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 $


    Change = 0.0;


    //Manipulate Data
    if (NormalizeData) {
      m_Filter = new Normalize();
      m_Filter.setInputFormat(m_Instances);
      m_Instances = Filter.useFilter(m_Instances, m_Filter);
    }


    //Set the intecept coefficient.

View Full Code Here

    train = Filter.useFilter( train, m_ConvertToSI);


    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) {
      // normalize/standardize the converted training dataset

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    // transformed instances and we can check header compatibility
    m_trainHeader = new Instances(m_trainInstances, 0);
    
    // normalize data if desired
    if (m_normalize) {
      m_normalizeFilter = new Normalize();
      m_normalizeFilter.setInputFormat(m_trainInstances);
      m_trainInstances = Filter.useFilter(m_trainInstances, m_normalizeFilter);
    }
    
    // convert any nominal attributes to binary numeric attributes

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    /* 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);

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      attributesToRemove.addElement(new Integer(m_classIndex));
    }
    
    // normalize data if desired
    if (m_normalize) {
      m_normalizeFilter = new Normalize();
      m_normalizeFilter.setInputFormat(m_trainInstances);
      m_trainInstances = Filter.useFilter(m_trainInstances, m_normalizeFilter);
    }
    
    // convert any nominal attributes to binary numeric attributes

View Full Code Here

    m_replaceMissingFilter.setInputFormat(m_trainInstances);
    m_trainInstances = Filter.useFilter(m_trainInstances, 
                                        m_replaceMissingFilter);


    if (m_normalize) {
      m_normalizeFilter = new Normalize();
      m_normalizeFilter.setInputFormat(m_trainInstances);
      m_trainInstances = Filter.useFilter(m_trainInstances, m_normalizeFilter);
    }


    m_nominalToBinFilter = new NominalToBinary();

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;
    }

View Full Code Here

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

View Full Code Here


    Change = 0.0;


    //Manipulate Data
    if (NormalizeData) {
      m_Filter = new Normalize();
      m_Filter.setInputFormat(m_Instances);
      m_Instances = Filter.useFilter(m_Instances, m_Filter);
    }


    //Set the intecept coefficient.

View Full Code Here

    // remove instances with missing class
    insts = new Instances(insts);
    insts.deleteWithMissingClass();
    
    if (getNormalize()) {
      m_Filter = new Normalize();
      m_Filter.setInputFormat(insts);
      insts = Filter.useFilter(insts, m_Filter);
    }
    
    Vector vy = new Vector();

View Full Code Here

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