Examples of Normalize

com.blitline.image.functions.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 $

Examples of weka.filters.unsupervised.attribute.Normalize


    /* 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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Examples of weka.filters.unsupervised.attribute.Normalize

      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_numInstances = data.numInstances();

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Examples of weka.filters.unsupervised.attribute.Normalize

      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;

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Examples of weka.filters.unsupervised.attribute.Normalize

    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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Examples of weka.filters.unsupervised.attribute.Normalize

      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;

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Examples of weka.filters.unsupervised.attribute.Normalize

    // 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();

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Examples of weka.filters.unsupervised.attribute.Normalize

      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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Examples of weka.filters.unsupervised.attribute.Normalize

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


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


        int[] vy = new int[insts.numInstances()];

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