Examples of weka.core.Instances.classAttribute()

weka.core.Instances.classAttribute()
Returns the class attribute. @return the class attribute @throws UnassignedClassException if the class is not set

      if (!ok) {
        continue;
      }
      // first the class value (if nominal)
      String cVal = (miningSchemaI.classAttribute().isNominal() || 
          miningSchemaI.classAttribute().isString())
        ? miningSchemaI.classAttribute().value(i)
        : " ";
      buff.append(PMMLUtils.pad(cVal, " ", maxClassWidth - cVal.length(), false));     
      buff.append("\n");
      for (int j = 0; j < m_parameterList.size(); j++) {

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        continue;
      }
      // first the class value (if nominal)
      String cVal = (miningSchemaI.classAttribute().isNominal() || 
          miningSchemaI.classAttribute().isString())
        ? miningSchemaI.classAttribute().value(i)
        : " ";
      buff.append(PMMLUtils.pad(cVal, " ", maxClassWidth - cVal.length(), false));     
      buff.append("\n");
      for (int j = 0; j < m_parameterList.size(); j++) {
        PCell p = m_paramMatrix[i][j];

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        TargetMetaInfo targetData = m_miningSchema.getTargetMetaData();
        if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
          preds[0] = targetData.getDefaultValue();
        } else {
          Instances miningSchemaI = m_miningSchema.getFieldsAsInstances();
          for (int i = 0; i < miningSchemaI.classAttribute().numValues(); i++) {
            preds[i] = targetData.getPriorProbability(miningSchemaI.classAttribute().value(i));
          }
        }
        return preds;
      }

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        if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
          preds[0] = targetData.getDefaultValue();
        } else {
          Instances miningSchemaI = m_miningSchema.getFieldsAsInstances();
          for (int i = 0; i < miningSchemaI.classAttribute().numValues(); i++) {
            preds[i] = targetData.getPriorProbability(miningSchemaI.classAttribute().value(i));
          }
        }
        return preds;
      }
    } else {

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    Random random = new Random(m_seed);
    Instances dataCopy = new Instances(data);
    dataCopy.randomize(random);


    if (dataCopy.classAttribute().isNominal()) {
      dataCopy.stratify(m_numFolds);
    }


    for (int f = 0; f < m_numFolds; f++) {
      trainData[f] = dataCopy.trainCV(m_numFolds, f, random);

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    // check class attribute (if set)
   if (fieldsI.classIndex() >= 0) {
      if (dataSet.classIndex() < 0) {
  // first see if we can find a matching class
  String className = fieldsI.classAttribute().name();
  Attribute classMatch = dataSet.attribute(className);
  if (classMatch == null) {
    throw new Exception("[MappingInfo] Can't find match for target field " + className
        + "in incoming instances!");
  }

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  if (classMatch == null) {
    throw new Exception("[MappingInfo] Can't find match for target field " + className
        + "in incoming instances!");
  }
  dataSet.setClass(classMatch);
      } else if (!fieldsI.classAttribute().name().equals(dataSet.classAttribute().name())) {
        throw new Exception("[MappingInfo] class attribute in mining schema does not match "
            + "class attribute in incoming instances!");
      }
    }

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    props = new Vector();
    props.add(Messages.getInstance().getString("ArffViewerMainPanel_ShowProperties_Filename_Text") + panel.getFilename());
    props.add(Messages.getInstance().getString("ArffViewerMainPanel_ShowProperties_Filename_Text") + inst.relationName());
    props.add(Messages.getInstance().getString("ArffViewerMainPanel_ShowProperties_Instances_Text") + inst.numInstances());
    props.add(Messages.getInstance().getString("ArffViewerMainPanel_ShowProperties_Attributes_Text") + inst.numAttributes());
    props.add(Messages.getInstance().getString("ArffViewerMainPanel_ShowProperties_ClassAttribute_Text") + inst.classAttribute().name());
    props.add(Messages.getInstance().getString("ArffViewerMainPanel_ShowProperties_ClassLabels_Text") + inst.numClasses());
    
    dialog = new ListSelectorDialog(getParentFrame(), new JList(props));
    dialog.showDialog();
  }

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    Instances newData = new Instances(data);
    newData.deleteWithMissingClass();
    
    Random random = new Random(m_Seed);
    newData.randomize(random);
    if (newData.classAttribute().isNominal() && (m_NumXValFolds > 1)) {
      newData.stratify(m_NumXValFolds);
    }
    Instances train = newData;               // train on all data by default
    Instances test = newData;               // test on training data by default
    Classifier bestClassifier = null;

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    data = Filter.useFilter(instances, m_Filter);
  
    if(data == null)
      throw new Exception(" Unable to randomize the class orders.");
    
    m_Class = data.classAttribute();  
    m_Ruleset = new FastVector();
    m_RulesetStats = new FastVector();
    m_Distributions = new FastVector();


    // Sort by classes frequency

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