Examples of NominalToBinary

weka.filters.supervised.attribute.NominalToBinary
Converts all nominal attributes into binary numeric attributes. An attribute with k values is transformed into k binary attributes if the class is nominal (using the one-attribute-per-value approach). Binary attributes are left binary, if option '-A' is not given.If the class is numeric, k - 1 new binary attributes are generated in the manner described in "Classification and Regression Trees" by Breiman et al. (i.e. taking the average class value associated with each attribute value into account)

For more information, see:

L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone (1984). Classification and Regression Trees. Wadsworth Inc.
BibTeX:
```
 @book{Breiman1984, author = {L. Breiman and J.H. Friedman and R.A. Olshen and C.J. Stone}, publisher = {Wadsworth Inc}, title = {Classification and Regression Trees}, year = {1984}, ISBN = {0412048418} } 
```
Valid options are:
```
 -N Sets if binary attributes are to be coded as nominal ones.
```
```
 -A For each nominal value a new attribute is created,  not only if there are more than 2 values.
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5541 $
weka.filters.unsupervised.attribute.NominalToBinary
Converts all nominal attributes into binary numeric attributes. An attribute with k values is transformed into k binary attributes if the class is nominal (using the one-attribute-per-value approach). Binary attributes are left binary, if option '-A' is not given.If the class is numeric, you might want to use the supervised version of this filter.
Valid options are:
```
 -N Sets if binary attributes are to be coded as nominal ones.
```
```
 -A For each nominal value a new attribute is created,  not only if there are more than 2 values.
```
```
 -R <col1,col2-col4,...> Specifies list of columns to act on. First and last are  valid indexes. (default: first-last)
```
```
 -V Invert matching sense of column indexes.
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5543 $

Examples of weka.filters.supervised.attribute.NominalToBinary

     * by a pseudo-class variable that is used by LogitBoost.
     */
    protected Instances getNumericData(Instances train) throws Exception{
  
  Instances filteredData = new Instances(train);  
  m_nominalToBinary = new NominalToBinary();      
  m_nominalToBinary.setInputFormat(filteredData);
  filteredData = Filter.useFilter(filteredData, m_nominalToBinary);  


  return super.getNumericData(filteredData);
    }

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Examples of weka.filters.supervised.attribute.NominalToBinary


    m_replaceMissing = new ReplaceMissingValues();
    m_replaceMissing.setInputFormat(m_instances);
    m_instances = Filter.useFilter(m_instances, m_replaceMissing);


    m_nominalToBinary = new NominalToBinary();
    m_nominalToBinary.setInputFormat(m_instances);
    m_instances = Filter.useFilter(m_instances, m_nominalToBinary);


    m_removeUseless = new RemoveUseless();
    m_removeUseless.setInputFormat(m_instances);

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Examples of weka.filters.supervised.attribute.NominalToBinary

      data.deleteWithMissingClass();
    }


    // Preprocess instances
    if (!m_checksTurnedOff) {
      m_TransformFilter = new NominalToBinary();
      m_TransformFilter.setInputFormat(data);
      data = Filter.useFilter(data, m_TransformFilter);
      m_MissingFilter = new ReplaceMissingValues();
      m_MissingFilter.setInputFormat(data);
      data = Filter.useFilter(data, m_MissingFilter);

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Examples of weka.filters.supervised.attribute.NominalToBinary

   * by a pseudo-class variable that is used by LogitBoost.
   */
  protected Instances getNumericData(Instances train) throws Exception{
  
    Instances filteredData = new Instances(train);  
    m_nominalToBinary = new NominalToBinary();      
    m_nominalToBinary.setInputFormat(filteredData);
    filteredData = Filter.useFilter(filteredData, m_nominalToBinary);  


    return super.getNumericData(filteredData);
  }

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Examples of weka.filters.supervised.attribute.NominalToBinary

      data.deleteWithMissingClass();
    }


    // Preprocess instances
    if (!m_checksTurnedOff) {
      m_TransformFilter = new NominalToBinary();
      m_TransformFilter.setInputFormat(data);
      data = Filter.useFilter(data, m_TransformFilter);
      m_MissingFilter = new ReplaceMissingValues();
      m_MissingFilter.setInputFormat(data);
      data = Filter.useFilter(data, m_MissingFilter);

View Full Code Here

Examples of weka.filters.supervised.attribute.NominalToBinary


    m_replaceMissing = new ReplaceMissingValues();
    m_replaceMissing.setInputFormat(m_instances);
    m_instances = Filter.useFilter(m_instances, m_replaceMissing);


    m_nominalToBinary = new NominalToBinary();
    m_nominalToBinary.setInputFormat(m_instances);
    m_instances = Filter.useFilter(m_instances, m_nominalToBinary);


    m_removeUseless = new RemoveUseless();
    m_removeUseless.setInputFormat(m_instances);

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Examples of weka.filters.unsupervised.attribute.NominalToBinary

    m_stopIt = true;
    m_stopped = true;
    m_accepted = false;
    m_numeric = false;
    m_random = null;
    m_nominalToBinaryFilter = new NominalToBinary();
    m_sigmoidUnit = new SigmoidUnit();
    m_linearUnit = new LinearUnit();
    //setting all the options to their defaults. To completely change these
    //defaults they will also need to be changed down the bottom in the 
    //setoptions function (the text info in the accompanying functions should

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Examples of weka.filters.unsupervised.attribute.NominalToBinary

    m_instances = new Instances(i);
    m_random = new Random(m_randomSeed);
    m_instances.randomize(m_random);


    if (m_useNomToBin) {
      m_nominalToBinaryFilter = new NominalToBinary();
      m_nominalToBinaryFilter.setInputFormat(m_instances);
      m_instances = Filter.useFilter(m_instances,
             m_nominalToBinaryFilter);
    }
    m_numAttributes = m_instances.numAttributes() - 1;

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Examples of weka.filters.unsupervised.attribute.NominalToBinary

        }
      }
    }


    if (!onlyNumeric) {
      m_NominalToBinary = new NominalToBinary();
      m_NominalToBinary.setInputFormat(insts);
      insts = Filter.useFilter(insts, m_NominalToBinary);
    }
    return insts;
  }

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Examples of weka.filters.unsupervised.attribute.NominalToBinary

    m_Train = new Instances(insts);
    
    m_ReplaceMissingValues = new ReplaceMissingValues();
    m_ReplaceMissingValues.setInputFormat(m_Train);
    m_Train = Filter.useFilter(m_Train, m_ReplaceMissingValues);
    m_NominalToBinary = new NominalToBinary();
    m_NominalToBinary.setInputFormat(m_Train);
    m_Train = Filter.useFilter(m_Train, m_NominalToBinary);


    /** Randomize training data */
    if(m_Seed != -1) {

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