Examples of weka.core.Instances.deleteAttributeAt()

weka.core.Instances.deleteAttributeAt()
Deletes an attribute at the given position (0 to numAttributes() - 1). A deep copy of the attribute information is performed before the attribute is deleted. @param position the attribute's position (position starts with 0) @throws IllegalArgumentException if the given index is out of rangeor the class attribute is being deleted

      if(m_DataBaseConnection.getUpperCase()) {
        m_idColumn = m_idColumn.toUpperCase();
      }
      
      if(result.attribute(0).name().equals(m_idColumn)){
        result.deleteAttributeAt(0);
      }
      
      m_structure = new Instances(result,0);
      
    } catch (Exception ex) {

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    if (m_Filter!=null) 
      insts = Filter.useFilter(insts, m_Filter);     


    //calculate the distance from each single instance to the ball center
    int numInsts = insts.numInstances();     
    insts.deleteAttributeAt(0); //remove the bagIndex attribute, no use for the distance calculation


    for (int i=0; i<numInsts; i++){
      distance =0;     
      for (int j=0; j<insts.numAttributes()-1; j++)
        distance += (insts.instance(i).value(j) - m_Center[j])*(insts.instance(i).value(j)-m_Center[j]);

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      m_DataBaseConnection.disconnectFromDatabase();
      // get rid of m_idColumn
      if (m_DataBaseConnection.getUpperCase())
        m_idColumn = m_idColumn.toUpperCase();
      if (result.attribute(0).name().equals(m_idColumn)) {
        result.deleteAttributeAt(0);
      }
      m_structure = new Instances(result, 0);
    } catch (Exception ex) {
      logger.error(ex);
      printException(ex);

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    Instances test = data.testCV(m_NumFolds, i);
    
    // Make class numeric
    Instances trainN = new Instances(train);
    trainN.setClassIndex(-1);
    trainN.deleteAttributeAt(classIndex);
    trainN.insertAttributeAt(new Attribute("'pseudo class'"), classIndex);
    trainN.setClassIndex(classIndex);
    m_NumericClassData = new Instances(trainN, 0);
    
    // Get class values

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                               datePredictor       ? getNumDate()        : 0, 
                               relationalPredictor ? getNumRelational()  : 0, 
                               multiInstance);
      if (nominalPredictor && !multiInstance) {
        train1.deleteAttributeAt(0);
        train2.deleteAttributeAt(0);
      }
      if (missingLevel > 0) {
        addMissing(train1, missingLevel, predictorMissing);
        addMissing(train2, missingLevel, predictorMissing);
      }

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                              stringPredictor     ? getNumString()      : 0, 
                              datePredictor       ? getNumDate()        : 0, 
                              relationalPredictor ? getNumRelational()  : 0, 
                              multiInstance);
      if (nominalPredictor && !multiInstance)
        train.deleteAttributeAt(0);
      if (missingLevel > 0)
        addMissing(train, missingLevel, predictorMissing);
      clusterers = AbstractClusterer.makeCopies(getClusterer(), 2);
      evaluationB = new ClusterEvaluation();
      evaluationI = new ClusterEvaluation();

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    m_OriginalHeader = new Instances(data, 0);
    
    // remove class attribute for clusterer
    clusterData = new Instances(data);
    clusterData.setClassIndex(-1);
    clusterData.deleteAttributeAt(m_OriginalHeader.classIndex());
    m_ClusteringHeader = new Instances(clusterData, 0);


    if (m_ClusteringHeader.numAttributes() == 0) {
      System.err.println("Data contains only class attribute, defaulting to ZeroR model.");
      m_ZeroR = new ZeroR();

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                              stringPredictor     ? getNumString()     : 0, 
                              datePredictor       ? getNumDate()       : 0, 
                              relationalPredictor ? getNumRelational() : 0, 
                              multiInstance);
      if (nominalPredictor && !multiInstance)
        train.deleteAttributeAt(0);
      if (missingLevel > 0)
        addMissing(train, missingLevel, predictorMissing);
      clusterer = AbstractClusterer.makeCopies(getClusterer(), 1)[0];
    } catch (Exception ex) {
      throw new Error("Error setting up for tests: " + ex.getMessage());

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                              stringPredictor     ? getNumString()     : 0,
                              datePredictor       ? getNumDate()       : 0,
                              relationalPredictor ? getNumRelational() : 0,
                              multiInstance);
      if (nominalPredictor && !multiInstance)
        train.deleteAttributeAt(0);
      if (missingLevel > 0)
        addMissing(train, missingLevel, predictorMissing);
      clusterer = AbstractClusterer.makeCopies(getClusterer(), 1)[0];
    } catch (Exception ex) {
      ex.printStackTrace();

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            if (iAttribute != instances.classIndex()) {
                FastVector values = new FastVector();
                values.addElement("0");
                values.addElement("1");
                Attribute a = new Attribute(instances.attribute(iAttribute).name(), (FastVector) values);
                instances.deleteAttributeAt(iAttribute);
                instances.insertAttributeAt(a,iAttribute);
            }
        }
        
        for (int iInstance = 0; iInstance < bayesNet.m_Instances.numInstances(); iInstance++) {

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