Examples of weka.filters.unsupervised.attribute.NumericToBinary

Package weka.filters.unsupervised.attribute

Examples of weka.filters.unsupervised.attribute.NumericToBinary

weka.filters.unsupervised.attribute.NumericToBinary
Converts all numeric attributes into binary attributes (apart from the class attribute, if set): if the value of the numeric attribute is exactly zero, the value of the new attribute will be zero. If the value of the numeric attribute is missing, the value of the new attribute will be missing. Otherwise, the value of the new attribute will be one. The new attributes will be nominal.
Valid options are:
```
 -unset-class-temporarily Unsets the class index temporarily before the filter is applied to the data. (default: no)
```
@author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5543 $

      Discretize disTransform = new Discretize();
      disTransform.setUseBetterEncoding(true);
      disTransform.setInputFormat(data);
      data = Filter.useFilter(data, disTransform);
    } else {
      NumericToBinary binTransform = new NumericToBinary();
      binTransform.setInputFormat(data);
      data = Filter.useFilter(data, binTransform);
    }      
    int numClasses = data.attribute(classIndex).numValues();


    // Reserve space and initialize counters

View Full Code Here

      Discretize disTransform = new Discretize();
      disTransform.setUseBetterEncoding(true);
      disTransform.setInputFormat(data);
      data = Filter.useFilter(data, disTransform);
    } else {
      NumericToBinary binTransform = new NumericToBinary();
      binTransform.setInputFormat(data);
      data = Filter.useFilter(data, binTransform);
    }      
    int numClasses = data.attribute(classIndex).numValues();


    // Reserve space and initialize counters

View Full Code Here

      Discretize disTransform = new Discretize();
      disTransform.setUseBetterEncoding(true);
      disTransform.setInputFormat(data);
      data = Filter.useFilter(data, disTransform);
    } else {
      NumericToBinary binTransform = new NumericToBinary();
      binTransform.setInputFormat(data);
      data = Filter.useFilter(data, binTransform);
    }      
    int numClasses = data.attribute(classIndex).numValues();


    // Reserve space and initialize counters

View Full Code Here

      Discretize disTransform = new Discretize();
      disTransform.setUseBetterEncoding(true);
      disTransform.setInputFormat(data);
      data = Filter.useFilter(data, disTransform);
    } else {
      NumericToBinary binTransform = new NumericToBinary();
      binTransform.setInputFormat(data);
      data = Filter.useFilter(data, binTransform);
    }      
    int numClasses = data.attribute(classIndex).numValues();


    // Reserve space and initialize counters

View Full Code Here

      Discretize disTransform = new Discretize();
      disTransform.setUseBetterEncoding(true);
      disTransform.setInputFormat(data);
      data = Filter.useFilter(data, disTransform);
    } else {
      NumericToBinary binTransform = new NumericToBinary();
      binTransform.setInputFormat(data);
      data = Filter.useFilter(data, binTransform);
    }      
    int numClasses = data.attribute(classIndex).numValues();


    // Reserve space and initialize counters

View Full Code Here

      Discretize disTransform = new Discretize();
      disTransform.setUseBetterEncoding(true);
      disTransform.setInputFormat(data);
      data = Filter.useFilter(data, disTransform);
    } else {
      NumericToBinary binTransform = new NumericToBinary();
      binTransform.setInputFormat(data);
      data = Filter.useFilter(data, binTransform);
    }
    int numClasses = data.attribute(classIndex).numValues();
        runAttributeSelection(data, classIndex, rnd);

View Full Code Here

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