Examples of weka.filters.unsupervised.attribute.Remove

Package weka.filters.unsupervised.attribute

Examples of weka.filters.unsupervised.attribute.Remove

weka.filters.unsupervised.attribute.Remove
A filter that removes a range of attributes from the dataset. Will re-order the remaining attributes if invert matching sense is turned on and the attribute column indices are not specified in ascending order.
Valid options are:
```
 -R <index1,index2-index4,...> Specify list of columns to delete. First and last are valid indexes. (default none)
```
```
 -V Invert matching sense (i.e. only keep specified columns)
```
@author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 7107 $

      deleteCols.addElement(new Integer(m_classIndex));
    }


    // remove columns from the data if necessary
    if (deleteCols.size() > 0) {
      m_attributeFilter = new Remove();
      int [] todelete = new int [deleteCols.size()];
      for (int i=0;i<deleteCols.size();i++) {
        todelete[i] = ((Integer)(deleteCols.elementAt(i))).intValue();
      }
      m_attributeFilter.setAttributeIndicesArray(todelete);

View Full Code Here

    m_decisionFeatures = new int [selected.length+1];
    System.arraycopy(selected, 0, m_decisionFeatures, 0, selected.length);
    m_decisionFeatures[m_decisionFeatures.length-1] = m_theInstances.classIndex();


    // reduce instances to selected features
    m_delTransform = new Remove();
    m_delTransform.setInvertSelection(true);


    // set features to keep
    m_delTransform.setAttributeIndicesArray(m_decisionFeatures); 
    m_delTransform.setInputFormat(m_theInstances);

View Full Code Here

   */
  private void buildClusterer() throws Exception {
      if(m_trainingSet.classIndex() < 0)  
        m_Clusterer.buildClusterer(m_trainingSet);
      else{ //class based evaluation if class attribute is set
        Remove removeClass = new Remove();
  removeClass.setAttributeIndices(""+(m_trainingSet.classIndex()+1));
  removeClass.setInvertSelection(false);
  removeClass.setInputFormat(m_trainingSet);
  Instances clusterTrain = Filter.useFilter(m_trainingSet, removeClass);
  m_Clusterer.buildClusterer(clusterTrain);
      }
  }

View Full Code Here

   */
  private void buildLinearModel(int [] indices) throws Exception {
    // copy the training instances and remove all but the tested
    // attributes
    Instances reducedInst = new Instances(m_instances);
    Remove attributeFilter = new Remove();
    
    attributeFilter.setInvertSelection(true);
    attributeFilter.setAttributeIndicesArray(indices);
    attributeFilter.setInputFormat(reducedInst);


    reducedInst = Filter.useFilter(reducedInst, attributeFilter);
    
    // build a linear regression for the training data using the
    // tested attributes

View Full Code Here

    m_RemoveButton.setEnabled(false);
    m_RemoveButton.setToolTipText("Remove selected attributes.");
    m_RemoveButton.addActionListener(new ActionListener() {
  public void actionPerformed(ActionEvent e) {
    try {
      Remove r = new Remove();
      int [] selected = m_AttPanel.getSelectedAttributes();
      if (selected.length == 0) {
        return;
      }
      if (selected.length == m_Instances.numAttributes()) {
        // Pop up an error optionpane
        JOptionPane.showMessageDialog(PreprocessPanel.this,
              "Can't remove all attributes from data!\n",
              "Remove Attributes",
              JOptionPane.ERROR_MESSAGE);
        m_Log.logMessage("Can't remove all attributes from data!");
        m_Log.statusMessage("Problem removing attributes");
        return;
      }
      r.setAttributeIndicesArray(selected);
      applyFilter(r);
      m_RemoveButton.setEnabled(false);
    } catch (Exception ex) {
      if (m_Log instanceof TaskLogger) {
        ((TaskLogger)m_Log).taskFinished();

View Full Code Here

         +m_upperBoundMinSupport);
      }
    }


    if (deleteString.toString().length() > 0) {
      Remove af = new Remove();
      af.setAttributeIndices(deleteString.toString());
      af.setInvertSelection(false);
      af.setInputFormat(instances);
      Instances newInst = Filter.useFilter(instances, af);


      return newInst;
    }
    return instances;

View Full Code Here

    // If class is set then do class based evaluation as well
    if (hasClass) {
      if (testRaw.classAttribute().isNumeric())
  throw new Exception("ClusterEvaluation: Class must be nominal!");


      filter = new Remove();
      ((Remove) filter).setAttributeIndices("" + (testRaw.classIndex() + 1));
      ((Remove) filter).setInvertSelection(false);
      filter.setInputFormat(testRaw);
    }

View Full Code Here

  else {
    clusterer.buildClusterer(source.getDataSet());
  }
      }
      else {
  Remove removeClass = new Remove();
  removeClass.setAttributeIndices("" + theClass);
  removeClass.setInvertSelection(false);
  removeClass.setInputFormat(train);
  if (updateable) {
    Instances clusterTrain = Filter.useFilter(train, removeClass);
    clusterer.buildClusterer(clusterTrain);
          trainHeader = clusterTrain;
    while (source.hasMoreElements(train)) {
      inst = source.nextElement(train);
      removeClass.input(inst);
      removeClass.batchFinished();
      Instance clusterTrainInst = removeClass.output();
      ((UpdateableClusterer) clusterer).updateClusterer(clusterTrainInst);
    }
    ((UpdateableClusterer) clusterer).updateFinished();
  }
  else {

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      : 0;
    int overall_length = RESULT_SIZE+addm;


    if (m_removeClassColumn && train.classIndex() != -1) {
      // remove the class column from the training and testing data
      Remove r = new Remove();
      r.setAttributeIndicesArray(new int [] {train.classIndex()});
      r.setInvertSelection(false);
      r.setInputFormat(train);
      train = Filter.useFilter(train, r);
      
      test = Filter.useFilter(test, r);
    }
    train.setClassIndex(-1);

View Full Code Here

      m_RunThread.start();
    }
  }


  private Instances removeClass(Instances inst) {
    Remove af = new Remove();
    Instances retI = null;
    
    try {
      if (inst.classIndex() < 0) {
  retI = inst;
      } else {
  af.setAttributeIndices(""+(inst.classIndex()+1));
  af.setInvertSelection(false);
  af.setInputFormat(inst);
  retI = Filter.useFilter(inst, af);
      }
    } catch (Exception e) {
      e.printStackTrace();
    }

View Full Code Here

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