weka.core.Instances.enumerateAttributes()方法的使用及代码示例

x33g5p2x  于2022-01-21 转载在 其他  
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本文整理了Java中weka.core.Instances.enumerateAttributes()方法的一些代码示例，展示了Instances.enumerateAttributes()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.enumerateAttributes()方法的具体详情如下：
包路径：weka.core.Instances
类名称：Instances
方法名：enumerateAttributes

Instances.enumerateAttributes介绍

[英]Returns an enumeration of all the attributes. The class attribute (if set) is skipped by this enumeration.
[中]返回所有属性的枚举。此枚举将跳过类属性（如果已设置）。

代码示例

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

@SuppressWarnings("unchecked")
@Override
public Enumeration enumerateAttributes() {
 return super.enumerateAttributes();
}

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

@Override
public Enumeration enumerateAttributes() {
 return dataset().enumerateAttributes();
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Returns an enumeration of all the attributes.
 * 
 * @return enumeration of all the attributes
 * @throws UnassignedDatasetException if the instance doesn't have access to a
 *           dataset
 */
// @ requires m_Dataset != null;
@Override
public/* @pure@ */Enumeration<Attribute> enumerateAttributes() {
 if (m_Dataset == null) {
  throw new UnassignedDatasetException(
   "Instance doesn't have access to a dataset!");
 }
 return m_Dataset.enumerateAttributes();
}

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

/**
 * Returns an enumeration of all the attributes.
 * 
 * @return enumeration of all the attributes
 * @throws UnassignedDatasetException if the instance doesn't have access to a
 *           dataset
 */
// @ requires m_Dataset != null;
@Override
public/* @pure@ */Enumeration<Attribute> enumerateAttributes() {
 if (m_Dataset == null) {
  throw new UnassignedDatasetException(
   "Instance doesn't have access to a dataset!");
 }
 return m_Dataset.enumerateAttributes();
}

代码示例来源：origin: de.tudarmstadt.ukp.dkpro.tc/de.tudarmstadt.ukp.dkpro.tc.weka-gpl

/**
 * @return The offset of the instanceId attribute within the weka instance
 */
@SuppressWarnings("unchecked")
public static int getInstanceIdAttributeOffset(Instances data)
{
  int attOffset = 1;
  Enumeration<Attribute> enumeration = data.enumerateAttributes();
  while (enumeration.hasMoreElements()) {
    Attribute att = enumeration.nextElement();
    // System.out.println(att.name());
    if (att.name().equals(AddIdFeatureExtractor.ID_FEATURE_NAME)) {
      return attOffset;
    }
    attOffset++;
  }
  return -1;
}

代码示例来源：origin: sc.fiji/Trainable_Segmentation

/**
 * Check if a set of Weka instances has 3D image attributes
 * (given by the presence of the attribute names from FeatureStack3D
 * which are not present in FeatureStack).
 * @param data set of Weka instances
 * @return true if the set contains exclusive 3D attributes, false otherwise
 */
public boolean containsExclusive3DFeatures( Instances data )
{
  // make list of exclusive 3D feature names
  final ArrayList<String> names3D = new ArrayList<String>();
  for( int i=0; i<FeatureStack3D.availableFeatures.length; i++ )
  names3D.add( FeatureStack3D.availableFeatures[ i ] );
  for( int i=0; i<FeatureStack.availableFeatures.length; i++ )
  {
  if( names3D.contains( FeatureStack.availableFeatures[ i ] ) )
    names3D.remove( FeatureStack.availableFeatures[ i ] );
  }
  Enumeration<Attribute> attributes = data.enumerateAttributes();
  while(attributes.hasMoreElements())
  {
  final Attribute a = attributes.nextElement();
  for( String s : names3D )
    if( a.name().startsWith( s ) )
    return true;
  }
  return false;
}
/**

代码示例来源：origin: fiji/Trainable_Segmentation

/**
 * Check if a set of Weka instances has 2D image attributes
 * (given by the presence of the attribute names from FeatureStack
 * which are not present in FeatureStack3D).
 * @param data set of Weka instances
 * @return true if the set contains exclusive 2D attributes, false otherwise
 */
public boolean containsExclusive2DFeatures( Instances data )
{
  // make list of exclusive 2D feature names
  final ArrayList<String> names2D = new ArrayList<String>();
  for( int i=0; i<FeatureStack.availableFeatures.length; i++ )
  names2D.add( FeatureStack.availableFeatures[ i ] );
  for( int i=0; i<FeatureStack3D.availableFeatures.length; i++ )
  {
  if( names2D.contains( FeatureStack3D.availableFeatures[ i ] ) )
    names2D.remove( FeatureStack3D.availableFeatures[ i ] );
  }
  Enumeration<Attribute> attributes = data.enumerateAttributes();
  while(attributes.hasMoreElements())
  {
  final Attribute a = attributes.nextElement();
  for( String s : names2D )
    if( a.name().startsWith( s ) )
    return true;
  }
  return false;
}
/**

代码示例来源：origin: sc.fiji/Trainable_Segmentation

/**
 * Check if a set of Weka instances has 2D image attributes
 * (given by the presence of the attribute names from FeatureStack
 * which are not present in FeatureStack3D).
 * @param data set of Weka instances
 * @return true if the set contains exclusive 2D attributes, false otherwise
 */
public boolean containsExclusive2DFeatures( Instances data )
{
  // make list of exclusive 2D feature names
  final ArrayList<String> names2D = new ArrayList<String>();
  for( int i=0; i<FeatureStack.availableFeatures.length; i++ )
  names2D.add( FeatureStack.availableFeatures[ i ] );
  for( int i=0; i<FeatureStack3D.availableFeatures.length; i++ )
  {
  if( names2D.contains( FeatureStack3D.availableFeatures[ i ] ) )
    names2D.remove( FeatureStack3D.availableFeatures[ i ] );
  }
  Enumeration<Attribute> attributes = data.enumerateAttributes();
  while(attributes.hasMoreElements())
  {
  final Attribute a = attributes.nextElement();
  for( String s : names2D )
    if( a.name().startsWith( s ) )
    return true;
  }
  return false;
}
/**

代码示例来源：origin: fiji/Trainable_Segmentation

/**
 * Check if a set of Weka instances has 3D image attributes
 * (given by the presence of the attribute names from FeatureStack3D
 * which are not present in FeatureStack).
 * @param data set of Weka instances
 * @return true if the set contains exclusive 3D attributes, false otherwise
 */
public boolean containsExclusive3DFeatures( Instances data )
{
  // make list of exclusive 3D feature names
  final ArrayList<String> names3D = new ArrayList<String>();
  for( int i=0; i<FeatureStack3D.availableFeatures.length; i++ )
  names3D.add( FeatureStack3D.availableFeatures[ i ] );
  for( int i=0; i<FeatureStack.availableFeatures.length; i++ )
  {
  if( names3D.contains( FeatureStack.availableFeatures[ i ] ) )
    names3D.remove( FeatureStack.availableFeatures[ i ] );
  }
  Enumeration<Attribute> attributes = data.enumerateAttributes();
  while(attributes.hasMoreElements())
  {
  final Attribute a = attributes.nextElement();
  for( String s : names3D )
    if( a.name().startsWith( s ) )
    return true;
  }
  return false;
}
/**

代码示例来源：origin: amidst/toolbox

/**
 * Creates a set of {@link Attributes} from a given {@link weka.core.Instances} object.
 * @param modelContext a {@link weka.core.Instances} object.
 * @return a set of {@link Attributes}.
 */
public static Attributes convertAttributes(Instances modelContext){
  Enumeration attributesWeka = modelContext.enumerateAttributes();
  return convertAttributes(attributesWeka, modelContext.classAttribute());
}

代码示例来源：origin: sc.fiji/Trainable_Segmentation

/**
 * Check if a set of Weka instances has color attributes
 * (given by the presence of the attribute names "Hue",
 * "Saturation" or "Brightness").
 * @param data set of Weka instances
 * @return true if the set contains color attributes, false otherwise
 */
public boolean containsColorFeatures( Instances data )
{
  Enumeration<Attribute> attributes = data.enumerateAttributes();
  while(attributes.hasMoreElements())
  {
    final Attribute a = attributes.nextElement();
    if( a.name().equals( "Hue" ) ||
      a.name().equals( "Saturation" ) ||
      a.name().equals( "Brightness" ) )
        return true;
  }
  return false;
}
/**

代码示例来源：origin: fiji/Trainable_Segmentation

/**
 * Check if a set of Weka instances has color attributes
 * (given by the presence of the attribute names "Hue",
 * "Saturation" or "Brightness").
 * @param data set of Weka instances
 * @return true if the set contains color attributes, false otherwise
 */
public boolean containsColorFeatures( Instances data )
{
  Enumeration<Attribute> attributes = data.enumerateAttributes();
  while(attributes.hasMoreElements())
  {
    final Attribute a = attributes.nextElement();
    if( a.name().equals( "Hue" ) ||
      a.name().equals( "Saturation" ) ||
      a.name().equals( "Brightness" ) )
        return true;
  }
  return false;
}
/**

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

/**
 * Constructor.
 * @param inst a Weka Instances instance
 */
@SuppressWarnings("unchecked")
public WekaObviousTable(Instances inst) {
 this.instances = inst;
 this.schema = new SchemaImpl();
 Enumeration attributes = instances.enumerateAttributes();
 while (attributes.hasMoreElements()) {
  Attribute att = (Attribute) attributes.nextElement();
  Class<?> c = checkClass(att);
  schema.addColumn(att.name(), c, null);
 }
}

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

/**
 * Compute the number of all possible conditions that could appear in a rule
 * of a given data. For nominal attributes, it's the number of values that
 * could appear; for numeric attributes, it's the number of values * 2, i.e.
 * <= and >= are counted as different possible conditions.
 * 
 * @param data the given data
 * @return number of all conditions of the data
 */
public static double numAllConditions(Instances data) {
 double total = 0;
 Enumeration<Attribute> attEnum = data.enumerateAttributes();
 while (attEnum.hasMoreElements()) {
  Attribute att = attEnum.nextElement();
  if (att.isNominal()) {
   total += att.numValues();
  } else {
   total += 2.0 * data.numDistinctValues(att);
  }
 }
 return total;
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Compute the number of all possible conditions that could appear in a rule
 * of a given data. For nominal attributes, it's the number of values that
 * could appear; for numeric attributes, it's the number of values * 2, i.e.
 * <= and >= are counted as different possible conditions.
 * 
 * @param data the given data
 * @return number of all conditions of the data
 */
public static double numAllConditions(Instances data) {
 double total = 0;
 Enumeration<Attribute> attEnum = data.enumerateAttributes();
 while (attEnum.hasMoreElements()) {
  Attribute att = attEnum.nextElement();
  if (att.isNominal()) {
   total += att.numValues();
  } else {
   total += 2.0 * data.numDistinctValues(att);
  }
 }
 return total;
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Updates the classifier with the given instance.
 * 
 * @param instance the new training instance to include in the model
 * @exception Exception if the instance could not be incorporated in the
 *              model.
 */
public void updateClassifier(Instance instance) throws Exception {
 if (!instance.classIsMissing()) {
  Enumeration<Attribute> enumAtts = m_Instances.enumerateAttributes();
  int attIndex = 0;
  while (enumAtts.hasMoreElements()) {
   Attribute attribute = enumAtts.nextElement();
   if (!instance.isMissing(attribute)) {
    m_Distributions[attIndex][(int) instance.classValue()].addValue(
     instance.value(attribute), instance.weight());
   }
   attIndex++;
  }
  m_ClassDistribution.addValue(instance.classValue(), instance.weight());
 }
}

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

/**
 * Updates the classifier with the given instance.
 * 
 * @param instance the new training instance to include in the model
 * @exception Exception if the instance could not be incorporated in the
 *              model.
 */
public void updateClassifier(Instance instance) throws Exception {
 if (!instance.classIsMissing()) {
  Enumeration<Attribute> enumAtts = m_Instances.enumerateAttributes();
  int attIndex = 0;
  while (enumAtts.hasMoreElements()) {
   Attribute attribute = enumAtts.nextElement();
   if (!instance.isMissing(attribute)) {
    m_Distributions[attIndex][(int) instance.classValue()].addValue(
     instance.value(attribute), instance.weight());
   }
   attIndex++;
  }
  m_ClassDistribution.addValue(instance.classValue(), instance.weight());
 }
}

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

/**
 * ensure that all variables are nominal and that there are no missing values
 * 
 * @param instances data set to check and quantize and/or fill in missing
 *          values
 * @return filtered instances
 * @throws Exception if a filter (Discretize, ReplaceMissingValues) fails
 */
protected Instances normalizeDataSet(Instances instances) throws Exception {
 m_nNonDiscreteAttribute = -1;
 Enumeration<Attribute> enu = instances.enumerateAttributes();
 while (enu.hasMoreElements()) {
  Attribute attribute = enu.nextElement();
  if (attribute.type() != Attribute.NOMINAL) {
   m_nNonDiscreteAttribute = attribute.index();
  }
 }
 if ((m_nNonDiscreteAttribute > -1)
  && (instances.attribute(m_nNonDiscreteAttribute).type() != Attribute.NOMINAL)) {
  m_DiscretizeFilter = new Discretize();
  m_DiscretizeFilter.setInputFormat(instances);
  instances = Filter.useFilter(instances, m_DiscretizeFilter);
 }
 m_MissingValuesFilter = new ReplaceMissingValues();
 m_MissingValuesFilter.setInputFormat(instances);
 instances = Filter.useFilter(instances, m_MissingValuesFilter);
 return instances;
} // normalizeDataSet

代码示例来源：origin: Waikato/weka-trunk

/**
 * ensure that all variables are nominal and that there are no missing values
 * 
 * @param instances data set to check and quantize and/or fill in missing
 *          values
 * @return filtered instances
 * @throws Exception if a filter (Discretize, ReplaceMissingValues) fails
 */
protected Instances normalizeDataSet(Instances instances) throws Exception {
 m_nNonDiscreteAttribute = -1;
 Enumeration<Attribute> enu = instances.enumerateAttributes();
 while (enu.hasMoreElements()) {
  Attribute attribute = enu.nextElement();
  if (attribute.type() != Attribute.NOMINAL) {
   m_nNonDiscreteAttribute = attribute.index();
  }
 }
 if ((m_nNonDiscreteAttribute > -1)
  && (instances.attribute(m_nNonDiscreteAttribute).type() != Attribute.NOMINAL)) {
  m_DiscretizeFilter = new Discretize();
  m_DiscretizeFilter.setInputFormat(instances);
  instances = Filter.useFilter(instances, m_DiscretizeFilter);
 }
 m_MissingValuesFilter = new ReplaceMissingValues();
 m_MissingValuesFilter.setInputFormat(instances);
 instances = Filter.useFilter(instances, m_MissingValuesFilter);
 return instances;
} // normalizeDataSet

代码示例来源：origin: amidst/toolbox

@Override
public void buildClusterer(Instances data) throws Exception {
  attributes_ = Converter.convertAttributes(data.enumerateAttributes());
  Variables modelHeader = new Variables(attributes_);
  clusterVar_ = modelHeader.newMultinomialVariable("clusterVar", this.numberOfClusters());
  inferenceAlgorithm_ = new ImportanceSampling();
  inferenceAlgorithm_.setSeed(this.getSeed());
  dag = new DAG(modelHeader);
  /* Set DAG structure. */
  /* Add the hidden cluster variable as a parent of all the predictive attributes. */
  dag.getParentSets().stream()
  .filter(w -> w.getMainVar().getVarID() != clusterVar_.getVarID())
  .filter(w -> w.getMainVar().isObservable())
  .forEach(w -> w.addParent(clusterVar_));
  System.out.println(dag.toString());
  parameterLearningAlgorithm_ = new SVB();
  parameterLearningAlgorithm_.setDAG(dag);
  DataOnMemoryListContainer<DataInstance> batch_ = new DataOnMemoryListContainer(attributes_);
  data.stream().forEach(instance ->
      batch_.add(new DataInstanceFromDataRow(new DataRowWeka(instance, this.attributes_)))
  );
  parameterLearningAlgorithm_.setDataStream(batch_);
  parameterLearningAlgorithm_.initLearning();
  parameterLearningAlgorithm_.runLearning();
  bnModel_ = parameterLearningAlgorithm_.getLearntBayesianNetwork();
  System.out.println(bnModel_);
  inferenceAlgorithm_.setModel(bnModel_);
}

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