Examples of weka.filters.unsupervised.attribute.Reorder

Package weka.filters.unsupervised.attribute

Examples of weka.filters.unsupervised.attribute.Reorder

weka.filters.unsupervised.attribute.Reorder
A filter that generates output with a new order of the attributes. Useful if one wants to move an attribute to the end to use it as class attribute (e.g. with using "-R 2-last,1").
But it's not only possible to change the order of all the attributes, but also to leave out attributes. E.g. if you have 10 attributes, you can generate the following output order: 1,3,5,7,9,10 or 10,1-5.
You can also duplicate attributes, e.g. for further processing later on: e.g. 1,1,1,4,4,4,2,2,2 where the second and the third column of each attribute are processed differently and the first one, i.e. the original one is kept.
One can simply inverse the order of the attributes via 'last-first'.
After appyling the filter, the index of the class attribute is the last attribute.
Valid options are:
```
 -R <index1,index2-index4,...> Specify list of columns to copy. First and last are valid indexes. (default first-last)
```
@author FracPete (fracpete at waikato dot ac dot nz) @version $Revision: 6249 $

   * it moves it to the end of the attributes
   * 
   * @param columnIndex    the index of the column
   */
  public void attributeAsClassAt(int columnIndex) {
    Reorder     reorder;
    String      order;
    int         i;
    
    if ( (columnIndex > 0) && (columnIndex < getColumnCount()) ) {
      addUndoPoint();
      
      try {
        // build order string (1-based!)
        order = "";
        for (i = 1; i < m_Data.numAttributes() + 1; i++) {
          // skip new class
          if (i == columnIndex)
            continue;
          
          if (!order.equals(""))
            order += ",";
          order += Integer.toString(i);
        }
        if (!order.equals(""))
          order += ",";
        order += Integer.toString(columnIndex);
        
        // process data
        reorder = new Reorder();
        reorder.setAttributeIndices(order);
        reorder.setInputFormat(m_Data);
        m_Data = Filter.useFilter(m_Data, reorder);
        
        // set class index
        m_Data.setClassIndex(m_Data.numAttributes() - 1);
      }

View Full Code Here

      if (nNode >= getNrOfNodes()) {
        throw new Exception("Cannot find node named [[[" + sName + "]]] in the data");
      }
      order[iNode] = nNode;
    }
    Reorder reorderFilter = new Reorder();
    reorderFilter.setAttributeIndicesArray(order);
    reorderFilter.setInputFormat(instances);
    instances = Filter.useFilter(instances, reorderFilter);
    // filter using discretization/missing values filter
    Instances newInstances = new Instances(m_Instances, 0);
    if (m_DiscretizeFilter == null && m_MissingValuesFilter == null) {
      newInstances = normalizeDataSet(instances);

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      if (nNode >= getNrOfNodes()) {
        throw new Exception("Cannot find node named [[[" + sName + "]]] in the data");
      }
      order[iNode] = nNode;
    }
    Reorder reorderFilter = new Reorder();
    reorderFilter.setAttributeIndicesArray(order);
    reorderFilter.setInputFormat(instances);
    instances = Filter.useFilter(instances, reorderFilter);
    // filter using discretization/missing values filter
    Instances newInstances = new Instances(m_Instances, 0);
    if (m_DiscretizeFilter == null && m_MissingValuesFilter == null) {
      newInstances = normalizeDataSet(instances);

View Full Code Here

   * it moves it to the end of the attributes
   *
   * @param columnIndex    the index of the column
   */
  public void attributeAsClassAt(int columnIndex) {
    Reorder     reorder;
    String      order;
    int         i;


    if ( (columnIndex > 0) && (columnIndex < getColumnCount()) ) {
      addUndoPoint();


      try {
        // build order string (1-based!)
        order = "";
        for (i = 1; i < m_Data.numAttributes() + 1; i++) {
          // skip new class
          if (i == columnIndex)
            continue;


          if (!order.equals(""))
            order += ",";
          order += Integer.toString(i);
        }
        if (!order.equals(""))
          order += ",";
        order += Integer.toString(columnIndex);


        // process data
        reorder = new Reorder();
        reorder.setAttributeIndices(order);
        reorder.setInputFormat(m_Data);
        m_Data = Filter.useFilter(m_Data, reorder);


        // set class index
        m_Data.setClassIndex(m_Data.numAttributes() - 1);
      }

View Full Code Here

   * it moves it to the end of the attributes
   * 
   * @param columnIndex    the index of the column
   */
  public void attributeAsClassAt(int columnIndex) {
    Reorder     reorder;
    String      order;
    int         i;
    
    if ( (columnIndex > 0) && (columnIndex < getColumnCount()) ) {
      addUndoPoint();
      
      try {
        // build order string (1-based!)
        order = "";
        for (i = 1; i < m_Data.numAttributes() + 1; i++) {
          // skip new class
          if (i == columnIndex)
            continue;
          
          if (!order.equals(""))
            order += ",";
          order += Integer.toString(i);
        }
        if (!order.equals(""))
          order += ",";
        order += Integer.toString(columnIndex);
        
        // process data
        reorder = new Reorder();
        reorder.setAttributeIndices(order);
        reorder.setInputFormat(m_Data);
        m_Data = Filter.useFilter(m_Data, reorder);
        
        // set class index
        m_Data.setClassIndex(m_Data.numAttributes() - 1);
      }

View Full Code Here

      if (nNode >= getNrOfNodes()) {
        throw new Exception("Cannot find node named [[[" + sName + "]]] in the data");
      }
      order[iNode] = nNode;
    }
    Reorder reorderFilter = new Reorder();
    reorderFilter.setAttributeIndicesArray(order);
    reorderFilter.setInputFormat(instances);
    instances = Filter.useFilter(instances, reorderFilter);
    // filter using discretization/missing values filter
    Instances newInstances = new Instances(m_Instances, 0);
    if (m_DiscretizeFilter == null && m_MissingValuesFilter == null) {
      newInstances = normalizeDataSet(instances);

View Full Code Here

   * it moves it to the end of the attributes
   * 
   * @param columnIndex    the index of the column
   */
  public void attributeAsClassAt(int columnIndex) {
    Reorder     reorder;
    String      order;
    int         i;
    
    if ( (columnIndex > 0) && (columnIndex < getColumnCount()) ) {
      addUndoPoint();
      
      try {
        // build order string (1-based!)
        order = "";
        for (i = 1; i < m_Data.numAttributes() + 1; i++) {
          // skip new class
          if (i == columnIndex)
            continue;
          
          if (!order.equals(""))
            order += ",";
          order += Integer.toString(i);
        }
        if (!order.equals(""))
          order += ",";
        order += Integer.toString(columnIndex);
        
        // process data
        reorder = new Reorder();
        reorder.setAttributeIndices(order);
        reorder.setInputFormat(m_Data);
        m_Data = Filter.useFilter(m_Data, reorder);
        
        // set class index
        m_Data.setClassIndex(m_Data.numAttributes() - 1);
      }

View Full Code Here

      if (nNode >= getNrOfNodes()) {
        throw new Exception("Cannot find node named [[[" + sName + "]]] in the data");
      }
      order[iNode] = nNode;
    }
    Reorder reorderFilter = new Reorder();
    reorderFilter.setAttributeIndicesArray(order);
    reorderFilter.setInputFormat(instances);
    instances = Filter.useFilter(instances, reorderFilter);
    // filter using discretization/missing values filter
    Instances newInstances = new Instances(m_Instances, 0);
    if (m_DiscretizeFilter == null && m_MissingValuesFilter == null) {
      newInstances = normalizeDataSet(instances);

View Full Code Here

    toUse.setSuppressMappingReport(true);
    return toUse;
  }
  
  protected Instances reorderAtts(Instances data) throws Exception {
    Reorder r = new Reorder();
    String range = "last";
    for (int i = data.numAttributes() - 1; i > 0; i--) {
      range += "," + i;
    }
    r.setAttributeIndices(range);
    r.setInputFormat(data);
    data = Filter.useFilter(data, r);
    
    return data;
  }

View Full Code Here

                            int[] shuff_withClass = new int[filt_learn.numAttributes()];
                            for (int jj = 0; jj < filt_learn.numAttributes()-1; jj++) {shuff_noClass[jj]=String.valueOf(jj);}
                            Collections.shuffle(Arrays.asList(shuff_noClass), new Random(42));
                            for (int kk = 0; kk < filt_learn.numAttributes()-1; kk++) {shuff_withClass[kk]=Integer.valueOf(shuff_noClass[kk]);}
                            shuff_withClass[filt_learn.numAttributes()-1] = filt_learn.numAttributes()-1;
                            Reorder reo = new Reorder();
                            reo.setAttributeIndicesArray(shuff_withClass);
                            Instances shuffled_learn = new Instances(filt_learn);
                            reo.setInputFormat(shuffled_learn);
                            shuffled_learn = Filter.useFilter(shuffled_learn, reo);
                            Instances shuffled_hold = new Instances(filt_hold);
                            reo.setInputFormat(shuffled_hold);
                            shuffled_hold = Filter.useFilter(shuffled_hold, reo);


                            ///////////Randomizes the order of the instances (Doesn't matter here, because we're using the whole set!)


                            Instances randData = new Instances(shuffled_learn);
                            cpyCls[0].buildClassifier(randData);
                            




                            ///// Figure out who stays and who goes ////////////
                           
                            WinningShuffle_Learn = shuffled_learn;
                            WinningShuffle_Hold = shuffled_hold;
                           
                        }


                        for (int PII_iter_count = 0; PII_iter_count < numIters; PII_iter_count++){








                            //Randomize the ORDER of the ATTRIBUTES (not the instances, because that would be stupid//
                            String[] shuff_noClass = new String[filt_learn.numAttributes()-1];
                            int[] shuff_withClass = new int[filt_learn.numAttributes()];
                            for (int jj = 0; jj < filt_learn.numAttributes()-1; jj++) {shuff_noClass[jj]=String.valueOf(jj);}
                            if (PII_iter_count!=0){Collections.shuffle(Arrays.asList(shuff_noClass), new Random(PII_iter_count+1));}
                            for (int kk = 0; kk < filt_learn.numAttributes()-1; kk++) {shuff_withClass[kk]=Integer.valueOf(shuff_noClass[kk]);}
                            shuff_withClass[filt_learn.numAttributes()-1] = filt_learn.numAttributes()-1;
                            Reorder reo = new Reorder();
                            reo.setAttributeIndicesArray(shuff_withClass);
                            Instances shuffled_learn = new Instances(filt_learn);
                            reo.setInputFormat(shuffled_learn);
                            shuffled_learn = Filter.useFilter(shuffled_learn, reo);
                            Instances shuffled_hold = new Instances(filt_hold);
                            reo.setInputFormat(shuffled_hold);
                            shuffled_hold = Filter.useFilter(shuffled_hold, reo);


                            ///////////Randomizes the order of the instances (Doesn't matter here, because we're using the whole set!)


                            Instances randData = new Instances(shuffled_learn);

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