Examples of weka.core.SelectedTag

weka.core.SelectedTag
Represents a selected value from a finite set of values, where each value is a Tag (i.e. has some string associated with it). Primarily used in schemes to select between alternative behaviours, associating names with the alternative behaviours. @author Len Trigg @version $Revision: 1.11 $

    else
      setC(1.0);
    
    tmpStr = Utils.getOption('N', options);
    if (tmpStr.length() != 0)
      setFilterType(new SelectedTag(Integer.parseInt(tmpStr), TAGS_FILTER));
    else
      setFilterType(new SelectedTag(FILTER_NORMALIZE, TAGS_FILTER));


    tmpStr = Utils.getOption('I', options);
    if (tmpStr.length() != 0)
      setMaxIterations(Integer.parseInt(tmpStr));
    else

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   *
   * @return the filtering mode
   */
  public SelectedTag getFilterType() {


    return new SelectedTag(m_filterType, TAGS_FILTER);
  }

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        instLabels.addElement(new Double(classes[h]));             
    }


    // convert the training dataset into single-instance dataset
    m_ConvertToProp.setWeightMethod(
        new SelectedTag(
          MultiInstanceToPropositional.WEIGHTMETHOD_1, 
          MultiInstanceToPropositional.TAGS_WEIGHTMETHOD)); 
    m_ConvertToProp.setInputFormat(train);
    train = Filter.useFilter( train, m_ConvertToProp);
    train.deleteAttributeAt(0); //remove the bagIndex attribute;


    if (m_filterType == FILTER_STANDARDIZE)  
      m_Filter = new Standardize();
    else if (m_filterType == FILTER_NORMALIZE)
      m_Filter = new Normalize();
    else 
      m_Filter = null;


    if (m_Filter!=null) {
      m_Filter.setInputFormat(train);
      train = Filter.useFilter(train, m_Filter);
    }  


    if (m_Debug) {
      System.out.println("\nIteration History..." );
    }


    if (getDebug())
      System.out.println("\nstart building model ...");


    int index;
    double sum, max_output; 
    Vector max_index = new Vector();
    Instance inst=null;


    int loopNum=0;
    do {
      loopNum++;
      index=-1;
      if (m_Debug)
        System.out.println("=====================loop: "+loopNum);


      //store the previous label information
      pre_instLabels=(Vector)instLabels.clone();   


      // set the proper SMO options in order to build a SVM model
      m_SVM = new SVM();
      m_SVM.setC(getC());
      m_SVM.setKernel(Kernel.makeCopy(getKernel()));
      // SVM model do not normalize / standardize the input dataset as the the dataset has already been processed  
      m_SVM.setFilterType(new SelectedTag(FILTER_NONE, TAGS_FILTER));  


      m_SVM.buildClassifier(train); 


      for(int h=0; h<numBags; h++)  {//h_th bag
        if (classes[h]==1) { //positive bag

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  public Classifier getClassifier() {
    PLSClassifier classifier = new PLSClassifier();
    
    PLSFilter filter = new PLSFilter();
    filter.setReplaceMissing(true);
    filter.setPreprocessing(new SelectedTag(PLSFilter.PREPROCESSING_CENTER, PLSFilter.TAGS_PREPROCESSING));
    filter.setNumComponents(NUM_COMPONENTS);
    filter.setAlgorithm(new SelectedTag(PLSFilter.ALGORITHM_SIMPLS, PLSFilter.TAGS_ALGORITHM));
    try {
      classifier.setFilter(filter);
    }
    catch (Exception e) {
      e.printStackTrace();

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  public void setOptions(String[] options) throws Exception {
    setDebug(Utils.getFlag('D', options));


    String nString = Utils.getOption('N', options);
    if (nString.length() != 0) {
      setFilterType(new SelectedTag(Integer.parseInt(nString), TAGS_FILTER));
    } else {
      setFilterType(new SelectedTag(FILTER_STANDARDIZE, TAGS_FILTER));
    }     
  }

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   * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
   *
   * @return the filtering mode
   */
  public SelectedTag getFilterType() {
    return new SelectedTag(m_filterType, TAGS_FILTER);
  }

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  public void setOptions(String[] options) throws Exception {
    setDebug(Utils.getFlag('D', options));


    String nString = Utils.getOption('N', options);
    if (nString.length() != 0) {
      setFilterType(new SelectedTag(Integer.parseInt(nString), TAGS_FILTER));
    } else {
      setFilterType(new SelectedTag(FILTER_STANDARDIZE, TAGS_FILTER));
    }     
  }

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   * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
   *
   * @return the filtering mode
   */
  public SelectedTag getFilterType() {
    return new SelectedTag(m_filterType, TAGS_FILTER);
  }

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   * @see RandomProjection#TAGS_DSTRS_TYPE
   */
  protected void checkDistributionType(int type) {
    m_Filter = getFilter();
    ((RandomProjection) m_Filter).setDistribution(
        new SelectedTag(type, RandomProjection.TAGS_DSTRS_TYPE));
    Instances result = useFilter();
    checkResult(result);
  }

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      minSupportString = Utils.getOption('M', options),
      significanceLevelString = Utils.getOption('S', options),
      classIndexString = Utils.getOption('c',options);
    String metricTypeString = Utils.getOption('T', options);
    if (metricTypeString.length() != 0) {
      setMetricType(new SelectedTag(Integer.parseInt(metricTypeString),
            TAGS_SELECTION));
    }
    
    if (numRulesString.length() != 0) {
      m_numRules = Integer.parseInt(numRulesString);

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