Examples of weka.filters.unsupervised.attribute.Normalize

Package weka.filters.unsupervised.attribute

Examples of weka.filters.unsupervised.attribute.Normalize

weka.filters.unsupervised.attribute.Normalize
Normalizes all numeric values in the given dataset (apart from the class attribute, if set). The resulting values are by default in [0,1] for the data used to compute the normalization intervals. But with the scale and translation parameters one can change that, e.g., with scale = 2.0 and translation = -1.0 you get values in the range [-1,+1].
Valid options are:
```
 -unset-class-temporarily Unsets the class index temporarily before the filter is applied to the data. (default: no)
```
```
 -S <num> The scaling factor for the output range. (default: 1.0)
```
```
 -T <num> The translation of the output range. (default: 0.0)
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @author FracPete (fracpete at waikato dot ac dot nz) @version $Revision: 5543 $

    if (getConvertNominalToBinary()) {
      insts = nominalToBinary(insts);
    }


    if (getNormalize()) {
      m_Filter = new Normalize();
      m_Filter.setInputFormat(insts);
      insts = Filter.useFilter(insts, m_Filter);
    }


    List<Integer> vy = new ArrayList<Integer>(insts.numInstances());

View Full Code Here

    if (m_filterType == FILTER_STANDARDIZE) {
      m_Filter = new Standardize();
      m_Filter.setInputFormat(insts);
      insts = Filter.useFilter(insts, m_Filter); 
    } else if (m_filterType == FILTER_NORMALIZE) {
      m_Filter = new Normalize();
      m_Filter.setInputFormat(insts);
      insts = Filter.useFilter(insts, m_Filter); 
    } else {
      m_Filter = null;
    }

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


      m_normalize = new Normalize();
      m_normalize.setInputFormat(data);
      data = Filter.useFilter(data, m_normalize);
    }
    
    m_weights = new double[data.numAttributes() + 1];

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    // replace missing values filtering, it will fail
    // if the data actually does have missing values
    getCapabilities().testWithFail(insts);
        
    if (getNormalize()) {
      m_Filter = new Normalize();
      m_Filter.setInputFormat(insts);
      insts = Filter.useFilter(insts, m_Filter);
    }
    
    Vector vy = new Vector();

View Full Code Here

      m_Filter = new Standardize();
      ((Standardize)m_Filter).setIgnoreClass(true);
      m_Filter.setInputFormat(instances);
      instances = Filter.useFilter(instances, m_Filter);      
    } else if (m_filterType == FILTER_NORMALIZE) {
      m_Filter = new Normalize();
      ((Normalize)m_Filter).setIgnoreClass(true);
      m_Filter.setInputFormat(instances);
      instances = Filter.useFilter(instances, m_Filter);
    } else {
      m_Filter = null;

View Full Code Here


    m_RemoveUseless = new RemoveUseless();
    m_RemoveUseless.setInputFormat(data);
    data = Filter.useFilter(data, m_RemoveUseless);


    m_Normalize = new Normalize();
    m_Normalize.setInputFormat(data);
    data = Filter.useFilter(data, m_Normalize);


    if(m_NumberOfGroups) {
      generateGroupsFromNumbers(data, random);

View Full Code Here

    }


    if (m_filterType == FILTER_STANDARDIZE) 
      m_Filter = new Standardize();
    else if (m_filterType == FILTER_NORMALIZE)
      m_Filter = new Normalize();
    else 
      m_Filter = null;




    Instances transformedInsts;

View Full Code Here

    train.deleteAttributeAt(0); //remove the bagIndex attribute;


    if (m_filterType == FILTER_STANDARDIZE)  
      m_Filter = new Standardize();
    else if (m_filterType == FILTER_NORMALIZE)
      m_Filter = new Normalize();
    else 
      m_Filter = null;


    if (m_Filter!=null) {
      m_Filter.setInputFormat(train);

View Full Code Here


    /* filter the training data */
    if (m_filterType == FILTER_STANDARDIZE)  
      m_Filter = new Standardize();
    else if (m_filterType == FILTER_NORMALIZE)
      m_Filter = new Normalize();
    else 
      m_Filter = null; 


    if (m_Filter!=null) {
      m_Filter.setInputFormat(datasets);

View Full Code Here


    /* filter the training data */
    if (m_filterType == FILTER_STANDARDIZE)  
      m_Filter = new Standardize();
    else if (m_filterType == FILTER_NORMALIZE)
      m_Filter = new Normalize();
    else 
      m_Filter = null; 


    if (m_Filter!=null) {
      m_Filter.setInputFormat(datasets);

View Full Code Here

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