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

Instances.classAttribute介绍

[英]Returns the class attribute.
[中]返回class属性。

代码示例

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

/**
 * Constructor for numeric attribute.
 * 
 * @param data the data to work with
 * @param attribute the attribute to use
 * @param nBreaks the break point
 * @throws Exception if something goes wrong
 */
public OneRRule(Instances data, Attribute attribute, int nBreaks) throws Exception {
 m_class = data.classAttribute();
 m_numInst = data.numInstances();
 m_attr = attribute;
 m_correct = 0;
 m_classifications = new int[nBreaks];
 m_breakpoints = new double[nBreaks - 1]; // last breakpoint is infinity
}

代码示例来源：origin: Waikato/wekaDeeplearning4j

/**
 * Fixes nominal label indices. Dl4j sorts them during training time. A mapping from weka-labels
 * resorted labels is stored in {@link this.labelsSortIndex}.
 *
 * @param j Original index
 * @param insts Test dataset
 * @return Remapped index if test dataset has nominal label. Else return {@code j}
 */
protected int fixLabelIndexIfNominal(int j, Instances insts) {
 if (insts.classAttribute().isNominal()) {
  return labelSortIndex[j];
 } else {
  return j;
 }
}

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

@Override
public List<String> getPredictionLabels() {
 if (m_modelHeader == null) {
  return null;
 }
 if (m_modelHeader.classAttribute().isNominal()) {
  if (m_predictionLabels == null) {
   m_predictionLabels = new ArrayList<String>();
   for (int i = 0; i < m_modelHeader.classAttribute().numValues(); i++) {
    m_predictionLabels.add(m_modelHeader.classAttribute().value(i));
   }
  }
 }
 return m_predictionLabels;
}

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

@Override
public void buildClassifier(Instances D) throws Exception {
   testCapabilities(D);
   
  int L = D.classIndex();
  if(getDebug()) System.out.print("Creating "+L+" models ("+m_Classifier.getClass().getName()+"): ");
  m_MultiClassifiers = AbstractClassifier.makeCopies(m_Classifier,L);
  m_Templates = new Instances[L];
  for(int j = 0; j < L; j++) {
    //Select only class attribute 'j'
    m_Templates[j] = MLUtils.keepAttributesAt(new Instances(D),new int[]{j},L);
    m_Templates[j].setClassIndex(0);
    //Build the classifier for that class
    m_MultiClassifiers[j].buildClassifier(m_Templates[j]);
    if(getDebug()) System.out.print(" " + (m_Templates[j].classAttribute().name()));
    m_Templates[j] = new Instances(m_Templates[j], 0);
  }
}

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

/**
 * Makes the format for the level-1 data.
 *
 * @param instances the level-0 format
 * @return the format for the meta data
 * @throws Exception if the format generation fails
 */
protected Instances metaFormat(Instances instances) throws Exception {
 ArrayList<Attribute> attributes = new ArrayList<Attribute>();
 Instances metaFormat;
 for (int k = 0; k < m_Classifiers.length; k++) {
  Classifier classifier = (Classifier) getClassifier(k);
  String name = classifier.getClass().getName() + "-" + (k+1);
  if (m_BaseFormat.classAttribute().isNumeric()) {
 attributes.add(new Attribute(name));
  } else {
 for (int j = 0; j < m_BaseFormat.classAttribute().numValues(); j++) {
  attributes.add(
    new Attribute(
    name + ":" + m_BaseFormat.classAttribute().value(j)));
 }
  }
 }
 attributes.add((Attribute) m_BaseFormat.classAttribute().copy());
 metaFormat = new Instances("Meta format", attributes, 0);
 metaFormat.setClassIndex(metaFormat.numAttributes() - 1);
 return metaFormat;
}

代码示例来源：origin: Waikato/weka-trunk

instances = new Instances(instances);
instances.deleteWithMissingClass();
m_ClassType = instances.classAttribute().type();
m_Train = new Instances(instances, 0, instances.numInstances());
if ((m_WindowSize > 0) && (instances.numInstances() > m_WindowSize)) {
 m_Train = new Instances(m_Train, 
       m_Train.numInstances()-m_WindowSize, 
       m_WindowSize);
 if ((i != m_Train.classIndex()) && 
 (m_Train.attribute(i).isNominal() ||
  m_Train.attribute(i).isNumeric())) {
m_NumAttributesUsed += 1.0;

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

/**
 * Filter the data according to the ruleset and compute the basic stats:
 * coverage/uncoverage, true/false positive/negatives of each rule
 */
public void countData() {
 if ((m_Filtered != null) || (m_Ruleset == null) || (m_Data == null)) {
  return;
 }
 int size = m_Ruleset.size();
 m_Filtered = new ArrayList<Instances[]>(size);
 m_SimpleStats = new ArrayList<double[]>(size);
 m_Distributions = new ArrayList<double[]>(size);
 Instances data = new Instances(m_Data);
 for (int i = 0; i < size; i++) {
  double[] stats = new double[6]; // 6 statistics parameters
  double[] classCounts = new double[m_Data.classAttribute().numValues()];
  Instances[] filtered = computeSimpleStats(i, data, stats, classCounts);
  m_Filtered.add(filtered);
  m_SimpleStats.add(stats);
  m_Distributions.add(classCounts);
  data = filtered[1]; // Data not covered
 }
}

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

/**
 * generates a consequence of length 1 for a class association rule.
 * 
 * @param instances the instances under consideration
 * @return FastVector with consequences of length 1
 */
public static ArrayList<Object> singleConsequence(Instances instances) {
 ItemSet consequence;
 ArrayList<Object> consequences = new ArrayList<Object>();
 for (int j = 0; j < (instances.classAttribute()).numValues(); j++) {
  consequence = new ItemSet(instances.numInstances());
  int[] consequenceItems = new int[instances.numAttributes()];
  consequence.setItem(consequenceItems);
  for (int k = 0; k < instances.numAttributes(); k++) {
   consequence.setItemAt(-1, k);
  }
  consequence.setItemAt(j, instances.classIndex());
  consequences.add(consequence);
 }
 return consequences;
}

代码示例来源：origin: Waikato/meka

/**
 * mapBack: returns the original indices (encoded in the class attribute).
 */
private int[] mapBack(Instances template, int i) {
  try {
    return MLUtils.toIntArray(template.classAttribute().value(i));
  } catch(Exception e) {
    return new int[]{};
  }
}

代码示例来源：origin: Waikato/weka-trunk

@SuppressWarnings("unchecked")
public void setup(Instances inputFormat) throws Exception {
 m_inputContainsStringAttributes = inputFormat.checkForStringAttributes();
 m_inputFormat = inputFormat.stringFreeStructure();
 if (!m_inputContainsStringAttributes) {
  return;
 }
 m_numClasses =
  !m_doNotOperateOnPerClassBasis && m_inputFormat.classIndex() >= 0 && m_inputFormat.classAttribute().isNominal() ?
      m_inputFormat.numClasses() : 1;
 m_dictsPerClass =
  m_sortDictionary ? new TreeMap[m_numClasses]
   : new LinkedHashMap[m_numClasses];
 m_classIndex = m_inputFormat.classIndex();
 for (int i = 0; i < m_numClasses; i++) {
  m_dictsPerClass[i] =
   m_sortDictionary ? new TreeMap<String, int[]>()
    : new LinkedHashMap<String, int[]>();
 }
 determineSelectedRange(inputFormat);
}

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

/**
 * Returns a string representation of the classifier.
 * 
 * @return a string representation of the classifier
 */
public String toString()
{
 StringBuffer result = new StringBuffer("The class counts (including Laplace correction)\n-----------------------------------------------\n");
 
 for(int c = 0; c<m_numClasses; c++)
  result.append(m_headerInfo.classAttribute().value(c)).append("\t").
      append(Utils.doubleToString(m_probOfClass[c], getNumDecimalPlaces())).append("\n");
 
 result.append("\nThe probability of a word given the class\n-----------------------------------------\n\t");
 for(int c = 0; c<m_numClasses; c++)
  result.append(m_headerInfo.classAttribute().value(c)).append("\t");
 
 result.append("\n");
 for(int w = 0; w<m_numAttributes; w++)
 {
  if (w != m_headerInfo.classIndex()) {
   result.append(m_headerInfo.attribute(w).name()).append("\t");
   for(int c = 0; c<m_numClasses; c++)
    result.append(Utils.doubleToString(m_probOfWordGivenClass[c][w] / m_wordsPerClass[c], getNumDecimalPlaces())).append("\t");
   result.append("\n");
  }
 }
 return result.toString();
}

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: stackoverflow.com

BufferedReader datafile = readDataFile(TrainingFile);
Instances train = new Instances(datafile);
data.setClassIndex(data.numAttributes() - 1);

Classifier cls = new J48();
cls.buildClassifier(train);

DataSource testDataset = new DataSource(Test);
Instances test = testDataset.getDataSet();
Testdata.setClassIndex(Testdata.numAttributes() - 1);

for(int i = 0; i < test.numInstances(); i++){

  Instance inst = test.instance(i);

  double actualClassValue  = test.instance(i).classValue();

  //it will print your class value
  String actual=test.classAttribute().value((int)actualClassValue);

  double result = cls.classifyInstance(inst);

  //will print your predicted value
  String prediction=test.classAttribute().value((int)result );

}

代码示例来源：origin: org.dkpro.tc/dkpro-tc-ml-weka

public static List<String> getClassLabels(Instances data, boolean isMultilabel)
{
  List<String> classLabelList = new ArrayList<String>();
  if (!isMultilabel) {
    Enumeration<Object> classLabels = data.classAttribute().enumerateValues();
    while (classLabels.hasMoreElements()) {
      classLabelList.add((String) classLabels.nextElement());
    }
  }
  else {
    int numLabels = data.classIndex();
    for (int i = 0; i < numLabels; i++) {
      classLabelList.add(data.attribute(i).name());
    }
  }
  return classLabelList;
}

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

/**
 * compares the generated dataset with the original one
 */
protected void performTests(Instances result) {
 assertEquals(m_Instances.numAttributes(), result.numAttributes());
 assertEquals(m_Instances.numInstances(), result.numInstances());
 // None of the attributes should have changed
 for (int i = 0; i < result.numAttributes(); i++) {
  assertEquals(m_Instances.attribute(i).type(), result.attribute(i).type());
  assertEquals(m_Instances.attribute(i).name(), result.attribute(i).name());
 }
 // did the order change?
 boolean orderEqual = true;
 for (int i = 0; i < result.numClasses(); i++) {
  if (!m_Instances.classAttribute().value(i).equals(
     result.classAttribute().value(i))) {
   orderEqual = false;
   break;
  }
 }
 if (orderEqual)
  fail("Order wasn't changed!");
}

代码示例来源：origin: Waikato/weka-trunk

if (data.classAttribute().isNominal()) {
 classifier = new weka.classifiers.trees.J48();
} else {
result = new double[data.numInstances()];
for (i = 0; i < result.length; i++) {
 result[i] = classifier.classifyInstance(data.instance(i));

代码示例来源：origin: Waikato/meka

@Override
public void buildClassifier(Instances D) throws Exception {
  testCapabilities(D);
   
  int L = D.classIndex();
  if(getDebug()) System.out.print("Creating "+L+" models ("+m_Classifier.getClass().getName()+"): ");
  m_MultiClassifiers = AbstractClassifier.makeCopies(m_Classifier,L);
  m_InstancesTemplates = new Instances[L];
  for(int j = 0; j < L; j++) {
    //Select only class attribute 'j'
    Instances D_j = F.keepLabels(new Instances(D),L,new int[]{j});
    D_j.setClassIndex(0);
    //Build the classifier for that class
    m_MultiClassifiers[j].buildClassifier(D_j);
    if(getDebug()) System.out.print(" " + (D_j.classAttribute().name()));
    m_InstancesTemplates[j] = new Instances(D_j, 0);
  }
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Determines the format of the level-1 data.
 *
 * @param instances the level-0 format
 * @return the format for the meta data
 * @throws Exception if the format generation fails
 */
protected Instances metaFormat(Instances instances) throws Exception {
 ArrayList<Attribute> attributes = new ArrayList<Attribute>();
 Instances metaFormat;
 for (int k = 0; k < m_Classifiers.length; k++) {
  Classifier classifier = getClassifier(k);
  String name = classifier.getClass().getName() + "-" + (k + 1);
  if (m_BaseFormat.classAttribute().isNumeric()) {
   attributes.add(new Attribute(name));
  } else {
   for (int j = 0; j < m_BaseFormat.classAttribute().numValues(); j++) {
    attributes.add(new Attribute(name + ":" + m_BaseFormat.classAttribute().value(j)));
   }
  }
 }
 attributes.add((Attribute) m_BaseFormat.classAttribute().copy());
 metaFormat = new Instances("Meta format", attributes, 0);
 metaFormat.setClassIndex(metaFormat.numAttributes() - 1);
 return metaFormat;
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Constructor for numeric attribute.
 * 
 * @param data the data to work with
 * @param attribute the attribute to use
 * @param nBreaks the break point
 * @throws Exception if something goes wrong
 */
public OneRRule(Instances data, Attribute attribute, int nBreaks) throws Exception {
 m_class = data.classAttribute();
 m_numInst = data.numInstances();
 m_attr = attribute;
 m_correct = 0;
 m_classifications = new int[nBreaks];
 m_breakpoints = new double[nBreaks - 1]; // last breakpoint is infinity
}

代码示例来源：origin: Waikato/weka-trunk

/**
 * Filter the data according to the ruleset and compute the basic stats:
 * coverage/uncoverage, true/false positive/negatives of each rule
 */
public void countData() {
 if ((m_Filtered != null) || (m_Ruleset == null) || (m_Data == null)) {
  return;
 }
 int size = m_Ruleset.size();
 m_Filtered = new ArrayList<Instances[]>(size);
 m_SimpleStats = new ArrayList<double[]>(size);
 m_Distributions = new ArrayList<double[]>(size);
 Instances data = new Instances(m_Data);
 for (int i = 0; i < size; i++) {
  double[] stats = new double[6]; // 6 statistics parameters
  double[] classCounts = new double[m_Data.classAttribute().numValues()];
  Instances[] filtered = computeSimpleStats(i, data, stats, classCounts);
  m_Filtered.add(filtered);
  m_SimpleStats.add(stats);
  m_Distributions.add(classCounts);
  data = filtered[1]; // Data not covered
 }
}