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 $

    double errorRate = 0;
    int numAttributes = 0;
    Instances trainCopy=null;
    Instances testCopy=null;


    Remove delTransform = new Remove();
    delTransform.setInvertSelection(true);
    // copy the training instances
    trainCopy = new Instances(m_trainingInstances);
    
    if (!m_useTraining) {
      if (m_holdOutInstances == null) {
  throw new Exception("Must specify a set of hold out/test instances "
          +"with -H");
      } 
      // copy the test instances
      testCopy = new Instances(m_holdOutInstances);
    }
    
    // count attributes set in the BitSet
    for (i = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        numAttributes++;
      }
    }
    
    // set up an array of attribute indexes for the filter (+1 for the class)
    int[] featArray = new int[numAttributes + 1];
    
    for (i = 0, j = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        featArray[j++] = i;
      }
    }
    
    featArray[j] = m_classIndex;
    delTransform.setAttributeIndicesArray(featArray);
    delTransform.setInputFormat(trainCopy);
    trainCopy = Filter.useFilter(trainCopy, delTransform);
    if (!m_useTraining) {
      testCopy = Filter.useFilter(testCopy, delTransform);
    }

View Full Code Here


    if (m_trainingInstances.equalHeaders(holdOut) == false) {
      throw new Exception("evaluateSubset : Incompatable instance types.");
    }


    Remove delTransform = new Remove();
    delTransform.setInvertSelection(true);
    // copy the training instances
    trainCopy = new Instances(m_trainingInstances);
    
    testCopy = new Instances(holdOut);


    // count attributes set in the BitSet
    for (i = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        numAttributes++;
      }
    }
    
    // set up an array of attribute indexes for the filter (+1 for the class)
    int[] featArray = new int[numAttributes + 1];
    
    for (i = 0, j = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        featArray[j++] = i;
      }
    }
    
    featArray[j] = m_classIndex;
    delTransform.setAttributeIndicesArray(featArray);
    delTransform.setInputFormat(trainCopy);
    trainCopy = Filter.useFilter(trainCopy, delTransform);
    testCopy = Filter.useFilter(testCopy, delTransform);


    // build the classifier
    m_Classifier.buildClassifier(trainCopy);

View Full Code Here


    if (m_trainingInstances.equalHeaders(holdOut.dataset()) == false) {
      throw new Exception("evaluateSubset : Incompatable instance types.");
    }


    Remove delTransform = new Remove();
    delTransform.setInvertSelection(true);
    // copy the training instances
    trainCopy = new Instances(m_trainingInstances);
    
    testCopy = (Instance)holdOut.copy();


    // count attributes set in the BitSet
    for (i = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        numAttributes++;
      }
    }
    
    // set up an array of attribute indexes for the filter (+1 for the class)
    int[] featArray = new int[numAttributes + 1];
    
    for (i = 0, j = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        featArray[j++] = i;
      }
    }
    featArray[j] = m_classIndex;
    delTransform.setAttributeIndicesArray(featArray);
    delTransform.setInputFormat(trainCopy);


    if (retrain) {
      trainCopy = Filter.useFilter(trainCopy, delTransform);
      // build the classifier
      m_Classifier.buildClassifier(trainCopy);
    }


    delTransform.input(testCopy);
    testCopy = delTransform.output();


    double pred;
    double [] distrib;
    distrib = m_Classifier.distributionForInstance(testCopy);
    if (m_trainingInstances.classAttribute().isNominal()) {

View Full Code Here

    double errorRate = 0;
    double[] repError = new double[5];
    int numAttributes = 0;
    int i, j;
    Random Rnd = new Random(m_seed);
    Remove delTransform = new Remove();
    delTransform.setInvertSelection(true);
    // copy the instances
    Instances trainCopy = new Instances(m_trainInstances);


    // count attributes set in the BitSet
    for (i = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        numAttributes++;
      }
    }


    // set up an array of attribute indexes for the filter (+1 for the class)
    int[] featArray = new int[numAttributes + 1];


    for (i = 0, j = 0; i < m_numAttribs; i++) {
      if (subset.get(i)) {
        featArray[j++] = i;
      }
    }


    featArray[j] = m_classIndex;
    delTransform.setAttributeIndicesArray(featArray);
    delTransform.setInputFormat(trainCopy);
    trainCopy = Filter.useFilter(trainCopy, delTransform);


    // max of 5 repititions ofcross validation
    for (i = 0; i < 5; i++) {
      m_Evaluation = new Evaluation(trainCopy);

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

  ((Randomizable) m_Classifiers[j]).setSeed(random.nextInt());
      }
      FilteredClassifier fc = new FilteredClassifier();
      fc.setClassifier(m_Classifiers[j]);
      m_Classifiers[j] = fc;
      Remove rm = new Remove();
      rm.setOptions(new String[]{"-V", "-R", randomSubSpace(indices,subSpaceSize,classIndex+1,random)});
      fc.setFilter(rm);


      // build the classifier
      m_Classifiers[j].buildClassifier(data);
    }

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 {

View Full Code Here

      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

      : 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

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