Examples of weka.filters.unsupervised.attribute.NominalToBinary

Package weka.filters.unsupervised.attribute

Examples of weka.filters.unsupervised.attribute.NominalToBinary

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 $

    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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    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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        }
      }
    }


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

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    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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  m_ReplaceMissingValues = new ReplaceMissingValues();
  m_ReplaceMissingValues.setInputFormat(data);
  data = Filter.useFilter(data, m_ReplaceMissingValues);
  
  //convert nominal attributes
  m_NominalToBinary = new NominalToBinary();
  m_NominalToBinary.setInputFormat(data);
  data = Filter.useFilter(data, m_NominalToBinary);
  
  //create actual logistic model
  m_boostedModel = new LogisticBase(m_numBoostingIterations, m_useCrossValidation, m_errorOnProbabilities);

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

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    m_AttFilter = new RemoveUseless();
    m_AttFilter.setInputFormat(train);
    train = Filter.useFilter(train, m_AttFilter);
  
    // Transform attributes
    m_NominalToBinary = new NominalToBinary();
    m_NominalToBinary.setInputFormat(train);
    train = Filter.useFilter(train, m_NominalToBinary);
    
    // Save the structure for printing the model
    m_structure = new Instances(train, 0);

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        }
      }
    }
    
    if (!onlyNumeric) {
      m_nominalToBinary = new NominalToBinary();
      m_nominalToBinary.setInputFormat(data);
      data = Filter.useFilter(data, m_nominalToBinary);
    }
    
    if (!m_dontNormalize && data.numInstances() > 0) {

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    m_Missing = new ReplaceMissingValues();
    m_Missing.setInputFormat(instances);
    instances = Filter.useFilter(instances, m_Missing);
    
    if (!m_onlyNumeric) {
      m_NominalToBinary = new NominalToBinary();
      m_NominalToBinary.setInputFormat(instances);
      instances = Filter.useFilter(instances, m_NominalToBinary);
    } else {
      m_NominalToBinary = null;
    }

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    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 */
    m_Train.randomize(new Random(m_Seed));

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