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

      while (lastInstance < originalDataSet.numInstances()
    && sequenceID == originalDataSet.instance(lastInstance).value(dataSeqID)) {
  lastInstance++;
      }
      Instances dataSequence = new Instances(originalDataSet, firstInstance, (lastInstance)-firstInstance);
      dataSequence.deleteAttributeAt(dataSeqID);
      dataSequences.addElement(dataSequence);
      firstInstance = lastInstance;
    }
    return dataSequences;
  }

View Full Code Here

    FastVector dataSequences = extractDataSequences(m_OriginalDataSet, m_DataSeqID);
    long minSupportCount = Math.round(m_MinSupport * dataSequences.size());
    FastVector kMinusOneSequences;
    FastVector kSequences;


    originalDataSet.deleteAttributeAt(0);
    FastVector oneElements = Element.getOneElements(originalDataSet);
    m_Cycles = 1;


    kSequences = Sequence.oneElementsToSequences(oneElements);
    Sequence.updateSupportCount(kSequences, dataSequences);

View Full Code Here

    }


    //convert the training dataset into single-instance dataset
    m_ConvertToSI.setInputFormat(train);
    Instances data = Filter.useFilter( train, m_ConvertToSI);
    data.deleteAttributeAt(0); //remove the bagIndex attribute;




    // Assume the order of the instances are preserved in the Discretize filter
    if(m_DiscretizeBin > 0){
      m_Filter = new Discretize();

View Full Code Here

    Instances insts = new Instances(exmp.dataset(), 0);
    insts.add(exmp);


    // convert the training dataset into single-instance dataset
    insts = Filter.useFilter( insts, m_ConvertToSI);
    insts.deleteAttributeAt(0); //remove the bagIndex attribute  


    double n = insts.numInstances();


    if(m_DiscretizeBin > 0)
      insts = Filter.useFilter(insts, m_Filter);

View Full Code Here

    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]);

View Full Code Here

      int numInstances = boostData.numInstances();
      
      // Temporarily unset the class index
      int classIndex = data.classIndex();
      boostData.setClassIndex(-1);
      boostData.deleteAttributeAt(classIndex);
      boostData.insertAttributeAt(new Attribute("'pseudo class'"), classIndex);
      boostData.setClassIndex(classIndex);
      double [][] trainFs = new double [numInstances][m_NumClasses];
      double [][] trainYs = new double [numInstances][m_NumClasses];
      for (int j = 0; j < m_NumClasses; j++) {

View Full Code Here

    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) {
  printException(ex);

View Full Code Here

    // Create a copy of the data with the class transformed into numeric
    boostData = new Instances(data);


    // Temporarily unset the class index
    boostData.setClassIndex(-1);
    boostData.deleteAttributeAt(classIndex);
    boostData.insertAttributeAt(new Attribute("'pseudo class'"), classIndex);
    boostData.setClassIndex(classIndex);
    m_NumericClassData = new Instances(boostData, 0);


    data.randomize(m_RandomInstance);

View Full Code Here

    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

View Full Code Here

            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++) {

View Full Code Here

0 1 2 3 4

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.