumClasses];
   for (int j = 0; j < m_NumClasses; j++) {
     for (int i = 0, k = 0; i < numInstances; i++, k++) {
m_trainYs[i][j] = (m_data.instance(k).classValue() == j) ? 
  1.0 - m_Offset: 0.0 + (m_Offset / (double)m_NumClasses);
     }
   }

   // Make class numeric
   m_data.setClassIndex(-1);
   m_data.deleteAttributeAt(classIndex);
   m_data.insertAttributeAt(new Attribute("'pseudo class'"), classIndex);
   m_data.setClassIndex(classIndex);
   m_NumericClassData = new Instances(m_data, 0);

   // Perform iterations
   m_probs = initialProbs(numInstances);
   m_logLikelihood = logLikelihood(m_trainYs, m_probs);
   m_NumGenerated = 0;
   if (m_Debug) {
     System.err.println("Avg. log-likelihood: " + m_logLikelihood);
   }
   m_sumOfWeights = m_data.sumOfWeights();
 }

项目：WekaJSATBridge 文件：WekaClassifier.java

@Override
public boolean supportsWeightedData()
{
    return wekaClassifier instanceof WeightedInstancesHandler;
}

项目：WekaJSATBridge 文件：WekaRegressor.java

@Override
public boolean supportsWeightedData()
{
    return wekaClassifier instanceof WeightedInstancesHandler;
}